Screening and Replication using the Same Data Set: Testing Strategies for Family-Based Studies in which All Probands Are Affected

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Screening and Replication using the Same Data Set: Testing Strategies for Family-Based Studies in which All Probands Are Affected

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dc.contributor.author Murphy, Amy
dc.contributor.author Weiss, Scott Tillman
dc.contributor.author Lange, Christoph
dc.date.accessioned 2011-05-10T00:27:16Z
dc.date.issued 2008
dc.identifier.citation Murphy, Amy, Scott T. Weiss, and Christoph Lange. 2008. Screening and Replication using the Same Data Set: Testing Strategies for Family-Based Studies in which All Probands Are Affected. PLoS Genetics 4(9): e1000197. en_US
dc.identifier.issn 1553-7390 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4885954
dc.description.abstract For genome-wide association studies in family-based designs, we propose a powerful two-stage testing strategy that can be applied in situations in which parent-offspring trio data are available and all offspring are affected with the trait or disease under study. In the first step of the testing strategy, we construct estimators of genetic effect size in the completely ascertained sample of affected offspring and their parents that are statistically independent of the family-based association/transmission disequilibrium tests (FBATs/TDTs) that are calculated in the second step of the testing strategy. For each marker, the genetic effect is estimated (without requiring an estimate of the SNP allele frequency) and the conditional power of the corresponding FBAT/TDT is computed. Based on the power estimates, a weighted Bonferroni procedure assigns an individually adjusted significance level to each SNP. In the second stage, the SNPs are tested with the FBAT/TDT statistic at the individually adjusted significance levels. Using simulation studies for scenarios with up to 1,000,000 SNPs, varying allele frequencies and genetic effect sizes, the power of the strategy is compared with standard methodology (e.g., FBATs/TDTs with Bonferroni correction). In all considered situations, the proposed testing strategy demonstrates substantial power increases over the standard approach, even when the true genetic model is unknown and must be selected based on the conditional power estimates. The practical relevance of our methodology is illustrated by an application to a genome-wide association study for childhood asthma, in which we detect two markers meeting genome-wide significance that would not have been detected using standard methodology. en_US
dc.language.iso en_US en_US
dc.publisher Public Library of Science en_US
dc.relation.isversionof doi:10.1371/journal.pgen.1000197 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2529406/pdf/ en_US
dash.license LAA
dc.subject genetics and genomics en_US
dc.subject complex traits en_US
dc.subject disease models en_US
dc.subject genetics of disease en_US
dc.subject mathematics en_US
dc.subject statistics en_US
dc.title Screening and Replication using the Same Data Set: Testing Strategies for Family-Based Studies in which All Probands Are Affected en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal PLoS Genetics en_US
dash.depositing.author Weiss, Scott Tillman
dc.date.available 2011-05-10T00:27:16Z
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US
dash.affiliation.other SPH^Molecular+Integrative Physiological Sci Prog en_US
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US
dash.affiliation.other SPH^Biostatistics en_US

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