Sclerotinia sclerotiorum resistance in soybean, which have not been previously reported. At a significance level of α = 0.05, haplotype-trait association showed that the most prominent signal originated from a haplotype with 4-SNP (single nucleotide polymorphism) on chromosome 17, and single SNP-trait analysis located a nucleotide polymorphism at position rs34387780 on chromosome 3. All of the peak-SNPs  of each chromosome also appeared in their respective haplotypes. Samples with extreme phenotypes were singled-out for association studies, 25–30% from each end of the phenotypic spectrum appeared in the present investigation to be the most appropriate sample size. Some key genes were identified by epistasis interaction analysis. By combining information on the nearest positional genes indicated that most loci have not been previously reported. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggest potential candidate genes underlying callose deposition in the cell wall and mitogen-activated protein kinase (MAPK) signaling pathway-plant, as well as plant-pathogen interaction pathway, were activated. Integration of multi-method genome-wide association study (GWAS) revealed novel genomic regions and promising candidate genes in novel regions, which include Glyma.01g048500, Glyma.03g129100, Glyma.17g072200, and the Dishevelled (Dvl) family of proteins on chromosomes 1, 3, 17, and 20, respectively.Quantitative trait locus (QTL) mapping often yields associations with dissimilar loci/genes as a consequence of diverse factors. One trait for which very limited agreement between mapping studies has been observed is resistance to white mold in soybean. To explore whether different approaches applied to a single data set could lead to more consistent results, haplotype-trait association and epistasis interaction effects were explored as a complement to a more conventional marker-trait analysis. At least 10 genomic regions were significantly associated with The online version contains supplementary material available at 10.1007/s13353-021-00654-z. Sclerotinia sclerotiorum (Lib.) de Bary. In 1986, the incidence of this pathogen in Canadian legume production reached 25%  in soybean is a global soybean disease that emerged in the mid-80 s and spread across countries such as the USA, Canada, Argentina, Turkey, Japan, and India . Markers that are in high LD within a particular set of materials form haplotypes. The diversity of haplotypes in a genomic region is not limited to the two alleles typically seen for a single SNP locus. As a consequence, haplotypes may more accurately capture the underlying allelic diversity at loci controlling a trait of interest. When used in association tests, haplotypes may therefore better capture the association to the studied trait  and only extreme phenotypes (n = 76), which comparing single SNP-trait association tests only by Bastien et al. The detected QTL were compared with the results of other studies, and candidate genes were identified by GO enrichment and KEGG pathway enrichment analyses.To further investigate the QTL conferring partial resistance to WM in soybean, In this study, we employ 20,691 SNPs to reanalysis a previously published dataset that used 7864 SNPs analysis  were provided by Bastien was not considered. A total of 20,691 SNPs passed the MAF  lower limit of 5%; for association, α = 0.05 was used as the cut-off significance, which corresponded to a non-adjusted p-value of 2.42 × 10−6 . The 126 samples provided a total of 2,607,066 loci, among which 213,559 (8.192%) were heterozygous.Single-end sequencing was performed on three lanes of an Illumina HiSeq2000 . Data quality control is essential to GWAS. In the present investigation, the http://taylor0.biology.ucla.edu/structureHarvesteroybase.org/tools.php) for population structure analysis with the MCMC algorithm and NJ algorithm was used to perform clustering analysis, and phylogenetic reconstruction was conducted using MEGA5.05 (http://www.megasoftware.net/). SPSS was used for principal component analysis . The Bayesian clustering algorithm implemented in STRUCTURE 2.3.4 was used to simulate population genetic structure. According to the Q value distribution of each population, it is considered that the sample has a single blood relationship when the Q value of a sample in a population is greater than or equal to 0.6, and it is considered that the sample has mixed sources when Q value is less than 0.6. To obtain an estimate of the most probable number of population (K), K values from 1 to 10 were simulated with 20 iterations for each K, using 10,000 burn-in periods followed by 10,000 Markov Chain Monte Carlo iterations. Delta K was plotted against K values and the best number of clusters was determined following the method proposed by Evanno et al.  in max(T) permutation mode using a permutation test with 10,0000 times, and for haplotype analysis using PLINKv1.07 (http://www.softpedia.com/get/Science-CAD/PLINK.shtml), the command line “–blocks” was used to assign SNPs to their respective haplotypes. Then, analysis for haplotype-trait association was performed, and haplotype-trait association analysis only for additive effects.For complete phenotype set, single SNP-trait association tests, with additive effects only and additive effects plus dominant effects of GLM  or MLM (mixed linear model) model, were run in TASSEL v.5.0 (http://www.broadinstitute.org/haploview), because sample size was reduced without a decrease in the number of markers used, an increase in the number of false positives resulted from higher inflation; p-values were thus adjusted through a 10,000-time permutation test approach. With the 95% CI of D’ value bound between 0.70 and 0.98 for adjacent SNPs to infer haplotype blocks, then carry out haplotype-phenotype association tests of extreme phenotype set.The frequency distribution of the genotypic segregation in the two groups  will thus deviate from the Mendelian law. Using a chi-square test to assess this deviation for either or both groups, it was then possible to infer whether the marker is linked to the QTL. In the analysis with extreme phenotypes as implemented in HAPLOVIEW4.2  all SNP-by-all SNP two-locus epistasis test between all of the 205,284,045 SNP pairs; (2) SNP-by-SNP epistasis test between only significant SNPs pre-filtered by genome-wide association analyses; and (3) SNP-by-all epistasis test between significant SNPs and all SNPs. CYTOSCAPE v3.7.1 was used to visualize the interaction network.r2 > 0.2, 84,255 pairs of SNPs were found to be in LD. Among the 84,255 marker pairs in LD, an average r2 of 0.73 was observed; 46,322 pairs (55.0%) had r2 ≥0.8, and 7841 pairs (9.3%) showed an r2 value of 1. The distribution of LD along each chromosome was uneven. The average physical distance between pairs of loci decreased as their LD increased. Pairs with r2 < 0.8 were 154.1 kb apart, pairs with r2 ≥ 0.8 were 139.7 kb apart, and pairs with r2 = 1 were separated by 99.6 kb. The 20 chromosomes yielded a total of 2,858 predicted haplotypes   were 6.24%, 5.44%, and 5.16%, respectively, and the 126 lines cannot be clearly grouped by PC method, while the 126 soybean genotypes into two groups using NJ (neighbor joining). However, the genetic distance between these two groups was not significant, indicating that the samples have a low level of genetic stratification.Sclerotinia infection among the 126 soybean lines was significantly different, in which varied from 28.6 to 192.4 mm, with the traits of continuous distribution and quantitative inheritance.The results showed that the resistance to Q); (4) MLM (K); (5) MLM ; and (6) MLM (Q + K). In this study, the number of principal components is selected according to Q-Q Plot (quantile–quantile plots) , and Q-Q Plot revealed that the model using the first 79 PCs, which altogether captured 85% of the population structure, produced results that correlated most closely with the expected p-values (TableSix different modes were tested: 1) GLM; (2) GLM (PC); (3) GLM The loci showing the most significant association signals included one SNP located at position rs34387780 on chromosome 3 and five other markers in close vicinity .The strongest association was identified for a haplotype formed by 12 SNPs on chromosome 1; haplotypes on chromosomes 13, 4, 10, 20, and 17 were also detected as having significant association to soybean lesion length (Table S5).p-value threshold for the haplotype-based analysis was lowered to α = 0.01 for subsequent candidate gene selection.The analyses using extreme phenotype groups (both single SNP-trait and haplotype-trait) revealed that the peak-SNPs in the single SNP-trait analysis also appeared in their respective haplotypes. Because the method using extreme groups suffered from a diminished number of soybean genotypes, the analysis using haplotypes had proven to be advantageous by reducing the degree of freedom; hence, the adjusted S.sclerotiorum resistance in soybean using the abovementioned methods are shown in Fig. The results of QTL mapping for p-value thresholds for declaring significant association and for defining significant SNP groups yielded different results. With a single-SNP cut-off p-value of 1 × 10−4, neither method (2) nor method (3) produced significant epistatic interactions. Thus, we used the ALL × ALL epistasis mode by PLINK v1.07 and performed a total of 205,284,045 valid SNP × SNP tests. A total of 220 SNPs were involved in the interaction network  (Table S5), with 112 SNPs in the first three subnetworks. Multiple interactions were found between Gm01 and Gm15 and within Gm06  protein, respectively.With methods (2) and (3), the use of different p-values produced by the all SNP-by-all SNP interaction analysis that were smaller than 1 × 10−8 were considered  and the candidate genes potentially involved in the defense mechanism of soybean to Sclerotinia infection    Fig.  and KEGGml) Fig. , includirelated such as GO:0,004,735 ~ pyrroline-5-carboxylate reductase.”Sclerotinia infection.The phenotypic data identification is very important in genome-wide association studies, because the determination of the phenotypic data of 126 soybean lines used in this study had previously been performed in a greenhouse to ensure equal selective pressure, environmental variability that affects phenotypic variation can be ignored, and that ensure the reliability of phenotypic value, those disease resistance lines such as Karlo RR, PRO 275, and Toma, PS73, Nattosan, and Supra as susceptible ones; however, Williams 82 was in the middle phenotypic value on stem lesion lengths in response to Punadjusted-value = 0.06; Gm1: Punadjusted-value = 0.5189). The other two SNPs (on chromosomes 20 and 19), similar to the four QTL identified by Iquira et al. -ATPase C (V-H + -ATPase C) gene on chromosome 6 and the S-adenosyl-L-methionine-dependent methyltransferases superfamily protein (SAM-Mtases) gene on chromosome 18. The V-H + -ATPase C subunit is the most sensitive subunit in the plant’s response to stress. During periods of stress, the number of C subunits and their mRNA levels drastically and rapidly increase , the enzyme that catalyzes the final step of proline synthesis through single-SNP association analysis, is also corroborated by the fact that remorin is a proline-rich protein. The interaction between plant remorin protein and bacteria has already been investigated , and glycosyltransferases (levansucrase and GDP-fucose protein O-fucosyltransferase) are involved in plant signal transduction. Cellulase synthase, glutamate synthase, P5CR, lycopene β-cyclase, polyamine oxidase, SAM-Mtases, glucose methylcholine oxidoreductase family protein, glutathione-S-transferase, cytochrome P450, NBS-LRR disease resistance protein, and Argonaute family protein probably participate in the disease resistance mechanisms of soybean to ays Zhao .. The roted Wang . GlutathSclerotinia induces cellulose fiber degradation to soluble disaccharide through secretion of cellolase, and then degradation of cellobiose to glucose by β-glucosidase. Experiments show that the pathogenicity of the S.sclerotiorum strain in rape is positively correlated with its cellulase activity  Below is the link to the electronic supplementary material.


