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.

LDpred-funct: incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets

2018 ◽  
Author(s):  
Carla Márquez-Luna ◽  
Steven Gazal ◽  
Po-Ru Loh ◽  
Samuel S. Kim ◽  
Nicholas Furlotte ◽  
...  
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Causal Effect ◽  
Complex Trait ◽  
Training Data ◽  
Data Sets ◽  
Uk Biobank ◽  
Validation Data ◽  
Functional Regions ◽  
The Uk

AbstractGenetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a new method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, which includes coding, conserved, regulatory and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. LDpred-funct attained higher prediction accuracy than other polygenic prediction methods in simulations using real genotypes. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank. We used association statistics from British-ancestry samples as training data (avg N=373K) and samples of other European ancestries as validation data (avg N=22K), to minimize confounding. LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R2=0.144; highest R2=0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (total N=1107K; higher heritability in UK Biobank cohort) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.

scholarly journals Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Marion Patxot ◽  
Daniel Trejo Banos ◽  
Athanasios Kousathanas ◽  
Etienne J. Orliac ◽  
Sven E. Ojavee ◽  
...  
Keyword(s):  
Open Source Software ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Functional Enrichment ◽  
Regulatory Regions ◽  
Coding Regions ◽  
Heritability Estimation ◽  
The Uk ◽  
Marker Discovery

AbstractWe develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

scholarly journals Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

2018 ◽  
Author(s):  
Guiyan Ni ◽  
Julius van der Werf ◽  
Xuan Zhou ◽  
Elina Hyppönen ◽  
Naomi R. Wray ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Interaction Analysis ◽  
Complex Trait ◽  
Reaction Norm ◽  
Significant Heterogeneity ◽  
Association Analyses ◽  
Norm Model ◽  
The Uk ◽  
Residual Variances

ABSTRACTThe genomics era has brought useful tools to dissect the genetic architecture of complex traits. We propose a reaction norm model (RNM) to tackle genotype-environment correlation and interaction problems in the context of genome-wide association analyses of complex traits. In our approach, an environmental risk factor affecting the trait of interest can be modeled as dependent on a continuous covariate that is itself regulated by genetic as well as environmental factors. Our multivariate RNM approach allows the joint modelling of the relation between the genotype (G) and the covariate (C), so that both their correlation (association) and interaction (effect modification) can be estimated. Hence we jointly estimate genotype-covariate correlation and interaction (GCCI). We demonstrate using simulation that the proposed multivariate RNM performs better than the current state-of-the-art methods that ignore G-C correlation. We apply the method to data from the UK Biobank (N= 66,281) in analysis of body mass index using smoking quantity as a covariate. We find a highly significant G-C correlation, but a negligible G-C interaction. In contrast, when a conventional G-C interaction analysis is applied (i.e., G-C correlation is not included in the model), highly significant G-C interaction estimates are found. It is also notable that we find a significant heterogeneity in the estimated residual variances across different covariate levels probably due to residual-covariate interaction. Using simulation we also show that the residual variances estimated by genomic restricted maximum likelihood (GREML) or linkage disequilibrium score regression (LDSC) can be inflated in the presence of interactions, implying that the currently reported SNP-heritability estimates from these methods should be interpreted with caution. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses and that the failure to do so may lead to substantial biases in inferences relating to genetic architecture of complex traits, including estimated SNP-heritability.

scholarly journals Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carla Márquez-Luna ◽  
Steven Gazal ◽  
Po-Ru Loh ◽  
Samuel S. Kim ◽  
Nicholas Furlotte ◽  
...  
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Causal Effect ◽  
Complex Trait ◽  
Training Data ◽  
Data Sets ◽  
Uk Biobank ◽  
Functional Regions ◽  
The Uk

AbstractPolygenic risk prediction is a widely investigated topic because of its promising clinical applications. Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, including coding, conserved, regulatory, and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank (avg N = 373 K as training data). LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R2 = 0.144; highest R2 = 0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (N = 1107 K) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.

