Genetic diversity studies provide important details on target trait availability and its variability, for the success of breeding programs. In this study, GBS approach was used to reveal a new structuration of genetic diversity and population structure of pigeonpea in Benin. We used a total of 688 high-quality Single Nucleotide Polymorphism markers for a total of 44 pigeonpea genotypes. The distribution of SNP markers on the 11 chromosomes ranged from 14 on chromosome 5 to 133 on chromosome 2. The Polymorphism Information Content and gene diversity values were 0.30 and 0.34 respectively. The analysis of population structure revealed four clear subpopulations. The Weighted Neighbor Joining tree agreed with structure analyses by grouping the 44 genotypes into four clusters. The PCoA revealed that genotypes from subpopulations 1, 2 and 3 intermixed among themselves. The Analysis of Molecular Variance showed 7% of the total variation among genotypes while the rest of variation (93%) was within genotypes from subpopulations indicating a high gene exchange (Nm = 7.13) and low genetic differentiation (PhiPT = 0.07) between subpopulations. Subpopulation 2 presented the highest mean values of number of different alleles (Na = 1.57), number of loci with private alleles (Pa = 0.11) and the percentage of polymorphic loci (P = 57.12%). We discuss our findings and demonstrate how the genetic diversity and the population structure of this specie can be used through the Genome Wide Association Studies and Marker-Assisted Selection to enhance genetic gain in pigeonpea breeding programs in Benin. Cajanus cajan [L.] Millspaugh) is an important food legume crop widely grown in tropical and subtropical climates [Pigeonpea  and extract purity using the absorbance ratio 260nm to 230nm. According to the DNA concentration, different dilution rates were applied to each sample to normalize each sample at 10 ng/μl for further use.GBS libraries were prepared following Elshire et al.  and in aThe raw sequence data generated was filtered using Tassel v5.2.63  analysisMajor Allele Frequency (MAF), gene diversity, and Polymorphic Information Content (PIC) values for all SNP markers, were calculated using Power Marker v.3.25 software .http://taylor0.biology.ucla.edu/structureHarvester/) available online was used to calculate the final population structure. The likelihood value Ln (K) (mean + SD) method [The Structure v2.3.4 Software  was used) method  was used) method . GenotypTo assess genetic diversity, genetic distances across genotypes were calculated using Tassel v5.2.63 . The disGenotyping by sequencing (GBS) of the 50 genotypes provided 18.7 Gb data containing 3.16 million sequence reads. The sequencing data was mapped on to the reference pigeonpea genome and the alignment sequences provided 71,712 SNPs across 50 genotypes. Considering the filtering criteria applied both to samples and SNP, six genotypes  were excluded from further analysis and a total of 688 high quality SNPs were retained for further analysis. The number of SNPs per chromosome ranged from 14 on chromosome 5 to 133 on chromosome 2 . The ratThe likelihood value method used showed a first clear peak at K = 2 . HoweverThe Neighbor Joining tree based on the genetic distances analysis showed four major clusters within all the 44 pigeonpea analyzed genotypes . ClusterFst values for subpopulations pop1 pop2 pop3 and pop4 were 0.46; 0.48; 0.57 and 0.40 respectively. We observed a low average distance (HE) between individuals in same cluster in subpopulation 3 (0.09)  were 0.07 and 7.13 respectively . The mea3 (0.09) .The knowledge of genetic diversity is of paramount importance not only for the development of conservation strategies but also and more importantly the selection of parents to be used in genetic improvement –48. In tPopulation structure analysis clarifies genetic diversity studies . This stThe diversity pattern revealed by the mean values of total number of alleles, number of loci with private alleles and percentage of polymorphic loci within subpopulations provided insight to genetic diversity within populations  indicateIn this study, GBS approach was used to determine the level of genetic diversity, population structure of a collection of pigeonpea landraces grown in Benin. Results showed significant genetic diversity structured and maintained by the selection of cultivars based on specific traits. Four clear subpopulations were identified. The subpopulation 2 exhibited the highest mean values of total number of alleles, number of loci with private alleles and percentage of polymorphic loci. It represented the main gene pool in the analyzed collection and could contain desirable traits, such as biotic or abiotic stress tolerance. These findings laid the basis for Genome Wide Association Studies and consequently Marker-Assisted Selection to enhance genetic gain in pigeonpea breeding programs in Benin.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Juniperus deppeana, a conifer distributed from the Southwestern United States to the highlands of Central America. We combined information of one plastid marker and two nuclear markers to infer phylogeographic structure, genetic diversity and demographic changes. We also characterized the climatic niche for each variety to infer the plausible area of suitability during past climatic conditions and to evaluate climatic niche discontinuities along with the species distribution. We found a marked phylogeographic structure separating the populations North and South of the Isthmus of Tehuantepec, with populations to the South of this barrier forming a distinct genetic cluster corresponding to Juniperus deppeana var. gamboana. We also found signals of population expansion in the Northern genetic cluster. Ecological niche modeling results confirmed climatic niche differences and discontinuities among J. deppeana varieties and heterogeneous responses to climatic oscillations. Overall, J. deppeana’s genetic diversity has been marked by distribution shifts, population growth and secondary contact the North, and in situ permanence in the South since the last interglacial to the present. High genetic variation suggests a wide and climatically diverse distribution during climatic oscillations. We detected the existence of two main genetic clusters, supporting previous proposals that Juniperus deppeana and Juniperus gamboana may be considered two separate species.The most recent glacial cycles of the Pleistocene affected the distribution, population sizes, and levels of genetic structure of temperate-forest species in the main Mexican mountain systems. Our objective was to investigate the effects these cycles had on the genetic structure and distribution of a dominant species of the “mexical” vegetation across North and Central America. We studied the genetic diversity of  Mountains play a critical role in the maintenance and generation of biodiversity. This is mainly due to their unstable physiographic nature, highly variable climatic conditions and recent tectonic activity which together promote evolutionary processes in populations such as genetic differentiation, local adaptation, demographic changes, and distribution shifts   . Mexicoerus L.) , 1979.Juniperus, Caryocedrus and Sabina, the latter being the most diverse  and which along with other conifer genera such as Abies and Pinus has gone through increased diversification during the Miocene (23–5 Mya)  .Juniperus deppeana Steud.), a widespread serrate leaf juniper frequent in the Southwestern pinyon-juniper woodlands in the USA and the Mexican open conifer forests from 1,900 to 3,200 m (formae (Juniperus deppeana var. deppeana Steud), across both the Sierra Madre Occidental (SMOcc)  and the Sierra Madre Oriental (SMOr) (J. deppeana var. deppeana) to the Trans-Mexican Volcanic Belt (TMVB) in central Mexico and the Chiapan-Guatemalan highlands between Mexico and Guatemala (J. deppeana var. gamboana (Martínez) R.P. Adams). There are also some varieties and formae recorded from a small number of scattered localities with not enough individuals to be considered populations (J. deppeana var. patoniana (Martinez) Zanoni, J. deppeana f. sperryi (Correll) R.P. Adams and J. deppeana f. elongata R.P. Adams).Here we focused on the alligator bark juniper  glacial oscillations affected the Southern populations of J. deppeana differently than Northern populations, due to more pronounced range shifts in the latter, therefore, we expect higher genetic diversity, lower genetic structure and evidence of population growth in the Northern populations compared to the South; (2) the broad geographic range and morphological heterogeneity could indicate ecological divergence among varieties and forms, which would in turn exhibit genetic differentiation.We aimed to describe the processes that have created the genetic diversity in the varieties of We tested the first hypothesis by estimating genetic diversity and structure with both cpDNA and nrDNA markers. The second hypotesis was tested by modeling climatic niche under current and past conditions , and performing a background test of niche differentiation to determine if the climatic niche is significantly different tan expected by chance.J. deppeana (J. flaccida and J. durangensis as outgroups (Collection permit no. SGPA/DGGFS/712/1768/16 issued by SEMARNAT). Samples were sorted by variety according to i.e. considering J. gamboana as a variety of J. deppeana)  Martinez)  . Neverthartinez) ; thereflocality .J. durangensis and J. flaccida: Myb transcription factor (Myb) and the second intron of the gene LEAFY (Lfy) for nuclear representation and trnL-trnF spacer from cpDNA. These markers were selected because they have shown relatively high variability in other studied Juniperus species and due to their single-copy nature  modified protocol  and DNALfy .PCR protocols for each marker were as follows: a 30 s initial period at 98 °C, 40 cycles of 10 s at 98 °C, 40 s at 55 °C, 90 s at 72 °C and a final extension of 7 min at 72 °C for Lfy ; an iniLfy . For nuclear loci, heterozygous sites were detected and coded using the IUPAC ambiguity code. We aligned the sequences together using the MUSCLE algorithm . Haploid sequence reconstruction was further carried out for nuclear markers (i.e. Myb and Lfy) using the PHASE 2.1 algorithm  between localities and a geographic distance matrix was approximated by calculating the euclidean distance between points using the ‘geodist’ R package  to confirm the convergence of the MCMC in the replicates combined. The resulting tree with highest posterior probability was obtained with TreeAnnotator  to vnnotator  and postLfy, JC69 + G(0.34) for Myb and JC69 for trnL-trnF. Two independent replicates were run with 20 million generations setting a 10% burn-in, and a strict clock model fixing the mutation rate of the trnL-trnF spacer set at 1.12 × 10−10 substitutions yr−1 as in To detect changes in effective population size, a multilocus Extended Bayesian Skyline Plot (EBSP)  was genJ. deppeana’s varieties, as well as the extent of their suitability areas under past climatic conditions, i.e. Last Interglacial  and the Last Glacial Maximum , we use species distribution modeling (SDM) using the pseudo-absence algorithm Maxent 3.4.1  as well as collection records from the MEXU herbarium  and grouped them into subsets according to variety distribution. We used 10 climatic variables from the 19 bioclimatic layers available at WorldClim (at a 30 arc-sec resolution)   and testolution)  to find olution)  and the olution) .https://chelsa-climate.org/last-glacial-maximum-climate) database at a 2.5 min resolution. Bioclimatic layers used for the LIG   into binary maps using a 10-percentile of the training records threshold, and areas with climatic stability between current conditions and the LGM were defined in terms of each group’s predicted suitability.We used bioclimatic layers for the LGM for two general circulation models (GCM), the Model for Interdisciplinary Research on Climate (MIROC-ESM)  and the  the LIG  were relim.org)  at a 2.5J. deppeana’s varieties are better explained by historical/random causes or by climatic niche differences. Background test is based on the comparison of niche overlap metrics such as I , because background test is more informative about the climatic niche of the geographic distribution of the records used than the true ecological niche of the entities involved  (deppeana (North) vs. deppeana (South), robusta vs. deppeana (North), zacatecensis vs. robusta, zacatecensis vs. deppeana (South) and deppeana (South) vs. gamboana. All tests were run symmetrically, meaning that every comparison resulted in two null distributions  and using a general rectangular extent of −119°, −90° long and 13°, 34° lat. As the number of occurrences for each group varied considerably, we standardized comparisons by setting the number of random background points to equal the group with fewer records. For this analysis, we used the total joint datasets (i.e. training and test data combined) and background areas between groups were defined as the different biotic communities shared among groups in order to incorporate plausible information about dispersal ability to the test, as well as climatic and evolutionary affinities of each group  . We ran ibutions . All coch group . We usewww.ncbi.nlm.nih.gov/genbank) under the popset numbers: 2208845402 (trnL-trnF), 2208845248 (Lfy) and 2208844880 (Myb). Total length of sequences were as follows: 302 pb (trnL-trnF), 775 pb (Lfy) and 607 pb (Myb). Polymorphism and sites with indels were highly variable among loci, as well as the number of parsimony-informative sites, being the nuclear marker Lfy the most variable , and multiple low-frequency haplotypes connected by one or two mutational steps in a star-like shape  and the second one is formed by the populations assigned as var. gamboana. The resulting plot for K = 3 (∆K = 9.837 & LnP(K) = −990.8100), is also shown, where the first genetic cluster is further divided into two genetic clusters that correspond each one to the northern and southern extremes of var. deppeana distribution range, and mixed clustering for the populations of var. robusta and var. zacatecensis, as well as a well-defined cluster for var. gamboana (p = 0.056)  = −1,001.7300; Likelihood and ∆K values for all K values tested can be seen in gamboana . The Nor= 0.056) .J. deppeana var. gamboana populations  that started somewhere between 0.12 and 0.14 Mya and continued up to the current time (Ne) from 0.14 Mya to today. For the second cluster (i.e. var. gamboana) the results support the hypothesis of constant population size given that the confidence interval (95% CPD) includes zero changes in effective population size , 77 for deppeana (South), 109 for robusta, 32 for zacatecensis and 44 for gamboana and the optimal level of complexity varied between each group in terms of feature class and regularization multiplier , mean diurnal range (Bio2), isothermality (Bio3), maximum temperature of the warmest month (Bio5), mean temperature or the driest quarter (Bio9), mean temperature of the coldest quarter (Bio11), annual precipitation (Bio12), precipitation seasonality (Bio15), precipitation of the driest quarter (Bio17) and precipitation of the coldest quarter (Bio19) which together account for >85% of the total variability of the whole unsorted data set. The number of final occurrence records considered varied between geographic groups. A total of 162 occurrence records for ltiplier .gamboana, in which a much smaller distribution during the LGM is predicted under the CCSM4 conditions , robusta and gamboana, and more subtle differences for the rest when comparing both GCMs used. While with the CCSM4 model the projected distribution of varieties deppeana (North) and robusta were more concentrated in the South Mexican Plateau and the East portion of today’s Chihuahuan Desert, with the MIROC-ESM model the predicted distribution of var. robusta for the LGM was wider in the northern part of the SMOcc with small patches surrounding today’s Chihuahuan Desert in northeast Mexico  and zacatecensis only a small portion of the area was mapped on the east part of the TMVB and at the lower part of the SMOcc for each group respectively (i.e. deppeana (North) and robusta) the potential distribution map during the LIG was concentrated in the southern portion of the Mexican plateau, the main SMOcc and a scattered distribution along the west portion of the TMVB.For the LIG  the projections were unable to map any suitable area for the presence of var. ectively . For thegamboana, in which a conflict between metrics and a lack of significance in at least one of the symmetrical comparisons of each metric evaluated were found (deppeana (North) vs deppeana (South), robusta vs deppeana (North), and zacatecensis vs deppeana (South)–meaning that observed values for overlap metrics, D and I, are lower than expected by chance. The relationship between zacatecensis and robusta SDMs was more complex, while the comparison of group robusta vs the background resulted in overlap metrics higher than expected by chance, when comparing zacatecensis against the same background area the overlapping metrics were lower than what is expected by chance.The overall overlap metrics were significant and consistent for most of the comparisons except the ones involving group re found . We founJ. deppeana are contrasting to previous reports for Mexican rare juniper J. blancoi. J. blancoi has an overlapping distribution range with J. deppeana along the TMVB and the SMOcc, therefore, we would expect that it has been exposed to the same climatic oscillations during the Pleistocene in these particular regions. While J. deppeana showed higher levels of genetic diversity than J. blancoi in nuclear markers  . The zoJ. deppeana var. gamboana, which has been historically considered a different species mainly due to morphological differences (e.g. the number of seeds per cone) (i.e. J. deppeana and J. gamboana as two different species). Moreover, it is worthy to note that J. deppeana and J. gamboana as two different lineages with historically limited gene flow with the Isthmus of Tehuantepec acting as a barrier between them.In this study, the populations at the east side of the Isthmus of Tehuantepec correspond to er cone) . More reer cone) , 2013, tJ. blancoi with a genetic structure that correspond with elevation differences and environmental factors  J. deppeana has a wider distribution and is known to grow in a wider elevation range . Accordingly, species with a less restrictive elevation range are more likely to find wider suitability areas during glacial cycles and therefore to show more connectivity and less genetic structure, for restricted taxa, only elevational shifts are expected along with local extinction , robusta and zacatecensis projected to the LGM showed areas of high suitability in a large portion of the Mexican Central Plateau along with the dry slopes of the SMOcc and SMOr. According to palynological data, pinyon-juniper woodlands and open conifer forests dominated most of today’s Chihuahuan Desert during the LGM and until approximately 11,000 years ago, when its modern xeric composition took place and the conifer forests were displaced to higher elevations in its surrounding slopes  and possibly this was followed by a dispersal event from the Chihuahuan Desert populations. For variety deppeana (South) in the TMVB both MIROC-ESM and CCSM4 projections show a wider distribution during the LGM that extended from the TMVB to the Sierra Madre del Sur in southern Mexico. The overlapping areas  between the current and LGM predicted areas suggest that the current potential distribution of J. deppeana var. deppeana along the east portion of the TMVB has remained constant since at least the LGM to the present. In addition, the relatively low number of low-copy haplotypes found in the nuclear haplotype networks for this variety are consistent with constant population sizes. Other studies  also support the idea that vegetation in the TMVB suffered elevation shifts during the glacial cycles with relatively little effect on effective population sizes, especially for medium to high elevation plants . Nonetheless, the projections for varieties deppeana (North) and robusta for this period showed a displacement to the South of the Mexican plateau and the SMOcc. Our results suggest that the southern Mexican plateau was consistently inhabited by J. deppeana in both glacial and interglacial periods and only became uninhabitable for the species after the LGM ca. 11,000 years ago. Projections also suggest a northward range expansion that took place between the LIG and LGM either from the populations on the SMOcc or the ones in the southern portion of the Chihuahuan Desert. Climate conditions during LIG were warmer and wetter than present ones . Similar distribution shifts had been proposed as a response to climate changes associated to glacial-interglacial events for other temperate and arid taxa in surrounding regions; these distributional shifts are also accompanied by demographic changes and local extinction in some cases  than the populations in the SMOcc , suggestme cases .deppeana as well as the climatic differentiation between each variety with their respective adjacent varieties (i.e. var. robusta and f. zacatecensis)  did not show significant support for either disparity or similarity in the climatic niches evaluated and there is no reason to believe that they occupy different climatic niches, at least in the geographic space (Our results for the background test support a north-south climatic differentiation between the populations of var. ecensis) . This suecensis) . This antability . On the ic space . Althougic space  and the ic space .deppeana, var. robusta and zacatecensis. Other common “mexical” species with similar distributions, such as the North American pinyon pines (Pinus L. subsection Cembroides) show similar patterns of climatic differentiation in the geographic space (i.e. ENM based approaches) only compare groups in the geographic space and do not incorporate explicit information about climatic tolerances of each group, it is not possible to infer a process of niche divergence or tolerance evolution from these results  and show the climatic heterogeneity that this species exhibits in the geographic space. Future efforts should focus on the extent to which these climatic differences can shape the distribution of genetic diversity and how well they could predict genetic structure.Interestingly, the genetic groups recovered in the clustering analysis West to the Isthmus of Tehuantepec  do not ric space . Neverth results . Our resJ. deppeana  increased their distribution range and shifted southward, reaching the TMVB; meanwhile, the more “tropical” J. gamboana showed little change. These distribution changes are also supported by the amount of genetic variation and negative values of Tajima’s D found in the varieties North of the Isthmus of Tehuantepec. It has already been reported that the communities of the Chihuahuan Desert and adjacent areas have changed a lot since de Miocene  with a complex evolutionary history marked by distributional shifts, demographic changes and a process of genetic divergence driven mainly by the existence of a geographic barrier (Isthmus of Tehuantepec). Following these results, the general taxonomy of J. deppeana and its varieties should be revisited using a more integral view and taking into consideration intraspecific variation at genetic level. It is possible, given the climatic heterogeneity occupied by J. deppeana’s populations, that other causes beyond the historical ones could explain the genetic structure found in populations in Mexico like contemporary gene flow or human influences, but these hypotheses still have to be tested. It is also worthy to note that while the genetic data showed an important demographic expansion, the projected distributions for LGM and LIG showed that this expansion possibly was not homogeneous across the species range. Therefore future studies should explore and test more complex demographic and migration scenarios.Our results from genetic and ecological niche analyses suggest the existence of two different lineages (10.7717/peerj.13802/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13802/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.13802/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.13802/supp-4Supplemental Information 4Click here for additional data file.


