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Statistical models for genomic prediction in animals and plants (2019)

ECTS credits:
3 ECTS

Course parameters:
Language: English
Level of course: PhD course (also available for MSc’s – see comment)
Time of year: Summer 2019
No. of contact hours/hours in total incl. preparation, assignment(s) or the like: 35/80
Capacity limits: 20 students

Objectives of the course:
The course focuses on the quantitative genetics and statistical background of different genomic prediction models, covering also estimation of variance components, theory on genomic heritabilities, Bayesian statistics, estimation of hyper parameters in Bayesian models, multitrait models and simple genomic feature models. Use of all models will be trained in computer practicals with the objective that students obtain an understanding of the statistical principles of the different models, and can analyse data with a critical assessment of the results from different statistical approaches.

Learning outcomes and competences:
At the end of the course, the student should be able to:

  • describe the common uses of genomic prediction in animal and plant breeding
  • analyse and discuss the statistical problems arising with large sets of predictors and common ways to handle these problems
  • structure and explain strengths and weaknesses of various statistical and computational tools to build prediction models from high dimensional data
  • apply software tools for mixed models, ridge regression, LASSO and Bayesian and machine learning methods
  • perform cross validation studies and assess predictive ability of models by prediction correlation and accuracy
  • explain and evaluate consequences of the data and population factors affecting predictive ability
  • apply prediction tools in an empirical data set

Compulsory programme:

  • A set of key papers (approx 5) is distributed that students are expected to study as preparation and that are discussed in a ‘journal club’ during the course
  • 5 full days of lectures, computer exercises, and review / discussion of the exercises. Students should actively participate in the discussions, after each practical a few students will be asked to present their results which will be discussed with the other students.

Course contents:
Teaching sessions are schedule for 5 days:

  • Day 1: background on genomic prediction and genomic selection in animals and plant; simple approaches using GWAS results and introduction to mixed models for whole-genome prediction.
  • Day 2: tackling large p-small n using random/shrinkage effects and cross-validation. Building of the G-matrix and the GBLUP model.
  • Day 3: Details on adjustments and scaling of G- matrices, interpretation of relationships and inbreeding in the G-matrix and comparison and combination of G and A, and the single step GBLUP model. Journal club / literature review by students. General introduction to Bayesian statistics.
  • Day 4: Bayesian shrinkage models: BayesA and LASSO and their hyper parameters; Bayesian variable selection models and their hyper parameters. Background on implementation of Bayesian methods using MCMC and MCMC post-analysis and convergence assessment. GBLUP and Kernel methods to capture epistasis and special combining ability.
  • Day 5: Estimation of variance components and genomic heritability from genomic models, multi-trait models and genomic feature models. Practical analysis details: repeated and weighted records. Presentations by student on exercise results (MSc students add their report outline).

Prerequisites:
Background in linear models (regression, multiple regression) and preferably in mixed models (random effects, variance components) and basic mathematical statistics (joint, marginal, conditional distribution) and linear (matrix) algebra.

Name of lecturer:
Luc Janss

Type of course/teaching methods:
Lectures, computer exercises, literature review and presentations by students

Literature:
Approx. 5 key papers and class notes

Course homepage:
None
 

Course assessment:
Assessment is based on presentations and active participation in the discussion of the exercises. PhD students receive a course certificate when successfully completing the course.

Provider:
Department of Molecular Biology and Genetics, Aarhus University

Special comments on this course:
The course is also available as 5 ECTS MSc course with a higher workload (adding a project report and oral exam). MSc students get a certificate with a grade in the Danish 12-point scale.

Course fee: There is no course fee for students enrolled at a Danish university. For all others, there is a course fee of 1,000 DKK.

Time:
12 – 16 Aug 2019, every day from 9.00-12.00 and 13.00-16.00.

Place:
AU central campus in Aarhus. Exact location will be announced later, as it depends on number of students and room size needed.

Registration:
Course fee: There is no course fee for students enrolled at a Danish university. For all others, there is a course fee of 1,000 DKK.

Registration until 1 July 2019 by sending an email to the course coordinator Luc Janss (luc.janss@mbg.au.dk).

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