ORCID Profile
0000-0001-9701-2718
Current Organisations
University College London
,
University of South Australia
,
University of London
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Genomics | Genetics | Quantitative Genetics (incl. Disease and Trait Mapping Genetics) | Animal Breeding | Computer Software not elsewhere classified |
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Biological Sciences | Application Software Packages (excl. Computer Games) | Expanding Knowledge in the Agricultural and Veterinary Sciences | Environmentally Sustainable Animal Production not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences
Related Links
Publications
Low‐cost and Non‐flammable Eutectic Electrolytes for Advanced Zn‐I2 Batteries
Publisher: Wiley
Date: 18-08-2023
Abstract: As a burgeoning electrolyte system, eutectic electrolytes based on ZnCl 2 /Zn(CF 3 SO 3 ) 2 /Zn(TFSI) 2 have been widely proposed in advanced Zn‐I 2 batteries however, safety and cost concerns significantly limit their applications. Here, we report new‐type ZnSO 4 ‐based eutectic electrolytes that are both safe and cost‐effective. Their universality is evident in various solvents of polyhydric alcohols, in which multiple −OH groups not only involve in Zn 2+ solvation but also interact with water, resulting in the high stability of electrolytes. Taking propylene glycol‐based hydrated eutectic electrolyte as an ex le, it features significant advantages in non‐flammability and low price that is /200 cost of Zn(CF 3 SO 3 ) 2 /Zn(TFSI) 2 ‐based eutectic electrolytes. Moreover, its effectiveness in confining the shuttle effects of I 2 cathode and side reactions of Zn anodes is evidenced, resulting in Zn‐I 2 cells with high reversibility at 1 C and 91.4 % capacity remaining under 20 C. After scaling up to the pouch cell with a record mass loading of 33.3 mg cm −2 , super‐high‐capacity retention of 96.7 % is achieved after 500 cycles, which exceeds other aqueous counterparts. This work significantly broadens the eutectic electrolyte family for advanced Zn battery design.
Implications of simplified linkage equilibrium SNP simulation
Publisher: Proceedings of the National Academy of Sciences
Date: 28-09-2015
Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects
Publisher: Springer Science and Business Media LLC
Date: 21-11-2016
DOI: 10.1038/NG.3725
Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept
Publisher: Springer Science and Business Media LLC
Date: 02-2016
DOI: 10.1038/NN.4228
Integrative analysis of genomic and exposomic influences on youth mental health
Publisher: Wiley
Date: 10-08-2022
DOI: 10.1111/JCPP.13664
Abstract: Understanding complex influences on mental health problems in young people is needed to inform early prevention strategies. Both genetic and environmental factors are known to influence youth mental health, but a more comprehensive picture of their interplay, including wide‐ranging environmental exposures – that is, the exposome – is needed. We perform an integrative analysis of genomic and exposomic data in relation to internalizing and externalizing symptoms in a cohort of 4,314 unrelated youth from the Adolescent Brain and Cognitive Development (ABCD) Study. Using novel GREML‐based approaches, we model the variance in internalizing and externalizing symptoms explained by additive and interactive influences from the genome (G) and modeled exposome (E) consisting of up to 133 variables at the family, peer, school, neighborhood, life event, and broader environmental levels, including genome‐by‐exposome (G × E) and exposome‐by‐exposome (E × E) effects. A best‐fitting integrative model with G, E, and G × E components explained 35% and 63% of variance in youth internalizing and externalizing symptoms, respectively. Youth in the top quintile of model‐predicted risk accounted for the majority of in iduals with clinically elevated symptoms at follow‐up (60% for internalizing 72% for externalizing). Of note, different domains of environmental exposures were most impactful for internalizing (life events) and externalizing (contextual including family, school, and peer‐level factors) symptoms. In addition, variance explained by G × E contributions was substantially larger for externalizing (33%) than internalizing (13%) symptoms. Advanced statistical genetic methods in a longitudinal cohort of youth can be leveraged to address fundamental questions about the role of ‘nature and nurture’ in developmental psychopathology.
Using dominance relationship coefficients based on linkage disequilibrium and linkage with a general complex pedigree to increase mapping resolution
Publisher: Oxford University Press (OUP)
Date: 10-2006
DOI: 10.1534/GENETICS.106.060806
Abstract: Dominance (intralocus allelic interactions) plays often an important role in quantitative trait variation. However, few studies about dominance in QTL mapping have been reported in outbred animal or human populations. This is because common dominance effects can be predicted mainly for many full sibs, which do not often occur in outbred or natural populations with a general pedigree. Moreover, incomplete genotypes for such a pedigree make it infeasible to estimate dominance relationship coefficients between in iduals. In this study, identity-by-descent (IBD) coefficients are estimated on the basis of populationwide linkage disequilibrium (LD), which makes it possible to track dominance relationships between unrelated founders. Therefore, it is possible to use dominance effects in QTL mapping without full sibs. Incomplete genotypes with a complex pedigree and many markers can be efficiently dealt with by a Markov chain Monte Carlo method for estimating IBD and dominance relationship matrices ($\\batchmode \\documentclass[fleqn,10pt,legalpaper]{article} \\usepackage{amssymb} \\usepackage{amsfonts} \\usepackage{amsmath} \\pagestyle{empty} \\begin{document} \\(D_{\\mathrm{RM}}\\) \\end{document}$). It is shown by simulation that the use of $\\batchmode \\documentclass[fleqn,10pt,legalpaper]{article} \\usepackage{amssymb} \\usepackage{amsfonts} \\usepackage{amsmath} \\pagestyle{empty} \\begin{document} \\(D_{\\mathrm{RM}}\\) \\end{document}$ increases the likelihood ratio at the true QTL position and the mapping accuracy and power with complete dominance, overdominance, and recessive inheritance modes when using 200 genotyped and phenotyped in iduals.
Methods and experimental designs for detection of QTL in sheep and goats
Publisher: Elsevier BV
Date: 06-2007
Using an evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker loci
Publisher: Springer Science and Business Media LLC
Date: 11-04-2008
Research Review: Polygenic methods and their application to psychiatric traits.
Publisher: Wiley
Date: 08-2014
DOI: 10.1111/JCPP.12295
Abstract: Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pregenomics era the genetic architecture (number, frequency and effect size of risk variants) of complex genetic disorders was unknown. Empirical evidence for the genetic architecture of psychiatric disorders is emerging from the genetic studies of the last 5 years. We review the methods investigating the polygenic nature of complex disorders. We provide mini-guides to genomic profile (or polygenic) risk scoring and to estimation of variance (or heritability) from common SNPs a glossary of key terms is also provided. We review results of applications of the methods to psychiatric disorders and related traits and consider how these methods inform on missing heritability, hidden heritability and still-missing heritability. Genome-wide genotyping and sequencing studies are providing evidence that psychiatric disorders are truly polygenic, that is they have a genetic architecture of many genetic variants, including risk variants that are both common and rare in the population. S le sizes published to date are mostly underpowered to detect effect sizes of the magnitude presented by nature, and these effect sizes may be constrained by the biological validity of the diagnostic constructs. Increasing the s le size for genome wide association studies of psychiatric disorders will lead to the identification of more associated genetic variants, as already found for schizophrenia. These loci provide the starting point of functional analyses that might eventually lead to new prevention and treatment options and to improved biological validity of diagnostic constructs. Polygenic analyses will contribute further to our understanding of complex genetic traits as s le sizes increase and as s le resources become richer in phenotypic descriptors, both in terms of clinical symptoms and of nongenetic risk factors.
Erratum: Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs
Publisher: Springer Science and Business Media LLC
Date: 27-06-2012
DOI: 10.1038/NG0712-831A
Deeply understanding the Zn anode behaviour and corresponding improvement strategies in different aqueous Zn-based batteries
Publisher: Royal Society of Chemistry (RSC)
Date: 2020
DOI: 10.1039/D0EE02162H
Abstract: The differences and similarities of the Zn electrode in both alkaline and mild electrolytes have been thoroughly clarified.
Supercapacitive behavior of electrostatic self-assembly reduced graphene oxide/CoAl-layered double hydroxides nanocomposites
Publisher: Elsevier BV
Date: 06-2016
Suppressing Hydrogen Evolution via Anticatalytic Interfaces toward Highly Efficient Aqueous Zn-Ion Batteries
Publisher: American Chemical Society (ACS)
Date: 06-02-2023
An In-Depth Study of Zn Metal Surface Chemistry for Advanced Aqueous Zn-Ion Batteries
Publisher: Wiley
Date: 08-07-2020
Pseudocapacitive Zinc Cation Intercalation with Superior Kinetics Enabled by Atomically Thin V2O5 Nanobelts for Quasi-Solid-State Microbatteries
Publisher: Elsevier BV
Date: 09-2022
Genetic biomarkers for endometriosis
Publisher: Springer International Publishing
Date: 2017
Ultrathin Few-Layer GeP Nanosheets via Lithiation-Assisted Chemical Exfoliation and Their Application in Sodium Storage
Publisher: Wiley
Date: 03-2020
Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology
Publisher: Springer Science and Business Media LLC
Date: 21-06-2022
DOI: 10.1038/S42003-022-03554-Y
Abstract: Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.
Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood
Publisher: Elsevier BV
Date: 06-2018
Effect of selection on bias and accuracy in genomic prediction of breeding values
Publisher: Cold Spring Harbor Laboratory
Date: 09-04-2018
DOI: 10.1101/298042
Abstract: Reference populations for genomic selection (GS) usually involve highly selected in iduals, which may result in biased prediction of estimated genomic breeding values (GEBV). In the present study, bias and accuracy of GEBV were explored for various genetic models and prediction methods when using selected in iduals for a reference. Data were simulated for an animal breeding program to compare Best Linear Unbiased Prediction of breeding values using pedigree based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single Step approach (SSGBLUP), where information on genotyped in iduals was used to infer a matrix H with relationships among all available genotyped and non-genotyped in iduals that were linked through pedigree. In SSGBLUP, various weights (α=0.95, 0.80, 0.50) for the genomic relationship matrix ( G ) relative to the numerator relationship matrix ( A ) were applied to construct H and in another version (SSGBLUP_F), inbreeding was accounted for while computing A -1 . With GBLUP, accuracy of GEBV prediction increased linearly with an increase in the number of animals selected in reference. For the scenario with no-selection and random mating (RR) prediction was unbiased. For GBLUP, lower accuracy and bias observed in the scenarios with selection and random mating (SR) or selection and positive assortative mating (SA), in which prediction bias increased when a smaller and highly selected proportion genotyped. Bias disappeared when all in iduals were genotyped. SSGBLUP_F showed higher accuracy compared to GBLUP and bias of prediction was negligible even with selective genotyping. However, PBLUP and SSGBLUP showed bias in SA owing to not fully accounting for allele frequency changes because of selection of quantitative trait loci (QTL) with larger effects and also due to high inbreeding rate. In genetic models with fewer QTL but each with larger effect, predictions were less accurate and more biased for selection scenarios. Results suggest that prediction accuracy and bias is affected by the genetic architecture of the trait. Selective genotyping lead to significant bias in GEBV prediction. SSGBLUP with appropriate scaling of A and G matrices can provide accurate and less biased prediction but scaling requires careful consideration in populations under selection and with high levels of inbreeding.
Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep
Publisher: Springer Science and Business Media LLC
Date: 26-06-2019
Defections induced hydrogenation of silicene: a density functional theory calculation study
Publisher: Royal Society of Chemistry (RSC)
Date: 2016
DOI: 10.1039/C6RA11885B
Abstract: Vacancy defects significantly depress the energy barrier for dissociative adsorption of H 2 on silicene, which can open the band gap of silicene.
A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts
Publisher: Elsevier BV
Date: 11-2021
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Publisher: Springer Science and Business Media LLC
Date: 02-02-2015
DOI: 10.1038/NG.3211
Preparation of three-dimensional nitrogen-doped graphene layers by gas foaming method and its electrochemical capactive behavior
Publisher: Elsevier BV
Date: 03-2016
Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222596
Abstract: Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.
