SNP分子标记及其在木本植物遗传育种的应用

木本植物因其生命周期长、基因组杂合度高、基因组较大、遗传背景不清晰等特性,制约了其研究进程。随着现代生物技术的发展,DNA分子标记技术在木本植物研究领域的应用越来越多,其中单核苷酸多态性(SNP)作为第三代分子标记技术以其高效、快速、稳定、可靠等诸多优点得到广泛应用。本文简述SNP标记的特点、开发方法、检测方法及其在木本植物遗传多样性和亲缘关系分析、品种鉴定、连锁图谱构建和辅助育种等方面的研究进展,为更好地应用SNP技术开展木本植物研究提供参考。     

Comparison of microsatellite and single nucleotide polymorphism markers for the genetic analysis of a Galloway cattle population

Highly informative genetic markers are essential for efficient management of cattle populations, as well as for food safety. After a decade of domination by microsatellite markers, a new type of genetic marker, single nucleotide polymorphism (SNP), has recently appeared on the scene. In the present study, the exclusion power of both kinds of markers with regards to individual identification and parental analysis was directly compared in a Galloway cattle population. Seventeen bovine microsatellites were distributed in three incremental marker sets (10, 14 and 17 microsatellite markers) and used for cattle genotyping. A set of 43 bovine SNP was used for genotyping the same cattle population. The accuracy of both kinds of markers in individual identification was evaluated using probability of identity estimations. These were 2.4 x 10(-8) for the 10 microsatellite set, 2.3 x 10(-11) for the 14 microsatellite set, and 1.4 x 10(-13) for the 17 microsatellite marker set. For the 43 SNP markers, the estimated probability of identity was 5.3 x 10(-11). The exclusion power of both kinds of markers in parental analysis was evaluated using paternity exclusion estimations, and, in addition to this, by estimation of the parental exclusion probability in 18 Galloway family trios. Paternity exclusion was estimated to be over 99% for microsatellites, and approx. 98% for SNP. Both, microsatellite and SNP sets of markers showed similar parental exclusion probabilities.     

山羊基因组与遗传变异图谱研究进展

高通量测序技术和生物信息学的发展极大的促进了山羊分子生物学研究。山羊参考基因组的不断完善以及基因组重测序技术的应用,在全基因组水平上发现了大量的遗传变异信息(SNP、Indel和CNV),丰富了山羊分子群体遗传学研究利用的分子标记。综述了山羊参考基因组组装和全基因组变异图谱的构建及其在山羊上的研究进展,以期为进一步利用分子遗传标记进行山羊的各种性状的遗传基础研究和遗传资源保护利用提供科学依据和参考。     

Genome-wide association mapping of flowering time and northern corn leaf blight (Setosphaeria turcica) resistance in a vast commercial maize germplasm set

ABSTRACT: BACKGROUND: Setosphaeria turcica is a fungal pathogen that causes northern corn leaf blight (NCLB) which is a serious foliar disease in maize. In order to unravel the genetic architecture of the resistance against this disease, a vast association mapping panel comprising 1487 European maize inbred lines was used to (i) identify chromosomal regions affecting flowering time (FT) and northern corn leaf blight (NCLB) resistance, (ii) examine the epistatic interactions of the identified chromosomal regions with the genetic background on an individual molecular marker basis, and (iii) dissect the correlation between NCLB resistance and FT. RESULTS: The single marker analyses performed for 8 244 single nucleotide polymorphism (SNP) markers revealed seven, four, and four SNP markers significantly (alpha D 0.05, amplicon wise Bonferroni correction) associated with FT, NCLB, and NCLB resistance corrected for FT, respectively. These markers explained individually between 0.36 and 14.29% of the genetic variance of the corresponding trait. DISCUSSION: The very well interpretable pattern of SNP associations observed for FT suggested that data from applied plant breeding programs can be used to dissect polygenic traits. This in turn indicates that the associations identified for NCLB resistance might be successfully used in marker-assisted selection programs. Furthermore, the associated genes are also of interest for further research concerning the mechanism of resistance to NCLB and plant diseases in general, because some of the associated genes have not been mentioned in this context so far.     

Genomic and genetic variability of six chicken populations using single nucleotide polymorphism and copy number variants as markers

Genomic and genetic variation among six Italian chicken native breeds (Livornese, Mericanel della Brianza, Milanino, Bionda Piemontese, Bianca di Saluzzo and Siciliana) were studied using single nucleotide polymorphism (SNP) and copy number variants (CNV) as markers. A total of 94 DNA samples genotyped with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) were used in the analyses. The results showed the genetic and genomic variability occurring among the six Italian chicken breeds. The genetic relationship among animals was established with a principal component analysis. The genetic diversity within breeds was calculated using heterozygosity values (expected and observed) and with Wright’s F-statistics. The individual-based CNV calling, based on log R ratio and B-allele frequency values, was done by the Hidden–Markov Model (HMM) of PennCNV software on autosomes. A hierarchical agglomerative clustering was applied in each population according to the absence or presence of definite CNV regions (CNV were grouped by overlapping of at least 1 bp). The CNV map was built on a total of 1003 CNV found in individual samples, after grouping by overlaps, resulting in 564 unique CNV regions (344 gains, 213 losses and 7 complex), for a total of 9.43 Mb of sequence and 1.03% of the chicken assembly autosome. All the approaches using SNP data showed that the Siciliana breed clearly differentiate from other populations, the Livornese breed separates into two distinct groups according to the feather colour (i.e. white and black) and the Bionda Piemontese and Bianca di Saluzzo breeds are closely related. The genetic variability found using SNP is comparable with that found by other authors in the same breeds using microsatellite markers. The CNV markers analysis clearly confirmed the SNP results.     

Estimating population structure from AFLP amplification intensity

In the last decade, amplified fragment length polymorphisms (AFLPs) have become one of the most widely used molecular markers to study the genetic structure of natural populations. Most of the statistical methods available to study the genetic structure of populations using AFLPs consider these markers as dominant and are thus unable to distinguish between individuals being heterozygous or homozygous for the dominant allele. Some attempts have been made to treat AFLPs as codominant markers by using AFLP band intensities to infer the most likely genotype of each individual. These two approaches have some drawbacks, the former discarding potentially valuable information and the latter being sometimes unable to correctly assign genotypes to individuals. In this study, we propose an alternative likelihood‐based approach, which does not attempt at inferring the genotype of each individual, but rather incorporate the uncertainty about genotypes into a Bayesian framework leading to the estimation of population‐specific F_IS and F_ST coefficients. We show with simulations that the accuracy of our method is much higher than one using AFLP as dominant markers and is generally close to what would be obtained by using the same number of Single‐Nucleotide Polymorphism (SNP) markers. The method is applied to a data set of four populations of the common vole (Microtus arvalis) from Grisons in Switzerland, for which we obtained 562 polymorphic AFLP markers. Our approach is very general and has the potential to make AFLP markers as useful as SNP data for nonmodel species.     

家蚕非滞育红卵突变体Re-nd突变基因的定位克隆

【目的】家蚕Bombyx mori非滞育红卵突变体Re-nd是唯一在非滞育状态下卵色呈现鲜红色的突变品种。本研究通过基因连锁分析和定位克隆的方法确定Re-nd的突变基因所在的染色体及紧密连锁位置,为后续Re-nd的功能研究及应用奠定基础。【方法】以家蚕卵色突变体Re-nd和野生型大造进行杂交,配制基因连锁分析群体材料和定位克隆群体材料;针对家蚕全染色体进行SNP标记开发,利用BC₁代群体材料进行基因连锁分析,确定Re-nd的突变基因所在的染色体;针对定位的Re nd的突变基因所在染色体进行SNP标记开发,利用BC₁群体材料对Re-nd的突变基因进行定位克隆。【结果】基因连锁分析结果显示Re-nd的突变表型与第6号染色体上的SNP标记完全连锁;初步定位克隆结果显示Re-nd的突变基因位于SNP标记SNP7和SNP17之间,物理距离4.04 Mb;以SNP7和SNP17之间筛选出的6个SNP标记和25个重组个体进行精细定位克隆,结果显示Re-nd的突变基因所在的区域位于SNP10和SNP12两个SNP标记之间的nscaf2853上,物理距离949.3 kb左右。【结论】将Re-nd的突变基因定位于第6号染色体的2个SNP标记SNP10和SNP12之间,物理距离约949.3 kb。本研究为后续Re-nd突变基因的精细定位及功能应用研究奠定了基础。     

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.     

Inferences from Genomic Models in Stratified Populations

Unaccounted population stratification can lead to spurious associations in genome-wide association studies (GWAS) and in this context several methods have been proposed to deal with this problem. An alternative line of research uses whole-genome random regression (WGRR) models that fit all markers simultaneously. Important objectives in WGRR studies are to estimate the proportion of variance accounted for by the markers, the effect of individual markers, prediction of genetic values for complex traits, and prediction of genetic risk of diseases. Proposals to account for stratification in this context are unsatisfactory. Here we address this problem and describe a reparameterization of a WGRR model, based on an eigenvalue decomposition, for simultaneous inference of parameters and unobserved population structure. This allows estimation of genomic parameters with and without inclusion of marker-derived eigenvectors that account for stratification. The method is illustrated with grain yield in wheat typed for 1279 genetic markers, and with height, HDL cholesterol and systolic blood pressure from the British 1958 cohort study typed for 1 million SNP genotypes. Both sets of data show signs of population structure but with different consequences on inferences. The method is compared to an advocated approach consisting of including eigenvectors as fixed-effect covariates in a WGRR model. We show that this approach, used in the context of WGRR models, is ill posed and illustrate the advantages of the proposed model. In summary, our method permits a unified approach to the study of population structure and inference of parameters, is computationally efficient, and is easy to implement.     

GVCBLUP: a computer package for genomic prediction and variance component estimation of additive and dominance effects

Background

Dominance effect may play an important role in genetic variation of complex traits. Full featured and easy-to-use computing tools for genomic prediction and variance component estimation of additive and dominance effects using genome-wide single nucleotide polymorphism (SNP) markers are necessary to understand dominance contribution to a complex trait and to utilize dominance for selecting individuals with favorable genetic potential.

Results

The GVCBLUP package is a shared memory parallel computing tool for genomic prediction and variance component estimation of additive and dominance effects using genome-wide SNP markers. This package currently has three main programs (GREML_CE, GREML_QM, and GCORRMX) and a graphical user interface (GUI) that integrates the three main programs with an existing program for the graphical viewing of SNP additive and dominance effects (GVCeasy). The GREML_CE and GREML_QM programs offer complementary computing advantages with identical results for genomic prediction of breeding values, dominance deviations and genotypic values, and for genomic estimation of additive and dominance variances and heritabilities using a combination of expectation-maximization (EM) algorithm and average information restricted maximum likelihood (AI-REML) algorithm. GREML_CE is designed for large numbers of SNP markers and GREML_QM for large numbers of individuals. Test results showed that GREML_CE could analyze 50,000 individuals with 400 K SNP markers and GREML_QM could analyze 100,000 individuals with 50K SNP markers. GCORRMX calculates genomic additive and dominance relationship matrices using SNP markers. GVCeasy is the GUI for GVCBLUP integrated with an existing software tool for the graphical viewing of SNP effects and a function for editing the parameter files for the three main programs.

Conclusion

The GVCBLUP package is a powerful and versatile computing tool for assessing the type and magnitude of genetic effects affecting a phenotype by estimating whole-genome additive and dominance heritabilities, for genomic prediction of breeding values, dominance deviations and genotypic values, for calculating genomic relationships, and for research and education in genomic prediction and estimation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-270) contains supplementary material, which is available to authorized users.     

Dealing with paralogy in RADseq data: in silico detection and single nucleotide polymorphism validation in Robinia pseudoacacia L.

The RADseq technology allows researchers to efficiently develop thousands of polymorphic loci across multiple individuals with little or no prior information on the genome. However, many questions remain about the biases inherent to this technology. Notably, sequence misalignments arising from paralogy may affect the development of single nucleotide polymorphism (SNP) markers and the estimation of genetic diversity. We evaluated the impact of putative paralog loci on genetic diversity estimation during the development of SNPs from a RADseq dataset for the nonmodel tree species Robinia pseudoacacia L. We sequenced nine genotypes and analyzed the frequency of putative paralogous RAD loci as a function of both the depth of coverage and the mismatch threshold allowed between loci. Putative paralogy was detected in a very variable number of loci, from 1% to more than 20%, with the depth of coverage having a major influence on the result. Putative paralogy artificially increased the observed degree of polymorphism and resulting estimates of diversity. The choice of the depth of coverage also affected diversity estimation and SNP validation: A low threshold decreased the chances of detecting minor alleles while a high threshold increased allelic dropout. SNP validation was better for the low threshold (4×) than for the high threshold (18×) we tested. Using the strategy developed here, we were able to validate more than 80% of the SNPs tested by means of individual genotyping, resulting in a readily usable set of 330 SNPs, suitable for use in population genetics applications.     

Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism panels

The genomic era has led to an unprecedented increase in the availability of genome‐wide data for a broad range of taxa. Wildlife management strives to make use of these vast resources to enable refined genetic assessments that enhance biodiversity conservation. However, as new genomic platforms emerge, problems remain in adapting the usually complex approaches for genotyping of noninvasively collected wildlife samples. Here, we provide practical guidelines for the standardized development of reduced single nucleotide polymorphism (SNP) panels applicable for microfluidic genotyping of degraded DNA samples, such as faeces or hairs. We demonstrate how microfluidic SNP panels can be optimized to efficiently monitor European wildcat (Felis silvestris S.) populations. We show how panels can be set up in a modular fashion to accommodate informative markers for relevant population genetics questions, such as individual identification, hybridization assessment and the detection of population structure. We discuss various aspects regarding the implementation of reduced SNP panels and provide a framework that will allow both molecular ecologists and practitioners to help bridge the gap between genomics and applied wildlife conservation.     

Characterization of 18 SNP markers for sperm whale (Physeter macrocephalus)

We report the characterization of 18 new single nucleotide polymorphism (SNP) markers for an endangered species, the sperm whale (Physeter macrocephalus), developed using a targeted gene approach. SNP markers were derived from autosomal regions of the genome using primers originally characterized for genome mapping in other mammals. These SNP markers are the first to be designed for genotyping sperm whale populations and will provide a necessary addition to the genetic tools employed for understanding population structure on a global scale and for developing a conservation management strategy for this endangered species.     

An efficient haplotyping method with DNA pools

Determination of haplotype frequencies (the joint distribution of genetic markers) in large population samples is a powerful tool for association studies. This is due to their greater extent of polymorphism since any two bi-allelic single nucleotide polymorphisms (SNPs) generate a potential four-allele genetic marker. Therefore, a haplotype may capture a given functional polymorphism with higher statistical power than its SNP components. The statistical estimation of haplotype frequencies, usually employed in linkage disequilibrium studies, requires individual genotyping for each SNP in the haplotype, thus making it an expensive process. In this study, we describe a new method for direct measurement of haplotype frequencies in DNA pools by allele-specific, long-range haplotype amplification. The proposed method allows the efficient determination of haplotypes composed of two SNPs in close vicinity (up to 20 kb).     

Construction of a high-density genetic map for grape using next generation restriction-site associated DNA sequencing

ABSTRACT: BACKGROUND: Genetic mapping and QTL detection are powerful methodologies in plant improvement and breeding. Construction of a high-density and high-quality genetic map would be of great benefit in the production of superior grapes to meet human demand. High throughput and low cost of the recently developed next generation sequencing (NGS) technology have resulted in its wide application in genome research. Sequencing restriction-site associated DNA (RAD) might be an efficient strategy to simplify genotyping. Combining NGS with RAD has proven to be powerful for single nucleotide polymorphism (SNP) marker development. RESULTS: An F1 population of 100 individual plants was developed. In-silico digestion-site prediction was used to select an appropriate restriction enzyme for construction of a RAD sequencing library. Next generation RAD sequencing was applied to genotype the F1 population and its parents. Applying a cluster strategy for SNP modulation, a total of 1,814 high-quality SNP markers were developed: 1,121 of these were mapped to the female genetic map, 759 to the male map, and 1,646 to the integrated map. A comparison of the genetic maps to the published Vitis vinifera genome revealed both conservation and variations. CONCLUSIONS: The applicability of next generation RAD sequencing for genotyping a grape F1 population was demonstrated, leading to the successful development of a genetic map with high density and quality using our designed SNP markers. Detailed analysis revealed that this newly developed genetic map can be used for a variety of genome investigations, such as QTL detection, sequence assembly and genome comparison. KEYWORDS: Grape; Genetic map; Next generation sequencing (NGS); Restriction-site associated DNA (RAD).     

SNP markers: methods of analysis, ways of development, and comparison on an example of common wheat

SNPs (single nucleotide polymorphisms), which belong to the last-generation molecular markers, occur at high frequencies in both animal and plant genomes. The development of SNP markers allows to automatize and enhance tenfolds the effectiveness of genotype analysis. This review summarizes literature data on methods of SNP polymorphism analysis. Various methods of developing SNP markers are considered, taking common wheat Triticum aestivum L. as an example. These markers are compared to other DNA markers, in order to ensure adequate choice of marker type for solving various molecular genetic problems.     

