The investigation of the genetic basis of local adaptation in non-model species is an interesting focus of evolutionary biologists and molecular ecologists. Identifying these adaptive genetic variabilities on the genome responsible can provide insight into the genetic mechanism of local adaptation.Pterocarya stenoptera across its distribution area in China to provide insights into the complex interplay between multiple environmental variables and adaptive genetic differentiation. The Bayesian analysis of population structure showed that the 22 populations of P. stenoptera were subdivided into two groups. Redundancy analysis demonstrated that this genetic differentiation was caused by the divergent selection of environmental difference. A total of 44 outlier loci were mutually identified by Arlequin and BayeScan, 43 of which were environment-associated loci (EAL). The results of latent factor mixed model analysis showed that solar radiation in June (Sr6), minimum temperature of the coldest month (Bio6), temperature seasonality (Bio4), and water vapor pressure in January (Wvp1) were associated with the highest numbers of EAL. Sr6 was associated with the ecological habitat of “prefered light”, and Bio6 and Wvp1 were associated with the ecological habitat of “warm and humid environment”.We investigated the spatial distribution of genetic variation in 22 natural populations of Our results provided empirical evidence that environmental variables related to the ecological habitats of species play key roles in driving adaptive differentiation of species genome.The online version of this article (10.1186/s12870-018-1524-x) contains supplementary material, which is available to authorized users. Recently, the investigation of the genetic basis of local adaptation in non-model species has become an interesting focus of evolutionary biologists and molecular ecologists , 2. LocaLandscape genomics was proposed by Joost et al. . It has A large number of landscape genomic studies have proved that environmental variables would drive the adaptive differentiation of some loci on the genome of locally adapted species , 13. HowPterocarya stenoptera C. DC (Juglandaceae) is a deciduous broad-leaved tree growing in forests below 1500 m above sea level along the stream bank or wet hillside land. It is widely distributed in warm temperate and subtropical zones of China. Pterocarya stenoptera prefers light, tolerates waterlogging, likes to grow in warm and humid environment, and can grow on acidic to slightly alkaline soil [P. stenoptera across its distribution region in China were sampled to investigate the relationship between adaptive genetic variations on the genome and environmental variables by using landscape genomic approach.ine soil . Here, 2P. stenoptera and identified the adaptive loci by performing correlations between local environmental variables and selected SCoT alleles. The present study aimed to (i) identify the spatial genetic structure of P. stenoptera, (ii) evaluate the role of environmental variations on the spatial genetic structure of P. stenoptera, and (iii) examine the effects of environmental variables on adaptive differentiation of P. stenoptera genome.In this study, we employed SCoT markers to scan the genome of P. stenoptera from 22 wild populations were successfully scored using the 9 SCoT primers, and 1006 unambiguous fragments were identified with sizes varying from 100 bp to 1200 bp. The number of alleles in 9 primers ranged from 53 (SCoT35) to 156 (SCoT25). The lowest number and percentage of polymorphic alleles  were found in AHXN (P5) population and the highest  in JSBH (P16) population. Nei’s genetic diversity (HE) per population varied from 0.0459 in AHXN (P5) to 0.083 in HNTM (P20). Overall, the summary statistics of the genetic diversity analyses of 22 populations of P. stenoptera are shown in Table A total of 510 individuals of P. stenoptera  among all populations was 2.022. To detect the roles of the 30 environmental variables in this genetic differentiation and their relative contribution on this differentiation, a constrained linear ordination analysis, redundancy analysis (RDA), was performed. The results of RDA are shown in Table P. stenoptera into two groups, whereas the genetic differentiation between them is very weak  with uin Fig. . Among tP. stenoptera across its distribution area in China to provide insights into the complex interplay between multiple environmental variables and adaptive genetic differentiation of this widespread broad-leaved tree species and thus improve our understanding of the genetic mechanism of local adaptation. Landscape genomics has developed rapidly in the past decade, which has been proved to be an effective method for studying adaptive evolution of species [P. stenoptera in response to environmental variables by using 1006 SCoT alleles.In this study, we investigated the spatial distribution of genetic variation in wild populations of  species . Here, wP. stenoptera seems not suitable for this situation, due to a large population size. Meanwhile, there is high gene flow among populations of P. stenoptera (Nm = 2.022). The efficient gene flow (Nm > 1) would avoid the isolation between populations [P. stenoptera based on SCoT marker can hardly be attributed to genetic drift and inbreeding. Our survey showed that the 22 populations of P. stenoptera were divided into two groups, i.e., Group A (P1 to P11) and Group B (P12 to P22). Three possible reasons can be used to explain this intraspecific population differentiation. The first is caused by population demographic history. There were two refugia for P. stenoptera during the Quaternary glacial period; the present spatial genetic pattern was formed by the spread and redistribution of the population from the two refugia after glaciation. The second is due to geographical or ecological barriers. There is a geographical or ecological barrier between the two groups, which blocks or interferes with the gene flow and leads to the genetic differentiation between the two groups. The third is caused by divergent selection of environmental factors. The two groups are in different habitats, and different environmental factors lead to the allele frequency difference of the naturally selected genes, which eventually leads to the genetic differentiation between the two groups. Assuming that the first reason is true for P. stenoptera, the populations as refugia have the highest genetic diversity, and the population that is away from the refugia will gradually reduce its genetic diversity because of the founder effect. However, neither Group A nor Group B has gradient descent from the population with the highest genetic diversity. More importantly, Group A is discontinuous, which is separated by Group B. Thus, it is impossible to have two refugia for P. stenoptera. Overall, the first possible reason does not explain the genetic differentiation between the two groups of P. stenoptera. The second possible reason is considered to explain the genetic differentiation between the two groups of P. stenoptera. There must be ecological or geographical barriers between the two groups. In fact, the populations of the two groups are continuously distributed, and there are no geographical or geographical barriers. Even if the differentiation is caused by the second possible reason, the differentiation between them must be significant. Our results of hierarchical AMOVA  do not support this interpretation. By assuming the genetic divergence of P. stenoptera caused by the third possible reason, a weak genetic differentiation between the two groups would be expected because of the interaction between strong gene flow and divergent selection of environmental factors. The results of hierarchical AMOVA were consistent with this expectation. Our RDA results  between the two groups, which promoted the adaptive differentiation of these related adaptive loci. Bio6 and Wvp1 refer to the minimum temperature of the coldest month and water vapor pressure in January, respectively. Similarly, there are significant differences of the two environmental variables between the two populations. In China, January is the driest month. These two variables were associated with larger numbers of adaptive loci, suggesting that temperature and water vapor in extreme environments were the main causes of their adaptive differentiation. In fact, previous studies have proved that there is a significant difference in the cold resistance of P. stenoptera from different provenance [P. stenoptera. Overall, the hypothesis that environmental variables related to ecological habitats of species play key roles in driving adaptive differentiation of species genome is also suitable for P. stenoptera.Because of the inherent limitations of SCoT markers, sequence information cannot be obtained . Thus, ts genome , 14, 15.ovenance . SurprisP. stenoptera. Our survey showed that the 22 populations of P. stenoptera were divided into two groups. Although spatial genetic structure of species is the result of the interaction of multiple factors, our results suggested that the divergent selection of environmental differences play a major role on the genetic differentiation of P. stenoptera. Our results also provided empirical evidence that environmental variables related to ecological habitats of species play key roles in driving adaptive differentiation of species genome.In the present study, SCoT markers were used to investigate adaptive genetic differentiation in P. stenoptera in China  protocol. DNA was then quantified using the ND5000 Ultra Micro UV-Vis spectrophotometer . All individuals were genotyped using SCoT markers. Despite its inherent defects lacking DNA sequence information, the SCoT marker has been used for landscape genomic studies because it has the advantages of non-requirement for genomic information, high repeatability, and high throughput , 14, 15.The SCoT fragments were identified based on the presence or absence of peaks viewed in GeneMarker 2.2.0 . The raw information was then transformed into a 1/0 matrix. To reduce the error reading rate of SCoT fragments, the peaks within 100–1200 bp and relative fluorescent units above 200 were scored. Subsequent population genetic analyses were carried out with the basis of the 1/0 matrix from SCoT markers.NA), allele frequencies, Nei’s measure of gene diversity (HE) [PPA), were calculated using AFLP-SURV 1.0 [P. stenoptera was assessed using the Bayesian-based program STRUCTURE 2.3.4 [K values from 1 to 10 with 10 replicates for each K, and a burn-in period of 10,000 and 10,000 Markov chain Monte Carlo iterations. The admixture model with independent allele frequencies was used for this analysis. The optimal K value with the most suitable population clusters was judged according to the ∆K values introduced by Evanno et al. [P. stenoptera was characterized using hierarchical AMOVA within Arlequin 3.5 [Nm) among populations, we also calculated FST based on neutral loci . The value of gene flow was estimated according to 1/4(1/FST − 1). A total of 43 environmental variables  from 1970 to 2000 at 2.5 arcmin resolution and further extracted using DIVA-GIS 7.5 [Genetic parameters, including polymorphic allele number (ity (HE) , and perSURV 1.0 . GeneticRE 2.3.4 . The proo et al. , and thio et al. . The aveo et al. . The baro et al. . The disquin 3.5 . To calc-GIS 7.5 . The str-GIS 7.5 . Here, aFSTP-value below 0.05 were considered as outlier loci, whereas those with total allele frequencies below 0.05 or above 0.95 were removed from the final results. The second method is a Bayesian method in BayeScan 2.0 [P below 0.001 were considered as EAL.To minimize the false positive rate, two methods were used to identify the mutual outlier loci for subsequent environmental association analysis. The first method is a hierarchical island model in Arlequin 3.5 . The advScan 2.0 . The advScan 2.0 . The proScan 2.0 . This meAdditional file 1:The outlier loci identified by Arlequin and BayeScan. (DOCX 31 kb)Additional file 2:Fifty-five environmental variables used in this study. (DOCX 16 kb)Additional file 3:Environmental variables for each location from the WorldClim database. (DOCX 26 kb)Additional file 4:Allele frequencies of 1006 alleles for each population. (DOCX 253 kb)


