From Genome Analysis Wiki
Jump to navigationJump to search
95 bytes removed
, 10:02, 27 March 2014
Line 11: |
Line 11: |
| | | |
| == Single Variant Score Tests == | | == Single Variant Score Tests == |
− | Our single variant association test is the score test using linear mixed model, treating single variants as fixed effects. The alternative model is:
| + | |
| + | We used the following model for the trait: |
| | | |
| <math> \mathbf{y}=\mathbf{X}\boldsymbol{\beta}+\gamma_i(\mathbf{G_i}-\bar{\mathbf{G_i}})+\mathbf{g}+\boldsymbol{\varepsilon} </math>. | | <math> \mathbf{y}=\mathbf{X}\boldsymbol{\beta}+\gamma_i(\mathbf{G_i}-\bar{\mathbf{G_i}})+\mathbf{g}+\boldsymbol{\varepsilon} </math>. |
| | | |
− | In this model, the scalar parameter <math>\gamma_i</math> is to measure the additive genetic effect of the <math>i^{th}</math> variant. As usual, the score statistic for testing <math>H_0:\gamma_i=0</math> is: | + | Here, [explain the formula]. |
| + | |
| + | In this model, <math>\gamma_i</math> is to measure the additive genetic effect of the <math>i^{th}</math> variant. As usual, the score statistic for testing <math>H_0:\gamma_i=0</math> is: |
| | | |
| <math> U_i=(\mathbf{G_i}-\mathbf{\bar{G_i}} )^T \hat{\boldsymbol{\Omega}}^{-1}(\mathbf{y}-\mathbf{X}\boldsymbol{\beta}) </math> | | <math> U_i=(\mathbf{G_i}-\mathbf{\bar{G_i}} )^T \hat{\boldsymbol{\Omega}}^{-1}(\mathbf{y}-\mathbf{X}\boldsymbol{\beta}) </math> |