Common bean is a legume of social and nutritional importance as a food crop, cultivated worldwide especially in developing countries, accounting for an important source of income for small farmers. The availability of the complete sequences of the two common bean genomes has dramatically accelerated and has enabled new experimental strategies to be applied for genetic research. DArTseq has been widely used as a method of SNP genotyping allowing comprehensive genome coverage with genetic applications in common bean breeding programs.HE of 0.442 for the whole collection, 0.102 for Andean and 0.168 for Mesoamerican gene pools (FST = 0.747 between gene pools), 0.440 for the group of cultivars and lines, and 0.448 for the group of landrace accessions (FST = 0.002 between cultivar/line and landrace groups). The SNP effects were predicted with predominance of impact on non-coding regions (77.8%). SNPs under selection were identified within gene pools comparing landrace and cultivar/line germplasm groups  and between the gene pools (59 SNPs), predominantly on chromosomes 1 and 9. The LD extension estimate corrected for population structure and relatedness (r2SV) was ~ 88 kbp, while for the Andean gene pool was ~ 395 kbp, and for the Mesoamerican was ~ 130 kbp.Using this technology, 6286 SNPs (1 SNP/86.5 Kbp) were genotyped in genic (43.3%) and non-genic regions (56.7%). Genetic subdivision associated to the common bean gene pools (K = 2) and related to grain types (K = 3 and K = 5) were reported. A total of 83% and 91% of all SNPs were polymorphic within the Andean and Mesoamerican gene pools, respectively, and 26% were able to differentiate the gene pools. Genetic diversity analysis revealed an average For common bean, DArTseq provides an efficient and cost-effective strategy of generating SNPs for large-scale genome-wide studies. The DArTseq resulted in an operational panel of 560 polymorphic SNPs in linkage equilibrium, providing high genome coverage. This SNP set could be used in genotyping platforms with many applications, such as population genetics, phylogeny relation between common bean varieties and support to molecular breeding approaches.The online version of this article (doi:10.1186/s12864-017-3805-4) contains supplementary material, which is available to authorized users. Phaseolus vulgaris L. [Phaseolus vulgaris varieties [It is estimated that approximately 150 plant species are grown directly for human consumption or animal feed worldwide, and 30 of them contribute to 95% of the calories and protein in the human diet . Among lgaris L. , being carieties . In BrazThere is consensus regarding the predominant genetic structure of common bean in the Andean and Mesoamerican gene pools , 8, due Molecular markers have been very helpful in efforts to detect gaps and redundancies in germplasm collections , to elucHigh-density genotyping, combining genome complexity reduction with next-generation sequencing (NGS), allows the identification of an almost unlimited number of SNPs for any species at low cost. The strategies of restriction site-associated DNA sequencing (RADseq)  and genoIn this study, DArTseq derived SNPs were used for the genetic analysis of a common bean germplasm collection of Andean and Mesoamerican origin, being each origin further stratified into cultivar/line and landrace groups. This study also made advances in the detection and characterization of genomic regions with signals of selection imposed by the domestication and breeding of common bean in Brazil. In addition, a set of SNPs with high discriminatory value between gene pools, as well as between groups (landraces and cultivars/lines) within gene pools, was proposed for routine use for the characterization of gene bank accessions and in breeding programs.A total of 188 common bean accessions, including 91 landraces and 97 Brazilian and international cultivars/lines belonging to the Andean and Mesoamerican gene pools, were used . The DArTseq quality markers were determined by the parameters “reproducibility”  and “call-rate” (percentage of samples for which a given marker was scored”) [DArTseq™ represents a combination of DArT complexity reduction methods, based on methyl filtration, and next-generation sequencing platforms . The teca et al. . The ampscored”) .P. vulgaris v 1.0 [Genomic regions flanking SNPs were aligned against the reference genome of is v 1.0  using BLis v 1.0 . Annotatis v 1.0  based onis v 1.0 . The SNPSNPs with putative effects predicted to be moderate or high were functionally annotated using the Blast2GO tool v 3.2  and char2 < 0.5) identified using Golden Helix SNP & Variation Suite v 8  through the LD Prunning command. A population number (K) ranging from 1 to 20, with 20 interactions each, was assumed. The admixture model was applied using a 500,000 burn-in periods followed by 1,000,000 Markov Chain Monte Carlo (MCMC) replications. The most likely K was determined, as proposed by [The genetic structure, based on the Bayesian clustering approach, was implemented by Structure v 2.3.4 . This anposed by  using Stposed by , followeposed by . The orgposed by . Discrimposed by  was perfposed by , 59 to pposed by , based oposed by . The Anaposed by  using SNFST for each window of the genome [π, average pairwise differences among individuals chosen randomly from the sample population) [W, estimation of population mutation rate calculated on the basis of the number of segregating sites) [http://ggplot2.org/) was used to create the graphs for patterns of variation [The e genome , Tajima’e genome , diversiulation) , nucleotulation) , 67 and g sites) , were esg sites) , 69. Theariation .FST joint distribution versus heterozygosity. Those loci that fall outside the 95% confidence interval were considered outliers.The outlier SNPs were detected using two methods: 1) Method proposed by Foll and Gaggiotti  implemen2) and corrected for bias due to population structure (K = 2) and relatedness (r2SV) using the LDcorSV package for R [LD was estimated using SNPs with MAF ≥ 0.05 and the pairwise LD measures were calculated by the usual method  was performed applying an individual-centered approach as described by [HE estimates were obtained using a hierarchical procedure, with a 150 km neighborhood grid used to avoid spatial autocorrelation between groups. In cases in which only one accession was represented in a given region, HE represents diversity only for this accession. This analysis was performed using the “sHe” function of the R package “biotools” [Spatial analysis of genetic diversity . Data from rainfall and climate/temperature were obtained from the Institute of Forest Research and Studies (IPEF). The software ArcGIS, based on Geographic Information System (SIG), was used to define areas on the maps. Landraces were geographically placed on the maps using the associated coordinate information.The 188 beans analyzed by DArTseq comprised a mini core group derived from the Brazilian common bean core collection (600 accessions) and are representative of the most genetically diverse accessions identified by microsatellite markers analysis (data not shown). For the SNP markers generated in DArTseq  showed alignment in the genome, of which 5311 (89.09%) aligned to a single region and 650 (10.90%) presented multiple alignments (ranging from two to 88). The sequences aligned to the 11 chromosomes and 12 scaffolds. The average number of SNPs per chromosome was 541, ranging from 389 on chromosome 4 to 792 on chromosome 2 , low impact (14.22%), moderate impact (7.92%), and high impact (0.05%). Most SNPs with predicted effects were observed in genic regions (6950), of which 20.82% and 17.30% were observed within exons and introns, respectively, with the remaining in non-translated regions. In genic flanking sequences (5 kb window) 5,267 effects were identified, of which 58.21% and 41.79% occurred in downstream and upstream regions, respectively. SNP effects categorized as moderate and high, were identified in 901 transcripts, of which 810 were mapped and 777 were fully annotated  and Mesoamerican (111) gene pools and six genotypes (3.87%) as admixture Fig. . Five ofThe tools implemented in DAPC revealed a more complex population structure in the Mesoamerican by landraces and lines/cultivars Fig. . The denFST value between groups (0.747). The combined set of markers generated an overall exclusion power of 100%, whereas 28 SNPs distinguished all genotypes  and 0.012 (p >0.002) for Mesoamerican and Andean, respectively. Within the Andean gene pool, cultivars/lines presented 1,217 private alleles, while in landraces it was 533 (Table High numbers of polymorphic SNPs were identified for Mesoamerican (87.51%) and Andean 88.39%) cultivars/lines compared to the landraces (Mesoamerican = 90.78%. Andean = 73.49%). The 9% cultivFST was high between the gene pools for the majority of the chromosomes, with the highest level at chromosomes 1 and 9  compared to the Mesoamerican  and was greatly reduced for the Andean  and Mesoamerican  groups  for the cultivars/lines and 0.0043 for the landraces of Mesoamerican origin, with a similar distribution of SNPs into MAF classes. Reduced values were observed for the cultivars/lines (0.0022) and landraces (0.0013) of Andean origin, probably due to the additional set of SNPs with MAF > 0.1 and ≤ 0.2  for all individuals was 0.00071 (±0.00001), with a lower value estimated for the 33 Andean landraces (0.00058). The θW, Tajima’s D, and FST estimates were highly variable across the P. vulgaris genome  and reduced θW and Tajima’s D  and Arlequin (p < 0.05), respectively, of which 16 loci were common to both analyses. From the 59 SNPs, 54 aligned over the 11 chromosomes (with the highest abundance on chromosomes 1 and 9), with an average of one SNP every 8.6 million bases. Across the genome, ~41% of SNPs were identified within genes , while the remaining (~59%) were in intergenic regions. The analysis of SNP effect on the outliers revealed a total of 110 effects predicted for 54 outliers, of which 11% were low-impact, 10% moderate and 79.09% modifier type. We identified 91 transcripts affected by 54 SNPs under selection, of which 82 presented homology to the non-redundant (nr) protein database and 71 were annotated , 15 outlier SNPs common to both analyses were identified distributed around chromosomes 2, 7, 8, and 9. 