Genome-wide association analyses of expression phenotypes.
A number of issues arise when analyzing the large amount of data from high-throughput genotype and expression microarray experiments, including design and interpretation of genome-wide association studies of expression phenotypes. These issues were considered by contributions submitted to Group 1 of the Genetic Analysis Workshop 15 (GAW15), which focused on the association of quantitative expression data. These contributions evaluated diverse hypotheses, including those relevant to cancer and obesity research, and used various analytic techniques, many of which were derived from information theory. Several observations from these reports stand out. First, one needs to consider the genetic model of the trait of interest and carefully select which single nucleotide polymorphisms and individuals are included early in the design stage of a study. Second, by targeting specific pathways when analyzing genome-wide data, one can generate more interpretable results than agnostic approaches. Finally, for datasets with small sample sizes but a large number of features like the Genetic Analysis Workshop 15 dataset, machine learning approaches may be more practical than traditional parametric approaches.
Chen
Gary K
GK
Department of Epidemiology and Biostatistics, Institute for Human Genetics, University of California at San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143, USA.
Zheng
Tian
T
Witte
John S
JS
Goode
Ellen L
EL
Gao
Lei
L
Hu
Pingzhao
P
Suh
Young Ju
YJ
Suktitipat
Bhoom
B
Szymczak
Silke
S
Woo
Jung Hoon
JH
Zhang
Wei
W
D002874:Chromosome Mapping; D005786:Gene Expression Regulation; D015894:Genome, Human; D006801:Humans; D018579:Patient Selection; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide