Four other markers surpassed the proposed threshold (P = 2.5 × 10–5). Additionally, fourteen genomic regions accounted for more than 0.1% of the variance explained by all genome windows. Corresponding markers were located within or in close vicinity to several candidate genes, such as ARRB1, RELT, ATG16L2, IGSF21, UBR4, ULK1, DCN, MAPB1, NAIP, CD26, IFIH1, NDFIP2, DOK4, MAF, IL2RB, USP18, ARID5A, ZAP70, CNTN5, PIK3AP1, DNTT, BLNK, and NHLRC3. These genes play important roles in the regulation of immune responses to the infections through several biological pathways. Similar vaginal bacterial community was observed in both cases and controls while the fecal bacterial composition and diversity differed between the groups (P < 0.05). Faeces from the control does showed a higher relative abundance of the phylum Bacteroidota compared to cases (P < 0.05), while the latter showed more Firmicutes, Spirochaetota, Planctomycetota, and Proteobacteria. On the genus level, the control does exhibited higher abundances of Rikenellaceae RC9 gut group and Christensenellaceae R-7 group (P < 0.05), while the infected does revealed higher Bacteroides, Alistipes, and Prevotellaceae UCG-003 (P < 0.05). This information increases our understanding of the genetics of the susceptibility to Brucella in goats and may be useful in breeding programs and selection schemes that aim at controlling the disease in livestock.Goats contribute significantly to the global food security and industry. They constitute a main supplier of meat and milk for large proportions of people in Egypt and worldwide. Brucellosis is a zoonotic infectious disease that causes a significant economic loss in animal production. A case–control genome-wide association analysis (GWAS) was conducted using the infectious status of the animal as a phenotype. The does that showed abortion during the last third period of pregnancy and which were positive to both rose bengal plate and serum tube agglutination tests, were considered as cases. Otherwise, they were considered as controls. All animals were genotyped using the Illumina 65KSNP BeadChip. Additionally, the diversity and composition of vaginal and fecal microbiota in cases and controls were investigated using PCR-amplicone sequencing of the V4 region of 16S rDNA. After applying quality control criteria, 35,818 markers and 66 does were available for the GWAS test. The GWAS revealed a significantly associated SNP (P = 5.01 × 10 Brucella species1. It is categorized as a complex disease due to its wide range of hosts and the variable clinical signs1. It is a major zoonotic disease that infects domesticated and wild animals3, and is classified as a serious challenge and a neglected disease in developing countries, infecting about half a million people yearly5. Brucellosis is endemic and a highly contagious disease in the mediterranean countries, the middle east, south and central Asia and central and south America7. The disease causes significant economic losses to the animal production industry due to reducing animal productivity and fertility, fetuses’ losses, vaccination and veterinary care and culling costs10.Brucellosis is a bacterial infectious disease caused by various Brucella melitensis is the most causative prevalent species of the disease in small ruminants11. Animals become infected after the ingestion of contaminated milk, feed, water or grazing forage, close contact with infected animals, uterine secretions, or aborted foetuses14. The most notable clinical signs of the disease are abortion during the last third part of pregnancy, damages in male congenital tract with sterility, reduced fertility, reduced weight gain, a substantial decline in milk production and serious clinical complications to humans as well15. Humans acquire the disease through direct contact with infected animals or consumption of the contaminated animal products. Different Brucella spp. may infect humans16, however, B. melitensis transmitted from sheep and goats is the most contagious virulent pathogen18. However, data for the Brucella prevalence in Egypt are scarce, recent studies stated that the disease is endemic with high prevalence21. The prevalence of brucellosis in Egyptian goats ranged from 3.5521 to 11.3% according to different resources23 and causes a severe reduction in the profitability of animal production18.Brucella vaccine available for use in humans24. Vaccines are available for animals but do not differentiate between infected and vaccinated animals on serological diagnostic tests25. Other tests, including molecular methods, have been proposed for the diagnosis of brucellosis in farm animals, although a combination of different methods appears most reliable for definitive identification so far26. It is well-known that genetics contribute significantly to complex traits27, including animal health and welfare29. Improvement in animal health through genetic selection is advantageous, because genetic gain is accumulative and permanent, as the polymorphisms introduced by breeding into a population can persist for many generations30. Identification of the host genetic predisposition to disease susceptibility could be a useful aid in programs that focus on management, screening, and culling of diseased animals31. It is worth to note that the vaginal and fecal microbiota has direct relationships to animal performance as it affects animal fertility32, and thus the profitability of animal farms. Therefore, exploring the vaginal and fecal microbiome could assist to predict the reproduction success and may be considered in selection programs34.Currently, there is no safe and effective 38 and livestock43. This suggests that there are individual variations in the degree of susceptibility or resistance to the disease44. Besides, understanding the changes in the vaginal and gut microbiota in case and control animals could improve their reproductive performance45. Limited information is available for the disease related traits and its effect on the vaginal and gut microbiome in livestock. The objective of this study was to scan animals’ genomes to identify genetic markers and candidate genes underlying susceptibility to brucellosis infection in Egyptian goats. Also, the effect of brucellosis infection on the diversity and composition of vaginal and fecal microbial community was considered. This may help in management system and controlling programs of the disease.Few genetic variants and candidate genes have been identified to contribute to brucellosis in humanshttps://arriveguidelines.org/), all methods were performed in accordance with the relevant guidelines and regulations. A total number of 96 Damascus goats from two independent flocks located in the North-costal region of Egypt, were included in this study. The most obvious sign of brucellosis infection was recorded in the first flock (n = 76). More than half of the does have aborted at the last third period of pregnancy with retained placenta, while the remaining does have delivered their kids successfully. The second flock consisted of 20 does were included in the analysis as controls because they all gave successful birth, and they were negative to all the subsequent brucellosis serological tests. All the animals received a similar diet consisting of alfalfa hay (50%) and barley grains (50%). All the samples used in this study were collected during the first week after abortion or delivery.All animal procedures included in the current study were approved by the animal breeding committee at the Desert Research Center (DRC) in Egypt. Along with the relevant ARRIVE 46. Samples that showed positive reaction to RPPT were tested using serum tube agglutination test (STAT)47 to confirm the infection. For RPPT, equal volumes (30 μL) of standardized B. melitensis antigen and test serum were mixed thoroughly. Any appearance of agglutination was recorded as a positive result. According to the degree of agglutination, positive samples were classified as weakly positive ( +) to strongly positive (+ +  + +). The samples in which agglutination was not observed within 4 min were assigned to be negative ( −) to Brucella infection. Using STAT, significant titers were those determined to be ≥ 1/80, which were considered positive to Brucella infection, while the 1/40 titers were considered negative. According to the World Organization of Animal Health protocol, RPPT and STAT tests were repeated for the same animals twice with a period of 2 weeks between the two tests to confirm