By structuring farmers’ informal networks of seed exchange, kinship systems play a key role in the dynamics of crop genetic diversity in smallholder farming systems. However, because many crop diseases are propagated through infected germplasm, local seed systems can also facilitate the dissemination of seedborne pathogens. Here, we investigate how the interplay of kinship systems and local networks of germplasm exchange influences the metapopulation dynamics of viruses responsible for the cassava mosaic disease (CMD), a major threat to food security in Africa. Combining anthropological, genetic and plant epidemiological data, we analyzed the genetic structure of local populations of the African cassava mosaic virus (ACMV), one of the main causal agents of CMD. Results reveal contrasted patterns of viral diversity in patrilineal and matrilineal communities, consistent with local modes of seed exchange. Our results demonstrate that plant virus ecosystems have also a cultural component and that social factors that shape regional seed exchange networks influence the genetic structure of plant virus populations. This study combines ethnobotanical and epidemiological data to understand how social networks of seed exchange influence the genetic structure of the African cassava mosaic virus in Gabon. Results reveal contrasted patterns of viral diversity in patrilineal and matrilineal communities, consistent with cultural differences in modes of seed exchange. Informal seed systems account for the majority of crop germplasm planted by smallholder farmers3. Exchanging “seeds” (here understood as propagules sensu lato) allows farmers to acquire new varieties, recover lost types or compensate for seed shortage4. By maintaining large portfolios of crop varieties, farmers accommodate different cultural needs or preferences and buffer the effects of unpredictable climatic or epidemiological shocks5. However, seed exchange networks can also make smallholder farming systems more vulnerable if they facilitate the spread of seedborne plant diseases.Every year, 10–40% of the global crop harvest is lost to plant pathogens7.Few studies have explored in detail the importance of seed exchanges on endemic propagation of plant pathogens through landrace populations. Understanding how social networks of seed exchanges influence the population dynamics of plant diseases is key to designing effective disease management programs, which increasingly rely upon community-based approaches to curb the spread of crop diseases12. Kinship systems are cultural representations of relationships between individuals based on the notion of clan membership. By defining rules of descent and incest prohibitions, kinship systems structure matrimonial networks between communities and normalize social interactions between kin (related by descent) and affine (related by marriage).In smallholder farming communities, kinship systems play an important role in promoting seed exchanges between villages11. Gabon is characterized by a strong cultural geographical contrast, with matrilineal societies occupying the southern side of the Ogooué River while patrilineal societies are predominant on the northern side , marriages play an important role in regulating exchanges of cassava varieties between smallholder farmer communities11. Here, we investigate whether this southern-matrilineal/northern-patrilineal contrast also influences the spatial structure of viruses responsible for the cassava mosaic disease (CMD), a major pandemic that threatens regional food security in Africa.Cassava diversity in Gabon exhibits a strong phylogeographic structure, with high varietal diversity in the south and low diversity in the north, resulting from the strong matrilineal/patrilineal geographic divide that has contributed to maintaining regional patterns of genetic diversity that mirror the geographic distribution of patrilineal and matrilineal societiesBegomovirus (family Geminiviridae), seven of which are endemic to Africa13. Cassava mosaic geminiviruses (CMGs) are naturally transmitted by whiteflies (Bemisia tabaci Gennadius [Aleyrodidae: Hemiptera]), which play a key role in the epidemiology of CMD14, and through infected stem cuttings, which contribute to maintaining high prevalence of CMD15. Since the early 1990s, a severe form of CMD that originated in East Africa from a synergistic interaction between the African cassava mosaic virus (ACMV) and Uganda strain of East African cassava mosaic virus (EACMV-UG) has been steadily expanding towards Central and West Africa16. Today, CMD is considered one of the most damaging plant diseases in the world15. Cultivars resistant to CMD have been widely deployed in an effort to contain the pandemic but limited knowledge about the role of local seed systems in the circulation of germplasm infected with CMD and the rate of adoption of disease-resistant cultivars has been a major barrier to their success17.CMD is caused by a complex of viruses of the genus −3 to 10−5 substitutions/site/year, CMGs evolve at molecular rates comparable to that of RNA viruses18. Because of this rapid evolutionary rate, it is possible to investigate factors that impact the dynamics of CMGs transmission by analyzing the shape of viral phylogenies20.With DNA substitution rates in the order of 1021. Unlike other CMG species, in which interspecific recombination and pseudo-recombination (the reassortment of heterologous genome components) is frequent24, the ACMV genome presents little evidence of recombination15 , making it easier to infer movements of viral lineages between communities of farmers using phylodynamic methods.ACMV is an excellent model for studying how seed exchange networks influence CMG diversity in cassava landrace populations. ACMV is omnipresent in sub-Saharan Africa where cassava is cultivatedHere, we extend the phylodynamic inference framework to include social factors that shape regional networks of seed exchange and demonstrate how social rules that control seed movements within and between farmer communities influence functional connectivity between local populations of ACMV in Gabon.Viral DNA was recovered from dried cassava leaves collected between 2004 and 2015 across 11 villages chosen to represent contrasted situations in terms of social structure, ethnolinguistic diversity, accessibility, and degrees of insertion with local/regional markets . BAPS identified four plant clusters: (i) a southern cluster, predominantly associated with matrilineal villages; (ii) a northern cluster, predominantly associated with patrilineal villages, and (iii) an eastern cluster, predominant in IMB, MOP and ODJ, with a fourth separate cluster mostly associated with CCB . Analysis of the viral phylogeny revealed 29 phylotypes and 20 “singleton” haplotypes that were not assigned to any cluster  was strongly correlated with cassava varietal diversity   but not with precipitation seasonality .To estimate the diversity of local viral assemblages, we used phylogenetic clustering methods to measure the dispersion of viral isolates across the phylogenetic tree and identify clusters of viral sequences that share a common evolutionary history , where 33% of viral isolates did not relate to any phylotype (singletons), and the least diverse in Mbong-Ete , where 80% of isolates clustered within a single phylotype (P20)  and 1D , but not for 2D , which emphasizes abundant types  compared to 80% (SD = 8.3%) in matrilineal communities . The most diverse assemblage was observed in Nombedouma  are comparatively less common in patrilineal societies where farmers, unless single, divorced or widowed, rely mostly on the varieties gifted to them by their mother-in-law , viral diversity evolves primarily through the build-up of mutations from local founders maintained in the population as the same landraces are passed down generations, resulting in higher genetic relatedness among viral haplotypes. This was particularly well illustrated in Mbong-Ete, where 75% of the farmers interviewed had received their manioc cuttings from their mother-in-law. Mbong-Ete was revisited in 2015 and 20 farmers were interviewed, including 11 farmers whose farms were surveyed in 2006. While cultural attachment to heirloom landraces remains very strong, field surveys showed that varietal diversity almost quintupled at the village level in the nine-year interval (from 3 to 14 cassava varieties). However, half of the new varieties were private (grown by only one farmer), and on average farmers’ portfolio of cassava varieties increased from three to four varieties only , but besides anecdotal evidence data on densities of whitefly populations are not available for Gabon. Seasonal variations of temperature and rainfall greatly influence cassava growth and have been associated with changes in the abundance of whiteflies and in the incidence of ACMV33. In Gabon, both temperature and precipitation seasonality show greater variability in the southern part of the country than in the north . Dramatic increase in whitefly population density has been shown to drive the epidemic front of severe CMD pandemics across East and Central Africa36. Geographic isolation has important implications for farmers’ access to seeds and exposure to plant pathogens. With better transportation links, villages may be more exposed to movements of infected germplasm compared to more secluded communities that rely mostly on a smaller set of local varieties. However, local patterns of viral diversity were not concordant with either the size or geographic accessibility of villages. Nombedouma, where we recorded the highest viral diversity  in villages bordering Cameroon  and the lowest (2%) in Cocobeach (CCB), near the border with Equatorial Guinea where the virus had just been reported40.When asked what triggered them to solicit cassava cuttings from outside their village, farmers replied it was the increasing incidence in their fields of a severe form of CMD, to which “Adzoro”, one of the three staple cassava varieties in the region, seems particularly susceptible. Mixed infection is an important feature of the severe form of CMDWhile open seed exchange between matrilineal villages may have facilitated the spread of EACMV-UG across southern Gabon, prevalent endogamy  and the predominance of affinal transmission in patrilineal societies could have, in contrast, contributed to the delay of the arrival of EACMV-UG in northern Gabon by limiting exchanges with other communities. A nonmetric multidimensional scaling (NMDS) analysis of the phylogenetic composition of viral assemblages revealed that while local ACMV populations were generally distinct, irrespective of the geographic distance separating villages where they were collected, in northern Gabon viral populations almost overlapped  that play key administrative roles in controlling the flow of people and goods across the borders with Equatorial Guinea and Cameroon. Despite active cross-border trade exchanges that could encourage the circulation of viral variants, however, ACMV diversity was low in Cocobeach and Minvoul compared to matrilineal villages, where even small communities showed higher levels of phylogenetic diversity. The lack of correlation between accessibility and viral diversity is further evidence that kinship systems and rules that control seed movements within and between farmer communities influence local landrace populations’ exposure to new ACMV variants. It highlights, in particular, the role of affinal transmission in limiting the inflow of plant pathogens in patrilineal villages.42. Our results suggest that plant virus ecosystems have also a cultural component, and that social factors—in particular kinship systems and social networks of seed exchange—also influence the spatial structure of plant pathogens.The structure and dynamics of plant virus diversity are shaped by the modes of transmission of the disease, the host’s level of resistance or tolerance to the virus, and the ecology, population structure, and genetic diversity of the host plant43 whose transmission over long distances is primarily borne by infected propagules44. Originally confined to East Africa, the CBSD pandemic has been expanding rapidly since 2004, causing repeated crop failures and severe food shortages across East Africa43. An expansion of the CBSD epidemics to Central and West Africa would have dramatic economic and socio-political consequences for Africa (43). In Tanzania, efforts to control the CBSD outbreak through community phytosanitation, which is focused on replacing local landraces with virus-free clones in areas severely affected by the disease7, were partly compromised after farmers re-introduced heirloom varieties using infected propagules obtained through informal seed exchange networks6.In this study we focused on the intraspecific diversity of viral communities as a proof of concept, using ACMV as a model. Although a similar approach may be difficult to apply to other CMGs, the ubiquity of ACMV in Africa makes it an interesting proxy for monitoring viral movements between communities of farmers. Ultimately, it could be used to anticipate the geographic spread of emerging diseases such as the cassava brown streak disease (CBSD), another devastating disease45, but for disease management programs to benefit local development in the long term, we need to recognize the important social role played by seed exchanges in smallholder farming communities47. Formally testing the role of seed exchange networks in the epidemiology of CMD will require additional data collection and developing spatially explicit phylodynamic models to disentangle the effects of social factors from other environmental parameters. However, we believe that the notion of social epidemiology, which investigates the influence of social factors on the distribution of diseases in human populations, should be extended to seedborne pathogens in cultivated plants, whose transmission depends also on cultural factors that govern social interactions between farmer communities and determine connectivity between populations of the host plant.Community-based approaches focused on promoting virus-free clones from local landraces can be an effective strategy to offset the detrimental effects of the virus build-up in clonally propagated landrace populations11 was reanalyzed for the presence of cassava mosaic geminiviruses using diagnostic PCR. One village (MBG) was revisited and three additional villages were surveyed in 2014 and 2015 to study the evolution of cassava diversity and CMD prevalence in northern Gabon in the nine-year interval  and landraces (GiL) in R 3.5.148 using the package ineq49 . In each village, ~100 plants  were screened for the presence of ACMV, EACMV and East African cassava mosaic Cameroon virus (EACMCV) in single and mixed infection using a multiplex-PCR assay50. Samples for which PCRs revealed single infection by ACMV were selected for sequencing. Samples with mixed infection were additionally screened for the presence of EACMV-UG using specific primers22.Total DNA was extracted from 20 mg of dried leaves using DNeasy36. Details protocols for all PCR assays are provided in Supplementary Data 51 and alignments were refined manually. Tests performed with RDP452 showed no evidence of recombination.To characterize ACMV diversity, a set of degenerate primers was used that amplifies ~528 bp of the replication-associated protein (Rep) open reading frame (ORF) AC153 was used to build a maximum-likelihood (ML) tree from the 2006–2007 and 2014–2015 datasets combined under the GTR + CAT model. Branch support was estimated using nonparametric approximate likelihood-ratio tests (aLRT SH-like)54. TempEst 1.555 was used to test for the presence of a temporal signal in the molecular phylogeny by performing regression analyses of genetic divergence between viral sequences against sampling dates.FastTree 2.1.956 was used to measure the dispersion of haplotypes across the tree and identify clusters of viral sequences that share a common evolutionary history . Clusters were assigned with branch support (aLRT) >70% and maximum pairwise genetic distance between taxa ≤4.5% to minimize the number of singletons, i.e., DNA sequences not assigned to any cluster. Viral diversity was then evaluated using three measures of Hill numbers qD with q = 0 (species richness), q = 1  and q = 2 (inverse Simpson index). Sample-size- and coverage-based rarefaction and extrapolation curves57 were generated using the R package iNEXT58. Extrapolated data was calculated up to a base sample double the size of the smallest reference sample and 95% confidence intervals were derived from 100 bootstrap replicates.To evaluate the diversity of viral communities, Cluster Picker 1.359 was used to perform Tukey-like contrast tests based on resampling (1000 iterations). Unlike ANOVA, which assumes normality and homoscedasticity, this method does not make any distributional assumptions but accounts for correlations among variables and the distributional characteristics of the data. Following Pallman et al.29, p-values were adjusted for multiple comparisons across groups and across diversity measures for integral Hill numbers of orders 0 ≤ q ≤ 2. To compare the composition of viral assemblages across regional clusters, the R package vegan60 was used to perform a nonmetric multidimensional scaling analysis (NMDS) using Bray–Curtis dissimilarities.To compare diversity measures between villages clustered by kinship, the mcpHill function implemented in the R package simboot61 to analyze topological properties of local networks of viral haplotypes. Although less accurate than character-based approaches, distance-based methods are useful to study intraspecific genetic variation in small viral populations, in which ancestral haplotypes often coexist along with their descendants, resulting in polytomies which violate assumptions of phylogenetic reconstruction methods62. Networks based on genetic similarity between viral isolates are an intuitive approach to derive information on the local dynamics of seedborne pathogens. Relationships among viral haplotypes can be represented as undirected networks in which nodes (haplotypes) are connected by edges whose length corresponds to the shortest genetic distance between nodes.At the village level, statistical parsimony networks were constructed using the R script TempNet64 was used to explore the population genetic structure of ACMV in Gabon. For the genetic mixture analysis, five independent runs were performed using the “spatial clustering of individuals” option with an upper limit of 15 for the maximum number of clusters (K). Population admixture analysis was performed using the “admixture based on mixture clustering” module, with a minimum population size of two, 100 iterations to calculate the admixture coefficient for individuals and 200 reference individuals from each population. Twenty iterations were used to calculate the admixture coefficient for the reference individuals. For comparison, BAPS was also used to explore patterns of genetic diversity in cassava host plants using multilocus genotypic data from six nuclear microsatellite markers  were obtained from WorldClim v2.166. Vegetation cover data was obtained from Mayaux et al.67. The effect of environmental parameters on the prevalence and diversity of ACMV was tested using Pearson’s correlation coefficient r.To test whether ACMV population structure is influenced by environmental factors, elevation data and climatic rasters for temperature seasonality [BIO4 (standard deviation × 100)], total annual rainfall [BIO12] and precipitation seasonality [BIO15 (coefficient of variation)] at 30 s resolution (~1 kmFurther information on research design is available in the Supplementary Information NewSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting SummaryPeer Review FileDescription of Additional Supplementary Files


Research over the past two decades has identified a group of common genetic variants explaining a portion of variance in native language ability. The present study investigates whether the same group of genetic variants are associated with different languages and languages learned at different times in life. We recruited 940 young adults who spoke from childhood Chinese and English as their first (native) (L1) and second (L2) language, respectively, who were learners of a new, third (L3) language. For the variants examined, we found a general decrease of contribution of genes to language functions from native to foreign (L2 and L3) languages, with variance in foreign languages explained largely by non-genetic factors such as musical training and motivation. Furthermore, genetic variants that were found to contribute to traits specific to Chinese and English respectively exerted the strongest effects on L1 and L2. These results seem to speak against the hypothesis of a language- and time-universal genetic core of linguistic functions. Instead, they provide preliminary evidence that genetic contribution to language may depend at least partly on the intricate language-specific features. Future research including a larger sample size, more languages and more genetic variants is required to further explore these hypotheses. In the two decades since the first studies concerning FOXP2 and apraxia of speech were made2, a series of studies  have identified new genes that explained a small portion of variation in spoken and written language functions and disorders6. These latter studies often focused on common genetic variants and their associations with language-related traits . Though the effect sizes are small, the study of common variants offers an important opportunity to investigate variation of language functions on a continuum. Subtle differences in language functions  are more likely to be associated with primary language impairment and variations in success in acquiring foreign languages. These subtle differences differ from severe forms of speech and language impairment  that are more likely to be associated with rarer genetic mutations . The focus of the present study is on common variants and subtle differences in language.Even before the publication of the first studies on the molecular genetics of a speech disorder10 and to identifying more new genes, we argue that the genetic studies of language should consider two additional questions concerning variation on a continuum. First, what can the genetics of language inform us about how languages are learned? Second, if an ultimate translational goal of the study of genetics of language is to develop a screening tool for primary language impairment, how can it be used for the more than 7000 languages that are currently spoken and languages that are learned at different times in life?In addition to investigating the molecular pathways that give rise to the neurological functions of genes associated with language functions and disorders12 and foreign14 language learning, a large degree of individual variability in learning success has been observed . Many factors have been attributed to individual variability, including socioeconomic background for native languages16, and memory17, music experience20 and subtle neuroanatomical differences21 for foreign languages. At the lower end of individual variability is primary language impairment, which includes Developmental Language Disorder (formerly known as Specific Language Impairment) and dyslexia, which concerns