Parastagonospora nodorum is a necrotrophic fungal pathogen of wheat (Triticum aestivum L.), one of the world’s most important crops. P. nodorum mediates host cell death using proteinaceous necrotrophic effectors, presumably liberating nutrients that allow the infection process to continue. The identification of pathogen effectors has allowed host genetic resistance mechanisms to be separated into their constituent parts. In P. nodorum, three proteinaceous effectors have been cloned: SnToxA, SnTox1, and SnTox3. Here, we survey sensitivity to all three effectors in a panel of 480 European wheat varieties, and fine-map the wheat SnTox3 sensitivity locus Snn3-B1 using genome-wide association scans (GWAS) and an eight-founder wheat multi-parent advanced generation inter-cross (MAGIC) population. Using a Bonferroni corrected P ≤ 0.05 significance threshold, GWAS identified 10 significant markers defining a single locus, Snn3-B1, located on the short arm of chromosome 5B explaining 32% of the phenotypic variation . Single marker analysis of SnTox3 sensitivity in the MAGIC population located Snn3-B1 via five significant SNPs, defining a 6.2-kb region that included the two peak SNPs identified in the association mapping panel. Accordingly, SNP Excalibur_c47452_183 was converted to the KASP genotyping system, and validated by screening a subset of 95 wheat varieties, providing a valuable resource for marker assisted breeding and for further genetic investigation. In addition, composite interval mapping in the MAGIC population identified six minor SnTox3 sensitivity quantitative trait loci, on chromosomes 2A , 2B , 3B , 4D , 6A , and 7B , each accounting for between 3.1 and 6.0 % of the phenotypic variance. Collectively, the outcomes of this study provides breeders with knowledge and resources regarding the sensitivity of European wheat germplasm to P. nodorum effectors, as well as simple diagnostic markers for determining allelic state at Snn3-B1. Parastagonospora  nodorum (Berk.) Quaedvlieg, Verkley, and Crous is the causal agent of the disease SNB and glume blotch in wheat (Triticum aestivum L.), a disease of significant economic importance in Australia, Europe, North America, and Northern Africa  toxins, to induce a hypersensitive response in the host, which takes the form of programmed cell death  and Pirus serotine  domain, a nucleotide-binding site (NBS), and leucine-rich repeats (LRRs), with deletion of Tsn1 resulting in SnToxA insensitivity , with yeast two-hybrid analysis showing the Snn1 and SnTox1 proteins interact directly in vitro , located on the short arm of chromosome 5B. Culture filtrate containing SnTox3 was produced using a wildtype pathogen isolate, SN15, and host sensitivity was mapped using the BR34 × Grandin wheat population, accounting for 17% of the phenotypic variation  populations were used for effector sensitivity screening and genetic mapping. The first was an AM panel, representing a diverse collection of 480 elite, predominantly British, wheat varieties drawn from historic collections and National Lists, encompassing varieties released between 1916 and 2007 (Supplementary Table http://www.niab.com/pages/id/326/Resources). The second was an eight-founder MAGIC population, termed the “NIAB Elite MAGIC” population  12 (SnTox1 and SnTox3 were expressed in escribed . For SnT (GS) 12 , as prev (GS) 12 . Briefly (GS) 12 . A water (GS) 12 .h2 = σ2G/(σ2G + σ2e) where σ2G is the genetic variation between line means and σ2e is the error variance appropriate to those means. Calculations were carried out in GenStat SMA: a simple linear model test in R/lme4 using all 20,643 SNPs. After finding of a major QTL, the analysis was repeated with the major QTL as a covariate.(1)Haplotype analysis using a subset of 7,369 uniquely mapped SNPs from the MAGIC genetic map .P threshold of 0.05. A full QTL model was then fitted with all QTL using R/fit.mpQTL. For the AM panel, the difference between homozygous marker classes was estimated as twice the linear regression coefficient from a regression of the trait on the marker classes . The coefficient of determination (or r-squared) was used as a measure of the proportion of variance explained by the marker. Genetic markers were anchored using BLASTn (https://wheat-urgi.versailles.inra.fr/), and where explicitly stated in the text, against the TGACv1 cv. Chinese Spring 42 physical map  genotyping platform . SNP flanking DNA sequences were used to design KASP primers using the software PolyMarker . GenomicFigure 1 and Supplementary Table h2 = 0.92), followed by SnToxA (h2 = 0.83) and SnTox1 (h2 = 0.77). For SnToxA, a separation into insensitive/weakly sensitive (0 ≤ score < 1) and strongly sensitive (score ≥ 3) was observed, accounting for 73% (338/460 varieties) and 10% (47/460 varieties) of all lines successfully screened, respectively . While spring cultivars represent just 6% of the complete AM panel, 23% (11/47 cultivars) of all SnToxA sensitive lines are spring cultivars, as is the oldest SnToxA sensitive variety, Garnet (released in 1926). Analysis of the wheat pedigree  shows the SnToxA sensitive varieties Axona (released in 1984) and Tonic (released in 1983) to be the most prominent in the transmission of strong sensitivity (due to the prominence of their offspring Cadenza in the wheat pedigree), accounting for 23% of varieties with a sensitivity score ≥ 3. Indeed, the four most recent SnToxA sensitive varieties  all have Cadenza in their pedigree. Response to SnTox1 is more evenly spread across sensitivity classes, with 24% insensitive (111/461 varieties), 28% sensitive (130/461), and 48% (220/461) showing intermediate sensitivity . No pedigree relationships of note were observed for SnTox1 and SnTox3 sensitivity. The most common effector sensitivity identified in the AM population was to SnTox3, with 42% of varieties found to be strongly sensitive (192/457), including the recent varieties Cocoon, KWS Santiago, Orator, Rainbow, and Tuxedo, all released in 2007. Insensitivity to SnTox3 was found in 25% (114/457) of lines, including six released in 2007, while intermediate sensitivity was observed in 33% of varieties. All possible combinations of sensitivity between the three effectors were identified, with 18 varieties displaying high sensitivity (score ≥ 3) to all three effectors.The AM panel, consisting of 480 varieties and breeding lines released between 1916 and 2007, was phenotyped for sensitivity to SnToxA, SnTox1, and SnTox3 via leaf infiltration, and the severity of host response scored using a 0 (insensitive) to 4 (extensive necrosis) scale , GWAS identified 30 highly significant markers associated with SnToxA sensitivity (Supplementary Table 10P ≥ 31.34), six are located within a gene model encoding a potassium transporter (TraesCS5B01G368500), just two genes proximal to the S/TPK-NBS-LRR gene underlying Tsn1 (10P > 6.41) SNPs. These accounted for between 8.3% and 14.3% of the variation (average 11.6%) with differences in score between the two homozygous classes ranging from 0.9 to 1.6 (mean 1.4). All seven SNPs were located at the Snn1 locus, with SNP Excalibur_c21898_1423 located 25 genes distant from the WAK gene underlying Snn1 in cv. Chinese Spring . Of these, seven were located within a single region on the short arm of chromosome 5B in the IWGSC RefSeq v1.0 physical map. While the remaining three SNPs  returned hits on unallocated chromosomes in IWGSC RefSeq v1.0, all three were localized to chromosome 5B on the TGACv1 wheat reference sequence (Table 1A), and have been previously allocated to the short arm of chromosome 5B by treating the SNP as a trait, and locating its estimated position by trait mapping (10P = 20.44) and explained 32% of the phenotypic variation, explaining a phenotypic difference between homozygous allele classes of 2.1. Anchoring previous markers identified as flanking Snn3-B1 to the IWGSC RefSeq v1.0 wheat genome reference sequence confirmed we had identified Snn3-B1 in the GWAS panel. Using our peak Snn3-B1 marker Excalibur_c47452_183 as a cofactor in GWAS found no additional significant genetic loci outside of the Snn3-B1 region  markers (Supplementary Table Table 1B). SMA identified four additional QTL. The first was on chromosomes 2B , located at 356.66 cM by SNP Kukri_c9898_1766. The second, at 40.61 cM on chromosome 4D , was identified by SNP BS00036421_51, the third (QTox3.niab-6B.1) was identified by three SNPs on chromosome 6B, with wsnp_Ku_c2119_4098330 showing the highest significance (P = 0.003), and the fourth (QTox3.niab-7B.1) was identified by four chromosome 7B SNPs, with BS00022127_51 showing the highest significance (P = 0.038). Using the most significant peak SNP GENE_3324_338 as a covariate in SMA analysis did not identify any additional genetic loci, and resulted in the disappearance of all four minor QTL.While screening the AM panel for SnTox3 sensitivity, the eight founders of the NIAB Elite MAGIC population were found to contrast for SnTox3 sensitivity , we again detected QTox3.niab-2B.1  and QTox3.niab-7B.1 . In addition, three further QTL were discovered, distinct to those identified by SMA (Table 2). The first, QTox3.niab-2A.1, mapped to chromosome 2A at 234.62 cM  with a P-value = 9.17-7, and explained 6.0% of the variation with 0 covariates. The second, QTox3.niab-3B.1, was located on 3B at 84.11 cM , and explained 3.1% of the variation (only found with 5 or 10 covariates). Finally, QTox3.niab-6A.1 at 65.6 cM on chromosome 6A explained 4.2% of the variation with 0 covariates  and the MAGIC population by SAM  were aligned to the IWGSC RefSeq v1.0 wheat reference genome. The seven SNPs from the AM panel delineated a physical region of 326 kb (∼6.648–6.975 Mb), while the five MAGIC SNPs delineated physical regions of 6.2 kb (6.648–6.654 Mb), located within the physical interval as defined in the AM panel. The 326-kb region was predicted to contain seven gene models, representing three high-confidence and four low-confidence gene models  delimiting a 327-kb interval containing seven genes. This was achieved without the need to develop populations specifically to investigate SnTox3 sensitivity, demonstrating the efficacy of using genetic resources such as AM panels and MAGIC populations for rapid genetic dissection of target traits , a triple knock-out strain lacking SnToxA, SnTox1, and SnTox3 (toxa13), and seedling inoculation with culture filtrate from isolate toxa13 (QTox3.niab-3B.1) appears to correspond to a QTL for adult plant SNB resistance in the wheat SHA3/CBRD x Naxos grown under field conditions in Norway  to rapidly create near isogenic lines through selfing , it did not accurately locate Snn3-B1. This is due to a 5BS/7BS translocation , involved in the accumulation of chloroplast proteins and the salt stress response pathway in Arabidopsis (-66) to single, unannotated genes in rice and brachypodium. Further work is needed to investigate whether any of these genes underlie Snn3-B1.The physical region, as defined by the most significant SNPs identified in the AM panel and MAGIC population, was predicted to contain seven gene models. Gene model TraesCS5B01G005000  is similar to bidopsis . The SNPbidopsis , SPL11, bidopsis , and Plabidopsis . Genes Tbidopsis . HoweverSnn3-B1, Tsn1, and Snn2 – although when infected with P. nodorum isolates lacking SnToxA. This is consistent with the notion that the SnToxA–Tsn1 interaction is epistatic to the SnTox3–Snn3-B1 interaction . Nevertheless, the KASP marker for SNP Excalibur_c47452_183 developed here will be of use in tracking SnTox3 sensitivity alleles where the sensitivity of the founders is known  for SnTox3, this marker could be used to remove the majority of highly sensitive varieties from a breeding program.SnTox3 sensitivity and disease susceptibility had previously been reported to be poorly correlated , only aceraction . Howevern Europe . It is tf SnTox3 . This mapathogen . NeverthRD, LB, GM, and GR undertook research. RD, KG, NG, CCM, IM, and JC analyzed the data. EF, JC, KG, HP, IM, and RO provided scientific input and project resources. JC, K-CT, NG, and RO provided supervision and project management. RD and JC wrote the manuscript. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AT and handling Editor declared their shared affiliation.


HOXA3, HOXA7, HOXA10, and HOXA11), meat quality , appetite (NUCB2) and disease resistance traits  were identified in ROH islands. Moreover, several quantitative trait loci for ham weight and ham fat thickness were detected. Genes in ROH islands suggested, at least partially, a selection for economic traits and environmental adaptation, and should be subject of future investigation. These findings contribute to the understanding of the effects of environmental and artificial selection in shaping the distribution of functional variants in the pig genome.Jinhua pig, a well-known Chinese indigenous breed, has evolved as a pig breed with excellent meat quality, greater disease resistance, and higher prolificacy. The reduction in the number of Jinhua pigs over the past years has raised concerns about inbreeding. Runs of homozygosity (ROH) along the genome have been applied to quantify individual autozygosity to improve the understanding of inbreeding depression and identify genes associated with traits of interest. Here, we investigated the occurrence and distribution of ROH using next-generation sequencing data to characterize autozygosity in 202 Jinhua pigs, as well as to identify the genomic regions with high ROH frequencies within individuals. The average inbreeding coefficient, based on ROH longer than 1 Mb, was 0.168 ± 0.052. In total, 18,690 ROH were identified in all individuals, among which shorter segments (1–5 Mb) predominated. Individual ROH autosome coverage ranged from 5.32 to 29.14% in the Jinhua population. On average, approximately 16.8% of the whole genome was covered by ROH segments, with the lowest coverage on SSC11 and the highest coverage on SSC17. A total of 824 SNPs (about 0.5%) and 11 ROH island regions were identified (occurring in over 45% of the samples). Genes associated with reproduction ( It can result from several different phenomena, such as genetic drift, consanguineous matings, population bottleneck, as well as natural and artificial selection . Jinhua ic drift . In addiic drift . For theG Morgan, where G is the number of generations since the common ancestor (Runs of homozygosity (ROH) are contiguous homozygous segments of the genome where the two haplotypes inherited from the parents are identical . The devancestor , the numancestor . The autancestor  and alsoancestor . In addiancestor . Recentlancestor , chickenancestor , and sheancestor , but lese and different measures of inbreeding from pedigree and genomic information, were also investigated. For that, we used genotyping by genome reducing and sequencing (GGRS)  , which wg (GGRS) .Ear tissue samples were collected from 202 Jinhua pigs  from conservation pig farms in Zhejiang province. Those pigs were born between 2014 and 2017, with an average depth of about four generations. A commercial kit  was used to extract genomic DNA, and verified the integrity and purity of DNA by agarose gel electrophoresis and the A260/280 ratio. The Genomic DNA samples were genotyped using the GGRS protocol . Quality1). These SNPs were annotated according to the Ensembl pig gene annotation set (Ensembl release 922) as previously reported by To determine novel variants in our sequence data, we compared the identified SNPs with the dbSNP data (Build 152o) and expected heterozygosity (He) were estimated using PLINK v1.07  is the effective population size t generations ago, calculated as t = (2f (tc))-1; ct is the recombination rate for a specific physical distance between SNPs, which was estimated using f is the Haldane mapping function built between recombination rate and genetic distance measured by Morgan; α is a correction for the occurrence of mutations.where Linkage disequilibrium between SNP pairs was estimated using PLINK v1.07 . Haplotyhomozyg were used to define ROH  thefine ROH , a minimPED) for all pigs were estimated using R package “pedigree.”Different types of inbreeding coefficients were estimated based on pedigree and genomic information. Pedigree-based inbreeding coefficients (FROH), as the ratio of the total length of genome covered by ROH to the total length of the genome covered by SNPs or sequences, as proposed by Genomic inbreeding for each animal was estimated from ROH , 5–10 Mb (FROH5-10Mb), 1–5 Mb (FROH1-5Mb), and ROH > 1 Mb , corresponding to 5 generations, 5 to 10 generations, 10 to 50 generations, and 50 generations, respectively.in which LSNP1, was based on the variance of the additive genotypes , and n is the total number of SNPs. Note that these coefficients were corrected by the allele frequencies of the current population and they can take negative values  - 0.5|], where Φ(x) represents the Gaussian cumulative distribution function and piHS is the two sided p-value associated with the neutral hypothesis of no selection (p < 0.005) were considered as putative signatures of selection  test was performed within Jinhua pigs. The iHS is a measure of the amount of extended haplotype homozygosity at a given SNP, designed to use phased genotypes to identify putative regions of recent or ongoing positive selection in genomes . The hap package  as previelection . Correspelection . In thisA total of 166,661 informative SNPs satisfying the quality filters were obtained, 26,458 of which were identified as unreported in the pig SNP database of NCBI. The SNP density was about 1 SNP per 13.6 kb and they were equally distributed on each chromosome, with the exception of some isolated regions . Accordio) and expected (He) heterozygosities in this Jinhua pig population were 0.312 ± 0.070 and 0.429 ± 0.057, respectively. The value of He was slightly higher than that reported by e was estimated from five to 1000 generations ago in our study. The results show that Ne has decreased through time, at a faster rate at 1,000 to 970 generations ago (The average observed (Hions ago . This fiROH based on larger segments (0.041–0.053) was closer to FPED (0.01) than the average FSNP. This was to some extent expected, given that the pedigree depth (about four generations) is in agreement with larger ROH segments. In addition, these two coefficients vary in the same range (0–1), while SNP-by-SNP coefficients estimated here can take lower or larger values. These low average FROH values of inbreeding suggest that recent inbreeding was low. However, according to SNP-by-SNP based coefficients, which reflect deviations of the observed inbreeding from the expected values in the current population, recent inbreeding seems to be considerably higher (0.262) than that based on pedigree and ROH.The average inbreeding coefficients estimated using different approaches are shown in PED with all the genomic coefficients was low , increasing between coefficients computed from the same source of information, as expected , thus suggesting that genotype-based estimates provide greater accuracy on relatedness as supported by previous studies  had higher correlations with FSNP1, FSNP2, and FSNP3. A similar trend was also reported by SNP2 had higher correlations with FROH1-5Mb, FROH5-10Mb, FROH>10Mb and all FROH_all. These results corroborate previous results observed in cattle (SNP2 based on excess of homozygosity correlated relatively highly with FROH detected from 50k and sequence data. This trend may be due to the fact that both FROH and FSNP2 directly reflect homozygosity on the genome (SNP2 (to some extent) capture all of the homozygosity, whereas, the FROH uses only ROH. Furthermore, the moderate to high correlations between FROH and the three other estimates of genomic inbreeding  suggested that the proportion of the genome in ROH can be used as an accurate estimate of individual inbreeding levels  in a ROH were considered as candidate SNPs . A totalChromosome position, start and end position of ROH, ROH length, number of SNPs, and number of genes within the genomic regions of extended homozygosity are reported in ADCK1, involved in phosphate metabolism, which was in the selection signature region between Berkshire and Korean native pig breeds   . The MYOds group .HOXA3, HOXA7, HOXA10, and HOXA11 on SSC18, which affects embryo implantation and prolificacy traits  with F4 fimbriae (F4ac) receptor  database islands . In partIn summary, the results show that the genomic regions that display autozygosity in the Jinhua pig breed are related to important production traits under selection, and possibly also help improve their adaptability to survive in hot and very humid environments.Results from the iHS test revealed that this coefficient had an average value of 0.77, with a maximum value of 4.69 on SSC7, thus indicating that the iHS values were not uniform across the genome. p-value < 0.005 that harbored signatures of selection, 92 of which were found in ROH islands  Sequence Read Archive (SRA). 