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imtoken钱包安卓版下载|yak

作者： imtoken钱包安卓版下载

2024-03-10 00:02:51

Yak：致力于安全能力融合的语言 | Yak Program Language

Skip to main content官方文档Yak 语言YAK IDE关于我们Ya! 一刻YakLab合作伙伴下载白皮书GithubSearchYak：致力于安全能力融合的语言旅程伊始：Yak 语言环境安装与搭建环境Yaklang 语法教程Yaklang 编程实战渐进式安全研发教程Yak 中的常见库使用教程API 接口完全手册Yak 的前世今生On this pageYak：致力于安全能力融合的语言我们要解决什么问题？#当我们提到黑客编程，可能大家想起得最多是 Python。凭借简单的语法和丰富的安全工具生态基础，Python 被安全从业人员视为必学必会的技能。随着大家技术的深入，我们不再满足于 Python 编写脚本来服务于自己，大量工具/平台/安全产品的出现，规模化的需求已经被提上了日程， Golang

慢慢进入了大家的视野。作为一门效率更高，更适合产品分发，工程研发和平台搭建的语言，很快各种安全组织和白帽子们发挥自己的研发能力实现了很多工具和系统的研发。在这个阶段中，我们开始关注更加专业的 "安全研发"。与此同时，"安全研发不光包含安全平台的研发，也包含安全能力的研发"，这个理念慢慢被大家所接受。我们常用合适的语言编写平台去处理业务需求，但是安全能力的研发往往更加复杂，一般来说不同的安全工具安全能力会采用"最合适"的语言来完成，

这就造成了安全平台与安全能力模块的割裂。为什么安全能力要放在不同项目中呢？不能使用同一个平台吗？关于 "最合适" 的说明很大一部分原因是 "历史原因"，和没有专人去做新的场景适配，导致 "老代码" 越来越多。为了搞定这个问题，我们从事了很多工作在 Yak 中，我们希望他能承担 "安全能力融合" 的职责，你的 PoC，你的扫描器，你的扫描模块，漏洞扫描算法都可以用它来解决。我们想提供 "一站式" 的安全能力基座。Yak 发展史核心理念：安全基础能力融合#完善的内容生态提供入门/保姆级别的安全研发教程长期支持，具有成功的企业实践经验高级功能自由度极高，独一无二的 Fuzz 体验底层融合多种安全能力/工具，打破次元壁集成 MIT 协议的高质量工具提升行业整体安全水平速览：像搭积木一样编写扫描工具#我们创建一个 service_scan.yak 内容如下// 极简获取参数，--target xxxx --port 80scanTarget, scanPorts = cli.String("target"), cli.String("port")

// 默认批量进行服务扫描results, err = servicescan.Scan(scanTarget, scanPorts)die(err)

// 取出扫描结果（异步扫描结果）for result = range results { println(result.String())}Copy于是我们执行 yak service_scan.yak --target 192.168.1.1/24 --port 22,80 之后，将会看到如下输出tcp://192.168.1.32:22 open openssh[6.6.1]tcp://192.168.1.21:22 open openssh[7.4]tcp://192.168.1.40:22 open openssh[6.6.1]tcp://192.168.1.43:22 open openssh[5.3]tcp://192.168.1.44:22 open openssh[5.3]tcp://192.168.1.46:22 open openssh[5.3]tcp://192.168.1.60:22 open openssh[5.3]tcp://192.168.1.48:22 open openssh[5.3]tcp://192.168.1.66:22 open linux_kernel[*]/openssh[7.2p2]/ubuntu_linux[*]tcp://192.168.1.80:22 open openssh[5.3]...............tcp://192.168.1.83:80 open apache_tomcat[1.1]/coyote[1.1]/coyote_http_connector[1.1]/java[*]/jquery[*]/jquery[1.3.2]tcp://192.168.1.99:80 opentcp://192.168.1.122:80 open nginx[*]tcp://192.168.1.125:80 open linux_kernel[*]/nginx[1.10.3]/ubuntu[*]/ubuntu_linux[*]tcp://192.168.1.126:80 open nginx[*]/php[5.4.45]Copy语言基础架构#Edit this pageNext旅程伊始：Yak 语言环境安装与搭建环境 »我们要解决什么问题？核心理念：安全基础能力融合速览：像搭积木一样编写扫描工具语言基础架构Copyright © 2024 for Yak Project. 京ICP备17047700号-3官方文档关于我们

YAK中文(简体)翻译：剑桥词典

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yak 在英语-中文（简体）词典中的翻译

yaknoun [ C ] uk

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/jæk/ us

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a type of cattle with long hair and long horns, found mainly in Tibet

（主要见于中国西藏的）牦牛

yakverb [ I ]

informal uk

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/jæk/ us

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/jæk/ -kk-

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yack

（同 yack）

同义词

gab informal

jaw informal

yap informal disapproving

(yak在剑桥英语-中文（简体）词典的翻译 © Cambridge University Press)

yak的例句

yak

Large piles of dried yak dung are stored close to the tent as an important source of fuel.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

Milk from water buffalo, goats, ewes, mares, camels, and yaks however, is also used to produce yogurt in various parts of the world.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

As a result, many supposedly pure yak or pure cattle probably carry each other's genetic material.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

The head of the saihai had a hole with a cord attached to a tassel of strips of lacquered paper, leather, cloth or yak hair.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

The principal economic activity is animal husbandry, pastoral yak, goat, sheep, and so on.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

In addition, the area is now known for production of exotic meats such as yak, bison, and elk.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

Generally they rear cattle, sheep, goats, camels and/or yaks for milk, skin, meat and wool.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

Meat dishes are likely to be yak, goat, or mutton, often dried or cooked in a spicy stew with potatoes.

来自 Wikipedia

该例句来自维基百科，在CC BY-SA许可下可重复使用。

示例中的观点不代表剑桥词典编辑、剑桥大学出版社和其许可证颁发者的观点。

yak的翻译

中文（繁体）

（主要見於中國西藏的）犛牛, （同 yack）…

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yak, yac…

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in Ukrainian

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Tibet öküzü, yak…

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ตัวจามรีอยู่ในฑิเบต…

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bò Tây Tạng…

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yada yada yada

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yahoo

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yakka

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flexitarian

A flexitarian way of eating consists mainly of vegetarian food but with some meat.

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下载安装与更新配置 | Yak Program Language

Skip to main content官方文档Yak 语言YAK IDE关于我们Ya! 一刻YakLab合作伙伴下载白皮书GithubSearchYakit: 集成化单兵安全能力平台下载安装与更新配置本地模式与远程模式基础功能模块使用专项漏洞检测自动化漏洞检测中间人攻击简介MITM 中间人代理与劫持Web FuzzerWebsocket劫持插件使用及编写反连管理数据处理数据库设置和其他辅助工具FAQOn this page下载安装与更新配置Yakit 是 Yak 引擎的客户端，它提供了一个图形化用户界面（GUI）来操控 Yak 引擎的能力。同时，Yakit也是一款集成化的渗透测试工具，它提供了一系列安全工具和功能，包括MITM劫持操作台、Web Fuzzer、Yak Cloud IDE、ShellReceiver等，使我们能够更好地完成对应用的安全测试，无论是自动化地还是手工地。在渗透测试中，我们可以使用Yakit来简化测试工作，即使在没有熟练技巧的情况下，只需熟悉Yakit的使用即可轻松高效地完成渗透测试工作在使用 Yakit 之前，您需要先下载和安装 Yakit。本章将提供Yakit的安装更新及配置更新说明，请按照下面的步骤进行下载和安装。下载#下载 Yakit 的方式有两种：分别为官网下载和Github下载1.官网下载：https://yaklang.com2.Github下载:https://github.com/yaklang/yakit/releasesinfo国内网速不佳的用户的可以通过阿里云oss下载，地址如下：https://yaklang.oss-cn-beijing.aliyuncs.com/yak/${version}/Yakit-${version}-windows-amd64.exe其中${version}表示的是yakit版本比如当前最新版本为1.1.8则下载链接为：https://yaklang.oss-cn-beijing.aliyuncs.com/yak/1.1.8/Yakit-1.1.8-windows-amd64.exe安装#windows端#目前 Yakit 支持 Windows、Mac OS X 和 Linux 平台，根据自己的系统下载对应的版本，然后点击安装包进行安装,同意协议后即可完成安装。Linux端#Linux无需安装可视化页面Yakit，只需要按照核心引擎Yak大多数情况下，用户可以通过以命令来安装，Linux x64 版本的 Yak 发行版。bash <(curl -sS -L http://oss.yaklang.io/install-latest-yak.sh)Copy或者手动下载安装#点击这里下载 yak_linux_amd64 在下载完成之后，执行chmod +x yak_linux_amd64 && ./yak_linux_amd64 installCopy即可安装在确认 yak version 命令执行成功之后，一般来说即可删除下载的文件 rm ./yak_linux_amd64Mac端#打开下载好的安装包，拖动软件到右边的文件夹中进行安装如果在较新的 macOS 系统中，用户安装后提示如图所示“已损坏”情况，解决办法请参考：“安装常见问题”至此，完成了Yakit客户端的安装。下载核心引擎#因为 Yakit 的核心并不在工具本身上，而是依托于 Yak gRPC 接口；也就是说，我们可以仅仅只把 Yakit 当作一个 "视窗" 来操纵 Yak 引擎来完成我们想要实现的安全能力。1.1.6版本之后引擎下载方式为自动下载安装，通过安装包安装即可同时安装客户端和引擎，无需单独安装引擎。1.1.6版本之前引擎需要单独安装，各平台安装方式一致（建议使用最新版本）：更新#Windows和Mac更新#点击小铃铛按钮提示更新yakit和yaklang核心引擎（Windows在左上角，Mac在右上角）Yakit启动页面直接也可以更新引擎Linux更新#Linux版本无yakit可视化页面，所以只需要更新yak核心引擎直接执行命令，就可以发现更新到最新版本yak upgradeCopy安装失败？常见安装问题 Q&A：#MacOS 安装问题#在 MacOS 下安装常见会遇到两个安装问题安装 "可信来源" 的问题提示 "Yakit 已损坏"针对 "可信来源" 安装的解决办法#Reference: https://zhuanlan.zhihu.com/p/51328476在Terminal / iTerm2 中执行 sudo spctl --master-disable 即可在 App Store 中可选 "任何来源"针对 MacOS 12 版本或高版本 Silicon-Chip 系列的 "Yakit 已损坏" 的处理办法在新版本的 MacOS 或 M1 芯片的系统下，安装会提示问题：苹果系统有一个 GateKeeper 保护机制。从互联网上下载来的文件，会被自动打上 com.apple.quarantine 标志，我们可以理解为 "免疫隔离"。系统根据这个附加属性对这个文件作出限制。

随着版本不同，MacOS 对 com.apple.quarantine 的限制越来越严格，在较新的 MacOS 中，会直接提示 "映像损坏" 或 "应用损坏" 这类很激进的策略。

我们可以通过手动移除该选项来解决此问题，在 Terminal 中执行sudo xattr -r -d com.apple.quarantine /Applications/Yakit.app即可其他问题#Yak 安装的原理是啥？安装脚本托管在阿里云OSS上，大家可以通过下载 http://oss.yaklang.io/install-latest-yak.sh 这个脚本查看具体的安装方法。安装脚本会下载 yak 主程序到本地yak 程序会把自己的二进制复制到可执行的目录中(例如：/usr/local/bin 或者 C:\Windows\System32\)，然后执行 yak version 来验证自己的安装过程。如何删除 Yak ？找到 Yak 的安装路径，删除即可。例如：which yak 在 *unix 系统下可以找到安装路径安装失败应该如何操作？手动下载最新版本的 Yak 发行程序（选择合适自己的）。在本机的管理员权限/root权限下，执行 yak_{{GOOS}}_amd64 install 命令。执行成功之后，查看 yak version 命令是否能成功执行。pcap/libpcap 依赖出现问题？不同平台的解决方式不一样，但是 *unix 操作系统下，一般来说 brew/apt/yum install libpcap 即可解决。windows 平台下，大家可以通过下载并安装 npcap for Common Windows Version 来解决。danger如果遇到 pcap 依赖问题，请遵循上面的步骤解决。至此，我们完成了Yakit的安装，进入到了初始页面，右键点击默认项目，点击进入项目即可进入主页接下来让我们一起开始Yakit的探索之旅吧～用户登录设置通知漏洞和风险通知引擎官方网站主页当前引擎的IP和端口CPU实时占用率功能菜单栏子功能菜单栏导入协作资源字典管理Yak IDE --Yak Runner自定义功能菜单插件商店信息概览Previous« Yakit: 集成化单兵安全能力平台Next本地模式与远程模式 »下载安装windows端Linux端Mac端下载核心引擎更新Windows和Mac更新Linux更新安装失败？常见安装问题 Q&A：MacOS 安装问题其他问题Copyright © 2024 for Yak Project. 京ICP备17047700号-3官方文档关于我们