impairment of language in the written modality. The vast majority of the studies were conducted to examine individual variability in native language, and more specifically English and other European languages as a native language.In both native22. If that is the case, the genes that explain individual differences in native language would not be the same as those in foreign languages. In fact, it may be the case that the genetic effects on native language would be larger than the effects on foreign languages. A better understanding would address long-standing debates in language learning about whether the learning of native and foreign languages is fundamentally different23. As far as we are aware, with the exception of Waye et al.24, who examined Chinese and English literacy in bilingual children and one gene, no genetic studies of language have yet investigated foreign language learning. Rimfeld et al.25 examined the genetic contributions to foreign language learning using a twins sample and did not examine the molecule genetics of such contributions.To obtain a more comprehensive understanding of the genetic basis of language, research must consider not only genetic associations with native language on a continuum of proficiency level, but also foreign languages learned at different time points in life. Such an understanding would give us a clearer idea of whether the genetic effects on language functions are subject to developmental and learning factors. Languages that are learned later in life may require a different set of cognitive resources than languages learned in infancy, which may have been contributory sources of individual variability in L2 attainment26. The genes that have been attributed to language could be those that subserve language functions independent of language features  or functions that are specific to a linguistic feature . A real-world implication for understanding the language universal or specific nature of genetic association concerns whether the same genetic diagnosis of language impairment can be made only for a specific language or for any language. In recent years, genetic research has been extended to the examination of non-European languages such as Chinese28. However, with the notable exception of the work of Waye et al.24, these studies of non-European languages did not examine the genetic associations of two languages within the same population. This makes it difficult to tease apart language and population specific effects, because these two factors often co-vary.A more comprehensive understanding of the genetic basis of language must also investigate languages other than European languages. More than 7000 languages are spoken worldwide29 for a review), we asked whether the same genetic variants collectively demonstrate an extended effect on language ability that is measured in early adulthood in speakers of Chinese. To answer this question, we surveyed the literature on the genetic basis of language and identified a group of 28 genetic variants , would exert similar effects on a foreign, second language (L2) that was learned since early childhood with a relatively high proficiency. Foreign language proficiency was measured by the English subject test of the same college entrance examination in Hong Kong from the same group of participants. Third, we investigated whether the same genetic variants contribute to the learning of a new, third language (L3) in adulthood. We used the same group of participants, namely students at college-level modern language courses whose L3 ability was measured comprehensively by a composite series of classroom and laboratory tests . This group of genetic variants would contribute to the learning of L1, L2 and L3. Alternatively, we argue that different languages and languages learned at different times have different genetic underpinnings. As different language features are associated with different brain functions 57 which resulted in fewer participants than the entire set , family SES , two SNPs of DCDC2  , and one SNP of DRD2 (rs1800497)  were significantly predicting L1 proficiency , family SES , two SNPs of FOXP2   and one SNP of CATNAP2 (rs2538991)  were significant predictors of L2 proficiency  and S100B (rs9722)  were significant predictors of L3 proficiency (Table Stepwise procedure in both directions was implemented to determine which hypothesized SNPs (if any) significantly explained the variation in language proficiency. In the first step, all 28 hypothesized SNPs were included as predictors of language proficiency, along with non-genetic variables. The final model had the best combination of independent variables for predicting the language proficiency. Gender  = 0.011, the robust Comparative Fit Index (CFI) = 1.000, the robust Tucker-Lewis Index (TLI) = 1.040, and the Yuan–Bentler scaling correction factor = 1.024. Table DRD2 (rs1800497) (standardized path coefficient 0.110), but negatively associated with Family SES (standardized path coefficient − 0.122) and DCDC2 (rs6940827) (standardized path coefficient − 0.123). L2 proficiency was positively associated with L1 proficiency (standardized path coefficient 0.253), Family SES (standardized path coefficient 0.256), music (standardized path coefficient 0.150), CNTNAP2 (rs2538991) (standardized path coefficient 0.094), but negatively associate with FOXP2 (rs1852469) (standardized path coefficient − 0.169). L3 proficiency was positively associated with L2 proficiency (standardized path coefficient 0.295), internal motivation (standardized path coefficient 0.243), S100B (rs9722) (standardized path coefficient 0.112), and DCDC2 (rs6456593) (standardized path coefficient 0.106). Generally speaking, the SEM results converged with the stepwise regression results, even when proficiency levels for all three languages were considered together.The stepwise regression approach reported above provided information about which ones of the 28 hypothesized genetic variants as well as non-genetic factors contributed to each language individually. In order to examine the contribution of genetic and non-genetic factors simultaneously for the three languages, we used SEMWe found little overlap in the genetic associations among the three languages that our participants learned at different times in life. This pattern of results can be seen when the three languages were examined individually or simultaneously. Instead, we found that different common genetic variants contribute to explaining variance of the three languages. The effects of genes on language seem to be language specific and are stronger for native than foreign languages. By contrast, the effects of non-genetic factors seem to be stronger for foreign than native languages.DCDC2 and DRD2. Importantly, the significant DCDC2 variants were those found in other studies of Chinese, including rs645659334, and rs694082735, each contributing to about 1% of the variance in L1. DRD2 (rs1800497) was found to contribute significantly to about 1.6% of variance in our study. In a previous study, the same variant was found to explain variance in bilingual proficiency52, which confirmed the results of a previous artificial language learning study where young adults learned a morpho-phonological grammar42. We found two different genes associated with L2, namely CNTNAP2 and FOXP2, which combined explained about 3.5% of variance. CNTNAP2 (rs2538991), which is downregulated by FOXP2, is associated with non-word repetition in English5. Non-word repetition is a predictor of language impairment in English-speaking children59. Interestingly, in Chinese, non-word repetition did not predict language impairment60. Thus, the association of CNTNAP2 (rs2538991) with English only may support the language-specific hypothesis. The specific genetic variants of FOXP2 that we found to be associated with L2 included rs6980093, which was associated with verbal fluency (naming as many words as possible in a semantic category within 60 s) in two Italian samples45, and rs1852469, which has been associated with speech sound disorders in a Chinese population44. Compared to L1, the genetic effects on L3 is much weaker. For the common variants examined, S100B (rs9722) was the only significant contributor to L3 proficiency in the stepwise regression analysis, which explained about 1% of variance. S100B are highly expressed in the hippocampus61. Its association with the learning of a new language is consistent with the role of declarative memory in early stages of language learning62. The pattern of results of the SEM converged with those of the stepwise regression, except that rs6456593 was also found to be associated with L3 but not L1. This difference does not change the preliminary conclusions of the study.We found two genes that contributed to explaining variance in L1 ability in our stepwise regression, 44 and Vernes et al.5, respectively. In both cases, the allele frequencies in our sample were different from what was reported in those studies. While the allele frequencies we found for rs1852469 was consistent with what was reported in dBSNP (https://www.ncbi.nlm.nih.gov/snp/) (A>T), the opposite was found in Zhao et al.44 (T>A), even though both samples were East Asian. For rs2538991, the allele frequencies were roughly equal for the European population that Vernes et al.5 studied, but for our sample of East Asian, the A allele was clearly the minor allele.Table 63). Only 28 SNPs were examined in the present study, and it is likely that overlapping variance with other SNPs that we did not investigate would be revealed should a GWAS study was conducted. Furthermore, because our candidate SNPs have been studied extensively in other studies, they represent those of larger effects and our replication here speaks to that. In addition to these explanations, it is important to acknowledge that smaller studies such as this one often results in overestimation of effect sizes64 and even false positives.The amount of variance explained by any single SNP was about 1 to 2% in this study, which is seemingly large when compared to those effects found in GWAS studies  and family SES16 (1.4%). For L2, the best predictor was family SES66 (5%), followed by music training20 (2.6%). For L3, the best predictor was clearly the non-genetic factor of motivation (5.4%). Again, this finding is consistent with the results of previous non-genetic studies69, which found motivation to be the best predictor of learning a new language.Taken as a whole, the results may support the hypothesis that genetic associations are strongest for a specific language. Furthermore, genetic effects seem to be strongest for native than foreign languages. For L1, the amount of variance explained by genetic factors combined (3.7%) was much stronger than that of any one of the significant non-genetic factors, including gender70. In early adulthood, this trend may lead to a negative association between family SES and L1 because of an emphasis on learning English for families of higher SES background, as learners from higher SES families are more likely to attend English-medium schools.It is worth noting that the effect of family SES on L1 is in the negative direction in our sample. This is likely a unique finding to learning L1 and L2 in Hong Kong. In a longitudinal study in school children in Hong Kong, family income only predicted L2 (English) but not L1 (Chinese) proficiency24 are the only other researchers who have examined L1 and L2 within the same population. However, only the genetic variant rs3743205 of DYX1C1 was studied. Vaughn and Hernandez52 also examined two languages but did not report association results for each language independently, focusing instead only on bilingual proficiency, a measure of the balance of two languages.An important feature of our study is that we examined the genetic associations of three languages all within a single (Han Chinese) population and investigated the contributions of a group of genes that have found to be related to language. This design allows us to more clearly study how the same group of genes are associated with different languages and languages learned at different times, without contamination by the co-varying factors of population and language. As far as we know, Waye et al.71 and the Fundamental Difference Hypothesis (FDH)72. Under LCDH, a set of identical “core languages functions” such as phonological and syntactic processes are required for the successful learning of any languages at any time in life. In terms of genetics, this implies the same set of genetic variants for native and foreign languages. FDH hypothesizes an innate language learning system that is only accessible at the earliest time in life for learning an infant’s native language. Foreign language learning lacks access to this innate system. In genetic terms, it implies a group of genetic variants that are only associated with L1.Our study contributes to the decades-long debate in language learning about whether native and foreign languages are learned primarily with the same mental mechanisms. Our two hypotheses were aligned with the Linguistic Coding Differences Hypothesis (LCDH)73 hypothesized that dopamine-related genes are linked to individual differences in language learning. Vaughn and Hernandez52 tested this hypothesis and found a significant association between the dopamine-related genes COMT (rs4680) and DRD2 (rs1800497), and individual differences in achieving balanced bilingual proficiency. Wong et al.42 who used an artificial language in laboratory conditions rather than an authentic language, found a significant association between DRD2 (rs1800497) and the learning of morphophonology. Stein et al.74 found a significant association between several SNPs of DRD2 (including rs1800497) and measures of native language but only the vocabulary measure reached statistical significance after correction for multiple comparisons. Nevertheless, the findings from these previous studies are consistent with those of the present study. The dopamine hypothesis concerns a language universal mechanism. Future research will need to explore why the present study only found a significant association with native language.Wong et al.75 and Becker et al.76 for examples of studies of European samples) who may have different, subtle genetic differences which may not occur in such a restricted sample. Fourth, we did not collect data on participants’ time on L3, which may explain some of the variance in L3 proficiency.Our study has several limitations. First, although the genetic variants we examined were those that have been reported (and sometimes replicated) in research studies during the past two decades and are the most promising candidates for language, many more potential genetic variants remain to be examined. It is very likely that those genetic variants may show an overlap across three languages. But based on the best available information we have about genes and language, we designed our study and found interpretable findings to confirm one of the two hypotheses. A GWAS with a very large sample size is needed in the future. Second, although we have found differences in genetic associations across languages, it is still unclear whether they occur because of language features or because they are languages learned at different points in life. Our evidence provides support for both explanations. A much larger-scale study with a much larger sample size in the future would control for the different grouping of languages and when they are learned, which would allow for a more precise delineation of these two factors. Third, only Han Chinese participants were studied. Future research will need to sample different populations . L3 is most strongly related to the motivation of the learners who learn the new language. Our results did not lend support to the hypothesis that a common set of genetic factors contribute to all language learning. It is likely that language learning at different times in life requires different processing demandsMean = 19.98, SD = 1.28) for our study through mass emails and advertisements in their language classes, after obtaining permission from the class teachers. Written informed consent was obtained from all participants. The research protocol was approved by the Joint Chinese University of Hong Kong—New Territories East Cluster Clinical Research Ethics Committee and the research was performed in accordance with the Declaration of Helsinki. All participants were native speakers of Cantonese of Han Chinese descent without any self-reported neurological or psychiatric disorders. They all scored within normal limits (at least 85) of the nonverbal intelligence measured by the Test of Nonverbal Intelligence (4th Ed)78 and passed the hearing screening at the frequencies of 500, 1 k, 2 k and 4 k Hz at 30 dBH. All learned English as L2, and French, German, or Spanish as L3. Because these participants enrolled in this study over a 4-year period, not all variables were collected from every participant. Some data was also missed due to fatigue, coding errors and genotyping failures. Table We recruited a total of 940 participants  between 18 to 25 years of age  Learner Questionnaire80 to indicate their internal motivation, external motivation, anxiety, and attitudes to learning the L3. A data reduction process was used to derive four metrics related to this questionnaire , and musical experiences. Family SES was determined by following the Hollingshead indexaire see .81. For both Chinese and English, the composite scores were calculated using subtests on reading, writing, speaking, and listening skills on a scale from 1 (lowest) to 7 (highest).The L1 and L2 proficiency of participants were measured by the composite scores of each of the Chinese and English language subjects of the Hong Kong Diploma of Secondary Education Examination (HKDSE), the public examination for university entrance in Hong Kong, administered by the Hong Kong Examinations and Assessment Authority (HKEAA). HKDSE implements an annual calibration exercise to ensure that scores across years reflect the same levels of performanceFrog, Where Are You?” story82 was transcribed, morphosynatically tagged, and analyzed using the CLAN program of the TalkBank project83. Second, the pronunciation of speech production was assessed by native speakers based on excerpts from the storytelling sample. Third, lexical access was calculated by using the accuracy rates of a picture naming task. Classroom-based measures were participants’ z-transformed exam scores of the L3 class. The final L3 proficiency index, known as the L3 Global score, was calculated by using the Principal Component Analysis based on these measures. Details regarding to data collection, analysis, and reduction procedures for L3 proficiency are given in SI Materials and Methods.To obtain an overall measure of L3 proficiency, we collected laboratory-based and classroom-based data which covered reading, writing, speaking, and listening abilities for each third language, similar to L1 and L2. Laboratory-based measures included three types of data. First, a sample of passages read aloud from the “https://www.ncbi.nlm.nih.gov/snp/) for East Asians.Saliva samples were collected using Oragene (DNA Genotek) and used to extract the genomic DNA of participants. A NanoDrop Spectrophotometer was used to quantify Extracted DNA samples, and was normalized to 5 ng/μl for use in genotyping. A commercially available Sequenom MassARRAY platform was used to genotype the SNPs. Table FOXP2 were included so far as they were common variants and were associated with speech44. We conducted a literature search for studies that had investigated individual differences in typical language functions or language impairment. For genetic variants associated with language impairment, we only considered language impairment as a primary condition , excluding studies of autism, intellectual disability, and other neurodevelopmental disorders where language impairment of any modality is a secondary condition90. We also excluded studies that examined rare deletions7, along with studies of genetic variants that are linked to stuttering without other traits related to abstract linguistic structures91. We only included variants of CNTNAP2 that have been associated with primary language conditions5. CNTNAP2 has been associated with language functions in Autism Spectrum Disorder (ASD) in children of European backgrounds92. In Chinese children with ASD, there are conflicting findings regarding the role of CNTNAP2 polymorphisms94. Given these uncertainties, SNPs that were associated with language in ASD but not language as a primary condition were excluded. We also excluded SNPs due to linkage disequilibrium with other SNPs in the study. Linkage disequilibrium (LD) among the SNPs on the same chromosome was calculated using snpStats95 package of R96 . We began our analysis with stepwise regression. For each language of a stepwise regression model, we used the 28 SNPs as predictors, and used family SES, gender, and musical training as non-genetic predictors. For L3, we also analyzed the data with motivation measures as additional predictors. Standard linear additive SNP encoding was used to code the alleles. The major alleles were given a value of 2, the heterozygous alleles a value of 1, and the minor alleles a value of 0. Thus, a positive statistical relationship between SNP and language means a higher load of the major alleles for better language.97 of R96 to remove and add predictors based on their improvement to the Akaike information criterion (AIC). Final models of stepwise regression included all predictors that showed improvement to the AIC. Statistical significance of each variable was also indicated by the false discovery rate (FDR) corrected p values, which were calculated using the Benjamini–Hochberg method.We included all 28 SNPs and non-genetic variables  in stepwise regression models for L1, L2, and L3 separately. Stepwise regression is a method of fitting regression models in which the choice of predictive variables is made by an automatic procedure. The final model had the best combination of independent variables for predicting the dependent variables. For all models, stepwise procedure in both directions was implemented via MASS package98 of R96. Demographic characteristics, including gender, music training, and family SES, and genetic variants that were associated with each language separately from stepwise regression models were considered independent variables in the data analysis. Proficiency in each language was treated as a latent variable. In the metamodels, we hypothesized that both non-genetic  and genetic variables had effects on proficiency of each language  to account for missing data and robust SEs accounting for non-normality. The goodness of fit for the tested model was established by the following indices: (i) χ2 test with an estimated significance level P ≥ 0.05, (ii) χ2/df < 2, (iii) robust root mean square error of approximation (robust RMSEA) < 0.05 and an upper limit of the 95% confidence interval (CI) for robust RMSEA < 0.08, (iii) robust comparative fit index (robust CFI) and robust Tucker–Lewis Index (robust TLI) with values ≥ 0.90, and (iv) standardized root mean square residual (SRMR) with a value lower than 0.10. We reported both unstandardized and standardized path coefficients  using the lavaan packageage Fig. . For L3,Supplementary Information.


