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From FAANG to fork: application of highly annotated genomes to improve farmed animal production
Genome Biology ( IF 12.3 ) Pub Date : 2020-11-24 , DOI: 10.1186/s13059-020-02197-8
Emily L Clark 1 , Alan L Archibald 1 , Hans D Daetwyler 2, 3 , Martien A M Groenen 4 , Peter W Harrison 5 , Ross D Houston 1 , Christa Kühn 6, 7 , Sigbjørn Lien 8 , Daniel J Macqueen 1 , James M Reecy 9 , Diego Robledo 1 , Mick Watson 1 , Christopher K Tuggle 9 , Elisabetta Giuffra 10
Affiliation  

The Food and Agriculture Organisation of the United Nations (FAO) reports that by the year 2050 the global human population is likely to reach 9.7 billion, rising to 11.2 billion by 2100 (https://population.un.org/wpp/Publications/Files/Key_Findings_WPP_2015.pdf). This population growth poses several challenges to the global food system, which will need to produce more healthy food using fewer natural resources, reducing the environmental impact, conserving biodiversity and flexibly adjusting to changing societal expectations. Meeting this demand requires environmentally sustainable improvements to farmed animal health and welfare, and of efficiency and diversification (e.g. to include a broader range of locally adapted species) [1]. The changes in breeding strategies and management practises required to meet these goals will need to build on an improved ability to accurately use genotype to predict phenotype in the world’s farmed animal species, both terrestrial and aquatic (Fig. 1).

Fig. 1
figure1

Addressing the challenges of global food production in the 21st Century

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Here we describe a set of research priorities to meet such present and future challenges that build on progress, successes and resources from the Functional Annotation of ANimal Genomes (FAANG) project [2]. The first stages of FAANG focused on foundational data generation to characterise expressed and regulatory genomic regions, curation and provision of annotated farmed animal genomes [2, 3]. These were largely based on individual level, high depth approaches [3]. The primary challenge facing this community now is harnessing these resources to link genotype, phenotype and genetic merit in order to translate this research out of the laboratory and into industry application in the field. To achieve this effectively, we will need to generate functional genomic information for large populations of animals, rather than relying on a small number of deeply annotated individuals. Furthermore, to date, most of the datasets are from tissues consisting of heterogeneous cell populations, hindering the resolution of functional information and limiting our ability to understand the fundamental cellular and subcellular processes underlying phenotypes. Since the original FAANG white paper was published in 2015 [2], exciting new opportunities have arisen to tackle these challenges. We describe a set of research action priorities for FAANG for the next decade (Fig. 2), in each of the sections below.

Fig. 2
figure2

Priorities for the next decade of FAANG research

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In the past 20 years, genomic selection has substantially increased genetic gain in some farmed animal species through the use of large training populations [4]. However, prediction accuracy in genetically distant populations (i.e. across populations, breeds and generations) remains limited due in part to the current reliance on neutral markers in incomplete linkage disequilibrium with causative genetic variants in the breeding population of interest [4]. Using variants more tightly linked to causative polymorphisms and supported by genomic information in a multi-breed training population can partially alleviate these limitations [5]. Large-scale whole-genome resequencing has produced inventories of many millions of variants for thousands of animals [6]. In such sequence datasets, the causative variants are directly genotyped among millions of neutral markers. This reduces the signal-to-noise ratio when all the data are used for genomic prediction without prior biological information. Efforts to detect causative variants have been successful for variants with large phenotypic effects, often deleterious, using a combination of quantitative, population and molecular genetics [4]. However, economically important traits have a polygenic architecture and causative variants are expected to have small effects, which makes their detection and quantification difficult. Most of these causal variants, with small effects, are likely to be located in regulatory sequences and impact complex traits through changes in gene expression [4]. Thus, it is expected that improvements in prediction accuracy can be achieved by filtering the genetic marker information based upon whether the genetic variants reside in functional sequences and developing robust prediction models that can accommodate the biological priors. As functional (expressed and regulatory) genomic elements are not easy to predict from sequence alone, FAANG will enhance current genome annotation with functional information from a range of relevant tissues, cell types and developmental stages. Recently, novel methods for the integration of biological information (e.g. methylation of regions of predicted functionality) into genomic prediction have been proposed, e.g. [5]. These models, which are based on the combination and ranking of many diverse datasets from multiple animals, could facilitate further improvements in predicting genetic merit and consequently on genomic selection, as has been demonstrated in cattle [5]. As many more suitable datasets will become available in the next 5 years, improving and adapting these methods to enhance genomic prediction accuracy, whilst conserving genetic diversity, across farmed animal species will be a priority for FAANG.

