Approximately 50% of the causes of infertility are of genetic origin. The objective of this study was to analyze the role of genetics in human reproduction by reviewing the main genetic causes of infertility and the use of preimplantation genetic testing in Brazil. This literature review comprised articles in English and Portuguese published on databases PubMed, Scielo, and Bireme from 1990 to 2019. Randomized clinical trials and specialized guidelines were given preference whenever possible. Genetic cause can be traced back to up to 20% of the cases of severe azoospermia or oligozoospermia. Subjects with these conditions are good candidates for genetic screening. In women, genetic causes of infertility  culminate with premature ovarian failure. Genetic screening helps advise couples of the risk of experiencing early reproductive capacity loss and of the chances of their offspring carrying genetic disorders. In addition to enhancing the prevention of serious diseases in the offspring of couples at increased risk of genetic diseases, preimplantation genetic screening improves the success rates of assisted reproduction procedures by allowing the selection of euploid embryos for transfer. The interface between genetics and human reproduction has gained significant relevance, but discussions are still needed on which procedures are clinically and ethically acceptable and how they should be regulated. According to the World Health Organization, infertility is a disease of the reproductive system defined by the inability of sexually active couples to get pregnant within a period of one year, without the use of contraceptive methods. In women over 35 years of age or couples with known infertility-related comorbidities, this period is six months  gene, and microdeletion on the Y chromosome. Genetic cause can be traced back to up to 20% of the cases of severe azoospermia or oligozoospermia. Subjects with these conditions are good candidates for genetic screening. Although the detection of genetic alterations does not substantially alter treatment, they must be analyzed for two main reasons: to achieve a conclusive causal diagnosis and assess the genetic risk to the offspring in case of successful treatment .In women, genetic causes of infertility  culminate with premature ovarian failure. Complex multifactorial conditions such as endometriosis and polycystic ovary syndrome have been associated with gene alterations .Recurrent miscarriage, defined as three or more pregnancy losses before 20 weeks of gestation, may also have a genetic etiology. Between a quarter and 51% of the cases of recurrent miscarriage have been associated with chromosomal anomalies of the fetus. Karyotyping of miscarriage products should be performed to determine the cytogenetic reasons for the pregnancy loss. Karyotyping of the parents is also recommended .in vitro development . Preimplantation genetic testing (PGT) is a clinical application of genetics that enables the examination of a limited number of embryonic cells during their Many recent studies in the field of genetics have mentioned the transition from traditional "monogenic genetics" to comprehensive testing of the human genome through the integration of Next Generation Sequencing (NGS), which allows complete DNA sequencing in a single day with bioinformatics techniques. Novel technologies have shed light on the variations of the human genome and extended the application of genetics. Recent technological advances are already being used to investigate the underlying causes of male and female infertility and in preimplantation genetic testing, both subjects of this paper  is the most common cause of male infertility of genetic origin. Its prevalence reaches 5% in men with severe oligozoospermia and increases up to 10% in subjects with spermogram-documented azoospermia. Infertility occurs due to changes in spermatogenesis and testicular injuries in individuals with progressive syndrome, progressive fibrosis, degeneration of germinal cells and Sertoli cells .There is consensus around the correlation between testicular phenotype severity and frequency of chromosomal abnormalities such as KS. For this reason, G-banding is recommended in Europe for men with sperm concentrations of less than 10 million/mL. This cut-off point was established based on the fact that the incidence of chromosomal alterations is ten times higher in these patients compared with the general population .The American Society for Reproductive Medicine recommended karyotyping to screen males with non-obstructive azoospermia (absence of azoospermia) or severe oligozoospermia (concentration <5 million/mL) for KS, especially before ICSI .Peripheral blood karyotyping and identification of KS are fundamental in the differential diagnosis of obstructive azoospermia  and non-obstructive azoospermia . In this case, genetic testing is a valuable tool to achieve proper patient management  may retrieve tubules with active spermatogenesis. The combination of TESE and ICSI, in which the spermatozoon is injected directly into the egg, allowed men with KS previously considered sterile to father children  to form an anomalous chromosome. A reciprocal translocation, on the other hand, occurs when genetic material is exchanged usually between non-homologous chromosomes  was identified in the long arm of the Y chromosome in 1996 and deletions in this region were identified in 13 of 370 men with severe oligozoospermia or azoospermia. Although microdeletions are too small to be found in conventional karyotyping, they can be diagnosed via polymerase chain reaction , AZFb , and AZFc  and contain many of the genes needed in spermatogenesis. Males with deletions in the AZFc region may have sperm in ejaculate. Some with the same deletion are azoospermic, but may have sufficient sperm production to allow TESE. TESE is contraindicated for patients with deletions in the AZFa and AZFb regions, since their chances of having a successful sperm extraction procedure are extremely slim ; and 2-3% in ICSI candidates. Differences in frequency might be due to poor patient selection, differences in the ethnicity of the studied population, sample size, and differences in study design  for their adolescent children to consider the possibility of cryopreserving sperm as a measure of fertility preservation .The main known monogenic disease is congenital bilateral absence of the vas deferens (CBAVD) with obstructive azoospermia. Mutations in the CFTR gene are found in more than 90% of the patients with agenesis of the vas deferens .The CFTR gene is located on chromosome 7. In its homozygous form, the gene causes cystic fibrosis, one of the most common and severe autosomal recessive diseases to affect Caucasians. One in 2,500 individuals is affected and one in 25 is an asymptomatic carrier of mutation. The presence of mutations that do not completely impair the expression of the CFTR gene causes CBAVD in men, with consequent obstructive azoospermia. CBAVD is found in 6% of the patients with obstructive azoospermia and in about 2% of infertile individuals. Infertility caused by obstructive azoospermia is observed in more than 95% of the males with cystic fibrosis, while 60-70% of the patients with CBAVD have mutations on the CFTR gene without manifesting clinical symptoms of cystic fibrosis , and hypoestrogenism. It occurs in 1% of all women and in 0.1% of women under the age of 30. POF has been associated with increased risk of osteoporosis, osteoarthritis, and cardiovascular disease, all of which related to hypoestrogenism. In addition to experiencing typical postmenopausal symptoms, women with POF suffer from early loss of reproductive capacity. Therefore, women at high risk for POF and who delay pregnancy to after the age of 30 may experience difficulty conceiving and maintaining pregnancy to term , chromosome X trisomy , and Turner’s syndrome , to name a few - are still controversial.  and endometriosis.Although PCOS appears to follow a pattern of dominant inheritance, no specific gene has been linked to the disease. Therefore, genetic testing for this condition loses its purpose. There is a known association between PCOS and the following genes: FBN3, FST, INS, INSR, TCF7L2, CAPN10, FTO, SHBG, PCOS1, SRD5A1, SRD5A2, and CYP11A. However, they have also been associated with obesity, diabetes, and insulin resistance, alterations commonly associated with PCOS. To date, only the insulin receptor gene (INSR) has demonstrated a more significant association with susceptibility to PCOS as described in the GWAS study .The presence of disease in first-degree relatives increases the risk of endometriosis by five to eight times. In 1999, a study described gene changes in three chromosomal regions  in patients with endometriosis  is a clinical application of genetics that allows the examination of a limited number of embryo cells harvested by biopsy during in vitro embryo development. Assisted reproduction procedures involve the in vitro management of oocytes, spermatozoa or human embryos with the objective of achieving pregnancy (There currently are two types of PGT: preimplantation genetic testing for monogenic diseases (PGT-M) and preimplantation genetic testing of aneuploidies (PGT-A). PGT-M is designed to diagnose a specific Mendelian genetic disorder in the embryo for which the parents are at high risk, as in the case of multiple sclerosis. PGT-A is used to detect chromosomal aneuploidies  and select embryos free from conditions such as Down syndrome (trisomy 21) .Likewise, there have been significant advances in techniques used for genetic and chromosomal analysis using small amounts of DNA, including polymerase chain reaction (PCR), fluorescence in situ hybridization (FISH), single nucleotide polymorphism (SNP) microarrays, comparative genomic hybridization (aCGH), and new generation sequencing (NGS) .Polar body biopsyThe procedure involves the removal of the first and second polar bodies  prior to the initiation of embryo cleavage. Although polar body biopsy precludes the removal of embryo cells, it is limited by the fact that only maternal genes and chromosomes can be analyzed, thus excluding possible paternal contributions to the embryo. In addition, the amount of material obtained from a single cell is small and subject to limitations. For these reasons, this technique is not widely used  from an embryo in the cleavage stage, which has between six and eight cells. This technique is more advantageous than polar body biopsy, since maternal and paternal contributions can be analyzed. However, its limitations include the small amount of genetic material available for study and the presence of mosaicism .The presence of mosaicism in early-stage embryos is significant. A 1994 study described a 50% rate of mosaicism in cleavage-stage embryos. Recent studies claim that the proportion may be as high as 60%. Therefore, it is possible that the cell biopsied and tested during PGT-A might not represent the ploidy status of other embryo cells. Mosaicism potentiates the occurrence of diagnostic error and undesired clinical outcomes even in cases where a precise cellular genetic diagnosis has been performed  and monosomy (one copy less). It is currently believed to occur in most embryos. Frequency of aneuploidy increases with maternal age. Aneuploidies are the most common cause of early miscarriage and usually halt embryo development before implantation , the American College of Obstetrics and Gynecology (ACOG), and the European Society for Human Reproduction and Embryology (ESHRE) issued formal opinions discouraging the use of PGT-A .However, the development of single-cell genome amplification allowed the use of new technologies to quantify all 24 chromosomes, known as comprehensive chromosome tracking (CCS), which includes microarrays with SNP matrices, aCGH, PCR, and NGS  Brezina. The clivs. 