家蚕核型多角体病毒水平转移基因分析

为了探讨杆状病毒基因组的遗传进化模式,文章利用家蚕核型多角体病毒(BmNPV)和其宿主家蚕全基因组数据,进行了全基因组的同源性搜索和系统进化分析,结果显示,BmNPV的几丁质酶(Chi)基因、凋亡抑制蛋白3(IAP3)基因和尿苷二磷酸葡萄糖转移酶(UGT)基因为水平转移基因。这3个基因都来源于其宿主昆虫。通过核苷酸组成、密码子偏好性、选择压力等基因特征分析,发现BmNPV水平转移基因与其基因组序列存在明显差异,进一步验证水平转移基因的外源性。对3个水平转移基因的功能分析发现它们有利于杆状病毒在宿主昆虫中的侵染与繁殖,并提高杆状病毒在昆虫中的生存能力。     

SNP芯片数据估计动物个体基因组品种构成的方法及应用

自然和人工选择、地理隔离和遗传漂移等原因使动物基因组中许多位点的等位基因频率在群体间会产生差异。源于不同品种(祖先)杂交(交配)的动物个体,其基因组与这些品种(祖先)的基因频率(基因型)会存在一定的相关性。因此采用合适的统计模型和分析方法,可以估计出每个品种(祖先)对于个体基因组的遗传贡献比例,又称为个体的基因组品种构成(genomic breed composition, GBC)。本文介绍了利用SNP芯片数据估计动物个体GBC的原理、方法及步骤,并且通过对198头待鉴定的日本红毛和牛GBC的评估,演示了用回归模型和混合分布模型估计动物个体GBC的具体步骤,其中包括SNP子集的筛选、参考群体中动物个体选择以及待测定动物GBC的计算。参考动物群体选自日本红毛和牛(Akaushi)、安格斯牛(Angus)、海福特牛(Hereford)、荷斯坦牛(Holstein)和娟珊牛(Jersey) 5个品种共36 574头,每个个体有40K或50K芯片数据。本文在现有商用 SNP芯片基础上筛选用于品种鉴定和估计动物个体GBC的SNP子集,是对现有SNP芯片功能的拓展和深入开发利用。此外,在基因组选择中如何利用SNP基因型估计动物个体GBC的结果,提高纯种和杂种动物的预测准确度,也是值得深入研究的领域。     

推测187份玉米自交系基因组血统与分子亲缘关系

为提高育种效率以及开展重要 QTL 和关键基因关联性分析研究, 以 187 份生产上重要玉米自交系为材料, 以 70 个均匀分布于全基因组简单重复序列(SSR)基因座鉴定出的290个等位基因多态性为分析数据, 采用联合连锁位点与混合模型分析, 推测这些自交系的基因组血缘构成以及分子亲缘关系, 并分析了全基因连锁不平衡。当亚群数目 K=5 时, 导致似然值 P 明显下降, 亚群数据 K > 6 时, 似然值 P 没有明显上升, 表明群体结构的亚群数 K 最佳推测为 6。六个亚群分别为 PA、BSSS (含 Reid)、PB、兰卡斯特 (Lancaster)、旅大红骨 (旅大红骨及其衍生系)、四平头 (唐四平头及其衍生系)。亚群间的Kullback-Leibler 距离自 0.13 至 1.06 不等, 平均为 0.599, 各亚群间区分度较好。全基因组连锁不平衡(LD)分析表明: 与四平头种质类群内相比, 遗传基础宽泛种质的基因组内存在 LD"区块"(LD block)少且小, 对重要 QTLs 与基因的关联性分析可以避免假阳性。本研究群体结构与基因组构成分析数据为这些材料育种应用与改良提供了重要信息, 也为基于这些材料的关联性分析奠定了分析基础。     

野牦牛线粒体基因组序列测定及其系统进化

野牦牛属高寒地区的特有物种,是我国最珍贵的野生动物遗传资源之一,已被列为国家一级重点保护动物。对野牦牛mtDNA进行全序列测定和结构分析,并基于线粒体基因组序列对其系统发生进行了探讨。结果表明:(1)野牦牛线粒体基因组全序列的大小为16 322 bp,整个基因组由37个编码基因和D-loop区组成;22个tRNA基因序列长度为1 524 bp、2个RNA基因序列长度为2 528 bp、13个编码蛋白基因序列长度为11420 bp、D-loop区长度为892 bp。基因组中无间隔序列,基因间排列紧密,基因内无内含子。(2)野牦牛具有较丰富的遗传多样性。(3)分子系统发生关系显示牦牛为牛亚科中的一个独立属,即牦牛属(Poephagus),牦牛属包括家牦牛(Poephagus grunniens)和野牦牛(Poephagus mutus)2个种。野牦牛线粒体基因组全序列的获得和结构解析对研究牦牛的起源、演化和分类,以及野牦牛遗传资源的保护、开发和利用均具有重要的理论和实际意义。     

