Genome Analysis Wiki - User contributions [en]

Meta Analysis of SNPxEnvironment Interaction

2011-01-10T16:24:43Z

Amanning:

= Introduction =

The meta-analysis of SNP-Environment interaction regression models is possible by jointly meta-analyzing and testing the SNP and SNPxE beta coefficients from an interaction regression model:

<math>E(Y)=\beta_0 + \beta_1 E + \beta_2 \text {SNP} + \beta_3 E \times \text {SNP}</math>

 

The joint meta-analysis:

*produces summary estimates of β2 and β3
*produces the estimated covariance matrix of <math>\hat \beta_2 </math> and <math>\hat \beta_3 </math>
*computes a test statistics for the null hypothesis: β2=0 and β3=0
*computes a test of homogeneity of the regression slopes

 
Reference Manning AK, Lavalley M, Liu CT, Rice K, An P, Liu Y, et al. Meta-analysis of gene-environment interaction: Joint estimation of SNP and SNP x environment regression coefficients. Genet Epidemiol. 2011 Jan;35(1):11-8.
 

The joint meta-analysis method has been implemented though a patch to METAL available from [http://www.sph.umich.edu/csg/abecasis/Metal http://www.sph.umich.edu/csg/abecasis/Metal] ([[Metal_Documentation|Metal Documentation]]). Individual studies can obtain the required beta and covariance estimates for interaction regression models using QUICKTEST version 0.95 and later ([http://toby.freeshell.org/software/quicktest/ http://toby.freeshell.org/software/quicktest/]) and ProbABEL version 0.1-3 and later ([http://mga.bionet.nsc.ru/~yurii/ABEL/GenABEL/ http://mga.bionet.nsc.ru/~yurii/ABEL/GenABEL/]). 

= Implementation =

The joint meta-analysis is executed through the SCHEME INTERACTION command. The following commands must be used for each data file:

MARKERLABEL [LABEL]
ALLELELABELS [LABEL1 LABEL2]
EFFECTLABEL [LABEL]
STDERRLABEL [LABEL]
INTEFFECTLABEL [LABEL]
INTSTDERRLABEL [LABEL]
INTCOVLABEL [LABEL]

If MINMAXFREQUENCY ON is used, FREQLABEL must be specified. If AVERAGEFREQUENCY ON is used, in addition to FREQLABEL, WEIGHTLABEL also must be specified to calculate the average frequencies of alleles. Using the sample size as the weight to calculate average allele frequency is strongly suggested. In this case, frequency results in the output will be the same as frequency results from SCHEME SAMPLESIZE.

Two columns of directions will appear in the output of SCHEME INTERACTION, one for the direction of the main effect, one for the direction of the interaction term.

The output columns are:

; Effect : Summarized estimate of the SNP beta coefficient
; StdErr : Estimated standard error of the SNP beta coefficient
; IntEffect : Summarized estimate of the SNPxE beta coefficient
; IntStdErr : Estimated standard error of the SNPxE beta coefficient
; IntCov : Estimated covariance between the SNP beta coefficient and the SNPxE beta coefficient
; ChiSq2df : Joint test of the SNP beta coefficient and SNPxE beta coefficient
; P-value : P-value of ChiSq2df
; Direction : Study-specific direction of the SNP beta-coefficient
; DirectionInt : Study-specific direction of the SNPxE beta-coefficient
; HetChiSq : Heterogeneity test statistic
; HetDf : Heterogeneity test statistic degrees of freedom
; HetPVal : Heterogeneity p-value

If ANALYZE HETEROGENEITY is specified, the heterogeneity test statistic, degrees of freedom and p-value will be produced in the output columns: HetChiSq, HetDf and HetPVal.

The command, GENOMICCONTROL ON, adjusts the test statistics within each sample by λ''GC'', which is either pre-specified or calculated.

