Literature DB >> 12687648

Diagnostic tools in linkage analysis for quantitative traits.

Mariza de Andrade1, Brooke Fridley, Eric Boerwinkle, Stephen Turner.   

Abstract

Diagnostic methods are key components in any good statistical analysis. Because of the similarities between the variance components approach and regression analysis with respect to the normality assumption, when performing quantitative genetic linkage analysis using variance component methods, one must check the normality assumption of the quantitative trait and outliers. Thus, the main purposes of this paper are to describe methods for testing the normality assumption, to describe various diagnostic methods for identifying outliers, and to discuss the issues that may arise when outliers are present when using variance components models in quantitative trait linkage analysis. Data from the Rochester Family Heart Study are used to illustrate the various diagnostic methods and related issues. Copyright 2003 Wiley-Liss, Inc.

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Year:  2003        PMID: 12687648     DOI: 10.1002/gepi.10236

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  4 in total

1.  Case-deletion diagnostics for maximum likelihood multipoint quantitative trait locus linkage analysis.

Authors:  Maria C B Mendoza; Trudy L Burns; Michael P Jones
Journal:  Hum Hered       Date:  2009-01-27       Impact factor: 0.444

2.  A Variance-Component Framework for Pedigree Analysis of Continuous and Categorical Outcomes.

Authors:  Michael P Epstein; Jessica E Hunter; Emily G Allen; Stephanie L Sherman; Xihong Lin; Michael Boehnke
Journal:  Stat Biosci       Date:  2009-11

3.  Genome-wide linkage screen for stature and body mass index in 3.032 families: evidence for sex- and population-specific genetic effects.

Authors:  Sampo Sammalisto; Tero Hiekkalinna; Karen Schwander; Sharon Kardia; Alan B Weder; Beatriz L Rodriguez; Alessandro Doria; Jennifer A Kelly; Gail R Bruner; John B Harley; Susan Redline; Emma K Larkin; Sanjay R Patel; Amy J H Ewan; James L Weber; Markus Perola; Leena Peltonen
Journal:  Eur J Hum Genet       Date:  2008-09-10       Impact factor: 4.246

4.  Comparison of longitudinal variance components and regression-based approaches for linkage detection on chromosome 17 for systolic blood pressure.

Authors:  Mariza de Andrade; Curtis Olswold
Journal:  BMC Genet       Date:  2003-12-31       Impact factor: 2.797

  4 in total

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