Rachel A Hoffing, Aimee M Deaton, Aaron M Holleman, Lynne Krohn, Philip J LoGerfo, Mollie E Plekan, Sebastian Akle Serrano, Paul Nioi, Lucas D Ward
{"title":"Transcript-aware analysis of rare predicted loss-of-function variants in the UK Biobank elucidate new isoform-trait associations.","authors":"Rachel A Hoffing, Aimee M Deaton, Aaron M Holleman, Lynne Krohn, Philip J LoGerfo, Mollie E Plekan, Sebastian Akle Serrano, Paul Nioi, Lucas D Ward","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>A single gene can produce multiple transcripts with distinct molecular functions. Rare-variant association tests often aggregate all coding variants across individual genes, without accounting for the variants' presence or consequence in resulting transcript isoforms. To evaluate the utility of transcript-aware variant sets, rare predicted loss-of-function (pLOF) variants were aggregated for 17,035 protein-coding genes using 55,558 distinct transcript-specific variant sets. These sets were tested for their association with 728 circulating proteins and 188 quantitative phenotypes across 406,921 individuals in the UK Biobank. The transcript-specific approach resulted in larger estimated effects of pLOF variants decreasing serum cis-protein levels compared to the gene-based approach (pbinom ≤ 2x10-16). Additionally, 251 quantitative trait associations were identified as being significant using the transcript-specific approach but not the gene-based approach, including PCSK5 transcript ENST00000376752 and standing height (transcript-specific statistic, P = 1.3x10-16, effect = 0.7 SD decrease; gene-based statistic, P = 0.02, effect = 0.05 SD decrease) and LDLR transcript ENST00000252444 and apolipoprotein B (transcript-specific statistic, P = 5.7x10-20, effect = 1.0 SD increase; gene-based statistic, P = 3.0x10-4, effect = 0.2 SD increase). This approach demonstrates the importance of considering the effect of pLOFs on specific transcript isoforms when performing rare-variant association studies.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"247-260"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
A single gene can produce multiple transcripts with distinct molecular functions. Rare-variant association tests often aggregate all coding variants across individual genes, without accounting for the variants' presence or consequence in resulting transcript isoforms. To evaluate the utility of transcript-aware variant sets, rare predicted loss-of-function (pLOF) variants were aggregated for 17,035 protein-coding genes using 55,558 distinct transcript-specific variant sets. These sets were tested for their association with 728 circulating proteins and 188 quantitative phenotypes across 406,921 individuals in the UK Biobank. The transcript-specific approach resulted in larger estimated effects of pLOF variants decreasing serum cis-protein levels compared to the gene-based approach (pbinom ≤ 2x10-16). Additionally, 251 quantitative trait associations were identified as being significant using the transcript-specific approach but not the gene-based approach, including PCSK5 transcript ENST00000376752 and standing height (transcript-specific statistic, P = 1.3x10-16, effect = 0.7 SD decrease; gene-based statistic, P = 0.02, effect = 0.05 SD decrease) and LDLR transcript ENST00000252444 and apolipoprotein B (transcript-specific statistic, P = 5.7x10-20, effect = 1.0 SD increase; gene-based statistic, P = 3.0x10-4, effect = 0.2 SD increase). This approach demonstrates the importance of considering the effect of pLOFs on specific transcript isoforms when performing rare-variant association studies.