Yak | Wild Ox of Asia, Himalayas & Tibet | Britannica

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牦牛（牛科牛属下的一种动物）_百度百科

科牛属下的一种动物）_百度百科网页新闻贴吧知道网盘图片视频地图文库资讯采购百科百度首页登录注册进入词条全站搜索帮助首页秒懂百科特色百科知识专题加入百科百科团队权威合作下载百科APP个人中心牦牛是一个多义词，请在下列义项上选择浏览（共4个义项）展开添加义项牦牛[máo niú]播报讨论上传视频牛科牛属下的一种动物收藏查看我的收藏0有用+10本词条由“科普中国”科学百科词条编写与应用工作项目审核。牦牛是偶蹄目牛科牛属哺乳动物。全身黑色长毛，尤其是脖颈、前胸、腹部处。脸不长；眼如鸡卵；鼻孔很粗。寿命约二三十年。 [13-14]牦牛少量分布于蒙古、印度、阿富汗等国。主要分布于中国青藏高原及其周围海拔3000米以上的高寒地区。草食性反刍家畜， [15]生命力顽强。性情凶猛。冬季聚集到湖滨平原，夏秋到高原的雪山附近交配繁殖。 [16-17]在繁殖季节，雄兽有争夺配偶行为；雌兽孕期大约为258天，每胎仅生一只幼崽，每年可生产一胎。 [14]牦牛是中国的主要牛种之一，自古至今是青藏高原牧区的优势种家畜和当家畜种。 [16-17]有识途的本领，善走险路和沼泽地，并能避开陷阱择路而行，可作为旅游者的前导， [18]有“高原之舟”之称。毛可以制作成衣服或帐篷，皮是制革的好材料；还可以用来农；粪被晒成粪饼可以用来烧火。 [19]中国国家一级保护动物； [20]野生牦牛于2014年被国际自然保护联盟濒危物种红色名录（IUCN）列为易危（VU）。 [21]中文名牦牛拉丁学名Bos mutus别名亚归、猪声牛、西藏牛、马尾牛、旄牛、柞牛、摩牛、毛犀、毛牛外文名wild yak界动物界门脊索动物门Chordata纲哺乳纲Mammalia目偶蹄目Artiodactyla科牛科Bovidae属牛属Bos种牦牛亚门脊椎动物亚门亚属牦牛亚属亚目反刍亚目亚纲真兽亚纲亚科牛亚科族牛族亚种野牦牛分布区域青藏高原海拔3000米以上地区，中国川、青、藏、新等省区均有分布二名法Bos grunniens目录1分类地位2物种学史▪起源演化▪古今命名3形态特征4生活习性5分布范围6栖息环境7繁殖方式8主要变种▪白牦牛▪九龙牦牛▪高山牦牛▪高原牦牛▪尼泊尔牦牛▪印度牦牛▪塔吉克牦牛▪俄罗斯牦牛▪吉尔吉斯牦牛9保护级别10饲养管理▪牦牛放牧▪牦牛管理▪牦牛疾病11物种文化分类地位播报编辑牦牛牦牛的分类地位一直存在着争议，牦牛究竟是属于牛亚科牛属还是属于牛亚科牦牛属，还没有形成一个明确的定论。通过对牦牛与牛亚科其他属在古生物学证据、形态学特征、血液蛋白多态性、微卫星多态性、mtDNA序列变异、rDNA的RFLP数据和功能基因序列信息等各方面研究资料的比较分析，发现牦牛无论在古生物学证据、形态学特征，还是在分子生物学特征上均表现出与牛属中的普通牛Bostaurus、瘤牛Bosindicus不同，而与美洲野牛Bisonbison的亲缘关系更近一些，因此将牦牛划分为牛亚科中1个独立属（即牦牛属），似乎比将牦牛作为牛属中的1个亚属或1个种更合适。牦牛起源于中国，是一种古老而原始的物种。牦牛Bos grunniens、Poephagus mutus或Bos mutus是唯一能在青藏高原的高寒牧区繁衍的牛亚科动物，是当地人民必不可少的生活资料和生产资料，有“高原之舟”和“全能家畜”的美誉(Wieneret al.,2003)。 [2]物种学史播报编辑起源演化牦牛能充分利用高寒草地牧草资源，对其生态环境条件具有极强适应性，耐粗、耐劳，在空气稀薄、寒冷、牧草生长期短等恶劣环境条件下能生活自如、繁衍后代。牦牛根据达尔文学说，一切动物或牛种，不管是现存的，或在古代存在而现已灭绝的，彼此都有着不同远近程度的亲缘关系。如果它们的形态和内部结构等相似之处越多，生殖隔离程度越小，它们的亲缘关系就越近，或他们从一个共同祖先进化而来的时间就越近。否则，它们脱离开共同祖先后，经过了一段相当长的进化时期，有的在万年甚至百万年。 [3]家养牦牛，在国内外的一些文献上，都说是起源于中国西藏；野牦牛，是家养牦牛的祖先。但从中国华北、内蒙，以及西伯利亚、阿拉斯加等地发现的牦牛化石考证，不论现今分别在中国藏北高原昆仑山区的野牦牛，或是由野牦牛驯养而来的家牦牛，都是距今三百多万年前（更新世）生存并广为分布在欧亚大陆东北部的原始牦牛，后来，由于地壳运动、气候变迁而南移至现世界屋脊--中国青藏高原地区，并能适应高寒气候而延续下来的牛种。因此，可以这样说，牦牛起源于欧亚大陆的东北部；现今的家养牦牛和野生牦牛，都是同一祖先的后代，他们之间不存在先代、后代的关系。野牦牛，也不是家牦牛的始祖、始源或祖先。另外，在中国历史上，殷周时期即开始用牦牛与普通牛、瘤牛进行杂交，现今沿青藏高原边缘还有一个广阔的接触地带，他们之间通过能育的母犏牛进行基因交流。因此，可以这样认为，现存的牦牛在其起源和形成的一定程度上吸收了普通牛及瘤牛的一些基因。 [3]古今命名牦牛(4张)世界上所有国家和民族，对牦牛的称呼都是藏语的Yag音译，英语、法语叫Yak，俄语叫як，尼泊尔语叫Yakpho。这在世界浩繁的词汇语音中极为少见。汉语称之牦牛，这是读了别字，以讹传讹的结果。在有文字之前，中原华夏人民称牦牛为“雅牛”，就是藏语的音译，以后造字“氂”，读“雅”。可后来，这个“氂”字别读为毛，而牦牛也全身长有长毛，于是牦牛的称谓就流传至今；人类驯养牦牛的历史，在牛属(Bos)中最为悠久，但人类对它的选择作用很小，保持有许多原始性状和品质，是畜牧学研究极好的理想材料。形态特征播报编辑牦牛野牦牛是大型偶蹄类动物。身体强健，肩部显著隆起。耳较小；雌雄均有角，角黑色，雄性角大，角末端距离远。四肢短而强健。躯体上方被毛短而光滑，体侧、腹面、及尾部毛长而下垂，常常接近地面。体长约250厘米，肩高约170厘米。全身毛色以深黑褐色为主。牦牛牦牛头大，角粗，皮松厚，髻甲高长宽，前肢短而端正，后肢呈刀状，体侧下部逆生粗长毛，尾短并着生蓬松长毛，公牦牛头粗重，呈长方形，颈短厚且深，睾丸较大，接近腹部，不下垂；母牦牛头长，眼大而圆，额宽，有角，颈长而薄，乳房小，呈碗碟状，乳头短小，乳静脉不明显。牦牛头较粗重，额宽平，面稍凹，眼圆存在神，嘴方大，唇薄，绝大多存在角，角形向外折向上、开张，角间距大，母牦牛角很细。公、母均无肉垂。前胸开阔，胸深，肋开张，背腰平直，腹大而不下垂，尻部较窄、倾斜。尾根低，尾短。四肢强健存在力，蹄小而圆，蹄叉紧，蹄质坚实。前胸、臂胸腹体侧着成长毛及地，尾毛丛生帚状。牦牛和普通牛相像，但是有很多独特的特征，高大威猛，算得上是牛中的高富帅。牦牛的形状如水牛，牦牛体长2～3米，尾长37～46厘米，肩高1.3米以上；公牦牛体重一般为300～490公斤，母牦牛为210～358公斤。 [22]躯体强健，颈短，头大，额长而平，四肢短粗；雌雄均具角；全身褐黑色或棕黑色，天祝白牦牛是牦牛中最特别一种，全身呈白色；牦牛的皮毛粗硬，体侧、胸部、肩部、四肢上部和尾部密生长毛，长一尺左右，尤其体侧的毛被几可及地。牦牛的尾巴很长。生活习性播报编辑牦牛牦牛体形防寒保暖，体躯紧凑，颈短耳小，皮厚表面积小；汗腺机能极不发达，被毛长度、细度不等且随季节变化，体侧及下部裙毛密而长，可御寒防湿，适宜寒冷气候。胸廓大，心胸发达，气管粗短，红细胞大，血红蛋白含量高，呼吸、脉搏快，适应高原缺氧环境。嘴巴宽大、嘴唇灵活，能啃食矮草；蹄质坚实且有软垫，性情温顺，反应灵敏，建立的条件反射比较巩固，容易调教；抗病力强、抗逆性强、合群性强、食性广、耐饥渴、耐粗放的饲养管理条件。牦牛能适应海拔3200～4800米，大气压68420.85～55435.28Pa，氧分压14505.43～11679.01Pa，含氧量14.9%～11.44%的生态环境。其胸廓发达，心肺发育指数大，借以保护胸、腹内脏器官、外生殖器官、乳房及各关节，以防受冻。据测定，在海拔3800米的草甸草场上日放牧9.5h，牦牛日采食鲜草27.86±1.42kg。牦牛还有识途的本领，善走陡坡险路、雪山沼泽，能游渡江河激流，并能避开陷阱择路而行，可作旅游者的前导。分布范围播报编辑分布于中国四川、青海、西藏、新疆、甘肃等省（区）。除中国外，与中国毗邻的蒙古、原苏联中亚地区以及印度、不丹、锡金、阿富汗、巴基斯坦等国家均有少量分布。中国是世界牦牛的发源地，全世界90%的牦牛生活在中国青藏高原及毗邻的6个省区。其中青海490万头，占全国牦牛总数的38%，居全国第一；西藏390万头，占30%，居全国第二；四川310万头，占23%，居全国第三；甘肃88万头，占7%，居全国第四；新疆17万头，占1.3%，居全国第五；云南5万头，占0.4%，居全国第六。栖息环境播报编辑野牦牛栖息于海拔4000～5000米的高原草甸、灌丛、荒漠等地，适应性强，耐风雪严寒，嗅觉较灵敏，多成群活动，喜晨昏觅食。一般年末至次年年初发情交配，怀孕期约9个月，胎产1仔，幼仔2～3年性成熟。野牦牛原是我国青藏高原一带的特产动物，是典型的高寒动物，性极耐寒。分布于新疆南部、青海、西藏、甘肃西北部和四川西部等地。栖息于海拔3000 —6000米的高山草甸地带，人迹罕至的高山大峰、山间盆地、高寒草原、高寒荒漠草原等各种环境中，夏季甚至可以到海拔5000—6000米的地方，活动于雪线下缘。野牦牛具有耐苦、耐寒、耐饥、耐渴的本领，对高山草原环境条件有很强的适应性，所以很多野生有蹄类和家畜难以利用和到达的灌木林地、高山草场，它却能登临受用。 [4]野牦牛野牦牛一年四季生活的地方不一样，冬季聚集到湖滨平原，夏秋到高原的雪线附近交配繁殖。野牦牛性情凶猛，人们一般不敢轻易触动它，触怒了它会以10倍的牛劲疯狂冲上来，有时还会把汽车撞翻。中国牦牛占世界总数的90%，其中多数生长在西藏高原。繁殖方式播报编辑野牦牛野生牦牛交配季节在9月份，孕期大约260天，每胎产1仔，小牛1岁后断奶，3～4岁性成熟。野生牦牛寿命超过23岁。 [5]成年体高：公129.2厘米母110.9厘米；成年体重：公443.4千克母256.7千克；性成熟年龄：12月龄；适配年龄：2岁；平均单产：274千克；乳脂率：6.37%～7.2%；适应性：适应高海拔，耐严寒，耐粗饲，耐艰苦。主要变种播报编辑中国牦牛可分为“横断高山型”和“青藏高原型”两大类型，共有九龙牦牛、麦洼牦牛、天祝白牦牛、青海高原牦牛、西藏高山牦牛、木里牦牛、香格里拉（中甸）牦牛、帕里牦牛、斯布牦牛、娘亚牦牛、新疆牦牛等11个地方品种和大通牦牛1个培育品种。其中，列入《中国牛品种志》的有九龙牦牛、麦洼牦牛、天祝白牦牛、青海高原牦牛、西藏高山牦牛等5个品种。国外牦牛主要尼泊尔牦牛、印度牦牛、塔吉克牦牛、俄罗斯牦牛、吉尔吉斯牦牛等。除中国以外，饲养牦牛的国家还有蒙古、吉尔吉斯斯坦、俄罗斯、塔吉克斯坦、印度、尼泊尔、哈萨克斯坦、不丹、（原）锡金、阿富汗、巴基斯坦、克什米尔等国家和地区。白牦牛分布于甘肃省武威市天祝县西大滩、抓喜秀龙滩（汉语称永丰滩）和阿崐沿沟草原。 [6]天祝白牦牛体质结实紧凑，体表少褶皱，耳垂皮小，体表散热面积小，寒冷气候条件下体表散热少；被毛由不同毛纤维类型组成，具有保温性良好的空气层，保暖性高；白牦牛鼻孔大，气管粗短；胸腔大而发达，心脏、肺脏发育好，血液中红细胞（红血球）数量多（天祝白牦牛为6.60百万/立方毫米，黄牛为4.50百万/立方毫米），血红蛋白含量高，携氧量多，从而增加血液中的氧容量。天祝白牦牛(4张)上述种种适应寒冷气候、缺氧条件和气压低等生态环境的特性，保证了白牦牛生理或生命的正常活动。天祝白牦牛产品以肉分类的有白牦牛分割肉系列，以及白牦牛熟食品系列，如香酱牛肉、香酱熏牛肉、牛三珍、牛蹄、牛肚等真空包装的十多个品种；以白牦牛骨髓为原料开发的有高级白牦牛骨髓油、骨髓壮骨粉、骨髓营养粉、骨髓油茶粉等产品；因白牦牛的纯白色毛、尾能染色，可制成各式古典戏装及假发。天祝白牦牛是中国青藏高原 [1]型牦牛中的一个珍贵而特异的地方良种。其产区是中国白牦牛毛、绒及尾毛的主要产地，甚至牦牛尾是重要外贸物资，经济价值高，是中国的特产。 [6]九龙牦牛分布于四川省甘孜州、阿坝州等地。九龙牦牛是世界上体型最大、肉用性能好、毛绒产量高的国内外最优秀的牦牛品种，分为高大和多毛两个类型，毛多型产绒量比一般牦牛高5～10倍。额宽头较短，额毛丛生卷曲，公母有角，角间距大。四肢、胸前、腹侧裙毛着地，全身背毛为多（3/4）黑色，少数黑白相间。颈粗短，鬐甲稍高，有肩峰，胸极深，背腰平直，尻欠宽而略斜，尾根着生低，尾短。四肢相对较短。3.5岁公牛体高114厘米，母牛为110厘米，公牛体重为270千克，母牛为240千克。成年阉割牛屠宰率为55﹪，净肉率为46﹪，骨肉比为1:5.5,眼肌面积为88.6平方厘米；公牛分别为58﹪、48﹪、1:4.8和83.7平方厘米；母牛分别为56﹪、49﹪、1:6.0和58.3平方厘米。驮载60～70千克。泌乳期5个月，产奶量为350千克，乳脂率5～7.5﹪。公牛产毛量为13.9千克，母牛为1.8千克，阉牛为4.3千克，绒、毛各半。母牛初配年龄为2～3岁，公牛为4～5岁，一般3年2胎，繁殖率为68﹪，成活率为62﹪。高山牦牛高山牦牛主要产于西藏自治区东部高山深谷地区的高山草场。以嘉黎县产的牦牛最为优良。西藏自治区东部、南部山原地区，海拔4000米以上的高寒湿润草场上均有分布。头较粗重，额宽平，面稍凹，眼圆有神，嘴方大，唇薄，绝大多数有角，角形向外折向上、开张，角间距大，母牦牛角较细。公、母均无肉垂。前胸开阔，胸深，肋开张，背腰平直，腹大而不下垂，尻部较窄、倾斜。尾根低，尾短。四肢强健有力，蹄小而圆，蹄叉紧，蹄质坚实。前胸、臂胸腹体侧着生长毛及地，尾毛丛生帚状。高山牦牛产乳高峰期为每年的七八两月牧草茂盛期，以第二胎的产乳量最高。每年六七月份剪毛一次，毛和绒的比例为1：1~2。经调教的阉牦牛，性温驯，驮力强，耐劳，供长途驮载货物运输。一般参100~200千克，边走边放牧采食，日行15公里左右，可连续驮运数月，往返行程一二千公里。高山牦牛对其分布区高海拔、低含氧、温差大牧草生长期短的不良环境适应性很强，是当地人民生产、生活所不可缺的重要畜种。产肉性能：经草地放牧不同，11月上旬在嘉黎测定的成年阉牛之头，达中等瞟情，平均体重379.1千克，平均体重208.5千克。役用性能：经调教的阉牦牛，性温驯，驮力强，耐劳，供长途驮载货物运输。产毛性能：西藏高山牦牛每年六七月份剪毛一次（带犊阳孕后期母牦牛，只抓绒不剪毛）。高原牦牛牦牛形态图(5张)产于青海南部、北部两高寒地区，包括果洛藏族自治州和玉树藏族自治州两个州的十二个县，黄南藏族自治州的泽库县和河南蒙古族自治县，海西蒙古族藏族自治州的天峻县和格尔木市唐古拉山公社，海北藏族自治州的祁连县和海南藏族自治州的兴海县西的公社，大多在海拔3700米，甚至4000米以上的高寒地区。体型外貌上带有野牦牛的特征。体态结构紧凑，前躯发达，后躯较差。头大，额宽。角粗；皮松厚；耆甲高长宽，前肢短而端正，后肢呈刀状；体侧下部密生粗长毛，犹如穿统裙，尾短并生蓬松长毛。公牦牛头粗重，呈长方形；，颈短厚且深，睾丸较小，接近腹部、不下垂；母牦牛头长，眼大而圆，额宽、有角、颈长而薄，乳房小、呈碗碟状，乳头短小，乳静脉不明显。毛色多为黑褐色，占71.8%，嘴唇、眼眶周围和背线处的短毛为灰白色或污白色。尼泊尔牦牛分布于与中国接壤的尼泊尔北部高山地区。它和印度、不丹、锡金的牦牛均来源于中国西藏。当地牧民还经常与中国西藏自治区的牧民友好往来，交换种公牦牛。印度牦牛主要分布于印度北部喜马偕尔邦及克什米尔地区和东北部阿萨姆邦北部地区海拔3000～5000米的高山区。蔡立认为，是牦牛在青藏高原驯化后，翻过喜马拉雅山脉的一些山口，进入南坡高山草地后形成的。与中国西藏的牦牛有较近的亲缘关系。塔吉克牦牛主要分布于塔吉克斯坦的帕米尔地区。其来源与以上吉尔吉斯牦牛、俄罗斯牦牛类同。俄罗斯牦牛主要分布在西伯利亚南部与蒙古接壤地带的阿尔泰和布里亚特地区。蔡立认为中国青藏高原的牦牛翻过昆仑山脉进入阿尔泰地区后形成的。吉尔吉斯牦牛主要分布于与中国新疆维吾尔自治区相邻的吉尔吉斯东南部地区。蔡立认为是牦牛在中国青藏高原被驯化后，越过昆仑山脉，经由帕米尔进入吉尔吉斯的。保护级别播报编辑野牦牛（Bos mutus, Bos grunniens）是一濒危灭绝品种，在中国被列为一类保护野生动物。 [4]世界自然保护联盟红色名录列为：易危（VU） [5]中国国家一级保护动物饲养管理播报编辑牦牛饲养管理的水平和方法，受牦牛分布地区生态环境条件、生产方式、生产者的科学文化水平、宗教信仰等因素的制约和影响，不同地区甚至同一地区的不同牦牛群间都有差异。绝大多数地区的牦牛仅只靠天然草地牧草，获取它维持生命、生长发育和繁殖等所需的营养。就是在冬春冷季，天然草地牧草枯萎、饲草缺乏的情况下，除少数体弱、难以度春的幼龄牦牛和母牦牛，补给少量的干草或青贮牧草外，一律不给补饲。牦牛群的管理，则随气候季节而定。牦牛放牧牦牛的放牧多数地区采用根据不同季节划分放牧草地，然后分群放牧的方式；少数地区实行围栏分群放牧。1．牧场的划分牦牛分布区的气候条件属于高寒草地气候，只有冷、暖季节之别，无明显的四季之分。因而一般将牧场划分为夏秋、冬春两季，即夏秋（暖季）和冬春（冷季）牧场。划分的依据主要是牧场的海拔高度、地形地势、离定居点的远近和交通条件等。夏秋牧场选在远离定居点，海拔较高，通风凉爽，蚊虻较少，有充足水源的阴坡山顶地带；冬春牧场则选在定居点附近，海拔较低，交通方便，避风雪的阳坡低地。牦牛牧场牦牛分布的有些地区，属高山狭谷地貌，牦牛可利用的草地总面积虽很广阔，但被深谷分隔为相对“零星”的草地，往往一个村、组、户使用的草地分散在几条山梁上，每一牦牛群只使用其中一、二个山梁的草地。因此，也有将牧场划分为春、夏、秋、冬四季牧场的。只是春、秋牧场使用的时间短、面积较小，似由冬牧场去夏牧场，或由夏牧场回冬牧场的过渡性牧场。该地区多以山沟、林边草地为冬牧场，以岭端草甸为夏牧场，山坡地带为春、秋牧场。2．牛群组织为了放牧管理和合理利用草场，提高牦牛生产性能，对牦牛应根据性别、年龄、生理状况进行分群，避免混群放牧，使牛群相对安静，采食及营养状况相对均匀，减少放牧的困难。牦牛群一般分为：①泌乳牛群，又称为奶牛群。是指由正在泌乳的牦牛组成的牛群。每群100头左右。对泌乳牦牛群，应分配给最好的牧场，有条件的地区还可适当补饲，使其多产乳，及早发情配种。在泌乳牦牛群中，有相当一部分是当年未产犊仍继续挤乳的母牦牛，数量多时可单独组群。②干乳牛群，又称为干巴牛群。该牛群是指由未带犊牛而干乳的母牦牛，以及已经达到初次配种年龄的母牦牛组成的牛群，每群150～200头。③幼牛群。是指由断奶至周岁以内的牛只组成的牛群。幼龄牦牛性情比较活泼，合群性差，与成年牛混群放牧相互干扰很大。因此，一般单独组群，且群体较小，以50头左右为宜。④青年牛群。是指由周岁以上至初次配种年龄前的牛只组成的牛群。每群150～200头。这个年龄阶段的牛已具备繁殖能力，因此，除去势小公牛外，公、母牛最好分别组群，隔离放牧，防止早配。⑤育肥牛群。是指由将在当年秋末淘汰的各类牛只组成，育肥后供肉用的牛群。每群150～200头，在牛只数量少时，种公牛也可并入此群。对于这部分牦牛可在较边远的牧场放牧，使其安静，少走动，快上膘。有条件的地区还可适当补饲，加快育肥速度。不过上述牦牛群的组织和划分，以及群体的大小并不是绝对的，各地区应根据地形、草场面积、管理水平、牦牛数量的多少，来因地制宜地合理组群和放牧，才能提高牦牛生产的经济效益。3．牧场利用夏秋和冬春牧场的利用时间，主要根据牧草生长情况和气候而定，一般各用半年。每年夏初（4～5月），整群分群后开始出牧，由冬春牧场转入夏秋牧场；每年冬前（11～12月），清点圈存数后，转入冬春牧场。但由于受草场面积和气候的影响，往往夏秋牧场利用不足，而冬春牧场利用过度，其结果反应出草、畜供需间的矛盾大，出现季节性的不平衡，在冬春季牦牛基本上呈半饥饿状态，掉膘严重。