基于高密度SNP标记估计群体间遗传关联

联合育种的准确性受到群体间遗传关联程度的影响.本研究通过比较基于系谱数据和基因组数据计算的群体遗传关联,探究高密度SNP标记在遗传关联估计中的应用前景.本研究同时使用了模拟数据和真实数据,采用6种不同的遗传关联计算方法,包括PEVD(prediction error variance of differences)、P...     

基因组选择技术在农业动物育种中的应用

基因组选择(genomic selection, GS)是畜禽经济性状遗传改良的重要方法。随着高密度SNP芯片和二代测序价格的下降,GS技术越来越多被应用于奶牛、猪、鸡等农业动物育种中。然而,降低全基因组SNP分型成本、提高基因组育种值(genomic estimated breeding value,GEBV)估计准确性仍然是GS研究的主要难题。本文从全基因组SNP分型策略和GEBV估计模型两个方面进行了综述,并对目前GS技术在主要畜禽品种中的应用现状进行了介绍,以期为GS在农业动物育种中的深入开展提供借鉴和参考。     

植物全基因组选择技术的研究进展及其在玉米育种上的应用

全基因组选择技术通过全基因组中大量的单核苷酸多态性标记（SNP）和参照群体的表型数据建立 BLUP 模型估计出每一标记的育种值,称为估计育种值（GEBV）,然后仅利用同样的分子标记估计出后代个体育种值并进行选择。该文就近年来国内外有关影响基因组选择效率的主要因素——参考群体的类型与大小、模型的建立方法、标记的类型及其数目、性状遗传力,以及对基因组选择效率的影响等方面的研究进展进行综述,并介绍了全基因组选择技术在玉米育种上应用概况以及对未来的展望。     

植物全基因组选择技术的研究进展及其在玉米育种上的应用（英文）

全基因组选择技术通过全基因组中大量的单核苷酸多态性标记(SNP)和参照群体的表型数据建立BLUP模型估计出每一标记的育种值,称为估计育种值(GEBV),然后仅利用同样的分子标记估计出后代个体育种值并进行选择。该文就近年来国内外有关影响基因组选择效率的主要因素——参考群体的类型与大小、模型的建立方法、标记的类型及其数目、性状遗传力,以及对基因组选择效率的影响等方面的研究进展进行综述,并介绍了全基因组选择技术在玉米育种上应用概况以及对未来的展望。     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状, 随着平衡育种理念的提出和发展, 繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展, 全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状,随着平衡育种理念的提出和发展,繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展,全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

桃基因组及全基因组关/联分析研究进展

桃(Prunus persica [L.] Batsch)是蔷薇科重要的核果类果树, 适应性强, 栽培范围广, 果实口感好, 深受消费者喜欢。提高桃果实品质及增加抗病、抗虫性一直是桃遗传育种者关注的焦点。文章对近年来桃遗传分子标记连锁图谱和物理图谱构建、分子标记开发应用、全基因组和转录组测序工作中所取得的最新成果进行综述, 同时阐述了高密度SNP芯片标记技术在桃以及其它作物上所开展的全基因组关联分析应用实例, 为桃进一步开展全基因组关联分析, 挖掘目标性状QTLs以及高效育种选择标记提供理论基础     

猪的全基因组水平SNP研究现状

SNP（single nucleotide polymorphism,单核苷酸多态）在猪基因组中的分布极其广泛,平均分布间隔为300～400 bp,相关数据库收录已达55万条。猪基因组测序已取得实质性进展,大规模搜索发现基因组及EST（expressed sequence tag）序列中的SNP已展开,应用于猪全基因组水平的SNP芯片已建立。在此基础上,基于猪SNP标记的遗传图谱绘制、QTL（quantitative trait loci）定位、遗传多样性检测及全基因组关联分析等也都相继出现。     

家畜全基因组选择中的单步贝叶斯方法

随着标记信息可以被越来越多的应用在家畜育种中,许多基因组选择(GS)方法使得育种工作者可以利用家畜早期的基因型数据提前对其进行选择。结合系谱、表型和基因型数据,我们可以缩短家畜的世代间隔,提高家畜遗传价值估计的准确性,进而加速其遗传改良速度。近年来,和广泛使用的多步基因组选择策略相比,业界更推崇基于在系谱关系矩阵中增加基因组信息的单步遗传评估方法。即使通常的基因组选择方法依然是多步方法,如GBLUP法,但是基于单步基因组模型进行的基因组评估能提供更为准确的结果。本研究的目的是引入单步贝叶斯方法,此方法可以用贝叶斯回归模型直接计算单核苷酸多态性(SNP)的效应,同时我们使用模拟方法评估模型的性能。研究结果显示:QTL数目对单步贝叶斯方法的准确性无影响,但其准确性受遗传力的影响。同时,其准确性随着测序个体数的增加而增加。我们也讨论了与使用单步贝叶斯方法相关的问题,并详细描述了一些与之有关的统计理论和算法问题。     

