Hypoglycemia prognostic models contingent on prospective, self-reported survey data offer a powerful avenue for determining real-world event susceptibility and interventional targets.This protocol describes the design and implementation of the 1-year iNPHORM  study, which aims to measure real-world self-reported severe and nonsevere hypoglycemia incidence  in American adults with type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues, and develop and internally validate prognostic models for severe, nonsevere daytime, and nonsevere nocturnal hypoglycemia. As a secondary objective, iNPHORM aims to quantify the effects of different antihyperglycemics on hypoglycemia rates.iNPHORM is a prospective, 12-wave internet-based panel survey that was conducted across the United States. Americans (aged 18-90 years) with self-reported type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues were conveniently sampled via the web from a pre-existing, closed, probability-based internet panel (sample frame). A sample size of 521 baseline responders was calculated for this study. Prospective data on hypoglycemia and potential prognostic factors were self-assessed across 14 closed, fully automated questionnaires  that were piloted using semistructured interviews (n=3) before fielding; no face-to-face contact was required as part of the data collection. Participant responses will be analyzed using multivariable count regression and machine learning techniques to develop and internally validate prognostic models for 1-year severe and 30-day nonsevere daytime and nocturnal hypoglycemia. The causal effects of different antihyperglycemics on hypoglycemia rates will also be investigated.Recruitment and data collection occurred between February 2020 and March 2021 . A total of 1694 participants completed the baseline questionnaire, of whom 1206 (71.19%) were followed up for 12 months. Most follow-up waves  were completed, translating to a participation rate of 179% relative to our target sample size. Over 70.98% (856/1206) completed wave 12. Analyses of sample characteristics, quality metrics, and hypoglycemia incidence and prognostication are currently underway with published results anticipated by fall 2022.iNPHORM is the first hypoglycemia prognostic study in the United States to leverage prospective, longitudinal self-reports. The results will contribute to improved real-world hypoglycemia risk estimation and potentially safer, more effective clinical diabetes management.ClinicalTrials.gov NCT04219514; https://clinicaltrials.gov/ct2/show/NCT04219514DERR1-10.2196/33726 Although prognostic models can complement clinical decision-making and risk-tailored interventions -5, theirProspective, web-based survey data, especially when collected anonymously , can revIn 2020, our team launched iNPHORM —the first prospective (1-year) survey of hypoglycemia risk in the American public with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) prescribed insulin and/or secretagogues. The results of this study will culminate in real-world hypoglycemia prognostic models that are readily compatible with and complementary to routine practice. Here, we detail the design and implementation protocol of iNPHORM. The paper has been structured according to established guidelines ,29 and tThe primary objectives are as follows:To determine the real-world incidence of self-reported 1-year severe and 30-day nonsevere daytime and nocturnal hypoglycemia among American adults with T1DM or T2DM prescribed insulin and/or insulin secretagoguesTo develop and internally validate real-world hypoglycemia risk prediction models for 1-year severe, 30-day nonsevere daytime, and 30-day nonsevere nocturnal hypoglycemia, which will be converted into a user-friendly, clinic-based toolThe secondary objective is to assess treatment-related causes of hypoglycemia among American adults with T1DM or T2DM prescribed insulin and/or insulin secretagogues.iNPHORM is an internet-based panel survey that was conducted across the United States. Repeated self-assessed measures were taken over 12 monthly interwave intervals via web-based questionnaires. Prospective longitudinality allowed us to (1) obtain data not reliably collected retrospectively or cross-sectionally , (2) assess within-person changes or stability masked by aggregate statistics, and (3) narrow the SE between measurements.Participants were recruited via the web from an established, closed, probability-based internet panel. The internet panel comprised 5 vendor samples of the United States public consenting to receive survey notifications by email (sample frame). Vendor partners used random probability sampling and, when necessary, validity checks, quotas, and multidimensional calibration. These approaches helped maintain fair and representative  sampling within communities . The intInternet panelists could enroll if they were (1) aged 18 to 90 years, (2) living in the United States (past year), (3) self-reporting a diagnosis of T1DM or T2DM , and (4)On the basis of recent conservative techniques ,34, N≥52A and B) were recruited into the prospective, 12-wave iNPHORM study using convenience sampling. First, vendor partners emailed a generally worded study invitation to a randomly selected subset of the internet panel (subset A). Those interested were emailed a link to a screener. To enroll, eligible respondents were required to provide consent , complete a baseline questionnaire (accessible by the emailed link), and register with iNPHORM using a confirmed, valid email address and unique username/password. Enrollees were hosted and monitored by Ipsos Interactive Services (IIS) ; .At baseline and at each follow-up .within the past 30 days (if the last scheduled questionnaire was not completed) or since the last time an iNPHORM survey was completed (if the last scheduled questionnaire was completed). Given its relative infrequency and saliency, severe daytime and nocturnal hypoglycemia data were captured since the last time an iNPHORM survey was completed.In line with past research ,65-67, wBesides hypoglycemia frequency, closed- and open-ended items assessed event detection methods , symptom severity , causes , treatments, hypoglycemia-specific self-management behaviors/social support, and experiences with continuous/flash glucose monitoring. We also investigated the type of assistance required for severe hypoglycemia recovery . Each month, modified Clarke  and GoldSeverephysically unable to treat your hypoglycemia by yourself, it is considered a Severe Hypoglycemia event. You may be severely disorientated, unable to swallow, or unconscious. As a result, you are likely to need the help of another person to recover. This person may need to administer glucagon or a glucose injection to treat your severe hypoglycemia event. Emergency medical services may be called, and hospitalization may be required. Severe events can arise when your low blood glucose is left untreated and continues to drop. The early signs and symptoms of severe hypoglycemia typically include blurred vision, difficulty concentrating, confused thinking, slurred speech, numbness, and/or drowsiness. If your blood glucose stays low for too long, it can result in seizures, comas, and in rare cases, death. Consequently, severe hypoglycemia is a medical emergency.”“When you are Mild/moderate physically able to treat your hypoglycemia by yourself, it is considered a Mild/Moderate Hypoglycemia event. Treatment can include taking a glucose or sucrose tablet, drinking a glass of juice, or eating some food. Mild/moderate hypoglycemia events can be identified by symptoms such as shakiness, sweatiness or chills, irritability, feeling nervous or anxious, hunger, weakness, mild confusion, forgetfulness, fast heartbeat, feeling dizzy, and color draining from the skin. Mild/moderate hypoglycemia events can be identified from these symptoms or by a measured blood glucose level taken from a self-monitoring blood glucose (SMBG) meter or continuous/real-time glucose monitoring (CGM) device. You are still conscious and able to swallow.”“When you are DaytimeDaytime events (mild/moderate or severe) occur while you are awake.”“NocturnalNocturnal events (mild/moderate or severe) occur while you are sleeping or attempting to sleep. In addition to the symptoms described above, nocturnal hypoglycemia can be marked by symptoms such as vivid dreams/nightmares, restless sleep, morning headaches, night sweats, tiredness, irritability/confusion upon waking, convulsions, and talking/shouting while sleeping.”“iNPHORM was funded by an investigator-initiated grant from Sanofi Global . Before recruitment, we obtained ethics approval from the Western University health sciences research ethics board  and registered the study with ClinicalTrials.gov . The COVID-19 subquestionnaire was approved as an ethics amendment before fielding.I agree to participate or I do not agree to participate.A letter of information was emailed to all eligible respondents  and 6. TParticipation was voluntary. Enrollees could withdraw at any time by informing the IIS interviewer (pilot participants only), clicking an unsubscribe button provided in each email, or by emailing IIS directly. Privacy breaches and technical problems were monitored by IIS. Personally identifiable data  were encrypted automatically by the IIS platform and kept confidential from IIS and research personnel. IIS transferred deidentified data files to the Western University research team using a secure file transfer protocol on a password-protected network drive. All deidentified data will be stored for 7 years on a password-protected network drive at the Department of Family Medicine at Western University and on encrypted password-protected external drives; storage devices will be erased after this time. The iNPHORM assessments and data are owned by Western University.Complying with US Food and Drug Administration postmarket safety reporting regulations , we emaiUnique IDs, randomly assigned by IIS at the study outset, were used to tether the participants’ data across waves. Closed-ended responses were directly precoded, and a data dictionary and map have been developed. Repair rules addressing impossible, implausible, and discordant values will be documented in iNPHORM’s metadata . Both the raw and repaired data