For sample i, the Wald test statistic for each SNP is calculated to test the joint significance of βSNP,''i'' and βSNPxE,''i'':

<math>W_i=\bold \beta^T_i \Sigma^{-1}_i \bold \beta_i</math>

Under the null hypothesis, these ''W''''i'''s follow a chi-square distribution of 2 df. The median statistic from this distribution is 1.386294, so

<math>\lambda_{GC}=\frac{\text{median observed} W_i}{1.386294}</math>

for this sample.

= Example =
<pre>CUSTOMVARIABLE TotalSampleSize
LABEL TotalSampleSize as N
SCHEME INTERACTION

SEPARATOR TAB
MARKER SNP
ALLELE EFFECT_ALLELE NON_EFFECT_ALLELE
EFFECT BETA
STDERR SE
INTEFFECT BETA_INT
INTSTDERR SE_INT
INTCOV COV
WEIGHT N

PROCESS results1.txt
PROCESS results2.txt
PROCESS results3.txt
PROCESS results4.txt
PROCESS results5.txt

OUTFILE jointmeta .txt
ANALYZE

OUTFILE jointmeta_het .txt
ANALYZE HETEROGENEITY

QUIT
</pre>

Joint Meta Analysis

2010-05-26T15:46:52Z

Amanning: moved Joint Meta Analysis to Meta Analysis of SNPxEnvironment Interaction

#REDIRECT [[Meta Analysis of SNPxEnvironment Interaction]]

Meta Analysis of SNPxEnvironment Interaction

2010-05-26T15:46:52Z

Amanning: moved Joint Meta Analysis to Meta Analysis of SNPxEnvironment Interaction

= Introduction =

The meta-analysis of SNP-Environment interaction regression models is possible by jointly meta-analyzing and testing the SNP and SNPxE beta coefficients from an interaction regression model:

<math>E(Y)=\beta_0 + \beta_1 E + \beta_2 \text {SNP} + \beta_3 E \times \text {SNP}</math>

 

The joint meta-analysis:

*produces summary estimates of β2 and β3
*produces the estimated covariance matrix of <math>\hat \beta_2 </math> and <math>\hat \beta_3 </math>
*computes a test statistics for the null hypothesis: β2=0 and β3=0
*computes a test of homogeneity of the regression slopes

 

The joint meta-analysis method has been implemented in METAL available from [http://www.sph.umich.edu/csg/abecasis/Metal http://www.sph.umich.edu/csg/abecasis/Metal] ([[Metal_Documentation|Metal Documentation]]). Individual studies can obtain the required beta and covariance estimates for interaction regression models using QUICKTEST version 0.95 and later ([http://toby.freeshell.org/software/quicktest/ http://toby.freeshell.org/software/quicktest/]) and ProbABEL version 0.1-3 and later ([http://mga.bionet.nsc.ru/~yurii/ABEL/GenABEL/ http://mga.bionet.nsc.ru/~yurii/ABEL/GenABEL/]). 

= Implementation =

The joint meta-analysis is executed through the SCHEME INTERACTION command. The following commands must be used for each data file:

MARKERLABEL [LABEL]

ALLELELABELS [LABEL1 LABEL2]

EFFECTLABEL [LABEL]

STDERRLABEL [LABEL]

INTEFFECTLABEL [LABEL]

INTSTDERRLABEL [LABEL]

INTCOVLABEL [LABEL]

 

If MINMAXFREQUENCY ON is used, FREQLABEL must be specified. If AVERAGEFREQUENCY ON is used, in addition to FREQLABEL, WEIGHTLABEL also must be specified to calculate the average frequencies of alleles. Using the sample size as the weight to calculate average allele frequency is strongly suggested. In this case, frequency results in the output will be the same as frequency results from SCHEME SAMPLESIZE.

 

Two columns of directions will appear in the output of SCHEME INTERACTION, one for the direction of the main effect, one for the direction of the interaction term.

