Genomic selection to improve production from livestock

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从基因工程获益的英语作文

从基因工程获益的英语作文英文回答：Genetic engineering offers a myriad of potential benefits that could revolutionize various aspects of human life, including:1. Improved Healthcare: Genetic engineering holds the promise of curing and preventing genetic diseases through gene therapy. By manipulating genes, scientists can correct defective genes or introduce new genes that confer resistance to specific diseases.2. Enhanced Food Production: Genetic engineering can optimize crop yields, improve nutritional value, and enhance resistance to pests and diseases. This has the potential to address global food security and malnutrition.3. Renewable Energy: Scientists are exploring the use of genetically modified organisms (GMOs) to producebiofuels and other renewable energy sources. Harnessing the power of biochemistry, genetic engineering can contribute to reducing carbon emissions and promoting sustainability.4. Environmental Remediation: Genetic engineering can be employed to develop microorganisms that can biodegrade pollutants, remediate contaminated sites, and mitigate environmental damage. This has significant implications for environmental conservation and restoration.5. Advanced Materials: Genetic engineering can be applied to create novel materials with tailored properties and applications. By manipulating the genetic makeup of organisms, scientists can synthesize advanced materials for fields such as aerospace, medicine, and electronics.6. Scientific Research: Genetic engineering provides powerful tools for studying gene function, exploring biological mechanisms, and modeling diseases. It has revolutionized our understanding of genetics andfacilitated advancements in various scientific disciplines.中文回答：转基因工程的益处。

猪的基因序列英语作文

猪的基因序列英语作文Title: The Genomic Blueprint of Pigs: Unraveling the Genetic Sequence。

Pigs, as essential livestock animals, have been a cornerstone of human civilization for thousands of years. Their genetic makeup holds the key to understanding various aspects of their biology, evolution, and potential applications in agriculture and biomedicine. In this essay, we delve into the intricate world of pig genomics, exploring the significance of their genetic sequence andits implications.Introduction to Pig Genomics:The field of genomics, particularly pig genomics, encompasses the study of the entire genetic material of pigs, including their DNA sequence, gene structure, and function. The completion of the pig genome sequencing project in 2012 marked a significant milestone inunderstanding the genetic blueprint of these animals. With approximately 2.7 billion base pairs, the pig genome is comparable in size to the human genome, comprising a complex network of genes and regulatory elements.Genetic Diversity and Evolution:The genetic diversity among pig breeds is extensive, reflecting centuries of selective breeding by humans for various traits such as meat quality, disease resistance, and reproductive performance. By analyzing the genetic variation present within and between different pig populations, researchers can reconstruct the evolutionary history of pigs and uncover the genetic basis of traitsthat have been shaped by natural selection and artificial selection.Functional Genomics:Functional genomics aims to decipher the functions of genes and their interactions within biological systems. Through techniques such as transcriptomics, proteomics, andmetabolomics, scientists can elucidate how genes are expressed, regulated, and ultimately contribute to the phenotype of an organism. In pigs, functional genomics studies have provided insights into important biological processes such as growth, development, immunity, and reproduction.Applications in Agriculture:The knowledge gained from pig genomics has practical implications for agricultural practices aimed at improving pig breeding, production efficiency, and disease management. Genomic selection, for instance, allows breeders toidentify superior individuals for breeding based on their genetic potential for desirable traits, leading to accelerated genetic progress and increased productivity. Additionally, genomic technologies enable the detection of genetic markers associated with disease resistance,enabling more targeted breeding strategies to mitigate the impact of infectious diseases in pig populations.Biomedical Relevance:Beyond agriculture, pig genomics has relevance in biomedical research, particularly in the field of comparative genomics and xenotransplantation. Pigs share physiological and anatomical similarities with humans, making them valuable models for studying human diseases and developing novel therapies. By editing pig genomes using advanced gene editing techniques such as CRISPR-Cas9, researchers can generate pig models with genetic modifications that mimic human disease conditions, facilitating the development of new treatments and therapies.Challenges and Future Directions:Despite the significant progress in pig genomics, several challenges remain, including the identification of functional elements within the genome, understanding the complex regulatory networks that govern gene expression, and addressing ethical considerations associated with genetic manipulation in pigs. Future research efforts will likely focus on integrating genomic data with other omicsdata sets, developing innovative breeding strategies, and advancing genome editing technologies to further harness the potential of pig genomics in agriculture, medicine, and beyond.Conclusion:In conclusion, the genomic sequence of pigs holds immense promise for advancing our understanding of pig biology, evolution, and applications in agriculture and biomedicine. By unraveling the complexities of the pig genome, scientists can unlock new opportunities for enhancing pig breeding, improving production efficiency, and addressing global challenges such as food security and human health. The journey of exploration into pig genomics continues to evolve, promising exciting discoveries and innovations in the years to come.。

农业技术职称主要业绩英语

农业技术职称主要业绩英语Major Achievements in Agricultural Technology.1. Introduction.Agricultural technology plays a crucial role in enhancing agricultural productivity, ensuring food security, and promoting sustainable farming practices. Agricultural professionals with specialized knowledge and skills are essential for driving innovation and adopting bestpractices in the field. To recognize their contributions, professional titles and certifications are awarded based on their major achievements and experience. This article highlights the key achievements of individuals who have earned recognition in agricultural technology.2. Crop Production.Development and implementation of precision farming techniques: Using advanced sensors, data analysis, andautomation to optimize crop production, reduce environmental impact, and increase yields.Breeding and cultivation of high-yielding, disease-resistant crop varieties: Utilizing genetic engineering, biotechnology, and traditional breeding methods to develop crops that are resilient to pests, diseases, and climate change.Optimization of irrigation and water management practices: Employing efficient irrigation systems, soil moisture monitoring, and drought-tolerant crops to conserve water resources and maximize crop growth.Advancements in crop protection: Developing and implementing integrated pest management (IPM) strategies, biological control agents, and precision pesticide application to minimize crop losses while preserving biodiversity.3. Livestock Production.Genetic improvement of livestock breeds: Using selective breeding, genomic selection, and reproductive technologies to enhance livestock productivity, health, and welfare.Development of innovative feeding and nutrition strategies: Optimizing animal diets, utilizing precision feeding systems, and incorporating novel feed ingredients to improve animal growth and performance.Advancements in animal health management: Implementing preventive healthcare programs, developing vaccines and therapeutics, and utilizing technology for early disease detection and monitoring.Sustainable livestock production systems: Designing and implementing integrated livestock-crop systems, reducing greenhouse gas emissions, and promoting animal welfare practices.4. Agricultural Engineering.Design and development of agricultural machinery: Creating efficient and innovative farm equipment for tillage, planting, harvesting, and processing.Advancements in irrigation technology: Developing advanced irrigation systems, sensors, and automation to optimize water use and reduce energy consumption.Renewable energy applications in agriculture:Utilizing solar, wind, and biomass energy sources to power agricultural operations and reduce carbon emissions.Precision agriculture technologies: Integrating sensors, data analysis, and control systems to optimize crop production, livestock management, and environmental sustainability.5. Agricultural Economics.Analysis of agricultural markets and policy: Conducting research on agricultural markets, trade, and policy to inform decision-making and ensure fair returnsfor farmers.Development of farm management strategies: Providing farmers with economic tools, models, and advice to improve financial performance and risk management.Assessment of agricultural technology adoption: Evaluating the economic impact of new technologies on farm productivity, profitability, and environmental sustainability.Policy recommendations for sustainable agriculture: Advocating for policies that promote agricultural innovation, protect natural resources, and address challenges facing the industry.6. Conclusion.The achievements of individuals in agricultural technology are essential for addressing global food security challenges, ensuring sustainable agricultural practices, and driving economic growth. By recognizing andrewarding these professionals, we encourage continued innovation, knowledge transfer, and the adoption of best practices in the field. The advancements highlighted in this article demonstrate the transformative power of agricultural technology and its potential to shape a more sustainable and food-secure future.。

猪育种新技术

有关猪的育种
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A
全基因组选择
育种目标的变化
测定技术的变化
育种新技术
全基因组选择
什么叫全基因组选择为了进一步提高育种的效率，T.H,Meuwissen等提出了一种新的标记辅助选择方法，即全基因组选择（Genomic selection,GS）。该方法假设覆盖全基因组上的高密度SNPs标记中至少有１个SNP与 QTL处于连锁不平衡关系，利用SNP估计每个QTL的效应，从而获得个体的全基因组估计育种值（Genomic estimated breeding value, GEBV）。
对猪体进行一系列扫描之后，可以精确预测胴体的组成成分。与传统的超声波技术相比，用这种技术预测胴体肌肉、脂肪和骨骼产量的准确率分别可提高6.4％、5.6％和15.0％。
测定技术的变化
对于屠宰商和零售商来说，CT还有一个更加突出的优点，那就是可以测出不同大分切部位（背脊部、后腿部、腹胁部和肩胛部）当中的肌肉产量。
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全基因组选择的优点不仅可以提高选择的准确性，尤其是一些低遗传力性状、难以测量的性状、限性性状、生长后期测定的性状、屠宰性状和免疫力等，还可以在动物出生时或者胚胎期即可预测 GEBV，从而缩短世代间隔，大大提高遗传进展。
全基因组选择商业化应用在猪的全基因组测序完成后，A.M.Ramos等对商业品种杜洛克（34头）、皮特兰（23头）、长白猪（29头）、大白猪（36头）和野猪（36头）共5个品种的DNA分别混池，采用全基因组重测序方法鉴定出数十万个SNPs，并从中选择设计出PorcineSNP60 Beadchip。该SNP芯片共有64232个SNPs，检出率可达97.5%，SNP信息可靠。

