17th Summer Institute in Statistical Genetics: Module Descriptions
Module 1: Probability and Statistical Inference
Instructors: J. Hughes and D. Yanez
This module covers the laws of probability and the binomial, multinomial, and normal distributions. It covers descriptive statistics and methods of inference, including maximum likelihood, confidence intervals and simple Bayes methods. Classical hypothesis testing topics, including type I and II errors, two-sample tests, chi-square tests and contingency table analysis, and exact and permutation tests. Resampling methods, such as the bootstrap and jackknife, are covered as well. This module serves as a foundation for almost all of the later modules.
Module 2: Computing for Statistical Genetics
Instructors: T. Lumley and K. Rice
This module introduces software for analysis of genetic data, in the R statistical environment. Data management in R, programming concepts for R, and standard regression analyses will be discussed. These topics will be followed by analysis more specific to genetic data, including association analysis, and handling large date files. Use of the extensive collection of genomics packages from the Bioconductor project will be introduced. Finally, the use of R as an interface to other more specialized, ‘legacy’ software will be demonstrated. Reference will be made to current analyses of whole-genome association study data. This module assumes no prior knowledge of R. It will provide a foundation for computation for later modules.
Module 3: Bayesian Statistics for Genetics
Instructors: P. Hoff and J. Wakefield
The use of Bayesian methods in genetics has a long history. In this introductory module we will begin by discussing introductory probability. We will then describe Bayesian approaches to binomial proportions, multinomial proportions, two-sample comparisons (binomial, Poisson, normal), the linear model, and Monte Carlo methods of summarization. Advanced topics will be touched on, including hierarchical models, generalized linear models, and missing data. Illustrative applications will include: Hardy-Weinberg testing and estimation, detection of allele-specific expression, QTL mapping, testing in genome-wide association studies, mixture models, multiple testing in high throughput genomics. Background Reading: P.D. Hoff (2009). A First Course in Bayesian Statistical Methods. Springer-Verlag.
SISMID Module 2: Evolutionary Dynamics and Molecular Epidemiology of Viruses
Instructors: P. Lemey and M.A. Suchard
This module covers the use of phylogenetic and bioinformatic tools to analyze pathogen genetic variation and to gain insight in the processes that shape their diversity. The module focuses on phylogenies and how these relate to population genetic processes in infectious diseases. In particular, the module will cover Bayesian Evolutionary Analysis by Sampling Trees (BEAST). This software will be used in class exercises that are mainly focused on estimating epidemic time scales, reconstruction of changes in viral population sizes through time and inference of spatial diffusion of viruses. Evolutionary processes including recombination and selection will also be considered. Assumes material in Module 1. Co-listed with Summer Institute in Statistical Genetics.
Module 4: Regression and Analysis of Variance
Instructors: R. Hubbard and L. Inoue
This module is designed as a foundation for the quantitative genetics and QTL modules as well as for the association mapping modules. It assumes the material in Module 1 and it will cover the basic commands in R. It covers linear regression and analysis of variance. This module includes both lectures and interactive data analysis using R. Specific topics discussed are: simple linear regression; multiple linear regression; residual analysis; transformations; one-way ANOVA; two-way ANOVA; analysis of covariance; multiple comparisons.
Module 5: Molecular Genetics and Genomics
Instructors: J. Akey and G. Gibson
This module provides an overview of the basic principles of molecular genetics, but also incorporates an introduction to the latest genomic approaches. Starts with the laws of Mendelian inheritance and the roles of DNA and RNA as genetic material, discuss mutations and transmission genetics, and moves on to describe the foundations of population and quantitative genetics. This course builds the necessary concepts and introduces the methodologies that will enable students to take more detailed modules dealing with the structure and distribution of molecular variation, linkage and association studies for dissection of quantitative traits, phylogeny reconstruction, and gene or protein expression profiling. Also touches on such topics as comparative genomics, mutational genetic analysis, and regulation of gene expression. Recommended text: Gibson, G. and Muse, S. (2009). “A Primer of Genome Science.” 3rd edition, Sinauer Associates.
Module 6: Population Genetic Data Analysis
Instructors: J. Goudet and B. Weir
This module overlaps substantially with Module 8. It 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 Laurie, C.C. 2011. Statistical genetics in the genome era. Genetics Research 92:461–470.
Module 7: Quantitative Genetics
Instructors: W. Muir and B. Walsh
Assumes the material in Modules 1, 4 and 5. Provides a foundation for modules 11, 12 and 18. 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 cross-breeding, 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.
