Plasmodium falciparum genome can be used to explore parasite population dynamics, with practical applications to malaria control. The ability to identify the geographic origin and trace the migratory patterns of parasites with clinically important phenotypes such as drug resistance is particularly relevant. With increasing single-nucleotide polymorphism (SNP) discovery from ongoing Plasmodium genome sequencing projects, a demand for high SNP and sample throughput genotyping platforms for large-scale population genetic studies is required. Low parasitaemias and multiple clone infections present a number of challenges to genotyping P. falciparum. We addressed some of these issues using a custom 384-SNP Illumina GoldenGate assay on P. falciparum DNA from laboratory clones (long-term cultured adapted parasite clones), short-term cultured parasite isolates and clinical (non-cultured isolates) samples from East and West Africa, Southeast Asia and Oceania. Eighty percent of the SNPs (n = 306) produced reliable genotype calls on samples containing as little as 2 ng of total genomic DNA and on whole genome amplified DNA. Analysis of artificial mixtures of laboratory clones demonstrated high genotype calling specificity and moderate sensitivity to call minor frequency alleles. Clear resolution of geographically distinct populations was demonstrated using Principal Components Analysis (PCA), and global patterns of population genetic diversity were consistent with previous reports. These results validate the utility of the platform in performing population genetic studies of P. falciparum.The diversity in the  Plasmodium falciparum continues to impose a substantial public health burden across the globe, causing an estimated 500 million clinical cases and 1–2 million deaths each year Subsets of genetic polymorphisms can be used to explore the dynamics of parasites with important phenotypes such as drug resistance, with practical applications to malaria control and elimination. Key to the success of malaria control strategies is an understanding of the parasite genetic diversity and dynamics/exchange of gene flow between human populations. With this knowledge, effective spatial and temporal boundaries for intervention can be implemented. The ability to identify the geographic origin and monitor the migration patterns of clinically important parasite genetic traits should greatly facilitate control efforts, with important applications for drug resistance surveillance. With sufficient genetic information, we may be able to use the spectrum of genetic variation, or in a more simplified form, a “molecular barcode”, to elucidate the geographic origin of an infection. Molecular barcodes have already been used successfully by Daniels and colleagues Plasmodium sequencing efforts providing a valuable resource of SNPs , and enhancing the feasibility of large scale genotype-based population genetic studies of the parasite, a demand for effective high sample and SNP throughput genotyping platforms is anticipated. However, genotyping P. falciparum samples presents a number of challenges including limited DNA quantity, an abundance of contaminating human DNA, extensive polymorphism that may occur in primer binding sites, and multiple clone infections in which multiple parasite clones may be present at any number and ratio.With ongoing whole-genome P. falciparum clones and isolates from East and West Africa, Southeast Asia and Oceania, we measured the specificity and sensitivity of the GoldenGate platform on various preparations of P. falciparum DNA. In addition, we characterise the genetic structure of parasite populations and assess the potential of molecular barcoding approaches to identify the likely geographic origin of an infection.To address some of these limitations, we developed a customized Illumina GoldenGate 384-SNP genotyping assay. The GoldenGate platform was chosen for the study owing to the moderately high sample  and SNP (up to 1536 loci) throughput, and the custom design feature, which enables users to tailor the SNP genotyping assay to meet the specific requirements of their study. Using P falciparum DNA was obtained from either i) long-term cultured parasite clones , ii) short-term culture parasite isolates obtained from patients with malaria , and (iii) non-cultured parasite isolates obtained directly from patient's blood  (in vitro as previously described ea (PNG) . Laboratet al., submitted). Genomic DNA was extracted from infected red blood cell pellets using the QIAamp DNA Blood Midi Kit according to the manufacturer's protocol (QIAGEN). Whole genome amplification was undertaken on DNA template  using Genomiphi's Multiple Displacement Amplification kit as per protocol (Amersham Biosciences). Human DNA extracted from an EBV-transformed B cell line obtained from the Centre d'Etude du Polymorphisme Humain collection  was used as a control and in preparations of human-spiked Plasmodium samples for the assessment of the effect of human DNA presence on Plasmodium genotyping efficacy. To address the quantities of human and Plasmodium DNA in each sample, quantitative real time PCR (QRT-PCR) analysis using human and parasite-specific primers was undertaken using the Applied Biosystems StepOne RT-PCR system .Prior to DNA extraction, all blood samples were subject to white blood cell depletion to reduce human DNA contamination using a combination of lymphoprep density gradient centrifugation (Axis-Shield) followed by Plasmodipur filtration (Euro-Diagnostica), as described in  P. falciparum laboratory clones  with at least 1 observation of each allele, (b) Illumina assay design score >1  (c) uniform spacing across the P.falciparum genome. The final set of 384 SNPs consisted of 31 well-known candidate gene SNPs and 353 SNPs chosen to achieve on average 1 marker per 60 kb  that range from 0 to 1 and intensity values (R) with values from 0 onwards. Theta values approximating 0 and 1 reflect different alleles and, thus, theta is used to score genotypes as heterozygous or homozygous for each allele EH) at each locus was calculated as EH = n/(n-1)](2pq), where n is the number of infections sampled, p is the frequency of allele 1 and q is the frequency of allele 2. The formula [n/(n-1)] was included to enable adjustment for sample size. At the population level, EH was averaged across all loci Expected heterozygosity (ST), using both theta values and genotype calls ST statistics (Cockerham and Weir) Principal component analysis (PCA) was performed by calculating the eigenvalues and their vectors (PCs) of a matrix consisting of average number of pairwise genotype or theta differences between each isolate. The proportion of total variation explained by each principal component was estimated using eigenvalues. The genetic differentiation between populations was assessed using pair-wise measures of Wright's Fixation index (F2>0.99) was observed for both inter- and intra-plate replicates for this set of SNPs, demonstrating overall low experimental variability on the GoldenGate platform  allelic concordance with the reference sequence platform . The perplatform . Approxiplatform .The ability of the assay to perform at lower DNA quantities than recommended by Illumina  was investigated by assessing the intensity and genotype concordance between 250 ng, 25 ng, 2.5 ng and 0.25 ng templates of the clones 3D7, HB3 and IT. For all three parasite clones, the performance of the 25 ng and 2.5 ng templates was similar to the 250 ng template . AlthougP. falciparum genotyping accuracy was also assessed by comparison of the genotype calls between samples containing only parasite DNA with samples containing a mixture of parasite and human DNA at different concentrations (2>0.96).The influence of human DNA on trations . GenotypP. falciparum samples subject to whole genome amplification (WGA) using the multiple displacement amplification (MDA) method Genotyping efficacy was also assessed on Given that SNP selection and primer design were based on the SNP information available in PlasmoDB version 4.4, it is possible that unidentified SNPs might reduce the genotyping efficacy at a number of loci, particularly in sample populations that were not well represented in this version of the database. The genotyping efficacy at the 319 reliable SNPs was assessed in 143 short-term or clinical (non-cultured) samples sourced from Burkina Faso, Mali, Kenya, Cambodia, Thailand and Papua New Guinea. As summarized in P. falciparum clones. The presence of heterozygous genotypes is indicative of the presence of more than one clone (haploid genome) in a single infection . Examples of the genotype profiles (as theta scores) of 2 clinical samples (non-cultured) described in this study are presented in et al., under review). Whole genome sequence data generated on the Illumina Genome Analyzer platform was available for 105 of the samples described in this study. Thus, it was possible to assess the reliability of the GoldenGate genotype calls, by comparison with the Genome Analyzer calls at 106 of the globally reliable SNPs. . The full set of globally reliable SNPs could not be compared owing to regions of low sequence coverage. At the 106 loci, an average genotype concordance of 97.8% (including both homozygous and heterozygous genotypes) was observed between the two datasets .Clinical malaria samples obtained directly from patient's blood may have multiple genetically distinct loci should exhibit heterozygous genotypes in the mixtures. At ratios of 1∶1, only 5 SNPs expected to exhibit heterozygous genotypes could not be distinguished from homozygous loci. All other expected heterozygous loci were clearly distinguishable from the homozygous loci, and exhibited theta values between 0.1–0.9. At ratios of 1∶2 and 1∶4, it was still possible to discriminate 84.7% and 71.5% of the expected heterozygous SNPs (theta between 0.1–0.9). At ratios of 1∶10, discrimination had decreased to 34.7%. At lower ratios, the assay clearly tended toward the genotype calls of the predominant parasite and it was not possible to discriminate different clones within the mixture.In contrast to clonal laboratory parasites et al., under review).Using the data from the 143 clinical and short-term cultured samples at the 306 globally reliable SNPs , we asseP. falciparum diversity were consistent with previous reports EH = 0.23, 0.24, and 0.24, respectively), than in the Thailand, Cambodia and Papua New Guinea samples . This would fit with the view that P. falciparum originated in Africa, and that some of the ancestral diversity has been lost as it has spread to other parts of the world; an alternative explanation might be that the level of diversity is related to historical levels of transmission intensity, which have tended to be higher in Africa than in Southeast Asia.The level of genetic diversity in each population was assessed using a measure of the expected heterozygosity with adjustment for sample size . Global ST) ST<0.1), particularly between Mali and Burkina Faso (FST<0.011). High levels of differentiation were observed between Papua New Guinea and all other populations, with a trend of increasing differentiation by distance . A similar trend of increasing differentiation by distance was observed between the Asian and African populations. High levels of differentiation were observed between Kenya and each of Cambodia (FST = 0.326) and Thailand (FST = 0.329), but the highest levels of differentiation were observed between the Asian and West African populations (FST range 0.454–0.463). As with the heterozygosity patterns, these global patterns of population differentiation are consistent with previous reports for P. falciparumPair-wise estimates of the differentiation between the parasite populations were calculated using Wright's Fixation index  was used to assess the ability to assign the geographic origin of a given sample on the basis of its clustering pattern and underlying molecular barcode . Using tmsp typing P. falciparum parasites.To successfully implement and monitor malaria control and elimination strategies, we need a greater understanding of the dynamics of parasite gene flow between populations and effective and rapid tools for tracing important parasite genetic traits such as anti-malarial drug resistance. Microsatellite-based approaches are common-place for elucidating the population genetic structure of parasite populations P. falciparum SNP datasets  P. falciparum samples is challenging owing to features such as limitations in the quantity of parasite DNA, contamination with human DNA from white blood cells, and the presence of extensive polymorphism, including multiple clone infections.Rapidly accumulating genome-wide P. falciparum DNA, and to apply these data to population genetic studies of the parasite. The Illumina BeadXpress platform, which primarily uses the Golden Gate SNP genotyping assay but genotypes lower number of SNPs, has been previously successful on genotyping 96 SNPs in P. falciparumWe assessed the utility of the high-throughput Illumina GoldenGate platform to genotype various preparations of 2>0.95), 84% (306) of SNPs demonstrated reliable genotyping performance on both clinical and cultured samples from a range of geographic locations, including samples with abundant human contamination and with low quantities of genomic or whole genome amplified (WGA) DNA.Using a 384-SNP custom-design GoldenGate assay, under strict criteria for genotype intensity (R>0.1) and concordance between sample replicates (rlinkage disequilibrium (LD). Patterns of LD underlie methods to detect signatures of recent positive selection, and candidate loci underlying traits such as antimalarial drug resistance P. falciparum populations from a range of geographic locations, including highly diverse and mutliclonal populations from Africa Multiple clone infections present a challenge to parasite genetic studies owing to sensitivity of detection of minor frequency variants and potential ambiguity in haplotype reconstruction. Accurate haplotype reconstruction is important for certain genetic metrics which require an understanding of genetic phase, such as et al., under review). Using artificial mixes of laboratory clones, the 306-SNP assay demonstrated high specificity (high concordance between replicates) and modest sensitivity to detect minor frequency variants . At this level of sensitivity, using the 306 validated SNPs on 143 clinical and short-term cultured samples from across the globe, we observed global patterns of population genetic diversity and differentiation consistent with previous reports using data generated on other platforms.In our study, we were able to detect multiclonal infections  in clinical samples. In addition to identifying whether a sample carried more than one genetic variant, we were able to describe the extent of diversity within a sample (i.e. how different the parasites within a multiple infection are), as defined by the proportion of heterozygous loci within a sample. Using this method, we observed higher levels of diversity within the clinical African samples than in the clinical PNG samples, consistent with observations from datasets produced on the Illumina Genome Analyzer platform  DNA templates, typical of filter paper extracts. In addition to the high sample and SNP throughput, an important feature of the GoldenGate platform is its custom-design potential. This feature enables researchers to select the specific SNPs which best facilitate their research objective. With ongoing Plasmodium whole-genome sequencing efforts and the identification of novel candidate disease and drug resistance loci , researches will have a large resource of SNPs from which to select optimal subsets for their specific study design.In summary, we discuss the challenges of genotyping in Figure S1Distribution of theta values  at 306 SNPs in (left to right) the laboratory clone 3D7 and 2 clinical samples from Burkina Faso and PNG. Values approximating 0 and 1 correspond to homozygous calls while other values represent heterozygous calls.(TIF)Click here for additional data file.Figure S2Principal Components Analysis plots  using molecular barcodes to differentiate the 143 samples and assign geographic origins to the infections. Green = Mali Orange = Burkina Faso, Blue = Kenya, Black = Cambodia, Grey = Thailand, Red = Papua New Guinea. Using a 9-SNP molecular barcode, samples can clearly be assigned continental origins (a) Clear sample assignment to East or West Africa is possible using a 4-SNP molecular barcode, although Mali and Burkina Faso cannot be resolved (b). Using a 6-SNP molecular barcode, samples can be assigned to Thailand or Cambodia with moderate confidence (c). The highest resolution of Mali and Burkina Faso is possible with a 13-SNP barcode , but assignment to either population remains moderately ambiguous (d).(TIF)Click here for additional data file.Table S1Sample details.(DOCX)Click here for additional data file.Table S2Golden Gate custom assay information.(XLSX)Click here for additional data file.Table S3Assessment of assay performance using 319 “reliable” SNPs in the presence of human DNA.a Total 250 ng genomic DNA. b Filtration criteria A: SNPs with genotype concordance between replicates >0.95. c Filtration criteria B: SNPs with R>0.1 and with genotype concordance between replicates >0.95. d Mean correlation between replicates.(DOCX)Click here for additional data file.Table S4Assessment of assay performance using 319 “reliable” SNPs on whole genome amplified (WGA) samples.a Filtration criteria A: SNPs with genotype concordance between replicates >0.95. b Filtration criteria B: SNPs with R>0.1 and with genotype concordance between replicates >0.95.(DOCX)Click here for additional data file.


Canis lupus) and 2) compare the genetic affinities of modern breeds to regional indigenous village dog populations. The Y chromosome markers indicated that village dogs in the two regions corresponded to reciprocally monophyletic clades, reflecting several to many thousand years divergence, predating the Neolithic ages, and indicating long-indigenous roots to those regions. As expected, breeds of the Middle East and East Asia clustered within the respective regional village dog clade. Australian dingoes also clustered in the Southeast Asian clade. However, the European and American breeds clustered almost entirely within the Southeast Asian clade, even sharing many haplotypes, suggesting a substantial and recent influence of East Asian dogs in the creation of European breeds. Comparison to 818 published breed dog Y STR haplotypes confirmed this conclusion and indicated that some African breeds reflect another distinct patrilineal origin. The lower-resolution mtDNA marker consistently supported Y-chromosome results. Both marker types confirmed previous findings of higher genetic diversity in dogs from Southeast Asia than the Middle East. Our findings demonstrate the importance of village dogs as windows into the past and provide a reference against which ancient DNA can be used to further elucidate origins and spread of the domestic dog.Modern genetic samples are commonly used to trace dog origins, which entails untested assumptions that village dogs reflect indigenous ancestry or that breed origins can be reliably traced to particular regions. We used high-resolution Y chromosome markers (SNP and STR) and mitochondrial DNA to analyze 495 village dogs/dingoes from the Middle East and Southeast Asia, along with 138 dogs from >35 modern breeds to 1) assess genetic divergence between Middle Eastern and Southeast Asian village dogs and their phylogenetic affinities to Australian dingoes and gray wolves ( Canis lupus) 12,000–40,000 years BP, and that they spread rapidly throughout Eurasia and the Americas at the end of the last ice age Archaeology and DNA studies indicate that dogs evolved from or share a recent common ancestor with the gray wolf  limit the practical utility of this approach. An alternative approach is to assess the genetic divergence between relatively large samples of extant village dogs from multiple regions to infer population ages. Specifically, if populations reflect primarily indigenous ancestry, their genetic divergence should reflect thousands of years' isolation, whereas if they are heavily admixed with modern Western breeds, they should reflect little genetic divergence. Mitochondrial DNA (mtDNA) sequences are commonly used in phylogeographic studies of plants and animals, including dogs 2–104 generations.DNA markers on the non-recombining region of the Y chromosome (NRY) have been used to great advantage in studies of several domestic species and humans Our first objective was to determine whether village dogs from the Middle East and Continental and Island Southeast Asia were indigenous to those regions or, alternatively, secondary products of a post-Victorian expansion of Western breed dogs. The second objective was to determine whether modern breeds could be traced to either of these putative indigenous village dog populations. We used highly resolved Y-chromosome SNP-STR haplotypes to assess the approximate minimum age and genetic similarity of these village dog populations. To address uncertainty in STR mutation rates, estimates of divergence time were calibrated using Australian dingoes and Bali dogs, both of which are known to have been isolated for several thousand years based on independent evidence All procedures involving animals were reviewed and approved by the University of California, Davis, Animal Care and Use Committee (Protocol No. 16643).Canis mesomelas) and a dhole  from captivity. Buccal swabs were collected from dogs. Most village dogs (mainland and Island Southeast Asia) were captured in the course of spay-neuter programs  from the Middle East and Southeast Asia, 15 Australian dingoes , 45 desert-bred Salukis , and 93 male breed dogs from 35 additional breeds or mixtures of breeds. Blood, tissue, or buccal swabs were obtained from the wild canids, including gray wolves from Iran (n = 3), China (n = 1), and the Yukon, Canada (n = 3), along with a black-backed jackal  and CTTATATGCATGGGGCAAACC (reverse) and Big Dye sequencing chemistry . Males were genotyped using 5 dinucleotide-repeat STRs from the NRY, including 650−79.2, 650−79.3, 990−35 DNA was extracted from buccal swabs using a standard protocol Male samples were genotyped at 11 NRY SNP loci Mitochondrial DNA sequences were used both as a means of assessing the similarity of our samples to the nearby samples of Pang et al. We constructed phylogenetic networks based on Y-SNPs, Y-STRs, and the combination of both markers. The Y-SNP networks were used to coarsely characterize the deeper phylogenetic structure in village dogs, whereas the Y STR networks provided far greater resolution with respect to recent divergence in the same village dogs and enabled us to directly compare 818 previously published breed dog haplotypes r = 2 and є = 0. We first constructed median-joining networks The Y chromosome networks were constructed using program Network 4.50 In principle, use of coalescent models and Markov chain Monte Carlo (MCMC) approaches can be used to estimate population (or clade) splitting times n = 200) and Southeast Asia (n = 231) were sequenced at the mtDNA locus. This analysis revealed a total of 54 HVI haplotypes, of which 17 were novel (Genbank Accession Nos. HQ287728–HQ287744). The 15 Australian dingoes from Fraser Island all had indigenous dingo haplotypes  and Southeast Asia . The 402-bp equivalents of all of the widespread ancestral haplotypes previously identified as universally occurring haplotypes (UTs) were found in at least one of the two populations (n = 12) than the Middle East (n = 4) (n = 612 village dogs) than the Middle East (n = 37 village dogs) in the past Village dogs from the Middle East (ulations . In totaulations  were fou (n = 4) , S4, eve−8 per year It is noteworthy that village dogs from Bali (n = 94) exhibited a lower haplotype diversity than dogs from other islands in Southeast Asia or sampling sites in the Middle East . This waThe more ancient phylogenetic relationships of the Southeast Asian and Middle Eastern village dog patrilines were revealed by the NRY SNPs in 300 village dogs and dingoes, along with 7 gray wolves, and an out-group of 1 dhole and 1 black backed jackal. The 8 resulting haplotypes  fell intThe 5 NRY STRs revealed considerably more Y chromosome diversity, including 95 haplotypes in the 300 village dogs and dingoes (and 5 more in the 7 wolves). The diversity of village dog STR haplotypes also was higher than previously found in a much larger sample of breed dogs . AlthougCombining the two marker types corrected inconsistencies due to ambiguous positioning of distinct STR haplotypes, revealed a greater mutational distance between the Southeast Asian and Middle Eastern clades, equalized the apparent divergence of both dog clades from the basal wolf clade, and revealed that several Middle Eastern village dog haplotypes clustered with the wolf clade, suggestive of recent wolf introgression in Middle Eastern village dogs. The 4 haplotypes exhibited by Australian dingoes clustered more closely with Southeast Asian village dogs, including a basal haplotype shared with some village dogs. The haplotypes of Bali dogs also clustered within the Southeast Asian clade. The haplotypes of Southeast Asian village dogs clustering in the Middle Eastern clade were linked by long branches, indicating ancient derivation. Additionally, the SNP-STR network revealed a diversity of STR haplotypes corresponding to each of the SNP haplotype. Only SNP haplotypes 7 and 11 shared a STR haplotype, indicating that the SNP separating these haplotypes was likely a recent mutation. Thus, the SNP- STR data set provided a powerful basis for assessing ancient vs. recent divergence between village dog populations and for comparing to the haplotypes of breed dogs to infer their proximate origins.−4 per year, fell within the expected range for mutation of human Y STRs , each haplotype was rooted to its most proximate common ancestral node  breeds, 3 from 2 East Asian breeds, 31 from a Middle Eastern breed, desert-bred (Bedouin) Salukis from Israel, and 28 of various mixed breeds . The Saln = 818; n = 60) from these 818 breed dogs clustered primarily in the Southeast Asian village dog clade, including all but 12 European and American breed dogs  sharing 2 haplotypes  that are representative of their indigenous ancestry. Thus, our first question in this study was whether contemporary village dogs in Asia (including parts of Southeast Asia and the Middle East) primarily exhibited indigenous ancestry or, alternatively, whether they reflected admixture among recently created breeds . Second, because breeds are so commonly used to trace ultimate origins e.g., , we wishOur findings indicated that Middle Eastern and Southeast Asian village dog populations must have originated either from a common gene pool thousands of years before present or from distinct groups of wolf or wolf-like founders, but are clearly not the product of a post-Victorian expansion of dog breeds. First, the monophyly of Y chromosome clades associated with the two populations, and their long and similar divergence from wolves, suggest that most of the extant Y chromosome diversity evolved after these populations were established. The numbers of mutations separating haplotypes and their ancestral nodes in each of these clades provided a measure of time to most recent common ancestor, which also reflected the minimum time since separation. The time estimates, while necessarily imprecise, were qualitatively robust because they were based on calibration to known-age indigenous, insular dog populations of Bali and Australia. These Island populations were known to be founded on the order of 3,000–5,000 years BP, based on archaeological evidence Having established that village dogs used in this study reflect indigenous populations, it was possible to compare ancestry of breeds with respect to these indigenous populations. Although we could not trace modern breeds to a precise location of origin because we did not sample village dogs from a large intervening portion of Asia, the considerable genetic distance between these village dog populations, even if ends of a continuum, enabled inference about the relative regional affinities. As expected, Middle Eastern breeds clustered with Middle Eastern village dogs and East Asian breeds clustered with Southeast Asian village dogs. However, putative European and American breeds also clustered in the Southeast Asian clade, which ran counter to expectations. It would not have been surprising to find some Eastern influence in Western breeds, as many breeds developed during the Victorian dog-fancy era either were initiated from East Asian stock If Western breeds do not derive from their putative regions of origin, this warrants reconsideration of some previous conclusions about dog origins. In particular, Savolainen et al. On the other hand, our findings with both mtDNA and Y chromosome analyses provided strong confirmation of higher diversity previously observed in Southeast Asia than the Middle East Up to now, use of ancient DNA approaches to the study of dog origins have been limited to mtDNA, which has been most useful in showing that modern dogs do not necessarily reflect the ancient ones inhabiting those same regions Figure S1Sampling locations throughout the Middle East and Southeast Asia. Yangtze River is indicated by blue line. Israeli desert-bred Saluki , Iranian village dogs , Southeast Asian village dogs .(TIF)Click here for additional data file.Figure S2Y chromosome SNP-STR haplotype network depicting haplotype names. Network of dog (circles) and wolf (squares) NRY SNP-STR haplotypes, including 300 village dogs, 124 breed dogs, and 7 wolves. Haplotype names are beside their respective haplotype. Size of circle is proportional to sample size, except that the larges circle represents 18–50 individuals. NRY SNP-STR subclades corresponding to numbered SNP haplotypes in (TIF)Click here for additional data file.Table S1a.List of Sequenom PCR primers developed for SNP loci, which correspond to Natanaelsson et al. 2006(DOC)Click here for additional data file.Table S2a.Sequenom SNP extension primer sequences and expected extension products, developed for SNP loci, which correspond to Natanaelsson et al. 2006(DOC)Click here for additional data file.Table S3Frequency of 402 bp mtDNA haplotypes of village dogs sampled in 7 populations. Haplotypes are named as the lowest-numbered previously named synonymous 582 bp haplotype Savolainen et al. 2002a and Pang et al. 2009b or are novel in this study (haplotype names beginning with “V”); see (DOCX)Click here for additional data file.Table S4Seventeen novel mtDNA haplotypes, location of sample, and number of substitutions differing from the nearest published haplotype. Sequences were deposited in Genbank .(DOC)Click here for additional data file.Table S5Number of individuals (n), number (No.) of mtDNA haplotypes, and rarified haplotype richness (corrected for differing sample sizes to n = 10) for 402 bp mtDNA D-loop haplotypes of village dog sampling locations in the Middle East and Southeast Asia.(DOC)Click here for additional data file.Table S6a).Y chromosome SNP haplotypes as resolved from 11 “Ydog” loci Click here for additional data file.Table S7Imputed Y-SNP haplotypes corresponding to the 23  Y-STR haplotypes for which 1 or more of the 11 SNPs failed (indicated by “-”) or for which no SNP genotyping was attempted. All but one incomplete SNP haplotype corresponded to the same failed locus and the same haplotype ambiguity (7 or 11), suggesting a mutation in the priming region associated with this clade. The SNP haplotypes are indicated in (DOCX)Click here for additional data file.Table S8Allelic composition of NRY STR haplotypes and corresponding SNP haplotypes. An asterix identifies a SNP haplotype with one or more positions imputed (see a.uted see . Previou(DOCX)Click here for additional data file.Table S9a), and gray wolves.Frequency of NRY STR and SNP haplotypes observed in Middle Eastern and Southeast Asian village dog populations, Australian Dingoes, breed dogs (including published ones An asterix identifies a SNP haplotype with one or more positions imputed (see uted see .(DOCX)Click here for additional data file.Table S10a, breed class, and haplotype name of STR haplotypes included in NRY STR and SNP-STR analyses.Breed, number (No.) of individuals, putative region of breed origin Parentheses indicate individuals and haplotypes found in breed dogs that were genotyped in the present study and used in SNP-STR analysis; otherwise entries refer to published datab.(DOCX)Click here for additional data file.


