Gene mapping by association: genome-wide association studies

Question 1

What do genome-wide association studies (GWAS) typically examine?

Answer 1

Single nucleotide polymorphisms (SNPs) and copy number variants (CNVs)

GWAS involve testing associations between disease phenotypes and millions of marker loci across the genome.

Question 2

What is the purpose of comparing SNP allele frequencies in cases versus controls in GWAS?

Answer 2

To identify SNPs that may be located near genes contributing to disease susceptibility

A statistically significant difference in SNP frequencies suggests a potential association with the disease.

Question 3

What is the average distance between SNPs in a GWAS?

Answer 3

1 kb to 3 kb

This close proximity increases the likelihood that an identified SNP is near a disease-causing variant.

Question 4

What is a significant advantage of GWAS compared to traditional linkage analysis?

Answer 4

No assumptions about the biology of the disease are made when choosing genes to study

This allows for the testing of variants near every gene.

Question 5

What type of populations are typically used in GWAS?

Answer 5

Unrelated cases and controls

This approach is often easier than collecting family data.

Question 6

What can produce spurious associations in GWAS?

Answer 6

Ethnic stratification, imprecise disease definitions, inadequate sample sizes, and improper matching

These factors can lead to false associations between a disease and potential risk factors.

Question 7

What is linkage disequilibrium?

Answer 7

The non-random association of alleles at different loci

It allows researchers to infer the presence of one SNP based on another linked SNP.

Question 8

What is the International Haplotype Map Project (HapMap)?

Answer 8

A project that established linkage disequilibrium patterns for millions of SNPs in various populations

This aids in focusing gene-finding efforts on a reduced number of informative SNP markers.

Question 9

What limitation exists in most GWAS based on microarray data?

Answer 9

They typically omit rare variants with allele frequencies less than 5%

These rare variants may significantly contribute to disease causation.

Question 10

What must be closely matched in case-control studies to avoid spurious results?

Answer 10

Cases and controls

Proper matching is crucial for the validity of GWAS results.