Summary Oxford University FHS revision notes: Genomic Studies of Disease
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Course
Genetics and Evolution
Institution
Oxford University (OX)
My Oxford University notes for the FHS exam in Genetics and Evolution. Useful for Biology, Biomedical Sciences and Human Sciences. I achieved a first and multiple academic prizes. Includes descriptions of concepts and key references/experiments.
To GWAS or not to GWAS? Discuss the merits of Genome-Wide Association Studies.
What has society gained from genome-wide association studies?
Background
Genomics: the study of entire genomes.
Complex trait: bell-shaped
Extreme phenotypes lead to clinical conditions/disease traits
Variation due to age, sex, environmental factors (e.g. diet) and genetic variation
May be an effect of multiple common variants that slightly alter normal physiological processes
The complex path from genotype to phenotype complicates the identification of disease genes
Complex traits are defined as any trait which does not follow mendelian inheritance patterns, and
as such almost all traits can be considered complex traits, not only are they often polygenic, but
also there are often strong gene-environment interactions which affect the expressed phenotype
General pros
Genetic factors are particularly interesting because (unlike environmental factors) they are:
Inherited at birth
Essentially unchanging
(Often) easily measurable
-> makes inferences about causation particularly simple
Overarching reason to look for disease genes: understand the biology of disease/
Potential benefits
Disease diagnosis (genetic tests developed for >2000 human conditions, >990 currently available on
NHS)
Rational drug design (using structural information about drug targets (or their ligands) to design
effective drugs)
Detecting disease predispositions (e.g. BRCA1 -> breast cancer)
Gene therapy and control systems for drugs (insertion of genes to treat disease. 2012: gene therapy
for cystic fibrosis, delivery of wtCFTR (wt = wild type) using nebuliser -> modest but noticeable
(roughly 4%) improvement in lung function)
Pharmacogenomics “custom drugs” (decrease adverse reactions, custom treatments e.g.
MammaPrint tests RNA samples to assess risk of growth and metastasis in early-stage breast cancer
tumours -> informs treatment decisions)
Wider implications e.g. agriculture, livestock breeding and bioprocessing (e.g. disease-resistant
crops and animals)
General cons
STRANGER AND DE JAGER (2012): follow-on functionality studies to identify the proteins and pathways
through which disease is expressed must consider that cellular environments can change the regulatory
processes of DNA expression -> attempts to elucidate mechanisms and pathways must be performed in a
cell specific manner.
, In order to fully understand the genetic susceptibility of disease we must take a wider biological
perspective.
Basics
Narrow down a disease-causing mutation by assessing where it lies among recombination events
observed in one or more families
Traditional approach: the probability that a gene important for a disease is linked to a genetic
marker is studied through the LOD score, which assesses the probability that a given pedigree,
where the disease and the marker are cosegregating, is due to the existence of linkage (with a given
linkage value) or to chance
Pros: not that many markers needed (100s - 1000s); useful for monogenic disorders; linkage analysis was
successful in identifying the genetic basis of many human diseases in which the disease penetrance
resembles a simple Mendelian model e.g. Huntington’s disease (HD 1993), Cystic Fibrosis, some forms of
breast cancer (BRCA1 1993), Alzheimers (APOE 1991).
Limitations: not that good resolution; will only work for rare mutations with strong effects; for common
and complex diseases the results of genetic linkage studies proved hard to reproduce; recombination
frequencies can establish only approximate map positions by using nearby loci. This means that no matter
how precisely its recombination frequencies are measured, the physical position of a new mutant locus on
the chromosome will be too uncertain to identify the DNA region that encodes gene X.
ASSOCIATION STUDIES
GWAS (inc. merits and limitations)
Basics
Observational study of a genome-wide set of genetic variants in different individuals to see if any
variant is associated with a trait
Methodology for discovering genotype-phenotype associations
NOT a sequencing technology/approach
Process
Collect large numbers of case individuals (1000s)
o The number of SNPs needed for association testing depends on demographic factors (e.g.
ancestral population sizes)
Collect large numbers of controls (perhaps randomly from the population)
(3. Get consent)
Extract DNA
Genotype individuals at lots of markers
Throw away data – poor quality samples, poorly genotyped SNPs
At each SNP do a test for allele frequency difference between cases and controls (chi squared,
logistic regression)
Look for small p-values (how small)?
Pros: more powerful than linkage studies for the study of complex disease.
Limitations
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