An emerging research area in genetics is to detect associations between complex traits and rare variants (RVs) with next-generation sequencing data. Due to extremely low minor allele frequencies (MAFs) of RVs, many existing tests for common variants (CVs), such as the univariate test on each individual variant, most popular in genome-wide association studies (GWAS), may no longer be suitable. To boost statistical power, a common theme of existing association tests is to aggregate information across multiple RVs in a gene. With sequencing data, since the majority of RVs may not be causal, in which case most, if not all, existing association tests have severely deteriorating performance. We propose developing and evaluating an adaptive test that can maintain high power across various situations, including in the presence of opposite association directions and of many non-associated RVs. We will extend the proposed adaptive test to pathway analysis and multi-trait analysis. The developed methods will be applied to detect associations of RV-cardiovascular traits with the sequencing data from the CHARGE-S and ESP cohorts. We will develop and distribute software implementing the proposed methods.

Public Health Relevance

This proposed research is expected not only to advance data analysis methodology and practice for DNA sequencing data, but also to contribute valuable analysis tools to the elucidation of genetic components of complex human diseases and traits.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL116720-01A1
Application #
8581698
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Applebaum-Bowden, Deborah
Project Start
2013-09-01
Project End
2016-05-31
Budget Start
2013-09-01
Budget End
2014-05-31
Support Year
1
Fiscal Year
2013
Total Cost
$345,566
Indirect Cost
$64,826
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
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Wei, Peng; Yang, Yang; Guo, Xinjian et al. (2016) Identification of an Association of TNFAIP3 Polymorphisms With Matrix Metalloproteinase Expression in Fibroblasts in an Integrative Study of Systemic Sclerosis-Associated Genetic and Environmental Factors. Arthritis Rheumatol 68:749-60
Yu, Bing; Li, Alexander H; Muzny, Donna et al. (2015) Association of Rare Loss-Of-Function Alleles in HAL, Serum Histidine: Levels and Incident Coronary Heart Disease. Circ Cardiovasc Genet 8:351-5

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