Gene expression data produced from expression microarrays have not only greatly improved our understanding of cell biology, but also provided invaluable resources to guide the diagnosis and treatment of human diseases. However, the pace of incorporating gene expression signatures into medical practice has been relatively slow. This is mainly due to the limitations of gene expression microarrays and the natural variation of gene expression across tissues or developmental stages. This research project aims to overcome these limitations by joint study of germline DNA polymorphisms and allele-specific expression (ASE) obtained from RNA-seq data. Since germline DNA polymorphisms are stable across tissues and developmental stages, inclusion of DNA information will help us establish more reliable biomarkers for patients' clinical care. More specifically, we will study the genetic basis of ASE in both normal and tumor tissues, dissect genetic and parent-of-origin effects on ASE in human cell lines, and identify genes that escape X inactivation in both mouse reciprocal cross and human cell lines.
We propose to develop statistical methods and software for RNA-seq data analysis, with specific aims on dissecting the genetic basis of allele-specific expression (ASE), quantitative assessment of autosomal imprinting in humans, as well as the genetically controlled measurement of escape from X-inactivation in mouse and human. The deliverables of this project will help biomedical researchers to harvest the huge amount of knowledge accumulated in DNA variations and RNA-seq data and translate them into strategies of personalized disease prevention and treatment.
| Liu, Yanyan; Xiong, Sican; Sun, Wei et al. (2018) Joint Analysis of Strain and Parent-of-Origin Effects for Recombinant Inbred Intercrosses Generated from Multiparent Populations with the Collaborative Cross as an Example. G3 (Bethesda) 8:599-605 |
| Sun, Wei; Bunn, Paul; Jin, Chong et al. (2018) The association between copy number aberration, DNA methylation and gene expression in tumor samples. Nucleic Acids Res 46:3009-3018 |
| He, Qianchuan; Liu, Yang; Sun, Wei (2018) Statistical analysis of non-coding RNA data. Cancer Lett 417:161-167 |
| Liu, Yang; He, Qianchan; Sun, Wei (2018) Association analysis using somatic mutations. PLoS Genet 14:e1007746 |
| Kirk, Jessime M; Kim, Susan O; Inoue, Kaoru et al. (2018) Functional classification of long non-coding RNAs by k-mer content. Nat Genet 50:1474-1482 |
| Chen, Ting-Huei; Sun, Wei (2017) Prediction of cancer drug sensitivity using high-dimensional omic features. Biostatistics 18:1-14 |
| Zhang, Yiwen; Zhou, Hua; Zhou, Jin et al. (2017) Regression Models For Multivariate Count Data. J Comput Graph Stat 26:1-13 |
| Zhou, Hua; Blangero, John; Dyer, Thomas D et al. (2017) Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data. Genet Epidemiol 41:174-186 |
| Richardson, Sylvia; Tseng, George C; Sun, Wei (2016) Statistical Methods in Integrative Genomics. Annu Rev Stat Appl 3:181-209 |
| Wang, Xuefeng; Chen, Mengjie; Yu, Xiaoqing et al. (2016) Global copy number profiling of cancer genomes. Bioinformatics 32:926-8 |
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