In charlotte-ngs/asmss2022: Applied Statistical Methods in Animal Science - Spring Semester 2022

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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Welcome to

751-7602-00L Applied Statistical Methods in Animal Sciences

Topics for Master Theses

Topics for Master Theses at Qualitas AG together with ETH are listed here.

Information

Lecturer: Peter von Rohr
Date: Montag 8-10
Mode: hybrid - in person and via zoom
Room: LFW C11

Exercise Platform

The exercise platform can be found at: http://2-htz.quagzws.com:<PORT>
The number that you have to insert for <PORT> was sent by e-mail, as well as the password
The username is: rstudio

Exam

Written exam
Date: 30.05.2022 - 08:15 - 10:00
Room: LFW C 11

Description

The following descriptions are taken from the course catalog

Applied Statistical Methods in Animal Science

Genomic selection is currently the method of choice for improving the genetic potential of selection candidates in livestock breeding programs. This lecture introduces the reason why regression cannot be used in genomic selection. Alternatives to regression analysis that are suitable for genomic selection are presented. The concepts introduced are illustrated by excersises in R.

The students are familiar with the properties of multiple linear regression and they are able to analyse simple data sets using regression methods. The students know why multiple linear regression cannot be used for genomic selection. The students know the statistical methods used in genomic selection, such as BLUP-based approaches, Bayesian procedures and LASSO. The students are able to solve simple exercise problems using the statistical framework R.

Questions

Questions can be asked during the lecture and during the exercise hour or via e-mail:

Peter von Rohr (peter.vonrohr at usys.ethz.ch)

Lecture Program

### # header names of tables
vecTableHeaders <- c("Week", "Date", "Topic")
# define course start date
dCourseStart <- as.Date("2022/02/21")
# set number of weeks in semester
nNrSemesterWeeks <- 15
# define columns for weaks, dates, and subjects
Week <- 1:nNrSemesterWeeks
Date <- format(seq(dCourseStart, by = "week", length.out = nNrSemesterWeeks), "%d.%m")
Topic <- vector(mode = "character", length = nNrSemesterWeeks)
# subjects per week
Topic[1] <- "Introduction to Applied Statistical Methods in Animal Science"
Topic[2] <- "Linear Regression Models"
Topic[3] <- "Linear Fixed Effect Models"
Topic[4] <- "Model Selection"
Topic[5] <- "Pedigree BLUP"
Topic[6] <- "Variance Components"
Topic[7] <- "GBLUP - Marker-Effects Models"
Topic[8] <- "GBLUP - Breeding Value Models"
Topic[9] <- "__Easter Monday__"
Topic[10] <- "Lasso"
Topic[11] <- "SVM"
Topic[12] <- "Bayesian Approaches in Linear Regression Models"
Topic[13] <- "Bayesian Approaches in Linear Mixed Effects Models"
Topic[14] <- "Questions, Test Exam"
Topic[15] <- "Exams" 

dfCnTable <- data.frame(Week, Date, Topic, stringsAsFactors = FALSE)
colnames(dfCnTable) <- vecTableHeaders
knitr::kable(dfCnTable)