基于DNA池测序法筛选奶牛高信息量SNP标记的可行性

首先选择139个牛SNP标记, 利用DNA池测序法, 根据测序峰图中不同碱基信号峰高的比值确定了92个SNP为高信息量标记(比值>1/2); 为了进一步验证筛选的准确性, 对其中59个标记采用基质辅助激光解析电离飞行时间质谱(Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry, MALDI-TOF MS)技术检测了122头荷斯坦牛的基因型。结果显示, 检出率高于85%的标记有56个, 其平均最小等位基因频率(Minor allele frequency, MAF)为0.41, 最小值为0.27, 最大值为0.5; MAF>0.3的标记有54个, 占96.4%(54/56)。文章结果表明, 采用DNA池测序法筛选高信息量SNP标记是可行和可信的。     

Ancestral informative marker selection and population structure visualization using sparse Laplacian eigenfunctions

Identification of a small panel of population structure informative markers can reduce genotyping cost and is useful in various applications, such as ancestry inference in association mapping, forensics and evolutionary theory in population genetics. Traditional methods to ascertain ancestral informative markers usually require the prior knowledge of individual ancestry and have difficulty for admixed populations. Recently Principal Components Analysis (PCA) has been employed with success to select SNPs which are highly correlated with top significant principal components (PCs) without use of individual ancestral information. The approach is also applicable to admixed populations. Here we propose a novel approach based on our recent result on summarizing population structure by graph laplacian eigenfunctions, which differs from PCA in that it is geometric and robust to outliers. Our approach also takes advantage of the priori sparseness of informative markers in the genome. Through simulation of a ring population and the real global population sample HGDP of 650K SNPs genotyped in 940 unrelated individuals, we validate the proposed algorithm at selecting most informative markers, a small fraction of which can recover the similar underlying population structure efficiently. Employing a standard Support Vector Machine (SVM) to predict individuals' continental memberships on HGDP dataset of seven continents, we demonstrate that the selected SNPs by our method are more informative but less redundant than those selected by PCA. Our algorithm is a promising tool in genome-wide association studies and population genetics, facilitating the selection of structure informative markers, efficient detection of population substructure and ancestral inference.     

Use of sequence data from rainbow trout and Atlantic salmon for SNP detection in Pacific salmon

Single nucleotide polymorphisms (SNPs) are a class of genetic markers that are well suited to a broad range of research and management applications. Although advances in genotyping chemistries and analysis methods continue to increase the potential advantages of using SNPs to address molecular ecological questions, the scarcity of available DNA sequence data for most species has limited marker development. As the number and diversity of species being targeted for large-scale sequencing has increased, so has the potential for using sequence from sister taxa for marker development in species of interest. We evaluated the use of Oncorhynchus mykiss and Salmo salar sequence data to identify SNPs in three other species (Oncorhynchus tshawytscha, Oncorhynchus nerka and Oncorhynchus keta). Primers designed based on O. mykiss and S. salar alignments were more successful than primers designed based on Oncorhynchus-only alignments for sequencing target species, presumably due to the much larger number of potential targets available from the former alignments and possibly greater sequence conservation in those targets. In sequencing approximately 89 kb we observed a frequency of 4.30 x 10(-3) SNPs per base pair. Approximately half (53/101) of the subsequently designed validation assays resulted in high-throughput SNP genotyping markers. We speculate that this relatively low conversion rate may reflect the duplicated nature of the salmon genome. Our results suggest that a large number of SNPs could be developed for Pacific salmon using sequence data from other species. While the costs of DNA sequencing are still significant, these must be compared to the costs of using other marker classes for a given application.     

High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform

Multiplexed single nucleotide polymorphism (SNP) markers have the potential to increase the speed and cost-effectiveness of genotyping, provided that an optimal SNP density is used for each application. To test the efficiency of multiplexed SNP genotyping for diversity, mapping and breeding applications in rice (Oryza sativa L.), we designed seven GoldenGate VeraCode oligo pool assay (OPA) sets for the Illumina BeadXpress Reader. Validated markers from existing 1536 Illumina SNPs and 44?K Affymetrix SNP chips developed at Cornell University were used to select subsets of informative SNPs for different germplasm groups with even distribution across the genome. A 96-plex OPA was developed for quality control purposes and for assigning a sample into one of the five O. sativa population subgroups. Six 384-plex OPAs were designed for genetic diversity analysis, DNA fingerprinting, and to have evenly-spaced polymorphic markers for quantitative trait locus (QTL) mapping and background selection for crosses between different germplasm pools in rice: Indica/Indica, Indica/Japonica, Japonica/Japonica, Indica/O. rufipogon, and Japonica/O. rufipogon. After testing on a diverse set of rice varieties, two of the SNP sets were re-designed by replacing poor-performing SNPs. Pilot studies were successfully performed for diversity analysis, QTL mapping, marker-assisted backcrossing, and developing specialized genetic stocks, demonstrating that 384-plex SNP genotyping on the BeadXpress platform is a robust and efficient method for marker genotyping in rice.     

Forensically relevant SNP classes

Forensic samples that contain too little template DNA or are too degraded require alternate genetic marker analyses or approaches to what is currently used for routine casework. Single nucleotide polymorphisms (SNPs) offer promise to support forensic DNA analyses because of an abundance of potential markers, amenability to automation, and potential reduction in required fragment length to only 60-80 bp. The SNP markers will serve an important role in analyzing challenging forensic samples, such as those that are very degraded, for augmenting the power of kinship analyses and family reconstructions for missing persons and unidentified human remains, as well as for providing investigative lead value in some cases without a suspect (and no genetic profile match in CODIS). The SNPs for forensic analyses can be divided into four categories: identity-testing SNPs; lineage informative SNPs; ancestry informative SNPs; and phenotype informative SNPs. In addition to discussing the applications of these different types of SNPs, this article provides some discussion on privacy issues so that society and policymakers can be more informed.     

Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.     

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.     

Tracing sub-structure in the European American population with PCA-informative markers

Genetic structure in the European American population reflects waves of migration and recent gene flow among different populations. This complex structure can introduce bias in genetic association studies. Using Principal Components Analysis (PCA), we analyze the structure of two independent European American datasets (1,521 individuals-307,315 autosomal SNPs). Individual variation lies across a continuum with some individuals showing high degrees of admixture with non-European populations, as demonstrated through joint analysis with HapMap data. The CEPH Europeans only represent a small fraction of the variation encountered in the larger European American datasets we studied. We interpret the first eigenvector of this data as correlated with ancestry, and we apply an algorithm that we have previously described to select PCA-informative markers (PCAIMs) that can reproduce this structure. Importantly, we develop a novel method that can remove redundancy from the selected SNP panels and show that we can effectively remove correlated markers, thus increasing genotyping savings. Only 150-200 PCAIMs suffice to accurately predict fine structure in European American datasets, as identified by PCA. Simulating association studies, we couple our method with a PCA-based stratification correction tool and demonstrate that a small number of PCAIMs can efficiently remove false correlations with almost no loss in power. The structure informative SNPs that we propose are an important resource for genetic association studies of European Americans. Furthermore, our redundancy removal algorithm can be applied on sets of ancestry informative markers selected with any method in order to select the most uncorrelated SNPs, and significantly decreases genotyping costs.     

MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression

The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs accurately representing the rest of the SNPs. Informative SNP selection can achieve (1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or (2) necessary reduction of the huge SNP sets (obtained, e.g. from Affymetrix) for further fine haplotype analysis. A novel informative SNP selection method for unphased genotype data based on multiple linear regression (MLR) is implemented in the software package MLR-tagging. This software can be used for informative SNP (tag) selection and genotype prediction. The stepwise tag selection algorithm (STSA) selects positions of the given number of informative SNPs based on a genotype sample population. The MLR SNP prediction algorithm predicts a complete genotype based on the values of its informative SNPs, their positions among all SNPs, and a sample of complete genotypes. An extensive experimental study on various datasets including 10 regions from HapMap shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. (2005). AVAILABILITY: MLR-Tagging software package is publicly available at http://alla.cs.gsu.edu/~software/tagging/tagging.html     

Differentiating salmon populations at broad and fine geographical scales with microsatellites and single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are appealing genetic markers due to several beneficial attributes, but uncertainty remains about how many of these bi-allelic markers are necessary to have sufficient power to differentiate populations, a task now generally accomplished with highly polymorphic microsatellite markers. In this study, we tested the utility of 37 SNPs and 13 microsatellites for differentiating 29 broadly distributed populations of Chinook salmon ( n  = 2783). Information content of all loci was determined by In and     , and the top 12 markers ranked by In were microsatellites, but the 6 highest, and 7 of the top 10     ranked markers, were SNPs. The mean ratio of random SNPs to random microsatellites ranged from 3.9 to 4.1, but this ratio was consistently reduced when only the most informative loci were included. Individual assignment test accuracy was higher for microsatellites (73.1%) than SNPs (66.6%), and pooling all 50 markers provided the highest accuracy (83.2%). When marker types were combined, as few as 15 of the top ranked loci provided higher assignment accuracy than either microsatellites or SNPs alone. Neighbour-joining dendrograms revealed similar clustering patterns and pairwise tests of population differentiation had nearly identical results with each suite of markers. Statistical tests and simulations indicated that closely related populations were better differentiated by microsatellites than SNPs. Our results indicate that both types of markers are likely to be useful in population genetics studies and that, in some cases, a combination of SNPs and microsatellites may be the most effective suite of loci.     

Use of genotyping by sequencing data to develop a high‐throughput and multifunctional SNP panel for conservation applications in Pacific lamprey

Next‐generation sequencing data can be mined for highly informative single nucleotide polymorphisms (SNPs) to develop high‐throughput genomic assays for nonmodel organisms. However, choosing a set of SNPs to address a variety of objectives can be difficult because SNPs are often not equally informative. We developed an optimal combination of 96 high‐throughput SNP assays from a total of 4439 SNPs identified in a previous study of Pacific lamprey (Entosphenus tridentatus) and used them to address four disparate objectives: parentage analysis, species identification and characterization of neutral and adaptive variation. Nine of these SNPs are F_ST outliers, and five of these outliers are localized within genes and significantly associated with geography, run‐timing and dwarf life history. Two of the 96 SNPs were diagnostic for two other lamprey species that were morphologically indistinguishable at early larval stages and were sympatric in the Pacific Northwest. The majority (85) of SNPs in the panel were highly informative for parentage analysis, that is, putatively neutral with high minor allele frequency across the species’ range. Results from three case studies are presented to demonstrate the broad utility of this panel of SNP markers in this species. As Pacific lamprey populations are undergoing rapid decline, these SNPs provide an important resource to address critical uncertainties associated with the conservation and recovery of this imperiled species.     

Informativeness of genetic markers for inference of ancestry

Inference of individual ancestry is useful in various applications, such as admixture mapping and structured-association mapping. Using information-theoretic principles, we introduce a general measure, the informativeness for assignment (I(n)), applicable to any number of potential source populations, for determining the amount of information that multiallelic markers provide about individual ancestry. In a worldwide human microsatellite data set, we identify markers of highest informativeness for inference of regional ancestry and for inference of population ancestry within regions; these markers, which are listed in online-only tables in our article, can be useful both in testing for and in controlling the influence of ancestry on case-control genetic association studies. Markers that are informative in one collection of source populations are generally informative in others. Informativeness of random dinucleotides, the most informative class of microsatellites, is five to eight times that of random single-nucleotide polymorphisms (SNPs), but 2%-12% of SNPs have higher informativeness than the median for dinucleotides. Our results can aid in decisions about the type, quantity, and specific choice of markers for use in studies of ancestry.     

Integrative analysis of single nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related ethnic populations

ABSTRACT: BACKGROUND: Ancestry informative markers (AIMs) are a type of genetic marker that is informative for tracing the ancestral ethnicity of individuals. Application of AIMs has gained substantial attention in population genetics, forensic sciences, and medical genetics. Single nucleotide polymorphisms (SNPs), the materials of AIMs, are useful for classifying individuals from distinct continental origins but cannot discriminate individuals with subtle genetic differences from closely related ancestral lineages. Proof-of-principle studies have shown that gene expression (GE) also is a heritable human variation that exhibits differential intensity distributions among ethnic groups. GE supplies ethnic information supplemental to SNPs; this motivated us to integrate SNP and GE markers to construct AIM panels with a reduced number of required markers and provide high accuracy in ancestry inference. Few studies in the literature have considered GE in this aspect, and none have integrated SNP and GE markers to aid classification of samples from closely related ethnic populations. RESULTS: We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy. By analyzing genome-wide SNP and/or GE markers in 210 independent samples from four ethnic groups in the HapMap II Project, we found that average testing accuracies for a majority of classification analyses were quite high, except for SNP-only analyses that were performed to discern study samples containing individuals from two close Asian populations. The average testing accuracies ranged from 0.53 to 0.79 for SNP-only analyses and increased to around 0.90 when GE markers were integrated together with SNP markers for the classification of samples from closely related Asian populations. Compared to GE-only analyses, integrative analyses of SNP and GE markers showed comparable testing accuracies and a reduced number of selected markers in AIM panels. CONCLUSIONS: Integrative analysis of SNP and GE markers provides high-accuracy and/or cost-effective classification results for assigning samples from closely related or distantly related ancestral lineages to their original ancestral populations. User-friendly BIASLESS (Biomarkers Identification and Samples Subdivision) software was developed as an efficient tool for selecting key SNP and/or GE markers and then building models for sample subdivision. BIASLESS was programmed in R and R-GUI and is available online at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.     

A set of polymorphic EST‐derived markers for Picea species

A pooled DNA method was used to produce fully informative EST (expressed sequence tag)‐derived markers for the Picea genus. Nine markers were produced from 10 cDNA identified as candidates for cold tolerance or embryogenesis. Indels and SNPs (single nucleotide polymorphisms) were characterized from sequences obtained from pools of 10 individuals for each of the three species: Picea glauca (white spruce), Picea mariana (black spruce) and Picea abies (Norway spruce). Indels were present in 28% of the sequences and SNPs with a frequency greater than 10% were present on average in 1.2% of the positions.     

Mapping and Validation of the Major Sex-Determining Region in Nile Tilapia (Oreochromis niloticus L.) Using RAD Sequencing

Sex in Oreochromis niloticus (Nile tilapia) is principally determined by an XX/XY locus but other genetic and environmental factors also influence sex ratio. Restriction Associated DNA (RAD) sequencing was used in two families derived from crossing XY males with females from an isogenic clonal line, in order to identify Single Nucleotide Polymorphisms (SNPs) and map the sex-determining region(s). We constructed a linkage map with 3,802 SNPs, which corresponded to 3,280 informative markers, and identified a major sex-determining region on linkage group 1, explaining nearly 96% of the phenotypic variance. This sex-determining region was mapped in a 2 cM interval, corresponding to approximately 1.2 Mb in the O. niloticus draft genome. In order to validate this, a diverse family (4 families; 96 individuals in total) and population (40 broodstock individuals) test panel were genotyped for five of the SNPs showing the highest association with phenotypic sex. From the expanded data set, SNPs Oni23063 and Oni28137 showed the highest association, which persisted both in the case of family and population data. Across the entire dataset all females were found to be homozygous for these two SNPs. Males were heterozygous, with the exception of five individuals in the population and two in the family dataset. These fish possessed the homozygous genotype expected of females. Progeny sex ratios (over 95% females) from two of the males with the “female” genotype indicated that they were neomales (XX males). Sex reversal induced by elevated temperature during sexual differentiation also resulted in phenotypic males with the “female” genotype. This study narrows down the region containing the main sex-determining locus, and provides genetic markers tightly linked to this locus, with an association that persisted across the population. These markers will be of use in refining the production of genetically male O. niloticus for aquaculture.     

A multiplex bead-based suspension array assay for interrogation of phylogenetically informative single nucleotide polymorphisms for Bacillus anthracis

Single nucleotide polymorphisms (SNPs) are abundant in genomes of all species and represent informative DNA markers extensively used to analyze phylogenetic relationships between strains. Medium to high throughput, open methodologies able to test many SNPs in a minimum time are therefore in great need. By using the versatile Luminex® xTAG technology, we developed an efficient multiplexed SNP genotyping assay to score 13 phylogenetically informative SNPs within the genome of Bacillus anthracis. The Multiplex Oligonucleotide Ligation-PCR procedure (MOL-PCR) described by Deshpande et al., 2010 has been modified and adapted for simultaneous interrogation of 13 biallelic canonical SNPs in a 13-plex assay. Changes made to the originally published method include the design of allele-specific dual-priming-oligonucleotides (DPOs) as competing detection probes (MOLigo probes) and use of asymmetric PCR reaction for signal amplification and labeling of ligation products carrying SNP targets. These innovations significantly reduce cross-reactivity observed when initial MOLigo probes were used and enhance hybridization efficiency onto the microsphere array, respectively. When evaluated on 73 representative samples, the 13-plex assay yielded unambiguous SNP calls and lineage affiliation. Assay limit of detection was determined to be 2 ng of genomic DNA. The reproducibility, robustness and easy-of-use of the present method were validated by a small-scale proficiency testing performed between four European laboratories. While cost-effective compared to other singleplex methods, the present MOL-PCR method offers a high degree of flexibility and scalability. It can easily accommodate newly identified SNPs to increase resolving power to the canSNP typing of B. anthracis.     

Analysis of genetic variation in the GenomEUtwin project.

Multiallelic short tandem repeat polymorphisms, or microsatellites, are useful markers in genome wide scans to identify chromosomal regions containing genes underlying disease loci. The biallelic single nucleotide polymorphism (SNP) can be used to fine map previously identified large candidate regions or to test functional candidate genes by association analysis. In the GenomEUtwin project the population based impact of susceptibility genes for six multifactorial traits will be studied. A genome wide panel of informative human microsatellite markers will be analyzed by fluorescent capillary electrophoresis in well characterized twin and population samples. Contrary to microsatellites, selection of the most informative panels of SNPs is hampered by imperfect data on the allele frequencies and population distribution of SNPs markers in the databases. Therefore, selection of SNPs requires a substantial amount of bioinformatics, and, the SNPs need to be validated experimentally in the relevant populations prior to genotyping large sample sets. In the GenomEUtwin project, large scale genotyping of SNPs will be performed using the SNPstreamUHT and MassARRAY genotyping systems that are based on the primer extension reaction principle combined with fluorescent and mass spectrometric detection, respectively. Production of the genotyping data will be a joint effort by GenomEUtwin partners at the University of Helsinki, the National Public Health Institute in Helsinki, Finland and Uppsala University, Sweden. All genotyping data will be stored in a common database established specifically for the GenomEUtwin project, from where it can be accessed by the twin research centres that provided the samples for genotyping.     

Sequences associated with human iris pigmentation

To determine whether and how common polymorphisms are associated with natural distributions of iris colors, we surveyed 851 individuals of mainly European descent at 335 SNP loci in 13 pigmentation genes and 419 other SNPs distributed throughout the genome and known or thought to be informative for certain elements of population structure. We identified numerous SNPs, haplotypes, and diplotypes (diploid pairs of haplotypes) within the OCA2, MYO5A, TYRP1, AIM, DCT, and TYR genes and the CYP1A2-15q22-ter, CYP1B1-2p21, CYP2C8-10q23, CYP2C9-10q24, and MAOA-Xp11.4 regions as significantly associated with iris colors. Half of the associated SNPs were located on chromosome 15, which corresponds with results that others have previously obtained from linkage analysis. We identified 5 additional genes (ASIP, MC1R, POMC, and SILV) and one additional region (GSTT2-22q11.23) with haplotype and/or diplotypes, but not individual SNP alleles associated with iris colors. For most of the genes, multilocus gene-wise genotype sequences were more strongly associated with iris colors than were haplotypes or SNP alleles. Diplotypes for these genes explain 15% of iris color variation. Apart from representing the first comprehensive candidate gene study for variable iris pigmentation and constituting a first step toward developing a classification model for the inference of iris color from DNA, our results suggest that cryptic population structure might serve as a leverage tool for complex trait gene mapping if genomes are screened with the appropriate ancestry informative markers.     

Delimiting species in recent radiations

Despite considerable effort from the systematics community, delimiting species boundaries in recent radiations remains a daunting challenge. We argue that genealogical approaches, although sometimes useful, may not solve this important problem, because recently derived species often have not had sufficient time to achieve monophyly. Instead, we suggest that population genetic approaches that rely on large sets of informative markers like single nucleotide polymorphisms (SNPs) provide an alternative framework for delimiting very recently derived species. We address two major challenges in applying such markers to species delimitation: discovering markers in nonmodel systems and using them to delimit recently derived species. Using turtles as a test case, we explore the utility of a single, relatively low-coverage genomic resource as an aid in gene and marker discovery. We exploit an end-sequenced bacterial artificial chromosome (BAC) library from an individual painted turtle (Chrysemys picta) and outline a novel protocol that efficiently identifies primer pairs that amplify homologous sequences across the tree of living turtles. Preliminary data using this library to discover SNPs in Emydura macquarii, a species that diverged from C. picta approximately 210 million years ago, indicate that sequences identified from the Chrysemys BAC library provide useful SNPs even in this very distantly related taxon. Several recent methods in wide use in the population genetics literature allow one to discover potential species, or test existing species hypotheses, with SNP data and may be particularly informative for very recently derived species. As BAC and other genomic resources become increasingly available for scattered taxa across the tree of life, we are optimistic that these resources will provide abundant, inexpensive markers that will help delimit boundaries in problematic, recent species radiations.     

Finding Markers That Make a Difference: DNA Pooling and SNP-Arrays Identify Population Informative Markers for Genetic Stock Identification

Genetic stock identification (GSI) using molecular markers is an important tool for management of migratory species. Here, we tested a cost-effective alternative to individual genotyping, known as allelotyping, for identification of highly informative SNPs for accurate genetic stock identification. We estimated allele frequencies of 2880 SNPs from DNA pools of 23 Atlantic salmon populations using Illumina SNP-chip. We evaluated the performance of four common strategies (global F _ST, pairwise F _ST, Delta and outlier approach) for selection of the most informative set of SNPs and tested their effectiveness for GSI compared to random sets of SNP and microsatellite markers. For the majority of cases, SNPs selected using the outlier approach performed best followed by pairwise F _ST and Delta methods. Overall, the selection procedure reduced the number of SNPs required for accurate GSI by up to 53% compared with randomly chosen SNPs. However, GSI accuracy was more affected by populations in the ascertainment group rather than the ranking method itself. We demonstrated for the first time the compatibility of different large-scale SNP datasets by compiling the largest population genetic dataset for Atlantic salmon to date. Finally, we showed an excellent performance of our top SNPs on an independent set of populations covering the main European distribution range of Atlantic salmon. Taken together, we demonstrate how combination of DNA pooling and SNP arrays can be applied for conservation and management of salmonids as well as other species.