Quantifying the impact of genetically regulated expression on complex traits and diseases

Cai, Mingxuan; Chen, Lin; Liu, Jin; Yang, Can

doi:10.1101/546580

Cited by 5 publications

(5 citation statements)

References 65 publications

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“…Two popular integrative gene-based association tests, PrediXcan (1) and TWAS (2), both of which will be called TWAS for simplicity in the sequel, leverage comprehensive regulatory knowledge and genome-wide association studies (GWAS) data either at the individual level or at the summary level, providing opportunities to evaluate or re-evaluate GWAS focusing on common variants (CVs), with minor allele frequency (MAF) higher than 5%. These methods and the related extensions (3,4) boost statistical power and provide important functional implications that usually lack in standard GWAS. For example, lipid traits, our phenotypes of interest, have estimated heritabilities of 30-70%, but the GWAS-identified CVs account for only 8-16% of the total phenotypic variation (5).…”

Section: Introductionmentioning

confidence: 99%

“…Since LFVs and CVs in close physical distance may be in linkage disequilibrium (LD), the observed contribution of LFVs could be masked or originated by CVs, while a recent study suggests additive contribution of de novo mutations and CVs to the risk of autism spectrum disorders (17). Therefore, we address two more aims: (3) what are the contributions of LFVs (or CVs), in addition to CVs (or LFVs), to predicting gene expression and (4) whether LFVs have a unique contribution to the association signal(s) of any identified gene(s) for lipid traits.…”

Section: Introductionmentioning

confidence: 99%

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Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

Yang

Wei

et al. 2020

Human Molecular Genetics

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Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and transcriptomic data to showcase their improved statistical power of identifying gene–trait associations while, importantly, offering further biological insights. TWAS have thus far focused on common variants as available from GWAS. Compared with common variants, the findings for or even applications to low-frequency variants are limited and their underlying role in regulating gene expression is less clear. To fill this gap, we extend TWAS to integrating whole genome sequencing data with transcriptomic data for low-frequency variants. Using the data from the Framingham Heart Study, we demonstrate that low-frequency variants play an important and universal role in predicting gene expression, which is not completely due to linkage disequilibrium with the nearby common variants. By including low-frequency variants, in addition to common variants, we increase the predictivity of gene expression for 79% of the examined genes. Incorporating this piece of functional genomic information, we perform association testing for five lipid traits in two UK10K whole genome sequencing cohorts, hypothesizing that cis-expression quantitative trait loci, including low-frequency variants, are more likely to be trait-associated. We discover that two genes, LDLR and TTC22, are genome-wide significantly associated with low-density lipoprotein cholesterol based on 3203 subjects and that the association signals are largely independent of common variants. We further demonstrate that a joint analysis of both common and low-frequency variants identifies association signals that would be missed by testing on either common variants or low-frequency variants alone.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

Yang

Wei

et al. 2020

Human Molecular Genetics

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“…First, MultiXcan and UTMOST cannot be used to identify the tissue-specific gene-trait associations. Many studies have shown that genes associated with complex traits are always regulated in a tissue-specific manner [16, 17, 18, 9]. For example, a recent study across 44 tissues confirmed this phenomenon in 18 complex traits [19], implying the persuasive role of tissue-specific regulatory effects in a wide range of complex traits.…”

