Modules
- Module 1: Introduction to Population and Quantitative Genetics and Genomics (July 31 - August 2)
Bioinformatics Research Center,
Department of Statistics,
Department of Biological Sciences,
North Carolina State University
szeng@ncsu.edu
Chau-Ti Ting
Department of Life Science,
Institute of Ecology and Evolutionary Biology,
National Taiwan University
[email protected]
The availability of large populations with dense genomic markers makes it possible the inference of total relationship between
phenotype(s) and a genome. This relationship is characterized by the whole set of underlying quantitative trait loci (QTL), big and
small. In this module, we present a theoretical framework that possesses the property of orthogonal partition of genetic effects and
variance, and statistical methods that aim to infer this relationship and estimate properties of this relationship, such as the
distribution of QTL effects, genomic partition of QTL additive, dominant and epistatic variation, genetic basis of trait correlation,
genotype-by-environment interaction, and heterosis. The emphasis is to infer the overall relationship. Only through this orthogonal
partition and genome-wide analysis, can we adequately study the genetic basis of some interesting biological problems, such as
heterosis, and assess the relative importance of different genetic components (additive, dominant and epistatic components) to the
problems. In addition, we will also introduce methods to estimate allele frequencies, Hardy-Weinberg, like disequilibrium and
F-statistics.
大族群之中高密度基因組標記可用來推斷基因表現和基因組的關係,這種關係的特性在於整體涵蓋大或小的數量性狀基因座(QTL)。此
課程將提到具有遺傳效應和差異的正交分割理論架構,以及推斷其中關係和估計關係屬性的統計方法,如QTL效應的分佈、QTL的基因組分
配、 顯性和基因的上位作用、基礎遺傳性狀相關性、基因型與環境相互作用和雜交優勢。重點內容在於推斷族群整體關係。只有通過此正交分
割和全基因組分析,我們可以充分研究一些有趣的生物基礎遺傳問題,如雜交優勢,並評估不同遺傳組成的相對重要性(遺傳累加姓、遺傳優勢
和上位效應)的問題。此外,我們還將介紹估算等位基因頻率的方法、哈溫定律,如不平衡檢定和F檢定。
- Module 2: Advanced Quantitative Genetics (August 2 - 4)
Department of Ecology and Evolutionary Biology,
University of Arizona
[email protected]
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 — disease susceptibility, crop yield, growth and reproduction in animals, human
and animal behavior, and all gene expression data (transcriptome and proteome) — a working knowledge of quantitative genetics is
critical in diverse fields from plant and animal breeding, human genetics, genomics, and behavior, to ecology and evolutionary biology.
The course will cover the basics of quantitative genetics including: genetic basis for complex traits, population genetic
assumptions including detection of admixture, Fisher’s variance decomposition, covariance between relatives, calculation of the
numerator relationship matrix based on IBD alleles and an arbitrary pedigree, the genomic relationship matrix based on AIS alleles,
heritability in the broad and narrow sense, inbreeding and cross-breeding, and response to selection.
The module also includes an introduction to advanced topics such as: Mixed Models, Best Linear Unbiased Prediction (BLUP),
Genomic selection (GBLUP), Genome Wide Association Analysis (GWAS), QTL mapping, detection of selection from genomic data,
correlated characters; and the multivariate response to selection.
數量遺傳學的分析對象為同時受到遺傳與環境因子影響而表現出不同特徵的複雜性狀。由於這些性狀包括了我們大多數的研究特徵,例如
疾病的感受性、作物產量、動物的生長與生殖、人類與動物的行為,和所有基因表現的數據(轉錄組及蛋白質組),因此在不同的領域,從植物與
動物的育種、人類遺傳、基因體學、與行為學到生態與演化學,對數量遺傳有基本的認知都是必要的。
本課程將涵蓋基礎的數量遺傳,包括複雜性狀的遺傳基礎、群遺傳的假設包括族群混和的檢測,Fisher的變異分析、親屬間的共變數、基
於同血緣關係等位基因和任意譜系的分子關係矩陣的計算,計算近親交配與隨機交配分子親緣親係數矩陣、基於狀態等位基因的基因體關係矩
陣、廣義與狹義的遺傳性,近親與雜交育種,與對選汰的反應。
此課程亦涵蓋進階的主題介紹,例如混合型模式,最佳線性不偏估預測,基因體選汰,全基因組關聯分析,數量表徵基因座定位法,以基因
體數據估計選汰,相關的性狀,與對選汰的多變量反應。
- Module 3: Association Mapping: GWAS and Sequence Data (August 7 - 9)
Department of Statistics, North Carolina State University
[email protected]
Po-Hsiu Kuo
Institute of Epidemiology and Preventive Medicine, National Taiwan University
[email protected]
This module will provide students with the basic tools to carry out genetic association analysis within the context of genome
wide association studies (GWAS) and next-generation sequencing (NGS) studies with considerable hands-on learning. Specific topics
covered include: introduction to genetic data and association testing; GWAS data management and analyses (e.g., quality control,
imputation, testing, result visualization); single-locus test; multi-loci test; population structure; multiple testing; NGS data
management; rare variant association analysis; association analysis using summary statistics; post-GWAS analysis using annotation
and pathway analysis; and other emerging topics. This module will include in-class software exercises which will provide students
with hands-on experiences. Many software applications are implemented in R and the module will be most useful for students with
basic familiarity with R, though all necessary R code will be provided. Other public domain software that will be used include
PLINK.
此課程以全基因組關聯分析(genome-wide association study, GWAS)及次世代定序(Next Generation Sequencing , NGS) 為基礎應用工
具,透過學生實際操作進行基因組關聯分析,具體的操作遺傳數據和關聯檢測,包含: GWAS數據分析(例如:品質控管、統計補植、統計檢
定、數據可視化)、單一位點檢測、多重基因座檢測、人口結構、多重檢驗、NGS數據整理、罕見變異分析、以概述統計量做關聯分析、途徑分
析與解釋GWAS數據、以及其他嶄新的主題。課堂中將使用軟體操作提供學生實作經驗,R軟體需要透過許多R指令,許多軟體的應用都是以R軟
體執行,此課程對於熟悉R軟體基礎應用的學生而言是相當有用的,此外,也將使用其他共通領域的軟體,包含命令提示列軟體PLINK。
- Module 4: Reproducible Research for Biomedical Big Data (August 9 - 11)
Iinumbers | Data Consultant Service
[email protected]