人们对牦牛乳、肉产品的需求量迅速增长，市场价格上升，且与绵羊毛、肉之间的比价失调，饲养牦牛比饲养其他家畜的经济效益高，收入多；加上受“牛”是财富的传统观念影响，牦牛的存栏数持续增长，绵羊、山羊饲养量却有所下降。导致牦牛在草地畜种结构中的比例失调，使本来已不太合理的牛、羊比例更为不合理；使冬春季牧场的草畜矛盾进一步扩大，出现了严重的超载、过牧现象。因此，在实际生产中，应优化牦牛种群结构，控制或减少牦牛数量的发展，才能合理利用草地资源，以求提高牦牛乳、肉等产品量及经济效益。牦牛群在夏秋季牧场上，根据牧草的生长状况及牛群的大小，每10～40天搬迁一次放牧地。其搬迁的方向和路线，应基本固定，年年如此。两放牧地的距离，以不超过20千米为宜。搬迁的方法有两种：一是人、畜、帐篷设施等同时迁移，牦牛群在迁移途中基本上不采食，待到新牧地后再放牧。另一种是人、帐篷设施等搬迁到新牧地，而牦牛群照常放牧，不随人、物一起走。只是出牧的方向朝着新的牧地，并逐渐向新牧地靠拢，晚上收牧于新牧地。冬春季牧场上牧地的搬迁，其间隔时间可延长一些，一般在一个冬春冷季里，搬迁2～5次。如牦牛群小，并有条件给以一定量的补饲的，在一个冷季里也可以不搬迁。4．棚圈放牧牦牛的草地上，除棚圈及一些简易的配种架、供预防接种的巷道圈外，一般很少有牧地设施。牦牛的棚圈只建于冬春牧地，仅供牛群夜间使用。多数是就地取材，有永久性、半永久性和临时性的几种类型。①泥圈。泥圈是一种比较永久性的牧地设施，一般应建在定居点或离定居点不远的冬春季牧场上。一户一圈或一户多圈。主要供泌乳牛群、幼牛群使用。泥圈墙高1～1.2米，大小以200～600米为宜，在圈的一边可用木板或柳条编织后上压粘土方式搭建棚架，棚背风向阳。泥圈可以单独建一圈，也可以二、三个或四、五个圈相连。圈与圈之间用土墙或木栏相隔，有栏内相通。在顶端的一个圈中，可建一个本栏巷道，供预防接种、灌药检查等用。②粪圈。是利用牛粪堆砌而成的临时性牧地设施。当牦牛群进入冬春冷季牧场时，在牧地的四周开始堆砌。方法是每天用新鲜牛粪堆积15～500px高的一层，过一昼夜，牛粪冻结而坚固，第二天又再往上堆一层，连续几天即成圈。粪圈有两种：一是无顶圈，如象四堵围墙那样，关栏成年牦牛，面积较大，可防风雪。另一种是有顶圈，关栏犊牦牛，其形状如象倒扣的瓦缸，基础如马蹄，直径约1米，层层上堆逐渐缩小，直至结顶，高约1米，正好可关1头犊牛。圈的开口处与主风向相反，外钉一木桩，犊牦牛栓系在桩上，可自由出入圈门。圈内可垫一些干草保暖。牦牛③草皮圈。是一种半永久性的，经修补后第二年仍可利用的牧地设施。在冬春季牧场上选择避风向阳处，划定范围，利用范围内的草皮，堆集而成的圈。草皮堆高60～2500px左右，供关栏公牦牛和驮牛。④木栏圈。用原木取材后的边角余料围成圈，上面可盖顶棚，用于关栏犊牦牛。木栏圈可建在泥圈的一角，形成圈中圈，即选取泥圈的一角，围以小木栏，开一低矮小门。圈内铺以垫草，让犊牦牛自由出入。夜间将犊牦牛关栏其中，同母牦牛隔离，母牦牛露营夜牧，以便第二天早上挤乳。5．牛群管理牦牛的气质属强健不平衡型，表现粗暴、性野、胆怯、易惊，但合群性强，经训练建立的条件反射不易消失，较能听从指挥。因而大群牦牛放牧，一般只需一个放牧员，不易发生丢失。根据牦牛易惊的特性，牦牛群进入放牧地后，放牧员不宜紧跟牦牛群，以免牦牛到处游走而不安静采食。为防止牛只越界和害狼偷袭，放牧员可选择一处与牦牛群有一定距离，能顾及全群的高地进行守护、瞭望。牦牛控制牦牛群使其听从指挥的方法是，放牧员用特定的呼唤、口令声，伴以甩出小石块。用小石块投击离群的牦牛，一般多采用徒手投掷，投掷距离远及数十米。距离较远时也可用放牧鞭投掷。石块的落地，以及它在空中飞行的“嗖嗖”声，和放牧鞭的抽鞭声，都是给牦牛的警告和信号。牦牛会根据石块落地点和声响的来源，判断应该前去的方向。放牧员利用放牧鞭驱使牦牛前进，集合或分散。走远离群的牦牛，听见鞭和飞石的声音，以及落石点，会很快地合群。牦牛群的放牧日程，因牦牛群类型和季节不同而有区别。总的原则是：“夏秋季早出晚归，冬春季迟出早归”，以利于采食，抓膘和提供产品。①夏秋季的放牧。夏秋季放牧的主要任务是提高产乳量，搞好抓膘和配种，使当年要屠宰的牦牛在入冬前出栏，其他牛只为越冬过春打好基础。进入夏秋季后，力争牦牛群早出冬春季牧场，在向夏秋季牧场转移时，牛群日行程以10～15千米为宜，边放牧边向目的地前进。夏秋季要早出牧、晚归牧，延长放牧时间，让牦牛多采食。天气炎热时，中午让牦牛在凉爽的地方反刍和卧息。出牧后由低逐渐向通风凉爽的高山放牧；由牧草质量差或适口性差的牧场，逐渐向牧草质量好的牧场放牧；可在头一天放牧过的牧场上让牦牛再采食一遍，这时牦牛因刚出牧而饥饿，选择牧草不严，能采食适口性差的牧草，可减少牧草的浪费。在牧草质量较好的牧地上放牧时，要控制好牛群，使牦牛成横队采食，保证每头牛能充分采食，避免乱跑践踏牧草或采食不均而造成浪费。夏秋季放牧根据安排的牧场或轮牧计划，要及时更换牧场和搬迁，使牛粪均匀地散布在牧场上，同时减轻对牧场特别是圈地周围牧场的践踏。这样可改善植被状态，有利于提高牧草产量，减少寄生虫病的感染。当定居点距牧场2千米以上时就应搬迁，以减少每天出牧、归牧赶路的时间及牦牛体力的消耗。带犊泌乳的牦牛，10d左右搬迁一次，3～5d更换一次牧地。应按牧场的放牧计划放牧，而不应该赶放好草或抢放好草地，以免每天驱赶牛群为抢好草而奔跑，造成对牦牛健康和牧场的不利影响。②冬春季的放牧。冬春季放牧的主要任务是保膘和保胎。防止牦牛乏弱，使牛只安全越冬过春，妊娠母牦牛安全产仔，提高犊牛的成活率。冬春季放牧要晚出牧，早归牧，充分利用中午暖和时间放牧和饮水。晴天放较远的山坡和阴山；风雪天近牧，放避风的洼地或山湾。放牧牛群朝顺风方向行进。怀孕母牦牛避免在冰滩地放牧，也不宜在早晨及空腹时饮水。刚进入冬春季牧场的牦牛，一般体壮膘肥，应尽量选择未积雪的边远牧地、高山及坡地放牧，推迟进定居点附近的冬春季牧地放牧的时间。冬春季风雪多，应注意气象预报，及时归牧。在牧草不均匀或质量差的牧地上放牧时，要采取散牧的方式，让牛只在牧地上相对分散自由采食，以便使牛只在较大的面积内每头牛都能采食较多的牧草。冬春季是牦牛一年中最乏弱的时间，除跟群放牧外，有条件的地区还应加强补饲。特别是大风雪天，剧烈降温，寒冷对乏弱牛只造成的危害严重，一般应停止放牧，在棚圈内补饲，使牛只安全越冬过春。 [7]牦牛管理管理牦牛的技术水平和方法，在不同地区和不同时期有较大的差异。多数地区采用的大致情况如下。1．挤乳牦牛挤乳是牦牛管理中劳动量很大的一项工作。牦牛挤乳分为犊牛吸吮和手工挤乳两个阶段。在每次挤乳过程中，吸吮和挤乳要重复两次或排乳反射分两期。因此，需要的时间长、劳动效率低。由于牦牛的乳头细短（长仅为2～75px），一般采用指擦法挤乳。牛群挤乳时间长短，影响到产乳量和牦牛全天的采食时间，所以挤乳速度要快，每头牛挤乳持续时间要短，争取每头牛在6分钟内挤完。泌乳母牦牛对生人、异味等很敏感，因此，挤乳时要安静，挤乳员、挤乳动作、口令、挤乳顺序和相关制度，不宜随意改变。牦牛的挤乳还无法采用机械挤乳的方法。因此，挤乳员要掌握正确的手工挤乳技术，才能提高挤乳速度和产乳量。挤乳员挤乳时，若双手的力量较均匀地分布在前膊、手指和手掌的肌肉上，并配合正确坐着挤的姿势，则能使肌肉在紧张工作中消耗的能量得以补充，可不觉困倦地挤乳。否则蹲着挤乳，肌肉过度紧张，用力不匀时，不仅挤乳速度慢，而且很快就觉得双手无力。挤乳时挤压乳头所需的肌肉力量约15～20千克，若每群牛挤乳2.5h ，挤乳速度80～140次/分钟，则每天手关节及肌肉的紧张动作达1.2～2.1万次，劳动强度是很大的，一定要注意保护双手。每天用温水（40℃）浸泡手、臂1～3次，每次10～15分钟。浸泡后擦少许护肤脂，然后用自己的手相互按摩手指、关节及上膊肌肉，以促进血液循环，增强肌肉新陈代谢，防止双手发病。2．育犊牦牛犊一般为自然哺乳，为保证犊牛的正常生长发育，必须根据牧地的产草量、犊牛的采食量及其生长发育、健康状况，调整对牦牛的挤乳量。犊牛在2周龄后即可采食牧草，3月龄左右时可大量采食，随年龄的增长哺乳量逐渐减少。同成年牛相比，牦牛犊每日采食时间较短，卧息时间多。因此，在放牧中要保证充分的卧息时间，防止驱赶或游走过多而影响生长发育。同时，不宜远牧，天气变冷，遇风雪时应及时收牧，应有干燥的棚圈供卧息。进入冬春季，牦牛犊哺乳至6月龄时，一般应断乳并分群饲养。如果一直随母牦牛哺乳，幼牦牛恋乳，母牦牛带犊，均不能很好采食，甚至拖到下胎产犊后还争食母乳。在这种情况下，母牦牛除冬春季乏弱干乳外，就无干乳期，不仅影响到母、幼牛的安全越冬过春，而且使怀孕母牛胎儿的生长发育受到影响，如此恶性循环，就很难提高牦牛的生产能力。对出生迟哺乳不足6月龄或乏弱的牦牛犊，可适当延长哺乳期后再断乳，但一定要对母牦牛在冬春季进行补饲。3．配种和去势配种和去势是牦牛繁殖技术的两个重要环节，它不仅直接影响牦牛的增殖和牦牛群的管理、产品的生产，而且与牦牛的选种选配、后代的品质等关系密切。因而在牦牛管理中应引起足够的重视。牦牛的配种一般采用自然交配的方法。根据公牦牛的性行为特点，充分利用处于优胜地位公牦牛的竟配能力而达到选配的目的；也注意及时淘汰虽居优胜地位而配种能力减退的公牦牛。公牦牛配种年龄为4～8岁，以4.5～6.5岁的配种能力最强，8岁以后很少能在大群中交配。母牦牛的初配年龄为3岁左右。公母牦牛的比例以1：14～25为宜。有条件的地区可采用人工辅助配种，来提高受胎率和进行选配。即当发现发情母牦牛后，将其系留于定居点，用绳捆绑其两肢，套于颈上，左、右二人牵拉保定，然后驱赶3头以上公牦牛来竟配。当母牦牛准确地受配两次后，将公牦牛驱散，并将新鲜牛粪涂抹在受配母牦牛臀部，防止公牦牛再次爬跨配种，松去绳索。牦牛成熟晚，去势年龄比普通牛迟，一般在2～3岁，不宜过早，否则影响生长发育。有围栏草场或管理好时，公牦牛可不去势而育肥。牦牛去势最好选在气温暖和、蚊蝇少的5～6月进行，以利于伤口愈合，并为暖季放牧育肥打好基础。去势手术应迅速，牛只放倒保定时间不应过长，手术后缓慢出牧，一周内就近放牧，不要剧烈驱赶，并每天检查伤口，发现出血、感染化脓时请兽医处理。有些地区采用非手术的提睾去势法，取得了较好的效果。该方法是将公牦牛保定后，用手将睾丸尽力挤向阴囊上端，使其紧贴腹壁，然后用弹性好的橡皮圈套紧睾丸下端阴囊，使睾丸不能再下降，因睾丸紧贴腹壁后温度升高，致使精子不能成活，生理上达到去势的目的。因雄性激素仍继续产生，公牦牛的生长速度比手术摘除睾丸的公牦牛要快，产肉量高，提睾去势的公牦牛仍有性欲，可作试情公牛，单独组群放牧时应加强管理，避免相互爬跨、离群等而造成的不安静。4．妊检母牦牛发情配种后，一般都能受孕，且较少发生流产等中止怀孕，加之牦牛孕后发情的病例不多，因而对牦牛一般可以不作妊娠检查。牧民判断牦牛是否受孕的标准是下一个情期是否再发情。若要进行妊娠检查，蔡立认为，以直肠检查最为简单易行。 [8]牦牛疾病牦牛炭疽牦牛炭疽是由炭疽杆菌引起的急性人畜共患病。本病呈散发性或地方性流行，一年四季都有发生，但夏秋温暖多雨季节和地势低洼易于积水的沼泽地带发病多。多年来，牦牛产区有计划、有目的地预防注射炭疽芽胞苗，取得良好的效果。由过去的地方性流行转为局部地区零星散发。发生疫情时，要严格封锁，控制隔离病牛，专人管理，严格搞好排泄物的处理及消毒工作，病牛可用抗炭疽血清或青霉素、四环素等药物治疗。牦牛布氏杆菌病布氏杆菌病简称布病，是由布氏杆菌引起的人畜共患病。在牦布病免疫学预防方面，先后用布氏杆菌M5号菌苗、19号菌苗、S2号菌苗等进行气雾或饮水免疫；用MB32弱毒菌苗，进行皮下接种，室内、外气雾免疫，免疫期达一年以上。巴氏杆菌病又称出血性败血症，是由多杀性巴氏杆菌引起的多种动物共患的一种败血性传染病。本病的特征，急性经过时呈败血性变化，慢性经过时则表现为皮下组织、关节、各脏器的局限性化脓性炎症。多呈散发性或地方流行性，一年四季均可发生，但秋冬季节发病较多。早期发现该病除隔离、消毒和尸体深埋处理外，可用抗巴氏杆菌病血清或选用抗生素及磺胺类药物治疗。牦牛沙门氏菌病沙门氏菌病又称副伤寒，是由沙门氏菌属的一种或多种血清型的沙门氏杆菌引起的人和动物的一种疾病的总称。尤其是对幼畜危害严重。犊牛大肠杆菌病是由病原性大肠杆菌(埃希氏大肠杆菌)引起的一种犊牛急性传染病。临床上主要表现为剧烈腹泻、脱水、虚脱及急性败血症。犊牦牛大肠杆菌病在牧区普遍存在，多发生于生后1～4日的犊牛。国内对犊牛大肠杆菌病的治疗，方法颇多。晏哲生等应用抗生素、呋喃类药物和分离的致病株自制高免血清；四川甘孜灌服三颗针液防治犊牦牛下痢，西藏昌都地区用复方黄莲治疗犊牛“拉稀病”，疗效均高。牦牛传染性胸膜肺炎是由牛丝菌霉形体引起的一种接触决策慢性或亚急性传染病，其特征主要是呈现纤维素性肺炎和胸膜肺炎症状。中国1958年研制成兔化牛肺疫疫苗，试验证明安全有效，免疫期为一年半。为了适应中国广大牧区不产兔的特点，接着又研制了绵羊反应苗，在牧区推广应用，控制了牦牛牛肺疫的发生。牦牛钩端螺旋体病牦牛钩端螺旋体病是由致病性的钩端螺旋体引起的人畜共患急性传染病。结核病结核病是由结核分枝杆菌引起的人和畜禽共患的一种慢性传染病。用结核菌素进行皮内变态反应是诊断牦牛(畜禽)结核病的主要方法，但由于牦牛个体不同，结核菌菌型不同等因素，还不能将病牦牛全部检出，有时还可能出现非特异性反应，因此在不同情况下要结合流行病学、临床症状、病理变化和病原学诊断等方法进行综合判断。曾经试用荧光抗体技术诊断结核病。应加强定期检疫，对检出的病牛要严格隔离或淘汰。若发现为开放性结核病牛时，要进行扑杀。除检疫外，为防止传染，要做好消毒工作。犊牛出生后进行体表消毒，与病牛隔离喂养或人工喂健康母牦牛的奶，断奶时及断奶后3～6个月检疫是阴性者，并入健康牛群。犊牦牛弯曲菌病弯曲菌病又称弯曲菌肠炎，是由空肠弯曲菌引起的一种新的人畜共患急性腹泻病，主要危害幼儿和幼畜。临床上以发热、腹泻、腹痛为主要特征。证明四环素、痢特灵等药物均有明显疗效，酸乳和乳清对犊牦牛弯曲菌病有防治效果。牦牛嗜皮菌病嗜皮菌病是由刚果嗜皮菌引起的一种人畜共患皮肤传染病。各种年龄的牦牛均可发病，主要表现为口唇、头颈、背、胸等部的皮肤出现豌豆大至蚕豆大的结节。发病后精神、食欲无显著变化，呈慢性经过，大多可自愈。牦牛皮霉菌病皮霉菌病是由多种皮霉菌引起的畜禽和人的体表质化组织(皮肤、毛发、指甲、爪、蹄等)的传染病，不侵害皮下深层组织。及时采取正确的治疗，用5%灰黄霉素液体石蜡油合剂涂擦，每日一次，一般7日可愈。牦牛肉毒梭菌中毒病肉毒梭菌中毒病简称肉毒中毒，是因吸收肉毒梭菌毒素而发生的一种人畜共患的中毒病。据观察牦牛梭菌中毒病多发生于成年母牦牛，尤其是泌乳期的母牦牛。在防治方面，青海省曾用自制高免血清治疗早期病牛。由青海省兽医生物药品厂制造的肉毒梭菌C型明胶菌苗，已列入部颁《兽医生物药品制造与检查规程》。现又试制小剂量的肉毒梭菌C型干粉苗，使用方便。牦牛口蹄疫口蹄疫是由口蹄疫病毒引起的急性传染病。主要侵害偶蹄兽，具有高度的接触传染性。牦牛极易感染口蹄疫，人也可感染发病。临床上以口腔粘膜、蹄部和乳房皮肤发生水泡和溃疡为主要特征。口蹄疫病毒具有多型性，在牦牛中流行的口蹄疫病毒型为O型和A型(A型死亡率低，O型死亡率高)。口蹄疫病毒对外界环境抵抗力很强，尤其能耐低温，在夏天草场上只能存活7天，而冬季可存活195天。牦牛粘膜病牛粘膜病又称牛病毒性腹泻，是由披风病毒科瘟疫病毒属的粘膜病毒引起的牛的急性或慢性传染病。多数呈隐性感染。急性病例呈现发热、白细胞数减少、口腔及其他消化道粘膜出现糜烂或溃疡、腹泻等症状。慢性病例常有持久感染症状。在免疫学预防研究方面，陈永等研制牛病毒性腹泻—粘膜病Oregon C24V冻干弱毒疫苗，用来预防牦牛粘膜病有很好的免疫效果。但该疫苗成本较昂，对怀孕母牛不够安全。西南民族学院试用猪瘟兔化弱毒疫苗免疫牦牛粘膜病取得满意效果。牦牛牛瘟牛瘟俗称炀肠瘟、胆张瘟。是由牛瘟病毒引起的偶蹄兽尤其是牛换刀性、发热性、败血性传染病。病的特征是各粘膜特别是消化道粘膜的发炎、出血、糜烂和坏死。政府组织大批兽医人员，参加牛瘟防治工作，并组织专门力量，研制适合于牦牛免疫的疫苗—绵羊适应山羊化兔化牛瘟苗(绵羊兔毒)，控制了牦牛牛瘟流行，至1955年在全国范围内消灭了牛瘟。牦牛传染性角膜结膜炎牦牛传染性角膜结膜炎是一种地方性流行性眼病。通常呈急性经过。临床特征为眼红膜和角膜眼显发炎、大量流泪、不同程度的角膜浑浊或呈乳白色。国内用3～5%弱蛋白银溶液或青霉素溶液滴眼均有效。物种文化播报编辑1973年在中国甘肃省天祝藏族自治县哈溪镇出土了一件硕大的牦牛青铜器。其身高为0.7米，腹径为0.3米，背高为0.51米，角长为0.4米，体重80千克，是一件保存非常完整的民族文物。这是中国出土的第一件以牦牛为造型的青铜器，该器形体结构严谨、准确，造型古拙、质朴，气势雄浑、凝重，雕塑风格概括、逼真，冶炼技术高超，勘称一绝，是研究藏族历史、文化、宗教的重要实物资料，也是一件不可多得的民族艺术瑰宝。牦牛青铜器牦牛这一年轻而又古老的动物是藏族先民最早驯化的牲畜之一。它伴随着这个具有悠久历史和灿烂文化的民族生存至今已有几千年的历史。牦牛性情温和、驯顺、善良，具有极强的耐力和吃苦精神，对于世代沿袭着游牧生活的藏民族来说，牦牛具有无可替代的重要地位。在高寒恶劣的气候条件下，无论烈日炎炎的盛夏，还是冰雪袭人的寒冬。牦牛均以其耐寒负重的秉性坚韧不拔地奔波在雪域高原，担负着“雪域之舟”的重任。可以说在藏民族的衣、食、住、行当中处处都离不开牦牛，牛乳、牛肉、牛毛，为在世界屋脊上勇敢而顽强地生存下来、历经艰难困苦的藏民族提供着生活、生产必需的资料来源，成为一代代在青藏高原上繁衍生息、发展成长起来的藏民族生命与力量的源泉，牦牛图腾崇拜。 [9]屋顶上供奉的牦牛角牦牛是藏族历史上重要的图腾崇拜物。图腾系印第安语TOTEM音译。其涵意为“他的亲族”。原始社会时期，人类认为其部落、氏族可能与某种动物、植物或其他比较亲近的自然物存在着某种特殊的血缘关系，于是他们便把这种与自已部落及氏族有密切关系的动物或植物尊崇为图腾，把它奉为本氏族的标志。世界上有许多以牛为图腾崇拜物的国家和民族。如古埃及人、波斯人视公牛为人类的祖先。印度人对牛的崇拜更是神圣无比，视牛为天神。 [9]藏族创世纪神话《万物起源》中说：“牛的头、眼、肠、毛、蹄、心脏等均变成了日月、星辰、江河、湖泊、森林和山川等”。这是藏族先民对其所崇拜的图腾牦牛加以神化或物化之后，驰骋其丰富的自然想象能力而产生的必然结果。如今还在安多藏族地区广为流传的藏族神话故事《斯巴宰牛歌》当中讲到：“斯巴最初形成时，天地混合在一起，分开天地是大鹏”。“斯巴宰小牛时，砍下牛头扔地上，便有了高高的山峰；割下牛尾扔道旁，便有了弯曲的大路；剥下牛皮铺地上，便有了平坦的原野”。又说“斯巴宰小牛时，丢下一块鲜牛肉，公鸡偷去顶头上；丢下一块白牛油，喜鹊偷去贴肚上；丢下一些红牛血，红嘴鸭偷去粘嘴上”。“斯巴”（SRID-PA）含义是“宇宙”、“世界”。由此可见牦牛不单纯是藏民族原始的图腾崇拜物。在藏族几千年的历史长河中，对牦牛的图腾崇拜不断发展和演化形成了一种既古老而又现代的文化形式枣牦牛文化。由此藏族历史上铸造如此硕大的牦牛青铜器自然与其牦牛图腾崇拜有着必然的联系。无论是藏区保留完整的有关牦牛题材的原始岩画，还是殷商时期雕刻在青铜器皿上的牛头纹饰，包括周朝时期绘制于彩陶上的牛形图案，以及迄今犹存的悬挂于藏族门宅屋顶上的牦牛头骨，甚至包括出土的这件举世无双、极为珍贵的牦牛青铜器，它们都可以追溯到远古时期人类的牛图腾崇拜的文化当中。 [9-12]新手上路成长任务编辑入门编辑规则本人编辑我有疑问内容质疑在线客服官方贴吧意见反馈投诉建议举报不良信息未通过词条申诉投诉侵权信息封禁查询与解封©2024 Baidu 使用百度前必读 | 百科协议 | 隐私政策 | 百度百科合作平台 | 京ICP证030173号京公网安备110000020000

