Recent advances of information technology in biomedical sciences and other
applied areas have created numerous large diverse data sets with a high
dimensional feature space, which provide us a tremendous amount of information
and new opportunities for improving the quality of human life. Meanwhile, great
challenges are also created driven by the continuous arrival of new data that
requires researchers to convert these raw data into scientific knowledge in
order to benefit from it. Association studies of complex diseases using SNP
data have become more and more popular in biomedical research in recent years.
In this paper, we present a review of recent statistical advances and
challenges for analyzing correlated high dimensional SNP data in genomic
association studies for complex diseases. The review includes both general
feature reduction approaches for high dimensional correlated data and more
specific approaches for SNPs data, which include unsupervised haplotype
mapping, tag SNP selection, and supervised SNPs selection using statistical
testing/scoring, statistical modeling and machine learning methods with an
emphasis on how to identify interacting loci.Comment: Published in at http://dx.doi.org/10.1214/07-SS026 the Statistics
Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical
Statistics (http://www.imstat.org