From Genome Analysis Wiki
Jump to navigationJump to search
2 bytes removed
, 11:38, 11 March 2014
Line 17: |
Line 17: |
| == 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: | | Our single variant association test is the score test using linear mixed model, treating single variants as fixed effects. The alternative model is: |
− |
| |
| | | |
| <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: | | 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: |
− |
| |
| | | |
| <math> U_i=(\mathbf{G_i}-\mathbf{\bar{G_i}} )^T \boldsymbol{\Omega}^(-1)(\mathbf{y}-\mathbf{X}\boldsymbol{\beta}) </math> | | <math> U_i=(\mathbf{G_i}-\mathbf{\bar{G_i}} )^T \boldsymbol{\Omega}^(-1)(\mathbf{y}-\mathbf{X}\boldsymbol{\beta}) </math> |