ORCID Profile
0000-0002-4272-9305
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Applied Statistics | Gene mapping | Statistical and quantitative genetics | Genomics | Bioinformatics and computational biology | Computational statistics | Genetics | Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Publisher: Springer Science and Business Media LLC
Date: 23-07-2018
Publisher: Springer Science and Business Media LLC
Date: 31-05-2021
Publisher: Springer Science and Business Media LLC
Date: 16-04-2018
DOI: 10.1038/S41588-018-0101-4
Abstract: We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated in iduals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.
Publisher: Springer Science and Business Media LLC
Date: 04-03-2021
DOI: 10.1038/S41598-021-84739-Z
Abstract: Genome-wide association studies (GWAS) in s les of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs ( $$r_{g}$$ r g ) or genome-wide significant SNPs ( $$r_{{g\\left( {GWS} \\right)}}$$ r g GWS ) for height and body mass index (BMI) in s les of European (EUR $$n = 49,839$$ n = 49 , 839 ) and African (AFR $$n = 17,426$$ n = 17 , 426 ) ancestry. The $$\\hat{r}_{g}$$ r ^ g between EUR and AFR was 0.75 ( $${\\text{s}}.{\\text{e}}. = 0.035$$ s . e . = 0.035 ) for height and 0.68 ( $${\\text{s}}.{\\text{e}}. = 0.062$$ s . e . = 0.062 ) for BMI, and the corresponding $$\\hat{r}_{{g\\left( {GWS} \\right)}}$$ r ^ g GWS was 0.82 ( $${\\text{s}}.{\\text{e}}. = 0.030$$ s . e . = 0.030 ) for height and 0.87 ( $${\\text{s}}.{\\text{e}}. = 0.064$$ s . e . = 0.064 ) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that $$\\hat{r}_{g}$$ r ^ g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.
Publisher: Cold Spring Harbor Laboratory
Date: 21-05-2019
DOI: 10.1101/643304
Abstract: Using data from 5,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health, Domingue et al. (2018) claimed to show that friends are genetically more similar to one another than randomly selected peers, beyond the confounding effects of population stratification by ancestry. The authors also claimed to show ‘social-genetic’ effects, whereby in iduals’ educational attainment (EA) is influenced by their friends’ genes. Neither claim is justified by the data. Mathematically we show that 1) although similarity at causal variants is expected under assortment, the genome-wide relationship between friends (and similarly between mates) is extremely small (an effect that could be explained by subtle population stratification) and 2) significant association between in iduals’ EA and their friends’ polygenic score for EA is expected under homophily with no socio-genetic effects.
Publisher: Springer Science and Business Media LLC
Date: 31-07-2020
DOI: 10.1038/S41467-020-17719-Y
Abstract: Polygenic scores (PGS) have been widely used to predict disease risk using variants identified from genome-wide association studies (GWAS). To date, most GWAS have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European ancestry populations. Here, we derive a theoretical model of the relative accuracy (RA) of PGS across ancestries. We show through extensive simulations that the RA of PGS based on genome-wide significant SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of causal SNP effects and heritability. We find that LD and MAF differences between ancestries can explain between 70 and 80% of the loss of RA of European-based PGS in African ancestry for traits like body mass index and type 2 diabetes. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWAS are mostly shared across continents.
Publisher: Wiley
Date: 20-07-2020
DOI: 10.1002/HBM.25109
Publisher: Springer Science and Business Media LLC
Date: 05-10-2014
DOI: 10.1038/NG.3097
Publisher: Springer Science and Business Media LLC
Date: 07-2019
DOI: 10.1038/S41598-019-45823-7
Abstract: Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic in iduals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P 5 × 10 −8 ) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.
Publisher: Oxford University Press (OUP)
Date: 14-09-2018
DOI: 10.1093/HMG/DDY327
Abstract: More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of in iduals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.