全基因组测序在重要家畜上的研究进展

全基因组测序研究主要包括通过不同测序技术和组装比对方法,获得某物种的全基因组序列图谱,及在此基础上构建物种全基因组遗传变异图谱进行个体或群体遗传多样性、选择信号或起源进化等方面的研究。利用单核苷酸多态性(SNP)、插入和缺失(Indel)和拷贝数变异(CNV)等遗传变异作为分子标记,全基因组测序研究已经在家畜起源进化、驯化、适应性机制、重要经济性状候选基因、群体历史动态等方面取得了许多重要的研究成果。本文主要对近几年全基因组测序在常见家畜(猪、马、牛、羊等及其近缘物种)的取得的重要研究成果进行了综述,并讨论了全基因组测序的优势、缺点及在生产中意义。此外,对基因组测序研究的未来发展进行了归纳及展望,以期为今后家畜重要经济性状的功能基因定位和物种起源、驯化研究提供参考。     

Otterlace软件在猪全基因组序列人工注释分析中的应用

2009年11月,美、英等国科学家宣布首次绘制出家猪的基因组草图。近两年,随着全基因组序列陆续释放,越来越多的测序片段得到正确拼接组装,从全基因组水平上对猪功能基因进行注释分析显得尤为迫切。文章以丝切蛋白1(Cofilin 1,CFL1)基因的注释过程为例,介绍了运用Sanger研究所开发的Otterlace软件对猪全基因组的免疫基因序列进行人工分析与注释。通过详细说明Zmap、Blixem和Dotter 3个注释工具的使用方法,并给出了注释过程的主要步骤,以期对Otterlace的应用起一个抛砖引玉的作用。运用Otterlace软件对243个免疫相关基因进行分析,其中180个基因得到完整或部分注释,这为后续深入开展这些基因的功能研究奠定了基础。     

鸡T细胞受体γ链基因位点重新测序和组装

动物T细胞受体(T cell receptor,TCR)基因由多个不同的高度同源的基因家族组成,通过全基因组测序很难获得准确的基因序列和排列位置。文章通过在NCBI中发布的鸡TCR的γ链(TCRγ或TRG)基因片段序列定位了鸡TRG基因所在区域,并确定了与鸡TRG基因位点对应的细菌人工染色体(BAC)克隆(CH261-174P24)。对该克隆进行高通量的重新测序和组装后,得到含有10个scaffolds的基因组草图,较完整地覆盖了鸡TRG基因位点及两侧区域。通过PCR扩增和测序证明了scaffold内部结构的正确性,校正了鸡参考基因组TRG基因位点一个可变基因和一个缺口序列(gap)附近各一处错误序列,以及可变基因区多处序列错误。文章通过校正鸡参考基因组TRG基因位点的序列,为鸡TRA/D和TRB基因位点的基因组序列分析提供了新方法。     

绿色性状的基因定位和基因组选择的多变量方法

绿色性状的遗传改良为绿色超级稻的培育奠定了坚实的基础。绿色性状,如高产、抗病、抗逆、氮磷高效利用等,大多是受多基因控制的复杂性状。关联分析和基因组选择是对植物复杂数量性状进行遗传解析和改良的重要方法。在绿色超级稻的育种实践中,需要同时改良多个绿色性状。然而,目前关联分析和基因组选择方法大多仍专注于对单个性状的分析,忽略了性状间的相关性。现分别提出关联分析和基因组选择的多性状方法,两者充分利用了性状之间的遗传相关和环境相关信息;而模拟研究和实证研究均表明,多性状方法能有效提高基因定位和表型预测的准确性,为绿色性状的遗传改良提供重要技术支撑。     