Section: Introductionmentioning

confidence: 99%

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

Shi

Chai

Yang

et al. 2019

Preprint

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Transcriptome-wide association studies (TWAS) integrate expression quantitative trait loci 1 (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target 2 genes for complex traits. Several statistical methods have been recently proposed to improve 3 the performance of TWAS in gene prioritization by integrating transcriptome information 4 imputed from multiple tissues, and made significant achievements in improving the ability 5 to detect gene-trait associations. The major limitation of these methods is that they cannot 6 be used to elucidate the specific functional effects of candidate genes across different tissues. 7 Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWAS, using a 8 * Correspondence should be addressed to Jin Liu (jin.liu@duke-nus.edu.sg) 1 computational framework that integrates multi-tissue eQTL analysis. TisCoMM leverages the 9 co-regulation of genetic variations across different tissues explicitly via a unified collaborative 10 mixed model. With the probabilistic framework, TisCoMM not only performs hypothesis testing 11 to prioritize gene-trait associations but also ascertains the tissue-specific role of candidate 12 target genes in complex traits. To make use of widely available GWAS summary statistics, we 13 extend TisCoMM to use summary-level data, namely, TisCoMM-S 2 . Using extensive simulation 14 studies, we demonstrate both the type I error control and the improved statistical power for 15 the proposed methods. We further illustrate the benefits of our methods in applications to 16 summary-level GWAS data for 35 traits. Notably, apart from better identifying potential 17 trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The 18 follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system 19 plays an essential function for asthma development in both thyroid and lung tissues. 20Over the last decade, GWASs have achieved remarkable successes in identifying genetic 22 susceptible variants for a variety of complex traits [1]. However, the biological mechanisms to 23 understand these discoveries remain largely elusive as majority of these discoveries are located in 24 non-coding regions [2]. Recent expression quantitative trait loci (eQTLs) studies indicate that 25 the expression regulatory information may play a pivotal role bridging both genetic variants 26 and traits [3, 4, 5]. Cellular traits in comprehensive eQTL studies can serve as reference data, 27 providing investigators with an opportunity to examine the regulatory role of genetic variants on 28 gene expression. For example, the Genotype-Tissue Expression (GTEx) Project [6] has provided 29 DNA sequencing data from 948 individuals and collected gene-expression measurements of 54 30 tissues from these individuals in the recent V8 release. 31Transcriptome-wide association studies (TWAS) has been widely used to integrate the 32 expression regulatory information from these eQTL studies with GWAS to ...

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“…Second, since MR Steiger's method is based on using only a single SNP as IV, to improve both its statistical power and applicability, we extend it to allow multiple, and possibly correlated, SNPs. In particular, there is increasing interest in applying transcriptome-wide association studies (TWAS, or PrediXcan) to identify causal genes or other molecular/imaging endophenotypes by integrating GWAS with eQTL/ xQTL data [11][12][13][14][15][16][17][18][19][20]. In these applications, multiple correlated cis-SNPs near a gene are used as IVs to impute or predict the gene's expression level (or another endophenotype) to infer whether the gene's expression has a causal effect on a trait, say coronary artery disease (CAD); it is assumed that the causal relationship (if existing) is from the gene to the trait.…”

Section: Introductionmentioning

confidence: 99%

Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data

Xue

Pan

2020

PLoS Genet

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Orienting the causal relationship between pairs of traits is a fundamental task in scientific research with significant implications in practice, such as in prioritizing molecular targets and modifiable risk factors for developing therapeutic and interventional strategies for complex diseases. A recent method, called Steiger’s method, using a single SNP as an instrument variable (IV) in the framework of Mendelian randomization (MR), has since been widely applied. We report the following new contributions. First, we propose a single SNP-based alternative, overcoming a severe limitation of Steiger’s method in simply assuming, instead of inferring, the existence of a causal relationship. We also clarify a condition necessary for the validity of the methods in the presence of hidden confounding. Second, to improve statistical power, we propose combining the results from multiple, and possibly correlated, SNPs as multiple instruments. Third, we develop three goodness-of-fit tests to check modeling assumptions, including those required for valid IVs. Fourth, by relaxing one of the three IV assumptions in MR, we propose several methods, including an Egger regression-like approach and its multivariable version (analogous to multivariable MR), to account for horizontal pleiotropy of the SNPs/IVs, which is often unavoidable in practice. All our methods can simultaneously infer both the existence and (if so) the direction of a causal relationship, largely expanding their applicability over that of Steiger’s method. Although we focus on uni-directional causal relationships, we also briefly discuss an extension to bi-directional relationships. Through extensive simulations and an application to infer the causal directions between low density lipoprotein (LDL) cholesterol, or high density lipoprotein (HDL) cholesterol, and coronary artery disease (CAD), we demonstrate the superior performance and advantage of our proposed methods over Steiger’s method and bi-directional MR. In particular, after accounting for horizontal pleiotropy, our method confirmed the well known causal direction from LDL to CAD, while other methods, including bi-directional MR, might fail.

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Quantifying the impact of genetically regulated expression on complex traits and diseases

Cited by 5 publications

References 65 publications

Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data

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