27.6%) and live birth (64.8% vs. 27.4%) rates compared with untested transferred blastocysts (vs. 41.8%) and lower miscarriage (10.5% vs. 24.8%) rates compared with untested embryo transfers (p=0.002), and the proportions of live births were 66.4% and 47.9%, respectively  (A 2017 study showed that the transfer of euploid blastocysts identified after PGT-A by aCGH increased implantation (52.8% p=0.001) .There are advantages and disadvantages to screening for aneuploidies in embryos. The procedure is known to reduce the risk of aneuploidies detected during pregnancy and after birth. In addition, the identification and subsequent disposal of aneuploid embryos may decrease the cost of excess frozen embryos . When usGiven the uncertainties around the supposed ability embryos have to autocorrect, the false-positive rates of PGT-A, and the accuracy of diagnoses of mosaicism, there is concern that embryos that might result in healthy babies are being discarded . HoweverPreimplantation genetic diagnosis determines whether embryo cells carry genetic anomalies associated with specific disorders known to affect one or both parents . GardnerThe first successful case of preimplantation genetic testing in humans was in fact a PGT-M. In 1990, the test was performed for adrenoleukodystrophy, an X-linked recessive condition. The cleavage-stage embryos were biopsied and PCR was performed to distinguish between male and female embryos. The female embryos were transferred and yielded two pregnancies. In 1992, PGT-M was performed after cleavage-stage embryo biopsy to detect a specific mutation associated with cystic fibrosis, an autosomal recessive disease, resulting in a live birth. Since then, PGT-M has been used to decrease the chances of propagation of known genetic diseases  or inversions   supposedly contain little genetic information of relevance. Chromosome inversions are two breaks in the same chromosome, either in the same arm (paracentric) or one in each arm (pericentric), with inversion of the segment between the points of interruption , and cost-effectiveness. This continually evolving field requires close communication between clinical genetics, IVF teams, and patients to ensure that everyone is fully informed and able to make well thought out choices.The success rates of assisted reproductive technology procedures are increasing, and genetic diagnosis is a fundamental element in the treatment of infertile couples. Further discussions are required about which procedures are clinically and ethically acceptable and how they should be regulated.


Adaptive radiations (ARs) frequently show remarkable repeatability where single lineages undergo multiple independent episodes of AR in distant places and long-separate time points. Genetic variation generated through hybridization between distantly related lineages can promote AR. This mechanism, however, requires rare coincidence in space and time between a hybridization event and opening of ecological opportunity, because hybridization generates large genetic variation only locally and it will persist only for a short period. Hence, hybridization seems unlikely to explain recurrent AR in the same lineage. Contrary to these expectations, our evolutionary computer simulations demonstrate that admixture variation can geographically spread and persist for long periods if the hybrid population becomes separated into isolated sub-lineages. Subsequent secondary hybridization of some of these can reestablish genetic polymorphisms from the ancestral hybridization in places far from the birthplace of the hybrid clade and long after the ancestral hybridization event. Consequently, simulations revealed conditions where exceptional genetic variation, once generated through a rare hybridization event, can facilitate multiple ARs exploiting ecological opportunities available at distant points in time and space. One remarkable observation in empirical AR research is that the same lineage often made multiple ARs at geographically distant places or recurrent radiations in the same site through time, while closely related and ecologically similar lineages fail to radiate –9. Why aGrowing empirical and theoretical evidence suggest that hybridization between genetically distant lineages can promote AR by assembling genes from distinct parental lineages into a wide array of novel genotypes –19 x. Trait x is controlled by L loci located at random positions in a genome consisting of 2n chromosomes. Each chromosome is l base-pairs long. All loci have additive effect; trait value of an individual i is given by kie1 and kie2 are phenotypic effect values of two alleles of the locus k. The phenotypic effect value of alleles can be altered by point mutations that occur at a rate μ per locus in meiosis. Each allele carries a set S of derived nucleotides and a mutation on the allele adds a new derived nucleotide to S. The effect of each derived nucleotide ε follows the normal distribution kije, is given by kijS is the set of derived nucleotides carried by the allele and uε is the effect of the derived nucleotide u. In addition to mutations, crossover recombination, which occurs both between and within loci at a rate r per base-pair in meiosis, creates chromosomes with novel combinations of derived nucleotides. More details of the model are described in the electronic supplementary material, appendix S1.The model considers diploid organisms with non-overlapping generations. Individuals are either female or male. To keep the model simple so that simulation results will be interpretable, we consider the evolution of only a single quantitative ecological trait H1,2,…,5) in which growth performance is maximized with habitat-specific optimal trait values x_Hopt . The growth performance iP of an individual i in a habitat H is given by exp{−φ(ix − x_Hopt)2}, where φ is a parameter controlling the strength of natural selection. Survival probability of an individual i is given by HiM is the number of individuals in the same habitat as the individual i, f is per-capita female fecundity (f > 2), and N is a constant that determines carrying capacity of the habitat. The biological and theoretical basis of this survival function is described in the electronic supplementary material, appendix S1. In short, this survival function assumes that both basal survival rate in the absence of competition and superiority in competition increases linearly with individuals' growth performance. Each surviving female selects a single mating partner randomly from males living in the same habitat of the same patch. Newborn individuals move to a neighbouring patch with probability Pm and to a randomly selected habitat in the same patch with probability Hm. We used Hm = 0.2, with which gene flow between populations in parapatric habitats is weakly limited. Gene flow among populations in distinct habitats can be further reduced through adaptive differentiation of the ecological trait x because immigrant inviability, the increased mortality of immigrants with locally maladaptive phenotypes [The model considers multiple habitable patches, each of which contains two or five parapatric habitats . There aenotypes , can red(b)kie1 = kie2 = 0 for any k). Two populations evolved independently for T0 generations under identical habitat conditions (H1) in which the optimal trait value was 0 and the selection strength was Pφ . The accumulated genetic differences between lineages can give rise to novel phenotypic variation when genomes of two lineages are recombined in a hybrid population (e.g. transgressive segregation) [Prior to simulating evolutionary dynamics caused by hybridization, we prepared two parental lineages for hybridization by simulating genomic evolution of two isolated populations starting from one common ancestor. In the common ancestral genome, effects of all alleles were set to 0 (h was φP . Althougegation) . SecondaIn simulations in the remaining parts of this paper, we limited the source of genetic variation to the initial hybridization event. That is, we did not allow any spontaneous mutations after the period of allopatric evolution and ruled out the effect of standing genetic variation within parental populations by simulating hybridization between two clonal populations each composed of a single haploid genome randomly sampled from one of two parental populations. Owing to these assumptions, if recurrent AR occurs in our simulations, we can safely conclude that genetic variation generated by the initial hybridization event can promote recurrent AR.