In-silico expression profiling based on the available transcriptome data revealed that 326 of these genes were differentially expressed under drought stress. KEGG analysis resulted in reduction of candidate genes from 369 to 99, revealing enrichment in various signaling pathways. Haplotype analysis confirmed 5 QTLs among the initially identified 9 QTLs. Two QTLs, qRWC1.1 and qYLD7.1, were chosen based on high SNP density. Candidate gene-based analysis revealed distinct haplotypes in qYLD7.1 associated with significant phenotypic differences, potentially linked to pathways for secondary metabolite biosynthesis. These identified candidate genes bolster defenses through flavonoids and phenylalanine-derived compounds, aiding UV protection, pathogen resistance, and plant structure.The study provides novel genomic regions and candidate genes which can be utilized in genomics-assisted breeding of superior drought tolerant chickpea cultivars.Elucidation of the genetic basis of drought tolerance is vital for genomics-assisted breeding of drought tolerant crop varieties. Here, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphisms (SNPs) in recombinant inbred lines (RILs) derived from a cross between a drought tolerant chickpea variety, Pusa 362 and a drought sensitive variety, SBD 377. The GBS identified a total of 35,502 SNPs and subsequent filtering of these resulted in 3237 high-quality SNPs included in the eight linkage groups. Fifty-one percent of these SNPs were located in the genic regions distributed throughout the genome. The high density linkage map has total map length of 1069 cm with an average marker interval of 0.33 cm. The linkage map was used to identify 9 robust and consistent QTLs for four drought related traits viz. membrane stability index, relative water content, seed weight and yield under drought, with percent variance explained within the range of 6.29%–90.68% and LOD scores of 2.64 to 6.38, which were located on five of the eight linkage groups. A genomic region on LG 7 harbors quantitative trait loci (QTLs) explaining > 90% phenotypic variance for membrane stability index, and > 10% PVE for yield. This study also provides the first report of major QTLs for physiological traits such as membrane stability index and relative water content for drought stress in chickpea. A total of 369 putative candidate genes were identified in the 6.6 Mb genomic region spanning these QTLs.  Cicer arietinum L.) is one of the most important food legume crops cultivated globally1. According to FAOSTAT (2020)2, among the major legume crops, soybeans lead with a production of approximately 355.37 million tons (mt), followed by peas at 34.99 mt, lima and common beans at 27.41 mt, chickpeas at 15.06 mt, broad beans at 7.47 mt s, cowpeas at 9.04 mt, and pigeonpeas at 5.05 mt. This positions chickpea as the fourth-largest legume crop globally2. India is the largest producer (11 million tons) where it produces approximately 73% of the total global production of chickpea2. The chickpea production globally is adversely affected by drought stress, which can cause up to 50% yield losses1. Prevalence of drought at pod filling stage, commonly referred to as terminal drought, results in reduced flower and pod production and reduced seed size in chickpea plants. With the ongoing global warming, the world is predicted to face even hotter climate and erratic rainfalls in the near future4. This will lead to an increase in both the incidence and intensity of drought stress during pod filling stage. Hence, it becomes imperative to understand the genetic basis and mechanisms for drought tolerance in chickpea for varietal improvement.Chickpea or Bengal gram  technologies, single nucleotide polymorphism (SNP) markers have become a favorite choice for genotyping. The advantages of SNP markers include high abundance in the genome, biallelic nature, high reproducibility, low mutation rates and amenability to automation12. Some SNPs in the protein coding regions may directly affect the agronomic traits by changing the amino acid sequence of the protein, hence these are known as functional SNPs or perfect markers. Due to these reasons, SNPs are being increasingly used for genotyping in many crops including rice, wheat, barley, sorghum and soybean16.Various physiological and morphological traits have been used to screen drought tolerance in plants, for example water use efficiency (WUE), stomatal conductance (SC), relative water content (RWC), membrane stability index (MSI), canopy temperature depression (CTD), root biomass and architecture, leaf chlorophyll content, leaf rolling, yield and yield components under stress18. The problem of poor uniformity in genome coverage in GBS analysis has been addressed by restriction site associated DNA (RAD) sequencing, which involves sequential steps of restriction enzyme digestion of genomic DNA for reduced representation, adapter ligation, PCR amplification and sequencing. The approach has been widely used for diversity assessment, trait mapping and genome wide association studies (GWAS) in several crops20. Further, QTLs for seed traits in chickpea have been mapped using GBS to generate a high-density linkage map of 3,363 SNP markers12.Genotyping by Sequencing (GBS) allows bar-coding for large-scale multiplexing of samples to achieve high throughput SNP genotyping with lower cost per sample as compared to high-density SNP chip arraysRealizing the importance of drought responsive morpho-physiological traits, which allow chickpea plants to withstand drought stress, the present study was undertaken to map the QTLs for drought stress responsive traits viz. membrane stability index (MSI), relative water content (RWC), 100 seed weight (SW) and seed yield per plant (YLD) under drought and to identify candidate genes underlying these QTL regions.ApeKI restriction enzyme overhangs, and base quality Phred score of ≥ 15 for at least 80% of the bases in a read. After QC, a total of 177 million high quality reads  were processed for further analysis. The average number of reads per genotype was 1,125,483 with a variation of 29.88% between individuals. After alignment to the reference genome a total of 35,502 raw SNPs were identified. Of these, 3237 were high quality SNPs identified after stringent filtering, and the sequence information of these 3237 SNPs is provided in Supplementary Table A total of 42 Gb data with 209 million reads were generated for the two parents and 186 RILs of the Pusa 362/SBD 377 mapping population. The sequence data has been deposited in the NCBI short reads archive (SRA) database  under accession numbers SRR13002229 and SRR13002230. The sequence reads were subjected to quality check (QC). Filtering criterion included removal of barcodes, Genomic distribution of the identified SNPs in intergenic, exonic, intronic and genic UTR regions was determined using the chickpea genome annotation project database . The highest proportion of the identified SNPs were located in the intergenic regions (48.52%), followed by introns (23.98%), exons (22.48%) and UTRs (5%) Fig. . The pen11 RIL population developed by crossing chickpea varieties Pusa 362  and SBD 377 , showed a considerable amount genetic variation for traits under drought stress. The RIL population showed segregation for all the four drought tolerance traits analyzed in this study. The 3 years descriptive statistics for these traits are summarized in Table A F12. The lengths of individual linkage groups ranged from 81.0 cm for LG8) to 178.1 cm for LG6 and the total map length of the eight linkage groups was 1069.1 cm. The number of SNPs mapped per linkage group varied from 128 (LG8) to 636 (LG6) followed closely by LG4 which had 633 mapped SNPs. The average marker interval for genome was 0.33 cm with LG4 exhibiting highest density linkage group (0.23 cm) and LG8 having the lowest marker density (0.63 cm). A summary of the marker information for the eight linkage groups of chickpea is presented in Table Genome wide RAD SNPs were utilized to generate a high-density linkage map of the RIL population. A total 3,237 SNPs identified by stringent filtering of data for SNP quality and goodness of fit to the expected 1:1 segregation ratio for RILs were used to develop linkage maps of the eight chickpea chromosomes Fig. . The linqRWC1.1 and qRWC5.1) identified on LG 1 and LG 5 were observed consistently for three years in the same physical position under drought stress  was found to be consistent across the year and at same physical position in pooled data analysis (Table qMSI2.1) LG5 (qMSI5.1), and on LG7 (qMSI7.1 and qMSI7.2) were detected in experimental year 2015–16 and in pooled data analysis. The LOD values for these QTLs ranged from 5.15 (qMSI4.1) to 2.64 (qMSI7.2) in pooled data analysis. These QTLs explained phenotypic variance of 90.68% (qMSI7.1), 0.2% (qMSI7.2), and 9.12% (qMSI4.1), respectively in pooled data analysis. The position of qMSI2.1 was at 107 cm with single peak marker S2_35782996 on LG2, similarly for qMSI4.1 peak marker was identified at 41.42 cm position on LG4. Other QTLs, qMSI7.1 and qMSI7.2 were mapped at 35.61 cm and 62.8 cm position within marker intervals of S7_11812441-S7_11870532 and S7_20579963-S7_21595527 respectively on LG7 (Table qSWD5.1) to 2.97 (qSWD7.1). The phenotypic variance explained (PVE) by these two QTLs ranged between 44.15% (qSWD5.1) and 49.58% (qSWD7.1)  except LG3 and LG8 Fig. , Table 3qYLD1.1, qYLD5.2, and qYLD7.1) were identified on three different linkage groups including LGs 1, 5, and 7 for the trait yield per plant in pooled data analysis. The LOD values ranged from 6.38 (qYLD1.1) to 2.885 (qYLD7.1). The phenotypic variance explained by these three mapped QTLs ranged from 67.81% (qYLD1.1) to 6.29% (qYLD5.2). The pooled data analysis of this result showed that the alleles from parent Pusa 362 favored yield at all the loci except qYLD5.2 and qYLD7.1  and Bivanij (drought tolerant). Out of 369 genes, 326 genes were differentially expressed under drought conditions. Among these 369 genes, 37 genes were identified for qMSI, 80 genes for qRWC, and 252 genes for qYLD QTL , relative water content (RWC), and yield per plant (YLD). A total number of 369 genes were identified in the 6.6 Mb regions QTL Fig. . These cQTL Fig. . PathwayQTL Fig. .Figure 6qRWC1.1 (10 SNPs) and qYLD7.1 (121 SNPs) were selected for haplotype analysis. In the qRWC1.1 region, six candidate genes were found of which only one gene, LOC101514724, encoding ATP-dependent DNA helicase Q-like 3, was associated with more than two SNPs. However, no significant difference was found in RWC between the two haplotypes of qRWC1.1 (64.7% vs 66.5%). Of the 74 candidate genes in qYLD7.1, four candidate genes, LOC101498779, LOC101501730, LOC101514620 and LOC101494835 harboured more than one SNP and hence were selected for haplotype analysis. Two haplotypes each, identified in LOC101494835 and LOC101498779, showed a significant difference in yield (65.31 g vs 89.51 g and 65.61 g vs 89.99 g grams). The haplotype GGTC/TACT were associated with loci S7_993031, S7_994976, S7_995674, and S7_995691 in LOC101498779, which encodes arogenate dehydratase/prephenate dehydratase 1 and KEGG pathways analysis showed that this gene came under metabolic pathways including biosynthesis of secondary metabolites and amino acids phenylalanine. Similarly, the haplotype CGAA/TTTG was associated with loci S7_2228317, S7_2228318, S7_2228372m, and S7_2230434 in LOC101494835 gene, whose orthologs in Glycine max encodes MHD domain-containing proteins that are associated with flavonoid biosynthesis. This gene was observed to be differentially expressed between the drought tolerant and drought sensitive varieties of chickpea desi chickpea varieties, Pusa 362 (tolerant) and SBD 377 (sensitive), with contrasting values for four drought tolerance parameters, namely MSI, RWC, SW and YLD under drought stress. The traits were selected keeping in view the impact of reproductive stage drought stress on drastic reduction of grain yield. MSI and RWC are physiological traits which measure the cellular response to drought stress and are considered as reliable parameters for quantifying the plant drought stress tolerance. SWD and YLD under drought stress are a direct measure of drought tolerance. Two parents (Pusa 362 and SBD 377) were selected due to their contrasting phenotypic traits associated with drought tolerance  for YLD (> 46%) across the consecutive three years within the F11 RIL population have observed. By the F11 generation, generally most loci in recombinant inbred lines (RILs) should be homozygous, effectively fixing most genetic variability31. However, despite these expectations, we observed a notable high CV, suggesting the influence of various intertwined factors. Residual heterozygosity, although expected to be minimal, could still persist, leading to some ongoing genetic segregation and, consequently, variability. Epistatic interactions are also important contributors, given that yield is a multifaceted trait, and interactions between various QTLs can manifest in broader phenotypic variability than individual QTL effects alone32. Additionally, while controlled environments, such as those under rainout shelters, minimize variability, micro-environmental fluctuations like subtle changes in temperature or humidity can still impact plant yield33. It's imperative to also consider that even in such controlled settings, the genetic makeup of plants can interact in complex ways with these subtle environmental nuances, resulting in gene-by-environment (GxE) interactions and the observed variability34. This interplay of genetic and environmental factors in our F11 RIL population underscores the challenges of interpreting high CV in legume yield studies, even under ostensibly controlled conditions. Significant positive correlations of MSI, RWC and SWD with grain yield under drought stress, suggesting that these are genuine parameters for assessing drought tolerance. Positive correlation between 100 seed weight and grain yield under drought stress were observed and it has also been reported earlier in chickpea35. It indicates that yield related traits could be vital parameters and primary objective for developing drought tolerant crop plants and legumes including chickpeas. The traits targeted for improving drought tolerance must have strong correlation with grain yield under drought stress conditions36.In the present study, GBS approach was used for high throughput SNP genotyping of a RIL population derived from cross between two 15, but only 3237 (9%) of these were suitable for linkage mapping, after filtering for missing values and segregation distortion. This underlines the limitation of GBS in finding commonly genotyped SNPs across individuals due to random Poison distribution of the sequence reads in the genome of individual samples. This can be improved by increasing the fold coverage of sequence data which adds to the sequencing and informatics costs. Hence, recently there has been a shift towards use of high-density chip array-based SNP genotyping for QTL mapping in crop plants including chickpeas38. The highest number of SNPs were mapped to chromosome 6 (636), followed closely by chromosome 4 (633). These account for 19% of the SNP markers mapped. The mapping of higher number of SNPs on the 4th chromosome (CaLG4) has previously been reported by Jaganathan et al. 201524 and has been attributed to the possible presence of repeat rich regions on the chromosome. About 51% of the SNPs were mapped to the genic regions of the genome with 23% of intronic SNPs. 22% of the genic SNPs mapped to the exons in the genome. Among the genes which carried highest number of exonic SNPs were the genes coding for pentatricopeptide repeat-containing proteins. These proteins have been shown to be involved in regulating plant responses to drought, salinity and cold39. The presence of large number of exonic SNPs in these genes might be indicative of their role in drought stress tolerance in chickpea. The average marker density across the chromosomes was 0.33 cm, which is similar to chickpea SNP maps reported by Verma et al. 201512, Barmukh et al. 2020, 202129, denser than inter and intra-specific maps reported by Gaur et al. 201240 , Hiremath et al. 201241 , Deokar et al. 201442 , but lower density than chip array-based linkage map reported by17 (13 679 SNPs spanning 1033.67 cm and 7769 SNPs spanning 1076.35 cm).A total of 35,502 SNPs were identified between Pusa 362 and SBD 377 by alignment of Illumina sequence reads to the chickpea reference genomeqRWC1.1, qRWC5.1), MSI , and YLD  which may be used for marker assisted breeding for drought tolerance in chickpea. In addition to mapping of QTLs for yield under drought stress environment, to the best of our knowledge, this is the first study that reports mapping of QTLs for physiological traits like MSI and RWC. Four QTLs for MSI, namely qMSI2.1, qMSI4.1, qMSI7.1, and qMSI7.2 with PVE range from 90.68% (qMSI7.1) to 0.2% (qMSI7.2) were found on linkage groups LG 2, LG 4 and LG 7, respectively, while the two consistent QTLs for RWC viz., qRWC1.1 (PVE- 80.76), and qRWC5.1 (PVE- 3.1), were identified on LG 1, and 5 respectively. Three major genomic regions spanning three robust QTLs (PVE: 67.81–6.29%) associated with yield per plant agronomic traits (YLD) were identified and mapped on three different Linkage Groups  of high density genetic map of chickpea. On the basis of physical position of SNPs marker covering the respective QTL intervals, the reliability of mapped QTLs for YLD were determined by comparing their underlying genomic region with earlier studies on genomic mapping44. In contrast to previous studies the QTLs identified in this study, especially the QTLs for yield were observed to overlap with previously mapped QTLs25. All these nine mapped QTLs reported by our study can be considered as robust QTLs showing phenotypic expression and have major effects except qRWC5.1 (PVE 3.1%), qMSI4.1 (PVE 9.12%), qMSI7.2 (PVE 0.2%), and qYLD5.2 (PVE 6.29%) on traits individually with PVE > 10% each. The consistent QTLs across different studies, can be of immense utility for pyramiding for reproductive stage drought tolerance in chickpea.Utilization of RIL population with high density genetic map and phenotyping under rainout shelter allowed to precise mapping of genomic region/s for drought stress in chickpea. A positive and significant correlation between the yield and yield related traits like seed weight could be an important parameter for developing drought resilient chickpea genotypes, which indicated that yield traits have considered to be the important objective for improving drought tolerance in agriculturally important crops including chickpea. We have identified 9 consistent QTLs for RWC (kabuli chickpea (CDF Frontier) genome15 was used for this analysis, because it is better assembled than the desi chickpea (ICC 4958) genome14 and therefore the possibility of locating the marker intervals on the chromosomes is higher. A total of 326 putative candidate genes showed differential gene expression. These genes encoded proteins associated with different metabolic processes and pathways such as biosynthetic pathways, signaling pathways, photosynthesis, etc. A large majority of these genes are involved in the biosynthesis of osmolytes like amino acids, polyamines and sugar alcohols. These genes have been shown to express under abiotic stress conditions as adaptive features to maintain plant water potential46. Similarly, the genes associated with photosynthetic, starch biosynthesis and UDP-glucose biosynthesis pathways, have been shown to be induced under drought stress in chickpea46. The synthesis of UDP-Glucose is a pre-requisite for imparting mechanical strength and cell-wall remodeling to protect plant from abiotic stress47. Genes coding for different TF families were identified and were differentially expressed under drought stress conditions. These are also involved in hormonal signaling, in response to hormones like abscisic acid, auxin, gibberellin and cytokinin, indicating a vital role of plant hormones and their cross-talk in drought stress51.Utilizing and predictive annotation approaches to identify candidate genes located at loci harboring SNPs has the potential to serve as a highly effective strategy in pinpointing causal genes52. To ascertain the veracity of the mapped QTLs and the potential candidate genes involved, haplotype analysis was performed, which revealed the presence of 5 QTLs among the 9 initially identified QTLs. From these, 2 QTLs, namely qRWC1.1 and qYLD7.1, were selected due to their high SNPs density within the QTL regions. While the candidate gene based haplotype analysis could not support the findings of qRWC1.1, two candidate genes in the qYLD7.1 displayed two distinct haplotypes that exhibited a substantial phenotypic differences. These two haplotypes could be associated with metabolic pathways for secondary metabolites biosynthesis including amino acid and flavonoid biosynthesis as found from the KEGG and GO analyses. Arogenate Dehydratase/Prephenate Dehydratase 1 (LOC101498779) gene encodes an enzyme involved in the terminal steps of phenylalanine biosynthesis. Phenylalanine is not just an essential amino acid; it is also a precursor to many secondary metabolites, including flavonoids, lignins, and alkaloids. Phenylalanine-derived compounds can play protective roles under stress. Lignins, for example, can contribute to cell wall rigidity and reduce water loss53. The exact function of MHD Domain-Containing Proteins (LOC101494835) in plant might be uncertain, the orthologs in Glycine max are associated with flavonoid biosynthesis. Flavonoids are versatile secondary metabolites playing roles in UV protection, signaling, and stress responses. Flavonoids antioxidant properties make them crucial in scavenging reactive oxygen species, which increase under stress conditions like drought. They can protect cellular structures from oxidative damage and maintain cell homeostasis54. Flavonoids have an antioxidant properties and have the ability to eliminate reactive oxygen species (ROS) in response to drought stress55. By impeding the metabolic functions of enzymes involved in ROS production pathways flavonoids induce activation of the antioxidant defense system. Given that phenylalanine is a precursor to flavonoids, these two genes might be part of a coordinated response to drought. Enhanced phenylalanine production could fuel flavonoid biosynthesis, boosting the plant's antioxidant capacity during drought56.These identified genes could impact other stress responses. For instance, flavonoids also play a role in UV protection, pathogen defense, and allelopathy. An upregulated flavonoid pathway could enhance multiple stress defenses simultaneously. Phenylalanine-derived compounds like lignins can also play roles in pathogen defense and structural integrity57.The two identified candidate genes play roles in interconnected pathways pivotal for plant stress responses. Beyond their direct metabolic functions, they contribute to a broader network of responses that can enhance plant resilience not just to drought but potentially other stresses as well. This holistic understanding underscores their significance and potential as targets for crop improvement. The QTL regions associated with these genes, whose role in drought stress has been well known, strongly support the hypothesis for association of the identified QTLs to drought stress tolerance.To identify the candidate genes in the QTL intervals, the genetic map developed in the present study was integrated with available genome and transcriptome resources of chickpea. The The high-density linkage map developed in this study is likely to be of immense utility for high resolution mapping of QTLs for drought tolerance traits in chickpea. This enabled identification of closely linked SNP markers, which can be used for the introgression of superior alleles of the genes for drought tolerance traits through MAS for developing drought tolerant cultivars. In this study we identified novel QTLs for physiological traits, membrane stability index (MSI), relative water content (RWC), and yield/plant (YLD) under drought stress. The QTLs for membrane stability index (MSI) and relative water content (RWC), have been mapped for the first time in chickpea. To validate the accuracy of the mapped QTLs and determine the potential candidate genes implicated, a comprehensive haplotype analysis was conducted. The analysis unveiled the existence of five QTLs out of the initial nine QTLs that were identified. The identification of differentially expressed candidate genes in these QTL regions, paves way for better understanding of the biological pathways and molecular mechanisms involved in drought stress response with ultimate aim of introgressing these genomic regions, using MABC, for developing superior drought tolerant chickpea genotypes.11 RIL population developed by crossing chickpea varieties Pusa 362  and SBD 377  comprising of 186 individuals were used to identification of genomic regions and candidate genes for drought tolerance. Fresh young leaves were used for genomic DNA isolation using the CTAB method with minor modifications58. The isolated DNA was checked for quality by electrophoresis in 0.8% agarose gel and quantified using Nanodrop 8000 spectrophotometer equipment (Thermo-Scientific).An Frabi season  under rainout shelter conditions at the ICAR-IIPR, Kanpur, Uttar Pradesh, India . Each experimental block consisted of a single 1 m plot with a spacing of 0.3 m between rows. In chickpea reproductive phase (particularly flowering and pod formation) is consider as a key step to evaluate drought traits. Exposure to water deficit at reproductive stage may cause yield loss through flower and pod abortion and is consider as a simple and effective screening techniques for terminal drought. Drought stress was imposed by withholding irrigation at the pod formation stage after 90 days after sowing. The parental lines and RILs were phenotyped for four drought tolerance parameters: (i) membrane stability index (MSI), (ii) relative water content (RWC), (ii) seed weight under drought (SWD) and yield /plant (YLD) under drought stress, over three consecutive years from 2014 to 2017. For determination of RWC, the third fully expanded leaf from top of the plants was collected before noon. The leaves were weighed to record the fresh weight (FW), then immersed in double distilled water in Petri plates for 4 h and weighed again to record turgid weight (TW). The samples were then dried at 65 °C for 48 h and weighed to record the dry weights (DW). The data was recorded in triplicates for each of the RILs and parental genotypes. The RWC (%) was calculated using the following formula:The set of 186 RILs along with the parents Pusa 362 and SBD 377 were grown for phenotyping in augmented block design for three consecutive years during the To measure the MSI, leaf tissue (0.5 g) was collected in glass vials containing 10 ml of double distilled water. These samples were incubated in a water bath at 40 °C for 30 min. After cooling the tubes to room temperature, electrical conductivity (C1) of the solution was recorded with a conductivity meter . The tubes were then kept in a boiling water (100 °C) bath for 10 min and electrical conductivity measured (C2), after the tubes attained the room temperature (25 °C). The membrane stability index (MSI) using the following formula:The seed and yield traits were recorded after threshing and cleaning of seeds. Average 100 seed weight under drought (SWD) was calculated by weighing 100 mature seeds from 10 representative plants of each RIL and the parental genotypes at a seed moisture content of 10%. Average seed yield per plant was recorded from the seed weights of five plants for each RIL and parental genotypes. Statistical analysis to compute mean, standard deviation (SD), coefficient of variation (CV), correlation coefficients, for the traits was performed by using XLSTAT/SPSS v17.0.ApeKI according to the protocol of Elshire et al.,13. All libraries were pooled and single end sequencing of the fragments was performed using Illumina TrueSeq V3.0 sequencing chemistry, with a read length of 100 bp. The raw sequence reads obtained in the FASTQ file format were processed by filtering through GBS analysis pipeline implemented in TASSEL v3.059. The reads were filtered for the criterion including the presence of perfectly matched barcodes with the four bps remnant sequences of the RE site and a minimum Phred score of 10 across the first 72 bases. The reads were sorted, de-multiplexed and further trimmed to 64 bases for analysis. The filtered reads were mapped to the reference chickpea genome15 using Burrows-Wheeler alignment software (BWA). The missing data were imputed using Beagle v3.3.260, while retaining the default parameters. The perfectly aligned sequences were used for SNP calling with a with a minor allele frequency (MAF) ≥ 0.2, minimum sequence read depth: 10X and SNP base quality ≥ 20. The distribution of the identified SNPs was visualized across the 8 linkage groups (LGs) using Circos v0.6161.To prepare libraries for GBS analysis, genomic DNA from the parental genotypes (Pusa 362 X SBD 377) and 186 RILs was subjected to restriction digestion with a type II restriction endonuclease 62. The Benjamini and Hochberg false discovery rate (FDR) correction at a significance level of 5% was done using the BiINGO plugin of Cytoscape v2.663. To identify SNPs present in the transcription factors (TFs) family genes, the peptide sequences of different TF belonging to five leguminous crops  were retrieved from the Plant Transcription Factor database. These sequences were utilized to build HMM profiles for all TF families and categorize the genes with SNPs into different TF families.The distribution of SNPs in different genomic regions, namely intergenic, exonic, intronic and UTR, was determined to analyse their functional or structural significance. Gene ontology (GO) analysis was performed for the genes containing SNPs to classify them into three principal GO categories, using in-house custom perl scripts and SNP effect predictor (snpEff c3.1 h)2-test (p < 0.05). Markers showing segregation distortion were excluded from the analysis. The linkage map was constructed using JOINMAP 4.1 program64. Ordering of the markers was done using regression mapping algorithm RECORD (REcombination Counting and ORDering)65. The Kosambi function was used to estimate the map distance between markers66 and MapChart67 was used for visualization the genetic map. The QTLs associated with the traits under consideration were mapped by integrating the genotypic and phenotypic data on the 186 RILs using QTL.gCIMapping68. The default settings were used with a walk speed of 1 cm, window size of 10 cm and a minimum LOD threshold score of 2.5 calculated at 10,000 permutations and significance level, (P ≤ 0.05) were used to identify significant QTLs for the traits. The phenotypic variance explained (PVE %) and the additive effect explained by the QTLs were also determined.The genome wide SNPs derived from the GBS data on Pusa 362/ SBD 377 RILs were analyzed for segregation distortion against the expected 1:1 ratio using χ15 sequence to identify the putative candidate genes controlling the traits The identified sequences were used for gene prediction using FGENESH69. In-silico expression analysis was performed for the identified candidate genes using available transcriptome data from the root and shoot tissues of chickpea genotypes ‘Bivanij’ (drought tolerant) and ‘Hashem’ (drought sensitive), exposed to drought stress 70. The transcriptome data was retrieved from NCBI and mapped to the putative candidate genes with the help of the 454 Roche gsMapper (Newblerv2.3.5). A heatmap was generated using the FPKM (Fragments per kilobase per million mapped reads) values to depict the differential gene expression by using the TIGR Multiple Experiment Viewer (MEV) software72. Biological significance of differentially expressed genes was explored by GO term enrichment analysis including biological processes, molecular functions and cellular components using Blast2GO 73. The KEGG enrichment was done using David v6.8. Haplotype analysis for selected genes were done using Haploview 74.The genomic sequence between the SNP loci flanking the identified QTL intervals were retrieved by BLAST search against the reference chickpea genomeThe authors declare that the experimental research work involving the growth of plants in this study, was conducted in compliance with relevant institutional, and national guidelines and legislation.Supplementary Figures.Supplementary Tables.


