,1,2,3, 陈斌,1,3, 冉茂良,1,3, 杨广民2, 曾诚2The application of genomic selection in pig cross breeding
Anqi Yang,1,2,3, Bin Chen,1,3, Maoliang Ran,1,3, Guangmin Yang2, Cheng Zeng2通讯作者: 陈斌，教授，博士生导师，研究方向：猪遗传育种。E-mail:chenbin7586@126.com; 冉茂良，博士，研究方向：猪遗传育种。E-mail:ranmaoliang0903@126.com
第一联系人:
编委: 李明洲
收稿日期:2019-08-28修回日期:2020-02-8网络出版日期:2020-02-17
| 基金资助: |
Received:2019-08-28Revised:2020-02-8Online:2020-02-17
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摘要
基因组选择是指在全基因组范围内通过基因组中大量的标记信息估计出个体全基因组范围的育种值,可进一步提升育种效率和准确性,目前在猪纯繁育种中得到广泛应用。但有研究表明,现有的基因组选择方法在猪杂交育种上的应用效果并不理想,在跨群体条件下预测准确性极低。杂交作为养猪业中最为广泛的育种手段之一,通过结合基因组选择理论进一步提升猪的生产性能,具有重要的经济和研究价值。本文综述了基因组选择的发展及其在猪育种中的应用现状,并结合国内外猪杂交育种的方式,分析了目前基因组选择方法在猪杂交育种应用方面的不足,旨在为未来基因组选择在猪杂交育种中的合理应用提供参考。
关键词：
Abstract
Genomic selection is a form of marker-assisted selection in which genetic markers covering the entire genome are used so that all quantitative trait loci are in linkage disequilibrium with at least one marker. Genomic selection improves the efficiency and accuracy of breeding and it is widely used in purebred breeding across many animal species. However, some studies indicate that the accuracy of genome selection in cross breeding needs to be improved,especially in cross population. As one of the most extensive breeding methods employed in the swine industry, cross breeding has significant, potential research and economic value to further improve its performance by combining with genomic selection. In this review, we summarize the application of genomic selection in pigs, and elucidate the genomic selection deficiencies in breeding hybrid pigs. This review will also provide valuable insights for the future application and improvement of genomic selection in pig cross breeding.
Keywords：
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本文引用格式
杨岸奇, 陈斌, 冉茂良, 杨广民, 曾诚. 基因组选择在猪杂交育种中的应用. 遗传[J], 2020, 42(2): 145-152 doi:10.16288/j.yczz.19-253
Anqi Yang.
动物育种学是以遗传学理论为基础,以经济养殖动物为研究对象,综合多种学科与技术以提升动物生产性能和经济效益的一门应用学科。总体而言,动物育种主要有两种途径——纯繁育种和杂交育 种。随着分子生物学相关技术的发展与计算机科学的广泛应用,更多的遗传信息被发掘且对信息的利用效率有了质的飞跃,动物育种从仅利用表型数据的传统选种选育方法发展为利用遗传信息结合表型数据的基因组选择法(genomic selection, GS)[1,2]。动物因产生经济效益的方式不同而导致育种途径不完全一致,研究认为基因组选择准确性受研究对象的群体结构、生产方式和技术手段的影响[3]。目前,基因组选择在奶牛中已有较为深入的研究和广泛应 用[4,5],奶牛经济效益主要来自对纯种产奶性能的筛选。猪作为国内外重要的食用动物且猪肉的供应绝大部分来源于杂交后代[6],因此将基因组选择应用于猪杂交育种以提升商品猪的性能还需要进行深入探讨。中国作为养猪大国,拥有全球最大的猪肉产品消费市场,为提升猪的生产性能和养猪产业的核心竞争力,目前基因组选择已初步应用于我国良种猪纯繁育种及地方猪改良并卓有成效[7,8]。杂交作为猪育种的重要手段之一,通过合理地筛选亲本组合,杂交后代可表现出杂种优势。杂种优势是不同品种杂交后,F1代生产性能高于亲本,但这种优势无法通过F1代互相交配而稳定遗传。针对杂种优势,研究人员分别提出显性假说、超显性假说等以解释这种特殊的遗传现象[9],同时也有研究对精确利用杂种优势提出参考意见并对在杂交育种中偏向于利用遗传加性效应的研究方法提出质疑[10]。目前杂种优势分子层面的作用机制尚未彻底明确,只能从多个角度进行阐述并加以分析[11],在宏观层面仅限于对不同品种杂交后代进行配合力分析而选出最优杂交组 合。充分利用现有的基因组信息,以统计学方法为指导,精准预测后进行人工干预动物繁育,从而实现获得更具经济效益的动物品种是动物育种的发展方向[12]。
基因组选择是目前最强有力的育种手段,随着分子生物学与统计学的发展,基因芯片费用逐步降低,科研人员越来越重视基因组选择在种用动物上的应用,但基因组选择在猪杂交育种中对杂种优势的利用报道偏少。因此,本文主要综述了基因组选择的发展现状和目前基因组选择在猪育种上的最新研究成果,以期为基因组选择在猪杂交育种中的应用提供参考。
1 基因组选择在猪育种中的发展现状
1.1 分子生物学技术与基因组选择的发展
20世纪90年代,Fujii等[13]首次使用标记辅助选择鉴定出与猪应激及屠宰后肉品质相关的氟烷基因,该方法效率高、对隐性基因型进行筛查可以避免后代出现猪恶性高温综合征(malignant hyperthermia syndrom)等优点,使标记辅助选择(marker assisted selection, MAS)在猪育种上的研究范围逐步扩大。但猪的经济性状以数量性状为主[14],Rothschild等[15]将猪第一个可用的数量性状基因座(quantitative trait locus, QTL)用于改良其繁殖性状时,发现该QTL对总产仔数表型方差的影响仅占12%,该结果证明早期遗传学家提出的微效多基因假说的合理性,同时间接说明利用标记辅助选择提高动物经济性状的方法适用性不强。基于标记辅助选择存在的优势与不足,科研人员综合标记辅助选择法与最佳线性无偏预测方法(best linear unbiased predicttion, BLUP),将所鉴定的不同基因型根据其遗传特性融入BLUP法中,将不同基因型设定为半随机效应[16],用于估算个体估计育种值(estimated breeding value, EBV)。分子生物学技术与数量遗传学方法的结合使用对基因组选择方法的提出有着重要影响。早期研究以与性状显著相关的基因为重点研究对象,但随着单核苷酸序列多态性(single nucleotide polymorphisms, SNP)基因分型技术的发展与应用,更多功能未知的基因型被发现,将大量功能暂未明确的基因用于动物育种成为新的研究方向。Meuwissen等[17]提出利用贝叶斯方法关联所有标记,基于足够的标记密度,该方法可以有效估计各基因型的效应,并提出以基因型与EBV关联的全基因组估计育种值(genomic estimated breeding value, GEBV),这标志着基因组选择开始应用于动物育种。基因组选择方法可以充分利用遗传信息,提高基因信息利用水平[18],从而使育种效率及基因育种值计算的准确性得到极大提高[19]。