Pityopsis ruthii (Ruth’s golden aster) is a federally endangered herbaceous perennial endemic to the Hiwassee and Ocoee Rivers in southeastern Tennessee, United States. Comprehensive genetic studies providing novel information to conservationists for preservation of the species are lacking. Genetic variation and gene flow were evaluated for 814 individuals from 33 discrete locations using polymorphic microsatellites: seven chloroplast and twelve nuclear. A total of 198 alleles were detected with the nuclear loci and 79 alleles with the chloroplast loci. Gene flow was estimated, with the Hiwassee River  showing higher levels of gene flow and lower levels of population differentiation than the Ocoee River . Population structure was examined using Bayesian cluster analyses. Nuclear and chloroplast analyses were incongruent. From the chloroplast microsatellites, three clusters were identified; all were present in sampling sites at both rivers, indicating a lack of allele fixation along rivers. Nuclear markers revealed two clusters and separated by river. When the Hiwassee River locations were analyzed, four clusters were identified for both the chloroplast and nuclear microsatellites, though the individuals clustered differently. Analysis of the Ocoee River revealed two clusters for the chloroplast microsatellites and three for the nuclear microsatellites. We recommend P. ruthii be managed as four populations for the Hiwassee River and three populations for the Ocoee River. Our results provide critical genetic information for P. ruthii that can be used for species management decisions to drive future population augmentation/reintroduction and ex situ conservation efforts. Pityopsis ruthii  Small, also known as Ruth’s golden aster, is endemic to southeastern Tennessee, United States, where it grows on unshaded phyllite rock boulders in and on the adjacent slopes of the Hiwassee and Ocoee Rivers (P. ruthii (2n = 2x = 18) that remain in the wild, one containing approximately 1,000 individuals along ∼3 km of the Ocoee River and a larger population of around 12,000 individuals along ∼6.5 km of the Hiwassee River . Sampling sites were established as groups of plants based on well delineated breaks in suitable habitat within the riparian area for each river. The average census counts (2011–2014) for the sampling sites sampled ranged from 15 to 1034 plants for the Hiwassee River and 12–491 plants for the Ocoee River . The de novo assembly yielded chloroplast genome reads that were then assembled and screened for microsatellites using the program Imperfect SSR Finder  according to the manufacturer’s protocol. The concentration and quality of the DNA was measured with a NanoDrop ND-1000 spectrophotometer . Twelve polymorphic microsatellite primer pairs for . ruthii  were useR Finder . Twenty-P. ruthii individuals were amplified using a 10 μl polymerase chain reaction with 4 ng genomic DNA, 2.5 mM MgCl2, 1X GeneAmp PCR Buffer , 0.2 mM dNTPs, 0.25 μM primer (forward and reverse), 0.4 U AmpliTaq Gold DNA polymerase (Applied Biosystems), 5% dimethyl sulfoxide (DMSO), and sterile water. The reaction was run using the following cycling conditions: 94°C for 3 min; 35 cycles at 94°C for 40 s, 55°C for 40 s, and 72°C for 30 s; and one cycle at 72°C. All individuals were amplified using 12 nuclear microsatellite loci and 7 chloroplast microsatellite loci. Amplicons were visualized using the QIAxcel Advanced Capillary Electrophoresis System (Qiagen) and sized using an internal 25–500 bp DNA size marker. Electropherograms were visualized using the software BioCalculator (Qiagen) for nuclear data and the next iteration of the software, ScreenGel version 1.4.0 (Qiagen) for chloroplast data. The raw allelic data was compiled into an Excel worksheet and all nineteen loci were binned using the Excel add-in FLEXIBIN (Supplementary Data Sheet S1). When both datasets were combined for Bayesian analyses, chloroplast data was coded as diploid rather than haploid, as STRUCTURE does not allow mixed ploidy data.For microsatellite analyses, FLEXIBIN , which bNA), effective number of alleles (NE), observed (HO) and expected (HE) heterozygosity, gene flow (Nm), and F statistics across all populations for each locus. The program HP-Rare (AR). A Bayesian analysis was performed using STRUCTURE version 2.3.4  repetitions were used in all analyses. Estimation of the best k value was determined using STRUCTURE Harvester (k) using the ad hoc statistic Δk. This is based on the second order rate of change in the log probability of the data between successive values of k. The analysis was performed three times: once with all individuals, once with individuals found along the Hiwassee River, and once with individuals found along the Ocoee River. Additionally, subsampling of populations over 25 individuals was performed using a random number generator for further Bayesian analyses, to ensure accuracy despite uneven sampling. Subsampling yielded a smaller dataset of 683 individuals.The Excel add-in GenAlEx 6.5  and Arle HP-Rare  was usedon 2.3.4  using tharvester , which iFST and gene flow estimates between populations. To determine the occurrence of isolation by distance (IBD), a Mantel test between the genetic and geographic distances was evaluated using GenAlEx with 9,999 permutations.The apportionment of genetic variation was determined by an analysis of molecular variance (AMOVA) using ARLEQUIN . The sigps = 0.9 (the frequency of single step mutations) and the variance of those mutations