The first phase of FAANG is using a specific set of transcriptomic and epigenomic assays to define functional regions of the genome in tissues [2]. Due to the significant investment per sample, this phase was limited to only a few individuals and ascribed function was averaged across these replicates [2]. Progress has been made in defining functional regions, and this should be built upon to ascertain the effect of genetic variation on genome function [3]. Collecting functional genomic data across many genetically diverse animals lends itself to the application of statistical genomics to detect quantitative trait loci (QTL) controlling molecular phenotypes. This is particularly powerful when done at sequence-level resolution to directly relate molecular phenotypes (e.g. gene expression or methylation information) to variants associated with complex traits. The GTEx consortium (https://gtexportal.org/home/) has achieved this very effectively across human tissues, enabling expression QTL (eQTL) studies linking gene expression to genetic variation [7] and providing a framework for FAANG to develop a similar project for farmed animals (FAANGGTEx). Large farmed animal cohorts in controlled and well-characterised environments with extensive pedigree information and molecular phenotypes would allow researchers, in partnership with industry, to (1) build better predictive models of genotype-to-phenotype, (2) better understand genotype-by-environment interactions and (3) prioritise functional variants for inclusion in breeding programmes [4]. Hundreds of thousands of farmed animals currently have imputed genotypes and extended pedigrees with deep phenotypic records [6]. A project analysing the relationship between SNPs from Genome Wide Association Studies and gene expression for cattle, mining publicly available sequence data, was published earlier this year, demonstrating the feasibility, timeliness and potential of a GTEx approach for farmed animals [8].

Beyond its use in genomic prediction, the functional data produced by FAANG will provide new perspectives for informed management decisions. Epigenetic and expression information for individual animals could be combined with microbiome data and high-throughput phenotypes from new management technologies (e.g. wearables, GPS, in-vivo imaging systems) [9]. These datasets from large cohorts of animals would enhance prediction of adaptive capacity at the individual, farm or population level through integration of prior environmental data with individual genome information. Thus, providing new opportunities for informed management decisions during an animal’s lifetime (e.g. to optimise diets or for steering animals into the most appropriate production systems). A genome enabled management approach (providing animals, within a production system, with their specific needs during their lifetime) will be beneficial to improving animal health and welfare, facilitate adaptation to changing environments and contribute to addressing public concerns related to animal production. Achieving this within the next 10 years may be possible, but the challenge will be to ensure it is practical and affordable for animal breeders and producers.

Through large-scale sequencing efforts by the farmed animal genomics community data are now accumulating that characterise the sequence diversity of farmed animals including locally adapted breeds/populations. As a consequence, future genetic management is likely to include the use of pangenomes that will capture all available population-level genomic information for a given farmed animal species. Using graph-based frameworks, we can more accurately genotype and annotate the genomic diversity present in any given individual [10]. In this way, pangenomes can reveal population- or breed-specific adaptations that could be used to tailor the genotypes chosen in future farming systems in order to conserve biodiversity whilst improving production efficiency and animal health [1]. Furthermore, the highly annotated genomes produced by FAANG allow evolutionary conservation across species to be defined for all genomic features [11]. Ongoing FAANG projects involve comparative analysis which will reveal the functional basis of phenotypes present in one species that are desirable in others. Such projects contribute to addressing the major opportunity that exists to enhance the sustainable production of a wider diversity of animal species, including numerous and diverse aquaculture species that are poised to exploit functional genomics to expedite genetic improvement, where tailored and cost-efficient approaches will be required [12]. Current FAANG-related projects already extend to several major farmed finfish species in Europe and North America. We envisage an increased representation of aquatic species, including shellfish, and further expansion to include invertebrates, within FAANG projects during the next 5 to 10 years.

The use of bulk tissue samples in the FAANG studies performed to date captures regulatory elements and expression signals averaged across all represented cell types but fails to reveal the cell-specific basis of the molecular phenotypes of interest. In order to more accurately link genotype to phenotype, data at the level of individual cell types are required. Single-cell sequencing technologies enable the deconvolution of the transcriptional and regulatory complexity in tissues made up of multiple cell types. New technologies to detect gene expression as well as chromatin accessibility, structure and interactions within single cells provide more comprehensive data to predict function and interaction partners for regulatory elements. As a consequence, one of the main priorities for FAANG within the next 5 to 10 years is to create single-cell atlases for the key tissues of farmed animal species (FAANGSingleCell). The organisational processes, standardisation and data sharing infrastructure established by the community for the first stages of FAANG [3] will provide a strong foundation for FAANGSingleCell to progress quickly and efficiently. The FAANGSingleCell project should build on existing functional tissue maps for other species [13] and will enable the identification of genomic variants underpinning trait-linked cell types/factors and causal variants. In the FAANGGTEx project described above, single-cell atlases will provide a powerful layer of resolution including cell-specific molecular phenotypes, enabling the fine-scale dissection of complex traits of interest.

