Yersinia pestis started with the Justinianic Plague (541–544) and continued for around 200 y as the so-called First Pandemic. To date, only one Y. pestis strain from this pandemic has been reconstructed using ancient DNA. In this study, we present eight genomes from Britain, France, Germany, and Spain, demonstrating the geographic range of plague during the First Pandemic and showing microdiversity in the Early Medieval Period. Moreover, we detect similar genome decay during the First and Second Pandemics (14th to 18th century) that includes the same two virulence factors, thus providing an example of potential convergent evolution of Y. pestis during large-scale epidemics.The first historically reported pandemic attributed to  Yersinia pestis began as the Justinianic Plague in 541 within the Roman Empire and continued as the so-called First Pandemic until 750. Although paleogenomic studies have previously identified the causative agent as Y. pestis, little is known about the bacterium’s spread, diversity, and genetic history over the course of the pandemic. To elucidate the microevolution of the bacterium during this time period, we screened human remains from 21 sites in Austria, Britain, Germany, France, and Spain for Y. pestis DNA and reconstructed eight genomes. We present a methodological approach assessing single-nucleotide polymorphisms (SNPs) in ancient bacterial genomes, facilitating qualitative analyses of low coverage genomes from a metagenomic background. Phylogenetic analysis on the eight reconstructed genomes reveals the existence of previously undocumented Y. pestis diversity during the sixth to eighth centuries, and provides evidence for the presence of multiple distinct Y. pestis strains in Europe. We offer genetic evidence for the presence of the Justinianic Plague in the British Isles, previously only hypothesized from ambiguous documentary accounts, as well as the parallel occurrence of multiple derived strains in central and southern France, Spain, and southern Germany. Four of the reported strains form a polytomy similar to others seen across the Y. pestis phylogeny, associated with the Second and Third Pandemics. We identified a deletion of a 45-kb genomic region in the most recent First Pandemic strains affecting two virulence factors, intriguingly overlapping with a deletion found in 17th- to 18th-century genomes of the Second Pandemic.The first historically documented pandemic caused by  Yersinia pestis, the causative agent of plague, is a Gram-negative bacterium that predominantly infects rodents and is transmitted by their ectoparasites such as fleas. As a zoonosis, it is also able to infect humans with a mortality rate of 50–100% without antibiotic treatment . For the remaining site, Edix Hill, Britain, the 22 samples only had shotgun sequencing data available, and therefore, pathogen DNA screening was performed using the metagenomic tool MALT . All 30 PCR-positive extracts and 5 of the Edix Hill samples were subsequently turned into double-stranded, double-indexed, and UDG-treated DNA libraries and were enriched for the Y. pestis genome following an in-solution capture approach . Five samples, EDI002.A, DIR002.A, LVC001.B, LVC001.C, and PEI001.A, were classified as outliers. Despite having chromosomal coverage, DIR002.A, EDI002.A, and PEI001.A had no or only a few reads mapping to the plasmids and were therefore considered as Y. pestis negative. LVC001.B and LVC001.C had an exceptionally high ratio of reads mapping to pPCP1 and are still considered positive. The remaining 33 samples come from four sites in Germany , two in France , one in Britain , and one in Spain . The raw reads of six positive samples of the individuals LVC001, LVC005, and LVC006 were combined to yield a single genome with a mean coverage of 6.7-fold for the site of Lunel-Viel after assuring that they represent an identical strain. From each site, only the genome with the highest coverage was used for phylogenetic analyses when multiple genomes were available but shown to be identical in the evaluation of their single-nucleotide polymorphism (SNP) profiles. As such, the genomes of EDI003.A, EDI004.A, and UNT004.A were omitted.After mapping to the chromosome, 10 genomes showed a higher than 4.5-fold mean coverage and were used for downstream analyses. These were DIT003.B (9.4-fold), EDI001.A (38.1-fold), EDI003.A (5.2-fold), EDI004.A (7.5-fold), LSD001.A (4.8-fold), LSD023.A (7.2-fold), PET004.A (5.6-fold), VAL001.B (9.6-fold), as well as UNT003.A and UNT004.A   and this phenomenon might be limited to chronic infections with pathogens such as Mycobacterium tuberculosis, where mixed infections have been previously documented (SI Appendix).Phylogenetic analyses based on SNP alignments are prone to wrong topologies and artificial branches introduced by false-positive SNPs. This is especially true for low-coverage genomes that derive from metagenomic specimens. In the context of ancient pathogen DNA, there are three main sources for false-positive SNPs: First, DNA damage such as deamination of cytosine to uracil can lead to misincorporation of nucleotides during sample processing . Second,cumented . The intY. pestis microdiversity present in Europe during the First Pandemic. Given that our genomes are of relatively low genomic coverage, we critically evaluated uniquely called and shared SNPs among the First Pandemic genomes to accurately determine their phylogenetic position. This analysis was performed for all genomes retrieved from UDG-treated libraries with higher than 4.5-fold mean coverage, including the previously published high-quality Altenerding genome (17.2-fold mean coverage).The retrieval of genomes that span a wide geographic area gives us the opportunity to assess Y. pestis) but the genomic region is similar enough in other environmental organisms so that their reads can map, they might mimic a SNP in Y. pestis when the contaminant species carries a different allele in that position. (B) Excluding all SNPs for which heterozygous calls were identified in the surrounding regions. Heterozygous calls accumulate in conserved regions due to the above-described effect. (C) Excluding all SNPs within regions that include positions that lack genomic coverage. Variants in genome architecture often appear as gaps in mapped data and are likely to cause mapping errors, potentially resulting in false-positive SNPs.For this, we developed the tool “SNPEvaluation” and defined three different criteria, all applying for a 50-bp window surrounding the SNP: (A) Comparing the mean coverage after BWA mapping with high and low stringency and excluding all SNPs that showed a higher coverage under low stringent mapping than in high stringent mapping. In metagenomic datasets, reads of related species map frequently to conserved regions in the reference genome. When the position is not covered by reads from the target organism (Y. pestis genotype (CO92) with different coverages and reads from a captured sample previously determined as Y. pestis-negative (DIR002.A) for representation of an environmental background that is typical for ancient DNA datasets. The SNP evaluation on the artificial datasets—applied with the same criteria as for the First Pandemic genomes presented in this study—showed a maximum sensitivity  and a high specificity .The performance of the presented tool and the validity of the selected criteria were assessed using simulated datasets, where each dataset consisted of both reads of a known SI Appendix, Table S8). Forty-four chromosomal SNPs, three SNPs on the pCD1 plasmids, and two on the pMT1 plasmid were classified as true positive across all 11 genomes . The following 39 chromosomal SNPs appear unambiguous and phylogenetically informative . The genomes of Lunel-Viel (LVC_merged) and Saint-Doulchard  share in total 12 SNPs and Lunel-Viel has one more unique derived SNP. The two genomes of Saint-Doulchard share two additional SNPs but are separated by branches of nine (LSD001.A) and seven SNPs (LSD023.A). Three SNPs, two of which were previously called in Altenerding , were identified as potentially shared among all First Pandemic genomes and therefore classified as uninformative for the microdiversity. One SNP might be shared among all genomes except Edix Hill, but cannot be reliably classified due to low coverage in multiple samples. Another SNP appears to be homoplastic since it appears in the Altenerding cluster and Saint-Doulchard, but not in Lunel-Viel. Whereas the low-coverage genomes of LSD007.A, LSD019.A, LSD020.A, LSD021.A, LSD022.A, and LSD024.A appear identical with LSD023.A by full or partial coverage of its unique SNPs, the genomes of LSD002.A, LSD013.A, and LSD026.A cannot be assigned to the genotype of LSD023.A or LSD001.A, since none of their unique SNPs is covered in these genomes.This evaluation was applied to all nonshared SNPs within the First Pandemic lineage, totaling between 1 and 87 chromosomal SNPs per genome , one as shared between both Saint-Doulchard genomes , and one as unique to the genome LSD001.A. One additional SNP was found on the pMT1 plasmid in the Valencia genome (VAL001.B). An analysis of the Aschheim genome as well as a SNP effect analysis is presented in SI Appendix, Table S11). Only SNPs that pass all three criteria of our SNP evaluation in at least half of the analyzed genomes  were accepted as true shared SNPs, reducing the number from 50 SNPs identified in a previous study  and one SNP absent in Edix Hill but potentially present in all other genomes. For both shared and unique SNPs, no conflicting positions were found. This strain could, therefore, be attributed to the First Pandemic lineage without, however, resolving its exact phylogenetic position .The Waging sample (WAG001.A) had a genomic coverage too low for inclusion in our phylogenetic analysis. Since it was the only sample giving evidence for Y. pestis genomes  as well as 7 Second Pandemic genomes, including a representative of the Black Death strain (London) and 7 post-Black Death genomes , to avoThe raw data of the Aschheim and Altenerding genomes were processed identically, however considering only the A120 sample for Aschheim instead of the combined A120+A76 data .All genomes recovered from UDG-libraries with higher than 4.5-fold mean coverage including the Altenerding genome were assessed in the SNP analysis. Additionally, the sample WAG001.A was evaluated to explore its phylogenetic position, since it was the only positive sample of the relevant site.Y. pestis genomes , one genome from second- to third-century Tian-Shan mountains , one ges [DA101 ], and ses [DA101 ] were ta regions , 40 as w regions , was usehttps://github.com/andreasKroepelin/SNP_Evaluation) and a comparative mapping, using BWA with high stringent  and low stringent  mapping parameters, allowing for more mismatches in the latter. SNPs were called true positive when meeting the following criteria within a 50-bp window: (A) the ratio of mean coverage of low-stringent to high-stringent mapping is not higher than 1.00, (B) no “heterozygous” positions, and (C) no noncovered positions . An assessment of this method is presented in SI Appendix, showing a maximal sensitivity  while accepting a high specificity (up to 3.49–8.57% of true positions filtered out).For the evaluation of unique and shared SNPs of First Pandemic genomes retrieved from UDG-treated libraries, we used the newly developed tool “SNPEvaluation” .SNP evaluation on the plasmids was done using the same criteria after mapping to the individual references as described above. For the SNP effect analysis, the remaining unique true SNPs were compared with the genome annotations of the CO92 SI Appendix, Table S11) were evaluated with the same criteria with minor modifications: The minimum threshold for calling a position was set to one read covering and SNPs were called true positive, if the SNP passed the criteria in more than half of the genomes under examination.Shared SNPs  but with the same criteria. As previously addressed , resulting in a total number of 6,580 SNPs. Robustness of all tree nodes was tested by the bootstrap methods using 1,000 pseudoreplicates.For the phylogenetic analyses, we aimed for one high coverage genome per site to minimize missing data in the SNP alignment, excluding the genome of EDI003.A, EDI004.A, and UNT004.A after assuring no conflicting positions with EDI001.A and UNT003.A, respectively, in the SNP evaluation. A maximum-likelihood tree [RAxML 8  using thA detailed tree of the First Pandemic lineage was drawn manually based on the performed SNP evaluation, excluding all potential false-positive SNPs B.The presence/absence analysis for genes was performed with BEDTools  by calcuSupplementary File