sets will be retained.iNPHORM longitudinal panel will be computed as the ratio of the observed number of completed waves to the maximum expected number (12 waves per participant). To evaluate the success of our completion rate against our predetermined sample size , the observed number of waves for which severe hypoglycemia information was available will be compared against the maximum expected number of completed follow-ups.The recruitment rate will be calculated as the ratio of consenting individuals to enrollees. The average total completion rates for the not applicable/prefer not to say/I don’t know or opt out) nonresponses. Missing data will be handled using multiple imputation by chained equations [All data were stored in real time for analysis, even if the questionnaire was incomplete . The completeness rate will be assessed after data cleaning and repair. Missing values will be coded as unit, block, item (because of skip logic), or residual  or medians and IQRs (nonparametric).Crude severe and nonsevere daytime and nocturnal hypoglycemia incidence proportions and densities with 95% CIs for overdispersed count data will be reported overall and by diabetes type, medication regimen, mode of detection (symptoms and/or blood glucose), symptom severity (unconsciousness), and health care use. Incidence density calculations will account for observation durations as an offset for zero-risk and/or unobserved periods.The following procedures comply with current guidelines ,71 and tPrognostic models will be developed for severe, nonsevere daytime, and nonsevere nocturnal hypoglycemia. Daytime and nocturnal severe events will be combined, given their nonspecific relevance and to ensure sufficient precision. Severe hypoglycemia will be modeled over 1 year using the Andersen-Gill Cox proportional hazards regression for recurrent events . NonseveCandidate prognostic factors will be selected a priori based on biological plausibility, previous literature, data quality, measurement reliability, and multicollinearity. Intrinsic, extrinsic, nonmodifiable, and modifiable predictors (including frequency of previous severe and nonsevere hypoglycemia) will be considered. To minimize overfitting ,76 and iBootstrapping will be used to determine the optimism-corrected performance of each final model ,77,81. DModels will be converted into a user-friendly, clinic-based tool to complement real-world practice. Back-end computations of patients’ prognostic factors will provide point-of-care assessments for 1-year severe and/or 30-day nonsevere daytime/nocturnal hypoglycemia. To aid interpretation, risk estimates will also be categorized .The tool will be streamlined for easy integration in clinicians’ existing electronic medical records (EMRs) and compatible with prepopulated EMRs and manually inputted data. A standalone internet application and paper-based nomogram will be developed for when EMR integration is not possible. Real-time imputation will be explored .Differential effects of antihyperglycemic regimens on hypoglycemia rates will be tested using causal analytic techniques . The results may help in identifying new and useful associations that can improve model performance or otherwise real-world event detection and management .iNPHORM commenced in February 2020 and concluded in March 2021. No bugs, downtimes, privacy breaches, or other unexpected events were reported/detected. Herein, we present the recruitment and completion rates . Analysesubpanel A). Individuals in subpanel A who failed to complete wave 1 were withdrawn  and systematically refreshed with subpanel B. From April 7 to April 23, 3197 individuals consented, of whom 437 (13.67%) were enrolled. Thus, as of April 2020, 1206 participants comprised the iNPHORM longitudinal panel.From February 10 to February 25, 2020, 2339 individuals consented to participate in iNPHORM; of these individuals, 1257 (53.74%) completed all actions to enroll . Dropout was highest at wave 1, with completion rates stabilizing thereafter. Across respondents, 71.89% (867/1206) completed ≥8 follow-ups, with 55.22% (666/1206) completing all 12 , and easy-to-complete questionnaires shored up high participation rates. Sample characteristics, quality metrics, and hypoglycemia incidence and prognostication will be published by fall 2022.Poor generalizability has been an ongoing problem in prognostic hypoglycemia research . To promData were collected over 12 one-month intervals, balancing the probability of observing events against participants’ abilities to recall them accurately. Frequent and long-term data capture enabled us to obtain maximally valid self-reported information on not only hypoglycemia occurrence but also a range of important, preselected factors commonly unavailable in secondary sources . The lonself-treated, [Our self-report study yields pertinent insights into the routinely uncaptured burden of hypoglycemia. Past prognostic hypoglycemia research has relied heavily on administrative, insurance-based claims records; however, these sources poorly represent events occurring outside the health care system. Recent evidence suggests that only 5% of severe events require hospitalization, and as many as 50% are treated at home by family/friends ,20. Moretreated,  is scarctreated,  and nonstreated,  events, treated, . Not surtreated, .iNPHORM builds on the methodological and economic advantages of real-time, web-based self-report to acquire instantaneous and representative ,26 data Certain limitations and safeguards warrant elaboration. Notwithstanding efforts to promote generalizability, selection biases could have arisen because of the nonrepresentativeness of the internet panel demography and/or of respondents/responses ,103,104.subpanel A were identified and removed at wave 1 via logic testing and noncompletion. One-time systematic refreshment, especially during the first interwave when attrition is highest, has been shown to reduce panel stagnation while improving study feasibility and analytic validity [subpanel B was recruited from a contemporaneous subgroup of the same frame population as subpanel A. Push factors were used to sustain participation [Another related limitation is the risk of attrition bias. To mitigate loss to follow-up, ostensibly unmotivated respondents in validity . To prevcipation . Remunercipation . Cash amcipation ,36 and rcipation -43.Although web-based  surveys have been shown to promote item completeness and accuracy ,24, theyBefore fielding, the assessments underwent pretesting and piloting to promote content usability and accuracy. A total of 3 individuals participated in the pilot process; this sample size aligned with established best practices at IIS while permitting parsimonious representativity and feasibility. Nevertheless, a larger pilot sample size may have yielded further meaningful feedback. Finally, despite the proven validity/reliability and/or widespread use of many iNPHORM items, no validated self-reported hypoglycemia measure exists yet. To attenuate instrumentation effects in our study , hypoglyiNPHORM promises important forward strides in real-world hypoglycemia detection and prevention. This protocol highlights the powerful application of an internet-based panel survey to assess long-term hypoglycemia risk in a large, community-based cohort of adult Americans with insulin- and/or secretagogue-treated T1DM and T2DM. To date, descriptive and prognostic hypoglycemia estimates have stemmed mainly from cross-sectional and short-term retrospective analyses of pre-existing databases subject to untenable bias. Pairing the importance of longitudinal, prospective self-reported hypoglycemia data with the advantages of web-based survey modes, iNPHORM aims to clarify putative epidemiological understandings and reveal opportune insights into point-of-care decision-making, research priorities, and effective interventional precision -111.


Seed flooding stress is one of the threatening environmental stressors that adversely limits soybean at the germination stage across the globe. The knowledge on the genetic basis underlying seed-flooding tolerance is limited. Therefore, we performed a genome-wide association study (GWAS) using 34,718 single nucleotide polymorphism (SNPs) in a panel of 243 worldwide soybean collections to identify genetic loci linked to soybean seed flooding tolerance at the germination stage.Cis-elements in promoter regions which may be involved in seed-flooding tolerance in soybeans and these warrant further screening and functional validation.In the present study, GWAS was performed with two contrasting models, Mixed Linear Model (MLM) and Multi-Locus Random-SNP-Effect Mixed Linear Model (mrMLM) to identify significant SNPs associated with electrical conductivity (EC), germination rate (GR), shoot length (ShL), and root length (RL) traits at germination stage in soybean. With MLM, a total of 20, 40, 4, and 9 SNPs associated with EC, GR, ShL and RL, respectively, whereas in the same order mrMLM detected 27, 17, 13, and 18 SNPs. Among these SNPs, two major SNPs, Gm_08_11971416, and Gm_08_46239716 were found to be consistently connected with seed-flooding tolerance related traits, namely EC and GR across two environments. We also detected two SNPs, Gm_05_1000479 and Gm_01_53535790 linked to ShL and RL, respectively. Based on Gene Ontology enrichment analysis, gene functional annotations, and protein-protein interaction network analysis, we predicted eight candidate genes and three hub genes within the regions of the four SNPs with Our findings demonstrate that GWAS based on high-density SNP markers is an efficient approach to dissect the genetic basis of complex traits and identify candidate genes in soybean. The trait associated SNPs could be used for genetic improvement in soybean breeding programs. The candidate genes could help researchers better understand the molecular mechanisms underlying seed-flooding stress tolerance in soybean.The online version contains supplementary material available at 10.1186/s12870-021-03268-z. Glycine max (L.) Merr.] is one of the most economically important legume crops, providing protein, oil, carbohydrates, minerals, vitamins, folic acid, fibre, isoflavones and other nutrients for humans and animals [https://www.statista.com). However, sustainable soybean production