OCA2-HERC2 locus is responsible for the greatest proportion of eye color variation in humans. Numerous studies extensively described both functional SNPs and associated patterns of variation over this region. The goal of our study is to examine how these haplotype structures and allelic associations vary when highly variable markers such as microsatellites are used. Eleven microsatellites spanning 357 Kb of OCA2-HERC2 genes are analyzed in 3029 individuals from worldwide populations. We found that several markers display large differences in allele frequency (10% to 35% difference) among Europeans, East Asians and Africans. In Europe, the alleles showing increased frequency can also discriminate individuals with (IrisPlex) predicted blue and brown eyes. Distinct haplotypes are identified around the variants C and T of the functional SNP rs12913832 (associated to blue eyes), with linkage disequilibrium r2 values significant up to 237 Kb. The haplotype carrying the allele rs12913832 C has high frequency (76%) in blue eye predicted individuals , while the haplotype associated to the allele rs12913832 T is restricted to brown eye predicted individuals. Finally, homozygosity values reach levels of 91% near rs12913832. Odds ratios show values of 4.2, 7.4 and 10.4 for four markers around rs12913832 and 7.1 for their core haplotype. Hence, this study provides an example on the informativeness of multiallelic markers that, despite their current limited potential contribution to forensic eye color prediction, supports the use of microsatellites for identifying causing variants showing similar genetic features and history.The  Genome-wide association studies in people of European decent3 have instead indicated eye color as a polygenic trait4 yet characterized by a limited number of major genes. The OCA2-HERC2 genes explain most of the blue and brown eye color inheritance5. Different polymorphisms in the regulatory and coding region of OCA2 are primarily associated with different eye, hair and skin pigmentation phenotypes9. These findings increased our understanding of the genetic basis of human pigmentation, and drew attention to their potential applications, such as forensic investigations11, historical and anthropological researches12.The human eye color trait is the most variable in European populations and it was for a long time considered a simple Mendelian trait with a brown eye color dominant allele and a blue eye color recessive alleleHERC2, is responsible for the greatest proportion of eye color predictability, this SNP together with five SNPs located in other genes have been brought together in the IrisPlex eye color prediction panel11. The accuracy rate of correctly predicting an individual's eye color as being blue or brown is on average 94% in Europe13. Additional variation has yet to be identified to account for the poor success rate for intermediate eye color predictions (73% accuracy) and in admixed populations15.One SNP in particular, rs12913832 in HERC2 gene, 21 kb upstream of the promoter of OCA2. The rs12913832 C-derived allele is highly associated with European blue eye color as a recessive trait9. The ability of this conserved element to act as an enhancer of OCA2 transcription has been confirmed in experiments using melanocyte cultures carrying either rs12913832 T/T or rs12913832 C/C genotypes17. The P protein, produced by OCA2, is thought to be a mature melanosomal membrane protein, with a potential role in trafficking other proteins to melanosomes19.The SNP rs12913832 is located in a (11 bp) conserved region of intron 86 of the OCA2-HERC2 region associated with blue eye color in Europeans has been strong20. This region encompass the third longest haplotype spam of diminished heterozygosity in the genome of modern Europeans21 which implies intense selection at this locus in ancestral European populations. Multiple factors possibly played a role such as sexual22, the ability to overcome seasonal affective disorder25 and associated light skin increased risk for developing melanoma and nonmelanoma skin cancer26. Several lines of research indicate that selective pressure for light pigmentation acted independently in Europeans and East Asians, yet with some genes in common. The brown-eyed associated SNPs frequent in Europeans are different from that of Asians, suggesting a population specific history of the genetic component of pigmentation27.Interestingly, the selection pressure on the 17. During the great agriculture migration to the northern part of Europe, the mutations spread out from the Black Sea region. Newer studies have also indicated that some OCA2 missense mutations give rise to blue eye color, when the genotype rs12913832 C/T predicts brown eye color and that these are only found in the Scandinavian population and not in individuals of Southern-European descent28. The OCA2-HERC2 is therefore a region of large interest due to its functional role and population genetics. Global SNP variation has been comprehensively described, yet more data are being available from ongoing projects of whole genome sequencing which indicate that multiallelic sites represent as much as 10% of the genomic variants.Evidences from haplotype analysis comparing Dutch and Mediterranean population samples suggest that blue eye color has only arisen once during the Neolithic period, past 6–10,000 years ago, as a founder mutation shared by diverse European populationsOCA2-HERC2 by using microsatellite markers (STRs) as an example of multiallelic variants. We are interested in exploring how the use of polymorphisms with a different mutational mechanism and higher rate, change the pattern of haplotype structures and allelic associations around functional alleles. Conveniently, the recent history of selection of OCA2 should enhance the degree of correlations of neutral variants even when highly variable polymorphisms are used. Our interest in these data is twofold: on the one hand, to provide an empirical example of the utility of STRs in studies of association mapping of similar traits, on the other hand, to search for rs12913832 proxy STR markers for a presumptive eye color DNA test to be included in conventional forensic STR multiplexes.In this study, we aim at further investigating the genetic variability of 31. Yet, several results suggest that association studies using not only SNPs but also multiallelic STRs within or near candidate loci would be useful to search for a disease susceptibility gene, especially in populations with unknown LD structure. The rationale behind this is that in non-African samples highly significant LD between microsatellite alleles and stable markers is preserved across relatively large genetic distances (about 100 Kb range)29 compared to the shorter range (about 30 Kb) for SNPs32. The average length of LD for microsatellites is ~ 100 Kb, which is considerably higher than that of SNPs34. Therefore, a single microsatellite captures a larger genomic region than does a single SNP35. One STR can harbor both types of alleles: those showing complete association with a SNP and alleles that show little or no association. STR outside LD blocks may still show LD with SNPs inside the LD block. In that case, for association studies we would not need an STR in each haplotype block as for SNPs. At the same time, microsatellites show a smaller interpolation variability37 and a single-step expansion or contraction of the tandem repeat on the background of ancestral SNP haplotypes can break up common haplotypes, leading to greater haplotype diversity within the linkage disequilibrium block of interest. The relative performance of STR and SNP in association mapping will also depend on the frequency of disease variants. Markers alleles achieve maximal power for detecting associations when disease alleles are at similar frequencies38. As a result, STR have the potential to find rare disease variants that common SNPs will miss40. Several genome wide association studies used tens of thousands of STRs to investigate the genetic basis of hypertension, narcolepsy, anorexia nervosa, mandibular prognathism, rheumatoid arthritis, type 2 diabetes and prostate cancer47.The known susceptibility gene for rheumatoid arthritis HLA-DRB1 was successfully confirmed and two new candidates (TNXB and NOTCH4)43 were identified through a combination of STR and SNP analysis. Microsatellites have starred in association studies leading to widely replicated discoveries of type 2 diabetes (TCF7L2)46 and prostate cancer genes (the 8q region)45. Moreover, the ability of microsatellite markers to capture information on coding SNPs was successfully tested for the human major histocompatibility complex to predict HLA functional SNPs49.The density of genetic markers required for successful association mapping of complex diseases depends on linkage disequilibrium between non-functional markers and functional variants. There are few reports about the pattern or extent of linkage disequilibrium (LD) between SNPs and STRs genomewide50. To increase the accuracy of power studies in STR based disease mapping it is therefore important to provide empirical data for genomic regions of large interest and well characterized as the OCA2-HERC2 locus.However, patterns of LD between SNPs and microsatellites markers may vary considerably between lociOCA2-HERC2 region and their correlation with the functional SNP rs12913832 and the eye color trait as predicted by the IrisPlex assay. We also examine degrees of LD, related microsatellites’ haplotype structures and homozygosity levels.Here we present our results on the global variation of eleven microsatellites spanning 357 Kb in the OCA2 to intron 5 of HERC2. Seven STRs are dinucleotide repeats, three are tetranucleotide and one is a pentanucleotide repeat sequence. Markers’ distances and number of alleles are indicated in Fig. A total of 11 microsatellites polymorphisms are the focus on this study. They span the region from intron 18 of Figure Results of Fig. 11. Based on these data we selected individuals of Europe with a probability of blue or brown eye color higher or equal to 0.8, this arbitrary cut-off was used to reduce the error rate associated to such indirect determination of the phenotype, together with eliminating all individuals with predicted “intermediate eye color”. This analysis determined two groups, 42 predicted blue eyes and 62 predicted brown eyes individuals. In the HGDP-CEPH collection, outside Europe, only two individuals of Central-South Asia and three of Middle-East had a value of blue eye color prediction higher or equal to 0.8. To avoid a population structure bias we limited our analysis to Europeans.The HGDP-CEPH European group includes 158 individuals. Prediction of eye color is possible using the IrisPlex marker set available for all DNA samplesWhen STRs’ allele frequencies are compared between predicted brown-eyed versus blue-eyed Europeans Fig.  several P values ranging from 10−3 to < 10−6 including the haplotype of STR 6–8–9–10 P value 10−6 (data not shown). Smaller P values ranging from 10−5 to < 10−6 are obtained for the same STRs in addition to STR A for the larger dataset when rs12913832 C and T containing haplotypes are compared.The statistical significance of the differences in allele frequency distribution of the data reported in Fig. 2 values for Europeans are shown in Fig. Significant linkage disequilibrium r2 values  and (-4-1-6-4) Fig. b that arHomozygosity values of STR markers in predicted blue eye Europeans are higher than in predicted brown eye individuals and increase for markers located around rs12913832, with values reaching 75% to 91%  polymorphisms, previously elucidated functional variants and patterns of allelic associations including signatures of natural selection in Europe. Here we further investigate this region with a set of microsatellites annotated from whole genome sequencing data. With respect to previous studies based on SNP data, this type of polymorphism (STR) should provide non-redundant genetic information due to the different mutational mechanisms and its effect on patterns of allelic associations and haplotype structure. Here we show for the first time the correlation between multiallelic markers and functional variants of the eye color phenotype.This study focuses on the most important genomic region for eye color variation in humans, which is OCA2-HERC2 locus were genotyped in 1,064 individuals from 52 populations around the