Communications in Information & Systems

Prediction of Phenotype Information from Genotype Data

Nir Yosef, Jens Gramm, Qian-fei Wang, William S. Noble, Richard M. Karp, and Roded Sharan

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The dissection of complex diseases is one of the greatest challenges of human genetics with important clinical and scientific applications. Traditionally, associations were sought between single genetic markers and disease. The availability of large scale SNP data makes it possible, for the first time, to study the predictive power of genotypes and haplotypes with respect to phenotype data. Here we present a novel method for predicting phenotype information from genotype data. The method is based on a support vector machine that employs new kernel functions for the similarity between genotypes or their underlying haplotypes. We demonstrate our approach on SNP data for the apolipoprotein gene cluster in baboons, predicting plasma lipid levels with significant success rates, and identifying associations that were not detected using extant approaches.

Article information

Commun. Inf. Syst., Volume 10, Number 2 (2010), 99-114.

First available in Project Euclid: 9 March 2010

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Machine learning (Computing Methodologies–Artificial Intelligence–Learning) Parameter learning (Computing Methodologies–Artificial Intelligence–Learning) Classifier design and evaluation (Computing Methodologies–Pattern Recognition–Design Methodology) Biology and genetics (Computer Applications–Life and Medical Sciences)


Yosef, Nir; Gramm, Jens; Wang, Qian-fei; Noble, William S.; Karp, Richard M.; Sharan, Roded. Prediction of Phenotype Information from Genotype Data. Commun. Inf. Syst. 10 (2010), no. 2, 99--114.

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