Bottom line p-values now available in the CVDKP
When genetic association analysis for a phenotype is performed in multiple studies, many different p-values representing the significance of that association are generated. How do we know which one is the most accurate?
To complicate things even further, the populations tested in different datasets often overlap with each other. How can we avoid double-counting associations?
Bottom line analysis provides an answer to both of these questions. It integrates results over multiple datasets and accounts for sample overlap between datasets to generate a single p-value representing the significance of the association between a variant and a phenotype.
Now, you can access bottom line p-values for individual variants on Variant pages in the Cardiovascular Disease Knowledge Portal as well as in the other portals of the Knowledge Portal Network: Type 2 Diabetes KP, Cerebrovascular Disease KP, and Sleep Disorder KP. To view bottom line p-values, open the "associations at a glance" section of the Variant page (see an example):
Choose to view "Bottom line analysis" in the PheWAS plot, and then mouse over a point to see the p-value:
We thank our colleagues at the University of Michigan, who developed the METAL method used in this analysis. Please note that this method as instantiated in the CVDKP is experimental; be sure to compare the results with those from individual datasets, and contact us with any questions.
To complicate things even further, the populations tested in different datasets often overlap with each other. How can we avoid double-counting associations?
Bottom line analysis provides an answer to both of these questions. It integrates results over multiple datasets and accounts for sample overlap between datasets to generate a single p-value representing the significance of the association between a variant and a phenotype.
Now, you can access bottom line p-values for individual variants on Variant pages in the Cardiovascular Disease Knowledge Portal as well as in the other portals of the Knowledge Portal Network: Type 2 Diabetes KP, Cerebrovascular Disease KP, and Sleep Disorder KP. To view bottom line p-values, open the "associations at a glance" section of the Variant page (see an example):
Choose to view "Bottom line analysis" in the PheWAS plot, and then mouse over a point to see the p-value:
We thank our colleagues at the University of Michigan, who developed the METAL method used in this analysis. Please note that this method as instantiated in the CVDKP is experimental; be sure to compare the results with those from individual datasets, and contact us with any questions.
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