Estimating maize genetic erosion in modernized smallholder agriculture

Replacement of crop landraces by modern varieties is thought to cause diversity loss. We studied genetic erosion in maize within a model system; modernized smallholder agriculture in southern Mexico. The local seed supply was described through interviews and in situ seed collection. In spite of the dominance of commercial seed, the informal seed system was found to persist. True landraces were rare and most informal seed was derived from modern varieties (creolized). Seed lots were characterized for agronomical traits and molecular markers. We avoided the problem of non-consistent nomenclature by taking individual seed lots as the basis for diversity inference. We defined diversity as the weighted average distance between seed lots. Diversity was calculated for subsets of the seed supply to assess the impact of replacing traditional landraces with any of these subsets. Results were different for molecular markers, ear- and vegetative/flowering traits. Nonetheless, creolized varieties showed low diversity for all traits. These varieties were distinct from traditional landraces and little differentiated from their ancestral stocks. Although adoption of creolized maize into the informal seed system has lowered diversity as compared to traditional landraces, genetic erosion was moderated by the distinct features offered by modern varieties

A sorghum practical haplotype graph facilitates genome‐wide imputation and cost‐effective genomic prediction

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome-wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage—only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57–.73 and are similar to prediction accuracies obtained with genotyping-by-sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low-coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost-effective option for genomic selection

Informal “Seed” Systems and the Management of Gene Flow in Traditional Agroecosystems: The Case of Cassava in Cauca, Colombia

Our ability to manage gene flow within traditional agroecosystems and their repercussions requires understanding the biology of crops, including farming practices' role in crop ecology. That these practices' effects on crop population genetics have not been quantified bespeaks lack of an appropriate analytical framework. We use a model that construes seed-management practices as part of a crop's demography to describe the dynamics of cassava (Manihot esculenta Crantz) in Cauca, Colombia. We quantify several management practices for cassava—the first estimates of their kind for a vegetatively-propagated crop—describe their demographic repercussions, and compare them to those of maize, a sexually-reproduced grain crop. We discuss the implications for gene flow, the conservation of cassava diversity, and the biosafety of vegetatively-propagated crops in centers of diversity. Cassava populations are surprisingly open and dynamic: farmers exchange germplasm across localities, particularly improved varieties, and distribute it among neighbors at extremely high rates vis-à-vis maize. This implies that a large portion of cassava populations consists of non-local germplasm, often grown in mixed stands with local varieties. Gene flow from this germplasm into local seed banks and gene pools via pollen has been documented, but its extent remains uncertain. In sum, cassava's biology and vegetative propagation might facilitate pre-release confinement of genetically-modified varieties, as expected, but simultaneously contribute to their diffusion across traditional agroecosystems if released. Genetically-modified cassava is unlikely to displace landraces or compromise their diversity; but rapid diffusion of improved germplasm and subsequent incorporation into cassava landraces, seed banks or wild populations could obstruct the tracking and eradication of deleterious transgenes. Attempts to regulate traditional farming practices to reduce the risks could compromise cassava populations' adaptive potential and ultimately prove ineffectual

Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

BACKGROUND: The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program. RESULTS: The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives. CONCLUSIONS: Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-2345-z) contains supplementary material, which is available to authorized users

Genomic variation in tomato, from wild ancestors to contemporary breeding accessions

