Brassica oleracea forms a diverse and economically significant crop group. Improvement efforts are often hindered by limited knowledge of diversity contained within available germplasm. Here, we employ genotyping-by-sequencing to investigate a diverse panel of 85 landrace and improved B. oleracea broccoli, cauliflower, and Chinese kale entries. Ultimately, 21,680 high-quality SNPs were used to reveal a complex and admixed population structure and clarify phylogenetic relationships among B. oleracea groups. Each broccoli landrace contained, on average, 8.4 times as many unique alleles as an improved broccoli and landraces collectively represented 81% of all broccoli-specific alleles. Commercial broccoli hybrids were largely represented by a single subpopulation identified within a complex population structure. Greater allelic diversity in landrace broccoli and 96.1% of SNPs differentiating improved cauliflower from landrace cauliflower were common to the larger pool of broccoli germplasm, supporting a parallel or later development of cauliflower due to introgression events from broccoli. Chinese kale was readily distinguished by principal coordinate analysis. Genotyping was accomplished with and without reliance upon a reference genome producing 141,317 and 20,815 filtered SNPs, respectively, supporting robust SNP discovery methods in neglected or unimproved crop groups that lack a reference genome. This work clarifies the population structure, phylogeny, and domestication footprints of landrace and improved B. oleracea broccoli using many genotyping-by-sequencing markers. Additionally, a large pool of genetic diversity contained in broccoli landraces is described which may enhance future breeding efforts. Brassica oleracea, or wild cabbage. They found 21,680 sequence variations among them all, which they used to look for patterns of relatedness between the plants. Their results support the possible domestication of cauliflower after or in parallel with broccoli. The study identifies a large pool of genetic diversity within broccoli landraces that could be used to improve future breeding efforts.A comparison of gene variations in modern-day offspring of wild cabbage shows that broccoli may have preceded the appearance of cauliflower in domesticated crops. Jian Hua, Thomas Bjorkman, and colleagues from Cornell University in the US examined the DNA sequences of 85 traditional “landrace” and improved broccoli, cauliflower, and Chinese kale plants, all of which descend from the species  Brassica oleracea is an economically important and outcrossing species domesticated as early as 2000 BCE and has been specialized into many unique botanical types such as broccoli, cauliflower, cabbage, kale, Chinese kale, and Brussels sprouts. Commercial production of these crops is frequently subject to abiotic stressors such as heat stress typically resulting in a reduction in horticultural quality. Improvement efforts for these crop groups are often limited by a lack of knowledge of available diversity or genetic bottlenecks that occurred during domestication or dispersal. Early B. oleracea vegetables were grown in close geographical proximity with several sexually compatible undomesticated relatives1 and are grouped into a larger coenospecies (2n = 18) capable of sharing genetic information and producing fertile offspring2. While a consensus theory of B. oleracea domestication has not been reached, it is generally assumed that early domestication of B. oleracea occurred in the Mediterranean and was ultimately spread by trade resulting in a proliferation of locally adapted landraces representing a complex genetic admixture5. It is currently unclear if broccoli and cauliflower were domesticated independently, if one was selected from within the other, or the degree to which mutual introgression has occurred8. While early work indicated a wider genetic basis for cauliflower implying that broccoli was selected from cauliflower, an opposite broccoli-first domestication model has been suggested7. Tonguç and Griffiths (2004)9 demonstrated that diversity was lower in cauliflower than broccoli. Studies of molecular10 and morphological5 markers indicate a closer relationship between undomesticated B. oleracea and broccoli than cauliflower and suggest cauliflower may have experienced introgression from broccoli.11. The large numbers of single-nucleotide polymorphism (SNP) markers generated by GBS provide a deeper understanding of population structure and genetic diversity, are suitable for genome-wide association studies (GWAS) and even candidate gene discovery. Typically, GBS studies have relied on a high-quality reference genome to align sequencing reads; however, a quality reference genome may be unavailable or difficult to assemble in certain minor or neglected crops. Therefore we are also interested in quantifying SNP production without the benefit of a reference genome.These studies have all relied on morphological observations or a small number of molecular markers (<200) that can lead to biased estimates based on analysis of limited genomic regions and weak inferences of population structure. Genotyping-by-Sequencing (GBS) is a technically straightforward, multiplexed approach that is highly suitable for population diversity studies by sequencing genomic subsets specifically targeted by restriction enzymes for fast, specific, and reproducible resultsThe main objectives of this study were (1) to investigate the diversity, population structure, and possible selection footprints of broccoli; (2) to address competing broccoli-first or cauliflower-first domestication models; and (3) to provide a comparison of SNP production without a high-quality reference genome for subsequent analyses of other neglected crop groups.Phylogeny reconstruction using 21,680 markers revealed several interesting patterns of relatedness among entries  populations. In general, improved broccoli entries were largely associated (85.5%) with a single theoretical population denoted in purple . The cauliflower group contained fewer unique SNPs than the broccoli group . Among these cauliflower-specific SNPs, 89 were landrace-specific  and 229 were improved specific . Eighty-five SNPs were found to be unique to Chinese kale entries . When comparing all landraces versus all improved entries, landraces contained on average 5.3 more unique SNPs than improved entries.When comparing broccoli, cauliflower, and Chinese kale, entries labeled as broccoli were the most diverse containing a total of 3543 unique SNPs, averaging 56.2 per entry . Mean fixation index was greater when comparing landrace and improved broccoli (FST = 0.12 ± 0.08) than when comparing landrace and improved cauliflower (FST = 0.00 ± 0.10). When comparing all landraces with all improved entries, mean adjusted fixation index was relatively low (FST = 0.05 ± 0.05)Mean adjusted fixation index, a measure of population differentiation due to genetic structure, was higher when comparing improved broccoli with improved cauliflower (FST > 0.35) identified more high-impact (HI) and moderate-impact (MI) coding variants between improved broccoli and improved cauliflower entries  would be divided into 976 equal haplotype blocks. On average, each haploblock would contain over 20 GBS markers using the 21,680 LD pruned markers. We observed non-uniform LD behavior within this study population most likely due to signatures of selection (e.g. domestication events) or genomic regions with reduced recombination rates such as centromeres. Several non-centromeric chromosomal regions  exhibited differential LD behavior when comparing landrace broccoli with improved broccoli  analysis in consideration of marker density required for GWAS or other mapping studies. On average, LD in landrace broccoli decayed below  kb Fig. , consideoli Fig. . GBS marWe compared SNP Discovery using the TASSEL Discovery pipeline versus the UNEAK pipeline. Discovery analysis found 203,091,048 total good barcode reads for all samples . After merging, 1,686,226 reads remained and 65.8% were aligned to unique positions. Filtering hapmap SNPs reduced the total count to 141,317. From the UNEAK pipeline, 1,686,226 tags remained after merging, and 680,277 total tag networks were identified. A total of 115,586 reciprocal tag pairs were identified and used for SNP calling. Filtering hapmap SNPs reduced the total to 20,815 (14.7% of Discovery pipeline).13. Phylogenetic analysis indicated two broccoli landraces “Cavolo Broccolo Ramoso Calabrese” and “Cavolo Broccolo Verde Calbrese Precoce” appeared to be highly similar to improved broccoli. It is possible that modern broccoli was derived from lines similar to these landraces. Indeed, morphologically similar open-pollinated broccoli marketed as “Cavolo Broccolo Ramoso Calabrese” is still commercially available in Italy. The glossy-leaf broccoli entries “Broc3” and “Broc5” as well as “Zeus F1” contain membership in the red group that may reflect a lineage from a distinct genetic background. A broccoli–cauliflower cross7, “Green Harmony”, exhibits either a cauliflower or broccoli phenotype depending on environmental conditions and contained nearly equal membership in both the improved broccoli purple group (43.3%) and the improved cauliflower red group (47.1%). Broccoli entries were shown to contain 16.8% more average unique alleles than cauliflower entries. Chinese broccoli is effectively differentiated from a larger pool of B. oleracea germplasm by a third principal-coordinate axis of variation.The pool of landrace broccoli was shown to contain 61% more total polymorphic sites and 8.4 times more unique alleles per entries than hybrid broccoli. Phylogenetic and population structure analyses indicated that broccoli hybrids appear to have undergone a population bottleneck and can largely be represented by a single subpopulation within a larger and more admixed pool of broccoli germplasm. Older commercial broccoli entries such as “Green Comet” (1968), “Packman” (1983), and “Big Sur” (1980) were located relatively distantly from a concentrated cluster of more recently developed (2000 to present) broccoli hybridsWe conclude that an earlier or parallel domestication