1.2 统计方法与基因组选择
基因组选择的迅速发展得益于科研人员对动物基因测序、SNPs功能鉴定和数量遗传学的深入研究。随着高通量测序费用下降及数量遗传学理论的发展,等位基因效应评估准确性也逐步提升[20]。方法的准确性与可靠性是基因组选择应用于动物育种的核心,因此根据信息来源准确快速计算动物估计育种值是目前基因组选择的研究热点之一。基因组选择是通过构造参考群,根据参考群体的SNPs信息并关联其性状表型值,对候选群体进行选种选育。基因组选择的优点在于可节约传统育种中性能测定所消耗的人力、物力和时间,只需对刚出生的后代遗传信息进行检测分析,结合参考群体表型信息计算GEBV,从而决定对待选动物是否留种。迄今为止,在基因组选择中广泛使用的计算方法为基因组最佳线性无偏预测法(genomic best linear unbiased prediction, GBLUP),该方法的统计模型与传统BLUP法无差异,GBLUP法假设各SNP的方差一致,仅需将传统BLUP法中的A矩阵替换为G矩阵。G矩阵的构建方法由Vanraden提出,在基因组选择中应用最为广泛,优点在于运算速度快、充分利用个体间的遗传差异信息、降低孟德尔抽样所引起的偏差[21],与传统BLUP法相比,GBLUP法大多数情况下的预测准确性更高[22]。除GBLUP法外,科研人员还开发了基于贝叶斯方法的基因组选择其他算法,如Bayes-A、Bayes-B、Bayes-Lasso和Bayes Cπ等[23,24,25,26]。在数理统计中,贝叶斯学派对事件概率引入先验概率的概念,认为所有事件发生前均有主观认知,通过主观认知构造先验分布,而后通过事件发生获得后验分布,再根据后验分布进行相关的
计算。与GBLUP法不同,贝叶斯方法基于等位基因方差的前提假设不一致,即等位基因频率和基因型效应方差不完全具有固定的分布[27]。频率学派与贝叶斯学派在现代统计学发展与应用中,不同情况下均可以对事件获得精准地预测和结果分析,因此基因组选择中算法的选择也只能根据数据的实际情况加以调整计算,不存在一种算法在任何条件下都适用[28]。虽然基因组选择的理论研究在不断深入和完善,但由于投资回报率、记录完整性及群体规模等问题,基因组选择的扩大应用仍存在阻力。而一步法(single step procedure)的提出从一定程度上克服了由于动物经济价值和群体相对较小等方面的应用难题[29,30],可以将没有进行基因分型的动物纳入遗传评估模型进而指导动物育种[31]。在获取动物表型信息与基因信息后,需要适时调整算法或模型,且研究人员仍需依照统计学原理继续开发新算法以解决新的问题。此外,部分科研人员开始尝试以基因组选择为基础,综合多组学信息进行猪的育种研究[32],该方向可能将会成为下一阶段猪育种研究热点。
1.3 基因组选择在猪重要经济性状的应用
在猪育种中,基因组选择应用最广的性状为繁殖性状、生长性状和胴体性状。较高的繁殖性能可以保证猪种在市场竞争中获得更多效益,同时可以减少母猪淘汰率,节约养殖成本。传统的BLUP育种方法提升繁殖性状主要通过增加其选择权重,而对EBV相近的后备猪,优先选留亲本繁殖性能较高的个体。相反,基因组选择可以更好地利用基因型信息,并有效控制群体近交程度 [17,33]。但基因组选择在猪繁殖性状上仍受到诸多因素影响,如不同的外部环境、统计模型、群体结构和不同群体之间的基因同源性等。基因组选择在猪繁殖性状上的主要研究结果见表1。猪的繁殖性状属于低遗传力性状和限性性状,受环境因素影响较大,计算结果的准确性和遗传进展的加快程度不尽相同。相反,遗传力较高的生长性状和胴体性状因其易于测定且可从半同胞或全同胞中获得更为丰富的信息,所以传统BLUP和基因组选择的预测结果相比繁殖性状更为准确。基因组选择在猪较高遗传力性状中的主要成果见表2。
Table 1
表1
表1基因组选择在猪繁殖性状方面的研究
Table 1
| 主要研究性状 | 主要结论 | 文献来源 |
|---|---|---|
| 总产仔数(h2=0.16); 流产率(h2=0.16) | 两种性状GEBV和EBV估计值的平均准确性分别为0.82、0.83;当不断有高胎次母猪作为研究对象时基因组选择准确性分别降低至0.33~0.65范围内。 | 34 |
| 窝总产仔数 | 利用一步法计算的母猪基因组育种值准确性为0.28~0.49,传统BLUP的育种值准确性为0.22;公猪无显著差异。 | 35 |
| 窝总产仔数 | GEBV值计算准确性比养殖公司宣称的计算结果准确性高20%。 | 36 |
| 总产仔数(不同群体h2分别 为0.21,0.14,0.19) | 表型值的实际值与预测值的平均相关性总是高于传统方法,杂交群体高0.26,纯繁群体高0.15~0.22。 | 37 |
| 繁殖阶段总产仔数, 窝总产仔数,死亡率 | 对比一步法、GBLUP、传统BLUP法等预测结果发现：一步法、GBLUP法预测准确性平均为0.171和0.209,BLUP法准确性平均为0.091。 | 38 |
| 总产仔数 | 与传统方法比,一步法使结果准确性提升19%且认为基因组选择的准确性高于传统的系谱指数和育种值选种,且低遗传力性状提高幅度最大。 | 39 |
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Table 2
表2
表2基因组选择在生长性状与肉质性状方面的研究结果
Table 2
| 主要研究性状 | 主要结论 | 文献来源 |
|---|---|---|
| 生长速度、背膘厚度、肉品质、饲料利用率、肌内脂肪含量 | GBLUP提升遗传进展,减少近交系数。基因组选择比传统选择方法对性状的遗传进展提升高27%~33%。候选群体中只有亲缘关系和表型值明确的条件下,基因组选择对遗传改良速度的增加效果不明显。 | 40 |
| 平均日增重、背膘厚 | 利用基因组选择和RRBLUP法对性状的遗传进展有很大促进作用。利用高密度芯片可以降低连锁不平衡对性状的影响。 | 41 |
| 采食量、平均日增重、背膘厚、肌肉厚度、肌内脂肪含量 | 使用Bayes-A法,无论高密度芯片和低密度芯片对基因进行分析都不能使GEBV值获得较高准确性。 | 42 |
| 日采食量、平均日增重、 背膘厚 | 日采食量、平均日增重、背膘厚GEBV准确性分别为0.508~0.531、0.506~0.532、0.308~0.362。 | 43 |
| 宰后45 min猪体温度和 眼肌pH值 | 相比于RRBLUP、Bayes-A和Bayes-B等模型,异方差统计模型可增强GEBV的准确性,但准确性提高幅度较小。 | 44 |
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2 猪杂交育种现状与基因组选择
杂交育种是猪育种的重要方法,但对比纯繁育种,基因组选择在猪杂交育种的研究深度有所欠缺。根据育种原则和市场需要,猪群分布一般为金字塔结构：顶层为原种代猪场,中间为父母代扩繁猪场,底层为商品代猪场,优良基因从顶层传递至底层而后提供肉产品至消费市场。遗传进展的提升是在原种代纯繁育种过程中获得,扩繁场与商品猪场完成传递,有研究认为仅依赖于不同群体的表型数据指导杂交育种会导致纯种猪所获遗传进展在商品代中有所衰退,因而在杂交育种中需要新的技术手段加强遗传进展传递效率[45,46]。2.1 基因组选择在良种猪杂交育种的应用
在养猪业中,主要的杂交方式包括二元杂交、三元杂交、双杂交、回交和轮回杂交等,其中以三元杂交(杜洛克♂×长白猪♂×大白猪♀)应用最为广泛。Xiang等[47]对Christensen等[48]提出的一步法加以改进,采用非线性模型作为主要的统计模型,对长白猪与大白猪双列杂交及其后代的总产仔数性状进行分析,结果表明改进的一步法在杂交育种遗传评估中有较高的准确率,长白猪、大白猪与杂交后代在总产仔数性状上的遗传相关约为0.57~0.78,认为利用非线性模型结合基因组选择理论进行杂交育种,可以有效提高预测准确性。André等[49]利用基因组信息计算多群体、不同性状(初次受精日龄、总产仔数、初生窝重、窝内仔猪体重变异等) GEBV,以纯种猪建立参考群,对纯繁和杂交后代性状GEBV进行预测并分析其准确性,结果表明纯种后代性状GEBV准确性为0.21~0.72,而杂交后代GEBV计算结果准确性有所下降,约为0.18~0.67;而对转群后代进行跨群体预测时,预测结果与真实表型数据相比,初次受精日龄和总产仔数GEBV的预测准确性极低,其他性状准确率较低且与传统方法预测结果差异不显著,该研究认为虽然根据基因信息可以提高猪育种中的预测准确性,但在某些条件下利用系谱信息进行预测仍有较高的准确率,这主要由性状与群体特性所决定。上述研究表明,目前的研究手段和方法可能对猪杂交育种信息的综合利用还不够全面,有待今后科研人员提供新的思路或算法以期进一步提高基因组选择在杂交育种预测方面的准确率。