131 transcripts were affected by these SNPs, and 116 of these have been annotated  at a distance of ~2055 Kb and ~395 Kb for r2 and r2SV, respectively, while for the Mesoamerican, half decay was observed at distances of ~312 Kb and ~130 Kb. For accessions overall without correction (r2), no decay was observed within the 3000 Kb window  LD decreased to half at 88 Kb  to 62 (chromosome 8) were identified. A total of 4354 SNPs (82.57%) were distributed in these blocks, with an average of ~10 SNPs per block. Chromosomes 9 (90.12%) and 4 (71.77%) had the highest and lowest percentage of SNPs within blocks, respectively. The average block size was 842.2 Kb, and the largest block was in chromosome 3, with 8634 Kb and 21 SNPs. The maximum and minimum frequency of haplotypes was 0.850 and 0.010, respectively, with the most common haplotype located on chromosome 7. On average, 71.66% of the genome was covered by the blocks , FIS = 0.826 (±0.001), and FIT = 0.897 (±0.001). DAPC revealed a structure similar to those obtained for the whole set of SNPs (5531)  was 0.411. For the regions with low precipitation (≤700 to 1000 mm), the HE of the seven accessions  was 0.419.The highest estimates of sis Fig. . Within P. vulgaris genome, supporting the analysis of population structure, LD, and identification of genomic regions under selection that have an impact on crop improvement research. The average call-rate was 92%, close to the value of 91.3% previously reported for watermelon [Pstl-Msel) resulted in the more frequent appearance of SNPs, as reported by Schröder et al. [The SNP markers derived using DArTseq demonstrated that this technology is an efficient method of genotyping with broad genome coverage and can be useful for analyses of genetic diversity in a common bean germplasm pool composed of landraces and cultivars/lines. The sequencing of two important varieties of common bean representative of the Andean and Mesoamerican groups , 18 has termelon , and thetermelon . A high termelon , increasr et al. . Within r et al. . The conn = 181, HE = 0.442), compared to the SNPs identified by RAD , Valdisser et al. [n = 88, HE = 0.390), Müller et al. [HE = 0.168; n = 111) was close to values obtained by Rodriguez et al. [HE = 0.157; n = 100); however, lower compared to the studies of Cichy et al. [HE = 0.233; n = 21), who characterized a more diverse set of accessions that are from different geographic origin, breeding program, grain type, and growth habit. In our Mesoamerican germplasm, the only structure observed was by grain type (K = 4) [π = 0.001781), compared to the Mesoamerican (π = 0.004541) germplasm. The lowest diversity in the Andean is expected  due to the prevalence of Mesoamerican introduction and domestication in Brazil [DArTseq also allowed the detection of SNPs with high diversity  , 31, witn Brazil , in addin Brazil . The intn Brazil , whereasn Brazil , 88.HE and π were reported for the Andean, compared to the Mesoamerican. Moreover, the diversity of the Andean Brazilian landraces  reduces only in the order of 1.3 x compared to the diversity estimated by Cichy et al. [HE = 0.128). Therefore, there is a strong indication that Brazil may be a center of secondary domestication with high diversity that deserves a further characterization of the remaining accessions and integrates into the Brazilian core collection. For the Andean cultivars/lines (n = 31), even represented by accessions of 22 countries, the πC/L (0.002171) was almost half the value for the Mesoamerican  used predominantly in breeding program for improving agronomic traits [In the present study, all landraces originated from Brazil and only the breeding germplasm included introductions. From the 87 Brazilian landraces, lower estimates of Regarding the spatial distribution of the genetic diversity, no significant relationship between genetic and physical distances was identified (data not shown). By overlapping the thematic and diversity maps, important sites to collect landraces, considering both genetic diversity and adaptability under hydric restriction and high temperature were identified. For breeding purposes, this is extremely valuable in terms of understanding adaptive responses and identification of target accessions genetically diverse with the potential to be integrated into bean pre-breeding programs, as well as germplasm resource management. Genetically diverse accessions originated from geographic areas with high temperature and reduced rainfall could be of potential use in common bean breeding programs in attempt to increase the frequency of favorable alleles, and consequently, increasing the potential to obtain inbred lines more tolerant to drought stress, which can be obtained, for example, through recurrent selection.As expected, the SNP effects categorized as modifiers was more abundant and SNP with impact on protein efficiency and loss-of-function, that have a direct impact on gene function with adaptive interference during the course of selection, were reported in a smaller proportion . ConsideFST = 0.86 and chromosome 9, FST = 0.87), suggesting that distinct and broad genetic mechanisms were involved in the process of common bean domestication. Similar finds were reported by Schmutz et al. [The process of domestication and artificial selection imposed by agriculture resulted in changes to allelic frequencies and allowed the identification of genomic regions under adaptive evolution using high-density SNP genotyping , 91, 92.z et al. . Among tz et al. , 22 and,z et al. .Outlier SNPs associated with the same GO terms were reported in both gene pools. Among these, we highlighted integral components of membranes that could respond to plant demand to be more efficient in the process of water and nutrient transport, as well as the location of photoassimilates. Furthermore, several common transcripts related to the development of morpho-anatomical structures were reported, corroborating previous studies of QTLs involved in the domestication and diversification processes . SelectiOur data showed that, several genes under selection in both pools were related to pathways of primary metabolism, such as sucrose, amino acids, lipids and starch metabolism. These pathways are source of energy and carbon that broadly affect a range of cellular mechanisms. Interestingly, 11 distinct putative genes under selection were identified in both pools, six Mesoamerican and five Andean genes, related to the same Purine and Thiamine metabolic pathway revealing different signatures of selection associated with the same processes. Products derived from the Thiamine biosynthesis play role as a cofactor in important metabolic pathways, such as glycolysis, Krebs cycle, nitrogen assimilation, and have been shown to have functions in response to abiotic and biotic stress in plants . The Pur2, and after the correction, an increase in decay was observed, showing that the evolutionary and breeding history [c/L = 4040 Kbp; Meso LDc/L = 428 Kbp) presented slower LD decay compared to the landraces , consistent with previous studies [A high proportion of alleles at low frequencies were observed within the gene pools, whereas for the whole set of accessions, most SNPs were present at high frequencies (≥0.3), reflecting the presence of fixed loci for alternative alleles between the gene pools. Without correcting for relatedness and structure, the LD presented elevates estimates of r history , 85 stro history  and lobl history . This wa studies . The pat studies , and thi studies . The mor studies . The red studies .Furthermore, the variation in size of the haplotype blocks across the common bean genome Table  revealedDArTseq analysis over a diverse group of common bean germplasm allowed the identification of a panel composed of 560 SNPs, selected from the whole set of 6286, with nearly 90% genome coverage. For breeding purposes, this panel of SNP, which allows identification of genetic intervals at low to moderate resolution, would be readily incorporated to routine genetic analysis of breeding programs. The benefits of marker-assisted breeding using this panel over a large set of SNPs are due to the increase in the efficiency of genome sampling at a lower cost. This panel certainly will be of great utility for germplasm characterization, linkage mapping, and assisted backcrossing, meeting the research demands with impacts on bean crop systems. For studies that demand improved genome resolution, such as association and genome selection, whole genome sequencing of multiple samples is the method we should use to allow the detection of the majority allelic variants for relevant traits.The present study has shown that the DArTseq approach generated a large set of useful SNPs for common bean with a comprehensive genome coverage, representative of coding and non-coding regions that allowed an accurate assessment of structuration and quantification of genetic diversity in the Brazilian core collection composed of landrace and improved germplasm. We also were able to identify genomic regions under selection in domesticated germplasm associated with molecular functions that could be used as target in further studies to determine the nature and relevance of these loci in the process of adaptation. In addition we observed that the extent of LD was variable throughout the genome and in different strata of germplasms, which was helpful for determination of a reduced set of SNPs useful for genetic analysis. Through this study, we are adding value to the common bean Genebank at Embrapa publicly available for worldwide signatory institutions of the International Treaty on Plant Genetic Resources. This information, in combination with phenotypic evaluation, hold much promise for breakthroughs in the elucidation of genetic control of complex traits.Additional