THI Modulation of Genetic and Non-genetic Variance Components for Carcass Traits in Hanwoo Cattle
Publisher: Frontiers Media SA
Date: 23-12-2020
DOI: 10.3389/FGENE.2020.576377
Abstract: The phenotype of carcass traits in beef cattle are affected by random genetic and non-genetic effects, which both can be modulated by an environmental variable such as Temperature-Humidity Index (THI), a key environmental factor in cattle production. In this study, a multivariate reaction norm model (MRNM) was used to assess if the random genetic and non-genetic (i.e., residual) effects of carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) were modulated by THI, using 9,318 Hanwoo steers ( N = 8,964) and cows ( N = 354) that were genotyped on the Illumina Bovine SNP50 BeadChip (50K). THI was measured based on the period of 15–45 days before slaughter. Both the correlation and the interaction between THI and random genetic and non-genetic effects were accounted for in the model. In the analyses, it was shown that the genetic effects of EMA and the non-genetic effects of CW and MS were significantly modulated by THI. No significant THI modulation of such effects was found for BFT. These results highlight the relevance of THI changes for the genetic and non-genetic variation of CW, EMA, and MS in Hanwoo beef cattle. Importantly, heritability estimates for CW, EMA, and MS from additive models without considering THI interactions were underestimated. Moreover, the significance of interaction can be biased if not properly accounting for the correlation between THI and genetic and non-genetic effects. Thus, we argue that the estimation of genetic parameters should be based on appropriate models to avoid any potential bias of estimates. Our finding should serve as a basis for future studies aiming at revealing genotype by environment interaction in estimation and genomic prediction of breeding values.
Genotype–covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model
Publisher: Springer Science and Business Media LLC
Date: 20-05-2019
DOI: 10.1038/S41467-019-10128-W
Abstract: The genomics era has brought useful tools to dissect the genetic architecture of complex traits. Here we propose a multivariate reaction norm model (MRNM) to tackle genotype–covariate (G–C) correlation and interaction problems. We apply MRNM to the UK Biobank data in analysis of body mass index using smoking quantity as a covariate, finding a highly significant G–C correlation, but only weak evidence for G–C interaction. In contrast, G–C interaction estimates are inflated in existing methods. It is also notable that there is significant heterogeneity in the estimated residual variances (i.e., variances not attributable to factors in the model) across different covariate levels, i.e., residual–covariate (R–C) interaction. We also show that the residual variances estimated by standard additive models can be inflated in the presence of G–C and/or R–C interactions. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses.
A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data
Publisher: Springer Science and Business Media LLC
Date: 09-02-2023
DOI: 10.1038/S41467-023-36281-X
Abstract: Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is erse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.
Adiposity and cancer: a Mendelian randomization analysis in the UK biobank
Publisher: Springer Science and Business Media LLC
Date: 27-08-2021
DOI: 10.1038/S41366-021-00942-Y
Abstract: Observational and Mendelian randomization (MR) studies link obesity and cancer, but it remains unclear whether these depend upon related metabolic abnormalities. We used information from 321,472 participants in the UK biobank, including 30,561 cases of obesity-related cancer. We constructed three genetic instruments reflecting higher adiposity together with either "unfavourable" (82 SNPs), "favourable" (24 SNPs) or "neutral" metabolic profile (25 SNPs). We looked at associations with 14 types of cancer, previously suggested to be associated with obesity. All genetic instruments had a strong association with BMI (p < 1 × 10 Higher adiposity associated with a higher risk of non-hormonal cancer but a lower risk of some hormone related cancers. Presence of metabolic abnormalities might aggravate the adverse effects of higher adiposity on cancer. Further studies are warranted to investigate whether interventions on adverse metabolic health may help to alleviate obesity-related cancer risk.
Dehydration-Triggered Ionic Channel Engineering in Potassium Niobate for Li/K-Ion Storage
Publisher: Wiley
Date: 24-04-2020
MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
Publisher: Oxford University Press (OUP)
Date: 10-01-2016
DOI: 10.1093/BIOINFORMATICS/BTW012
Abstract: Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations. Availability and implementation: MTG2 is available in ite/honglee0707/mtg2. Contact: hong.lee@une.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
Genome-wide association study identifies a locus at 7p15.2 associated with endometriosis
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 04-2011
Using information of relatives in genomic prediction to apply effective stratified medicine
Publisher: Springer Science and Business Media LLC
Date: 09-02-2017
DOI: 10.1038/SREP42091
Abstract: Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to in iduals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative in iduals in the data set used to generate the predictors (“discovery s le”) to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size ( N e ), i.e. when in iduals are closely related. For ex le, with the s le size of discovery set N = 3000, heritability h 2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.
Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method
Publisher: Springer Science and Business Media LLC
Date: 29-08-2017
Age at first birth in women is genetically associated with increased risk of schizophrenia
Publisher: Springer Science and Business Media LLC
Date: 05-07-2018
DOI: 10.1038/S41598-018-28160-Z
Abstract: Previous studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is −0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank s le. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.
Gene expression imputation across multiple brain regions provides insights into schizophrenia risk
Publisher: Springer Science and Business Media LLC
Date: 25-03-2019
Cross-ancestry genetic architecture and prediction for cholesterol traits
Publisher: Cold Spring Harbor Laboratory
Date: 02-02-2023
DOI: 10.1101/2023.01.31.23285307
Abstract: While cholesterol is essential for human life, a high level of cholesterol is closely linked with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have been successful to identify genetic variants associated with cholesterol, which have been conducted mostly in white European populations. Consequently, it remains mostly unknown how genetic effects on cholesterol vary across ancestries. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries for cholesterol. We find significant genetic heterogeneity between ancestries for total- and LDL-cholesterol. Furthermore, we show that single nucleotide polymorphisms (SNPs), which have concordant effects across ancestries for cholesterol, are more frequently found in the regulatory region, compared to the other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog ( www.ebi.ac.uk/gwas/ details are in web resources section). These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings
Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model
Publisher: Public Library of Science (PLoS)
Date: 07-04-2015
Novel genetic analysis for case-control genome-wide association studies: quantification of power and genomic prediction accuracy.
Publisher: Public Library of Science (PLoS)
Date: 19-08-2013
Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data
Publisher: Oxford University Press (OUP)
Date: 11-04-2014
DOI: 10.1093/HMG/DDU174
Single atom catalysts for triiodide adsorption and fast conversion to boost the performance of aqueous zinc–iodine batteries
Publisher: Royal Society of Chemistry (RSC)
Date: 2023
DOI: 10.1039/D3EE01453C
Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum
Publisher: Elsevier BV
Date: 09-2021
Global Trends in CD4 Cell Count at the Start of Antiretroviral Therapy: Collaborative Study of Treatment Programs
Publisher: Oxford University Press (OUP)
Date: 25-01-2018
DOI: 10.1093/CID/CIX915
Age at first birth in women is genetically associated with increased risk of schizophrenia
Publisher: Cold Spring Harbor Laboratory
Date: 27-09-2017
DOI: 10.1101/194076
Abstract: Previous studies have shown an increased risk for a range of mental health issues in children born to both younger and older parents compared to children of average-aged parents. However, until recently, it was not clear if these increased risks are due to psychosocial factors associated with age or if parents at higher genetic risk for psychiatric disorders tend to have children at an earlier or later age. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age of mothers at the birth of their first child (AFB). Here, we use independent data from the UK Biobank (N=38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, end to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value=1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value=3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE=0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE=0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.
Response to Browning and Browning
Publisher: Elsevier BV
Date: 07-2011
Genetic correlations of polygenic disease traits: from theory to practice
Publisher: Springer Science and Business Media LLC
Date: 06-06-2019
DOI: 10.1038/S41576-019-0137-Z
Abstract: The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
Metabolic profile predicts incident cancer: A large-scale population study in the UK Biobank
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.METABOL.2022.155342
Abstract: Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 in idual biomarkers and cancer risk. In total, 21,973 in iduals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of in idual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were in idually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.
Healthy lifestyle, genetic risk, and incidence of cancer: A prospective cohort study of 13 cancer types
Publisher: Cold Spring Harbor Laboratory
Date: 07-12-2021
DOI: 10.1101/2021.12.07.21267341
Abstract: Genetic and lifestyle factors are related to the risk of cancer, but it is unclear whether a healthy lifestyle can offset genetic risk. Our aim was to investigate this for 13 cancer types using data from the UK Biobank prospective cohort. In 2006-2010, participants aged 37-73 years were assessed and followed until 2015-2019. Analyses were restricted to those of European ancestries with no history of malignant cancer (n=195,822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined (‘overall cancer’), and a healthy lifestyle score was calculated from current recommendations. Relationships with cancer incidence were examined using Cox regression, adjusting for relevant confounders. Interactions between HLI and PRSs were assessed. There were 15,240 incident cancers during the 1,926,987 person-years of follow-up (median follow-up= 10.2 years). After adjusting for confounders, an unhealthy lifestyle was associated with a higher risk of overall cancer [lowest vs highest tertile hazard ratio (95% confidence interval) = 1.32(1.26, 1.37)] and eight cancer types. The greatest increased risks were seen for cancers of the lung [3.5(2.96,4.15)], bladder [2.03 (1.57, 2.64)], and pancreas [1.98 (1.54,2.55)]. Positive additive interactions were observed, suggesting a healthy lifestyle may partially offset genetic risk of colorectal, breast, and pancreatic cancers, and may completely offset genetic risk of lung and bladder cancers. A healthy lifestyle is beneficial for most cancers and may offset genetic risk of some cancers. These findings have important implications for those genetically predisposed to these cancers and population strategies for cancer prevention.
Heritability And Genetic Correlations For Hormone-Sensitive Cancers In The UK Biobank: A Molecular Evidence of Shared Aetiology
Publisher: Research Square Platform LLC
Date: 14-10-2021
DOI: 10.21203/RS.3.RS-926833/V1
Abstract: Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. To test this hypothesis, we analysed five hormone-sensitive cancers in the UK Biobank as a single disease. We observed that a significant proportion of variance in disease liability was explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale was estimated as 10.06% (SE 0.70%) for the disease. Moreover, we found 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.
Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank.
Publisher: Wiley
Date: 19-04-2022
DOI: 10.1002/GEPI.22449
Abstract: Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294-91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits-12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs-one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic-covariate and residual-covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.
Fine mapping of multiple interacting quantitative trait loci using combined linkage disequilibrium and linkage information.
Publisher: Zhejiang University Press
Date: 10-2007
Genetic mapping of quantitative trait loci for resistance to Haemonchus contortus in sheep
Publisher: Wiley
Date: 06-2009
DOI: 10.1111/J.1365-2052.2008.01836.X
Abstract: This paper presents results from a mapping experiment to detect quantitative trait loci (QTL) for resistance to Haemonchus contortus infestation in merino sheep. The primary trait analysed was faecal worm egg count in response to artificial challenge at 6 months of age. In the first stage of the experiment, whole genome linkage analysis was used for broad-scale mapping. The animal resource used was a designed flock comprising 571 in iduals from four half-sib families. The average marker spacing was about 20 cM. For the primary trait, 11 QTL (as chromosomal/family combinations) were significant at the 5% chromosome-wide level, with allelic substitution effects of between 0.19 and 0.38 phenotypic standard deviation units. In general, these QTL did not have a significant effect on faecal worm egg count recorded at 13 months of age. In the second stage of the experiment, three promising regions (located on chromosomes 1, 3 and 4) were fine-mapped. This involved typing more closely spaced markers on in iduals from the designed flock as well as an additional 495 in iduals selected from a related population with a deeper pedigree. Analysis was performed using a linkage disequilibrium-linkage approach, under additive, dominant and multiple QTL models. Of these, the multiple QTL model resulted in the most refined QTL positions, with resolutions of <10 cM achieved for two regions. Because of the moderate size of effect of the QTL, and the apparent age and/or immune status specificity of the QTL, it is suggested that a panel of QTL will be required for significant genetic gains to be achieved within industry via marker-assisted selection.
RICOPILI: Rapid Imputation for COnsortias PIpeLIne
Publisher: Oxford University Press (OUP)
Date: 08-08-2020
DOI: 10.1093/BIOINFORMATICS/BTZ633
Abstract: Genome-wide association study (GWAS) analyses, at sufficient s le sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work. RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: /broadinstitute.org/RICOPILI/home Supplementary data are available at Bioinformatics online.
