Integrating genomics and transcriptomics to empower dairy breeding for feed efficient animals

整合基因组学和转录组学，赋能乳品育种，提高饲料效率

• 批准号: BB/X009505/1
• 负责人: Bingjie Li
• 金额: $ 44.21万
• 依托单位: Scotland's Rural College
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX009505%2F1
• 关键词: Integrating; genomics; transcriptomics; empower; dairy

Integrating genomics and transcriptomics to empower dairy breeding for feed efficient animalsImproving feed efficiency (FE) of dairy cattle has been a major interest for animal scientists and dairy farmers over decades. Feed efficiency is a complex trait in dairy cattle and is highly linked to milk yield and methane emission in dairy production. Improving FE of dairy cattle will increase profits of dairy farmers and reduce methane emissions of UK dairy population. Including FE into genetic improvement scheme is one of the top priorities in the UK dairy breeding now.The overall aim of the proposal is to understand the genetic basis of dairy feed efficiency through integrating population-level phenotypic, genomic, and transcriptomic data, which will be exploited to empower dairy breeding for feed efficient and environmentally-friendly animals. The project builds on SRUC's award-wining Dairy Research Centre (Queen's Anniversary Prize) with 50-year data recording on dairy feed efficiency on a population level. We will apply new methods we recently published on Nature Genetics to this data, to fully utilize and integrate animals' genomic and transcriptomic profiles into understanding genetics of feed efficiency. To achieve the overall aim of the project, we will: (i) Identify cattle genes and genomic regions that are associated with FE using population-level phenotypic and genomic data;(ii) Characterize the genes and variants of the cattle genome whose expression is important to FE using genomic and RNA-sequencing information; (iii) Develop methods and apply the newly acquired genomic and regulatory variants to enhance genomic selection for FE in dairy breeding.The proposed project builds on SRUC's award-winning Dairy Research Centre with 50-year dairy recording on feed intake, milk production and composition, body weight (BW) and condition, health status, and reproductive events per animal. Three work packages (WP) will be conducted:(i) WP1: We will generate whole genome-sequencing (WGS) data for 40 representative animals in the study population, plus WGS data we previously sequenced or obtained from publicly-available database. We will use these sequence data to facilitate genotype imputation to obtain sequence-level genotypes in the study population. We will conduct association analyses to identify genes and regions associated with FE using sequence-level genotypes. (ii) WP2: We will obtain RNA-sequencing for 200 animals in the study population from blood samples. Animals' transcriptomic profiles will be integrated with their genomic data and FE phenotypes to identify regulatory variants associated with FE.(iii) WP3: We will apply the newly acquired genomic and regulatory variants from WP1 and WP2 into genomic prediction for FE, to develop and assess methods of genomic prediction for FE using the functional variants.The present project will facilitate improved FE and reduced methane emission in the dairy population, highly relevant to BBSRC's strategic priorities in bioscience for sustainable agriculture and priority areas in data-driven biology. This project has extremely important scientific, economic, and social impact on understanding complex trait of feed efficiency, increasing profits of farmers and dairy industry, and mitigating methane emissions of UK dairy population. The project uses high-quality data and innovative methods to provide the scientific community a role model of integrating population-level omics data into genetic research for complex traits in animals and plants. The research outcome will have great potential to be applied to the UK national genetic improvement programs of dairy cattle. The research outcome will facilitate at least 1% extra genetic progress for UK dairy breeding, worth millions of pounds per year for the UK dairy industry.

