학술논문
Analysis of Whole Exome Sequencing with Cardiometabolic Traits Using Family-Based Linkage and Association in the IRAS Family Study.
Document Type
Article
Author
Tabb, Keri L.; Hellwege, Jacklyn N.; Palmer, Nicholette D.; Dimitrov, Latchezar; Sajuthi, Satria; Taylor, Kent D.; Ng, Maggie C.Y.; Hawkins, Gregory A.; Chen, Yii‐der Ida; Brown, W. Mark; McWilliams, David; Williams, Adrienne; Lorenzo, Carlos; Norris, Jill M.; Long, Jirong; Rotter, Jerome I.; Curran, Joanne E.; Blangero, John; Wagenknecht, Lynne E.; Langefeld, Carl D.
Source
Subject
*NUCLEOTIDE sequencing
*LINKAGE (Genetics)
*ATHEROSCLEROSIS
*GENE frequency
*SINGLE nucleotide polymorphisms
*
*
*
*
Language
ISSN
0003-4800
Abstract
Family-based methods are a potentially powerful tool to identify trait-defining genetic variants in extended families, particularly when used to complement conventional association analysis. We utilized two-point linkage analysis and single variant association analysis to evaluate whole exome sequencing (WES) data from 1205 Hispanic Americans (78 families) from the Insulin Resistance Atherosclerosis Family Study. WES identified 211,612 variants above the minor allele frequency threshold of ≥0.005. These variants were tested for linkage and/or association with 50 cardiometabolic traits after quality control checks. Two-point linkage analysis yielded 10,580,600 logarithm of the odds (LOD) scores with 1148 LOD scores ≥3, 183 LOD scores ≥4, and 29 LOD scores ≥5. The maximal novel LOD score was 5.50 for rs2289043:T>C, in UNC5C with subcutaneous adipose tissue volume. Association analysis identified 13 variants attaining genome-wide significance ( P < 5 × 10−08), with the strongest association between rs651821:C>T in APOA5 and triglyceride levels ( P = 3.67 × 10−10). Overall, there was a 5.2-fold increase in the number of informative variants detected by WES compared to exome chip analysis in this population, nearly 30% of which were novel variants relative to the Database of Single Nucleotide Polymorphisms (dbSNP) build 138. Thus, integration of results from two-point linkage and single-variant association analysis from WES data enabled identification of novel signals potentially contributing to cardiometabolic traits. [ABSTRACT FROM AUTHOR]