GBSSI) is responsible for Waxy gene encoding the, which is involved in the amylose synthesis step of starch biosynthesis. We investigated the genotypic and haplotypic variations of GBSSI (Os06g0133000) gene, including its evolutionary relatedness in the nucleotide sequence level using single-nucleotide polymorphisms (SNPs), indels, and structural variations (SVs) from 475 Korean World Rice Collection (KRICE_CORE), which comprised 54 wild rice and 421 cultivated represented by 6 ecotypes  or in another way by 3 varietal types . The results revealed that 27 of 59 haplotypes indicated a total of 12 functional SNPs (fSNPs), identifying 9 novel fSNPs. According to the identified novel fSNPs, we classified the entire rice collection into three groups: cultivated, wild, and mixed (cultivated and wild) rice. Five novel fSNPs were localized in wild rice: four G/A fSNPs in exons 2, 9, and 12 and one T/C fSNP in exon 13. We also identified the three previously reported fSNPs, namely, a G/A fSNP (exon 4), an A/C fSNP (exon 6), and a C/T fSNP (exon 10), which were observed only in cultivated rice, whereas an A/G fSNP (exon 4) was observed exclusively in wild rice. All-against-all comparison of four varietal types or six ecotypes of cultivated rice with wild rice showed that the GBSSI diversity was higher only in wild rice (π = 0.0056). The diversity reduction in cultivated rice can be useful to encompass the origin of this gene GBSSI during its evolution. Significant deviations of positive  and negative  Tajima's D values from a neutral model can be informative about the selective sweeps of GBSSI genome insights. Despite the estimation of the differences in population structure and principal component analysis (PCA) between wild and subdivided cultivated subgroups, an inbreeding effect was quantified by FST statistic, signifying the genetic relatedness of GBSSI. Our findings of a novel wild fSNPS can be applicable for future breeding of waxy rice varieties. Furthermore, the signatures of selective sweep can also be of informative into further deeper insights during domestication.Granule-bound starch synthase I ( Starch, which serves as a reserve carbohydrate in plants, is a major food component for humans worldwide. It exists in the form of granules, which are composed almost entirely of two major polysaccharides: amylose and amylopectin Bertoft, . Eating GBSSI), which is encoded by the waxy gene  and is also found in the tropical japonica and indica varieties, which are classified as South and Southeast Asian varieties  and Wxb (12–19% AC in japonica rice) in rice  provided new insights of a unique phenotype into rice quality improvement  to detect the functional alleles GBSSI, its genetic diversity, population differentiation, and evolutionary relationship among the classified rice groups. Our results will contribute to rice breeding scientists, an understanding of the domestication signatures of waxy alleles related to their haplotypes as well as their distribution patterns among the classified populations, which in turn promotes the breeding program of new waxy rice cultivars.Much research has been performed on rice evolution over its long history Khush, . VariousGBSSI employing genetic linkage mapping have been carried out annually around the world. Nevertheless, the different alleles of GBSSI and their functional responses are different due to their domesticated ancestors in the group of rice accessions. In this study, we used 475 Korean rice accessions (KRICE_CORE) for the genetic identification of GBSSI in terms of diversity and relatedness among and between the classified subgroups as well as performing additional validation of functional waxy alleles. A series of analyses were conducted as follows.Many functional studies of A heuristic set of 421 rice accessions, which were represented by 3 original variety types  , previouFor these 421 Asian cultivated and 54 wild rice accessions, field experiments were conducted in the departmental field of the Plant Resources Department, Kongju National University (Yesan Campus), in 2016 and 2017. Within these three originally cultivated varietal types, there were 6 ecotypes, including 279 temperate japonica, 26 tropical japonica, 102 indica, 9 aus, 2 aromatic, and 3 admixed varieties . RecommeBankIt2477403: MZ514142–MZ514616). The decoded sequences were saved in the FastQ file format. VCFtools  version 0.1.15  were collected ~15 days after the transplantation from all tested plants. DNA extraction was performed by the cetyltrimethylammonium bromide (CTAB) method, and the genomic DNA was stored in a refrigerator at 4°C until use  were inferred using average Q-values by the POPHELPER version 2.3.1  displaying as a dimensional scale in RStudio version 1.4.1106. The list of principal components (PC1 and PC2) referring to variants was generated in TASSEL 5 version 20210408  were incorporated by using a Python script (structure.py) with fastStructure . Using VCFtools version 0.1.15, variant files were selected for the GBSSI gene region for the classified representative cultivated ecotypes/subpopulations to be compared. The sliding window sizes used for nucleotide diversity (π) and Tajima's D calculations were the same, at 1.5 kb, and the values were compared among the classified rice groups based on their varietal types or ecotypes.To determine the degree of polymorphism within the pairwise comparison of classified populations, we investigated their diversity by nucleotide diversity values (π). We calculated Tajima's GBSSI gene region. VCFtools were used to specify the selected gene regions of the tested samples, and an alignment with the reference gene sequences adapted from RAP-DB (https://rapdb.dna.affrc.go.jp/index.html) was performed in the Molecular Evolutionary Genetics Analysis (MEGAX version 10.1.8) , Tajima's GBSSI was 1,765,622.,…,1,770,653 on chromosome 6. Within this GBSSI gene region, we performed variant calling to identify genetic variants and classified the results as SNPs, InDels, structural variations (SVs), and different variations (DVs) (Oryza nivara and O. rufipogon (which are the most commonly used wild ancestors), from the remaining groups. We generated a complete list of the genetic variant types according to the subpopulations to which they belonged, including this group of wild rice. The total variant number was higher in the wild group than in the cultivated group. Within the cultivated subpopulation, indica rice harbored the most SNP variants (35), followed by admixture (27) and aromatic (21) rice.The genomic region of ns (DVs) . We counGBSSI (Os06g0133000), we conducted a haplotype analysis , we verified a total of 12 functional SNPs (fSNPs) located in exons 2, 4, 6, 9, 10, 12, and 13 among 475 diverse rice accession. For further insights, we specified three main haplotype groups, cultivated rice, wild rice (W), and their combination (cultivated and wild rice). According to the major findings for fSNPs, the haplotypes in the cultivated rice group were replaced by a parental ecotype or subcultivated haplotypes or haplotypes from temperate japonica (Te_J), indica (I), and subcultivated rice (ScR)  to serine (S) by a C/T substitution. An additional fSNP (C/T) was also observed in the haplotype ScR-1, representing 20 indica, and 1 tropical japonica accessions. We found two more haplotypes, ScR-2 and ScR-4, showing the same polymorphism (A/C SNP) as that of temperate japonica haplotypes. In brief, we observed that the cultivated group had only three fSNPs  in exons 4, 6, and 10, corresponding to different subpopulations of 73 rice accessions, namely 43 temperate japonica, 22 indica, 15 tropical japonica, 2 aromatic, and 1 admixture.Across the ce (ScR) . We idenIn the wild group, we observed six different positions showing fSNPs , which wConsidering the haplogroups classified by fSNPs, the number of positions with InDel variants was also selected for additional analysis of haplotype variations. The variation in differences categorized as InDels between the cultivated and wild groups were similar to those categorized as SNPs. For example, one InDel mutation (23 bp insertion) in exon 2 was identified only in the cultivated group, namely, in japonica (Te_J_1 and ScR-4). Similarly, different InDels at different positions in introns 1, 2, 5, and 12 were also observed only in the wild group. Specifically, W-1 showed three deletions  in introns 1 and 11. W-3, W-4 (A to D), and W-8 showed 16 and 5 bp deletions in intron 1, 13 bp deletions in intron 2, 17 bp deletions in intron 5, and 4 bp deletions in intron 12, respectively. One last finding from a haplotype analysis was a functional G to T mutation at the 5′ splice site of intron 1, which was a key to distinguishing waxy cultivars from nonwaxy cultivars.K = 2, and the remaining K-values, especially K = 6 and K = 7, created separate patterns of the classified types, but their internal clusters were mixed , the population structure of cultivated ecotypes was more or less mixed with that of the wild groups, especially tropical japonica and indica. In other words, the wild group seemed to be closely related to many cultivated subpopulations in complicated patterns because of that most of internal subgroups of cultivated subpopulation were associated with each other and then their admixed populations could be parts of the wild during this gene, GBSSI domestication.We did a population structure to observe the differences by genetic variants among and between the classified cultivated subpopulations, including wild rice, and hence their genetic relatedness were verified . Admixedre mixed . For K =ecotypes , we obseThe PCA for the descriptive components of the landrace, weedy, wild, and bred varieties  revealedGBSSI between the classified subgroups, we further calculated FST values. The greater the genetic distance between the populations, the less the breeding efficiency between them, and the more isolated they