is threatened by various abiotic stresses, including flooding. The soybean yield is reduced by flooding in the various stages of growth [Soybean [ animals , 2. The f growth –6. Effecf growth –9. Plantf growth , 11. TheR2) and log of odd (LOD) in the range of 5.8–33.3% and 3.59–19.73%, respectively. Out of these, chromosomes 10, 12, and 13 harbored relatively more stable QTLs. However, most of the earlier reported QTLs were detected by linkage mapping strategy with several limitations, making them challenging to use for breeding program. Genome-wide association study (GWAS) provides more extensive ancestral recombination events due to high density of SNP markers. Therefore, to overcome the limitations associated with bi-parental mapping, GWAS has proven to be more effective and efficient in unraveling the genetic architecture of complex traits in soybean [Over the past decades, extensive progress has been made in identifying essential parameters for assessing soybean flooding tolerance/susceptibility at germination or seedling stages –14. Floo soybean –25.TN13 on chromosome Chr13 detected by the two distinct models for germination rate (GR), electrical conductivity (EC), and normal seedling rate (NSR). From QTN13, one candidate gene, Glyma.13 g248000 (GmSFT) was predicted, which was found to have a nonsynonymous mutation in the seed flooding tolerant genotypes, resulting in an amino acid alternation in the protein. Wu et al. (2020) examined flooding tolerance when more than 50% of plants were in the reproductive growth stage with a blooming flower. Fourteen SNPs distributed on 4 chromosomes viz., Chr03 (8), Chr04 (1), Chr07 (2), Chr13 (1) and Chr19 (2) were detected simultaneously by 4 single-locus models.In soybean, there are only two independent studies on flooding tolerance with GWAS , 27. In www.soybase.org) [From the above, it is clear that less progress has been made in identifying genomic regions associated with soybean flooding tolerance, which is the first step toward identifying candidate genes. In order to speed up the genetic improvement of flooding tolerance in soybean, it is necessary to uncover the molecular mechanisms and develop gene-based functional markers suitable for marker-assisted selection (MAS). The present study used 243 diverse soybean plant introductions (PIs) obtained from the SoySNP50K BeadChip project available on SoyBase (http:ase.org)  and utilh2, and ANOVA of each trait for the 243 soybean accessions in 2018 and 2019 are presented in Table h2 for the four traits ranged from 0.78–0.99 (Table r2) with EC and the other three traits. On the other hand, significant positive r2 existed among the GR, ShL and RL , germination rate (GR), shoot length (ShL), and root length (RL) linked to seed flooding tolerance were measured at the germination stag. The descriptive statistics, r2 declined with increasing distance, and the average LD decay distance by 34,718 SNPs for LD analysis was about 400 kb, with r2 dropping to half of its highest value  with a maximum (2912) on the Chr18 and a minimum (1192) on Chr12  ≥ 3.5, a total of 20 SNPs were significantly associated with EC in 2018, 2019, plus the average across two environments on 4 chromosomes viz. Chr04, Chr08, Chr14, and Chr17  = 5.16) and GR (log10(P) = 5.06) met the Bonferroni correction criteria (≈ 4.54). SNPs related with GR Gm_08_46236506, Gm_08_46239716, and Gm_08_46242569 met the Bonferroni correction criteria as well  and LOD  = 3.71–10.17)  and one multi-locus model (mrMLM) were used to perform SNP-trait association to identify significant SNP associated with EC, GR, ShL and RL. In MLM with a threshold of -In the comparison of results from MLM and mrMLM, two major SNPs (Gm_08_11971416 and Gm_08_46239716) on Chr08 were detected concurrently for EC and GR (Table P < 0.05), thus, 476.60 ± 25.46, 594.1 ± 39.25, and 365.4 ± 28.26 μS·cm− 1 g− 1, respectively . The highest EC was observed in H1 (873.7 ± 35.11 μS·cm− 1 g− 1), while H3 had the least EC (226.3 ± 7.25 μS·cm− 1 g− 1)  around Gm_08_11971416 Fig. a. The mehttp://bioinfo.cau.edu.cn/agriGO). Of these genes, 61 had no functional annotations (representing NA) , Glyma.01G206300 (G-Type Lectin S-Receptor-Like Serine/Threonine-Protein Kinase), Glyma.05G006700 (Protein kinase superfamily protein) Glyma.05G008000 (CCCH-type zinc finger family protein), Glyma.08G152800 (Leucine-rich repeat family protein), Glyma.08G152900 (Tetratricopeptide repeat like superfamily protein), Glyma.08G344500 (GATA Transcription factor 26), and Glyma.08G348500 (UDP-glycosyltransferase) from the 422 genes , Glyma.05G016800  and Glyma.08G159800  that may be associated with soybean seed-flooding tolerance  networks associated with seed flooding tolerance, we used STRING online software. The possible network obtained from STRING was subsequently visualized in the Network Analyst platform. From PPI network analysis, we identified three hub genes viz., www.soybase.org). Based on RNA-seq analysis, all the predicted candidate genes showed significantly higher gene expression in the root tissues, root nodule, young leaf, flower, and pod shell as well as seed developmental stages except Glyma.08G344500 , jasmonic acid (CGTCA-motif), abscisic acid (ABRE), salicylic acid (TCA-element) and others  [Arabidopsis thaliana (10 kb) [As evident from the phylogenetic tree and kinship plot, the population structure divided the 243 lines into 2 sub-groups   and Arab (10 kb) . This is (10 kb) , 37.According to previous studies, there are 27 QTLs associated with flooding tolerance in the soybean genome, distributed across 15 chromosomes, suggesting that flooding tolerance is a complex quantitative trait regulated by several genes with both substantial and modest impacts in soybean.P = 2.88E-05 or -log10 = 4.54, in the current study) is overly strict when the linkage disequilibrium among genetic markers is large, which is generally the case with soybean [P ≤ 0.0003 or -log10(P) > 3.5 in the current study was used, which is less stringent than the Bonferroni-corrected threshold, but more stringent than the threshold used in the study by Wu et al. [10(P) > 2.5) and Song et al. [10(P) > 3.0) with accessions and SNPs from SoySNP50K BeadChip. The adopted threshold in study was similar to Kaler et al. (2017) who utilized 373 accessions and 31,260 SNPs and Chamarthi et al. (2021) with 200 accessions and 34,680 SNPs from SoySNP50K BeadChip to map genomic regions associated with soybean canopy wilting.Limited QTL mapping studies have been undertaken to detect the genomic regions linked with flooding tolerance using different bi-parental mapping populations. According to previous studies, there are 27 QTLs associated with flooding tolerance in the soybean genome, distributed across 15 chromosomes, suggesting that flooding tolerance is a complex quantitative trait and regulated by multiple genes with both major and minor effects in soybean –18. Howe soybean , 44. Theg et al.   with the LOD = 3 which has been recommended in multi-locus GWAS to balance high power and low false positive rate . Multi-l-log10(P) ≥ 3.5  [Flood tolerance 7–2 [Flood tolerance 6–3 [Flood tolerance 4–5 [qSFT_17–52) reported by Dhungana et al. [Flood tolerance 6–2 [The stability of QTL is a requisite for their use in a practical breeding program such as MAS. Therefore, the major and stable SNPs   showed significantly higher expression levels in tolerant genotypes than sensitive genotype under seed flooding stress [It is of great interest to identify the candidate genes underlying the genomic region for practical plant breeding. To date, the SoyBase has reported on 27 QTLs related to soybean flooding tolerance. However, none was used to mine putative candidate genes with the exception of recent studies , 27. Forg stress .(Glyma.01G198000), located on Chr01, encodes a basic helix-loop-helix (bHLH) transcription factor that is involved in plant adaptive responses to various abiotic stresses, including drought, salinity, heavy metal stress, oxidative stress, iron deficiency, low temperature stresses, and osmotic stress [(Glyma.01G206300) that codes for G-Type Lectin S-Receptor-Like Serine/Threonine-Protein Kinase and plays a crucial role in plant response to salt stress [(Glyma.05G006700) encodes a protein kinase that is involved in plant responses to drought, salt, and cold stress [(Glyma.05G008000) located on Chr05, encodes a CCCH-type zinc finger protein. CCCH genes contribute to seed germination by regulating abscisic acid (ABA), light and gibberellic acid in Arabidopsis [OsCCCH-Zn-1 is induced under hypoxia, submergence, and drought stresses [Glyma.08G152800 encodes a leucine-rich repeat protein. Overexpression of the leucine-rich repeat receptor-like kinase gene (LRK2) increased drought tolerance in rice [(Glyma.08G152900) is also located on Chr08 which encodes for a tetratricopeptide repeat like protein (TPR) involved in the plant hormonal regulation, such as ethylene biosynthesis, gibberellic acid and cytokinin responses [AtTPR10 functions as a molecular chaperone to protect plants from diverse abiotic stresses, such as low temperature, drought, and salinity [Based on the GO enrichment analysis, and gene functional annotations we predicted eight genes as the possible candidate genes Table . The genc stress –59. On Ct stress . Gene  encodes a GATA Transcription factor 26 that is involved in the regulation of growth processes and various environmental stresses, including salinity and drought [SlGATA17 (Solyc05g056120.2.1) enhances drought tolerance with increased activation of the phenylpropanoid biosynthetic pathway in transgenic tomato compared to the wild type [Glyma.08G348500 encodes a UDP-glycosyltransferase that is involved in the regulation of grain size and multiple abiotic stress tolerance  in rice [Glyma.01G207700, Glyma.05G016800, and Glyma.08G159800) as ribosomal protein genes in the functional co-expression network generated by network analyst that might regulate abiotic stress tolerance . These warrant functional validation of the eight candidate and three hub genes to unravel their actual regulatory mechanism in seed flooding tolerance.Gene  of Nanjing Agricultural University in Nanjing, Jiangsu Province of China. All the accessions were grown in a randomized complete block design with three replications every year. Additional