Common Genetic Diseases

Question 1

Describe the difference between rare monogenic disease and common polygenic disease

Answer 1

rare monogenic:
Monogenic diseases (table 1) are rare diseases attributable to genetic variants with large effects on disease status. Because of the high penetrance of such variants, the disease is typically inherited in a classical Mendelian fashion

common polygenic: On the other end, we have polygenic diseases, which are diseases influenced by the combined effects of many genes. This end of the spectrum includes more common diseases in the population, such as coronary heart disease and Type 2 diabetes

Question 2

Describe the key differences between the genetic variation leading to monogenic disease compared to the genetic variation leading to common disease

Answer 2

• common polygenic disease has interaction with the environment and have multiple common genes and inherited SNPs
• monogenic diseases are manifested through one gene, often through a SNV with typical medelian inheritance

Question 3

Describe how we measure the contribution of genetics to a disease using twin studies as an example

Answer 3

MZ = 100% shared geentic variation
DZ (2 eggs fertilised by different sperm simultaneously) = 50%
h^2 = 2 x (MZ correlation – DZ correlation)

Question 4

Describe the basic concepts underlying a Genome-Wide Association Study (GWAS)

Answer 4

• genotype thousands of SNPs
• assumption free genome wide study
• presence in majority of cases and not in controls could suggets candidate
• use SNP microarrays with probes complementary to SNPs of interest which have flourescence tags
• eg: if allele of interest is ‘G’
  AA = 0
  AG = 1
  GG = 2
• linkage disequilibrium = the difference between the observed frequency of a particular combination of alleles at two loci and the frequency expected for random association. (high LD = high occurence of co-segregation of specific SNPs)

Question 5

Describe how a GWAS can be used to identify genetic variants associated with common disease

Answer 5

• gwas
• manhattan plot (-log10 plot against position in chromosome)
• analyse SNPs with and refine candidates based on statistical significance
• greater the quantity of data = smaller threshold to refine from false positives