2.2 我国地方猪杂交育种与基因组选择
2.2.1 地方猪配套系杂交地方猪杂交育种目的不仅限于提升其生产性能,同时还可以有效地对珍稀地方猪种起到遗传资源保护作用。很多地方猪种的经济效益与投资回报率与良种猪相比劣势较大,在市场竞争中被边缘化,从而造成部分地方猪群体规模大量缩减。然而,地方猪遗传资源的保护需要足够的群体规模作支撑,因此扩大群体必须提升市场认可程度和养殖场对地方猪的饲养意愿,而以基因组选择方法指导地方猪杂交育种可以更为有效地保证后代具有地方猪和与之杂交品种双方的优良特性。2019年7月,湘沙配套系正式通过国家遗传资源委员会认定,开创了地方猪保护新模式[50]。该配套系采用沙子岭猪做为第一母本,巴克夏猪作为第二父本,大白猪作为终端父本进行杂交育种,将地方猪肉质优良和“洋猪”快速生长等特性较好地传递给商品代,其原理与三元杂交一致。国内对基因组选择在地方猪配套系杂交育种上的应用还处于空白状态,目前对于猪杂交育种的研究主要集中在利用不同品种进行杂交,测定后代生产性能并计算不同组合的配合力以筛选杂交最优组合或基因型与杂交后代性状的关联分析等。国外基因组选择在猪杂交育种方面的应用以科研为主,相对高昂的费用只有纯繁育种才能带来可观回报,
因而种猪企业偏向于将基因组选择用于纯繁育种。国内地方猪配套系杂交育种的研究深度有待加强,性能测定的规范性有所欠缺,基因组选择应用至配套系杂交所需费用使养殖场望而却步,并且研究表明湘沙配套系杂交后代很难出现生产性能均高于亲本的现象[51]。结合André等[49]研究结果,基因组选择对跨群体杂交后代性能的预测准确性存在与传统方法无差异的可能,可以推断基因组选择应用于地方猪配套系生产还需解决预测准确性不够和投资回报率低的问题。
2.2.2 地方猪杂交形成新品种
以地方猪为核心的配套系生产模式既能改善地方猪生产性能偏低的问题,又能保留地方猪优良特性,是兼顾市场与遗传资源保护的重要举措,但地方猪遗传资源的杂交利用并非仅限于此。湘村黑猪作为湖南省首个通过国家品种审定的新品种,是以桃源黑猪为母本、杜洛克为父本,通过导入杂交的方式培育而成。这种杂交方式对地方猪资源保护力度较前者虽有所降低,但在新品种形成后对群体的改良可参照纯繁育种方式进行,所以后期的育种工作效率会显著提升。体型、毛色的一致性是新品种通过审定的基本要素,传统的选育方法耗时长、效率低,因此通过基因组选择加快杂交后代的体貌性状同质化速度,对于节约选育成本和缩短育成时间具有重要意义。汪超[52]利用基因组选择对通城猪杂交后代的毛色遗传规律进行研究,通过构建杜洛克猪×通城猪回交群体及大白猪×通城猪F2群体,测定毛色所占体表覆盖面积且将毛色性状定性为数量性状,结果表明MITF和EDNRB这两个基因可能是控制中国花猪毛色的两个主效基因;此外,在毛色筛选区域发现与繁殖相关的基因。虽然该研究并非针对新品种形成,但其将毛色作为数量性状进行研究的思路为基因组选择应用至新品种形成提供新方向,间接说明了通过基因组选择可以使杂交后代的体貌性状更快地趋于稳定,以达到国家畜禽遗传资源委员会对新品种认定的基本要求。
3 结语与展望
猪杂交育种在国外良种猪杂交与地方猪杂交上有差异,与植物杂交育种有着更明显的区别,虽然有大量杂交育种的研究成果为未来精确利用杂种优势作参考,但目前还没有完整的模型能对杂交过程进行很好的诠释[53,54]。考虑到养殖成本和实际操作难度,地方猪杂交育种大部分以不完全双列杂交筛选亲本组合,且传统的杂交育种方法无法利用基因等分子水平上的信息,导致杂种优势不能被完全体现和合理利用。与传统方法相比,基因组选择在猪纯繁育种方面对育种值计算准确性有不同程度的提高[32],而猪杂交育种的分子机制更为复杂导致所包含的信息无法被现有的研究手段充分利用,因此将猪纯繁育种的基因组选择方法套用至杂交育种效果并不理想[49]。考虑到目前基因组选择的算法在杂交育种上的局限性,将神经网络等新算法结合基因组选择理论指导杂交育种,以提升计算机运算效率和结果准确性,也为基因组选择合理应用至杂交育种提供新的改进思路[55,56]。目前育种程序的选择可以通过使用表型(表型选择)、谱系关系(育种值选择)或分子标记(标记辅助选择或基因组选择)来完成,但所有这些方法都属于截断选择,即淘汰所有GEBV/ EBV低于某选择阈值的个体,然后用选留的个体来繁育下一代,且侧重于亲代配种前的最佳性能。这些技术手段能显著提高选择反应,但因此提高了亲属间联合选择的概率,进而导致近交速率(ΔF)的增加。近年来,基因组选配(genomic mating, GM)作为另一种基因组选择的方法被提出,它侧重于选配而不是截断选择,其使用估计育种值、风险预测(有用性)和亲本之间亲缘系数来优化配种[57]。因而基因组选配作为一种可利用大量信息的新型育种工具,可为育种者设计和管理育种方案提供参考,在猪杂交育种中也可以适当使用。基因组选择在猪杂交育种中的应用仅考虑基因加性效应会造成信息浪费且预测结果准确性不理想,应从模型、算法及信息使用率等方面合理优化,进而提升基因组选择猪杂交育种上的适用性。参考文献 原文顺序
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被引期刊影响因子
DOI:10.1111/j.1439-0388.2007.00702.xURLPMID:18076469 [本文引用: 1]
Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used so that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. This approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and new methods to efficiently genotype large number of SNP. Simulation results and limited experimental results suggest that breeding values can be predicted with high accuracy using genetic markers alone but more validation is required especially in samples of the population different from that in which the effect of the markers was estimated. The ideal method to estimate the breeding value from genomic data is to calculate the conditional mean of the breeding value given the genotype of the animal at each QTL. This conditional mean can only be calculated by using a prior distribution of QTL effects so this should be part of the research carried out to implement genomic selection. In practice, this method of estimating breeding values is approximated by using the marker genotypes instead of the QTL genotypes but the ideal method is likely to be approached more closely as more sequence and SNP data is obtained. Implementation of genomic selection is likely to have major implications for genetic evaluation systems and for genetic improvement programmes generally and these are discussed.
