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DTSTAMP:20250712T163914Z
DESCRIPTION:Simplified Power Calculations for Genetic Association Studies\n
 \nAndriy Derkach is currently conducting his post-doctoral research in Bio
 statistics Branch of Division of Cancer Epidemiology and Genetics at the N
 ational Cancer Institute. His doctoral and post-doctoral training have foc
 used on statistical methods that integrate high dimensional molecular and 
 genetics data within or between studies to improve probability of detectin
 g associations with disease. He received his doctoral training at Universi
 ty of Toronto\, where he worked on developing statistical tests and design
 s for association studies with rare variants. As post-doctoral fellow\, he
  has been involved in studies of continuous biomarkers\, such as metabolit
 e levels that reflect the effects of endogenous\, environmental and geneti
 c factors. Currently\, he is interested in detecting subsets of biomarkers
  mediating the relationship between a known risk factor and cancer.\n\n	Gen
 ome-wide association studies are now shifting focus from an analysis of co
 mmon to uncommon and rare variants with an anticipation to explain additio
 nal variation in complex traits. As power for association testing for indi
 vidual rare variants may often be low\, various aggregate level associatio
 n tests have been proposed to detect genetic loci that may contain cluster
 s of causal variants. We showed that these methods can be divided into two
  classes: tests based on linear and composite statistics (e.g. variance-co
 mponent tests). Typically power calculations for such tests require specif
 ication of a large number of parameters\, including effect sizes and allel
 e frequencies of individual markers\, making them difficult to use in prac
 tice. In this presentation\, we approximate power of linear and quadratic 
 tests to varying degree of accuracy using a smaller number of key paramete
 rs\, including the total genetic variance explained by multiple variants w
 ithin a locus. Using the simplified power calculation methods\, we then de
 velop a mathematical framework to obtain bounds on the genetic architectur
 e of an underlying trait given results from a genome-wide study. By using 
 proposed framework\, we observe important implications for lack or a limit
 ed number of findings in many currently reported studies. Finally\, we pro
 vide insights into the required quality of annotation/functional informati
 on for identification of likely causal variants to make meaningful improve
 ment in power of subsequent association tests.\n
DTSTART:20171122T193000Z
DTEND:20171122T203000Z
LOCATION:Room 25\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Andriy Derkach\, Biostatistics Branch\, National Cancer Institute
URL:/mathstat/channels/event/andriy-derkach-biostatist
 ics-branch-national-cancer-institute-282948
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