Myodes rufocanus fluctuate greatly within and across years in Japan. Here, to investigate the role of individual dispersal in maintaining population genetic diversity, we examined how genetic diversity varied during fluctuations in density by analyzing eight microsatellite loci in voles sampled three times per year for 5 years, using two fixed trapping grids (approximately 0.5 ha each). At each trapping session, all captured voles at each trapping grid were removed. The STRUCTURE program was used to analyze serially collected samples to examine how population crashes were related to temporal variability, based on local‐scale genetic compositions in each population. In total, 461 and 527 voles were captured at each trapping grid during this study. The number of voles captured during each trapping session  varied considerably at both grids. Although patterns in fluctuations were not synchronized between grids, the peak densities were similar. At both grids, the mean allele number recorded at each trapping session was strongly, positively, and nonlinearly correlated with density. STRUCTURE analyses revealed that the proportions of cluster compositions among individuals at each grid differed markedly before and after the crash phase, implying the long‐distance dispersal of voles from remote areas at periods of low density. The present results suggest that, in gray‐sided vole populations, genetic diversity varies with density largely at the local scale; in contrast, genetic variation in a metapopulation is well‐preserved at the regional scale due to the density‐dependent dispersal behaviors of individuals. By influencing the dispersal patterns of individuals, fluctuations in density affect metapopulation structure spatially and temporally, while the levels of genetic diversity are preserved in a metapopulation.Population densities of the gray‐sided vole  Allelic diversity was strongly, positively, and non‐linearly correlated with density at two trapping grids, although large fluctuations in density were observed. At each grid, if the density was similar, the mean number of alleles observed was also similar, irrespective of trapping sessions. Subsequently, as the populations increase to peak‐density levels, the populations expand spatially, facilitating the exchange of individuals between neighboring populations; thus, populations become more similar. Additionally, Berthier et al.  has collected voles three times per year at these grids since the late 1980s with the permission of the Hokkaido Government. Tissue samples were collected from these voles during 2002–2006 for DNA extraction. During each trapping session, 50 Sherman‐type traps were arranged in a 5 × 10 grid pattern at an interval of 10 m (~0.5 ha) with a handful of oatmeal in each trap for three nights . Under this sampling protocol, almost all weaned gray‐sided voles in the grid would be captured, allowing us to estimate vole density per 0.5 ha at the study sites. Upon capture, voles were euthanized by cervical spine dislocation. At the laboratory of the Nemuro Subprefectural Bureau of the Hokkaido Government, a few small pieces of liver per vole were fixed in 99.5% ethanol after biopsy. Liver samples were stored at −80°C in Sapporo, Hokkaido until DNA extraction. To determine whether similar density fluctuation occurs around the grids, we concurrently performed additional trapping in the areas surrounding grids A and I, during which voles were captured using 49 or 50 live traps on three nights; these voles were released shortly after capture.On the Nemuro Peninsula, the breeding season of the gray‐sided vole is from mid‐April to early October. To accurately identify changes in population density and collect liver samples during the breeding season, voles were captured three times per year  for 5 years 2002–2006) at two fixed sites on Nemuro Peninsula—grids A and I  as undergrowth. Although the middle of the peninsula has been urbanized, most of the peninsula remains vegetated  gelatin, 0.25 U AmpliTaq Gold DNA polymerase (Applied Biosystems), and approximately 100 ng genomic DNA. Two other loci, MSCRB‐09 and −10 (GenBank accession nos. AB248757 and AB248758), were amplified using the same reaction mixture mentioned above, with the primer sets 5′‐TAGGA ACAGC TGGAG AACCA C‐3′ and 5′‐TGCAC ACTCT AAATA CCATG TGCA‐3′ for MSCRB‐09 and 5′‐TGCCC CAATC TGTCT TCCAC‐3′ and 5′‐TCAGA CTCAG TACAG TGAAT CT‐3′ for MSCRB‐10. Both amplification processes used an annealing temperature of 54°C. The PCR process was as follows: incubation for 10 min at 95°C, 35–55 cycles of 20 s at 93°C, 15 s at a locus‐specific annealing temperature, and 20 s at 72°C. Genotyping was performed with an ABI PRISM 310 Genetic Analyzer (Applied Biosystems) using GeneScan Analysis 3.1.2 and Genotyper 2.5 software (Applied Biosystems).Genomic DNA was extracted from each liver sample using the conventional phenol‐chloroform method  were also calculated using GenAlEx; data were used from trapping sessions in which five or more voles were captured. Allelic richness, a measure of the number of alleles within samples that considers the differences in sample size , the presence of Hardy–Weinberg equilibrium was tested at each locus for each trapping session; linkage disequilibrium among loci was tested using samples from trapping sessions in which null alleles were likely to be absent. For each session, the presence of null alleles was assessed at each locus using the program MICRO‐CHECKER 2.2  varied from one to eight. Each run, replicated 100 times, comprised a burn‐in period of 104 and 2 × 104 iterations with an admixture model under default settings, with the exception of option LOCPRIOR, in which the order of trapping sessions was considered to represent sampling location. Using the R package pophelper 2.3.0  were drawn for the inferred K value, for which individual assignments to clusters were determined using the CLUMPP 1.1.2 program  varied considerably at both grids, despite constant trapping efforts Figure . We capt3.2SE) values of 0.850 ± 0.016 and 0.868 ± 0.009 for grids A and I, respectively. No correlation was observed between density and heterozygosity averaged among the eight loci at either grid  and I , respectively. Statistically significant departures from Hardy–Weinberg equilibrium were observed in three trapping sessions at grid A and four sessions at grid I. For each trapping session, significant departures from random union of gametes were observed at fewer than three loci at grid A and one locus at grid I . At grid I, an analysis of two trapping sessions (sessions 6 and 9), excluding possible null alleles, revealed significant differentiation in five locus combinations. However, of the locus combinations with significant nonrandom associations, no combinations were repeated across the sessions analyzed at each grid.Genotypes were determined based on the eight microsatellite loci for all collected samples (461 and 527 at grids A and I), with the exception of a single animal, which had a missing value for one locus, MSCRB‐11 Table  and S2. d Figure . ModeratThe results of MICRO‐CHECKER analysis suggested that null alleles were present at MSCRB‐06, ‐07, ‐09, ‐10, and ‐11 for samples collected at grid A and at MSCRB‐06 and ‐11 for samples collected at grid I Table  and S2; 3.3As density increased, the average number of alleles observed at the eight loci increased, after the number of alleles at loci suspected to have null alleles had been increased by one for each trapping session Figure . Howevernls command in R was applied to obtain nonlinear least‐squares estimates of parameters for three assumed models after Crawley (R2) were higher than 94% for all models, one model, Y = a + b × ln (X), where Y is the mean number of observed alleles and X is the density, produced both the highest R2 values and lowest AIC values for both trapping grids , three time‐series data sets were used for this analysis: sessions 11–15 for grid A and sessions 2–6 and 11–15 for grid I. This analysis revealed that more new alleles appeared during the early increasing phase immediately following a crash Figure .3.4K value was observed at K = 2  and after a crash (sessions 11–15), the other cluster accounted for a large amount  were more frequent at grid A.To clearly demonstrate temporal changes in cluster compositions, the frequency distributions of s Figure . Similart Figure . Althoug44.1Microtus arvalis, genetic diversity fluctuates at the local scale, while it remains constant at the regional scale. In gray‐sided vole populations, genetic diversity may be well‐preserved within metapopulations at the regional scale, whereas fluctuations in genetic diversity occur continuously at the local scale. Here, we observed that allele numbers were restored within a short period of time. The mutation rate for microsatellite loci is low  and voles were sorted into distinct groups based on the grid in which they were captured, independent of trapping session  in the Canadian Arctic. Genetic differentiation was clearly observed among geographical regions but weaker among localities within regions, suggesting gene flow within regions. Lagerholm et al. (L. lemmus) microsatellite loci exhibited high variability indicative of a weak population structure among Fennoscandian subregions and proposed that mass movements during periods of peak‐density act as pulses of gene flow between mountain tundra areas, thus helping to preserve genetic variation and counteract differentiation among subregions. However, these are snapshot studies of populations, and genetic diversity is not examined regularly in lemmings ; Formal analysis (lead); Investigation ; Writing‐original draft (lead); Writing‐review & editing (lead). Kenichi Takahashi: Investigation ; Writing‐review & editing (supporting).Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.Appendix S4Click here for additional data file.Appendix S5Click here for additional data file.


Comparisons of microsatellites and single‐nucleotide polymorphisms (SNPs) have found that SNPs outperform microsatellites in population genetic analyses, questioning the continued utility of microsatellites in population and landscape genetics. Yet, highly polymorphic markers may be of value in species that have reduced genetic variation. This study repeated previous analyses that used microsatellites with SNPs developed from ddRAD sequencing in the black‐capped vireo source‐sink system. SNPs provided greater resolution of genetic diversity, population differentiation, and migrant detection but could not reconstruct parentage relationships due to insufficient heterozygosities. The biological inferences made by both sets of markers were similar: asymmetrical gene flow from source sites to the remaining sink sites. With the landscape genetic analyses, we found different results between the two molecular markers, but associations of the top environmental features  with dispersal estimates were shared between marker types. Despite the higher precision of SNPs, we find that microsatellites effectively uncover population processes and patterns and are superior for parentage analyses in this species with reduced genetic diversity. This study illustrates the continued applicability and relevance of microsatellites in population genetic research. Comparisons of microsatellites and single‐nucleotide polymorphisms (SNPs) have found that SNPs outperform microsatellites in population genetic analyses, calling into the question the continued utility of microsatellites in population and landscape genetics. This study repeated analyses previously done using microsatellites with SNPs developed from ddRAD sequencing in the black‐capped vireo source‐sink system. Despite the higher precision of SNPs, we find that microsatellites effectively uncover population processes and patterns and are superior for parentage analyses in this species with reduced genetic diversity. In these and subsequent analyses, we corrected alpha levels for multiple comparisons using a standard Bonferroni correction  deviations and linkage disequilibrium (LD). Loci found to be in LD or deviating from HWE were omitted from further analysis. We calculated observed and expected heterozygosity  using the pairwise.WCfst function in R package hierfstat (v 3.5.0), estimating 95% confidence intervals with the boot.ppfst function in the same R package  to dete Goudet, .p‐value threshold. We used parentage assignments in CERVUS (v 3.0.7) to directly observe migration among sites  and fathers (n = 56), chosen by their relative age to a given SY offspring, using an SNP dataset with a 90% genotyping rate across the 178 individuals (N loci = 806), with a mean missingness of 8.3%. We additionally ran a small sensitive analysis to test ranges of parameters . A more stringent genotyping rate was used for this analysis to avoid biases associated with missing data and parentage analyses , we detected first‐generation migrants using “L_home/L_max” likelihood ratio, the Paetkau et al.  criterio2.2For all landscape genetics analyses, we used the population‐level proportion of shared alleles (Dps) as a metric of gene flow . We considered models with a ΔAICc < 2 to be competitive  presented in Hauser et al.  but not for He (p = .549). All sites, except BC and CB, had significantly lower H0 than He , but similar patterns in proportion of migrants in sites were found. Using SNPs, we found 82 migrants, with 33 detected in central Texas sites and 49 detected in Fort Hood sites using GENECLASS2  than those in Fort Hood sites (1.8–3.0%). Hauser et al.  and Hausn = 16), of which most were from Fort Hood (n = 14). We found directional migration from Fort Hood to central Texas (n = 4) compared with central Texas to Fort Hood (n = 1). The full microsatellite dataset assigned more parent–offspring pairs (n = 21) at the 95% confidence interval , specific results differed between the datasets. Among population genetic estimates, we found statistically significant heterozygosity deficiencies in many sites, higher allelic richness in ER and WR, statistically significant pairwise FST values compared with those of multiallelic microsatellites . Previous studies have found that SNPs are more accurate at estimating genetic diversity metrics  would be necessary to reconstruct 1st‐order relationships accurately. As the maximal heterozygosity value possible with SNP loci is 0.50  among the black‐capped vireo study sites were extremely small (on the order of thousandths) and may lack biological significance. When calculating population genetic metrics, large SNP datasets, such as ours, increase the chance of statistically significant results  and Type I error of results  that have been corroborated by observational and telemetry data , or where bioinformatics expertise or computational power is not accessible ; Data curation (lead); Formal analysis (lead); Methodology ; Software (lead); Validation (lead); Writing‐original draft (lead). Giridhar Athrey: Conceptualization ; Formal analysis (supporting); Methodology (supporting); Supervision (supporting); Validation (supporting); Writing‐review & editing (supporting). Paul L. Leberg: Conceptualization ; Funding acquisition (lead); Project administration (lead); Resources (lead); Supervision (lead); Writing‐review & editing (supporting).


Oryza sativa L.) is cultivated under a variety of climatic conditions. Due to the fragility of the coastal ecosystem, rice farming in these areas has lagged behind. Salinity coupled with floods has added to this trend. Hence, to prevent genetic erosion, conserving and characterizing the coastal rice, is the need of the hour. This work accessed the genetic variation and population structure among 2,242 rice accessions originating from India’s east coast comprising Andhra Pradesh, Orissa, and Tamil Nadu, using 36 SNP markers, and have generated a core set (247 accessions) as well as a mini-core set (30 accessions) of rice germplasm. All the 36 SNP loci were biallelic and 72 alleles found with average two alleles per locus. The genetic relatedness of the total collection was inferred using the un-rooted neighbor-joining tree, which grouped all the genotypes  into three major clusters. Two groups were obtained with a core set and three groups obtained with a mini core set. The mean PIC value of total collection was 0.24, and those of the core collection and mini core collection were 0.27 and 0.32, respectively. The mean heterozygosity and gene diversity of the overall collection were 0.07 and 0.29, respectively, and the core set and mini core set revealed 0.12 and 0.34, 0.20 and 0.40 values, respectively, representing 99% of distinctiveness in the core and mini core sets. Population structure analysis showed maximum population at K = 4 for total collection and core collection. Accessions were distributed according to their population structure confirmed by PCoA and AMOVA analysis. The identified small and diverse core set panel will be useful in allele mining for biotic and abiotic traits and managing the genetic diversity of the coastal rice collection. Validation of the 36-plex SNP assay was done by comparing the genetic diversity parameters across two different rice core collections, i.e., east coast and northeast rice collection. The same set of SNP markers was found very effective in deciphering diversity at different genetic parameters in both the collections; hence, these marker sets can be utilized for core development and diversity analysis studies.In India, rice ( Oryza sativa L.) is an important cereal crop which is a predominant food for over three billion people across the globe. Since it can adapt to an extensive spectrum of environmental conditions, it is therefore considered a varied crop species  are supposed to be the most bountiful variation found across the genome and therefore are excellent for high-resolution genotyping, in turn useful for analysis of genetic diversity and association mapping, linkage mapping, and marker-assisted selection (MAS) . Simple Plant Genetic Resources of native rice needs to be conserved promptly. A major conservation strategy counts on developing core collections, thereby screening gene bank accessions for important agronomic traits. To create a core collection, one needs a good construction strategy. In the present study, we demonstrate the identification of a diverse core set panel from the east coast region of India comprising 247 core accessions and a mini core set of 30 accessions to enable elite gene mining for breeding and conservation and on a long-term perspective to form a panel for association studies. We have also done a comparative analysis of genetic diversity parameters across two different rice collections , thus validating the use of SNP markers for studying diversity parameters across any other rice collection.A set of 2,242 seed samples of the east coast region of India  were procured from the Indian National Genebank, National Bureau of Plant Genetic Resources (NBPGR), New Delhi, with passport data  number) and the states to which they belong. This is given in www.sequenom.com). The information about the genetic location of the multiform assays designed for 36 SNPs having conserved single-copy rice genes was derived from The seed was de-husked, and genomic DNA was isolated using the QIAGEN DNeasy Plant Mini Kit. A tissue lyser  and a tissue lyser adapter set  was used to grind kernels into fine powder. Working stocks with 10 ng/μl of the genomic DNA samples was prepared, and 30 µl of the diluted sample was transferred to a 96-well plate to be run on the Sequenom MassARRAY which adopts the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometer for most authentic detection of SNPs (http://taylor0.biology.ucla.edu). Additional hierarchical structure analysis was done after observing additional peaks at two different K values to unmask the groups   to calcue groups . Accessie groups  was usedPowerCore 1.0 software  was usedhttps://build.ngchm.net/NGCHM-web-builder, NG-CHM BUILDER: Interactive Heat Map  was used to determine the dissimilarity matrix which was used to construct an unrooted phylogenetic tree using FigTree v1.4.3 . Total cThe rice collections of coastal states belonging to the east coast of India were analyzed for the genetic diversity and population structure study. The lowest PIC recorded was 0.0 for marker 04-19-4_C from the state of Orissa, and the highest was 0.37 for marker v03-3478-1_C from the state of Andhra Pradesh. Heterozygosity was observed to be 0.0 for three markers 04-19-4_C, 11-3935_C, and 12-3200-2_C from Orissa state, and the highest value observed was 0.69 for marker 04-1801-20_C from the state of Andhra Pradesh. The lowest value of genetic diversity was found to be 0.0 for marker 04-19-4_C, and the highest was 0.49 for marker 02-267_C both from Orissa. The lowest and highest major allele frequencies recorded were 0.52 for 02-267_C and 1.0 for 04-19-4_C, respectively, from Orissa state . For thehttp://taylor0. biology.ucla.edu). The number of populations was found to be four for the entire 2,242 east coast rice collection . In the first case, four populations were assumed and AMOVA analyses revealed that 29% diversity exists among populations, 20% within individuals, and 51% among individuals of the total east coast rice collection , while tThe AMOVA study of Andhra Pradesh revealed 23% variance within individuals and 30% and 47% variance among populations and among individuals, respectively . PCoA plOut of 2,242 rice accessions studied, a core set of 247 accessions  and a mini core set of 30 accessions were selected using POWERCORE . Thirty-The degree of genetic diversity of the core set was studied to find the degree of genetic diversity captured from the overall coastal collection. Major allele frequency, gene diversity, heterozygosity, and PIC were observed to be 0.75, 0.34, 0.12, and 0.27 respectively . The valThe population structure grouped the core set accessions into four populations , 8. The The AMOVA study of the core collection revealed 29% variance within individuals as well as among population and 42% variance among individuals . The PCoKinship analysis of the core set showed that more than 50% of the samples had a kinship value less than zero, and less than 10% of the accessions had kinship values between 0.5 and 0.75 . The kin). This means that markers 01-608-4_C and 03-3478-1_C had given the maximum PIC value of 0.37 in both the collections and marker 04-19-4_C had given the minimum PIC value of 0.01 in both collections. Similarly, markers 01-608-4_C and 03-3478-1_C had displayed the highest gene diversity with a value of 0.49 across both collections and a minimum value of gene diversity 0.02 with marker 04-19-4_C across both collections. Generally, line graphs for PIC and gene diversity were overlapping for both collections except at certain points where deviations were observed. For example, marker 03-1691-1_C gave a gene diversity value of 0.32 in the coastal rice collection and 0.4 in the northeast rice collection. The same marker gave a PIC value of 0.26 in the coastal rice collection and 0.32 in the northeast rice collection. Marker 11-522-1_C gave a gene diversity value of 0.45 in the coastal collection and 0.19 across the northeast collection; also, this marker 11-522-1_C gave PIC values of 0.35 and 0.17 across the coastal rice and northeast rice collections. A subsequent analysis between major allele frequency and heterozygosity from the current study and with the northeast rice collection was also evaluated , markers 01-608-4_C and 03-3478-1_C were found to give the highest PIC value of 0.37, while marker 04-19-4_C was least informative giving the lowest PIC of 0.01 in both collections. Marker 01-608-4_C is a locus of evolutionary conserved genes from the saponin family of proteins, which is involved in the sphingolipid metabolic process and active in extracellular space ; in the Validation of the same set of SNP markers on two collections (northeast rice and east coast rice collection) has established that the 36-plex SNP assay is sufficient and efficient for initial diversity analysis and core development. Hence, this 36-plex SNP assay can be exploited by researchers for the genetic diversity study and development of core based on their own collections, thus accelerating their breeding program.This is the first study where India’s east coast rice collections were characterized using SNP markers. The genetic diversity and population structure were studied, and core and mini core collections with maximum diversity and minimum redundancy were developed. A total of 2,242 east coast rice accessions from three different states of India, i.e., Andhra Pradesh, Orissa, and Tamil Nadu, have been characterized, and a wide range of gene diversity and PIC was observed. A phylogenetic analysis of the total east coast rice collection revealed three groups, and a population structure analysis revealed four populations. The 36-SNP assay used in this study was validated by comparing the genetic diversity parameters  across two different rice collections, i.e., east coastal rice and northeast rice collection, and it was observed the these markers were sufficient to decipher all genetic parameters very efficiently; hence, they can be effectively utilized for core development and diversity study of different rice genotypes.


Multimorbidities greatly increase the global health burdens, but the landscapes of their genetic risks have not been systematically investigated.We used the hospital inpatient data of 385,335 patients in the UK Biobank to investigate the multimorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify multimorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for the networks of genetically interpretable multimorbidities to detect the hub diseases and the involved molecules and functions in each module.In total, 11,285 multimorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture levels. Multimorbidities affecting the same and different physiological systems displayed different patterns of the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by multimorbidities converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable multimorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the multimorbidity modules could help provide useful insights for the genetic contributors of multimorbidities.https://multimorbidity.comp-sysbio.org).Our results provide a systematic resource for understanding the genetic predispositions of multimorbidities and indicate that hub diseases and converged molecules and functions may be the key for treating multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities (The online version contains supplementary material available at 10.1186/s13073-021-00927-6. Multimorbidity, the coexistence of more than one disease in a patient not by chance, presents great challenges for disease diagnosis and treatment , 2. CompDuring the last decade, large-scale genome-wide association studies (GWASs) have found overlapped genetic risks for a few frequently multimorbid diseases at the genomic loci level, i.e., single-nucleotide polymorphisms (SNPs) or genes, suggesting that there might be a molecular basis of multimorbidity. For example, GWASs have uncovered 38 SNPs associated with both asthma and allergic diseases , and 187The malfunctions caused by disease risk loci can spread via cellular networks owing to molecular interactions among genes. To this end, some studies captured the genetic overlaps between multimorbidities by network-level evidence, such as protein-protein interactions (PPIs) and molecular pathways , 12, 13.Due to the limited access to the matched epidemiological and genomic data of the same population group, existing studies either used matched data for a limited number of diseases , 14–17, https://multimorbidity.comp-sysbio.org). The converged biological molecules and functions identified in this study are responsible for many multimorbidities, which may serve as the key factors for the management and treatment of multimorbidities.In this study, we take advantage of the large-scale, matched epidemiological and genetic data hosted in the UKB to systematically investigate the multimorbid relationships among 439 common diseases as well as their shared genetic factors. We have identified the multimorbidity shared genetic components at the loci and network levels, and performed functional analyses on them to uncover the converged biological functions. Furthermore, the shared genetic patterns of the multimorbidities affecting the same and different physiological systems have been explored. Finally, we have constructed and decomposed two multimorbidity networks to find the hub diseases that mediate multimorbid relationships in multimorbidity modules and to highlight the corresponding molecular mechanisms. Our results provide a systematic resource of multimorbidities among common diseases, as well as their shared genetic risks  with prevalence > 0.1%, since most of the publicly available GWAS summary statistics of the UKB diagnoses are based on the level 2 codes . A. A. The ctively) . Taken tP value = 5e−5), and all the 4 nutritional diseases are multimorbid with one another . To get a visual and more confident observation, we have constructed a high-confidence multimorbidity network comprised of multimorbidities with RR > 15  by Goh et al., diseases are connected if they share at least one disease-associated gene . We haveInteresting, apart from the intra-category multimorbidities, we also find significantly more multimorbid relationships between 41 pairs of different categories , further confirming our findings [https://multimorbidity.comp-sysbio.org).All available GWAS summary statistics based on the UKB subjects are collected from geneAtlas , coverinfindings . As the P value = 4.6e−4 for SNPs; difference not significant for genes after Bonferroni correction; Fisher exact test) compared to inter-category multimorbidities, while the latter are more likely to share network-level genetic components . There is no significant difference in how likely multimorbidities of intra- and inter-categories have genetic correlations. These results suggest that multimorbidities affecting the same and different physiological systems may have different biological origins—the former tends to directly originate from pleiotropic loci and the latter tends to indirectly originate from converged biological functions.We then explore whether there are any specific patterns in shared genetic components for different types of multimorbidities, i.e., exploring the differences in the levels of genetic components mediating intra- and inter-category multimorbidities. Overall, we find that multimorbidities of 9 out of 24 (37.5%) intra-categories and 52 out of 276 (18.8%) inter-categories significantly share genetic components, suggesting a relatively high probability of genetic involvement for multimorbidities affecting the same physiological systems . In fact, 50% (8/16) of the multimorbidities within this category share disease-associated genes. A total of 20 genes are involved in these intra-category multimorbid relationships and are mainly related to the human leukocyte antigen (HLA) complex (such as HLA-DQA1 and HLA-DRB1), histone clusters (such as HIST1H1B and HIST1H2AJ), and tumors (such as TERT and NOTCH4 for prostate cancer) [The above statistical observation is best illustrated by the diseases from “Male genital organs” category Fig. b. The mu cancer) , 50. In To summarize, based on matched genetic and epidemiological data, we find that almost half (46%) of the multimorbidities identified in this study are genetically interpretable, indicating a strong genetic role in the origin of multimorbidities. Among these genetically interpretable multimorbidities, the intra-category and inter-category ones tend to share genetic components at different levels (loci VS. network), suggesting their different biology origins.To enhance the understanding on the biological mechanisms of multimorbidities, we