基于全基因组芯片开发水稻HRM特异分子标记

植物中广泛分布着单核苷酸多态性(SNP)位点。在此基础上发展而来的SNP标记因其具有高分辨率和共显性等优点,已成为当前作物遗传研究重要的分子工具。本研究拟建立基于高分辨率熔解曲线(HRM)技术的SNP分子标记,从而实现对栽培稻和野生稻的高效基因分型,为今后水稻的基因挖掘、品种鉴定以及分子育种等提供可靠、快捷的技术工具。利用水稻全基因组9 K SNP芯片对栽培稻品种黄华占和野生稻Y605进行扫描,寻找两者之间的SNP位点,并将其开发成基于HRM技术的特异分子标记。然后,利用这些分子标记对亲本黄华占、野生稻Y605以及两者的BC3回交群体进行分子检测,以验证其有效性。水稻9 K基因芯片在黄华占与野生稻Y605之间总共找到了4198个SNP位点,它们在12条染色体上较均匀分布。在水稻第1号染色体上随机挑选出5个SNP位点开发成基于HRM技术的特异分子标记。利用这些标记对黄华占与野生稻Y605的BC3F1和BC3F2群体进行检测分析,发现它们都能准确区分亲本的纯合与杂合基因型。并且,在回交后代的第1号染色体ZY1-1~ZY1-4标记区间检测到野生稻片段插入。水稻全基因组9 K SNP芯片可以很好地应用于水稻SNP标记的开发。开发的SNP特异标记能准确、高效地对栽培稻和野生稻进行基因分型。进一步完成基于HRM技术的水稻全基因组SNP标记的开发,可为今后野生稻的分子遗传研究、有利基因挖掘和育种应用提供高效的分子检测手段。     

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.     

Preselection statistics and Random Forest classification identify population informative single nucleotide polymorphisms in cosmopolitan and autochthonous cattle breeds

Commercial single nucleotide polymorphism (SNP) arrays have been recently developed for several species and can be used to identify informative markers to differentiate breeds or populations for several downstream applications. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this work, we compared several methods of SNPs preselection (Delta, F_st and principal component analyses (PCA)) in addition to Random Forest classifications to analyse SNP data from six dairy cattle breeds, including cosmopolitan (Holstein, Brown and Simmental) and autochthonous Italian breeds raised in two different regions and subjected to limited or no breeding programmes (Cinisara, Modicana, raised only in Sicily and Reggiana, raised only in Emilia Romagna). From these classifications, two panels of 96 and 48 SNPs that contain the most discriminant SNPs were created for each preselection method. These panels were evaluated in terms of the ability to discriminate as a whole and breed-by-breed, as well as linkage disequilibrium within each panel. The obtained results showed that for the 48-SNP panel, the error rate increased mainly for autochthonous breeds, probably as a consequence of their admixed origin lower selection pressure and by ascertaining bias in the construction of the SNP chip. The 96-SNP panels were generally more able to discriminate all breeds. The panel derived by PCA-chrom (obtained by a preselection chromosome by chromosome) could identify informative SNPs that were particularly useful for the assignment of minor breeds that reached the lowest value of Out Of Bag error even in the Cinisara, whose value was quite high in all other panels. Moreover, this panel contained also the lowest number of SNPs in linkage disequilibrium. Several selected SNPs are located nearby genes affecting breed-specific phenotypic traits (coat colour and stature) or associated with production traits. In general, our results demonstrated the usefulness of Random Forest in combination to other reduction techniques to identify population informative SNPs.     

Evaluating pedigree data. II. Identifying the cause of error in families with inconsistencies

Pedigree data can be evaluated, and subsequently corrected, by analysis of the distribution of genetic markers, taking account of the possibility of mistyping . Using a model of pedigree error developed previously, we obtained the maximum likelihood estimates of error parameters in pedigree data from Tokelau. Posterior probabilities for the possible true relationships in each family are conditional on the putative relationships and the marker data are calculated using the parameter estimates. These probabilities are used as a basis for discriminating between pedigree error and genetic marker errors in families where inconsistencies have been observed. When applied to the Tokelau data and compared with the results of retyping inconsistent families, these statistical procedures are able to discriminate between pedigree and marker error, with approximately 90% accuracy, for families with two or more offspring. The large proportion of inconsistencies inferred to be due to marker error (61%) indicates the importance of discriminating between error sources when judging the reliability of putative relationship data. Application of our model of pedigree error has proved to be an efficient way of determining and subsequently correcting sources of error in extensive pedigree data collected in large surveys.     