Publisher: Elsevier BV
Date: 2021
DOI: 10.1016/J.BIOPSYCH.2020.05.034
Abstract: The genetic contribution to psychiatric disorders is observed through the increased rates of disorders in the relatives of those diagnosed with disorders. These increased rates are observed to be nonspecific for ex le, children of those with schizophrenia have increased rates of schizophrenia but also a broad range of other psychiatric diagnoses. While many factors contribute to risk, epidemiological evidence suggests that the genetic contribution carries the highest risk burden. The patterns of inheritance are consistent with a polygenic architecture of many contributing risk loci. The genetic studies of the past decade have provided empirical evidence identifying thousands of DNA variants associated with psychiatric disorders. Here, we describe how these latest results are consistent with observations from epidemiology. We provide an R tool (CHARRGe) to calculate genetic parameters from epidemiological parameters and vice versa. We discuss how the single nucleotide polymorphism-based estimates of heritability and genetic correlation relate to those estimated from family records.
Publisher: Public Library of Science (PLoS)
Date: 21-06-2016
Publisher: Springer Science and Business Media LLC
Date: 21-10-2019
DOI: 10.1038/S41562-019-0757-5
Abstract: Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a s le of ~450,000 in iduals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. In iduals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene-environment correlations.
Publisher: Springer Science and Business Media LLC
Date: 31-10-2016
DOI: 10.1038/NG.3698
Publisher: Springer Science and Business Media LLC
Date: 19-02-2021
DOI: 10.1038/S41467-021-21446-3
Abstract: Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using in idual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k–547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of ~0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.
Publisher: Public Library of Science (PLoS)
Date: 14-09-2020
Publisher: Springer Science and Business Media LLC
Date: 11-11-2019
Publisher: Elsevier BV
Date: 08-2019
Publisher: Cold Spring Harbor Laboratory
Date: 25-03-2019
DOI: 10.1101/588020
Abstract: Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated in iduals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2–5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be largely recovered from whole-genome sequence (WGS) data on 25,465 unrelated in iduals of European ancestry. We assigned 33.7 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned genetic variance accordingly. The estimated heritability was 0.68 (SE 0.10) for height and 0.30 (SE 0.10) for BMI, with a range of ~0.60 – 0.71 for height and ~0.25 – 0.35 for BMI, depending on quality control and analysis strategies. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection thereon. Cumulatively variants with 0.0001 MAF 0.1 explained 0.47 (SE 0.07) and 0.30 (SE 0.10) of heritability for height and BMI, respectively. Our results imply that rare variants, in particular those in regions of low LD, is a major source of the still missing heritability of complex traits and disease.
Publisher: Wiley
Date: 09-2021
DOI: 10.1002/AJMG.B.32876
Abstract: This study investigates if genetic factors could contribute to the high rate of mood disorders reported in a U.S. community known to have a restricted early founder population (confirmed here through runs of homozygosity analysis). Polygenic scores (PGSs) for eight common diseases, disorders, or traits, including psychiatric disorders, were calculated in 274 participants (125 mood disorder cases) who each reported three or four grandparents born in the community. Ancestry‐matched controls were selected from the UK Biobank (UKB three sets of N = 1,822 each). The mean PGSs were significantly higher in the community for major depression PRS ( p = 2.1 × 10 −19 , 0.56 SD units), bipolar disorder ( p = 2.5 × 10 −15 , 0.56 SD units), and schizophrenia ( p = 3.8 × 10 −21 , 0.64 SD units). The PGSs were not significantly different between the community participants and UKB controls for the traits of body mass index, Type 2 diabetes, coronary artery disease, and chronotype. The mean PGSs for height were significantly lower in the community s le compared to controls (−0.21 SD units, p = 1.2 × 10 −5 ). The results are consistent with enrichment of polygenic risk factors for psychiatric disorders in this community.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Springer Science and Business Media LLC
Date: 27-07-2018
DOI: 10.1038/S41467-018-04951-W
Abstract: Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood ( n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation ( n = 1980) and epigenomic annotation data highlight 3 genes ( CAMK1D , TP53INP1 , and ATP5G1 ) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.