file 1:Identification of the common bean accessions used in the SNP analysis, the gene pool origin, type of germplasm, institution of origin, and the commercial type of grain. (XLSX 18 kb)Additional file 2:The genotyping data and SNP quality parameters for the whole set of accessions. (XLSX 5705 kb)Additional file 3:Functional annotation of transcripts affected by SNPs with high (*) or moderate predicted impact. (XLSX 75 kb)Additional file 4:Enzymes associated with SNP sequences with high and moderate impact predicted. (PDF 22 kb)Additional file 5:The KEGG pathway maps for SNPs with high or moderate predicted impact. (XLSX 25 kb)Additional file 6:Gene models representing all SNP with high impact effects predicted by SnpEff. A) High impact effects classified as “splice_acceptor_variant”, defined as two bases before exon start, except for the first exon. B) High impact effects classified as “stop_gained”, a sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon. The nucleotide described corresponds to the disruptive high impact allele. (PNG 3026 kb)Additional file 7:FST), Watterson’s θ (θW), and Tajima’s D for all 11 chromosomes in the P. vulgaris genome for each group. FST is given as an average across all pairwise comparisons between Andean cultivars/lines and landraces (green), and between Mesoamerican cultivars/lines and landraces (red). The results of Tajima’s D and θW are given for each group separately, Andean cultivars/lines (dark green) and landraces (green), and Mesoamerican cultivars/lines (red) and landraces (yellow). FST, Tajima’s D and θW related summary statistics were calculated for each 100 kb non-overlapping sliding window. (PDF 240 kb)Genome-wide loess curves for genetic differentiation  classes. (A) Distribution of the number of SNPs into MAF classes for the whole population (grey), Andean (green), and Mesoamerican (red) genotypes. (B) Distribution of the number of SNPs into MAF classes for each group: Andean cultivars/lines (dark green) and landraces (green) and Mesoamerican cultivars/lines (red) and landraces (yellow). (PDF 23 kb)Additional file 9:Functional annotation of the transcripts affected by outlier SNPs. (XLSX 21 kb)Additional file 10:Functional annotation showing the most relevant GO terms for the outlier SNPs. The terms were filtered according to the node score. The numbers represent the amount of transcripts related to each term. (PDF 122 kb)Additional file 11:The KEGG pathway associated with outlier SNPs. (XLSX 15 kb)Additional file 12:Functional annotation of transcripts affected by SNP outliers in the Mesoamerican gene pool. (XLSX 24 kb)Additional file 13:Functional annotation of transcripts affected by outlier SNPs in the Andean accessions. (XLSX 15 kb)Additional file 14:Functional annotation showing the most relevant GO terms for the outlier SNPs within each gene pool. (A) Mesoamerican; (B) Andean. The terms were filtered according to the node score. The numbers represent the amount of transcripts related to each term. (PDF 233 kb)Additional file 15:Information of the selected 560 SNPs to compose a panel for genetic analysis of common bean. (XLSX 56 kb)Additional file 16:DAPC analysis based on the 560 SNP panel showing the division between Mesoamerican cultivars/lines (red) and landraces (yellow). M_CL: Mesoamerican cultivars/lines; M_L: Mesoamerican landraces; A_CL (dark green): Andean cultivars/lines; A_L (light green): Andean landraces. (PDF 38 kb)


Solanum boliviense (PI 597736) was crossed with a low/medium folate diploid S. tuberosum clone USW4self#3. The resulting F1 progeny was intermated to generate an F2 population, and tubers from 94 F2 individuals were harvested for folate analysis and SNP genotyping using a SolCap 12K Potato SNP array. Folate content in the progeny ranged from 304 to 2,952 ng g-1 dry weight. 6,759 high quality SNPs containing 4,174 (62%) polymorphic and 2,585 (38%) monomorphic SNPs were used to investigate marker-trait association. Association analysis was performed using two different approaches: survey SNP-trait association (SSTA) and SNP-trait association (STA). A total of 497 significant SNPs were identified, 489 by SSTA analysis and 43 by STA analysis. Markers identified by SSTA were located on all twelve chromosomes while those identified by STA were confined to chromosomes 2, 4, and 6. Eighteen of the significant SNPs were located within or in close proximity to folate metabolism-related genes. Forty two SNPs were identical between SSTA and STA analyses. These SNPs have potential to be used in marker-assisted selection for breeding high folate potato varieties.Micronutrient deficiency, also known as the hidden hunger, affects over two billion people worldwide. Potato is the third most consumed food crops in the world, and is therefore a fundamental element of food security for millions of people. Increasing the amount of micronutrients in food crop could help alleviate worldwide micronutrient malnutrition. In the present study, we report on the identification of single nucleotide polymorphism (SNP) markers associated with folate, an essential micronutrient in the human diet. A high folate diploid clone Fol 1.6 from the wild potato relative  Folate plays an important role in overall cellular and organismal health. In the absence of sufficient folate intake, cellular processes such as nucleic acid biosynthesis, the metabolism and catabolism of amino acids, and the methylation cycle cannot take place efficiently , suggesting that the folate trait is highly heritable and that high folate plants tend to consistently have higher folate content than low folate plants.Folate content in the Fy weight . Approxiy weight . The parreported . Transgrreported . Note thpulation  is 0.67 A total of 6,759 high-quality SNPs  were selected for mapping after very stringent screening of the 12K SNP array using the GenTrain score in Genome Studio. Sample BRR80 was removed from further analysis as it behaved as an off type for the majority of monomorphic markers. All the SNPs selected showed less than 10% “No call rate”.S. boliviense parent Fol 1.6 is less heterozygous (5.3%) , and that the s (5.3%) . Our iniividuals . It is oividuals , 37.2 individuals was surveyed to identify any accidental selfing and unrelated outcrossing individuals. All the individuals were scanned using factorial analysis (based on dissimilarity with number of axes set at 5) and neighbor joining (based on unweighted pairwise). Both these analyses suggest the absence of any potential subgroups (outcrossing groups). However, four samples  clustered together to form a separate group  ≥ 1.3 (default parameter in JMP Genomics) . These rSSTA identified 489 significant markers located on all twelve chromosomes, with chromosome 3 containing the highest number of markers . The larOf particular interest are significant SNPs that were identified by both SSTA and STA methods. There were 50 and 35 significant markers on chromosome 6 identified by SSTA and STA analyses, respectively, out of which 35 were common in both analyses. Similarly, STA identified seven markers on chromosome 4, whereas SSTA identified 31 markers on the same chromosome, out of which seven were common in both analyses. This set of markers showed an average of 0.29 PIC, 0.43 Het, 0.36 Div, and 0.24% MAF . SimilarThese common significant SNPs span a region of 10.24 Mb in chromosome 6. In chromosome 4, the common SNPs are located in two hotspots that are 60 Mb apart, one of which spans 0.7 Mb and consists of six SNPs, whereas the second spot consisted of only one SNP marker. Hence, these 42 SNPs located on chromosome 6 and 4  could beS. tuberosum genome are more likely to address only the common alleles rather than the rare alleles from S. bolivense. This could result in loss of differential real folate alleles from S. bolivense. S. bolivense population shows segregation for folate trait, but the underlying alleles, even if segregating, are less likely to be marked up by the SNPs derived from S. tuberosum.One possible weakness of SNP as a marker is ascertainment bias . The useFolate biosynthesis is a well characterized pathway in plants . It invoIn this study, a diploid segregating population was developed and genotyped to find significant associations between SNPs and folate content. A total of 497 SNPs were identified with potential associations with folate content by using two analytical methods. A set of 42 identical SNPs located on chromosome 6 and 4 was identified by both methods. Additional significant SNPs were located within or in close proximity to folate metabolism-related genes. Considering high reliability of folate trait with presence of high frequency of minor alleles, the set of markers identified in this study could facilitate the systematic screening of high folate germplasm and the introgression of the high-folate trait into new potato varieties. Because folate analyses are tedious and materials used in this study cannot really be evaluated in the field, folate was quantified from a single tuber harvest. Better replication, validation, and further tests in a field-ready background are next steps to this preliminary work. Moreover, there may be variation between genotypes in terms of folate retention after cooking. It will be important to evaluate and select germplasm that maintains high folate content after cooking.S1 Fig(A) Survey SNP-trait association (SSTA) analysis. (B) SNP-trait association (STA) analysis. SNP, single nucleotide polymorphism.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableIn red are markers that displayed high p-values but were included in the study because they are common significant markers in both SSTA and STA. SSTA, survey SNP-trait association; STA, SNP-trait association; SNP, single nucleotide polymorphism.(XLSX)Click here for additional data file.S3 TableSTA, SNP-trait association; SNP, single nucleotide polymorphism.(XLSX)Click here for additional data file.S4 TableBiosynthesis genes are highlighted in blue while salvage and homeostasis genes are highlighted in green.(DOCX)Click here for additional data file.


