Statistical gene mapping in pedigrees advances our knowledge of the genetic architecture of complex diseases (e.g., diabetes, Alzheimer's disease, asthma) and quantitative traits (e.g., bone density, blood pressure, milk production) in human and animal populations. Unfortunately most observed pedigree data are not complete and have missing data. Efficiently inferring haplotype configurations and calculating identity-by-descent (IBD) probabilities for complex pedigrees with large numbers of linked loci and missing marker data by using the observed genotype data (especially dense single nucleotide polymorphism (SNP) markers) are critical components and remain challenging in statistical gene mapping. The broad, long-term objectives of the proposed work are to develop efficient statistical and computational methods for haplotyping and gene mapping in large pedigrees with missing and phase unknown marker data.
The specific aims are to 1) extend our conditional enumeration haplotyping method that currently works with complete pedigree data to pedigrees with missing marker data, and then improve the method so that it can handle linkage disequilibrium (LD) between markers;2) develop a computationally efficient method for estimating IBD probabilities in large pedigrees with large numbers of linked loci and with missing marker data, develop a fine mapping method by modeling LD information between dense (SNP) markers, and evaluate the performance of the IBD probability estimation method in terms of quantitative trait loci (QTL) mapping accuracy in linkage analysis and fine mapping;3) apply the proposed methods to linkage analysis and fine mapping of two large, real human pedigree data sets (a 1623-person Hutterite pedigree and a 1412- person Amish pedigree);and 4) develop computer software to implement aims 1 and 2. Our approach to these aims is based on the computation of conditional probabilities of possible ordered genotypes at phase unknown markers and the calculation of likelihood of haplotype configurations. By setting a threshold value for the conditional probabilities of ordered genotypes at phase unknown markers and a threshold value for the conditional probabilities of haplotype configurations, the proposed haplotyping method identifies a subset of haplotype configurations with the highest likelihoods for a pedigree. IBD probabilities are estimated based on this subset of haplotype configurations, and then are used as input to variance components based QTL mapping methods in large pedigrees. The methodologies developed in this research will enhance our ability to map QTL and complex disease genes in human and animal populations.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM073766-05
Application #
7849472
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Krasnewich, Donna M
Project Start
2007-06-01
Project End
2012-05-31
Budget Start
2010-06-01
Budget End
2011-05-31
Support Year
5
Fiscal Year
2010
Total Cost
$261,011
Indirect Cost
Name
Virginia Commonwealth University
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
105300446
City
Richmond
State
VA
Country
United States
Zip Code
23298
Yan, Qi; Weeks, Daniel E; Celedón, Juan C et al. (2015) Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method. Genetics 201:1329-39
Chen, Wenan; Ren, Chunfeng; Qin, Huaizhen et al. (2015) A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests. Hum Hered 79:80-92
Yan, Qi; Weeks, Daniel E; Tiwari, Hemant K et al. (2015) Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples. Hum Hered 80:126-38
Yan, Qi; Tiwari, Hemant K; Yi, Nengjun et al. (2015) A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model. Hum Hered 79:60-8
Yan, Qi; Tiwari, Hemant K; Yi, Nengjun et al. (2014) Kernel-machine testing coupled with a rank-truncation method for genetic pathway analysis. Genet Epidemiol 38:447-56
Lin, Wan-Yu; Lou, Xiang-Yang; Gao, Guimin et al. (2014) Rare variant association testing by adaptive combination of P-values. PLoS One 9:e85728
Chen, Wenan; Chen, Xiangning; Archer, Kellie J et al. (2013) A rapid association test procedure robust under different genetic models accounting for population stratification. Hum Hered 75:23-33
Lin, Wan-Yu; Yi, Nengjun; Lou, Xiang-Yang et al. (2013) Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants. Genet Epidemiol 37:560-70
Chen, Wenan; Gao, Guimin; Nerella, Srilaxmi et al. (2013) MethylPCA: a toolkit to control for confounders in methylome-wide association studies. BMC Bioinformatics 14:74
Lin, Wan-Yu; Tiwari, Hemant K; Gao, Guimin et al. (2012) Similarity-based multimarker association tests for continuous traits. Ann Hum Genet 76:246-60

Showing the most recent 10 out of 22 publications