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

Content Provider	Springer Nature : BioMed Central
Author	Goldstein, Benjamin A Hubbard, Alan E Cutler, Adele Barcellos, Lisa F
Abstract	Background As 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. Results Using 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. Conclusions This 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.
Related Links	https://bmcgenomdata.biomedcentral.com/counter/pdf/10.1186/1471-2156-11-49.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	27306844
DOI	10.1186/1471-2156-11-49
Journal	BMC Genomic Data
Issue Number	1
Volume Number	11
Language	English
Publisher	BioMed Central
Publisher Date	2010-06-14
Access Restriction	Open
Subject Keyword	Life Sciences Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Genetics and Population Dynamics Multiple Sclerosis Random Forest Variable Importance Random Forest Algorithm Data Configuration
Content Type	Text
Resource Type	Article
Subject	Health Informatics Genetics
Journal Impact Factor	1.9/2023
5-Year Journal Impact Factor	1.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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