Multifactorial traits Flashcards
How do we know if a genetic component is non-Mendelian?
Twin studies (concordance rate)
Common multifactorial diseases involve
many DNA changes, each with a predisposing effect
A gene variant is
a single nucleotide polymorphism (SNP) that is associated with an increased risk of developing disease
it is present more frequently in those with the phenotype
Polymorphism
the presence of genetic variation within a population, upon which natural selection can operate
single nucleotide polymorphism
single base change
most common genomic variation
a substitution of a single nucleotide that occurs at a specific position in the genome, where each variation is present to some appreciable degree within a population
can predispose to conditions
The additive model is
a linear effect whereby if you have two copies of the disease allele, your risk is doubled
(think of graph)
GG
examples of common multifactorial quantitative traits
BMI, height, BP, CRP, LDL, fasting glucose
quantitative traits have more frequent gene variants that are specifically associated with higher/lower levels of the trait
Additive model applies, though environment ultimately controls (e.g. obesity)
The heritability of multifactorial diseases and continuous traits is due to
a large number of variants (more variants = more polygenicity)
GWAS
genome wide association studies
Hypothesis Free Approach - no prior knowledge
Types of GWAS
SNP-chip microarrays
whole genome sequencing (millions of SNPs)
hybrid alternatives (imputation)
SNP-chips
500-1m SNPs
high-density oligo arrays containing up to several million probes of smaller length, which allow for the genotyping of hundreds of thousands of selected SNPs across all chromosomes in a single reaction
cheap but limited to variation known about
whole genome sequencing
Whole genome sequencing is ostensibly the process of determining the complete DNA sequence of an organism’s genome at a single time. This entails sequencing all of an organism’s chromosomal DNA as well as DNA contained in the mitochondria
capturs more variation than SNP microarrays including variation which might not be catalogued BUT expensive
Hybrid alternatives
Imputation: refers to the statistical inference of unobserved genotypes.
cheap but based on statistical modelling - assay preferred for very rare variation
the point of GWAS is to develop
personalised medicine
DNA variant > molecular biology > physiology > disease
Predicting disease with genetic variants
ROC is the receiver operating characteristic
The ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings
area under the curve (AUC) is the measure of accuracy: AUC=50 no better than flipping coin
AUC=0.8 clinically useful
Autoimmune diseases are heavily influence by
HLA
human leukocyte antigen
on chromosome 6
e.g type 1 diabetes exception to ROC curve
Linkage disequilibrium
the occurrence in members of a population of combinations of linked genes in non-random proportions.
Linkage disequilibrium creates
correlation among genetic variants
Missing heritability is
the fact that GWAS has identified 1000s of genetic associations for disease risk and quantitative traits
but most of the observed effects are small: 1.1x increase in type 2 diabetes for every risk increasing allele
Reasons for missing heritability (4)
larger sample sizes (resolution?)
rare/structural variants not tested
genetic interactions (epistasis, environment)
over-estimation of heritability
In contrast to Mendelian diseases which are largely caused by protein coding changes, complex traits are mainly driven by
non coding variants that affect gene regulation
Pleiotropy and epistasis
Pleiotropy:
a single gene at a give locus affects many phenotypes
e.g. albinism affects hair, eye and skin pigment
Epistasis:
2 or more alleles at one locus affect the expression of genes at a different locus
