LEC46: Genetics of Complex Disease

Question 1

what does the spectrum of disease etiology explain?

Answer 1

disease etiology ranges from conditions w/ purely genetic, single causes (hemophelia, CF - single gene memdelian disorders) to totally environmental (motor vehicle accident)

multifactoral causation, when multiple genetic loci in varying contributions contribute to complex disease risk, is most challenging to study re: overall risk of disease development

Question 2

difference between mendelian model and complex disease model re: “genetic” disease

Answer 2

mendelian: gene mutations are **causal - sufficient and necessary - **to cause disease

complex disease: gene muation/variation **predisposes **to certain disease, or increases chances; not sufficient or necessary

Question 3

how can one determine heritability?

Answer 3

genetic phenotype / total variance = degree of genetic determination = heritability

Question 4

what kind of study can determine if a phenotype of clinical interest is dependent on genetic variants’ heritable component?

Answer 4

family studies

compare heritability in monozygotic twins vs. dizygotic twins

MZ 100% twins share genes, intrauterine environment, household

DZ twins share 50% genes, 100% intrauterine environment, household

thus can **compare heritability btwn twin types **

Question 5

formula for heritability estimates w/ twin studies?

Answer 5

h² = 2*(r²_MZ- r²_DZ)

heritability estimate = correlation between concordance of having disease btewn monozigotic and dizigotic twins

Question 6

what is the heritability range?

Answer 6

0 < heritability < 1

heritability = 0 if trait is not genetic at all (offspring resemble general population more than they do the parents)

heritability = 1 if trait is totally genetic (offspring resemble parents more than they do the general population)

Question 7

if you think your phenotype is heritable, how can you test that hypothesis?

Answer 7

candidate gene approach:

1) look for candidate gene in known pathways and then
2) knock out that gene in animals w/ similar phenotypes

and

3) see effect of knock out

Question 8

how is insulin signaling pathway an example of the candidate gene approach?

Answer 8

to study diabetes, can look at genes involved w/ insulin signaling pathway, study how/if ligand binding works/is implicated

can select for candidate genes based on knowledge of pathway

Question 9

how does candidate gene study via genetically altered animal model work?

Answer 9

knock out insulin receptor to study its impact on growth/life

see at birth, IR-deficient pups are indistinguishable from WT or heterozygous pups

after suckling, see major metabolic alternations begin

IR^-pups develop severe form of diabetes, growth retardation, skeletal muscle hypotrophy, death 1 week post-birth

Question 10

what is best way to test if a particular variant is sssociated w/ a disease of interest?

Answer 10

case-control association study testing frequency of a specific allele between genotypes at the genomic position of interest among disease cases and matched (age, ethnicity, gender) cohort of unaffected individuals

Question 11

how are SNP distributions among cases’/controls’ genotypes experimentally compared in case-control association study?

Answer 11

association mapping

obtain genotypes at genomic positions of interest (e.g. polymorphic variants in a candidate gene coding region or in regulatory elements) in a pool of cases and of controls

see if there are differences in specific allele frequnecy btwn cases and controls; this would suggest an association btwn disease and allele, whereas if have similar frequency between cases and controls, no association btwn disease and allele

Question 12

what does an association studie determine?

Answer 12

if the variant increases the risk of disease and it’s associated

helps under disease’s etiology

is not predictive of having disease or not based on the variant

Question 13

GWAS purpose?

Answer 13

study in which density of genetic marker is sufficient to capture a large proportion of the **common **vartion in the human genome in the population under study

so can genotype a large number of variants genome-wide

Question 14

what does GWAS use to do study?

Answer 14

collects allele frequency data from common variants distributed across the entire genome in large cohorts of cases and controls for diseases of interest

computers significance scores for each variant

Question 15

what are affymetrix and illumina?

Answer 15

platform for high throughput genotyping in a GWAS

Question 16

what results does GWAS produce?

Answer 16

OR: ratio in differences in the frequencies of having the disease, between cases and controls; aka the effect of having the SNP

accompanied by p-value

plotted on a Manhattan Plot

Question 17

describe a manhattan plot

Answer 17

plots computed significance scores for each variant tested in a GWAS as a function of genomic location, aka chromosome and position

each dot = the p-value for a statistical test for 1 SNP

b/c log scale, higher plot = lower p-value = more significant

horizontal line: genome-wide statistical significance; look at results above the line

Question 18

what did the advanced macular degeneration GWAS look at / find?