鸡T细胞受体γ链基因位点重新测序和组装

动物T细胞受体(T cell receptor,TCR)基因由多个不同的高度同源的基因家族组成,通过全基因组测序很难获得准确的基因序列和排列位置。文章通过在NCBI中发布的鸡TCR的γ链(TCRγ或TRG)基因片段序列定位了鸡TRG基因所在区域,并确定了与鸡TRG基因位点对应的细菌人工染色体(BAC)克隆(CH261-174P24)。对该克隆进行高通量的重新测序和组装后,得到含有10个scaffolds的基因组草图,较完整地覆盖了鸡TRG基因位点及两侧区域。通过PCR扩增和测序证明了scaffold内部结构的正确性,校正了鸡参考基因组TRG基因位点一个可变基因和一个缺口序列(gap)附近各一处错误序列,以及可变基因区多处序列错误。文章通过校正鸡参考基因组TRG基因位点的序列,为鸡TRA/D和TRB基因位点的基因组序列分析提供了新方法。     

后基因组时代微生物遗传学教学的探讨

对后基因组时代"微生物遗传学"课程教学进行探讨。提出以故事化课堂和形象化讲解增加学生的学习乐趣。为了更好地帮助学生理解后基因组学方法,将后基因组学与微生物遗传学融合教学(包括正向遗传学方法与快速正向遗传学方法、单基因敲除和表型分析与全基因组规模基因敲除和表型分析、传统的遗传相互作用与全基因组的遗传相互作用融合讲授)。此外,生物信息学网络资源的介绍延伸了课堂教学并提高了课堂教学质量。     

Genome-wide detection of selective signatures in Simmental cattle

Artificial selection has greatly improved the beef production performance and changed its genetic basis. High-density SNP markers provide a way to track these changes and use selective signatures to search for the genes associated with artificial selection. In this study, we performed extended haplotype homozygosity (EHH) tests based on Illumina BovineSNP50 (54 K) Chip data from 942 Simmental cattle to identify significant core regions containing selective signatures, then verified the biological significance of these identified regions based on some commonly used bioinformatics analyses. A total of 224 regions over the whole genome in Simmental cattle showing the highest significance and containing some important functional genes, such as GHSR, TG and CANCNA2D1 were chosen. We also observed some significant terms in the enrichment analyses of second GO terms and KEGG pathways, indicating that these genes are associated with economically relevant cattle traits. This is the first detection of selection signature in Simmental cattle. Our findings significantly expand the selection signature map of the cattle genome, and identify functional candidate genes under positive selection for future genetic research.     

Genome‐wide association study reveals candidate genes associated with body measurement traits in Chinese Wagyu beef cattle

We performed a genome‐wide association study to identify candidate genes for body measurement traits in 463 Wagyu beef cattle typed with the Illumina Bovine HD 770K SNP array. At the genome‐wide level, we detected 18, five and one SNPs associated with hip height, body height and body length respectively. In total, these SNPs are within or near 11 genes, six of which (PENK, XKR4, IMPAD1, PLAG1, CCND2 and SNTG1) have been reported previously and five of which (CSMD3, LAP3, SYN3, FAM19A5 and TIMP3) are novel candidate genes that we found to be associated with body measurement traits. Further exploration of these candidate genes will facilitate genetic improvement in Chinese Wagyu beef cattle.     

A genome map of divergent artificial selection between Bos taurus dairy cattle and Bos taurus beef cattle

A number of cattle breeds have become highly specialized for milk or beef production, following strong artificial selection for these traits. In this paper, we compare allele frequencies from 9323 single nucleotide polymorphism (SNP) markers genotyped in dairy and beef cattle breeds averaged in sliding windows across the genome, with the aim of identifying divergently selected regions of the genome between the production types. The value of the method for identifying selection signatures was validated by four sources of evidence. First, differences in allele frequencies between dairy and beef cattle at individual SNPs were correlated with the effects of those SNPs on production traits. Secondly, large differences in allele frequencies generally occurred in the same location for two independent data sets (correlation 0.45) between sliding window averages. Thirdly, the largest differences in sliding window average difference in allele frequencies were found on chromosome 20 in the region of the growth hormone receptor gene, which carries a mutation known to have an effect on milk production traits in a number of dairy populations. Finally, for the chromosome tested, the location of selection signatures between dairy and beef cattle was correlated with the location of selection signatures within dairy cattle.     

