ArticleAmerican journal of human genetics2018
A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics.
Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A novel CFAP57 nonsense mutation causes asthenozoospermia in a consanguineous Emirati family.Molecular biology reports · 2026Article
- An integrative association analysis for complex diseases in underrepresented groups by leveraging the trans-ethnic genetic similarity.Briefings in bioinformatics · 2026Article
- Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.Journal of translational medicine · 2024Article
- On the interpretation of transcriptome-wide association studies.PLoS genetics · 2023Article
- MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits.Human molecular genetics · 2023Article
- Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data.Journal of the American Statistical Association · 2023Article
- eQTL Set-Based Association Analysis Identifies Novel Susceptibility Loci for Barrett Esophagus and Esophageal Adenocarcinoma.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies.BMC bioinformatics · 2022Article
- Genetic Regulation of DNA Methylation Yields Novel Discoveries in GWAS of Colorectal Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
- A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women.Cancer research communications · 2022Article
- Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies.Journal of translational medicine · 2021Article
- JEM: A joint test to estimate the effect of multiple genetic variants on DNA methylation.Genetic epidemiology · 2021Article
- Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects.Gastroenterology · 2021Article
- Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics.Frontiers in genetics · 2021Article
- Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data.PLoS genetics · 2020Article
- Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.American journal of human genetics · 2020Article
- A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study.PLoS genetics · 2020Article
- Integrating germline and somatic genetics to identify genes associated with lung cancer.Genetic epidemiology · 2020Article
- A powerful and versatile colocalization test.PLoS computational biology · 2020Article
- The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE.Annual review of genomics and human genetics · 2019Review
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Authors and funding
35 authors.
Funding
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate phenotypes such as imputed gene expression through fixed effects, while allowing residual effects of individual variants to be random. We consider a set-based score testing framework, MiST (mixed effects score test), and propose two data-driven combination approaches to jointly test for the fixed and random effects. We establish the asymptotic distributions, which enable rapid calculation of p values for genome-wide analyses, and provide p values for fixed and random effects separately to enhance interpretability over GWASs. Extensive simulations demonstrate that our approaches are more powerful than existing ones. We apply our approach to a large-scale GWAS of colorectal cancer and identify two genes, POU5F1B and ATF1, which would have otherwise been missed by PrediXcan, after adjusting for all known loci.
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