Single-cell sequencing technologies can also be used to deeply characterise cell and tissue complexity of in vitro systems such as organoids. Over the last 5 years, organoids for many different organ systems and for multiple farmed animal species have been developed [4]. Organoids provide ex vivo/in vitro systems for testing candidate causal variants by genome editing technologies and potentially a system for high-throughput, cost-effective, large-scale in vitro phenotyping. Importantly, given the ease of biobanking, organoids have a strong ethical benefit in reducing the number of animals used in experimentation [3]. Multiple organoid models can be derived from very small quantities of tissue or from induced pluripotent stem cells (iPSCs). They provide the potential to generate and test multiple phenotypes to unravel when, and under what conditions, a putative causal variant has an effect. Therefore, farm animal organoids will be valuable over the coming decade, providing information about fundamental biology to model the effects of changing environmental conditions and supporting immunology, vaccinology, physiology, nutritional and biodiversity conservation studies. The ability to decompose complex phenotypes into key processes will provide a means to robustly relate the deep phenotypes measured in these systems with the traits used for selection, opening to the possibility of using organoids for breeding purposes.

The application of genome editing to farmed animals is advancing rapidly, mainly due to development of CRISPR/Cas technologies [12, 14]. The CRISPR toolbox has expanded to improve precision, allow modulation of gene expression and epigenetic modifications, and now forms an integral part of the future FAANG roadmap [3]. CRISPR-mediated modification of putative genomic elements can confirm their functionality and reveal their roles in cellular (and organoid) function. Genome-wide multiplexed CRISPR approaches now enable the simultaneous interrogation of thousands of genomic features in cell lines, increasing the feasibility of this approach for genome-scale annotation [15]. These high-throughput approaches can also be used in combination with single-cell sequencing technologies to obtain high-resolution molecular phenotypes. In addition, genome editing represents a potential major route for the application of FAANG research in farmed animal breeding programmes via (1) detection and utilisation of causative variants affecting important traits, (2) targeted introgression, or ‘introgression-by-editing’, of favourable alleles from other strains or species into a closed breeding population, or (3) creation of de novo alleles with favourable effects, either predicted from unbiased genome-wide screens or from a priori knowledge of the biology of the trait in question. Public perception and regulatory hurdles remain and ongoing discussion through stakeholder engagement must continue and evolve to keep pace with technological advances. While the use of genome editing for the improvement of farmed animals may currently only be possible in some countries, its use in in vitro models, such as organoids, is not subject to the same legislation and ethical considerations as the use of whole animals and thus represents a new frontier for FAANG research.

As a scientific community, FAANG continues to develop a coordinated analysis and data collection infrastructure crucial for its success [3]. The FAANG bioinformatics community, including the centralised Data Coordination Centre (DCC), is focused on open reproducible science, the FAANG data portal (https://data.faang.org/home) is the focal point for this activity. Technological development, coordination and standardisation by the DCC will continue to be crucial for the shift towards population scale studies, single-cell datasets, cell atlases and pangenomes, across a growing number of species. This will require new reproducible analysis pipelines and infrastructure, metadata validation services, data portal features such as a centralised atlas browser and online training resources. Single-cell atlases and in vitro systems for farmed animal species will be accompanied by high quality metadata, archiving and visualisations across species, organ systems, tissues and cell types. As FAANG datasets continue to increase in complexity, there is a growing need for new methods of data visualisation and integration to be made available. These future developments, and the distributed data and analysis infrastructure, will be crucial for the successful application of functional data to farmed animal breeding programmes.

The research priorities we have outlined for FAANG for the coming decade are depicted in Fig. 2. The uptake by the farmed animal production industry and the expected outcomes of each prioritised action are summarised in Fig. 3. FAANG will improve our ability to more accurately use genotype to predict phenotype. This will directly contribute to addressing the challenges faced for sustainable and responsible global food production in the next decade (Fig. 1). However, whilst the molecular assays used to enable functional annotation can now be delivered at much lower cost, the costs for the research priorities outlined above remain substantial, especially considering the rapid increase in number and diversity of target species in the aquaculture sector. As such, a strong commitment to invest in research is needed. Persuading the US Department of Agriculture and the European Commission to include FAANG projects in NIFA-AFRI and Horizon 2020 funding calls, respectively (https://faang.org/proj.php) was a major success for the first stage of FAANG and its leadership. Current funding for FAANG supports the research community to improve the functional annotation of key farmed animal species and to facilitate more refined genomics-enabled animal breeding/genetic improvement. The research priorities outlined here are already strategically aligned to the objectives of the European Green Deal (https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en) and current USDA National Institute for Food and Agriculture programmes (e.g. https://nifa.usda.gov/program/genome-phenome-initiative; https://www.ag2pi.org). International cooperation will be essential to secure funding for their achievement. Given the scale and cost of the research involved, it will likely be necessary to initially prioritise the development of in vitro systems and the enhancement of data infrastructure to provide a solid foundation for FAANGSingleCell and FAANGGTEx.

