Changes

= Motivation and Rationale  =

= Motivation and Rationale  =

[[EPACTS|EPACTS]] is a software pipeline developed to perform various statistical tests for analysis of whole-genome / whole-exome sequencing data.  The main motivation for using EPACTS is to use a consistent analysis framework for association analysis in the DIAGRAM consortium.  In addition, for analysis of low frequency variants (minor allele frequency [MAF] < 5%), standard logistic regression Wald or likelihood ratio tests found in existing association software are conservative or anti-conservative respectively.  We implemented two statistical tests we will use for for analysis of low frequency variants: (1) logistic regresion-based score test and (2) Firth bias-corrected logistic regression [http://www.stat.duke.edu/~scs/Courses/Stat376/Papers/GibbsFieldEst/BiasReductionMLE.pdf (Firth, 1993)].  For analysis of common variants, any asyptotic logistic regression test has well-controlled type I error rates and asymptotically equivalent power.  For simplicity and consistency, we propose the use of both score and Firth tests for testing all allele frequencies.

[[EPACTS|EPACTS]] is a software pipeline developed to perform various statistical tests for analysis of whole-genome / whole-exome sequencing data.  The main motivation for using EPACTS is to use a consistent analysis framework for association analysis in the DIAGRAM consortium.  In addition, for analysis of low frequency variants (minor allele frequency [MAF] < 5%), standard logistic regression Wald or likelihood ratio tests found in existing association software are conservative or anti-conservative respectively.  We implemented two statistical tests we will use for for analysis of low frequency variants: (1) logistic regresion-based score test and (2) Firth bias-corrected logistic regression [http://www.stat.duke.edu/~scs/Courses/Stat376/Papers/GibbsFieldEst/BiasReductionMLE.pdf (Firth, 1993)].  For analysis of common variants, any asyptotic logistic regression test has well-controlled type I error rates and asymptotically equivalent power.  For simplicity and consistency, we propose the use of both score and Firth tests for testing all allele frequencies.  

= Outline of analysis protocol  =

= Outline of analysis protocol  =

M QT

M QT

M AGE

M AGE

</pre>

</pre>  

 

== 4. &nbsp;Run EPACTS association pipeline  ==

== 4. &nbsp;Run EPACTS association pipeline  ==

*Score test: &nbsp;32 seconds

*Score test: &nbsp;32 seconds

<br> Hence, we only ask for Firth test results for SNPs with MAC &lt;= 200.

<br> Hence, we only ask for Firth test results for SNPs with MAC &lt;= 200.  

=== 1. Typical DIAGRAM analysis using existing association pipeline<br>  ===

=== 1. Typical DIAGRAM analysis using existing association pipeline<br>  ===

-test b.firth -pheno DISEASE -cov AGE -sepchr -anno -min-mac 1 -max-mac 200  -field EC -run 10

-test b.firth -pheno DISEASE -cov AGE -sepchr -anno -min-mac 1 -max-mac 200  -field EC -run 10

</pre>  

</pre>  

<br>'''Important:''' &nbsp;To analyze dosages (not genotypes), you must specify the dosage field with the "--field EC" option. &nbsp;Without this option, you will be analyzing the hard genotypes (i.e. --field option defaults to "GT" or "genotypes")!<br>

<br>'''Important:''' &nbsp;To analyze dosages (not genotypes), you must specify the dosage field with the "--field EC" option. &nbsp;Without this option, you will be analyzing the hard genotypes (i.e. --field option defaults to "GT" or "genotypes")!<br>  

=== 3. Analysis of chromosome 20 using logistic regression score test  ===

=== 3. Analysis of chromosome 20 using logistic regression score test  ===

-test b.score -pheno DISEASE -cov AGE -chr 20 -anno -min-mac 1 -field EC -run 10

-test b.score -pheno DISEASE -cov AGE -chr 20 -anno -min-mac 1 -field EC -run 10

</pre>  

</pre>  

This command will run single variant analysis using the score test logistic regression on the DISEASE phenotype adjusting for AGE. Add the relevant additional covariates with additional "-cov" options. This assumes that the VCF files are separated by chromosomes (option -sepchr). All variants with at least one minor allele count will be analyzed (option -min-mac 1). It will annotate results by functional category (option -anno) and run the analysis on 10 parallel CPUs (option -run 10).

This command will run single variant analysis using the score test logistic regression on the DISEASE phenotype adjusting for AGE. Add the relevant additional covariates with additional "-cov" options. This assumes that the VCF files are separated by chromosomes (option -sepchr). All variants with at least one minor allele count will be analyzed (option -min-mac 1). It will annotate results by functional category (option -anno) and run the analysis on 10 parallel CPUs (option -run 10).  

== 5. &nbsp;Report EPACTS results<br>  ==

== 5. &nbsp;Report EPACTS results<br>  ==

*PVALUE&nbsp;: P-value

*PVALUE&nbsp;: P-value

The rest of columns varies by statistical tests. For example, in b.score test, SCORE represents score test statistics, N.CASE and N.CTRL represents the case/control counts, and AF.CASE and AF.CTRL represents the case/control allele frequencies.

The rest of columns varies by statistical tests. For example, in b.score test, SCORE represents score test statistics, N.CASE and N.CTRL represents the case/control counts, and AF.CASE and AF.CTRL represents the case/control allele frequencies.