as 12. These values are typical for many microsatellite markers.BOTTLENECK version 1.2.02  was usedFST) was calculated for all samples, and Shannon’s diversity index, diversity (h), and unbiased diversity (uh) were calculated for each population and locus. Nei’s gene diversity . For the 12 polymorphic nuclear loci, 198 alleles were detected (Supplementary Table S1). Loci demonstrated an overall departure from HWE due to significant heterozygote deficiency when all 814 samples were analyzed together. The number of alleles detected per locus (A) ranged from 9 (PR028) to 24 (PR029), with a mean allelic richness (AR) of 1.80, ranging from 1.68 (PR002) to 1.89 (PR029 and PR031). The observed heterozygosity (HO) was 0.49 and deviated from the expected heterozygosity (HE) of 0.65. Population differentiation was large (FST = 0.24) and the inbreeding coefficient was moderate (FIS = 0.22). Average gene flow (Nm) across loci was 0.90 and ranged from 0.45 (PR009) to 1.76 (PR035).A total of 814 Table 1). For individuals from the Hiwassee River sampling sites, mean allelic richness was 1.99 and ranged from 1.52 (H-02-01) to 4.22 (H-12-06), whereas among the Ocoee River individuals, mean allelic richness was 1.56 and ranged from 1.49 (O-04-01) to 1.60 (O-06-01). On the Hiwassee River observed heterozygosity (HO) was 0.53 and ranged from 0.38 (H-03-01) to 0.71 (H-09-02) whereas expected heterozygosity (HE) was 0.64 and ranged from 0.51 (H-02-01) to 0.74 (H-04-01). Across the Ocoee River sampling sites, HO was 0.36 and ranged from 0.32 (O-03-01) to 0.40 (O-02-01) while HE was 0.54 and ranged from 0.46 (O-04-01) to 0.59 (O-06-01). The inbreeding coefficient was higher for the Ocoee River individuals (0.35) than the Hiwassee River individuals (0.22) and the range of inbreeding coefficient values was much larger for the Hiwassee River individuals (0.22 to –0.01) compared to the Ocoee River individuals (0.27–0.44).The genetic variability of the 12 microsatellite loci was assessed for each sampling site and between the two rivers (Table 1). Private alleles were found at 12 loci, with PR002 (7) and PR031 (7) having the most and PR003 and PR035 having 1 each (Table 1). Thirty-four private alleles occurred at a frequency of <0.05, 5 at a frequency between 0.05 and 0.09, 5 at a frequency between 0.10 and 0.20, and 1 at a frequency greater than 0.25 .Forty-five private alleles were found in 4 Ocoee River and 13 Hiwassee River sampling sites, of which H-04-04 (11), O-05-01 (5), H-06-05 (4), H-07-01 (4), and H-11-01 (4) had the most, 5 locations  had 2 private alleles, and 7 locations had 1 private allele . Grouping of all sampling sites together indicated that most (68%) of the variation is explained within individuals, and 14 and 18% of the variation is due to differences among individuals within sampling sites and among sampling sites. Additional AMOVA grouping of sampling sites by river found similar results to the grouping of all sampling sites, with a greater percentage of the variation explained within individuals rather than among the individuals within the sampling site. Nearly 20% greater variation was found within individuals on the Hiwassee River as compared to the Ocoee River.The variance components of the AMOVA analyses were highly significant at all hierarchical levels (P. ruthii demonstrated a high level of differentiation (Supplementary Tables S3). Pairwise comparisons of FST measures were all significantly different from zero for the Hiwassee River except for comparisons between nine sampling sites , and the comparison for the Ocoee River were all significantly different than zero . The greatest differentiation for the Hiwassee River sampling sites was observed between H-03-01 and H-08-03 (FST= 0.31) and the lowest differentiation between H-06-05 and H-07-03 (FST= 0.02). For the 300 pairwise sampling site comparisons of FST values for the Hiwassee River, 77 were less than 0.10, 180 were between 0.10 and 0.19, and 43 were greater than 0.20. For the Ocoee River sampling sites the greatest differentiation was observed between O-01-01 and O-04-01 (FST= 0.28) and O-03-01 and O-04-01 (FST= 0.28) and the lowest between O-02-01 and O-02-02 (FST= 0.05), with 21 out of 28 comparisons greater than 0.15. Along the Hiwassee River, gene flow was highest between H-09-01 and H-09-03 (40.64) and lowest between H-02-01 and H-08-03 (0.60). Within the Ocoee River sampling sites, gene flow was highest between O-02-01 and O-02-02 and lowest between O-03-01 and O-04-01 (Supplementary Table S3). Overall, the Hiwassee River sampling sites had lower FST and much higher gene flow estimates than the Ocoee River sampling sites.Sampling sites of Supplementary Table S4). Loci cpPR002 and cpPR010 showed the highest diversity (h = 0.54) whereas cpPR004 (h = 0.38) showed the least. Private alleles were identified at every locus, with a total of 15 across all loci and locations (Table 3). Thirteen sampling sites had private alleles: four from the Ocoee River and eleven from the Hiwassee River. In site H-04-04 private alleles were detected at multiple loci, whereas the other sampling sites had a single private allele. Site H-04-04 had the highest number of private alleles in both the nuclear and chloroplast data sets. Private alleles were detected at six other sampling sites for both nuclear and chloroplast loci: O-05-01, O-04-01, O-01-01, H-06-07, H-05-01, and H-04-05. Shannon’s Information Index was highest for Ocoee River site O-02-01 (1.07) and lowest for O-02-03 (0.62), whereas for the Hiwassee River sampling sites it was highest for H-01-06 (1.21) and lowest for H-08-07 (0.28). Diversity and unbiased diversity among the Ocoee River sampling sites were greatest in O-03-01 and lowest in O-04-01, and highest in H-01-06 and lowest in H-12-06 among the Hiwassee River sampling sites.We detected a total of 79 alleles among the seven chloroplast loci, with an average of 11.3 alleles per locus, ranging from a minimum of 5 to a maximum of 17 (h = 0.49) showing slightly higher diversity per sampling site than the Hiwassee River (h = 0.45) (Table 3).The haplotype analysis in ARLEQUIN detected 102 unique haplotypes within the 237 individuals from the Ocoee River sampling sites and 176 unique haplotypes within the 582 individuals from the Hiwassee River sampling sites, with only 5 complete haplotypes shared between the two rivers. The diversity within the two rivers was comparable, with the Ocoee River (Table 4), Hiwassee and Ocoee Rivers, revealed that differences between river only explained 5% of variation, whereas differences among sampling sites within groups explained 32% of variation and differences within sampling sites explained 63% of variation (P = 0.03). Among the Hiwassee River sampling sites, 37% of all variation could be explained among sampling sites, whereas differences within sampling sites explained 63% of variation (P < 0.01). Analysis of the Ocoee River populations revealed 22% variation among sampling sites and 78% variation explained by differences within sampling sites (P < 0.01). The Hiwassee River sampling sites had a higher FST (0.37) than the Ocoee River sampling sites (0.22).A hierarchical AMOVA with two groups . The STRUCTURE results using the chloroplast microsatellites differ from the nuclear microsatellites when all populations are combined. Admixture is more apparent in the chloroplast data than in the nuclear data as Δk = 6 . Clusters one and two include only sampling sites from the Hiwassee River and exhibit very little admixture, whereas the remaining four clusters exhibit a great deal of admixture and are difficult to distinguish. Cluster one (green) is composed of H-07-03, H-07-02, H-06-05, and H-12-06. Cluster two (red) is composed of H-11-01, H-06-01, and portions of H-06-02 and H-06-04.When data for the Hiwassee and Ocoee Rivers were combined, STRUCTURE analysis of nuclear microsatellites found evidence for two distinct clusters, which are partitioned based on the two rivers that were sampled . Cluster one (green) is composed of individuals from H-09-03, H-09-02, H-09-01, H-05-01, and a portion of H-04-04, with the other cluster (red) composed of the remaining sampling sites. Analysis of the Hiwassee River sampling sites using chloroplast microsatellites revealed Δk = 4 . Cluster one (green) is composed of H-11-01, H-06-04, H-06-02, and part of H-02-01. Cluster two (yellow) is composed of H-09-03, H-09-01, H-09-02, H-07-03, H-12-04, H-04-04, H-03-01, H-02-01, H-01-06, and H-01-02. Cluster three (red) includes H-08-07, H-08-06, H-08-04, H-08-03, H-06-07, H-05-01, and H-04-05. Cluster four (blue) is composed of H-07-02, H-06-05, and H-12-06, with very little admixture shown. Although admixture is evident among all sampling sites, it is higher within H-07-03, H-06-04, H-06-02, H-04-04, H-03-01, and H-02-01.Analyzing the data from each river separately revealed differences between the nuclear and chloroplast microsatellite data. Analysis of the nuclear microsatellites for the Hiwassee River sampling sites with STRUCTURE identified two genetically distinct clusters . Sampling sites O-06-01 and O-03-01 were in one cluster (red), O-05-01 and O-04-01 grouped in another cluster (blue), whereas O-02-03, O-02-02, O-02-01, and O-01-01 clustered into a third group (green). Although admixture is evident among all Ocoee River sampling sites, it is highest within O-06-01, O-03-01, O-02-03, and O-01-01. The STRUCTURE analysis with chloroplast microsatellites identified two clusters for the Ocoee sampling sites . Cluster one (green) includes O-06-01, O-03-01, O-02-03, O-02-02, O-02-01, and O-01-01. Cluster two (red) is composed of O-05-01 and O-04-01, which also clustered together in cluster one of the combined analysis. Admixture is low throughout both clusters, with only a few scattered individuals evidencing crossover between clusters.The STRUCTURE analysis of nuclear microsatellites identified three clusters for the Ocoee River populations  separation the Hiwassee and Ocoee Rivers . The separation is also noticeable and significant (P < 0.001) between rivers when using the chloroplast data from both rivers . Isolation by distance is less apparent when the Hiwassee and Ocoee River populations are separated. For the Hiwassee River sampling sites, a significant relationship is apparent between geographic distance (km) and genetic distance in both the chloroplast (P < 0.001) and nuclear (P < 0.001) datasets . For the Ocoee River sampling sites, the Mantel test for the chloroplast microsatellites does not indicate a significant relationship between geographic and genetic distance, while the nuclear does .The pairwise correlation between geographic and genetic distance to determine isolation by distance using nuclear data shows a clear and significant . BOTTLENECK did not detect a bottleneck in the third cluster of the Ocoee River using any of the models.Wilcoxon tests to detect bottlenecks in Pityopsis ruthii is characterized by high levels of variation at nuclear microsatellite loci and moderate levels of variation for chloroplast microsatellite loci. Overall, P. ruthii has high levels of diversity compared to other species, both within Asteraceae and in other families. Expected heterozygosity is considerably higher (HE= 0.63) than that found in endemic cliff dwelling perennial species  from China (Spiraea virginiana (HE = 0.39), a rare riparian shrub from the Southern Appalachian region of the United States (FIS) were found in some peripheral populations and suggest a higher degree of inbreeding in these populations compared to more central populations, though analyses of chloroplast microsatellites did not show a lack of diversity in those same populations. Diversity of P. ruthii (h = 0.46) estimated from chloroplast microsatellites was higher than seen in O. longilobus (h = 0.07) or O. taihangensis (h = 0.02) (Ainsliaea faurieana (h = 