Fig. 3
figure3

How implementation of FAANG research priorities over the next decade will benefit farmed animal production

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The timely achievement of all of the research priorities we outline here for the next stages of FAANG will together increase the capacity of the farmed animal production industry to face the challenges of the future, empowering genomic selection, enhancing adaptation to changing environments, conserving biodiversity and bridging the gaps between cellular and whole animal scale knowledge.

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The work presented in this manuscript was initiated at discussions held at three scientific meetings held in early 2020. The authors would like to thank the FAANG Scientific Advisory Board for useful advice and the following people for providing comments on earlier drafts of this manuscript: Amanda J. Chamberlain (Agriculture Victoria; La Trobe University), Appolinaire Djikeng (Centre for Tropical Livestock Genetics and Health, University of Edinburgh), Denis J. Headon (The Roslin Institute, University of Edinburgh), Ian Johnston (Xelect Ltd), Andreas Kranis (Aviagen; The Roslin Institute, University of Edinburgh), Michèle Tixier-Boichard (Université Paris Saclay, INRAE), Stephen N. White (USDA-ARS; Washington State University), Ruidong Xiang (University of Melbourne) and Daniel Zerbino (EMBL-EBI).

In the EU, FAANG has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 817923 (AQUA-FAANG), 817998 (GENE-SWitCH) and 815668 (BovReg) and from European COST Action CA15112: Functional Annotation of Animal Genomes - European network, FAANG-Europe. At EMBL-EBI, the work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) (BB/N019563/1 and BB/N019202/1), Wellcome (WT108749/Z/15/Z) and the European Molecular Biology Laboratory. At the Roslin Institute, the work was supported through BBSRC Institute Strategic Programme Grants (BB/P013732/1 and BB/P013759/1). In the USA, FAANG has received funding from the US National Science Foundation (IOS-1548275) and the US Department of Agriculture (2015-68004-24104, 2018-67015-27501). In Australia, FAANG has received funding from the DairyBio project (a joint venture between Agriculture Victoria, Dairy Australia and the Gardiner Foundation) and through the University of Queensland.

Affiliations

  1. The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, EH25 9RG, UK

    Emily L. Clark, Alan L. Archibald, Ross D. Houston, Daniel J. Macqueen, Diego Robledo & Mick Watson

  2. Agriculture Victoria, AgriBio Centre for AgriBioscience, Bundoora, Victoria, 3083, Australia

    Hans D. Daetwyler

  3. School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3083, Australia

    Hans D. Daetwyler

  4. Animal Breeding and Genomics Centre, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands

    Martien A. M. Groenen

  5. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

    Peter W. Harrison

  6. Leibniz Institute for Farm Animal Biology (FBN), Institute of Genome Biology, Genome Physiology Unit, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany

    Christa Kühn

  7. Faculty of Agricultural and Environmental Sciences, University Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany

    Christa Kühn

  8. Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, NO-1432, Ås, Norway

    Sigbjørn Lien

  9. Department of Animal Science, Iowa State University, Ames, IA, 50011, USA

    James M. Reecy & Christopher K. Tuggle

  10. Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France

    Elisabetta Giuffra

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Contributions

All co-authors contributed to the writing of the manuscript. ELC assembled and curated the manuscript in collaboration with the co-authors, with joint editing by MW and FAANG co-coordinators (CKT and EG). All authors read and approved the final manuscript.

Corresponding author

Correspondence to Emily L. Clark.

Competing interests

The authors declare that they have no competing interests.

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Clark, E.L., Archibald, A.L., Daetwyler, H.D. et al. From FAANG to fork: application of highly annotated genomes to improve farmed animal production. Genome Biol 21, 285 (2020). https://doi.org/10.1186/s13059-020-02197-8

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中文翻译：
从FAANG到fork：应用高度注释的基因组来改善养殖动物的生产

联合国粮食及农业组织（FAO）报告称，到2050年，全球人口有望达到97亿，到2100年将增至112亿（https://population.un.org/wpp/Publications/文件/Key_Findings_WPP_2015.pdf）。人口的增长对全球粮食系统提出了若干挑战，全球粮食系统将需要使用更少的自然资源来生产更多健康的食物，减少对环境的影响，保护生物多样性，并灵活地适应不断变化的社会期望。要满足这一需求，就需要在环境上可持续改善养殖动物的健康和福利，并提高其效率和多样化（例如，包括范围更广的本地适应物种）[1]。

