Volume 9 Supplement 7
Evaluation of pooled allelotyping versus individual genotyping for genome-wide association analysis of complex disease
© Pratap et al; licensee BioMed Central Ltd. 2008
Published: 8 July 2008
Recent advances in genotyping techniques and genomic knowledge via the Hap Map and Human Genome projects allow for true Genome-Wide Association (GWA) analysis for common complex diseases such as heart disease, diabetes, and Alzheimer's. A major obstacle in GWA analysis is the prohibitively high cost of genotyping the possibly thousands of individuals necessary to achieve statistical significance of results. One potential solution is to pool the DNA of case and control populations and to determine the genotype allele frequency differences in these populations by pooled allelotyping. While pooling can dramatically save time and money, it also adds sources of error. Our work has created a system process that allows for direct evaluation and comparison of pooled allelotyping to individual genotyping for GWA association analysis of complex disease.
Materials and methods
Complex disease penetrance functions were calculated for a 3 locus bi-allelic model with additive or multiplicative allelic spectrums using GenomeSIM software . Penetrance probabilities were calculated for genotypes having from 0 to 6 disease-associated alleles. All probability functions used a base penetrance probability of 10% disease risk to account for environmental influences on disease risk. A total of 25,000 individual genotype files were created, each comprised of 10,000 SNPs with 3 disease-associated loci imbedded within. Custom MATLAB scripts were used to make in silico "pseudo-pools" for pooled allelotyping from the individual genotype files. HAPLOVIEW software was used to conduct individual genotyping association analysis . A modified version of the Pooled DNA Analyzer (PDA) program was used for pooled association analysis .
S.P. supported by the National Library of Medicine, National Institutes of Health Grant (T15 007450-03) at the Vanderbilt University Department of Biomedical Informatics.
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