0.24), though the range within A. faurieana was comparable to that seen in P. ruthii (h = 0.48) was higher than the Hiwassee River sampling site mean (0.45). When compared to similarly threatened species with narrow ranges (Opisthopappus spp. and S. virginiana), P. ruthii shows high levels of diversity for nuclear loci.om China  and Spird States . Within  = 0.02)  and the . ruthii . The OcoFST values. The seven choloroplast loci had a higher number of alleles per locus and allelic frequency than loci used to study the widespread species Chrysanthemum indicum and C. lavandulifolium  FIS values for all sampling sites regardless of river indicating an excess of homozygosity and inbreeding. Though significant, FIS values for P. ruthii were comparatively low when compared to other species such as A. faurieana . Germination of seedlings in wild populations has been observed and the mortality was higher than 90% after 1 year . Further studies are needed to determine if seedlings at P. ruthii sampling sites with high inbreeding coefficients are suffering from inbreeding depression or if seedling recruitment is limited.We found significant  showed no signs of a bottleneck, perhaps due to the inclusion of four sampling sites, two of which (O-01-01 and O-02-03) show high admixture with the nuclear data. Coupled with the higher levels of gene flow among these sampling sites and high admixture in the two sampling sites furthest upstream, the lack of bottlenecking may indicate a founder effect. The four sampling sites that make up cluster three are located upstream from the other sites along the Ocoee, allowing little gene flow from the downstream sampling sites, but also showing little gene flow from cluster three to other locations, resulting in bottlenecks downstream. Considering the geographical proximity to one another, the outcrossing breeding system, and habitat continuity, we should expect genetic exchange among populations through pollen or seed dispersal. However, asynchronous flowering within sampling sites could compound inbreeding and explain the high FIS values observed. Variation of flowering times is not uncommon; flowering starts as early as late July to early August and continues until late October to early November. Mating of individuals from each group could lead to increased inbreeding, explaining the inbreeding coefficients within sampling sites.Recent bottlenecks were detected in multiple clusters of sampling sites, indicating a loss of genetic diversity, which could impact adaptation. Cluster three of the Ocoee River from the nuclear microsatellites (P. ruthii (FST values. High admixture is apparent in this sampling site in both the nuclear and chloroplast datasets, which could indicate recruitment to this site from other populations, since conditions are more favorable for P. ruthii.Additionally, drought appears to be a factor in maintaining habitat for some sampling sites of . ruthii . Site H-. ruthii . This drP. ruthii were once a single, widespread population that fragmented due to unknown causes, leading to higher levels of genetic drift in the genome and lower levels in the more conserved plastome. The shorter-term scale of nuclear data shows the effects of more recent fragmentation of the species, which is ultimately more informative for conservation than the chloroplast data.Fewer alleles and lower overall diversity was seen in chloroplast microsatellites when compared to nuclear microsatellites. This was expected due to the lack of recombination and smaller effective population size of chloroplast loci. The clear admixture between rivers in the STRUCTURE results from the chloroplast data is not apparent in the nuclear data. Coupled with the AMOVA results indicating a lack of variance between the Ocoee and Hiwassee River sampling sites, the differences between chloroplast and nuclear data are evident. Due to a lack of recombination, mutation rates in chloroplast genomes are lower than those in nuclear genomes; chloroplast markers allow a glimpse into a longer-term evolutionary scale than provided by nuclear markers alone. One possibility for the lack of allelic fixation in the chloroplast data is that the two populations of P. ruthii occur, though it is not clear what effect damming of the river and augmented flows have on seed recruitment. The lack of information on seed recruitment and habitat loss, coupled with high mortality of seedlings within natural populations pose challenges to developing strategies to protect sustainability of these populations. Another scenario is that the detected gene flow levels at least in part reflect natural gene flow. Lower levels of gene flow in some sampling sites could also be attributed to geographic isolation, which could restrict effective pollen or seed exchange among sampling sites.Dams on the Hiwassee and Ocoee Rivers have altered the hydrology of both rivers where Pityopsis ruthii is becoming reality. Molecular markers are commonly used in maintenance and selection of germplasm collections, and chloroplast microsatellites in particular have been used to much advantage in several species (P. ruthii is curated at the North Carolina Botanical Garden. The diversity maintained in this collection has not been characterized and it is now possible to assemble a core germplasm collection for P. ruthii. Including accessions which show high polymorphism among our nuclear and chloroplast loci will allow greater levels of diversity to be captured for future use.Coupled with the molecular markers in this diversity study and the information now available on population structure, preventing further loss of diversity and protecting  species . A core P. ruthii, surrounded by high ridges and ineffective seed dispersal mechanisms, the species may not be able to migrate with warming climates. Additionally, the rivers show differing levels of growth, with sampling sites along the Ocoee River exhibiting greater numbers of