整合基因组学和转录组学，使奶牛育种成为饲料效率高的动物几十年来，提高奶牛的饲料效率（FE）一直是动物科学家和奶农的主要兴趣。饲料效率是奶牛的一个复杂性状，与奶牛生产中的产奶量和甲烷排放量密切相关。提高奶牛的FE将增加奶农的利润，减少英国奶牛群的甲烷排放。将FE纳入遗传改良计划是目前英国奶牛育种的首要任务之一，该提案的总体目标是通过整合群体水平的表型、基因组和转录组数据，了解奶牛饲料效率的遗传基础，并利用这些数据为奶牛育种提供饲料效率和环境友好型动物。该项目建立在SRUC屡获殊荣的乳制品研究中心（女王周年奖）的基础上，该中心记录了50年来人口水平上的乳制品饲料效率数据。我们将应用我们最近发表在《自然遗传学》上的新方法来分析这些数据，充分利用和整合动物的基因组和转录组图谱，以了解饲料效率的遗传学。为了实现该项目的总体目标，我们将：（i）使用群体水平的表型和基因组数据识别与FE相关的牛基因和基因组区域;（ii）使用基因组和RNA测序信息表征表达对FE重要的牛基因组的基因和变体;（iii）开发方法并应用新获得的基因组和调控变体，以加强奶牛育种中FE的基因组选择。获奖的乳品研究中心拥有50年的乳品记录，记录了每头动物的采食量、产奶量和成分、体重（BW）和状况、健康状况和生殖事件。将进行三个工作包（WP）：（i）WP 1：我们将生成研究群体中40只代表性动物的全基因组测序（WGS）数据，加上我们之前测序或从公开数据库获得的WGS数据。我们将使用这些序列数据来促进基因型插补，以获得研究人群中的序列水平基因型。我们将进行关联分析，以确定与FE使用序列水平的基因型相关的基因和区域。(ii)WP 2：我们将从血液样本中获得研究群体中200只动物的RNA测序。动物的转录组谱将与其基因组数据和FE表型整合，以识别与FE相关的调节变体。(iii)WP 3：我们将把新获得的WP 1和WP 2的基因组和调控变体应用于FE的基因组预测，开发和评估使用功能变体的FE基因组预测方法。本项目将促进改善FE和减少奶牛群中的甲烷排放，与BBSRC在可持续农业生物科学和数据驱动生物学优先领域的战略重点高度相关。该项目对了解饲料效率的复杂特性，增加农民和乳制品行业的利润，减少英国奶牛群的甲烷排放量具有极其重要的科学，经济和社会影响。该项目使用高质量的数据和创新方法，为科学界提供了一个将群体水平的组学数据纳入动植物复杂性状遗传研究的榜样。该研究成果将在英国国家奶牛遗传改良计划中具有很大的应用潜力。这项研究成果将为英国奶牛育种带来至少1%的额外遗传进步，每年为英国奶牛业带来数百万英镑的价值。

项目成果

A compendium of genetic regulatory effects across pig tissues.

• DOI: 10.1038/s41588-023-01585-7
• 发表时间: 2024-01
• 期刊: NATURE GENETICS
• 影响因子: 30.8
• 作者: Teng, Jinyan;Gao, Yahui;Yin, Hongwei;Bai, Zhonghao;Liu, Shuli;Zeng, Haonan;Bai, Lijing;Cai, Zexi;Zhao, Bingru;Li, Xiujin;Xu, Zhiting;Lin, Qing;Pan, Zhangyuan;Yang, Wenjing;Yu, Xiaoshan;Guan, Dailu;Hou, Yali;Keel, Brittney N.;Rohrer, Gary A.;Lindholm-Perry, Amanda K.;Oliver, William T.;Ballester, Maria;Crespo-Piazuelo, Daniel;Quintanilla, Raquel;Canela-Xandri, Oriol;Rawlik, Konrad;Xia, Charley;Yao, Yuelin;Zhao, Qianyi;Yao, Wenye;Yang, Liu;Li, Houcheng;Zhang, Huicong;Liao, Wang;Chen, Tianshuo;Karlskov-Mortensen, Peter;Fredholm, Merete;Amills, Marcel;Clop, Alex;Giuffra, Elisabetta;Wu, Jun;Cai, Xiaodian;Diao, Shuqi;Pan, Xiangchun;Wei, Chen;Li, Jinghui;Cheng, Hao;Wang, Sheng;Su, Guosheng;Sahana, Goutam;Lund, Mogens Sando;Dekkers, Jack C. M.;Kramer, Luke;Tuggle, Christopher K.;Corbett, Ryan;Groenen, Martien A. M.;Madsen, Ole;Godia, Marta;Rocha, Dominique;Charles, Mathieu;Li, Cong-jun;Pausch, Hubert;Hu, Xiaoxiang;Frantz, Laurent;Luo, Yonglun;Lin, Lin;Zhou, Zhongyin;Zhang, Zhe;Chen, Zitao;Cui, Leilei;Xiang, Ruidong;Shen, Xia;Li, Pinghua;Huang, Ruihua;Tang, Guoqing;Li, Mingzhou;Zhao, Yunxiang;Yi, Guoqiang;Tang, Zhonglin;Jiang, Jicai;Zhao, Fuping;Yuan, Xiaolong;Liu, Xiaohong;Chen, Yaosheng;Xu, Xuewen;Zhao, Shuhong;Zhao, Pengju;Haley, Chris;Zhou, Huaijun;Wang, Qishan;Pan, Yuchun;Ding, Xiangdong;Ma, Li;Li, Jiaqi;Navarro, Pau;Zhang, Qin;Li, Bingjie;Tenesa, Albert;Li, Kui;Liu, George E.;Zhang, Zhe;Fang, Lingzhao
• 通讯作者: Fang, Lingzhao