Tree-structured supervised learning and the genetics of hypertension.
This paper is about an algorithm, FlexTree, for general supervised learning. It extends the binary tree-structured approach (Classification and Regression Trees, CART) although it differs greatly in its selection and combination of predictors. It is particularly applicable to assessing interactions: gene by gene and gene by environment as they bear on complex disease. One model for predisposition to complex disease involves many genes. Of them, most are pure noise; each of the values that is not the prevalent genotype for the minority of genes that contribute to the signal carries a "score." Scores add. Individuals with scores above an unknown threshold are predisposed to the disease. For the additive score problem and simulated data, FlexTree has cross-validated risk better than many cutting-edge technologies to which it was compared when small fractions of candidate genes carry the signal. For the model where only a precise list of aberrant genotypes is predisposing, there is not a systematic pattern of absolute superiority; however, overall, FlexTree seems better than the other technologies. We tried the algorithm on data from 563 Chinese women, 206 hypotensive, 357 hypertensive, with information on ethnicity, menopausal status, insulin-resistant status, and 21 loci. FlexTree and Logic Regression appear better than the others in terms of Bayes risk. However, the differences are not significant in the usual statistical sense.
Huang
Jing
J
Affymetrix Inc., 3380 Central Expressway, Santa Clara, CA 95051, USA. jing_huang@affymetrix.com
Lin
Alfred
A
Narasimhan
Balasubramanian
B
Quertermous
Thomas
T
Hsiung
C Agnes
CA
Ho
Low-Tone
LT
Grove
John S
JS
Olivier
Michael
M
Ranade
Koustubh
K
Risch
Neil J
NJ
Olshen
Richard A
RA
D000465:Algorithms; D005260:Female; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D006973:Hypertension / Q000235:genetics*; D007333:Insulin Resistance; D007858:Learning; D008432:Mathematical Computing; D008957:Models, Genetic; D012044:Regression Analysis