随机SNP在全基因组关联研究人群分层分析中的应用

在复杂疾病的全基因组关联研究中,人群分层现象会增加结果的假阳性率,因此考虑人群遗传结构、控制人群分层是很有必要的。而在人群分层研究中,使用随机选择的SNP的效果还有待进一步探讨。文章利用HapMap Phase2人群中无关个体的Affymetrix SNP 6.0芯片分型数据,在全基因组上随机均匀选择不同数量的SNP,同时利用f值和Fisher精确检验方法筛选祖先信息标记（Ancestry Informative Markers,AIMs）。然后利用HapMap Phase3中的无关个体的数据,以F-statistics和STRUCTURE分析两种方法评估所选出的不同SNP组合对人群的区分效果。研究发现,随机均匀分布于全基因组的SNP可用于识别人群内部存在的遗传结构。文章进一步提示,在全基因组关联研究中,当没有针对特定人群的AIMs时,可在全基因组上随机选择3000以上均匀分布的SNP来控制人群分层。     

酵母全基因组芯片的应用

酵母作为最常用的模式生物,其全基因组测序最先完成。利用已知的酵母基因组信息,结合基因芯片技术,可进一步系统研究酵母的功能基因组表达。基因芯片技术是上世纪末发展起来的一项集分子生物学、生物信息学和电子学等科目的生物高新技术。酵母全基因组芯片,可以用以从基因表达水平,研究环境、物理、化学因子、毒理和药物作用的机制,在最终阐明酵母基因组功能的同时,为生物学研究提供更优化的模式生物模型。     

应用两种基因组快速扩增方法进行病毒芯片杂交鉴定

为了摸索均衡的病毒基因组扩增方法,建立高通量的病毒检测基因芯片技术平台,本研究以甲病毒属的辛德比斯病毒作为检测模型,分别以随机PCR扩增法和MDA( Multiple Displacement Amplification)扩增法扩增病毒基因组,并以两种扩增产物作为模板,扩增辛德比斯病毒的特异基因片段以验证基因组扩增的均衡性;然后将两种基因组扩增产物标记荧光染料后与基因芯片进行杂交;结果表明从两种基因组扩增产物中正确扩增出了辛德比斯的特定基因片段,作为探针可与基因芯片上的靶标基因特异性结合;基因组扩增产物与基因芯片进行杂交,可成功检测到甲病毒属的特异性信号,充分说明随机PCR扩增法和MDA扩增法用于扩增病毒基因组均具有良好的均衡性,扩增产物可用于病毒性病原体的基因芯片检测。     

基于SLAF-seq技术的动物基因组应用及进展

近年来随着测序技术的迅速发展,简化基因组中的SLAF-seq是一种既经济又高效的、且在全基因组范围内挖掘SNP位点的测序技术。该技术已广泛应用于群体进化分析、遗传图谱的构建和QTL定位等大样本群体研究。该文综合国内外文献介绍SLAF-seq技术在动物基因组研究方面的应用,分析SLAF-seq技术在动物学中的发展方向并做出展望,以期为动物的遗传学和物种保护等相关研究开启一扇新的大门。     

基于红外相机的不可个体识别动物种群密度估算方法

种群密度估计对野生动物的保护和管理至关重要,也是动物生态学和保护生物学备受关注的研究热点,但对大中型兽类种群数量的准确估算一直面临挑战。红外相机是哺乳动物调查中普遍采用的工具,也是克服这一挑战的一种经济有效的方法。目前国际上已有多种方法采用红外相机数据估算不可个体识别动物的种群密度,但相关技术在我国的应用案例较少,本文旨在为国内研究者应用红外相机数据估算动物种群密度提供参考。首先,我们介绍了随机相遇模型(randomencounter model, REM)、随机相遇与停留时间(random encounter and staying time, REST)模型、相机前停留时间(time in front of the camera,TIFC)模型以及红外相机距离取样(cameratrapdistancesampling,CTDS)这四种模型的基本原理和假设;其次,描述了这些模型在野外调查中的技术要点,并给出数据处理与分析的建议;最后,总结了每个模型的数据需求、优点和缺点。虽然我国目前拥有估算种群密度的大量红外相机数据源,但有很多物种的数量尚未知晓,也没有一种方法对所有红外相机数据都是...     

