The use of high inputs in agriculture resulted in few varieties (hybrids and pure lines) used in all agricultural systems. Also varieties of vegetables, including broccoli, for organic and low-input agriculture, are almost exclusively hybrids, since there are very few specific breeding programs and varieties for sustainable agriculture systems.A strategy to overcome this issue is the adoption of specific breeding programs for developing heterogeneous varieties . In fact, heterogeneous varieties are able to evolve and adapt to specific agro-climatic conditions.The aim of this study was to develop a method  for obtaining heterogeneous varieties of broccoli and test its efficiency in developing highly diverse varieties, as needed in sustainable agriculture.A synthetic variety originated from a landrace was multiplied in different environments for 3 cycles and morpho-phenological and genetic diversity of the derived populations were assessed.The presented results are the first and unique indication about the efficiency of a short-time EBP for an allogamous species like broccoli.Few morphological changes were observed among varieties multiplied in different environments with different agro-climatic conditions. This could be probably due to the initial genetic diversity of the landrace from which the populations were selected and also to the great plasticity of the crop. However, SSR data highlighted a genetic differentiation among populations multiplied for two/three years across Europe and in Central Italy, that was not so evident when considering morphological data only.Few years of multiplication in different environments resulted in genetically differentiated broccoli populations that nonetheless preserved the original genetic diversity and productivity level and appear to evolve in relationship to different environments: the applied EBP is useful for developing heterogeneous materials for sustainable agriculture. Brassica oleracea L. (2n = 2x = 18) is an economically important vegetable and its world annual commercial production (cauliflower and broccoli) is over 25 Mt, currently cultivated on about 1.4 Mha worldwide [B. oleracea subspecies, broccoli  convar. Botrytis (L.) Alef. var. italica Plenck [a Plenck ) is an iCurrently, organic farmers largely depend on broccoli varieties bred for high external input in conventional farming systems , 5, and Suitable varieties for sustainable agriculture should have specific characteristics  in order to avoid the utilisation of external input like pesticides, herbicides and chemical fertilizers , 6. In tAs a consequence, breeding programs specifically adopted for sustainable agriculture should consider all the above. Among many breeding approaches, the Evolutionary plant Breeding Program (EBP), combining natural and artificial selection in target environments, is suggested to be effective in obtaining heterogeneous varieties for organic and low-input agriculture –13.The success of an EBP, like that of any other breeding program, is mainly due the genetic diversity of the initial materials. For example, EBP in cereals has been based on composite cross populations (CCP) or mixtures , 13, 14 An assessment of genetic diversity is therefore essential for the organization, development and control of a breeding program. The genetic diversity and structure of populations under selection can be efficiently evaluated by molecular markers. Depending on their type  they can also provide information about the intensity of both natural and human selection and about the genetic divergence of populations developed across years and environments. To this purpose, different types of molecular markers have been used. For example, genetic changes in maize composite populations under Reciprocal Recurrent Selection (RRS) were analyzed by using Restriction Fragment Length Polymorphism (RFLP) , 18, SimAgronomic performances of different broccoli heterogeneous varieties or other broccoli breeding materials for sustainable agriculture have been already reported , 27–29, Considering this lack of knowledge, the aims of this paper were to i) describe a EBP method for developing flexible broccoli varieties , ii) assess the adaptation potential and the morpho-phenological and genetic changes of the populations developed with this method in different environments and iii) in the adaptation area across years.YLD) and number of secondary heads (HN) the populations derived from the initial synthetic by multiplication for two years were not different from each other. In particular, YLD values ranged from 11.05 g (Syn2-TER) to 324.58 g (HHH), as populations formed a little main head and many (up to 36) secondary heads while most of the hybrids did not form any secondary heads HHH, among the hybrids, and Syn2-FR, among the populations, showed the highest total yield .The ANOVA analysis showed that the entries differ for all of the morpho-phenological traits recorded with great differences generally observed between the populations and three of the hybrids  Table . For theDH range = 101–204 dd; DM range = 134–219 dd), generally, the latest were the populations, the earliest the hybrids, with the exception of H. LR, Syn1-PG, Syn2-FR, Syn2-UK and Syn2-GR were the latest for inflorescence emission date, with Syn2-GR also late in maturation. Regarding traits related to head characteristics, generally, hybrids showed higher values than populations: HeH range = 6.41–9.59 cm, , HeW range = 3.29–14.53 cm  and SW range = 0.94–3.69 cm . The hybrids showed the lowest plant height, while LR and Syn1-PG the highest . Regarding plant diameter , all the entries showed similar values, with the exception of H, HH and Syn2-TER showing the lowest values. No differences were found among populations regarding vigour with the only exception of Syn2-TER (4.0) which was the less vigorous one.Considering the inflorescence emission and maturation date . In general, Na was higher than Ne in all the entries. In populations, Na ranged from 2.45  to 3.50  and Ne ranged from 1.78  to 2.38 , hybrids generally had lower values of Na and Ne than the populations.All the entries were characterised by similar number of alleles  values were close to 0 for all the populations. The existence of negative values in some populations and in all the hybrids hinted to a heterozygosity excess.Fixation index , with the only exception of the comparison between LR and Syn1-PG .Finally, all PG Table . ConsideSimilarly, a crescendo of differentiation was found between Syn2-PG and the same generation of multiplication in different environments (from 0.046 between Syn2-PG and Syn2-GR to 0.103 between Syn2-PG and Syn2-TER). The highest differentiation value was found between Syn2-FR and Syn2-TER, populations which were multiplied in very contrasting environments. As expected, the genetic differentiation between hybrids and between populations and hybrids was very high.AMOVA (not shown) worked out on the total data showed that among- and within- population diversity accounted for 33 and 67% of total genetic variation, respectively. However, when it was worked out only considering the LR and its derived populations, the proportions were 11 and 82%, respectively.NJ tree results showed the presence of two main clusters  and the biplot showed that the three hybrids and hybrid H with populations clustered separately and that the variable clustering and relationship were similar to what previous observed  favoured plants with the largest heads and then the greatest production of seed in each environment.Although several experiences of evolutionary breeding for autogamous species have been reported by different authors  did not seem to have heavily changed the morpho-phenological traits and the performances of populations in comparison with LR from which they derived. The populations showed few morphological differences probably due to their intrinsic plasticity, the initial genetic diversity of LR and the mating system of the species, factors that, combined, maintain a large variability . In addiOnly populations multiplied in TER were morphologically well distinguished from the other populations. TER is an environment that showed minimum temperatures lower than the others among December and March: these environmental factors could have negatively influenced the vegetative stage of plants and consequently the reproductive stage, since from December to March the heads appear and then proceed to maturation.Also, the EBP here described (selection and mating of the best tree plants for two years) did not seem to have resulted in any inbreeding depression, suggesting that the genetic diversity remained high.In spite of that, genetic data showed a differentiation among populations that morphological data did not assess. Only two years of multiplication caused a low to moderate differentiation. Worth of note is the progressive increase in differentiation values observed passing from different population generations and from a multiplication environment to another.Of course, a longer breeding period or a different selection method than the one implemented in this study could have allowed a greater differentiation among populations, as it happens in the process of broccoli LR differentiation . For exaOur genetic results were similar to those found by   was less selective than the others. In fact, even if the genetic distance among the Syn1-PG and the derived populations was increasing along years, the increase was larger in the TER and GR environments, which are not the adaptation environments.During an EBP the selective pressure of environments is crucial, but time is also an important factor in driving the oscillations of the allele frequencies and the subsequent adaptation of the genotypes.One of the aims of this study was to assess the adaptation potential of a heterogeneous variety when multiplied in different environments. Although a longer multiplication period would be necessary to have a final confirmation, the obtained data show that a high genetic diversity is maintained all over the tested multiplication environments and in spite of their pedological and climatic differences in at least three years. This diversity can then continue to evolve in response to different selection pressures in each environment in the future, as it has always happened with any introduction of variable materials in different environments in the past.Few years of multiplication in different environments resulted in differentiated populations which, nonetheless, preserved the original genetic diversity and productivity level and appear able to evolve in relationship to different environments.As such the EBP here described also appears to be a good method to develop broccoli materials (populations) for sustainable agricultural systems, those systems where the use of external input  is limited or absent.In sustainable agricultural systems, protection from biotic and abiotic stress (which are variable from year to year and from a situation to another) should rely on the diverse response of the single different genotypes at a certain moment and, consequently, on the ability of the crop, as a whole, to change its composition across time, making the crop community diverse and resistant/tolerant across time and situation. This changing ability is also important because sustainable systems often have very specific characteristics in term of soil fertility, type of occurring biotic and abiotic stress, agronomic and protection techniques applied by farmers . In most cases none is similar to another. Finally, it is also important because we are living in a period of rapid and unpredictable climatic changes, with the connected changes of biological forms negatively affecting crops.For all these reasons, in sustainable agriculture populations like those developed in this study, which are able to evolve across time and locations, appear to be much more suitable materials than hybrids and greater efforts than presently carried out should be applied to develop and diffuse them. Of course, where external inputs can counteract biotic and abiotic constrains and the cultivation environment is uniform, hybrid varieties can be the most useful materials.Aiming to develop materials suitable for organic agriculture (i.e. possessing the adaptation and the intrinsic diversity needed to suit a particular environment) we specifically focused our work on a landrace (LR) instead of on other materials.We chose a sprouting broccoli LR from Umbria  that is named “Zolfino”, highly appreciated on the local market and generally grown under low-input conditions. The LR is characterised by late flowering and maturation: requiring low winter temperatures for flower induction, it is harvested in a period spanning from beginning of March to mid-April. Initially the main head, then the lateral buds are harvested in the period.B. oleracea plants, since all the species varieties are interfertile. It should be noted that this procedure is common across Europe for farmers reproducing their B. oleracea LR .The initial material of LR was obtained by a farmer family which cultivated it since generations. After the farmer abandoned his own population, this Department (DSA3) continued its cultivation by yearly reproducing the seed and applying the selection procedures of the mother plants (MP) suggested by the farmer: the most vigorous 2 or 3 MP, among the 25 usually grown, are chosen and intercrossed, while the field is cleared from other Starting a breeding program to develop an initial synthetic in 2009, we asked the farmer to choose 17 MP among the 25 grown instead of the usual number Fig. . The folThe multiplication of Syn0 produced a Syn1-PG (2009–2010), the initial (foundation) material of this study.i)for two consecutive years at DSA3  in the same area of its origin giving rise to two populations named Syn2-PG (2010–2011) and Syn3-PG (2011–2012), respectively;ii)for two consecutive years in two other areas of Central Italy with different pedo-climatic conditions from Perugia: Grosseto (Tuscany) and Terminillo (Lazio), giving the populations named Syn2-GR, Syn3-GR and Syn2-TER and Syn3-TER, respectively;iii)for one year only (2010–2011) in United Kingdom giving a population named Syn2-UK;iv)for one year only (2010–2011) in France giving a population named Syn2-FR.To start the EBP the Syn1-PG seed was then multiplied in isolation in different areas of Central Italy, in France and in United Kingdom as follows Fig. :i)for tEach multiplication environment  is different from PG for several pedo-climatic traits  were intercrossed in order to obtain seed. The inflorescences of the other plants were cut before flowering in order to evaluate agronomic traits of the commercial heads.Each year, the obtained seed was sent to DSA3 in order to store it in its Genebank (FAO code ITA363) for the successive analyses (see below).For each environment of multiplication and for the environment of final agronomic trial, climatic data, i.e. average daily temperature, average of maximum daily temperatures, average of minimum daily temperatures (°C) and total rainfall precipitations (mm) (obtained from near-by weather stations), were recorded from sowing to harvest , Ironman (Monsanto), Belstar (Bejo Seeds) and Natura Vallata (Cooperativa Agricola Cesenate), named H, HH, HHH and HHHH, respectively.Secondly, we assessed other three populations obtained in Central Italy with a further multiplication cycle  in order to obtain information about further evolutionary processes eventually occurred.In order to assess eventual morpho-phenological changes, all the obtained multiplications were evaluated in the same environment.− 1 of the commercial product NUTEX LETAME  that was incorporated by using a rotary cultivator. As above, this is the fertilization level usually applied by farmers. Weed control was always performed by hand-weeding and no chemical control of pathogens and insects was applied. Broccoli were irrigated immediately after transplanting and during the following month, aiming at fully restoring crop evapotranspiration.Seeds from the 14 broccoli entries were sown in pots at the beginning of July and the obtained plantlets were transplanted in a DSA3 experimental field in Perugia  at the beginning of September. A total of 448 individual plants were evaluated. Plants were arranged by using a randomised block design with four replications. In each plot, eight plants of each entry were grown, for a total of 32 plants evaluated per entry. Spacing among plants was set at 1 × 1 m because the plants are very vigorous and this spacing is generally used by farmers growing the “Zolfino” LR , and j of the effect “Block” (B), is: Tij = m + Gi + Bj + eij, where m is the grand mean and eij is the experimental error, and means were separated using the Tukey Honest Significant Difference (HSD), as implemented in the HSD.test function of the “agricolae” package in R [To determine the significance of the sources of variation, the recorded data were processed by analysis of variance (ANOVA) using a linear model, where an individual trait value Tage in R , 42.In order to study the correlation pattern of the considered morpho-phenological traits and the differences among entries, a principal components analysis (PCA) was performed by using the function PCA of the “FactoMineR” package . PCA is Fifty mg of fresh leaf tissue were collected from each of the 448 plants (32 plants for each of 14 entries) and high quality DNA was isolated using the DNeasy 96 Plant Kit (Quiagen). DNA quality and concentration were checked by spectrophotometry using NanoDrop 2000 (Thermo Scientific) and 1.0% (w/v) agarose gel separation.B. oleracea chromosomes, the selected markers are not in linkage . All the DNA amplifications were carried out as described in [Broccoli individuals were genotyped using 17 putative neutral microsatellite markers (SSR) –47 and fribed in . ObtaineN) and observed (Na) genotypes, the effective (Ne) number of observed alleles, observed (Ho), as well as the expected (He) heterozygosity and the Fixation Index (F), were worked out by entry  with the use of GENALEX software [For each SSR marker the number of alleles and allele size range were recorded. The number of successfully analysed  was worked out following the method of  as impleFst values and two Genetic Distance (GD) matrices between individuals following the formula of [Pairwise comparison rmula of  were calrmula of . Two GD-rmula of . To bettrmula of , 54.Additional file 1: Figure S1. Monthly precipitation (mm) and mean of minimum, maximum and average temperature (°C). Data registered during the multiplication seasons in Perugia in 2009/2010 (PG 2009/2010), in Central Italy  in 2010/2011 and 2011/2012 , in United Kingdom in 2010/2011 (UK 2010/2011) and in France in 2010/2011 (FR 2010/2011), and during the final agronomic trial carried out in Perugia (PG 2013/2014). The bar chart refers to monthly precipitation, lines with squares, diamonds and circles refer to maximum, average and minimum temperature, respectively.Additional file 2: Figure S2. Final agronomic trial carried out in the DSA3 experimental field in Perugia . Broccoli multiplications carried out across Europe and in Central Italy were grown in the DSA3 experimental field in Perugia. The populations tested were: LR, Syn1-PG, Syn2-PG, Syn3-PG, Syn2-GR, Syn3-GR, Syn2-TER, Syn3-TER, Syn2-UK, Syn2-FR. H, HH, HHH and HHHH are the hybrid varieties used as controls.Additional file 3: Table S1. SSR markers used for analyze genetic diversity of broccoli original LR, Syn1-PG, its derived populations and hybrid controls. Bibliographic reference codes, genebank entry, primer sequences, repeated motif, linkage group (LG), and band range (in base pairs) relative to the 22 microsatellites used .Additional file 4: Figure S3. Neighbour-joining tree of genetic distances of the entries (two years of multiplication). B. oleracea var. italica original LR, Syn1-PG, its derived population by two years of multiplication across Europe and hybrid controls are coded with abbreviations and coloured with different colours as follows: LR-black, Syn1-PG-red, Syn2-PG-yellow, Syn2-GR-purple, Syn2-TER-dark green, Syn2-UK-blue, Syn2-FR-orange, H-violet, HH-brown, HHH-pink, HHHH-saddlebrown. Each individual of each entry are code whit a different number.Additional file 5: Table S2. Average values and standard deviations of morpho-phenological traits for the entries (three years of multiplication). B. oleracea var. italica original LR, Syn1-PG, its derived populations by three years of multiplication in Central Italy and hybrid controls. Averages followed by the same letters are not significantly different at P < 0.05 (Tukey HSD).Additional file 6: Figure S4. PCA based on morpho-phenological traits recorded for each entries (three years of multiplication). B. oleracea var. italica original LR, Syn1-PG, its derived populations by three years of multiplication in Central Italy and hybrid controls. Coloured symbols represent the different entries analysed as from the legend, grey dotted arrow represent the morpho-phenological traits coded as from Table Additional file 7: Table S3. Averages and standard errors relative to the main genetic parameters calculated by entry (three years of multiplication). Number of successfully analysed genotypes (N), observed (Na), and effective alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), and Fixation Index (F) worked out for original LR, Syn1-PG, its derived populations by three years of multiplication in Central Italy and hybrid controls on the basis of data recorded overall for the 22 markers used.Additional file 8: Figure S5. Neighbour-joining tree of genetic distances of the entries (three years of multiplication). B. oleracea var. italica original LR, Syn1-PG, its derived populations by three years of multiplication in Central Italy and hybrid controls are coded with abbreviations and coloured with different colours as follows: LR-black, Syn1-PG-red, Syn2-PG-yellow, Syn3-PG-light green, Syn2-GR-purple, Syn3-GR-grey, Syn2-TER-dark green, Syn3-TER-light blue, H-violet, HH-brown, HHH-pink, HHHH-saddlebrown. Each individual of each entry are code whit a different number.Additional file 9: Figure S6. PCoA based on average genetic distance of the entries (three years of multiplication). B. oleracea var. italica original LR, Syn1-PG, its derived populations by three years of multiplication in Central Italy and hybrid controls. Coloured symbols represent the different entries analysed as from the legend.


Studies on the peopling of South America have been limited by the paucity of sequence data from Native Americans, especially from the east part of the Amazon region. Here, we investigate the whole exome variation from 58 Native American individuals (eight different populations) from the Amazon region and draw insights into the peopling of South America. By using the sequence data generated here together with data from the public domain, we confirmed a strong genetic distinction between Andean and Amazonian populations. By testing distinct demographic models, our analysis supports a scenario of South America occupation that involves migrations along the Pacific and Atlantic coasts. Occupation of the southeast part of South America would involve migrations from the north, rather than from the west of the continent. The peopling of the Americas remains a fascinating, still controversial, and topic. While there has being significant advances in the last decade mainly due to genomics approaches being used in contemporaneous and ancient samples, critical issues remain unanswered, and especially regarding South America.It is now widely accepted that Native American founders moved from East Asia through Beringia, a land bridge between Northeast Asia and the extreme Northwestern America, and rapidly populated the whole continent . ArcheolThe peopling of South America is, however, more obscure. While is clear that a Pacific coastal route was rapidly used to reach the extreme south, it is generally believed that there was also an Atlantic coastal route toward the east . How theTo make the scenario even more complex, recent reports  have ideSince there is an extensive admixture in the American population due to European colonization and African slave trade, uniparental genetic systems  have been used to reconstruct Native American genetic origin . More reTo contribute to the reconstruction of the genetic history of Native Brazilian populations and address issues related to the peopling of South America, we analyzed exome data from 58 individuals from 8 different tribes scattered through the east of the Amazonian region. We have also included in our analysis genomic data from several other projects covering extant and fossil samples from a broad geographic extension, covering North, Central and South America regions. A marginal Australasian signal was found in two of the Amazonian populations sequenced here: Araweté and Zo’é. We confirmed that there is a clear genetic distinction between populations from the east and west of South America. This comparative analysis also allowed us to model the demographic movements in the early occupation of South America and suggest that the southeast part of the continent was occupied mainly by migrations from the Amazonian region.We sequenced the exome of 58 Native Brazilians samples from eight populations located at the east part of the Amazon basin . These iVariant calling in the exome data identified 132,794 single nucleotide variations (SNVs) and 14,102 indels. The genetic structure of Native Brazilian populations was explored and compared to other worldwide populations, with emphasis to Native American populations, including contemporaneous and ancient individuals (see methods and K = 5), the other three components being African, European and East Asian , where X represents each one of the fourteen Brazilian populations (sequenced here or obtained from the public domain) and Australasian indicate one of the following populations: Andaman, Australian, Papuan New Guinea, Bougainville, Dusun, Igorot, and Maori D, which test the gene flow from Amazon to population X in regard to southern populations.(B)D, which tests the gene flow between Amazon and West Andes to population X.(C)D, which test the gene flow from West Andes to population X in regard to southern populations.D  . The samt Andes) .We can envision few demographic scenarios for the occupation of central parts of South America, including the Amazonian region. Four possible scenarios will be discussed: (1) groups from the Atlantic route eventually turned south to occupy the Amazon basin and the Southeast of the continent; (2) groups from the Pacific route eventually turned east and occupied the central parts of the continent (likely in different waves from north to south); (3) Andean groups in the south migrate east and eventually turned north to occupy the south/central part of Brazil and the Amazonian region; and (4) a mixed model involving at least two of the models above. Since scenario 4 above is difficult to test with the present limitation in sample size, this leaves us with scenarios 1–3.f4 statistic using qpGraph from admixtools v.2.0 (available at 1) while varying the population representing each leaf nodes. Overall model 1 better fitted the data with smaller likelihood scores and f4 statistics with few tests presenting the worst absolute f4 below 3  groups from the Atlantic route eventually turned south to occupy the Amazon basin and the Southeast of the continent; (2) groups from the Pacific route eventually turned east and occupied the central parts of the continent (likely in different waves from north to south); and (3) Andean groups in the south migrate east and eventually turned north to occupy the south/central part of Brazil and the Amazonian region. The clear genetic distinction between western and eastern populations in South America, seen by others  and confSince native populations in the southeast (southeast of Brazil and/or north of Argentina) are closer to the native populations in the Amazonian region than to Andean populations, as observed by many authors  and hereAll these results give support to scenario 1, in which the southeast part of continent was populated from the north, rather than the west. Due to the controversy regarding a possible Australasian genetic signal in Native Americans and ancient samples from South America , we deciThe present study explored genetic data of Native Americans through whole-exome sequencing and investigated the history of occupation and expansion of these populations in South America. Our data support an occupation model with separate migration waves, most likely through a Pacific and Atlantic route with the southeast part of the continent being occupied by migrations from the Amazonian region.o 1062/2006 and 123/98). All participants signed a free-informed consent as well as the tribe leaders and when necessary a translator explained the project and the importance of the research. Their materials were collected according to the Declaration of Helsinki.The samples were collected from adult individuals (between 18 and 50 years old) from eight Native American populations residents of the Brazilian Amazon. They were collected as part of two projects developed by the Laboratório de Genética Humana e Médica (LGHM) and approved by Brazilian National Committee on Research Ethics – CONEP  and SureSelect Human XT all exon V6 (Agilent) kits following the manufacturer recommendations. The libraries were sequenced in the NextSeq 550 sequencing platform (Illumina) in 4 NextSeq 500/550 High Output Kit runs with approximately 16 samples each.2) using BWA MEM v0.7  between 2 and 10. To improve FlashPCA performance, rare variants  were excluded since they have a limited contribution to the analysis.We broadly investigate the genetic structure between all samples using an unsupervised ADMIXTURE v.1.3  clusteriWe also investigated the relationship between the investigated populations running TreeMix v.1.13  includin3 and D-statistic were computed according to 3).The unbiased gene-flow fhttps://github.com/andremrsantos/paper-sa-population.A detailed description of all analysis conducted here is included to https://www.ebi.ac.uk/ena) under the accession numbers: PRJEB9586, PRJNA393593, PRJEB24629, PRJNA229448, PRJEB25445, PRJEB29074, PRJEB28961, and PRJEB12437 and the sequencing data generated here are available at the ENA database under the accession number PRJEB35045.The data obtained from the public domain are available at the European Nucleotide Archive . All participants signed a free-informed consent as well as the tribe leaders and when necessary a translator explained the project and the importance of the research.SdS, ÂR, and SS conceived the study. JG was responsible for collecting all samples. AMR was responsible for all bioinformatics analysis. AV and TV-S were responsible for sample processing and sequencing. SdS and AMR wrote the manuscript. All authors made contributions to the text and approved the final version.We would like to mention that SdS is co-founder of DUNA Bioinformatics. The remaining 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.


Vanilla species in a world biodiversity hotspot, Madagascar, using seven microsatellite loci and 26 morphometric variables. Additionally, analyses were performed to test for the occurrence of any patterns of isolation by distance, isolation by environment, and isolation by adaptation and to detect possible physical barriers that might have caused genetic discontinuities between populations. Positive inbreeding coefficients detected in 22 populations were probably due to the presence of null alleles, geitonogamy and/or some admixture (sympatric species). In contrast, the only high‐altitude population showed an important rate of clonality leading to heterozygote excess. Genetic diversity was maximum in western populations, suggesting a postglacial colonization to the north and south. Clustering analyses identified seven genetic groups characterized by specific floral traits that matched five botanical descriptions in the literature. A contribution of montane refugia and river barriers on population differentiation was detected. We also detected combined effects of IBD/IBE and IBE/IBA on genetic differentiation and suggested this pattern is more likely determined by ecological isolation, although pollinator‐mediated divergent selection could not be ruled out for some of the species. Overall, this study provides further insights on speciation in orchids, a group for which Madagascar shows one of the world's highest level of endemism and confirms the importance of the peculiar biogeography of the island in shaping species differentiation.With over 25,000 species, orchids are among families with remarkable high rate of diversification. Since Darwin's time, major advances attributed the exceptional diversity of orchids to plant–pollinator interactions. However, unraveling the processes and factors that determine the phenotypic and genotypic variation of natural orchid populations remains a challenge. Here, we assessed genetic population structure and floral differentiation in recently diverged leafless  The aim of our study was to resolve evolutionary processes in a group of recently diverged Vanilla species in Madagascar, using a population genetics approach, with a combination of molecular data (microsatellites), floral morphological data, ecological data, and geographic distances. Clustering analyses identified seven genetic groups characterized by specific floral traits that matched five botanical descriptions in the literature. A contribution of montane refugia and river barriers on population differentiation was detected. We also detected combined effects of IBD/IBE and IBE/IBA on genetic differentiation and suggested this pattern is more likely determined by ecological isolation, although pollinator‐mediated divergent selection could not be ruled out for some of the species. Eleven additional samples from the Manompana forest . Each individual was amplified and genotyped using seven microsatellite markers developed by Gigant et al.  for multiple hypothesis test of LD. The Waples method  and the product of two fixation index (FST) for pairs of loci, was applied across all populations to test for a Wahlund or population structure effect on LD  and the mean expected heterozygosity under HWE (He) over all loci using the R package GENEPOP v.4.7.5 . Null allele frequencies at each locus across all populations were checked by INEst v.2.2  among populations  between pairs of populations using the GENEPOP package under R software. Analysis of molecular variance (AMOVA) ,” implemented in STRUCTURE were also examined   color space according to the Kendal et al. (L*) dimension values vary between 0 (black) and 100 (white reference) and represent the luminance. The (a*) and (b*) dimension values vary between −100 and 100 and represent the colors between red and green, and between yellow and blue, respectively , a* of inside of labellum (AIL), b* of inside of labellum (BIL), L* of outside face of sepal (LOFS), a* of outside face of sepal (AOFS), b* of outside face of sepal (BOFS), L* of inside face of petal (LIFP), a* of inside face of petal (AIFP), and b* of inside face of petal (BIFP). In total, 26 morphometric parameters were used in principal component analysis (PCA) using FACTOMINER  was also conducted to test the statistical significance of overall floral differences based on a priori groupings. All analyses were done with R software V3.5.1.Thereafter, flower parts were digitalized using a canoScan LiDE 120. The resulting images were processed under IMAGEJ software  were extracted for each sampling sites in Madagascar (Tables Recorded GPS coordinates Table  were usePS Table . To highr Tables . In addir Tables  and Worlr Tables  (Ribeiror Tables . Pearsonp) was used to identify the top eleven potential barriers. Then, 100 resampled bootstrap Nei's standard genetic distances (Dn) matrices  and coefficient of determination (R2) were estimated by performing 9,999 randomized permutation procedure . For that, a Pearson's correlation test on the 26 morphometric variables was carried out to choose a combination of uncorrelated variables (cor < 0.3). The PC scores of the first three principal components resulting from the PCA of these selected variables were, then, used to calculate the phenotypic distance between the eight populations in order to perform MMRR and dbRDA.To detect possible patterns of IBD and IBE, we performed multiple matrix regression with randomization (MMRR) as implemented in the R function “MMRR” Wang,  on the 2re Wang, . MMRR ware Wang,  using thre Wang, . The geore Wang, , 2014. Fre Wang, , was appre Wang,  in R. Ag33.1N > 0.7) except for AND population where genotypic diversity was only 0.27  (Table Ho) was lower than the expected heterozygosity (He), with positive FIS values, while Ho was higher than He  for the AND population (Table He) increased slightly  but significantly (p < .04) with latitude, with southern populations showing the lowest diversity and western populations showing the highest diversity , representing approximately 9% of clonality. At the population level, the clonality rate was generally low  overall differentiation among populations. Pairwise FST was all significant (p < .001), with high amplitude values ranging from 0.01 between two northwestern populations (BBL and MSL) up to 0.60 between AND  and ANJ (southwestern) (Table p < .001) was obtained between r2 and the product of two FST values for pairs of loci, suggesting a substructure of the sample  indicated a significant (n) Table . A strone Figure .K values in the Bayesian assignment of STRUCTURE showed a modal value at K = 4 and the maximum value of the posterior probability “Ln P(X|K)” corresponded to K = 7 ” to find the best K. Consequently, seven distinct genetic clusters were retained as the optimal K in STRUCTURE analysis  was found among populations, and only 6% was found among ecoregions. The distribution of ∆7 Figure . Accordi7 Figure , with un7 Figure  based ons Figure . If mosts Figure .FST‐based analysis of genetic barriers revealed eleven boundaries. Six of them were well supported with bootstrap values exceeding 60%. These genetic barriers separated, in majority, the seven different genetic groups obtained from STRUCTURE Figure .3.3K = 7). PCA1 explained 53.7% of the total variation and separated three clusters from three others by the weight and size of the flowers . Means of the ten most explanatory variables of the first three PCA dimensions and results of pairwise t test are shown in Table The first three principal components of PCA Figure , explains Figure . Color os Figure . Color os Figure . Clusters Figure . This mo3.4β = 0.0001, p = .001) and with a higher coefficient of determination (R2 = 0.29) to environmental distances (IBE)   but a weak independent pattern of IBE   with high NDVI and precipitation, north of Madagascar characterized by high annual temperature, west of Madagascar with high pH and temperature, and finally population from AND  that can be distinguished from the others by its altitude (mean elevation: 887 m) Figure . Scores ) Figure . However) Figure . Margina1) Table . Analyse1) Table . The PC11) Table . However1) Table . The int1) Table .44.1Vanilla populations in Madagascar. The levels of genetic diversity were relatively high for 20 populations out of the 23 , with an overall low genetic differentiation (FST = 0.19). These results are compatible with what was expected for Vanilla species, which are long‐lived thanks to vegetative growth, mostly allogamous . Indeed, flower morphology was clearly specific for each of six STRUCTURE clusters  compared to all the other species  along the western coast of Madagascar. In addition to the probable effect of climatic oscillation explained previously, the lowest genetic diversity detected in southern populations might be partially linked to population size reductions (bottlenecks), as small and isolated populations are characterized by a low genetic diversity and linkage disequilibrium among loci  on genetic differentiation. However, partial dbRDA showed a relatively weak (R2adj = 3.4%), but significant influence of environmental factors on genetic differentiation and no contribution of IBD. The combined effect of geographic and environmental distance (IBD ∩ IBE) was the most explanatory (R2adj = 42.8%). Indeed, spatial autocorrelation was detected between geographic and environmental distance   (Table R2adj = 50.06) (concerning flower size‐related measures) was also revealed in this dataset. But, there was a strong correlation between environment and morphological variables, making it difficult to disentangle their relative contribution to the genetic differentiation. Two evolutionary patterns can possibly explain this situation: Either IBA of floral traits could indicate pollinator‐mediated ecological selection, or ecological isolation could have led to drift‐induced morphological variations in flower size  were shown to be unable to remove pollen from the large flowers of V. grandiflora, as opposed to Eulaema meriana , with the smallest flowers, could be concerned as well by a certain level of pollinator specificity. However, various arguments are more in favor of the fact that most Vanilla species probably share the same pollinators in Madagascar. For V. bosseri in the Kirindy forest (KRD), Petersson  all have potential pollinator species from the Allodapini group     have also been reported  and the absence of morphological data of one identified genetic group. The presence of new and significantly different genetic and morphological groups in the east, not yet botanically described, is interesting and will require further sampling and genetic studies in the eastern region, which is not specifically described as harboring leafless Vanilla species ; Formal analysis (lead); Investigation ; Methodology (lead); Writing‐original draft (lead); Writing‐review & editing . Aro V. Ramarosandratana: Conceptualization ; Data curation ; Funding acquisition ; Supervision ; Validation ; Writing‐review & editing . Michel Grisoni: Conceptualization ; Funding acquisition ; Supervision ; Validation ; Writing‐review & editing . Vololoniaina H. Jeannoda: Conceptualization ; Supervision ; Validation (supporting); Writing‐review & editing . Pascale Besse: Conceptualization (lead); Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Supervision ; Validation ; Writing‐original draft ; Writing‐review & editing .Appendix S1Click here for additional data file.


The South Asian subcontinent is characterized by a complex history of human migrations and population interactions. In this study, we used genome-wide data to provide novel insights on the demographic history and population relationships of six Indo-European populations from the Indian State of West Maharashtra. The samples correspond to two castes (Deshastha Brahmins and Kunbi Marathas) and four tribal groups . We show that tribal groups have had much smaller effective population sizes than castes, and that genetic drift has had a higher impact in tribal populations. We also show clear affinities between the Bhil and Pawara tribes, and to a lesser extent, between the Warli and Kokana tribes. Our comparisons with available modern and ancient DNA datasets from South Asia indicate that the Brahmin caste has higher Ancient Iranian and Steppe pastoralist contributions than the Kunbi Marathas caste. Additionally, in contrast to the two castes, tribal groups have very high Ancient Ancestral South Indian (AASI) contributions. Indo-European tribal groups tend to have higher Steppe contributions than Dravidian tribal groups, providing further support for the hypothesis that Steppe pastoralists were the source of Indo-European languages in South Asia, as well as Europe. Tribal populations are considered to be the indigenous populations of India and constitute approximately 8.63% of the total population2. The non-tribal populations consist of religious communities outside the caste system, and hierarchically arranged, endogamous social groups known as castes3. This fundamental division of the Indian population into castes, tribes and religious groups has resulted in a large number of effective populations or endogamous groups3.The South Asian subcontinent is characterized by a complex history of human migrations and interactions, as well as a variety of traditional practices, all of which have contributed to an extensive cultural and genetic diversity. One of the defining characteristics of the Indian human landscape is the presence of both tribal and non-tribal groups4. ANI ancestry is proportionally higher among Indo-European speakers and is also more prevalent in upper castes than in lower or middle castes4. Furthermore, the ANI component has a discernable geographic pattern in South Asia decreasing from the northwest. This gradient relating the ANI with the ASI is known as the “Indian Cline”4.India’s genetic diversity can be best described by a model of mixture between two statistically reconstructed ancestral populations: The Ancestral Northern Indians (ANI), which are related genetically to West Eurasians, including Middle Easterners, Central Asians and Europeans, and Ancestral South Indians (ASI), which are distantly related to Indigenous Andaman Islanders7. These studies suggest that a distinctive genetic mixture of ancient Iranians and AASI could have formed the genetic basis of the Indus Valley Civilization (IVC), thus this mixture was denoted the “Indus Periphery”. It has been hypothesized that the Indus Periphery played a pivotal role in the transformation of the three source populations into the two ancestral populations, ANI and ASI, which shape the current Indian Cline6. During the decline of the IVC, a portion of the Indus Periphery population admixed with the AASI to form the ASI in the Southern regions of India, whereas subsequent interaction between Indus Periphery and incoming Steppe-MLBA populations in the Northern regions resulted in the formation of the ANI6. Notably, following this period of admixture (4200-1900 years ago), Indian populations appear to have exhibited a shift toward endogamy, thereafter reducing gene flow8. In this context, a range of historic migrations and long-standing socio-cultural divisions have structured India’s genetic variation into a unique pattern of different endogamous groups.Recent studies incorporating genomic data from both ancient DNA (aDNA) and present-day South Asians have modeled the formation of the Indian Cline as a combination of three source populations: (1) the Ancient Ancestral South Indians (AASI), which represent a hypothesized South Asian Hunter-Gatherer lineage arising from a population split contemporaneous with the split of East Asian, Onge and Australian Aboriginal Ancestors; (2) Ancient Iranians and (3) Middle and Late Bronze Age Steppe populations (Steppe-MLBA)9. These samples were analyzed together with previously reported South Asian modern and ancient samples to infer their population-structure and demographic history.This study presents genome-wide data from six modern populations from the Indian State of West Maharashtra (WM). The samples correspond to two castes  and four tribal groups , all of which belong to the Indo-European language family10, and quantified using the EPPENDORF Biophotometer Plus. All methods of data collection were conducted as per ethical guidelines and regulations after receiving approval from the Institutional Ethics Committee (IEC) at the Savitribai Phule Pune University (Ethics/2012/16).The study participants included unrelated individuals representing four tribal populations  and two caste populations (Deshastha Brahmins and Kunbi Marathas) collected from West Maharashtra, India. Sampling of tribal populations was done from Jawhar  in the Palghar district and Dhadgaon  in the Nandurbar district of West Maharashtra. Caste populations were sampled from the city of Pune and adjoining villages  using purposive sampling . Authors MJ and SO travelled to these communities and advertised the study in the community looking for volunteers. Healthy individuals from both genders with no apparent skin disorders were included in the study. Details of age, sex, birthplace and clan were recorded using a questionnaire, and a sample of whole blood (5-8 mL) was collected in EDTA vials from each study participant after obtaining informed written consent. DNA was extracted using the phenol-chloroform extraction methodGenotyping was carried out for 480 DNA samples with the APPLIED BIOSYSTEM’S AXIOM Precision Medicine Research Array (PMRA) at the Imperial Life Sciences Pvt Ltd. Laboratory  using standard protocols. The PMRA array includes approximately 900,000 markers and was designed to capture common genome variation in diverse population groups. The program Axiom Analysis Suite was used to carry out basic quality-control (QC) steps. After these initial QC steps, 522,125 polymorphic markers and 478 samples were retained for further analyses.-6, 3) markers with minor allele count <4, 4) Insertion/Deletion (Indel) markers, 5) markers not present in the 1000 Genomes reference panel, or that did not match on chromosome, position and alleles, 6) A/T or G/C SNPs with MAF > 40% in the 1000 Genomes South Asian reference samples, and 7) SNPs with allele frequency differences> 20% between the study sample and the 1000 Genomes South Asian reference sample. After these QC steps, we retained 456 samples and 398,118 autosomal markers. The samples were phased using the program SHAPEIT211.We performed additional QC steps to remove samples and markers. Briefly, we filtered out: 1) samples with sex discrepancies 2) samples that were outliers for heterozygosity, 3) samples with missing call rates <0.95, 4) related individuals (pi-hat> 0.25), and 5) samples that were outliers in Principal Component Analysis (PCA) plots. We also removed: 1) markers with genotype call rate <0.95, 2) markers with Hardy-Weinberg (HW) p-values <1012. The PLINK conditions applied were based on previous studies14:–homozyg-snp 30;–homozyg-kb 300;–homozyg-density 30;–homozyg-window-snp 30;–homozyg-gap 1000;–homozyg-window-het 1;–homozyg-window-missing 5;–homozyg-window-threshold 0.05. A SNP coverage of 504 K for all the populations analysed enables a single SNP to be found, on average, in a track of 5.7 Kb. To obtain a window with 30 SNPs, on average , a track of 171 Kb is needed. A threshold of 300 Kb was set for the minimum length in order to capture small ROH that originated far in the past and also to ensure that these are true ROH that arose by genetic drift or consanguinity. An alternative source of homozygosity originates from linkage disequilibrium (LD), which typically produces tracts measuring up to 100 Kb, based on empirical studies16. By using a minimum-length cut-off of 300 Kb, most short ROH resulting from LD are removed.ROH were called using PLINK v1.917, or by genetic drift and low effective population size, also called panmictic inbreeding18. The two different sources of inbreeding, namely, systematic inbreeding or non-random mating (FIS), and genetic drift (denoted by FST), are both components of the total inbreeding coefficient (FIT). FIT is defined as the probability that an individual receives two alleles that are identical-by-descent. Sewall Wright developed an approach to consider these three different F coefficients in his F statistics (1 − FIT) = (1 − FIS)(1 − FST)19. First defined as correlations, Nei showed how these coefficients can be expressed in terms of allele frequencies, and observed and expected genotype frequencies21. Systematic inbreeding has a direct effect on the HW proportions of a population and can be measured using the Wright’s fixation index or FIS19. In this context FIS is the average SNP homozygosity within an individual relative to the expected homozygosity of alleles randomly drawn from the population. This component of total inbreeding coefficient is measured using the–het function in PLINK. Panmictic inbreeding due to genetic drift (FST) can be considered a measure of the genetic differentiation of a subpopulation in comparison with an ideal population with a large Ne. FIT is the total inbreeding coefficient, traditionally obtained using deep genealogies, and can be calculated using the sum of Runs Of Homozygosity (ROH) higher than 1.5 Mb22.Inbreeding can be produced by two different biological phenomena: deviation from panmixia, in what G. Malecot called systematic inbreeding23, defining 2 cM as the minimum length for reporting IBD segments. Default settings of the program were applied for all other remaining parameters. After detecting the IBD segments with Refined IBD, we removed breaks and short gaps in IBD segments using the merge-ibd-segments java program (12Jul18 version). Following authors’ recommendations, we removed gaps between IBD segments that had at most one discordant homozygote and that were less than 0.6 cM in length. Networks of individuals were generated based on their shared IBD lengths using Gephi software24. Each node represents an individual, and the length of an edge equals to 1/. Therefore, in these networks, individuals sharing more IBD segments are located closer than individuals with limited IBD sharing. The IBDNe program25 was used to estimate effective population sizes in the last 50 generations, applying the default settings of the program.To investigate identity-by-descent (IBD) segments in the phased genotype data we used the Refined IBD program (12Jul18 version)26 and the Simons Genome Diversity Project (SGDP)27, hereinafter referred to as the WM-1KG-SGDP dataset. We then merged the WM-1KG-SGDP dataset with ancient DNA data generated by Narasimhan et al.5. After merging, the number of autosomal markers in the WM-1KG-SGDP-aDNA dataset was ~107 K markers.To obtain additional insights about the demographic history of our WM samples, we merged the WM database with other available data from both modern and ancient samples. We first merged the genotype data for markers without missing data in the WM sample (~126 K markers) with data for South Asian samples of the 1000 Genomes Project29.For the WM-1KG-SGDP PCA, the number of markers after LD-pruning with PLINK 1.9 (r2 = 0.1) was ~73 K. For the WM-1KG-SGDP-aDNA PCA we only included markers with genotyping rates higher than 80%, and samples with genotyping rates higher than 80% (except for one ancient Indus Periphery sample that had a genotyping rate higher than 60%). The final number of markers after LD-pruning with PLINK 1.9 (r2 = 0.2) was ~70 K (genotyping rate = 95.9%). Ancient DNA samples were projected onto the PCA plot generated with the modern samples, using the lsqproject = YES and shrinkmode = YES options of smartpca. Another PCA analysis was also carried out without projecting the aDNA samples. Unsupervised clustering of the samples included in the LD-pruned WM-1KG-SGDP-aDNA dataset was carried out with the program ADMIXTURE30.PCA analyses based on LD-pruned markers were performed using the smartpca algorithm implemented in EIG v7.2.131 was used to carry out allele sharing analyses with the f3 and f4 statistics32 based on the WM-1KG-SGDP-aDNA dataset. First, we calculated the outgroup f3 statistic, which measures the amount of shared genetic drift between two South Asian populations using an African population (Mbuti) as an outgroup with the topology f3. Second, we applied f4 statistics to test for admixture in modern South Asian populations. For putative parental groups for this test, we selected the following samples based on our PCA and ADMIXTURE estimates: Ganj-Dareh , Steppe-MLBA (Steppe population) and Andamanese (AASI population). For these tests, we also used Mbuti as an outgroup, using the topology f4 . Negative values indicate that there is more allele sharing between population X and population A than between the population X and B. Positive values indicate that there is more allele sharing between population X and B than between population X and A. The significance of the f3 and f4 statistics is based on a Z-score calculated by block jackknife. Finally, we used the program qpAdm33 to estimate the relative contributions of AASI-related, Indus-Diaspora-related and Steppe-related groups to the WM tribal and caste populations, as well as other relevant populations from South Asia. For this analysis we merged the WM-1KG-SGDP-aDNA dataset with genotypes of present-day and ancient DNA data, resulting in a sample file with ~52 K markers. For this test we used the same samples as Narasimhan et al.5, i. e., Onge, Steppe-MLBA and Indus Periphery as source groups , and Mbuti, Iron_Gates_HG, Karelia_HG, Ganj_Dareh_N, Anatolia_N, West_Siberia_N, Han, and Karitiana as outgroups.The ADMIXTOOLS bundle implemented in the R package admixrWe first identified ROH in single individuals. The results are summarized in Fig. ROH larger than a second cousin (FROH = 0.0152), first cousin (FROH = 0.0625) and uncle and niece (FROH = 0.125). The Kunbi Marathas sample has the highest proportion of individuals with genomic inbreeding coefficients (FROH) higher than the offspring of both first cousin and uncle-niece mating  belong to both castes (Kunbi Marathas and Deshastha Brahmins respectively) and can explain why they have a larger burden of ROH > 8 Mb. Besides these exceptions, Kunbi Marathas and Deshastha Brahmins are the populations with the lowest median FROH (0.0092 for both). In comparison with admixed populations, such as ACB and ASW, West Maharashtrian populations show, on average, FRHO eight times larger. An important question to answer is whether this homozygosity is the result of consanguinity or isolation and genetic drift. In Fig. 34. At a population level, this question is answered in Fig. IS against the population median FROH along with the interquartile range (IQR) of both variables. All the West Maharashtrian populations analysed are located under the FIS = 0, which is consistent with a model based on isolation and genetic drift, and not systematic consanguinity.Population heterogeneity is better represented when the number of ROH and the total sum of ROH > 1.5 Mb are plotted for each individual Fig. . VerticaTable 35. Individuals that share higher proportions of their genome IBD are located closer in the graphs. In the plots based on the full IBD dataset, the four tribal groups are clearly separated from the two caste groups. In agreement with the results based on the average total length of IBD shared between pairs of individuals, members of the Pawara tribal group clearly cluster with members of the Bhil tribal group, and individuals from the Kokana tribal group are located close to those of the Warli tribal group. Further, the plots based on large IBD segments (e.g. ≥14 cM) highlight that individuals from the tribal groups share more recent common ancestry than the individuals of the two caste groups. Particularly, the shared recent common ancestry between individuals from the Pawara and Bhil tribes is clear in the figures.We generated a network of IBD sharing between individuals based on the full IBD dataset, and also on segments of different sizes . The lengths of IBD segments will depend on the number of generations separating the two haplotypes that share a segment from their common ancestorWe also estimated effective population sizes for the six WM groups . In general, effective population sizes have been lower in the tribal population groups than in the castes. Among the tribal groups, the Warli shows a larger increase in population size within the last 20 generations than the other three groups. The patterns observed for the two caste groups are quite different. In the Deshastha Brahmins group, there has been a relatively constant population size and then a major population expansion within the last ten generations. In the Kunbi Marathas, the most notable aspect of the plot is the sharp drop in effective population size that started approximately 15 generations ago, with a recent increase about six generations ago.st PC, with the Punjabi samples occupying the most positive regions and several Dravidian tribal and caste groups from the SGDP project  located on negative regions in the 1st PC. The two WM caste samples overlap with the 1KG samples. The four WM tribal groups are clearly separated from the two caste samples and all the 1KG and SGDP samples, occupying the most negative regions of the 1st PC. Additionally, there is a clear split between the tribal samples in the 2nd PC, with the Pawara and Bhil clustering together in positive regions and the Warli and Kokana in negative regions. As an alternative strategy to depict population relationships, Supplementary Figure 6 shows a Neighbor-Joining tree including the WM samples with the 1KG South Asian samples, using an African sample as an outgroup. The tree was based on FST distances. The dendrogram is very consistent with the PC plots, with the four WM tribal groups clustering apart from all the caste groups.Supplementary Figure 5 depicts the PCA plot of the WM samples with modern South Asian samples. The samples are distributed in a cline in the 1et al.5 referred to as “Indus Periphery” samples and show a complex mixture of ancestry related to Iranian agriculturalists and South Asian hunter-gatherers (but no Steppe ancestry); Middle-to-late Bronze age samples from the Steppe (Steppe-MLBA); South Asian samples from the Iron Age and the Holocene ; and an Iron Age outlier from the South Asian Iron Age sample without Steppe ancestry (Saidu_Sharif_IA_o). A cline that is consistent with the well-known ANI-ASI cline is observed in the 1st PC. The most negative regions of this axis are occupied by the WM tribal groups and also modern samples from South India, most of them Dravidian-speakers . The Steppe-MLBA samples and the Ganj-Dareh samples (and some Iran_Turan_BA samples) occupy the most positive regions of the 1st PC. The SAS_H samples tend to show higher ASI ancestry than the earlier SAS_IA samples. The Indus_Periphery samples are located within the region occupied by the modern Indian populations, but they do not cluster together in the plot. We also prepared a PCA plot without projecting the ancient DNA samples . In this plot, the Steppe-MLBA samples and the Ganj-Dareh samples are separated in the 2nd PC.We also carried out a PCA analysis projecting ancient DNA samples onto the modern South Asian samples Fig. . The ancet al.5 have shown that modern South Asian populations are primarily the result of admixture between three distinct population groups: AASI, Ancient Iranians, and Steppe groups. The results for the samples presented in Narasimhan et al.5 are fully concordant with the results described in their study. Steppe_MBLA samples primarily fall into a red component, whereas the samples from Ganj-Dareh in Iran form a different yellow component. There is also a blue component that appears in different proportions in many of the modern and ancient samples, which seems to represent the AASI contribution. Most of the samples in the analysis (except Steppe-MLBA and Ganj_Dareh) appear to be the result of admixture between these ancestral groups . The average contributions of each component to each sample are depicted in Supplementary Table To infer in more detail individual clustering and potential admixture patterns in the modern and ancient DNA samples, we used an unsupervised ADMIXTURE analysis at K = 3 . The lowest shared genetic drift is observed with the samples from Pakistan , and in India with the SGDP Brahmin sample, the WM Deshastha Brahmin sample and the 1KG Gujarati sample. The opposite patterns are observed when exploring shared drift with the ancient Iranians  and the Steppe samples . The f4-statistics highlight very similar patterns, but provide additional information , Indus-Diaspora-related and Steppe-related groups to the WM tribal and caste populations, as well as other relevant groups from South Asia we used the program qpAdm. Supplementary Table 46 and Y-chromosome54 markers and dense autosomal datasets in modern58 and ancient DNA samples60 have provided fascinating insights about the demographic history of the region. These studies have explored a wide range of topics, including the genetic relationships between populations belonging to distinct linguistic groups , the genetic diversity present in castes and tribal groups, as well as the history and pattern of migrations in this region. The recent availability of ancient DNA data has added an important temporal perspective, making it possible to study in much more detail the relative contributions of Ancient Ancestral South Indians (AASI), Ancient Iranians and Steppe populations to modern tribal and caste groups across South Asia7.Genetic studies in South Asia, including evidence from mtDNAIn this study we generated genome-wide data from two castes (Deshastha Brahmin and Kunbi Maratha) and four tribal groups  from the Indian state of West Maharashtra, and investigated the demographic history of this region in addition to the genetic relationships of these populations with other modern and ancient samples from the South Asian continent.61 but these are identified as different tribal groups by Enthoven62. The Kokana and Warli groups also live in the same geographic region in the Palghar district. Close affinity between Bhil and Pawara and to a lesser degree between Kokana and Warli was also previously shown by dental nonmetric traits63 and mitochondrial D-loop sequences64.We identified ROH in single individuals and IBD segments in pairs of individuals within and between groups. The homozygosity patterns observed in the six WM groups is primarily the result of isolation and genetic drift, rather than systematic inbreeding Fig. . Within-66.We observed clear differences in effective population sizes between the WM tribal and caste groups . The four tribal populations show substantially smaller effective population sizes than the caste groups. Furthermore, in three of the four groups  there is a decrease in effective population size starting less than 20 generations ago, prior to a rebound in effective size that is evident starting approximately ten generations ago. This decline in effective population size is not observed in the Warli tribal group. The effective sizes of the two castes are larger than the tribal groups, but their demographic patterns are quite different. In particular, in the Kunbi Marathas, there is evidence of quite a dramatic effective population drop starting 20 generations ago . This is consistent with our observation that the Kunbi Marathas sample is characterized by a relative enrichment in long ROH and IBD segments with respect to other groups. Although it is difficult to pinpoint the possible causes for the population declines of the groups analyzed here, particularly within the Kunbi Marathas, there have been several mega-famines in India as a result of multi-year failures of the monsoon, including the devastating Durga Devi famine that lasted more than a decade, from 1396 AD to 1409 AD6. The analyses using PCA, ADMIXTURE, f3 and f4 statistics, and qpAdm show very consistent patterns. In the PCA plots, the tribal groups from WM split from other tribal and caste South Asian groups in the second PC , as well as ancient DNA samples from this regionf3, f4 and qpAdm statistics. The program ADMIXTURE provides estimates of relative ancestral contributions using an unsupervised clustering strategy based on modern and ancient samples. The results obtained using a model with three parental groups identifies two ancestral components present in modern South Asian populations that are maximized in the Steppe pastoralists (Steppe_MLBA) and Ancient Iranians (Ganj_Dareh), and a third component that is also present in many ancient and modern South Asian populations, and is particularly prevalent in the WM tribal groups and in Dravidian tribes and castes. This third component seems to represent the AASI contributions, and our results for the ancient DNA samples and modern SGDP and 1KG samples are in broad agreement with those reported by Narashiman et al.6. Unfortunately, there are no unadmixed modern or ancient representatives of the AASI population. In contrast, the analyses using f3- and f4-statistics and qpAdm are driven by comparisons with reference samples. For the analyses with f3- and f4-statistics, we used the Andamanese as a population distantly related to the AASI group, and the Ganj_Dareh and Steppe_MLBA samples as representative of the Ancient Iranians and Steppe pastoralists, respectively. For the analyses with qpAdm we used the same source populations and outgroups as those used by Narasimhan et al.6. Using this admixture model we can compare the results obtained for the WM samples with the extensive dataset of approximately 140 South Asian groups presented in Narasimhan et al.6. When interpreting the qpAdm results, it is important to note that the Onge population is only very distantly related to the AASI population and that the Indus-Periphery samples have both Ancient Iranian and AASI contributions. In spite of these caveats, all our analyses indicate that the WM tribal groups have high AASI contributions, similarly to tribal and caste Dravidian populations . This is evident in the large putative AASI components in the ADMIXTURE plots  and Warli (N = 3) samples. In their analyses, the Warli had one of the highest Onge (AASI)-related contributions reported for the Indian cline groups, as well as the Kolcha (another Indo-european tribe) and Dravidian populations from South India such as the Kurchas, Irula, Malayan, Adiyan, Ulladan, Palliyar and Pulliyar. Although both Indo-European and Dravidian tribal groups have very high AASI contributions, Indo-European tribal groups tend to have higher Steppe contributions than Dravidian tribal groups. This provides indirect support for a model that explains the spread of the Indo-European languages as a result of the migration of the Steppe pastoralists to Europe and South Asia, in contrast to the alternative model that supports the spread of Indo-European languages as a consequence of the migration of Anatolian farmers. This Steppe hypothesis has also been recently supported by Shinde et al.7, who have shown that there is negligible ancestry from Anatolian farmers in the ancient South Asian farmers represented in the Indus Valley Civilization. Overall, our results are in agreement with recent ancient DNA studies providing support for Steppe pastoralists as a source of Indo-European languages in Europe and South Asia67. Comparisons of mtDNA, Y-chromosome and autosomal data indicate that this migration was primarily male-driven40.Our results are also consistent with the qpAdm results reported by Narasimhan In summary, in this study we present novel insights on the demographic history and population relationships of six Indo-European populations from the Indian state of West Maharashtra . We show that WM tribal groups have smaller effective population sizes than WM castes, and genetic drift has had a higher impact in tribal populations. We also show clear affinities between the Bhil and Pawara tribes, and to a lesser extent, between the Warli and Kokana tribes. Our comparisons with available genomic data from modern and ancient samples from South Asia indicate higher Ancient Iranian and Steppe pastoralist contributions in the WM Brahmin caste than in the WM Kunbi Marathas caste. In contrast to the two castes, WM tribal groups have very high AASI contributions. Although both Indo-European and Dravidian tribal groups from India have very high AASI contributions, Indo-European tribal groups tend to have higher Steppe contributions than Dravidian tribal groups, providing further support for the hypothesis that Steppe pastoralists were the source of Indo-European languages in South Asia.Supplementary information.


Neighboring population pairs of deciduous and evergreen habitats were weakly genetically differentiated (FST = 0.003 on average), nevertheless with a statistically significant effect of habitat type on the overall genetic structure. This low differentiation was consistent with the strong and long‐lasting gene flow between populations inferred by demographic modeling. In turn, insular and mainland populations were moderately differentiated (FST = 0.08 on average), in line with the inference of moderate ancestral migration, followed by isolation since the end of the last glaciation. Effective population sizes were large, yet smaller on the island than on the mainland. Weak and nonparallel footprints of divergent selection between deciduous and evergreen populations were consistent with their high connectivity and the probable polygenic nature of local adaptation in these habitats. In turn, stronger footprints of divergent selection were identified between long isolated insular versus mainland birds and were more often found in regions of low recombination, as expected from theory. Lastly, we identified a genomic inversion on the mainland, spanning 2.8 Mb. These results provide insights into the demographic history and genetic architecture of local adaptation in blue tit populations at multiple geographic scales.Understanding the genomic processes underlying local adaptation is a central aim of modern evolutionary biology. This task requires identifying footprints of local selection but also estimating spatio‐temporal variations in population demography and variations in recombination rate and in diversity along the genome. Here, we investigated these parameters in blue tit populations inhabiting deciduous  Populations of small insectivorous passerines have long been used to study local adaptation, in both quantitative genetics and population genetics frameworks . Overall, traits displaying these strong phenotypic differences had heritabilities ranging from 0.20–0.43  and in insular (Corsica island) and mainland areas (Mainland France) in order to better understand the determinants of their local adaptation. The analysis was based on Resequencing of birds sampled in four sites studied in the context of a long‐term project  were captured in 10 locations , and the other half are dominated by the evergreen holm oak Q. ilex (indicated by “E”). Four of these locations  are monitored as part of a long‐term research program started in 1976 and have been described in previous studies  and by the Direction Départementale des Services Vétérinaires (DDSV). Birds were captured during the reproductive period, from early April to late June, on their nesting territory. Birds were captured either in nest boxes during the feeding of their young (in the four sites studied on the long term) or using nets in the vicinity of their nest (in the other sites). Birds were banded with a unique metal ring provided by the CRBPO. Five to 20 μl of blood was sampled from a neck or a wing vein from breeding blue tits. Blood was stored at 4°C in Queen's buffer  platform . Each individual was identified using a unique six nucleotides tag, individuals were multiplexed in equimolar proportions by libraries of 36 individuals, and each library was sequenced on a lane of an Illumina HiSeq 2000. Single‐end sequencing was used to produce 100 bp reads. This design was used to obtain an average read depth of 50x. The DNA of three individuals was extracted twice and processed in different libraries to test for reliability of the genotyping process.DNA extractions were achieved using Qiagen DNeasy Blood & Tissue kits and were randomized across sites. DNA was quantified using first a NanoDrop ND8000 spectrophotometer and then a Qubit 2.0 fluorometer with the DNA HS assay kit (Life Technologies). DNA quality was examined on agarose gels. Library preparation using RAD‐seq , allowing for a 10% mismatch in the adapter sequence. Reads were filtered for overall quality, demultiplexed, and trimmed to 85bp using process_radtags, from the Stacks software pipeline 1.39  of 5, the SNP model, and alpha = 0.05 . cstacks was used to build the catalog of loci, allowing three mismatches between sample loci when building the catalog. sstacks was used to match loci against the catalog. Lastly, populations program in Stacks was used to genotype individuals. In this program, relatively permissive filters were applied, to retain SNP genotyped in at least 50% of individuals  and with a minimum read depth of 4. 350,941 SNP and 947 individuals were obtained in this “GeneralDataset.”Raw sequences were inspected with Andrews,  for qualVCFtools , geographic distance (latitude and longitude), and altitude, on genomic differentiation, was measured using a redundancy analysis (RDA)  were compared using an ABC framework modified from Roux et al. , and a model of strict isolation (SI). The prior and details of the simulation pipeline are fully described in the Note S1. The PAN model assumes that the two focal populations descent from a single panmictic population characterized by its effective size (Ne). The EQ model  assumes that the population is subdivided into two discrete populations of sizes Npop1 and Npop2 that are connected by continuous gene flow at a constant rate each generation. In this model, the divergence time is not a parameter. The IM, SI, SC, and AM models all assume that an ancestral population of size NANC splits at splitT into two daughter populations of sizes Npop1 and Npop2. Under SI, no subsequent gene flow occurs. Under AM model, gene flow occurs from splitT to amT and is followed by a period without gene flow. Under IM, gene flow is continuous after splitT. Under, the SC model, splitT is followed by a period of SI, after which a SC starts Tsc generations. The EQ, IM, AM, and SC models included migration as M = 4 0.m,N with M1←2 being the number of migrants from population 2 to population 1 and M2←1 being the reverse. The effect of linked selection and barriers to gene flow were accounted for, by modeling heterogeneous population size (Ne) and heterogeneous migration (m), respectively. Such heterogeneity was modeled using beta distributions as hyper‐prior on each of these two parameters. These resulted in four alternative versions for models with gene flow EQ, AM, IM, and SC  and two versions for PAN and SI (Nhomo and Nhetero). We used a modified ABC  and SI. The pipeline described in the above section, integrating linked selection and barriers to gene flow, was run between the A1D samples (chosen on the mainland because it had the largest sample size) and B4D (chosen in Corsica because it had both a large sample size and was from the same habitat as A1D). We used the same ABC pipeline for the model selection procedure and parameter estimation as described in the above section. Finally, we attempted to convert demographic parameter into biological units assuming a mutation rate of 1e‐8 mutations/bp/generations.2.6Plink and smoothed in R. To contrast with long‐term Ne estimates from coalescent simulation in our ABC modeling , we also inferred recent Ne for each population using SNeP V1.1 , proportion of polymorphic loci, and MAF spectrums. For each population and chromosome, and subsequently on average for the entire genome, linkage disequilibrium (LD) decay was measured with 2.7Bayescan V2.1  tests, and we represented the pattern using histograms.We investigated variation of  McVean,  using li2.9Lostruct .We searched for potential genomic inversions using a variety of descriptive statistics. First, we searched for genomic regions having a particularly low recombination rate and large long‐distance linkage disequilibrium, nevertheless associated with a high density of SNPs and therefore unlikely to correspond to peri‐centromeric regions but rather to local suppression of recombination that may be due to inversions. Second, we implemented a PCA sliding window analysis in order to identify portions of the genome with individuals carrying an inversion at the homozygous or heterozygous state or individuals exempt from the inversion  and π  along the genome between individuals that were inverted homozygous, noninverted homozygous, and heterozygous for the inversion, looking for potentially reduced diversity and increased differentiation at the inversion. We looked for potential salient variations in read depth in and around the putative inversion. We also tested whether the detected inversion was at Hardy–Weinberg equilibrium (using a common χ2 test) and whether the frequency of the inversion varied geographically and between evergreen and deciduous habitats.We detected one putative inversion. We verified, using admixture with PopGenome , assuming a standard mutation rate of 1e‐8 and with the simplifying assumption of no gene flow and no introgression. Lastly, we represented the divergence between these individuals using an UPGMA tree of bitwise distance using the R package poppr 1.1.1 . Second, we selected the SNPs from the region of the inversion. Third, Da was measured between the five aforementioned groups of individuals using 2.10We used the R package topGO . The full RDA model was significant , latitude (p < .001), longitude (p < .001), and altitude (p < .001). The first axis of the global RDA explained 4.54% of the variance and was correlated mainly with latitude (−0.987) and longitude (+0.999). The fourth axis of this global RDA displayed the strongest correlation with phenology (−0.856) and explained 0.27% of the variance. The partial RDA model conditioning for the effects of latitude, longitude, and altitude was globally significant (p < .001), as well as the effect of phenology (p < .001). The axis of the partial RDA explained 0.29% of the variance and was correlated with phenology (0.922).The admixture analysis suggested the existence of two main distinct genetic groups corresponding to mainland and island populations Figure . IncreasFST comparisons were significantly different from zero  was slightly higher in deciduous than in evergreen habitats . Effective population size of the mainland was 397,685 , around 7.5 times greater than the one of the island, 52,990   than the other way around (m ~ 3.8e‐5). However, this asymmetry did not generate a strong difference in gene flow between the two groups, given the lower effective population size on the island compared to the mainland,  strongly outperformed the SI models, with p(AM) = 0.99 Table . For the] Figure . Our ana3.3t tests’ p‐values <.001. On the contrary, there was no significant difference of observed heterozygosity at smaller spatial scale among populations within the island  or within the mainland . More SNPs were polymorphic in mainland populations  than in island populations . The MAF spectrum showed enrichment of variants with smaller frequencies in mainland populations versus island populations . LD decayed rapidly in the first 5kb and was lower in populations from the mainland  compared to island populations . This pattern of rapid LD decay was similar between chromosomes . Contemporary eN inferred from LD varied from 142 (in A2E) to 355 (in A1D)  than in deciduous ones (261 on average). The four largest eN (with an average of 318) were found for the four populations monitored on the long term in forest in which hundreds of artificial nest boxes have been installed for several decades (the eN in the 6 other populations was on average of 210). In the entire dataset, 2,599 SNPs were identified as nonsynonymous while 6,751 SNPs were identified as synonymous. We observed 1.18 times lower allele frequencies for nonsynonymous variants (average MAF = 0.09) than for synonymous ones (average MAF = 0.11) . This overall lower frequency of nonsynonymous compared to synonymous mutations was similar between populations from Corsica (1.19) and the mainland (1.16), as well as between deciduous (1.18) and evergreen (1.19) habitats . In contrast, FST between each pair of deciduous and evergreen populations was not correlated to recombination rate. FST outlier SNPs between Corsica and the mainland populations and identified by Bayescan or by the RDA were more often found in regions of low recombination . In contrast, FST outlier SNPs between deciduous and evergreen populations were not preferentially found in regions of low recombination  in this region of the chromosome compared to the rest of the chromosome . We did not find evidence for an increase of read depth at specific loci in the putative inversion or its vicinity in inverted individuals compared to normal ones. We did however notice the absence (read depth = 0) of three loci from the inversion, in the four inverted individuals. The sliding PCA detected either two groups  outside of the putative inversion, or four groups at the putative inversion location, separating individuals from Corsica and three groups for mainland individuals  than for SNPs outside of this region (FST = 5.8e‐4). π was lower . The tree of genetic distance illustrated the divergence of inverted blue tits from noninverted blue tits from both mainland and Corsica . The percentage of each genotype was similar across all mainland locations and both habitat types .We detected one putative inversion on chromosome 3, spanning 2.8 Mb, from position 11,838,789 to 14,661,550, and containing 390 SNPs. The estimated recombination rate was on average nine times lower  included the following: (a) for the deciduous–evergreen outlier tests, GO:0060385, axonogenesis involved in innervation; GO:2001013, epithelial cell proliferation; GO:0007194, negative regulation of adenylate cyclase; GO:0090647, modulation of age‐related behavioral; GO:1901379, regulation of potassium ion transmembrane transport, (b) for the Corsica–mainland outlier tests, GO:0008344, adult locomotory behavior; GO:2001224, positive regulation of neuron migration, (c) for the inversion, GO:0016446, somatic hypermutation of immunoglobulin; GO:0045910, negative regulation of DNA recombination; GO:0006298, mismatch repair; GO:0008340, determination of adult lifespan.None of the gene lists gathered with the different tests (each outlier test among deciduous and evergreen environment and between Corsica and the mainland and the inversion test) yielded any significantly enriched GO term after correction multiple testing (Table S14). The most promising GO , explaining the low genetic differentiation between these populations. Demographic modeling also revealed that each pair of deciduous and evergreen populations most likely never diverged and maintained high connectivity through migration. We identified slight and mostly unrepeated footprints of divergent selection across these evergreen and deciduous population pairs, which is consistent with their demography and the likely polygenic nature of several traits implicated in their local adaptation. In both insular and mainland populations, we found large Ne, although smaller for insular populations than mainland ones, resulting in different distributions and lengths of ROH. Gene flow between Corsica and the mainland most likely stopped at the end of the last glaciation. Both large Ne and gene flow until the end of the last glaciation hence explained moderate genome‐wide differentiation. We identified several genomic footprints of selection, enriched in regions of low recombination only in the case of the mainland/island divergence. Finally, we identified a putative genomic inversion spanning 2.8 Mb, polymorphic in mainland populations only. We discuss these results in the context of the vast scientific knowledge acquired on these blue tit populations during the last four decades, and more generally in the context of genomics of adaptation with gene flow.4.1FST ranging from 0.0006 to 0.0079). This result is in line with the primary observations realized earlier on a smaller set of populations  was relatively coherent with the diversification time found in the literature for the entire blue tit complex . However, this differentiation value contrasted with the much larger divergence at mitochondrial DNA observed for these populations  was probably low enough to detect divergent outlier loci potentially implicated in local adaptation. We identified several outlier SNPs and outlier 200 kb windows showing elevated differentiation, more often in regions of lower recombination than elsewhere in the genome (contrary to the outliers found for the deciduous vs. evergreen populations). Furthermore, FST was on average higher in these regions of low recombination compared to elsewhere in the genome (which was not the case for deciduous vs. evergreen populations). These patterns of increased divergence in regions with low recombination between divergent populations are commonly observed in other species (Cruickshank & Hahn, FST sliding window, sized in Kb, is expected to dilute signatures of selection in regions of the genome where LD is fast decaying while it is expected to over‐represent regions where LD decays much more slowly, hence increasing even more the potential false positives in regions with low recombination (Beissinger, Rosa, Kaeppler, Gianola, & De Leon, The contemporary demography inferred between the populations from Corsica and mainland France, especially the absence of gene flow for approximately 4,500 generations and the large effective population size, likely provides a suitable context for the build‐up of local adaptation. Moreover, the level of divergence (e.g., Jay et al., via gene flow from a distinct refugium with which connectivity could have been enhanced after deglaciation, hence after the stop of gene flow with Corsica. The age of this inversion, its origin, its biological effects, and the potential accumulation of deleterious mutations need to be inferred more thoroughly via genotyping full inversion sequences from individuals in diverse locations and using advanced statistical methods (Lohse, Clarke, Ritchie, & Etges, While the putative genomic inversion on chromosome 3 was absent from Corsica and detected in mainland individuals, its level of divergence from the noninverted sequence indicated that it was likely twice older than the beginning of divergence between blue tit populations from mainland and Corsica. This could first suggest that this polymorphism emerged in mainland blue tit populations and then did not introgress the Corsican populations, maybe due to a local disadvantage and/or genetic incompatibilities or simply due to drift coupled to little gene flow. A second hypothesis could be that this inversion was present in Corsican populations but had been purged out due to a local disadvantage. Such a disadvantage can be due to the typical accumulation of deleterious mutations in such nonrecombining inverted sequences (5Our study demonstrated the usefulness of demographic modeling and of the analysis of the variation of genomic diversity and recombination along the genome to uncover the genetic determinants of local adaptation in a small passerine with a large distribution, and occupying different forest habitats. Especially, demographic modeling rejected the hypothesis of a secondary contact between deciduous and evergreen populations and favored a situation with continuous gene flow. These results support the idea that blue tits have adapted to their habitats despite ongoing gene flow, while contextualizing how large gene flow most probably constrained local adaptation (Lenormand, None declared.CP conceived the study, conducted fieldwork, laboratory analyses, bioinformatics and statistical analyses, interpreted the results and wrote the manuscript. QR conducted bioinformatics and statistical analyses, interpreted the results and edited the manuscript. AC obtained the ERC grant, conceived the study, performed fieldwork, interpreted the results and edited the manuscript.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Fig S6Click here for additional data file.Fig S7Click here for additional data file.Fig S8Click here for additional data file.Fig S9Click here for additional data file.Fig S10Click here for additional data file.Fig S11Click here for additional data file.Table S1‐S12Click here for additional data file.Note S1Click here for additional data file.