Hypoglycemia is a modifiable condition that causes mortality and readmission rates to increase in a hospital setting. The condition is worsened by its complications, quick onset, and an absence of a protocol mapped out by hospitals. This complication does not come from one origin, but rather from a range of barriers. Research from a variety of sources, such as those found from the American Diabetes Association, have shown great promises in ways to treat and prevent hypoglycemia that are of importance to highlight for practitioners in the inpatient setting. Hypoglycemia is a condition wherein a person’s blood glucose level is below 70 mg/dL. The signs and symptoms of this disorder can range from feeling anxious or lightheaded to more severe indicators such as unconsciousness or even seizures. Due to its quick onset and the way an individual may experience the signs and symptoms of low blood glucose, hypoglycemia can turn deadly to those without proper treatment. Mortality and readmission rates are the main concern for patients being discharged with hypoglycemia. The issue that arises with hypoglycemia is that it is a modifiable variable that has increased mortality and readmission rates . With efThe prevalence of hypoglycemia is commonly found with individuals who have type 1 diabetes mellitus, especially in the elderly population. This condition is more likely caused by those who take insulin; however, other factors may cause hypoglycemia such as missing a meal, taking certain classes of anti-diabetic medications (i.e. sulfonylureas such as glipizide or glimepiride), or consuming excessive amounts of alcohol. It is important for individuals to always monitor and record their blood glucose levels regularly to make sure it is within their target range to prevent hypoglycemia.Patients who were hospitalized due to their complications of diabetes have shown that hypoglycemia is associated with an increase in the length of stay, mortality risk, and early readmission to an inpatient hospital stay. One study found that those suffering from a mild form of hypoglycemia have shown a 60% increase in inpatient mortality rate while patients with severe hypoglycemia had a 105% increase . FurtherA recent study was conducted by the Veteran Affairs hospital over the course of 14 years, with nearly 844,000 admissions and came to the conclusion that within a 30-day period, both post-discharge mortality and 30-day readmission rates were higher if a patient experienced hypoglycemia or glucose levels near the hypoglycemia threshold [Although there is no universal proven way to solve the dilemma described above, there are some tools to help prevent hypoglycemia altogether. According to the American Diabetes Association (ADA), hospitals should adopt a hypoglycemia management protocol that includes a dynamic approach to prevent hypoglycemia from occurring. One example includes whether patients affected could take medications by mouth or intravenously (IV). In response to this situation, a generic protocol was developed by the ADA that successfully utilized continuous monitoring of blood glucose every hour until levels reached 100 mg/dL. Once the patient was stabilized with either a carbohydrate snack or with intravenous dextrose, their blood glucose was rechecked before every meal, at bedtime, and at a set time early in the morning . This idRe-education of staff is another fundamental key to helping patients with hypoglycemia. One study showed that re-educating physicians and nurses in the emergency and internal medicine departments through seminars highlighting the ADA’s standards of medical care led to better glycemic control . There wHypoglycemia has resulted in an increase in mortality and readmission rates in hospitals. Developing a protocol for a glycemic management program in a health-system will allow swift action against the dangers of hypoglycemia. With proper training and education for both medical professionals and patients, hypoglycemia can be closely monitored in the inpatient and outpatient settings to ensure patient safety and overall wellness.


Phaseolus vulgaris is an important grain legume for human consumption. Recently, association mapping studies have been performed for the species aiming to identify loci underlying quantitative variation of traits. It is now imperative to know whether the linkage disequilibrium (LD) reflects the true association between a marker and causative loci. The aim of this study was to estimate and analyze LD on a diversity panel of common beans using ordinary P. vulgaris. Phaseolus that is of great agricultural interest. This genus is native to the American continent, and contains 55 species, five of which are extensively farmed: Phaseolus vulgaris L., Phaseolus lunatus L., Phaseolus coccineus L., Phaseolus acutifolius A., and Phaseolus polyanthus Greenman [P. vulgaris, 2n = 22) is the most important species, and two clearly distinct gene pools have been identified—one Andean and the other Mesoamerican—whose molecular diversity and phenotypical characteristics, as well as their population structure and evolutionary dynamics, have been fully described in the literature [The major plant groups traditionally consumed for nutrition include cereals, legumes, tubers, oleaginous plants, and fruits. With some 20,000 species, the legumes (Fabaceae) are the third largest botanical family of angiosperms , and incterature ,4,5,6. FA number of research groups around the world have been studying the common bean in order to improve the crop, with the aim of developing more productive cultivars that are tolerant to biotic and abiotic stresses, and to boost the nutritional and technological value of the grains ,8. In thXanthomonas axonopodis pv. phaseoli [Colletotrichum lindemuthianum, Pseudomonas syringae [Pseudocercospora griseola [Association mapping, also known as LD mapping or genome-wide association studies (GWAS), is one mapping alternative for linkage analysis, allowing better use of the genetic variation within the population and providing greater resolution in the identification of QTLs . Associaphaseoli , Colletosyringae  and Pseugriseola , water sgriseola , as wellgriseola ,25,26,27griseola .To carry out GWAS experiments, it is necessary to know the genomic extent of LD and thereby define the population size and marker density, as well as the size of the LD physical window that flanks the causal polymorphism ,30. In trelation .Solanum melongena L.) [Hordeum vulgare L.) [Beta vulgaris L.) [Avena sativa L.) [Pyrus spp.) [Vitis vinifera L.) [In regard to GWAS, only the LD produced by physical linkage is of interest. For this reason, the population structure and kinship among individuals within the population are commonly taken into account during mapping in order to avoid the detection of false associations . Howeveron tests . In an aon tests  proposedgena L.) , barley gare L.) , cultivaaris L.) , oat (Avtiva L.) , pear (Pus spp.) , and thefera L.) .P. vulgaris genome [Studies focused on estimating the LD extension based on s genome ,43,44,45s genome . Howevers genome . Valdisss genome  and Reses genome  also estIn this scenario, the aims of the present study are to investigate the relative influence of population structure, kinship, and the combination of both to the LD estimates in a common bean diversity panel, as well as to provide a detailed examination of the intra- and inter-chromosomal LD patterns based on thousands of single nucleotide polymorphisms (SNPs).10 recombinant inbred lines (RILs) derived from the ‘CAL 143’ × ‘IAC UNA’ cross [i) grain morphology, (ii) resistance to biotic factors such as pests and diseases, (iii) tolerance to abiotic factors such as drought, and (iv) the micronutrient composition of grains [i) institution of origin  and FEPAGRO , 2 from FT Sementes, 4 from IAPAR (Agronomic Institute of Parana), 1 from UEM (State University of Maringa), 1 from UFLA , and 9 landraces); (ii) type of phaseolin , following the methodology proposed by Kami et al. [iii) grain size , and (iv) commercial group , as shown in This study was based on a panel of 180 common bean genotypes, as shown in A’ cross . In addiA’ cross . Phenotyf grains . The 180i et al. ; (iii) gn = 180) was extracted from young leaves collected from 10 plants per accession using the DNeasy® Plant Mini Kit  according to the manufacturer’s instructions. DNA concentration was assessed using spectrophotometry  and agarose gel electrophoresis (0.8% w/v). Sample DNA intensities were compared to a DNA quantitation standard after staining with SYBR SAFE® . To check DNA integrity, 500 ng from 20 randomly selected genotypes was subjected to restriction digestion using HindIII  according to the manufacturer’s instructions, followed by electrophoresis on SYBR SAFE® (Invitrogen) stained agarose gel (0.8% w/v).Total genomic DNA from the IAC panel genotypes (ApeKI (5′ CWGC 3′) and barcoded adapters were ligated to individual samples. The samples were pooled by plate into libraries and amplified by polymerase chain reaction. Detailed protocols can be found in Elshire et al. [Two genotyping by sequencing (GBS) libraries were constructed in 95-plex. For each library, a single random blank well was included for quality control to ensure that libraries were not switched during construction, sequencing, and analysis. Genomic DNA was co-digested with the restriction enzyme e et al. . Each liApeKI-GBS libraries from the IAC panel can be downloaded from GenBank BioSample SAMN05513252 and SAMN05513251, both included in BioProject PRJNA336556.Sequences from two P. vulgaris (G19833) were set as the reference genome [i) minor allele frequency (MAF) ≥ 0.01 and (ii) minimum coefficient of inbreeding 0.9.The TASSEL-GBS bioinformatics pipeline , designee genome . InitialP. vulgaris were selected. Exploratory analyses were conducted in order to verify the proportion of missing and heterozygous data for each SNP data set. Because P. vulgaris is predominantly autogamous, the occurrence of heterozygotes is negligible [Only SNPs in the assembled chromosomal pseudomolecules of gligible , as showgligible  was usedgligible  was usedi) the proportion of variance explained by each PC and (ii) graphic visual inspection of the dispersion of PC scores. Therefore, we used the first four PC values as the ‘S’ matrix. In addition, nucleotide diversity π [Principal component analysis (PCA) was applied to investigate population structure, via the ‘prcomp’ function implemented in the R statistical package , and a Tersity π  was estiersity π .A relatedness matrix was estimated from genotype data using the simple matching coefficient, extended to include loci that are identical by state but not by descent , accordiplatform  was usedLinkage disequilibrium, the non-random association of alleles at two different loci, was estimated by squared allele-frequency correlations using R package “LDcorSV” . Four LDenetics” . In addienetics” .LD decay over genetic distance was investigated by plotting pairwise LD values against the distance between loci on the same chromosome. It was modelled by matching a modified recombination-drift