第一部分課程：8月24日(星期一)至8月28日 (星期五)
- 第一講：族群遺傳資料分析（Population Genetic Data Analysis）
時間: 8月24日(星期一)至8月26日(星期三)上午
講員: Bruce Weir 教授 (University of Washington)
Dahlia Nielsen 教授 (North Carolina State University)
本課程為後續課程的基礎課程，課程內容包括:等位基因頻度、Hardy-Weinberg和連鎖不平衡(linkage disequilibrium)係數之估計、描述族群結構之F統計量、親源關係估計(relationship estimation)、司法鑑識科學(forensic science)和關聯定位(association mapping)上之統計遺傳觀念介紹，均應用R舉例說明並演練。背景閱讀為: Holsinger, K. and Weir, B.S., 2009. Genetics in geographically structured populations: defining, estimating, and interpreting FST. Nature Reviews Genetics 10:639–650. Weir, B.S. and C.C. Laurie. 2011. Statistical genetics in the genome era. Genetics Research 92:461–470.
This module serves as a foundation for many of the later modules. Estimates and sample variances of allele frequencies, Hardy-Weinberg and linkage disequilibrium, characterization of population structure with F-statistics. Relationship estimation. Statistical genetic aspects of forensic science and association mapping. Concepts illustrated with R exercises. Background reading: Holsinger, K. and Weir, B.S., 2009. Genetics in geographically structured populations: defining, estimating, and interpreting FST. Nature Reviews Genetics 10:639–650. Weir, B.S. and C.C. Laurie. 2011. Statistical genetics in the genome era. Genetics Research 92:461–470. - 第二講：數量遺傳（Quantitative Genetics）
時間: 8月26日(星期三)下午至8月28日(星期五)
講員: Michael Morrissey 教授 (University of St Andrews)
Guilherme Rosa 教授 (University of Wisconsin-Madison)
數量遺傳的分析對象為表現兼受遺傳與環境因子影響的複雜特性。因為多數的重要性狀都是數量性狀，例如抗病性、作物產量、與全部的微晶片資料，因此對於很多大不相同的領域，如植物與動物育種、人類遺傳、基因體學、生態學、與演化生物學，對數量遺傳有相當的瞭解都是必要的。本課程將涵蓋基礎的數量遺傳，包括Fishers變方劃分，親屬間的共變方，遺傳率，近親交配與隨機交配，以及對於選拔的反應；以及較進階的內容，如混合型模式，最佳線性無偏預測，數量性狀基因座定位，相關的性狀，與對於選拔的多變數反應。背景閱讀為：Lynch, M. and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer Associates.
Quantitative Genetics is the analysis of complex characters where both genetic and environment factors contribute to trait variation. Since this includes most traits of interest, such as disease susceptibility, crop yield, and all microarray data, a working knowledge of quantitative genetics is critical in diverse fields from plant and animal breeding, human genetics, genomics, to ecology and evolutionary biology. The course will cover the basics of quantitative genetics including: Fishers variance decomposition, covariance between relatives, heritability, inbreeding and crossbreeding, and response to selection. Also an introduction to advanced topics such as: Mixed Models, BLUP, QTL mapping; correlated characters; and the multivariate response to selection. Background reading: Lynch, M. and Walsh, B., 1998. Genetics and analysis of quantitative traits. Sinauer Associates.
第二部分課程：8月31日(星期一)至9月4日(星期五)
- 第三講：數量性狀基因座定位（QTL Mapping）
時間: 8月31日(星期一)至9月2日(星期三)上午
講員: Rebecca Doerge 教授 (Purdue University)
Zhao-Bang Zeng 教授 (North Carolina State University)
本課程系統性地介紹應用試驗雜交族群來進行數量性狀基因座定位的統計方法。課程內容包括:數量性狀基因座定位之試驗設計、 連鎖圖之建構、單一標示分析法、區間定位法、組合區間定位法、多區間定位法、門檻值之選擇及模型選擇等。將以QTL Cartographer軟體實作分析結果。講授內容強調資料分析之程序及分析之結果闡釋，而非複雜的公式推導過程。
學員請自備筆電以便實作演練。
This module will systematically introduce statistical methods for mapping quantitative trait loci (QTL) in experimental cross populations. Topics include experimental designs, linkage map construction, single-marker analyses, interval mapping, composite interval mapping and multiple interval mapping. Significance thresholds for genome scan and model selection will also be discussed. Uses public domain software Windows QTL-Cartographer for computer lab exercises. Emphasis is on procedures for QTL mapping data analysis and appropriate interpretation of mapping results rather than on formulas.