Phaseolus vulgaris L.) was introduced in Europe after 1492. Here, by combining whole-genome profiling, metabolic fingerprinting and phenotypic characterisation, we show that the first common bean cultigens successfully introduced into Europe were of Andean origin, after Francisco Pizarro’s expedition to northern Peru in 1529. We reveal that hybridisation, selection and recombination have shaped the genomic diversity of the European common bean in parallel with political constraints. There is clear evidence of adaptive introgression into the Mesoamerican-derived European genotypes, with 44 Andean introgressed genomic segments shared by more than 90% of European accessions and distributed across all chromosomes except PvChr11. Genomic scans for signatures of selection highlight the role of genes relevant to flowering and environmental adaptation, suggesting that introgression has been crucial for the dissemination of this tropical crop to the temperate regions of Europe.Domesticated crops have been disseminated by humans over vast geographic areas. Common bean ( Common bean has two distinct domestication centers in Mesoamerica and in the Andes. The authors show that the Andean is the first gene pool successfully introduced in Europe and identify signature of pervasive introgression among gene pools and of selection for flowering underlying adaptation. The Columbian Exchange2 started in 1492 with the transatlantic journey of Christopher Columbus. This large-scale set of reciprocal biological introductions between continents provides a paradigm for the rapid adaptation of crop plants to changing environments. Changes in flowering time and photoperiod sensitivity were selected in parallel in the common bean (Phaseolus vulgaris), maize (Zea mays), potato (Solanum tuberosum), to name a few crops that have undergone selection for these adaptive traits3. Among crops originating from the Americas, the common bean was rapidly adopted and successfully disseminated across Europe4 and it is now possible to identify local European varieties with Andean and Mesoamerican origins10. Nowadays, common bean, as other food legumes, is crucial for main societal challenges and to promote the transition to plant-based diets11.Following the process of domestication, crops were spread by humans over vast geographic areas, where they adapted to new and often extreme environments9. Studies of common bean evolution in Europe can exploit the parallel domestication processes and the major genetic differences between the two American genepools. This provides an ideal model to study the role of introgression during the adaptation of common bean accessions to European environments12.The introduction of the common bean to Europe from two distinct centres of origin offered an opportunity for widespread genepools hybridisation and recombinationIn this work, we combine whole-genome analysis and metabolic fingerprinting in 218 common bean landraces, integrated with genome-wide association (GWA) to characterise the genetic basis of multiple traits, including flowering time and growth habit in different environments with contrasting photoperiod conditions. We have used the combined results to characterise the effects of selection and inter-genepool introgression, and to test the occurrence of adaptive introgression associated with the development and adaptation of common bean accessions in Europe.P. vulgaris) (Supplementary Data 13 (Supplementary Note n = 114), based on nuclear  and three main Andean groups   Fig. . In the n = 14), the nuclear and chloroplast assignments were inconsistent , the Andean chloroplast genome was combined with a Mesoamerican nuclear genome (n = 6) or vice versa (n = 5). This suggests, at least in some cases, the occurrence of chloroplast capture17 as a result of inter-genepool hybridisation and subsequent backcrossing. Moreover, when we explored the molecular phenotypic diversity of the American and European accessions (Supplementary Note 2 > 0.65) confirmed the admixture scenario. Several intermediate phenotypes between Mesoamerican and Andean accessions were observed in European landraces, but these were absent in accessions collected in the Americas  Fig. . In a feent Fig. : considecas Fig. . Notablycas Fig. , and it 18 M1 corresponded to the higher-altitude Durango and Jalisco races, which originated primarily in northern and southern Mexico, respectively; M2 corresponded to the lower-altitude Mesoamerican race, which is mostly photoperiod-insensitive and is distributed in lowland Mexico, in Central America and in the Caribbeans; A1 corresponded to the generally photoperiod-insensitive Nueva Granada race; A2 corresponded to the Peru race, which includes entries with vigorous climbing growth habits and photoperiod sensitivity; and A3 corresponded to the Chile race, which has also been identified in archaeological samples from northern Argentina that date from 2500 to 600 years ago19. The identification of these well-defined ancestral genetic groups in the Americas offers a robust basis to study the inter-genepool and inter-race introgression that may have facilitated adaptation to European environments.Finally, to provide insight into the population structure of the American pure accessions, we considered their passport data (geographic distribution and country of origin) and phenotypic data on growth habits and photoperiod sensitivities. We identified a clear correspondence between the genetic groups in our sample from the Americas and the well-known common bean eco-geographic races:20 , showing median proportions of 4.7% and 9.2%, respectively. In the EU_MES accessions, 0.01–44.9% of the genome had introgressed from the other genepool, with only one EU_MES accession showing <1% genome introgression .The median length of the introgressed genomic segments was higher for the EU_AND accessions , which had been donated to the same pope by Emperor Charles V’s Spanish emissaries from Sicily (where the bean seeds were harvested). The very detailed writing of Piero Valeriano Bolzanio refers to common bean seeds, describing in depth several phenotypic traits supporting their Andean origin, as also recently proposed23. Valeriano documented his efforts, along with a network of collaborators in the north-east of Italy, Slovenia and Dalmatia, to grow and reproduce beans starting in 153222, with the first report of a putative Mesoamerican genotype in Europe dated 154223. Historical information and timelines, together with our data showing asymmetric introgression, suggest an earlier successful introduction and spread of the Andean genepool into Europe. This may also explain the high frequency of A1/Nueva Granada Andean ancestries  and were never observed above the 47th parallel . Although A3/Chile did not appear any more photoperiod-sensitive than A1/Nueva Granada  in the Andean compared to the Mesoamerican accessions. Very similar results were obtained when we repeated the analysis by excluding six accessions showing an admixture proportion > = 40% , due to the admixture, European diversity was always higher than American diversity, but the opposite was found when using the masked dataset  compared to our FST distribution, suggesting that flowering is likely a candidate trait that underwent a selection process34. Considering the Andean genepool, the successful introduction in Europe was connected to the domestication pattern at the centre of origin. The earlier, photoperiod-sensitive domesticated genotypes were less successfully disseminated in Europe. Indeed, the relationship between the American and European genetic groups of Andean origin (as defined by ChromoPainter), coupled with the phenotypic data for flowering  in American genotypes. In contrast, there was faster LD decay over greater distances (>3 Mb) in European populations  decay in accessions from Europe and the Americas Fig.  is consions Fig. . This reons Fig. . Howevervs AM_AND; Mann–Whitney p = 0.03) and the missense mutations . Conversely, we did not observe a reduction of genetic load in EU_MES for both missense and loss-of-function mutations  an outlier FST value between Europe and the Americas, suggesting different patterns of diversity between these regions. The level of genetic differentiation (FST) among genetic groups is reported in Supplementary Table FST value was used to define which selection signature represents significant differentiation at the genomic level between American and European populations, suggesting selection in Europe. The identification of excess introgression independently of hapFLK provides evidence for adaptive introgression and the identification of PAIL. We also considered the occurrence of inter-chromosomal LD across genomic regions, private to European genepools   as those showing signatures of adaptive introgression meeting the following requirements: (a) an excess of introgression based on Chromopainter, (b) a signature of selection detected using the hapFLK method, which analyses multiple populations, jointly considering their hierarchical structureols Fig.  as an inols Fig. –36 genomols Fig. . Finallyols Fig.  that shofin locus controlling determinacy, and explaining phenotypic variation also for flowering time40. The region also co-maps with Phvul.001G189200 41 and shows linkage40 to the Ppd locus controlling photoperiod sensitivity42. Wu et al.25 recently identified several markers on chromosome Pv01 associated with flowering under different conditions, with one at ~45.5 Mb. In our GWA, we identified a significant association between photoperiod, flowering time  a gene that has been proposed as a candidate for the common bean Ppd locus44.This adaptive introgression region in the common bean genome is close to known regions associated with flowering time, such as the n = 681) showing signatures of selection in Europe  and/or excessintrogression (n = 404). Accordingly, 277 genes show selection in Europe but not excess introgression, and 327 show excess introgression but not selection in Europe. The 77 PAIL show enrichment in seven Gene Ontology categories including GO:0048523, negative regulation of cellular processes; GO:0010228, vegetative to reproductive phase transition of the meristem; GO:0042445, hormone metabolic processes; GO:0009657, plastid organisation; GO:0042440, pigment metabolic processes; GO:0009733, response to auxin; and GO:0070647, protein modification by small protein conjugation or removal , both located within the same adaptive introgressed region of chromosome PvChr09 45  and RELATED TO VERNALISATION1 1 (RTV1)  and NUTCRACKER (NUC), respectively. Both are located in a region of PvChr01 as described above, and are in private inter-chromosomal LD with Phvul.003G137100 , and CYTOKININ-RESPONSIVE GATA FACTOR 1 (CGA1). LD is one of the eight genes identified so far in the A. thaliana autonomous pathway and it represses FLOWERING LOCUS C (FLC) to promote the transition from vegetative growth to flowering  gene49. The paralogous GNC and CGA1 genes act redundantly to promote greening downstream of the gibberellin signalling network50.Adaptive introgression signals were identified in many ays Fig. . SignifiOTU5 gene involved in the inorganic phosphate starvation response. These are interesting candidate loci for further validation. Finally, we propose that the dissemination of common bean accessions in Europe may have been influenced by political factors and constraints in the 16th century, including the interaction between political and religious powers in Western Europe and the subdivision of the European continent into Islamic and Christian countries.We have shown that adaptive introgression was necessary for the successful dissemination and adaptation of the common bean in Europe. We combined genome resequencing, metabolomics, classical phenotyping and data analysis for chromosome-level genetic assignment and environmental association. Our data indicate that the Andean genepool was the first to be successfully introduced in Europe, most likely from Francisco Pizarro’s expedition to northern Peru in 1529. Most of the Andean