model , includiP. vulgaris and 163,651 (5.4%) aligned to multiple regions. Finally, 83,364 SNPs were identified on chromosomal pseudomolecules and 684 on scaffold sequences from the reference genome.After sequencing the two GBS libraries from the IAC panel, a total of 428,404,611 reads were obtained, of which 399,296,160 (93.2%) were of high quality, and 3,018,395 tags were identified. Regarding alignment, 1,678,051 (55.6%) aligned to single positions on the reference genome of As the frequency of heterozygous genotypes was practically null, as shown in In the process of replacing missing data, the number of loci per window size varied from 10 (Pv09) to 22 (Pv07), as shown in P. vulgaris, although not uniformly (a higher SNP density was found near the chromosome ends). The chromosome position, alleles, and MAFs of filtered SNPs are given in Finally, after filtering, a total of 10,362 SNPs were identified, as shown in According to PCA, the first PC accounted for the majority (37.4%) of genetic variation, as shown in A further two separate groups were, for the most part, accessions originating from CIAT. One of them includes only “S” accessions, and the other consists mainly (9 accessions) of “T” accessions of Andean origin, with the large grains typical of this gene pool. In addition, nucleotide diversity analysis, as shown in 10 recombinant inbred lines (RILs) produced by bi-parental crossing of the accessions ‘CAL 143’ (Andean) and ‘IAC Una’ (Mesoamerican) [Finally, there was a separate group including the 14 Fmerican) ,17,68,69Analyzing the In contrast, distinct LD patterns were obtained when the measurements that control the bias introduced by population structure  with recombination rates in these regions [Estimates of LD varied from one chromosomal region to another and were higher in pericentromeric regions. Similarly, in the soybean, it has been reported that LD is negatively correlated  [Vitis vinifera L) [As expected, when we analyzed the highly structured IAC panel, the aris L.) , oat [38ifera L) .Interestingly, even working on a highly structured panel, P. vulgaris decayed to 0.1 at a distance of 1 Mb between loci, indicating fairly widespread LD within the species, in contrast to that observed in cultivars of soybean and rice. In these cases, the authors reported decays at a distance between loci of 133 kb , 123 kba Indica ), and 16Japonica ). For thJaponica  and 700 Japonica . NonetheJaponica . These aP. vulgaris, as well as epistatic effects, which have been reported to control seed yield and other agronomic traits in an Andean × Mesoamerican cross [Inter-chromosomal LD was recently reported by Campa et al.  in the can cross . Furtheran cross ,55.The GWAS method is based on the detection of genotype–phenotype associations in populations consisting of genotypes originating from natural populations and germplasm bank accessions. These populations often span many generations and it is assumed that multiple recombination events have occurred, reducing the extent of genomic regions affected by LD. The results of this study show that the marker density required for reasonable coverage of the genome depends on the particular features of the common bean genome and the chromosomal context. Additional studies to explore patterns of specific genomic segments with reduced LD could be conducted to carry out LD mapping based on a candidate gene approach, for example, as used to dissect the genetic architecture of plant shape in rice . In addiOur findings have fundamental implications for the development of association mapping in the common bean. In particular, it is already evident that the careful evaluation of population structure is a key element. Furthermore, our findings suggest that the degree of kinship among individuals in the population under study is also worthy of special attention. In panels substantially consisting of improved lines, such as those used herein, the bias induced by kinship may be stronger than that of population structure itself.Finally, we would stress the need to take account of one-off LD patterns in genomic regions in which studies have detected significant associations with phenotypic traits. Based on this analysis, it will be possible to outline strategies for exploiting these regions, with the aim of identifying candidate genes involved in trait inheritance.P. vulgaris.This study shows that LD estimates decay dramatically if population structure is taken into account, and especially the degree of kinship among accessions of cultivated common beans on the basis of information on thousands of SNPs. LD measurements vary from one chromosomal region to another and, as expected, are higher in pericentromeric regions. We also found evidence of LD between inter-chromosome loci, suggesting that the breeding process and the species crossing system may have contributed to LD magnitude, since the bias due to population structure and kinship was corrected for. Importantly, the LD estimates herein may influence our ability to localize important genes on the basis of association mapping studies in


Juglans regia L.) has grown enormously, likely reflecting increased consumption due to its numerous benefits to human health. However, advances in genome‐wide association (GWA) studies and genomic selection (GS) for agronomically important traits in walnut remain limited due to the lack of powerful genomic tools. Here, we present the development and validation of a high‐density 700K single nucleotide polymorphism (SNP) array in Persian walnut. Over 609K high‐quality SNPs have been thoroughly selected from a set of 9.6 m genome‐wide variants, previously identified from the high‐depth re‐sequencing of 27 founders of the Walnut Improvement Program (WIP) of University of California, Davis. To validate the effectiveness of the array, we genotyped a collection of 1284 walnut trees, including 1167 progeny of 48 WIP families and 26 walnut cultivars. More than half of the SNPs (55.7%) fell in the highest quality class of ‘Poly High Resolution’ (PHR) polymorphisms, which were used to assess the WIP pedigree integrity. We identified 151 new parent‐offspring relationships, all confirmed with the Mendelian inheritance test. In addition, we explored the genetic variability among cultivars of different origin, revealing how the varieties from Europe and California were differentiated from Asian accessions. Both the reconstruction of the WIP pedigree and population structure analysis confirmed the effectiveness of the Applied Biosystems™ Axiom™ J. regia 700K SNP array, which initiates a novel genomic and advanced phase in walnut genetics and breeding.Over the last 20 years, global production of Persian walnut ( Juglans regia L., also known as Persian or common walnut, is the only species of the genus Juglans widely cultivated for nut production . The number of SNPs was then reduced by almost 50% (9 582 656 variants) after applying quality filters  the potential SNP effect on genic regions or its genotype within the discovery panel, and (ii) the SNP distribution throughout the walnut genome. We selected 66 280 SNPs falling in 23 984 predicted genic regions and featuring mainly mis‐sense or non‐sense genic changes (HIGH_MOD_EFF). These SNPs, which cover 73.8% of all predicted genes in the walnut genome, are of particular interest for marker‐trait association studies, as any change in the amino acid sequence of a protein can potentially have phenotypic effects  by Bianco et al.  to select SNPs evenly distributed in the genome, similar to the strategy used in apple . This high‐density SNP array represents a great advance in walnut genetics, based to date on either a handful of markers  of the M. domestica; Bianco et al., Prunus persica; Verde et al., Vitis vinifera; LePaslier et al., o = 0.5–0.6; Ruiz‐Garcia et al., et al., in silico SNP genotypes of our discovery panel was on average 0.26  distribution, 160 473 SNPs showed a MAF value within the 5%–9% range, 156 359 in 10%–19%, 106 164 in 20%–29%, 101 743 in 30%–39% and 84 919 in the 40%–50% range. A common issue with array‐based technologies is the introduction of ascertainment bias in favour of common alleles during the SNP selection process . These samples likely failed due to low‐quality DNA or the genetic distance from the discovery panel, as might be the case for ‘VX211’ and ‘RX1’, two hybrids of J. regia with J. hindsii and J. microcarpa respectively, which were released as rootstocks from the UC Davis WIP  based on clustering performance and quality‐control measures: Poly High Resolution (PHR), No Minor Homozygote (NMH), Off‐Target Variant (OTV), Mono High Resolution (MHR), Call Rate Below Threshold (CRBT) and Other. Table . AfterwaCicerSNP array , Other (16.1%) and NMH (13.9%). PHR SNPs are polymorphic with good cluster resolution and high‐quality control measures, and are usually recommended for downstream analysis. The class ‘Other’ included low‐quality SNPs, which showed one or more cluster properties below thresholds and, therefore, did not convert well on the array. As with the PHR SNPs, the NMH showed good clustering performance but did not present any minor homozygous genotypes. A small proportion of variants (4.5%) was monomorphic (MHR) in the genotyping panel, which likely indicates false‐positives due to sequence errors in the discovery panel. The percentage of walnut variants falling into the more thoroughly defined class of PHR SNPs was higher than the one reported for the et al., et al., SNPs identified in predicted genic regions had the highest rate of conversion into PHR 72%) and NMH (14%) polymorphisms. This is consistent with previous observations in peach and apple in which genic SNPs were also the best performers due to the lower level of undetected polymorphisms in the sequences flanking the target SNP  than for the technical replicates (97.6%) of ‘Chandler’. The lower concordance among technical replicates can be explained by low quality DNA, as is also suggested by their higher missing rate (0.39% on average) compared to the biological replicates (0.16%). Moreover, we also compared the genotype data of the Axiom™ et al., Since its establishment in late 1948, the UC Davis WIP has worked to address the needs of California walnut growers by emphasizing yield, harvest date, kernel colour and in‐shell traits. All controlled crosses made in the WIP have been meticulously registered in a historical pedigree, which was used recently to estimate breeding values (EBVs) for four important traits in the WIP, such as lateral fruit bearing and kernel colour  for all pairwise comparisons between individuals  from full‐sib pairs (IBS0 > 0.03). In this way, we identified both new parents for 17 individuals. In particular, ‘Eureka’ and ‘Payne’ were confirmed as parents of the cultivar ‘Marchetti’, providing genetic evidence for the phenotypic similarity between these walnut varieties, as already predicted by Harold I. Forde, one of the pioneer plant breeders of the UC Davis WIP , OTV (152), AAvar (1380), ABvar (2192) and BBvar (906) polymorphisms showing no Mendelian errors. In this way, we removed most of the SNPs with an excess of genotyping errors.et al., We evaluated the missing rate and basic metrics of genetic diversity for PHR, robust PHR and robust NMH + OTV polymorphisms separately. As shown in Table FIS) values of WIP families with more than five seedlings each. FIS was negative when estimated with PHR (−0.015) and robust PHR (−0.028) and positive when only NMH + OTV (0.032) were considered. This can be explained by the fact that, while PHR variants capture most of the genetic diversity throughout the genome, the NMH and OTV polymorphisms might target specific genomic regions with excess homozygosity due to past selection within the UC Davis WIP. Moreover, the values of FIS and heterozygosity for the two PHR classes may derive from bias towards intermediate allele frequencies, commonly introduced