Genotype‐specific SNP map based on whole chromosome 3B sequence information from wheat cultivars Arina and Forno

Agronomically important traits are frequently controlled by rare, genotype‐specific alleles. Such genes can only be mapped in a population derived from the donor genotype. This requires the development of a specific genetic map, which is difficult in wheat because of the low level of polymorphism among elite cultivars. The absence of sufficient polymorphism, the complexity of the hexaploid wheat genome as well as the lack of complete sequence information make the construction of genetic maps with a high density of reproducible and polymorphic markers challenging. We developed a genotype‐specific genetic map of chromosome 3B from winter wheat cultivars Arina and Forno. Chromosome 3B was isolated from the two cultivars and then sequenced to 10‐fold coverage. This resulted in a single‐nucleotide polymorphisms (SNP) database of the complete chromosome. Based on proposed synteny with the Brachypodium model genome and gene annotation, sequences close to coding regions were used for the development of 70 SNP‐based markers. They were mapped on a Arina × Forno Recombinant Inbred Lines population and found to be spread over the complete chromosome 3B. While overall synteny was well maintained, numerous exceptions and inversions of syntenic gene order were identified. Additionally, we found that the majority of recombination events occurred in distal parts of chromosome 3B, particularly in hot‐spot regions. Compared with the earlier map based on SSR and RFLP markers, the number of markers increased fourfold. The approach presented here allows fast development of genotype‐specific polymorphic markers that can be used for mapping and marker‐assisted selection.     

Dissecting complex phenotypes using the genomics of twins

Genetics in the post-genomic period is shifting from structural to functional genetics or genomics. Meanwhile, the use of twins is largely expanding from traditional heritability estimation for disease phenotypes to the study of both diseases and various molecular phenotypes, such as the regulatory phenotypes in functional genomics concerning gene expression and regulation, by engaging both classical twin design and marker-based gene mapping techniques in genetic epidemiology. New research designs have been proposed for making novel uses of twins in studying the molecular basis in the epigenetics of human diseases. Besides, twins not only serve as ideal samples for disease gene mapping using conventional genetic markers but also represent an excellent model for associating DNA copy number variations, a structural genetic marker, with human diseases. It is believed that, with the rapid development in biotechniques and new advances in bioinformatics, the unique samples of twins will make new contributions to our understanding of the nature and nurture in complex disease development and in human health. This paper aims at summarizing the new uses of twins in current genetic studies and suggesting novel proposes together with useful design and analytical strategies.     

SNP markers: Methods of analysis, ways of development, and comparison on an example of common wheat

SNPs (single nucleotide polymorphisms), which belong to the last-generation molecular markers, occur at high frequencies in both animal and plant genomes. The development of SNP markers allows to automatize and enhance tenfolds the effectiveness of genotype analysis. This review summarizes literature data on methods of SNP polymorphism analysis. Various methods of developing SNP markers are considered, taking common wheat Triticum aestivum L. as an example. These markers are compared to other DNA markers, in order to ensure adequate choice of marker type for solving various molecular genetic problems.     

1 + 1 = 3: Development and Validation of a SNP-Based Algorithm to Identify Genetic Contributions from Three Distinct Inbred Mouse Strains

State-of-the-art, genome-wide assessment of mouse genetic background uses single nucleotide polymorphism (SNP) PCR. As SNP analysis can use multiplex testing, it is amenable to high-throughput analysis and is the preferred method for shared resource facilities that offer genetic background assessment of mouse genomes. However, a typical individual SNP query yields only two alleles (A vs. B), limiting the application of this methodology to distinguishing contributions from no more than two inbred mouse strains. By contrast, simple sequence length polymorphism (SSLP) analysis yields multiple alleles but is not amenable to high-throughput testing. We sought to devise a SNP-based technique to identify donor strain origins when three distinct mouse strains potentially contribute to the genetic makeup of an individual mouse. A computational approach was used to devise a three-strain analysis (3SA) algorithm that would permit identification of three genetic backgrounds while still using a binary-output SNP platform. A panel of 15 mosaic mice with contributions from BALB/c, C57Bl/6, and DBA/2 genetic backgrounds was bred and analyzed using a genome-wide SNP panel using 1449 markers. The 3SA algorithm was applied and then validated using SSLP. The 3SA algorithm assigned 85% of 1449 SNPs as informative for the C57Bl/6, BALB/c, or DBA/2 backgrounds, respectively. Testing the panel of 15 F2 mice, the 3SA algorithm predicted donor strain origins genome-wide. Donor strain origins predicted by the 3SA algorithm correlated perfectly with results from individual SSLP markers located on five different chromosomes (n=70 tests). We have established and validated an analysis algorithm based on binary SNP data that can successfully identify the donor strain origins of chromosomal regions in mice that are bred from three distinct inbred mouse strains.