Abstract
In hybrid maize breeding, the doubled haploid technique is increasingly replacing conventional recurrent selfing for the development of new lines. In addition, novel statistical methods have become available as a result of enhanced computing facilities. This has opened up many avenues to develop more efficient breeding schemes and selection strategies for maximizing progress from selection. The overall aim of the present study was to compare the selection progress by employing different breeding schemes and selection strategies. Two breeding schemes were considered, each involving selection in two stages: (i) developing DH lines from S0 plants and evaluating their testcrosses in stage one and testcrosses of the promising DH lines in stage two (DHTC) and (ii) early testing for testcross performance of S1 families before production of DH lines from superior S1 families and then evaluating their testcrosses in the second stage (S1TC-DHTC). For both breeding schemes, we examined different selection strategies, in which variance components and budgets varied, the cross and family structure was considered or ignored, and best linear unbiased prediction (BLUP) of testcross performance was employed. The specific objectives were to (1) maximize through optimum allocation of test resources the progress from selection, using the selection gain (ΔG) or the probability to select superior genotypes (P(q)) as well as their standard deviations as criteria, (2) investigate the effect of parental selection, varying variance components and budgets on the optimum allocation of test resources for maximizing the progress from selection, (3) assess the optimum filial generation (S0 or S1) for DH production, (4) compare various selection strategies - sequential selection considering or ignoring the cross and family structure - for maximizing progress from selection, (5) examine the effect of producing a larger number of candidates within promising crosses and S1 families on the progress from selection, and (6) determine the effect of BLUP, where information from genetically related candidates is integrated in the selection criteria, on the progress from selection. For both breeding schemes, the best strategy was to select among all S1 families and/or DH lines ignoring the cross structure. Further, in breeding scheme S1TC-DHTC, the progress from selection increased with variable sizes of crosses and S1 families, i.e., larger numbers of DH lines devoted to superior crosses and S1 families. Parental cross selection strongly influenced the optimum allocation of test resources and, consequently, the selection gain ΔG in both breeding schemes. With an increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progenies, the superiority in progress from selection compared to randomly chosen parents increased markedly, whereas the optimum number of parental crosses decreased in favor of an increased number of test candidates within crosses. With BLUP, information from genetically related test candidates resulted in more precise estimates of their genotypic values and the progress from selection slightly increased for both optimization criteria ΔG and P(q), compared with conventional phenotypic selection. Analytical solutions to enable fast calculations of the optimum allocation of test resources were developed. This analytical approach superseded matrix inversions required for the solution of the mixed model equations. In breeding scheme S1TC-DHTC, the optimum allocation of test resources involved (1) 10 or more test locations at both stages, (2) 10 or fewer parental crosses each with 100 to 300 S1 families at the first stage, and (3) 500 or more DH lines within a low number of parental crosses and S1 families at the second stage. In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5 to 10 times larger, whereas the number of test locations at the first stage and the number of DH lines at the second stage was strongly reduced compared with S1TC-DHTC. The possibility to reduce the number of parental crosses by selection among parental lines is of utmost importance for the optimization of the allocation of test resources and maximization of the progress from selection. Further, the optimum allocation of test resources is crucial to maximize the progress from selection under given economic and quantitative-genetic parameters. By using marker information and BLUP-based genomic selection, more efficient selection strategies could be developed for hybrid maize breeding.
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Claims 17
Claim · model-written summary
Selection across all S1 families or DH lines while ignoring cross structure was best for both schemes.
- Treatment
- select among all S1 families and/or DH lines ignoring the cross structuree0
- Outcome
- best strategye0
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“For both breeding schemes, the best strategy was to select among all S1 families and/or DH lines ignoring the cross structure”
t0“hybrid maize”
Claim · model-written summary
In S1TC-DHTC, allocating more DH lines to superior crosses and families increased selection progress.
- Treatment
- larger numbers of DH lines devoted to superior crosses and S1 familiese0
- Outcome
- progress from selectione0
- Direction
- increasede0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in breeding scheme S1TC-DHTC, the progress from selection increased with variable sizes of crosses and S1 families, i.e., larger numbers of DH lines devoted to superior crosses and S1 families”
t0“hybrid maize”
Claim · model-written summary
Parental cross selection strongly affected optimal test-resource allocation and selection gain in both schemes.
- Treatment
- Parental cross selectione0
- Outcome
- optimum allocation of test resources and, consequently, the selection gain ΔGe0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Parental cross selection strongly influenced the optimum allocation of test resources and, consequently, the selection gain ΔG in both breeding schemes”
t0“hybrid maize”
Claim · model-written summary
Greater parent-progeny mean testcross correlation increased the progress advantage over random parents.