the infection (case vs control). Additionally, another criterion was taken into account, which was the reproductive status of the animal (abortion vs birth).Blood samples for each animal in the study were collected from the jugular vein in vacutainer tubes containing EDTA and other plain tubes without anticoagulant. Subsequently, serum samples were initially tested against Finally, animals were considered cases if they have aborted at the last third period of pregnancy and were positive to both RPPT and STAT tests. Otherwise, they were considered control and matched with cases on the flock and proximal birth date.Genomic DNA was extracted from the whole blood of each doe using a Puregen Core Genomic DNA extraction from blood Kit A  according to the manufacturer’s protocol. The quantity and quality of extracted DNA was assessed using Nanodrop spectrophotometer. High-quality DNA samples (≥ 50 ng/µL) were genotyped at the Research Institute for Farm Animal Biology, Dummerstorf, Germany, using the Illumina®inc. Goat_IGGC_65K_v2 Infinium HD SNP chip , which contains 59,727 SNPs in total, evenly distributed throughout the caprine genome using iScan Reader (Illumina). The ARS1 goat assembly was used as a reference genome in this study. Genotype calling was performed using GenomeStudio software (Illumina) according to the manufacturer’s protocols.48, using the following parameters: (i) significant deviation from Hardy–Weinberg Equilibrium (HWE) p < 10−6; (ii) Minor Allele Frequency (MAF ≤ 0.01); (iii) genotype call rate < 0.99 for markers and < 90% for individuals. Furthermore, SNPs with unknown or identical chromosomal positions were also excluded from the subsequent analyses. A relatedness test was done using PLINK v1.9 to verify independence among the individuals. To avoid genomic inflation , pairwise identity-by-decent (IBD) was estimated for each pair of individuals in the population. Individuals of a pair that had a pi-hat value greater than 0.45 were considered as closely related, and thus were removed from the analysis.A principal component analysis (PCA) was performed to illustrate the relationship between individuals using R software. The PC1 and PC2  and genome-wide association analysis were implemented in SNPRelate package in R49 and with the –assoc function in PLINK software v1.9, respectively. The Bonferroni correction of P-values was applied by accounting for the number of performed tests, suggesting a significance threshold of P < 5 × 10–6. To avoid false negative SNPs, another threshold of P < 5 × 10–5 was used following the Welcome Trust Case Control Consortium suggestion (WTCCC)50, which was also suggested by Duggal P. et al.51 as a conservative way to correct for the SNPs that are not truly independent. A quantile–quantile (Q-Q) plot of the observed P-values against expected P-values was created to evaluate the overall GWAS associations. A Manhattan plot of the negative common logarithm of SNP-specific P-values versus the chromosomal location was drawn using qqman package in R52.Two statistical approaches based on fixation index (2) was estimated based on the genomic information only using the Genome-wide complex trait analysis (GCTA) software53. First, restricted maximum likelihood (REML) analysis was performed until convergence of the likelihood ratio test (LRT). This option was followed by the option ‘–grm’ to estimate the variance explained by the SNPs that were used to estimate the genomic relationship matrix (GRM). Second, random effects were predicted by the best linear unbiased prediction (BLUP) method using ‘–reml-pred-rand’ to estimate the breeding value for each animal in this study attributed by the aggregative effect of the SNPs used to estimate the GRM followed by ‘–blup-snp’ flags implemented in GRMEL tool, which calculates the BLUP solutions for the SNP effects.Due to the absence of pedigree data for animals included in this study, variance was proportioned, and SNP-based heritability  of genotyped animals using the postGSf90 software of the BLUPF90 package55. SNP effects were calculated as: û = DZ' [ZDZ'] –1 âg, where û is the vector of SNP effect; D is the diagonal matrix for weighting factors of the SNP effect; Z is the matrix of genotypes, and âg is the vector of breeding values predicted for genotyped animals.The variance explained by each SNP was calculated as: σ2 = û2 2p(1 − p), where û is the SNP effect described above and p is the allele frequency of the SNP56. The percentage of genetic variance explained by a window segment of 5 adjacent SNPs was calculated as: (Var (ai))/(σ2a) × 100%, where ai is the genetic value of the i-th region that consists of 5 adjacent SNP and σ2a is the total genetic variance57.The single-step GBLUP implemented in the BLUPF90 family–5) and the top 10 SNPs that accounted for the highest Fst values, a defined 2 Mb region (1 Mb on each side) was considered as a QTL interval. SNP locations reported in this paper are based on the genome version of Capra hircus available from the National Center for Biotechnology Information . Information on the SNPs and functional annotation of genes were obtained from BioMart at the Ensembl Genome Browser (http://www.ensembl.org/biomart)58. Functions of genes and encoded proteins were investigated using UniProt, OMIA  and the GeneCards databases59.For each significant SNP .g for 20 min to collect cell pellets that were used in DNA isolation using QIAamp DNA Stool Mini Kit  according to the manufacturer’s instructions.The vaginal samples were collected from each animal independently by inserting a sterile cotton swab into the vagina and rolled on the surface of vaginal epithelium for 30 s. The collected swabs were then stored at − 20 °C until subsequent analyses. The frozen swabs were thawed, re-suspended in 5 ml dissociation solution , pH = 2), vortexed for 1 min and the supernatant solution was subsequently transferred to another sterile 50 ml tube. This step was further repeated twice and supernatants were collected. Then, the liquid was centrifuged at 12,000×Fecal samples were collected from the rectum of the goats using clean gloves and samples were placed in sterilized 50 ml tubes that were frozen immediately at − 20 °C. Subsequently, 0.2 g fecal samples were used in DNA extraction using QIAamp DNA Stool Mini Kit  according to the manufacturer’s instructions.The quality and quantity of extracted nucleic acid were checked by Nanodrop spectrophotometer. The composition and diversity of microbial communities were studied by amplification of variable region V4 of the 16S rDNA gene by 515F and 926R primer sets using the following PCR conditions: 94 °C for 3 min; 35 cycles of 94 °C for 45 s, 50 °C for 60 s, and 72 °C for 90 s; and 72 °C for 10 min. The purified PCR-amplicons were sequenced using the Illumina MiSeq system at Integrated Microbiome Resource .60. The Fastq files of paired-end reads were demultiplexed and their quality was checked. Then after, the sequences were filtered, trimmed, and dereplicated followed by merging R1 and R2 reads together to get denoised sequences. The denoised sequences were subjected to removing the chimeras; then Amplicon Sequence Variants (ASVs) were obtained. Taxonomic assignment of ASVs was conducted using “assign Taxonomy” and “addSpecies” functions and microbial taxa were identified using SILVA reference database (version 138). Alpha diversity indices, including Chao1, Shannon, and InvSimpson were calculated. Moreover, beta diversity of microbial communities was calculated as principal coordinate analysis (PCoA) using bray–curtis dissimilarity. The differences in diversity indices and the relative abundances of bacterial phyla and genera were estimated by unpaired T-test. The raw sequence reads were deposited to SRA at https://www.ncbi.nlm.nih.gov/sra/PRJNA910086.The generated paired-end (PE) Illumina raw sequences were analyzed in R software using the DADA2 pipeline2 > 0.5. Finally, 66 individuals (12 cases and 54 controls) genotyped at 35,818 SNPs were used in the subsequent GWAS analyses.The quality control filtration process removed 15,051 SNPs and 10 animals, while 44,353 variants and 66 does passed QC criteria. The remaining SNPs were pruned for linkage disequilibrium using a window size of 1 Mb and a threshold of rsnp1723-scaffold1048-1212160; P-value < 5.0 × 10–7) on chromosome 15 at 29 Mb surpassed the suggested genome-wide significance threshold level. The top 10 SNPs, some of which reach the WTCCC suggested threshold were located on chromosomes 