Glycine max L. Merr.). A total of 498,921,777 reads 35–45bp in length were obtained from DNA sequence analysis of reduced representation libraries from several soybean accessions which included six cultivated and two wild soybean (G. soja Sieb. et Zucc.) genotypes. These reads were mapped to the soybean whole genome sequence and 209,903 SNPs were identified. After applying several filters, a total of 146,161 of the 209,903 SNPs were determined to be ideal candidates for Illumina Infinium II BeadChip design. To equalize the distance between selected SNPs, increase assay success rate, and minimize the number of SNPs with low minor allele frequency, an iteration algorithm based on a selection index was developed and used to select 60,800 SNPs for Infinium BeadChip design. Of the 60,800 SNPs, 50,701 were targeted to euchromatic regions and 10,000 to heterochromatic regions of the 20 soybean chromosomes. In addition, 99 SNPs were targeted to unanchored sequence scaffolds. Of the 60,800 SNPs, a total of 52,041 passed Illumina’s manufacturing phase to produce the SoySNP50K iSelect BeadChip. Validation of the SoySNP50K chip with 96 landrace genotypes, 96 elite cultivars and 96 wild soybean accessions showed that 47,337 SNPs were polymorphic and generated successful SNP allele calls. In addition, 40,841 of the 47,337 SNPs (86%) had minor allele frequencies ≥10% among the landraces, elite cultivars and the wild soybean accessions. A total of 620 and 42 candidate regions which may be associated with domestication and recent selection were identified, respectively. The SoySNP50K iSelect SNP beadchip will be a powerful tool for characterizing soybean genetic diversity and linkage disequilibrium, and for constructing high resolution linkage maps to improve the soybean whole genome sequence assembly.The objective of this research was to identify single nucleotide polymorphisms (SNPs) and to develop an Illumina Infinium BeadChip that contained over 50,000 SNPs from soybean ( Glycine max (L). Merr.] genome, a large number of molecular markers are required to cover the genome for the purpose of genome-wide genetic analysis. Single nucleotide polymorphisms (SNPs) are the most abundant form of DNA polymorphism in eukaryotic genomes http://www.illumina.com/). Thus, the objective of this research was to efficiently identify large numbers of SNPs in soybean using advanced sequencing technologies and SNP identification pipelines; to develop a custom high resolution Illumina Infinium BeadChip containing SNPs with high minor allele frequency, even distribution across the euchromatic and heterochromatic regions of the soybean genome; to evaluate the performance of the chip in a set of diverse landrace and elite soybean genotypes as well as in a diverse set of wild soybean [G. soja (Sieb. et Zucc.)] genotypes and to identify candidate regions that may be associated with domestication and recent selection imposed by plant breeding.Even with the extensive linkage disequilibrium (LD) in the soybean [The total number of short reads and total bases obtained from the eight genotypes were 498,921,777 and 18,313 Mb, respectively and the total bases obtained from the eight genotypes ranged from 277 Mb (0.3×) to 6,473 Mb (6.8×) . FollowiOf the 209,903 SNPs, a total of 28,891 were A vs. T or G vs. C SNPs and thus, the remaining 188,272 SNPs were a potential source for Infinium II assay . After eliminating SNPs with N’s or ambiguity codes in the 60 nt of flanking sequence and SNPs residing within 25 nt of another SNP, a total of 150,171 of the 188,272 SNPs for Infinium II assay remained. After elimination of the SNPs with flanking sequences that were not unique in the genome, a total of 146,161 SNPs, including 785 SNPs from unanchored scaffolds, was retained and these SNPs were ideal candidates for Infinium II assay design.i) for the corresponding priority groups for calculation of the SNP selection index.Of the 146,161 SNPs, a total of 23,721, 50,229, 58,281 and 13,930 were assigned to priority groups of A, B, C, and D, respectively. Initial validation of 767 SNPs via Sanger sequencing gave validation rates of 100%, 83%, 77% and 67% for the SNPs with the A, B, C, and D scores, respectively . These rBecause Illumina only allowed 60,800 SNPs in the bead pool at the time the chip was developed, a total of 60,701 SNPs were selected from the 20 chromosomes (Gm01–Gm20) and 99 SNPs were selected from 71 unanchored sequence scaffolds . Of the After SNP selection, the SNP selection index score of the 60,800 selected SNPs was increased versus the source set of 146,161 SNPs. The average SNP selection index score of the selected SNPs was 0.69 vs. 0.60 for the complete set of 146,161 SNPs. Of the 146,161 SNPs, 73,084 were located in euchromatic DNA and 72,272 were located in heterochromatic regions. A total of 50,701 SNPs were selected from the euchromatic regions and 10,000 were selected from the heterochromatic regions . In the A total of 16,381 (27%) of the 60,800 SNPs resided in genes and, of these, 6,820 SNPs were located in coding DNA sequence, 8,103 SNPs in introns, 752 SNPs in 5′untranslated regions and 706 SNPs in 3′UTR . Within Of the 60,800 SNPs submitted to Illumina for inclusion in the SNP assay, 52,041 SNPs, or 86% of the 60,800 passed Illumina manufacturing quality control and were included in the final bead pool of the SoySNP50K Infinium BeadChip. Validation of the SoySNP50K Infinium BeadChip was performed via the analysis of diverse germplasm including 96 landraces, 96 elite cultivars and 96 wild soybean accessions. Of the 52,041 SNPs, 47,337 (91%) were polymorphic and yielded successful SNP allele calls based upon clear separation between the fluorescent signals from the two homozygous genotypic classes for a given SNP as assessed with the normalized theta analysis of the Illumina GenomeStudio SNP analysis software.The rate of SNPs producing successful SNP allele calls was associated with the SNP selection index score, the Illumina Infinium SNP design score and the priority group . The corOf the 47,337 SNP assays that produced successful allele calls, 40,841 (86%) had a minor allele frequency ≥10% when tested on the 96 landraces, 96 elite cultivars and 96 wild soybeans. Within the individual groups, the landraces had the most SNPs with a minor allele frequency ≥10% while the elite cultivars had the fewest SNPs with a minor allele frequency ≥10% . Only 3,Genetic distances of 47.6% of the pairs of genotypes among the elite cultivars, 68.1% among the landraces and 48.1% among the wild accessions were greater than 0.3 based on the SNPs with minor allele frequency (MAF) >0.05 . The meast≥0.6) between G. soja and landrace and between landrace and elite populations, respectively , contained loci with high average fixation indices  Helianthus annuus L.) Malus×domestica) Solanum lycopersicum) A number of options exist for high throughput SNP genotyping and the Illumina GoldenGate platform is a commonly used assay in plants. Examples of its application in plants include 1536-SNP assays in barley The justification for the reduced representation library used in the SNP discovery was to reduce the complexity of the subset of the genome that was sequenced for SNP discovery and to generate large sets of common fragments for alignment. This approach may leave portions of the genome without sequence and thus without SNPs. However, due to the use of 10 different restriction enzymes and the resulting diversity of restriction sites, the identified SNPs were, for the most part, randomly distributed in the soybean genome.The density of genes and the recombination rate in euchromatic and heterochromatic regions differ dramatically with much lower recombination and a lower gene density in the heterochromatic vs. the euchromatic regions http://www.illumina.com/Documents/products/technotes/technote_iselect_design.pdf). The 86% bead type success rate for the SoySNP50K beadchip is in the normal range defined by Illumina, Inc. The allele call success rate is the proportion of the total number of assays generating reliable allele calls among the total number of assays in the final product. Reliability of the SNP allele call is dependent on the intensity of fluorescent signals from both alleles in the SNP Graph displayed by the GenomeStudio software and the distinct separation of the clusters of the allelic classes i.e., the two homozygotes and the heterozygotes, as displayed in the normalized theta output of GenomeStudio. Because the fluorescent signal of SNPs may vary with DNA samples and assays, the incidence of poor allele clustering such as multiple or indistinguishable clusters of samples may occur and lead to inaccurate allele calls. Based on our standard to define SNPs with successful allele calls, the rate of successful allele calls using the SoySNP50K beadchip was 91%, which is comparable to the rate of 77–91% reported in human populations using the Illumina Infinium 550 K beadchip During the SoySNP50K BeadChip development, account was taken of the factors that were essential for a successful assay design i.e., high allele call success rate, high minor allele frequency and the even distribution of SNPs in heterochromatic and euchromatic regions across the soybean genome. The success rate of a beadchip is subject to the bead type success rate during manufacture and the rate of successful allele calls in genotyping. The bead type success rate is the proportion of the total number of attempted assays present in the final product. Illumina guarantees a bead type success rate of >80% but not 100% due to the imperfect efficiency of manufacturing steps such as synthesizing oligonucleotide probes, attaching probes to silica beads, pooling beads, loading bead pools and decoding the beads identified on the beadchip  between G. soja and landrace in the 13–30 Mb region on Gm14 .Knowledge of the positions of the regions associated with domestication and with gene frequency changes associated with N. American soybean breeding is critical for the identification of genes controlling important agronomic traits. However, due to limited sample size, the span of the regions identified in the current study are still quite large. As the analysis of all of the more than 19,000 annual bp fraction was purified and prepared for Illumina GA sequencing . Protocols for adapter ligation, cluster generation and sequencing by synthesis on the Illumina GAII were followed and read lengths of 35–45bp were obtained. The Goat Pipeline (v 1.3) was used for base calling and quality assessment. A total of 56 lanes of sequence data were collected from the eight genotypes, in addition, 15 lanes of sequence data were collected from mixed DNA of Archer, Evans, Noir 1, Minsoy and Peking.Six soybean genotypes, Essex, Evans, Archer, Minsoy, Noir 1, and Peking and two wild soybean accessions, PI 468916 and PI 479752, were used for re-sequencing. The eight accessions are parents of soybean mapping populations. DNA of each of the eight genotypes was extracted from leaf or root tissues and each was digested separately with two sets of five restriction enzymes: Set 1: HaeIII, PsiI, SspI, RsaI; and Ms1I and Set 2: Cvik1–1, DraI, SnaB1, PshA1, and Stu1. The resulting digests were combined and the restriction fragments were size selected on a 1.0% agarose gel and the 110–140The whole genome sequence (WGS) of Williams 82 (Glyma1.01) http://primer3.sourceforge.net/releases.php). The targeted PCR product length ranged from 400–600bp, with the annealing temperature from 55–62°C and the primer length from 18–27 nucleotides. In order to reduce the likelihood of non-specific annealing of primers, all primer sequences were examined with e-PCR software In order to optimize the selection criteria to effectively design the Infinium BeadChip, a total of 767 SNP-containing loci were randomly selected for preliminary validation of the accuracy of SNP calls. A PCR primer set targeting the SNP position was designed to each of the loci. Primers were designed using standalone Primer 3 (http://www.ncbi.nlm.nih.gov/blast/megablast.shtml) and the specific positions of SNPs in the genome were determined with W = 50, cutoff percentage of alignment = 98 and low complexity filtered as described previously A total of 5300 SNPs in soybean had previously been genetically mapped and assayed in multiple genotypes using Sanger sequencing i) was then calculated for each SNP as the ratio of verified to examined SNPs in each priority group among the 767 SNP-containing loci in the verification of SNP calls using ABI 3730 Sanger sequence analysis. In addition, the Illumina Infinium design score provided by Illumina, which is a predictor of assay success, was assigned for each SNP using Illumina’s Assay Design Tool for Infinium . SNPs with a design score less than 0.4 were eliminated. For each SNP, a SNP selection index score (Ii) was calculated by multiplying the priority score (Ri) with the Illumina design score (Di), i.e. Ii = Ri×Di.SNPs were assigned a priority group of A, B, C or D as follows: A. SNPs were called from at least one genotype with read depth greater than or equal to 1, and the two SNP alleles from the reference and non-reference sequences were consistent with the IUB code called from mixed DNA or the SNPs were already present in dbSNP at NCBI and had previously been successfully assayed in multiple genotypes. B. SNPs were identified in at least two genotypes. C. SNPs were detected in only one genotype but the read depth was greater than 3. D. SNPs were called from only one genotype with read depth less than three, and the non-reference SNP allele was also present in the mixed DNA. A priority score  of the euchromatic (j = 1) or heterochromatic region (j = 2) for the ith chromosome and Tj and Wj were the total sequence length (bp) and total number of SNPs to be selected in the euchromatic or heterochromatic regions across the 20 soybean chromosomes, respectively. For each iteration of the algorithm, gaps (sequence length) between adjacent SNPs were calculated along each chromosome. Subsequently, the smallest gap along each chromosome was identified and the SNP selection index scores of the SNPs flanking the gap were calculated. The SNP with the smallest SNP selection index score was removed. If the SNP selection index scores of the two SNPs were identical, the SNP with the smallest gap to another adjacent SNP was eliminated. If two or more gaps in a single iteration were identical, the SNP with the smallest SNP selection index score among the SNPs flanking the gaps was eliminated. The iteration continued until the desired number of SNPs in euchromatic and heterochromatic regions was reached. In addition, the whole genome sequence of Williams 82 (Glyma1.01) contains 236 unanchored scaffolds with lengths from 10kb to 100 kb and 51 unanchored scaffolds with lengths greater than 100kb. SNPs identified in these scaffolds were also included in order to facilitate anchoring of these scaffolds for the next genome assembly.Based on the SNP selection index scores, an iteration algorithm was developed to select the desired number of SNPs with desired physical distance between adjacent SNPs in euchromatic and heterochromatic regions and with elevated index scores. The total number of SNPs to be selected from the euchromatic and the heterochromatic regions was determined based upon the ratio of genetic map distance to physical distance in bp in the euchromatic versus the heterochromatic regions. The definition of the physical lengths of the euchromatic and heterochromatic regions was as follows: The physical positions of mapped SNPs and simple sequence repeats (SSRs) G. soja accessions were used to determine the allele frequency of selected SNPs on the SoySNP50K iSelect SNP BeadChip. The 96 landraces G. soja accessions were from China, Korea, Japan and Russia and were selected based upon the wide ranges of latitude and longitude from where they were collected  is a measure of population differentiation. Fst values range from 0 to 1, with zero representing no allele differentiation and 1 representing complete allele differentiation between the two populations. The Fst between G. soja and the landraces and between the landraces and elite cultivars were calculated for each locus using the software Arlequin v3.1 and the significance of the Fst was tested using a non-parametric permutation approach st of SNP loci in 100kb windows along each chromosome was calculated.The fixation index , MAF in elite, landrace and wild soybean populations.(XLSX)Click here for additional data file.Table S2stAverage FG. sojast Elite cultivars vs. Landraces in 100 kbp windows along each chromosome. vs. Landraces and F(XLSX)Click here for additional data file.Table S3Accession IDs, province and country of origin, and latitude and longitude at which the 96 G. soja accessions used in this study were collected.(XLSX)Click here for additional data file.


ST, informativeness (In), 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 FST, In, 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.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 Molecular markers are widely used in the fields of ecology, evolution, and resource management The use of high-throughput SNP panels for the study of non-model organisms has primarily been limited by the cost and difficulties of discovering new SNPs, and consequently, the number of available assays has been low or nonexistent for many species. However, technological advances and innovative methodologies are enabling rapid SNP discovery Population studies in non-model organisms that used high-throughput assays for SNPs typically went through an initial discovery phase where every new assay was precious and every available marker was used e.g., . IncreasOncorhynchus nerka) this is already the case. At present, a limited set of 45 SNPs provides insight into life history, migration, and harvest In sockeye salmon  454 assemblies Discovery methods were iterative and adapted for different transcriptome datasets as they emerged from our laboratory. First, primers were selected directly from chum salmon  were discarded . The Sanger sequences used for 5′-nuclease assay design were used to annotate validated markers using the NCBI sequence database and Blastx. Only assays where the most similar sequence hit had an e-value <1.0E-10 were annotated.Six pairs of population samples (hereafter referred to as assessment populations) were chosen from throughout the species' range to assess within and among region variability . All fisTissues  or genomic DNA were obtained from archived samples at the University of Washington (UW), the Alaska Department of Fish and Game (ADF&G), and the Washington Department of Fish and Wildlife (WDFW). Genomic DNA was extracted as necessary using the DNeasy96 Blood and Tissue Kit .The markers were first evaluated using standard population genetic indices using the 12 assessment populations  as folloSTPopulation differentiation was measured as FSTnST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. Each locus was ranked according to five measures: FBELS and WHICHLOCI provide each locus a rank based on the accuracy of individual assignment for that locus and the value lost when the locus is removed from the panel in a jackknife fashion. Loci that result in the greatest loss in individual assignment performance when removed receive the highest score. Both of these locus-ranking programs were run with resampling for a simulated population size of 200 individuals and with 250 iterations. No critical population was defined. In WHICHLOCI, minimum correct assignment was set at 95.0%. In BELS, the performance measure was designated to maximize mean individual assignment accuracy for 100% correct assignment. For BELS, the role of locus input order was explored by running the analyses with four different locus orders: alphabetical, reverse alphabetical, and two randomly generated locus orders. Differences in locus ranks for each input order were tested in a pairwise fashion using the Wilcoxon Signed Rank test.Initially, each locus was ranked using all individuals available (full set) for the twelve SNP assessment populations . HoweverSNP panels were designed to assess the value of increasing the number of markers included in a panel and to evaluate the different measures for ranking SNPs using the 12 assessment populations. Two panel sizes were selected, 48 and 96 SNPs, to test for differences in resolving power when the number of markers was increased. These panel sizes represent the capabilities of high-throughput genotyping platforms commonly in use at that time e.g. .We assembled seven pairs of 48-SNP and 96-SNP panels. Using the training set, five pairs were created from the top ranked loci for each locus measure. A sixth pair of panels was constructed from top ranked loci (based on their average rank). Finally, a seventh pair of panels was constructed from randomly selected loci.fORCAEach panel was tested for performance with two different methods. Using the program ONCOR Differences in panel performance for both assessment methods were tested for using an ANOVA and the post hoc Tukey's Honestly Significant Difference test (α = 0.01).st and 3rd quartiles were calculated from the resulting probabilities in Excel (Microsoft for Macs 2011).In addition to panel testing, we examined the value of using the full set of loci and the change in assignment accuracy with decreasing panel size after the subsequent removal of loci. Beginning with the full set of 110 polymorphic loci, ONCOR was used to determine probability of correct assignment similarly to 96- and 48- SNP panel assessment. Loci were then excluded five at a time by lowest average rank  until onWe developed 5′-nuclease assays for SNP genotyping of sockeye salmon from both ascertainment sources: chum salmon contigs originating from 454 assemblies Each fish from the 12 populations used for SNP assessment was genotyped at 114 nuclear loci . These wRe-genotyping discrepancies were less than 1% in all populations. Individuals missing genotypes at more than 10% of the loci were excluded from analyses. Sample sizes reported in One_zP3b, Damdochax Creek at One_U1202-105, Pick Creek at One_Tf_ex3-182, and Baker Lake at One_ U1102-220. In each of these collections there was a rare homozygote genotype at each of these loci and average minor allele frequencies less than 0.03 with one exception. There was an excess of heterozygotes in the Baker Lake collection for One_U1102-220. Mean allelic richness varied across locations and ranged from 1.8 in Lake Kulik to 1.95 in Main Bay . Significant deviation from linkage equilibrium was observed in over half of the collections for only three pairs of loci: One_aldB-152 & One_ALDOB-135, One_GPDH-201 & One_GPDH2-187, and One_MHC2_190 & One_MHC_251. These loci were treated as independent for the remaining analyses because we wanted ranking and panel testing to include all available loci for the species. Retaining these loci in the data set could lead to upward bias in assignment success due to redundant information. However, there are only three pairs of loci which were not in linkage equilibrium, and these loci were not in disequilibirium in all populations, warranting their retention in downstream analyses.In four collections, there were deviations from HWE at a single locus after correction for multiple tests: Ozernaya River at ST was 0.114 for all 110 polymorphic SNPs across all collections .The average Flections . There wlections . The genlections  where thlections . Two of n) were highly correlated with the locus contribution (LC)  to FST as shown by Rosenberg et al. One_gadd45-269 and One_parp3-170). Often these loci received a high rank (low number) from BELS and a lower rank (high number) from FST, In, and LC measures . BELS rank did vary with input order  despite differences in panel composition with two different population sizes (full set vs. training set). Since the purpose of splitting the SNP assessment populations into a training set for SNP ranking and a holdout set for assessing SNP performance was to reduce upward bias only, training set ranks were used for panel testing.There were fewer loci shared between the 48 SNP panels. The FfORCA) for both the 96-SNP panel (0.96) and the 48-SNP panel (0.85).There was a significant difference in mean assignment scores using empirical  and simulated  data . In the ST and In panels when using the empirical data  and the randomly generated panel (0.84) and the BELS panel (0.72) had the lowest average.Most of the 96-SNP panels performed similar to and significantly better than the 48-SNP panels (P<0.001) except for the BELS and randomly generated 96-SNP panels; these performed similar to the 48-SNP Fcal data . The 48-cal data  except fcal data . The FSTst quartile nearly flanks the average probability of correct assignment until only 75 loci remain and is higher than the mean in some cases where the median of the 25th percentile data is actually higher than data mean. The probability of correct assignment at the 75th quantile remained nearly as high as 1.0 for some individuals until only 30 loci remain . Unsuccessfully amplified loci may have been adjacent to intron-exon boundaries resulting in PCR failure. This source of putative SNP drop-out is difficult to avoid when using transcriptome without a reference genome to identify intron-exon boundaries, and the lack of a reference genome may continue to present challenges for future SNP discovery. However, despite these challenges, improved NGS technologies and improved bioinformatics will continue to accelerate SNP discovery in non-model organisms One facet of SNP discovery that warrants attention is ascertainment bias which is introduced during the SNP discovery process because the variation being captured is usually only representative of a small number of individuals www.pwsac.com/mbh.htm). The high FST values observed between all other populations and regions in this study reflect the large geographic range surveyed in addition to the extreme philopatry of the species which results in strong genetic differences across even small geographic scales Most populations were easily differentiated except for the Main Bay Hatchery-Coghill Lake pair in Southcentral Alaska. The exception may be attributed to the fact that fertilized eggs from Coghill Lake fish were introduced into the Main Bay Hatchery population during the last three decades . Interestingly, differences in locus ranks based on diversity indices  versus the likelihood-based ranking programs BELS and WHICHLOCI were greater using the training set. Sample size may have a greater impact on ranking when using these programs. Although the ranking strategies used here are not novel, we believe that showing a comparison of ranking approaches for the same data is informative and will be of value once researchers have access to hundreds of SNPs.SNPs can be ranked in a variety of ways. Computer programs such as WHICHLOCI generate optimized SNP panels using genetic data, rigorous statistical algorithms, and general objective functions. Alternatively, ranking procedures developed for specific applications might consider everything from laboratory performance to accuracy of individual assignment. Unsurprisingly, sample size does impact ranking as we observed greater variation in locus ranks using the training set , which hIncreasing the panel size from 48 to 96 significantly improved individual assignment ; when loTesting panel performance with both empirical and simulated data yielded slightly different results. The greater variation in probability of correct assignment observed using empirical data may be partially attributed to individual differences in DNA quality. Some of the samples may have suffered tissue and DNA degradation; missing genotypes in these increase assignment difficulty. With simulated individuals there no variation in data quality, explaining the low variance and higher average probability of correct assignment. Simulated data provide a better idea of which panel performs best based solely on SNP composition because sample quality is not a source of variation; empirical data provides a better idea of panel performance in an actual study. There would be less variation in the probability of correct assignment with larger sample sizes for SNP ranking (training set) and evaluation (holdout set).ST, In, and LC panels were often the most similar and had the highest average probability of correct assignment. The similarity between these panels is expected since these ranking methods are all highly correlated . The stability of highest locus ranks and variability of mid- and low-performing locus ranks might be an artifact of the program's intent to determine a minimum set of loci that maximizes performance. Once the best performing loci, for example the top 40, have been identified, the addition or removal of the remaining loci results in minimal changes in performance resulting in arbitrary ranks.There was a pattern in panel performance for both panel-testing approaches and for panel sizes. Fted e.g. . These tted e.g. , 5. The ted e.g. . Some ofThe popularity of a given type of molecular marker has changed repeatedly over recent history. Regardless of the marker type or discovery method, there is continued interest in developing methods for ranking and evaluating markers, hence the design of locus selection programs such as BELS and WHICHLOCI. SNPs have recently become a marker of choice for several non-model species, and there is growing interest in methods to evaluate the ever-increasing number of SNPs. Here we not only describe an effective method for SNP discovery in the culturally and commercially important non-model sockeye salmon, but we also demonstrate how common locus-ranking methods perform differently when developing a SNP panel. Although our investigations explore the role of loci for use across a large geographic scale with high overall differentiation, the same approach can be applied and optimized for finer geographic resolution. The steps outlined here provide a starting place for developing a minimum panel size for maximum assignment accuracy for any specific system or question. Here we recommend panels of 48 or 96 SNPs that will expand the options for improved management and conservation of the iconic sockeye salmon.Table S1ST) for 12 SNP-assessment populations. Average rank based on training set individuals for five ranking approaches: genetic differentiation (FST), Rosenberg's informativeness (In), average contribution of locus to principal components (LC), and ranks from the locus selection programs BELS and WHICHLOCI. The numeral 1 indicates the highest rank. Locus ranks for each approach are based on a training set of the 12 SNP-assessment populations.All 114 loci in alphabetic order with descriptive statistics Click here for additional data file.Table S2Forward and reverse primer and probe sequences for all newly developed SNPs featured in this paper with SNP characterization and gene annotation when possible.(DOC)Click here for additional data file.