畜牧业现代化的概念

畜牧业现代化的概念英文回答：Concept of Livestock Modernization.Livestock modernization encompasses a wide range of practices and technologies aimed at enhancing the efficiency, sustainability, and profitability of livestock production systems. It involves the adoption of advanced breeding and genetic techniques, improved nutrition and feeding strategies, and sophisticated management practicesto optimize animal performance and reduce environmental impact.Key Elements of Livestock Modernization:Genetic Improvement: Utilizing advanced breeding methods, such as artificial insemination, embryo transfer, and genomic selection, to improve the genetic traits of livestock for desirable characteristics such as growth rate,feed efficiency, and disease resistance.Precision Nutrition: Tailoring diets to meet the specific nutritional requirements of different livestock species and ages, using advanced feed analysis and ration formulation tools to optimize feed utilization and reduce environmental emissions.Automated Management: Implementing automated systems for tasks such as milking, feeding, and waste removal to improve efficiency, reduce labor costs, and enhance animal welfare.Data Analytics: Utilizing data collection and analysis tools to monitor animal performance, identify trends, and make informed management decisions to optimize production outcomes.Sustainability: Focusing on practices that minimize environmental impact, such as reducing greenhouse gas emissions, conserving water resources, and promoting animal welfare, to ensure the long-term viability of livestocksystems.Benefits of Livestock Modernization:Increased production efficiency and profitability.Improved product quality and safety.Reduced environmental footprint.Enhanced animal welfare.Improved traceability and transparency in the supply chain.Challenges of Livestock Modernization:Cost of implementation.Access to advanced technologies and expertise.Skilled labor shortages.Public perception and market acceptance.Regulatory compliance.中文回答：畜牧业现代化的概念。

2020年生信SCI大赏-最全生信文章、最友好生信期刊、最详情发文趋势都在这里！

2020年⽣信SCI⼤赏-最全⽣信⽂章、最友好⽣信期刊、最详情发⽂趋势都在这⾥！i⽣信专注⽣物分析最前沿定期解读⽣信⽂章提供⽣信分析思路和套路⽅便⼤家短平快发SCI时光如⽔，即便是如此艰⾟坎坷的2020也已悄然离去。

⽣活在继续，舞会从来不曾停⽌！在这不平凡的2020年，⽣信SCI发⽂量如何？发⽂趋势⼜如何？有哪些⽣信友好期刊？今天我们就来整体回顾⼀下2020年⽣信SCI⽂章进展！2020年⽣信SCI发⽂量统计⼩编在Pubmed中，通过“gene expression omnibus”、“TCGA”、“bioinformatics”、“biomarker”、“differentially expressed”、“protein protein interaction”、“ROCanalysis”“signature”等关键词进⾏组合检索，再经过筛选、去重复，最终检索到2020年发表的所有⽣信SCI共4165篇，其中纯⽣信（或仅含少量表达检测）⽂章3194篇，剩下的971篇⽣信实验类⽂章，其包含实验占⽐＞30%。

看到这个数据你是不是有疑惑“听说⽣信⽂章很难发了，怎么还有这么多的⽂章呢，⽽且还不缺乏⾼分⽂章”。

那别⼈的⽣信⽂章都是怎么设计的呢？纯⽣信⽂章投哪些期刊⽐较容易接收呢？下⾯我们来⼀探究竟！⽣信友好期刊推荐对2020年所有⽣信⽂章的发表期刊进⾏统计，以下列举出接收⽣信⽂章量⼤于30篇的期刊，根据接收量排序如下：2020⽣信友好期刊接收量2020-IF因⼦Front Oncol181 4.85Biomed Res Int154 2.28Front Genet152 3.26Aging (Albany NY)129 4.83Oncol Lett101 2.31PeerJ93 2.38Cancer Cell Int87 4.18Cancers (Basel)84 6.13Sci Rep77 4.00J Cancer71 3.57Biosci Rep69 2.94BMC Cancer67 3.15Med Sci Monit65 1.92Medicine (Baltimore)62 1.55Cancer Manag Res61 2.89Cancer Med58 3.49J Cell Mol Med57 4.49Ann Transl Med51 3.30PLoS One44 2.74J Cell Physiol39 5.55J Comput Biol37 1.05Mol Med Rep36 2.10J Cell Biochem35 4.24Int J Mol Sci34 4.56J Transl Med34 4.12Am J Transl Res33 3.38DNA Cell Biol33 3.19DNA Cell Biol33 3.19Technol Cancer Res Treat33 2.07Front Mol Biosci30 4.19猫头鹰博⼠（微信：ipaper360）根据接收量>20篇，影响因⼦>3筛选条件，按照期刊影响因⼦排序如下：2020⽣信友好期刊2020-IF因⼦接收量Theranostics8.5821Cancers (Basel) 6.1384Bioinformatics 5.6120J Cell Physiol 5.5539Front Cell Dev Biol 5.2023Front Immunol 5.0922Front Oncol 4.85181Aging (Albany NY) 4.83129Int J Mol Sci 4.5634J Cell Mol Med 4.4957J Cell Biochem 4.2435Front Mol Biosci 4.1930Cancer Cell Int 4.1887J Transl Med 4.1234Sci Rep 4.0077J Cancer 3.5771Cancer Med 3.4958Cancer Biomark 3.4426Oncol Rep 3.4227Am J Transl Res 3.3833Ann Transl Med 3.3051Front Genet 3.26152DNA Cell Biol 3.1933BMC Cancer 3.1567猫头鹰博⼠（微信：ipaper360）以上期刊列表绝对都能称作“⽣信友好期刊”了，⼤家有需要投稿的⽣信⽂章，可以根据影响因⼦在列表中选择合适的期刊哦。

医学遗传名词解释

染色质（chromatin）是由DNA、RNA、蛋白质等组成的复合物，是核基因的载体，在真核细胞间期呈伸展状态。

染色体（chromosome ）细胞在有丝分裂或减数分裂过程中，由染色质凝缩而成的棒状结构。

常染色质(euchromatin)：细胞间期核内纤维折叠盘曲程度小，分散度大，染色较浅且具有转录活性的染色质。

异染色质(heterochromatin)：细胞间期核内纤维折叠盘曲紧密，呈凝集状态，染色较深且很少有转录活性的染色质。

由于雌性细胞中的两条X染色体中的一条发生异固缩，失去转录活性，这保证了雌雄两性细胞中都只有一条X染色体保持转录活性，使两性X连锁基因产物的量保持在相同水平上，这种效应称为X染色体的剂量补偿(dosage compensation)。

基因(Gene）：遗传的基本单位，含有编码一种RNA，大多数情况是编码一种多肽的信息单位；负载特定遗传信息的DNA片段，其结构包括由DNA编码序列、非编码调节序列和内含子组成的DNA区域。

微卫星DNA（Microsatellite）、STR（Short Tandem Repeat）、DNA指纹是由2—6bp重复单位构成核心序列，也称短串联重复序列(STR)，是一种广泛分布在人类基因组中的DNA片段，主要由核心序列拷贝数目的变化产生长度多态性，其在人群中存在个体间的高度变化，是DNA指纹的形成基础。

指基因组水平上由单个核苷酸的变异所引起的DNA 序列多态性,在群体中的发生频率不小于1 %，就称为单核苷酸多态性（Single Nucleotide Polymorphism, SNP）。

外显子（exon）内含子（intron）突变(mutation)：遗传物质所发生的可被检测和可遗传的改变；通常对机体有害。

广义：包括染色体和DNA的改变；狭义：指基因组DNA分子的碱基组成或其顺序的改变。

转换（transition）颠换（transversion）错义突变（missense mutation）同义突变（same sense mutation）无义突变（nonsense mutation）动态突变（Dynamic Mutation）人类基因组中的短串联重复序列，尤其是基因编码区或侧翼序列的三核苷酸重复，在一代代传递过程中重复次数发生明显增加，从而导致基因功能改变而产生疾病。

基因组选择在绵羊育种中的应用

Hereditas (Beijing)2019年4月, 41(4): 293―303 收稿日期: 2018-11-14; 修回日期: 2019-02-05基金项目:中央级公益性科研院所基本科研业务费专项(编号：Y2017XM02)资助[Supported by the Central Public-interest Scientific InstitutionBasal Research Fund (No. Y2017XM02)]作者简介: 赵志达，硕士研究生，专业方向：羊分子育种及生产。

E-mail: 707187879@通讯作者:张莉，博士，研究员，研究方向：畜禽分子遗传育种及生产，畜禽遗传资源保存和评价。

E-mail: zhangli07@DOI: 10.16288/j.yczz.18-251网络出版时间: 2019/4/1 14:01:37URI: /kcms/detail/11.1913.R.20190401.1401.003.html综述基因组选择在绵羊育种中的应用赵志达，张莉中国农业科学院北京畜牧兽医研究所，北京 100193摘要: 基因组选择是一种利用高密度芯片全部位点与目的基因存在的连锁不平衡估计基因组育种值的方法，目前已相继在英国、法国、澳大利亚和新西兰等国家的畜禽育种中得到应用并有效提升了育种效率。

