Diagnostic tools in linkage analysis for quantitative traits

Mariza De Andrade, Brooke Fridley, Eric Boerwinkle, Stephen T Turner

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)302-308
Number of pages7
JournalGenetic Epidemiology
Volume24
Issue number4
DOIs
StatePublished - May 2003

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Genetic Linkage
Analysis of Variance
Regression Analysis

Keywords

  • Diagnostics
  • Pedigree analysis
  • Variance components

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Diagnostic tools in linkage analysis for quantitative traits. / De Andrade, Mariza; Fridley, Brooke; Boerwinkle, Eric; Turner, Stephen T.

In: Genetic Epidemiology, Vol. 24, No. 4, 05.2003, p. 302-308.

Research output: Contribution to journalArticle

De Andrade, Mariza ; Fridley, Brooke ; Boerwinkle, Eric ; Turner, Stephen T. / Diagnostic tools in linkage analysis for quantitative traits. In: Genetic Epidemiology. 2003 ; Vol. 24, No. 4. pp. 302-308.
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