凝集素芯片技术检测糖蛋白方法的建立及初步应用

建立了凝集素芯片技术检测糖蛋白的方法,对实验条件进行了优化,应用凝集素芯片初步检测分析了Chang?蒺s liver正常肝细胞总蛋白中的糖蛋白糖链构成．将凝集素ConA、GNA固定于环氧化修饰的玻片表面,用Cy3标记标准糖蛋白RNaseB,利用凝集素识别特异糖链的原理建立凝集素芯片检测糖蛋白的方法．摸索出最佳封闭剂是含1% BSA的磷酸缓冲液,最佳孵育时间及温度为3 h和室温,最佳孵育缓冲液为含1% BSA和0.05% Tween-20的磷酸缓冲液,并用甘露糖抑制实验验证了凝集素芯片结合的特异性．用包含10种凝集素的芯片,成功解析了标准糖蛋白RNaseB、Fetuin的糖链构成,证实了凝集素芯片检测糖蛋白糖链的可行性．最后用凝集素芯片初步检测分析了Chang?蒺s liver正常肝细胞总蛋白中的糖蛋白糖链构成,发现 Chang's liver正常肝细胞总蛋白中的糖蛋白可能有多价 Sia或GlcNAc、terminalα-1,3 mannose、GalNAc、Galβ-1,4GlcNAc这些糖链结构的存在．蛋白质糖基化是一种重要的翻译后修饰,它在微生物感染、细胞分化、肿瘤转移、细胞癌变等生命活动中起着重要作用,因此近年来蛋白质的糖基化研究受到广泛的重视,但由于缺乏一种简便、快速、高通量的检测手段,蛋白质糖基化修饰的研究发展缓慢．凝集素芯片技术的出现实现了对糖蛋白的快速、准确、高通量的检测 分析．     

一类非连续数据的GLM模型的应用

分析了因变量取有限个不连续点值的一种情况,给出了因变量Ｙ取值城中任一点值 的概率估计．并运用这种方法,对ＲＫ手术的结果作出了预测．     

不同单细胞全基因组扩增方法的比较及MALBAC在辅助生殖中的应用

单细胞全基因组扩增(whole genome amplification, WGA)是指在单细胞水平对全基因组进行扩增的新技术,其原理是将分离的单个细胞的微量全基因组DNA进行扩增,获得高覆盖率的完整的基因组后进行高通量测序,用于揭示细胞异质性。目前,WGA方法主要包括引物延伸预扩增(primer extension preamplification PCR, PEP-PCR)、简并寡核苷酸引物PCR (degenerate oligonucleotide primed PCR, DOP-PCR)、多重置换扩增(multiple displacement amplification, MDA)、多次退火环状循环扩增(multiple annealing and looping-based amplification cycles, MALBAC)等。本文对不同的单细胞WGA方法的原理及应用情况分别进行了阐述,并对其扩增效率进行评价和比较,包括基因组覆盖度、均一性、重现性、SNV (single-nucleotide variants)和CNV (copy number variants)检测力等。综合对比不同单细胞WGA方法后发现,MALBAC的扩增均一性最高、等位基因脱扣率最低、重现性最好,且对于CNV和SNV的检测效果最好。本文还阐述了MALBAC技术在人类单精子减数重组、非整倍体分析以及人类卵细胞基因组研究中的应用。     