world (HGDP–CEPH panel). Data available from whole genome sequencing projects (gnomAD) were retrieved for the same STR when possible, this allowed us to work with larger sample collections from Europe (n = 876) East Asia (n = 801) and Africa (n = 896). Haplotypes were reconstructed integrating the two SNPs of the IrisPlex assay that are located on this chromosome, rs1800407 and rs12913832 and the eye color phenotype was predicted using the 6 SNP panel of IrisPlex.Eleven microsatellites spanning 357 Kb across the The results showed that nine STRs were polymorphic across major population groups. Allele frequency distributions showed as expected, Africa as the most variable region. Europe, Middle-East and Central-South Asia form an undistinguishable homogeneous Eurasian group different from East Asians, Oceanians and Amerindians.51. Although, only samples showing a prediction value for blue or brown higher or equal to 0.8 were included, the phenotyping procedure may have an error rate estimated to be 3–4%28. Because of this limitation, the subsequent analyses of allele imbalance and LD using the larger population data from gnomAD rather focused on the correlation between STRs and the functional variant rs12913832.Interestingly, large differences in allele frequency distributions (up to 36%) were found between predicted blue and brown eyed individuals of Europe with often one allele showing increased frequency in predicted blue eye color. It should be noted here that the actual phenotype data of the individuals was not available and the IrisPlex method was reported to have lower prediction accuracy for intermediate eye colors (non-blue and non-brown) and heavily relies on rs1291383252 to 54%9 including four SNPs in a region from STR 4 (17 Kb upstream) to STR 9 (22.6 Kb downstream) and to rs12913832, respectively.Differences in frequency values go up to 48% when considering two groups of haplotypes containing the variants C or T of rs12913832 in 876 Europeans, with markers STR 5, 6, 8, 9, 10 showing statistical significance. LD analysis indicated some degree of allelic associations among SNPs and STRs, these are more abundant in Europe, where the associated alleles are those that showed skewed allele frequencies between predicted blue and brown eye individuals. Previous studies also showed large difference in SNP haplotypes frequencies between blue and brown eye color individuals going from 362 values are significant up to 295 Kb from rs1800407 to STR 10 and values range from 0.1 to 0.5. Note that r2 values higher than 0.1 should allow identifying a disease susceptibility variation in an association study53. This block includes the functional variant rs12913832, three different haplotypes are associated to the allele C and T of rs12913832, one is most frequent in predicted blue eye individuals 76% (30% in blue) and two others exclusive to predicted brown eye individuals with 7.3% and 4% frequency. Finally, homozygosity values reach levels of 75% to 91% around rs12913832 in predicted blue eye individuals, which are consistent with the recessive mode of inheritance of the blue eye color. In addition, elevated odds ratios values up to 8 and 10.4 were obtained around rs12913832 and for the core haplotype RRRR of STR 6–8–9–10.Two haplotype blocks were identified from STR A to rs1800407 and from STR 6 to STR 10. Linkage disequilibrium r52. These blocks are also detected by this STR based study with some allelic associations going beyond originally defined blocks LD2 and LD3 up to 237 Kb. Eiberg and colleagues17 described a 175 kb long haplotype of 13 SNPs, 97% frequent in Danish individuals with blue eye color. The corresponding position of this block with respect to our data is 27 Kb upstream rs12913832 to 22.5 Kb downstream STR 9.Previous studies using SNPs genotyped in a large population of Netherland indicated three LD blocks going from the position corresponding to STR 2 to rs1800407 for 27 kb (LD1), around STR 6 for 13 Kb (LD2) and around STR 9 for 70.5 kb (LD3)34. This is because the higher mutation rates of STRs underlie a stronger statistical significance of LD. Therefore the importance of using STR also for the first screen of association study to reduce the number of initial association tests or when LD blocks are not known. Although many studies support these predictions theoretically, few have attempted to provide empirical data on the extent of LD detectable at STR and SNP sites at defined distance flanking unknown functional variants of significant effect. In the context of alcohol dependence syndrome studies, the persistence of allele association and differences in allele frequencies of a range of STR markers was investigated around the functional locus aldehyde dehydrogenase ALDH2 known to be under selective constraint in Japanese alcoholic populations33. This study showed the persistence of LD over distances up to 400 Kb for pairs of loci including at least one STR. It follows that the comparison of allele frequency differences for the STR markers in the case  and control populations would have detected the ALDH2 marker as a putative susceptibility locus. It should be noted that the recent origin of the ALDH2 functional markers is very likely to be a major factor determining the strength of the association observed. With regard to the observation of the allele frequency pattern difference between the cases and controls, population demography, allele frequencies and degree of natural selection are additional important factors in determining the success of gene hunting using this type of approach. Similarly, the genetic influence of IL10 SNP allele on HIV-1 infection and AIDS progression was first deduced by observations of epidemiology associations of two STR loci within 4 Kb of the IL10 gene54. In the same way, the data presented here show that considering the two extremes of brown and blue predicted eye color (most robust prediction by IrisPlex) and OCA2-HERC2 as candidate genes, simple parameters of skewed allele frequencies of STRs and levels of homozygosity would allow us to point out the most relevant genetic region carrying the functional variant rs12913832, even with a relatively small sample size and a degree (3–4%) of mislabeled phenotype due to the indirect ascertainment.This study designed around known causal variants confirms that allelic associations with functional SNPs can be detected over greater distance with STRs than with SNPsIn addition, these data confirm the different genetic history of eye color in Asians that showed no allelic associations around rs12913832 and one frequent haplotype that is rare in Europe. The patterns of variability we observed also support previous data indicating strong natural selection in this genomic region in Europe.OCA2-HERC2, markers mutating more rapidly than SNPs also capture the effect of demographic history and selective pressure by showing marked genetic differences between predicted blue and brown eyed individuals within the same population. These data support the hypothesis that with STR markers, besides SNPs, it is possible to investigate the genetics of eye color and effectively use this type of variation as marker for mapping causing variants of similar traits, appeared recently, with large penetrance and under positive selection.This study further describe novel SNP-STR correlations spanning a region of large interest for human genetics, population genetics and forensic science. These results show that in this region of Models for detecting variants responsible of genetic traits have been poorly constrained by available data leading to large uncertainties in model predictions. Studies like our go toward the effort of providing a portion of such empirical results. We hope that these data can help validating novel statistical methods aiming at using multiallelic markers for detecting functional variants.HERC2 previously showed OR of about 3055, due to the difference in ORs and therefore effects sizes of markers, the prediction using STR markers would be lower than using functional SNPs. Conversely, STRs alleles around rs12913832, are not as randomly distributed as one could expect from highly variable loci. In particular, for individuals found to be homozygous RRRR/RRRR around rs12913832 C/C the blue eye color would be predicted in 90% of the cases. Similarly, individuals that lack the haplotype RRRR around rs12913832 T/T are predicted brown eyed color in 89% of the times. Unfortunately, no indications of the phenotype can be obtained from all the other combinations of heterozygous haplotypes. Finally, there is great potential in investigating STRs in the HERC2-OCA2 region to explain the eye color that is wrongly predicted by rs11913832. These data may provide the basis of such future studies that should rely on precise and direct eye color phenotype determination.Finally, the idea of possibility replacing functional SNP genotyping with linked STRs for roughly predicting the eye color phenotype in Europe, is poorly supported by the data. A small set of STRs could be easily added to current forensic DNA profiling multiplex, yet none of the STRs analyzed is strongly associated to rs12913832 to work as proxy. This is not surprising since the highest OR observed with these STR data is about 10 while a single SNP in All methods were performed in accordance with the relevant guidelines and regulations of the journal.56. For all data analyses purposes we considered only 952 individuals (H952 subset) after exclusion of duplicates, first- and second-degree relatives57. Populations were combined into continental-based groups which have been previously established58 with the following composite populations, sample sizes and labels: 6 African (n = 105 AFR), 8 European (n = 158 EUR), 4 North African/Middle Eastern (n = 162 ME), 9 Central-South Asian (n = 202 CSA), 17 East Asian (n = 230 EAS), 2 Oceanian (n = 28 OCE) and 5 Native American (n = 64 NAM). A second population sample included 84 unrelated European individuals with Caucasian appearance and Swiss parents since three generations. DNA was extracted using the QIAamp DNA Mini kit (Qiagen AG Switzerland) according to the manufacturer’s guidelines and quantified using the Quantifiler Human DNA Quantification Kit (Life Technologies).The CEPH Human Genome Diversity panel (HGDP-CEPH) contains 1,064 individuals from African, European, North African/Middle Eastern, Central-South Asian, East Asian, Native American and Oceanian populationsCommission cantonale d'éthique de la recherche sur l'être humain (CER-VD), and were labelled with an anonymous code number linked only to demographic information and sex.For all subjects, blood cell samples were obtained according to protocols and informed-consent procedures approved by the institutional review board https://gnomad.broadinstitute.org/downloads) 59. The gnomAD v3.1.1 track shows variants from 143,150 unrelated individuals sequenced as part of various population-genetic and disease-specific studies, some markers have only frequency values while others show single individual genotypes. All variants are mapped to the GRCh38/hg38 reference sequence. From this database, markers STR 2 and STR B showed low quality genotypes with more than 50% of missing data. The population samples with individual genotypes from gnomAD used in this study include 634 Europeans (Finnish ‘fin’ and Non-Finnish European ‘nfe’), 791 Africans and 571 East-Asians. In summary, the largest sample collections considered in this study include 876 individuals from Europe, 896 form Africa and 801 from East-Asia.The genotypes of additional individuals were obtained from the Genome Aggregation Database , 125 μM dNTP (Thermo Fisher), 1.2 U AmpliTaq Gold DNA Polymerase (Thermo Fisher) and 0.5 ng DNA. Primers’ sequences, quantities and multiplexes are indicated in Supplementary Table STR markers were identified by searching the PCR fragments were separated by capillary electrophoresis after adding 1 μl PCR amplicon to 8.5 μl deionized formamide HI-DI (Thermo Fisher) and to 0.5 μl 600 LIZ size standard (Thermo Fisher). Capillary electrophoresis was performed using an ABI PRISM 3130xl Genetic Analyzer (Thermo Fisher) according to the manufacturer's instruction and analyzed using the GeneMapper® ID v3.2.1 software (Thermo Fisher), with a minimum peak height threshold of 50 RFU. The commercial DNA CEPH 1347-02 (Thermo Fisher) was added to two empty positions in each PCR plate as positive control