Yakit: 集成化单兵安全能力平台 | Yak Program Language

Skip to main content官方文档Yak 语言YAK IDE关于我们Ya! 一刻YakLab合作伙伴下载白皮书GithubSearchYakit: 集成化单兵安全能力平台下载安装与更新配置本地模式与远程模式基础功能模块使用专项漏洞检测自动化漏洞检测中间人攻击简介MITM 中间人代理与劫持Web FuzzerWebsocket劫持插件使用及编写反连管理数据处理数据库设置和其他辅助工具FAQOn this pageYakit: 集成化单兵安全能力平台Yakit 简介#基于安全融合的理念，Yaklang.io 团队研发出了安全领域垂直语言Yaklang，对于一些无法原生集成在Yak平台中的产品/工具，利用Yaklang可以重新编写他们的“高质量替代”。对于一些生态完整且认可度较高的产品，Yaklang能直接编译融合，并对源码进行必要修改，更好地适配Yaklang语言。但是限于使用形式，用户想要熟练使用Yak 语言，需要学习Yak语言并同时具备对安全的一定理解。Yak 语言核心提供了非常强大的安全能力，为了让 Yak 本身的安全能力更容易贴近大家的实际使用，降低使用的门槛，我们在为 Yak 编写了 gRPC 服务器，并使用这个服务器实现 / 构建了一个客户端：Yakit。Yakit 是 Yak 的衍生项目，对于一些不想写代码的安全从业者，Yakit会为Yak中所有的能力提供合适的GUI，通过Yakit的GUI去操控引擎的能力，随着版本更迭，GUI会更加成熟。Yakit的gRPC服务器，让用户部署更加方便快捷，与平台无关，可选择远程部署或直接本地启动在主机中使用。Yakit 可以做什么#Yakit 是一个高度集成化的 Yak 语言安全能力的输出平台，使用 Yakit，我们可以做到：类 Burpsuite 的 MITM 劫持操作台查看所有劫持到的请求的历史记录以及分析请求的参数全球第一个可视化的 Web 模糊测试工具：Web FuzzerYak Cloud IDE：内置智能提示的 Yak 语言云 IDEShellReceiver：开启 TCP 服务器接收反弹交互式 Shell 的反连第三方 Yak 模块商店：社区主导的第三方 Yak 模块插件，你想要的应有尽有...点此进入下载页独一无二的 Yakit 架构#正如我们在上面提到的，Yakit 的核心并不在工具本身上，而是依托于 Yak gRPC 接口；也就是说，我们可以仅仅只把 Yakit 当作一个 "视窗" 来操纵 Yak 引擎来完成我们想要实现的安全能力。我们可以用一张图来简单解释一下 Yakit 的架构是怎么样与传统安全工具有所区别的：依赖说明#Yakit 的能力需要 Yak 引擎Yakit 所有的能力都建立在 Yak 引擎提供对应版本的 gRPC 接口支持的基础上。所以，Yak 引擎启动之后，Yakit 才能连接执行各种各样的安全能力。Yak 模块与专项漏洞检测#专项漏洞检测 PoC 使用 nuclei 生态下的 yaml templates，

