The """"""""evidential"""""""" paradigm is a statistical paradigm, an alternative to frequentist and Bayesian paradigms for statistical inference. The evidential paradigm provides 4 major advantages over these other paradigms when analyzing genetic data: It (1) provides an objective measure of evidence;(2) has good operating characteristics (low error probabilities);(3) decouples the error probabilities from the measure of evidence;and - perhaps the advantage with the greatest potential impact - (4) provides better approaches to deal with the multiple testing problem. In Strug &Hodge (2006a, b) we quantified these advantages for linkage analysis of several simple genetic models, mostly with known parameters. Here we propose to extend our investigations to more complex genetic models, also with unknown parameters, and to association analysis: Specifically, we will (1) Extend linkage findings to association analysis, including genome-wide association studies;(2) Quantify error probabilities for linkage of more complex disease models;and (3) Develop and test new evidential methodology for linkage analysis of complex unknown traits. We will test and characterize new methods via rigorous theoretical analyses, supplemented by realistic computer simulations. /Relevance The long-term objective is to make the evidential paradigm, with its multiple testing advantages, available for use when analyzing all types of genetic data. As a result, some current problems imposed by conducting multiple tests on genetic data, for example, investigators'reluctance to thoroughly analyze their collected data, will no longer bedevil the field or stunt the advancement of knowledge.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Small Research Grants (R03)
Project #
5R03HG004314-02
Application #
7546976
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Ramos, Erin
Project Start
2008-01-01
Project End
2010-12-31
Budget Start
2009-01-01
Budget End
2010-12-31
Support Year
2
Fiscal Year
2009
Total Cost
$61,812
Indirect Cost
Name
Hospital for Sick Chldrn (Toronto)
Department
Type
DUNS #
208511808
City
Toronto
State
ON
Country
Canada
Zip Code
M5 1-X8
Blackman, Scott M; Commander, Clayton W; Watson, Christopher et al. (2013) Genetic modifiers of cystic fibrosis-related diabetes. Diabetes 62:3627-35
Sun, Lei; Rommens, Johanna M; Corvol, Harriet et al. (2012) Multiple apical plasma membrane constituents are associated with susceptibility to meconium ileus in individuals with cystic fibrosis. Nat Genet 44:562-9
Strug, Lisa J; Addis, Laura; Chiang, Theodore et al. (2012) The genetics of reading disability in an often excluded sample: novel loci suggested for reading disability in Rolandic epilepsy. PLoS One 7:e40696
Taylor, Chelsea; Commander, Clayton W; Collaco, Joseph M et al. (2011) A novel lung disease phenotype adjusted for mortality attrition for cystic fibrosis genetic modifier studies. Pediatr Pulmonol 46:857-69
Wright, Fred A; Strug, Lisa J; Doshi, Vishal K et al. (2011) Genome-wide association and linkage identify modifier loci of lung disease severity in cystic fibrosis at 11p13 and 20q13.2. Nat Genet 43:539-46
Hodge, Susan E; Baskurt, Zeynep; Strug, Lisa J (2011) Using parametric multipoint lods and mods for linkage analysis requires a shift in statistical thinking. Hum Hered 72:264-75
Li, W; Sun, L; Corey, M et al. (2011) Understanding the population structure of North American patients with cystic fibrosis. Clin Genet 79:136-46
Strug, L J; Suresh, R; Fyer, A J et al. (2010) Panic disorder is associated with the serotonin transporter gene (SLC6A4) but not the promoter region (5-HTTLPR). Mol Psychiatry 15:166-76
Pal, D K; Li, W; Clarke, T et al. (2010) Pleiotropic effects of the 11p13 locus on developmental verbal dyspraxia and EEG centrotemporal sharp waves. Genes Brain Behav 9:1004-12
Strug, Lisa J; Hodge, Susan E; Chiang, Theodore et al. (2010) A pure likelihood approach to the analysis of genetic association data: an alternative to Bayesian and frequentist analysis. Eur J Hum Genet 18:933-41

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