File Soybean PIs accessions were phenotypically evaluated for seed flooding stress following a previously described method . Brieflyh2) of each trait was estimated for the combined environments as α = 0.01 using OriginPro 9.0 software .Analysis of variance (ANOVA) of the phenotypic data was performed using the PROC GLM procedure in SAS 9.4 . Broad-sense heritability (ironment . Pearsonhttps://soybase.org/snps/index.php). The details on genotyping and SNP calling procedures for the 42,449 SNPs as described by Song et al. (2013). After removing SNPs with minor allele frequency (MAF) < 0.05, a total of 34,718 SNPs were used for further analysis in this study.SNP data of the selected PIs is available at Illumina Infinium SoySNP50K BeadChip database on SoyBase .A neighbor-joining tree was constructed together with Principal component analysis (PCA) was calculated using TASSEL 5.0 software . The 34,r2) using the RTM-GWAS V1.1 software [r2 fell to half its highest value. The average LD decay figure was drawn by GraphPad Prism version 5.01  using r2 for SNPs with pairwise distances less than 5 Mb in each chromosome.Pairwise linkage disequilibrium (LD) between 34,718 SNPs with a missing rate < 10% and MAF ≥ 0.05 was estimated using squared allele frequency correlations (software . The panmrMLM.GUI version 2.1 [10 (1/m) where m = the number of markers, thus -log10 = 4.54 as the Bonferroni correction line, however this threshold was adjusted to -log10(P) ≥ 3.5 and logarithm of odd (LOD) = 3 were used to declare SNP-trait association in MLM [An association analysis was performed for four traits viz., EC, GR, ShL, and RL through two distinct models, Mixed Linear Model (MLM) and Multi-Locus Random-SNP-Effect Mixed Linear Model (mrMLM) for each year plus average of phenotypic data across the 2 years. The MLM was run with the TASSEL 5.0 software, and the K matrix serves as a random effect in this model. However, mrMLM was carried out using the R package sion 2.1 . The sign in MLM , 42 and n in MLM  respectiP ≤ 0.05.Haplotype block analysis of the stable SNPs was investigated with Haploview software version 4.2 with the four-gamete rule method , 84. Dunwww.soybase.org). The model genes were retrieved from each of the regions for candidate gene analysis. Gene ontology (GO) enrichment analysis was conducted for all the model genes within the four SNPs (±500 kb) using agriGO (http://bioinfo.cau.edu.cn/agriGO) [G. max gene model as a reference background. Candidate genes associated with seed flooding tolerance were predicted based on GO enrichment analysis and gene functional annotations from the Phytozome (https://phytozome.jgi.doe.gov) and SoyBase (http://www.soybase.org) databases. These model genes were also used to construct probable protein-protein interaction (PPI) network with a publicly available online database Search Tool for Retrieval of Interacting Genes/Proteins (STRING) [https://soybase.org/soyseq/) developed by Severin et al. [The stable SNPs (detected for at least 2 traits by either of the models or both) were used to mine putative candidate genes at 500 kb up- and down-stream of the SNP position by using Glyma2.0 models on SoyBase (http:/agriGO) , in whic(STRING) . Credibl(STRING) . The expn et al.  and heatn et al. . In addin et al. Additional file 1: Table S1. SNPs markers associated with electrical conductivity (EC), germination rate (GR), Shoot Length (ShL), and Root Length (RL) via MLM.Additional file 2: Table S2. SNPs markers associated with electrical conductivity (EC), germination rate (GR), Shoot Length (ShL), and Root Length (RL) via mrMLM.Additional file 3: Table S3. One hundred fifteen (115) model genes were found within the region of Gm_08_11,971,416 SNP using Glyma2.0 models in SoyBase. Table S4. One hundred thirty (130) model genes were found within the region of Gm_08_46,239,716 SNP using Glyma2.0 models in SoyBase. Table S5. One hundred thirteen (113) model genes were found within the region of Gm_05_1,000,479 SNP using Glyma2.0 models in SoyBase. Table S6. One hundred twenty-five (125) model genes were found within the region of Gm_01_53,535,790 SNP using Glyma2.0 models in SoyBase.Additional file 4: Figure S1. Heat map exhibiting the expression profiles of candidate genes among the different soybean tissues and development stages.Additional file 5: Table S7. Cis-acting regulating elements related to seed flooding tolerance obtained from PlantCare database.Additional file 6: Table S8. A core set of 243 soybean plant introductions (PIs) selected from the USDA Soybean Germplasm Collection.Additional file 7: Table S9. Phenotypic data (average) of 243 PI accessions used in this study.


Mycobacterium bovis. Our data presented here suggests that the response to mycobacterial antigens varies in macrophages generated from Brown Swiss (BS) and Holstein Friesian (HF) cattle, two breeds belonging to the Bos taurus family. Whole genome sequencing of the Brown Swiss genome identified several potential candidate genes, in particular Toll-like Receptor-2 (TLR2), a pattern recognition receptor (PRR) that has previously been described to be involved in mycobacterial recognition. Further investigation revealed single nucleotide polymorphisms (SNP) in TLR2 that were identified between DNA isolated from cells of BS and HF cows. Interestingly, one specific SNP, H326Q, showed a different genotype frequency in two cattle subspecies, Bos (B.) taurus and Bos indicus. Cloning of the TLR2 gene and subsequent gene-reporter and chemokine assays revealed that this SNP, present in BS and Bos indicus breeds, resulted in a significantly higher response to mycobacterial antigens as well as tri-acylated lipopeptide ligands in general. Comparing wild-type and H326Q containing TLR2 responses, wild-type bovine TLR2 response showed clear, diminished mycobacterial antigen responses compared to human TLR2, however bovine TLR2 responses containing H326Q were found to be partially recovered compared to human TLR2. The creation of human:bovine TLR2 chimeras increased the response to mycobacterial antigens compared to the full-length bovine TLR2, but significantly reduced the response compared to the full-length human TLR2. Thus, our data, not only present evidence that TLR2 is a major PRR in the mammalian species-specific response to mycobacterial antigens, but furthermore, that there are clear differences between the response seen in different cattle breeds, which may contribute to their enhanced or reduced susceptibility to mycobacterial infection.Recent evidence suggests that several cattle breeds may be more resistant to infection with the zoonotic pathogen  Early recognition of pathogens and activation of the innate immune response is critical in determining the outcome of infection. Pathogens are recognized by evolutionary conserved, germline-encoded and cell-surface expressed pattern recognition receptors (PRR) . One of M. bovis, encoded by a subfamily of ESAT-6 and also present in M. tuberculosis and M. bovis BCG  production through activation of NF-κB and MAPK pathways –26. Furtoduction , 33. In oduction . Furtheroduction , 35. Thuoduction , 36  293 cell assay. Similar as in the human system, several SNPs have also been identified within the bovine TLR2 orthologue. Some of these have been indicated to contribute to disease susceptibility in cases of paratuberculosis  and Holstein Friesian (HF) and determine the genotypic frequency of a H326Q SNP that occurs in the ligand-binding region of the ECD. We report on the species- and breed- specific phenotypic consequence of this mutation in response to canonical TLR2 ligands and in the context of challenge with M. tuberculosis and M. bovis. We present evidence that TLR2 is a major PRR in the mammalian species-specific response to mycobacterial antigens, that there are clear differences in species-specific responses that may influence disease susceptibility and host tropism of Mycobacterium tuberculosis complex members.Given the importance of TLR2, it is perhaps not surprising that polymorphisms within the TLR2 gene have been shown to affect the immune response to mycobacterial infection in both humans and cattle , 37, 38 rculosis , clinicarculosis , 43, andrculosis –46. SNPsrculosis . CharactM. bovis strain AF2122/97 and M. bovis BCG Tokyo; M. tuberculosis H37Rv. Strains were cultured in BD Middlebrook 7H9 media , supplemented with OADC and 0.05% Tween 80 . For M. bovis culture only, growth media was additionally supplemented with sodium pyruvate; for M. tuberculosis culture only, media was additionally supplemented with glycerol .The following strains were used for all assays as indicated: de novo using the 10x Genomics assembly tool Supernova v2.0.1, using the option to output a phased genome, which generates two homologous assemblies where each scaffold in the assembly has one of the paternal or maternal haplotypes (Four lanes of 10x Genomics data were generated using an Illumina HiSeq. This provided ~1200 million reads with a 150bp read length. The data was assembled plotypes . Using oplotypes . We testplotypes .B. taurus genome annotation (ARS-UCD1.2.103) to that of the Brown Swiss, noting any genes that were unmapped in Brown Swiss (B. taurus ARS-UCD1.2.103) and downloaded the functional information for these genes using ENSEMBL BioMart database (The LiftOff tool was used to lift over the wn Swiss . We thendatabase . For genComplementary DNA (cDNA) from TLR2 mRNA sequences isolated from PBMCs from six clinically healthy pedigree BS and four HF were investigated for sequence comparison. All cows were female and housed under Home Office License PPL7009059. Age and lactation cycle of sampled cows were recorded. After identification and selection of candidate SNP H326Q within the coding sequence (CDS) of TLR2 in the BS samples, further cDNA was generated from nine BS and 13 HF PBMC samples from the above-mentioned farms to be assessed for the presence of this SNP. Additionally, 17 Sahiwal and 17 Boran cDNA samples were kindly provided by Drs Thomas Tzelos and Tim Connelley  for TLR2 SNP analysis.d = 1.077 g mL-1, StemCell Technologies ™, Canada) was underlaid and PBMC were isolated by density gradient centrifugation. PBMC were washed and red blood cells lysed, resultant cells were cultured in RPMI 1640 with GlutaMAX™ , supplemented with FCS  and