Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

Hao, Xingjie; Zeng, Ping; Zhang, Shujun; Zhou, Xiang

doi:10.1371/journal.pgen.1007186

Cited by 30 publications

(42 citation statements)

References 97 publications

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“…Although the linear (weighted) kernels are still most widely applied in genetic association studies, owing to its biological interpretability and computational efficiency, there have been recent attempts to design kernels that adapt to more complex signal structures by taking into account prior biological knowledge. For example, new variance component kernels have been proposed to accommodate functional annotations of noncoding variations in the mixed‐effects model framework (Hao, Zeng, Zhang, & Zhou, ; Z. He, Xu, Lee, & Ionita‐Laza, ).…”

Section: Kernel Designs and Multiple Kernelsmentioning

confidence: 99%

A review of kernel methods for genetic association studies

Larson

Chen

Schaid

2019

Genetic Epidemiology

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Evaluating the association of multiple genetic variants with a trait of interest by use of kernel‐based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.

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Section: Kernel Designs and Multiple Kernelsmentioning

confidence: 99%

A review of kernel methods for genetic association studies

Larson

Chen

Schaid

2019

Genetic Epidemiology

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“…Following [8], we partially validated the identified trait-relevant tissue/cell types for the GWAS diseases by searching PubMed. We reasoned that, if the tissue or cell type is relevant to the disease of interest, then there would be previous publications studying the disease in the particular tissue or cell type.…”

Section: Relevance Between Traits and Tissues/cell Types From Pubmed mentioning

confidence: 99%

“…Next, we attempted to validate the identified trait-relevant tissue by performing a PubMed search following the main idea in [8]. Specifically, we reasoned that, if a tissue is relevant to the disease of interest, then there would be previous publications studying the disease on the particular tissue.…”

Section: Real Data Application: Inferring Trait-relevant Tissues Withmentioning

confidence: 99%

“…However, these previous methods have ignored an important biological feature of gene expression data; that is, genes are interconnected with each other and are co-regulated together. Such gene co-expression patterns occur in a tissue specific or cell type specific fashion [8]. Certain gene co-expression sub-networks have been shown to contain valuable information for predicting gene-level association effect sizes on diseases in GWASs [8,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Such gene co-expression patterns occur in a tissue specific or cell type specific fashion [8]. Certain gene co-expression sub-networks have been shown to contain valuable information for predicting gene-level association effect sizes on diseases in GWASs [8,[20][21][22]. In addition, genes with high network connectivity are enriched for heritability of common diseases and disease related traits [23].…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

Shang

Smith

Zhou

2019

Preprint

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AbstractGenome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect sizes for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases. Our results also provide empirical evidence supporting one hypothesis of the omnigenic model: that trait-relevant gene co-expression networks underlie disease etiology.

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Incorporating multiple sets of eQTL weights into gene‐by‐environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

Yang

Tang

Risch

et al. 2020

Genetic Epidemiology

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It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene‐by‐environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data‐adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case–Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome‐wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking‐related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.

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Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

Cited by 30 publications

References 97 publications

A review of kernel methods for genetic association studies

A review of kernel methods for genetic association studies

Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

Incorporating multiple sets of eQTL weights into gene‐by‐environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

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