202 samples are available under the Bioproject No. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University, and all methods involved pigs were in accordance with the agreement of Institutional Animal Care and Use Committee of Shanghai Jiao Tong University (Contract No. 2011–0033).YP, QW, and ZX designed the experiments. ZX, HS, QZ, XZ, QL, and YY performed the experiments. ZX, ZZ, and PM developed some of the analysis software. ZX wrote the manuscript with the help of BO. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The current high error rate often requires substantial post-sequencing correction and would appear to prevent the adoption of this system for SNP genotyping, but nanopore sequencing errors are largely random. Using low coverage ONT minION sequencing for genotyping of pre-validated SNP loci was examined in 9 canola doubled haploids. The minION genotypes were compared to the Illumina sequences to determine the extent and nature of genotype discrepancies between the two systems. The significant increase in read length improved alignment to the genome and the absence of classical SRS biases results in a more even representation of the genome. Sequencing errors are present, primarily in the form of heterozygous genotypes, which can be removed in completely homozygous backgrounds but requires more advanced bioinformatics in heterozygous genomes. Developments in this technology are promising for routine genotyping in the future.Despite the high accuracy of short read sequencing (SRS), there are still issues with attaining accurate single nucleotide polymorphism (SNP) genotypes at low sequencing coverage and in highly duplicated genomes due to misalignment. Long read sequencing (LRS) systems, including the Oxford Nanopore Technologies (ONT) minION, have become popular options for  Brassica napus is a recent allotetraploid arising from natural hybridisation events between B. rapa and B. oleracea, with high collinearity to both progenitor genomes1. As such, not only are the progenitor genomes highly homoeologous, but there is also extensive within genome duplication due to numerous ancestral duplication events including a Brassiceae specific triplication event3, and frequent homoeologous recombination4. This high level of duplication across the genome leads to misalignment of short sequencing reads, complicating the distinction between true single nucleotide polymorphisms (SNPs) and differentiation between sub-genomes or paralogues. In a comparison between two cultivars, c. 90% of SNPs identified were found to be caused by variation between homoeologous regions and were termed “hemi-SNPs”5. Short probe-based methods face similar issues, with up to 62% of SNPs in a pair of doubled haploids (DHs) found to be heterozygous when using the Brassica 6 K SNP array6. In fact, it is common practise when using the Brassica SNP arrays to remove SNP loci whose flanking sequence aligns to multiple regions in the reference genome, typically leaving 20–30 K SNPs from the 60 K array which are informative and can be uniquely aligned7. Numerous studies in canola, as well as other polyploid or paleopolyploid crop species including soybean, maize and peanut, have encountered this issue and routinely apply filtering strategies aimed at removing SNPs caused by misalignment in an attempt to minimise the number of false-positive SNPs16, demonstrating the difficulty of accurately genotyping highly duplicated genomes. Ultimately, the accuracy of association studies and genomic prediction is increased when all present variation is captured, including heterozygotes19.de novo genome assembly25 and identification of structural variants due to the ability to span repetitive regions and complete variants29. However, the current error rate of c. 10%31, requires the use of more accurate short read sequencing (SRS) to polish such assemblies to ensure the final sequence is correct and has likely prevented the assessment of LRS for routine SNP genotyping purposes. Since skim whole genome re-sequencing (skim WGR) using SRS has been found to be highly cost-effective, high-throughput and a relatively accurate genotyping-by-sequencing method35, LRS must be sufficiently accurate at skim levels without correction in order to compete with current SRS technology. The SRS data generated for error correction would likely in itself be sufficient for genotyping purposes in many species. In addition, performing such error correction has the potential to introduce the same misalignment errors which are typical of SRS, as polishing involves the alignment of SRS to LRS. While skim WGR using LRS is probably currently unsuitable for de novo SNP discovery without correction or substantial sequencing depth, there is the potential that LRS can accurately genotype pre-determined SNP positions, as long as alignment algorithms are able to correctly break and align reads to a reference genome around short insertions and deletions, which are the predominant form of error in ONT minION sequencing30. However, for minION LRS to be realistically used for genotyping purposes, the resulting genotype calls need to contain fewer errors than the overall 10% error rate, although this may be tolerable for genomic selection, which has been found to be relatively robust to error rates of up to c. 10%36.Long read sequencing (LRS) technology generates reads which may span far enough to uniquely align to a reference genome. LRS has quickly become the technology of choice for https://nanoporetech.com/resource-centre/videos/sub1000). Although the PacBio system is absent of systemic errors and the circular consensus of fragments between 3 and 20 kb can be highly accurate, the ONT minION and promethION systems are preferable due to superior cost structures, sequencing outputs and ease of library preparation that typically takes less than one day. And while the nanopore base-calling algorithm does have some systemic errors, the majority of errors are non-systemic31 and can be improved with more sequencing depth, unlike in Illumina sequencing, and future improvements to quality scoring based on the behaviour of these systemic errors will further minimise this issue. Additionally, existing third-party software can be applied and does not rely on platform provided pipelines, also suggesting future independent development of genotyping pipelines targeted at nanopores sequencing.The steady decline in the cost of sequencing coupled with the advantages of a whole genome approach suggests that WGR will become the predominant form of genotyping in the future, whether based on LRS or SRS. At current cost structures and outputs, neither the PacBio or ONT LRS can compete with the Illumina HiSeq/NovaSeq systems. However, developments in these technologies are rapid and the ONT promethION system is projected to rival Illumina HiSeqxTEN system in the near future  assess the effect of long reads on misalignment to the reference genome, ii) determine if the overall confidence in the resulting genotypes can be improved by applying various filtering treatments, and iii) describe the nature of discrepancies between Illumina and minION sequencing derived genotypes.A total of four minION sequencing runs were prepared from the same extracted genetic material and were processed for library preparation twice. Runs 1 and 2 were generated from the same pool prepared using the 108 chemistry and runs 3 and 4 from the same pool using the 109 chemistry, with the final adapter ligation performed just prior to each sequencing run. A reduction in yield is observed in the second runs (runs 2 and 4) and a minor reduction in mean Q score in run 4 Table , probablde novo SNP discovery.Without additional filtering, the percentage of sequencing reads which align to the reference genome is similar between Illumina and minION, but this figure reduces to between 51.1% and 66.0% when a minimum q score of 30 is applied to the Illumina sequences, while between 97.5% and 98.8% of minION reads, which are not filtered based on q score, aligned to the reference genome Table . Visual The validity of using minION sequencing for genotype calling is assessed by comparing the genotype calls at known SNP positions between Illumina SRS and minION LRS generated in the same 9 DH samples. Any genotype call which is concordant between the two systems is deemed to be accurate. Genotype calls which are discordant between the two systems are generally assumed to be true in the Illumina sequences, due to the greater accuracy of Illumina SRS. However, as these are DH samples which are expected to be fully homozygous, this study suggests that genotype calls which are homozygous minION but heterozygous in Illumina are more likely to be correct in the minION sequences. Unless otherwise specified, references to “accurate” genotype calls refers to calls which are consistent between the two sequencing systems.The 9 samples which were sequenced are