ArticleHuman heredity2008
Haseman-Elston regression in ascertained samples: importance of dependent variable and mean correction factor selection.
Article in Human heredity, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Correlations between relatives: From Mendelian theory to complete genome sequence.Genetic epidemiology · 2019Article
- Some capabilities for model-based and model-free linkage analysis using the program package S.A.G.E. (Statistical Analysis for Genetic Epidemiology).Human heredity · 2011Article
- Confirmation of linkage to and localization of familial colon cancer risk haplotype on chromosome 9q22.Cancer research · 2010Article
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2 authors.
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Abstract
objectiveOne 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).
methodsUsing 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.
resultsWhen 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.
conclusionsAscertained 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.
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