Article

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

Applied Biosystems, Foster City, CA 94404, USA.
Human Heredity (Impact Factor: 1.47). 02/2005; 60(1):43-60. DOI: 10.1159/000087918
Source: PubMed

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.

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    • "Single Nucleotide Polymorphisms (SNP) case-control studies have historically been carried out in the context of binary phenotypes. Power and sample-size calculation methods and software for designing case-control studies have been published extensively (e.g., Michael and Otta (2002); Lange et al. (2004); Hao et al. (2004); De La Vega et al. (2005); Edwards et al. (2005); Skol et al. (2006); Klein (2007); Menashe et al. (2008); Spencer et al. (2009)). "
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    ABSTRACT: For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided.
    Full-text · Article · Sep 2012 · Genetic Epidemiology
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    • "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. "
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    Full-text · Article · Apr 2010 · BMC Medical Genetics
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    • "Research suggesting that association studies may have success in finding complex trait genes (De La Vega et al., 2005 ; Risch and Merikangas, 1996) and the discovery and validation of large numbers of SNPs in the human genome (HapMap, 2003), have elicited a growing interest in the performance of large-scale genetic association studies using SNPs at the candidate-gene , whole-chromosome, and eventually wholegenome level (De La Vega et al., 2005). Genetic association studies require a large sample size and strict investigation methodologies (Risch and Merikangas, 1996) given that the reliably observed gene variants odd ratios range around 1.2–1.5 (Kendler, 2005; Lasky-Su et al., 2005). "
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    Full-text · Article · Sep 2008 · The International Journal of Neuropsychopharmacology
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