Widespread signatures of negative selection in the genetic architecture of human complex traits

AbstractEstimation of the joint distribution of effect size and minor allele frequency (MAF) for genetic variants is important for understanding the genetic basis of complex trait variation and can be used to detect signature of natural selection. We develop a Bayesian mixed linear model that simultaneously estimates SNP-based heritability, polygenicity (i.e. the proportion of SNPs with nonzero effects) and the relationship between effect size and MAF for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752), and show that on average across 28 traits, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (p < 0.05/28 =1.8×10−3) signatures of natural selection for 23 out of 28 traits including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. We further apply the method to 27,869 gene expression traits (N = 1,748), and identify 30 genes that show significant (p < 2.3×10−6) evidence of natural selection. All the significant estimates of the relationship between effect size and MAF in either complex traits or gene expression traits are consistent with a model of negative selection, as confirmed by forward simulation. We conclude that natural selection acts pervasively on human complex traits shaping genetic variation in the form of negative selection.

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Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2018.09.007 ◽

2018 ◽

Vol 103 (5) ◽

pp. 654-665 ◽

Cited By ~ 29

Author(s):

Eilis Hannon ◽

Tyler J. Gorrie-Stone ◽

Melissa C. Smart ◽

Joe Burrage ◽

Amanda Hughes ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Complex Traits ◽

Trait Loci ◽

The Relationship ◽

Methylation Quantitative Trait Loci

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Opportunities and limits of combining microbiome and genome data for complex trait prediction

10.1101/2020.10.05.325977 ◽

2020 ◽

Author(s):

Miguel Pérez-Enciso ◽

Laura M. Zingaretti ◽

Yuliaxis Ramayo-Caldas ◽

Gustavo de los Campos

Keyword(s):

Complex Traits ◽

Variance Components ◽

Complex Trait ◽

Parameter Estimates ◽

Phenotypic Variance ◽

Genome Data ◽

Individual Snps ◽

Simulation Strategy ◽

Microbiome Data ◽

Trait Prediction

AbstractThe analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

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Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits

10.1101/297176 ◽

2018 ◽

Author(s):

Eilis Hannon ◽

Tyler J Gorrie-Stone ◽

Melissa C Smart ◽

Joe Burrage ◽

Amanda Hughes ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Genetic Variation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Complex Traits ◽

Common Genetic Variation ◽

Online Data ◽

The Relationship ◽

Methylation Quantitative Trait Loci

ABSTRACTCharacterizing the complex relationship between genetic, epigenetic and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. In this study, we describe the most comprehensive analysis of common genetic variation on DNA methylation (DNAm) to date, using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (P < 6.52x10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified using previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits, using Summary data–based Mendelian Randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression, identifying 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database (http://www.epigenomicslab.com/online-data-resources/) as a resource to the research community.

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Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples

Genetics ◽

10.1534/genetics.118.301865 ◽

2019 ◽

Vol 212 (3) ◽

pp. 919-929

Author(s):

Daniel A. Skelly ◽

Narayanan Raghupathy ◽

Raymond F. Robledo ◽

Joel H. Graber ◽

Elissa J. Chesler

Keyword(s):

Gene Expression ◽

Canonical Correlation ◽

Complex Traits ◽

Behavioral Genetics ◽

Association Studies ◽

Complex Trait ◽

Integrated Analysis ◽

Data Set ◽

Trait Analysis ◽

Molecular Features

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

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Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

10.1101/808295 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yuhua Zhang ◽

Corbin Quick ◽

Ketian Yu ◽

Alvaro Barbeira ◽

Francesca Luca ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Large Scale ◽

Molecular Mechanisms ◽

Association Studies ◽

Complex Trait ◽

Causal Effects ◽

Biological Mechanisms ◽

Integrative Framework ◽

Eqtl Data

AbstractTranscriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

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CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

10.1101/652263 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yi Yang ◽

Xingjie Shi ◽

Yuling Jiao ◽

Jian Huang ◽

Min Chen ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Mixed Model ◽

Association Studies ◽

Gwas Data ◽

Supplementary Information ◽

Summary Statistics ◽

Individual Level ◽

The Relationship

AbstractMotivationAlthough genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required.ResultsIn this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS [email protected] and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.Supplementary informationSupplementary data are available at Bioinformatics online.

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

10.1101/420166 ◽

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.

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Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection

Nature Genetics ◽

10.1038/ng.3954 ◽

2017 ◽

Vol 49 (10) ◽

pp. 1421-1427 ◽

Cited By ~ 163

Author(s):

Steven Gazal ◽

Hilary K Finucane ◽

Nicholas A Furlotte ◽

Po-Ru Loh ◽

Pier Francesco Palamara ◽

...

Keyword(s):

Linkage Disequilibrium ◽

Complex Traits ◽

Negative Selection ◽

Human Complex

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Opportunities and limits of combining microbiome and genome data for complex trait prediction

Genetics Selection Evolution ◽

10.1186/s12711-021-00658-7 ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Miguel Pérez-Enciso ◽

Laura M. Zingaretti ◽

Yuliaxis Ramayo-Caldas ◽

Gustavo de los Campos

Keyword(s):

Complex Traits ◽

Variance Component ◽

Reproducing Kernel ◽

Reproducing Kernel Hilbert Space ◽

Complex Trait ◽

Bayesian Variable Selection ◽

Phenotypic Variance ◽

Genotype Data ◽

Microbiome Data ◽

Trait Prediction

Abstract Background Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host’s genome, microbiome, and phenome be recovered? Methods Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data. Results Using real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms’ taxa that influence the phenotype. Conclusions While we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome.

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