This project will investigate several issues arising from the statistical and computational analysis of whole genome sequencing (WGS) based genomics studies. In the area of data management in WGS studies, we address the rapidly increasing cost associated with the transfer and storage of the massive files for the sequence reads and their associated quality scores. We will develop data compression methods to achieve a further compression of several folds beyond current standards, with minimal incurred errors. In the area of secondary analysis, we will develop new statistical learning methods to improve variant quality score recalibration and to filter out unreliable calls. This will improve te reliability of the key information provided by the WGS data, which are the variants calls indicating the locations where the genome differs from the reference and the nature of the differences. We will study methods for case-control studies based on WGS. In particular, we will develop statistical models to enable the integrating of information from multiple types of variants to obtain more powerful tests of association. We will apply the methods developed in this aim to the analysis of WGS data from a study on abdominal aortic aneurysm. Finally, we will address selected new questions associated with population scale WGS projects. Several national programs have recently been initiated to generate WGS data for hundreds of thousands of individuals with longitudinal medical records. The availability of this comprehensive data on a population scale will open up a rich frontier for genome medicine and will pose many new challenges for statistical analysis. We will formulate some of these new challenges and develop the statistical methods needed to meet these challenges.
The research in this project concerns the design and implementation of statistical and computational methods for the analysis of data from whole genome sequencing studies. Methods will be developed for sequence quality score compression, variant call filtering, and methods for case-control association analysis and mega-cohort analysis based on whole genome sequencing.
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|Daley, Timothy P; Lin, Zhixiang; Lin, Xueqiu et al. (2018) CRISPhieRmix: a hierarchical mixture model for CRISPR pooled screens. Genome Biol 19:159|
|Zhou, Bo; Arthur, Joseph G; Ho, Steve S et al. (2018) Extensive and deep sequencing of the Venter/HuRef genome for developing and benchmarking genome analysis tools. Sci Data 5:180261|
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|Wu, Mengmeng; Lin, Zhixiang; Ma, Shining et al. (2017) Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks. J Mol Cell Biol 9:436-452|
|Zamanighomi, Mahdi; Lin, Zhixiang; Wang, Yong et al. (2017) Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data. Nucleic Acids Res 45:5666-5677|
|Afshar, Pegah Tootoonchi; Wong, Wing Hung (2017) COSINE: non-seeding method for mapping long noisy sequences. Nucleic Acids Res 45:e132|
|Carter, Ava C; Chang, Howard Y; Church, George et al. (2017) Challenges and recommendations for epigenomics in precision health. Nat Biotechnol 35:1128-1132|
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|Chen, Xi; Yang, Hong; Wong, Wing Hung (2017) Phased Genome Sequencing Through Chromosome Sorting. Methods Mol Biol 1551:171-188|
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