For a complex trait, the pedigree-based llinkage analysis is effective for localizing disease-related genes to within 1cM. Fine-mapping of the disease mutation within this region is often achieved by linkage disequilibrium analysis using haplotypes. Haplotypes can also be very useful for stratifying patients and directing drugs to appropriate subpopulation. The discovery of human single nucleotides polymorphism (SNP) as genetic marker's greatly increases the density of the genetic map and enhances our ability in developing novel disease diagnostic tools and individualized drug therapies. However, with 1.65 million human SNPs single nucleotides polymorphism (SNP) markers identified, the haplotype determination and analysis present a daunting challenge to scientists. The general goal of this research is to advance our computational capability for both the construction of population haplotypes and the utilization of the whole haplotype information in LD mapping.
The specific aims of the proposed research are (a) to propose more efficient and scalable computational strategies for haplotype determination, (b) to demonstrate that in silico approaches to haplotype determinations are effective and economical alternatives to experimental ones, (c) to establish a computational platform that allows for a direct output of haplotypes from high-throughput genotyping readouts, and (d) to design a Bayesian framework for simultaneous haplotype determination, LD mapping, and patients clustering.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG002518-03
Application #
6765828
Study Section
Genome Study Section (GNM)
Program Officer
Brooks, Lisa
Project Start
2002-07-01
Project End
2006-06-30
Budget Start
2004-07-01
Budget End
2005-06-30
Support Year
3
Fiscal Year
2004
Total Cost
$326,000
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
082359691
City
Cambridge
State
MA
Country
United States
Zip Code
02138
Hu, Ming; Deng, Ke; Qin, Zhaohui et al. (2013) Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data. Quant Biol 1:156-174
Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu et al. (2012) CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS. J R Stat Soc Series B Stat Methodol 74:849-870
Balaji, Sentil; Mcclendon, Charles; Chowdhary, Rajesh et al. (2012) IMID: integrated molecular interaction database. Bioinformatics 28:747-9
Zhang, Yu; Jiang, Bo; Zhu, Jun et al. (2011) Bayesian models for detecting epistatic interactions from genetic data. Ann Hum Genet 75:183-93
Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S et al. (2011) Integrated bio-entity network: a system for biological knowledge discovery. PLoS One 6:e21474
Zhang, Wei; Zhu, Jun; Schadt, Eric E et al. (2010) A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules. PLoS Comput Biol 6:e1000642
Zhang, Jing; Hou, Tingjun; Wang, Wei et al. (2010) Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance. Proc Natl Acad Sci U S A 107:1321-6
Chowdhary, Rajesh; Bajic, Vladimir B; Dong, Difeng et al. (2010) Genome-wide analysis of regions similar to promoters of histone genes. BMC Syst Biol 4 Suppl 1:S4
Chowdhary, Rajesh; Zhang, Jinfeng; Liu, Jun S (2009) Bayesian inference of protein-protein interactions from biological literature. Bioinformatics 25:1536-42
Hsu, Yi-Hsiang; Niu, Tianhua; Song, Yiqing et al. (2008) Genetic variants in the UCP2-UCP3 gene cluster and risk of diabetes in the Women's Health Initiative Observational Study. Diabetes 57:1101-7

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