Literature DB >> 11359067

Weighting improves the "new Haseman-Elston" method.

W F Forrest1.   

Abstract

Elston et al. [Genet Epidemiol, in press] apply the results of Wright [Am J Hum Genet 1997;60:740-742] and Drigalenko [Am J Hum Genet 1998;63:1242-1245] to extend the traditional Haseman-Elston regression scheme [Haseman and Elston, Behav Genet 1972;2:3-19] to include not only linkage information contained in the sib pair's squared difference, but also information in their mean-corrected squared sum. The new algorithm detects linkage to a quantitative trait locus by modelling sib pair trait covariance as a function of identity-by-descent status. We demonstrate why this new estimator is suboptimal and can in some cases be inferior to the original Haseman-Elston method. We also describe a simple approach to estimation which improves on this new Haseman-Elston method by incorporating variance-based weights into the test statistic while staying within the linear modelling framework. In support of our theoretical claim, we conduct both a sib pair simulation and an application to GAW 10 sib pair data showing that our new estimator is superior to both the old and new Haseman-Elston schemes currently implemented in the analysis package S.A.G.E. 4.0. Copyright 2001 S. Karger AG, Basel

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Year:  2001        PMID: 11359067     DOI: 10.1159/000053353

Source DB:  PubMed          Journal:  Hum Hered        ISSN: 0001-5652            Impact factor:   0.444


  18 in total

1.  A score-statistic approach for the mapping of quantitative-trait loci with sibships of arbitrary size.

Authors:  K Wang; J Huang
Journal:  Am J Hum Genet       Date:  2001-12-27       Impact factor: 11.025

2.  Linkage analysis of extremely discordant and concordant sibling pairs identifies quantitative-trait loci that influence variation in the human personality trait neuroticism.

Authors:  Jan Fullerton; Matthew Cubin; Hemant Tiwari; Chenxi Wang; Amarjit Bomhra; Stuart Davidson; Sue Miller; Christopher Fairburn; Guy Goodwin; Michael C Neale; Simon Fiddy; Richard Mott; David B Allison; Jonathan Flint
Journal:  Am J Hum Genet       Date:  2003-02-20       Impact factor: 11.025

3.  Powerful regression-based quantitative-trait linkage analysis of general pedigrees.

Authors:  Pak C Sham; Shaun Purcell; Stacey S Cherny; Gonçalo R Abecasis
Journal:  Am J Hum Genet       Date:  2002-07-05       Impact factor: 11.025

4.  Regression-based quantitative-trait-locus mapping in the 21st century.

Authors:  Eleanor Feingold
Journal:  Am J Hum Genet       Date:  2002-08       Impact factor: 11.025

5.  Bias toward the null hypothesis in model-free linkage analysis is highly dependent on the test statistic used.

Authors:  Heather J Cordell
Journal:  Am J Hum Genet       Date:  2004-04-29       Impact factor: 11.025

6.  A latent class model for testing for linkage and classifying families when the sample may contain segregating and non-segregating families.

Authors:  Laurel A Bastone; Richard S Spielman; Xingmei Wang; Thomas R Ten Have; Mary E Putt
Journal:  Hum Hered       Date:  2010-06-17       Impact factor: 0.444

7.  A powerful and robust method for mapping quantitative trait loci in general pedigrees.

Authors:  G Diao; D Y Lin
Journal:  Am J Hum Genet       Date:  2005-05-25       Impact factor: 11.025

8.  Haseman-Elston regression in ascertained samples: importance of dependent variable and mean correction factor selection.

Authors:  Ritwik Sinha; Courtney Gray-McGuire
Journal:  Hum Hered       Date:  2007-09-26       Impact factor: 0.444

9.  Recent advances in human quantitative-trait-locus mapping: comparison of methods for selected sibling pairs.

Authors:  Karen T Cuenco; Jin P Szatkiewicz; Eleanor Feingold
Journal:  Am J Hum Genet       Date:  2003-09-10       Impact factor: 11.025

10.  Recent advances in human quantitative-trait-locus mapping: comparison of methods for discordant sibling pairs.

Authors:  Jin P Szatkiewicz; Karen T Cuenco; Eleanor Feingold
Journal:  Am J Hum Genet       Date:  2003-09-10       Impact factor: 11.025

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