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. Recent 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 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. Our goal is development of robust, powerful trait-association data analysis methods that will be useful for a wide variety of genetic and other """"""""omics"""""""" predictors in a full spectrum of study designs, ranging from unrelated samples with population structure to individuals sampled from a complex, inbred pedigree. Specifically, we propose development of novel methods for binary, quantitative, and longitudinal trait mapping with a range of predictors, such as genotype, genomic sequence, transcriptome, expression, metabolomic, methylation, proteomic or other data, where these methods incorporate relevant covariates and account for population structure and/or relatedness of individuals in the sample.
Common, complex diseases together account for a large portion of the health care burden in the United States. Recent advances 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 identification and characterization of the genetic components of complex disorders, which has the potential to lead to improvements in preventative and personalized medical care.
|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|
|Wang, Miaoyan; Roux, Fabrice; Bartoli, Claudia et al. (2018) Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes. Proc Natl Acad Sci U S A 115:E5440-E5449|
|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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