Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Dec;43(8):1030-1045.
doi: 10.1002/gepi.22249. Epub 2019 Sep 10.

Genome-wide association study of circulating folate one-carbon metabolites

Jun Wang  1 , Isaac Asante  2 , John A Baron  3 , Jane C Figueiredo  4 , Robert Haile  4   5 , A Joan Levine  4 , Polly A Newcomb  6 , Allyson S Templeton  6 , Fredrick R Schumacher  7 , Stan G Louie  2 , Graham Casey  8 , David V Conti  1
Affiliations

Genome-wide association study of circulating folate one-carbon metabolites

Jun Wang et al. Genet Epidemiol. 2019 Dec.

Abstract

Experimental, observational, and clinical trials support a critical role of folate one-carbon metabolism (FOCM) in colorectal cancer (CRC) development. In this report, we focus on understanding the relationship between common genetic variants and metabolites of FOCM. We conducted a genome-wide association study of FOCM biomarkers among 1,788 unaffected (without CRC) individuals of European ancestry from the Colon Cancer Family Registry. Twelve metabolites, including 5-methyltetrahydrofolate, vitamin B2 (flavin mononucleotide and riboflavin), vitamin B6 (4-pyridoxic acid, pyridoxal, and pyridoxamine), total homocysteine, methionine, S-adenosylmethionine, S-adenosylhomocysteine, cystathionine, and creatinine were measured from plasma using liquid chromatography-mass spectrometry (LC-MS) or LC-MS/MS. For each individual biomarker, we estimated genotype array-specific associations followed by a fixed-effect meta-analysis. We identified the variant rs35976024 (at 2p11.2 and intronic of ATOH8) associated with total homocysteine (p = 4.9 × 10-8 ). We found a group of six highly correlated variants on chromosome 15q14 associated with cystathionine (all p < 5 × 10-8 ), with the most significant variant rs28391580 (p = 2.8 × 10-8 ). Two variants (rs139435405 and rs149119426) on chromosome 14q13 showed significant (p < 5 × 10-8 ) associations with S-adenosylhomocysteine. These three biomarkers with significant associations are closely involved in homocysteine metabolism. Furthermore, when assessing the principal components (PCs) derived from seven individual biomarkers, we identified the variant rs12665366 (at 6p25.3 and intronic of EXOC2) associated with the first PC (p = 2.3 × 10-8 ). Our data suggest that common genetic variants may play an important role in FOCM, particularly in homocysteine metabolism.

Keywords: colorectal cancer; epidemiology; folate; genome-wide association analysis; one-carbon metabolism.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest: The authors declare no potential conflicts of interest.

Figures

Figure 1.
Figure 1.
Meta-analysis of three or more arrays for circulating folate one-carbon metabolites. Manhattan plots for a) total Homocysteine, b) Cystathionine and c) S-adenosylhomocysteine and d) biomarker PC1.
Figure 1.
Figure 1.
Meta-analysis of three or more arrays for circulating folate one-carbon metabolites. Manhattan plots for a) total Homocysteine, b) Cystathionine and c) S-adenosylhomocysteine and d) biomarker PC1.
Figure 1.
Figure 1.
Meta-analysis of three or more arrays for circulating folate one-carbon metabolites. Manhattan plots for a) total Homocysteine, b) Cystathionine and c) S-adenosylhomocysteine and d) biomarker PC1.
Figure 1.
Figure 1.
Meta-analysis of three or more arrays for circulating folate one-carbon metabolites. Manhattan plots for a) total Homocysteine, b) Cystathionine and c) S-adenosylhomocysteine and d) biomarker PC1.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Amos CI, Dennis J, Wang Z, Byun J, Schumacher FR, Gayther SA, … Easton DF (2017). The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers. Cancer Epidemiol Biomarkers Prev, 26(1), 126–135. doi: 10.1158/1055-9965.epi-16-0106 - DOI - PMC - PubMed
    1. Asante I, Pei H, Zhou E, Liu S, Chui D, Yoo E, & Louie SG (2018). Simultaneous quantitation of folates, flavins and B6 metabolites in human plasma by LC-MS/MS assay: Applications in colorectal cancer. J Pharm Biomed Anal, 158, 66–73. doi: 10.1016/j.jpba.2018.05.030 - DOI - PMC - PubMed
    1. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, … Snyder M (2012). Annotation of functional variation in personal genomes using RegulomeDB. Genome Res, 22(9), 1790–1797. doi: 10.1101/gr.137323.112 - DOI - PMC - PubMed
    1. Charles D, Ness AR, Campbell D, Davey Smith G, & Hall MH (2004). Taking folate in pregnancy and risk of maternal breast cancer. BMJ, 329(7479), 1375–1376. doi: 10.1136/bmj.329.7479.1375 - DOI - PMC - PubMed
    1. Cheng TY, Makar KW, Neuhouser ML, Miller JW, Song X, Brown EC, … Ulrich CM (2015). Folate-mediated one-carbon metabolism genes and interactions with nutritional factors on colorectal cancer risk: Women’s Health Initiative Observational Study. Cancer, 121(20), 3684–3691. doi: 10.1002/cncr.29465 - DOI - PMC - PubMed

Publication types