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

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 Revision as of 11:30, 21 November 2012 (view source) (→Entry title: move info about help2man and groff to the "templates" page)← Previous diff Revision as of 12:38, 21 November 2012 (view source) (→Entry title: first version)Next diff → Line 6: Line 6: | colspan="2"| | colspan="2"| - ==Entry title== + ==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 $\beta_1$ is in fact the additive effect of the SNP, noted $a$ from now on, and $\beta_2$ is the dominance effect of the SNP, $d = a k$. + + Let's now write in matrix notation: + + $Y = X B + E$ + + 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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## 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

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

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