[EN] Background: Domestication modifies the genomic variation of species. Quantifying this variation provides insights into the domestication process, facilitates the management of resources used by breeders and germplasm centers, and enables the design of experiments to associate traits with genes. We described and analyzed the genetic diversity of 1,008 tomato accessions including Solanum lycopersicum var. lycopersicum (SLL), S. lycopersicum var. cerasiforme (SLC), and S. pimpinellifolium (SP) that were genotyped using 7,720 SNPs. Additionally, we explored the allelic frequency of six loci affecting fruit weight and shape to infer patterns of selection. Results: Our results revealed a pattern of variation that strongly supported a two-step domestication process, occasional hybridization in the wild, and differentiation through human selection. These interpretations were consistent with the observed allele frequencies for the six loci affecting fruit weight and shape. Fruit weight was strongly selected in SLC in the Andean region of Ecuador and Northern Peru prior to the domestication of tomato in Mesoamerica. Alleles affecting fruit shape were differentially selected among SLL genetic subgroups. Our results also clarified the biological status of SLC. True SLC was phylogenetically positioned between SP and SLL and its fruit morphology was diverse. SLC and “cherry tomato” are not synonymous terms. The morphologically-based term “cherry tomato” included some SLC, contemporary varieties, as well as many admixtures between SP and SLL. Contemporary SLL showed a moderate increase in nucleotide diversity, when compared with vintage groups. Conclusions: This study presents a broad and detailed representation of the genomic variation in tomato. Tomato domestication seems to have followed a two step-process; a first domestication in South America and a second step in Mesoamerica. The distribution of fruit weight and shape alleles supports that domestication of SLC occurred in the Andean region. Our results also clarify the biological status of SLC as true phylogenetic group within tomato. We detect Ecuadorian and Peruvian accessions that may represent a pool of unexplored variation that could be of interest for crop improvement.We are grateful to the gene banks for their collections that made this study possible. We thank Syngenta Seeds for providing genotyping data for 42 accessions. We would like to thank the Supercomputing and Bioinnovation Center (Universidad de Malaga, Spain) for providing computational resources to process the SNAPP phylogenetic tree. This research was supported in part by the USDA/NIFA funded SolCAP project under contract number to DF and USDA AFRI 2013-67013-21229 to EvdK and DF.Blanca Postigo, JM.; Montero Pau, J.; Sauvage, C.; Bauchet, G.; Illa, E.; Díez Niclós, MJTDJ.; Francis, D.... (2015). Genomic variation in tomato, from wild ancestors to contemporary breeding accessions. BMC Genomics. 16(257):1-19. https://doi.org/10.1186/s12864-015-1444-1S11916257Tanksley SD, McCouch SR. Seed banks and molecular maps: unlocking genetic potential from the wild. Science (80-). 1997;277:1063–6.Doebley JF, Gaut BS, Smith BD. The molecular genetics of crop domestication. Cell. 2006;127:1309–21.Gepts P. A comparison between crop domestication, classical plant breeding, and genetic engineering. Crop Sci. 2002;42:1780.Weigel D, Nordborg M. Natural variation in Arabidopsis. How do we find the causal genes? Plant Physiol. 2005;138:567–8.Peralta IE, Spooner DM, Knapp S, Anderson C. Taxonomy of wild tomatoes and their relatives (Solanum sect. Lycopersicoides, sect. Juglandifolia, sect. Lycopersicon; Solanaceae). Syst Bot Monogr. 2008;84:1–186.Rick CM, Fobes JF. Allozyme variation in the cultivated tomato and closely related species. Bull Torrey Bot Club. 1975;102:376–84.Zuriaga E, Blanca J, Nuez F. Classification and phylogenetic relationships in Solanum section Lycopersicon based on AFLP and two nuclear gene sequences. Genet Resour Crop Evol. 2008;56:663–78.Zuriaga E, Blanca J, Cordero L, Sifres A, Blas-Cerdán WG, Morales R, et al. Genetic and bioclimatic variation in Solanum pimpinellifolium. Genet Resour Crop Evol. 2008;56:39–51.Blanca J, Cañizares J, Cordero L, Pascual L, Diez MJ, Nuez F. Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato. PLoS One. 2012;7:e48198.Rick CM. Natural variability in wild species of Lycopersicon and its bearing on tomato breeding. Genet Agrar. 