||5R03CA099844-02 Interpret this number
||University Of Utah
||Complex Multiple Snp Analysis with Pedigree Data
DESCRIPTION (provided by applicant):
Extended pedigree resources ascertained for disease, and previously used for linkage analysis, contain individuals with high likelihood of being genetic in nature. Sampling individuals from these pedigrees for association or other linkage-disequilibrium (LD) based methods therefore increases the likelihood that genetic cases are selected and thus increases the power to detect such genetic factors involved in the disease. Unfortunately, the relatedness of these individuals invalidates standard statistical analysis techniques, which can lead to inflated type I errors. Methods that appropriately account and correct for the inherent bias in using correlated data will allow for maximal and powerful use of already ascertained pedigree resources for LD-based analyses.
It is already clear that many small effect genes, in addition to environmental factors, are involved in common disease, and that both inter-genic and intra-genic epistatic interactions exist. Strategies to efficiently test complex interaction hypotheses, in conjunction with appropriate corrections for multiple testing are needed. Knowledge of underlying haplotype blocks will help minimize the number of tests, but efficient sequential methods will still be required to maximize power, especially when a-priori knowledge of interactions is absent, as is usually the case.
We aim to develop a flexible analysis tool and distribute a user-friendly, freely available software package that incorporates a broad range of statistical tests and strategies to test complex interactions in pedigree data. With the availability of such software it is anticipated that many researchers, including our own Genetic Epidemiology group at the University of Utah, with already ascertained resources will be able to begin new analyses and that the resources will gain new, previously unrealized, value.
Characterization of linkage disequilibrium structure, mutation history, and tagging SNPs, and their use in association analyses: ELAC2 and familial early-onset prostate cancer.
Camp NJ, Swensen J, Horne BD, Farnham JM, Thomas A, Cannon-Albright LA, Tavtigian SV
Genet Epidemiol, 2005 Apr;28(3), p. 232-43.
Graphical modeling of the joint distribution of alleles at associated loci.
Thomas A, Camp NJ
Am J Hum Genet, 2004 Jun;74(6), p. 1088-101.
2004 Apr 26.
Principal component analysis for selection of optimal SNP-sets that capture intragenic genetic variation.
Horne BD, Camp NJ
Genet Epidemiol, 2004 Jan;26(1), p. 11-21.