图。1
图1

解决全球粮食生产的挑战，在21日世纪

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在这里，我们根据动物基因组功能注释（FAANG）项目[2]的进展，成功和资源，描述了一系列研究重点，以应对当前和未来的挑战。FAANG的第一阶段专注于基础数据的生成，以表征表达和调控的基因组区域，管理和提供带注释的农场动物基因组[2，3]。这些主要基于个人层面的深度方法[3]。现在，这个社区面临的主要挑战是如何利用这些资源将基因型，表型和遗传价值联系起来，以便将这项研究从实验室转移到该领域的工业应用中。为了有效实现这一目标，我们将需要为大量动物生成功能基因组信息，而不是依赖少数带有深层注释的人。此外，迄今为止，大多数数据集均来自由异质细胞群体组成的组织，这阻碍了功能信息的解析，并限制了我们了解潜在的表型基础细胞和亚细胞过程的能力。自从最初的FAANG白皮书于2015年发布[2]以来，已经出现了令人兴奋的新机遇来应对这些挑战。我们在以下每个部分中介绍了FAANG在未来十年中的一系列研究行动重点（图2）。阻碍了功能信息的解析，并限制了我们了解潜在的表型基本细胞和亚细胞过程的能力。自从最初的FAANG白皮书于2015年发布[2]以来，已经出现了令人兴奋的新机遇来应对这些挑战。我们在以下每个部分中介绍了FAANG在未来十年中的一系列研究行动重点（图2）。阻碍了功能信息的解析，并限制了我们了解潜在的表型基本细胞和亚细胞过程的能力。自从最初的FAANG白皮书于2015年发布[2]以来，已经出现了令人兴奋的新机遇来应对这些挑战。我们在以下每个部分中介绍了FAANG在未来十年中的一系列研究行动重点（图2）。

图2
图2

FAANG研究未来十年的重点

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在过去的20年中，通过使用大量的训练种群，基因组选择大大增加了某些养殖动物的遗传增益[4]。然而，在遗传上遥远的种群中（即在种群，品种和世代之间）的预测准确性仍然受到限制，部分原因是当前对目标育种种群中因果遗传变异不完全连锁不平衡的中性标记的依赖[4]。在多品种训练群体中使用与致病多态性紧密联系并受基因组信息支持的变体可以部分缓解这些局限性[5]。大规模的全基因组重测序已为数千种动物提供了数百万个变种的清单[6]。在这样的序列数据集中，致病变异直接在数百万个中性标记中进行基因分型。当所有数据都用于基因组预测而没有先验生物学信息时，这降低了信噪比。利用定量，种群和分子遗传学的组合，对于具有大表型效应（通常是有害的）的变异体，已经成功检测出致病变异体[4]。但是，经济上重要的性状具有多基因结构，并且致病性变异预期影响很小，这使其难以检测和定量。这些因果变体中大多数具有很小的影响，很可能位于调节序列中，并通过基因表达的变化影响复杂的性状[4]。从而，期望通过基于遗传变异体是否存在于功能序列中并过滤遗传标记信息并开发能够适应生物学先验的鲁棒预测模型，可以实现预测准确性的提高。由于仅靠序列不容易预测功能性（表达的和调节性的）基因组元件，FAANG将利用来自一系列相关组织，细胞类型和发育阶段的功能性信息来增强当前的基因组注释。最近，已经提出了将生物学信息（例如，预测功能区域的甲基化）整合到基因组预测中的新方法，例如[5]。这些模型基于多种动物的多种多样的数据集的组合和排名，如在牛中所证明的那样，它可以促进预测遗传价值的进一步改进，进而促进基因组选择[5]。未来5年内将有更多合适的数据集出现，因此，改进和适应这些方法以提高基因组预测的准确性，同时又能保护跨养殖动物物种的遗传多样性，将是FAANG的工作重点。

FAANG的第一阶段是使用一组特定的转录组学和表观基因组测定法来定义组织中基因组的功能区域[2]。由于每个样品的大量投资，该阶段仅限于少数几个人，并且归因于这些重复实验的功能的平均值[2]。在定义功能区方面已经取得了进展，应该以此来确定遗传变异对基因组功能的影响[3​​]。收集许多遗传多样的动物的功能基因组数据有助于统计基因组学的应用，以检测控制分子表型的数量性状基因座（QTL）。当以序列级分辨率完成以直接关联分子表型时（例如，基因表达或甲基化信息）。GTEx联盟（https://gtexportal.org/home/）已在整个人类组织中非常有效地实现了这一目标，从而使表达QTL（eQTL）研究能够将基因表达与遗传变异联系起来[7]，并为FAANG提供了开发类似框架的框架养殖动物项目（FAANGGTEx）。在受控且特征明确的环境中具有广泛的谱系信息和分子表型的大型养殖动物群将使研究人员与行业合作，可以（1）建立更好的基因型对表型的预测模型，（2）更好地了解-环境相互作用；（3）优先考虑将功能变体纳入育种程序[4]。目前有成千上万的家畜具有估算的基因型和扩展的谱系，具有很深的表型记录[6]。今年早些时候发布了一个项目，该项目分析了全基因组关联研究的SNP与牛的基因表达之间的关系，并挖掘了公开可用的序列数据，证明了GTEx方法用于养殖动物的可行性，及时性和潜力[8]。

