Sarah Hearne

A consensus genetic map of cowpea [Vigna unguiculata (L) Walp.] and synteny based on EST-derived SNPs

Consensus genetic linkage maps provide a genomic framework for quantitative trait loci identification, map-based cloning, assessment of genetic diversity, association mapping, and applied breeding in marker-assisted selection schemes. Among “orphan crops” with limited genomic resources such as cowpea [Vigna unguiculata (L.) Walp.] (2n = 2x = 22), the use of transcript-derived SNPs in genetic maps provides opportunities for automated genotyping and estimation of genome structure based on synteny analysis. Here, we report the development and validation of a high-throughput EST-derived SNP assay for cowpea, its application in consensus map building, and determination of synteny to reference genomes. SNP mining from 183,118 ESTs sequenced from 17 cDNA libraries yielded ≈10,000 high-confidence SNPs from which an Illumina 1,536-SNP GoldenGate genotyping array was developed and applied to 741 recombinant inbred lines from six mapping populations. Approximately 90% of the SNPs were technically successful, providing 1,375 dependable markers. Of these, 928 were incorporated into a consensus genetic map spanning 680 cM with 11 linkage groups and an average marker distance of 0.73 cM. Comparison of this cowpea genetic map to reference legumes, soybean (Glycine max) and Medicago truncatula, revealed extensive macrosynteny encompassing 85 and 82%, respectively, of the cowpea map. Regions of soybean genome duplication were evident relative to the simpler diploid cowpea. Comparison with Arabidopsis revealed extensive genomic rearrangement with some conserved microsynteny. These results support evolutionary closeness between cowpea and soybean and identify regions for synteny-based functional genomics studies in legumes.

Novel Methods to Optimize Genotypic Imputation for Low-Coverage, Next-Generation Sequence Data in Crop Plants

Next‐generation sequencing technology such as genotyping‐by‐sequencing (GBS) made low‐cost, but often low‐coverage, whole‐genome sequencing widely available. Extensive inbreeding in crop plants provides an untapped, high quality source of phased haplotypes for imputing missing genotypes. We introduce Full‐Sib Family Haplotype Imputation (FSFHap), optimized for full‐sib populations, and a generalized method, Fast Inbred Line Library ImputatioN (FILLIN), to rapidly and accurately impute missing genotypes in GBS‐type data with ordered markers. FSFHap and FILLIN impute missing genotypes with high accuracy in GBS‐genotyped maize (Zea mays L.) inbred lines and breeding populations, while Beagle v. 4 is still preferable for diverse heterozygous populations. FILLIN and FSFHap are implemented in TASSEL 5.0.

Control—the Striga conundrum

There is a wide range of existing and potential control options for Striga. This paper describes and discusses many of the control options, with a focus on technology limitations, adoption limitations (real or potential) and, in the case of novel technologies, development limitations. The paper addresses the question as to why, after many years of research, control method testing, piloting and technology dissemination, the wide-scale effective control of Striga hermonthica (Del.) Benth. and Striga asiatica (L.) Kuntze is so elusive. Limitations, including variable technology reliability, poor access to control technology, costs (monetary, labour, skills) associated with control technology, limited practicality of methods and poor information, all hamper the adoption and impact of existing control methods. Some of the same issues may impact upon novel control technologies, and this needs careful consideration. Additional issues surround other potential technologies, especially so in the case of transgenic approaches. Suggestions are made as to how the impasse of effective Striga control can be overcome. More effective use of integrated control approaches, improved crop germplasm phenotyping, enhanced understanding of the host/non-host--parasite interaction and better integration and communication among the parasitic plant research, development and extension community are among the suggestions made.

AlphaSim : software for breeding program simulation

AlphaSim allows breeders and researchers to simulate genomic data with specific user criteria. AlphaSim is flexible, computationally efficient, and easy to use for a wide range of possible scenarios. AlphaSim can also be used in animal breeding, human genetics, and population genetics.

Identification, validation and high-throughput genotyping of transcribed gene SNPs in cassava

The availability of genomic resources can facilitate progress in plant breeding through the application of advanced molecular technologies for crop improvement. This is particularly important in the case of less researched crops such as cassava, a staple and food security crop for more than 800 million people. Here, expressed sequence tags (ESTs) were generated from five drought stressed and well-watered cassava varieties. Two cDNA libraries were developed: one from root tissue (CASR), the other from leaf, stem and stem meristem tissue (CASL). Sequencing generated 706 contigs and 3,430 singletons. These sequences were combined with those from two other EST sequencing initiatives and filtered based on the sequence quality. Quality sequences were aligned using CAP3 and embedded in a Windows browser called HarvEST:Cassava which is made available. HarvEST:Cassava consists of a Unigene set of 22,903 quality sequences. A total of 2,954 putative SNPs were identified. Of these 1,536 SNPs from 1,170 contigs and 53 cassava genotypes were selected for SNP validation using Illumina’s GoldenGate assay. As a result 1,190 SNPs were validated technically and biologically. The location of validated SNPs on scaffolds of the cassava genome sequence (v.4.1) is provided. A diversity assessment of 53 cassava varieties reveals some sub-structure based on the geographical origin, greater diversity in the Americas as opposed to Africa, and similar levels of diversity in West Africa and southern, eastern and central Africa. The resources presented allow for improved genetic dissection of economically important traits and the application of modern genomics-based approaches to cassava breeding and conservation.

