Person: Franco, J.
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Franco
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Franco, J.
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0000-0001-8082-4007 13 results
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- Genetic variation among elite inbred lines suggests potential to breed for BNI-capacity in maize(Nature Publishing Group, 2023) Petroli, C.; Guntur Venkata Subbarao; Burgueño, J.; Tadashi Yoshihashi; Huihui Li; Franco, J.; Pixley, K.V.
Publication - Strategic use of Iranian bread wheat landrace accessions for genetic improvement: core set formulation and validation(Wiley, 2021) Vikram, P.; Franco, J.; Burgueño, J.; Huihui Li; Sehgal, D.; Saint Pierre, C.; Ortiz, C.; Singh, V.K.; Sneller, C.; Sharma, A.R.; Tattaris, M.; Guzman, C.; Peña-Bautista, R.J.; Sansaloni, C.; Campos, J.; Thiyagarajan, K.; Fuentes Dávila, G.; Reynolds, M.P.; Sonder, K.; Velu, G.; Ellis, M.H.; Bhavani, S.; Jalal Kamali, M.R.; Roostaei, M.; Singh, S.; Basandrai, D.; Bains, N.; Basandrai, A.K.; Payne, T.S.; Crossa, J.; Singh, S.
Publication - Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints(Nature Publishing Group, 2020) Sansaloni, C.; Franco, J.; Santos, B.; Percival-Alwyn, L.; Singh, S.; Petroli, C.; Campos, J.; Dreher, K.; Payne, T.S.; Marshall, D.S.; Kilian, B.; Milne, I.; Raubach, S.; Shaw, P.D.; Stephen, G.; Carling, J.; Saint Pierre, C.; Burgueño, J.; Crossa, J.; Huihui Li; Guzman, C.; Kehel, Z.; Amri, A.; Kilian, A.; Wenzl, P.; Uauy, C.; Banziger, M.; Caccamo, M.; Pixley, K.V.
Publication - The impact of sample selection strategies on genetic diversity and representativeness in germplasm bank collections(BioMed Central, 2019) Franco, J.; Crossa, J.; Jiafa Chen; Hearne, S.Background: Germplasm banks maintain collections representing the most comprehensive catalogue of native genetic diversity available for crop improvement. Users of germplasm banks are interested in a fixed number of samples representing as broadly as possible the diversity present in the wider collection. A relevant question is whether it is necessary to develop completely independent germplasm samples or it is possible to select nested sets from a pre-defined core set panel not from the whole collection. We used data from 15,384, maize landraces stored in the CIMMYT germplasm bank to study the impact on 8 diversity criteria and the sample representativeness of: (1) two core selection strategies, a statistical sampling (DM), or a numerical maximization method (CH); (2) selecting samples of varying sizes; and (3) selecting samples of different sizes independently of each other or in a nested manner. Results: Sample sizes greater than 10% of the whole population size retained more than 75% of the polymorphic markers for all selection strategies and types of sample; lower sample sizes showed more variability (instability) among repetitions; the strongest effect of sample size was observed on the CH-independent combination. Independent and nested samples showed similar performance for all the criteria for the DM method, but there were differences between them for the CH method. The DM method achieved better approximations to the known values in the population than the CH method; 2-d multidimensional scaling plots of the collection and samples highlighted tendency of sample selection towards the extremes of diversity in the CH method, compared with sampling more representative of the overall genotypic distribution of diversity under the DM method. Conclusions: The use of core subsets of size greater than or equal to 10% of the whole collection satisfied well the requirement of representativeness and diversity. Nested samples showed similar diversity and representativeness characteristics as independent samples offering a cost effective method of sample definition for germplasm banks. For most criteria assessed the DM method achieved better approximations to the known values in the whole population than the CH method, that is, it generated more statistically representative samples from collections.
Publication - Genetic diversity and population structure of native maize populations in Latin America and the Caribbean(Public Library of Science, 2017) Bedoya-Salazar, C.A.; Dreisigacker, S.; Hearne, S.; Franco, J.; Mir, C.; Prasanna, B.M.; Taba, S.; Charcosset, A.; Warburton, M.This study describes the genetic diversity and population structure of 194 native maize populations from 23 countries of Latin America and the Caribbean. The germplasm, representing 131 distinct landraces, was genetically characterized as population bulks using 28 SSR markers. Three main groups of maize germplasm were identified. The first, the Mexico and Southern Andes group, highlights the Pre-Columbian and modern exchange of germplasm between North and South America. The second group, Mesoamerica lowland, supports the hypothesis that two separate human migration events could have contributed to Caribbean maize germplasm. The third, the Andean group, displayed early introduction of maize into the Andes, with little mixing since then, other than a regional interchange zone active in the past. Events and activities in the pre- and post-Columbian Americas including the development and expansion of pre-Columbian cultures and the arrival of Europeans to the Americas are discussed in relation to the history of maize migration from its point of domestication in Mesoamerica to South America and the Caribbean through sea and land routes.