by the filtering steps during array development  along with its progeny ‘Robert Livermore’ formed a separate group from ‘Lara’, ‘Fernor’ and ‘Meylan’, which were instead admixed with Californian genotypes. The closer relatedness of California and French genotypes can be explained by the series of controlled crosses made in the WIP between French cultivars, chosen for quality and late leafing traits, and the precocious and lateral fruitful varieties of California . ‘Sorrento’ is more accurately considered as a landrace rather than a variety. It is a mixture of genotypes, showing a heterogeneous phenotypic variability for important commercial traits, such as fruit size and yield. It was common practice in Campania  to label plants originating from ‘Sorrento’ seeds as Sorrento , formed a second subgroup with one of the walnut genotypes from Veneto . Interestingly, in cluster ii the American accession ‘Idaho’ grouped together with the walnut accessions from Bulgaria. This finding may be explained by the cold‐hardy Carpathian nature of ‘Idaho’, originally kept in the UC Davis collection just as a curiosity for its very large ‘Bijou’ type nuts but with poor quality  to explore the genetic variability among walnut cultivars of different origin, mostly from the United States , China, France and Italy Table . Considee Figure . In partJ. regia 700K array will transform many aspects of walnut genetics. In the UC Davis WIP, availablity of an accurate pedigree will drive the selection of appropriate parents for future artificial crosses, making the best use of genetic variability while avoiding inbreeding depression. At the same time, advanced genetic analysis can now be performed for traits of interest in the WIP to more thoroughly understand their genetic basis and to predict individual performance. The Axiom™ J. regia 700K array is also being used in France and Iran to genotype local Persian walnut germplasm collections with interesting phenotypic variation related to biotic and abiotic stresses  using BWA (v0.7.13) with default parameters  existence of other polymorphisms within the 35‐bases adjacent to the target SNP; (ii) high sequence similarity between all 16‐mer sequences flanking the SNPs and the rest of the genome (16‐mer count); (iii) the predicted probability that the SNP will convert on the array (P‐convert score). Entries with additional predicted variants in both flanking regions, a 16‐mer count > 300 and a P‐convert score ≤ 0.6 for both the forward and reverse probes were removed.Variants were further filtered following the recommendations of Affymetrix  were selected.SNP effect on predicted genes was assessed with SNPeff (Cingolani ALL_HOM).SNPs with homozygous genotypes for either the reference or the alternative allele, and with no more than three missing values were selected . In particular, the initial lysis incubation was performed with the addition of proteinase K and polyvinylpyrrolidone (PVP) and extended to 90 min. Up to 50 μL of DNA (≥ 20 ng/μL) from each sample were submitted to Affymetrix for genotyping using the Affymetrix GenTitan platform. The same DNA sample from the cultivar ‘Chandler’ was included twelve times as technical replicate (one per plate). Four independent DNA extractions were also performed from leaves of the same ‘Chandler’ tree, and were considered to be biological replicates.A total of 1284 walnut accessions of the UC Davis WIP and ten Italian walnut accessions were genotyped using the Axiom™ ay Table . Young l® Genotyping Console™ software (GTC) (v4.2) for clustering and genotype calling. Samples with a Dish Quality Control (DQC) value < 0.82 and call rate < 0.97 were excluded from further genotyping analysis. APT (v1.19.0) was used for post‐processing the results of GTC and classifying the SNPs into six major classes: PHR, NMH, OTV, MHR, CRBT and Other. A second run of filtering further processed the PHR SNPs, identifying seven additional subclasses to a new, more thoroughly defined PHR class  framework  was higher than 0.45. Known pedigree relationships were used to calibrate the kinship coefficients, and define novel parent‐offspring pairs. First, all pairwise relationships were considered to be first‐degree relatives if k ≥ 0.16. The proportion of SNPs with zero IBS0 was then used to further distinguish parent‐offspring (IBS0 ≤ 0.03) from full‐sib pairs (IBS0 > 0.03). The proportion of SNPs consistent with Mendelian inheritance for all identified parent‐offspring relationships was estimated using the function mendel implemented in Plink  were calculated separately for the PHR, robust (no Mendelian errors) PHR and robust ‘NMH + OTV’ SNPs , using the commands freq, freqx and missing in PLINK .Missing rate, MAF, heterozygosity and fixation index .Table S1 List of the walnut accessions included in the SNP discovery panel.Click here for additional data file.Table S2 List of the 609 658 SNPs tiled on the Axiom™ J. regia 700K SNP array. Details about the SNP position, alleles and classification after genotyping are provided.Click here for additional data file.Table S3 List of the walnut accessions genotyped with the Axiom™ J. regia 700K SNP array.Click here for additional data file.


Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references  and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat ( Triticum aestivum L.) is a major staple food crop in the world. The fast-growing world population demands wheat production to be increased up to 70% by 2050 to feed estimated world population of approximately nine billion . The purpose of using the barley reference genome is to show efficiency of using reference genomes of closely related species to impute missing data in cases where reference genome sequence is absent in some species. If a SNP could be mapped in multiple chromosome positions, the position with the lowest E-value was used to represent the SNP location.Sequence reads were first trimmed to 64 bp, and identical reads were grouped into sequence tags. Unique sequence tags were aligned internally to identify SNPs within the tags allowing mismatches of up to 3 bp. SNPs were called using the Universal Network Enabled Analysis Kit (UNEAK) GBS pipeline  in TASSEIn this study, imputation was performed using BEAGLE v3.3.2  and the Imputation accuracy was calculated by comparing the imputed SNPs after five, six and seven sequencing runs to the actual SNPs called from eight sequencing runs. Two files including one file of the actual SNP data from five (359 million reads), six (421 million reads), seven (488 million reads) and eight (566 million reads) sequencing runs, and an imputed data file generated from the five, six and seven sequencing runs were compared to calculate the number of correctly imputed data points. The ratio between correctly imputed and total imputed data points was used to estimate imputation accuracy . To evalTwo GBS libraries were generated for the 384 wheat accessions with 276 landraces and 12 cultivars in library 1 and 96 cultivars in library 2. To minimize missing data, an average of two sequencing runs was performed for each plate of 96 samples, therefore, library 1 with three plates of samples was run a total of six times and library 2 with one plate of samples was run twice. Eight sequencing runs generated a total of 566,439,207 reads from the two libraries with 81%  of high-quality barcoded reads, from which 133,039 unique SNPs were identified including 16,506, 38,642 and 65,560 SNPs with <20%, <50% and <80% missing data, respectively. To determine the relationship between number of GBS-SNPs and number of sequencing runs, numbers of SNPs were calculated for each increased run from first to six sequencing runs of the library 1 . The numThe average SNP density was 3.87 SNPs per Mbp when the SNPs with <80% missing data were counted . The seqTransitions were the most observed nucleotide variations (68.63%) including A/G 32.27%), C/T (28.36%), C/G (9.61%), A/C 7.34%), G/T (6.31%) and A/T (4.43%) transition types . Using t.27%, C/T%, G/T  in all four references. Although they were all significant, the correlations were much lower for the barley reference genome (RFive sequencing runs (three runs of the library 1 and two runs of the library 2) generated 355,197,375 total reads and 5,571 SNPs with less than 20% missing data. The number of SNPs was almost doubled  after adding one additional sequencing run of library 1  and tripled  after adding three sequencing runs of library 1 . For the number of SNPs with <80% missing data, increasing number of sequencing runs from five  to eight  only increased 44% SNPs. However, imputation reduced much more missing data than increasing sequencing runs from five to eight although imputation efficiency is different among the four references . The numImputation accuracy was calculated for each reference by comparing the SNP data imputed from five, six or seven sequencing runs to the real SNP data from eight runs . All fouThe imputation accuracy increased with the increase in allele frequency from 25.7% accuracy for allele frequency < 5% and 99.7% accuracy for allele frequency > 95% . The posPstI and MspI. A combination of PstI and MspI enzymes has been successfully used for high quality wheat library construction [Advancements in next-generation sequencing technology and high-throughput SNP genotyping can greatly accelerate crop breeding process if properly deployed . GBS tectruction . The seqtruction . In the Typically, two strategies can be used to reduce missing data: increasing sequencing depth or imputing missing data using a reference genome. Increasing sequence depth can be achieved through lowering multiplexing level in a fixed run number or increasing sequencing runs of a highly multiplexed library. Both methods will result in an increase in per-sample cost. As indicated previously, lowering multiplex level may not increase SNP number as expected. Thus, increasing number of sequencing runs can be an option. In this study, the first library was run six times and the number of SNPs was significantly increased especially for the number of SNPs with <20% missing data , indicatT. aestivum and tetraploid emmer wheat (AABB), while gene flow between the hexaploid and Ae. tauschii (DD) might have not occurred [Before imputation, we were able to bioinformatically map 15,172 , 36,278 (~55.34%), 56,125 (~85.61%) and 62,241 (94.94%) SNPs out of 65,560 SNPs with <80% missing data to the barley reference genome , CSSS T, W7984  and IWGSoccurred –47, whicoccurred , 48. TheThe number of transition-type SNPs (68.63%) with majority of A/G (32.27%) and C/T (28.36%) transition was much higher than transversion-type SNPs with an average Ts/Tv SNP ratio of 2.19 , which aAmong the four reference genomes, IWGSC RefSeq v1.0 has the best wheat genome sequence coverage and assembly quality therefore it is expected that the IWGSC RefSeq v1.0 generated the most imputed data points  from the five sequencing runs and the barley reference genome 464,174) generated the least . However,174 geneA high positive correlation was observed between imputation accuracy and allele frequency. Based on different references and runs, the greatest number of imputed data points was observed in allele frequencies of 0.55 to 0.95 with imputation accuracy from 88 to 99% . AlthougImputation using genome references is an effective tool to fill up massive missing genotypic data generated from GBS. Among the four references  used for imputation, IWGSC RefSeq v1.0 imputed the greatest number of missing data points with adequate imputation accuracy, especially for those alleles with high frequencies. For those alleles with low allele frequency, W7984 assembly showed the best imputation accuracy although imputed number of missing data points was slightly lower than the IWGSC RefSeq v1.0 reference. Therefore, they both can be used as reference genomes to impute missing GBS data in wheat breeding and genetic research.


