Monday 21st – Classes from 09:30 to 17:30 

 

 

Morning

 

CONCEPTS IN QUANTITATIVE GENETICS

 

 

·         Basics of quantitative genetics. Genotypic and phenotypic values.

 

·         Breeding value (additivity), dominance [and epitasis].

 

·         The basic genetic model and genotypic effects among a single cross.  

 

·         Phenotypic and genetic variances. Additive and dominance variances. Genetic variance from a factorial model.

 

·         Covariance between relatives.

 

·         Why to estimate genetic variances? Heritability concepts and applications.

 

·         The breeder’s equation. Response to selection. Theoretical equations. How to improve response to selection?

 

 

 

 

Afternoon

 

FUNDAMENTALS OF R PROGRAMMING

 

 

·         Introduction to R and fundamentals of R programming.

 

·         Calculating selection gradients and applying the breeder’s equation in R.

 

 

 

Suggested reading

 

·         Bruce Walsh (2001) Quantitative genetics in the age of genomics. Theor. Pop. Biol. 59:175–184

 

·         Heather Merk (2009) Introduction to R stat software application to plant breeding. http://www.extension.org/pages/60427

 

·         William G. Hill (2012) Quantitative genetics in the genomics era. Current Genomics 13:196–206

 

 

 

 

 

Tueasday 22nd– Classes from 09:30 to 17:30

 

Morning

 

QTL MAPPING, MULTI‐ENVIRONMENT TRIALS AND G×E

 

 

·         What is QTL mapping?

 

·         Why Multi‐Environment Trials?

 

·         What is G×E?

 

·         Dealing with G×E: Ignore it? Reduce it? Exploit it! How G×E can be studied and quantified?

 

·         Error variance, G×E variance, number of environments and number of replicates.  

 

·         Components of G×E and its influence in heritability and genetic gains.

 

 

 

Afternoon

 

STATISTICAL METHODS FOR STUDYING G×E

 

 

·         Methods for assessing G×E.

 

·         Simple Linear regression – regression on the site mean.

 

·         Advantages and disadvantages of simple linear regression.

 

·         Introduction to multivariate methods for assessing G×E.

 

 

 

Suggested reading

 

·         J.C. Bowman (1972) Genotype × environment interactions. Ann. Génet. Sél. Anim. 4:117–122

 

·         Walter T. Federer, and José Crossa (2012) I.4 screening experimental designs for quantitative trait loci, association mapping, genotype‐by environment interaction, and other investigations. Front. Physiol. 3:156 doi: 10.3389/fphys.2012.00156

 

 

 

 

 

Wednesday  23rd – Classes from 09:30 to 17:30 

 

 

Morning

 

ON THE UTILITY OF GENOME-WIDE SCANS FOR SELECTION AND ASSOCIATION STUDIES

 

 

·         Introduction and concepts of GWAS and scans for selection.

 

·         Single‐marker regression.

 

·         Single‐marker regression, accounting for population stratification and relatedness.

 

·         Models for variable selection.

 

·         Coupling GWAS and scans for selection.

 

 

 

Afternoon

 

IMPLEMENTING GWAS AND SCANS FOR SELECTION

 

 

·         Performing GWAS and scans for selection in Tassel.

 

·         Customizing scripts in R for making window analyses and Manhattan plots.

 

 

 

Suggested reading

 

·         José Crossa et al. (2007) Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics

 

·         Zeratsion Abera Desta, and Rodomiro Ortiz (2014) Genomic selection: genome‐wide prediction in plant improvement. Trends Plant Sci. 19:592–601

 

·         H. P. Piepho, J. Möhring, A.E. Melchinger, and A. Büchse (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209–228

 

 

 

 

 

 Thursday 24th – Classes from 09:30 to 17:30

 

Morning

 

STATISTICAL METHODS FOR PREDICTING BREEDING VALUES

 

 

·         Principles of genomic prediction and selection (GS) as a tool to accelerate genetic gains. What does genomic selection do?

 

·         The complexity of GS data, the curse of dimensionality.  

 

·         How can GS be implemented?

 

·         What can be and cannot be estimated through GS?

 

 

 

 Afternoon

 

PACKAGES FOR PREDICTING BREEDING VALUES

 

 

·         BGLR in R.

 

 

 

Suggested reading

 

·         Juan Burgueño et al. (2008) Using factor analytic models for joining environments and genotypes without crossover genotype × environment interaction. Crop Sci. 48:1291–1305

 

·         Hugh G. Gauch, Jr. (2006) Winning the accuracy game. Amer. Scientist 94:134–143

 

·         Rodomiro Ortiz et al. (2007) Studying the effect of environmental variables on the genotype × environment interaction of tomato. Euphytica 153:119–134

 

·         Alison B. Smith et al. (2015) Factor analytic mixed models for the provision of grower information from national crop variety testing programs. Theor. Appl. Genet. 128:55–72

 

 

 

Friday 25th – Classes from 09:30 to 17:30

 

Morning

 

EXTENSIONS OF GENOMIC PREDICTION (GS)

 

 

·         How to integrate GWAS and scans for selection with GS.

 

·         Statistical models for incorporating G×E into genomic prediction.

 

·         The Reaction Norm model for incorporating G×E.  

 

·         Practical examples and results for models with genomic G×E.

 

 

 

Afternoon

 

APPLYING AND MODELING FURTHER EXTENSIONS

 

 

·         GWAS and scans for selection with GS in R.

 

·         Hybrid prediction.

 

·         Closing.

 

 

Suggested reading

 

·         Filippo Bassi et al. (2015) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.).

 

·         Yoseph Beyene et al. (2015) Genetic gains in grain yield through genomic selection in eight bi‐parental maize populations under drought stress

 

·         Shizhong Xu et al. (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc. Natl. Acad. Sci. (USA) www.pnas.org/cgi/doi/10.1073/pnas.1413750111