Details

Autor(en) / Beteiligte

• McCartney, Daniel L
• Hillary, Robert F
• Stevenson, Anna J
• Ritchie, Stuart J
• Walker, Rosie M
• Zhang, Qian
• Morris, Stewart W
• Bermingham, Mairead L
• Campbell, Archie
• Murray, Alison D
• Whalley, Heather C
• Gale, Catharine R
• Porteous, David J
• Haley, Chris S
• McRae, Allan F
• Wray, Naomi R
• Visscher, Peter M
• McIntosh, Andrew M
• Evans, Kathryn L
• Deary, Ian J
• Marioni, Riccardo E

Titel

Epigenetic prediction of complex traits and death

Ist Teil von

• Genome Biology, 2018-09, Vol.19 (1), p.136-136, Article 136

Ort / Verlag

England: BioMed Central

Erscheinungsjahr

2018

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• 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. 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. 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.