DOI:10.3724/sp.j.1005.2011.01308URLPMID:22207376 [本文引用: 1]
Selective breeding is very important in agricultural production and breeding value estimation is the core of selective breeding. With the development of genetic markers, especially high throughput genotyping technology, it becomes available to estimate breeding value at genome level, i.e. genomic selection (GS). In this review, the methods of GS was categorized into two groups: one is to predict genomic estimated breeding value (GEBV) based on the allele effect, such as least squares, random regression - best linear unbiased prediction (RR-BLUP), Bayes and principle component analysis, etc; the other is to predict GEBV with genetic relationship matrix, which constructs genetic relationship matrix via high throughput genetic markers and then predicts GEBV through linear mixed model, i.e. GBLUP. The basic principles of these methods were also introduced according to the above two classifications. Factors affecting GS accuracy include markers of type and density, length of haplotype, the size of reference population, the extent between marker-QTL and so on. Among the methods of GS, Bayes and GBLUP are usually more accurate than the others and least squares is the worst. GBLUP is time-efficient and can combine pedigree with genotypic information, hence it is superior to other methods. Although progress was made in GS, there are still some challenges, for examples, united breeding, long-term genetic gain with GS, and disentangling markers with and without contribution to the traits. GS has been applied in animal and plant breeding practice and also has the potential to predict genetic predisposition in humans and study evolutionary dynamics. GS, which is more precise than the traditional method, is a breakthrough at measuring genetic relationship. Therefore, GS will be a revolutionary event in the history of animal and plant breeding.
DOI:10.3724/sp.j.1005.2011.01308URLPMID:22207376 [本文引用: 1]
Selective breeding is very important in agricultural production and breeding value estimation is the core of selective breeding. With the development of genetic markers, especially high throughput genotyping technology, it becomes available to estimate breeding value at genome level, i.e. genomic selection (GS). In this review, the methods of GS was categorized into two groups: one is to predict genomic estimated breeding value (GEBV) based on the allele effect, such as least squares, random regression - best linear unbiased prediction (RR-BLUP), Bayes and principle component analysis, etc; the other is to predict GEBV with genetic relationship matrix, which constructs genetic relationship matrix via high throughput genetic markers and then predicts GEBV through linear mixed model, i.e. GBLUP. The basic principles of these methods were also introduced according to the above two classifications. Factors affecting GS accuracy include markers of type and density, length of haplotype, the size of reference population, the extent between marker-QTL and so on. Among the methods of GS, Bayes and GBLUP are usually more accurate than the others and least squares is the worst. GBLUP is time-efficient and can combine pedigree with genotypic information, hence it is superior to other methods. Although progress was made in GS, there are still some challenges, for examples, united breeding, long-term genetic gain with GS, and disentangling markers with and without contribution to the traits. GS has been applied in animal and plant breeding practice and also has the potential to predict genetic predisposition in humans and study evolutionary dynamics. GS, which is more precise than the traditional method, is a breakthrough at measuring genetic relationship. Therefore, GS will be a revolutionary event in the history of animal and plant breeding.
DOI:10.2527/jas.2009-1975URLPMID:19749023 [本文引用: 1]
In livestock, genomic selection (GS) has primarily been investigated by simulation of purebred populations. Traits of interest are, however, often measured in crossbred or mixed populations with uncertain breed composition. If such data are used as the training data for GS without accounting for breed composition, estimates of marker effects may be biased due to population stratification and admixture. To investigate this, a genome of 100 cM was simulated with varying marker densities (5 to 40 segregating markers per cM). After 1,000 generations of random mating in a population of effective size 500, 4 lines with effective size 100 were isolated and mated for another 50 generations to create 4 pure breeds. These breeds were used to generate combined, F(1), F(2), 3- and 4-way crosses, and admixed training data sets of 1,000 individuals with phenotypes for an additive trait controlled by 100 segregating QTL and heritability of 0.30. The validation data set was a sample of 1,000 genotyped individuals from one pure breed. Method Bayes-B was used to simultaneously estimate the effects of all markers for breeding value estimation. With 5 (40) markers per cM, the correlation of true with estimated breeding value of selection candidates (accuracy) was greatest, 0.79 (0.85), when data from the same pure breed were used for training. When the training data set consisted of crossbreds, the accuracy ranged from 0.66 (0.79) to 0.74 (0.83) for the 2 marker densities, respectively. The admixed training data set resulted in nearly the same accuracies as when training was in the breed to which selection candidates belonged. However, accuracy was greatly reduced when genes from the target pure breed were not included in the admixed or crossbred population. This implies that, with high-density markers, admixed and crossbred populations can be used to develop GS prediction equations for all pure breeds that contributed to the population, without a substantial loss of accuracy compared with training on purebred data, even if breed origin has not been explicitly taken into account. In addition, using GS based on high-density marker data, purebreds can be accurately selected for crossbred performance without the need for pedigree or breed information. Results also showed that haplotype segments with strong linkage disequilibrium are shorter in crossbred and admixed populations than in purebreds, providing opportunities for QTL fine mapping.
[本文引用: 1]
DOI:10.3168/jds.2008-1514URL [本文引用: 1]
Abstract
Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R2) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R2 values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R2 values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.[本文引用: 1]
[本文引用: 1]
DOI:10.1186/1471-2156-15-7URLPMID:24422716 [本文引用: 1]
Modern breeding and artificial selection play critical roles in pig domestication and shape the genetic variation of different breeds. China has many indigenous pig breeds with various characteristics in morphology and production performance that differ from those of foreign commercial pig breeds. However, the signatures of selection on genes implying for economic traits between Chinese indigenous and commercial pigs have been poorly understood.
[本文引用: 1]
DOI:10.1111/j.1469-8137.2010.03574.xURLPMID:21166808 [本文引用: 1]
Hybrids between genetically diverse varieties display enhanced growth, and increased total biomass, stress resistance and grain yield. Gene expression and metabolic studies in maize, rice and other species suggest that protein metabolism plays a role in the growth differences between hybrids and inbreds. Single trait heterosis can be explained by the existing theories of dominance, overdominance and epistasis. General multigenic heterosis is observed in a wide variety of different species and is likely to share a common underlying biological mechanism. This review presents a model to explain differences in growth and yield caused by general multigenic heterosis. The model describes multigenic heterosis in terms of energy-use efficiency and faster cell cycle progression where hybrids have more efficient growth than inbreds because of differences in protein metabolism. The proposed model is consistent with the observed variation of gene expression in different pairs of inbred lines and hybrid offspring as well as growth differences in polyploids and aneuploids. It also suggests an approach to enhance yield gains in both hybrid and inbred crops via the creation of an appropriate computational analysis pipeline coupled to an efficient molecular breeding program.