of amplification and internal standard for allele calls, at least one empty well per plate was used a negative control of amplification.Markers were grouped in three multiplexes as indicated in the table. For allele names, ‘R’ indicates the number of repeats of the reference genome and additional repeats are indicated by ‘+ n’ or’-n’ consistent with gnomAD. Considering all populations, 723 HGDP-CEPH samples genotypes are also reported by gnomAD. These data were used for confirmation and harmonization of allele calls. All the genotypes produced here corresponded to the data available in gnomAD except for STR 9 where we could distinguish the alleles -8 and R, while these two are the same allele R in gnomAD. The allele − 8 was observed only in the HGDP-CEPH Eurasian populations. This is probably due to a neighboring deletion linked to the allele R coamplified by the PCR primers used in this study. It is also possible that, because of the complexity of the tandem repeat sequence, that includes both variable CA repeats and variable TA repeats, two different neighboring sites instead of one are indicated in gnomAD.11 and by using the model from the online tool (https://hirisplex.erasmusmc.nl/). The IrisPlex consists of 6 SNPs, rs12913832 (HERC2), rs1800407 (OCA2), rs12896399 (SLC24A4), rs16891982 (SLC45A2/MATP), rs1393350 (TYR) and rs12203592 (IRF4).The eye color prediction for the 84 Swiss Europeans samples was done by IrisPlex typing according to the protocol described in Walsh et al. 20112, 200 µM of each dNTP and uses adjusted thermocycling conditions for increased specificity: (1) 95 °C for 10 min, (2) 33 cycles of 95 °C for 30 s and 61 °C for 30 s, (3) 5 min at 61 °C. Each primers concentration used was 0.208 µM except for rs1800407 used at 0.104 µM. This was followed by product purification (2.5 µl of PCR product) using an Exo I / SAP treatment (NEB) in CutSmart 10 × Buffer for an incubation time of 90 min at 37 °C and inactivation step of 15 min at 80 °C. Further multiplex single base extension (SBE) reaction using the ABI Prism1 SNaPshot kit (Applied Biosystems) was performed, 1 µl of purified product, SNaPShot reaction mix and Sequencing Primer mix were used in a final volume of 5 µl. Thermocycling conditions used were: (1) 96 °C for 2 min, (2) 25 cycles of 96 °C for 10 s and 50 °C for 5 s, (3) 30 s at 60 °C. The SNaPShot PCR product was then treated with SAP (NEB) for 90 min at 37 °C and inactivation for 15 min at 80 °C. One microliter of cleaned products was analyzed on the ABI 3130xl Genetic Analyser (Applied Biosystems) with POP-4 on a 36 cm capillary length array. Run parameters were optimized to increase sensitivity, with an injection voltage of 6 kV for 11 s, and run time of 1000 s at 60 °C.As previously published, the protocol consists of a single multiplex two step PCR using 1 µl genomic DNA extract (concentration of 0.5 ng/µl) and primers in a 12 µl reaction which includes 1 × PCR buffer, 2.7 mM MgCl61, which implements a Monte Carlo approach by performing repeated simulations.Allele frequencies were estimated by gene counting. The statistical significance of the difference in allele frequency between predicted blue and brown eye color individuals was assessed with the CLUMP program62 as well as pairwise linkage disequilibrium r2 values. Odds ratios (OR) 95% confidence interval (CI) and P values of Table The EM algorithm was used to estimate maximum likelihood haplotype frequencies by using Arlequin version 3.5.1.2The current study was approved by the Centre Hospitalier Universitaire Vaudois and Université de Lausanne institutional review board. Genomic DNA samples fully-consenting individuals were collected by the Human Genome Diversity Project (HGDP), in a collaboration with the Centre Etude Polymorphism Humain (CEPH) in Paris. For all subjects, blood cell samples were obtained according to protocols and informed-consent procedures approved by institutional review boards, and were labelled with an anonymous code number linked only to demographic information and sex. Besides the HGDP-CEPH diversity panel human cell line samples, all other samples involved in the study are long lasting anonymized DNA extracts previously obtained with informed written consent from healthy individuals for research purposes.Each blood sample used was freely donated under conditions of informed consent to participate.Each blood sample used was freely donated under conditions of informed consent to publish.Supplementary Figure 1.Supplementary Table S1.


Four loci were significantly associated with reduction in BCVA: two loci on chromosome 11, downstream of NTM  and intronic in RP11-744N12.3 ; one near PGAM1P1 on chromosome 5 ; and one near TBC1D32 on chromosome 6 . In silico investigations of each locus identified multiple expression quantitative trait loci and potentially relevant candidate genes warranting further analysis. Thus, we identified multiple genetic loci predicting treatment outcomes for anti-VEGF therapies in DME. This work may potentially lead to managing DME using personalized treatment approaches.Intraocular anti-vascular endothelial growth factor (VEGF) therapies are the front-line treatment for diabetic macular edema (DME); however, treatment response varies widely. This study aimed to identify genetic determinants associated with anti-VEGF treatment response in DME. We performed a genome-wide association study on 220 Australian patients with DME treated with anti-VEGF therapy, genotyped on the Illumina Global Screening Array, and imputed to the Haplotype Reference Consortium panel. The primary outcome measures were changes in central macular thickness (CMT in microns) and best-corrected visual acuity (BCVA in ETDRS letters) after 12 months. Association between single nucleotide polymorphism (SNP) genotypes and DME outcomes were evaluated by linear regression, adjusting for the first three principal components, age, baseline CMT/BCVA, duration of diabetic retinopathy, and HbA1c. Two loci reached genome-wide significance ( Diabetic macular edema (DME) is the most common cause of visual impairment in patients with diabetes and can affect patients with either Type 1 (T1) or Type 2 (T2) diabetes mellitus (DM). It is the leading cause of central vision loss in the working-age population with a prevalence of 1.4–12.8% in patients with diabetes . It is cp < 0.05 for both measures). Demographic information and clinical parameters of the study cohort are described in A total of 248 diabetic (T1 + T2) patients receiving anti-VEGF injections were identified with complete clinical data. Of these, genome-wide genotyping data were available for 234 individuals. After quality control (QC), 220 individuals with data for 2,581,674 autosomal SNPs were available for analysis. The details of the individual level and single nucleotide polymorphism (SNP) level QC are provided in the Materials and Methods section and p < 5 × 10−8 (CASC15) gene on chromosome 6  gene. A second locus on chromosome 11, rs57801753 in the RP11-744N12.3 gene, a single SNP on chromosome 5, rs187876551 near Phosphoglycerate Mutase 1 Pseudogene 1 (PGAM1P1), and a locus on chromosome 6, rs118074968 near theTBC1 Domain Family Member 32 (TBC1D32) also reached significance. Details of all SNPs are given in Four loci reached genome-wide significance for association with change in BCVA . The mosp values that were suggestive of association, but that did not reach genome-wide significance (≤5 × 10−7). Supplementary Materials: SNP.xlsx (sheet 1–2) summarizes the lead SNPs found in each of these suggestive loci with details of SNPs in LD. There were two suggestive loci for change in CMT: chromosome 4, EPH Receptor A5 (EPHA5) gene, lead SNP rs139041797, and chromosome 16, Deoxyribonuclease I (DNASE1) gene, lead SNP rs35275535. For change in BCVA, there were five suggestive loci: chromosome 11, Apolipoprotein O 3733.1 (APOO3733.1) gene, lead SNP rs78772036; chromosome 11, RP11-179A16.1 gene, lead SNP rs75537672 and four other individuals SNPs .We also identified several other loci with Conditional analyses were applied to identify secondary association signals across the entire genome. For the change in CMT, analysis was performed conditioned on the lead SNPs, rs78466540, and rs11614480. For the change in BCVA, analysis was performed conditioned on the lead SNPs rs148980760, rs187876551, rs57801753, and rs118074968. No additional SNPs reached genome-wide or suggestive significance thresholds for either of the outcomes. The resulting Manhattan and Q–Q plots for the conditional analyses are presented in A logistic regression model (responders vs. non-responders) was implemented stratifying outcomes on the basis of the functional and anatomical response. The baseline and clinical characteristics of responders and non-responders for both outcomes are shown in RP11-116D17.3 gene expression in several tissues in the Genotype-Tissue Expression (GTEx) database  corresponding to multiple tissues including the pancreas and thyroid [GJA1) gene in a single tissue [The results of functional annotation for the genome-wide significant loci are given in database . Further: SNP.xlsx (Sheet 1–3)].We also performed eQTL analysis for significant loci in retinal tissue using the Eye Genotype Expression (EyeGEx) database; however, no associations were observed between these variants and the expression of retinal genes  (accessed on 12 January 2021). Using the Haplotype Reference Consortium panel , the allele dosage for over two million SNPs (after QC) was imputed for the 22 autosomal chromosomes. Post imputation, SNPs were excluded according to the following parameters: imputation quality score R2 < 0.8, MAF < 0.02, missing genotype rate > 0.03, heterozygosity ±6 SDs from the mean, and HWE deviation p < 1 × 10−06 (Imputation was performed using the Michigan imputation server [Minimac4 (1.5.7);  × 10−06 . This rehttp://www.R-project.org/) with the GWASTools (Bioconductor: http://www.bioconductor.org) [p ≤5 × 10−8 for genome-wide significance and at p ≤ 5 × 10−7 for suggestive significance. Local/regional association plots were constructed with FUMA v1.3.6 online tool (https://fuma.ctglab.nl/) (accessed on 18 January 2021) [2 ≥ 0.4) and the gene nearest to the lead SNP. We also performed conditional analysis to determine independent association signals across the entire genome, conditioning on the genome-wide significant SNPs.Associations between SNP genotypes (including imputed genotypes) and DME outcomes (change in CMT/BCVA) were evaluated by linear regression for continuous traits and logistic regression for categorical (responder/non-responder) groupings using PLINK2.0. Evidence for association was assessed using an additive model for risk by allele dose. The resulting residuals for change in CMT/BCVA distributions were approximately normally distributed. The original scales were used when reporting effect sizes . Age, HbA1c, duration of diabetic retinopathy (DR), injection type, and baseline CMT/BCVA were included in the model as covariates as they are consistently described in previous clinical studies as influencing change in CMT or BCVA following anti-VEGF treatment ,12. The tor.org)  and qqmator.org) , respectry 2021) . A locusAdditionally, associations between SNP genotypes and secondary outcomes  were evaluated using logistic regression, adjusting for covariates . p < 0.05 was considered statistically significant. Results are presented as the mean ± SD for continuous variables and as proportions (%) for categorical variables.For the demographic data, the mean change in BCVA and CMT from baseline to 12 months visits were conducted using paired Student t-tests in R version 4.0.2. http://www.gtexportal.org/home/datasets) (accessed on 18 January 2021) with a false discovery rate (FDR) < 0.05. As the GTEx database does not include ocular tissues, expression summary statistics for the candidate genes in the retina from the EyeGEx database  (accessed on 18 January 2021) were used to evaluate whether any of our variants showed significant effects on expression levels at FDR < 0.05 [https://genome.uiowa.edu/otdb/) (accessed on 20 January 2021) [To discern key genes and variants at the identified GWAS loci, we conducted functional follow-up analyses through the FUMAgwas online portal. The following criteria incorporated in the annotation tool were applied: (1) CADD score (range 1–99) to determine the deleteriousness of the variant , (2) RegR < 0.05 . Finallyry 2021) .


