CellGeneticsPeer-reviewed journal article
Influences of rare copy number variation on human complex traits
Margaux L.A. HujoeliD, Maxwell A. ShermaniD, Alison R. BartoniD, Ronen E. MukameliD, Vijay G. SankaraniD, Chikashi C. TeraoiD, Po‐Ru LohiD
Abstract
The human genome contains hundreds of thousands of regions exhibiting copy number variation (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs (spanning tens of kilobases) have been ascertainable from the SNP-array data generated by large biobanks. We developed a new computational approach that leverages abundant haplotype-sharing in biobank cohorts to more sensitively detect CNVs co-inherited within extended SNP haplotypes. Applied to UK Biobank, this approach achieved 6-fold increased CNV detection sensitivity compared to previous analyses, accounting for approximately half of all rare gene inactivation events produced by genomic structural variation. This extensive CNV call set enabled the most comprehensive analysis to date of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (P < 5 x 10-8) - involving 97 loci - that rigorous statistical fine-mapping analyses indicated were likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling new insights into dosage-sensitivity of these genes and implicating several novel gene-trait relationships. CNVs at several loci created extended allelic series including deletions or duplications of distal enhancers that associated with much stronger phenotypic effects than SNPs within these regulatory elements. These results demonstrate the ability of haplotype-informed analysis to empower structural variant detection and provide insights into the genetic basis of human complex traits.
- organism: human
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- research focus: fine-mapping
- approach: statistical model
- Citations
- 59
- FWCI
- 7.39
- Field percentile
- top 2%
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