Zea mays L.) is mainly distributed in tropical and temperate regions. However, with the changes of the environments, chilling stress has become a significantly abiotic stress affecting seed germination and thus the reproductive and biomass accumulation of maize. Herein, we investigated five seed germination-related phenotypes among 300 inbred lines under low-temperature condition (10 °C). By combining 43,943 single nucleotide polymorphisms (SNPs), a total of 15 significant (P < 2.03 ×  10-6) SNPs were identified to correlate with seed germination under cold stress based on the FarmCPU model in GWAS, among which three loci were repeatedly associated with multiple traits. Ten gene models were closely linked to these three variations, among which Zm00001d010454, Zm00001d010458, Zm00001d010459, and Zm00001d050021 were further verified by candidate gene association study and expression pattern analysis. Importantly, these candidate genes were previously reported to involve plant tolerance to chilling stress and other abiotic stress. Our findings contribute to the understanding of the genetic and molecular mechanisms underlying chilling germination in maize.As one of the major crops, maize ( Zea mays L.) is one of the major food, economic and energy crops globally, which is mainly distributed in tropical and temperate regions. Seed germination rate and seedling emergence rate are closely related to crop yield. With the environmental changes, chilling stress caused by the low temperatures (0−15 °C) has become one of the main abiotic stresses that affects seed germination, the reproductive and biomass accumulation of maize (Maize (of maize . In highof maize . Howeverof maize . ImproviERD (early responsive to dehydration), RAB (responsive to abscisic acid), LTI (low temperature-induced) and KIN (cold-induced). The proteins encoded by these genes, and the osmolytes  can stabilize cell membrane structure, remove excess ROS, and stabilize osmotic pressure, thereby improving plant tolerance to freezing stress (ICE1 (Inducer of CBF Expression1). In Arabidopsis and rice, MAP kinase cascades, MPK3, MPK6 and OsMAPK3 were proven to modulate ICE1 stability and thereby regulate the response of plants to chilling stress , and protein instability . A numbeg stress . The MAPg stress .The genetic and molecular mechanisms on seed germination traits under chilling stress are still obscure, although a few potential cold-tolerant genes and QTL have been identified in maize . In this2O2 for 30 min, washed with distilled water three times, incubated in saturated CaSO4⋅2H2O for 6 h, and finally washed by distilled water three times. A total of 30 seeds were germinated in a petri dish (130*130 mm) that was beforehand placed with a piece of sterilized wet filter paper on the bottom. Three replicates were conducted for each line. Following the ISTA protocol, seed germination experiment was conducted in a cultivation box  under 16 h/ 8 h, light/dark at 10 °C  germination rate  at 5 d (FG); (2) germination ratio at 10 d (TG); (3) root length at 10 d (RL); (4) shoot length at 10 d (SL) and (5) ratio of root length to shoot length (RRS).https://www.ibm.com/support/pages/node/230551) was used to process the phenotypic data, including the range, mean, and standard deviation. SAS v 9.3 was used to estimate the broad heritability. The ‘PerformanceAnalytics’ package in R software was employed to analyze the phenotypic correlations. The analysis of variance (ANOVA) was performed in R software using the package ‘lme4’. The broad-sense heritability (HB2) estimations for each trait was calculated as following: HB2 = σG2/ σP2 (σG2 = ((MSG –MSE)/n) and σP2 = ((MSG –MSE)/n + MES) are estimates of the genetic and phenotypic, respectively, where MSG and MSE represent the mean square for genotypes and the mean square error, respectively, n is the number of independent replications.For each trait, SPSS statistics version 20.0 software   was set as a significance threshold. Herein, Meff_G = 24,630, and thus, the P-valu e = 0.05∕24, 630 = 2.03 ×10−6. Based on the observation of P-values in different models under Q −Q diagrams (The whole-genome SNPs of the 300 accessions were corresponded to the previous study . The SNPMeff_G)  and multMeff_G)  to calcudiagrams , the mosdiagrams , thus, “P <  2.03 ×10−6) were therefore functionally annotated based on B73 AGPv4 genome (https://www.maizegdb.org/genome/assembly/Zm-B73-REFERENCE-GRAMENE-4.0). Among them, the gene models that were simultaneously associated with multiple chilling-germination traits were considered as the prioritized candidate genes in this study. To identify the intragenic variations associated with chilling-germination, sixty-three lines from the association pool were randomly selected for PCR-amplification of these candidate genes.. The primers of PCR amplification are shown in P < 0.05 set as the significance level  was defined as a favorable allele. For each significant SNP, the ratio of elite allele was calculated by using the formula as follows: RE (%) = N (E)/ N (T) ×100%. Herein, RE is the ratio of elite allele, N (E) and N (T) represent the number of lines containing elite alleles and the total number of lines, respectively. We employed the ‘pheatmap’ package in the R software  to displ® Plus All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge) . qRT-PCR was performed using an Applied Biosystems™ 7500  with NovoStart® SYBR qPCR SuperMix Plus . To standardize the results, ZmActin1 (Zm00001d010159) was used to serve as a control  and SCL326 (CC/CG/GG/TC) were selected for qRT-PCR . In detail, maize seeds were germinated under 16 h/ 8 h, light/dark at 10 °C and the seed samples were collected at 0 h, 12 h, 24 h, 72 h, 120 h, with the normal condition  as control. Total RNA was isolated from these samples using TRIzol  according to the manufacturer’s instructions. For each sample, 1.5 µg of RNA was reversely transcribed to cDNA with NovoScript control  . Three  control .P < 0.01) positive correlations were observed between each pair of FG, TG, RL, and SL, among which TG and FG showed the strongest correlation with the correlation coefficient of 0.81. However, RRS displayed a significantly (P < 0.01) negative correlation with each of the other traits except SL  SNPs were identified for these investigated traits (Zm00001d050021) was detected, which encodes an abscisic acid 8′-hydroxylase3 (abh3). A total of 4 candidate genes  were identified for PZE-107018981, among which Zm00001d019116 and Zm00001d019123 were annotated as ethylene-responsive transcription factor RAP2-11, and survival protein SurE-like phosphatase/nucleotidase, respectively, whereas Zm00001d019117 and Zm00001d019122 both encode unknown proteins. PZE-108063385 was associated with 5 gene models, of which Zm00001d010454, Zm00001d010458, and Zm00001d010459 were individually annotated as mannosyl-oligosaccharide 12-alpha-mannosidase MNS3, protein kinase superfamily protein, and putative CBL-interacting protein kinase family protein, respectively. Zm00001d010455 and Zm00001d010456 were not functionally annotated based on the current B73 genome information.In this study, we employed the FarmCPU model to detect the associations between the SNPs and the germination traits under chilling stress . A totald traits . Among td traits . For thed traits . For PZEThirty inbred lines that have been used as the parents of commercial varieties were included in the association panel, which enables us to evaluate the utilization of the superior alleles in maize breeding programs . In the P < 0.05) correlate with chilling-germination, involving two (SNP-4-60282052 and SNP-4-60288956) in Zm00001d050021, two (SNP-8-115913550 and SNP-8-115914606) in Zm00001d010454, two (SNP-8-116147619 and SNP-8-116148568) in Zm00001d010458, one (SNP-7-17937736) in Zm00001d019123 and one (SNP-8-116262789) in Zm00001d010459.For the 6 functionally annotated candidate genes, we further identified the intragenic variations that influenced seed germination under chilling stress by candidate gene association analysis. A total of 70 variations (67 SNPs and 3 InDels) were found within the gene bodies and their promoter regions (the upstream 2000bp) of the 6 genes . Eight SZm00001d010458, respectively, among which SNP-8-116147619 was associated with SL and RL and SNP-8-116148568 was related to all the five traits. Two haplotypes  formed in these 63 lines according to the two SNPs, and all the phenotypic values of Hap1 were significantly lower than those of Hap2  and the other groups including the alleles CC (Hap2) and TC (Hap3), respectively. T-test indicated that these lines with Hap3 had significantly (P < 0.05) larger SL and RL than those with Hap1 and Hap2 , Hap2 (AC). Hap2 showed a significantly larger SL value than Hap3  smaller in group I than in group II  and SCL326 (a chilling-tolerant line) during chilling-germination . Under control condition, the expression of t stages . Under Condition . In the to 100 h . Interesn SCL127 . These oB2 of these investigated traits ranged from 86.96%-96.56%  family, its homologous gene OsCIPK31 is involved in germination and seedling growth under abiotic stress conditions in rice were repeatedly associated with multiple chilling-germination traits and were thereby considered as the prioritized candidate genes mediating chilling germination in maize. More intriguingly, the intragenic variations from 4 (66.67%) candidate genes were further proven to influence these chilling-germination traits by candidate gene association studies and expression pattern analysis. Among them, ic cells , which hic cells . Zm00001d stress . The encs in rice. In wheaolerance . Similared genes . Zm00001bidopsis . It was bidopsis . In wheabidopsis . Taken t10.7717/peerj.11707/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.11707/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.11707/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.11707/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.11707/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.11707/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.11707/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.11707/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.11707/supp-9Supplemental Information 9P < 0.05) associated SNPs. The structure of the gene Zm00001d050021 is displayed in the middle. The extrons are represented by filled dark boxes, the promoter and 5′-UTR are shown by the left dark line, the introns were denoted by the dark lines between the filled dark boxes, and the 3′-UTR are displayed by the right dark line. The bottom image shows the pairwise LDs between the markers. (B) Comparison of chilling-germination phenotypic performance between three haplotypes. * and *** represent the significant levels of P < 0.05 and P < 0.001, respectively. Hap, haplotypes; FG, Germination rate at 5 d; TG, Germination rate at 10 d; RL, Root length at 10 d; SL, Shoot length at 10 d; RRS, Ratio of root length to shoot length.(A) Dark dots above the dotted line display the significantly  associated SNPs. The structure of the gene Zm00001d010454 is displayed in the middle. The extrons are represented by filled dark boxes, the promoter and 5′-UTR are shown by the left dark line, the introns were denoted by the dark lines between the filled dark boxes, and the 3′-UTR are displayed by the right dark line. The bottom image shows the pairwise LDs between the markers. (B) Comparison of chilling-germination phenotypic performance between three haplotypes. ** represent the significant level of P < 0.05. Hap, haplotypes; FG, Germination rate at 5 d; TG, Germination rate at 10 d; RL, Root length at 10 d; SL, Shoot length at 10 d; RRS, Ratio of root length to shoot length.(A) Dark dots above the dotted line display the significantly  SNP. The structure of the gene Zm00001d010459 is displayed in the middle. The extrons are represented by filled dark boxes, the promoter and 5′-UTR are shown by the left dark line, the introns were denoted by the dark lines between the filled dark boxes, and the 3′-UTR are displayed by the right dark line. The bottom image shows the pairwise LDs between the markers. (B) Comparison of chilling-germination phenotypic performance between two haplotypes. *** represent the significant level of P < 0.001. Hap, haplotypes; FG, Germination rate at 5 d; TG, Germination rate at 10 d; RL, Root length at 10 d; SL, Shoot length at 10 d; RRS, Ratio of root length to shoot length.(A) Dark dot above the dotted line shows significant ( Click here for additional data file.10.7717/peerj.11707/supp-12Supplemental Information 12Click here for additional data file.10.7717/peerj.11707/supp-13Supplemental Information 13Click here for additional data file.10.7717/peerj.11707/supp-14Supplemental Information 14Click here for additional data file.10.7717/peerj.11707/supp-15Supplemental Information 15Click here for additional data file.