在我国，基因组选择已在奶牛、生猪和肉鸡的育种中开始应用并取得了一定的成效。

我国是世界养羊大国，但在羊的养殖管理、育种水平以及生产效率等方面依旧与发达国家存在较大差距。

目前，已有育种工作者尝试对绵羊开展基因组选择育种研究，但至今尚未有比较系统的应用案例。

基于绵羊育种基础薄弱的现状，开展基因组选择对我国肉羊产业发展具有重要作用。

本文综述了畜禽基因组选择的研究进展及其在绵羊育种中的应用，并对该技术在今后绵羊生产中的指导作用进行了展望。

关键词: 基因组育种技术；全基因组选择；绵羊育种Applications of genome selection in sheep breedingZhida Zhao, Li ZhangInstitute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaAbstract: Genome selection is a method for estimating genomic breeding values using linkage disequilibrium betweenall sites of high-density chips and target genes. Genome selection has been applied to enhance livestock breeding efficiency in countries such as UK, France, Australia and New Zealand. In China, this technique has been successfully practiced in dairy cattle, swine, and broiler, and achieved significant progress. China raises the majority of sheep throughout the world. However, a large technological gap in sheep breeding and production efficiency exists between China and other developed countries. Some breeders have attempted to use genome selection in sheep breeding; however, there are no good cases till now. Based on the current level of animal breeding, the genome selection method should play an important role in the future of the meat sheep industry. In this review, we summarize the research of genome selection in livestock and poultry as well as some applications in sheep breeding. We also describe the prospects for the future application of this technology to improve the efficiency, quality and outcomes of sheep production.Keywords: genomic breeding technology; genomic selection; sheep breeding294 Hereditas(Beijing) 2019第41卷畜禽育种通常通过估算育种值(estimated bree-ding values, EBVs)来预测后代性状，以期得到符合人类需求的产品。

限制性两阶段多位点全基因组关联分析方法的特点与计算程序

作物学报 ACTA AGRONOMICA SINICA 2018, 44(9): 1274 1289/ISSN 0496-3490; CN 11-1809/S; CODEN TSHPA9E-mail:*********************.cn本研究由国家自然科学基金项目(31701447, 31671718), 国家重点研发计划项目(2017YFD0101500), 教育部111项目(B08025), 教育部长江学者和创新团队项目(PCSIRT_17R55), 国家现代农业产业技术体系建设专项(CARS-04), 江苏省优势学科建设工程专项, 中央高校基本科研业务费和江苏省JCIC-MCP 项目资助。

This study was supported by the National Natural Science Foundation of China (31701447, 31671718), the National Key R&D Program for Crop Breeding in China (2017YFD0101500), the MOE 111 Project (B08025), the MOE Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT_17R55), the China Agriculture Research System (CARS-04), the Jiangsu Higher Education PAPD Program, the Fundamental Research Funds for the Central Universities and the Jiangsu JCIC-MCP.*通信作者(Corresponding author): 盖钧镒,E-mail:************.cn第一作者联系方式:E-mail:****************Received(收稿日期): 2018-03-19; Accepted(接受日期): 2018-06-12; Published online(网络出版日期): 2018-06-29. URL: /kcms/detail/11.1809.S.20180629.1035.002.htmlDOI: 10.3724/SP.J.1006.2018.01274限制性两阶段多位点全基因组关联分析方法的特点与计算程序贺建波刘方东邢光南王吴彬赵团结管荣展盖钧镒*南京农业大学大豆研究所 / 农业部大豆生物学与遗传育种重点实验室 / 国家大豆改良中心 / 作物遗传与种质创新国家重点实验室, 江苏南京 210095摘要: 全基因组关联分析(genome-wide association study, GWAS)的理论及应用是近十几年来国内外数量性状研究的热点, 但是以往GWAS 方法注重于个别主要QTL/基因的检测与发掘。

评价养殖基围虾的遗传选择与改良策略英语作文

The genetic selection and improvement strategies in the cultivation of freshwater prawns play a crucial role in enhancing the overall productivity and quality of the species. By employing selective breeding techniques and genetic manipulation, farmers can effectively enhance desirable traits in the prawns, such as growth rate, disease resistance, and overall adaptability to different environmental conditions.One of the key strategies in genetic selection is the identification and breeding of superior broodstock. By carefully selecting and breeding prawns with desirable traits, such as faster growth, larger size, and higher survival rates, farmers can gradually improve the genetic makeup of their prawn populations. This can lead to increased productivity and profitability in the long run.Furthermore, genetic manipulation techniques, such as gene editing, can be employed to introduce specific genetic traits into prawn populations. For example, researchers can modify the genes responsible for disease resistance or growth hormone production, leading to prawns with enhanced resistance to common diseases and faster growth rates.In addition to genetic selection and manipulation, the implementation of advanced breeding programs can also contribute to the improvement of freshwater prawn populations. By carefully monitoring and selecting breeding pairs based on their genetic traits, farmers can gradually improve the overall genetic quality of their prawn populations.However, it is important to note that ethical considerations and environmental impacts should be carefully evaluated when implementing genetic selection and improvement strategies. It iscrucial to ensure that the genetic manipulation and selective breeding methods do not result in negative consequences for the natural environment or the welfare of the prawns.In conclusion, genetic selection and improvement strategies play a vital role in the cultivation of freshwater prawns. By employing selective breeding, genetic manipulation, and advanced breeding programs, farmers can enhance the genetic traits of their prawn populations, leading to improved productivity and overall quality. However, it is essential to approach these strategies with caution and consideration for ethical and environmental implications.。

浅谈杜洛克公猪的精液特性与精子形态对其精液质量的影响

112猪业科学 SWINE INDUSTRY SCIENCE 2016年33卷第6期遗传改良GENETIC IMPROVEMENT浅谈杜洛克公猪的精液特性与精子形态对其精液质量的影响蒋腾飞，楼平儿，曾新斌，范权飚（福建一春农业发展有限公司，福建南平 353000）中国的猪人工授精技术始于20世纪50年代。

随着人工授精技术的广泛普及，人们对公猪精液的要求也越来越高。

实际生产中，单次采精所能稀释的精液份数由公猪射精量、精液品质（如活力、密度等）和每份精液所需的精子数量所决定。

如果公猪单次射精量高且品质好，就能给猪场带来更多的效益。

杜洛克公猪作为终端父本，其精液使用量远超其他品种的公猪，但其精液品质往往不如其他品种公猪。

这迫使人工授精站或猪场饲养更多的杜洛克公猪，以满足生产所需，导致生产成本增加。

如何提高杜洛克公猪的精液品质是当前亟待解决的问题。

公猪的精液品质受到许多因素的影响，本文重点讨论精液特性及精子形态对杜洛克公猪精液质量的影响，为提高杜洛克公猪的精液产量及质量提供理论摘要：在生产实践中，杜洛克公猪的精液受到长途运输等应激后会较其他品种公猪的精液品质下降更快。

该文旨在通过比较不同品种的精液特性及精子形态，找到杜洛克公猪的精液品质较其他品种差的根本原因。

并利用以往的研究结果，对精子特性与精子形态的关系及精子形态与精子活力的关系展开讨论。

结果表明，与皮特兰、长白和大白等猪种相比，杜洛克猪的射精量少、精液浓度高、精子头部大而精子尾巴短。

借此能吸引更多的同行对此进行专门的研究。

关键词：杜洛克公猪；品种；精液特性；精子形态；精液质量基础。

1 精液特性与精子形态之间的关系精子的形状、大小与精液的特性（如精液浓度、精子数量）有关，不同的品种其精液特性也不一样。

下面从品种内部和品种间2个方面来阐述精液特性与精子形态之间的关系。

1.1 品种内Kondracki 等[1]针对杜洛克公猪的精液特性及精子形态展开了深入的研究，他们利用8头7-9月龄的青年杜洛表1 杜洛克猪不同原精浓度的精液特性与精子形态的关系注：角标为不同的小写字母，表示同行数据差异显著（P <0.05）；角标为不同的大写字母，表示差异极显著（P <0.01），下同。

农学博士英语作文题

农学博士英语作文题英文回答：Introduction.As an agricultural scientist, I am passionate about addressing the challenges facing global food security. With the world's population projected to reach 9 billion by 2050, we must develop sustainable and innovative solutions tofeed a growing population while protecting the environment. My doctoral research in Agricultural Sciences has equipped me with the knowledge and skills necessary to makesignificant contributions in this field.Research Highlights.My dissertation research focused on developing novel crop production systems that optimize yield whileminimizing environmental impact. I conducted extensivefield experiments to evaluate the effects of differentmanagement practices, such as cover cropping, reduced tillage, and precision agriculture, on soil health, crop productivity, and water quality. My findings have significant implications for improving the sustainability of agricultural production systems and reducing their environmental footprint.Furthermore, I have a strong foundation in plant breeding and genetics. I have developed expertise in using molecular markers and genomic selection tools to improve the traits of crops, such as yield, disease resistance, and drought tolerance. This knowledge will enable me to develop new crop varieties that are better adapted to changing climatic conditions and meet the demands of a growing global population.Research Impact.My research has been published in high-impactscientific journals and presented at international conferences. It has been cited by other researchers and has contributed to the advancement of scientific knowledge inthe field of agricultural sciences. My work has also received recognition from the scientific community, as evidenced by my receipt of several awards and research grants.Career Goals and Objectives.My long-term career goal is to become a leading researcher in the field of agricultural sciences. I aspire to make a significant contribution to global food security by developing innovative and sustainable crop production systems. I am confident that my doctoral degree will provide me with the foundation necessary to achieve my career aspirations.In the short term, I plan to pursue a postdoctoral research position at a renowned research institution. This will allow me to further develop my research skills and expand my network in the scientific community. I am particularly interested in exploring the potential of gene editing technologies to improve crop traits and address global challenges.中文回答：引言。

