Using structural equation models, we estimated associations between measurement error-free latent genetic and phenotypic variables. We found evidence of genetic similarity between partners for educational attainment (rg = 0.37), height (rg = 0.13), and depression (rg = 0.08). Common genetic variants associated with educational attainment correlated between siblings above 0.50 (rg = 0.68) and between siblings-in-law (rg = 0.25) and co-siblings-in-law (rg = 0.09). Indirect assortment on secondary traits accounted for partner similarity in education and depression, but not in height. Comparisons between the genetic similarities of partners and siblings indicated that genetic variances were in intergenerational equilibrium. This study shows genetic similarities between extended family members and that assortative mating has taken place for several generations.Assortative mating on heritable traits can have implications for the genetic resemblance between siblings and in-laws in succeeding generations. We studied polygenic scores and phenotypic data from pairs of partners ( Assortative mating could violate the assumption of random mating used in many genetic studies. Here, the authors study more than 25,000 Norwegian families to find genetic similarity between partners, siblings, and in-laws in genetic factors related to educational attainment, height, and depression. However, partners resemble each other for many traits, including educational attainment6, height8, and psychopathology10. The selection of partners based on similarity is known as assortative mating. Partner similarity can have genetic consequences in the following generations if the assortment is based on heritable traits11, which almost all human traits are13. When offspring inherit genetic variants from both parents that deviate in the same direction from the population mean, otherwise independent genetic variants can become correlated (gametic phase disequilibrium). This results in the elevated resemblance between siblings and increased genetic variation between families15. Increased genetic variation translates into larger variation between individuals in phenotypic expression. Assortment based on educational attainment may have particularly broad consequences. It could pose a societal challenge by concentrating human and economic resources16 and could present a health challenge because genetic influences on educational attainment correlate with most health phenotypes17. Furthermore, educational attainment has increased massively over the last few generations18. Therefore, the genetic consequences may not yet have fully unfolded. Studies of assortative mating are needed to increase our understanding of social inequalities.Although humans put considerable effort into finding suitable mates, genetic studies typically assume that mating is randomr = 0.65) and height (r = 0.20)6. Assortative mating in previous generations can also be detected in samples of genomes from unrelated individuals by estimating covariance between trait-associated loci in distant parts of the genome15. Previous studies on assortative mating have mainly analyzed partners only. Another source of evidence for assortative mating—as yet little exploited in the literature – is systematic inflation of sibling correlations. Genetic correlations between siblings are expected to be 0.50 under random mating but could increase under assortment in previous generations. When the assortment level is stable, the sibling correlation and the genetic variance increase for each generation until an equilibrium is reached19. Simultaneous use of partner and sibling correlations can be informative regarding whether this equilibrium has been reached or whether one can expect increasing genetic variation in the future.There is evidence for substantial correlations between partners at trait-associated genetic loci for educational attainment  and by extension between co-siblings-in-law, who are the respective partners of siblings. For brevity, we refer to these relationship types as in-laws and co-in-laws, respectively. In-laws and co-in-laws are only indirectly related, and are therefore informative for understanding the mechanisms leading to the resemblance between the partners that connect them. With direct assortment , the similarity between partners results from assortment based on the phenotype in question. This can be distinguished from indirect assortment , where partners resemble each other because they assort on one or more traits associated with the trait of primary interest26. Polygenic scores correlate with a trait to the degree that the trait is heritable, and the polygenic scores capture that heritable component. Polygenic scores capture the heritable component when they include SNPs in linkage disequilibrium with causal variants weighted according to the true associations with the trait in the target sample. The variance in a trait explained by polygenic scores is typically lower than its heritability, indicating that polygenic scores usually do not fully succeed in capturing the heritable component27. As long as polygenic scores are imperfectly correlated with the genetic predispositions to a trait, the correlation between multiple individuals’ polygenic scores will likely be lower than the correlations between the true set of trait-associated genetic variants. For instance, correlations between spouses’ polygenic scores could be underestimated if one partner has many trait-increasing alleles included in the polygenic score whereas the other has many trait-increasing alleles not included in the polygenic score. Polygenic scores for educational attainment have been found to correlate lower in partners, at only r = 0.1828 or r = 0.1129, than adjusted estimates of genetic correlations based on genetic predictors and the phenotypes of partners (r = 0.65)6. Several approaches to similar scaling issues have been proposed31, including structural equation modelling32.One way to investigate genetic resemblance between individuals is to calculate correlations between their polygenic scores. A polygenic score summarizes an individual’s genetic predisposition to a trait across many single nucleotide polymorphisms (SNPs)) model, and demonstrate that phenotypic data and polygenic scores from relatives can be used to estimate signal versus noise in polygenic scores, account for the noise, and estimate the correlation between their genetic signals. We estimate the phenotypic and genetic similarity between 26,681 pairs of spouses, 2170 pair of siblings, 3905 pair of in-laws, and 1763 pairs of co-in-laws participating in the population-based Norwegian Mother, Father, and Child Cohort Study (MoBa). We investigate assortment on educational attainment, height, and depression (symptoms of major depressive disorder), which are phenotypes differentially influenced by genetic and environmental factors and with varying levels of partner similarity. We find genetic similarity between partners in all three traits, elevated (>0.50) genetic correlations between siblings, and that the genetic similarity extends to in-laws and co-in-laws. The inclusion of in-laws also allows us to separate between direct assortment on the observed traits and indirect assortment on secondary traits. We show that there is indirect assortment on secondary traits for education and depression, whereas direct assortment underlies partner similarity in height. Because siblings provide information on the level of assortment in previous generations and partners provide information on the level of assortment in the current generation, we test whether the results are consistent with stable levels of genetic assortment across generations. The results suggest no deviations from intergenerational equilibrium, indicating that assortment on these traits has been going on for at least five generations and that one should not expect further genetic consequences for succeeding generations with the present level of assortment.In the present study, we introduce a model, the Correlation in Genetic Signals  and co-in-laws’  educational attainment. The polygenic scores were correlated between partners for educational attainment  and height  but not depression . The polygenic scores were also correlated between in-laws for educational attainment  and height , and between co-in-laws for educational attainment .Figure a measures the association between the observed phenotype and the latent phenotype that is the basis of partner selection. To the extent that a < 1, this is interpretable as evidence of indirect assortment operating alongside direct assortment. The results of fitting to the data rGenSi models are presented in Supplementary Note a either freely estimated or fixed to 1.00 are presented in Supplementary Table a = 0.77, 95% CI 0.75, 0.78) and depression  appeared to result from indirect assortment on secondary phenotypes highly correlated with, but not identical to these primary phenotypes. Partner similarity in height resulted from assortment directly based on observed height .Figure c measures the influence of environments that siblings shared. Supplementary Tables h2) and genetic signal (s2) than the model without c, but better fit. The shared environment did not influence height and depression (c fixed to 0 in the best-fitting models).The parameter r = 0.11, 95% CI 0.10, 0.12). Genetic correlations were also found for in-laws  and co-in-laws . For height, the partner correlation was estimated at 0.13 , the in-law correlation at 0.07 , and the co-in-law correlation at 0.01 . For depression, the partner correlation was estimated at 0.08 , the in-law correlation at 0.05 , and the co-in-law correlation at 0.00 . Genetic correlations between siblings were estimated above expectation (>0.50) for all three phenotypes, but only for educational attainment  did the confidence interval exclude 0.50. Results were similar when using alternative p-value cut-offs for including SNPs in the polygenic scores . Similarly, the correlations between the polygenic scores also fit well with the intergenerational equilibrium, with no deviations detected at the 5% significance level .Correlations between partners in the genetic signal result from mating in the present generation, and correlations between siblings in the genetic signal result from mating in previous generations. Figure We found genetic similarity between partners for educational attainment, height, and depression and elevated genetic correlations between siblings. The genetic similarity extended to in-laws and co-in-laws. The partner similarity was particularly high for educational attainment, which resulted from an assortment on traits correlated with educational attainment. No deviations from intergenerational equilibrium were found in the level of genetic assortment in the three traits.29. The rGenSi adjusted genetic partner correlation was lower than Robinson et al.’s6 (0.37 vs 0.65), but the confidence intervals overlapped with their estimate. High correlations between partners in genetic factors for educational attainment have now been found using several independent methods and in different populations. This strengthens our confidence that the results reflect the actual similarity between partners on common genetic variants associated with educational attainment. The genetic correlation of 0.37 is higher than what would be expected among half-siblings (0.25) under no assortment and can induce bias in genetic studies of educational attainment if not accounted for3. For height, the