Protomognathus americanus and its two host species Temnothorax longispinosus and T. curvispinosus. We sampled throughout their range, genotyped ants on six to eight microsatellite loci and an MtDNA sequence and found high levels of genetic variation and strong population structure in all three species. Interestingly, the most abundant species and primary host, T. longispinosus, is characterized by less structure, but lower local genetic diversity. Generally, differences between the species were small, and we conclude that they have similar evolutionary potentials. The coevolutionary interaction between this social parasite and its hosts may therefore be less influenced by divergent evolutionary potentials, but rather by varying selection pressures. We employed different methods to quantify and compare genetic diversity and structure between species and genetic markers. We found that Jost D is well suited for these comparisons, as long as mutation rates between markers and species are similar. If this is not the case, for example, when using MtDNA and microsatellites to study sex-specific dispersal, model-based inference should be used instead of descriptive statistics (such as D or GST). Using coalescent-based methods, we indeed found that males disperse much more than females, but this sex bias in dispersal differed between species. The findings of the different approaches with regard to genetic diversity and structure were in good accordance with each other.The spatial structure of host–parasite coevolution is shaped by population structure and genetic diversity of the interacting species. We analysed these population genetic parameters in three related ant species: the parasitic slavemaking ant  Host–parasite relationships are among the most widespread of all species interactions and important for the ecology and evolution of the interacting species. Host–parasite interactions can lead to coevolutionary arms races, where patterns of local adaptation can change over time and space. This spatial structure is often referred to as a geographic mosaic of coevolution . We are ST measures depend on levels of diversity  T. curvispinosus and 78 (60) P. americanus individuals .For the microsatellite data, the observed allele lengths were rounded to integers (binned) using the software TANDEM . Summaryseqinr , APE : S was calculated following ne, because, unlike heterozygosity (H), it changes linearly with diversity. If a population goes from 4 to 2 to 1 alleles , heterozygosity goes from 0.75 to 0.5 to 0 . The effective number of alleles would go from 4 to 2 to 1, which better reflects genetic diversity . To get an intuitive understanding of D, it is useful to rewrite ne).Jost D depends on the ratio between neS and neT. In the finite-island model, D depends on the mutation rate and the migration rate between any two subpopulations  values from the randomized datasets that are higher than or equal to the real D (or GST) value. We used the same randomized datasets for D and GST and the resulting P-values are exactly the same for D and GST. D and GST. Note that this permutation test is not the same as the bootstrapping procedure as implemented in the web-based smogd software (We tested the significance of the software . The booD or GST) is reduced when some of the samples are very small , we took a fragment from our dataset, starting at a random location in the sequence and calculated D, GST and P-values. We repeated this 20 times for each fragment length and averaged over these 20 runs. For each randomly selected fragment, a P-value was estimated by 1000 permutations. We estimate the power to detect population structure as the percentage of these 20 fragments which showed a P-value of < 0.05.For the MtDNA sequences, we calculated D, there is no elegant way to include genetic distances, because it is based on the effective number of alleles.The previous analyses of the microsatellites and MtDNA data are based on alleles, not genetic distance. When calculating D for each nucleotide and then average over nucleotides, so that when more nucleotides have high D values, the average D value will be high. However, D per nucleotide is not very informative. For most nucleotides D will be 0, because any sequence which can be aligned will consist mostly of nucleotides which are monomorphic. Even for sites that are polymorphic, D will still be low because there are usually a maximum two states at each nucleotide. D is highest if each subpopulation carries unique alleles, but for single nucleotides this is not possible. In practice, this means that D per nucleotide must go down as more locations are sampled.In principle, one can calculate ST analyses by using heterozygosities (HT and HS) per nucleotide and averaging over all nucleotides (HTnuc and HSnuc). These heterozygosities increase with genetic distance between individuals. Using HTnuc and HSnuc, one can calculate ST does not suffer from the same problems as GST does because there are usually only two and definitely not more than four allelic states per nucleotide, so heterozygosity cannot be high and is unlikely to reach a level where it does not increase (approximately) linearly with diversity. Significance can be estimated with the same permutation test as described before.By contrast, genetic distances can be easily integrated in Glculate  . Note thibdws  directly.To test for isolation by distance, a Mantel test was performed over the whole dataset using the web based software K (number of clusters) ranged from 1 to 6 for all species and we did four repeats per K value and used delta K (K. To determine delta K we used Structure Harvester (http://taylor0.biology.ucla.edu/struct_harvest/). We compared the outcome of Structure with pairwise D values averaged over loci.We analysed the microsatellite data with the program Structure 2.3.1 (θ (population-wide mutation rate) per subpopulation and NT (the number of subpopulations).The previous analyses describe the observable pattern of population structure, which is created by processes such as mutation and migration. Ideally, we would like to infer the parameters of these underlying processes using a model based approach  or MigraMigraine . Two aspects of our data make us confident that the finite-island model is not too far from reality for our species. (i) Our samples from different locations do not differ significantly in genetic diversity, which indicates that local population sizes are similar. (ii) Values of pairwise differentiation are similar between all locations , the original range did not include the whole confidence interval, so we reran with 2NmN, so we have to multiply the output by N to get θ*NT. For the migration rate, the output is also divided by N, which we use as is.For the microsatellite data, we use Migraine , which iMigraine ,b;, to eions see , which sNT. We therefore multiply the estimated θ value with N (the number of subpopulations). Unlike Migraine, migrate-n estimates migration rates between all sampled populations (e.g. the migration rate from subpopulation 1 to subpopulation 2). We multiply the estimated migration rate with the number of neighbouring populations to get the total migration rate M.For the MtDNA data, we run migrate-n , which uNT or M are different between the species of interest, we fit a linear model with the estimated values for θ*NT and M as response variables and species and locus as explanatory variables using R.To test whether θ*TANDEM suggest that good loci have an average rounding error which is below 10% of the repeat size. Only three of the eight loci were below this threshold for T. longispinosus , two of the six for P. americanus and also two of eight for T. curvispinosus were below this threshold (L5 and L18 for both species). Nevertheless, we decided to keep all loci in the analysis. For the demography and the population structure analysis, we found no differences between the high and the low quality loci. For the genetic diversity analysis, the data of only the high quality loci are not enough to make a comparison between the species, but also for this analysis, the results seem consistent over loci.The authors of P< 0.0001). The underlying reason could be the presence of null-alleles and/or inbreeding. We proceeded to estimate the proportion of null-alleles for each species, locus and population, using the method by D and GST we will therefore permute individuals and not alleles.Hardy–Weinberg equilibrium over all subpopulations and loci was rejected because of heterozygote deficiency for all three species (T. longispinosus had six significant locus pairs (P< 0.01) out of 28 , T. curvispinosus seven of 28  and P. americanus nine of 15 . This could mean that the different loci are not independent and not too much weight should be placed on evidence which comes from different loci.Significant linkage disequilibrium was found in all three species: ne) averaged over loci and locations varied between 7 and 11 for the three species for the microsatellites (T. longispinosus has significantly lower ne than the other two species (P< 0.005). The different loci had different values of ne , but the study sites were not significantly different (P= 0.98). We found no effect of longitude and latitude (P= 0.60). For the MtDNA dataset, we found no effect of species or location .The mean effective number of alleles , indicating a higher migration rate for this species. For the MtDNA data, both GST and D values were consistently higher than for the microsatellite loci  result. The number of base pairs that were needed for consistently significant results was 500 for P. americanus, 300 for T. longispinosus and 900 for T. curvispinosus.For short fragment lengths, ericanus . The resST slowly goes down because heterozygosity goes up when longer sequences are considered. D goes up with longer fragments, as it is more likely that mutations which make subpopulations differentiated are found. However, if the mutation rate increases even more (had we sampled an even longer fragment), the P-values would go up and power goes down (unpublished data). The reason is that if  every individual carries a different allele, there is no power to detect population structure. This is not the case in our dataset.From 300 to 1305 base pairs, GT. longispinosus shows the highest γST (P= 0.01), T. curvispinosus has a lower value of γST which is borderline significant (P= 0.05). P. americanus has even lower γST, which is not significant (P= 0.25).We find that hest γST , which iT. longispinosus or P. americanus for the microsatellite or the MtDNA data . For T. curvispinosus, we found a negative correlation for the microsatellites: subpopulations that are further away from each other tend to have lower D values , but a positive one for the MtDNA . The latter, however, is based on only three subpopulations .We found no significant correlations between genetic and geographic distances in K for K = 4 . The colours in the population labels in Using pairwise ng trees , which cwell see . Both StNT, averaged over loci as estimated with Migraine is 2.7 for T. longispinosus, 4.0 for T. curvispinosus and 6.3 for P. americanus (. Population-wide mutation rates (θ*NT) did not differ between species (P= 0.2), but locus had an almost significant effect (P= 0.05). The maximum likelihood estimates for M, averaged over loci, is 32.0 for T. longispinosus, 17.6 for T. curvispinosus and 6.5 for P. americanus (P = 0.02), although none of the one-by-one comparisons between the three species were significant. As expected, locus did not affect migration rate (P= 0.08).For the microsatellites, the maximum likelihood estimate for θ*ericanus . Populatericanus . We founNT (per nucleotide), as estimated by migrate-n is 0.004 for T. longispinosus, 0.005 for T. curvispinosus and 0.003 for P. americanus  and its estimated migration rates were lowest. Only the MtDNA analyses based on genetic distances revealed the expected pattern of structure in the host species but not in the parasite.Albeit the overall pattern was one of similarity, the species did differ from each other in some surprising aspects. For example, although There are several limitations to the inferences we can make based on our data. Naturally, we have data from a limited number of subpopulations and a limited number of microsatellite loci. The mitochondrial genome is nonrecombining and therefore only ‘shows’ one genealogical history, which can be greatly influenced by chance events. In addition, we detected linkage disequilibrium and an excess of homozygotes. The presence of linkage disequilibrium means that different loci are not entirely independent, and evidence based on different loci should therefore be treated with caution. The excess of homozygotes could be attributed to null-alleles or inbreeding, which could make subpopulations look more differentiated than they really are. Keeping in mind these limitations, we will now focus on each of the species separately.ne) for the host species T. longispinosus compared with the other two species. The same trend (though not significant) was found for the estimates of θ*NT by the software Migraine. This is surprising, given that T. longispinosus occurs at 10-fold higher densities than the parasite P. americanus , which can be interpreted as evidence for a population size expansion in this species. In the other two species, we found no significant deviation from expectations . It is therefore likely that the T. longispinosus population has recently increased in size in which case the observed genetic diversity will reflect ancient population sizes, rather than current. The evolutionary potential, however, is determined largely by its current population size  we find a negative correlation between geographic distance and D values; the populations that are further away tend to be closer genetically. This is clearly a surprising finding. However, other phylogeographic studies found that postglacial resettlement routes can lead to a divergent evolutionary history of close populations, for example, because they stem from different pleistocene refuges. For example, a population genetic analysis of the shrew Sorex antinorii detected two resettlement routes in the European Alps. These two genetic lineages came into secondary contact in the Rhone valley and neighbouring shrew populations are genetically clearly distinct .The host D values , lower mD values  and the D values . HoweverP. americanus has a slightly smaller range than T. longispinosus and only 10% of its host density. Still, we find that it has as high diversity as T. curvispinosus and higher genetic diversity in the subpopulations than T. longispinosus for the microsatellites . Strongly structured populations have been found for example in polygynous ant species of the genus Myrmica and Formica  were not different between the three species   , and thepinosus) . We hypoST and D values for MtDNA than microsatellites, as did many other population genetic studies on ants , whereas for P. americanus the difference is not so large (6.5 vs. 3.4). Note that M for microsatellites depends on the average migration rates of males and females. Under the assumption of similar effective population sizes for males and females, the comparisons suggest that in the two host species males have migration rates that are between 6 and 15-fold higher than that of queens. In the monogynous parasite, this difference appears to be only three-fold.If one is interested in sex-specific dispersal, one could compare the maximum likelihood estimates of migration rates between marker systems. For this comparison, we have to multiply the estimate of M for MtDNA with a factor 4 to adjust for the four-fold lower population size. If we do that we find that In our population genetic analysis we used several approaches: the newly introduced D statistic , the BayD is by far the easiest of the methods we used, as it can simply be calculated from the data and it does not require elaborate computations. However, there are two main drawbacks of using D. First, it depends on both migration and mutation rates (ne). The first drawback is not too severe for our dataset because we used the same loci for different species and found similar mutation rates between the loci we used. However, it makes comparisons between different marker systems (MtDNA vs. nuclear markers) difficult. We stress that whenever D values are compared between different loci, species or studies, it is necessary to consider the effect of mutation rates. The variability of the loci determines part of the outcome: very variable loci or long sequences lead to high D values, suggesting strong differentiation, whereas less variable loci or short sequences tend to underestimate structuring. Moreover, permutation tests are needed to determine whether D values are significantly higher than expected under panmixia.The statistic on rates , which mD, but this may not be necessary because there are elegant ways to include genetic distances in GST-like statistics. We used γST . However, the neighbour-joining tree for T. curvispinosus shows that the clustering of subpopulations is not very strong: the tree is quite starlike and no two populations are really close to each other, but this information is not clear from the Structure analysis. For the data and the questions we have, the Structure analysis does not provide novel insights.We analysed the microsatellite data with Structure and found evidence for four clusters in each of the three species see . Only inTo estimate population wide mutation and migration rates, we used two programs, Migraine and migrate-n, which are both based on coalescent theory and which use a maximum likelihood framework to estimate population demographic parameters from genetic data from different locations. In principle, the two programs should find the same results. However, we found that for the microsatellite data Migraine performed better. For migrate-n, even when running the program for 1 000 000 steps and with adding heated chains, outcomes still depended on starting values for the MCMC chain and the program did not converge. We did not experience this problem for the MtDNA data. Results from Migraine and migrate-n will always be biased because of model misspecification. For example, we do not know how many subpopulations there really are in the species we study, and we chose to ignore all unsampled populations in the statistical model. Model misspecification is a difficult problem, but several studies suggest that inference can still be made . AdditioDrosophila melanogaster and humans  and it is likely to be the case for many other species. However, to take into account the demographic history of the species of interest, much more data are needed and more complex models would need to be used to estimate the relevant parameters.Theoretical studies suggest that population size and migration rates are key parameters in coevolutionary arms races, because these parameters together determine how many new beneficial mutations enter a population in a given generation . In this


The historical origins of classical laboratory mouse strains have led to a relatively limited range of genetic and phenotypic variation, particularly for the study of behavior. Many recent efforts have resulted in improved diversity and precision of mouse genetic resources for behavioral research, including the Collaborative Cross and Diversity Outcross population. These two populations, derived from an eight way cross of common and wild-derived strains, have high precision and allelic diversity. Behavioral variation in the population is expanded, both qualitatively and quantitatively. Variation that had once been canalized among the various inbred lines has been made amenable to genetic dissection. The genetic attributes of these complementary populations, along with advances in genetic and genomic technologies, makes a systems genetic analyses of behavior more readily tractable, enabling discovery of a greater range of neurobiological phenomena underlying behavioral variation. The historical development of the laboratory mouse from the hands of collectors to the laboratory has been particularly consequential to the study of behavior. First noted in early Indian, Chinese, and Japanese historical records, mice were bred, collected and traded for unusual coat appearance and behaviors. These two selection criteria affected mouse behavior in the resulting population for two reasons, (1) the intentional selection for unusual behavioral characteristics such as ‘waltzing’ behaviors, and (2) the ontogenic similarity of the skin and central nervous system resulting in co-selection of abnormal behavioral characteristics with coat color and skin related phenotypes Wahlsten . As collThese inbred stocks later became the basis for virtually all experimental intercrosses, heterogeneous stocks, and other research populations that have been used to study genetic variation in behavior, derive selected lines, and develop mutant stocks. The result has been relatively low allelic diversity in the most widely used mouse populations  linkage disequilibrium across the genome for accurate localization of genetic loci, and high allelic diversity to maximize the chances of detecting variation among traits. New mouse resources have been continually developed to bring increased allelic diversity, either through a greater number of founders or increased evolutionary distance of founders, and/or increased precision through the assessment of mice with increased numbers of generations, each resulting in additional meiotic recombination events.The simplest strategy to improve mapping precision is simply to increase the number of recombinations that occur. Advanced intercross lines (AILs) were derived from the continued intercrossing of two founder strains to increase recombination precision  and Diversity Outcross (DO) populations were designed as complementary resources for the precision mapping and correlation of behavioral phenotypes and underlying biomolecular, physiological, and morphological characteristics of the central nervous system and the organismal milieu. Originally, envisioned as a community resource for precision QTL analysis and later as a genetic reference population for multiplicative data aggregation .Our early behavioral studies in the CC breeding population (Philip et al. The impact of low behavioral diversity and poorly randomized allele segregation among existing inbred strains is readily apparent in strain surveys of behavior. Surveys of the common inbred strains (Wahlsten et al. In almost all the cases, the range of phenotypic variation in the CC and DO matches or exceeds than that of the founder stocks (Philip et al. These properties indicate the value CC and DO mice as a selection base. The CC and its derivative DO population possess greater allelic diversity than the outbred stocks or existing selection base populations. The response to selection for home cage activity was found to be greater in CC than the Hsd:ICR strain, a conventional outbred stock (Zombeck et al. QTL mapping in these new populations requires careful modeling of the large number of allelic effects, and consideration of relations among the individuals in the mapping studies. In some cases, existing loci have been replicated, but more often, novel loci, particularly those driven by wild-derived alleles are detected. Perhaps the most striking findings are the high precision with which QTLs for behavioral phenotypes could be mapped. The CC and DO population have been used to map QTL to intervals that are typically less than 5 Mb and in the DO less than 2 Mb with conventional sample sizes of ~300 mice (Philip et al. The most exciting opportunity for behavioral science in the DO population is the expanded repertoire and range of behaviors observed in these mice. It is important to note that behavioral anomaly is not unique to the DO, but may be more prevalent, and in some cases is amenable to direct genetic analysis. The influence of wild-derived allelic variants in the CC and the DO mouse population has led some to question their utility in conventional mouse assays. In our experience, the mice are amenable to conventional assays, but one must carefully evaluate the validity of conventional interpretation. For example, some mice exhibit high exploratory activity, and in many cases, will spend equal amounts of time in both halves of the light-dark box or novelty preference apparatus. Similarly, mice may not habituate to these split-chamber tests of anxiety, risk taking, and novelty seeking over the duration of the test. When a locus is identified that appears to influence outcomes such as percent time in the light, or open field habituation, it is essential to determine whether this is due to a failure to apprehend or attend to the difference between the compartments of the test, or whether it truly reflects the desired measure of avoidance. Therefore, to properly interpret allelic effects on chamber avoidance, one must assess whether there was discrimination between the chambers by mice possessing each allele. Likewise, we assessed whether the difference in rate of change in activity is correlated with overall high activity (Logan et al. High genetic diversity means that some of the mice are wilder than typically observed in the lab. For technicians inexperienced with mouse handling in general, there may be added stress induced by prolonged capture and restraint procedures. DO mice exhibit high rates of inter-male aggression. This is easily avoided by restricting study to females, or by singly housing males, but neither of these are ideal solutions. Many influences of post-weaning handling, introduction of non-sib cage mates and age at various housing and husbandry events are suspected to influence aggression related behavior. Systematic studies both on a practical level and in the genetics of aggression related behavior are warranted and will likely be quite fruitful.In contrast to many conventional mouse populations, the extreme range of variation of behavior in the DO may be a substantial source of variation in cage mate behavior. Dominance hierarchies, social behavior and a number of other behaviors may be influenced by the behavior of the other mice in the cage. Tracking and analyzing these effects using social network graph and other analysis strategies may reveal exquisite impacts of naturalistic social and behavioral effects, e.g., enabling modeling of chronic familial stress. Our initial studies of aggressors vs. non-aggressors from separated cages did not reveal behavioral differences. However, further analysis and systematic evaluation are warranted. Often the unusual or prodigious behavior can be mapped to a single wild-derived allele, as we have done with tail-climbing during the tail-suspension assay of depression related behavior (Logan et al. Expression QTL analyses in the CC and DO mice promise to alleviate the challenge of large linked co-expression clusters and lack of precision. In the CC and DO, the high allelic diversity presents problems for microarray and conventional RNAseq analyses alike in their bias toward the C57BL/6J genome. The high allelic diversity in the population leads to many cis expression QTLs, and a virtual absence of the trans QTL bands (Ferris et al. The expected benefits of the CC and DO population lie in their high recombinational precision, and in the case of the CC population, as an improved genetic reference population in which repeated sampling of the fixed panel of individuals will enable large scale analysis of trait correlations. Studies in BXD recombinant inbred lines reveal the promise of multiplicative data aggregation (Chesler et al. The CC lines, though fewer in number than originally envisioned, have several advantages for behavioral scientists over the existing population of inbred strains. Allelic variants are distributed throughout the panel. Therefore, many mice comprise novel combinations of alleles across loci, increasing the range of observable behavioral variation, previously compressed by selection. Alleles from wild-derived strains are distributed across the panel such that they occur in multiple strains, enabling the variation resulting from these variants to be detected and mapped with precision. The uniformity of genetic relations across the strains reduces bias in genetic correlation and heritability estimates. Furthermore, by sampling mice from multiple strains, one could perform more readily generalizable studies of drug effects, behavioral differences across conditions and all manner of experimental paradigms, rather than constrain one’s research to the often maligned C57BL/6 or outbred stock. The same simple treatment vs. control paradigm is used, but the findings become less limited to a single biological context. As the number of finished CC lines increases, the population is projected to exceed the size of the most existing genetic reference populations. High-density genotypes are available for the CC and ultimately each line may be fully sequenced. Widespread distribution and coherent phenotyping efforts will be required to realize the potential of the CC as a genetic reference population.Precision in genetic populations depends on the actual size of recombinant haplotype fragments from the founder stocks, and the observed recombination density based on the diversity of founders, population size and the number of generations in which meiotic recombination occurred. Highly outbred stocks contain phenomenal precision and the DO provides both the high sample size and breeding history required for high precision genetics. The precision genetics enabled by the DO population even in the early generations which we profiled can shorten the process of fine-mapping by nearly a decade relative to a conventional F2 population. It is of course necessary to obtain high-density genotypes from each mouse in the population, and it is not possibly to directly correlate measures across distinct individuals. Instead transitive strategies must be developed to relate data through genotypic or phenotypic measures. Although the DO population is not a model of human genetic diversity in that the precise polymorphisms are not necessarily conserved, it possesses naturally occurring perturbation in loci throughout the genome, allowing evaluation of the effects of heterozygosity and polymorphism on a wider range of behaviors and genes than previously accessible. This high diversity will require high sample sizes for many phenotypes, and to fully realize the potential of mapping the additive, dominant, and epistatic effects of many low frequency alleles.The availability of sequence data from all the founders coupled with dense genotyping and resequencing of the CC lines themselves will enable researchers to make customized behavioral models with particular allelic variants at known loci, and to evaluate systematically the effects of heterozygosity. Through the production of a recombinant inbred cross, commonly referred to as a ‘RIX’ (Tsaih et al. Behavioral scientists have been early adopters and developers of methods and resources in quantitative genetic analysis for the simple reason that the phenotypes under investigation are highly complex under the influence of many genes and many environmental effects. Experimental paradigms and resources that recognize this complexity have been embraced to enable the discovery of novel mechanisms and interventions for disorders of behavior and to understand the relations among the processes of behavioral variation and behavioral change. Multiple advances in genetic strategies, genome and sequence analysis, and the methods for computational integration of experimental data are all required to fully realize the potential of systems genetic analyses of behavior. Improved quantitation of gene expression, metabolites, proteomes, and all the manner of biological entities have significantly improved the facility of complex trait studies in all populations, and have enabled improved prioritization and validation of biological candidate genes and mechanisms. A wealth of complementary resources and gene manipulation technologies expand the arsenal of genetics tools for the study of brain and behavior. The DO and CC represent resources that will match the challenges of behavioral genetics through constrained, yet unprecedented variation in mouse mapping panels, high-precision genetic mapping and network refinement. Distinctive behaviors in these mice present new opportunities to escape the confines of the conventional behavioral tests, unlocking opportunities to find the genetic basis of more naturalistic mouse behaviors, develop new mouse models through selected breeding and discover the relations among behavioral and neurobiological characters.


Symphodus melops, Labridae), a rocky shore species inhabiting North Sea waters and temperate northeastern Atlantic Ocean from Norway to Morocco including the Azores, using a fragment of the mitochondrial control region and the first intron of the nuclear S7 ribosomal protein gene. We found that S. melops shows a clear differentiation between the Atlantic and the Scandinavian populations and a sharp contrast in the genetic diversity, high in the south and low in the north. Within each of these main geographic areas there is little or no genetic differentiation. The species may have persisted throughout the last glacial maximum in the southern areas as paleotemperatures were not lower than they are today in North Scandinavia. The North Sea recolonization most likely took place during the current interglacial and is dominated by a haplotype absent from the south of the study area, but present in Plymouth and Belfast. The possibility of a glacial refugium in or near the English Channel is discussed.Pleistocene climate changes have imposed extreme conditions to intertidal rocky marine communities, forcing many species to significant range shifts in their geographical distributions. Phylogeographic analyses based on both mitochondrial and nuclear genetic markers provide a useful approach to unravel phylogeographic patterns and processes of species after this time period, to gain general knowledge of how climatic changes affect shifts in species distributions. We analyzed these patterns on the corkwing wrasse ( Symphodus melops , seem to have existed in broad areas of the Mediterranean and in the Atlantic, from central Portugal southwards  and from where the recolonization of more northerly areas took place when temperatures arose in the interglacials e.g., . Regardiuthwards . Indeed,uthwards .Recent studies on the phylogeography of West European marine organisms often attempted to address the following issues: (1) Did warm temperate organisms of western Europe survived in refugia inside the Mediterranean and recolonized the north east Atlantic after the end of the last glaciation, about 10,000 years ago e.g., ? (2) DidHomarus gammarusNephrops norvegicus, Sprattus sprattus, Lipophrys pholis, In this respect, the phylogeographic studies of West European marine organisms have yielded mixed results. Some studies demonstrate the role of the Mediterranean as a refugium and source of recolonization of the Atlantic e.g., , 2008. HThis diversity of phylogeographic patterns means that more species must be studied if we are to understand the relative importance of each pattern and identify ecological and life-history traits and historical processes that can generate them.S. melops with samples ranging from South Portugal to Norway and Sweden. We aim to find out if there was evidence of post glacial expansion and identify potential or probable refugia from where recolonization took place. Such questions will shed light upon how coastal species with low dispersal potential were affected by temperature changes in this time period.In the present paper, we use one mitochondrial and one nuclear marker to assess the phylogeographic patterns and processes of Symphodus melops is an interesting species that can add to our understanding of the patterns of recolonization of West European shores after the last glaciation. It is a rocky shore species inhabiting temperate Atlantic waters from Norway to Morocco and the Azores  following the manufacturers instructions. Voucher specimens are deposited in ISPA (ethanol preserved tissues). PCR amplification of mitochondrial control region (CR) and the first intron of the nuclear S7 ribosomal protein gene (S7) was performed with the following pairs of primers: dloop—LPro1 (5′-ACTCT CACCC CTAGC TCCCA AAG-3′) and HDL1 (5′-CCTGA AGTAG GAACC AGATG CCAG-3′) , 0.8 µl of each primer (10 µM), 4.4 µl of Sigma-water, and 4 µl of template DNA. An initial denaturation at 94°C for 7 min was followed by 35/30 cycles  and a final extension at 72°C for 7 min on a BioRad Mycycler thermal cycler. The same primers were used for the sequencing reaction, and the PCR products were purified and sequenced in STABVIDA (http://www.stabvida.net/).Total genomic DNA was extracted from fin or muscle samples preserved in 96% ethanol with the REDExtract-N-Amp kit   and the CCAG-3′) . PCR ampAll sequences were aligned using Clustal X .FSTs. Significance levels of all multiple statistical tests were corrected using the false discovery rate (FDR) approach   was usedFCT (see the results section), the data of the populations included in each group were pooled and mismatch analysis  (tMRCA) and the median and corresponding credibility intervals of the BSP were depicted using Tracer v.1.4   model asot (BSP)  employiner v.1.4 . mtDNA Cer v.1.4 , 3.6%/MYer v.1.4 , 15–20%/er v.1.4 . In the .2–2%/MY . For theMigration rates among populations were estimated by the MCMC approach implemented in Lamarc v. 2.1.3 , using 1The CR dataset consisted of a total of 364 characters , representing a total of 50 haplotypes. The amplification of the first intron of the S7 gene yielded a fragment of 520 base pairs, with 10 SNPs . Only two samples from Belfast were amplified for the S7 gene, thus this population was excluded from the subsequent analyses.The CR haplotype network shows two dominant haplotypes, separated by two mutational steps, with low frequency of rare alleles connected by only a few mutational steps . These tFSTs and corrected average pairwise differences are presented in FSTs and mean numbers of corrected pairwise differences that are not significant among themselves. They are all significantly different from the non-Scandinavian populations, except for the FST between Oslo and Roscoff for the S7 gene. Another cluster of populations that do not differ significantly are those from South and Central Portugal and Galicia. The populations of Belfast, Plymouth, and Roscoff occupy a somewhat intermediate position in the CR. Indeed, for the CR, while the Iberian cluster  displays significant FSTs and corrected average pairwise differences with Plymouth and Scandinavian populations, the populations of Roscoff and Belfast do not display significant differences when compared with Plymouth, also failing to display significant differences when compared with the Portuguese and Galician populations. For the S7, all populations from South Portugal to Plymouth display no significant differences (Belfast could not be included due to low sample size for this marker).For the CR fragment, the genetic diversity indices for each population are summarized in 2-test developed by 2 = 152.7, P < 0.001). Pairwise comparisons between localities showed that Algarve, Lisbon, Galicia, Roscoff, Plymouth, and Belfast all have significantly higher haplotype diversities than those found in northern locations .For the CR data, haplotype diversity values were significantly different among populations according to the χFCT (0.639) for two groups: the four Scandinavian locations versus the six Atlantic locations (P = 0.000). Given the concordance between the pairwise FSTs and SAMOVA, we decided to pool together Egersund, Kristiansand, Oslo, and Gullmar Fjord (Scandinavia) and Plymouth, Belfast, Roscoff, Galicia, Lisbon, and Algarve (Atlantic) for further analyses. An hierarchical AMOVA for the S7 data using the same two groups yielded a relatively low but significant difference among groups and lack of differentiation among populations: percentage of variation among groups = 7. 24 (P = 0.010); percentage of variation among populations within groups = – 0.36 (P = 0.768); and percentage of variation within populations = 93. 12 (P = 0.000) (FST = 0.0688)SAMOVA (CR dataset) yielded a maximized Fs tests, for the CR dataset, yielded negative and significant results both for Scandinavia  and the Atlantic , both supporting for population expansion. The best fit for the Atlantic was the sudden expansion model , while for Scandinavia was the spatial expansion with constant deme size . The results of the mismatch analysis showed that the Scandinavian population as a whole displays a very shallow mismatch distribution from zero to two differences, while the Atlantic mismatch distribution is deeper (from 0 to 10 differences).Tajima and Fu’Using a mutation rate of 5%, we conclude that the estimated age of the Scandinavian population is 2250 years , while for the Atlantic the estimated age of the population is about 91,800 years . For the Scandinavian population, the corresponding values for the mutation rates of 3.5% and 10% were 3200 years  and 1130 years . For the Atlantic population and for the same mutation rates, the numbers varied between 131,160 years  and 45,900 years . In summary, although the estimated ages of the expansions vary a lot and have large confidence intervals, the origin of the Scandinavian expansion tends to fall well after the LGM, likely in the current interglacial, while in the Atlantic there are strong indications that the expansion predates the LGM.S. melops. The plot reveals that the corkwing wrasse as a whole experienced a faster population growth in the past 15,000 years (∼13-fold), reaching an Nef of 13 million fish in the present day. The tMRCA from the Scandinavian population  is roughly half of the tMRCA obtained for the Atlantic fish .Lamarc analyses yielded higher theta for the Atlantic population (average 0.107) in comparison with the Scandinavian (average 0.027). The migration rate was higher northwards, from Atlantic into Scandinavia (average 17.885), than in the opposite direction (average 10.230). We could not perform estimations of population age because the model including growth did not converge.FST values , while for the Atlantic the age estimated is much older than the LGM.S. melops belongs to a genus mostly restricted to the Lusitanian province, with several species confined to the Mediterranean and its surroundings  that they could be a potential source of the recolonization of the far north. However, the absence of these haplotypes north of Plymouth and Belfast raises doubts about their role as founders of the new northern populations.As the North Sea was glaciated during the LGM e.g., , a questPalmaria palmata (Fucus serratus (S. melops if the colonization of the North Sea followed a path around western Britain and from Scotland to Skagerrak. Both Belfast and Plymouth show a mixture of Scandinavian and Atlantic haplotypes. It is possible that a finer scale analysis using microsatellites will help to track the pathway of recolonization of northern areas. Is or was it through the English Channel or rather west of United Kingdom reaching Norway through Scotland and surrounding islands? Considering the Scandinavian locations, it would also be very interesting to test if, at present, gene flow takes place between our sampling sites, a task that microsatellites would help to clarify.In our view, it seems more likely that the refugium or refugia were located in the vicinity of the North Sea. The Hurd Deep, a marine unglaciated lake in the bottom of the English Channel during the last glaciation, has been postulated as a possible refugium when the surrounding sea was glaciated e.g., . Other a palmata  and Fucuserratus , respectSymphodus melops stands out, like A. presbyter (A. presbyter, the eggs are demersal and the young hatch at a rather advanced stage of development, so that a typical larval stage does not exist (L. pholis, another West European fish whose males guard eggs like those of S. melops, the larvae stay a minimum of 29 days in the plankton before settling (To conclude, is it worth comparing our results with those for other inshore organisms of West Europe. resbyter , as an eot exist  making tsettling . This fisettling .N. norvegicus, H. gammarus, Petromyzon marinus, S. sprattus, L. pholis, S. melops and the already mentioned A. presbyter may represent exceptions. They may be very recent arrivals in northern areas or they may display slow dispersal so that a transient gradient in diversity was not yet erased. Further studies with organisms with varied life histories and ecology and population simulations are clearly needed to clarify this issue.Although as stated above that the literature has revealed contradictory results, there is an increasing number of papers reporting cases where diversity remains almost at the same level across the latitudinal range of marine species (e.g.,


Trypanosoma cruzi infection that has been assessed for genetic influences on differential disease progression in Chagas disease. For measures of Ascaris worm burden, within-population host genetic effects are generally more important than host population structure factors in determining patterns of infectious disease. No significant influences of population structure on measures associated with progression of cardiac disease in individuals who were seropositive for T. cruzi infection were found.Host genetic factors exert significant influences on differential susceptibility to many infectious diseases. In addition, population structure of both host and parasite may influence disease distribution patterns. In this study, we assess the effects of population structure on infectious disease in two populations in which host genetic factors influencing susceptibility to parasitic disease have been extensively studied. The first population is the Jirel population of eastern Nepal that has been the subject of research on the determinants of differential susceptibility to soil-transmitted helminth infections. The second group is a Brazilian population residing in an area endemic for  Our g(b)The Jiri Helminth Project was established in 1995 with the goal of determining the genetic components of susceptibility to helminthic infection in an endemic population . The stuet al. [Phenotypic information was determined for members of the population in the following manner as is described by Williams-Blangero et al. . First, et al. .The Jirel study participants have been genetically characterized as part of a long-term genome-wide scan study –8, and a(c)Trypanosoma cruzi and subsequent cardiac disease progression in American trypanosomiasis or Chagas disease. The rural Posse region of Goiás, Brazil is endemic for Chagas disease with approximately 50 per cent of the adult population being seropositive for T. cruzi infection [T. cruzi is transmitted to humans by triatomid or kissing bugs [T. cruzi [The Posse Family study was initiated in 1996 to characterize genes that influence susceptibility to infection with the parasitic organism ing bugs . The bugT. cruzi .T. cruzi infection and for genetic assessments. Additionally, we collected electrocardiograms (ECGs) from each sampled individual in order to measure aspects of cardiac disease progression in T. cruzi-infected individuals [Sampling for our study of Chagas disease was performed on a house-to-house basis in the villages of the Posse region. We collected blood samples to determine seropositivity for ividuals ,16. Pediividuals . Althoug(d)Our work on Chagas disease and intestinal helminth infections has resulted in the mapping of genes that influence helminthic infection and in the characterization of genetic components that influence Chagas disease. However, these are complex diseases with multi-factorial determinations and the question of whether or not underlying population structure influences infectious disease patterns is an intriguing one. In addition, parasite genes and population structuring of the parasites themselves may be important determinants of disease patterns in the host population ,14.Populations are substructured along many lines, including family structure which we study extensively as part of our genetic studies. However, they are also substructured at higher levels including by geographical distance, by differential admixture and by genetic drift among subpopulations.2.(a)Ascaris worm burden in the Jirel population. For this analysis, we used data available for 320 individuals . The 1094 Ascaris worms collected from these individuals were characterized for a total of 23 genetic markers [We first wanted to determine if both host genes and worm genes are important for determining the distribution of  markers ,14.n host individuals, the n × n phenotypic covariance matrix (Ω) is given by the following variance component model:Φ is twice the kinship matrix (i.e. the coefficient of relationship matrix) among individuals which is derived from the host pedigree structure, Rw represents the estimated coefficient of relationship [et al. [Ascaris worms collected from the Jirel human host population, and I is the identity matrix. The phenotypic variance of the focal trait is given by h2), the additive genetic heritability in the worms (w2) and the proportion of phenotypic variance owing to random environmental factors (e2 = 1 − h2 − w2).Using variance decomposition theory, we derived a model that allowed the infection-related phenotypic variance to be decomposed into components that are owing to host genetic factors, genetic variation among the worms and random environmental factors. For tionship  between  [et al. ) from hiUsing a standard maximum-likelihood variance estimation method as implemented in the statistical genetics computer package, SOLAR , we can w2 = 0), the LRT is distributed as 50 : 50 mixture of a point mass at zero and a chi-squared distribution with 1 d.f. When comparing the more general model with a random environmental model in which both h2 and w2 are constrained to zero, the resulting LRT is distributed as Using this approach, we tested whether nested models that eliminate specific variance components adequately captured the variation accounted for in the most general model (shown above). Likelihood ratio tests (LRTs) were formed as twice the difference in ln likelihoods. Because of boundary conditions , the resultant test statistics are distributed as mixtures of chi-squared distributions . For exaAscaris total count included sex, age and number of days of sampling post-albendazole. Only age was significant . Covariates for worm burden as measured by total Ascaris weight included sex, age, number of days of sampling post-albendazole and sex ratio of worms. Age , number of sampling days , and sex ratio  were significant.All worm burden traits were normalized using an inverse Gaussian transformation prior to analysis. Covariates for worm burden as assessed by (b)Ascaris counts, and sex, age, number of days sampling and for Ascaris weight, sex ratio of the worms.Because our sample size of genotyped worms by host was relatively small, we also decided to assess potential spatial effects influencing host worm burden. The inclusion of a random spatial process may also allow inference on worm population structure effects, such as inference of isolation by distance or spatial autocorrelation effects on worm kinship . For thiD is a matrix of geographical distances (in kilometres) between individuals as assessed from geographical coordinates of the houses that they live in, λ is the exponential decay parameter and s2 is a new relative variance component that measures the relative proportion of variance accounted for by the random spatial process. The term exp(−λD) generates a valid spatially decaying positive correlation matrix that structures the spatial variance component. As λ → ∞, this model becomes a standard shared household model in which individuals within a household exhibit a correlation of 1, while across households the resulting correlation is 0. This model can also be expanded to allow for host genetic × spatial interactions.We employed a standard exponential decay model to parametrize the expected correlations between hosts as a function of geographical distance. Because host genetic factors were explicitly accounted for by the pedigree information, any resulting spatial component may represent either: (i) a geographical component of exposure patterns owing to local environmental conditions such as temperature or moisture or (ii) a component resulting from worm population differentiation resulting from an isolation by distance process. In order to account for such a potential spatial process, we amend our previous variance component models as follows:(c)While our models specifically included host within-population genetic variation, there is also the possibility that some genetic between-group variation influences worm burden measures. Such between-group variation may have resulted from admixture or gene flow with other ethnic populations or be the result of microevolutionary processes that lead to differentiation between Jirel villages. Given the clan-structured nature of Jirel villages and mate choice , we may ρ| = 0.15 within a sliding 1 Mb window. For the Jirel cohort, genotypes for 26 272 polymorphic SNPs  were used in 200 founders and other unrelated persons selected from 916 genotyped subjects. The principal components (PCs) were derived only from unrelated individuals in order to avoid detecting structure induced by close biological relationships. SNPs were scored for dosage (zero to two copies) of the minor allele of each SNP. PCA was performed using the prcomp procedure in R (http://www.r-project.org), and scores were predicted for the remaining related individuals based on their genotypes. The resulting PCs should only reflect population genetic structure such as systematic gene flow or local admixture or intra-population differentiation such as between-village differences owing to drift and migration patterns. In the unrelated Jirel individuals, the cumulative proportion of total genotypic variance explained by the first four PCs was 4.2 per cent, consistent with the very flat distribution of variance across PCs. This effectively uniform distribution suggests that there is relatively little structure in the Jirel population and the structure that is observed probably reflects local admixture with relatively closely related populations and/or Jirel microdifferentiation owing to non-random migration among villages [In order to test for the potential effects of Jirel population structure, we performed principal component analysis (PCA) on SNP genotypes obtained from Illumina 660 BeadChip. SNP data were available for 819 individuals . A PCA panel of SNPs was selected from a full panel of haplotype-tagging SNPs based on the following criteria: minimum 1 kb spacing across the autosomal genome; minor allele frequency greater than or equal to 10 per cent; and pair-wise linkage disequilibrium ≤ |villages .Ascaris weight only). These analyses were embedded in a variance component framework in which we explicitly allowed for host additive genetic effects. This allows for adequate handling of the non-independence among individuals and also eliminates the potential for the test of the PCs to be biased owing to their reflection of close biological kinship. A likelihood ratio test was calculated for each tested PC by comparing a model in which the regression coefficient on the PC was estimated with one in which the regression coefficient was constrained to zero. This provided a LRT that is distributed as a chi-square with a single degree of freedom.In order to test whether Jirel population structure influences worm burden, we performed fixed-effect tests on worm burden using a linear regression model in which SNP-derived PCs were employed as covariates. Because of the relative lack of evidence for embedded genetic structure in the Jirel population , we limited formal testing of population structure to the first two PCs. As with our previous analyses, we also included covariates such as sex, age, number of days sampled post-albendazole treatment and sex ratio of worms (for (d)T. cruzi-infected individuals. Using the same SNP selection process as described above, we selected 25 740 out of 1.14 M total SNPs (obtained using the Illumina HumanOmni1-Quad chip). The currently genotyped sample is substantially smaller than our Jirel example. For this smaller cohort, we identified 49 unrelated individuals  to perform the PCA. From these results, we used the estimated PC coefficients to project PCs for the rest of the related sample. As expected, there was substantially greater genetic population structure in the Posse sample relative to that observed in the Jirels, with the first four PCs of the SNP variation cumulatively explaining 13.4 per cent of total variance (i.e. greater than three times that observed in the Jirels). This greater evidence for systematic population structure is presumably the result of larger scale admixture in the Brazilian population.Similar to our above analysis of the Jirel population, we performed PCA on high-density SNP data on samples obtained from the Posse population. These samples were limited to a priori evidence for population structure in the Posse population, we performed fixed effect-based effects on the first four PCs allowing for residual non-independence among related individuals owing to additive genetic effects. For each phenotype, we also simultaneously allowed for covariate effects including sex, age, sex × age, age2, sex × age2 and height in all analyses. The sample consists of 297 females and 262 males with a mean age of 51.5 years. Besides quantitative measures from ECGs Ascaris burden. For Ascaris total count, the model which included only host genes fits as well as the most general model which included both host genetic and worm genetic effects. Thus, the host genetic model represents a parsimonious model that fits as well as a more general model that included both host and worm genetic factors. Additive genetic host factors account for approximately 54 per cent of the total variation in worm counts. Models that only included worm genetic factors or no genetic factors were both rejected as being significantly different from the general model.One might expect host genetic effects to be the primary influence on total worm count because each worm represents an independent infection. There would be strong selection for infectivity in the worms because infection must occur in order for the worm to reproduce. Hence, worm counts are a function of the survival of individual worms and therefore reflect fitness-related traits that would be expected to have low levels of genetic variation.Ascaris weight, models allowing for only worm genetic factors or only host genetic factors could not be rejected. However, the worm genetics only model fits slightly better than the host genetics model. The environmental model could be rejected. Thus, models with either host or worm genetic factors alone fit these data. However, in the general model in which both host and worm genetic factors were fit, these parameters seemed somewhat confounded, exhibiting an error correlation of −0.953. Examination of the structure of the data revealed some confounding between host and worm coefficient of relationships, indicating that worm genetic effects and host genetic effects are highly correlated.A characteristic that is more likely to be influenced by worm genetic factors is total worm weight in the host, because weight variation in the common host environment is a characteristic associated with the worms themselves. For host worm burden as measured by (b)Ascaris worm count, these analyses show that both a host genetic factor and a random spatial component are required to best fit the data. All nested models are rejected when compared with the more general model in which all three variance components are estimated. The estimate of the exponential decay parameter in the general model was 3.92 which suggests that the expected spatially varying correlation is halved by a distance of 0.177 km. In the general model, host genetics accounts for approximately 27.2 per cent (±9.6%) of the total variation in Ascaris worm count, whereas spatial factors account for an additional 9.6 per cent (±4.1%). Thus, host genetic factors appear to be the single largest determinant of phenotypic variation. The observed spatial component may reflect the worm genetic component but this still remains to be resolved.For total Ascaris weight, the data suggest that a spatial process alone can account for the observed variation. In fact, the estimated decay parameter for the spatial process only model is very high (69.68), indicating very rapid decay and suggesting that the model has reduced to a basic household sharing model where correlations between individuals are only observed within household. However, such a model also is confounded by close biological relationships within households and therefore captures some host genetics along with local exposure or extremely rapidly decaying worm population structure.For worm burden as measured by total (c)We examined the population structure of the Jirels agnostically using high-density SNP information. PCs are routinely used to remove confounding owing to latent population structure in genome-wide association studies . Here, hOur previous work in anthropological genetics has demonstrated that there is structuring in the Jirel population ,11,21,22In order to test whether Jirel population structure influences worm burden, we performed fixed effect tests on worm burden using a linear regression model in which the SNP-derived PCs were employed as covariates. Because of the relative lack of evidence for embedded genetic structure in the Jirel population as observed by the small eigenvalues obtained during the PCA, we limited formal testing of population structure to the first two PCs. As with our previous analyses, we included covariates such as sex, age, number of days sampled after treatment, and for the worm weight phenotype, within-host sex ratio of the worms was also considered as a covariate. These analyses were embedded in a variance component framework in which we explicitly allowed for host genetic effects.Ascaris burden traits showed some evidence of being influenced by Jirel population structure. Host Ascaris total count was significantly influenced by the first PC (p = 0.01592). It accounted for about 1.1 per cent of the total phenotypic variation in worm count. This estimate probably estimates latent between-group variation that is indexed by PC1. Given that the Jirels have been speculated to be an admixed population with genetic input from both Sherpas and Sunwars, admixture may be the source of this variation. This population structure-induced variance can be contrasted with the additive genetic heritability estimated simultaneously which was estimated to account for 47.4 per cent of the total phenotypic variation in worm count. Thus, genetic factors including both between- and within-group variation appear to be important influences on total worm burden. Similarly, host worm burden as assessed by total Ascaris weight also showed a significant relationship with the first PC (0.0128). Again, approximately 1.1 per cent of the phenotypic variation in Ascaris weight could be attributed to the population structure-induced genetic variation. Overall, these analyses suggest that some of the observed phenotypic variation in worm burden can be ascribed to underlying Jirel genetic structure.The cumulative proportion of total genotypic variance explained by the first four PCs was 4.2 per cent, consistent with the very flat distribution of variance across PCs. Despite this relatively slight stratification, both focal host These analyses then suggest that some of the observed phenotypic variation in worm burden can be ascribed to underlying genetic structure in the Jirel population reflective of some type of latent between-group variation. However, our results overall suggest that host genetic factors are more important than population structure factors in determining patterns of helminthic infection.(d)p = 0.043) and for the ECG-derived parameter PR interval . Given the larger number of phenotypes and the number of PCs, these marginal results would fail to achieve experiment-wide significance. Thus, even though there seems to be substantially more structure in the Brazilian sample, it appears that the underlying genetic structure variation within the Posse population is not important for the infectious disease traits under consideration.4.The results of the analyses presented here demonstrate the value of considering population structure effects on variation in parasitic disease burden. Evidence for population structure effects may be informative for understanding the importance of genetic variation in the parasite itself. However, population structure effects on infectious disease burden are unpredictable. Even in two populations with known structuring, evidence for population structure effects on patterns of parasitic disease is limited. It is important to consider simultaneously the effects of population structure and of host and parasite variation in determining disease patters. Our results suggest that within-population host genetic factors are generally more important than host population structure factors in determining patterns of infectious disease.FST between the appropriate ancestral or current groups involved in gene flow. Even for major ethnic group differentiation, this parameter rarely is as large as 15 per cent of the within-group variation [We primarily view the observed population structure effects as reflective of underlying between-group genetic variation and hence would not expect between-group genetic variation to be a larger determinant than within-group genetic variation. In classical population genetic models, between-group genetic variation is expected to be proportional to within-group genetic variance, with the relationship being a scalar function of Wright's ariation . In the ariation ,25 and tariation  lead to In our Brazil study population, we fully expected to see larger admixture-related between-group genetic effects for our disease-related variables. The Brazilian population exhibits substantial admixture between local populations of very diverse ancestry including major African and Amerindian components against a European ancestral background. Our lack of finding major between-group genetic variation in Chagas disease-related phenotypes may suggest lack of differences in susceptibility/response to this infection across ethnic groups. However, there is still strong evidence for major within-group genetic variation influencing these phenotypes .Ascaris infection on chromosome 1. In general, we expect the underlying genes whose sequence variation leads to differential infection susceptibility or response to play major roles in immunological response to pathogens. However, we are still in the early days of causal gene identification although we are confident that the growing availability of inexpensive high-throughput next-generation sequencing will speed the pace of such discovery. Regardless of our long-term aims for uncovering the specific genes involved in differential response to infection, in this study, we focused on a secondary use of high-density genetic marker data that is generally employed to correct for the potentially adverse effects of latent population structure in gene localization studies. Thus, what is typically a nuisance factor in a study of within-group genetic variation becomes the focal factor in a study of between-group genetic variance.In this study, we have focused on genetic factors in aggregate, which is sometimes termed the polygenic component of phenotypic variation. Our variance component models incorporate aggregate additive genetic effects in both host and parasite populations . However, the central goal of our ongoing studies of these two isolated human populations is the identification of specific host genes lying in the causal pathway of susceptibility to infection or the phenotypic response to infection. In fact, the high-density genetic markers that we use in this study were originally generated specifically to perform quantitative trait locus (QTL) localization for host genes influencing infection. Their primary use has been to track segments of DNA that are passed identical-by-descent across the generations in order to co-localize them with phenotypic variation. We have previously localized major QTLs influencing helminthic infections in the very large Jirel pedigree ,7, inclu


