Cedrela lilloi C. DC. , an important south American timber species, has been historically overexploited through selective logging in Argentine Yungas Rainforest. Management and conservation programs of the species require knowledge of its genetic variation patterns; however, no information is available. Molecular genetic variability of the species was characterized to identify high-priority populations for conservation and domestication purposes. Fourteen native populations  along a latitudinal gradient and with different logging's intensities were assessed by 293 polymorphic AFLP (amplified fragment length polymorphism) markers. Genetic diversity was low (Ht = 0.135), according to marginal location of the species in Argentina. Most of the diversity was distributed within populations (87%). Northern populations showed significant higher genetic diversity (R2= 0.69) that agreed with latitudinal pattern of distribution of taxonomic diversity in the Yungas. Three clusters were identified by Bayesian analysis in correspondence with northern, central, and southern Yungas. An analysis of molecular variance (AMOVA) revealed significant genetic differences among latitudinal clusters even when logging (ΦRT = 0.07) and unlogging populations (ΦPT = 0.10) were separately analyzed. Loss of genetic diversity with increasing logging intensity was observed between neighboring populations with different disturbance (ΦPT = 0.03–0.10). Bottlenecks in disturbed populations are suggested as the main cause. Our results emphasize both: the necessity of maintaining the genetic diversity in protected areas that appear as possible long-term refuges of the species; and to rescue for the national system of protected areas some high genetic diversity populations that are on private fields. The Yungas, a subtropical montane rainforest from Northwest of Argentina (NWA), is one of the sites of highest biodiversity in this country, especially on the Upper Bermejo River Basin (UBRB). It is known that Yungas plays an important role in providing many environmental services such as watershed protection. This ecosystem is between 400–3000 meters of elevation, and it is located on the southernmost limit of the Neotropical Cloud Forests, a major system in Latin America . It occuCedrela lilloi C. DC. (cedro coya), Meliaceae  according to warmth-loving habit to C. lilloi, the genetic variation in the species in Argentina should follow a latitudinal pattern with a decrease in genetic diversity with increasing latitude toward less benign weather conditions and (2) severely disturbed populations through logging should have decreased the genetic diversity. The central aim of this work was to identify priority areas for conservation and use of the species in Argentine Yungas Rainforest as potential variability sources for domestication, breeding, and conservation programs.Genetic diversity is essential to ensure evolutionary processes of adaptation of forest genetic resources to changing environmental conditions and social requirements . Fortuna are few  for stud are few . This te are few . In ordeC. lilloi is a canopy, long-lived and deciduous tree species (30–40 m) that occurs along Argentine Yungas between 900–2500 meters of elevation in the upper level of montane rainforest and the temperate montane forest. The populations of this species show trees at low density and aggregated distribution with a gap-dependent recruitment , with present logging or until 20 years without logging; low disturbance populations (LDP), with 30–50 years without logging; and undisturbed populations (UP), with over 50 years without logging (unlogging). Additionally, information from interviews with forest producers and visual assessments during sampling was added to include the “intensity of logging” and the “history of use”  as others factors that allowed us to achieve a better approximation to disturbance level of logging populations. In protected areas, disturbance was estimated by logging history (prior to its creation) in addition to their age. La Florida Provincial Park  and Baritú National Park  were classified as UP, whereas Calilegua National Park (1979) and El Nogalar Natural Reserve  were classified as DP according to history of intense cedar logging. Trees were georeferenced by GPS (Geographic Position System) and identified by individual/population code.Young leaves from 160 trees randomly selected were collected from 14 native populations with different histories of use throughout natural range of i in NWA , Fig. 2.i in NWA . Previoustrategy . To assestrategy , 2006. IEcoRI/MseI) and selective amplification was performed using primers having two and four additional nucleotides, respectively (Eco + 2/Mse + 4), as suggested by C. odorata. Twelve AFLP primer combinations from Eco + 2  and Mse + 4  were examined on 16 individuals and those five most clear and polymorphic were selected: Eco+AT/Mse+ACCA, Eco+AC/Mse+AATA, Eco+AC/Mse+AACA, Eco+AT/Mse+AACA, and Eco+AG/Mse+ACCA, coded E14M115, E12M107, E12M99, E14M99, and E13M115, respectively. Amplification products were separated by electrophoresis on 6% (w/v) polyacrylamide gels and detected by silver staining .Total genomic DNA was extracted from 70–80 mg of dried leaf material following the standard CTAB procedure described by C) and the percentage of polymorphic loci (PPLC) per primer combination.AFLP products were scored as 1 or 0 for presence or absence of bands, respectively, and a binary data matrix was generated. It was assumed that each amplified fragment corresponded to dominant allele (for both heterozygous and homozygous states) and absent fragment corresponded to null homozygote. Only polymorphic bands were included and no homoplasy was assumed. In each gel, samples of known genetic profiles were included as controls. We estimated the total number of polymorphic loci, as well as the number of polymorphic loci (PLP), the percentage of polymorphic loci (PPLP), and the number of exclusive bands (EB), as well as the total average for the species. In addition, trees that showed exclusive bands at individual level (EBi) were identified, and the number of exclusive bands per individual and the number of trees with EBi per population were recorded.Genetic diversity  of populPT statistic. Statistical significance of test was assessed from 999 random permutations across full data set. In addition, total genetic diversity (Ht) and its partition within (Hw) and among (Hb) populations, as well as FST, were calculated according to m) among populations were obtained from Nm = (1−FST)/4 α FST, where Nm is the number of migrants per generation, and α is a correction factor that depends on the number of sampled populations, α = [n/(n−1)]2, according to K value, which is the number of genetically homogeneous clusters of populations was estimated. Five replications for each proposed K value (from K = 2 to K = 6) for 500 000 iterations after a 50 000 burn-in period was run. Program setting was performed under no-admixture ancestry model and correlated allele frequencies. We finally selected K value that maximized likelihood of data and ΔK modal values, and minimized variability of likelihood values, as recommended by To examine distribution of genetic variation among and within populations, a hierarchical analysis of molecular variance AMOVA,  was perfRT), among populations within sectors (ΦPR), and within populations. Additionally, intrapopulation genetic diversity was tested against latitude with a linear regression analysis performed by The effect of latitude on genetic diversity was assessed by He, PPL, and EB for each of three population groups, according to latitudinal sectors of Yungas , Fig. 2.RT or ΦPT statistics (for comparing by populations pairs). Particularly, under the second approach, several comparisons were performed: (a) among three disturbance groups, (b) between logged (LDP and DP) and unlogged groups (UP), and (c) between DP and LDP groups.Logging effects on genetic diversity was assessed by He, PPL, and EB for each disturbance group, after grouping populations according to this factor  from difC. lilloi, of which 293 (49%) were polymorphic among or within examined populations. Primers combinations differed in their ability to detect polymorphism; thus, E13M115, E14M115, and E12M99 were the most polymorphic, with 70, 70, and 65 PLC and 51%, 52%, and 60% PPLC, respectively. In contrast, E12M107 and E14M99 were the least polymorphic combinations, with 51 and 37 PLC and 43% and 41% PPLC, respectively.A total of 592 AFLP fragments were amplified over 160 individuals of P, PPLP, and EB were on average 80 (ranged from 36–192), 27.3% (ranged from 12.3–65.5), and 6 (ranged from 0–36), respectively. As a general pattern, genetic diversity of cedar populations decreased with increasing latitude and disturbance level  and PLce level . All genPT = 0.130, p ≤ 0.001). When Lynch & Milligan' restriction was applied, total genetic diversity (Ht) was 0.135, most of which was distributed within populations (Hw = 0.119). Genetic diversity among populations was Hb = 0.015 and FST = 0.115 was similar to ΦPT statistic from AMOVA. Estimated historical gene flow among populations was Nm = 1.67. Genetic clusters estimated by Bayesian model was K = 3, principally in accordance with the three latitudinal sectors . However, a 13% of the variance was partitioned among populations (Φ sectors . All sour = 0.90), , . Overallr = 0.35, p = 0.004), and this increased significantly when undisturbed populations were excluded from analysis . This increase is consistent with the greater genetic distances of La Florida with southern-central populations than with northern populations (separated over 500 km).A positive correlation between geographic and genetic distance was detected . Genetic differentiation among populations within sectors was ΦPR = 0.09 (p ≤ 0.001) and 86% of variance was concentrated within populations. For analysis of only disturbance and low disturbance populations, genetic differentiation among latitudinal sectors increased in 2% , which is consistent with the assignment of undisturbed population of Baritú (north) and La Florida (south) to the same group by Structure. Finally, genetic differentiation increased to 10%  for only undisturbed populations. Different AMOVA corresponding to three comparative assessments according to latitude are presented in 2 = 0.19, p > 0.01; PPL, R2 = 0.27, p > 0.01). However, it was high, negative, and significant in the analysis of only populations with some disturbance level, with R2 = 0.69 (p < 0.001) and R2 = 0.63 (p < 0.01) for He and PPL, respectively. For each disturbance group, relationship between genetic diversity and latitude increased in all cases, except in LDP for PPL . It was followed by southern , which is consistent to the high genetic diversity of La Florida. Central Yungas, where all sampled populations are disturbed (LDP and DP), showed the lowest genetic diversity . When only populations with some disturbance level were examined, we found a clear decrease in genetic diversity from northern to central Yungas and the same trend although less pronounced from central to southern Yungas. The He was 0.181, 0.104, 0.093 and the PPL was 89.3%, 40.9%, 38.6%, for northern, central, and southern Yungas, respectively. Only for EB, central sector showed a lower value (6) compared with northern (68) and southern (11). For assessment of only undisturbed populations, northern population of Baritú again had higher genetic diversity than southern population of La Florida . The AMO for PPL . These rC. lilloi populations was confirmed by obtaining a significant genetic differentiation between disturbance groups. For the total region, AMOVA detected that 2% of total variance corresponded to variation between logged and unlogged populations groups , which was low but highly significant. Within each latitudinal sector, a significant genetic differentiation associated with logging only was observed in south , and it became high  in the evaluation of disturbed and low-disturbed populations together as logged group. Only at south, LDP and DP groups showed significant genetic differentiation . Genetic differentiation between pairs of neighboring populations with different disturbance was high in south and low to moderate in north and center .The data revealed that logging decreased population genetic diversity. For the total region, unlogged group showed higher genetic diversity  than logged group . Within each latitudinal sector, the same trend of greater genetic diversity in undisturbed populations was observed. The clearest pattern of genetic diversity decrease with increasing logging was detected in southern Yungas. In this sector, we observed the highest genetic diversity for UP group , which was followed by LDP group , and DP group had the lowest values , for all estimated parameters. At center, LDP group was more diverse  than DP group . In northern Yungas, UP was the most diverse group , while the unique LDP population  was less diverse than DP group , which could be due to its most southerly location. Finally, comparative analysis of neighboring populations showed clearly that increased logging leads to an important loss in genetic diversity. Populations with lower disturbance level had higher genetic diversity for all compared pairs in three latitudinal sectors, corresponding to Baritú with 3R, Argencampo, and El Nogalar (north); La Ramada with Calilegua (center); and La Florida with El Siambón (south), . Effect in south . Geneticd center . The BarCedrela lilloi populations in Argentine Yungas. According to previous studies, 200 AFLP is an adequate starting point to explore genetic diversity and differentiation  and less than that reported in other AFLP studies for trees , as suggested by ST slightly below ΦPT is consistent with studies that have used both approaches (m = 1.67) is above to minimum proposed (Nm = 1) to reduce genetic structuring by drift . This is very important to develop future management and conservation plans of forest restoration at ecosystem level.A clear pattern of decline of genetic variability of  Forests  and Arge Forests . A signi Forests . C. lillronments , and theC. lilloi during the glacial times of Pleistocene , which are known to have affected the Neotropics (C. lilloi suggests that populations could have persisted at lower latitudes during cooling periods, and they have expanded toward southern latitudes during warmer periods. Then, loss of diversity is predicted as a result of demographic and genetic processes, such as population bottlenecks and genetic drift, occurring throughout range expansion. This may explain lower genetic diversity and divergence toward southern Yungas, which is expected in more recently colonized areas (Austrocedrus chilensis populations of northern Patagonia in Argentina to possible glacial refuges. Greater changes on polymorphic loci compared with heterozygosity are because the loss of any allele leads to change the number of polymorphic loci, whereas only those with higher frequency lead to modify heterozygosity (If we consider climate variability as historical processes, latitudinal shifts may also reflect long-term consequences of geological disturbances and refugial isolation . Our finotropics . Warmth-ed areas . Similared areas  related zygosity . HoweverC. lilloi in Argentine Yungas during extremely dry periods of Pleistocene. They are the UBRB in northern and the wet slopes of Sierra de Aconquija in Tucumán and may explain the high genetic variability of La Florida and populations of northern edge of Salta that are located precisely on the Aconquija system and the UBRB, respectively. This approximation is strongly in agreement with two areas with high concentration of endemic species proposed by During Quaternary, glacial cycles had pronounced periods of environmental aridity . TherefoC. lilloi populations, as has been reported for other forest species (C. lilloi populations in Salta (In addition to latitudinal pattern, results indicate that logging reduces genetic diversity of  species . The cle species , this wo species . Finally species . Therefo species . Structuin Salta .S. humilis populations related to smaller fragment. Second, although the historical gene flow estimated would not still reflect the human impact on Yungas forest, decrease in heterozygosity may suggest that long-term processes, associated with loss of higher frequency alleles by restricted gene flow and drift, are taking place. This is consistent with heterozygosity reductions by logging detected in S. macrophylla populations from Mexico to Panama with random amplification polymorphic DNA (C. lilloi (C. lilloi populations isolated and in small slope areas, thus suggesting crosses among relationship trees. In C. fissilis populations, increases in genetic structure (Loss of genetic diversity process involves several steps . First, phic DNA  and fromphic DNA . Progresphic DNA . Genetic. lilloi . Fornes tructure  and inbrtructure  associatC. lilloi are pollinated by small generalist insects (An adequate level of gene flow among remnant trees is a key factor to counteract probable genetic drift . It shou insects , with mo insects . AdditioC. lilloi has important implications for its conservation and use. To assign populations to different priority areas in Yungas Rainforest, we evaluated contribution from each one to the total genetic diversity from genetic diversity and uniqueness, as suggested by C. lilloi and Metán can act as stepping-stone for gene flow between central and southern Yungas.Genetic variation of Argentine populations of To conclude, conservation and use of endangered species should follow some statements. Assessment of genetic variation in species is essential to know the current gene pool to design appropriates conservation strategies. A maximum of populations prioritizing those more diverse and divergent through different environments should be preserved. Conservation only with protected areas is not enough. It is urgent to apply sustainable forest management that will protect populations on private fields. Finally, we expect that genetic and ecological studies about this valuable timber species begin to guide present practices of management and help to take political measurements at regional level, thus showing how knowledge can be translated into effective conservation plans.


Tenualosa ilisha  is an important anadromous clupeid species from the Western division of the Indo-Pacific region. It constitutes the largest single fishable species in Bangladesh. Information on genetic variability and population structure is very important for both management and conservation purposes. Past reports on the population structure of T. ilisha involving morphometric, allozyme and RAPD analyses are contradictory. We examined genetic variability and divergence in two riverine (the Jamuna and the Meghna), two estuarine (Kuakata and Sundarbans) and one marine (Cox's Bazar) populations of T. ilisha by applying PCR-RFLP analysis of the mtDNA D-loop region. The amplified PCR products were restricted with four restriction enzymes namely, XbaI, EcoRI, EcoRV, and HaeIII. High levels of haplotype and gene diversity within and significant differentiations among, populations of T. ilisha were observed in this study. Significant FST values indicated differentiation among the river, estuary and marine populations. The UPGMA dendrogram based on genetic distance resulted in two major clusters, although, these were subsequently divided into three, corresponding to the riverine, estuarine and marine populations. The study underlines the usefulness of RFLP of mtDNA D-loop region as molecular markers, and detected at least two differentiated populations of T. ilisha in Bangladesh waters. The hilsa shad,  Tenualosa ilisha, belonging to the sub-family Alosinae of the family Clupeidae , occurs in foreshore areas, estuaries, brackish-water lakes and freshwater rivers of the western division of the Indo-Pacific faunistic region. Its marine distribution extends from Iran and Iraq in the Persian Gulf to the west coast of India in the Arabian Sea and the Bay of Bengal , disruption of migration routes, loss of spawning, feeding and nursing grounds, increased fishing pressure, etc., have all contributed to this decline.Hilsa shad is the largest single fishable species in Bangladesh, present in almost all the major river systems, estuaries and marine environments , and at present contributing to approximately 12% of the total fish production and 20% of fishery-capture (inland and marine) with a biomass of 78, 273 metric tons from inland fisheries and 198, 850 metric tons from marine . Hilsa iThe hilsa shad is largely an anadromous species, but two other ecotypes - a fluvial potamodromous type and a marine type - have been recognised. The potamodromous stocks appear to remain in the middle reaches of the rivers throughout the year and breed therein. The anadromous stocks, whose normal habitat is the lower region of the estuaries and the foreshore areas, ascend the rivers during the breeding season and return to the original habitat after spawning . The upsT. ilisha, based on samples collected from Bangladesh and India and through morphometric, allozyme and RAPD analyses, are contradictory. PGM, T. ilisha in India, one comprising the Ganges and Yamuna rivers and the other the Hooghly and Narmada rivers, by using RAPD markers.Reports on the stock structure of the valuable tropical shad Among the different markers available for population genetic analysis, differences in mitochondrial DNA are probably the most widely used, since they follow maternal inheritance, do not undergo rearrangements or recombination and present higher mutation rates than those of nuclear genes . RFLP anT. ilisha, using PCR-RFLPs of the mtDNA D-loop region as genetic markers. The aim of the present study was to compare mtDNA genetic diversity in T. ilisha samples collected from rivers, estuaries and the sea, and to delineate genetic differentiation among populations of the three major aquatic environments. We also report here whether there are any genetic differences between distant river populations of T. ilisha, as well as between estuarine populations. The purpose of the present study is to examine the usefulness of mitochondrial D-loop region diversity to supplement allozyme and RAPD data on the population genetic structure of T. ilisha.In this study, we analyzed haplotype and gene diversity in five samples of T. ilisha), with an average body weight of about 400 g, were collected from five geographic locales: Balashi ), Chandpur (middle of the Meghna River), Kuakata (estuaries), Sundarbans (estuaries) and Cox's Bazar  with the help of artisanal fishers , 3 μL of 2.5 mM dNTPs , 1 μM of each primer and 2 units of Taq DNA polymerase . An oil-free thermal cycler  was used for PCR amplification with the following cycle parameters: initial denaturation at 94 °C for 3.0 min, followed by 35 cycles of denaturation at 94 °C for 45 s, annealing at 45 °C for 45 s and extension at 72 °C for 2.0 min. A final extension step at 72 °C for 7 min followed the last cycle. The PCR products were confirmed by electrophoresis on agarose gel and subjected to digestion with restriction endonucleases.The 2.2 kb D-loop region of the mtDNA, including a part of the cytochrome b and 12SrRNA genes, was amplified from each of the 90 samples by using the primer-pairs L-12260  and H-1067   were used for digesting the amplified fragments. Ten microlitres of the PCR product were used for each digestion following manufacturer's (Promega) recommended conditions. The restricted PCR products were electrophoresed on 1.4% agarose gel containing ethidium bromide and photographed under UV light using a Geldoc camera (UVP Inc). Two molecular weight markers, λ-DNA-HindIII digest and 100 bp DNA ladder were run on each gel along with the digested PCR products. The sizes of mtDNA restriction fragments were measured by using the software, DNAfrag . The presence or absence of restriction sites in the control region was inferred for each of the four enzymes from a series of restriction fragment patterns. The site codes across the amplified region for a restriction enzyme were concatenated and each fish was assigned a code of four letters that described its composite, multi-enzyme haplotype.Restriction patterns generated from each restriction endonuclease were given letter designations in the order of their frequencies. Haplotype A refers to the most common digestion pattern in the analyzed samples. The remaining alphabetical profile names  indicate variant digestion patterns reflecting their frequencies in order. Composite haplotypes were constructed from all the enzymes used and arranged from the enzyme generating the fewest polymorphic patterns to that generating the most  = -log(1 - FST)  were selected to obtain RFLP markers. All enzymes produced polymorphic banding patterns and restriction sites. Restriction of the PCR fragment with the four restriction endonucleases resulted in a total of 35 restriction profiles in the samples collected from five locations in Bangladesh. The cleavage patterns and estimated lengths of the restriction fragments for different restriction enzymes are shown in XbaI and EcoRI, five for EcoRV and seven for HaeIII. In some cases, however, the sum of the fragment sizes did not exactly equal the total size of the amplified region, probably due to small fragments being lost or bands of similar size co-migrating. The 35 different haplotypes (composite genotypes) detected in 90 individuals of the five populations, along with their numerical frequencies, are presented in PCR amplification of the D-loop region resulted in a product of approximately 2.2 kb with no detectable size differences between Out of the 35 haplotypes, only two, VI and XI, were distributed randomly among the five populations, the others being either specific for a particular population or shared by two or three (mostly two) populations . HaplotyST) across the populations was 0.092 and the population specific FST indices ranged from 0.085 (Sundarbans) to 0.105 (Chandpur). Tajima's D test  ranged from 0.500 (Chandpur) to 0.722 (Sundarbans) . Haplotys D test  and Fu'ss D test  were notST) values were found among the five populations of T. ilisha. The FST value between the Chandpur and Sundarbans populations was the highest and that between the Chandpur and Balashi the lowest. The FST values between the Balashi-Chandpur, Cox's Bazar-Kuakata, and Sundarbans-Kuakata population pairs were insignificant, while these values between the remaining seven were found to be significant  were calculated with AMOVA. No significant differences among samples were found. Hierarchical analyses indicated that 90.78% variation was contained within local populations while 9.22% was distributed among the populations. However, significant differentiation  for some other fishes as reported by T. ilisha is quite high. High levels of genetic diversity appear to be commonly observed in migratory fishes with large panmictic populations  and Chandpur (the Meghna) suggest a single panmictic riverine population of T. ilisha in Bangladesh. However, this conclusion may be contradicted through the presence of some private haplotypes in the Balashi and Chandpur populations. These results also run counter to those of T. ilisha, the Meghna and the Padma were different stocks, though they observed certain alleles shared by some individuals of these two river samples. The presence of private haplotypes indicates the extent of mixing between populations. Due to their locations , indicating that composite haplotypes were not distributed randomly with respect to locality. F-statistics  yielded ocations  and the ocations , which iT. ilisha estuarine spawning in Bangladesh (We observed significant differentiation between the riverine and marine (Cox's Bazar) populations, but not between the marine and one of the estuarine. There is no evidence of spawning in the sea, but there is evidence of ngladesh . Thus, fngladesh .The five samples were grouped into two clusters on the basis of genetic distance among population-pairs. Nevertheless, the two clusters were finally divided into three groups corresponding to the three different environments, riverine, estuarine and marine (sea). The slight distance between the Balashi and Chandpur population indicates their belonging to the same stock. However, the sharing of two haplotypes by all the populations has led us to postulate the presence of a certain degree of gene flow among those studied.T. ilisha) is the national fish of Bangladesh. The delicious taste and contribution of this fish has lead to its position as one of the most economically important fish in this country. Furthermore, hilsa fishery constitutes the largest single species fishery in the riverine, estuarine and marine ecosystems of the country. It is essential to recognize that geographically and genetically different populations as different stocks should be managed separately (T. ilisha indicate that population sub-division does indeed exist in this species. On the basis of the AMOVA, we can conclude that there are three stocks of hilsa with a substantial level of inter-population genetic divergence among river, estuarine and marine populations. The high level of haplotype variability found in only five populations underlines the usefulness of RFLPs of the D-loop region as molecular markers to investigate the geographic structure of T. ilisha. We analyzed only 18 individuals from each population, and the electrophoretic analysis of restriction fragments is also not so perfect as sequencing the PCR product. Therefore, to reach a more definite conclusion, larger samples from throughout the distribution range of the species in the country should be analyzed by sequencing the mtDNA D-loop region and/or with faster evolving molecular markers, such as micro-satellite loci.Hilsa shad (parately . Our stu


Fifteen guanacos were introduced to Staats Island in the Falklands/Malvinas archipelago from Patagonia in the 1930s. Twenty five years later, the population was culled from 300 to 10–20 individuals, but quickly rebounded to a population of almost 400 animals that today retain the genetic signature of the founding event and later bottleneck. The goals of this study were to (i) make a genetic assessment of this island population through comparisons with mainland populations and simulations, and (ii) assess the likely source-population of the introduced guanacos. Genetic variation was estimated from 513 bp of mitochondrial DNA sequence and 15 microsatellite loci among 154 guanacos collected from eight localities, including the adjacent mainland and the islands of Tierra del Fuego and Staats Island. Of the 23 haplotypes observed among our samples, the Staats Island population only contained three haplotypes, all of which were shared with the coastal Monte Leon population in southern Patagonia. Mitochondrial DNA and microsatellite variations on Staats Island were comparable to most mainland populations and greater than those observed on Tierra del Fuego. Patterns of genetic structure suggest that the Staats Island guanaco population was founded with animals from southern Patagonia (as opposed to northern Patagonia or Tierra del Fuego), but that effective reductions in population size lasted only a few generations and that surviving animals were a random sample of the pre-bottleneck genetic variation. Genetic analysis is a well-used tool for the conservation and management of animal populations and can help in determining genetic patterns, differentiating populations, resolving taxonomic uncertainties, and addressing evolutionary questions Lama guanicoe) is a native ungulate of South America. It is distributed widely from Perú (8° S) in the north, through Bolivia, Paraguay and Argentina until the southernmost part of Chile (55°S) The guanaco  in the South Atlantic Ocean, offers a natural and on-going laboratory for testing the effects of a founder event on a guanaco population. This unique population has remained small and completely isolated from other populations for over 70 years. Together with several other Patagonian species, guanacos were introduced in the late-1930s by John Hamilton in an attempt to diversify the local economy Here we present the first genetic assessment of a small-island population of guanacos. We hypothesize that the population is characterized by low levels of genetic diversity as a result of two demographic bottlenecks in its short history as impacted by inbreeding, when each time it was reduced to less than 20 surviving animals. We also establish the phylogenetic relationship between this population and mainland populations in order to determine the most likely source of guanaco-genetic variation on Staats Island. Finally, based on these results we propose alternatives for the management of guanaco populations on the continent and Staats Island itself.Guanaco samples did not come from any endangered guanaco populations in Chile or Argentina, where the guanaco is classified as “Least Concern” by the Red List, IUCN Staats Island is a small (500 ha) isle on the far western edge of the 750-island Falkland/Malvinas archipelago in the South Atlantic Ocean (51°53’ S latitude and 61°11’ W longitude) 600 km from the South American coast. Six research expeditions were conducted on Staats Island in December (early summer and beginning of guanaco birth season) from 1999 to 2008 and biological samples were collected for genetic analyses in 2004 and 2005. Staats Island is treeless and hilly. The south end is dominated by the Staats Plateau, a tableland surrounded by a coastline of formidable cliffs and monoliths. The island is characterized by large sink holes, cuts and gulches that are caused by connections to the sea along the coastline, especially on Staats Plateau. North of the plateau the balance of the island is dominated by domed peaks (max. 140 m) separated by steep slopes and four valleys of short grass and forb meadows. Dominant plant communities are Oceanic Heath, Grass, Cushion Plants, and Greens  For comparison, material suitable for DNA analysis (170 samples) was collected from eight localities throughout southern Patagonia and Staats Island , Table 1C TTT TTC GGC TTA CAA GAC C 3′, Hloop550G 5′ ATG GAC TGA ATA GCA CCT TAT G 3′, Lloop0007G 5′ GTA CTA AAA GAA AAT ATC ATG TC 3′ and H362 5′ GGT TTC ACG CGG CAT GGT GAT T 3’ and H15998 5′ CCA GCT TCA ATT GAT TTG ACT GCG 3′ et al.STФ) calculated with ARLEQUIN 3.5.1.2 A 513 bp long fragment of the 5’ side of the mitochondrial Control Region was amplified from 156 samples using the primers LThr-ARTIO 5′ TC2, 0.325 μm of each primer, 0.2 mm dNTP, 1X polymerase chain reaction (PCR) buffer (QIAGEN) and 0.4 U Taq polymerase (QIAGEN). All PCR amplifications were performed in a PE9700 (Perkin Elmer Applied Biosystems) thermal cycler with the following cycling conditions: initial denaturation at 95°C for 15 min, followed by 40 cycles of 95°C for 30 s, 52–57°C for 90 s and 72°C for 60 s, and a final extension of 72°C for 30 min Fifteen autosomal dinucleotide microsatellite loci were analysed: YWLL08, YWLL29, YWLL36, YWLL38, YWLL40, YWLL43, YWLL46 OH), and expected heterozygosity (EH) were estimated using FSTAT STF values with 10,000 permutations to assess significance. The difference between the expected heterozygosity in Staats Island and mainland populations were statistically tested using Welch’s t-test to account for sample heteroskedasticity utilizing R software (http://www.R-project.org). The inbreeding coefficient ISF was calculated using GENETIX version 4.05 We identified multiple samples that came from the same individual by searching for matching microsatellite genotypes using the Excel Microsatellite Toolkit The program STRUCTURE 2.3.3 q). An individual was considered assigned with high confidence if it presented a q ≥ 0.75 for a single cluster. The second method consisted of assigning individuals to STRUCTURE’s clusters using the likelihood-based method of Three methods were used to assign individuals to the clusters found by STRUCTURE. The first method is based on the probabilities of admixture inferred for each individual by STRUCTURE  than the observed data it is considered that the analysed population passed through a bottleneck. The simulations are performed under the TPM model parameterised by the proportion of single step mutations (sp), the size of the multistep mutations (Δg) and the neutral evolution rate (θ = 4Neμ). As the Mc threshold is sensitive to these parameters and there is no species-specific information on Ne, we performed simulations under various biologically plausible θ values from 0.01 to 20. To ensure this range of values was relevant, we estimated θ from the expected heterozygosity  for the initial population and each of the 65 years of simulation.Current genetic diversity of the Staats Island population was compared with those of simulated populations using the software BOTTLESIM h) and nucleotide diversity (π) are detailed in h; h = 0.61) to the Bosques Petrificados population in southern Argentina and was higher than the island population of Tierra del Fuego (h = 0.36), which has a large population of more than 80,000 individuals Among the 156 samples, there were 23 haplotypes and 15 polymorphic sites (2.9%) from the 514 bp Control-Region fragment . A dominP>0.0011 after Bonferroni correction), and no significant evidence of linkage disequilibrium among loci . Consistent with the other measures of genetic variability, we found high levels of population expected heterozygosity  and high values of mean number of alleles per locus (6.2±1.17)  .eH between populations, it is still interesting that, consistent with the mtDNA results, the Staats Island population seems to present a higher eH and average number of alleles per locus than the Tierra del Fuego population , or between the latter population and any of the continental populations  likely to be the source for the Staats Island population . While opulation . Howeverion  had the highest posterior probability, as measured with the ΔK Evanno method. The three clusters corresponded to the group of samples from i) northern Patagonia, ii) the samples from southern Patagonia including the guanacos from Staats Island, and iii) the samples from the island of Tierra del Fuego .Haplotypic diversity indicated that Monte Leon is the most similar continental population to Staats Island. The island population had three haplotypes, which were observed together only in the Monte Leon population. In comparison to Monte Leon, no other population presented more than one haplotype shared with the Staats Island. Moreover, the dominant haplotype in Staats Island (frequency of Hap_20 = 0.54) was only recorded in the Monte Leon population, where it occurred at a moderately low frequency (0.09). This finding was supported by assignments tests, which showed that most of the Staats Island individuals could be assigned to the Monte Leon population.q value higher than 75%  population showed no evidence of bottleneck for any of the eN values tested. In contrast, all other populations show M-ratio values below cM indicating they may have passed through a bottleneck. However, these analyses seem to become only significant for large values of simulated eN. Although the observed eN estimates of our populations based on the expected heterozygosity sometimes fell within the range of the simulated eN values that were significant, it is unlikely that such a large eN currently characterizes our populations. This is particularly true for the Staats Island population, since the demographic records since its establishment have never exceeded more than 400 animals.The BOTTLENECK analysis was complemented with the M-ratio method EH decreased by only 3.5% from 0.84 (SE = 0.01) to 0.81 (SE = 0.01)  at the end of the simulations.A population of 10,000 individuals was simulated on the basis of the allele frequencies detected in Patagonia. This population was bottlenecked to 15 animals  in order to mimic the founding of the Staats Island population 65 years ago . As expe = 0.01) . A seconThe guanacos of the Staats Island were introduced to Falkland/Malvinas archipelago ∼75 years ago, with our samples representing animals ∼65 years after the original introduction. This population was started from a founding group of animals no larger than 15 animals The Staats Island guanaco population is currently at similar levels of genetic diversity compared to continental natural populations in Patagonia and is higher than those measured in the Tierra del Fuego population despite its large size of ∼80,000 animals. Although there are various mechanisms that could explain the retention of high genetic diversity in Staats Island , the most likely candidate is the high genetic diversity due to a random sampling of individuals from the continental population, which were already diverse .The guanaco of southern South America were subdivided into three genetic populations that present various levels of divergence. The Staats Island was assigned to the clusters of South Patagonia, which presents the potential source populations from which its founder individuals were suspected have been taken The guanaco population on Staats Island surprisingly presented similar patterns of divergence as were observed in the various continental populations. Its nuclear DNA showed an average divergence from the continental populations similar to that between continental populations. This observation likely reflects the large genetic variation of Staats Island’s founding population and a reduced effect of genetic drift on the distribution of allelic frequencies over the past ∼65 years, as observed in other species eN reduction was not very large and/or the reduction was not sufficiently long), the signature of a distant bottleneck cannot be picked up anymore Nevertheless, this finding was unexpected due to the recorded demographic history of Staats Island’s guanaco population, which was founded by few animals eN than the nuclear DNA in diploid species STF = 0.303 and 0.026 respectively). Moreover, it is expected that the two island populations, with their smaller eN (relative to the continent), have drifted from each other even more than they did from the continental population, and as expected, they presented the highest pairwise population differentiation (STφ = 0.53, As revealed by the nuclear DNA analysis, genetic drift appears to have not played a major role in the microevolution of the guanaco population on Staats Island, yet it certainly had a role as observed in the mtDNA analyses. The mtDNA typically has a fourfold smaller A continental-coastal origin of current guanacos in Staats Island is supported by shared haplotypes and results of assignment tests using microsatellites markers. The animals that were brought to Staats Island were shipped from the Rio Gallegos port in Argentina in two separate years, i.e. 5 animals in 1938 and 10 animals in 1939, but it is not clear from where they were collected The Staats Island guanaco population presents a similar amount of genetic variation as continental guanaco populations. Interestingly and unexpectedly, this guanaco island population has been resilient to the effect of dramatic demographic changes as its genetic variation is not yet depauperated, i.e. become impoverished. Nevertheless, this by no means implies that potential severe cullings or hunts would not diminish the population’s fitness. It is likely that the two dramatic population size reductions did not significantly affect the Staats Island population because the highly reduced numbers lasted only a few generations and surviving animals were a sufficiently large random sample of the pre-bottleneck genetic variation.Similarly, continental guanaco populations also harbor high levels of genetic variation despite of the extensive pressure that was put on these populations in the recent past. Continental guanacos were hunted to increase the area intended for sheep flocks, because of a perceived forage competition between guanacos and sheep, and for the economic value of their pelts Compared with the mainland, the relative population decrease on Staats due to killing is substantially higher when it was reduced to 15–20 animals. Therefore, it is surprising that this guanaco population has maintained genetic diversity much more than expected according its demographic history of two dramatic reductions in size. This has revealing implications and hope for surviving small remnant populations on the mainland that could be maintaining genetic diversity in spite of their low numbers. Nevertheless, it is important that although Staats Island and Patagonia guanacos harbor relatively high genetic variation, that their population numbers not be allowed to decline to severely low levels to avoid unforeseen, adverse-stochastic ecological events. Currently, guanacos in Staats Island are suffering neonatal malformations and mortality A genetic assessment, ideally with genomic-level data, of critically small and isolated guanaco populations from throughout their natural distribution


The forensic genetics field is generating extensive population data on polymorphism of short tandem repeats (STR) markers in globally distributed samples. In this study we explored and quantified the informative power of these datasets to address issues related to human evolution and diversity, by using two online resources: an allele frequency dataset representing 141 populations summing up to almost 26 thousand individuals; a genotype dataset consisting of 42 populations and more than 11 thousand individuals. We show that the genetic relationships between populations based on forensic STRs are best explained by geography, as observed when analysing other worldwide datasets generated specifically to study human diversity. However, the global level of genetic differentiation between populations (as measured by a fixation index) is about half the value estimated with those other datasets, which contain a much higher number of markers but much less individuals. We suggest that the main factor explaining this difference is an ascertainment bias in forensics data resulting from the choice of markers for individual identification. We show that this choice results in average low variance of heterozygosity across world regions, and hence in low differentiation among populations. Thus, the forensic genetic markers currently produced for the purpose of individual assignment and identification allow the detection of the patterns of neutral genetic structure that characterize the human population but they do underestimate the levels of this genetic structure compared to the datasets of STRs (or other kinds of markers) generated specifically to study the diversity of human populations. Short Tandem Repeats (STRs) or microsatellites are popular genetic markers in many applications of genetics, from population characterisation to individual identification and they have also been widely used for gene mapping clusteredeness among populations, while the opposite effect is obtained by increasing the samples sizes as well as the number of clusters tested. In another study, the type of STRs (number of bases per repeat) was shown to influence the resulting population structure, and the geographic dispersion of the samples was also claimed to be an important factor Such features have led to the use of extensive sets of STRs distributed across the genome to characterize patterns of human genetic diversity and population structure, as a means to understand the history of past migrations, the relatedness between populations and associations between genotypes and phenotypes The forensic genetics field has generated numerous sample sets typed for a few STRs, distributed over the entire world, in order to assemble a database of genetic profiles ready to be used for individual identification. The number of globally dispersed samples and the high number of individuals screened are interesting aspects of these datasets which may potentially constitute an important source of information about human genetic diversity, despite the relatively low number of markers typed clusteredeness, as was shown by Rosenberg et al. One complex issue distinguishing the population and forensic genetics datasets is the fact that the markers have been chosen differently, namely at random in order to avoid any bias for the former, and for the purpose of individual identification for the latter. These choices may affect the results when both kinds of data compilations are analyzed with identical methods. Moreover, the fields of population and forensic genetics differ basically in two measurable characteristics of their datasets: the number of loci and the sample sizes. Both features can affect population www.strdna-db.org), that reports STR population data published in the main forensic science journals. As very few of these publications provide individual genotype profiles, the database reports only allelic frequencies and information on geographic location and ethnicity of the samples. Presently, strdna-db sums up a total of 842,826 individuals from 92 countries . The second computer tool, PopAffiliator http://cracs.fc.up.pt/popaffiliator), provides 61,212 individual genotype profiles, from more than 40 different studies. This database is still very unbalanced, with a high shift towards Central and South American samples , but it constitutes the most extensive dataset available so far for analysing diversity at the genotype level with the STRs used in forensics.The question of how much information is contained in forensic genetic datasets with respect to issues about human evolution has been debated for some time e.g. . While sThus, despite the low number of loci that are typed, the STR data collected and published by the forensic genetics community cover a considerable amount of globally distributed samples, which suggests that these databases could eventually contain useful information on worldwide patterns of population diversity and may be of interest for making inferences on human evolution. Such a goal calls, beforehand, for a better evaluation and quantification of potential biases introduced in population genetics analyses based on these markers, which have been primarily chosen for other purposes, in particular to meet the forensics interests of individual identification and assignment (therefore leading to ascertainment bias). In this study we present the results of analyses performed to describe the patterns and levels of genetic diversity and structure of human populations inferred from each of the two worldwide forensic datasets described above, i.e. the frequency distributions compiled in strdna-db and the genotype profiles assembled in PopAffiliator. These results are then compared to those obtained with other worldwide non-forensics datasets, in order to highlight possible discrepancies, to quantify them and to determine the likely reasons for these.The complete datasets provided by strdna-db and PopAffiliator online resources were retrieved and named, respectively, “Frequency”  and “Genotype” (genotype profiles) datasets. Both datasets were subjected to a phase of maximization of comparable data, leading to the inclusion of only those samples that present information on the 13 CODIS loci commonly tested with the commercial forensic kits. The details and reference of each sample used in this study are given in well-defined, being representatives of a given location presumably since a relatively long time, and the other 49 were considered as possibly admixed populations, having limited information about geographic/ethnic origin. This led us to consider only the well-defined samples  than in the full database.Further filters were then applied separately to each dataset. For the Frequency dataset, we controlled that the sum of frequencies was equal to 1 for each sample, which led to 190 globally distributed population samples  fitting this requirement . The use samples  for the well-defined population samples, summing up 11,132 individuals, comprising almost all the inhabited continents except Australia  Vp), as defined in For both datasets, genetic diversity within populations was estimated by two indices: the expected heterozygosity (t-test as provided in We tested the correlation between population genetic diversity and geographic distance from East Africa (Ethiopia), based on the assumption that this latter region is the most likely place of origin of anatomically modern humans STR indices , by using the software Arlequin 3.5 STR values were directly calculated for the Genotype data and their significance tested with the permutation procedure implemented in Arlequin . For the Frequency dataset, multi-locus STR between each pair of samples was computed using the Michalakis and Excoffier approach STR index is the ratio of the genetic variance due to differences between populations to the total genetic variance, locus-specific variance components were computed using Arlequin, and then summed over all loci so as to obtain a multi-locus STR value. For each locus, STR significance was tested through the permutation procedure of Arlequin . Population pairwise STR values inferred from each of both datasets were then used to calculate coancestry coefficients, , between geographic groups of populations (CTR), and globally among all populations (STR). The significance of these fixation indices was tested by 10,000 iterations of the permutation procedure implemented in Arlequin. For the Frequency dataset, all the AMOVA computations were performed for each locus independently and, in a similar way as was done for populations pairwise STR (see above), the various components of variance were combined across loci to infer multi-locus fixation indices. The statistical significance of the global multi-locus fixation indices were obtained using Fisher’s combined probability test.The levels of genetic differentiation between all populations and between geographic groups of populations were assessed in both datasets through analyses of molecular variance (AMOVA) a priori assignment of individuals to populations. We tested up to nine clusters (K) with 10 replicates for each run of 100,000 iterations after a burn-in step of 10,000 iterations. The Evanno approach was applied to determine the number of clusters K that best fit the data The Genotype dataset was also analysed with the STRUCTURE software which infers population clusters that maximize Hardy-Weinberg and linkage equilibrium et al. He across populations and geographic groups, and performed an AMOVA analysis of these data. Nine geographic groups were defined, still following the criteria adopted by the immunogenetics community He over all samples, respectively. These two extreme subsets of markers were taken as representatives of a highly biased choice of markers, either towards high or towards low heterozygosity, and were used in comparisons with our results by means of Wilcoxon and pairwise Levene tests.The STR dataset published by Pemberton Tests to detect significant departures from Hardy-Weinberg equilibrium (HWE) were carried out on the Genotype dataset. Among 546 tests, 13% rejected HWE at the 5% level and 5% at the 1% level, both proportions being above the false positive threshold. Except the Chinese sample from Chongming island, for which all the cases were significant even after Bonferroni correction for multiple tests, no specific pattern emerged from this analysis. Indeed, the number of rejection cases was evenly distributed among loci and populations. Moreover, rejections due to excess or deficit in heterozygotes were in similar proportions. When applying a Bonferroni correction with respect to the number of loci tested, cases of HW disequilibrium were still found in 8 (respectively 3) populations out of 42 at the 5% (respectively 1%) level. When applying Bonferroni correction to each locus separately, 3 out of the 13 loci were found in disequilibrium at the 5% level, but none at 1%. Note that two of these loci (D3S1358 and vWA) were in HWE in the study of Sun et al. Vp) and the expected heterozygosity (He), were used to investigate the general pattern of genetic diversity across the world for the set of markers analysed. Average values over geographic groups of populations are reported in Two measures of intra-population diversity, the variance in allele sizes  is relatively variable among population groups, especially so for the Frequency dataset (P<0.001). When groups are compared two by two  group (except with North Africa (NAF), Australia (AUS), and Central and South America (CSAM)), as well as for the comparison of Europe (EUR) with both South and East Asia (SAS and EAS). For the Genotype dataset , probably due to the effect of a high variance of Vp within groups.The variance in allele sizes , the global comparison is significant for both the Frequency and Genotype datasets  in the Genotype dataset, but only with He in the Frequency dataset  than with Vp . Thus, the distance from East Africa is more influential on the variation of He presented by the Genotype dataset.The correlation between intra-population diversity and geographic distance from East Africa was found to be significant with both measures  dataset . In bothAdditional tests of inter-population diversity were performed in order to evaluate the genetic differentiation between populations and between geographic groups. r) of genetic and geographic distances between populations is of 0.52 in the Frequency dataset, and of 0.64 in the Genotype dataset, and is clearly significant in both cases (P<0.001).The correlation coefficient  are between one third and one half the values usually measured with worldwide datasets of STRs .The comparison with HGDP tetra STRs datasets (i.e. the complete set of 434 tetra STRs and the two subsets of 13 tetra STRs each) shows that the markers used in forensics present a shift towards higher average number of alleles per sample, although this shift fails to reach statistical significance . Globally, STF values are lower than STR values but display again a similar trend, in that the levels of genetic differentiation estimated with the two forensic datasets (2.7% and 2.3% for the Frequency and Genotype dataset respectively) are a half of those measured in the HGDP dataset (5.3%).Given the complexity in the repeat structure of some of the 13 CODIS loci analysed in this work, namely FGA, D21S11 and TH01, we repeated all analyses without considering those three loci (10 CODIS loci only). We also computed tion see . When reThe intensive use of STRs to resolve forensic casework has made the forensic community a main producer of worldwide genetic data. A vast amount of this published data has been recently organized in online databases The datasets used in this work were carefully checked regarding two main issues: how well-defined the population samples are in terms of ethnicity and geographic location, and the amount of missing data and loci typed. In order to be considered in population genetics analyses, a sample should be, as much as possible, representative of a geographic region or of a cultural entity (population). Consequently we did not include in the analyses the population samples for which the origin of individuals was either not defined with enough precision or if the sample was mixed. For frequency data , the compilation of a dataset of well-defined samples was necessary in order to avoid poorly defined or probably admixed samples which were quite numerous. Regarding the genotype data, most of the samples presented already satisfactory definition but the differential loci typed across profiles and missing data were the main criteria to discard some samples from the analyses.He) and variance in number of repeats (Vp), show relatively low variation between population groups, although significant overall differences are observed. Despite differences between the two measures, both reveal a tendency to decrease from Africa to America. This tendency, consistent with the putative way of migration of modern humans out of Africa He, distance from East Africa explains 57% of the variation in genetic diversity among populations in the Genotype dataset. Even if this determination coefficient is higher than those obtained with the Vp measure or with the Frequency dataset, it is still substantially lower than the values obtained in other studies In agreement with expectations on forensics data e.g. , we founHe and Vp) are consistent with a neutral model of human evolution that assumes increased genetic drift with distance from Africa. Indeed, genetic drift leads to reduced heterozygosity, but since it is a stochastic process, the alleles that will drift need not to be the same in different populations. Hence, two populations can end up with similarly low numbers of alleles and heterozygotes (similar He), but in one population these alleles could be quite distant in repeat numbers (high Vp), whereas in the other population not (low Vp). Note that we are describing indices of diversity computed as averages over the 13 CODIS loci while some variance may exist when considering each locus independently.In turn, the differences between the two estimators of diversity used here  is similar in both datasets . The datasets differ, however, in the proportion of variation among groups relative to that among populations within groups, which is found higher for the Genotype dataset, probably due to a poorer geographic sampling coverage, particularly so for populations located at the crossroads of continental regions  the purpose for which STRs have been designed ; and ii) few loci (13) but many samples and individuals versus many loci (434) but less samples and individuals. The STR values for the 13 forensics STRs analysed here  are about half the values found with the data of i) the number and location of samples; ii) a high intra-population diversity; iii) a low variance in heterozygosity across populations. The first explanation may be discarded as both Frequency and Genotype datasets give STR values of the same magnitude (5% and 4.4%) despite a reduced number of samples in the Genotype dataset. We investigated in depth the two other explanations.Our results were compared with those obtained with a dataset made up of genome-wide distributed tetra STRs typed for the populations in the HGDP panel studied by Pemberton He. It is expected that higher diversity within populations is concomitant with reduced magnitude of differentiation among populations, unless the spectrum of extant alleles in diverse populations is substantially different He is slightly higher in our two datasets (0.77) than the value corresponding to the 434 tetra-STRs from the HGDP dataset (0.71), but it is still lower than the extreme value (0.85) obtained with the biased HGDP-extracted subset of 13 loci with the highest He  from the worldwide HGDP dataset published in t He see . The samt He see . The facHe among samples (0.00201) is three to five times higher than those inferred from both the forensics datasets analysed here . This observation is probably the consequence of an important ascertainment bias in the choice of the 13 CODIS STRs, which have been independently selected in order to be the most discriminating ones. Consequently, this ascertainment bias resulted in a reduced variance between samples compared to STRs randomly chosen in the genome, and thus the genetic differentiation measured by fixation indices is much lower. This fact is strengthened by the results of the comparisons with the two HGDP subsets of 13 loci, picked up to have the highest and the lowest heterozygosities , as both have a much higher variance in He than those measured in our datasets (and significantly higher than that of the Frequency dataset). This ascertainment bias could also explain the significant number of rejection cases of Hardy-Weinberg equilibrium, which exceed the type-I error threshold. However, to address this hypothesis, the proportion of Hardy-Weinberg disequilibrium should be evaluated in the other published datasets.However, when considering all 434 tetra STRs in Pemberton’s HGDP dataset the variance of In conclusion, we show that the two forensic datasets investigated contain valuable, albeit limited, information on worldwide genetic diversity, even after a careful selection of well-defined samples as explained in Figure S1Geographic distribution of the 141 (blue) and 42 (orange) samples of the Frequency and Genotype datasets, respectively. The possibly admixed samples discarded from the starting datasets are also represented in grey. Numbers correspond to the populations’ ID codes listed in (TIF)Click here for additional data file.Figure S2Results obtained with STRUCTURE on the Genotype dataset. A: Evanno’s estimation of the number of clusters K that better fits the data, K ranges from 1 to 9. B: Graphical representation of the inferred ancestry of individuals for a K value equal to three.(TIF)Click here for additional data file.Figure S3STR/STF indices computed with different AMOVA/ANOVA analyses. Many tests were performed with various group structures, considering or not the Oceania group, with or without complex loci and with or without large samples. For the group structures, three definitions were used, either following the immunogenetics community criterion as defined in the main text, or as defined in Rosenberg et al’s article (Science 2002), or as inferred in the same study using the program STRUCTURE.(TIF)Click here for additional data file.Table S1www.strdna-db.org). Geographic coordinates were assigned in this work.Frequency dataset description. The designations and information presented for populations are based on the original publications and the online source of the data ((PDF)Click here for additional data file.Table S2http://cracs.fc.up.pt/popaffiliator). Geographic coordinates were assigned in this work.Genotype dataset description. The designations and information presented for populations are based on the original publications ((PDF)Click here for additional data file.Table S3Vp and He in the Frequency dataset. Tables C and D: average genetic diversity measured by Vp and He in the Genotype dataset. The p-values below 0.05 are represented in bold and italic.Comparison of average genetic diversity among geographic groups. Pairwise Wilcoxon tests of the difference in average genetic diversity between geographic groups. Tables A and B: average genetic diversity measured by (PDF)Click here for additional data file.Table S4Comparison of number of alleles among datasets. Pairwise Wilcoxon tests of the distributions of the mean number of alleles per sample over loci presented in (PDF)Click here for additional data file.Table S5Comparison of expected heterozygosity among datasets. Pairwise Wilcoxon tests of the distributions of the expected heterozygosity per sample over loci presented in (PDF)Click here for additional data file.Table S6He in both forensic datasets, in the dataset constituted of all Pemberton et al. (2009) tetra loci, and in two subsets of 13 tetra loci of Pemberton et al. (2009) showing highest and lowest average He among populations. B: pairwise Levene tests for the variance in He between the datasets described in A, corrected for multiple tests. The p-values below 0.05 are represented in bold and italic.Comparison of expected heterozygosity between the two forensic datasets and subsets of HGDP data. A: mean, standard deviation and variance of (PDF)Click here for additional data file.


Genetic epidemiological studies of complex diseases often rely on data from the International HapMap Consortium for identification of single nucleotide polymorphisms (SNPs), particularly those that tag haplotypes. However, little is known about the relevance of the African populations used to collect HapMap data for study populations conducted elsewhere in Africa. Toll-like receptor (TLR) genes play a key role in susceptibility to various infectious diseases, including tuberculosis. We conducted full-exon sequencing in samples obtained from Uganda (n = 48) and South Africa (n = 48), in four genes in the TLR pathway: TLR2, TLR4, TLR6, and TIRAP. We identified one novel TIRAP SNP  and a novel TLR6 SNP (MAF 8%) in the Ugandan population, and a TLR6 SNP that is unique to the South African population (MAF 14%). These SNPs were also not present in the 1000 Genomes data. Genotype and haplotype frequencies and linkage disequilibrium patterns in Uganda and South Africa were similar to African populations in the HapMap datasets. Multidimensional scaling analysis of polymorphisms in all four genes suggested broad overlap of all of the examined African populations. Based on these data, we propose that there is enough similarity among African populations represented in the HapMap database to justify initial SNP selection for genetic epidemiological studies in Uganda and South Africa. We also discovered three novel polymorphisms that appear to be population-specific and would only be detected by sequencing efforts. The International HapMap Consortium Human genetic studies of diseases with complex inheritance involve analysis of single nucleotide polymorphisms (SNPs) which are present at a range of population-specific frequencies. The proper selection of SNPs for genetic studies requires either discovering the SNPs in the population of interest with Toll-like receptors (TLRs) play a key role in the innate immune response to a variety of pathogens. Mutations and polymorphisms in TLR genes have been associated with susceptibility to various infectious diseases, including Mendelian disorders with mutations in IRAK4, MyD88, TLR3, and Unc93b In this study, we conducted full exon sequencing of TLR2, TLR4, TLR6, and TIRAP in samples obtained as part of ongoing studies in Kampala, Uganda, and Cape Town, South Africa. Our objective in this study was first to examine whether there were any novel TLR gene polymorphisms in these study populations, and second, to compare the genotype and haplotype frequencies between these populations and the African HapMap populations.To understand haplotype structure and population polymorphism diversity, we sequenced the coding region of four candidate genes in two populations and compared it to four populations of African ancestry in the HapMap database. Specifically, we sequenced gene regions from the Kampala Ugandans  and Cape Town South Africans  and compared findings to four populations composed of unrelated individuals from the HapMap database: the Maasai in Kinyawa, Kenya , the Luhya in Webuye, Kenya , the African Ancestry in Southwest USA , and Yoruba in Ibadan, Nigeria . Four candidate genes were considered in our analysis: TLR2 (chromosome 4q32), TLR4 (9q32– q33), TLR6 (4p14), and TIRAP (11q23– q24). Genotype frequencies for these populations are provided in Comparison of genotype frequencies across these six populations suggested that there were a range of frequencies that were often different among the groups (p<0.05 by chi-square test) . When co2<0.2 for 76.9% of comparisons). Haplotypes were constructed for the SNPs that were common between our sequencing study and HapMap, and haplotype frequencies were compared between the populations  analysis, which combined the TLR2, TLR4, TLR6, and TIRAP data, was used to illustrate how UG and SA Mixed Ethnicity populations clustered with the HapMap populations. We plotted the first two dimensions from MDS . Visual The primary finding of our study was that the genotype frequency and haplotype structure of Ugandans and South Africans of Mixed ethnicity are similar to those in the HapMap database among the Kenyan and Yoruba Nigerian reference populations. A practical issue for genetic association studies is to determine whether tag SNPs identified using HapMap data adequately capture patterns of variation in other populations Many studies Another noteworthy finding of our study was that a small number of novel polymorphisms were detected in TLR6 and TIRAP. The relevance of rare variants in complex trait susceptibility is gaining attention The occurrence of novel polymorphisms, such as in the Ugandan population on TLR6, results in unique haplotypes not seen in other populations, which is consistent with a potential effect of selection There are a few limitations with this analysis. We restricted our analysis to TLR pathway genes, because of their key role in the innate immune response. Generalizations to the rest of the genome cannot be made based on only four genes, and selective pressure on immunity genes may result in different population genetic parameters than the rest of the genome. Second, our haplotype and LD analyses were restricted to SNPs that were common to both our exomic sequencing efforts and the HapMap. Furthermore, some SNPs were represented in Phase I and II of the HapMap, but not Phase III, and vice versa. Because of the differences seen in haplotype frequencies and LD, some information may have been lost by virtue of this aspect of study design. Finally, our sample size was underpowered to detect small differences between populations. We had 70–80% power to detect a difference of 0.2 of allele frequencies, but only had 20–30% power to detect differences of 0.1.In conclusion, we found that there is more similarity across African populations than there is dissimilarity, though patterns of similarity do not necessarily reflect geographic proximity. Thus, HapMap provides a good starting point for genetic association studies. However, one must be mindful of possible LD differences between specific populations and those represented on the HapMap. Selective pressure by TB and other infectious diseases may have influenced differential LD structure across Africa. For this reason, we suggest using all three African HapMap populations as the reference for tag SNP selection, as has been advocated by other studies Samples were obtained as part of two ongoing studies in Uganda and South Africa. Ugandan samples were initially collected as part of the Household Contact Study The South African samples were collected from healthy adults enrolled at the South African Tuberculosis Vaccine Initiative clinical site near Cape Town in South Africa The institutional review boards at University Hospitals of Cleveland and the Uganda Council for Science and Technology approved the Ugandan study. All individuals in the Ugandan study provided written informed consent. For the South African study, all protocols for this study were approved by the Research Ethics Committee of the University of Cape Town and the Institutional Review Boards at the University of Washington and University of Medicine and Dentistry of New Jersey. Ethical guidelines of the US Department of Health and Human Services and the South African Medical Research Council were adhered to, including written informed consent from parents of study participants.Genomic DNA was purified from peripheral blood by QIAamp DNA blood kit (Qiagen). We sequenced the coding region to look for polymorphisms and obtained high quality sequence of the entire coding region in 96 subjects for TLR2 , 76 for TLR4 , 96 for TLR6 , and 87 for TIRAP . The coding region was amplified by PCR, sequenced with Big Dye Terminator v3.0, and analyzed on an ABI PRISM 3730 capillary sequencer (Applied Biosystems). Sequence was aligned and analyzed with the programs PHRED/PHRAP and CONSED http://krunch.med.yale.edu/hwsim/) with 10,000 iterations. Genotype frequencies were compared across groups using a chi-squared test (or Fisher’s exact test when appropriate) in SAS PROC FREQ. LD was assessed using Haploview software, calculating both r2 and D’. Haplotypes were estimated using DECIPHER (S.A.G.E. version 6.0), using the most likely phase for each individual. Differences in haplotype frequencies across populations were evaluated using chi-square or Fisher’s exact tests, as well as for the genotype comparison tests.Hardy-Weinberg proportions were tested within healthy populations for each SNP using HWSIM (http://pngu.mgh.harvard.edu/purcell/plink/). MDS is similar to principal components analysis in that it utilizes SNP genotype data to estimate a matrix of allele sharing identical by state (IBS) and constructs a similarity matrix, then represents each subject by a vector of coordinates To examine how our populations clustered with the HapMap populations, we conducted multidimensional scaling (MDS) using PLINK Click here for additional data file.Table S22) for each gene  for the Ugandan, South African, and HapMap populations.Pairwise linkage disequilibrium (D’ and r(DOC)Click here for additional data file.


Outcrossing determines the level of introgression but little is known about the relationships of outcrossing rates, genetic diversity, and differentiation particularly in a weedy taxon that coexists with its conspecific crop.Introgression plays an important role in evolution of plant species Oryza sativa f. spontanea) populations from China were analyzed using microsatellite (SSR) fingerprints to study outcrossing rate and its relationship with genetic variability and differentiation. To estimate outcrossing, six highly polymorphic SSR loci were used to analyze >5500 progeny from 216 weedy rice families, applying a mixed mating model; to estimate genetic diversity and differentiation, 22 SSR loci were analyzed based on 301 weedy individuals. Additionally, four weed-crop shared SSR loci were used to estimate the influence of introgression from rice cultivars on weedy rice differentiation. Outcrossing rates varied significantly (0.4∼11.7%) among weedy rice populations showing relatively high overall Nei's genetic diversity (0.635). The observed heterozygosity was significantly correlated with outcrossing rates among populations  although no obvious correlation between outcrossing rates and genetic diversity parameters was observed. Allelic introgression from rice cultivars to their coexisting weedy rice was detected. Weedy rice populations demonstrated considerable genetic differentiation that was correlated with their spatial distribution , and possibly also influenced by the introgression from rice cultivars.Eleven weedy rice (Outcrossing rates can significantly affect heterozygosity of populations, which may shape the evolutionary potential of weedy rice. Introgression from the conspecific crop rice can influence the genetic differentiation and possibly evolution of its coexisting weedy rice populations. Introgression, as defined in its broad sense “the transfer of genes between genetically distinguishable populations” vice versa. A recent study on modeling of rice gene flow indicated that the outcrossing ability of a pollen-recipient is an important determinant for the level of gene flow The extent of introgression between species (or populations) relies essentially on their outcrossing ability, which is usually determined by the genetic nature of a plant species Oryza sativa f. spontanea; Poaceae) provides an ideal material for studying such relationships, because outcrossing rate, genetic diversity, and differentiation of weedy populations can be estimated relatively easily.Theoretical and empirical studies have indicated evident association between outcrossing and genetic diversity/differentiation of populations Weedy rice can be defined as any spontaneously and strongly shattering rice that occurs in cultivated rice fields. It is distributed all over the world, where rice is grown. Weedy rice can be discriminated from wild rice species by its habitats because weedy rice only occurs in or nearby rice fields and the wild species in natural habitats. Weedy rice is characterized by its strong seed shattering and dormancy, in addition to a red pericarp, which serves as a key to distinguish weedy rice from cultivated rice. Groups of weedy rice from various parts of the world may have separate evolutionary origins Weedy rice flowers almost simultaneously in many rice planting regions with its cultivated counterpart occurring in the same fields, and spontaneous hybridization (or gene flow) between cultivated rice and weedy rice is frequently reported Using microsatellite (SSR) fingerprints, we estimated outcrossing rates, genetic diversity, and differentiation of 11 weedy rice populations collected from NE and E China. In addition, we also examined the possible allelic introgression from cultivated rice grown in the same fields. The primary objectives of this study were to (1) determine outcrossing rates and genetic diversity of the 11 weedy rice populations, and the correlation between outcrossing rates and genetic diversity; (2) estimate genetic differentiation among the weedy rice populations across a geographical range; and (3) detect the possible allelic introgression from cultivated rice at different locations that may have affected the differentiation of weedy rice.2. The estimated densities of weedy rice populations varied between 2∼60 plants per 100 m2  , and ther 100 m2 . In HeilJaponica rice cultivars (pure lines) coexisting in the same fields were also sampled from each of the 11 sites, making up a total of 106 rice plants from the sampled sites. One to three panicles from each cultivar and weedy plant were collected and placed separately in a paper seed-bag. Panicles were air dried under natural conditions.To meet our objectives, three independent sets of materials were included in this study. To estimate outcrossing rates, 16∼20 weedy rice plants (as family) were collected from each population, with a spatial distance of >2 m between sampled plants. About 20∼25 mature seeds (as progeny) from each plant were randomly collected, and a total of 400∼600 seeds from each population were sampled. Consequently, a total of 5527 progeny (seeds) from the 11 weedy rice populations were included for analyses . To estiSeeds of weedy rice and cultivated rice were germinated in the laboratory of Fudan University at the room temperature (25±3°C) about four months after the samples were collected to release potential seed dormancy. The total genomic DNA was extracted from 10-day-old fresh seedlings following a modified cetyltrimethyl ammonium bromide (CTAB) protocol http://www.gramene.org) were randomly selected and used for analyzing weedy rice populations and rice cultivars. To estimate outcrossing rates and crop specific alleles that were defined as the alleles absent in families (mother plants) but present in progeny (seeds), six highly polymorphic SSR loci were selected, as recommended by Jarne and David SSR primer pairs designed based on published genome sequence of cultivated rice (Polymerase chain reactions (PCR) were carried out in a volume of 20 µL containing 1× buffer, 1 mM each of dATP, dCTP, dGTP and dTTP, 10 mM of SSR primer, 50 ng of genomic DNA and 0.6 unit of Taq polymerase Given the co-dominant nature of SSR markers, the amplified DNA fragments of a given locus were scored as genotype data for each weedy rice individual. For outcrossing analyses, genotype