系统比较方法的原理及应用

物种的大部分性状与其系统进化过程相联系,亲缘关系近的物种,其性状差异通常较小.因此在种间或更高分类单元层次研究性状之间的关系,或性状与环境间关系时需要考虑系统进化的影响,以满足常规统计分析对于样本独立性的要求.自20世纪80年代以来国际上已经陆续推出一系列的系统比较方法,其共同原理是:在推断物种间系统关系的基础上,在种间水平上比较,将原本不符合样本独立性的物种性状或环境变量数据,将其转化为适用于常规统计分析方法的彼此独立的数据,然后运用常规统计方法分析,得到排除了系统进化历史影响的物种性状间或者物种性状与环境变量间的关系.首先简单介绍了运用系统比较方法之前的建立系统关系和数据诊断这两个步骤,在此基础上阐述简单独立对比分析、Felsenstein的独立比较方法和自回归方法这3种常用的系统比较方法的基本原理、各自的特点以及它们在生态学、进化生物学等领域的应用.系统比较方法已经获得广泛的应用和认可,发现了应用常规统计分析所没有能发现的问题和规律,但在构建准确反映系统进化过程的系统关系、进化模型的选择等方面仍具有一定的局限性;而生物信息学、生物系统学的发展,以及各种相关软件的开发为系统比较方法的进一步完善发展和更为广泛的应用创造了条件.     

Estimation of genomic breed composition of individual animals in composite beef cattle

Three statistical models (an admixture model, linear regression, and ridge-regression BLUP) and two strategies for selecting SNP panels (uniformly spaced vs. maximum Euclidean distance of SNP allele frequencies between ancestral breeds) were compared for estimating genomic-estimated breed composition (GBC) in Brangus and Santa Gertrudis cattle, respectively. Animals were genotyped with a GeneSeek Genomic Profiler bovine low-density version 4 SNP chip. The estimated GBC was consistent among the uniformly spaced SNP panels, and values were similar between the three models. However, estimated GBC varied considerably between the three methods when using fewer than 10 000 SNPs that maximized the Euclidean distance of allele frequencies between the ancestral breeds. The admixture model performed most consistently across various SNP panel sizes. For the other two models, stabilized estimates were obtained with an SNP panel size of 20 000 SNPs or more. Based on the uniformly spaced 20K SNP panel, the estimated GBC was 69.8–70.5% Angus and 29.5–30.2% Brahman for Brangus, and 63.9–65.3% Shorthorn and 34.7–36.1% Brahman in Santa Gertrudis. The estimated GBC of ancestries for Santa Gertrudis roughly agreed with the pedigree-expected values. However, the estimated GBC in Brangus showed a considerably larger Angus composition than the pedigree-expected value (62.5%). The elevated Angus composition in the Brangus could be due to the mixture of some 1/2 Ultrablack animals (Brangus × Angus). Another reason could be the consequences of selection in Brangus cattle for phenotypes where the Angus breed has advantages.     

Prediction of breed composition in an admixed cattle population

Swiss Fleckvieh was established in 1970 as a composite of Simmental (SI) and Red Holstein Friesian (RHF) cattle. Breed composition is currently reported based on pedigree information. Information on a large number of molecular markers potentially provides more accurate information. For the analysis, we used Illumina BovineSNP50 Genotyping Beadchip data for 90 pure SI, 100 pure RHF and 305 admixed bulls. The scope of the study was to compare the performance of hidden Markov models, as implemented in structure software, with methods conventionally used in genomic selection [BayesB, partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO) variable selection)] for predicting breed composition. We checked the performance of algorithms for a set of 40 492 single nucleotide polymorphisms (SNPs), subsets of evenly distributed SNPs and subsets with different allele frequencies in the pure populations, using F_ST as an indicator. Key results are correlations of admixture levels estimated with the various algorithms with admixture based on pedigree information. For the full set, PLSR, BayesB and structure performed in a very similar manner (correlations of 0.97), whereas the correlation of LASSO and pedigree admixture was lower (0.93). With decreasing number of SNPs, correlations decreased substantially only for 5% or 1% of all SNPs. With SNPs chosen according to F_ST, results were similar to results obtained with the full set. Only when using 96 and 48 SNPs with the highest F_ST, correlations dropped to 0.92 and 0.90 respectively. Reducing the number of pure animals in training sets to 50, 20 and 10 each did not cause a drop in the correlation with pedigree admixture.     