除了将其用于基因组预测之外，FAANG产生的功能数据还将为明智的管理决策提供新的视角。个体动物的表观遗传和表达信息可以与微生物组数据和来自新管理技术（例如可穿戴设备，GPS，体内成像系统）的高通量表型结合[9]。通过将先前的环境数据与单个基因组信息相集成，来自大型动物群的这些数据集将增强对个人，农场或种群水平的适应能力的预测。因此，为在动物的一生中做出明智的管理决策提供了新的机会（例如，优化饮食或将动物引入最合适的生产系统）。支持基因组的管理方法（在生产系统内提供动物，及其一生中的特定需求）将有助于改善动物的健康和福利，促进对不断变化的环境的适应，并有助于解决与动物生产有关的公众关注。在未来十年内可能实现这一目标，但是挑战将是确保它对动物育种者和生产者而言切实可行且负担得起。

通过农场动物基因组学的大规模测序工作，目前正在积累表征农场动物序列多样性特征的数据，包括本地适应的品种/种群。结果，未来的遗传管理可能包括使用全基因组，它将捕获给定养殖动物物种的所有可用种群水平的基因组信息。使用基于图的框架，我们可以更准确地进行基因分型并注释任何给定个体中存在的基因组多样性[10]。这样，全基因组可以揭示针对特定种群或品种的适应性变化，这些适应性变化可用于定制未来农作系统中选择的基因型，以保护生物多样性，同时提高生产效率和动物健康[1]。此外，FAANG产生的高度注释的基因组允许针对所有基因组特征定义物种间的进化保守性[11]。正在进行的FAANG项目涉及比较分析，该分析将揭示一种物种中存在的表型的功能基础，而另一种物种则具有此表型。这些项目有助于解决存在的主要机会，以增强可持续发展的多种动物物种的生产，其中包括准备利用功能基因组学来加速遗传改良的众多不同水产养殖物种，在这种情况下将采用量身定制的，具有成本效益的方法。必需的[12]。当前与FAANG相关的项目已经扩展到欧洲和北美的几种主要养殖有鳍鱼类。我们设想增加包括贝类在内的水生物种的数量，

迄今为止，在FAANG研究中使用大块组织样品捕获了所有代表细胞类型的平均调节因子和表达信号，但未能揭示感兴趣分子表型的细胞特异性基础。为了更准确地将基因型与表型联系起来，需要单个细胞类型水平的数据。单细胞测序技术可以使多种细胞类型组成的组织中的转录和调节复杂性解卷积。用于检测基因表达以及染色质可及性，结构和单个细胞内相互作用的新技术提供了更全面的数据，以预测调控元件的功能和相互作用伙伴。作为结果，SingleCell）。社区为FAANG [3]的第一阶段建立的组织过程，标准化和数据共享基础结构将为FAANG SingleCell快速高效地发展提供坚实的基础。FAANG SingleCell项目应以其他物种的现有功能组织图为基础[13]，并将能够识别支持性状相关细胞类型/因子和因果变异的基因组变异。在上述的FAANG GTEx项目中，单细胞图谱将提供包括细胞特异性分子表型在内的强大分辨率，从而能够对感兴趣的复杂性状进行精细的解剖。

单细胞测序技术还可用于深入表征体外系统（如类器官）的细胞和组织复杂性。在过去的五年中，针对许多不同器官系统和多种养殖动物的类器官已被开发[4]。有机体提供了用于通过基因组编辑技术测试候选因果变体的离体/体外系统，以及潜在的高通量，经济高效的大规模体外表型分析系统。重要的是，考虑到生物库的便捷性，类器官在减少实验中使用的动物数量方面具有强大的伦理益处[3]。可以从非常少量的组织或诱导的多能干细胞（iPSC）衍生出多个类器官模型。它们提供了产生和测试多种表型以解散的潜力，在什么条件下，推定的因果变量会产生影响。因此，在未来十年中，家畜类动物器官将具有重要价值，可提供有关基础生物学的信息，以对环境条件的变化进行建模，并支持免疫学，疫苗学，生理学，营养学和生物多样性保护研究。将复杂表型分解为关键过程的能力将提供一种将这些系统中测得的深表型与用于选择的性状牢固地联系起来的方法，这为将类器官用于育种目的提供了可能性。疫苗学，生理学，营养和生物多样性保护研究。将复杂表型分解为关键过程的能力将提供一种将这些系统中测得的深表型与用于选择的性状牢固地联系起来的方法，这为将类器官用于育种目的提供了可能性。疫苗学，生理学，营养和生物多样性保护研究。将复杂表型分解为关键过程的能力将提供一种将这些系统中测得的深表型与用于选择的性状牢固地相关联的方法，这为将类器官用于育种目的提供了可能性。

