APSC 5984/20816 Complex Trait Genomics
Spring 2020
Instructor
- Name: Gota Morota
- Office: 368 Litton Reaves Hall
- Email: morota@vt.edu
- Web: http://morotalab.org/
- Office Hours: By appointment
Teaching assistant
Time and Location
- Mon/Wed/Fri 12:20-1:10pm
- Wallace Hall 244
Course Description
This course will cover quantitative genetic analysis of complex trait genomics 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 in 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.
- 1/22 (W): Course overview and Introduction [HTML1][HTML2]
- 1/24 (F): Ordinary least-squares and the curse of dimensionality
- 1/27 (M): Ordinary least-squares and the curse of dimensionality
- 1/29 (W): Ordinary least-squares and the curse of dimensionality [R]
- 1/31 (F): Review of allele and genotypic frequencies [R]
- 2/3 (M): Variance of allelic counts [HW1]
- 2/5 (W): Linkage disequilibrium [R]
- 2/7 (F): Life Science Seminar by Dr. Scott Lowman [WWW1][WWW2]
- 2/10 (M): Covariance between allelic counts
- 2/12 (W): Correlation between allelic counts & Marker genotype imputation [R]
- 2/14 (F): Multi-locus additive genetic variance in the presence of linkage disequilibrium
- 2/17 (M): Whole-genome regression - ridge regression 1 [R][HW2]
- 2/19 (W): Whole-genome regression - ridge regression 2 [R]
- 2/21 (F): Whole-genome regression - ridge regression 3 [R]
- 2/24 (M): LASSO & Additive genomic relationship matrix [R]
- 2/26 (W): Additive genomic relationship matrix & Guest lecture by Dr. Luiz Peternelli - An overview of the sugarcane breeding program and its challenges [R]
- 2/28 (F): Genomic best linear unbiased prediction [R]
- 3/2 (M): Single-marker linear mixed model GWAS - 1
- 3/4 (W): Single-marker linear mixed model GWAS - 2
- 3/6 (F): Single-marker linear mixed model GWAS - 3 and Generalized least squares [R][R]
- 3/9 (M): Spring break
- 3/11 (W): Spring break
- 3/13 (F): Spring break
- 3/16 (M): Extended spring break
- 3/18 (W): Extended spring break
- 3/20 (F): Extended spring break
- 3/23 (M): HW1 review and Single-marker linear mixed model GWAS - 4 (moved to an online format due to COVID-19 pandemic) [HW3]
- 3/25 (W): Mixed model equations for single-marker linear mixed model GWAS & Ridge regression BLUP [R][R]
- 3/27 (F): Genomic BLUP MME & Weighted least squares single-marker GWAS [R][R]
- 3/30 (M): Decoding mixed model equations [R]
- 4/1 (W): Decoding mixed model equations & Single-marker MLM GWAS using GAPIT [R]
- 4/3 (F): Cross-validation for genomic prediction - 1 [R]
- 4/6 (M): Compressed mixed linear model GWAS & Cross-validation for genomic prediction - 2 [R][HW4]
- 4/8 (W): A list of papers for Zoom presentations [TXT]
- 4/10 (F): Multiple testing correction in GWAS [HTML]
- 4/13 (M): Variance components
- 4/15 (W): Variance components
- 4/17 (F): Variance components [HTML]
- 4/20 (M): HW5 [HW5]
- 4/22 (W): Bayesian ridge regression & Bayesian LASSO [HTML]
- 4/24 (F): Bayesian alphabet [HTML]
- 4/27 (M): Estimating genomic heritability from Bayesian marker-based regression models [HTML]
- 4/29 (W): Student presentations
- 5/1 (F): Student presentations
- 5/4 (M): Final exam [HTML]