From these, 14 markers were located near 12 known genes  in RB cattle, 3 markers were close to ZDHHC19, DAPK1 and MMP7 genes, while one SNP overlapped the HERC3 gene in RS cattle. Four genes  associated with SCS in this study were previously reported in different studies. The most significant SNP (rs110749552) associated with SCS was located within the HERC3 gene. In both breeds, the SNPs and position of association signals were distinct among the three parities, denoting that mastitis is controlled by different genes that are dependent according to parity. The current results contribute to an expansion in the body of knowledge regarding the proportion of genetic variability explained by SNPs for SCS in dairy cattle.Mastitis is one of the most frequently encountered diseases in dairy cattle, negatively affecting animal welfare and milk production. For this reason, contributions to understanding its genomic architecture are of great interest. Genome-wide association studies (GWAS) have identified multiple loci associated with somatic cell score (SCS) and mastitis in cattle. However, most of the studies have been conducted in different parts of the world on various breeds, and none of the investigations have studied the genetic architecture of mastitis in Romanian dairy cattle breeds up to this point in time. In this study, we report the first GWAS for SCS in dairy cattle breeds from Romania. For GWAS, we used an Axiom Bovine v3 SNP-chip  and 33,330 records from 690 cows belonging to Romanian Spotted (RS) and Romanian Brown (RB) cattle. The results found one SNP significantly associated with SCS in the RS breed and 40 suggestive SNPs with −log In recent decades, the prevalence of clinical mastitis in cattle has increased significantly  in most Bos taurus genome in 2009 [https://www.animalgenome.org/cgi-bin/QTLdb/BT/index, accessed on 20 May 2021 [In dairy cattle, many studies have been conducted in different parts of the world on various breeds in order to investigate the genetic base of mastitis. Research results from more than two decades have shown the association between the major histocompatibility complex (MHC) and susceptibility or resistance to intramammary infection . In the  in 2009 , enabled in 2009 . By the  in 2009  used 773 in 2009 , by usin in 2009  conducte in 2009  analyzedMay 2021 ), a totahttp://afs.okstate.edu/breeds/cattle/baltataromaneasca/index.html/, accessed on 25 August 2021 [Mastitis resistance is a complex trait and although important progress has been made in this area, the research efforts should be intensified in order to better understand the genetic architecture of this important trait and, also, to find new possible markers in order to improve the possibilities of genetic progress and enable more accurate selection against mastitis. Moreover, although GWAS was widely used to identify the genetic variants associated with mastitis and its indicator traits such as SCS, to the best of our knowledge, none of the studies investigated the genetic architecture of SCS in Romanian dairy cattle breeds. To address this issue, in the current study, we used the GWAS approach in 723 Romanian Spotted  breed  and RomThis study was conducted on 723 dairy cattle out of which 601 were RS and 122 were RB cattle. The animals involved were managed in three breeding herds: two experimental herds belonging to the Romanian Academy of Agricultural Science (the Research and Development Station for Bovine Arad and the Research and Development Station for Bovine Sighetu Marmatiei) and one commercial herd located in Berliste. All cows involved in the study were kept under comparable housing and feeding conditions, with similar milking and sanitary conditions, and were mechanically milked twice a day. All the animals had at least 3 monthly test-day records per lactation. Only data corresponding to the first three lactations were included in the analysis.Phenotypic data consisted of 33,330 SCC records, of which 24,295 were for RS and 9035 were for RB cattle. For SCC determination, milk records were conducted every 28 days by the Official Dairy Control service. The milk samples were analyzed for SCC determination in the laboratory of the Milk Quality Control Foundation  by using CombiFoss™ FT + integrating MilkoScan™ FT + and Fossomatic™ FC.SCS = log2  + 3, specified by the international standard .n = 627) were collected from the tail vein in vacutainers containing K3EDTA as an anticoagulant, and the hair roots (approximately 20–40 hairs with follicles attached) were collected from the tail of the animals (n = 96). All protocols used for collection of the biological samples were approved by the Ethics Committee of the Research and Development Station for Bovine Arad (Statement no 88/04.10.2019). After collection, the samples were transferred to the laboratory and stored at 4 °C, and they were subsequently sent to IFN Schönow GmbH  for DNA extraction and genotyping using an Axiom Bovine v3 microarray (based on the reference genome Bos_taurus_UMD_3.1.1), which includes >63,000 SNPs. The used SNP chip included a high percentage of SNPs that are found distributed on all chromosomes. The SNPs on the array include 44,000 markers from the Council on Dairy Cattle Breeding (CDCB), International Society for Animal Genetics (ISAG) core parentage markers, VIP trait-associated SNPs and SNPs optimized for STR imputation. In total, 723 samples were genotyped.Biological samples needed for genotyping consisted of whole blood and hair roots that were collected by trained veterinarians. Blood samples (p > 10−6) were eliminated.Data quality control was performed in order to remove SNPs and individuals with insufficient genotyping quality. QC procedures were carried out by using PLINK v1.90b6.16 64-bit . The SNPPrincipal component analysis (PCA) was performed for each breed-specific subpopulation in order to check for population stratification. In both cases, the first 20 principal components were computed by using the R package argyle v0.2.2 . Based oremlf90 command, after which the estimated breeding values (EBVs) and prediction error variances and covariances (PEVs) were computed using the blupf90 command with the store_pev_pec option. PEVs were used to compute EBV reliabilities by using the following formula:AV is the additive genetic variance.The GWAS was performed for the SCS trait by using two-stage analysis. First, a random-regression test-day model using only phenotypes and pedigree data was implemented in Blupf90  by usingwideDRP function was used by setting the heritability to its estimated value and using the default value of 0.5 for the c parameter, which represents the fraction of genetic variance not explained by markers.EBV reliabilities were then used to compute de-regressed proofs (DRPs) by using the R package DRP , which iGWAS function, with only the first 8 principal components included as fixed effects. SNP effects were computed within the same R package by using the mixed.solve function. A genome-wide significance threshold of p < 1.00 × 10−4 was used, and SNPs exceeding this threshold were considered as candidate variants. Manhattan plots were created by using the R package qqman v.0.1.4 [For the second GWAS stage, DRPs were used as phenotypes in the rrBLUP package v.4.6.1  by using v.0.1.4 .IntRegionalPlot function of the R package IntAssoPlot v.0.99.21 [10 (p) values for all the SNPs in a selected region, with the chromosomal position on the x-axis. The bottom layer depicts a heat map of LD blocks for each of the pairs of SNPs within the region. In addition, the function adds lines between the two layers to show which SNP corresponds to which LD block.Linkage disequilibrium analyses for some of the significant SNPs on chromosome 7 and 9 in RB breed and chromosome 6 in RS breed and SNPs from the surrounding regions  were performed by using the .0.99.21 . This fuFinally, significant SNPs were annotated with overlapped and downstream/upstream genes within a ± 1 MBp range  by using the R package VariantAnnotation v.1.28.13 , and ann1–L3), the corresponding percentages were as follows: 45.61%, 30.78% and 23.61% in RS and 38.32%, 34.00% and 27.68% in the RB breed, respectively. The descriptive statistics of the observed phenotypes for each breed and lactation are presented in A total number of 690 cows with 33,330 test-day milk records were used in this study. The number of records for second (31.66%) and third parity (24.71%) was lower than those for the first lactation (43.63%). For the two breed-specific subpopulations by parity (L1–L3) were 2.81, 3.06 and 3.27 in RS and 4.53, 4.51 and 4.62 in RB cattle, respectively. The maximum level of SCS was 8.84; nevertheless, the number of cows with this level of SCS was low in both breeds. The overall mean SCS for RS was 2.99 ± 1.82 and 4.55 ± 1.89 in RB cattle.According to our results, the average SCSs among parities  was higher than compared to RB . The highest heritability was recorded for RS cattle in the third lactation (0.11). Between breeds, a distinct pattern of variation in heritability values was observed. The shape of the SCS heritability curves  and then remained on the plateau in mid-lactation (up to 200 days in milk), after which it gradually increased in the last part of the lactation (up to 305 days in milk). This trend appears similar to that of the incidence of mastitis.Estimates of heritability for the SCS showed different variation between breeds over the 305 days of lactation. The range of SCS heritability values for Ly curves  was simip > 10−6). Finally, after the quality control analysis, a data set of 40,305 SNPs across the entire bovine genome from 690 cows (571 RS and 119 RB) was used in the subsequent analysis to carry out genotype association tests.After computing descriptive statistics for the phenotypic data, quality control checking of genotyping data was the second step in our data analysis. Since the