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VISG – LARGE DATASETS
Literature Review
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Introduction – Genome Wide Selection
Aka Genomic SelectionSet of Markers
• 10,000’s - enough to capture most genetic information
‘Training set’ of animals• phenotyped and genotyped• representative of industry
Predictor• Over-specified – e.g. 10000 variables, 1000 individuals• Robust model selection required
Application• Predict in selection candidates
– Maybe no phenotypes– Maybe no pedigrees
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Introduction – Genome Wide SelectionPrediction Methods
• Stepwise Regression• gBLUP
– Fit all markers as a random effect– gi ~ N(0,g
2)
• BayesA– gi ~ N(0,gi
2)– prior : gi
2~ S/2 (choose S and )
• BayesB– similar to BayesA, except– proportion of effects are zero
Most investigations compare theseMany variations (sometimes with the same name)
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Literature
Dairy applications review (Hayes et al., 2009)GWS in crops (Heffner, Sorrells, Jannick, 2009)Prediction in unrelateds (Meuwissen, 2009) Marker panels (Habier, 2009)Phenotypes (Harris & Johnson, unpub)+ ...
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Issues
National evaluationsLong term gainsLD or relationship trackingMultiple breedsDistance from Training to ApplicationMarker Panels (subsets)Phenotypes (EBV-based)Non-additive effectsComputing requirements
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Methods
gBLUP almost as good as Bayes(A) (dairy)• Interpretation(?): many genes of small effect
Bayes methods better at using real LD (vs relatedness)Bayes(B) advantage greater with
• Higher marker density• Higher Training Application distance• Smaller Training set
Mixture of 2 normals ~ BayesBPartial Least SquaresMachine LearningHaplotype methods not used in practice yet
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Marker Panels
Evenly spaced panels• Track inheritance from parents (both SNP-chipped)• Will work with new traits
Lasso methods popular• Shrinks small effects to zero
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Other
Combining marker and other information• Phenotype info, parent info• Index methods; ‘blending’• Important for seamless national evaluations
Computing strategies• Tricks to reduce computation• Approximation rather than Iterative (MCMC) methods
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Online resources
Conferences• Statistical Genetics of Livestock for the Post-Genomic Era.
UW-Madison, May, 2009. http://dysci.wisc.edu/sglpge/index.html
• QTL/MAS Workshops. 2008: http://www.computationalgenetics.se/QTLMAS082009: http://www.qtlmas2009.wur.nl/UK/
Courses• Whole Genome Association and Genomic Selection.
September 1-8, 2008, Salzburg, Austria. http://www.nas.boku.ac.at/12100.html?&L=0
• Use of High-density SNP Genotyping for Genetic Improvement of Livestock . Iowa State, June, 2009. http://www.ans.iastate.edu/stud/courses/short/
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Toy example
• 5 SNP / 1000 individuals• y = mu + SNP1 + e
– mu = 10– SNP1 substitution effect = 10 / p = 0.5– Var(e) = 1
• 1 block / 1000 iterations• Runs in ~ 5 secs
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