How many SNPs are enough?

Since the days of allozyme analysis, we have been enamored with the idea that if we just had enough polymorphic mendelian loci, we could gauge the inbreeding level of individuals by measuring heterozygosity and simultaneously measure the degree of genetic relatedness between pairs of individuals. Given Mendel’s Laws, we have always known that we would need numerous independently segregating loci to achieve any reasonable degree of accuracy. Santure et al. (2010, this issue) use a 771 marker SNP panel to assess heterozygosity levels and to assess pairwise relatedness, and compare both with theoretical expectations obtained from a carefully recorded pedigree of a zebra finch breeding colony, as a function of increasing numbers of SNP markers. They also compare the SNP results with those from a 20‐locus microsatellite panel, showing that adding SNPs to a fairly large microsatellite panel improves accuracy, but given an existing panel of 125 SNPs, little is to be gained by adding microsatellites. They show that the accuracy available for estimating individual levels of inbreeding is somewhat limited. They also show that the average pairwise relatedness measures bracket pedigree relationship very nicely, but the variances for individual pairs remain substantial, even with a very large panel.     

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.     

Comparing RADseq and microsatellites for estimating genetic diversity and relatedness — Implications for brown trout conservation

The conservation and management of endangered species requires information on their genetic diversity, relatedness and population structure. The main genetic markers applied for these questions are microsatellites and single nucleotide polymorphisms (SNPs), the latter of which remain the more resource demanding approach in most cases. Here, we compare the performance of two approaches, SNPs obtained by restriction‐site‐associated DNA sequencing (RADseq) and 16 DNA microsatellite loci, for estimating genetic diversity, relatedness and genetic differentiation of three, small, geographically close wild brown trout (Salmo trutta) populations and a regionally used hatchery strain. The genetic differentiation, quantified as F_ST, was similar when measured using 16 microsatellites and 4,876 SNPs. Based on both marker types, each brown trout population represented a distinct gene pool with a low level of interbreeding. Analysis of SNPs identified half‐ and full‐siblings with a higher probability than the analysis based on microsatellites, and SNPs outperformed microsatellites in estimating individual‐level multilocus heterozygosity. Overall, the results indicated that moderately polymorphic microsatellites and SNPs from RADseq agreed on estimates of population genetic structure in moderately diverged, small populations, but RADseq outperformed microsatellites for applications that required individual‐level genotype information, such as quantifying relatedness and individual‐level heterozygosity. The results can be applied to other small populations with low or moderate levels of genetic diversity.     

SSRs,SNPs and DArTs comparison on estimation of relatedness and genetic parameters’ precision from a small half-sib sample population of <Emphasis Type="Italic">Eucalyptus grandis</Emphasis>

Simple sequence repeats (SSR) are the most widely used molecular markers for relatedness inference due to their multi-allelic nature and high informativeness. However, there is a growing trend toward using high-throughput and inter-specific transferable single-nucleotide polymorphisms (SNP) and Diversity Arrays Technology (DArT) in forest genetics owing to their wide genome coverage. We compared the efficiency of 15 SSRs, 181 SNPs and 2816 DArTs to estimate the relatedness coefficients, and their effects on genetic parameters’ precision, in a relatively small data set of an open-pollinated progeny trial of Eucalyptus grandis (Hill ex Maiden) with limited relationship from the pedigree. Both simulations and real data of Eucalyptus grandis were used to study the statistical performance of three relatedness estimators based on co-dominant markers. Relatedness estimates in pairs of individuals belonging to the same family (related) were higher for DArTs than for SNPs and SSRs. DArTs performed better compared to SSRs and SNPs in estimated relatedness coefficients in pairs of individuals belonging to different families (unrelated) and showed higher ability to discriminate unrelated from related individuals. The likelihood-based estimator exhibited the lowest root mean squared error (RMSE); however, the differences in RMSE among the three estimators studied were small. For the growth traits, heritability estimates based on SNPs yielded, on average, smaller standard errors compared to those based on SSRs and DArTs. Estimated relatedness in the realized relationship matrix and heritabilities can be accurately inferred from co-dominant or sufficiently dense dominant markers in a relatively small E. grandis data set with shallow pedigree.     

A High-Density Single Nucleotide Polymorphism Map for Neurospora crassa

We report the discovery and validation of a set of single nucleotide polymorphisms (SNPs) between the reference Neurospora crassa strain Oak Ridge and the Mauriceville strain (FGSC 2555), of sufficient density to allow fine mapping of most loci. Sequencing of Mauriceville cDNAs and alignment to the completed genomic sequence of the Oak Ridge strain identified 19,087 putative SNPs. Of these, a subset was validated by cleaved amplified polymorphic sequence (CAPS), a simple and robust PCR-based assay that reliably distinguishes between SNP alleles. Experimental confirmation resulted in the development of 250 CAPS markers distributed evenly over the genome. To demonstrate the applicability of this map, we used bulked segregant analysis followed by interval mapping to locate the csp-1 mutation to a narrow region on LGI. Subsequently, we refined mapping resolution to 74 kbp by developing additional markers, resequenced the candidate gene, NCU02713.3, in the mutant background, and phenocopied the mutation by gene replacement in the WT strain. Together, these techniques demonstrate a generally applicable and straightforward approach for the isolation of novel genes from existing mutants. Data on both putative and validated SNPs are deposited in a customized public database at the Broad Institute, which encourages augmentation by community users.     

Linking adaptation,delimitation of evolutionarily significant units (ESUs), and gene function: a case study using hemlock looper ecotypes

Developing genetic markers for the identification of recently diverged groups, such as ecotypes or species complexes, remains difficult due to challenges with incomplete lineage sorting, hybridization and introgression. Genome‐wide scans of single nucleotide polymorphisms (SNPs) have proven useful for inferring patterns of genetic differentiation at the population level. In combination with a new analytical technique, the discriminant analysis of principal components (DAPC), and within the framework of iterative taxonomy, it may also be possible to extract a combination of SNPs as markers for the delimitation of closely related groups. In addition, since DAPC identifies the loci contributing the most to group clustering, it may be possible to link putative biological function to differences that define group boundaries. We tested this technique on two ecotypes of the hemlock looper (Lambdina fiscellaria), which differ in terms of number of larval stadia, developmental rate and fecundity. It was possible to separately cluster the two ecotypes with 95% correct assignment using 27 SNPs. We also determined that a storage hexamerin carried eight of these SNPs, including the two highest contributing loci, of which the top contributor was nonsynonymous. Other studies have found this protein to be highly expressed just before metamorphosis, pointing to a possible connection between its role in clustering ecotypes and its biological function. These SNP markers can now be further developed for high throughput delimitation of individuals of unknown ecotype identity.     

A comparison of approaches to estimate the inbreeding coefficient and pairwise relatedness using genomic and pedigree data in a sheep population

Genome-wide SNP data provide a powerful tool to estimate pairwise relatedness among individuals and individual inbreeding coefficient. The aim of this study was to compare methods for estimating the two parameters in a Finnsheep population based on genome-wide SNPs and genealogies, separately. This study included ninety-nine Finnsheep in Finland that differed in coat colours (white, black, brown, grey, and black/white spotted) and were from a large pedigree comprising 319 119 animals. All the individuals were genotyped with the Illumina Ovine SNP50K BeadChip by the International Sheep Genomics Consortium. We identified three genetic subpopulations that corresponded approximately with the coat colours (grey, white, and black and brown) of the sheep. We detected a significant subdivision among the colour types (F _ST = 5.4%, P<0.05). We applied robust algorithms for the genomic estimation of individual inbreeding (F _SNP) and pairwise relatedness (Φ _SNP) as implemented in the programs KING and PLINK, respectively. Estimates of the two parameters from pedigrees (F _PED and Φ _PED) were computed using the RelaX2 program. Values of the two parameters estimated from genomic and genealogical data were mostly consistent, in particular for the highly inbred animals (e.g. inbreeding coefficient F>0.0625) and pairs of closely related animals (e.g. the full- or half-sibs). Nevertheless, we also detected differences in the two parameters between the approaches, particularly with respect to the grey Finnsheep. This could be due to the smaller sample size and relative incompleteness of the pedigree for them.We conclude that the genome-wide genomic data will provide useful information on a per sample or pairwise-samples basis in cases of complex genealogies or in the absence of genealogical data.     