genetic background of the European common bean was contributed by the A1/Nueva Granada and A3/Chile races after secondary domestication, whereas the more photoperiod-sensitive A2/Peru race contributed little to European common bean germplasm. The secondary domestication of these Andean races, related to the latitudinal expansion of the cultivation areas from the Andean centres of origin, facilitated the successful dissemination of the Andean common bean in the Old World. However, we propose that the adaptive introgression observed in Europe for individuals mainly belonging to the M1/Durango-Jalisco race was an important event that underpinned the successful dissemination of this Mesoamerican ancestry in Europe. Indeed, genomic analysis indicated that Andean genotypes were rapidly disseminated, whereas Mesoamerican genotypes were eventually disseminated in Europe following introgression from the Andean types. In addition to the flowering time data gathered from the European and American accessions, we also identified clear signatures of selection in common bean orthologues of genes representing the major flowering pathway and environmental adaptations, such as the P. vulgaris) were collected from international gene banks or individual institutional collections. We produced 199 single seed descent (SSD) lines by performing at least three cycles of self-fertilisation. For the remaining 19 accessions, one seed per accession was sampled directly from original seeds provided by the donor.Original seeds for 218 common bean accessions , short-day (2) and intermediate photoperiod conditions. During the summers of 2016 and 2017, four field trials were carried out in Italy  and in Germany (Gatersleben IPK). Six additional greenhouse experiments were performed under controlled conditions in Golm , Potenza  and Villaviciosa (Spain) during 2016, 2017 and 2018 (Supplementary Note vs indeterminacy on a single plant basis. Photoperiod sensitivity (PS) was calculated as the ratio between DTF in long-day and short-day experiments.Classical phenotyping was carried out on the 199 SSD lines, focusing on two main traits: days to flowering (DTF), defined as the number of days from sowing until 50% of plants showed at least one open flower; and growth habit (GH), defined as determinacy https://cran.r-project.org/) or JMP 7.0.051. The restricted maximum likelihood (REML) model implemented in JMP 7.00 was used to calculate least square means (LSM) and the best linear unbiased predictors (BLUPs) of each genotype. The REML model was also used to calculate the broad-sense heritability (h2B) for each quantitative trait by considering genotypes and environments as random effects. The distribution of DTF in each environment and Pearson’s pairwise correlation between environments were calculated using the corrplot and PerformanceAnalytics R packages53.Descriptive statistics were calculated for the different phenotypic traits using R . To explore the molecular phenotypic diversity, we performed non-targeted metabolic fingerprinting by high-throughput LC-MS analysis. Mass signals that were not detected in ≥50% of the samples and/or those with a peak intensity ≤1000 were excluded. Heritability was analysed as stated above, setting genotype and continent as random effects. The heritability was calculated based on 190 accessions (94 from the Americas and 96 from Europe) with more than one replicate.Molecular phenotyping . It was sheared using a Covaris E220 device to fragments of ~550 bp and PCR-free libraries were constructed using the KAPA HyperPrep Kit. Paired-end sequencing libraries were sequenced on Illumina HiSeq2500 or HiSeq4000 devices and labelled with different barcodes.15 using BWA-mem v0.7.1555. Unmapped reads were mapped to the P. vulgaris chloroplast genome (NCBI NC_009259). In both cases, SNPs were called using SAMtools56 and Genome Analysis ToolKit (GATK) v3.658. In SAMtools, duplicated reads were removed with rmdup and SNPs were discovered with mpileup for filtered high-quality alignments (–q 10) and bases (–Q 20) and then genotyped with BCFtools59. In GATK, duplicated reads were sorted and filtered with Picard v2.4.1 (http://broadinstitute.github.io/picard). Variants were then called according to GATK best practices and pre-filtered using the recommended parameters for hard filtering. Chromosomal and overlapping SNPs reported by both methods were retained and the genotypes produced by GATK were selected. We applied additional filtering with VCFtools60 (–minDP 3–max-missing 0.5–maf 0.05) and excluded SNPs with proportions of heterozygous genotypes >0.01. SNPs were annotated with SnpEff v4.3s61.Sequencing data were aligned to the common bean reference genome v2.062. ADMIXTURE v1.313 was used for population structure analysis. The unphased variants were filtered by taking one SNP every 250 kb using VCFtools. In ADMIXTURE, we varied K from 1 to 20 in 20 replicates and applied the analysis independently over the whole sample of American and European (n = 218) accessions or using the American accessions only . We dealt with potential cryptic population structures within each pool as previously described32.Population structure analysis was applied to the SAMtools/GATK overlapping SNP callset, followed by filtering to retain only genomic positions with a QUAL score ≥30 and a global depth of coverage between 1/3 and 4 times the mean value. Individual genotypes called using two reads or fewer were marked as missing data. Imputation and phasing were performed with Beagle64. Mixture analysis was used to determine the most probable K value according to the data. Clustering with linked loci analysis was chosen to account for the linkage between sites. Ten repetitions of the algorithm for each K value from 2 to 20 were applied. The relationships between genotypes were determined using the neighbour-joining method in MEGA X65 with a bootstrap value of 1000. Gaps and missing data were excluded.Population structure was inferred from chloroplast data using Bayesian Analysis of Population Structure (BAPS) v5.320. The effective population size (Ne) and mutation rates (Mu) were estimated individually for each accession using 10 iterations of the expectation–maximisation algorithm in ChromoPainter. The estimated parameters were fixed in a new round of analysis producing the final chromosome painting of the recipient haplotypes. Donor individuals were chosen as follows, according to their ancestry proportion inferred by admixture: (a) Mesoamerican individuals with a q value >0.99 in the admixture run , and (b) Andean individuals with a q value consistently >0.99 from K = 2 to K = 4 in the admixture run restricted to Andean accessions. Donors were subdivided into the five groups inferred by ADMIXTURE  and were used to estimate their contribution to the ancestry of each SNP in the recipient individuals. Individual SNP probabilities were then combined in 10 kb non-overlapping sliding windows along chromosomes and each window in each recipient haplotype was assigned to one of the five donor groups if a probability ≥0.8 was observed . The final within-individual distribution of lengths was characterised by the median value due to the non-normal distribution of the data.The attribution of each genomic window to the seven groups was also used to estimate the length of the introgressed blocks within each European accession. Each haplotype of the EU_AND accessions was traversed, merging consecutive windows attributed to any of the Mesoamerican clusters. Bedtoolshttp://www.worldclim.org)67 for a total of 19 bioclimatic variables and 24 monthly variables  assigned to each European accession and the ecological variables registered at the collection site.For spatial analyses, the ecological data (resolution ~1 km2) were downloaded from WorldClim  in callable regions, (b) in coding regions, and/or (c) in neutral regions  of the windows-based distribution were used as a point estimate of the differentiation between groups and to evaluate its dispersion.The magnitude of the genomic differentiation between and within America and Europe was evaluated using the Weir & Cockerham estimator 2 > 0.65  was used to evaluate differences between the genetic subgroups using the first principal component related to DTF and PS , AM_M (n = 36), EU_A (n = 71) and EU_M (n = 43) groups and VCFtools was used to sample a single SNP every 250 kb. This set of SNPs was used to construct a neighbour-joining tree and a kinship matrix according to the Reynolds’ genetic distance matrix between the four groups of accessions, constituting a genome-wide estimate of population structure. The hapFLK statistics were then computed independently on each chromosome over the complete SNP dataset and averaged over 20 expectation–maximisation cycles to fit the LD model. Initial analysis fixed the number of haplotype clusters to five. A second run was conducted, selecting the appropriate number of haplotype clusters based on the fastPHASE71 cross-validation procedure, implemented in the imputeqc R package (https://github.com/inzilico/imputeqc). VCFtools was used to extract a subset of SNPs spaced at least 100 kb apart on each chromosome, and five independent copies of the SNP set were generated, randomly masking 10% of the variants. We then used fastPHASE v1.4.8 to impute the missing genotypes in each dataset, setting the number of haplotype clusters to 5, 10, 20, 30, 40 and 50. The EstimateQuality function was used to compute the proportion of wrongly imputed genotypes (Wp) for each combination, and the K value, minimising the mean Wp proportion across the five SNP set replicates, was selected as the most supported number of haplotype clusters. The analysis was replicated using all or only American accessions. The scaling_chi2_hapflk.py script was used to scale hapFLK values and compute the corresponding p values. The significant SNPs  were extracted and bedtools was used to create a 10 kb region centred on each significant SNP and to merge overlapping regions within a maximum distance of 5 kb. The two sets of regions were merged forming the extended set (constituted by the union of the two sets) and the restricted set (containing only regions supported by both runs). To pinpoint putative regions under selection in Europe, the extended and restricted sets were intersected with the FST window analysis, and only regions containing at least one FST window located in the top 5% or top 1% were retained.The hapFLK72 was used to compute the correlation (r2) between allele frequencies at pairs of SNPs along the chromosomes, setting a minimum minor allele frequency of 0.1 and a maximum distance between SNPs of 5 Mb.The relationship between linkage disequilibrium (LD) and physical distance along chromosomes was evaluated in America and Europe, and successively within the American subgroups. PopLDdecay2 between pairs of markers located on different chromosomes. The analysis was performed independently over the American subgroups, using only SNPs that were segregating within each group of accessions with a minor allele frequency >0.05, and only pairs of SNPs showing an r2 value ≥0.8 were retained. Multiple pairs of SNPs pointing to the same chromosomal regions were merged if within a distance of 100 kb from each other and only pairs of regions spanning at least 500 kb on each side were retained. The whole analysis was also repeated including only SNPs falling in the putative regions under selection, decreasing the minimum width of retained regions from 500 kb to 50 kb. Link plots showing high-LD regions were produced using Rcircos73.The level of inter-chromosomal LD was also evaluated. VCFtools was used to sample one SNP every 10 kb and compute r74 (https://github.com/XiaoleiLiuBio/MVP). Bonferroni correction at α = 0.05 was applied as the significance threshold for each trait. The analysis was then conducted using the multi-locus stepwise linear mixed-model (MLMM)75 (https://github.com/Gregor-Mendel-Institute/MultLocMixMod). By applying a stepwise approach, this includes the most significant SNPs as cofactors in the mixed model. The mBonf criterion was used to identify the optimal results with Bonferroni correction at α = 0.05.Genome-wide association study (GWAS) was carried out for the growth habit, flowering time and photoperiod sensitivity data .Common bean genes orthologous to Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-13Reporting Summary


