Association between triglycerides, known risk SNVs and conserved rare variation in SLC25A40 in a multi-ancestry cohort

• Main Authors: Elisabeth A. Rosenthal, David R. Crosslin, Adam S. Gordon, David S. Carrell, Ian B. Stanaway, Eric B. Larson, Jane Grafton, Wei-Qi Wei, Joshua C. Denny, Qi-Ping Feng, Amy S. Shah, Amy C. Sturm, Marylyn D. Ritchie, Jennifer A. Pacheco, Hakon Hakonarson, Laura J. Rasmussen-Torvik, John J. Connolly, Xiao Fan, Maya Safarova, Iftikhar J. Kullo, Gail P. Jarvik
• Format: Article
• Language: English
• Published: BMC 2021-01-01
• Series: BMC Medical Genomics
• Subjects: Genetics; Cardiovascular disease; Triglycerides; Electronic health records
• Online Access: https://doi.org/10.1186/s12920-020-00854-2

Abstract Background Elevated triglycerides (TG) are associated with, and may be causal for, cardiovascular disease (CVD), and co-morbidities such as type II diabetes and metabolic syndrome. Pathogenic variants in APOA5 and APOC3 as well as risk SNVs in other genes [APOE (rs429358, rs7412), APOA1/C3/A4/A5 gene cluster (rs964184), INSR (rs7248104), CETP (rs7205804), GCKR (rs1260326)] have been shown to affect TG levels. Knowledge of genetic causes for elevated TG may lead to early intervention and targeted treatment for CVD. We previously identified linkage and association of a rare, highly conserved missense variant in SLC25A40, rs762174003, with hypertriglyceridemia (HTG) in a single large family, and replicated this association with rare, highly conserved missense variants in a European American and African American sample. Methods Here, we analyzed a longitudinal mixed-ancestry cohort (European, African and Asian ancestry, N = 8966) from the Electronic Medical Record and Genomics (eMERGE) Network. We tested associations between median TG and the genes of interest, using linear regression, adjusting for sex, median age, median BMI, and the first two principal components of ancestry. Results We replicated the association between TG and APOC3, APOA5, and risk variation at APOE, APOA1/C3/A4/A5 gene cluster, and GCKR. We failed to replicate the association between rare, highly conserved variation at SLC25A40 and TG, as well as for risk variation at INSR and CETP. Conclusions Analysis using data from electronic health records presents challenges that need to be overcome. Although large amounts of genotype data is becoming increasingly accessible, usable phenotype data can be challenging to obtain. We were able to replicate known, strong associations, but were unable to replicate moderate associations due to the limited sample size and missing drug information.