[本文引用: 1]
DOI:10.1038/nrg3503URLPMID:23752794 [本文引用: 1]
Heterosis, also known as hybrid vigour, is widespread in plants and animals, but the molecular bases for this phenomenon remain elusive. Recent studies in hybrids and allopolyploids using transcriptomic, proteomic, metabolomic, epigenomic and systems biology approaches have provided new insights. Emerging genomic and epigenetic perspectives suggest that heterosis arises from allelic interactions between parental genomes, leading to altered programming of genes that promote the growth, stress tolerance and fitness of hybrids. For example, epigenetic modifications of key regulatory genes in hybrids and allopolyploids can alter complex regulatory networks of physiology and metabolism, thus modulating biomass and leading to heterosis. The conceptual advances could help to improve plant and animal productivity through the manipulation of heterosis.
DOI:10.1186/s12859-018-2450-5URLPMID:30642239 [本文引用: 1]
Selecting animals for breeding in the optimum way plays an essential role for the management of genetic resources and in selective breeding of livestock species. It requires to compute the optimum genetic contribution of each selection candidate to the next generation. Current software packages for optimum contribution selection (OCS) are not able to handle the main conflicting objectives of animal breeding programs simultaneously, which includes to increase genetic gain, to increase or to maintain genetic diversity, to recover the original genetic background of endangered breeds with historic introgression, and to maintain or increase genetic diversity at native alleles.
DOI:10.1126/science.1862346URLPMID:1862346 [本文引用: 1]
Malignant hyperthermia (MH) causes neurological, liver, and kidney damage and death in humans and major economic losses in the swine industry. A single point mutation in the porcine gene for the skeletal muscle ryanodine receptor (ryr1) was found to be correlated with MH in five major breeds of lean, heavily muscled swine. Haplotyping suggests that the mutation in all five breeds has a common origin. Assuming that this is the causal mutation for MH, the development of a noninvasive diagnostic test will provide the basis for elimination of the MH gene or its controlled inclusion in swine breeding programs.
DOI:10.1007/BF00281673URLPMID:24420173 [本文引用: 1]
The method described uses monogenic inherited characters as markers by which the transmission of homologous chromosome sections from parents to progeny can be controlled. The procedure then attributes effects on continuously varying characters to marked chromosome sections. It allows the mapping of effects having a share in quantitative characters in natural or breeding populations.
DOI:10.1073/pnas.93.1.201URLPMID:8552604 [本文引用: 1]
Identification of individual major genes affecting quantitative traits in livestock species has been limited to date. By using a candidate gene approach and a divergent breed cross involving the Chinese Meishan pig, we have shown that a specific allele of the estrogen receptor (ER) locus is associated with increased litter size. Female pigs from synthetic lines with a 50% Meishan background that were homozygous for this beneficial allele produced 2.3 more pigs in first parities and 1.5 more pigs averaged over all parities than females from the same synthetic lines and homozygous for the undesirable allele. This beneficial ER allele was also found in pigs with Large White breed ancestory. Analysis of females with Large White breed background showed an advantage for females homozygous for the beneficial allele as compared to females homozygous for the other allele of more than 1 total pig born. Analyses of growth performance test records detected no significant unfavorable associations of the beneficial allele with growth and developmental traits. Mapping of the ER gene demonstrated that the closest known genes or markers were 3 centimorgans from ER. To our knowledge, one of these, superoxide dismutase gene (SOD2), was mapped for the first time in the pig. Analysis of ER and these linked markers indicated that ER is the best predictor of litter size differences. Introgression of the beneficial allele into commercial pig breeding lines, in which the allele was not present, and marker-assisted selection for the beneficial allele in lines with Meishan and Large White background have begun.
DOI:10.2527/2001.7951166xURLPMID:11374535 [本文引用: 1]
Utility of the preconditioned conjugate gradient algorithm with a diagonal preconditioner for solving mixed-model equations in animal breeding applications was evaluated with 16 test problems. The problems included single- and multiple-trait analyses, with data on beef, dairy, and swine ranging from small examples to national data sets. Multiple-trait models considered low and high genetic correlations. Convergence was based on relative differences between left- and right-hand sides. The ordering of equations was fixed effects followed by random effects, with no special ordering within random effects. The preconditioned conjugate gradient program implemented with double precision converged for all models. However, when implemented in single precision, the preconditioned conjugate gradient algorithm did not converge for seven large models. The preconditioned conjugate gradient and successive overrelaxation algorithms were subsequently compared for 13 of the test problems. The preconditioned conjugate gradient algorithm was easy to implement with the iteration on data for general models. However, successive overrelaxation requires specific programming for each set of models. On average, the preconditioned conjugate gradient algorithm converged in three times fewer rounds of iteration than successive overrelaxation. With straightforward implementations, programs using the preconditioned conjugate gradient algorithm may be two or more times faster than those using successive overrelaxation. However, programs using the preconditioned conjugate gradient algorithm would use more memory than would comparable implementations using successive overrelaxation. Extensive optimization of either algorithm can influence rankings. The preconditioned conjugate gradient implemented with iteration on data, a diagonal preconditioner, and in double precision may be the algorithm of choice for solving mixed-model equations when sufficient memory is available and ease of implementation is essential.
URLPMID:11290733 [本文引用: 2]
Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of approximately 50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size N(e) = 100, the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
DOI:10.1016/j.tig.2013.02.002URL [本文引用: 1]
Thousands of quantitative trait loci (QTL) have been identified for a wide range of economically important phenotypes in pigs. Recently, QTL analyses have begun to use high-density single nucleotide polymorphism (SNP) panels and applications have extended beyond experimental intercrosses to outbred populations by exploiting long-range linkage disequilibrium that results in higher resolution QTL mapping. Relevant phenotypes generally fall under categories of growth and body composition, carcass and meat quality, reproduction, and disease resistance. A few expression QTL (eQTL) studies have been performed that integrate transcriptional profiles with genotype data by considering expression levels as response variables in OIL analyses for identifying genes controlling important trait phenotypes. Rapidly evolving genomics technologies, including RNAseq, provide tremendous opportunities for QTL and eQTL discovery. In this review, we discuss recent progress in pig QTL and eQTL discovery, including approaches for allele-specific expression, and implications of these discoveries for pig breeding and genetics.
DOI:10.3168/jds.2008-1646URL [本文引用: 1]
Abstract
A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams. The increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBV only, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.[本文引用: 1]
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DOI:10.3168/jds.2007-0980URL [本文引用: 1]
Abstract
Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.DOI:10.1017/S1751731116002366URLPMID:27869042 [本文引用: 1]
The purpose of this study is review and evaluation of computing methods used in genomic selection for animal breeding. Commonly used models include SNP BLUP with extensions (BayesA, etc), genomic BLUP (GBLUP) and single-step GBLUP (ssGBLUP). These models are applied for genomewide association studies (GWAS), genomic prediction and parameter estimation. Solving methods include finite Cholesky decomposition possibly with a sparse implementation, and iterative Gauss-Seidel (GS) or preconditioned conjugate gradient (PCG), the last two methods possibly with iteration on data. Details are provided that can drastically decrease some computations. For SNP BLUP especially with sampling and large number of SNP, the only choice is GS with iteration on data and adjustment of residuals. If only solutions are required, PCG by iteration on data is a clear choice. A genomic relationship matrix (GRM) has limited dimensionality due to small effective population size, resulting in infinite number of generalized inverses of GRM for large genotyped populations. A specific inverse called APY requires only a small fraction of GRM, is sparse and can be computed and stored at a low cost for millions of animals. With APY inverse and PCG iteration, GBLUP and ssGBLUP can be applied to any population. Both tools can be applied to GWAS. When the system of equations is sparse but contains dense blocks, a recently developed package for sparse Cholesky decomposition and sparse inversion called YAMS has greatly improved performance over packages where such blocks were treated as sparse. With YAMS, GREML and possibly single-step GREML can be applied to populations with &gt;50 000 genotyped animals. From a computational perspective, genomic selection is becoming a mature methodology.