Publisher: Cold Spring Harbor Laboratory
Date: 09-11-2020
DOI: 10.1101/2020.11.09.375501
Abstract: Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random s le of unrelated in iduals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive , dominance and additive-by-additive genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated in iduals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits . In contrast, the average estimate of across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance across the traits was 0.058, not significantly different from zero because of the large s ling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive, and that s le sizes of many millions of unrelated in iduals are needed to estimate epistatic variance with sufficient precision.
Publisher: Springer Science and Business Media LLC
Date: 08-11-2019
DOI: 10.1038/S41467-019-12653-0
Abstract: Accurate prediction of an in idual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful in idual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 in iduals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis ( n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R 2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.
Publisher: Cold Spring Harbor Laboratory
Date: 16-04-2020
DOI: 10.1101/2020.04.16.044685
Abstract: Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where s le sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on s les sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized s ling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random s les of genes in the cattle genome (p=0.01). Randomly s led SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly s led SNPs within random cattle genes (p=0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p=0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.
Publisher: Springer Science and Business Media LLC
Date: 23-09-2020
DOI: 10.1038/S41467-020-18534-1
Abstract: Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P -threshold ( P optimal ) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and in iduals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.
Publisher: Springer Science and Business Media LLC
Date: 03-09-2019
DOI: 10.1038/S41467-019-11724-6
Abstract: In most human societies, there are taboos and laws banning mating between first- and second-degree relatives, but actual prevalence and effects on health and fitness are poorly quantified. Here, we leverage a large observational study of ~450,000 participants of European ancestry from the UK Biobank (UKB) to quantify extreme inbreeding (EI) and its consequences. We use genotyped SNPs to detect large runs of homozygosity (ROH) and call EI when % of an in idual’s genome comprise ROHs. We estimate a prevalence of EI of ~0.03%, i.e., ~1/3652. EI cases have phenotypic means between 0.3 and 0.7 standard deviation below the population mean for 7 traits, including stature and cognitive ability, consistent with inbreeding depression estimated from in iduals with low levels of inbreeding. Our study provides DNA-based quantification of the prevalence of EI in a European ancestry s le from the UK and measures its effects on health and fitness traits.
Publisher: Cold Spring Harbor Laboratory
Date: 02-03-2018
DOI: 10.1101/274654
Abstract: Genome-wide association studies (GWAS) stand as powerful experimental designs for identifying DNA variants associated with complex traits and diseases. In the past decade, both the number of such studies and their s le sizes have increased dramatically. Recent GWAS of height and body mass index (BMI) in ∼250,000 European participants have led to the discovery of ∼700 and ∼100 nearly independent SNPs associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450,000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N∼700,000 in iduals and substantially increases the number of GWAS signals associated with these traits. We identified 3,290 and 716 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of p × 10 −8 ), including 1,185 height-associated SNPs and 554 BMI-associated SNPs located within loci not previously identified by these two GWAS. The genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼5% of the variance of BMI in an independent s le from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were 0.44 and 0.20, respectively. From analyses of integrating GWAS and eQTL data by Summary-data based Mendelian Randomization (SMR), we identified an enrichment of eQTLs amongst lead height and BMI signals, prioritisting 684 and 134 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS s le sizes continues to deliver, by discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow up studies.
Publisher: Springer Science and Business Media LLC
Date: 02-2016
DOI: 10.1038/NCOMMS10495
Abstract: To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 in iduals. Twelve loci reached genome-wide significance ( P × 10 −8 ), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14 , IGF2BP1 , PLA2G6 , CRTC1 ) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.
