Laura Harris, Ellen M. McDonagh, Xiaolei Zhang, Katherine Fawcett, Amy Foreman, Petr Daneck, Panagiotis I. Sergouniotis, Helen Parkinson, Francesco Mazzarotto, Michael Inouye, Edward J. Hollox, Ewan Birney, Tomas Fitzgerald
{"title":"Genome-wide association testing beyond SNPs","authors":"Laura Harris, Ellen M. McDonagh, Xiaolei Zhang, Katherine Fawcett, Amy Foreman, Petr Daneck, Panagiotis I. Sergouniotis, Helen Parkinson, Francesco Mazzarotto, Michael Inouye, Edward J. Hollox, Ewan Birney, Tomas Fitzgerald","doi":"10.1038/s41576-024-00778-y","DOIUrl":null,"url":null,"abstract":"<p>Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.</p>","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":null,"pages":null},"PeriodicalIF":39.1000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41576-024-00778-y","RegionNum":1,"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
Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.
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