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High-throughput Phenotyping Driven Quantitative Genetics @CMA-FCT-NOVA

October 20 and 22, 2021



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Course description

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. 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. The course will use R/RStudio and Julia for statistical computing tools. 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.

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