Publisher: Springer Science and Business Media LLC
Date: 29-06-2018
Publisher: Springer Science and Business Media LLC
Date: 11-06-2018
DOI: 10.1038/S41467-018-04558-1
Abstract: Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis -expression or -DNA methylation (DNAm) quantitative trait loci ( cis -eQTLs or cis -mQTLs) between brain and blood ( r b ). Using publicly available data, we find that genetic effects at the top cis -eQTLs or mQTLs are highly correlated between independent brain and blood s les ( $$\\hat r_b = 0.70$$ r ^ b = 0.70 for cis -eQTLs and $$\\hat r_ b = 0.78$$ r ^ b = 0.78 for cis -mQTLs). Using meta-analyzed brain cis -eQTL/mQTL data ( n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger s le sizes ( n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis -eQTL/mQTL data with large s le sizes.
Publisher: Springer Science and Business Media LLC
Date: 16-02-2021
DOI: 10.1038/S41467-021-21283-4
Abstract: Attributing the similarity between in iduals to genetic and non-genetic factors is central to genetic analyses. In this paper we use the genomic relationship ( $$\\pi$$ π ) among 417,060 in iduals to investigate the phenotypic covariance between pairs of in iduals for 32 traits across the spectrum of relatedness, from unrelated pairs through to identical twins. We find linear relationships between phenotypic covariance and $$\\pi$$ π that agree with the SNP-based heritability ( $$\\hat h_{SNP}^2$$ h ̂ S N P 2 ) in unrelated pairs ( $$\\pi \\, \\, 0.02$$ π 0.02 ), and with pedigree-estimated heritability in close relatives ( $$\\pi \\, \\, 0.05$$ π 0.05 ). The covariance increases faster than $$\\pi \\hat h_{SNP}^2$$ π h ̂ S N P 2 in distant relatives ( $$0.02 \\, \\, \\pi \\, \\, 0.05$$ 0.02 π 0.05 ), and we attribute this to imperfect linkage disequilibrium between causal variants and the common variants used to construct $$\\pi$$ π . We also examine the effect of assortative mating on heritability estimates from different experimental designs. We find that full-sib identity-by-descent regression estimates for height (0.66 s.e. 0.07) are consistent with estimates from close relatives (0.82 s.e. 0.04) after accounting for the effect of assortative mating.
Publisher: Wiley
Date: 08-09-2015
DOI: 10.1002/OBY.21199
Abstract: This study characterized the kynurenine pathway (KP) in human obesity by evaluating circulating levels of kynurenines and the expression of KP enzymes in adipose tissue. Tryptophan and KP metabolite levels were measured in serum of in iduals from the D.E.S.I.R. cohort (case-cohort study: 212 diabetic, 836 randomly s led) and in women with obesity, diabetic or normoglycemic, from the ABOS cohort (n = 100). KP enzyme gene expressions were analyzed in omental and subcutaneous adipose tissue of women from the ABOS cohort, in human primary adipocytes and in monocyte-derived macrophages. In the D.E.S.I.R. cohort, kynurenine levels were positively associated with body mass index (BMI) (P = 4.68 × 10(-19) ) and with a higher HOMA2-IR insulin resistance index (P = 6.23 × 10(-4) ). The levels of kynurenine, kynurenic acid, and quinolinic acid were associated with higher BMI (P < 0.05). The expression of several KP enzyme genes (indoleamine 2,3-dioxygenase 1 [IDO1], kynureninase [KYNU], kynurenine 3-monooxygenase [KMO], and kynurenine aminotransferase III [CCBL2]) was increased in the omental adipose tissue of women with obesity compared to lean (P < 0.05), and their expression was induced by proinflammatory cytokines in human primary adipocytes (P < 0.05), except for KMO that is not expressed in these cells. The expressions of IDO1, KYNU, KMO, and CCBL2 were higher in proinflammatory than in anti-inflammatory macrophages (P < 0.05). In the context of obesity, the presence of macrophages in adipose tissue may contribute to erting KP toward KMO activation.