转基因食品增产英语作文

转基因食品增产英语作文Here is an English essay on the topic of genetically modified foods and increased production, with more than 1000 words in the main body of the text, as per your instructions. No additional title has been included, and there are no extra punctuation marks in the main text.Genetically modified foods have been a topic of intense debate and discussion in recent years. On one side, proponents argue that these technologies have the potential to significantly increase agricultural productivity and address the growing global demand for food. On the other hand, critics raise concerns about the potential risks and long-term consequences of introducing genetically modified organisms (GMOs) into the food supply. As the world's population continues to grow, the need to find innovative solutions to food security has become increasingly urgent, and the role of genetically modified crops in meeting this challenge is a critical area of exploration.One of the primary arguments in favor of genetically modified foods is their ability to increase crop yields and production. Through genetic engineering, scientists can introduce desirable traits intoplants, such as resistance to pests, diseases, and environmental stresses. This can lead to higher crop yields, reduced losses due to pest infestations or adverse weather conditions, and ultimately, a greater supply of food to meet the demands of a growing population. For example, the development of drought-resistant crops can be particularly beneficial in regions where water scarcity is a significant challenge, allowing farmers to maintain productivity even during periods of limited rainfall.In addition to increased yields, genetically modified crops can also be engineered to have enhanced nutritional profiles. By modifying the genetic makeup of plants, scientists can increase the levels of essential vitamins, minerals, and other beneficial compounds. This can be especially important in regions where access to a diverse and nutritious diet is limited, as it can help address issues of malnutrition and micronutrient deficiencies. The development of "Golden Rice," a genetically modified rice variety fortified with beta-carotene, is a prime example of how genetic engineering can be used to tackle the problem of vitamin A deficiency, a leading cause of preventable blindness in developing countries.Furthermore, genetically modified crops can be designed to be more resilient to pests and diseases, reducing the need for the application of harmful pesticides and herbicides. This not only benefits the environment by reducing the chemical load in the ecosystem butalso has the potential to improve human health by reducing exposure to these potentially toxic substances. Additionally, the increased efficiency and reduced waste associated with genetically modified crops can lead to lower food prices, making essential nutrients more accessible to populations with limited resources.However, the proponents of genetically modified foods acknowledge that there are valid concerns and potential risks that must be carefully considered. One of the primary concerns is the potential for unintended consequences, such as the development of herbicide-resistant weeds or the inadvertent transfer of modified genes to non-target organisms. There are also concerns about the long-term effects of consuming genetically modified foods on human health, as the long-term impacts are not yet fully understood.Another significant concern is the potential for genetically modified crops to disrupt traditional agricultural practices and threaten the livelihoods of small-scale farmers. In some cases, the introduction of patented genetically modified seeds can lead to increased costs for farmers, potentially pricing them out of the market and undermining local food production systems. There are also concerns about the concentration of power and control in the hands of a few multinational corporations that dominate the genetically modified seed market.To address these concerns, proponents of genetically modified foods argue that rigorous safety testing and regulatory oversight are essential. They emphasize the need for thorough, independent scientific research to assess the potential risks and benefits of these technologies, and the importance of transparent and inclusive decision-making processes that involve a wide range of stakeholders, including farmers, consumers, and environmental advocates.Additionally, some proponents suggest that the development of genetically modified crops should be coupled with efforts to support sustainable and equitable agricultural practices, such as the preservation of biodiversity, the promotion of traditional farming methods, and the empowerment of small-scale producers. By taking a comprehensive and holistic approach to agricultural innovation, they argue that the benefits of genetically modified foods can be harnessed while mitigating the potential risks and addressing the concerns of various stakeholders.Ultimately, the debate over genetically modified foods is a complex and multifaceted issue that requires careful consideration of the potential benefits and risks. As the global population continues to grow and the challenges of food security become more pressing, the role of genetically modified crops in addressing these challenges will likely remain a topic of intense discussion and ongoing research. By fostering open and informed dialogues, encouraging rigorousscientific inquiry, and prioritizing the needs and concerns of diverse stakeholders, it may be possible to find a balanced and sustainable approach to the use of genetically modified technologies in the food system.。

优良育种的重要性和优良育种的方法英语作文

优良育种的重要性和优良育种的方法英语作文The Importance of Selective Breeding and Its MethodsSelective breeding, also known as artificial selection, is a fundamental practice in agriculture and animal husbandry that has played a crucial role in the development of various plant and animal species. This process involves the intentional selection and breeding of individuals with desirable traits to produce offspring with improved characteristics. The importance of selective breeding cannot be overstated as it has significantly contributed to the enhancement of food production, the development of disease-resistant and high-yielding crops, the improvement of livestock for meat, dairy, and other purposes, and the preservation of endangered species.One of the primary reasons why selective breeding is so important is its ability to enhance the desirable traits of a population. By carefully selecting parents with the most favorable characteristics, breeders can gradually improve the overall quality and performance of a particular species. This can lead to increased crop yields, improved nutritional value, enhanced resistance to pests and diseases, andeven the development of new and unique varieties. For example, the selective breeding of wheat has resulted in varieties that are more resistant to drought, have higher protein content, and produce higher yields, making it a crucial tool in addressing global food security challenges.In addition to improving the overall quality of a species, selective breeding also plays a crucial role in the preservation of endangered species. By carefully selecting individuals with the most desirable genetic traits, breeders can work to maintain the genetic diversity of a population and ensure its long-term survival. This is particularly important in the face of environmental threats, habitat loss, and other factors that can contribute to the decline of certain species. Through selective breeding, conservationists can work to reintroduce and repopulate threatened species, helping to ensure their continued existence.The methods of selective breeding are varied and can be tailored to the specific needs of different species and breeding programs. One of the most common approaches is to identify individuals with the desired traits and selectively breed them to produce offspring with those same characteristics. This can involve the use of various technologies, such as genetic testing and advanced reproductive techniques, to ensure the most effective selection and breeding processes.Another important method of selective breeding is the use of hybrid vigor, or heterosis. This phenomenon occurs when the offspring of two genetically distinct individuals exhibit superior traits comparedto their parents. By carefully selecting parents with complementary genetic profiles, breeders can produce offspring that exhibit enhanced growth, disease resistance, or other desirable characteristics. This approach is particularly useful in the development of new crop varieties and the improvement of livestock.In addition to these traditional methods, modern advancements in biotechnology and genetic engineering have also opened up new possibilities for selective breeding. Techniques such as marker-assisted selection, genome editing, and cloning can provide breeders with even more precise and targeted ways to manipulate the genetic makeup of a population. These technologies allow for the identification and selection of specific genes or genetic markers associated with desirable traits, enabling breeders to more efficiently and effectively produce offspring with the desired characteristics.Despite the many benefits of selective breeding, it is important to consider the potential ethical and environmental concerns that may arise from its use. Breeders must be mindful of the potential for unintended consequences, such as the loss of genetic diversity or the creation of organisms that may have negative impacts on theenvironment. Additionally, there are ongoing debates around the ethical implications of some of the more advanced biotechnological techniques used in selective breeding, such as genome editing.In conclusion, the importance of selective breeding cannot be overstated. This practice has been instrumental in the development of many of the plant and animal species that we rely on for food, fiber, and other essential resources. By continuing to refine and improve the methods of selective breeding, we can work to address global challenges, preserve endangered species, and ensure the long-term sustainability of our agricultural and natural systems. As we move forward, it is crucial that we carefully consider the ethical and environmental implications of these practices, and strive to use them in a responsible and sustainable manner.。

单细胞测序在农业领域的应用

单细胞测序在农业领域的应用英文回答：Single-cell sequencing, also known as single-cell genomics, is a powerful technique that allows researchersto study the gene expression profiles of individual cells.It has a wide range of applications in various fields, including agriculture.In agriculture, single-cell sequencing can be used to gain insights into plant development and improve crop breeding. By analyzing the gene expression patterns of individual plant cells, researchers can identify genes that are involved in important agricultural traits such as yield, disease resistance, and stress tolerance.For example, let's say I am a plant breeder working on developing drought-tolerant crops. I can use single-cell sequencing to identify the genes that are activated in response to drought stress in individual plant cells. Bycomparing the gene expression profiles of drought-tolerant and drought-sensitive plants, I can identify key genes that are associated with drought tolerance. This information can then be used to develop new crop varieties with improved drought tolerance.In addition to crop breeding, single-cell sequencing can also be used to study the microbiome of plants. The microbiome refers to the community of microorganisms that live in and on plants. These microorganisms play a crucial role in plant health and can have a significant impact on crop productivity. By analyzing the gene expressionprofiles of individual microbial cells, researchers can gain a better understanding of the interactions between plants and their associated microorganisms.For example, let's say I am a plant pathologist studying the interactions between plants and fungal pathogens. I can use single-cell sequencing to analyze the gene expression patterns of individual fungal cells during infection. This can help me identify the genes that are responsible for the virulence of the pathogen andunderstand how it interacts with the plant's immune system. This knowledge can then be used to develop new strategies for disease control in agriculture.中文回答：单细胞测序，也被称为单细胞基因组学，是一种强大的技术，可以让研究人员研究单个细胞的基因表达谱。