 

The output columns are:

Effect - Summarized estimate of the SNP beta coefficient

StdErr - Estimated standard error of the SNP beta coefficient

IntEffect - Summarized estimate of the SNPxE beta coefficient

IntStdErr - Estimated standard error of the SNPxE beta coefficient

IntCov - Estimated covariance between the SNP beta coefficient and the SNPxE beta coefficient

ChiSq2df - Joint test of the SNP beta coefficient and SNPxE beta coefficient

P-value - P-value of ChiSq2df

Direction - Study-specific direction of the SNP beta-coefficient

DirectionInt - Study-specific direction of the SNPxE beta-coefficient

HetChiSq - Heterogeneity test statistic

HetDf - Heterogeneity test statistic degrees of freedom

HetPVal - Heterogeneity p-value

 

If ANALYZE HETEROGENEITY is specified, the heterogeneity test statistic, degrees of freedom and p-value will be produced in the output columns: HetChiSq, HetDf and HetPVal.

 

The command, GENOMICCONTROL ON, adjusts the test statistics within each sample by λ''GC'', which is either pre-specified or calculated.

 

For sample i, the Wald test statistic for each SNP is calculated to test the joint significance of βSNP,''i'' and βSNPxE,''i'':

<math>W_i=\bold \beta^T_i \Sigma^{-1}_i \bold \beta_i</math>

 

Under the null hypothesis, these ''W''''i'''s follow a chi-square distribution of 2 df. The median statistic from this distribution is 1.386294, so

<math>\lambda_{GC}=\frac{\text{median observed} W_i}{1.386294}</math>

 for this sample.
 
= Example =
<pre>CUSTOMVARIABLE TotalSampleSize
LABEL TotalSampleSize as N
SCHEME INTERACTION

SEPARATOR TAB
MARKER SNP
ALLELE EFFECT_ALLELE NON_EFFECT_ALLELE
EFFECT BETA
STDERR SE
INTEFFECT BETA_INT
INTSTDERR SE_INT
INTCOV COV
WEIGHT N

PROCESS results1.txt
PROCESS results2.txt
PROCESS results3.txt
PROCESS results4.txt
PROCESS results5.txt

OUTFILE jointmeta .txt
ANALYZE

OUTFILE jointmeta_het .txt
ANALYZE HETEROGENEITY

QUIT
</pre>

Meta Analysis of SNPxEnvironment Interaction

2010-05-26T15:46:23Z

Amanning:

= Introduction =

The meta-analysis of SNP-Environment interaction regression models is possible by jointly meta-analyzing and testing the SNP and SNPxE beta coefficients from an interaction regression model:

<math>E(Y)=\beta_0 + \beta_1 E + \beta_2 \text {SNP} + \beta_3 E \times \text {SNP}</math>

 

The joint meta-analysis:

*produces summary estimates of β2 and β3
*produces the estimated covariance matrix of <math>\hat \beta_2 </math> and <math>\hat \beta_3 </math>
*computes a test statistics for the null hypothesis: β2=0 and β3=0
*computes a test of homogeneity of the regression slopes

 

The joint meta-analysis method has been implemented in METAL available from [http://www.sph.umich.edu/csg/abecasis/Metal http://www.sph.umich.edu/csg/abecasis/Metal] ([[Metal_Documentation|Metal Documentation]]). Individual studies can obtain the required beta and covariance estimates for interaction regression models using QUICKTEST version 0.95 and later ([http://toby.freeshell.org/software/quicktest/ http://toby.freeshell.org/software/quicktest/]) and ProbABEL version 0.1-3 and later ([http://mga.bionet.nsc.ru/~yurii/ABEL/GenABEL/ http://mga.bionet.nsc.ru/~yurii/ABEL/GenABEL/]). 