SNPs by AFLP (SBA): a rapid SNP isolation strategy for non-model organisms

Despite the great potential of single nucleotide polymorphism (SNP) markers in evolutionary studies, in particular for inferring population genetic parameters, SNP analysis has almost exclusively been limited to humans and ‘genomic model’ organisms, due to the lack of available sequence data in non-model organisms. Here, we describe a rapid and cost effective method to isolate candidate SNPs in non-model organisms. This SNP isolation strategy consists basically in the direct sequencing of amplified fragment length polymorphism bands. In a first application of this method, 10 unique DNA fragments that contained 24 SNPs were discovered in 11.11 kb of sequenced genomic DNA of a non-model species, the brown trout (Salmo trutta).     

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.     

Discovery,validation and characterization of 1039 cattle single nucleotide polymorphisms

We identified ～13 000 putative single nucleotide polymorphisms (SNPs) by comparison of repeat‐masked BAC‐end sequences from the cattle RPCI‐42 BAC library with whole‐genome shotgun contigs of cattle genome assembly Btau 1.0. Genotyping of a subset of these SNPs was performed on a panel containing 186 DNA samples from 18 cattle breeds including 43 trios. Of 1039 SNPs confirmed as polymorphic in the panel, 998 had minor allele frequency ≥0.25 among unrelated individuals of at least one breed. When Btau 4.0 became available, 974 of these validated SNPs were assigned in silico to known cattle chromosomes, while 41 SNPs were mapped to unassigned sequence scaffolds, yielding one SNP every ～3 Mbp on average. Twenty‐four SNPs identified in Btau 1.0 were not mapped to Btau 4.0. Of the 1015 SNPs mapped to Btau 4.0, 959 SNPs had nucleotide bases identical in Btau 4.0 and Btau 1.0 contigs, whereas 56 bases were changed, resulting in the loss of the in silico SNP in Btau 4.0. Because these 1039 SNPs were all directly confirmed by genotyping on the multi‐breed panel, it is likely that the original polymorphisms were correctly identified. The 1039 validated SNPs identified in this study represent a new and useful resource for genome‐wide association studies and applications in animal breeding.     

Rank and Order: Evaluating the Performance of SNPs for Individual Assignment in a Non-Model Organism

Single nucleotide polymorphisms (SNPs) are valuable tools for ecological and evolutionary studies. In non-model species, the use of SNPs has been limited by the number of markers available. However, new technologies and decreasing technology costs have facilitated the discovery of a constantly increasing number of SNPs. With hundreds or thousands of SNPs potentially available, there is interest in comparing and developing methods for evaluating SNPs to create panels of high-throughput assays that are customized for performance, research questions, and resources. Here we use five different methods to rank 43 new SNPs and 71 previously published SNPs for sockeye salmon: F_ST, informativeness (I_n), average contribution to principal components (LC), and the locus-ranking programs BELS and WHICHLOCI. We then tested the performance of these different ranking methods by creating 48- and 96-SNP panels of the top-ranked loci for each method and used empirical and simulated data to obtain the probability of assigning individuals to the correct population using each panel. All 96-SNP panels performed similarly and better than the 48-SNP panels except for the 96-SNP BELS panel. Among the 48-SNP panels, panels created from F_ST, I_n, and LC ranks performed better than panels formed using the top-ranked loci from the programs BELS and WHICHLOCI. The application of ranking methods to optimize panel performance will become more important as more high-throughput assays become available.     

Empirical comparison of Simple Sequence Repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness

While Simple Sequence Repeats (SSRs) are extremely useful genetic markers, recent advances in technology have produced a shift toward use of single nucleotide polymorphisms (SNPs). The different mutational properties of these two classes of markers result in differences in heterozygosities and allele frequencies that may have implications for their use in assessing relatedness and evaluation of genetic diversity. We compared analyses based on 89 SSRs (primarily dinucleotide repeats) to analyses based on 847 SNPs in individuals from the same 259 inbred maize lines, which had been chosen to represent the diversity available among current and historic lines used in breeding. The SSRs performed better at clustering germplasm into populations than did a set of 847 SNPs or 554 SNP haplotypes, and SSRs provided more resolution in measuring genetic distance based on allele-sharing. Except for closely related pairs of individuals, measures of distance based on SSRs were only weakly correlated with measures of distance based on SNPs. Our results suggest that 1) large numbers of SNP loci will be required to replace highly polymorphic SSRs in studies of diversity and relatedness and 2) relatedness among highly-diverged maize lines is difficult to measure accurately regardless of the marker system.     

