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Using genomic relationship likelihood for parentage assignment
BACKGROUND: Parentage assignment is usually based on a limited number of unlinked, independent genomic markers (microsatellites, low-density single nucleotide polymorphisms (SNPs), etc.). Classical methods for parentage assignment are exclusion-based (i.e. based on loci that violate Mendelian inheri...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960170/ https://www.ncbi.nlm.nih.gov/pubmed/29776335 http://dx.doi.org/10.1186/s12711-018-0397-7 |
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author | Grashei, Kim E. Ødegård, Jørgen Meuwissen, Theo H. E. |
author_facet | Grashei, Kim E. Ødegård, Jørgen Meuwissen, Theo H. E. |
author_sort | Grashei, Kim E. |
collection | PubMed |
description | BACKGROUND: Parentage assignment is usually based on a limited number of unlinked, independent genomic markers (microsatellites, low-density single nucleotide polymorphisms (SNPs), etc.). Classical methods for parentage assignment are exclusion-based (i.e. based on loci that violate Mendelian inheritance) or likelihood-based, assuming independent inheritance of loci. For true parent–offspring relations, genotyping errors cause apparent violations of Mendelian inheritance. Thus, the maximum proportion of such violations must be determined, which is complicated by variable call- and genotype error rates among loci and individuals. Recently, genotyping using high-density SNP chips has become available at lower cost and is increasingly used in genetics research and breeding programs. However, dense SNPs are not independently inherited, violating the assumptions of the likelihood-based methods. Hence, parentage assignment usually assumes a maximum proportion of exclusions, or applies likelihood-based methods on a smaller subset of independent markers. Our aim was to develop a fast and accurate trio parentage assignment method for dense SNP data without prior genotyping error- or call rate knowledge among loci and individuals. This genomic relationship likelihood (GRL) method infers parentage by using genomic relationships, which are typically used in genomic prediction models. RESULTS: Using 50 simulated datasets with 53,427 to 55,517 SNPs, genotyping error rates of 1–3% and call rates of ~ 80 to 98%, GRL was found to be fast and highly (~ 99%) accurate for parentage assignment. An iterative approach was developed for training using the evaluation data, giving similar accuracy. For comparison, we used the Colony2 software that assigns parentage and sibship simultaneously to increase the power of the likelihood-based method and found that it has considerably lower accuracy than GRL. We also compared GRL with an exclusion-based method in which one of the parameters was estimated using GRL assignments.This method was slightly more accurate than GRL. CONCLUSIONS: We show that GRL is a fast and accurate method of parentage assignment that can use dense, non-independent SNPs, with variable call rates and unknown genotyping error rates. By offering an alternative way of assigning parents, GRL is also suitable for estimating the expected proportion of inconsistent parent–offspring genotypes for exclusion-based models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12711-018-0397-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5960170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59601702018-05-24 Using genomic relationship likelihood for parentage assignment Grashei, Kim E. Ødegård, Jørgen Meuwissen, Theo H. E. Genet Sel Evol Research Article BACKGROUND: Parentage assignment is usually based on a limited number of unlinked, independent genomic markers (microsatellites, low-density single nucleotide polymorphisms (SNPs), etc.). Classical methods for parentage assignment are exclusion-based (i.e. based on loci that violate Mendelian inheritance) or likelihood-based, assuming independent inheritance of loci. For true parent–offspring relations, genotyping errors cause apparent violations of Mendelian inheritance. Thus, the maximum proportion of such violations must be determined, which is complicated by variable call- and genotype error rates among loci and individuals. Recently, genotyping using high-density SNP chips has become available at lower cost and is increasingly used in genetics research and breeding programs. However, dense SNPs are not independently inherited, violating the assumptions of the likelihood-based methods. Hence, parentage assignment usually assumes a maximum proportion of exclusions, or applies likelihood-based methods on a smaller subset of independent markers. Our aim was to develop a fast and accurate trio parentage assignment method for dense SNP data without prior genotyping error- or call rate knowledge among loci and individuals. This genomic relationship likelihood (GRL) method infers parentage by using genomic relationships, which are typically used in genomic prediction models. RESULTS: Using 50 simulated datasets with 53,427 to 55,517 SNPs, genotyping error rates of 1–3% and call rates of ~ 80 to 98%, GRL was found to be fast and highly (~ 99%) accurate for parentage assignment. An iterative approach was developed for training using the evaluation data, giving similar accuracy. For comparison, we used the Colony2 software that assigns parentage and sibship simultaneously to increase the power of the likelihood-based method and found that it has considerably lower accuracy than GRL. We also compared GRL with an exclusion-based method in which one of the parameters was estimated using GRL assignments.This method was slightly more accurate than GRL. CONCLUSIONS: We show that GRL is a fast and accurate method of parentage assignment that can use dense, non-independent SNPs, with variable call rates and unknown genotyping error rates. By offering an alternative way of assigning parents, GRL is also suitable for estimating the expected proportion of inconsistent parent–offspring genotypes for exclusion-based models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12711-018-0397-7) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-18 /pmc/articles/PMC5960170/ /pubmed/29776335 http://dx.doi.org/10.1186/s12711-018-0397-7 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Grashei, Kim E. Ødegård, Jørgen Meuwissen, Theo H. E. Using genomic relationship likelihood for parentage assignment |
title | Using genomic relationship likelihood for parentage assignment |
title_full | Using genomic relationship likelihood for parentage assignment |
title_fullStr | Using genomic relationship likelihood for parentage assignment |
title_full_unstemmed | Using genomic relationship likelihood for parentage assignment |
title_short | Using genomic relationship likelihood for parentage assignment |
title_sort | using genomic relationship likelihood for parentage assignment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960170/ https://www.ncbi.nlm.nih.gov/pubmed/29776335 http://dx.doi.org/10.1186/s12711-018-0397-7 |
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