Staphylococcus aureus can colonize the human vestibulum nasi for many years. It is unknown whether and, how S. aureus adapts to this ecological niche during colonization. We determined the short (1 and 3 months) and mid-term (36 months) genomic evolution of S. aureus in natural carriers and artificially colonized volunteers. Eighty-five S. aureus strains were collected from 6 natural carriers during 3 years and 6 artificially colonized volunteers during 1 month. Multi-locus sequence typing (MLST) and single nucleotide polymorphism (SNP) analysis based on whole-genome sequencing (WGS) were carried out. Mutation frequencies within resident bacterial populations over time were quantified using core genome SNP counts (comparing groups of genomes) and pairwise SNP divergence assessment . SNP counts (within 1–3 months) in all naturally colonizing strains varied from 0 to 757 (median 4). These strains showed random and independent patterns of pairwise SNP divergence . When the different core genome SNP counts over a period of 3 years were considered, the median SNP count was 4 (range 0–26). Host-specific pairwise SNP divergence for the same period ranged from 9 to 57 SNPs (median 20). During short term artificial colonization the mutation frequency was even lower  and the pairwise SNP distances were 0 to 5 SNPs (median 2). Quantifying mutation frequencies is important for the longitudinal follow-up of epidemics of infections and outbreak management. Random pattern of pairwise SNP divergence between the strains isolated from single carriers suggested that the WGS of multiple colonies is necessary in this context. Over periods up to 3 years, maximum median core genome SNP counts and SNP divergence for the strains studied were 4 and 20 SNPs or lower. During artificial colonization, where median core genome SNP and pairwise SNP distance scores were 2, there is no early stage selection of different genotypes. Therefore, we suggest an epidemiological cut off value of 20 SNPs as a marker of S. aureus strain identity during studies on nasal colonization and also outbreaks of infection. Staphylococcus aureus (S. aureus) is among the bacterial species associated with increasing drug resistance, morbidity, invasive disease, and mortality in humans as well as animals drug resistant bacterial pathogens. This has become a global threat . Staphyl animals . S. aureolonized . S. aurefections . S. aurenfection . The prent years .S. aureus expresses adherence genes  and several immune-modulating genes   . During and scn) . Host fa as well .S. aureus secretes a number of immune-modulating proteins. Staphylococcal complement inhibitor (SCIN), encoded by the scn gene, can efficiently protect S. aureus by inhibiting the innate immune response mediated by human neutrophils. SCIN and other immune modulating proteins are encoded on the immune evasion cluster (IEC)  . The scn all IEC . To test all IEC .S. aureus strains during natural and artificial nasal colonization during periods ranging between 1 and 36 months. The numbers of human volunteers and hence the overall number of S. aureus nasal isolates are limited due to the technical and logistic complexity of the studies involved (We have here used whole genome sequencing (WGS) to quantify the mutational changes occurring in involved . In addiStaphylococcus aureus strain collection was carried out as described earlier . The latter strains were susceptible to all common antibiotics and were free from staphylococcal toxin genes . Raw reads were assembled using the A5 MiSeq-20140604 assembler. Datasets for strains cultured in 2007 and 2010 contained 35 and 22 isolates, respectively, involving natural nasal colonization in 6 persistent carriers. The dataset from 2008 (28 isolates) was collected for strains from 6 different volunteers artificially colonized with iage see . The seq1. The MLST method is known to have a higher discriminatory power for S. aureus strains than PFGE  was performed using BioNumericshan PFGE . For cla PFGE . The average number of contigs generated per genome was 64 contigs (ranging from 40 to 315 contigs). The average N50 contig length was 171778 bp . IsolateStaphylococcus aureus isolates from 2007 and 2010  from carriers A to F were analyzed for short term genomic changes, over 3 months (in 2007), and 1 month (in 2010). ST30  and ST20  were found to be the dominant MLST types followed by ST8 and ST5 (18% each) in 2007 and ST22 and ST7 (20% each) in the 2010 subgroups  and 0 to 11 SNPs (median 3.5 SNPs), respectively , 2. We oEvolutionary analysis over a period of 3 years (2007–2010) could only be done for the isolates from two persistent carriers, A, and C. In these carriers the MLST type remained unchanged over time, suggesting persistent colonization with the same strains . All isoS. aureus nasal carriers  were identified in the current sequence dataset . Two strains (1410054 and 1410055) were shown to have acquired the cna gene during colonization of host E  and collagen adhesions (cna) may represent risk for subsequent invasive infections in carriers  term colonization (up to 3 years) the number of accumulating SNPs was low as well. We here suggest an epidemiological median cut off value of 20 SNPs as a marker of The datasets for this manuscript are not publicly available because we are still in the process of submitting data on NCBI. Requests to access the datasets should be directed to manisha.goyal@biomerieux.com.S. aureus strains. MG, FJ, MP, and CM carried out the whole genome sequencing studies. MG interpreted the sequence data and wrote the first version of the manuscript. All authors discussed the results and edited the manuscript.WvW, NV, and AvB conceived the study. MT conducted the microbiological experimentation for AvB, MP, CM, FJ, and MG were employees of bioMérieux, a company designing, developing, and marketing tests in the domain of infectious diseases. The company was not involved in the design of the current review and the opinions expressed are those of the authors and may be different from formal company opinions and policies. The remaining 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.