Genome‐wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness
Publisher: Wiley
Date: 11-12-2015
DOI: 10.1002/AJMG.B.32402
Dominance genetic variation contributes little to the missing heritability for human complex traits
Publisher: Elsevier BV
Date: 03-2015
A simple and fast two-locus quality control test to detect false positives due to batch effects in genome-wide association studies
Publisher: Wiley
Date: 22-11-2010
DOI: 10.1002/GEPI.20541
Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship
Publisher: Public Library of Science (PLoS)
Date: 21-12-2017
Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes
Publisher: Springer Science and Business Media LLC
Date: 18-01-2012
Geographical genomics of human leukocyte gene expression variation in southern Morocco
Publisher: Springer Science and Business Media LLC
Date: 06-12-2009
DOI: 10.1038/NG.495
Heterogeneity of genetic architecture of body size traits in a free-living population
Publisher: Wiley
Date: 30-03-2015
DOI: 10.1111/MEC.13146
Contextualizing genetic risk score for disease screening and rare variant discovery
Publisher: Springer Science and Business Media LLC
Date: 20-07-2021
DOI: 10.1038/S41467-021-24387-Z
Abstract: Studies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a s le prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.
Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank
Publisher: Cold Spring Harbor Laboratory
Date: 04-08-2021
DOI: 10.1101/2021.08.02.21261499
Abstract: Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N=61294-91644), we investigate whether the polygenic and residual variation of depressive symptoms are modulated by 25 a-priori selected covariate traits: 12 environmental variables, 5 biomarkers and polygenic risk scores for 8 mental health disorders. MRNMs provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. Of the 25 selected covariates, 11 significantly modulate depressive symptoms, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic score, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably non-zero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% CI [0.54,1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.
Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women
Publisher: American Medical Association (AMA)
Date: 05-2016
Lifestyle modifies the diabetes-related metabolic risk, conditional on individual genetic differences
Publisher: Frontiers Media SA
Date: 09-03-2022
DOI: 10.3389/FGENE.2022.759309
Abstract: Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include in idual self-management of environmental risk factors, such as improving diet quality, increasing physical activity, and reducing smoking and alcohol consumption, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the in idual level. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 16 GxE interactions. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure) and by alcohol and smoking for three (BMI, glucose, waist–hip ratio and BMI and diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on in idual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of in idual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.
A Robust Coin-Cell Design for In Situ Synchrotron-based X-Ray Powder Diffraction Analysis of Battery Materials
Publisher: Wiley
Date: 06-11-2021
Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index
Publisher: Springer Science and Business Media LLC
Date: 31-08-2015
DOI: 10.1038/NG.3390
Building fast and selective Zn ion channels for highly stable quasi-solid-state Zn-ion batteries
Publisher: Royal Society of Chemistry (RSC)
Date: 2023
DOI: 10.1039/D3TA02866F
Lifestyle modifies the diabetes-related metabolic risk, conditional on individual genetic differences
Publisher: Cold Spring Harbor Laboratory
Date: 23-11-2020
DOI: 10.1101/2020.11.22.20236505
Abstract: Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include in idual self-management of environmental risk factors such as improving diet quality, increasing physical activity and reducing smoking and alcohol consumptions, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the in idual level. In this study, we applied a whole-genome genotype-by-environment (GxE) interaction approach to describe how intermediate traits reflecting metabolic risk are affected by genetic variations and how this genetic risk can interact with lifestyle, which can vary, conditional on in idual genetic differences. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 17 GxE interactions, of which four modulated BMI and the others distributed across other traits. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure), and by alcohol and smoking for three (BMI, glucose, waist-hip ratio and BMI, diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on in idual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of in idual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all. This study has been supported by the Australian Research Council (DP 190100766, FT 160100229).
Lifestyle, genetic risk and incidence of cancer: a prospective cohort study of 13 cancer types
Publisher: Oxford University Press (OUP)
Date: 18-01-2023
DOI: 10.1093/IJE/DYAC238
Abstract: Genetic and lifestyle factors are associated with cancer risk. We investigated the benefits of adhering to lifestyle advice by the World Cancer Research Fund (WCRF) with the risk of 13 types of cancer and whether these associations differ according to genetic risk using data from the UK Biobank. In 2006–2010, participants aged 37–73 years had their lifestyle assessed and were followed up for incident cancers until 2015–2019. Analyses were restricted to those of White European ancestry with no prior history of malignant cancer (n = 195 822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined (‘overall cancer’), and a lifestyle index was calculated from WCRF recommendations. Associations with cancer incidence were estimated using Cox regression, adjusting for relevant confounders. Additive and multiplicative interactions between lifestyle index and PRSs were assessed. There were 15 240 incident cancers during the 1 926 987 person-years of follow-up (median follow-up = 10.2 years). After adjusting for confounders, the lifestyle index was associated with a lower risk of overall cancer [hazard ratio per standard deviation increase (95% CI) = 0.89 (0.87, 0.90)] and of eight specific cancer types. There was no evidence of interactions on the multiplicative scale. There was evidence of additive interactions in risks for colorectal, breast, pancreatic, lung and bladder cancers, such that the recommended lifestyle was associated with greater change in absolute risk for persons at higher genetic risk (P & 0.0003 for all). The recommended lifestyle has beneficial associations with most cancers. In terms of absolute risk, the protective association is greater for higher genetic risk groups for some cancers. These findings have important implications for persons most genetically predisposed to those cancers and for targeted strategies for cancer prevention.
Detection of genomic regions underlying resistance to gastrointestinal parasites in Australian sheep
Publisher: Springer Science and Business Media LLC
Date: 03-07-2019
Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes
Publisher: Elsevier BV
Date: 06-2018
Whole‐Genome Approach Discovers Novel Genetic and Nongenetic Variance Components Modulated by Lifestyle for Cardiovascular Health
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 21-04-2020
Abstract: Both genetic and nongenetic factors can predispose in iduals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention. We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For ex le, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified in iduals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance. Lifestyle changes are relevant to cardiovascular disease prevention. In idual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.
Polygenic scores associated with educational attainment in adults predict educational achievement and ADHD symptoms in children
Publisher: Wiley
Date: 05-07-2014
DOI: 10.1002/AJMG.B.32254
Abstract: The American Psychiatric Association estimates that 3 to 7 per cent of all school aged children are diagnosed with attention deficit hyperactivity disorder (ADHD). Even after correcting for general cognitive ability, numerous studies report a negative association between ADHD and educational achievement. With polygenic scores we examined whether genetic variants that have a positive influence on educational attainment have a protective effect against ADHD. The effect sizes from a large GWA meta-analysis of educational attainment in adults were used to calculate polygenic scores in an independent s le of 12-year-old children from the Netherlands Twin Register. Linear mixed models showed that the polygenic scores significantly predicted educational achievement, school performance, ADHD symptoms and attention problems in children. These results confirm the genetic overlap between ADHD and educational achievement, indicating that one way to gain insight into genetic variants responsible for variation in ADHD is to include data on educational achievement, which are available at a larger scale.
Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis
Publisher: Springer Science and Business Media LLC
Date: 02-11-2015
DOI: 10.1038/NG.3431
Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood.
Publisher: Oxford University Press (OUP)
Date: 26-07-2012
DOI: 10.1093/BIOINFORMATICS/BTS474
Abstract: Summary: Genetic correlations are the genome-wide aggregate effects of causal variants affecting multiple traits. Traditionally, genetic correlations between complex traits are estimated from pedigree studies, but such estimates can be confounded by shared environmental factors. Moreover, for diseases, low prevalence rates imply that even if the true genetic correlation between disorders was high, co-aggregation of disorders in families might not occur or could not be distinguished from chance. We have developed and implemented statistical methods based on linear mixed models to obtain unbiased estimates of the genetic correlation between pairs of quantitative traits or pairs of binary traits of complex diseases using population-based case–control studies with genome-wide single-nucleotide polymorphism data. The method is validated in a simulation study and applied to estimate genetic correlation between various diseases from Wellcome Trust Case Control Consortium data in a series of bivariate analyses. We estimate a significant positive genetic correlation between risk of Type 2 diabetes and hypertension of ~0.31 (SE 0.14, P = 0.024). Availability: Our methods, appropriate for both quantitative and binary traits, are implemented in the freely available software GCTA (oftware/gcta/reml_bivar.html). Contact: hong.lee@uq.edu.au Supplementary Information: Supplementary data are available at Bioinformatics online.
Biological insights from 108 schizophrenia-associated genetic loci
Publisher: Springer Science and Business Media LLC
Date: 07-2014
DOI: 10.1038/NATURE13595
The genetic relationship between female reproductive traits and six psychiatric disorders
Publisher: Cold Spring Harbor Laboratory
Date: 03-10-2018
DOI: 10.1101/433946
Abstract: Female reproductive behaviors have an important implication in evolutionary fitness and health of offspring. Previous studies have shown that age at first birth of women (AFB) is genetically associated with schizophrenia (SCZ). However, for most other psychiatric disorders and reproductive traits, the latent shared genetic architecture is largely unknown. Here we used the second wave of UK Biobank data (N=220,685) to evaluate the association between five female reproductive traits and polygenetic risk scores (PRS) projected from genome-wide association study summary statistics of six psychiatric disorders (N=429,178). We found that the PRS of attention-deficit/hyperactivity disorder (ADHD) were strongly associated with AFB (genetic correlation of −0.68 ± 0.03 with p-value = 1.86E-89), age at first sexual intercourse (AFS) (−0.56 ± 0.03 with p-value = 3.42E-60), number of live births (NLB) (0.36 ± 0.04 with p-value = 4.01E-17) and age at menopause (−0.27 ± 0.04 with p-value = 5.71E-13). There were also robustly significant associations between the PRS of eating disorder (ED) and AFB (genetic correlation of 0.35 ± 0.06), ED and AFS (0.19 0.06), Major depressive disorder (MDD) and AFB (−0.27 ± 0.07), MDD and AFS (− 0.27 ± 0.03) and SCZ and AFS (−0.10 ± 0.03). Our findings reveal the shared genetic architecture between the five reproductive traits in women and six psychiatric disorders, which have a potential implication that helps to improve reproductive health in women, hence better child outcomes. Our findings may also explain, at least in part, an evolutionary hypothesis that causal mutations underlying psychiatric disorders have positive effects on reproductive success.
Genome-wide association analysis identifies 13 new risk loci for schizophrenia
Publisher: Springer Science and Business Media LLC
Date: 25-08-2013
DOI: 10.1038/NG.2742
Three-dimensional nitrogen-doped graphene hydrogels prepared via hydrothermal synthesis as high-performance supercapacitor materials
Publisher: Elsevier BV
Date: 03-2016
Correction: Novel Genetic Analysis for Case-Control Genome-Wide Association Studies: Quantification of Power and Genomic Prediction Accuracy
Publisher: Public Library of Science (PLoS)
Date: 28-08-2013
DOI: 10.1371/ANNOTATION/0B29C9C7-A86D-4E0F-BBB8-5C29B16E2884
3D-Printed Wearable Electrochemical Energy Devices
Publisher: Wiley
Date: 29-07-2022
Abstract: Emerging markets for wearable electronics have stimulated a rapidly growing demand for the commercialization of flexible and reliable energy storage and conversion units (including batteries, supercapacitors, and thermoelectrochemical cells). 3D printing, a rapidly growing suite of fabrication technologies, is extensively used in the above‐mentioned energy‐related areas owing to its relatively low cost, freedom of design, and controllable, reproducible prototyping capability. However, there remain challenges in processable ink formulation and accurate material/device design. By summarizing the recent progress in 3D‐printed wearable electrochemical energy devices and discussing the current limitations and future perspectives, this article is expected to serve as a reference for the scalable fabrication of advanced energy systems via 3D printing.
Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture
Publisher: Springer Science and Business Media LLC
Date: 07-04-2013
DOI: 10.1038/NG.2606
Harnessing Plasma-Assisted Doping Engineering to Stabilize Metallic Phase MoSe2 for Fast and Durable Sodium-Ion Storage
Publisher: Wiley
Date: 03-2022
Abstract: Metallic‐phase selenide molybdenum (1T‐MoSe 2 ) has become a rising star for sodium storage in comparison with its semiconductor phase (2H‐MoSe 2 ) owing to the intrinsic metallic electronic conductivity and unimpeded Na + diffusion structure. However, the thermodynamically unstable nature of 1T phase renders it an unprecedented challenge to realize its phase control and stabilization. Herein, a plasma‐assisted P‐doping‐triggered phase‐transition engineering is proposed to synthesize stabilized P‐doped 1T phase MoSe 2 nanoflower composites (P‐1T‐MoSe 2 NFs). Mechanism analysis reveals significantly decreased phase‐transition energy barriers of the plasma‐induced Se‐vacancy‐rich MoSe 2 from 2H to 1T owing to its low crystallinity and reduced structure stability. The vacancy‐rich structure promotes highly concentrated P doping, which manipulates the electronic structure of the MoSe 2 and urges its phase transition, acquiring a high transition efficiency of 91% accompanied with ultrahigh phase stability. As a result, the P‐1T‐MoSe 2 NFs deliver an exceptional high reversible capacity of 510.8 mAh g −1 at 50 mA g −1 with no capacity fading over 1000 cycles at 5000 mA g −1 for sodium storage. The underlying mechanism of this phase‐transition engineering verified by profound analysis provides informative guide for designing advanced materials for next‐generation energy‐storage systems.
A genetic investigation of sex bias in the prevalence of attention deficit hyperactivity disorder
Publisher: Cold Spring Harbor Laboratory
Date: 23-06-2017
DOI: 10.1101/154088
Abstract: Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is 2-7 times more common in males than females. We examined two putative genetic mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases. We analyzed genome-wide common variants from the Psychiatric Genomics Consortium and iPSYCH Project (20,183 cases, 35,191 controls) and Swedish population-registry data (N=77,905 cases, N=1,874,637 population controls). We find strong genetic correlation for ADHD across sex and no mean difference in polygenic burden across sex. In contrast, siblings of female probands are at an increased risk of ADHD, compared to siblings of male probands. The results also suggest that females with ADHD are at especially high risk of comorbid developmental conditions. Overall, this study supports a greater familial burden of risk in females with ADHD and some clinical and etiological heterogeneity. However, autosomal common variants largely do not explain the sex bias in ADHD prevalence.
Crystallographic‐Site‐Specific Structural Engineering Enables Extraordinary Electrochemical Performance of High‐Voltage LiNi0.5Mn1.5O4 Spinel Cathodes for Lithium‐Ion
Publisher: Wiley
Date: 04-09-2021
Abstract: The development of reliable and safe high‐energy‐density lithium‐ion batteries is hindered by the structural instability of cathode materials during cycling, arising as a result of detrimental phase transformations occurring at high operating voltages alongside the loss of active materials induced by transition metal dissolution. Originating from the fundamental structure/function relation of battery materials, the authors purposefully perform crystallographic‐site‐specific structural engineering on electrode material structure, using the high‐voltage LiNi 0.5 Mn 1.5 O 4 (LNMO) cathode as a representative, which directly addresses the root source of structural instability of the Fd m structure. By employing Sb as a dopant to modify the specific issue‐involved 16 c and 16 d sites simultaneously, the authors successfully transform the detrimental two‐phase reaction occurring at high‐voltage into a preferential solid‐solution reaction and significantly suppress the loss of Mn from the LNMO structure. The modified LNMO material delivers an impressive 99% of its theoretical specific capacity at 1 C, and maintains 87.6% and 72.4% of initial capacity after 1500 and 3000 cycles, respectively. The issue‐tracing site‐specific structural tailoring demonstrated for this material will facilitate the rapid development of high‐energy‐density materials for lithium‐ion batteries.
CORE GREML: Estimating covariance between random effects in linear mixed models for genomic analyses of complex traits
Publisher: Cold Spring Harbor Laboratory
Date: 25-11-2019
DOI: 10.1101/853515
Abstract: Linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) are a key variance partitioning tool, where effects of multiple sources, such as different functional genomic regions, on phenotypes are treated as random. Classic LMMs assume independence between random effects, which can cause biased estimation of variance components. Here, we relax this independence assumption by introducing a generalised GREML, called CORE GREML, that can explicitly estimate the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing unbiased estimates of variance and covariance components. Using data from the UK biobank, we demonstrate that CORE GREML is useful for genomic partitioning analyses and for genome-transcriptome partitioning of phenotypic variance. For ex le, we found that the transcriptome, imputed using genotype data, explained a significant proportion of phenotypic variance for height (0.15, se = 5.4e-3, p -value = 1.5e-283), and that these transcriptomic effects on phenotypes correlated with effects of the genome (r = 0.35, se = 4.6e-2, p -value = 1.2e-14). We conclude that the covariance between random effects is a key parameter that needs to be estimated, especially when partitioning phenotypic variance by omic layer.
Educational attainment: A genome wide association study in 9538 Australians
Publisher: Public Library of Science (PLoS)
Date: 09-06-2011
Improving genetic prediction by leveraging genetic correlations among human diseases and traits
Publisher: Springer Science and Business Media LLC
Date: 07-03-2018
DOI: 10.1038/S41467-017-02769-6
Abstract: Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.
GWAS of 126,559 individuals identifies genetic variants associated with educational attainment
Publisher: American Association for the Advancement of Science (AAAS)
Date: 21-06-2013
Abstract: Many genomic elements in humans are associated with behavior, including educational attainment. In a genome-wide association study including more than 100,000 s les, Rietveld et al. (p. 1467 , published online 30 May see the Perspective by Flint and Munafò ) looked for genes related to educational attainment in Caucasians. Small genetic effects at three loci appeared to impact educational attainment.
Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index
Publisher: Public Library of Science (PLoS)
Date: 31-07-2014
Studying the Conversion Mechanism to Broaden Cathode Options in Aqueous Zinc‐Ion Batteries
Publisher: Wiley
Date: 20-10-2021
Abstract: Aqueous Zn‐ion batteries (ZIBs) are regarded as alternatives to Li‐ion batteries benefiting from both improved safety and environmental impact. The widespread application of ZIBs, however, is compromised by the lack of high‐performance cathodes. Currently, only the intercalation mechanism is widely reported in aqueous ZIBs, which significantly limits cathode options. Beyond Zn‐ion intercalation, we comprehensively study the conversion mechanism for Zn 2+ storage and its diffusion pathway in a CuI cathode, indicating that CuI occurs a direct conversion reaction without Zn 2+ intercalation due to the high energy barrier for Zn 2+ intercalation and migration. Importantly, this direct conversion reaction mechanism can be readily generalized to other high‐capacity cathodes, such as Cu 2 S (336.7 mA h g −1 ) and Cu 2 O (374.5 mA h g −1 ), indicating its practical universality. Our work enriches the Zn‐ion storage mechanism and significantly broadens the cathode horizons towards next‐generation ZIBs.
Genetic polymorphisms of the bovine Fatty acid binding protein 4 gene are significantly associated with marbling and carcass weight in Hanwoo (Korean Cattle)
Publisher: Wiley
Date: 03-2010
DOI: 10.1111/J.1365-2052.2010.02024.X
Abstract: The objective of this study was to investigate an association between polymorphisms in the FABP4 gene and phenotypic variation for marbling and carcass weight (CWT) in a population of Hanwoo steers. We re-sequenced 4.3 kb of the FABP4 gene region in 24 Hanwoo bulls and identified 16 SNPs and 1 microsatellite polymorphism. Of these 16 SNPs, three SNPs [g.2774G>C (intron I), g.3473A>T (intron II) and g.3631G>A (exon III, creating a p.Met >Val amino acid substitution)] were genotyped in 583 steers to assess their association with carcass traits. The g.3473A allele showed a significant increasing effect on CWT (P = 0.01) and the g.3631G allele was associated with higher marbling score (P = 0.006). One haplotype of these three SNPs (CAG) was significantly associated with CWT (P = 0.02) and marbling score (P = 0.05) and could potentially be of value for marker assisted selection in Hanwoo cattle. The CAG haplotype effect for CWT was larger (11.14 +/- 5.03 kg) than the largest single locus effect of g.3473A>T (5.01 +/- 2.2 kg).
Heterostructure Manipulation via in Situ Localized Phase Transformation for High-Rate and Highly Durable Lithium Ion Storage
Publisher: American Chemical Society (ACS)
Date: 25-09-2018
MnO2-introduced-tunnels strategy for the preparation of nanotunnel inserted hierarchical-porous carbon as electrode material for high-performance supercapacitors
Publisher: Elsevier BV
Date: 07-2017
The impact of genomic and traditional selection on the contribution of mutational variance to long-term selection response and genetic variance
Publisher: Oxford University Press (OUP)
Date: 10-2019
DOI: 10.1534/GENETICS.119.302336
Abstract: De novo mutations (DNM) create new genetic variance and are an important driver for long-term selection response. We hypothesized that genomic selection exploits mutational variance less than traditional selection methods such as mass selection or selection on pedigree-based breeding values, because DNM in selection candidates are not captured when the selection candidates’ own phenotype is not used in genomic selection, DNM are not on SNP chips and DNM are not in linkage disequilibrium with the SNP on the chip. We tested this hypothesis with Monte Carlo simulation. From whole-genome sequence data, a subset of ∼300,000 variants was used that served as putative markers, quantitative trait loci or DNM. We simulated 20 generations with truncation selection based on breeding values from genomic best linear unbiased prediction without (GBLUP_no_OP) or with own phenotype (GBLUP_OP), pedigree-based BLUP without (BLUP_no_OP) or with own phenotype (BLUP_OP), or directly on phenotype. GBLUP_OP was the best strategy in exploiting mutational variance, while GBLUP_no_OP and BLUP_no_OP were the worst in exploiting mutational variance. The crucial element is that GBLUP_no_OP and BLUP_no_OP puts no selection pressure on DNM in selection candidates. Genetic variance decreased faster with GBLUP_no_OP and GBLUP_OP than with BLUP_no_OP, BLUP_OP or mass selection. The distribution of mutational effects, mutational variance, number of DNM per in idual and nonadditivity had a large impact on mutational selection response and mutational genetic variance, but not on ranking of selection strategies. We advocate that more sustainable genomic selection strategies are required to optimize long-term selection response and to maintain genetic ersity.
A polygenic resilience score moderates the genetic risk for schizophrenia
Publisher: Springer Science and Business Media LLC
Date: 06-09-2019
DOI: 10.1038/S41380-019-0463-8
Abstract: Based on the discovery by the Resilience Project (Chen R. et al. Nat Biotechnol 34:531–538, 2016) of rare variants that confer resistance to Mendelian disease, and protective alleles for some complex diseases, we posited the existence of genetic variants that promote resilience to highly heritable polygenic disorders1,0 such as schizophrenia. Resilience has been traditionally viewed as a psychological construct, although our use of the term resilience refers to a different construct that directly relates to the Resilience Project, namely: heritable variation that promotes resistance to disease by reducing the penetrance of risk loci, wherein resilience and risk loci operate orthogonal to one another. In this study, we established a procedure to identify unaffected in iduals with relatively high polygenic risk for schizophrenia, and contrasted them with risk-matched schizophrenia cases to generate the first known “polygenic resilience score” that represents the additive contributions to SZ resistance by variants that are distinct from risk loci. The resilience score was derived from data compiled by the Psychiatric Genomics Consortium, and replicated in three independent s les. This work establishes a generalizable framework for finding resilience variants for any complex, heritable disorder.
Body mass index and complications following major gastrointestinal surgery: a prospective, international cohort study and meta‐analysis
Publisher: Wiley
Date: 09-07-2018
DOI: 10.1111/CODI.14292
Abstract: Previous studies reported conflicting evidence on the effects of obesity on outcomes after gastrointestinal surgery. The aims of this study were to explore the relationship of obesity with major postoperative complications in an international cohort and to present a meta-analysis of all available prospective data. This prospective, multicentre study included adults undergoing both elective and emergency gastrointestinal resection, reversal of stoma or formation of stoma. The primary end-point was 30-day major complications (Clavien-Dindo Grades III-V). A systematic search was undertaken for studies assessing the relationship between obesity and major complications after gastrointestinal surgery. In idual patient meta-analysis was used to analyse pooled results. This study included 2519 patients across 127 centres, of whom 560 (22.2%) were obese. Unadjusted major complication rates were lower in obese vs normal weight patients (13.0% vs 16.2%, respectively), but this did not reach statistical significance (P = 0.863) on multivariate analysis for patients having surgery for either malignant or benign conditions. In idual patient meta-analysis demonstrated that obese patients undergoing surgery for malignancy were at increased risk of major complications (OR 2.10, 95% CI 1.49-2.96, P < 0.001), whereas obese patients undergoing surgery for benign indications were at decreased risk (OR 0.59, 95% CI 0.46-0.75, P < 0.001) compared to normal weight patients. In our international data, obesity was not found to be associated with major complications following gastrointestinal surgery. Meta-analysis of available prospective data made a novel finding of obesity being associated with different outcomes depending on whether patients were undergoing surgery for benign or malignant disease.
GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data
Publisher: Springer Science and Business Media LLC
Date: 21-06-2021
DOI: 10.1186/S13059-021-02403-1
Abstract: Genetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 in iduals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.
Genetic studies of body mass index yield new insights for obesity biology
Publisher: Springer Science and Business Media LLC
Date: 11-02-2015
DOI: 10.1038/NATURE14177
Designing Dendrite-Free Zinc Anodes for Advanced Aqueous Zinc Batteries
Publisher: Wiley
Date: 05-06-2020
Detecting genotype-population interaction effects by ancestry principal components
Publisher: Cold Spring Harbor Laboratory
Date: 30-07-2019
DOI: 10.1101/719948
Abstract: Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups of population stratified by different countries or cohorts, which ignored the difference of population characteristics for the in iduals within each group and resulted in loss of information. Here we introduce a novel concept of genotype-by-population (G×P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G×P interactions across erse populations including white British and other white Europeans from the UK Biobank ( N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioural traits such as age first had sexual intercourse and qualifications. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations.
GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data
Publisher: Cold Spring Harbor Laboratory
Date: 06-2020
DOI: 10.1101/2020.05.31.122549
Abstract: Genetic variation in response to the environment is fundamental in the biology of complex traits and diseases, i.e. genotype-by-environment interaction (GxE). However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 in iduals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.
Bio-inspired design of anin situmultifunctional polymeric solid–electrolyte interphase for Zn metal anode cycling at 30 mA cm−2and 30 mA h cm−2
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1EE01851E
Abstract: We report a bio-inspired design strategy for constructing an in situ polymeric SEI in aqueous Zn chemistry. This SEI can restrain interfacial side reactions, facilitate a uniform Zn 2+ flux, and consequently endow a highly stable Zn metal anode.
A better coefficient of determination for genetic profile analysis.
Publisher: Wiley
Date: 04-2012
DOI: 10.1002/GEPI.21614
Abstract: Genome-wide association studies have facilitated the construction of risk predictors for disease from multiple Single Nucleotide Polymorphism markers. The ability of such "genetic profiles" to predict outcome is usually quantified in an independent data set. Coefficients of determination (R(2) ) have been a useful measure to quantify the goodness-of-fit of the genetic profile. Various pseudo-R(2) measures for binary responses have been proposed. However, there is no standard or consensus measure because the concept of residual variance is not easily defined on the observed probability scale. Unlike other nongenetic predictors such as environmental exposure, there is prior information on genetic predictors because for most traits there are estimates of the proportion of variation in risk in the population due to all genetic factors, the heritability. It is this useful ability to benchmark that makes the choice of a measure of goodness-of-fit in genetic profiling different from that of nongenetic predictors. In this study, we use a liability threshold model to establish the relationship between the observed probability scale and underlying liability scale in measuring R(2) for binary responses. We show that currently used R(2) measures are difficult to interpret, biased by ascertainment, and not comparable to heritability. We suggest a novel and globally standard measure of R(2) that is interpretable on the liability scale. Furthermore, even when using ascertained case-control studies that are typical in human disease studies, we can obtain an R(2) measure on the liability scale that can be compared directly to heritability.
Intrinsically Stretchable Electroluminescent Elastomers with Self‐Confinement Effect for Highly Efficient Non‐Blended Stretchable OLEDs
Publisher: Wiley
Date: 07-12-2022
Abstract: Ultra‐flexible stretchable organic light‐emitting diodes (OLEDs) are emerging as a basic component of flexible electronics and human‐machine interfaces. However, the brightness and efficiency of stretchable OLEDs remain still far inferior to their rigid counterparts, owing to the scarcity of satisfactory stretchable electroluminescent materials. Herein, we explore a general concept based on the self‐confinement effect to dramatically improve the stretchability of elastomers, without affecting electroluminescent properties. The balanced rigid/flexible chain dynamics under self‐confinement significantly reduces the modulus of the elastomers, resulting in the maximum strain reaching 806 %. Ultra‐flexible stretchable OLEDs have been constructed based on the resulting ISEEs, achieving unprecedented high‐performance non‐blended stretchable OLEDs. The results suggest an effective molecular design strategy for highly deformable stretchable displays and flexible electronics.
Complement genes contribute sex-biased vulnerability in diverse disorders
Publisher: Springer Science and Business Media LLC
Date: 11-05-2020
Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders
Publisher: Elsevier BV
Date: 2022
Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs
Publisher: Springer Science and Business Media LLC
Date: 19-02-2012
DOI: 10.1038/NG.1108
Preparation of Lithium Titanate/Reduced Graphene Oxide Composites with Three-Dimensional “Fishnet-Like” Conductive Structure via a Gas-Foaming Method for High-Rate Lithium-Ion Batteries
Publisher: American Chemical Society (ACS)
Date: 28-11-2017
Abstract: With use of ammonium chloride (NH
EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations.
Publisher: Springer Science and Business Media LLC
Date: 04-05-2016
DOI: 10.1038/HDY.2016.25
Combining the meiosis Gibbs sampler with the random walk approach for linkage and association studies with a general complex pedigree and multimarker loci
Publisher: Oxford University Press (OUP)
Date: 12-2005
DOI: 10.1534/GENETICS.104.037028
Abstract: A linkage analysis for finding inheritance states and haplotype configurations is an essential process for linkage and association mapping. The linkage analysis is routinely based upon observed pedigree information and marker genotypes for in iduals in the pedigree. It is not feasible for exact methods to use all such information for a large complex pedigree especially when there are many missing genotypic data. Proposed Markov chain Monte Carlo approaches such as a single-site Gibbs s ler or the meiosis Gibbs s ler are able to handle a complex pedigree with sparse genotypic data however, they often have reducibility problems, causing biased estimates. We present a combined method, applying the random walk approach to the reducible sites in the meiosis s ler. Therefore, one can efficiently obtain reliable estimates such as identity-by-descent coefficients between in iduals based on inheritance states or haplotype configurations, and a wider range of data can be used for mapping of quantitative trait loci within a reasonable time.
Achieving High-Performance Metal Phosphide Anode for Potassium Ion Batteries via Concentrated Electrolyte Chemistry
Publisher: Wiley
Date: 31-12-2021
Detecting Genotype-Population Interaction Effects by Ancestry Principal Components
Publisher: Frontiers Media SA
Date: 21-04-2020
Evidence for multiple alleles effecting muscling and fatness at the Ovine GDF8 locus
Publisher: Springer Science and Business Media LLC
Date: 08-11-2007
Heterostructure Manipulation toward Ameliorating Electrodes for Better Lithium Storage Capability
Publisher: American Chemical Society (ACS)
Date: 16-10-2018
Recent progress and perspectives on aqueous Zn-based rechargeable batteries with mild aqueous electrolytes
Publisher: Elsevier BV
Date: 07-2019
Dual‐Function Electrolyte Additive for Highly Reversible Zn Anode
Publisher: Wiley
Date: 25-08-2021
Abstract: Practical application of aqueous Zn‐ion batteries (AZIBs) is significantly limited by poor reversibility of the Zn anode. This is because of 1) dendrite growth, and 2) water‐induced parasitic reactions including hydrogen evolution, during cycling. Here for the first time an elegantly simple method is reported that introduces ethylene diamine tetraacetic acid tetrasodium salt (Na 4 EDTA) to a ZnSO 4 electrolyte. This is shown to concomitantly suppress dendritic Zn deposition and H 2 evolution. Findings confirm that EDTA anions are adsorbed on the Zn surface and dominate active sites for H 2 generation and inhibit water electrolysis. Additionally, adsorbed EDTA promotes desolvation of Zn(H 2 O) 6 2+ by removing H 2 O molecules from the solvation sheath of Zn 2+ . Side reactions and dendrite growth are therefore suppressed by using the additive. A high Zn reversibility with Coulombic efficiency (CE) of 99.5% and long lifespan of 2500 cycles at 5 mAh cm −2 , 2 mAh cm −2 is demonstrated. Additionally, the highly reversible Zn electrode significantly boosts overall performance of VO 2 //Zn full‐cells. These findings are expected to be of immediate benefit to a range of researchers in using dual‐function additives to suppress Zn dendrite and parasitic reactions for electrochemistry and energy storage applications.
Kaposi Sarcoma Risk in HIV-Infected Children and Adolescents on Combination Antiretroviral Therapy From Sub-Saharan Africa, Europe, and Asia
Publisher: Oxford University Press (OUP)
Date: 30-08-2016
DOI: 10.1093/CID/CIW519
Estimation of SNP heritability from dense genotype data
Publisher: Elsevier BV
Date: 12-2013
Surface engineering enables highly reversible lithium-ion storage and durable structure for advanced silicon anode
Publisher: Elsevier BV
Date: 07-2021
Significance tests for R2 of out-of-sample prediction using polygenic scores
Publisher: Cold Spring Harbor Laboratory
Date: 10-06-2022
DOI: 10.1101/2022.06.08.495250
Abstract: The coefficient of determination ( R 2 ) is a well-established measure to indicate the predictive ability of polygenic scores (PGS). However, the s ling variance of R 2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGS based on different discovery s les, the s ling covariance of R 2 is necessary to test the difference between them. Here, we show how to estimate the variance and covariance of R 2 values to assess the 95% CI and p-value of the R 2 difference. We apply this approach to real data to predict into 28,880 European participants using UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGS compared to BBJ PGS (p-value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGS significantly improves the predictive ability, compared to a model of UKBB PGS only (p-value 3.5e-05 for cholesterol and 1.3e-28 for BMI). The proposed approach can also be applied to testing a significant difference between R 2 values across different p-value thresholds. We also show that the predictive ability of regulatory SNPs is significantly enriched than non-regulatory SNPs for cholesterol (p-value 2.6e-19 for UKBB and 8.7e-08 for BBJ). We suggest that the proposed approach (available in R package ‘r2redux’) should be used to test the statistical significance of difference between pairs of PGS, which may help to draw a correct conclusion about the predictive ability of PGS.
Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples
Publisher: Public Library of Science (PLoS)
Date: 10-04-2014
Across-cohort QC analyses of GWAS summary statistics from complex traits
Publisher: Springer Science and Business Media LLC
Date: 24-08-2017
Simultaneous fine mapping of closely linked epistatic quantitative trait loci using combined linkage disequilibrium and linkage with a general pedigree
Publisher: Springer Science and Business Media LLC
Date: 10-04-2008
DOI: 10.1051/GSE:2008002
Additive Genetic Variation in Schizophrenia Risk Is Shared by Populations of African and European Descent
Publisher: Elsevier BV
Date: 09-2013
Boosting the energy density of supercapacitors by designing both hollow NiO nanoparticles/nitrogen-doped carbon cathode and nitrogen-doped carbon anode from the same precursor
Publisher: Elsevier BV
Date: 03-2022
A novel method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data
Publisher: Cold Spring Harbor Laboratory
Date: 20-09-2021
DOI: 10.1101/2021.09.16.460619
Abstract: Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups for a complex trait. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is erse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific causal effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to six anthropometric traits from the UK Biobank data across 5 ancestry groups. One of our findings is that for obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestry groups.