rGenSi genetic partner correlation was consistent with Robinson et al.’s6 results from a different population. For depression, we have less basis for direct comparison of genetic partner correlations, but previous studies indicate low assortment on this trait15. The observed resemblance between in-laws or co-in-laws in measured genetic factors for educational attainment was theoretically expected and implicit in quantitative genetic models of siblings and their partners. Confirming that they exist by using genomic data aligns with our expectations and highlights the wide implications of assortative mating. This implies that health and resources are concentrated not only at the couple level but also in wider family networks. The mating within subgroups of the population is an example of endogamy that can concentrate resources and maintain social inequality.All three polygenic scores correlated with the phenotype of partners and indicated correlations between partners’ genetic factors. For educational attainment, the correlations between partners’ polygenic scores were close to the results of the previous studies33 (p. 382) and that there is nothing more to partner similarity in height. The direct assortment on height is in line with previous genetic studies6. For educational attainment and depression, the story is more complex. The association between observed education and the latent mated phenotype was not perfect, indicating that other traits than the observed played a role. The results for educational attainment are in line with previous studies finding that partner similarity in education results from the indirect assortment on correlated traits34. They are also consistent with reports of social homogamy in twin samples35 because social homogamy and indirect assortment can be indistinguishable in phenotypic data20. If unobserved secondary traits underlie the partner similarity, individuals with highly educated families are likely to find highly educated partners even if they do not have high education themselves. Our results cannot tell us what these secondary traits might be, but plausible candidates for educational attainment include cognitive abilities, conscientiousness, other indicators of academic abilities37, and the obtained education itself; most likely a combination of these. This could be clarified by investigating an assortment on these traits in tandem with educational attainment. Our depression measure was intended to assess lifetime history of major depression but might more strongly capture recent than past episodes38. It therefore seems plausible that factors underlying the observed partner similarity in depression could include symptoms at the time of couple formation rather than the time of observation, or phenotypes associated with depression, such as neuroticism, other mental disorders, or a general risk of psychopathology40.Our results favored models where partner similarity in educational attainment and depression was accounted for by indirect assortment on secondary traits. For partner similarity in height, our results were consistent with partner selection directly on manifest height. This means that “tall people choose other tall people because they are tall”15. Higher education has been widely available in Norway for only a few generations, but people may have always selected partners based on traits that are correlated with educational attainment today. Our results on direct versus indirect assortment indicate that such factors were important, meaning that assortment on genetic factors correlated with educational attainment may be older than assortment on education itself. The genetic partner correlations could be predicted from the genetic sibling correlations and vice versa. The results thus indicate that an assortment on genetic factors correlated with educational attainment has occurred long enough for it to be in or close to equilibrium. Results from the polygenic scores and the rGenSi model both supported this. The genetic variance increases fastest in the first generations with assortment. Genetic variance close to equilibrium can be observed after only five generations, whereas 60% of the increase in variance is seen after two generations . Hence, it is possible that education became relevant as an assortment factor later in Iceland than in Norway. A weakness with this explanation is that Icelanders could have chosen partners based on factors that are related to education today even with low access to formal education. Rurality and unidentified cultural differences are therefore alternative explanations. In addition, we cannot exclude that the contrast in results is related to differences in methods, such as our use of siblings and in-laws rather than only partners. This finding therefore calls for further replication. Alternative versions of our model, presented in the Supplementary Table 19 and associated health outcomes17 unless the level of assortment changes. For height and depression, the results also did not indicate deviations from intergenerational equilibrium.A genetic partner correlation can lead to increased genetic variation in the next generation and a sibling correlation elevated above 0.50. This is exactly what we found for educational attainment, with a genetic sibling correlation of 0.68 . This indicates that the participants’ parents were also engaged in assortative mating, a finding that is in line with previous findings of correlations between trait-associated alleles in different parts of the genome42. This allowed us to scale up the genetic correlations to infer associations between the full set of genetic variants giving rise to the phenotypes rather than between the polygenic scores. The model disentangles the proportion of genetic signal (s2) in the polygenic scores and may be informative about the comprehensiveness of the polygenic score as an indicator of genetic risk in the target sample. Because of this, the results were consistent across different versions of the polygenic scores. Ideally, the latent genetic factor should be modelled as the sum of two components—trait-increasing alleles included in the polygenic score (A) plus trait-increasing alleles not included in the polygenic (B) score34. The polygenic score itself should then be modelled as the sum of trait-increasing alleles (A) and measurement error. This would have enabled us to distinguish measurement error from partialness of the polygenic scores. However, there were not enough degrees of freedom in the present data, and estimating the resulting correlations was sufficient to reach our objectives. Estimating direct versus indirect mating and shared environmental influences provides a more comprehensive understanding of the processes that give rise to phenotypic and genetic similarity. The inclusion of shared environment did, however, increase the width of the confidence intervals. Structural equation models can flexibly be adapted. We therefore believe that variations of the model can be applied to other research questions than ours.We used a structural equation model to estimate the correlation between partners and relatives in their genetic signal, or underlying genetic factors, for a trait. This approach is computationally efficient and can be applied in cases where only polygenic scores are available but not raw genomic data. Nevertheless, simulations showed that the model could reconstruct full genetic correlations, as well as other parameters. Correcting for measurement error is one of the main advantages of using structural equation modelling. Correcting polygenic scores for measurement error to extract the genetic signal is not yet common practice, but approaches for estimating latent genetic effects have been describedh and s may be smaller and the residuals larger. Thus, the interpretation of the model depends on the similarity between the GWASed and target phenotype and is only straightforward when these are the same. Second, the rGenSi model makes certain simplifying assumptions. Extensive simulations indicated that the model provided the expected results. In addition, the empirical results that could be compared with previous studies were mainly in line with these. However, we did not include covariance between an individual’s latent genetic and environmental factors, which would be expected under vertical transmission. Assuming independence is a limitation of the current model and it remains to be determined how this influences the results in various scenarios. We also relied on previous GWAS to construct the polygenic scores. Assortative mating and parental indirect genetic effects may have influenced these studies and thereby also inflated our estimates of heritability. If we have overestimated heritability, genetic correlations between relatives will most likely also be inflated. Recent studies are exploring how these phenomena influence genomic studies and how to counteract this45. Third, all participants were expecting parents as our sample was based on a pregnancy cohort, and we could not study the role of selective fertility for genetic variance. Fourth, there is some selective participation in MoBa46 that is plausibly enhanced among families where both parents participate. Fifth, although we attempted to adjust for population structure by including the first 50 principal components, we cannot rule out the effects of residual population structure47. For example, some parts of the spousal correlations could be due to assortment on social factors such as place of birth rather than assortment on phenotypes. Sixth, the MoBa sample is homogenous and mostly of European ancestry, potentially constraining generalizability to similar ethnicities.Our study has several advantages, such as a large population-based sample with a high participation rate. Nevertheless, the findings must be interpreted in the light of some limitations: First, the latent genetic factor includes variance shared by the polygenic score and the mated phenotype. If the phenotypes are differentially operationalized or assessed in discovery and target samples, This paper has four key findings. First, correlations between relatives’ polygenic scores may reflect only a part of the genetic similarity arising from assortative mating. We have presented a structural equation modelling approach to estimate genetic correlations between individuals when phenotypic data is available. Second, the application of this model to partners, siblings, and in-laws indicated high levels of genetic partner similarity for educational attainment and some partner similarity for height and depression. Phenotypes were correlated with partners’ and, in some cases, in-laws’ polygenic scores. Third, partner similarity in educational attainment and, to a smaller degree, in depression appeared not to be based directly on these phenotypes but rather on correlated phenotypes that we did not observe. Partner similarity in height resulted from direct phenotypic assortment only. Fourth, the genetic variances of educational attainment, height, and depression appeared to be in intergenerational equilibrium. Even though higher education has only been widespread for a few generations, individuals may in previous times have assorted on traits that predict educational attainment today. Hence, this study shows genetic similarities between extended family members and that assortative mating has taken place for several generations.The establishment of The Norwegian Mother, Father, and Child Cohort Study (MoBa) and initial