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

L31 Functional Human Genetics 1 Flashcards

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USMLE® Step 1 flashcards Biology 101 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
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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)
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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
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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

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5
Q

Heritability in a changing environment

A

see onenote

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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
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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
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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
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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)

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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
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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
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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

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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
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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
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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
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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)

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