conduct functional analyses of the loci and network-level genetic components. The overall genetic architecture is not analyzed here, as it reflects statistical correlations but not detailed functions (see “Discussion”).P values = 1.2e−44 and 4.1e−147) and intergenic regions   than other disease SNPs and non-disease SNPs  and still have significantly higher CADD scores  than other disease SNPs and non-disease SNPs, but are no longer overrepresented in intergenic regions  Fig. a, but wiNPs see “b. These P values = 2.9e−38 and 8.8e−15, respectively). Essential genes are reported to have a tendency to encode hub proteins in the human interactome and play important roles in maintaining normal developmental and/or physiological functions [P values = 0.01 and 3.6e−11, respectively, t test; Fig. P values = 4.7e−4 and 8.1e−44, respectively, two-sided Mann–Whitney U test; Fig. P values = 0.04 and 2.9e−10) and express in more tissues  compared with other disease genes and non-disease genes  disease genes recorded in Online Mendelian Inheritance in Man are not essential genes critical for survival, and these disease genes are less likely to be housekeeping genes that express in all tissues . We thenunctions . These rFor the network-level genetic components, the genes involved in the top 10 PPIs shared by the most numbers of multimorbidities are significantly enriched in GO terms related to biological processes of gene silencing and protein metabolism  see “”. These sigma = 1.15 and 1.03, respectively; see “The fact that a small number of genetic components can interpret a large portion of the genetically interpretable multimorbidities, inspires us to examine whether the “small world” property exists in these genetically interpretable multimorbidities. Therefore, we construct two multimorbidity networks by connecting multimorbid diseases that share the loci-level genetic components (denoted as the LG network) and the network-level genetic components (denoted as the NG network), respectively. As expected, for the LG and NG networks, their node degrees follow the power law distribution  connected with more than 25% of all nodes in each network as “universal hub diseases.” It is not appropriate to assign the “universal hub diseases” into any single multimorbidity module, as each module usually contains far fewer than 25% of all nodes. Seven and thirteen “universal hub diseases” are found for the LG network and the NG network, respectively  . Within each module (size > 5), if more than half of the within-module edges can be mediated by three or less top-degree diseases, we define them as “local hub diseases” of the module. As shown in Table Next, based on the remained nodes that are not “universal hub diseases,” we have detected 11 multimorbidity modules for the LG network and 10 multimorbidity modules for the NG network  that regulates the iron metabolism [CHRNA3 encodes the neuronal nicotinic acetylcholine receptor, and its mutation is associated with lung function and COPD severity in ever-smokers [. Though there are many other possible pathways individually associated with the above diseases, our analysis indicates that iron metabolism and the neuronal nicotinic acetylcholine receptor pathways may be the top candidates to examine when study the multimorbidities of these diseases with F17. Lastly, “universal hub diseases” connect to multiple modules, sometimes through different genetic components. In the NG network, the universal hub disease E66 (Obesity) have multiple connections to NG-module1  and NG-module6 (“Hepatobiliary pancreas”)  Fig. b. PathwaIn this study, we have profiled the multimorbid relationships among the common diseases in the UKB and systematically investigated the genetic risks shared by multimorbidities. We report an atlas of 11,285 multimorbid disease pairs among 438 common diseases, which is by far the largest in scale. We find that 46% of the multimorbidities with available genetic information share genetic components in at least one of the three levels—loci, network, or overall genetic architecture, and show that multimorbidities affecting the same and different physiological systems tend to share different levels of genetic components. Functional analyses show that the loci-level genetic components shared by multimorbidities tend to be deleterious (for SNPs) and affect multiple tissues (for genes), and both loci- and network-level genetic components mainly converge on cell immunity, protein metabolism, and gene silencing related functions. We have also constructed two multimorbidity networks of genetically interpretable multimorbidities and show that hub diseases mediating the majority of within-module connections can provide useful insights into the genetic contributors for multimorbidities affecting different physiological systems. Therefore, our results provide a detailed multimorbid and genetic landscapes of common diseases, which may be valuable for guiding the early diagnosis, management, and treatment of multimorbidities.EDNRA, a known target of aspirin, is shared by the multimorbidity of I20 (Angina pectoris) and I25(Chronic ischaemic heart disease), and we find that 65% of the people suffering from both diseases report usage of aspirin in the UKB. Moreover, the indication of aspirin for I20 (Angina pectoris) and I25 (Chronic ischaemic heart disease) individually have been reported by the Comparative Toxicogenomics Database  and J84 . 16.7% of the people suffering from both diseases used lansoprazole according to the UKB. We find the indications of lansoprazole only include E66 (Obesity) in CTD . Notably, as much as 1970 out of the 8218 (24%) multimorbidities have significant genetic correlations, supporting the polygenic architecture of complex diseases, while nearly half of them cannot be readily interpretable at either the loci or the network level .In summary, we have performed, for the first time, a systematic analysis of multimorbid relations among common diseases as well as their shared genetic components based on the matched epidemiological and genetic data of the same subjects from the UKB. Our results illustrate the multimorbidity tendency and the genetic association patterns of multimorbidities of intra- and inter-physiological systems and indicate that the hub diseases and converged biological molecules and functions may be the key for managing multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities . Size distributions of disease and multimorbidity pathways (reactome and non-reactome) when only using pathways with size <= 200 (bottom) . Correlation between the prevalence of diseases and the number of multimorbidities . Splicing score of multimorbidity SNPs, other disease SNPs and non-disease SNPs . Characteristics of the genetic components shared by multimorbidities when removing HLA-region variants . pLIs of multimorbidity genes, other disease genes and non-disease genes when removing essential genes . Degree distributions of diseases in multimorbidity networks that share loci and network level genetic components . Multimorbidity overlaps interpreted by SNPs, genes, PPIs, pathways and genetic correlations . Correlations among genetic correlation (rg), relative risk (RR), and phenotype similarity (PheSim) of multimorbidities . Number of multimorbidities with time windows of 1 day, 0.5 year, 1 year, 2 years, 3 years, 4 years, 5 years and with no time limit . Multimorbidity tendency of intra- and inter-categories based on the new chapters (NC) of diseases .Additional file 3: Supplementary Tables. Common disease summary information (Table S1). Multimorbidities among common diseases in the UK Biobank (Table S2). Multimorbidities interpreted by SNPs (Table S3). Multimorbidities interpreted by genes (Table S4). Multimorbidities interpreted by PPIs (Table S5). Multimorbidities interpreted by pathways (Table S6). Multimorbidities interpreted by genetic correlations (Table S7). Summary of universal hub diseases in the LG- and NG-networks (Table S8). Multimorbidity modules in the LG-network (Table S9). Multimorbidity modules in the NG-network (Table S10). Multimorbidity-drug-prescription relations derived from multimorbidity genes, drug targets and the UK Biobank self-report medications (Table S11). Multimorbidities among all diseases (including both common and rare diseases) in the UK Biobank (Table S12). Multimorbidity comparison with Jensen et al. [et al. [n et al.  and Blai [et al.  at diffe


FMDV serotypes O and A were reacted with pan-FMDV antibodies that recognize all seven FMDV serotypes . The antigen–antibody complex was then immobilized on magnetic nanoparticles and reacted with β-glc-conjugated FMDV type O or type A antibodies. Subsequently, the addition of res-β-glc resulted in the release of fluorescent resorufin and glucose owing to catalytic hydrolysis by β-glc. The detection limit of fluorescent signals using a fluorescence spectrophotometer was estimated to be log(6.7) and log(5.9) copies/mL for FMDV type O and A, respectively, while that of electrochemical signals using a glucometer was estimated to be log(6.9) and log(6.1) copies/mL for FMDV type O and A, respectively. Compared with a commercially available lateral flow assay diagnostic kit for immunochromatographic detection of FMDV type O and A, this dual-modal detection platform offers approximately four-fold greater sensitivity. This highly sensitive and accurate dual-modal detection method can be used for effective disease diagnosis and treatment, and will find application in the early-stage diagnosis of viral diseases and next-generation diagnostic platforms.Foot-and-mouth disease virus (FMDV) is a highly contagious disease that affects cloven-hoofed animals. The traditional diagnostic methods for FMDV have several drawbacks such as cross-reactivity, low sensitivity, and low selectivity. To overcome these drawbacks, we present an optical and electrochemical dual-modal approach for the specific detection of FMDV serotypes O and A by utilizing a magnetic nanoparticle labeling technique with resorufin β- Aphthovirus, family Picornaviridae) and has high morbidity and low mortality rates in infected animals. As FMDV can disseminate over long distances and cause acute epidemics in FMD-free areas, outbreaks of FMD severely restrict international trade in animals and related materials, triggering massive economic damage [Foot-and-mouth disease (FMD) is a highly transmissible and fatal disease of wild and domestic cloven-hoofed animals such as cattle, sheep, goat, and swine. It is caused by Foot-and-mouth disease virus (FMDV)  , lateralLFAs are a well-established and valuable tool for point-of-care testing in the biomedicine, agriculture, food, and environmental sciences fields, as they are inexpensive, easy to use, and portable . MoreoveRecently, various approaches have emerged for efficient virus detection based on the signal outputs of different chemical and biological sensors. These methods, including surface-enhanced Raman spectroscopy (SERS), fluorescence, electrochemistry, and colorimetry , have red-glucopyranoside (res-β-glc) and β-glucosidase (β-glc) that produces both fluorescent and electrochemical signals. Res-β-glc is a stable fluorogenic galactosidase substrate that generates fluorescent resorufin and glucose molecules upon interaction with enzymes such as β-glc. β-glc catalytically hydrolyzes the glycosidic bonds in res-β-glc to form a terminal non-reducing residue of β-d-glucosides and oligosaccharides, followed by the release of resorufin and glucose molecules, which can be detected by optical and electrical analyses, respectively. Magnetic nanoparticles (MNPs) conjugated with pan-FMDV antibodies (pan-Ab), which can react with all FMDV serotypes, were reacted with different concentrations of FMDV type O or A. Then, β-glc-conjugated FMDV type O or A antibodies (β-glc–O-Ab or β-glc–A-Ab) were treated with the solution containing pan-FMDV antibody-conjugated MNPs (pan-Ab–MNPs)  a that waAb–MNPs) b. FinallAb–MNPs) c. The anRes-β-glc, β-glc, bovine serum albumin (BSA), N-hydroxysuccinimide (NHS), tris(2-carboxyethyl)phosphine hydrochloride (TCEP), 2-(N-morpholino)ethanesulfonic acid (MES) buffer, and MNPs were purchased from Sigma-Aldrich . Sulfosuccinimidyl 4-cyclohexane-1-carboxylate (sulfo-SMCC) was obtained from Thermo Fisher Scientific . N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide (EDC) was purchased from Tokyo Chemical Industry . 