To better understand the genetic factors responsible for this tumor regression property, whole genome resequencing was conducted using Illumina Hi-Seq 2 × 100 bp paired-end read method  with AR (confirmed tumor regression property) and AP chickens. Sequence reads were aligned to the chicken reference genome  and produced coverage of 11× and 14× in AR and AP, respectively. A total of 7.1 and 7.3 million single nucleotide polymorphisms (SNPs) were present in AR and AP genomes, respectively. Through a series of filtration processes, a total of 12,242 SNPs were identified in AR chickens that were associated with non-synonymous, frameshift, nonsense, no-start and no-stop mutations. Further filtering of SNPs based on read depth ≥ 10, SNP% ≥ 0.75, and non-synonymous mutations identified 63 reliable marker SNPs which were chosen for gene network analysis. The network analysis revealed that the candidate genes identified in AR chickens play roles in networks centered to ubiquitin C (UBC), phosphoinositide 3-kinases (PI3K), and nuclear factor kappa B (NF-kB) complexes suggesting that the tumor regression property in AR chickens might be associated with ubiquitylation, PI3K, and NF-kB signaling pathways. This study provides an insight into genetic factors that could be responsible for the tumor regression property.Arkansas Regressor (AR) chickens, unlike Arkansas Progressor (AP) chickens, regress tumors induced by the  Chickens are not only an important supply of proteins for the human population but are also outstanding animal models in several fields of biology. They provide excellent opportunities for unraveling the genetic basis of phenotypic variations . Their lv-src oncogene of RSV, unlike the AP birds which develop malignant tumors upon v-src activation in connective tissue known as sarcoma. The tumor regression process in AR chickens may be due to the suppression of cell division, induction of apoptosis, DNA damage repair, or inhibition of metastasis by various tumor suppressor genes. In chicken models, tumor regression has been found to be strongly associated with both B complex haplotype, that encodes major histocompatibility complex (MHC), and non-MHC molecules which include T-lymphocytes and B-cell alloantigens [Arkansas Regressor (AR) and Arkansas Progressor (AP) chickens are important animal models for studying the molecular basis of disease resistance and susceptibility. The AR and AP chickens were developed in 1965 by inbreeding of White Leghorn (susceptible to Rous Sarcoma virus: RSV) and Giant Jungle Fowl strains (resistant to RSV) . The AR antigens ,12,13.Single nucleotide polymorphisms (SNPs) are very common genetic alterations and occur at a rate of ~5 SNPs per kilobase (kb) in chickens  and ~1 SSeveral genetic variation analyses are performed to identify SNPs associated with disease resistance traits in chickens ,18,19,20Adult AP and AR chickens that are maintained by Dr. N. Anthony at the University of Arkansas  were used for this study. A blood specimen (1 mL) was collected from 12 birds from each line following an animal use protocol approved by the University of Arkansas Institutional Animal Care and Use Committee . Genomic DNA was extracted from whole blood samples using a QiaAmp DNA mini kit  following manufacturer’s instructions. The quality of the DNA was checked using a NanoDrop 1000 spectrophotometer  and agarose gel electrophoresis. Then, the 10 best quality samples from each line were pooled to represent each chicken line.Library construction and whole genome sequencing for the pooled DNA samples were performed by the National Center for Genome Resources . Illumina HiSeq 2 × 100 bp paired-end read method was used for genome sequencing. The quality of raw sequencing data was determined using FastQC toolkit  and low-The JMP genomics  program was used for filtering unique SNPs for tumor regression in the AR chickens. Single nucleotide polymorphism (SNPs) occurring in both AR and AP lines were removed, leaving behind the unique SNPs for each line. To identify highly fixed and homozygous SNPs, the SNPs were filtered based on SNP percentages (SNP%). The SNPs with a SNP% of ≥ 0.75  were chosen. The 75% cutoff for SNP selection was set by considering potential sequencing errors that can be generated by the massively parallel sequencing method. Potential causal tumor regressing SNPs that induce non-synonymous changes in CDS  regions were chosen and unique SNPs in either AP or AR showing ≥ 10 read depths were selected as reliable SNPs. To reduce false positives, reliable SNPs chosen by criteria described above were confirmed by double-checking the initial assembly results with alignment view in SeqMan Pro program of Lasergene package (DNAStar).For validation purposes, nine different SNPs associated with the induction of amino acid changes in CDS regions were randomly selected and subjected to allele-specific polymerase chain reaction (PCR) using a greater number of birds. The intent of this assay is to validate the identified SNPs in the broader population. The validation assay of SNPs confirms: (1) That the nucleotide differences seen in the sequenced bird`s also occur in other birds of the same group; (2) that the sequence variants do in fact represent fixed differences. For this, 96 phenotypically verified birds each from AR and AP lines were used for blood sampling and then genomic DNA isolation. Genomic DNA was purified from whole blood using the Wizard SV 96 Genomic DNA Purification System  following the manufacturer’s instructions. The quality and quantity of isolated DNA were determined using a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific) and agarose gel electrophoresis. DNA from all samples were then diluted to 1 ng/µL in 96-well PCR formats. Allele-specific primers were designed corresponding to nine different SNPs based on the Red Jungle Fowl genome sequence . Two sets of primers were designed with a common reverse primer for each SNP. The forward primer designated as F1 is the reference type whose terminal nucleotide at the 3’ end matches with a base in the reference genome. The forward primer designated as F3 is the SNP type and terminal nucleotide at the 3’ end matches with a base in the SNP. The third nucleotide from the 3’ end of both the forward primers (F1 and F3) were intentionally changed so that they mismatch with the genome in that position . All prin = 58) retaining SNPs (n = 63) associated with the regression of tumors after the filtering process were analyzed using Ingenuity Pathway Analysis  for understanding the gene ontology and molecular networks. Since IPA uses mechanistic pathways derived from human, mouse, and rat bioinformatics, functionalities for genes containing SNP in chicken were interpreted based primarily on mammalian biological mechanisms. The number of molecules in the network was set to the limit of 35, leaving only the most important ones based on the number of connections for each focus gene (a subset of uploaded significant genes having direct interactions with other genes in the database) to other significant genes [Candidate genes  study in chickens . About 4To verify the SNPs identified by genome resequencing, nine SNPs were randomly chosen from the 63 reliable candidate SNPs and subjected to allele-specific PCR in a greater number of birds, see The IPA program was used to determine functional groups and networks analysis for genes containing amino acid changes in AR chickens. We used several online analytical tools , in addition to IPA, to analyze functions of the genes containing SNPs and obtained similar results of gene-to-gene interactions, function, and disease categories. Thus, the only bioinformatic pathway results generated by IPA will be discussed here in order to more clearly present the mechanistic results. The 63 SNPs were found in 58 genes associated with chromosomal open reading frames, and hypothetical proteins, see Using IPA, molecular networks were generated using the interacting genes associated with amino acid changes in AR chickens based on functional knowledge inputs. A summary of the associated network functions of candidate genes is presented in The major functions of molecules associated with Network #1 are related to developmental, heredity, and metabolic disorders. Similarly, the top functions of molecules in Network #2 include cell death and survival, hematological system development and function, and humoral immune response, see BMX gene, the oncogenic functions of the BMX gene may have been turned off along with its regulatory action on the PI3K signaling pathway. This may be another reason for the tumor regression character of AR chickens.The candidate genes in Network #3, see In this study, several candidate SNP markers which alter the amino acids residues associated with the tumor regression trait in AR chickens were detected through high-throughput genome re-sequencing. Based on bioinformatic studies, the reliable candidate genes containing SNPs were involved in ubiquitylation, PI3K, and NF-kB signaling pathways, suggesting their role in tumor regression in AR chickens. Future studies including screening custom SNP arrays that contain 12,242 SNPs representing nonsynonymous amino acid changes, allele-specific expression of the marker genes with candidate SNPs in target tissues, and more thorough bioinformatic pathway analyses will be performed to gain better insight into the mechanism of tumor regression in AR chickens.


Homarus gammarus), genetic structure was explored across the species' range using a small panel of single nucleotide polymorphisms (SNPs) previously isolated from restriction‐site‐associated DNA sequencing; these SNPs were selected to maximize differentiation at a range of both broad and fine scales. After quality control and filtering, 1,278 lobsters from 38 sampling sites were genotyped at 79 SNPs. The results revealed a pronounced phylogeographic break between the Atlantic and Mediterranean basins, while structure within the Mediterranean was also apparent, partitioned between lobsters from the central Mediterranean and the Aegean Sea. In addition, a genetic cline across the north‐east Atlantic was revealed using both putatively neutral and outlier SNPs, but the precise driver(s) of this clinal pattern—isolation by distance, secondary contact, selection across an environmental gradient, or a combination of these factors—remains undetermined. Putatively neutral markers differentiated lobsters from Oosterschelde, an estuary on the Dutch coast, a finding likely explained by past bottlenecks and limited gene flow with adjacent North Sea populations. Building on the findings of our spatial genetic analysis, we were able to test the accuracy of assigning lobsters at various spatial scales, including to basin of origin (Atlantic or Mediterranean), region of origin and sampling location. The predictive model assembled using 79 SNPs correctly assigned 99.7% of lobsters not used to build the model to their basin of origin, but accuracy decreased to region of origin and again to sampling location. These results are of direct relevance to managers of lobster fisheries and hatcheries, and provide the basis for a genetic tool for tracing the origin of European lobsters in the food supply chain.Resolving stock structure is crucial for fisheries conservation to ensure that the spatial implementation of management is commensurate with that of biological population units. To address this in the economically important European lobster ( The current range of H. gammarus extends over most of the north‐east Atlantic, from northern Norway to northern Morocco , and includes parts of the Mediterranean and the western Black Sea where they are considerably rarer  restriction fragment length polymorphism (RFLP) markers have found that lobsters from northern Norway, Oosterschelde (Netherlands) and the Mediterranean are genetically differentiated from each other and all other samples analysed , intermediate‐scale region and fine‐scale location (sampling location). Finally, we discuss the applications of these results to inform the management, supplementation and conservation of European lobster populations.22.1H. gammarus. The majority of sites were sampled in 2016–2018; however, due to the rarity and difficulty of obtaining Mediterranean samples, some DNA samples analysed in previous studies , 96 SNPs were used to compose the panel. After ranking SNPs in both data sets by 2.3missingno function from poppr v2.8.0  in more than 50% of