Breeding Platform Research Articles

AutoGP: An intelligent breeding platform for enhancing maize genomic selection.

In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding. Furthermore, recently developed genomic selection (GS) models, such as deep learning (DL), are complicatedand inconvenient for breeders to navigate and optimize within their breeding programs. Here, we present the development of an intelligent breeding platform named AutoGP (http://autogp.hzau.edu.cn), which integrates genotype extraction, phenotypic extraction, and GS models of genotype-to-phenotype data within a user-friendly web interface. AutoGP has three main advantages over previously developed platforms: 1) an efficient sequencing chip to identify high-quality, high-confidence SNPs throughout gene-regulatory networks; 2) a complete workflow for extraction of plant phenotypes (such as plant height and leaf count) from smartphone-captured video; and 3) a broad model pool, enabling users to select from five ML models (support vector machine, extreme gradient boosting, gradient-boosted decision tree, multilayer perceptron, and random forest) and four commonly used DL models (deep learning genomic selection, deep learning genomic-wide association study, deep neural network for genomic prediction, and SoyDNGP). For the convenience of breeders, we use datasets from the maize (Zea mays) complete-diallel design plus unbalanced breeding-like inter-cross population as a case study to demonstrate the usefulness of AutoGP. We show that our genotype chips can effectively extract high-quality SNPs associated with days to tasseling and plant height. The models show reliable predictive accuracy on different populations and can provide effective guidance for breeders. Overall, AutoGP offers a practical solution to streamline the breeding process, enabling breeders to achieve more efficient and accurate genomic selection.

Enhanced Typhoon-Induced Design Wave Height Estimation for Ocean Engineering Applications Using High Performance Computing

Coastal construction heavily depends on accurately estimating design wave parameters. This paper presents a technique for calculating ocean engineering design wave heights that addresses the challenges of inadequate wind speed and measured wave data in the field using High Performance Computing (HPC). The proposed method combines the Simulating WAves Nearshore (SWAN) wave model driven by wind with two different wind field models: the NCEP/NCAR reanalysis wind field and the Jelesnianski typhoon model wind field. By combining these models, the study reduces the underestimation of wind speed and inaccurate depiction of typhoon-related details. The method's effectiveness is demonstrated by applying it to calculate the design wave elements of the deep-sea breeding platform in Dongluo Island, Fujian. The study results show that the platform can experience a maximum wave height of 9.42 m during a 50-year recurrence period. The research shows that the platform's southwest corner is the most vulnerable location. This new calculation method is a significant improvement in ocean engineering design since it overcomes the limitations that come with the lack of wind speed and measured waves. By utilizing multiple wind field models, the calculated wave heights' accuracy and reliability is considerably enhanced. The application of this method to the breeding platform in Dongluo Island has yielded valuable insights for designing resilient structures in similar oceanic environments. This study demonstrates the important application of computational numerical simulations in the field of ocean engineering, providing high-precision predictive capabilities for structural design and risk assessment.

Smart Breeding Platform: A web-based tool for high-throughput population genetics, phenomics, and genomic selection

Smart Breeding Platform: A web-based tool for high-throughput population genetics, phenomics, and genomic selection

Research on Key Technologies of Smart Breeding Platform Based on 5G

Abstract In modern animal husbandry, the application of 5G technology opens a new era of farming management, especially in intelligent farming. The high-speed and low-latency communication characteristics make remote monitoring, data transmission, and thoughtful decision-making possible, significantly improving breeding efficiency and management. It has great potential for monitoring animal health, preventing and controlling diseases, and improving production efficiency. This paper explores the application of 5G technology in an intelligent farming management platform and its key technologies. By analyzing the needs of smart farming, we clarify the key application areas of 5G technology. To identify sick pigs, measure their weight accurately using binocular vision technology, and combine with machine learning algorithms, the YOLO v3 algorithm is utilized. The accuracy of sick pig identification based on YOLO v3 reaches 98%, the error of pig weight measurement is controlled within ±2%, and the real-time data transmission and processing realized by 5G technology significantly improves the efficiency of disease prevention and control.5G technology can effectively support the efficient operation of the intelligent farming platform, which is of great significance to enhance the level of farming management and promote the sustainable development of the animal husbandry industry.

