association analysis

Question 1

Genetic Association

Answer 1

presence of a variant allele in the genome at a higher frequency in unrelated subjects with a particular disease of interest/trait (cases) compared to those that do not have the trait/disease (controls)

Question 2

with disease =

Answer 2

cases

Question 3

without disease =

Answer 3

controls

Question 4

Controls vs case in association analysis

Answer 4

must be identical to the cases APART from not having the disease = well matched

e.g. same age, sex, ethnicity, location etc.

Question 5

Difference in variant frequency between cases and controls

Answer 5

in cases, the gene variant is at a higher frequency than in the controls and is associated with the disease

Question 6

What confirms the strength of association between a gene variant and the disease?

Answer 6

Statistics e.g. p-value

Question 7

Quality case-control genetic studies must have:

Answer 7

• large numbers of well defined cases (1000s)
• equal numbers of matched controls
• reliable genotyping technology (SNP array)
• standard statistical analysis (PLINK)
• positive associations should be replicated

Question 8

What allows us to capture genetic diversity?

Answer 8

The use of genetic markers

Question 9

Features of an ideal genetic marker (e.g. SNP)

Answer 9

• polymorphic (more than 1 form)
• randomly distributed across the genome
• fixed location in genome
• frequent in the genome
• frequent in the population
• stable with time
• easy to assay (genotype)

Question 10

How are SNPs generated?

Answer 10

though mismatch repair during DNA replication

Question 11

Possible location of an SNP

Answer 11

Gene (coding region)

• no amino acid change (synonymous)
• amino acid change (non-synonymous)
• new stop codon (nonsense)

Gene (non-coding region)

• promoter: mRNA and protein changed
• terminator: mRNA and protein changed
• splice site: altered mRNAm altered protein

Intergenic Region
-98% of SNPs in this region

Question 12

dbSNP

Answer 12

online database of SNPs and multiple small-scale variations that include insertions/deletions, microsatellites and non-polymorphic variants

Question 13

Minor allele frequency (MAF)

Answer 13

the frequency of the less common variant in a population

SNP will have two alleles:

• major allele
• minor allele

they must add up to 1

Question 14

Genome Wide Association Studies (GWAS)

Answer 14

is the presence of an allele. at a higher frequency in unrelated subjects with a particular trait, compared to those that do not have the trait

• recruit large numbers of cases and controls
• SNP markers are used across the whole genome, and these are genotyped using SNP microarrays.
• We look for association between disease and each marker by doing a chi-square test

Question 15

Steps of GWAS

Answer 15

Obtain DNA from people with disease of interest (cases) and unaffected people (controls)

Run each DNA sample on a SNP chip to measure genotypes at 300,000-1,000,000 SNPs in cases and controls

Identify SNPs where one allele is significantly more common in cases than controls
-the SNP is associated with disease

Question 16

How is GWAS data presented?

Answer 16

on a Manhattan plot

Question 17

what is a Manhattan plot in relation to the GWAS results

Answer 17

The end result from GWAS that shows the score for each SNP marker tested in the study, across each chromosome in the genome - a peak indicates that the location of that particular SNP may be very close to a disease-related locus - low p-value = high peak –> reject null hypothesis for that SNP - shows evidence of correlation of each marker tested with a disease phenotype across the genotype

*highest point is MOST significant SNP with disease state

X-axis is position of SNP on chromosome

Y-axis is -log10(p-value) of the association between each marker and the disease, calculated using a chi-squared test

Question 18

Why are chromosomes more solid at the bottom of the Manhattan plot?

Answer 18

because the vast majority of SNPs are not associated with disease

*associations are further up the graph

Question 19

What does the peak on a Manhattan plot show?

Answer 19

The peak does not identify the gene causing the disease, instead it only identifies the genomic REGION associated with the disease and this is usually very small (<100kb)

-if you zoom in on an SNP with strong disease association (high up in plot), you will find lots of other associated SNPs in the adjacent gene and therefore more work has to be done to work out which SNP is correct and where the actual disease-causing variant is

Question 20

Disadvantage of GWAS + what is meta analysis

Answer 20

Difficult to do very large association studies (>1K cases), and therefore meta-analysis of GWAS is done to combine statistical results from multiple smaller studies

Pre-experiment: consortium
Post-experiment: meta analysis

Question 21

Problems with GWAS

Answer 21

Expensive

requires large sample size,

only explains a small part of the disease phenotype, meaning their contribution to the genetic component of the disease is estimated to be low (<5%), could be because:

• many common SNPs of small effect
• rare SNPs
• don’t look at copy number variation or epigenetic variation

Question 22

Common Obesity GWAS

Answer 22

Obesity is strongly genetic

Though GWAS we can clearly see genes associated with obesity