SNP分型检测技术及其在畜禽遗传和育种中的应用研究进展

单核苷酸多态性(SNP)属于第三代遗传标记,因其二态性、广泛性、易于自动化检测等特性而成为研究热点。SNP广泛应用于遗传图谱构建、基因定位、进化分析研究以及标记辅助选择和亲缘关系鉴定等方面。本文对SNP的形成、分类、检测方法以及SNP检测技术的发展和在畜禽中的研究与应用进行综述,分析了各类SNP检测方法的优缺点,为推动SNP检测技术的快捷化、精确化、经济化及拓展其应用领域提供参考。     

采用单碱基延伸法检测家蚕单核苷酸多态性位点

单核苷酸多态性(SNP)分型可以作为丰富的分子标记用于家蚕群体遗传学、基因组注释以及功能基因研究。利用SNaPshot试剂盒的SNP分型方法,对以单碱基延伸法检测家蚕SNP位点进行了探索。首先对SNP位点进行PCR扩增,然后以PCR产物为模板,以4种荧光标记ddNTPs为底物,采用延伸引物扩增SNP位点处的一个碱基,最后用377测序仪进行基因分型。通过对家蚕2个SNP位点的分析结果表明,利用单碱基延伸法可以准确地检测SNP位点,并且可以知道该位点的碱基。由于单碱基延伸法可以通过多重PCR在1个反应体系检测多至10个位点,因此该方法不仅方便快捷,并且可以实现高通量检测SNP位点。     

单核苷酸突变的检测及其在畜牧业中的应用前景

单核苷酸多态性 ( SNP)是最新发展起来的第 3代分子标记 ,具有密度高、双等位基因、易实现自动化检测的特点。本文重点介绍了 SNP检测的方法和原理及其在畜牧业研究生产中的广泛利用前景。并提出 SNP研究中遇到的一些问题     

单核苷酸多态性及其在畜牧兽医领域的研究进展

研究介绍了单核苷酸多态性(SNP)的定义、类型等相关概念,以及SNP常用的检测方法,并综述SNP在动物分子标记辅助育种、品种鉴定以及疾病相关方面的研究现状,为下一步拓展SNP应用提供参考。     

基于SNP芯片技术对猪性状的应用及其研究进展

SNP具有分布广、数量多,易检测和便于分型等优点,在动植物分子育种方面已经得到广泛应用,并不断出现新的分析检测方法。相对全基因组重测序的成本而言,SNP芯片检测的成本较低,使得利用SNP芯片技术在全基因组范围内寻找与人类疾病和动植物性状相关的SNPs成为可能。利用SNP芯片技术结合全基因组关联分析(GWAS),已经检测到了与猪性状显著相关的SNPs位点和候选基因,为未来猪的分子育种提供理论依据。该文主要就SNP芯片技术的特点、原理和方法以及在猪性状方面的应用加以综述。     

鸡NLRC5基因启动子多态性分析

为了研究NLRC5基因启动子区的SNP及其对启动子功能元件的影响,采用目标捕获测序和PCR产物直接测序的方法对27个鸡品种和2种细胞的NLRC5基因启动子区进行SNP检测,总共检测到37个SNP位点,其中斗鸡未发现SNP存在,SNP9存在于除斗鸡以外的其他鸡种和细胞。生物信息学软件预测得到NLRC5基因启动子区转录因子结合位点和CpG岛,SNP位点对转录因子结合位点和CpG岛大小均有影响,表明NLRC5启动子区SNP可能通过不同方式影响基因表达调控。     

单核苷酸突变的检测及其在畜牧业中的应用前景

单核苷酸多态性（SNP）是最新发展起来的3代分子标记，具有密度高，双等位基因，易实现自动化检测的特点，本文重点介绍了SNP检测的方法和原理及其在畜牧业研究生产中的广泛利用前景，并提出SNP研究中遇到的一些问题。     