Development of an efficient breeding platform for fruit trees using data science and statistical and molecular genetics

Development of an efficient breeding platform for fruit trees using data science and statistical and molecular genetics

Adaptive mechanisms of Bacillus to near space extreme environments

Earth's near space is an extreme atmosphere environment with high levels of radiation, low atmospheric pressure and dramatic temperature fluctuations. The region is above the flight altitude of aircraft but below the orbit of satellites, which has special and Mars-like conditions for investigating the survival and evolution of life. Technical limitations including flight devices, payloads and technologies/methodologies hinder microbiological research in near space. In this study, we investigated microbial survival and adaptive strategies in near space using a scientific balloon fight mission and multi-omics analyses. Methods for sample preparation, storage, protector and vessel were optimized to prepare the exposed microbial samples. After 3 h 17 min of exposure at a float altitude of ~32 km, only Bacillus strains were alive with survival efficiencies of 0–10−6. Diverse mutants with significantly altered metabolites were generated, firstly proving that Earth's near space could be used as a new powerful microbial breeding platform. Multi-omics analyses of mutants revealed cascade changes at the genome, transcriptome and proteome levels. In response to environmental stresses, two mutants had similar proteome changes caused by different genomic mutations and mRNA expression levels. Metabolic network analysis combined with proteins' expression levels revealed that metabolic fluxes of EMP, PPP and purine synthesis-related pathways were significantly altered to increase/decrease inosine production. Further analysis showed that proteins related to translation, molecular chaperones, cell wall/membrane, sporulation, DNA replication/repair and anti-oxidation were significantly upregulated, enabling cells to efficiently repair DNA/protein damages and improve viability against environmental stress. Overall, these results revealed genetic and metabolic responses of Bacillus to the harsh conditions in near space, providing a research basis for bacterial adaptive mechanisms in extreme environments.

Targeting market segment needs with public-good crop breeding investments: A case study with potato and sweetpotato focused on poverty alleviation, nutrition and gender.

Crop breeding programs have often focused on the release of new varieties that target yield improvement to achieve food security and reduce poverty. While continued investments in this objective are justified, there is a need for breeding programs to be increasingly more demand-driven and responsive to the changing customer preferences and population dynamics. This paper analyses the responsiveness of global potato and sweetpotato breeding programs pursued by the International Potato Center (CIP) and its partners to three major development indicators: poverty, malnutrition and gender. The study followed a seed product market segmentation blueprint developed by the Excellence in Breeding platform (EiB) to identify, describe, and estimate the sizes of the market segments at subregional levels. We then estimated the potential poverty and nutrition impacts of investments in the respective market segments. Further, we employed the G+ tools involving multidisciplinary workshops to evaluate the gender-responsiveness of the breeding programs. Our analysis reveals that future investments in breeding programs will achieve greater impacts by developing varieties for market segments and pipelines that have more poor rural people, high stunting rates among children, anemia prevalence among women of reproductive age, and where there is high vitamin A deficiency. In addition, breeding strategies that reduce gender inequality and enhance appropriate change of gender roles (hence gender transformative) are also required.

The giant diploid faba genome unlocks variation in a global protein crop

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity1. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value2. Faba bean (Vicia faba L.) has a high yield potential and is well suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has expanded to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, although with substantial copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association analysis to dissect the genetic basis of seed size and hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate the improvement of sustainable protein production across the Mediterranean, subtropical and northern temperate agroecological zones.

A Genome-Wide Scan Divulges Key Loci Involved in Resistance to Aphids (Aphis craccivora) in Cowpea (Vigna unguiculata).

Cowpea aphids (Aphis craccivora Koch) double as a direct damaging pest and a virus vector to cowpea, threatening the economic yield of the crop. Given the multiple ecotypes, different alleles have been implicated in aphid resistance, necessitating the identification of key genes involved. The present study implemented a genome-wide scan using 365 cowpea mini-core accessions to decipher loci involved in resistance to aphid ecotype from Kano, Nigeria. Accessions were artificially inoculated with A. craccivora in insect-proof cages and damage severity assessed at 21 days after infestation. Significant phenotypic differences based on aphid damage severity were registered among the accessions. Skewed phenotypic distributions were depicted in the population, suggesting the involvement of major genes in the control of resistance. A genome-wide scan identified three major regions on chromosomes Vu10, Vu08 and Vu02, and two minor ones on chromosomes Vu01 and Vu06, that were significantly associated with aphid resistance. These regions harbored several genes, out of which, five viz Vigun01g233100.1, Vigun02g088900.1, Vigun06g224900.1, Vigun08g030200.1 and Vigun10g031100.1 were the most proximal to the peak single nucleotide polymorphisms (SNPs) positions. These genes are expressed under stress signaling, mechanical wounding and insect feeding. The uncovered loci contribute towards establishing a marker-assisted breeding platform and building durable resistance against aphids in cowpea.

Aquaculture Molecular Breeding Platform (AMBP): a comprehensive web server for genotype imputation and genetic analysis in aquaculture.

