Statistical methods for analyzing high-throughput genotype and sequencing data. Recent advances in sequencing technologies make it possible to sequence a large number of subjects and test many low frequency or rare genetic variants. Many new statistical methods have been developed to analyze sequencing data and the majority of these methods focus on population based samples. It is well- known that rare variants segregate in families and population based methods will suffer lower power when studying rare variants. In addition, how to control population stratification has not been well studied for rare variant analysis. As such, this renewal application requests support to continue developing statistical methods for analyzing large scale genetic data sets. In particular, we propose to 1) Develop unified statistical methods to detect rare genetic variants from sequencing data using both families and unrelated subjects;2) Develop statistical methods of joint modeling of local ancestry and association in rare variant analysis, at the same time controlling for population stratification;3) Develop statistical association methods that can be applied to multiple traits using summary statistics from GWAS or whole sequencing data;4) Develop corresponding software that will be made publicly available. We will collaborate with laboratory-based investigators to obtain appropriate data sets and apply our new analytic methods to this crucial practical problem in genetic epidemiology. In particular, we will be analyzing the GWAS and sequencing data on traits related to hypertension and sleep apnea.
The propose of this application is to develop new statistical methods for analyzing high- throughput genotyping and sequence data. We will develop statistical methods to detect rare genetic variants using both family and unrelated subjects, to jointly model admixture mapping and association in order to search for rare potential causal variants contributing to the admixture mapping signals and to analyze multiple traits using summary statistics from GWAS or whole sequencing data. Finally we will develop corresponding software to these new methods.
|Wang, Heming; Nandakumar, Priyanka; Tekola-Ayele, Fasil et al. (2018) Combined linkage and association analysis identifies rare and low frequency variants for blood pressure at 1q31. Eur J Hum Genet :|
|Wang, Tao; Xue, Xiaonan; Xie, Xianhong et al. (2018) Adjustment for covariates using summary statistics of genome-wide association studies. Genet Epidemiol 42:812-825|
|Kayima, J; Liang, J; Natanzon, Y et al. (2017) Association of genetic variation with blood pressure traits among East Africans. Clin Genet 92:487-494|
|Ouyang, Weiwei; Zhu, Xiaofeng; Qin, Huaizhen (2017) Detecting Multiethnic Rare Variants. Methods Mol Biol 1666:527-538|
|Li, Xiaoyin; Redline, Susan; Zhang, Xiang et al. (2017) Height associated variants demonstrate assortative mating in human populations. Sci Rep 7:15689|
|Qin, Huaizhen; Zhu, Xiaofeng (2017) Calibrating Population Stratification in Association Analysis. Methods Mol Biol 1666:441-453|
|Wang, Heming; Choi, Yoonha; Tayo, Bamidele et al. (2017) Genome-wide survey in African Americans demonstrates potential epistasis of fitness in the human genome. Genet Epidemiol 41:122-135|
|Liu, Ching-Ti; Raghavan, Sridharan; Maruthur, Nisa et al. (2016) Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin. Am J Hum Genet 99:56-75|
|Liang, Jingjing; Cade, Brian E; Wang, Heming et al. (2016) Comparison of Heritability Estimation and Linkage Analysis for Multiple Traits Using Principal Component Analyses. Genet Epidemiol 40:222-32|
|Shetty, Priya B; Tang, Hua; Feng, Tao et al. (2015) Variants for HDL-C, LDL-C, and triglycerides identified from admixture mapping and fine-mapping analysis in African American families. Circ Cardiovasc Genet 8:106-13|
Showing the most recent 10 out of 64 publications