How can a high-quality genome assembly help plant breeders?

Benevenuto, Juliana; Ferrão, Luís Felipe Ventorim; Amadeu, Rodrigo R.; Muñoz, Patricio

doi:10.1093/gigascience/giz068

Cited by 48 publications

(57 citation statements)

References 11 publications

(17 reference statements)

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“…Markers associated with QTLs explained low-to-moderate portions of the phenotypic variances, which suggest a quantitative nature of these traits. In accordance to this, previous GWAS analyses in blueberry also detected few and scattered associations between SNPs and fruit firmness using dominant gene action models, which also explained a small proportion of the trait variation (Ferrão et al, 2018; Benevenuto et al, 2019).…”

Section: Discussionsupporting

confidence: 84%

“…Library preparation and sequencing were performed by RAPiD Genomics (Gainesville, FL, USA) using a sequence capture approach. Briefly, 6,000 custom-designed biotinylated probes of 120-mer were selected based on the distribution on the ‘Draper’ reference genome (Benevenuto et al, 2019), and previously tested on the parental cultivars. Sequencing of the entire population was carried out in the Illumina HiSeq platform using 150 cycle paired-end runs.…”

Section: Methodsmentioning

confidence: 99%

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High-Resolution Linkage Map and QTL Analyses for Machine Harvest Traits in Autotetraploid Blueberry

Cappai

Amadeu

Benevenuto

et al. 2020

Preprint

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2Blueberry (Vacciunium corymbosum and hybrids) is an autotetraploid crop whose commercial 3 relevance has been growing steadily during the last twenty years. However, the ever-increasing 4 cost of labor for hand-picking blueberry is one main constraint in competitive marketing of the 5 fruit. Machine harvestability is, therefore, a key trait for the blueberry industry. Understanding 6 the genetic architecture of traits through quantitative trait locus (QTL) mapping is the first step 7 towards implementation of molecular breeding for faster genetic gains. Despite recent advances 8 in software development for autotetraploid genetic mapping, a high-resolution map is still not 9 available for blueberry. In this study, we crafted a map for autotetraploid low-chill highbush 10 blueberry containing 11,292 SNP markers and a total size of 1,953.97 cM (average density of 11 5.78 markers/cM). This map was subsequently used to perform QTL analyses for traits relevant 12 to machine harvesting: firmness, firmness retention, and fruit detachment force. Significant 13 QTL peaks were identified for all the traits. The QTL intervals were further explored for putative 14 candidate genes. Genes related to cell wall remodeling were highlighted in the firmness and 15 firmness retention intervals. For fruit detachment force, transcription factors involved in fruit 16 abscission were detected. Altogether, our findings provide the basis for future fine-mapping and 17 molecular breeding efforts for machine harvesting in blueberry. 18 Keywords: Vaccinium, southern highbush blueberry, firmness, detachment force, polyploid 19 20chromosomal alterations are not only tolerated by plant genomes, but can also drive evolution, for example, 21 promoting speciation (Adams and Wendel, 2005). Polyploid organisms are classified as either allopolyploids 22 or autopolyploids, depending on the degree of divergence between their subgenomes (Brubaker et al., 1999). 23 Autopolyploids, such as blueberry (Vaccinium corymbosum L.), potato (Solanum tuberosum L.), and alfalfa 24 (Medicago sativa L.), contain multiple copies of the same chromosome set, which can all pair and exchange 25 1 Cappai et al. QTL Map for Blueberry Harvest genetic material during gamete formation. In contrast, allopolyploids, such as wheat (Triticum aestivum 26 L.), coffee (Coffea arabica L.), and strawberry (Fragaria ananassa Duch.) contain two or more divergent 27 genomes, which usually show bivalent pairing and have disomic inheritance, like diploid organisms. 28 While in diploid systems the study of allelic inheritance is relatively simple, polysomic inheritance in 29 autopolyploids increases the number of possible genetic configurations and impacts downstream genetic 30 analyses, including linkage map construction and quantitative trait loci (QTL) mapping (Bever and Felber, 31 1992). 32 QTL mapping is the association study between phenotypic and genetic variants usually performed by 33 tracking recombination events of the possible QTL along chromosomes (Broman and Sen, 2009). In 34 b...

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Section: Discussionsupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

High-Resolution Linkage Map and QTL Analyses for Machine Harvest Traits in Autotetraploid Blueberry

Cappai

Amadeu

Benevenuto

et al. 2020

Preprint

Self Cite

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“…Phenotypes were pre-corrected for fixed year effects, as detailed in Amadeu et al (2019) and de Bem Oliveira et al (2019). A total of 73,045 SNPs was obtained using sequence capture by Rapid Genomics (Gainesville, FL), after aligning the reads against the high-quality "Draper" genome assembly (Colle et al, 2019) as described in Benevenuto et al (2019). Marker filtering followed these criteria: biallelic, mean coverage > 40, minimum allele frequency > 0.01; maximum missing data = 0.5%; minimum quality = 20.…”

Section: Plant Materials and Genotypesmentioning

confidence: 99%

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

et al. 2020

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“…In GBS experiments, the consensus of read clusters at cutting sites could be adopted as a reference in cases where there is no appropriate sequence to use 50 . However, genome assembly is a difficult task when dealing with repetitive regions and polyploids 110 . With the aim of reducing possible biases, we decided not to use de novo approaches, which were previously described as inappropriate for sugarcane GBS data 105 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Aono

Costa

Rody

et al. 2020

Preprint

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Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane. 3/154/15 10/15

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How can a high-quality genome assembly help plant breeders?

Cited by 48 publications

References 11 publications

High-Resolution Linkage Map and QTL Analyses for Machine Harvest Traits in Autotetraploid Blueberry

High-Resolution Linkage Map and QTL Analyses for Machine Harvest Traits in Autotetraploid Blueberry

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

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