ORCID Profile
0000-0002-0096-9765
Current Organisation
Aarhus University
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Biostatistics | Genetics | Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Animal Production and Animal Primary Products not elsewhere classified | Plant Production and Plant Primary Products not elsewhere classified | Health not elsewhere classified |
Publisher: Cold Spring Harbor Laboratory
Date: 10-10-2020
DOI: 10.1101/2020.10.09.333542
Abstract: Following years of epigenome-wide association studies (EWAS), traits analysed to date tend to yield few associations. Reinforcing this observation, we conducted EWAS on 400 traits and 16 yielded at least one association at the conventional significance threshold (P ×10 −7 ). To investigate why EWAS yield is low, we formally estimated the proportion of phenotypic variation captured by 421,693 blood derived DNA methylation markers (h 2 EWAS ) across all 400 traits. The mean h 2 EWAS was zero, with evidence for regular cigarette smoking exhibiting the largest association with all markers (h 2 EWAS =0.42) and the only one surpassing a false discovery rate 0.1. Though underpowered to determine the h 2 EWAS value for any one trait, h 2 EWAS was predictive of the number of EWAS hits across the traits analysed (AUC=0.7). Modelling the contributions of the methylome on a per-site versus a per-region basis gave varied h 2 EWAS estimates (r=0.47) but neither approach obtained substantially higher model fits across all traits. Our analysis indicates that most complex traits do not heavily associate with markers commonly measured in EWAS within blood. However, it is likely DNA methylation does capture variation in some traits and h 2 EWAS may be a reasonable way to prioritise traits that are likely to yield associations.
Publisher: Cold Spring Harbor Laboratory
Date: 09-09-2016
DOI: 10.1101/074310
Abstract: SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (SD 3) higher than those obtained from the widely-used software GCTA, and 25% (SD 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability using our improved heritability model their estimated contribution is only 24%.
Publisher: Springer Science and Business Media LLC
Date: 18-04-2012
DOI: 10.1038/NATURE10983
Publisher: Cold Spring Harbor Laboratory
Date: 24-06-2014
Abstract: BLUP ( b est l inear u nbiased p rediction) is widely used to predict complex traits in plant and animal breeding, and increasingly in human genetics. The BLUP mathematical model, which consists of a single random effect term, was adequate when kinships were measured from pedigrees. However, when genome-wide SNPs are used to measure kinships, the BLUP model implicitly assumes that all SNPs have the same effect-size distribution, which is a severe and unnecessary limitation. We propose MultiBLUP, which extends the BLUP model to include multiple random effects, allowing greatly improved prediction when the random effects correspond to classes of SNPs with distinct effect-size variances. The SNP classes can be specified in advance, for ex le, based on SNP functional annotations, and we also provide an adaptive procedure for determining a suitable partition of SNPs. We apply MultiBLUP to genome-wide association data from the Wellcome Trust Case Control Consortium (seven diseases), and from much larger studies of celiac disease and inflammatory bowel disease, finding that it consistently provides better prediction than alternative methods. Moreover, MultiBLUP is computationally very efficient for the largest data set, which includes 12,678 in iduals and 1.5 M SNPs, the total analysis can be run on a single desktop PC in less than a day and can be parallelized to run even faster. Tools to perform MultiBLUP are freely available in our software LDAK.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 29-12-2017
DOI: 10.1212/WNL.0000000000004853
Abstract: To characterize, among European and Han Chinese populations, the genetic predictors of maculopapular exanthema (MPE), a cutaneous adverse drug reaction common to antiepileptic drugs. We conducted a case-control genome-wide association study of autosomal genotypes, including Class I and II human leukocyte antigen (HLA) alleles, in 323 cases and 1,321 drug-tolerant controls from epilepsy cohorts of northern European and Han Chinese descent. Results from each cohort were meta-analyzed. We report an association between a rare variant in the complement factor H–related 4 ( CFHR4 ) gene and phenytoin-induced MPE in Europeans ( p = 4.5 × 10 –11 odds ratio [95% confidence interval] 7 [3.2–16]). This variant is in complete linkage disequilibrium with a missense variant (N1050Y) in the complement factor H ( CFH ) gene. In addition, our results reinforce the association between HLA-A*31:01 and carbamazepine hypersensitivity. We did not identify significant genetic associations with MPE among Han Chinese patients. The identification of genetic predictors of MPE in CFHR4 and CFH, members of the complement factor H–related protein family, suggest a new link between regulation of the complement system alternative pathway and phenytoin-induced hypersensitivity in European-ancestral patients.
