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PRODID:https://murmitoyen.com/events/vanille/udem/
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UID:69dae7d00d983
DTSTAMP:20260411T203112
DTSTART:20180126T153000
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TRANSP:OPAQUE
DTEND:20180126T163000
URL:https://murmitoyen.com/events/vanille/udem/detail/805607-back-to-the-fu
 ture-why-i-think-regression-is-the-new-black-in-genetic-association-studie
 s
LOCATION:Université de Montréal - Pavillon André-Aisenstadt\, 2920\, che
 min de la Tour\, Montréal\, QC\, Canada\, H3T 1N8
SUMMARY:Back to the future: why I think REGRESSION is the new black in gene
 tic association studies
DESCRIPTION:Linear regression remains an important framework in the era of 
 big and complex data. In this talk I present some recent examples where we
  resort to the classical simple linear regression model and its celebrated
  extensions in novel settings. The Eureka moment came while reading Wu and
  Guan's (2015) comments on our generalized Kruskal-Wallis (GKW) test (Elif
  Acar and Sun 2013\, Biometrics). Wu and Guan presented an alternative â
 €œrank linear regression model and derived the proposed GKW statistic a
 s a score test statistic'\, and astutely pointed out that “the linea
 r model approach makes the derivation more straightforward and transparent
 \, and leads to a simplified and unified approach to the general rank base
 d multi-group comparison problem.' More recently\, we turned our attention
  to extending Levene's variance test for data with group uncertainty and s
 ample correlation. While a direct modification of the original statistic i
 s indeed challenging\, I will demonstrate that a two-stage regression fram
 ework makes the ensuing development quite straightforward\, eventually lea
 ding to a generalized joint location-scale test (David Soave and Sun 2017\
 , Biometrics). Finally\, I will discuss on-going work\, with graduate stud
 ent Lin Zhang\, on developing an allele-based association test that is rob
 ust to the assumption of Hardy-Weinberg equilibrium and is generalizable t
 o complex data structure. The crux of this work is\, again\, reformulating
  the problem as a regression!
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