Gene pools and the genetic architecture of domesticated cowpea

Cowpea [Vigna unguiculata (L.) Walp.] is a major tropical legume crop grown in warm to hot areas throughout the world and especially important to the people of sub‐Saharan Africa where the crop was domesticated. To date, relatively little is understood about its domestication origins and patterns of genetic variation. In this study, a worldwide collection of cowpea landraces and African ancestral wild cowpea was genotyped with more than 1200 single nucleotide polymorphism markers. Bayesian inference revealed the presence of two major gene pools in cultivated cowpea in Africa. Landraces from gene pool 1 are mostly distributed in western Africa while the majority of gene pool 2 are located in eastern Africa. Each gene pool is most closely related to wild cowpea in the same geographic region, indicating divergent domestication processes leading to the formation of two gene pools. The total genetic variation within landraces from countries outside Africa was slightly greater than within African landraces. Accessions from Asia and Europe were more related to those from western Africa while accessions from the Americas appeared more closely related to those from eastern Africa. This delineation of cowpea germplasm into groups of genetic relatedness will be valuable for guiding introgression efforts in breeding programs and for improving the efficiency of germplasm management.

A study of allelic diversity underlying flowering-time adaptation in maize landraces

Landraces (traditional varieties) of domesticated species preserve useful genetic variation, yet they remain untapped due to the genetic linkage between the few useful alleles and hundreds of undesirable alleles. We integrated two approaches to characterize the diversity of 4,471 maize landraces. First, we mapped genomic regions controlling latitudinal and altitudinal adaptation and identified 1,498 genes. Second, we used F-one association mapping (FOAM) to map the genes that control flowering time, across 22 environments, and identified 1,005 genes. In total, we found that 61.4% of the single-nucleotide polymorphisms (SNPs) associated with altitude were also associated with flowering time. More than half of the SNPs associated with altitude were within large structural variants (inversions, centromeres and pericentromeric regions). The combined mapping results indicate that although floral regulatory network genes contribute substantially to field variation, over 90% of the contributing genes probably have indirect effects. Our dual strategy can be used to harness the landrace diversity of plants and animals.

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

BackgroundThe 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.ResultsThe 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.ConclusionsResults 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.

Out of America: tracing the genetic footprints of the global diffusion of maize

Maize was first domesticated in a restricted valley in south-central Mexico. It was diffused throughout the Americas over thousands of years, and following the discovery of the New World by Columbus, was introduced into Europe. Trade and colonization introduced it further into all parts of the world to which it could adapt. Repeated introductions, local selection and adaptation, a highly diverse gene pool and outcrossing nature, and global trade in maize led to difficulty understanding exactly where the diversity of many of the local maize landraces originated. This is particularly true in Africa and Asia, where historical accounts are scarce or contradictory. Knowledge of post-domestication movements of maize around the world would assist in germplasm conservation and plant breeding efforts. To this end, we used SSR markers to genotype multiple individuals from hundreds of representative landraces from around the world. Applying a multidisciplinary approach combining genetic, linguistic, and historical data, we reconstructed possible patterns of maize diffusion throughout the world from American “contribution” centers, which we propose reflect the origins of maize worldwide. These results shed new light on introductions of maize into Africa and Asia. By providing a first globally comprehensive genetic characterization of landraces using markers appropriate to this evolutionary time frame, we explore the post-domestication evolutionary history of maize and highlight original diversity sources that may be tapped for plant improvement in different regions of the world.

Maize Landraces and Adaptation to Climate Change in Mexico

Mexico is the primary center of origin and diversity for maize (Zea mays L.). Farmers grow the crop largely under rain-fed conditions. Mexico is at considerable risk from climate change because of predicted rising temperatures, declining rainfall, and an increase in extreme weather events. Small-scale maize farmers are particularly vulnerable because of their geographical location as well as their limited adaptive capacity. Recommended climate change adaptation strategies include farmers’ increased use of heat and drought stress-tolerant maize. Farmer adoption of improved germplasm has been disappointing because of inefficient seed input chains and farmers’ preference for landraces for culinary, agronomic, and cultural reasons. Scientists have tended to overlook the fact that maize landraces have a critical role to play in climate change adaptation. Landraces may already exist that are appropriate for predicted climates. Furthermore, within the primary gene pool of maize and its wild relatives there exists unexploited genetic diversity for novel traits and alleles that can be used for breeding new high yielding and stress-tolerant cultivars. The breeding component of adaptation strategies should focus more on improving farmers’ landraces. The desired result would be a segmented maize seed sector characterized by both (improved) landraces and improved maize varieties. The public and private sector could continue to provide farmers with improved maize varieties and different actors, including farmers themselves, would generate seed of improved landraces for sale and/or exchange.