Genome-wide association testing beyond SNPs

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

Genome-wide association testing beyond SNPs

Nat Rev Genet. 2024 Oct 7. doi: 10.1038/s41576-024-00778-y. Online ahead of print.

Authors

Laura Harris^#¹, Ellen M McDonagh^#¹, Xiaolei Zhang^#¹, Katherine Fawcett^{1

2}, Amy Foreman¹, Petr Daneck³, Panagiotis I Sergouniotis^{1

4}, Helen Parkinson¹, Francesco Mazzarotto^{5

6}, Michael Inouye^{7

8

9}, Edward J Hollox¹⁰, Ewan Birney¹, Tomas Fitzgerald¹¹

Affiliations

¹ European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK.
² Department of Population Health Sciences, University of Leicester, Leicester, UK.
³ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
⁴ Division of Evolution, Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, UK.
⁵ Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
⁶ National Heart and Lung Institute, Imperial College London, London, UK.
⁷ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁸ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁹ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia.
¹⁰ Department of Genetics and Genome Biology, University of Leicester, Leicester, UK.
¹¹ European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK. [email protected].

^# Contributed equally.

PMID: 39375560
DOI: 10.1038/s41576-024-00778-y

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.

Publication types

Review