PigVar collects parameters of the variations including summary lists of the locations of the variations within protein-coding and long intergenic non-coding RNA (lincRNA) genes, whether the SNPs are synonymous or non-synonymous, their ancestral and derived states, geographic sampling locations, as well as breed information. The PigVar database will be kept operational and updated to facilitate medical research using the pig as model and agricultural research including pig breeding.Pigs are excellent large-animal models for medical research and a promising organ donor source for transplant patients. Next-generation sequencing technology has yielded a dramatic increase in the volume of genomic data for pigs. However, the limited amount of variation data provided by dbSNP, and non-congruent criteria used for calling variation, present considerable hindrances to the utility of this data. We used a uniform pipeline, based on GATK, to identify non-redundant, high-quality, whole-genome SNPs from 280 pigs and 6 outgroup species. A total of 64.6 million SNPs were identified in 280 pigs and 36.8 million in the outgroups. We then used LUMPY to identify a total of 7 236 813 structural variations (SVs) in 211 pigs. Positively selected loci were identified through five statistical tests of different evolutionary attributes of the SNPs. Combining the non-redundant variations and the evolutionary selective scores, we built the first pig-specific variation database, PigVar (Database URL: http://www.ibiomedical.net/pigvar/ However, the SNP list in dbSNP lacks sample information on individuals, which limits the ability to conduct population genetic analyses based on these SNPs. With the development of high-throughput sequencing techniques, genome sequence data from hundreds of pigs, representing various breeds, has been generated , Sus barbatus, Sus verrucosus, Sus cebifrons, Sus celebensis and Phacochoerus africanus). The samples have wide geographic origins, and include all locations where pigs were domesticated in Eurasia  with the parameters ‘-M -t 5’ to the pig reference genome (Sus scrofa 10.2)  with the parameter ‘-b’  with the parameters ‘REMOVE_DUPLICATES = true VALI DA T I ON  _STRINGENCY = SILENT  MAX _FILE _HANDL ES _ FOR _READ_ENDS_MAP = 1000’ was then used to remove PCR duplicates. To obtain high-quality SNPs, samtools mpileup  and the Genome Analysis Toolkit  , following a standard processing pipeline that included realignment of the reads around indels  and recalibration of the base quality scores to obtain more accurate quality scores for each base, followed by UnifiedGenotyper  to call SNPs. We identified at total of 64 582 787 and 36 810 479 SNPs in the pigs and their outgroup species, respectively. The number of SNPs that we identified is greater than the number found in the latest version of dbSNP .All genome resequencing data used here were generated using the Illumina platform. Data from Ai et al. , Li et a  , 23. Forfa 10.2) , mappingf57256b) , 25 was ftp://ftp.ensembl.org/pub/release-80/gtf/sus_scrofa; last accessed January 18, 2017). In our previous study. SNPs were then annotated with protein-coding genes, transcripts and proteins, downloaded from the Ensembl FTP site  of the allelic change for each SNP was deduced by comparing the bases in 280 pigs versus 7 outgroup individuals from 6 species closely related to pigs. All outgroup individuals were considered for determining the direction of the SNP change. A SNP needed to have been sequenced in at least three of the seven outgroup individuals, and only those unambiguously called were classified. A total of 39 767 799 SNPs were classified into their ancestral and derived states from the 64.6 million pig SNPs.et al.  that are associated with human diseases and traits to the mouse and pig genomes using LiftOver , and found that the number of SNPs identified in disease and trait genes in pig is more than that found in mouse . We use Java Server Pages  to implement data visualization, search and download. Gbrowse (v2.55) was used for chromosome-based data visualization  database , and a specific location in one of four input formats, PigVar SNP ID, dbSNP ID, genomic region or gene name . GenomicAll SNP data called by samtools mpileup, GATK, phased SNPs, and the structure variations identified using LUMPY can be downloaded from the database.PigVar is the first database that brings together SNPs called from 287 whole genome sequenced pigs and relatives, their selection scores, as well as structural variations from 211 pigs. Previous studies , 20, 21 Pigs are gaining increasing consideration as a model for medical research owing to the similarity of their metabolic characteristics, cardiovascular systems and organ size to those of humans , especiaTo ensure the long time efficiency of this database, we will regularly update the PigVar database as new pig whole-genome re-sequencing data by our own research group or from publicly available resources becomes available. PigVar will also undergo continuous improvements, for instance to enable downloading of intermediate processing files and submitting whole-genome sequence data or SNP list directly to our database by researchers. Additional statistical genetic parameters, such as CMS , iHS 3434 and reIn conclusion, PigVar represents the first comprehensive genomic pig database containing 64.6 million SNPs from 280 pigs, 36.8 million SNPs from their outgroup species, accurate structural variations in 211 pigs, as well as positive selection analysis. PigVar’s user friendly functionalities should enable researchers to obtain detailed information on specific SNPs, including location within protein-coding or lincRNA genes, synonymous or non-synonymous substitutions, ancestral or derived state, plus their associated positive selection scores. PigVar should accelerate medical research using pigs as a model, and enhance genomic studies including pig breeding.Database Online.Supplementary DataClick here for additional data file.


Sparus aurata) is a species of paramount importance to the Mediterranean aquaculture industry, with an annual production exceeding 140,000 metric tons. Pasteurellosis due to the Gram-negative bacterium Photobacterium damselae subsp. piscicida (Phdp) causes significant mortality, especially during larval and juvenile stages, and poses a serious threat to bream production. Selective breeding for improved resistance to pasteurellosis is a promising avenue for disease control, and the use of genetic markers to predict breeding values can improve the accuracy of selection, and allow accurate calculation of estimated breeding values of nonchallenged animals. In the current study, a population of 825 sea bream juveniles, originating from a factorial cross between 67 broodfish , were challenged by 30 min immersion with 1 × 105 CFU virulent Phdp. Mortalities and survivors were recorded and sampled for genotyping by sequencing. The restriction-site associated DNA sequencing approach, 2b-RAD, was used to generate genome-wide single nucleotide polymorphism (SNP) genotypes for all samples. A high-density linkage map containing 12,085 SNPs grouped into 24 linkage groups (consistent with the karyotype) was constructed. The heritability of surviving days (censored data) was 0.22  and 0.28  using the pedigree and the genomic relationship matrix respectively. A genome-wide association study did not reveal individual SNPs significantly associated with resistance at a genome-wide significance level. Genomic prediction approaches were tested to investigate the potential of the SNPs obtained by 2b-RAD for estimating breeding values for resistance. The accuracy of the genomic prediction models (r = 0.38–0.46) outperformed the traditional BLUP approach based on pedigree records (r = 0.30). Overall results suggest that major quantitative trait loci affecting resistance to pasteurellosis were not present in this population, but highlight the effectiveness of 2b-RAD genotyping by sequencing for genomic selection in a mass spawning fish species.Gilthead sea bream ( Sparus aurata) is one of the most important farmed fish in Mediterranean countries, with an annual production of ∼146,000 metric tons (Photobacterium damselae subsp. piscicida (Phdp) is one of the primary disease problems faced by the sea bream aquaculture industry. High levels of mortality (∼90–100%) are frequently observed, especially in periods where water temperature rises above 18°, with larvae and juveniles being most susceptible  breeding programs  families (Previous studies into the genetic resistance to pasteurellosis in sea bream have detected resistance quantitative trait loci (QTL) using microsatellite markers . Mortality levels were nearly identical for both tanks, and only fish from tank A were included in the current study. Fish used in the challenge originated from 67 broodfish . As already described in Table S2).All fish were then transferred to the Istituto Zooprofilattico Sperimentale delle Venezie  for the experimental challenge. Fish were divided into two aerated tanks (A and B), each with 800 liter of recirculating seawater . Water temperature was maintained at 19°. After 1 wk of acclimation, fish were experimentally infected with a highly virulent strain of Phdp (strain 249/ittio 99), as described in A total of 892 2b-RAD libraries (67 parents and 825 juveniles) were constructed by following the protocol reported by http://comailab.genomecenter.ucdavis.edu/index.php/Barcode_generator). PCR reactions (50 μl) were prepared containing 12 μl of ligated DNA product, 0.2 μM of each primer, 0.3 mM dNTP, 5× Phusion HF buffer, and 2 U Phusion high-fidelity DNA polymerase (New England Biolabs). Each library was PCR amplified using the following conditions: 13 cycles of 95° for 5 sec, 60° for 20 sec, and 72° for 5 sec.Sample-specific barcodes were designed through a Barcode Generator program  and offspring (48 libraries per pool). The quality of each pool was verified on Agilent 2100 Bioanalyzer. Finally, pooled libraries were sequenced on an Illumina HiSeq2500 platform  using 50 base single-end sequencing (v3 chemistry).et al. 2013).Quality and adapters trimming of sequenced reads were performed by running a customized script -passed reads using parameters -m 10 -M 2 -N 3 for parents and -m 5 -M 2 -N 3 for offspring, (ii) a catalog of loci unique for all families/crosses was constructed by using all parents’ reads on cstacks program, then (iii) each set of parents/offspring per cross was matched separately against such a catalog (sstacks program), followed by genotype assignment by setting the following parameters on the genotypes program: -c–min_hom_sequations 7–max_het_sequations 0.05.For each family/cross, individual genotypes were constructed using components of the STACKS pipeline as follows: (i) for each individual, S. aurata genome . Mapping analysis was carried out by means of CLC Genomic Workbench 7.5, with stringent criteria .Unique tags created by STACKS were mapped against a draft assembly of P < 0.001). Linkage groups