Publisher: Springer Science and Business Media LLC
Date: 27-01-2023
DOI: 10.1038/S41467-023-36013-1
Abstract: The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal ( h 2 = 11% standard error: 1%). The genetic correlation between different fasting times is 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent s les. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose ( N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 24-09-2021
Abstract: The sequencing of the human genome has allowed the study of the genetic architecture of common diseases: the number of genomic variants that contribute to risk of disease and their joint frequency and effect size distribution. Common diseases are polygenic, with many loci contributing to phenotype, and the cumulative burden of risk alleles determines in idual risk in conjunction with environmental factors. Most risk loci occur in noncoding regions of the genome regulating cell- and context-specific gene expression. Although the effect sizes of most risk alleles are small, their cumulative effects in in iduals, quantified as a polygenic (risk) score, can identify people at increased risk of disease, thereby facilitating prevention or early intervention.
Publisher: Oxford University Press (OUP)
Date: 24-11-2015
DOI: 10.1093/HMG/DDV472
Publisher: Public Library of Science (PLoS)
Date: 12-10-2020
Publisher: Springer Science and Business Media LLC
Date: 11-07-2016
DOI: 10.1038/NATURE18642
Publisher: Research Square Platform LLC
Date: 10-03-2022
DOI: 10.21203/RS.3.RS-1409164/V1
Abstract: Hypertension is a leading cause of premature death affecting more than a billion in iduals worldwide. Here we report on the genetic determinants of blood pressure (BP) traits (systolic, diastolic, and pulse pressure) in the largest single-stage genome-wide analysis to date (N = 1,028,980 European-descent in iduals). We identified 2,103 independent genetic signals (P 5x10 − 8 ) for BP traits, including 113 novel loci. These associations explain ~ 40% of common SNP heritability of systolic and diastolic BP. Comparison of top versus bottom deciles of polygenic risk scores (PRS) based on these results reveal clinically meaningful differences in BP (12.9 mm Hg for systolic BP, 95% CI 11.5–14.2 mm Hg, p = 9.08×10 − 73 ) and hypertension risk (OR 5.41 95% CI 4.12 to 7.10 P = 9.71×10 − 33 ) in an independent dataset. Compared with the area under the curve (AUC) for hypertension discrimination for a model with sex, age, BMI, and genetic ancestry, adding systolic and diastolic BP PRS increased discrimination from 0.791 (95% CI = 0.781–0.801) to 0.814 (95% CI = 0.805–0.824, ∆AUC = 0.023, P = 2.27x10 − 22 ). Our transcriptome-wide association study detected 2,793 BP colocalized associations with genetically-predicted expression of 1,070 genes in five cardiovascular tissues, of which 500 are previously unreported for BP traits. These findings represent an advance in our understanding of hypertension and highlight the role of increasingly large genomic studies for development of more accurate PRS, which may inform precision health research.
Publisher: American Medical Association (AMA)
Date: 09-2018
Publisher: Springer Science and Business Media LLC
Date: 12-08-2012
DOI: 10.1038/NG.2385
Publisher: Proceedings of the National Academy of Sciences
Date: 21-02-2018
Publisher: Public Library of Science (PLoS)
Date: 10-2015
Publisher: Springer Science and Business Media LLC
Date: 23-12-2012
DOI: 10.1038/NG.2500
Publisher: Proceedings of the National Academy of Sciences
Date: 26-07-2017
Abstract: Inbreeding depression (ID) is the reduction of fitness in offspring of related parents. This phenomenon can be quantified from SNP data through a number of measures of inbreeding. Our study addresses two key questions. How accurate are the different methods to estimate ID? And how and why should investigators choose among the multiple inbreeding measures to detect and quantify ID? Here, we compare the behaviors of ID estimates from three commonly used SNP-based measures of inbreeding and provide both theoretical and empirical arguments to answer these questions. Our work illustrates how to analyze SNP data efficiently to detect and quantify ID, across species and traits.