Module 8: Population Genetics and Association Mapping
Instructors: K. Kerr and T. Thornton
This module overlaps substantially with Module 6. It assumes the material in Module 1 and it serves as the foundation for many later modules. Topics covered include: basic probability and Mendelian genetics; Hardy-Weinberg equilibrium; inbreeding coefficients; population structure; recombination and genetic linkage; linkage disequilibrium; measures of relatedness; haplotype frequency estimation with unphased genotypes, genetic association testing; association testing in the presence of population structure and/or relatedness. Many concepts are illustrated with public domain software such as R and HAPLOVIEW. Background reading: Weir, B.S. (1996). “Genetic Data Analysis II.” Sinauer Associates; Thornton and McPeek (2010) “ROADTRIPS: Case-Control Association Testing with Partially or Completely Unknown Population and Pedigree Structure.” American Journal of Human Genetics 86:172–184.
Module 9: Gene Expression Profiling
Instructors: G. Gibson and J. Storey
This course covers all aspects of the statistical analysis of gene expression profiling; the methods are also relevant to analysis of proteomic and metabolomic data. Theory will be integrated with case studies demonstrating the principles of quality control, normalization, analysis of variance and hypothesis testing, time series, surrogate variable analysis, and optimal discovery procedures. Discussion will include microarray and nextgen sequencing applications, downstream data-mining and network analysis approaches, and relevant statistical software will be demonstrated.
Module 10: MCMC for Genetics
Instructors: E. Anderson and J. Novembre
This module examines the use of Bayesian Statistics and Markov chain Monte Carlo methods in modern analyses of genetic data. It assumes a solid foundation in basic statistics and the concept of likelihood as well as some population genetics. A basic familiarity with the R statistical package, or other computing language, will be helpful. The first day includes an introduction to Bayesian statistics, Monte Carlo, and MCMC. Mathematical concepts covered include expectation, laws of large numbers, and ergodic and time-reversible Markov chains. Algorithms include the Metropolis-Hastings algorithm and Gibbs sampling. Some mathematical detail is given; however, there is considerable emphasis on concepts and practical issues arising in applications. Mathematical ideas are illustrated with simple examples and reinforced with a computer practical using the R statistical language. With that background, two applications of MCMC are investigated in detail: inference of population structure (using the program STRUCTURE) and haplotype inference (using the program PHASE). Computer practicals using both programs are included. Further topics include the use of MCMC in model evaluation and model checking, strategies for assessing MCMC convergence and diagnosing MCMC mixing problems, importance sampling, and Metropolis-coupled MCMC. Software used: R, STRUCTURE, PHASE. Background reading: Shoemaker, J.S., Painter, I.S. and Weir, B.S. (1999). Bayesian statistics in genetics. Trends in Genetics 15:354–358. Beaumont, M.A. and Rannala, B. (2004). The Bayesian revolution in genetics. Nature Reviews Genetics 5:251–261. Gilks, W.R., Richardson, S. and Spiegelhalter, D.J.. (1996). “Markov Chain Monte Carlo in Practice.” Chapman and Hall.
Module 11: Introduction to QTL Mapping
Instructors: R. Doerge and Z-B. Zeng
Assumes the material in Modules 1,4,5 and 8. Material in Modules 6 or 8 would be helpful. 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.
Module 12: Mixed Models in Quantitative Genetics
Instructors: W. Muir and B. Walsh
The analysis of linear models containing both fixed and random effects. Topics to be discussed include a basic matrix algebra review, the general linear model, derivation of the mixed model, BLUP and REML estimation, estimation and design issues, Bayesian formulations. Applications to be discussed include estimation of breeding values and genetic variances in general pedigrees, association mapping, genomic selection, direct and associative effects models of general group and kin selection, genotype by environment interaction models. Background reading: Lynch, M. and Walsh, B. 1998. Genetics and analysis of quantitative traits. Sinauer Associates.
Module 13: Molecular Phylogenetics
Instructors: J. Felsenstein, M. Holder and J. Thorne
Assumes the material in Modules 1 and 5. Overview of methods for analysis of interspecific DNA and protein sequence data. Coverage will include parsimony, maximum likelihood, distance-based, and Bayesian methods for phylogenetic estimation. Probabilistic models for sequence change will be emphasized. Related topics that will be presented are the comparative method, divergence time estimation, phylogenetic hypothesis testing, and detection of positive selection. Statistical methodology will be a focus and some related computational algorithms will be outlined. Brief introductions will be made to software packages such as PAUP*, Beast, PHYLIP, and MrBayes. Background readings: Felsenstein, J. (2004) “Inferring Phylogenies.” Sinauer Associates, or Yang, Z. (2006) “Computational Molecular Evolution (Oxford Series in Ecology and Evolution).” Oxford University Press.
