Statistical Developments in Analysis of Genomics Data for Disease Phenotype and Drug Response
Organizer and Chair: Jun Xie, Professor of Statistics and Graduate Chair, Department of Statistics, Purdue University
Speakers
- Ching-Ti Liu, Associate Professor of Biostatistics, Department of Biostatistics, Boston University
- Wei Sun, Associate Member of Biostatistics Program and Public Health Sciences Division, Fred Hutchinson Cancer Research Center
- Yaowu Liu, Postdoctoral Research Fellow, Department of Biostatistics, Harvard University
Wednesday, June 6, 10:00 a.m.-12:00 p.m. in STEW 214 CD
| Time | Speaker | Title |
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| 10:00-10:40 a.m. | Ching-Ti Liu | Genetic Fine Mapping Incorporating Functional Annotation: A Random Effects Approach |
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Abstract: Genome-wide association studies (GWAS) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes, and functional annotation from external databases to prioritize the most plausible causal SNPs in each trait-associated locus. Our proposed method improves upon previous fine mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine mapping studies helps to distinguish between variants in high LD, and to identify promising targets for follow-up studies. |
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| 10:40-11:20 a.m. | Wei Sun |
Estimation of Intra-Tumor Heterogeneity and Assessing Its Impact on Survival Time |
| Abstract: A tumor sample often includes a conglomerate of heterogeneous tumor cells and different types of normal cells. Understanding genetic heterogeneity of tumor cells (intra-tumor heterogeneity) may help us identify useful biomarkers to guide the practice of precision medicine. For example, intra-tumor heterogeneity, combined with tumor mutation burden, can be used to predict response to cancer immunotherapy, which is now shaping the landscape of tumor therapy. While popular methods exist to study intra-tumor heterogeneity, they usually do not jointly consider copy number aberrations and somatic point mutations and their timings under a valid statistical framework. We have developed two statistical methods to study ITH: SMASH (Subclone Multiplicity Allocation and Somatic Heterogeneity) and SHARE (Statistical method for Heterogeneity using Allele-specific REads and somatic point mutations). I will discuss these two methods and some applications of these methods, including association with cancer patients' survival time. | ||
| 11:20 a.m. -12:00 p.m. | Yaowu Liu |
Cauchy Combination Test: a Powerful Test with Analytic P-value Calculation under Arbitrary Dependency Structures |
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Abstract: Combining individual p-values to aggregate multiple small effects has a long-standing interest in statistics, dating back to the classic Fisher's combination test. In modern large-scale data analysis, correlation and sparsity are common features, and efficient computation is a necessary requirement for dealing with massive data. To overcome these challenges, we propose a new test that takes advantage of the Cauchy distribution. We prove a non-asymptotic result that the tail of the null distribution of our proposed test statistic can be well approximated by a Cauchy distribution under arbitrary dependency structures. Based on this theoretical result, the p-value calculation of our proposed test is not only accurate, but also as simple as the classic z-test or t-test, making our test well suited for analyzing massive data. We adapt the proposed test for analyzing rare variants in sequencing studies and demonstrate the promising performance and usefulness of the new method through extensive simulations and analysis of sequencing data from the Atherosclerosis Risk in Communities (ARIC) study. |
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