Publisher: Cold Spring Harbor Laboratory
Date: 26-10-2021
DOI: 10.1101/2021.10.26.21265507
Abstract: S les can be prone to ascertainment and attrition biases.The Australian Genetics of Depression Study is a large publicly recruited cohort (n=20,689) established to increase the understanding of depression and antidepressant treatment response. As part of the recruitment, participants donated a saliva s le and were given the option to consent to linkage of prescription records for research purposes. This study investigates differences between participants who donated a saliva s le or agreed to linkage of their records compared to those who did not. We observed that older, male participants with a higher education were more likely to donate a saliva s le. Self-reported bipolar disorder, ADHD, panic disorder, PTSD, substance use disorder and social anxiety disorder were associated with lower odds of donating a saliva s le whereas anorexia was associated with higher odds of donation. Male and younger participants showed higher odds of agreeing to record linkage. Participants with higher neuroticism scores and those with a history of bipolar disorder were also more likely to agree to record linkage whereas participants with a diagnosis of anorexia were less likely to agree. Increased likelihood of consent was also associated with increased genetic susceptibility to anorexia and reduced genetic risk for depression, and schizophrenia whereas there was no significant genetic effect for neuroticism. Overall, our results show moderate differences among these subs les. Most current epidemiological studies do not adjust, nor search, for attrition biases at the genetic level. The possibility to do so is a strength of s les such as the AGDS. Our results suggest that analyses can be made more robust by identifying attrition biases both on the phenotypic and genetic level, and either contextualising them as a potential limitation or performing sensitivity analyses adjusting for them.
Publisher: Springer Science and Business Media LLC
Date: 12-10-2022
DOI: 10.1038/S41586-022-05275-Y
Abstract: Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge s le sizes 1 . Here, using data from a genome-wide association study of 5.4 million in iduals of erse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-s le estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller s le sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.
Publisher: Wiley
Date: 21-12-2013
DOI: 10.1002/IJC.28655
Abstract: Coffee and tea contain numerous antimutagenic and antioxidant components and high levels of caffeine that may protect against colorectal cancer (CRC). We investigated the association between coffee and tea consumption and CRC risk and studied potential effect modification by CYP1A2 and NAT2 genotypes, enzymes involved in the metabolization of caffeine. Data from 477,071 participants (70.2% female) of the European Investigation into Cancer and Nutrition (EPIC) cohort study were analyzed. At baseline (1992-2000) habitual (total, caffeinated and decaffeinated) coffee and tea consumption was assessed with dietary questionnaires. Cox proportional hazards models were used to estimate adjusted hazard ratio's (HR) and 95% confidence intervals (95% CI). Potential effect modification by genotype-based CYP1A2 and NAT2 activity was studied in a nested case-control set of 1,252 cases and 2,175 controls. After a median follow-up of 11.6 years, 4,234 participants developed CRC (mean age 64.7 ± 8.3 years). Total coffee consumption (high vs. non/low) was not associated with CRC risk (HR 1.06, 95% CI 0.95-1.18) or subsite cancers, and no significant associations were found for caffeinated (HR 1.10, 95% CI 0.97-1.26) and decaffeinated coffee (HR 0.96, 95% CI 0.84-1.11) and tea (HR 0.97, 95% CI 0.86-1.09). High coffee and tea consuming subjects with slow CYP1A2 or NAT2 activity had a similar CRC risk compared to non/low coffee and tea consuming subjects with a fast CYP1A2 or NAT2 activity, which suggests that caffeine metabolism does not affect the link between coffee and tea consumption and CRC risk. This study shows that coffee and tea consumption is not likely to be associated with overall CRC.
Publisher: Springer Science and Business Media LLC
Date: 23-04-2019
DOI: 10.1038/S41467-019-09572-5
Abstract: Genome-wide association studies (GWASs) of medication use may contribute to understanding of disease etiology, could generate new leads relevant for drug discovery and can be used to quantify future risk of medication taking. Here, we conduct GWASs of self-reported medication use from 23 medication categories in approximately 320,000 in iduals from the UK Biobank. A total of 505 independent genetic loci that meet stringent criteria ( P 10 −8 /23) for statistical significance are identified. We investigate the implications of these GWAS findings in relation to biological mechanism, potential drug target identification and genetic risk stratification of disease. Amongst the medication-associated genes are 16 known therapeutic-effect target genes for medications from 9 categories. Two of the medication classes studied are for disorders that have not previously been subject to large GWAS (hypothyroidism and gastro-oesophageal reflux disease).