17 (25 Mb), 5 (21 Mb), 20 (9 Mb), 3 (54 Mb), 2 (102 Mb), 12 (30 Mb), 18 (7 Mb), 5 (75 Mb) and 30 (39 Mb)  and Chr15 (29 Mb) and these genomic regions harbor important functional candidate genes for the disease.The top 10 SNPs that accounted for the highest Fudy Fig. B were obudy Fig.  and accoly Table . Informa2g), residual (σ2e) and phenotypic (σ2p) variances and corresponding standard errors (SE) were estimated based on genomic information as 0.038 ± 0.039, 0.1 ± 0.04 and 0.14 ± 0.025, respectively. SNP-based h2 for susceptibility to brucellosis infection in goats based on genomic information was estimated as 0.26 ± 0.26.Genotypic , 15 (72 Mb), 7 (40 Mb), 21 (46 Mb), 13 (48 Mb), 16 (43 Mb), 18 (15 Mb), 30 (22 Mb), 12 (0 Mb), 6 (84 Mb), 1 (66 Mb), 5 (20 Mb), 23 (4 Mb) and 30 (21 Mb) explaining about 0.47%, 0.43%, 0.2%, 0.12%, 0.12%, 0.12, 0.12%, 0.11%, 0.1%, 0.1%, 0.1%, 0.1%, 0.1% and 0.1% of the genetic variance, respectively  reads per sample. The number of ASVs and alpha diversity indices, Chao1, Invsimpson, and Shannon, were greater in vaginal positive (VP) than vaginal negative (VN) group but this difference was not significant (P > 0.05) Table .Table 4TBurkholderia-Caballeronia-Paraburkholderia, Alcaligenes, Halomonas, Ralstonia, Delftia, Pseudochrobactrum, Pseudoxanthomonas, Bradyrhizobium and Paracoccus. Only genus Pseudochrobactrum was significantly higher (P < 0.05) in VP group compared to VN group. Additionally, unclassified bacteria in this phylum represented more than 5%  reads per sample. The number of ASVs as well as alpha diversity indices, Chao1, Inverse Simpson, and Shannon, were significantly (P < 0.05) higher in the cases  than in the control   in FP group compared to FN (Table Phascolarctobacterium and NK4A214 group were significantly higher (P < 0.05) in FP group. Unclassified bacteria within Firmicutes represented 23.6% of the bacterial community  and was higher in FP group compared with FN group without significant difference. This phylum was dominated by 0.41% andty Table .Table 9TRikenellaceae RC9 gut group that was declined in FP group. In addition, genera Bacteroides, Alistipes, and Prevotellaceae UCG-003 were higher abundant in FP group , was significantly higher abundant in FN group than FP group Table . On the %, was siup Table . ArchaeaSLC11A1, TLR1, and TLR4) and cytokine  responses62. Hence, polymorphisms in the coding genes or genes of related pathways may contribute to the immune response capacity of the animal (i.e. disease tolerance or susceptibility). The genetic basis for variations in the animal response to the infectious diseases was previously reported in cattle suggesting the feasibility of genetic selection to improve resistance/tolerance to the disease30. In the current study, some of the significantly identified chromosomal regions harbor genes with biological roles in the innate immune response of the animal. For instance, the snp57399-scaffold913-2774950 (rs268288924) on goat chromosome 20 was located in the intronic region of MAP1B (microtubule associated protein 1B) gene, which acts as a positive cofactor in DAPK1 (death associated protein kinase)-mediated autophagic vesicle formation and membrane blebbing63. The snp20448-scaffold202-3614967 (rs268252959) on chromosome 2 mapped to the intronic region of KCNH7 (potassium voltage-gated channel subfamily H member 7) gene. This gene was identified in the plasma membrane as a potassium channel64 to be involved in multiple cellular processes affect the immune system.The GWAS for brucellosis infection in Egyptian goats identified novel genetic markers, which may contribute to brucellosis susceptibility. It is important to understand the biological background of the candidate genes identified in the current study. The animal's response to the pathogens relies on the induction of multiple cell-mediated immune systems, such as the innate immune : (1) the arrestin beta 1 (ARRB1) gene, which is involved in Toll-like and IL-1 receptors signaling. This gene was found to be highly expressed in peripheral blood leukocytes and plays a major role in regulating receptor-mediated immune functions66. (2) The RELT-TNF receptor (RELT) gene, which activates the NF-kappaB pathway and binds TNF receptor-associated factor 1 (TRAF1). This receptor is capable of stimulating T-cell proliferation in the presence of CD3 signaling, which suggests its regulatory role in immune response67. (3) The autophagy related 16 like (ATG16L2) gene, which is involved in biological pathways related to autophagy. In agreement, several immunity-related genes have been identified for resistance/susceptibility to brucellosis infection in humans, such as IL-1735, IFN-R136, TGF-β137 and IL6 and IL1038. In contrast, four alleles in the TLR5 gene were identified to be correlated with a seroprevalence of brucellosis in the Saanen goats43, as well as the innate immunity genes in cattle68. A haplotype in the PTPRT gene was associated with resistance to Brucella infection in Argentinian goats41. Associations of TNF rs668920841 and INRA111 polymorphisms with caprine brucellosis was also reported69. Additionally, variability at the SLC11A1 locus has been linked to resistance to brucellosis in Algerian goat70. Few genetic variants and candidate genes have been associated with antibody response in feral swine infected with brucellosis42.Importantly, three potential candidate genes were located within 1 Mb window of the most significant associated SNP on chromosome 15 (ULK1 (Unc-51 Like autophagy activating kinase 1) gene , which is involved in several processes including autophagosome assembly71. The IFI44L and IFI44 (interferon induced proteins) genes at CHR3 (54 Mb) play a critical role in antiviral and antibacterial activity72. They promote macrophage differentiation and facilitate inflammatory cytokine secretions in the immune response to bacterial infection. The snp20448-scaffold202-3614967 (P = 6.86 × 10–5) on chromosome 2 was in close proximity to three candidate genes , which are involved in the immune regulation and response to infections74. Similarly, snp21290-scaffold208-1536919 on chromosome 18 at 70 Mb indicated two potential candidate genes: docking protein (DOK4) and transcription factor (MAF), which are involved in the regulation of the immune response induced by T-cells and activation of the expression of IL4 in T helper 2 cells76. The window at 75 Mb on chromosome 5 harbored snp32384-scaffold368-1088126 indicating the interleukin 2 receptor subunit beta (IL2RB) gene, which is involved in IL2 and T cell-mediated immune responses62, and the ubiquitin specific peptidase (USP18) gene, which is involved in regulation of the inflammatory response triggered by type I interferon77.Likewise, other significant SNPs were located in intergenic regions of important candidate genes. For instance, PIK3AP1) gene that contributes to B-cell development, activation of PI3K in natural killer cells and TLR signaling pathways, (2) DNA Nucleotidylexotransferase (DNTT) gene, which encodes a protein expressed in malignant pre-B and pre-T lymphocytes during early differentiation78, and (3) B Cell Linker (BLNK) gene that encodes a cytoplasmic linker or adaptor protein and plays a critical role in B-cell development79. Further chromosomal regions were identified on chromosomes 5 (72 MB), 12 (63 Mb) and 24 (46 Mb) comprising contactin (CNTN5), NHL repeat containing (NHLRC3) and ectopic P-Granules 5 autophagy tethering factor (EPG5) genes that encode proteins involved in the immunoglobulin superfamily80, autophagy and pathways related to the innate immune system81. A considerable overlap was observed between GWAS and Fst approaches pointing to chromosomes 18 (7 Mb) and 15 (29 Mb) with important candidate genes for the disease as shown above.An interesting genomic region on chromosome 26 (34 Mb) included 3 potential candidate genes that are involved in the immune system: (1) Phosphoinositide-3-Kinase Adaptor Protein  using either pedigree or genomic information was reported for paratuberculosis in dairy cattle85. This suggests that brucellosis infection may be heritable and could respond well to genetic improvement programs.SNP heritability measures the proportion of phenotypic variance explained by all markers without pedigree86. In our study, we identified 14 genome windows each explained more than 0.1% of the total genetic variance and all together explained 2.32% of the genetic variance. These regions harbored genes of immunity, which may be suitable