DHs, such that no heterozygous genotypes are expected. To determine the effect of both sequencing systems on misalignment, the percentage of heterozygous genotypes in each individual was examined in the Illumina and minION sequencing, both filtered for a read depth between 2 and 5 for consistency. As well as an examination of all combined minION runs, each sequencing run was examined separately, especially as runs 3 and 4 were unevenly pooled, resulting in more variable genome coverage between individuals, which allows the association between sequencing coverage and heterozygosity to be examined. The average percentage of heterozygous genotype calls was similar between the Illumina data and all combined minION runs, at 4.6% and 4.2% respectively Table . HoweverThe overall accuracy of minION genotypes was improved in two ways. Firstly, removing all heterozygous genotypes increased accuracy by 2.8–4.5% Fig. , such thB. napus , were examined. The percentage of accurate genotype calls remains similar between the full c. 4 million SNP set used in this study and the 900 K subset . In this situation, the minION genotypes are more likely to be correct, as it is expected that these samples are fully homozygous. The second most common situation was where the genotype is homozygous in Illumina but heterozygous in minION (38.9%) and may be caused by sequencing error in the minION sequencing. Finally, 18.1% were homozygous in Illumina but the alternative homozygous class in minION. This last situation is more difficult to classify and may be due to the introduction of errors in minION sequencing, however either technology could be correct in this instance. However, it is unlikely that heterozygous minION genotypes will be retained in DHs, and as such, if all heterozygous genotypes are excluded, an examination of discrepant genotype calls (5.3%) found that 70.3% are heterozygous in Illumina but homozygous in minION, and the remaining 29.7% were homozygous in Illumina but the other homozygous class in minION. As these are DHs, the genotypes which are heterozygous in Illumina but homozygous in minION are assumed to be correct in the latter, increasing the total percentage of correct genotypes across all samples from 94.7% to 98.4% .Conversely, examining all the Illumina genotypes which are homozygous, 95% are concordant in the minION sequences Table . The majAs previous studies have found a nucleotide bias in minION sequencing, all genotypes which were homozygous in the Illumina sequences were examined based on nucleotide type, and the proportion of discrepant versus correct genotypes determined in the minION sequencing Fig. . SNP genAlthough the simplest approach is to discard any heterozygous genotype calls, this is only feasible in fully homozygous samples. As plant breeding often uses heterozygotes, approaches to improve the accuracy of heterozygous genotype calls was examined. Firstly, VCF files contain PL values, describing the phred-scaled likelihoods of the possible genotypes, with a value of 0 indicating the most likely genotype. There are scenarios where a SNP has been called heterozygous, but the most likely genotype is one of the homozygous genotypic classes according to the PL values, which is calculated based on numerous factors. Converting heterozygous genotypes to the homozygous genotype with the best PL value was tested. A total of 272,940 genotypes were heterozygous in minION across all SNP loci and individuals (dp 2–5), and if minION heterozygous calls are converted to the homozygous genotype class with the lowest PL value when the heterozygous PL value is not 0 (the most likely genotype), 41.3% of minION heterozygotes are converted to a homozygous class. Of these PL adjusted genotypes, 67.8% are concordant, 25% discrepant due to heterozygosity in Illumina and 7.2% are discrepant as the Illumina genotype is the alternative homozygous class.Secondly, the allelic depth of the reference and alternative allele can be used to adjust heterozygous genotypes. As such an approach is likely to require greater sequencing depth, it was tested at regions with more supporting reads, dp 4–6, as well as dp 2–5. Filtering for regions with more supporting reads did not improve accuracy of allelic depth-based adjustment Table , perhapsLastly, the effect of altering the minimum base quality score required by Samtools mpileup for inclusion in genotyping calling was examined. The default score of 13, as has been used throughout this study, was compared to a less stringent minimum base score of 1, and a more stringent minimum of 20. Applying harsher minimum base quality score does marginally improve the overall concordance of genotype calls between Illumina and minION sequencing Table . Howeverde novo SNP discovery, but will require substantial advances in sequencing accuracy or bioinformatic handling before it is suitable for routine SNP genotyping outside of homozygous germplasm. Although the majority of B. napus cultivars are DHs or highly homozygous due to their breeding history, filtering on excess heterozygosity will not always be feasible, as some genuine heterozygosity is likely present, including recent alleles which have not yet become fixed, and it may be necessary to genotype heterozygous populations. Ultimately, the accuracy of association studies and genomic prediction is increased when all present variation is captured, including heterozygotes19. At current error rates and using the simple genotype calling techniques employed in this study, it is necessary to remove all heterozygous genotype calls to achieve sufficient accuracy for use in downstream applications. Unless employed in completely homozygous genomes, methods to distinguish between true heterozygotes and false positives need to be developed, preventing the implementation of this method in species and samples expected to display heterozygous genotypes.Unpolished ONT minION LRS has the potential to be used as a SNP genotyping platform when using a list of known SNP loci, rather than 38. The extremely low sequencing depth requires the use of advanced bioinformatic techniques including a sliding-window approach to correctly assign genotypes based on either known or inferred parental genotypes. Another potential technique would be to use a haplotype-based approach, assigning a parental haplotype to each sample rather than genotyping individual SNPs and reducing the effect of individual sequencing errors as long as selected haplotypic regions are sufficiently divergent.Although heterozygous samples will be bioinformatically more difficult, structured heterozygous populations will be easier to work with and are likely to benefit from the techniques currently applied in skim WGR of bi-parental populations, particularly in rice30, and for these reasons results in a more even representation of the genome. It appears that the kurtosis of the normal distribution of genome coverage is reduced in minION compared to Illumina sequencing, such that the significant variation in realised sequencing coverage across the genome which is observed in other SRS systems39 is not present to the same extent. The effect of this was clearly seen on the accuracy of genotype calls at different numbers of supporting reads, with a perceptible decrease in accuracy at greater than expected coverage levels. This highlights the importance of interpreting sequencing data with the effect of sequencing platform in mind and in the case of minION LRS, results in a benefit to filtering depth around realised coverage, with the optimal number of supporting reads correlating with coverage.MinION LRS avoids biases which affect other SRS systems including PCR bias, GC-content and mappability41. Conversely, increasing sequencing coverage did not reduce heterozygosity in the Illumina SRS and is consistent with the high accuracy but short read length of Illumina sequencing resulting in misalignment. In addition, common quality filtering applied to SRS aimed at minimising this issue removes a large proportion of reads which cannot be uniquely aligned to the reference genome, as was observed in this study a well as others43. As the nanopore long reads cannot be quality filtered in the same way due to the high overall error rate, more of the sequencing data is utilised and therefore, potentially less sequencing data can generate the same outcome compared to traditional SRS systems. An investigation into the authenticity of SNPs identified by ddRAD found 41% of fragments did not yield expected results due to the presence of homologous sequences44, which is likely the primary cause of genotyping errors when using SRS in duplicated genomes. Both short reads and probes have been repeatedly shown to struggle with sequence similarity, resulting in frequent misalignment in duplicated genomes including canola45 but many other crop species are also highly duplicated, including soybean47, maize12, peanut48 and cotton51. Duplication is prevalent across all plant species, with plant genomes containing an average of 64.5% paralogous genes52 and therefore it is likely that the development of LRS based genotyping protocols will be of benefit across not only crop species but all plants.Another advantage of LRS over SRS is the potential for the improvement of sequencing read alignment in highly duplicated genomes. Although there is not a significant reduction in the number of heterozygous genotype calls in the minION LRS in comparison to Illumina SRS, the relatively high negative correlation between genome coverage and heterozygosity in minION, as opposed to Illumina, is indicative of improved alignment accuracy. Essentially, the issue of identifying erroneous SNPs caused by misalignment is removed before the SNP calling step is performed. This suggests that the heterozygous genotypes observed in the minION LRS are a result of sequencing error, as heterozygosity is reduced with additional sequencing depth. Although nanopore sequencing results in some systemic errors, it