Use of locally weighted scatterplot smoothing (LOWESS) regression to study selection signatures in Piedmontese and Italian Brown cattle breeds

Selection is the major force affecting local levels of genetic variation in species. The availability of dense marker maps offers new opportunities for a detailed understanding of genetic diversity distribution across the animal genome. Over the last 50 years, cattle breeds have been subjected to intense artificial selection. Consequently, regions controlling traits of economic importance are expected to exhibit selection signatures. The fixation index (F_st) is an estimate of population differentiation, based on genetic polymorphism data, and it is calculated using the relationship between inbreeding and heterozygosity. In the present study, locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach were used to investigate selection signatures in two cattle breeds with different production aptitudes (dairy and beef). F_st was calculated for 42 514 SNP marker loci distributed across the genome in 749 Italian Brown and 364 Piedmontese bulls. The statistical significance of F_st values was assessed using a control chart. The LOWESS technique was efficient in removing noise from the raw data and was able to highlight selection signatures in chromosomes known to harbour genes affecting dairy and beef traits. Examples include the peaks detected for BTA2 in the region where the myostatin gene is located and for BTA6 in the region harbouring the ABCG2 locus. Moreover, several loci not previously reported in cattle studies were detected.     

Genetic association of marbling score with intragenic nucleotide variants at selection signals of the bovine genome

Selection signals of Korean cattle might be attributed largely to artificial selection for meat quality. Rapidly increased intragenic markers of newly annotated genes in the bovine genome would help overcome limited findings of genetic markers associated with meat quality at the selection signals in a previous study. The present study examined genetic associations of marbling score (MS) with intragenic nucleotide variants at selection signals of Korean cattle. A total of 39 092 nucleotide variants of 407 Korean cattle were utilized in the association analysis. A total of 129 variants were selected within newly annotated genes in the bovine genome. Their genetic associations were analyzed using the mixed model with random polygenic effects based on identical-by-state genetic relationships among animals in order to control for spurious associations produced by population structure. Genetic associations of MS were found (P<3.88×10⁻⁴) with six intragenic nucleotide variants on bovine autosomes 3 (cache domain containing 1, CACHD1), 5 (like-glycosyltransferase, LARGE), 16 (cell division cycle 42 binding protein kinase alpha, CDC42BPA) and 21 (snurportin 1, SNUPN; protein tyrosine phosphatase, non-receptor type 9, PTPN9; chondroitin sulfate proteoglycan 4, CSPG4). In particular, the genetic associations with CDC42BPA and LARGE were confirmed using an independent data set of Korean cattle. The results implied that allele frequencies of functional variants and their proximity variants have been augmented by directional selection for greater MS and remain selection signals in the bovine genome. Further studies of fine mapping would be useful to incorporate favorable alleles in marker-assisted selection for MS of Korean cattle.     

Genome-wide association study of economically important traits in Charolais and Limousin beef cows

Genomic selection has proven effective for advancing genetic gain for key profit traits in dairy cattle production systems. However, its impact to-date on genetic improvement programs for beef cattle has been less effective. Despite this, the technology is thought to be particularly useful for low heritability traits such as those associated with reproductive efficiency. The objective of this study was to identify genetic variants associated with key determinants of reproductive and overall productive efficiency in beef cows. The analysis employed a large dataset derived from the national genetic evaluation program in Ireland for two of the most predominant beef breeds, viz. Charolais (n = 5 244 cows) and Limousin (n = 7 304 cows). Single nucleotide polymorphisms (SNPs) were identified as being statistically significantly associated (adj. P < 0.05) with both reproductive and productive traits for both breed types. However, there was little across breed commonality, with only two SNPs (rs110240246 and rs110344317; adj. P < 0.05) located within the genomic regions of the LCORL and MSTN genes respectively, identified in both Charolais and Limousin populations, associated with traits including carcass weight, cull-cow weight and live-weight. Significant SNPs within the MSTN gene were also associated with both reproduction and production related traits within each breed. Finally, traits including calving difficulty, calf mortality and calving interval were associated with SNPs within genomic regions comprising genes involved in cellular growth and lipid metabolism. Genetic variants identified as associated with both important reproductive efficiency and production related traits from this study warrant further analyses for their potential incorporation into breeding programmes to support the sustainability of beef cattle production.     