populations. Although only one target SNP per RAD‐tag was considered to compose the SNP panel in the RADseq study  to detect loci under selection and assumes that markers excessively related to population structure are candidates for local adaptation. For all selection tests, an alpha of 0.05 was used, and loci that were identified as an outlier in two or more tests were considered outlier SNPs.Outlier selection tests were conducted on the original RAD data from Jenkins et al.,  using th2.5Fst  and testing whether values were significantly different from zero. In addition, to explore patterns of isolation by distance (IBD), Mantel tests were conducted on genetic distances (Fst) and geographical distances (km) using the mantel.rtest function from the R package ade4 v1.7.11  from multilocus allele frequencies, with the assumption that loci are in HWE and linkage equilibrium. STRUCTURE was executed using the admixture model, with 105 MCMC repetitions and a burn‐in of 105. The locprior option was selected, meaning sampling locations were used as a priori information; all other parameters were set to default values. To statistically compare different values of K, the mean value of L (K) , region and sampling location of origin was assessed using the R package assignPOP v1.1.4  were randomly selected in R to compose a file representing “unknown” individuals, whereby the basin of origin was considered to be unknown. Due to the potential bias of unequal sample size in assignment analyses Wang, , 250 indassign.MC from assignPOP. Resampling was repeated 100 times for each combination of training individuals and loci. The proportion of individuals from each source population randomly allocated to the baseline data set was set to 0.5, 0.7 and 0.9. Lastly, the support vector machine (SVM) classification function was used to build predictive models; after building predictive models based on the baseline data set, the origin of the “unknown” individuals was assessed to further evaluate the performance of the predictive model.A Monte Carlo cross‐validation procedure was used to group individuals into baseline and test data sets using the function 33.1p < .05) from equilibrium between several pairs of loci. In particular, LD was detected between SNPs 15531 and 53935, between SNPs 28357 and 56785, and between SNPs 22740, 33066, 51507, 53052, 53263 and 65064; the extent of LD among these pairs of loci was apparent when visualizing the population allele frequencies for each SNP  did not work consistently on the Fluidigm EP1 system, possibly due to inadequate assay design, poor STA performance or ascertainment bias. One locus (22365) contained 28.3% missing data and two loci (8953 and 21197) deviated significantly from HWE in 22 and 34 sampling sites, respectively; all three loci were subsequently removed from the data set. Two loci (21880 and 22323) exhibited unexpectedly high proportions of observed heterozygosity ; these loci were removed because they could contain paralogs, as true variants are often considered to have a maximum frequency of 0.50 heterozygous genotypes  to 0.246 (Oos‐Sar13) and from zero (e.g. Ale‐Sky) to 0.030 (Oos‐Ale), respectively. The highest values for both statistics were between Atlantic sites and Mediterranean sites, of which many values were significantly different from zero. Within the Atlantic, Oosterschelde consistently yielded the highest pairwise values with other Atlantic sites in both statistics , although when the Mediterranean samples were removed, this correlation was much weaker . However, removal of Oosterschelde lobsters from the analysis of Atlantic sites vastly increased the strength and significance of the correlation . Analysis with only the Mediterranean samples also produced a positive correlation, but this was not significant .Global values of s Figure . Pairwiss Figure . The low3.3Fst for the outlier SNP data set was 0.310, while global Fst for the neutral SNP data set was 0.024. The DAPC using all 79 SNPs showed that lobsters originating from the Atlantic and the Mediterranean were genetically distinct . Global K clusters ranging from 3 to 5 using 79 SNPs  correctly assigned on average 98% of Atlantic individuals and 97% of Mediterranean individuals to their basin of origin. The population allele frequency of one allele for each of these top eight SNP loci was visualized  using the baseline data was extremely accurate, ranging from 97% to 100% depending on the proportion of individuals used in the training data set and the number of loci used for the assignment tests Figure a. Overald Figure ; this mod Figure .In contrast, assigning individuals from the Atlantic back to their sampling location of origin was not accurate using all 79 SNPs Figure A. AssignFst loci, assignment of Scandinavian individuals was improved across all training proportions , but assignment among the remaining Atlantic individuals was generally unchanged  from one RAD screen and then genotyped those markers in all individuals at all sites sampled using a relatively inexpensive and high‐throughput SNP genotyping approach. This approach enabled a large number of individuals to be genotyped, increasing the likelihood of accurately representing allele frequencies at each site sampled, while still retaining a subset of informative SNPs to facilitate the exploration of basin‐wide patterns, and differentiation patterns at fine spatial scales in the geographical areas of highest population abundance (i.e. the north‐east Atlantic).This study comprises the first application of SNP markers isolated from RAD sequencing to investigate population genetic structure and assignment in the European lobster. Moreover, as opposed to sequencing multiple RAD libraries, this study isolated the most informative markers , a finding the authors linked to reduced larval dispersal across the Almeria–Oran front, located in the western Mediterranean between Spain and Algeria. Although the Almeria–Oran front has been reported to impede gene flow in a number of marine species . The authors attributed this differentiation to the geographical isolation of Aegean Sea populations which could be caused by bathymetric and oceanographic conditions. Our study also found that populations from the Aegean Sea had in general the lowest values of observed heterozygosity compared with other sites sampled  within the Mediterranean, separated into sites from the central Mediterranean (Sardinia and Lazio) and the Aegean Sea . Furthermore, this pattern of differentiation was much stronger when analysed using only putatively neutral SNPs Figures c and 3c.ed Table , which med Table . Overall4.2H. gammarus, and suggests that lobster populations across the north‐east Atlantic are not in complete panmixia. The most commonly proposed causes of clinal patterns in allele frequencies are as follows: (a) IBD caused by limited dispersal; (b) secondary contact and introgression between previously isolated and genetically divergent populations; and (c) selection across an environmental gradient , whose north‐west Atlantic range spans an extensive temperature gradient of −1°C to 26°C  to the Skagerrak region (1°C minima in winter). Moreover, as with H. americanus  and H. americanus , which accords with the results for American lobster, whereby assignment success to population level achieved 25%–30% on average using 10,156 SNPs  offers some respite to lobster populations , this may have reduced the power to detect genuine outliers because many sites included in this SNP study were not included in the original RAD analysis. All outliers detected originated from the SNP loci selected for the panel to maximize differentiation between the Atlantic, Skagerrak and the Mediterranean Figure , of whicH. gammarus, but, uniquely, the additional resolution provided by this study revealed a genetic cline across the north‐east Atlantic. Analyses of neutral SNPs suggested that this cline could have been produced in part by IBD or secondary contact, or both, as there is evidence that restrictions in contemporary gene flow can maintain neutral nonequilibrium clines formed by postglacial expansions and secondary contact  generally accord with previous genetic studies of t Dayan, . HoweverNone declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Pyrus; 2n = 34), the third most important temperate fruit crop, has great nutritional and economic value. Despite the availability of many genomic resources in pear, it is challenging to genotype novel germplasm resources and breeding progeny in a timely and cost‐effective manner. Genotyping arrays can provide fast, efficient and high‐throughput genetic characterization of diverse germplasm, genetic mapping and breeding populations. We present here 200K AXIOM® PyrSNP, a large‐scale single nucleotide polymorphism (SNP) genotyping array to facilitate genotyping of Pyrus species. A diverse panel of 113 re‐sequenced pear genotypes was used to discover SNPs to promote increased adoption of the array. A set of 188 diverse accessions and an F1 population of 98 individuals from ‘Cuiguan’ × ‘Starkrimson’ was genotyped with the array to assess its effectiveness. A large majority of SNPs (166 335 or 83%) are of high quality. The high density and uniform distribution of the array SNPs facilitated prediction of centromeric regions on 17 pear chromosomes, and significantly improved the genome assembly from 75.5% to 81.4% based on genetic mapping. Identification of a gene associated with flowering time and candidate genes linked to size of fruit core via genome wide association studies showed the usefulness of the array in pear genetic research. The newly developed high‐density SNP array presents an important tool for rapid and high‐throughput genotyping in pear for genetic map construction, QTL identification and genomic selection.Pear ( It has a small genome size of 527 Mb with approximately 53.1% (271.9 Mb) repetitive sequences  was constructed using 335 SSRs and AFLP markers. Recently, high‐density genetic maps were constructed using SNP and SSR markers for mapping several fruit quality traits in Asian pear  for synteny analysis between European and Asian pear  cultivars and 7692 SNPs from the IRSC apple Infinium® II 8K array have demonstrated marker transferability across Maloideae species   Table  with dete et al. . A totale et al.  uniforml® PyrSNP array with 188 diverse pear accessions  and call rate (>95%). Use of duplicated samples identified 99.4% SNP reproducibility. The SNP genotyping results from the 286 accessions were initially classified into six categories based on SNP quality control metrics (http://www.affymetrix.com). Only 17% of the SNPs (33 339 of 200 481 SNPs) could be converted based on the most stringent criteria described in method section  of the total array SNPs were converted. Moreover, approximately 17% of the SNPs on the array that belonged to the ‘Other’ category were filtered out due to below‐threshold quality of genotypes. The remaining 166 335 high‐quality SNPs, including 140 632 SNPs on 17 chromosomes and 25 703 SNPs on unanchored scaffold sequences, provided the most reliable genotypes and were used for further analyses.In the validation of the 200K AXIOMns Table  and 98 Fet al., 2013; Chen et al., et al., et al., ® PyrSNP array corresponds with gene richness along the chromosomes. Thus, based on these, SNP distribution helped to propose putative centromeric regions for each of the pear chromosomes  genome showed that about 85% (n = 119 622) of these SNPs were present in the 23 006 genes or their 2 kb flanking promoter regions. Similarly, about 68% (17 362 out of 25 703) of SNPs located in scaffolds were mapped to 2383 predicted genes. Overall, the converted SNPs covered about 60% of the annotated genes (42 341) in ‘Dangshansuli’ genome (http://peargenome.njau.edu.cn). Further annotation of these SNPs showed that 9.39% (12 869) of the SNPs (136 984) in genic and promoter regions were non‐synonymous sites, 25.4% were synonymous sites and 65.62% were located in intronic region of genes. Among 12 869 non‐synonymous sites, we identified 240 SNPs involved in early termination or elongation of the transcripts.Across 17 pear chromosomes, 140 632 high‐quality SNPs were distributed, with an average spacing of 3.6 kb. About 95.22% of the adjacent SNP pairs were spaced at <10 kb, whereas 78.35% were shorter than 2 kb and 11.25% were located within 2–5 kb Figure . Telomers Figure . For insK = 2 separated the 286 pear accessions into two groups: Group I from the diverse germplasm and Group II from the hybrid F1 progeny of ‘Cuiguan’  × ‘Starkrimson’  , new groups split from group I showing admixture in the population. Group II still remained isolated during the K = 2–4 increase. These results suggest that Group I exhibit higher genetic diversity in comparison to Group II; most likely due to bi‐parental nature of the latter population. Similar results were obtained from PCA , which were far away from Asian pear species , indicating that they belong to or are more closely related to European pear species  algorithms, principal component analysis (PCA) and a maximum‐likelihood (ML) tree construction. Population structure analysis using ) Figure a. With ss Figure . Combine® PyrSNP array to construct genetic map and to improve Asian pear genome assembly. After filtering for quality and missing data, a total of 7173 high‐quality including 6083 and 1090 SNPs across 17 pear chromosomes and unanchored scaffolds respectively, were polymorphic in this population. At LOD score >4.0, 2388 SNPs out of 7173 SNP markers were grouped and ordered to obtain 17 linkage groups (LGs) . The consistency between the order of markers on linkage map and genome sequence assembly was 71% on average across 17 LGs  were present between single fruit weight, fruit size, core size, shape and roughness of fruit stem, while these traits had a negative correlation (r < −0.25) with the stone cell and fruit hardness , as well as between stone cell and fruit hardness (r > 0.38)  Figure .