Cinnamomum kanehirae has been exploited for timber and to obtain medicinal fungi for the past 100 years. Understanding anthropogenic factors influencing the demography of this species after the last glacial maximum (LGM) is critically important for the conservation of this species.Species in the varied geographic topology of Taiwan underwent obvious demographic changes during glacial periods. C. kanehirae were classified into four geographic regions: northwestern (NW), west-central (WC), southwestern (SW), and southeastern (SE). In total, 113 individuals from 19 localities were sampled, and variations in the chalcone synthase gene (Chs) intron and leafy (Lfy) intron-2 sequences of nuclear DNA were examined in order to assess phylogeographic patterns, the timescales of demographic and evolutionary events, and recent anthropogenic effects. In total, 210 Chs and 170 Lfy sequences, which respectively constituted 36 and 35 haplotypes, were used for the analyses. Estimates of the migration rate (M) through time revealed a pattern of frequent gene flow during previous and the present interglacials. The isolation-by-distance test showed that there generally was no significant correlation between genetic and geographic distances. The level of among-region genetic differentiation was significant when comparing eastern to western populations. However, no significant among-region genetic differentiation was found in comparisons among the four geographic regions. Moreover, essentially no genetic structuring was found for the three regions west of the CMR. A fit of spatial range expansion was found for pooled and regional samples according to the non-significant values of the sum of squared deviations. Using the Bayesian skyline plot (BSP) method, a recent bottleneck after the LGM expansion was detected in both regional and pooled samples.Populations of C. kanehirae samples.Common haplotype distributions among geographic regions and the relatively shallow genetic structuring displayed are the result of historical gene flows. Southward dispersals in an earlier time frame from the NW region and in a later time frame from the SE region were inferred. The BSP analysis suggested a postglacial expansion event. Recent trends, however, refer to a bottleneck due to human interventions observed for both pooled and regional  Climate change is an important factor influencing organisms' evolutionary history, especially their demographic dynamics . The PleCinnamomum spp.), banyan (Ficus spp.), beech (Fagus spp.), etc. During glacial periods, conifers which were distributed at high elevations in Taiwan spread to lower elevations to escape the ice-covered mountaintops . The ge0.00007) , and theChs) and the second intron of the Leafy gene (Lfy) of nuclear DNA were sequenced to assess the phylogeographic structure of C. kanehirae in Taiwan. Population genetic analyses of C. kanehirae should provide more insights into geographic influences on migratory events . Furthermore, a coherent timescale for the geologic time and demographic events as well as climate change was established using the coalescent-based Bayesian framework. The ancestral effective population size of C. kanehirae was estimated in order to understand the trend of historical demography. Such a study is vital to our understanding of the process of demographic changes corresponding to how climate changes affected subtropical tree species in the Pleistocene.In this study, the intron of chalcone synthase , southwestern Taiwan  procedure . In. InC. kars. 3.11  in orderFST) was calculated by Arlequin [C. kanehirae (and then the reinforcement of the geographic distance was necessary when estimating using MIGRATE), the IBD pattern was assumed. The IBD model was examined by the Mantel test  to analyze the geographic effect on the genetic distribution. A reduced major axis (RMA) regression [http://ibdws.sdsu.edu/~ibdws/).Genetic differentiation between populations (pairwise Arlequin  based onArlequin  equationgression  was usedgression  indicategression  (availabChs and Lfy) suggested an intralocus recombination, we discarded the sites within the recombination block and retained the longest possible contiguous unrecombined sequence for subsequent analyses.In the following analyses, all sites within studied loci were assumed to share the same genealogical history. However, recombination is believed to be common in nuclear loci and to influence the outcome of genealogically based analyses . TherefoNm) estimated by the reciprocal of pairwise FST by Wright's [θ and equal migration rates were incorporated into the Bayesian approach in the MIGRATE algorithm. FST estimates were used as starting values for the initial analysis. For all other analyses, parameters at the end of the previous run were used as starting values for the next run until results converged at approximately the same values. We used settings of three long chains with 5 × 106 sampled and 5 × 103 recorded genealogies and discarded 25% of them at the beginning of a chain. Multiple long chains were combined for estimates. Adaptive heating was applied with temperature specifications of 1.0, 1.2, 1.5, and 3.0. Three replicates of the dataset were analyzed to calculate the average and confidence intervals. Because gene flow between populations estimated by pairwise M, which indicates the migration rate over the mutation rate, revealed confusing results (data not shown), four geographic regions based on the determination of Lin et al. [Because the incomplete migration-drift equilibrium was inferred based on the IBD results, the gene flow , the demographic expansion factor, with 95% confidence intervals, and the mutation parameter, θ, as well as the number of migrants (M). This least-squares estimation of τ is reliable but was found to be unreliable for estimating the migration parameter, M [M estimate of > 1 together with a non-significant SSD value as evidence of spatial expansion. The time at which the expansion event took place was dated following the expression, t = τ/2 μk, where τ is the estimated number of generations since the expansion, μ is the mutation rate per site per generation, and k is the sequence length. A mutation rate of 1.5% per 106 years per site was used for both Chs [Lfy. We assumed a mean generation time of 20 years for C. kanehirae in converting the time to expansion to years.Signatures of population demographic changes (bottlenecks or expansions) in jima's D  and Fu's's D [Fs  statisti's D [Fs  to see wquin 3.1 . This mequin 3.1 ,59. ThirArlequin ,60-62. WArlequin . The modArlequin . It estimeter, M . Therefoboth Chs  and Lfy.7 generations and were sampled every 1000 generations, with the first 2500 samples discarded as burn-in. Three replicates were run and combined to separately analyze the Chs and Lfy datasets. Other parameters were set as default values. TRACER vers. 1.4 [Patterns of historical demography can also be inferred from estimates of the effective population size over time using the BSP method  as impleers. 1.4  was usedChs (72.5%) and 41 of 94 samples of Lfy (43.6%) were heterozygotes. Although all 226 alleles of Chs and 188 alleles of Lfy were unambiguously identified, 16 and 18 of them, respectively, were probably derived from Taq errors and were thus excluded from further analysis. PCR amplification resulted in 637 and 657 bp of the Chs gene sequence spanning exon 1 (1~18 bp), intron 1 (19~594 bp or a 20-bp indel), and exon 2 (595~637 bp), and 741 bp of the Lfy gene sequence spanning exon 2 (1~75 bp), intron 2 (76~692 bp), and exon 3 (693~741 bp). All Chs variations occurred within the region of the intron. In aligned Lfy sequences, two variable sites were found within exon 2, 29 variable sites were found within intron 2, and three variable sites were found within exon 3  was 0.00716 and ranged from 0.00372 (population CP in the SE region) to 0.00865 (population ALS in the WC region) in Chs and was 0.00479 and ranged from 0 (population YF in the SE region) to 0.00838 (population FL in the SE region) in Lfy. Haplotype diversity (Hd) was 0.841 and ranged from 0.515 (population CP in the SE region) to 1.000 (population HNS in the SW region) in Chs and was 0.895 and ranged from 0.000 (population YF in the SE region) to 1.000 (population WC in the WC region) in Lfy. Haplotype and nucleotide diversities of the four geographic regions were also calculated: haplotype diversities (Hd) ranged from 0.679 (SE region) to 0.906 (WC region) in Chs and from 0.674 (NW region) to 0.896 (SE region) in Lfy; nucleotide diversities ranged from 0.00511 (SE region) to 0.00784 (SW region) in Chs and from 0.00260 (WC region) to 0.00625 (SE region) in Lfy. When comparing the genetic diversity of regions to each other, the haplotype diversity and nucleotide diversity of the SE region estimated for Chs were significantly lower than those of the other three regions, but the nucleotide diversity of the SE region estimated for Lfy was significantly higher than those of the other regions. In addition, the haplotype diversity of the NW region estimated for Lfy was significantly lower than that of the SE region  estimated using Wright's model [M) calculated by the Bayesian approach indicated frequent gene flow, which was not hindered by geological interruption  and geographic distances  was a common phenomenon according to the Mantel test, and only the SE-SW comparison showed a marginally significant correlation between genetic distance and linear geographic distance . If we consider the altitudinal slope, then the NW-SE and NW-WC comparisons showed significant correlations between genetic and geographic distances for Chs , whereas no significant correlation was found based on Lfy sequences. In addition, we also estimated the most probable time at which gene flow occurred by the Bayesian approach performed using MIGRATE [At the entire population and regional scales, lack of correlations between genetic . Significant genetic differentiation was also found in both sets of comparisons at the hierarchical level among populations within regions . Moreover, genetic differentiation among populations was also significant for both sets of comparisons . When comparing the three geographic regions west of the CMR, both Chs and Lfy revealed no genetic structuring among regions, but a certain level of significant genetic structuring was found at the hierarchical levels among populations within regions and among populations . Although most comparisons of genetic structure were significant, the relatively shallow genetic structure on average especially when populations west of the CMR were compared represents a scenario of unhindered gene flow incompatible with the rugged geographic topology. The relatively shallow population structure was also supported by the mixed clustering relationships of populations (data not shown) and again reflects the large, contiguous distribution of this species in the past.The AMOVA among the four geographic regions and between the east-west regions had similar results of a high proportion of the overall genetic variation being attributed to within-population levels Table . On averD revealed non-significant negative values in many individual populations, pooled samples and the four regional samples for both the Chs and Lfy datasets  in the KS population, SW regions, and pooled samples were found  implies a far-distant spatial expansion for older lineages of NW regional samples. In contrast to NW samples, SE regional samples revealed a relatively shorter expansion time and larger migration rate , which indicates a recent spatial expansion.Neutrality tests of Tajima's ts Table . Non-signd Table . Fu's Fsatistics ,68. Non-ex Table . We furts P < 0.0 in the Ks P < 0.0 in the KC. kanehirae for both nuclear markers  expansion. In addition, regional samples of western, WC, and SE revealed a more-obvious population decline for Chs than for Lfy  gene in Q. glauca [G3pdh in Manihot esculenta [PHYP and PHYO loci in Pinus sylvestris [Solanum pimpinellifolium [C. kanehirae [C. kanehirae in the past [Historical demographic events and population sizes of most natural populations fluctuated and left traces in patterns of their genetic diversity. In this study,  complex , the gly. glauca  and the sculenta , the PHYlvestris , and thelifolium . Althouganehirae . High nuanehirae -77 and ithe past ,26.C. kanehirae is far more complex than previously thought, with evidence of historical range expansion and a very recent bottleneck  ,80, and riations ,37,40,81f Taiwan . CharactC. kanehirae inflicted by anthropogenic factors. The past increase in gene flow and spatial expansion should have assisted the dispersal of population- and region-specific haplotypes; however, human interventions including over-deforestation and habitat destruction caused declining of population size and fewer opportunities for colonization of these specific haplotypes.Loss of genetic diversity due to anthropogenic factors might not be apparent because of the high genetic diversity that accumulated during past range expansions and resulted in only a slight decline in the recent effective population size as estimated by the BSP analysis. Nevertheless, this small decline in the recent population size reveals a commencing phase of a bottleneck in Cinnamomum kanehirae is a plant species with bird-dispersed seeds. Forest birds in the warm period of the last interglacial [C. kanehirae populations. Cinnamomum kanehirae has a similar pattern of diversity distribution as that of Machilus thunbergii (Lauraceae) based on cpDNA variations with most populations possessing the ancestral haplotype [FST) and a high degree of gene flow were also found by estimates of M based on nuclear DNA variations. Moreover, the haplotype network depicted no clear patterns of geographic structuring, further supporting the regional migrations of haplotypes . The onset of a temperature rise after the LGM for populations located in the SE region preceded that of populations located in other regions, thereby facilitating the migration from SE to SW regions by C. kanehirae.Historical and extrinsic ecological events can leave signatures in the spatial genetic structure of a species and provide insights into understanding the species' evolutionary history . In Taiw regions ,81,84,89ns Table . Howeverns Table . The treCinnamomum kanehirae is confronted with conditions of being rare, fragmented, and threatened at the present time, consistent with the genetic survey that revealed a pattern of population growth followed by an ongoing bottleneck. Despite the declining effective population size of C. kanehirae, the genetic diversity of remnant individuals showed no dramatic reduction in genetic diversity. In C. kanehirae, a tree with a long generation time, the rapid population decline probably began decades ago due to anthropogenic interventions for two or more generations. Thus information on the loss of genetic diversity might not be spontaneously revealed, but will probably be seen within two or more generations if the populations are not restored. In light of the declining effective population size, the condition of the true population size of C. kanehirae at present may be far more severely threatened. In addition to the continuing illegal selective cutting for cultivating the medicinal fungus, A. cinnamomea, the loss of habitat is another serious issue contributing to population declines of C. kanehirae. Elevations where C. kanehirae grows range 200~2000 m, and overdevelopment for farming vegetables, tea, and fruit crops has destroyed the natural habitats of primeval forests, including habitats of C. kanehirae. Therefore, loss of the population size of C. kanehirae was mainly caused by human overexploitation. In consequence, it is essential that this valuable species be conserved through habitat restoration, which can advance the restoration of the effective population size of C. kanehirae. Further field surveys to locate plants and grafts for ex situ conservation are another requirement to conserve this species. Management strategies should also consider reinforcing local populations with individuals originating from populations showing the closest genetic relatedness to produce larger population with higher genetic variability. In summary, conservation strategies must therefore aim for habitat restoration and remnant diversity preservation, and avoid the occurrence of genetic inbreeding in populations of C. kanehirae.Distributions of genetic variations in wild populations provide insights into a species' evolutionary history and its future continuity ,91. CinnSYH and TPL conceived the research; DCK, CCL, and KCH collected the data; PCL analyzed the data and wrote the first draft; SYH led the writing. All authors read and approved the final manuscript.Chs and LfyPolymorphic sites among haplotypes at . The table provides polymorphic information for both the Chs and Lfy loci for each haplotype.Click here for fileST of 19 Cinnamomum kanehirae populationsPairwise F. The table provides the pairwise genetic distance matrix obtained using Chs and Lfy DNA.Click here for fileChs and Lfy sequencesPhylogenetic network constructed by both . The figure shows the haplotype networks constructed using a statistical parsimony approach. The long-fragment indel of Chs sequences was treated as one-mutation step and recoded. The limit of 90% connection was used to estimate the network. Four geographic regions were indicated proportionally with four colors.Click here for fileChanges in effective population size in regional and pooled samples of Cinnamomum kanehirae. The figure shows the effective population size per generation for regional and pooled samples of Cinnamomum kanehirae based on Chs and Lfy datasets. The solid line represents mean values of the estimated effective population size with lower (0.05) and upper (0.95) bounds.Click here for file


BMC Evolutionary Biology suggest that a major genetic turnover took place in Eurasian wolves after the Pleistocene. These results add to the growing evidence that large mammal species surviving the late Pleistocene extinctions nevertheless lost a large share of their genetic diversity.Population genetic analyses of Eurasian wolves published recently in See research article http://www.biomedcentral.com/1471-2148/10/104 Unraveling the population history of a species is a tricky business. The only archives left are the fossil record, and the traces that population processes have left in the species' gene pool. Both have been fundamental in developing our understanding of how populations existed in the past, but both are ultimately unsatisfying when we come to address how organisms deal - in the long term - with change.Fossils are prone to the vagaries of taphonomy (the processes by which fossils form), with few individuals even entering the record, and only a small fraction of those surviving and being recovered and analyzed. More problematically, few organisms leave sufficiently robust and informative fossils from which we can readily identify population-level differences. Genetic analyses, on the other hand, are perfectly capable of providing that level of resolution. But genetic data are difficult to interpret. A given gene genealogy may provide little information about past processes or events. Even if the data can be shown to be informative, it may prove impossible to probe in any sort of depth into the past, as populations can readily become admixed, bottlenecked, translocated, or worse still, just flat-out extinct.Homo sapiens) and a complex series of climate shifts yet to be convincingly resolved.The analysis of ancient DNA has done much to bring these two fields of research together. Having struggled in the limelight for much of the 1990s, first as an answer looking for a question, then latterly with some significant issues about data reproducibility, ancient DNA seems more recently to have found itself very much at home in facilitating comparisons between current and past populations, with a timescale of around the past 50,000 to 100,000 years. Conveniently situated in the midst of this period is a suite of large mammal extinctions and range changes. Even better, the megafaunal extinctions that began at the end of the Pleistocene  are well studied, have a well-established chronology (due to the ready availability of radiocarbon dating) and remain controversial, with the relative roles of a globally expanding, top predator  is a tiny fraction of what it had been during the Pleistocene. A more recent study on American wolves demonstrated a complete replacement of one haplogroup ) by a second one some 11,000 years ago [et al. [Therefore, comparisons between the genetic diversity of past and present populations of a species, such as the study on Eurasian wolves  from Alaska and Canada [Ovibos moschatus), bison or spotted hyena (Crocuta crocuta), for which Eurasian populations disappeared and the species only survived in North America (muskox and bison) and Africa (spotted hyena), this may not come as a complete surprise [The first study to demonstrate the complexity of past population history was published in 2002 on brown bears (d Canada . In thisd Canada ,7. In fasurprise ,9. Howevet al. [So far, we know very little about the potential factors that drive reduction in genetic diversity for the extant large mammals of the northern continents - the Holarctic. It does not help that the causes of megafaunal extinctions at the end of the Pleistocene are also still unresolved in that region . In partet al.  this facet al.  but alsoet al.  coincideIt may come as a surprise that these wolves were neither able to adapt to a new prey spectrum nor did their mtDNA sequences survive as a result of gene flow with neighboring or incoming wolves. However, although wolves and carnivores in general are usually highly mobile, ecological factors, including specializations on certain prey, seem to play an important role in shaping the geographical structure of genetic diversity in extant wolf populations ,13. TherThe observed losses of genetic and ecomorphological diversity are not restricted to carnivores, as similar observations have been made for bison. Not only did the species as a whole lose a large part of its genetic diversity, modern bison are also surprisingly homogeneous in their morphology when compared to their Pleistocene counterparts, which were diverse enough to have been placed into multiple species. The same seems to be true for horses, the Pleistocene representatives of which have also been divided into numerous species. However, a recent ancient DNA study has suggested that the morphological range of extinct horse species was much larger than has been assumed taking modern horses as a baseline .It therefore seems that extant species - at least in the northern Holarctic - are depleted in both genetic and ecomorphological diversity when compared with their Pleistocene counterparts. Given that the last glaciation ended only some 10,000 years ago, this is not entirely surprising. If we assume that diversity is lost during glacial maxima and builds up between them, then we would expect Late Pleistocene populations to show more diversity in both respects, as they had almost 100,000 years to accumulate it. The question is what lessons we can draw from this?et al. [Compared with the Late Pleistocene, we have undoubtedly lost a lot of biodiversity, and it seems unlikely that we will develop the technology to bring it back in the near future. However, studies like that by Pilot et al.  are starConverting this observation into conservation policy requires a more extensive discussion between some apparently quite disparate members of the scientific community - paleontologists, conservation biologists and ancient DNA specialists among others. One possibility is that the best way to preserve biodiversity for the future is not to try to maximize mitochondrial sequence diversity in a population, but rather to take an active role in encouraging species to colonize as broad a habitat as naturally possible, drawing from the Late Pleistocene records. Although none of us will live long enough to witness the result, such a strategy could allow species not only to build up genetic diversity but also to again reach the ecomorphological variability so aptly demonstrated by the fossil record.


Phylogenetic mitochondrial DNA haplogroups are highly partitioned across global geographic regions. A unique exception is the X haplogroup, which has a widespread global distribution without major regions of distinct localization.We have examined mitochondrial DNA sequence variation together with Y-chromosome-based haplogroup structure among the Druze, a religious minority with a unique socio-demographic history residing in the Near East. We observed a striking overall pattern of heterogeneous parental origins, consistent with Druze oral tradition, together with both a high frequency and a high diversity of the mitochondrial DNA (mtDNA) X haplogroup within a confined regional subpopulation. Furthermore demographic modeling indicated low migration rates with nearby populations.These findings were enabled through the use of a paternal kindred based sampling approach, and suggest that the Galilee Druze represent a population isolate, and that the combination of a high frequency and diversity of the mtDNA X haplogroup signifies a phylogenetic refugium, providing a sample snapshot of the genetic landscape of the Near East prior to the modern age. Phylogenetic clustering of mtDNA haplogroups has been found to correlate with geography, such that different haplogroups often correspond to specific geographic origins Haplogroup X is further divided into two subclades. Subhaplogroup X1 was found to be largely restricted to the Afro-Asiatic-speaking populations of northern Africa and the neighboring areas, suggesting a possible geographic diffusion of X1 along the coast of the Mediterranean and the Red Sea. Subhaplogroup X2 is characterized by a much wider geographic range, but at the same time by very low frequencies in the populations of the regions where it is foundThe Druze population has a unique historical, social and demographic structure, which is closely connected with their religion. The contemporary Druze population constitutes a small minority in four countries of the Near East: Syria, Lebanon, Israel and Jordan. In total, the estimated population number is fewer than 1,000,000 in the Near East and fewer than 100,000 in the Druze Diaspora. The Israeli Druze population is estimated at 150,000, and is distributed over three geographical subregions: the Carmel, the Galilee, and the Golan Heights. It has been postulated according to historical records that the origin of the Druze in each of these regions is different. Although the Druze represent a small percentage of the total population of the countries of the Near East in which they reside, their concentration in mountainous districts has produced a compact social structure, resulting in a nearly exclusive majority in some geographical regions, and therefore a low frequency of admixture with other populations. Druze customs strongly favor marriage within the same village or the same geographical areaPrevious studies described a high frequency with a low diversity of both the X1 and X2 subhaplogroups in the Druze populationAltogether we sampled 311 different paternal households from 20 Druze villages in Northern Israel, , and 208^ = 0.979±0.002).In 311 samples, comprising 208 surnames. 280 males were typed for 12 short tandem repeats (STR) and haplogroup defining SNPs see . We foun^) among the different Druze subregions for both NRY lineages (for definition see −9). There were 10 STR haplotypes and 7 lineages among the 24 haplogroup K individuals (H^ = 0.71). In contrast to the overall high NRY haplogroup diversity for the entire sample, village based pockets of low diversity were observed, consistent with local paternal founder effects. For example, the village of Beit -Jaan included 5 samples from haplogroup K, all of which belonged to the same lineage (H^ = 0). Similar phenomena were observed in the village Sajur, in which all 5 haplogroup K samples belonged to the same lineage (H^ = 0). This pattern of overall high diversity with local regions of low diversities was also evident for other villages and other NRY haplogroups, as well mtDNA haplogroups (data not shown).Analysis of Nei's genetic diversity (H^ = 0.979±0.003) . It is clear that the calculated genetic diversity for a given genomic region will depend upon the sampling approach and sample size. A small sample set which might include individuals from the same kindred or from a confined geographic region in which there is a high rate of consanguinity and endogamy is expected to yield a lower calculated genetic diversity than a larger sample set gathered from individuals belonging to different parental households from different subpopulations.Mitochondrial DNA analysis (nucleotides 16024–16569 and 1–310) carried out on all samples, revealed 106 haplotypes (H9±0.003)  and S3. In order to exclude the effect of sample size alone in generating this difference, we conducted a bootstrap analysis designed to account for the different sample sizes in the two studies see , and fou16) , and proposed a bottleneck or founder effect. Given the different sampling strategies, we sought to determine the total number of different mtDNA X haplogroup lineages in our sample set. In order to determine the total number of distinct lineages, we followed the approach previously described for several other populationsThe effect of sampling strategy was particularly evident for the mtDNA X-haplogroup, in which we found both a high frequency  together with a high diversity of haplotypes, as defined in Macaulay et al(7) on the basis of sequence variation at the HVS-I region (nucleotides 16090–16365) . MacaulaAllele frequency approaches are also often used to ascertain population bottlenecks and expansionsP = 9.3*10^−5 and  = 3*10^−4, respectively). This finding remained significant after Bonferroni correction for multiple hypothesis testing. In another independent unpublished large Near East sample set, which also included 77 Israeli Druze samples from the Carmel region, only one sample belonged to the X haplogroup . One particularly exceptional example of both high diversity and high frequency of haplogroup X was the Galilee heights village of Peq'in, where 6 of 17 households (35.3%) belonged to four distinct lineages of haplogroup X , yielding a calculated genetic diversity for both X1 and X2 of 0.667±0.02. This village, is believed to be one of the oldest Druze villages in Israel, and is mentioned in historical records dating from the 13th century. These records specifically note that certain villages in the Galilee accepted the Druze religion during the “Dawa” and never left this geographic regionThe finding of a surprisingly high frequency and high diversity of X haplogroup lineages, and the rejection of a recent bottleneck or founder effect, encouraged us to conduct a more detailed analysis of the distribution of the X haplogroup lineages according to geographic sub-regions. Using a hypergeometric testAn attractive hypothesis to explain this geographic concentration of lineages which diverged remotely within an ancient haplogroup (and which are not identified in other populations of the region) is that these Galilee Druze individuals represent the refugium of an ancestral group with high diversity and high frequency of haplogroup X, which was more prevalent in the region in antiquity, and from which the global diversity of X mtDNA haplogroup emerged. Alternative hypotheses could be non-random sampling effects during the original establishment of the Druze population. We now consider these alternatives.−5), using a wide range of both source and sampled populations. The probability for the migrant pool model was even lower when calculated for Egyptians, Armenians and for different combinations of these postulated source populations for such a colonization process. Furthermore, comparison of the Druze X haplogroup sequences to Turkish and Egyptian X haplogroup sequencesFirst consider the hypothesis that the current population genetic structure is the result of a random or non-random migration process contributing to original members of the Druze religion. Indeed, Druze tradition considers the population to have been constituted from many diverse source populations. Various studies have shown that when a new colony is formed from several potential source populations, either lowerTo further exclude the possibility of migration as a dominant force shaping the genetic landscape of the Druze, we have applied a demographic modeling strategy. We have used the isolation with migration model We also used a numerical model to test and exclude the possibility of non-random migration and colonization A clear expectation for a population refugium as suggested by the Druze mtDNA haplogroup X analysis is the finding of high diversity also among other haplogroups that were more prevalent in the past and which have survived in the population refugium. Also, this high diversity specific to the refugium population, should not be shared with nearby populations which are not part of the refugium. Therefore we extended the mtDNA analysis to other Druze haplogroups. Complete mtDNA sequencing for each of the D-loop defined haplotypes was carried out for haplogroups H and K samples, which together with haplogroup X were the 3 most prevalent haplogroups among the Druze population. For 32 D-loop defined haplotypes within haplogroup H, we observed 26 different coding region variant defined lineages, of which 12 represent novel lineages . For 10 Our findings of high diversity and high frequency of X haplogroup concentrated among the Galilee Druze provides new insights regarding both sampling methodologies in population genetics, and the understanding of the effects of sociological patterns on the population genetic landscape of the Near East.The findings provide a formal demonstration that in an endogamous and consanguineous population, calculated mtDNA genetic diversity is significantly lower using random sampling than using paternal or maternal household directed sampling. Therefore, in order to obtain a comprehensive phylogenetic picture of lineage diversity in a sample set from the Druze population or populations with similar socio-demographic patterns, it is necessary to either collect a very large random sample, or to use a directed sampling strategy to ensure coverage of all of the regionally endogamous cryptic subpopulations. Random sampling taken from a regional and small sample within an endogamous subpopulation, will be relevant only for that specific subpopulation as might have been the case in the Macaulay et.alThe absence of a recent demographic bottleneck or population founder effect for the Druze population is consistent with their oral tradition of heterogeneous ancestral origins, and also is consistent with the pattern of Mendelian genetic disease in the population. Rather than finding an excess of genetic disease with monogenic inheritance distributed throughout the population, rare monogenic recessive diseases are kindred or community specific and causative mutations have been successfully mapped in many cases using homozygosity mapping strategiesLineage analysis within the mtDNA X-haplogroup was particularly enlightening. It should be noted that the estimated coalescence times for the major mtDNA X subhaplogroups X1 and X2 are 42,900±18,100 and 17,900±2,900 respectivelyFurthermore, the formal rejection of the alternate hypothesis relating to immigration to the region of individuals sharing the same mtDNA haplogroup but with lineages that diverged in antiquity, further strengthens the Druze refugium model. Although, we cannot exclude the possibility that some ratio between non-random migration and colonization did occur during and following the “Dawa” period and generated the differences in haplogroup frequencies among the current Druze subregions, and between the Druze and other populations, this explanation is highly unlikely in face of the demographic modeling results. The overall low migration rate between the Druze and all other nearby populations  cannot eDemographic modeling can also provide estimates of divergence times for populations with shared ancestries. The demographic modeling in the current study indicates most recent divergence of the Druze from an ancestral population shared with Egyptians, Ashkenazi Jews, Adygeis and Greeks . The EgyThe historical events and time frame for the loss or dilution of haplogroup X individuals is consistent with the population upheavals and patterns of migration that have characterized the Near East during the past two millennia at least Our findings suggest that the Near East maternal genetic landscape differed substantially in the past from its current structure, and was enriched in diverse lineages of the mtDNA X haplogroup. These findings have been uncovered due to the unique demographic features of the Druze population, and the adjusted sampling method employed in the current study. The combination of a high frequency and diversity of the Druze mtDNA haplogroup X lineages, in a confined geographic region, and the low migration rate with nearby populations make it unlikely that this diversity was imported. It is thus likely that the global diversity of this haplogroup evolved in the Near East and adjacent regions of western Eurasia, during a long incubation period coinciding with and following the most recent out of Africa expansion as dated by mtDNA coalescence simulationsWe employed a paternal household-based sampling method  from 20 Sequences were determined using the ABI Prism Dye Terminator cycle-sequencing protocols developed by Applied Biosystems (Perkin-Elmer), and according to previously reported protocolsDYS19, DYS388, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS426, DYS439, DYS438 and DYS457) were genotyped in two multiplex reactions following the protocol of Redd et al. DYS389CD as equivalent to DYS389I, and we define DYS389AB as equivalent to DYS389II minus DYS389I.We followed the recommendations of Hammer and Zegura 2002We adopted the consensus haplogroup nomenclature schemes for both the NRY and mtDNA In the case of mtDNA, haplotypes and lineages can relate to either the control region or the complete mtDNA sequence datan = 45) from the current data set and calculated the resulting gene diversity. The p-value is the fraction of samples in which the gene diversity was lower than that in the previous study.In order to evaluate the differences in mtDNA genetic diversity between the current dataset and that reported previouslyWe used a simplified analytical model to assess the probability of obtaining a diversity level with random sampling, comparable to that obtained by surname directed sampling. For simplicity we use the number of haplotypes, rather than gene diversity, as a measure of diversity. We consider an ideal situation where surname households are completely correlated with mitochondrial lineages. Considering a population of N haplotypes , each containing x individuals. We sample m individuals . Given a non-random sample according to surname k = N haplotypes are expected for any sample set. The probability of obtaining k haplotypes using random sampling is:The Arlequin software packages of which are X haplogroup, and a target population  of size n, ft of which are X haplogroup (for example fs = 6/218 for the Turks and ft = 41/311 for the Druze). The distribution of X haplogroup individuals in such a sampling scheme follows a hypergeomtric distribution, from which a p-value can be obtained. We estimated this for source populations of 10,000–10,000,000 and target population of size 100–100,000.To assess the probability that the fraction of X haplogroups in the Druze population may arise from a sampling of a larger source population, we assumed a source population of size N, fThe simulation tool IM Table S1(0.05 MB DOC)Click here for additional data file.Table S2(0.52 MB DOC)Click here for additional data file.Table S3(0.04 MB DOC)Click here for additional data file.Table S4(0.05 MB DOC)Click here for additional data file.Table S5(0.04 MB DOC)Click here for additional data file.Table S6(0.04 MB DOC)Click here for additional data file.Figure S1(0.28 MB DOC)Click here for additional data file.Figure S2(0.14 MB DOC)Click here for additional data file.


Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities  can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation. Airline networks provide fast transportation every day for goods and people; however, these connections also provide pathways for the spread of diseases In their study, Guimerà et al. Thus, in the present research we address two questions related to the network characteristics of disease spread on the samples of cities connected by air travel:(Q1)How does sampling affect the main network characteristics of the entire network?Although the small samples (representing major cities and continents) can produce strongly biased network characteristics, they might still be successfully used in adequate representation of global disease transmission. Thus, we ask the second question:(Q2)Can small samples capture enough network information to adequately describe the global spread of a disease such as influenza, and if so, then how should researchers select those samples?In this paper, we describe the data and sample selection rules, we describe network characteristics of the entire network and of the samples, we provide an analysis of disease spread on sampled networks, and, finally, we discuss the results and practical implications as they relate to the two research questions.Following Guimerà et al. Because of the epidemiological implications, the unit of analysis in our study was a city rather than an airport. Most of the cities represented in the data set correspond to a single airport. However, for some larger cities, OAG has aggregated data from multiple airports. For example, New York City includes data from John F. Kennedy International Airport, LaGuardia Airport, and Newark Liberty International Airport. As another example, Washington, DC includes data from Dulles International Airport and Ronald Reagan National Airport but not Baltimore/Washington International Airport. The full OAG data set contains 3883 airport codes; however, a number of codes refer to train stations or bus stops. Additionally, some small airports (<100 passengers/day) are located on small islands for which population size was not available, and some airports are disconnected  from the main connected component. In our analysis, we have included only those locations from the largest connected network component for which we could obtain information on both flight volume and population size, resulting in a list of 2904 locations. We will refer to this reduced list as the OAG list.We also considered a cruder aggregation of U.S. cities into larger metropolitan areas using the U.S. Census Bureau definitions of Combined Statistical Areas. For example, we aggregated Washington, DC, and Baltimore into a single Washington listing. This additional level of aggregation was performed to create a list of cities matching the one used in Epstein et al. population-based, sampling the cities with the greatest airline seat traffic as volume-based, sampling the Rvachev and Longini Rvachev-based, and sampling the Epstein et al. Epstein-based. Our set of samples included a sample that matched, or at least closely approximated, the list of cities used in Rvachev and Longini From the OAG and Epstein lists of cities, we selected several samples so that we may compare their network structures and their effects on a model of global disease spread. The samples varied by city list , sampling method  airline transport is critical for disease transmission.In addition to regional coverage, global flight volume coverage may also be an important factor in modeling disease spread since the largest flight volumes may be more indicative of potential major disease routes. For example, a sample of 52 cities with the largest flight volume (which represents just 1.7% of the cities in the OAG list) accounts for about 40% of the total traffic. The coverage increases to about 75% for n = 155 and to about 90% for n = 500.A simple correlation analysis shows that city sizes and flight volumes are not strongly correlated to each other . The lowOne would expect that as the sample size increases and includes more peripheral cities, the average shortest paths between the cities (the geodesics) would become longer and betweenness (the average number of geodesics passing through a node) would increase because a larger number of geodesics would pass through a node. These effects are in fact observed in the samples . At the Although the trends are similar for the samples selected by volume and population size, there are differences in quantitative values. The peak of the mean degree for the volume-based samples is around 155 cities and the average degree in the samples is about 32. For the population-based samples, the peak corresponds to the largest degree of 500, and the average degree is around 17. This discrepancy reflects the fact that the largest cities are not necessarily the most connected nor do they have the largest flight volumes. The same argument applies to the clustering coefficient and the geodesic.As mentioned in Guimerà et al. The presented plots indicate that inclusiveness of volume, degree, and betweenness is fairly stable across the range of sample sizes. In particular, volume-based samples consistently contain more than 80% of the cities selected based on the highest degree and vice versa. The highest volume and highest betweenness samples share no less than 50%. In fact, the percentage of shared cities increases as the sample size gets larger than 1000. The highest betweenness and degree cities show a similar pattern. On the other hand, samples based on the population show little commonality (less than 30%) with samples based on other characteristics when the sample size is smaller than 100 cities. This percentage increases as the sample size increases. It is monotonic for population-volume and population-degree, but is not monotonic for population-betweenness. A sample as large as 1500 cities  is needed to ensure that 50% of the cities appear on all four lists. If a researcher wants to include the cities that are within the top 100 of both population size and flight volume, he or she will need to have a sample size of about 165 cities . The mulhttps://www.epimodels.org/midas/globalmodel.do. Thus, our model represents a larger group of robust, equation-based models describing the global spread of infectious disease. Using the GEM as a common modeling framework for all samples allows us to study the influence of sample size and sampling method on disease dynamics.The Global Epidemic Model (GEM) has been developed as a tool for studying the global spread of influenza and consists of a number of coupled stochastic differential equations with parameters corresponding to the epidemiology and transmission of influenza We tested the samples that include 52, 155, and 500 cities selected by population and volume, as well as the samples used in Rvachev and Longini Assuming that the flu epidemic started in Hong Kong on January 1, we used the GEM to calculate first passage times (FPT) to each city in the sample. The FPT to a particular city is defined as the number of days between the epidemic origination  and the moment when the number of infectious people in the destination city reaches or exceeds 1. The FPTs are presented in Although the data are available for all cities in the samples, we have chosen just a few for illustration. We chose cities that represent major world regions  that are key entry ports to the United States , or that are among those identified by Guimerà et al. Our simulation results show that for most large and well-connected cities there is little difference in disease dynamics with respect to the sample size. A large expected difference is regional coverage. When the sample size is small some critical cities such as Cape Town or Lagos would be left out of the sample. An example of selection discrepancy between population size vs. flight volume is Washington, DC, which is not a part of the 52 largest city sample but is a part of the 52 largest volume sample. The results also show that larger sample sizes tend to produce smaller FPTs, possibly due to the additional routes through which the disease can travel. Furthermore, when the sample size is large enough, there is no difference between the results from the volume-based and the population-based samples. However, when the sample size is small, air traffic connections to some cities can be inadequately represented, and the disease transmission time can be significantly biased. Most of the discrepancies occur in the small, population-based samples. This is especially true for the large cities that are relatively weakly connected, such as St. Petersburg, Russia, where the FPT is about 130 days for a sample of 52 population-based cities, compared to 94 days in the full network. This difference is more than a month, which is very important for public health preparedness. Similar differences are observed for better-connected cities such as Cape Town (98 vs. 54 days), Bogotá and Washington, DC, although for the latter two the differences are not as dramatic.These results suggest that ignoring a large number of short connections produces artificial delays in disease transmission. Thus, to avoid bias in disease spread, a reliable sample should include at least the 100–150 cities with the largest flight volume and cities that are well connected. The addition of cities with large population sizes or those in remote locations will improve regional coverage. For example, Fairbanks, Alaska would not be selected in a population-based or a volume-based sample of size n<1000, but given its regional importance, it should be considered for inclusion regardless of the sample size in studies where the Alaskan region is of interest.The sample of 204 cities adequately represents global disease dynamics while also being reasonably small and providing reasonable regional coverage.  We base our assessment of its ability to represent global disease dynamics on the observation that for our cities of interest it reproduced the FPTs found in the entire list of 2904 cities (within random error). The samples of size 500 and the volume-based sample of size 155 also share this same result. Choosing between these samples may depend on other factors such as size (larger samples require more processing time) and coverage of particular regions.We have reviewed a number of published global epidemic models and analyzed the global airline transportation network data with respect to its use in epidemic modeling. We have shown that in order to reduce bias in the estimation of global disease dynamics, a network of connected cities should be based on the volume-based core that also includes the most-connected cities. About 150 cities are sufficient for adequate coverage of the major world regions. The results of the simulations suggest that samples based on air traffic volume better describe sample characteristics but can have poor regional coverage and thus need to be complemented by the samples based on population size, betweenness, and clustering characteristics. Next, one could add remote locations for answering questions about specific regions. We have shown through dynamic simulations that a relatively small number of cities (around 200 or 300) can capture enough network information to adequately describe the global spread of a disease such as influenza.Although the use of 52 cities was justified for the purposes of previous studies In the present study, we have performed a systematic analysis of network characteristics such as average degree, betweenness, clustering coefficient, and geodesic length, and have shown how these characteristics depend not only on the sample size but also on how the sample is selected. We have provided guidance for the sample size selection when certain network characteristics need to be preserved.Major network characteristics such as degree, clustering, betweenness, and regional coverage become more biased compared to the entire network as the sample size decreases. The results also show that the bias is smoothly dependent on the population size for all sampling methods and there is no “natural” threshold suggesting the optimal choice of the sample size. For each objective, the “optimal” sample size should be the balance of the scale at which the conclusions are made and the appropriateness of using airline transportation as the basis for human travel flows. One should keep in mind that very small airports can reflect insignificant private traffic flows, while most of the regional transportation is based on ground traffic. Although a large number of network characteristics could be studied with respect to the network samples, we have selected the ones that are clearly defined and were shown to be related to the spread of diseases, innovation, or information. Some measures were inevitably left out. For example, Ghani et al. While the field of network analysis is quickly developing, there is still not much literature about the effects of different sampling methods on network characteristics. Most works in network sampling have focused on the problem of estimating how the network measures will change in the sample given a particular sampling scheme such as a snowball, random sampling of nodes and links, or random walk The current study has a number of limitations. The analysis is based on the airline network, and its generalizability to other networks such as social or ground transportation might be limited. However, this study is the first attempt to use a systematic approach for the selection of a network sample for dynamic modeling. Although the actual curves and the quantitative results would be different for each specific study, the methodology of selecting a sample based on individual and network characteristics will guide future studies. Another limitation is that in this study the entire network of connected cities is known. Although complete or nearly complete network information is sometimes available in transportation studies, this is usually not the case in social network studies. Nevertheless, having an estimate of which percent of the population the sample represents allows one to extrapolate the network characteristic curves to what they are likely to be in the entire population.


Determining the genetic architecture of complex traits is a necessary step to understand phenotypic changes in natural, experimental and domestic populations. However, this is still a major challenge for modern genetics, since the estimation of genetic effects tends to be complicated by genetic interactions, which lead to changes in the effect of allelic substitutions depending on the genetic background. Recent progress in statistical tools aiming to describe and quantify genetic effects meaningfully improves the efficiency and the availability of genotype-to-phenotype mapping methods. In this contribution, we facilitate the practical use of the recently published ‘NOIA’ quantitative framework by providing an implementation of linear and multilinear regressions, change of reference operation and genotype-to-phenotype mapping in a package (‘noia’) for the software R, and we discuss theoretical and practical benefits evolutionary and quantitative geneticists may find in using proper modeling strategies to quantify the effects of genes. Quantitative genetics aim at providing models that describe the genetic architecture of complex phenotypic traits. These so-called multifactorial characters are generally underlain by several genes and by environmental factors, that can potentially interact in many ways. One of the most insightful applications of dissecting the genetic architecture of a quantitative trait, e.g. through QTL mapping experiments, is to determine individual genetic effects. However, the proper quantification of allelic effects from experimental data, as well as the description of the Genotype-to-Phenotype (GP) map, remains challenging. In particular, it is well known that the effect of a particular allele often depends on the genetic background, i.e. the genotype at other loci.sensu Genetic interactions (or epistasis), when measured from a polymorphic population, change with allelic frequencies. 2 population or to Hardy-Weinberg proportions). Furthermore, once the estimates of genetic effects have been obtained using the proper statistical formulation of genetic effects, researchers might want to express them in a different way, e.g. describing the average effects of allele substitutions at a different population than the sample under study, or describing natural effects of allele substitutions from an individual genotype—i.e. functional estimates of genetic effects as described above. NOIA provides tools to transform the estimates obtained from the data into these other estimates with different useful meanings  when estimating the genetic architecture of a trait after a QTL mapping experiment, and (ii) when genotyping loci known to underlie a complex trait in a natural population. The aim of this communication is to present statistical and bioinformatic tools designed to use NOIA in practice. First, we propose to extend the NOIA framework to handle the decomposition of the genetic variance and the computation of confidence intervals of genetic effects. We then describe how to simplify the analysis of complex GP maps through the multilinear model of genetic interactions proposed by G) to the vector of genetic effects E by:R being the genetic-effect design matrix for the reference point R. If the model is orthogonal, genetic effects are ‘statistical’, and the reference point is μ, the mean of the population. According to A and a , the orthogonal decomposition of genetic effects is:SThe genetic effects α and δ correspond to the additive and dominance effects. Assuming linkage equilibrium, the extension of equation 2 to any number of loci through simple matrix algebra is straightforward .RB), EBR, can be obtained by the ‘change of reference’ operation:When a specific genotype is chosen as a reference, the corresponding effects will be ‘functional’, and they no longer depend on genotypic frequencies in the population. Genetic effects from a specific genetic background reference .where The decomposition of variance components, a classical procedure in quantitative genetics, can be performed in a straightforward way by summing up the variances of the effects at the same level: the additive variance will be the sum of the variances of single additive effects, etc. If the model is orthogonal, then the sum of all effect variances should add up to the total genetic (explained) variance.E, are provided directly by R  regression coefficients, σy by R R . The equi and j their respective effects on the phenotype, the multilinear model predicts that both substitutions occuring together will result in a genotypic value of: i + j + ɛij · i · j.The multilinear model  providesL genetic effects for L loci, which becomes increasingly difficult to handle with high number of loci. The multilinear approximation leads to a simplification of the models by reducing the number of parameters. For instance, for a 2-locus (A and B) model,The general model provided by NOIA generates 3nls procedure, showed that the numerical convergence was generally not problematic when proper starting values are provided .the right-hand side vector contains only 6 parameters to estimate, compared to the 9 of the full model. Transferring this in equation 5 leads to a non-linear regression. Its implementation in the software R R , throughlm and nls (library stats) functions of R. The noia package is released under the General Public Licence, and can be freely downloaded from the CRAN depository http://cran.r-project.org/web/packages/noia/index.html.The NOIA framework has been implemented as a fully documented package for the free software R, which is available on most common operating systems including Linux, Microsoft Windows and Macintosh. The linear and non-linear regressions rely repectively on the In the ideal situation, the exact genotype is known at the expected location of the genetic factors that are thought to influence the trait. This reduces uncertainties and increases the power of the analysis. However, in many cases, the genetic information consist of markers that are close to, but not exactly at the quantitative trait locus. In this latter situation, it is possible to calculate the probability of the genotype at the locus from the genotype at flanking markers, knowing the recombination rate between markers, the most common method being the Haley-Knott regression . The extL columns, L being the number of loci . The linear and multilinear regression functions (linearRegression and multilinearRegression respectively) estimate genetic effects in the NOIA statistical framework, and display the estimated values of genetic effects, the corresponding part of genetic variance explained, the standard deviation of the estimate and the probability that the corresponding effect is 0 . Missing of genotypes . Increasing the number of loci in the model may therefore lead to an increasing complexity (such as a three or four-way interactions) associated to huge confidence intervals, and possibly overparameterization (more parameters than what can be possibly etimated from the dataset). Moreover, large datasets may lead to computational problems . The anaOur framework provides two ways of achieving this. The first possibility is to restrict the analysis to a subset of the possible genetic effects, by removing some of them from the analysis. The expected orthogonality of the linear model guarantees that removing any parameter does not impact the other ones, and the hierarchy of genetic effects suggests to get rid of high order effects first, since they are calculated from the residuals of lower-order effects. Dominance, which may not be of interest in all situations, can be removed as well.The other possibility is to reduce the number of genetic effects without losing all information about high-order epistasis. This can be achieved through the multilinear regression, which