Abstract

Genome-wide association methods based on linkage disequilibrium (LD) offer a promising approach to detect genetic variations that are responsible for complex human diseases, such as hypertension, diabetes, obesity, cancers, etc. Approaches based on haplotypes may provide additional power to map disease genes than those based on single markers. More importantly, haplotypes may lead to insights on the factors influencing the dependencies among genetic markers, i.e. linkage disequilibrium (LD), and such insights may provide information essential to understand human evolution and may capture cis-interactions between two or more causal variants. However, the haplotype analysis using a large number of tightly linked SNPs is just being developed and poses great challenges to scientists. Furthermore, most existing methods have not considered the haplotype structure that will soon be provided by the HapMap project and have not been evaluated in this context. The overall goal of this project is to develop statistical and computational tools and methods for the analysis of haplotypes in linkage disequilibrium mapping of complex disease genes. The specific objectives of this project are: (1) Develop efficient algorithms to estimate haplotype frequencies and determine haplotype configurations in general pedigrees for a large number of tightly linked genetic markers with recombinants. (2) Define new test statistics based on haplotype sharing for mapping genes responsible for complex human diseases. (3) Assess the power using tag SNPs in linkage disequilibrium mapping of genes that are responsible for qualitative and quantitative traits. In this context, different methods for tag SNP selection will be compared and the effect of several critical issues in designing efficient and effective algorithms for tag SNP selection will be investigated. (4) Release user-friendly software to the scientific community. The proposed methods are expected to aid the discovery of genes that are responsible for complex human diseases and finally enhance our ability to understand them. ? ? ?

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM074913-03
Application #
7455906
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Anderson, Richard A
Project Start
2006-07-01
Project End
2011-06-30
Budget Start
2008-07-01
Budget End
2009-06-30
Support Year
3
Fiscal Year
2008
Total Cost
$211,921
Indirect Cost

  Institution

Name
University of Alabama Birmingham
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
063690705
City
Birmingham
State
AL
Country
United States
Zip Code
35294

  Publications

Zhang, Kui; Zhi, Degui (2013) Joint haplotype phasing and genotype calling of multiple individuals using haplotype informative reads. Bioinformatics 29:2427-34
Rao, Weinian; Ma, Yamin; Ma, Li et al. (2013) High-resolution whole-genome haplotyping using limited seed data. Nat Methods 10:6-7
Li, Qiling; Kang, Ting; Tian, Xiaohua et al. (2013) Multimeric stability of human C-reactive protein in archived specimens. PLoS One 8:e58094
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
Zhi, Degui; Wu, Jihua; Liu, Nianjun et al. (2012) Genotype calling from next-generation sequencing data using haplotype information of reads. Bioinformatics 28:938-46
Lin, Wan-Yu; Yi, Nengjun; Zhi, Degui et al. (2012) Haplotype-based methods for detecting uncommon causal variants with common SNPs. Genet Epidemiol 36:572-82
Li, Jun; Zhang, Kui; Yi, Nengjun (2011) A Bayesian hierarchical model for detecting haplotype-haplotype and haplotype-environment interactions in genetic association studies. Hum Hered 71:148-60
Gao, Liyan; Fang, Zhide; Zhang, Kui et al. (2011) Length bias correction for RNA-seq data in gene set analyses. Bioinformatics 27:662-9
Zhang, Boshao; Zhi, Degui; Zhang, Kui et al. (2011) Practical Consideration of Genotype Imputation: Sample Size, Window Size, Reference Choice, and Untyped Rate. Stat Interface 4:339-352
Kaklamani, Virginia; Yi, Nengjun; Zhang, Kui et al. (2011) Polymorphisms of ADIPOQ and ADIPOR1 and prostate cancer risk. Metabolism 60:1234-43

Showing the most recent 10 out of 18 publications