Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Human and Medical Genetics > L31 Functional Human Genetics 1 > Flashcards

L31 Functional Human Genetics 1 Flashcards

You may prefer our related Brainscape-certified flashcards:
Biology 101 flashcards USMLE® Step 1 flashcards
1
Q

The case of the missing heritability

A

see onenote

  • traits are more complicated than we thought
  • decyphering how genotypes impact phenotypes is hard
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What do we mean by heritable?

A

see onenote

Heritable traits

  • E.g. height, eye colour
  • Proportion of phenotypic variation due to genetic variation in a population
  • Broad sense heritability H2
  • Narrow sense heritability h2
  • Trait needs to be variable for it to be heritable (due to the way we define heritability)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

How much of a trait is due to genetics?

- how do we measure

A

see onenote

  • Partition phenotypic variation into genetic and environmental variation
  • Twin studies
  • Monozygotic twins - Vg = 0, any differences would be due to the environment
  • Dizygotic twins don’t have the same genotype but presumably they have the same environment
  • comparison between parent and offspring
  • More heritability, more additive variance = steeper slope
  • E.g. h2 = 0.8, 80% of variation in height is due to genetics
  • Heritability of trait changes as environment changes, heritability is not a fixed value through time and space
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Broad-sense and narrow-sense heritability

A

see onenote

Vg = Va + Vd + Vi
Vi = epistatic variance

narrow sense heritability
h^2 = Va/Vg

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Heritability in a changing environment

A

see onenote

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Heritability estimates for some complex traits

A

see onenote

Simple (mendelian): 1 locus

Complex

  • oligogenic: 2-10 loci
  • multigenic: 10-100 loci
  • polygenic: 100+ loci
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Means of going from phenotype to genotype

A

see onenote

linkage mapping

  • Use a segregating pedigree to construct a linkage map
  • Requires extensive pedigree to reach significance
  • Trait with complex architecture difficult to identify causal variants

candidate gene association mapping
- test one or a handful of pre-selected loci for association with trait in cases and control

GWAS

  • Test for association with many markers e.g. SNPs
  • SNPs are either the disease causing variant or is in LD with the disease causing variant
  • Region in LD with the SNP is significant in the trait
  • Cryptic carriers in control group will decrease power e.g. don’t have diabetes yet but will soon get it
  • Super control = low insulin resistance values (not resistant to insulin), probably won’t get diabetes
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Pitfalls of linkage and association mapping

A

see onenote

linkage mapping requires pedigrees to reach significance
- suited to monogenic disorders

candidate gene mapping relies on a prior assumptions about trait aetiology and causality

  • meaningless if loci is misidentified
  • much contributing variation can be missed
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

GWAS for human traits

A
  • allows us to test millions of polymorphisms for association with our chosen trait/disease
  • we should be able to identify statistically significant associations between variants and trait in the sampled population

crucial assumptions
- significant SNPs are either the disease-causing variant (rare) or in LD with the disease causing variant (common)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

The HapMap empowered GWAS

A

see onenote

  • to successfully conduct GWAS we need a precise map of the LD structure of the genome to map traits back to genotypes
  • by including trios (father-mother-child), HapMap enabled us to generate one
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Inferring haplotypes and linkage blocks

A

see onenote slides

  • phasing is the act of deducing haplotype structure from genotype data
  • once we have identified haplotypes empirically using our trios, we can use probability to phase unrelated samples
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

From haplotypes to LD

A

see onenote slides

  • haplotype blocks can be formalised into LD blocks
  • strength of LD is capture by r^2

r^2 = the square of the correlation coefficient between two loci

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Why is accurate phasing important?

A

see onenote

  • GWAS rely on tag SNPs
  • tag SNP = a SNP that summarises variation within an LD block
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Case-control GWAS - the classic design

A

see onenote

  • compare cases and control sampled from the same population
  • cryptic carriers in control group will also decrease power e.g. pre-symptomatic individuals
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Association testing

A

see onenote

The success of GWAS depends on”

  • well differentiated case and controls drawn from the same population
  • sufficient statistical power to detect significant associations
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Matters of power and significance

A

see onenote

  • community thresholds for significance are now p<5x10^-8
  • should lower the rate of false positives to almost zero
17
Q

Manhattan plots

A

see onenote

  • provide an easy to visualise the results
18
Q

Early GWAS was promising

A

Mycordial infarction

  • age-related macular degeneration
  • identified a small number of loci with large effects on disease risk
  • these loci could explain a substantial fraction of the h^2 estimates
19
Q

Was the human genome project worth it?

A

see onenote

20
Q

Wellcome Trust Case Control Consortium Phase 1

A

see onenote slides

14000 cases across seven diseases, plus 3000 shared healthy controls

21
Q

The GIANT consortium

A

see onenote

Defining the role of common variation in genomic and biological architecture of adult human height
- subsequent GWAS failed to replicate early successes in the field,, even as sample sizes increased

22
Q

GWAS catalog

A
  • publicly available curated resource of all published GWAS and association results
23
Q

The heritability of most studied traits remains poorly explained by GWAS hits

A

see onenote

some possible explanations:

  • rare variants of large effect
  • low penetrance
  • epistasis
  • etc.

GWAS often gives little insight into the biological mechanism underlying that association

24
Q

A matter of power

A

see onenote

  • early GWAS were underpowered to detect most associations

strength of association between trait and genotype depends on:

  • effect size of variant
  • penetrance of variant
  • freq of variant
  • quality of case/control separation

all of these interact in complex ways

  • a rare variant of large effect will not be detected in a GWAS, unless the sample size is very large
  • nor will a common variant of large effect but low penetrance
25
Q

A closer look at height

A

see onenote

  • many of the SNPs and loci were in genes that had been previously implicated in skeletal disorders

Human and Medical Genetics (32 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions