Chromosomal polymorphism due to inversions is one of the best-studied systems in Drosophila population genetics (1-3). In natural populations of Drosophila, it is common and is an adaptive trait so subject to natural selection (4-6). There is genetic differentiation of inversion polymorphism in D. ananassae, which suggests that chromosomal polymorphism may be adaptively important in a widespread domestic species and populations may undergo evolutionary divergence as a consequence of their adaptation to varying environments (7-8). The idea is that the greater environmental diversity a population faces, the more inversions it can maintain due to diversifying selection (Ludwig effect).

D. ananassae is a cosmopolitan and human commensal species distributed in the tropical, subtropical and mildly temperate regions (Figure 1). The genome of D. ananassae harbors a large number of inversions in its natural populations (9). Out of these reported from various parts of the world, three paracentric inversions namely, Alpha (AL) in 2L, Delta (DE) in 3L and Eta (ET) in 3R show worldwide distribution hence named cosmopolitan inversions by Futch (1966) (10). Population genetics of chromosomal polymorphism in Indian natural populations of D.ananassae has been extensively studied (7-8, 11). The results have clearly shown that there is geographic differentiation of inversion polymorphism. D.ananassae populations provide a unique and interesting opportunity to know the patterns of distribution of genetic variability and its relation to various eco-geographical factors, which has not been examined earlier to the present study. Earlier to this study, quantitative differences in frequencies of chromosome arrangements from different eco-geographic regions were taken as an evidence for geographic differentiation of inversion polymorphism (7-8). In another study, Nei’s (1973) genetic identity estimates (12) were applied to reveal the distribution of genetic diversity by clustering populations according to state / province (13). In study by Singh and Singh (14), pairwise F_ST estimates and gene flow were calculated to deduce population sub-structuring and gene flow (14).

Figure 1

Drosophila ananassae, male and female.

In order to understand the community structure in natural of populations of D.ananassae, network analysis was employed. Network analysis is useful tool to understand the complex system, where the system can be represented as nodes and their mutual interaction by edges. Complex networks are generally partitioned into the classes. These classes contain sets of nodes such that edges within the class are highly dense as compared to the edges outside the class. These classes are known as modules or community. Community detection is a fundamental problem in the network analysis. It provides better understanding of the organizational structure of the network. A vast variety of real-world problems such as co-authorship groups, metabolic networks, web communities etc. has been identified using various community detection algorithms. In the present problem, locations are considered as nodes. An edge is placed between nodes if the difference between two inversions (e.g. alpha (AL)) is less than a predefined number. The lesser difference is an indicative of the similarity between the populations of two locations, which is represented by the existence of edge between two nodes in our network. We construct these networks for alpha (AL), delta (DE) and eta (ET) frequencies. After construction of networks, communities are identified. The communities aim to characterize the groups of similar populations (15-16). Unlike conventional techniques such as principal component analysis or Bayesian approach, networks do not deal with the problem of dimension reduction. Thus, network analysis serves as a robust tool to analyze the communities based on AL, DE and ET frequencies.

Present communication employs network analysis and community detection to the frequencies of three cosmopolitan inversions to determine i) the genetic diversity maintained in the species, ii) pattern of distribution of genetic variation within and among populations, and iii) more specifically, the role of geographical regions, time of collection and seasons in influencing genetic variation in populations of D.ananassae.

In one of the largest spatio-temporal study done to date, D. ananassae flies were collected from different eco-geographical regions of India. Nei’s gene diversity statistics, network analysis and modularity were applied to the frequencies of chromosomal inversions to arrive at the estimates of genetic diversity; distribution of genetic variation, community structure analysis and strength of communities formed in the populations of D.ananassae. Analysis show spatial and temporal characteristics of inversion polymorphism in Indian natural populations of D.ananassae. Network analysis and modularity has allowed the quantification of degree of clustering of D.ananassae populations. Spatial association or clustering provides the measure of geographical ‘closeness’ or spatial proximity. Earlier to this study, D. ananassae populations have not been investigated anywhere on such an enormous scale both spatially and temporally with respect to chromosomal polymorphism. This is, despite the fact that these flies are domestic and cosmopolitan in distribution. The present study tries to fill the void, by doing the collections from different corners of the country thus including the whole range of geo-climatic heterogeneity. This study has adopted mathematical analysis like Community detection and modularity to corroborate the findings from genetical analysis (Nei’s gene diversity estimates).

When populations were grouped by geographic regions of India (East, West, North, North-east, South, coastal and mainland) the diversity (H_S) among populations from a particular region comes lower than among-group diversity (G_ST) between populations from different regions as given in Table 2. This means that populations from northern region are genetically more identical as compared to populations from northeast, west or east. Similarly, populations from coastal region differ from the populations from mainland region suggesting the role of eco-geographical parameters on the patterns of inversion frequencies and distribution of genetic diversity. This supports the theory that populations from a particular region are adapted to the microhabitat/niche of that region and inversions being recombination-suppressors have evolved to safeguard the co-adapted gene complexes from undergoing recombination and disrupting the co-adapted gene complexes. In the earlier companion study, we have the similar finding where populations from the similar eco-geographic regions i.e. from the same state or province show more or less similar trend in inversion frequencies and the level of inversion heterozygosity (13). Nei’s genetic identity estimates also reveal that populations from similar eco-geographical regions are more identical compared to those belonging to different regions (13). Some populations irrespective of occupying similar regions show genetic dissimilarity from one another, which is contrary to the pattern under environment-specific selection. This might be due to the inter-habitat/niche differences or environmental heterogeneity to which populations are exposed since historical past. By and large, populations from the identical geographic regions and so grouped together show higher genetic similarity than populations of different groups. This strengthens the role of natural selection in geographical differentiation of inversion polymorphism as also shown by previous studies on Indian natural populations of D.melanogaster (26-27). This reaffirms the theory that Indian natural populations of D.ananassae show geographical differentiation with respect to the inversion polymorphism. Da Cunha and Dobzhansky (28) reported the similar phenomena in D. willistoni where levels of inversion polymorphism in populations are directly related to the diversity of the habitat occupied by the populations. In Drosophila, fitness related traits show geographical variations as an adaptive response of plasticity to the experienced environment (29-30). Many studies have empirically established the linkage or epistatic association of inversions with fitness related traits and hence impact of natural selection on genetic variation. Adaptive response to the ecological variations driven by ecological factors that vary to a great extent in the region leads to marked genetic diversity within the species (31). The association of genetic variation with environmental and geographical heterogeneity could be due to natural selection operating on chromosomal variability in D. ananassae. Natural selection may be involved in generating and maintaining the genetic differentiation among populations (32-34).

The development of polymorphism through natural selection is one of the way through which a population may improve its capacity to utilize the environment and survive through temporal changes of it. In natural populations of Drosophila, chromosomal polymorphism due to inversions is common and is an adaptive trait (4-6). A number of adaptive functions have been found to be associated with inversion polymorphism. Inversions have also been used to study geographical clines, temporal cycles, meiotic drive and natural selection (2-3). These are of interest because of their unique origin and also because of the fact that functional coadaptations are likely to occur within an inversion so that rearrangements might also be involved in an adaptive polymorphism (35). The inversion karyotypes may differ in certain components of fitness such as fecundity, viability, rate of development, fertility, hatchability and sexual activity (4).

Season wise (summer, monsoon and winter) grouping of populations led to almost equal mean values of H_S and G_ST. However, individual results for monsoon season show higher G_ST compared to H_S as given in Table 2. This indicates the overall effect of humidity and temperature on the genetic variability in populations through its effect on type of vegetation. On the other hand, D. ananassae populations undergo drastic reduction during seasonal extremes (summer and winter) (36). The first unambiguous indication that inversions were subject to strong selection came from studies of temporal shifts in the inversion frequencies. Dobzhansky and coworkers have also reported similar seasonal and altitude dependent fluctuations in the frequencies of various gene arrangements of D. pseudoobscura from a number of different localities (37-44), whereas no such changes were observed at other localities (45). Other species in which seasonal changes in inversion frequencies have been reported are D. melanogaster (46-48). Further, Indian localities are highly variable in latitude and altitude and there are significant seasonal variations moving from south to north (29-30).

When populations were grouped according to the time of collection (in years and months), the overall diversity (H_T) came similar in both the cases. This suggests the small effect of collection time on the overall genetic variability. This could be due to geo-climatic homogeneity in a particular collection time or year resulting into similar patterns of variability among populations belonging to a particular region collected and sampled at the same time. This suggests that sampling could affect the overall pattern of genetic diversity (49). One very important comparison that comes out from this study is that diversity among populations in a particular collection year is lower (H_S=0.305; also, overall diversity, H_T=0.455) than diversity among populations in a particular collection month (H_S=0.321; also, total diversity H_T=0.456). This is reasonable as seasonal fluctuations are more or less constant over a year than in a single month, thus leading to more or less similar patterns of genetic variability across a particular collection year. This testifies the ‘local adaptations’ theory leading to ‘local selection’. The overall value of gene diversity estimates for the two temporal parameters corroborate these findings. We have the similar findings when populations were grouped according to regions (mainland and coastal regions), i.e. the diversity among sites in a particular region comes lower (H_S=0.297, while H_T= 0.457) than when populations were grouped into mainland and coastal regions (H_S=0.309, while H_T= 0.457). Here also, the geographical factors over a broad region remain more or less similar but these may vary over small coastal or mainland regions, thus, affecting the variability pattern accordingly. D. ananassae being cosmopolitan in distribution have a broad geographic range, which itself testifies to the ‘local adaptations’ across the environmental heterogeneity. Variation in the degree of inversion polymorphism can be accounted for by genetic and demographic factors (50-53).

Selection history hypothesis explain the increase in genetic variance in novel and stressful environment as such situations are not encountered in historical past (50-53). Stressful environment induces an increase in genetic and phenotypic variations in fitness related quantitative characters (54-55). Geographical gradients are of special interest in the climatic adaptation because the climate varies strongly with geographical variables (56-57). Several environmental factors may impact the physiology of individuals; temperature is thought to be one of the strongest, and thereby of great selective importance (56-57). Thermal variations are linked with changes in relative humidity along altitudinal and latitudinal gradients. Localities with higher elevations along the south-north transect have lower relative humidity and ambient temperature than many other localities. By contrast, in the Indian tropical peninsula, low altitude localities are characterized by ambient temperatures of 25-30ºC and high relative humidity. Thus, Drosophila species and ectothermic insects face locality specific environmental stress (58-59). Further, natural populations display geographic population sub-structure, which is due to the differences in alleles and genotype frequencies from one geographic region to other. In addition, natural habitats are typically patchy with favorable areas intermixed with unfavorable areas. When there is population subdivision, there is almost inevitably some genetic differentiation among the subpopulations, which is the acquisition of allele frequencies that differ among the subpopulations (60). D. ananassae occurs in highly structured population throughout its geographic range (14, 31, 61-62). Ecological and demographic factors may have significant consequences for the short and long term evolutionary dynamics of inversion polymorphism and the manner with which they co-evolve with the rest of the genome (63).

Nei diversity index does not address the problem of community structure of population collected from various locations. Similarity, based on the difference between AL, DE and ET is not an indicative of high dimensional data. Thus, widely used methods such as principal component analysis and Bayesian technique are not suitable for this problem. To this end, network analysis serves as a potential tool to analyze the structure of populations and identification of similar groups of population. The communities detected at various difference scale of AL, DE and ET tends to encompass the identical populations. Further, these communities are largely consists of sites located nearby to each other with few exceptions. These findings connotes to the impact of regional specific factors such as climatic conditions on the populations of Drosophila.

Larger values of d delineate the weak level of similarity among the populations. More edges can be seen in the networks generated over the larger values of d in AL, DE and ET and yields a small number of communities with more dense edges.

In one of the largest spatio-temporal study, gene-diversity statistics, Community structure analysis and Modularity have revealed the presence of significant variability in the Indian natural populations of D.ananassae. In most of the cases the among-group diversity i.e. distribution of diversity among populations with respect to different regions; different seasons; different collection time was more compared to within-group diversity. Among the parameters chosen to analyze the components of genetic diversity, geographical attributes seems to have maximum, while the time of collection and seasons have minimum influence on the genetic variability in Indian natural populations of D.ananassae. Genetic variability associated with environmental heterogeneity clearly reveals the role of environment specific natural selection; however, the homogenizing effects observed could be due to genetic hitchhiking and canalization (i.e. genetic differences are canalized via rigid polymorphic system that represses deviation from phenotype that is optimal in common selecting environment.

Pranveer Singh and Pankaj Narula contributed equally to the work. Author thanks Prof. B.N. Singh, Banaras Hindu University (BHU), India for providing lab facility and guidance and Prof. B. Charlesworth, University of Edinburgh, UK, for suggesting Nei’s gene diversity statistics for the study.