학술논문

Rare coding variants in RCN3 are associated with blood pressure
Document Type
article
Source
BMC Genomics. 23(1)
Subject
Biological Sciences
Genetics
Cardiovascular
Human Genome
Clinical Research
Aetiology
2.1 Biological and endogenous factors
Good Health and Well Being
Blood Pressure
Genetic Linkage
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Polymorphism
Single Nucleotide
Precision Medicine
Whole Genome Sequencing
Rare variant analysis
Blood pressure
Whole genome sequencing
Samoan Obesity
Lifestyle and Genetic Adaptations Study (OLaGA) Group
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Information and Computing Sciences
Medical and Health Sciences
Bioinformatics
Biological sciences
Biomedical and clinical sciences
Language
Abstract
BackgroundWhile large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.ResultsAssociations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7).ConclusionsLow frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.