data collection was based on a license from the Norwegian Data Protection Agency and approval from The Regional Committees for Medical and Health Research Ethics. The MoBa cohort is now based on regulations related to the Norwegian Health Registry Act. The current study was approved by The Regional Committees for Medical and Health Research Ethics, Southern and Eastern Norway (project# 2017/2205). Informed consent was obtained from all study participants. The consent allows linking with data from other sources. The participants did not receive monetary compensation.48. Participants were recruited from all over Norway from 1999 to 2008. The women consented to participation in 41% of the pregnancies. The cohort now includes 114,500 children, 95,200 mothers and 75,200 fathers. The current study is based on version 12 of the quality-assured data files.MoBa is a population-based pregnancy cohort study conducted by the Norwegian Institute of Public HealthWe use data on the parent generation in the MoBa sample, that is, mothers and fathers but not children, and study the following relationship types among them, detailed in Fig. 50.Blood samples were obtained from both parents during pregnancy. After birth, a second blood sample was taken from the mother. Genotyping of the entire MoBa cohort is ongoing. We used genotype data based on 32,000 family trios. The analytic sample included genotype data on 30,197 mothers and 28,691 fathers. There were valid genomic data on 26,681 complete couples, 2170 complete sibling pairs, 3905 in-law pairs, and 1763 co-in-law pairs (after removing 4 extended families where the co-in-laws were siblings). Information about the genotyping, imputation, and quality control is available in the Supplementary Methods 51. Seven categories were in use, corresponding to 1 = Lower secondary school , 2 = Upper secondary school, basic (10-11 years), 3 = Upper secondary school, completed (12 years), 4 = Post-secondary non-tertiary education, 5 = Bachelor’s degree or equivalent, 6 = Master’s degree or equivalent, 7 = PhD or equivalent. Level of education (1–7) was used as the unit on these variables.Educational attainment of the participants at age 30 was gathered from registers at Statistics Norway and coded according to the Norwegian Standard Classification of EducationHeight in centimeters was self-reported by mothers and fathers in the first questionnaire (15 weeks of gestation).52. The average of the items is used as an individual’s score on depression. The genetic association between major depressive disorder and broader definitions of depression has been reported to be very high (r = 0.86)53. The internal consistency in this sample was Cronbach’s α = 0.82 for women and Cronbach’s α = 0.81 for men.Symptoms of major depressive disorder were measured with 6 items constituting the Life-Time History of Major Depression, which is based on DSM-III54, height55, and major depressive disorder56. The main results are based on SNPs associated with the phenotype at p<0.05 in the GWAS. The Supplementary Methods Polygenic scores were calculated using PRSice2, based on European samples from the most recent GWAS of educational attainmentmean = 0, SD = 1) adjusted for population structure  and batch effects for polygenic scores. We estimated Pearson correlations between relatives’ phenotypes and polygenic scores and across phenotypes and polygenic scores. Including the principal components in the analyses only marginally changed the correlations  the correlation between the latent genetic variable and the phenotype, h. Usually, the correlation between phenotype and the polygenic score (the product h * s) can be observed, but separating the two components is impossible. Using data from relatives, we can identify h and s and provide correlations in the genetic signal free from the residual noise. We call this model rGenSi because we use it to study correlations in genetic signals among relatives. The model estimates the genetic correlation (2), the proportion of noise (n2 = 1-s2), and the heritability (h2) of a phenotype, and it can be extended to distinguish between direct and indirect assortment.The correlation between two individuals’ polygenic scores may not correspond to the full resemblance on causal variants for the trait because a polygenic score may not be identical to the weighted sum of true trait-associated genetic variants in the target sample. We introduce a structural equation model (SEM), illustrated in Fig. m, that results from direct (primary phenotypic) assortment. The correlation between siblings’ genetic signals is estimated in the model as 32. They contribute to the co-variance between variables, but not their variance. They are useful for analyzing assortment processes, which do not change the values of variables. For instance, the correlation between partners’ genetic signals is The association between partners’ phenotypes is modelled as a co-path, s, h, m, and rs. It can be extended with two additional parameters, a and c, which are fixed to 1 and 0, respectively, in the basic model. The association between the observed phenotype and a latent phenotype on which assortment takes place is represented by a. When a = 1, assortment takes place directly on the observed phenotype (primary phenotypic assortment). When it is lower, the assortment is indirect, as a correlated phenotype underlies the assortment (secondary assortment). For instance, academic abilities rather than obtained length of education could lead to partner similarity in education, and partner similarity in manifest depression might be due to similarity in neuroticism. The phenotype on which partners assort is modelled as a latent variable, like in the Cascade model22. When a is freely estimated, m corresponds to the correlation between a polygenic score and the partner’s phenotype divided by the correlation with one’s own phenotype.  The a parameter reduces all correlations between observed phenotypes to the same degree, that is, partner and co-in-law correlations are equally reduced by a2.The basic version of the rGenSi model has four free parameters, Finally, siblings’ phenotypes may be more similar than indicated by their genetic similarity, which means that they may be shaped by having similar environments. This can be modelled with a factor that is shared by siblings and that influences their phenotypes (c). The genetic residual is defined as not being associated with the phenotype. It is therefore assumed not to be a basis for partner selection and to not correlate between partners. Likewise, we assume that assortment in the previous generation is unrelated to the genetic residual, allowing us to set the residual genetic correlation to the uninflated value of 0.50 in siblings. The model is further explained in the Supplementary Methods rg,partner, the additive genetic correlation between siblings in an equilibrium population is expected to be 19 (p. 158). , but we define ρzh2= rg,partner). This formula allows us to compare partner correlations, which result from the assortment in the current generation, with sibling correlations, which results from the assortment in previous generations. An observed partner correlation higher than expected in equilibrium indicates that the level of genetic assortment is increasing compared to previous generations. We investigated deviations from intergenerational equilibrium by constraining correlations between partners’ and siblings’ polygenic scores according to the above formula and by adding this as a constraint to the rGenSi model. We compared models that assumed equilibrium to the less restricted models that did not assume equilibrium and tested whether they had a significantly worse fit to the data.Assortative mating can lead to genetic correlations between partners and elevated genetic correlations between siblings in succeeding generations until an equilibrium is reached. In the rGenSi model, the genetic correlations between partners and between siblings are estimated independently. In equilibrium, however, the genetic correlation between partners can easily be predicted from the genetic correlation between siblings and vice versa. If the correlation between partners in additive genetic factors is Further information on research design is available in the Supplementary informationPeer Review FileDescription of Additional Supplementary FilesSupplementary Software 1Reporting Summary


Rhyacophila tristis, common and widespread representative of mountain freshwater fauna. Analysis of the COI mitochondrial marker revealed presence of the western and eastern lineages, with samples from both lineages being grouped in BOLD (Barcode of Life Data System) into separate BINs (Barcode Index Numbers). Our data indicates that eastern lineage (BIN_E) is more closely related to the Balkan populations than to co-occurring western lineage (BIN_W), and that the contact zone of the lineages passes through the W Carpathians. The study revealed phylogeographic and demographic differences between lineages, supporting hypothesis of their evolutionary independence and specific ecological preferences. The obtained genetic data of the R. tristis population from W Carpathians improved our knowledge about population genetics of this aquatic species and can contribute to understanding the state and evolution of biodiversity of freshwater ecosystems in Europe.The region of the Western Carpathians is, among other aspects, very important for survival and diversity of European freshwater fauna due to the presence of a large number of (sub)mountain springs and streams. However, these ecologically and faunistically diversified habitats are still understudied in the context of genetic diversity and population structure of their inhabitants. This study focuses on genetic diversity and distribution patterns of the caddisfly  The acquired results may subsequently help to estimate evolution of species affected by current climate change, as they provide valuable information on the size of the gene pool or the degree of species adaptation in relation to environmental changes16. Some studies also document the major role of the W Carpathians as a glacial refugia for various species or genetic lineages22. The W Carpathians form a huge reservoir of freshwater, including a rich system of submountain and mountain springs and streams, which are still understudied in the context of the genetic diversity and population structure of their inhabitants. However, those few studies dealing with populations of aquatic species in the W Carpathian mountains have confirmed significantly different patterns of their genetic diversity than in the rest of Europe25.Recent molecular data on aquatic and terrestrial taxa show that the Western Carpathians montane species on the European level. The study revealed strong genetic differences between populations from the western and eastern part of Europe, to which, among other factors, large distances between sites as well as sparse sampling may have contributed. It was suggested that R. tristis survived in an independent Pleistocene refugia both in the Alps and the Carpathians. Unfortunately, the area of the W Carpathians was not covered in this study, so the data from an important part of the species area of distribution was missing.The knowledge on the spatial distribution of genetic diversity at a species level is an essential prerequisite for understanding the resilience of biota in the changing environment across geological periods30. Due to high habitat diversity of this