Application of two machine learning algorithms to genetic association studies in the presence of covariates.
BACKGROUND
Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized.


RESULTS
In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided.


CONCLUSIONS
Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation.
Nonyane
Bareng A S
BA
Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, MA, USA. aletta@schoolph.umass.edu
Foulkes
Andrea S
AS
D000465:Algorithms; D001185:Artificial Intelligence; D008076:Cholesterol, HDL / Q000097:blood; D019295:Computational Biology; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease; D005838:Genotype; D015658:HIV Infections / Q000097:blood / Q000188:drug therapy / Q000208:ethnology / Q000235:genetics; D006801:Humans; D008049:Lipase / Q000235:genetics; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012044:Regression Analysis
D008076:Cholesterol, HDL; C504173:LIPC protein, human; C410382:LIPG protein, human; D008049:Lipase