Person: Li, Zilin
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Li
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Zilin
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Li, Zilin
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Publication Genome sequencing analysis identifies Epstein–Barr virus subtypes associated with high risk of nasopharyngeal carcinoma(Springer Science and Business Media LLC, 2019-06-17) Xu, Miao; Yao, Youyuan; Chen, Hui; Zhang, Shanshan; Cao, Su-Mei; Zhang, Zhe; Luo, Bing; Liu, Zhiwei; Li, Zilin; Xiang, Tong; He, Guiping; Feng, Qi-Sheng; Chen, Li-Zhen; Guo, Xiang; Jia, Wei-Hua; Chen, Ming-Yuan; Zhang, Xiao; Xie, Shang-Hang; Peng, Roujun; Chang, Ellen T.; Pedergnana, Vincent; Feng, Lin; Bei, Jin-Xin; Xu, Rui-Hua; Zeng, Mu-Sheng; Ye, Weimin; Adami, Hans-Olov; Lin, Xihong; Zhai, Weiwei; Zeng, Yi-Xin; Liu, JianjunEpstein-Barr virus (EBV) infection is ubiquitous worldwide and associated with multiple cancers including nasopharyngeal carcinoma (NPC). The role of EBV viral genomic variation in NPC development and its striking endemicity in southern China has been poorly explored. Through large-scale genome sequencing and association study of EBV isolates from China, we identified two non-synonymous EBV variants within BALF2 strongly associated with NPC risk (conditional P value 1.75 X 10-6 for SNP162476_C and 3.23 X 10-13 for SNP163364_T), whose cumulative effects contributed to 83% of the overall risk in southern China. Phylogenetic analysis of the risk variants revealed a unique origin in southern China followed by clonal expansion. EBV BALF2 haplotype carrying the risk variants were shown to reduce viral lytic DNA replication, as a result potentially promoting viral latency. Our discovery has not only provided insight to the unique endemic pattern of NPC occurrence in southern China, but also paved the way for the identification of individuals at high risk of NPC and effective intervention program to reduce the disease burden in southern China.Publication Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies(Elsevier BV, 2019-05) Li, Zilin; Li, Xihao; Liu, Yaowu; Shen, Jincheng; Chen, Han; Zhou, Hufeng; Morrison, Alanna C.; Boerwinkle, Eric; Lin, XihongLarge-scale whole genome sequencing (WGS) studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests (RVATs) have limited scope to leverage variant functions. We propose STAAR (variant-Set Test for Association using Annotation infoRmation), a scalable and powerful RVAT method by effectively incorporating both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce “annotation Principal Components”, multi-dimensional summaries of in-silico variant annotations. STAAR accounts for population structure and relatedness, and is scalable for analyzing very large cohort and biobank WGS studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery samples and 17,822 replication samples from the Trans-Omics for Precision Medicine program. We discovered and replicated novel RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.Publication A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies(Springer Science and Business Media LLC, 2022-10-27) Li, Zilin; Xihao, Li; Zhou, Hufeng; Gaynor, Sheila M.; Arapoglou, Theodore; Quick, Corbin; Dey, Rounak; Xihong, LinLarge-scale whole-genome sequencing (WGS) studies have enabled analysis of noncoding rare variant (RV) associations with complex human diseases and traits. Variant set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association-detection framework, STAARpipeline, to automatically annotate a WGS study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed-window and dynamic-window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.Publication Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies(Springer Science and Business Media LLC, 2022-12-23) Li, Xihao; Quick, Corbin; Zhou, Hufeng; Gaynor, Sheila M.; Liu, Yaowu; Dey, Rounak; Li, Zilin; Lin, XihongMeta-analysis of whole-genome/exome sequencing (WGS/WES) studies provides an attractive solution to obtain large sample sizes from multiple studies for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to large WGS data. Here we propose MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large WGS/WES data. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits, and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans-Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.