Complex Trait Genetics

Course

URL study guide

https://studiegids.vu.nl/en/courses/2024-2025/P_MCTRGEN

Course Objective

Provide the theoretical background into human population and biometrical genetics, as they to the genetic analysis of human complex traits. Following this course, students will understand the standard biometrical model underlying common analyses in complex trait genetics. Common analyses include 1) the basic application of the biometrical model to a measured genotype; 2) decomposition of phenotypic variance based on expected allele shared identically by descent (IBD; e.g. the classical twin design), observed allele sharing IBD, and the average genetic correlation, based on allele sharing identically by state (IBS; GCTA), 3) Fixed and random effects models that leverage observed genetic information to detect genotype-environment covariance and interaction (by modeling the direct and indirect effects of genes on phenotype). The analyses are explained with explicit reference to the underlying statistical models, with the emphasis on the dominant role of the linear mixed model (LLM). The LLM is presented in terms of matrix algebra, but no prior knowledge of matrix algebra is assumed.

Course Content

1) The biometrical model (following the exposition of Falconer and MacKay). 2) Fixed and random effects, as modeled in the linear mixed model (LLM), including the required matrix algebraic representation of the LLM. 3) LLM and the decomposition of phenotypic variance using genetic information concerning IBD or IBS, emphasizing the common underlying method (linking the classical twin design and GCTA). 4) Direct and indirect genetic effects in fixed effects models using polygenic risk scores 5) Direct and indirect genetic effects in random effects models (variations on GCTA).

Teaching Methods

The course comprises 7 lectures. Attendence is recommended, but not mandatory. Each week there is an assignment which involved doing exercises and analyses in R, and reporting the results.

Method of Assessment

Examination consists of two components: 1) Weekly assignments, which are graded. Submitting the assignments by the given deadline is a course requirement. 2) An paper and pencil exam. The final grade is based on the the grades for the assignments (A) and the grade for the PP exam (B): final grade = .3*A + .7*B. To pass, students require a final grade ≥ 5.5, with A ≥ 5.5 and B ≥ 5.5 (i.e., A and B are non-compensatory).

Literature

1) Practical workbook used for the assignments. 2) Lectures notes based on the lectures 3) Lecture notes on basic matrix algebra and the linear mixed model 4) Lecture PowerPoint presentations 5) Articles All material will be available on Canvas

Target Audience

This is an obligatory course for students in the research master Genes in Behavior and Health.

Entry Requirements

Students are expected to have 1) active statistical knowledge concerning linear regression analysis, measures of association (covariance and correlation), and statistical inference and null-hypothesis testing; 2) working knowledge of the R and the R environment (R or R studio), and practical R programming skills, 3) Knowledge of genetics and genetical methods based on the MA1 year of the research master GHB.
Academic year1/09/2431/08/25
Course level6.00 EC

Language of Tuition

  • English

Study type

  • Master