Tag SNP Selection in Genotype
Data for Maximizing SNP Prediction Accuracy
Gad Kimmel, Tel Aviv University
The search for genetic regions associated with complex disease, such as
cancer or Alzheimer's disease, is an important challenge that may lead
to better diagnosis and treatment. The existence of millions of DNA
variations, primarily single nucleotide polymorphisms (SNPs), may allow
the fine dissection of such associations. However, studies seeking
disease association are limited by the cost of genotyping SNPs.
Therefore, it is essential to find a small subset of informative SNPs
(tag SNPs) that may be used as good representatives of the rest of the
SNPs.
We define a new natural measure for evaluating the prediction accuracy
of a set of tag SNPs, and use it to develop a new method for tag SNPs
selection. Our method is based on a novel algorithm that predicts the
values of the rest of the SNPs given the tag SNPs. In contrast to most
previous methods, our prediction algorithm uses the genotype
information and not the haplotype information of the tag SNPs. Our
method is very efficient, and it does not rely on having a block
partition of the genomic region.
We compared our method to two state of the art tag SNP selection
algorithms on 58 different genotype data sets from four different
sources. Our method consistently found tag SNPs with considerably
better prediction ability than the other methods.
Joint work with Eran Halperin and Ron Shamir.