Whole genome sequence analysis of serum amino acid levels.

TitleWhole genome sequence analysis of serum amino acid levels.
Publication TypeJournal Article
Year of Publication2016
AuthorsYu, B, de Vries, PS, Metcalf, GA, Wang, Z, Feofanova, EV, Liu, X, Muzny, DM, Wagenknecht, LE, Gibbs, RA, Morrison, AC, Boerwinkle, E
JournalGenome Biol
Volume17
Issue1
Pagination237
Date Published2016 Nov 24
ISSN1474-760X
Abstract

BACKGROUND: Blood levels of amino acids are important biomarkers of disease and are influenced by synthesis, protein degradation, and gene-environment interactions. Whole genome sequence analysis of amino acid levels may establish a paradigm for analyzing quantitative risk factors.

RESULTS: In a discovery cohort of 1872 African Americans and a replication cohort of 1552 European Americans we sequenced exons and whole genomes and measured serum levels of 70 amino acids. Rare and low-frequency variants (minor allele frequency ≤5%) were analyzed by three types of aggregating motifs defined by gene exons, regulatory regions, or genome-wide sliding windows. Common variants (minor allele frequency >5%) were analyzed individually. Over all four analysis strategies, 14 gene-amino acid associations were identified and replicated. The 14 loci accounted for an average of 1.8% of the variance in amino acid levels, which ranged from 0.4 to 9.7%. Among the identified locus-amino acid pairs, four are novel and six have been reported to underlie known Mendelian conditions. These results suggest that there may be substantial genetic effects on amino acid levels in the general population that may underlie inborn errors of metabolism. We also identify a predicted promoter variant in AGA (the gene that encodes aspartylglucosaminidase) that is significantly associated with asparagine levels, with an effect that is independent of any observed coding variants.

CONCLUSIONS: These data provide insights into genetic influences on circulating amino acid levels by integrating -omic technologies in a multi-ethnic population. The results also help establish a paradigm for whole genome sequence analysis of quantitative traits.

DOI10.1186/s13059-016-1106-x
Alternate JournalGenome Biol.
PubMed ID27884205
PubMed Central IDPMC5123402
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
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / 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
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