但是由于 Yakit 接管了 nuclei 的 templates 的管理，可以为 nuclei 模块生成 Yak 模块的执行过程。所以用户一般在 Yakit(>=1.0.8-beta3 版本) 的右上角点击 "一键更新"操作步骤如下：点击之后，我们将会看到 PoC 更新的整个流程，更新成功后，结果如下没有更新成功？一般情况下，没有更新成功可能的原因有两个网络状况Yakit 本地资源目录权限/Owner 配置不合理如果是第二个原因，可以参考下一节的相关解释来进行操作。用户数据与本地文件存储#一般来说，本地文件与用户存储数据将会存储在 $HOME/yakit-projects/ 目录下。目录中的文件包括MITM 需要用到的根证书与根证书的密钥sqlite3 格式的数据库，数据库的内容是 Yakit 使用的记录，包括劫持到的请求，PoC等权限说明用户需要保持这个本地用户文件的目录权限为 0755，并且与日常使用用户在同一个组或者同一个用户下如果权限配置不合理，将会导致 yak grpc 引擎无法正常启动，Yakit 功能失效。如果本地数据库权限配置不合理，将会导致无法写入数据等问题造成 Yakit 功能不可用。可以通过如下方式修复macOS 下调整资源目录的 ownersudo chown -R user ~/yakit-projectsCopymacOS 下调整资源目录的读写权限sudo chmod 0755 ~/yakit-projectsCopyHappy Hunting!#当我们更新完 PoC 重启之后，点击 "专项漏洞检测"，任意打开一个专项漏洞可以看到如下内容则可以说明 Yakit 的模块与正常功能，我们都已经加载完毕了，大家可以愉快的使用了！Next下载安装与更新配置 »Yakit 简介Yakit 可以做什么独一无二的 Yakit 架构依赖说明用户数据与本地文件存储Happy Hunting!Copyright © 2024 for Yak Project. 京ICP备17047700号-3官方文档关于我们

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Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions | Nature Communications

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Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions

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Published: 22 December 2015

Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions

Qiang Qiu1 na1, Lizhong Wang1 na1, Kun Wang2 na1, Yongzhi Yang1 na1, Tao Ma2, Zefu Wang1, Xiao Zhang1, Zhengqiang Ni1, Fujiang Hou1, Ruijun Long1, Richard Abbott3, Johannes Lenstra4 & …Jianquan Liu1,2 Show authors

Nature Communications

volume 6, Article number: 10283 (2015)

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Animal behaviourBehavioural ecologyGenetic variationSequencing

AbstractYak domestication represents an important episode in the early human occupation of the high-altitude Qinghai-Tibet Plateau (QTP). The precise timing of domestication is debated and little is known about the underlying genetic changes that occurred during the process. Here we investigate genome variation of wild and domestic yaks. We detect signals of selection in 209 genes of domestic yaks, several of which relate to behaviour and tameness. We date yak domestication to 7,300 years before present (yr BP), most likely by nomadic people, and an estimated sixfold increase in yak population size by 3,600 yr BP. These dates coincide with two early human population expansions on the QTP during the early-Neolithic age and the late-Holocene, respectively. Our findings add to an understanding of yak domestication and its importance in the early human occupation of the QTP.

