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Whole-genome sequencing coupled to imputation discovers genetic signals for anthropometric traits.

Am. J. Hum. Genet. 100, 865-884 (2017)
Publ. Version/Full Text DOI PMC
Free by publisher
Open Access Green as soon as Postprint is submitted to ZB.
Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Dxa Traits ; Uk Biobank ; Uk10k ; Anthropometry ; Genetic Association Study ; Imputation ; Next-generation Whole-genome Sequencing
ISSN (print) / ISBN 0002-9297
e-ISSN 1537-6605
Journal American Journal of Human Genetics, The
Quellenangaben Volume: 100, Issue: 6, Pages: 865-884 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Translational Genomics (ITG)