- 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

- STAT 801 or equivalent
- STAT 970 recommended
- 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]

- 1/12 (T): Ordinary least-squares and the curse of dimensionality - prediction vs. inference [HTML]
- 1/14 (R): Linkage disequilibrium [HTML]
- 1/19 (T): One locus to the infinitesimal model - 1 [HTML]
- 1/21 (R): One locus to the infinitesimal model - 2 [HTML][HW1]
- 1/26 (T): Whole-genome regression - ridge regression 1 [HTML][R]
- 1/28 (R): Whole-genome regression - ridge regression 2 [HTML][R]
- 2/2 (T): UNL classes cancelled due to inclement weather
- 2/4 (R): Overview of likelihood-based inference [HW2]
- 2/9 (T): Prediction of random effects - best prediction (BP)
- 2/11 (R): Prediction of random effects - best linear prediction (BLP) and best linear unbiased prediction (BLUP)
- 2/16 (T): Prediction of random effects - mixed model equations (MME)
- 2/18 (R): Iterative methods for solving MME
- 2/23 (T): Relatedness due to genetic markers - additive genomic relationship [HTML][R][R]
- 2/25 (R): Relatedness due to genetic markers - dominance genomic relationship 1
- 3/1(T): Relatedness due to genetic markers - dominance genomic relationship 2 [HTML][HW3]
- 3/3(R): Whole-genome regression - Genomic BLUP (GBLUP) and ridge regression BLUP (RR-BLUP) [R]
- 3/8(T): Whole-genome regression - single-step GBLUP and cross-validation
- 3/10 (R): Genetic variance estimation with restricted maximum likelihood (REML)
- 3/15 (T): Whole-genome regression - Bayesian penalized regression models - Bayesian ridge regression
- 3/17 (R): Whole-genome regression - Bayesian penalized regression models - Bayesian LASSO / Bayesian alphabet - Bayes A, B, C, and Cpi [R]
- 3/22 (T): Spring break
- 3/24 (R): Spring break
- 3/29 (T): UNL Plant Breeding Symposium 2016 [WWW][HTML]
- 3/31 (R): Whole-genome regression - Semi-parametric regression - Reproducing kernel Hilbert spaces regression 1 [HW4]
- 4/5 (T): Whole-genome regression - Semi-parametric regression - Reproducing kernel Hilbert spaces regression 2 [R]
- 4/7 (R): Deterministic equations for genome-enabled prediction
- 4/12 (T): Population stratification - Mixed-linear-model association
- 4/14 (R): Multi-trait model and pleiotropy [HW5]
- 4/19 (T): Liability threshold model
- 4/21 (R): Student presentations 1
- 4/26 (T): Student presentations 2
- 4/28 (R): Student presentations 3