大豆作物遗传育种英语作文

大豆作物遗传育种英语作文Soybean is one of the most important agricultural crops in the world, providing a valuable source of protein, oil, and other nutrients. As the global population continues to grow, the demand for soybean and soybean-derived products is also increasing. To meet this growing demand, researchers and breeders have been working tirelessly to improve the genetic makeup of soybean crops through various breeding techniques.Soybean genetic breeding is a complex and multifaceted process that involves the manipulation of the plant's genetic material to achieve desired traits. These traits can include higher yield, improved disease and pest resistance, enhanced nutritional value, and increased tolerance to environmental stresses such as drought, salinity, and temperature extremes.One of the key approaches in soybean genetic breeding is the use of traditional breeding methods. This involves the crossing of elite soybean lines with desirable traits to create new genetic combinations. Breeders carefully select the parent lines based ontheir performance and the specific traits they wish to incorporate into the offspring. Through a process of repeated selection and evaluation, breeders can gradually enhance the desired characteristics of the soybean plants.In addition to traditional breeding, modern biotechnological tools have revolutionized the field of soybean genetic breeding. The advent of molecular markers, such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs), has enabled breeders to identify and track specific genes or genomic regions associated with important traits. This knowledge can then be used to accelerate the breeding process through marker-assisted selection (MAS).MAS allows breeders to select for desired traits at an early stage of plant development, without having to wait for the full expression of the trait in the mature plant. This can significantly reduce the time and resources required for the breeding process, leading to more efficient and effective development of new soybean varieties.Another important aspect of soybean genetic breeding is the exploration and utilization of genetic diversity. Soybean is a highly diverse crop, with a wide range of genetic variation both within and among different soybean cultivars and wild relatives. By tapping into this genetic diversity, breeders can access a vast pool of geneticresources that can be used to introduce new and beneficial traits into soybean breeding programs.One approach to harnessing genetic diversity is the use of genome-wide association studies (GWAS). GWAS involves the analysis of genetic markers across the entire soybean genome to identify associations between specific genomic regions and desired traits. This information can then be used to identify and select for the most promising genetic variants for further breeding efforts.In addition to GWAS, the use of advanced sequencing technologies, such as next-generation sequencing (NGS), has greatly expanded our understanding of the soybean genome. By deciphering the complete genetic code of soybean, researchers can identify key genes and regulatory regions that control important agronomic traits. This knowledge can then be leveraged to develop more targeted and efficient breeding strategies.Another exciting development in soybean genetic breeding is the use of gene editing technologies, such as CRISPR-Cas9. These tools allow for the precise modification of specific DNA sequences, enabling breeders to introduce or remove desired traits with unprecedented precision. This can lead to the development of soybean varieties with enhanced characteristics, such as improved disease resistance, increased nutritional value, or better adaptationto changing environmental conditions.While the advancements in soybean genetic breeding have been remarkable, there are still numerous challenges and opportunities that lie ahead. One of the key challenges is the need to develop soybean varieties that can withstand the increasing threats posed by climate change, such as drought, heat stress, and emerging pests and diseases.To address these challenges, soybean breeders and researchers are exploring various strategies, including the identification of novel genetic sources of stress tolerance, the development of multi-trait breeding approaches, and the integration of precision phenotyping techniques to accurately evaluate plant performance under different environmental conditions.Moreover, the successful deployment of soybean genetic breeding innovations will require close collaboration among researchers, breeders, farmers, and other stakeholders in the agricultural value chain. This collaborative effort will ensure that the benefits of these advancements reach the end-users, ultimately leading to improved food security, sustainability, and the overall well-being of communities around the world.In conclusion, the field of soybean genetic breeding is a dynamic andrapidly evolving area of research and innovation. By harnessing the power of modern biotechnological tools, exploring genetic diversity, and fostering collaborative efforts, soybean breeders are poised to develop new and improved soybean varieties that can meet the growing global demand for this versatile and nutritious crop. As we continue to push the boundaries of soybean genetic breeding, we can look forward to a future where soybean plays an even more pivotal role in ensuring food security, promoting sustainable agriculture, and contributing to the overall prosperity of our planet.。

生产更好的产品英语作文

生产更好的产品英语作文In order to produce better products, companies need to focus on several key areas to ensure quality and customer satisfaction. This includes investing in research and development, improving production processes, and implementing quality control measures.First and foremost, companies must invest in research and development to stay ahead of the competition and meet the evolving needs of customers. By conducting market research and analyzing trends, companies can identify opportunities for innovation and product improvement. This can involve developing new technologies, materials, or designs to enhance the performance, durability, and functionality of products.Furthermore, companies need to continuously improve their production processes to ensure efficiency and consistency. This can involve streamlining operations, optimizing workflows, and investing in automation andtechnology. By reducing waste, minimizing errors, and increasing productivity, companies can produce better products at a lower cost.Quality control is another crucial aspect of producing better products. Companies must implement rigorous testing and inspection procedures to ensure that products meet quality standards and specifications. This can involve conducting thorough checks at each stage of the production process, from raw materials to finished goods. By detecting and resolving defects early on, companies can preventcostly recalls and maintain customer trust.In addition to these key areas, companies should also focus on sustainability and social responsibility in their production processes. By adopting environmentally friendly practices, such as using recycled materials and reducing waste, companies can minimize their impact on the environment and contribute to a more sustainable future. Furthermore, companies should ensure fair labor practices and ethical sourcing to support workers' rights and uphold ethical standards.In conclusion, producing better products requires a comprehensive approach that encompasses research and development, production processes, quality control, sustainability, and social responsibility. By investing in these key areas, companies can create products that meet customer needs, exceed expectations, and drive business success. Ultimately, the pursuit of excellence in product quality is essential for long-term growth and competitiveness in today's global marketplace.。

全基因组选择及其在奶牛育种中应用进展

·2011·20·科技Research on X-sex Control of Frozen Semen for Jersey and Holstein-FriesianWEI Huan 1,LI Ming 2,LI Xiu-liang 2,LIU Rui-xin 2,LUO Meng-huo 2（1.Technical Extension Station of Animal Production in Hechi City,Hechi,quangxi547000;2.Guangxi Institute of Animal Science,Nanning,Guangxi 530001）Abstract ：A trial of artificial insemination(AI)has been carried out with frozen semen of X-sex control for the cows of Jersey andHolstein -Friesian,as well as the donators of embryo transfer to evaluate the effect on this AI research work.Results from the synchronous estrus,superoulation,AI and embryo collection in this trial showed that the conception rate and the amount and rate of available embryo from the donators were affected by the frozen semen of X-sex control;higher conception rate was found in heifers other than delivered cows and Holstein -Friesian other than Jersey cows in different breeds.However,the results of amount of available embryo and rate of available embryo from the younger donators were much better than the delivered ones in the same breed,and the better result also occurred in Holstein-Friesian when the comparison was done in the same age of different breed cows.Key words ：Jersey ；Holstein-Friesian ；Frozen semen of X-sex control ；Evaluation of results在生产上采用CIDR+PG+LHRH-A 3的方法更经济实惠。

植物全基因组选择技术的研究进展及其在玉米育种上的应用

植物全基因组选择技术的研究进展及其在玉米育种上的应用孙琦;李文兰;陈立涛;赵勐;李文才;于彦丽;孟昭东【摘要】全基因组选择技术通过全基因组中大量的单核苷酸多态性标记(SNP)和参照群体的表型数据建立 BLUP模型估计出每一标记的育种值，称为估计育种值(GEBV)，然后仅利用同样的分子标记估计出后代个体育种值并进行选择。

该文就近年来国内外有关影响基因组选择效率的主要因素———参考群体的类型与大小、模型的建立方法、标记的类型及其数目、性状遗传力，以及对基因组选择效率的影响等方面的研究进展进行综述，并介绍了全基因组选择技术在玉米育种上应用概况以及对未来的展望。

