Recent Literature coming from HeDWIC (and a few key older references) – updated June, 2022

Abu-Zaitoun, S.Y., K. Chandrasekhar, S. Assili, M.J. Shtaya, R.M. Jamous, et al. 2018. Unlocking the Genetic Diversity within A Middle-East Panel of Durum Wheat Landraces for Adaptation to Semi-arid Climate. Agronomy 8(10). doi: 10.3390/agronomy8100233.

Alvarado, G., F.M. Rodríguez, A. Pacheco, J. Burgueño, J. Crossa, et al. 2020. META-R: A software to analyze data from multi-environment plant breeding trials. Crop J. 8(5). doi: 10.1016/j.cj.2020.03.010.

Asseng, S., D. Cammarano, B. Basso, U. Chung, P.D. Alderman, et al. 2017. Hot spots of wheat yield decline with rising temperatures. Glob. Chang. Biol. doi: 10.1111/gcb.13530.

Asseng, S., F. Ewert, P. Martre, R.P. Rötter, D.B. Lobell, et al. 2015. Rising temperatures reduce global wheat production. Nat. Clim. Chang. 5: 143–147. doi: 10.1038/nclimate2470.

Braun, H.J., G. Atlin, and T. Payne. 2010. Multi-location testing as a tool to identify plant response to global climate change. In: Reynolds, M.P., editor, Climate change and crop production. CABI, Wallingford. p. 115–138

Caamal-Pat, D., P. Pérez-Rodríguez, J. Crossa, C. Velasco-Cruz, S. Pérez-Elizalde, et al. 2021. lme4GS: An R-Package for Genomic Selection. Front. Genet. 12. doi: 10.3389/fgene.2021.680569.

Camarillo-Castillo, F., T.D. Huggins, S. Mondal, M.P. Reynolds, M. Tilley, et al. 2021. High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat. Plant Methods 17(1): 1–17. doi: 10.1186/s13007-021-00759-w.

Coast, O., S. Shah, A. Ivakov, O. Gaju, P.B. Wilson, et al. 2019. Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. Plant Cell Environ. 42: 2133–2150. doi: 10.1111/pce.13544.

Comastri, A., M. Janni, J. Simmonds, C. Uauy, D. Pignone, et al. 2018. Heat in Wheat: Exploit Reverse Genetic Techniques to Discover New Alleles Within the Triticum durum sHsp26 Family. Front. Plant Sci. 9(September): 1–16. doi: 10.3389/fpls.2018.01337.

Costa-Neto, G., R. Fritsche-Neto, and J. Crossa. 2021a. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity (Edinb). 126(1). doi: 10.1038/s41437-020-00353-1.

Costa-Neto, G., G. Galli, H.F. Carvalho, J. Crossa, and R. Fritsche-Neto. 2021b. EnvRtype: A software to interplay enviromics and quantitative genomics in agriculture. G3 Genes, Genomes, Genet. 11(4). doi: 10.1093/g3journal/jkab040.

Crespo-Herrera, L.A., J. Crossa, J. Huerta-Espino, S. Mondal, G. Velu, et al. 2021. Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains. Front. Plant Sci. 12. doi: 10.3389/fpls.2021.638520.

Crossa, J., R. Fritsche-Neto, O.A. Montesinos-Lopez, G. Costa-Neto, S. Dreisigacker, et al. 2021. The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data. Front. Plant Sci. 12(April): 1–6. doi: 10.3389/fpls.2021.651480.

Cuevas, J., O.A. Montesinos-López, J.W.R. Martini, P. Pérez-Rodríguez, M. Lillemo, et al. 2020. Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions. Front. Genet. 11. doi: 10.3389/fgene.2020.567757.

Dreccer, M.F., G. Molero, C. Rivera-Amado, C. John-Bejai, and Z. Wilson. 2019. Yielding to the image: How phenotyping reproductive growth can assist crop improvement and production. Plant Sci. 282: 73–82. doi: 10.1016/j.plantsci.2018.06.008.

Du, Y., L. Chen, Y. Wang, Z. Yang, I. Saeed, et al. 2018. The combination of dwarfing genes Rht4 and Rht8 reduced plant height, improved yield traits of rainfed bread wheat (Triticum aestivum L.). F. Crop. Res. 215: 149–155. doi: 10.1016/j.fcr.2017.10.015.

Dwivedi, S.L., M.P. Reynolds, and R. Ortiz. 2021. Mitigating tradeoffs in plant breeding. iScience 24(9). doi: 10.1016/j.isci.2021.102965.