Dicamptodon tenebrosus) in the Pacific Northwest of the United States. We consider predictions based on the contemporary landscape , in addition to relatively ancient post-Pleistocene range expansion . To assess local versus larger-scale effects, we sampled 318 individuals across 23 sites, which were clustered in five sampling regions. Genetic variation was assessed using five microsatellite markers. We found evidence of (i) historical regional isolation, with decreased genetic diversity among more recently colonized northern sites, as well as (ii) high levels of inbreeding and loss of heterozygosity at local scales, despite relatively low overall population differentiation (FST) or strong evidence for population bottlenecks. Genetic diversity was not associated with contemporary disturbances (logging or fire), and there were no detectable effects on the genetic connectivity of populations based on intervening landscape features (habitat fragmentation and topography). However, lower genetic diversity in more northern regions indicates a lag in recovery of genetic diversity following post-Pleistocene expansion. Additionally, some populations had evidence of having undergone a recent genetic bottleneck or had high inbreeding (FIS) values. Lower genetic diversity in more northern sites means populations may be more vulnerable to future environmental changes, and managing for connectivity alone may not be sufficient given low mobility. Recent apparent reductions in some populations were not clearly linked to anthropogenic disturbances we examined. This suggests the type of disturbances this species is sensitive to may not be well understood.Determining the genetic consequences of both historical and contemporary events can clarify the effects of the environment on population connectivity and inform conservation decisions. Historical events (like glaciations) and contemporary factors (like logging) can disrupt gene flow between populations. This is especially true among species with specialized ecological requirements and low dispersal ability, like amphibians. We test for the genetic consequences of historical and contemporary disturbances in the coastal giant salamander ( Both contemporary and historical factors can contribute to the geographic distribution of genetic variation. These factors can increase the relative isolation of individuals, consequently reducing gene flow, population abundance, and genetic diversity. Considering genetic signatures across landscapes can help link causes to consequences, and further guide conservation efforts by identifying populations with low genetic diversity that have recently experienced habitat disturbances. Genetic connectivity and diversity can be precursors for sensitivity to demographic changes, disease events, or future climatic changes e.g., –4). If g. If g4])The degree to which contemporary and historical factors contribute to population isolation will vary across taxonomic and geographic scales. Historical and contemporary factors may be important for species that have persisted or expanded into areas that have undergone pronounced climatic shifts, such as previously glaciated areas. Species with limited dispersal and specialized habitat associations may be particularly susceptible to the negative consequences of reduced connectivity.Amphibians are often reliant on spatially patchy and ephemeral habitats , and have high habitat specificity and strict ecophysiological requirements , 21. TheDicamptodon tenebrosus) from the Pacific Northwest (PNW) of the United States. The species breeds in naturally fragmented and disturbance-prone headwater habitats [Here, we test hypotheses about the effects of contemporary and historical factors on within- and among-population connectivity in the coastal giant salamander . The cohabitats –34. Potehabitats , 36, andhabitats ). Naturahabitats  and unsuhabitats , 41). Mohabitats .Rhyacotriton spp.) to land-cover type and the presence of roads [Rhyacotriton spp. hypothesized to result in varying vulnerabilities to disturbance . Th. ThRhyac the PNW –51, undeWe test hypotheses based on the population genetic variation of the coastal giant salamander to evaluate the potential effects of contemporary and historical factors on species vulnerability. We focus our sampling on the Oregon Coast Range within the PNW. By sampling at both regional and local  scales, we were able to test both historical  and contemporary  factors. Specifically, we explore genetic diversity and connectivity as related to land-use, logging, fire, as well across different river drainages.Tail or toe clips were collected from individual coastal giant salamanders across 23 headwater sites (designated 1–23) from five broadly separated regions  upon collection. DNEasy Tissue kits  were used for DNA extractions, following manufacturer guidelines. Individuals were genotyped for five microsatellite loci  developer), were conducted for each locus using GenePop (v. 4.1) [Ho), expected heterozygosity (He), and genetic differentiation between populations (FST) were calculated using Fstat (v. 2.9.3) [PCR products were genotyped, and alleles were manually scored using GeneMarker software . Potential genotyping errors, including null alleles, scoring errors, and allele dropout, were assessed using Micro-Checker (v. 2.3.3) . Tests f(v. 4.1) ; signifi. 2.9.3) .Structure analysis (v. 2.3.4) [prcomp function and visualized using the package ggplot2 [Structure analyses, K-values of 1 to 24 were run in the global analysis  without conditioning on geographic sites, using the admixture model with a 50,000 burn-in, followed by 500,000 repetitions. Ten independent runs were conducted for each K-value. We also conducted additional Structure analyses using a priori assignments. For these analyses, we conditioned by (a) population (K = 1–24), (b) drainage (K = 1–13), and (c) region (K = 1–6). The most likely K-value was estimated using Structure Harvester [Clumpak [Geographic structuring of genetic variation was evaluated in two complementary approaches: by principal component analysis (PCA) and a . 2.3.4) , 61. The. 2.3.4)  using RS. 2.3.4)  using th ggplot2 . Missingarvester , and ass[Clumpak , 67.FST versus the minimum distances between sites using RStudio [ggplot2 [adegenet [We also evaluated isolation by distance (IBD) based on  RStudio , 63 and [ggplot2 . We evaladegenet  using 99FST -values versus geographic distance, then testing whether these residuals were explained by boundaries between drainage basins. Specifically, differences among residuals based on drainage association  were analyzed using a Kruskal-Wallis test [D. tenebrosus (explained below). The different categories for water catchments included: Hydrological Units (HUs) at three levels  [We examined the structuring of genetic variation as a function of watershed and drainage , controllis test  with a Blis test , 71 for  and 12) , drainag and 12) . Larger  and 12) , 75. How and 12) . ApplyinFST). Conversely, habitat fragmentation and less desirable land cover types would increase genetic differentiation among sampling sites.The extent of different landcover classes was obtained from the National Land Cover Database (NLCD) from 2006 , given tFST-values and five different predictors: distance, proportion of evergreen forest, proportion of shrub/scrub, and patch cohesion of evergreen forest between sites, as well as whether sites were in the same watershed. We performed analyses in RStudio [To quantify the landcover effects of areas separating sampled sites, we identified regions representing the shortest distances between each pair of sites in a region and created 80-m wide rectangles between the two sample sites . The width of rectangles  was chosen by quadrupling the maximum observed movement distance (of 20 m)  to hedge RStudio , 63. Onl RStudio .He) is significantly higher than the equilibrium heterozygosity (Heq). Significance was evaluated using a one-sided Wilcoxon sign rank test based on 1,000 iterations. Note that bottlenecks in this test are defined as severe population contractions resulting in a reduction of effective population size, not as a population contraction followed by an expansion. We used the developer’s recommended settings for microsatellite data for our loci and individual sample size. Specifically, the settings were a two-phase mutation model with 95% of single mutations, a multi-step variance of 12, and the mode shift turned off. In addition, based on program guidelines and subsequent methodological studies [We tested for the potential effects of disturbance events on genetic diversity using genetic bottleneck-tests within sites at each site using BOTTLENECK (1.2.02) . This ap studies , 83, we We also tested for correlations between genetic diversity  and whether a disturbance event had occurred in the last 70 years via Kruskal-Wallis tests . We compAll microsatellite loci were polymorphic. There were no consistent patterns of deviation from HWE, null alleles, or linkage disequilibrium across sites or across microsatellites, with the exception of the D6 locus (which was out of HWE). Consequently, all analyses were performed with and without the D6 locus, and because results were qualitatively similar, analyses using all loci are presented here (see He) ranged from 0.07 to 1.00, and observed heterozygosity (Ho) ranged from 0.00 to 1.00. The FIS-index per-population (averaged across loci) had a mean of 0.37 (SD ± 0.096) and ranged from 0.14 (site 11) to 0.57 .Genetic diversity was generally high  did not overlap with individuals from the southern region (region E) on PC2. The northern region (A) also tended towards less genetic variation , but not within regions (P = 0.454). Within regions, there is pronounced variation in the levels of genetic differentiation across sites (P = 0.044) before, but not after Bonferroni correction for multiple comparisons.IBD was significant across regions , our analysis did not identify a significant predictor of this differentiation. Of the five predictor variables, the best fit model included only one predictor–the proportion of shrub/scrub area between sampled locations (P = 0.055).Although Both types of disturbance–fire and logging–were common in the study area. Eleven sites had a recent history of fire, and eight sites had a recent history of logging. Five sites had been both burned and logged, and only one logged site had forested buffers left along the waterway.P = 0.031, 0.047, 0.016, and 0.016, respectively; Evidence of recent bottlenecks was apparent in four sampled sites: sites 2, 12, 20, and 23  sensitivity in this species is more context-dependent; 2) there has not been sufficient time for disturbances to leave a genetic signature; 3) signals of historical disturbance have overpowered those of more recent disturbances; 4) our results are statistical false negatives; or 5) some combination of these. Indeed, the first reason is perhaps the primary driver for the lack of a strong detected effect of a particular recent disturbance regime  must also be compatible with the lack of strong evidence for population bottlenecks, moderate levels of inbreeding, and loss of heterozygosity , expounded upon above, might contribute to unclear, mixed genetic signals.When these findings are considered jointly, they are consistent with a general lack of population persistence, and specifically, locally fluctuating population sizes. Such fluctuations would drive down the effective population sizes, which could explain the inbreeding and loss of heterozygosity. Fluctuating population sizes, in particular, could also lead to the variation in Rhyacotriton spp.) [Rhyacotriton spp. in contrast to coastal giant salamanders [Whereas other studies of PNW salamanders have shown that genetic structure is affected by habitat disturbance associated with logging   or land-on spp.) , we founon spp.) . The difamanders  may indiFIS) levels, coastal giant salamanders may be vulnerable to genetic losses from unspecified causes.Similarly, it would be a mistake to make management decisions based on the presumption that gene flow will resolve negative impacts of disturbance . The sometimes-high genetic differentiation within a region Click here for additional data file.S1 Tablen), observed heterozygosity (Ho), expected heterozygosity (He), expected heterozygosity under Hardy-Weinberg equilibrium (HWE), the number of alleles (NA), the allelic richness (AR), and inbreeding coefficient (FIS).Loci (far left column) are summarized per sampled site (1–23). The summary statistics described are number of individuals with information on that locus ((PDF)Click here for additional data file.S2 TableThese include corresponding watershed units: , drainage basin, or watershed subdivided by Level 1 streams . For HUs, numbers correspond to the unique HU identifier codes designated by the U.S. Geological Survey . Numbers(PDF)Click here for additional data file.S3 TableHo, mean He, and mean FIS, and the total number of alleles (NA). Estimates for expected heterozygosity given mutation-drift equilibrium (Heq) and whether the populations has likely experienced a genetic bottleneck  are based on the program BOTTLENECK [For each site (1–23), mean TTLENECK . Dashes (PDF)Click here for additional data file.S1 FigK = 4 or K = 3) as shown by the different colors. The three scenarios include a prior categorization of individuals (i.e. conditioning) by (i) watershed (based on HU 08 categorization) and (ii) population, as well as with (iii) no a priori information for individuals.Individuals  are grouped by population and organized according to sampled site Click here for additional data file.S2 FigAllelic richness (y-axis) refers to the average allelic richness for each site, and this was compared to whether or not the sites experienced a disturbance event  within the last 70 years. Kruskal-Wallis tests (three) did not identify statistically significant effects of these factors on allelic richness.(PDF)Click here for additional data file.