were formed using a minimum LOD threshold value of 8 in the SeparateChromosomes module, allowing a maximum distance between consecutive SNPs of 50 cM. Marker order within each linkage group was performed using the OrderMarkers module, where the likelihood of marker order is computed by using a hidden Markov model , μ is the vector of the intercept, Z is the incidence matrix relating phenotypes with the random animal effects, u is the vector of animal effects ∼N with either A corresponding to the pedigree-based relationship matrix or G corresponding to the genomic relationship matrix, and σg2 corresponding to the additive genetic variance. Finally, e is the vector of residuals. The G matrix was estimated according to kin function of the R/synbreed package (File S1). The additive genetic variance was estimated by applying Markov Chain Monte Carlo (MCMC) algorithm, using a prior that followed an inverse scaled x2 distribution (df = 5). The MCMC used 10-Million iterations, first 10% of which were discarded, and values were stored every 1000 iterations thereafter. Convergence of the resulting posterior distribution was assessed both visually (inspecting the resulting MCMC plots) and analytically with Geweke’s diagnostic using R/boa v1.1.7 , α is the vector of unknown marker effects, u is the vector of animal random effects ∼N, and e is the vector of residuals. The matrix G represents the genomic relationship matrix as described above, and X and Z are incidence matrices relating y to α and u, respectively. According to the above model, additive SNP effects are treated as fixed effects, with the inclusion of the random animal effect to decrease spurious associations due to (genomic) relationships between the animals (N), where N represents the number of QC-filtered SNPs across the entire genome.To test the association between individual SNPs and resistance to pasteurellosis (measured as surviving days), a GWAS was performed using R/rrBLUP . The mixt distribution induces marker size effect shrinkage, allowing for variable marker effect sizes. Models BayesB and BayesC also perform variable selection, with the difference between the two being the usage of a scaled-t or a Gaussian prior density, respectively . SNPs with >15% missing genotypes were removed in order to minimize impact of imputed genotypes, since used software cannot handle missing genotypes. Missing values of the remaining SNPs were imputed using R/synbreed  softwareectively  was apply is the vector of phenotypic records, η is the linear predictor, and e is the vector of residuals.The general form of the fitted models was the following:η, in the case of rrBLUP, BayesA, BayesB, and BayesC, had the following general form:μ is the intercept, 1X is the design matrix relating the phenotypes to the markers, and β1 is the vector of marker effects with corresponding priors depending on the model used. Marker coding followed the format where the heterozygotes were coded as 1 and the two alternate homozygotes as 0 and 2.The linear predictor, η, in the case of pedigree BLUP, had the following form:u is the animal random effect vector ∼N with the matrix A representing the pedigree estimated relationship matrix. The parameters of the above models were estimated through MCMC .The linear predictor, n = 578 animals) and a validation set (n = 200). The above was repeated 100 times, with the obtained prediction accuracies being adjusted for the trait heritability for each tested model. The GEBVs for each replicate of the validation data set were estimated as:X is the incidence matrix relating GEBV with SNP genotypes and u is the vector of estimated SNP effects from the corresponding training data set.Assessment of the accuracy of breeding value predictions was conducted according to the following procedure. The data set was randomly split into a training set  SNPs spaced >1 cM apart (2614 SNPs) or (ii) SNPs spaced >5 cM apart (705 SNPs) on the linkage map.Table S1 contains the phenotypic data. Table S2 contains the pedigree. Table S3 and Table S4 contain summaries of obtained reads for parental and offspring samples respectively. Table S5 contains the location of the linkage map SNPs. Table S6 contains the genotypic data. File S1 contains the R script used for genomic prediction.Raw reads were deposited in the European Bioinformatics Institute (EBI) repository under project ID SRP081498. The challenged population consisted of 75 full sibling families, with a mean family size of 10, originating from a factorial cross between 67 broodfish . The largest full-sib family consisted of 114 animals, while the smallest had only two animals (three full-sib families). The overall survival at the end of the pasteurellosis challenge was 4.7%. Observed mortality levels showed three distinct peaks on day 7 (10.4% loss), day 11 (14% loss), and day 15 (5.7% loss), followed by a steady reduction in daily mortality rate .Table S3) and offspring (Table S4), respectively. The STACKS catalog consisted of 202,598 unique 2b-RAD loci, of which 73,876 contained at least one SNP in the parents (EBI repository SRP081498). To confirm the identity of loci created by STACKS, the 202,598 tags of the catalog were mapped against an S. aurata draft reference genome assembly . A high percentage of 2b-RAD loci were successfully mapped, with 93.5% of tags showing a unique match to the reference genome. In order to maximize the number of informative SNPs and minimize the amount of missing or erroneous data, RAD-tags that were retrieved in at least 75% of the samples, and that carried only one or two SNPs were retained. A total of 48 animals with missing data >30% were excluded from subsequent analysis. A total of 21,974 putative SNPs were finally used for construction of the genetic map from 777 disease challenged offspring (genotypic missing data <30%) and their corresponding 67 parents.The mean number of raw reads was 8.76 million and 4.51 million, while the number of reads passing QC was 7.74 million (88%) and 3.69 million (81%) for parents (Table S5). The remaining SNPs (9889) either failed to pass QC filters, or were not placed on the resulting linkage groups during mapping, and these were discarded. The female and male maps were comparable with total lengths of 3822 and 4010 cM, respectively. The number of SNPs per chromosome ranged from 366 to 607 , while linkage group length ranged from 115 to 202 cM . The correlation between number of SNPs and corresponding chromosome map length was 0.74 (n = 24 linkage groups).The linkage map consisted of 12,085 SNPs that were grouped into 24 linkage groups, in accordance with gilthead sea bream karyotype, with a total map length of 3899 cM . The SNPs with the lowest P values (P < 10−3) were located in linkage groups 1–3, 10, 17, 20, and 21 (The heritability of surviving days (censored data) was 0.22  and 0.28  using the pedigree and the genomic relationship matrix, respectively, of the 777 disease challenged offspring. No SNPs surpassed the Bonferroni-corrected genome-wide significance threshold . The prediction was conducted using genotype information from 11,239 SNP markers (passed QC filters set for genomic prediction) for the 777 disease challenged animals that were randomly split in training (n = 578) and validation (n = 200) datasets. The application of all the genomic prediction models resulted in higher accuracies than those achieved using PBLUP. Prediction accuracy with PBLUP was 0.3 vs. 0.38–0.46 for the genomic prediction models, with highest accuracy being observed using the BayesA method  is a farmed species of paramount importance for Mediterranean aquaculture. While vaccines can offer some protection against pasteurellosis, the low immune competence observed in larval and juvenile stages renders this protection temporary , which is currently lacking in sea bream. This is likely to change in the near future as the reference genome sequence and associated genomic tools/data become available. In the meantime, RAD sequencing and similar techniques can readily be applied to generate genome-wide SNP marker datasets, even in the absence of such genomic resources (Gilthead sea bream (Dicentrarchus labrax) linkage maps  compared to those previously reported for disease resistance traits in various aquaculture species , the lowP values indicative of putative suggestive QTL on linkage groups 1–3, 10, 17, 20, and 21. Unfortunately, the lack of an integrated linkage map for sea bream does not allow direct comparison of the previously described resistance QTL  and overall survival is unknown.The GWAS results pointed to a polygenic or oligogenic genetic architecture for resistance to pasteurellosis, with no genome-wide significant QTL identified, with the lowest ance QTL , interprThe results from the genomic prediction approach were encouraging for practical implementation of selective breeding for genetic resistance in sea bream, with the genomic prediction models outperforming traditional BLUP. The advantage of the genomic-based models was retained also when only SNPs at 1-cM location intervals were used, which may be useful for reducing genotyping costs and improving cost effectiveness. However, due to the limited number of families in the current study, the training and validation sets contain closely related animals, which will increase the accuracy of prediction. Additional testing of genomic prediction at varying marker densities on separate and preferably larger populations would be required to ascertain the appropriate density for commercial application of genomic selection. The benefit of genomic prediction over a pedigree-based approach is likely because of the ability to capture within-family genetic variation. In mass-spawning species such as sea bream, in which family size and structure is difficult to control, this approach is likely to be particularly advantageous. Overall, the current study demonstrates that SNP markers generated via 2b-RAD are effective at capturing the genetic variation in a complex trait in a sea bream breeding population. This approach is likely to be useful in other species with less-developed genomic tools, and provides further evidence that incorporation of genomic selection is likely to result in significant improvement in selection accuracy and genetic gain compared to traditional family selection in aquaculture breeding.2b-RAD sequencing was applied to investigate genetic resistance to pasteurellosis in gilthead sea bream. The SNP data generated were applied to create the first high-density linkage map for sea bream. Only suggestive QTL were detected, implying that resistance to pasteurellosis has an oligogenic-polygenic architecture for the studied population. Genomic prediction using the 2b-RAD genotype data were effective, with substantial improvement in prediction accuracy over the pedigree-based model. This highlights the utility of genotyping by sequencing for genomic prediction of disease resistance in aquaculture species, and its potential to apply genomic selection in commercial breeding programs.


