Abstract:
BACKGROUND:Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications. RESULTS:Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios. CONCLUSIONS:DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.
journal_name
Genome Bioljournal_title
Genome biologyauthors
McCartney DL,Hillary RF,Stevenson AJ,Ritchie SJ,Walker RM,Zhang Q,Morris SW,Bermingham ML,Campbell A,Murray AD,Whalley HC,Gale CR,Porteous DJ,Haley CS,McRae AF,Wray NR,Visscher PM,McIntosh AM,Evans KL,Deary IJ,Mardoi
10.1186/s13059-018-1514-1subject
Has Abstractpub_date
2018-09-27 00:00:00pages
136issue
1eissn
1474-7596issn
1474-760Xpii
10.1186/s13059-018-1514-1journal_volume
19pub_type
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