(c)k tandemly connected small patches  (a). This landscape structure represents, for example, two large lakes connected by rivers. Each of regions 1 and 2 contained five distinct habitats , whereas each corridor patch contained two habitats AH and BH. We assumed AH = BH = H1 for cases with an environmentally homogeneous corridor, whereas AH = H1 and BH = H3 for cases with an environmentally heterogeneous corridor. In cases with a single corridor, CP,j1 and CP,j2 were connected by migration at rate 0.5 for any j , whereas in cases with two geographically isolated corridors, there was no migration between CP,j1 and CP,j2. Natural selection strength φ was set to Bφ in regions 1, 2 and Cφ in corridor patches. Each simulation was started by introducing two parental lineages into habitat 1 of region 1 and continued for 5000 generations after the colonization.The spatially repeated AR scenario considered two large patches (region 1 and 2) connected by two lines of expansion corridors, each of which consists of 2, …, k) a. This lk), the strength of stabilizing selection in corridor patches (Cφ) and the carrying capacity of corridor patches (CN). With each parameter set, we performed 30 simulation replications. For this analysis, we first simulated evolution of 30 independent pairs of two parental lineages with the default parameter set . Then, for all parameter sets, we performed 30 simulations of hybridization and evolution that follow using the same set of 30 parental lineage pairs to form the initial hybrid populations.We explored geographic and ecological conditions under which genetic variation generated by hybridization occurring in the region 1 can promote recurrent AR in both region 1 and 2. For this purpose, we performed simulations systematically varying values of the following five parameters: the number of geographically isolated expansion corridors (1 or 2), the number of distinct habitats in each corridor (1 or 2), the length of the corridor(s) ((d)H1, 2, … 5). Three patches form a single large region as they are connected by the migration of individuals at a high rate (pm = 0.1). We simulated a geologically transient environmental change, such as climate change or geographic dynamics, that causes contraction and subdivision of the habitable zone and loss of divergent ecological selection (b). We modelled transient environmental change as a reduction of values of the following parameters: (i) the maximum carrying capacity in single habitat (from BN to RN), (ii) migration rate between three patches (from Pm to Rm) and (iii) strength of natural selection (from Bφ to Rφ). We used Rφ = 0 (i.e. no selection), and thus the environmental change always led to the loss of ecological diversity that has evolved before the environmental change. Changes in values of these three parameters proceeded gradually between generations 10 000 and 10 100. The altered environmental condition continued for the following 9900 generations . Then, a renewed environmental change took place between generations 20 000 and 20 100 during which the values of the three parameters returned to the original values . Each simulation was continued until generation 30 000.The temporally repeated AR scenario considered three patches, each of which contains five types of habitat k) . This was because the high genetic/phenotypic variation generated by hybridization was not maintained in the corridor population due to the combination of stabilizing selection in corridor patches and genetic drift associated with successive founder events at the front of the range expansion. Consequently, the lack of genetic/phenotypic variation prevented AR in the second region despite ecological opportunity afforded by the available resources and environmental heterogeneity. In line with this interpretation, the likelihood of recurrent AR was further suppressed both by increasing the strength of stabilizing selection in corridor patches and by decreasing the population size in corridor patches .In all our simulation runs, hybridization between two colonizing lineages in region 1 was followed by rapid AR within region 1. However, when there was only a single corridor without environmental heterogeneity, AR was not repeated in region 2 unless two regions were close to each other . Isolation of two range expansion waves in these corridors led to the formation of two genetically distinct sub-lineages because the populations in the two corridors fixed different sets of alleles during their range expansion. Although phenotypic/genetic variation eroded within both sub-lineages during range expansion, secondary admixture between them in region 2 reestablished high standing genotypic/phenotypic variation, which permitted AR in region 2.By contrast, AR did occur in region 2 even after a long-distance range expansion when there were two geographically isolated expansion corridors (figures 2b and S7). In this case, the arrival of lineage 2 in region 1 led to hybridization-induced AR there, but genetic material from the parental lineage 2 did not spread through corridors to region 2. This was because most of the hybrid genotypes and associated phenotypes were maladaptive in corridor patches compared to those of the resident lineage 1 population. However, our additional simulations revealed that admixture variation can spread to region 2 if migration in corridors is unidirectional . Unidirectionality of migration is often the case in dendritic river–lake systems [AR in region 2, however, often failed to occur when one expansion front reached region 2 well before the other expansion front. In this situation, migration from region 2 into the second corridor led to the situation where the contact and hybrid zone between the two lineages resides inside one of the corridors rather than in region 2 . Despite the presence of ecological opportunity in region 2, no radiation occurs because the necessary genetic variation never arrives there. Similarly, our additional simulations revealed that the recurrence of AR mediated by two isolated expansion corridors was hindered if the parental lineage 1 was introduced to region 1 and spread across the entire system of connected patches before the parental lineage 2 arrived in region 1 . Corridors with two environmentally distinct habitats allowed two ecologically distinct incipient species, which had arisen in the AR of hybrid origin in region 1, to independently disperse to region 2 through the different habitats in the corridor. The two incipient species could, nonetheless, form a hybrid population upon the invasion of unoccupied habitats in region 2 because immigrant inviability, the only mechanism for ecological speciation in our model, is strongly context-dependent and is absent in habitats that hold no locally adapted resident competitors. Admixture in these cases reestablished phenotypic variation that often exceeded the range of that of the two incipient species combined (i.e. transgressive segregation) and permitted the evolution of new species adapting to habitats H2, H4 and H5. Different from the case with two geographically isolated corridors, recurrent AR mediated by an environmentally heterogeneous corridor was not hindered by the time lag between the introductions of two parental lineages  because habitat H3 in the corridor had not been occupied until a specialist of this habitat evolved through the hybridization-enabled AR in region 1.Environmental heterogeneity within a single dispersal corridor could also facilitate recurrent AR even with long corridors . With supplementary simulations, we confirmed that our conclusions are valid also when (i) regions 1 and 2 have different sets of habitats with different fitness optima , and when (ii) the ecological trait axis subjected to divergent selection in the region 2 is different from that in the region 1 .(b)a shows an example of evolutionary dynamics. Hybridization of two parental lineages produced large standing genotypic/phenotypic variation leading to rapid AR. This AR, however, collapsed after environmental changes caused the relaxation of divergent ecological selection and the associated loss of reproductive isolation. Formerly ecologically differentiated incipient species merged into a hybrid swarm and lost their ecological differentiation . As three patches  were isolated during the refugial phase, different sets of genes became fixed in the three refugial patches. Although standing genetic variation within each refugial sub-lineage was lost during the refugial phase through genetic drift , subsequent admixture between them after reconnecting of the refugial patches recovered large standing genetic/phenotypic variation, which was available to natural selection when environmental conditions returned to the original state (generations 20 000–30 000 approx.). Consequently, AR could be reestablished quickly despite the absence of spontaneous mutations.Our simulations demonstrated that ecological speciation and geographical isolation of lineages can both facilitate long-term maintenance of hybridization-induced adaptive genetic variation, thereby enabling a renewed hybridization-enabled AR after the transient environmental change had destroyed earlier AR. Figure 4Bφ; figures 4b,d and 5b). By contrast, a large fraction of SNPs could persist throughout this period if strong divergent selection between habitats generated reproductive isolation between ecologically differentiating sub-lineages . Second, genetic variation at the meta-community level could decline during the refugial phase (generations 10 000–20 000) due to global fixation of same genes by genetic drift when there was gene flow between the patches . This effect could be slightly mitigated if refugia had large population sizes . If the three patches were strongly isolated , many polymorphisms could persist in the form of genetic differentiation between sub-lineages (figures 4d and 5b). Under the conditions where ecological and geographical isolation of lineages could maintain large fractions of the old admixture variation, AR was reestablished when the environment returned to the original state (generations 20 000–30 000 approx.) .Simulations with varying parameter values revealed conditions under which AR can be reestablished after the collapse of original radiation. To evaluate the sustainability of high evolvability under the conditions given by different parameter combinations, we tracked the number of polymorphic nucleotide sites (SNPs) among all individuals in the entire meta-community. Hybridization-induced genetic variation could be lost at the meta-community level in two different phases. First, in the period before the refugial phase (generations 0–10 000) if divergent ecological selection between habitats was too weak to generate reproductive isolation between ecologically differentiating sub-lineages . An alternative mechanism for the loss of divergent selection is an environmental change that causes a shift of the optimal trait values in the different habitats towards the same value. We confirmed that our results were not qualitatively changed under this alternative scenario .e–h. These simulations revealed that physical genetic linkage that had built up adaptively may promote recurrent evolution of phenotypes that had once evolved in the past, although the effect was slight in our simulations.While the number of polymorphisms explained the likelihood of the recurrence of AR well in our simulations, this ignores effects of relics of physical genetic linkage that may have built up adaptively in the first AR. We investigated this in additional simulations that we summarize in electronic supplementary material, figure S114.Our results demonstrate that genetic variation generated by a single geographically localized hybridization episode can promote multiple episodes of AR far apart in space and time. Our simulations did not allow any spontaneous mutations after the hybrid swarm origin of the radiating lineage. This way we made sure that the origin of genetic variation that enabled the second AR was the initial hybridization event. If spontaneous mutations are allowed after the initial hybridization event, admixture between sub-lineages will not only restore old but also generate a new genetic variation by reshuffling de novo mutations after they had arisen in isolation in either sub-lineage. In this respect, our simulations are conservative regarding