The main objectives of the current study were to assess the feasibility and accuracy of genotype imputation in the context of a salmon breeding program. The specific aims were: (i) to measure the accuracy of genotype imputation using medium (25 K) and high (78 K) density mapped SNP panels, by masking varying proportions of the genotypes and assessing the correlation between the imputed genotypes and the true genotypes; and (ii) to assess the efficacy of imputed genotype data in genomic prediction of key performance traits (sea lice resistance and body weight). Imputation accuracies of up to 0.90 were observed using the simple two-generation pedigree dataset, and moderately high accuracy (0.83) was possible even with very low density SNP data (∼250 SNPs). The performance of genomic prediction using imputed genotype data was comparable to using true genotype data, and both were superior to pedigree-based prediction. These results demonstrate that the genotype imputation approach used in this study can provide a cost-effective method for generating robust genome-wide SNP data for genomic prediction in Atlantic salmon. Genotype imputation approaches are likely to form a critical component of cost-efficient genomic selection programs to improve economically important traits in aquaculture.Genomic selection uses genome-wide marker information to predict breeding values for traits of economic interest, and is more accurate than pedigree-based methods. The development of high density SNP arrays for Atlantic salmon has enabled genomic selection in selective breeding programs, alongside high-resolution association mapping of the genetic basis of complex traits. However, in sibling testing schemes typical of salmon breeding programs, trait records are available on many thousands of fish with close relationships to the selection candidates. Therefore, routine high density SNP genotyping may be prohibitively expensive. One means to reducing genotyping cost is the use of genotype imputation, where selected key animals ( Modern genetic studies typically require high density genome-wide SNPs for mapping variants underlying complex traits, or predicting breeding values from genotype data. Genomic selection has transformed terrestrial and aquatic animal breeding programs, and relies on capturing accurate realized genetic relationships between animals, and linkage disequilibrium (LD) between SNP markers and causative mutations underlying economically important traits . These genotype data are then used to impute the nongenotyped markers for the individuals genotyped at low density  for many aquaculture species . A large proportion of SNP genotypes were masked in the offspring, resulting in “pseudo” low density panels. The correlation between true genotypes and imputed genotypes was then assessed for the masked SNPs under various scenarios. Finally, the imputed SNP data were used in genomic prediction for key economic traits, and prediction accuracy was assessed relative to pedigree-based approaches, and genomic approaches using the full genotype dataset.L. salmonis) copepods as described in The genotype and trait data used in the current study were from 624 Atlantic salmon postsmolts, which was a sample from a specific year group subset of a large commercial breeding program  hatched in the spring of 2008. The samples comprised 59 nuclear families, derived from 30 sires and 59 dams. At ∼1 yr posthatching, juvenile fish were challenged with sea lice  under approval of the ethics review committee of the University of Stirling , and according to Home Office license requirements. All animals were reared in accordance with relevant national and European Union (EU) legislation concerning health and welfare. Landcatch are accredited participants in the Royal Society for the Prevention of Cruelty to Animals (RSPCA) Freedom Foods Standard, the Scottish Salmon Producers Organization Code of Good Practice, and the EU Code-EFABAR Code of Good Practice for Farm Animal Breeding and Reproduction Organizations.File S1 and File S2, respectively. As a result, all parents and offspring samples had genotypes for both SNP panels (the genotype data are provided in File S3), and these formed the basis of the imputation analyses.All samples were genotyped using the Affymetrix Axiom 132 K Atlantic salmon SNP chip developed by in silico. For the individuals chosen to have LD SNP panel data, two settings determining the content of the LD SNP panel were applied by masking either 90 or 99% of the markers. The remaining SNPs  were selected to be evenly spaced throughout the genome, based on physical distance according to the Atlantic salmon reference genome assembly [GenBank accession GCA_000233375.4  all offspring had LD SNP panel data, or (ii) 75% of offspring had LD SNP panel data, and the remaining 25% had MD or HD SNP panel data. The latter scenario was applied to measure the impact of including a proportion of offspring with complete genotype information on the phasing and imputation accuracy. The 75% of offspring chosen for LD panel data in scenario (ii) were evenly distributed across all nuclear families in the population.r) between the allele dosage of the true genotype and the most likely imputed genotype, averaged across all SNPs and all animals.The genotype imputation analyses were performed using the AlphaImpute v1.3.2 software  are sufficient for achieving maximum genomic prediction accuracy in the current experimental set up , only thTo test the accuracy of genomic and pedigree-based prediction, a cross-validation approach was applied (as described in File S1 contains details of the SNPs used for the medium density (25 K) SNP platform. File S2 contains details of the SNPs used for the high density (78 K) SNP platform. File S3 contains the family and phenotype data used in the analysis. File S4 contains the genotype data used in the analysis.The data used in this study are available as supplementary files. e.g.The accuracy of imputation of high density genotypes was assessed as the correlation between the imputed genotypes and the true genotypes in the offspring, where varying proportions of genotypes had been masked. The imputation accuracy ranged from 0.62 to 0.85 for the MD SNP panel (25 K), and from 0.76 to 0.90 for the HD SNP panel (78 K). The higher imputation accuracy based on the HD panel compared to the MD panel may be explained by more accurate resolution of haplotypes, especially for short chromosome segments. Higher imputation accuracy with increased marker density has been shown previously in simulated and experimental populations in livestock  may have been a limiting factor in determining the imputation accuracy. In addition, the lack of genotyped ancestral generations may have impaired the phasing of the parental haplotypes for whole chromosomes. When genotype imputation is employed in livestock populations, multiple generations of ancestral genotyped individuals, and pedigree information are typically available. Likewise, in the study of The imputation accuracies achieved in the present study (ranging from 0.62 to 0.90) were generally lower that that achieved in previous studies in farmed animals and crops  resulted in higher imputation accuracy at all MAF , and the proportion of offspring designated as being genotyped for the LD panels (100 or 75%). Under these scenarios, the correlation between true and imputed genotypes increased with higher MAF . The equivalent figures for the MD SNP panel were 0.62 and 0.76, respectively. However, there was a large degree of variability of imputation accuracy across individuals, demonstrating a negatively skewed distribution . While tvs. 0.60 for lice resistance, 0.67 vs. 0.69 for body weight), but substantially higher than pedigree-based method for both phenotypes   was tested, based on previous studies which suggested that between 5 and 20 K SNPs is adequate for maximum prediction accuracy in a typical salmon breeding set up . The impctively) . Taking ctively) . This hii.e., 256 SNPs) was also compared to prediction accuracy using the LD SNP panel plus imputation. For the trait of sea lice resistance, genomic prediction using 256 SNPs was inferior to pedigree-based prediction (accuracy ∼0.40 vs. 0.48), while 256 SNPs with imputation increased the accuracy to 0.58 , while SNP densities >∼3000 SNPs can be genotyped most cost-effectively using SNP arrays. The cost per sample of genotyping for a medium density SNP chip is several fold higher than the cost of genotyping for a 256 SNP panel. Assuming an approximate price for the former of £40 per sample, and an approximate price for the latter of £5 per sample, the total cost of the genotyping for genomic prediction using the imputation described herein is ∼60% lower than genotyping all samples at MD. Furthermore, the efficacy of genotype imputation (and therefore genomic prediction using imputed data) is likely to increase as high density genotype data are collected on additional generations, especially for grandparents of the population where imputation is being applied. The current study used SNP array genotyping data as the basis for imputation, but genotyping by sequencing approaches such as RAD-Seq , there is a nonlinear relationship between SNP panel density and cost per sample. This relationship depends on several factors, including the technology, the company, and the number of genotyped samples. However, in general terms, SNP densities of <∼3000 SNPs can be genotyped most cost-effectively using individual targeted assays, for example using KASP technology , or targeted genotyping by sequencing  of selection candidates. Prediction accuracy in this scenario is likely to benefit large sample sizes for the training populations, high marker density , and/or prioritization of putative functional variants in the SNP panel used for prediction. The latter may be enhanced by initiatives such as the Functional Annotation of All Salmonid Genomes . There are a number of other routes to improving cost-efficiency of genomic selection; for example by preselecting candidates for genotyping based on trait or breeding values e.g., . AnotherGenotype imputation approaches were tested in a sample of Atlantic salmon from a commercial breeding program, and the efficacy of using imputed genotype data for genomic prediction was evaluated. Using a two-generation design, with parents genotyped at medium or high density, and offspring genotyped at a lower density, imputation accuracy of up to 0.90 was possible. Genomic prediction accuracy using imputed genotype data were comparable to true genotype data with a ∼250 SNP panel used on 75% of the offspring. However, overall improvement in imputation accuracy may be expected by genotyping additional ancestral generations in the pedigree. Genomic prediction accuracies using imputed genotypes were very close to those using true genotypes, for both growth and sea lice resistance traits. Given that low density genotyping is substantially cheaper than medium or high density, imputation approaches may contribute to the widespread and cost-effective generation of genome-wide SNP data for genomic selection in aquaculture breeding programs.www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.040717/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Uria lomvia) is a highly philopatric colonial arctic seabird that occupies a significant