Comparison of SSRs and SNPs in assessment of genetic relatedness in maize

Advances in high-throughput SNP genotyping and genome sequencing technologies have enabled genome-wide association mapping in dissecting the genetic basis of complex quantitative traits. In this study, 82 SSRs and 884 SNPs with minor allele frequencies (MAF) over 0.20 were used to compare their ability to assess population structure, principal component analysis (PCA) and relative kinship in a maize association panel consisting of 154 inbred lines. Compared to SNPs, SSRs provided more information on genetic diversity. The expected heterozygosity (He) of SSRs and SNPs averaged 0.65 and 0.44, and the polymorphic information content of these two markers was 0.61 and 0.34 in this panel, respectively. Additionally, SSRs performed better at clustering all lines into groups using STRUCTURE and PCA approaches, and estimating relative kinship. For both marker systems, the same clusters were observed based on PCA and the first two eigenvectors accounted for similar percentage of genetic variations in this panel. The correlation coefficients of each eigenvector from SSRs and SNPs decreased sharply when the eigenvector varied from 1 to 3, but kept around 0 when the eigenvector were over 3. The kinship estimates based on SSRs and SNPs were moderately correlated (r (2)?=?0.69). All these results suggest that SSR markers with moderate density are more informative than SNPs for assessing genetic relatedness in maize association mapping panels.     

Using relatedness networks to infer contemporary dispersal: Application to the endangered mammal Galemys pyrenaicus

Information about the degree of contemporary dispersal is important when trying to understand how populations interchange individuals and identify the specific barriers that prevent these movements. In the case of endangered species, this can represent crucial information when designing appropriate conservation strategies. Here we analyse relatedness between individuals from different localities and use these data to infer whether dispersal occurred in recent generations. We applied this approach to the Pyrenean desman (Galemys pyrenaicus), a semiaquatic and endangered species endemic to the Iberian Peninsula. We studied this species in four primary rivers in the Iberian Range, where two ancient mitochondrial lineages are separated by a strict contact zone, suggesting the existence of complex dispersal patterns. Using next‐generation sequencing, we obtained 912 SNPs from each specimen and estimated relatedness values between them. While relatedness networks were dense within each river, we found surprisingly few relationships between individuals from different rivers despite their close proximity in some cases, indicating much lower dispersal between rivers compared to dispersal within a single river. In agreement with this result, the degree of inbreeding was exceedingly high in most individuals. These data show that relatedness information can be crucial to understand the contemporary dispersal patterns and conservation status of specific populations of endangered species.     

A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals

We present methods for imputing data for ungenotyped markers and for inferring haplotype phase in large data sets of unrelated individuals and parent-offspring trios. Our methods make use of known haplotype phase when it is available, and our methods are computationally efficient so that the full information in large reference panels with thousands of individuals is utilized. We demonstrate that substantial gains in imputation accuracy accrue with increasingly large reference panel sizes, particularly when imputing low-frequency variants, and that unphased reference panels can provide highly accurate genotype imputation. We place our methodology in a unified framework that enables the simultaneous use of unphased and phased data from trios and unrelated individuals in a single analysis. For unrelated individuals, our imputation methods produce well-calibrated posterior genotype probabilities and highly accurate allele-frequency estimates. For trios, our haplotype-inference method is four orders of magnitude faster than the gold-standard PHASE program and has excellent accuracy. Our methods enable genotype imputation to be performed with unphased trio or unrelated reference panels, thus accounting for haplotype-phase uncertainty in the reference panel. We present a useful measure of imputation accuracy, allelic R², and show that this measure can be estimated accurately from posterior genotype probabilities. Our methods are implemented in version 3.0 of the BEAGLE software package.     

