单核苷酸多态性在茶树中应用的研究进展

茶树原产于中国西南地区,是一种重要的经济作物。茶叶作为中国的传统饮品,是世界上三大无酒精饮料之一。长期的自然演化及自交不亲和使得茶树的种质资源极其丰富。单核苷酸多态性(single nucleotide polymorphism,SNP)是基因组水平上单核苷酸突变引起的基因序列多样性,是继限制性酶切片段多态性(restriction fragment length polymorphism,RFLP)和简单序列重复(simple sequence repeat,SSR)的第3代分子标记,广泛存在于生物基因组中。SNP分子标记作为最重要的分子标记技术对遗传学研究有极其重要的作用。本文介绍了SNP分子标记的特征,综述了SNP在茶树中的开发检测方法,研究应用于分子标记、基因定位、关联分析等,旨在为理解和研究SNP分子标记在茶树中的研究应用提供参考。     

全基因组关联分析馒头比容与质构SNP标记

为从分子水平研究控制馒头比容与质构性状的基因位点,以205份不同小麦品种为实验材料,利用分布于小麦全基因组的24 355个单核苷酸多态性(SNP)标记对馒头比容与质构性状进行关联分析。共检测到42个比容性状显著关联位点,其中8个极显著关联位点(P0.000 1),同时也是高遗传贡献率位点(R~210%),2个位点至少在2个环境中稳定表达;检测到313个质构性状显著关联位点,其中31个极显著关联位点,46个高遗传贡献率位点,11个位点至少在2个环境中稳定表达。同时,发掘了5个质构性状主效关联位点,如3B染色体上黏聚性关联位点Kukri_c13329_800等。本研究所得到的这些标记为在分子水平上研究馒头品质性状提供了有价值的参考。     

基于单核苷酸多态性位点的多重实时荧光定量聚合酶链式反应法鉴别当雄高山牦牛肉

通过比对5个不同地区牦牛线粒体基因的差异,筛选出用于鉴定当雄高山牦牛的11 个单核苷酸多态性（single nucleotide polymorphisms,SNP）位点,将这些SNP位点作为引物3′端,同时引入引物碱基错配理念,设计出8 对特异性引物,构建出2 个三重和1 个二重实时荧光定量聚合酶链式反应（real-time polymerase chain reaction,RT-PCR）体系,建立一种基于SNP位点和多重RT-PCR技术鉴别当雄高山牦牛肉的方法。结果表明：该方法特异性较好、简便快捷、结果直观可靠,能够有效鉴别当雄高山牦牛肉;方法中3 个RT-PCR反应体系扩增产物对应的熔解温度（melting temperature,Tm）范围分别为反应体系A：73.1～75.2、76.0～77.3、79.0～80.0 ℃,反应体系B：69.5～72.8、75.0～77.9、78.5～80.0 ℃,反应体系C：76.1～78.1、79.5～80.7 ℃;根据3 个反应体系中熔解曲线峰的有无以及Tm是否符合取值范围来判定样品是否为当雄高山牦牛肉;市售样品测定结果表明,该方法可用于实际样品中当雄高山牦牛肉的快速鉴别。     

烤烟烟碱含量的全基因组关联分析

烟碱是烟草属特有的一类生物碱,为加深对烟碱遗传调控机制的解析,充分挖掘高烟碱优异等位。本研究选取219份烤烟品种开展了RAD重测序研究,获得了384,904个高质量SNP位点的群体分型数据。同时在四川西昌和山东诸城2个试验点,于2012-2015年相同栽培管理条件下对供试品种的烟叶烟碱含量进行了表型鉴定。利用全基因组关联分析的策略,共发掘到2个与烟碱含量显著关联的SNP位点,分别位于1号染色体的69,912,063bp处和2号染色体的81,115,794bp处。基于上述SNP位点预测到2个候选基因,并将目标SNP位点转化3对可通过PCR检测的功能标记。通过以上研究,加深了烟碱遗传调控机制的解析,为高烟碱优异等位基因型的发掘和高烟碱新品种的分子标记辅助选择提供了可用的标记和基因资源。      

