LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations

Abstract Motivation Much effort has been made toward understanding the genetic architecture of complex traits and diseases. In the past decade, fruitful GWAS findings have highlighted the important role of regulatory variants and pervasive pleiotropy. Because of the accumulation of GWAS data on a wide range of phenotypes and high-quality functional annotations in different cell types, it is timely to develop a statistical framework to explore the genetic architecture of human complex traits by integrating rich data resources. Results In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPM’s performance and compared it with related methods. Then, we applied it to analyze 44 GWASs with 9 genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits. Availability and implementation The LPM package, all simulation codes and real datasets in this study are available at https://github.com/mingjingsi/LPM. Supplementary information Supplementary data are available at Bioinformatics online.

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LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations

10.1101/439133 ◽

2018 ◽

Author(s):

Jingsi Ming ◽

Tao Wang ◽

Can Yang

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Association Studies ◽

Probit Model ◽

Genome Wide Association Studies ◽

Computational Framework ◽

Functional Annotations ◽

Risk Variants ◽

Genome Wide ◽

Cell Type Specific

AbstractMuch effort has been made toward understanding the genetic architecture of complex traits and diseases. Recent results from genome-wide association studies (GWASs) suggest the importance of regulatory genetic effects and pervasive pleiotropy among complex traits. In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes, but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPM’s performance and compared it with related methods. Then we applied it to analyze 44 GWASs with nine genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits. The LPM package is available at https://github.com/mingjingsi/LPM.

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Exposing flaws in S-LDSC; reply to Gazal et al.

10.1101/280784 ◽

2018 ◽

Cited By ~ 5

Author(s):

Doug Speed ◽

David J Balding

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Real Data ◽

Dnase I ◽

Gwas Data ◽

Functional Annotations ◽

Highly Sensitive ◽

Hypersensitive Sites ◽

Simple Demonstration ◽

Better Than

In our recent publication,1 we examined the two heritability models most widely used when estimating SNP heritability: the GCTA Model, which is used by the software GCTA2 and upon which LD Score regression (LDSC) is based,3 and the LDAK Model, which is used by our software LDAK.4 First we demonstrated the importance of choosing an appropriate heritability model, by showing that estimates of SNP heritability can be highly sensitive to which model is assumed. Then we empirically tested the GCTA and LDAK Models on GWAS data for a wide variety of complex traits. We found that the LDAK Model fits real data both significantly and substantially better than the GCTA Model, indicating that LDAK estimates more accurately describe the genetic architecture of complex traits than those from GCTA or LDSC.Some of our most striking results were our revised estimates of functional enrichments (the heritability enrichments of SNP categories defined by functional annotations). In general, estimates from LDAK were substantially more modest than previous estimates based on the GCTA Model. For example, we estimated that DNase I hypersensitive sites (DHS) were 1.4-fold (SD 0.1) enriched, whereas a study using GCTA had found they were 5.1-fold (SD 0.5) enriched,5 and we estimated that conserved SNPs were 1.3-fold (SD 0.3) enriched, whereas a study using S-LDSC (stratified LDSC) had found they were 13.3-fold (SD 1.5) enriched.6In their correspondence, Gazal et al. dispute our findings. They assert that the heritability model assumed by LDSC is more realistic than the LDAK Model, and that estimates of enrichment from S-LDSC7 are more accurate than those from LDAK. Here, we explain why their justification for preferring the model used by LDSC is incorrect, and provide a simple demonstration that S-LDSC produces unreliable estimates of enrichment.

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TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

10.1101/507525 ◽

2018 ◽

Cited By ~ 3

Author(s):

Sini Nagpal ◽

Xiaoran Meng ◽

Michael P. Epstein ◽

Lam C. Tsoi ◽

Matthew Patrick ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Bayesian Model ◽

Genetic Architecture ◽

Bayesian Method ◽

Association Studies ◽

Gwas Data ◽

Nonparametric Bayesian ◽

Transcriptomic Data ◽

Special Cases

AbstractThe transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool “TIGAR” (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS 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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SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations

10.1101/2021.10.04.463133 ◽

2021 ◽

Author(s):

Wenmin Zhang ◽

Hamed S Najafabadi ◽

Yue Li

Keyword(s):