Admixture and Gene Flow from Russia in the Recovering Northern European Brown Bear (Ursus arctos)

Large carnivores were persecuted to near extinction during the last centuries, but have now recovered in some countries. It has been proposed earlier that the recovery of the Northern European brown bear is supported by migration from Russia. We tested this hypothesis by obtaining for the first time continuous sampling of the whole Finnish bear population, which is located centrally between the Russian and Scandinavian bear populations. The Finnish population is assumed to experience high gene flow from Russian Karelia. If so, no or a low degree of genetic differentiation between Finnish and Russian bears could be expected. We have genotyped bears extensively from all over Finland using 12 validated microsatellite markers and compared their genetic composition to bears from Russian Karelia, Sweden, and Norway. Our fine masked investigation identified two overlapping genetic clusters structured by isolation-by-distance in Finland (pairwise F_ST = 0.025). One cluster included Russian bears, and migration analyses showed a high number of migrants from Russia into Finland, providing evidence of eastern gene flow as an important driver during recovery. In comparison, both clusters excluded bears from Sweden and Norway, and we found no migrants from Finland in either country, indicating that eastern gene flow was probably not important for the population recovery in Scandinavia. Our analyses on different spatial scales suggest a continuous bear population in Finland and Russian Karelia, separated from Scandinavia.     

Population structure, habitat features and genetic structure of managed red deer populations

Management of game ungulates alters population structure and habitat features, with potential effects on genetic structure. Here, we study 26 red deer (Cervus elaphus) populations in Spain. We used census data and habitat features as well as genetic information at 11 microsatellite markers from 717 individuals. We found that metapopulations presented a distribution associated with forest interruptions. Within metapopulations, fences did not have a significant effect on red deer genetic structure. The metapopulations we studied presented similar population structure, but they differed in habitat features and genetic structure. The metapopulation with higher resource availability showed a genetic structure pattern in which genetic relatedness between geographically close individuals was high while relatedness between geographically distant individuals was low. Contrarily, the metapopulation with lower resource availability presented a genetic structure pattern in which the genetic relatedness between individuals of different populations was independent of the geographic distance. We discuss the possible connection between resource availability and genetic structure. Finally, we did not find any population or environmental variable related to genetic differentiation within metapopulations.     

Construction and Application of a Korean Reference Panel for Imputing Classical Alleles and Amino Acids of Human Leukocyte Antigen Genes

Genetic variations of human leukocyte antigen (HLA) genes within the major histocompatibility complex (MHC) locus are strongly associated with disease susceptibility and prognosis for many diseases, including many autoimmune diseases. In this study, we developed a Korean HLA reference panel for imputing classical alleles and amino acid residues of several HLA genes. An HLA reference panel has potential for use in identifying and fine-mapping disease associations with the MHC locus in East Asian populations, including Koreans. A total of 413 unrelated Korean subjects were analyzed for single nucleotide polymorphisms (SNPs) at the MHC locus and six HLA genes, including HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1. The HLA reference panel was constructed by phasing the 5,858 MHC SNPs, 233 classical HLA alleles, and 1,387 amino acid residue markers from 1,025 amino acid positions as binary variables. The imputation accuracy of the HLA reference panel was assessed by measuring concordance rates between imputed and genotyped alleles of the HLA genes from a subset of the study subjects and East Asian HapMap individuals. Average concordance rates were 95.6% and 91.1% at 2-digit and 4-digit allele resolutions, respectively. The imputation accuracy was minimally affected by SNP density of a test dataset for imputation. In conclusion, the Korean HLA reference panel we developed was highly suitable for imputing HLA alleles and amino acids from MHC SNPs in East Asians, including Koreans.     

Detection of selection signatures in the genome of a farmed population of anadromous rainbow trout (Oncorhynchus mykiss)

Domestication processes and artificial selection are likely to leave signatures that can be detected at a molecular level in farmed rainbow trout (Oncorhynchus mykiss). These signatures of selection are genomic regions that contain functional genetic variants conferring a higher fitness to their bearers. We genotyped 749 rainbow trout from a commercial population using a rainbow trout Axiom 57 K SNP array panel and identified putative genomic regions under selection using the pcadapt, Composite Likelihood Ratio (CLR) and Integrated Haplotype Score (iHS) methods. After applying quality-control pipelines and statistical analyses, we detected 12, 96 and 16 SNPs putatively under selection, associated with 96, 781 and 115 candidate genes, respectively. Several of these candidate genes were associated with growth, early development, reproduction, behavior and immune system traits. In addition, some of the SNPs were found in interesting regions located in autosomal inversions on Omy05 and Omy20. These findings could represent a genome-wide map of selection signatures in farmed rainbow trout and could be important in explaining domestication and selection for genetic traits of commercial interest.