supposes that genetic interactions are proportional to the product of marginal effects. The resulting estimate is the directionality of epistasis, and can be used as a proxy for complex GP maps .Model orthogonalization is a general issue in statistics, and is thus not restricted to the decomposition of genetic effects. In any case, getting genetic estimates as independent as possible remains desirable for at least three reasons: (i) it enables straightforward model selection strategies, (ii) it describes the genetic system as average effects of allele substitutions in the sample under study, and (iii) it leads to a proper decomposition of genetic variances. The aim of a research project could however be to focus on a population with properties different from those of the sample under study. Using the change-of-reference tools of NOIA it is possible to transform the obtained estimates from the data into the ones that correspond to the desired reference population, and to obtain the orthogonal decomposition of variance in that population. Furthermore, it is possible to use not only reference populations but also reference individual genotypes. The genetic effects obtained using individual genotypes as reference points make it possible to describe the genetic system as sets of allele substitutions from those individuals. This so-called ‘functional modeling of genetic effects’ enables researchers to use real data to study interesting aspects of evolution such as hybrid incompatibilities and domestication processes e.g. .The NOIA model is perfectly orthogonal at the level of a single locus, whatever the genotypic frequencies. However, it assumes linkage equilibrium (as many previous models for variance decomposition, e.g. Further improvement of the NOIA framework include the implementation of a multi-allele model, the current model providing the matrix algebra for only two alleles. This two-allele case is satisfactory in many situations, in which the population under study is the result of a cross between two divergent parental populations. This pattern maximizes the power of gene location, and is thus frequently used, both for domestic populations and natural species surveys. However, many natural populations show a high degree of polymorphism, and would benefit from a more general model. In any case, studying multiple alleles will generate many new genetic parameters to estimate, and would rapidly reach a limit due to the quality and the size of the data set. Practical use of such more general model would thus be doubtful without the development of tools and concepts aiming at simplifying the description of genetic architectures.Statistical tools such as those described in this contribution aim at providing meaningful and unbiased estimates for many genetic parameters. However, the number of parameters underlying a GP map increases exponentially with the number of genetic factors involved: more loci means more pairwise interactions between loci, and much more high-order interactions. It is likely that most quantitative traits are underlined by large and complex networks of interacting genes, and the development of new technologies in genetics and molecular biology may bring an enormous amount of data on the structure and effects of genetic polymorphisms in such networks. Our ability to extract and summarize information that is relevant for geneticists and evolutionary biologists thus requires the development of tools aiming at manipulating huge datasets not only mathematically, but also conceptually.


Drosophila virilis is a cosmopolitan species belonging to the virilis group, where divergence times between different phylads go back to the early Miocene. We analysed mitochondrial DNA sequence variation among 35 Drosophila virilis strains covering the species' range in order to detect demographic events that could be used to understand the present characteristics of the species, as well as its differences from other members of the group.The pattern of genetic variation within and among populations of a species is strongly affected by its phylogeographic history. Analyses based on putatively neutral markers provide data from which past events, such as population expansions and colonizations, can be inferred. Drosophila virilis showed very low nucleotide diversity with haplotypes distributed in a star-like network, consistent with a recent world-wide exponential expansion possibly associated either with domestication or post-glacial colonization. All analyses point towards a rapid population expansion. Coalescence models support this interpretation. The central haplotype in the network, which could be interpreted as ancestral, is widely distributed and gives no information about the geographical origin of the population expansion. The species showed no geographic structure in the distribution of mitochondrial haplotypes, in contrast to results of a recent microsatellite-based analysis.D. virilis haplotypes indicate a pattern of global demographic expansion, probably related to human movements, although this interpretation cannot be distinguished from a selective sweep in the mitochondrial DNA until nuclear sequence data become available. The particular behavioural traits of this species, including weak species-discrimination and intraspecific mate choice exercised by the females, can be understood from this perspective.The lack of geographic structure and the star-like topology depicted by the  The development of methods to analyse intraspecific phylogenies has provided very valuable tools to understand how populations have been influenced by historical and contemporary processes ,2. MitocThe pattern of variation of mitochondrial DNA, permits inferences about a species' demographic history, including events such as population expansions and colonizations. The application of coalescent theory ,15 for tDrosophila virilis group comprises 13 species and subspecies divided into two clades, the virilis and montana phylads . Alt. AltDroses [e.g. . D. viriolerance  and higholerance . Like senditions , which mD. virilis using mitochondrial DNA sequence data, in order to provide an historical framework for evolutionary studies on life history and behavioural traits of the species and comparisons with other species in the group, especially D. montana, the most divergent species within the virilis group.In this paper, we report an analysis of the demographic history of We examined a total of 35 lines covering the species range: 9 from Japan, 9 from Eastern Europe and Central Asia, 5 from Western Europe, 2 from the United States and 10 from China  was 0.00185 ± 0.00114. The haplotype network . However, it is not possible to rule out alternative models, such as recent expansion from a smaller but more stable refugial population and time estimation should be treated with caution given the wide confidence interval on the expansion rate estimate and uncertainty about the substitution rate in D. virilis.The In natural populations, ancestral haplotypes may persist and be sampled together with their descendants . The coaD. virilis strains from different continents, Vieira and Charlesworth [D. virilis originated from an ancestral form in Asia, since the most primitive species of the virilis-repleta section of the genus Drosophila have been observed in Southeast Asia. This hypothesis recently received further support from a molecular phylogeny of the virilis section [In their study on sequence variation in six X-linked genes of 21 lesworth  found nolesworth . Throckmlesworth  has prev section .et al. [D. virilis strains, many of which were analysed here for mitochondrial DNA. Although a phylogenetic tree and STRUCTURE analysis showed only moderate clustering of the strains originating from Continental Asia, Europe, America and Japan, an assignment test using a priori information about the geographical origin of the strains gave high posterior probabilities for their correct assignment. Microsatellite variation also showed significant population differentiation, as measured by FST, with evidence for isolation by distance. Variability detected by microsatellites can have a more recent origin than variation measured by mitochondrial DNA. Consequently, the lack of population differentiation in mitochondrial DNA could indicate shared ancestry while microsatellite variation between populations indicates that differentiation is in progress because current gene flow is restricted. Alternatively, the homogeneity of mitochondrial DNA could be the result of a recent selective sweep which did not disturb the pre-existing population structure for nuclear loci. Unfortunately, the use of laboratory strains prohibited testing for a demographic expansion using the microsatellite data.In a recent study, Huttunen et al.  analysedD. americana, which, together with D. lummei, represents dispersal of the virilis group into the Neartic region [Drosophila americana is represented by two chromosomal forms, D. a. americana and D. a. texana, differing by a fusion between the X and fourth chromosome and the frequency of chromosomal inversions. As in the case of D. virilis, phylogenetic analysis failed to resolve distinct clades between the chromosomal forms and also between geographic regions sampled, with Fst values not significantly different from zero [D. virilis [D. americana showed no differentiation between populations [There are several papers describing genetic variation in another species of the virilis group, c region -46. Drosrom zero ,44. The rom zero . Further virilis , an analD. virilis, and in D. americana, is in sharp contrast with the pattern recently described for another species of the group, D. montana [D. virilis and D. montana belong to the same species group in the genus Drosophila, they represent different phylads and differ in many characteristics, including chromosomal variation, habitat preferences, and courtship behaviour. Differences in biogeographic history, reflected in the pattern of mitochondrial variation in the two species, could have been important in the origin of these characteristics and constitute the basis for the interpretation of their evolution.The pattern of geographic variation in haplotypes of  montana . In thisDrosophila virilis suggests a worldwide exponential population expansion during the Pleistocene, with extensive migration between demes. Slight differentiation at microsatellite markers results either from bottlenecking during or after expansion, or accumulation of differentiation across current barriers to gene exchange. The alternative possibility, that a selective sweep has homogenised mitochondrial sequences without disturbing pre-existing population structure for nuclear markers, cannot be excluded. However, a recent demographic expansion is consistent with the domestic habitat of D. virilis and helps to explain its lack of chromosomal polymorphism as well as evidence for a history of weak selection on mating behaviour by comparison with related species.Mitochondrial DNA sequence variation in Drosophila virilis strains, covering the species' range, were selected for analysis  in reaction buffer. Initial denaturation was for 7 minutes at 94°C followed by 35 cycles of 1 minute at 94°C, 1 minute at the annealing temperature (54°C for COI and 56°C for COII) and 1 minute at 72°C, and a final incubation of 5 minutes at 72°C. The products were purified using QIAquick columns (QIAGEN) and sequenced using the forward primer. No ambiguous sites were found in the sequences. Sequences (GenBank accession nos. DQ426800 to DQ426823) were aligned with CLUSTAL-V [DNA was extracted from ethanol-preserved flies following a standard protocol , where ttRNALEU [; TL2: 5'AAATC-3' ,51 whichLUSTAL-V .COI and COII data sets. There was congruence between the data-sets and therefore the two fragments were combined for all subsequent analyses. ARLEQUIN 2.0 [D [F [The partition homogeneity test, PHT , as implQUIN 2.0  was usedN 2.0 [D  and Fu's.0 [D [F ).θ and the population growth rate g, assuming an exponential model of growth and using a maximum likelihood approach. The parameters used for the simulations were obtained by running a hierarchy of likelihood-ratio tests in Modeltest 3.0 [The program FLUCTUATE  was usedtest 3.0  to choostest 3.0 . The statest 3.0 . GENIE wNetworks of haplotypes were constructed based on statistical parsimony using the program TCS 1.06 .PMM carried out the molecular genetic studies, participated in the analyses and drafted the manuscript. JR participated in the molecular genetic studies. AH participated in the design of the study and helped to draft the manuscript. RKB participated in the design and in the analyses and helped to draft the manuscript. All authors read and approved the final manuscript.


