- Name: Gota Morota
- Office: A218f Animal Science Building
- Email: morota@unl.edu
- Web: http://morotalab.org/
- Office Hours: By appointment

- Tues./Thurs. 9:30-10:45am
- Animal Science Building, Room A228

- 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]

- 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

- [PDF]

- 1/9 (T): Course overview [HTML]
- 1/11 (R): Ordinary least-squares and the curse of dimensionality
- 1/16 (T): Variance, covariance, and linkage disequilibrium
- 1/18 (R): Covariance between allelic counts [HW1]
- 1/23 (T): Review of average effect of allele substitution
- 1/25 (R): Multi-locus additive genetic variance in the presence of linkage disequilibrium
- 1/30 (T): Whole-genome regression - ridge regression [R] [HW2]
- 2/1 (R): Whole-genome regression - ridge regression [R] [P]
- 2/6 (T): Whole-genome regression - ridge regression
- 2/8 (R): Overview of likelihood-based inference; Prediction of random effects - best prediction (BP) [R][P]
- 2/13 (T): Prediction of random effects - best linear prediction (BLP); best linear unbiased prediction (BLUP); mixed model equations (MME) (Guest lecture - Mr. Haipeng Yu) [PDF][R][P]
- 2/15 (R): HW1 review & HW2 review (Guest lecture - Dr. Malachy Campbell)
- 2/20 (T): Relatedness due to genetic markers - additive genomic relationship
- 2/22 (R): Relatedness due to genetic markers - dominance genomic relationship
- 2/27 (T): Relatedness due to genetic markers - dominance genomic relationship
- 3/1 (R): Whole-genome regression - Genomic BLUP (GBLUP) [HW3]
- 3/6 (T): Whole-genome regression - Ridge regression BLUP (RR-BLUP) & Cross-validation
- 3/8 (R): Iterative methods for solving MME 1
- 3/13 (T): Iterative methods for solving MME 2; Genetic variance estimation with maximum likelihood (ML) [HW4]
- 3/15 (R): Genetic variance estimation with restricted maximum likelihood (REML)
- 3/20 (T): Spring break
- 3/22 (R): Spring break
- 3/27 (T): No class
- 3/29 (R): Population stratification - Mixed-linear-model association (Guest lecture - Dr. Waseem Hussain)
- 4/3 (T): Whole-genome regression - Bayesian penalized regression models - Bayesian ridge regression
- 4/5 (R): Whole-genome regression - Bayesian penalized regression models - Bayesian ridge regression; Bayesian LASSO [R] [HW5]
- 4/10 (T): Priors in whole-genome regression [R] [D]
- 4/12 (R): HW3 review; Linear mixed model in the presence of population stratification [HW6]
- 4/17 (T): HW4 review; Whole-genome regression - Semi-parametric regression - Reproducing kernel Hilbert spaces regression [R]
- 4/19 (R): Dr. Jose Crossa - Trait Prediction in Agriculture Workshop [WWW]
- 4/24 (T): HW5 review; Whole-genome regression - Semi-parametric regression - Reproducing kernel Hilbert spaces regression
- 4/26 (R): HW6 review; Student presentations
- 5/3 (R): Final exam - 10:00 a.m. to 12:00 p.m.