Capsicum annuum L. available at the German Federal ex situ genebank at IPK Gatersleben. C. annuum (Capsicum) is the most economically important species of the genus Capsicum. So far, there is no report that has addressed the genetic basis of seed longevity in Capsicum. Here, we convened a total of 1152 Capsicum accessions that were deposited in Gatersleben over forty years (from 1976 to 2017) and assessed their longevity by analyzing the standard germination percentage after 5–40 years of storage at −15/−18 °C. These data were used to determine the genetic causes of seed longevity, along with 23,462 single nucleotide polymorphism (SNP) markers covering all of the 12 Capsicum chromosomes. Using the association-mapping approach, we identified a total of 224 marker trait associations (MTAs)  on all the Capsicum chromosomes. Several candidate genes were identified using the blast analysis of SNPs, and these candidate genes are discussed.Seed longevity is the most important trait in the genebank management system. No seed can remain infinitely viable. There are 1241 accessions of  Seeds are considered the building blocks of genebanks, which came in to being to preserve plant genetic resources and avoid the risk of extinction and of genetic erosion . Seed stGlobal food supply relies on the availability of viable seeds . To mainhttps://gbis.ipk-gatersleben.de/gbis2i/faces/index.jsf, accessed on 20 October 2022), there were 1241 accessions of Capsicum annuum L. available. C. annuum is the most economically important species of the genus Capsicum  carried 21 significant MTAs, and the variance explained was 1.8–3.9%. Finally, another 32 MTAs (including 2 highly significant MTAs) were discovered for longevity after storage for 36–40 years, which involved all the Capsicum. A direct comparison among the results of different intervals is not possible because of the involvement of dissimilar accessions tested during each interval. However, some accessions were found to be common across different intervals. For example, there were 171, 261, 419, 163, 108, 318 and 54 accessions from 1- to 5-year intervals that were common in the 6–10-year, 11–15-year, 16–20-year, 21–25-year, 26–30-year, 31–35-year and 36–40-year intervals, respectively. ANOVA results indicated that germination % among common accessions was significantly different in all such cases (data not shown). No reports exist in which such comparisons were made in any crop. It is, however, known that different factors are involved in different aging procedures such as accelerated aging (AA) and controlled deterioration (CD) methods  (ACPs) synthase I (associated with SNPs S1_1430591 and S1_1435099), phosphatidylinositol 4-kinase gamma 2 (associated with SNP S1_1921287) and B3-domain-containing transcription factor NGA1 isoform X2 (associated with SNP S1_133318). Subtilisin-like proteases (subtilases) are serine proteases that play specific roles in plant development and signaling cascades. Several subtilases are specifically induced following pathogen infection or under stress . They ars system . On the s system . In addis system . NGA traleucine-rich repeat receptor-like protein kinase (associated with SNP S2_59895267), putative uroporphyrinogen decarboxylase, chloroplastic-like (associated with SNP S2_117264835), putative aspartic proteinase nepenthesin-2-like (associated with SNP S2_157010230) and THO complex subunit 2-like (associated with SNP sS2_165845447 and S2_165845425). Aspartic proteinase nepenthesin-2-like was reported during the periods of 11–15 years and 20–25 years of storage. The nepenthesin aspartic proteases, which are produced by specialized cells in the lower part of the pitchers, are aimed primarily at the digestion of prey trapped by the plant  family are sequence-specific RNA-binding proteins that play crucial roles in organelle RNA metabolism . PPRs hacysteine synthase (associated with SNP S8_140480324) and syntaxin-32-like (associated with SNP S8_119314147) were the candidate genes on chromosome 8. The former is an enzyme responsible for the formation of cysteine from O-acetyl-serine and hydrogen sulfide with the concomitant release of acetic acid [Putative tic acid , and thetic acid .S-adenosyl-l-methionine as the methyl donor to form methyl salicylate. SAMT appears to play an important role in plant response to drought stress by modulating the SA-signaling pathway [The most important candidate genes on chromosome 9 include salicylate O-methyltransferase-like (SAMT) (associated with SNPs S9_91765981 and S9_91766032), solute carrier family 35 member F1-like (associated with SNP S9_649935), cell division cycle protein 48-like protein (associated with SNP S9_5554728), superoxide dismutase (associated with SNP S9_93034919) and eukaryotic translation initiation factor isoform 4G-1-like isoform 1 (associated with SNP S9_270289444). SAMT regulates the SA signaling pathway and catalyzes the methylation of SA with  pathway . Solute  pathway . Cell di pathway . Likewis pathway  and PHS histone H3.3-like (associated with SNP S10_374592 and multiple seed storage durations) and high mobility group B protein 6 (associated with SNP S10_16302829). Histone H3.3-like is a candidate gene for longevity in wheat [high-mobility group B protein 6” is a WRKY transcription factor involved in the nucleosome/chromatin assembly [On chromosome 10, the most important candidate genes include putative assembly .Chromosome 11 carries longevity genes such as putative flavin-containing monooxygenase 1-like (FMO) (associated with SNP S11_152967951), protein YAE1 isoform X3 (associated with SNP S11_257151051) and putative F-box protein-like (associated with SNP S11_257213134). FMOs are oxidoreductases and possess remarkable diversity and functionality in the oxygenation reactions, which are crucial steps within hormone metabolism, pathogen resistance, signaling and chemical defense . YAE1 prCapsicum accessions was convened in this investigation. These were deposited in the IPK-Gatersleben over a number of years (from 1976 to 2017) . Their details can be accessed through the Gatersleben genebank information system by providing the ID (identity number) of the accessions.A total of 1152 to 2017)  and keptto 2017) . A largeCapsicum accessions from 1990 to 2022. Not all accessions were assessed after each year. For easy understanding, we divided the germination assays into 8 intervals: (1) germination assays performed between 1 and 5 years of storage (812 tests), (2) germination assays performed between 6 and 10 years of storage (320 tests), (3) germinations assays performed between 11 and 15 years of storage (389 tests), (4) germinations assays performed between 16 and 20 years of storage (506 tests), (5) germination assays performed between 21and 25 years of storage (163 tests), (6) germination assays performed between 26 and 30 years of storage (169 tests), (7) germination assays performed between 31 and 35 years of storage (604 tests) and (8) germinations assays performed between 36 and 40 years of storage (140 tests) (We performed 3103 germination assays on 1152 0 tests) . Stored Capsicum chromosomes. Other relevant details including the details of bioinformatics techniques and tools are available from Tripodi et al. [For the purpose of genotyping, 100 mg fresh leaf tissue that was collected from individual plants upon germination was used for DNA extraction. DNeasy Plant Mini Kit  or the Sbeadex maxi plant kit  was used to extract the highest-quality DNA, the quantity and quality parameters of which were determined using both spectrometry  and fluorometry  methods. Samples with 260/280 and 230/260 ratios ranging between 1.8 to 2.2 and 1.8 to 2.0, respectively, and with a less-than-twofold deviation between fluorimetric and spectrophotometric readings were subjected to genotyping-by-sequencing (GBS). Genotyping was carried out using an Illumina HiSeq2500 platform generating 1 × 107-bp single-end reads version 3 chemistry , which resulted in the generation of 23,462 single nucleotide polymorphism (SNP) markers covering all the 12 i et al. .TASSEL v5.2.43 [TASSEL v5.2.43. We ran each of the analyses using the PC = 3, PC = 4 and PC = 5 options for correct estimates. We found that 3, 4 or 5 PCs yielded 90–95% similar marker trait association with slight variation in the p-values of the associated SNPs. Thus, we kept PC = 5 for the final analysis. We claimed the SNPs in significant association with longevity that gave a p-value of 0.001 . In addition, highly significant p-values were calculated by taking the reciprocal of the number of markers [p-values of 4.26 × 10−5 were considered to be highly significantly associated.We performed the GWA analyses by utilizing the data of 23,462 high-quality SNP markers  and the  v5.2.43  software markers . Thus, SC. annuum CM334 [https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastx&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome, accessed on 3 January 2023) research tool database for functional gene annotations. The topmost hits with the smallest E-value and a high percentage of query coverage were reported as potential candidate genes.In order to look for the candidate genes linked with the associated SNPs, sequence retrieval of each significant or highly significant SNP was performed. This was achieved by retrieving a raw sequence of 301 nucleotides considering 150 bases upstream and 150 bases downstream of each candidate SNP. The sequence was generated from the reference genome um CM334  version um CM334  via a blCapsicum using the real-time data after long-term cold storage and the untapped natural genetic diversity. Several candidate genes have been reported for seed longevity in Capsicum. Some of them have already been reported for longevity in wheat, barley or other crops, whereas others are novel. Our findings lay the foundation for the comprehensive future studies of seed longevity in Capsicum.To conclude, we presented the very first comprehensive genetic analyses of seed longevity in


