Difference between revisions of "EPACTS for DIAGRAM"
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= Motivation and Rationale = | = Motivation and Rationale = | ||
| − | 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 recommended 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 all allele frequencies. | + | 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 recommended 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 all allele frequencies. |
= Outline of analysis protocol = | = Outline of analysis protocol = | ||
This is the analysis protocol for analysis of imputed DIAGRAM datasets using the EPACTS pipeline. | This is the analysis protocol for analysis of imputed DIAGRAM datasets using the EPACTS pipeline. | ||
Revision as of 10:49, 18 September 2012
Motivation and Rationale
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 recommended for analysis of low frequency variants: (1) logistic regresion-based score test and (2) Firth bias-corrected logistic regression (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 all allele frequencies.
Outline of analysis protocol
This is the analysis protocol for analysis of imputed DIAGRAM datasets using the EPACTS pipeline.
