Gene-based association tests using GWAS summary statistics and incorporating eQTL

PDF Version Also Available for Download.

Description

Article proposes a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. The results show that this newly developed method can identify more significant genes than other methods.

Physical Description

14 p.

Creation Information

Cao, Xuwei; Wang, Xuexia; Zhang, Shuanglin & Sha, Qiuying March 3, 2022.

Context

This article is part of the collection entitled: UNT Scholarly Works and was provided by the UNT College of Science to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 16 times. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Authors

Publisher

Provided By

UNT College of Science

The College of Science provides students with the high-demand skills and knowledge to succeed as researchers and professionals. The College includes four departments: Biology, Chemistry, Math, and Physics, and is also home to a number of interdisciplinary programs, centers, institutes, intercollegiate programs, labs, and services.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

Article proposes a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. The results show that this newly developed method can identify more significant genes than other methods.

Physical Description

14 p.

Notes

Abstract: Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.

Source

  • Scientific Reports, 12, Springer Nature, March 3 2022

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

Publication Information

  • Publication Title: Scientific Reports
  • Volume: 12
  • Article Identifier: 3553 (2022)
  • Pages: 14
  • Peer Reviewed: Yes

Collections

This article is part of the following collection of related materials.

UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • March 3, 2022

Added to The UNT Digital Library

  • June 29, 2022, 7:32 p.m.

Description Last Updated

  • Nov. 1, 2022, 9:57 a.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 16

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Cao, Xuwei; Wang, Xuexia; Zhang, Shuanglin & Sha, Qiuying. Gene-based association tests using GWAS summary statistics and incorporating eQTL, article, March 3, 2022; (https://digital.library.unt.edu/ark:/67531/metadc1954014/: accessed April 15, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Science.

Back to Top of Screen