- Outcome
- superiority in progress from selectione0
- Direction
- increased markedlye0
- Qualifier
- increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progeniese0
- Comparator
- randomly chosen parentse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“With an increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progenies, the superiority in progress from selection compared to randomly chosen parents increased markedly”
t0“hybrid maize”
Claim · model-written summary
Greater parent-progeny testcross correlation decreased the optimal number of parental crosses.
- Material
- parental linese1
- Outcome
- optimum number of parental crossese0
- Direction
- decreasede0
- Qualifier
- increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progeniese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the optimum number of parental crosses decreased in favor of an increased number of test candidates within crosses”
e1“With an increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progenies”
t0“hybrid maize”
Claim · model-written summary
Greater parent-progeny testcross correlation increased optimal candidate numbers within crosses.
- Material
- parental linese1
- Outcome
- number of test candidates within crossese0
- Direction
- increasede0
- Qualifier
- increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progeniese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“an increased number of test candidates within crosses”
e1“With an increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progenies”
t0“hybrid maize”
Claim · model-written summary
BLUP gave more precise genotypic-value estimates than conventional phenotypic selection.
- Treatment
- BLUPe0
- Outcome
- estimates of their genotypic valuese0
- Direction
- more precisee0
- Comparator
- conventional phenotypic selectione0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“With BLUP, information from genetically related test candidates resulted in more precise estimates of their genotypic values and the progress from selection slightly increased for both optimization criteria ΔG and P(q), compared with conventional phenotypic selection”
t0“hybrid maize”
Claim · model-written summary
BLUP slightly increased selection progress under both ΔG and P(q) criteria.
- Treatment
- BLUPe0
- Outcome
- progress from selectione0
- Direction
- increasede0
- Comparator
- conventional phenotypic selectione0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“With BLUP, information from genetically related test candidates resulted in more precise estimates of their genotypic values and the progress from selection slightly increased for both optimization criteria ΔG and P(q), compared with conventional phenotypic selection”
t0“hybrid maize”
Claim · model-written summary
Analytical solutions enabled fast calculation of optimal test-resource allocation and superseded mixed-model matrix inversions.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Analytical solutions to enable fast calculations of the optimum allocation of test resources were developed. This analytical approach superseded matrix inversions required for the solution of the mixed model equations”
t0“hybrid maize”
Claim · model-written summary
S1TC-DHTC optimal allocation used at least 10 test locations at both stages.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In breeding scheme S1TC-DHTC, the optimum allocation of test resources involved (1) 10 or more test locations at both stages”
t0“hybrid maize”
Claim · model-written summary
S1TC-DHTC optimal first-stage allocation used at most 10 parental crosses with 100 to 300 S1 families each.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“10 or fewer parental crosses each with 100 to 300 S1 families at the first stage”
t0“hybrid maize”
Claim · model-written summary
S1TC-DHTC optimal second-stage allocation used at least 500 DH lines in few crosses and families.
- Outcome
- DH linese0
- Value
- 500 or moree0
- Qualifier
- within a low number of parental crosses and S1 families at the second stagee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“500 or more DH lines within a low number of parental crosses and S1 families at the second stage”
t0“hybrid maize”
Claim · model-written summary
DHTC required 5 to 10 times more first-stage test candidates than S1TC-DHTC.
- Outcome
- optimum number of test candidates at the first stagee0
- Direction
- largere0
- Value
- 5 to 10 timese0
- Comparator
- S1TC-DHTCe1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5 to 10 times larger”
e1“compared with S1TC-DHTC”
t0“hybrid maize”
Claim · model-written summary
DHTC had fewer first-stage test locations and second-stage DH lines than S1TC-DHTC.
- Outcome
- number of test locations at the first stage and the number of DH lines at the second stagee0
- Direction
- strongly reducede0
- Comparator
- S1TC-DHTCe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the number of test locations at the first stage and the number of DH lines at the second stage was strongly reduced compared with S1TC-DHTC”
t0“hybrid maize”
Claim · model-written summary
The authors identify selecting parental lines to reduce parental crosses as important for optimal resource allocation and selection progress.
- Outcome
- reduce the number of parental crosses by selection among parental linese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The possibility to reduce the number of parental crosses by selection among parental lines is of utmost importance for the optimization of the allocation of test resources and maximization of the progress from selection”
t0“hybrid maize”
Claim · model-written summary
Optimal test-resource allocation is crucial to maximizing selection progress under the given parameters.
- Outcome
- optimum allocation of test resourcese0
- Qualifier
- under given economic and quantitative-genetic parameterse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the optimum allocation of test resources is crucial to maximize the progress from selection under given economic and quantitative-genetic parameters”
t0“hybrid maize”
Claim · model-written summary
Marker information and BLUP-based genomic selection could enable more efficient hybrid maize selection strategies.
- Treatment
- marker information and BLUP-based genomic selectione0
- Outcome
- more efficient selection strategiese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“By using marker information and BLUP-based genomic selection, more efficient selection strategies could be developed for hybrid maize breeding”