中国肉牛分子与基因修饰育种研究进展

随着世界肉牛产业科技的快速发展,我国肉牛产业的整体水平得到明显提高并取得丰硕成果。肉牛育种技术实现了由常规杂交育种向分子标记辅助育种、全基因组选择育种和基因组修饰育种的技术跨越,揭示出大量与生长发育、肉质品质、繁殖与疾病等相关的候选基因与分子标记,并逐步应用于肉牛育种实践。与生长发育性状相关的基因或分子标记主要集中在生长激素基因、生肌调节因子家族、肌肉生长抑制因子和胰岛素样生长因子等;参与肉质形成的基因主要集中在脂肪酸运输与沉积相关信号通路、钙蛋白酶信号通路、生肌调节因子家族与肌肉生长抑制因子等;繁殖性状相关基因或分子标记主要集中在GnRH-FSHR-LH、生长分化因子9、催乳素受体和FoxO1等;抗病相关基因主要有MHC基因家族、TOLL样受体4基因等。目前,利用精准基因编辑技术已培育出促生长发育与提高肉品质的肉牛育种新材料。本文总结了近年来我国在肉牛分子与基因组修饰育种领域取得的研究进展,以期为我国肉牛遗传育种技术研究提供参考和借鉴。     

Integration of selection signatures and multi-trait GWAS reveals polygenic genetic architecture of carcass traits in beef cattle

Carcass merits are widely considered as economically important traits affecting beef production in the beef cattle industry. However, the genetic basis of carcass traits remains to be well understood. Here, we applied multiple methods, including the Composite of Likelihood Ratio (CLR) and Genome-wide Association Study (GWAS), to explore the selection signatures and candidate variants affecting carcass traits. We identified 11,600 selected regions overlapping with 2214 candidate genes, and most of those were enriched in binding and gene regulation. Notably, we identified 66 and 110 potential variants significantly associated with carcass traits using single-trait and multi-traits analyses, respectively. By integrating selection signatures with single and multi-traits associations, we identified 12 and 27 putative genes, respectively. Several highly conserved missense variants were identified in OR5M13D, NCAPG, and TEX2. Our study supported polygenic genetic architecture of carcass traits and provided novel insights into the genetic basis of complex traits in beef cattle.     

Genome‐wide association study for body weight in cattle populations from Siberia

Body weight is a complex trait in cattle associated with commonly used commercial breeding measurements related to growth. Although many quantitative trait loci (QTL) for body weight have been identified in cattle so far, searching for genetic determinants in different breeds or environments is promising. Therefore, we carried out a genome‐wide association study (GWAS) in two cattle populations from the Russian Federation (Siberian region) using the GGP HD150K array containing 139 376 single nucleotide polymorphism (SNP) markers. Association tests for 107 550 SNPs left after filtering revealed five statistically significant SNPs on BTA5, considering a false discovery rate of less than 0.05. The chromosomal region containing these five SNPs contains the CCND2 gene, which was previously associated with average daily weight gain and body mass index in US beef cattle populations and in humans respectively. Our study is the first GWAS for body weight in beef cattle populations from the Russian Federation. The results provided here suggest that, despite the existence of breed‐ and species‐specific QTL, the genetic architecture of body weight could be evolutionarily conserved in mammals.     

An infinitesimal model for quantitative trait genomic value prediction

We developed a marker based infinitesimal model for quantitative trait analysis. In contrast to the classical infinitesimal model, we now have new information about the segregation of every individual locus of the entire genome. Under this new model, we propose that the genetic effect of an individual locus is a function of the genome location (a continuous quantity). The overall genetic value of an individual is the weighted integral of the genetic effect function along the genome. Numerical integration is performed to find the integral, which requires partitioning the entire genome into a finite number of bins. Each bin may contain many markers. The integral is approximated by the weighted sum of all the bin effects. We now turn the problem of marker analysis into bin analysis so that the model dimension has decreased from a virtual infinity to a finite number of bins. This new approach can efficiently handle virtually unlimited number of markers without marker selection. The marker based infinitesimal model requires high linkage disequilibrium of all markers within a bin. For populations with low or no linkage disequilibrium, we develop an adaptive infinitesimal model. Both the original and the adaptive models are tested using simulated data as well as beef cattle data. The simulated data analysis shows that there is always an optimal number of bins at which the predictability of the bin model is much greater than the original marker analysis. Result of the beef cattle data analysis indicates that the bin model can increase the predictability from 10% (multiple marker analysis) to 33% (multiple bin analysis). The marker based infinitesimal model paves a way towards the solution of genetic mapping and genomic selection using the whole genome sequence data.