Natasha Santhanam, Sandra Sanchez-Roige, Sabrina Mi, Yanyu Liang, Apurva S Chitre, Daniel Munro, Denghui Chen, Jianjun Gao, Angel Garcia-Martinez, Anthony M George, Alexander F Gileta, Wenyan Han, Katie Holl, Alesa Hughson, Christopher P King, Alexander C Lamparelli, Connor D Martin, Festus Nyasimi, Celine L St Pierre, Sarah Sumner, Jordan Tripi, Tengfei Wang, Hao Chen, Shelly Flagel, Keita Ishiwari, Paul Meyer, Oksana Polesskaya, Laura Saba, Leah C Solberg Woods, Abraham A Palmer, Hae Kyung Im
{"title":"RatXcan: A framework for cross-species integration of genome-wide association and gene expression data.","authors":"Natasha Santhanam, Sandra Sanchez-Roige, Sabrina Mi, Yanyu Liang, Apurva S Chitre, Daniel Munro, Denghui Chen, Jianjun Gao, Angel Garcia-Martinez, Anthony M George, Alexander F Gileta, Wenyan Han, Katie Holl, Alesa Hughson, Christopher P King, Alexander C Lamparelli, Connor D Martin, Festus Nyasimi, Celine L St Pierre, Sarah Sumner, Jordan Tripi, Tengfei Wang, Hao Chen, Shelly Flagel, Keita Ishiwari, Paul Meyer, Oksana Polesskaya, Laura Saba, Leah C Solberg Woods, Abraham A Palmer, Hae Kyung Im","doi":"10.1371/journal.pgen.1011583","DOIUrl":null,"url":null,"abstract":"<p><p>Genome-wide association studies (GWAS) have implicated specific alleles and genes as risk factors for numerous complex traits. However, translating GWAS results into biologically and therapeutically meaningful discoveries remains extremely challenging. Most GWAS results identify noncoding regions of the genome, suggesting that differences in gene regulation are the major driver of trait variability. To better integrate GWAS results with gene regulatory polymorphisms, we previously developed PrediXcan (also known as \"transcriptome-wide association studies\" or TWAS), which maps SNPs to predicted gene expression using GWAS data. In this study, we developed RatXcan, a framework that extends this methodology to outbred heterogeneous stock (HS) rats. RatXcan accounts for the close familial relationships among HS rats by modeling the relatedness with a random effect that encodes the genetic relatedness. RatXcan also corrects for polygenic-driven inflation because of the equivalence between a relatedness random effect and the infinitesimal polygenic model. To develop RatXcan, we trained transcript predictors for 8,934 genes using reference genotype and expression data from five rat brain regions. We found that the cis genetic architecture of gene expression in both rats and humans was sparse and similar across brain tissues. We tested the association between predicted expression in rats and two example traits (body length and BMI) using phenotype and genotype data from 5,401 densely genotyped HS rats and identified a significant enrichment between the genes associated with rat and human body length and BMI. Thus, RatXcan represents a valuable tool for identifying the relationship between gene expression and phenotypes across species and paves the way to explore shared biological mechanisms of complex traits.</p>","PeriodicalId":49007,"journal":{"name":"PLoS Genetics","volume":"21 3","pages":"e1011583"},"PeriodicalIF":4.0000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1371/journal.pgen.1011583","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Genome-wide association studies (GWAS) have implicated specific alleles and genes as risk factors for numerous complex traits. However, translating GWAS results into biologically and therapeutically meaningful discoveries remains extremely challenging. Most GWAS results identify noncoding regions of the genome, suggesting that differences in gene regulation are the major driver of trait variability. To better integrate GWAS results with gene regulatory polymorphisms, we previously developed PrediXcan (also known as "transcriptome-wide association studies" or TWAS), which maps SNPs to predicted gene expression using GWAS data. In this study, we developed RatXcan, a framework that extends this methodology to outbred heterogeneous stock (HS) rats. RatXcan accounts for the close familial relationships among HS rats by modeling the relatedness with a random effect that encodes the genetic relatedness. RatXcan also corrects for polygenic-driven inflation because of the equivalence between a relatedness random effect and the infinitesimal polygenic model. To develop RatXcan, we trained transcript predictors for 8,934 genes using reference genotype and expression data from five rat brain regions. We found that the cis genetic architecture of gene expression in both rats and humans was sparse and similar across brain tissues. We tested the association between predicted expression in rats and two example traits (body length and BMI) using phenotype and genotype data from 5,401 densely genotyped HS rats and identified a significant enrichment between the genes associated with rat and human body length and BMI. Thus, RatXcan represents a valuable tool for identifying the relationship between gene expression and phenotypes across species and paves the way to explore shared biological mechanisms of complex traits.
期刊介绍:
PLOS Genetics is run by an international Editorial Board, headed by the Editors-in-Chief, Greg Barsh (HudsonAlpha Institute of Biotechnology, and Stanford University School of Medicine) and Greg Copenhaver (The University of North Carolina at Chapel Hill).
Articles published in PLOS Genetics are archived in PubMed Central and cited in PubMed.