candidates for the susceptibility to brucellosis. Interestingly, the genome window located on chromosome 18 (15 Mb) harbored interleukin 17C (IL17-C) and ring finger protein 166 (RNF166) genes that play a crucial role in the innate immunity87 and autophagy87, respectively. Likewise, the genomic window on chromosome 1 (66 Mb) harbored CD86, poly (ADP-ribose) polymerase family (PARP9), and deltex E3 ubiquitin ligase (DTX3L) candidate genes, which are involved in the immune response89. Corresponding to Fst results, the region at 72 Mb on chromosome 15 that harbors the CNTN5 gene was identified here to explain 0.43% of genetic variance. This suggests that this region may plays an important role in the disease susceptibility and may be worth to be involved in further investigations. Otherwise, none of the top significant SNPs were reported in the top genome windows that explain > 1% of phenotypic variance, suggesting that the trait is likely controlled by a multiple SNPs with small to medium effects85.Quantitative traits may be affected by a few genes with large or modest effects plus genes with small effects, otherwise, they are affected by many genes with small effects90. This is due to the direct effects of the intestinal microbiota on diet fermentation and animal feed efficiency92. Genetic polymorphisms were identified to influence the microbiome in humans. Noteworthy, the vaginal and fecal microbiome is significantly associated with animal performance32. For instance, it was reported that dysbiosis in the vaginal microbiome causes inflammation and declines reproductive efficiency93. In goats, no information is available on the vaginal and fecal microbiome and their relations with reproductive diseases such as brucellosis.The immune system has a complex bidirectional relationship with the microbiome of the organism. The diversity of the fecal microbial community showed a crucial association with variations in the immune response, which in turn may alter the microbiome94. This is consistent with our findings in the vaginal and fecal microbial communities and supported by the results of PCoA. In agreement, Lui et al.95 showed that the vaginal microbiome of aborted women showed higher diversity compared to healthy women. Additionally, the vaginal microbiome in this study was dominated by phylum Firmicutes and Proteobacteria, which agrees with previous studies on cattle32 and sheep34. In this study, the vaginal bacteria were dominated by microbes assigned to genera covering Burkholderia, Caballeronia and Paraburkholderia, which were lower abundant in the infected does compared to the non-infected. Consistently, the abundance of these genera was declined in the aborted women patients with recurrent spontaneous abortion (RSA)96. Moreover, corresponding species were observed in the semen microbiome and showed a positive correlation with the healthy status of the genital tract97. Infected animals showed a higher relative abundance of the genus Halomonas, which is a pathogen bacterium95. Genus Alcaligenes was higher abundant in the infected does which agrees with the pathogenic attributes assigned to species of this genus98. Furthermore, Alcaligenes is a drug-resistant genus and susceptible to a specific combination of antibiotics99. These findings highlight the importance of investigating the vaginal microbiome in animals with reproductive disorders to characterise the microbial milieu and identify the best suitable antimicrobial treatment. Genera Streptococcus, Salinicoccus and Staphylococcus cause vaginal disorders and abortion, which may explain their higher proportion in the infected does101. Staphylococcus was previously observed in ewes34 and dominated the vaginal microbiome of goat in estrus synchronization33. Unlike Human vaginal microbiome, Lactobacillus represented a small proportion in the goats’ vaginal microbiome102. Importantly, some of bacterial genera found exclusively in the infected does are pathogens (e.g. genus Neisseria), which may causes sexually transmitted diseases103.Reproductive disorders increase the microbial diversity in the reproductive tract104. Most of bacteroidetes’ members are specialized in the degradation of lignocellulose and soluble polysaccharides in the animal gut105, which may be a reason for decreasing the dietary fiber digestion in the infected animals. The dominant genera in the fecal microbiome were Rikenellaceae RC9 gut group and Christensenellaceae R-7 group, which is consistent with previous studies on goat fecal microbiome104 and the rumen microbiome of cattle92. These genera showed higher relative abundances in the non-infected animals. Members of family Christensenellaceae were related to a healthy phenotype in humans106. Christensenellaceae R-7 group abundance was positively correlated with animal health, feed efficiency, animal’s body mass index, rumen digestion and absorption of nutrients, fiber digestion, and protein metabolism; and this genus produces acetic and butyric acids92. Moreover, Rikenellaceae RC9 gut group plays a critical role in the digestion of crude fiber105. Accordingly, reproductive disorders could impair animal performance and gut fermentation. Specifically, fiberolytic bacteria not only digest dietary fiber but some also represent a barrier against pathogens107.The differences in the diversity and structure of fecal microbiome were significant between infected and non-infected animals. Also, the bacterial community in fecal samples was dominated by phylum Firmicutes and Bacteroidetes, which agrees with previous studies on fecal microbiota in goats and cattle108. Despite GWAS highlights signals in the entire genome that help to understand the biology of disease infection, they do not pinpoint the causative variants. There are many limitations of complex traits (e.g. disease susceptibility) analysis with GWAS. The small sample size, low or missing heritability109 and the phenotype definition (e.g. examining different stages of the disease progression) are of these limitations110. Therefore, it is difficult to identify SNPs with larger effects and there is a lot of discrepancies between GWAS results111. Besides, the identified variants do not capture most of the additive genetic variations due to the disease. However, these investigations are important to clarify the biological basis of disease, providing information about the mechanisms underlying the disease process83. In the current study, the small sample size and identification of the infectious status of brucellosis based on serological test only are limiting our results. Future studies with larger sample size are needed to confirm the identified SNPs that arise from a particular biological pathway. Alongside, combining multiple datasets may increase the statistical power and exhibit the overlapped chromosomal regions that was marginally identified in the previous analysis112. This may also encompass interdisciplinary molecular approaches, such as microbiology and PCR-based identification of the causative agent with different isolates to classify and identify the virulent strain of the disease113.Generally, identifying the causative variants for a specific disease is challengingARRB1, RELT and ATG16L2), 20 , 2 , 17 , 3 , 18 , 5  and 26  as the most important genomic regions potentially contributing to the incidence of brucellosis in the examined goat population. Some of the identified QTLs were overlapped between Fst and GWAS approaches (CHR18: 7 Mb and CHR15: 29 Mb) and all of them are novel. Furthermore, our results suggested that brucellosis infection is heritable (h2 = 0.26) and may respond well to genetic improvement programs. The GWAS analysis may support the development of markers to be used as useful adjunct in controlling the disease. In addition, new perspectives into the interaction between the infection and the diversity and composition of gut and vaginal were obtained.Lack of enough information concerning the genetics of brucellosis in livestock is a real barrier towards animal welfare, diagnosis, vaccination, and management of the disease. The previous studies pointed out polymorphisms in immunity-related genes as putative candidate genes for disease susceptibility in humans and livestock. Accordingly, the identified variants and candidate genes from our GWAS analysis may contribute to the phenotypic variations between cases and controls of Brucella infection. This included chromosome 15 (29 Mb; Supplementary Information 1.Supplementary Information 2.