DOI:10.1017/S1751731109991352URLPMID:22443868 [本文引用: 1]
Animal breeding faces one of the most significant changes of the past decades - the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.
DOI:10.1534/genetics.109.103952URLPMID:19620397 [本文引用: 1]
The use of all available molecular markers in statistical models for prediction of quantitative traits has led to what could be termed a genomic-assisted selection paradigm in animal and plant breeding. This article provides a critical review of some theoretical and statistical concepts in the context of genomic-assisted genetic evaluation of animals and crops. First, relationships between the (Bayesian) variance of marker effects in some regression models and additive genetic variance are examined under standard assumptions. Second, the connection between marker genotypes and resemblance between relatives is explored, and linkages between a marker-based model and the infinitesimal model are reviewed. Third, issues associated with the use of Bayesian models for marker-assisted selection, with a focus on the role of the priors, are examined from a theoretical angle. The sensitivity of a Bayesian specification that has been proposed (called &quot;Bayes A&quot;) with respect to priors is illustrated with a simulation. Methods that can solve potential shortcomings of some of these Bayesian regression procedures are discussed briefly.
DOI:10.1534/genetics.107.085589URLPMID:18505874 [本文引用: 1]
The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-chi(2) distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.
DOI:10.1186/1471-2105-12-186URLPMID:21605355 [本文引用: 1]
Two bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.
DOI:10.3724/SP.J.1005.2014.0111URL [本文引用: 1]
Estimation of genomic breeding values is the key step in genomic selection. The successful application of genomic selection depends on the accuracy of genomic estimated breeding values, which is mostly determined by the estimation method. Bayes-type and BLUP-type methods are the two main methods which have been widely studied and used. Here, we systematically introduce the currently proposed Bayesian methods, and summarize their effectiveness and improvements. Results from both simulated and real data showed that the accuracies of Bayesian methods are higher than those of BLUP methods, especially for the traits which are influenced by QTL with large effect. Because the theories and computation of Bayesian methods are relatively complicated, their use in practical breeding is less common than BLUP methods. However, with the development of fast algorithms and the improvement of computer hardware, the computational problem of Bayesian methods is expected to be solved. In addition, further studies on the genetic architecture of traits will provide Bayesian methods more accurate prior information, which will make their advantage in accuracy of genomic estimated breeding values more prominent. Therefore, the application of Bayesian methods will be more extensive.
DOI:10.3724/SP.J.1005.2014.0111URL [本文引用: 1]
Estimation of genomic breeding values is the key step in genomic selection. The successful application of genomic selection depends on the accuracy of genomic estimated breeding values, which is mostly determined by the estimation method. Bayes-type and BLUP-type methods are the two main methods which have been widely studied and used. Here, we systematically introduce the currently proposed Bayesian methods, and summarize their effectiveness and improvements. Results from both simulated and real data showed that the accuracies of Bayesian methods are higher than those of BLUP methods, especially for the traits which are influenced by QTL with large effect. Because the theories and computation of Bayesian methods are relatively complicated, their use in practical breeding is less common than BLUP methods. However, with the development of fast algorithms and the improvement of computer hardware, the computational problem of Bayesian methods is expected to be solved. In addition, further studies on the genetic architecture of traits will provide Bayesian methods more accurate prior information, which will make their advantage in accuracy of genomic estimated breeding values more prominent. Therefore, the application of Bayesian methods will be more extensive.
DOI:10.1016/j.cell.2018.05.051URLPMID:29906445 [本文引用: 1]
The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent &quot;omnigenic&quot; or &quot;core genes&quot; model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.
DOI:10.3168/jds.2009-2061URL [本文引用: 1]
Abstract
Dense molecular markers are being used in genetic evaluation for parts of the population. This requires a two-step procedure where pseudo-data (for instance, daughter yield deviations) are computed from full records and pedigree data and later used for genomic evaluation. This results in bias and loss of information. One way to incorporate the genomic information into a full genetic evaluation is by modifying the numerator relationship matrix. A naive proposal is to substitute the relationships of genotyped animals with the genomic relationship matrix. However, this results in incoherencies because the genomic relationship matrix includes information on relationships among ancestors and descendants. In other words, using the pedigree-derived covariance between genotyped and ungenotyped individuals, with the pretense that genomic information does not exist, leads to inconsistencies. It is proposed to condition the genetic value of ungenotyped animals on the genetic value of genotyped animals via the selection index (e.g., pedigree information), and then use the genomic relationship matrix for the latter. This results in a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H. In this matrix, genomic information is transmitted to the covariances among all ungenotyped individuals. The matrix is (semi)positive definite by construction, which is not the case for the naive approach. Numerical examples and alternative expressions are discussed. Matrix H is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients.DOI:10.3168/jds.2009-2730URL [本文引用: 1]
Abstract
The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2 h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes.[本文引用: 1]
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DOI:10.1017/S1751731113001389URLPMID:23880322 [本文引用: 1]
Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r = 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r = 0.31) when G was used in crossbreds, but the worst (r = 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.
DOI:10.2527/jas.2014-8331URLPMID:25549983 [本文引用: 1]
A single-step method allows genetic evaluation using information of phenotypes, pedigree, and markers from genotyped and nongenotyped individuals simultaneously. This paper compared genomic predictions obtained from a single-step BLUP (SSBLUP) method, a genomic BLUP (GBLUP) method, a selection index blending (SELIND) method, and a traditional pedigree-based method (BLUP) for total number of piglets born (TNB), litter size at d 5 after birth (LS5), and mortality rate before d 5 (Mort; including stillbirth) in Danish Landrace and Yorkshire pigs. Data sets of 778,095 litters from 309,362 Landrace sows and 472,001 litters from 190,760 Yorkshire sows were used for the analysis. There were 332,795 Landrace and 207,255 Yorkshire animals in the pedigree data, among which 3,445 Landrace pigs (1,366 boars and 2,079 sows) and 3,372 Yorkshire pigs (1,241 boars and 2,131 sows) were genotyped with the Illumina PorcineSNP60 BeadChip. The results showed that the 3 methods with marker information (SSBLUP, GBLUP, and SELIND) produced more accurate predictions for genotyped animals than the pedigree-based method. For genotyped animals, the average of reliabilities for all traits in both breeds using traditional BLUP was 0.091, which increased to 0.171 w+hen using GBLUP and to 0.179 when using SELIND and further increased to 0.209 when using SSBLUP. Furthermore, the average reliability of EBV for nongenotyped animals was increased from 0.091 for traditional BLUP to 0.105 for the SSBLUP. The results indicate that the SSBLUP is a good approach to practical genomic prediction of litter size and piglet mortality in Danish Landrace and Yorkshire populations.