® PyrSNP array to perform GWAS. Results showed that nine of 18 trait associations identified a total of 42 significant  loci (Table P = 2.08 × 10−5) linked with early bloom showed association with a known flowering gene   Figure . In addi0 Figure , a gene ® PyrSNP array ideal for genetic studies, genomic selection and breeding of pear worldwide. The array has shown its usefulness and robustness in providing a large number of markers for dense genetic map construction and for identifying marker‐trait associations in several desirable pear traits. Such high‐density and genome‐wide coverage of SNP markers in 200K AXIOM® PyrSNP array can provide opportunities to fine map genetic loci associated with several economical traits in different pear genetic backgrounds.A diverse SNP discovery panel and the inclusion of SNPs from Asian and European pears make 200K AXIOMet al., et al., et al., Asian pear has higher genetic diversity and a more complicated genetic than European pear , likely due to the large population used  of MonoHighResolution was seen, compared to the PolyHighResolution category (6.5%). Recently, development and validation of a 70K Affymetrix Axiom Genotyping Array for pear was presented at Plant and Animal Genome XXVI conference and 9th International Rosaceae Genomics Conference (RGC9) without data publication . The array is based on re‐sequencing data of 55 accessions and showed more than 90% of the SNPs as high quality and polymorphic (PolyHighResolution) on The National Clonal Germplasm Repository pear accessions.The high proportion (36%) of MonoHighResolution SNPs compared to the rather limited proportion of PolyHighResolution (17%) and NoMinorHom (16%) was likely due to the population group used to validate the array in the study and its size. The SNPs designed on 200K Axiomet al., et al., et al., et al., et al., 2013; Chen et al., et al., et al., Use of dense genetic maps to improve the quality of a genome assembly has been widely applied across plant species  with the longitudinal and transverse fruit core diameter have been detected in this study. These loci correspond to candidate genes with homologs related to seed size in other plant species  with early bloom in pear. It is a homolog of Lhcb8, a gene that participates in light harvesting and transfer in high plants. It collects and transfers light energy to the reaction centres of photosystem II and controls plant flowering   bases and fewer than 50% of bases with a quality score <5 were retained and used for alignment and SNP detection in later steps  ≥ 0.1, missing genotypes <10% and LD r2 < 0.4 based on the four groups . A set of 222 526 quality filtered SNPs from 17 chromosomes and another set of 44 001 SNPs from unanchored scaffolds were selected. These SNPs were primarily selected from the coding sequences (CDS) of genes identified genome‐wide. Additionally, 29 298 SNPs from the CDS regions of 1204 genes annotated as the trait‐associated genes by Wu et al.  were genotyped for the initial validation of the 200K AXIOM® PyrSNP array. In F1 progeny, maternal parent ‘Cuiguan’ is an Asian cultivar pear (P. pyrifolia); while male parent ‘Starkrimson’ is a European cultivated pear (P. communis). It has been reported that both Asian and European species are distantly related but underwent independent domestication . The sample quality was controlled using both the value of Dish Quality Control (DQC) and sample call rate in the Affymetrix® Power Tools (APT) software package. Briefly, the QC and genotype calls were generated from the hybridization signals from the CEL files created by the GeneTitan Multi‐Channel Instrument. The DQC value is the intensity contrast between signal and noise as measured by non‐polymorphic probes. A value of 1 for DQC indicates perfect resolution. In general, high DQC value represents the high call rate. However, sample call rate must be considered along with DQC values since the latter are insensitive to sample and DNA contamination. All accessions genotyped on the array were analyzed using Axiom Analysis Suite v.1.1.1  using diploid threshold configurations. In addition, two duplicated samples were included in each 96‐well plate for analysis of data reproducibility. Each SNP was classified into one of six categories with gradual reduction in reliability using ‘SNPolisher’ R Package designed by Affymetrix (http://www.affymetrix.com). The six categories are: ‘PolyHighResolution’, ‘NoMinorHom’, ‘OTV’, ‘MonoHighResolution’, ‘CallRateBelowThreshold’ and ‘Other’. ‘PolyHighResolution’ is the most reliable category for which the SNP passes all quality control criteria, including call rate (CR) ≥97%, Fisher's linear discriminant (FLD) ≥3.6, heterozygous strength offset (HetSO) ≥−0.1 and homozygote ratio offset (HomRO) values ≥0.3, whereas SNPs in other categories pass only some of these criteria. The CR value is the ratio of the number of genotyped samples against the number of expected samples for a probe. The FLD value is used to measure the cluster quality of a SNP based on the distance between the clusters and the variance within a cluster. The HetSO value measures the distance of the heterozygous cluster centre above or below from the homozygous cluster centres in the y dimension. Low HetSO values result from mis‐clustering events or inclusion of samples that contain variation from the reference genome that was used to design the array probe. The HomRO value is the distance to zero in the x dimension from the centre of the homozygous cluster. It indicates the shift of clusters from their expected positions. SNPs identified as ‘Other’ category were excluded from further analysis. The remaining SNPs were annotated using in‐house python script, and retained for further analyses. SNP count and distribution patterns across the pear genome were analyzed using a 150 kb sliding window to predict centromeric regions on pear chromosomes using R package.A panel of 188 diverse pear accessions Table  and an Fhttp://membres-timc.imag.fr/Olivier.Francois/tutoRstructure.pdf) in R software to calculate the population structure with default parameters: maximum number of iterations, 200; regularization parameter, 100; and tolerance error, 1e‐5. The number of clusters (K) was set from 2–4. Population structure was displayed using barplot function in R package. Principal component analysis was performed using PLINK V1.90  and Genome‐wide Complex Trait Analysis (GCTA) version 1.26.0  package in R software. We used SNPhylo . All analyses were performed using the 166 335 high‐quality SNPs, and MAF ≥5% and missing rates <0.2 was used to further filter this SNP set.We converted the VCF file to GENO using an in‐house python script, and then used the LEA package , only included ‘PolyHighResolution’ and ‘NoMinorHom’ categories, were converted into the marker code system of three segregation types, ‘lmxll’, ‘nnxnp’ and ‘hkxhk’ as described in the cross‐pollinated (CP) model of JoinMap v4.1 software . The outliers were checked for each trait individually using boxplot charts. Trait correlations were calculated and plotted in using cor and heatmap.2 in gplots package , σg2 is the genotypic variance, σe2 is the environmental variance and n the mean number of replicate per genotype.Phenotypic data was collected for 18 phenological and fruit traits from 286 pear accessions in 2016 and 2017. The pear trees were planted in the experimental orchard of the College of Horticulture at Nanjing Agricultural University. Most grown for about 10 years in an orchard with minimal management. Three to six biological replicates of fruits from each genotype were collected at the maturation stage for phenotypic evaluation and quality assessment. A Vernier caliper was used to measure the longitudinal and transverse diameter (unit: cm) of fruit core and periphery. An electronic balance was used to measure the single fruit weight (unit: g). Digital display Atago PAL‐1 was used to measure the soluble solid content in fruit flesh. Fruit hardness was measured using hardness tester CT3‐10K (unit: g)   and total 31 388 SNPs with a MAF ≥5% from 286 pear accessions were used for GWAS. Individuals with 100% missing trait data were removed for further analysis. Genomic Association and Prediction Integrated Tool (GAPIT), an R package developed by Lipka et al. , with a X.L.L. carried out data analysis, prepared the figures and drafted the manuscript. J.S. provided support for data analysis, interpretation of results and manuscript writing. M.F.Q. constructed the genetic linkage map. S.W.L. and X.Z. collected samples and performed the experiments to measure the physiological data of all the samples. M.Y.Z. discussed the results from GWAS. A.K. helped with the interpretation of results and writing manuscript. J.W. and S.L.Z managed and designed the research.The authors declare no competing financial interests.Figure S1 The distribution of 200 481 SNPs selected for pear array design on the physical maps of the 17 pear chromosomes.Click here for additional data file.Figure S2 Population structure analysis of 188 accessions from Group I.Click here for additional data file.Figure S3 Distribution of the converted SNPs on the array in sliding 150 kb windows along the each of 17 pear chromosomes.Click here for additional data file.Figure S4 The normal distribution (blue bar) and boxplot charts (orange box) of phenotypic data from 18 traits.Click here for additional data file.Figure S5 Correlation between 18 fruit quality and phenological traits assessed in a 188 diverse accessions of Asian pear.Click here for additional data file.Table S1 The origin of sequencing 113 pear accessions.Click here for additional data file.Table S2 List of the 200 481 SNP markers that were put on the array.Click here for additional data file.Table S3 The origin of 188 pear accessions.Click here for additional data file.Table S4 The details of 2388 SNPs mapped on the 17 linkage groups.Click here for additional data file.Table S5 The consistency between the order of markers on the linkage map and genome sequence assembly.Click here for additional data file.Table S6 The summary of 355 SNPs mapped the unanchored scaffolds on each linkage group.Click here for additional data file.Table S7 The 18 imputed phenotypic data from 219 pear accessions.Click here for additional data file.Table S8 The Pearson correlation of 18 traits from 219 accessions.Click here for additional data file.Table S9 The information of 42 SNP markers significantly associated with nine traits.Click here for additional data file.Table S10 The candidate genes in the flanking region of significant SNP markers associated with 12 traits.Click here for additional data file.Table S11 The candidate genes in the flanking region of significant SNP markers associated with early bloom and their annotation information.Click here for additional data file.Table S12 The candidate genes in the flanking region of significant SNP markers associated with core size and their annotation information.Click here for additional data file.