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Lernmaterialien für Week4 an der Vrije Universiteit Amsterdam

Greife auf kostenlose Karteikarten, Zusammenfassungen, Übungsaufgaben und Altklausuren für deinen Week4 Kurs an der Vrije Universiteit Amsterdam zu.

TESTE DEIN WISSEN

How can SNPs be mapped to genes?

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All of the above

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multifinality

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= pleiotropy

= the same genotypic networks may lead to different clinical outcomes



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What are other factors that complicate polygenicity?

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  • Gene-gene interactions
  • Gene-environment interactions
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Additive vs. non-additive effects

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  • Additive: the respective contributions of all the genes are summed to get expression of a trait or gene
  • Non-additive: gene-gene interactions are what drive the expression of a trait or disease
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How does the convergence of multiple SNPs manifest/

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  • The genes on which the SNPs are situated may contribute to a specific biological function or pathway;
  • May be expressed in a specific tissue type; or
  • May be expressed in a specific cell type
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On which levels can convergence occur?

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  • SNP-gene level: all SNPs are part of the same gene;
  • Gene-function level: all genes are part of the same function;
  • Gene-tissue level: all genes are expressed in the same tissue
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Key observations that lead to the formulation of the omnigenic model

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  • Common variants tend to have small effects and rare variants tend to have large effects
  • Most GWAS hits are in noncoding regions -> regulatory function
    • There must be a small set of core genes and a larger set of regulatory genes
  • GWAS hits seem to spread broadly across the genome and tend not to converge in pathways
    • We haven’t characterized all biological pathways
  • Genetic variants that have broad functions are more often associated with disease than those that have more specialized function
    • More broadly expressed in multiple tissue types
    • If these genes are broken, they will have more widespread and devastating effects than genes only expressed in a single tissue type.
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TESTE DEIN WISSEN

What is the relationship between rare variants and the omnigenic model?

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  • Rare variants have much larger effects than common variants (as they haven't yet dropped out of the genome due to selective pressure)
  • Therefore rare variants are more likely to be on one of the core genes directly influencing/disrupting a biological function in a disease, as the main variant that causes the phenotypic effect
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​How do we account for confounding among gene sets when using the MAGMA software?

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In a linear regression framework in MAGMA, we add confounders as covariates to account for their influence.

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What are possible confounders that have to be dealt with statistically?

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- Linkage Disequilibrium (between SNPs) 

- nr. of genes (in a gene set) 

- nr. of SNPs (in a gene) 

- background h²



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What is background h²?

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= overall heritability of a trait
(compared to heritability of each SNP for a trait)

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equifinality

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= heterogeneity

= different genotypic networks lead to the same outcome




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Q:

How can SNPs be mapped to genes?

A:

All of the above

Q:

multifinality

A:

= pleiotropy

= the same genotypic networks may lead to different clinical outcomes



Q:

What are other factors that complicate polygenicity?

A:
  • Gene-gene interactions
  • Gene-environment interactions
Q:

Additive vs. non-additive effects

A:
  • Additive: the respective contributions of all the genes are summed to get expression of a trait or gene
  • Non-additive: gene-gene interactions are what drive the expression of a trait or disease
Q:

How does the convergence of multiple SNPs manifest/

A:
  • The genes on which the SNPs are situated may contribute to a specific biological function or pathway;
  • May be expressed in a specific tissue type; or
  • May be expressed in a specific cell type
Mehr Karteikarten anzeigen
Q:

On which levels can convergence occur?

A:
  • SNP-gene level: all SNPs are part of the same gene;
  • Gene-function level: all genes are part of the same function;
  • Gene-tissue level: all genes are expressed in the same tissue
Q:

Key observations that lead to the formulation of the omnigenic model

A:
  • Common variants tend to have small effects and rare variants tend to have large effects
  • Most GWAS hits are in noncoding regions -> regulatory function
    • There must be a small set of core genes and a larger set of regulatory genes
  • GWAS hits seem to spread broadly across the genome and tend not to converge in pathways
    • We haven’t characterized all biological pathways
  • Genetic variants that have broad functions are more often associated with disease than those that have more specialized function
    • More broadly expressed in multiple tissue types
    • If these genes are broken, they will have more widespread and devastating effects than genes only expressed in a single tissue type.
Q:

What is the relationship between rare variants and the omnigenic model?

A:
  • Rare variants have much larger effects than common variants (as they haven't yet dropped out of the genome due to selective pressure)
  • Therefore rare variants are more likely to be on one of the core genes directly influencing/disrupting a biological function in a disease, as the main variant that causes the phenotypic effect
Q:

​How do we account for confounding among gene sets when using the MAGMA software?

A:

In a linear regression framework in MAGMA, we add confounders as covariates to account for their influence.

Q:

What are possible confounders that have to be dealt with statistically?

A:

- Linkage Disequilibrium (between SNPs) 

- nr. of genes (in a gene set) 

- nr. of SNPs (in a gene) 

- background h²



Q:

What is background h²?

A:

= overall heritability of a trait
(compared to heritability of each SNP for a trait)

Q:

equifinality

A:

= heterogeneity

= different genotypic networks lead to the same outcome




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