penicillin/streptomycin  at 37 °C with 5% CO2. To derive MØ, PBMC were incubated additionally with 10% filtered L929 supernatant. Media was replaced after three days and adherent cells were harvested after 6 days. Matured MØ were phenotypically assessed by flow cytometry using antibodies specific for bovine cell surface expressed markers including CD14, CD16, CD32, CD11b, CD80, CD163 and MHC class II. All antibodies and isotype controls were purchased from Bio-Rad, UK  isolation and subsequent macrophage (MØ) generation was collected from clinically healthy pedigree HF and BS cows. All procedures were carried out under the Home Office license (PPL7009059) approved by the RVC’s AWERB Committee. Blood was drawn into sterile glass vacuum bottles containing 10% v/v acid citrate and centrifuged to separate the buffy coat, which was then diluted in phosphate buffered saline  with 2% foetal calf serum . Lymphoprep  with on-column gDNA digestion  according to the manufacturer’s instructions. Total RNA was reverse transcribed to cDNA using iScript cDNA Synthesis Kit  according to the manufacturer’s instructions. cDNA samples were checked for concentration and purity via the A260/280 ratio using a Nanodrop ND-1000 Spectrophotometer . A RT-negative control of each RNA sample was analysed by polymerase chain reaction (PCR) for expression of β-actin to confirm lack of gDNA contamination of RNA.For sequencing and cloning of the tlr2 sequences were amplified using primers designed using Primer-BLAST  and Primer3Plus  or pGEM-T plasmid , manufacturer’s instructions were followed. Plasmids were transformed into NEB® 10-beta competent E. coli  and colony PCR performed to verify the presence of the tlr2 insert. Sequences were validated using Sanger sequencing; DNA sequencing was performed by DNA Sequencing & Services  using the corresponding vector-specific sequencing primers. A minimum of 4 different plasmids derived from unique colonies were sequenced for each animal. Bovine TLR2 contigs were assembled using the bovine TLR2 sequence Bos taurus Hereford breed NM_174197.2  as a reference. TLR2 nucleotide sequences were translated into corresponding amino acid sequences and aligned in CLC Main Workbench V7.6.4 . Detected SNPs were classified according to the dbSNP database . Analysis of SNP data was curated and as at present, the NCBI dbSNP database contains only human data, allele changes, CDS position and function of each SNP was manually cross-checked and referenced against Ensemble and the European Variation Archive (EVA).Full length mer3Plus Table 2 The Human Embryonic Kidney (HEK) 293T cell line, stably transfected with the gene encoding NF-κB inducible secreted embryonic alkaline phosphatase (SEAP), was cultured SEAP media as described recently . Bovine -1 diacylated FSL-1 ; 1 mg mL-1 of Pam3CSK4 ; 19 kDa lipoprotein antigen (represents Rv3763 or Mb3789) , M. bovis BCG (MOI of 10) or MTB H37Rv at (MOI of 5). The direct NF-κB activator phorbol 12-myristate 13-acetate  was used at 200 ng mL-1 as a positive control. Sterile culture media and mock transfection with an empty vector plasmid were used as negative controls. Supernatants were harvested for CXCL8 ELISA as described by Cronin et al.  followed by incubation for 24 h at 37°C. For both assays, optical density was measured using a SpectraMax M2 spectrometer .We recently described the SEAP assay and the correlation between SEAP and CXCL8 production in detail , see alsn et al.  for boviWe used the 10x Genomics tool Supernova to assemble a haplotype-phased Brown Swiss cow genome. After trimming, the mean read length was 138.5 bases, with a raw read coverage of 59.64. The genome size was calculated to be 2.66Gb (of which 98.08% of the genome was present in scaffolds >10Kb) with a contig N50 of 523Kb and a scaffold N50 of 26Mb  comprised of that of a B. taurus Hereford breed bull  in order to identify potential functional SNP sites. A total of 19 SNP variants were detected across the breeds, comprising both synonymous and missense mutations  in B. indicus, thus potentially indicating the presence of a heterozygous allele in B. indicus. Of all SNPs identified across the breeds, two of the non-synonymous SNPs have been already reported in the literature  was selected for further investigation using a larger sample size, including cDNA from two additional breeds, Boran and Sahiwal, from the B. indicus species. A total of 66 cDNA samples  were assessed for H326Q SNP analysis and frequency analysis . Of the BS samples, six samples were homozygous for the RefSeq sequence, whereas the majority (n=9) were found to be heterozygous (TA genotype), containing both, the RefSeq sequence and the H326Q SNP. Of the Bos indicus breeds, the Boran samples revealed two samples homozygous for the RefSeq sequence, eight heterozygous and seven homozygous for the SNP. Thus, the heterozygote genotype was most abundant in the BS and Boran population. All Sahiwal samples (n=17) were homozygous for the SNP variant (AA genotype).Interestingly, all HF samples tested as part of the present study were homozygous for the B. taurus as well as B. indicus. Of note are positions H326Q as reported here and by others, as well as positions 417, 563 and 665; these latter 3 amino acid positions are particularly interesting as like H326Q these SNPs were found to introduce identical residue changes to our full length TLR2 analysis. Further, these SNPs were found to be present in BS cattle investigated  highlights an alignment of BS with B. taurus as TLR2 is derived from a single transcript in both breeds , we assessed in the next step this association with TLR2 gene models and global sequence alignments. From the genotype analysis BS and Boran cattle represent an intermediate between the genotype extremes, perhaps representing genomic heritage; for example, Boran cattle are predominantly . taurus , 60. TLRHaving identified the H326Q candidate SNP and its representation in the present study population of different cattle breeds, specifically in the BS population, the potential impact of this SNP on boTLR2 function in this breed was assessed. We utilised a previously published HEK cell assay to express the different TLR2 constructs in the absence of additional PRR . As wellTo confirm that the different TLR2 constructs were similarly expressed on the cell surface of the SEAP HEK cells, immunostaining was performed using bovine and human TLR2 specific antibodies. The mean fluorescence intensity (MFI) values of the positively stained population were similar for all constructs  than with the M. bovis BCG infection (MOI of 10).Having assessed the activity of TLR2 variant HEK cells using TLR2-specific agonists and determining good correlation between SEAP activity and CXCL8 secretion  genes, in line with cattle belonging to “high γδ species” compared to human counterparts  to the uncharged glutamine (Q) at position 326, which is located in the ligand binding region LRR11 of the ECD of the receptor . Using an traits  and moren traits  comparedmigenius , our resthe host –70.in vitro phenotypic assessment. Initially, we used the SEAP assay that has been previously reported as a quantitative indicator of NF-κB activation during TLR stimulation  approved by the RVC’s AWERB Committee.Writing proposal: AG, SW, and DW. Experimental work and scientific conceptualization: M-CB, AG, SW, AH, and DW. Genome assembly, analyses, and annotation: GE and FP. All authors contributed to the article and approved the submitted version.The project was funded by a Bloomsbury PhD studentship to AG, SW and DW, as well as BBSRC grant BB/P008461/1 to DW. The manuscript is ID No 1550801 of the RVC. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Arabidopsis thaliana, ribosomal cistrons are present in Fagus sylvatica only in four major regions, in line with FISH studies. On most assembled chromosomes, telomeric repeats were found at both ends, while centromeric repeats were found to be scattered throughout the genome apart from their main occurrence per chromosome. The genome-wide distribution of SNPs was evaluated using a second individual from Jamy Nature Reserve (Poland). SNPs, repeat elements and duplicated genes were unevenly distributed in the genomes, with one major anomaly on chromosome 4. The genome presented here adds to the available highly resolved plant genomes and we hope it will serve as a valuable basis for future research on genome architecture and for understanding the past and future of European Beech populations in a changing climate.The European Beech is the dominant climax tree in most regions of Central Europe and valued for its ecological versatility and hardwood timber. Even though a draft genome has been published recently, higher resolution is required for studying aspects of genome architecture and recombination. Here, we present a chromosome-level assembly of the more than 300 year-old reference individual, Bhaga, from the Kellerwald-Edersee National Park (Germany). Its nuclear genome of 541 Mb was resolved into 12 chromosomes varying in length between 28 and 73 Mb. Multiple nuclear insertions of parts of the chloroplast genome were observed, with one region on chromosome 11 spanning more than 2 Mb which fragments up to 54,784 bp long and covering the whole chloroplast genome were inserted randomly. Unlike in  Fagus sylvatica)  . Europealvatica) . Beech tlvatica) . Under olvatica) , but botlvatica) . Particulvatica) , will inlvatica) . In ordelvatica) . Howeverlvatica) . Such gelvatica) ].Camellia sinensis  and at the National Forest Office nursery in Guémené-Penfao in January 2019 . For relatedness assessment among the half-sib progeny of MSSB, young leaves after bud burst were sampled from saplings in the nursery in spring 2014 (1st campaign) and 2017 (2nd campaign), immediately frozen in dry ice and then stored at −80°C before subsequent genetic analyses. Likewise, leaves were sampled on the mother tree and 19 surrounding adult trees (expected fathers). Nuclear DNA was extracted individually from 10 mg of tissue using the DNeasy Plant Mini Kit  following the manufacturer’s instructions. DNA concentration was measured on a ND-8000 NanoDrop spectrophotometer . For additional transcriptome construction a total of six different organs were sampled on the MSSB accession, including: two types of buds (quiescent buds and swelling buds just before bud break) during dormancy release on the 15th March, 2017, male flowers and female flowers collected on the 3rd of May 2017, leaves, and xylem collected on the 28th of June 2017. Each organ was immediately flash-frozen in liquid nitrogen and stored at −80°C before RNA extraction. For short read sequencing (Illumina), total RNA was extracted from these six samples following the procedure described in For a progeny trial establishment seeds were sampled from a single mother tree (accession MSSB). About 1,000 beechnuts were collected during two successive campaigns in the fall 2013 and 2016 using a net under the mother tree located in the southern range of the species in the south-west of France (Saint- Symphorien 44° 25′ 41.138″ N 0° 29′ 23.125″ W). Seeds were germinated and raised the following springs at the National Forest Office nursery in Guémené-Penfao (47° 37′ 59.99″ N -1° 49′ 59.99″ W) and then planted at the Nouzilly (47° 32′ 36″ N 0° 45′ 0″ E) experimental unit PAO of INRAE in February 2017 , were frozen on dry ice, and then stored at −80°C. RNA was extracted from bud-scale-free leaves following the procedure described above. These 200 genotypes included two relatively large full-sib families comprising 49 full-sibs (family MSSBxSSP12) and 36 full-sibs (family MSSBxMSSH) (see results section).The previous scaffold-level assembly was constructed with Illumina shotgun short reads and PacBio long reads . For a cThe completeness of the assembly was evaluated with plant-specific (viridiplantae_odb10.2019-11-20) and eudicot-specific (eudicots_odb10.2019-11-20) Benchmarking Universal Single-Copy Orthologs (BUSCO v4.1.4) .Arabidopsis thaliana as the closest reference-level organism.Cleaned transcriptomic Illumina reads  were aligned to the assembly using Hisat  in orderA. thaliana, Vitis vinifera, and Eucalyptus grandis were downloaded from the plaza v4.5 dicots dataset  and were used along with the predicted proteins from the current assembly to build protein clusters using cd-hit v.4.8.1  , which wFagus sylvatica were identified by a combination of homology-based and de novo approaches using RepeatModeler 2.0   was usedSeparately assembled chloroplast  and mitoThe DNA isolated from the Polish individual Jamy was shipped to Macrogen Inc.  for library preparation with 350 bp targeted insert size using a TruSeq DNA PCR Free preparation kit  and sequencing on HiSeq X device  using PE-150 mode. The generated 366,127,860 raw read pairs (55.3 Gb) were processed with AfterQC v 0.9.1  for qualhttp://beechgenome.net.A genome browser was set up using JBrowse v.1.16.10 . Tracks st campaign, and 19 putative fathers. PCR and extension primers were designed from flanking sequences (60 bp of either side) of 40 loci  was used to genotype the mother tree (MSSB), its half-sib progeny from the 1 40 loci  availablPaternity analysis was carried out using Cervus 3.0  to checkSix Illumina RNA-Seq libraries (one for each organ) were constructed from 500 ng total RNA using the TruSeq Stranded mRNA kit , which allows for mRNA strand orientation (the orientation of sequences relative to the antisense strand is recorded). Each library was sequenced using 151 bp paired-end read chemistry on a HS4000 Illumina sequencer .One Nanopore cDNA library was also prepared from entire female flower RNA. The cDNA library was obtained from 50 ng RNA following the Oxford Nanopore Technologies  protocol “cDNA-PCR Sequencing (SQK-PCS108)” with a 14 cycle PCR (6 min for elongation time). ONT adapters were ligated to 190 ng of resulting cDNA. The Nanopore library was sequenced using a MinION Mk1b and R9.4.1 flowcells.Short-read RNA-Seq data (Illumina) from the six tissues were assembled using Velvet  1.2.07 aLong-read RNA-Seq data (Oxford Nanopore Technologies) from female flowers were corrected using NaS  with defQuercus robur or Castanea mollissima proteins was kept. This procedure yielded 34,987 unigenes (below referred to as the 35 K unigene set).Contigs obtained from short reads as well as corrected long reads were then aligned on a draft version of the MSSB genome assembly (unpublished) using BLAT . The besThe 200 RNA samples were prepared as described above (Unigene Set Construction section), using the TruSeq Stranded mRNA kit , starting from 500 ng total RNA. Libraries were multiplexed onto an Illumina Novaseq 6,000 sequencer , using the S4 chemistry (2 × 150 read length), targeting approximately 30 million reads per sample.First, SNPs in the MSSB reference unigenes were identified. To this end, a trimming procedure was applied to the MSSB sequences to remove adapters, primers, ribosomal reads and nucleotides with quality value lower than 20 from both ends of the reads, and to discard reads shorter than 30 nucleotides as described previously . TrimmedSNPs were called and bioinformatically genotyped for the mapping population at each MSSB polymorphic site, based on the paired-end Illumina sequencing of 200 RNAseq libraries. The 200 raw-read datasets were trimmed following the same procedure used for MSSB. Reads were aligned to the 35 K unigene set using bwa mem 0.7.17. Genotypes were recovered from the 200 libraries at the 507,905 polymorphic positions identified in MSSB using GATK 3.8.Subsequently, the following four-step filtering procedure was applied: 1) for each SNP of a given half-sib, polymorphic genotypes were set to monomorphic if the sequencing depth for this individual at a given position was lower than 20; 2) SNPs were kept only if at least 50% of the mapping population  were heterozygous at that site; 3) only polymorphic sites consistent with a 1:1 heterozygote:homozygote genotype ratio were kept, according to a Chi-square test with a 90% confidence interval , corresponding to heterozygous loci in the mother tree and monomorphic in all possible fathers; 4) finally, for each contig, only the SNP with fewest missing data in the mapping population was retained.Half-sibs presenting a high amount of missing data were discarded. As a result, 182 individuals (out of 200 selected from the first and second campaign) with valid genotypes for at least 4,127 loci were kept for further analyses. A preliminary analysis was then performed using R-qtl package to group linked SNP markers into robust linkage groups (LG) (LOD = 8) . Given tSequences of the unigenes encompassing SNP markers included in the linkage map were aligned on the genome assembly using BLAT with default parameters, except for applying “-minScore = 80”. Unigenes presenting more than one alignment were filtered out. In other words, when a second best match having a score equal to or greater to 90% of the best score, the marker was tagged as ambiguous. For all the remaining alignments only the alignment with the best score was kept.Fagaceae , surpassing that of the previous assembly and other genome assemblies available for members of the Fagaceae . Of the Fagus sylvatica genome 4.9 exons per gene were found on average, corresponding well to other high-quality plant genome drafts. The distribution of exons and introns in comparison to J. regia and A. thaliana is presented in F. sylvatica, out of which 1,776 were present in the three other species used for comparison  correlates with the features reported above, they were identified on the basis of the comparison of the two individuals investigated in this study, Bhaga and Jamy. A total of 2,787,807 SNPs were identified out of which 1,271,410 were homozygous  and 1,582,804 were heterozygous . A total of 269,756 SNPs fell inside coding regions out of which 119,946 were homozygous.Heterozygous SNPs were very unequally distributed over the chromosomes . SeveralHomozygous SNPs differentiating Bhaga and Jamy often followed a different pattern. All regions with low heterozygous SNP frequency longer than 5 Mb had an above-average homozygous SNP frequency, with the exception of the anomalous repeat-rich region on chromosome 4, which had very low frequencies for both homozygous and heterozygous SNPs. However, there were also two regions of more than 1 Mb length on chromosome 11 that also showed low frequencies of both SNP categories .Generally, the frequency of overall and intergenic SNPs per 100 kb window corresponded well for both heterozygous and homozygous SNPs, suggesting neutral evolution. However, there were some regions in which genic and intergenic SNP frequencies were uncoupled. For example, on chromosome 1 a high overall heterozygous SNP frequency was observed at 37.7, 48.2, and 56 Mb, but genic heterozygous SNP frequency was low despite normal gene density, suggesting the presence of highly conserved genes. In line with this, also the frequency of homozygous genic SNPs was equally low in the corresponding areas. Similarly, homozygous SNP frequencies were also decoupled on chromosome 1, where a low frequency was observed at 4.2, 7.1, 38.2, 62.1, and 64.8 Mb, but a high genic SNP frequency was observed. This suggests the presence of diversifying genes in the corresponding 100 kb windows, such as genes involved in coping with biotic or abiotic stress.In line with the different distribution over the chromosomes, with large areas poor in heterozygous SNPs, there were much more windows with low numbers of heterozygous SNPs than windows with homozygous SNPs . Notablybeechgenome.net. Predicted genes, annotated repeat elements, homozygous SNPs, and heterozygous SNPs are available in “B. Annotations”. The telomeric and centromeric locations, as well as the GC content details are available in “C. Other Details”.A genome browser for the genome of Bhaga, with the various genomic features outlined above annotated, is available at The analysis of the 36 SNPs using Cervus allowed the identification of candidate fathers and reconstruct full-sib families. For 317 of the 537 offspring a likely father was identified. The 19 candidate fathers were represented in the progeny, although their contributions were variable (0.8–21%). For the other offspring, no father could be assigned, i.e., the pollen donor is not present among the surrounding trees . The two largest families comprised 68 (MSSBxMSSH) and 86 (MSSBxSSP12) full-sibs. Few years after plantations, 36 genotypes for the former and 49 for the latter survived .Fagus sylvatica based on short and long NGS reads obtained from cDNA libraries constructed from six different tissues. The first unigene set for this species was established back in 2015 using a combination of Sanger and Roche-454 reads (Fagaceae species), this new reference provides the most complete transcript catalog  using an RNA-Seq approach, led to 408,111,505 Illumina paired-end reads. A total of 383,149,091 trimmed sequences were used to identify putative segregating SNPs in MSSB.On average, 82.67% of the reads were properly aligned on the reference unigene, ranging from 72.94% for the male flowers to 86.46% for leaves. In total, 613,885 and 507,905 SNPs were identified using Samtools/bcftools and GATK, respectively. A total of 507,905 SNPs in MSSB were finally identified by both methods.Sequencing of the 200 siblings, followed by trimming of the raw data, led to a total of 9,155,925,565 reads. On average, 78.64% of the reads were properly aligned on the reference unigene (min. 72.6%—max. 83.04%). Overall, 267,361 polymorphic sites in at least one out of the 200 half-sibs were found. Our four-step filtering process yielded a final set of 6,385 SNPs spread over 6,385 contigs, with at least 20× coverage.Beech is a diploid species with 2n = 2x = 24. The 12 expected linkage groups (LG) were retrieved using SNPs from set #1 using the R-qtl package. The number of SNP markers per LG ranged from 231 to 412. However, the detailed linkage analysis, carried out with JoinMap for each LG, revealed an unexpectedly high number of crossing-overs and oversized LGs compared to previous linkage mapping analyses performed in beech  or oak , probablFagus sylvatica genome assembly. The alignments were filtered and congruence between scaffolds and linkage groups was checked. Most of the markers from a given LG mapped on a single scaffold  of the scaffold  providinFagaceae genomes, both in terms of size and gene space. Here the base chromosome number of 12 could be confirmed, as previously reported based on chromosome counts .Telomeres are inherently difficult to resolve because of long stretches of GC-rich repeats that can cause artefacts during library preparation  and can Centromere repeats were identified by screening the genome for repeats of intermediate sizes, and were found to be present predominantly within a single location per chromosome. However, lower amounts of centromeric repeat units were also observed to be scattered throughout the genome. The function of the centromeric repeats outside of the centromere remains largely enigmatic but could be associated with chromosome structuring  or centrOrganelle DNA integration has been frequently found in all kingdoms of life for which high-resolution genomes are available . It can An anomaly regarding organelle DNA insertion was observed on chromosome 11. Around a central insertion of mitochondrion DNA, multiple insertions of chloroplast DNA were found. The whole region spans more than 2 Mb, which is significantly longer than the organelle integration hotspots reported in other species . The evoSNP content was found to vary across all chromosomes leading to a mosaic pattern. While most of the areas of high or low SNP density were rather short and not correlated to any other patterns, there were several regions >1 Mb that exhibited a similar polymorphism type, suggesting non-neutral evolution.The longest of those stretches poor in both heterozygous and homozygous positions was found on chromosome 4, and corresponded to a region rich in both genes and repeat elements. This is remarkable and probably due to a recent proliferation, as repeat-rich regions are usually less stable and more prone to accumulate mutations .Most regions with lower abundance of heterozygous SNPs than on average were found to be particularly high in homozygous SNPs. The longest of such stretches was found on chromosome 6, comprising about two thirds of the entire chromosome. Three more such regions longer than 5 Mbp were found on other chromosomes. The evolutionary significance of this is unclear, but it is conceivable that these areas contain locale specific variants for which no alternative alleles are shared within the same stand. For confirmation of this hypothesis, it would be important to evaluate genetic markers from additional individuals of the same stand. Locally adaptive alleles could be fixed relatively easy by local inbreeding , consideOverall, homozygous and heterozygous SNPs were rather uniformly distributed throughout the major part of the genome, suggesting neutral evolution or balancing selection.The chromosome-level assembly of the ultra-centennial individual Bhaga from the Kellerwald-Edersee National Park in Germany and its comparison with the individual Jamy from the Jamy Nature Reserve in Poland revealed several notable genomic features. The prediction of the telomeres and centromeres as well as ribosomal DNA corresponded well with data gained from chromosome imaging , suggestTaken together, the data presented here suggest a strongly partitioned genome architecture and potentially divergent selection regimes in the stands of the two individuals investigated here. Future comparisons of additional genomes to the reference will help understanding the significance of the variant sites identified in this study and shed light on the fundamental processes involved in local adaptation of a long-lived tree species exposed to a changing climate.


With advances in next-generation sequencing technologies, an unprecedented amount of soybean accessions has been sequenced by many individual studies and made available as raw sequencing reads for post-genomic research.To develop a consolidated and user-friendly genomic resource for post-genomic research, we consolidated the raw resequencing data of 1465 soybean genomes available in the public and 91 highly diverse wild soybean genomes newly sequenced. These altogether provided a collection of 1556 sequenced genomes of 1501 diverse accessions (1.5 K). The collection comprises of wild, landraces and elite cultivars of soybean that were grown in East Asia or major soybean cultivating areas around the world. Our extensive sequence analysis discovered 32 million single nucleotide polymorphisms (32mSNPs) and revealed a SNP density of 30 SNPs/kb and 12 non-synonymous SNPs/gene reflecting a high structural and functional genomic diversity of the new collection. Each SNP was annotated with 30 categories of structural and/or functional information. We further identified paired accessions between the 1.5 K and 20,087 (20 K) accessions in US collection as genomic “equivalent” accessions sharing the highest genomic identity for minimizing the barriers in soybean germplasm exchange between countries. We also exemplified the utility of 32mSNPs in enhancing post-genomics research through in-silico genotyping, high-resolution GWAS, discovering and/or characterizing genes and alleles/mutations, identifying germplasms containing beneficial alleles that are potentially experiencing artificial selection.The comprehensive analysis of publicly available large-scale genome sequencing data of diverse cultivated accessions and the newly in-house sequenced wild accessions greatly increased the soybean genome-wide variation resolution. This could facilitate a variety of genetic and molecular-level analyses in soybean. The 32mSNPs and 1.5 K accessions with their comprehensive annotation have been made available at the SoyBase and Ag Data Commons. The dataset could further serve as a versatile and expandable core resource for exploring the exponentially increasing genome sequencing data for a variety of post-genomic research.The online version contains supplementary material available at 10.1186/s12864-022-08326-w. Glycine max (L.) Merr.] is one of the most economically important field crops for its high seed protein (40%) and oil (20%), which are primarily used for animal feed, human consumption and industrial use. Soybean also plays a vital and sustainable role in agriculture through fixing atmospheric nitrogen. The worldwide soybean production has been tripled and its growing acreage has doubled since 1993  were wereG. sns Table . ConsideG. max cv. Williams 82.a2 v1) available at Phytozome v12 [https://broadinstitute.github.io/picard/). SNPs were called using UnifiedGenotyper function of Genome Analysis Toolkit (GATK) (Version: 3.4) with default parameters [etc) of all 56,044 gene models from G. max cv. Wm82.a2.v1 reference genome [The raw sequencing reads were aligned with soybean reference genome  [SoySNP50K iSelect Bead Chip based genotyping results for 20,087 (20 K) accessions of the US Soybean Collection were downloaded from the SoyBase (ase.org) . Using chttps://www.ncbi.nlm.nih.gov/SNP) and compared with the 32mSNPs from 1.5 K genomes. The coordinates of 32mSNPs that did not correspond with those in dbSNP database were regarded as newly identified SNPs.The 15,623,492 registered soybean SNP coordinates were downloaded from the NCBI’s dbSNP Database  was used to analyze population structure and relation between re-sequenced 1556 accessions and the 20,087 accessions in the GRIN database using filtered 32mSNPs and SoySNP50K SNPs, respectively. PCA was conducted using the SNPRelate package [packages . All SNPAF ≥0.05 .p < 0.05/ total amount of SNPs used in the analysis. The regional LD heatmap was generated using the LDheatmap package [The phenotypic data for stem determinate was downloaded from the GRIN database. SNP data were filtered to keep only biallelic SNP and minor allele frequency ≥ 0.01. GWAS was performed using a linear mixed model in GAPIT , 54 with package .Additional file 1: Table S1. Summary for the collection of 1.5 K accessions: For each accession, the columns showed its common name, PI, maturity group (MG), origin and geographic location (latitude and longitude), coverage of its genome sequencing, percent aligned to the reference genome sequence, total number of reads , percentage of identity in comparison with its own 50 K SNP genotype, the PI with the highest Percentage of Identity (PID) in comparison with 50 K SNP genotypes of all accessions including itself and not including itself. Table S2. Best match of each of the 20 K accessions to the 1.5 K accessions: The best match accession and the percentage of identity is listed for each accession in US collection of 20 K accession.