will be from another. We calculated the FST values of pairwise comparisons of the variety types, namely, landraces, weedy rice, bred rice, and wild rice  between the wild and bred rice, followed by that (0.256) for the landrace-wild comparison and then that between landraces and weedy rice (−0.008). All cultivated varieties indicated their lower FST values among and between themselves than their values from the wild. We estimated FST values also for pairwise comparisons of cultivated ecotypes together with wild rice  between aromatic and aus rice was higher than the other FSTvalues. Despite the highest FST value (0.547) between the wild and landrace variety types, some cultivated ecotypes showed sizeable pairwise distances from wild rice, such as 0.703 (temperate japonica), 0.524 (aromatic), and 0.510 . Interestingly, the classified cultivated subgroups were admixed with the wild rice in PCA, but in some classified subgroups, especially some ecotypes, their genetic distances were considerably distant from the wild in FST test.To verify the level of genetic differentiation of ild rice . We obseild rice . The valGBSSI gene region. In a comparison of the wild and cultivated subgroups , we found the highest nucleotide diversity value  in the wild group. All the calculated diversity values (π) at the fixed segregation sites in GBSSI region were < 0.0100 , followed by landraces (0.0039), weedy varieties (0.0037), and bred varieties (0.0013). A comparison of the average diversity values of these groups  at 1,765,500 , followed by the landrace group (0.2764), and the lowest value was observed in the bred group (−1.0488)  . The wee−1.0488) .D value (2.6890) at the genome position of 1,768,500, and the lowest value was observed in tropical japonica accessions (−2.0771) at the genome position of 1,770,000. Within GBSSI genomic region, indica and admixture indicated only positive Tajima's D values whereas the remaining ecotypes showed both positive and negative Tajima's D values based on the identified positions. We compared the classified ecotypes according to their average D values, which revealed that indica accessions had the highest D value (1.8349)   and trop(1.8349) . In the GBSSI, we used the entire GBSSI region for a haplotype network construction. After a series of analyses, we identified 59 haplotypes (hereafter referred to as “Hap”) belonging to the classified ecotypes. According to the network, Hap_1 was a major haplotype (291 accessions), indicating the close genetic relatedness of GBSSI in most temperate japonica (230), indica (44), tropical japonica (10), and wild (7) rice accessions. As shown in figure, most of the haplotypes belonging to cultivated subpopulations are closely associated, with a few mutational steps . Moreover, there were additional predominant haplotypes composed of identical genomic sequences, e.g., Hap_41, belonging to temperate and tropical japonica accessions and Hap_36, belonging to tropical japonica, temperate japonica, aromatic, and admixed accessions. However, some haplotypes belonging to cultivated indica, admixed, and aus accessions were very far in terms of GBSSI genetic distance from japonica haplotypes. Most of the wild haplotypes were diverse in the GBSSI network and not only distant from the cultivated haplotypes but also separated from each other. This diverse display of wild rice indirectly indicated that wild rice accessions had different rare alleles of GBSSI.To visualize the genetic relatedness of the classified subpopulations of all tested rice accessions, we constructed and analyzed a haplotype network . To coveGBSSI among cultivated subpopulations resulting from domestication  in rice has been described by many research groups, where the functional responses of waxy alleles were different based on their specific polymorphic sites. These research groups discovered a G/T SNP  have collectively been discovered in rice genome  these days, many researchers have been increasing their interests on other starch synthesis-related genes (SSRGs), like SSII/ALK  genes for the improvement of ECQ rice  and 27 InDels, representing 139 haplotypes along chromosome 6  in coding exons 2, 4, 6, 9, 10, 12, and 13. Of these, three fSNPs, namely, A/G in exon 4, A/C in exon 6, and C/T in exon 10, were found in temperate japonica and indica, were in agreement with previous findings, and confirmed the strong association of SNPs with a waxy gene function. Cultivated haplotypes (ScR-2 and ScR-4) also showed an A/C SNP in exon 6 in tropical japonica, aromatic, and admixed accessions , whereasmosome 6 . After fmosome 6 .Wx protein, resulting in chain termination  was identified as a unique coding sequence in exon 2 of the waxy gene, originating from tropical glutinous rice, and it can explain the lack of amylose and GBSSI-encoded waxy gene   content in rice is controlled by GBSSI in cultivated rice, especially in japonica ecotypes or bred varieties by their considerable low diversity values  have been reported in previous studies, such as 0.87 for an elite rice genotype from Chile  located on exon 3 of SSIIIa gene  were detected, indicating that the wild and cultivated rice groups independently arose based on the GBSSI waxy allele function. Moreover, it seemed to be that the cultivated rice accessions were not hybridized with any wild rice for the purposes of glutinous rice production or that no wild rice was grown in Korea. Despite these findings, the wild accessions with the higher nucleotide diversity can serve as potential donors of functional waxy alleles under a directional balancing selection.This study has given an account of collective and supportive information about functional variants, genetic diversity, and genetic distance (relationship) within the GBSSI in 475 rice accessions and its genetic distance and relatedness between and among the populations. In this study, domestication signatures were obviously indicated by a diversity reduction using the classified cultivated subgroups compared to the higher nucleotide diversity of wild rice. Different Tajima's D values of cultivated rice groups were additional indicators for GBSSI evolution under different selective sweeps, indicating the GBSSI gene region under selective sweep. In spite of complicated associations between and among the cultivated subgroups and wild in PCA, clearly differentiated cultivated varieties or ecotypes were stratified as separate populations in PCA. Greater isolations of cultivated subgroups from one another and genetic distance of each subgroup from the wild were clearly indicated by their inferred FST values. Through these our findings, further research program on breeding of waxy rice can be promoted.Evolutionary analyses revealed the genetic diversity of BankIt2477403: MZ514142–MZ514616).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: NCBI with accession number (TZM and Y-JP: conceptualization. S-HC and I-MC: methodology. TZM, J-MY, and S-HC: investigation. TZM and K-WK: data curation and writing—review and editing. TZM and J-MY: writing—original draft preparation. Y-JP: project administration. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sus scrofa). Here, we assessed the genetic distinctness and genome-wide domestic pig ancestry levels of the Corsican wild boar subspecies S. s. meridionalis, with reference to its Sardinian conspecifics, employing a genome-wide single nucleotide polymorphism (SNP) assay and mitochondrial control region (mtCR) haplotypes. We also assessed the reliance of morphological criteria and the melanocortin-1 receptor (MC1R) coat colour gene to identify individuals with domestic introgression. While Corsican wild boars showed closest affinity to Sardinian and Italian wild boars compared to other European populations based on principal component analysis, the observation of previously undescribed mtCR haplotypes and high levels of nuclear divergence (Weir’s θ > 0.14) highlighted the genetic distinctness of Corsican S. s. meridionalis. Across three complementary analyses of mixed ancestry , proportions of domestic pig ancestry were estimated at 9.5% in Corsican wild boars, which was significantly higher than in wild boars in Sardinia, where free-range pig keeping was banned in 2012. Comparison of morphologically pure- and hybrid-looking Corsican wild boars suggested a weak correlation between morphological criteria and genome-wide domestic pig ancestry. The study highlights the usefulness of molecular markers to assess the direct impacts of management practices on gene flow between domestic and wild species.Owing to the intensified domestication process with artificial trait selection, introgressive hybridisation between domestic and wild species poses a management problem. Traditional free-range livestock husbandry, as practiced in Corsica and Sardinia, is known to facilitate hybridisation between wild boars and domestic pigs ( Domestication exerts a strong selective pressure on species through genetic processes, such as inbreeding, genetic drift, natural selection to captivity, and artificial selection of desirable traits  and domestic pigs (S. s. domesticus) are characterised by a complex interplay of domestication, feralisation, and introgressive hybridisation . Following Frantz et al. (MC1R) coat colour gene that included the single nucleotide polymorphisms between codon positions 95 and 166 , were genotyped using the Porcine SNP60 v2 BeadChip (Illumina Inc.) following manufacturer’s instructions. GenomeStudio 2.0 software (Illumina Inc.) was employed to call genotypes using a custom cluster file to improve call rates. Only autosomal SNPs, mapping to chromosomes 1–18 on the reference genome Sscrofa build 11.1, were retained for analysis. The resulting 60K genotypes were merged with publicly available data from 44 domestic pigs and 60 wild boars sampled in France, Iberia, Italy and Sardinia  samples. The resulting dataset (50K SNP panel set hereafter) was