Variable Ranking by Random Forests Model for Genome-Wide Association Study
نویسنده
چکیده
An important step in the genome-wide association study (GWAS) is the ranking of single nucleotide polymorphisms (SNPs). We propose a method based on the variable importance measure from the random forests model. SNPs in the entire genome region are randomly divided into subsets. We then fit the random forests model to each subset to compute subranks for the SNPs. The ranks of the SNPs are defined based on these subranks and then iteratively improved. We study the impact of the parameters and show that our method performs well in comparison to popular existing methods. We apply our method to select SNPs in a real-data study of the link between SNPs and human fingerprint ridge counts.
منابع مشابه
Maximal conditional chi-square importance in random forests
MOTIVATION High-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. W...
متن کاملDetecting gene-by-smoking interactions in a genome-wide association study of early-onset coronary heart disease using random forests
BACKGROUND Genome-wide association studies are often limited in their ability to attain their full potential due to the sheer volume of information created. We sought to use the random forest algorithm to identify single-nucleotide polymorphisms (SNPs) that may be involved in gene-by-smoking interactions related to the early-onset of coronary heart disease. METHODS Using data from the Framing...
متن کاملUnveiling the genetic loci for a panicle developmental trait using genome-wide association study in rice
Panicle size has a high correlation with grain yield in rice. There is a bottleneck to identify the additional quantitative trait loci (QTL) for panicle size due to the conventional traits used for QTL mapping. To identify more genetic loci for panicle size, a panicle developmental trait (LNTB, the length from panicle neck-knot to the first primary branch in the rachis) related to panicle size ...
متن کاملFinding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data
BACKGROUND Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity. METHODS We aim to dete...
متن کاملEvaluation of random forests performance for genome-wide association studies in the presence of interaction effects
Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with...
متن کامل