An expression-directed linear mixed model discovering low-effect genetic variants

Author:

Li Qing1,Bian Jiayi2,Qian Yanzhao2,Kossinna Pathum1,Gau Cooper2,Gordon Paul M K3,Zhou Xiang4,Guo Xingyi5ORCID,Yan Jun67,Wu Jingjing2,Long Quan12378ORCID

Affiliation:

1. Department of Biochemistry & Molecular Biology, University of Calgary , Calgary T2N 1N4 , Canada

2. Department of Mathematics and Statistics, University of Calgary , Calgary T2N 1N4 , Canada

3. Alberta Children's Hospital Research Institute, University of Calgary , Calgary T2N 1N4 , Canada

4. School of Public Health, University of Michigan , Ann Arbor 48109 , USA

5. Department of Medicine & Biomedical Informatics, Vanderbilt University Medical Center , Nashville 37203 , USA

6. Physiology and Pharmacology, University of Calgary , Calgary T2N 1N4 , Canada

7. Hotchkiss Brain Institute, University of Calgary , Calgary T2N 1N4 , Canada

8. Department of Medical Genetics, University of Calgary , Calgary T2N 1N4 , Canada

Abstract

Abstract Detecting genetic variants with low-effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model. Our linear mixed model estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model, enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying expression-directed linear mixed model to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by expression-directed linear mixed model substantially improved estimation of missing heritability. Expression-directed linear mixed model moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.

Funder

New Frontiers in Research Fund and an HBI pilot

Alberta Innovates LevMax-Health Program Bridge Funds

Canada Foundation for Innovation

NSERC Discovery

Campbell McLaurin Chair for Hearing Deficiencies

Alberta Innovates Graduate Student Scholarships

Publisher

Oxford University Press (OUP)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3