Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline

Kadam, Dnyaneshwar C.; Potts, Sarah M.; Bohn, Martin; Lipka, Alexander E.; Lorenz, Aaron J.

doi:10.1534/g3.116.031286

Cited by 108 publications

(128 citation statements)

References 46 publications

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“…Therefore, increasing TP size will increase the precision of estimating GCA effects (Technow et al, 2014). Although the genetic covariance between inbreds was used for hybrid performance prediction in this study, recent research showed that applying genomic-estimated GCA and SCA might result in higher prediction accuracy when using T0 hybrids in the TP (Kadam et al, 2016). This heterotic pattern condition may be one explanation why prediction accuracy benefits more from increasing TP size using T2 hybrids than T0 hybrids.…”

Section: Prediction Accuracy Response To Increased Training Sizementioning

confidence: 92%

“…This heterotic pattern condition may be one explanation why prediction accuracy benefits more from increasing TP size using T2 hybrids than T0 hybrids. Although the genetic covariance between inbreds was used for hybrid performance prediction in this study, recent research showed that applying genomic-estimated GCA and SCA might result in higher prediction accuracy when using T0 hybrids in the TP (Kadam et al, 2016).…”

Section: Prediction Accuracy Response To Increased Training Sizementioning

confidence: 99%

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Genomic Selection Using Maize Ex‐Plant Variety Protection Germplasm for the Prediction of Nitrogen‐Use Traits

et al. 2019

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Maize (Zea mays L) yield increases associated with better usage of N fertilizer, (i.e., increased N use efficiency [NUE]), will require innovative breeding efforts. Genomic selection (GS) for N‐use traits (e.g., uptake or utilization efficiency) may speed up the breeding cycle of programs targeting NUE in maize. We evaluated the GS accuracy of 12 N‐use traits for training populations (TPs) varying in composition (TC) and size, predicted yield performance under different N fertilizer rates, and investigated the usefulness of GS for NUE in maize breeding programs. A total of 552 maize hybrids were planted under low (0 kg N ha−1) and high N fertilizer (252 kg N ha−1) conditions across 10 environments. Training composition scenarios included T0 (hybrids in which none of the parents were included in the random subset of inbreds), T1 (hybrids in which one of their parents were included in the random subset of inbreds), and T2 (hybrids in which both of their parents were included in the random subset of inbreds). Training population sizes ranged from 10 to 40 or 30 to 90 hybrids, depending on the N‐use trait. Across different TC, TP sizes, and N‐use traits, GS accuracy ranged from −0.12 to 0.78 and was greatest with larger TP sizes when both parents of untested hybrids appeared in the training and validation sets (T2 hybrids). Moreover, GS accuracy in response to different TC and TP sizes was dependent on the N‐use trait. Successful breeding for N stress tolerance or improved yield response to N fertilizer level will require selection of specific N‐use traits.

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Section: Prediction Accuracy Response To Increased Training Sizementioning

confidence: 92%

Section: Prediction Accuracy Response To Increased Training Sizementioning

confidence: 99%

Genomic Selection Using Maize Ex‐Plant Variety Protection Germplasm for the Prediction of Nitrogen‐Use Traits

et al. 2019

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“…These results 654 contrast with those from previous studies on hybrid maize, which showed low contribution of 655 non-additive genetic effects to genotypic variability. Critically, those studies were based on 656 panels derived solely from crosses between different heterotic groups, e.g., Flint×Dent (Technow 657 et al 2014, Giraud et al 2017 or SS×NSS (Kadam et al 2016). Therefore, complementation 658 effects were relatively consistent across hybrids, such that variability for specific combining 659 ability (contributed by dominance and/or epistasis) was low.…”

Section: Partition Of Polygenic Effects By Gene Proximity Increased Pmentioning

confidence: 99%

The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

et al. 2019

Preprint

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25Heterosis has been key to the development of maize breeding but describing its genetic basis has 26 been challenging. Previous studies of heterosis have shown the contribution of within-locus 27 complementation effects (dominance) and their differential importance across genomic regions. 28 However, they have generally considered panels of limited genetic diversity and have shown 29 little benefit to including dominance effects for predicting genotypic value in breeding 30 populations. This study examined within-locus complementation and enrichment of genetic 31 effects by functional classes in maize. We based our analyses on a diverse panel of inbred lines 32 crossed with two testers representative of the major heterotic groups in the United States (1,106 33 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1,640 34 hybrids). We assayed three agronomic traits: days to silking (DTS), plant height (PH) and grain 35 yield (GY). Our results point to the presence of dominance for all traits, but also among-locus 36 complementation (epistasis) for DTS and genotype-by-environment interactions for GY. 37Consistently, dominance improved genomic prediction for PH only. In addition, we assessed 38 enrichment of genetic effects in classes defined by genic regions (gene annotation), structural 39 features (recombination rate and chromatin openness), and evolutionary features (minor allele 40 frequency and evolutionary constraint). We found support for enrichment in genic regions and 41 subsequent improvement of genomic prediction for all traits. Our results point to mechanisms by 42 which heterosis arises through local complementation in proximal gene regions and suggest the 43

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“…However, apart from gain per unit cost, GS still proved advantageous at lower values in cases, where lines do not produce enough seed for actual testcrossing, and when field testing is reduced due to resource constraints. It also increased efficiency in cross prediction, especially early on in breeding programs (Kadam et al, 2016). Likewise, under drought, GS proved highly effective in identifying parental hybrid combinations that increased genetic gain in maize hybrids (Beyene et al, 2015).…”

Section: Hybrid‐enabled Line Profiling (Help)mentioning

confidence: 99%

Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing Crops

Ginkel¹,

Ortíz

2018

Crop Science

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P lant breeding, certainly in the era before genomics and marker-assisted selection, was often referred to as a "numbers game." The more crosses, the more likely it is to find improved genetic combinations. We propose a newly integrated breeding strategy for self-fertilizing crops that dramatically reduces the number of crosses being promoted while increasing the likelihood of obtaining superior new cultivars. This strategy emerges from experiments in the Bread Wheat Program of the International Center for the Improvement of Maize and Wheat (CIMMYT).In the late 1990s we noticed that ~8% of the elite new lines in the International Bread Wheat Screening Nursery (IBWSN) for international distribution were derived from <20 crosses. The remaining 92% of elite lines came from 5000 to 10,000 crosses. These few original crosses, and the lines derived from them, represented a strategy that we now describe as hybrid-enabled line profiling (HELP). This strategy offers breeding programs for selfing crops enormous benefits through improved performance. It Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing CropsMaarten van Ginkel* and Rodomiro Ortiz ABSTRACT Hybrid-enabled line profiling (HELP) is a new integrated breeding strategy for self-fertilizing crops that combines existing and recently identified elements, resulting in a strategy that synergistically exceeds existing breeding concepts. Heterosis in selfing crops is often driven by additive and additive ´ additive gene action, the molecular basis of which is increasingly being revealed. Unlike nonadditive heterosis, additive forms can be relatively easily fixed in homozygous lines, meaning that their seed can simply be resown to express the same "heterosis." Crossing diverse, complementary "selfing" parents to create the desired trait or allele line profile requires strict male sterility of the female; this can now be achieved relatively easily through present and emerging chemical, environmental, or genetic techniques. Fairly small amounts of hybrid seed are needed, with no need to scale up seed production, as it is not the hybrid that will be commercialized. After multilocation testing, homozygous lines from only the most superior hybrids, driven mainly by additive effects and additive ´ additive gene action, are rapidly derived using techniques such as doubled haploids. Multilocation testing and molecular confirmation of target line profiles then identify superior lines for release to farmers. The HELP strategy integrates modern high-throughput versions of existing and new concepts and methodologies into a breeding system strategy that focuses on the most superior crosses, <10% of all crosses. This focus results in significant increases in efficiency and can reverse the edible yield plateauing seen or feared in some of our major selfing food crops.M. van Ginkel,

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Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline

Cited by 108 publications

References 46 publications

Genomic Selection Using Maize Ex‐Plant Variety Protection Germplasm for the Prediction of Nitrogen‐Use Traits

Genomic Selection Using Maize Ex‐Plant Variety Protection Germplasm for the Prediction of Nitrogen‐Use Traits

The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing Crops

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