Constructing genetic and physical maps for all the human chromosomes are fundamental aims of the Human Genome Project, and the promise to map and identify genes for human diseases is a principal reason why the Genome Project has widespread support. Efficient statistical methods help to insure that these goals are met in a timely, cost-effective manner. In this proposal, the investigators address a set of methodological problems that arise in radiation hybrid and linkage map construction, and the mapping of human disease genes by linkage and association analysis. First, the investigators will develop and test methods for linkage mapping of genes for complex human disease. The investigators extend multipoint affected sib pair (ASP) linkage analysis to allow explicitly for genotyping error and assess the value of this extension, evaluate the robustness of ASP linkage analysis to errors in order and intermarker distance in genetic maps, and examine the variability of the multipoint lod score function in the presence of a disease locus to address the issue of replication of linkage findings. Second, the investigators will develop a Bayesian method of point and interval estimation of marker position for radiation hybrid and linkage mapping that takes into account ordering uncertainty. Third, the investigators will develop family-based association methods for gene mapping based on a discordant sib pair (DSP) design and evaluate an extension to parent-offspring trio association methods that uses both affected and unaffected offspring. Fourth, the investigators will develop, test, distribute, and support computer software based on the methods that arise from the other goals of this project, and will continue to update, distribute, and support their current software including SIMLINK, RHMAP, RELPAIR, and several modules of the MENDEL package. Fifth, the investigators will continue to be opportunistic in identifying and addressing important statistical modeling and analysis problems that are related to the other goals of this project.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG000376-12
Application #
2889647
Study Section
Genome Study Section (GNM)
Program Officer
Brooks, Lisa
Project Start
1988-09-28
Project End
2001-08-31
Budget Start
1999-09-01
Budget End
2000-08-31
Support Year
12
Fiscal Year
1999
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
791277940
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Wojcik, Genevieve L; Fuchsberger, Christian; Taliun, Daniel et al. (2018) Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies. G3 (Bethesda) 8:3255-3267
Reppell, M; Zöllner, S (2018) An efficient algorithm for generating the internal branches of a Kingman coalescent. Theor Popul Biol 122:57-66
Jiang, Yu; Chen, Sai; McGuire, Daniel et al. (2018) Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes. PLoS Genet 14:e1007452
Dutta, Diptavo; Scott, Laura; Boehnke, Michael et al. (2018) Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes. Genet Epidemiol :
Ray, Debashree; Boehnke, Michael (2018) Methods for meta-analysis of multiple traits using GWAS summary statistics. Genet Epidemiol 42:134-145
Scott, Robert A; Scott, Laura J; Mägi, Reedik et al. (2017) An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans. Diabetes 66:2888-2902
Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei et al. (2017) Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models. Eur J Hum Genet 25:350-359
Chiu, Chi-Yang; Jung, Jeesun; Wang, Yifan et al. (2017) A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing. Genet Epidemiol 41:18-34
Taliun, Daniel; Chothani, Sonia P; Schönherr, Sebastian et al. (2017) LASER server: ancestry tracing with genotypes or sequence reads. Bioinformatics 33:2056-2058
McCarthy, Shane; Das, Sayantan; Kretzschmar, Warren et al. (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279-83

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