Person: Sansaloni, C.
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Sansaloni
First Name
C.
Name
Sansaloni, C.
ORCID ID
0000-0003-2675-45249 results
Search Results
Now showing 1 - 9 of 9
- A comparison of the adoption of genomic selection across different breeding institutions(Frontiers, 2021) Gholami, M.; Wimmer, V.; Sansaloni, C.; Petroli, C.; Hearne, S.; Covarrubias, E.; Rensing, S.; Heise, J.; Pérez-Rodríguez, P.; Dreisigacker, S.; Crossa, J.; Martini, J.W.R.
Publication - Harnessing translational research in wheat for climate resilience(Oxford University Press, 2021) Reynolds, M.P.; Lewis, J.; Ammar, K.; Basnet, B.R.; Crespo Herrera, L.A.; Crossa, J.; Dhugga, K.; Dreisigacker, S.; Juliana, P.; Karwat, H.; Kishii, M.; Krause, M.; Langridge, P.; Lashkari, A.; Mondal, S.; Payne, T.S.; Pequeno, D.N.L.; Pinto Espinosa, F.; Sansaloni, C.; Schulthess, U.; Singh, R.P.; Sonder, K.; Sukumaran, S.; Wei Xiong; Braun, H.J.
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 - Harnessing genetic potential of wheat germplasm banks through impact-oriented-prebreeding for future food and nutritional security(Nature Publishing Group, 2018) Singh, S.; Vikram, P.; Sehgal, D.; Burgueño, J.; Sharma, A.R.; Singh, S.K.; Sansaloni, C.; Joynson, R.; Brabbs, T.; Ortiz, C.; Solís Moya, E.; Velu, G.; Gupta, N.; Sidhu, H.S.; Basandrai, A.K.; Basandrai, D.; Ledesma-Ramires, L.; Suaste-Franco, M.P.; Fuentes Dávila, G.; Ireta Moreno, J.; Sonder, K.; Vaibhav K. Singh; Sajid Shokat; Shokat, S.; Mian A. R. Arif; Khalil A. Laghari; Puja Srivastava; Bhavani, S.; Satish Kumar; Pal, D.; Jaiswal, J.P.; Kumar, U.; Harinder K. Chaudhary; Crossa, J.; Payne, T.S.; Imtiaz, M.; Sohu, V.S.; Singh, G.P.; Bains, N.; Hall, A.J.W.; Pixley, K.V.The value of exotic wheat genetic resources for accelerating grain yield gains is largely unproven and unrealized. We used next-generation sequencing, together with multi-environment phenotyping, to study the contribution of exotic genomes to 984 three-way-cross-derived (exotic/elite1//elite2) pre-breeding lines (PBLs). Genomic characterization of these lines with haplotype map-based and SNP marker approaches revealed exotic specific imprints of 16.1 to 25.1%, which compares to theoretical expectation of 25%. A rare and favorable haplotype (GT) with 0.4% frequency in gene bank identified on chromosome 6D minimized grain yield (GY) loss under heat stress without GY penalty under irrigated conditions. More specifically, the ‘T’ allele of the haplotype GT originated in Aegilops tauschii and was absent in all elite lines used in study. In silico analysis of the SNP showed hits with a candidate gene coding for isoflavone reductase IRL-like protein in Ae. tauschii. Rare haplotypes were also identified on chromosomes 1A, 6A and 2B effective against abiotic/biotic stresses. Results demonstrate positive contributions of exotic germplasm to PBLs derived from crosses of exotics with CIMMYT’s best elite lines. This is a major impact-oriented pre-breeding effort at CIMMYT, resulting in large-scale development of PBLs for deployment in breeding programs addressing food security under climate change scenarios.
Publication - Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones(Nature Publishing Group, 2016) Saint Pierre, C.; Burgueño, J.; Fuentes Dávila, G.; Figueroa, P.; Solís Moya, E.; Ireta Moreno, J.; Hernández Muela, V.M.; Zamora Villa, V.; Vikram, P.; Mathews, K.L.; Sansaloni, C.; Sehgal, D.; Jarquin, D.; Wenzl, P.; Singh, S.; Crossa, J.Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines’ performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha−1 across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.
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.; 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 - 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
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, Roberto; 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