results in errors which are more randomly spread throughout the genome than those caused by misalignment. Random errors are less likely to impact downstream applications compared to consistent errors53, and is especially relevant for minION, which has a higher error rate than current SRS technologies31. Secondly, sequence collapse, incomplete or missing regions of the reference genome resulting in the absence of one or more duplicated regions may lead to heterozygous genotype calls. For instance, if there are two highly similar regions in the genome, but only one is present in the reference sequence, all reads arising from both of these homoeologues will align uniquely to a single region of the reference and may lead to false-positive SNPs if there is differentiation between the two regions. Additionally, gene transfer from organelles to nuclear DNA is common54 such that organelle DNA will align to these regions and may cause heterozygotes. Such misalignment can only be removed by incorporating the organelle sequences into the reference genome. Lastly, these genotypes may be genuinely heterozygous, as the process of producing DHs is not always completely effective55. Currently, the most practical method is to remove any heterozygous genotypes, but this approach will not be suitable in genomes expected to contain genuine heterozygosity. Additionally, c. 1.5% of all genotype calls were one homozygous genotype class in the Illumina SRS and the alternate homozygous genotype class in the minION LRS. Genotypes of this sort cannot be confidently classified as correct or incorrect, nor can they be identified in data sets based only on a single sequencing platform. Thankfully this represents only a small percentage of genotype calls but will inevitably be included in any SNP data sets generated from minION sequencing.Despite this improvement in read alignment, numerous heterozygous genotypes are observed in the minION sequencing, with a small number of genotypes found to be heterozygous in both sequencing systems. Although heterozygous genotype calls in DHs can be indicative of misalignment, there are other possible causes. Firstly, sequencing error can cause heterozygous genotypes, particularly in low coverage samples lacking sufficient reads to drown out noise35. Additionally, choice of SNP calling software has a substantial effect on genotypes56, with concordance ranging anywhere from c. 92% to over 99%58.As minION LRS results in improved alignment in duplicated genomes, complete concordance between Illumina and minION sequencing is not expected. Instead, by calculating the accuracy of minION sequencing as the percentage of homozygous genotype calls which are the same in both systems plus the genotypes which are heterozygous in Illumina SRS but homozygous in minION LRS, accuracy as high as 98.4% can be achieved in nanopore LRS of DHs. For context, genotypes compared between more deeply sequenced (10x genome coverage) Illumina SRS samples and those same samples with skim sequencing coverage can contain between 1.2% discrepant genotypes at 5x coverage and 7.7% discrepant genotypes at 0.25x coveragede novo assembly, were examined but did not affect genotyping accuracy. It has become common practise to improve nanopore based de novo assemblies by polishing the sequences with more accurate SRS as it greatly improves sequence accuracy and consequently genome completeness62. However, the amount of Illumina SRS required for polishing will also likely be sufficient for SNP genotyping in most species35, such that any nanopore LRS based genotyping strategy must be sufficiently accurate without polishing. In addition, polishing involves the alignment of SRS to the LRS and will have similar issues with misalignment between homoeologous regions as observed when aligning SRS to a reference genome and may introduce errors which will appear to be SNPs. Recent studies have found that nanopore sequencing errors are not completely random, with a substitution bias of G and C nucleotides, and the deletion bias for A and T nucleotides63. The effect of this on overall genotyping accuracy was negligible with minimal improvement when using a restricted list of A or T only SNPs. Nor did trimming the more error prone ends of reads31 have any effect since, realistically, removing a total of 200 bp from reads averaging 7,525 bp in length (Q >= 7) equates to approximately 2.7% of sequencing data, such that there is minimal likelihood of pre-define SNPs falling within the flanking regions of reads. Additionally, nanopore LRS based de novo assemblies have benefitted from targeting longer read fragments29 and excluding reads below a minimum length threshold, resulting in less fragmented assemblies despite incorporating less sequencing data overall61, but this approach did not improve overall genotyping accuracy. Even small fragments (c. 500 bp) sequenced on the minION are longer than the 150–250 bp produced by Illumina sequencing and provide sufficiently long reads for improved alignment to the reference genome.A number of techniques commonly applied to improve minION LRS for other applications, such as 64. This study found the QIAGEN DNeasy 96 Plant Kit to produce sufficiently high quality DNA, such that the ability to use cost effective, high-throughput DNA extraction methods, coupled with relatively quick library preparation (less than one day) suggests ONT LRS has the potential to become a routine genotyping method. However, ONT library preparation protocols currently have a number of limitations that would prevent the adoption of this technology for high-throughput genotyping. Currently, there are only 24 ligation barcodes available for minION and promethION, and while there are 96 PCR-based barcodes available for minION sequencing, the use of PCR will introduce classic PCR biases which also effect Illumina sequencing. Similarly, sequencing outputs are increasing steadily but do not yet match the same price structure as Illumina.As there is no benefit in excluding shorter fragments, more sequence reads can be utilised for genotyping and efforts to increase fragment length at the DNA extraction and size selection steps can largely be omitted. Nonetheless, input DNA quality should be considered a priority as it has been consistently found to have a significant effect on sequencing yield65, which was found to have a high detection rate of true SNPs but has a substantial false positive rate which is affected by GC error bias issues30, and currently does not allow genotyping of known variant sites or homozygous reference genotypes. The consensus-based approach of Nanopolish66 has been found to result in highly accurate genotyping of pre-defined SNPs. For instance, applying Nanopolish for variant genotyping of c. 30x coverage of the human genome allowed 99.16% of genotypes to be called correctly, including many homozygous reference sites, which translated to 94.83% accuracy of variant genotypes29. In order to minimise the effect of noise, this study required the log-likelihood ratio of a variant call to be at least 30, or it was considered to be homozygous reference. These methods may require substantial sequencing depth, and in the case of Nanopolish, requires the fast5 sequences which are substantially larger than fastq/a formats. The development of genotyping algorithms with the behaviour of ONT sequencing error and approaches recalling heterozygous genotypes based on allelic proportions, which was found to be moderately accurate in this study, could be of benefit but may require greater sequencing coverage to ensure recovery of true allelic proportions. Adjustment of heterozygous genotypes based on genotype likelihoods defined in the VCF PL field was also moderately accurate. However, less than half of heterozygous calls were converted, while in comparison, adjusting based on allelic depth resulted in similar accuracy but converted just over 70% of heterozygous calls. Nonetheless, PL values are calculated based on a number of factors, such as base quality, allelic depth and population data, making this approach reasonable and computationally simple for minION users, and may be improved by the use of larger sample sets. Additionally, filtering minION reads on a higher minimum Q score cut-off than the default of 7 used in this study may have some benefit. At the genotype calling stage, solely applying a higher minimum base quality during genotype calling did marginally improve the overall accuracy but was outweighed by the significant reduction in marker numbers. With the steady increase in sequencing accuracy likely to continue as sequencing chemistry and pore technology improves, the development of easy to implement bioinformatic strategies for genotyping of ONT LRS is probable.Bioinformatic tools for ONT sequencing face similar issues, as some software does exist for variant genotyping, but is often originating from SRS and has not been optimised for LRS. However, specific software does exist including marginCallerEven with the simple bioinformatic pipeline used in this study, overall genotype accuracy as high as 98.4% is the maximum accuracy achieved, as stated in the results. 