Answer 18

a common coding variant, Y402H, in the complement factor H (CFH) gene on chromosome 1 (1q31) increases the risk of developing AMD

studies estimated the odds ratio associated with this variant for all categories of AMD to be between 2.45 to 3.33

the odds ratios were higher, between 3.5 and 7.4, for advanced dry and wet forms of AMD

CFH inhibits the formation and accelerates the decay of alternative pathway C3 convertases and serves as a cofactor for the factor-1 mediated cleavage and inactivation of C3b

Question 19

how does sample size pose a challenge to GWAS?

Answer 19

need very large sample b/c have low threshold for p-values

if effect is small, = 1.2, means carrying variant increases your risk of disease by 20%

to detect such a rare variant, may need as many as 7,000 ppl in study

if effect size is large (i.e. OR=2.0), could have smaller sample size (i.e. n=1,000)

Question 20

are GWAS effective at explaining common variants? why/why not?

Answer 20

no

GWAS analysis of common diseases has shown that it explains only a few percent(s) of the genetic component of disease heritability, = “missing heritability”

aka other things are contributing to heritability that the GWAS cannot show

GWAS explains only a few genetic components

Question 21

what does the common disease-common variant vs. common disease-rare variant hypothesis explain?

Answer 21

why GWAS are only somewhat informative, and unhelpful for common diseases’ explanation

hypothesis is: common variants will explain phenotypes in common diseases

actually: we cannot explain lots of heritability

very rare variants that cause disease, we know about = mendelian disease

and common variants w/ small effect size have been described by GWAS

however GWAS cannot explain low-frequency variants w/ intermediate penetrance/effect size

Question 22

what are challenges of association studies?

Answer 22

1) require lare sample sizes (thousands of individuals) to attain significance
2) require phenotypically homogenous groups of cases & controls
3) sensitive to populations stratification
4) produce false positive results
5) hard to replicate

Question 23

what’s the most important predictive tool? why?

Answer 23

_family history _

family history is risk factor for many chronic diseases

family history useful to assess health risk, initiate interventions, motivate behavioral changes

lower cost, greater acceptability, reflection of shared genetic and environmental factors make it better than genomic tools

also see that genetic tests add very little predictive value compared to using clinical risk scores based on demographic, family history, and clinical/laboratory data

Question 24

challenges for next gen sequencing?

Answer 24

1) rare variants require larger sample sizes
2) millions of variants require multiple testing
3) lack of good annotation
4) limited statistical approaches exist to identity associations w/ phenotypes of interest
5) value comes from **analysis, annotation, and association **w/ other data; raw genetic data has little value

Question 25

how did GWAS reposition Crohn’s disease pharma work?

Answer 25

determined new target, IL23R gene on chromosome 1, significant in Crohn’s association study

used Stelara, drug for cirrhosis, for Crohns b/c targeted interleukin 12 and interleukin 23 - gave Crohns patients who’d been untreatable improvements in symptoms w/in 6 weeks

Question 26

PCSK9 / LDL

Question 27

explain how PCSK9 works re: LDL

what did GWAS show

Answer 27

LDL taken into cell for degredation by the LDL receptor

usually, LDL receptor is recycled back to cell surface to endocytose more LDL after takes in some LDL

PCSK9 is gene that causes degredation of LDL receptors

in people who have high levels of LDL in plasma, want to thus inhibit PCKS9 activity so that LDL receptors aren’t destroyed, and instead are recycled to endocytose/degrade more LDL

thus individuals w/ high cholesterol who have PCSK9 mutation have lower LDL eventually; aka could block PCSK9 in people with high cholesterol to uptake more LDL

Question 28

are SNPs predictive of ype 2 diabetes risk?

Answer 28

number of risk alleles counted in diabetes vs non-diabetes persons

lower genetic risk score calculated, = more likely to be a control

if have very high genetic risk count score, almost definitively a carrier w/ a genetic risk

Question 29

what does this show?

Answer 29

gene count does not predict as well for diabetes as framingham standards do

family heritability thus is most important predictor of inheritance, more than gene count score

Question 30

what can modify genetic risk/effect?

Answer 30

gene-environment interactions:

gene-diet - nutrigenomics

gene-drug - pharmacogenetics

gene-lifestyle - smoking, exercise

Question 31

what does FTO gene study show?

Answer 31

FTO: fat mass and obesity-associated protein

AA: carries 2 risk variants for obesity

GA: carry 1 risk allele

GG: not at risk

individuals w/ risk allele have highest BMI, those w/o risk allele have lowest BMI

however, those who’re carriers of 2 risk alleles benefit most from doing physical activity; those who’re homozygous or heterozygous for WT, no risk allele, do not benefit from exercise, to lower their BMI

Question 32

why is knowledge of genetic risk important/helpful?

Answer 32

1) can inform personalized medicine, leading to prediction of disease risk & prognosis or adapting therapy to individual patients
2) expands our understanding of biological pathways of disease, leading to development of targeted therapies