pruned for minor allele frequency (MAF > 0.01) and linkage disequilibrium (LD) in PLINK. SNPs with r2 > 0.5 were removed from sliding windows of 50 SNPs and with 10 SNPs of overlap using the indep-pairwise function. The pruned dataset is hereafter referred to as 30K SNP panel set and was used in analyses assuming independent SNP loci.Quality control filtering  was carried out in PLINK 1.9  and expected heterozygosity (He) were estimated from the 50K SNP panel set in PLINK for each domestic pig breed and regional wild boar population. The degree of genetic divergence was estimated using Weir and Cockerham’s θ (θ) as implementing in the StAMPP package  and 25 samples from domestic pig breeds ‘Large-White’, ‘Duroc’, and Sardinian domestic pigs as putative ancestral reference populations given the putative origin of the insular wild boar populations and documented cross-breeding in the Nustrale breed  in PCADMIX. The window size was set to 20 SNPs. Results were plotted in R using a custom script by Barbato et al.  and one in a wild boar and a domestic pig. One domestic pig carried a haplotype that had only been recorded in domestic pigs (mainly Large White) and a further three pigs carried a haplotype previously observed in a Croatian wild boar  revealed a total of 11 different haplotypes, with a total of 25 variable sites consisting of 20 transitions, one transversion and four insertions/deletions Table . We obseMC1R gene in 76 samples  revealed the presence of five different alleles. While the majority of the wild boar  were homozygous for the wild type 0101/E + allele, this was only the case for four hybrid boars  than domestic pig samples  were retained. The data set consisted of 85 wild boars , which was lower than the genetic divergence estimated among Corsican and Sardinian wild boars  and Corsican and Italian wild boars . The genetic divergence estimates among wild boar populations ranged between Weir’s θ = 0.107,  and Weir’s θ = 0.209 . The lowest degree of pairwise genetic divergence was estimated between Sardinian feral pigs and the Large White breed . The divergence between the Nustrale and Large White breeds was similarly one of the lowest observed in the study . The highest degree of divergence included the Mora Romagnola breed.The overall degree of population divergence between our study populations was estimated to be Weir’s In the PCA, the first principal component accounted for 9.36% of the variance and discriminated between wild boars and domestic pigs, with the Mora Romagnola and Duroc breeds clustering away from the remaining domestic pig clusters Fig. . The secΔK method suggested K = 2 as the most likely number of clusters, separating wild boars from domestic pigs  compared to Sardinian wild boars (average 5.49%). Blocks of domestic pig ancestry were distributed across all chromosomes, with only a few blocks showing converging ancestry across all individual haplotypes  in ELAI, the amount of inferred domestic pig ancestry was estimated at 6.47% (100 mg) and 10.01% (1000 mg) in Corsican wild boars. In comparison, Sardinian wild boar samples were inferred to comprise only 2.42% (100 mg) and 5.47% (1000 mg) domestic pig ancestry. Nustrale breed samples showed high levels of inferred wild boar ancestry of 44.32% (100 mg) and 40.23% (1000 mg).Across all three approaches , individual-level differences in estimates of local ancestry proportions were observed among Corsican wild boars Fig. . CorsicaSPATA17, SPATA18), visual perception (CRB1), and hearing development (LRIG3). The outlier loci were distributed across 14 chromosomes and their position did not coincide with areas of increased estimated domestic pig ancestry proportions  and a contemporary Corsican S. scrofa. Here, a mtCR haplotype belonging to the Asian clade (A) was only observed in two domestic pigs. However, it cannot be excluded that clade A or E2 haplotypes also occur at low frequency in Corsica and remained undetected due to insufficient sampling intensity. Conversely, over half of the sampled Corsican wild boars carried mtCR haplotypes that had not previously been described in Sardinian or other wild boar populations, indicative of a clear divergence of Corsican wild boars from other European wild boar populations.This study aimed to fill the knowledge gaps surrounding the genetic distinctiveness of Corsican wild boars, with respect to its Sardinian conspecifics and other European wild boar populations. In line with Sardinian wild boars  comparable to the levels of divergence estimated among continental wild boar populations, which was also reflected in clear clustering in the Bayesian clustering analysis.Nuclear markers provided complementary insights into the genetic distinctiveness of Corsican wild boars. The principal component analysis and TREEMIX divided the wild boars into a western  and a southern  cluster, in agreement with a postglacial demographic expansion from an Iberian refugium, and the Alps hindering a northward range expansion  and in Sardinia (ban on free-range pigs since 2012) were reflected in the extent of domestic pig ancestry in the local wild boar populations. The average proportion of genome-wide domestic pig ancestry was estimated to be significantly higher in Corsican than in Sardinian wild boars. Compliance to the ban on traditional free-range pig farming has been problematic in Sardinia , the intentional hybridisation between domestic sows and wild boars has been reported Dulat . This coMC1R coat colour gene, given its known effects on phenotypic diversity in pigs  require the specification of pure reference populations. The choice and size of reference populations was previously shown to affect results  may make the comparison at K = 2 suboptimal. The difference in domestic reference populations likely explains the marked difference in estimated domestic pig ancestry in Sardinian wild boars among STRUCTURE, ELAI, and PCADMIX.We deduced the amount of domestic ancestry from the STRUCTURE result at S. s. meridionalis and traditional farming practices hold socio-economic and cultural values in Corsica, persistent interactions between wild boars and domestic pigs pose several management challenges. Importantly, wild boar and domestic pig interactions were previously deemed responsible for the maintenance and transmission of several infectious diseases, such as Hepatitis E virus, bovine tuberculosis, trichinellosis, or Aujeszky’s disease virus  in Europe (Le Corre et al. Sus verrucosus; Drygala et al. The rate of hybridisation events is increasing globally due to habitat change and introductions of non-native species (Crispo et al. Corsican wild boars and domestic pigs show clear genetic differentiation despite extensive introgressive hybridisation. This result is in line with the paradigm that divergence can be upheld even in the presence of gene flow (Pinho and Hey This study has highlighted that livestock management practices can have far-reaching effects on wild populations. Our results validate the importance of molecular markers to formally estimate the potentially damaging effects of domestic introgression into wild populations with the sole reliance on external morphological criteria proving to be an unreliable predictor of genome-wide domestic ancestry.Supplementary Material


The concept of Healthy Cities, introduced by the World Health Organization, demonstrates the value of health for the whole urban system. As one of the most important components of urban systems, transportation plays an important role in Healthy Cities. Many transportation evaluation systems focus on factors such as road networks, parking spaces, transportation speed, accessibility, convenience, and commuting time, while the vulnerability and resilience of urban transportation are rarely evaluated. This study presents the preliminary progress in the evaluation of traffic vulnerability and resilience during precipitation events in 39 Chinese cities. Traffic congestion index data, derived from the Baidu Map Smart Transportation Platform, and rainfall data, derived from NASA’s global precipitation measurement, are utilized. Traffic vulnerability index, traffic resilience index, and the corresponding quantitative methods are proposed, and the analysis results are presented. This study is of value in improving the understanding of urban traffic vulnerability and resilience, and in enabling the quantitative evaluation of them in urban health assessment and the Healthy Cities program. The rapid advance of urbanization has brought new opportunities, as well as challenges, for urban residents ,3,4,5,6.Transportation is one important component of urban systems. Due to the large population and active economic activities in cities, the transportation system is often under pressure, resulting in unhealthy conditions, such as traffic congestion ,13,14,15The rest of this paper is organized as follows: The study area and data are introduced, followed by the method of evaluating traffic vulnerability and resilience. The next section focuses on the analysis results. The advantages and limitations of this study are discussed, leading to the conclusions.This study selected 39 major cities in China, covering Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta, Pearl River Delta, and several other densely populated urban agglomerations. They include four first-tier cities , 12 new first-tier cities , and 23 second-tier cities . These cities are located in multiple climatic zones, from the rainy southern part of China to the less rainy northern part .https://jiaotong.baidu.com/, accessed on 9 January 2021), and the rainfall data are derived from NASA’s global precipitation measurement .The data used in this study mainly include urban traffic data and rainfall data. The urban traffic data are derived from the Baidu Map Smart Transportation Platform , with a time resolution of one hour. To eliminate the influence of periodic changes  on