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IntroductionDomestication of livestock species was a key factor in triggering the socioeconomic transition in humans from a hunter–gatherer lifestyle to one of nomadic pastoralism or agricultural settlement1,2. This process occurred first in the Middle East ∼11,000 years ago and later in other parts of the world. The Qinghai-Tibet Plateau (QTP) is the world’s largest and highest plateau with an area of ∼2.5 million square kilometres and an average elevation of 4,200 m. Humans are known to have colonized this vast area of Asia by at least 20,000 years ago and subsequent large-scale human population expansions occurred during the early Neolithic (10,000–7,000 years before present (yr BP)) and late Holocene (4,000–3,000 yr BP)3,4,5. The bovine yak species is endemic to this region. Historical records and archaeological evidence suggest that yak pastoralist societies were established in the QTP by ∼4,500 yr BP (refs 6, 7) and previous analyses of mitochondrial DNA variation indicate that yaks were domesticated during the early Neolithic period, some time between 6,000 and 12,000 yr BP (refs 8, 9). Since then, yak has become the mainstay of Tibetan pastoral society and >14 million domestic yaks are currently kept on the QTP, providing food, shelter, fuel and transport for the indigenous human population10,11. The ancestral wild yak population is threatened, but still exists with regular gene flow occurring between wild and domestic populations (Supplementary Note 1). Because yak domestication preceded the development of a human pastoral lifestyle in the QTP, a plausible hypothesis is that yak domestication was closely associated with early human population expansion in the region.To examine the domestication of yak in more detail, we compare the genomes of wild and domestic yaks and investigate genetic changes underlying domestication. We use coalescent modelling to date yak domestication and population expansions more precisely than can be inferred from available archaeological and palaeontological evidence, and relate our findings to information on the prehistoric development of human society on the QTP.ResultsGenome resequencing and genetic variationWe analysed genome sequences from 13 wild yaks, representing three highly diverged mitochondrial lineages8,9, and 59 domestic yaks from different locations on the QTP (Fig. 1a) representing 48 animals from unselected landraces (D2 population) as well as 11 Tianzhu white yaks (D1 population), which since 130 years ago11,12 have been bred by strict selection of coat colour (Fig. 1b). Genome resequencing accomplished an average depth of 6.7 × and average genome coverage of 98% (Supplementary Table 1). We detected a total of 14.56 million high-quality single nucleotide polymorphisms (SNPs), most of which (76.4%) are located in intergenic regions (Supplementary Table 2).Figure 1: Phylogenetic and population genetic analyses of wild and domestic yaks.(a) The geographic distribution of the sampling locations for wild (dark red) and domestic (dark blue) yaks. The coloured areas indicate geographic distribution of wild yaks (light red), Tianzhu county (green) and the QTP (grey). (b) A neighbour-joining phylogenetic tree constructed using whole-genome SNPs data. The scale bar represents level of similarity. W: wild yaks; D1, the Tianzhu white breed; and D2: all of the remaining domestic yaks. (c) Principal component (PC) analysis plots of the first two components. The fraction of the variance explained is 3.24% for PC1 and 1.36% for PC2, with Tracy–Widom P<10−44 (Supplementary Table 3). (d) Population structure plots with K=2–5. The y axis quantifies the proportion of the individual’s genome from inferred ancestral populations, and x axis shows the different populations. Geographic information is provided in Supplementary Table 1. (e) Decay of linkage disequilibrium of D1, D2 and W populations measured by r2.Full size imageGenetic changes underlying domesticationTo examine the genome-wide relationships and divergence between wild and domestic yak populations, we visualized pairwise genetic distances in a neighbour-joining tree (Fig. 1b, consensus tree based on 1,000 bootstrap replicates shown in Supplementary Fig. 1). This revealed a clear split between wild and domestic yaks despite continuing gene flow between them, and also separation of Tianzhu white yaks within the domestic population (Supplementary Note 1). Principal component analysis as well as model-based clustering yielded similar results (Fig. 1c,d; Supplementary Note 1; and Supplementary Table 3). Domestication often reduces effective population size (Ne) and genetic diversity13,14; but we obtained similar sequence diversity (π) values of 0.0013 and 0.0014 for wild and domestic yaks, respectively (Supplementary Fig. 2 and Supplementary Table 4). We further found that the estimated population-differentiation statistic (FST) between wild and domestic yaks is only 0.058 (Supplementary Table 4), which is smaller than between taurine and zebu cattle or between diverged taurine cattle breeds15. FST estimates supported the gene flow occurring between wild and domestic yaks (Supplementary Note 1).We detected genomic regions that have been subject to selection as inferred from high wild/domestic π log-ratios and an extreme divergence of allele frequencies of wild and domestic yaks16,17 (Fig. 2a and Supplementary Fig. 3). We identified 182 potential selective-sweep regions with an average size of 79.5 kb, together comprising around 14.5 Mb or 0.54% of the assembled genome. The role of these regions is confirmed by significantly lower values of Tajima’s D and higher linkage disequilibrium patterns (P values 2.7 × 10−12 and 1.5 × 10−4, respectively, Wilcoxon rank-sum test, Supplementary Note 2) in domestic populations. These regions harbour 209 annotated protein-coding genes (Supplementary Data 1), which are expected to represent targets of selection. Among these, GO group GO:0051969 (regulation of transmission of nerve impulse) was overrepresented (P<0.05, Supplementary Table 5 and Supplementary Data 1) with eight genes affecting synaptic circuitry and neurological processes (Arc, ASPA, ATP2B2, MYO6, NTRK2, Rab40c, SNCA and TG). From these genes and 30 other genes (Supplementary Data 1) involved in brain and neuronal development, 19 are considered to be associated with behaviour. ADCYAP1R1 (Fig. 2b) encodes a pituitary adenylate cyclase-activating polypeptide receptor that in humans is strongly expressed in the amygdala and hippocampus, and is associated with fear response, threat stimuli, post-traumatic stress disorder and other anxiety disorders. Adcyap1r1-deficient mice exhibit strongly reduced anxiety-like behaviour18. SCRIB (Fig. 2c) encodes the scribbled planar cell polarity protein, which is a key regulator of brain development and spine morphology. Scrib1 knockout mice exhibit enhanced learning and memory abilities and impaired social behaviour correlated with altered neuronal morphology19. PLXNB1 encodes a neuronal receptor for semaphorins and has an important role in developing nervous systems and controlling axon guidance20. A recent quantitative trait loci study in rat identified PLXNB1 as a candidate gene contributing to differences in tameness and aggression21, which are expected to be important during the early phase of animal domestication2. The pathways of brain and neuronal development identified here to be under selection during yak domestication are similar to those reported previously for rabbit22 and cat23, suggesting common features of domestication in these unrelated species.Figure 2: Genomic regions with selection sweep signals in domestic yaks.(a) Distribution of ln ratio (θπ,wild/θπ,domestic) and FST of 50 kb windows with 10 kb steps. Red dots represent windows fulfilling the selected regions requirement (corresponding to Z test P<0.005, where FST≥0.17 and ln ratio≥0.65). Example of genes (b,c) with selection sweep signals in domestic yaks. FST, θπ and Tajima’s D values are plotted using a 5-kb sliding window. Wild (green) and domestic (blue) yaks are represented by different colours. Horizontal dashed lines represent mean whole-genome of corresponding values. Genes are shown at the bottom (black rectangle, coding sequences; red line, introns).Full size imageOnly a few genes subject to selection were associated with specific physical characteristics or economically significant traits, such as TTLL1 and RHPN1 associated with sperm development and RHOD with early pregnancy. Also, a limited number of sweeps associated with coat colour were detected from an examination of genetic divergence between Tianzhu white yaks and other domestic yaks (Supplementary Note 3). In line with the low level of genetic and morphological differentiation recorded between wild and domestic yaks (Supplementary Table 4 and Supplementary Fig. 4), our analyses confirm that the effects of domestication in yaks are not as marked as for most other domestic species1,22. This may reflect a trade-off between survival of yaks in a harsh high-altitude environment and performance under pastoral conditions.Demographic historyWe employed the pairwise sequentially Markovian coalescent (PSMC) method24 to examine changes in effective population size (Ne) of the ancestral population of both wild and domestic yaks in response to Quaternary climatic change. We applied this method to our deep-coverage (>20 ×) yak genomes from three wild and four domestic yaks, including the reference genome. Both wild and domestic yaks exhibited similar demographic trajectories until about 20,000 years ago (Fig. 3a and Supplementary Fig. 5). The ancestral Ne of yaks shows a peak at ∼1 Myr ago followed by two distinct declines. The first decline occurred ∼0.9 Myr ago, coinciding with extensive glaciation during the mid-Pleistocene25, with three highly divergent mitochondrial lineages known to have survived this decline9. Other animal species such as giant panda and golden snub-nosed monkeys living in the southern and southeastern QTP also suffered during the same period decreases in effective population size26,27. The second decline involved at least a threefold decrease in Ne, and occurred ∼40,000 years ago coinciding with the last glaciation25.Figure 3: Demographic history of yak.(a) Demographic history inferred by PSMC. The period of the Xixiabangma Glaciation (XG, 1,170–800 thousand years ago, kya), Naynayxungla Glaciation (NG, 780–500 kya) and the last glacial maximum (LGM, ∼20 kya) are shaded in grey. (b) Schematic of demographic scenario modelled in Fastsimcoal2. The ancestral population is in grey, wild yak in brown and domestic yak in blue. The width shows the relative effective population size. The figures at the arrows indicate the average number of migrants per generation between wild and domestic yaks. The folded genome-wide SFS from 13 wild yaks (c) and 59 domestic yaks (d). Different colours represent data before (blue) and after (orange) impute filtering of sites for which the correlation of observed and imputed date was <0.9.Full size imageWe used the joint site frequency spectrum (SFS) approach implemented in fastsimcoal2 (ref. 28) to simulate more recent demographic fluctuations. Thirty alternative models of historical divergence were fitted to the allele-frequency spectrum of domestic and wild yak populations, incorporating strict isolation, isolation-with-migration, bottlenecks and/or growth (Supplementary Fig. 6). A demographic model in which domestic and wild yaks diverged through a dynamic process involving population bottlenecks in both wild and domestic yaks and extensive post-domestication gene flow produced a significantly better fit than alternative models (Fig. 3b). The allele-frequency spectrum simulated with the best model was very close to the spectrum generated from real data (Supplementary Fig. 7), demonstrating the accuracy of the calculations. Thus in the best fitting model domestication of yaks occurred ∼7,300 yr BP, with a 95% confidence interval of 7,227–7,914 yr BP, slightly later than the domestication of many other livestock species (10,000–8,000 yr BP), but preceding the introduction of taurine cattle to China 5,400–4,700 yr BP (ref. 29).Analyses of mitochondrial, Y-chromosomal and autosomal DNA data suggest that modern humans began colonizing the QTP ∼30,000 yr BP and that their population size expanded rapidly first between 10,000 and 7,000 yr BP and later between 4,000 and 3,000 yr BP (refs 5, 30, 31). However, archaeological and anthropological evidence indicates that the earliest agricultural settlements in the northeastern QTP were established 5,200 yr BP or later3,32. During the early-Neolithic age, the climate on the QTP was warmer than today25, which may have favored persistence of a hunter–gatherer population in the region. Our results suggest that the yak was domesticated by 7,300 yr BP and may have been triggered by and facilitated the first expansion of human population size on the QTP at this stage. Given the absence of agricultural settlements at this time, the first pastoralists were probably nomadic herders. A similar domestication by nomadic people in another extreme environment has been described for reindeer33.Later in the Holocene agriculture was established on the QTP, for example, the introduction of barley cultivation 4,000–3,600 yr BP (ref. 30). This coincided with a second human population expansion3,4,5,30,32 despite the colder climate of the late Holocene25. Interestingly, our coalescence analyses revealed a sixfold increase in population size of the domestic yak (Ne, from 1,100 to 6,500) during the same period (3,600 yr BP, Fig. 3b), which might have resulted from the second human population expansion on the QTP following the introduction of agriculture or contributed to this second expansion by providing a reliable resource of food, hides and transportation. According to our coalescent analysis, ∼500 years ago the Ne of the wild yak population seriously declined from 21,200 to 1,700, which is consistent with a loss of low-frequency variants (Fig. 3c,d) and a lower genetic diversity in current wild yaks (Supplementary Fig. 2 and Supplementary Table 4). This possibly resulted from habitat loss due to increasing human activities.DiscussionDespite low morphological divergence and continuing gene flow, we detected a clear genetic split between wild and domestic yaks. We found that the genomes of domestic yaks exhibit clear signatures of selection at genes that probably affect animal behaviour and tameness according to previous reports on other animals22,23. These findings suggest that parallel processes of evolution have occurred during the domestication of unrelated animals across different localities of the world. Our study further indicates that the yak is likely to have been domesticated before 7,000 years ago and that domestication was closely associated with the expansion of the human population on the QTP during the early Neolithic period31. Moreover, following the introduction of agriculture30, a further increase in the effective population size of domestic yaks later in the Holocene may have resulted from or contributed to causing a second human population expansion and the subsequent development of human society on the QTP during this period3,4,5,30,32MethodsSample collection and sequencingA total of 84 individuals (15 wild yaks and 69 domestic yaks, Supplementary Table 1) were collected and sequenced, yielding a data set of genomes from 13 wild and 59 domestic yaks without close relatives and with <50% missing data. The wild samples were collected from corpses of wild yaks in the central Kokohili region, which were identified as wild yaks because of their long hair and large skeletons. Domestic yaks were sampled across the species main geographic distribution. Samples were collected under the supervision of ethical committees and permission was obtained when necessary. For each yak, genomic DNA was extracted from muscle samples using a standard phenol/chloroform extraction34. The quality and integrity of the extracted DNA was checked by measuring the A260/A280 ratio and by agarose gel electrophoresis. Paired-end sequencing libraries with an insert size of 500 bp were constructed according to the Illumina manufacturer’s instructions for sequencing on the Hiseq 2,000 platform. Sequencing and base calling were performed according to the standard Illumina protocols.Sequence quality checkingDuplicate reads caused by base-calling and adaptor contamination were removed. Reads with (i) ≥10% unidentified nucleotides (N), (ii) with a phred quality ≤7 for >65% of the read length or with (iii) a stretch of >10 bp identical to the adaptor sequence with up to two mismatches were removed or corrected using a k-mer frequency-based methodology35. Reads were also trimmed if they had three consecutive bp with a phred quality of ≤13, and discarded if they were shorter than 45 bp.Sex-linked scaffoldsWe used Blastz (ref. 36) to perform whole-genome alignment of the yak and taurine cattle genomes and to identify yak sex chromosomes (downloaded from National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov, UMD3.1.1, GCA_000003055.4). All hits against the cattle sex chromosome were treated as sex-linked scaffolds. A total of 186 scaffolds with a combined size of ∼134 Mb were aligned to the cattle sex chromosome and omitted from subsequent analyses.Read mappingHigh-quality reads were aligned to the Bos grunniens reference genome37 and mitochondrial reference genome (accession number: JQ692071.1) using BWA-MEM (0.7.10-r789) with default parameters38. Sequence Alignment/Map (SAM) format files were imported to SAMtools (v0.1.19)39 for sorting and merging and Picard (http://broadinstitute.github.io/picard/, version 1.92) to assign read group information containing library, lane and sample identity. The Genome Analysis Toolkit (GATK, version 2.6–4-g3e5ff60)40 was used to perform local realignment of reads to enhance the alignments in the vicinity of indel polymorphisms. Realignment was performed with GATK in two steps. The first step used the RealignerTargetCreator to identify regions where realignment was needed, and the second step used IndelRealigner to realign the regions found in the first step, generating for each individual a realigned Binary sequence Alignment/Map (BAM) file.Filtering alignmentsWe removed all alignments that were not of sufficiently high quality for SNP detection and subsequent analyses. Alignments to be removed were identified using the following stepwise protocol: (i) discard reads that do not map uniquely; (ii) only use reads for which a mate can be mapped; (iii) discard ‘bad’ reads with flag ≥255; (iv) discard bases with a quality <20; and (v) discard reads with a mapping quality <30. We also adjusted the quality scores around indels using SAMtools and removed the alignments that anchored short scaffolds of <2 kb.Filtering sitesTo minimize the influence of sequencing and mapping bias, the following site types were discarded: (i) sites with unbalanced quality scores as determined using Wilcoxon rank-sum test with threshold of P<10−5; (ii) sites with strand bias (P<10−5); (iii) sites with extremely low (<2 ×) or extremely high (>18 ×) coverage, both thresholds being defined after investigating the coverage distribution empirically; (iv) sites that failed the Hardy–Weinberg Equilibrium test and P<10−3, using SAMtools and BCFtools39; and (v) sites for which the available information derived from <90% of the sampled domestic and/or wild populations. The combined application of these filters left us with a data set comprising ∼2.2 Gb, representing 81.7% of the genome.SNP and genotype callingVariant discovery analysis was conducted at the population level for wild and domestic yak samples separately. We used the SAMtools model41 implemented in analysis of next generation sequencing data (ANGSD)42 to estimate genotype likelihoods and generated Beagle files. A maximum likelihood approach43 was then used to infer major/minor states based on the genotype likelihoods. Minor allele-frequency polarized by major/minor state was also estimated from the genotype likelihoods based on Kim’s method44. A likelihood ratio test statistic for the allele-frequency based on a χ2 distribution with one degree of freedom and a P-value threshold of 1 × 10-6 was used as an SNP discovery criterion. SNPs were retained only if they could be genotyped in at least 90% of the sampled individuals from both domestic and wild populations. This yielded a total of 14.6 million SNPs.A two-step procedure implemented in ANGSD was used to estimate the SFS: (i) sample allele-frequency likelihood files (.saf) were generated using the option ‘–doSaf 1’, with ancestral state being assigned by a cattle genome45 (17.4 ×); (ii) the allele-frequency likelihood files were optimized using the realSFS (ref. 46) programme in order the estimate the SFS. Genotypes were called using the full set of genotype likelihoods data. Using the sample allele frequency as a prior for genotype frequencies under the assumption of Hardy–Weinberg equilibrium, we then computed the posterior probabilities of the genotypes at each site for each individual.RelationshipsTo identify closely related individuals, the programme PLINK v1.07 (ref. 47) was used to obtain pairwise estimates of Identity-By-State (IBS) scores between all samples. One wild and seven domestic individuals were excluded due to their high pairwise genetic similarity with another sampled individual (IBS>0.9), leaving only unrelated samples for use in the downstream analyses. We also discarded one wild and three domestic individuals with >50% missing data (Supplementary Table 1).Genome-wide identity scoresTo visualize genetic relatedness between domestic and wild populations, we calculated for individual SNPs identity scores as the sum of the products of the frequencies of both alleles with the frequencies of the same allele in the reference genome. Identity scores for 50 kb windows along the genome were averaged over the SNPs within the window (Supplementary Fig. 4).Population genetics analysisAfter mapping sequencing data against the reference yak mitochondrial genome (accession number: JQ692071.1), only positions covered by a minimum number of three independent unique reads with base qualities of ≥30 were used to call the consensus sequences. Eighty yak mitochondrial sequences were generated and aligned together with 81 sequences from B. grunniens, one from Bos taurus, one from Bos indicus, one from Bison bison and one from Bison bonasus (see the labels of external branches on Supplementary Fig. 8 for accession numbers). We partitioned the alignment into six main regions: the D-loop, ribosomal RNA, tRNA and the first, second and third codon positions for Coding DNA Sequence (CDS). The initiation and termination codons and overlapping regions between CDSs were excluded. We also removed sites with missing genotypes in >10% of the sampled individuals. The best mutational model for each of the partitions was then selected using ModelGenerator v851 (ref. 48) with eight rate categories. The partitions and their corresponding mutational models were used for Bayesian phylogenetic inference with MrBayes v3.22 (ref. 49), running two analyses in parallel, each with four Markov Chain Monte Carlo (MCMC) chains. The final tree topology was recovered after a total of 100,000,000 generations, sampling 1 in every 1,000 generations after discarding the first 25% as burn-in. The s.d. of split frequencies was below 0.01 after 100,000,000 generations, indicating the convergence of the four chains to the stationary distribution. The resulting tree, as drawn with MEGA v5.0 (ref. 50), is shown in Supplementary Fig. 1. This tree topology is consistent with the presence of distinct phylogeographical patterns and multiple divergent lineages in yaks as determined in previous studies8,9 based on D-loop and mitochondrial genomic sequences from a wide range of samples.For autosomal genome data, a neighbour-joining tree was constructed with PHYLIP v3.695 (http://evolution.genetics.washington.edu/phylip.html) using the matrix of pairwise genetic distances. The ancestral states of the SNPs were determined using the close relative of the yak, B. taurus45, as the outgroup. A second frequency tree (Supplementary Fig. 1) was generated based on 1,000 bootstrap replicates using the consensus module of PHYLIP. FigTree (http://tree.bio.ed.ac.uk/software/figtree/) and MEGA v5.0 were used to visualize the phylogenetic trees. Principal component analysis of the SNPs was performed using the smartpca programme in EIGENSOFT v5.0 (ref. 51). A Tracy–Widom test was used to determine the significance level of the eigenvectors.Geographic distances in km between individuals were calculated via the formula: Distance=acos(sin(lat1 × π/180)sin(lat2 × π/180)+cos(lat1 × π/180)cos(lat2 × π/180)cos(lon2 × π/180−lon1 × π/180)) × 6378.135, in which lat1 and lat2 are latitudes in degrees of the two individuals and lon1 and lon2 their longitudes (Supplementary Fig. 9).Genome-wide patterns of heterozygosity and neutrality testsThe nucleotide diversity (π), population-differentiation statistic (FST), Tajima’s D statistic and Watterson estimator (θw) were calculated using a sliding window approach (50 kb window sliding in 10 kb steps)52,53,54. To compensate for missing data and variations in the depth-of-coverage across the different genomes (4.5–8.8 × , average coverage 6.7 ×), an empirical Bayesian method was used to calculate the posterior probabilities for the sample frequency spectrum using a maximum likelihood estimate of the SFS as the prior. The method takes genotype uncertainty into account and is based directly on genotype likelihoods rather than called genotypes. Only genomic windows in which at least 80% of bases were covered were considered to avoid coverage-related bias, leaving 207,111 windows with an average SNPs number of 346 per-window (min 34, max 3,298).Screening for selective sweepsTo identify genomic regions that may have been subject to selection during domestication, we combined the two domestic yak populations (D1 and D2) as a single domestic gene pool. We scanned the genome for regions with the highest differences in genetic diversity (π log-ratio wild/domestic) and extreme divergence in allele frequency between wild and domestic populations using a genome-wide sliding window strategy. More specifically, we calculated the sequence diversity statistics (π), and the population-differentiation statistic (FST) using a 50 kb window with a 10-kb step. The π log-ratio was calculated as ln(πW)−ln(πD), where πW and πD are the nucleotide diversity values for the wild and domestic yaks, respectively. At a significance level of P<0.005 (Z test, with π log-ratio >0.65 and FST>0.17, Fig. 2a and Supplementary Fig. 3), we identified a total of 182 potential selective-sweep regions (with an average size of 79.5 kb, range from 10 to 450 kb) overlapping with 209 candidate genes, used for subsequent analysis and discussion.To test whether the candidate selective-sweep regions had an excess of singleton polymorphisms, we computed the Tajima’s D value for domestic yaks using the same sliding window approach. Regions under selective sweeps had very significantly lower values of Tajima’s D (P=2.7 × 10−12, Wilcoxon rank-sum test). In addition, pairwise r2 values showed that the candidate regions exhibited significantly extended linkage disequilibrium (P=1.5 × 10−4, Wilcoxon rank-sum test). These results confirm the occurrence of selective sweeps in the identified regions.The impact of population structure to selection signal was tested by repeating the sweep detection by comparing the two domestic populations D1 and D2 separately to the wild population (W). For 196 (93.8%) of 209 genes selection signals were statistically significant in both domestic populations; relatively strong selection signals were evident for the other 13 candidate genes but did not reach the significance threshold (Supplementary Data 1).Functional classification of GO categories was performed using the Blast2GO programme55. Enrichment analysis was performed and the χ2 test was used to calculate the statistical significance of enrichment. The P values were adjusted by FDR and the adjusted P value cut-off was 0.05.Demographic historyWe inferred a demographic history for B. grunniens by applying the Pairwise Sequentially Markovian Coalescence model24 to the complete diploid genome sequences, excluding sexual chromosomes/scaffolds. This method reconstructs the history of changes in population size over time using the distribution of the most recent common ancestor (tMRCA) between two alleles in an individual. PSMC has high false-negative rates at low depth, which leads to a systematic underestimation of true event times. To ensure the quality of consensus sequences, we sequenced three wild and three domestic yaks to a high coverage of 20 × . DNA was prepared and libraries were built using the protocols described above. Consensus sequences were obtained using SAMtools and divided into non-overlapping 100 bp bins. Bases of low sequencing depth (less than a third of the average depth) or high depth (twice the average depth) were masked. The analysis was performed using the following parameters: −N25 −t15 −r5 −p ‘4+25 × 2+4+6’. The mutation rate per generation per site was estimated as: μ=D × g/2 T where D is the observed frequency of pairwise differences between two species, T is the estimated divergence time and g is the estimated generation time for the two species. The estimated generation time (g) was set to 3 years and the estimated divergence time was set to 4.7 Myr based on a previous study on cattle and yak56. These values yielded an estimated mutation rate of 5.84 × 10−9 mutations per generation per site for the yak. PSMC modelling was done using a bootstrapping approach, with sampling performed 100 times to estimate the variance of the simulated results.As PSMC inference is known to be inaccurate for recent datings, we also inferred the joint demographic histories of the wild and domestic yak using the flexible and robust simulation-based composite-likelihood approach implemented in the fastsimcoal2 programme28, which infers demographic parameters from the SFS. The analysis was performed for 13 wild samples and 59 domestic samples. To improve the genotype accuracy and infer missing genotypes, we used BEAGLE (ref. 57) to infer the haplotypes of wild and domestic individuals from previously estimated genotypes. After investigating the empirical minor allele frequency distributions, we inferred haplotypes for non-coding sites alone with estimated minor allele frequency values of >0.038 for wild yak and >0.008 for domestic yak. Only sites for which the correlation between the observed and imputed data (r2) was >0.9 were retained. To examine potential bias introduced by impute filtering, we compared the SFS before and after filtering. No potential bias was found (Fig. 3c,d). The joint SFS of wild and domestic yaks was used to estimate evolutionary scenario parameters. We used the folded spectrum to minimize potential biases when determining the ancestral allelic states. Alternative models of historical events were fitted to the joint SFS of wild and domestic yak and we allowed only instantaneous population size changes (Supplementary Fig. 6). For each model, we ran the programme 50 times with varying starting points to ensure convergence, and retained the fitting with the highest likelihood. Estimates were obtained from 100,000 simulations per likelihood estimation (-n100,000, -N100,000), 40 Expectation/Conditional Maximization (ECM) cycle (-L40) and 50 runs per data set. The best model was addressed through the maximum value of the likelihoods and Akaike information criterion28. Parametric bootstrap estimates were obtained by parameter estimation based on 100 data sets simulated according to CML estimates in best model (model15) estimation parameters (Supplementary Data 2). The population history and parameters from the best model were used to perform forward simulation and residuals analysis with ∂a∂i (ref. 58) to check the accuracy of the demographic model.Additional informationAccession codes: The sequencing data for this project have been deposited in the European Nucleotide Archive (EMBL-EBI) under accession code PRJNA285834.How to cite this article: Qiu, Q. et al. Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions. Nat. Commun. 6:10283 doi: 10.1038/ncomms10283 (2015).