success of a genome-wide association study is considerably influenced by appropriate study design and data analysis workflow , extra aThe animals involved in this study came from two breeds and three breeding herds. Thus, in order to assess population stratification, we performed principal component analysis. The PCA plot revealed a clear population structure for the animals in the two cattle breeds included in the study, showing a clear separation between individuals, according to breed . Cluster10 (p) ≥ 5.89, and the inferior horizontal lines represent the suggestive threshold of −log10 (p) ≥ 4.00, which was set to report further significant associations that were not observed under Bonferroni corrections. The suggestive threshold was chosen according to previous studies where researchers defined a suggestive threshold between p < 10−5 and p < 10−3 [Bos taurus [To investigate the genetic variation that underlies the SCS, GWAS was performed in roder to identify the associated SNP loci in cattle. The Manhattan plots for SCS in the RS and RB breeds are shown in p < 10−3 ,29. The s taurus .1–L3) are presented in 3  was located on chromosome 6 within the HERC3 gene (HECT and RLD domain containing E3 ubiquitin protein ligase 3) and was the most significant SNP for SCS (log10 (p) = 6.37). In the RB breed, the genome-wide analysis detected 26 SNPs that reached genome-wide significance for association with SCS  and AX-117085949 (rs43585209) were located near the COL12A1 gene, respectively. None of the 15 detected SNPs in the RS breed were in common with the detected 26 SNPs that reached genome-wide significance for association with SCS in the RB breed.The genome-wide SNPs detected for SCS among parities  on chromosome 7 in the RB breed for a region of 1 MBp interval , which covers seventeen SNPs  was located in a block along with another significant SNP (AX-115114947). The two significant SNPs in block 2 were located upstream of the AKAP8 gene at distances of 514,640 and 511,009 Bp, respectively. A second linkage disequilibrium analysis for a total of twenty SNPs on chromosome 9 in the RB breed was performed and showed three LD blocks , which was the only marker in our study that had a significant association according to Bonferroni correction. For the RS breed, the LD analysis revealed three high LD blocks  to 0.11 (RS), which are in agreement with previous studies. For example, for the Simmental breed, Wei et al.  reportedHERC3, LUZP2, AKAP8 and MEGF10 genes located near significant SNPs on chromosome 6, 29 and 7 are consistent with other studies [Genome-wide association studies provide a valuable source of information for the comprehension of genetic mechanisms that point out the various phenotypes in dairy cattle. Nevertheless, when looking at the GWAS results so far, it is clear that identifying specific SNPs and genes located around them linked to SCS is difficult. As observed in the present study , from a  studies ,48,49, wZDHHC19, DAPK1 and MMP7 genes, while one SNP overlapped the HERC3 gene. The most significant SNP in our study, rs110749552,  = 6.36) associated with SCS in RS breed in L3 was located within the HERC3 gene that encodes a member of the HERC ubiquitin ligase family. Significant associations of the HERC3 gene with different traits have been previously reported by several authors in cattle or sheep populations. This gene was previously found to be related to mastitis and somatic cell count in Ethiopian cattle populations [HERC3 gene was associated with milk protein percentage in Chinese Holstein cattle. In French Holstein, the HERC3 gene had highly significant effects on αs1-casein [HERC3 as a candidate gene for yearling weight. In addition, Al Kalaldeh [HERC3 gene on Ovis aries chromosome 6 fell within the QTL region that underlies genetic variation for parasite resistance in sheep and also noticed that the genes from the HERC family of ubiquitin ligases are associated with antigen processing: ubiquitination and proteasome degradation; immune system; class I MHC mediated antigen processing and presentation; and adaptive immune system pathways. However, this is the second study where this gene has been found to be involved in the genetic makeup of mastitis.In the RS breed, the GWAS results pointed out 15 SNPs associated with SCS. Out of the significant SNPs, three markers were close to the ulations . Accordiulations , the HER1-casein . Porto-NKalaldeh  showed tAKAP8, CLHC1, MEGF10, SATB2, GATA6, SPATA6, COL12A1, EPS8, LUZP2, RAMAC, IL12A and ANKRD55. On chromosome 7, the rs42228650 SNP was close to MEGF10, a gene that was found to be associated with SCS in dairy cows in a previous investigation [AKAP8 gene, which was associated with somatic cell count in seven different populations of dairy cows in a complex study that used the combined approach of examining previous results from differential gene expression analysis and GWAS studies to identify the key pathways and candidate genes that potentially confer genetic resistance to mastitis in dairy cows [LUZP2 on chromosome 29, which has been associated with SCS [With regards to the RB breed, the GWAS results revealed 26 promising SNPs associated with SCS. Out of the significant SNPs, 14 were located near 12 known genes, including tigation  and was tigation . On the iry cows . Furtherwith SCS  and milkSATB2, a gene that encodes a DNA binding protein that specifically binds nuclear matrix attachment regions. A previous study disclosed the SATB2 gene to be associated with tick burden, and the author mentioned a potential link of this gene to the immune system [EPS8 gene associated with fat yield, covered by SNP AX-106726354 located near EPS8 in this study. The SNP AX-124381310 on chromosome 24 was close to the GATA6 gene. The GATA gene family has six members (GATA1–GATA6) in vertebrates and plays a critical role in controlling the growth, differentiation and survival of diverse cells, as well as maintaining the body’s functions [GATA6 were reported to be associated with microtia in Awassi sheep in ruminants [In addition to the genes known to be associated with mastitis, the present study identified novel candidate genes associated with SCS in Romanian cattle. Some of these genes have been shown to be linked with other traits in ruminants. For instance, the rs109189476 SNP on chromosome 2 was close to e system . Jiang ee system  conducteunctions . Currentuminants .COL12A1 (Collagen Type XII α 1 Chain) gene, it is known that degradation of the extracellular matrix and collagen chain trimerization is common among the related pathways of this gene. The Gene Ontology annotations associated with this gene include extracellular matrix structural constituent conferring tensile strength. Collagens consist of 28 members in vertebrates, and they are the most abundant extracellular matrix proteins [COL4A1) were significantly downregulated in the inflammation-associated fibroblasts from bovine mammary glands compared to normal fibroblasts, suggesting an implication of collagen members in immune response since one of the related pathways include extracellular matrix–receptor interaction. Moreover, Lu et al. [COL12A1 is upregulated in late lactation, suggesting that the collagen genes had varied biological functions at different phases of lactation. In the present study, we identified two significant SNPs associated with SCS (rs43585636 and rs43585209) located close to COL12A1. All of the aforementioned aspects suggest that the COL12A1 gene might be involved in SCS.Regarding the proteins . Chen etproteins  showed tu et al.  revealedLUZP2, AKAP8 and MEGF10 were also identified in our study as candidate genes for SCS in the RB breed. For this specific case in the RB breed, further studies using larger sample sizes should be performed in order to validate previous results. Finally, an additional factor that may influence the different sets of candidate SNPs/breed is the distinct genetic backgrounds of the two breeds as they have different levels of clustering, as shown in previous studies on Romanian cattle breeds [This study revealed that different markers and candidate genes were found for the two breeds in our study. In addition, all identified SNPs have been distinct among the three parities, denoting that SCS is influenced by different genes according to parity. The different sets of markers discovered in this work compared to studies reported in the literature can be attributed to various factors. We can assume that the power of detecting SNPs can be lower in the RB breed compared to the RS breed as a consequence of the comparatively lower number of genotyped RB animals used in the analysis. Furthermore, previously known mastitis related genes such as e breeds ,63.The specific novel SNPs and candidate genes located around them reported in the present study can be considered as candidates involved in SCS and mastitis resistance; however, these need to be kept in perspective, and polymorphisms in those genes should be further analyzed to highlight whether they influence the ability of dairy cows to resist mastitis.Our study represents the first GWAS for SCS in Romanian dairy cattle and, thus, provides new perspective into the genetic architecture of udder infections in these native dairy cattle. We identified 41 SNPs and detected their significant associations with variation in SCS in dairy cattle. In both breeds, the SNPs and position of association signals were distinct among the three parities, denoting that mastitis is influenced by different genes according to parity. The results contribute to an increase in knowledge regarding the proportion of genetic variability explained by SNPs for SCS in dairy cattle and, particularly, in Romanian native cattle. However, further large-scale studies of a vast number of native dairy cattle breeds are needed in order to investigate others markers and genes that could be involved in mastitis.