traffic conditions, the study focuses on the evening rush hours of working days, and selects congestion index data from 5 pm to 8 pm on weekdays in 2018 and 2019.The global precipitation measurement provides a set of global rainfall data based on satellite observations. It has the advantages of high spatial resolution, high temporal resolution , and wide spatial coverage. In addition, it is also highly accurate and has a strong ability to capture solid-state precipitation, heavy rainfall, and low precipitation .This study adopts global rainfall data from 1 January 2018 to 31 December 2019. The latitude and longitude coordinates of the cities examined in this study are used to obtain the daily precipitation data of the grid point where each city is located. The time resolution is half an hour, with the spatial resolution being the 0.1° × 0.1° grid and the data unit being mm/hour. Two adjacent rainfall datum with the half-hour resolution are added together in order to enable the time resolution to be one hour. The time zone of the data is adjusted from UTC + 0 to UTC + 8, in order to be compatible with the traffic congestion index data.The evening rush of the working day refers to the increase in traffic behavior caused by people coming home from get off work or participating in evening. If precipitation occurs in this period, or stops shortly before this period, its impact on urban traffic will be very significant. Thus, this research mainly focuses on this situation by proposing vulnerability index and resilience index, and performing quantitative evaluation of the vulnerability and resilience of urban traffic to precipitation in 39 cities in China. The traffic vulnerability index refers to the changes in the urban traffic congestion index under different rainfall intensities, and the traffic resilience index refers to the time window allowing urban traffic to recover to the historical congestion index after precipitation ends.The traffic vulnerability index is established to evaluate the response of different cities to different rainfall intensities, measuring urban traffic during precipitation. The basic idea of establishing this index is described as follows.Firstly, precipitation intensity classification. Precipitation intensity refers to the amount of precipitation per unit time, which, in this study, is an hour and the unit of intensity is mm/h. The precipitation intensity, obtained based on the data from 2018 to 2019, is divided evenly by uniform interval, and the average value of urban traffic congestion index under each precipitation category is calculated as follows:In this equation, max and min represent the maximum and minimum hourly precipitation for the time period selected by the study, respectively, while interval means the grading interval. In this study, the interval is 0.1 mm. N is the grading number of a city.p < 0.05 is considered statistically significant.The next step is regression analysis. Based on the results of the above classification, scatter plots are drawn and regression analysis is conducted to obtain the regression coefficient, which is also the vulnerability index of the city traffic under precipitation. This study adopts the ordinary least squares (OLS) method to estimate the regression coefficient. The hypothesis testing is performed and Traffic resilience index is established to measure the ability of the urban traffic to return to a normal state after precipitation. The basic idea of establishing this index is as follows: the traffic recovery time of the city after each precipitation event is calculated, then the average value of the traffic recovery time after all precipitation in two years (from 2018 to 2019) is obtained. The equation is as follows:In this equation, Time represents the time it takes for the city’s traffic to return to its normal state. In this equation, ΔI is the change in the congestion index, I is the actual value of the congestion index, μ is the average of the historical congestion index and σ is the standard deviation of the historical congestion index. When ΔI > 0, the congestion index is higher than the historical congestion index. In this case, the urban traffic is still congested and is still recovering from precipitation. ΔI ≤ 0 means that the congestion index is equal to or lower than the historical congestion index. In this case, the urban traffic is no longer in congestion and this moment is considered as the ending moment of the recovery process.p < 0.05 is considered statistically significant, that is, the urban traffic is strongly affected by precipitation and the traffic vulnerability is relatively high. p > 0.05 is considered statistically insignificant, that is, the urban traffic is not affected by precipitation. The subfigures of the cities, where p < 0.05 are marked in red, are shown in To eliminate the influence of the periodic changes in traffic conditions, the study uses traffic data and precipitation data of evening rush hours (from 5 pm to 8 pm) of the working days from 1 January 2018 to 31 December 2019. Based on the traffic vulnerability evaluation method mentioned above, the traffic vulnerability index and significance test results of 39 first-tier and second-tier cities in China are obtained, and scatter plots and OLS fitting line graphs are constructed . The subp value of each city are obtained  The size of the city: Megacities, such as Shanghai, or province capitals, such as Nanning, Jinan, Changsha, Zhengzhou, Hefei, and Lanzhou, usually cover an urban area of more than 3000 square kilometers. These large cities are weak in alleviating traffic congestion and in traffic recovery after precipitation. In contrast, cities such as Nantong, Yangzhou, Shaoxing, Wuxi, Jiaxing, and Haikou cover an urban area of less than 3000 square kilometers. They are relatively strong in alleviating traffic congestion and in traffic recovery .(2) The level of economic development: Cities such as Nantong, Yangzhou, Shaoxing, Wenzhou, Wuxi, Jiaxing, and Haikou are located in the southeast coastal areas. These cities have relatively high levels of economic development, and their infrastructure is relatively complete. Although Shanghai is also highly developed in its economy, it is poor in traffic recovery after precipitation, due to its large size and population ,31.Cities are complex ecosystems combining nature, society, and economy. Under the impact of precipitation, it is usually small-scale developed cities that are better in traffic resilience. The lower population density of small-scale cities alleviates the pressure on easing traffic congestion. Cities that are highly economically developed are also better in traffic resilience due to their relatively complete infrastructure, dense road network, and good drainage capacity for water on the road. These factors contribute to a faster recovery of traffic to pre-precipitation levels.Normalization is carried out based on the traffic vulnerability index and the traffic recovery time. The vulnerability index and the recovery index are normalized to be in the range of 0–1. Let the horizontal axis be the normalized vulnerability index and the vertical axis be the normalized recovery time, and draw the NVI–NRT  diagram, which shows the comprehensive traffic evaluation results of the 39 cities mentioned above . The diaIn the NVI–NRT diagram, the greater the abscissa, the greater the degree of vulnerability; the greater the ordinate, the longer the recovery time. The dotted line in the diagram represents the average level of the vulnerability index and the resilience index of the cities mentioned above. It is shown that cities with more complex topographical features, such as Beijing, Qingdao, Chongqing, Wuhan, Lanzhou, and Guiyang, have a high vulnerability index. Cities that are larger in size, or less economically developed, such as Shanghai, Nanning, Lanzhou, Changsha, Xuzhou, Zhengzhou, Hefei, and Jinan, have a long recovery time. The cities in the lower left corner of the diagram, such as Nantong, Jiaxing, Hangzhou, Foshan, Xi’an, Wenzhou, Ningbo, Haikou, Shaoxing, Wuxi, Yangzhou, and Taizhou, have a low vulnerability index and a short recovery time. These cities are better in traffic resilience.In 2019, the Central Committee of the Communist Party of China and the State Council issued the Constructing Transportation Power Outline, stating that “we should construct a modern high-quality integrated three-dimensional transportation network, enhancing the flexibility of the system. We should build a scheme preventing and controlling the damages caused by natural disasters to the transportation system, increasing its resistance to natural disasters” ,23,24. TOn the other hand, although the proposed indicators are useful in reflecting the vulnerability and resilience of traffic systems, they are far from a comprehensive evaluation. Enrichment of the indicators ,34,35,36In this study, a traffic vulnerability index and a traffic resilience index are proposed and utilized to assess the vulnerability and resilience of traffic affected by precipitation in 39 major cities. The results show that Beijing, Qingdao, Wuhan, Chongqing, Lanzhou, and Guiyang are very vulnerable to precipitation events, due to their relatively rugged terrain and obvious topographical obstacles, such as a large rivers or high mountains. On the other hand, Nantong, Hangzhou, Jiaxing, Foshan, Yangzhou, Shaoxing, Wuxi, and Haikou exhibit good resilience during precipitation events. The major reasons for this are that the area of these cities is not large, and the transportation infrastructure is relatively complete.The indicators and the corresponding quantitative methods are valuable for others to refer when evaluating the vulnerability and resilience of transportation systems. Enrichment of the indicators for vulnerability and resilience, proposing new quantitative method, and performing pilot studies in other cities are expected.