Plant variety identification is the one most important of agricultural systems. Development of DNA marker profiles of released varieties to compare with candidate variety or future variety is required. However, strictly speaking, scientists did not use most existing variety identification techniques for “identification” but for “distinction of a limited number of cultivars,” of which generalization ability always not be well estimated. Because many varieties have similar genetic backgrounds, even some essentially derived varieties (EDVs) are involved, which brings difficulties for identification and breeding progress. A fast, accurate variety identification method, which also has good performance on EDV determination, needs to be developed.In this study, with the strategy of “Divide and Conquer,” a variety identification method Conditional Random Selection (CRS) method based on SNP of the whole genome of 3024 rice varieties was developed and be applied in essentially derived variety (EDV) identification of rice. CRS is a fast, efficient, and automated variety identification method. Meanwhile, in practical, with the optimal threshold of identity score searched in this study, the set of SNP (including 390 SNPs) showed optimal performance on EDV and non-EDV identification in two independent testing datasets.This approach first selected a minimal set of SNPs to discriminate non-EDVs in the 3000 Rice Genome Project, then united several simplified SNP sets to improve its generalization ability for EDV and non-EDV identification in testing datasets. The results suggested that the CRS method outperformed traditional feature selection methods. Furthermore, it provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties and be useful for crop breeding.The online version contains supplementary material available at 10.1186/s12859-022-04562-9. Fast, accurate, and efficient varieties or cultivars identification and characterization are essential for crop varieties' breeding, registration process, seed production, trade, inspection, and patents protection . As a stThe traditional species or variety identification approach involves observing and recording morphological characters , 6. SincGossypium hirsutum L.) germplasm using the CottonSNP63K Array and screened out SNPs to efficiently discern differences among cultivars. In 2018, fifty core markers from 2.54 million SNPs obtained by aligning resequencing data of cabbage inbred lines were selected and used to establish a DNA fingerprint database of 59 cabbage varieties. An artificially mixed population validated the core SNP markers. The SNP fingerprinting constructed with core markers could identify the cabbage variety's distinctness and authenticity [In 2010, Jung et al.  first reenticity . The manRecently, with the development of sequencing technology, high-throughput SNPs have been widely generated in lots of crops . Zhang eThe objectives of this study were to (1) develop a fast, cost-effective screening SNP markers procedure for varieties identification without manual operation process; (2) construct a rice fingerprint map and apply it for EDV and non-EDV discrimination with independent test datasets; (3) code the method with R language for easy application.https://snp-seek.irri.org/), including ~ 4.04 million core SNPs of 3024 rice accessions from 89 different countries or regions [http://www.ncbi.nlm.nih.gov/, update to Nov. 6th, 2020). Then 99,253 SNP markers located in the genes' coding region (cSNPs) were extracted and used following rice fingerprint mapping modeling. The training dataset contains 3024 varieties with 99,253 SNPs, denoted as Train_Data .There are three rice SNP genotype datasets involved in this study. We downloaded the first dataset from the 3000 Rice Genome Project ( regions . The genhttps://snp-seek.irri.org/). The same 99, 253 cSNPs as in Train_Data were extracted to form the first independent test dataset, denoted as Test_Data1 .The second dataset contains 1568 rice varieties genotyped using a genome-wide high-density rice array (HDRA). Wang et al.  imputed The third dataset contains 401 rice varieties. Their seeds were obtained from the China rice breeders and planted in Wuhan in 2013. On average, these materials were sequenced on the Illumina HiSeq2500 platform at 11 × genome coverage. By quality control, 1,894,012 high quality SNPs with minor allele frequency (MAF) > 5% and missing rate < 20% were obtained. Among them, there are 319,624 SNPs also detected in 3K-RGP, respectively. The 85, 860 cSNPs that were also identified in Train_Data were extracted to form the second independent test dataset, denoted as Test_Data2 .The composition of the training dataset and two independent test datasets are shown in Fig. The software R language was used to calculate each cSNP marker's polymorphism information content (PIC) in the Train_Data. The calculation formula isP site i , 27.Identity scores (IS) of SNPs were used to calculate the genomic similarity of varieties , which aIn this formula, Dsi refers to the distance value of sample allele to the reference allele at a given SNP site i; Dsi is 0, 0.5, and 1, separately for the homozygous allele that same as the reference allele, heterozygous allele, and homozygous allele that different from the reference allele at given SNP site i. N is the total number of SNPs within a 20-kb window. In this study, taking Oryza Sativa L. cv. Nipponbare as reference genome, IS between every two varieties in the Train_Data were calculated with the 404 k core SNPs. According to Longping Yuan's suggestion, IS = 97.5% was set as the threshold for EDVs' determination of rice varieties with SNPs on the whole genome . If two This study proposed a new method, Conditional Random Selection (CRS), for fast and accurate identifying varieties. The flowchart of the CRS method was shown in Fig. Stage 1: SNPs Preliminary screening. At first, randomly selected three SNPs to separate accessions by constructing specific haplotypes. Assuming that SNP2, SNP3 and SNP4 were selected in the first round. The genotype combinations of the three SNPs were different from each other among varieties V1, V2, and V3, which denoted as specific haplotypes. In contrast, the situation is different in V4–V7. The three SNPs' genotype combination for V4 and V5 was , and for V6 and V7 were , referred to as a non-specific haplotype  identification with the CRS method. Since there were multiple random selection procedures involved in the CRS method, multiple simplified SNP combination sets can distinguish all distinct varieties in Train_Data in theory. Then the CRS method was applied five times to obtain five simplified SNP combination sets without duplicating SNP.Uniting multiple sets of SNP combinations can improve independent identification reliability and fault tolerance. Still, on the other hand, to save the identification cost as much as possible, it is necessary to determine the optimal number set of SNP combinations. It should contain a relatively small size of markers and can well distinguish EDV and non-EDV in practical application.Compared with the 404 k SNPs, the number of selected SNPs with CRS has been dramatically reduced. There would result in a high false-positive if we still use IS = 97.5% as the threshold to judge whether the variety in Train_Data was EDV or not. Testing multiple IS value levels is necessary to determine the optimal IS threshold for selected SNPs in EDV and non-EDV identification. In this study, one to four SNP combination sets from the above five simplified SNP combination sets were selected to form a union SNP combination set. The threshold of IS 95.5% to 99.5% by the step of 1% was set to identify EDV for the optimal threshold of IS determination. At each threshold of IS, all varieties can be partitioned into four categories, which are quantified as the numbers of true positives (TP), true negatives (TN), false positives (FP), or false negatives (FN). By comparing the results with EDV identified with 404 k SNPs, the parameters Precision (PR) (formula ), and F-Meanwhile, the reference methods Random Selection  and high PIC selection  were also applicated for results comparison. After that, we calculated the optimal union SNP combination's PIC value and their distribution on the chromosome with R language.To test the varieties' identification ability of the three methods CRS, RS, and HPS, with the optimal IS threshold obtained from the above analysis, we separately used the optimal union SNP combination set, the same amount of SNPs selected by RS or HPS, for independent datasets' EDV identification. Then by comparing these results with EDV and non-EDV discriminated with 404 k SNPs under the threshold of IS = 97.5%, the PR, RC, and F1 of CRS, RS, and HPS methods were calculated. The RS and HPS methods were repeated applicated five times, and the highest PR, RC, and F1 of them were kept for comparing with that of the CRS. Test_Data1 and Test_Data2 were separately used for independent external varieties and internal varieties identification.https://github.com/KnessKness/Li_1).The CRS method was compiled with the R language and could be downloaded free on Github (www.barcode-generator.org) [We used an online tool (available at tor.org)  to generThe 99, 253 cSNPs in the Train_Data were uniformly distributed on 12 chromosomes Fig. B. The PIBy application of the CRS method five times, at last, we obtained five simplified SNP combination sets separately involving 118, 136, 121, 116, and 136 SNPs, without any duplicate SNP among these SNP combination sets. In order to improve the generalization and robustness of a single SNP combination set, one to four simplified SNP combination sets were united to determine EDV with different IS levels Fig. . The EDVEDV determination performance with CRS, RS, and HPS methods is shown in Fig. Meanwhile, the PIC value of the 390 SNPs with the best performance in EDV determination selected with the CRS method ranged from 0.096 to 0.652. The 390 SNPs were distributed on 12 chromosomes, and most of them with relatively low PIC value Fig. , less thwww.barcode-generator.org).At last, the SNPs of the union of three simplified SNP combination sets (118 + 136 + 136 = 390) were selected for rice fingerprint map construction of 3024 varieties in Train_Data. The united SNP combination sets of 3024 varieties were listed in Additional file By comparing the varieties in Test_Data1 and Train_Data with 404 k SNPs, there were 263 varieties in Test_Data1 determined as EDV of the varieties in Train_Data at the threshold of IS = 97.5%. While with the 390 SNPs selected with the CRS method, 129 varieties were determined as EDV at the threshold of IS = 98.5%. The precision and recall of the EDV determination with the CRS method were 83.72% and 41.06%, respectively. While there were 21 and 41 EDVs determined by HPS and RS method, respectively. The order of the F-score of the three methods is CRS (56.5%) > RS (27.0%) > HPS (14.8%)  > RS (99.9%) > CRS (99.5%), CRS (99.2%) > RS (95.2%) > HPS (81.4%), CRS (99.3%) > RS (97.5%) > HPS (89.8%), respectively  or tri-allelic (including hybrid lines), in theory, if there were n distinct varieties in the training dataset, at least p (2p = n or 3p = n) makers needed for distinguishing all varieties. For example, the combination of at least ten bi-allelic SNPs can discriminate 3024 rice varieties in the ideal situation. However, because of the enormous SNP markers contained in the genome sequencing data (m), selecting p markers from m is too large to obtain the optimal SNP combination set. Except for this big challenge, accurate