%Marker-assisted selection (MAS)technology could realize direct genetic selection,but it must base on QTL mapping.Genomic selection (GS),as the newest MAS method,has much advantage com-pared to traditional MAS technology,especially QTL mapping not necessary.Inthis paper,the factors af-fecting prediction accuracy of GS were reviewed,including training population type,prediction model, marker number,population size,population structure,hereditary of traits and so on.The application of GS in maize breeding was also introduced as well as hybrids performance prediction.We then predicated the future research and application of GS in maize breeding.【期刊名称】《西北植物学报》【年(卷),期】2016(036)006【总页数】9页(P1269-1277)【关键词】全基因组选择;玉米;估计育种值【作者】孙琦;李文兰;陈立涛;赵勐;李文才;于彦丽;孟昭东【作者单位】山东省农业科学院玉米研究所，济南 250100;山东省农业科学院玉米研究所，济南 250100;莱阳市种子公司，山东莱阳 265200;山东省农业科学院玉米研究所，济南 250100;山东省农业科学院玉米研究所，济南 250100;山东省农业科学院玉米研究所，济南 250100;山东省农业科学院玉米研究所，济南250100【正文语种】中文【中图分类】Q789With rapid development of the molecular biology and genomics, marker-assisted selection(MAS) emerged as the times require. MAS technology is as a kind of crop genetic improvement method combing the phenotypic and genetic value, which can realize genetic direct selection and effective polymerization[1] . When complex traits controlled by multiple genes need to be improved, MAS has two aspects of flaws. First, selection of the progeny population is established on the quantity traits location (QTL) mapping. But the result of QTL mapping basing on the bi-parental populations has no universality and couldn’t be applied accurately in breeding[2]. Second, the important traits were controlled by lots of small effective genes,lack of appropriate statistic method and breeding technology which will apply quantity genes to complex traits improvement[3]. New MAS technology-genomic selection (GS) emerged asthe times require.Meuwissen first put forward genomic selection (GS) breeding strategy. GS uses a “training population” of individuals that have been genotyped and phenotyped. Best linear unbiased prediction (BLUP) model is established on the basis of the genotyped result of an individual and its breeding value (Mean performance of crosses with same tester) for the training population. The breeding value of “Candidate population” is estimated by BLUP model and genotypic data.without cross to tester and phenotypes record[4]. BLUP model takes genotypic data of untested individuals and produces genomic estimated breeding values (GEBVs). These GEBVs say nothing of the function of the underlying genes as the ideal selection criterion[5] . Genomic selection basis of GEBVs is superior to traditional breeding for increasing gains per unit time even if both models show the same efficiency. In principle, phenotypes value of the candidate individuals is non-essential for the selection, hence shortening the length of the breeding cycle[6].Genomic selection have several merits compared to the traditional MAS. (1) QTL mapping is not necessary for GS. Genomic selection differs from previous strategies such as linkage and association mapping in that it abandons the objective to map the effect of single gene and instead of focusing on the efficient estimation of breeding values on the basis of a large number of molecular markers, ideally covering the full genome[5] . (2) Genomic selection is more precision especially for early selection. Genotyping uses high density molecular markers which can estimate all ofthe QTL effects and explain the genetic variance for most of the traits. But MAS only uses several markers in traits selection. So genomic selection is more accurate than MAS[7] . (3) Genomic selection can shorten generation interval, accelerate genetic progress and reduce production cost. Genetic progress of GS is more than phenotypic selection 4%-25%. Cost of GS is less than traditional breeding 26%-56%[8] . (4) Selection efficiency of low heritability traits is higher for GS than MAS. (5) The criterion of GS is breeding value, sum of all of the allele genetic effects for each individual. It is judged by the mean performance of its cross progeny, not the performance of itself. So GS is more accurate[9].Genomic selection originated from animal breeding during last century. It has been widely used in dairy cattle breeding in America, Australia, New Zealand and so on[10-11] . It was also applied in broiler chickens and pigs breeding[12-13] . GS’ application in plant breeding was developed in recent years, which focused on simulation studies. It is used in maize[14] , wheat[15] , tree[16] , sugar beet[17] , Barley[18] , triticale[19] and so on. Empirical study is performed in larger companies such as Monsanto and Pioneer-Dupond. Mark Sorrells and Jean-Luc Jannink are trying to use GS to increase the speed of variety improvement 3-4 times. The work is carried out with CYMMIT and performed four aspects to improve the yield of maize and wheat[20].Under the above context, the objective of this study is to review the essential factors affecting the GS in plant breeding. Maize is essential for global food security. More research of genomic selection on maize lauchedin recent years[21-23]. The paper will introduce the advance on the application of GS in maize breeding. We than put forward the future research which should be carried out in maize breeding in China. Factors that affect GS prediction accuracy of include the number of markers used for estimating the GEBVs[10] , trait heritability[7] , calibration population size[5] , statistical models[24] , number and type of molecular markers[25-26] , linkage disequilibrium[27] , effective population size[28], relationship between calibration and test set (TS)[29-31] and population structure[32-34] .2.1 Training population of genomic selectionIn animal breeding, we only discussed GS in the context of population-wide linkage disequilibrium, where the population might be defined as an entire breed of cattle, pig, or chicken. The need for high marker densities in GS may be reduced if the candidate population consists of progeny of the training population. In that case, an evenly spaced low-density subset of the markers typed on the training population can be used on the candidates, and scores for the full complement of markers can be inferred by cosegregation[35] . Because plants often produce very large full sibships (an F2 population derived from a single F1 by selfing is an example of such a sibship), however, there is also a tradition of QTL detection, MAS and GS within such sibships[5] . Bernardo compared F2, BC1, and BC2 populations from an adapted×exotic maize cross as training population in the simulation experiment[14]. The result indicates that genomewide selection should start at F2 rather than backcross population,even when the number of favorable alleles is substantially larger in the adapted parent than in the exotic parent. Compared to natural populations, genetic basis of F2 populations is simpler because F2 populations derive from only two inbred lines. So the biparental population size might be smaller than that of natural populations. Simulation studies have previously indicated that for three cycles of genomewide selection in an adapt ed×exotic cross, a population size of NC0 = 144 was generally sufficient[21] . Low density markers are suitable to F2 populations[22] . But two disadvantages of F2 populations exist. Biparental population requires separate model for training within each cross.The BLUP model is only suit for the progenies selection from the two parental lines. The progeny of F2 population must be selected by the phenotypic value of F3 testcrosses. Following progeny selection may be only according to BLUP model afterF3.F2 as training population often be suilt for cross-pollinated plant such as maize. Yusheng Zhao based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program[23]. In the study of Vannesa etal.[36] , marker effects estimated in 255 diverse maize hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations.Wegenast et al. suggested that genomic selection was applied in plant breeding, however, not only within a specific bi-parental cross or within adiverse panel of elite lines but also rather within and among crosses[37]. Self-pollination plant often adopt natural population such as wheat or sugar. Würschum et al used 924 sugar beet lines as training population. The results suggest that a training population derived from intensively phenotyped and genotyped diverse lines from a breeding program does hold potential to build up robust calibration models for genomic selection[17]. Hans et al. accessed the accuracy of GEBVs for rust resistance in 206 hexaploid wheat landraces[15].2.2 Prediction model of genomic selectionGenomic selection modeling takes advantage of the increasing abundance of molecular markers through modeling of many genetic loci with small effects[26,35,38] . Over the last decade, simulation and empirical cross-validation studies in plants have shown GS is more effective than traditional MAS strategies that use only a subset of markers with significant effects[5-7,39] .Estimation methods of allelic effects include least squares regression[40], ridge regression BLUP (RR-BLUP), principle component analysis[41-42] and Bayes regression[43]. In essence for least squares, chromosome fragments or markers are selected associated to the traits by genome-wide association studies (GWAS) at the same time and then the effect of the fragments is estimated[44]. RR-BLUP method regards the fragment effects as random effects. The marker effect was estimated by linear mixed models. The sum of fragments effect is breeding value for an individual[43]. Bayes methods combines the prior distribution of marker effect varianceand data collection. Frenquently used Bayes methods conclude Bayes A and Bayes B. Main difference between Bayes A and Bayes B is that Bayes A permits different variance for different markers and Bayes B permits that the variance of some markers is zero[45].Simulation studies show that the prediction accuracy of Bayes method is best and least squares is weakest. The accuracy rate of RR-BLUP is slightly smaller than Bayes A. Even so, RR-BLUP has four aspects superior to Bayesian method. First, Bayesian method is complex and need super computer. But computer requirement is lower and calculation speed is higher for RR-BLUP. Marker effects are estimated by RR-BLUP in SAS PROC IML[46]. Second, prediction within families was more accurate in BLUP than Bayes B. Regression coefficient b of RR-BLUP is nearer to 1 than BayesA[47]. Habier et al. showed that RR-BLUP is more effective at capturing genetic relationships because it fits more markers into the prediction Model[27]. In contrast, Bayes B is more effective at capturing LD between markers and QTL. Third, RR-BLUP is more accurate than other method when the number of QTLs increases or the heredity is higher[18] . Fourth, BLUP led to lower inbreeding and a smaller reduction of genetic variance compared to Bayes and PLS [48]. From above, we can conclud that BLUP methods is better than Bayesian regression for plant models.In addition, machine-learning methods also can be used to predict the marker effect, including support vector machine (SVM) , booting and random forest (RF). Ogutu et al. compared these methods for genomic selection. The result shows that the correlation between the predicted andtrue breeding values is 0.547 for boosting, 0.497 for SVMs,and 0.483 for RF, indicating better performance for boosting than for SVMs and RF[49].2.3 Other factors affecting prediction accuracyIn genome-wide selection methods, prediction accuracy is affected by population size (N), average hereditary of traits (h2) and markernumbers(NM)[50]. Simulation studies showed that the population structure is also crucial for the prediction accuracy in genomicselection[27].Prediction accuracy increases with markers density. Markers number on a certain length genome also directly affects total information of genetic markers. If SSR markers density increases from 0.25 Ne/morgan (Ne, effective population size) to 2 Ne/morgan, prediction accuracy will be improved from 0.63 to 0.83. If SNP markers density increases from 1Ne/morgan to 8 Ne/morgan, prediction accuracy will be improved from 0.69 to 0.86. Even at the highest tested densities of 2 Ne SSR markers per Morgan or 8 Ne SNP markers per Morgan, accuracy had not reached a plateau[5] . Meanwhile, more markers number, more easy to get the Linkage disequilibrium(LD) markers. Emily found that in the biparental populations, there was no consistent gain in genome-wide prediction (rmp) from increasing marker density above one marker per 12.5 cM[22]. Zhao et al. revealed that the accuracy was nearly reaching a plateau at 800 SNPs when the number of markers varied from 100 to 800 [23]. The reason is that genome is sufficiently saturated with markers when the prediction accuracy arrives at a plateau[28,50]. The number of markers needed foraccurate predictions of genotypic values depends on the extent of linkage disequilibrium (LD) between markers and QTL[4] and also on the germplasm under consideration[18] .Different marker type has different polymorphism information content (PIC). Comparing SSR and SNP markers, they found that for similar accuracies, the SNP markers required a density of 2 to 3 times that of the SSR[5].Simulation studies showed that the population size is crucial for the prediction accuracy in genomic selection[27]. The result of Emily et al. indicated that prediction accuracy rmp increased as population size N increased. In the biparental maize population and with the highest markers number NM, (1 213 markers) and hereditary h2 = 0.30, the prediction accuracy for grain yield was rmp = 0.19 with N= 