We explore the prediction of individuals' phenotypes for complex traits using genomic data.
We compare several widely used prediction models, including Ridge Regression, LASSO …

Genomic prediction of complex human traits (eg, height, cognitive ability, bone density) and
disease risks (eg, breast cancer, diabetes, heart disease, atrial fibrillation) has advanced …

Despite rapid advances in genomic technology, our ability to account for phenotypic
variation using genetic information remains limited for many traits. This has unfortunately …

We report a new method to estimate the predictive performance of polygenic models for risk
prediction and assess predictive performance for ten complex traits or common diseases …

Modern biobanks that collect genotype-phenotype information from hundreds of thousands
of individuals bring unprecedented opportunities for genomic... Despite the important …

In this study, we investigated the effect of five feature selection approaches on the
performance of a mixed model (G-BLUP) and a Bayesian (Bayes C) prediction method. We …

Abstract Machine-learning techniques have helped solve a broad range of prediction
problems, yet are not widely used to build polygenic risk scores for the prediction of complex …

Most existing tools for constructing genetic prediction models begin with the assumption that
all genetic variants contribute equally towards the phenotype. However, this represents a …

Phenotype prediction is a key goal for medical genetics. Unfortunately, most genome-wide
association studies are done in European populations, which reduces the accuracy of …

Hsu et al. used advanced methods from machine learning to analyze almost half a million
genomes. They produced, for the first time, accurate genomic predictors for complex traits …