Speaker: Professor Gota Morota (www.morotalab.org), from Virginia Polytechnic Institute and State University
Dates: 20th (Wednesday) and 22nd (Friday) of October from 10:00-12:30 (morning) and from 15:00-16:30 (afternoon)
Where: ONLINE!! Please register at https://forms.gle/ycJjBN4GsziZoG6Z6 until the 15th of October. The ZOOM link for the workshop and password will be sent to all on the 18th of October.
(Note that, this workshop will be recorded so that it can then be shared with you and anyone who may be interested in the topic. If you do not abide by this requirement then just keep your webcam off throughout the entire time.)
Target audience: The course is aimed at students, researchers and professionals interested in the quantitative genetic analysis of high-throughput phenotyping data. Some basic understanding of genetics, statistics and programming will be beneficial.
Abstract: This course will cover quantitative genetic analysis of complex trait genomics with an emphasis on image-derived high-throughput phenotyping data. The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. We will discuss statistical methodologies for connecting phenotypes with high-dimensional genomic information to better understand polygenic traits from prediction and inference perspectives. Topics will include phenotyping, genomic relatedness, linkage disequilibrium, population stratification, genomic heritability, genome-wide association study, genomic prediction, causal inference, and statistical learning. We will use examples from the plant and animal genetics literature.
Workshop specifics:
(1) This is a two-day course. Each day will include lectures, hands-on data analysis sessions and class discussions. Hands-on sessions will involve the data analysis of simulated and real genomic data available in public repositories.
(2) The course will use R/RStudio and Julia for statistical computing tools. Please make sure you have the latest versions of R/RStudio and Julia installed prior to the workshop. Professor Gota Morota will inform you beforehand of the additional packages you also need to install.
(3) Professor Gota Morota will create a course website using a GitHub page and upload all the materials there.
General program:
Day 1 (morning)
Introduction to Genome to Phenome (1 hour)
Decoding best linear unbiased prediction – lecture (1.5 hours)
Introduction to Genome to Phenome (1 hour)
Decoding best linear unbiased prediction – lecture (1.5 hours)
Day 1 (afternoon)
Decoding best linear unbiased prediction – hands-on (1.5 hours)
Decoding best linear unbiased prediction – hands-on (1.5 hours)
Day 2 (morning)
Deterministic formulas for genomic prediction – lecture and hands-on (1 hour)
Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data – lecture (1.5 hours)
Day 2 (afternoon)
Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data – hands-on (1.5 hours)
Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data – hands-on (1.5 hours)