identifying varieties in independent tests is another crucial challenge for crop variety identification, 28. HowWith the development and wide application of machine learning methods , 33, sevIn this study, we proposed the Conditional Random Selection (CRS) method for effectively identifying rice variety. This method screened out a few SNPs evenly distributed in the coding region of the whole genome of rice varieties in the 3 K rice project with the strategy of "divide and conquer" could discriminate all distinct varieties successfully, which greatly save the cost of variety distinction in the Training set. Compared to the SNP located in the non-coding region, the SNP in the coding region (cSNP) are more stable and trend related to genes and traits of crops, which will facilitate the varieties discrimination . MeanwhiThere were three advantages of the CRS method compared to traditional methods. First, we chose a SNP combination set that can distinguish all training set samples as much as possible. Previous studies often kept several high polymorphic SNPs and then clustered the samples to check the resolution of these SNPs. If some samples could not be distinguished, then find out those SNPs with the highest resolution for particular sample pairs and add them to the original list of SNPs to improve the resolution . HoweverSecond, by uniting multiple sets of simplified SNP combinations and searching the optimal threshold of IS, the set with a suitable number of SNPs showed sound performance on variety identification (EDVs and non-EDVs determination) in the training set. IS of SNP on the whole genome was a good index for estimating the genomic similarity . CompareThird, the SNPs selected with the CRS method showed good performance on variety identification in the testing set. As well-known as that, the constancy of a variety is provided by a set of genes specific to it, while variations of genes make the variety is different from generation to generation. In other words, the constancy of a variety is not stable. Thus, the SNP combinations even performed well on distinguishing EDV and non-EDV in the Training dataset but not necessarily well on identifying EDV and non-EDV in the testing set. With the CRS method, we initially retained the polymorphic markers in EDVs, then screened out the optimal SNP combination to distinguish non-EDVs in the training set. This strategy possibly significantly reduced the probability of mis-determination on EDVs and non-EDVs in the testing set.Although CRS methods showed well performance on variety identification in both Training and Testing datasets, it still could not grant 100% of identification accuracy in the testing set due to the limitation of sample size in the training set. As more and more varieties are developed, the need to properly identify and categorize them increases. Besides that, according to the International Union for the Protection of New Varieties of Plants (UPOV) convention, a variety is deemed to be essentially derived from an initial variety if it is (1) predominantly derived and (2) clearly distinguishable from the initial variety and (3) genetically conform to the initial variety (UPOV1991) . AlthougIn this study, we developed an effective variety identification method. We first selected a minimal set of SNPs to discriminate non-EDVs in the 3000 Rice Genome Project, then united several sets of simplified SNP combinations to improve its generalization ability for EDV and non-EDV identification in testing datasets. The method outperformed traditional feature selection methods. Furthermore, it provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties and be useful for crop breeding.Additional file 1: Table S1. The detail information of united SNP combinations.Additional file 2: Table S2. The information of the united SNP combination sets of 3024 varieties.Additional file 3: Table S3. Metadata information for the 3K RG accessions.


Next-generation sequencing is a promising approach for the detection of causal variants within previously identified quantitative trait loci. Because of the costs of re-sequencing experiments, this application is currently mainly restricted to subsets of animals from already genotyped populations. Imputation from a lower to a higher marker density could represent a useful complementary approach. An analysis of the literature shows that several strategies are available to select animals for re-sequencing. This study demonstrates an animal selection workflow under practical conditions. Our approach considers different data sources and limited resources such as budget and availability of sampling material. The workflow combines previously described approaches and makes use of genotype and pedigree information from a Landrace and Large White population. Genotypes were phased and haplotypes were accurately estimated with AlphaPhase. Then, AlphaSeqOpt was used to optimize selection of animals for re-sequencing, reflecting the existing diversity of haplotypes. AlphaSeqOpt and ENDOG were used to select individuals based on pedigree information and by taking into account key animals that represent the genetic diversity of the populations. After the best selection criteria were determined, a subset of 57 animals was selected for subsequent re-sequencing. In order to evaluate and assess the advantage of this procedure, imputation accuracy was assessed by setting a set of single nucleotide polymorphism (SNP) chip genotypes to missing. Accuracy values were compared to those of alternative selection scenarios and the results showed the clear benefits of a targeted selection within this practical-driven approach. Especially imputation of low-frequency markers benefits from the combined approach described here. Accuracy was increased by up to 12% compared to a randomized or exclusively haplotype-based selection of sequencing candidates. Next-generation sequencing (NGS) experiments provide data that are of increasing importance in animal genetics with the potential to improve knowledge of the genetic background of complex traits with societal and economical interest . In addUntil now, many of the genetic analyses in pigs are based on single nucleotide polymorphism (SNP) genotype data from chip arrays e.g., , 4). How. How4]).Available NGS data are used to enhance information content of routinely phenotyped and genotyped animals, especially in terms of imputation from a lower to a higher marker density. An efficient approach is to select a highly informative and representative subset of animals for re-sequencing and to use it as reference panel for imputation of a population that is genotyped at a lower marker density . SeveraIn a simulated data set, five approaches to select candidates for re-sequencing were discussed by Druet et al. . ResultsNGS data and imputed whole-genome sequence data are assumed to include causative variants and to enable their detection e.g., , 14, 15), 1514, 1The aim of this study was to evaluate alternative strategies for the choice of animals for re-sequencing in the maternal Landrace (LR) and Large White (LW) pig lines. Selection was conducted by applying several theoretical concepts and approaches under practical conditions. Selection steps were built on existing pedigree and real SNP genotype information within the pigFit project which focuses on piglet survival and immunocompetence , 21. PerThe aim of the project ’pigFit-Molecular genetic and immunological analysis of survival and postnatal growth of piglets’ is to identify biological relevant regions associated with survivability, health and immune traits in piglets and growing pigs. Within this project, phenotyping and genotyping were performed in the two maternal pig lines LR and LW. Blood and tissue samples were collected from piglets and their biological dams. SNP genotype data, based on the PorcineSNP60v2 BeadChip , were used to conduct the animal selection process. In total, SNP genotype information for 944 LR and 800 LW animals that were genetically linked to the pigFit data set was available. Before starting the analysis, breed-specific quality control was performed for SNPs on autosomes. Markers showing a high linkage disequilibrium , as recommended for a 60k SNP density [Haplotype-based selection aims at maximizing the diversity of haplotypes with or without weighting the haplotype frequency . Our hap density . Hickey  density  demonstrIn general, the aim of the H approach is to cover the maximum proportion of haplotype diversity within the population. The results obtained with simulated data led to the assumption that these animals will enable accurate imputation from chip genotype data to sequence data .To carry out the selection step, we used the AlphaSeqOpt-Method 1 . In a nuGeneralCoreLength used in the AlphaPhase software. Since the H approach aimed at covering a large amount of haplotypes, the threshold for haplotype frequency in the population was set to 0 to take all the haplotypes into account, regardless of their frequency. In both lines, the availability of sample material was a key limiting factor, particularly in previous generations. Thus, the target number of animals to select for re-sequencing was set to 100. Animals were added to the candidate set in an iterative manner. Although the AlphaSeqOpt software includes an additional approach, Method 2 [The settings for the AlphaSeqOpt software were modified according to the porcine genome structure with 18 autosomes and to the chip structure with a varying number of SNPs per chromosome. The call rate of SNPs and individuals had to exceed 90% to be included in the construction of the haplotype library. Information on the number of cores and the core length was adapted to the Method 2 , this meOptimisationMethod was set to Pedigree. A subset of 100 candidates from the genotyped animals was selected based on pedigree relationships [One of the strategies discussed by Druet et al.  maximizeionships , 25.Ancestors (P2) were identified as the animals that best explained the genetic variability of both populations considering their unbalanced use in breeding [The ENDOG v.4.8 software  was usedbreeding , 27.and at least by one of the P approaches Step 1: Animals that were selected by the H approach Step 2: Animals that were selected in both P approaches and both parents, sire and dam, were identified as important ancestors (P2)Step 3: Animals that were selected by the H approach and one parent was identified as an important ancestor (P2)Step 4: Animals that were selected by the H approach As the starting point for the C approach, all animals from the H, P1, and P2 approaches were taken into account to select animals for re-sequencing. According to the available budget, the target number of animals was set to 57, divided into 28 LR and 29 LW animals. The C approach is practical-driven and aims at optimizing imputation accuracy of common and low-frequency variants by combining haplotype- and pedigree-based criteria. The findings shown by Druet et al.  providedcombined sample) was compared to the set of the top candidates from the H approach (haplotype sample) and a set of randomly selected animals in a 30-fold repetition (random sample). Genotype data in the reference panel contained information on all markers that passed quality control .Evaluation of alternative selection strategies was performed based on the 60k SNP genotype data in a 0, 1, 2 coding as obtained from the PorcineSNP60v2 BeadChip . The workflow for the validation is shown in Fig. 1) or half of the number of SNPs (LD2) to missing. Imputation was performed from LD to HD for each of the