Phenotypic variance partitioning by transcriptomic gene expression levels and environmental variables for anthropometric traits using GTEx data
Publisher: Wiley
Date: 15-06-2023
DOI: 10.1002/GEPI.22531
Abstract: Phenotypic variation in human is the results of genetic variation and environmental influences. Understanding the contribution of genetic and environmental components to phenotypic variation is of great interest. The variance explained by genome‐wide single nucleotide polymorphisms (SNPs) typically represents a small proportion of the phenotypic variance for complex traits, which may be because the genome is only a part of the whole biological process to shape the phenotypes. In this study, we propose to partition the phenotypic variance of three anthropometric traits, using gene expression levels and environmental variables from GTEx data. We use the gene expression of four tissues that are deemed relevant for the anthropometric traits (two adipose tissues, skeletal muscle tissue and blood tissue). Additionally, we estimate the transcriptome–environment correlation that partly underlies the phenotypes of the anthropometric traits. We found that genetic factors play a significant role in determining body mass index (BMI), with the proportion of phenotypic variance explained by gene expression levels of visceral adipose tissue being 0.68 (SE = 0.06). However, we also observed that environmental factors such as age, sex, ancestry, smoking status, and drinking alcohol status have a small but significant impact (0.005, SE = 0.001). Interestingly, we found a significant negative correlation between the transcriptomic and environmental effects on BMI (transcriptome–environment correlation = −0.54, SE = 0.14), suggesting an antagonistic relationship. This implies that in iduals with lower genetic profiles may be more susceptible to the effects of environmental factors on BMI, while those with higher genetic profiles may be less susceptible. We also show that the estimated transcriptomic variance varies across tissues, e.g., the gene expression levels of whole blood tissue and environmental variables explain a lower proportion of BMI phenotypic variance (0.16, SE = 0.05 and 0.04, SE = 0.004 respectively). We observed a significant positive correlation between transcriptomic and environmental effects (1.21, SE = 0.23) for this tissue. In conclusion, phenotypic variance partitioning can be done using gene expression and environmental data even with a small s le size ( n = 838 from GTEx data), which can provide insights into how the transcriptomic and environmental effects contribute to the phenotypes of the anthropometric traits.
Population‐based identity‐by‐descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia
Publisher: Wiley
Date: 23-02-2019
DOI: 10.1002/AJMG.B.32716
Electrolyte Design for In Situ Construction of Highly Zn2+-Conductive Solid Electrolyte Interphase to Enable High-Performance Aqueous Zn-Ion Batteries under Practical Conditions
Publisher: Wiley
Date: 12-02-2021
First Principles Study on the Electronic Structure and Interface Stability of Hybrid Silicene/Fluorosilicene Nanoribbons
Publisher: Springer Science and Business Media LLC
Date: 26-10-2015
DOI: 10.1038/SREP15734
Abstract: The interface stability of hybrid silicene/fluorosilicene nanoribbons (SFNRs) has been investigated by using density functional theory calculations, where fluorosilicene is the fully fluorinated silicene. It is found that the diffusion of F atoms at the zigzag and armchair interfaces of SFNRs is endothermic and the corresponding minimum energy barriers are respectively 1.66 and 1.56 eV, which are remarkably higher than the minimum diffusion energy barrier of one F atom and two F atoms on pristine silicene 1.00 and 1.29 eV, respectively. Therefore, the thermal stability of SFNRs can be significantly enhanced by increasing the F diffusion barriers through silicene/fluorosilicene interface engineering. In addition, the electronic and magnetic properties of SFNRs are also investigated. It is found that the armchair SFNRs are nonmagnetic semiconductors and the band gap of armchair SFNRs presents oscillatory behavior when the width of silicene part changing. For the zigzag SFNRs, the antiferromagnetic semiconducting state is the most stable one. This work provides fundamental insights for the applications of SFNRs in electronic devices.
Estimating missing heritability for disease from genome-wide association studies.
Publisher: Elsevier BV
Date: 03-2011
Studying the Conversion Mechanism to Broaden Cathode Options in Aqueous Zinc‐Ion Batteries
Publisher: Wiley
Date: 21-10-2021
Abstract: Aqueous Zn‐ion batteries (ZIBs) are regarded as alternatives to Li‐ion batteries benefiting from both improved safety and environmental impact. The widespread application of ZIBs, however, is compromised by the lack of high‐performance cathodes. Currently, only the intercalation mechanism is widely reported in aqueous ZIBs, which significantly limits cathode options. Beyond Zn‐ion intercalation, we comprehensively study the conversion mechanism for Zn 2+ storage and its diffusion pathway in a CuI cathode, indicating that CuI occurs a direct conversion reaction without Zn 2+ intercalation due to the high energy barrier for Zn 2+ intercalation and migration. Importantly, this direct conversion reaction mechanism can be readily generalized to other high‐capacity cathodes, such as Cu 2 S (336.7 mA h g −1 ) and Cu 2 O (374.5 mA h g −1 ), indicating its practical universality. Our work enriches the Zn‐ion storage mechanism and significantly broadens the cathode horizons towards next‐generation ZIBs.
The role of pedigree information in combined linkage disequilibrium and linkage mapping of quantitative trait loci in a general complex pedigree
Publisher: Oxford University Press (OUP)
Date: 2005
DOI: 10.1534/GENETICS.104.033233
Abstract: Combined linkage disequilibrium and linkage (LDL) mapping can exploit historical as well as recent and observed recombinations in a recorded pedigree. We investigated the role of pedigree information in LDL mapping and the performance of LDL mapping in general complex pedigrees. We compared using complete and incomplete genotypic data, spanning 5 or 10 generations of known pedigree, and we used bi- or multiallelic markers that were positioned at 1- or 5-cM intervals. Analyses carried out with or without pedigree information were compared. Results were compared with linkage mapping in some of the data sets. Linkage mapping or LDL mapping with sparse marker spacing (∼5 cM) gave a poorer mapping resolution without considering pedigree information compared to that with considering pedigree information. The difference was bigger in a pedigree of more generations. However, LDL mapping with closely linked markers (∼1 cM) gave a much higher mapping resolution regardless of using pedigree information. This study shows that when marker spacing is dense and there is considerable linkage disequilibrium generated from historical recombinations between flanking markers and QTL, the loss of power due to ignoring pedigree information is negligible and mapping resolution is very high.
Genetic correlation between amyotrophic lateral sclerosis and schizophrenia
Publisher: Springer Science and Business Media LLC
Date: 21-03-2017
DOI: 10.1038/NCOMMS14774
Abstract: We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique in iduals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05–21.6 P =1 × 10 −4 ) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS ( P =8.4 × 10 −7 ). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08–1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.
An opportunity for primary prevention research in psychotic disorders
Publisher: Elsevier BV
Date: 05-2022
QTL and gene expression analyses identify genes affecting carcass weight and marbling on BTA14 in Hanwoo (Korean Cattle)
Publisher: Springer Science and Business Media LLC
Date: 30-07-2011
DOI: 10.1007/S00335-011-9331-9
Abstract: Causal mutations affecting quantitative trait variation can be good targets for marker-assisted selection for carcass traits in beef cattle. In this study, linkage and linkage disequilibrium analysis (LDLA) for four carcass traits was undertaken using 19 markers on bovine chromosome 14. The LDLA analysis detected quantitative trait loci (QTL) for carcass weight (CWT) and eye muscle area (EMA) at the same position at around 50 cM and surrounded by the markers FABP4SNP2774C>G and FABP4_μsat3237. The QTL for marbling (MAR) was identified at the midpoint of markers BMS4513 and RM137 in a 3.5-cM marker interval. The most likely position for a second QTL for CWT was found at the midpoint of tenth marker bracket (FABP4SNP2774C>G and FABP4_μsat3237). For this marker bracket, the total number of haplotypes was 34 with a most common frequency of 0.118. Effects of haplotypes on CWT varied from a -5-kg deviation for haplotype 6 to +8 kg for haplotype 23. To determine which genes contribute to the QTL effect, gene expression analysis was performed in muscle for a wide range of phenotypes. The results demonstrate that two genes, LOC781182 (p = 0.002) and TRPS1 (p = 0.006) were upregulated with increasing CWT and EMA, whereas only LOC614744 (p = 0.04) has a significant effect on intramuscular fat (IMF) content. Two genetic markers detected in FABP4 were the most likely QTL position in this QTL study, but FABP4 did not show a significant effect on both traits (CWT and EMA) in gene expression analysis. We conclude that three genes could be potential causal genes affecting carcass traits CWT, EMA, and IMF in Hanwoo.
Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives
Publisher: Springer Science and Business Media LLC
Date: 17-06-2020
DOI: 10.1038/S41467-020-16829-X
Abstract: Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target in iduals, typically using unrelated in iduals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 in iduals with first-degree relatives of target in iduals can achieve a prediction accuracy similar to that of around 220,000 unrelated in iduals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in s le size. For lifestyle traits, the prediction accuracy with 5,000 in iduals including first-degree relatives of target in iduals is significantly higher than that with 220,000 unrelated in iduals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention.
An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
Publisher: Springer Science and Business Media LLC
Date: 02-11-2021
DOI: 10.1038/S41598-021-00427-Y
Abstract: Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For ex le, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.
Metal organic frameworks derived Ni-doped hierarchical NiXCo1-XS@C bundled-like nanostructures for enhanced supercapacitors
Publisher: Elsevier BV
Date: 02-2022
Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes
Publisher: Cold Spring Harbor Laboratory
Date: 08-08-2017
DOI: 10.1101/173435
Abstract: Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2×10 −6 ) while the inverse was not significant (19 regions, p=0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p=3.6×10 −6 ) and manic symptoms (BD, p=2×10 −5 ), 2) BD subphenotypes: psychotic features (SCZ p=1.2×10 −10 , BD p=5.3×10 −5 ) and age of onset (SCZ p=7.9×10 −4 ). Finally, we show that psychotic features in BD has significant SNP-heritability (h 2 snp =0.15, SE=0.06), and a significant genetic correlation with SCZ (r g =0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p=0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes ( DARS2 , ARFGEF2 , DCAKD and GATAD2A ) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.
Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion
Publisher: Springer Science and Business Media LLC
Date: 17-04-2012
DOI: 10.1038/TP.2012.27
Three-dimensional graphene layers prepared by a gas-foaming method for supercapacitor applications
Publisher: Elsevier BV
Date: 11-2015
Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction
Publisher: Wiley
Date: 19-06-2019
DOI: 10.1111/JBG.12420
Abstract: Reference populations for genomic selection usually involve selected in iduals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with in iduals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree-based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single-Step approach (SSGBLUP) using both. For a scenario with no-selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all in iduals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single-Step approach to obtain accurate and unbiased prediction of GEBV.
Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: Genome‐wide association study of both common and rare variants
Publisher: Wiley
Date: 31-05-2013
DOI: 10.1002/AJMG.B.32169
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs
Publisher: Springer Science and Business Media LLC
Date: 11-08-2202
DOI: 10.1038/NG.2711
Genome-wide association study identifies a locus at 7p15.2 associated with endometriosis
Publisher: Springer Science and Business Media LLC
Date: 12-12-2011
DOI: 10.1038/NG.731
Genotype-environment interaction on human cognitive function conditioned on the status of breastfeeding and maternal smoking around birth
Publisher: Springer Science and Business Media LLC
Date: 20-07-2017
DOI: 10.1038/S41598-017-06214-Y
Abstract: We estimated genotype by environment interaction (G × E) on later cognitive performance and educational attainment across four unique environments, i.e. 1) breastfed without maternal smoking, 2) breastfed with maternal smoking, 3) non-breastfed without maternal smoking and 4) non-breastfed with maternal smoking, using a novel design and statistical approach that was facilitated by the availability of datasets with the genome-wide single nucleotide polymorphisms (SNPs). There was significant G × E for both fluid intelligence (p-value = 1.0E-03) and educational attainment (p-value = 8.3E-05) when comparing genetic effects in the group of in iduals who were breastfed without maternal smoking with those not breastfed without maternal smoking. There was also significant G × E for fluid intelligence (p-value = 3.9E-05) when comparing the group of in iduals who were breastfed with maternal smoking with those not breastfed without maternal smoking. Genome-wide significant SNPs were different between different environmental groups. Genomic prediction accuracies were significantly higher when using the target and discovery s le from the same environmental group than when using those from the different environmental groups. This finding demonstrates G × E has important implications for future studies on the genetic architecture, genome-wide association studies and genomic predictions.
Integrating thermal physiology within a syndrome
Publisher: Wiley
Date: 15-01-2018
Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture
Publisher: Public Library of Science (PLoS)
Date: 24-10-2013
Runs of Homozygosity Implicate Autozygosity as a Schizophrenia Risk Factor
Publisher: Public Library of Science (PLoS)
Date: 12-04-2012
Regulation methods for the Zn/electrolyte interphase and the effectiveness evaluation in aqueous Zn-ion batteries
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1EE02021H
Abstract: The working principles of interphase strategies to enhance Zn reversibility are discussed. The effectiveness evaluation techniques, including electrochemical methods, characterization measurements, and computational simulations, are proposed.
