The Genetics Of Complex Diseases Flashcards
Complex disorders
Common Polygenic-> individual genetic variants of low penetrance Gene-gene or gene-environment interactions Multiple genes contribute to disease susceptibility Severity of symptoms/age of onset Aetiological mechanisms may differ Eg Heart disease Diabete Alzheimer's Bipolar Chrons
Mendelian disorders
Rare
Monogenci
Highly penetrant genetic variations
Clear models of inheritance
Familial tendency
Relative risk ratio-> risk in relatives of affected subjects/risk in population Sibling relative risk (ds) Cystic fibrosis ds 500, life time 0.05% Huntingtons ds 5000, life time 0.01% Coronary heart disease ds 2-7 T1DM ds 15 T2Dm ds 3 Alzheimer's ds 3 Bipolar ds 7-10 Chrons 17-34
Factors influencing relative risk
Shared environment factors Shared genes Use twin studies to differentiate T1DM mz twins 50% Chrons mz twins 50% dz 10%
Heritability
The phenotypic variability due to genetic variation T1DM 85% T2DM 30-70% Alzheimer's 60-80% Bipolar 80-90%
Human genome
3,000,000,000 nucleotide bases
95% of genome is interagenic-> som RNA used to dampen other RNA action-> not all RNA produces protiens
DNA variation
Polymorphism
Common
Variable number of tandem repeats-> highly polymorphic, function uncertain, valuable genetic markers
-> repeats can have no phenotypic effect, no difference in having a larger number of repeats->above 30 is unstable-> expansion or repeat during mitosis-> cause disease phenotype
Single nucleotide polymorphism-> over 10 million confirmed-> 1 per 500 bases-> many appear to have no function-> some effect encoded protein or gene regulation
Using Polymorphisms as genetic markers
Genetic variants with similar chromosomal positions are seldom separated by recombination at mitosis
Increased distance between snaps increased chance of recombination
Co inheritance of a genetic marker and a disease suggests that the marker is close to a disease gene
Genome wide screening
Panel of Polymorphisms spread throughout genome
Genotype affected and unaffected individuals
Identify genetic Polymorphisms which segregate with disease
Assume that causal genetic variants lie close to this marker
-> genome wide linkage analysis (family based)
-> genome wide association screen (case control)
Genome wide linkage screens
Multicase families
VNTR marker genotyping -> success in families with uncommon monogenic variants of the disease
Monogenic Alzheimer’s-> deposition of amyloid plaques in brain -> early age onset-> rare, highly penetrant mutations in amyloid
Monogenic T2DM-> MODY-> 2-5% MODY genes identified so far-> all affect insulin by B cells
Monogenic coronary vascular disease-> related to lipoprotien metabolism, familial hypercholesterolemia-> LDL R gene mutation -> myocardial infarction before 50 in males
Less success in complex disease
HLA regions of T1DM
Apo E in Alzheimer’s
Genes with unusually large effect
Patterns of complex diseases
Minority of affected individuals -> multicast families, early onset severe disease, monogenic inheritance -> genes with large effect size
Majority of individuals-> gene-gene and gene-environment interaction, phenotypic diversity, genes with individually small effect size, not detectable by genome wide linkage analysis
Genome wide association screening
Compare frequency of gene variants, usually SNP’s in cases and controls
Doesn’t require multicast families
Compare SNPs across pop
Don’t need to do all of them due to correlation between nearby SNPs
May find causative SNP and associated SNP
Linkage disequilibrium
Combinations of alleles in a segment of the genome are know as halo types
Nearby SNPs not inherited independently -> limited number of halo types observed
Geneotyping a carefully select panel for tagSNPs can capture most of the variation in a single halo type block-> requires map of genetic variation and linkage disequilibrium -> hap map project-> map of human genetic variation in 4 populations
Genotyping platforms
High throughput array technology genotyping put to 1 million SNPs in a single experiment
Identified 5 new genes for T1DM, 3 for T2DM
GWAS design
Case selection-> defined phenotype
Control selection-> match pop, possibility for miss classification bias! issue for common traits
Biggest sample size possible -> more likely to detect complex diseases with small effect sizes
Population substructure-> minimise phenotype