Publisher: Springer Science and Business Media LLC
Date: 07-2015
DOI: 10.1038/NATURE14618
Publisher: Cold Spring Harbor Laboratory
Date: 15-01-2020
DOI: 10.1101/2020.01.14.905927
Abstract: Polygenic scores (PGS) have been widely used to predict complex traits and risk of diseases using variants identified from genome-wide association studies (GWASs). To date, most GWASs have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European populations. Here, we develop a new theory to predict the relative accuracy (RA, relative to the accuracy in populations of the same ancestry as the discovery population) of PGS across ancestries. We used simulations and real data from the UK Biobank to evaluate our results. We found across various simulation scenarios that the RA of PGS based on trait-associated SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of SNP effect sizes and heritability. Altogether, we find that LD and MAF differences between ancestries explain alone up to ~70% of the loss of RA using European-based PGS in African ancestry for traits like body mass index and height. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWASs are mostly shared across continents.
Publisher: Public Library of Science (PLoS)
Date: 08-08-2013
Publisher: Springer Science and Business Media LLC
Date: 26-11-2018
Publisher: Cold Spring Harbor Laboratory
Date: 10-01-2022
DOI: 10.1101/2022.01.07.475305
Abstract: Common SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge s le sizes. Here we show, using GWAS data from 5.4 million in iduals of erse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-s le estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller s le sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.
Publisher: Cold Spring Harbor Laboratory
Date: 20-12-2018
DOI: 10.1101/501049
Abstract: It is common that one medication is prescribed for several indications, and conversely that several medications are prescribed for the same indication, suggesting a complex biological network for disease risk and its relationship with pharmacological function. Genome-wide association studies (GWASs) of medication-use may contribute to understanding of disease etiology, generation of new leads relevant for drug discovery and quantify prospects for precision medicine. We conducted GWAS to profile self-reported medication-use from 23 categories in approximately 320,000 in iduals from the UK Biobank. A total of 505 independent genetic loci that met stringent criteria for statistical significance were identified. We investigated the implications of these GWAS findings in relation to biological mechanism, drug target identification and genetic risk stratification of disease. Amongst the medication-associated genes were 16 known therapeutic-effect target genes for medications from 9 categories.
Publisher: Springer Science and Business Media LLC
Date: 12-09-2016
DOI: 10.1038/NG.3667
Publisher: Springer Science and Business Media LLC
Date: 09-02-2014
DOI: 10.1038/NG.2897
Publisher: Springer Science and Business Media LLC
Date: 11-02-2015
DOI: 10.1038/NATURE14177
Publisher: Springer Science and Business Media LLC
Date: 14-04-2013
DOI: 10.1038/NG.2610
Publisher: Springer Science and Business Media LLC
Date: 11-02-2015
DOI: 10.1038/NATURE14132
Publisher: Springer Science and Business Media LLC
Date: 19-12-2017
Abstract: To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European in iduals and exome sequencing of 12,940 in iduals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1–5%) non-coding variants in the whole-genome sequenced in iduals and 99.7% of low-frequency coding variants in the whole-exome sequenced in iduals. Each variant was tested for association with T2D in the sequenced in iduals, and, to increase power, most were tested in larger numbers of in iduals ( % of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
Publisher: Elsevier BV
Date: 11-2018
Publisher: Springer Science and Business Media LLC
Date: 18-09-2023
No related organisations have been discovered for Loic Yengo.
Start Date: 2023
End Date: 12-2026
Amount: $904,843.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2020
End Date: 12-2022
Amount: $409,364.00
Funder: Australian Research Council
View Funded Activity