Pelochelys cantorii is critically endangered and rarely seen in the wild, and only 13 adults are being kept in captivity in China. For the purpose of reinforcing the conservation and management of P. cantorii, the Pearl River Fisheries Research Institute (Chinese Fishery Academy of Sciences) successfully bred more than 800 turtles from 2015 to 2020. In this study, we developed and characterized 10 simple sequence repeat markers from the RNA transcriptome of P. cantorii, established two multiplex PCR systems, and obtained the genetic structure and genetic diversity of the artificially conserved population. The aim was to obtain viable second-generation P. cantorii with the highest genetic diversity to implement population recovery plans for this species.Pelochelys cantorii we used transcriptome data to design more than 30,000 tri- and tetranucleotide repeat microsatellite primer pairs, of which 230 pairs were used for laboratory experiments. After two screenings, only 10 microsatellite markers with good specificity, high amplification efficiency, and polymorphisms were obtained. Using the selected primers, two multiplex PCR systems were established to compare and analyze the genetic diversity of artificially assisted breeding generations from four parents  continuously bred over two years. A total of 25 alleles were detected among the 10 microsatellite loci of the offspring. The polymorphism information content (PIC) was 0.313–0.674, with an average of 0.401, among which two loci were highly polymorphic (PIC ≥ 0.5). The number of alleles was 2–5 and the number of effective alleles was 1.635–3.614. The observed heterozygosity was 0.341–0.813, with an average of 0.582, whereas the average expected heterozygosity was 0.389–0.725, with an average of 0.493. Moreover, on the basis of Nei’s genetic distance and the Bayesian clustering algorithm, the 182 offspring were divided into two subgroups, and the results corresponded to the two maternal lines. This is the first study to investigate the molecular markers of P. cantorii, and the results obtained can be used to protect genetic resources and provide a genetic basis for the design of population recovery plans.To understand the genetic structure of the protected turtle species  Pelochelys cantorii) belongs to the order Testudines (family: Trionychidae), one of the largest inland aquatic turtle species. It is an important indicator of ecological health in the Pearl River and the south of the Yangtze River in China. This species also has a long history and is of great scientific value in paleogeography and paleontological evolution. In the past, P. cantorii was widely distributed in southeastern China, but due to excessive economic development, its habitat has continuously deteriorated, and its population has been greatly reduced. Currently, P. cantorii is critically endangered and rarely seen in the wild, and only 13 adults are kept in captivity [P. cantorii as an endangered species in 2000, and it was later added to a page in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora treaty in 2003.The Asian giant soft-shelled turtle  successfully bred more than 800 turtles from 2015 to 2020 using four sexually mature turtles . Twenty healthy P. cantorii, aged 45 years and weighing 1.5–2 kg, were selected for a rewilding adaptive protection test in 2020 [P. cantorii with high genetic diversity and restore the wild population through artificial propagation and release.For the purpose of reinforcing the conservation and management of Microsatellite markers are codominant markers with a high degree of polymorphism ,4,5. TheP. cantorii. The main goal was to develop microsatellite multiplexes for P. cantorii to evaluate the genetic diversity and structure of artificially assisted breeding generations and to design the most feasible population recovery plan for this species.In this study, we obtained transcriptomic data for P. cantorii breeding and protection base in Gaoming, China, which maintains four parents . There were a total of 182 offspring . All of them underwent artificial incubation, whereafter umbilical cords were collected after hatching. The umbilical cords were soaked in anhydrous ethanol and stored at −20 °C.The experimental materials were obtained from a Permission for this work was obtained from the relevant staff at the Gaoming breeding and protection base. We only used the umbilical cord as experimental material, which can naturally fall off, to prevent influencing individual biological behaviors.Umbilical cords were cut to a mung bean size (approximately 30 μg), and a MicroElute Genomic DNA Kit  was used for DNA extraction according to the manufacturer’s specifications. The optical density (DNA absorbance ratio) and concentration of the extracted DNA were measured using a Nano Q microspectrophotometer . Then, 1% agar gel electrophoresis was performed to test DNA integrity. The DNA solutions were stored at −20 °C until use.Using transcriptome data produced in our laboratory (unpublished), tri- and tetranucleotide repeat microsatellite sequences were identified, and primers were designed using Primer Premier 5.0 . ThereafBefore PCR amplification, all primers were diluted to 10 μmol/L and mixed at a 1:40 ratio for each pair of forward and reverse primers. All three fluorescent connectors were diluted to 20 μmol/L. The established 10 μL multiplex PCR system comprised the following: 5 μL Applied Biosystems Multiplex PCR Master Mix; 2 μL mixed primers (forward primer and reverse primer); 0.2 μL fluorescent connector, 1.8 μL deionized water; 30–150 μmol/L DNA; 1 μL. The amplification procedure was as follows: initial denaturation at 94 °C for 5 min; denaturation at 94 °C for 30 s, annealing at 60 °C for 45 s, and extension at 72 °C for 70 s (24 cycles); denaturation at 94 °C for 30 s, annealing at 53 °C for 40 s, and extension at 72 °C for 30 s (eight cycles); final extension at 72 °C for another 10 min. A 2 μL volume of PCR product was mixed with 8 μL of molecular weight marker (GeneScan 500 LIZ)™ and a deionized formamide mixture (1:100). After further incubation for 5 min at 95 °C, the mixture was cooled on an ice plate for rapid denaturation. Thereafter, capillary electrophoresis was performed using an Abi 3130 multifunctional genetic analyzer . Finally, Peak Scanner Software v1.00 was used for genotyping.a), effective alleles (Ae), observed heterozygosity (Ho), expected heterozygosity (He), and Shannon diversity index of the microsatellite loci. The polymorphism information content (PIC) of the microsatellite loci was calculated using CERVUS v3.0 [IS) among turtles was performed using GenAlEx 6 [POPGENE v1.32  was usedVUS v3.0 . CalculaenAlEx 6 .POPGENE v1.32 was used to treat each sample as a population to calculate Nei’s standard genetic distance for each turtle, and dichotomous difference clustering (evolutionary tree) was constructed with MEGA 5.0  to obtaihttp://taylor0.biology.ucla.edu/structureHarvester/ (accessed on 6 January 2020) and concluded as the best K value.Structure v2.3.4  was usedDNA integrity was evaluated using 1% agarose gel electrophoresis. When the band pattern was clear and bright , DNA intn sequence motifs was the largest, accounting for 6.00%. Tetranucleotide microsatellite primers consisted of 32.27% of the total, and (AAAG)n sequence motifs were the most numerous, accounting for 6.67%. Among these, 230 primer pairs were randomly selected. In total, 153 pairs were amplified to form stable bands after the first screening. Eight individual samples were randomly selected for detection, and 10 pairs were polymorphic, with a polymorphism ratio of 6.53%. Relevant information on the 10 microsatellite primer pairs is shown in More than 30,000 tri- and tetranucleotide repeat microsatellite primer pairs were designed. Trinucleotide repeat microsatellite primers comprised 67.73% of the total, and the number of (AAT)By optimizing the parameters of multiplex PCR, two groups of microsatellite multiplex PCR systems were established, each containing five microsatellite loci. The specific parameters of the system are shown in IS was −0.611–0.310. The Na in the 2019 samples was 2–5, with an average of 2.5. The Ne was 1.606–0.377, and Ho was 0.359–0.854, with an average of 0.609. The He was 0.379–0.737, and the mean was 0.498. The Shannon diversity index ranged from 0.565–1.353, with an average value of 0.764. The Na in the 2020 samples was 2–4, with an average of 2.3. The Ne was 1.576–3.346 and the Ho was 0.317–0.760, with an average of 0.547. He was 0.368–0.706, with an average of 0.482. The Shannon diversity index was 0.552–1.291, with an average value of 0.731. The genotyping call rate of the 10 microsatellite loci  ranged from 34.07% to 81.87%. The number of alleles and available alleles, Ho, He, and Shannon diversity index in 2019 were all higher than those in 2020. The statistical results of the observational data are shown in All 10 microsatellite markers used in both PCR combinations amplified stable and clear DNA bands in the 182 turtle samples. The band size was 114–190 bp, and the number of alleles detected at each site was 2–5. The PIC was 0.313–0.674, of which two were highly polymorphic (PIC ≥ 0.5) and eight were intermediate polymorphic markers (0.25 ≤ PIC ≤ 0.5). The Shannon diversity index was 0.577–1.335, with an average value of 0.755. The FThe genetic distance between each individual was calculated using POPGENE. The individual clustering diagram based on the evolutionary tree method using MEGA showed that the entire progeny population could be divided into two subgroups . Beyond Cyprinus carpio var. singuonensis and screened a large number of polymorphic microsatellite loci [Tachypleus tridentatus, which could also be applied to the analysis of its genetic diversity [C. carpio had more abundant polymorphisms. In vertebrate genomes, microsatellite tetranucleotides (GATA/AGAT) are the most common [P. cantorii. The AAAG(n) sequence motif was the most prevalent, accounting for 6.67% of the total, whereas GATA and AGAT sequence motifs only represented 1.92% and 1.57%, respectively. This may be related to the study design criteria and species specificity.Yue et al. (2016) performed transcriptome sequencing of the reproductive tissues of ite loci . Twelve iversity . Accordiiversity , microsaiversity  also shot common ; therefoP. cantorii observed in this study had fewer alleles. For example, genetic differentiation of Geochelone nigra using 10 microsatellite loci showed 12–37 alleles in each microsatellite seat in various groups, with an average of 21.1 [Malaclemys terrapin ranged from 8–14 in five microsatellite loci, which was an approximate average of 10.67 [Mauremys mutica ranged from 5–26, with an average of 14.190 [Compared with other testudines, the population of  of 21.1 . In addiof 10.67 . The numf 14.190 .o was similar to that of nine populations of T. tridentatus (0.46–0.57) and higher than that of the endangered Cuora flavomarginata (0.032–0.936), with an average value of 0.329 [o (0.512–0.627) of 218 individuals from four Pelodiscus sinensis populations [P. cantorii is moderate. However, it is worth noting that the number of alleles and available alleles, Ho, He, Shannon diversity index, and other data from 2019 were all higher than those from 2020. The reasons for this may be the following: (1) the number of umbilical cord samples used varied—in 2019 and 2020, there were 103 and 79 samples, respectively; (2) these experiments took samples from the same four parents , and every parent mating combination was haphazard; (3) genetic diversity analysis was performed only