Enterococcus faecium (VREfm) is a major nosocomial pathogen. Identifying VREfm transmission dynamics permits targeted interventions, and while genomics is increasingly being utilised, methods are not yet standardised or optimised for accuracy. We aimed to develop a standardized genomic method for identifying putative VREfm transmission links. Using comprehensive genomic and epidemiological data from a cohort of 308 VREfm infection or colonization cases, we compared multiple approaches for quantifying genetic relatedness. We showed that clustering by core genome multilocus sequence type (cgMLST) was more informative of population structure than traditional MLST. Pairwise genome comparisons using split k-mer analysis (SKA) provided the high-level resolution needed to infer patient-to-patient transmission. The more common mapping to a reference genome was not sufficiently discriminatory, defining more than three times more genomic transmission events than SKA (3729 compared to 1079 events). Here, we show a standardized genomic framework for inferring VREfm transmission that can be the basis for global deployment of VREfm genomics into routine outbreak detection and investigation.Vancomycin-resistant  Enterococcus faecium is an important healthcare-associated pathogen and genomic analyses could inform targeted interventions. Here, the authors optimise an analysis pipeline for identification of putative transmission events using core genome multilocus sequence type clustering and split kmer analysis.Vancomycin-resistant  Enterococcus faecium comprises part of the normal gastrointestinal tract flora in humans, but in recent years, has become an increasingly important healthcare-associated pathogen, causing a range of infections, including bloodstream and urinary tract infections3. Enterococcus faecium is particularly difficult to treat due to intrinsic resistance to a range of antibiotics, and although vancomycin was once the treatment of choice for E. faecium, vancomycin-resistant E. faecium is now common in many countries4. Consequently, the World Health Organisation (WHO) listed vancomycin-resistant Enterococcus faecium (VREfm) as a “high priority” in its recent list of priority bacterial pathogens due to the limited treatment options available5.6. Conventional genomic approaches for determining genetic relatedness and transmission links include multilocus sequence type (MLST) and single nucleotide polymorphism (SNP) distances, generated from core genome alignments usually using recombination masking. However, these methods are not optimal for highly recombinogenic and very genetically diverse species, such as E. faecium, and may result in isolates being labelled as more closely/less closely related than they actually are7. More recently, alternative approaches such as core genome MLST (cgMLST) and kmer-based methods  have been developed to deal with some of the pitfalls of the conventional approaches. In all approaches, genomic data are combined with epidemiological information for a more accurate determination of transmission links and outbreak status.Whole-genome sequencing (WGS) allows high-resolution investigation of pathogen outbreaks and transmission networks, facilitating accurate targeting of infection control interventions and consequently reducing the number of infections10. Despite this proven utility, there is yet to be standardisation in the methods and thresholds used to identify outbreaks and transmission links. The lack of standardisation is a key factor identified by the WHO limiting the use of whole-genome sequencing in routine surveillance of antimicrobial resistance11. Multiple thresholds have been proposed for pairwise SNP distances to identify putative transmission links in E. faecium based on within-patient isolate diversity (ranging from 6 to 17 SNPs)13. However, these thresholds are not accompanied by methods to standardise the preceding steps in the analyses. A recent study from our group demonstrated the large variation in SNP distances caused by changes in analysis approach, such as reference choice, masking for recombination and subsampling of datasets7. For analysis of VREfm, this study recommended using a closely related reference genome and not masking for prophage/recombination regions to ensure consistent and accurate pairwise SNP distances.Multiple studies have demonstrated the utility of these methods for VREfm, especially when there are complex transmission networks that involve multiple wards or when hospitals have a high VREfm burdenStaphylococcus aureus (MRSA) were sequenced and the data interpreted using an automated pipeline14. Promisingly, the data showed that even with current technology, the limiting step in real-time outbreak detection using WGS is the data analysis step and not the turnaround time of sequencing. For a bioinformatic analysis of genomic transmission to be implemented as standard practice it needs to: (i) be stable over time as additional isolates are added to the analysis; (ii) be standardised to allow for comparison across sites or hospitals; (iii) be computationally efficient; and (iv) allow for automation and require minimal intervention and interpretation.Current genomic studies on transmission and outbreak analysis are primarily focussed on retrospective analysis that only occurs once an outbreak is suspected or has been resolved. Laboratory and bioinformatic workflows that identify potential transmission events in closer to real time are still being developed. One potential system was described by Brown et al., in which methicillin-resistant The aim of this study was to determine the best genomic approach for identifying putative VREfm transmission links in as close to real-time as possible and develop an analysis pipeline that could be implemented as standard practice. Using WGS and comprehensive patient location data, we compared genomic variant calling methods and pairwise SNP distance thresholds for transmission inference to develop a method that best fulfilled these requirements.E. faecium isolates and using split kmer analysis (SKA) to identify putative transmission clusters based on genomic similarity. In addition, we demonstrate how the use of these methods in a health care scenario provide accurate information on transmission events and allow for the specific targeting of infection control resources.Here, we show the superiority of using cgMLST clustering as an alternative to MLST to group genetically similar MLST has traditionally been used to broadly group genetically similar isolates together within a species, including when identifying isolates worth investigating for possible transmission. We aimed to compare groups identified using traditional MLST and those identified using cgMLST to see which best matched the population structure.vanA VRE population , both within and between STs. The distribution of cgMLST pairwise allelic difference values across the species shows a clear peak at the lower values that only contains isolate pairs from the same ST  through a single-linkage cluster approach. This meant no between-ST isolate pairs were clustered together while still maximising the number of within-ST isolate pairs.cgMLST was tested as an alternative approach. The number of cgMLST allele differences was calculated between all isolate pairs , this allelic difference threshold also separated isolate pairs of the same ST in a manner that better corresponded with the population structure , the gold standard comparison method.After genetically similar isolates have been broadly grouped together, i.e. with MLST or cgMLST clustering, a finer-scale comparison is needed to identify possible transmission events. Traditionally, this involves constructing core genome alignments and then calculating a pairwise SNP distance between all isolate pairs. Numerous fine-scale genetic comparison approaches were compared in order to determine which correlated best to pairwise comparison using R2 > 0.83) , core genome MLST core alignments (cgMLSTCA) and core genome MLST with cluster references core alignments (cgMLSTCRCA) approaches did not correlate well with PCDR distances. , however, this was not true of split kmer analysis (SKA) which consistently correlated well with PCDR   across all ST backgrounds, representing the strong correlation between the two methods.This was true even when considering isolate pairs that are closely related (PCDR pairwise SNP distance ≤ 50), the R2 values. MLSTCA had the smallest average core alignment length as a percentage of reference (66%), while cgMLSTCA and cgMLSTCRCA were comparable with an average of approximately 80% . This threshold differed between genomic methods with cgMLSTCRCA and SKA having the most similar thresholds (≤6 SNPs and ≤7 SNPs respectively) and PCDR having a threshold of ≤10 SNPs and cgMLSTCA ≤44 SNPs , five were linked to other isolates by SKA and 13 were linked to other isolates by cgMLSTCRCA. Of the SKA (n = 44) and cgMLSTCRCA (n = 42) clusters with one isolate, one and seven respectively were genomically linked to another isolate by PCDR. Of the PCDR clusters containing more than one isolate, cgMLSTCRCA split 7 while SKA split only two   Fig. .Fig. 6Me2 values), reflective of this change in clustering method, cgMLSTCRCA  distances had an even stronger correlation to PCDR  distances , reflective of the more closely related reference genomes. This is consistent with a recent study by Gorrie et al. that detailed the large effects that reference choice and sample diversity can have on core alignments and showed that there were significant differences in the distribution of pairwise distances depending on how closely related the reference genome was7. Although generating reference genomes for each cgMLST cluster is possible, it is an additional step that adds to the time and cost of the analysis pipeline. As such, a method that does not require a within cgMLST cluster de novo reference but can still accurately replicate PCDR  results without the intense computational requirements is preferred. Across all the major ST backgrounds, SKA (direct pairwise kmer) and PCDR  pairwise distances were consistently well correlated, even when comparing isolates that were very closely related. SKA (direct pairwise kmer) is significantly faster than PCDR  and requires much fewer computational resources, both in terms of processing and memory. Unlike cgMLSTCA , SKA (direct pairwise kmer) does not require de novo reference genomes and the pairwise distances are not affected by changes in sample diversity, making it an ideal alternative.It was hypothesised that cgMLSTCA  pairwise distances would more closely reflect the PCDR  distances compared to MLSTCA  distances. Although cgMLSTCA  pairwise distances better correlated with PCDR  distances compared to MLSTCA  and SKA (direct pairwise kmer) clusters compared to PCDR . Minor errors in genomic putative transmission linkage were classified as isolates identified as being involved in transmission by the respective genomic method but not by the gold standard (PCDR). SKA (direct pairwise kmer) had five minor errors and cgMLSTCRCA  had 13. Major errors in genomic putative transmission linkage were classified as isolates identified as being involved in transmission by the gold standard genomic method (PCDR) but not by the respective genomic method. SKA (direct pairwise kmer) had one major error and cgMLSTCRCA  had seven. However, the practical benefits of SKA  and the extra sequencing requirements for cgMLST cluster references in cgMLSTCRCA  mean that SKA (direct pairwise kmer) is likely to be a much more desirable method in a real-world scenario.13. However, we have shown that the methods used in these studies  do not accurately reflect the degree of relatedness between all isolate pairs and consequently can result in mislabelling isolates as involved or not involved in transmission. This can have large implications in the hospital environment where the use of finite infection control resources needs to be as targeted as possible. This phenomenon was observed in the cgMLSTCA transmission threshold, which was considerably higher than the other methods and subsequently