area, including numerous montane springs and streams, we hypothesize that the genetic patterns and level of the genetic diversity found by Bálint et al. in 201129 at the large geographic scale could be also detected at a much smaller area of the W Carpathians. It could also be expected that this mountain range may include a contact zone between the Alpine (western) and Carpathian (eastern) lineages of the R. tristis. To test these assumptions, we generated and analysed a large dataset of R. tristis samples from the springs and streams in the W Carpathians and adjacent areas, which also allowed us to fill the gap in the phylogeographic puzzle of this species in the European mountains.W Carpathians represent the northernmost part of the Alpine-Carpathian mountain chainR. tristis had a relatively wide distribution in the studied area, both in streams and springs, although not in high numbers. The BIN (Barcode Index Number) algorithm implemented in BOLD (Barcode of Life Data System) and ASAP (Assemble Species by Automatic Partitioning) analysis resulted in identical partitioning of the 5′ COI dataset into two genetic lineages, whose distribution was largely disjunct , was recorded at 16 localities in the west, while the eastern lineage, equal to BIN BOLD:ADL4166 (BIN_E), was found at 44 localities in the east. Both lineages (BINs) were found in sympatry only at two localities in the Fatra-Tatra area: the Biela Voda stream (T180) and Jazierce spring (V033). In the Fatra-Tatra area, where both BINs met, the highest levels of molecular divergence and a relatively clear boundary between the two genetic lineages were found  occurring, e.g., in the Bulgarian Rila Mts and the BIN_A (BOLD:AAD5573) from the Swiss and Italian Alps  revealed that divergence of the species likely started some 2.5 million years ago (Myr) and the western and eastern BINs diverged ca. one and a half million years later. Possible misidentification of sequences (marked as R. pubescens) downloaded from public databases was also noted within the analysis  was shared with German, Austrian, and Italian localities. This BIN included eight private haplotypes, six of them were found only in the Fatra-Tatra Area and two were private for the Western Beskids. Within the BIN_E, the total number of private haplotypes was 14, four private haplotypes were identified in the Fatra-Tatra Area, the Slovak Ore Mts, and the Vihorlat Mts, one was found in the Poloniny Mts and the Apuseni Mts. The most common haplotype of the BIN_E (RT12 with 55 sequences) occurred at 32 sites. Only a single haplotype (RT06) was shared among the Inner and Outer Eastern Carpathians  and more distant localities in Romania  also confirms such survival pattern in the periglacial areas32.Our results show that the presence of the western (BIN_W) and the eastern (BIN_E) lineage was also detected in the W Carpathians themselves, and based on the genetic landscape approach, we proved that W Carpathians form a contact zone between these two lineages (BINs). Reconstruction of phylogeny suggests that the two BINs separated relatively long ago , indicating  that in fact they could represent two separate species. However, to make a sound conclusion on their taxonomy, additional studies including more molecular markers as well as morphological feature analysis involving the study of adults would be needed. The analysis also showed that the eastern BIN_E is more closely related to the Balkan samples  than to the BIN_W co-occuring in the same mountain system (W Carpathians).Drusus discolor very likely persisted in the Tatra Mts in numerous refugia over multiple glacial cycles23. Biodiversity richness of the W Carpathians is also supported by studies of the cold-adapted gammarids Gammarus balcanicus5, G. fossarum14 or G. jazdzewskii33. Although no endemic lineages of R. tristis were found in the W Carpathians, we have detected the presence of 21 private haplotypes within BIN_W and BIN_E, which have not yet been confirmed anywhere else.Many freshwater species contain endemic genetic lineages or at least private haplotypes occuring in different European mountain systems, including the W Carpathians. For example, the caddisfly R. tristis revealed distinct variation among physiographic units and/or river basins. This is, however, very likely due to separate phylogeography, i.e., history and distribution of both detected BINs. On the contrary, the results of AMOVA on the level of individual BINs were more consistent with respect to PU or RB division, which suggests that considering BINs as separate evolutionary units is more natural. In both BINs, the highest portion of the variability was assigned to differences within subpopulations , suggesting relatively recent colonization of the studied area. In the western BIN_W, however, a reasonable part of the genetic variability was attributed to differences among subpopulations within PU/RB, similarly as in aquatic beetle Elmis aenea from springs of the same area34. On the other hand, AMOVA of BIN_E revealed no variability among localities within PU/RB, but slight differences between PU/RB were detected. This was similar to the more fluvial riffle beetle Limnius perrisi from the same study or the blackfly Simulium degrangei35 studied also in the W Carpathians. FST values of both BINs agreed with the results of AMOVA. The differences between W Carpathian R. tristis lineages on the level of genetic variation between localities within the PU/RB could indicate a lower degree of local isolation and/or higher dispersal ability of BIN_E than BIN_W.The AMOVA of the whole W Carpathian population of R. tristis from the W Carpathians differed concerning elevation intervals in which they were found. Based on our data, we cannot unambiguously confirm whether this is a stable autecological characteristic of the lineages, because R. tristis has a significantly larger area of distribution than the studied area and to confirm or refute the possible difference, we would need more samples. On the other hand, in all sampled localities with an elevation above 800 m a.s.l. in the western part of the studied area, R. tristis was missing, which also suggests that the western lineage (BIN_W) could be characteristic for lower altitudes. Elevation influences a series of other factors, such as the proportion of suitable habitats or migration patterns, and indirectly it shapes species population genetics1. Moreover, high-elevation sites are often reservoirs of a specific part of the species’ genetic diversity38, but their vulnerability is high and the loss of isolated populations from higher elevations could result in a loss of important, locally adapted genotypes39. Vice versa, adaptation potential and long-term survival of species or genetic lineages are dependent on sufficient intraspecific genetic diversity41, and from this perspective, i.e., more tolerant to different environmental conditions in different elevations, BIN_E could be at an advantage. On the contrary, if the BIN_W preference for lower elevation is confirmed, this lineage deserves more attention from the point of view of its protection due to higher anthropogenic pressure on lower-situated springs and streams.The lineages (BINs) of R. tristis and, conversely, similar patterns of distribution of genetic variability as in beetles from the same area34, but also in other insect species42. These findings emphasize the need to study populations in detail at the level of natural evolutionary units. This allows us to better understand the evolution and phylogeography of individual taxa and to look for general trends in the development of the fauna of entire ecosystems.By analysis of population genetics, we found the presence of differentiated genetic lineages (BINs) in R. tristis lineages detected in the W Carpathians differs. The BIN_W started to expand roughly around 40–80 thousand years ago (ka), while BIN_E around 25 ka during the Last Glacial Maximum (LGM). In that time, latitudinal temperature gradient existed across Europe, when winter soil temperatures were 10–20 °C colder in Central and Northern Europe and around 2–4 °C in Southern Europe than today43. Such conditions could suit the up-stream caddisfly R. tristis, and so its glacial expansion could be expected44. The population of the W Carpathian BIN_E grew sharply compared to BIN_W, and its present effective population size is around five times higher. We assume that this difference could be linked to the different distribution of the BINs. Differences in eBSP and AMOVA could indicate survival of the BIN_W in the study area for a longer period and occurrence of genetic drift after the colonisation of individual sites, causing genetic differences among populations within the PU/RB. In contrast, the results for BIN_E suggest that it spread to the W Carpathians more abruptly and relatively rapidly after LGM. Subtle differences in the variability among PU/RB suggest that individual units/basins were colonized by a part of the originally homogeneous population, which has brought with it specific, although not significantly different compositions of haplotypes. However, the relatively short time of occurrence in the studied area has not yet allowed BIN_E to fix more apparent differences between local populations, as very likely happened within BIN_W.Beside genetic variability distribution, also the demography of the R. tristis caddisfly from the (sub)montane springs and streams in the W Carpathians showed that these habitats form an important and unique type of the freshwater ecosystems producing or harbouring substantial  intraspecific genetic diversity. It can be assumed that these habitats play an equal role also in other areas. The comparison with co-occurring species revealed very interesting results regarding the differences or similarities of their genetic diversity as well as history. This study did not have the ambition to address the taxonomy of the species of interest, but more attention should be paid to this issue in the future, because, as genetic and geographical data have suggested, the possibility that the two identified lineages (BINs) represent separate cryptic species cannot be ruled out. The eastern lineage (BIN_E) most likely colonized the W Carpathians from Southeast Europe, probably at the end of the LGM, while BIN_W appears to originate in areas west of the W Carpathians and it survived and developed its population in the study area for a longer time. The Western Carpathians are the place where the two lineages meet. What role do W Carpathian springs and streams play in survival and preservation of R. tristis genetic diversity, whether the lineages hybridize in the contact zone or what are their specific ecological requirements, will need further study including samples from a wider area of distribution and additional genetic markers.The detailed population study of the 45. Most of the W Carpathian ranges are of moderate elevation, ranging mostly from 500 to 1300 m a.s.l., only sparsely exceeding 1500 m a. s. l.45. Geologically, the W Carpathians are diversified, especially in the inner part of the arc, with various kinds of bedrock . The outer W Carpathians are built almost solely from flysch46. During the Pleistocene cycles, this area remained mostly unglaciated. The continental ice sheet was only located in the northernmost foothills of the Polish part during its maximal extent in the Middle Pleistocene and the mountain glaciers covered valleys in the highest ranges (above 1700 m a.s.l.)47. The mountain glaciers completely disappeared