Power and Sample Size Calculations for Genetic Case/Control Studies Using Gene-Centric SNP Maps: Application to Human Chromosomes 6, 21, and 22 in Three Populations

Abstract

Power and sample size calculations are critical parts of any research design for genetic association. We present a method that utilizes haplotype frequency information and average marker-marker linkage disequilibrium on SNPs typed in and around all genes on a chromosome. The test statistic used is the classic likelihood ratio test applied to haplotypes in case/control populations. Haplotype frequencies are computed through specification of genetic model parameters. Power is determined by computation of the test’s non-centrality parameter. Power per gene is computed as a weighted average of the power assuming each haplotype is associated with the trait. We apply our method to genotype data from dense SNP maps across three entire chromosomes (6, 21, and 22) for three different human populations (African-American, Caucasian, Chinese), three different models of disease (additive, dominant, and multiplicative) and two trait allele frequencies (rare, common). We perform a regression analysis using these factors, average marker-marker disequilibrium, and the haplotype diversity across the gene region to determine which factors most significantly affect average power for a gene in our data. Also, as a ‘proof of principle’ calculation, we perform power and sample size calculations for all genes within 100 kb of the PSORS1 locus (chromosome 6) for a previously published association study of psoriasis. Results of our regression analysis indicate that four highly significant factors that determine average power to detect association are: disease model, average marker-marker disequilibrium, haplotype diversity, and the trait allele frequency. These findings may have important implications for the design of well-powered candidate gene association studies. Our power and sample size calculations for the PSORS1 gene appear consistent with published findings, namely that there is substantial power (>0.99) for most genes within 100 kb of the PSORS1 locus at the 0.01 significance level.

Glucocorticoid receptor polymorphisms and haplotypes associated with chronic fatigue syndrome

“…This study is limited by the small sample size and by the use of mostly intronic markers that have unknown functional impact on the HPA axis. It is interesting to note that SNP browser software, which was used for SNP selection, showed > 90% power to detect an association with NR3C1 polymorphisms in Caucasian subjects with 250 cases and 250 controls, assuming a disease allele frequency of 0.20 (De La Vega et al . 2005a,b).…”

Promoter polymorphism -119C/G in MYG1 (C12orf10) gene is related to vitiligo susceptibility and Arg4Gln affects mitochondrial entrance of Myg1

“…For the SNPs rs2694861, rs1465073, rs1534284, rs4759054, rs4325348, rs2279025, rs1545650, and 4759281 the Applied Biosystems (Foster City, California) SNPlex assay pool was used. The ZipCode probes were detected with an Applied Biosystems 3730 DNA Analyzer, and data interpretation was performed with the Applied Biosystems Genemapper v4.0 software [13]. SNPbrowser version 3.5 was used for SNP selection and SNPlex assay pool design.…”

Testing for Linkage and Association Across the Dihydrolipoyl Dehydrogenase Gene Region with Alzheimer’s Disease in Three Sample Populations

“…These factors include genotype relative risks [43], difference between disease and marker locus frequencies [44][45][46][47][48][49], and other factors. Gordon and Finch [50] provide an overview of these factors.…”