Simultaneous fine mapping of multiple closely linked quantitative trait loci using combined linkage disequilibrium and linkage with a general pedigree
Publisher: Oxford University Press (OUP)
Date: 08-2006
DOI: 10.1534/GENETICS.106.057653
Abstract: Within a small region (e.g., & cM), there can be multiple quantitative trait loci (QTL) underlying phenotypes of a trait. Simultaneous fine mapping of closely linked QTL needs an efficient tool to remove confounded shade effects among QTL within such a small region. We propose a variance component method using combined linkage disequilibrium (LD) and linkage information and a reversible jump Markov chain Monte Carlo (MCMC) s ling for model selection. QTL identity-by-descent (IBD) coefficients between in iduals are estimated by a hybrid MCMC combining the random walk and the meiosis Gibbs s ler. These coefficients are used in a mixed linear model and an empirical Bayesian procedure combines residual maximum likelihood (REML) to estimate QTL effects and a reversible jump MCMC that s les the number of QTL and the posterior QTL intensities across the tested region. Note that two MCMC processes are used, i.e., an (internal) MCMC for IBD estimation and an (external) MCMC for model selection. In a simulation study, the use of the multiple-QTL model clearly removes the shade effects between three closely linked QTL located at 1.125, 3.875, and 7.875 cM across the region of 10 cM, using 40 markers at 0.25-cM intervals. It is shown that the use of combined LD and linkage information gives much more useful information compared to using linkage information alone for both single- and multiple-QTL analyses. When using a lower marker density (11 markers at 1-cM intervals), the signal of the second QTL can disappear. Extreme values of past effective size (resulting in extreme levels of LD) decrease the mapping accuracy.
No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study
Publisher: Public Library of Science (PLoS)
Date: 28-10-2016
GxE PRS: Genotype-environment interaction in polygenic risk score models for quantitative and binary traits
Publisher: Cold Spring Harbor Laboratory
Date: 21-07-2023
DOI: 10.1101/2023.07.20.549816
Abstract: The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an in idual’s genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing GxE PRS models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose a novel GxE PRS model that correctly incorporates the GxE component to analyze complex traits and diseases. Through extensive simulations, we demonstrate that our proposed model outperforms existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed model to real data, and report significant GxE effects. Specifically, we highlight the impact of our model on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index (BMI) by alcohol intake frequency (ALC). In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension (HYP) by waist-to-hip ratio (WHR). These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxE PRS, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, while providing a practical tool to support further research in this area.
Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits
Publisher: Springer Science and Business Media LLC
Date: 15-06-2010
Genetic and brain similarity independently predict childhood anthropometrics and socioeconomic markers
Publisher: Cold Spring Harbor Laboratory
Date: 21-08-2023
DOI: 10.1101/2023.08.18.553837
Abstract: Linking the developing brain with in idual differences in clinical and demographic traits is challenging due to the substantial interin idual heterogeneity of brain anatomy and organization. Here we employ a novel approach that parses in idual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance, with the assumption that similarity in cortical thickness may in part be driven by similarity in genetic variants. We employ this approach in a s le of 7760 unrelated children in the ABCD cohort baseline s le (mean age 9.9, 46.8% female). In general, significant associations between cortical thickness similarity and traits were limited to anthropometrics such as height (r 2 = 0.11, SE = 0.01), weight (r 2 = 0.12, SE = 0.01), and birth weight (r 2 = 0.19, SE = 0.01), as well as markers of socioeconomic status such as local area deprivation (r 2 = 0.06, SE = 0.01). Analyses of the contribution from common genetic variants to traits revealed contributions across included outcomes, albeit somewhat lower than previous reports, possibly due to the young age of the s le. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as socioeconomic factors and the developing brain, which appear to be independent from in idual differences in common genetic variants in this population-based s le. The approach provides a promising framework for analyses of neuroimaging genetics cohorts, which can be further expanded by including imaging derived phenotypes beyond cortical thickness.
A Genetic Investigation of Sex Bias in the Prevalence of Attention-Deficit/Hyperactivity Disorder
Publisher: Elsevier BV
Date: 06-2018
A genome-wide association study for extremely high intelligence
Publisher: Springer Science and Business Media LLC
Date: 04-07-2017
DOI: 10.1038/MP.2017.121
Multivariate Genetic Analyses of Cognition and Academic Achievement from Two Population Samples of 174,000 and 166,000 School Children
Publisher: Springer Science and Business Media LLC
Date: 15-06-2012
DOI: 10.1007/S10519-012-9549-7
Abstract: The genetic influence on the association between contemporaneously measured intelligence and academic achievement in childhood was examined in nationally representative cohorts from England and The Netherlands using a whole population indirect twin design, including singleton data. We identified 1,056 same-sex (SS) and 495 opposite-sex (OS) twin pairs among 174,098 British 11 year-olds with test scores from 2004, and, 785 SS and 327 OS twin pairs among 120,995 Dutch schoolchildren, aged 8, 10 or 12 years, with assessments from 1994 to 2002. The estimate of intelligence heritability was large in both cohorts, consistent with previous studies (h (2) = 0.70 ± 0.14, England h (2) = 0.43 ± 0.28-0.67 ± 0.31, The Netherlands), as was the heritability of academic achievement variables (h (2) = 0.51 ± 0.16-0.81 ± 0.16, England h (2) = 0.36 ± 0.27-0.74 ± 0.27, The Netherlands). Additive genetic covariance explained the large majority of the phenotypic correlations between intelligence and academic achievement scores in England, when standardised to a bivariate heritability (Biv h (2) = 0.76 ± 0.15-0.88 ± 0.16), and less consistent but often large proportions of the phenotypic correlations in The Netherlands (Biv h (2) = 0.33 ± 0.52-1.00 ± 0.43). In the British cohort both nonverbal and verbal reasoning showed very high additive genetic covariance with achievement scores (Biv h (2) = 0.94-0.98 Biv h (2) = 0.77-1.00 respectively). In The Netherlands, covariance estimates were consistent across age groups. The heritability of intelligence-academic achievement associations in two population cohorts of elementary schoolchildren, using a twin pair extraction method, is at the high end of estimates reported by studies of largely preselected twin s les.
Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model
Publisher: Cold Spring Harbor Laboratory
Date: 26-07-2018
DOI: 10.1101/377796
Abstract: The genomics era has brought useful tools to dissect the genetic architecture of complex traits. We propose a reaction norm model (RNM) to tackle genotype-environment correlation and interaction problems in the context of genome-wide association analyses of complex traits. In our approach, an environmental risk factor affecting the trait of interest can be modeled as dependent on a continuous covariate that is itself regulated by genetic as well as environmental factors. Our multivariate RNM approach allows the joint modelling of the relation between the genotype (G) and the covariate (C), so that both their correlation (association) and interaction (effect modification) can be estimated. Hence we jointly estimate genotype-covariate correlation and interaction (GCCI). We demonstrate using simulation that the proposed multivariate RNM performs better than the current state-of-the-art methods that ignore G-C correlation. We apply the method to data from the UK Biobank (N= 66,281) in analysis of body mass index using smoking quantity as a covariate. We find a highly significant G-C correlation, but a negligible G-C interaction. In contrast, when a conventional G-C interaction analysis is applied (i.e., G-C correlation is not included in the model), highly significant G-C interaction estimates are found. It is also notable that we find a significant heterogeneity in the estimated residual variances across different covariate levels probably due to residual-covariate interaction. Using simulation we also show that the residual variances estimated by genomic restricted maximum likelihood (GREML) or linkage disequilibrium score regression (LDSC) can be inflated in the presence of interactions, implying that the currently reported SNP-heritability estimates from these methods should be interpreted with caution. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses and that the failure to do so may lead to substantial biases in inferences relating to genetic architecture of complex traits, including estimated SNP-heritability.
New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis
Publisher: Oxford University Press (OUP)
Date: 18-08-2015
DOI: 10.1093/IJE/DYV136
R2ROC: An efficient method of comparing two or more correlated AUC from out-of-sample prediction using polygenic scores
Publisher: Cold Spring Harbor Laboratory
Date: 03-08-2023
DOI: 10.1101/2023.08.01.551571
Abstract: Polygenic risk scores (PRSs) enable early prediction of disease risk. Evaluating PRS performance for binary traits commonly relies on the area under the receiver operating characteristic curve (AUC). However, the widely used DeLong’s method for comparative significance tests suffer from limitations, including computational time and the lack of a one-to-one mapping between test statistics based on AUC and R 2 . To overcome these limitations, we propose a novel approach that leverages the Delta method to derive the variance and covariance of AUC values, enabling a comprehensive and efficient comparative significance test. Our approach offers notable advantages over DeLong’s method, including reduced computation time (up to 150-fold), making it suitable for large-scale analyses and ideal for integration into machine learning frameworks. Furthermore, our method allows for a direct one-to-one mapping between AUC and R 2 values for comparative significance tests, providing enhanced insights into the relationship between these measures and facilitating their interpretation. We validated our proposed approach through simulations and applied it to real data comparing PRSs for diabetes and coronary artery disease (CAD) prediction in a cohort of 28,880 European in iduals. The PRSs were derived using genome-wide association study summary statistics from two distinct sources. Our approach enabled a comprehensive and informative comparison of the PRSs, shedding light on their respective predictive abilities for diabetes and CAD. This advancement contributes to the assessment of genetic risk factors and personalized disease prediction, supporting better healthcare decision-making.
Hepatitis C virus infection in children and adolescents
Publisher: Elsevier BV
Date: 06-2019
Boosting Zinc Electrode Reversibility in Aqueous Electrolytes by Using Low-Cost Antisolvents
Publisher: Wiley
Date: 24-02-2021
Significance tests for R2 of out-of-sample prediction using polygenic scores
Publisher: Elsevier BV
Date: 02-2023
A Whole-Genome Approach Discovers Novel Genetic and Non-Genetic Variance Components Modulated by Lifestyle for Cardiovascular Health
Publisher: Cold Spring Harbor Laboratory
Date: 14-07-2019
DOI: 10.1101/700617
Abstract: Both genetic and non-genetic factors can predispose in iduals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease (CVD) prevention. Here, we use a novel whole-genome framework to estimate genetic and non-genetic effects on—hence their predispositions to—cardiovascular risk and determine whether they vary with respect to lifestyle factors. We performed analyses on the Atherosclerosis Risk in Communities Study (ARIC, N=6,896-7,180) and validated findings using the UK Biobank (UKBB, N=14,076-34,538). Cardiovascular risk was measured using 23 traits in the ARIC and eight traits in the UKBB, such as body mass index (BMI), resting heart rate, white blood cell count and blood pressure and lifestyle factors included information on physical activity, smoking, alcohol consumption and dietary intake. Physical activity altered both genetic and non-genetic effects on heart rate and BMI, genetic effects on HDL cholesterol level, and non-genetic effects on waist-to-hip ratio. Alcohol consumption altered both genetic and non-genetic effects on BMI, while smoking altered non-genetic effects on heart rate, pulse pressure, and white blood cell count. In addition, saturated fat intake modified genetic effects on BMI, and total daily energy intake modified non-genetic effects on waist-to-hip ratio. These results highlight the relevance of lifestyle changes for CVD prevention. We also stratified in iduals according to their genetic predispositions and showed notable differences in the effects of lifestyle on cardiovascular risk across stratified groups, implying the need for in idualizing lifestyle changes for CVD prevention. Finally, we showed that neglecting lifestyle modulation of genetic and non-genetic effects will on average reduce SNP heritability estimates of cardiovascular traits by a small yet significant amount, primarily owing to overestimation of residual variance. Thus, current SNP heritability estimates for cardiovascular traits, which commonly do not consider modulating effects of lifestyle covariates, are likely underestimated.