on offspring from 2019 and 2020, which is a short sampling period.Schultz et al. (2009) believed that a PIC > 0.5 is high, 0.5 > PIC > 0.25 is moderate, and a PIC < 0.25 is low. In this experiment, the number of alleles detected at each locus was 2–5 . The PICof 0.329 ,34. The ulations . These r1 × F1, M1 × F2, M2 × F1, and M2 × F2). The fact that the four subgroups had different numbers of progeny may be due to male competition during mating, including female selection preference, sperm motility difference, fixed collocation, and other factors. This phenomenon indicates that there are dominant individuals among the parents, but the emergence of such individuals may cause problems, such as migration and a reduction in genetic diversity in the offspring population. For the breeding of second-generation offspring in future strategies, individuals of different subgroups can be selected according to clustering data, and gene exchange within the population can be artificially mediated to prevent inbreeding decline.In this experiment, MEGA and Structure software were used to cluster the conserved population. The results showed that the population could be divided into two subgroups, which was consistent with the actual situation of producing offspring from only two maternal parents. MEGA’s evolutionary tree results show that the population could be further divided into four subsets, consistent with the actual situation in which all four parents participate in reproductive activity and produce theoretical offspring groups  collaborating with Gaoming’s breeding center for P. cantorii has had no genotype supplementation from individuals of other regions in the short term. For all offspring derived from the four initial parents, germplasm resources are very limited, and the number of close relatives may increase in the future. Therefore, the introduction of sexually mature individuals from other regions may be an effective method to improve the level of genetic diversity, strengthen gene exchange among populations, and maintain the genetic potential of the population in prospective studies. At the same time, on the basis of molecular markers of parental genotype files and the genetic distances and relationships of the parents used to set up the scientific breeding program, we propose that it is possible to ensure that the offspring inherit the full genetic variation of the parents. This will allow them to produce offspring with rich genetic diversity and facilitate the supplementation of artificial breeding populations into the natural population.The breeding period we used to develop a conservation population of


Individual, provider, clinic, and societal level barriers have been shown to undermine the potential impact of genetic testing. The current approach in the primary care setting places an exorbitant burden on both providers and patients. Current literature provides insight into how to address barriers across multiple levels  and at multiple stages in the testing process  but interventions have had limited success. After outlining the current approach to genetic testing in the primary care setting, including the barriers that prevent genetic testing uptake and the methods proposed to address these issues, we recommend integrating genetic testing into routine medical care through population-based testing. Success in efforts to increase the uptake of genetic testing will not occur without significant changes to the way genetic services are delivered. These changes will not be instantaneous but are critical in moving this field forward to realize the potential for cancer risk genetic assessment to reduce cancer burden. There is a pressing need to better integrate genetic testing for hereditary cancer risk into primary care settings. Genetic testing is the future of preventative medicine, but currently remains an untapped resource for patients who may benefit from additional cancer surveillance or risk-reduction. The clinical impact of this testing depends on the effective identification of interested at-risk patients, successful delivery of testing, and follow-up care for individuals with positive results.Individual interventions have had varying degrees of success in identifying strategies that are capable of increasing hereditary risk assessment and genetic testing uptake , 2. HoweThe purpose of this paper is to provide an overview of the current model of genetic testing in the primary care setting, describe multilevel barriers to genetic testing, and assess proposed interventions from the existing body of literature. We conclude with suggestions for a future action plan that promotes population-based genetic testing and informs practical decisions about how to integrate genetic testing for cancer into the primary care setting.The current primary care model relies on a patient-provider interaction to start the testing process, an interaction that often occurs by chance. A patient may fill out a family history or the patient may ask a direct question about their risk that cues the provider to ask about family history and other risk factors. Sometimes, a patient-provider general discussion reveals clues that may lead an astute provider to pursue a cancer-risk conversation , 4. Any If a provider does determine that a patient would benefit from genetic testing, they can either directly order a test or refer the patient to a genetics professional. Even if they choose referral, extensive patient education may be needed regarding purpose, benefit, and risk of genetic testing to motivate the patient to complete the genetics consultation. If the provider orders the test directly, they are responsible for choosing the correct test, answering any patient concerns, including those surrounding privacy, cost, and explaining implications of positive and negative results –7. When Barriers, as it applies to genetic screening and testing, refers to the individual, provider, system, and policy level hurdles an individual must overcome to access genetic testing and navigate the process of genetic testing. Investigators have done extensive work identifying the multilevel barriers within the current model of genetic testing for cancer risk . Shen anIndividual-level barriers include the perceived cost of genetic testing, knowledge and beliefs about the genetic testing process, fear related to being at an increased risk for cancer, fear of genetic discrimination, and distrust in the medical system –18. AddiAt the level of the provider, primary care providers (PCPs) play an important role in helping patients overcome individual-level barriers. However, to do so they need to prioritize conversations about cancer risk during appointments, consistently identify high-risk patients, clarify how to obtain and order genetic testing, learn the nuances of result interpretation, improve communication with the clinical genetics services, and establish how to utilize test results to inform patient care , 20–22. An additional barrier is that many PCPs do not have a thorough understanding of the role and availability of genetics professionals to effectively collaborate with them or connect patients to these resources , 21. FurAt the systems level, genetic testing processes have not been integrated fully into EHR systems. The nuanced nature of genetic testing information, referral recommendations, coordination of communication between providers and specialists, and post-test care pathways for patients identified with hereditary risk all pose challenges to the production of an effective EHR . AlongsiEven if an individual patient overcomes these multilevel barriers, they may still face policy-level barriers related to lack of insurance coverage and the cost of genetic testing. The cost of genetic testing services is not well understood by patients or providers. Although the cost has decreased dramatically in recent history, out-of-pocket costs of genetic testing can still range anywhere from $100 to $2,000 depending on the type of test that is ordered and how much is covered by insurance . AdditioAdditionally, concerns about discrimination by insurers make individuals hesitant to undergo genetic testing . While tAll these factors are intricately intertwined and contribute to a low uptake rate of genetic testing for hereditary cancer, particularly amongst cancer-free patients. Failure to address these issues across multiple levels has contributed to an inability to reduce cancer mortality rates . A comprStill, this is a prolific field of study with many suggested solutions existing at each level. Bednar and colleagues  conducteWhile ideally patients will be captured prior to a cancer diagnosis, this has not always been the case under our current model of genetic testing. As such, interventions have been tested to address this issue, also with varying success. For example, Uyar et al.  recruiteEffective aspects of all 16 interventions included direct patient engagement through mass marketing or online testing information, integration of a genetic counselor or health navigator into the genetic testing process, and physician education and support. Importantly, an aspect of physician support that will be critical as we move towards a population-based screening model is effective integration of genetic information in the EHR –43. AddiStill, there are remaining limitations, including: a lack of follow-up time with patients, lack of detail in risk assessment strategies, largely White and high-income study populations , and privacy concerns , 47, 48.To build on previous efforts, we have implemented the EDGE Study (NCT04746794) which is a multilevel intervention with input from varied stakeholders, to improve population-based screening methods for hereditary cancer . All actGenetic testing, generally, has been historically inaccessible to underrepresented and underserved populations , 50, 51,During the EDGE study, individuals are offered the opportunity to discuss their concerns or questions regarding genetic testing with research or clinic staff . PCPs doWhile these interventions have been successful in increasing genetic testing uptake, their success has ultimately been limited by the multilevel barriers prolific in our current testing model. Offering genetic testing on a case-by-case basis, as we do currently, relies on the ability of healthcare providers to identify eligible patients with fidelity and move them through the process with ease. Meeting these conditions has proven untenable in many healthcare systems, even with certain interventions. As such, we must consider shifting to a population-based model in which all patients are offered genetic testing for hereditary cancer risk as part of their routine medical care.A population-based testing model will address some testing barriers by design and others through systematization. Currently, providers fail to identify patients who meet eligibility criteria, and the eligibility criteria fail to capture all individuals who would benefit from genetic testing . These bStudies evaluating population-based models have found that population-based testing identifies carriers that would be missed by traditional criteria, does not result in increased psychosocial concerns, can result in clinically actionable information, and can be cost-effective if implemented at 30 years old or earlier , 54–56. Until the scaffolding exists to establish population-based testing, streamlined genetic testing for patients with certain cancers  must remain a priority for interventions. Additionally, it is possible that individuals opt out of genetic testing as a preventative tool but