around 8.500 years ago48. The precipitation in the area is determined by differences in elevation and geomorphological relief. Hydrologically, the W Carpathian rivers have a rain-snow regime with floods in springs and summers.The study area includes the W Carpathians, one of the major physiographic units of the Carpathian mountain range Fig. A. It covThe studied localities include 15 springs and 43 streams situated mainly in Slovakia, partially in Czechia and Poland, within the Inner and Outer Western Carpathian physiographic units/subunits. These localities were supplemented by sampling sites in the Inner Eastern Carpathians  and the Outer Eastern Carpathians (Low Beskids—LB) Fig. .49, using a hydrobiological hand-net with a mesh size of 0.5 mm. In the field, specimens were fixed in 96% ethanol. In the laboratory, the invertebrates were sorted into higher taxonomic groups using stereomicroscope, prefixed with clean ethanol and stored in a freezer at − 25 °C. Samples were supplemented by additional specimens of R. tristis from the ZooLab collection of the Plant Science and Biodiversity Centre, Slovak Academy of Sciences (Bratislava). The individuals of R. tristis were morphologically identified using the determination keys Sedlák from 198050 and Waringer and Graf from 201151.The qualitative sampling of macrozoobenthos was performed in 2016–2017, within the framework of a broader hydrobiological research focused on karst springs and diversity of the W Carpathian streams. It was carried out by a multi-habitat kick-sampling technique52, followed by PCR amplification of ca. 650 bp-long barcoding fragment of the mitochondrial cytochrome c oxidase subunit I (5′ COI) using the primer pair LCO1490–HCO219853. The PCR was performed in a total volume of 25 μl containing 5 μl of 5 × DreamTaq™ Buffer, 1.5 μl of Mg2+ (25 mM), 0.5 μl of each primer (concentration 5 mM), 0.5 μl of dNTP Mix (20 mM), 0.125 μl (0.625 U) DreamTaq DNA Polymerase, 11.875 μl ultra-pure H2O and 5 μl of DNA template. The PCR cycling consisted of a 2-min initial denaturation at 94 °C, followed by 40 cycles of 94 °C (40 s) denaturation, 46 °C (40 s) annealing and 72 °C (1 min) extension and termination at 72 °C (10 min) for a final extension. A 4 μl aliquot of the PCR products were visualized by GoldView (Solarbio) in electrophoresis on a 1% agarose gel and GelLogic imaging equipment to check PCR products quality and length. The PCR products were purified with Exo-FastAP Thermo Scientific and were sent for sequencing to Macrogen Europe Inc., Amsterdam.DNA was extracted from a total of 161 individuals, two legs of each individual, using the Chelex protocol29 and 200954, the non-barcoding 3′ COI marker using primers Jerry and Pat55 was amplified. We sequenced the necessary number of individuals (six specimens selected from different physiographic units) to create a phylogenetic tree together with 19 sequences (3′ COI) of R. tristis and 15 sequences (3′ COI) of congeneric species R. aquitanica McLachlan, 1879 (8) and R. carpathica Botoșaneanu, 1995 (7) from Bálint et al. 201129 and 200954 , the Poloniny Mts (3 loc.), the Vihorlat Mts (7 loc.) and Low Beskids (1 loc.) , used for the reconstruction of haplotype networks and phylogenetic tree. These sequences were obtained from the BOLD (http://www.boldsystems.org).The obtained sequences were edited in SEQUENCHER v5.1 and aligned using the MUSCLE algorithmR. tristis samples for presence of the deeper (genetic/evolutionary) lineages. The first was the BIN algorithm implemented in BOLD (http://www.boldsystems.org), where every uploaded sequence is compared to all records and assigned to an existing or a newly created Barcode Index Number (BIN)58. The BIN system clusters are unique and include sequences that presumably represent a single species. The second approach utilized the Assemble Species by Automatic Partitioning (ASAP), a novel and powerful method to build species partitions, based on pairwise genetic distances from single locus sequence alignments 59.Two methods were used to test 60 based on 5′ COI data. We calculated haplotype diversity (H), nucleotide diversity (π), number of polymorphic sites (S) and average number of nucleotide differences (K) per individual BINs of R. tristis species within the W Carpathians. Statistical comparison of the genetic indices between BINs was computed with the Wilcoxon signed rank test for paired data in R v4.0.2 (http://www.r-project.org).The haplotype data files and the diversity indices were generated in DnaSP v5.1061. The networks include sequences outside the W Carpathians to explain the phylogeographic relationships and haplotype distribution in the broader context.Haplotype networks were reconstructed using the median-joining method (MJN) in PopART v1.762 and, separately, for the 3′ COI marker also including published sequences54. As an outgroup, the European congeners R. aquitanica and R. carpathica were included. The nucleotide substitution model was set through bModelTest63. The tree prior was set to Birth–Death and a strict molecular clock was used following the Path Sampling Selection. The strict molecular clock was calibrated with the standard mitochondrial rate for arthropod COI equal to 0.0115 substitutions/site/Myr64. Two runs of Markov chain Monte Carlo (MCMC), each 20 million iterations long and sampled every 1000 iterations, were performed for both fragments. Runs were examined using Tracer v1.765, and all sampled parameters achieved a sufficient sample size (ESS > 200). Tree files were combined using Log-Combiner v2.5.262, with the removal of the non-stationary 25% burn-in phase. The maximum clade credibility chronogram was generated using TreeAnnotator v2.5.262. The same methodology was used in case of the non-barcoding 3′ COI marker phylogeny reconstruction  software to produce a genetic divergence surface image using the inverse distance weighted algorithm. The resulting image was plotted onto a relief map to create a final map in which the hypsometric tints (red-blue) reflect the genetic distance between population pairs.The spatial diversity pattern in the studied area of Carpathians was illustrated with a genetic landscape approach generated with Alleles in Space (AIS)67 and general spatial autocorrelation test using Alleles in Space (AIS)66. Both tests analyse correlation between spatial and molecular distance. To assess the significance, tests were run with 1000 permutations.To test if spatial distance is structuring the molecular diversity of 5′ COI fragments, we run two types of isolation by distance tests: Mantel testR. tristis in the W Carpathians based on 5′ COI was characterized by the analysis of molecular variance (AMOVA) and fixation indices (FST) using Arlequin v3.568. The AMOVA was used to estimate whether the observed genetic diversity may be attributed to the geographical or river basin partitioning of populations in three levels: among physiographic units (PU)/river basins (RB), among sampling sites within PU/RB and within sampling sites. We also performed AMOVA and FST separately for individual BINs of the R. tristis, to find out the differences between them. 138 sequences of R. tristis  were included to calculate the FST index, localities with only 1 sequence were excluded. To test the significance of covariance components and fixation indices, 1,000 permutations were performed.The population structure of R. tristis in the studied area of Carpathians, based on 5′ COI, were examined in three different approaches. First, Tajima’s D69, Fu’s Fs70 and Fu and Li’s D71 neutrality tests with 10,000 permutations were calculated in DnaSP v5.10 to test the selective neutrality and population stability. Secondly, nucleotide mismatch distribution was performed in Arlequin v3.568 to detect the demographic and spatial dynamics of population expansion history in the W Carpathians. The fit between the observed and expected distributions was evaluated by the test statistics of goodness-of-fit, including the sum of squared deviation (SSD) and Harpending’s raggedness index (r).Historical expansion patterns of the 62, was used to show the fluctuations of R. tristis demography in the W Carpathians over time. The model of substitution and molecular clock were set up identical as in the case of the phylogeny reconstruction. For comparison, two runs of Monte Carlo Markov Chains (MCMC) were performed, each 40 million iterations long and sampled every 10,000 iterations. The runs were examined in Tracer v1.7 and all the parameters reached the effective sampling size (ESS) above 200. The eBSP plots were produced using R v4.0.2 software (http://www.r-project.org). Both runs showed complementary results, thus only one is presented.Finally, the extended Bayesian skyline plot (eBSP), implemented in BEAST v2.6.2 software package Supplementary Information.


We estimate annual increases in urban population exposure to air pollutants of 1 to 18% for fine particles (PM2.5) and 2 to 23% for NO2 from 2005 to 2018 and attribute 180,000  additional premature deaths in 2018 (62% increase relative to 2005) to this increase in exposure. These cities are predicted to reach populations of up to 80 million people by 2100, so regulatory action targeting emerging anthropogenic sources is urgently needed.Tropical cities are experiencing rapid growth but lack routine air pollution monitoring to develop prescient air quality policies. Here, we conduct targeted sampling of recent (2000s to 2010s) observations of air pollutants from space-based instruments over 46 fast-growing tropical cities. We quantify significant annual increases in nitrogen dioxide (NO A total of 180,000 additional premature deaths are attributable to rapid air quality degradation in fast-growing tropical cities. More than 40% of the global population resides in the tropics  is already a leading environmental health risk in many countries in the tropics  guideline of 5 μg m−3 (Exposure to ambient fine particulate pollution (PM2) and ammonia (NH3) abundances as well as aerosol optical depth (AOD) reproduce long-term trends in surface observations of NO2, NH3, and PM2.5, respectively  trends in air pollution abundance and precursor emissions and to discern the relative role of traditional and emerging pollution sources. We go on to estimate the increase in urban population exposure to the hazardous pollutants PM2.5 and NO2 and the associated health burden of exposure to PM2.5 using a health risk assessment model constrained with epidemiological data representative of the range of PM2.5 concentrations in the tropics.Here, we conduct targeted sampling of more than a decade of satellite observations of NO−1) in Africa, 1% a−1 for Riyadh and 8% a−1 for Sana’a in the Middle East, 0.8 to 3% a−1 in South Asia, and 0.5 to 7% a−1 in Southeast Asia  by 2100 are shown in 2 for 2005–2018, NH3 for 2008–2018, and HCHO as a proxy for reactive nonmethane VOCs (NMVOCs) for 2005–2018 over these cities obtained from a long-term, consistent record of satellite observations  cities, by 0.1 to 14.1% a−1. The increase is significant for 34 of these 41 cities (2.5 by forming aerosol nitrate. NO2 triples over Chittagong (Bangladesh) and more than doubles over Antananarivo (Madagascar), Hanoi (Vietnam), Luanda (Angola), and Dhaka (Bangladesh). NO2 declines in five cities, although the downward trend is only appreciable and significant for Jakarta (−2.0% a−1). This decline, already identified using an earlier record of observations from the same satellite instrument  and Asia . The directions of our NO2 trends are consistent with previous studies that have focused on large cities around the world  emissions of acidic sulfate aerosols , Mumbai (1.7% a−1), and Kinshasa (2.0% a−1) support sustained increases in reactive NMVOCs identified for 1997–2009 using an earlier record of satellite observations (−1), although the HCHO column densities there are close to the instrument detection limit and very small (<1 × 1015 molecules cm−2) at the beginning of the record after removing the background contribution . The population in Sana’a has increased by 4.7% a−1, but the country has also been embroiled in conflict and political instability, and the increase in the other pollutants is marginal (0.07 to 1.15% a−1) (table S1).Trends in HCHO as a proxy for reactive NMVOCs  are not  for NO2 . This co2 increases, consistent with a shift to more efficient combustion sources, as is suggested by regional anthropogenic emission inventories (−1 are less steep than trends in NO2 (0.8 to 14.1% a−1). The generally positive, steeper, and greater number of significant trends in NO2 (and thus NOx) compared to those of reactive NMVOCs has implications for surface ozone formed from photochemical oxidation of NMVOCs in the presence of NOx. In most of the tropics, surface ozone is near the WHO 8-hour mean guideline of 100 μg m−3  in 2005 exceed values of 2 in almost all cities (reaching 23 in Conakry and Nairobi), indicative of chemical regimes in which ozone formation is sensitive to NOx sources. During the record of NO2 and HCHO observations used here (2005–2018), most of these cities are transitioning to a regime in which ozone production is sensitive to NMVOC sources, synonymous with many megacities in the United States, Europe, and China. If the future mimics the past, then the transition to sensitivity to NMVOCs could occur as soon as 2025 in Chittagong and Dhaka and 2030 in Addis Ababa, Hanoi, and Luanda. As NMVOCs are especially challenging to monitor and regulate , reactive NMVOCs decline at the same time as NO2.5  and significant. AOD more than doubles in Bangalore (7.8% a−1) and Hyderabad (7.3% a−1). Earlier studies have reported similar positive trends for these cities (2.5). Desert dust likely does not contribute to trends in AOD over South Asian cities, as desert dust optical depth has declined over the Thar Desert and makes a negligible contribution to AOD trends across the rest of India , while, in Africa, there are significant increases in Luanda (2.3% a−1), Addis Ababa (3.3% a−1), and Dar es Salaam (3.3% a−1). The significant increase in AOD for Addis Ababa may be influenced by the positive trend in desert dust optical depth over Ethiopia  with steep increases in biomass burning have much shallower trends in anthropogenic activity. These same three cities exhibit positive trends in burned fraction but not in biomass burning NO2  that has been applied in the Global Burden of Disease (GBD) studies (GBD-MAPS NOx emission trends reproduce the direction of trends in satellite NO2 for most cities (GBD-MAPS does not capture the policy-driven decline in emissions in Jakarta (+4.7% a−1 in CEDSGBD-MAPS and −2.0% a−1 in the observations) or the marginal, although nonsignificant, increase in NOx in Sana’a (−3.8% a−1 in CEDSGBD-MAPS and +0.07% a−1 in the observations). According to CEDSGBD-MAPS, the steepest increases in NOx emissions in African cities are from nonresidential energy generation (4.6% a−1) and industry (4.2% a−1). These sources together account for 65 to 70% of CEDSGBD-MAPS NOx in Indian cities, but the steepest increases are for off-road transport (8.6% a−1) and a mix of nontraditional combustion sources (2.8% a−1). In Asian cities, excluding India, the largest growth in CEDSGBD-MAPS NOx is from the dominant sectors’ energy generation (4.2% a−1), industry (5.3% a−1), and off-road transport (6.3% a−1).We also assess the skill of a contemporary anthropogenic emission inventory at reproducing trends in t cities , althougx lifetime and the relative contribution of the free tropospheric portion of the column may also contribute to divergence in column and emission trends. Most studies assessing these effects have focused on cities in the northern hemisphere that have experienced substantial policy-driven decline in NOx emissions  should have opposing effects on agreement between trends in NO2 columns and NOx emissions. Trends in the column are likely steeper than trends in emissions because of decline in NOx lifetime, whereas trends in the NO2 column should be dampened relative to trends in emissions because of decline in the relative contribution of the NOx background.Changes in NOGBD-MAPS NH3 emissions are positive for all cities, but two to five times less than the observed trends  a−1 during 2005–2017] compared to the other tropical cities (30 to 80 kt a−1 for the same time period) and mainly come from waste processing.Trends in CEDSd trends . Underes sources . SimilarGBD-MAPS. Trends diverge for Sana’a (−1.1% a−1 for CEDSGBD-MAPS and +10.6% a−1 for the observations), but observed values are small and may be prone to error. As with NO2, CEDSGBD-MAPS trends in reactive NMVOCs in Jakarta (+2.6% a−1) are opposite to the observations (−1.7% a−1). Significant decline in reactive NMVOCs over Mombasa in the observations (−2.7% a−1), but not in the inventory (+1.8% a−1), may be due to a shift toward combustion-efficient sources due to substantial development of the Mombasa seaport to meet increasing demand for imported goods  are shown in 2.5 increases in all cities except Manila (−0.8% a−1), where a 2.0% a−1 decline in PM2.5 , although the absolute health gains due to decline in exposure to NO2 in Jakarta will be more than offset by a 1.8% a−1 increase in exposure to PM2.5 due to a 1.3% a−1 increase in population and a small, although not significant, increase in PM2.5 , Abuja (10.7% a−1), Antananarivo (13.0% a−1), Luanda (11.7% a−1), and Dar es Salaam (14.8% a−1) in Africa. These same African cities also experience the steepest increases in NO2 exposure of >12% a−1. Across Africa, the increase in exposure to NO2 ranges from 2.0% a−1 in Blantyre to 21.3% a−1 in Luanda and 23.0% a−1 in Antananarivo.The trends in exposure to PMin PM2.5  countersa−1; 2.5  and NO2 ; 2.5  and applying these to a health risk assessment model that relates the risk of premature mortality to ambient PM2.5 . We focus on PM2.5 because of its much greater health risk than NO2  in 2005 to 470,000  in 2018. The contribution to total premature deaths increases from 27% in 2005 to 32% in 2018, and the number of cities where at least one-third of deaths is attributable to exposure to PM2.5 increases from 14 in 2005 to 20 in 2018. PM2.5 exposure persists as the leading cause (>50%) of premature deaths in Kano (Nigeria), N’Djamena (Chad), and Kolkata (India).s in AOD  and PM2. and NO2 . The inc and NO2 , is 180,2.5 > 50 μg m−3 . The total increase in premature deaths from 2005 to 2018 without Kolkata is 165,000, and the lower limit of the CI exceeds zero .Premature mortality estimates for 2005 and for 2008 are significant for almost all cities (table S2). The wide range in the premature mortality 95% CIs is due to large uncertainty in the health risk assessment model for annual mean PM2.5, population, and baseline mortality) to changes in total premature mortality . African cities experience substantial decline in baseline mortality that reduces the impact of increases in population and PM2.5 on premature mortality. Decline in baseline mortality more than offsets increases in the other factors in Blantyre, Lilongwe, and Kaduna, leading to decline in premature mortality attributable to PM2.5. The significant 3.1% a−1 increase in NO2 in Kaduna may still lead to severe public health outcomes. The increase in premature mortality in South Asian cities is due exclusively to population and PM2.5. Although trends in premature mortality are positive for all Southeast Asian cities, the relative contribution of each factor is mixed. The contribution of factors in Phnom Penh and Yangon is similar to African cities. Decline in PM2.5 in Bangkok, Hanoi, Ho Chi Minh City, and Manila does not mitigate the influence of increases in population and baseline mortality.We also determine the contribution of individual factors , yield premature mortality estimates that are more than double those from the GBD  model that is also applied to GBD studies  in these cities at rates two to three times faster than or opposite in sign to national and regional trends. The degradation in air quality in the past two decades is mostly influenced by anthropogenic activity rather than traditional widespread open burning of biomass. Only Jakarta shows evidence of air quality improvements due to policy measures, and those improvements have had a limited effect, leading to decline in NO2 and reactive VOCs but not in NH3 or PM2.5.With our targeted sampling of satellite observations over 46 densely populated fast-growing tropical cities, we find large and significant increases in NOx-sensitive to the more challenging to regulate VOC-sensitive regime. In some cities, this may occur as early as 2025. We find large increases in population exposure to hazardous air pollutants PM2.5 and NO2 and estimate 13,000 additional premature deaths each year from this increase in population exposure to air pollution. These trends will almost certainly be sustained in the future because of projected population growth  pandemic, but unemployment, inequitable health care access, and reduced fertility resulting from the pandemic , attenuation of light by aerosols throughout the atmospheric column or AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS), and total column NH3 from the Infrared Atmospheric Sounding Interferometer (IASI) to determine trends in air quality, precursor emissions, and population exposures over the rapidly growing tropical cities identified in 2 and total column NH3 reproduce month-to-month variability in surface concentrations of these and that satellite observations of AOD reproduce long-term trends in surface observations of PM2.5 . Satellite data coverage can be low for smaller cities, exacerbated by persistent clouds in the tropics. We address this by extending the sampling domain beyond the city boundaries relative to the instrument pixel size (6.5 km for OMI and MODIS and 10 km for IASI), as in  version 3.6  to total column HCHO by subtracting the background column component due to oxidation of methane and other long-lived VOCs  if the CI range does not intersect zero.All atmospheric components of interest in this work exhibit a distinct seasonality in the tropics due to seasonality in photochemistry, planetary boundary layer dynamics, synoptic meteorological events such as monsoons, and sources such as biomass burning  , HCHO (lifetime of 2 to 3 hours), and NH3 (lifetime of 2 to 15 hours) provide constraints on precursor emissions of NOx, reactive NMVOCs, and NH3  developed for the U.S. Health Effects Institute (HEI) Global Burden of Disease–Major Air Pollution Sources (GBD-MAPS) project. It extends the record of emissions of the original Community Emissions Data System (CEDS) inventory (GBD-MAPS are provided as monthly gridded (0.5° by 0.5°) values for 11 broad source sectors  formulaN is the total population from the United Nations (UN)  and end (2018) to estimate relative trends in exposure. This approach draws on our previous findings that trends in satellite observations of column values are consistent with trends in surface concentrations (AOD for surface PM2.5 and tropospheric column NO2 for surface NO2), even for cities where seasonality in AOD and PM2.5 are decoupled  of the time period that we use here (2005–2018) and a risk assessment model that relates PM2.5 exposure to risk of premature mortality. A description and validation of GEOS-Chem PM2.5 is in (2.5 for 2012 in each city by sampling GEOS-Chem grid cells in the same way as we do GFEDv4.1s burned fraction and CEDSGBD-MAPS. We then use these values with the trends in AOD (2.5 to determine concentrations of PM2.5 in 2005 and 2018. The health risk assessment model that we use is from a recent meta-analysis (2.5 concentration range, and more health end points than previous approaches (2.5 in 2005 and 2018 using the GEOS-Chem and satellite AOD–derived PM2.5 as input. We then convert this to the number of premature deaths attributable to exposure to PM2.5 for people older than 14 years in each city in 2005 and 2018 using country-level age-specific baseline mortality rates from the GBD (2.5, to population, and to baseline mortality rates. We also quantify the relative role of each of these factors as the log transform of the ratio of premature mortality obtained with each of these parameters held fixed at 2005 values to premature mortality due to the combination of all factors, as in (We estimate the increase in premature mortality due to the increase in exposure to PMs in AOD  as proxi


