Faculty, Staff and Student Publications
Publication Date
8-22-2022
Journal
Communications Biology
Abstract
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.
Keywords
Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Machine learning, Genome-wide association studies
DOI
10.1038/s42003-022-03812-z
PMID
35995843
PMCID
PMC9395509
PubMedCentral® Posted Date
8-22-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes