Modeling copy number variation in the genomic prediction of maize hybrids

Lyra, Danilo Hottis; Galli, Giovanni; Alves, Filipe Couto; Granato, Ítalo Stefanine Correia; Vidotti, Miriam Suzane; Sousa, Massáine Bandeira e; Morosini, Júlia Silva; Crossa, José; Fritsche‐Neto, Roberto

doi:10.1007/s00122-018-3215-2

Cited by 20 publications

(11 citation statements)

References 101 publications

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“…However, previous CGH and SNP arrays did not design probes to target breakpoints and detected InDels by analyzing the variation of fluorescent intensity signals of ordered probes [30–32]. Consequently, these technologies targeted exclusively low copy regions of the genome, excluding InDels containing repeats, such as transposable elements (TEs) [2, 8, 42]. This is a strong drawback for maize and many other crops since a large part of their sequence is composed of transposable elements [28, 57] which may be highly variable between individuals [4, 24, 58] and may impact phenotypes [59–61].…”

Section: Discussionmentioning

confidence: 99%

“…Although our array was not designed to genotype duplications and inversions, our approach could be easily extended to genotype breakpoints of inversions, but further development of the pipeline for genotyping duplications using internal probes would be required. This powerful approach opens the way to studying the contribution of InDels and other SVs to trait variation and heterosis in maize [42] and should contribute to decipher the biological impact of InDels and other SVs at a larger scale in different species.…”

Section: Discussionmentioning

confidence: 99%

“…While these approaches proved to be useful, there is a strong risk of false positive detection of PAVs using OTV patterns, mainly because these arrays were not designed to target PAVs. In order to reduce this risk of false positive detection of PAVs and more largely CNVs, several methods based either on segmentation or Hidden Markov Chain have been developed to use variation of fluorescent intensity signal of contiguous probes along the genome [36–41] These kind of approaches have been used on 600 K Affymetrix® Axiom® SNP array to detect several hundreds of CNVs and to explore the contribution of CNV to phenotypic variation [42].…”

Section: Introductionmentioning

confidence: 99%

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High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array

et al. 2019

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BackgroundInsertions/deletions (InDels) and more specifically presence/absence variations (PAVs) are pervasive in several species and have strong functional and phenotypic effect by removing or drastically modifying genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection.ResultsWe have developed, as a proof of concept, a new high-throughput and affordable approach to genotype InDels. We first identified 141,000 InDels by aligning reads from the B73 line against the genome of three temperate maize inbred lines (F2, PH207, and C103) and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these InDels, with a combination of probes selected at breakpoint sites (13%) or within the InDel sequence, either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is composed of 662,772 probes and targets 105,927 InDels, including PAVs ranging from 35 bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 86,648 polymorphic InDels (82% of all InDels interrogated by the array) on 445 maize DNA samples with 422,369 probes. Genotyping InDels using this approach produced a highly reliable dataset, with low genotyping error (~ 3%), high call rate (~ 98%), and high reproducibility (> 95%). This reliability can be further increased by combining genotyping of several probes calling the same InDels (< 0.1% error rate and > 99.9% of call rate for 5 probes). This “proof of concept” tool was used to estimate the kinship matrix between 362 maize lines with 57,824 polymorphic InDels. This InDels kinship matrix was highly correlated with kinship estimated using SNPs from Illumina 50 K SNP arrays.ConclusionsWe efficiently genotyped thousands of small to large InDels on a sizeable number of individuals using a new Affymetrix® Axiom® array. This powerful approach opens the way to studying the contribution of InDels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of InDels at a larger scale.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array

et al. 2019

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“…Our second strategy was combining whole-genome marker effects with causative gene variants, and we observed that predictive ability did not improve significantly in any of the traits, except for grain weight per spike (Table S1). On one hand, genome-wide marker effects are most likely capturing the variation from other core genes, and possibly the information of both types of kinship would be redundant, consequently not effectively contributing to improving prediction (Lyra et al, 2019). On the other hand, incorporating gene effects considerably increased predictive ability for grain weight, suggesting that the associated regulatory pathway highly impacted the grain weight phenotype (see the proportion of heritability per single gene and subpopulation), thus adding extra information to the model.…”

Section: Trehalose Pathway Genes Revealed Substantial Contributions Tmentioning

confidence: 99%

Gene-based mapping of trehalose biosynthetic pathway genes reveals association with source- and sink-related yield traits in a spring wheat panel

Lyra

Griffiths

Watson

et al. 2020

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SummaryTrehalose 6-phosphate (T6P) signalling regulates carbon use and allocation and is a target to improve crop yields. However, the specific contributions of trehalose phosphate synthase (TPS) and trehalose phosphate phosphatase (TPP) genes to source- and sink-related traits remain largely unknown. We used exome-capture sequencing on TPS and TPP genes to estimate and partition the genetic variation of yield-related traits in a spring wheat (Triticum aestivum) breeding panel with diverse genetic heritage. Twelve phenotypes were directly correlated to TPS and TPP genes including final biomass (source) and spikes and grain numbers and grain filling traits (sink) showing indications of both positive and negative gene selection. Additionally, individual genes explained a substantial proportion of heritability (e.g. 3, 12, and 18% of the variance in gene homeologues most closely related to Arabidopsis TPS1 for final biomass), indicating a considerable contribution of this regulatory pathway to the phenotypic variation. Most importantly, two significant missense point mutations in the exon 6 of the TPS1 gene on chromosome 1D substantially increased plant height and peduncle length which was inversely related to grains per m2. Gene-based prediction resulted in significant gains of predictive ability (6% improvement) for grain weight when gene effects were combined with the whole genome markers, potentially helping breeding programs in designing strategic crosses. Three TPS1 homeologues were particularly significant in trait variation. Our study has generated a wealth of information on the role of natural variation of TPS and TPP genes related to yield potential.

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“…Recent studies have indicated the usefulness of various genomic prediction models involved in ridge regression best linear unbiased prediction (rrBLUP), genomic BLUP (GBLUP), and the general combining ability (GCA) model in prediction of yield-related traits and biomass-related traits (BRTs) in the harvested mature maize materials based on genomics, transcriptomics, and metabolites data [5][6][7][8][9][10][11][12][13]. Compared to the traditional pedigree approaches in plant breeding, predictive ability based on genomic prediction could be improved to different extents [14,15].…”

Section: Introductionmentioning

confidence: 99%

Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations

et al. 2021

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Background Heterosis has been widely used in maize breeding. However, we know little about the heterotic quantitative trait loci and their roles in genomic prediction. In this study, we sought to identify heterotic quantitative trait loci for seedling biomass-related traits using triple testcross design and compare their prediction accuracies by fitting molecular markers and heterotic quantitative trait loci. Results A triple testcross population comprised of 366 genotypes was constructed by crossing each of 122 intermated B73 × Mo17 genotypes with B73, Mo17, and B73 × Mo17. The mid-parent heterosis of seedling biomass-related traits involved in leaf length, leaf width, leaf area, and seedling dry weight displayed a large range, from less than 50 to ~ 150%. Relationships between heterosis of seedling biomass-related traits showed congruency with that between performances. Based on a linkage map comprised of 1631 markers, 14 augmented additive, two augmented dominance, and three dominance × additive epistatic quantitative trait loci for heterosis of seedling biomass-related traits were identified, with each individually explaining 4.1–20.5% of the phenotypic variation. All modes of gene action, i.e., additive, partially dominant, dominant, and overdominant modes were observed. In addition, ten additive × additive and six dominance × dominance epistatic interactions were identified. By implementing the general and special combining ability model, we found that prediction accuracy ranged from 0.29 for leaf length to 0.56 for leaf width. Different number of marker analysis showed that ~ 800 markers almost capture the largest prediction accuracies. When incorporating the heterotic quantitative trait loci into the model, we did not find the significant change of prediction accuracy, with only leaf length showing the marginal improvement by 1.7%. Conclusions Our results demonstrated that the triple testcross design is suitable for detecting heterotic quantitative trait loci and evaluating the prediction accuracy. Seedling leaf width can be used as the representative trait for seedling prediction. The heterotic quantitative trait loci are not necessary for genomic prediction of seedling biomass-related traits.

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Modeling copy number variation in the genomic prediction of maize hybrids

Cited by 20 publications

References 101 publications

High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array

High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array

Gene-based mapping of trehalose biosynthetic pathway genes reveals association with source- and sink-related yield traits in a spring wheat panel

Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations

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