It is of vital importance to understand the population structure, dissect the genetic bases of performance traits, and make proper strategies for selection in breeding programs. However, there is no single webserver covering the specific needs in aquaculture. We present Aquaculture Molecular Breeding Platform (AMBP), the first web server for genetic data analysis in aquatic species of farming interest. AMBP integrates the haplotype reference panels of 18 aquaculture species, which greatly improves the accuracy of genotype imputation. It also supports multiple tools to infer genetic structures, dissect the genetic architecture of performance traits, estimate breeding values, and predict optimum contribution. All the tools are coherently linked in a web-interface for users to generate interpretable results and evaluate statistical appropriateness. The webserver supports standard VCF and PLINK (PED, MAP) files, and implements automated pipelines for format transformation and visualization to simplify the process of analysis. As a demonstration, we applied the webserver to Pacific white shrimp and Atlantic salmon datasets. In summary, AMBP constitutes comprehensive resources and analytical tools for exploring genetic data and guiding practical breeding programs. AMBP is available at http://mgb.qnlm.ac.

Integrative Pre-Breeding for Biotic Resistance in Forest Trees.

Climate change is unleashing novel biotic antagonistic interactions for forest trees that may jeopardize populations’ persistence. Therefore, this review article envisions highlighting major opportunities from ecological evolutionary genomics to assist the identification, conservation, and breeding of biotic resistance in forest tree species. Specifically, we first discuss how assessing the genomic architecture of biotic stress resistance enables us to recognize a more polygenic nature for a trait typically regarded Mendelian, an expectation from the Fisherian runaway pathogen–host concerted arms-race evolutionary model. Secondly, we outline innovative pipelines to capture and harness natural tree pre-adaptations to biotic stresses by merging tools from the ecology, phylo-geography, and omnigenetics fields within a predictive breeding platform. Promoting integrative ecological genomic studies promises a better understanding of antagonistic co-evolutionary interactions, as well as more efficient breeding utilization of resistant phenotypes.

Integration of meta-QTL discovery with omics: Towards a molecular breeding platform for improving wheat resistance to Fusarium head blight

Fusarium head blight (FHB) is a global wheat disease that devastates wheat production. Resistance to FHB spread within a wheat spike (type II resistance) and to mycotoxin accumulation in infected kernel (type III resistance) are the two main types of resistance. Of hundreds of QTL that have been reported, only a few can be used in wheat breeding because most show minor and/or inconsistent effects in different genetic backgrounds. We describe a new strategy for identifying robust and reliable meta-QTL (mQTL) that can be used for improvement of wheat FHB resistance. It involves integration of mQTL analysis with mQTL physical mapping and identification of single-copy markers and candidate genes. Using meta-analysis, we consolidated 625 original QTL from 113 publications into 118 genetic map-based mQTL (gmQTL). These gmQTL were further located on the Chinese Spring reference sequence map. Finally, 77 high-confidence mQTL (hcmQTL) were selected from the reference sequence-based mQTL (smQTL). Locus-specific single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) markers and 17 genes responsive to FHB were then identified in the hcmQTL intervals by combined analysis of transcriptomic and proteomic data. This work may lead to a comprehensive molecular breeding platform for improving wheat resistance to FHB.

Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia.

Much of the world’s population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.

A biotechnology-based male-sterility system for hybrid seed production in tomato.

SummaryIncorporating male sterility into hybrid seed production reduces its cost and ensures high varietal purity. Despite these advantages, male‐sterile lines have not been widely used to produce tomato (Solanum lycopersicum) hybrid seeds. We describe the development of a biotechnology‐based breeding platform that utilized genic male sterility to produce hybrid seeds. In this platform, we generated a novel male‐sterile tomato line by clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR‐associated protein 9 (Cas9)‐mediated mutagenesis of a stamen‐specific gene SlSTR1 and devised a transgenic maintainer by transforming male‐sterile plants with a fertility‐restoration gene linked to a seedling‐colour gene. Offspring of crosses between a hemizygous maintainer and the homozygous male‐sterile plant segregated into 50% non‐transgenic male‐sterile plants and 50% male‐fertile maintainer plants, which could be easily distinguished by seedling colour. This system has great practical potential for hybrid seed breeding and production as it overcomes the problems intrinsic to other male‐sterility systems and can be easily adapted for a range of tomato cultivars and diverse vegetable crops.

Gene Pool Subdivision of East African Sweetpotato Parental Material.