The present study was designed to investigate the patterns of genetic distinction across a hypervariable region of the CYP2B6 gene, known to contain important SNPs, i.e. rs4803419 and rs3745274, among five major ethnic groups of the Pakistani population.Some single nucleotide polymorphisms (SNPs) in the cytochrome P450 CYP2B6 gene sequences obtained from 104 human samples belonging to Punjabi, Pathan, Sindhi, Seraiki and Baloch ethnicities of Pakistan. The partial CYP2B6 gene region analyzed in the current study is previously known to possess important SNPs.Arlequin v3.5.DnaSPv6.12. and network 5 resources were used to analyze population genetic variance in the partial P-value = 0.026) as well as between Seraiki and Balochi groups . However, the degree of genetic distinction was low between Pathan and Punjabi ethnic groups. Some SNPs, including rs3745274 and rs4803419, which are previously shown in strong association with increased plasma Efavirenz level, were found in high frequency. Besides, a novel SNP, which was not found in dbSNP and Ensemble databases, was identified in the Balochi ethnicity. This novel SNP is predicted to affect the CYP2B6 splicing pattern.The data analyses revealed that genetic variance among samples mainly came from differentiation within the ethnic groups. However, significant genetic variation was also found among the various ethnic groups. The high pairwise Fst genetic distinction was observed between Seraiki and Sindhi ethnic groups (Fst = 0.13392, CYP2B6 and, therefore, may be implicated in non-responder phenomenon.These results may have significant implications in Pakistani ethnicities in the context of drugs metabolized by CYP2B6, especially in Seraiki and Balochi ethnicity. The novel heterogeneous SNP, found in the present study, might lead to altered drug-metabolizing potential of  CYP2A, CYP2B, CYP2F, CYP2G, CYP2S and CYP2T genes  is a major family of the cytochrome enzymes, comprising of several subfamilies that aggregated together in the form of clusters in the genome. The CYP2 subfamilies include the 2T genes . Among t protein . The humkb block . The CYPosfamide , antimalosfamide , antivirosfamide . Althougosterone , in conjCYP2B6 gene may affect the expression and enzyme activity of the translated protein, resulting in significant differences in the pharmacokinetics of CYP2B6-metabolized drugs among individuals and ethnicities, in turn, leading to variations in efficacy and toxicity , *3 (C777A), *4 (A785G), *5 (C1459T), *6 (G516T and A785G), *7  , (The single nucleotide polymorphisms (SNPs) in the toxicity . Several C1459T) , *8 (A41 C1459T) , besidesB6.htm), . GeneticCYP2B6 enzyme expression result in altered drug responses  leading to CYP2B6*6 allele was alone responsible for aberrant splicing, resulting in a low-CYP2B6 expression phenotype. In recent years, studies have identified CYP2B6*6 in association with enhanced plasma concentrations of certain drugs  were also approved by the same committee. Urdu is understood throughout Pakistan. For DNA extraction, five ml of venous blood was collected in EDTA tubes and stored in a refrigerator. DNA extraction kits  wer used to isolate the genomic DNA from the blood samples. The quantity and quality of the isolated DNA was analyzed by running it on a 1% agarose gel and by using ultraviolet spectrophotometer at 260 and 280 nm wavelength. The ratio for most samples was found in the range of 1.80 ± 0.05. Isolated genomic DNA was stored at −20 °C until further processing.CYP2B6 target region holding important and significant genetic variants was amplified from the purified genomic DNA. The PCR products were purified and submitted for commercial DNA sequencing services to Tisngka Biotechnology, China. Each of the samples was sequenced in triplicate (3×) for sequence validation.The The quality of the DNA sequences was checked using the Staden package and Finch TV v1.4 . High-quality sequence reads were assembled using the Lasergene v7.1 package (DNASTAR Inc UA) as described previously A’ (to estimate the nucleotide differences among the population). The haplotypes network plot was developed using the median-joining method introduced in the NETWORK 10.0 package.The high-quality draft sequences were compared with the Human reference genome (GRch38) coordinates deposited in the ensemble database. The SNPs positions were identified and scanned against the genome-wide association study (GWAS) database. The GWAS database holds specific markers reported in association with a particular trait. Besides, comparative analyses were performed against different worldwide populations genetic variants data acquired from the 1000 Genomes database  using FeThe functional prediction of the novel SNP was assessed by different in silico tools, including MutationTaster-2, PolyPhen2, SIFT and PredictSNP2. These tools can predict the non-synonymous and synonymous mutation of the coding and non-coding regions. PredictSNP2 was used to find out deleterious, neutral, and unknown mutations. This resource provides a predictive score based on various parameters like combine annotation dependent depletion (CADD), deleterious annotation neural networks (DANN), computational framework for annotation and prioritization in coding and non-coding region (FunSeq2), genome-wide annotation of variants (GWAVA), fitness consequence (FitCons), and functional analysis through hidden Markov models (FATHAMM).CYP2B6 was generated for 104 individuals belonging from major ethnic groups of the Pakistani population. The target region was spanning genomic coordinates, i.e. 19:41006882–41007421 of the Human reference genome GRch38. The sequenced data completely covered the exon 4 and intron 4 and partially covered the third intron region of the CYP2B6 gene. The data analysis in the context of human reference genome GRch38 identified total three polymorphic loci. The annotations and allele frequencies of all the three polymorphic loci are given in High-quality sequenced data of CYP2B6 gene among ethnic groups’ samples. The high pairwise Fst genetic distinction was observed between Seraiki and Sindhi ethnic groups  as well as between Seraiki and Balochi groups . However, the degree of genetic distinction was low between Pathan and Punjabi ethnic groups  in comparison with the East Asian: Han Chinese (CHB), Japanese (JPT); South Asian: Bengali (BEB), Sri Lankan Tamil (STU), Indian Telugu (ITU), Gujrati Indian (GIH); and European populations: CEU. (Utah Residents CEPH with Northern and Western European Ancestry) (The Fixation index (Fst) statistic was employed to investigate the genetic composition across c groups . Moreovencestry) .P-value = 0.28615). Similarly, a variance factor (Est. Var) of 0.50731 was observed within population; however, among-ethnic groups variance factor was found low 0.00554. The among-ethnic group based genetic variance in this model was although low but still significant, suggesting that ethnic groups are genetically distinct. High pairwise nucleotide differences (DA) were observed between Seraiki and Sindhi, Seraiki and Balochi ethnic groups.AMOVA was used to estimates the genetic deviation at a molecular level among and within various ethnic groups samples. The significance of AMOVA was assessed by 99,000 permutations . The AMOA) during comparison of Pakistani ethnicities CYP2B6 SNPs with additional Asian and European populations data acquired from the 1000 Genomes Project  was found in the Seraiki and Sindhi, Seraiki, and Balochi ethnic groups. The highest within-population genetic distinction  was observed for Balochi ethnic group samples . Moreove Project .A total of five haplotypes with haplotype diversity (Hd) of 0.7276 were identified for all the 104 sample sequences. The haplotype-1 was found with predominant frequency in Sindhi, Pathan, Punjabi, and Balochi samples. The haplotype-2 was also found in the same ethnic groups including Seraiki, whereas, haplotype-3 was found at high frequency only in the Seraiki group . The AMOCYP2B6 in different ethnicities of the Pakistani population is expected to be a complex but an interesting project to study.The DNA sequencing approach can be implemented effectively to assess the allelic distribution of the human DNA sequences and to estimate interethnic genetic variation among populations. This approach has been frequently implemented across the world to identify specific individual polymorphisms. The Pakistani population is a heterogeneous admixture and exhibits high ethnic diversity due to complex demography and various invasions, including Central Asian, Arabs and British colonial invasions . TherefoCYP2B6 gene in major ethnic groups across the Pakistani population. The enzyme encoding from CYP2B6 is responsible for the metabolism of various pharmacological drugs including efavirenz  and black African mixed ancestries . TherefoAMOVA, which was performed based on haplotypes significance analysis to determine the degree of regional differentiation and homogeneity within and among Pakistani ethnic groups, found that the majority of the genetic variance is attributed due to within-population factors. Genetic variance among populations was only 1.08%, as inferred during AMOVA analysis . This vaCYP2B6*6 allele has been found variable throughout the world. Globally, G allele frequency is 0.73 and that of T allele is 0.26  Wild type (B) Heterozygous novel SNP (Chr19: 41007072 G→A) detected in a sample from Balochi ethnic group.Click here for additional data file.10.7717/peerj.11149/supp-3Supplemental Information 3Pairwise differences (πxy) among the Pakistani ethnic groups—Between sampled population groups ; within-population πxx  and the net number of nucleotide differences among population’s groups (Nei distance DA) .Click here for additional data file.10.7717/peerj.11149/supp-4Supplemental Information 4Detected haplotype frequencies in five ethnic groups samples from the Pakistani population.Click here for additional data file.10.7717/peerj.11149/supp-5Supplemental Information 5HapDistMatrix: Depicted pairwise difference among haplotypes of five ethnic groups of Pakistani population.Click here for additional data file.


