Common, complex diseases together account for a large portion of the health care burden in the United States, and genetic analysis of these traits remains one of the major challenges facing biomedical researchers. Advances in high- throughput technologies have led to increasing availability of large-scale genetic sequence information and other related biological data sets. If robust, powerful statistical and computational methods and tools are developed to analyze these data, then additional progress can be made on identifying and characterizing the genetic components of complex disorders. This, in turn, has the potential to (1) lead to better understanding of the biology of such disorders, (2) clarify the role of environmental risk factors, which could be targets of cost-effective treatment and prevention strategies, and (3) lead to improvements in personalized medical care. The goal of the project is development of robust, powerful trait-association data analysis methods that will be useful for a wide variety of complex traits in a full spectrum of study designs, including unrelated samples with mild population structure, samples of related individuals, and individuals from admixed or founder populations. Speci?c aims of the project are development of (1) more powerful association methods for binary traits, including joint analysis of multiple phenotypes and multiple genetic variants; (2) fast, robust methods for assessing signi?cance in a wide variety of association studies, including methods to detect sparse and weak association signals; and (3) methods to analyze genetic interaction in an association analysis, for one genome or a pair of interacting genomes. The proposed methods incorporate relevant covariates, allow ascertainment, and account for population structure and relatedness of individuals in the sample. Together, the insights attained from the proposed methods and their application to current genetic questions will drive further discoveries into and create greater understanding of the genetics of complex traits. 1

Public Health Relevance

Common, complex diseases together account for a large portion of the health care burden in the United States. Ad- vances in high-throughput technologies have led to increasing availability of large-scale genetic sequence information and other related biological data sets. If suitable statistical and computational methods and tools are developed to analyze these data, then progress can be made on identi?cation and characterization of the genetic components of complex disorders, which has the potential to lead to improvements in preventive and personalized medical care. 1

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG001645-21
Application #
9986857
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Li, Rongling
Project Start
1997-09-01
Project End
2024-05-31
Budget Start
2020-06-01
Budget End
2021-05-31
Support Year
21
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Chicago
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
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Wu, Xiaowei; McPeek, Mary Sara (2018) L-GATOR: Genetic Association Testing for a Longitudinally Measured Quantitative Trait in Samples with Related Individuals. Am J Hum Genet 102:574-591
Zhong, Sheng; Jiang, Duo; McPeek, Mary Sara (2016) CERAMIC: Case-Control Association Testing in Samples with Related Individuals, Based on Retrospective Mixed Model Analysis with Adjustment for Covariates. PLoS Genet 12:e1006329
Jiang, Duo; Zhong, Sheng; McPeek, Mary Sara (2016) Retrospective Binary-Trait Association Test Elucidates Genetic Architecture of Crohn Disease. Am J Hum Genet 98:243-55
Wang, Miaoyan; Jakobsdottir, Johanna; Smith, Albert V et al. (2016) G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies. Genet Epidemiol 40:446-60
Jiang, Duo; Mbatchou, Joelle; McPeek, Mary Sara (2015) Retrospective Association Analysis of Binary Traits: Overcoming Some Limitations of the Additive Polygenic Model. Hum Hered 80:187-95
Jiang, Duo; McPeek, Mary Sara (2014) Robust rare variant association testing for quantitative traits in samples with related individuals. Genet Epidemiol 38:10-20
Jakobsdottir, Johanna; McPeek, Mary Sara (2013) MASTOR: mixed-model association mapping of quantitative traits in samples with related individuals. Am J Hum Genet 92:652-66
McPeek, Mary Sara (2012) BLUP genotype imputation for case-control association testing with related individuals and missing data. J Comput Biol 19:756-65
Thornton, Timothy; Zhang, Qian; Cai, Xiaochen et al. (2012) XM: association testing on the X-chromosome in case-control samples with related individuals. Genet Epidemiol 36:438-50

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