Users require a laptop and will use it in all sessions. - 第四講：基因表現輪廓分析（Gene Expression Profiling）
時間: 9月2日(星期三)下午至9月4日(星期五)
講員: Gregory Gibson 教授 (Georgia Institute of Technology)
Wen-Ping Hsieh 教授 (National Tsing Hua University)
本課程講授有關轉錄組學(transcriptomics)之應用與原理，內容包括微陣列(microarray)與RNA-Seq的分析方法。課程著重於假設檢驗的統計基礎、資料標準化的戰略與R程式實作、基本ANOVA與錯誤發現率的計算程序等。此外，我們將討論下游分析工具如分群、模組檢測、eQTL、與整合基因體推斷路徑結構等。
The gene expression module will cover the theory and application of transcriptomics, including both microarray and RNA-Seq methodologies. The focus of the module is on the statistical basis of hypothesis testing, covering the central role of normalization strategies with the opportunity for students to work examples using the SNM module in R, as well as the fundamentals of ANOVA and the False Discovery Rate procedure. In addition, we will discuss options for downstream processing by clustering and module detection, finishing with expression QTL analysis and integrative genomics to infer pathway structure. - 第五講：人類關聯性基因定位統計方法（Human Association Mapping）
時間: 8月31日(星期一)至9月2日(星期三)上午
講員: Timothy Thornton 教授 (University of Washington)
Michael Wu 教授 (Fred Hutchinson Cancer Research Center in Seattle)
本課程將介紹以人類群體為研究對象之關聯性基因定位(association mapping)，並整理過去在HapMap計畫、WTCCC研究和其他大型全基因體關聯性研究(genome-wide association study)中所獲得之成果。課題將含括：(1)連鎖不平衡(linkage disequilibrium)及如何利用連鎖不平衡來進行關聯性基因定位、(2)介紹如何以群體為基礎（population-based）和以家庭為基礎（family-based）的關聯性分析，進行離散型或是連續型性狀之基因定位、(3)介紹族群結構（population structure）的偵測及如何在有族群結構的情況下進行基因定位、(4)多重檢定（multiple testing）的相關議題、(5)不同基因型鑑定的使用時機與策略、(6)拷貝數分析(copy number analysis)、大量的單一核苷酸多型性(single nucleotide polymorphism; SNP)分析、單套型區塊（haplotype block）分析等。
This module is an introduction to association mapping, focusing on human populations and informed by findings from the HapMap project, the Wellcome Trust Case Control Consortium and other whole-genome association studies. Topics include theory of linkage disequilibrium and mapping, population and family-based association techniques for discrete and continuous traits, methods for detecting and accounting for population structure, multiple testing issues, and genotyping strategies. Association of Copy Number Variants with human disease. Analysis of dense SNP maps. Haplotype blocks. - 第六講：基因體學資料之路徑和網路分析(Pathway and Network Analysis for Omics Data)
時間: 9月2日(星期三)下午至9月4日(星期五)
講員: Alison Motsinger-Reif 教授 (North Carolina State University)
Ali Shojaie 教授 (University of Washington)
生物系統之中各個元件的交互作用可用網路來呈現，而高維度的基因體學資料(omics data)中常應用於解構基因調控網路(gene regulatory network)、蛋白質交互作用網路(protein-protein interaction network)、代謝體網路(metabolic networks)等，上述網路除了有助於理解生物系統與複雜疾病外，也常用於分析生物功能的調節。除此之外，生物路徑分析也可驗證前人研究發表或基因本體資料庫的資訊，藉以鑑別與疾病或性狀相關的生物路徑。本課程將討論重建與分析基因體網路的統計方法，如生物路徑富集分析(pathway enrichment analysis)等，所有統計方法將利用R進行分析，並特別著重於大量變數與少量樣本情境下之基因體資料的應用。參與者必須自備電腦，且熟悉R或其他命令提示字元的程式語言自備電腦，並應具備迴歸分析等基本統計基礎。
學員請自備筆電以便實作演練。
Networks represent the interactions among components of biological systems. In the context of high dimensional omics data, relevant networks include gene regulatory networks, protein-protein interaction networks, and metabolic networks. These networks provide a window into biological systems as well as complex diseases, and can be used to understand how biological functions are implemented and how homeostasis is maintained. On the other hand, pathway-based analyses can be used to leverage biological knowledge available from literature, gene ontologies or previous experiments in order to identify the pathways associated with disease or an outcome of interest. In this module, various statistical learning methods for reconstruction and analysis of networks from omics data are discussed, as well as methods of pathway enrichment analysis. Particular attention will be paid to omics datasets with a large number of variables, e.g. genes, and a small number of samples, e.g. patients. The techniques discussed will be demonstrated in R. This course assumes a previous course in regression and familiarity with R or other command line programming languages.
Users require a laptop and will use it in all sessions.