基因组编辑在饲养动物中的应用正在迅速发展，这主要是由于CRISPR / Cas技术的发展[12，14]。CRISPR工具箱已经扩展，可以提高精度，允许基因表达的调节和表观遗传修饰，现在已成为未来FAANG路线图不可或缺的一部分[3]。CRISPR介导的假定基因组元件的修饰可以确认其功能，并揭示其在细胞（和类器官）功能中的作用。全基因组多重CRISPR方法现在可以同时询问细胞系中成千上万的基因组特征，从而提高了该方法在基因组规模注释中的可行性[15]。这些高通量方法也可以与单细胞测序技术结合使用，以获得高分辨率的分子表型。此外，基因组编辑代表FAANG研究在农场动物育种计划中应用的潜在主要途径，途径是：（1）检测和利用影响重要性状的致病性变体；（2）有利的等位基因定向渗入或“通过编辑渗入”。从其他品系或物种到封闭的繁殖种群，或（3）从有利的全基因组筛选或从有关性状的生物学先验知识预测产生具有良好作用的从头等位基因。公众的认知和监管障碍仍然存在，通过利益相关方参与进行的持续讨论必须继续并发展，以与技术进步保持同步。虽然目前仅在某些国家/地区可以使用基因组编辑来改善养殖动物，但它可以用于体外模型，

作为一个科学界，FAANG继续开发对其成功至关重要的协调分析和数据收集基础设施[3]。FAANG生物信息学社区，包括中央数据协调中心（DCC），致力于开放可复制的科学，FAANG数据门户（https://data.faang.org/home）是此活动的重点。DCC的技术发展，协调和标准化对于在越来越多的物种中进行人口规模研究，单细胞数据集，细胞图谱和全基因组研究将继续至关重要。这将需要新的可重现的分析管道和基础架构，元数据验证服务，数据门户功能（例如集中式地图集浏览器）和在线培训资源。用于饲养动物的单细胞地图集和体外系统将伴随着高质量的元数据，跨物种，器官系统，组织和细胞类型的存档和可视化。随着FAANG数据集的复杂性不断增加，人们越来越需要提供新的数据可视化和集成方法。这些未来的发展以及分布式数据和分析基础结构，对于将功能数据成功应用于养殖动物育种计划至关重要。

图2描绘了我们在未来10年中为FAANG概述的研究重点。图3总结了养殖动物生产行业的吸收以及每个优先行动的预期成果。FAANG将提高我们更准确地开展工作的能力。使用基因型来预测表型。这将直接有助于应对未来十年全球可持续和负责任的全球粮食生产所面临的挑战（图1）。然而，尽管现在可以以较低的成本交付用于进行功能注释的分子测定，但上述研究重点的成本仍然很高，尤其是考虑到水产养殖部门目标物种的数量和多样性的迅速增长。因此，需要坚定地投资于研究。说服美国农业部和欧盟委员会分别将FAANG项目纳入NIFA-AFRI和Horizo​​n 2020资助电话（https://faang.org/proj.php）是FAANG及其第一阶段的重大成功领导。FAANG的当前资金支持研究界改善主要农场动物物种的功能注释，并促进更精细的基因组学驱动的动物育种/遗传改良。此处概述的研究重点已经在战略上与欧洲绿色协议（https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en）和美国农业部国家研究所的目标保持战略一致。粮食和农业计划（例如https://nifa.usda.gov/program/genome-phenome-initiative；https://www.ag2pi.org）。国际合作对于确保实现其成就至关重要。考虑到所涉及研究的规模和成本，可能有必要首先对体外系统的开发和数据基础结构的增强进行优先排序，从而为FAANG奠定坚实的基础。SingleCell和FAANG GTEx。

图3
图3

未来十年实施FAANG研究重点将如何使养殖动物生产受益

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我们在此处概述的FAANG下一阶段的所有研究优先事项的及时实现，将共同提高养殖动物生产业应对未来挑战的能力，增强基因组选择的能力，增强对变化的环境的适应性，保护生物多样性和缩小细胞和整个动物规模知识之间的差距。

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下载参考

本手稿中提出的工作是在2020年初举行的三场科学会议上进行的讨论中启动的。作者在此感谢FAANG科学顾问委员会的有益建议，并感谢以下人员对本手稿的早期草案提出意见：Amanda J 。张伯伦（维多利亚农业；拉筹伯大学），Appolinaire Djikeng（爱丁堡大学热带畜牧遗传与健康中心），丹尼斯·J·海顿（爱丁堡大学罗斯林研究所），伊恩·约翰斯顿（Xelect Ltd），安德烈亚斯·克拉尼斯（Andreas Kranis） （Aviagen；爱丁堡大学罗斯林研究所），MichèleTixier-Boichard（INRAE巴黎萨克莱大学），Stephen N. White（USDA-ARS；华盛顿州立大学），Ruidong Xiang（墨尔本大学）和Daniel Zerbino（EMBL） -EBI）。