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DOI:10.2527/jas.2012-5107URL [本文引用: 1]
Stochastic simulation was used to compare the efficiency of 3 pig breeding schemes based on either traditional genetic evaluation or genomic evaluation. The simulated population contained 1,050 female and 50 male breeding animals. It was selected for 10 yr for a synthetic breeding goal that included 2 traits with equal economic weights and heritabilities of 0.2 or 0.4. The reference breeding scheme, named BLUP-AM, was based on the phenotyping of all candidates (13,770 animals/yr) for Trait 1 and of relatives from 10% of the litters (270 animals/yr) for Trait 2 and on BLUP-Animal Model genetic evaluations. Under the first alternative scenario, named GE-1TP, selection was based on genomic breeding values (GBV) estimated with one training population (TP) made up of candidate relatives phenotyped for both traits, with a size increasing from 1,000 to 3,430 over time. Under the second alternative scenario, named GE-2TP, the GBV for Trait 2 were estimated using a TP identical to that of GE-1TP, but the GBV for Trait 1 were estimated using a large TP made up of candidates that increased in number from 13,770 to 55,080 over time. Over the simulated period, both genomic breeding schemes generated 39 to 58% more accurate EBV for Trait 2 than the reference scheme, resulting in 78 to 128% (GE-1TP) and 63 to 84% (GE-2TP) greater average annual genetic trends for this trait. For Trait 1, GE-1TP was 18 to 24% less accurate than BLUP-AM, reducing average annual genetic trends by 27 to 44%. By contrast, GE-2TP generated 35 to 43% more accurate EBV and 8 to 22% greater average annual genetic trends for Trait 1 than the reference scheme. Consequently, GE-2TP was 27 to 33% more efficient in improving the global breeding goal than BLUP-AM whereas GE-1TP was globally as efficient as the reference scheme. Both genomic schemes reduced the inbreeding rate, the greatest decrease being observed for GE-2TP (-49 to -60% compared with BLUP-AM). In conclusion, genomic selection could substantially and durably improve the efficiency of pig breeding schemes in terms of reliability, genetic trends, and inbreeding rate without any need to modify their current structure. Even though it only generates a small TP, limited annual phenotyping capacity for traits currently only recorded on relatives would not be prohibitive. A large TP is, however, required to outperform the current schemes for traits recorded on the candidates in the latter.
DOI:10.2527/jas.2013-6102URL [本文引用: 1]
Genetic improvement of exotic and indigenous pigs in tropical developing countries is desired. Implementations of traditional selection methods on tropical pig populations are limited by lack of data recording and analysis infrastructure. Genome-wide selection (GS) provides an approach for achieving faster genetic progress without developing a pedigree recording system. The implications of GS on long-term gain and inbreeding should be studied before actual implementation, especially where low linkage disequilibrium (LD) is anticipated in the target population. A simulation case study of this option was performed on the basis of the available 60,000 SNP panel for porcine genome. Computer simulation was used to explore the effects of various selection meth-ods, trait heritability, and different breeding programs when applying GS. Genomic predictions were based on the ridge regression method. Genome-wide selection performed better than BLUP and phenotypic selection methods by increasing genetic gain and maintaining genetic variation while lowering inbreeding, especially for traits with low heritability. Indigenous pig populations with low LD can be improved by using GS if high-density marker panels are available. The combination of GS with repeated backcrossing of crossbreds to exotic pigs in developing countries promises to rapidly improve the genetic merit of the commercial population. Application of this novel method on a real population will need to be performed to validate these results.
DOI:10.2527/jas.2014-7337URL [本文引用: 1]
Efficient use of feed resources has become a clear challenge for the U.S. pork industry as feed costs continue to be the largest variable expense. The availability of the Illumina Porcine60K BeadChip has greatly facilitated whole-genome association studies to identify chromosomal regions harboring genes influencing those traits. The current study aimed at identifying genomic regions associated with variation in feed efficiency and several production traits in a Duroc terminal sire population, including ADFI, ADG, feed conversion ratio, residual feed intake (RFI), real-time ultrasound back fat thickness (BF), ultrasound muscle depth, intramuscular fat content (IMF), birth weight (BW at birth), and weaning weight (BW at weaning). Single trait association analyses were performed using Bayes B models with 35,140 SNP on 18 autosomes after quality control. Significance of nonoverlapping 1-Mb length windows (n = 2,380) were tested across 3 QTL inference methods: posterior distribution of windows variances from Monte Carlo Markov Chain, naive Bayes factor, and nonparametric bootstrapping. Genes within the informative QTL regions for the traits were annotated. A region ranging from 166 to 140 Mb (4-Mb length) on SSC 1, approximately 8 Mb upstream of the MC4R gene, was significantly associated with ADFI, ADG, and BF, where SOCS6 and DOK6 are proposed as the most likely candidate genes. Another region affecting BW at weaning was identified on SSC 4 (84-85 Mb), harboring genes previously found to influence both human and cattle height: PLAG1, CHCHD7, RDHE2 (or SDR16C5), MOS, RPS20, LYN, and PENK. No QTL were identified for RFI, IMF, and BW at birth. In conclusion, we have identified several genomic regions associated with traits affecting nutrient utilization that could be considered for future genomic prediction to improve feed utilization.
DOI:10.2527/jas.2014-8640URLPMID:26020301 [本文引用: 1]
The study investigated genetic architecture and predictive ability using genomic annotation of residual feed intake (RFI) and its component traits (daily feed intake [DFI], ADG, and back fat [BF]). A total of 1,272 Duroc pigs had both genotypic and phenotypic records, and the records were split into a training (968 pigs) and a validation dataset (304 pigs) by assigning records as before and after January 1, 2012, respectively. SNP were annotated by 14 different classes using Ensembl variant effect prediction. Predictive accuracy and prediction bias were calculated using Bayesian Power LASSO, Bayesian A, B, and Cπ, and genomic BLUP (GBLUP) methods. Predictive accuracy ranged from 0.508 to 0.531, 0.506 to 0.532, 0.276 to 0.357, and 0.308 to 0.362 for DFI, RFI, ADG, and BF, respectively. BayesCπ100.1 increased accuracy slightly compared to the GBLUP model and other methods. The contribution per SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from randomized SNP groups. Genomic prediction has accuracy comparable to observed phenotype, and use of genomic prediction can be cost effective by replacing feed intake measurement. Genomic annotation had less impact on predictive accuracy traits considered here but may be different for other traits. It is the first study to provide useful insights into biological classes of SNP driving the whole genomic prediction for complex traits in pigs.
DOI:10.1534/g3.115.022897URLPMID:26564950 [本文引用: 1]
Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit.
