課程大綱

課程資訊

課程名稱

遺傳資料統計分析
Statistical Analysis of Genetic Data

開課學期

111-2

授課對象

公共衛生學院流行病學與預防醫學研究所

授課教師

馮嬿臻

課號

EPM5019

課程識別碼

849EU0470

班次

學分

2.0

全/半年

半年

必/選修

選修

上課時間

星期三3,4(10:20~12:10)

上課地點

公衛211

備註

本課程以英語授課。與蕭朱杏、曾仲瑩合授
總人數上限：30人

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課程概述

This course will focus on introducing the statistical principles and methods underlying human population and medical genetics research. To understand how genetic variation affects human health and disease, we will cover quantitative topics including linkage disequilibrium, population structure and stratification, genetic association tests, statistical fine-mapping, heritability estimation, and advanced topics in genetic risk prediction and sequence data analysis. This course will involve analysis of real-world or simulated genetics datasets and students are required to participate in group projects of selected topics.

課程目標

After this course, we expect the students to gain the following core knowledge and competencies:
1. Understand the definition and estimation of key genetic parameters underlying population and medical genetics
2. Understand the data types, study design, and the statistical and computational aspects of modern human genetics research
3. Apply statistical genetic methods to their own genetic data exploration, analysis, and interpretation in related to health
4. Describe the strengths and limitations of the potential use of genetic information in medicine and public health practices

課程要求

EPM7138 (Principle of Genetic Epidemiology), EPM7181 (Special topics in genomic studies), or the equivalent thereof.
Programming experience in R or Python. Familiarity with the UNIX/LINUX environment is preferred but not required.

預期每週課後學習時數

Office Hours

另約時間備註： By appointment.

指定閱讀

參考書目

1. The International HapMap 3 Consortium. An integrated haplotype map of rare and common genetic variation in diverse human populations. Nature 467, 52-8 (2010).
2. Conrad, D.F. et al. A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nat Genet 38, 1251-60 (2006).
3. Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98-101 (2008).
4. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904-9 (2006).
5. Winkler, T.W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat Protoc 9, 1192–1212 (2014).
6. Duggal, P. et al. The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing. Am J Epidemiol, 188(12), 2069-2077 (2019).
7. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42, 565-9 (2010).
8. Bulik-Sullivan, B.K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291-5 (2015).
9. Khera, A.V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50, 1219-24 (2018).
10. Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71-6 (2019).
11. Ge, T. et al. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun 10, 1-10 (2019)
12. Martin, A. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51, 584–591(2019)

評量方式
(僅供參考)

No.	項目	百分比	說明
1.	Homework assignments	50%
2.	Group project	40%
3.	Class participation	10%

課程進度

週次	日期	單元主題
第1週	02/22	Course overview & key concepts in molecular and population genetics
第2週	03/01	Linkage disequilibrium: measures and applications
第3週	03/08	Population structure, stratification, and admixture: concepts and methods (I)
第4週	03/15	Population structure, stratification, and admixture: concepts and methods (II)
第5週	03/22	Fundamentals and approaches of genome-wide association studies (GWAS)
第6週	03/29	Introduction to statistical fine mapping
第7週	04/05	Qingming Festival (Tomb Sweeping Day)
第8週	04/12	All those heritabilities: estimation and interpretation
第9週	04/19	Genetic risk prediction of complex traits and diseases
第10週	04/26	Causal inference using Mendelian randomization
第11週	05/03	Variant-set association analysis (I)
第12週	05/10	Variant-set association analysis (II)
第13週	05/17	Set-based association analysis: pathway, network, and differential network analysis
第14週	05/24	Guest lecture: Gene expression, regulation, and its application in disease gene discovery
第15週	05/31	Guest lecture: NGS applications in medical genetics
第16週	06/07	Project presentation