Hypoglycemia remains a challenge for roughly 25% of people with type 1 diabetes (T1D) despite using advanced technologies such as continuous glucose monitors (CGMs) or automated insulin delivery systems. Factors impacting hypoglycemia self-management behaviors (including reduced ability to detect hypoglycemia symptoms and unhelpful hypoglycemia beliefs) can lead to hypoglycemia development in people with T1D who use advanced diabetes technology.This study aims to develop a scalable, personalized mobile health  behavioral intervention program to improve hypoglycemia self-management and ultimately reduce hypoglycemia in people with T1D who use advanced diabetes technology.We  jointly developed an mHealth text messaging hypoglycemia behavioral intervention program based on user-centered design principles. The following five iterative steps were taken: (1) conceptualization of hypoglycemia self-management processes and relevant interventions; (2) identification of text message themes and message content development; (3) message revision; (4) patient partner assessments for message readability, language acceptability, and trustworthiness; and (5) message finalization and integration with a CGM data–connected mHealth SMS text message delivery platform. An mHealth web-based SMS text message delivery platform that communicates with a CGM glucose information-sharing platform was also developed.The mHealth SMS text messaging hypoglycemia behavioral intervention program HypoPals, directed by patients’ own CGM data, delivers personalized intervention messages to (1) improve hypoglycemia symptom detection and (2) elicit self-reflection, provide fact-based education, and suggest practical health behaviors to address unhelpful hypoglycemia beliefs and promote hypoglycemia self-management. The program is designed to message patients up to 4 times per day over a 10-week period.A rigorous conceptual framework, a multidisciplinary team (including patient partners), and behavior change techniques were incorporated to create a scalable, personalized mHealth SMS text messaging behavioral intervention. This program was systematically developed to improve hypoglycemia self-management in advanced diabetes technology users with T1D. A clinical trial is needed to evaluate the program’s efficacy for future clinical implementation. Hypoglycemia episodes, especially those with blood glucose <54 mg/dL, can cause acute problems including brain dysfunction ,2 and caSeveral technologies developed over the past decade have led to major breakthroughs in diabetes management, including in reducing hypoglycemia . In partEven with these innovations, data from clinical trials -31 and fMobile health  uses mobile communication  to deliThis formative research was intended to develop a scalable, personalized mHealth behavioral intervention program, HypoPals, focusing on reducing hypoglycemic episodes, particularly those with blood glucose <54 mg/dL, among people with T1D who use CGMs.A multidisciplinary team  convened to develop the HypoPals mHealth SMS text messaging behavioral intervention program. User-centered design principles  were adoWe previously conducted survey ,37 and qTo develop hypoglycemia symptom detection training messages, concepts from blood glucose awareness training   were revDuring the message drafting, message series focusing on separate themes were jointly compiled by an endocrinologist (YKL) and an mHealth interventionist and health psychologist (JEA). Rigorous discussions were held regarding concepts, writing styles, behavior change strategies, and message length and reading level when creating each message series. Experiences with hypoglycemia and hypoglycemia self-management from people living with T1D  were incorporated into the messages. Two-way interactive messaging techniques were incorporated for increasing the engagement with and the efficacy of intervention messages . Weekly An endocrinologist (SAA) and a diabetes-specialized clinical psychologist (NDZ), 2 HARPdoc developers, individually reviewed the messages and suggested revisions. Messages were then edited by the message developers (JEA and YKL) and rereviewed by SAA and NDZ for additional input and modification as needed. A diabetes care and education specialist team  also met with the message developers to discuss and iteratively revise each message. Conversations focused on the message content, consistency with current diabetes education materials, readability, language acceptability, and use of behavior change techniques. The revision team offered more feedback during the revisions with patient partners (described below) until no further concerns or suggestions were raised.To ensure user-level message readability, language acceptability, and trustworthiness , 14 focuInterview guideTo you, what were the messages trying to say? (to determine if messages were perceived as intended)How do you think of the messages in general?What are the things you don’t like as much about how the messages were written?Do you trust what the messages were telling you?What else came to your mind when reading these messages?Each focus group convened for up to 2 hours to ensure participants remained engaged in the web-based interview. Each patient partner could only attend up to 2 focus group interviews to prevent feedback from a small portion of respondents skewing the evaluation and revision processes, which would limit the messages’ generalizability. All draft messages were reviewed, and about 70% were edited for phrasing or content based on patient partners’ suggestions. All revised messages were then rereviewed with other patient partners to enhance generalizability.An mHealth web-based SMS text messaging platform that communicates with the Dexcom CGM glucose information-sharing platform was developed using Twilio . DatabasDexcom CGMs were used in this phase of HypoPals development because Dexcom Developer is a Food and Drug Administration–approved platform for securely sharing patients’ CGM data with third-party app developers . The higThe University of Michigan Institutional Review Board approved this study .A CGM data–directed mHealth SMS text messaging program, HypoPals, was created to enhance hypoglycemia self-management and reduce hypoglycemia in people with T1D who continue to develop severe hypoglycemia or spend time with glucose <54 mg/dL above the recommended target  despite Hypoglycemia symptom detection training includes 27 message series. Each series typically contains a pair of messages. The first message in each series normally presents information about hypoglycemia symptoms and outlines symptom detection behavior in a stepwise fashion by covering (1) the importance and use of hypoglycemia symptoms as biological feedback for decision-making; (2) types of hypoglycemia symptoms, such as bodily sensations or sensory, mental, and mood changes, along with quick self-tests that patients can perform to detect psychological changes during hypoglycemia; (3) creation of a list of personal hypoglycemia symptoms; and (4) the need to routinely self-check hypoglycemia symptoms upon receiving CGM hypoglycemia data. The second message in the series generally indicates the time of a recent CGM-detected hypoglycemic episode and encourages users to recall their hypoglycemia symptoms at that time. All messages are meant to encourage habitual self-checking of hypoglycemia symptoms when patients receive CGM information about ongoing hypoglycemia. Sample messages are displayed in The mHealth SMS text messaging technology scans for CGM hypoglycemic episodes  3 timesHypoPals’s hypoglycemia psychoeducation includes 77 message series. Each series contains between 1 and 3 messages, and 14 series contain an additional 2-way interactive message. The messages are intended to (1) calibrate hypoglycemia risk perceptions, (2) improve hypoglycemia self-management outcome expectancies, and (3) increase hypoglycemia self-management coping efficacy. Methods such as eliciting self-reflection, providing fact-based education, and suggesting practical health behaviors are included in the program to address unhelpful hypoglycemia beliefs and promote hypoglycemia self-management. Chronologically, message topics first entail (1) identifying internal motivations to increase hypoglycemia self-management outcome expectancies and (2) eliciting perceived hypoglycemia risks to mitigate associated risk perceptions. The topics then evolve to focus on (3) addressing strong perceptions of hyperglycemia risks and social risks from self-management to further improve hypoglycemia management outcome expectancies and (4) suggesting practical behaviors related to hypoglycemia self-management to reinforce self-management self-efficacy. Every 7 days, based on the time spent in hypoglycemia, the user receives either an encouraging message or a gentle reminder to promote self-monitoring and hypoglycemia self-management. Sample messages are shown in During the focus group interviews with patient partners , participants generally reported to have found the intervention messages trustworthy. At the early phase of this step, comments about the message readability often focused on the message length or the content itself: “I think this is way too long. I don’t know how to fix it but you better shorten it” and “I don’t understand what this means. Tell me what it was trying to say.” Many patient partners actively offered suggestions on how to improve the readability of the messages: “Perhaps you can shorten this by saying...”; “I don’t know anything about psychology but I will better understand it if you say...”; and “Maybe you should break down these messages to make them shorter.” Critical comments on the acceptability were also made early in the revision phase: “I don’t like how this was written; it makes feel that I am not in control. Maybe you can change this to...” and “This kind of feedback makes me uncomfortable. You should try to make it more positive.” No concerns about readability or acceptability emerged during the later phase of this revision step. Instead, comments from patient partners began to focus on the perceived effectiveness while this concept was not specifically assessed: “This is really, really helpful; you guys are doing a great job!”; “This is giving me goosebumps – it is just talking about me!”; “I am the only person I know who has diabetes, and this is helping me know that there are also others around experiencing what I experience”; and “I wish people [had] told me about [this information] years ago. I probably could’ve lived a better life.”In terms of technological processes, the mHealth platform regularly retrieves users’ CGM glucose data to deliver intervention messages and achieve a closed-loop CGM-mHealth SMS text message intervention cycle  73]..73].Advanced diabetes technologies have helped reduce hypoglycemia in people living with T1D. However, no widely available interventions exist to further assist patients who continue to develop this dangerous complication. The literature suggests that a lowered ability to detect hypoglycemia symptoms and unhelpful hypoglycemia beliefs hinder hypoglycemia self-management among T1D advanced diabetes technology users. We developed HypoPals, a CGM data–directed hypoglycemia behavioral intervention program, to address these gaps. The program is meant to promote hypoglycemia self-management and ultimately minimize hypoglycemia among people with T1D who use advanced diabetes technologies.HypoPals has several strengths. First, we developed a detailed conceptual framework grounded in quantitative and qualitative research. The model was adapted from established theories, including a hypoglycemia biopsychobehavioral model . ProgramHypoPals is innovative in several ways. To our knowledge, this theory-driven program is the first to adopt mHealth as delivery vehicle for a hypoglycemia behavioral intervention. This technique could promote scalability and consistent delivery (and thus fidelity and reproducibility) in the future . FurtherNotably, HypoPals was not created to replace conventional diabetes self-management and support programs. People with T1D presumably need comprehensive diabetes self-management training on issues including insulin dosing, meal content, exercise, and fast-acting carbohydrates to adequately process the program’s content. Thus, HypoPals may play an adjunctive role—particularly for patients who continue to experience severe hypoglycemia  or are at high risk due to impaired hypoglycemia awareness or unhelpful hypoglycemia beliefs despite the current standard of care, including completion of comprehensive diabetes education and using advanced diabetes technologies.It is important to point out that, while HypoPals was designed to address multiple barriers to hypoglycemia self-management in advanced diabetes technology users, HypoPals alone is unlikely to resolve all hypoglycemia on CGM. For example, social determinants of health, including limited access to food, have been related to hypoglycemia development . While HHypoPals’s efficacy in reducing hypoglycemia has not been formally tested . The proIf this program’s efficacy is confirmed, then further modifications could make HypoPals more scalable. For instance, while the current iteration relies on Dexcom CGM data, the program’s conceptualization and methods could also apply to users of other CGM systems. Moreover, because this intervention was developed with US patient partners, cultural and linguistic adaptations may be needed to deploy it more globally.A CGM data–directed mHealth hypoglycemia behavioral intervention program was developed to improve hypoglycemia self-management among people with T1D using advanced diabetes technologies. Efficacy testing is the next step. To date, this theory-based, scalable program has potential for improving hypoglycemia symptom detection and addressing unhelpful hypoglycemia beliefs in order to reduce hypoglycemia. If proven to be effective, HypoPals can lessen hypoglycemia-related complications and burdens among people who continue to struggle with these concerns despite the current standard of care.


