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

Abstract

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Polygenic Risk Scores in Neurodegenerative Diseases: a Review

“…To date, no PRS for ND that combines both common and rare variants has been calculated, which may lead to an increase of their accuracy. Novel machine learning methods are being developed to capture compact representations of GWAS [79] and coupled with powerful classification methods, namely deep neural networks, to produce highly accurate predictions [80][81][82]. The parallel development of larger cohorts, molecular phenotyping and the advancement of novel and more sophisticated methods to represent and operate multi-variant models, will allow a more precise discernment of the overlap of the genetic architecture among ND and predict individuals at risk with the accuracy required in clinical settings.…”

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

“…When tailored for understanding GWAS data, ML predictions can provide an improved statistical foundation of evidence to support or improve GWAS results. For instance, ML in GWAS has been applied to identify loci, increase the statistical power of GWAS (Mieth et al, 2016), detect epistatic interactions (Leem et al, 2014), improve polygenic risk scoring produced from GWAS (Pare et al, 2017), and prioritize genes and variants on post-GWAS analysis (Vitsios and Petrovski, 2019). Here we will focus on the ML applications developed for post-GWAS prioritization.…”

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

“…Taking advantage of it will depend on state-of-the-art mathematical and statistical approaches capable of incorporating large numbers of single-nucleotide polymorphisms into risk models, artificial intelligence, and supervised machine learning approaches. 98,99…”