ARA and DHA primary production was restricted to the summer months, however, perch required larger amounts of these PUFA during winter, as their ARA and DHA muscle content was higher compared to summer. The expression of fads2 in perch liver increased during winter and was higher in mature females. Mature females stored DHA in their gonads already in late summer and autumn, long before the upcoming spring spawning period in May. Lower δ13CDHA values in the gonads in September suggest that these females actively synthesized DHA as part of this reproductive investment. Lower δ13CARA values in the liver of all individuals during winter suggest that perch were synthesizing essential FA to help cope with over-wintering conditions. Perch seem able to modulate its biosynthesis of physiologically required PUFA in situations of stress (fasting or cold temperatures) or in situations of high energetic demand . Biosynthesis of physiologically required PUFA may be an important part of survival and reproduction in aquatic food webs with long cold periods.Long-chain polyunsaturated fatty acids (PUFA) are critical for reproduction and thermal adaptation. Year-round variability in the expression of The online version contains supplementary material available at 10.1007/s00442-023-05480-0. At high latitudes, the winter lasts longer than the growing season and has a disproportionally large impact on organisms’ growth, reproduction, survival, and fitness  involves a series of desaturation and elongation reactions catalyzed by fatty acid desaturase (Fads) and elongation of very long-chain fatty acid (Elovl) enzymes  is a cool water species with an optimal temperature of 23 °C  in the liver of the European perch in a boreal lake in relation to individual variation in size, sex, and maturity. Our objectives were to assess; (i) seasonal changes in the expression of fads2 in the liver of P. fluviatilis, and; (ii) how such changes were related to environmental  or biological variation . Together with fads2 activity we report values of carbon stable isotopes as well as the content of fatty acids (FA) in perch tissues  and potential perch prey items that were sampled simultaneously.In the present study, we investigated the year-round variability in the expression of 2, an average depth of 14.4 m (maximum depth of 85 m), and a shoreline length of 33 km   by sieving 2 L of water through 50 µm mesh size and then filtering it through GF/C filter paper. Filter papers were immediately stored at − 80 °C. Zooplankton samples were taken from the same location from August 2020 to September 2021 using vertical tows of 0–80 m with 1 m diameter and 3 m long plankton net with a mesh size of 500 µm. Animals were sorted in the laboratory to Calanoida and M/TL3) × 100, where M is the fish mass in grams, and TL is the total fish length in cm. For the perch captured between September 2020 and April 2021, a piece of fresh liver tissue of each fish was collected and immediately homogenized in 750 μL of DNA/RNA Shield using a TerraLyzer™ Cell Disruptor and BashingBead™ (2.0 mm) lysis tubes , for later gene expression analyses. A second piece of liver tissue was sampled and kept separately for FA analyses. White dorsal muscle tissue was sampled for the same purpose, next to the anterior dorsal fin and above the lateral line of each fish. Samples were stored at − 80 °C, while the remaining carcasses of each fish were stored at − 20 °C. Later, fish were thawed, and stomach content was analyzed using the points method  and mass (accuracy 0.1 g) of each perch were measured. Fish were selected to cover different size categories and both sexes, except in February 2021 when total sampling was limited. Length categories were split into small perch (< 12 cm), medium perch (12–20 cm), and large perch (> 20 cm). Fulton’s condition factor was calculated for each perch: CF =  was calculated using the CFX Maestro™ software.Detailed methods are available in the supplementary materials. Briefly, RNA was extracted from liver samples using a Chemagic™ 360 and the Chemagic™ Viral DNA/RNA 300 Kit. The RNA was treated with DNAse and reverse transcribed to cDNA. cDNA samples were stored at − 20 °C. The primer sequences for Detailed methods are available in the supplementary materials. Total lipids were extracted from seston, zooplankton, and fish tissues using the protocol described by Folch et al. . Analyse13C values of FAs were determined using a GC-MSD  connected to an Isotope Ratio Mass Spectrometer  via combustion interface. Fatty acids were separated using a 30 m ZB-23 column (Phenomenex). The samples were run against an internal standard, FAME. The δ13C values of FAME were run with EA-SIRMS system (Thermo Fisher Scientific), and were used to calculate the δ13C value of individual FA.The δfads2 gene expression in the perch liver and the FA content (ARA or DHA) in the perch tissues were assessed using the Pearson’s correlation coefficient (r) test.The normal distribution of the datasets was tested with the Shapiro–Wilk test. As most datasets were not normally distributed, differences among the groups of individuals or time points were investigated using non-parametric Kruskal–Wallis tests (KW). When significant differences were found, it was followed by a Dunn's post hoc test. For most comparisons, individuals were grouped by sampling months, sex and maturity stages . Correlations between the 12C faster than 13C , medium (12–20 cm), and large (> 20 cm). Due to strong female-biased sexual dimorphism in perch  were used as a proxy of potential fish prey items for the larger individuals. The δ13C values obtained from the seston and pooled zooplankton samples  were grouped and plotted by season (summer and winter) since, for several sampling months, only one sample was collected. Summer included months from May to September and winter from October to April. To be coherent for all prey items, the δ13C values in the muscle tissue of small perch were grouped the same way when used as a proxy of fish prey for large perch.Enzymatic processes usually use α = 0.05.All statistical analyses were done using R 4.2.0  with the significant level set at p ≤ 0.05). It then declined slightly during the winter months while remaining relatively stable until the spawning period, before being at its lowest during summer, as observed with mature females (Supplementary Table S2). Juveniles were only available in February (N = 2) and were the smallest individuals captured.Mature females were the largest individuals captured all year long (Supplementary Table S2). The gonadosomatic index (GSI) of mature females increased gradually between September and its peak in April (before spawning in May), then decreasing to the lowest level in the summer months (June and August) (Supplementary Table S2). Mature males, on the other hand, had their highest GSI in September  (Supplementary Table S2). Analysis of stomach content revealed that Mysis relicta was an important part of the diet for both small (< 12 cm) and medium-sized (12–20 cm) perch during the winter months  (Supplementary Table S3). During spring and summer, small perch preyed mostly on small zooplankton . The summer diet of medium-sized perch was more diverse and included small zooplankton, insects, as well as other fish (Supplementary Table S3). Large individuals were almost exclusively piscivorous all year long, especially large females. Smaller perch were an important diet item for larger piscivorous perch, representing up to 60–70% of the observed diet in February and August, for example (Supplementary Table S3).For all individuals, the percentage of recorded empty stomachs was at its highest in September, February and April and close to 0% for the other sampling months . Accordingly, the average stomach fullness index score was higher in December, June, and August compared to September, February, and April  lower during the summer (June and August) compared to the rest of the year, with maximum values ranging between 2 and 3 μg per mg. Similarly, the DHA content in perch muscle was lowest in August and September, but peaked during winter: it was higher in February compared to any other month . Interestingly, juveniles that were sampled in February (N = 2) had the highest DHA content (~ 14 μg per mg) compared to larger individuals.The ARA and DHA content in perch muscle followed similar trends between small-medium invertivorous perch and larger piscivorous perch Fig. . The ARAAll individuals taken together, the content of ARA and DHA in perch liver followed a similar trend: it was higher in September and gradually decreased until August of the following year when it was the lowest Fig. . In the p ≤ 0.05, r = 0.66) with their fasd2 expression in the liver. The DHA content of muscles correlated positively with the liver fads2 expression for mature males and immature females , but not for immature males and mature females. Finally, the liver ARA content was negatively correlated  with fads2 expression for mature males but not for females or immature males.The DHA content in the gonad for mature females was positively correlated  in the muscle were similar all year long and followed those of the prey items, except for September 2020, when values were higher  during the winter months (December and February) compared to September and April , but not in February.Two clear trends were observed regarding the ril Fig. . Second,ber Fig. . This wa13CDHA values were lower in the gonads of the large mature females in September compared to other tissues or their prey of choice, small perch  compared to other tissues or prey items , and often characterized by hyperphagia (pre-winter fattening), but also by physiological mechanisms favorizing lipid storage before winter, and lipid rather than protein depletion during hibernation , there is experimental evidence suggesting possible endogenous DHA biosynthesis by perch   populations and explained by the linkage of fads2 to the X chromosome  individuals were shown to have a higher fads2 gene expression in blood cells compared to males , there was no isotopic evidence that synthesized DHA was further incorporated into the gonads, as no significant differences in δ13CDHA values were observed between gonads and other tissues or prey items. With decreasing temperatures and light conditions, foraging activity decreased from September onwards, as illustrated by the overall lower stomach fullness index between September and April compared to June and August. During the summer period, when primary production is high and when food is more easily available, perch need to recover from the demanding spawning period first. It is then possible that the dietary DHA alone is not enough to cover the cost of the high gonadal DHA requirements of mature females and/or that the dietary DHA is (at least partly) stored elsewhere for other purposes (such as lipids reserves), forcing mature females to synthesize a large part of the DHA destined to the gonad, when possible . Indeed, perch store most of their dietary fat in their perivisceral fat, including DHA, rather than in their muscle tissue  until spawning. However, even though fads2 in the perch. The overall expression of fads2  was higher during the winter months: this was especially noticeable in February, which was the coldest sampling month. The fact that the expression of fads2 increases during the winter months even for the immature individuals strongly implies that fads2 has important physiological implications other than reproduction for the perch of Lake Pääjärvi, such as over-wintering. In winter, light conditions are poor due to the shorter days but also due to the ice and snow cover, decreasing the visual light penetration to at least half  into ARA has been shown as an important alternative synthesis pathway for ARA in the Crustacea Daphnia magna  suggested that ARA might be important in stress conditions involving sub-optimal temperatures, as the pro-inflammatory eicosanoids derived from ARA are crucial when fish are exposed to low temperature for a long period , the hormone produced by vitellogenic follicles undergoing final meiotic maturation , where cubs are born during the winter, the proportion of DHA and ARA in the plasma of adults is at its maximum in winter   was able to induce the production of 17,20fads2  and confirm these findings. The long winter season in boreal lakes is characterized by low temperatures, low light conditions, reduced foraging activity, and thus long fasting periods for perch. Nonetheless, the fads2 expression was at its highest in winter, including both sex and all size categories, suggesting that the biosynthesis of physiologically required PUFA, such as DHA or ARA, might be needed to cope with these environmental and physiological stressful conditions. The inclusion of other tissues  in future studies could help better understand the seasonal dynamics of PUFA in perch or other fish capable of synthesizing these LC-PUFA. Studying fads2 in fish inhabiting different lake ecosystems would increase our understanding how other fish intrinsically respond to the paucity of dietary PUFA to optimize their fitness. Terrestrial animals also do possess the desaturase genomic architecture required to convert LA and ALA to LC-PUFA, however, the related literature is largely biased towards laboratory-raised, domesticated or hatchery-reared species . As observed with other species, mature female perch appeared to have a higher capability for the synthesis of these physiologically required PUFA compared to other individuals. This is likely explained by their high requirement of DHA storage in the gonads for later successful embryogenesis. However, more research is needed to fully functionally characterize perch Supplementary file1 (DOCX 463 KB)Below is the link to the electronic supplementary material.