单核苷酸多态性在多倍体作物油菜中的研究进展

单核苷酸多态性（single nucleotide polymorphism,SNP）是新型分子标记,广泛存在于生物基因组中,在人类及动物的许多研究领域得到了应用,但是在油菜等一些重要的多倍体植物中应用较少。本文以油菜为例,对作物中常用的SNP开发及检测方法作了概述,其中包括第二代测序方法和SNP芯片等快速发展的新技术;综述了SNP在油菜基因定位、遗传多样性研究、全基因组关联分析等研究中的应用进展;讨论了作物SNP研究的发展趋势;以期为SNP在油菜等重要多倍体作物中广泛应用提供参考。     

馒头主要品质性状SNP标记全基因组关联分析

为从分子水平研究控制馒头主要品质性状的基因位点,以205?份不同小麦品种为实验材料,利用分布于小麦全基因组的24?355?个单核苷酸多态性（single nucleotide polymorphism,SNP）标记对馒头比容、色泽与质构性状进行关联分析。检测到8?个比容性状极显著关联位点（P＜0.000?1）,同时也是高遗传变异贡献率位点（R2＞10%）,2?个位点至少在2?个环境中稳定表达;23?个馒头色泽性状极显著关联位点,30?个高遗传变异贡献率位点,9?个位点在两个以上环境中检测到;31?个馒头质构性状极显著关联位点,46?个高遗传变异贡献率位点,11?个位点在两个以上环境中检测到。同时,发掘了5?个质构性状主效关联位点,如5D染色体上胶着性位点BS00000020_51。本研究所得到的这些位点为结合分子育种技术改良馒头用小麦粉品质提供了有价值的参考。     

肉牛PPARG基因第1内含子单核苷酸多态性的研究

采用PCR—SSCP技术与DNA测序相结合的方法,以延边黄牛、德国黄牛×西门塔尔、利木赞×西门塔尔、夏洛莱×西门塔尔和红安格斯×西门塔尔5个肉牛群体为研究对象,对PPARG基因第1内含子单核苷酸多态性进行检测。测序发现,在PPARG基因第1内含子区发现了A26106G突变,形成1个SNP位点,表现出TT、CT、CC 3种基因型:4个二元杂交群体的CC基因型频率显著高于延边黄牛群体(P0.05),且等位基因C为优势等位基因;TT基因型只存在于延边黄牛群体;经卡方(X2)检验发现5个肉牛群体均处于Hardy-Weinberg平衡状态;群体遗传多态性分析表明,安格斯、德国黄、利木赞和延边黄牛4个肉牛群体的PIC(多态信息含量)均为中度多态,处于0.25和0.50之间;夏洛莱牛的PIC小于0.25,为低度多态。     

单核苷酸多态性技术在羊绒、羊毛纤维鉴别中的应用

随着羊毛纤维剥鳞、拉伸等先进技术的发展,原先一直使用的根据鳞片结构不同,鉴别羊绒羊毛纤维的光学显微镜法逐渐显现出局限性.文章介绍了一种利用多态性技术(SNP),通过聚合链式反应(PCR)的基因扩增和限制性片段长度多态性(RFLP)技术的酶切,根据各种纤维的线粒体DNA经扩增酶切后基因序列的不同,来鉴定山羊绒与绵羊毛的新技术.     

基于RAD重测序技术开发烟草品种SNP位点

利用限制性内切酶位点标签（RAD）技术,通过对10份供试烟草材料的基因组简化重测序,发掘了烟草高通量SNP位点,为烟草基因组学提供标记信息。结果表明,本研究共获得了44.33 Gb的Clean data数据,平均覆盖度1.01 X,共鉴定到291 770个SNP位点,SNP位点间的平均间距为10.066±29.801 kb。发掘到的SNP位点能够覆盖整个基因组,但在不同染色体部位上的分布密度存在一定差异,在17号染色上半臂的存在一段大范围的SNP密集区域。SNP变异类型以转换为主,通过功能注释在基因区域发现45 049处SNP位点。利用SNP分型信息,计算了供试品种间的遗传距离,平均为0.29,台烟8号的遗传背景与其他品种相对最远。该结果将为烟草QTL定位、候选基因发掘、亲本组配等研究提供科研依据。     