Fine Mapping ◽

Complex Traits ◽

Genetic Architecture ◽

Association Studies ◽

Computational Cost ◽

Mapping Method ◽

Genome Wide Association Studies ◽

Functional Annotations ◽

Genome Wide ◽

Causal Variants

Identifying causal variants from genome-wide association studies (GWASs) is challenging due to widespread linkage disequilibrium (LD). Functional annotations of the genome may help prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. However, classical fine-mapping methods have a high computational cost, particularly when the underlying genetic architecture and LD patterns are complex. Here, we propose a novel approach, SparsePro, to efficiently conduct functionally informed statistical fine-mapping. Our method enjoys two major innovations: First, by creating a sparse low-dimensional projection of the high-dimensional genotype, we enable a linear search of causal variants instead of an exponential search of causal configurations used in existing methods; Second, we adopt a probabilistic framework with a highly efficient variational expectation-maximization algorithm to integrate statistical associations and functional priors. We evaluate SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved more accurate and well-calibrated posterior inference with greatly reduced computation time. We demonstrate the utility of SparsePro by investigating the genetic architecture of five functional biomarkers of vital organs. We identify potential causal variants contributing to the genetically encoded coordination mechanisms between vital organs and pinpoint target genes with potential pleiotropic effects. In summary, we have developed an efficient genome-wide fine-mapping method with the ability to integrate functional annotations. Our method may have wide utility in understanding the genetics of complex traits as well as in increasing the yield of functional follow-up studies of GWASs.

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Global genetic heterogeneity in adaptive traits

10.1101/2021.02.26.433043 ◽

2021 ◽

Cited By ~ 1

Author(s):

William Andres Lopez-Arboleda ◽

Stephan Reinert ◽

Magnus Nordborg ◽

Arthur Korte

Keyword(s):

Genetic Heterogeneity ◽

Complex Traits ◽

Genetic Architecture ◽

Human Genetics ◽

Association Studies ◽

Local Environment ◽

Genome Wide Association Studies ◽

Major Objective ◽

Genome Wide ◽

Wide Range

AbstractUnderstanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS for a variety of phenotypes in the model plant species Arabididopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, while others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be incredibly sensitive to sampling and population structure.

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Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning

Bioinformatics ◽

10.1093/bioinformatics/btaa229 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3811-3817 ◽

Cited By ~ 3

Author(s):

Songshan Yang ◽

Jiawei Wen ◽

Scott T Eckert ◽

Yaqun Wang ◽

Dajiang J Liu ◽

...

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Association Studies ◽

Superior Performance ◽

Supplementary Information ◽

Tuning Parameter ◽

Snp Analysis ◽

Genome Wide Association Studies ◽

Multiple Testing Correction ◽

Wide Range

Abstract Motivation Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype–phenotype association. Results In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. Availability and implementation R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. Supplementary information Supplementary data are available at Bioinformatics online.

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

Bioinformatics ◽

10.1093/bioinformatics/btz880 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2009-2016 ◽

Cited By ~ 6

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

Abstract Motivation Although 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) 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. Results In 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 data. Availability and implementation The implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM. Supplementary information Supplementary data are available at Bioinformatics online.

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Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009483 ◽

2021 ◽

Vol 17 (10) ◽

pp. e1009483

Author(s):

Ruth Johnson ◽

Kathryn S. Burch ◽

Kangcheng Hou ◽

Mario Paciuc ◽

Bogdan Pasaniuc ◽

...

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Complex Trait ◽

Causal Variant ◽

Causal Snps ◽

Fundamental Parameter ◽

A Genome ◽

Wide Range ◽

Zero Effect ◽

Genomic Regions

The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs.

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Motif-Raptor: A Cell Type-Specific and Transcription Factor Centric Approach for Post-GWAS Prioritization of Causal Regulators

Bioinformatics ◽

10.1093/bioinformatics/btab072 ◽

2021 ◽

Author(s):

Qiuming Yao ◽

Paolo Ferragina ◽

Yakir Reshef ◽

Guillaume Lettre ◽

Daniel E Bauer ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Association Studies ◽

Cell Types ◽

Chromatin Accessibility ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Systematic Understanding ◽

Coding Variants

Abstract Motivation Genome-wide association studies (GWAS) have identified thousands of common trait-associated genetic variants but interpretation of their function remains challenging. These genetic variants can overlap the binding sites of transcription factors (TFs) and therefore could alter gene expression. However, we currently lack a systematic understanding on how this mechanism contributes to phenotype. Results We present Motif-Raptor, a TF-centric computational tool that integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. Given trait associated non-coding variants, Motif-Raptor can recover relevant cell types and critical TFs to drive hypotheses regarding their mechanism of action. We tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated. Availability Motif-Raptor is freely available as a Python package at: https://github.com/pinellolab/MotifRaptor. Supplementary information Supplementary data are available at Bioinformatics online.

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