of broccoli compared with cauliflower is far more probable given the following observations: (a) improved cauliflower entries share more unique alleles with broccoli entries (64.3%) than cauliflower landraces share with broccoli entries (11.6%), suggesting introgression events of broccoli during the development of modern cauliflower, consistent with a broccoli-first domestication model; (b) broccoli landraces are considerably more genetically diverse in both site variants and unique alleles than other botanical groups included in this study; and (c) improved cauliflower shares a considerable population structure component with landrace broccoli.r2 = 0.5 (2 and 20 kb) similar to other outcrossing crop groups such as maize15 (2 kb) and grape16 (15 kb) but considerably slower than a collection of highly interrelated B. oleracea collard landraces17 (0.5 kb). LD in landrace broccoli plateaued roughly 2.5 times faster than improved broccoli. Given the number of high-quality SNPs and overall linkage decay, the marker saturation is suitable for future GWAS, although resolution may vary from genic to megabase levels. Moreover, this work may prove useful for identification of genes fixed during domestication that contribute to overall horticultural quality.LD decays rapidly in landrace and improved broccoli below Although a high-quality reference genome proved useful for diversity analysis, 20,815 filtered markers were generated at an average density of one marker per 22.5 kb without such a resource. This result may be useful for neglected crop groups where a reference genome is unavailable or difficult to assemble.B. oleracea entries; of these, 63 samples were classified by passport data as broccoli , consisting of 37 landraces and 26 improved entries such as F1 hybrids and doubled haploid breeding lines from public and private sources (Supplementary Table STbotrytis), 6 of which were described as landraces, and 13 as improved. All cauliflower entries were either summer or fall maturing. Three entries classified as Chinese kale —“TO1000” the reference genome taxa, “A12DHd Chinese Kale” an entry used in several mapping populations8, and “Bugh Gana” (1958) were included as a phylogenetic outgroup.The GBS diversity panel included 85 unique 18. Genomic DNA of each entry was digested with ApeKI and used for library construction. The barcoded libraries from each entry was mixed and subjected to Illumina Next-Generation sequencing at the Cornell University Biotech Institute11.Seeds from each entry were germinated and grown in growth chambers under standard conditions. Young leaves were collected and subject to DNA extraction as previously described19. Minimum quality score in the GBSSeqToTagDB plugin was adjusted to 20. Chromosomes in the reference genome v2.1 (ref. 12) were renamed for pipeline compatibility and indexed and aligned with BWA v.0.7.8 (ref. 20). The BWA option samse was invoked to generate alignments in SAM format using single-end reads and randomly choosing repetitive hits. Imputation was accomplished using a K nearest neighbors21 approach . After filtering monomorphic loci and sites with more than 25% missing data, 64,323 SNPs remained. LD pruning was accomplished with PLINK (v1.90b3.46)22 using the indep-pairwise function with a step size of 50, a variant count of 5, and r2 threshold of 0.5, leaving 21,680 high-quality SNPs in low LD .The Tassel5 GBSv2 pipeline was run using default settings except for several modifications25. Evidence of selection26, allele frequency differences between genetic groups  at each locus and also across all loci were determined by the R package hierfstat (v.0.04.22)28.We partitioned our data into several datasets for subsequent analyses 33 and was permuted using a Large K Greedy algorithm with random input order and 2000 repeats. CLUMPP then aligned the multiple runs of clustering to generate the best value of K using the Evanno ΔK method35. Membership coefficients for individuals and were visualized using DISTRUCT36.To estimate the number of theoretical populations, the values of K between 1 and 10 were tested 10 times using the clustering algorithm STRUCTURE (v2.3.4) to group all varieties into the optimal population number using an admixture model assuming correlated allele frequencies24 using a maximum likelihood phylogeny reconstruction approach with 2000 bootstrap replications. All sites were used and algorithm parameters included nucleotide substitution using a general time reversible model, gamma distributed rates among sites, with four discrete gamma categories for invariant sites. The maximum likelihood heuristic was nearest-neighbor interchange with a moderate branch swap filter. The tree was visualized in FigTree37 using the color scheme from principal coordinate analysis.An unrooted neighbor-joining tree was generated with MEGA19) was used with default values with several exceptions. Minimum number of tag presence was adjusted to 3 in the MergeMultipleTagCount Plugin. Maximum tag number was increased to 300,000,000 in the MergeTagsByTaxaPlugin. The proportion of taxa with at least one tag per locus was set to 0.1. Finally, the GBSHapMapFilters plugin was set to allow minimum site coverage of 0.8, maximum minor allele frequency of 1, minimum taxa coverage of 0.1, and minimum minor allele frequency of 0.01. Chromosomes in the reference genome12 were renamed for pipeline compatibility and indexed and aligned with BWA20. The BWA option samse was invoked to generate alignments in the SAM format using single-end reads and randomly choosing repetitive hits.To compare reference and non-reference SNP discovery, the GBS Discovery pipeline in Tassel v3.0.166 39 was used to calculate depth and missingness from the output VCF files for both pipelines.The non-reference based UNEAK pipelineThe FASTQ files of the sequencing data were deposited to NCBI SRA. SNP data are available upon request.Supplemental Figure 1: Three-dimensional plot of first three PCoA axes using 21,680 SNPsSupplemental Figure 2: Intersection of alleles between landrace broccoli, improved broccoli, improved cauliflower, and landrace cauliflowerSupplemental Figure 3: Analyses of variant consequences for regions where FST > |0.35| between datasetsSupplemental Figure 4: Genome-wide Fst analysis between datasets plotted against physical positionSupplemental Table 1. Germplasm used within studySupplemental Table 2. Number of high-quality LD pruned SNPs identified by Tassel 5 GBSv2 per chromosome across diversity panel compared against summary statistics from reference genomeSupplemental Table 3. Classifications of accessions used within study and number of unique 21,680 SNP datasetSupplemental Table 4. Variants between all broccoli and all cauliflower entriesSupplemental Table 5. Variants between improved broccoli and improved cauliflower entriesSupplemental Table 6. Variants between broccoli and landrace broccoli entriesSupplemental Table 7. Variants between improved cauliflower with landrace cauliflower entriesSupplemental Table 7. Variants between landrace and improved entriesMovie. Principle component analysis


Plasmodium falciparum genes msp1 and msp2 to distinguish recrudescence from reinfection. A more recently developed potential alternative to this method is a molecular genotyping assay based on a panel of 24 single nucleotide polymorphism (SNP) markers.Current World Health Organization guidelines for conducting anti-malarial drug efficacy clinical trials recommend genotyping msp genotyping and the 24 SNP assay. Additionally, to conserve time and resources, the statistical program R was used to select the most informative SNPs for a set of unrelated Malawian samples to develop a truncated SNP-based assay for the region surrounding Blantyre, Malawi. The ability of this truncated assay to distinguish reinfection from recrudescence when compared to the full 24 SNP assay was then analysed using data from the therapeutic trials.Performance parameters of these two genotyping methods were compared using data from two recently completed drug efficacy trials. Blood samples from two anti-malarial therapeutic trials were analysed by both msp and SNP barcoding methodologies, and 294 for assessing the most informative of the 24 SNP markers. SNP genotyping performed comparably to msp genotyping, with only one case of disagreement among the 50 interpretable results, where the SNP assay identified the sample as reinfection and the msp typing as recrudescence. Furthermore, SNP typing was more robust; only 6% of samples were uninterpretable by SNP typing, compared to 19.7% when msp genotyping was used. For discriminating reinfection from recrudescence, a truncated 6 SNP assay was found to perform at 95.1% the accuracy of the full 24 SNP bar code.A total of 360 samples were analysed; 66 for concordance of msp genotyping in determining recrudescence from reinfection. Although more expensive, SNP typing is faster and less work intensive. Limiting the assay to those SNPs most informative in the geographical region of interest may further decrease the workload and the cost, making this technique a feasible and affordable alternative in drug efficacy trials.The use of SNP analysis has similar sensitivity to the standard The online version of this article (10.1186/s12936-019-2695-0) contains supplementary material, which is available to authorized users. In the face of continuing emergence of anti-malarial drug resistance, therapeutic trials of novel drugs, or combinations of known drugs, remain a critical part of a defensive strategy for combating this high-morbidity disease. A challenge with such studies arises in areas where malaria is endemic, such as sub-Saharan Africa, where individuals experience frequent infections. For example, the Chikhwawa district in southern Malawi has an estimated inoculation rate of 183 infective bites per person per year [To distinguish between these two possibilities, each infection is characterized or “fingerprinted” using molecular genotyping of highly polymorphic alleles. Genotypes of the infection at enrollment, prior to the administration of the trial drug, are then compared with the post-treatment infection. Recrudescence is