Ursus arctos) within Scandinavia. We sequenced reduced representation libraries from nine individuals throughout the geographic range. With consensus reads containing putative SNPs, we applied strict filtering criteria with the aim of finding only high-quality, highly-informative SNPs. We tested 150 putative SNPs of which 96% were validated on a panel of 68 individuals. Ninety-six of the validated SNPs with the highest minor allele frequency were selected. The final SNP panel includes four mitochondrial markers, two monomorphic Y-chromosome sex-determination markers, three X-chromosome SNPs and 87 autosomal SNPs. From our validation sample panel, we identified two previously known parent-offspring dyads with reasonable accuracy. This panel of SNPs is a promising tool for inferring relatedness in the brown bear population in Scandinavia.Information about relatedness between individuals in wild populations is advantageous when studying evolutionary, behavioural and ecological processes. Genomic data can be used to determine relatedness between individuals either when no prior knowledge exists or to confirm suspected relatedness. Here we present a set of 96 SNPs suitable for inferring relatedness for brown bears ( Ursus arctos). Among many uses, genomic markers can help determine genetic relatedness between individuals in a population, which is key for determining many evolutionary, behavioural or ecological processes )tructure . Therefotructure  considertructure ). An informative SNP panel is one in which each SNP maximizes the differences in allelic representation across individuals within a population when compared to all other SNPs. Hence, SNPs with higher minor allele frequencies (MAF) and that are not in linkage with each other are more informative for relatedness inference . The numHistorically, over-hunting and habitat fragmentation have negatively affected many brown bear populations, a trend that led to the loss of much of the historical geographic range in Europe . Currentde novo a set of 96 high quality SNPs by applying an NGS-based RRL approach with an ascertainment panel of brown bears across the geographic range in Scandinavia. A SNP-chip was designed primarily to facilitate relatedness studies, although it can be useful for a wider range of studies. In addition to autosomal SNPs and Y-chromosome sex-determination markers, we included mitochondrial (mtDNA) and X-chromosome SNPs to further facilitate determination of parental ancestry. Our approach to reducing data complexity allowed for efficient and simplified ascertainment of a medium-throughput panel of highly informative SNPs. In this study, we developed We obtained 68 samples from the National Veterinary Institute , Uppsala, Sweden) from bears deceased either through a licensed hunt or that were found dead through other causes . No bears were killed for the purpose of this study or for other research endeavours. Samples were obtained with full consent by SVA. Samples were chosen to represent an even sex ratio and the full geographic range of brown bear throughout Sweden . The majDNA was extracted using the QIAsymphony SP and the QIAsymphony DNA kit  according to manufacturer’s instructions. Nucleotide quantity and purity were assessed using a spectrophotometer . DNA quality for extractions used for sequencing was visually assessed by gel electrophoresis using the Kodak Electrophoresis Documentation and Analysis System 120 .We used a combination of targeted and anonymous sequencing approaches to identify SNPs that are informative of parental lineage and that are autosomal respectively. The targeted approach involved mitochondria and the Y-chromosome, while the anonymous approach involved high-throughput sequencing of reduced representation libraries.Four primer pairs were designed from the published mitochondrial genome (NCBI Accession # EU497665.1) of a European brown bear using Primer3 . Each prTaq buffer*, 1.6μl 2.0mM MgCl2* and 0.17μl Taq DNA polymerase* (*Fermentas Taq DNA Polymerase – native). The optimized PCR conditions for all primer pairs include 1 cycle of 94°C for 3 min; 20 cycles of 94°C for 20 s, 60°C less 0.5°C/cycle for 30 s, 72°C for 30 s; 15 cycles of 94°C for 20 s, 50°C for 30 s; and 72°C for 5 min. PCR amplification was confirmed through gel electrophoresis. The remainder of the product was Sanger sequenced by Medicinsk och klinisk genetik  on a 3730 xl DNA analyzer . Sequences were aligned using BioEdit  and manually screened to identify SNPs.Each of ten samples were PCR amplified in a total reaction volume of 20 μl consisting of 2.5μl, 10-40ng/μl DNA, 0.5μl 10μM each of the forward and reverse primer, 12.23μl distilled water, 0.5μl 2.5mM dNTP’s, 2.0μl 10X Four published Y-chromosome primer pairs were selected . The toTaq buffer*, 1.6μl and 1.2μl 2.0mM MgCl2* for DBY3, DBY5 and DBY8, SMCY7 respectively and 0.17μl Taq DNA polymerase* (*Fermentas Taq DNA Polymerase – native). The PCR conditions were optimized and the resulting products confirmed, sequenced and processed following the same conditions and steps as for the mtDNA. Each of 12 samples from males were PCR amplified in a total reaction volume of 20μl consisting of 2.5μl, 10-100 ng/μl DNA, 0.5μl of 10μM each of forward and reverse primer, 12.23μl and 12.63μl distilled water for DBY3, DBY5 and DBY8, SMCY7 respectively, 0.5μl 2.5mM dNTP’s, 2.0μl 10X A/GATCT) restriction enzyme, based on [in silico digestion with BglII to test the above assumptions.To determine an appropriate balance between genomic coverage and read depth, we performed preliminary calculations for developing a reduced representation library using BglII  and thabased on ) relativbased on . With thhttp://www.ncbi.nlm.nih.gov/sra/?term=srp023544).We digested 0.5 μg each of ten DNA samples (liver) individually for 16 hours with BglII  according to manufacturer’s instructions. To remove the activated enzyme, samples were purified using the MinElute Reaction Cleanup kit  in two elutions. The second elution was visualized by gel electrophoresis to assess the quality of the digestion. Digested DNA samples were sent to the Science for Life Laboratories  for library construction and preparation. Fragments 100 to 700 bp were excised and blunt end repaired. Paired-end, multiplexed adapters were ligated to the fragments by sample and equimolar concentrations were measured and sequenced on one lane of Illumina HiSeq2000 resulting in 2x100 bp paired-end reads with insert sizes ranging from zero to 500 bp (mean 249.01 +- 130.06). Sequence data has been submitted to the NCBI Sequence Read Archive (SRA) under the study accession number SRP023544 (http://hannonlab.cshl.edu/fastx_toolkit/) and adapters removed with cutadapt . Reads v 0.9.3; ) with thv 0.9.3; ) with thThe results from Stacks were imported into a custom MySQL  database where, in combination with python scripts, putative SNPs were filtered to remove ones of low quality . First, E and HO), and using Genepop v. 4.2 [ST. SNPs were selected for the final SNP-chip (96 SNPs) if they were among those with the highest MAF. It is important to note that depending on time since divergence, ascertainment bias may affect the utility of these SNPs in brown bear populations outside of Scandinavia. These SNPs were analyzed for linkage disequilibrium with D’ statistics in the R packages genetics v 1.3.8 (http://cran.r-project.org/web/packages/genetics//index.html) and LDheatmap v 0.99 [Genotyped individuals were analyzed to determine both relatedness using the Graphical Representation of Relationship errors approach  and the p v. 4.2 ,43, Hardp v 0.99 . The 90 To determine how informative the final set of SNPs would be in assessing relatedness, we conducted two additional analyses. First, we incorporated all autosomal SNPs (n=87) and unrelated individuals n=50) and ran Structure –48 using and ran A total of 2015 and 1489 bp were sequenced in the mtDNA and the Y-chromosome respectively. Sequencing of the RRLs generated ~20 Gbp of data from nine samples. One sample failed to sequence for unknown reasons. After quality filtering and removal of reads not containing the restriction cut site, approximately 30 million paired reads (32%) remained. We suspect that the low retention rate is a result of unintended sequencing of degraded DNA as indicated by the gel visualization of the restriction digest. However, 82% of retained reads (unpaired) aligned to the draft genome suggesting that the sequence data used in downstream analysis was of high quality. This is promising for sequencing projects that are dependent on low quality DNA . in silico digestion of the draft genome  using tA total of 57 haploid SNPs were identified from the mtDNA sequences. Fifty-four of these separated the two major maternal haplotypes that distinguish the eastern European lineage from the western European lineage. We chose four of these mtDNA SNPs for lineage identification in the final SNP-chip.The Y-chromosome sequences showed no variable sites, concurring with the theory that mammalian Y-chromosomes have low levels of nucleotide diversity . While tA total of 1.4M stacks  were created with aligned reads both within and among individuals (n=9). Of these, 105k (14%) contained at least one putative SNP, although this is likely to be an overestimate of the true number of SNPs due to presence of sequencing errors. Mean read depth within each individual ranged from 3X to 8X and is likely to be underestimated due to duplicate stacks resulting from the use of stringent parameters. Although we estimated an expected read depth of 40X, only 32% of the sequence data generated was utilized for creating stacks and, as such, expected depth was reduced to approximately 12X per individual. Nevertheless, read depth was sufficient to reliably call SNPs as shown by our validation results below.After the initial filtering criteria  were applied, 4,612 putative SNPs remained. These SNPs were then further reduced to 1,162 after application of the additional filtering criteria using the draft genome assembly. Our final panel of putative SNPs included 144 nuclear SNPs, four mtDNA markers and two Y-chromosome sex-determination markers. We used a panel of 68 individuals from throughout Sweden  for validation. A total of 144 of the 150 SNPs (96%) produced good results. Of the six that failed, one gave no signal, one was monomorphic, and the remaining four did not pass the control checks. Since we ran two chips with 96 SNPs each, we effectively ran 56 of the SNPs twice as a control. In addition, we included both negative (water in place of DNA) and positive controls. The positive controls included duplication of some of the samples including those that were originally sequenced. The working SNPs passed all of the control checks and we did not detect a single error (error rate < 0.001). The four mitochondrial markers and two Y-chromosome markers segregated according to expectations. Since mitochondria are haploid, there is no possibility for heterozygotes to exist. As expected, scatterplots of these SNPs display only two distinct clusters each representing one of the two possible alleles . SimilarWhile it has been documented that there are several subpopulations within the northern population , analysiE and HO, HWD and FST   and that demonstrated a clear divergence of clusters in the scatterplot. These 96 SNPs were further analyzed for MAF (mean= 0.39), H and FST . After r and FST  revealedWe determined that three of the 96 SNPs most likely occur on the X-chromosome. In all three cases, all male samples (n=36) were homozygous for the same allele whereas female samples (n=32) were either homozygous or heterozygous. The chance of a Type I error  in inducing loci that are on the X-chromosome with 36 male samples and a MAF of 0.31 (our lowest MAF for X-chromosome SNP) is one in 535 million. We therefore feel confident in stating that these SNPs occur on the X-chromosome. These SNPs will be advantageous when determining parentage by allowing additional exclusion power in cases where alleles are not in concordance with putative parent-offspring pairs. The population structure analysis based on Structure –48 resulTo assess the performance of the SNP set to determine a minimum of first-order relatedness , we calculated the Lynch-Ritland  relatednWe present a new panel of 96 SNPs suitable for assaying the Scandinavian brown bear for relatedness and other ecological and evolutionary analyses. Through application of an NGS based RRL approach, we successfully reduced the computational power required to the extent that most analyses were performed on a standard-specification personal computer. This was made possible by eliminating sequences (within the limits of the study) that did not meet strict quality control  and avoiding the often problematic, computationally demanding and error-prone step of sequence assembly through the use of one restriction enzyme. While some applications require a greater number of SNPs, other applications may actually become disadvantaged by too much information. This is likely the case for relatedness studies and thus allowed us the freedom to rapidly decrease the amount of data we analyzed. This SNP-chip holds much promise for conservation of the Scandinavian brown bear, particularly for the southern population, which is one of the few relic western European populations. There are many potential uses for this SNP-chip including the use of relatedness estimates to monitor the genetic health, identify mating patterns and reproductive success, and track individual movements. It can also be useful for estimating population size based on individual identification, detecting hybridization events between the northern and southern populations, and confirming paternity in possible multiple paternity events or cases of infanticide. Table S1dbSNP submitted SNP (ss#) numbers and descriptive statistics for autosomal SNPs.(DOCX)Click here for additional data file.


Acropora millepora and Pocillopora damicornis (types α and β). In A. millepora, Single Nucleotide Polymorphisms (SNPs) were pre-selected by targeting genes believed to be involved in the coral thermal stress responses. In P. damicornis (type α and β), SNPs showing varying allele frequencies between two populations from distinct environments were pre-selected. Allele frequencies at nine, five and eight of the pre-selected SNP loci were correlated against gradients of water clarity and temperature in a large number of populations along the Great Barrier Reef.Knowledge of genetic markers that are correlated to stress tolerance may improve spatial mapping of reef vulnerability and can inform restoration efforts, including the choice of genotypes for breeding and reseeding. In this manuscript we present two methods for screening transcriptome data for candidate genetic markers in two reef building corals, P. damicornis, SNP allele frequencies of β-hexosaminidase and Elongation factor 1-α were significantly correlated to temperature in type α and to temperature and/or water clarity respectively in type β. Type α also showed a correlation between water clarity and SNP allele frequency in a gene of unknown function. In A. millepora, allele frequencies at five  SNP loci showed significant correlations.A significant correlation between environmental category and SNP allele frequency was detected in up to 55% of the tested loci, which is an exceptional success rate for these types of tests. In After validation of these candidate loci through laboratory or field assessment of relative stress tolerance of colonies harbouring different alleles, it is anticipated that a proportion of these markers may represent the first coral candidate Quantitative Trait Loci for environmental stress tolerance and provide an important genetic tool that can be incorporated into spatial management decisions and restoration efforts of coral reefs. One pertinent example would be to combine spatial data of tolerant populations with genetic connectivity and thus identify high priority conservation reefs and implement targeted coral husbandry and active restoration efforts that use locally- and stress-adapted genotypes. Current models of the Earth’s climate over the coming decades predict rapid changes in a range of parameters including temperature, rainfall, and ocean pH . Given tST outliers) [There are numerous approaches to detect adaptive genetic variation and signatures of local adaptation (reviewed in ). In nonutliers) -14, elucutliers) , and assutliers) ,17. To dutliers) -20.Fundulus heteroclitus, where the gene Lactate dehydrogenase-B shows differences in allele frequencies between populations from distinct thermal environments [Dopamine receptor D4 and personality in great tits (Parus major) [Melancortin-1 receptor gene (Mc1r) across several species, including moth, mice and humans [Adaptive genetic variation and its link to phenotypic traits has been widely studied in forestry (reviewed in . Howeverronments . This vas major) , and thed humans . In the d humans . The disSymbiodinium) [The Great Barrier Reef (GBR) is the largest coral reef ecosystem in the world. It spans over 2300 km along the East Coast of Australia and encompasses almost 3000 reefs . Like moodinium)  and thriodinium) ,26 and dodinium) ,28.Symbiodinium and the coral host tissue [Symbiodinium. In the coral Acropora millepora, thermal stress tolerance has been shown to be correlated with ambient water temperatures in three thermally distinct populations [Symbiodinium type harboured [Pocillopora damicornis, laboratory studies have shown a latitudinal difference in bleaching sensitivity that was not correlated to the type of Symbiodinium harboured, indicative of localised adaptation of the coral host to varying thermal regimes [The most commonly reported thermal stress response in corals is coral bleaching, which is the dissociation between the t tissue ,29-34. Vulations . While tarboured , patternarboured , suggest regimes .Acropora millepora has emerged as one of the model species for coral genetics and genomics. The species has been the target of multiple experiments on transcriptome effects of environmental stress [Acropora digitifera[A. millepora . However, the concept of identifying loci, which can be used as markers for relative stress tolerance, remains untested in corals.The first studies of gene expression in corals emerged around the turn of the century . Since tl stress ,43,46-50l stress ,52, incligitifera and A. mThis paper presents the first putative genetic markers for environmental stress tolerance in corals. The genetic markers were identified using two novel transcriptome-based strategies. Both strategies make use of next generation sequencing and large scale Single Nucleotide Polymorphism (SNP) genotyping across environmental gradients and build on the concept of identifying GEAs . These sTwo strategies were followed to identify SNPs that show a significant correlation with environmental gradients, which differ in their use of newly generated versus already available transcriptome data. The strategies are not compared with each other for efficiency, rather presented as two alternative approaches to generate a similar outcome.The first strategy involved three steps: (1) generate population-tagged transcriptome data from a small number of different environmental zones; (2) mine transcriptome data for SNPs that show a distinct difference in allele frequency between the targeted populations; (3) test the selected SNPs on population samples from a more extensive range of the environmental variable. In the second strategy, step (1) was excluded and step (2) was modified to mining already published transcriptome data for SNPs in pre-selected genes; step (3) was the same in both strategies. Both strategies generated genotypic data across a large number of populations from which allele frequencies were calculated. Correlative methods were used to detect significant genome environment associations (GEAs) as per below.Pocillopora damicornis were collected from Miall Island in the southern GBR (Keppel Islands), Pelorus Island in the central GBR , and two sites in the far northern GBR, Wallace Islet and Wilke Reef  from Wilke and Pelorus Island reefs were used, respectively. These samples were pooled into two population specific samples. For P. damicornis (type α), mRNA from three Wallace Islet and two Miall Island samples were pooled into a third sample and included in the 454 analysis. The final sequencing was done for three pooled mRNA samples using the 454® Genome Sequencer FLX Titanium Series reagents according to the manufacturer’s instructions (Roche Diagnostics Corporation) by the Australian Genome Research Facility (AGRF).Messenger RNA was isolated using a modified protocol of the Invitrogen™ Dynabeads® Oligo(dT)25. Two exthttp://www.clontech.com/) using the SmartScreener script from within PipeMeta and then reads and quality scores were trimmed and quality filtered . The remaining 653,814 reads and quality scores  were then tagged (i.e. α-numeric strings added to titles) by population to differentiate them after assembly. The sample containing the partial transcriptome of P. damicornis (type α) was not included in the final assembly, which was done using the sequences from P. damicornis (type β) only.The 947,623 resulting sequences and quality scores were analysed using PipeMeta, a menu based, desktop interface pipeline toolset for Windows ,60. Sequin silico predicted proteins from the starlet sea anemone Nematostella vectensis[Acropora digitifera[P. damicornis (type β) with sequenced and publically available genomes. In order to better estimate transcriptome coverage of the sequencing effort, both sets of predicted genes were modified to remove shorter protein sequences with greater than 90% similarity to longer sequences in the set.Assembly of the timed reads and quality scores (fasta files) was performed using commercially available assembly software . To further assess the performance of the assembly, all resulting unigenes (Contigs and Singletons over 50 bp) were Blast aligned to a modified set of vectensis and Acroigitifera, the twoP. damicornis (type β) unigenes were Blast aligned to the Uniprot protein database (http://www.expasy.uniprot.org/database/download.shtml) to annotate the assembly.All P. damicornis (type β) sequence data were searched for functional SNPs using the SNP hunter extension of the PipeMeta software , a tool specifically designed to find SNPs in de novo transcriptome assemblies using a variety of customizable settings and filters. About 24,000 SNP candidates were located within the partial de novo transcriptome assembly and screened to identify potential biomarkers.The Symbiodinium sequences). Second, all SNPs that did not have a frequency of > 10% in at least one of the two populations were removed to minimize false positives due to sequencing errors. After this, a search was made for bi-allelic SNPs for which allele frequencies differed by > 20% between the two populations . Based on these criteria, a final list of 80 SNPs distributed among 67 contigs was created.The candidate SNPS were narrowed down by applying the following filters. First, all non-annotated contigs were removed, followed by any contigs that did not align to ESTs or genes of known function that were derived from other metazoans (to avoid http://frodo.wi.mit.edu/primer3/) to amplify a 75 – 150 base pair PCR product. PCR amplification was performed using DNA extracts from three colony samples of P. damicornis (type β) and the product was visualized on a 1% TAE-agarose gel. Any primer pair that produced products that were longer than predicted and hence possibly included intron sequences, yielded inconsistent amplification success or displayed any other signs of poor quality, were discarded. A final selection of eight SNPs was made to use on the larger, GBR wide sample set of P. damicornis (type β) (Table P. damicornis (type α).Primers for the 80 SNPs were designed using Primer 3  to category 9 (21.7 – 28 m).These datasets divide the environmental range into seven temperature and nine secchi disc depth categories, where seven is the highest temperature and nine is the deepest secchi disc depth. All temperature zones were included in the collections. Due to the photosynthetic properties of the coral-P. damicornis and A. millepora collected for GBR connectivity studies [P. damicornis[Existing samples of  studies  were useamicornis all sampP. damicornis (type β) were chosen based on having a sample size of ≥ 15 colonies. The larger sample collection of P. damicornis (type α) allowed a more comprehensive selection of populations to be made for this species. Ten populations with a sample size of ≥20 colonies, spanning a broader range of thermal categories and at least two replicates of each water clarity category, were chosen for the environmental correlation analyses  and (type α), respectively, against the two environmental categories, making a total of 26 tests. For A. millepora, 9 SNP loci were tested against the same environmental categories  across 17 populations, all with sample sizes of ≥ 20   in the following manner: The PCR step was set up in 10 μL reactions incorporating Accumelt HRM supermix (Quanta Biosciences©). Each sample was set up in triplicate with a primer concentration of between 200 nM and 500 nM, and 1 μl of 1/50 diluted gDNA. No template (negative) and known genotype (positive) controls were included in all runs. PCR and HRM were carried out under the conditions recommended by the instrument manufacturer. PCR commenced with an initial 5 minute hold at 95°C followed by 40 cycles of a 10 second, 95°C denaturation and 30 second, 55°C annealing and extension. For the HRM analysis the temperature was ramped from 65°-95°C, rising by 0.1°C each step with a 90 second pre-melt conditioning cycle on the first step and a 2 second wait every cycle thereafter. Prior to HRM, the gain was optimised to set the greatest fluorescence to less than 90 fluorescence units.DNA was extracted from a small fragment of coral, following a slightly modified method by Wilson et al. . For botControl samples of identified genotypes were included in each PCR /HRM run. CT values and amplification completeness were checked using the Rotorgene Q series software V2.02 and samples showing late or poor amplification were excluded. HRM profiles were normalised against average signatures within a pre-selected temperature range. The control samples were used as references to automatically determine sample genotypes. All genotype calls were checked manually, and confirmed by further analysis using ScreenClust (Qiagen) software.A. millepora, genotyping was conducted at the AGRF using a Sequenom® MassArray on an Autoflex Spectrometer using iPLEX GOLD chemistry.For Correlations between environmental variables and SNP allele frequency were tested by using a logistic regression with a logit link and a binomial error distribution to relate the probability of allele 1 in each locus to the two environmental variables, temperature and secchi depth . The likA. millepora microsatellite data set from the same coral samples [To test for the proportion of random correlations, 159 alleles from 11 putatively neutral microsatellite loci were also tested for these environment correlations using the already published  samples .After preliminary machine quality filtering, 947,623 sequences were obtained. Additional primer screening and quality filtering through PipeMeta removed 27.4% of all sequenced nucleotides. The remaining 653,814 reads were used in the assembly. Roughly 67% of the trimmed, filtered reads (64% of the sequenced nucleotides) assembled to form 87,520 contigs with an average length of 584 (±296) base pairs (bp), with the 244,541 remaining singletons averaging 304 (±85) bp in length (Table Nematostella vectensis and Acropora digitifera modified in silico predicted proteins). Out of these, 10,021 (23.4%) and 11,148 (52.4%) were unique hits to N. vectensis and A. digitifera, respectively. Using a conservative approach to estimate transcriptome coverage length, approximately 16.0% of N. vectensis total amino acids and 25.3% of the A. digitifera total amino acids were covered  by P. damicornis (type β) unigene sequence, with median coverage rates of 35.6% and 38.7% per predicted gene with a strong alignment for N. vectensis and A. digitifera proteins. Less than 6.8% (N. vectensis) or 10.2% (A. digitifera) of the above reference gene amino acid coverage was from more than one P. damicornis (type β) unigene (i.e. duplicate or overlapping amino acid coverage by multiple unigenes).To further assess the quality and coverage achieved by the assembly, the unigenes were BLAST aligned to two reference gene sets from closely related species with sequenced genomes  for 54,094 or 61.8% of the Contigs, out of which 44.5% had top alignments to proteins expressed in metazoans , 15.2% aligned to plant, 8.4% aligned to bacteria, and 0.3% aligned to virus  only one was a transversion (24_CA_2390). This SNP occurred at Night and Tydeman reefs with the rare allele (A) occurring at a frequency of 18.1% and 7.6% respectively. In the original transcriptome data, this allele occurred at Wilke Islet reef, which is located in the same geographic region as Night and Tydeman  was based on the allele frequencies at eight SNP loci. In P. damicornis (type α), five loci were amplified using the primers developed for P. damicornis (type B) (Table P. damicornis (type α) due to ambiguous scoring patterns of the HRM curves in this species  Table . The othP. damicornis type α or type β (Table P. damicornis (type β) and three out of five (60%) were significant in P. damicornis (type α). These correlations were found between temperature and SNP allele frequency in β-hexosamidase in P. damicornis (type α) and for water clarity in P. damicornis (type β). Temperature and allele frequency was also significantly correlated to SNP allele frequency in Elongation factor 1-α_2631 for type α and β as well as a gene of unknown function (Contig_24_847) in P. damicornis (type β). P. damicornis (type β) also showed a correlation between water clarity and SNP allele frequencies in Elongation factor 1-α_2631  was found to be monomorphic and excluded from further analyses. The remaining eight SNPs were successfully genotyped in the majority of the colony samples. Allele frequencies in five (55%) of these loci showed a significant correlation with environmental gradients. β-gamma crystallin, Galaxin and Ubiquitin were correlated to temperature and Thioredoxin and Ligand of Numb X2 were found to correlate to water clarity  were found to have significant correlations purely by chance  for environmental stress tolerance through subsequent correlative tests of phenotypic response. Both methods revealed likely signatures of selection in up to 55% of the loci tested. This is a considerable improvement compared to the previously reported 2-10% in non-targeted, genome-wide scans for outliers .Acropora millepora, Pocillopora damicornis (type α) and Pocillopora damicornis (type β), respectively. These represent the first genetic markers in corals for environmental stress tolerance. After validation of these candidate loci, i.e., through laboratory or field assessment of relative stress tolerance of colonies harbouring different alleles at these loci, it is anticipated that a proportion of these loci can be used as de facto QTLs.Of a small subset of SNPs screened across a large spatial scale on the Great Barrier Reef (GBR), four, two and three SNP(s) were found to be correlated to either temperature or water clarity in the reef-building coral species A. millepora provided an estimated the occurrence of false positives to be a maximum of ~10%.The risk of confounding effects  causing false positives as discussed in Bierne et al.  is reducA. millepora and at least one (EF-1-α ) of the three significant SNPs in P. damicornis were non-synonymous mutations , indicative of the existence of adaptive variation, and have been correlated to variations in larval responses to elevated temperatures and settlement cues [. The protein this genes encodes was originally characterised from the coral Galaxea fascicularis and is a soluble protein from the coral exoskeleton, where it is involved in calcification [A. millepora. The two amgalaxin like proteins are expressed exclusively during the early stages of calcification in the newly settled coral polyp, while amgalxin is continuously expressed also in the adult colony [Porites lobata, with corals from environmentally variable back reef sites showing consistently higher levels, regardless of transplant location [Ligand of numb X2 and Thioredoxin genes correlated to water clarity. Ligand of numb X2 is a close homologue to the Ligand of numb X, which is an important ligase for ubiquitin [All the SNPs that were selected in t one EF--α  of ths Tables , hence aent cues . Anotherfication . Three gt colony . Recent t colony , supportt colony . Thus, mt colony . Differelocation , which abiquitin  while thbiquitin . Furtherbiquitin , indicatP. damicornis, two genes of known function were found to have significant correlations with temperature and/or water clarity, namely EF 1-α and β-hexosaminidase. Apart from its role in the process of cellular binding of actin [EF 1-α has been shown to be significantly down-regulated during cancer cell senescence [In of actin , EF 1-α nescence  and durinescence . Hence, nescence , which snescence -86, hencF. heteroclitus[in situ observations.Although phenotypic responses are rarely determined by a single locus, but rather by the cumulative small effects of multiple loci , it has eroclitus. The useeroclitus. The linDeveloping and publishing efficient methods to screen genomes and transcriptomes for candidate genetic markers for stress tolerance remains an important scientific endeavour, and the development of temperature and water clarity-associated genetic markers in corals is both timely and relevant. Climate change does not only mean increasing sea surface temperatures, it is also predicted to come with an increasing occurrence of extreme weather events , includiP. damicornis samples. All authors read and approved the final manuscript.The manuscript is the outcome of the postdoctoral research of PL, who conceptualised the idea and acquired funding in collaboration with MvO, collected field material, conducted a majority of the pre-transcription laboratory work, was involved in the bioinformatics and subsequent genotyping work and did the majority of the manuscript writing. In addition to the above conceptualisation and fund acquisition, MvO provided core funds and supervision. JCV did the majority of the bioinformatics on the transcriptomes, SM provided insights and support on the statistical analyses, wrote the scripts for the logistic regression analyses and interpreted the results and LP did a large part of the HRM analyses for the Examples of good and ambiguous HRM melt curves.Click here for filePocillopora damicornis.SNP allele frequencies in each population of the two types of Click here for fileAcropora millepora.SNP allele frequencies in each population of Click here for fileAcropora millepora that showed a significant correlation, presumably by chance.Microsatellite alleles from the tested colonies and populations Click here for file


Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small. Genomic estimated breeding values (GEBVs) are at the core of genomic selection. The GEBV is calculated as the sum of all SNP effects; the estimation of SNP effects therefore plays an important role in genomic selection. In the SNP genotype data obtained from the SNP chip technique, missing genotype information is a common phenomenon that leads to a low call rate for some SNPs and for some animals. The routine data quality control procedure in genomic selection eliminates SNPs and animals with low call rates from the data sets, resulting in the loss of information and a decrease in the accuracy of the GEBV. Imputation can be used to deduce the missing genotypes and could be helpful in increasing the accuracy of genomic selection. Imputation also allows for the use of low-density chips that may be more cost-effective, facilitating the widespread implementation of whole-genome selection [Genomic selection is becoming prevalent and practicable in dairy cattle breeding, where genomic breeding values of animals are estimated using high density single nucleotide polymorphisms (SNPs) and are the basis for the selection of elite animals . Genomicelection ,6.Several imputation methods have been proposed and are implemented in programs like fastPHASE , Beagle In genomic selection, quality control of the SNP data is a necessary step before the SNP effects are estimated ,11. Two Bos taurus autosomes and the × chromosome using the Illumina Bovine SNP50TM chip . The 2, 093 cows formed the reference population. Their 13 sires were used for imputation of the missing SNP genotypes. The average number of daughters per sire was 161; the range was from 83 to 358. The cows came from 14 Holstein cattle farms in Beijing, China, where regular and standard performance testing  has been conducted since 1999 [A total of 2, 180 Chinese Holstein Cattle  born between 2001 and 2006 were genotyped for 54, 001 SNP markers distributed across the 29 nce 1999 . The remTwo imputation methods were implemented: 1) the findhap program v1  was usedThree criteria of minor allele frequency (MAF) of SNPs were considered, > 3% (QC3%), > 5% (QC5%) and > 0% (QC0), for marker filtration. For all of the three criteria, the SNP call rate and individual call rates were required to be more than 90%.To study the order of imputation and quality control, six strategies were designed . All marker effects i gwere also assumed to be normally distributed, i ~ N g. The marker effects were assumed to be zero with a probability of π or non zero with a variance that followed a scaled inverse chi-square distribution with a probability of (1 - π) [where In this study, the prior probability π was assumed to be 0.95 based on the results of Zhang et al. , BayesB The GEBVs for the bulls in the validation population were calculated as the sum of all estimated SNP effects according to their marker genotypes . The accThe original SNP dataset for the reference population consisted of the genotypes of 54, 001 SNPs for the 2, 093 cows. The total proportion of missing genotypes in this population was 1.92%. The call rates for the SNPs ranged from 0% to 100% with an average of 97.98%. The call rates for the animals ranged from 70.27% to 99.43% with an average of 98.05%; one cow for which the call rate was extremely low (43.52%) was discarded from the dataset. The proportions of animals and SNPs with call rate less than 90% were 3.01% and 2.18%, respectively. The proportions of SNPs with MAFs less than 0.03 and less than 0.05 were 17.23% and 20.53%, respectively. Of the SNPs with call rates less than 90% (average 67.83%), the average MAF was 0.165 and the proportions with MAFs of less than 0.03 and less than 0.05 were 26.06% and 35.89%, respectively. For the SNPs with call rates greater than 90% (average 98.77%), the average MAF was 0.228 and the proportions with MAFs less than 0.03 and less than 0.05 were 17.03% and 20.18%, respectively. This result indicates that SNPs with low call rates tend to have low MAFs. After data editing using the six imputation and quality control strategies, six different SNP datasets were generated  and the same accuracies of the GEBVs. The likely reason for the high similarity of the two datasets after imputation is that the missing SNP rate in the original dataset was relatively small (1.92%) and the majority of the missing genotypes were probably real heterozygotes. Indeed, the two datasets shared 99.13% identical SNP genotypes in each individual. When the missing SNP rate is high, based on our result, it is reasonable to suppose that Impute A would result in a higher accuracy for the GEBVs because this method imputes the missing genotypes more accurately.In this study, two criteria for MAF, > 3% and > 5% were applied for quality control either after or before imputation. Obviously, with the stricter criterion for MAF ( > 5%) more SNPs would be excluded from the dataset. Our results showed that, when imputing before quality control, the dataset with MAF > 3% contained 1, 362 more SNPs than the dataset with MAF > 5%; when imputing after quality control, the dataset with MAF > 3% contained 1, 615 more SNPs than the dataset with MAF > 5%. This difference is because when imputing before quality control, the MAF of the SNPs would increase along with the increased call rate as a result of imputation, so that some SNPs with MAF < 5% in the original dataset become > 5% after imputation. Our results show that, when imputing before quality control, there is no difference in the accuracies of the predicted GEBVs from datasets with MAF > 3% and MAF > 5% for all traits; when imputing after quality control, the accuracies from the dataset with MAF > 3% are slightly higher than those from the dataset with MAF > 5%. This observation suggests that using MAF > 3% is more appropriate than using MAF > 5%.In conclusion, we found that conducting imputation before quality control could be a useful strategy to increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small.The authors declare that they have no competing interests.ZW carried out the design of the study, participated in the statistical analysis and drafted the manuscript. ZZ carried out the statistical analysis. WF participated in the design of the study and statistical analysis. PM and CW participated in the statistical analysis. QZ and XD conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.


