Literature DB >> 16189542

Biased estimators of quantitative trait locus heritability and location in interval mapping.

M Bogdan1, R W Doerge.   

Abstract

In many empirical studies, it has been observed that genome scans yield biased estimates of heritability, as well as genetic effects. It is widely accepted that quantitative trait locus (QTL) mapping is a model selection procedure, and that the overestimation of genetic effects is the result of using the same data for model selection as estimation of parameters. There are two key steps in QTL modeling, each of which biases the estimation of genetic effects. First, test procedures are employed to select the regions of the genome for which there is significant evidence for the presence of QTL. Second, and most important for this demonstration, estimates of the genetic effects are reported only at the locations for which the evidence is maximal. We demonstrate that even when we know there is just one QTL present (ignoring the testing bias), and we use interval mapping to estimate its location and effect, the estimator of the effect will be biased. As evidence, we present results of simulations investigating the relative importance of the two sources of bias and the dependence of bias of heritability estimators on the true QTL heritability, sample size, and the length of the investigated part of the genome. Moreover, we present results of simulations demonstrating the skewness of the distribution of estimators of QTL locations and the resulting bias in estimation of location. We use computer simulations to investigate the dependence of this bias on the true QTL location, heritability, and the sample size.

Mesh:

Year:  2005        PMID: 16189542     DOI: 10.1038/sj.hdy.6800747

Source DB:  PubMed          Journal:  Heredity (Edinb)        ISSN: 0018-067X            Impact factor:   3.821


  8 in total

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2.  Locating multiple interacting quantitative trait Loci using rank-based model selection.

Authors:  Małgorzata Zak; Andreas Baierl; Małgorzata Bogdan; Andreas Futschik
Journal:  Genetics       Date:  2007-05-16       Impact factor: 4.562

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4.  Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis.

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Journal:  Genetics       Date:  2006-12-18       Impact factor: 4.562

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Journal:  Curr Opin Behav Sci       Date:  2015-12-01

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7.  Identification of QTLs for rice grain size and weight by high-throughput SNP markers in the IR64 x Sadri population.

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Journal:  Front Genet       Date:  2022-08-19       Impact factor: 4.772

8.  Variable selection for large p small n regression models with incomplete data: mapping QTL with epistases.

Authors:  Min Zhang; Dabao Zhang; Martin T Wells
Journal:  BMC Bioinformatics       Date:  2008-05-29       Impact factor: 3.169

  8 in total

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