Accession codes

Accessions

European Nucleotide Archive

PRJNA285834

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Download referencesAcknowledgementsThe project was supported by the National Natural Science Foundation of China (31322052 and 91331102), the National High-Tech Research and Development Program of China (863 Program, 2013AA102505 3-2), Ministry of Science and Technology of the People's Republic of China (2010DFA34610), International Collaboration 111 Projects of China, Fundamental Research Funds for the Central Universities, 985 and 211 Projects of Lanzhou University.Author informationAuthor notesQiang Qiu, Lizhong Wang, Kun Wang and Yongzhi Yang: These authors contributed equally to this work.Authors and AffiliationsState Key Laboratory of Grassland Agro-Ecosystem, College of Life Science, Lanzhou University, Lanzhou, 730000, ChinaQiang Qiu, Lizhong Wang, Yongzhi Yang, Zefu Wang, Xiao Zhang, Zhengqiang Ni, Fujiang Hou, Ruijun Long & Jianquan LiuMOE Key Laboratory for Bio-resources and Eco-environment, College of Life Science, Sichuan University, Chengdu, 610064, ChinaKun Wang, Tao Ma & Jianquan LiuSchool of Biology, University of St Andrews, St Andrews, Fife, KY16 9TH, UKRichard AbbottFaculty of Veterinary Medicine, Utrecht University, Yalelaan 8, 3584 CM, Utrecht, The NetherlandsJohannes LenstraAuthorsQiang QiuView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsJ.Li. designed and supervised the project. Q.Q., L.W., K.W., Y.Y., T.M., Z.W., X.Z., Z.N., F.H., R.L. and J.Li. collected and generated the data, and performed the preliminary bioinformatic analyses. Q.Q. and L.W. filtered the data and performed the majority of the population genetic analysis with some contributions from K.W. and Y.Y., Q.Q., L.W. and J.Li. wrote the manuscript with critical input from all the authors, whereas R.A. and J.Lo. revised the manuscript.Corresponding authorCorrespondence to

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Supplementary informationSupplementary InformationSupplementary Figures 1-13, Supplementary Tables 1-6, Supplementary Notes 1-3 and Supplementary References (PDF 1755 kb)Supplementary InformationPutative regions identified to be under domestication sweeps (XLSX 45 kb)Supplementary InformationParameters of models estimated in Fastsimcoal2 (XLSX 14 kb)Supplementary InformationSummary statistics of ABBA statistics (XLSX 181 kb)Rights and permissions

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Reprints and permissionsAbout this articleCite this articleQiu, Q., Wang, L., Wang, K. et al. Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions.

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Amazing Facts about Yaks | OneKindPlanet Animal Education

ing Facts about Yaks | OneKindPlanet Animal Education

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OneKindPlanet | Animals | Herbivore, IUCN: Vulnerable, Mammal | YakAnimal A-ZYakYaks are able to withstand temperatures of -40degrees C (–40 degrees F). At this temperature, they have even been seen bathing in lakes and rivers. The yak’s warm coat provides insulation through a thick outer coating of long hair and a dense inner coating of matted, shorter fur.

Amazing Facts About the YakMost yaks are domesticated although there is also a small, vulnerable wild yak population.Yaks are herd animals. Herds can contain several hundred individuals, although they are often much smaller.The herds consist primarily of females and their young, with a smaller number of adult males.A great deal of their time is spent grazing on mountain plains, eating grass, herbs and wild flowers.Yaks live at the highest altitude of any mammal.Similar to other cow species, the yak has more than one stomach which it uses to successfully get all the nutrients out of the plants it eats.Yaks have firm, dense horns which they use to break through snow in order to get the plants that are buried underneath. They will also use their horns in defence.They have a dense undercoat covered by outer hair which is generally dark brown to black in color, which almost reaches to the ground.In winter a yak can survive temperatures as low as -40 degrees C (-40 degrees F).At night and in snowstorms they will protect themselves from the cold by huddling up together with their calves in the warmer centre.ShareYaks usually give birth in June. A female yak gives birth to a single calf every other year. The mother will find a secluded spot to give birth. Once born, the calf is able to walk within about ten minutes and the pair will rejoin the herd.Yaks are very friendly in nature and there has been very little documented aggression from yaks towards human beings, although mothers can be extremely protective of their young and will bluff charge if they feel threatened.Contrary to popular belief, yaks and their manure have little to no detectable odor when maintained appropriately in pastures or paddocks with adequate access to forage and water.Yaks grunt and, unlike cattle, are not known to produce the characteristic bovine lowing (mooing) sound.Historically, the main natural predator of the wild yak has been the Tibetan Wolf, but Brown Bears and Snow Leopards have also been reported to predate on Yak in some areas.Wild yak are threatened by habitat loss and over-hunting by humans.Photo: travelwayoflife – wikimedia commonsShare

Find more animals like thisHerbivoreIUCN: VulnerableMammal

Quick FactsType: MammalDiet: HerbivoreLifespan: 15-20 yearsSize: 2-2.2 metresHabitat: Grassy plains in mountainous regionsRange: The Himalayan region of south Central Asia, the Tibetan Plateau and as far north as Mongolia and Russia.Scientific name: Bos grunniens (domesticated), Bos mutus (wild)

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The yak genome and adaptation to life at high altitude

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Published: 01 July 2012

The yak genome and adaptation to life at high altitude

Qiang Qiu1 na1, Guojie Zhang2 na1, Tao Ma1 na1, Wubin Qian2 na1, Junyi Wang2 na1, Zhiqiang Ye3,4 na1, Changchang Cao2, Quanjun Hu1, Jaebum Kim5,6, Denis M Larkin7, Loretta Auvil8, Boris Capitanu8, Jian Ma5,9, Harris A Lewin10, Xiaoju Qian2, Yongshan Lang2, Ran Zhou1, Lizhong Wang1, Kun Wang1, Jinquan Xia2, Shengguang Liao2, Shengkai Pan2, Xu Lu1, Haolong Hou2, Yan Wang2, Xuetao Zang2, Ye Yin2, Hui Ma1, Jian Zhang1, Zhaofeng Wang1, Yingmei Zhang1, Dawei Zhang1, Takahiro Yonezawa11, Masami Hasegawa11, Yang Zhong11, Wenbin Liu2, Yan Zhang2, Zhiyong Huang2, Shengxiang Zhang1, Ruijun Long1, Huanming Yang2, Jian Wang2, Johannes A Lenstra12, David N Cooper13, Yi Wu1, Jun Wang2,14, Peng Shi3, Jian Wang2 & …Jianquan Liu1,15 Show authors

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Adaptive radiationComparative genomicsHypoxia

AbstractDomestic yaks (Bos grunniens) provide meat and other necessities for Tibetans living at high altitude on the Qinghai-Tibetan Plateau and in adjacent regions. Comparison between yak and the closely related low-altitude cattle (Bos taurus) is informative in studying animal adaptation to high altitude. Here, we present the draft genome sequence of a female domestic yak generated using Illumina-based technology at 65-fold coverage. Genomic comparisons between yak and cattle identify an expansion in yak of gene families related to sensory perception and energy metabolism, as well as an enrichment of protein domains involved in sensing the extracellular environment and hypoxic stress. Positively selected and rapidly evolving genes in the yak lineage are also found to be significantly enriched in functional categories and pathways related to hypoxia and nutrition metabolism. These findings may have important implications for understanding adaptation to high altitude in other animal species and for hypoxia-related diseases in humans.