environmental gradient, shows marked phenotypic differences among colonies, and has large effective population sizes. To test whether thick‐billed murres from five colonies along the eastern Canadian Arctic coast show genomic signatures of local adaptation to their breeding grounds, we analyzed geographic variation in genome‐wide markers mapped to a newly assembled thick‐billed murre reference genome. We used outlier analyses to detect loci putatively under selection, and clustering analyses to investigate patterns of differentiation based on 2220 genomewide single nucleotide polymorphisms (SNPs) and 137 outlier SNPs. We found no evidence of population structure among colonies using all loci but found population structure based on outliers only, where birds from the two northernmost colonies (Minarets and Prince Leopold) grouped with birds from the southernmost colony (Gannet), and birds from Coats and Akpatok were distinct from all other colonies. Although results from our analyses did not support local adaptation along the latitudinal cline of breeding colonies, outlier loci grouped birds from different colonies according to their non‐breeding distributions, suggesting that outliers may be informative about adaptation and/or demographic connectivity associated with their migration patterns or nonbreeding grounds.Investigating the extent (or the existence) of local adaptation is crucial to understanding how populations adapt. When experiments or fitness measurements are difficult or impossible to perform in natural populations, genomic techniques allow us to investigate local adaptation through the comparison of allele frequencies and outlier loci along environmental clines. The thick‐billed murre ( Individuals can also experience different selective pressures within a lifetime across different environmental patches and different seasons. In migratory species, conditions at the breeding vs. non‐breeding grounds can be very different and expose organisms to different sources of mortality. If “local habitat” is defined as the conditions at a given point in time and space  and tawny owl  seem to be maintained by environmental selection  provides a useful model for studying local adaptation. It is a long‐lived colonial seabird with a wide distribution in arctic and subarctic regions of the Northern Hemisphere. In the summer, during the breeding season, thick‐billed murres nest on coastal cliffs. After chicks fledge, thick‐billed murres migrate south toward ice‐free areas. As a migratory species with a wide distribution, thick‐billed murres experience spatial and temporal variation in environmental conditions. We hypothesize that thick‐billed murres are adapted to local conditions at the breeding grounds because selection should act most strongly during the breeding time due to (1) the energy demands of breeding, (2) high overwinter survival , the sister species of the thick‐billed murre, as an out group to test for the presence of hybrids , known migratory behavior , and morphometric distinctiveness . Short‐insert paired‐end libraries were prepared using Illumina TruSeq DNA Sample Prep Kit v2 targeting inserts of 320 bp. To improve scaffolding, long‐insert libraries of three different insert sizes  were prepared with Illumina Nextera Mate Pair Sample Preparation Kit. All libraries were sequenced in three lanes of Illumina HiSeq2500 with 100 bp paired‐end sequencing. We trimmed short‐insert paired‐end reads from the 3′ end until a minimum phred score of 30 was reached, filtered for a minimum read length of 50, and adapter‐clipped using cbase is the base coverage, ck‐mer is the k‐mer coverage, L is the read length, and K is the k‐mer size.where 2.3SpH1 and EcoR1. We ligated each sample to adapters barcoded with a set of eight sequences (8 bp long) that differed by at least two base positions to maximize assignment probability. We pooled uniquely barcoded samples and extracted 300 to 350 bp fragments using a Pippin Prep . To add Illumina indices with a unique sequence for each pooled library, and to increase concentrations of sequencing libraries, we amplified a total of 26–190 ng of pooled, size‐selected DNA using high‐fidelity PCR for 16 cycles . We cleaned PCR products with AMPure beads  and pooled libraries in equimolar amounts. We sequenced 93 samples using one lane of Illumina HiSeq2500 paired‐end 150 bp sequencing from a rapid run flow cell at the Genome Quebec Innovation Centre.We prepared RAD libraries using the ddRADseq protocol as described in Peterson et al. . Briefly2.4geneious v.6.1.5  for downstream analyses with the program populations. Loci included in the final dataset had at least 10× depth of sequencing and were present in at least 90% of the individuals in each of the five colonies included in this study. To test for presence of common murre X thick‐billed murre hybrids, we repeated the analysis including the four samples of common murres with the same settings. This dataset included only one SNP per locus, retrieved in stacks using the option “–write_single_snp.”We used F‐statistics for each SNP using genodive  and a locus‐specific component . A positive value of alpha at a given locus suggests positive selection. Estimated model parameters were obtained by running the analysis using 20 pilot runs, each consisting of 5,000 iterations, followed by 100,000 iterations with a burn‐in of 50,000 iterations. lositan uses coalescent simulations to generate a null distribution of FST values, controlling for the corresponding expected heterozygosity value. The loci that lie outside the expected range under a neutral model of evolution are considered candidates for positive selection. We ran lositan using 50,000 simulations, a “neutral” forced mean FST, confidence interval of 99%, and false discovery rate of 10%.We applied further filtering to our SNP dataset by discarding loci that had minor allele frequencies <5% in at least one population to minimize detection of false positives in subsequent outlier analyses, and we intersected lists of loci from each colony to minimize the occurrence of missing values in the final dataset. Then, we created 10 subsets of data representing each of the 10 possible colony pairs. To detect loci putatively under selection, we performed outlier analyses for each pairwise comparison using two different approaches: the Bayesian method implemented in the program 2.6r = −0.97, p < 0.01, Adegenet, and the program Structure v.2.3.2  between 1 and 6 using prior population information, with a correlated allele frequency model, and a burn‐in of 50,000 generations followed by 500,000 generations. We used the same settings to analyze the dataset that included common murres. The most likely number of genetic populations was calculated using Evanno's ΔK . PCA of environmental variables showed that PC1 explained 88.5% of the variation, and was highly correlated with latitude  annotation terms to all outlier SNPs using the Gene Ontology database . FIS calculated across all loci and all colonies was significantly >0 . The global FST was not different from 0, with the highest FST for an individual SNP being 0.112 and most loci showing low levels of differentiation. Pairwise FST estimates between colonies were not different from 0 for any comparison  and common murres (n = 4) contained 2279 loci. Average FST between thick‐billed murres (n = 86) and common murres (n = 4) was high and significantly different from 0 .The SNP dataset that included samples of both thick‐billed (3.4bayescan did not detect any outlier loci. The number of outlier SNPs identified by lositan ranged from 14 to 28 among runs. Akpatok showed the highest number of outliers when compared to Prince Leopold (28 SNPs) and Minarets (24 SNPs), respectively. The 137 SNPs identified among all runs were distributed among 111 loci, and 38 SNPs were indicated as potentially under positive selection in two or more pairwise comparisons, indicating either that populations may be diverging in parallel . Similarly, the DAPC showed a high degree of overlap among colonies . These results are consistent with previous analyses of mitochondrial and microsatellite data, which also failed to detect population structure in the Atlantic Ocean  for instance, genotyping of thousands of genomewide markers revealed weak but significant population structure among colonies in Japan and Hawaii, where previous results from traditional genetic markers were contradictory . The presence of even subtle population structure did not receive support from any of our analyses run on the entire dataset, including estimates of colony pairwise ST Table , and clu220 SNPs.FIS ; locus‐specific FST between colonies never exceeded 0.307; and although structuring based on the outlier loci identified with lositan was indicated by structure when we used the admixture model, clear separation between colonies was evident only when we used the no admixture model, which is more powerful at detecting weak structure. The genomic architecture of traits under selection can also give the appearance of weak selection. If a trait is polygenic, the selection coefficient s will be divided among the SNPs that contribute to that trait, so that the more polygenic a trait is, the lower s will be on each SNP, thereby hindering the ability to identify loci under selection  a group that included samples from Gannet, Minarets, and Prince Leopold; (2) Coats; and (3) Akpatok. Similarly, birds from Gannet, Minarets, and Prince Leopold clustered together in the DAPC, whereas birds from Coats and Akpatok clustered separately from the rest of the colonies. The fact that the southernmost colony (Gannet), which is climatically the most different from the rest of the colonies, grouped together with the two northernmost colonies (Minarets and Prince Leopold) in both clustering analyses rejects our hypothesis of local adaptation to the breeding grounds along a latitudinal cline.Sporophila), this approach revealed that putative differentiated loci were not really informative of differentiation among species because the same pattern could be obtained from a randomized dataset , a smaller Alcid seabird, mainly due to air temperature and wind speed  of outlier loci assigned with GO terms, neither enrichment analyses nor any other inference regarding the biological function of outlier loci was possible. However, the low assignment rate could indicate that (1) outlier loci may be located in regulatory regions or other regions of functional importance that are under selection but are not annotated; (2) several genes of small effect may have not been detected due to low sensitivity of outlier detection methods Yeaman, ; (3) somAlthough lack of population structure in seabirds and other marine organisms is very common (e.g., Friesen, 5Lack of population structure despite high philopatry is observed in many other seabird species (reviewed in Friesen, None declared.A.T., A.J.S, S.V.E., and V.L.F. designed research. G.J.R facilitated funding and provided expertise on the study system and its environment. A.T. and A.J.S collected data; A.T. analyzed the data with the help of A.J.S. and wrote the manuscript with the help of all co‐authors.10.5061/dryad.7182c.The thick‐billed genome assembly and input files for all the analyses conducted in this study are available from the Dryad Digital Repository: doi: Click here for additional data file.


