会场介绍 | 第15届中国 R 会（北京）——生物统计专场

孙韬，中国人民大学统计学院讲师，博士毕业于匹兹堡大学生物统计系，主要研究方向为复杂生存数据模型，老年慢性病预防与管理。主持国自然青年基金项目与国家统计局重点项目，学术论文发表于Science, Biometrics, Biostatistics, Statistics in Medicine, Statistical Methods in Medical Research等期刊。担任中国老年学和老年医学学会老龄经济学分会理事。

Alzheimer's disease (AD) is a progressive and polygenic disorder that affects millions of individuals each year. Given that there have been few effective treatments yet for AD, it is highly desirable to develop an accurate model to predict the full disease progression profile based on an individual's genetic characteristics for early prevention and clinical management. This work uses data composed of all four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, including 1740 individuals with 8 million genetic variants. We tackle several challenges in this data, characterized by large-scale genetic data, interval-censored outcome due to intermittent assessments, and left truncation in one study phase (ADNIGO). Specifically, we first develop a semiparametric transformation model on interval-censored and left-truncated data and estimate parameters through a sieve approach. Then we propose a computationally efficient generalized score test to identify variants associated with AD progression. Next, we implement a novel neural network on interval-censored data (NN-IC) to construct a prediction model using top variants identified from the genome-wide test. Comprehensive simulation studies show that the NN-IC outperforms several existing methods in terms of prediction accuracy. Finally, we apply the NN-IC to the full ADNI data and successfully identify subgroups with differential progression risk profiles.

祁婷博士，西湖大学副研究员。主要研究方向是统计遗传学，通过整合多组学数据解析人类复杂性状和常见疾病的遗传机制。相关工作已经发表在Nature Genetics，Nature Communications等杂志。

生物学中心法则描述了遗传信息的传递过程，包括基因的转录、RNA的剪接、修饰和翻译。在此过程中，RNA聚合酶以DNA为模板合成前体信使RNA，开启遗传信息的传递。前体信使RNA通过不同的剪接方式（即：在不同的剪接位点剪掉内含子，连接外显子），生成多样化的成熟信使RNA，这个过程称之为可变剪接（或选择性剪接）。据统计，约95%的人类基因存在可变剪接，有些基因的剪接方式多达数百种，这是基因表达调控和蛋白质组多样性形成的重要机制。可变剪接的异常会导致生理状态的失衡和疾病的发生。因此，建立全面的可变剪接遗传调控图谱及其与常见疾病之间的关联图谱对可变剪切的分子机制研究以及疾病治疗新靶点的发掘有着重要意义。研究团队开发了一款高效的RNA可变剪接遗传调控位点（splicing QTL或sQTL）定位新方法，将其命名为THISTLE；利用该方法系统地分析了2865个脑组织的转录组和遗传学数据，绘制了迄今为止最全面的可变剪接遗传调控图谱；通过将该sQTL图谱映射到精神分裂、阿尔兹海默症、帕金森氏症等大脑相关性状和疾病的全基因组关联分析数据中，鉴定出244个易感基因，其中61%基因的机制无法被基因转录水平的遗传调控所解释，揭示了RNA可变剪接在复杂性状和疾病遗传机制中独特的重要作用。

李子林，印第安纳大学医学院生物统计与健康数据科学系助理教授。历任哈佛大学陈曾熙公共卫生学院生物统计系研究员、副研究员和博士后，本科与博士毕业于清华大学数学科学系，师从林希虹院士。主要研究方向为高维数据中的统计方法理论和遗传统计学。相关研究成果在Journal of American Statistical Association、Nature Methods、Nature Genetics、The American Journal of Human Genetics等国际学术期刊发表。入选首批美国国家心肺血液研究所生物数据云计算平台研究员（National Heart, Lung and Blood Institute BioData Catalyst Cohort I Fellow），获得国际数理统计协会颁发的2021年度New Researcher Travel Award。

We applied the STAARpipeline to analyze the total cholesterol in 30,138 samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. All analyses scale well in computation time and memory. We discover several potentially new significant associations with lipids, including a finding of rare variants in an intergenic region near JKAMPP1 associated with total cholesterol.

Xihao Li is a postdoctoral research fellow in the Department of Biostatistics at Harvard T.H. Chan School of Public Health, mentored by Professor Xihong Lin. Prior to this, he received his Ph.D. in Biostatistics at Harvard University. Dr. Li's research interests lie in developing novel statistical methodologies that enable scalable and integrative analysis of large-scale whole-genome/whole-exome sequencing data and multi-omics data. He has also worked on methodological projects to develop statistical approaches for rare disease clinical trials and real-world evidence studies.

MetaSTAAR dynamically incorporates multiple functional annotations to empower RV association meta-analysis, and enables conditional analyses to identify RV-set signals independent of nearby common variants. We applied MetaSTAAR to identify RV-sets associated with four quantitative lipid traits in a meta-analysis of 30,138 related samples from the NHLBI TOPMed Program Freeze 5 data, consisting of 14 ancestrally diverse studies and 255 million variants in total, as well as a meta-analysis of TOPMed data and the UK Biobank WES data of ~200,000 related samples.