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
110-2 
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
星期五3,4(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:20人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1102EPM5019_ 
課程簡介影片
 
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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: Office hours can be scheduled upon request. 
參考書目
Laird, N. M., & Lange, C. (2011). The fundamentals of modern statistical genetics. Springer Science 
指定閱讀
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 (5 in total) 
50% 
 
2. 
Group project  
40% 
 
3. 
Class participation and discussions  
10% 
 
 
課程進度
週次
日期
單元主題
第1週
2/18  Course overview & key concepts in molecular and population genetics 
第2週
2/25  Linkage disequilibrium: measures and applications 
第3週
3/04  Population structure, stratification, and admixture (I) 
第4週
3/11  Population structure, stratification, and admixture (II) 
第5週
3/18  Fundamentals and approaches of genome-wide association studies (GWAS) 
第6週
3/25  Introduction to statistical fine mapping 
第7週
4/01  All those heritabilities: estimation and interpretation 
第8週
4/08  Genetic risk prediction of complex traits and diseases 
第9週
4/15  Causal inference using Mendelian randomization  
第10週
4/22  Variant-set association analysis (I) 
第11週
4/29  Variant-set association analysis (II) 
第12週
5/06  Guest lecture: Gene expression, regulation, and its application in disease gene discovery 
第13週
5/13  Set-based association analysis: pathway, network, and differential network analysis  
第14週
5/20  Guest lecture: Biobank research to empower genetic discovery 
第15週
5/27  Project presentation 
第16週
6/03  Dragon Boat Festival (holiday)