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Unit outline_

BSTA5013: Statistical Genomics (SGX)

Semester 2, 2024 [Online] - Camperdown/Darlington, Sydney

Statistical genomics is the application of statistical methods to understand genomes, their structure, function and history, in many different scientific contexts, including understanding biological mechanisms in health and disease. Statistical genomics is characterised by large datasets, high-dimensional regression models, stochastic processes, and computationally-intensive statistical methods. The aim of this unit is to learn about relevant biology and terminology, to understand the most important mathematical models and inference methods in statistical genetics, to be able to test for association between genetic variants and outcomes of interest, and to use genome-wide statistical models to help understand the genetic mechanisms underlying a trait and to predict outcomes. The statistical package R will be used to perform regression-based analyses of genetic data.

Unit details and rules

Academic unit Public Health
Credit points 6
Prerequisites
? 
BSTA5004 and (BSTA5210 or BSTA5211 or BSTA5007)
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Erin Cvejic, erin.cvejic@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Assignment Assignment 1
Written assignment covering Modules 1 and 2
20% Week 04
Due date: 23 Aug 2024 at 23:59
6-8 pages
Outcomes assessed: LO1 LO4 LO8
Assignment Assignment 2
Written assignment covering Modules 3 and 4
20% Week 07
Due date: 13 Sep 2024 at 23:59
6-8 pages
Outcomes assessed: LO1 LO8 LO2 LO5 LO6
Assignment Assignment 3
Written assignment covering Modules 5 and 6
20% Week 11
Due date: 18 Oct 2024 at 23:59
6-8 pages
Outcomes assessed: LO1 LO8 LO7
Short release assignment Take-home written assessment
At-home written assessment available over 4 days covering all unit content
40% Week 13
Due date: 01 Nov 2024 at 17:00
10-12 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8

Assessment summary

  • Assessment 1 to 3 are written assignments covering Modules 1-2, 3-4, and 5-6 respectively. 
  • Assessment 4 is a short-release written assessment available over 4 days assessing all unit content.

Detailed information for each assessment will be provided on Canvas. 

Assessment criteria

Grade

Mark Range

Description

AF

Absent fail

Range from 0 to 49

To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level. 

FA

Fail

Range from 0 to less than 50

To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard.

PS

Pass

Range from 50 to less than 65

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard.

CR

Credit

Range from 65 to less than 75

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard

D

Distinction

Range from 75 to less than 85

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard.

HD

High distinction

Range from 85 to 100 inclusive

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 50%.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Multiple weeks Module 2 - Genetic association analysis including GWAS Independent study (20 hr) LO3 LO4 LO6 LO8
Module 4 - Sequence analysis using hidden Markov models Independent study (20 hr) LO2 LO8
Module 5 - Introduction to Transcriptomics Data Independent study (20 hr) LO7 LO8
Module 7 - Introduction to single-cell omics data Independent study (20 hr) LO7 LO8
Week 01 Module 1 - Basics of human genetics and genetic epidemiology, Review of R Independent study (10 hr) LO1
Week 04 Module 3 - Heritability and genomic prediction Independent study (10 hr) LO5 LO6
Week 09 Module 6 - Introduction to Epigenetics Data Independent study (10 hr) LO7 LO8
Week 12 Module 8 - Genomic Medicine Independent study (10 hr) LO3 LO7 LO8

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Textbook: Handbook of Statistical Genomics (Eds: Balding, Marioni and Moltke, 4th ed, Wiley 2019).

This text has 36 chapters summarising the start-of-art in the field, as well as an extensive glossary. The Handbook is available online through your university library, please check as soon as possible that you can access it. The Handbook is huge at well over 1,000 pages and we will only examine a small fraction of it in this course, but you should take the opportunity to browse other chapters/sections to get a fuller understanding of the field.

 

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Describe core mechanisms and central dogma of genetics
  • LO2. Perform sequence analysis using hidden Markov models
  • LO3. Access genomic data from public databases
  • LO4. Perform a genetic association analysis, including the assessment of possible confounding
  • LO5. Explain the concept of heritability and its estimation
  • LO6. Use genome-wide SNP data to develop prediction models
  • LO7. Explain key features of data and statistical models used in the fields of transcriptomics, epigenetics, and single-cell omics
  • LO8. Effectively communicate results of statistical analyses in genomics and related areas

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

This unit was last delivered in Semester 2, 2023. Based on student feedback from this delivery, the number of tutorials will be increased from three to five tutorials.

This unit is delivered externally through the Biostatistics Collaboration of Australia (BCA). 

Software Requirements
The statistical software package R is used in this unit of study. 

Required Mathematical Background:
Students should be familiar and comfortable with the statistical concepts and models covered as part of MFB (or MBB and PDT), PSI, and RM1 (or LMR and CDA). 

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.