48, rmp = 0.26 with N = 96, and rmp = 0.33 with N = 192[22]. Zhao Yusheng observed a monotonic increase in the prediction accuracy for grain yield with increasing population size without any substantial decrease in the slope [23] . The study of Bernardo also indicated that lager poluation size would get higher prediction precision[14]. But F2 population size of NC0 = 144 was generally sufficient[21].Training population structure is also an important factor affecting prediction accuracy of genomic selection for multi-parental populations. Training population structure set methods conclude random sampling, unidirectional sampling (selecting individuals with highest genotypic values), bidirectional sampling (selecting individuals with highest or lowestgenotypic values)[50-51]. This bidirectional selection showed to be much more powerful than random sampling[52] . Yusheng Zhao observed a substantial loss in the accuracy to predict genomic breeding values in unidirectional selected populations. Bidirectional selection is a valuable approach to efficiently implement genomic selection in applied plant breeding programs[53].For the same trait within the same population, prediction accuracy(rmp) will remain unchanged for different combinations of population size (N) and trait hereditary (h2). Decrease on h2 can be compensated by a proportional increase in N (and vice versa) so that rmp is maintained. On the other hand, traits with initially low h2 can be evaluated with larger N or the h2 for a subset of traits can be increased by the use of additional testing resources. Different traits, however, vary in their prediction accuracy even when N, h2, and NM (markers number) are constant. Yield traits had lower prediction accuracy than other traits despite the constant N, h2, and NM. Simulation results indicated that rmp is also lowest for yield traits even when its h2 is as high as other traits. Plant height and lodging are always predicted most accurately followed by floweringtime[22] . Empirical evidence and experience on the predictability of different traits are necessary in designing training populations.3.1 Origination of GS in maizeThe key technology of GS is the maize hybrid prediction by BLUP model with markers effects or coefficient of parentage. It was used to predict the single-cross performance in maize hybrid breeding at first. The BLUPmodel is established based on the tested hybrids data and the markers information of their parents. The performance of untested hybrids is predicted by the BLUP model and the markers data of the parents[54]. Bernardo devoted himself to hybrids prediction by BLUP model inmaize[55-58]. The coefficient of relative between theory and actual observation was 0.688～0.800 by RFLP markers[54] . BLUP is suitable for hybrid performance prediction since the trait only has moderate heritability. Prediction accuracy of molecular marker effects is higher than phylogenetic relationship[58]. With the development of molecular markers, new molecular marker type emerged. Simple sequence repeats (SSR) and single nucleotide polymorphism (SNP) were widely used. Manje Gowda et al. found that prediction accuracy of flower time and plant height was above 0.8 with SSR markers in maize[19]. Research of Massman et al. indicated that prediction accuracy of grain yield was 0.8, and root logging ratio was 0.87 using SSRmarkers[59]. But the prediction effect of grain yield was only 0.50～0.66, and root logging ratio was only 0.31～0.45 with coefficient of parentage[55] . Then it indicated that molecular markers was more suitable for hybrid performance prediction than coefficient of parentage.Then scientists found that BLUP was not only used to hybrid performance prediction, but also the breeding value of individuals among the maize population. So BLUP was used to individuals selection of F2 population in selection and breeding of inbred lines. Hybrid performance prediction lay the foundation for the genome-wide selection in maize.3.2 Application of genomic selection in maizeBernardo’s laboratory began to study applying GS to maize breeding in Minnesota University of America[21] . They did plenty of simulation and empirical experiments. Piepho in German and Robert in Brazil also tried to study using GS in maize breeding[60-61]. GS utility in maize breeding consist of two sides, hybrids performance prediction and improvement of inbred lines. He devoted to inbred lines improvement using GS. The BLUP model of biparental populations from two inbred lines is only suit for the progeny of the parents. Genomewide selection as proposed in maize involves two steps[21]. First, a segregating maize population is genotyped and evaluated for testcross performance of F3 family. Based on the genotypic and phenotypic data, breeding values associated with a large set of markers (e.g., 256 to 512 markers) are calculated for the traits of interest. Significance tests for markers are not used, and the effects of all markers are fitted as random effects in a linear model by best linear unbiased prediction (BLUP). Second, two or three generations of selection based on all markers are conducted in a year-round nursery (e.g., Hawaii or Puerto Rico) or greenhouse. Trait values are predicted as the sum of an individual plant’s marker values across all markers, and selection is subsequently based on these genomewide prediction. According to the steps, Emily (2013b) introgressed semidwarf germplasm to U.S. Corn belt inbred and found that genomewide selection from Cycle 1 until Cycle 5 either maintained or improved on the gains from phenotypic selection achieved in Cycle 1[62].The results of Bernardo indicated that a useful strategy for the rapid improvement of an adapted×exotic cross involves 7 to 8 cycles of genomewide selection starting in the F2[14]. Benjamin et al. demonstrated that progressive selfing had a significant and positive impact on genomic selection gains. In particular, selfing to the F8 produced a 72% increase over F2 gains[63]. However, most of the gains are realized by the F5 generation (95% of the F8 gains). Also note that the F8 and DH performed similarly, consistent with previous observations[64] .In the research of Bernardo, the training population is the specific bi-parental populations from the two parental lines, so the BLUP model is suit for the progeny of the two inbred lines. Other experiments of GS in maize are about multi-parental populations as training population. Study of Yusheng Zhao was based on experimental data of six segregating populations from a half-diallel mating design. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs[23]. These result of the study might be as genomic prediction model for further breeding elite maize lines between the six populations. In the study of Vanessa et al., marker effects estimated in 255 diverse maize hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations[36]. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomicprediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.GS is just beginning to be implemented, but it will take long time to be used in maize breeding. In previous study, training population was only from several inbred lines, even if two inbred lines. It couldn’t be implemented by other breeding program. Future research should focus on two sides of work. First, we should commit to build a generalized prediction model for some kinds of inbred lines such as yield, quality and so on. But these traits were complex composed of a great deal of genes. Traditional MAS technology couldn’t realize the traits selection in maize breeding. 973 Plan “Basic study on breeding of geno me-wide selection of yield and quality traits in maize” has been carried out in 2014. The plan will systematicly analyze the genetic basis of maize yield and quality, and then build genome-wide selection breeding model. It will afford new technology for maize breeding. Seond,in China, abiotic stress tolerance also reduces the yield seriously in maize especially drought tolerance. Drought is the foremost factor restricting maize production, often resulting in 20-50% maize yield reduction every year in China[65] . If we establish prediction model of drought tolerance, it will afford the theory and technology support of maize breeding. Consequently, our research team will carried out study on the genomic selection program of drought tolerance.References:[1] STUBER C W, POLACCO M, SENIOR M L. Synergy of empirical breeding, marker-assisted selection, and genomics to increase crop yield potential[J]. Crop Science, 1999,39:1 571-1 583.[2] MOOSE S P, MUMM R H. Molecular plant breeding as the foundation for 21st century crop improvement[J]. Plant Physiology, 2008, 147: 969-977.[3] BERNARDO R. Molecular markers and selection for complex traits in plants: learning from the last 20 years[J].Crop Science, 2008, 48:1 649-1 664.[4] MEUWISSENT H, HAYES B J, GODDARD M E. Prediction of total genetic value using genome-wide dense marker maps[J]. Genetics, 2001, 157: 1 819-1 829.[5] JANNINK J L, LORENZ A J, IWATA H. Genomic selection in plant breeding: from theory to practice[J]. Briefings in Functional Genomics, 2010, 9(2):166-177.[6] HEFFNER E L, JANNINK J L, IWATA H, et al. Genomic selection accuracy for grain quality traits in biparental wheat populations[J]. Crop Science, 2011, 51: 2 597-2 606.[7] HEFFNER E L, SORRELLS M E, JANNINK J L. Genomic selection for crop improvement[J]. Crop Science, 2009, 49: 1-12.[8] MAYOR P J , BERNARDO R. Genomewide selection and marker-assisted recurrent selection in doubled haploid versus F2 populations[J]. Crop Science, 2009, 49:1 719-1 725.[9] MASSMAN J M, JUNG H J G, BERNARDO R. Genomewide selectionversus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize[J]. Crop Science, 2012, 53(1): 58-66.[10] SCHAEFFER L R. Strategy for applying genome-wide selection in dairy cattle[J]. Journal of Animal Breeding Genetic, 2006, 123: 218-223.[11] GODDARD M E, HAYES B J. Genomic selection[J]. Journal of animal Breeding Genetics, 2007, 124: 323-330.[12] DAETWYLER H D, VILLANUEVA B, BIJMA P. Inbreeding in genome-wide selection[J]. Journal of Animal Breeding Genetic, 2007, 124: 369-376.[13] TU L, WOOLLIAMS J A, SIGBJORN L. The accuracy of genomic selection in norwegian red cattle assessed by cross validation[J]. Genetics, 2009, 183: 1 119-1 126.[14] BERNARDO R. Genomewide selection for rapid introgression of exotic germplasm in maize[J]. Crop Science, 2009, 49: 419-425.[15] HANS D D, BANSAL U K, BARIANA H S, et al. Genomic prediction for rust resistance in diverse wheat landraces[J]. Theory and Applied Genetics, 2014, 127: 1 795-1 803.[16] MARIE D, BOUVET J M. Genomic selection in tree breeding: testing accuracy of prediction models including dominance effect[J]. BMC Proceedings, 2011, 5(Supply7): 1-2.[17] WÜRSCHUM T, REIF J C , KRAFT T, et al. Genomic selection in sugar beet breeding populations[J]. BMC Genetics, 2013, 14: 85-92.[18] ZHONG S Q, DEKKERS J C M, FERNANDO R L, et al. Factors affecting accuracy from genomic selection in populations derived from multipleinbred lines: a barley case study[J]. Genetics, 2009, 182(1): 355-364. [19] GOWDA M, ZHAO Y S , MAURER H P, et al. Best linear unbiased prediction of triticale hybrid performance[J]. Euphytica, 2013, 191: 223-230.[20] 吴永升, 邵俊明, 周瑞阳, 等. 植物数量性状全基因组选择研究进展[J]. 西南农业学报, 2012，25(4): 1 510-1 514.WU Y S, SHAO J M, ZHOU R Y, et al. Reviews of genome- wide selection for quantitative traits in plants[J]. Southwest China Journal of Agricultural Sciences, 2012, 25(4): 1 510-1 514.[21] BERNARDO R, YU J. Prospects for genome-wide selection for quantita-tive traits in maize[J]. Crop Science,2007, 47: 1 082-1 090.[22] EMILY C, BERNARDO R. Accuracy of genomewide selection for different traits with constant population size, heritability, and number of markers[J]. Plant Genome, 2013a, 6(1): 1-7.[23] ZHAO Y S, GOWDA M, LIU W X, et al. Accuracy of genomic selection in European maize elite breeding populations[J].Theoretical and Appllied Genetics, 2012a, 124: 769-776.[24] HESLOT N, YANG H P, SORRELLS M E, et al. Genomic selection in plant breeding: a comparison of models[J]. Crop Science, 2012, 52: 146-160.[25] CHEN X, SULLIVAN P F. Single nucleotide polymorphism genotyping: biochemistry, protocol, cost and throughput[J]. Pharmaco Genetics, 2003, 3: 77-96.[26] POLAND J, RIFE T W. Genotyping-by-sequencing for plant breeding and genetics[J]. Plant Genetics, 2012, 5: 92-102.[27] HABIER D, FERNANDO R L, DEKKERS J C M. The impact of genetic relationship information on genome-assisted breeding values[J]. Genetics, 2007, 177: 2 389-2 397.[28] DAETWYLER H D, VILLANUEVA B, WOOLLIAMS J A. Accuracy of predicting the genetic risk of disease using a genome-wide approach[J]. PLoS One, 2008, 3: 3 395.[29] ALBRECHT T, WIMMER V, AUINGER H J, et al.Genome-based prediction of testcross values in maize[J]. Theoretical and Appllied Genetics, 2011, 123: 339-350[30] CLARK S, HICKEY J, WERF J. Different models of genetic variation and their effect on genomic evaluation[J]. Genetic Selection Evolution, 2011, 43: 18.[31] PSZCZOLA M, STRABEL T, MULDER H A, et al.Reliability of direct genomic values for animals with different relationships within and to the reference population[J]. Journal of Dairy Science, 2012, 95z: 389-400. [32] SAATCHI M, MCCLURE M C, MCKAY S D, et al. Accuracies of genomic breeding values in American Angus beef cattle using k-means clustering for cross-validation[J]. Genetic Selection Evolution, 2011, 43: 40.[33] WINDHAUSEN V S, ATLIN G N, CROSSA J, et al. Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments[J]. Genes Genomes Genetic, 2012, 2:1 427-1 436.[34] GUO Z, TUCKER D M, BASTEN C J, et al. The impact of population structure on genomic prediction in stratified populations[J]. Theoretical。