classified a much higher number of isolates as involved in transmission. Additionally, sequencing multiple colonies from individual cultures would have provided a more accurate assessment of the level of intra-patient genomic diversity. Although this was not performed in our study, we used data from Gouliouris et al.13, who sequenced extensively from the same patient, along with our preferred genomic comparison method SKA and found an equivalent threshold of ≤5 SNPs. Although slightly lower than our SKA threshold of ≤7 SNPs, we felt it appropriate for the threshold to remain at ≤7 SNPs to ensure no potential transmission pairs were missed.The pairwise SNP distance genomic transmission thresholds derived from intra-patient diversity for cgMLSTCRCA , PCDR  and SKA (direct pairwise kmer) were similar in magnitude to those previously describedIn summation, to identify genomic putative transmission clusters we propose using cgMLST clustering to broadly group genetically similar isolates and then split kmer analysis on the isolates within these cgMLST clusters (SKA) to identify putative transmission links. This proposed pipeline is outlined in Fig. Implementation of this proposed method into routine use relies on it having added clinical value which was demonstrated in the hospital case study. Use of cgMLST was able to differentiate the ST78 isolates into two distinct clusters (cgMLST cluster 3 and cgMLST cluster 5). These well-defined transmission clusters allowed for easy identification of high-risk wards which may inform targeted interventions. The use of SKA to identify genomic clusters allowed the differentiation of singular cgMLST clusters into multiple distinct genomic transmission clusters (ST78) . In the Controlling Superbugs study, sampling strategies differed between hospitals, with some hospitals sampling patients more extensively than others and hence making it more likely that putative transmission links are identified. Although the ward move data is highly detailed, it can contain errors and may miss other patient contacts. These errors may explain some of the genomic putative transmission links that were not supported by epidemiological data. However, the purpose of this study was focussed on exploring the differences in genomic methods rather than quantifying the exact number of the transmission events that have occurred and their directionality and source. Hence these limitations have minimal impact on the results in this case. Differences in genomic relatedness caused by variant calling software (snippy and SKA) and input parameters may also have resulted in the erroneous classification of isolates as linked. However, snippy has been shown to be among the best bacterial SNP callers in a comparison by Bush et al. and the purpose of this study was to explore the key issues with using core alignments to identify putative transmission links rather than the effect of changing the variant callerE. faecium isolates. We have then explored how these clusters can be analysed to identify putative transmission links based on genomic similarity and suggest that the optimal method is SKA. These data will inform the future translation of VREfm sequencing data into routine outbreak detection and investigation.In conclusion, we demonstrate the benefits of using cgMLST clustering as an alternative to MLST to cluster genetically similar 16 and October 2017—November 2018) prospective study that aimed to investigate the use of genomics to predict in-hospital multidrug-resistant organism transmission for a range of species, including vanA VREfm. Eight hospital sites across four hospital networks were involved in the study, resulting in 346 vanA VREfm isolates (308 patients) sent for WGS (pilot study16 and implementation study17). Isolates were collected from patient samples  collected routinely from hospital inpatients (>24 h) at any time during their admission. Duplicate isolates were excluded as defined by; screening isolates of the same type (species and multilocus sequence type) already included in the study (first isolate collected included) or clinical isolates of the same type collected within 14 days of the previous clinical sample. An overview of the VRE screening and infection control practices can be found in Supplementary Table 16. Briefly, patient pairs were classified according to previously published definitions18 as ‘probable transmission’ if on the same ward at the same time, ‘possible transmission’ if admitted to the same ward at a different time (within 60 days), or admitted to the same hospital at the same time; all other patients were classified as ‘unlikely transmission’. An overview of the epidemiological data categorisation decision tree is provided in Supplementary Fig. This project used data arising from the “Controlling Superbugs” study; a 15-month  and Chemagic magnetic bead technology (PerkinElmer). Genomic DNA libraries were then prepared using the Nextera XT kit according to the manufacturer’s instructions (Illumina Inc.). Whole-genome sequencing was performed on the Illumina NextSeq platform using 2 ×150 bp paired-end chemistry as beforemlst tool (v2.19.0) (https://github.com/tseemann/mlst) and the pubMLST database (https://pubmlst.org)19 for E. faecium.All isolates underwent in silico MLST, to determine their sequence type (ST), using the E. faecium cgMLST scheme, created by de Been et al20. and chewBBACA (v2.0.16)21, implemented locally in the COREugate pipeline (v2.0.4) (https://github.com/kristyhoran/Coreugate). This pipeline determines the alleles of each core gene for every isolate as defined by the specific pathogen scheme. The E. faecium cgMLST scheme contains 1,423 genes. The number of allelic differences between each isolate within this core set of genes is then determined. The cgMLST clusters were then determined using single linkage clustering and a pairwise allelic difference threshold of ≤ 25. This threshold was determined based on the distribution of pairwise allelic differences and the inter-MLST pairwise allelic difference (see results).cgMLST alleles of each isolates were defined using the public The MLST core alignments (MLSTCA) used an “MLST reference” . The cgMLST core alignments (cgMLSTCA) also used an “MLST reference” based on the ST of the cgMLST cluster. The cgMLST within-cluster reference core alignments (cgMLSTCRCA) used a “cgMLST cluster reference” (first isolate collected from each of the cgMLST clusters). A complete list of reference genomes is available in Supplementary Data https://github.com/rrwick/Porechop) (barcode threshold 90 and two barcodes required) and filtlong (v.0.2.0) (https://github.com/rrwick/Filtlong) (minimum length 1000 bp and keep percent 95). Reads were then assembled using the Trycycler pipeline (v.0.3.3) (https://github.com/rrwick/Trycycler). This involved first subsampling the reads based on an estimated genome size of 3 Mb using the default method provided by Trycycler. Three different assemblers were used to generate nine draft assemblies (three with each assembler) – flye (v2.8.1)22, miniasm+minipolish (v2.17) (https://github.com/lh3/miniasm)23 and raven (v1.1.0) (https://github.com/lbcb-sci/raven). All assemblers were used with default settings. The contigs in all assemblies were then clustered and any cluster containing contigs from six or more assemblies proceeded to the reconcile step. Any contigs that prevented the reconcile step from completing were removed from the cluster. Following the reconcile, sequence alignment and the consensus step, the consensus sequences were polished with Illumina short reads using snippy (v4.6.0) (https://github.com/tseemann/snippy), the same program used in the core and pairwise alignments. Two rounds of polishing using short read data were completed with bcftools (v1.9) (https://github.com/samtools/bcftools) being used to edit the consensus sequence based on the SNPs identified using snippy.The earliest collected isolate from each of the cgMLST groups with more than two isolates were chosen for Oxford Nanopore sequencing (GridION). These isolates are listed in Supplementary Data 24. (n = 297) and had been divided into clade A1, A2 and B based on their genomic similarity. After constructing a phylogenetic tree using study and global context isolates, the divergent study isolates were found to cluster with clades A2 and B . For the species alignment, the core alignment length was 1,059,458 base pairs, the core SNP alignment was 4,020 sites and the core SNP alignment after masking for recombination using Gubbins26 was 39 sites. All trees were made using core SNP alignments that did not have recombination masking due to the highly variable effect that omitting recombination regions can have on pairwise distances7 and the small size of the resulting core alignment. All trees were midpoint rooted and visualised in R  using phangorn (v2.5.5)27, ape (v5.4)28, ggtree (v2.3.4)29 and ggplot (v.3.3.2)30.When generating the species phylogenetic tree, three isolates were excluded from the study isolate core alignment analysis due to their divergence from the rest of the study isolate . The four major STs were chosen as they comprise 88% of all study isolates. Only cgMLST clusters with three or more isolates can be used in subsequent analyses due to the requirements of the core genome alignments. An overview of the following methods is shown in Fig. Only isolates from the four major STs  with more than three isolates in their respective cgMLST clusters were used in the genomic comparisons (https://github.com/tseemann/snippy) with minfrac set to 0.9 and mincov set to 10. Pairwise SNP distances were calculated in R using harrietr . Recombination masking was not performed for any core alignments to allow for comparison between all methods (PCDR and SKA cannot be screened for recombination). It has also been shown that recombination screening can have a highly variable effect on isolate pairwise distances, often inflating the number of closely related pairs7. An overview of these methods is shown in Fig. Multi-locus sequence type core alignments (MLSTCA) were performed on all isolates in the same ST using a reference of the same ST. Core genome MLST core alignments (cgMLSTCA) were performed on all cgMLST clusters with greater than two isolates using reference genome of the same ST. Core genome MLST with cluster reference core alignments (cgMLSTCRCA) were core alignments performed on all cgMLST clusters with greater than two isolates using a reference of the same cgMLST cluster. All core genome alignments were performed using snippy v4.6.0) (.0 (https31 (v2.3.0) with default settings. SKESA was chosen due to its speed and high sequence quality. Mapping and variant calling was performed using snippy (v4.6.0) with minfrac set to 0.9 and mincov set to 10. Firstly, each isolate read set was first mapped to its corresponding SKESA assembly to identify any “self SNPs” and these “self SNPs” were removed from any subsequent pairwise comparisons based on their location in each contig. A breakdown of the number of “self SNPs” identified can be found in Supplementary Fig. de novo references for the pairwise comparison, with the reads from each isolate in the pair, being mapped to the de novo assembly of the other (non-self) isolate. The average number of SNP variants (excluding any “self SNPs”), calculated from these two alignments, was used as the pairwise SNP distance between two isolates. All PCDR distances presented and referenced in the paper are those that were adjusted for “self SNPs”. PCDR was used as the gold standard for determining isolate relatedness, as this method is consistent with the traditional mapping and core alignment approach but also maximises the proportion of genome considered in each pairwise alignment  for each of the input isolates from the short-read data, using the fastq subcommand . Isolate-to-isolate pairwise SNP distances were then calculated using ska distance with default settings. When using ska distance, a SNP is defined as “Number of split kmers found in both samples where the middle base is an A, C, G or T but differs between files”. In contrast to a traditional core genome alignment, SKA compares isolate pairs based on all genomic data contained in the read files, which is equivalent to a whole-genome comparison.The SKA (v1.0) packagen = 30 isolate pairs) to avoid comparing distant isolates from different acquisition events. Within patient pairs from different cgMLST clusters (n = 10) were shown to have a much greater SNP distance using SKA (distances ranging from 396 to 3176 SNPs) than those of the same cgMLST cluster (0–34 SNPs). These pairwise distances can be found in Supplementary Data n = 2 isolate pairs excluded) . Only isolate pairs arising from the same patient and from within the same cgMLST cluster were considered  from 45 patients. Isolates were assessed for genomic transmission links using cgMLST clustering  and SKA. A pairwise genomic distance of ≤7 SNPs was used to define SKA genomic transmission links. Any pairs that were identified as being genomically linked were then assessed for epidemiological links. Bed move data was collected for all patients between July 2019 and July 2021 (2 years). The same decision tree that was used in the main study was used to categorise the degree of epidemiological links, except as all isolates in the case study were from the same hospital, the hospital unit was used instead of hospital to provide greater resolution in links  using tidyverse33, networkD334, naniar35, ggpubr36, patchwork37, htmlwidgets38 and htmltools39. Flow diagrams were generated using LucidChart (https://www.lucidchart.com). The coefficient of determination (R2) was calculated using ggpmisc40 and was based on a linear regression model.Figures were generated in R  and endorsed by the corresponding HREC at each participating site (HREC/13/MH/326). This ethical approval also covered the hospital case study.