scholarly journals Epistasis within the MHC contributes to the genetic architecture of celiac disease

2014 ◽  
Author(s):  
Ben Goudey ◽  
Gad Abraham ◽  
Eder Kikianty ◽  
Qiao Wang ◽  
Dave Rawlinson ◽  
...  
Keyword(s):  
Celiac Disease ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Class Iii ◽  
Case Control Studies ◽  
Model Free ◽  
Genome Wide ◽  
Hla Haplotypes ◽  
The Uk ◽  
Single Snps

Epistasis has long been thought to contribute to the genetic aetiology of complex diseases, yet few robust epistatic interactions in humans have been detected. We have conducted exhaustive genome-wide scans for pairwise epistasis in five independent celiac disease (CD) case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 20 significant epistatic signals within the HLA region which achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD epistatic signal corresponded to genes in the HLA class III region, in particular PRRC2A and GPANK1/C6orf47, which are known to contain variants for non-Hodgkin's lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for epistatic variants outside the MHC was not observed. Both within and between European populations, we observed striking consistency of epistatic models and epistatic model distribution. Within the UK population, models of CD based on both epistatic and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. Models of only epistatic pairs or additive single-SNPs showed similar levels of CD variance explained, indicating the existence of a substantial overlap of additive and epistatic components. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.

scholarly journals Expanding the Genetic Architecture of Nicotine Dependence and its Shared Genetics with Multiple Traits: Findings from the Nicotine Dependence GenOmics (iNDiGO) Consortium

2020 ◽  
Author(s):  
Bryan C. Quach ◽  
Michael J. Bray ◽  
Nathan C. Gaddis ◽  
Mengzhen Liu ◽  
Teemu Palviainen ◽  
...  
Keyword(s):  
Nicotine Dependence ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
African Ancestry ◽  
Multiple Traits ◽  
Expression Of Genes ◽  
Genome Wide ◽  
A Genome ◽  
The Uk ◽  
Smoking Index

AbstractCigarette smoking is the leading cause of preventable morbidity and mortality. Knowledge is evolving on genetics underlying initiation, regular smoking, nicotine dependence (ND), and cessation. We performed a genome-wide association study using the Fagerström Test for ND (FTND) in 58,000 smokers of European or African ancestry. Five genome-wide significant loci, including two novel loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416) were identified, and loci reported for other smoking traits were extended to ND. Using the heaviness of smoking index (HSI) in the UK Biobank (N=33,791), rs2714700 was consistently associated, but rs1862416 was not associated, likely reflecting ND features not captured by the HSI. Both variants were cis-eQTLs (rs2714700 for MAGI2-AS3 in hippocampus, rs1862416 for TENM2 in lung), and expression of genes spanning ND-associated variants was enriched in cerebellum. SNP-based heritability of ND was 8.6%, and ND was genetically correlated with 17 other smoking traits (rg=0.40–0.95) and co-morbidities. Our results emphasize the FTND as a composite phenotype that expands genetic knowledge of smoking, including loci specific to ND.

scholarly journals Variable prediction accuracy of polygenic scores within an ancestry group

2019 ◽  
Author(s):  
Hakhamanesh Mostafavi ◽  
Arbel Harpak ◽  
Dalton Conley ◽  
Jonathan K Pritchard ◽  
Molly Przeworski
Keyword(s):  
Prediction Accuracy ◽  
Human Genetics ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Gwas Study ◽  
Ancestry Group ◽  
Genome Wide ◽  
Polygenic Scores ◽  
The Uk

AbstractFields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

scholarly journals Global genetic heterogeneity in adaptive traits

2021 ◽  
Author(s):  
William Andres Lopez-Arboleda ◽  
Stephan Reinert ◽  
Magnus Nordborg ◽  
Arthur Korte
Keyword(s):  
Genetic Heterogeneity ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Association Studies ◽  
Local Environment ◽  
Genome Wide Association Studies ◽  
Major Objective ◽  
Genome Wide ◽  
Wide Range

AbstractUnderstanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS for a variety of phenotypes in the model plant species Arabididopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, while others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be incredibly sensitive to sampling and population structure.

scholarly journals Phenome-wide search for pleiotropic loci highlights key genes and molecular pathways for human complex traits

2019 ◽  
Author(s):  
Anton E. Shikov ◽  
Alexander V. Predeus ◽  
Yury A. Barbitoff
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Genome Wide ◽  
Mhc Locus ◽  
High Degree ◽  
Human Complex

AbstractOver recent decades, genome-wide association studies (GWAS) have dramatically changed the understanding of human genetics. A recent genetic data release by UK Biobank has allowed many researchers worldwide to have comprehensive look into the genetic architecture of thousands of human phenotypes. In this study, we developed a novel statistical framework to assess phenome-wide significance and genetic pleiotropy across the human phenome based on GWAS summary statistics. We demonstrate widespread sharing of genetic architecture components between distinct groups of traits. Apart from known multiple associations inside the MHC locus, we discover high degree of pleiotropy for genes involved in immune system function, apoptosis, hemostasis cascades, as well as lipid and xenobiotic metabolism. We find several notable examples of novel pleiotropic loci (e.g., the MIR2113 microRNA broadly associated with cognition), and provide several possible mechanisms for these association signals. Our results allow for a functional phenome-wide look into the shared components of genetic architecture of human complex traits, and highlight crucial genes and pathways for their development.