在欧盟，FAANG已获得欧盟Horizo​​n 2020研究与创新计划的资助，资助计划的编号为817923（AQUA-FAANG），817998（GENE-SWitCH）和815668（BovReg），并获得了COST行动CA15112：动物基因组-欧洲网络，FAANG-欧洲。在EMBL-EBI，这项工作得到了生物技术和生物科学研究委员会（BBSRC）（BB / N019563 / 1和BB / N019202 / 1），惠康（WT108749 / Z / 15 / Z）和欧洲分子生物学实验室的支持。在罗斯林研究所，这项工作得到了BBSRC研究所战略计划补助金（BB / P013732 / 1和BB / P013759 / 1）的支持。在美国，FAANG已获得美国国家科学基金会（IOS-1548275）和美国农业部的资助（2015-68004-24104、2018-67015-27501）。在澳大利亚，

隶属关系

  1. 爱丁堡大学罗斯林研究所和皇家（迪克）兽医学院，英国爱丁堡EH25 9RG

    艾米丽·克拉克（Emily L. Clark），艾伦·阿奇博尔德（Alan L. Archibald），罗斯·休斯顿（Ross D.Houston），丹尼尔·麦克昆（Daniel J.

  2. 维多利亚农业，澳大利亚农业生物科学中心，农业生物科学中心，邦多拉，维多利亚州3083，澳大利亚

    汉斯·戴特威勒

  3. 拉筹伯大学应用系统生物学学院，维多利亚，邦多拉，3083，澳大利亚

    汉斯·戴特威勒

  4. 瓦赫宁根大学和研究中心动物育种和基因组学中心，6708 PB，荷兰瓦赫宁根

    马丁·格罗宁（Martien AM Groenen）

  5. 英国剑桥，汉克斯顿，惠康基因组校区，欧洲生物信息学研究所欧洲分子生物学实验室，英国CB10 1SD

    彼得·哈里森

  6. 莱布尼茨农场动物生物学研究所（FBN），基因组生物学研究所，基因组生理学部门，德国威廉斯堡-斯塔尔-阿利2号，18196，德国杜默斯托夫

    克里斯塔·库恩

  7. 罗斯托克大学农业与环境科学学院，Justus-von-Liebig-Weg 6，18059，德国罗斯托克

    克里斯塔·库恩

  8. 挪威生命科学大学动物与水产养殖科学系整合遗传学中心（CIGENE），挪威Ås，NO-1432

    西格比昂·连恩（SigbjørnLien）

  9. 爱荷华州立大学动物科学系，爱荷华州埃姆斯，50011，美国

    詹姆斯·里西（James M. Reecy）和克里斯托弗·K·图格（Christopher K.

  10. 巴黎萨克莱大学，INRAE，AgroParisTech，GABI，78350，Jouy-en-Josas，法国

    伊丽莎白·朱芙拉（Elisabetta Giuffra）

s
  1. 艾米莉·克拉克（Emily L. Clark）查看作者出版物

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  2. 艾伦·阿奇博尔德（Alan L. Archibald）查看作者出版物

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  3. Hans D. Daetwyler查看作者出版物

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会费

所有合著者都为手稿的撰写做出了贡献。ELC与合著者合作，由MW和FAANG共同协调者（CKT和EG）共同编辑并策划了手稿。所有作者阅读并认可的终稿。

通讯作者

对应于艾米莉·克拉克（Emily L. Clark）。

利益争夺

作者宣称他们没有竞争利益。

发行人须知

对于已发布地图和机构隶属关系中的管辖权主张，Springer Nature保持中立。

开放存取本文是根据知识共享署名4.0国际许可许可的，该许可允许以任何媒介或格式使用，共享，改编，分发和复制，只要您对原始作者和出处提供适当的信誉，链接到知识共享许可，并指出是否进行了更改。本文的图像或其他第三方材料包含在该文章的知识共享许可中，除非在该材料的信用栏中另有说明。如果该材料未包含在该文章的创用CC许可中，并且您的预期用途未得到法律法规的许可或超出了许可的用途，则您需要直接获得版权所有者的许可。要查看此许可证的副本，请访问http://creativecommons.org/licenses/by/4.0/。

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引用本文

Clark，EL，Archibald，AL，Daetwyler，HD等。从FAANG到fork：应用高度注释的基因组来改善养殖动物的生产。基因组生物学 21， 285（2020）。https://doi.org/10.1186/s13059-020-02197-8

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  • DOI ：https ：//doi.org/10.1186/s13059-020-02197-8
更新日期：2020-11-25
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