OBJECTIVE: One of the first tools for performing linkage analysis, Haseman-Elston regression (HE), has been successfully used to identify linkages to several disease traits. A recent explosion in extensions of HE leaves one faced with the task of choosing a flavor of HE best suited for a given situation. This paper puts this dilemma into perspective and proposes a modification to HE for highly ascertained samples (BLUP-PM). METHODS: Using data simulated for a range of models, we evaluated type I error and power of several dependent variables in HE, including the novel BLUP-PM. RESULTS: When analyzing a continuous trait, even in highly ascertained samples, type I error is stable and approximately nominal across dependent variables. When analyzing binary traits in highly ascertained samples, type I error is elevated and unstable for all except BLUP-PM. Regardless of trait type, the optimally weighted HE regression and BLUP-PM have the greatest power. CONCLUSIONS: Ascertained samples do not always reflect the population from which they are drawn and therefore choice of dependent variable in HE becomes increasingly important. Our results do not reveal a single, universal choice, but offer criteria by which to choose and demonstrate BLUP-PM performs well in most situations. (c) 2007 S. Karger AG, Basel.
OBJECTIVE: One of the first tools for performing linkage analysis, Haseman-Elston regression (HE), has been successfully used to identify linkages to several disease traits. A recent explosion in extensions of HE leaves one faced with the task of choosing a flavor of HE best suited for a given situation. This paper puts this dilemma into perspective and proposes a modification to HE for highly ascertained samples (BLUP-PM). METHODS: Using data simulated for a range of models, we evaluated type I error and power of several dependent variables in HE, including the novel BLUP-PM. RESULTS: When analyzing a continuous trait, even in highly ascertained samples, type I error is stable and approximately nominal across dependent variables. When analyzing binary traits in highly ascertained samples, type I error is elevated and unstable for all except BLUP-PM. Regardless of trait type, the optimally weighted HE regression and BLUP-PM have the greatest power. CONCLUSIONS: Ascertained samples do not always reflect the population from which they are drawn and therefore choice of dependent variable in HE becomes increasingly important. Our results do not reveal a single, universal choice, but offer criteria by which to choose and demonstrate BLUP-PM performs well in most situations. (c) 2007 S. Karger AG, Basel.
Authors: Courtney Gray-McGuire; Kishore Guda; Indra Adrianto; Chee Paul Lin; Leanna Natale; John D Potter; Polly Newcomb; Elizabeth M Poole; Cornelia M Ulrich; Noralane Lindor; Ellen L Goode; Brooke L Fridley; Robert Jenkins; Loic Le Marchand; Graham Casey; Robert Haile; John Hopper; Mark Jenkins; Joanne Young; Daniel Buchanan; Steve Gallinger; Mark Adams; Susan Lewis; Joseph Willis; Robert Elston; Sanford D Markowitz; Georgia L Wiesner Journal: Cancer Res Date: 2010-06-15 Impact factor: 12.701
Authors: Catherine M Stein; Sarah Zalwango; LaShaunda L Malone; Sungho Won; Harriet Mayanja-Kizza; Roy D Mugerwa; Dmitry V Leontiev; Cheryl L Thompson; Kevin C Cartier; Robert C Elston; Sudha K Iyengar; W Henry Boom; Christopher C Whalen Journal: PLoS One Date: 2008-12-31 Impact factor: 3.240