ASCI 896-004 Statistical Genomics
Spring 2017
Instructor
- Name: Gota Morota
- Office: A218f Animal Science Building
- Email: morota@unl.edu
- Web: http://morotalab.org/
- Office Hours: By appointment
Time and Location
- Tues./Thurs. 9:30-10:45am
- Animal Science Building, Room A228
Prerequisites
- ASCI 861U, 931, or equivalent
- STAT 802, 821, or equivalent
- Knowledge of statistical programming language R
- Searle, S.R. (1982) Matrix Algebra Useful for Statistics. Wiley, New York. [Amazon]
Course Description
This course will cover quantitative genetic analysis of complex trait genetics with emphasis on the use of molecular markers spanning the entire genome. We will discuss statistical methodologies for connecting phenotypes with high-dimensional genomic information to better understand polygenic traits from both prediction and inference perspectives. Topics will include genomic relatedness, linkage disequilibrium, population stratification, genomic heritability, missing heritability, genome-wide association study, genomic prediction, causal inference, and statistical learning. We will use examples from the animal, plant, and human genetics literature. Additional topics will be briefly touched upon, including sequence data, gene expression, epigenetics, and bioinformatics. Homework assignments involve hands-on analysis of simulated and real genomic data available at public repositories. The course will use R/Bioconductor software for statistical computing tools.
Learning Objectives
After taking this course, the student will be able to:
- understand the statistical theory behind commonly used quantitative methods in genomics
- apply statistical methods to high-dimensional genomic data and analyze them using statistical computing tools
- critically review current literature in statistical and quantitative genetics
Texts and Reading Materials
Lecture slides will be provided on the class website. There will be no required textbook.
Syllabus
Schedule
Lectures will be delivered using a whiteboard and presentation slides.
- 1/10 (T): Course overview [HTML]
- 1/12 (R): Ordinary least-squares and the curse of dimensionality
- 1/17 (T): UNL classes canceled due to road conditions
- 1/19 (R): Variance, covariance, and linkage disequilibrium [HTML]
- 1/24 (T): Covariance between allelic counts [HW1]
- 1/26 (R): Review of average effect of allele substitution and breeding value
- 1/31 (T): Multi-locus additive genetic variance in the presence of linkage disequilibrium [HW2]
- 2/2 (R): Whole-genome regression - ridge regression 1 [R][P]
- 2/7 (T): Whole-genome regression - ridge regression 2
- 2/9 (R): HW1 review & Overview of likelihood-based inference
- 2/14 (T): Prediction of random effects - best prediction (BP) and best linear prediction (BLP)
- 2/16 (R): Prediction of random effects - best linear unbiased prediction (BLUP) and mixed model equations (MME) [HW3] [P]
- 2/21 (T): Prediction of random effects - mixed model equations (MME) [P]
- 2/23 (R): Relatedness due to genetic markers - additive genomic relationship
- 2/28 (T): Relatedness due to genetic markers - dominance genomic relationship 1
- 3/2 (R): Relatedness due to genetic markers - dominance genomic relationship 2 [HW4]
- 3/7 (T): HW2 review & Whole-genome regression - Genomic BLUP (GBLUP)
- 3/9 (R): Whole-genome regression - Ridge regression BLUP (RR-BLUP) & Cross-validation
- 3/14 (T): UNL Plant Breeding Symposium 2017 [WWW]
- 3/16 (R): HW3 review & Iterative methods for solving MME
- 3/21 (T): Spring break
- 3/23 (R): Spring break
- 3/28 (T): HW4 review and R packages to fit GBLUP/RR-BLUP [HW5][R]
- 3/30 (R): Iterative methods for solving MME & Genetic variance estimation with maximum likelihood (ML)
- 4/4 (T): Genetic variance estimation with restricted maximum likelihood (REML) & Whole-genome regression - Bayesian penalized regression models - Bayesian ridge regression [D]
- 4/6 (R): Whole-genome regression - Bayesian penalized regression models - Bayesian LASSO [HW6][R]
- 4/11 (T): Whole-genome regression - Bayesian penalized regression models - Bayesian alphabet - Bayes A, B, C, and Cpi & Population stratification - Mixed-linear-model association
- 4/13 (R): Population stratification - Mixed-linear-model association
- 4/18 (T): Whole-genome regression - Semi-parametric regression - Reproducing kernel Hilbert spaces regression [HW7][R]
- 4/20 (R): Guest lecture - Dr. Diego Jarquin
- 4/25 (T): Student presentations - 1
- 4/27 (R): Student presentations - 2 [HTML]
- 5/2 (T): Final exam - 10:00 a.m. to 12:00 p.m.