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Mastitis causes economic loss due to discarded milk and reduced milk production and quality, increased medical care costs and somatic cell count (SCC) penalties. The use of genetic markers associated with the variability of this trait through marker-assisted selection (MAS) could help traditional methods. Our objectives were to identify new single nucleotide polymorphisms (SNPs) and genes associated with mastitis resistance in Assaf sheep by using the Illumina Ovine Infinium® HD SNP BeadChip (680K). Firstly, corrected phenotype estimates for somatic cell score (SCS) were calculated using 6173 records from 1894 multiparous Assaf ewes, and were used to select 192 extreme animals  for the genome-wide association study (GWAS). Four SNPs , three of them totally linked, were found to be significant at the chromosome level (FDR 10%) in two different regions of OAR19 close to genes related to the immune system response. Validation studies of two SNPs (rs419096188 and rs424642424) by Kompetitive Allele-Specific PCR (KASP) genotyping in the total population (n = 1894) confirmed previous GWAS association results for the SCS trait. Finally, the SNP rs419096188 was also associated with lactose content trait.® HD SNP BeadChip (680K). In total, 6173 records from 1894 multiparous Assaf ewes with at least three test day records and aged between 2 and 7 years old were used to estimate a corrected phenotype for somatic cell score (SCS). Then, 192 ewes were selected from the top (n = 96) and bottom (n = 96) tails of the corrected SCS phenotype distribution to be used in a GWAS. Although no significant SNPs were found at the genome level, four SNPs  were significant at the chromosome level (FDR 10%) in two different regions of OAR19. The SNP rs419096188 was located in intron 1 of the NUP210 and close to the HDAC11 genes (61 kb apart), while the other three SNPs were totally linked and located 171 kb apart from the ARPP21 gene. These three genes were related to the immune system response. These results were validated in two SNPs (rs419096188 and rs424642424) in the total population (n = 1894) by Kompetitive Allele-Specific PCR (KASP) genotyping. Furthermore, rs419096188 was also associated with lactose content.A genome-wide association study (GWAS) was performed to identify new single nucleotide polymorphisms (SNPs) and genes associated with mastitis resistance in Assaf sheep by using the Illumina Ovine Infinium Escherichia coli, Streptococcus uberis, and Staphylococcus aureus  and the residual [e ~N ]. Corrected phenotypes estimates adjusted by the factors included in the model were used to select 192 animals from the upper and bottom tails of the corrected phenotypes distribution of the whole population . QC was performed using PLINK 1.9 [We applied the QC criteria to the raw genotypes as follows: (i) individuals with low call rates (<0.97) were excluded from additional analysis; and (ii) SNPs were also excluded if they had a low call rate (<0.97), a minor allele frequency (MAF) < 0.01, or significant deviations from Hardy-Weinberg equilibrium (HWE)  analyses were performed on genome-wide identity-by-state (IBS) pair-wise distances to check outliers and population stratification and the appropriate choice of high- and low-SCS animals within the population. Plotting the component C1 values against component C2 allows us to identify clustering of samples using standard classical (metric) multidimensional scaling.Pair-wise linkage disequilibrium (LD) measured by the rLINK 1.9 . All autThe GWAS for estimated SCS data was performed using the mixed linear model-based association analysis (MLMA) of the genome-wide complex trait (GCTA) analysis software  for the software . The numThe Hardy–Weinberg equilibrium exact test values, observed and expected heterozygosities, and minor allele frequency (MAF) for each SNP were calculated using PLINK 1.9 software . The relTo test differences between genotypes, the least squares means (LSMs) for each pair-wise comparison were estimated.In addition, the relationships between the SNPs rs419096188 and rs424642424 and MY, PC, FC, LC, and SC were also estimated as described above.n = 1894). The corrected phenotype obtained was the permanent effect of each ewe, which is a new phenotype without the known effects. Corrected phenotype estimates for the somatic cell score (SCS) were calculated for all ewes considered in this study  on the y-axis. However, 4 SNPs in OAR19 were significant at the chromosome level (FDR p < 0.10)  (2 = 1).The genomic inflation factor was used to assess bias in the test statistics. The average genomic inflation factors were 1.012 and 1.016 for MLMA and LOCO approaches, respectively, which suggests that any potential bias due to population stratification was addressed. No significant genome-wide results were found. 4642424) . As the ARPP21 gene was annotated in this interval in OAR 3.1. The SNP rs415580501 was located 171 kb apart from the ARPP21 gene. Furthermore, a miRNA (miR-128-2) is located in an intron of this gene. For the SNP rs419096188, 4 coding genes  and 3 long noncoding RNAs  were annotated in the 250 kb interval. The SNP rs419096188 was located in the 5’ region or in intron 1 of the NUP210 gene according to the OAR 3.1 and Oar_Rambouillet_v1.0 (GCF_002742125.1) genome versions, respectively, while the FBLN2, Ensoarg00000001587, and HDAC11 genes were located at 164, 153, and 61 kb, respectively, in Oar3.1.Putative causal genes located in the 250 kb region on both sides of the significant SNPs for the SCS trait are also indicated in p = 0.011) and GG (p = 0.002) genotypes, respectively. In the case of rs424642424, the SCS showed an increase of 0.16 ± 0.05 in AG ewes compared to GG (p = 0.002) animals. These results confirm those obtained by the GWAS. Finally, for rs419096188, the LC from animals carrying the AA genotype was 0.046 ± 0.016% higher than that from ewes with the GG genotype (p <0.05).In total, 1824 ewes belonging to the three flocks were genotyped by KASP for both SNPs. Only one genotype failure in one ewe for rs424642424 was found. The SNPs studied were in Hardy-Weinberg equilibrium. The association studies showed that the HTD and Dim effects were significant for the two SNPs for all traits . The genARPP21 gene, which includes a miRNA (mir128-2) located in one of its introns. Mammals have two genes for miR-128 that are located in introns of two conserved, orthologous protein-coding host genes, R3HDM1 and ARPP21, that harbor miR-128-1 and miR-128-2, respectively. The SNP rs419096188 is located in the interval where four coding genes  and three long noncoding RNAs were annotated (NUP210 gene according to the OAR 3.1 (Texel) or Oar_Rambouillet_v1.0 (GCF_002742125.1) sheep genome versions, respectively, while FBLN2, Ensoarg00000001587, and HDAC11 genes were located at 164, 153, and 61 kb, respectively, in Oar3.1.This study identified two different genomic regions in OAR19 associated with the SCS, a trait that is highly correlated with ovine mastitis and that has high negative effects on farm economy, animal welfare, and milk product health for the dairy industry. According to our findings, no significant SNPs at the genome-wide level were found; however, four SNPs were found to be significant at the chromosome-wide level in OAR19. Three of these SNPs were completely linked   and werennotated . The SNPsuppressor of cytokine signaling 2 (SOCS2) gene has been shown to be particularly important for the trait being related to the inflammatory response and has been detected in Lacaune [Different results were obtained in previous GWASs for mastitis resistance in sheep. In Sarda , Lacaune Lacaune  and Chio Lacaune  sheep brARPP21, NUP210, and HDAC11) and one miRNA (miR-128-2) could be functionally related to the trait studied. Hosting miR-128-2 is related to breast and prostate cancer in humans [miR-128-2 in cultured bovine mammary epithelial cells after six hours of infection with S. uberis. Recently, Ren et al. [miR-128 inhibits the expression of inflammatory cytokines to avoid excessive inflammation by regulating the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway. The ARPP21 gene was thought to be related to the response to stimulus, triggering a cellular response to heat at any temperature higher than the optimal temperature for the organism [ARPP21 gene was downregulated after 2 h of infection with S. Typhi [ARPP21 could antagonize miRNA function in general and miR-128 targeting in particular [ARPP21 gene was upregulated in a mouse miR-128 knockout [In our study, three genes , located near rs419096188 and involved in the immune system process [HDAC11 also has important roles in initiating adaptive immune responses, such as type I interferon (IFN) signaling and CD4+ T cells [NUP210 gene, which is involved in the immune response, cholesterol homeostasis, and stress response. The NUP210 gene belongs to the nucleoporin (NUP) protein family and is involved in nucleocytoplasmic transport through nuclear pore complexes (NPCs). The expression of this gene has been found to be related to tube morphogenesis and mammary gland development in French dairy cattle breeds [NUP210 deletion revealed a cell intrinsic role in the regulation of CD4+ T cell homeostasis, specifically reducing the number of circulating naïve CD4+ T lymphocytes and establishing tissue-specific NPCs as key modulators of T cell receptor (TCR) signaling in mice.The second coding gene was  process . HDAC11  process ,39. This process . HDAC11 e breeds . Furthere breeds . Borlidoe breeds  showed tn = 1824). The validation results confirmed those of the GWAS. Both SNPs were associated with the SCS trait, and rs419096188 was also significant for lactose content (The two significant genomic regions associated with mastitis resistance were validated by genotyping one SNP of each region in the whole ewe population ( content . Animals content . This isThe results reported in the present study suggested that two different regions of OAR19 influence the SCS. Although none of them were reported in previous studies, it should be kept in mind that resistance to mastitis is a polygenic and complex trait that is difficult to explain considering only a few markers. Further studies will be needed to isolate the functional SNPs responsible for the phenotypic variation observed for the SCS trait in this study.