still need a route to testing should they develop cancer. As such, there must be an effective clinical care pathway to receive streamlined genetic testing for those with a personal history of cancer. Hopefully, as the fear and stigma associated with genetic testing decreases and population-based testing expands, the need for streamlined testing will become less significant.Many cancer prevention or surveillance tools are used in routine medical care  but genetic testing for cancer risk has not been utilized in the same way, thus limiting our capacity to prevent and detect hereditary cancer. Genetic indicators of risk should not be treated as though they are significantly different from other risk indicators and genetic testing should be integrated into primary care accordingly.The joint guidelines put forth by the American College of Medical Genetics (ACMG) and the National Society of Genetic Counselors (NSGC), the President's Cancer Panel, and the extensive work in intervention research highlight the urgency of increasing genetic testing uptake rates for individuals who are eligible for, and interested in, genetic testing , 58. TheThe current model in the primary care setting is ineffective and fails to provide testing to eligible, interested patients. It places the burden of identifying high-risk patients on the provider or on the motivated patient, ignoring larger systemic and societal barriers an individual must overcome to access genetic testing and exacerbating existing healthcare disparities. Primary care providers, in our current medical landscape, are not given the tools and information to facilitate genetic testing for their patients. An existing body of work offers solutions to address multilevel barriers. However, there are limitations and testing uptake remains low. As demonstrated in previous work and by the methodology of the EDGE Study, future interventions must simultaneously address these barriers at as many levels as possible. Most of these barriers could be eliminated through a systematic population-based model.Larger systemic and societal barriers, as well as the interaction between these levels, will take more time and effort to address, with input necessary from multiple stakeholders. Future interventions should target the challenges in expanding to population-based genetic testing and can start from the ground up - working within the system to address patient, provider, and clinic barriers while pushing for systemic- and policy-level change.


Capsella bursa-pastoris. We installed two large common gardens of the shepherd’s purse, a young, self-fertilizing, allopolyploid weed with a worldwide distribution. One common garden was located in Europe, the other in Asia. We used accessions from three distinct genetic clusters  that reflect the demographic history of the species. Several life-history traits were measured. To explain the phenotypic variation between and within genetic clusters, we analysed the effects of (i) the genetic clusters, (ii) the phenotypic plasticity and its association to fitness and (iii) the distance in terms of bioclimatic variables between the sampling site of an accession and the common garden, i.e. the environmental distance. Our experiment showed that (i) the performance of C. bursa-pastoris is closely related to its high phenotypic plasticity; (ii) within a common garden, genetic cluster was a main determinant of phenotypic differences; and (iii) at the scale of the experiment, the effect of environmental distance to the common garden could not be distinguished from that of genetic clusters. Phenotypic plasticity and demographic history both play important role at different stages of range expansion. The success of the worldwide expansion of C. bursa-pastoris was undoubtedly influenced by its strong phenotypic plasticity.The colonization success of a species depends on the interplay between its phenotypic plasticity, adaptive potential and demographic history. Assessing their relative contributions during the different phases of a species range expansion is challenging, and requires large-scale experiments. Here, we investigated the relative contributions of plasticity, performance and demographic history to the worldwide expansion of the shepherd’s purse,  Capsella bursa-pastoris, the shepherd’s purse, at different stages of expansion. This study provides insight into the causes of the ecological success of a plant species during range expansion.The respective role of demography, plasticity and adaptation in the colonization success of plant species remains an intense topic of investigation in evolutionary ecology and genomics. A screening of phenotypic traits of hundreds of genotypes in large-scale common garden experiments in Eastern Asia and Europe shows that both demography and a high phenotypic plasticity underlie the success of the tetraploid and self-fertilizing species,  Range expansion can leave a strong footprint on current patterns of genetic and phenotypic variation . The shepherd’s purse is a self-fertilizing colonizer of recent allopolyploid origin. The shepherd’s purse arose some 100 000 years ago from hybridization between the diploid self-fertilizing Capsella orientalis and the outcrossing Capsella grandiflora . First, to capture the diversity of environmental conditions that C. bursa-pastoris faced during its range expansion, we installed two common gardens at extreme latitudes in Eurasia, one in East Asia and one in Northern Europe  and 1091 in Guangzhou .We used a collection of 232 accessions—offspring of a single self-fertilizing mother plant—from 59 sites distributed across Europe, Asia, North Africa and the Middle East , the number of basal inflorescences and the number of fruits along a section of 10 cm in the middle of the main inflorescence. To estimate the overall performance of each individual, and as a proxy for fitness, we calculated the total number of fruits per individual as the product of the aforementioned three traits.After senescence, we recorded for each plant several morphological traits: the germination time, bolting time (i.e. time until differentiation of the bud from vegetative parts indicating the initiation of the reproductive period), flowering time (i.e. time until the appearance of the first opened flower) and senescence time (i.e. time until the drying state of the plant). In addition, we calculated the four inter-event periods: time between sowing and germination (GP), time between germination and bolting (BP), time between bolting and flowering (FP) and time between flowering and senescence (SP), also called flowering duration. At flowering time, we recorded two additional morphological traits: the number of rosette leaves and the maximum diameter of the rosette (in cm).We also measured four phenological traits: C. bursa-pastoris in each common garden: for 139 days from May to September 2014 in Uppsala, and for 193 days from November 2014 to May 2015 in Guangzhou. Throughout the experimental period, day length becomes longer in Uppsala, and shorter in Guangzhou.One common garden was located in Uppsala , and the second one in Guangzhou . Experiments were conducted at the natural growing period of andandEnvironmental conditions were monitored daily at ground level using temperature and humidity sensors . The overall environmental conditions were reported in geosphere . Their progenies were then used to establish the common gardens. More specifically, about 20 seeds per accession were surface-sterilized the same day and germinated in Petri dishes (one per accession), with Murashige and Skoog (MS) medium and agar  containing standard plant nursery soil mixed with water . The pots were randomly placed in the greenhouse, and left 7 days. During the 7 days, the pots were watered automatically twice a week, and randomly moved every day to avoid the impact of micro-environmental variation in the greenhouse. After 7 days, the pots were pierced on the bottom and placed outside in the common garden, so that the plants were using the soil from Uppsala or Guangzhou regardless of the initial nursery soil. The pots were dispatched into six blocks  arranged at 2 m spacing and containing standard soil. Each block contained one replicate per accession, with a total of six replicates per accession. When less than six seedlings from an accession germinated, seedlings from another accession of the same sampling site complemented the block, in order to keep a uniform individual density. Apart from the initial shading and watering, no additional support was provided to the seedlings. The experiment lasted until the senescence of the last plant, i.e. when the last plant dried up but had not yet shed its fruits.The common gardens were first analysed with the following mixed model:Yijklm’ is a phenotype of the individual m from the accession l of the genetic cluster i in the block k of the common garden j, ‘μ’ is the overall mean, ‘ci’ is the fixed effect of the ith genetic cluster, sj is the fixed effect of the jth common garden , ‘Bk’ and ‘Al’ are the uncorrelated random effects of the kth blocks and the lth accession, respectively (the accession’s effect is nested in the cluster’s effect) and ‘eijklm’ is the residual. The residual was fitted to different distributions according to the lowest Akaike information criterion and depending on the trait .For the four inter-event phenological periods, the starting date of each time span was included in the model as an additional fixed effect. Statistical significance of the genetic cluster effect was assessed with a type II Wald chi-square test, and the difference between genetic clusters was assessed with Tukey’s HSD (honestly significant difference) tests  at an adjusted Bonferroni cut-off of 0.0036 . The PCA of the phenotypes also supported this trend . The environment was much more decisive than genetic differentiation in explaining phenotypic differences, as the genetic cluster effect explained significantly less than the site effect in model [see], and as the PCA revealed the genetic differentiation only from the third component [see].The phenotypes of is trend , as the height , number of fruits over 10 cm  and number of primary branches . Similarly, the phenology which is much less correlated to the number of fruits  shifted significantly between Uppsala and Guangzhou [see], with bolting time, flowering time and senescence time being longer in Guangzhou  than in Uppsala . Since C. bursa-pastoris is a facultative long-day species , but significant for none of the phenotypes in Uppsala . In Uppsala, compared to Guangzhou, the most plastic accessions in terms of number of fruits had smaller height , number of fruits over 10 cm  and number of primary branches . In other words, plasticity in some morphological traits was beneficial in terms of number of fruits only in Guangzhou. In Uppsala, plasticity only affected morphology, but not the number of fruits. None of the associations between the number of fruits and the P-scores were significantly specific to a genetic cluster , and 9 out of 14 in Guangzhou . Genetic cluster had a significant effect in both common gardens for most of the traits , while significant in Guangzhou ; not significant in Guangzhou for the number of secondary inflorescences , while significant in Uppsala . Across the common gardens, the relation of phenotypes between the genetic clusters remained stable between ASI and EUR, and stable for some traits  with ME  than the other clusters in Guangzhou  can explain the variation among accessions. Statistical analyses of model  with ME . For theuangzhou , althougnumber of rosette leaves and maximum rosette diameter) were much stronger than those of other phenotypes in Uppsala , though less pronounced in Guangzhou . Rosettes of European accessions were significantly larger and had more leaves than those of Asian accessions in both common gardens . The Middle Eastern accessions had significantly larger rosettes than Asian accessions in both common gardens .Genetic cluster effects for rosette characters  and in Guangzhou . The Asian cluster exhibited late germination, early bolting, early flowering and late senescence, and therefore a long flowering duration. In contrast, the European cluster germinated early, but bolted, flowered and withered late. European accessions’ late flowering , although this trend was no longer significant (P = 0.38) after dowmsampling. In Guangzhou the Middle Eastern cluster outperformed both the Asian and the European clusters . Results suggested that the interplay between common garden and genetic cluster for the total number of fruits was due to the height and the number of primary branches: the Middle Eastern accessions were significantly smaller than the European accessions in Uppsala , and significantly taller in Guangzhou ; the Middle Eastern accessions did not have significantly more primary branches than European accessions in Uppsala , while they did in Guangzhou . On the other hand, some patterns were consistent across common gardens: Asian accessions were significantly smaller than European and the Middle Eastern clusters in both common gardens , and likewise, the Middle Eastern cluster had significantly more fruits over a segment of 10 cm than the European cluster in both common gardens . However, Asian accessions were no longer significantly smaller in Guangzhou than the European accessions (P = 0.25) after downsampling. Variance of number of fruits was also higher for the Middle Eastern cluster than that of other clusters  and the shortest lifespan .The constraining environment of Uppsala (lower performance on average for most of the traits) compared to Guangzhou probably exacerbated the effect of environmental distance uangzhou . In UppsCapsella bursa-pastoris is a tetraploid and self-fertilizing species that expanded worldwide relatively recently  genetic variation had a strong effect on phenotypic variation within a common garden and (iii) the effect of environmental distance was confounded by genetic clusters, impeding a proper assessment of the role of local adaptation.C. bursa-pastoris is a species with a worldwide distribution, thus a priori facing very contrasted environments, which is expected to favour high phenotypic plasticity for traits associated to fitness , or r = 0.52). C. bursa-pastoris, and likewise, in Arabidopsis thaliana, rosette leaf number has been shown to be sensitive to stimuli of flowering, such as shading  a response to avoid biotic competition  and maize (Zea mays), which is the main cue to the alternation of dry and humid seasons in these climates , noted R above. Table S5. Significance of the link between phenotypic plasticity (P-score) and the number of fruits, as described in model (5). Table S6. Variance ratio of the number of fruits for different genetic clusters : statistics and degree of freedom (df) of the Fisher’s F-test. Table S7. Effects of the environmental distance on each trait in Capsella bursa-pastoris in each common garden , with an analysis of variance of model (3). Table S8. Effect of environmental distance within genetic clusters  of Capsella bursa-pastoris in each common garden , with the model (3). Figure S1. Multi-dimensional scaling of the genotypes . Figure S2. Principal component analysis (PCA) of the environmental data: day length, temperature and humidity. Figure S3. Partial leverage per sampling site for the number of fruits in Guangzhou in model (2). Figure S4. Reaction norm of the number of fruits in Uppsala and Guangzhou. Figure S5. Principal Component Analysis of Figure 2, with PCA 3 and 4, showing the genetic differentiation. Figure S6. Histograms of the phenological traits of Capsella bursa-pastoris  according to the common garden and the genetic cluster.Figure S7. Box plots of the number of fruits per environmental distance , per common gardens, and categorized per genetic cluster: red for ASI, green for EUR, and blue for ME.Figure S8. Table of significance of the model (S1) for each phenotype and for each bioclimatic variable, in Uppsala. Figure S9. Table of significance of the model (S1) for each phenotype and for each bioclimatic variable, in Guangzhou.plac011_suppl_Supplementary_MaterialClick here for additional data file.


Australian life insurance companies can legally use genetic test results in underwriting, which can lead to genetic discrimination. In 2019, the Financial Services Council  introduced a partial moratorium restricting the use of genetic testing in underwriting policies ≤ $500,000 (active 2019–2024). Health professionals (HPs), especially clinical geneticists and genetic counsellors, often discuss the implications of genetic testing with patients, and provide critical insights into the effectiveness of the moratorium. Using a sequential explanatory mixed methods design, we interviewed 23 Australian HPs, who regularly discuss genetic testing with patients and had previously completed an online survey about genetic testing and life insurance. Interviews explored views and experiences about the moratorium, and regulation, in greater depth. Interview transcripts were analysed using thematic analysis. Two key themes emerged from views expressed by HPs during interviews (about matters reported to or observed by them): 1) benefits of the moratorium, and 2) concerns about the moratorium. While HPs reported that the moratorium reassures some consumers, concerns include industry self-regulation, uncertainty created by the temporary time period, and the inadequacy of the moratorium’s financial limits for patients’ financial needs. Although a minority of HPs felt the current industry self-regulated moratorium is an adequate solution to genetic discrimination, the vast majority (19/23) expressed concern with industry self-regulation and most felt government regulation is required to adequately protect consumers. HPs in Australia are concerned about the adequacy of the FSC moratorium with regards to consumer protections, and suggest government regulation is required. Genetic testing can help identify individuals’ risk of developing future disease, including some cancers , and canDebate exists regarding whether the use of genetic test results by life insurers should be restricted . Some coDisability Discrimination Act 1992 (Cth)  sorted out before I have a genetic test?” Some HPs also commented that not needing to have insurance in place prior to genetic testing removed pressure from some patients, who may otherwise have delayed or declined genetic testing.“I don’t have much trust in the insurance industry as a whole so I guess anything to regulate [the industry] that’s from an external body [would be better] - government would be the best, because that is our structure of the law… to keep things in check and make it fairer for people.” The vast majority of interviewed HPs (19/23) expressed concerns about the FSC’s self-regulation of the industry’s adherence to the moratorium, making it the most frequently expressed concern. HPs viewed self-regulation by the insurance industry as inadequate, due to a lack of trust in life insurance companies to abide by the moratorium without any government regulation.“I think there should be somebody overseeing it rather than just the life insurance companies. It doesn’t make sense because they are – I mean they have a commercial interest in what they’re doing so why should we trust them to do the right thing really?” Concerns from HPs included the potential for non-compliance with the moratorium by insurance companies due to lack of government regulation, and lack of penalties for non-compliance. HPs also described their distrust in insurance companies to self-regulate properly because their commercial interests are in direct conflict with patient interests.“This is self-monitored, there is no set legislative regulations that the insurance companies, by the law, have to abide by. Or there is no check, per se, on it, and having legislation would make that happen. There is a bit more responsibility.” “If it’s law, [insurance companies] have to follow it and then if they choose not to, then there are the repercussions of that. They can be liable to criminal charges, I guess. So, yeah, I like that idea in that it holds them accountable for their actions.” Many HPs felt that insurers’ use of genetic information should be regulated by the government through legislation. HPs expressed a view that legislation would hold insurance companies accountable to limitations on their use of patients’ genetic information, helping to ensuring fair treatment of all patients applying for insurance.has been working well, in that I’m not aware of any discriminatory cases that have come up … I guess the guidelines [insurance industries] are guided by with their current regulatory bodies have worked.” “[Self-regulation] A minority of HPs (4/23) expressed a view that government regulation is not needed at this time, as there is no evidence that the self-regulated moratorium is inadequate. Several HPs cited the lack of discrimination witnessed by them personally since the moratorium started as the reason why further regulation was currently unnecessary.“So, whenever you raise the insurance question, I know [patients say],’It’s only helpful up to half a million.’ And half a million isn’t as much as you used to think it was.” Another significant concern reported by HPs is the effect on their patients of the moratorium’s financial limits. Of the 23 HPs interviewed, 18 discussed concerns with the financial limits, with 13 HPs expressing that such limits create a barrier for their patients in accessing testing.“There’s lots of people out there who mentioned that, if they were insuring their current income and there was a complete loss of income from this point onwards, that $500,000 [would not be sufficient] - they’d be looking at a much larger policy.” HPs commented that patients often require polices worth more than $500,000, and that the current financial limit provides no reassurance to these patients.“I’ve had a few people say it’s a bit low, this $500,000. Particularly I guess if you were someone who had your own business, or even a mortgage, and you wanted to make sure you were covered for that … So, it does seem a bit low when you consider what things cost.” Several HPs mentioned that the financial limit does not reflect the current cost of living. Particular references were made to the current property prices, and maintaining a mortgage and/or a personal business.“It’s very difficult to know because I think the term [of the] moratorium means that they’re building something that’s temporary … If someone gets insurance through the moratorium and then the moratorium ends, what does all of that mean?” Many HPs expressed concerns about the temporary nature of the moratorium, and how the uncertainty of its duration leaves HPs unable to advise or reassure clients with confidence. Many HPs mentioned that they are unable to provide patients with clarity around what will happen after the moratorium ends.We really don’t know what’s going to happen after 2024 … and nobody really knows what the impact’s going to be – what the insurance industry or anybody else is doing with that information in those years to come.” “Many HPs commented that they could not provide patients with any information or reassurance relating to how insurance companies will use patients’ information in the future.