Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models.

ABSTRACT: We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated

SUBMITTER: Spiliopoulou A 

PROVIDER: S-EPMC4476450 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature