Whole-genome sequencing analysis identifies rare, large-effect noncoding variants and regulatory regions associated with circulating protein levels
Year: 2025
Type: article
Study design: Observational Study
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.
Source: Nature Genetics
Institutions: University of Exeter, University of Geneva
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Funders: Wellcome Trust, European Federation of Pharmaceutical Industries and Associations, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, Diabetes UK, Research Councils UK, University of Exeter, European Commission, Innovative Medicines Initiative, Medical Research Council, Biotechnology and Biological Sciences Research Council
Awards: Using genetics to test the disease consequences of higher adiposity uncoupled from its adverse metabolic effects, G1909691036, G2412805558, G2890765493, G2921871229, Using whole genome sequencing to identify non-coding elements associated with diabetes and related traits across ancestries, G434337789, G4559153192, G4807641478, G4844573334, Stratification of Obesity Phenotypes to Optimize Future Therapy, G6825567342, G7762657818, Accelerated discovery of functional non-coding genomic variation using single molecule real-time (SMRT) sequencing., G936437485
OpenAlex ID: W4407884005
