An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings

BMC Genetics

Research Article

Adele Cutler¹  Lisa F Barcellos²  Alan E Hubbard²  Benjamin A Goldstein³ 

[1] Department of Mathematics & Statistics, Utah State University, Logan, UT, USA;Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA;Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA;Genetic Epidemiology and Genomics Laboratory, Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA;

关键词: Multiple Sclerosis;    Random Forest;    Variable Importance;    Random Forest Algorithm;    Data Configuration;   

DOI  :  10.1186/1471-2156-11-49

 received in 2010-01-14, accepted in 2010-06-14,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundAs computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (GWA) datasets becomes possible. While most traditional statistical methods can only elucidate main effects of genetic variants on risk for disease, certain machine learning approaches are particularly suited to discover higher order and non-linear effects. One such approach is the Random Forests (RF) algorithm. The use of RF for SNP discovery related to human disease has grown in recent years; however, most work has focused on small datasets or simulation studies which are limited.ResultsUsing a multiple sclerosis (MS) case-control dataset comprised of 300 K SNP genotypes across the genome, we outline an approach and some considerations for optimally tuning the RF algorithm based on the empirical dataset. Importantly, results show that typical default parameter values are not appropriate for large GWA datasets. Furthermore, gains can be made by sub-sampling the data, pruning based on linkage disequilibrium (LD), and removing strong effects from RF analyses. The new RF results are compared to findings from the original MS GWA study and demonstrate overlap. In addition, four new interesting candidate MS genes are identified, MPHOSPH9, CTNNA3, PHACTR2 and IL7, by RF analysis and warrant further follow-up in independent studies.ConclusionsThis study presents one of the first illustrations of successfully analyzing GWA data with a machine learning algorithm. It is shown that RF is computationally feasible for GWA data and the results obtained make biologic sense based on previous studies. More importantly, new genes were identified as potentially being associated with MS, suggesting new avenues of investigation for this complex disease.

【 授权许可】

CC BY   
© Goldstein et al; licensee BioMed Central Ltd. 2010