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MainThe yak (B. grunniens) is an iconic symbol of Tibet and of high altitude. More than 14 million domestic yaks provide the basic resources (such as meat, milk, transportation, dung for fuel and hides for tented accommodation) that are necessary for Tibetans and other nomadic pastoralists in high-altitude environments1. In contrast, closely related and cross-fertile taurine cattle (B. taurus) suffer from severe pulmonary hypertension when reared in the yak habitat2,3,4. Yaks have numerous anatomical and physiological traits that equip them for life at high altitude, including large lungs and hearts1, lack of hypoxic pulmonary vasoconstriction5, increased foraging ability6, strong environmental sense1 and high energy metabolism1,7. Thus, comparing yak and cattle contributes to the understanding of evolutionary adaptation to high altitude5,6,7.Genomic comparisons between closely related species provide insights into the genetic basis of mammalian divergence and adaptation8,9. In this study, we sequenced the genome of a female domestic yak using a whole-genome shotgun strategy and the Illumina HiSeq 2000 platform. De novo assembly of 4.4 billion reads from paired-end libraries (Supplementary Table 1) yielded a draft assembly (65-fold coverage) with a total length of 2,657 Mb, close to the 2,649 Mb of sequence obtained for the cattle genome10 (UMD 3.1), and contig and scaffold N50 sizes of 20.4 kb and 1.4 Mb, respectively (Supplementary Table 2). Approximately 90% of the total sequence was covered by 2,083 scaffolds of >307 kb, with the largest scaffold spanning 8.8 Mb. The assembly metrics of the yak genome were comparable to those of other animal genome assemblies generated by next-generation sequencing technology (Supplementary Table 3). The sequencing depth of 98% of the assembly was more than 20-fold (Supplementary Fig. 1), ensuring high accuracy at the nucleotide level11. The genome assembly covered, without any obvious errors in assembly, 97% of 6 fosmids sequenced by Sanger sequencing and 98% of 81,020 Unigenes assembled from Illumina RNA sequencing (RNA-seq) data for 5 tissues (Supplementary Fig. 2 and Supplementary Tables 4 and 5), indicating coverage of most of the euchromatic regions. GC content distributions were similar to those of the cattle genome (Supplementary Fig. 3 and Supplementary Note). We predicted 22,282 protein-coding genes in yak on the basis of RNA-seq, homology and ab initio gene prediction (Supplementary Fig. 4 and Supplementary Table 6).High coverage11,12 permitted the identification of 2.2 million heterozygous single-nucleotide variants (SNVs) within the sequenced individual (Supplementary Figs. 5 and 6 and Supplementary Tables 7 and 8). The heterozygosity rate (0.89 × 10−3) was approximately 1.5 times higher than that estimated for cattle (0.59 × 10−3) (refs. 10,13). This may be due to a longer and more systematic selection in cattle and/or to introgression from wild yaks still living on the Qinghai-Tibetan Plateau14,15. Yak and cattle both have 30 chromosomes and have similar karyotypes1, which allows chromosomal assignments on the basis of homology, despite a lack of physical maps for yak chromosomes. Using the human genome as an outgroup, we reconstructed 207 conserved ancestral homologous synteny blocks (aHSBs)16 covering 2.51 Gb (94%) of the yak genome. The existence of these blocks confirms the extensive synteny of the cattle and yak genomes and also allows the detection of breakpoint regions (Supplementary Table 9).Frequent turnover of gene copy and/or protein domain number has been proposed as a major mechanism underlying the adaptive divergence of closely related species9,17,18,19. First, we used TreeFam20 to identify 13,810 homologous gene families shared by 4 species (yak, cattle, human and dog): 362 gene families were specific to yak and cattle, and 100 were found only in yak (Fig. 1). The sequence depth of these multiple-copy genes was in the same range as for single-copy genes (Supplementary Fig. 7). The yak-specific gene families contained 170 genes, 75 of which have known InterPro domains. Compared with the cattle-specific gene families, the yak-specific families were significantly over-represented in two major functional categories: olfactory sensation (14 genes; P < 0.01) and host defense and immunity (11 genes; P < 0.01) (Supplementary Table 10). Next, we identified 596 gene families that were substantially expanded in yak compared to other mammals (Fig. 2a). Functional categories that were enriched for significant gene family expansions mainly included sensory perception (gene ontology (GO) 0004984, olfactory receptor activity, P < 0.01; GO 0050909, sensory perception of taste, P < 0.01) and energy metabolism (GO 0004129, cytochrome-c oxidase activity, P < 0.01; GO 0015986, ATP synthesis coupled proton transport, P < 0.01) (Supplementary Table 11). Third, matching of ORFs to PFAM domain families at the protein level showed expansion in the numbers of specific domains (Supplementary Table 12). Thus, with regard to sensory perception receptors (olfactory and taste) and other G protein–coupled receptor (GPCR) rhodopsin-like receptors, known to be involved in sensing of the extracellular environment21, we found significantly more GPCR transmembrane domains in yak than in cattle (1,558 versus 1,358). Categories related to hypoxic stress seemed also to have enriched expansions of the corresponding domains in yak. For example, genes with the Hig_1_n (PF04588.6 in Pfam) domain were highly expressed under hypoxic stress22,23 and also had expanded copy numbers in yak (13 copies) relative to cattle (9 copies) and other mammals. Phylogenetic analysis of genes that encoded this domain showed an expansion in the numbers of the closely related Hig_1_n domains in both yak and cattle, as well as three additional copies in yak (Fig. 2b).Figure 1: Venn diagram showing unique and shared gene families between the yak, cattle, dog and human genomes.The number of gene families is listed in each of the diagram components and the total number for each animal is given in parentheses.Full size imageFigure 2: Gene expansion and contraction in the yak genome.(a) Dynamic evolution of orthologous gene families. The proportions of expanded (red) and contracted (blue) gene families are shown as pie charts at each branch terminus. MRCA, most recent common ancestor. (b) A neighbor-joining tree of mammalian Hig domain sequences.Full size imageAdaptive divergence at the molecular level may also be expressed by an increased rate of nonsynonymous changes within genes involved in adaptation9,24. We identified 8,923 high-confidence 1:1:1 orthologous genes in the yak, cattle and human genomes, most of which also correspond to genes in the horse, dog, mouse and chimpanzee genomes (Supplementary Fig. 8). Overall, yak and cattle genes were highly similar, with 45% of encoded proteins identical and mean protein similarity approximating 99.5% (Supplementary Figs. 9 and 10). Average synonymous (Ks) and nonsynonymous (Ka) gene divergence values between yak and cattle were 0.0114 and 0.00207, respectively, close to the values between human and chimpanzee genes (Supplementary Fig. 11). Yak and cattle were estimated to have diverged approximately 4.9 million years ago, which is comparable to the time at which humans and chimpanzees diverged9 (Supplementary Fig. 12). Ka/Ks ratios of nonsynonymous-to-synonymous substitutions for different GO categories revealed an enrichment of elevated pairwise Ka/Ks values in the hypoxia response25,26,27 and energy metabolism28,29 categories, including in 'regulation of blood vessel size', 'regulation of angiogenesis', 'heme binding', 'glycerolipid biosynthetic process' and 'electron carrier activity' (Supplementary Table 13). Analysis of Ka/Ks ratios in the cattle and yak lineages verified that genes with elevated Ka/Ks values in yak were significantly enriched for these functions (Fig. 3a).Figure 3: Adaptive evolution in the yak genome.(a) Data points represent pairs of yak and cattle mean Ka/Ks ratios by GO category. GO categories with putatively accelerated (P < 0.05, binomial test) nonsynonymous divergence in the yak lineage (red) and in the cattle lineage (blue) are highlighted. A complete list of categories is provided in Supplementary Table 17. (b) Comparison of the proportions of genes showing evidence for positive selection in the yak and cattle lineages. The numbers of positively selected genes are given in parentheses. (c) Five genes involved in integrated nutrition pathways (according to KEGG pathway: map04971 and map00020) were found to show evidence of positive selection in the yak lineage. Solid lines indicate direct relationships between enzymes and metabolites. Dashed lines indicate that more than one step is involved in the process.Full size imageTo test the hypothesis that these rapidly evolving genes in yak have been under positive selection, we used the branch-site likelihood ratio test to identify positively selected genes (PSGs) in both the yak and cattle lineages. We identified 85 PSGs (in yak) and 95 PSGs (in cattle) (Fig. 3b and Supplementary Table 14). The PSGs detected in yak were enriched for genes involved in the hypoxia response and energy metabolism (Supplementary Tables 15 and 16). Of 81 genes examined in the response to hypoxia functional category (GO 0001666), 3 (3.7%) showed evidence of positive selection in yak (compared to none in cattle), which is significantly higher than the background level of positive selection across the genome (P < 0.05) (Fig. 3b). The three yak PSGs comprise two important regulators (Adam17 and Arg2) and one target gene (Mmp3) of hypoxia-inducible factor–1α (Hif-1α). As a master regulator of the cellular response to hypoxia, Hif-1α triggers wide transcription of genes involved in angiogenesis, vasodilatation and energy metabolism30,31,32,33,34,35. Notably, alleles of human ADAM17 were previously shown to be present at significant different frequencies in Tibetans and low-altitude dwellers36, indicating a possible role for this gene in altitude adaptation. The Adam17 and Arg2 proteins affect Hif-1α stability and activity by regulating production of tumor necrosis factor α (TNF-α)31,32 and nitric oxide, respectively33,34, whereas Mmp3 has key roles in numerous physiological processes35. In their high-altitude environments, yaks must not only maintain normal energy production under hypoxic pressure7,37 but must also optimize nutritional assimilation, as a consequence of the limited herbal resources available1. Indeed, we found five key genes that show signs of positive selection in yak nutrition pathways (Fig. 3c): Camk2b regulates the secretion of gastric acid in the rumen, which contributes to the assimilation of volatile fatty acids produced by ruminal fermentation38,39,40,41,42, and Gcnt3, Hsd17b12, Whsc1 and Glul have important roles in polysaccharide, fatty acid and amino-acid metabolism, respectively43,44,45,46. In addition, the positively selected changes in Glul may be important for the high level of nitrogen utilization in yak7.Our evolutionary analyses based on genomic data have provided important insights into adaptation to high altitude in yak. Further understanding may be gained by functional analysis of the identified genes with signs of adaptive evolution in comparative stress studies of yak and other animals living at high altitude. The identification of genes required for natural high-altitude adaptation may help to improve current understanding, treatment and prevention of altitude sickness and other hypoxia-related diseases in humans. In addition, this report of the yak genome sequence, together with the many SNVs identified, will facilitate genetic dissection of agronomically important traits in the species and will accelerate the genetic improvement of milk and meat production in this animal that is essential to the lifestyle and economy of the Tibetan people.URLs.SOAP, http://soap.genomics.org.cn/; Ensembl, http://www.ensembl.org/; TimeTree, http://www.timetree.org/.MethodsGenome sequencing and assembly.Genomic DNA was extracted from the liver of a female yak with an estimated inbreeding coefficient of 0.094 (ref. 1) that lived above 3,700 m in Huangyuan County of Qinghai Province, China. Sequencing libraries were constructed with multiple insert sizes (200 bp to 20 kb) according to the Illumina protocol. For short insert libraries (200 to 800 bp), 6 μg of DNA was fragmented to the desired insert size, end-repaired and ligated to Illumina paired-end adaptors. Ligated fragments were size selected at 200, 500 and 800 bp on agarose gels and were purified by PCR amplification to yield the corresponding libraries. For long insert sizes (2, 5, 10 and 20 kb) mate-pair library construction, 60 μg of genomic DNA was used; we circularized DNA, digested linear DNA, fragmented circularized DNA and purified biotinylated DNA and then performed adaptor ligation. All libraries were sequenced on an Illumina HiSeq 2000 platform.Whole-genome shotgun assembly of the yak was performed using short oligonucleotide analysis package (SOAP)denovo47 (Supplementary Note). First, a series of strict filtering steps were performed before assembly to avoid artifactual duplication, adaptor contamination and low-quality reads. Second, reads from the short-insert (≤800-bp) libraries were assembled into distinct contigs on the basis of k-mer overlap information. Third, reads from the long-insert (≥800-bp) libraries were aligned to the contig sequence, and the paired-end relationships between reads were used to construct scaffolds. We used a hierarchical assembly strategy in which we added data step by step from short paired-end reads to long paired-end reads. Finally, in order to fill gaps between scaffolds, we used the paired-end information to retrieve read pairs that had one read well aligned on the contigs and another read located within the gap region. We then performed a local assembly of the collected reads.Transcriptome and fosmid sequencing and assembly.From the same yak animal, RNA was extracted from fresh heart, liver, brain, stomach and lung tissues for the generation of transcriptome data. The quality and integrity of the RNA samples were examined using the Agilent 2100 Bioanalyzer, their RNA integrity number (RIN) values ranged from 8.6–10.0, with no sign of degradation. Approximately 20 μg of total RNA (at a concentration of ≥400 ng/μl) from each tissue was used to construct cDNA libraries. Poly(A) mRNA was isolated using beads conjugated to oligo(dT), mRNA was fragmented, and cDNA was synthesized using random hexamer primers and reverse transcriptase (Invitrogen). After end repair, adaptor ligation and PCR amplification, the libraries were sequenced using the Illumina HiSeq 2000 platform. Libraries that gave reads that were unevenly distributed among the gene regions (for example, showing a strong bias toward 5′ or 3′ regions) were discarded and replaced. Transcripts were assembled using SOAPdenovo. Reads for the assembly were filtered as for the genome assembly, and duplicate reads were removed. For further study, we used only those transcripts that were longer than 150 bp and that were covered at least twice.We also constructed a fosmid (inset size of ∼40 kb) library from the same DNA resource and randomly selected six clones for Sanger sequencing. These fosmids were assembled using the Celera Assembler48. We then evaluated the completeness and accuracy of the genome assembly by comparing the assembled scaffolds with 6 complete fosmid clones and 81,020 Unigene sequences using BLAST searches.Heterozygous SNV detection.To evaluate the heterozygosity rate and its distribution, high-quality reads (average quality score of >30) from short-insert libraries were realigned to the assembly with SOAP (see URLs). The probabilities of each possible genotype at every position on the reference genome were calculated, and a statistical model based on Bayesian theory and the Illumina quality system was used to call SNVs. The allelic sequence with the highest probability was used as the reference sequence, and heterozygous SNVs were called if other alleles also had high probability11. To estimate the accuracy of the identified heterozygous SNVs, we randomly selected 150 heterozygous SNVs and validated them by PCR amplification and Sanger sequencing. In 146 of the 150 sequences, SNVs were validated by double sequence peaks (Supplementary Fig. 6 and Supplementary Table 8).Annotation.Transposable elements in the yak genome assembly were first identified using a combination of homology-based and de novo approaches at both the DNA and protein levels47. We then used homology and ab initio prediction, as well as RNA-seq to identify protein-coding genes, building a consensus gene set by merging all predicted genes. For homology-based gene prediction, we aligned human and cattle protein sequences to the repeat-masked yak genome using TBLASTN and Genewise49 for fast alignment and accurate spliced alignment, respectively. Next, we used the ab initio gene prediction methods Genscan50 and Augustus51 to predict protein-coding genes, using parameters trained from a set of high-quality homolog prediction proteins. RNA-seq–based gene prediction was performed by aligning all RNA-seq data against the assembled genome using TopHat52, and Cufflinks53 was used to predict cDNAs from the resultant data. The final gene set was generated by merging these three gene prediction resources by GLEAN54. Gene functions were assigned according to the best match of the alignment to the SwissProt and Translated EMBL Nucleotide Sequence Data Library (TEMBL) databases, using BLASTP. For yak reference genes, motifs and domains were determined by searches in InterProScan55 of the sequences against publicly available databases, including Pfam, PRINTS, PROSITE, ProDom and SMART. Gene Ontology56 IDs for each gene were obtained from the corresponding InterPro entry. We also mapped yak reference genes to KEGG57 pathway databases and identified the best match for each gene.Gene families.The protein-coding genes from 6 mammalian species (Canis familiaris, Homo sapiens, Mus musculus, B. taurus, Equus caballus and Monodelphis domestica) downloaded from Ensembl (release 56; see URLs) were used in addition to yak genes to define gene families that descended from a single gene in the last common ancestor20. The longest translation form was chosen to represent each gene, and stretches of genes encoding fewer than 30 amino acids were filtered out. The 9,393 single-copy families obtained from this analysis were used to reconstruct phylogenies and estimate the times since divergence. Data from fourfold-degenerate sites were extracted from each family and attributed to one 'super gene' for each species. Modeltest58 was used to select the best substitution model (GTR + gamma + I), and Mrbayes59 was used to reconstruct the phylogenetic tree. The MCMCtree program implemented in the Phylogenetic Analysis by Maximum Likelihood (PAML)60 package was used to estimate the time since cattle-yak divergence. Calibration time was obtained from the TimeTree database (see URLs). To identify gene families that had undergone expansion or contraction, we applied the Café program, which is based on a probabilistic graphical model61, to infer the rate and direction of change in gene family size over a given phylogeny.Evolutionary analyses.We used conserved genome synteny methodology62 to establish a high-confidence orthologous gene set that included yak, cattle (UMD 3.1), horse (EquCab2.0), dog (CanFam2.0), mouse (mm9), chimpanzee (panTro2) and human (hg19) genes. Briefly, whole-genome multiple alignments were constructed for the relevant genomes using the MULTIZ63 alignment pipeline, with the human genome serving as the reference genome. To minimize the effect of annotation errors, variations in sequence quality and changes in gene structure on subsequent evolutionary rate analyses, we mapped all the human protein-coding genes from RefSeq64, KnownGene65 and VEGA66 to each of the other species via their syntenic alignments, then passed the resulting blocks through a series of rigorous filters that selected for large-scale synteny, high alignment quality and conservation of exon-intron structure. All orthologs were aligned using the codon option in the Probabilistic Alignment Kit (PRANK)67 program, and alignments shorter than 150 bp were discarded. The values of Ka and Ks and the Ka/Ks ratio were estimated for each gene using the Codeml program with the free-ratio model in the PAML package, and 10,000 concatenated alignments constructed from 150 randomly chosen genes were used to estimate lineage-specific mean values. The human GO annotation download from Ensembl was used to assign GO categories to 8,923 orthologs. The binomial test9 was used to identify GO categories with more than 20 orthologs that had an excess of nonsynonymous changes in either yak or cattle lineages. To detect genes evolving under positive selection in either yak or cattle (Supplementary Table 17), we used the optimized branch-site model68 in which likelihood ratio test (LRT) P values were computed assuming that the null distribution was a 50:50 mixture of a chi-squared distribution with 1 degree of freedom and a point mass at zero. Fisher's exact tests were used to test for over-represented functional categories among positively selected genes8. For each category C and set of PSGs S, a 2 × 2 contingency table was constructed for the numbers of genes assigned or not assigned to C and within or outside S. Then, (one-sided) P values for the independence of rows and columns were computed by Fisher's exact test. In addition, the distributions of LRT P values among the genes assigned and not assigned to C were compared by a (one-sided) Mann-Whitney U test. To test whether the unique mutations in yak, which resulted in the detected signal of positive evolution in the yak lineage, were specific to yak, we amplified and sequenced the DNA fragments encompassing the candidate yak-specific mutations in 15 genes (including the 8 genes shown in Fig. 3b,c) in 5 randomly selected yaks and 5 cattle. All mutations were confirmed to be specific to yak.Accession codes.The yak whole-genome shotgun project has been deposited at the DNA Data Bank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL) nucleotide sequencing database and GenBank under the same accession, AGSK00000000. The version of the genome described in this paper is the first version, AGSK01000000 (available at DDBJ, EMBL and GenBank). The mitochondrial sequence has been deposited at GenBank under accession JQ692071. All short-read data have been deposited at the Short Read Archive (SRA) under accession SRA047288. Raw sequencing data for the transcriptome have been deposited in the Gene Expression Omnibus (GEO) under accession GSE33300.

Accession codes

Primary accessions

DDBJ/GenBank/EMBL

DDBJ

EMBL/GenBank/DDBJ

EMBL

Gene Expression Omnibus

GSE33300

NCBI Reference Sequence

AGSK00000000

AGSK01000000

GenBank

JQ692071

Sequence Read Archive

SRA047288

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Download referencesAcknowledgementsThe project was supported by the Natural Science Foundation of China (30725004 and 40972018), the Ministry of Science and Technology of China (2010DFB63500), the International Collaboration 111 Projects of China, the 985 and 211 Projects of Lanzhou University, the Shenzhen Municipal Government (ZYC200903240077A) and the Hundreds-Talent Program from the Chinese Academy of Sciences.Author informationAuthor notesQiang Qiu, Guojie Zhang, Tao Ma, Wubin Qian, Junyi Wang and Zhiqiang Ye: These authors contributed equally to this work.Authors and AffiliationsState Key Laboratory of Grassland Agro-Ecosystem, College of Life Science, Lanzhou University, Lanzhou, ChinaQiang Qiu, Tao Ma, Quanjun Hu, Ran Zhou, Lizhong Wang, Kun Wang, Xu Lu, Hui Ma, Jian Zhang, Zhaofeng Wang, Yingmei Zhang, Dawei Zhang, Shengxiang Zhang, Ruijun Long, Yi Wu & Jianquan LiuBeijing Genomics Institute (BGI)-Shenzhen, Shenzhen, ChinaGuojie Zhang, Wubin Qian, Junyi Wang, Changchang Cao, Xiaoju Qian, Yongshan Lang, Jinquan Xia, Shengguang Liao, Shengkai Pan, Haolong Hou, Yan Wang, Xuetao Zang, Ye Yin, Wenbin Liu, Yan Zhang, Zhiyong Huang, Huanming Yang, Jian Wang, Jun Wang & Jian WangState Key Laboratory of Genetic Resources and Evolution, Institute of Kunming Zoology, Chinese Academy of Sciences, Kunming, ChinaZhiqiang Ye & Peng ShiGraduate School of Chinese Academy of Sciences, Beijing, ChinaZhiqiang YeInstitute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USAJaebum Kim & Jian MaDepartment of Animal Biotechnology, Konkuk University, Seoul, KoreaJaebum KimInstitute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion, UKDenis M LarkinNational Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USALoretta Auvil & Boris CapitanuDepartment of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USAJian MaDepartment of Evolution and Ecology, University of California, Davis, California, USAHarris A LewinSchool of Life Sciences, Fudan University, Shanghai, ChinaTakahiro Yonezawa, Masami Hasegawa & Yang ZhongFaculty of Veterinary Medicine, Utrecht University, Utrecht, The NetherlandsJohannes A LenstraInstitute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UKDavid N CooperDepartment of Biology, University of Copenhagen, Copenhagen, DenmarkJun WangKey Laboratory for Bio-resources and Eco-environment, College of Life Science, Sichuan University, Chengdu, ChinaJianquan LiuAuthorsQiang QiuView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsJ.L. designed and managed the project. Jian Wang and Jun Wang led the genome sequencing. P.S. designed the related analyses of the gene families. Z.W. prepared the yak nucleic acid samples. G.Z., Y.L., W.Q., H,Y., Junyi Wang, X.Q., Y. Wang, X.Z. and Y.Y. performed the DNA sequencing. W.Q., Junyi Wang, S.L., S.P. and C.C. performed the genome assembly. W.Q., C.C., S.L., Yan Zhang, J.X., H.H. and G.Z. performed the genome annotation. Q.Q. and T.M. designed evolutionary analyses. T.M., Q.Q., Q.H., M.H., Yingmei Zhang, R.Z., X.L., L.W., H.M., K.W., D.Z., S.Z., R.L., T.Y., W.Q., C.C., G.Z. and Y. Wu performed evolutionary analyses. Z.Y. and P.S. independently verified all analyses. J.K., D.M.L., L.A., B.C., J.M., H.A.L. and W.L. performed the synteny analyses. Q.H., Q.Q., J.Z. and Z.H. carried out data submission and database construction. Q.Q. and J.L. wrote the paper. H.A.L., Jun Wang, P.S., D.N.C., G.Z., J.A.L., J.M. and Y. Zhong revised the paper.Corresponding authorsCorrespondence to

Jun Wang, Peng Shi, Jian Wang or Jianquan Liu.Ethics declarations

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The authors declare no competing financial interests.

Supplementary informationSupplementary Text and FiguresSupplementary Figures 1–12, Supplementary Tables 1–16 and Supplementary Note (PDF 2056 kb)Supplementary Table 17Identification of lineage-specific accelerated GO categories (XLS 48 kb)Rights and permissions

This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial-ShareAlike license (http://creativecommons.org/licenses/by-nc-sa/3.0/), which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation, and derivative works must be licensed under the same or similar license.

Reprints and permissionsAbout this articleCite this articleQiu, Q., Zhang, G., Ma, T. et al. The yak genome and adaptation to life at high altitude.

Nat Genet 44, 946–949 (2012). https://doi.org/10.1038/ng.2343Download citationReceived: 29 November 2011Accepted: 06 June 2012Published: 01 July 2012Issue Date: August 2012DOI: https://doi.org/10.1038/ng.2343Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard

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