Segregation distortion: Utilizing simulated genotyping data to evaluate statistical methods

Alexander Coulton; Alexandra M. Przewieslik-Allen; Amanda J. Burridge; Daniel S. Shaw; Keith J. Edwards; Gary L. A. Barker

doi:10.1371/journal.pone.0228951

Segregation distortion: Utilizing simulated genotyping data to evaluate statistical methods

Alexander Coulton, Alexandra M. Przewieslik-Allen, Amanda J. Burridge, Daniel S. Shaw, Keith J. Edwards, Gary L. A. Barker

Department of Life Sciences

University of Bristol

Research output: Contribution to journal › Article › peer-review

9 Citations (SciVal)

Abstract

Segregation distortion is the phenomenon in which genotypes deviate from expected Mendelian ratios in the progeny of a cross between two varieties or species. There is not currently a widely used consensus for the appropriate statistical test, or more specifically the multiple testing correction procedure, used to detect segregation distortion for high-density single-nucleotide polymorphism (SNP) data. Here we examine the efficacy of various multiple testing procedures, including chi-square test with no correction for multiple testing, false-discovery rate correction and Bonferroni correction using an in-silico simulation of a biparental mapping population. We find that the false discovery rate correction best approximates the traditional p-value threshold of 0.05 for high-density marker data. We also utilize this simulation to test the effect of segregation distortion on the genetic mapping process, specifically on the formation of linkage groups during marker clustering. Only extreme segregation distortion was found to effect genetic mapping. In addition, we utilize replicate empirical mapping populations of wheat varieties Avalon and Cadenza to assess how often segregation distortion conforms to the same pattern between closely related wheat varieties.

Original language	English
Article number	e0228951
Pages (from-to)	e0228951
Journal	PLoS ONE
Volume	15
Issue number	2
DOIs	https://doi.org/10.1371/journal.pone.0228951
Publication status	Published - 19 Feb 2020

Bibliographical note

Funding Information:
AC is supported by the BBSRC funded South West Biosciences Doctoral Training Partnership (BB/M009122/1). We are grateful to the Biotechnology and Biological Sciences Research Council - https://bbsrc.ukri.org/ - (BBSRC), UK, for funding this work under the Designing Future Wheat program (BBS/E/C/ 000I0280). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
Copyright: © 2020 Coulton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

General Biochemistry,Genetics and Molecular Biology
General Agricultural and Biological Sciences
General

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https://dx.plos.org/10.1371/journal.pone.0228951

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abstract = "Segregation distortion is the phenomenon in which genotypes deviate from expected Mendelian ratios in the progeny of a cross between two varieties or species. There is not currently a widely used consensus for the appropriate statistical test, or more specifically the multiple testing correction procedure, used to detect segregation distortion for high-density single-nucleotide polymorphism (SNP) data. Here we examine the efficacy of various multiple testing procedures, including chi-square test with no correction for multiple testing, false-discovery rate correction and Bonferroni correction using an in-silico simulation of a biparental mapping population. We find that the false discovery rate correction best approximates the traditional p-value threshold of 0.05 for high-density marker data. We also utilize this simulation to test the effect of segregation distortion on the genetic mapping process, specifically on the formation of linkage groups during marker clustering. Only extreme segregation distortion was found to effect genetic mapping. In addition, we utilize replicate empirical mapping populations of wheat varieties Avalon and Cadenza to assess how often segregation distortion conforms to the same pattern between closely related wheat varieties.",

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Segregation distortion: Utilizing simulated genotyping data to evaluate statistical methods

Abstract

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