Detailed information about the course
Mixed Models in Quantitative Genetics - Swiss Institute in Statistical Genetics - Module 4
13-15 September 2017
Prof. Bruce Walsh, University of Arizona (US)
Prof. Guilherme J. M. Rosa, University of Wisconsin (US)
"Mixed models” refers to the analysis of linear models with arbitrary (co)variance structures among and within random effects and may be due to such factors as relationships or shared environments, cytoplasm, maternal effects and history. Mixed models are utilized in complex data analysis where the usual assumption(s) of independence and/or homogenous variances fail. Mixed models allow effects of nature to be separated from those of nurture and are emerging as the default method of analysis for human data. These issues are pervasive in human studies due to the lack of ability to randomize subjects to households, choice, and prior history. In plant breeding, growth and yield data are correlated due to shared locations, but diminish by distance resulting in spatial correlations. In animal breeding, performance data is correlated because individuals maybe related and may share common material environment as well as common pens or cages. Further, when individuals share a common space, they may experience indirect genetics effects (IGEs), which is an inherited effect in one individual experienced as an environmental effect in an associated individual. The evolution of cooperation and competition is based on IGEs, the estimation of which require mixed model analysis. Detection of cytoplasmic and epigenetic effects rely heavily on mixed model methods because of shared material or parental histories. Topics to be discussed include a basic matrix algebra review, the general linear model, derivation of the mixed model, BLUP and REML estimation, estimation and design issues, Bayesian formulations. Applications to be discussed include estimation of breeding values and genetic variances in general pedigrees, association mapping, genomic selection, spatial correlations and corrections, maternal genetic effects, detecting selection from genomic data, admixture detection and correction, direct and indirect genetic effects, models of general group and kin selection, genotype by environment interaction models.
University of Lausanne
Full attendance and active participation
- a course on basic statistics
- the course "An Introduction to R" or a similar course.
September 14th: 09h00-12h00 and 13h30-17h30
September 15th: 09h00-12h00 and 13h30-17h30
Biophore building, Amphitheater
Free for participants belonging to a CUSO University (UniBE, UniFR, UniGE, UniL & UniNE) and PhD students of the DP-biol.
250CHF for Academic participants
500CHF for Non-Academic participants
The registration fees do not comprise accomodation and food.
PhD students of the DPEE are eligible for reimbursement of incurred travel expenses by train (half-fare card, and 2nd class) and meals (up to 25 CHF). Please send the original tickets (no copies, except for the general abonnement) with the reimbursement form to:
Doctoral Program in Ecology and Evolution
DEE- Biophore Building
University of Lausanne
Regarding reimbursement of accomodation, please contact Caroline Betto-Colliard (ecologie-evolution(at)cuso.ch) BEFORE the beginning of the course (only for PhD students of the DPEE ).
For all other PhD students, please check reimbursement conditions on the website of your doctoral program.
CUSO PhD students: through your MyCUSO account.
External participants (non-CUSO PhD students, post-docs, etc...): use the icon "registration" at top of page and the last gray box "non-CUSO student" ("personne hors myCUSO").
|Deadline for registration||13.08.2017|