优良育种的重要性和发展途径的英语作文

优良育种的重要性和发展途径的英语作文The Importance and Development Path of Superior BreedingBreeding plays a crucial role in agricultural advancement and food security. Superior breeding not only improves the quality, yield, and resistance of crops and livestock but also provides a foundation for sustainable agricultural development. Over the years, advancements in breeding techniques have greatly improved the efficiency and precision of breeding, leading to the development of superior varieties that meet the demands of a growing population. In this essay, the importance of superior breeding and its development pathways will be discussed.The Importance of Superior BreedingSuperior breeding is essential for improving agricultural productivity and ensuring food security. By developing new varieties that are more resistant to pests and diseases, have higher yields, and better nutritional content, superior breeding plays a crucial role in increasing the overall output of crops and livestock. This is especially important in the face of the challenges posed by climate change, which can disrupt traditional agricultural practices and threaten food security. Additionally, superior breeding can help reduce the reliance onchemical inputs such as fertilizers and pesticides, thus promoting sustainable agricultural practices.Furthermore, superior breeding can also help address issues related to poverty and malnutrition. By developing crop varieties that are more nutritious and have higher yields, superior breeding can help improve the quality of food available to vulnerable populations, thus reducing the prevalence of malnutrition and related health issues. Moreover, by increasing agricultural productivity, superior breeding can help lift smallholder farmers out of poverty and improve their livelihoods.Overall, superior breeding plays a crucial role in ensuring food security, promoting sustainable agricultural practices, and improving the quality of food available to vulnerable populations. Therefore, it is essential to invest in research and development efforts to further advance breeding techniques and develop superior varieties that meet the demands of a growing population.Development Pathways of Superior BreedingThere are several pathways through which superior breeding can be developed and implemented. These include:1. Genetic Engineering: Genetic engineering involves the manipulation of an organism's genetic material to introduce specific traits or characteristics. This technique has been widely used in crop breeding to develop varieties that are resistant to pests and diseases, have higher yields, and better nutritional content. Genetic engineering has the potential to significantly improve agricultural productivity and address the challenges posed by climate change.2. Marker-Assisted Selection: Marker-assisted selection is a breeding technique that utilizes genetic markers to identify and select plants or animals with desired traits. This technique allows breeders to more efficiently select for specific traits, thus speeding up the breeding process. Marker-assisted selection has been successfully used in crop breeding to develop varieties that are more resistant to pests and diseases, have higher yields, and better nutritional content.3. Genomic Selection: Genomic selection is a breeding technique that involves analyzing an organism's entire genome to identify genes associated with specific traits. This technique allows breeders to more accurately predict an organism's performance based on its genetic makeup, thus speeding up the breeding process. Genomic selection has the potential torevolutionize breeding efforts by allowing breeders to develop superior varieties with increased precision and efficiency.4. Participatory Plant Breeding: Participatory plant breeding involves involving farmers in the breeding process by soliciting their input and feedback on new varieties. This approach helps ensure that new varieties meet the needs and preferences of local farmers, thus increasing the likelihood of adoption. Participatory plant breeding has been successfully used in developing countries to develop varieties that are well-adapted to local growing conditions and farming practices.5. Bioinformatics: Bioinformatics involves the use of computer algorithms and software to analyze and interpret biological data. This technique has been widely used in breeding to analyze genetic data, identify genes associated with specific traits, and predict an organism's performance based on its genetic makeup. Bioinformatics has the potential to significantly improve breeding efforts by providing breeders with valuable insights into an organism's genetic makeup and its potential for performance.In conclusion, superior breeding is essential for improving agricultural productivity, ensuring food security, and promoting sustainable agricultural practices. By investing in research anddevelopment efforts to further advance breeding techniques and develop superior varieties, we can address the challenges posed by climate change, poverty, and malnutrition, and ensure a more secure and sustainable food supply for future generations.。

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Dairy breeding program
Current......
Year 1 ~ 100 bull calves selected Year 2 Daughters born Year 3 Daughters mated Year 4 Daughters milking Year 5 ~ 20 Bulls chosen for industry wide semen sales
G
iR g L
Double rate of genetic gain
Genomic selection in action
GENE TEAM: GENETICS AUSTRALIA'S FIRST GENETIC MARKER BULLS INFUSE, DEFIER AND WATCHDOG.
• Alternative -> use all SNP information • Track all chromosome segments, capture all variants affecting trait • Termed Genomic selection
Reference Population
Reference Population MQMQMQMQMQMQMQMQMQM
Genotypes Phenotypes
w1 w2 w3 w4 w5 w6 w7 w8 w9 w10
Reference Population MQMQMQMQMQMQMQMQMQM
Genotypes Phenotypes
Animal breeding
Powerful science
Time to market weight for meat chickens decreased from 16 to 5 weeks in 30 years
Feed conversion efficiency improved by 140% in salmon in seven generations
Cows: milk production Bulls: average of daughters milk production
In dairy data
• Split data into two sub-populations
• Reference: Cows, bulls born < 2003
Marker assisted selection
• Find SNPs linked to loci explaining variation in economic traits Genome wide association (GWAS)
• Use markers with largest effects to increase accuracy of selection • Benefit depends on proportion of variance explained by the markers……
Screen ’000s of calves on genomic breeding value
~ 20 elite bull calves selected on GEBV
Semen from elite bulls for industry wide sale
3 yrs advanced genetic gain
Screen ’000s of calves on genomic breeding value
~ 20 elite bull calves selected on GEBV
Semen from elite bulls for industry wide sale
3 yrs advanced genetic gain
Proportion of black….
70 KIT 60 50
F-value
40 30 20 10 0
0 20000000 40000000
9% of phenotypic variance
<1% of phenotypic 60000000 80000000 100000000 variance Position (bp)
Genomic selection across species
Breeding objective traits
Increased genetic gain from genomic selection
Industry Dairy Cattle Pigs Beef cattle Layer chickens Potential increase 60-120% (Pryce et al. 2011)
Proportion of black….
70 KIT 60 50
F-value
40 30 20 10 0
0 20000000 40000000
<1% of phenotypic 60000000 80000000 100000000 variance Position (bp)
120000000
Current......
Screen ’00s of calves on parental average ebv (accuracy 0.35)
Year 1 ~ 100 bull calves selected Year 2 Daughters born Year 3 Daughters mated Year 4 Daughters milking Year 5 ~ 20 Bulls chosen for industry wide semen sales
• Accuracy = r(Genomic breeding value,phenotype) 274 bulls
In dairy data
• Genomic breeding value accuracy 0.75 • Parent average accuracy 0.35
Dairy breeding program
120000000
Using the SNP information
• The proportion of variance explained by individual SNP small
Limited extra response from marker assisted selection
The opportunity
• New genomic technologies
– Cost of DNA markers 1,000 times cheaper than 3 years ago – “SNP chips” -> 800,000 DNA markers at once
• Can we use this technology to greatly increase genetic gain in animal breeding?
Image 1
Image 2
Image 3
Genomic selection to improve production from livestock
Ben Hayes
Outline
• The challenges and an opportunity
• Genomic selection • The future
Genotypes
Prediction equation
Genomic Breeding Value = w1x1+w2x2+w3x3……..
Selected Breeders
Estimated breeding values
In dairy data
• 3,800 Australian dairy bulls and 9,890 cows • Genotyped for 56 000 genome wide SNP6 w7 w8 w9 w10
Prediction equation
Genomic Breeding Value = w1x1+w2x2+w3x3……..
Reference Population
Genotypes Phenotypes
Selection candidates
• Genomic selection • The future
The future
• More accurate genomic breeding values
• At lower cost
The future
• More accurate genomic breeding values
– Larger reference populations? – Whole genome sequencing?
• At lower cost
Bigger reference populations
Bigger reference populations
International collaboration!
The future
• Better DNA markers?
~ 20 elite bull calves selected on GEBV
Semen from elite bulls for industry wide sale
3 yrs advanced genetic gain
Dairy breeding program
Current......
Year 1 ~ 100 bull calves selected Year 2 Daughters born Year 3 Daughters mated Year 4 Daughters milking Year 5 ~ 20 Bulls chosen for industry wide semen sales