Sweetpotato [Ipomoea batatas (L.) Lam] breeding is important for food security and health in East Africa (EA), and a breeding platform in Uganda provides national researchers and breeders in EA with true seed. Our objectives were to characterize genetic relationships among parental material used at the EA breeding platform. There were 135 parents and six check clones analyzed using 31 simple sequence repeat primers. An average of 7.13 alleles per primer was found, and Jaccard similarity coefficients were in the range of 0.298 to 1.00 with a mean of 0.542. Unweighted pair group cluster analysis placed most African parents in two main subclusters showing no association with morphology or geographical origin. The subclusters were also supported by principal coordinate analysis, derivative analysis of principal components, and population structure simulations. The analyzed breeding material from EA was highly genetically variable, grouped in two distinct genetic pools, and suitable to study heterosis exploiting breeding schemes.

Effects of a vegetable-eel-earthworm integrated planting and breeding system on bacterial community structure in vegetable fields

Agricultural production combined with planting and breeding, which can reduce chemical fertilizer and pesticide applications, reduce losses due to natural disasters, and improve the output and quality of agricultural products, is an important way to achieve green, circular and efficient production. To assess effects on soil bacterial community structure, a vegetable-eel-earthworm integrated planting and breeding platform (VEE-IPBP) combined with experiment planting was established at Chongming Island, Shanghai and compared to traditional planting. High-throughput sequencing to reveal soil bacterial community structure was performed on samples collected at 0, 3 and 6 years after implementation of the two models. Over time, the Shannon index first increased and then decreased in the VEE-IPBP system and was reduced by 3.2% compared to the traditional planting (In the same time and space scale, the single-degree planting method of dryland vegetables under mechanical cultivation is adopted) (p < 0.05). In contrast, Chao and Ace indices were increased by 2.4% and 3.2%. Thus, soil bacterial diversity was markedly different in the two planting models. The abundance of Proteus, Cyanophyta and Cyanophyta in soil increased after 6 years, and the proportion of Lysinibacillus increased significantly, contributing to improvement in soil disease resistance. Redundancy analysis (RDA) showed that the soil pH and water content were the main factors influencing the change in soil bacterial community structure in the two planting models, and the dominant species of soil bacteria were Lysobacter and Bacillus.

Why a hill country symposium?

Introduction When I was young the hill country was where the store lambs came from. But with the flatter and generally better land being lost to the likes of urban and dairy farming expansion, New Zealand's hill country is now the key breeding platform, and is increasingly being required to finish stock for the red meat sector - a sector that generates nearly $8 billion/year in export earnings for the country. Hill country farmers, their service providers and local communities face notable challenges including continuing pressure on profitability, rural depopulation and climate change, as well as environmental pressure around the 'right to farm' from the greater New Zealand population. However, there are also great success stories in terms of the improvements in productivity and environmental stewardship that have been made in the hill country. The Hill Country Symposium (HCS) will update relevant research and present some of the industry's successes. However, our main aim is as a group to answer two questions: What does a profitable and resilient future for our hill country farming look like? What do we, collectively

Taking the Phenomics Revolution into the Field

Taking the <i>Phenomics Revolution</i> into the Field

Facing the challenges of global agriculture today: what can we do about drought?

Facing the challenges of global agriculture today: what can we do about drought?

Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice

The Crop Ontology (CO) of the Generation Challenge Program (GCP) (http://cropontology.org/) is developed for the Integrated Breeding Platform (IBP) (http://www.integratedbreeding.net/) by several centers of The Consultative Group on International Agricultural Research (CGIAR): bioversity, CIMMYT, CIP, ICRISAT, IITA, and IRRI. Integrated breeding necessitates that breeders access genotypic and phenotypic data related to a given trait. The CO provides validated trait names used by the crop communities of practice (CoP) for harmonizing the annotation of phenotypic and genotypic data and thus supporting data accessibility and discovery through web queries. The trait information is completed by the description of the measurement methods and scales, and images. The trait dictionaries used to produce the Integrated Breeding (IB) fieldbooks are synchronized with the CO terms for an automatic annotation of the phenotypic data measured in the field. The IB fieldbook provides breeders with direct access to the CO to get additional descriptive information on the traits. Ontologies and trait dictionaries are online for cassava, chickpea, common bean, groundnut, maize, Musa, potato, rice, sorghum, and wheat. Online curation and annotation tools facilitate (http://cropontology.org) direct maintenance of the trait information and production of trait dictionaries by the crop communities. An important feature is the cross referencing of CO terms with the Crop database trait ID and with their synonyms in Plant Ontology (PO) and Trait Ontology (TO). Web links between cross referenced terms in CO provide online access to data annotated with similar ontological terms, particularly the genetic data in Gramene (University of Cornell) or the evaluation and climatic data in the Global Repository of evaluation trials of the Climate Change, Agriculture and Food Security programme (CCAFS). Cross-referencing and annotation will be further applied in the IBP.