Publication - Unlocking the genetic diversity of Creole wheats(Nuture Publishing Group, 2016) Vikram, P.; Franco, J.; Burgueño, J.; Huihui Li; Sehgal, D.; Saint Pierre, C.; Ortiz, C.; Sneller, C.; Tattaris, M.; Guzman, C.; Sansaloni, C.; Ellis, M.; Fuentes Dávila, G.; Reynolds, M.P.; Sonder, K.; Singh, P.K.; Payne, T.S.; Wenzl, P.; Sharma, A.R.; Bains, N.; Singh, G.P.; Crossa, J.; Singh, S.Climate change and slow yield gains pose a major threat to global wheat production. Underutilized genetic resources including landraces and wild relatives are key elements for developing high-yielding and climate-resilient wheat varieties. Landraces introduced into Mexico from Europe, also known as Creole wheats, are adapted to a wide range of climatic regimes and represent a unique genetic resource. Eight thousand four hundred and sixteen wheat landraces representing all dimensions of Mexico were characterized through genotyping-by-sequencing technology. Results revealed sub-groups adapted to specific environments of Mexico. Broadly, accessions from north and south of Mexico showed considerable genetic differentiation. However, a large percentage of landrace accessions were genetically very close, although belonged to different regions most likely due to the recent (nearly five centuries before) introduction of wheat in Mexico. Some of the groups adapted to extreme environments and accumulated high number of rare alleles. Core reference sets were assembled simultaneously using multiple variables, capturing 89% of the rare alleles present in the complete set. Genetic information about Mexican wheat landraces and core reference set can be effectively utilized in next generation wheat varietal improvement.
Publication - 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.
Publication - 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.
Publication - User's manual for the LCDMV software (calculation software of molecular distances between varieties): for fingerprinting and genetic diversity studies(CIMMYT, 2003) Dubreuil, P.; Dillmann, C.; Warburton, M.; Crossa, J.; Franco, J.; Baril, C.LCDMV (in English, known as the Calculation Software of Molecular Distances between Varieties) is a computer program developed in the SAS language (SAS Institute Inc., version 6.12), with the help of the modules SAS-STAT, and SAS-IML. It was written to analyze biochemical markers (isozymes) or molecular markers (RFLP, STS, SSR, RAPD, AFLP) obtained on homogenous or heterogeneous varieties. Its main function is to estimate genetic distances between varieties, and to analyze the structure of the genetic makeup of a given collection of OTU’s (Operational Taxonomic Units).
Publication - Comparison of the performance of synthetic maize varieties created based on either genetic distance or general combining ability of the parents(Consiglio per la Ricerca e la sperimentazione in Agricoltura, Unità di Ricerca per la Maiscoltura, 2012) Narro, L.A.; Franco, J.; George, M.L.C.; Arcos, A.L.; Osorio, K.V.; Warburton, M.Synthetics varieties are grown by farmers and used by breeders to select new inbred lines. In countries unable to market hybrids, use of synthetics leads to yield improvements over landraces. Synthetics are derived from intercrossing inbred lines known to possess high general combining ability (GCA) as measured via crossing with testers and phenotyping for yield in multiple environments. Genetic similarity (GS) between lines measured by molecular markers may efficiently estimate GCA. Although the prediction of specific combining ability (SCA) of lines via GS has not been successful, it may have potential to predict the suitability of lines to form a synthetic variety. As this has not been reported, the objective of this research was to compare the performance of four synthetic maize varieties developed using GS calculated between parents using SSR markers with the performance of synthetics developed using GCA based on yield. Synthetics were phenotyped for yield and other agronomic traits in replicated field trials in several environments. The two synthetics formed based on low GS (0.34 and 0.33) performed better than all other synthetics in yield and most agronomic traits. The synthetics formed based on high GS (0.77 and 0.53), performed worst for nearly all traits. The GCA-based synthetics were generally intermediate for all traits. Response of synthetics to environmental variation and efficiencies gained via use of molecular markers in synthetic formation is discussed.
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