Computational Methods in Plant & Animal Quantitative Genetics
Why: Quantitative genetic variation is the substrate for phenotypic evolution in natural populations and for selective breeding of domestic crop and animal species. It also underlies susceptibility to common complex diseases and behavioral disorders in humans, as well as responses to pharmacological therapies. As such, a thorough understanding of the underlying genetic control of these traits can help alleviate complex diseases and develop new and more personalized therapeutic interventions to improve human health. The genetic framework is that such characters are controlled by many genes and in which non-genetic environmental factors may also influence the trait.
Given the wealth of raw genomics data, the challenge is now to get the most of observations. Large amounts of genomic data allow unprecedented opportunities to map the genotypes and the traits. This raises the question of the most suitable computational methods to implement. A cornerstone of the analytical tools is the Linear Model. The matrix form of the Mixed Linear Model is an unprecedented and successful tool to decipher the phenotypes – genotypes relationships. This is due to its extraordinary flexibility, the improvement of computational resources, and the inclusion of genetic relationships among individuals into the models.
How: The course is implemented in a skill-based learning approach based on analysis of real data sets and case studies in a variety of biological questions. Practices within team projects are presented to the class. Individual reports using reproducible research framework are also expected.
What: The current statistical framework for plant and animal quantitative genetics will be presented. The students will learn the most up-to-date computational and statistical methods in quantitative genetics, from in-depth phenotypic analysis to high-density genetic maps and Quantitative Trait Loci (QTL) mapping, to GWAS (Genome-Wide Association Studies) and to Genomic Prediction. Frequentist and some Bayesian methods will be demonstrated. The course will address Plant and Animal Genetics in a balanced way.
Where and When: The course is scheduled in Term 3 (Feb – March) at the Skolkovo Institute of Science and Technology.
Who: Prof. Laurent Gentzbittel & Prof. Cécile Ben
Given the wealth of raw genomics data, the challenge is now to get the most of observations. Large amounts of genomic data allow unprecedented opportunities to map the genotypes and the traits. This raises the question of the most suitable computational methods to implement. A cornerstone of the analytical tools is the Linear Model. The matrix form of the Mixed Linear Model is an unprecedented and successful tool to decipher the phenotypes – genotypes relationships. This is due to its extraordinary flexibility, the improvement of computational resources, and the inclusion of genetic relationships among individuals into the models.
How: The course is implemented in a skill-based learning approach based on analysis of real data sets and case studies in a variety of biological questions. Practices within team projects are presented to the class. Individual reports using reproducible research framework are also expected.
What: The current statistical framework for plant and animal quantitative genetics will be presented. The students will learn the most up-to-date computational and statistical methods in quantitative genetics, from in-depth phenotypic analysis to high-density genetic maps and Quantitative Trait Loci (QTL) mapping, to GWAS (Genome-Wide Association Studies) and to Genomic Prediction. Frequentist and some Bayesian methods will be demonstrated. The course will address Plant and Animal Genetics in a balanced way.
Where and When: The course is scheduled in Term 3 (Feb – March) at the Skolkovo Institute of Science and Technology.
Who: Prof. Laurent Gentzbittel & Prof. Cécile Ben