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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
2R01HG001645-16
Application #
8760899
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Brooks, Lisa
Project Start
1997-09-01
Project End
2018-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
16
Fiscal Year
2014
Total Cost
$366,400
Indirect Cost
$134,501
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
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
Thornton, Timothy; McPeek, Mary Sara (2010) ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure. Am J Hum Genet 86:172-84
Zhang, Jun; Niyogi, Partha; McPeek, Mary Sara (2009) Laplacian eigenfunctions learn population structure. PLoS One 4:e7928
Wang, Zuoheng; McPeek, Mary Sara (2009) An Incomplete-Data Quasi-likelihood Approach to Haplotype-Based Genetic Association Studies on Related Individuals. J Am Stat Assoc 104:1251-1260
Wang, Zuoheng; McPeek, Mary Sara (2009) ATRIUM: testing untyped SNPs in case-control association studies with related individuals. Am J Hum Genet 85:667-78
Weiss, Lauren A; Veenstra-Vanderweele, Jeremy; Newman, Dina L et al. (2004) Genome-wide association study identifies ITGB3 as a QTL for whole blood serotonin. Eur J Hum Genet 12:949-54