scholarly journals A scalable method for estimating the regional polygenicity of complex traits

2020 ◽  
Author(s):  
Ruth Johnson ◽  
Kathryn S. Burch ◽  
Kangcheng Hou ◽  
Mario Paciuc ◽  
Bogdan Pasaniuc ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Effect Sizes ◽  
Marginal Effect ◽  
High Cholesterol ◽  
Genome Wide ◽  
Unbiased Estimates ◽  
Genomic Regions ◽  
Insight Into

AbstractA key question in human genetics is understanding the proportion of SNPs modulating a particular phenotype or the proportion of susceptibility SNPs for a disease, termed polygenicity. Previous studies have observed that complex traits tend to be highly polygenic, opposing the previous belief that only a handful of SNPs contribute to a trait. Beyond these genome-wide estimates, the distribution of polygenicity across genomic regions as well as the genomic factors that affect regional polygenicity remain poorly understood. A reason for this gap is that methods for estimating polygenicity utilize SNP effect sizes from GWAS. However, estimating regional polygenicity from GWAS effect sizes involves untangling the correlation between SNPs due to LD, leading to intractable computations for even a small number of SNPs. In this work, we propose a scalable method, BEAVR, to estimate the regional polygenicity of a trait given marginal effect sizes from GWAS and LD information. We implement a Gibbs sampler to estimate the posterior distribution of the regional polygenicity and derive a fast, algorithmic update to circumvent the computational bottlenecks associated with LD. The runtime of our algorithm is 𝒪(MK) for M SNPs and K susceptibility SNPs, where the number of susceptibility SNPs is typically K ≪ M. By modeling the full LD structure, we show that BEAVR provides unbiased estimates of polygenicity compared to previous methods that only partially model LD. Finally, we show how estimates of regional polygenicity for BMI, eczema, and high cholesterol provide insight into the regional genetic architecture of each trait.

scholarly journals Variable prediction accuracy of polygenic scores within an ancestry group

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Hakhamanesh Mostafavi ◽  
Arbel Harpak ◽  
Ipsita Agarwal ◽  
Dalton Conley ◽  
Jonathan K Pritchard ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Human Genetics ◽  
Association Studies ◽  
Economic Status ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Ancestry Group ◽  
Genome Wide ◽  
Polygenic Scores ◽  
The Uk

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Migraine, Human Genetics and a Passion for Science

2020 ◽  
Vol 23 (2) ◽  
pp. 105-106
Author(s):  
Dale R. Nyholt
Keyword(s):  
Genetic Architecture ◽  
Latent Class ◽  
Human Genetics ◽  
Polygenic Risk Score ◽  
Association Analyses ◽  
Polygenic Risk ◽  
The Past ◽  
Risk Variants ◽  
Genome Wide ◽  
Wide Range

AbstractThis note reflects on my collaborations with Nick Martin and the GenEpi group over the past 20 years. Over the past two decades, our work together has focused on gene mapping and understanding the genetic architecture of a wide range of traits with particular foci on migraine and common baldness. Our migraine research has included latent class and twin analyses cumulating in genome-wide association analyses which had identified 44 (34 new) risk variants for migraine. Leveraging these results through polygenic risk score analyses identified subgroups of patients likely to respond to triptans (an acute migraine drug), providing the first step toward precision medicine in migraine [Kogelman et al. (2019) Neurology Genetics, 5, e364].

Integrating disease and drug-related phenotypes for improved identification of pharmacogenomic variants

2021 ◽  
Vol 22 (5) ◽  
pp. 251-261
Author(s):  
Tom W Ouellette ◽  
Galen EB Wright ◽  
Britt I Drögemöller ◽  
Colin JD Ross ◽  
Bruce C Carleton
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Genome Wide Association ◽  
Research Network ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Inflammatory Bowel ◽  
The Uk ◽  
Shared Genetic ◽  
New Onset

Aim: To improve the identification and interpretation of pharmacogenetic variants through the integration of disease and drug-related traits. Materials & methods: We hypothesized that integrating genome-wide disease and pharmacogenomic data may drive new insights into drug toxicity and response by identifying shared genetic architecture. Pleiotropic variants were identified using a methodological framework incorporating colocalization analysis. Results: Using genome-wide association studies summary statistics from the UK Biobank, European Bioinformatics Institute genome-wide association studies catalog and the Pharmacogenomics Research Network, we validated pleiotropy at the ABCG2 locus between allopurinol response and gout and identified novel pleiotropy between antihypertensive-induced new-onset diabetes, Crohn’s disease and inflammatory bowel disease at the IL18RAP/SLC9A4 locus. Conclusion: New mechanistic insights and genetic loci can be uncovered by identifying pleiotropy between disease and drug-related traits.

scholarly journals Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture

2019 ◽  
Author(s):  
Kangcheng Hou ◽  
Kathryn S. Burch ◽  
Arunabha Majumdar ◽  
Huwenbo Shi ◽  
Nicholas Mancuso ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Accurate Estimation ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Genome Wide ◽  
Wide Range ◽  
Fundamental Quantity ◽  
The Uk ◽  
Scale Data

AbstractThe proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-heritability reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.

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