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Petroli, C.

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Petroli
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Petroli, C.
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0000-0003-0502-248X

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2015 - 20186
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End date: 2019 × Start date: 2015 ×

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Now showing 1 - 6 of 6
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    Publication
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    Capacity Development: Collaborative Projects
    (CIMMYT, 2018) Petroli, C.
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    From genebank to field-leveraging genomics to identify and bring novel native variation to breeding pools
    (CIMMYT, 2016) Romero, A.; Hickey, J.; Kilian, A.; Buckler, E.; Marshall, D.S.; Crossa, J.; Petroli, C.; Sansaloni, C.; Molnar, T.L.; Pixley, K.V.; Wenzl, P.; Singh, S.; Burgueño, J.; Charles Chen; Salinas García, G.; Willcox, M.; Saint Pierre, C.
    Potentially valuable genetic variation, the raw material for crop improvement, remains untapped on genebank shelves, at a time when challenges to crop production are unprecedented. Genebanks should NOT be museums. They should enable breeders worldwide to use high-value genetic diversity to meet tomorrow’s challenges
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    The development of quality control genotyping approaches: a case study using elite maize lines
    (Public Library of Science, 2016) Jiafa Chen; Zavala Espinosa, C.; Ortega, N.; Petroli, C.; Franco, J.; Burgueño, J.; Costich, D.E.; Hearne, S.
    Quality control (QC) of germplasmidentity and purity is a critical component of breeding and conservation activities. SNP genotyping technologies and increased availability of markers provide the opportunity to employ genotyping as a low-cost and robust component of this QC. In the public sector available low-cost SNP QC genotypingmethods have been developed from a very limited panel ofmarkers of 1,000 to 1,500 markers without broad selection of the most informative SNPs. Selection of optimal SNPs and definition of appropriate germplasm sampling in addition to platform section impact on logistical and resource-use considerations for breeding and conservation applications when mainstreaming QC. In order to address these issues, we evaluated the selection and use of SNPs for QC applications from large DArTSeq data sets generated from CIMMYT maize inbred lines (CMLs). Two QC genotyping strategies were developed, the first is a “rapid QC”, employing a small number of SNPs to identify potential mislabeling of seed packages or plots, the second is a “broad QC”, employing a larger number of SNP, used to identify each germplasm entry and tomeasure heterogeneity. The optimal marker selection strategies combined the selection ofmarkers with high minor allele frequency, sampling of clustered SNP in proportion tomarker cluster distance and selecting markers thatmaintain a uniform genomic distribution. The rapid and broad QC SNP panels selected using this approach were further validated using blind test assessments of related re-generation samples. The influence of sampling within each line was evaluated. Sampling 192 individuals would result in close to 100% possibility of detecting a 5%contamination in the entry, and approximately a 98%probability to detect a 2%contamination of the line. These results provide a framework for the establishment of QC genotyping. A comparison of financial and time costs for use of these approaches across different platforms is discussed providing a framework for institutions involved inmaize conservation and breeding to assess the resource use effectiveness of QC genotyping. Application of these research findings, in combination with existing QC approaches, will ensure the regeneration, distribution and use in breeding of true to type inbred germplasm. These findings also provide an effective approach to optimize SNP selection for QC genotyping in other species.
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    Genomic prediction of gene bank wheat landraces
    (Genetics Society of America, 2016) Crossa, J.; Jarquin, D.; Franco, J.; Pérez-Rodríguez, P.; Burgueño, J.; Saint Pierre, C.; Vikram, P.; Sansaloni, C.; Petroli, C.; Akdemir, D.; Sneller, C.; Reynolds, M.P.; Tattaris, M.; Payne, T.S.; Guzman, C.; Peña, R.; Wenzl, P.; Singh, S.
    This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH) and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G×E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections were formed. Accounting for population structure decreased prediction accuracy by 15%-20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections but slightly lower for Iranian collections. Prediction accuracy when incorporating G×E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G×E term. For Iranian landraces, accuracies were 0.55 for the G×E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.
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    Exploring and mobilizing the Gene Bank Biodiversity for wheat improvement
    (Public Library of Science, 2015) Sehgal, D.; Vikram, P.; Sansaloni, C.; Ortiz, C.; Saint Pierre, C.; Payne, T.S.; Ellis, M.H.; Amri, A.; Petroli, C.; Wenzl, P.; Singh, S.
    Identifying and mobilizing useful genetic variation from germplasm banks to breeding programs is an important strategy for sustaining crop genetic improvement. The molecular diversity of 1,423 spring bread wheat accessions representing major global production environments was investigated using high quality genotyping-by-sequencing (GBS) loci, and gene-based markers for various adaptive and quality traits. Mean diversity index (DI) estimates revealed synthetic hexaploids to be genetically more diverse (DI= 0.284) than elites (DI = 0.267) and landraces (DI = 0.245). GBS markers discovered thousands of new SNP variations in the landraces which were well known to be adapted to drought (1273 novel GBS SNPs) and heat (4473 novel GBS SNPs) stress environments. This may open new avenues for pre-breeding by enriching the elite germplasm with novel alleles for drought and heat tolerance. Furthermore, new allelic variation for vernalization and glutenin genes was also identified from 47 landraces originating from Iraq, Iran, India, Afghanistan, Pakistan, Uzbekistan and Turkmenistan. The information generated in the study has been utilized to select 200 diverse gene bank accessions to harness their potential in pre-breeding and for allele mining of candidate genes for drought and heat stress tolerance, thus channeling novel variation into breeding pipelines. This research is part of CIMMYT’s ongoing ‘Seeds of Discovery’ project visioning towards the development of high yielding wheat varieties that address future challenges from climate change.
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