the effects of hybridization between sub-lineages in promoting recurrent radiation. This also means that our results do not generally deny the possibility of recurrent AR with spontaneous mutations alone, although AR was extremely unlikely without hybridization-induced genetic variation under our simulation conditions .The present study extends and integrates the hybrid swarm origin hypothesis of AR and the syngameon hypothesis . The synAnother relevant theory is the transporter hypothesis that has been invoked to explain the repeated evolution of freshwater stickleback from marine ancestors. The transporter hypothesis argues that recurrent hybridization between marine and freshwater stickleback may enable transporting of freshwater alleles among geographically isolated freshwater habitats through the spatially contiguous marine population allowing the latter to recurrently invade freshwater habitats  through (a)Simulations of the spatially repeated AR scenario provide a theoretically feasible explanation for the highly recurrent occurrence of AR in one lineage of African cichlid fish in the Lake Victoria region  and alsoThe spatially repeated AR mediated by colonization through two independent expansion corridors failed when range expansion in one of the parental lineages occurred already before its localized hybridization with the second lineage . This result suggests that spatially repeated AR is more likely to occur when hybridization itself triggers range expansion ,32 or whSimulations of the temporally repeated AR scenario offer a mechanistic hypothesis for the understanding of some ARs of African lake cichlid fish in which mass extinction due to lake desiccation and re-evolution of species diversity are thought to have occurred. All African Great Lakes experienced massive water level fluctuations driven by climate oscillations . The ass5.While the evidence is growing that hybridization can be a source of genetic variation that has facilitated adaptive radiations ,17–19,34


Dear Readers,The Journal of Innovations in Cardiac Rhythm Management contains an important article by Hylind et al.1 titled “Genetic Testing for Inherited Cardiac Arrhythmias: Current State-of-the-Art and Future Avenues.” In it, the authors discuss the basic genetic concepts underlying long QT syndrome (LQTS) and catecholaminergic polymorphic ventricular tachycardia (CPVT), two arrhythmias with well-defined genetic mechanisms. They furthermore describe the predictive value of genetic testing and, most importantly, review genotype-guided treatment strategies.This issue of Genetic testing as a means to better understand patients with inherited arrhythmias has become widely used. Many centers now have inherited arrhythmia clinics, with genetic counselors and physicians working closely together to provide patients and their families with individualized data regarding the possible onset of various cardiac diseases generated from the results of genetic testing as well as risk assessment services for family members and, to a lesser degree, guidance for treatment strategies. These facilities are not limited only to evaluating arrhythmias with well-defined genetic mechanisms such as LQTS and CPVT; they also strive to elucidate the genetic underpinnings of other cardiac conditions with less clear mechanisms, such as inherited cardiomyopathies and other arrhythmias that can cause sudden cardiac death.One of the limitations of genetic testing, however, is the existing variation in the degree of the penetrance and expressivity of a gene mutation in affected individuals. The clinical presentation of a certain condition is often not just determined by a patient’s genetic profile: other factors such as environmental influences may also play a part. As a result, the role and success of genetic testing in the formulation of treatment plans remain limited in scope. Still, with the ongoing advances in technology continually allowing for a faster, more detailed, and increasingly accessible analysis of the genetic profile, it is perhaps foreseeable that clinical studies where genetic testing is investigated not only as a diagnostic tool but also as a major determinant of the treatment plan may exist in the near future.I hope that you find the article by Hylind et al. and the rest of this issue educational and of value in your practices.Sincerely,md, fhrs, faccMoussa Mansour, Editor in ChiefThe Journal of Innovations in Cardiac Rhythm ManagementMMansour@InnovationsInCRM.comDirector, Atrial Fibrillation ProgramJeremy Ruskin and Dan Starks Endowed Chair in CardiologyMassachusetts General HospitalBoston, MA 02114


Etheostoma lemniscatum), a habitat specialist that is presumed to be dispersal‐limited. We predicted that the interplay between these intrinsic and extrinsic factors would limit dispersal and lead to genetic structure even at the small spatial scale of the species range (a 38.6 km river reach). The simple linear distribution of E. lemniscatum allowed for an ideal test of how these factors acted on gene flow and allowed us to test expectations  of linearly distributed species. Using 20 microsatellites from 163 individuals collected from 18 habitat patches, we observed low levels of genetic structure that were related to geographic distance and rapids, though these factors were not barriers to gene flow. Pools separating habitat patches did not contribute to any observed genetic structure. Overall, E. lemniscatum maintains gene flow across its range and is comprised of a single population. Due to the linear distribution of the species, a stepping‐stone model of dispersal best explains the maintenance of gene flow across its small range. In general, our observation of higher‐than‐expected connectivity likely stems from an adaptation to disperse due to temporally unstable and patchy habitat.Genetic connectivity is expected to be lower in species with limited dispersal ability and a high degree of habitat specialization (intrinsic factors). Also, gene flow is predicted to be limited by habitat conditions such as physical barriers and geographic distance (extrinsic factors). We investigated the effects of distance, intervening pools, and rapids on gene flow in a species, the Tuxedo Darter ( We examine whether physical obstacles and geographic distance influence genetic structure in a presumably dispersal‐limited, habitat specialist riverine fish. We found minimal levels of genetic structure associated with rapids and distance. So, we conclude that there are minimal filters to gene flow in this endangered species and that higher‐than‐expected connectivity likely stems from an adaptation to disperse due to temporally unstable and patchy habitats. Intrinsic species traits  and extrinsic habitat conditions  often dictate the amount of population connectivity and gene flow in a species. Organisms with moderate to high dispersal have higher recolonization potential  and Salmo trutta (Brown trout) were found to be due to unsuitable habitat in dispersal corridors overriding their presumed inherent high dispersal ability  and Xenicus gilviventris   indicative of an elevated extinction risk and vulnerability to anthropogenic habitat fragmentation . PCRs were 10 μl total volume. For samples above 5 ng/μl DNA concentration, the PCR contained 5.65 μl PCR pure water, 1.00 μl 10X concentrate standard Taq reaction buffer‐Mg free , 1.20 μl 25 mM MgCl2 , 0.20 μl 10 mM dNTPs , 0.25 μl 10 p.m. forward primer, 0.50 μl 10 p.m. reverse primer, 0.10 μl 5,000 U/ml Taq Polymerase , 0.10 μl M13‐labeled primer , and 1.00 μl DNA. Samples with DNA concentrations below 5 ng/μl differed in containing 4.65 μl PCR pure water, and 2.00 μl DNA, with all other reagent amounts the same as those with higher DNA concentrations. PCR cycle conditions were as follows: initial denaturation at 94°C for 1‐min; 35 cycles each of denaturation at 94°C for 30‐s, primer specific annealing temperature ranging from 60°C–65°C  developed species‐specific microsatellite primers, which identified over 6,500 potential loci for this study. Of these, we optimized 92 loci for PCR products were genotyped using an ABI3730 sequencer with LIZ600 as the size standard at the University of Florida Interdisciplinary Center for Biotechnology Research. Allele sizes were scored automatically using the panel editor function in GeneMarker v1.6 (SoftGenetics LLC) and then confirmed manually. Any allele that did not fit the expected repeat pattern for that locus was closely examined and then, if necessary, edited manually or reamplified and regenotyped. To ensure consistency of allele sizes across reactions, three positive controls were included on each genotyping run.2.3n ≤ 6 individuals were grouped together with the closest adjacent site(s) to create 10 stream reaches  and downstream (R8–R10) of DJD were grouped prior to analyses.To meet sample size requirements for some analyses used, sites where s Figure . To examp‐values had a sequential Bonferroni correction applied to minimize type‐I errors , and observed (Ho) and expected heterozygosity (He) in GenAlEx v6.503  in GENETIX v4.05  at each reach against the calculated distances in R v3.4.2  method in NeESTIMATOR v2.01  for our 10 reaches to test for isolation‐by‐distance (IBD) using IBD v1.52 , and 10,000 burn‐in Markov chain Monte Carlo (MCMC) steps followed by 100,000 MCMC steps. Since our initial STRUCTURE runs failed to detect any population structure, subsequent STRUCTURE analyses employed the LOCPRIOR model using our 10 reaches and reaches upstream and downstream of DJD as priors. Hubisz, Falush, Stephens, and Pritchard (K)])  for individuals within a specified distance class is an indication of deviation (r ≠ 0) from the null hypothesis of no spatial genetic pattern (r = 0)  between our original 18 sites. A significant and positive genetic autocorrelation coefficient  compared to other stream reaches  showed no significant downstream increase in genetic diversity . Most reaches had FIS values that did not significantly differ from zero; however, R3 and R9 had significant negative FIS values, indicating outbreeding occurring in those reaches . Measures of allelic diversity were low overall, but fairly uniform among our ten reaches; 3.2FST values ranging from 0.0002 to 0.040  recovered. The results of our Mantel test in the program IBD recovered a significant IBD relationship among our ten stream reaches , but distance explained only 5.6% of the total genetic variation, suggesting that distance plays only a small role in creating genetic structure in E. lemniscatum .We observed low differentiation between the ten reaches, with pairwise K = 1 as the most likely number of clusters using the mean log‐likelihood method  at the first two distance classes  at the 0, 1.35, and 2.45 km distance classes , these abiotic factors were not barriers to gene flow. We found that gene flow occurs at high levels up to 2.45 km and is well maintained up to 5.8 km, distances greater