PHPS41180 Statistics for Human Genomics

Academic Year 2022/2023

This module aims to develop skills in the analysis of human genetic data. It will focus on 1) estimation of heritability and genetic architecture; 2) gene-mapping in family, case-control, and population-representative samples; 3) the use of R statistical software and specialised genetic analysis software.

Largely based on self-directed and problem-based learning, the module will be supported by extensive online exercises, online asynchronous lecture materials and links to online resources via the UCD library and beyond. Students will be expected to engage in online discussions, and attend regular, scheduled synchronous virtual classroom sessions.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module students will be able to:
* Understand the application of statistical methods to estimate heritability in twin studies
* Use statistical software for the analysis of gene-mapping studies with genetic linkage and genetic association
* Apply corrections for the control of confounding by non-genetic covariates or population stratification in gene-mapping studies
* To use appropriate methods to characterise the predictive properties of genetic tests, either based on single variants, or polygenic scores

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

4
Specified Learning Activities

40
Autonomous Student Learning

42
Total

110
Approaches to Teaching and Learning:
Highly practical and hands-on, this module consists of an online distance course in data analysis for human genetic researchers. The predominant modes of delivery are live lectures. These are accompanied by short video segments, guided exercises, and guided reading. Active learning is encouraged through asynchronous group discussions online, and the guided computational exercises and self-assessments provide continuous feedback. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Additional Information:
Students are required to have taken PHPS41150 Introduction to Biostatistics, or similar instruction in introductory statistics and the R statistical environment


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Final integrative assignment. Coursework (End of Trimester) n/a Standard conversion grade scale 40% No

50
Class Test: MCQ of the theoretical course materials Week 12 n/a Standard conversion grade scale 40% No

20
Project: Practical report on computational tasks Varies over the Trimester n/a Standard conversion grade scale 40% No

30

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Feedback for the in-semester project will be given individually to students, electronically. There will also be general feedback on the class performance, on areas of weakness and strength. The MCQ exam will have immediate feedback. There will be regularly spaced summative short MCQs to provide on-going feedback on learning progression.