Mixed Models in Quantitative Genetics - Swiss Institute in Statistical Genetics - Module 4

13-15 September 2017

A joint course with the Doctoral Program in Organismal Biology
Prof. Jérôme Goudet, University of Lausanne (CH)
Prof. Bruce Weir, University of Washington (US)

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.

Background reading: Lynch, M. and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer Associates."

University of Lausanne

Map

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Full attendance and active participation

!PREREQUISITE!
Participants should have attended :

- a course on basic statistics

- the course "An Introduction to R" or a similar course. 

When?
13-15 September 2017
September 13th: 13h30-17h30

September 14th: 09h00-12h00 and 13h30-17h30

September 15th: 09h00-12h00 and 13h30-17h30

Where?
University of Lausanne

Biophore building, Amphitheater

Information:
- Please bring your laptop to the course!

Questions?
Caroline Betto-Colliard
@: [email protected]
Phone: +41 (0)21 692 42 44

Registration Fees: 

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.

Reimbursement:

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:

 Caroline Betto-Colliard

 Doctoral Program in Ecology and Evolution

 DEE- Biophore Building

 University of Lausanne

 CH-1015 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").

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