伊犁马MyoG基因外显子1多态性与肉质性状及肌纤维性状关联分析

为寻找伊犁马肉质性能的分子标记,试验以38匹伊犁马为材料,测定肉质性状（失水率、熟肉率、剪切力）和肌纤维性状（肌纤维横截面积、肌纤维直径、肌纤维密度）,利用PCR直接测序法检测肌细胞生成素（meyogenin,MyoG）基因外显子1在伊犁马群体中的多态性,并对MyoG基因SNPs不同基因型与肉质、肌纤维性状进行关联分析。结果表明,MyoG基因外显子1检测出5个突变位点,分别为SNP1（g.31187343 A>C）、SNP2（g.31187333 G>A）、SNP3（g.31187132 C>T）、SNP4（g.31187105 C>G）和SNP5（g.31187099 C>T）,其中SNP1为错义突变,碱基A突变为C使得氨基酸由苏氨酸突变为脯氨酸,其他位点均为无义突变。SNP3和SNP4为中度多态位点,SNP1和SNP2为低度多态位点,这4个位点均处于Hardy-Weinberg平衡状态。MyoG基因外显子1中SNP1和SNP4不同基因型个体失水率、熟肉率、肌纤维横截面积、肌纤维直径、肌纤维密度差异显著（P<0.05）;SNP3不同基因型个体熟肉率、肌纤维横截面积、肌纤维密度差异显著（P<0.05）;SNP2不同基因型个体各指标差异均不显著（P>0.05）。综上,伊犁马MyoG基因外显子1检测到5个多态位点,其中SNP1（g.31187343 A>C）、SNP3（g.31187132 C>T）和SNP4（g.31187105 C>G）位点不同基因型对肉质及肌纤维性状有显著影响,这些位点可作为伊犁马肉质性能潜在分子标记。     

SNP分子标记的研究及应用

SNP作为一种新的遗传标记，越来越受到世人的关注。本文主要介绍了SNP的概念、特点、检测分析技术、存在的问题及应用前景。     

SNP技术在猪肉溯源过程中的应用

为筛选可用于梅山猪和申农猪肉质溯源的SNP位点标记,采用PCR-RFLP方法检测13个基因在梅山猪和申农猪群体内14个SNP位点的多态性,以期寻找多态性丰富的SNP位点,通过再次采集样品,进行溯源验证试验,最终确定可用于梅山猪和申农猪肉溯源的SNP位点组合。结果表明：MMP19基因、GRN基因、NR4A1基因的SNP位点和PSMB10基因的2个SNP位点杂合度（H值）均大于0.30,符合肉质溯源要求。可根据MMP19、PSMB10、NR4A1和GRN 4个基因共5个SNP位点编制用于检测梅山猪和申农猪肉质溯源的条形码。     

Assessing single-nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a <3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.     

A genome-wide association study of meat and carcass traits in Australian cattle

Chromosomal regions containing DNA variation affecting the traits intramuscular fat percentage (IMF), meat tenderness measured as peak force to shear the LM (LLPF), and rump fat measured at the sacro-iliac crest in the chiller (CHILLP8) were identified using a set of 53,798 SNP genotyped on 940 taurine and indicine cattle sampled from a large progeny test experiment. Of these SNP, 87, 64, and 63 were significantly (P < 0.001) associated with the traits IMF, LLPF, and CHILLP8, respectively. A second, nonoverlapping sample of 1,338 taurine and indicine cattle from the same large progeny test experiment genotyped for 335 SNP, including as a positive control the calpastatin (CAST) c.2832A > G SNP, was used to confirm these locations. In total, 37 SNP were significantly (P < 0.05) associated with the same trait and with the same favorable homozygote in both data sets, representing 27 chromosomal regions. For the trait IMF, the effect of SNP in the confirmation data set was predicted from the discovery set by multiplying the estimated allele effect of each SNP in the discovery set by the number of copies of the reference allele of each SNP in the confirmation set. These weighted effects were then summed over all SNP to generate a molecular breeding value (MBV) for each animal in the confirmation data set. Using a bivariate analysis of MBV and IMF phenotypes of animals in the confirmation set, a panel of 14 SNP explained 5.6 and 15.6% of the phenotypic and genetic variance of IMF, respectively, in the confirmation data set. The amount of variation did not increase as more SNP were added to the MBV and instead decreased to 1.2 and 3.8% of the phenotypic and genetic variance of IMF, respectively, when 329 SNP were included in the analysis.     

