Linkage disequilibrium (LD, the non-random association of alleles at two or more loci) provides valuable information for detecting genetic variations that are responsible for complex human diseases such as hypertension, diabetes, obesity, and stroke. Haplotypes, the combinations of alleles on the same chromosome that were inherited as a unit, may offer valuable insights on the LD structure of the human genome and may provide additional power for mapping disease genes. Such insights may be useful not only in disease gene mapping, but also in other fields such as population genetics, where haplotype information has been used to study migration and immigration rates, genetic demography, and human evolutionary history. The international HapMap project, which aims to develop a haplotype map of the human genome, has already begun to provide valuable resources that can in turn motivate the development and testing of new haplotype methods. Although haplotype analysis using a large quantity of single nucleotide polymorphisms (SNPs) is in great need, it also poses great challenges. The overall goal of this project is to develop novel statistical and computational methods and software tools for the analysis of haplotypes in mapping of complex human disease genes. The specific objectives of this project are: (1) to develop efficient algorithms to estimate haplotype frequencies and determine individual haplotype configurations in the presence of informatively missing genotypes and genotyping errors in samples of unrelated individuals;(2) to develop statistical methods to identify a set of candidate genomic regions for use in disease association mapping;(3) to develop new haplotype-based disease gene mapping methods that can handle informatively missing genotypes and genotyping errors, that can combine information from multiple regions of interest, and that are robust to population heterogeneity;and (4) to release robust and user-friendly software, which implements the proposed methods, to the scientific community at no charge. The proposed methods will be performed on the publicly available data (e.g. data from the HapMap project), as well as other human data generated in our collaborators'ongoing projects, including data sets concerning genetic effects on left ventricular hypertrophy, rheumatoid arthritis, and obesity. The proposed project is closely related to NIH's mission in that the accomplished methods will be useful to the broad biomedical research community and will greatly facilitate the study of human genetic variation and its association with complex diseases. This will help in pursuit of new knowledge about these diseases. Relevance: The proposed methods are expected to aid the discovery of the genes that are responsible for complex human diseases, help us to better understand them, and finally enhance our ability to prevent, diagnose, and treat these diseases.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-GGG-H (90))
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Krasnewich, Donna M
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University of Alabama Birmingham
Biostatistics & Other Math Sci
Schools of Public Health
United States
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Shendre, Aditi; Parmar, Gaurav M; Dillon, Chrisly et al. (2018) Influence of Age on Warfarin Dose, Anticoagulation Control, and Risk of Hemorrhage. Pharmacotherapy 38:588-596
Liu, Nianjun; Irvin, Marguerite R; Zhi, Degui et al. (2017) Influence of common and rare genetic variation on warfarin dose among African-Americans and European-Americans using the exome array. Pharmacogenomics 18:1059-1073
Limdi, Nita A; Brown, Todd M; Shendre, Aditi et al. (2017) Quality of anticoagulation control and hemorrhage risk among African American and European American warfarin users. Pharmacogenet Genomics 27:347-355
Ather, Sameer; Shendre, Aditi; Beasley, T Mark et al. (2016) Effect of Left Ventricular Systolic Dysfunction on Response to Warfarin. Am J Cardiol 118:232-6
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
Liu, Nianjun (2015) QTL mapping - Current status and challenges: Comment on ""Mapping complex traits as a dynamic system"" by L. Sun and R. Wu. Phys Life Rev 13:194-5
Zhi, Degui; Liu, Nianjun; Zhang, Kui (2015) On the design and analysis of next-generation sequencing genotyping for a cohort with haplotype-informative reads. Methods 79-80:41-6
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
Limdi, Nita A; Brown, Todd M; Yan, Qi et al. (2015) Race influences warfarin dose changes associated with genetic factors. Blood 126:539-45
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

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