Person:
Franco, J.

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Franco
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Franco, J.
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0000-0001-8082-4007

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    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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    Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures
    (BioMed Central, 2009) Thachuk, C.; Crossa, J.; Franco, J.; Dreisigacker, S.; Warburton, M.; Davenport, G.
    Background: Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeder's preference) or allele richness (taxonomist's preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both. RESULTS: We present Core Hunter, an advanced stochastic local search algorithm for selecting core subsets. Core Hunter is able to find core subsets having more genetic diversity and better average genetic distance than the current state-of-the-art algorithms for all genetic distance and diversity measures we evaluated. Furthermore, Core Hunter can attempt to optimize any number of genetic measures simultaneously, based on the preference of the user. Notably, Core Hunter is able to select significantly smaller core subsets, which retain all unique alleles from a reference collection, than state-of-the-art algorithms. CONCLUSION: Core Hunter is a highly effective and flexible tool for sampling genetic resources and establishing core subsets. Our implementation, documentation, and source code for Core Hunter is available at http://corehunter.org.
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    Sampling strategies for conserving maize diversity when forming core subsets using genetic markers
    (Crop Science Society of America (CSSA), 2006) Franco, J.; Crossa, J.; Warburton, M.; Taba, S.
    Core subsets can be formed on the basis of molecular markers and different sampling strategies. This research used genetic markers on three maize data sets for studying 24 stratified sampling strategies to investigate which strategy conserved the most diversity in the core subset as compared with the original sample. The strategies were formed by combining three factors: (i) two clustering methods (UPGMA and Ward), based on (ii) two initial genetic distance measures, and using (iii) six allocation criteria [two based on the size of the cluster and four based on maximizing distances in the core (the D method) used with four diversity indices]. The objectives were (i) to study the influence of these factors and their interaction on the diversity of the core subsets and (ii) to compare the 24 stratified sampling strategies with the M strategy implemented in the MSTRAT algorithm. Success of each strategy was measured on the basis of maximizing genetic distances (Modified Roger and Cavalli‐Sforza and Edwards distances) and genetic diversity indices (Shannon index, proportion of heterozygous loci, and number of effective alleles) in each core. Twenty independent stratified random samples were obtained for each strategy using a sampling intensity of 20% of the collection. For the three data sets, the UPGMA with D allocation methods produced core subsets with significantly more diversity than the other methods and were better than the M strategy for maximizing genetic distance. For most of the diversity indices, the M strategy outperformed the D method.
    Publication