Fine mapping and detection of the causative mutation underlying Quantitative Trait Loci

The effect on power and precision of including the causative SNP amongst the investigated markers in Quantitative Trait Loci (QTL) mapping experiments was investigated. Three fine mapping methods were tested to see which was most efficient in finding the causative mutation: combined linkage and linkage disequilibrium mapping (LLD); association mapping (MARK); a combination of LLD and association mapping (LLDMARK). Two simulated data sets were analysed: in one set, the causative SNP was included amongst the markers, while in the other set the causative SNP was masked between markers. Including the causative SNP amongst the markers increased both precision and power in the analyses. For the LLD method the number of correctly positioned QTL increased from 17 for the analysis without the causative SNP to 77 for the analysis including the causative SNP. The likelihood of the data analysis increased from 3.4 to 13.3 likelihood units for the MARK method when the causative SNP was included. When the causative SNP was masked between the analysed markers, the LLD method was most efficient in detecting the correct QTL position, while the MARK method was most efficient when the causative SNP was included as a marker in the analysis. The LLDMARK method, combining association mapping and LLD, assumes a QTL as the null hypothesis (using LLD method) and tests whether the ‘putative causative SNP’ explains significantly more variance than a QTL in the region. Thus, if the putative causative SNP does not only give an Identical‐By‐Descent (IBD) signal, but also an Alike‐In‐State (AIS) signal, LLDMARK gives a positive likelihood ratio. LLDMARK detected less than half as many causative SNPs as the other methods, and also had a relatively high false discovery rate when the QTL effect was large. LLDMARK may however be more robust against spurious associations, because the regional IBD is largely corrected for by fitting a QTL effect in the null hypothesis model.     

Detection of runs of homozygosity in Norwegian Red: Density,criteria and genotyping quality control

Runs of homozygosity (ROH) are long, homozygote segments of an individual’s genome, traceable to the parents and might be identical by descent. Due to the lack of standards for quality control of genotyping and criteria to define ROH, Norwegian Red was used to find the effects of SNP density, genotyping quality control and ROH criteria on the detection of ROH. A total of 384 bulls were genotyped with the Illumina HD-chip containing 777,962 SNP markers. A total of 22 data subsets were derived to examine the effects of SNP density, quality control of genotyping and ROH criteria. ROH was detected by PLINK. High SNP density led to increased resolution, fewer false-positive ROH segment, and made it possible to detect shorter ROH. Considering the ROH criteria, we demonstrated that allowing for heterozygote SNP could generate false positives. Furthermore, genotyping quality control should be tuned towards keeping as many SNP as possible, also low minor allele frequency SNP, as otherwise many ROH segments will be lost.     