鼠李糖乳杆菌L-乳酸脱氢酶的生物信息学分析和基因克隆

本研究以鼠李糖乳杆菌（Lactobacillus rhamnosus）L-乳酸脱氢酶（Lr-L-LDH）为研究对象,以研究较多的Lr-L-LDH1为对照,对基因组注释的两个Lr-L-LDH1和Lr-L-LDH2基因,进行异同分析。采用在线网站和专业软件对Lr-L-LDH1和Lr-L-LDH2的一级结构、基本特性、亲疏水性、二级结构进行分析和预测,对三级结构进行同源建模以及酶和底物的分子对接分析,并对酶的编码基因进行系统发育分析,克隆表达及酶活性检测。结果显示：相较于Lr-L-LDH1,Lr-L-LDH2有着相似的分子特性,二级和三级结构,但Lr-L-LDH2编码序列短,氨基酸序列同源性低（48.08%）,有不同的进化地位;Lr-L-LDH2也含有乳酸脱氢酶催化活性位点序列和保守的NAD+结合位点序列（GXGXXG）,能够形成典型的活性三维口袋域,是NAD+依赖型四聚体结构L-乳酸脱氢酶,在细胞中需要果糖1,6-二磷酸（FBP）来激活,催化丙酮酸还原为L-乳酸;体外克隆表达和酶学分析表明,Lr-L-LDH2酶活力极显著低于Lr-L-LDH1(P<0.01)。Lr-L-LDH1和Lr...     

A genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using two methods and a dense single nucleotide polymorphism map

Genome scans for detection of bovine quantitative trait loci (QTL) were performed via variance component linkage analysis and linkage disequilibrium single-locus regression (LDRM). Four hundred eighty-four Holstein sires, of which 427 were from 10 grandsire families, were genotyped for 9,919 single nucleotide polymorphisms (SNP) using the Affymetrix MegAllele GeneChip Bovine Mapping 10K SNP array. A hybrid of the granddaughter and selective genotyping designs was applied. Four thousand eight hundred fifty-six of the 9,919 SNP were located to chromosomes in base-pairs and formed the basis for the analyses. The mean polymorphism information content of the SNP was 0.25. The SNP centimorgan position was interpolated from their base-pair position using a microsatellite framework map. Estimated breeding values were used as observations, and the following traits were analyzed: 305-d lactation milk, fat, and protein yield; somatic cell score; herd life; interval of calving to first service; and age at first service. The variance component linkage analysis detected 102 potential QTL, whereas LDRM analysis found 144 significant SNP associations after accounting for a 5% false discovery rate. Twenty potential QTL and 49 significant SNP associations were in close proximity to QTL cited in the literature. Both methods found significant regions on Bos taurus autosome (BTA) 3, 5, and 16 for milk yield; BTA 14 and 19 for fat yield; BTA 1, 3, 16, and 28 for protein yield; BTA 2 and 13 for calving to first service; and BTA 14 for age at first service. Both approaches were effective in detecting potential QTL with a dense SNP map. The LDRM was well suited for a first genome scan due to its approximately 8 times lower computational demands. Further fine mapping should be applied on the chromosomal regions of interest found in this study.     

Efficient parentage assignment and pedigree reconstruction with dense single nucleotide polymorphism data

Large numbers of dairy cattle are now routinely genotyped for dense single nucleotide polymorphism (SNP) arrays for the purpose of predicting genomic estimated breeding values. Such SNP arrays contain very good information for parentage assignment and pedigree reconstruction. The main challenge in using this information for parentage assignment and pedigree reconstruction is development of computationally efficient strategies that enable a candidate animal to be assigned its sire and dam with the large volume of data. Here we describe an efficient algorithm for parentage assignment with SNP data and demonstrate very accurate assignment with 50,000-SNP and 3,000-SNP panels. The computer code implementing the algorithm is given in the Appendix.     

Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms

The availability of dense single nucleotide polymorphism (SNP) genotypes for dairy cattle has created exciting research opportunities and revolutionized practical breeding programs. Broader application of this technology will lead to situations in which genotypes from different low-, medium-, or high-density platforms must be combined. In this case, missing SNP genotypes can be imputed using family- or population-based algorithms. Our objective was to evaluate the accuracy of imputation in Jersey cattle, using reference panels comprising 2,542 animals with 43,385 SNP genotypes and study samples of 604 animals for which genotypes were available for 1, 2, 5, 10, 20, 40, or 80% of loci. Two population-based algorithms, fastPHASE 1.2 (P. Scheet and M. Stevens; University of Washington TechTransfer Digital Ventures Program, Seattle, WA) and IMPUTE 2.0 (B. Howie and J. Marchini; Department of Statistics, University of Oxford, UK), were used to impute genotypes on Bos taurus autosomes 1, 15, and 28. The mean proportion of genotypes imputed correctly ranged from 0.659 to 0.801 when 1 to 2% of genotypes were available in the study samples, from 0.733 to 0.964 when 5 to 20% of genotypes were available, and from 0.896 to 0.995 when 40 to 80% of genotypes were available. In the absence of pedigrees or genotypes of close relatives, the accuracy of imputation may be modest (generally <0.80) when low-density platforms with fewer than 1,000 SNP are used, but population-based algorithms can provide reasonably good accuracy (0.80 to 0.95) when medium-density platforms of 2,000 to 4,000 SNP are used in conjunction with high-density genotypes (e.g., >40,000 SNP) from a reference population. Accurate imputation of high-density genotypes from inexpensive low- or medium-density platforms could greatly enhance the efficiency of whole-genome selection programs in dairy cattle.     

Short communication: Genotyping and single nucleotide polymorphism analysis of bovine leukemia virus in Chinese dairy cattle

Bovine leukemia virus (BLV) causes enzootic leucosis in cattle and is classified into 10 genotypes with a worldwide distribution, except for several European countries, Australia, and New Zealand. Although BLV is widespread in Chinese cows with the positive rate of 49.1% at the individual level, very little is known about the BLV genotype in dairy cattle in China. To determine BLV genetic variability in cows in China, 112 BLV-positive samples from 5 cities in China were used for BLV molecular characterization in this study. Phylogenetic analysis using the neighbor-joining method on partial env sequence encoding gp51 obtained from 5 Chinese cities and those available in GenBank (n = 53, representing BLV genotype 1–10) revealed the Chinese strains belonged to genotype 6. Seven unique SNP were identified among Yancheng, Shanghai, and Bengbu strains out of the total 12 SNP identified in Chinese strains. The genotyping coupled with SNP analysis of BLV can serve as a useful molecular epidemiological tool for tracing the source of pathogens. This study highlights the importance of genetic analysis of geographically diverse BLV strains to understand BLV global genetic diversity.     

Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers

The objective of the present study was to assess the predictive ability of subsets of single nucleotide polymorphism (SNP) markers for development of low-cost, low-density genotyping assays in dairy cattle. Dense SNP genotypes of 4,703 Holstein bulls were provided by the USDA Agricultural Research Service. A subset of 3,305 bulls born from 1952 to 1998 was used to fit various models (training set), and a subset of 1,398 bulls born from 1999 to 2002 was used to evaluate their predictive ability (testing set). After editing, data included genotypes for 32,518 SNP and August 2003 and April 2008 predicted transmitting abilities (PTA) for lifetime net merit (LNM$), the latter resulting from progeny testing. The Bayesian least absolute shrinkage and selection operator method was used to regress August 2003 PTA on marker covariates in the training set to arrive at estimates of marker effects and direct genomic PTA. The coefficient of determination (R²) from regressing the April 2008 progeny test PTA of bulls in the testing set on their August 2003 direct genomic PTA was 0.375. Subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP were created by choosing equally spaced and highly ranked SNP, with the latter based on the absolute value of their estimated effects obtained from the training set. The SNP effects were re-estimated from the training set for each subset of SNP, and the 2008 progeny test PTA of bulls in the testing set were regressed on corresponding direct genomic PTA. The R² values for subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP with largest effects (evenly spaced SNP) were 0.184 (0.064), 0.236 (0.111), 0.269 (0.190), 0.289 (0.179), 0.307 (0.228), 0.313 (0.268), and 0.322 (0.291), respectively. These results indicate that a low-density assay comprising selected SNP could be a cost-effective alternative for selection decisions and that significant gains in predictive ability may be achieved by increasing the number of SNP allocated to such an assay from 300 or fewer to 1,000 or more.     