1X phosphate-buffered saline  solution  was used to dissolve β-glc. Distilled water from a Milli-Q water purification system was used to prepare all chemical solutions. FMDV  antibodies were provided from the Korea Research Institute of Bioscience and Biotechnology. Inactivated antigens of serotype O (O1/Manisa) and A (A22/Iraq) were purchased from The Pirbright Institute .To prepare pan-Ab–MNPs, carboxylated MNPs (7.0 mg) were washed successively with 1 mL deionized water and 1 mL MES buffer , and collected using a magnetic separator. Then, the particles were incubated at 37 °C for 30 min with 1 mL MES buffer  containing a mixture (10 μL) of 800 mM EDC and 1 M NHS, followed by washing three times in the same buffer. Partially carboxylated MNPs were incubated with a 100 μg/mL pan-Ab solution for 1.5 h at 37 °C. Subsequently, the remaining carboxyl-activated groups were blocked by incubation with 1 mL of 1% BSA in MES buffer  for 2 h at 25 °C. Finally, the pan-Ab–MNPs were washed three times with the same buffer and stored at 4 °C for further experiments. The optimal EDC/NHS ratio for efficient antibody conjugation see  was dete® Ultra Centrifugal Filters, 30 K) by centrifugation at 16,000 rpm for 50 min at 4 °C to remove the unreacted sulfo-SMCC. To activate the antibodies, a mixture of FMDV type O or A antibodies  and TCEP (20 μM) was reacted for 2 h at 25 °C to break the disulfide bonds within the antibodies. Finally, to prepare β-glc–O-Ab and β-glc–A-Ab, TCEP-treated O- and A-Ab were treated with β-glc, followed by filtering and incubation for 12 h at 4 °C. The conjugation of β-glc to O- and A-Ab was confirmed by using high-performance liquid chromatography .β-glc (0.8 mg) was dissolved in 1 mL 1X PBS solution (pH 7.4) to prepare 6 μM β-glc solution. The solution was incubated at 25 °C for 1 h in 50 μL 1X PBS solution (pH 7.4) containing 1 mM sulfo-SMCC, and then filtered  and collected using a magnetic separator.Next, β-glc–O-Ab and β-glc–A-Ab were successively reacted with pan-Ab–MNPs for 2 h at 25 °C, forming MNP-based antigen–antibody aggregations b, followThe conjugation of pan-Ab to the surface of the MNPs was characterized by zeta potential measurements, UV–Vis spectroscopy, Bradford assays, and FT-IR spectroscopy. As shown in −1 corresponding to Fe–O–Fe, which indicates the characteristic absorption of the MNPs, as well as O–H and C=O stretching vibrations at ~1710 and ~3500 cm−1 corresponding to COOH, which indicates the chemical bonding of the MNPs. The N–H peak of the CONH group at 3100–3600 cm−1 was observed in the spectra of pan-Ab and pan-Ab–MNPs, further verifying the successful immobilization of pan-Ab onto the MNP surface.To quantify the loading of covalently immobilized antibodies on the MNPs, we drew a standard calibration curve using Bradford assays, as shown in The conjugation of β-glc to O- and A-Ab was confirmed by sodium dodecyl sulphate (SDS)-polyacrylamide gel electrophoresis (PAGE) and Native-PAGE. The results are shown in d-glucosides and oligosaccharides  and electrochemical (glucose) detection methods by using res-β-glc and β-glc. In this approach, FMDV was reacted with pan-Ab functionalized on the surface of MNPs, after which they were treated with β-glc–O- or β-glc–A-Ab. Res-β-glc is catalytically hydrolyzed by β-glc and releases fluorescent resorufin and glucose molecules. The detection efficiency for the two products was evaluated by using a fluorescence spectrophotometer and portable glucometer, respectively. The sensitivity for FMDV types O and A reached log(6.7) and log(5.9) copies/mL through fluorescence measurements, respectively, and log(6.9) and log(6.1) copies/mL through electrochemical measurements, respectively. Importantly, we confirmed that O- and A-Ab-sensitized systems could clearly distinguish between FMDV types O and A in the dual-modal system with high selectivity. Thus, the proposed dual-modal sensing system offers improved sensitivity and selectivity for FMDV detection. ELISA, RT-PCR, and RT-LAMP, widely used to diagnose FMDV, often suffer from false-positive reactions. However, our dual-modal sensing system has the advantage of being able to reduce such fatal errors generated from single diagnosis methods. Even though, compared to fluorescent detection, it was confirmed that the electrochemical signal could not be measured to a lower concentration due to the limitation of the performance range of the glucometer, our strategy has the possibility to be improved with sensitive detecting devices. This study provides an easy assay for the highly reliable detection of FMDV in real samples. In addition, it presents a strong example of the potential of dual-modal sensing for the development of portable point-of-care devices and next-generation diagnostic platforms.


Salamandra salamandra co-occur: the pueriparous S. s. bernardezi that is mostly parapatric to its three larviparous subspecies neighbours. We used a landscape genetics framework to: (i) characterise the spatial location and extent of each contact zone; (ii) assess patterns of introgression and hybridization between subspecies pairs; and (iii) examine the role of environmental heterogeneity in the evolutionary dynamics of hybrid zones. We found high levels of introgression between parity modes, and between distinct phenotypes, thus demonstrating the evolution to pueriparity alone or morphological differentiation do not lead to reproductive isolation between these highly divergent S. salamandra morphotypes. However, we detected substantial variation in patterns of hybridization across contact zones, being lower in the contact zone located on a topographically complex area. We highlight the importance of accounting for spatial environmental heterogeneity when studying evolutionary dynamics of hybrid zones.Explicitly accounting for phenotypic differentiation together with environmental heterogeneity is crucial to understand the evolutionary dynamics in hybrid zones. Species showing intra-specific variation in phenotypic traits that meet across environmentally heterogeneous regions constitute excellent natural settings to study the role of phenotypic differentiation and environmental factors in shaping the spatial extent and patterns of admixture in hybrid zones. We studied three environmentally distinct contact zones where morphologically and reproductively divergent subspecies of  Environmental variation  may also promote selection for or against hybrids6, or ecological segregation of parent taxa7, while landscape features  can act as barriers to dispersal and gene flow  across heterogeneous landscapes10. Additionally, the historical context underlying the formation and maintenance of hybrid zones can also affect the extent to which contemporary gene flow occurs between species, with past demography and age of contact zones comprising relevant factors13.The spatial extent of hybrid zones (i.e. areas where gene flow between lineages occurs), and the degree of admixture between taxa in these areas are influenced by endogenous (genetic factors) and exogenous  selection, but also through extrinsic neutral processes (e.g. landscape barriers to gene flow). It has been shown that genomic incompatibilities between parent taxa at prezygotic (e.g. reduced fertilization success) and/or postzygotic level (e.g. reduce hybrid fitness) influence evolutionary dynamics in hybrid zones14. These lineages seldom achieve reproductive isolation and continue to hybridize in secondary contact zones15. The evolutionary fate of hybrid zones depends on the strength of barriers to gene flow between diverged lineages. On the one hand, secondary contact zones can result in extensive admixture of parental genotypes or lineage fusion when there are not effective barriers to gene flow16. On the other hand, selection against hybrids can favour the evolution of barriers to gene flow and ultimately lead to reproductive isolation and speciation17. However, an intermediate scenario commonly found in secondary contact zones involves the formation of tension zones often characterized by an equilibrium between dispersal and selection18.Contact zones involving intraspecific evolutionary units (e.g. subspecies) often resulted from allopatric differentiation of neighbouring lineages in distinct refugia during unfavourable climatic periods of the Pleistocene, followed by population expansions during climatically favourable periods19, ultimately dictating the extent and population dynamics of hybrid zones through processes such as assortative mating and overdominance21. Thus, gene flow between subspecies or lineages occurring in secondary contact zones is also determined by their genomic and phenotypic differentiation24. Accounting for this phenotypic differentiation together with the bioclimatic gradients and landscape heterogeneity across contact zones is crucial to understanding the evolution of phenotypic traits and patterns of introgression. This is especially relevant under a scenario of pronounced climate and landscape changes driven by anthropogenic activities because they can alter the evolutionary trajectories of hybridizing taxa25 and the fate of evolutionary significant units26.Phenotypes are targets of selection, playing a major role in species’ performance , recognition, and diversificationSalamandra salamandra , is widely distributed in Europe and offers an exceptional model system for studying neutral and adaptive processes, particularly in the Iberian Peninsula, where shows its highest intraspecific genetic and phenotypic variation30. This likely resulted from the complex Iberian physiography and the climatic oscillations during the Pleistocene, which promoted allopatric divergence in populations isolated within Iberian glacial refugia31. In this region, S. salamandra shows a remarkable intraspecific variation in external morphology 30, and more strikingly, in reproductive modes27. Two viviparous strategies co-occur27: (1) an aquatic-breeding and widespread reproductive mode, larviparity (parturition of aquatic larvae); and (2) a terrestrial-breeding and spatially restricted reproductive mode, pueriparity . Pueriparity in S. salamandra evolved at least twice: in the subspecies S. s. bernardezi along the Cantabrian Mountains during the Pleistocene31, and in two Atlantic islands in the subspecies S. s. gallaica during the Holocene33. Salamandra s. bernardezi is currently surrounded by larviparous populations of S. s. gallaica and S. s. bejarae along its western and southern range, respectively. Whether S. salamandra populations across the southern slopes of the Cantabrian Mountains belong to S. s. bejarae , or to an undescribed subspecies within the S. s. gallaica complex, needs taxonomic revision. However, they both represent independent and closely related lineages within one of the two main S. salamandra nuclear clades30 and, hence, we opted to consider those populations as traditionally S. s. bejarae. The second nuclear clade includes the Iberian S. s. bernardezi and S. s. fastuosa, together with the Appenninic subspecies S. s. gigliolii. On the easternmost range, S. s. bernardezi (Asturias-Cantabria border) meets S. s. fastuosa, which is considered larviparous despite exhibiting both reproductive strategies in some populations34  northern Galicia, where S. s. bernardezi is in contact at west of its range with S. s. gallaica35; (2) the Cantabrian Mountains, where southern populations of S. s. bernardezi meet S. s. bejarae at relatively high elevations; and (3) the Asturias-Cantabria border where admixture between eastern populations of S. s. bernardezi and S. s. fastuosa was previously reported31. We used a fine-scale individual sampling, and combined one mitochondrial gene (cytochrome b) and 15 microsatellites markers in a landscape genetics approach to: (1) delimit and characterise the range of S. s. bernardezi; (2) identify patterns of introgression and quantify levels of admixture between pueriparous and larviparous subspecies in each contact zone; and (3) evaluate the role of environmental and geographic factors in explaining genetic admixture between the different subspecies. Considering previous studies examining phenotypic and/or genetic patterns across northern Iberian populations of S. salamandra38, we hypothesize that some degree of genetic admixture occurs between subspecies in all contact zones. However, we expect the level of admixture to depend upon the environmental heterogeneity in each area and, therefore, we expect reduced rates of hybridization under IBE or IBR scenarios. Moreover, because of the reduced population connectivity among pueriparous S. s. bernardezi populations38, we hypothesise that introgression between larviparous and pueriparous populations will be asymmetric.Here we investigate patterns of gene flow and introgression, and the underlying environmental drivers, between the pueriparous Salamandra s. bernardezi and S. s. fastuosa are relatively small salamanders (body size up to 180 mm27), with stripe colouration pattern , and round snout29. While the former is strictly pueriparous37, the latter is mostly larviparous despite also presenting pueriparity across its range34 , show a spotted or blotched colouration pattern and a pointed snout29. These two subspecies are larviparous, although insular pueriparous populations of S. s. gallaica exist33.Quercus spp. and Fagus spp.) with nearby aquatic systems (streams or ponds) where larviparous females can give birth to aquatic larvae39. They can also be found in other terrestrial habitats, such as scrublands and coniferous forests, alpine scrublands, and more rarely in eucalyptus  plantations40. Previous studies showed agricultural and urban areas comprise strong barriers to dispersal and gene flow in both larviparous and pueriparous fire salamanders42. However, pueriparous salamanders can also survive in harsh environments where water bodies are absent42, despite water courses (streams and rivers) comprising semi-permeable obstacles to dispersal due to their fully terrestrial life style38.Despite being phenotypically different in northern Spain, these subspecies inhabit similar habitats, particularly, deciduous woodlands  where different subspecies, morphotypes and the two distinct reproductive modes meet, thus covering a large portion of the secondary contact zones between S. s. bernardezi and its neighbouring lineages , where intermixed phenotypes between S. s. bernardezi and S. s. gallaica were previously observed35 , where we collected samples of 73 individuals from 38 localities on both sides of the mountains, representing S. s. bernardezi and S. s. bejarae from the northern and southern slopes, respectively  within each contact zone because this sampling approach was shown to provide greater statistical power in detecting landscape genetic patterns compared to population-based samplingges Fig. . We sampd35 Fig.  and is eely Fig. , and is rae Fig. , the mouhttps://arriveguidelines.org/arrive-guidelines).Fieldwork for obtaining tissue samples was done with the corresponding permits from the regional administrations . Sampling procedures were carried out following the Guidelines for Use of Live Amphibians and Reptiles in Field and Laboratory Research, 2nd Edition, revised by the Herpetological Animal Care and Use Committee (HACC) of the American Society of Ichthyologists and Herpetologists, 2004. All procedures complied with the country legal requirements on animal welfare (RD 53/2013) and were conducted in accordance with the guidelines of the Research Ethics Committee of the University of Oviedo  under authorisation #8-INV-2012. The members of the research team have approved licenses by the Service of Animal Welfare and Production of the Principality of Asturias to conduct experimental protocols with animals (license types C and D to A.G.N). This study was carried out in compliance with the ARRIVE guidelines  for how to report animal research in scientific publications , and inspected and aligned resulting chromatograms using GENEIOUS version 11.1.4 (http://www.geneious.com). Extracted products were also used to amplify and genotype 15 microsatellite loci  distributed in five optimized multiplexes (see47 for details about the protocol optimisation and genotyping).We extracted genomic DNA using the Genomic DNA Tissue Kit (EasySpin), following the manufacturer’s protocol, and verified both the quantity and quality of extracted products in a 0.8% agarose gel. We used the DNA extract as template for polymerase chain reactions (PCR) to amplify and sequence one mitochondrial (mtDNA) fragment (ca. 1100 bp) of the cytochrome b and adjacent tRNAs (hereafter cytb). We used the primers Glu14100L and Pro15500H50 on Iberian populations of S. salamandra support the validity of these markers for population and landscape genetic analyses.Our individual-based approach, together with some degree of continuous phenotypic variation in the contact zones, prevent us from grouping samples into a priori populations or genetic units, and thus from assessing accurately Hardy–Weinberg equilibrium (HWE) and linkage equilibrium (LE). However, previous phylogeographic and population genetic studies using the same set of markers51 and identify the mitochondrial lineage (mtDNA) of each sample, which was then used to estimate the extent of cyto-nuclear discordance across each contact zone. However, we note that mtDNA phylogenetic tree does not represent the true phylogenetic relationships among S. salamandra subspecies. In particular, S. s. fastuosa (with embedded S. s. gigliolii) was placed as the sister lineage to S. s. bernardezi and formed a well differentiated clade within S. salamandra according to phylogenomic data30. Yet, both S. s. gallaica and S. s. bejarae fall within the second clade and are sister lineages in both mtDNA and phylogenomic trees. We selected the optimal nucleotide substitution model (TrN) with JMODELTEST version 2.1.452, under the Bayesian information criterion (BIC). To estimate the time to the most recent common ancestor (TMRCA) of mtDNA lineages we used both a prior substitution rate and fossil calibrations. We set the divergence rate for cytb estimated in a 2.7% Myr for Mediterranean amphibians53. We used a normal distribution with a mean of 0.0134 and SD of 0.0009. We also used two calibration points (divergence date estimates) based on the split between Chioglossa and Mertensiella  of 14.2 and 27.5, respectively), and the split between Lyciasalamandra and Salamandra . These estimates were obtained from a two-step protocol based on a combination of primary fossil calibrations and secondary calibrations from a multilocus nuclear tree on a multilocus mitochondrial tree53. However, we avoided the use of internal nodes provided within Salamandra due to some incorrect phylogenetic relationships at both inter-specific  and intra-subspecific (e.g. S. s. morenica) levels30. We obtained cytb sequences from GenBank for Chioglossa lustitanica (AF329314), Mertensiella caucasica (EU880319), Lyciasalamandra flavimembris (EU880318) and Lyciasalamandra atifi (AF154053), which were added to our cytb alignment. We performed three independent runs using an uncorrelated relaxed clock and a constant population size model as the coalescent tree prior, with a total of 100 million generations (burn-in: 10%). We combined tree files of all runs using the software LOGCOMBINER version 1.7.5, and verified parameter convergence by examining the effective sample sizes (ESSs) in TRACER version 1.6. We obtained a maximum clade credibility summary tree with Bayesian posterior probabilities (BPP) for each node using TREEANNOTATOR version 1.8.4, and edited the resulted tree in FIGTREE version 1.4.3 (http://tree.bio.ed.ac.uk/software/figtree). We ran BEAST analyses in the CIPRES Science Gateway54. We estimated genetic distances (uncorrected p-distances) between subspecies in GENEIOUS 11.1.4 (www.geneious.com).We used the cytb sequences to perform coalescent-based Bayesian analyses in BEAST version 1.8.455 to assess the individual genetic membership coefficient  for each subspecies in each contact zone. Because we were not interested in determining the genetic structure of each study area but rather assigning individuals to one of the two co-occurring subspecies, we ran STRUCTURE for K = 2 (two clusters). We ran 10 replicates and used the correlated allele frequency and admixture models, without location prior, and with a burn-in period of 100,000 iterations followed by 1,000,000 iterations. We combined the results from the 10 replicate runs using CLUMPP56. To complement STRUCTURE, we used the spatially explicit method of spatial Principal Component Analyses (sPCA)57, which does not assume HWE or LE expectations. To identify if the sPCA scores were able to assess global (patches and clines where neighbours tend to be similar) and local (strong genetic differences between neighbours) structure, we used a Delaunay triangulation connection network and two separate Monte Carlo tests with 100 permutations.We first used STRUCTURE 2.3.459. We used six classes in each analysis: pure individuals of each subspecies , first generation hybrid (F1), second-generation hybrid (F2), and first-generation backcrosses to each subspecies. To increase the power to detect hybrids, we also assigned all individuals with > 90%  estimated ancestry in STRUCTURE from a single subspecies to the appropriate pure parental category using the ‘z’ option. We performed three independent runs per contact zone and we set 1 million generations with a burn-in of 10% per run. We used two random starting values in each NEWHYBRIDS run, and the “Jeffreys-like” prior for the estimates of θ  and π . We used a posterior probability threshold of P ≥ 0.760 to assign potential hybrids identified in STRUCTURE to each of the six classes.Because each individual has two q-values (one for each of K=2 clusters), we considered individuals as admixed when showing low levels of coefficient membership for either of the two clusters . Then, we used these potential hybrids to estimate the probability of assignment (Z) of early-generation hybrid or backcrossed individuals between subspecies within each contact zone in NEWHYBRIDS v.1.1The workflow for the landscape genetic analyses is summarised in Fig. 61. We used COANCESTRY62 to calculate pairwise relatedness matrices between individuals in each studied contact zone. First, we used the relatedness coefficient from Queller & Goodnight (1989) (RQG)63, which does not assume Hardy–Weinberg equilibrium. We also used the triadic likelihood relatedness (RTRI) estimate64, which calculates pairwise relatedness between two individuals while using a third one as control, thus reducing the identical by descent error rate. The matrices were linearly scaled to the range between 0 and 1, where higher values indicate higher similarity. To obtain an indication of dissimilarity, we found the complement of each matrix element by subtracting to 1.The use of individual-based distances is recommended to avoid potential violations of the assumption of discrete populations that can lead to erroneous estimates of genetic parametersInstituto Geográfico Nacional (https://www.ign.es) at a 5 m resolution and aggregated to a 250 m resolution for downstream analyses. We produced two variables using a different aggregating function: (1) the average to represent mean elevation at each 250 m pixel, and (2) the standard deviation as a representation of the topographic heterogeneity. This collection of two variables is hereafter referred to as elevation data. We used the Normalized Difference Vegetation Index (NDVI) as a continuous surrogate of habitat availability. The NDVI imagery was obtained with a 16-day period covering a range of 10 years (from January 2009 to December 2018) that  includes the field work period. The MOD13Q1 product was downloaded from the EarthData website (https://earthdata.nasa.gov/) with a spatial resolution of 250 m and 16-day temporal resolution, resulting in a time-series with 230 measurement layers. To provide summaries of the 16-day NDVI for a few variables, we processed the data with a harmonic regression and extracted three spatial coefficients representing the periodic behaviour in each pixel as continuous surrogates of local habitat65. The collection of three variables is hereafter referred as NDVI data. All data was processed in R with packages rgdal (v.1.5.8), raster (v.3.3.13), TSA (v.1.2.1) and zoo (v.1.8.8). Climate data was obtained from the CHELSA database (https://chelsa-climate.org/66) as 19 bioclimate variables at 30” (~ 1 km) spatial resolution. We further summarized the climate data by means of a PCA after centering and scaling. Resulting spatial components from PCA are hereafter referred to as the climate data. We projected all spatial data to the WGS84 coordinate system.We evaluated the three main environmental features that can influence spatial connectivity and gene flow of fire salamanders across the study region: elevation, habitat and climate. Elevation data was obtained from the Spanish We generated geographic, environmental and resistance distance matrices to test for the dominant model of isolation by distance (IBD), environment (IBE), or resistance (IBR) in each contact zone. We produced IBD matrices by calculating Euclidean geographic distances between genotyped individuals in each contact zone. We transformed coordinates to the ETRS89 Lambert Azimuthal Equal-Area projection before calculating the straight line distance between sampling localities.CLIM) and another based on NDVI (IBENDVI) for each contact zone , climate (IBRCLIM), and NDVI (IBRNDVI). We used a common dataset combining the presence locations from the three contact zones to determine the usage of each variable in each category. Pixel duplicates were removed independently in each category due to the different spatial resolution. We extracted variable data at the presence locations and calculated a vector of average values per variable and the covariance matrix. We used this information to combine the different variables by means of a multivariate normal distribution, generating a map representing the likelihood of finding a presence for each variable category. Hence, this value is related to the usage of the multidimensional space of each combination of variables by the species. We used these maps as a resistance surfaces, after a minmax scaling, in order to generate resistance distances with the ‘commuteDistance’ function in ‘gdistance’ R package (v.1.3.6).We generated IBR matrices in relation to descriptive categories: elevation  to the genetic distance matrices in each contact zone. It is expected that genetic differentiation increases with geographical distance. Yet, due to limited movement and finite population sizes and genomes, this increase cannot occur indefinitely and tends to plateau72. The same reasoning can be applied to the other models, including resistance and environmental distances. We optimized a logistic function with two parameters (curvature and inflection point), allowing for a near linear arrangement with original distances to a full logistic relation with steep transition. In the latter case, larger distances tend to plateau after the inflection point thus allowing a scenario where genetic differentiation accumulates until a maximum limit. We performed optimizations with genetic algorithms using the R package GA (v.3.2) to maximize the logLikelihood of a Generalized Least Squares (GLS) regression using the genetic matrix as a dependent variable and an isolation matrix as an independent variable. Data in pairwise distance matrices show high degrees of non-independence which hampers the direct application of regression-based algorithms. The GLS regression incorporates correlation structures informing about the lack of independence between samples. We used a maximum-likelihood population effects (MLPE) model that generates such correlation structure from pairwise data71. We used the package nlme (v.3.1.140) for GLS with corMLPE . All optimizations were done with a maximum of 1000 iterations or 200 runs without change of the optimization score. All isolation model matrices were min–max scaled to the range 0–1 in order to facilitate the comparison of the optimized parameters.Parameter optimization procedures are common in landscape genetic studiesWe used the Akaike Information Criterion (AIC) to rank univariate models and to eliminate those isolation models that were contributing less to the overall fit of the regression Table . Models S. s. bernardezi, while the second clade includes three monophyletic lineages of the three focal larviparous subspecies   with an earlier diversification within S. s. bernardezi (ca. 1 Mya [0.8–1.4]) compared to the S. s. gallaica-bejarae-fastuosa clade (ca. 0.3 Mya [0.15–0.4]).We obtained a total of 302 sequences after discarding sequences of poor quality and trimming some sequences (from ca. 1070 bp to 350 bp). Phylogenetic analyses identified two main monophyletic (Posterior Probability > 0.95) clusters within the three studied contact zones: one includes seven well supported mtDNA lineages of putative pueriparous After discarding individuals with missing data > 10% in the microsatellite dataset, we obtained a fully comparative mtDNA-nuDNA datasets for a total of 302 samples .STRUCTURE recovered two well defined genetic groups in each contact zone that largely match the distribution of each mitochondrial lineage Figs. , 3, 4. TS. s. gallaica  or S. s. bernardezi , while the remaining 35 salamanders (28%) were inferred as potential hybrids . Among these potential hybrids, 29 salamanders were labelled as F2 hybrids in NEWHYBRIDS. Similarly, in AC, from the 107 genotypes, 57 salamanders (53%) were assigned either to the subspecies S. s. fastuosa  or S. s. bernardezi . The remaining 50 salamanders comprised potential hybrids (47%), from which, 38 individuals were categorised as F2 hybrids, two salamanders as pure S. s. bernardezi, and two others as S. s. fastuosa in NEWHYBRIDS. Unlike the latter two contact zones, CM was characterized by a very low proportion of admixed individuals. From the 68 studied salamanders, a total of 65 individuals (96%) were assigned either to the subspecies S. s. bejarae  or S. s. bernardezi , while the remaining three salamanders (4%) that were inferred as potential hybrids were later assigned as pure S. s. bejarae in NEWHYBRIDS. Furthermore, the number of cyto-nuclear discordances was higher for NG and AC  than for CM (6%).We found relatively similar patterns of hybridization in the contact zones of NG and AC Table , in whicCLIM and IBRNDVI for RQG genetic distances, and IBRCLIM with IBD for RTRI (Table QG and RTRI) and pairwise geographic and environmental distances for each contact zone are represented in Supplementary Figs. The PCA of climate data showed the first three components accounted for 91.4% of total variation. Optimizations of IBD and IBE models showed a high degree of success, with the ΔAIC of the optimized model showing values higher than 15 compared to the non-optimized version . We detected clear signs of gene flow and introgression in two out of three regions, thus supporting the hypothesis of incomplete reproductive isolation between these highly divergent S. salamandra morphotypes. Despite the extensive patterns of introgression and admixture in two contact zones (NG and AC), the Cantabrian Mountains (CM) likely reduce gene flow between the larviparous S. s. bejarae and pueriparous S. s. bernardezi. This study suggests the combined effect of landscape and climate barriers shape patterns of hybridization, thus highlighting the need of genetic studies across multiple and heterogeneous contact zones to better delineate the spatial patterns of gene flow and hibridisation between non-reproductively