Gareth Hawkes, Kartik Chundru, Leigh Jackson, Kashyap A. Patel, Anna Murray, Andrew R. Wood, Caroline F. Wright, Michael N. Weedon, Timothy M. Frayling, Robin N. Beaumont
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引用次数: 0
Abstract
The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants. We identified 604 independent rare noncoding single-variant associations with circulating protein levels. Unlike protein-coding variation, rare noncoding genetic variation was almost as likely to increase or decrease protein levels. Rare noncoding aggregate testing identified 357 conditionally independent associated regions. Of these, 74 (21%) were not detectable by single-variant testing alone. Our findings have important implications for the identification, and role, of rare noncoding genetic variation associated with common human phenotypes, including the importance of testing aggregates of noncoding variants.
期刊介绍:
Nature Genetics publishes the very highest quality research in genetics. It encompasses genetic and functional genomic studies on human and plant traits and on other model organisms. Current emphasis is on the genetic basis for common and complex diseases and on the functional mechanism, architecture and evolution of gene networks, studied by experimental perturbation.
Integrative genetic topics comprise, but are not limited to:
-Genes in the pathology of human disease
-Molecular analysis of simple and complex genetic traits
-Cancer genetics
-Agricultural genomics
-Developmental genetics
-Regulatory variation in gene expression
-Strategies and technologies for extracting function from genomic data
-Pharmacological genomics
-Genome evolution