Difference between revisions of "Biostatistics 666: Power of Genomewide Association Studies"
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== Lecture Notes == | == Lecture Notes == | ||
| − | [[ | + | [[666.2010.01_-_Power_of_Genomewide_Studies.pdf |Slides in PDF Format]] |
== Recommended Reading == | == Recommended Reading == | ||
Skol el al (2006) Joint analysis is more efficient than replication based analysis for two-stage genomewide association studies. ''Nature Genetics'' '''38''':209-13 | Skol el al (2006) Joint analysis is more efficient than replication based analysis for two-stage genomewide association studies. ''Nature Genetics'' '''38''':209-13 | ||
Latest revision as of 04:28, 1 November 2017
Objective
This lecture discusses power calculations in the context of genomewide association studies. It also introduces the concept of two stage designs and the tradeoffs between replication based and two stage analyses. The "Winner's Curse" makes a brief cameo appearance.
Lecture Notes
Recommended Reading
Skol el al (2006) Joint analysis is more efficient than replication based analysis for two-stage genomewide association studies. Nature Genetics 38:209-13