“So some [counsellors] are saying, ‘So I don’t know what will happen after that, it might be wiped.’ And so I think for some, that’s a reason to say, ‘Look, this protection may not apply soon.’ So there’s a little uncertainty there, and a little less, I guess, reassurance that we can provide.” The temporary nature of the moratorium featured in a number of HPs’ descriptions of the difficulty of explaining the moratorium to patients, and its effect on the reassurance they can provide to patients, as well as their own uncertainty about whether the moratorium will continue to apply in the future.“It’s a short-term thing and it’s not entirely clear what it means. So I think that makes it difficult to explain.” Some HPs said that the temporary nature of the moratorium created further complexities, not only for their own understanding about how the moratorium is applied, but also difficulties explaining this limitation to patients.Our study provides an in-depth assessment of Australian HPs’ views and experiences regarding the current FSC moratorium on genetic testing and life insurance.Interviews with 23 of the 166 previous participants of our published online survey , allowedWhile HPs generally consider that the moratorium is an important first step in reducing genetic discrimination in life insurance, most HPs expressed continuing concerns about the temporary nature of the moratorium, its financial limits and industry self-regulation. These concerns mirror those expressed by HPs in the previous online survey , where >Many HPs expressed discomfort with their inability to reassure patients due to the temporary nature of the moratorium. Despite a recommendation from the Parliamentary Joint Committee (PJC) in 2018 that any ban should apply indefinitely to genetic tests taken before the moratorium is lifted, to ensure certainty for consumers , this prHPs who are involved in organising genetic testing must provide patients with information regarding the medical and familial implications of a genetic condition, while working collaboratively to plan the next healthcare steps . This roOur interview data demonstrates that many Australian HPs are now unsure how to have conversations about genetic testing and life insurance with patients, given the uncertainty around the future of the moratorium, and the possible future insurance implications of having genetic testing at this time. Given the possibility that the moratorium may not be continued beyond 2024, it is indeed impossible for HPs or any person to provide reliable or guaranteed information about the future insurance risks of genetic testing.Pre-moratorium, Australian life insurance companies could ask applicants about genetic test results regardless of the amount of cover being applied for. Under the partial current moratorium, patients can apply for life insurance policies up to $ 500,000 without disclosing genetic test results . As indiConcerns with the moratorium’s financial limits were similarly reflected in our previously published survey, where almost half of the responding HPs made comments in the optional comments section regarding the moratorium’s financial limits being too low . These cn = 166) felt that government oversight of the moratorium is required [When exploring HPs’ views on the moratorium’s regulation, a majority of participants voiced the need for life insurers’ use of genetic test results to be regulated by government. This result was consistent with our previous online survey, in which 95% , with criminal penalties. This issue was also raised by the Australian life insurance industry in the recent Australian Parliamentary Joint Committee inquiry [Discussions about regulation in this area sometimes raise queries about consequences of restricting insurers’ access to genetic information. Some insurers and authors have raised concerns that the restriction on the use of genetic test results by insurers may lead to “adverse selection”, whereby the purchase of insurance by individuals with genetic predisposition to disease could lead to unsustainability of the insurance sector . This is inquiry . The ComLimitations of this study include the relatively small number of interviewed participants, and the potential for self-selection bias that may have influenced participation . Nonetheless, we interviewed all HPs who consented to an interview, and continued interviewing participants until after data saturation was reached, to capture as many viewpoints as possible. A further limitation of our study is the secondary nature of reports by HPs about patient views and experiences. Some of our findings are therefore limited to HPs’ understanding and experience of patient views, rather than the collection of direct evidence. Past research by this group has demonstrated significant consumer concerns regarding the use of genetic test results by consumers existed before the implementation of the moratorium . FurtherStrengths of the study include the sequential, explanatory mixed method design, which allows for both quantitative analysis of survey data (previously published) and in-depth exploration of the previous survey responses to obtain a more complete picture of HPs’ views and opinions.In conclusion, our study demonstrates that although Australian HPs consider the FSC moratorium to be a positive step that provides benefits for some patients, ultimately the majority of HPs remain concerned about the overall adequacy of the current moratorium as a long-term regulatory solution. Major concerns raised with the moratorium include its industry self-regulation; low financial limits; and temporary nature, resulting in uncertainty of future applicability. Most HPs consider that government regulation and intervention is required to adequately protect Australian consumers long-term. The findings of this study indicate the need for a more stable, independent and long-term policy solution for the regulation of genetic testing and life insurance in Australia.Supplementary File S1Supplementary File S2


Stock structure is of paramount importance for sustainable management of exploited resources. In that context, genetic markers have been used for more than two decades to resolve spatial structure of marine exploited resources and to fully fathom stock dynamics and interactions. While genetic markers such as allozymes and RFLP dominated the debate in the early era of genetics, technology advances have provided scientists with new tools every decade to better assess stock discrimination and interactions (i.e. gene flow). Here, we provide a review of genetic studies performed to understand stock structure of Atlantic cod in Icelandic waters, from the early allozyme approaches to the genomic work currently carried out. We further highlight the importance of the generation of a chromosome‐anchored genome assembly together with whole‐genome population data, which drastically changed our perception of the possible management units to consider. After nearly 60 years of genetic investigation of Atlantic cod structure in Icelandic waters, genetic (and later genomic) data combined with behavioural monitoring using Data Storage Tags shifted the attention from geographical population structures to behavioural ecotypes. This review also demonstrates the need for future research to further disentangle the impact of these ecotypes (and gene flow among them) on the population structure of Atlantic cod in Icelandic waters. It also highlights the importance of whole‐genome data to unravel unexpected within‐species diversity related to chromosomal inversions and associated supergenes, which are important to consider for future development of sustainable management programmes of the species within the North Atlantic. These potential new management units revealed in Icelandic waters represent parallel evolutionary adaptive cod lineages that were recently found across the species range . The popularity of the Pan I locus lasted more than a decade and was probably linked to the fact that this gene contains two major alleles, Pan IA and Pan IB, and to the ease of obtaining genotypes. The two alleles Pan IA and Pan IB differ by six fixed nonsynonymous DNA substitutions, clustering in the first intravesicular loop (IV1 domain) of the protein , known to discriminate between the iconic migratory Northeast Arctic cod (NEAC) and the stationary Norwegian coastal cod , characterized by seasonal trend in temperature , while other made feeding migrations to deeper waters where they foraged in thermal fronts , demonstrated that almost no Pan IBB genotypes were captured in the north and that the relationship between the Pan I genotypes and the behavioural ecotypes varied among regions . However, only two of these polymorphic sites had high MAFs that markedly differed between behavioural ecotypes (one synonymous SNP at site 459 [AA153] and one nonsynonymous at site 1295 in the 3′‐UTR).Since these ecotypes are occurring at different depths during most of the year, they are clearly exposed to different light conditions. In the rhodopsin pigment, which is involved in light detection, several amino acid substitutions have been shown to affect the spectral sensitivity in several teleost species .The Atlantic cod was one of the first nonmodel organism for which a chromosome‐anchored draft genome assembly was available  should ideally join behavioural ecology, life‐portfolio diversity and other disciplines to ensure an improved knowledge of diversity within the harvested species and develop efficient plans for conservation. The DSTs experiment of Pálsson and Thorsteinsson  and the Nevertheless, the story of the Icelandic cod stock genetic structure is far from being fully understood. At present, and before any management measures are taken, there is an urgent need to assess the relative role of the behavioural ecotypes versus the observed geographical partitioning (Southwest vs. Northeast) in maintaining the observed stock structure in Icelandic waters. While genomics has contributed to a better knowledge of the within‐species diversity in Icelandic cod and on the evolutionary processes behind it , but no real measures are taken on the feeding grounds. In addition, despite the numerous structure studies performed in Iceland, several crucial questions remain, and it is therefore premature to draw conclusion on the dynamic of the cod stock in this region. The primary remaining question to resolve is to understand the role of the inverted variants in the maintenance of geographical versus ecotypes divergence both during spawning time (population differentiation) and during the rest of the cod life cycle . Once this crucial question is resolved, the temporal stability of the observed structure and the effect of fisheries can be investigated further.To conclude, the advancement of genome sequencing technologies in the last decades has drastically redirected genomic investigations in nonmodel organisms, such as the Icelandic cod behavioural ecotypes. The recent use of reference genomes of coastal versus frontal Icelandic cod and of stationary versus migratory individuals of cod across the North Atlantic has confirmed the presence of supergenes under natural selection, which shaped the architecture of local adaptation of the species in Icelandic waters for the last 30,000 years and in the North Atlantic for the last 0.4–1.66 million years (Matschiner et al., The authors declare no competing interests.