Hernandez-Ochoa, I.M., S. Asseng, B.T. Kassie, W. Xiong, R. Robertson, et al. 2018. Climate change impact on Mexico wheat production. Agric. For. Meteorol. doi: 10.1016/j.agrformet.2018.09.008.

Hunt, J.R., P.T. Hayman, R.A. Richards, and J.B. Passioura. 2018. Opportunities to reduce heat damage in rain-fed wheat crops based on plant breeding and agronomic management. F. Crop. Res. 224(January): 126–138. doi: 10.1016/j.fcr.2018.05.012.

Joynson, R., G. Molero, B. Coombes, L.-J. Gardiner, C. Rivera-Amado, et al. 2021. Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat. Plant Biotechnol. J. 19(8): 1537–1552. doi: 10.1111/pbi.13568.

Juliana, P., R.P. Singh, J. Poland, S. Shrestha, J. Huerta-Espino, et al. 2021. Elucidating the genetics of grain yield and stress-resilience in bread wheat using a large-scale genome-wide association mapping study with 55,568 lines. Sci. Rep. 11(1). doi: 10.1038/s41598-021-84308-4.

Laddomada, B., A. Blanco, G. Mita, L. D’amico, R.P. Singh, et al. 2021. Drought and heat stress impacts on phenolic acids accumulation in durum wheat cultivars. Foods 10(9). doi: 10.3390/foods10092142.

Langridge, P., and M. Reynolds. 2021. Breeding for drought and heat tolerance in wheat. Theor. Appl. Genet. 134(6): 1753–1769. doi: 10.1007/s00122-021-03795-1.

Li, C., L. Li, M. Reynolds, J. Wang, X. Chang, et al. 2021. Recognizing the hidden half in wheat: root system attributes associated with drought tolerance. J. Exp. Bot.: erab124.

Li, L., Z. Peng, X. Mao, J. Wang, X. Chang, et al. 2019. Genome-wide association study reveals genomic regions controlling root and shoot traits at late growth stages in wheat. Ann. Bot.: 1–14. doi: 10.1093/aob/mcz041.

Liu, C., F. Pinto, C.M. Cossani, S. Sukumaran, and M.P. Reynolds. 2019a. Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: Heritability estimates and marker-trait associations. Front. Agric. Sci. Eng. 6(3). doi: 10.15302/J-FASE-2019269.

Liu, C., S. Sukumaran, E. Claverie, C. Sansaloni, S. Dreisigacker, et al. 2019b. Genetic dissection of heat and drought stress QTLs in phenology-controlled synthetic-derived recombinant inbred lines in spring wheat. Mol. Breed. 39: 1–18. doi: 10.1007/s11032-019-0938-y.

Lyra, D.H., C.A. Griffiths, A. Watson, R. Joynson, G. Molero, et al. 2021. Gene-based mapping of trehalose biosynthetic pathway genes reveals association with source- and sink-related yield traits in a spring wheat panel. Food Energy Secur. doi: 10.1002/fes3.292.

Martini, J.W.R., T.L. Molnar, J. Crossa, S.J. Hearne, and K. V. Pixley. 2021. Opportunities and Challenges of Predictive Approaches for Harnessing the Potential of Genetic Resources. Front. Plant Sci. 12. doi: 10.3389/fpls.2021.674036.

Molero, G., R. Joynson, F.J. Pinera‐Chavez, L. Gardiner, C. Rivera‐Amado, et al. 2019. Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotechnol. J. 17(7): 1276–1288. doi: 10.1111/pbi.13052.

Montesinos-López, A., H. Gutierrez-Pulido, O.A. Montesinos-López, and J. Crossa. 2020a. Maximum a posteriori threshold genomic prediction model for ordinal traits. G3 Genes, Genomes, Genet. 10(11). doi: 10.1534/g3.120.401733.

Montesinos-López, O.A., A. Montesinos-López, C.M. Hernandez-Suarez, J.A. Barrón-López, and J. Crossa. 2021a. Deep-learning power and perspectives for genomic selection. Plant Genome. doi: 10.1002/tpg2.20122.

Montesinos-López, A., O.A. Montesinos-López, J.C. Montesinos-López, C.A. Flores-Cortes, R. de la Rosa, et al. 2021b. A guide for kernel generalized regression methods for genomic-enabled prediction. Heredity (Edinb). 126(4). doi: 10.1038/s41437-021-00412-1.