= Implementation =

The joint meta-analysis is executed through the SCHEME INTERACTION command. The following commands must be used for each data file:

MARKERLABEL [LABEL]

ALLELELABELS [LABEL1 LABEL2]

EFFECTLABEL [LABEL]

STDERRLABEL [LABEL]

INTEFFECTLABEL [LABEL]

INTSTDERRLABEL [LABEL]

INTCOVLABEL [LABEL]

 

If MINMAXFREQUENCY ON is used, FREQLABEL must be specified. If AVERAGEFREQUENCY ON is used, in addition to FREQLABEL, WEIGHTLABEL also must be specified to calculate the average frequencies of alleles. Using the sample size as the weight to calculate average allele frequency is strongly suggested. In this case, frequency results in the output will be the same as frequency results from SCHEME SAMPLESIZE.

 

Two columns of directions will appear in the output of SCHEME INTERACTION, one for the direction of the main effect, one for the direction of the interaction term.

 

The output columns are:

Effect - Summarized estimate of the SNP beta coefficient

StdErr - Estimated standard error of the SNP beta coefficient

IntEffect - Summarized estimate of the SNPxE beta coefficient

IntStdErr - Estimated standard error of the SNPxE beta coefficient

IntCov - Estimated covariance between the SNP beta coefficient and the SNPxE beta coefficient

ChiSq2df - Joint test of the SNP beta coefficient and SNPxE beta coefficient

P-value - P-value of ChiSq2df

Direction - Study-specific direction of the SNP beta-coefficient

DirectionInt - Study-specific direction of the SNPxE beta-coefficient

HetChiSq - Heterogeneity test statistic

HetDf - Heterogeneity test statistic degrees of freedom

HetPVal - Heterogeneity p-value

 

If ANALYZE HETEROGENEITY is specified, the heterogeneity test statistic, degrees of freedom and p-value will be produced in the output columns: HetChiSq, HetDf and HetPVal.

 

The command, GENOMICCONTROL ON, adjusts the test statistics within each sample by λ''GC'', which is either pre-specified or calculated.

 

For sample i, the Wald test statistic for each SNP is calculated to test the joint significance of βSNP,''i'' and βSNPxE,''i'':

<math>W_i=\bold \beta^T_i \Sigma^{-1}_i \bold \beta_i</math>

 

Under the null hypothesis, these ''W''''i'''s follow a chi-square distribution of 2 df. The median statistic from this distribution is 1.386294, so

<math>\lambda_{GC}=\frac{\text{median observed} W_i}{1.386294}</math>

 for this sample.
 
= Example =
<pre>CUSTOMVARIABLE TotalSampleSize
LABEL TotalSampleSize as N
SCHEME INTERACTION

SEPARATOR TAB
MARKER SNP
ALLELE EFFECT_ALLELE NON_EFFECT_ALLELE
EFFECT BETA
STDERR SE
INTEFFECT BETA_INT
INTSTDERR SE_INT
INTCOV COV
WEIGHT N

PROCESS results1.txt
PROCESS results2.txt
PROCESS results3.txt
PROCESS results4.txt
PROCESS results5.txt

OUTFILE jointmeta .txt
ANALYZE

OUTFILE jointmeta_het .txt
ANALYZE HETEROGENEITY

QUIT
</pre>

Meta Analysis of SNPxEnvironment Interaction

2010-05-26T15:39:34Z

Amanning: The meta-analysis of SNP-Environment interaction regression models

= Introduction =

The meta-analysis of SNP-Environment interaction regression models is possible by jointly meta-analyzing and testing the SNP and SNPxE beta coefficients from an interaction regression model:

<math>E(Y)=\beta_0 + \beta_1 E + \beta_2 \text {SNP} + \beta_3 E \times \text {SNP}</math>

 

The joint meta-analysis:

*produces summary estimates of β2 and β3
*produces the estimated covariance matrix of <math>\hat \beta_2 </math> and <math>\hat \beta_3 </math>
*computes a test statistics for the null hypothesis: β2=0 and β3=0
*computes a test of homogeneity of the regression slopes

 