97.2% is the maximum percentage of SNPs which are correct in all individuals under optimal filtering conditions and assumptions was achieved in a set of 9 DH canola samples. More advanced approaches can realistically be expected to lead to reliable genotyping of even more complex germplasm in the future. Currently, this technology could reasonably be used to genotype known SNP loci in DHs and the improvement of existing SNP lists in species with highly duplicated genomes. The availability of some genomic resources, specifically in the form a reference genome and a pre-defined list of SNPs is still required. The expected cost-structure and throughput of ONT sequencing systems in the near future is promising not only for canola but many crop and plant species of interest, due improvement in misalignment between duplicated regions and more even representation of the genome.B. napus DHs using the DNeasy 96 Plant Kit , according to the manufacturer’s instructions. Multiple elutions per sample were pooled and concentrated using a 1.8x ratio of Agencourt AMPure XP beads . Resulting DNA was not size selected.Genomic DNA was extracted from leaf tissue of 9 Australian spring type Libraries were prepared for minION sequencing according to the manufacturers protocol, using the Native Barcoding expansion pack (EXP-NBD103) and the 1D Sequencing kit, with either the SQK-LSK108 or SQK-LSK109 chemistry. No additional shearing was performed, but the DNA repair step was performed according to the protocol, starting with 1.5 µg of DNA per sample. A total of 4 flowcells (FLO-MIN106) were each run for the full 48 hours, generating c. 7 Gbp (SQK-LSK108), c. 4 Gbp (SQK-LSK108), c. 10 Gbp (SQK-LSK109) and c. 9 Gbp (SQK-LSK109) of sequencing respectively.35.Each of the 9 DH samples were also whole genome sequenced to between 4.4–14.4x coverage on an Illumina Hiseq3000 using a previously described library preparation method for whole genome sequencing of DHs67 to determine whether trimming the ends of reads had any effect on accuracy.Basecalling was performed using Albacore (v2.3.1) for all minION sequencing data, applying the default minimum Q score of 7. Adapters were trimmed using Porechop (v0.2.3) and a range of minimum read lengths applied using Filtlong (v0.2.0) to determine whether minimum read length has any effect on overall genotyping accuracy. End trimming was performed using cutadaptbzh whole genome reference1 using minimap268 and converted to BAM files using SAMtools view69. SNPs were called using SAMtools mpileup (v1.3.1) supplied with a list of c. 4 million pre-validated SNPs16 and converted to a VCF using BCFtools call69 and VCFtools70, ignoring indels and removing triallelic SNPs but otherwise retaining all genotypes whether variant or not. Resulting VCF files were converted to genotype and depth matrices, and the accuracy of genotype calls in the long reads sequences was evaluated in R71. The minION-based genotype calls were compared to the genotype calls of the same samples sequenced using Illumina SRS and filtered to a minimum read depth of 2 unless otherwise stated. Not all SNPs could be compared due to missing data in either the LRS or SRS sequences, and so were excluded from this analysis. As filtering for both a minimum and maximum read depth was found to be beneficial for minION sequencing, instances where all samples were required to undergo the same treatment for consistency, a read depth range between 2 and 5 supporting reads was applied due to the balance between overall accuracy and number of available data points. Alignments of sequencing reads to the reference genome were visualised using either Tablet72 or IGV73. Adjustment of heterozygous genotypes based on PL value and allelic depth were performed in R, by extracting the PL and AD fields from the VCF file.The resulting fastq files were aligned to the Darmor-16 and set of SNP loci which were also identified across the diversity of canola74 were examined. There is higher confidence that these SNPs are true variants in canola as they’ve been independently verified in two studies, across a range of germplasm types.To address the possibility that the SNP positions used in this study may contain some loci which are not genuine, c. 900 K SNPs which were common between the SNP list used in this studySupplementary Materials


Although harness racing is of high economic importance to the global equine industry, significant genomic resources have yet to be applied to mapping harness racing success. To identify genomic regions associated with harness racing success, the current study performs genome-wide association analyses with three racing performance traits in the Norwegian-Swedish Coldblooded Trotter using the 670 K Axiom Equine Genotyping Array.Following quality control, 613 horses and 359,635 SNPs were retained for further analysis. After strict Bonferroni correction, nine genome-wide significant SNPs were identified for career earnings. No genome-wide significant SNPs were identified for number of gallops or best km time. However, four suggestive genome-wide significant SNPs were identified for number of gallops, while 19 were identified for best km time. Multiple genes related to intelligence, energy metabolism, and immune function were identified as potential candidate genes for harness racing success.Apart from the physiological requirements needed for a harness racing horse to be successful, the results of the current study also advocate learning ability and memory as important elements for harness racing success. Further exploration into the mental capacity required for a horse to achieve racing success is likely warranted.The online version of this article (10.1186/s12863-018-0670-3) contains supplementary material, which is available to authorized users. Regardless of horseracing discipline, speed, or perhaps more appropriately, unparalleled speed, is the “holy grail” of almost every horse owner, trainer, and breeder. However, speed alone does not necessarily equate to success on the racecourse. The manner in which a horse demonstrates speed is critical to its racing success –3. For eDMRT3) gene had a major effect on the pattern of locomotion in the horse, resulting in some horses possessing the ability to trot or pace at high speed without transitioning into gallop [DMRT3 mutation was associated with racing success (e.g. increased earned prize money) [To date, significant genomic resources have been applied in studies attempting to map speed and racing success in TBs and QHs –18. Whilo gallop . Follow-e money) , 23–26. e money) , 26. Cone money) –38. NeveDespite the fact that harness racing success is of high economic importance to the global equine industry, genome scans for performance appear to predominantly target Thoroughbreds and sport horses (e.g. Warmbloods) , 39–41. Motivated by these facts, we conducted a GWA study to identify genomic regions and genes associated with harness racing success using the Norwegian-Swedish Coldblooded Trotter (NSCT). Norwegian-Swedish Coldblooded Trotters are ideally suited for GWA analyses of harness racing performance as their small population is not only likely to correspond with low within breed genetic variation, but the limited region in which NSCTs are eligible to race is also likely to reduce environmental variation. Thus, a more accurate assessment of the relationship between genomic regions and harness racing performance is achievable.Pedigree information and performance data on all raced and unraced NSCTs born between 1 January 2000 and 31 December 2009 were provided by the trotting associations in both Norway and Sweden (Norsk Rikstoto and Svensk Travsport). Pedigree information included horse name, horse id, date of birth, country of birth, sex, breeder, sire id, and dam id. Performance data, as of 8 February 2017, was presented per race per horse . This data included non-competitive premie and qualification races, with each record containing information on horse id, race date, race track, race type, race distance, trainer, owner, driver, finish position, prize money earned, gallop status, and average km time , 49.In order to reflect the raced population as accurately as possible, a list of raced individuals was randomly generated from the data described above using the statistical software R . In addiDeoxyribonucleic acid was prepared from the hair roots using a standard hair-preparation procedure. Briefly, 186 μL Chelex 100 Resin  and 14 μL of proteinase K  were added to the sample. The mix was incubated at 56 °C for 2 h and the proteinase K was inactivated for 10 min at 95 °C. For DNA preparation from blood samples, 350 μL of blood was used and isolated on the Qiasymphony instrument using the Qiasymphony DSP DNA mini kit . A summary of the final horses selected for genotyping is shown in Table n = 570) and the 670 K+ Axiom Equine Genotyping Array (n = 91). The data from the two arrays were subsequently merged based on SNP name, chromosome, and position, yielding a combined SNP data set of 611,888 SNPs for 661 horses. Iterative QC was then performed with the GenABEL package in R to remove poorly genotyped and noisy data using the following thresholds: minor allele frequency (MAF) (< 0.5%), missing genotypes per single nucleotide polymorphism (SNP) (> 5%), missing SNPs per sample (> 15%), and Hardy-Weinberg equilibrium (HWE)  [Prior to quality control (QC) the data set consisted of individuals genotyped using the 670 K Axiom Equine Genotyping Array (R < 0.2) .Pedigree and performance data for the 661 genotyped horses were structured for analyses using custom scripts written in Perl v5.20.1. Sex classifications were based on the official sex of the horse at the end of the study. Number of career starts was defined as the total number of competitive races for each individual . Number of gallops (NG) was defined as the total number of competitive races in which the horse was recorded as galloping at some point during the race. Career earnings (CE) were calculated as the total amount of prize money won for each horse as of 8 February 2017. Prize money won in Sweden (SEK) was converted to Norwegian currency (NOK) based on the average exchange rate for the year in which the race occurred . Horses Genome-wide association (GWA) analyses were performed using the GenABEL package in R . The pacp < 1.39 × 10− 7, corrected for total number of SNPs post QC) while a “suggestive” genome-wide significance threshold was also set at 1.0 × 10− 5. Manhattan and quantile-quantile plots were generated in R while the extent of linkage disequilibrium (LD) was estimated by calculating r2 values between all pairs of SNPs with inter-SNP distances of less than 1 Mb using PLINK v1.09 (http://zzz.bwh.harvard.edu/plink/). The effB in the GenABEL result was regarded as the allele substitution