Vigna aconitifolia) is an underutilized, protein-rich legume that is grown in arid and semi-arid areas of south Asia and is highly resistant to abiotic stresses such as heat and drought. Despite its economic importance, the crop remains unexplored at the genomic level for genetic diversity and trait mapping studies. To date, there is no report of SNP marker discovery and association mapping of any trait in this crop. Therefore, this study aimed to dissect the genetic diversity, population structure and marker-trait association for the flowering trait in a diversity panel of 428 moth bean accessions using genotyping by sequencing (GBS) approach.Moth bean  were discovered by genotyping of 428 moth bean accessions. Model-based structure analysis and PCA grouped the moth bean accessions into two subpopulations. Cluster analysis revealed accessions belonging to the Northwestern region of India had higher variability than accessions from the other regions suggesting that this region represents its center of diversity. AMOVA revealed more variations within individuals (74%) and among the individuals (24%) than among the populations (2%). Marker-trait association analysis using seven multi-locus models including mrMLM, FASTmrEMMA FASTmrEMMA, ISIS EM-BLASSO, MLMM, BLINK and FarmCPU revealed 29 potential genomic regions for the trait days to 50% flowering, which were consistently detected in three or more models. Analysis of the allelic effect of the major genomic regions explaining phenotypic variance of more than 10% and those detected in at least 2 environments showed 4 genomic regions with significant phenotypic effect on this trait. Further, we also analyzed genetic relationships among the Our study shows that the north-western regions of India represent the center of diversity of the moth bean. Further, the study revealed flowering-related genomic regions/candidate genes which can be potentially exploited in breeding programs to develop early-maturity moth bean varieties.The online version contains supplementary material available at 10.1186/s12870-023-04215-w. V. aconitifolia (Jacq.) Marechal], zombie pea (Vigna vexillata (L.) A. Rich), riceban [Vigna umbellata (Thunb.) Ohwi & H. Ohashi] which are rich in protein as well as other nutrients. These can serve as an alternative source to counter the demand–supply imbalance of conventional legume crops , [A/G], [A/T], [C/G], [C/T] and [G/T] having a share of 7.89%, 36.15%, 6.54%, 5.78%, 36.20%, and 7.4% of the total SNPs, respectively. Amongst these, SNP types [A/G] and [C/T] shared the highest percentage while other types contained less than 10% of total SNPs.To understand the population structure of the moth bean diversity panel, 9078 SNPs were employed in STRUCTURE software using an admixture-based model. The maximum peak value for Delta K was observed at K = 2 Fig. A. The acAllelic Distance Matrix for F-Statistics (Fst) analysis in the present study showed a larger variation within the individuals 74%) than among the individual (24%)  and are ancestrally related. Further, localization of these SNP on other Vigna species genomes was expected to shed some light on the relatedness of moth bean with other Vigna species at the DNA level. Therefore, chromosomal localization of significant SNPs (9078) was performed on the genomes of various Vigna species such as V. angularis, V. mungo, V. radiata, V. umbellata, V. unguiculata. BLASTn analysis was performed by using the 100 bp flanking sequences for 9078 SNPs to identify the distribution on Vigna genomes. A total of 3721 (40.99%), 6155 (76.80%), 3807 (41.94%), 3681 (40.55%), and 1391 (15.32%) of moth bean SNPs were mapped to the genomes of V. angularis, V. mungo, V. radiata, V. umbellata, and V. unguiculata, respectively. Based on these sequence-based mapping analyses, the moth bean was found to be closer to V. mungo and least close to V. unguiculata  followed by chromosome 1 . Further, to get the tentative physical location (order and position on chromosome) of the moth bean SNPs, the chromosomes of V. mungo which was found to be most closely related to the moth bean were used as an anchor. These have been depicted in Supplementary Figure SOur study, reports SNP variants in the moth bean genome for the first time. The chromosomal locations for these SNP could not be assigned due to the non-availability of moth bean reference genome. However, to have a fair idea of their tentative physical location and chromosomal distribution, these can be localized on the genomes of other mber n = 0 and arember n = 0 and are2 values for the SNPs identified by mrMLM and the P-value range for the SNPs identified by GAPIT were summarized in Table The days to 50% flowering phenotyping data of the moth bean diversity panel across the five environments and 9078 SNPs identified through GBS were used in GWAS analysis. GWAS was implemented using various multi-locus models, 4 models  in the mrMLM package and 3 models  in GAPIT software package. A high significant cut-off logarithm of odds (LOD ≥ 3.0) Figs.  and 7 waP-values of the association that exposed a good fitting for the used methods. Manhattan and QQ plots for days to 50% flowering at Bikaner and Jodhpur (the year 2022) generated by various models in the mrMLM package have been shown in Figs. The fitting of the multi-locus GWAS methods was tested using the quantile–quantile (QQ) plots between observed and expected the year 022 generP-value, these SNPs were considered top SNPs.Out of total 34 consistent SNPs detected for days to 50% flowering Table , three SCandidate genes were investigated for the SNPs (marker-trait associations) significantly associated with days to 50% flowering. A total of 34 SNPs were identified across all environments and in three or more methods. However, out of 34 SNPs, three SNPs  were present in more than one environment. Thus, essentially, there were only 29 unique SNPs representing 34 associations for flowering traits across environments. These 29 SNPs were subjected to annotation analysis using the BLASTx program. Out of 29 significant unique SNPs, 7 SNPs were found in the vicinity of annotated genes, and the rest were having no hits or were found to be hypothetical  between the two groups of phenotypes. The analysis demonstrated that four SNPs had a significant effect (P < 0.01) on the flowering trait  across the 2 locations (Bikaner and Jodhpur)  using the admixture model. For a better representation of genetic variability within the population, individuals within populations were categorized as pure and admixtures Fig. A. MajoriVigna genomes along with model plants. Seven contigs with an e-value below 0.05 showed significant annotation with the genome of other closely related Vigna Spp. These were found to show significant association with the flowering mechanisms. The receptor-like serine/threonine-protein kinases belong to the largest group of family of receptor-like kinases (RLKs) and receptor-like proteins (RLPs) which are actively involved in sensing cell’s external and internal signals [BIK1 mutant has been reported to show defects at the later developmental stages with early flowering [Rauvolfia serpentine [O-ß-D-glucoside of hydroquinone) with the help of the enzyme hydroquinone:O-glucosyltransferase. Glycosyltransferases (GTs) are enzymes that glycosylate plant compounds that in turn are directly or indirectly involved in the production of plant secondary compounds. A study involving Arabidopsis glycosyltransferase (UGT87A2) depicted regulation of flowering time involving flowering repressor gene FLOWERING LOCUS C [UGT87A2) extended the flowering time in Arabidopsis for both long and short days [V. unguiculata (Table V. unguiculata [Arabidopsis has been reported [Brassica napus contained a gene annotated as pectin esterase [PPE1) gene in mature pollen and germinated pollen