Low‐cost and Non‐flammable Eutectic Electrolytes for Advanced Zn‐I2 Batteries
Publisher: Wiley
Date: 17-08-2023
Abstract: As a burgeoning electrolyte system, eutectic electrolytes based on ZnCl 2 /Zn(CF 3 SO 3 ) 2 /Zn(TFSI) 2 have been widely proposed in advanced Zn‐I 2 batteries however, safety and cost concerns significantly limit their applications. Here, we report new‐type ZnSO 4 ‐based eutectic electrolytes that are both safe and cost‐effective. Their universality is evident in various solvents of polyhydric alcohols, in which multiple −OH groups not only involve in Zn 2+ solvation but also interact with water, resulting in the high stability of electrolytes. Taking propylene glycol‐based hydrated eutectic electrolyte as an ex le, it features significant advantages in non‐flammability and low price that is /200 cost of Zn(CF 3 SO 3 ) 2 /Zn(TFSI) 2 ‐based eutectic electrolytes. Moreover, its effectiveness in confining the shuttle effects of I 2 cathode and side reactions of Zn anodes is evidenced, resulting in Zn‐I 2 cells with high reversibility at 1 C and 91.4 % capacity remaining under 20 C. After scaling up to the pouch cell with a record mass loading of 33.3 mg cm −2 , super‐high‐capacity retention of 96.7 % is achieved after 500 cycles, which exceeds other aqueous counterparts. This work significantly broadens the eutectic electrolyte family for advanced Zn battery design.
Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis
Publisher: Oxford University Press (OUP)
Date: 28-11-2012
DOI: 10.1093/HMG/DDS491
A novel hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation
Publisher: Cold Spring Harbor Laboratory
Date: 03-07-2022
DOI: 10.1101/2022.07.03.498620
Abstract: The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped in iduals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method (e.g., that implemented in BLUPf90 software) requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning and scale factor in simulated as well as real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter 1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α , which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase the prediction accuracy, in addition to blending and tuning processes, when using HBLUP. Despite significant advancements in genotyping technologies, the capability to predict the phenotypes of complex traits is still limited. H-matrix best linear unbiased prediction (HBLUP) method has been used to tackle this limitation to demonstrate a promising prediction accuracy. However, the performance of HBLUP depends heavily on the optimisation of hyper-parameters (e.g. blending and tuning). In this study, we introduce a scale factor ( α ), as a new hyper-parameter in HBLUP, which accounts for the relationship between allele frequency and per-allele effect size. Using simulation and real data analysis, we investigate the impact of the hyper-parameters (blending, tuning, and scale factor) on the performance of HBLUP. In general, the blending process may not improve the prediction accuracy for simulation and cattle data although a marginally improved prediction accuracy is observed with a blending hyper-parameter = 0.86 for one of carcass traits in the cattle data. In contrast, the tuning process can increase the HBLUP accuracy particularly in simulated data. Furthermore, we observe that an optimal scale factor plays a significant role in improving the prediction accuracy in both simulated and real data, and the improvement is relatively large compared with blending and tuning processes. In this context, we propose considering the scale factor as a hyper-parameter to increase the predictive performance of HBLUP.
GCTA: A Tool for Genome-wide Complex Trait Analysis
Publisher: Elsevier BV
Date: 2011
Cross-disorder GWAS meta-analysis for Attention Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, Obsessive Compulsive Disorder, and Tourette Syndrome
Publisher: Cold Spring Harbor Laboratory
Date: 16-09-2019
DOI: 10.1101/770222
Abstract: Attention Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Obsessive-Compulsive Disorder (OCD), and Tourette Syndrome (TS) are among the most prevalent neurodevelopmental psychiatric disorders of childhood and adolescence. High comorbidity rates across these four disorders point toward a common etiological thread that could be connecting them across the repetitive behaviors-impulsivity-compulsivity continuum. Aiming to uncover the shared genetic basis across ADHD, ASD, OCD, and TS, we undertake a systematic cross-disorder meta-analysis, integrating summary statistics from all currently available genome-wide association studies (GWAS) for these disorders, as made available by the Psychiatric Genomics Consortium (PGC) and the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH). We present analysis of a combined dataset of 93,294 in iduals, across 6,788,510 markers and investigate associations on the single-nucleotide polymorphism (SNP), gene and pathway levels across all four disorders but also pairwise. In the ADHD-ASD-OCD-TS cross disorder GWAS meta-analysis, we uncover in total 297 genomewide significant variants from six LD (linkage disequilibrium) -independent genomic risk regions. Out of these genomewide significant association results, 199 SNPs, that map onto four genomic regions, show high posterior probability for association with at least three of the studied disorders (m-value .9). Gene-based GWAS meta-analysis across ADHD, ASD, OCD, and TS identified 21 genes significantly associated under Bonferroni correction. Out of those, 15 could not be identified as significantly associated based on the in idual disorder GWAS dataset, indicating increased power in the cross-disorder comparisons. Cross-disorder tissue-specificity analysis implicates the Hypothalamus-Pituitary-Adrenal axis (stress response) as possibly underlying shared pathophysiology across ADHD, ASD, OCD, and TS. Our work highlights genetic variants and genes that may contribute to overlapping neurobiology across the four studied disorders and highlights the value of re-defining the framework for the study across this spectrum of highly comorbid disorders, by using transdiagnostic approaches.
Hidden heritability due to heterogeneity across seven populations
Publisher: Springer Science and Business Media LLC
Date: 11-09-2017
DOI: 10.1038/S41562-017-0195-1
Abstract: Meta-analyses of genome-wide association studies (GWAS), which dominate genetic discovery are based on data from erse historical time periods and populations. Genetic scores derived from GWAS explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the 'hidden heritability' puzzle. Using seven s ling populations (N=35,062), we test whether hidden heritability is attributed to heterogeneity across s ling populations and time, showing that estimates are substantially smaller from across compared to within populations. We show that the hidden heritability varies substantially: from zero (height), to 20% for BMI, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results more likely reflect heterogeneity in phenotypic measurement or gene-environment interaction than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene-environment interaction may be a central challenge for genetic discovery.
Genome wide QTL mapping to identify candidate genes for carcass traits in Hanwoo (Korean Cattle)
Publisher: Springer Science and Business Media LLC
Date: 31-01-2012
Constructing Layered Nanostructures from Non-Layered Sulfide Crystals via Surface Charge Manipulation Strategy
Publisher: Wiley
Date: 02-06-2021
Abstract: 2D non‐layered metal sulfides possess intriguing properties, rendering them bright application prospects in energy storage and conversion, however, the synthesis of non‐layered metal sulfide nanosheets is still significantly challenging. Herein, a surface‐charge‐regulating strategy is developed to fabricate microsized 2D non‐layered metal sulfides via manipulation of the isoelectric point, which can easily modulate the manner of surface charge arrangement during the growth of crystal nuclei. The result of this strategy are materials that are completely assembled with a preferred orientation but comprise a large lateral size with maintaining atomic thickness. A series of modified sulfides are successfully synthesized, demonstrating that their microarchitectures are shifted in an expected manner. Then, one of these materials, In 4 SnS 8 , approaches a promising candidate for sodium storage by means of its structural integrity, boosted transfer kinetics, and abundant active sites. The proposed synthetic protocol can open up a new opportunity to explore 2D non‐layered materials for energy‐related applications.
Mortality and AIDS‐defining events among young people following transition from paediatric to adult HIV care in the UK
Publisher: Wiley
Date: 03-05-2021
DOI: 10.1111/HIV.13096
Abstract: To investigate risk of AIDS and mortality after transition from paediatric to adult care in a UK cohort of young people with perinatally acquired HIV. Records of people aged ≥ 13 years on 31 December 2015 in the UK paediatric HIV cohort (Collaborative HIV Paediatric Study) were linked to those of adults in the UK Collaborative HIV Cohort (CHIC) cohort. We calculated time from transition to a new AIDS event/death, with follow‐up censored at the last visit or 31 December 2015, whichever was the earliest. Cumulative incidence of and risk factors for AIDS/mortality were assessed using Kaplan–Meier and Cox regression. At the final paediatric visit, the 474 participants [51% female, 80% black, 60% born outside the UK, median (interquartile range) age at antiretroviral therapy (ART) initiation = 9 (5–13) years] had a median age of 18 (17–19) years and CD4 count of 471 (280–663) cell/μL 89% were prescribed ART and 60% overall had a viral load ≤ 400 copies/mL. Over median follow‐up in adult care of 3 (2–6) years, 35 (8%) experienced a new AIDS event ( n = 25) or death ( n = 14) (incidence = 1.8/100 person‐years). In multivariable analyses, lower CD4 count at the last paediatric visit [adjusted hazard ratio = 0.8 (95% confidence interval: 0.7–1.0)/100 cells/μL increment] and AIDS diagnosis in paediatric care [2.7 (1.4–5.5)] were associated with a new AIDS event/mortality in adult care. Young people with perinatally acquired HIV transitioning to adult care with markers of disease progression in paediatric care experienced poorer outcomes in adult care. Increased investment in multidisciplinary specialized services is required to support this population at high risk of morbidity and mortality.
Polyiodide Confinement by Starch Enables Shuttle-Free Zn–Iodine Batteries
Publisher: Wiley
Date: 02-05-2022
Abstract: Aqueous Zn–iodine (Zn–I 2 ) batteries have been regarded as a promising energy‐storage system owing to their high energy ower density, safety, and cost‐effectiveness. However, the polyiodide shuttling results in serious active mass loss and Zn corrosion, which limits the cycling life of Zn–I 2 batteries. Inspired by the chromogenic reaction between starch and iodine, a structure confinement strategy is proposed to suppress polyiodide shuttling in Zn–I 2 batteries by hiring starch, due to its unique double‐helix structure. In situ Raman spectroscopy demonstrates an I 5 − ‐dominated I − /I 2 conversion mechanism when using starch. The I 5 − presents a much stronger bonding with starch than I 3 − , inhibiting the polyiodide shuttling in Zn–I 2 batteries, which is confirmed by in situ ultraviolet–visible spectra. Consequently, a highly reversible Zn–I 2 battery with high Coulombic efficiency (≈100% at 0.2 A g −1 ) and ultralong cycling stability ( 000 cycles) is realized. Simultaneously, the Zn corrosion triggered by polyiodide is effectively inhibited owing to the desirable shuttling‐suppression by the starch, as evidenced by X‐ray photoelectron spectroscopy analysis. This work provides a new understanding of the failure mechanism of Zn–I 2 batteries and proposes a cheap but effective strategy to realize high‐cyclability Zn–I 2 batteries.
Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data
Publisher: Public Library of Science (PLoS)
Date: 24-10-2008
Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood
Publisher: Cold Spring Harbor Laboratory
Date: 27-09-2017
DOI: 10.1101/194019
Abstract: Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e. linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual s le and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ~150,000 in iduals give a higher accuracy than LDSC estimates based on ~400,000 in iduals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-data sets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if s le sizes are lesser.
Phenome-wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers
Publisher: Cold Spring Harbor Laboratory
Date: 08-09-2023
CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
Publisher: Springer Science and Business Media LLC
Date: 21-08-2020
DOI: 10.1038/S41467-020-18085-5
Abstract: As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named CORE GREML, that explicitly estimates the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing variance and covariance estimates free from bias due to correlated random effects. Applying CORE GREML to UK Biobank data, we find, for ex le, that the transcriptome, imputed using genotype data, explains a significant proportion of phenotypic variance for height (0.15, p -value = 1.5e-283), and that these transcriptomic effects correlate with the genomic effects (genome-transcriptome correlation = 0.35, p -value = 1.2e-14). We conclude that the covariance between random effects is a key parameter for estimation, especially when partitioning phenotypic variance by multi-omics layers.
New genetic loci link adipose and insulin biology to body fat distribution
Publisher: Springer Science and Business Media LLC
Date: 11-02-2015
DOI: 10.1038/NATURE14132
Hepatitis B virus infection in children and adolescents
Publisher: Elsevier BV
Date: 06-2019
An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
Publisher: Cold Spring Harbor Laboratory
Date: 10-11-2020
DOI: 10.1101/2020.11.09.373704
Abstract: Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI & height for N ∼ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For ex le, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome & exposome). We also show, using established theories, integrating genomic and exposomic data is essential to attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a great potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.
Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion (vol 2, e125, 2012)
Publisher: Springer Science and Business Media LLC
Date: 29-05-2012
DOI: 10.1038/TP.2012.49
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University College London
Location: United Kingdom of Great Britain and Northern Ireland
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University Of London
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Related Funding Activities
ARC Future Fellowships - Grant ID: FT160100229
Start Date: 2017
End Date: 12-2020
Amount: $719,313.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP160102126
Start Date: 2016
End Date: 04-2019
Amount: $331,600.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP190100766
Start Date: 06-2019
End Date: 06-2024
Amount: $404,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Early Career Researcher Award - Grant ID: DE130100614
Start Date: 2013
End Date: 06-2016
Amount: $375,000.00
Funder: Australian Research Council
View Funded Activity