scholarly journals Leveraging molecular QTL to understand the genetic architecture of diseases and complex traits

2017 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Steven Gazal ◽  
Bryce van de Geijn ◽  
Hilary Finucane ◽  
Chelsea J.-T. Ju ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Complex Trait ◽  
Effect Sizes ◽  
Joint Analysis ◽  
Loss Of Function ◽  
Tissue Specific ◽  
Functional Annotations ◽  
Using Data

AbstractThere is increasing evidence that many GWAS risk loci are molecular QTL for gene ex-pression (eQTL), histone modification (hQTL), splicing (sQTL), and/or DNA methylation (meQTL). Here, we introduce a new set of functional annotations based on causal posterior prob-abilities (CPP) of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that these annotations are very strongly enriched for disease heritability across 41 independent diseases and complex traits (average N = 320K): 5.84x for GTEx eQTL, and 5.44x for eQTL, 4.27-4.28x for hQTL (H3K27ac and H3K4me1), 3.61x for sQTL and 2.81x for meQTL in BLUEPRINT (all P ≤ 1.39e-10), far higher than enrichments obtained using stan-dard functional annotations that include all significant molecular cis-QTL (1.17-1.80x). eQTL annotations that were obtained by meta-analyzing all 44 GTEx tissues generally performed best, but tissue-specific blood eQTL annotations produced stronger enrichments for autoimmune dis-eases and blood cell traits and tissue-specific brain eQTL annotations produced stronger enrich-ments for brain-related diseases and traits, despite high cis-genetic correlations of eQTL effect sizes across tissues. Notably, eQTL annotations restricted to loss-of-function intolerant genes from ExAC were even more strongly enriched for disease heritability (17.09x; vs. 5.84x for all genes; P = 4.90e-17 for difference). All molecular QTL except sQTL remained significantly enriched for disease heritability in a joint analysis conditioned on each other and on a broad set of functional annotations from previous studies, implying that each of these annotations is uniquely informative for disease and complex trait architectures.

scholarly journals Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

2018 ◽  
Author(s):  
Andrew D. Grotzinger ◽  
Mijke Rhemtulla ◽  
Ronald de Vlaming ◽  
Stuart J. Ritchie ◽  
Travis T. Mallard ◽  
...  
Keyword(s):  
Complex Traits ◽  
Structural Equation ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Covariance Structure ◽  
Equation Modeling ◽  
Joint Analysis ◽  
Summary Statistics ◽  
Individual Traits ◽  
Polygenic Scores

AbstractMethods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

scholarly journals Negative short-range genomic autocorrelation of causal effects on human complex traits

2020 ◽  
Author(s):  
Armin P. Schoech ◽  
Omer Weissbrod ◽  
Luke J. O’Connor ◽  
Nick Patterson ◽  
Huwenbo Shi ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Causal Effect ◽  
Complex Trait ◽  
Minor Allele ◽  
Effect Sizes ◽  
Point Estimate ◽  
Genetic Risk Prediction ◽  
Heritability Estimation ◽  
The Uk

AbstractMost models of complex trait genetic architecture assume that signed causal effect sizes of each SNP (defined with respect to the minor allele) are uncorrelated with those of nearby SNPs, but it is currently unknown whether this is the case. We develop a new method, autocorrelation LD regression (ACLR), for estimating the genome-wide autocorrelation of causal minor allele effect sizes as a function of genomic distance. Our method estimates these autocorrelations by regressing the products of summary statistics on distance-dependent LD scores. We determined that ACLR robustly assesses the presence or absence of nonzero autocorrelation, producing unbiased estimates with well-calibrated standard errors in null simulations regardless of genetic architecture; if true autocorrelation is nonzero, ACLR correctly detects its sign, although estimates of the autocorrelation magnitude are susceptible to bias in cases of certain genetic architectures. We applied ACLR to 31 diseases and complex traits from the UK Biobank (average N=331K), meta-analyzing results across traits. We determined that autocorrelations were significantly negative at distances of 1-50bp (P = 8 × 10−6, point estimate −0.35 ±0.08) and 50-100bp (P = 2 × 10−3, point estimate −0.33 ± 0.11). We show that the autocorrelation is primarily driven by pairs of SNPs in positive LD, which is consistent with the expectation that linked SNPs with opposite effects are less impacted by natural selection. Our findings suggest that this mechanism broadly affects complex trait genetic architectures, and we discuss implications for association mapping, heritability estimation, and genetic risk prediction.