data of progeny was recorded as homozygous genotypes  or heterozygous genotypes  and formatted according to the MLTR requirement to be used for outcrossing rates estimation under MLTR model mt); (2) single-locus outcrossing rate (st); (3) the proportion of biparental inbreeding to the total outcrossing rate [(mt-st)/mt] Pa) as estimated by inverse of multilocus paternity correlation Subs) estimated by the differences between correlation of pollen genotypes calculated by multilocus and single-locus The mating system of weedy rice populations was calculated from 5527 progeny using a multilocus mixed mating model f) was calculated to estimate whether the observed outcrossing rates of weedy rice populations based on SSR markers and MLTR model deviated from theoretically expected outcrossing rates under inbreeding equilibrium (IE). Inbreeding coefficient (f) was obtained as follows: (1) calculating the most possible maternal genotypes observed in each weedy rice population based on the MLTR model; (2) calculating inbreeding coefficient (Mf) of maternal plants in each population based on maternal genotypes by the formula: Mf  = 1 - oH/eH, where oH and eH indicated the observed heterozygosity and expected heterozygosity, respectively; (3) calculating the expected inbreeding coefficient (eqf) in each population by the formula: eqf  =  (1 - mt)/(1+mt) t-test of paired samples was performed to detect the significant differences between the observed (Mf) and expected (eqf) coefficients.The parameter of inbreeding coefficient (isF) A); (3) the percentage of polymorphic loci (P); (4) Nei's genetic diversity (GD) oH). Private alleles of each weedy rice population were analyzed using the GDA  software ver. 1.1 Genetic diversity was estimated from 301 plants of 11 weedy rice populations based on 22 SSR loci using the MSA  software ver. 4.05 A, GD, and oH) were analyzed using the Pearson's correlation method packaged in the SPSS software ver. 12.0 (http://www.spss.com/). The correlation was illustrated by a linear model (y = a+ bx) using the STATISTICA ver. 6.0 .Correlations between obtained outcrossing rates from mating estimation and genetic diversity parameters  estimating frequency of alleles from paternal and maternal plants by MLTR software; (2) determining paternal specific alleles by comparing alleles from both parents, alleles that only appear in pollen donors were defined as paternal specific allele; (3) determining the paternal specific alleles that could only come from the crop by examining alleles in cultivated rice coexisting in the same fields, as crop specific alleles.Nei's unbiased genetic distances of 11 weedy rice populations (301 plants) and rice varieties (106 plants) that coexisted in the same fields were calculated together based on four shared SSR primer pairs  that highly represented genetic diversity and structure of the crop and weed, using the MSA software ver. 4.05. An UPGMA tree of weedy and cultivated rice was constructed based on the cluster analysis of Nei's genetic distances, using the software NTSYS ver. 2.02 mt), and 2.9% by single-locus outcrossing rate (st), indicating in general very low frequency of outcrossing in weedy rice populations, which correspond to the self-pollinating nature of weedy rice. A relatively high level of variation in outcrossing rates was found among populations (mt ranging from 0.4∼11.7% and st from 0.3∼8.6%). The lowest multilocus outcrossing rate was found in the JL-4 population and the highest in the HLJ-4 population  and expected inbreeding coefficient  between Mf and eqf, suggesting in general that the outcrossing rates detected in this study were reliable (Average outcrossing rates of the 11 weedy rice populations were 4.1% as estimated by multilocus outcrossing rate (pulation . To examreliable . The inbreliable . A low lreliable .Pa), indicating the average number of genetically distinct pollen donors (father plants) involved in hybridization, was nearly four plants in weedy rice populations, with the highest number of five plants and the lowest of one plant (Average effective paternity (ne plant . In addine plant .isF) estimated from 22 SSR primer pairs of 301 plants was as high as 0.987 (GD) of the 11 weedy rice populations was 0.635, with the highest value in the HLJ-4 population (0.453) and the lowest in the JL-2 population (0.145) (oH) varied considerably among the 11 weedy rice populations, with the highest value of oH = 0.023 in the HJL-4 and JL-1 populations, and the lowest value as oH = 0.000 in JL-4, JS-2, and JS-3 populations  . The obsulations .mt, r2 = 0.783; P<0.001; for st, r2 = 0.895; P<0.001) between outcrossing rates and observed heterozygosity in the 11 weedy rice populations. The correlation between multilocus outcrossing rates and observed heterozygosity could be well described by a linear model y = 0.1937x+0.0003  between the genetic distances and spatial distances of weedy rice populations, indicating that genetic differentiation among weedy rice populations was significantly correlated with their geographical distribution. The possible flow of SSR alleles from cultivated rice to coexisting weedy rice populations was examined by analyzing the paternity in weedy rice seedlings (progeny), given that a weedy rice mother plant (family) was always surrounded by cultivated rice plants . When the 11 coexisting rice cultivars were included in the cluster analysis, the structure of weedy rice populations indicated by the UPGMA tree was slightly altered . In genet-test of paired samples demonstrated that the observed inbreeding coefficients (Mf) were in significant accordance with the expected inbreeding coefficients (eqf) in all 11 weedy rice populations. This statistic showed strong reliability of the estimated outcrossing rates of the self-pollination weedy rice based on the SSR fingerprints and MLTR modeling in this study. Therefore, the detected variation in outcrossing rates among weedy rice populations may well reflect the true nature of outcrossing in weedy rice, with a low possibility of errors from the experimental procedures.Results from the present study based on >200 families  and >5500 progeny (seeds) indicated a low to moderate level of outcrossing rates (0.4∼11.7%) of weedy rice populations, although with considerable variation. The Outcrossing rate is usually determined by the genetic ability to outcross (which is a relatively stable character for a particular plant population or species) and the environmental conditions under which the outcrossing events occur In addition to variation in outcrossing rates, we also found that the proportion of biparental inbreeding  to the total outcrossing was considerably variable among weedy rice populations (ranging from ca.11∼65%). Usually, biparental inbreeding is associated with the fine-scale spatial genetic structure, in which the neighboring individuals are genetically identical or extremely close oH) in the corresponding weedy rice populations, although no such a correlation between outcrossing rates and Nei's genetic diversity or average number of alleles per locus was observed. In other words, outcrossing can considerably promote heterozygosity in weedy rice populations. Wright et al. Our results, based on the fingerprinting of 22 SSR loci from another independent set of weedy rice samples with >300 individuals from the 11 populations, demonstrated a considerable among-population variation in genetic diversity, as estimated by the average number of alleles per locus, the percentage of polymorphic loci, and Nei's genetic diversity. Interestingly, these results showed a significantly positive correlation between outcrossing rates and observed heterozygosity  although the detected geographical effect might be associated with differences between north (Heilongjiang and Jilin) and south (Jiangsu) regions. Genetic differentiation of weedy rice populations has been reported by many authors et al. Our results from the cluster analysis based on the coefficients of genetic distances showed evident differentiation among the 11 weedy rice populations. The Mantel test further indicated a significant correlation between the genetic and spatial distances of weedy rice populations across a geographical region (isF) Japonica rice fields from NE and E China, where the farming conditions for rice are very similar in the irrigated eco-system. Normally, rice was cultivated once a year or rotated with wheat or oil rape at the following seasons, but rice varieties used across the region can vary greatly. Given the frequent gene flow from cultivated rice to weedy rice Commonly, the causes of genetic differentiation among plant populations across a given geographical range can be associated with factors such as immigration/migration and environmental heterogeneity among populations As a conspecific weed of cultivated rice, individuals of weedy rice are commonly surrounded by densely populated crop in the field. Recurrent gene flow from a particular rice variety or different varieties grown at different years in the same fields can considerably shape the genetic composition of weedy rice populations, and enhance genetic differentiation among weedy rice populations by incorporating crop alleles from different varieties 2) of weedy rice plants that were surrounded by cultivated rice plants in the fields. More importantly, comparison between the paternal-specific alleles found in weedy rice and alleles detected in cultivated rice from the same fields demonstrated some paternal-specific alleles that were also detected in rice varieties. This finding confirms the allelic introgression from diverse rice cultivars to weedy rice, supporting our hypothesis of introgression from rice crop that may influence genetic structure and differentiation of weedy rice populations. In fact, farmers frequently change rice varieties between years for cultivation among regions, where considerable genetic diversity of rice varieties has been detected Data from outcrossing rate estimation in this study revealed some paternal-specific SSR alleles (paternity) that was not detected in the genotypes of maternal plants (weedy rice families). This finding suggests that the pollen donors of many outcrossing (or hybridization) events were most likely from cultivated rice, given the relatively low density (∼10 plants/100 mThe above findings have implications for understanding adaptive evolution of weedy rice in human-influenced agro-ecosystems. Weedy rice always occurs sympatrically with cultivated rice, and genes from cultivated rice can be easily incorporated into the gene pool of weedy rice through recurrent crop-to-weed gene flow and introgression. This progress may promote the persistence and better adaptation of weedy rice to the human-influenced habitats via gene flow and introgression although other factors such as selection and habitat heterogeneity may also play their roles. The knowledge of biological bases and the relationships among outcrossing, genetic diversity, and differentiation in weedy populations is essential for addressing adaptive evolution of weedy rice, in addition to assessing environmental biosafety impacts caused by crop-to-weed transgene flow from GM rice.In summary, results from this study demonstrate a considerable variation in outcrossing rates and genetic diversity among weedy rice populations from a given geographical range. The correlation between outcrossing rates and observed heterozygosity provides evidence that a high level of outcrossing may influence genetic variability and potential evolution of weedy rice populations. This might be true for other self-pollinating plant species. Our results also show considerable genetic differentiation among weedy rice populations across a given geographical range. Noticeably, the genetic structure and differentiation of weedy rice populations could be affected by cultivated rice