tubes [PPE 1 gene in the subsequent developmental stage of anther which was highly positively correlated with the size of the flower. Contig id 104,203 (SNP6919) was annotated as Actin-related protein 8 like. It belongs to actin-related protein (ARP) family that are represented proteins having similar identity between 17 and 60% with conventional actins. In accordance to the subcellular localization, it is categorized either as cytoplasmic  or nuclear  [Arabidopsis [Flowering time is a key phenological trait that governs plant growth, plant height, and grain quality. Early flowering varieties generally mature earlier that save input costs and it also helps the plant to escape abiotic stress such as drought and heat , 41. The signals . These p signals , 44. Lin signals . The BIKlowering . The funrpentine . The stu LOCUS C . The stuort days . The annuiculata . Howeveruiculata , 51. Thereported . Contig esterase  which shen tubes . The stu, and 9) . The fun, and 9) ; howeverbidopsis , 57. ManVigna species such as V. angularis, V. mungo, V. radiata, V. umbellata, V. unguiculata was performed for deducing genome level relatedness among these species. It resulted in the localization of 3721 (40.99%), 6155 (76.80%), 3807 (41.94%), 3681 (40.55%), and 1391 (15.32%) with the genome of V. angularis, V. mungo, V. radiata, V. umbellata and V. unguiculata, respectively. The result therefore clearly demonstrates a high level of genomic similarity of the moth bean genome with that of V. mungo followed by V. radiata while the lowest similarity was observed to be with genome of V. ungiculata. The result is line with the phylogenetic positioning of Vigna species reported by Takahashi et al. [V. aconitifolia with V. radiata followed by V. umbellta and V. unguiculata using DNA sequences of nuclear rDNA-ITS and chloroplast atpB-rbcL spacer regions. Genomic synteny of moth bean performed by Yundaeng et al. [Vigna species such as azuki bean, rice bean, mungbean, black gram and yardlong bean. The study demonstrated a high level of moth bean genomic synteny with Vigna species, especially with azuki bean. Nearly 76.2% of azuki bean SSRs were found to be amplified while accessing parental polymorphism within moth bean parents.Chromosomal localization of significant SNPs 9078) on the genomes of closely related i et al.  that sho8 on the V. mungo was the closest species to moth bean among all Vigna species. The potential candidate genes/ SNPs identified in this study may be further validated for their role in determining days to 50% flowering and used for the development of markers that may be exploited in the marker-assisted program for breeding early-maturity moth bean varieties.The characterization of the genetic diversity of moth bean accessions using GBS showed that accessions from the northwest part of India were highly diverse and this region may represent the center of diversity and domestication. GWAS analysis for the flowering trait in moth bean using seven multi-locus-based models identified a total of 29 significant SNPs from five different flowering datasets. These significant SNPs were identified by three or more methods. A total of four major genomic regions showed a significant effect on the flowering trait. Further, the sequence-based localization analysis revealed that A total of 428 moth bean accessions were analyzed in the present study. The seeds of all accessions were collected from the genebank of ICAR-NBPGR, New Delhi. These 428 moth bean accessions were collected from various locations such as 160 from Rajasthan, 56 from Gujarat, 14 from Haryana, 4 from Madhya Pradesh, 5 from Maharashtra, 4 from Himachal Pradesh, 1 from Tamil Nadu, 2 from Thailand and 2 from United States of America (USA) Fig. . DetailsAll materials were planted in two different locations. The first location was Bikaner (28°1′37.60′′ N latitude and 73°18′7.76′′ E longitude) during the growing session in 2021 and 2022. The second location was Jodhpur (26°15′ 49.91′′ N latitude and 73° 0′ 32.25′′ E longitude) during the growing session in 2019, 2021 and 2022. During the flowering period, the average maximum and minimum temperature was 35.61 °C and 24.68 °C in 2021 and 35.88 °C and 25.2 °C in 2022 at Bikaner respectively. The average maximum and minimum temperatures at Jodhpur were 34.6 °C and 26.4 °C in 2019, 35.2 °C and 26.9 °C in 2021, and 33.6 °C and 26.3 °C in 2022 Fig. . The expMse1 and HindIII_EcoR1 restriction enzymes and resulting fragments were ligated with two barcoded adapters as well as a universal sequencing adapter. The library was prepared by pooling segments of desired fragment size selected after gel electrophoresis. The library was quantified on Bioanalyzer using high sensitivity DNA kit (Agilent Technologies) and sequenced on the Illumina HiSeq2500 platform. The raw sequence files were parsed based on the barcode, low-quality reads were removed and data was analyzed using ‘Stacks’ software [Genomic DNA was isolated from the leaf of two-week-old moth bean seedlings using CTAB (cetyl trimethyl ammonium bromide) method  and treasoftware . SNPs wesoftware . All SNPPopulation structure was estimated using Bayesian model-based approach implemented through the STRUCTURE program version 2.2 . The puthttps://cran.r-project.org/package=mrMLM) and MLMM [P-value. SNPs detected in three or more methods were considered as significant SNPs reliably associated with flowering trait.GWAS was carried out using various multi-locus methods such as mrMLM , FASTmrMand MLMM , BLINK [and MLMM  and Farmand MLMM  implemenand MLMM  R packagand MLMM . SNPs asVigna species. The standalone BLASTn program was performed with the genome of Vigna angularis (adzuki bean), V mungo (black gram), Vigna radiata (mung bean), V. umbellata (rice bean), and Vigna unguiculata (cowpea). The blast parameters were set as, Best hit score = 0.05, and Best hit overhang = 0.1. The top hits based on e-value and identity > 77% were selected as the final hits. The top hits and their respective coordinates were used to plot the circus map using the python package pycircos 1.0.2 (https://pypi.org/project/pycircos/). Chromosomal mapping of SNP markers was performed using the web tool MG2C (http://mg2c.iask.in/mg2c_v2.1/) to represent their physical position on V. mungo chromosomes [The flanking sequences (100 bp) from each SNP’s position for all 9078 SNPs were used to search against the genome of other omosomes .Vigna species as well as in other model crops. The putative candidate genes were identified using the BLASTx program from NCBI-BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) with default parameters. Functional annotations of each putative candidate gene were also investigated using Vigna species along with the other model crops.The significantly associated SNPs with flowering trait were searched for the orthologous sequences in genomes of other Additional file 1: Supplementary Figure S1. Localization of moth bean SNPs (6155 SNPs) on 11 V. mungo chromosomes.Additional file 2: Supplementary Figure S2. Manhattan and QQ plots generated by the total used model for all environments.Additional file 3: Supplementary Table S1. List of common early flowering accessions across five different environments.Additional file 4: Supplementary Table S2. Distribution of accessions according to population structure and phylogenetic relationship analyses.Additional file 5: Supplementary Table S3. Blast analyses for flanking sequence of all SNPs with other Vigna species such as V. angularis, V. mungo, V. radiata, V. umbellate, and V. unguiculata, representing their genomic association.Additional file 6: Supplementary Table S4. All significant SNPs identified by each models across the all environment.Additional file 7: Supplementary Table S5. All moth bean accessions used in this study along with their distributed location.