reference panels combined sample, haplotype sample, and random sample, using FImpute v3 [The remaining animals  were allocated to the target panel. The assessed imputation scenarios are shown in Fig. mpute v3 . ImputatThe H approach resulted in 100 candidates per breed that maximized the diversity of haplotypes across the population. As shown in Fig. The P1 and H approaches are based on the same animals originating from the most recent generations. Within the P1 approach, generations 3 and 4 (relative to the most recent generation being generation 1) comprised about two thirds of the 100 candidates in each breed see Fig. .The P2 approach included 17 generations of pedigree for both populations. In total, 148 LR and 117 LW animals were selected based on pedigree relationships. Selected LR animals were from 13 of the 17 generations, with the largest number from generations 6 to 8  and all MAF classes . Imputation using the random sample  and haplotype sample  as reference panel had a lower mean accuracy.Imputation accuracy was calculated to evaluate and quantify the performance of the selected animals for re-sequencing in a practical data set based on 60k SNP genotypes. The detailed results are in Table Assessment of the imputation accuracy was performed by imputing masked genotypes from the PorcineSNP60v2 BeadChip  in a 20-fold repetition and reflect the accuracy of imputation to a HD density in these populations. However, this study cannot make a conclusive statement regarding the accuracy for imputation to sequence data.1) led to higher accuracies compared to imputation of 50% of the SNP set (LD2). Exceptions were found for the random sample in LR and the MAF class >5% in the combined sample in LR.Overall, imputation of 10,000 masked SNPs . This may be due to the prioritization of haplotype diversity in the selection process for the combined sample and to the imputation to 60k level. However, in terms of low-frequency variants, imputation to the sequence level remains a major challenge, especially with a small number of sequenced animals.The strategy for selecting animals is very important, especially to enable an accurate imputation of low-frequency variants . As expeIn addition, our study observed differences in imputation accuracy between LR and LW. For low-frequency (1–3%) and higher-frequency markers (>5%), imputation performance was better in LW. In addition to breed-specific genetic aspects, one possible explanation is the larger number of animals within the reference panel for LW. It is important to underline that both nucleus populations are genetically distinct and differ in their breeding objectives to a considerable extent. Moreover, genotyped animals were assumed to be representative samples of both nucleus populations.combined sample as reference panel for LR .Compared to random selection, targeted selection approaches achieved up to 12.4% higher imputation accuracy using the In general, imputation accuracy varied a lot between replicates of randomly selected reference panels. This emphasizes the importance of the structure of reference panels for re-sequencing experiments, especially when resources are limited . Our res1 in LR. Nevertheless, an increase of about 2.4 to 8.1% was found with low-frequency markers of the C over the H approach.Assuming the availability of sample material from all prioritized candidates, the H approach was tested as an alternative approach, by considering haplotype-based criteria, only. It should be noted that the C approach is a heuristic, practical-driven approach and its results differ slightly from those using exclusively the H approach. For markers with a higher MAF (>5%), we observed no improvement in imputation accuracy of the C approach compared to the H approach. The latter achieved an even slightly higher accuracy for LDTargeted selection using the C approach with haplotype- and pedigree-based selection criteria, as described in this paper, shows that it is superior compared to alternative approaches to select animals for a re-sequencing experiment using data from the LR and LW populations investigated here.The respective characteristics of the LR and LW populations are common for commercial maternal nucleus breeding populations. Moreover, we had to consider the practical constraints  within our C approach. Against this background, our practical-driven approach and its results could be considered for similar situations in pig breeding using the data of nucleus herds.combined sample will be used to supplement the pigFit project in future investigations. Making use of highly informative genotype data, genome-wide association studies (GWAS) that use highly informative genotypes are a highly efficient approach to map loci and to detect QTL and candidate genes for complex traits . This as .Targeted animal selection for re-sequencing is recommended. Taking into account all available information on population structure, when selecting animals for re-sequencing, including pedigree information and additional SNP genotype information, increased the imputation accuracy of masked SNP chip genotypes, measured as the correlation between true and imputed genotypes, by an average of around 7.3 to 12.4% for low-frequency markers and up to 5.3% for markers with a MAF >5%. The combined use of pedigree- and genotype-based approaches in practical data sets is particularly suitable for improving imputation accuracies for low-frequency markers.


Nonetheless, frequent testing of ABHRs should be routinely conducted to reduce the risk of consumer exposure to non-compliant ABHRs.The COVID-19 pandemic led to panic-buying of alcohol-based hand rubs (ABHRs). In response, governmental agencies  permitted the sale of ABHRs formulated with “technical-grade” ethanol to alleviate the growing demand. Technical-grade ethanol contains elevated concentrations of impurities , which may exhibit dose-dependent toxicity. In this study, a rapid solvent extraction was employed to analyze gelled ABHRs via gas chromatography with flame ionization detection. In total, 26 liquid and 16 gelled ABHRs were analyzed for nine common impurities to determine compliance with Health Canada interim guidelines. Of 42 samples analyzed, 11 ABHRs appear to be non-compliant with interim Health Canada guidelines. Non-compliant ABHRs exhibited elevated concentrations of acetaldehyde, with a maximal concentration observed of 251 ± 10 µL L Community transmission of infectious disease remains a significant concern , especian-propanol), and presence of additives  [v/v) have superior antibacterial properties [Alcohol  can be produced by fermentation of biomass or synthetically via direct or indirect ethylene hydration  ,4. Howev., H2O2)  are critoperties  with higoperties . Concentoperties , includioperties .Alcohol-based hand rubs (ABHRs) can be available in liquid, gel, or foamable formats and are the first choice for hand hygiene in healthcare settings as they afford more effective antimicrobial activity than antiseptic soaps ,9, improTypically, raw materials destined for the pharmaceutical and food industry must meet specific standards for composition, such as those outlined in monographs available through the Food Chemicals Codex (FCC) and the United States Pharmacopeia (USP) ,24. CompAlcohol standards  are char−1), was used as the extraction solvent for gelled ABHRs. Gelled ABHRs were vortexed for 20 sec with this solution at a ratio of 1:1 , in 2 mL VWR microcentrifuge tubes . The samples were then centrifuged at 10,000 rpm  for 5 min at room temperature to facilitate phase separation. The supernatant was then filtered using VWR 0.2 µm polytetrafluoroethylene (PTFE) syringe filters  and spiked with 3-pentanol (10 µL·L−1), then analyzed directly via GC-FID. Liquid ABHRs were filtered with 0.2 µm PTFE syringe filters , spiked with 4-methyl-2-pentanol (10 µL·L−1), and analyzed directly via GC-FID with no prior treatment. Each sample was measured in triplicate, with the exception of sample 26 (due to inadequate sample amount). Results are measured as mean ± standard deviation.A standard spiking solution of dichloromethane (DCM) containing the recovery standard, 4-methyl-2-pentanol  at 140 °C and 1 µL of sample was injected. The syringe was thoroughly washed with acetonitrile between injections to avoid cross-contamination. A five-point calibration curve was derived for all the impurities listed in 2 > 0.99 and an RSD ≤ 25%.Analyses of ABHR were conducted using an Agilent 7890 GC  equipped with a flame ionization detector (FID). Samples were injected using an Agilent 7693A  automatic liquid sampler with a 10 µL Hamilton cemented needle syringe . Volatile organic compounds were separated on a 30-m Agilent J&W DB-624 Ultra Inert column . The FID was operated at 250 °C with flow rates of 400 mL minThe unprecedented impact of the COVID-19 pandemic has led to a global crisis resulting in widespread panic buying of essential items  includin−1 methanol, ≤2 µL·L−1 benzene, and ≤10 µL·L−1 combined acetal and acetaldehyde [−1 [Typically, ABHRs are formulated using pharmaceutical-grade ethanol, examined against the USP ethanol monograph. This monograph specifically requires quantification of methanol, benzene, acetal , and acetaldehyde in addition to a sum of all other organic compounds detected by GC. Traditionally benzene has been used as an entrainer in azeotropic distillation systems for ethanol dewatering , howeveraldehyde , and thehyde [−1 .Extraction of compounds of interest from gelled ABHRs using dichloromethane was successful, achieving a mean recovery of 105.7% ± 9.1%. However, 1-butanol co-eluted with the internal standard in many of the gelled samples. Therefore, to obtain an approximate concentration for 1-butanol, the average area of the internal standard was subtracted from the total peak area at the retention time of 5 min. An ethanol blank containing 10 ppm of 3-pentanol was injected every 10 samples to compensate for instrument drift.In total, 31 of the 42 samples complied with the interim guidelines outlined by Health Canada . The rem−1 in accordance with USP criteria, ranging from below limited of detection (LOD) to 47 ± 2 µL L−1  (v/v). The current formulations of hand sanitizer suggested by the World Health Organization are either 80% (v/v) of ethanol or 75% (v/v) isopropanol mixed with 1.45% glycerol (v/v) [Many of the ABHRs in this study exhibited the presence of humectants . Humectants are typically added to ABHRs as a moisturizing agent and to encourage hand hydration . In the v/v) . Howeverol (v/v) . Most ofol (v/v) . Similarol (v/v) . Neverthv/v) to 90 ± 3% (v/v), although sample 26 had an ethanol content of 50% , while four ABHRs contained an elevated mixture of ethanol and isopropanol as the active ingredient . Finally, the remaining six ABHRs  appeared to have minimal amounts of this compound. Ethanol is the most effective alcohol against virus, whereas isopropanol is considered to be a better bactericidal alcohol . TherefoThe permittance of technical-grade ethanol in ABHRs enabled small, non-traditional businesses to manufacture ABHRs to meet market demands. However, the new manufacturers implemented non-conventional packaging for ABHRs . For exaFinally, analyses of ABHRs can be further complicated by the presence of additive ingredients, such as fragrances. Fragrances may be mixed into ABHR products as a possible attempt to mask odours related to fusel alcohols , many of the ABHRs appear to meet Health Canada’s interim guidelines on technical-grade ethanol, with only six of the ABHRs studied meeting USP limitations on organic impurities in ethanol.