In addition to heterogeneity and artificial selection, natural selection is one of the forces used to combat climate change and improve agrobiodiversity in evolutionary plant breeding. Accurate identification of the specific genomic effects of natural selection will likely accelerate transfer between populations. Thus, insights into changes in allele frequency, adequate population size, gene flow and drift are essential. However, observing such effects often involves a trade-off between costs and resolution when a large sample of genotypes for many loci is analysed. Pool genotyping approaches achieve high resolution and precision in estimating allele frequency when sequence coverage is high. Nevertheless, high-coverage pool sequencing of large genomes is expensive.2F21) has undergone 18 generations of natural adaption to conventional farming practice. The accuracies of estimated pool-based allele frequencies and genome coverage yields were compared using three next-generation sequencing genotyping methods. To achieve accurate allele frequency estimates with low sequence coverage, we employed a haplotyping approach. Low coverage allele frequencies of closely located single polymorphisms were aggregated into a single haplotype allele frequency, yielding 2-to-271-times higher depth and increased precision. When we combined different haplotyping tactics, we found that gene and chip marker-based haplotype analyses performed equivalently or better compared with simple contig haplotype windows. Comparing multiple pool samples and referencing against an individual sequencing approach revealed that whole-genome pool re-sequencing (WGS) achieved the highest correlation with individual genotyping (≥ 0.97). In contrast, transcriptome-based genotyping (MACE) and genotyping by sequencing (GBS) pool replicates were significantly associated with higher error rates and lower correlations, but are still valuable to detect large allele frequency variations.Three pool samples  from a barley backcross population were generated to assess the population's allele frequency. The tested population (BCThe proposed strategy identified the allele frequency of populations with high accuracy at low cost. This is particularly relevant to evolutionary plant breeding of crops with very large genomes, such as barley. Whole-genome low coverage re-sequencing at 0.03 × coverage per genotype accurately estimated the allele frequency when a loci-based haplotyping approach was applied. The implementation of annotated haplotypes capitalises on the biological background and statistical robustness.The online version contains supplementary material available at 10.1186/s13007-022-00852-8. Next-generation sequencing (NGS) technology is routinely used in plant research to detect nucleotide polymorphisms between genotypes at genomic and transcriptomic levels –3. NGS cHowever, pooled sequencing has undeniable disadvantages, as the information of haplotypes and heterozygosity is lost. Some approaches try to recover this information –20, wherIn contrast to a static coverage level, Rellstab et al.  proposedHordeum vulgare L., a crop species with a large genome. This study evaluates three next-generation sequencing-based genotyping techniques on a low-level coverage to distinguish the most reliable method with the best costs and coverage trade-offs. Furthermore, four haplotyping approaches were established and compared to precisely estimate the allele frequency of genes or small genomic regions on a low coverage sequencing level.Our goal was to make a reliable allele frequency estimate in 2F21 barley population, grown in conventional agriculture for eighteen generations, was used to test the hypothesis of accurate allele frequency estimation for an entire population , DNA and RNA extracted and genotyped by three low coverage sequencing strategies . For each of these three methods, the allele frequency was accessed on SNP and haplotype scales. We used three different haplotyping approaches  to estimate and improve a haplotype’s allele frequency in calculated genomic window sizes .A BCThe coverage levels and numbers of detected polymorphic loci varied with the genotyping approaches. MACE produced on average 8 million reads across the three pool samples, ranging from 17 to 68 bp length. The reads were trimmed before alignment by removing the ten bases on the head of each read. Additionally, all reads shorter than 40 bp were omitted. We identified 13,079 SNPs  between the two parents. These SNPs were merged into 5919 gene-derived haplotypes (GHs) and 3275 marker-derived haplotypes (MHs). The average values of the SNP counts per haplotype were 2 and 3.5, respectively, yielding read coverages of 42 and 70 for GHs and MHs, respectively. We detected 7.06% (± 0.265 SD) genome-wide wild-type donor allele frequency across the three pool samples , and those for MH increased fourfold (14.33). The read count of MHs was approximately sixfold higher for the GBS-derived haplotype compared with MACE , respectively. The high saturation of haplotypes yielded high haplotype read coverages of 963 and 5443 for GHs and MHs, respectively. The average genome-wide allele frequency measured over the two replicates was 6.5% (± 0.14) and was accordingly 0.5% to 0.9% lower than GBS and MACE, respectively  was determined. MACE only covers expressed 3′ ends of genes and is based on expressed genes. Therefore, the bias may be related to this biological background. For GBS, no regression could be calculated.To evaluate the benefits of the haplotyping approach based on genotyping strategies, we asked whether the sizes of constructed haplotypes affect the HAF precision. The extension of haplotypes does not follow a sliding window approach. The haplotype window size is relatively large (illustrated above), raising the question of whether the accuracy of the haplotype frequency depends on the extension size and the chromosomal position. To test this possibility, the deviation of the pool's estimated frequency from the actual frequency was tested using a linear regression analysis compared, where the extension size and chromosomal position were included as cofactors. As a result of this test, the position did not influence the deviation for WGS and MACE haplotypes or the extension size of the WGS and marker MACE haplotypes. For GHs of MACE, a regression with One pool P1) was individually genotyped for 21 loci to validate pool haplotype allele frequencies. KASP markers had a failure rate < 0.015 (19 of 21) and were compared with the corresponding pool of genotypes using the Pearson correlation, root-mean-square-error (RMSE), and a negative binomial zero-inflated linear model   was not observed for GHs, whereas the correlation was 0.834 on the MH scale  while haplotyping in the MLW to a HAF value increased the Pearson correlation .Although allele frequencies of KASP highly correlate with haplotype-based frequencies, comparing a higher number of loci would be preferred. For this purpose, the allele frequencies of 300 genotypes of a BCip array . The popion Fig. . DependiA simplified contig-based haplotyping approach (CH)  was compNevertheless, a marginally higher RMSE of the CH was observed compared with MACE and WGS. Similarly, the correlation to individual genotyping was slightly lower compared with GHs and MHs. A relevant improvement over MHs and GHs was observed only for the GBS pools Table . These fAccording to Antonovics , we assuPool sequencing can be used to identify the rate of gene flow, random drift, and natural selection. The applied pool genotyping approaches' reliabilities were tested by calculating the average allele frequency distance between two neighbouring haplotypes.2-derived population of self-pollinating barley depends on the history of BC2 families. For example, BC2F1 possesses approximately 25% heterozygous regions, organised through recombination between distinct blocks during crossing. These heterozygous regions are integrated into the remaining homozygous regions. During subsequent generations, these heterozygous blocks will follow the degradation of heterozygosity through selfing as well as through a putative effect of selection or other forces that alter allele frequency  as donor parent. The population was established according to ! = null or [SA]! = null)"sambamba makrdup and then sorted using sort. Variant calling employed samtools mplieup and bcftools call  on minision 1.8 . Finallysion 1.8 , the proAdditional file 1:Figure S1. Distribution and coverage of detected polymorphisms over the barley chromosomes. A whole-genome sequencing; B MACE transcriptome sequencing; C genotyping by sequencing. Values illustrate the average across the replicates (P1–P3). Figure S2. KASP individual genotyping allele frequency results against the measured allele frequency in P1 pool sample for the MACE transcriptome. The dashed line indicates the optimal match, where pool obtained values match the individual genotyping ideally. The read curve is a regression smooth curve using all points. The color of the points indicates the coverage per locus, which ranges from 1 to several thousand. Error bars highlight the entire margin of single SNP allele frequency that contributed to the overall allele frequency of the haplotype. If no error bar is visible, there is only one SNP contribution information to the haplotype. A the single SNP comparison. Only one SNP is detected having the exact same position as the KASP markers. B the gene-based haplotype allele frequency compared to the individual genotyping. C marker-based haplotype pool allele frequency in comparison to true allele frequency (measured by KASP). D contig haplotype-based comparison to the individual genotyping. The pool sequenced sample contains the same 288 genotypes that have been tested individually for the 21 KASP loci. As two KASP markers did not meet the quality threshold, they were omitted from the analysis. Figure S3. KASP individual genotyping allele frequency results against the measured allele frequency in P1 pool sample for the GBS data. The dashed line indicates the optimal match, where pool obtained values match the individual genotyping ideally. The red curve is a regression smooth curve using all points. The color of the points indicates the coverage per locus, which ranges from 1 to several thousand. Error bars highlight the entire margin of single SNP allele frequency that contributed to the overall allele frequency of the haplotype. If no error bar is visible, there is only one SNP contribution information to the haplotype. A The single SNP comparison. Only one SNP was detected having the exact same position as the KASP markers. B The gene-based haplotype allele frequency compared to the individual genotyping. C Marker-based allele frequency. D Contig haplotype-based comparison to the individual genotyping. The pool sequenced sample contains the exact same 288 genotypes that have been tested individually for the 21 KASP loci. As two KASP marker did not meet the quality threshold, they were omitted from the analysis. Figure S4. KASP individual genotyping allele frequency results against the measured allele frequency in P1 pool sample for the WGS data. The dashed line indicates the optimal match, where pool obtained values match the individual genotyping ideally. The read curve is a regression smooth curve using all points. The color of the points indicates the coverage per locus, which ranges from 1 to several thousand. Error bars highlight the entire margin of single SNP allele frequencies that contributed to the overall allele frequency of the haplotype. If no error bar is visible, there is only one SNP contribution information to the haplotype. A The single SNP comparison. 10 SNP are detected to have the exact same position as the KASP markers. B The gene-based haplotype allele frequency compared to the individual genotyping. C Marker-based haplotype pool allele frequency comparison to true allele frequency. D Contig haplotype-based comparison to the individual genotyping. The pool sequenced sample contains the exact same 288 genotypes that have been tested individually for the 21 KASP loci. As two KASP marker did not meet the quality threshold, they were omitted from the analysis. Figure S5. Median haplotype allele frequency (HAF) difference of two neighbouring haplotypes (blue) and the share of haplotypes being highly different to their physical neighbours . All tested pool genotyping approaches are illustrated with their replicates. Figure S6 Genome-wide allele frequency on a physical map for gene-based haplotypes. The donor allele frequency is plotted in % (y-axis) against the genomic position (x-axis), split by chromosome and illustrated in base pairs. Each dot represents a gene haplotype and the color is related to the read coverage. The orange line indicates the expected allele frequency in the BC2F1. A MACE RNA sequencing output, B WGreS, C GBS. Values are the average across all replicates. Figure S7. Donor allele frequency in the region of brt1 and brt2 brittleness genes. The donor allele frequency is illustrated for each gene-based haplotype. The color illustrate the coverage per haplotype, while the shape separated the pool genotyping approaches. For each pool genotyping approach, all pools sequenced are illustrated. Phenotypic data of the population indicates unmeasurable levels of brittleness alleles in the population. All three pool genotyping approaches highlight similar observations on haplotype allele frequency levelsAdditional file 2.Table S2 Information on the 21 CASP markers used for validation of pool sequencing. Column 'Marker' gives the unique naming of the CASP; Allel e 1 & Allel 2 denote how many of the 288 homozygote genotypes were observed to carry the respective allele. . 'failed' indicated for how many genotypes the allele identification failed. 'heterozygot' presents the number of genotypes being heterozygote. The 'ratio' illustrated the allele frequency of the ISR42-8 alleles for each CASP marker. 'Chr' and 'Pos' describe the physical position of the CASP markers on the barley reference genome. 'Quality' denotes the observed SNP calling quality from MACE RNAseq sequencing