26th Summer Institute in Statistical Genetics (SISG)


This module is currently full. Registrations are closed at this time.

Module 14: Mixed Models in Quantitative Genetics

Mon, July 19 to Wed, July 21
Instructor(s):
Registration for this module closes July 12. 

 

 

Live session timeframe (exact schedule with live sessions will be posted by module instructors prior to the start of the module): Monday: 8 a.m. – 2:30 p.m. Pacific (11 a.m. – 5:30 p.m. Eastern); Tuesday: 8 a.m. – 2:30 p.m. Pacific (11 a.m. – 5:30 p.m. Eastern); Wednesday: 8 a.m. – 11 a.m. Pacific (11 a.m. – 2 p.m. Eastern).

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 homogeneous 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 are 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. Suggested pairing: Modules 9 and 15.

Learning Objectives: After attending this module, participants will be able to:

  1. Understand the key idea from matrix theory for linear models (multiplication, inverse, rank, eigenstructure, quadratic forms).
  2. Understand the construction general linear models.
  3. Understand the construction of mixed models.
  4. Apply mixed models for estimating breeding values.
  5. Apply mixed models for GWAS.
  6. Apply mixed models for genomic selection and genomic prediction.