JARID2, PDIA6, FLRT2 and DICER1). The results of this study provide a theoretical basis for subsequent molecular breeding.Sow reproductive performance is crucial in the entire pig industry. However, the heritability of reproductive traits is slowthat it is difficult to make great genetic progress by traditional breeding methods. Therefore, in this study, we performed genome-wide association analyses of litter traits in French White sows to explore genes related to sow reproductive traits and we finally detected a notable region (10.72–11.06 Mb on chromosome 7) and four promising genes , number born alive (NBA), number healthy piglets (NHP) and litter weight born alive (LWB). To identify genomic regions and genes for these traits, we used two approaches: a single-locus genome-wide association study (GWAS) and a single-step GWAS (ssGWAS). Overall, a total of five SNPs and 36 genomic regions were identified by single-locus GWAS and ssGWAS, respectively. Notably, fourof all five significant SNPs were located in 10.72–11.06 Mb on chromosome 7, were also identified by ssGWAS. These regions explained the highest or second highest genetic variance in the TNB, NBA and NHP traits and harbor the protein coding gene  The reproductive capacity of sows plays an important role in the composition of the economic benefits of the pig industry. In order to obtain more profit, it is especially important to improve the sow reproductive performance. Litter traits, such as total number born (TNB), the number born alive (NBA), the number of healthy piglets (NHP)  and the ESR, FSHβ, PRLR and RFRP [During the recent decades, with the development of molecular breeding technology, genome-wide association study (GWAS) has been widely used to identify quantitative trait loci (QTL) and quantitative trait nucleotides (QTN) in complex traits. In pigs, GWAS has become one of the most powerful methods for exploring single nucleotide polymorphisms (SNPs) and candidate genes associated with complex traits ,6,7,8. Iand RFRP ,10,11,12and RFRP ,13,14. Fand RFRP . We use However, reproduction traits are controlled by many minor genes, there are only few candidate genes that have been identified until now and just explain a small proportion of genetic variance. Therefore, it is necessary to further investigate the genetic mechanism for reproductive traits. In this study, to identify candidate regions/genes and explore genetic architecture of litter traits , the traditional GWAS and ssGWAS methods were performed on 895 French Large White pigs genotyped by GeneSeek Genomic Profiler (GGP) 50K Porcine Beadchip.In this study, a total of 1407 French Large White pigs were collected from a core pig breeding farm and were grouped in standard commercial pens. Each pig was bred under the same forage and feeding management conditions. All phenotypic records came from the first parity. The traits included TNB, NBA, NHP and LWB. NHP is the number of piglets with a birth weight above 1 kg. Four traits were recorded from January 2018 to March 2022. All 1407 sows have pedigree information. The NBA <3 and TNB >25 were excluded  and the p < 10−6 for Hardy–Weinberg equilibrium test were filtered out. Individuals were filtered out with SNP marker call rates <0.90. All SNPs located in unmapped regions were also excluded. Subsequently, a final set of 895 individuals and 42726 SNPs was retained for subsequent analyses.Genomic DNA of 895 sows was extracted from the ear tissue using a genome extraction kit  and genotyped using the GGP 50K SNP Beadchip . Data quality control was performed by PLINK v1.90 . SNP marThe AIREMLF90 module of BLUPF90 software family was used to estimate the genetic parameters of all traits . The varputed as :H−1=A−1+The GWAS was performed by the R package rMVP using the mixed linear model . The staThe ssGWAS was performed using BLUPF90 software family . The RENj-th SNP for all animals and j-th SNP.The process ofcalculating SNPs effects was iterated twice. The percentage of genetic variance explained by i-th consecutive SNPs (SNP window) was calculated as described by Wang et al. :Var, and only the protein coding genes were used for the following analysis. Linkage disequilibrium (LD) analysis of overlapping regions identified by two methods was performed by Haploview 4.2 software [For GWAS, candidate genes were defined as the genes that were nearest the significant SNPs. In the ssGWAS, genomic windows that explained >1% were considered to be relevant for all traits . All typsoftware . We manuThe descriptive statistics of four reproductive traits are listed in The heritability, genetic and phenotypic correlations of the four litter traits were estimated . In geneThere were 2, 2, 1 and 0 SNPs that reached a chromosome-level significance for TNB, NBA, NHP and LWB, respectively . These sThere were 42,726 windows for ssGWAS analysis after dividing five consecutive SNPs into one window, of which, the last nine windows contained fewer than five SNPs. We identified 32, 26, 28 and 22 relevant 5-SNP windows which explained 1% or more of the total genetic variance for TNB, NBA, NHP and LWB, respectively . Table 4JARID2 gene that was located 0.5 Mb downstream of these two SNPs. Another candidate gene was GMNN, which was located 0.07 Mb upstream of the SNP (H3GA0020211). In ssGWAS, we obtained 82 genes totallyfor all four reproductive traits, including 49 protein coding genes  were related to the TNB, NBA and LWB traits and the DGK1 and CREB3L2 genes were associated with the NBA, NHP and LWB traits. Two genes (ENSSSCG00000042180 and NEDD1) were simultaneously associated with the TNB, NBA and NHP traits. For the location of 10.8 Mb, which was detected in both methods, we selected the 10.5–11.5 Mb region of the NBA trait in its vicinity for analysis  located on 10.8 Mb of SSC7. We annotated the ng genes . In whicanalysis . We annoLitter traits contained the TNB, NBA, NHP and LWB of piglets and are closely associated with sow reproductive performance, affecting pig production efficiency and economic profit. Hence, exploring the underlying genetic architecture of litter traits in pigs is extremely important. In this study, we explored the genetic variation of TNB, NBA, NHP and LWB in Large White pigs using GWASs and then further explored the genes related to reproduction.Heritability of reproductive traits is low, with usually around 0.1. Previous research also reported that TNB, NBA, NHP and LWB were low heritability traits, and their heritability was approximately 0.05–0.19 in the French Large White population ,40. In oENSSSCG00000044834, but this gene has not been studied so far. Subsequently, we searched the nearby genes for this region and we finally mapped JARID2, which was located at 11.35–11.60 Mb on SSC 7. JARID2 is a member of the jumonji demethylase protein family. It was reported that the protein encoded by JARID2 interacts with the polycomb repressive complex 2 (PRC2), which plays an essential role in regulating gene expression during embryonic development [In traditional GWAS, although we only detected five significant SNPs, it was interesting that two of them were repetitive and both located around 10.8 Mb on SSC7. In ssGWAS, the genomic regions that explained the highest genetic variance of TNB and NBA were both located at 10.72–11.06 Mb on SSC 7. Then, the region that explained the second highest genetic variance of NHP was similarly located at 10.72–11.03 Mb on SSC7 and the difference with the highest genetic variance was only 0.02%. The nearest gene around 10.8 Mb was elopment . Additioelopment ,43, one GMNN was annotated at 19.59–19.60 Mb on SSC 7 near the SNP (H3GA0020211). The GMNN gene encodes a protein that plays a critical role in cell cycle regulation. It was reported that deleting the GMNN gene in oocytes from the primordial follicle stage could impair early embryo development and implantation [PDIA6, ODC1, KLF11 and GRHL1. The PDIA6 gene is a member of the protein disulfide isomerase (PDI) family. Ye et al. showed that the PDIA6 gene could affect oocyte quality by regulating redox homeostasis [ODC1) gene encodes the rate-limiting enzyme of the polyamine biosynthesis pathway. It was reported that ODC1 inhibited polyamine synthesis and caused uterine quiescence, which led to entry of the blastocyst into embryo diapause [KLF11 gene could adjust the expression of cytochrome (CYP) p450 enzymes to realize the genetic regulation of uterine biology [GRHL1 gene was reported to play a critical role in placenta development [FLRT2 gene. This gene is a member of the fibronectin leucine rich transmembrane (FLRT) family and it can regulate early embryonic vascular and neural development. It was reported that mouse embryos lacking FLRT2 expression arrest at mid-gestation owing to cardiac insufficiency [FLRT2 gene was also identified to be related to the NBA trait of pigs [DICER1 and CLMN genes in the 116.39–116.50 Mb region. The DICER1 gene encodes a protein possessing an RNA helicase and the encoded protein functions as a ribonuclease. It has been reported that DICER1 expression was important for early embryos in animals, including domestic livestock such as pigs and cattle [DICER1 in mouse causes morphologic abnormalities and stunted growth and lethality [CLMN gene can affect the development of nervous system in early embryos [WWOX and UBR4. The WWOX gene is a member of the short-chain dehydrogenases/reductases (SDR) protein family. In the study of Chen et al., the WWOX gene was identified as a candidate gene for calf traits in dairy cows [UBR4 gene played a significant role in embryonic development by affecting angiogenesis of the yolk sac [Next, we totally annotated 11 candidate genes for two methods. The antation . In TNB,eostasis . The orndiapause . It was  biology . The GRHelopment . Additio of pigs . Also ond cattle . In addiethality . Mark etiry cows . It was yolk sac .JARID2, PDIA6, FLRT2 and DICER1. Notably, in the two results, we found two overlapping SNPs (WU_10.2_7_11370371 and ASGA0031202) associated with TNB, NBA and NHP in the 10.72–11.06 Mb region on SSC 7, where we mapped the ENSSSCG00000044834 gene. Our study screened out 11 novel genes related to reproduction and some of them have not been studied in livestock, and therefore, the function of these candidate genes needs to be further explored. The results of this study are encouraging in the search for a new major gene regulating litter size traits in pigs and provide a reference for pig genetic mechanism analysis and breeding.In this study, we performed GWAS and ssGWAS analyses for TNB, NBA, NHP and LWB in French Large White pigs using a 50K SNP chip. Overall, five significant SNPs and 36 genomic regions were identified, respectively in two methods. By manually searching the literature, we identified four potential candidate genes for reproductive traits including