Approximately, 6,683 (shotgun) and 14,724 (RAD) SNPs were detected within our elephant sequence dataset. Genotyping of a representative sample of 194 SNPs resulted in a SNP validation rate of ∼ 83 to 94% and 17% of the loci were polymorphic with a low diversity (Ho = 0.057). Different numbers of microsatellites were identified through shotgun  and RAD (868) techniques. Out of all di-, tri-, and tetra-microsatellite loci, 1,706 loci had sufficient flanking regions (shotgun) while only 7 were found with RAD. All microsatellites were monomorphic in the Bornean but polymorphic in another elephant subspecies. Despite using different sample sizes, and the well known differences in the two platforms used regarding sequence length and throughput, the two approaches showed high validation rate. The approaches used here for marker development in a threatened species demonstrate the utility of high throughput sequencing technologies as a starting point for the development of genomic tools in a non-model species and in particular for a species with low genetic diversity.High throughput sequencing technologies are being applied to an increasing number of model species with a high-quality reference genome. The application and analyses of whole-genome sequence data in non-model species with no prior genomic information are currently under way. Recent sequencing technologies provide new opportunities for gathering genomic data in natural populations, laying the empirical foundation for future research in the field of conservation and population genomics. Here we present the case study of the Bornean elephant, which is the most endangered subspecies of Asian elephant and exhibits very low genetic diversity. We used two different sequencing platforms, the Roche 454 FLX (shotgun) and Illumina, GAIIx  to evaluate the feasibility of the two methodologies for the discovery of  The field of population genetics has been dominated for many years by the use of microsatellites which have been developed and tested across a wide range of species with enormous success de novo SNPs including the targeted gene approach using ‘CATS’ (comparative anchor tagged sequences) or ‘EPIC’ primers (exon priming intron crossing) A number of approaches have been used to discover Eucalyptus grandisThe development of high throughput sequencing platforms has emerged as a new tool to develop genomic markers and is already showing great promise for fast and efficient marker detection for model and non-model species While high throughput SNP genotyping platforms can in principle rapidly and efficiently genotype many loci on hundreds of samples identifying SNPs that fit the criteria for assay development can be challenging in organisms without a reference genome. Two reasons are that SNP genotyping assays often require SNPs to have large windows free of nearby polymorphisms and SNPs should be evenly spaced throughout the genome Elephas maximus borneensis) is a subspecies of mainland Asian elephant which differs from other subspecies in its morphology and behaviour et al., de novo markers (SNPs and microsatellites) for the Bornean elephant by using the advantages offered by two different high throughput sequencing methodologies , (ii) develop a genotyping assay for a reasonably large subset of identified SNPs, and (iii) validate a subset of these markers on high quality (blood) samples.The Bornean elephant  under the study accession number ERP001879 (http://www.ebi.ac.uk/ena/data/view/ERP001879). The four samples chosen for de novo assembly performed better than the other samples in terms of high output of sequence, coverage, quality , and in terms of the total number of RAD-tags generated. The variability seen in read numbers observed for each sample was mostly due to the overall quality of the input material. Some samples were slightly more degraded than others. The number of sequenced RAD-tags obtained was highly dependent on the sequence amount and number of reads per cluster   were used in local contigs) . A breakDe novo contig lengths ranged from 150  to 560 bp (sample 2) with mean length between 259 and 288 bp (sample 4 and 2). The average depth of coverage per base of contigs was 12.9x. The contigs assembled from each RAD-sequence had an N50 length between 279 and 320 bp  SNPs were identified as heterozygotes (using the threshold parameter as implemented in SAMtools) and 9,676 as homozygotes (monomorphic). Altogether 20%  were identified as variant loci. Out of these we obtained a genotype for all individuals (100%) for 1,200 loci/RAD tags, and for 75% of the individuals (i.e. six or more) for 1,700 loci (d 320 bp . N50 is http://www.ebi.ac.uk/ena/data/view/ERP001879). Altogether 219,157 (19.17%) out of 1,144,367 reads aligned to each other from elephant sample A and B and were assembled into contigs. This was the total amount of data used for contig assembly. The total number of de novo assembled contigs (>100 bp) obtained was 16,857 with an average contig length of 328.13 bases. There were 3,267 large contigs (>500 bp) with an average contig size of 841 bases, with the largest contig size identified as 6,407 bp. The length distribution of the 454 and RAD-seq (Illumina) assembled contigs are shown in A full run on the Roche 454 GS-FLX system yielded a total of 1,144,367 reads for the two elephants used with elephant sample A and B generating 509,030 and 635,337 reads, respectively . The aveL. africana) genome and 497,169 (97.6%) reads from sample A and 617,930 (97.2%) reads from sample B were successfully mapped  was higher than that of transversions , which corresponds to a ts/tv ratio of 1.6∶1. Our results showed that the number of A–G substitutions almost equaled the number of C–T substitutions in the transitions class. Moreover, the substitutions within the transversions class occurred in comparable frequencies, with the exception of A–T.Filtering of the contigs containing SNPs in RAD-seq data set resulted in identification of 518 (24.6%) putative SNPs with phred quality score, >q20  out of the 2,100 variant loci with favorable characteristics for downstream genotyping design. 491 out of 518 loci were identified with quality score, >q30 . The observed SNP density (number of SNPs/bp) was 0.00081(∼8/10 kb). These SNPs were identified in all eight Bornean elephant lines and were used for Sequenom assay design. We then eliminated 11 contigs because 10 of them were identified with a third allele  and one contig indicated primer design error. This left a total of 507 independent SNPs for further validation and characterization in 19 multiplexes . In addiScreening of 10,352 contigs from elephant sample 4 resulted in the identification of 868 SSR with 844 mono-, and 24 di-nucleotide repeats in 837 contigs  with 31 (3.7%) contigs that contained more than one type of repeat motif. 17 SSR were present in compound formation. Out of 24 di-nucleotide repeats, 7 loci (29%) with ≥3 repeats were identified as ‘potentially amplifiable loci’ (PAL). No tri-and tetra-nucleotide repeats were identified in this limited sequence data set from sample 4 .The shotgun genome sequencing of the two elephant samples resulted in identification of 18,206 variants  residing in 5,885 contigs . 1,753 (10%) contigs out of 16,857 contigs were identified to contain these 6,683 SNPs . The calA comparison of 454 and RAD-seq (Illumina) contigs revealed that two loci were common to both data sets (using mapping criteria of 95% identity and at least a stretch of 20 bp perfectly matching). These loci were not included in the assay design.E. m. borneensis, we identified 18,195 contigs  that contained SSRs or microsatellites, 8,432 contigs obtained from elephant sample A, and 9,763 contigs from elephant sample B. 27,226 microsatellites (with ≥3 repeats) were identified in the 18,195 contigs, of which 18,188, 7,241, 1,471 and 326 contained mono-, di-, tri-, and tetra-nucleotide repeats, respectively , 15 (45%) of these loci were also found as fixed heterozygotes. Of the remaining 18 loci, 11 loci were found as homozygote for one of the two alleles and were recorded with high confidence . For the remaining 7 loci, only one of the alleles (homozygote) was recorded with high probability. Further investigation on a large sample size is required to confirm the status of these duplicated loci in Asian elephants. Also, a blast search of these 33 contigs against L. africana genomic data produced significant hits for all except one. Twenty one loci (63%) aligned to two different positions in the same or different scaffolds of L. africana genome. Eleven loci (33%) showed unique alignment. It has been recognized that the BLAST algorithm can be rather inefficient in identifying homologous sequences when short contigs are involved, i.e. those originating from the Illumina platform Ho = 0.057 respectively. Elephant sample 7 displayed observed heterozygosity (Ho) higher than the other individuals (Ho = 0.10) (The seven Bornean elephant samples (same samples that were used in the discovery panel of RAD-seq) were genotyped targeting 161 putative SNPs (124 from RAD-seq and 37 from 454) using five multiplex assays with 3–5 replicates per individual and generating 3,903 genotypes . 54% of calls in Sequenom were recorded as “conservative”, 15% “moderate”, 9% “aggressive”, 18% low probability , 3% with no alleles and 1% were user calls . In addition, we assigned a ‘no-call’ to the genotype calls at 7 different loci in sample number 7 due to bad spectrum (non-predictable variation in peak height). 99% of the genotyped data successfully achieved a consensus. Seven genotypes were non-reproducible as they consistently failed to amplify and did not yield a consensus. Positive PCRs (number of PCRs giving correct or consensus genotype) were reliably obtained for all seven elephant samples with PCR success rate ranging from 83 to 94% (average across each sample) and 86–95% (average across each plex). The possible errors detected and resolved by repeated genotyping were mainly caused by: (1) allelic dropout leading to detection of false homozygotes in six elephant samples with highest allelic dropout rate observed in sample 1 (27%), (2) false alleles were detected in only one elephant (sample 3) and were in low proportion (0.16%).  = 0.10) .Out of 55 potential microsatellites, 20 primer pairs did not amplify on seven elephant samples and 10 produced unclear patterns in the electropherograms and hence discarded. Of the remaining 25 microsatellites  producing scorable peaks, all were found to be monomorphic across the seven Bornean elephant samples tested.de novo SNPs and microsatellites developed using both methods (RAD-seq and 454 sequencing) were also successfully genotyped on E.m. indicus samples  showed a significant blast match and were successfully mapped to different L. africana genomic positions . In an effort to identify or predict the possible functions of the SNPs  the analysis revealed that only 8% (3 out of 37) of the 454-contigs could be associated with one or more functions. The function was putatively associated to a SNP if the region surrounding the SNP, when blasted against L. africana, overlaps an L. africana gene, in which case a gene's GO (Gene Ontology) term (from Ensembl annotation) was associated to the SNP. If the contig is nearby a gene, but it does not overlap that SNP, no GO term was associated. That is why we have so few SNPs ‘annotated’. 24 unique GO terms  were represented in these 454-contigs . For the remaining contigs, no significant matches were obtained using Blastn. 23% of SNP containing contigs (113 out of 491) could be associated with one or more GO terms .All 454-contigs (100%) containing SNPs  in Bornean elephants and 17% of the loci were polymorphic. Our results are in agreement with the previous study (and only available genetic data on this species) of Fernando et al., He = 0.041 using five microsatellites). But further genotyping and investigation of these loci on a large number of elephants from different populations is required.We are currently witnessing a revolution in the field of population and conservation genetics and molecular ecology due to the easy access to high throughput sequencing platforms, which have hastened the development of molecular markers in silico assuming equal and random frequency of cut sites; but in practice, some restriction enzymes depart considerably from this expectation in silico  on the basis of the genome size of L. africana and its GC content. This discrepancy observed was most likely due to the quality of the samples, and the genomic DNA concentration.In theory, the number of RAD-tag fragments can be roughly predicted L. africana genome.Using the 454 sequencing system, we also identified a reasonably large number of SNPs  despite using low sequence coverage and only two individuals. Again, a significant number of polymorphisms in our samples may have been overlooked because of the stringent methodology used to declare SNPs. The advantage of the stringent filtering of the reads is that it provided a higher quality data set. Reads used for contigs assembly shared high sequence identity (around 97% mapped accurately) with the Previous attempts to identify SNPs in other non-model species using high throughput sequencing technology have been of two kinds: (1) full genome sequencing using very high level of coverage One of the major challenges as identified in our data was that RAD-seq yielded a high number of candidate SNPs with high coverage but a large proportion (around 75%) of the identified SNPs loci were found inappropriate for SNP assay design. This was due to their proximity to the ends of the large contigs and/or rarely due to the presence of other SNPs around them. Thus, in the absence of a reference genome the number of SNPs with sufficient flanking sequence for designing a genotyping assay remains limited et al., E. m. indicus) even though we only had 3 individuals. This again confirms that Bornean elephants have extremely low genetic diversity in comparison to other Asian elephant subspecies. Since these data on Bornean elephant were collected, the high throughput sequencing technologies have evolved significantly, most importantly with the improvement in Illumina read lengths (now up to 150 bases with the GAIIx) and accommodate paired-end sequencing from both ends of ∼200–600 bp fragments While our primary goal was the identification of SNP markers, we also identified SSR motifs using both approaches. However, there were important differences in the results. A much high number of microsatellites  were identified using 454 sequencing with ∼18% of these loci which had primer design sites that could potentially be used to amplify and score microsatellite alleles based on length variation (repeat number). In the case of RAD-seq, 29% of loci  were found suitable for primer design. This indicates that the major drawback with high throughput sequencing approaches is the current technical limitation introduced by sequence read lengths Another potential issue related with the high throughput sequencing  is the assembly of paralogous regions In conclusion, our study (results from both sequencing methodologies) suggests that despite substantial differences in sample size, sequence throughput, read length, and average sequencing depth, similar results were obtained after the validation in terms of SNPs genotyping success rate. Our ability to identify thousands of genomic markers with relative ease will also make it possible to estimate many important parameters, related to conservation, such as inbreeding coefficients or relatedness. This will clearly allow conservation biologists to enter the new genomic era of non-model species.Samples were collected by the veterinarians at Lok Kawi Wildlife Park and Oregon zoo during the routine health checks. Ethics approval was not required or sought for this research, as the sample were not collected specifically for this study. All samples from Sabah, Malaysia were exported under CITES  permit obtained from Sabah Wildlife Department.Eight fresh whole blood samples (hereafter sample 1 to 8) were collected directly from Bornean elephants. Seven samples (sample 1 to 7) were collected from the Lok Kawi Wildlife Park, Sabah, Malaysia in January 2010 and one additional Bornean elephant sample (sample 8) was obtained from the Oregon zoo, Portland in the United States. Genomic DNA was extracted using DNeasy Blood & Tissue Kit (QAIGEN). 10–12 µg of gDNA was isolated from each sample at a concentration of 20 ng/ul that was quantified using Nanodrop, ND-1000 spectrophotometer. High quality genomic DNA is the most crucial aspect for the success of high throughput sequencing methods. Therefore, several rounds of DNA extraction were performed and pooled for the downstream applications.Two Bornean elephant whole blood samples (hereafter sample A and B) were collected along the Kinabatangan River in Sabah, Malaysia in March 2009 . DNA was prepared as mentioned above. The shotgun sequencing was performed at the Microarray and Next Gen Sequencing Facility at the Center for Applied Genetics and Technology, University of Connecticut, USA. Sample A and B were run separately in a 2-region PTP plate.etal.,EagI and processed into short DNA fragments or ‘tags’. The enzyme EagI  was selected for RAD profiling of the 8 elephant sample lines. DNA was digested for 60 min at 37°C in a 50 µL reaction containing 20 U of EagI . The reactions were stopped by holding the temperature at 65°C for 20 min. The P1 adapter , according to the protocol described by Baird A RAD library containing each sample was sequenced on a Genome Analyzer  in two lanes using an asymmetric sequencing strategy: paired end 40 × 80 bp sequences were obtained for all 8 samples.de novo assembly http://www.illumina.com), which identified SNP loci free of other polymorphisms 60 bp either upstream or downstream. A quality score, based on the probability of good performance using the Illumina Golden Gate assay, was assigned to each SNP, where a score >0.6 indicated a high probability of success.The paired-end sequence reads from each sample were manually collected and segregated by barcode. Once samples were demultiplexed the quality and quantity of sequence obtain from each sample was assessed. We identified sample number ‘4’ as having the highest quality and quantity of sequence and therefore began our analysis with this sample. First, a set of unitag, or unique RAD single end sequences were identified with coverage ranging from 5x–500x. Next, all paired-end reads that were associated with each single-end unitag sequence obtained above were collected and, the program velvet E.m. borneensis genomic DNA samples (∼20 ng DNA /in 30 µl of water). For allele separation, the Sequenom MassARRAY™ Analyzer (Autoflex mass spectrometer) was used. Genotypes were assigned by the MassARRAY SpectroTYPER RT v3.4 software (Sequenom) based on the mass peaks present. The resulting data were manually inspected using SEQUENOM System Typer 4.0. The software uses a three parameter model to calculate significance of each putative genotype. A final genotype is called and assigned as ‘conservative’, ‘moderate’, ‘aggressive’, ‘low probability’, and ‘user call ’ based on degree of confidence. SNPs were classified as “failed assays” when the majority of genotypes could not be scored due to low probability or when the samples did not cluster well according to genotype. SNPs that were out of Hardy-Weinberg Equilibrium (HWE) in one or more samples were cross-checked. Multiplex assays (plex 1 to plex 4) targeting 124 unique SNPs randomly selected from the set of identified SNPs were genotyped across a panel of seven elephants which are also the individuals used to discover the markers. Each elephant blood sample was amplified in 3–5 different replicates (average 4 replicates for each plex). After repeated genotyping, it is necessary to construct a consensus multilocus genotype  amplifications of a sample) and to estimate the genotyping error rates The identified contigs were used to design a group of multiplexed genotyping assays using the Sequenom MassArray Assay Design software . To facilitate assay design, we screened the complete SNP panel to identify those alleles free of flanking polymorphisms . Up to 40 different SNPs can be multiplexed in one assay, if primers are designed by the custom software to give unique mass range for each SNP. For contigs containing more than one SNP, only one SNP was selected per contig for validation. In addition, SNPs were discarded if the contig did not have at least 30 bp on either side of the SNP to allow for amplification. Multiplexes and primer sets are available from the first author upon request. Once the assays were identified, large-scale surveys of their frequencies were performed using the Sequenom iPLEX genotyping platform 500 ng of starting DNA was used in the 454 FLX shotgun library preparation, following the manufacturer’s protocol and quality control steps.de novo assembly using the program GS Assembler