The purpose of this project is to develop methodology for analyzing molecular population genetics data. Work has focused on the use of nonparametric methods for localizing susceptibility loci for complex diseases in humans. A strategy for mapping disease loci is to test for association with case-control samples, and follow up with a family-based test of association to confirm positive results. One way to improve the power of the first test is to increase sample size by combining the case-control and family data. To deal with the correlation between the two tests that this strategy introduces, we have developed a Monte Carlo procedure that always gives a valid test. For late-onset diseases it is common that parental genetic data are not available. Three recent family -based tests of association and linkage utilize an unaffected sibling as a surrogate for untyped parents. We have extended one of these tests and have compared the properties of the four tests in the context of a complex disease for both biallelic and multiallelic markers, as well as for sibships of different sizes. We have also examined the consequences of having some parental data in the sample. Two family-based tests of association and linkage to quantitative traits were developed that do not use parental data. One procedure assumes a mixed effects model in which the sibship is the random factor, the genotype is the fixed factor and the continuous phenotype is the dependent variable. Covariates can be easily accommodated and the procedure can be implemented using available statistical software. The second is a permutation-based procedure. We conducted a simulation study to illustrate the relative power of each test for a variety of quantitative genetic models. Optimal marker selection for the transmission/disequilibrium test (TDT) when applied to admixed populations was investigated. We found that collapsing a microsatellite marker to two alleles can increase the power of the TDT, and a method was developed for finding the optimal collapsing the uses estimates of alleles frequencies in the admixed population.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Intramural Research (Z01)
Project #
1Z01ES044004-02
Application #
6106653
Study Section
Special Emphasis Panel (BB)
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
1998
Total Cost
Indirect Cost
City
State
Country
United States
Zip Code
Xu, Zongli; Kaplan, Norman L; Taylor, Jack A (2007) Tag SNP selection for candidate gene association studies using HapMap and gene resequencing data. Eur J Hum Genet 15:1063-70
Xu, Zongli; Kaplan, Norman L; Taylor, Jack A (2007) TAGster: efficient selection of LD tag SNPs in single or multiple populations. Bioinformatics 23:3254-5
Taylor, Jack A; Xu, Zong-Li; Kaplan, Norman L et al. (2006) How well do HapMap haplotypes identify common haplotypes of genes? A comparison with haplotypes of 334 genes resequenced in the environmental genome project. Cancer Epidemiol Biomarkers Prev 15:133-7
McBride, Kim L; Pignatelli, Ricardo; Lewin, Mark et al. (2005) Inheritance analysis of congenital left ventricular outflow tract obstruction malformations: Segregation, multiplex relative risk, and heritability. Am J Med Genet A 134A:180-6
Morris, Richard W; Kaplan, Norman L (2004) Testing for association with a case-parents design in the presence of genotyping errors. Genet Epidemiol 26:142-54
Rieger, R H; Kaplan, N L; Weinberg, C R (2001) Efficient use of siblings in testing for linkage and association. Genet Epidemiol 20:175-91
Kaplan, N; Morris, R (2001) Issues concerning association studies for fine mapping a susceptibility gene for a complex disease. Genet Epidemiol 20:432-57
Martin, E R; Bass, M P; Kaplan, N L (2001) Correcting for a potential bias in the pedigree disequilibrium test. Am J Hum Genet 68:1065-7
Martin, E R; Monks, S A; Warren, L L et al. (2000) A test for linkage and association in general pedigrees: the pedigree disequilibrium test. Am J Hum Genet 67:146-54
McIntyre, L M; Martin, E R; Simonsen, K L et al. (2000) Circumventing multiple testing: a multilocus Monte Carlo approach to testing for association. Genet Epidemiol 19:18-29