Loss-of-function variants influence the human serum metabolome.

TitleLoss-of-function variants influence the human serum metabolome.
Publication TypeJournal Article
Year of Publication2016
AuthorsYu, B, Li, AH, Metcalf, GA, Muzny, DM, Morrison, AC, White, S, Mosley, TH, Gibbs, RA, Boerwinkle, E
JournalSci Adv
Volume2
Issue8
Paginatione1600800
Date Published2016 08
ISSN2375-2548
KeywordsAfrican Americans, Aged, Biomarkers, European Continental Ancestry Group, Exome, Female, Genetic Variation, Heart Failure, High-Throughput Nucleotide Sequencing, Humans, Hypertension, Liver-Specific Organic Anion Transporter 1, Male, Metabolome, Middle Aged, Mutation, Risk Factors
Abstract

The metabolome is a collection of small molecules resulting from multiple cellular and biological processes that can act as biomarkers of disease, and African-Americans exhibit high levels of genetic diversity. Exome sequencing of a sample of deeply phenotyped African-Americans allowed us to analyze the effects of annotated loss-of-function (LoF) mutations on 308 serum metabolites measured by untargeted liquid and gas chromatography coupled with mass spectrometry. In an independent sample, we identified and replicated four genes harboring six LoF mutations that significantly affected five metabolites. These sites were related to a 19 to 45% difference in geometric mean metabolite levels, with an average effect size of 25%. We show that some of the affected metabolites are risk predictors or diagnostic biomarkers of disease and, using the principle of Mendelian randomization, are in the causal pathway of disease. For example, LoF mutations in SLCO1B1 elevate the levels of hexadecanedioate, a fatty acid significantly associated with increased blood pressure levels and risk of incident heart failure in both African-Americans and an independent sample of European-Americans. We show that SLCO1B1 LoF mutations significantly increase the risk of incident heart failure, thus implicating the metabolite in the causal pathway of disease. These results reveal new avenues into gene function and the understanding of disease etiology by integrating -omic technologies into a deeply phenotyped population study.

DOI10.1126/sciadv.1600800
Alternate JournalSci Adv
PubMed ID27602404
PubMed Central IDPMC5007069
Grant ListHHSN268201100012C / HL / NHLBI NIH HHS / United States
RC2 HL102419 / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
U54 HG006542 / HG / NHGRI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
R01 HL059367 / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
R01 HL086694 / HL / NHLBI NIH HHS / United States
U01 HG004402 / HG / NHGRI NIH HHS / United States
UM1 HG006542 / HG / NHGRI NIH HHS / United States
U54 HG003273 / HG / NHGRI NIH HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
R01 HL087641 / HL / NHLBI NIH HHS / United States