Ovis canadensis californiana) in Oregon, USA, which previously experienced poor productivity and numerical declines. In the experiment, two declining populations were supplemented with ewes from a more genetically diverse population of California bighorn sheep in Nevada. We incorporated analysis of genetic samples representing both experimental populations prior to supplementation, samples from the supplemented individuals, and samples collected from both experimental populations approximately one generation after supplementation. We used genetic analyses to assess the integration of supplemented and resident populations by identifying interpopulation hybrids. Further, we incorporated demographic simulations to assess the risk of outbreeding depression as a result of the experimental augmentation. Finally, we used data from microsatellites and mitochondrial sequences to determine if genetic management increased genetic diversity in the experimental populations. Our analyses demonstrated the success of genetic management by documenting interpopulation hybrids, identifying no evidence for outbreeding depression as a result of contact between the genetically distinct supplemented and resident populations, and by identifying increased population-level metrics of genetic diversity in postsupplementation populations compared with presupplementation levels.Positive demographic responses have been reported in several species where the immigration or supplementation of genetically distinct individuals into wild populations has resulted in a genetic rescue effect. However, rarely have researchers incorporated what could be considerable risk of outbreeding depression into planning for genetic management programs. We assess the genetic effects of an experiment in genetic management involving replicate populations of California bighorn sheep ( An infusions of genetic diversity that results in reduced evidence of inbreeding depression, as measured by various demographic parameters , has been termed “genetic rescue” , and theOvis canadensis), which once existed throughout their range in natural metapopulations characterized by the patchy distribution of suitably rugged habitat  likely were pregnant when released.SM is located in Harney County of southeastern Oregon. Bighorn sheep primarily inhabit the east face of SM, which rises a vertical mile from the surrounding landscape . The popLG is a rocky gorge that branches from the Lower Owyhee River canyon in Malheur County of eastern Oregon . CaliforThe source population used for both experimental supplementations was the SR herd of northeastern Nevada . Sheep wn = 72 total). We collected additional tissue samples opportunistically from hunter-killed sheep. SM and LG were resampled in 2006  and 2007 , approximately one generation following experimental genetic management to track the progress of introgression. In both postsupplementation sampling events  and rehydrated in 100-µl TLE . We assessed extraction success by visualizing the product of gel electrophoresis on a Gel Doc XR system . DNA concentrations were quantified in successful extractions using a NanoDrop 8000 spectrophotometer . We diluted working stocks of genomic DNA to 20 ng/µl and stored the original extractions at –80° C to slow the degradation of extracted DNA.2 (Table S1), 1× reaction buffer , and 1 unit of Taq DNA polymerase. We used the following thermocycler profile for all loci: 94°C for 2 min; 30 or 35 cycles of 94°C for 30 sec, optimized annealing temperature for 30 sec (Table S1), and 72°C for 30 sec; then 72°C for 10 min and a final extension at 60°C for 45 min. Successful amplification was verified by electrophoresis of PCR products (we included the PCR positive and negative controls from each reaction) through 2% agarose gel stained with ethidium bromide. Amplified fragments were electrophoresed using an ABI 3730xl automated sequencer . We assigned genotypes after importing the electrophoretic data into GeneMapper version 3.7 (Applied Biosystems). We employed the standard quality control measures for microsatellite datasets in our lab (described inWe used polymerase chain reaction (PCR) to amplify 23 microsatellite loci from each sample, including the 11 markers used by2, 1× reaction buffer , and 1 unit of Taq DNA polymerase. Amplification success was verified via electrophoresis as above. PCR products were cleaned by precipitating the DNA with a sodium acetate solution (0.12 mM NaOAc in 100% ethanol), centrifuging to form a pellet, washing the pellet with 70% ethanol, and resuspending the PCR product in water. We then cycle-sequenced approximately 10 ng of cleaned PCR product in 10 µl reaction volumes containing 5 pmol forward or reverse primer, 1 µl Big Dye Terminator version 3.1 (Applied Biosystems), and 3 µl of 5× buffer (Applied Biosystems). We used the following sequencing-reaction profile: 98°C for 5 min, followed by 60 cycles of 98°C for 30 sec, 50°C for 15 sec, and 60°C for 2 min. Sequencing products were cleaned using the same sodium acetate protocol as above prior to being rehydrated in 30 µl sterile water. Cleaned sequencing products were run on an ABI 3730xl automated DNA sequencer (Applied Biosystems). We aligned and manually edited the resulting sequences using Sequencher version 4.1 . To ensure the quality of our dataset, all samples were sequenced in forward and reverse directions.We amplified a 515 base pair fragment of the mitochondrial control region using the following PCR conditions: an initial denature step of 5 min at 94°C, 35 cycles of 94°C for 60 sec, 65°C for 70 sec, and 72°C for 90 sec, followed by a final 72°C extension step of 5 min. Each 10-µl reaction volume contained 20 ng template DNA, 0.25 µM forward and reverse primer (Table S1), 0.2 mM of each dNTP, 1.8 mM MgClcreate version 1.33  between resident populations (SM and LG) and the supplemented SR individuals using information from both mitochondrial and nuclear DNA. First, we identified all individuals in postsupplementation samples (SMP and LGP) that contained mitochondrial haplotypes unique to SR prior to supplementation. Since only ewes were supplemented from SR, unique-SR haplotypes could only have occurred in postsupplementation sampling if individuals were part of a maternal lineage tracing back to a supplemented ewe. We assumed the assignment of mitochondrial haplotypes to individuals was error free.newhybrids version 1.1  and the actual supplemented individuals from SR. Categories of descent were pure-resident, pure-supplemented, F1 , F2 , and two backcrosses: F1× pure resident (BXres) and F1 × pure supplemented (BXsup). Further hybrid classes were not considered because they are progressively more difficult to diagnose using molecular markers  many times using different, over-dispersed random seeds after verifying that the sampler was mixing properly as recommended by4 burn-in sweeps followed by 105 sweeps on which data were collected.Second, we used the Bayesian clustering approach implemented in program sion 1.1  to assig markers  and ultinewhybrids. To that number we added individuals that harbored an mtDNA haplotype unique to SR prior to supplementation if their multilocus genotype profile (via newhybrids) indicated that they were of pure resident descent . Individuals similarly harboring SR haplotypes, but with multilocus genotypes assigning back to the supplemented population, also were possible because the supplemented ewes ≥2 years old probably were pregnant with pure-SR offspring at the time of their release. Thus, pure-supplemented individuals (≤6 years old) could then be detected in postsupplementation sampling if a pure-supplemented ram, carried in utero by a supplemented ewe, survived to later breed with a supplemented ewe. However, we consider this biological scenario to be unlikely.We then enumerated hybrid individuals from SMP and LGP samples based on the following criteria. We counted all individuals identified as hybrids using their multilocus microsatellite genotypes in hybridlab version 1.0 (res = F1 × pure-resident and BXsup = F1 × pure-supplemented). Two sets of simulated hybrid individuals were created using the appropriate resident (SM or LG) and supplemented (SR) genotypes as starting parent populations. We then assessed the ability of the program newhybrids to correctly identify hybrid individuals and to assign hybrid individuals to the appropriate category of descent using identical program parameters to those described above . We then calculated the number of correct and incorrect assignments made by newhybrids for each category of simulated hybrids.Finally, to assess the power of our suite of microsatellite markers to identify various hybrids, we used program sion 1.0  to simulWe constructed stochastic, transition-matrix population models to simulate the range of lamb survival values that could account for the observed proportion of hybrid individuals sampled from SMP and LGP one generation after supplementation. We chose to investigate lamb survival because juvenile viability is the demographic parameter most affected by inbreeding and outbreeding depression . Thus, iWe simulated survival and reproduction of the supplemented ewes at SM and LG in separate spreadsheet models through 6 years, which corresponded with the number of years between the actual supplementations (2000 and 2001) and postsupplementation sampling . Starting conditions for each simulation were the specific ages of the supplemented ewes (different for SM and LG). We assumed the supplemented ewes ≥2 years old could be pregnant upon their arrival, but also that we could detect pure-supplemented individuals in postsupplementation sampling using molecular methods and remove them from further calculations. Inference into lamb survival rates for pure-supplemented individuals was beyond the scope of this study.5 times each for SM and LG to generate measures of central tendency for lamb survival at any given value of the number of individuals ≤6 years old  by the population size of SMP in 2006 and LGP in 2007. Population size estimates  followingWe determined the number of individuals in the actual population that were hybrids by multiplying the proportion of hybrid individuals from postsupplementation sampling (see diagnosing introgression above) by the adjusted population size to obtain the expected number of hybrid individuals in the actual SM and LG herds 6 years after supplementation. The mean ± 2 standard deviation  of lamb survival values from all simulations resulting in populations within ±10% of the expected number of hybrids were calculated for comparisons with observed, baseline  lamb survival values in both SM and LG. We chose to incorporate this ±10% adjustment a priori to account for any unmeasured deviations from our assumptions, for example, those deviations due to variance underlying our original population size estimates or divergence from a stable age distribution.We then multiplied the observed proportion of hybrids , and therefore provide a conservative comparison for our model-predicted lamb survival values.t-tests in SAS version 9.1  to determine if locus-specific expected heterozygosity  calculated in program fstat version 2.9  haplotypes, haplotype diversity . There was no evidence for linkage disequilibrium among microsatellite loci in the populations (data not shown).We successfully extracted genomic DNA from 171 samples . We exclWe acquired forward and reverse mtDNA control region sequences from 162 of 171 samples . We docuUsing information from mitochondrial haplotypes and analyses of multilocus microsatellite genotypes, we documented nine hybrids from SMP . In addinewhybrids was effective at detecting hybrid individuals using our molecular data. We calculated 1.1% and 0.7% overall error rates  for detecting hybrid individuals using simulated hybrids from the SM and LG supplementations, respectively . We calculated the expected number of hybrid individuals as 25.7 in SMP (20.5%× adjusted population size of 126) to compare against the stochastic lamb survival models, or 23.1 to 28.3 hybrid individuals within ±10% of our expectation. Similarly, we calculated the expected number of hybrid individuals in LGP as 42.1 (23.4%× adjusted population size of 180) or 37.9 to 46.3 hybrid individuals within ±10% of our expectation. The average and central 95% of lamb survival values for simulations in which the number of hybrid individuals fell between ±10% calculated values for SMP did not fully extend beyond presupplementation baseline values, but generally was higher , but AR did increase after genetic management  and AR increased in LGP  and the average number of pairwise nucleotide differences among haplotypes within each population (k) increased beyond the presupplementation levels  in both SMP and LGP  and Leslie Gulch (LGP) approximately one generation after supplementation: 20.5%  in SMP and 23.4% (11 of 47) in LGP. In fact, additional introgression seems probable based on the detection of a small number of pure-supplemented individuals in postsupplementation samples from both treatment populations. Further, simulations incorporating stochastic demographic parameters demonstrated that lamb survival—a life-history parameter often impacted by inbreeding and outbreeding depression —was at lBecause interpopulation hybrids from both experimental populations exhibited equal or greater lamb survival than lambs in SM and LG populations prior to supplementation, we found no evidence of immediate outbreeding depression. Moreover, our results could suggest that admixed individuals were more fit than residents indicating the possibility of a genetic rescue effect . OutbreeAt the population level, our analyses revealed a consistent signature of increased genetic diversity in both treatment populations postsupplementation . Specifically, sampling of SMP revealed 15% higher heterozygosity , 24% higher allelic richness, and increased mitochondrial haplotype diversity and, in keeping with our expectations, average pairwise relatedness decreased from significant levels in SM to nonsignificant levels in SMP. In the replicate population, LG, expected heterozygosity and allelic richness increased 12% and 23%, respectively. Similar to results in SM, average pairwise relatedness decreased in LGP and the number of mtDNA haplotypes and measures of mitochondrial diversity in LG increased after genetic management.In conclusion, we found that genetic management in a replicated, experimental framework successfully increased genetic diversity in California bighorn sheep populations in Oregon by facilitating the introgression of immigrant genotypes. In addition, this study was unique in its progression through steps recently advocated by