than the pool lengths separating most adjacent habitat patches. This suggests that most pools between localities do not constrain dispersal. Additionally, we found little evidence for a bias in downstream dispersal since there was no significant trend of DIGD and only minimal signals of increased genetic structure in upstream areas. Overall, E. lemniscatum maintains gene flow across its range and is comprised of a single population. Given the small range and linear distribution of the species, we propose a stepping‐stone model of dispersal best explains the observed gene flow across its range, a phenomenon observed in other linearly distributed taxa  like other imperiled darters, when compared to allelic diversity in nonimperiled darters . High allelic diversity at small numbers of neutral markers  is predictive of increased adaptive potential for quantitative traits  of 300–600 individuals, and a total of 200 and 100 individuals were estimated for the species in 2008 and 2009, respectively  and the total number of individuals observed in the course of our study , extending the known range of the species by approximately 8 km   or actions that reduce the permeability of intervening pool habitats, such as increased sedimentation, would negatively impact genetic exchange and diversity.Given evidence of overall depressed genetic diversity, low E. lemniscatum is experiencing population growth and expansion as our data may indicate, the species should remain relatively stable as long as its habitat remains protected by the National Park Service and water quality keeps improving  has reduced genetic input from across the rest of the species range. This is likely a signal of the expectation for increased genetic differentiation in upstream river sites, possibly due to more restricted gene flow in upstream directions from a downstream dispersal bias.Unlike other riverine species that exhibit a downstream increase in intraspecific genetic diversity (DIGD) . In general, mainstem‐dwelling species  display higher levels of connectivity due to the simplicity of dispersal in a mainstem linear habitat compared to dispersal in dendritic stream networks, which require longer and more complex movements between headwater areas to maintain population connectivity  is not a common occurrence. Additionally, most darters are not known to make large seasonal long‐distance migrations  can indicate low dispersal ability and gene flow (Turner & Trexler, E. lemniscatum to more readily populate new habitat patches as they become available in its temporally variable environment and explain how the species was able to relatively quickly recolonize habitat that became free‐flowing during the lowered reservoir levels on Lake Cumberland.When compared to other members of the E. lemniscatum, favorable or near‐favorable habitat patches may be more continuous through its range than we currently realize, which could aid generalist dispersal. Additionally, few surveys for E. lemniscatum have searched the deep, intervening pool habitats, and the species may occupy these deep pools more than currently acknowledged. There is precedent for this among darters: Percina pantherina is found in pools up to 5 m deep (Schaefer, Marsh‐Matthews, Spooner, Gido, & Matthews, E. lemniscatum has been documented is 1.2 m (Davis & Cook, Habitat specialists may exhibit relaxed habitat requirements when dispersing (Fisher, Lambin, & Yletyinen, 5Etheostoma lemniscatum seems to have evolved dispersal adaptations in response to the patchy and, presumably, temporally variable habitat in its big river environment. Also, unlike many other riverine organisms, E. lemniscatum does not show strong signals of the expected distribution of genetic diversity that occurs due to asymmetric downstream dispersal. Its small range and linear distribution have likely contributed to the lack of these strong signals and the minimal levels of genetic differentiation observed. Our study and others (Ginson et al., We found that a habitat specialist with presumed low dispersal ability displays a remarkably low level of genetic structure. The minor limits to gene flow observed were primarily attributable to distance, and possibly rapids, rather than intervening pool habitats. None declared.R.E.B. and M.F.C. designed the study. B.A.W. performed molecular work and data analysis with help from R.E.B. and M.F.C. The manuscript was written by B.A.W. All authors participated in fieldwork and edited the manuscript. Click here for additional data file.


We tested for sex-specific substructure with individual-based Bayesian assignment tests and spatial autocorrelation analyses. Differences between the sexes in genetic differentiation, relatedness, inbreeding, and diversity were analysed using population-based AMOVA, F-statistics, and assignment indices. Our results showed two different genetic clusters that were spatially structured for females but admixed for males. Similarly, spatial autocorrelation and relatedness was significantly higher in females than males. However, we found weaker sex-specific patterns for the Eurasian lynx when the data were separated in three geographical regions than when divided in the two genetic clusters. Overall, our results suggest male-biased dispersal and female philopatry for the Eurasian lynx in Southern Finland. The female genetic structuring increased from west to east within our study area. In addition, detection of male-biased dispersal was dependent on analytical methods utilized, on whether subtle underlying genetic structuring was considered or not, and the choice of population delineation. Conclusively, we suggest using multiple genetic approaches to study sex-biased dispersal in a continuously distributed species in which population delineation is difficult.Conservation and management of large carnivores requires knowledge of female and male dispersal. Such information is crucial to evaluate the population’s status and thus management actions. This knowledge is challenging to obtain, often incomplete and contradictory at times. The size of the target population and the methods applied can bias the results. Also, population history and biological or environmental influences can affect dispersal on different scales within a study area. We have genotyped Eurasian lynx  continuously distributed in southern Finland (~23,000 km Individual dispersal and gene flow into a breeding area impacts the evolutionary potential, long-term viability, and distribution ranges of populations and species –5. ThereFelidae [Lynx genus, sex-biased dispersal has been reported for bobcats (Lynx rufus) [Lynx canadensis) or Iberian lynx (Lynx pardinus) [Dispersal varies with several factors, including population size, landscape features, various anthropogenic disturbances, and especially by social systems of the species , 11. LarFelidae , partly Felidae . Sex-biaFelidae . In the x rufus) , 16, 17,ardinus) –20 and iPreviously, telemetry studies of Eurasian lynx have documented dispersal of both sexes but cannot consistently ascertain that dispersal is sex-biased. In a Scandinavian telemetry study, males dispersed further and more frequently than females, of which one third remained philopatric . In a PoGenetic studies can detect the amount and direction of sex-biased gene flow as an indicator of sex-biased dispersal . Eurasiaversus genetic, see [There may be several reasons for the observed variation across studies. The Eurasian lynx populations in Europe differ in size and level of connectedness with other populations , and, intic, see , 35–39, tic, see . Sex-biatic, see , 41, anatic, see , and asstic, see , 37. Sectic, see . The lattic, see , 44.a priori delineation of groups to be analyzed may result in genetic structure and F-statistics estimates that may reflect historical rather than contemporary gene flow [a priori delineation of populations difficult [For population-based statistical methods, the required ene flow , 45. Furifficult , 46. Twoifficult , 46. Firifficult , 46, 47.a priori groupings when local conditions are to be analyzed.Individual-based statistical methods allow for the analysis of sex-biased dispersal by investigating the relationship between genetic and geographic distance for each of the sexes and require geographical coordinates . These mST-values [IS-values than in the philopatric sex [We have shown in a previous study on Eurasian lynx that underlying geographical structure can change the strength of the signal in spatial autocorrelation analyses . TherefoT-values . In the tric sex .In addition, the dispersing sex should display generally weaker structure in Bayesian clustering methods than the philopatric sex because the former will homogenize the gene pool across the landscape , 36. Forth century, the population received natural recruitment from Russian Karelia [In this study, we have used both population-based and individual-based statistical analyses to infer sex-specific dispersal patterns from genetic data in a continuously distributed population of Eurasian lynx in southern Finland. Eurasian lynx inhabit all of Finland, and forms a continuously distributed population in connection with that of Russian Karelia,  but rema Karelia . Lynx re Karelia . Yearly  Karelia . The hun Karelia .We applied 21 bi-parentally inherited short tandem repeat (STR) markers to individuals sampled after dispersal (> 1 year), to obtain unbiased estimates  of sex-b2. Forest dominated by pine, spruce, and birch constitutes about 70% of the terrestrial area. Lakes encompass about 10% and the rest consists of mires, farmland, and urban areas, creating a blend of different land uses. Despite the high forest cover, the area holds five of the 15 largest urban areas in Finland, including the capital area of Helsinki, which lies about 100 km east from the western-most point of the study area. Human population density varies between 6 to 3,044 individuals / km2. Our initial data set consisted of all 369 legally hunted lynx during 2003–2017 . Tissue samples for genetic analysis were collected from all individuals at the Natural Research Institute Finland’s (Luke) lab in Taivalkoski Research Station. No ethic permit was required, as the samples did not involve live animals. A CITES permit was obtained for shipping the samples to the Norwegian Institute of Bioeconomy Research (NIBIO) lab in Svanvik, Norway. The tissue samples were stored in ethanol until analysis. The age of the individuals was determined from cementum annuli analysis of tooth samples in a laboratory specialized in age determination  [Our study area is in southern Finland  . Before na, USA) . In the Procedures for DNA extraction and genotyping are outlined in detail in . BrieflyIS) as well as significance levels with FSTAT 2.9.3.2 [We used Micro-Checker 2.2.3  to detec 2.9.3.2 –38. Fina 2.9.3.2 , 58 was https://www.phylo.org/; [We applied a Bayesian clustering algorithm using the software package STRUCTURE 2.3.4 , 59 