In Germany, oral emergency contraception (EC) with the active ingredients levonorgestrel (LNG) and ulipristal acetate (UPA) is available as over-the-counter (OTC) medicine only from community pharmacies (CPs). Because of the window of effect, which is limited to only a few days, CPs have a great responsibility to provide rapid and unimpeded access, while also ensuring “adequate” counseling. The aim was—for the first time in Europe and thus also in Germany for the methodology used in this study—to investigate immediate availability, pricing, and aspects of counseling.®, citing a contraceptive failure one day ago as the reason.Covert mystery calls were conducted in a random sample of CPs stratified by districts in the German capital Berlin. Each of the 263 CPs included was called once at random by one of two trained female student mystery callers. They simulated a product-based scenario for the UPA original ellaOneOf 257 successfully called CPs, UPA preparations were immediately available in 98.4% (253/257) and LNG preparations in 86.8% (184/212) of CPs. Prices for UPA preparations varied from €15.95 to €42.95  €5.91]) and for LNG preparations from €10.60 to €32.49 . Information about the correct different window of effect of UPA and LNG preparations was provided in 69.8% (127/182) of CPs. UPA preparations were recommended in 63.1% (111/176) and LNG preparations in 17.2% (30/174) of CPs. Information was provided on how to take them as soon as possible in 30.8% (44/143) of CPs and on how to use them after vomiting in 46.0% (64/139).Berlin CPs support access through high immediate availability, especially to UPA preparations. However, access is hampered by high absolute price ranges of both UPA and LNG preparations, which could ideally be minimized by a comparison app. It is positive that CPs promote the benefits of UPA preparations by recommending them noticeably more often than LNG preparations. However, there are deficiencies in giving advice, so there is a need to raise awareness among pharmacy staff to ensure “adequate” counseling in advance over the phone.The online version contains supplementary material available at 10.1186/s40545-023-00565-w. The UPA original ellaOne® (n = 224) had a median price of €35.07 [IQR €2.07] with a price range from €25.50 to €42.95  and UPA generics (n = 50) had a median price of €24.90 [IQR €1.38] with a price range from €15.95 to €32.00 . For LNG preparations (n = 164), the median price was €22.00 [IQR €5.76] with a price range from €10.60 to €32.49 . Thus, the median price for UPA preparations was 59.1% higher than the median price for LNG preparations. Prices for Berlin districts are reported in Additional file The median price for UPA preparations (ellaOne® and generics) n = 41 was €369.8% (127/182), and thus the majority of CPs, provided information about the correct different window of effect of UPA and LNG preparations. UPA preparations were recommended far more frequently  than LNG preparations . In 30.8% (44/143), information was given about taking the drug as soon as possible and in 46.0% (64/139) about retaking after vomiting.p = 0.002, V = 0.309), with an effect size corresponding to a “medium” effect according to Cohen [. In post hoc analyses, a call duration between 2:01 and 3:00 min (Benjamini–Hochberg adjusted p = 0.044), between 3:01 and 4:00 min (Benjamini–Hochberg adjusted p = 0.010), or above 4:00 min (Benjamini–Hochberg adjusted p = 0.010) compared to 1:01 to 2:00 min were significantly associated with increased “giving advice on the correct different window of effect”. Similarly, the “call duration” and the “giving advice on retaking after vomiting”  showed a significant association, which also had a “medium” effect size [p = 0.012) or above 4:00 min (Benjamini–Hochberg adjusted p = 0.012) compared to 1:01 to 2:00 min were significantly associated with increased “giving advice on retaking after vomiting”. For “giving advice on retaking after vomiting”, there was also a significant association with “districts of the CPs” , also with a “medium” effect [2 [p = 0.013, V = 0.210), although the effect size was considered “small” [There were no significant associations between the availability and prices of UPA and LNG preparations (items 1–8 of the assessment form) and the possible influencing factors. Table to Cohen . In postect size . Post ho” effect . In post “small” .Table 6AIn line with a German self-reported study , the CPsIn this MC study, the median price for UPA preparations was more than half higher than the median price for LNG preparations. Although comparative studies from Germany are lacking, it is known at least from international SPM studies that in the USA  and in Within UPA and LNG preparations, enormous price ranges were present in this MC study, analogous to two German SPM studies on acute diarrhea. Thus, in a medium-sized city with 84 on-site visits in 21 CPs each, these two determined prices, which also included both originals and generics, from €2.36 to €8.49 Δ €6.13, 260%)  and from0%  and f® versus UPA generics). However, higher-priced medicines can represent a financial burden for patients above a certain price threshold and thus a barrier to access to oral EC, which is urgently needed at a given time.A certain price difference between UPA and LNG preparations does not seem surprising at first, as UPA preparations are superior to LNG preparations with regard to efficacy and window of effect , 7. HoweIn particular, to minimize the high absolute price ranges for oral EC and their potential consequences, price transparency should be increased —despite UPA preparations were recommended noticeably more frequently than LNG preparations in this MC study in line with a Turkish SPM study  and a GeGiving advice was overall—as in international MC studies with comparable counseling items in the USA , 56, 58,Furthermore, in this MC study, certain factors had an influence on some assessment items. For example, giving advice on the correct different window of effect and the retaking after vomiting of the oral EC were significantly associated with the call duration. This could also be expected, since longer duration of conversation may also result in more giving advice. Unlike other indications , the SPMTo the authors’ knowledge, this is the first SPM study in Europe, and thus also in Germany, to examine practices of CPs on oral EC, including immediate availability, pricing as well as preparation recommendations and giving advice as aspects of counseling. The SPM in the form of mystery calls, internationally referred to as the “gold standard” , was appThe study only covered the city of Berlin in the northeast of Germany, which means that the results are not transferable to other cities, especially in geographically different regions, and also not to rural regions. In addition, although the minimum sample size was slightly undercut with a subsequent continuous decrease in the sample sizes of the items of the assessment form, the results can still show a realistic picture of the practices of the Berlin CPs with regard to oral EC.®, an assessment of LNG preparations divided into original and generic products was not possible.The respective mystery calls were conducted by two different MCs, which may have led to an averaging of the MCs' personal characteristics (voice) and thus to the depiction of even more realistic conversational situations. In addition, only objective items and dichotomous scales were used in order to avoid a subjective assessment and thus the MCs' leeway in assessment, which is typical for SPM studies . Nevertheless, the results of the mystery calls conducted may differ from those obtained during on-site visits. Furthermore, due to the execution of one mystery call per CP, the answers refer to only one member of the pharmacy staff. Due to the demand of the MCs explicitly for ellaOneThe inclusion in the assessment form of the possible questions for information gathering of the pharmacy staff provided for in the scenario could have enabled a fully comprehensive assessment of the counseling and also a better assessment of the respective preparation recommendation. However, due to the large number of items to be remembered, there would then have been a risk of bias in the results due to possible missing or faulty memories  of the MCs.No specific measures were taken to find out whether mystery callers were identified by the pharmacy staff. This would have required the willingness to cooperate of the CPs studied , which iBerlin CPs support access through high immediate availability, especially to UPA preparations. However, access is hampered by high absolute price ranges of both UPA and LNG preparations, which could ideally be minimized by a comparison app. It is positive that CPs promote the benefits of UPA preparations by recommending them noticeably more often than LNG preparations. However, there are deficiencies in giving advice, so there is a need to raise awareness among pharmacy staff to ensure "adequate" counseling in advance over the phone.Additional file 1. Example for a written, pharmacy-specific performance feedback using graphically edited benchmarking.Additional file 2. Price of UPA medications (n = 241) by district, median price (minimum price - maximum price). Price of ellaOne® (n = 224) by district, median price (minimum price - maximum price). Price of UPA generics (n = 50) by district, median price (minimum price - maximum price). Price of LNG medications (n = 160) by district, median price (minimum price - maximum price).