Publisher: Wiley
Date: 13-03-2022
Abstract: Complex‐trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome‐wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP‐heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on in idual genotype data and association test statistics, highlighting the role of a low‐dimensional model for the heritability of each SNP. We use state‐of‐art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: youtu.be/WC2u03V65MQ
Publisher: Elsevier BV
Date: 12-2013
Publisher: Springer Science and Business Media LLC
Date: 31-08-2023
DOI: 10.1038/S41588-023-01485-W
Abstract: Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.
Publisher: Cold Spring Harbor Laboratory
Date: 13-07-2023
DOI: 10.1101/2023.07.13.23292588
Abstract: We propose TetraHer, a method for estimating the liability heritability of binary phenotypes. TetraHer has five key features. Firstly, it can be applied to data from complex pedigrees, that contain multiple types of relationships. Secondly, it can correct for ascertainment of cases or controls. Thirdly, it can accommodate covariates. Fourthly, it can model the contribution of common environment. Fifthly, it produces a likelihood, that can be used for significance testing. We first demonstrate the validity of TetraHer on simulated data. We then use TetraHer to estimate liability heritability for 229 codes from the tenth International Classification of Diseases (ICD-10). We identify 118 codes with significant heritability (P .05/229), which can be used in future analyses for investigating the genetic architecture of human diseases.
Publisher: Elsevier BV
Date: 12-2012
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 2023
Publisher: Wiley
Date: 20-09-2019
DOI: 10.1002/GEPI.22259
Abstract: Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.
Publisher: Cold Spring Harbor Laboratory
Date: 18-09-2018
DOI: 10.1101/420703
Publisher: Cold Spring Harbor Laboratory
Date: 15-08-2019
DOI: 10.1101/736496
Abstract: There is currently much debate regarding the best way to model how heritability varies across the genome. The authors of GCTA recommend the GCTA-LDMS-I Model, the authors of LD Score Regression recommend the Baseline LD Model, while we have instead recommended the LDAK Model. Here we provide a statistical framework for assessing heritability models using summary statistics from genome-wide association studies. Using data from studies of 31 complex human traits (average s le size 136,000), we show that the Baseline LD Model is the most realistic of the existing heritability models, but that it can be improved by incorporating features from the LDAK Model. Our framework also provides a method for estimating the selection-related parameter α from summary statistics. We find strong evidence (P e-6) of negative genome-wide selection for traits including height, systolic blood pressure and college education, and that the impact of selection is stronger inside functional categories such as coding SNPs and promoter regions.
Publisher: Springer Science and Business Media LLC
Date: 2012
DOI: 10.1186/GM360
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2013
DOI: 10.1161/STROKEAHA.113.002186
Abstract: Visit-to-visit variability in blood pressure (vBP) is associated with ischemic stroke. We sought to determine whether such variability has genetic causes and whether genetic variants associated with BP variability are also associated with ischemic stroke. A Genome Wide Association Study (GWAS) for loci influencing BP variability was undertaken in 3802 in iduals from the Anglo-Scandinavian Cardiac Outcome Trial (ASCOT) study, in which long-term visit-to-visit and within-visit BP measures were available. Because BP variability is strongly associated with ischemic stroke, we genotyped the sentinel single nucleotide polymorphism in an independent ischemic stroke population comprising 8624 cases and 12 722 controls and in 3900 additional (Scandinavian) participants from the ASCOT study to replicate our findings. The ASCOT discovery GWAS identified a cluster of 17 correlated single nucleotide polymorphisms within the NLGN1 gene (3q26.31) associated with BP variability. The strongest association was with rs976683 ( P =1.4×10 −8 ). Conditional analysis of rs976683 provided no evidence of additional independent associations at the locus. Analysis of rs976683 in patients with ischemic stroke found no association for overall stroke (odds ratio, 1.02 95% CI, 0.97–1.07 P =0.52) or its subtypes: cardioembolic (odds ratio, 1.07 95% CI, 0.97–1.16 P =0.17), large vessel disease (odds ratio, 0.98 95% CI, 0.89–1.07 P =0.60), and small vessel disease (odds ratio, 1.07 95% CI, 0.97–1.17 P =0.19). No evidence for association was found between rs976683 and BP variability in the additional (Scandinavian) ASCOT participants ( P =0.18). We identified a cluster of single nucleotide polymorphisms at the NLGN1 locus showing significant association with BP variability. Follow-up analyses did not support an association with risk of ischemic stroke and its subtypes.