to alo.org/; ) that pelo.org/;  to reduclo.org/; . Followilo.org/; , the modlo.org/; . HoweverA second set of analyses served as an additional test to determine the most likely number of K and was based on pre-defined sub-populations along the distribution continuum and along the recolonization axis from east to west. We did this by pooling the individuals according to their sampling location into three regions; western, central, and eastern; each containing 1/3 of the samples , which mFinally, because unequal sample sizes occurred in the geographical STRUCTURE analyses, we accounted for that by determining the most likely number of K with STRUCTURESELECTOR .r, we ran the analysis on the regional level  using the multiple genetic cluster approach [We performed spatial autocorrelation analyses using GenAlEx 6.51b2 with 9,999 permutations , 58 to capproach , treatinapproach , 58, we In addition, we performed a Principal Coordinate Analysis (PCoA) for all samples, divided by sex, with GenAlEx 6.51b2 , 58. StaWe performed an AMOVA  to inveST), inbreeding coefficient (FIS), mean (mAIc) and variance (vAIc) of the corrected assignment index AI (AIc) with the software package FSTAT 2.9.3.2 [We assessed differences between sexes in five genetic diversity estimators , genetic differentiation  with average observed and expected heterozygosity of 0.651 (standard error: 0.03) and 0.662 (standard error: 0.03) Accordingly, only one locus  showed a highly significant FFor all three data sets , two distinct genetic clusters were identified by the Evanno method (ΔK = 2) when running the analysis without predefined populations  cluster, located in the eastern part of the study area . The STRfemales = 0.106, P value: 0.000; males = -0.002; P value: 0.028; cluster1 = 0.055; P value: 0.001, but error bars were regularly below r = 0; rcluster2 = 0.121, P value: 0.000; radmixed = 0.142, P value: 0.001; cluster1 = -0.022, P value: 0.017, rcluster2 = -0.006, P value: 0.363, radmixed = 0.006, P value: 0.008, but error bars were regularly below r = 0; females = 0034, P value = 0.000; rmales = -0.004, P value = 0.000, but error bars were regularly below r = 0; WEST = 0.034, P value = 0.001, but error bars were regularly below r = 0; rCENTRAL = 0.030, P value = 0.011, rEAST = 0.062, P value = 0.006; WEST = -0.003, P value = 0.034; rCENTRAL = -0.004, P value = 0.000, but error bars were regularly below r = 0; rEAST = -0.004, P value = 0.019; Across genetic clusters, females showed significant decreasing spatial autocorrelation coefficient, r, up to 45 km  values with higher variance  and two additional estimators at the geographical region level , and analysis scale .The observed spatial gradient in sex-biased dispersal indicated regional differences in the spatial organization of female relatives, since no similar pattern was observed for males. Males showed spatial admixture between genetic clusters, while females were spatially structured. Spatial autocorrelation coefficient, r, and relatedness (REL) were also significantly stronger in females than males. However, we found weaker sex-specific patterns when the data were separated in three geographical regions than when divided into the two genetic clusters based on the spatial autocorrelation coefficient, r, and relatedness (REL) obtained in the FSTAT analysis, although the latter was significant based on the group-based analysis in terms of the difference between sexes. Assignment indices were also consistently larger when the analysis was based on genetic clusters than on geographic regions. In contrast, molecular variance showed higher structuring for females for the three geographical regions than for the two genetic clusters. FSTAT estimators supported male-biased dispersal, particularly the analysis based on geographical regions. Thus, the ability to detect sex-biased dispersal was dependent on the analytical methods applied, on whether underlying genetic structuring was considered or not, and the choice of groupings.Both individual-based methods  clearly indicated male dispersal and female philopatry. Although seldom applied to test for sex-biased dispersal, Bayesian individual assignment methods like STRUCTURE may thus reveal sex-specific structure and help define genetic clusters to be used in population-based methods . SiIn addition, we used three different population-based statistical methods. While the AMOVA based on geographical regions indicated higher structuring and philopatry in females than in males, the cluster-based analysis showed similar estimates for both sexes. This suggests that the latter approach may underestimate the sex-bias if the genetic clusters show spatial overlap, since genetic variation between sexes may be geographically distributed more evenly than among genetic clusters. Excluding admixed individuals and first-generation migrants (which are presumably more common in the dispersing sex) in the genetic cluster analysis may also lead to a downwards bias in the molecular variance difference between the sexes. Therefore, AMOVA is probably most effective if applied directly on geographical sampling locations in continuous populations. For the assignment indices, we found that even subtle sex-specific substructure can reduce the power of the analysis for sex-biased dispersal testing. Female individuals from a different genetic cluster located only in part of the study area, lowered the positive assignment index, both overall and particularly in cluster 2/eastern geographical region as those females were identified as immigrants. By excluding those individuals, the dispersal asymmetry increased, which also increased the power of the tests . This biIS and observed heterozygosity, appeared less powerful in our study independent of the groupings assessed. This result is consistent with tests by [ST and relatedness perform particularly well when the proportion of dispersers is high in the sample, which is facilitated by using a geographical region approach rather than a genetic cluster approach. Estimators like mAIc, vAIc, and HS appear less sensitive to this type of bias and may more readily be applied to genetic clusters identified by Bayesian individual assignment tests. On the contrary, our results suggest that population-level metrices, like FST, heterozygosity, and relatedness may be less powerful in situations where population genetic differentiation is generally very low and proportion of dispersers high, leading to weakly differentiated female and male genotypes among populations. This may be better captured in the geographical region approach in our study, by likely including a higher proportion of dispersers. Estimators like mAIc, vAIc, and HS appear less sensitive to this type of bias and may more readily be applied to genetic clusters identified by Bayesian individual assignment tests.Some tests of sex-biased dispersal, such as Ftests by , who furFinally, to test whether the higher proportion of males in the dataset could have an impact on the results, we subsampled the males and ran two additional analyses with GENALEX with two different male subsets, which resulted in higher female means as expected (results not shown). Since the datasets were initially sorted based on geographical coordinates, the subsampled datasets were generated by deleting every second individual. This ensured that the reduced dataset still had a relatively even geographical distribution. Therefore, we consider a potential sampling effect caused by the higher number of males as negligible.While earlier telemetry studies of Eurasian lynx reported conflicting evidence of sex-biased dispersal, we consistently found male-biased dispersal, which is typical for mammals –8, 67. TLack of dispersal barriers may result in long-distance dispersal of both sexes . On the While short-distance dispersal may be the ultimate cause of inbreeding avoidance and kin competition, colonization of new patches is likely the cause for long-distance dispersal and hence independent of sex , 72, 73.unpublished). In contrast, in the western part of our study area, the process of lynx recolonization was still ongoing in the beginning of the sampling period, and thus probably did not represent a saturated population with a stable social organization until around 2010 . These different colonization stages may explain the observed spatial variation in sex-specific autocorrelation, which was mainly due to spatial variation in the organization of the females caused by differences in population density and resource availability [unpublished) and the potential founder effects therein. Relatively high, slightly male-biased hunting pressure on lynx in Finland constantly provides vacant territories weakening the competition between males, which could lead to less pressure to disperse [The recolonization of lynx in Finland took place from east to west mainly before our sampling period, and the population was at its highest in 2014 . In the lability , 75. Eurlability , 25, 76.lability . Therefolability , 75. We disperse . We founOngoing anthropogenic pressures can considerably affect future connectivity and viability of the Eurasian lynx population studied, and more broadly recolonizing large carnivore populations that display sex-biased dispersal. Intensifying urbanization, agriculture, and forestry activities are all factors leading to increased habitat patchiness that are more likely to affect the philopatric sex . In addiIf hunting more heavily targets the dispersing sex, it may reduce connectivity and increase genetic population structuring and inbreeding , 79. MalResearch on the Eurasian lynx in the past has shown contradictory evidence concerning sex-biased dispersal. Our results point to flexible dispersal behaviour and spatial organization in female Eurasian lynx, which may reflect the influence of factors such as density, adult turnover rate , 24, 71 We systematically applied different genetic analyses and groupings and recorded the power of detection of sex-biased dispersal. Our results indicate that combining a series of analytical approaches and groupings to evaluate potential biases of estimators is recommended. This may be especially important for species with continuous distribution ranges in which populations cannot readily be delineated and where subtle population genetic structure may be present . Further, careful consideration should be given on how sampling biases may affect the results.Considering future research objectives, constructing a pedigree may expand our knowledge about inbreeding avoidance and the prerequisites of philopatry, and the extent to which these behavioural traits drive evolutionary dynamics. Also, the effects of hunting on these features, and more profoundly, the survival of matrilineages and genetic variance in the population needs to be investigated when hunting remains the predominant population management tool.S1 Material(DOCX)Click here for additional data file.