Honey bee subspecies originate from specific geographical areas in Africa, Europe and the Middle East, and beekeepers interested in specific phenotypes have imported genetic material to regions outside of the bees' original range for use either in pure lines or controlled crosses. Moreover, imported drones are present in the environment and mate naturally with queens from the local subspecies. The resulting admixture complicates population genetics analyses, and population stratification can be a major problem for association studies. To better understand Western European honey bee populations, we produced a whole genome sequence and single nucleotide polymorphism (SNP) genotype data set from 870 haploid drones and demonstrate its utility for the identification of nine genetic backgrounds and various degrees of admixture in a subset of 629 samples. Five backgrounds identified correspond to subspecies, two to isolated populations on islands and two to managed populations. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long and represents 21% of chromosome 11, with two major haplotypes corresponding to the two dominant genetic backgrounds identified. This large naturally phased data set is available as a single vcf file that can now serve as a reference for subsequent populations genomics studies in the honey bee, such as (i) selecting individuals of verified homogeneous genetic backgrounds as references, (ii) imputing genotypes from a lower‐density data set generated by an SNP‐chip or by low‐pass sequencing, or (iii) selecting SNPs compatible with the requirements of genotyping chips. Thus, although 642 colonies were included for population genomics analyses, in total 870 samples were used for SNP detection  and then centrifuged again, after which the DNA in the supernatant was precipitated with absolute ethanol and resuspended in 100 μl TE 10/0.1. Pair‐end sequencing was performed on Illumina HiSeq 2000, 2500 and 3000 sequencing machines with 20 samples per lane, or on a NovaSeq machine with 96 samples per lane, following the manufacturers' protocols for library reparations.DNA was extracted from the thorax of adult bees or from pupae as described in Wragg et al. . Briefly2.2bwa‐mem (version 0.7.15) . Libraries that were sequenced in multiple runs were merged with samtools (version 1.8)   or could highlight copy number variants (CNVs) in the genome. Individual gVCF files were combined with combinegvcfs and then jointly genotyped with genotypegvcfs, resulting in a single VCF file for the 870 samples containing 14,990,574 raw variants. After removing Indels with gatk selectvariants, 10,601,454 SNPs remained. Sequencing depth was estimated using mosdepth  Li, , and dup2.3https://github.com/avignal5/SeqApiPop/blob/v1.5/SeqApiPop_2_VcfCleanup.md.The first run of filters concerns technical issues related to the sequencing and alignment steps and was therefore used for the total data set of 870 samples, to benefit from its larger size for SNP detection and validation Figure . These f2.4plink (version 1.9)  using the Evanno method provided . Further details are given in https://github.com/avignal5/SeqApiPop/blob/v1.5/SeqApiPop_5_TreeMix.md. Local ancestry inference and positioning of haplotype switches were performed with rfmix version 2.03‐r0 , aligned to the reference genome for SNP detection and recombinants were detected with the custom script find_crossing_overs.py to produce a genetic map. Further details on the rfmix analysis are given in https://github.com/avignal5/SeqApiPop/blob/v1.5/SeqApiPop_6_RFMix.md.Haplotype blocks were detected with u et al. . Briefly33.1Sequencing of the honey bee drones for the SeqApiPop diversity project began in 2014 on Illumina HiSeq instruments and some of the first samples had such low coverage that a second run (or even three in the case of OUE8) of sequencing was performed. For these samples, the resulting BAM files were merged prior to variant calling. Only four samples of the diversity project were sequenced on Novaseq instruments, for which higher sequencing depths were achieved. Therefore, to improve the robustness of the SNP detection pipeline, we included drone genome sequences from other ongoing projects using the same genetic types, which had the advantage of all being produced with a Novaseq instruments and at a higher depth. Samples sequenced with the HiSeq and NovaSeq instruments had mean sequencing depths of 12.5 ± 6.1 and 33.5 ± 10.2 respectively Table  and S4.gatk pipeline allowed the detection of 10,601,454 raw SNPs . We performed 50 independent runs with the admixture software for each value of 2 ≤ K ≤ 16 on the LD‐pruned data set, totalling 750 independent analyses. Cross‐validation (CV) error estimates of the results computed by the software are shown in Figure K = 8 having runs with the lowest CV values overall, and K = 9 having the lowest median CV value over its 50 runs. The resulting Q matrices were jointly analysed using pong , C lineage  and O lineage (A. m. caucasia). For K = 2, A. m. caucasia bees are considered as having the same genetic background as the A. m. ligustica and A. m. carnica bees, also reflecting the results from the PCA , and the Corsican breeder's organization (AOP Corse), appear quite homogenous Figure , with GPs Figure , K = 9 sl Figure . As expeA Figure . Some adK = 9 genetic backgrounds, the three main groups are now further subdivided. The M lineage group from the K = 3 backgrounds is now composed of four genetic backgrounds: A. m. iberiensis is separated from A. m. mellifera, and the A. m. mellifera bees are separated into three groups from mainland France, and the two islands of Ouessant and Colonsay. The other three subspecies A. m. ligustica, A. m. carnica and A. m. caucasia each have their own genetic background. An eighth background corresponds to the samples from the bees selected for the production of royal jelly and a ninth appears in the two populations that were noted as Buckfast bees. Although it is a major background in these two populations, a majority of samples have also a large proportion of A. m. carnica and, to a lesser extent, of A. m. ligustica backgrounds. This ninth background can also be found in other breeders' populations, principally in Hérault and Tarn1  between the populations in the data set, based on the Evanno method .To investigate further the haplotype blocks detected, we performed a local ancestry inference on our data set with 3 Figure , resulti3 Figure  aligned 3 Figure . When co44.1Our complete data set of haploid drones is composed of 870 samples sequenced using Illumina's HiSeq and NovaSeq technologies. The results clearly show that although a few of the early sequences produced on the HiSeq are of lower depth, only 15 samples were eliminated due to the fraction of missing genotypes exceeding 10%. By contrast, the fraction of missing genotypes over the ~7 million SNPs detected was considerably lower in samples sequenced on the NovaSeq sequencing platform. Having sequenced haploids, the removal of heterozygote SNPs in individual samples is recommended to reduce the likelihood of “pseudo SNPs,” as we have shown previously that heterozygote SNPs tend to cluster together  off the coast of western Brittany, isolated from the rest of the French honey bee population since its installation in 1987 and the prohibition of imports since 1991 mostly for sanitary reasons. In contrast, the mode with the eight runs and lowest CV presents a better separation of A. m. mellifera and A. m. iberiensis, which is also found in the major mode at K = 9 backgrounds. A smaller level of admixture can still be found between A. m. mellifera and A. m. iberiensis, which is quite likely to be due to the shared ancestry between these two subspecies.We explored a range of n Figure . Our resa Figure . This isK = 9, represented by 33 out of 50 runs, returned the lowest mean CV value. This mode identifies mainland France A. m. mellifera samples as having a distinct genetic background and suggests that honey bees from Ouessant may have been re‐introduced in the mainland conservatories. This mode also identifies a distinct genetic background in French and Swiss Buckfast bees. Buckfast bees were developed by Brother Adam, and are described in page 14 of “Beekeeping at the Buckfast Abbey” as a cross performed around 1915 between “the leather‐coloured Italian bee and the old native English variety” , have very different genetic management strategies. The Tarn1 breeder produces Buckfast and  Figures , 4. This Figures . InteresApis mellifera, a topic still under debate. Our intentions here are different, being targeted towards managed populations, for which detailed knowledge of genetic makeup is essential for further work concerning traits of interest to queen breeders and beekeepers and the interaction between different subspecies present within a territory. Typically, a refined description of admixture and of its distribution along chromosomes is essential to avoid confounding effects in genome‐wide association studies (GWAS).Previous analyses on worldwide data sets were published, either by whole genome sequencing of workers  and southeastern Europe (C lineage). These regions were noted to coincide with haplotype blocks detected with AvaI repeats, which were previously assigned to centromeres by cytogenetic analysis, and of a low GC content  in the South African Cape honey bee Apis mellifera capensis showed it was in a nonrecombining region over 100 kb long on chromosome 1, although long‐read mapping failed to detect any inversion  for download, together with the list of the 629 unique samples used for the diversity analysis. Scripts and supplementary description of bioinformatic analyses are available in GitHub:  at Figures S1–S20Click here for additional data file.Tables S1–S7Click here for additional data file.