Publisher: Cold Spring Harbor Laboratory
Date: 04-10-2021
DOI: 10.1101/2021.10.04.462983
Abstract: Advances in whole-genome genotyping and sequencing have allowed genome-wide analyses of association, prediction and heritability in many organisms. However, the application of such analyses to bacteria is still in its infancy, being limited by difficulties including the plasticity of bacterial genomes and their strong population structure. Here we propose, and validate using simulations, a suite of genome-wide analyses for bacteria. We combine methods from human genetics and previous bacterial studies, including linear mixed models, elastic net and LD-score regression, and introduce innovations such as frequency-based allele coding, testing for both insertion/deletion and nucleotide effects and partitioning heritability by genome region. We then analyse three phenotypes of a major human pathogen Streptococcus pneumoniae , including the first analyses of minimum inhibitory concentrations (MIC) for each of two antibiotics, penicillin and ceftriaxone. We show that these are highly heritable leading to high prediction accuracy, which is explained by many genetic associations identified under good control of population structure effects. In the case of ceftriaxone MIC, these results are surprising because none of the isolates was resistant according to the inhibition zone diameter threshold. We estimate that just over half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes around a quarter of the heritability of ceftriaxone MIC. For the within-host survival phenotype carriage duration, no reliable associations were found but we observed moderate heritability and prediction accuracy, indicating a polygenic trait. While generating important new results for S. pneumoniae , we have critically assessed existing methods and introduced innovations that will be useful for future large-scale population genomics studies to help decipher the genetic architecture of bacterial traits. Genome-wide association, prediction and heritability analyses in bacteria are beginning to help unravel the genetic underpinnings of traits such as antimicrobial resistance, virulence, within-host survival and transmissibility. Progress to date is limited by challenges including the effects of strong population structure and variable recombination, and the many gaps in sequence alignments including the absence of entire genes in many isolates. More work is required to critically asses and develop methods for bacterial genomics. We address this task here, using a range of existing methods from bacterial and human genetics, such as linear mixed models, elastic net and LD-score regression. Using simulations, we first validate and then adapt these methods to introduce new analyses, including separate assessment of gap and nucleotide effects, a new allele coding for association analyses and a method to partition heritability into genome regions. We analyse within-host survival and two antimicrobial response traits of Streptococcus pneumoniae , identifying many novel associations while demonstrating good control of population structure and accurate prediction. We present both new results for an important pathogen and methodological advances that will be useful in guiding future studies in bacterial population genomics.
Publisher: Springer Science and Business Media LLC
Date: 23-03-2020
Publisher: Wiley
Date: 17-02-2019
DOI: 10.1111/ACPS.13009
Publisher: Oxford University Press (OUP)
Date: 19-08-2013
DOI: 10.1093/HMG/DDT403
Publisher: Springer Science and Business Media LLC
Date: 03-12-2018
Publisher: American Association for the Advancement of Science (AAAS)
Date: 06-09-2019
Abstract: Longitudinal data find a new variant controlling BMI in infancy and reveal genetic differences between infant and adult BMI.
Publisher: Springer Science and Business Media LLC
Date: 10-12-2018
DOI: 10.1038/S41467-018-07524-Z
Abstract: The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 in iduals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have erse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.
Publisher: Springer Science and Business Media LLC
Date: 22-05-2017
DOI: 10.1038/NG.3865
Publisher: Cold Spring Harbor Laboratory
Date: 05-07-2022
DOI: 10.1101/2022.07.01.22277161
Abstract: We present LDAK-GBAT, a novel tool for gene-based association testing using summary statistics from genome-wide association studies. We first evaluate LDAK-GBAT using ten phenotypes from the UK Biobank. We show that LDAK-GBAT is computationally efficient, taking approximately 30 minutes to analyze imputed data (2.9M common, genic SNPs), and requiring less than 10Gb memory. In total, LDAK-GBAT finds 680 genome-wide significant genes ( P ≤2.8×10 −6 ), which is at least 25% more than each of five existing tools (MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, sumFREGAT-PCA and sumFREGAT-ACAT), and 48% more than found by single-SNP analysis. We then analyze 99 additional phenotypes from the UK Biobank, the Million Veterans Project and the Psychiatric Genetics Consortium. In total, LDAK-GBAT finds 7957 significant genes, which is at least 24% more than the best existing tools, and 42% more than found by single-SNP analysis.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 06-2019
End Date: 11-2022
Amount: $410,000.00
Funder: Australian Research Council
View Funded Activity