characterized by identical molecular fingerprints at both time points. In reinfection, on the other hand, the molecular fingerprints at the two-time points differ.Plasmodium falciparum parasites, which are responsible for the majority of human malaria morbidity and mortality. Genetic diversity within this species is augmented by recombination events during its obligate sexual stage within the mosquito vector. Genetic diversity is particularly evident in high transmission settings like Malawi and other sub-Saharan African countries.Genotyping to distinguish between reinfection and recrudescence is an effective approach because of the wide range of genetic heterogeneity among individual msp1 and msp2) genes to identify length polymorphisms [msp genotyping is widely used and, because of the extensive polymorphism of msp1 and msp2 [The current gold standard recommended by the World Health Organization (WHO) for distinguishing genetically distinct parasites involves genotyping the merozoite surface protein 1 and 2  barcode  has the notyping . The bararasites . Before msp genotyping to the originally designed 24 SNP barcode, an attempt was made to customize the barcode for Malawi. The original 24 SNP barcode was developed using sequences from parasites from West Africa and Southeast Asia. For drug efficacy studies in a smaller geographical area, a smaller number of SNPs might be sufficient to distinguish recrudescence from reinfection; this would increase the efficiency and decrease the costs of the assay.In addition to comparing In addition to its usefulness in distinguishing recrudescence from reinfection, the 24 SNP barcode is also often used to determine whether an infection represents a single genotypic clone or a mixture of multiple clones. Given that the malaria parasite is haploid during the blood stage in the human host, a single clone will reveal homozygous results at all 24 SNPs, whereas a mixed infection would reveal heterozygosity at one or more of the SNPs. Although identifying mixed infections was not the focus of this study, the truncated assay was also evaluated for this application as this is a common use of the 24 SNP assay.msp genotyping, this study utilized 66 paired samples from cases of recurrent malaria infection during two therapeutic efficacy studies conducted among children age 6 to 59 months in Malawi between July 2011 and November 2012 at Machinga District Hospital and between March and July of 2014 at Machinga, Nkhotakota, and Karonga District Hospitals [P. falciparum mono-infections with 1000–200,000 asexual parasites/µl were enrolled (day 0) and followed on days 1, 2, 3, 7, 14, 21, 28, and 42 in the earlier study, and through day 28 in the later study. In addition, children were seen on any other day if they were ill. At each visit, blood was collected for thick and thin smears and for molecular testing on filter papers (Whatman 3MM). Patients with microscopically detected parasites on any day after day 3 were deemed to have recurrent parasitaemia and samples were tested by both msp and SNP genotyping methods as described below to differentiate recrudescence from reinfection. All genotyping was performed blinded to patient information.To compare the barcode method versus ospitals . ConsentFor the evaluation of a geographically relevant truncated bar code, samples were obtained from patients (n = 294) admitted to the pediatric research ward at Queen Elizabeth Central Hospital in Blantyre, Malawi from January to June during the years 2009 and 2011. Peripheral blood was collected on FTA cards (Whatman 3MM) at admission and SNP genotyping was performed as described below.DNA was extracted from blood spots using QIAamp DNA Mini and Blood Mini kit  as per manufacturer’s instructions.msp1 [msp2 [A nested PCR was used to amplify the polymorphic repetitive regions block 2 of msp1  and blocp1 [msp2  of all dp1 [msp2 . Parasitp1 [msp2 . In the msp1 block 2, and the FC27 and 3D7/IC families of the msp2 repeats as listed elsewhere [msp1 and msp2. Amplifications were performed on a BIO-RAD T100TM thermocycler  [msp1 and msp2.Using a template from the product generated in the first reaction, five separate second nested reactions were then performed, using in each case specific primer pairs for MAD20, K1, and RO33 families of the lsewhere , in ordeles, CA)  and sepaSamples from all cases of recurrent parasite infection were genotyped using the molecular barcode assay as described previously . The posIn brief, a master mix consisting of 2.95 µl of commercial grade nuclease-free water, 0.05 µl of the 40× Taqman SNP assay and 5 µl of the Taqman Universal PCR Master Mix  per reaction was added to each well of the 96 well real-time PCR plate pre-loaded with 2 µl (10 ng) of each parasite DNA sample. Parasite DNA samples were then amplified on the Step One Plus real-time PCR instrument .msp1 and msp2 genotyping, the number and size of the electrophoresed bands on the day of recurrent parasitaemia and day 0 were compared. Bands less than 500 bp in size were considered the same if they were within 10 bp difference in size. If the number and size of bands representing allelic types on day 0 was equal to the number and size of bands observed on the day of recurrent parasitaemia, the recurrent episode of parasitaemia was considered a recrudescence (treatment failure). Otherwise, it was considered a new infection. Samples lacking a distinct band in any of the five amplification reactions were re-tested. Samples that twice failed to amplify were considered uninterpretable.Samples were classified as either recrudescence, reinfection, or uninterpretable by each method, as follows. For For SNP analysis, PCR amplification results of both the full 24 SNP barcode and truncated barcodes were completed using Applied Biosystem’s Proprietary Allelic Discrimination software version 2.2.2. Where the software did not give genotype calls directly, allele calls were made manually by examining both the amplification plot and the multi-component plot. A single allelic difference between day 0 and the day of recurrent infection was considered sufficient to call the infections different (i.e. reinfection) due to the high-resolution power of the allelic discrimination software used. Similarly, if a single allele was homozygous at day 0 but heterozygous at follow up, it was considered a new infection because the addition of a new allele represents a new parasite clone. Conversely, a heterozygous allele at day 0 changing to a homozygous allele at follow up could be due to the persistence of one strain of a mixed infection, with simultaneous clearance of the second strain and, therefore, in the absence of any other allelic differences, the case was designated as recrudescence and not reinfection.For the SNP genotyping assays, if more than 2/3 of the SNPs (i.e. 17 of the full 24 SNP assay and 5 of the 6 SNPs of the truncated assay) were identical and the remaining SNPs failed to amplify, then the case was labelled recrudescent. Sensitivity analysis showed that variation of this cutoff by 3 SNPs in either direction resulted in the reclassification of only one case (a recrudescent sample being reclassified as uninterpretable). If 1/3 or more SNPs (8 for the full barcode and 2 for the truncated assay) failed to amplify for at least one of the time points of a matched sample, and there were no other SNP differences indicating a reinfection, then testing was repeated. If no additional SNPs were amplified the sample was labelled as uninterpretable.msp genotyping was considered the gold standard, and results obtained from the barcode method were compared to the msp method using Stata version 10.1  to assess sensitivity and specificity of the barcode method.Samples uninterpretable by either genotyping method were excluded from the final analysis. For the purposes of this study, msp genotyping.The 24 SNP barcode was optimized for Malawi using the statistical package R . SNP calls were formatted into a 294 × 24 matrix where the 294 rows contained SNP calls from individual samples and the 24 columns represented SNPs 1–24. A function was created that would analyse and compare all pairs of rows (samples) using all possible combinations of 6, 8, 10, and 12 SNPs. The SNP combinations that yielded the maximum number of different rows  were subsequently labelled as truncated versions of the full 24 SNP assay optimized for our Malawian data set. The R functions specifically used are outlined in Additional File msp1 and msp2 classified 5 (7.6%), 48 (72.7%), and 13 (19.7%) as recrudescence, reinfection, and uninterpretable, respectively , specificity was 75.0% (95% CI 21.9–98.7%), positive predictive value was 97.9% (95% CI 87.3–99.9%), and negative predictive value was 100% (95% CI 31.0–100%)  , they were then used to analyse the matched sample sets from the two therapeutic drug trials. The results were compared to both msp genotyping, 17 samples were excluded because they were uninterpretable by msp (n = 13) or by the truncated barcode (n = 5), with one sample overlapping. The overall sensitivity for detecting reinfection using the 6 SNP barcode compared to the full 24 SNP assay was 94.8%, and specificity was 100%.For comparison of the full 24 SNP barcode and the truncated barcodes, a total of five uninterpretable samples from the original 66 samples were excluded. For comparison of the truncated barcode and msp1 and msp2 genotyping as a gold standard for distinguishing recrudescence from reinfection in areas with endemic malaria [msp genotyping for distinguishing recrudescence from reinfection (sensitivity 100% and specificity 75%).Molecular genotyping is a reliable method of distinguishing recrudescence from reinfection in anti-malarial drug efficacy trials. Although the WHO recommends  malaria , this me malaria . The novP. falciparum genome and not in linkage disequilibrium, to clearly discriminate alleles in mixed genome samples. Daniels et al. demonstrated that no two parasites known to be of independent origin had the same allele signature. In addition, they showed that there was generally good agreement between the barcode and msp1 and msp2 genotyping with respect to identifying mixed parasite