Module 14: Inference of Relationships and Relatedness
Instructors: E. Anderson and E. Thompson
This module focuses on methods for inferring relationships and relatedness between individuals in natural populations using multi-locus genetic data. Emphasis is given to applications in managed or endangered populations of plant and animal species. Topics covered in the underlying theory include: gene identity by descent (ibd) versus gene identity in state (iis); calculation of probabilities of gene ibd conditional on relationships and genetic data; coefficients of inbreeding and kinship; information gain by considering joint relationships of additional (more than two) relatives; linked loci, genome scans, and the lengths of chromosomal ibd segments. Estimation problems covered include: estimation of pairwise relatedness and relationships; parentage and paternity inference and pedigree reconstruction in natural populations; inference of sibling groups in the absence of parental information; relationship inference and validation from linked loci; the estimation of population mixtures and hybrid individuals. The focus will be primarily on likelihood and Bayesian methods of estimation. This module assumes knowledge of the material in basic statistics (module 1) and basic population genetics (modules 6 or 8). Module 10 would be helpful, but it is not a strict prerequisite.
Module 15: Systems Genetics for Experimental Crosses
Instructors: E. Chaibub Neto and B. Yandell
This module will take a holistic, technical look at systems genetics for experimental crosses. The field of systems genetics, also known as “genetical genomics,” views “omics” molecular phenotypes such as mRNA expression, protein and metabolite levels as quantitative traits, amenable to quantitative genetical analyses. We begin with model selection for the genetic architecture of a single trait, building on the Introduction to QTL Mapping module. We then address QTL mapping of multiple correlated traits, modeling the correlation structure of the traits. Extensions of the Churchill-Doerge permutation tests are developed to assess the legitimacy of alleged QTL hotspots, i.e., loci where many traits have LOD peaks. The remainder of the module concerns causal phenotype models driven by QTL. For pairs of molecular phenotypes mapping to the same locus, we compare models using one trait as a covariate of the other to assess the causal ordering among the phenotypes. This analysis is used to identify key drivers of subsets of traits, i.e., traits that seem to have a causal effect on most of the other co-mapping traits. The key drivers and their co-mapping traits are then organized into causal phenotype networks. Finally, we show how biological pathway information, coming from GO, KEGG, TF or PPI databases can improve causal models. This module presumes material covered in modules 1, 2, 4 and 11.
Module 16: Coalescent Theory
Instructors: P. Awadalla and M. Kuhner
This module is an introduction to the coalescent and its applications to modern population genetics and genomics. Assumes material in Modules 1 and 5. Material in Module 6 or 8 and 13 would be helpful. Derivation and properties of basic coalescent model and extension to include factors such as recombination, geographic structure and natural selection. Use of the coalescent in analyzing data for disease gene mapping, recombination rate estimation, and detection of recent adaptive evolution. Use of coalescent methodologies in large-scale surveys of genetic variation. Applications to standard or next-generation sequencing data for inferences from natural populations and disease cohorts. Use of public domain software.
Module 17: High-Dimensional Omics Data
Instructors: A. Shojaie and D. Witten
In this course, we will cover a number of statistical machine learning methods for the analysis of high-dimensional biological data, often referred to as “omics.” Examples include genomic, transcriptomic, metabolomic, proteomic, and other large-scale data sets, typically characterized by a huge number of molecular measurements (such as genes) and a relatively small number of samples (such as patients). In the first half of the course, we will cover supervised learning methods that are useful in the analysis of omics data. These include penalized approaches for performing regression, classification, and survival analysis in the high-dimensional setting. In the second half of the course, we will discuss unsupervised approaches for the analysis of omics data, such as clustering, principal components analysis, and network estimation techniques. Throughout the course, we will highlight the effects of high dimensionality and focus on common pitfalls in the analysis of omics data, and how to avoid them.
Module 18: Human Quantitative Genetics
Instructors: P. Visscher and B. Weir
This module assumes the material in Modules 1 and 6 or 8. Material in Module 7 would be helpful. A quantitative genetic framework for association mapping. Topics include: genetic correlations for individuals and for traits; Haseman-Elston regression for linkage analysis for quantitative traits; experimental design; estimating genetic variance associated with genome-wide identity by descent; estimation of heritability. The use of relatives to estimate heritability within families. The use of GWAS data to estimate the contribution of all SNPs simultaneously. Background reading: Visscher, P.M. et al. (2007) Genome partitioning of genetic variation for height from 11,214 sibling pairs. American Journal of Human Genetics 81:11041110 (2007); Visscher, P.M. 2009. Whole genome approaches to quantitative genetics. Genetica 136:351–357; Weir, B.S. 2008. Linkage disequilibrium and association tests. Annual Reviews of Genomics and Human Genetics 9:129–142.
Module 19: Advanced R Programming for Bioinformatics
Instructors: T. Lumley and K. Rice
This module covers object-oriented programming, SQL database use, some of the Bioconductor data infrastructure, and calling C code from R. The module is aimed at people who have either substantial R experience or programming experience in other languages. Module 2 would not be sufficient preparation. Background reading: Gentleman. R. (2008) R Programming in Bioinformatics. Taylor & Francis.
SISMID Module 13: Introduction to Metagenomic Data Analysis
Instructors: A.V. Alekseyenko and S.P. Holmes
This course is concerned with analysis of microbial community data generated by next-generation sequencing technologies. These high-throughput methods allow for deep surveying of microorganisms inhabiting their biological hosts. We will cover the steps for preprocessing Roche 454 sequencing data, necessary to produce abundance tables. We will then examine methodology for associating microbial abundance data with experimental factors and outcomes. Programming will be done in R, and overview of available programs for preprocessing the data will be provided. Prerequisites: Module 1: Probability and Statistical Inference. Co-listed with Summer Institute in Statistical Genetics.
Module 20: GWAS Data Cleaning
Instructors: S. Gogarten and C. Laurie
Genome-wide Association Studies need to take care with genotypic data quality in order to maximize power and reduce false positives. The GENEVA Coordinating Center at the University of Washington has developed a comprehensive protocol that addresses issues of sample and SNP quality. The process begins with formatting SNP intensity and annotation data in the netCDF format and then using the R packages GWASTools and SNPRelate. The procedures include examination of: missing call rates; heterozygosity; gender and sex chromosome aneuploidy; relatedness estimation; principal component analysis to detect population stratification; Hardy-Weinberg testing; and basic association testing. Participants will apply these procedures to HapMap genotypic data. Basic proficiency in R is required (as in Module 2). Advanced R is not necessary, but would help participants who need to extend or modify the code in this module (see Module 19). Background Reading: Laurie et al. 2010. Quality control and quality assurance in genotypic data for genome-wide association studies. Genetic Epidemiology 34,591-602.
Module 21: Network and Pathway Analyses of Omics Data
Instructors: Gaiteri, Guinney, Motsinger-Reif and Sieberts
This module covers a range of commonly used and newly emerging data-mining approaches for genetic and genomic data analysis. Coexpression networks based on gene-gene correlations are a tool that can be used to sample many regulatory networks and provide a window into complex diseases. The network structure itself can be used to understand how biological functions are implemented and homeostasis is maintained. We will review strategies for leveraging coexpression network structure to detect the collective influence of multiple contributing factors in complex diseases. We will discuss both pattern recognition and dimensionality reduction approaches, and will discuss details of highly successful methods like Classification and Regression Trees, Random Forest, and Multifactor Dimensionality Reduction. Throughout the course we will cover general issues with data-mining approaches such as variable selection, hypothesis testing, multiple comparisons, and predictive modeling. Finally, we will cover pathway-based analyses both in the gene expression study and GWAS frameworks. Pathway-based analyses can be used to leverage biological knowledge available from literature, gene ontologies or previous experiments to identify the pathways associated with disease or outcome. This approach can be illuminating in the case where each individual gene or loci shows small-to-moderate association, which might not overcome the significance burden of multiple testing which accompanies high-dimensional -omics analyses. Software tools for implementing the analyses discussed will be emphasized.
Module 22: Plant and Animal Association Mapping
Instructors: P. Bradbury
This module is an introduction to association mapping, focusing on plant and animal populations. 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, issues in polyploid organisms, multiple testing issues, and genotyping strategies. Examples for real data, including a discussion of linkage disequilibrium in plant and animal populations. Hands-on experience with publicly available software packages, including TASSEL. Assumes material in Modules 1, 4 and 5. Material in Modules 6 or 8 would be useful.