Aspergillus fumigatus is a potentially lethal opportunistic pathogen that infects over ~200,000 people and causes ~100,000 deaths per year globally. Treating A. fumigatus infections is particularly challenging because of the recent emergence of azole-resistance. The majority of studies focusing on the molecular mechanisms underlying azole resistance have examined azole-resistant isolates. However, isolates that are susceptible to azoles also display variation in their sensitivity, presenting a unique opportunity to identify genes contributing to azole sensitivity. Here, we used genome-wide association (GWA) analysis to identify loci involved in azole sensitivity by analyzing the association between 68,853 SNPs and itraconazole (ITCZ) minimum inhibitory concentration (MIC) in 76 clinical isolates of A. fumigatus from Japan. Population structure analysis suggests the presence of four distinct populations, with ITCZ MICs distributed relatively evenly across populations. We independently conducted GWA when treating ITCZ MIC as a quantitative trait and a binary trait, and identified two SNPs with strong associations in both analyses. These SNPs fell within the coding regions of Afu2g02220 and Afu2g02140. We functionally validated Afu2g02220 by knocking it out using a CRISPR/Cas9 approach, because orthologs of this gene are involved in sterol modification and ITCZ targets the ergosterol biosynthesis pathway. Knockout strains displayed no difference in growth compared to the parent strain in minimal media, yet a minor but consistent inhibition of growth in the presence of 0.15 μg/ml ITCZ. Our results suggest that GWA paired with efficient gene deletion is a powerful and unbiased strategy for identifying the genetic basis of complex traits in A. fumigatus. Aspergillus fumigatus is one of the most deadly fungal pathogens and results in more than 100,000 deaths per year  which are directly involved in the biosynthesis of ergosterol  and triazoles  target ergosterol , Japan (http://nbrp.jp/) .Sixty-five Japanese brp.jp/) . These s4 conidia per l mL were incubated in RPMI 1640 broth (pH=7) with a range of ITCZ concentrations  at 35°C for 48 h. MIC values represent the lowest ITCZ concentrations that completely inhibited growth.Minimal inhibitory concentration (MIC) of ITCZ for each isolate was determined following the Clinical and Laboratory Standards Institute (CLSI) M38-A2 broth microdilution method John, . Before https://en.novogene.com/) on an Illumina NovaSeq 6000.Genomic DNA (gDNA) isolation was performed as previously described  to trim residual adapter sequences from reads, and trim reads where quality score was below 30, with the parameters “–stringency 5” and “-q 30,” respectively. Trimmed reads shorter than 50 bp were then discarded using the option “–length 50.” Next, the deduplicated and trimmed read set was mapped to the A fumigatus Af293 reference genome  with options “–maf 0.05 –max-missing 1 –thin 3500,” to filter the full set of SNPs and require a minor allele frequency ≥ 5%, no missing data across all samples, and at least 3.5 Kb distance between SNPs. 6,324 SNPs remained after filtering, and subsequent population structure analysis was conducted with this marker set. In addition, to test the consistency of population assignments with different number of SNPs, population structure analysis was conducted with a dense SNP set where thinning was not applied  and an additional thinned SNP set where markers were spaced apart by at least 35 Kb (756 SNPs).To investigate the population structure of the K = 1–10, where K indicates the number of populations. The 5-fold cross-validation (CV) procedure was calculated to find the most likely K with option “–cv = 5.” For each K the CV error was calculated and the K with lowest CV error indicated the most likely population number. Additionally, we used the non-model based population structure software DAPC  analysis was conducted to identify genetic variants that were significantly correlated with ITCZ MIC. For GWA analysis, we filtered our complete set of SNPs with VCFtools to include SNPs with a minor allele frequency ≥5%, SNP sites with ≤ 10% missing data, and SNPs that were biallelic. This filtering procedure resulted in 68,853 SNPs that were used for GWA.p-value inflation. The potential functional effects of candidate SNPs were predicted using SnpEff v4.3t  /Cas9-mediated protocol for gene editing, as previously described . Complete guideRNAs (gRNAs) were then assembled in vitro using the custom designed crRNA coupled with a commercially acquired tracrRNA. The assembled gRNAs were then combined with commercially purchased Cas9 to form ribonucleoproteins for transformation, as previously described : GGATGCGTTGTTCCTGTGCGAfu2g02220 KO Reverse Screening Primer (P2): AACGAGGGCTGGAGTGCCHygR Reverse Screening Primer (P3): ACACCCAATACGCCGGCCCommon 4 conidia were inoculated onto glucose minimal media (GMM) agar plates without ITCZ or with 0.15 μg/ml ITCZ. We used 0.15 μg/ml ITCZ because the parent strain (CEA10) is sensitive to ITCZ and did not grow in ITCZ concentrations ≥ 0.30 μg/ml. GMM was prepared as previously described .Colony diameter was used as an estimate of growth rate to compare KO and WT strains in the presence and absence of ITCZ. For each strain, 10A. fumigatus from Japan and analyzed them in combination with an additional 11 previously sequenced isolates  for 65 isolates of A. fumigatus isolates using the model-based approach implemented in ADMIXTURE . K = 4 was also the most likely scenario as evaluated by BIC in DAPC . For reference, ITCZ resistance is typically defined by MIC ≥ 4  in each analysis as significant (Afu2g02220) while the remaining three SNPs were non-synonymous  (N = 5), 3 (N = 8), 4 (N = 2), 5 (N = 2), 6 (N = 1), and 8 (N = 1)  . Signifi (N = 1) .Afu2g02220, Afu2g02140, Afu2g02290, Afu2g02170, and Afu2g01910) while the remaining five were non-synonymous    . Interes579,284) .Afu2g02220 encodes a synonymous variant and had the ninth lowest and lowest p-values in the quantitative trait and binary trait analyses, respectively (Afu2g02220 is annotated as a sterol 3-β-glucosyltransferase (Afu2g02140 encodes a non-synonymous variant (A233G) and had the 10th lowest and seventh lowest p-values in the quantitative trait and binary trait analyses, respectively (Afu2g02140 contains a CUE domain (as predicted by PFAM) , oxidative stress induced by H2O2 , iron starvation + H2O2 , and during exposure to ITCZ in strain A1160   . Afu2g02u2g02220 .Afu2g02220 because (i) the SNP located in this gene had highly significant p-values in both GWA analyses (ii) Afu2g02220 has a predicted functional role in sterol metabolism, and ITCZ targets the ergosterol biosynthesis pathway and (iii) Afu2g02220 was up-regulated during ITCZ exposure (Afu2g02220 by replacing it with the indicator gene hygromycin B phosphotransferase (hygR) in the A. fumigatus CEA10 genetic background  and ΔAfu2g02220 strains in the presence of 0.15 μg/ml of ITCZ and measured colony diameter after 72 h of incubation at 37°C. We observed a qualitative reduction in conidia production in KO strains    (Afu2g02220 plays a minor role in ITCZ sensitivity.To test the effect of  strains . In mini.024 mm) . This reoth KOs) . These rA. fumigatus to identify loci involved in ITCZ sensitivity. MIC values fell within a relatively tight range of 0.125–1 μg/ml [for reference, ITCZ resistant strains are defined by MICs ≥ 4 μg/ml , Candida albicans, Pichia pastoris, as well as the amoeba Dictyostelium discoideum can use various sterols, including ergosterol, as sugar acceptors    (Kikuma et al., A. oryzae, ΔAoAtg26 shows deficiency in degradation of peroxisomes, mitochondria, and nuclei and localizes to vacuoles (Kikuma et al., AoAtg26 also shows reductions in conidiation and impairment of aerial hyphae formation (Kikuma et al., Afu2g02220 compared to the WT (In addition to its role in sterol modification, o the WT .Afu2g02220. We analyzed A. fumigatus RNA-seq data during growth under iron depletion, oxidative stress, ITCZ exposure and growth in blood and minimal media (Stajich et al., Afu2g02220 expression was significantly up-regulated during oxidative stress, iron depletion and ITCZ exposure (Afu2g02220 in stress response and ITCZ sensitivity.The fungal cell wall is rigid but also dynamic in order to respond to environmental stress. Because Afu2g02220 may directly interact with ergosterol, we hypothesized that environmental stress could alter the expression of exposure . Howeverexposure  or proteexposure  during eexposure  (anotherN. crassa. This study used RNA-seq data to identify genetic variants, Fisher's exact tests to perform GWA in a closely related group of 112 isolates, and existing deletion mutants generated by the Neurospora Genome Project (Colot et al., S. cerevisiae used a mixed linear model to identify correlations between genotype and tolerance to hydrolysate toxins, and used homologous recombination to knockout candidate genes in two independent genetic backgrounds (Sardi et al., Previously, Palma-Guerrero et al.  used a sRaw whole-genome Illumina data for the 65 isolates are available through NCBI BioProject PRJNA638646 and the 11 previously sequenced isolates by Takahashi-Nakaguchi et al.  through A. fumigatus isolates. WG and JF conducted CRISPR and growth rate experiments. All authors contributed to writing the manuscript.SZ and JG designed the study and analyzed the data. AW determined itraconazole MIC and provided The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.