scholarly journals Cross-population Joint Analysis of eQTLs: Fine Mapping and Functional Annotation

2014 ◽  
Author(s):  
Xiaoquan Wen ◽  
Francesca Luca ◽  
Roger Pique-Regi
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Statistical Approach ◽  
Molecular Level ◽  
Joint Analysis ◽  
P Value ◽  
Analysis Framework ◽  
Population Groups ◽  
Functional Annotations ◽  
Eqtl Data

Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at the molecular level. In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach effectively identifies multiple independent {\it cis}-eQTL signals that are consistently presented across populations, accounting for heterogeneity in allele frequencies and patterns of linkage disequilibrium. Furthermore, our analysis framework enables integrating high-resolution functional annotations into analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from five population groups. From this analysis, we concluded that i) joint analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTLs; ii) many genes harbor multiple independent eQTLs in their {\it cis} regions; iii) genetic variants that disrupt transcription factor binding are significantly enriched in eQTLs (p-value = 4.93 × 10-22).

scholarly journals GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Wei ◽  
Paula S. Ramos ◽  
Kelly J. Hunt ◽  
Bethany J. Wolf ◽  
Gary Hardiman ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Association Studies ◽  
Genetic Relationships ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Risk Variants ◽  
Novel Approach ◽  
Medical Benefits ◽  
Rigorous Framework

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes.

scholarly journals Polygenicity of complex traits is explained by negative selection

2018 ◽  
Author(s):  
Luke J. O’Connor ◽  
Armin P. Schoech ◽  
Farhad Hormozdiari ◽  
Steven Gazal ◽  
Nick Patterson ◽  
...  
Keyword(s):  
Complex Traits ◽  
Negative Selection ◽  
Genetic Architecture ◽  
Low Frequency ◽  
Effect Sizes ◽  
Common Disease ◽  
Common Variants ◽  
Robust Statistical Method ◽  
Genetic Signal ◽  
Definition Of

Complex traits and common disease are highly polygenic: thousands of common variants are causal, and their effect sizes are almost always small. Polygenicity could be explained by negative selection, which constrains common-variant effect sizes and may reshape their distribution across the genome. We refer to this phenomenon as flattening, as genetic signal is flattened relative to the underlying biology. We introduce a mathematical definition of polygenicity, the effective number of associated SNPs, and a robust statistical method to estimate it. This definition of polygenicity differs from the number of causal SNPs, a standard definition; it depends strongly on SNPs with large effects. In analyses of 33 complex traits (average N=361k), we determined that common variants are ∼4x more polygenic than low-frequency variants, consistent with pervasive flattening. Moreover, functionally important regions of the genome have increased polygenicity in proportion to their increased heritability, implying that heritability enrichment reflects differences in the number of associations rather than their magnitude (which is constrained by selection). We conclude that negative selection constrains the genetic signal of biologically important regions and genes, reshaping genetic architecture.

scholarly journals Evolution of polygenic traits under global vs local adaptation

Genetics ◽  
2022 ◽  
Vol 220 (1) ◽  
Author(s):  
Sam Yeaman
Keyword(s):  
Local Adaptation ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Population Genomics ◽  
Natural Populations ◽  
Evolutionary Process ◽  
Frequency Effect ◽  
Theoretical Predictions ◽  
Using Data ◽  
And Migration