Montesinos-López, O.A., A. Montesinos-López, B.A. Mosqueda-Gonzalez, J.C. Montesinos-López, J. Crossa, et al. 2021c. A zero altered Poisson random forest model for genomic-enabled prediction. G3 Genes, Genomes, Genet. 11(2). doi: 10.1093/g3journal/jkaa057.

Montesinos-López, O.A., A. Montesinos-López, P. Pérez-Rodríguez, J.A. Barrón-López, J.W.R. Martini, et al. 2021d. A review of deep learning applications for genomic selection. BMC Genomics 22(1). doi: 10.1186/s12864-020-07319-x.

Montesinos-López, O.A., J.C. Montesinos-López, P. Singh, N. Lozano-Ramirez, A. Barrón-López, et al. 2020b. A multivariate poisson deep learning model for genomic prediction of count data. G3 Genes, Genomes, Genet. 10(11). doi: 10.1534/g3.120.401631.

Montesinos-López, A., D.E. Runcie, M.I. Ibba, P. Pérez-Rodríguez, O.A. Montesinos-López, et al. 2021e. Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials. G3 Genes|Genomes|Genetics 11(10). doi: 10.1093/g3journal/jkab270.

Pequeno, D.N.L., I.M. Hernandez-Ochoa, M.P. Reynolds, K. Sonder, A. Molero-Milan, et al. 2021. Climate impact and adaptation to heat and drought stress of regional and global wheat production. Environ. Res. Lett. 16: 054070. doi: 10.1088/1748-9326/abd970.

Puhl, L.E., J. Crossa, S. Munilla, P. Pérez-Rodríguez, and R.J.C. Cantet. 2021. Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels. Genetics 217(2). doi: 10.1093/genetics/iyaa048.

Quintero, A., G. Molero, M.P. Reynolds, and D.F. Calderini. 2018. Trade-off between grain weight and grain number in wheat depends on GxE interaction: A case study of an elite CIMMYT panel (CIMCOG). Eur. J. Agron. 92. doi: 10.1016/j.eja.2017.09.007.

Reynolds, M., O.K. Atkin, M. Bennett, M. Cooper, I.C. Dodd, et al. 2021a. Addressing Research Bottlenecks to Crop Productivity. Trends Plant Sci. 26(6): 607–630. doi: 10.1016/j.tplants.2021.03.011.

Reynolds, M., A. Borrell, H. Braun, G. Edmeades, R. Flavell, et al. 2019. Translational Research for Climate Resilient, Higher Yielding Crops. Crop Breeding, Genet. Genomics 1: e190016. doi: 10.20900/cbgg20190016.

Reynolds, M.P., and H.J. Braun, editors. 2022. Wheat Improvement: Food Security in a Changing Climate. 1st ed. Springer Cham. doi: 10.1007/978-3-030-90673-3

Reynolds, M.P., H.J. Braun, A.J. Cavalieri, S. Chapotin, W.J. Davies, et al. 2017a. Improving global integration of crop research. Science (80-. ). 357(6349): 359–360. doi: 10.1126/science.aam8559.

Reynolds, M., and P. Langridge. 2016. Physiological breeding. Curr. Opin. Plant Biol. 31: 162–171. doi: 10.1016/j.pbi.2016.04.005.

Reynolds, M.P., and J.M. Lewis. 2022. The Future of Climate Resilience in Wheat. SocArXiv. doi: 10.31235/

Reynolds, M.P., J.M. Lewis, K. Ammar, B.R. Basnet, L. Crespo-Herrera, et al. 2021b. Harnessing translational research in wheat for climate resilience. J. Exp. Bot. 72(14): 5134–5157. doi: 10.1093/jxb/erab256.

Reynolds, M.P., A.J.D. Pask, W.J.E. Hoppitt, K. Sonder, S. Sukumaran, et al. 2017b. Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat. Euphytica 213: 257. doi: 10.1007/s10681-017-2040-z.

Rivera-Amado, C., G. Molero, E. Trujillo-Negrellos, M. Reynolds, and J. Foulkes. 2020. Estimating organ contribution to grain filling and potential for source upregulation in wheat cultivars with a contrasting source-sink balance. Agronomy 10: 1527. doi: 10.3390/agronomy10101527.

Rivera‐Amado, C., E. Trujillo-Negrellos, G. Molero, M.P. Reynolds, R. Sylvester-Bradley, et al. 2019. Optimizing dry-matter partitioning for increased spike growth, grain number and harvest index in spring wheat. F. Crop. Res. 240: 154–167. doi: 10.1016/j.fcr.2019.04.016.

