Heritability and Risk Factors Flashcards
What are the 2 types of risk factors for developing T2D and CVD?
- Modifiable: Diet, physical activity, smoking, high BP, high cholesterol, high body fat or body weight
- Non-modifiable: Race, Gender, Heredity/ family history of T2D or gestational diabetes, age over 45 years
Is hypercholesterolemia a modifiable or non-modifiable risk factor?
a hereditary (non-modifiable) risk factor but is modifiable with medication
How are risk factors identified?
Cross-sectional studies
* Measure variables of interest at one specific time point
* Derive associations (linear regression analysis): eg. BMI and glucose tolerance
Population studies
* Series of cross-sectional studies in the population
* Study a group of people from the general population: eg. Distance between school and fast food restaurants vs children obesity
Prospective cohort studies
* Measure variables of interest at study entry and over a period of time (i.e. outcome at defined follow-up intervals): Follow a cohort of children over the next 10 years
Others: Retrospective studies, …
correlation does not imply ….?
causation
* correlation is simply a relationship where action A relates to action B—but one event doesn’t necessarily cause the other event to happen.
* Likely there are other reasons invovled in the outcome.
What are common limitations in studies assessing risk factors in genetics?
- Biases occur in the planning, data collection, analysis, and publication stages of research (entire process): In research, bias occurs when systematic error [is] introduced into sampling or testing by selecting or encouraging one outcome or answer over others.
- Confounding occurs when an extraneous variable in a statistical model correlates with both the dependent variable and the independent variable → i.e. when an observed association is due to a third factor which is independently associated with both the outcome of interest and the exposure.
Heritability: Mendelian vs Complex inheritance
- Mendelian: a single genetic defect, usually highly deleterious → dominant/ recessive such as brown eyes vs. blue eyes
- Complex: polymorphisms (SNPs) with weak effects that, alone or in combination, modulate the risk of diseases (most genes fall into this)
What are SNPs that result in gene variants with resulting biological consequences?
Protein-coding region variant
* Non-synonymous (C/D): Changes one amino acid and potentially protein activity
* Gain of Stop codon (I/J). Deletion of a part of the protein
* Frameshift variant: Insertion, introduce AA that shifts everything (K/KL) or deletion, removing a base and causes shift (M/) which modify the amino acid sequence after the variant.
Synonymous (G/H): No change in sequence. No alteration of protein function
Non-protein coding region variant:
* 5’ region variant (A/B): Can alter promoter activity
* Intron region (E/F): Can alter RNA splicing
* 3’ region variant (N/O): Can alter mRNA stability
How are gene variants determined?
Each identified variant carries a small but significant increased risk of developing the disease
* Take a control population who does not have disease (hypercholesterolemia) and then sequence entire genome then take people with disease (hypercholesterolemia) being studied and sequence their entire genome.
* Find where the variation occurs which would be the SNPs and there will probably be in the thousands.
* Look at where the mutation is and then determine if the difference between those without disease and mutation and those with disease and mutation is significant or not
* The graph then contains all the identified SNPs and can then report the p-value to find significance (< 0.05). Typically the SNPs seen higher up have greater association with the disease.
* Would then do further studies on the identified gene to figure out where it is impacting the metabolic pathways
regional association study
How are associations of genetic variants (SNPs) with trait investigated and what are the limitations/ struggles of this)?
Can identify the different types of SNPs on single nucleotides and those that are associated with the disease (black ones)
What are some of the problems in identifying SNPs?
SNPs travel together so the SNP actually associated with the disease may not be the one that is known
* black SNP associated with disease but in reality it is travelling with others so possible that the star SNP is what is causing the disease by no longer being able to be activated
* SNPs travel together so may know the black ones but then other ones travel with it (grey) which don’t really know about and white is very rare and often come alone and so often ignored
* DNA is 3D structure so though that some SNPs are not even on straight line and travel far away
* Might get a gene that dont even know what it does
what is the most common type of gene variant?
Single nucleotide polymorphisms (SNPs) are the most frequent type of variation in the genome (50M) and are conserved during evolution
Forward vs. reverse genetics
- forward genetics: Look for diseases and take population and sequence then find genes associated with it → May not know exactly what they do though, as it may be associated with something else and some are unknown and dont know how they ar expressed or what they do so need functional assay to better understand them
- reverse genetics: Have clue about gene manipulation disease and trying to reproduce genotype
Why is estimating a dose-relationship between gene variant and disease important?
To determine how much of risk the mutation may be
* If only 1% not that much
Genetics of T1D
Several regions of the genome have been linked to type 1 diabetes (>20); not everyone who carries the gene will have the disease
* The most studied region, called insulin-dependant diabetes mellitus (IDDM1), involve the human leukocyte antigen (HLA) genes (MHC proteins; pertaining to immunity)
* The IDDM2 locus contains the insulin gene
Genetics of type 2 diabetes over the years
Tons of gene variants associated with T2D discovered over the years and still being discovered
* Shows the genome with all the chromosomes and see SNPs and where located and statisical relevance