Genomic prediction in family bulks using different traits and cross-validations in pine
| Autor: | Patricio R. Munoz, Matias Kirst, Janeo Eustáquio de Almeida Filho, Marcio F. R. Resende, Marcos Deon Vilela de Resende, Esteban F. Rios, Salvador A. Gezan, Mario H. M. L. Andrade |
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| Přispěvatelé: | ESTEBAN FERNANDO RIOS, UNIVERSITY OF FLORIDA, MARIO H M L ANDRADE, UNIVERSITY OF FLORIDA, MARCIO F R RESENDE JR, UNIVERSITY OF FLORIDA, MATIAS KIRST, UNIVERSITY OF FLORIDA, MARCOS DEON VILELA DE RESENDE, CNPCa, JANEO E DE ALMEIDA FILHO, BAYER CROP SCIENCE, SALVADOR A GEZAN, VSN INTERNATIONAL, PATRICIO MUNOZ, UNIVERSITY OF FLORIDA. |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
0301 basic medicine AcademicSubjects/SCI01140 Genotype AcademicSubjects/SCI00010 In silico Population Reprodução Vegetal Unit of selection Melhoramento Genético Vegetal QH426-470 Biology AcademicSubjects/SCI01180 Loblolly pine 01 natural sciences Polymorphism Single Nucleotide training population 03 medical and health sciences Statistics Genetics Humans Selection Genetic education Molecular Biology Allele frequency Genetics (clinical) Selection (genetic algorithm) genomic prediction Investigation Pineus education.field_of_study Models Genetic fungi predictive ability food and beverages Variance (accounting) Genomics Mating system family selection Statistical models Plant Breeding 030104 developmental biology Phenotype Genetic gain AcademicSubjects/SCI00960 010606 plant biology & botany |
| Zdroj: | G3: Genes|Genomes|Genetics G3: Genes, Genomes, Genetics, Vol 11, Iss 9 (2021) Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
| ISSN: | 2160-1836 |
| Popis: | Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5?20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations. Made available in DSpace on 2022-01-21T01:17:09Z (GMT). No. of bitstreams: 1 Genomic-prediction-in-family-bulks.pdf: 944283 bytes, checksum: 8d30a3d9d2120f1b7c162d051e07c5ac (MD5) Previous issue date: 2021 |
| Databáze: | OpenAIRE |
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