v2.5.3 (454 Life Sciences). To explore data quality and relevance, we used draft genome sequence of African savanna elephant (Loxodonta africana)  to map the reads. A recent study by Rohland et al., Loxodonta and Eurasian elephantids as 4.2–9 Ma (million years). For SNP prediction, reads from the two elephant samples were then mapped to the combined assembly contigs using the Burrows-Wheeler Aligner (BWA) and the SW algorithm longer reads Reads generated from whole genome shotgun sequencing of the two elephants (sample A and B) were aligned to generate a combined 8,432 contigs and 9,763 contigs from assemblies for elephant samples A and B, respectively, were converted into single FASTA format files. To develop a set of about 20–30 microsatellite markers, we conducted several screenings with different search criteria, i.e. using different minimum repeat lengths  in order to extract a total of 50–100 sequences with microsatellite repeats using MSATCOMMANDER version 0.8.1 2, 0.2 mM of each dNTP, 0.2 mM of both forward and reverse primer, and 0.75 U Taq polymerase (QBIOgene). Amplifications were performed using touchdown PCR in a BioRad (My Cycler) thermocycler with the following reaction profile: initial denaturation at 95°C for 3 min, 15 cycles: 95°C for 30 s, an elevated locus-specific annealing temperature Ta +7.5°C for 45 s, 72°C for 1 min; 20 cycles: 95°C for 30 s, Ta°C for 45 s, 72°C for 1 min; and a final extension at 72°C for 5 min. Fragment size was determined on a 3130 Genetic Analyzer (Applied Biosystems) multicapillary automatic sequencer using the GENEMAPPER version 3.7 and an internal size standard . The newly designed microsatellite primers for di-, tri-, and tetra-nucleotide loci were screened on seven Bornean and three other Asian elephant samples (E. m. indicus) and genotypes were recorded.The PCR amplified products were sequenced to confirm the microsatellite repeat before fluorescent tagging. Variability of microsatellite loci was analyzed by PCR using a 5′-fluorescence-labelled forward primer by using 20–30 ng of genomic DNA as template. PCR was performed in a total volume of 10 µL, containing 10 mM Tris-HCl, pH 9.0, 50 mM KCl, 2 mM MgCli.e., 37 from the 454 run and 518 from RAD-seq) were used for Blastn searches and gene ontology (GO) annotation. Also, a precise blast was performed by using FASTA files containing just short sequences of 120 bp SNP flanking sequences to determine if the putative SNPs were associated with genes, not to provide a complete annotation of these sequences. All contigs were queried against the sequence database of L. africana genome (Ensembl genome assembly for L. africana) using NCBI Basic Local Alignment Search Tool . The gene ontology annotation files linking the gene ontology terms to the Ensembl L. africana gene identifiers were obtained from biomart (http://www.biomart.org). The contigs from Bornean elephant were annotated with the gene ontology terms associated with the orthologous L. africana gene. The Blastn searches were performed using e-value cut-off of 10−5 and the best alignment match and overlapping positions were noted.Contigs containing potential SNPs including those validated by genotyping (Table S1List of RAD tags/loci with genotypes. A complete list (vcf file) of all RAD tag/loci where genotypes were obtained for all individuals (100%) for 1,200 loci/RAD tags, and for 75% of the individuals (i.e. six or more) for 1,700 loci. For the remaining 400 loci, the SNPs were shared by less than six individuals. Eight E. m. borneensis samples were used in the discovery panel of RAD-sequencing using Illumina platform.(XLSX)Click here for additional data file.Table S2Summary of SSRs (simple sequence repeats) identified in the Bornean elephant sequence dataset. The number of SSRs or microsatellite loci identified in E. m. borneensis (n = 2) using the 454 shotgun sequencing approach, and the subset of these that are potentially amplifiable (containing suitable PCR priming sites) and validated. The di- and tri-nucleotide microsatellite repeat classes (≥3) were more abundant than the tetra-nucleotides.(XLSX)Click here for additional data file.Table S3Estimation of genetic diversity and genotyping error rates. Comparison of observed heterozygosity (Ho), missing genotypes, allelic dropout, number of positive PCRs, false alleles, and mean number of allele (MNA) for the genotyped samples of E. m. borneensis (n = 7) and E. m. indicus (n = 3). Multiplex assays (five plexes) targeting 194 unique SNPs were validated across all elephant samples using Sequenom iPLEX platform. Note that plex1 to plex 4 (124 loci) represent assays developed for SNPs identified using RAD-sequencing and plex 5 (37 loci) represent SNPs identified through 454 shotgun sequencing (see cing see .(DOC)Click here for additional data file.Table S4Gene Ontology (GO) term representation of 454-contigs. The GO terms were obtained for 454 contigs containing SNPs using Ensembl gene annotation of L. africana. In these 454-contigs, 24 unique GO terms were represented.(XLSX)Click here for additional data file.Table S5Gene Ontology (GO) term representation of RAD-seq contigs. The GO terms were obtained for RAD-contigs containing SNPs using Ensembl gene annotation of L. africana. In these RAD-seq contigs, 35 unique GO terms were represented.(XLSX)Click here for additional data file.Table S6Recent SNP discovery efforts in non-model species. A literature review of recent SNP discovery efforts in non-model species using high throughput sequencing platforms.(XLSX)Click here for additional data file.


An association study of 50,000 single nucleotide polymorphisms (SNPs) was performed for even-chain saturated fatty acids (C4:0-C18:0), even-chain monounsaturated fatty acids (C10:1-C18:1), and the polyunsaturated C18:2Bos taurus autosomes (BTA) 14, 19, and 26 showed highly significant associations with seven to ten traits, explaining a relatively large percentage of the total additive genetic variation. Many additional regions were significantly associated with the fatty acids. Some of the regions harbor genes that are known to be involved in fat synthesis or were previously identified as underlying quantitative trait loci for fat yield or content, such as ABCG2 and PPARGC1A on BTA 6; ACSS2 on BTA 13; DGAT1 on BTA 14; ACLY, SREBF1, STAT5A, GH, and FASN on BTA 19; SCD1 on BTA26; and AGPAT6 on BTA 27.The two-step single SNP association analysis found a total of 54 regions on 29 chromosomes that were significantly associated with one or more fatty acids. DGAT1 and SCD1. Other regions also showed significant associations with the fatty acids studied. These additional regions explain a relatively small percentage of the total additive genetic variance, but they are relevant to the total genetic merit of an individual and in unraveling the genetic background of milk fat composition. Regions identified in this study can be fine mapped to find causal mutations. The results also create opportunities for changing milk fat composition through breeding by selecting individuals based on their genetic merit for milk fat composition.Medium chain and unsaturated fatty acids are strongly influenced by polymorphisms in  Genetic1 K232A)  and stea1 A293V)  have imp1 A293V) -12. ManyIn order to identify genomic regions involved in the biosynthesis of milk fat, Schennink et al.  and StooRecent developments in molecular genetics have made it possible to perform genome-wide association studies using thousands of single nucleotide polymorphism (SNP) markers to detect QTL. A genome-wide association study has higher power to detect QTL and provides more precise estimates of QTL locations compared to a linkage study. Some genome-wide associations for routinely evaluated traits in dairy cattle, such as milk production and fertility, have been published -22. To tcis9,trans11 (CLA).The aim of this study was to perform a genome-wide association analysis using 50,000 SNP markers to identify QTL for individual fatty acids in bovine milk. Associations were studied for even-chain saturated fatty acids (C4:0-C18:0), even-chain monounsaturated fatty acids (C10:1-C18:1), and the polyunsaturated fatty acid C18:2The fat composition of winter milk samples from 1,905 first-lactation Dutch Holstein Friesian cows was available for this study. The cows were housed on 398 commercial farms throughout the Netherlands. At least three cows were sampled per farm. The cows were between 63 and 282 days in milk. The period of negative energy balance in early lactation was avoided by choosing cows over 63 days in lactation. The population consisted of five large paternal half-sib families from proven sires  and 50 small paternal half-sib families from test-sires (10-24 daughters per sire), as well as 190 cows descending from 45 other proven sires (1-30 daughters per sire). The pedigree of the cows was supplied by CRV  and consisted of 26,300 animals.cis9) monounsaturated fatty acids C10:1 to C18:1, and the polyunsaturated fatty acid CLA. The fatty acids were expressed in terms of weight-proportion of total fat weight (w/w%). In total, these fatty acids made up 89% of the total fat. Table Milk fat composition was measured by gas chromatography. Many fatty acids were measured, but only the major fatty acids are reported here: even-chain saturated fatty acids C4:0 to C18:0, even-chain (Bos taurus (BTA) chromosomes and were assigned to BTA 0. In addition, 591 of the SNPs were located on the X chromosome. The SNPs on BTA 0 and the X chromosome were included in the study. The average distance between SNPs was 52,452 bp. Monomorphic SNPs (n = 245), SNPs with a genotyping rate < 80% (n = 383), and SNPs with a genotype frequency < 0.006  were discarded from the original SNP set of 50,855 SNPs, resulting in the final marker set of 44,733 SNPs used for the association analysis. Table Blood samples were collected from the cows for DNA isolation. The cows were genotyped using a custom Infinium Array  designed by CRV. In total, 1,810 cows were successfully  genotyped. The cows were genotyped for 50,855 technically successful SNPs. The assumed map positions of the SNPs were based on the bovine genome assembly BTAU 4.0 . From thFor the association study, both phenotype and genotype information was available for 1,706 individuals. A two-step single SNP association analysis was performed. In the first step, the genome was screened for interesting regions using a general linear model. In the second step, the interesting regions were verified using an animal model.In