Robles-Zazueta, C.A., G. Molero, F. Pinto, M.J. Foulkes, M.P. Reynolds, et al. 2021. Field based remote sensing models predict radiation use efficiency in wheat. J. Exp. Bot. doi: 10.1093/jxb/erab115.

Roitsch, T., L. Cabrera-Bosquet, A. Fournier, K. Ghamkhar, J. Jiménez-Berni, et al. 2019. Review: New sensors and data-driven approaches—A path to next generation phenomics. Plant Sci. 282: 2–10. doi: 10.1016/j.plantsci.2019.01.011.

Roncallo, P.F., A.O. Larsen, A.L. Achilli, C. Saint Pierre, C.A. Gallo, et al. 2021. Linkage disequilibrium patterns, population structure and diversity analysis in a worldwide durum wheat collection including Argentinian genotypes. BMC Genomics 22(1). doi: 10.1186/s12864-021-07519-z.

Sehgal, D., S. Mondal, L. Crespo-Herrera, G. Velu, P. Juliana, et al. 2020. Haplotype-Based, Genome-Wide Association Study Reveals Stable Genomic Regions for Grain Yield in CIMMYT Spring Bread Wheat. Front. Genet. 11. doi: 10.3389/fgene.2020.589490.

Sierra-Gonzalez, A., G. Molero, C. Rivera-Amado, M.A. Babar, M.P. Reynolds, et al. 2021. Exploring genetic diversity for grain partitioning traits to enhance yield in a high biomass spring wheat panel. F. Crop. Res. 260. doi: 10.1016/j.fcr.2020.107979.

Silva-Pérez, V., R.T. Furbank, A.G. Condon, and J.R. Evans. 2017. Biochemical model of C3 photosynthesis applied to wheat at different temperatures. Plant Cell Environ. 40: 1552–1564. doi: 10.1111/pce.12953.

Silva-Perez, V., G. Molero, S.P. Serbin, A.G. Condon, M.P. Reynolds, et al. 2018. Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. J. Exp. Bot. 69(3): 483–496. doi: 10.1093/jxb/erx421.

Singh, S., A. Jighly, D. Sehgal, J. Burgueño, R. Joukhader, et al. 2021. Direct introgression of untapped diversity into elite wheat lines. Nat. Food 2: 819–827. doi: 10.1038/s43016-021-00380-z.

Subbarao, G. V., M. Kishii, A. Bozal-Leorri, I. Ortiz-Monasterio, X. Gao, et al. 2021. Enlisting wild grass genes to combat nitrification in wheat farming: A nature-based solution. Proc. Natl. Acad. Sci. U. S. A. 118(35). doi: 10.1073/pnas.2106595118.

Sukumaran, S., M.P. Reynolds, and C. Sansaloni. 2018. Genome-Wide Association Analyses Identify QTL Hotspots for Yield and Component Traits in Durum Wheat Grown under Yield Potential, Drought, and Heat Stress Environments. Front. Plant Sci. 9(February): 1–16. doi: 10.3389/fpls.2018.00081.

Valluru, R., M.P. Reynolds, W.J. Davies, and S. Sukumaran. 2017. Phenotypic and genome-wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phytol. 214(1): 271–283. doi: 10.1111/nph.14367.

Vikram, P., J. Franco, J. Burgueño, H. Li, D. Sehgal, et al. 2020. Strategic use of Iranian bread wheat landrace accessions for genetic improvement: Core set formulation and validation. Plant Breed. 140(1): 87–99. doi: 10.1111/pbr.12885.

Villar-Hernández, B. de J., S. Pérez-Elizalde, J.W.R. Martini, F. Toledo, P. Perez-Rodriguez, et al. 2021. Application of multi-trait Bayesian decision theory for parental genomic selection. G3 (Bethesda). 11(2). doi: 10.1093/g3journal/jkab012.

Volpato, L., F. Pinto, L. González-Pérez, I.G. Thompson, A. Borém, et al. 2021. High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation. Front. Plant Sci. 12(February). doi: 10.3389/fpls.2021.591587.

Webber, H., J.W. White, B.A. Kimball, F. Ewert, S. Asseng, et al. 2018. Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions. F. Crop. Res. doi: 10.1016/j.fcr.2017.11.005.

Xiong, W., M.P. Reynolds, J. Crossa, U. Schulthess, K. Sonder, et al. 2021. Increased ranking change in wheat breeding under climate change. Nat. Plants 7(9). doi: 10.1038/s41477-021-00988-w.