samples.The barcode method designed by Daniels et al.  uses 24 msp genotyping method resulted in more uninterpretable calls in our hands, with more reactions failing to generate distinct bands. Our levels of uninterpretable results were similar to those in other large scale studies [msp method and $11.45 for the SNP-based assay. Although the ease of the SNP assay allows for higher throughput, when technician salaries as well as the cost of repeating uninterpretable results are included in the per assay estimate, the msp method is still cheaper with a cost of approximately $14.36 per sample as compared to $18.66 for the SNP based method. However, truncation of the SNP assay from 24 to 6 locations decreases the overall cost to $10.25 per sample, less than the msp method. Furthermore, the SNP assay requires more specialized equipment, with the requirement of a real-time PCR machine. In contrast, the msp based assay relies on the more universally available conventional PCR machines and standard gel electrophoresis. The SNP based assay has the advantage of being higher throughput—requiring only one amplification step, whereas two are required in the msp method. The SNP method also avoids the tedious step of loading samples onto an agarose gel.This study established that the two methods are comparable in their ability to distinguish recrudescence from reinfection. Both assays have advantages and disadvantages. The  studies , 14. HowP. falciparum isolates from the same geographic region [In an effort to minimize time and resources while maintaining the equal discriminatory ability of the assay, the possibility of truncating the barcode was explored. Various studies have demonstrated surprisingly high levels of genetic relatedness among c region –18. Thismsp genotyping than by SNP barcode. Uninterpretable results seen in the msp1 and 2 based assays were due to poor amplification or inability to determine amplicon size on gel electrophoresis, while uninterpretable results in the SNP based assay were due to poor amplification. Of the 13 samples that were uninterpretable by msp genotyping and 4 by the full barcode, only one sample was uninterpretable by both methods, suggesting assay specific failures rather than general poor quality of DNA. In the analysis of the truncated barcode, this led to a large apparent drop in the accuracy of the 6 SNP barcode in determining reinfection when using msp genotyping rather than the full 24 SNP barcode as the gold standard—from 95.1 to 91.8%. Using the full 24 SNP panel as the gold standard, the 6 SNP barcode only missed three cases of reinfection within the total 61 samples analysed in that comparison. Using the msp genotyping as the gold standard, even though there was only one additional sample disagreement, the drop in total sample number from 61 to 49 led to a 45/49 agreement, i.e. a 91.8% accuracy.A significantly higher proportion of samples were uninterpretable by msp results as the gold standard. For instance, the 6 SNP assay’s accuracy in discriminating unique infections dropped from 98.5 to 95.1%. One possible explanation may be that the 6 SNP optimized barcode was proposed based on a large data set of unique genotypes from a geographic area immediately surrounding Blantyre. This SNP set was then used to analyse samples from Machinga, Nkhotakota, and Karonga district hospitals, which are 120, 375, and 825 km away from Blantyre, respectively. Geographic genotype variance has been shown to be very focal [In comparing Tables ry focal –18. Whilmsp and the SNP based methods. Although this non-amplification was not biased towards any specific location, the cutoff of allowing up to seven non-amplified alleles could result in the misclassification of reinfections as recrudescence. Although this limitation would be equally present with either technique, it is of increased concern in the msp assay where there was a high percent of non-amplification (19%). Finally, the choice of samples from geographically separated regions for design and validation of the truncated bar code may have resulted in less than ideal performance of the truncated barcode.The major limitation of this study is the small sample size. This limitation is magnified by the fact that there are very few recrudescent infections by either measurement method. This leads to an exceedingly low number of recrudescent infections to be compared by the two methods. An additional limitation is the inability to amplify all alleles in all samples—in both the msp1 and msp2 genotyping and the SNP-based barcodes. The major downfall of the SNP-based method—increased cost—could be effectively ameliorated with the design of a truncated set of SNPs. In Malawi, a subset of 6 SNPs performed almost as well as the originally designed 24 SNP method, thus substantially decreasing cost without compromising data quality.This study showed comparable results from Additional file 1. R code for generating truncated bar code.


Hordeum vulgare ssp. vulgare) is cultivated from the equator to the Arctic Circle. The wild progenitor species, Hordeum vulgare ssp. spontaneum, occupies a relatively narrow latitudinal range (∼30 - 40° N) primarily at low elevation . Adaptation to the range of cultivation has occurred over ∼8,000 years. The genetic basis of adaptation is amenable to study through environmental association. An advantage of environmental association in a well-characterized crop is that many loci that contribute to climatic adaptation and abiotic stress tolerance have already been identified. This provides the opportunity to determine if environmental association approaches effectively identify these loci of large effect. Using published genotyping from 7,864 SNPs in 803 barley landraces, we examined allele frequency differentiation across multiple partitions of the data and mixed model associations relative to bioclimatic variables. Using newly generated resequencing data from a subset of these landraces, we tested for linkage disequilibrium (LD) between SNPs queried in genotyping and SNPs in neighboring loci. Six loci previously reported to contribute to adaptive differences in flowering time and abiotic stress in barley and six loci previously identified in other plant species were identified in our analyses. In many cases, patterns of LD are consistent with the causative variant occurring in the immediate vicinity of the queried SNP. The identification of barley orthologs to well-characterized genes may provide a new understanding of the nature of adaptive variation and could permit a more targeted use of potentially adaptive variants in barley breeding and germplasm improvement.Barley ( When cultigens are disseminated from their region of origin, they must undergo adaptation to new climatic conditions cf. . CorrelaqSH1, shows evidence of selection in both the japonica and indica subspecies of rice, suggesting that only qSH1 played a direct role in the domestication of both subspecies , the progenitor of cultivated barley (Hordeum vulgare ssp. vulgare), occurs primarily within a limited latitudinal range of 30 - 40° N  on Oct 7, 2015 using the getData function from R package ‘raster’ . PCA was performed using the SmartPCA program from the EIGENSOFT package (We generated exome resequencing from 62 landrace accessions from a randomly chosen subset of landraces in the core collection. This includes 37 six-row spring and 25 two-row spring accessions (Table S2). DNA was extracted from leaf tissue collected from a single plant per accession at the 4-5 leaf stage using a standard 2X CTAB isolation protocol  algorithm , and (2) within the wild range of the species (30° N – 40° N) vs. landraces at latitudes lower than 30° N (low latitude). High latitude includes the northern extent of the range of wild barley and extends across Eurasia from the Central Iberian Peninsula to the Northern Japanese Archipelago. Low latitude includes the southern extent of the range of wild barley. For landraces, this extends from northwestern Africa to just south of the Japanese Archipelago. We also compared low and high latitude vs. the wild range in a single comparison.We compared allele frequencies at two latitudinal ranges: (1) within the wild range of the species (30° N – 40° N) vs. winter growth habit, with assignment based on USDA passport information.For a longitudinal comparison, we divided the sample at 48° E, roughly through the Zagros Mountains, which coincides with the major axis of population structure in wild barley  (A literature search was used to identify genes previously reported to contribute to plant flowering time and cold or drought tolerance. Google Scholar searches were performed with the terms “cold OR freezing OR drought tolerance” or “flowering time” and “plant” or “barley” (Table S5-7). For each publication with these keywords in the title or abstract, we looked for evidence that individual genes were reported to contribute to flowering time, cold, or drought tolerance. The protein-coding sequences (CDS) of identified genes were used as the query or subject in BLASTN against the barley high-confidence CDS in May 2016 on the IPK Barley BLAST Server  and resequencing datasets  was inferred using whole-genome resequencing data from https://github.com/vcflib/vcflib). Missing genotypes were imputed using PHASE  file using ‘vcf-intersect‘ from vcflib , including a VCF file of exome capture SNPs and the full results for environmental association and We selected 803 barley landrace accessions that are a portion of the core collection within the United States Department of Agriculture, National Small Grain Collection . The site frequency spectrums for SNPs in both the 9K and exome panels are shown in Figure S4. Average inbreeding coefficients estimated from SNP genotyping data and exome resequencing data are 0.996 (± 0.025) and 0.981 (± 0.008) respectively.To examine associations between environmental variables and SNP diversity, we downloaded the latitude, longitude, elevation, and 19 bioclimatic variables of the collection locations for each landrace from “WorldClim” . Because there is expected to be correlation among bioclimatic variables , we performed independent component analysis on