Abstract
Every known link between a genetic variant and blood pressure improves the understanding and potentially the risk assessment of related diseases such as hypertension. Genetic data have become increasingly comprehensive and available for an increasing number of samples. The availability of whole-genome sequencing data means that statistical genetic models must evolve to meet the challenge of using both rare variants (RVs) and common variants (CVs) to link previously unidentified genome loci to disease-related traits. Penalized regression has two features, variable selection and proportional coefficient shrinkage, that allow researchers to build models tailored to hypothesized characteristics of the genotype-phenotype map. The following work uses the Genetic Analysis Workshop 18 data to investigate the performance of a spectrum of penalized regressions using at first only CVs or only RVs to predict systolic blood pressure (SBP). Next, combinations of CVs and RVs are used to model SBP, and the impact on prediction is quantified. The study demonstrates that penalized regression improves blood pressure prediction for any combination of CVs and RVs compared with maximum likelihood estimation. More significantly, models using both types of variants provide better predictions of SBP than those using only CVs or only RVs. The predictive mean squared error was reduced by up to 11.5% when RVs were added to CV-only penalized regression models. Elastic net regression with equally weighted LASSO and ridge components, in particular, can use large numbers of single-nucleotide polymorphisms to improve prediction.
Highlights
The potential number of lives affected by successful early identification of patients at high risk for hypertension has motivated researchers across a spectrum of fields
The strongest conclusion can be drawn about the effect of including rare variants (RVs) in addition to common variants (CVs) when predicting systolic blood pressure (SBP)
The predictive mean squared error (PMSE) was reduced by up to 11.5%, and generally reduced between 4% and 9%, when RVs were added to CV-only penalized regression models
Summary
The potential number of lives affected by successful early identification of patients at high risk for hypertension has motivated researchers across a spectrum of fields. On the frontier of risk prediction is the identification of genetic variants linked to traits such as high blood pressure. Advancements in sequencing have fostered the identification of a growing number of loci related to blood pressure. A second compelling study concluded that perhaps as many as hundreds of SNPs affect blood pressure; rare variants (RVs) One promising tool that may be able to leverage risk information simultaneously in both CVs and RVs is penalized regression. The following work systematically measured the advantages of the different types of penalized regression methods in the prediction of SBP using only CVs, only RV, or combinations of the two
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