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Genome-enabled predictions for binomial traits in sugar beet populations
BACKGROUND: Genomic information can be used to predict not only continuous but also categorical (e.g. binomial) traits. Several traits of interest in human medicine and agriculture present a discrete distribution of phenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an examp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4113669/ https://www.ncbi.nlm.nih.gov/pubmed/25053450 http://dx.doi.org/10.1186/1471-2156-15-87 |
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author | Biscarini, Filippo Stevanato, Piergiorgio Broccanello, Chiara Stella, Alessandra Saccomani, Massimo |
author_facet | Biscarini, Filippo Stevanato, Piergiorgio Broccanello, Chiara Stella, Alessandra Saccomani, Massimo |
author_sort | Biscarini, Filippo |
collection | PubMed |
description | BACKGROUND: Genomic information can be used to predict not only continuous but also categorical (e.g. binomial) traits. Several traits of interest in human medicine and agriculture present a discrete distribution of phenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an example of binomial trait of agronomic importance. In this paper, a panel of 192 SNPs (single nucleotide polymorphisms) was used to genotype 124 sugar beet individual plants from 18 lines, and to classify them as showing “high” or “low” root vigor. RESULTS: A threshold model was used to fit the relationship between binomial root vigor and SNP genotypes, through the matrix of genomic relationships between individuals in a genomic BLUP (G-BLUP) approach. From a 5-fold cross-validation scheme, 500 testing subsets were generated. The estimated average cross-validation error rate was 0.000731 (0.073%). Only 9 out of 12326 test observations (500 replicates for an average test set size of 24.65) were misclassified. CONCLUSIONS: The estimated prediction accuracy was quite high. Such accurate predictions may be related to the high estimated heritability for root vigor (0.783) and to the few genes with large effect underlying the trait. Despite the sparse SNP panel, there was sufficient within-scaffold LD where SNPs with large effect on root vigor were located to allow for genome-enabled predictions to work. |
format | Online Article Text |
id | pubmed-4113669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41136692014-08-05 Genome-enabled predictions for binomial traits in sugar beet populations Biscarini, Filippo Stevanato, Piergiorgio Broccanello, Chiara Stella, Alessandra Saccomani, Massimo BMC Genet Research Article BACKGROUND: Genomic information can be used to predict not only continuous but also categorical (e.g. binomial) traits. Several traits of interest in human medicine and agriculture present a discrete distribution of phenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an example of binomial trait of agronomic importance. In this paper, a panel of 192 SNPs (single nucleotide polymorphisms) was used to genotype 124 sugar beet individual plants from 18 lines, and to classify them as showing “high” or “low” root vigor. RESULTS: A threshold model was used to fit the relationship between binomial root vigor and SNP genotypes, through the matrix of genomic relationships between individuals in a genomic BLUP (G-BLUP) approach. From a 5-fold cross-validation scheme, 500 testing subsets were generated. The estimated average cross-validation error rate was 0.000731 (0.073%). Only 9 out of 12326 test observations (500 replicates for an average test set size of 24.65) were misclassified. CONCLUSIONS: The estimated prediction accuracy was quite high. Such accurate predictions may be related to the high estimated heritability for root vigor (0.783) and to the few genes with large effect underlying the trait. Despite the sparse SNP panel, there was sufficient within-scaffold LD where SNPs with large effect on root vigor were located to allow for genome-enabled predictions to work. BioMed Central 2014-07-22 /pmc/articles/PMC4113669/ /pubmed/25053450 http://dx.doi.org/10.1186/1471-2156-15-87 Text en Copyright © 2014 Biscarini et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 Biscarini, Filippo Stevanato, Piergiorgio Broccanello, Chiara Stella, Alessandra Saccomani, Massimo Genome-enabled predictions for binomial traits in sugar beet populations |
title | Genome-enabled predictions for binomial traits in sugar beet populations |
title_full | Genome-enabled predictions for binomial traits in sugar beet populations |
title_fullStr | Genome-enabled predictions for binomial traits in sugar beet populations |
title_full_unstemmed | Genome-enabled predictions for binomial traits in sugar beet populations |
title_short | Genome-enabled predictions for binomial traits in sugar beet populations |
title_sort | genome-enabled predictions for binomial traits in sugar beet populations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4113669/ https://www.ncbi.nlm.nih.gov/pubmed/25053450 http://dx.doi.org/10.1186/1471-2156-15-87 |
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