scholarly journals Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

2018 ◽  
Author(s):  
Alvaro N. Barbeira ◽  
Milton D. Pividori ◽  
Jiamao Zheng ◽  
Heather E. Wheeler ◽  
Dan L. Nicolae ◽  
...  
Keyword(s):  
Complex Traits ◽  
Target Genes ◽  
Association Studies ◽  
Test Methods ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Genome Wide ◽  
Gene Level ◽  
The Individual ◽  
The Uk

AbstractIntegration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association test methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restricts our ability to detect associations. Here we propose an efficient statistical method that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes: MulTiXcan. MulTiXcan integrates evidence across multiple panels while taking into account their correlation. We apply our method to a broad set of complex traits available from the UK Biobank and show that we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed an extension to work on summary statistics: S-MulTiXcan, which we show yields highly concordant results with the individual level version. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

scholarly journals Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

2021 ◽  
Author(s):  
Steven Gazal ◽  
Omer Weissbrod ◽  
Farhad Hormozdiari ◽  
Kushal Dey ◽  
Joseph Nasser ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Common Disease ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Functional Interpretation ◽  
Genome Wide

Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies. 

scholarly journals Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies

2021 ◽  
Vol 118 (25) ◽  
pp. e2023184118
Author(s):  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
Yunong Lin ◽  
Zijie Zhao ◽  
Jiawen Chen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Phenotypic Data ◽  
Individual Level ◽  
Indirect Genetic Effects ◽  
Genome Wide

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

scholarly journals Dissecting the genetics of complex traits using summary association statistics

2016 ◽  
Author(s):  
Bogdan Pasaniuc ◽  
Alkes L. Price
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Privacy Concerns ◽  
Individual Level ◽  
The Past ◽  
Genome Wide ◽  
Association Data ◽  
Limit Access

AbstractDuring the past decade, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced vast repositories of genetic variation and trait measurements across millions of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyze summary association statistics. Here we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

scholarly journals Nonlinear post-selection inference for genome-wide association studies

2020 ◽  
Author(s):  
Lotfi Slim ◽  
Clément Chatelain ◽  
Chloé-Agathe Azencott
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Association Testing ◽  
Testing Tools ◽  
Disease Mechanisms ◽  
Novel Approach ◽  
Genome Wide ◽  
Gene Level ◽  
The Uk

AbstractAssociation testing in genome-wide association studies (GWAS) is often performed at either the SNP level or the gene level. The two levels can bring different insights into disease mechanisms. In the present work, we provide a novel approach based on nonlinear post-selection inference to bridge the gap between them. Our approach selects, within a gene, the SNPs or LD blocks most associated with the phenotype, before testing their combined effect. Both the selection and the association testing are conducted nonlinearly. We apply our tool to the study of BMI and its variation in the UK BioBank. In this study, our approach outperformed other gene-level association testing tools, with the unique benefit of pinpointing the causal SNPs.

scholarly journals Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies

2020 ◽  
Author(s):  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
Yunong Lin ◽  
Zijie Zhao ◽  
Jiawen Chen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Phenotypic Data ◽  
Individual Level ◽  
Indirect Genetic Effects ◽  
Genome Wide

AbstractMarginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a novel statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower BMI, less active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. Polygenic transmission disequilibrium test showed a significant over-transmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

scholarly journals Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

2020 ◽  
Author(s):  
Ying Wang ◽  
Jing Guo ◽  
Guiyan Ni ◽  
Jian Yang ◽  
Peter M. Visscher ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
African Ancestry ◽  
Real Data ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Causal Variants ◽  
The Uk

AbstractPolygenic 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.

Advancements of transcriptome imputation and related transcriptome-wide association studies

2019 ◽  
Vol 1 (1) ◽  
pp. 14-16
Author(s):  
Wen Zhang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
Joint Tissue ◽  
New Era ◽  
Risk Variants ◽  
Genome Wide ◽  
Gene Level ◽  
Genetic Mechanisms

Despite the progresses of genome-wide association studies (GWASs) in revealing genetic mechanisms of human complex traits, the basis through which most identified risk variants function are highly unknown and need further investigations as well as discoveries. Recent advancements of transcriptome predictions put the transcriptome-wide association studies (TWASs) forward into a new era. TWAS through imputed transcriptomes could discover more gene-trait associations and relevant joint-tissue TWAS via eQTL analysis provide insights into furthering elucidations about gene-level association studies in difficult-to-acquire tissues. This mini-review goes over the recent advancements of gene expression imputations as well as the gene-trait association studies, which highlight the importance of genetically regulated expression (GReX) in this area.

scholarly journals VAMPIRE: A model blood cell trait focused annotation tool for interpretation and prioritization of complex trait genome-wide association study results

2021 ◽  
Author(s):  
Cheynna Crowley ◽  
Quan Sun ◽  
Le Huang ◽  
Erik L. Bao ◽  
Paul Auer ◽  
...  
Keyword(s):  
Blood Cell ◽  
Association Study ◽  
Complex Traits ◽  
Web Application ◽  
Target Genes ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractThousands of genetic loci have been identified as associated with hematological indices (red blood cell, white blood cell, and platelet related traits), as well as other complex traits and disease. However, most loci identified are noncoding and not clearly linked to target genes, and tools are needed to prioritize the most likely functional variants for experimental follow-up. We here describe VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny (http://shiny.bios.unc.edu/vampire/) for blood cell trait associated loci from recent large multi-ethnic genome-wide association studies (GWAS). This tool efficiently displays information from blood cell relevant tissues on epigenomic signatures, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information from Hi-C and similar technologies, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Variants are classified into multiple prioritization categories according to these functional signatures. Leveraging data generated from independent functional validation experiments, we demonstrate that our prioritized variants are enriched within experimentally validated variant sets. VAMPIRE allows rapid prioritization and interpretation of blood cell trait GWAS variants and could be easily adapted for use with other complex trait GWAS results and extended to new annotation sources.Author SummaryMany large genome-wide association studies (GWAS) have recently been performed for blood cell traits, with thousands of associations identified. However, most of the associated variants are in noncoding regions and are often hard to interpret, link to genes, and prioritize for functional follow-up. Similar challenges exist for genetic studies of many other traits and diseases. Trying to translate knowledge of GWAS significant variants to target genes and biological insights, we here describe VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny (http://shiny.bios.unc.edu/vampire/) for blood cell trait associated loci from recent large multi-ethnic GWAS. This tool displays a variety of information including epigenomic signatures, variant impact on protein and gene expression, chromatin conformation information, and publicly available GWAS and phenome-wide association study (PheWAS) results for other traits. We classified variants into annotation categories using this information, and show that variants in the highest priority categories are enriched in likely causal variant sets from previous functional experiments. We anticipate this tool will guide appropriate variants to prioritize for experimental validation for researchers studying blood cell traits, as well as providing an easily adaptable model for the creation of similar annotation tools for other complex traits and diseases.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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