Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

Abstract

BackgroundThrough the wealth of information contained within them, genome-wide association studies (GWAS) have the potential to provide researchers with a systematic means of associating genetic variants with a wide variety of disease phenotypes. Due to the limitations of approaches that have analyzed single variants one at a time, it has been proposed that the genetic basis of these disorders could be determined through detailed analysis of the genetic variants themselves and in conjunction with one another. The construction of models that account for these subsets of variants requires methodologies that generate predictions based on the total risk of a particular group of polymorphisms. However, due to the excessive number of variants, constructing these types of models has so far been computationally infeasible.ResultsWe have implemented an algorithm, known as greedy RLS, that we use to perform the first known wrapper-based feature selection on the genome-wide level. The running time of greedy RLS grows linearly in the number of training examples, the number of features in the original data set, and the number of selected features. This speed is achieved through computational short-cuts based on matrix calculus. Since the memory consumption in present-day computers can form an even tighter bottleneck than running time, we also developed a space efficient variation of greedy RLS which trades running time for memory. These approaches are then compared to traditional wrapper-based feature selection implementations based on support vector machines (SVM) to reveal the relative speed-up and to assess the feasibility of the new algorithm. As a proof of concept, we apply greedy RLS to the Hypertension – UK National Blood Service WTCCC dataset and select the most predictive variants using 3-fold external cross-validation in less than 26 minutes on a high-end desktop. On this dataset, we also show that greedy RLS has a better classification performance on independent test data than a classifier trained using features selected by a statistical p-value-based filter, which is currently the most popular approach for constructing predictive models in GWAS.ConclusionsGreedy RLS is the first known implementation of a machine learning based method with the capability to conduct a wrapper-based feature selection on an entire GWAS containing several thousand examples and over 400,000 variants. In our experiments, greedy RLS selected a highly predictive subset of genetic variants in a fraction of the time spent by wrapper-based selection methods used together with SVM classifiers. The proposed algorithms are freely available as part of the RLScore software library at http://users.utu.fi/aatapa/RLScore/.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

“…These approaches can be classified into two groups:Methods that construct a model from genetic data in order to carry out accurate predictions on a phenotype4344454647484950515253545556.Methods that use machine learning to construct a statistical association test or rank genetic markers according to their predicted association with a phenotype30315758596061626364656667.…”

“…Methods that use machine learning to construct a statistical association test or rank genetic markers according to their predicted association with a phenotype30315758596061626364656667.…”

“…These approaches, however, are unable to identify correlation structures of SNPs that have been excluded in the first step and neither method is validated on real data in terms of a comparison to the GWAS database. Similarly, Pahikkala et al 59. and He and Lin60 develop methods for ranking genetic markers based on the sure independence screening strategy70 and stability selection analyzing only one SNP at a time.…”

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

“…The best performing approach for sub-challenge 3 applied a greedy regularized least squares (RLS) model (Pahikkala et al, 2012) to solve the prediction task as a multilabel learning problem. Multitarget greedy regularized least-squares (MT-GRLS) (Naula et al, 2014) is a wrapper-based learning algorithm that constructs multilabel ridge regression model based on a given budget restriction on the number of common features to be selected.…”

A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders

“…With nested-CV, an inner-CV loop is used for model selection while an outer-CV is used to compute an estimate of the error with a completely new dataset. Through the use of nested FS, such ubiquitous problems can be automatically detected and removed, resulting in more reliable subset or pattern discovery in many fields [11][12][13][14][15]. Within this context, a wrapper-based system is generally used combining a classifier and a meta-heuristic algorithm to identify the best subset of features without sacrificing prediction accuracy.…”