the first step, a genome-wide association study was performed with a general linear model using the R package 'SNPassoc' . In thiswhere y* was the phenotype adjusted for the systematic environmental effects; sire was the fixed effect of sire; SNP was the fixed effect of SNP genotype; and e was the random residual. Sire effect was included in the SNPassoc model to account for paternal half-sib relations. Phenotypes were adjusted for days in milk, age at first calving, calving season, and herd. Adjusted phenotypes were obtained from the phenotypes of 1,905 cows using an animal model in ASReml :(2)where y was the (unadjusted) phenotype; μ was the overall mean; dim was the covariate describing the effect of days in milk; afc was the covariate describing the effect of age at first calving; season was the fixed effect of the class of calving season ; scode was the fixed effect accounting for differences in genetic level between groups of proven bull daughters, young bull daughters, and other bull daughters; herd was the random effect of herd, distributed as N was controlled according to the method described by Storey and Tibshirani , by sepaThe first step was performed to identify interesting regions, which were then further analyzed with an animal model to account for all relationships among individuals. Including a polygenic effect and accounting for genetic relationships would be more appropriate . The modA region started at the first significant SNP on a chromosome that was followed by an additional significant SNP within 10 Mbp; the region was extended as long as another significant SNP occurred within 10 Mbp from the previous one and ended at the last significant SNP that was not followed by another significant SNP within the next 10 Mbp. Thus, a region contained at least two significant SNPs. More than one region could be present on the same chromosome when there were groups of significant SNPs located within 10 Mbp from each other but further than 10 Mbp from the other region(s) on the chromosome. The 10 Mbp distance between significant SNPs is rather large, but it was chosen to prevent having many small regions on one chromosome, each containing a small number of significant SNPs.10 ≥ 3 were considered significant.In the second step, all SNPs in regions with significant effects were analyzed using animal model (2) extended with an SNP effect in ASReml . In thisThe genetic variance explained by an SNP was calculated from the estimated genotype effects from animal model (2) extended with an SNP effect and the observed genotype frequencies. The result was expressed as a percentage of the total additive genetic variance. These percentages can be overestimated, especially when the effect of an SNP is small, this is due to the so called Beavis effect . The per10 for all of the studied fatty acids. All analyzed fatty acids showed significant associations with at least one genomic region.In the first step of the single SNP association study, all SNPs were analyzed using a general linear model. The analysis resulted in many significant (FDR < 0.05) associations between SNPs and the studied fatty acids. Figure 10 ≥ 3. Thus, most of the regions identified with the general linear model were confirmed with the animal model. The correlation between P-values in both analyses was 0.90. All results mentioned hereafter originate from the animal model and concern the 54 regions that remained significant at -log10 ≥ 3 with the animal model.From the results of the general linear model, a total of 64 significant regions were defined, affecting one or more (up to 10) traits. Significant associations with SNPs were detected on all chromosomes; however, on BTA 29 we identified only a single SNP associated with a trait instead of multiple SNPs in a region. Therefore, no regions were defined on BTA 29. These 64 regions were analyzed with an animal model and 54 remained significant at -logSignificant associations were detected with several regions for most of the fatty acids, especially C14:0 (19 regions) and C16:1 (18 regions), whereas C18:0 and C12:0 were associated with only one region Table . The X cULGR_BTA-38166 and ULGR_BTA-38169) were significantly associated with C10:0 and located at 24.2 Mbp on BTA 16 according to the UMD 3.0 genome assembly [Sixteen of the unmapped SNPs showed significant associations with one or more of the studied fatty acids. Comparing the sequence of these significant unmapped SNPs against genome assembly UMD 3.0  identifiassembly .For each region, we estimated the variance explained by the most significant SNP for each trait. Within a region, this most significant SNP can be a different SNP per trait. The percentage of total additive genetic variance explained by the most significant SNP per region ranged from 0.9 for C10:0 on BTA 1 to 61.9 for C18:1 on BTA 14 Table . These pDGAT1 and region 26 harbors SCD1; these genes are known to affect fat composition [Three chromosomes showed highly significant regions, which were associated with multiple fatty acids: region 14a showed significant associations with 10 traits, region 19b with 8 traits, and region 26 with 7 traits. The region 14a harbors position . These r10 = 36.6), C16:0  = 51.3), and C16:1  = 54.1) were found with two SNPs (ULGR_SNP_AJ318490_1b and ULGR_SNP_AJ318490_1c), located at 0.4 Mbp on BTA 14, that are responsible for the DGAT1 K232A polymorphism. For C6:0  = 8.4), C18:1  = 48.6), and CLA  = 15.8) the DGAT1 K232A SNPs were the most significant SNPs immediately after an unmapped SNP (ULGN_SNP_AJ318490_2). After comparing this unmapped SNP sequence against the sequence of DGAT1, we identified it as an SNP located in DGAT1 that is in high LD (r2 = 0.99) with the DGAT1 K232A SNPs. The strongest associations for C10:1  = 41.1), C12:1  = 22.8), and C14:1  = 80.0) were found with an unmapped SNP (ULGR_SNP_SCD). Also, C16:1 showed strong association  = 34.8) with this unmapped SNP on BTA 26, but C16:1 showed the strongest association with the DGAT1 K232A SNPs on BTA 14 as mentioned above. After comparing this unmapped SNP sequence against the sequence of SCD1, we identified it as the SNP underlying the SCD1 A293V polymorphism. Although SCD1 was not mapped on genome assembly BTAU 4.0, it was mapped on genome assembly UMD 3.0 at 21.1 Mbp on BTA 26 [10 = 11.0) and C12:0  = 9.7) were found with an SNP (ULGR_MARC_10099_486) located at 52.5 Mbp on BTA 19. The strongest association for C4:0  = 6.6) was found with an SNP (ULGR_BTA-45866) located at 54.6 Mbp on BTA 19, and the strongest association for C8:0  = 6.2) was found with an SNP (ULGR_BTA-45847) located at 55.1 Mbp on BTA 19. Also, C14:0 showed strong association  = 22.0) with an SNP (ULGR_BTA-45758) on BTA 19 located at 52.1 Mbp, but C14:0 showed the strongest association with the DGAT1 K232A SNPs on BTA 14 as mentioned above. The strongest association for C18:0  = 5.8) was found with an SNP (ULGR_BTA-28678) located at 64.2 Mbp on BTA 7.In this section the SNP showing the strongest association of the whole genome for each trait is reported. Strongest associations for C14:0 , with significant effects on milk fat composition. These regions showed highly significant associations with several fatty acids. The regions on BTA 14 and 26 are regions that have been studied previously, and our results confirm the earlier findings -12. The DGAT1, which is known to influence milk production traits [DGAT1 resulting in a K to A amino acid substitution (DGAT1 K232A). The K allele is associated with larger fractions of C6:0, C8:0, C16:0, and C16:1, and with smaller fractions of C14:0, C18:1, and CLA.The region on BTA 14 showing an association with C6:0, C8:0, C14:0, C16:0, C10:1, C12:1, C14:1, C16:1, C18:1, and CLA is the region harboring n traits  and milkn traits ,10,12. FULGR_BTC-068225) was located at 3.0 Mbp, and for C12:1 the most significant SNP (ULGR_BTC-067423) was located at 3.7 Mbp. However, for C10:1 and C12:1, the DGAT1 K232A SNPs were also significant. After correcting the phenotypes for the effect of the DGAT1 K232A polymorphism, the -log10 of these most significant SNPs decreased from 6.50 to 2.30 for C10:1, from 5.44 to 2.52 for C12:1, and from 4.63 to 2.40 for C14:1. The LD between these most significant SNPs and the DGAT1 K232A SNPs was moderate (r2 = 0.26 and 0.31). Although the associations of C10:1, C12:1, and C14:1 were just below the significance threshold after correcting for the DGAT1 K232A genotype, the findings still suggest that an additional QTL may be present for the medium chain unsaturated fatty acids on BTA 14.For C10:1 and C14:1, the most significant SNP  at 21.1 Mbp. The A allele was associated with larger fractions of C10:1, C12:1, and C14:1, and with smaller fractions of C10:0, C14:0, and C16:1. Thus, the A allele resulted in more C10:1 and C14:1 at the cost of C10:0 and C14:0. A similar effect was found for C12:0 and C12:1, though C12:0 was not significantly associated with the SNP. The opposite effect was found for C16:0 and C16:1; the A allele resulted in less C16:1 and more C16:0, though C16:0 was not significantly associated with the SNP. The SCD1 gene codes for the SCD enzyme, which desaturates saturated fatty acids to Δ9 unsaturated fatty acids in the mammary gland [SCD1 A293V polymorphism on milk fatty acids [The region on BTA 26 that showed an association with C10:0, C14:0, C16:0, C10:1, C12:1, C14:1, and C16:1 is the region harboring ty acids ,8,10,11.ry gland . The assSCD1 A293V SNP was not significant for C16:0  = 1.09), which also confirms previous studies [SCD1 A293V SNP (r2 = 0.08), and correcting for SCD1 A293V had little effect on the significance of the SNP associated with C16:0  decreased from 5.52 to 4.46). Also, one allele of this SNP is associated with larger fractions of C10:1, C12:1, C14:1, and C16:0, and with smaller fractions of C10:0, C12:0, C14:0, and C16:1, suggesting that it has something to do with desaturation, but it was only significantly associated with C16:0. We did not identify obvious candidate genes in this region.The  studies ,10,11. Tfatty acid synthase  as a candidate gene responsible for the observed effect. In addition to FASN, several other genes located within the region on BTA 19 are involved in the biosynthesis of milk fat, including sterol regulatory element binding transcription factor 1 , ATP citrate lyase , signal transducer and activator of transcription 5A , and growth hormone . These genes are all candidate genes because SNPs in the whole region showed an association with the traits, perhaps in LD