Using the theory of added-variable plot for linear mixed models to decompose genetic effects in family data

Nubia E. Duarte, Suely R. Giolo, Alexandre C. Pereira, Mariza De Andrade, Júlia P. Soler

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Effective analytical tools are highly desirable for data analysis and for making the biological link between genotypic and phenotypic measures. In family data it is important to reconcile the methods that explain the phenotypic variability through fixed genetic effects and ones that estimate variance components using classical heritability methods. Thus, in this paper, we propose a method based on added-variable plot for polygenic linear mixed models applied to genome wide association studies in family-based designs. Our goal is to be able to discriminate genetic predictor variables in effects due to random polygenic and residual components. We also propose an index to detect influential families for each predictor variable identified with genetic effect. We assess the performance of our proposed method using our own family simulated data and the Genetic Analysis Workshop 17 family simulated data.

Original languageEnglish (US)
Pages (from-to)359-378
Number of pages20
JournalStatistical Applications in Genetics and Molecular Biology
Volume13
Issue number3
DOIs
StatePublished - Jun 2014

Keywords

  • Association analysis
  • Influential families
  • Polygenic models
  • Variance components

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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