RMja (nematode resistance) and SK (bitterness) genes were consistent. We also genotyped 49 hybrids from an almond F2 progeny and could build a genetic map with a set of 1159 SNPs. Error rates, less than 1%, were evaluated by comparing replicates and by detection of departures from Mendelian inheritance in the F2 progeny. This almond array is commercially available and should be a cost-effective genotyping tool useful in the search for new genes and quantitative traits loci (QTL) involved in the control of agronomic traits.A high-density single nucleotide polymorphism (SNP) array is essential to enable faster progress in plant breeding for new cultivar development. In this regard, we have developed an Axiom 60K almond SNP array by resequencing 81 almond accessions. For the validation of the array, a set of 210 accessions were genotyped and 82.8% of the SNPs were classified in the best recommended SNPs. The rate of missing data was between 0.4% and 2.7% for the almond accessions and less than 15.5% for the few peach and wild accessions, suggesting that this array can be used for peach and interspecific peach × almond genetic studies. The values of the two SNPs linked to the  The detection of single nucleotide markers (SNPs) has been enhanced with the Next-Generation Sequencing (NGS) technologies ,2. SNPs Several high density SNP arrays have been developed in fruit species, either with the Illumina technology in the 9K  and 18K Prunus persica) and almond (Prunus dulcis), the Illumina 18K peach SNP array was used in a GWAS analysis of a collection of Italian almond germplasm [Based on the strong synteny between peach , and recently another whole genome cv “Nonpareil” was also published [The whole genome sequences of  “Texas”  and Prunauranne”  were pubublished . In addiIn this study, we present the development of an Axiom 60K almond SNP array (PdSNP_V1) based on the SNP polymorphism of a set of 81 resequenced almond accessions and the validation and accuracy of the genotyping data generated from the array. Alignment of the NGS reads of the 81 almond accessions to the “Texas” reference genome  and SNP The final filter on step 3 was based on the p-convert score estimated by Affymetrix. Prior to chip production, an in-silico validation was performed to estimate the p-convert value for the forward and reverse probes for each SNP. The p-convert value is derived from a random forest model to predict the probability of SNP conversion on the array. A high p-convert value means that we expect a high probability of conversion for the SNP probes on the array. SNPs with a p-convert of less than 0.666 were removed, discarding 11,265 SNPs. In addition, Affymetrix had classified SNPs into three categories, recommended, not recommended and neutral . Only SNTo explore the distribution of the 60,581 selected SNPs, SNP frequency was plotted at every one million base pairs along each chromosome. . We haveP. dulcis and 23 genotypes of species of the subgenus Amygdalus including some peach genotypes and interspecific hybrids. A total of 47,012 SNPs (77.6%) were classified in the Poly High Resolution (PHR) Affymetrix category, considered the most informative with good cluster resolution , the array data are also valuable with a similar frequency of missing data as P. dulcis, but with a lower percentage of heterozygous SNPs. For accessions of the two peach species, P. persica and P. davidiana, the rate of missing data was slightly higher (11.7–15.5%), but it is still a low rate. With a relatively high number of heterozygous SNPs, this suggests that the array has the potential to be used for diversity analysis and linkage mapping of peach trees, and specifically for linkage mapping of peach x almond progenies, useful for gene transfers from almond to peach or vice versa [For all 60,581 SNPs, we observed between 262 (0.4%) and 1665 (2.7%) missing data for the 185 cessions . In compher 11.7–.5%, but RMja gene [RMja gene is a dominant gene, these genotypes would be resistant, which will be validated by an inoculation test with root-knot nematodes. All other almond accessions were genotyped homozygous susceptible. This is consistent with published data [M. javanica (data not published). The RMja gene is an orthologue of the plum gene Ma [MA/MA), P2175 (Ma/ma) and P2980 (Ma/ma) was well determined by the SNP AX-599403222.In the almond diversity panel analysis, the three additional SNPs were classified in the category Poly High Resolution (PHR) and were considered “best recommended” . For theMja gene , only thMja gene . Three whed data  and resu gene Ma  and the Prudu_000307-V1.0 gene, which could be associated with kernel weight [For the SNP AX-599403226, including in the l weight , we founSk gene involved in the bitter taste of almond [Sk) in the breeding program. Twenty accessions were scored BB (bHLH2) could be the main reason for the sweet phenotype according these authors. In the case of “Texas” and the other sweet accessions that scored ‘BB’, the observed discrepancy could be explained by a different origin of the sweet phenotype, as result of a different SNP mutation in the same or other unknown gene; this still remains unclear. In conclusion, this marker can confirm that with the genotype AA or AB, the variety is sweet, but with the genotype BB we could not confirm it is bitter. However, this SNP has a good profile and high resolution, and a good marker of this region  in JoinMap®4.1 [® were applied to order and determine the genetic location of markers. In order to keep the most reliable markers, we chose to remove markers mapping far from any other marker with a threshold of 5 cM (centimorgan). Finally, the selection and the construction of the genetic map were performed in JoinMap®4.1, resulting in a final set of 1159 SNPs   and No Minor Hom (50 SNPs). We retained only 7234 intercross SNPs with a co-dominant 1:2:1 segregation, with a p-value less than or equal to 0.01 for a chi-squared goodness-of-fit test . Followi159 SNPs . The mapOur results show that the almond chip PdSNPV1 can be useful for constructing high-density linkage maps for almond and peach × almond populations. Some linkage maps with SNP markers have already been constructed in almond and peach × almond populations using the SNP Illumina peach chip  or genotThe genotypic array data from the “Texas” accession was compared to the data of the “Texas” reference genome. To assess the repeatability of the genotyping, we tested three “Ferrastar” samples. The differences of allelic state between each replicate of Ferrastar were the consequence of the natural error rate belonging to the Axiom technology. There were 0.90% and 0.95% error rates between the first replicate and the two other replicates and 1.01% between the second and the third replicate , which iFor all homozygous SNP types detected in the F1 parent “Penta”, the 49 F2 hybrids were expected to have the same allelic status as the parent. With 20,462 monomorphic markers AA in Penta for the A allele of “Texas”, we expected only AA datapoints for these 20,462 markers in the 49 F2 hybrids, but 494 were genotyped AB and 2167 were BB on the 1,002,203 observed data points corresponding to 0.27% erroneous data . For theP dulcis and 5 of wild species, two of P. webbii and one of P. bucharica, P. kuramica, and an interspecific hybrid P. fenzliana × P. bucharica. Forty-two accessions were resequenced on Illumina® HiSeq2000 by the CNAG-CRG Barcelona (https://www.cnag.crg.eu/ accessed on 1 June 2020) and the 14 others were resequenced on Novaseq 6000 Illumina at the MGX platform at Montpellier (https://www.mgx.cnrs.fr/ accessed on 1 January 2021)). For increasing the panel, we downloaded the sequences available on NCBI, 14 published by Yu et al. [We resequenced 50 accessions of u et al. , 3 by Kou et al. , and 9 bu et al.   using BWA version 0.7.16a-r1181 [All fastq files were trimmed using Trim Galore! version 0.6.1  and thei6a-r1181 . Samtool6a-r1181  was usedhttps://www.rosaceae.org/ accessed on 1 January 2021) using BWA version 0.7.16a-r1181 [All fastq files were trimmed using Trim Galore! version 0.6.1  and thei6a-r1181 . We used6a-r1181  to perfoRMja gene [Sk) involved in the bitter taste of almond [After this initial filtering, we applied three more selection steps . In the Mja gene . The AX-f almond . The lasf almond . We wereP. dulcis from the INRAE and IRTA collections, and other accessions of species of the subgenus Amygdalus .Genomic DNA was extracted from young leaves according to the protocol of Antanaviciute et al. . The amoAfter genotyping accessions, genotype calling and QC metrics were performed with SNP Axiom Analysis Suite v5.1 (Affymetrix) using diploid threshold configurations and default DishQC settings .The software classified SNPs into six categories: Poly High Resolution (PHR) when we observed a good resolution for the two homozygous clusters and at least two occurrences of the minor allele, Mono High Resolution (MHR) when the SNP passes all thresholds except the number of minor alleles., meaning that all genotyped samples are monomorphic, No Minor Hom (NMH) when one of the homozygote clusters was not observed; Off-Target Variant (OTV) when the SNP has a sequence significantly different from the sequence of the hybridization probes caused for example by double deletions or sequence non-homology, Call Rate Below Threshold (CBT) when the SNP call rate is below the ratio of 97, meaning that the software has failed to identify clusters, “Other” when the SNP does not satisfy all quality thresholds.We have developed and validated a new Axiom 60K almond SNP array (PdSNP_V1) and our studies have shown that the genotyping is reliable, with few missing data and erroneous points. Based on F2 progeny, we found only a few errors indicating departures from Mendelian inheritance of homozygote SNP loci.Amygdalus species. The PdSNP_V1 array would be suitable for Prunus rootstock breeding, notably for linkage mapping of peach × almond progenies. The PdSNP_V1 array is the first developed almond array and is commercially available from Thermofischer Scientific. SNP genotyping can be performed on INRAE’s Gentyane platform or on other platforms equipped with Axiom genotyping instruments.The PdSNP_V1 array could be used for almond diversity analysis, association studies and genetic mapping for breeding purposes, as a large number of breeding genitors were included in the set of accessions resequenced for the detection of SNPs. Our data showed that this array is also effective for peach species and some wild