with mutations in genes not captured by our marker set. The strongest association was found near FASN, but also near some other candidate genes as discussed below.The region on BTA 19, at 32.7-61.8 Mbp, showed associations with C4:0, C8:0, C10:0, C12:0, C14:0, C16:1, C18:1, and CLA, i.e. mainly with the short and medium chain saturated fatty acids and with the long chain unsaturated fatty acids. No significant effects were found for C6:0, but a QTL was indicated below the threshold in the region on BTA 19 Figure . Morris 10 = 22.04) was also the most significant SNP for C18:1  = 4.52) and located at 52.1 Mbp on BTA 19 (ULGR_BTA-45758 in LOC518878), which is 71,862 bp from FASN. This SNP also showed significant effects on C4:0  = 3.57), C10:0  = 7.01), C12:0  = 5.96), and CLA  = 3.67), whereas the association with C8:0  = 2.96) was just below the threshold. The effects of SNPs on C8:0, C10:0, C12:0, and C14:0 were in opposite direction of the effects on C4:0, C16:1, C18:1, and CLA. Fatty acid synthase (encoded by FASN) is a multi-enzyme system involved in de novo fatty acid synthesis. The SNP effects suggest that less C4:0 and more C8:0, C10:0, C12:0, and C14:0 are produced by fatty acid synthesis, or vice versa, which is in line with the function of FASN. Three SNPs in FASN were included in the marker set used in our study; however, two of them (FASN16009 and FASN763) were monomorphic for our population. The third SNP, FASN17924, showed association with C14:0  = 3.05). Schennink et al. [FASN16024, finding a significant association with C14:0 for the same population as in our study. Morris et al. [FASN17924 and C14:0, but did find a significant association between C14:0 and FASN15531 and FASN15603.The region on BTA 19 was strongly associated with C14:0 and explained a large portion of the total additive genetic variation of C14:0. The SNP most significant for C14:0  and showed significant association with C18:1  = 3.81). The neighboring SNP showed even more associations: with C8:0  = 3.48), C10:0  = 5.13), C12:0  = 3.86), and C14:0  = 7.41).This region on BTA 19 also harbors at yield . One SNPBFGL-NGS-111365), which is 13,873 bp from STAT5A. The SNPs neighboring ACLY and STAT5A showed significant associations with several of these traits, especially C14:0. All SNPs in the regions seem to be detecting the same effect, which is strongest with C14:0. Although previous studies suggested FASN as the candidate gene for association, which of the candidate genes causes the effect remains debatable. The causal mutation might even be in a gene not considered here.No SNPs were located in the other candidate genes on BTA 19. The SNP showing the strongest association with C16:1 was located at 43.8 Mbp on BTA 19  and peroxysome proliferator-activated receptor-gamma coactivator-1alpha . The SNPs most significantly associated with C12:1  = 5.03, at 44.3 Mbp), C14:1  = 4.39, at 41.2 Mbp), and C16:1  = 4.63, at 40.2 Mbp) were located between these candidate genes. ABCG2 has been associated with milk fat yield and percentage [ABCG2 and showed no significant associations with the studied fatty acids. PPARGC1A has been associated with milk fat yield in German Holsteins [PPARGC1A, but none of these were significantly associated with C12:1, C14:1, or C16:1. However, one of the 10 SNPs in PPARGC1A showed an association with C16:1, which was just below the significance threshold  = 2.86). In a candidate gene study, Schennink et al. [PPARGC1A that were not included in our marker set: PPARGC1A1790+514 with C16:1 and PPARGC1A1892+19 with C14:1. Our genome-wide scan indicates that not PPARGC1A, but another region has the strongest effect on the unsaturated medium chain fatty acids.On BTA 6, region 6a was associated with C12:1, C14:1, and C16:1 Table . The SNPrcentage ,36. One 2 ABCG2, .4 Mbp anolsteins  in two lk et al.  found a On BTA 7, two regions showed an association with several fatty acids: region 7a associated with C10:1, C12:1, C14:1, and C18:1; and region 7b associated with C14:0, C12:1, C14:1, and C18:0 Table .Region 7a showed an association with almost all unsaturated fatty acids. In general, the SNP effects on C18:1 were in the opposite direction of the SNP effects on C10:1, C12:1, and C14:1. SNPs in this region were either associated with C10:1, C12:1, and C14:1, or with C18:1, suggesting one QTL for the medium chain unsaturated fatty acids and another for C18:1. The SNP effects on C10:0, C12:0, and C14:0 were in the same direction as the effects on C10:1, C12:1, and C14:1, but they were not significant. The SNP effects on C18:0 were in the same direction as the effects on C18:1, but they were not significant. Although only unsaturated fatty acids were significantly associated with this region, the SNP effects suggested that this QTL has nothing to do with desaturation because the SNP effects on unsaturated fatty acids were in the same direction as the effects on their saturated equivalents.2 = 0.53-0.97). This finding indicates the likelihood of one QTL in this region with an effect on these four traits. The effects of these most significant SNPs were in the opposite direction for C18:0 compared to C14:0, C12:1, and C14:1. The SNP effects on C10:0 and C12:0 were in the same direction as the effects on C14:0, C12:1, and C14:1, but they were not significant. The SNP effects on C16:0, C18:1, and CLA were in the same direction as the effects on C18:0, but they were not significant. These SNP effects suggest that this QTL has something to do with a trade-off between long chain fatty acids and de novo synthesis of medium chain fatty acids. No candidate genes were located in this region.Region 7b showed associations with C14:0, C18:0, C12:1, and C14:1. The most significant SNP for each trait was different, but they were located in the same neighborhood, around 64.0-64.1 Mbp, and were in high LD with one another (rULGR_SNP_BES11_Contig346_1209), was the most significant SNP in the region, and the SNP effect was in the same direction for all three traits. These traits have a high genetic correlation [acyl-CoA synthetase short-chain family member 2 (ACSS2), which activates acetate for de novo fatty acid synthesis [ACSS2 is a good candidate gene for a QTL with an effect on C6:0, C8:0, and C10:0. Given that this particular significant SNP is located in an intron (between exon 16 and 17) of ACSS2, this SNP is not likely the causal mutation, but it can be in high LD with the causal mutation. In addition, the region also had an effect on C14:1 and C16:1. The SNP effects for C14:1 and C16:1 were in the same direction, but these effects were in opposite direction of SNP effects on C6:0-C10:0. The SNP in ACSS2 had no significant association with C14:1 or C16:1, which indicates an additional QTL with an effect on these unsaturated fatty acids.On BTA 13, region 13 was associated with C6:0, C8:0, C10:0, C14:1, and C16:1 (Table 1-acylglycerol-3-phosphate O-acyltransferase 6 (AGPAT6), which is the most abundant isoform of all AGPAT mRNA (~60%) in the mammary gland and involved in positioning fatty acids on the second position of the triglyceride backbone [AGPAT6 knock-out mice, the composition of the triacylglycerol is altered and contains proportionally more polyunsaturated fatty acids at the expense of monounsaturated fatty acids [On BTA 27, region 27 was associated with C14:0, C16:0, C10:1, C12:1, C14:1, and C16:1 Table . The regbackbone , this gety acids , which mDGAT1 and SCD1. For other traits, however, no regions had such large effects. The sum of the total additive genetic variance explained by the most significant SNP per region for C4:0-C12:0 and C18:0 was less than 20% of the total additive genetic variance. This finding suggests that these traits are influenced by many genes with small effects. Regions explaining roughly 1% of the total additive genetic variation or more were detected by the studied design , but additional regions with either undetectable small effects or that were not dense enough in our marker set to detect the effect is likely.Table FASN plays an important role in de novo synthesis, but less than 6.5% of the total additive genetic variance of short chain fatty acids is explained by the region harboring FASN. De novo synthesis of fatty acids requires several compounds in addition to FASN enzymes to elongate fatty acids. One of the compounds is acetate, which is activated by ACSS2 for de novo synthesis, ACSS2 was indicated as a candidate gene associated with C6:0-C10:0 in this study. More genes like this that assist FASN in de novo synthesis and, therefore, explain a portion of the total additive genetic variation is likely. Also, genes involved in transport and triacylglyceride production might explain some of the total additive genetic variation.Even though short chain fatty acids are produced by de novo synthesis, less than 20% of the total additive genetic variance was explained by the analyzed regions. DGAT1 and SCD1, genes that have large effects on medium chain fatty acids and unsaturated fatty acids. Several regions showed associations with these milk fatty acids, but with smaller effects. The short chain fatty acids, C12:0 and C18:0, are not strongly affected by genes with large effects, but are influenced by regions with small effects. Some regions included candidate genes involved in milk fat synthesis pathways. On BTA 19, there were several genes involved in fat synthesis underlying the region associated with multiple fatty acids. Only in a few cases was an SNP associated with fatty acids actually located in a candidate gene. Regions identified in this study can be fine mapped to find causal mutations. The results also create opportunities for changing milk fat composition through breeding by selecting individuals based on their genetic merit for milk fat composition, which can be retrieved from the estimated SNP effects and the individual's genotype.A genome-wide association study of 50,000 SNPs was performed for milk fatty acids, resulting in many QTL. All over the genome regions were associated with milk fatty acids, some regions with just one fatty acid and other regions with multiple fatty acids. Milk fat composition is strongly influenced by polymorphisms in ACB carried out the analysis, prepared and drafted the manuscript. HB participated in the design of the study, the coordination and helped to draft the manuscript. MHPWV helped to draft and edit the manuscript. JAMA initiated and established the overall project design. All authors read and approved the final manuscript.