From microsatellites to single nucleotide polymorphisms for the genetic monitoring of a critically endangered sturgeon

The use of genetic information is crucial in conservation programs for the establishment of breeding plans and for the evaluation of restocking success. Short tandem repeats (STRs) have been the most widely used molecular markers in such programs, but next‐generation sequencing approaches have prompted the transition to genome‐wide markers such as single nucleotide polymorphisms (SNPs). Until now, most sturgeon species have been monitored using STRs. The low diversity found in the critically endangered European sturgeon (Acipenser sturio), however, makes its future genetic monitoring challenging, and the current resolution needs to be increased. Here, we describe the discovery of a highly informative set of 79 SNPs using double‐digest restriction‐associated DNA (ddRAD) sequencing and its validation by genotyping using the MassARRAY system. Comparing with STRs, the SNP panel proved to be highly efficient and reproducible, allowing for more accurate parentage and kinship assignments' on 192 juveniles of known pedigree and 40 wild‐born adults. We explore the effectiveness of both markers to estimated relatedness and inbreeding, using simulated and empirical datasets. Interestingly, we found significant correlations between STRs and SNPs at individual heterozygosity and inbreeding that give support to a reasonable representation of whole genome diversity for both markers. These results are useful for the conservation program of A. sturio in building a comprehensive studbook, which will optimize conservation strategies. This approach also proves suitable for other case studies in which highly discriminatory genetic markers are needed to assess parentage and kinship.     

Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach

Advancements in next-generation sequencing technology have enabled whole genome re-sequencing in many species providing unprecedented discovery and characterization of molecular polymorphisms. There are limitations, however, to next-generation sequencing approaches for species with large complex genomes such as barley and wheat. Genotyping-by-sequencing (GBS) has been developed as a tool for association studies and genomics-assisted breeding in a range of species including those with complex genomes. GBS uses restriction enzymes for targeted complexity reduction followed by multiplex sequencing to produce high-quality polymorphism data at a relatively low per sample cost. Here we present a GBS approach for species that currently lack a reference genome sequence. We developed a novel two-enzyme GBS protocol and genotyped bi-parental barley and wheat populations to develop a genetically anchored reference map of identified SNPs and tags. We were able to map over 34,000 SNPs and 240,000 tags onto the Oregon Wolfe Barley reference map, and 20,000 SNPs and 367,000 tags on the Synthetic W9784 × Opata85 (SynOpDH) wheat reference map. To further evaluate GBS in wheat, we also constructed a de novo genetic map using only SNP markers from the GBS data. The GBS approach presented here provides a powerful method of developing high-density markers in species without a sequenced genome while providing valuable tools for anchoring and ordering physical maps and whole-genome shotgun sequence. Development of the sequenced reference genome(s) will in turn increase the utility of GBS data enabling physical mapping of genes and haplotype imputation of missing data. Finally, as a result of low per-sample costs, GBS will have broad application in genomics-assisted plant breeding programs.     

Evaluating outlier loci and their effect on the identification of pedigree errors

Homozygosity outlier loci, which show patterns of variation that are extremely divergent from the rest of the genome, can be evaluated by comparison of the homozygosity under Hardy-Weinberg proportions (the sum of the squares of allele frequencies) with the expected homozygosity under neutrality. Such outlier loci are potentially under selection (balancing selection or directional selection) when genome-wide effects (such as bottleneck and rapid population growth) are excluded. Outlier loci show skewed allele frequencies with respect to neutrality and may therefore affect the identification of pedigree errors. However, choosing neutral markers (excluding outlier loci) for the identification of pedigree errors has been neglected thus far. Our results showed that 4.1%, 5.5%, and 1.5% of the microsatellite markers, Illumina single-nucleotide polymorphisms (SNPs), and Affymetrix SNPs, respectively, on the autosomes appear to be under balancing selection (p or=40%) appear to be under balancing selection. Pedigree structure errors in 15 of 143 pedigrees were detected using microsatellite markers from the autosomes and/or selected SNPs from chromosomes 1 to 18 of the Illumina and/or selected SNPs from chromosomes 1 to 16 of the Affymetrix. Outlier loci did not make a major difference to the identification of pedigree errors. The Collaborative Study on the Genetics of Alcoholism data has pedigree errors and some of them may be due to sample mix up.