the 19 variables to identify the subset of variables that best summarizes the range of environments occupied by barley landraces . Combined with the 9K genotype data, we performed environmental association using a mixed linear model in GAPIT. The population structure and the kinship matrix were used in all mixed-model associations with environmental variables and ICs. Relatively few individuals showed close kinship; 95% of comparisons had pairwise distance > 0.1 based on the Manhattan distance between accessions . Initially, we identified 32 SNPs with the first three ICs with FDR ≤ 0.25. Loosening FDR to ≤ 0.3 or ≤ 0.4 identified an additional 45 SNPs or 77 in total. The first three ICs provide a limited summary of bioclimatic variation because they incorporate only eight bioclimatic variables (Table S3). The eight variables are not closely correlated to other bioclimatic variables . Limiting the analysis to ICs potentially excludes some of the bioclimatic signal associated with the remaining variables. Thus, we also examined each of the bioclimatic and geographic variables independently. The environmental association with bioclimatic and geographic variables and three ICs identified 155 SNPs in significant associations (with FDR ≤ 0.25) . Since wild barley typically grows below 1,500 m . While FST values for pairwise comparisons include many barley genes previously associated with adaptive phenotypes (see below), the outliers in the single elevation FST comparison did not include these candidate loci (Table S10-12). Thus, we focused on reporting outlier results on the two-level comparisons . FST comparisons for elevation, latitude, and growth habit identified 203 outliers  . For both elevation and latitude, we calculated a single ng panel .FST outliers shared nine SNPs in the coding portion of 11 annotated genes. The only characterized gene found in both analyses is HvPhyC in barley. For details regarding overlapping results see Table S11.Environmental associations and FST threshold limited to the top 1% of values. However, we found six genes previously identified in barley as contributing to flowering time, four genes contributing to cold tolerance, and none of the 13 genes contributing to drought tolerance , include four loci identified as FST outliers. HvPhyC  (HvPpd-H2 (HvFT3)  (HvVrn-H1 (HvAP1)  .FST values in the upper 2.5%  related to cold tolerance were also identified. TaWCI16 was characterized in wheat and involved in freezing tolerance (OsiSAP8 is a rice (Oryza sativa) locus, which has been associated with cold, drought, and salt stress response  . A primary partitioning of barley populations by longitude, reported as eastern and western populations, has been reported previously (FST was for elevation at 0.089 (± 0.10) . The three-level comparison of FST from 0 - 1,500 m, 1,501 - 3,000 m, and >3,000 m resulted in a slightly lower average FST = 0.0826 (± 0.0745) . A three-level comparison of latitude with comparisons above and below the range of wild barley  was similar to elevation with average FST = 0.087 (±0.082) . Pairwise comparisons of the wild range to high latitude, wild to low latitude, and plant growth habit as either spring or winter barley resulted in much lower average FST values (Table S9).Comparison of average STF outliers. We obtained results from the two-level comparisons for high and low latitude, elevation, and growth habit. The upper 1% of FST values from each comparison yielded 55 outlier SNPs for a total of 203 unique SNPs . The ancestral allele dominates within the wild barley geographic range for this SNP. Whereas the derived allele is more prevalent in higher latitude regions including the northern extent of the range of wild barley. This range extends across Eurasia from the Central Iberian Peninsula to the Northern Japanese Archipelago . The ancestral state for the SNP predominates within the geographic range of wild barley and higher latitudes. Whereas the derived allele is more prevalent in lower latitudes, which includes the southern extent of the range of wild barley. For landraces, this extends from northwestern Africa to just south of the Japanese Archipelago . The highest FST between samples from the elevation comparison is for SNP 12_20648 with FST = 0.594 . This SNP’s ancestral allele occurs at high elevations, such as the Himalayan Mountains, while the derived allele tends to occur at lower elevation  . The ancestral SNP state is C, and the derived state is T. The CC genotype is observed in 60.5% of winter barley while TT is observed in 88.2% of spring barley .SNPs with the most extreme p-values and Benjamini-Hochberg FDR-values were reported in Supplemental data 9.Using a mixed linear model, we identified 155 unique SNPs significantly associated with at least one environmental factor with the threshold of FDR < 0.25 . The largest proportion of unique SNPs were found for precipitation (33.55%), followed by geographic variables (18.71%), temperature variables (18.06%), and then ICs (1.29%) . The aggregated independent components generally did not identify novel variants.We found 81 SNPs associated with precipitation (variables BIO12 to BIO19) and 51 with temperature  for individual environmental variables . We alsoFST outliers have average MAF = 0.330 (± 0.101) vs. a sample-wide average of 0.262 (± 0.140) a highly significant difference . SNPs with significant environmental associations have an average MAF = 0.251 (± 0.137), which is slightly lower than the full SNP data set but not statistically significant . While MAF limits the potential to associate genotype to phenotype for association analysis; the relatively large sample represented here does not suffer from this major limitation of detection. The high MAF contrasts with expectations that adaptive variants for less frequently occupied habitats, such as high elevation sites of cultivation, should be relatively uncommon (average MAF = 0.240 (± 0.091) of outliers from elevation comparison). A relatively low MAF might be expected under models where adaptive variants in a particular environment exhibit antagonistic pleiotropy, and thus confer lower fitness away from habitats in which they are adaptive  on chromosome arms.As previously reported, SNP density is highest on chromosome arms and lower in pericentromeric regions  and were on average 32.9 Kb (± 31.5 Kb) from the physical position of the queried SNPs . For 123 (34.4%) SNPs, LD with r2 > 0.45 (90th percentile) occurred within the same gene .An examination of p-value = 8.56 × 10−4), which is in the HvPRR1/HvTOC1 gene  ” (OC1 gene . TOC1 isthe gene . Five SN > 0.45) . Reseque > 0.45) .p-value = 4.95 × 10−4). The SNP is in the barley gene HORVU3Hr1G030150.1, which is an ortholog of the wheat gene WCI16 (Wheat Cold Induced 16) . TaWCI16 encodes a putative transcription factor involved in stomata development. It represents a novel class of late embryogenesis abundant (LEA) proteins in response to cellular dehydration and is involved in freezing tolerance ” and “mean temperature of the coldest quarter (BIO11)” identified an association on chromosome 3H with SNP 11_10380  associated with “precipitation of driest months (BIO14)” hit the barley gene HORVU1Hr1G008120.1. This is an ortholog that produces dehydroascorbate reductase  in Arabidopsis thaliana and bread wheat. It is one of two important enzymes functioning in the regeneration of ascorbate (AsA) which plays a role in protection against oxidative stress . Previous results suggest that overexpression of DHAR can protect plants against drought, salt, and polyethylene glycol-induced stress in tobacco and bread wheat . Six SNPs are in high LD with SCRI_RS_235243 (r2 > 0.45), all are noncoding variants within DHAR . The derived variant at SCRI_RS_235243 occurs within two haplotypes  that occur in high latitude regions.Environmental association analysis suggested that the SNP SCRI_RS_235243 significantly  (HvVrn-H1 (HvAP1)  locus OsiSAP8 which has been associated with cold, drought, and salt stress response . All nine of these genes have been identified in previous geographic comparisons in barley . Genes can contribute to phenotypic variation without having played a role in previous rounds of adaptation (Why were more previously identified genes not detected? Not every gene was genotyped by the 9K SNPs and many genes genotyped are represented by a single SNP or a small number of SNPs . The surFST outliers. They contribute to geographic differentiation in barley breeding populations in North America (HvCbf4B, HvPpd-H1, and HvVrn-H1. They were also found as an FST outlier and in association with temperature adaptation in comparisons of wild barley populations (HvPpd-H2/HvFT3 and HvCbf4, were also found as outliers in allele frequency in barley landraces in Spain (Three of the loci we identified as contributing to adaptive differentiation in Old World landraces were previously reported as FST of ∼0.40 in the elevation comparison. All occur at the consensus genetic map position of 663.25 cM, which is consistent with the regions reported in barley landraces from Spain (vs. perennial growth habit in Mimulus guttattus (We focused on SNP comparisons, but also found evidence that a large chromosomal inversion has contributed to elevational adaptation in barley. On chromosome 5H, 15 SNPs have uttattus , and temSNP density is a limitation in our study. With ∼40,000 annotated genes in the barley genome .Our study benefits from large sample size. Despite limited SNP density and the sampling of relatively common variants, our comparative analyses identified a number of previously identified barley loci and many plausible candidate loci from other plant species. Better coverage of barley gene space through exome capture or whole genome resequencing in a relatively deep panel of accessions would likely uncover a much more comprehensive set of variants contributing to environmental adaptation. This could contribute to the targeted use of variants for adaptation to environmental and climatic conditions for barley breeding and germplasm improvement, with the potential to improve the understanding of loci that contribute to climatic adaptation in wheat and other cereals.


How DNA sequence variation influences gene expression remains poorly understood. Diploid organisms have two homologous copies of their DNA sequence in the same nucleus, providing a rich source of information about how genetic variation affects a wealth of biochemical processes. However, few computational methods have been developed to discover allele specific differences in functional genomic data. Existing methods either treat each SNP independently, limiting statistical power, or combine SNPs across gene annotations, preventing the discovery of allele specific differences in unexpected genomic regions. Here we introduce AlleleHMM, a new computational method to identify blocks of neighboring SNPs that share similar allele specific differences in mark abundance. AlleleHMM uses a hidden Markov model to divide the genome into three hidden states based on allele frequencies in genomic data: a symmetric state (state S) which shows no difference between alleles, and regions with a higher signal on the maternal (state M) or paternal (state P) allele. AlleleHMM substantially outperformed naive methods using both simulated and real genomic data, particularly when input data had realistic levels of overdispersion. Using global run-on sequencing (GRO-seq) data, AlleleHMM identified thousands of allele specific blocks of transcription in both coding and non-coding genomic regions. AlleleHMM is a powerful tool for discovering allele specific regions in functional genomic datasets. DNA encodes the blueprints for making an organism, in part by coordinating a complex cell-type and condition-specific gene expression program. Regulatory DNA affects gene expression by controlling the rates of a variety of steps during the transcription cycle, including opening chromatin, decorating core histones and DNA with chemical modifications, initiating RNA polymerase II (Pol II) at transcription start sites (TSSs), and releasing Pol II from a paused state into productive elongation . In addiFinding allele specific differences in the distribution of marks along the genome is a powerful strategy for understanding the link between DNA sequence and the various biochemical processes that regulate gene expression ,4. DiploDespite the general utility of allele specific expression measurements, surprisingly few computational methods have been proposed to detect allelic differences. Current methods that examine allele specific enrichment either test single-nucleotide polymorphisms (SNPs) independently ,5 or comHere, we introduce AlleleHMM, a novel computational tool that was designed to address these limitations. AlleleHMM identifies genomic blocks of SNPs that share the same allele specificity in mark abundance using a hidden Markov model (HMM). We show that AlleleHMM has significantly higher sensitivity and specificity when compared to tests that treat each SNP independently. AlleleHMM has similar statistical power compared to the practice of merging reads inside of gene annotations, and can also identify allele specific differences in unannotated non-coding RNAs when running genome-wide. When applied to publicly available global run-on sequencing (GRO-seq) data, a direct measurement of RNA polymerase, AlleleHMM discovers the location of the vast majority of genes discovered by merging gene annotations, and also identified over one thousand allele specific blocks that lie in unannotated genomic regions. Blocks of allele specific transcription are inversely correlated with the allele specific differences in repressive chromatin marks. Thus, AlleleHMM is a powerful new strategy to identify allele specific differences in functional genomic data.The primary goal of AlleleHMM is to identify allele specific blocks of signal in distributed functional genomic data. AlleleHMM relies on the key assumption that contiguous genomic regions share the same allele specificity Figure . This maWe developed an HMM  that rephttps://github.com/Danko-Lab/AlleleHMM.AlleleHMM can be downloaded from: There are three hidden states in AlleleHMM Figure : (S) a sTransitions are permitted between all of the hidden states Figure . To contEach hidden state in AlleleHMM is associated with a separate probability distribution  that was used to represent the input data. The emission probabilities for all three states were calculated using the binomial distribution. The input to AlleleHMM is the total read count and maternal read count for each SNP in the genome. The input data is provided as:j is defined as:Given these input data, the emission probability for state The value et al.  dataset, as τ approached 1e-05, the fraction of AlleleHMM blocks beginning within 5 kb of a dREG site saturated at ∼50%  , because at ∼50% . Althoug at ∼50% . In analTo provide an orthogonal validation for the value of τ selected using the TSS strategy described above, we measured the sensitivity and specificity of AlleleHMM over different values τ in each dataset, using gene annotations as a gold-standard  (overdispersion of 0.26)  .To test how AlleleHMM performance varied with the length, signal level, and the degree of allele specificity when the input data was overdispersed, we fixed overdispersion to 0.25 . Using gene annotations as a surrogate for a ground truth, we found that AlleleHMM had a higher sensitivity than independent binomial tests, especially within the range of allele specificity magnitudes that was most likely to contain biologically relevant allelic differences  or between >0.45 and <0.55 (true negatives). Using these highly imbalanced datasets (ratio of positive to negative examples < 0.05), AlleleHMM retained a high precision (>0.66) at relatively high sensitivity (>0.76). AlleleHMM outperformed independent binomial tests by a wide margin in this task . To investigate whether these SNPs were false negative calls by AlleleHMM or false positives by the binomial test, we again used SNPs within annotated genes under the assumption that RNA polymerase across a gene shares the same allele specificity. The majority of allele specific SNPs identified by AlleleHMM were found to have the same direction  of allele specificity as the gene annotation, henceforth called ‘concordant’ . We show that AlleleHMM provides substantial improvements in both sensitivity and specificity for detecting allele specific SNPs compared with existing computational tools, using both simulation studies and analyses of real GRO-seq data. AlleleHMM is applicable to any type of functional genomic data and to any diploid species with a high-quality phased reference genome. AlleleHMM can now be deployed to understand the interplay between chromatin environment, transcription, and mRNA across a wide variety of organisms, providing new insights into how DNA sequences influence biochemical processes in the nucleus.Although there has been extensive interest in using allele specific information to understand the interplay between functional marks, surprisingly few computational methods have been developed for this task. One of the current methods to identify allelic differences requires performing independent statistical tests at each SNP ,5. This de novo.An alternative approach that is commonly used to identify allele specific differences in mark abundance is to use pre-established boundaries of genes or other genomic features as a way to pool SNPs within regions of the genome . This alAlthough AlleleHMM is a powerful tool that makes significant improvements compared with existing strategies, it does have several limitations. Chiefly among these, AlleleHMM will provide the most significant benefit for functional assays where marks are spread broadly across the genome, rather than focused within specific functional regions. This provides the broadest benefit for assays such as GRO-seq, RNA-seq, and several of the broadly distributed ChIP-seq marks. AlleleHMM may provide less benefit for assays such as 3′ mRNA-seq or chromatin accessibility assays , where signals are distributed within a specific position of the genome. Nevertheless, AlleleHMM will still work under these settings, and may still provide a substantial benefit for detecting expression differences that span multiple genes or chromatin accessible regions.Using AlleleHMM, we have identified thousands of regions harboring allele specific differences in human GM12878 and murine ESCs. We have found that allelic differences tend to occur over large genomic regions that harbor multiple transcription units, often sharing the same gene annotation. This finding is reminiscent of the shared architecture of quantitative trait loci (QTLs) across broad genomic regions . This figkz176_Supplemental_FileClick here for additional data file.