= Implementation =

The joint meta-analysis is executed through the SCHEME INTERACTION command. The following commands must be used for each data file:

MARKERLABEL [LABEL]

ALLELELABELS [LABEL1 LABEL2]

EFFECTLABEL [LABEL]

STDERRLABEL [LABEL]

INTEFFECTLABEL [LABEL]

INTSTDERRLABEL [LABEL]

INTCOVLABEL [LABEL]

 

If MINMAXFREQUENCY ON is used, FREQLABEL must be specified. If AVERAGEFREQUENCY ON is used, in addition to FREQLABEL, WEIGHTLABEL also must be specified to calculate the average frequencies of alleles. Using the sample size as the weight to calculate average allele frequency is strongly suggested. In this case, frequency results in the output will be the same as frequency results from SCHEME SAMPLESIZE.

 

Two columns of directions will appear in the output of SCHEME INTERACTION, one for the direction of the main effect, one for the direction of the interaction term.

 

The output columns are:

Effect - Summarized estimate of the SNP beta coefficient

StdErr - Estimated standard error of the SNP beta coefficient

IntEffect - Summarized estimate of the SNPxE beta coefficient

IntStdErr - Estimated standard error of the SNPxE beta coefficient

IntCov - Estimated covariance between the SNP beta coefficient and the SNPxE beta coefficient

ChiSq2df - Joint test of the SNP beta coefficient and SNPxE beta coefficient

P-value - P-value of ChiSq2df

Direction - Study-specific direction of the SNP beta-coefficient

DirectionInt - Study-specific direction of the SNPxE beta-coefficient

HetChiSq - Heterogeneity test statistic

HetDf - Heterogeneity test statistic degrees of freedom

HetPVal - Heterogeneity p-value

 

If ANALYZE HETEROGENEITY is specified, the heterogeneity test statistic, degrees of freedom and p-value will be produced in the output columns: HetChiSq, HetDf and HetPVal.

 

The command, GENOMICCONTROL ON, adjusts the test statistics within each sample by λ''GC'', which is either pre-specified or calculated.

 

For sample i, the Wald test statistic for each SNP is calculated to test the joint significance of βSNP,''i'' and βSNPxE,''i'':

<math>W_i=\bold \beta^T_i \Sigma^{-1}_i \bold \beta_i</math>

 

Under the null hypothesis, these ''W''''i'''s follow a chi-square distribution of 2 df. The median statistic from this distribution is 1.386294, so

<math>\lambda_{GC}=\frac{\text{median observed} W_i}{1.386294}</math>

 for this sample.

 

The joint meta-analysis method has been implemented in METAL available from [http://www.sph.umich.edu/csg/abecasis/Metal http://www.sph.umich.edu/csg/abecasis/Metal] ([[Metal Documentation|Metal Documentation]]). Individual studies can obtain the required beta and covariance estimates for interaction regression models using QUICKTEST version 0.95 and later ([http://toby.freeshell.org/software/quicktest/ http://toby.freeshell.org/software/quicktest/]) and ProbABEL version 0.1-3 and later ([http://mga.bionet.nsc.ru/ yurii/ABEL/ http://mga.bionet.nsc.ru/ yurii/ABEL/]).

= Example =
<pre>
CUSTOMVARIABLE TotalSampleSize
LABEL TotalSampleSize as N
SCHEME INTERACTION

SEPARATOR TAB
MARKER SNP
ALLELE EFFECT_ALLELE NON_EFFECT_ALLELE
EFFECT BETA
STDERR SE
INTEFFECT BETA_INT
INTSTDERR SE_INT
INTCOV COV
WEIGHT N

PROCESS results1.txt
PROCESS results2.txt
PROCESS results3.txt
PROCESS results4.txt
PROCESS results5.txt

OUTFILE jointmeta .txt
ANALYZE

OUTFILE jointmeta_het .txt
ANALYZE HETEROGENEITY

QUIT
</pre>