Accurate Genomic Prediction Of Human Height

TL;DR

This paper develops highly accurate genomic predictors for human height and other traits using advanced machine learning, capturing most of the heritability and revealing insights into genetic architecture with large-scale UK Biobank data.

Contribution

It introduces novel machine learning methods that predict human height with accuracy close to heritability estimates, addressing the missing heritability problem.

Findings

Predictors explain ~40% of height variance

Predicted height correlates ~0.65 with actual height

Identified ~20,000 SNPs related to height

Abstract

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, $\sim$40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate $\sim$0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the "missing heritability" problem -- i.e., the gap between prediction R-squared and SNP heritability. The $\sim$20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.