Armidale Animal Breeding Summer Course 2019

Venue: University of New England, Armidale, NSW Australia

Course Audience: Postgraduate students and other professionals

 

 

 

Introduction to Graphical Models with Applications to Quantitative Genetics and Genomics 

 

Teachers:

 

Dr Guilherme J. M. Rosa

University of Wisconsin-Madison (http://www.ansci.wisc.edu/Facultypages/rosa.html)

 

Dr Francisco Peñagaricano,

Univ. of Florida-Gainesville (http://animal.ifas.ufl.edu/faculty/penagaricano/index.shtml)

 

Dates:     Tuesday 29 January 2019 (9am) – Friday 1 February 2019 (4pm)

 

Course Description

 

Graphical models comprise a set of data analysis tools that allow the investigation and representation of interconnected components in complex systems. In genetics and genomics, for example, graphical models can be used to study recursive and simultaneous relationships among phenotypes, or to investigate gene-phenotype networks. Graphical models explore conditional independencies between variables to detect those that are directly linked to each other, and to infer the directional flow of information (e.g. causal effects) between them. As such, they can produce an interpretation of relationships among variables which differs from that obtained with traditional multivariate models, in which all relationships are represented by symmetric linear associations among random variables, such as covariances and correlations. This course will provide an introduction to graphical models, including techniques such as path analysis, Bayesian networks (BNs), and structural equation models. Some theoretical background will be presented, and key concepts will be introduced, such as the concept of d-separation, causal sufficiency, instrumental variable, and Markov blanket. All the material will be illustrated with applications in quantitative genetics and genomics, with examples including the prediction of phenotypes using earlier expressed traits, genome-enabled prediction, genome-wide association analysis (GWAS) and quantitative trait loci (QTL) mapping for multiple traits, and the analysis of multiple layers of omics information.

 

Target audience and prerequisites

 

The course is guided to graduate students and researchers interested on the analysis of genetics and genomics data, including complex traits, molecular markers and gene expression.

Some basic knowledge of quantitative and molecular genetics, linear models, and elementary probability and statistics is expected. However, a brief overview of matrix algebra, probability distributions, and statistical inference will be provided at the beginning of the course. In addition, a working knowledge of R is desirable but an introduction will be offered prior to the use of specific R packages.

 

 

COURSE OUTLINE

 

Correlation and Causation

Sewall Wright and path analysis

Observational and experimental data

Confounding and selection bias

Randomization

 

Basics of Matrix Algebra

Definitions and matrix operations

Systems of equations

Linear regression and least squares

 

Aspects of Multivariate Distributions

Density function or mass function

Marginal and conditional distributions

Expectation and variance

Covariance and independence

The multivariate normal distribution

 

Inference with Multivariate Models

Likelihood principle

Parameter estimation, Hypothesis test

Independence tests (Discrete, Continuous, and Mixed cases)

 

Introduction to Graphical Models

Basic concepts; network topology features

Correlation networks

Marginal and partial correlations

Conditional independence and the concept of d-separation

 

Structural Equation Models in Quantitative Genetics

Traditional multi-trait mixed effects model (MTM)

Genetic and phenotypic correlation

Basics of structural equation models (SEM)

SEM with latent variables

SEM embedded in MTM; direct and indirect genetic effects

 

Bayesian Networks

Introduction

Structure learning (constraint- and score-based algorithms)

Parameter learning

The concept of Markov blanket

Causal inference

 

Applications in Genetics and Genomics

Building parsimonious models

Genome-enabled prediction

Instrumental variable and Mendelian randomization

Multiple-trait QTL mapping

Combining multiple layers of omics information

 

R packages: Rgraphviz, pcalg, bnlearn, qtlnet, sem, lavaan, among others

 

 

 

Registration details: A link will be open around November 2018


 

Material of previous years:

 

Armidale Genetics Summer Course 2018    Materials

·       Mathematical modeling of infection dynamics in genetically diverse livestock populations: Andrea Doeschl-Wilson and Osvaldo Anacleto        

 

Armidale Genetics Summer Course 2017    Materials

·       Genotype by environment interaction in breeding programs: Piter Bijma and Han Mulder   

 

Armidale Genetics Summer Course 2016    Materials

 

Investigating the Genetic Architecture of Complex Traits  & Prediction of Phenotype

from Genome-wide SNPs - Doug Speed and David Balding

 

Armidale Animal Breeding Summer Course 2015   Materials

                 Primer to genomic analysis using R:    Cedric Gondro

                 From Sequence Data to Genomic Prediction:   Ben Hayes and Hans Daetwyler

 

Armidale Animal Breeding Summer Course 2014    Materials

Breeding Program Design with Genomic Selection: Jack Dekkers, Julius van der Werf

 

Armidale Animal Breeding Summer Course 2012    Materials

Statistical Methods for Genome-Enabled Selection: Daniel Gianola, Gustavo de los Campos 

 

Armidale Animal Breeding Summer Course 2011    Materials

·       Statistical methods and design in plant breeding and genomics: Ian Mackay

·       IBD inference in genome association studies: Elizabeth Thompson

 

Armidale Animal Breeding Summer Course 2010    Materials

·       Application of evolutionary algorithms to solve complex problems in quantitative genetics

and bioinformatics:   Brian Kinghorn, Cedric Gondro

·       Bayesian methods in genome association studies:  Dorian Garrick,  Rohan Fernando

 

Armidale Animal Breeding Summer Course 2009    Materials

·       Quantitative Genetic Theory and Analysis- Selection Theory:    Bruce Walsh

·       Quantitative Genetic Models for social interaction and inherited variability: Piter Bijma

 

Armidale Animal Breeding Summer Course 2008      Materials 

·       Genomic Selection:       Ben Hayes

 

Armidale Animal Breeding Summer Course 2007      Materials

·       Generalized Linear Mixed Models:        Steve Kachman

 

Armidale Animal Breeding Summer Course 2006    Materials

·       Gene Expression:          Toni Reverter

·       Breeding Program Design:         Graser, James, Van der Werf

 
Armidale Animal Breeding Summer Course 2005    Materials
·       Breeding Objectives: Gibson, Van der Werf, Kinghorn
·       Scientific Writing:          David Lindsay
·       ASReml:           Arthur Gilmour
 
Armidale Animal Breeding Summer Course 2004    Materials
·       Bayesian for Beginners               Kerrie Mengersen
·       Bayesian Models for QTL analysis        Michel Perez-Enciso
·       Bioinformatics   John McEwan
 

Armidale Animal Breeding Summer Course 2003    Materials

·       Scientific Writing:  David Lindsay
·       Linear Models for animal breeding:   Julius van der Werf, Mike Goddard
·       QTL mapping for practitioners, from linkage to gene: Ben Hayes, Julius van der Werf
 
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