Fwd: COURSE: Prediction of Complex Traits Using Genomic and Environmental Big Data

[Michal Vinkler]

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Předmět:	COURSE: Prediction of Complex Traits Using Genomic and Environmental Big Data
Přeposláno - Datum:	Tue, 7 Jan 2025 14:49:14 -0600 (CST)
Přeposláno - Od:	ange...@animalgenome.org
Datum:	Tue, 07 Jan 2025 14:49:13 -0600
Od:	DANIEL GIANOLA <gia...@ansci.wisc.edu>
Komu:	Members of AnGenMap <ange...@animalgenome.org>

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SECOND ANNOUNCEMENT

If interested, please register promptly as indicated below. Registration will close in mid-February 2025

INTERNATIONAL COURSE

Prediction of Complex Traits Using Genomic and Environmental Big Data:
Concepts, models, software and practicum.

March 10-14, 2025
Instituto Nacional de Investigación Agropecuaria (INIA)
Montevideo, Uruguay

Instructor:

Gustavo de los Campos
Professor, Departments of Epidemiology & Biostatistics, and Statistics & Probability, Michigan State University, USA. Email: gust...@msu.edu .

Course Description:

Would you like to learn how to predict complex agricultural (plants, trees, animals) and disease phenotypes and risks in humans and animals using DNA markers? In this five-day course, key concepts underlying prediction and the most commonly used models will be introduced, including genomic BLUP, penalized, and Bayesian linear regression, variable selection methods, non-linear methods (kernel regressions, neural networks, and XGBoost). Factors affecting prediction performance will be discussed.

The course consists of modules, each including a brief introduction, followed by hands-on group work using publicly available data and open-source software.
Focus is primarily on genome-assisted prediction using DNA markers but examples using environmental data are covered. A good understanding of probability and inference in linear models and some experience working with R is required. Please bring your laptop with a current version of R installed.

Course Content:

1- Genomic Prediction of Complex Traits: Principles
2- Variable Selection using Single-Marker Regression
3- Shrinkage Estimation (Ridge Regression and Early Stopping)
4- Bayesian shrinkage and Variable Selection Models
5- Semi-parametric regression using Reproducing Kernel Hilbert Spaces
6- Genomic Prediction using XGBoost
7- Sparse GBLUP
8- Multi-trait/multi-environment Genomic Prediction
9- Integrating Environmental Covariates into Genomic Prediction
10- Transfer Learning

Language of instruction: English

Registration and accommodation:

Information will be forthcoming soon. The course will be free but there will be a fee for coffee breaks and lunch.

Please indicate your intention of attending by sending an e-mail to each of the contacts provided below. Since there will be a cap on the number of participants, first come, first served.

VENUE: Institut Pasteur de Montevideo, Uruguay. https://pasteur.uy/en/
PARTICIPANTS: Must bring a laptop and arrange accommodation and travel.

CONTACT:
Daniel Gianola (dgia...@inia.org.uy)
Olga Ravagnolo (orava...@inia.org.uy)


Daniel Gianola

Sewall Wright Professor Emeritus of Animal Breeding and Genetics
Department of Animal and Dairy Sciences
University of Wisconsin-Madison
256 Animal Sciences Building
1675 Observatory Dr.
Madison, WI 53706
USA

https://andysci.wisc.edu/directory/dan-gianola/
https://en.wikipedia.org/wiki/Daniel_Gianola
https://scholar.google.com/citations?user=bikTUXkAAAAJ&hl=en&oi=ao
https://qbi.wisc.edu/research/all-faculty/

"Nature is written in mathematical language (Galileo). If nothing else works, try Bayes theorem"
"Bayesian methods with subjective priors may butcher the processes of science" (adapted from Oscar Kempthorne)


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