effect and the proportion of phenotypic variance explained by each significant SNP was estimated as follows:To account for any population stratification, genome-wide association analyses of CE, BT, and NG were performed using a mixed model-structured association approach (“mmscore” function with the “strata” option in GenABEL). Preliminary analyses indicated significant effects of sex, birth year, and number of career starts on all three traits. Country of birth was also shown to influence CE. As a result, sex, birth year, number of career starts, and country of birth were included as co-variants in the final analyses accordingly. Genome-wide significance for each of the analyses was determined by Bonferroni correction  for each trait using the lm function in R [http://www.ensembl.org/) was used for candidate gene screening. Genomic coordinates of genome-wide significant and suggestive genome-wide signficant SNPs +/− 500 kb were used as inputs to generate a list of annotated genes using the Ensembl Biomart function. The PANTHER Classification system was then used to obtain an overview of the biological processes, molecular functions, and pathways known to be affected by these genes [Where ion in R . The biose genes , 55.2 dropping below 0.20 by 3 kb , with the majority of these SNPs residing on Equus caballus chromosome (ECA) 6  and presented an estimated allele substitution effect of − 7079.46 NOK. The favourable allele appeared to be the T allele, with each C allele resulting in a negative effect on CE. Despite the fact that the frequency of the T allele was 98.2% and the frequency of the C allele was only 1.8%, the percentage of phenotypic variance explained by this SNP was 3.85%. The 32 SNPs in total were estimated to explain 18.34%, 18.71% and 33.17% of the variation for CE, NG and BT, respectively, in the population studied. Consequently, after LD pruning, only 17 SNPs remained – explaining 14.17%, 18.38% and 33.13% of the variation for CE, NG and BT, respectively  and 547 dams (IQR 1–1), were included in the final GWA analyses  gene, manifests intermittently with clinical signs ranging from muscle fasciculation to signs of paresis [Also of note is the role voltage-gate potassium channels play in Hyperkalemic periodic paralysis (HYPP), a genetic disorder predominantly seen in Quarter Horses. The condition, caused by a mutation in the sodium voltage-gated channel alpha subunit 4  gene effects and DOCK8 [DMRT3 and harness racing performance, additional research of DMRT3 in horses strongly suggest that the mutation is unlikely to be the single cause of gaiting ability [DOCK8 also significantly contributes to gaiting ability, ultimately playing some role in a harness racing horse’s propensity to exhibit speed at trot or pace.Perhaps the most obvious candidate gene for harness racing success in the current study was ardation –83. Whilnd DOCK8 , 82, 84. ability , 85–88. PDE3A gene, a gene on ECA6 in which a single genome-wide significant SNP associated with career earnings is located, plays a critical role in cardiovascular function [PDE3A gene that ultimately alters cardiovascular function could potentially prevent a horse from meeting the higher metabolic demands of racing, thus decreasing his/her chances of winning and limiting his/her career earnings. On the contrary, an advantageous mutation in the gene could allow some horses to perform at an even greater cardiovascular level, increasing their likelihood of winning races and earning more prize money.The protein encoded by the function –91. The function –92. HealINPP5D and the SOX5 genes. The INPP5D gene is an important regulator of immune cell signaling, while the SOX5 gene is involved in embryonic development and has been associated with multiple human diseases and disorders [SOX5 have also been theorized to disrupt neuronal development and function [Also identified by single genome-wide significant SNPs on ECA6 were the isorders –99. Moreisorders , 100. Hofunction , 102.GRIN2B) likely play an important role in controlling the fundamental biological processes underlying adaptation to equine athletic performance.Regions on ECA1, ECA7, and ECA16 have also previously been described as important for endurance performance traits, while regions on ECA14 and ECA18 associated with gallop racing in other studies do not appear to play a significant role in harness racing , 39, 66.GRIN2B, DOCK8, ATP7B, KCNRG, PIP5K1B, PDE3A, INPP5D, SOX5) suggested as strong candidate genes for harness racing success. Apart from the physical attributes required to achieve racing success, multiple candidate genes identified in the current study also advocate learning ability and memory as critical to success. However, further analyses of these genes based on additional genetic and functional studies are required to explore this notion in greater detail. Moreover, future studies should also consider a validation study with an independent population as well as sequencing of candidate genes to better identify causal alleles.After strict Bonferroni correction, 9 genome-wide significant and 23 suggestive genome-wide significant SNPs associated with harness racing success were identified. These SNPs were located on ECA1, ECA2, ECA6, ECA7, ECA16, ECA17, ECA23, ECA25, ECA28, ECA29, and ECA31 with eight genes Extent of linkage disequilibrium of the final sample of horses. Pairwise rAdditional file 2:QQ plots for earnings, best km time, and number of gallops analyses. Top panel - corrected QQ plot for earnings analysis (Uncorrected λ = 1.0532); Middle panel – corrected QQ plot for best km time analysis (λ = 1.0902); Bottom panel – corrected QQ plot for number of gallops analysis (λ = 1.0256). (TIF 111 kb)Additional file 3:Functional classification gene list from PANTHER analysis. (XLSX 27 kb)


Objective: Hypoglycemia has been shown to promote inflammation, a common pathogenic process, in many chronic health conditions including diabetes and cardiovascular disease. The aim of this study was to investigate the association of hypoglycemia, assessed by continuous glucose monitoring (CGM) with major adverse cardiovascular event (MACE) outcomes and all-cause mortality.Methods: A retrospective cohort study was conducted with 1,520 patients with type 2 diabetes mellitus (T2DM). The severity of hypoglycemia event was assessed by CGM system.Results: Three hundred and forty-seven participants experienced hypoglycemia events (323 with mild hypoglycemia and 24 with severe hypoglycemia). A fraction of 72.62% hypoglycemia was asymptomatic. During a median follow-up of 31 months, 380 participants reached the primary outcome of MACE , 80 participants died before the end of the study. In multivariate Cox regression models, hypoglycemia was associated with cardiovascular death (HR 2.642[95CI% 1.398–4.994]), non-fatal stroke (HR 1.813 [95CI% 1.110–2.960]) and all-cause mortality (HR 1.960 [95 CI% 1.124- 3.418]) after the full adjustment. Hypoglycemia was not associated with non-fatal MI and unstable angina. The HR of severe hypoglycemia was higher than mild hypoglycemia for cardiovascular death. Patients with symptomatic and asymptomatic hypoglycemia had similar MACE outcomes and all-cause mortality.Conclusions: CGM is effective to detect asymptomatic and nocturnal hypoglycemia. Hypoglycemia is associated with an increased risk of non-fatal stroke, cardiovascular related death, and total mortality. The cardiovascular mortality is dose-dependent on the severity of hypoglycemia. Chronic low grade inflammation is a common feature underlying many chronic diseases and conditions such as insulin resistance and cardiovascular disease. Studies have suggested that hypoglycemia is able to promote inflammatory processes in patients with or without diabetes , 2. HypoIn the present study, using glucose data collected from CGMs, we investigated the relationships of hypoglycemia with total mortality and major adverse cardiovascular events (MACE), including non-fatal stroke, non-fatal myocardial infarction (MI), unstable angina leading to hospitalization, and cardiovascular death.In this retrospective cohort study, we recruited 1,520 patients admitted to our hospital between January 2013 and December 2017.The inclusion criteria were: (1) T2DM according to 2013 American Diabetes Association standards . The glucose level was determined every 5 min, 288 times maximum per day. The CGM system was calibrated with blood glucometer measurements  four times daily according to the manufacturer's instruction. Participants were instructed to keep a diary about the occurrence of hypoglycemic symptoms. The CGM system used in our study measured interstitial glucose in a blinded manner, and data analysis was performed after disconnection of the device.This clinical study was approved by the ethics committee boad of The Central Hospital of Wuhan. All participants had signed the informed consent form during the enrollment and before the start the study.A clinical data warehouse was created as a collaborative program between Wuhan Central Hospital  and Shanghai Lejiu Healthcare Technology Co., Ltd. Data were extracted every 24 h by Lex Clinical Data Application 3.2  from the Hospital Health Information System (HIS) to a designated clinical data warehouse including admission/transfer/discharge, laboratory orders/results, medication orders, discharge summary, administration events, flow sheet entries, procedures, medical reports, etc. All original unstructured data  were exclusively converted to a uniformed structured format. Core elements of the data warehouse were completely de-identified so that all queries and analytics could be carried out without exposing the confidential health data, allowing the investigators with sufficient privilege to re-identify data. Lex Clinical Data Application 3.2 was a self-service data access tool designed to query the clinical data warehouse and return tabular data for analysis and visualization.A hypoglycemia event was defined as an interstitial glucose