The primary goal of this proposal is to aid Dr. Seunggeun (Shawn) Lee in becoming an independent researcher with expertise in sequencing association studies of complex lung, heart, and blood diseases such as ARDS/ALI, OSA, and type 2 diabetes. Dr. Lee is currently a postdoctoral research fellow in the Department of Biostatistics at the Harvard School of Public Health, where he has begun developing statistical methods for sequence association studies. Complex diseases not only incur tragic human cost but also impose a substantial financial burden on society. Each year, ARDS/ALI alone is responsible for 75,000 deaths and over $20 billion in medical costs in the US. The tremendous development of next generation sequencing technology will improve our understanding of the complex disease etiology by identifying the disease susceptibility rare genetic variants. However, statistical development lags behind the development of sequencing technology, and we need to develop advanced statistical methods to fill this gap. Specifically, the applicant proposes to develop statistical methods to: 1) adjust genotyping errors caused by cost-effective sequencing designs such as low coverage sequencing;and 2) test an association between multivariate correlated phenotypes and rare genetic variants. The developed methods will be applied to ongoing sequencing association studies of ARDS/ALI, OSA, and type 2 diabetes. During the mentored period, the applicant will learn modern statistical methods such as the kernel machine, measurement error model and generalized estimating equation, as well as to establish fundamental statistical framework of the proposed research under the guidance of Dr. Xihong Lin (mentor), and to expand knowledge on complex diseases under the guidance of Dr. David Christiani (co-mentor). In addition, the applicant will broaden his background in heart and lung disorders, modern parallel computing, and Next Generation Sequencing through rigorous coursework and participation in workshops and seminars. Building upon skills acquired in the mentored period, Dr. Lee will expand the established statistical framework to diverse models, and apply them in ARDS, OSA, and type 2 diabetes studies to identify disease susceptibility rare genetic variants. After the completion of this award, the applicant will have developed into an independent and productive researcher with expertise in the application of sequencing technologies to genetic epidemiology research.

Public Health Relevance

Complex disease such as ARDS/ALI, OSA, and type 2 diabetes are major public health concerns. The proposed research will develop advanced statistical methods to analyze Next Generation Sequencing data and apply them to real sequencing association studies to identify disease susceptibility rare genetic variants. The results from this study will improve our understanding of complex disease etiology.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Transition Award (R00)
Project #
5R00HL113164-04
Application #
8723875
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Wolz, Michael
Project Start
2012-06-15
Project End
2016-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
4
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Lee, Seunggeun; Fuchsberger, Christian; Kim, Sehee et al. (2016) An efficient resampling method for calibrating single and gene-based rare variant association analysis in case-control studies. Biostatistics 17:1-15
Lin, Xinyi; Lee, Seunggeun; Wu, Michael C et al. (2016) Test for rare variants by environment interactions in sequencing association studies. Biometrics 72:156-64
Shi, Jingchunzi; Lee, Seunggeun (2016) A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis. Biometrics 72:945-54
Ware, Erin B; Smith, Jennifer A; Mukherjee, Bhramar et al. (2016) Applying Novel Methods for Assessing Individual- and Neighborhood-Level Social and Psychosocial Environment Interactions with Genetic Factors in the Prediction of Depressive Symptoms in the Multi-Ethnic Study of Atherosclerosis. Behav Genet 46:89-99
Ma, Clement; Boehnke, Michael; Lee, Seunggeun et al. (2015) Evaluating the Calibration and Power of Three Gene-Based Association Tests of Rare Variants for the X Chromosome. Genet Epidemiol 39:499-508
Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab et al. (2015) Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT). Stat Interface 8:495-505
He, Zihuai; Zhang, Min; Lee, Seunggeun et al. (2015) Set-based tests for genetic association in longitudinal studies. Biometrics 71:606-15
He, Zihuai; Payne, Erin K; Mukherjee, Bhramar et al. (2015) Association between Stress Response Genes and Features of Diurnal Cortisol Curves in the Multi-Ethnic Study of Atherosclerosis: A New Multi-Phenotype Approach for Gene-Based Association Tests. PLoS One 10:e0126637
Lee, Seunggeun; Zou, Fei; Wright, Fred A (2014) Convergence of Sample Eigenvalues, Eigenvectors, and Principal Component Scores for Ultra-High Dimensional Data. Biometrika 101:484-490
Lee, Seunggeung; Abecasis, Gonçalo R; Boehnke, Michael et al. (2014) Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 95:5-23