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Detecting rare variant associations: methods for testing haplotypes and multiallelic genotypes

Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095-7088, USA.
Genetic Epidemiology (Impact Factor: 2.95). 01/2011; 35 Suppl 1(S1):S85-91. DOI: 10.1002/gepi.20656
Source: PubMed

ABSTRACT We summarize the work done by the contributors to Group 13 at Genetic Analysis Workshop 17 (GAW17) and provide a synthesis of their data analyses. The Group 13 contributors used a variety of approaches to test associations of both rare variants and common single-nucleotide polymorphisms (SNPs) with the GAW17 simulated traits, implementing analytic methods that incorporate multiallelic genotypes and haplotypes. In addition to using a wide variety of statistical methods and approaches, the contributors exhibited a remarkable amount of flexibility and creativity in coding the variants and their genes and in evaluating their proposed approaches and methods. We describe and contrast their methods along three dimensions: (1) selection and coding of genetic entities for analysis, (2) method of analysis, and (3) evaluation of the results. The contributors consistently presented a strong rationale for using multiallelic analytic approaches. They indicated that power was likely to be increased by capturing the signals of multiple markers within genetic entities defined by sliding windows, haplotypes, genes, functional pathways, and the entire set of SNPs and rare variants taken in aggregate. Despite this variability, the methods were fairly consistent in their ability to identify two associated genes for each simulated trait. The first gene was selected for the largest number of causal alleles and the second for a high-frequency causal SNP. The presumed model of inheritance and choice of genetic entities are likely to have a strong effect on the outcomes of the analyses.

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Available from: Marsha Wilcox, Dec 18, 2013
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    ABSTRACT: Genetic Analysis Workshop 17 (GAW17) focused on the transition from genome-wide association study designs and methods to the study designs and statistical genetic methods that will be required for the analysis of next-generation sequence data including both common and rare sequence variants. In the 166 contributions to GAW17, a wide variety of statistical methods were applied to simulated traits in population- and family-based samples, and results from these analyses were compared to the known generating model. In general, many of the statistical genetic methods used in the population-based sample identified causal sequence variants (SVs) when the estimated locus-specific heritability, as measured in the population-based sample, was greater than about 0.08. However, SVs with locus-specific heritabilities less than 0.03 were rarely identified consistently. In the family-based samples, many of the methods detected SVs that were rarer than those detected in the population-based sample, but the estimated locus-specific heritabilities for these rare SVs, as measured in the family-based samples, were substantially higher (>0.2) than their corresponding heritabilities in the population-based samples. Substantial inflation of the type I error rate was observed across a wide variety of statistical methods. Although many of the contributions found little inflation in type I error for Q4, a trait with no causal SVs, type I error rates for Q1 and Q2 were well above their nominal levels with the inflation for Q1 being higher than that for Q2. It seems likely that this inflation in type I error is due to correlations among SVs.
    Genetic Epidemiology 01/2011; 35 Suppl 1:S107-14. DOI:10.1002/gepi.20659 · 2.95 Impact Factor