Abstract
BackgroundGenome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations. Feature screening has proved to be an effective and powerful approach to handle ultrahigh dimensional data statistically, yet it has not received much attention in GWAS. Feature screening reduces the feature space from millions to hundreds by removing non-informative noise. However, the univariate measures used to rank features are mainly based on individual effect without considering the mutual interactions with other features. In this article, we explore the performance of a random forest (RF) based feature screening procedure to emphasize the SNPs that have complex effects for a continuous phenotype.ResultsBoth simulation and real data analysis are conducted to examine the power of the forest-based feature screening. We compare it with five other popular feature screening approaches via simulation and conclude that RF can serve as a decent feature screening tool to accommodate complex genetic effects such as nonlinear, interactive, correlative, and joint effects. Unlike the traditional p-value based Manhattan plot, we use the Permutation Variable Importance Measure (PVIM) to display the relative significance and believe that it will provide as much useful information as the traditional plot.ConclusionMost complex traits are found to be regulated by epistatic and polygenic variants. The forest-based feature screening is proven to be an efficient, easily implemented, and accurate approach to cope whole genome data with complex structures. Our explorations should add to a growing body of enlargement of feature screening better serving the demands of contemporary genome data.
Highlights
Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits
Most complex traits are regulated by polygenetic variants, which decreases the power of most popular traditional p-value based approaches [4,5,6,7]
Power simulation To illustrate the power of random forest (RF) as a feature screening tool for detecting correlative, nonlinear, and interactive effects, we designed four different simulation settings to control linear vs nonlinear, constant vs functional, and additive vs interactive features
Summary
Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations. Feature screening has proved to be an effective and powerful approach to handle ultrahigh dimensional data statistically, yet it has not received much attention in GWAS. High-throughput genotyping techniques and large data repository capability give genome-wide association studies (GWAS) great power to unravel the genetic etiology of complex traits. With the number of Single Nucleotide Polymorphisms (SNPs) per DNA array growing from 10,000 to 1 million [1], ultra-high dimensionality is one of the grand challenges in GWAS. Most complex traits are regulated by polygenetic variants, which decreases the power of most popular traditional p-value based approaches [4,5,6,7].
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