Next-generation sequencing technologies coupled with the efficient DNA capture methods provide exome sequencing approach to investigate the genetic basis of complex phenotypes. Unlike whole genome association studies (GWAS) which can only discover variation in DNA that is frequent in the population (great than 1%), exome sequencing is a great choice for scientists today who might be interested in looking for rare mutations. Furthermore, exome sequencing has the advantage of testing comprehensively the role of coding variation, both common and rare. It is anticipated that every gene may harbor functionally relevant variants. Recently, a number of statistical methods become available for analyzing the contribution of rare variants to the development of complex traits. These methods include Combined Multivariate and Collapsing (CMC) Method, Multivariate test of collapsed sub-groups Hotelling T2 test, MANOVA, Fishers product method, Weighted Sum Method and Kernel-based adaptive test. While the merits of these methods have been evaluated extensively for population-based association studies, none of these methods in their current form can be used to analyze the pedigree based association analysis using exome sequencing data. We will develop pedigree-based rare variants analysis approach by treating each affected relatives as dependent pairs and the dependency will be accounted for using correlation matrix. Under the null hypothesis of no association of a set of rare variants with the diseases, the new statistic we have developed is asymptotically distributed as a central distribution. Further, we will use the estimated IBD based weights to account for the dependency of the related affected or unaffected pairs generated from same pedigrees. This method will be used to analyze approximately bipolar disorder pedigrees with exome data. Simulation studies will be used for determining power and type I errors. This method will be used to analyze approximately 100 bipolar disorder pedigrees with exome data as well as data sets with other mental disorders in the future.

Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
U.S. National Institute of Mental Health
Zip Code
Guo, W; Samuels, J F; Wang, Y et al. (2017) Polygenic risk score and heritability estimates reveals a genetic relationship between ASD and OCD. Eur Neuropsychopharmacol 27:657-666
Tao, Yu; Gao, Hui; Ackerman, Benjamin et al. (2016) Evidence for contribution of common genetic variants within chromosome 8p21.2-8p21.1 to restricted and repetitive behaviors in autism spectrum disorders. BMC Genomics 17:163
Qin, H; Samuels, J F; Wang, Y et al. (2016) Whole-genome association analysis of treatment response in obsessive-compulsive disorder. Mol Psychiatry 21:270-6
Qin, Hai-De; Liao, Xiao-Yu; Chen, Yuan-Bin et al. (2016) Genomic Characterization of Esophageal Squamous Cell Carcinoma Reveals Critical Genes Underlying Tumorigenesis and Poor Prognosis. Am J Hum Genet 98:709-27
Wang, Zhiqiang; Yang, Bixiu; Liu, Yansong et al. (2015) Further evidence supporting the association of NKAPL with schizophrenia. Neurosci Lett 605:49-52
Xu, Meng Yuan; Umbach, David M; Murphy, Eleanor et al. (2015) A Novel Mixture Model to Estimate the Time to Drug Effect Onset and Its Association with Covariates. Hum Hered 80:90-9
Wang, Yi; Li, Yi; Cao, Hongbao et al. (2015) Efficient test for nonlinear dependence of two continuous variables. BMC Bioinformatics 16:260
Wang, Jicai; Cao, Hongbao; Liao, Yanhui et al. (2015) Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings. Neuroimage Clin 8:95-103
Zhang, Fuquan; Xu, Yong; Cao, Hongbao et al. (2015) Mapsnp: an R package to plot a genomic map for single nucleotide polymorphisms. PLoS One 10:e0123609
Zhang, Fuquan; Xu, Yong; Shugart, Yin Yao et al. (2015) Converging evidence implicates the abnormal microRNA system in schizophrenia. Schizophr Bull 41:728-35

Showing the most recent 10 out of 31 publications