Literature DB >> 8817796

Mixture distributions in human genetics research.

N J Schork1, D B Allison, B Thiel.   

Abstract

The use of mixture distributions in genetics research dates back to at least the late 1800s when Karl Pearson applied them in an analysis of crab morphometry. Pearson's use of normal mixture distributions to model the mixing of different species of crab (or 'families' of crab as he referred to them) within a defined geographic area motivated further use of mixture distributions in genetics research settings, and ultimately led to their development and recognition as intuitive modelling devices for the effects of underlying genes on quantitative phenotypic (i.e. trait) expression. In addition, mixture distributions are now used routinely to model or accommodate the genetic heterogeneity thought to underlie many human diseases. Specific applications of mixture distribution models in contemporary human genetics research are, in fact, too numerous to count. Despite this long, consistent and arguably illustrious history of use, little mention of mixture distributions in genetics research is made in many recent reviews on mixture models. This review attempts to rectify this by providing insight into the role that mixture distributions play in contemporary human genetics research. Tables providing examples from the literature that describe applications of mixture models in human genetics research are offered as a way of acquainting the interested reader with relevant studies. In addition, some of the more problematic aspects of the use of mixture models in genetics research are outlined and addressed.

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Year:  1996        PMID: 8817796     DOI: 10.1177/096228029600500204

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  6 in total

1.  Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure.

Authors:  D B Allison; M C Neale; R Zannolli; N J Schork; C I Amos; J Blangero
Journal:  Am J Hum Genet       Date:  1999-08       Impact factor: 11.025

2.  Testing the robustness of the new Haseman-Elston quantitative-trait loci-mapping procedure.

Authors:  D B Allison; J R Fernández; M Heo; T M Beasley
Journal:  Am J Hum Genet       Date:  2000-05-11       Impact factor: 11.025

3.  A mixture model approach in gene-gene and gene-environmental interactions for binary phenotypes.

Authors:  Lang Li; Menggang Yu; Robarge D Jason; Changyu Shen; Faouzi Azzouz; Howard L McLeod; Silvana Borges-Gonzales; Anne Nguyen; Todd Skaar; Zeruesenay Desta; Christopher J Sweeney; David A Flockhart
Journal:  J Biopharm Stat       Date:  2008       Impact factor: 1.051

4.  A penalized mixture model approach in genotype/phenotype association analysis for quantitative phenotypes.

Authors:  Lang Li; Silvana Borges; Robarge D Jason; Changyu Shen; Zeruesenay Desta; David Flockhart
Journal:  Cancer Inform       Date:  2010-04-27

5.  Linkage disequilibrium analysis of biallelic DNA markers, human quantitative trait loci, and threshold-defined case and control subjects.

Authors:  N J Schork; S K Nath; D Fallin; A Chakravarti
Journal:  Am J Hum Genet       Date:  2000-10-13       Impact factor: 11.043

6.  Evolution of DNA Methylation Across Ecdysozoa.

Authors:  Jan Engelhardt; Oliver Scheer; Peter F Stadler; Sonja J Prohaska
Journal:  J Mol Evol       Date:  2022-01-28       Impact factor: 2.395

  6 in total

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