Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation

Abstract Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on p...

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Autores principales:	N. Khalilisamani, P. C. Thomson, H. W. Raadsma, M. S. Khatkar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ac93fe8cbbd410d827ecf165dec1428
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spelling	oai:doaj.org-article:7ac93fe8cbbd410d827ecf165dec14282021-12-02T18:02:06ZImpact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation10.1038/s41598-021-97873-52045-2322https://doaj.org/article/7ac93fe8cbbd410d827ecf165dec14282021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97873-5https://doaj.org/toc/2045-2322Abstract Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn, Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.N. KhalilisamaniP. C. ThomsonH. W. RaadsmaM. S. KhatkarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q N. Khalilisamani P. C. Thomson H. W. Raadsma M. S. Khatkar Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation
description	Abstract Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn, Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.
format	article
author	N. Khalilisamani P. C. Thomson H. W. Raadsma M. S. Khatkar
author_facet	N. Khalilisamani P. C. Thomson H. W. Raadsma M. S. Khatkar
author_sort	N. Khalilisamani
title	Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation
title_short	Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation
title_full	Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation
title_fullStr	Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation
title_full_unstemmed	Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation
title_sort	impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7ac93fe8cbbd410d827ecf165dec1428
work_keys_str_mv	AT nkhalilisamani impactofgenotypicerrorswithequalandunequalfamilycontributiononaccuracyofgenomicpredictioninaquacultureusingsimulation AT pcthomson impactofgenotypicerrorswithequalandunequalfamilycontributiononaccuracyofgenomicpredictioninaquacultureusingsimulation AT hwraadsma impactofgenotypicerrorswithequalandunequalfamilycontributiononaccuracyofgenomicpredictioninaquacultureusingsimulation AT mskhatkar impactofgenotypicerrorswithequalandunequalfamilycontributiononaccuracyofgenomicpredictioninaquacultureusingsimulation
_version_	1718378935640653824

Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation

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