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DOI:10.2527/jas.2006-683URLPMID:17504955 [本文引用: 1]
Several studies have shown that selection of purebreds for increased performance of their crossbred descendants under field conditions is hampered by low genetic correlations between purebred and commercial crossbred (CC) performance. Although this can be addressed by including phenotypic data from CC relatives for selection of purebreds through combined crossbred and purebred selection (CCPS), this also increases rates of inbreeding and requires comprehensive systems for collection of phenotypic data and pedigrees at the CC level. This study shows that both these limitations can be overcome with marker-assisted selection (MAS) by using estimates of the effects of markers on CC performance. To evaluate the potential benefits of CC-MAS, a model to incorporate marker information in selection strategies was developed based on selection index theory, which allows prediction of responses and rates of inbreeding by using standard deterministic selection theory. Assuming a genetic correlation between purebred and CC performance of 0.7 for a breeding program representing a terminal sire line in pigs, CC-MAS was shown to substantially increase rates of response and reduce rates of inbreeding compared with purebred selection and CCPS, with 60 CC half sibs available for each purebred selection candidate. When the accuracy of marker-based EBV was 0.6, CC-MAS resulted in 34 and 10% greater responses in CC performance than purebred selection and CCPS. Corresponding rates of inbreeding were 1.4% per generation for CC-MAS, compared with 2.1% for purebred selection and 3.0% for CCPS. For marker-based EBV with an accuracy of 0.9, CC-MAS resulted in 75 and 43% greater responses than purebred selection and CCPS, and further reduced rates of inbreeding to 1.0% per generation. Selection on marker-based EBV derived from purebred phenotypes resulted in substantially less response in CC performance than in CC-MAS. In conclusion, effective use of MAS requires estimates of the effect on CC performance, and MAS based on such estimates enables more effective selection for CC performance without the need for extensive pedigree recording and while reducing rates of inbreeding.
DOI:10.2527/jas.2015-9930URLPMID:27065256 [本文引用: 1]
Crossbreding is predominant and intensively used in commercial meat production systems, especially in poultry and swine. Genomic evaluation has been successfully applied for breeding within purebreds but also offers opportunities of selecting purebreds for crossbred performance by combining information from purebreds with information from crossbreds. However, it generally requires that all relevant animals are genotyped, which is costly and presently does not seem to be feasible in practice. Recently, a novel single-step BLUP method for genomic evaluation of both purebred and crossbred performance has been developed that can incorporate marker genotypes into a traditional animal model. This new method has not been validated in real data sets. In this study, we applied this single-step method to analyze data for the maternal trait of total number of piglets born in Danish Landrace, Yorkshire, and two-way crossbred pigs in different scenarios. The genetic correlation between purebred and crossbred performances was investigated first, and then the impact of (crossbred) genomic information on prediction reliability for crossbred performance was explored. The results confirm the existence of a moderate genetic correlation, and it was seen that the standard errors on the estimates were reduced when including genomic information. Models with marker information, especially crossbred genomic information, improved model-based reliabilities for crossbred performance of purebred boars and also improved the predictive ability for crossbred animals and, to some extent, reduced the bias of prediction. We conclude that the new single-step BLUP method is a good tool in the genetic evaluation for crossbred performance in purebred animals.
DOI:10.1186/1297-9686-46-23URLPMID:24666469 [本文引用: 1]
For a two-breed crossbreeding system, Wei and van der Werf presented a model for genetic evaluation using information from both purebred and crossbred animals. The model provides breeding values for both purebred and crossbred performances. Genomic evaluation incorporates marker genotypes into a genetic evaluation system. Among popular methods are the so-called single-step methods, in which marker genotypes are incorporated into a traditional animal model by using a combined relationship matrix that extends the marker-based relationship matrix to non-genotyped animals. However, a single-step method for genomic evaluation of both purebred and crossbred performances has not been developed yet.
DOI:10.1534/g3.115.018119URLPMID:26019187 [本文引用: 3]
Genomic selection has been widely implemented in dairy cattle breeding when the aim is to improve performance of purebred animals. In pigs, however, the final product is a crossbred animal. This may affect the efficiency of methods that are currently implemented for dairy cattle. Therefore, the objective of this study was to determine the accuracy of predicted breeding values in crossbred pigs using purebred genomic and phenotypic data. A second objective was to compare the predictive ability of SNPs when training is done in either single or multiple populations for four traits: age at first insemination (AFI); total number of piglets born (TNB); litter birth weight (LBW); and litter variation (LVR). We performed marker-based and pedigree-based predictions. Within-population predictions for the four traits ranged from 0.21 to 0.72. Multi-population prediction yielded accuracies ranging from 0.18 to 0.67. Predictions across purebred populations as well as predicting genetic merit of crossbreds from their purebred parental lines for AFI performed poorly (not significantly different from zero). In contrast, accuracies of across-population predictions and accuracies of purebred to crossbred predictions for LBW and LVR ranged from 0.08 to 0.31 and 0.11 to 0.31, respectively. Accuracy for TNB was zero for across-population prediction, whereas for purebred to crossbred prediction it ranged from 0.08 to 0.22. In general, marker-based outperformed pedigree-based prediction across populations and traits. However, in some cases pedigree-based prediction performed similarly or outperformed marker-based prediction. There was predictive ability when purebred populations were used to predict crossbred genetic merit using an additive model in the populations studied. AFI was the only exception, indicating that predictive ability depends largely on the genetic correlation between PB and CB performance, which was 0.31 for AFI. Multi-population prediction was no better than within-population prediction for the purebred validation set. Accuracy of prediction was very trait-dependent.
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[学位论文].
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DOI:10.1186/s12863-014-0126-3URLPMID:25421851 [本文引用: 1]
Genomic selection and genomic wide association studies are widely used methods that aim to exploit the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL). Securing a sufficiently large set of genotypes and phenotypes can be a limiting factor that may be overcome by combining data from multiple breeds or using crossbred information. However, the estimated effect of a marker in one breed or a crossbred can only be useful for the selection of animals in another breed if there is a correspondence of the phase between the marker and the QTL across breeds. Using data of five pure pig (Sus scrofa) lines (SL1, SL2, SL3, DL1, DL2), one F1 cross (DLF1) and two commercial finishing crosses (TER1 and TER2), the objectives of this study were: (i) to compare the equality of LD decay curves of different pig populations; and (ii) to evaluate the persistence of the LD phase across lines or final crosses.
DOI:10.1105/tpc.110.076133URLPMID:20622146 [本文引用: 1]
Heterosis refers to the phenomenon that progeny of diverse varieties of a species or crosses between species exhibit greater biomass, speed of development, and fertility than both parents. Various models have been posited to explain heterosis, including dominance, overdominance, and pseudo-overdominance. In this Perspective, we consider that it might be useful to the field to abandon these terms that by their nature constrain data interpretation and instead attempt a progression to a quantitative genetic framework involving interactions in hierarchical networks. While we do not provide a comprehensive model to explain the phenomenology of heterosis, we provide the details of what needs to be explained and a direction of pursuit that we feel should be fruitful.
DOI:10.2527/jas.2012-6162URL [本文引用: 1]
In recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees, or both. These models include, among others, the Bayesian regularized neural networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R package described here (brnn) implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples.
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DOI:10.3389/fgene.2016.00210URLPMID:27965707 [本文引用: 1]
Selection in breeding programs can be done by using phenotypes (phenotypic selection), pedigree relationship (breeding value selection) or molecular markers (marker assisted selection or genomic selection). All these methods are based on truncation selection, focusing on the best performance of parents before mating. In this article we proposed an approach to breeding, named genomic mating, which focuses on mating instead of truncation selection. Genomic mating uses information in a similar fashion to genomic selection but includes information on complementation of parents to be mated. Following the efficiency frontier surface, genomic mating uses concepts of estimated breeding values, risk (usefulness) and coefficient of ancestry to optimize mating between parents. We used a genetic algorithm to find solutions to this optimization problem and the results from our simulations comparing genomic selection, phenotypic selection and the mating approach indicate that current approach for breeding complex traits is more favorable than phenotypic and genomic selection. Genomic mating is similar to genomic selection in terms of estimating marker effects, but in genomic mating the genetic information and the estimated marker effects are used to decide which genotypes should be crossed to obtain the next breeding population.
