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 GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

Genetic Methodologies and Applications

2017 ◽  
Author(s):  
Shaun M. Purcell
Keyword(s):  
Mental Illness ◽  
Genetic Risk ◽  
Genetic Basis ◽  
Common Mental Disorders ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
The Past ◽  
Genome Wide ◽  
Experimental Approaches ◽  
Large Families

Mental illness is highly heritable, yet it has been difficult historically to identify the specific genes that comprise that risk. This difficulty resides in the fact that the genetic risk for all common mental disorders is polygenic, with perhaps hundreds of genetic variations, each of small effect, contributing to the overall risk. Despite these challenges, the field has made dramatic advances over the past decade in beginning to understand the genetic basis of mental illness. This chapter provides an overview of the experimental approaches used, beginning with epidemiology and population genetics to define the heritability of an illness, to classic studies of large families and linkage disequilibrium analysis, to genome-wide investigations including genome-wide association studies (GWAS), exome sequencing, and whole genome sequencing. Increasingly, these genetic advances are being understood within the biological context of disease pathophysiology.

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 Unravelling the complex genetic basis of growth in New Zealand silver trevally (Pseudocaranx georgianus)

2021 ◽  
Author(s):  
Noemie Valenza-Troubat ◽  
Sara Montanari ◽  
Peter Ritchie ◽  
Maren Wellenreuther
Keyword(s):  
Qtl Mapping ◽  
Complex Traits ◽  
Genetic Basis ◽  
Growth Traits ◽  
Association Studies ◽  
Snp Markers ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Powerful Approach ◽  
Trait Locus

AbstractGrowth directly influences production rate and therefore is one of the most important and well-studied trait in animal breeding. However, understanding the genetic basis of growth has been hindered by its typically complex polygenic architecture. Here, we performed quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) for 10 growth traits that were observed over two years in 1,100 F1 captive-bred trevally (Pseudocaranx georgianus). We constructed the first high-density linkage map for trevally, which included 19,861 single nucleotide polymorphism (SNP) markers, and discovered eight QTLs for height, length and weight on linkage groups 3, 14 and 18. Using GWAS, we further identified 113 SNP-trait associations, uncovering 10 genetic hot spots involved in growth. Two of the markers found in the GWAS co-located with the QTLs previously mentioned, demonstrating that combining QTL mapping and GWAS represents a powerful approach for the identification and validation of loci controlling complex traits. This is the first study of its kind for trevally. Our findings provide important insights into the genetic architecture of growth in this species and supply a basis for fine mapping QTLs, marker-assisted selection, and further detailed functional analysis of the genes underlying growth in trevally.

scholarly journals Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

2019 ◽  
Vol 20 (1) ◽  
pp. 461-493 ◽  
Author(s):  
Guy Sella ◽  
Nicholas H. Barton
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Practical Importance ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Heritable Variation ◽  
Genome Wide ◽  
Polygenic Adaptation

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Core Gene ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Gene Sets ◽  
Genome Wide ◽  
Trait Variance ◽  
Core Genes

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

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 Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits

2018 ◽  
Author(s):  
Malika Kumar Freund ◽  
Kathryn Burch ◽  
Huwenbo Shi ◽  
Nicholas Mancuso ◽  
Gleb Kichaev ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Mendelian Disorder ◽  
Mendelian Disorders ◽  
Gene Sets ◽  
Genome Wide ◽  
Significant Enrichment

ABSTRACTAlthough recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we quantify the overlap of genes identified through large-scale genome-wide association studies (GWAS) for 62 complex traits and diseases with genes known to cause 20 broad categories of Mendelian disorders. We identify a significant enrichment of phenotypically-matched Mendelian disorder genes in GWAS gene sets. Further, we observe elevated GWAS effect sizes near phenotypically-matched Mendelian disorder genes. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of phenotypically-matched Mendelian disorder genes. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by noncoding variants in complex traits, and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.

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 A Bayesian Method to Incorporate Hundreds of Functional Characteristics with Association Evidence to Improve Variant Prioritization

2013 ◽  
Author(s):  
Sarah A Gagliano ◽  
Michael R Barnes ◽  
Michael E Weale ◽  
Jo Knight
Keyword(s):  
Complex Traits ◽  
Bayesian Method ◽  
Association Studies ◽  
Bayes Factors ◽  
Genome Wide Association Studies ◽  
Functional Characteristics ◽  
Genome Wide ◽  
Dna Elements ◽  
Association Data ◽  
Predicted Values

The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals (“hits”) to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL) and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype-specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher predicted values showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in psoriasis GWAS data and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data.

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