Human milk oligosaccharides (OS) play a key role in brain and gut microbiota development of the neonate, but the underlying biosynthetic steps of OS in the mammary gland are still largely unknown. As bovine milk contains OS with somewhat similar structures and functionalities there is increased interest in further understanding the genetic basis underlying the OS content of milk for eventual extraction and generation of value-added ingredients for infant formulas and nutraceuticals. The present study is the first to report on genetic parameter estimation as well as on a genome wide association study (GWAS) from the largest bovine milk OS dataset analyzed to date.B3GNT5, a gene encoding a glycosyltransferase involved in glycan synthesis. In Danish Jersey, a very strong QTL was detected for the OS with composition 2 Hex 1 HexNAc (isomer 1) on BTA11. The most significant SNP had –log10 of 52.88 (BOVINEHD1100030300) and was assigned to ABO, a gene encoding ABO blood group glycosyltransferases. This SNP has been reported to be a missense mutation and explains 56% of the OS variation. Other candidate genes of interest identified for milk OS were ALG3, B3GALNT2, LOC520336, PIGV, MAN1C1, ST6GALNAC6, GLT6D1, GALNT14, GALNT17, COLGALT2, LFNG and SIGLEC.In total 15 different bovine milk OS were monitored. Heritabilities ranged from 0 to 0.68 in Danish Holstein and from 0 to 0.92 in Danish Jersey. The GWAS identified in total 1770 SNPs (FDR < 0.10) for five different OS in Danish Holstein and 6913 SNPs (FDR < 0.10) for 11 OS in Danish Jersey. In Danish Holstein, a major overlapping QTL was identified on BTA1 for LNH and LNT explaining 24% of the variation in these OS. The most significant SNPs were associated with To our knowledge, this is the first study documenting a solid breeding potential for bovine milk OS and a strong indication of specific candidate genes related to OS synthesis underlying this genetic influence. This new information has the potential to guide breeding strategies to achieve production of milk with higher diversity and concentration of OS and ultimately facilitate large-scale extraction of bovine milk OS.The online version of this article (10.1186/s12864-019-5786-y) contains supplementary material, which is available to authorized users. Free oligosaccharides (OS) are bioactive molecules present in human milk that provide numerous health benefits to developing infants, including stimulating growth of selected beneficial bacteria in the gut, participating in development of the brain and exerting anti-pathogenic activity by preventing pathogen binding to intestinal epithelial cells , 2. The Bovine milk OS are generally smaller in size than those of human milk, with less complex structures and fewer isomers for each composition . Milk OSWickramasinghe et al.  examinedr = 0.91 for DH and r = 0.89 for DJ). Further, Lacto-N-hexaose (LNH) and Lacto-N-tetraose (LNT) were strongly positively correlated especially in DJ (r = 0.55 for DH and r = 0.90 for DJ) and the four fucosylated OS were strongly positively correlated in DJ, though not in DH.In Table The heritability estimates and the genetic variance for 15 individual OS for DH and DJ are presented in Table p-value) and percentage of explained phenotypic variance are presented in Table The entire set of GWAS results are presented in Additional file 10 of 21.63. This SNP was not assigned to any gene. In total 19 SNPs were significant for 5 Hex 4 HexNAc on BTA10, but none were assigned to genes for OS synthesis.For the non-fucosylated neutral OS, a total of 1755 significant SNPs were detected including 390 for LNT, 112 for 4 Hex 1 HexNAc, 736 for LNH and 517 for 5 Hex 4 HexNAc  of 20.78, and the most significant SNP for LNH was BOVINEHD0100024179 with a –log10 of 19.77. Both these SNPs were assigned to B3GNT5, which encodes UDP-GlcNAc:β-Gal β-1-3-N-acetylglucosaminyltransferase 5  of 18.69). The top SNPs explained 24% of the phenotypic variance for LNT and 23% of the phenotypic variance for LNH  was found on BTA1 with a –log10 of 6.31 for LNH and a –log10 of 5.05 for LNT, respectively. This SNP was assigned to ALG3, a gene encoding α-1-3-mannosyltransferase and explained 7.3% of the phenotypic variance for LNH and 5.8% for LNT  of 16.53 (BOVINEHD2300013179). This SNP was not assigned to any gene, but the region contained two significant SNPs assigned to LOC520336  with –log10 of 6.32 and 6.03, respectively. This gene encodes N-acetyllactosaminide β-1-6-N-acetylglucosaminyl-transferase, isoform C and explained 7.3% of the phenotypic variance for LNH. Other candidate genes of interest include B3GALNT2 on BTA28 for 5 Hex 4 HexNAc, which encodes β-1-3-N-acetylgalactosaminyltransferase 2. In total, 11 SNPs were significant for 5 Hex 4 HexNAc on BTA28, and the two most significant SNPs , which both had a –log10 of 5.54, were assigned to B3GALNT2 and explained 6.4% of the phenotypic variance  of 8.82. This SNP was not assigned to any gene. The 10 significant SNPs for 4 Hex 1 HexNAc on BTA1 did not overlap with the common QTL for LNT and LNH, and none of the SNPs could be assigned to a gene. Fifteen significant SNPs were identified on BTA11, and the two most significant SNPs , which both had –log10 of 5.12, were assigned to GLT6D1, a gene encoding glycosyltransferase 6 domain containing 1. Together these SNPs explained 5.9% of the phenotypic variance . None of the 10 overlapping SNPs common between 4 Hex 1 HexNAc and 5 Hex 4 HexNAc could be assigned to any gene, but seven of these were located on BTA1. The three common SNPs between 5 Hex 4 HexNAc and LNT were all located on BTA25, but could not be assigned to any gene.For 4 Hex 1 HexNAc, 112 significant SNPs were distributed over 18 chromosomes with no clear QTLs. The most significant SNP was on BTA1 (BOVINEHD0100014437) with a –log10 of 7.29, but could not be assigned to any gene.For the fucosylated neutral OS 5 Hex 4 HexNAc 1 Fuc, 15 significant SNPs were detected , 2 Hex 1 HexNAc isomer 2 (16), LNT (1039), 3 Hex 2 HexNAc (122), 4 Hex 1 HecNAc (623), LNH (1000) and 5 Hex 4 HexNAc (356)  of 52.88 (BOVINEHD1100030300) and explained 56% of the trait variance. This SNP was assigned to ABO, a gene which encodes the ABO blood group . In total, 19 SNPs within this QTL were assigned to the gene with –log10 ranging from 9.98 to 52.88. The BTA11 QTL for 2 Hex 1 HexNAc isomer 1 also contained significant SNPs assigned to two other candidate genes (ST6GALNAC6 and GLT6D1) in addition to numerous other genes  of 6.60 was assigned to ST6GALNAC6, a gene encoding ST6 N-acetylgalactosaminide α-2-6-sialyltransferase 6 explaining 8.5% of the trait variance  of 5.56 and 4.56, respectively, were assigned to GLT6D1, a gene encoding glycosyltransferase 6 domain containing 1. Together these explained 7.1% of the phenotypic variance. In addition, one SNP on BTA11 was assigned to PAEP. For 2 Hex 1 HexNAc isomer 2 only 16 significant SNPs were detected, all located on BTA1, but with no assignment to any known genes.A very strong QTL was detected for 2 Hex 1 HexNAc isomer 1 on BTA11 Fig. . The mosce Table . Further10 of 57.84 (BOVINEHD0200034794), while for LNH the most significant SNP had a –log of 52.68 (BOVINEHD0200034794), explaining 56% of the trait variance. This SNP could not be assigned to any gene. The QTL contains numerous genes, including two candidate genes related to OS synthesis . The most significant of these SNPs was BOVINEHD0200037177 in both traits with –log10 of 6.68 for LNT and 5.65 for LNH, respectively. This gene encodes mannosidase alpha class 1C member 1 and explained 8.6% of the phenotypic variance for LNT and 7.2% of the phenotypic variance for LNH , a gene encoding phosphatidylinositol glycan anchor biosynthesis class V. These SNPs had –log10 of 5.53 and 4.45, respectively. BOVINEHD0200036859 was also significant for LNT with a –log10 of 5.33  of 3.98, 3.75 and 3.75, respectively and explained 4.9% of the phenotypic variance.In total, 848 of the 880 significant SNPs common for LNT and LNH were located within a very strong QTL on BTA2  of 20.35 (BOVINEHD1600014578) and was assigned to AGRN, a gene encoding agrin  of 10.42. The 122 significant SNPs for 3 Hex 2 HexNAc were scattered over 13 chromosomes, but 57 SNPs were overlapping with 4 Hex 1 HexNAc, and likewise the most significant SNP for this OS was BOVINEHD1600014578 with a –log10 of 9.75. For 4 Hex 1 HexNAc, two significant SNPs  were assigned to the candidate gene COLGALT2 within the BTA16 QTL, which encodes collagen beta(1-O)galactosyltransferase 2. These both had a –log10 of 3.91 and explained 4.8% of the trait variance.For 4 Hex 1 HexNAc in DJ, a strong QTL was found on BTA16, and the most significant SNP had –log10 of 31.65 and 22.51, respectively. Likewise, 4 Hex 4 HexNAc 1 Fuc, 5 Hex 4 HexNAc, and 5 Hex 4 HexNAc 1 Fuc also had a strong QTL on BTA25 and a high number of shared significant SNPs with the other fucosylated OS. BOVINEHD4100017380 was the most significant SNP for 4 Hex 4 HexNAc 1 Fuc, 5 Hex 4 HexNAc, and 5 Hex 4 HexNAc 1 Fuc with –log10 of 16.79, 13.44 and 24.06, respectively. This SNP could not be assigned to any gene. However, for the four fucosylated OS and 5 Hex 4 HexNAc a high number of significant SNPs on BTA25 were assigned to WBSCR17 alias GALNT17), which encodes polypeptide N-acetylgalactosaminyltransferase 17 like. The most significant SNP assigned to this gene was BOVINEHD2500008220 for 3 Hex 6 HexNAc 1 Fuc, 5 Hex 4 HexNAc 1 Fuc, 4 Hex 5 HexNAc 1 Fuc, 4 Hex 4 HexNAc 1 Fuc, and 5 Hex 4 HexNAc with –log10 of 27.69, 23.71, 20.33, 14.22 and 13.21, respectively  of 3.85 was