Artocarpus annulatus, the closest known wild relative of two important and underutilized fruit tree crops, jackfruit (A. heterophyllus) and cempedak (A. integer). Identifying and conserving crop wild relatives is critical for the conservation of crop genetic diversity and breeding. In 2016 and 2017, five A. annulatus populations were located, and leaf material, locality information, and demographic data were collected. Microsatellite markers were used to assess genetic diversity and structure among populations, and to compare levels of genetic diversity to closely related congeneric species. Results indicate no evidence of inbreeding in A. annulatus, and there is no genetic structure among the five populations. However, diversity measures trended lower in seedlings compared to mature trees, suggesting allelic diversity may be under threat in the youngest generation of plants. Also, genetic diversity is lower in A. annulatus compared to closely related congeners. The present study provides a baseline estimate of A. annulatus genetic diversity that can be used for comparison in future studies and to other species in the unique limestone karst ecosystems. Considerations for in situ and ex situ conservation approaches are discussed.Limestone karsts of Southeast Asia can harbor high levels of endemism, but are highly fragmented, increasingly threatened, and their biodiversity is often poorly studied. This is true of the Padawan Limestone Area of Sarawak, Malaysia, home to the endemic  One of  forests . This sp) Merr.) . Artocarears ago  but remaears ago . AccordiA. annulatus has broad implications beyond karst systems, as the genetic diversity of crop wild relatives can prove a useful source of novel traits  found substantial differentiation and significant isolation by distance across populations  destruction of A. annulatus habitat, the known populations of A. annulatus will show recent evidence of gene flow and as there will not be population specific alleles; due to pressures of increased fragmentation and habitat loss, recruitment of younger individuals may be more tenuous and lead to decreased genetic diversity in younger individuals compared to mature trees; and as a critically endangered species with restricted distribution, A. annulatus will harbor less genetic diversity than its widely distributed congeners. It is hoped that the results can inform management plans for in situ and ex situ conservation efforts and provide much needed information about the unique biota of the Southeast Asian karst formations. Understanding how diversity is distributed among populations will provide insight into the importance of whether in situ or ex situ conservation efforts should be targeted toward all or some populations to capture the existing diversity of this critically endangered species.Depending on what we know about the genetic diversity and structure of endangered species, different approaches to conservation may be warranted . Due to  recover . While tulations . UnderstArtocarpus annulatus is a small to mid-sized evergreen tree, growing up to 10 m tall on karst outcrops in Sarawak, Malaysia. It is monoecious, with separate male and female inflorescences occurring on the same individual tree. The male inflorescences have rings around them, for which the specific epithet “annulatus” is named  and A. integer (cempedak). They also bear cauliflorous fruit structures, which are edible and much larger than A. annulatus (reaching up to 45 kg in A. heterophyllus). Jackfruit is thought to be native to the western Ghats of India (with a possible secondary center of diversity in Bangladesh) (A. integer var. integer) is thought to be native to peninsular Malaysia and possibly Borneo; bangkong (A. integer var. silvestris) is a close wild relative native to peninsular Malaysia  . It is gMalaysia .Artocarpus annulatus is known only from the Padawan limestone area in Kuching District, Sarawak, Malaysia , as well as four additional sites in surrounding areas of limestone hill forest  were visited to search for A. annulatus individuals  (A. annulatus was found (pers. obs. Zakaria). For all sites where A. annulatus was found, GPS coordinates, images, and herbarium collections were taken for representative samples. In addition, leaf tissue samples (dried in silica), for 131 A annulatus trees were taken across the five sites , cempedak , and A. integer var. silvestris   . Represeg Bedoh) . At one ve sites . For mosempedak) .2 dried leaf using either the Qiagen DNeasy kits  following standard protocol or a modified cetyltrimethylammonium bromide (CTAB) method  and cempedak (A. integer) (Genomic DNA was extracted from 1 cm) method . Nine di altilis  were useinteger) . The micThe PCR reactions contained 5 µL of 2x myTaq Mix , 0.15 µL 20 mg/ml BSA , 0.25 µL each of labelled forward and reverse primers (10 µm concentration), 3.35 µL of DNA-free water, and 1 µL of DNA (with concentrations between 10 and 80 ng/µL). The PCR cycles were: 2 min at 94 °C for initial denaturization, followed by 34 cycles of 94 °C for 30 s of denaturation, 52 °C for 1 min of annealing, then 72 °C for 2 min of extension; the 34 cycles were then followed by 72 °C for 10 min of final extension .The PCR products were run on a Beckman Coulter CEQ 8000 Genetic Analysis System  following the methods of Artocarpus annulatus is thought to be functionally diploid, and as such each region should display either one (homozygous) or two (heterozygous) alleles. Previous studies have revealed that some primers (including primers MAA196 and MAA156) may amplify two regions, and up to four alleles can be present   .a), number of effective alleles (Ne), observed and expected heterozygosities (Ho and He), number of private alleles (Pa), inbreeding coefficient (FIS) and fixation index (FST). Allelic richness was calculated in the R package Hierfstat  implementing the Evanno method   consistently amplified two readily distinguishable regions, which were scored as separate loci and differentiated as primer MAA178A and primer MAA178B (following species) . Althougnnulatus , suggestntiation . This thntiation . The folFIS) for all populations was negative (indicating no signs of inbreeding), or in the case of Gunung Mentawa it was barely positive (0.05). The population at Gunung Mentawa had no private alleles, while the other four populations had between one (Gunung Manok) and six (Gunung Bedoh) private alleles. Pairwise comparisons of FST showed limited levels of genetic differentiation across populations . The most likely number was K = 3. The structure plot shows that the genetic clusters are restricted to neither distinct geographic populations nor size classes of trees, with all three clusters being fairly equally present in each population and each size class  and inbreeding coefficients (FIS), based on the same six microsatellite loci, were calculated for A. annulatus and its closest relatives: A. integer var. integer (cempedak),A. integer var. silvestris  (Data from A. heterophyllus (jackfruit) (Data from p = 0.113) across the species for He, though A. annulatus tended to have the lowest values (p = 0.020) across species for FIS, and a posthoc Tukey HSD test then found significant differences between A. annulatus and the other three taxa Expected heterozygosities  of its most closely related congeners  using the same six microsatellite markers, A. annulatus tends to have lower measures, but there is no significant difference (p = 0.597)  . We alsonnulatus . This coy chance , which mA. annulatus does not show signs of inbreeding, as evidenced by the negative value of the FIS compared to its three relatives—displaying FIS values between 0.149 and 0.314 (A. annulatus is distributed within populations (93%), and because A. annulatus is a long-lived, likely outcrossing tree (as are other members of the genus), these results are put into perspective. Genetic diversity levels could be remnant from when the population of A. annulatus may have been larger and may have had higher levels of diversity, and the remaining individuals still retain this because of their lifespan  are under human selection and are frequently vegetatively propagated  to maintain desired traits  and cempedak (A. integer). Both of these monoecious species  are thought to be pollinated via a three-way mutualistic relationship between the plant itself, gall midges , and a fungus  that grows on the male inflorescences  is about 3 km to the closet population (Gunung Bedoh) and about 6 km to the next closet populations. Most of what is known about flight range of members of the gall midge family comes from studies of pests. For example, studies of Hessian flies  suggest that they choose when, where, and how far to move based on cues from the environment, and are also affected by wind  is also the most genetically differentiated from the others.It is of interest to consider methods of gene flow to understand what might be causing low levels of genetic differentiation across populations in such a dramatic landscape as karst forests, where dispersal ability of inhabitants may be limited . The twoescences . The aduescences , and in echanism . Gall miechanism . Some of by wind . They ca by wind . Additio by wind . We stilA. annulatus are large (ca. 6 × 8 cm) and distinctive with bumpy or spiky skin and a strong fragrance , domestic cows (Bos primigenius), buffalo , and rhesus macaques (Macaca mulatta) as seed dispersers  and the serow (Capricornis sumatraensis)  among them , Fig. S1ragrance . Just liragrance , but theelatives . While rspersers . The sturaensis) . While ass sites . Future A. annulatus gene pool could be reduced due to random, stochastic loss of individuals, leading to a decreased level of diversity in younger generations and a genetic bottleneck. When A. annulatus individuals were analyzed by size class , diversity measures tended to be lower in seedlings compared to saplings and mature trees. However, only the number of private alleles was significantly less in seedlings  measurements. Tree heights are estimates. For the purposes of differentiating age, any sample less than 1 meter tall was considered a seedling; any sample taller than 1 meter and with a dbh less than 5.5 cm was considered a sapling; and any sample with a dbh of 5.5 cm or more was considered mature due to the observation that trees of that size often had signs of past reproduction. All other samples were considered saplings. Collection numbers in bold have voucher specimens deposited in SAR. For all other samples, only dried leaf material was collected.Click here for additional data file.10.7717/peerj.9897/supp-3Table S2 Loci in bold italics were excluded from further analysis due to being monomorphic (MAA122 and MAA140), functionally monomorphic (MAA54) or having evidence of null alleles (MAA105).Microsatellite marker MAA178 amplified two regions and is notated as two loci, 178A and 178B.Click here for additional data file.10.7717/peerj.9897/supp-4Figure S1The population names are indicated above the plot.Click here for additional data file.


