Chromis multilineata in different areas along the northeast coast of Brazil and in the waters around the oceanic islands of Fernando de Noronha (FNA) and Saint Peter and Saint Paul Archipelago (SPSPA) through analysis of the HVR1 mtDNA sequence of the control region. The remote SPSPA exhibits the highest level of genetic divergence among populations. Conventional and molecular cytogenetic analysis showed similar karyotype patterns (2n = 48 acrocentrics) between these insular areas. Our estimates reveal three genetically different population groups of C. multilineata on the Brazilian coast. The level of genetic structure is higher than previous data suggested, indicating complex panel of interactions between the oceanic island and coastal populations of Brazil.Connectivity levels among Brazilian reef fish fauna populations have attracted growing interest, mainly between mainland shores and oceanic islands. The Pomacentridae, whose phylogeographic patterns are largely unknown in the Atlantic, are a family of dominant fish in reef regions. We present data on the variability and population structure of damselfish  Despite improvements in recent years, the aspects associated with dispersion, genetic variability, speciation, and evolution in fish populations distributed along the Brazilian coast and oceanic islands, for example, –6 continAmong the typical species in sea reef environments are the Pomacentridae, one of the four most widely found families inhabiting tropical and subtropical reefs and one of the top 10 richest families in terms of diversity. It is the dominant group in number and diversity in most reef environments –9. Abudefduf, Stegastes, Microspathodon, and Chromis [ C. multilineata dominant among pelagic holoplanktonic species [On the Brazilian coast, the family Pomacentridae is represented by the genera Chromis , 10. The Chromis , 10–12,  species –18. The  species  and colo species , 21.With amphi-Atlantic distribution, Brown chromis in the Western Atlantic extends from northern Florida to southern Brazil. It is one of the dominant species in insular regions, which show a sequential geographic position in relation to Brazil's Northeast coast, with different distances from the continent, such as the Rocas Atoll (267 km), Fernando de Noronha Archipelago (365 km), and the Saint Peter and Saint Paul Archipelago . More southward, it is found in the vicinity of Trindade Island and in the Meso-Atlantic region, in the waters around Ascension and Santa Helena islands –17. Chromis with insular distribution have shown a pronounced divergence from a number of areas of the African coast. On the other hand, cytogenetic data of populations from Brazilian insular areas have largely improved [ Caranx lugubris karyomorphs in SPSPA [Some reports involving fish populations of Fernando de Noronha Archipelago showed no evidence of genetic structuring in regard to the Northeastern coastal areas of Brazil . In the improved , 24, shoimproved , and incin SPSPA  or conspin SPSPA . C. multilineata on a geographic macroscale  shows no genetic structuring between the Saint Peter and Saint Paul Archipelago and the Brazilian coast [The body of the species is greenish brown on the back and sides and white or silver on the abdomen, the edges of the dorsal and anal fins and the central part of the caudal fin tips are light yellow, while the edges of the dorsal fin are dark, often exhibiting a bright yellow spot immediately after the last ray of the dorsal fin . Howeveran coast . Althougan coast , the chr Chromis multilineata and a variable relationship between insular and coastal populations.Considering the evidence of gene flow restriction in marine organisms from oceanic islands in relation to the Brazilian coast –32, we sChromis multilineata samples were collected by scuba diving in different Western Atlantic regions, encompassing coastal regions and oceanic islands in Brazil ; 1.25 U of Taq polymerase (Gibco BRL); and 0.3 mM of each primer, with the addition of MilliQ water for a final volume of 25 μL. The thermal cycling parameters were an initial denaturation step at 94°C for 2 min, followed by 35 cycles of 45 s and denaturation at 94°C, 45 s annealing at 52°C, and 1 min of extension at 72°C. Excess primers and dNTPs were removed with ExoSAP-IT . PCR products were sequenced on the Embrapa-Cenargen Sequencing Platform (Brasília/DF), with the BigDye Terminator v3.1 reaction kit, using the ABI 3130XL Genetic Analyzer .The HVR-1 sequences of the D-loop region were amplified following the methodology proposed by Domingues et al. , using u C. multilineata were edited and checked through BioEdit 5.0.9 [FST) and molecular variance (AMOVA) were established using DnaSP 4b10.9 [The nucleotide sequences for the HVR1 region of mtDNA init 5.0.9  and the it 5.0.9 . Modelteit 5.0.9  was applit 5.0.9 . Haplotyit 5.0.9 , while tP 4b10.9  and ArleP 4b10.9 . The ManP 4b10.9 . Chromis multilineata from the Fernando de Noronha (n = 12) and Saint Peter and Saint Paul (n = 14) Archipelagos, including a semialbino specimen from the SPSPA, were used for cytogenetical characterization. All specimens were submitted to in vivo mitotic stimulation overnight by intramuscular inoculation with a complex of bacterial and fungal antigens [3 (CMA3) and 4′,6-diamidino-2-phenylindole (DAPI) fluorochromes [Live individuals ofantigens . Mitoticantigens . Cell suantigens , specifiochromes , in supeochromes , 48, res Lutjanus analis , using the primers A 5′-TAC GCC CGA TCT CGT CCG ATC-3′ and B 5′-CAG GCT GGT ATG GCC GTA AGC-3′ [The 5S and 18S rDNA probes, containing approximately 200 bp and 1400 bp, respectively, were obtained by PCR from the nuclear DNA ofA AGC-3′ , NS1 5′-A AGC-3′ , respect in situ hybridization (FISH) was performed on mitotic chromosome spreads [µg/mL) for 1.5 h at 37°C. After denaturation of chromosomal DNA in 70% formamide (pH 7.0), the spreads were incubated in 2xSSC for 4 min at 70°C. Hybridization mixtures containing 100 ng of the denatured probe, 10 mg/mL dextran sulfate, 2xSSC, and 50% formamide (pH 7.0) in a final volume of 30 µL were applied to the slides. Hybridization was conducted overnight at 37°C in a 2xSSC moist chamber. Posthybridization washes were carried out at 37°C in 2xSSC, 50% formamide (pH 7.0) for 15 min, followed by a second wash in 2xSSC for 15 min, and a final wash at room temperature in 4xSSC for 15 min. Signal detection was performed using avidin-FITC  for the 5S rDNAprobe and anti-digoxigenin-rhodamine  for the 18S rDNA probe. Posthybridization washes were carried out in a shaker (150 rpm) and chromosomes were counterstained with DAPI (1.2 µg/mL).Fluorescence spreads  and dualThe slides were screened with an Olympus BX51 epifluorescence microscope and chromosomal plates were captured using a DP73 Olympus high resolution camera and CellSens© 1.9 Digital Imaging software. A total of 20–30 metaphases were analyzed for each specimen. Chromosome morphology was determined according to . C. multilineata for each collection region produced partial sequences with 312 pb (GenBank accession numbers KM211594 and KM211689). Although the presence of tandem repetitions has been commonly described in a number of fish [The sequencing of the amplified mtDNA HVR1 region of of fish –56, theyP < 0.05) in this insular region in relation to others , evidencing the presence of genetic structure. Coastal populations also showed structuring between each other. In fact, the RN population shows structuring in relation to BA . The level of gene flow between sites, suggested by the number of migrants per generation (Nm), showed Nm = 4.9 individuals between RN and SPSPA and unrestricted gene flow between RN and FNA .The best evolutionary model determined for the sequences was TrN + I + G. The populations analyzed showed partially recognizable groups using the NJ method. Although tree topology has shown a number of branches with significant bootstrap values, most exhibit a mixture of individuals from different regions . Populato others . FST va and FNA . The Man C. multilineata from the SPSPA and FNA had a karyotype with 48 acrocentric chromosomes , indicating an excess of low frequency polymorphisms, which, in historic demographic terms, suggests recent population expansions of the species. Geographic isolation cycles and secondary contacts are favorable for increased genetic diversity [In Atlantic species ofl events . In C. mc events , 60. In c events  showed siversity . C. multilineata using mtDNA (cyt b) and involving geographic regions partially overlapping those covered by our study or in similar areas (SPSPA) showed an absence of population structure [ C. multilineata.Previous population analysis intructure . The mortructure , revealsThe causes of genetic structure in reef environments may result from a historic interaction complex of different factors, such as physical connectivity between habitats, ocean currents, ecological patterns of species, larval behavior, and dispersal potential . In geneChromis multilineata was genetically subdivided into three population groups in the sample areas of the Western Atlantic. The genetic profiles show one group composed of the SPSPA population, another formed by FNA and RN individuals, and a third by BA individuals  and nearest geographic locations , such as the FNA (650 km). The genetic differences reported here indicate a genetic condition peculiar to this region, compared to Brazilian coastal areas.ividuals . PopulatHaplotypic diversity and specific biological conditions classify the SPSPA as an evolutionarily independent geographic province. The exclusive genetic profile of the SPSPA population is reinforced by considerable biological evidence that highlights the effects of geographic isolation on connectivity. This includes the elevated level of endemism in many animal groups, such as mollusks, sponges (11.5%), and fish (8.3%) –32, whicInterpopulation chromosomal analyses have been increasingly used in marine fish species with wide geographic distribution . The dat C. jubauna and C. flavicauda result from centric fusions. Such specific polymorphisms are evidence of a transitory condition in Chrominae [ Chromis multilineata, on the other hand, shows a similar karyotype in all populations, on both the coast of Bahia [In contrast to most Pomacentridae species, which show a numerically conserved karyotype of 48 chromosomes, with differences in the number of arms (NF = 48–90) , 69, Chrhrominae . Robertshrominae  and, in of Bahia  and the  Chromis multilineata individuals [ C. multilineata from the SPSPA was the first survey performed in a natural population where semialbinism is recurrent. Although we performed classical cytogenetic procedures and the physical mapping of two ribosomal genes (18S and 5S rRNA), known polymorphic chromosome regions [In the SPSPA, semialbinism has been observed in severalividuals . Albinisividuals –74. Karyividuals . In mari regions , the chr C. multilineata semialbino phenotype in this area supports the occurrence of a larval autorecruitment process in this insular region.Atypical body color variants may also be associated with extrinsic causes, such as pollution, dietary deficiency, and parasites –79. HoweOceanographic conditions play an important role in the dispersion and colonization process in sea organisms, particularly in fish species that spend all their lives on reefs, where dispersion is restricted to the pelagic larval stage. Although the action of ocean currents is not the only factor that promotes dispersion of individuals in marine environments, their impact must always be considered. In the Tropical Atlantic the circulation of surface waters is complex and consists of an Equatorial gyre formed by North Equatorial Counter Currents (NECC), Guinea and three branches  of the South Equatorial Current (SEC), which forms the North Brazil Current (NBC) and Brazil Current (BC) . C. multilineata data.Even though the South Equatorial Current flows through the SPSPA towards the South American continent, a low gene flow from this region to other areas on the Brazilian coast was observed, especially to RN. In addition to distance, this finding could also be explained by the existence of larval retention around the islands. This hypothesis is plausible and in accordance withResults of the numerical simulation of ocean circulation suggest an interaction between the Equatorial Current and the SPSPA . This ef C. multilineata are similar to those found in other Atlantic species of Chromis, analyzed by HVRI mtDNA sequences. In C. limbata, a drastic effect of Pleistocenic glaciation on genetic patterns was observed in the species [ C. multilineata for all populations analyzed may be related to the same events.Genetic patterns identified in species . This co species . Thus, tSimilarities in ichthyofauna allow dividing the Brazilian coast into two subgroups . The fir C. multilineata from these regions.The Brazilian continental shelf (2°S–8°S) is characterized by low temperature variation and high salinity variation, especially at the mouth of large rivers, such as the Amazon. Brazil's NE coast consists of a vast oligotrophic region, under the influence of Tropical Atlantic gyre circulation, including the north and south branches of the SEC, which flows westward to the Brazilian coast, where it bifurcates between 12° and 14°S (Bahia coast), forming the NCB to the north and CB to the south , 84. CirC. multilineata shows a higher level of genetic structure than previous data suggested for this species along the Brazilian coast. Although all populations exhibit similar cytogenetical traits, unsuspected levels of genetic structure were identified among them. Indeed, some restrictions to gene flow are present between populations of the insular regions (FNA × SPSPA) and between coastal populations (RN × BA). The population of SPSPA exhibits a more conspicuous divergence from the others. These genetic variations suggest that they were established in recent times and indicate a complex scenario of interactions between populations of oceanic islands and those along the coast of Brazil.


Drosophila brain have typically been considered from either a developmental or functional perspective without reference to how the circuits might have been inherited from ancestral forms. For example, two candidate pathways for ON- and OFF-edge motion detection in the visual system act via circuits that use respectively either T4 or T5, two cell types of the fourth neuropil, or lobula plate (LOP), that exhibit narrow-field direction-selective responses and provide input to wide-field tangential neurons. T4 or T5 both have four subtypes that terminate one each in the four strata of the LOP. Representatives are reported in a wide range of Diptera, and both cell types exhibit various similarities in: (1) the morphology of their dendritic arbors; (2) their four morphological and functional subtypes; (3) their cholinergic profile in Drosophila; (4) their input from the pathways of L3 cells in the first neuropil, or lamina (LA), and by one of a pair of LA cells, L1 (to the T4 pathway) and L2 (to the T5 pathway); and (5) their innervation by a single, wide-field contralateral tangential neuron from the central brain. Progenitors of both also express the gene atonal early in their proliferation from the inner anlage of the developing optic lobe, being alone among many other cell type progeny to do so. Yet T4 receives input in the second neuropil, or medulla (ME), and T5 in the third neuropil or lobula (LO). Here we suggest that these two cell types were originally one, that their ancestral cell population duplicated and split to innervate separate ME and LO neuropils, and that a fiber crossing—the internal chiasma—arose between the two neuropils. The split most plausibly occurred, we suggest, with the formation of the LO as a new neuropil that formed when it separated from its ancestral neuropil to leave the ME, suggesting additionally that ME input neurons to T4 and T5 may also have had a common origin.Synaptic circuits for identified behaviors in the  The problem of how a complex brain with highly organized pathways and centers could have arisen from simpler forms lacking clear structural compartments and with less differentiated cellular components, has generally attracted discussion chiefly in seeking a transition from very basal forms to those in which brain structure assumes some canonical stage —and the outer part of the second neuropil, or medulla (ME), neuropil splitting is said to have occurred among neuroblast proliferation centers, or anlagen, and to be accompanied by the formation of a chiasma of axons connecting between the two new descendant neuropils  form a chiasma, those from neighboring transmedulla (Tm) cells inverting their retinotopic order within alternating fiber sheets, first depicted by Braitenberg  as sheetThe morphologies of T4 and T5 cells are highly characteristic, and each has unmistakable similarities to the other Figures  especialIn a further final similarity, T4 and T5 cells are both also innervated by a complex tangential cell (CT1), a newly discovered giant contralateral tangential neuron from the central brain. This cell has laterally directed neurites that split and innervate terminals in both the ME and LO, one per column in all columns of the M10 and Lo1 strata , and that receive reciprocal input from the corresponding T4 (ME) and T5 (LO) cells  reconstruction of synaptic circuits , but a lack of immunoreactivity to the vesicular GABA transporter VGAT  how the LO arose, and whether, as we are about to suggest, this could have been from an ancestral neuropil fused with what then became the modern proximal medulla (PM); (2) the topological requirements for this transition, especially those of axon trajectories within the internal chiasma  the eyeless sine oculis mutant lacks both LA and outer ME, whereas the inner ME, LO and LOP are all still present  and the new LO, share some significant anatomical features in common. These include a population of visual projection neurons (VPNs) conveying visual inputs to the central brain, and the nearly-symmetrical innervation of both neuropils by LOP Y cells  in the central brain Figure . We firsThe only significant cell population with a major innervation site in the LO is in fact that of the VPNs, which are actually not optic lobe neurons at all, but develop from neuroblasts in the central brain  circuit, comparing this against an input from a unicolumnar partner, Mi1  diverged further from each other than Tm4 did from Tm3. Two broad options for a unicolumnar input exist: (1) Either there was a single input terminal in each column, one that terminated in ME strata M9, M10, and possibly Lo1, and this became Mi1 for T4, with its own input in turn from the L1 pathway. For input to a new T5 cell population from the L2 pathway, an Mi1-like ancestor would then have had either to extend deeper to LO stratum Lo1 or to retract selectively from M9 and M10 Figures , and thiThe options for the “VPN-duplication” model Figure  are muchDid L1 and L2 arise by duplication in the LA to initiate the segregation between T4 and T5? The circuits driving modern T4 and T5 differ in receiving respective inputs from the L1 and L2 pathways. How might these have arisen as separate pathways? Evidence is mostly only suggestive, but both L-cells are similar in two important ways. First, their dendrites cooperate closely in occupying two of the four postsynaptic sites at all photoreceptor tetrads ; both thus have a cholinergic phenotype. This might be held to support their common ancestral origin, but the weakness in this argument is that most elements of the L2 pathway are also cholinergic as well. L2 and Tm2 (Takemura et al., cha transcripts and ChAT immunoreactivity, and Tm1 is likewise ChAT positive (Shinomiya et al., cha transcripts and ChAT immunoreactivity (Shinomiya et al., Finally, what can be learned from the neurotransmitter phenotype of these cells? From a survey of molluskan species, Sakharov  viewed tDrosophila. It remains to be determined whether during the course of evolution the dichotomy arose first with L1 and L2 in the LA or, deeper in the optic lobe, with the origin of T4 and T5 as evolutionary siblings. Looking yet further afield, it seems plausible that an evolutionary driving force toward ON- and OFF-coding pathways arose more generally, because this dichotomy appears very widely in sensory systems of many stripes. Other benefits may result from an ON/OFF split in sensory pathways, in particular that such a split may simplify downstream computations (Gjorgjieva et al., The existence of dichotomously antagonistic ON- and OFF- pathways has of course long been known in the vertebrate RE. There, an ON-OFF split in the visual pathways already occurs at the first synapse (Schiller, For all that we hope it to be closely reasoned, most of what we propose is entirely speculative and unlikely to receive detailed support from comparative anatomy or the fossil record. On the other hand, we can anticipate that future comparative transcriptome analyses among identified neurons may bear with greater force on our proposals, and help address the question of how neural circuits might have arisen from among identified neurons of the same or neighboring neuropils. In parallel, we may also anticipate that more detailed studies on the development of the neurons of the fly’s ON- and OFF-edge motion pathways may also help contribute additional support for our hypothesis. For example, knowing the lineage relationships between T4 and T5 may help to consolidate further the relationship we propose for these cells as evolutionary siblings, as well as it would provide support for the relationships between neurons of their input pathways. In particular, such data could help demonstrate an evolutionary relationship between T4 and T5’s upstream partner neurons. For example the lineage and genetic relationships that we may propose from morphological evidence alone, between Tm cells such as Tm3 and Tm4, or between Mi and Tm cells, such as Mi1 and Tm1/Tm2, could help to arbitrate evolutionary relationships as robustly as we hope to have demonstrated these for their target neurons, T4 and T5. Unlike the latter pair, comparative anatomy and the fossil record are, we think, unlikely to further our understanding of the evolutionary relationships of these ME cells beyond the realm of suggestion.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The North-Eastern region (NER) of India, comprising of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland and Tripura, is a hot spot for genetic diversity and the most probable origin of rice. North-east rice collections are known to possess various agronomically important traits like biotic and abiotic stress tolerance, unique grain and cooking quality. The genetic diversity and associated population structure of 6,984 rice accessions, originating from NER, were assessed using 36 genome wide unlinked single nucleotide polymorphism (SNP) markers distributed across the 12 rice chromosomes. All of the 36 SNP loci were polymorphic and bi-allelic, contained five types of base substitutions and together produced nine types of alleles. The polymorphic information content (PIC) ranged from 0.004 for Tripura to 0.375 for Manipur and major allele frequency ranged from 0.50 for Assam to 0.99 for Tripura. Heterozygosity ranged from 0.002 in Nagaland to 0.42 in Mizoram and gene diversity ranged from 0.006 in Arunachal Pradesh to 0.50 in Manipur. The genetic relatedness among the rice accessions was evaluated using an unrooted phylogenetic tree analysis, which grouped all accessions into three major clusters. For determining population structure, populations K = 1 to K = 20 were tested and population K = 3 was present in all the states, with the exception of Meghalaya and Manipur where, K = 5 and K = 4 populations were present, respectively. Principal Coordinate Analysis (PCoA) showed that accessions were distributed according to their population structure. AMOVA analysis showed that, maximum diversity was partitioned at the individual accession level . Using POWERCORE software, a core set of 701 accessions was obtained, which accounted for approximately 10% of the total NE India collections, representing 99.9% of the allelic diversity. The rice core set developed will be a valuable resource for future genomic studies and crop improvement strategies. Plant genetic resources are of paramount importance for the future and to ensure the food and nutritional security of an increasing population. A large number of genetic materials have been conserved in Genebank, but their use is being limited due to an unmanageable number of accessions and the continuous expansion of accessions numbers. Core germplasm development has been proposed for better management and use of collections available in Genebank et al. (1995) to adequately sample a given collection The sampling percentage of a core set has long been under debate O. rufipogon in the region South of Himalayan mountain range, likely in the present day eastern and North-Eastern (NE) India, extending eastward to Nepal, Myanmar and Thailand to Southern China The rice genetic resource is an important source for rice breeding and makes a valuable contribution to global wealth and food security A large collection of the rice genetic resource from NE India is currently conserved at National Genebank (NGB), National Bureau of Plant Genetic Resources (NBPGR), New Delhi. Since these germplasm contains unique traits which can be exploited for future crop improvement programme but has not been properly characterized at molecular level. Therefore, in the present study this collection was analyzed (i) to study the genetic diversity and population structure of all 6,984 rice germplasm accessions using 36 SNP markers, which were developed and used earlier for diversity and population structure analysis of rice varieties www.nbpgr.ernet.in) and presented in A total of 6,984 accessions of NE India  were drawn from NGB, NBPGR, New Delhi. The NE India germplasm collection also included 24 accessions with EC ID from IRRI, Philippines and two accessions from Ghana which were growing in farmer's field since their introduction in 1991. The purpose of their inclusion was to analyse their clustering in relation to Indian germplasm collection. The details of each accession along with passport information were obtained from the online database of NGB, (Seeds of each genotype (10–12 seeds) were dehusked and used for DNA isolation using QIAGEN DNeasy plant mini kit. Kernels were ground into fine powder using tissue lyser  with a tissue lyser adapter set (QIAGENq). DNA was extracted following the procedures described by manufacturers.Genomic DNA of all the 6,984 accessions was diluted to prepare working stocks of 10 ng/µl. Sequenom Mass ARRAY multiplex assays were designed for 36 SNPs (iPLEX gold chemistry), representing conserved single-copy rice genes http://taylor0.biology.ucla.edu) to determine the final population. POWERCORE software The major allele frequency, gene diversity, heterozygosity and Polymorphic Information Content (PIC) for each locus were calculated for SNP markers using Power Marker 3.5 Genetic diversity of NE India rice collections , available at NGB, was estimated using 36 SNP markers. Alleles generated with all 36 SNP markers were scored to study the genetic diversity. Since these accessions cover seven states of NE India, they were analyzed state-wise for precise estimation of the level of their genetic diversity. The accessions collected from Arunachal Pradesh (663), Assam (2635), Manipur (549), Meghalaya (2427), Mizoram (107), Nagaland (377) and Tripura (226) were analyzed for major allele frequency, heterozygosity, gene diversity and PIC . The higCluster analysis was performed using the NJ method for all the collections from seven states and unrooted phylogenetic trees were constructed . Rice cohttp://taylor0. biology.ucla.edu) was used to determine final number of populations. The number of populations in Meghalaya collections was estimated as five, in Manipur collections as four and three for collections belonging to remaining states, i.e. Arunachal Pradesh, Assam, Mizoram, Nagaland and Tripura  showed a very distinct PCoA plot, population1 in Meghalaya collections concentrated only in quadrant 3 and 4, whereas population4 concentrated in 1 and 2 quadrant and unlike population3, population4 and population5 did not show intermixing with each other . PCoA ofOf the 6,984 rice accessions studied, a core set of 701 accessions  were selected using POWERCORE . Nine alThe genetic diversity of the core set was estimated to know the extent of diversity captured from total collection. Major allele frequency; gene diversity, heterozygosity and PIC were separately estimated for core-sets of individual states . CompariSTRUCTURE analysis grouped core set of 701 rice accessions into three populations . Populatversus core set showed that nearly all alleles with similar frequency were represented in the core collection  State wise analysis of the genetic diversity of the NE India collection showed interesting results. The average PIC values for these SNP markers did not vary much across the states and ranged from 0.23 (Manipur) to 0.27 . The average PIC value recorded in the present study was in the range reported by researchers in previous studies. Singh et al., (1973) et al., (2013) et al., (2013) Analysis of statistical genetic parameters across the collections from different states indicated a high level of variability in the NE India collection. There is relatively a high level of genetic diversity among the NE India rice collections, as reported by others using morphological Population structure study has been reported for the first time in NE rice collections and the number of populations estimated five for Meghalaya, four for Manipur and three for the remaining states. Admixture has been reported in all the states besides pure lines and this may be due to germplasm taken in this study which is more heterogeneous in comparison to varieties.et al., (2013) There have been no other studies for characterization of a large number of NE India rice accessions at the molecular level that could be used for comparison of our results. AMOVA analysis showed maximum variation among individuals for accessions belonging to Nagaland (75%) and minimum for Meghalaya collection (37%). Choudhury The genomic characterization revealed huge variability among the NE India rice collection; therefore, we decided to identify a core set for the NE India accessions to facilitate the rice breeders to effectively use the same in their crop improvement programmes and efficient conservation and management of germplasm in the genebank. In recent past, various types of molecular markers have been used to develop core sets for different annual crops. These markers include SNPs for Arabidopsis In the current study, we divided the NE India rice collection into seven (on the basis of source states) subsets and later all the accessions of subsets were pooled to form a core set of NE India rice germplasm. Statistical parameters, genetic distance and cluster analysis were taken into consideration to develop this core set; these methods were followed in accordance with other studies In present study, we developed a core set of 10% of total collection; while there is no perfect core set constructing theory or universal methods to fit all crops, which might be due to large number of germplasm resources, absence of data for accessions, or complication and diversification of data type, and so on In conclusion, SNP marker based molecular characterization of NE India rice collection revealed that large variation exists among the accessions and the pattern of the genetic diversity and population structure varied across the seven states. Collections from Meghalaya were found to be the most diverse and possessed maximum number of populations. Core sets have been developed state wise as well as for the entire NE collection that can be potentially exploited by rice breeders in conventional breeding programmes and molecular breeders/researchers for genomic studies such as association mapping for gene discovery for various desirable traits and identification of suitable parents for their use in rice improvement. Further development of core set and sub-sets for NE India rice is likely to significantly reduce the task of the genebank curators as they will need to multiply and maintain small number accessions in the medium term storage as active collections to meet the requirement of the rice breeders.Figure S1Circular phylogenetic trees of NE rice collection constructed based on SNP data using MEGA software (a) Arunachal Pradesh, (b) Assam, (c) Manipur, (d) Meghalaya, (e) Mizoram, (f) Nagaland and (g) Tripura. (Red marked accessions was selected for core by Power Core).(DOCX)Click here for additional data file.Figure S2Estimation of populations in NE rice collections using LnP(D) derived Δk for k from 1 to 20 in (a) Arunachal Pradesh, (b) Assam, (c) Manipur, (d) Meghalaya, (e) Mizoram, (f) Nagaland and (g) Tripura.(DOCX)Click here for additional data file.Figure S3Population structure of NE rice collection based on SNP data (a) Arunachal Pradesh, (b) Assam, (c) Manipur, (d) Meghalaya, (e) Mizoram, (f) Nagaland and (g) Tripura.(DOCX)Click here for additional data file.Figure S4Analysis of Molecular Variance in NE rice collection based on SNP data (a) Arunachal Pradesh, (b) Assam, (c) Manipur, (d) Meghalaya, (e) Mizoram, (f) Nagaland and (g) Tripura.(DOCX)Click here for additional data file.Figure S5Principal Coordinate Analysis in NE rice collection based on SNP data (a) Arunachal Pradesh, (b) Assam, (c) Manipur, (d) Meghalaya, (e) Mizoram, (f) Nagaland and (g) Tripura.(DOCX)Click here for additional data file.Table S1List of NE rice collections studied using SNP markers.(XLSX)Click here for additional data file.Table S2List of SNP primers used for genotyping of NE rice collection (6984 rice accessions) alongwith gene diversity, PIC and Major allele frequency.(DOCX)Click here for additional data file.Table S3Output Table generated from Structure Harvester software for Evanno method. Yellow highlight shows the largest value in the Delta K column.(DOCX)Click here for additional data file.Table S4List of rice accessions identified as NE core set based on SNP markers.(XLSX)Click here for additional data file.


Haloptilus longicornis in both the Atlantic and Pacific Oceans, and we hypothesized that populations within each gyre represent distinct gene pools that persist over time. We tested this expectation through basin-scale sampling across the Atlantic Ocean in 2010 and 2012. Using both mitochondrial (mtCOII) and microsatellite markers (7 loci), we show that the genetic composition of populations was stable across two years in both the northern and southern subtropical gyres. Genetic variation in this species was partitioned among ocean gyres , suggesting strong spatial population structure, but no significant partitioning was found among sampling years. This temporal persistence of population structure across a large geographic scale was coupled with chaotic genetic patchiness at smaller spatial scales, but the magnitude of genetic differentiation was an order of magnitude lower at these smaller scales. Our results demonstrate that genetically distinct plankton populations persist over time in highly-dispersive open ocean habitats, and this is the first study to rigorously test for temporal stability of large scale population structure in the plankton.Although stochasticity in oceanographic conditions is known to be an important driver of temporal genetic change in many marine species, little is known about whether genetically distinct plankton populations can persist in open ocean habitats. A prior study demonstrated significant population genetic structure among oceanic gyres in the mesopelagic copepod  Understanding the ecology and evolution of marine species requires knowledge of the extent to which conspecific populations are genetically differentiated across space, and whether these spatial patterns are stable through time. At large geographic scales, marine species with large and stable population sizes, broad species ranges, long-lived planktonic larvae, and high fecundity are expected to exhibit temporal stability in allele frequencies coupled with weak or no genetic structure among populations , due toEucalanus spinifer, Eucalanus hyalinus, and Pleuromamma xiphias H. longic v7.1.7, . A phylo v7.1.7, , and theH. longicornis [H. longicornis species 1 or H. longicornis species 2 based on factorial correspondence analysis (FCA) using GENETIX v4.05 [H. longicornis species 1, and were included in all subsequent analyses  and linkage disequilibrium were examined using ARLEQUIN v3.5.1.3 and GENEPOP v4.2 for all microsatellite loci [FST values with correction for null alleles in FreeNA [FST values were calculated among all sample sites using both microsatellite and mtCOII data, as a measure of population subdivision across samples  based on FST were carried out to partition the genetic variance across both space (ocean gyres) and time (sampling years), for both marker types. In these analyses, we tested for population structure under the following groupings: with samples stratified by (1) northern and southern subtropical gyres (2 gyres), and (2) across two sampling years . Global FST values were estimated using non-hierarchical AMOVAs among all samples, as well as among subsets of the data across ocean gyres and sampling years. Significance was tested with 10,000 permutations of genotypes or haplotypes among populations. Principal coordinate analysis (PCA) plots of linearized pairwise FST values based on both mtCOII and microsatellite data were used to visualize spatial and temporal genetic differentiation among samples. Population structure was further examined using a Bayesian clustering method implemented in STRUCTURE [5 steps followed by 106 steps, with and without using sampling location as a prior. We ran these analyses for each of the 2010 and 2012 datasets using K = 1 to K = 10, and for the dataset of combined years using K = 1 to K = 20. We ran three separate replicates for each K to investigate consistency of Pr(X|K). The true K was evaluated by visual inspection of barplots and comparing Pr(X|K) across K values.For ite loci –38. We tite loci , and estn FreeNA . Microsan FreeNA ,41. Pair3.5.1.3, ,43). Pait v2.1.4 , and theTRUCTURE ,46 for mH. longicornis sp. 1 animals sequenced, with an average of 7 haplotypes observed at each sampling site . However, there were significant differences between the northern and southern subtropical gyres in the number of mtCOII haplotypes , haplotype diversity , and nucleotide diversity , with higher diversity observed in the southern gyre.A total of 58 mtCOII haplotypes occurred among the 472 ing site . AverageFST values are calculated both with and without correction for null alleles, higher values are observed in the corrected FST values. For this reason, we consider it conservative to present these values as uncorrected for null alleles, as this is least likely to identify spurious, elevated structure among populations.Our seven microsatellite markers were moderately polymorphic, with the average number of alleles per locus ranging from 4.9 to 6.9 across all samples . The aveFST = 0.175, P < 0.0001, global ΦST = 0.048, P = 0.0001) and microsatellite markers , while no significant results were found among the two sampling years , 2012 only , or 2010 only for the mtCOII data , than were within-gyre comparisons  and 55.3% (msat) of the variation explained by the first principal coordinate ; however, this type of analysis lacks power for levels of divergence comparable to our microsatellite dataset [There was significant genetic differentiation among samples at both mtCOII  or circum-antarctic (Euphausia superba) distribution patterns and temporal stability was only examined for a subset of geographic samples. The shared observation of genetically distinct populations that persist through time in the open sea suggests that temporal stability in large-scale population structure may not be uncommon for holozooplankton in a variety of habitats. Our results support our initial expectations that plankton populations within subtropical gyres represent distinct gene pools that persist over time.Our results suggest that temporal genetic change in zooplankton populations is heavily influenced by the spatial scale of study. At large spatial scales, we observed temporal stability in the common mesopelagic copepod Metridia pacifica populations across mesoscale coastal upwelling filaments and eddies in the California Current [FST values unrelated to geographic distance or ocean habitat among sites in several other studies . Genetic patchiness also can be seen in the data reported here for Haloptilus longicornis. There are population samples within both the northern and southern gyres that show weak but significant differentiation relative to other samples within the same gyre , and Current ,53, as wxiphias, ,21). A nxiphias, –59); how km; ,51,Operationally, our results suggest that the broad-scale genetic patterns of population structure within holozooplankton species can be captured with limited temporal sampling given sufficient sampling intensity (sample size). This result is reassuring, as the majority of prior studies have used samples from a wide range of sampling dates, and interpreted the patterns observed exclusively in a spatial context . Our results demonstrate temporal stability in the genetic structure of a ‘high dispersal’ marine species, and are particularly important given the dispersive nature of the pelagic habitat and very high vagility of planktonic species. Additional work is needed to understand the extent and underlying causes of fine-scale genetic patchiness in the plankton, as well as to interpret its biological impact on species. Confirmation of the observations of temporally stable genetic structure over large spatial scales from other zooplankton species also would lend greater support to the conclusion that these patterns are the norm for the holozooplankton.S1 Fileegoetze@hawaii.edu).Please contact the corresponding author regarding subsequent use of these data ((CSV)Click here for additional data file.S1 Tableh = haplotype diversity; π = nucleotide diversity.Collection sites for plankton samples included in this study, from (A) the 2010 AMT cruise (AMT20), and (B) the 2012 AMT cruise (AMT22). Pop ID = the population identifier referred to throughout the manuscript; Station = the cruise and station number of each sample; N = the number of adult females included; H = the number of haplotypes; (PDF)Click here for additional data file.S2 TableHo and He are the observed and expected heterozygosity averaged across all microsatellite loci for each population. k = average number of alleles per locus.Collection sites for plankton samples included in this study, from (A) the 2010 AMT cruise (AMT20), and (B) the 2012 AMT cruise (AMT22). Pop ID = the population identifier referred to throughout the manuscript; Station = the cruise and station number of each sample; N = the number of adult females included. (PDF)Click here for additional data file.S3 TableP < 0.05) are shaded grey, bold indicates significance following correction for multiple comparisons (FDR).Samples are grouped by ocean gyres, with sample site numbers as in Tables (PDF)Click here for additional data file.S4 Tablebold indicates significance following correction for multiple comparisons (FDR).Samples are grouped by ocean gyres, with sample site numbers as in Tables (PDF)Click here for additional data file.


Bhutan is a small Himalayan country lying within the region considered to be the origin of citrus. Diverse citrus wild types grow naturally in different climates, elevations and edaphic conditions, but only mandarin is cultivated commercially. The first report of Huanglongbing  in Bhutan in 2003, and the threat it posed to the country’s citrus orchards prompted the collection of mandarin germplasm from across the country. This paper describes the genetic diversity of mandarin accessions in Bhutan using amplified fragment length polymorphic (AFLP) markers.Tsirang1, Tsirang3, Sarpang1, Dagana4, Samtse4, Dagana1, and Trongsa2) from five districts  and their grouping was strongly supported by bootstrap analysis . Cluster B consisted of 14 accessions divided into three sub-groups . However, bootstrap value supported significantly for subgroup1  and subgroup3 .Twenty three accessions of Bhutanese mandarin were analyzed using AFLP markers to assess the genetic variability that is believed to exist only in Bhutan and some parts of North East India and South China. Five primer pairs  were identified  and used for the analyses. A total of 244 bands were scored visually of which 126 (52%) were polymorphic with an average polymorphism information content of 0.95 per marker. A cluster dendrogram based on multiscale bootstrap sampling categorized twenty three accessions into two broad groups containing eight and 14 accessions, respectively. Group A consisted accessions (This study indicates that Bhutanese mandarin germplasm collected from across the country are genetically diverse although the level of variability differed among the accessions assessed. The variation in genetic variability was observed irrespective of where the accessions were collected suggesting that phenotype and geographical location can serve a basis for future germplasm collection in Bhutan. Further, five primer pair combinations could separate 23 mandarins accessions considered in this study, suggesting that AFLP markers can be a useful tool for future identification. Bhutan is a small landlocked Himalayan country between China and India. This region is believed to be the most likely origin of citrus , and a rCitrus reticulata Blanco) is believed to be one of the three true Citrus species [e.g. “Dorokha local” and “Tsirang local”) applied to mandarins in the different districts suggest variability, and that notion is supported to some extent by morphological studies [Mandarin  and trifoliate orange (Citrus trifoliata) [The AFLP markers approach is a powerful molecular tool used widely in phylogenetics, population genetics, genetic mapping, and cultivar identification. The technique provides highly stable and reproducible information  without d genera , and homd genera . AFLP mad genera , and in d genera . The tecfoliata) . Thus, tThe process of germplasm collection would be more efficient and more likely to result in the collection of genuinely diverse genotypes if it was based on better knowledge of the level of genetic variability present among a population and between populations. Genetic evaluation of accessions from various locations prior to germplasm collection would not only provide information about the geographic distribution of genetic diversity but also help to identify areas of focus for further collection. To date there has been no reports of any assessment based on molecular markers of the genetic variability of mandarins growing in Bhutan. The level of genetic variability remains unknown even for the germplasm already collected.Therefore, a preliminary population genetic analysis was conducted using the AFLP marker technique to determine the level of genetic polymorphism and variability among 23 mandarin samples that were collected in Bhutan, their selection being based on phenotype and geographic location. The study also assessed the usefulness of AFLP markers to identify mandarins grown in Bhutan.EcoR1 and Mse1 primer generated a total of 244 bands of which 126 were polymorphic. On average, 52% polymorphism was obtained from each primer combination. The band intensity within a locus also varied among the accessions. The primer pair E-ACA/M-CAG  that enabled accessions to be discriminated from each other.p-value = 96%, AU p-value = 86%) and sub-group 1  and sub-group 2  under group B. Group A contained accessions from all five different locations . The hierarchical dendrogram suggested a complicated relationship among the mandarin accessions tested. Accessions from Tsirang appeared in both the major groups A and B. Under group B, Tsirang 2 and Tsirang 4 along with Sarpang 6 formed a small clade (subgroup 1). Likewise, Tsirang 1 and Tsirang 3 formed a separate clade within Group A. Bootstrap analysis failed to provide evidence of a third cluster under group B.The cluster analysis, represented as a hierarchical dendrogram Figure , separatThe range of polymorphism (41–70%) indicated by AFLP analysis suggests the possibility of there being more than one mandarin type, as well as several hybrids, amongst the accessions assessed. The ability of AFLP primer combinations to produce high numbers of polymorphic bands suggests it is a useful tool to identify mandarins grown in Bhutan. This genetic variability and relationship could form a basis for further collection of accessions and genetic improvement strategies. Many species are collectively known as mandarins, but differ in their origin, morphology, distribution and adaptation to the environment ,28. HistNo geographical affinity was shown between the accessions collected from Trongsa, Tsirang, Dagana, Samtse, or Sarpang. In other words, with the exception of accessions from Zhemgang, accessions from the same district differed genetically. This result is not unsurprising given, as indicated previously, that most mandarin trees in Bhutan are seedlings; being either of gametic or nucellar origin. The genetic variability within districts and between districts is in accordance with the morphological variability reported earlier . The nonet al. [The ability of AFLP to separate closely related accessions supports the findings of Colletta Filho et al. . Bhutaneet al.  is suppoSamtse2) was genetically similar to an accession from Dagana district (Dagana4) despite the distance separating the two districts. This similarity may be due to the trees in each district having been grown from seedlings originating from the National Seed Center, which is the only nursery authorised to supply citrus seedlings. Most of the other accessions were grown from unknown seedling sources. On the other hand, it would seem that the accessions from Tsirang (which has an elevation of 1480 masl) may have resulted from out crossing or might have been driven by environmental factors to adapt. The cluster tree analysis showed limited affinity relating to the accessions’ sources (districts). The arrays of minor groups are difficult to interpret because the dendrogram may have been complicated by homoplasy—a shared character state that is reported to be due either to co-migration of non-homologous fragments or the loss of fragments — which can result in an underestimation of genetic variability [An accession from Samtse  growing in large pots in an insect-proof facility at the Renewable Natural Resources and Development Center (RNRRDC) at Wengkhar. Details of the location for the accessions are shown in , thick DNA bands were re-suspended in 500 to 800 μl TE buffer, and thinner bands were re-suspended in 50 to 300 μl of buffer. Based on earlier studies [et al. [EcoRI/MseI (2.5 U each) in a restriction buffer 50 mM  in a final volume of 25 μl. EcoRI and MseI adapters were subsequently ligated to the digested DNA fragments. The adapter-ligated DNA (diluted 1:9) was pre-amplified with AFLP primers each having one selective nucleotide using the following cycling parameters: 20 cycles of 30 sec at 94°C, 60 sec at 56°C and 60 sec at 72°C. The pre-amplified DNA was diluted (1:9) based on an assay of the concentration of pre-amplified DNA and the amount needed for band visibility on 6% polyacrylamide gel following electrophoresis. The aliquot was subsequently used for selective amplification with EcoRI and MseI primers having three selective nucleotides at the 3’ ends. The cycling parameters for selective amplification were as follows: 1 cycle of 30 sec at 94°C, 30 sec at 65°C and 60 sec at 72°C. The annealing temperature was then lowered by 0.7°C per cycle during the first 12 cycles and then 23 cycles were performed at 94°C for 30 sec, 56°C for 30 sec and 72°C for 60 sec. The reaction products were resolved on 6% polyacrylamide sequencing gels followed by silver staining.The quantity of the DNA was determined by comparing the extracted DNA with 50 ng lamda (λ) DNA, and quality was judged by the presence of smears in 1% agarose gel electrophoresis. Depending on band size  were subjected to cluster analysis using the “pvclust” package  of “R”  and foll


Considering how popular grapes are in terms of their antioxidant benefits, we compared concord, purple, red, and green grapes for total antioxidant capacity (TAC) and carbohydrate concentration. All grapes were acquired from commercial sources and samples of each were separated into skinned and not skinned groups. Each whole grape and the skins were individually homogenized and then separated into pulp and supernatant fractions. Each fraction was analyzed for total TAC and carbohydrates. The concord grapes and purple grapes had significantly higher TAC in the homogenates than did the red or green grapes. The concord grapes and green grapes had significantly higher TAC in the pulp than in the cytosol whereas the red and purple grapes had approximately the same amount. The majority of the TAC of the purple and red grapes was in the skin whereas the concord and green grapes had approximately the same TAC in the skin and pulp. The concord and purple grapes had the highest TAC when compared to the red and green grapes, whereas the red and green grapes had approximately the same total TAC. Grapes are frequently touted as excellent sources of antioxidants ,2,3 whicIn the case of grapes, people tend to associate the differently colored grapes with varying levels of antioxidant capacity (TAC) with the rule of thumb being that the darker grapes have more TAC than the lighter ones. This general consensus is worth evaluating because the different colors may be more indicative of different classes of antioxidants rather than varying levels of antioxidants. It is known that the polyphenols in purple and red grapes consist predominantly of anthocyanins whereas in green grapes flavanols constitute the majority of the polyphenols ,14,15.In the current study, four different groups of grapes were analyzed by the CUPRAC biochemical assay for total TAC : concordin vivo, which is to say that total antioxidant capacity does not necessarily correlate directly with how beneficial different antioxidants may be in the human body [Due to the potential for antioxidants to protect against the onset of various diseases, it is important to better understand grapes as a vehicle for these antioxidants. The popularity of grapes makes it worthwhile to analyze and categorize the differences in antioxidants between the different colors of grapes in order to satiate the burgeoning demand of health awareness regarding common foods. Many different types of foods are commonly advertised as being rich in antioxidants, yet the amount of antioxidants is never explicitly stated. This fact might compel a curious person to wonder what the antioxidant activities are for the most common foods promoted for their antioxidants, such as grapes. It should also be noted that antioxidants have variable effectiveness man body ,22.g for 20 min and separated into the pellet (pulp) and supernatant (cytosol). The skin was also homogenized and analyzed as stated above.All methods were approved by the Stratton VA Medical Center R&D Committee. Four different grapes  were obtained from the supermarket for analysis using the CUPRAC biochemical assay for total TAC ,23,24,25l-ascorbic acid. The concentrations were 1000, 500, 250, 125, 62.5, and 31.25 μM, as well as a blank (Tris buffer). Once the ammonium acetate, neocuproine, and copper (II) chloride dihydrate were added to the samples and standards, the test tubes were incubated at room temperature for thirty minutes. At this point, they were analyzed in a Hitachi U-2001 Spectrophotometer set to read at 450 nm.For the CUPRAC assay, all samples and standards were prepared in duplicates in which 150 μL of the sample or standard was mixed with 150 μL each of 1 M ammonium acetate, 7.5 mM neocuproine, and 10 mM copper (II) chloride dihydrate. The standard curve was established using 1 mM For the carbohydrate analysis, the samples and standards were also prepared in duplicate by adding 20 μL of the samples or standards to each pair of test tubes containing 1 mL anthrone solution, which was prepared daily at a ratio of 1 mg of anthrone per 1 mL of 2.3:1 sulfuric acid in water. The standard curve was created using six glucose concentrations diluted in water with concentrations of 5, 2.5, 1.25, 0.6, 0.3, and 0.15 mg glucose per milliliter with the seventh standard being a blank (water). This glucose solution was prepared daily. 20 μL of each sample or standard was mixed with 1 mL of the anthrone solution, and the test tubes were incubated in a water bath at a temperature of 90 °C for five minutes. They were then placed in cold water for five minutes and then removed from the water and allowed to warm to room temperature for five more minutes. Once stabilized to room temperature, all standards and samples were read in the Hitachi U-2001 Spectrophotometer set to read at 630 nM.p < 0.05 was required for statistical significance.One way analyses of variance was used followed by the Tukey test for individual differences. A p < 0.05.The carbohydrate concentrations of the grapes were: Concord = 10.3 ± 1.0 μg glucose/mL; purple = 27.2 ± 1.3 μg glucose/mL; red = 25.4 ± 2.4 μg glucose/mL; and green = 26.0 ± 2.5 μg glucose/mL. The carbohydrate concentration of the green grapes was significantly different from all other grapes; The antioxidant capacity normalized by carbohydrate content is presented in The Concord grapes and green grapes had significantly higher TAC in the pulp than in the cytosol whereas the red and purple grapes had approximately the same amount.We also analyzed the data by separating the four grapes into skin, pulp, and cytosol and normalizing to percent of the total TAC of the grape . The redIn the pulp, which included the seeds of the grapes, the concord grapes had 48% and the green grapes 38% of the total TAC. The purple and red grapes had very low antioxidant capacity in the pulp. The cytosol showed activities ranging from 15% to 26% of the total TAC.Our data shows that the Concord and purple grapes have a significantly higher TAC than the red or green grapes. This supports the general belief that darker grapes have more antioxidants. However, whereas the purple grapes had most of the antioxidant capacity in the skin, the Concord grapes have approximately 45% of the TAC in the skin and 45% of the TAC in the pulp. Thus, juicing these grapes and getting rid of the skin would give the Concord grapes a higher TAC than juice made from the purple grapes. It is not known whether any commercial grape juice tries to extract the antioxidants from the skin.Interestingly, the red and green grapes did not show different antioxidant activities. Red grapes share some commonalities with purple grapes but also some others with the green. For example, the red have the majority of their polyphenols in the form of anthocyanins like the purple do while the green have mainly flavanols. When dissected, the shape, location, and size of the seeds of the red grape were virtually identical to the green grape. This contrasted with the purple grapes which had extremely inconspicuous seeds in a slightly different location.in vivo than flavanols (green grapes), and studies have demonstrated that anthocyanins are strongly detected using the CUPRAC test, thus the ratio of TAC for purple grapes may be somewhat skewed [Two hypotheses concerning the relationship between the color of the grapes and TAC are: (1) the differences in TAC are caused by either differences in the concentration of antioxidants or(2) the differences in TAC are caused by the differences in the forms of the antioxidants. It may well be that both are important. If color was a major determining factor, than it would have been expected that the red grapes would have a higher TAC than the green instead of seeing approximately the same total capacity in our results. However, similar to the differences in distribution shown between Concord grapes and purple grapes, most of the TAC of the red grapes was in the skin whereas much of the TAC of the green grapes was in the pulp. It should be noted that anthocyanins (purple and red grapes) behave differently t skewed ,27,28. Rt skewed ,30,31,32The percentage of antioxidant capacity in the cytosol showed few noticeable differences between the four grapes. This suggests that the antioxidants in the four types of grapes tend to solubilize into the cytosol in the same proportions.in vivo and in vitro ischemia/reperfusion and partial outlet obstruction with all models producing significant oxidative damage to the urinary bladder. These studies clearly demonstrated that oral grape ingestion reduced both oxidative damage to proteins and lipids and significantly protected the rabbit urinary bladder from both structural and contractile damage [The TAC of grapes in general has been effectively demonstrated in studies where a grape powder suspension has been fed orally to rabbits subjected to a variety of forms of e damage ,34,35,36When comparing the carbohydrates among the grapes we found that the purple, green, and red grapes have essentially the same concentration of carbohydrates whereas the Concord grapes had approximately half the carbohydrates. Since the TAC was normalized to carbohydrates, the Concord grapes would have less antioxidants per weight of grapes than the purple grapes.in vivo.Concord and purple grapes have significantly higher total antioxidant activities than red or green grapes which were very similar. The antioxidants in the purple and red grapes were found primarily in the skin, whereas, in the Concord and green grapes, the TAC was equally divided between skin and pulp. This study suggests that the dark color Concord and purple grapes are more beneficial in terms of antioxidants. Further studies are needed to confirm whether this is also true


According to the nearly-neutral theory of evolution, the relative strengths of selection and drift shift in favour of drift at small population sizes. Numerous studies have analysed the effect of bottlenecks and small population sizes on genetic diversity in the MHC, which plays a central role in pathogen recognition and immune defense and is thus considered a model example for the study of adaptive evolution. However, to understand changes in genetic diversity at loci under selection, it is necessary to compare the genetic diversity of a population before and after the bottleneck. In this study, we analyse three fragments of the MHC DQA gene in woolly mammoth samples radiocarbon dated to before and after a well-documented bottleneck that took place about ten thousand years ago. Our results indicate a decrease in observed heterozygosity and number of alleles, suggesting that genetic drift had an impact on the variation on MHC. Based on coalescent simulations, we found no evidence of balancing selection maintaining MHC diversity during the Holocene. However, strong trans-species polymorphism among mammoths and elephants points to historical effects of balancing selection on the woolly mammoth lineage. Small populations face an increased risk of extinction due to loss of genetic diversity, which is associated with inbreeding depression and loss of adaptive variationIn vertebrates, the capability to resist pathogens is determined by the variability of genes of the major histocompatibility complex (MHC)4689What effect a bottleneck has on MHC diversity has been a long-standing topic of discussion in research on natural selection131517A meta-analysis of studies on the effect of bottlenecks on MHC polymorphism1921In recent years, however, analyses of DNA from historical samples have been applied to directly study pre- and post-bottleneck MHC diversity in prairie-chickensMammuthus primigenius) to study the change in diversity in a MHC locus as the species went through a bottleneck and subsequently persisted at a small population size until its extinction ~4 cal ka . The woolly mammoth was a large herbivore with a widespread Holarctic distribution during the Pleistocene, but retreated to the northern edge of its range following the climatic changes at the Pleistocene-Holocene boundary27292931To date, no studies have examined changes in MHC diversity leading up to a species’ extinction. In this study, we have used ancient DNA from the woolly mammoth (i) the Pleistocene continental population in Chukotka and Wrangel while these areas were still a part of Beringia , and ii) the Wrangel Island population after rising sea levels led to its isolation  HotStar Taq DNA Polymerase (Qiagen), 2 μL of DNA extract and distilled water up to the total volume of 25 μL. Amplifications were carried out under following conditions: 10 min of initial denaturation at 95 °C; 55 cycles of denaturation of 30 sec at 94 °C, annealing of 30 sec at 50–54 °C  or 52 (both intron 2a and exon 4) PCR products. Intron 2b worked only for 26 samples, likely due to the fact that this fragment was longer than the other fragments that were targeted.To estimate the concentration of the cleaned pool, we ran 2 μL of the pool on an agarose gel and compared it to a dilution series of a GeneRuler 100 bp Plus DNA Ladder (Fermentas). Subsequently, we followed a modified versionWe conducted all pre-PCR steps in a dedicated ancient DNA laboratory at the Swedish Museum of Natural History in Stockholm, which is physically separated from the modern DNA facilities. We used protective gloves, suits and face masks to prevent contamination. All surfaces were regularly cleaned with sodium hypochlorite and tools and reagents were cleaned by ultraviolet irradiation. We used negative controls with all extractions and PCRs (at least one per eight samples).For samples that were scored as homozygotes, we added a third replicate to further reduce the risk that allelic dropout led to erroneous genotypes. These third replicates were sequenced by Sanger sequencing on the ABI 3130xl sequencer (Applied Biosystems) with primers MPDQA-1F, -1R and -3F being replaced by internal primers see  to securMHC is recognized as one of the hotspots of gene duplication in various taxonomic unitshttps://github.com/josephhughes/Sequence-manipulation/blob/master/fastaQual2fastq.pl) to convert the fasta and quality files into fastq files. At this point, each fastq file represented (one replicate of) one individual and contained reads for all loci that were successfully amplified for that particular sample.We used the 454 Sequencing System Software 2.9 to process the sequencing data generated in the GS Junior run. Reads that passed system filters were sorted according to 5′ and 3′ tags using the sfffile command and allowing for one mismatch. The sorted files in .sff format were converted into fasta and quality files using the sffinfo command. We used a perl script fastaQual2fastq.pl (The reads were mapped to the reference Asian elephant sequence (GenBank no. GU369701) following steps 4–6.6 of a mapping pipeline for 454 sequencing dataSince ancient DNA is generally of low quality, we used a rigorous step-by-step approach to score alleles for each sample: 1) We extracted one read for each cluster of identical reads that constituted at least 5% of the reads; 2) We aligned putative alleles from both replicates; 3) We discarded all singletons differing only by C/T and G/A changes, unless an identical sequence was found in any other of the samples or among the elephant MHC alleles.O and HE), the number of alleles (NA) and the number of unique alleles per population in Arlequin 3.5.2.12 2 × 2 contingency table, where groups were defined as the mainland and Wrangel populations and categories were defined as the number of homozygotes and heterozygotes. Allelic richness was calculated with rarefaction as implemented in adze 1.0For the diversity analyses we included three loci – exon 2, intron 2a and exon 4. Intron 2b was omitted because only half as many samples were successfully amplified and sequenced as for the other loci. We calculated genetic diversity of the mainland and Wrangel populations as observed and expected heterozygosity (HdN and dS) using the Nei-Gojobori/Jukes-Cantor methoddN/dS above 1 are considered as evidence of positive selection, while values below 1 indicate purifying selection. We also performed codon-based Z-tests of selection to estimate the probabilities of positive, neutral and purifying selection. We tested selection on entire fragments of exon 2 and exon 4. Moreover, since exon 2 contains antigen-binding regions (ABR) we also analysed exon 2 split into ABR and non-ABR codons. ABR were selected according to elephant ABRWe scanned for signals of selection in MEGA 6EB: 2–10,000 individuals) for a bottleneck of five generations of duration and a constant population size for the Wrangel population  until extinction, both on an equally spaced log-scale. For each combination of parameters we performed 1,000 simulations for three DNA fragments of lengths 99, 89 and 92 base pairs representing the three MHC loci analyzed. In each simulation, we sampled individuals at the same time as the mean calibrated age of our samples  and marginally significant in intron 2a , whereas it was non-significant in exon 2 . Plots of the mean number of distinct alleles per locus against sample size  in exons 2 and 4. Since exon 2 contains ABR, we performed analyses on ABR and non-ABR codons separately .


Anastrepha fraterculus is one of the most important fruit fly plagues in the American continent and only chemical control is applied in the field to diminish its population densities. A better understanding of the genetic variability during the introduction and adaptation of wild A. fraterculus populations to laboratory conditions is required for the development of stable and vigorous experimental colonies and mass-reared strains in support of successful Sterile Insect Technique (SIT) efforts.The present study aims to analyze the dynamics of changes in genetic variability during the first six generations under artificial rearing conditions in two populations: a) a wild population recently introduced to laboratory culture, named TW and, b) a long-established control line, named CL.Results showed a declining tendency of genetic variability in TW. In CL, the relatively high values of genetic variability appear to be maintained across generations and could denote an intrinsic capacity to avoid the loss of genetic diversity in time.A. fraterculus strains for SIT programs are discussed.The impact of evolutionary forces on this species during the adaptation process as well as the best approach to choose strategies to introduce experimental and mass-reared  The introduction of species into artificial conditions with the aim to establish laboratory lines is necessary and useful in numerous situations for experimental or mass rearing purposes. Laboratory conditions are different from the ones a species encounters in nature and often favor a small group of individuals with specific reproductive advantages . For insThe adaptation of wild populations to laboratory conditions is a crucial step in modern biological control programs, such as in the case of the Sterile Insect Technique (SIT) -17. The Anastrepha fraterculus Wiedemann (Diptera: Tephritidae), is among the targeted species of integral pest control programs in Latin America, given its great economic importance. Although SIT is currently being applied in Central America to control the population density of other species of Anastrepha genus . To apply the test, ratios of expected heterozygosities were calculated for each locus using data from Gn and Gn+6 (Gn+6/Gn ratios) and for each line (CL and TW). Since Ln RH values are expected to follow a normal distribution for neutrality evolving microsatellite loci [Effects of positive selection were tested for each microsatellite locus by applying the Ln RH test ,50. Thisite loci , signifiite loci . These aite loci .P > 0.05 in all cases). After the sequential Bonferroni correction, most of the populations sampled conformed to HWE at most of the loci. Out of the 120 tests performed (4 populations × 3 generations × 10 loci), only eight showed deviations from HWE (P < 0.05), measured using the fixation index (FIS) , the ten microsatellite loci had different levels of polymorphism in terms of number of alleles, ranging from 2 to 15 and with a mean of 5.71 alleles per locus (data not shown). No linkage disequilibrium was detected with consistency across the populations , with 2% of genetic variability explained by this source of variation. The results from the Exact G test considering all loci in this generation also showed that genotypic frequencies differ between the lines (P<0.05). In addition, the genetic variability parameters showed high initial values for these lines. Particularly, TW seemed to show higher diversity in terms of mean number of alleles per locus (Na), allelic richness (AR) and expected heterozygosity (He) than CL in the parental generation Gn .The two lines analyzed here, the long-established CL and the recently introduced TW, showed genetic heterogeneity. The AMOVA performed in GP = 0.04 for Na; Q = 7.74, P = 0.02, for AR). Although these parameters showed a similar trend to decline through generations in R2, no significant differences were detected for this replicate. For CL, no significant differences were detected in genetic diversity indexes among generations .The genetic diversity in CL seemed to be more stable through generations than that in TW, specifically in terms of AR Table A. He forP<0.05, Table AMOVA results Table  support Figure A. fraterculus sp.1 population during early adaptation. We discuss our data in the frame of recent studies on other Tephritid species and explored the relative impact of evolutionary forces during laboratory adaptation. Our results indicate that during introduction to laboratory conditions A. fraterculus suffers a clear genetic differentiation, even in the very early steps of the adaptation process. Genetic drift appeared to be the main evolutionary force impacting on the genetic variability of the A. fraterculus during the introduction to laboratory rearing conditions.We presented here the first exploratory analysis of the dynamics of change in genetic variability for a wild Bactrocera dorsalis Hendel [A. fraterculus experimental line. Moreover, the expected heterozygosity values were comparable between our CL line and the IGEAF established strain that gave origin (results obtained for 14 microsatellites markers) [A. fraterculus even under laboratory conditions.The genotypic differences exhibited in the parental generation between CL and TW reflect the occurrence of genotypic variation between the long-established population and the wild population that gave origin to it. In line with previous studies, the wild population showed higher values in genetic diversity parameters. Our lab-adapted line also showed an overall high genetic variability in the 10 microsatellite loci, as described for laboratory strains of s Hendel , and a smarkers) , suggestBactrocera spp. In larger-scale studies, these authors followed the fluctuation of genotypic frequencies of microsatellite markers during the colonization of replicated lines of Bactrocera tryoni Froggat (Diptera: Tephritidae) and a wild population of B. oleae Rossi (Diptera: Tephritidae) in laboratory conditions, respectively. Substantial changes in the genetic variability during the initial period of adaptation were observed for these species, particularly between generations 4 and 10 for B. tryoni [B. oleae [The declining patterns in genetic variability through early adaptation to laboratory conditions observed for TW are consistent with the results obtained by Gilchrist et al.  and ZigoB. oleae .B. tryoni and Simões et al. [Drosophila subobscura Collin (Diptera: Drosophilidae) populations. In agreement with the study by Hopper with small population sizes [The high inter-replicate differences observed particularly for our TW line suggest the occurrence of genetic drift with detectable impact on the genetic variability of the replicates, as described by Gilchrist et al.  for B. ts et al. ,51,53 foon sizes , geneticA. fraterculus experimental and mass-rearing programs. The approach proposed by these authors relates to the fragmentation of a population to diminish the genetic adaptation to captivity [B. oleae mass-reared colony and would represent a good strategy to maintain variability in this species. In the case of our A. fraterculus line, the adequate number of generations between replicate crosses or refreshments with wild material and the evolutionary forces driving genetic changes in the adaptation process of A. fraterculus deserve further evaluation including a larger number of generations and a larger number of replicates. The possible combination of these studies with other management strategies for A. fraterculus remains as a challenge for future research and will provide useful information to establish improved and efficient experimental and mass-rearing colonies for the development of SIT for this pest.We found no strong evidence of selection with the Ln RH test applied to the microsatellite data. Only one locus A112) showed slight deviations from neutral expectations at one TW replicate (R1) between Gn and Gn+6. Our results point out the relative importance of genetic drift, particularly, through the smaller effective population sizes at early stages of adaptation, molding genetic molecular diversity in our recently introduced TW line, although certain genome region (A112 locus) could reflect selection during laboratory adaptation. In this context, we suggest that the implementation of strategies like the cross of replicates, as proposed by Gilchrist et al. ,55, coulaptivity , which maptivity . Additio2 showed A. fraterculus populations during adaptation to laboratory conditions. Our results indicate the relative importance of genetic drift as a principal driving evolutionary force in early laboratory adaptation that must be further analyzed. Given our findings of the dissimilarities between replicates, we suggest that the fragmentation and crosses among replicates after several generations may be a suitable procedure to maintain the genetic diversity of the population in captivity. Future challenges should include analyzing further generations and establishing the association between long-term dynamics of change in the genetic variability and in biological or behavioral parameters of relevance to the quality assessment of mass-reared colonies  across generations for SIT implementation against this pest.The present study represents a first insight into the dynamics of change in the genetic variability of A. fraterculus experimental rearing. We introduced here a first approach to the genetic changes of a wild population of A. fraterculus during the first generations under artificial rearing. Based on recent studies on other Tephritidae species, we discuss the possible evolutionary forces driving the genetic changes observed here. Although our investigations are based on an experimentally reared strain, the results provide useful information about the genetic aspects of this species that could be useful in the development of larger-scale rearing to maximize its potential in the field when specific control strategies are applied.Studies about changes in genetic variability during adaptation to artificial rearing are of fundamental importance to the monitoring of biological and genetic parameters applicable to mass-reared strains in the frame of SIT control strategies. These studies bring useful information about genetic aspects of foundation and management strategies for A. fraterculus, all the investigations focused on this direction, as are biological and genetic studies performed in natural and lab strains, will be of paramount importance to help governmental decisions to solve the important economic problem posed by this fruit pest in South America.As SIT has not yet been developed for The authors declare that they have no competing interests.MAP performed the experimental procedures, molecular genetic studies and participated in the drafting of the manuscript. ACS performed the genetic and statistical analysis and drafted the manuscript. MIR performed the statistical analysis and helped to draft the manuscript. MJ participated in the characterization of the microsatellite markers and molecular genetic studies. MTV and DFS participated in the experimental design and coordinated the management of insect strains. JLC participated in the design of the study and helped to draft the manuscript. SBL participated in the design of the study, genetic analysis and drafting of the manuscript. All authors read and approved the final version of the manuscript.IS and null allele frequency at each locus for all populations  and generations . Bold values represent significant frequencies of null alleles after Bonferroni correction (P < 0.05). FIS values with asterisk represent significant deviations from HWE, *P < 0.05.Table S1 . Table S1. Fixation index FClick here for file


STF = 0.025). One cluster included Russian bears, and migration analyses showed a high number of migrants from Russia into Finland, providing evidence of eastern gene flow as an important driver during recovery. In comparison, both clusters excluded bears from Sweden and Norway, and we found no migrants from Finland in either country, indicating that eastern gene flow was probably not important for the population recovery in Scandinavia. Our analyses on different spatial scales suggest a continuous bear population in Finland and Russian Karelia, separated from Scandinavia.Large carnivores were persecuted to near extinction during the last centuries, but have now recovered in some countries. It has been proposed earlier that the recovery of the Northern European brown bear is supported by migration from Russia. We tested this hypothesis by obtaining for the first time continuous sampling of the whole Finnish bear population, which is located centrally between the Russian and Scandinavian bear populations. The Finnish population is assumed to experience high gene flow from Russian Karelia. If so, no or a low degree of genetic differentiation between Finnish and Russian bears could be expected. We have genotyped bears extensively from all over Finland using 12 validated microsatellite markers and compared their genetic composition to bears from Russian Karelia, Sweden, and Norway. Our fine masked investigation identified two overlapping genetic clusters structured by isolation-by-distance in Finland (pairwise  Habitat fragmentation and anthropogenic disturbance is a global threat to wildlife, with impacts such as declining population sizes and reduced gene flow among populations. Both effects are widely reported to promote genetic drift and oppose long-term population viability During the last century, large terrestrial carnivores declined both in numbers and geographic distribution see e.g. . Even thUrsus arctos) was distributed throughout the country until the beginning of the 19th century In Finland, the brown bear  and the existence of separate genetic populations Since the recovery of brown bears in Finland is assumed to be explained by high gene flow from Russian Karelia, one should expect a low degree of genetic differentiation between brown bears from these areas today, which has not been sufficiently tested. In addition, the results of the previous studies suggesting more than one subpopulation of bears in Finland All samples were collected from dead animals, harvested legally in Finland and Russia. Legal harvest of bears in Finland in the different hunting districts follows an annual quota corresponding to the estimated abundance and distribution of brown bears in those areas N = 97), Sweden (N = 84) and Russia (N = 22);  and 28 additional tissue samples from Russian Karelia  from the same time period.In our study, we analyzed the data of a total of 517 bears from 2006 to 2010 , includiImmediately after collection, tissue samples were stored in 95% ethanol until extraction. Samples were extracted with DNeasy Tissue Kit (Qiagen), following the manufacturer’s instructions, and genotyped using 12 different dinucleotide markers  developed for bears: G1A, G1D, G10B, G10L K = 10. In Structure version 2.3.3 K value between one and ten were performed. For each run, we set a burn-in period of 100,000 Markov Chain Monte Carlo (MCMC) iterations, followed by sampling of 1,000,000 iterations. The results were post-processed with the ad-hoc approach of Evanno et al. q) above 0.6 has been considered as a feasible cut-off membership value to assign individuals to a population with confidence, since more than 50% of the genome is assigned to a group and therefore suggests inferred ancestry q) of 0.7 q>0.7 in this study.We tested for genetic structure using two Bayesian assignment algorithms (Structure and Geneland) and factorial correspondence analysis (FCA). Since earlier studies have indicated a restricted number of genetic clusters in Northwestern Europe K = 1 to 10, and the number of MCMC iterations was 1,000,000, with a thinning of 100. The maximum rate of Poisson process was set to 100, and the maximum number of nuclei was 300. Geographical location of the samples  was included into the analysis. FCA was performed with the program Genetix 4.05.2 In Geneland STF values were calculated with the program Arlequin version 3.5.1.2 To determine the degree of differentiation among genetic clusters, AMOVA analyses and pairwise We calculated IBD among pairs of brown bears in Finland and Russian Karelia using the software Spagedi version 1.3 Nm) was estimated using the private allele method To further test the east-west gene flow hypothesis we estimated the amount of migration between the bears in Finland and Russian Karelia as well as between Finland and Scandinavia using two different methods. Firstly, the effective number of migrants , and those were mainly found in the zone where the clusters overlapped (K = 3 to 5) had lower likelihoods and showed substantially higher numbers of unassigned individuals up to 72%  suggested three genetic clusters: a western one, including mainly genotypes from Scandinavia, namely Västerbotten and Troms, a northern one, including genotypes from Pasvik and northern Finland and Murmansk, and a southern cluster containing genotypes from middle and southern Finland as well as Russian Karelia . In compSTF values between the two subpopulations were significant (P<0.001), with STF = 0.025 between the northern and southern cluster found in Finland and STF = 0.026 between the two clusters identified when Finnish and Russian samples were analyzed together. AMOVA analysis within Finland showed that 2.54% of the variation was between the clusters and 97.46% within them (P<0.01). For the north-south division in Finland and Russian Karelia, 2.59% variation was between the clusters and 97.41% variation within the them, respectively (P<0.01).The pairwise P<0.001) between kinship and spatial distance between pairs of individuals sampled continuously in Finland and Russian Karelia, providing evidence of an influence of IBD on the degree of genetic structuring. All distance classes showed significant deviation of kinship from the population mean  deviated as well and showed an elevated, albeit low, positive value of ozygotes . This ovnificant .P<0.01) after sequential Bonferroni correction in 40 out of 66 marker pairs. Notable is that out of these, 29 pairs were solely found in the Scandinavian cluster (Västerbotten and Troms). Significant LD found was not consistent across all samples and all genetic clusters identified.We found significant linkage disequilibrium (P = 0.034) for the genetic cluster identified in Scandinavia (Västerbotten and Troms). This cluster showed also the lowest value for the Garza-Williamson index with M = 0.64, which is just below Mcrit of M<0.68 proposed by Garza and Williamson We detected a genetic bottleneck . The ambiguously assigned individuals were mainly from the admixture zone in the southern part of Finland along the border to Russia (and in Russian Karelia) and raise the question on their origin. However, note that for the cluster assignment analysis on the large scale (incl. Scandinavia), the number of ambiguously assigned individuals was rather low (3.5%). Hence, bears that could not be assigned unambiguously on the intermediate scale were assigned almost completely when compared to a more distant population (i.e. Scandinavia). We believe that these results suggest the existence of a continuous bear population structured by IBD in Finland and Russian Karelia. Consequently, we interpret the low membership coefficients as a likely result of admixture between subpopulations. This might have led to difficulties in clearly assigning individual genotypes to one of the identified clusters during the analyses. However, influence of other bear populations to the east and south, e.g. towards St. Petersburg and further south to Estonia No genetic bottleneck was detected for the bears in Finland and Russian Karelia, as previously indicated for a small part of the population OH = 0.66) Connectivity of the Finnish and Russian Karelian brown bears with populations in the west towards Scandinavia seemed more restricted, as our previous study has indicated We propose that future studies should analyze historical samples to elucidate the history of the brown bears in Finland, Russian Karelia and Scandinavia during the time of persecution and initial phases of recovery. Further, analyses on recent migration should be monitored and focus more intensively on bears in different regions by applying noninvasive genetic sampling and estimation of capture-mark-recapture probabilities. This can result in feasible estimations on possible demographical changes, such as reproduction and turn-over rates as well as the ratio between effective and census population sizes.Figure S1Bayesian clustering results of Northern European bears with the program Structure . Samples were collected from 2006 to 2010. (a) Finnish samples only; (b) Finnish and Russian Karelian brown bear samples together as well as (c) brown bear samples from all over the sampling range of northern Scandinavia, Finland and north western Russia of the. Presented are the mean likelihoods L(K) and standard deviations for K = 1 to 10 clusters over 10 independent runs  and the estimate of ΔK using the approach described by Evanno et al. (2006). Graphs were plotted using the web based analysis Structure Harvester (Earl and vonHoldt 2012).(TIF)Click here for additional data file.Figure S2Bayesian clustering results of the Northern European bears with Structure . Bar plots showing the assignment probabilities for each bear to the identified clusters from K = 2 to 5 when only samples from Finland were analyzed (a) and samples from Finland and Russian Karelia pooled together (b). Individuals are arranged by latitude from north (left) to south (right). Bar plots for K = 2 to 5 when all data  is analyzed together (c). Notable is the increase of unassigned individuals (q<0.7) with increasing K. For Finland, the number for K = 2 was 60 (20.98%), while for K = 3 to 5 the number increased from 103 (36.01%) to 206 (72.03%).(ZIP)Click here for additional data file.Table S1Brown bear genotypes (N = 517) used in this study.(XLSX)Click here for additional data file.Table S2The estimated number of effective migrants per generation (Nm). Number of migrants between Finland and Russian Karelia as well as between Finland and Scandinavia. Nm results are shown after correction for different sample sizes of 10, 25 and 50 as well as for the mean sample size.(XLSX)Click here for additional data file.


One of the goals in genetic research aims at identifying genes in biochemical and physiological processes to reveal genetic causes of rare and common diseases. Previous obstacles such as costly genotyping or sequencing have been reduced with the chip-based genomewide association studies (GWAS), now culminating with the latest toy—next generation sequencing methodologies (NGS). Concomitantly, computer technologies have evolved to an increasing use of multicore processors and distributed computing on large networks or grids. Although the technologies are not perfect, we now have unprecedented opportunities to perform genetic studies not possible just 10 years ago. The hype about these new technologies have been large, but all the promises have however not been fulfilled entirely as hoped for. Maybe because the hype has been more about the technologies as such, and less about their intended use. Millions of genetic variations have been detected by GWAS and NGS, but only a few have been linked to diseases—with almost no practical clinical significance. A major reason for this apparent deadlock is the inadequacy of the models used, which are based on the traditional “Mendelian” approach, in which one gene is supposed to have a main effect on a trait or a disease. However, most genes claimed to be associated with a disease have small effects and only a tiny fraction of the genetic variance has been captured.In this short notice it is argued, why this traditional approach should be supplemented or even replaced by modeling approaches in accordance with the complexity of biological systems, if we shall have any reasonable hope to understand the genetics behind any trait and bring genetics into practical use in medicine for common diseases  is the prevalent driving force in evolution. The effect of any mutation (genic as well as exgenic including possible changes in epigenetic processes) may be increased, buffered, or ameliorated in particular genetic backgrounds, while having negative  effect in other genetic backgrounds. Buffering is the essence in evolutionary theory of canalization and organismal robustness, in which the phenotype appears robust to mutational perturbations. Mutations may accumulate and appear as cryptic or neutral variations as long as they are not selected against  implies that the phenotypic mean tends to be preserved when a mutation occurs, but the cost is diminished variation of the phenotype, as new (deleterious) mutations are buffered leaving less degrees of freedom of variation compared to the pre-mutational genotype. Thus, a particular phenotype representing a local maximum in the phenotypic landscape, is generated by an ensemble of genotypes, each depicting a path or trajectory of the genetic network. Generally, the probability of a given genetic path being accessible to generate a phenotype decreases with the number of mutations. However, as the number of paths increases exponentially with the number of mutations a large and increasing number of paths may eventually generate a phenotype. This hypothesis has been confirmed empirically , epigenetic modifications, non-genic regulatory and genome-organizing structures, and not the least interactions and communications between cells in a multicellular organisms like humans, the combinatorial space of interactions and hence phenotypes is  infinite.Similarly, in a physiological process like blood pressure, which are regulated by say 100 interacting genes, more than 10Two basic aspects must be addressed in population genetics: (1) biological processes even in its most simple forms are blue-printed in the genome of interacting networks of genes; (2) the expression of the biological processes and phenotypes are conditional on the genetic variations and their inherent epistasis.The genetic networks coding a trait can be mapped as a “continuum” reflecting the physiological states they define  in a structural equation modeling framework (SEM) . A gargantuan endeavor and at the moment maybe wild-fetched, but eventually any genetic variation has to be substantiated in a real biological context - not just as a statistical phenomenon.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.