Abstract Observations about the number, frequency, effect size, and genomic distribution of alleles associated with complex traits must be interpreted in light of evolutionary process. These characteristics, which constitute a trait’s genetic architecture, can dramatically affect evolutionary outcomes in applications from agriculture to medicine, and can provide a window into how evolution works. Here, I review theoretical predictions about the evolution of genetic architecture under spatially homogeneous, global adaptation as compared with spatially heterogeneous, local adaptation. Due to the tension between divergent selection and migration, local adaptation can favor “concentrated” genetic architectures that are enriched for alleles of larger effect, clustered in a smaller number of genomic regions, relative to expectations under global adaptation. However, the evolution of such architectures may be limited by many factors, including the genotypic redundancy of the trait, mutation rate, and temporal variability of environment. I review the circumstances in which predictions differ for global vs local adaptation and discuss where progress can be made in testing hypotheses using data from natural populations and lab experiments. As the field of comparative population genomics expands in scope, differences in architecture among traits and species will provide insights into how evolution works, and such differences must be interpreted in light of which kind of selection has been operating.

scholarly journals Disease heritability enrichment of regulatory elements is concentrated in elements with ancient sequence age and conserved function across species

2018 ◽  
Author(s):  
Margaux L.A. Hujoel ◽  
Steven Gazal ◽  
Farhad Hormozdiari ◽  
Bryce van de Geijn ◽  
Alkes L. Price
Keyword(s):  
Complex Traits ◽  
Negative Selection ◽  
Target Gene ◽  
Regulatory Element ◽  
Complex Trait ◽  
Regulatory Elements ◽  
Mean Value ◽  
Regulatory Function ◽  
Loss Of Function ◽  
The Mean

AbstractRegulatory elements, e.g. enhancers and promoters, have been widely reported to be enriched for disease and complex trait heritability. We investigated how this enrichment varies with the age of the underlying genome sequence, the conservation of regulatory function across species, and the target gene of the regulatory element. We estimated heritability enrichment by applying stratified LD score regression to summary statistics from 41 independent diseases and complex traits (average N =320K) and meta-analyzing results across traits. Enrichment of human enhancers and promoters was larger in elements with older sequence age, assessed via alignment with other species irrespective of conserved functionality: enhancer elements with ancient sequence age (older than the split between marsupial and placental mammals) were 8.8x enriched (vs. 2.5x for all enhancers; p = 3e-14), and promoter elements with ancient sequence age were 13.5x enriched (vs. 5.1x for all promoters; p = 5e-16). Enrichment of human enhancers and promoters was also larger in elements whose regulatory function was conserved across species, e.g. human enhancers that were enhancers in ≥5 of 9 other mammals were 4.6x enriched (p = 5e-12 vs. all enhancers). Enrichment of human promoters was larger in promoters of loss-of-function intolerant genes: 12.0x enrichment (p = 8e-15 vs. all promoters). The mean value of several measures of negative selection within these genomic annotations mirrored all of these findings. Notably, the annotations with these excess heritability enrichments were jointly significant conditional on each other and on our baseline-LD model, which includes a broad set of coding, conserved, regulatory and LD-related annotations.

scholarly journals Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

2019 ◽  
Author(s):  
Huwenbo Shi ◽  
Kathryn S. Burch ◽  
Ruth Johnson ◽  
Malika K. Freund ◽  
Gleb Kichaev ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Empirical Bayes ◽  
Genetic Correlations ◽  
Causal Snps ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Snps ◽  
Genome Wide ◽  
Summary Data

AbstractDespite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

scholarly journals A polygenic p factor for major psychiatric disorders

2018 ◽  
Author(s):  
Saskia Selzam ◽  
Jonathan R. I. Coleman ◽  
Avshalom Caspi ◽  
Terrie E. Moffitt ◽  
Robert Plomin
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Genetic Research ◽  
Strong Support ◽  
Principal Component ◽  
Complex Trait ◽  
Genome Wide ◽  
P Factor ◽  
First Time

AbstractIt has recently been proposed that a single dimension, called the p factor, can capture a person’s liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by using principal component analysis on matrices of genetic correlations between major psychiatric disorders estimated by three methods – family study, Genome-wide Complex Trait Analysis, and Linkage-Disequilibrium Score Regression – and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7,026 unrelated individuals. All disorders loaded on a first unrotated principal component, which accounted for 57%, 43%, 34% and 19% of the variance respectively for each method. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.

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