Sus scrofa) belongs among the most important game species both ecologically and economically, therefore knowing more about the basics of the genetics of the species is a key factor for accurate and sustainable management of its population. The aim of this study was to estimate the genetic diversity and to elucidate the genetic structure and location of wild boar populations in the Carpathian Basin. A total of 486 samples were collected and genotyped using 13 STR markers. The number of alleles varied between 4 and 14, at 9 of the 13 loci the observed heterozygosity was significantly different (p < 0.05) from the expected value, showing remarkable introgression in the population. The population was separated into two groups, with an Fst value of 0.03, suggesting the presence of two subpopulations. The first group included 147 individuals from the north-eastern part of Hungary, whereas the second group included 339 samples collected west and south of the first group. The two subpopulations’ genetic indices are roughly similar. The lack of physical barriers between the two groups indicates that the genetic difference is most likely caused by the high reproduction rate and large home range of the wild boars, or by some genetic traces’ having been preserved from both the last ice age and the period before the Hungarian water regulation.In the Carpathian Basin the wild boar ( Sus scrofa) is one of the most common big game species in Europe and worldwide. The origin of the species lives in East Asia, where the wild boar was separated from its closest relatives (Sus verrucosus) some 0.9–0.5 million years ago. Under the influence of the last glacial a strong decrease in numbers happened, but the Carpathian Mountains functioned as a refugia, thus many species, including wild boars found an area to survive here. When the ice age ended, these species rapidly recolonized the neighbouring areas and nowadays wild boars are widely distributed across Europe, Asia and Northern Africa [The wild boar  and tissue samples (n = 423) were collected in individually marked collection tubes. Muscle tissue samples were preserved in 96% ethanol, and all samples were stored at −20 °C until processing. For each sample, collection date and location were noted. The GPS coordinates of the kill sites were used if possible. In case if the exact location was unknown, the GPS coordinates of the centre of the hunting range were assigned to the sample in order to reach the highest possible accuracy. A few samples were also collected from neighbouring countries, namely Romania (n = 5), Croatia (n = 4), and Slovakia (n = 4) for comparison with the Hungarian samples.A total of 486 samples were collected from wild boars shot at hunting sites by licensed hunters. Samples originated from free-ranging wild boar were legally shot during organized hunting events. For this no specific approval was needed, all applicable international, national and institutional guidelines were followed. This study was carried out in strict accordance with the recommendations and rules in the Hungarian Code of Practice for the Care and Use of Animals for Scientific Purposes. The protocol was approved by the Animal Care and Ethics Committee of the Agricultural Biotechnology Institute, NAIK. Hair follicles  and from hair follicles using the QIAamp DNA Investigator Kit . The manufacturer’s instructions were followed in both cases. The quantity and purity of isolated DNA were checked by spectrophotometry in a NanoDrop ND-1000 machine . The DNA was stored at −20 °C until PCR amplification.st value) the Geneland v.3.4.2. [PCR amplification was performed using a multiplexed microsatellite marker set with 13 markers described by Lin et al. . Amplifiv.3.4.2.  program v.3.4.2. . To detev.3.4.2. ,A total of 291 male and 195 female wild boars were genotyped, and the STR markers used showed fairly high polymorphisms .p < 0.001 . A single He < Ho with p < 0.05 was also found (PigSTR14B). These results suggest strong introgression in the population.The number of alleles varied between 4 (PigSTR14A) and 14 (PigSTR11B), with an average of 7.62. The effective number of alleles was between 1.162 and 5.095 , with an average of 2.29. Significant deviation from the Hardy-Weinberg equilibrium with He > Ho occurred in eight cases with st value was 0.02975, suggesting that gene flow between the groups is high enough for the two groups to belong into one population, but there are genetic differences between them. Most likely this means that there are two different subpopulations present in the region.Geneland detected a genetic structure and clustered the samples into two groups (with 80% probability). The first group included 147 samples from the eastern part of Hungary, whereas the second, larger one consisted of 339 individuals from the western part of the country . The FstThe subpopulations were almost perfectly separated geographically, but no physical barriers seemed to be present between them. There was one sample in the north-western region that clearly did not fit into the surrounding cluster, and there were four samples in the south-western region that differed from the surrounding samples. In the next step the output of the Barrier software was put on the map, but the results are not consistent with either genetic segregation or topography or the major road network of the area .One of the greatest changes in the environment of Hungary was water management in the late 1800′s, therefore the previous results were put on a hydrographic map from the mid-1800’s .No clear correlation can be detected in this case either; however, the location of the smaller subpopulation is roughly the same as the floodplain in the north-eastern part of the country bordered by the Mátra mountains.Per population diversity values were similarly high as that for the whole sample set .p < 0.001 significance level  and in one case He < Ho with p < 0.05 significance level (PigSTR11A) were found. In group 2, significant deviation from the equilibrium with He > Ho occured in eight cases with p < 0.001 , and with He < Ho in a single case with p < 0.05 level of significance (PigSTR14B).The number of alleles varied between 3–12 (PigSTR4B & PigSTR14A and PigSTR15A) and 3–13 (PigSTR14A & PigSTR1A and PigSTR11B & PigSTR15A) with an average of 5.62 and 6.54, respectively. The effective number of alleles was between 1.10 and 4.84 (PigSTR14B and PigSTR15A) and between 1.19 and 5.15 (PigSTR14B and PigSTR15A). In group 1 five loci showed deviation from the Hardy-Weinberg equilibrium; in four cases He > Ho with Our extensive sampling and the higher number of STR markers used resulted in 486 individual multilocus genotypes of wild boars across the Carpathian Basin. The genetic diversity revealed corresponds very well to the results of other studies shown below, although the markers used, and sample numbers differ greatly between studies. Frantz et al.  found anThe heterozygosity values found in our work are similar to the results of previous European studies but differ slightly from the results of Hungarian samples. The Bulgarian wild boars’ observed heterozygosity values were lower than expected in nine markers, and in Germany at eight loci . Sprem eThe population genetic indices of subpopulation 2 are the same as the whole populations’ results, probably because 69.47% of the individuals belongs here.No significant correlation between genetic and geographic distances was found, probably because of the high motility and reproduction rate of the wild boar. The lack of different populations in Hungary is consistent (i) with our previous results, where we focused on the effects of Hungary’s busiest highway  and (ii)Genetic characterization is essential for recognizing species diversity as well as for rationalizing managing activities. By the analysis of 13 STR markers of 486 wild boars, the genetic diversity is shown to be similarly high as found all across Europe. Although the marker set used was originally designed for Asian wild boars, with some optimizations it can also be used perfectly for European boars as a faster and cheaper alternative to previously used STR marker sets. In Hungary two subpopulations were found, which were almost completely separated by regions. The separation seems to be related only with the characteristics of the species and not with the recent geographical features, but traces of the period before water regulation seem to be visible. The genetical parameters of the subpopulations (and the whole Carpathian population) are diverse, but the high number of deviations from the Hardy-Weinberg equilibrium may cause greater differences between the subpopulations. The clarification of the exact location of the southern border between subpopulations necessitates the examination of more samples from Croatia, Serbia and Romania. In addition, our study has direct relevance for hunting strategies in wild boar. It is clear that, rather than relying on a general/national management strategy, subpopulations should be managed in an integrative way, strengthened with biological data.