FST; up to 61%). Herbs lacking adaptations for long-distance dispersal were estimated to lose genetic diversity at higher rate than dwarf shrubs adapted to long-distance dispersal. The expected range reduction in these 27 northern species was larger than reported for temperate plants, and all were predicted to lose genetic diversity according to at least one scenario. SDM combined with FST estimates and/or with species trait information thus allows the prediction of species' vulnerability to climate change, aiding rational prioritization of conservation efforts.Climate change will lead to loss of range for many species, and thus to loss of genetic diversity crucial for their long-term persistence. We analysed range-wide genetic diversity (amplified fragment length polymorphisms) in 9581 samples from 1200 populations of 27 northern plant species, to assess genetic consequences of range reduction and potential association with species traits. We used species distribution modelling  to predict loss of range and genetic diversity by 2080. Loss of genetic diversity varied considerably among species, and this variation could be explained by dispersal adaptation (up to 57%) and by genetic differentiation among populations ( MaintSalix herbacea, a loss of 50 per cent of its European range was estimated to cause a loss of only 5 per cent of its genetic diversity because of its high dispersal ability and history of broad-fronted postglacial colonization [Most species adapted to cold environments are expected to suffer range reduction following climate warming ,10. In anization . Thus, snization ,20 may anization .FST), which is available for thousands of species, can be used as a proxy for predicting the genetic vulnerability of species to climate change. Finally, we use an ensemble of species distribution models (SDMs) to estimate the expected range reduction and associated loss of genetic diversity under two different global circulation models (GCMs) and two emission scenarios. Our results show that the expected genetic consequences of climate change differ markedly among species according to their adaptations to seed dispersal and growth forms, and that it is possible to predict the genetic consequences of range reduction by combining species modelling approaches with prior knowledge on species traits and/or FST estimates.Here, we use published and new amplified fragment length polymorphism (AFLP) data combined with a randomization procedure to estimate loss of genetic diversity under increasing loss of range for 27 northern plant species, and explore the effect of different species traits. We also explore whether genetic differentiation among populations (2.(a)We analysed 27 plant species typically occurring in the bioclimatic zones at the tree line and beyond, i.e. the alpine and arctic zones. Information on the following species traits was compiled from the literature and databases: dispersal adaptation, growth form, pollination mode, breeding system, northernmost bioclimatic zone where it occurs, temperature tolerance and current geographical distribution type .(b)We analysed 73–958 individual plants from 14 to 131 local populations of each species and assessed levels of genetic diversity from AFLP data . For 24 of the species, full details of data collection and genetic structuring have been published elsewhere .rcMap v. 9.2 and the Lambert azimuthal-equal area projection of the Northern Hemisphere  was thus expressed relative to this total loss as: Gloss = average number of markers lost/. We investigated the sensitivity of our estimates to variation in grid sizes and to geographical patterns in loss of genetic diversity .We expressed the number of AFLP markers lost relative to genetic diversity among individuals. Considering that a species would have lost all its genetic diversity when the last remaining population consists of identical individuals, we defined total loss of diversity as the total number of markers minus average number of markers per individual. The estimated loss of genetic diversity ((c)http://www.cru.uea.ac.uk/) and the Worldclim (http://www.worldclim.org/) data centre at a 10′ resolution. Future climates were represented by Community Climate Model version 3 (CCM3) and Hadley Centre Coupled Model version 3 (HadCM3) GCMs for A2 (+3.2°C) and B2 (+1.0°C) emission scenarios [To estimate range reduction and gain, we used SDMs relating observed species occurrences to environmental variables . These mcenarios .2). We also added validated occurrence points from the Global Biodiversity Information Facility (http://www.gbif.org/), and from our own field sampling locations . Selected climatic variables were extracted for these points. We created spatially random pseudo-absences (same number as presences) outside of these climatic boundaries [Observed distributions were digitized at the circumboreal/circumarctic scale from Hultén & Fries'  distribuundaries . All prebiomod package in R [SDMs were calibrated within the age in R , which cage in R  and the age in R . CalibraBecause the above-mentioned models can be overly complex, we also performed a simple rectilinear envelop model (surface range envelop ). The en(d)The proportion of genetic diversity likely to be lost in the year 2080 was determined by evaluating the number of grid cells corresponding to the estimated range reduction see , and the(e)FST) was estimated by an analysis of molecular variance using the software Arlequin v. 3.0 [For each species, genetic differentiation among all sampled populations . As dispersal has higher impact on genetic diversity than growth form [We tested for correlations among species' traits using linear models for correlations involving the continuous variables like northernmost bioclimatic zone and temperature, and wth form ,20, we kp > 0.05) except for at 10 per cent range reduction.Expected loss of genetic diversity, which was expressed as proportion, was arcsine (square root) transformed in all analyses, which according to Shapiro–Wilk tests normalized distribution in all cases (FST) as a predictor for the rate of genetic loss, and (iii) the effect of range reduction by 2080 on loss of genetic diversity. Model selection was performed by Akaike's Information Criterion corrected (AICc) for small sample size  to investigate the effect of dispersal adaptations, pollination mode, breeding system, temperature tolerance and current geographical distribution type on loss of genetic diversity given a certain range loss, (ii) the usefulness of genetic differentiation  and choosing the angiosperm consensus tree [et al. [Juniperus communis was regarded as sister to all angiosperms. All branch lengths were assigned a value of 1. The categorical variables such as dispersal, range, breeding, pollination and life form were coded as dummy variables . The PIC analyses were run in Compare v. 4.6b [A possible problem with the above-mentioned analyses is that species are treated as statistically independent units. This approach compares the extant species situated at the tip of the phylogenies (TIP). As plant traits are likely to be correlated for phylogenetically related species, analyses taking into account phylogeny may reveal different results. Therefore, we also tested the effect of traits using phylogenetic-independent contrasts (PICs) . We conssus tree . A polyt [et al. . The gym v. 4.6b . A rough3.Arabis alpina and Ranculus glacialis . These results were robust to differences in number of populations, mean number of individuals per population and total number of polymorphic markers . Our estimates indicated that species without adaptations to long-distance dispersal (and/or herbaceous species) will lose genetic diversity at about twice the rate of species adapted to long-distance dispersal by animals or wind , as expected as the A2 scenario anticipates a more severe climate change than the B2 scenario . For 18 of the species, the predicted range reduction (median based on seven techniques) exceeded 40 per cent for at least one emission scenario and circulation model . Range rscenario . The ranJ. communis and Vaccinium uliginosum) were predicted to lose some of their present genetic diversity by 2080 Carex bigelowii and S. herbacea [A. alpina and R. glacialis [The precision of our predicted loss of genetic diversity largely depended on the geographical distribution of genetic diversity within the species. For species with a rather even level of genetic diversity throughout most of its distribution range, as e.g. in herbacea ,44,45, tlacialis ,47, the FST values than the species lacking such adaptations. However, given the strong correlation between dispersal adaptations and growth form in our dataset, the differences we observed among short- and long-distance dispersers may be based on a combination of these two species traits. Growth form may indeed also contribute to shaping genetic patterns, as woody species  often have a larger stature and lower population densities, which could result in higher pollen and seed dispersal [Dispersal adaptations appear to be important in determining the rate of loss of genetic diversity. This is not surprising, as dispersal adaptations influence the level of genetic differentiation among plant populations ,20,48. Aispersal . A recenispersal . Woody sispersal . In addiispersal ,20 creatispersal ,51, shorFST and loss of genetic diversity, suggests that FST values can be used as a proxy for predicting the genetic vulnerability of species to climate change. However, as estimates of FST, GST or related measures of population differentiation depend on the type of genetic markers used, the geographical scale of the study, the number of individuals and populations sampled, and the estimator used [FST estimates should be evaluated in the light of these sources of bias.The strong correlation we found between tor used –54, the Our AFLP-based predicted loss mainly represents loss of neutral genetic diversity , whereas(b)Overall, the range changes modelled for these northern species in 2080  were slightly higher than those modelled for temperate tree species under the HadCM3 GCM and A2 (30% versus 22%) or B2 (22% versus 19%) emission scenarios , and thaCurrently, The International Union for Conservation of Nature  red listSDMs are widely used for management purposes . The pre5.FST values, which are available for a wide range of species, as well as species traits such as dispersal adaptation and growth form, can be used to predict a species' susceptibility to loss of genetic diversity following climate change. As it is important to assess and commence management actions before genetic diversity is lost [Our study shows that  is lost , we advo