Journal
HUMAN GENETICS
Volume 131, Issue 10, Pages 1639-1654Publisher
SPRINGER
DOI: 10.1007/s00439-012-1194-y
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Funding
- National Institutes of Health [NO1-AR-2-2263, RO1-AR-44422]
- National Arthritis Foundation
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After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.
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