The present study was designed with the objectives of identifying genetic polymorphism of the PDE3A, PDGFRB and MARCH1 genes and their effects on semen production traits in a Holstein bull population.Using a genome-wide association study strategy, our previous study discovered 19 significant single-nucleotide polymorphisms (SNPs) related to semen production traits in Chinese Holstein bulls. Among them, three SNPs were within or close to the P < 0.05): one SNP was located downstream of PDGFRB and three SNPs were located in the promoter of MARCH1. Interestingly, for MARCH1, haplotype-based analysis revealed significant associations of haplotypes with semen volume per ejaculate. Furthermore, high expression of the MARCH1 gene was observed in sperm cells. One SNP (rs43445726) in the regulatory region of MARCH1 had a significant effect on gene expression.A total of 20 SNPs were detected and genotyped in 730 bulls. Association analyses using de-regressed estimated breeding values of each semen production trait revealed four statistically significant SNPs for one or more semen production traits (PDGFRB and MARCH1 genes with semen production traits. The identified SNPs may serve as genetic markers to optimize breeding programs for semen production traits in Holstein bull populations.Our study demonstrated the significant associations of genetic variants of the The online version of this article (doi:10.1186/s12863-017-0527-1) contains supplementary material, which is available to authorized users. In livestock breeding, the diagnosis of male fertility is very important because about half of pregnancy failures can be attributed to decreased male fertility or male factor infertility . Sires wFSHR, INHA, TNP1, TNP2, CAPN1 and SPAG11 have been widely studied as candidate genes for semen production traits of bulls , the number of sperm per ejaculate  and the number of motile sperm per ejaculate . The breeding values (EBVs) and accuracies of EBVs of selected bulls for five semen production traits were calculated using the AI_REML procedure in the DMU package based on records of 335,005 ejaculate samples from 1450 Chinese Holstein bulls aged 14–144 months. The linear model used was as follows:ijklmnopq is the phenotypic value of each trait of bulls; μ is the overall mean; Fi represents the fixed effect of farm; Hij represents handlers of semen collection, which is nested in the farm effect; Ak represents the fixed effect of age; Sl represents the fixed effect of the season when frozen semen samples were collected; Tm represents the number of collections on one day; In represents the interval (in days) between collections; αo is the random polygenic effect, distributed as N ], the initial sperm motility [SMOT (%)], the sperm concentration per ejaculate [SCPE  . The desThe levels of heritability of SVPE, SMOT, SCPE, NSPE and NMSPE were estimated to be 0.15, 0.12, 0.22, 0.16 and 0.12, respectively. Positive genetic correlations were observed among all traits, and the highest correlation was observed between NSPE and NMSPE . PCR amplifications for pooled DNA were performed in a reaction volume of 20 μL comprising 2 μL of 50 ng/μL DNA, 1 μL of each primer, 10 μL of premix (containing dNTPs and DNA polymerase)  and 6 μL of ddH2O. The amplification procedures were as follows: 10 min at 95 °C for initial denaturing; followed by 35 cycles at 95 °C for 30 s, 60 °C for 30 s and 72 °C for 30 s; and a final extension at 72 °C for 10 min. Amplification products were confirmed by gel electrophoresis on 2% agarose gels and sequenced using ABI3730XL (Applied Biosystems). Furthermore, the identified SNPs were genotyped in 730 Chinese Holstein bulls using a matrix-assisted laser desorption/ionization time of flight mass spectrometry assay .Genomic DNA was isolated from the frozen semen of 730 bulls using a standard phenol-chloroform method. The quality and quantity of extracted genomic DNA were measured with a NanoDrop™ Spectrophotometer (ND-2000c)  and gel electrophoresis. Then, each DNA sample was diluted to 50 ng/μL and stored at −20 °C for subsequent use. A DNA pool was constructed from 50 randomly selected samples with equal amounts of DNA (50 ng/μL). A total of 83 pairs of primers were designed to amplify entire coding regions, partial introns, and 5′ upstream (3000 bp) and 3′ downstream regions (3000 bp) based on the genomic sequences of the bovine P-value cut-off of 0.01. To estimate the extent of LD for the three genes, pairwise LD was measured among the SNPs of each gene based on the criterion of D’ using the software Haploview 4.2  [Hardy–Weinberg equilibrium was tested on each identified SNP using the chi-squared test at a MA, USA) . AccordiMA, USA) . A haploijk is the de-regressed EBVs; μ is the overall mean of de-regressed EBVs; Gi is the fixed effect corresponding to the genotype of polymorphisms or haplotypes; αj is the random familial polygenic effect, distributed as N , dominance (d) and allele substitution (α) were calculated according to the equation proposed by Falconer & Mackay [p − q). Here, AA and BB are the genotype frequencies of the two homozygotes; AB is the heterozygous genotype frequency; and p and q are the allele frequencies at the corresponding locus.Pedigree information of the resource population was traced back for three generations to construct the numerator relationship matrix. The associations of SNPs and haplotypes with the five semen production traits were evaluated using the mixed procedure in SAS 9.3 . The model was performed as follows:& Mackay , namely,i-th SNP was estimated according to the formula below [pi and qi are the allele frequencies for the significant i-th SNP, i-th SNP on the trait under analysis and The percentage of genetic variance accounted for by the significant la below :\documenPDE3A, PDGFRB and MARCH1 genes, we conducted gene expression analyses of different genotypes. Fresh semen samples were collected from ten fully genotyped bulls.To further confirm the potential functions of the PDE3A, PDGFRB, MARCH1 and the housekeeping gene GAPDH were designed by Primer-Blast on NCBI and synthesized by Beijing Genomics Institute Tech. , following the manufacturer’s instructions. The primers for To further detect the effect of variants of significantly associated genes, the mRNA expression of sperm cells with different genotypes at sites of functionally important mutations was analyzed. The results of mRNA expression were analyzed by the GLM procedure in SAS 9.3 software.PDE3A, being distributed in exons (n = 2), introns (n = 3) and the 3′ untranslated region (n = 3). In addition, five SNPs were located in MARCH1, being distributed in the promoter (n = 3), an exon (n = 1) and an intron (n = 1). Furthermore, seven SNPs were located in PDGFRB, being distributed in exons (n = 2), introns (n = 2) and downstream of the gene (n = 3). The identified SNPs were then subjected to genotyping in 730 bulls. However, not all individuals were successfully genotyped at all SNPs. The number of remaining individuals as well as genotype frequencies, allele frequencies, primers for amplification and results of the chi-squared tests for each SNP are shown in Additional file P < 0.01).Sequence analysis revealed that a total of 20 SNPs were detected using the pooled DNA of 50 bulls. Of those, eight SNPs were located in Association studies showed that four statistically significant SNPs associated with at least one semen production trait. The estimated effects of the four significant SNPs on semen production traits are shown in Table PDGFRB was significantly related to SVPE (P = 0.0052), NSPE (P = 0.0206) and NMSPE (P = 0.0195). In particular, SNP rs110305039 had been revealed as an SNP significantly associated with sperm volume in our previous GWAS [One SNP (rs110305039) located downstream of ous GWAS .MARCH1 were significantly associated with SVPE (P = 0.0246); SVPE (P = 0.0341); and all three of SVPE (P = 0.0001), NSPE (P = 0.0203) and NMSPE (P = 0.0189), respectively. In addition, the results showed that homozygous genotypes of all the significant SNPs were dominant for semen production traits. The dominant, additive and allele substitution effects of the significant SNPs on the target semen production traits are presented in Table Three SNPs  located in the promoter of PDE3A, one block consisting of three SNPs  was inferred and three haplotypes were formed in the studied population. Association analysis revealed that haplotypes of PDE3A did not reach significance for five semen production traits. For PDGFRB, four haplotypes in one block with seven SNPs did not significantly associate with semen production traits. For MARCH1, three SNPs  constituted a block in the studied population. The main haplotypes of TTT (H1), CCT (H2) and CCC (H3) accounted for frequencies of 57.2%, 20.7% and 19.5% of the total, respectively. Haplotype association study of MARCH1 demonstrated a significant association with SVPE (P = 0.0013)  were subjected to a comparison with the reference transcription factor binding sites in the JASPAR CORE Vertebrata database (http://jaspar.genereg.net/cgi-bin/jaspar_db.pl), using a relative profile score threshold of 85%. As a result, the three regulatory SNPs were predicted to create some new transcription binding sites via the substitution of C to T. The details of this are shown in Fig. The mutations in the regulatory regions of a gene can affect the transcription rate by changing the transcription factor binding sites . Therefoant SNPs 1 bp wereGAPDH expression in sperm cells. MARCH1 had a higher expression than the other two genes in sperm cells  of rs43445726 were associated with a significant difference in gene expression of MARCH1 (P = 0.0035)   Fig. . Similar18) Fig. .Fig. 3NoPDGFRB and MARCH1 genes on semen production traits in another population of Chinese Holstein bulls.In support of our previous GWAS, we provide further evidence for the significant genetic effects of the PDE3A is only 0.1 Mb away from another SNP found to be significantly associated with sperm motility in our previous GWAS. It is also close to an SNP (1.8 Mb away) shown to be significantly associated with sperm motility in a Polish Holstein bull population [PDE3A and five semen production traits. We suspected that PDE3A might have genetic effects on semen production traits, but the causal mutation has not been detected in the studied population. Further studies should be conducted to confirm the genetic effects of PDE3A on semen production traits.The PDE3A enzyme, mainly located in the post-acrosomal segment of the sperm head , has beean SNP 1. Mb away pulation . HoweverPDGFRB was found to have significant associations with SVPE, NSPE and NMSPE, which had already been declared to be significantly associated with SVPE in our previous GWAS. The mRNA of PDGFRB has been detected in gonocytes [PDGFRB in sperm cells. Gonocytes, the precursors of spermatogonial stem cells, are located in the center of the seminiferous tubules. At a defined species-specific period of time (cattle: pre-puberty), their proliferation and migration to the basement membrane give rise to spermatogonial stem cells, which maintain spermatogenesis in the mature testis [One SNP situated downstream of onocytes , Leydig onocytes . Similare testis . The PDGe testis . The inhe testis .MARCH1 are seldom analyzed in relation to spermatogenesis. However, previous studies identified that three MARCH family members, MARCH-XI, MARCH10 and MARCH7, are highly expressed in developing spermatids [MARCH10 is abundantly expressed in elongated spermatids. Furthermore, immunohistochemical analysis of MARCH10 proteins revealed that they are predominantly located in the cytoplasmic lobes and the principal piece of the flagella. It is supposed that MARCH10 proteins are synthesized in the cytoplasm and then transported to the developing flagella [MARCH1 in spermatozoa and significant effects of MARCH1 on SVPE, SNPE and SNMPE support the asumption that MARCH1 functions in spermatogenesis, as MARCH-XI, MARCH10 and MARCH7 do.MARCH proteins are ubiquitin ligases and target glycoproteins for lysosomal destruction via ubiquitination of the cytoplasmic tail. Unlike the above two candidate genes, the functions of ermatids –32. The ermatids . MARCH10flagella . SimilarMARCH1 were predicted to add a series of transcription factor binding sites. Specifically, one of the added transcription factors, RHOXF1, is encoded by an X-linked reproductive homeobox gene and has been observed to be specifically expressed in testis, especially in pachytene spermatocytes and round spermatids [MARCH1 [MARCH1 on SVPE, NSPE and NMSPE. Furthermore, it was predicted that the SNP rs43445726 explained 12.22% of the genetic variance of SVPE, implying significant genetic effects of this mutation. However, the detected effects may be limited to the specific population studied here, so further analysis should be conducted to reveal the function of MARCH1 in spermatogenesis and verify the functional implications of its mutations.In the present study, alleles involving a substitution of C to T in the regulatory region of ermatids . RHOXF1  [MARCH1 . FurtherPDGFRB and MARCH1 were significantly associated with semen production traits and presented the high expression of MARCH1 in mature sperm, which were consistent with previous GWAS and functional analyses. Our results not only provide new insight into the functions of the PDGFRB and MARCH1 genes, but also contribute useful information for marker-assisted selection or genome selection strategies of genetic improvement programs for semen production traits.Our findings demonstrated that