Evidence map›Paper›PMID 34025719›Full record

ArticleFrontiers in genetics2021

BTOB: Extending the Biased GWAS to Bivariate GWAS.

Junxian Zhu, Qiao Fan, Wenying Deng, Yimeng Wang, Xiaobo Guo

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Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Junxian ZhuDepartment of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou, China.
Qiao FanCenter for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore, Singapore.
Wenying DengDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States.
Yimeng WangDepartment of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou, China.
Xiaobo GuoDepartment of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, a number of literatures published large-scale genome-wide association studies (GWASs) for human diseases or traits while adjusting for other heritable covariate. However, it is known that these GWASs are biased, which may lead to biased genetic estimates or even false positives. In this study, we provide a method called "BTOB" which extends the biased GWAS to bivariate GWAS by integrating the summary association statistics from the biased GWAS and the GWAS for the adjusted heritable covariate. We employ the proposed BTOB method to analyze the summary association statistics from the large scale meta-GWASs for waist-to-hip ratio (WHR) and body mass index (BMI), and show that the proposed approach can help identify more susceptible genes compared with the corresponding univariate GWASs. Theoretical results and simulations also confirm the validity and efficiency of the proposed BTOB method.

Indexed as

biasedbivariate GWASGWASheritable covariatesummary association statistics

Identifiers

PMID34025719
PMCPMC8134661

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.