1976;30:249–59.Rick CM, Holle M. Andean Lycopersicon esculentum var. cerasiforme: genetic variation and its evolutionary significance. Econ Bot. 1990;44:69–78.Nakazato T, Franklin RA, Kirk BC, Housworth EA. Population structure, demographic history, and evolutionary patterns of a green-fruited tomato, Solanum peruvianum (Solanaceae), revealed by spatial genetics analyses. Am J Bot. 2012;99:1207–16.Rick CM, Butler L. Cytogenetics of the Tomato. Adv Genet. 1956;8:267–382. Advances in Genetics.Jenkins JA. The origin of the cultivated tomato. Econ Bot. 1948;2:379–92.Nesbitt TC, Tanksley SD. Comparative sequencing in the genus lycopersicon: implications for the evolution of fruit size in the domestication of cultivated tomatoes. Genetics. 2002;162:365–79.Ranc N, Muños S, Santoni S, Causse M. A clarified position for Solanum lycopersicum var cerasiforme in the evolutionary history of tomatoes (solanaceae). BMC Plant Biol. 2008;8:130.De Candolle A. Origin of cultivated plants. 2nd ed. London: Trench, Paul; 1886.Miller JC, Tanksley SD. RFLP analysis of phylogenetic relationships and genetic variation in the genus Lycopersicon. Theor Appl Genet. 1990;80:437–48.Williams CE, Clair DAS. Phenetic relationships and levels of variability detected by restriction fragment length polymorphism and random amplified polymorphic DNA analysis of cultivated and wild accessions of Lycopersicon esculentum. Genome. 1993;36:619–30.Park YH, West MAL, St Clair DA. Evaluation of AFLPs for germplasm fingerprinting and assessment of genetic diversity in cultivars of tomato (Lycopersicon esculentum L). Genome. 2004;47:510–8.Sim S-C, Robbins MD, Van Deynze A, Michel AP, Francis DM. Population structure and genetic differentiation associated with breeding history and selection in tomato (Solanum lycopersicum L.). Heredity (Edinb). 2011;106:927–35.Sim S-C, Robbins MD, Chilcott C, Zhu T, Francis DM. Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L) reveals patterns of SNP variation associated with breeding. BMC Genomics. 2009;10:466.Sim S-C, Durstewitz G, Plieske J, Wieseke R, Ganal MW, Van Deynze A, et al. Development of a large SNP genotyping array and generation of high-density genetic maps in tomato. PLoS One. 2012;7:e40563.Frary A, Nesbitt TC, Grandillo S, Knaap E, Cong B, Liu J, et al. fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science. 2000;289:85–8.Liu J, Van Eck J, Cong B, Tanksley SD. A new class of regulatory genes underlying the cause of pear-shaped tomato fruit. Proc Natl Acad Sci U S A. 2002;99:13302–6.Xiao H, Jiang N, Schaffner E, Stockinger EJ, van der Knaap E. A retrotransposon-mediated gene duplication underlies morphological variation of tomato fruit. Science. 2008;319:1527–30.Cong B, Barrero LS, Tanksley SD. Regulatory change in YABBY-like transcription factor led to evolution of extreme fruit size during tomato domestication. Nat Genet. 2008;40:800–4.Muños S, Ranc N, Botton E, Bérard A, Rolland S, Duffé P, et al. Increase in tomato locule number is controlled by two single-nucleotide polymorphisms located near WUSCHEL. Plant Physiol. 2011;156:2244–54.Chakrabarti M, Zhang N, Sauvage C, Muños S, Blanca J, Cañizares J, et al. A cytochrome P450 regulates a domestication trait in cultivated tomato. Proc Natl Acad Sci U S A. 2013;110:17125–30.Rodríguez GR, Muños S, Anderson C, Sim S-C, Michel A, Causse M, et al. Distribution of SUN, OVATE, LC, and FAS in the tomato germplasm and the relationship to fruit shape diversity. Plant Physiol. 2011;156:275–85.Sim S-C, Van Deynze A, Stoffel K, Douches DS, Zarka D, Ganal MW, et al. High-density SNP genotyping of tomato (Solanum lycopersicum L) reveals patterns of genetic variation due to breeding. PLoS One. 2012;7:e45520.Sauvage C, Segura V, Bauchet G, Stevens R, Thi Do P, Nikoloski Z, et al. Genome Wide Association in tomato reveals 44 candidate loci for fruit metabolic traits. Plant Physiol. 2014;165:1120–32.Hamilton JP, Sim S-C, Stoffel K, Van Deynze A, Buell CR, Francis DM. Single nucleotide polymorphism discovery in cultivated tomato via sequencing by synthesis. Plant Genome J. 2012;5:17.Patterson NJ, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet. 2006;2:e190.Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9.Kosman E, Leonard KJ. Similarity coefficients for molecular markers in studies of genetic relationships between individuals for haploid, diploid, and polyploid species. Mol Ecol. 2005;14:415–24.Adler D. vioplot: Violin plot. 2005.Jost L. Gst and its relatives do not measure differentiation. Mol Ecol. 2008;17:4015–26.Excoffier L, Lischer H. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10:564–7.Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Ecol Evol. 2006;23:254–67.Knight R, Maxwell P, Birmingham A, Carnes J, Caporaso JG, Easton BC, et al. PyCogent: a toolkit for making sense from sequence. Genome Biol. 2007;8:R171.Szpiech ZA, Jakobsson M, Rosenberg NA. ADZE: a rarefaction approach for counting alleles private to combinations of populations. Bioinformatics. 2008;24:2498–504.Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23:2633–5.Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74:829.R Core Team. R: A Language and Environment for Statistical Computing. 2013.Sinnot RS. Virtues of the haversine. Sky Telesc. 1984;68:159.Hijmans RJ, Etten JV. raster: Geographic data analysis and Modeling. 2013.Bryant D, Bouckaert R, Felsenstein J, Rosenberg NA, RoyChoudhury A. Inferring species trees directly from biallelic genetic markers: bypassing gene trees in a full coalescent analysis. Mol Biol Evol. 2012;29:1917–32.Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007;7:214.Rambaut A. Tracer v.1.5. 2009.Huang Z, van der Knaap E. Tomato fruit weight 11.3 maps close to fasciated on the bottom of chromosome 11. Theor Appl Genet. 2011;123:465–74.Guo M, Rupe MA, Dieter JA, Zou J, Spielbauer D, Duncan KE, et al. Cell Number Regulator1 affects plant and organ size in maize: implications for crop yield enhancement and heterosis. Plant Cell. 2010;22:1057–73.Sambrook J, Fritsch EF, Maniatis T. Molecular cloning. New York: Cold Spring Harbor Laboratory Press; 1989.Lin T, Zhu G, Zhang J, Xu X, Yu Q, Zheng Z, et al. Genomic analyses provide insights into the history of tomato breeding. Nat Genet. 2014;46:1220–6.Platt A, Horton M, Huang YS, Li Y, Anastasio AE, Mulyati NW, et al. The scale of population structure in Arabidopsis thaliana. PLoS Genet. 2010;6:e1000843.Pressoir G, Berthaud J. Patterns of population structure in maize landraces from the Central Valleys of Oaxaca in Mexico. Heredity (Edinb). 2004;92:88–94.Koenig D, Jiménez-Gómez JM, Kimura S, Fulop D, Chitwood DH, Headland LR, et al. Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato. Proc Natl Acad Sci U S A. 2013;110:e2655–62.Nakazato T, Housworth EA. Spatial genetics of wild tomato species reveals roles of the Andean geography on demographic history. Am J Bot. 2011;98:88–98.United States. Office of Experimental Stations. Experimental Station Recod, Volumen 39. Volume 39. Washington, DC, USA: United States. Office of Experimental Stations; 1918.Merk HL, Yames SC, Van Deynze A, Tong N, Menda N, Mueller LA, et al. Trait diversity and potential for selection indeces based on variation among regionally adapted processing tomato germplasm. J Am Soc Hortic Sci. 2012;137:427–37

A crop population perspective on maize seed systems in Mexico

Improvement of local germplasm through artificial selection is regarded as the main force behind maize evolution and diversity in Mexico, the crop's center of origin. This perspective neglects the larger social context of maize evolution. Using a theoretical approach and Mexico-wide data, we show that farmer-led evolution of maize is largely driven by a technological diffusion and appropriation process that selectively integrates nonlocal germplasm into local seed stocks. Our approach construes farmer practices as events in the life history of seed to build a demographic model. The model shows how random and systematic differences in management combine to structure maize seed populations into subpopulations that can spread or become extinct, in some cases independently of visible agronomic advantages. The process involves continuous population bottlenecks that can lead to diversity loss. Nonlocal germplasm thus might play a critical role in maintaining diversity in individual localities. Empirical estimates show that introduction of nonlocal seed in Central and Southeastern Mexico is rarer than previously thought; prompt replacement further prevents new seed from spreading. Yet introduced seed perceived as valuable diffuses rapidly, contributing variation in the form of type diversity or through introgression into local seed. Maize seed dynamics and evolution are thus part of a complex social process driven by farmers' desire to appropriate the value in maize farming, not always achieved by preserving or improving local seed stocks