Genome-enable prediction for health traits using high-density SNP panel in US Holstein cattle

The objective of this study was to compare accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle. The dataset was composed of 2505 records reporting the occurrence of retained fetal membranes (RFM), metritis (MET), mastitis (MAST), displaced abomasum (DA), lameness (LS), clinical endometritis (CE), respiratory disease (RD), dystocia (DYST) and subclinical ketosis (SCK) in Holstein cows, collected between 2012 and 2014 in 16 dairies located across the US. Cows were genotyped with the Illumina BovineHD (HD, 777K). The quality controls for SNP genotypes were HWE P-value of at least 1 × 10⁻¹⁰; MAF greater than 0.01 and call rate greater than 0.95. The FImpute program was used for imputation of missing SNP markers. The effect of each SNP was estimated using the Bayesian Ridge Regression (BRR), Bayes A, Bayes B and Bayes Cπ methods. The prediction quality was assessed by the area under the curve, the prediction mean square error and the correlation between genomic breeding value and the observed phenotype, using a leave-one-out cross-validation technique that avoids iterative cross-validation. The highest accuracies of predictions achieved were: RFM [Bayes B (0.34)], MET [BRR (0.36)], MAST [Bayes B (0.55), DA [Bayes Cπ (0.26)], LS [Bayes A (0.12)], CE [Bayes A (0.32)], RD [Bayes Cπ (0.23)], DYST [Bayes A (0.35)] and SCK [Bayes Cπ (0.38)] models. Except for DA, LS and RD, the predictive abilities were similar between the methods. A strong relationship between the predictive ability and the heritability of the trait was observed, where traits with higher heritability achieved higher accuracy and lower bias when compared with those with low heritability. Overall, it has been shown that a high-density SNP panel can be used successfully to predict genomic breeding values of health traits in Holstein cattle and that the model of choice will depend mostly on the genetic architecture of the trait.     

Use of partial least squares regression to predict single nucleotide polymorphism marker genotypes when some animals are genotyped with a low-density panel

High-density single nucleotide polymorphism (SNP) platforms are currently used in genomic selection (GS) programs to enhance the selection response. However, the genotyping of a large number of animals with high-throughput platforms is rather expensive and may represent a constraint for a large-scale implementation of GS. The use of low-density marker (LDM) platforms could overcome this problem, but different SNP chips may be required for each trait and/or breed. In this study, a strategy of imputation independent from trait and breed is proposed. A simulated population of 5865 individuals with a genome of 6000 SNP equally distributed on six chromosomes was considered. First, reference and prediction populations were generated by mimicking high- and low-density SNP platforms, respectively. Then, the partial least squares regression (PLSR) technique was applied to reconstruct the missing SNP in the low-density chip. The proportion of SNP correctly reconstructed by the PLSR method ranged from 0.78 to 0.97 when 90% and 50%, respectively, of genotypes were predicted. Moreover, data sets consisting of a mixture of actual and PLSR-predicted SNP or only actual SNP were used to predict genomic breeding values (GEBVs). Correlations between GEBV and true breeding values varied from 0.74 to 0.76, respectively. The results of the study indicate that the PLSR technique can be considered a reliable computational strategy for predicting SNP genotypes in an LDM platform with reasonable accuracy.     

Population structure and breed composition prediction in a multi-breed sheep population using genome-wide single nucleotide polymorphism genotypes

