Cargando…
Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection
Recurrent selection (RS) has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents [Formula: see text] but little is known about how [Formula: see text] affects genomic selection (GS) in RS, especi...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Genetics Society of America
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345710/ https://www.ncbi.nlm.nih.gov/pubmed/28064189 http://dx.doi.org/10.1534/g3.116.036582 |
_version_ | 1782513769014886400 |
---|---|
author | Müller, Dominik Schopp, Pascal Melchinger, Albrecht E. |
author_facet | Müller, Dominik Schopp, Pascal Melchinger, Albrecht E. |
author_sort | Müller, Dominik |
collection | PubMed |
description | Recurrent selection (RS) has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents [Formula: see text] but little is known about how [Formula: see text] affects genomic selection (GS) in RS, especially the persistency of prediction accuracy ([Formula: see text]) and genetic gain. Synthetics were simulated by intermating [Formula: see text] = 2–32 parent lines from an ancestral population with short- or long-range linkage disequilibrium ([Formula: see text]) and subjected to multiple cycles of GS. We determined [Formula: see text] and genetic gain across 30 cycles for different training set (TS) sizes, marker densities, and generations of recombination before model training. Contributions to [Formula: see text] and genetic gain from pedigree relationships, as well as from cosegregation and [Formula: see text] between QTL and markers, were analyzed via four scenarios differing in (i) the relatedness between TS and selection candidates and (ii) whether selection was based on markers or pedigree records. Persistency of [Formula: see text] was high for small [Formula: see text] where predominantly cosegregation contributed to [Formula: see text] , but also for large [Formula: see text] where [Formula: see text] replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing [Formula: see text] > 4, given long-range LD(A) in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to [Formula: see text] for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size ([Formula: see text]) and higher marker density improved persistency of [Formula: see text] and hence genetic gain, but additional recombinations could not increase genetic gain. |
format | Online Article Text |
id | pubmed-5345710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-53457102017-03-21 Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection Müller, Dominik Schopp, Pascal Melchinger, Albrecht E. G3 (Bethesda) Genomic Selection Recurrent selection (RS) has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents [Formula: see text] but little is known about how [Formula: see text] affects genomic selection (GS) in RS, especially the persistency of prediction accuracy ([Formula: see text]) and genetic gain. Synthetics were simulated by intermating [Formula: see text] = 2–32 parent lines from an ancestral population with short- or long-range linkage disequilibrium ([Formula: see text]) and subjected to multiple cycles of GS. We determined [Formula: see text] and genetic gain across 30 cycles for different training set (TS) sizes, marker densities, and generations of recombination before model training. Contributions to [Formula: see text] and genetic gain from pedigree relationships, as well as from cosegregation and [Formula: see text] between QTL and markers, were analyzed via four scenarios differing in (i) the relatedness between TS and selection candidates and (ii) whether selection was based on markers or pedigree records. Persistency of [Formula: see text] was high for small [Formula: see text] where predominantly cosegregation contributed to [Formula: see text] , but also for large [Formula: see text] where [Formula: see text] replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing [Formula: see text] > 4, given long-range LD(A) in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to [Formula: see text] for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size ([Formula: see text]) and higher marker density improved persistency of [Formula: see text] and hence genetic gain, but additional recombinations could not increase genetic gain. Genetics Society of America 2017-01-04 /pmc/articles/PMC5345710/ /pubmed/28064189 http://dx.doi.org/10.1534/g3.116.036582 Text en Copyright © 2017 Müller et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article 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 the original work is properly cited. |
spellingShingle | Genomic Selection Müller, Dominik Schopp, Pascal Melchinger, Albrecht E. Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection |
title | Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection |
title_full | Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection |
title_fullStr | Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection |
title_full_unstemmed | Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection |
title_short | Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection |
title_sort | persistency of prediction accuracy and genetic gain in synthetic populations under recurrent genomic selection |
topic | Genomic Selection |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345710/ https://www.ncbi.nlm.nih.gov/pubmed/28064189 http://dx.doi.org/10.1534/g3.116.036582 |
work_keys_str_mv | AT mullerdominik persistencyofpredictionaccuracyandgeneticgaininsyntheticpopulationsunderrecurrentgenomicselection AT schopppascal persistencyofpredictionaccuracyandgeneticgaininsyntheticpopulationsunderrecurrentgenomicselection AT melchingeralbrechte persistencyofpredictionaccuracyandgeneticgaininsyntheticpopulationsunderrecurrentgenomicselection |