Identification of Disease-Causing Genetic Variants BIOL5300

  • Academic Session: 2023-24
  • School: School of Molecular Biosciences
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No

Short Description

Recent advances in our understanding of the genetic basis for predisposition to complex/multifactorial traits and disorders have enabled genetic mapping studies on a genomic scale to be followed through to identification of causal variants and characterisation of their molecular effects on phenotype. We shall provide theoretical and hands-on practical experience of how genetic analysis works in the post-genome era using examples from local research studies. The course will cover the genetic basis for traits and disorders, the analysis of genetic influences at the genomic level, the identification of variants underlying quantitative traits and the prediction of coding variant effects on protein structure and function.


The course will run in one of the four designated timetable blocks in Semester 2. Contact teaching for this course will take place over 3 weeks and will consist of a mixture of lecture, tutorial and computer practical sessions.

Requirements of Entry


Excluded Courses





a) a written assignment in the form of an essay item (25%);

b) a written assignment in the form of three items combined into a multipart report on analysis of computer lab practical data (75%; items will be aggregated to calculate the grade for this component).

The combined length for the two components will be 3000 words (+/-500 words), with further guidance given for word limits for component a).

Course Aims

This course aims to equip students with extensive, critical and integrative understanding of the route by which genetic predisposition to complex traits and disorders can be dissected through the application of a range of computing approaches. Students will have the chance to put these analysis concepts and approaches into practice during extensive computer lab practicals and will develop analytical skills, practical computing skills and the ability to assess critically, and in the appropriate biological context, procedures for the identification and characterisation of causative genetic variants.

Intended Learning Outcomes of Course

By the end of this course, students will be able to:

■ Critically discuss the basis for genetic predisposition to complex traits and disorders, and the role of linkage disequilibrium in procedures for locating disease-causing variants within the genome;

■ Creatively apply and critically discuss approaches for the analysis of genome-wide association study (GWAS) and candidate region association study data;

■ Critically discuss the basis for and approaches used in identifying likely genes affected by quantitative trait loci (QTLs) from gene expression data, identifying causative regulatory variants (quantitative trait nucleotides - QTNs)  and in predicting the effects of coding and other variants on protein structure and function;

■ Use computers creatively to execute a variety of planned analyses in several areas relating to analysis of genetic association and QTL mapping/gene expression data and in relation to homology modelling of protein structure and other tools for predicting protein functional changes;

■ Use computers creatively to execute a variety of planned genetic and statistical analyses using R;

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.