拜城油鸡FSHR基因多态性及其与生产性能的关联分析

【目的】探究促卵泡激素受体(follicle stimulating hormone receptor,FSHR)基因多态性与拜城油鸡产蛋性状、蛋品质及孵化性能的关系,为选育高生产性能的种鸡提供新的分子标记。【方法】以129只拜城油鸡为研究对象,利用DNA混池重测序技术筛选SNPs位点,采取Sequenom飞行时间质谱技术进行相关位点分型,通过SPSS 26.0软件的一般线性模型进行FSHR基因多态性与拜城油鸡生产性能的关联分析。【结果】FSHR基因外显子1上存在2个SNPs位点:SNP1(g.7640519 C＞A)、SNP2(g.7640639 T＞C);外显子4和5上分别存在1个SNP位点:SNP3(g.7692270 C＞T)、SNP4(g.7698478 A＞G);外显子10上存在3个SNPs位点:SNP5(g.7716725 G＞C)、SNP6(g.7715900 A＞G)、SNP7(g.7716470 T＞C),除SNP6外,检出率均在90%以上。SNP1位点仅检测到2个基因型:CC和CA,CC为优势基因型;SNP2、SNP3和SNP7位点均存在3种基因型:TT、TC和CC;SNP4位点存在3种基因型:AA、AG和GG;SNP5位点存在3种基因型:CC、CG和GG。相关分析结果表明,SNP2位点CC基因型个体开产日龄极显著高于TT基因型(P＜0.01),显著高于CT基因型(P＜0.05);TT基因型个体蛋重显著高于CT基因型(P＜0.05),入孵蛋死胚率显著高于CC基因型(P＜0.05)。SNP3位点TT基因型个体蛋壳厚度显著高于CC基因型(P＜0.05)。SNP5位点CG基因型个体300日龄总产蛋量极显著高于CC和GG基因型(P＜0.01),300日龄平均蛋重和开产蛋重均显著高于GG基因型(P＜0.05),开产体重显著高于CC基因型(P＜0.05);GG基因型个体蛋壳厚度显著低于CC和CG基因型(P＜0.05)。SNP7位点TT基因型个体开产蛋重和个体蛋重均显著高于CC和CT基因型(P＜0.05),蛋白高度和蛋黄重均显著高于CT基因型(P＜0.05),哈氏单位值极显著高于CC和CT基因型(P＜0.01)。【结论】FSHR基因SNP5(g.7716725 G＞C)位点CG基因型可作为影响拜城油鸡产蛋性能的标记基因型,SNP7(g.7716470 T＞C)位点TT基因型可作为影响拜城油鸡蛋品质的优势基因型。     

兴义鸭肌肉生长抑制素基因多态性与屠宰性状的相关性分析

为了解兴义鸭肌肉生长抑制素(myostatin,MSTN)基因SNPs与屠宰性状的相关性,本研究采用基因克隆及PCR产物直接测序的方法,将MSTN基因作为鸭屠宰性状的候选基因,对兴义鸭的MSTN基因外显子进行多态性检测.结果表明,在60个兴义鸭个体中筛选出8个SNPs,其中,第1外显子有5个突变位点:SNP1(G77A)、SNP2(A91G)、SNP3(G130A)、SNP4(C325T)和SNP5(C331T);在第2外显子中并未发现突变位点;第3外显子有3个突变位点:SNP6(C206T)、SNP7(A235G)和SNP8(C256A);对这8个SNPs与屠宰性状进行关联性分析,结果并未达到显著水平(P>0.05).本研究结果可丰富MSTN基因的研究数据,为鸭的育种提供参考.     

Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low‐density SNP panels: Evidence that long‐range LD is a major contributing factor

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium‐density single nucleotide polymorphism (SNP) array. Here, the impact of lower‐density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)‐flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree‐based prediction (0.50–0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL‐flanking SNP (0.65–0.72) was similar to the panel with 35K SNP (0.65–0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r² ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long‐range LD likely contributed to the accurate genomic predictions with the low‐density SNP panels. Population structure analysis supported the hypothesis that long‐range LD in this population may be caused by admixture. Results suggest that lower‐cost, low‐density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.     

Development of novel SNP system for individual and pedigree control in a Japanese Black cattle population using whole‐genome genotyping assay

Individual identification and parentage analysis using DNA markers are essential for assuring food identity and managing livestock population. The objective of this study was to develop a single nucleotide polymorphism (SNP) panel system for individual effective identification and parentage testing in a Japanese Black cattle population using BovineSNP50 BeadChip. On the basis of SNP frequencies, 60 unlinked informative SNPs were finally selected as candidate markers. The allelic frequencies for each SNP were estimated using additional individuals by PCR‐RFLP (restriction fragment length polymorphism). A total of 87 SNP markers added in conjunction with previously developed 27 SNPs were evaluated to assess the utility of the test. The estimated identity power was 2.01 × 10^?34. Parentage exclusion probabilities, when both suspected parents' genotypes were known and when only one suspected parent was genotyped, were estimated as 0.99999997 and 0.99998010, respectively. This developed SNP panel was quite powerful and could successfully exclude false sires with a probability of >0.9999 even if the dam's genotype information was not obtained. The SNP system would contribute to management of the beef industry in Japan.