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 Probabilistic inference of the genetic architecture of functional enrichment of complex traits

2020 ◽  
Author(s):  
Marion Patxot ◽  
Daniel Trejo Banos ◽  
Athanasios Kousathanas ◽  
Etienne J Orliac ◽  
Sven E Ojavee ◽  
...  
Keyword(s):  
Effect Size ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Penalized Regression ◽  
Functional Enrichment ◽  
Effect Sizes ◽  
Uk Biobank ◽  
Regulatory Regions ◽  
Coding Regions ◽  
The Uk

Due to the complexity of linkage disequilibrium (LD) and gene regulation, understanding the genetic basis of common complex traits remains a major challenge. We develop a Bayesian model (BayesRR-RC) implemented in a hybrid-parallel algorithm that scales to whole-genome sequence data on many hundreds of thousands of individuals, taking 22 seconds per iteration to estimate the inclusion probabilities and effect sizes of 8.4 million markers and 78 SNP-heritability parameters in the UK Biobank. Unlike naive penalized regression or mixed-linear model approaches, BayesRR-RC accurately estimates annotation-specific genetic architecture, determines the underlying joint effect size distribution and provides a probabilistic determination of association within marker groups in a single step. Of the genetic variation captured for height, body mass index, cardiovascular disease, and type-2 diabetes in the UK Biobank, only ≤ 10% is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, up to 40% to intronic regions, and 22-28% to distal 10-500kb upstream regions. ≥60% of the variance contributed by these exonic, intronic and distal 10-500kb regions is underlain by many thousands of common variants, each with larger average effect sizes compared to the rest of the genome. We also find differences in the relationship between effect size and heterozygosity across annotation groups and across traits. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance for just these four traits. In the Estonian Biobank, we show improved prediction accuracy over other approaches and generate a posterior predictive distribution for each individual.

scholarly journals On Using Local Ancestry to Characterize the Genetic Architecture of Human Phenotypes: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations as a Model

2018 ◽  
Author(s):  
Yizhen Zhong ◽  
Minoli Perera ◽  
Eric R. Gamazon
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Type I ◽  
Eqtl Mapping ◽  
Entire Genome ◽  
Local Ancestry ◽  
Heritability Estimation

AbstractBackgroundUnderstanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of use of local ancestry on high-dimensional omics analyses, including most prominently expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored.ResultsHere we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We estimate the trait variance explained by ancestry using local admixture relatedness between individuals. Using National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that use of local ancestry can substantially improve eQTL mapping and heritability estimation and characterize the sparse versus polygenic component of gene expression in admixed and multiethnic populations respectively. Using simulations of diverse genetic architectures to estimate trait heritability and the level of confounding, we show improved accuracy given individual-level data and evaluate a summary statistics based approach. Furthermore, we provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches.ConclusionOur study has important methodological implications on genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

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 The Critical Needs and Challenges for Genetic Architecture Studies in Africa

2018 ◽  
Author(s):  
Alicia R. Martin ◽  
Solomon Teferra ◽  
Marlo Möller ◽  
Eileen G. Hoal ◽  
Mark J. Daly
Keyword(s):  
Genetic Diversity ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Prediction Models ◽  
Research Capacity ◽  
Genetic Associations ◽  
Genetic Studies ◽  
Environmental Pressures ◽  
Homogeneous Population ◽  
Genetic Risk Prediction

Human genetic studies have long been vastly Eurocentric, raising a key question about the generalizability of these study findings to other populations. Because humans originated in Africa, these populations retain more genetic diversity, and yet individuals of African descent have been tremendously underrepresented in genetic studies. The diversity in Africa affords ample opportunities to improve fine-mapping resolution for associated loci, discover novel genetic associations with phenotypes, build more generalizable genetic risk prediction models, and better understand the genetic architecture of complex traits and diseases subject to varying environmental pressures. Thus, it is both ethically and scientifically imperative that geneticists globally surmount challenges that have limited progress in African genetic studies to date while meaningfully including African investigators, as greater inclusivity and enhanced research capacity affords enormous opportunities to accelerate genomic discoveries that translate more effectively to all populations. We review the advantages and challenges of studying the genetic architecture of complex traits and diseases in Africa. For example, with greater genetic diversity comes greater ancestral heterogeneity; this higher level of understudied diversity can yield novel genetic findings, but some methods that assume homogeneous population structure and work well in European populations may work less well in the presence of greater diversity and heterogeneity in African populations. Consequently, we advocate for methodological development that will accelerate studies important for all populations, especially those currently underrepresented in genetics.

scholarly journals Detection and quantification of inbreeding depression for complex traits from SNP data

2017 ◽  
Vol 114 (32) ◽  
pp. 8602-8607 ◽  
Author(s):  
Loic Yengo ◽  
Zhihong Zhu ◽  
Naomi R. Wray ◽  
Bruce S. Weir ◽  
Jian Yang ◽  
...  
Keyword(s):  
Inbreeding Depression ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Handgrip Strength ◽  
Uk Biobank ◽  
Snp Data ◽  
Causal Variants ◽  
Auditory Acuity ◽  
The Uk ◽  
Detection And Quantification

Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in ∼140,000 participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between −2.3 and −5.2 phenotypic SDs for complete inbreeding; P<0.001). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data.

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