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Academic Year 2021/2022

Data Science and Statistics for Human Genomics (PHPS40870)

Public Health & Population Sci
Health & Agricultural Sciences
Public Hlth, Phys & Sports Sci
4 (Masters)
Module Coordinator:
Dr Ricardo Piper Segurado
Mode of Delivery:
Internship Module:
How will I be graded?
Letter grades

Curricular information is subject to change.

This module aims to develop skills in the analysis of human genetic data, particular with respect to applications in biomedical research. It will focus 1) on the theoretical basis of genetic analysis of family data, of case-control studies and case-only designs; 2) on developing understanding of the techniques of statistical genetic analyses, including correlations and estimation of heritability, genetic linkage analysis, genetic association analysis, and tests of gene-environment and gene-gene interactions, as well as advanced methods such as polygenic and risk scores, and gene-set / network analysis; 3) the use of prevalent genetic analysis software programs such as PLINK, etc.

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.

About this Module

Learning Outcomes:

On completion of this module students will be able to:
• Understand the theory of, and choice of statistical test for the analysis of hypotheses concerning genetic effects on phenotypes
• Use dedicated software for the analysis of genetic linkage in pedigrees
• Use dedicated or generic software for the analysis of genetic association between a trait or disease status and single locus
• Be able to estimate haplotypes in pedigree or unrelated samples using dedicated software, and understand the limitations of the methods
• Understand the concept of population stratification and be aware of the methods used to model this
• Demonstrate understanding of the issues surrounding the analysis of rare variants or private mutations, and the methods developed to circumvent these

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning








Approaches to Teaching and Learning:
Highly practical and hands-on, this module consists of an online distance course in statistics and statistical software for human genetic researchers. The predominant modes of delivery are short video segments and guided reading. Active learning is encouraged through asynchronous group discussions online, guided exercises and self-assessments with feedback, and project work with real-world and simulated data.

Requirements, Exclusions and Recommendations
Learning Recommendations:

Students are recommended to be familiar with the basic principles of inheritance and human genetics.

Students are recommended to have taken an introductory statistics course or module prior to enrolling which covered basic probability and hypothesis testing at a minimum, and ideally up to linear and logistic regression.

Module Requisites and Incompatibles
Not applicable to this module.

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment: End-of-semester Assignment Coursework (End of Trimester) n/a Standard conversion grade scale 40% No


Class Test: Online MCQ Week 12 n/a Standard conversion grade scale 40% No


Project: Practical report 1 Varies over the Trimester n/a Standard conversion grade scale 40% No


Project: Practical report 2 Varies over the Trimester n/a Standard conversion grade scale 40% No



Carry forward of passed components

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 assignments 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.