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Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species
BACKGROUND: The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286500/ https://www.ncbi.nlm.nih.gov/pubmed/32520936 http://dx.doi.org/10.1371/journal.pone.0232201 |
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author | Thistlethwaite, Frances R. Gamal El-Dien, Omnia Ratcliffe, Blaise Klápště, Jaroslav Porth, Ilga Chen, Charles Stoehr, Michael U. Ingvarsson, Pär K. El-Kassaby, Yousry A. |
author_facet | Thistlethwaite, Frances R. Gamal El-Dien, Omnia Ratcliffe, Blaise Klápště, Jaroslav Porth, Ilga Chen, Charles Stoehr, Michael U. Ingvarsson, Pär K. El-Kassaby, Yousry A. |
author_sort | Thistlethwaite, Frances R. |
collection | PubMed |
description | BACKGROUND: The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ~ 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200–50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F(1) families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. RESULTS: As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000–15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. CONCLUSIONS: Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits. |
format | Online Article Text |
id | pubmed-7286500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72865002020-06-17 Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species Thistlethwaite, Frances R. Gamal El-Dien, Omnia Ratcliffe, Blaise Klápště, Jaroslav Porth, Ilga Chen, Charles Stoehr, Michael U. Ingvarsson, Pär K. El-Kassaby, Yousry A. PLoS One Research Article BACKGROUND: The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ~ 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200–50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F(1) families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. RESULTS: As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000–15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. CONCLUSIONS: Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits. Public Library of Science 2020-06-10 /pmc/articles/PMC7286500/ /pubmed/32520936 http://dx.doi.org/10.1371/journal.pone.0232201 Text en © 2020 Thistlethwaite et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Thistlethwaite, Frances R. Gamal El-Dien, Omnia Ratcliffe, Blaise Klápště, Jaroslav Porth, Ilga Chen, Charles Stoehr, Michael U. Ingvarsson, Pär K. El-Kassaby, Yousry A. Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species |
title | Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species |
title_full | Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species |
title_fullStr | Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species |
title_full_unstemmed | Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species |
title_short | Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species |
title_sort | linkage disequilibrium vs. pedigree: genomic selection prediction accuracy in conifer species |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286500/ https://www.ncbi.nlm.nih.gov/pubmed/32520936 http://dx.doi.org/10.1371/journal.pone.0232201 |
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