The UK can be proud of the fact that numerous native breeds of sheep have been developed here that possess unique phenotypic features and excellent productivity and are utilized throughout the world. Their remarkable popularity and further sustainable breeding on grass pastures of British Isles and elsewhere can benefit from genomic applications.Sheep farming has been an important sector of the UK’s economy and rural life for many centuries. It is the favored source of wool, meat and milk products. In the era of exponential progress in genomic technologies, we can now address the questions of what is special about UK sheep breed genotypes and how they differ genetically form one another and from other countries. We can reflect how their natural history has been determined at the level of their genetic code and what traces have been left in their genomes because of selection for phenotypic traits. These include adaptability to certain environmental conditions and management, as well as resistance to disease. Application of these advancements in genetics and genomics to study sheep breeds of British domestic selection has begun and will continue in order to facilitate conservation solutions and production improvement. Sheep are an integral element of UK countryside landscapes at a range of altitudes, with both modern and ancient breeds  represented . Their uThe UK is a largest producer of sheep than any EU country, counting nearly 34 million adult animals and lambs . Prior tA glance at the gene pool landscape for British sheep breeds from the National Sheep Association leads to a list of over 60 native breeds , whereasSheep breed gene pool of British Isles can be subdivided into geographical groups  accordinThe geographical concentration (endemism) of the British sheep breeds was suggested to be a major risk factor for breed endangerment .British sheep breeds are common in other parts of the world and have been exported from the UK to other countries to create new breeds and improve the extant ones, with Australian, USA and European breeds being prominent examples ,18. As iMany of British breeds are unique in terms of phenotypic traits and adaptation to local conditions  see exa but noneLaminaria [In terms of peculiar adaptability, specific phenotypes and features of the British sheep breeds, some of them are lowland breeds , some are hill breeds , and some are upland breeds , while some others are adapted to variable conditions  . An extraminaria .Body size varies from small (Black Welsh Mountain) to medium  to large . Some breeds are horned , others are hornless , and in some others only males have horns . The Border Leicester breed has a characteristic “Roman nose”.Most breeds are used for producing lamb meat, while some (Clun Forest) are dual purpose and can be used for meat, wool and milk. There are long-woolled breeds , and others  naturally shed wool, not requiring shearing. Females of some breeds  have a slightly shorter gestation period allowing 1.5 lambs a year. Some breeds  have a low disease risk. Previously, British sheep breeds and flocks were assessed, on the base of prion protein (PrP) genotypes, for risk of scrapie, also known as prion disease and transmissible spongiform encephalopathy ,33,34,35Over several decades, there has been a general trend in the world’s animal production to substitute or cross local breeds with productive commercial stocks that may lead to genetic diversity reduction . Such anThe Rare Breeds Survival Trust names 57 British native sheep breeds, including 25 breeds on the critical list, e.g., Border Leicester, Dorset Horn, Balwen Welsh Mountain, Hill Radnor and Llanwenog . Indeed,The collection of breed data including adaptation to specific habitats facilitates development of conservation solutions. Characterization of genetic originality and authenticity of a breed via its DNA analysis is a crucial component in informed decision making to decide whether a breed (or population) is worthy or not of conservation action. Information regarding the risk status and the level of inbreeding can be further used to choose respective conservation strategies . Novel molecular and advanced genomic tools can be effectively employed to incorporate genetic variability and genomic information into conservation planning and sustainable conservation programs ,41. CharCLPG) locus causing the muscle hypertrophy phenotype [At present, there is a rich arsenal of genetic and genomic resources, tools and applications used for livestock assessment, breeding and production including, first of all, genetic profiling of diverse breeds, and search for quantitative trait loci (QTLs) and candidate genes in farm animals. These genomic advances facilitate breed improvement and understanding of the genetic processes in the course of domestication and breed evolution. For example, the Dorset breed was found to be a carrier of a single polymorphism mutation at the callipyge  in three other candidate genes for nematode resistance and body weight were examined in populations of domestic Scottish Blackface and free-living Soay ewe lambs, and a nominally significant association between an IL23R SNP and body weight was found [Genetic resistance to nematode infection is an important target of selective breeding for this trait in the UK. This was studied within a purebred Scottish Blackface flock by partial resequencing genes in the Major Histocompatibility Complex (MHC) class II region . Causal as found .TMEM8B and SPAG8 that showed picks of a signature of selection at single SNPs in four sheep breeds, the Suffolk among them [Other examples of candidate genes, for example, associated with ewe mature weight are ong them .With the advent of next generation sequencing (NGS) technologies, SNP panels and a whole genome sequence draft became available for the sheep by 2010 ,60 that These state-of-the-art resources are crucial for genetic improvement of the existing sheep flock by implementing genome-wide association studies , analysAfter marker validation, genetic or genomic selection is feasible when targeting, for example, such sheep traits as footrot resistance  and matuThere are two major collaborative sheep genomics groups, the International Sheep Genomics Consortium  and an AUse of multiple SNP markers has substantially enhanced analysis of genetic diversity and population history ,72,73,74The earliest research of SNP-based diversity in UK sheep showed genetic distinctiveness of three English native hill breeds examined at three SNP loci associated with phenotypes ,50. In aSNPs have also become genetic markers of choice in searching for QTLs and conducting GWAS in sheep  and selection signatures in six commercial European meat breeds including the Suffolk sheep (of Irish population) . The SufFurther development of NGS platforms and reduction of their cost make it possible to implement whole genome sequencing for numbers of individuals within one or more species. Whole genome sequences seem to provide ultimate evaluation of genetic variability and candidate mutations that can be further used for genome-wide association studies, sequence genotype imputation and genomic prediction improvement as a component of genomic selection ,66.Using whole genome sequences of 21 Chinese native sheep breeds, Yang et al.  identifiNaval-Sanchez et al.  sequenceOn the basis of whole genome sequences for 145 wild and domestic sheep and goat samples, Alberto et al.  found seUsing SNP and whole genome sequence data for a large worldwide sample collection of wild and domestic animals, Chen et al.  found thThe ongoing Australia- and New Zeeland-based project SheepGenomesDB is aimed at sequencing 453 (Run1) and 935 (Run2) animals, which is supposed to cover a global sheep breed diversity in order to identify causative mutations and facilitate genomic selection ,66.To date, there has not been launched an overall, comprehensive genetic/genomic survey of the British sheep gene pool. Certain British native breeds have been part of either diversity studies using different molecular markers or whole genome sequences and often a limited number of samples per breed. For example, using the Illumina HiSeq 2000 platform, three individual animals, each representing one Welsh sheep breed , were sequenced in order to explore their demographic history .Recently, more whole genome sequences were generated for separate British breeds. For instance, the Illumina whole genome sequences for seven Cambridge sheep and one British du Cher individual were obtained in a comparative study including also the Romanov and two Iranian breeds . The CamWhole genome sequences of 17 Poll Dorset sheep was compared to those from two Tibetan breeds . SelectiPDGFD, XYLB, TSHR, and SGCZ, with PDGFD (platelet derived growth factor D) being a specially remarkable candidate for fat deposition in tail.There are also international projects that have generated whole genome sequences for British and non-British breeds and can be served as data sources for further studies ,89,93,94The UK sheep landscape is characterized by abundant breed history and genetic diversity, suggesting its preservation for sustainable use and advanced research. The latter can rely upon state-of-the-art molecular and genomic tools including SNPs and whole genome sequencing. This opens further opportunities to elucidate breed ancestry, variability, and genetic merit of particular markers and candidate genes for adaptation, genomic selection and production improvement. Further research is anticipated to understand comprehensively how genomic landscape of the British sheep has contributed to the fact that over the centuries they have thrived to current numbers and value, delighting our eyes on the green landscapes of today’s Great Britain.