Complex-Trait Prediction in the Era of Big Data.

Accurate prediction of complex traits requires using a large number of DNA variants. Advances in statistical and machine learning methodology enable the identification of complex patterns in high-dimensional settings. However, training these highly parameterized methods requires very large data sets. Until recently, such data sets were not available. But the situation is changing rapidly as very large biomedical data sets comprising individual genotype-phenotype data for hundreds of thousands of individuals become available in public and private domains. We argue that the convergence of advances in methodology and the advent of Big Genomic Data will enable unprecedented improvements in complex-trait prediction; we review theory and evidence supporting our claim and discuss challenges and opportunities that Big Data will bring to complex-trait prediction. Highlights Genome-wide association (GWA) studies have discovered thousands of variants associated with many important human traits and diseases. However, GWA-significant variants explain only a small fraction of the trait heritability. Achieving high genomic prediction accuracy requires using tens or hundreds of thousands of SNPs, including many that do not reach GWA significance. Penalized and Bayesian regressions can be used to fit high-dimensional regressions including hundreds of thousands of predictors. However, training these high-dimensional regressions requires using very large data sets. Until recently, such data sets were not available, but this situation is changing rapidly. We argue that the convergence of advances in methodology and the advent of very large biomedical data sets (comprising hundreds of thousands of genotypes linked to phenotypes) will enable unprecedented improvements in complex-trait prediction. [ABSTRACT FROM AUTHOR]