User:Timothee Flutre/Notebook/Postdoc/2011/11/10

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==Entry title==
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==Bayesian model of univariate linear regression for QTL detection==
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''See Servin & Stephens (PLoS Genetics, 2007).''
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* '''Data''': let's assume that we obtained data from N individuals. We note <math>y_1,\ldots,y_N</math> the (quantitative) phenotypes (e.g. expression level at a given gene), and <math>g_1,\ldots,g_N</math> the genotypes at a given SNP (as allele dose, 0, 1 or 2).
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* '''Goal''': we want (i) to assess the evidence in the data for an effect of the genotype on the phenotype, and (ii) estimate the posterior distribution of this effect.
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* '''Assumptions''': the relationship between genotype and phenotype is linear; the individuals are not genetically related; there is no hidden confounding factors in the phenotypes.
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* '''Likelihood''':
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<math>\forall i \in \{1,\ldots,N\}, \; y_i = \mu + \beta_1 g_i + \beta_2 \mathbf{1}_{g_i=1} + \epsilon_i</math>
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with: <math>\epsilon_i \overset{i.i.d}{\sim} \mathcal{N}(0,\tau^{-1})</math>
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where <math>\beta_1</math> is in fact the additive effect of the SNP, noted <math>a</math> from now on, and <math>\beta_2</math> is the dominance effect of the SNP, <math>d = a k</math>.
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Let's now write in matrix notation:
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<math>Y = X B + E</math>
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where <math>B = [ \mu \; a \; d ]^T</math>
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which gives the following conditional distribution for the phenotypes:
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<math>Y | X, B, \tau \sim \mathcal{N}(XB, \tau^{-1} I_N)</math>
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* '''Priors''': conjugate
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<math>\tau \sim \Gamma(\kappa/2, \, \lambda/2)</math>
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<math>B | \tau \sim \mathcal{N}(\vec{0}, \, \tau^{-1} \Sigma_B) \text{ with } \Sigma_B = diag(\sigma_{\mu}^2, \sigma_a^2, \sigma_d^2)</math>
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Revision as of 12:38, 21 November 2012

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Bayesian model of univariate linear regression for QTL detection

See Servin & Stephens (PLoS Genetics, 2007).


  • Data: let's assume that we obtained data from N individuals. We note y_1,\ldots,y_N the (quantitative) phenotypes (e.g. expression level at a given gene), and g_1,\ldots,g_N the genotypes at a given SNP (as allele dose, 0, 1 or 2).


  • Goal: we want (i) to assess the evidence in the data for an effect of the genotype on the phenotype, and (ii) estimate the posterior distribution of this effect.


  • Assumptions: the relationship between genotype and phenotype is linear; the individuals are not genetically related; there is no hidden confounding factors in the phenotypes.


  • Likelihood:

\forall i \in \{1,\ldots,N\}, \; y_i = \mu + \beta_1 g_i + \beta_2 \mathbf{1}_{g_i=1} + \epsilon_i

with: \epsilon_i \overset{i.i.d}{\sim} \mathcal{N}(0,\tau^{-1})

where β1 is in fact the additive effect of the SNP, noted a from now on, and β2 is the dominance effect of the SNP, d = ak.

Let's now write in matrix notation:

Y = XB + E

where B = [ \mu \; a \; d ]^T

which gives the following conditional distribution for the phenotypes:

Y | X, B, \tau \sim \mathcal{N}(XB, \tau^{-1} I_N)


  • Priors: conjugate

\tau \sim \Gamma(\kappa/2, \, \lambda/2)

B | \tau \sim \mathcal{N}(\vec{0}, \, \tau^{-1} \Sigma_B) \text{ with } \Sigma_B = diag(\sigma_{\mu}^2, \sigma_a^2, \sigma_d^2)


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