Diagnostic tools in linkage analysis for quantitative traits
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Updated: Sep 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Mariza de Andrade1, Brooke Fridley, Eric Boerwinkle
1Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA. mandrade@mayo.edu
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.
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