level below 3.9 mmol/L (70 mg/dl) for at least 15 min and recovery when the interstitial glucose concentration had been continuously above the threshold for 15 min or more . PercentAfter discharging from the hospital, participants were invited to join the out-patient blood glucose management system, which was run by the professional medical staff. Routine self-blood glucose (at least four times per week) and HbA1c (every 3 month) monitoring were demanded in order to know the glucose control condition and clinical medication. The median duration of follow-ups was 31 months . Eighteen patients (1.18%) with new episodes of severe hypoglycemic events during follow-up were excluded from the study, and 41 (2.70%) patients were lost before follow-ups due to various reasons.The primary outcome was the first occurrence of an adjudicated MACE, including non-fatal MI, non-fatal stroke, cardiovascular death, and unstable angina leading to hospitalization. The secondary outcome was death of any cause. The diagnoses of MACE outcomes were ascertained according to the hospitalization records, discharge summary and certification of death, which were adjudicated by an independent committee. The members of the committee were from the cardiovascular and neurology departments of our hospital, who were unaware of the CGM results. Follow-up time was calculated from the date of hypoglycemia event to the onset date of the MACE event, death, or end of study (31 August 2018). Cause of death was classified as cardiovascular death and all other causes of death.t-test for continuous variables and Chi-square test for categorical data. Cox proportional models were used to evaluate the association between hypoglycemia and either MACE or all-cause mortality. We progressively adjusted the models for potential confounders. Model 1 was a crude model. Model 2 included age, sex, eGFR, HbA1c, BMI, and duration of diabetes. Model 3 included all variables in model 2 plus smoking status, alcohol history, past medical history , all diabetic medications , hypertension medication, lipid-lowering medication, and antiplatelet agents.The differences between groups were compared using For further analysis, we evaluated the risk of MACE outcomes and total mortality according to the severity of hypoglycemia and the appearance of hypoglycemic symptoms. The severity of hypoglycemia was classified into three categories: no hypoglycemia, mild hypoglycemia, and severe hypoglycemia. The group of no hypoglycemia served as the reference group, and adjusted for models described before. The survival curves of the three groups were estimate by Kaplan-Meier method, and the homogeneity between survival curves was tested by log-rank test.P < 0.05 was considered as statistical significance.All analyses were performed using State software . Baseline characteristics of the study population were presented in Compared to patients without hypoglycemia, those who experienced hypoglycemia were significantly older, had a longer duration of diabetes, and a lower eGFR. They were more likely to have experienced hypoglycemia events previously. They also took less anti-diabetic medications of metformin, pioglitazone, and DPP-4 inhibitors; meanwhile they were more often treated with insulin and anti-platelet agents . PatientPatients with hypoglycemia were further divided into two groups according to the appearance of hypoglycemic symptoms. Compared to patients with symptomatic hypoglycemia, patients experiencing asymptomatic hypoglycemia had lower mean glucose of CGM and smaller glycemic variability, were more likely to experience nocturnal hypoglycemia, and had longer periods of hypoglycemic events . The othDuring the follow-up, 380 diabetic patients had developed MACE . Eighty patients died before the end of our study. Of the 347 patients with hypoglycemia, the median time between hypoglycemia events and MACE outcomes was 21  months.The crude incidence of MACE outcomes in people with hypoglycemia was higher than the incidence in people without hypoglycemia. These estimated results still remained significant  after further adjustments for potential confounding factors.Furthermore, we examined the findings by subtypes of MACE outcomes. Compared to patients without hypoglycemia, those with hypoglycemia had a higher rate for non-fatal stroke, cardiovascular death and total mortality . The assPatients with severe hypoglycemia had a higher risk of cardiovascular death than those with mild hypoglycemia . For subThe results of our study showed that hypoglycemia events detected by CGM were strongly associated with subsequent MACE outcomes and all-cause mortality. This association persisted after adjustment for a wide range of confounders. Furthermore, the risk of cardiovascular death and all-cause mortality was the highest after severe hypoglycemia events during CGM monitoring, especially in the first year, suggesting that health care providers should pay particular attention to the potential for morbidity and mortality after a severe hypoglycemic event. CGMs also detected a high proportion of asymptomatic hypoglycemic events, which appeared to have a similarly effect on MACE outcomes and all-cause mortality like the symptomatic hypoglycemia.n = 19), we were unable to analyze the association between the cause-specific mortality and hypoglycemia like the Trail Comparing Cardiovascular Safety of Insulin Degludec vs. Insulin Glargine in Patients with Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE)study (The Action to Control Cardiovascular Risk in Diabetes (ACCORD) study reported that intensive glycemic control was associated with increased risk of cardiovascular-related death , 4. SincTE)study . In analTE)study , 14.This can be explained by the different definitions of hypoglycemia, resulting in variable frequencies of hypoglycemia across studies. For most large epidemiological studies, hypoglycemia cases were collected by self-report , 6 or ICn = 24) in our study, these results may be limited by low statistical power.We found some evidence of a dose-dependent relationship between the severity of hypoglycemia and cardiovascular death and all-cause mortality. Our assumption is that the cardiovascular outcomes of severe hypoglycemia may be worse than that of mild hypoglycemia. A sub-analysis of the ACCORD study found that the protective effect of recurrent mild hypoglycemia was more pronounced than severe hypoglycemia . It is sThe clinical management of T2DM emphasizes the importance of glycemic control to reduce the risk of chronic complications associated with diabetes . HoweverCurrently, the standard of care in clinical practice is self-monitoring of capillary blood glucose (SMBG), which only provides a single point of time measurement and often fails to detect nocturnal and asymptomatic hypoglycemia. With the ability to measure glucose levels continuously and reflect glycemic variability, CGM technology is gaining increasing interest in clinical management. Numerous studies using CGM have demonstrated significant improvements in reducing hypoglycemia , 26. FutOur study has two important strengths. First, to our knowledge, this is the first study of applying CGM to reveal the relationship between hypoglycemia and the increased risk of CVD. Previous epidemiologic investigations without an accurate definition of hypoglycemia may underestimate the prevalence of hypoglycemia events. Both the severity and time of hypoglycemia can be collected precisely through the CGM system to make a precise diagnosis of hypoglycemia. Second, we were able to adjust for numerous standardized and high-quality covariates, including the duration of diabetes, personal habits (smoking and drinking), BMI, and kidney function. In most large clinical trials, the ICD-code extracted from electronic medical records may be inaccurate, possibly leading to misclassification of exposure and confounding factors.There were several limitations in our research. Firstly, the number of events for some outcomes may limit the precision of our estimations. Secondly, our study had a relatively short duration of follow-up with a median time of 31 months, which may limit the power to detect a significant association. Thirdly, the study only recruited participants of T2DM during hospitalization, which may not be generalizable beyond this population. Finally, the retrospective nature of our study precludes the possibility to explain the direct causal effect between hypoglycemia and MACE outcomes. Thus, well-designed prospective cohort studies with the primary intention are needed to evaluate the association between hypoglycemia and cardiovascular outcomes.In conclusion, through the analysis of glucose data collected by using CGMs, our results add to the accumulating evidence that hypoglycemia is associated with an increased risk of non-fatal stroke, cardiovascular death, and all-cause mortality. We also revealed a dose-dependent relationship between the severity of hypoglycemia and cardiovascular outcomes. Therefore, effective measurements should be taken to prevent severe hypoglycemia in patients with T2DM, especially those at high risk of cardiovascular problems.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.This clinical study was approved by the ethics committee broad of The Central Hospital of Wuhan. All participants had signed the informed consent form during the enrollment and before the start the study.WW and SZ conceived and designed the study. WW conducted the statistical analyses and wrote the manuscript. SZ provided guidance for the statistical analysis and made critical revisions to the manuscript for important intellectual content. HM provided guidance for the statistical analysis. GY collaborated with Shanghai Lejiu Healthcare Technology Co., Ltd. and took responsibility for the integrity of the data. QT and PX adjudicated the MACE outcomes and the cause of death. LY and SF took charge of the patients' follow-up.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.