Sorghum bicolor L. Moench) accessions were subjected to short-read genome sequencing to characterize the distribution of single-nucleotide polymorphisms (SNPs). Two strategies were used for DNA library preparation. Missing SNP genotype data were imputed by local haplotype comparison. The effect of library type and genomic diversity on SNP discovery and imputation are evaluated.Eight diverse sorghum  to the public reference genome revealed 283,000 SNPs at ≥82% confirmation probability. Sequencing from libraries constructed to limit sequencing to start at defined restriction sites led to genotyping 10-fold more SNPs in all 8 accessions, and correctly imputing 11% more missing data, than from semirandom libraries. The SNP yield advantage of the reduced-representation method was less than expected, since up to one fifth of reads started at noncanonical restriction sites and up to one third of restriction sites predicted BsrFI library would conservatively provide satisfactory genotyping of 96,000 sorghum SNPs. For most reliable SNP-genotype imputation in shallowly sequenced genomes, germplasm panels should consist of pairs or groups of genomically similar entries. These results may help in designing strategies for economical genotyping-by-sequencing of large numbers of plant accessions.A sequence quantity of 3 million 50-base reads per accession using a  Sorghum bicolor L. Moench) presents one of the more attractive targets for rapid genetic improvement: high abiotic-stress tolerance, the efficient C4 photosynthesis pathway, high genetic diversity, and an available genome sequence. A grass belonging to the Andropogonaceae, sorghum ranks fifth among world crops in weight of grain produced ) followed by heat inactivation at 80°C for 20 min. Samples were phenol:chloroform extracted, EtOH precipitated and resuspended in 15 μL Qiagen buffer EB. For RAD_B, 2.0 μL of 10 nM P1 , a modified Solexa© adapter  containing a unique MID (bar code) of 5 bases for RAD_B and 4 for RAD_P, were added to each sample along with 2 μL 10X T4 DNA ligase buffer  and 1.0 μL (600 U) T4 DNA ligase enzyme  and incubated at room temperature (RT) for 20 min. Samples were again heat-inactivated for 10 min at 65°C, randomly sheared with a Bioruptor (Diagenode) to an average size of 500 bp, and run out on a 1.5% agarose (Sigma), 0.5 × TBE gel. DNA 300 bp to 700 bp was isolated using a MinElute Gel Extraction Kit (Qiagen). T4 polymerase  was then used to polish the ends of the DNA. Samples were then purified using a Minelute column (Qiagen) and 15 U of Klenow exo- (Enzymatics) was used to add adenine (Fermentas) overhangs on the 3' end of the DNA at 37°C. After subsequent purification, 1 μL of 10 μM P2, a divergent modified Solexa adapter, was ligated to the obtained DNA fragments at 18°C. Samples were again purified and eluted in 12 μL. The eluate was quantified using a Qubit fluorimeter and 5 ng of this product was used in a PCR amplification with 25 μL Phusion Master Mix (NEB), 2.5 μL of 10 μM modified Solexa Amplification primer mix and up to 20.5 μL H2O. Phusion PCR settings followed product guidelines (NEB) for a total of 18 (13 for RAD_P) cycles. Samples were gel purified, excising DNA 300-600 bp, and diluted to 10 nM. Four accessions in each RAD_P library and two in each RAD_B library were pooled for sequencing. The three sets of libraries were sequenced by Illumina sequencing-by-synthesis technology with read lengths ranging from 36 to 76. From the RL, 54% of the sequence was from Sb2.Semirandom (SR) libraries were prepared by a commercial sequencing center from 3 μg of DNA digested with tocol of  with theribed by . Brieflyin silico with each of ten methylation-sensitive enzymes. Reads from both strands at each site were extracted and realigned with the genome and the proportion finding a unique best alignment recorded. Fragment sizes were tabulated. For the two enzymes then used to make RAD libraries, the correspondences between the unique-alignment rate and the fragment size were tabulated.In the process of selection of an optimal enzyme, the reference genome was digested http://www.novocraft.com) was used for alignment followed by application of SAMtools (http://samtools.sourceforge.net/) to identify candidate SNPs and indels. Initial parameter values were: minimum coverage depth 3, minimum SNP quality 20, minimum indel quality 50, with other parameters such as window size and nearby SNP and gap density left at their defaults. Average read coverage was computed as total bases sequenced divided by genomic bases covered by at least one read.Reads were separated by bar code if applicable and trimmed by 4 bases at 3' ends. Reads with average sequence quality at least 25 were retained. Two methods were used to identify candidate SNPs: 1) SOAP v2  was usedSb2 (BTx430), 70 fragments comprising more than 35 kb of DNA and showing the highest density of candidate SNPs were selected. This validation sequence contained 1106 SOAP-called unfiltered mismatches, 121 SNPs called by the simple filter, and 148 SNPs called by NovoAlign based on the parameters described above. PCR primers were designed to yield products of length 550 to 650 bases for Sanger sequencing and target sequence fragments were amplified from BTx430 and sequenced in both directions. Fragments were assembled with PolyPhred [From reads of http://www.phytozome.net/sorghum). SNPs were classified as synonymous or nonsynonymous by determination of their position in the CDS of genes followed by examination of the effect of codon change on amino-acid identity. Large-effect SNPs were defined as SNPs lying in exons or in splice sites and expected to lead to alteration of protein structure via a shift between sense and nonsense codons or splicing disruption. Large-effect SNP density was calculated for each PFAM protein family for whose members more than 100 kb of the genic region was covered by reads.SNPs were assigned to genomic feature groups and gene classes according to the sorghum genome annotation  sequenced in common in the two accessions.For a proportion statistic useful for comparison with genotype-imputation accuracy, pairwise similarity was calculated as the sum, over the Missing SNP genotypes were imputed by a method adapted from . This enSb2 and Sb8 were randomly resampled in increments of 1 million reads. To determine the effect of this sampling on imputation accuracy, SNPs were progressively sampled from the entire set based on the proportions predicted by the SNP yield curves and their imputation statistics were tabulated.In an effort to determine the RAD sequencing coverage required for a specified level of SNP genotyping yield, RAD_B reads for The authors declare that they have no competing interests.JCN, FFW, and JY conceived the study and participated in its design and coordination. JCN and SW performed all analyses. GA, YW, and XL assisted with DNA extraction and confirmation sequencing. JCN wrote the paper, with contributions from JY, FFW, and SW. All authors read and approved the final manuscript.Cumulative restriction-site coverage from two RAD sequencing libraries in sorghum. Distinct sites represented at either of two coverage depths were counted for successive samples of randomly ordered reads from all eight accessions.Click here for fileConfirmation proportions of candidate sorghum SNPs relative to SNP quality score. Source data are a set of 70 sequences in accession BTx430 containing 156 SNP candidates with Novoalign quality score at least 20. The figure projects on the entire SNP set the modest improvement in confirmation rate but sharp decline in available SNPs associated with increased SNP-calling stringency.Click here for fileGenotype distributions of SNPs from all sorghum sequencing libraries. RR, AA, AR: reference- and alternative-allele homozygotes and heterozygote.Click here for fileAccession coverage frequencies of indels for three sorghum sequencing libraries. Describes the allele frequencies of these polymorphisms taken over all accessions; compare with Figure 6a for individual accessions. Plot for full SNP set is included to show functional similarity. Log plot is used to show approximately exponential form of allele distributions.Click here for fileDistribution of genomic features on chromosomes. Features were counted in windows of length L kb at 10-kb offsets, where L was 5000 for SNPs, 500 for large-effect SNPs, and 100 for repetitive DNA content.Click here for fileSNP and indel density by genomic feature, for all three sorghum sequencing libraries combined. Only polymorphisms in reads from canonical restriction sites are included.Click here for fileIndel length distribution by genomic feature, all libraries. Only indels in reads from canonical restriction sites were used; others show the same trend but lower densities.Click here for fileLarge-effect SNP assignment to gene families. Density of large-effect (LE) SNPs (see text for definition) for all gene families with more than 100 kb of read coverage by the RAD_B library. Shown in the figure are the twenty families with highest density of LE SNPs.Click here for filePairwise comparison of chromosome-1 SNP haplotypes for eight sorghum accessions. Each main numbered horizontal plot represents one accession. Its seven minor plots represent pairwise genotype comparison of called SNPs with those in the other seven accessions. Only matches of the reference homozygote are shown. The color coding indicates windows of 50 SNPs with at least 30, 50, or 80% of genotypes in common between the two accessions (coded as S in the legend). Accessions 1-8 are BTx623, BTx430, P898012, Segaolane, SC35, SC265, PI653737 (S. propinquum), and 12-26 (S. bicolor ssp. verticilliflorum). Scale at bottom indicates physical positions of SNPs on chromosome.Click here for filePairwise comparison of chromosome-2 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-3 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-4 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-5 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-6 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-7 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-8 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-9 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for filePairwise comparison of chromosome-10 SNP haplotypes for eight sorghum accessions. Description is as for Additional file Click here for fileData completeness and imputation accuracy for SNPs from three sorghum sequencing libraries. Missing-data proportion for a library is calculated with respect to all SNPs identified in reads from the library.Click here for fileDependence of SNP genotype imputation accuracy in a sorghum accession on its maximum similarity to any of the remaining seven accessions. Pairwise similarity was defined on the basis of all SNPs assayed in the panel, as described in the text.Click here for file