Ciona intestinalis, we used a combination of natural variation and neutral population genetics as an approach for the systematic identification of new mutations. In addition to the significance of developmental variation for phenotype-driven studies, this approach can encompass important implications in evolutionary and population biology.The study of ascidians  has made a considerable contribution to our understanding of the origin and evolution of basal chordates. To provide further information to support forward genetics in C. intestinalis. The influence of historical, geographical and environmental factors on the distribution of abnormal phenotypes was assessed by means of 12 microsatellites. We identified 37 possible mutant loci with stereotyped defects in embryonic development that segregate in a way typical of recessive alleles. Local populations were found to differ in genetic organization and frequency distribution of phenotypic classes.Here, we report a preliminary survey for naturally occurring mutations in three geographically interconnected populations of C. intestinalis constitutes a valuable source of phenotypes for studying embryonic development in ascidians. Correlating genetic structure and the occurrence of abnormal phenotypes is a crucial focus for understanding the selective forces that shape natural finite populations, and may provide insights of great importance into the evolutionary mechanisms that generate animal diversity.Natural genetic polymorphism of  The primary objective of forward genetics is to provide a low-cost resource for mutants with interesting phenotypes that may be subsequently mapped and identified at the molecular level Mutant alleles occur in natural populations and are able to influence individual fitness differently. Genetic drift acts upon these alleles by causing rapid changes in their frequencies according to the rate with which these mutations are filtered by natural selection. This indicates that a high percentage of individuals should be free of lethal mutations and carry early-stage positively selected phenotypes. Mutational pressure coupled with random sampling drift contributes significantly to the genetic load, inbreeding depression and finally to the pool of genetic variability of a population Ciona intestinalis   in studies of development and evolution in Chordata C. intestinalis is a filter-feeding hermaphrodite invertebrate species that lives in shallow eutrophic waters, such as estuaries, lagoons and marinas, with a geographical distribution that spans most temperate to sub-boreal zones of both hemispheres. C. intestinalis forms dense coastal aggregates of ecological and economical relevance Ciona genomes analyzed so far display a high degree of DNA polymorphism, possibly associated with a large effective population size C. intestinalis sp. A ranges mostly within sites among regions, whereas population structuring appears to be higher in sp. B than in sp. A Numerous advances, including genome sequencing, have considerably enhanced the use of the model tunicate, Ciona, as other ascidians, accumulates toxic substances by acting as a biological filter for the purification of coastal waters and consequently as an important bio-indicator In the wild, the species is characterized by seasonal fluctuations in demography. Minos-specific electroporation and remobilization techniques are expected to provide higher proficiency and amenability of insertional mutagenesis screenings C. intestinalis sp. A populations from interconnected geographical locations. Twelve microsatellite loci were used to investigate the genetic structure of these populations and to compare it with rates and categories of the mutations encountered in sampling sites. Early emphasis on two mutant lines (omero and curly) provides model systems for addressing determination of sensory organs and neuro-muscular activity.Random germline mutagenesis has been pursued by means of chemical 1 scheme based on artificial self-fertilization in order to identify deleterious alleles acting during C. intestinalis sp. A embryogenesis. As already shown in zebrafish, a morphology-based mutation screen can isolate potentially important phenotypes per month.We used a simple Fper individual) gave rise to a high percentage (54.3–69.5%) of adults suitable for phenotype screening in F1 progeny (>30 early blastulae) , self-fertility  and reproductive capacity (∼1700–3000 eggs astulae) . In addicurly, levi, smerz)  . Whereas, smerz) .Ascidian development includes a wide set of variable traits, defined merely as the morphological and behavioural features that are determined throughout the life cycle. When different natural genotypes of the same species are directly compared, phenotypic variation may be observed for particular cell types. Deleterious alleles were classified into seven phenotypic classes. The most frequent type of mutations showed abnormal development of the larval sensory organs . Other categories were those affecting tail (7), multiple traits (7), trunk (5), brain patterning (2), cell death (1) and larval motility (1) .C. intestinalis is exemplified by three main anatomical features: a dorsal nervous system, a notochord and gill slits e.g. albus) (e.g. tasso) (e.g. pascoli)  (monkey) (shiva) (miller) (omero)  , few pig. tasso) , accumulpascoli) , normal )  , two oto(monkey) , larger  (shiva) , supranu(miller)  and abse (omero) . Other s  (e.g. mezacapa) (bermuda)  , anterioezacapa) , and thebermuda) .e.g. pigtail) (e.g. pavese) (e.g. fuoriditesta) (omero and fuoriditesta) was isolated, as shown by independent allele segregation in F2 outcross generation  ; trunk,  pavese) ; and taiditesta) . One douneration , 2G,H.e.g. uritsa) (bell and rohmer) (data not shown) and c) defects in notochord morphogenesis  (pasolini) and abnormal tip (junk) (data not shown).We identified seven broods with recessive alterations in tail formation . Most of uritsa) , b) kinktreveza) . Two defcurly phenotype was found to cause circling movements, likely due to defects in muscle contractions (see below).After hatching, normal larvae straighten out their tails and are then capable of symmetrical swimming by generating tail flicks (for orientation) and bilateral swimming movements of the tail Motility, trunk, brain, multiple and cell death categories  showed full lethality before settlement and metamorphosis. On the other hand, tail and sensory organ phenotypes were nearly viable , with inbred or outbred mutant homozygotes propagating until adulthood. Notably, the abundance of lethal- and non-lethal phenotypes varied sharply among populations, contributing a major portion in CdS (60%) and FuI (81.8%), respectively ,2.omero (omr) mutant was initially identified due to the absence of the pigmented sensory structures . Mutant larvae were analysed by in situ hybridisation, in order to verify if the omr mutation was accompanied by abnormalities in sensory vesicle structure and by impairment of ocellus photoreceptors. The genes taken into account were the sensory vesicle markers otx and six3/6, and the photoreceptor markers arrestin, opsin-1 and pitx-2. In comparison to control larvae, no detectable discrepancy in the expression of all the markers listed above was observed  were examined in at neurula stage embryos. Although at this stage omr mutant embryos are morphologically indistinguishable from wild-type embryos, any alterations in gene expression should be evident in ∼25% of the embryos. The expression of bmp5/7, mitf and tyr is lost in the pigment cell precursors of 30%, 35% and 35% of embryos, respectively  and during this period made swimming movements and tail twitches which resulted in larvae describing roughly circular tracks. The curled ‘posture’ of the curly larvae is similar to that of freshly hatched normal larvae. No obvious malformation of the notochord was observed in the mutants at the light microscope level. Superficially they resembled larvae that have experimentally reversed L-R asymmetry due to one-sided expression of the nodal gene Halocynthia roretziC. intestinalis, E.R. Brown, unpublished data), blocked swimming but did not result in tail ‘straightening’ even after exposure for several hours. Suction electrode recordings from larval tails showed that muscle potentials were detected at half the normal frequency for both light off and spontaneous swimming bursts , which has a role in maintaining the shape adopted by the normal larvae before hatching, rather than gross developmental differences such as changes in LR symmetry Addition of CNQX, an AMPA–like glutamate receptor blocker, instantaneously altered the swimming pattern producing normal muscle contraction frequencies and patterns. We speculate that the results are consistent with the idea that the per locus and per population is summarized in per locus ranging from 3 to 24 (see P<0.001) rather than between them . Genetic distance from pairwise comparisons It is assumed that the genetic variability of a population that is estimated from selectively neutral alleles is produced in a stepwise mode. This process is mathematically tested in locus-specific allelic frequencies in equilibrium conditions from natural finite random-mating populations o 24 see . HierarcThe genetic separation among sites as well as gene flow levels were not concordant with population geographic locations and distances : VC and p>0.05) in any of the populations, indicating independent assortment of alleles at all loci. Observed genotypic proportions deviated significantly from Hardy-Weinberg (HW) expectations at several loci mainly for the FuI population, suggesting that HW proportions may be altered by small population size, non-random mating and inbreeding.Fisher's pair-wise exact tests for linkage disequilibrium performed considering all loci combinations were not significant  is consistent with previous estimates (∼20%) DrosophilaArtificial mutagenesis is a powerful approach to study developmental, physiological, or pathological processes, as a forward genetic complement in defining novel molecular functions. However, this technique is available only for a small fraction of animal and vegetal species with strong genetics. Since C. intestinalis larvae attach to the substrate shortly after hatching In this report, the percentage of deleterious lethal phenotypes varied between the samples of specimens analyzed, with higher rates occurring in the CdS (6/10) and VC (8/16) harbour sites. In contrast, non-lethal alterations were largely dominant in FuI (9/11). Difference in mutant frequency correlated only between VC/CdS and FuI populations at a reduced geographic scale (tens of Km). This, together with the elevated migration rate between the CDS and the VC population, the results suggest that local biogeographic constraints do not contribute to isolation by distance in open water habitats. The genetic isolation of the FuI population and the predominance of early non-lethal mutant phenotypes indicated that the genetic variants responsible