isolated taxa.The degree of reproductive isolation is expected to be larger between lineages showing differentiation in parity modesS. s. gallaica and S. s. bernardezi32. The high proportion of admixed individuals inferred from our genetic analyses demonstrates the existence of a contact zone between western S. s. bernardezi and northern S. s. gallaica populations within a relatively homogenous landscape in northern Galicia (NG), thus indicating previous works were likely biased due to an insufficient sampling in this region. We detected a north–south contact zone between S. s. bernardezi and S. s. gallaica located ca. 10–30 kms from the northern Galician coast  corroborates the presence of a hybrid zone in NG.Previous phylogeographic studies suggested the eastern Galician mountains comprise a barrier to gene flow between S. s. bernardezi and northern S. s. bejarae populations across the Cantabrian Mountains  located between mountain chains . The deep divergence found in Rana points to an old split (4 Mya) and a narrow hybrid zone (25 km), which led the authors to conclude these two lineages represent distinct species. Salamandra, however, shows a more recent split (late Pleistocene). Despite this study showing the existence of a contact zone between these subspecies, previous phylogeograhic work placed this hybrid zone farther east to the Pyrenees31. The identification of the contact zone between S. s. bernardezi and S. s. fastuosa is not possible at the morphological level because S. s. fastuosa appears to be phenotypically indistinguishable from S. s. bernardezi. We have also detected females with mixed reproductive modes across AC, and also cases of larviparity and pueriparity within the S. s. bernardezi and S. s. fastuosa ranges, respectively, which again supports the presence of a relatively broad hybrid zone in this area. Nevertheless, future studies should be conducted to accurately delimit the range of S. s. fastuosa and the level of introgression across its range.Similar to NG, we also found a high proportion of admixed individuals along the Asturias-Cantabria border (AC), where eastern populations of 80, including salamanders. Conflicting geographic patterns between mitochondrial and nuclear genetic markers have been reported across the S. salamandra range  differences between S. s. gallaica and S. s. bernardezi, we found extensive gene flow across the entire NG, thus confirming the lack of reproductive isolation between these divergent taxa, and the widespread presence of admixed phenotypes in this contact zone. We found an asymmetric pattern of cyto-nuclear discordances between both lineages. The majority of introgressed individuals exhibited a S. s. bernardezi mtDNA, which could suggest a northward expansion by S. s. gallaica followed by a mitochondrial capture of S. s. bernardezi. This process is potentially in agreement with the male-biased dispersal hypothesis, in which, through population expansions, fire salamander males may disperse longer distances than females84 and move into their neighbouring subspecies range, thus capturing the local mitochondrial lineage after a few generations. Such a process was suggested as a potential driver of cyto-nuclear discordances among other S. salamandra subspecies81. However, S. s. gallaica mtDNA reached the northern Galician coast and it is known that S. s. gallaica mtDNA is present at low frequencies within the range of S. s. bernardezi38. Whether this is the result of a recent expansion of both males and females of S. s. gallaica into the range of S. s. bernardezi or a male-biased colonization of the latter into a historical and wider range of the former remains an open question, with important implications for the mid- and long-term viability of S. s. bernardezi, which warrants future research.Despite the marked geneticS. s. bejarae and the pueriparous and small S. s. bernardezi across the CM are likely due to unfavourable environmental conditions of high-elevation areas (see next section). These conditions likely preclude the presence or reduce population densities and activity of fire salamanders, which in turn hinder dispersal across complex topographic areas. However, valleys (1400–1700 m a.s.l.) oriented in a north–south axis might act as mountain passages allowing sporadic dispersal events of fire salamander across the CM.In CM, we found very few cases of genetic introgression at both windward and leeward sides of the Cantabrian Mountains, but no signs of hybridization. The negligible signs of admixture and hybridization between the larviparous and large S. s. bernardezi and S. s. fastuosa. This is an expected result because of their phenotypic similarity and shared recent evolutionary histories. The relatively high proportion of cyto-nuclear discordances found for both subspecies comprises another pattern in common with NG, although the underlying drivers responsible for this genetic pattern are likely different. Selection of traits encoded in the nuclear genome could have boosted such discordances in AC along the expansion wave. Nuclear phylogenomic analyses place S. s. fastuosa as the sister taxon of S. s. bernardezi, yet S. s. fastuosa mtDNA falls within the S. s. gallaica-bejarae clade30. This genetic discordance fits a demographic scenario where selected traits  encoded by nuclear genes in S. s. bernardezi spread eastwards into the S. s. fastuosa area during range expansions of favourable climatic cycles in the Pleistocene31. It is possible that only the original mitochondrial legacy of S. s. fastuosa was maintained though historical mitochondrial capture when both lineages introgressed, a process that has been suggested for Rana temporaria in this region79. Alternatively, S. s. fastuosa could first diverge from S. s. bernardezi, and then followed by a process of historical mitochondrial capture of S. s. gallaica-bejarae. Further sampling across this region and the use of genomic data and demographic models will allow testing the above contrasting hypotheses.Like NG, we found high levels of gene flow and hybridization across AC and, consequently, absence of endogenous reproductive barriers between S. s. bernardezi and S. s. bejarae along this CZ  across AC likely facilitate salamander dispersal.Landscape genetic analyses revealed the IBD model is a strong predictor of genetic differentiation in the NG and AC contact zones and, to a lesser extent, in CM. The marked genetic structure and absence of hybrids across the Cantabrian Mountains suggests this mountain chain is a major barrier to dispersal and gene flow between larviparous and pueriparous lineages, with mountain valleys probably allowing sporadic dispersal events. Interestingly, the observed pattern of genetic differentiation in CM could not be explained by the topographic complexity across this area, as we previously hypothesised, but it was influenced by the climate (IBRclim) and landcover (IBRndvi), albeit with some disagreement between the two genetic distance metrics used here. There is a strong environmental gradient acting between the southern and northern slopes of the Cantabrian Mountains, following the sharp transition between Atlantic and Mediterranean bioclimates, which t is reinforcing the effect that prevents rampant gene flow between 41, though the degree of fragmentation observed in NG and AC appears to be insufficient to prevent hybridization at the spatial scale of this study. Water courses are also known to affect gene flow in S. Salamandra, particularly, in S. s. bernardezi due to its terrestrial life cycle91. Two main rivers (Deva and Nansa) cross the AC hybrid zone in a north–south axis and their locations coincide with a phylogeographic break found in other amphibians79. Although these rivers might have a role in reducing gene flow between S. s. bernardezi and S. s. fastuosa, the observed patterns of admixture between these lineages suggest they could act as semi-permeable barriers to gene flow. Also, larviparous individuals would have a greater advantage to overcome these geographic barriers during the larval stage91, but our data prevent us from making robust inferences about crossing rates per reproductive mode.Other landscape barriers are likely affecting gene flow across the three studied contact zones. For instance, the reduction and conversion of woodlands to mosaics of agricultural fields, urban settlements, and plantations of exotic trees have been shown to hinder dispersal and enhance genetic differentiation among fire salamander populations92, but see93) and morphological (colour polymorphism94) differentiation in fire salamanders, and it may be a common underlying mechanism explaining the elevated intra-specific phenotypic variation that characterizes this species95. Our study did not identify divergent natural selection (i.e. support for an IBE scenario) as a driver of the distinct morphological and reproductive types across northern Iberian S. salamandra populations. Although environmental differentiation observed between southern and northern slopes of the Cantabrian Mountains , our study reinforces the intra-specific polymorphic nature of this species at various phenotypic levels. Finally, we cannot neglect potential historical differences underlying the formation and maintenance of these contact zones. These differences may involve an older and well established hybrid zone in northern Galicia , and a recent secondary contact zone where population edges are still expanding across the CM (S. s. bejarae–S. s. bernardezi).The barrier effect imposed by the Cantabrian Mountains, together with a potential ecological adaptation to the local habitat and climate present in the northern and southern slopes of this mountain chain, may also contribute to the reduced levels of admixture and introgression observed between S. salamandra subspecies are highly heterogeneous across space, thus emphasizing the role of environmental factors in shaping patterns of hybridization and reproductive isolation. This is best illustrated by the Cantabrian Mountains, which likely comprise an effective barrier to gene flow between the pueriparous S. s. bernardezi and larviparous S. s. bejarae. Despite the fact that prezygotic and postzygotic barriers were not detected in our study, it is unclear whether hybridization among these subspecies negatively affects survival and fertility of hybrids. This study also has important conservation and taxonomic implications, as the elevation of intra-specific lineages to species level is often based on the reduction of gene flow found between lineages across a small area of their entire contact zone, thus neglecting the environmental heterogeneity, and putative different outcomes of hybridization. Future multidisciplinary studies  are needed to evaluate the potential genomic consequences of hybridization in the fire salamander, the spatio-temporal dynamics of these hybrid zones , and the potential role of ecological divergence in the Cantabrian Mountains48.Our study shows that patterns of hybridization between Supplementary Information.