Knowledge of population structure and breed composition of a population can be advantageous for a number of reasons; these include designing optimal (cross)breeding strategies in order to maximise non-additive genetic effects, maintaining flockbook integrity by authenticating animals being registered and as a quality control measure in the genotyping process. The objectives of the present study were to 1) describe the population structure of 24 sheep breeds, 2) quantify the breed composition of both flockbook-recorded and crossbred animals using single nucleotide polymorphism BLUP (SNP-BLUP), and 3) quantify the accuracy of breed composition prediction from low-density genotype panels containing between 2000 and 6000 SNPs. In total, 9334 autosomal SNPs on 11 144 flockbook-recorded animals and 1172 crossbred animals were used. The population structure of all breeds was characterised by principal component analysis (PCA) as well as the pairwise breed fixation index (F_st). The total number of animals, all of which were purebred, included in the calibration population for SNP-BLUP was 2579 with the number of animals per breed ranging from 9 to 500. The remaining 9559 flockbook-recorded animals, composite breeds and crossbred animals represented the test population; three breeds were excluded from breed composition prediction. The breed composition predicted using SNP-BLUP with 9334 SNPs was considered the gold standard prediction. The pairwise breed F_st ranged from 0.040 (between the Irish Blackface and Scottish Blackface) to 0.282 (between the Border Leicester and Suffolk). Principal component analysis revealed that the Suffolk from Ireland and the Suffolk from New Zealand formed distinct, non-overlapping clusters. In contrast, the Texel from Ireland and that from New Zealand formed integrated, overlapping clusters. Composite animals such as the Belclare clustered close to its founder breeds (i.e., Finn, Galway, Lleyn and Texel). When all 9334 SNPs were used to predict breed composition, an animal that had a majority breed proportion predicted to be ≥0.90 was defined as purebred for the present study. As the panel density decreased, the predicted breed proportion threshold, used to identify animals as purebred, also decreased (≥0.85 with 6000 SNPs to ≥0.60 with 2000 SNPs). In all, results from the study suggest that breed composition for purebred and crossbred animals can be determined with SNP-BLUP using ≥5000 SNPs.     

Predictive ability of selected subsets of single nucleotide polymorphisms (SNPs) in a moderately sized dairy cattle population

Several studies have shown that computation of genomic estimated breeding values (GEBV) with accuracies significantly greater than parent average (PA) estimated breeding values (EBVs) requires genotyping of at least several thousand progeny-tested bulls. For all published analyses, GEBV computed from the selected samples of markers have lower or equal accuracy than GEBV derived on the basis of all valid single nucleotide polymorphisms (SNPs). In the current study, we report on four new methods for selection of markers. Milk, fat, protein, somatic cell score, fertility, persistency, herd life and the Israeli selection index were analyzed. The 972 Israeli Holstein bulls genotyped with EBV for milk production traits computed from daughter records in 2012 were assigned into a training set of 844 bulls with progeny test EBV in 2008, and a validation set of 128 young bulls. Numbers of bulls in the two sets varied slightly among the nonproduction traits. In EFF₁₂, SNPs were first selected for each trait based on the effects of each marker on the bulls’ 2012 EBV corrected for effective relationships, as determined by the SNP matrix. EFF₀₈ was the same as EFF₁₂, except that the SNPs were selected on the basis of the 2008 EBV. In DIF_max, the SNPs with the greatest differences in allelic frequency between the bulls in the training and validation sets were selected, whereas in DIF_min the SNPs with the smallest differences were selected. For all methods, the numbers of SNPs retained varied over the range of 300 to 6000. For each trait, except fertility, an optimum number of markers between 800 and 5000 was obtained for EFF₁₂, based on the correlation between the GEBV and current EBV of the validation bulls. For all traits, the difference between the correlation of GEBV and current EBV and the correlation of the PA and current EBV was >0.25. EFF₀₈ was inferior to EFF₁₂, and was generally no better than PA EBV. DIF_max always outperformed DIF_min and generally outperformed EFF₀₈ and PA. Furthermore, GEBV based on DIF_max were generally less biased than PA. It is likely that other methods of SNP selection could improve upon these results.     

An easier method to identify the individual genomic composition of allopolyploid complexes

A new method for the fast identification of the genomic composition of the cyprinid Squalius alburnoides is presented. The method is based on a length polymorphism detected in the β‐actin gene, which serves as the basis for the development of a semi‐quantitative PCR.     