assigned to SIGLEC1, which encodes sialic acid binding Ig like lectin 1  assigned to GLT6D1 for 2 Hex 1 HexNAc isomer 1 in DJ and two significant SNPs  assigned to GLT6D1 for 4 Hex 1 HexNAc in DH. As denoted, any OS, which were below the level of quantification had zeroes entered for the genetic analysis. In DH, four OS had zeroes in more than 10 milk samples, while this was the case for three OS in DJ. This slightly violated assumptions of normal distribution. However, no significant SNPs were identified in the GWAS and low to non-existing h2 were estimated for the OS in DH. Likewise, two of the OS had low to non-existing h2 in DJ, whereas for 5 Hex 4 HexNAc, where 15 samples were denoted with zero values h2 was 0.42 (SE 0.23).The GWAS revealed a large number of significant SNP in DH and DJ, and strong genetic association between specific OS within breeds. However, only 11 significant SNPs were common for DH and DJ. In both breeds, significant SNPs were assigned to the candidate gene 10 in the full data set was reported as a missense mutation. This is the SNP (BOVINEHD1100030300), which is significant for 2 Hex 1 HexNAc isomer 1 in DJ and is assigned to the ABO gene. The most promising candidate genes and their role for synthesis of specific OS in the mammary gland are illustrated in Fig. Significant SNPs assigned to candidate genes for OS synthesis were checked to see whether any of the SNPs were associated with interesting protein sequence variation. Most of the markers were assigned to intergenic or intronic regions, but the SNP with the highest –logAdvanced analytical techniques in combination with efficient labeling methods enabling sample multiplexing have ensured high-quality OS profiling data in the largest dataset reported to date. The phenotype evaluated was the relative content of each OS compared among the milk samples. In the current study, all cows were in mid-lactation and these results are therefore translatable to commercially available milk.2 > 0.4). For some OS the same trend was found in DH and DJ: most notably the low to insignificant heritabilities for the acidic OS were evident in both breeds. This suggests that the content of sialylated OS cannot be improved through selective breeding. However, this result does not represent a concern because bovine milk already contains a high proportion of sialylated OS , while Free milk OS synthesis is very complex: a literature review has identified more than 121 glycosylation-related genes which included several glycosyltransferases, glycosidases and glycan transporters . The conB3GNT5. This gene encodes a specific glycosyltransferase, β3-N-acetylglucosaminyltransferases, which is an enzyme catalyzing the elongation of a glycan by adding a β-N-acetylglucosamine residue at the C3 position of galactose [B3GNT5 are all listed as intronic variants, and a deeper exploration of the region and the traits association is thus needed to establish the direct relationship. The gene was found only to be weakly expressed in the study by [The most promising QTL in DH is on BTA1 and identified for LNT and LNH. The three top SNPs were shared for both OS and were assigned to alactose . This festudy by .B3GNT5 in DH, several other less significant candidate genes were also detected , and a lower activity of ST6GALNAC6 could thus lead to increased content of 2 Hex 1 HexNAc. Expression of ST6GALNAC6 has been reported earlier in Jersey and Holstein cows, but only ST6GALNAC2 and ST6GALNAC5 displayed significant change in expression from early to mid-lactation [In DJ, a major QTL was identified on BTA11 for 2 Hex 1 HexNAc isomer 1 and had 19 SNPs assigned to the n humans . In human humans . The actn humans . The moln humans . The ABOn humans . The bovn humans . The OS n humans , which in humans . Furtheron BTA11 . This ge species , and, in and ABO , suggestactation .GALNT17, which most likely encodes polypeptide N-acetylgalactosaminyltransferase 17. Apart from the fucosylated OS, significant SNPs for 5 Hex 4 HexNAc were also assigned to this gene. Compared to human milk OS, where the only HexNAc moiety is N-acetylglucosamine [GALNT17 in the current study could indicate that that the elongation of more complex OS structures in bovine milk are related to N-acetylgalactosamine. Furthermore, for 4 Hex 4 HexNAc 1 Fuc and 5 Hex 4 HexNAc 1 Fuc another significant SNP on BTA25 was assigned to LFNG, a candidate gene encoding O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase. This enzyme has a fucose-specific β-1,3-N-acetylglucosaminyltransferase activity that ensures elongation of O-linked fucose residues. One final significant SNP for 3 Hex 6 HexNAc 1 Fuc on BTA 13 was assigned to SIGLEC1, which encodes sialic acid binding Ig-like lectin 1. This enzyme mediates binding of sialic acid to surface proteins and therefore the reason for its association with this neutral OS is currently unclear.For the fucosylated OS a high number of significant SNPs were detected. The most significant SNPs were on BTA25 and a high number of these were assigned to cosamine , bovine cosamine , 29 and MAN1C1, encoding mannosidase alpha class 1C member 1, and PIGV, encoding phosphatidylinositol glycan anchor biosynthesis class V. Mannosidase is a glycosidase involved in the maturation and trimming of N-linked glycans [PIGV encodes a mannosyltransferase involved in involved in the biosynthesis of glycosylphosphatidylinositol (GPI), which is a membrane anchor for e.g. N-linked glycosylation [GALNT14 within the detected BTA11 QTL, which is a gene encoding polypeptide N-acetylgalactosaminyltransferase 14. This galactosaminyl transferase catalyses the addition N-acetylgalactosamine at especially serine and threonine positions in proteins and peptides and is thus involved in the formation of O-linked glycans, which have a core structure containing 1 N-acetylgalactosamine [COLGAT2, which encodes collagen beta(1-O)galactosyltransferase 2, was detected as a candidate gene within a QTL on BTA16 for 4 Hex 1 HexNAc. This gene is mainly related to the collagen pathway, but the galactosyltransferase may catalyze the addition of galactose to OS, where e.g. B4GALT1 encodes the galactosyltansferase being involved in biosynthesis of lactose in the mammary gland [Within the very strong and overlapping QTL for LNT and LNH on BTA2, significant SNPs were assigned to two candidate genes involved in glycan synthesis. These include  glycans , where tsylation . This agry gland .2 are generally reflected in strong SNP associations. Few significant SNPs were common for DH and DJ. However, within breeds the results are expected to be largely transferable, as also documented in a joint GWAS on Danish and Chinese Holstein for milk fatty acid traits, where a large number of overlapping SNPs between populations were identified [In the present study, specific candidate genes related to OS synthesis have been identified by GWAS in both DH and DJ and high hentified . Milk OSentified , 32, 33,entified , 35 and entified . Recent entified , 38. TheTo our knowledge, this is the first study documenting a solid breeding potential for bovine milk OS and a strong indication of specific candidate genes related to OS synthesis underlying this genetic influence. This new information has the potential to guide breeding strategies to achieve production of milk with a higher diversity and concentration of OS and to ultimately facilitate large-scale manufacture of bovine milk OS.Morning milk samples from 334 DH cows (19 herds) and 300 DJ cows (21 herds) were collected, as outlined by Poulsen et al. . In totaOS were extracted from bovine milk, labeled with isobaric reagents, and multiplexed as described previously by . To measwww.illumina.com/documents/products/datasheets/datasheet_bovineHD.pdf) [Bos taurus genome assembly (ARS-UCD1.2) [The genotyping was performed as described earlier . In shoreHD.pdf) . The geneHD.pdf)  and indieHD.pdf) . The SNP-UCD1.2) . Only maThe calculation of the genomic relationship matrix has been described in detail by . The genThe REML approach in DMU was used  to estimijkl represents the phenotype of individual l in herd i and lactation j, μ, herdi  and parityj  are fixed effects. b1 is a regression coefficient for DIMk, DIMk is a covariate of days in milk , and animal is a random additive genetic effect of animal l based on G of animal l [Yanimal l .A season effect was not fitted into the model, as the milk samples were collected once on each farm in a short period when the cows were housed indoor.2a was the genetic variation, and σ2e was the residual variation based on univariate analyses.Heritability estimate was defined as:2) and a covariate SNPm indicating if a SNP is heterozygote (1), or homozygote . The effect of the SNP was tested using a Wald test with a null hypothesis of H0: b = 0. To correct for multiple testing the false discovery rate (FDR) was used as implemented in the R package “qvalue” version 1.34.0. A SNP was considered significant at an FDR of P < 0.10. Proportion of variance explained (PVE) was calculated as:The association analysis was performed for each breed separately based on an extension of the linear model 1 with and allele substation effect  for milk oligosaccharides for Danish Holstein. (TXT 317 kb)Additional file 2:Table S2. Significant SNP markers (FDR < 0.10) for milk oligosaccharides for Danish Jersey. (TXT 1411 kb)Additional file 3:Figure S3. QQ-plots of P-values for milk oligosaccharides in Danish Holstein. (PDF 713 kb)Additional file 4:Figure S4. QQ-plots of P-values for milk oligosaccharides in Danish Jersey. (PDF 734 kb)