Application of single-step single nucleotide polymorphism best linear unbiased predictor model with unknown-parent groups for type traits in Japanese Holsteins

The objectives of this study were to investigate the computational performance and the predictive ability and bias of a single-step SNP BLUP model (ssSNPBLUP) in genotyped young animals with unknown-parent groups (UPG) for type traits, using national genetic evaluation data from the Japanese Holstein population. The phenotype, genotype, and pedigree data were the same as those used in a national genetic evaluation of linear type traits classified between April 1984 and December 2020. In the current study, 2 data sets were prepared: the full data set containing all entries up to December 2020 and a truncated data set ending with December 2016. Genotyped animals were classified into 3 types: sires with classified daughters (S), cows with records (C), and young animals (Y). The computing performance and prediction accuracy of ssSNPBLUP were compared for the following 3 groups of genotyped animals: sires with classified daughters and young animals (SY); cows with records and young animals (CY); and sires with classified daughters, cows with records, and young animals (SCY). In addition, we tested 3 parameters of residual polygenic variance in ssSNPBLUP (0.1, 0.2, or 0.3). Daughter yield deviations (DYD) for the validation bulls and phenotypes adjusted for all fixed effects and random effects other than animal and residual (Y_adj) for the validation cows were obtained using the full data set from the pedigree-based BLUP model. The regression coefficients of DYD for bulls (or Y_adj for cows) on the genomic estimated breeding value (GEBV) using the truncated data set were used to measure the inflation of the predictions of young animals. The coefficient of determination of DYD on GEBV was used to measure the predictive ability of the predictions for the validation bulls. The reliability of the predictions for the validation cows was calculated as the square of the correlation between Y_adj and GEBV divided by heritability. The predictive ability was highest in the SCY group and lowest in the CY group. However, minimal difference was found in predictive abilities with or without UPG models using different parameters of residual polygenic variance. The regression coefficients approached 1.0 as the parameter of residual polygenic variance increased, but regression coefficients were mostly similar regardless of the use of UPG across the groups of genotyped animals. The ssSNPBLUP model, including UPG, was demonstrated as feasible for implementation in the national evaluation of type traits in Japanese Holsteins.     

The number of single nucleotide polymorphisms and on-farm data required for whole-herd parentage testing in dairy cattle herds

New platforms utilizing single nucleotide polymorphisms (SNP) offer operational advantages over the conventional microsatellite-based ones, making them a promising alternative for parentage exclusion. Through simulation and empirical data, a 40-SNP panel (where the minor allele frequency was 0.35 on average) was shown to be a comparable or better diagnostic tool than the current 14-microsatellite panel that is used to parentage test New Zealand dairy animals. The 40 SNP alone did not have sufficient power of exclusion to match more than 75% of the progeny to the correct sire and dam. Utilizing mating records and grouping progeny and dams by birth and calving dates, respectively, decreased the number of sire-dam combinations that each progeny was tested against and dramatically increased the utility of the SNP. These results highlight the importance of combining genotypes with on-farm data to maximize the ability to assign parentage in the New Zealand dairy herd.     

Multi-locus genotyping of bottom fermenting yeasts by single nucleotide polymorphisms indicative of brewing characteristics

Yeast plays a capital role in brewing fermentation and has a direct impact on flavor and aroma. For the evaluation of competent brewing strains during quality control or development of novel strains it is standard practice to perform fermentation tests, which are costly and time-consuming. Here, we have categorized DNA markers which enable to distinguish and to screen brewing strains more efficiently than ever before. Sequence analysis at 289 loci in the genomes of six bottom fermenting Saccharomyces pastorianus strains revealed that 30 loci contained single nucleotide polymorphisms (SNPs). By determining the nucleotide sequences at the SNP-loci in 26 other S. pastorianus strains and 20 strains of the top fermenting yeast Saccharomyces cerevisiae, almost all these strains could be discriminated solely on the basis of the SNPs. By comparing the fermentative phenotypes of these strains we found that some DNA markers showed a strong association with brewing characteristics, such as the production of ethyl acetate and hydrogen sulphide (H2S). Therefore, the DNA markers we identified will facilitate quality control and the efficient development of brewing yeast strains.