Evidence map›Paper›PMID 26883044›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2016

Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML.

Angela-Maria Bernal-Vasquez, H-Friedrich Utz, Hans-Peter Piepho

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Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

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65citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

65 citing papers in PubMed.

  1. Genomic loci associated with Fusarium stalk rot resistance and related agronomic traits in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  2. Association mapping of hybrid seed set and heterosis in a central European multi-parental wheat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  4. Integrating genomic predictions into an applied Central European wheat breeding program.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  11. Harnessing historical genebank data to accelerate pea breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
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  12. Harnessing big data for enhanced genome-wide prediction in winter wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
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5 more citing papers are in PubMed but not listed here.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Angela-Maria Bernal-VasquezBiostatistics Unit, Institute of Crop Sciences, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany. bernalva@uni-hohenheim.de.ORCID http://orcid.org/0000-0003-0415-8318
H-Friedrich UtzPlant Breeding Institute, University of Hohenheim, Fruwirthstrasse 21, 70599, Stuttgart, Germany.
Hans-Peter PiephoBiostatistics Unit, Institute of Crop Sciences, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

key messageWe review and propose several methods for identifying possible outliers and evaluate their properties. The methods are applied to a genomic prediction program in hybrid rye. Many plant breeders use ANOVA-based software for routine analysis of field trials. These programs may offer specific in-built options for residual analysis that are lacking in current REML software. With the advance of molecular technologies, there is a need to switch to REML-based approaches, but without losing the good features of outlier detection methods that have proven useful in the past. Our aims were to compare the variance component estimates between ANOVA and REML approaches, to scrutinize the outlier detection method of the ANOVA-based package PlabStat and to propose and evaluate alternative procedures for outlier detection. We compared the outputs produced using ANOVA and REML approaches of four published datasets of generalized lattice designs. Five outlier detection methods are explained step by step. Their performance was evaluated by measuring the true positive rate and the false positive rate in a dataset with artificial outliers simulated in several scenarios. An implementation of genomic prediction using an empirical rye multi-environment trial was used to assess the outlier detection methods with respect to the predictive abilities of a mixed model for each method. We provide a detailed explanation of how the PlabStat outlier detection methodology can be translated to REML-based software together with the evaluation of alternative methods to identify outliers. The method combining the Bonferroni-Holm test to judge each residual and the residual standardization strategy of PlabStat exhibited good ability to detect outliers in small and large datasets and under a genomic prediction application. We recommend the use of outlier detection methods as a decision support in the routine data analyses of plant breeding experiments.

Indexed as

Models, GeneticPlant BreedingAnalysis of VarianceGenomicsLikelihood FunctionsModels, StatisticalSecaleSoftware

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.