for the phenotypic variation are maintained due to population isolation. In this situation, the dispersal capabilities of larvae may play an important role. Whereas Although microsatellite loci were generally in accordance with HW equilibrium for VC and CdS sites, the FuI population deviated from expectations in the majority of the loci when significance values were combined across them. Here, we attribute this deviation to a non-random mating among individuals followed by inbreeding and natural selection rather than the result of an excess of heterozygotes. The predominance of non-lethal phenotypes in the FuI population supports this view. Non-random mating and inbreeding, when outcrossing rates are low to absent, are able to increase the frequency of homozygous genotypes and therefore eliminate large part of deleterious mutations that are not completely dominant. Individuals carrying highly heritable deleterious allelic variants are hence quickly eliminated by natural selection, which is further supported by environmental quality and features. On the other hand, CdS and VC populations occupy open, circulating coastal waters and they display high levels of gene flow and predominant rates of lethal alterations . We assuIt is known that isolated populations are susceptible to genetic changes at higher rates. Moreover, genetic alterations promoted by pollutants have been inferred by means of molecular markers Ciona populations, high levels of genetic diversity may derive from several factors, including the mutation-selection balance between the reappearing phenotypes and their elimination by natural selection, and the population genetic structure driven by reproductive strategies, geographical connectivity and effective population size. In this report, we observe a high load of recessive alleles that affect distinct cell types, tissues and organs. These phenotypes are probably ephemeral entities with low fitness that contribute to local adaptation and do not represent potentially independent evolutionary lineages but are rapidly eliminated. This hypothesis is supported by the reduced overall morphological complexity of the species in an ecological scenario characterized by low intraspecific competition and high physical stress. Alternatively, these loci could represent young, deleterious and spatially restricted alleles with evolutionary potential.Current evidence suggests that mutations in key developmental genes that control morphogenesis can cause morphological discontinuities with variable levels of fitness, and that the selective advantage of such variation is primarily filtered by the ecological context. In C. intestinalis characterized by morphological alterations that are strikingly reminiscent of the larval anatomy in species belonging to different ascidian families. In tadpole larvae, the putative homologue of the vertebrate fore- and midbrain is the sensory vesicle, a structure that usually contains two sensory organs, the ocellus and the otolith, which are sensitive to light and gravity, respectively C. intestinalis larvae, the frequency of mutations displaying loss, size alteration or supranumerary sensory organs provides a number of phylo-mimicking phenotypes that may be used to uncover the changes in regulatory gene networks that underlie the morphological diversity. For instance, the absence of an ocellus in omero and tasso phenotypes is typical of many Molgulid species. Similarly, lack of an otolith in the larvae of Clavelinid taxa (Pycnoclavella spp.) is evocative of the abnormal sensory organs of pascoli and omero  differentiation are non-lethal is not surprising if one considers the independent evolutionary loss of these organs in different ascidian taxa. We postulate that such changes in sensory input are related to colonization of altered habitats. It is notable that abnormal phenotypes of the two brain sensory structures occur without correlated changes in other developmental programs and, as in the cases reported in this study, may be considered to be adaptations in a highly polymorphic species C. intestinalis at the population level, or it is obscured by random genetic drift, immigration and negative selection, still remains to be clarified. Indeed, we do not exclude the possibility that genetic patterning as well as frequency distribution of deleterious alleles may be selectively promoted by environmental stressors in dynamic habitats of extreme anthropogenic impact such as those analyzed in this study. The combination of marker-assisted analysis of population structure and screening of phenotypic classes is central not only in shaping models in developmental biology but also in evolutionary genetics of populations. The comparison between frequencies of natural variation and population genetic patterning represents a unique opportunity to quantify the importance of biogeographic and anthropogenic factors in affecting mutation–selection dynamics.Spatial structuring of interconnected populations and the distribution of mutant classes follow biogeographical expectations. Whether the impact of pollutant stressors is discernable in C. intestinalis is still scarce, short generation time, strong genetics and genomics, mode of reproduction, closely related sub-populations with high rates of genetic variance, and ways of retaining frozen stocks of genetic lines make this species a model with a future in evolution studies in the wild.Although the evolutionary population biology of Ciona once the gene responsible for the phenotype is identified. The occurrence of abnormalities in early determination, cell fate and differentiation of sensory organs in C. intestinalis populations may disclose hidden aspects of the genetic hardwiring , two located in the harbours of Villaggio Coppola (VC) and Castellamare di Stabia (CdS), and one in a brackish water lagoon, the Lake Fusaro (FuI). Wild-caught animals were maintained in 35 lt tanks with flow-through seawater and fed as previously described per 15 cm Petri dish) and monitored for cell division in order to measure self-fertilization rates. Only batches containing at least 30 developing embryos were kept for further observations. Embryos were separated from unfertilized eggs and grown at 18°C in FSW. At the larval stage (18 hours post fertilization), inbred offspring were visually scored for defective phenotypes by stereomicroscopy. Behavioral and neuro-muscular abnormalities were inspected by analysis of larval tail movements. Candidate heterozygote carriers of mutant alleles affecting 20–30% of the embryos were left to rest for two weeks before repeating the self-fertilization test. Unfit carriers  were anesthetized in order to collect sperm from the vas deferens with a Pasteur pipette. In this and in the case of confirmed carriers, sperm were outcrossed with wild-type eggs (of the same genetic background) for inheritance tests and/or cryopreserved. When individuals carried a limited amount of sperm, male gametes were used solely for outcrossing. Resulting F1 offspring were genotyped at the larval stage for mutant allele transmission by self-fertilization tests. Limited storage space and information from re-selfing limited the number of outcross populations for transmission analysis to 11 lines, while complementation tests of similar mutation phenotypes were not performed at this juncture.Specimens of Ciona intestinalis A individuals . Specimens were multilocus genotyped with 4 previously reported http://www.beckmancoulter.com) and electropherograms were analyzed with the Beckman CEQ 2000 v3.0 software. Quality of the allelic matrices and post-genotypic errors by population and locus were assessed in MICRO-CHECKER v2.2.1 per locus per population, partition of the genetic diversity and variation, gene-flow, F-statistics, number of migrants, analysis of molecular variance and factorial correspondence analysis of the microsatellite data, were explored as implemented in GENALEX v6 Total genomic DNA was extracted as previously described C. intestinalis cDNA library (http://ghost.zool.kyoto-u.ac.jp/indexr1.html). Clones of interest were isolated and sequenced using a 3730 DNA Analyzer apparatus . Sequence validation was performed by alignment with the C. intestinalis genome (http://genome.jgi-psf.org/ciona4/ciona4.home.html). RNA probes were synthesized using the DIG RNA labeling kit . Whole mount in situ hybridization was performed as previously reported Ci-opsin1 (ID: R1CiGC28m24), Ci-bmp5/7 (ID: R1CiGC04j07), Ci-MITF (ID: R1CiGC28k08) and Ci-tyrosinase (ID: R1CiGC33c19). Images of embryo were recorded using an AXIO Imager.M1 microscope connected to an AxioCam HRc camera  and elaborated with Adobe Photoshop v7.0 or vCS.Riboprobe preparation: cDNA clones were identified via bioinformatic screening of a curly mutants to continue circling for 24 hours, electrophysiological recordings were made from tail muscles as previously described http://www.sutter.com). The electrodes were mounted on a micromanipulator and their tips broken under microscopic control, so that the internal diameter was about four-fifths the diameter of the larval tail. Using coarse manipulation of the microscope stage and micromanipulation of the electrode, the larval tail was placed in close contact with the tip of the electrode. Then, negative suction was applied and the larval tail was drawn into the pipette to about two thirds of its length. Muscle action potentials were then recorded differentially between the inside of the pipette and the seawater of the bath, and amplified  10000 times with reference to a silver chloride pellet placed in the bath. Signals were AC-coupled, passed between 0.1 Hz and 10000 Hz and digitized and stored, using a Digidata 1200 data acquisition system. Analysis was carried out with Clampfit v9.0 software . A custom-built shutter was controlled by 5 V pulses delivered from the Digidata board, allowing a step-down in the light intensity to be applied for 3 s.To study in detail the defect that caused −1). Larval activity was recorded in a series of 1 min sweeps, one every 5·min, under constant light conditions with a 3 s light-off stimulus to test the shadow response. Plots of instantaneous frequencies of potentials vs time and mean frequency of potentials were obtained from raw traces. The duration of each interval of larval swimming activity, also termed ‘burst’, and the quantity of activity for each sweep , both with light-off stimulation and in constant light, was obtained. Recordings were made from newly hatched larvae and from larvae up to 6 hr post-hatching. The activity of each larva was recorded for a maximum period of 3 h.All experiments were carried out at 20°C and preparations were continuously superfused with FSW (8 ml/minhttp://www.tocris.com) and added prediluted from a concentrated stock solution and to the superfusate at the final concentrations indicated.Freshly 0.22 µm filtered sea-water was continuously perfused over the preparation at a rate of 8 mL/min. CNQX was purchased from Tocris (