用介导表达评分回归量化基因调控表达对复杂性状的介导作用的稳健性。

IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Biology Methods and Protocols Pub Date : 2023-10-17 eCollection Date: 2023-01-01 DOI:10.1093/biomethods/bpad024
Chen Lin, Wei Liu, Wei Jiang, Hongyu Zhao
{"title":"用介导表达评分回归量化基因调控表达对复杂性状的介导作用的稳健性。","authors":"Chen Lin, Wei Liu, Wei Jiang, Hongyu Zhao","doi":"10.1093/biomethods/bpad024","DOIUrl":null,"url":null,"abstract":"<p><p>Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between <i>cis</i>-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599978/pdf/","citationCount":"0","resultStr":"{\"title\":\"Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression.\",\"authors\":\"Chen Lin, Wei Liu, Wei Jiang, Hongyu Zhao\",\"doi\":\"10.1093/biomethods/bpad024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between <i>cis</i>-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.</p>\",\"PeriodicalId\":36528,\"journal\":{\"name\":\"Biology Methods and Protocols\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599978/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biology Methods and Protocols\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/biomethods/bpad024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology Methods and Protocols","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/biomethods/bpad024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

通过全基因组关联研究(GWAS),遗传关联信号大多在非编码区发现,表明基因表达调控在人类疾病和性状中的作用。然而,将表达数量性状基因座(eQTL)与疾病相关变异共定位的成功率有限。介导表达得分回归(MESC)是最近提出的一种量化由遗传调控基因表达介导的性状遗传力比例的方法。将MESC应用于GWAS结果,对许多性状的介导遗传力估计较低。由于MESC依赖于顺式eQTL效应、基因效应和非介导SNP效应之间的严格独立性假设,它可能无法表征这些效应大小之间的真实关系,从而导致有偏差的结果。在这里,我们考虑了MESC的稳健性,以研究由MESC推断的介导遗传力的低部分是否反映了复杂性状的生物学现实,或者是由模型错误指定引起的低估。我们的结果表明,MESC可能导致介导遗传力的估计有偏差,基因注释的错误指定导致低估,而SNP注释的错误标记可能导致高估。此外,eQTL效应估计的错误可能导致对介导遗传力的低估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression.

Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between cis-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
期刊最新文献
Optimizing Western blotting immunodetection: Streamlining antibody cocktails for reduced protocol time and enhanced multiplexing applications. Live cell fluorescence microscopy-an end-to-end workflow for high-throughput image and data analysis. A reproducible method to study traumatic injury-induced zebrafish brain regeneration. Cluster analysis identifies long COVID subtypes in Belgian patients. Unpacking unstructured data: A pilot study on extracting insights from neuropathological reports of Parkinson's Disease patients using large language models.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1