Person: Hearne, S.
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Hearne
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S.
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Hearne, S.
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0000-0003-2015-2450 27 results
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- Novel methods to optimize genotypic imputation for low-coverage, next- generation sequence data in crops plants(CSSA, 2014) Swarts, K.; Huihui Li; Romero Navarro, J.A.; Dong An; Romay, M.C.; Hearne, S.; Acharya, C.B.; Glaubitz, J.C.; Mitchell, S.; Elshire, R.; Buckler, E.; Bradbury, P.
Publication - Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation(CSSA, 2014) Hickey, J.; Dreisigacker, S.; Crossa, J.; Hearne, S.; Babu, R.; Prasanna, B.M.; Grondona, M.; Zambelli, A.; Windhausen, V.S.; Mathews, K.L.; Gorjanc, G.
Publication - Maize landraces and adaptation to climate change in Mexico(Taylor & Francis, 2014) Hellin, J.; Bellon, M.; Hearne, S.
Publication Publication - Gene action controlling normalized difference vegetation index in crosses of elite maize (Zea mays L.) inbred lines(Akadémiai Kiadó, 2017) Adebayo, M. A.; Menkir, A.; Hearne, S.; Kolawole, A. O.
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 - Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS(Wiley, 2019) Jing Li; Gou-Bo Chen; Rasheed, A.; Delin Li; Sonder, K.; Zavala Espinosa, C.; Jiankang Wang; Costich, D.E.; Schnable, P.S.; Hearne, S.; Huihui LiUnderstanding the genomic basis of adaptation in maize is important for gene discovery and the improvement of breeding germplasm, but much remains a mystery in spite of significant population genetics and archaeological research. Identifying the signals underpinning adaptation are challenging as adaptation often coincided with genetic drift, and the base genomic diversity of the species in massive. In this study, tGBS technology was used to genotype 1,143 diverse maize accessions including landraces collected from 20 countries and elite breeding lines of tropical lowland, highland, subtropical/midaltitude and temperate ecological zones. Based on 355,442 high-quality single nucleotide polymorphisms, 13 genomic regions were detected as being under selection using the bottom-up searching strategy, EigenGWAS. Of the 13 selection regions, 10 were first reported, two were associated with environmental parameters via EnvGWAS, and 146 genes were enriched. Combining large-scale genomic and ecological data in this diverse maize panel, our study supports a polygenic adaptation model of maize and offers a framework to enhance our understanding of both the mechanistic basis and the evolutionary consequences of maize domestication and adaptation. The regions identified here are promising candidates for further, targeted exploration to identify beneficial alleles and haplotypes for deployment in maize breeding.
Publication - Excellence in Breeding Platform: Linkage with STMA(CIMMYT, 2018) Olsen, M.; Quinn, M.; Hearne, S.; Kotch, G.P.; Vadez, V.; Robbins, K.; Banziger, M.
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