Estimating quantitative genetic parameters in wild populations: a comparison of pedigree and genomic approaches

The estimation of quantitative genetic parameters in wild populations is generally limited by the accuracy and completeness of the available pedigree information. Using relatedness at genomewide markers can potentially remove this limitation and lead to less biased and more precise estimates. We estimated heritability, maternal genetic effects and genetic correlations for body size traits in an unmanaged long‐term study population of Soay sheep on St Kilda using three increasingly complete and accurate estimates of relatedness: (i) Pedigree 1, using observation‐derived maternal links and microsatellite‐derived paternal links; (ii) Pedigree 2, using SNP‐derived assignment of both maternity and paternity; and (iii) whole‐genome relatedness at 37 037 autosomal SNPs. In initial analyses, heritability estimates were strikingly similar for all three methods, while standard errors were systematically lower in analyses based on Pedigree 2 and genomic relatedness. Genetic correlations were generally strong, differed little between the three estimates of relatedness and the standard errors declined only very slightly with improved relatedness information. When partitioning maternal effects into separate genetic and environmental components, maternal genetic effects found in juvenile traits increased substantially across the three relatedness estimates. Heritability declined compared to parallel models where only a maternal environment effect was fitted, suggesting that maternal genetic effects are confounded with direct genetic effects and that more accurate estimates of relatedness were better able to separate maternal genetic effects from direct genetic effects. We found that the heritability captured by SNP markers asymptoted at about half the SNPs available, suggesting that denser marker panels are not necessarily required for precise and unbiased heritability estimates. Finally, we present guidelines for the use of genomic relatedness in future quantitative genetics studies in natural populations.     

Identification of Yersinia enterocolitica using a random genomic DNA microarray chip

Aims: To fabricate a DNA chip containing random fragments of genomic DNA of Yersinia enterocolitica and to verify its diagnostic ability. Methods and Results: A DNA microarray chip was fabricated using randomly fragmented DNA of Y. enterocolitica. Chips were hybridized with genomic DNA extracted from other Y. enterocolitica strains, other Yersinia spp. and bacteria in different genera. Genomic DNA extracted from Y. enterocolitica showed a significantly higher hybridization rate compared with DNA of other Yersinia spp. or bacterial genera, thereby distinguishing it from other bacteria. Conclusions: A DNA chip containing randomly fragmented genomic DNA from Y. enterocolitica can detect Y. enterocolitica and clearly distinguish it from other Yersinia spp. and bacteria in different genera. Significance and Impact of the Study: A microarray chip containing randomly fragmented genomic DNA of Y. enterocolitica was fabricated without sequence information, and its diagnostic ability to identify Y. enterocolitica was verified.     

Possibilities to improve the genetic evaluation of a rare breed using limited genomic information and multivariate BLUP

The use of molecular genetic information in the evaluation of livestock has become more common. This study looks at the efficacy of using such information to improve the genetic evaluation of a rare breed of dual-purpose cattle. Data were available in the form of pedigree information on the Gloucester cattle breed in the United Kingdom and recorded milk and beef performance on a small number of animals. In addition, molecular genetic information in the form of multi-marker, multiple regression results converted to a 1 to 10 score (Igenity scores) and 123 single nucleotide polymorphism (SNP) genotypes for 199 non-recorded animals were available. Appropriate mixed-animal models were explored for the recorded traits and these were used to calculate estimated breeding values (EBV), and their accuracies, for 6527 animals in the breed’s pedigree file. Various ways to improve the accuracy of these EBV were explored. This involved using multivariate BLUP analyses, genomic estimated breeding values (GEBV) and combining Igenity scores with recorded traits in a series of bivariate genetic analyses. Using the milk recording traits as an example, the accuracy of a number of traits could be improved using multivariate analyses by up to 14%, depending on the combination of traits used. The level of increase in accuracy largely corresponded to the absolute difference between the genetic and residual correlations between two traits, but this was not always symmetrical. The use of GEBV did not increase the accuracy of milk trait EBV owing to the low proportion of variance explained by the 101 SNPs used. Using Igenity scores in bivariate analyses with the recorded data was more successful in increasing EBV accuracy. The largest increases were found in genotyped animals with no recorded performance (e.g. a 58% increase in fat weight in milk); however, the size of the increase depended on the level of the genetic correlation between the recorded trait and the Igenity score for that trait. Lower levels of improvements in accuracy were seen in animals that were recoded but not genotyped, and ancestors which were neither genotyped nor recorded. This study demonstrated that it was possible to improve the accuracy of EBV estimation by including Igenity score information in genetic analyses but it also concluded that increasing the level of performance recording in the breed would be beneficial.