flowers per plant and a lower level of competition with other herbaceous and woody plants (P. ruthii. Along with understanding the ecology of the species, a viable method of introducing diversity into the natural habitat is necessary. Cultivation of plants is possible through both stem cuttings and tissue culture, providing methods for reintroduction studies; it is feasible to grow P. ruthii seedlings in vitro and transplant seamlessly into the natural habitat (Ongoing pollination studies and reintroduction efforts add to the effort base of knowledge on the ecology of this endangered species . With thy plants . Moore e habitat , 2018.Pityopsis, and has provided a relatively easy and cost-effective way to follow genetic diversity in existing, augmented, or reintroduced populations over time.We reject our null hypothesis regarding population structuring and accept the alternative hypothesis that each river includes multiple viable breeding populations which are distinct from one another. Currently, each discrete site is managed as a separate population, though our study shows no evidence for such fragmentation. Rather, we posit that multiple sampling sites have similar genetics and therefore can be managed as one larger population. This is especially evident when viewing data from the nuclear microsatellite markers. Managing the species using a framework with two to four populations along the Hiwassee River and two to three along the Ocoee River will allow researchers to use plants in larger placement areas for augmentation, reintroduction, and/or translocation studies to add diversity to a particular population. Our work in identifying conservation units for this species and understanding the underlying genetics and ecology of the species will inform conservation practices in the future, as well as further study into the entire genus AD, ES, MS, RT, and PW conceived and designed the experiments. AD, EH, and PW performed the experiments. AD, EH, ÐH, TR, and PW analyzed the data. RT, TR, MS, and PW contributed reagents, materials, and analysis tools. EH, PW, and AD wrote the paper. EH, AD, ÐH, TR, ES, MS, RT, and PW read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We successfully established a uniform π criterion and developed a comprehensive plant genetic diversity database. The mean population-level and species-level π values across seed plants were 0.00374  and 0.00569 , respectively. Significant differences were recovered for breeding system (p < 0.001) at the population level and geographic range (p = 0.023) at the species level. Selfing taxa had significantly lower π values than outcrossing and mixed-mating taxa, whereas narrowly distributed taxa had significantly lower π values than widely distributed taxa. Despite significant differences between the two extreme threat categories , the genetic diversity reduction on the way to extinction was difficult to detect in early stages.Despite substantial advances in genotyping techniques and massively accumulated data over the past half century, a uniform measurement of neutral genetic diversity derived by different molecular markers across a wide taxonomical range has not yet been formulated. We collected genetic diversity data on seed plants derived by AFLP, allozyme, ISSR, RAPD, SSR and nucleotide sequences, converted expected heterozygosity ( According to the neutral theory of molecular evolution, current patterns of neutral genetic diversity that are maintained within species and populations reflect underlying molecular evolutionary processes involving mutation, genetic drift, and gene flow ,2. BecaueH) under Hardy-Weinberg equilibrium [eH estimates derived by different molecular markers could be uniformly transformed to π. Few studies have attempted such work, although an evaluation of the correlation between allozyme and DNA sequencing has been performed [All measured heritable polymorphisms using any molecular marker are derivatives of indirect measurements of nucleotide differences of genomic sequences . Nucleotilibrium , the mosilibrium ,16,17, terformed ,18.He and π, especially for different markers used in various genetic backgrounds across a wide spectrum of different taxa. The expected nucleotide diversity (π) under neutral equilibrium and infinite sites model is represented by 4Neμ0, where Ne is the effective population size and μ0 is the mutation rate per nucleotide [He) under Hardy-Weinberg equilibrium and the infinite allele model is represented by 4Neμ/(4Neμ + 1), where the mutation rate μ refers to the entire protein or amplified fragment [He depend on the effective population size and their corresponding mutation rates. Therefore the relationship between π and He is:He estimates derived by two different markers isTo date, no solid statistical relationship has been developed between cleotide . Alternafragment . The exp0/μ and μ2/μ1 are constant across species and treat He/(1 − He) as a single parameter, both equations become simple linear formulae, i.e., the correlation and transformation among the estimates derived by different molecular markers could be realized under the assumption of constant mutation rate ratios. The mutation rate ratios could be obtained by performing regression analysis with linear functions through empirical data. In this study, we aimed to test the putative relationship and deduce the He-π transformation equations in seed plants, on the basis of a comprehensive compilation of all available data of He estimates derived by different molecular markers.If we assume that the mutation rate ratios μetc.) have significant impacts on plant genetic diversity levels based on evidence from several molecular markers, including allozyme [He derived by allozyme [It is widely accepted that life history traits  Red List of Threatened species [There has been a great deal of controversy over the role of genetic factors in extinction risk in the past decades, especially after the “Lande scenario” , which hulchella  and Melia cinxia , and mos species , but limeH estimates using different molecular markers across a wide taxonomic spectrum of seed plants; (2) to reassess the relationship between plant genetic diversity and life history traits or extinction risk; (3) to provide baseline data for future evolutionary studies and conservation practice using NGS techniques.The purposes of the present study were three-fold: (1) to establish a uniform π criterion by transforming the eH estimates using the five different markers except SSR were quite similar, with mean values ranging 0.159–0.182 at the population level and 0.198–0.246 at the species level, whereas SSR-based eH estimates were approximately three-fold larger , with a mean value of 0.55 at the population level and 0.643 at the species level  values derived by any of the five markers is shown in p < 0.05), except for three pairs, Sequence/AFLP, Sequence/ISSR and ISSR/SSR, which was most likely due to limited data availability . Five marker pairs  were chosen for the regression analyses to convert eH to π. The Allozyme-, RAPD-, and SSR-based eH values were directly converted to π. However, the taxon numbers for the Sequence/AFLP and Sequence/ISSR pairs were too small; therefore, the AFLP- and ISSR-based eH values were indirectly converted via SSR and RAPD, respectively. As shown in p < 0.001), and the 2R values ranged from 0.669 to 0.905. The linear coefficients for the five markers in the eH-π conversion equation π = b eH/(1 − eH) were 0.021 (Allozyme), 0.013 (RAPD), 0.002 (SSR), 0.012  and 0.014 .A summary of all pairwise correlation analyses between the π values derived by nucleotide sequences and the sH and tH values derived by the five markers were converted to πs and πt with the deduced regression equations, whereas the π values measured by nucleotide sequences were directly used as πt. After removing redundancy, 1023 and 807 taxa were obtained in the πs and πt datasets, respectively. To eliminate the uncertainty of taxonomic status of cultivated taxa for the follow-up analyses, the cultivated taxa were removed and the taxon numbers were reduced to 966 and 728. The πs values ranged from 0.0000025 to 0.03285, with a mean value of 0.00374, involving 155 families and 47 orders, whereas the πt values ranged from 0.0000025 to 0.12900, with a mean value of 0.00569, involving 130 families and 46 orders , respectively  at the population level and geographic range (p = 0.023) at the species level, but not for extinction risk at either level. Multiple comparison analyses also revealed significant differences (p < 0.05) for several group pairs. Among the breeding system groups, selfing taxa had significantly lower values than other taxa, and asexual taxa had significantly lower values than outcrossing taxa at the population level. However, selfing taxa had significantly lower values than outcrossing and mixed-mating taxa at the species level. Narrowly distributed taxa had significantly lower values than widely distributed taxa at the species level. The CR category had significantly lower values than the LC category at both levels.The πion risk . One-waysH and tH estimates summarized by Hamrick and Godt [sH, 0.113 vs. 0.173; tH, 0.149 vs. 0.198). Such differences might be explained by the fact that we collected data accumulated until 2013 much later than those included in Hamrick and Godt [sH values  summarized by Nybom et al. [vs. 247; ISSR, 4 vs. 145; RAPD, 60 vs. 136; SSR, 104 vs. 260). Nevertheless, the comparison among different markers by Nybom et al. [The most extensive data compiled and analyzed in this review provided us with valuable insights on the magnitude and variability of genetic diversity in plants . Obviousand Godt  were smaand Godt . More rem et al.  were liket al. detected a significant (p < 0.001) relationship in a dataset from 27 plant species [p = 0.068) across 22 species studied by Leffler et al. [eH estimates derived by the most commonly used molecular markers with the assumption that the mutation rate ratios of different markers are constant across species. The successful eH-π conversion by using our deduced regression equations provided us with the first comprehensive plant nucleotide diversity database covering a wide range of plant families and orders.Our results demonstrated that most of the 15 pairwise correlation coefficients were significantly positive , confirm species , whereasr et al. . In our r et al. ,16,17 onp = 0.133) at the species level in one-way ANOVAs in this study, although selfing taxa (mean: 0.00427) had significantly lower values than outcrossing (mean: 0.00597) and mixed-mating taxa (mean: 0.00633) in the multiple comparison. It was probably due to confounded partition of genetic diversity within and between populations of plant taxa with certain breeding systems. For example, selfer species usually have low intra-population genetic diversity, but high inter-population diversity, such as selfing Arabidopsis thaliana compared with its outcrossing relative A. lyrata [The association of plant genetic diversity and life history traits  has been of great interest to evolutionary and conservation biologists. One-way ANOVAs in this study showed that genetic diversity revealed as π values was not significantly different among the taxa grouped by geographic range at the population level and by breeding system at the species level . Our res. lyrata ,27. Thuset al. compared genetic diversity levels between threatened species and their nonthreatened relatives and found that 77% of threatened species had lower diversity, providing convincing evidence for the importance of genetic factors in conservation [et al. [The role of genetic factors in extinction risk has been controversial in the past decades , especiaervation . Similarervation  and birdervation . However [et al.  were thas (πt) value than LC categorized taxa, whereas the genetic diversity level was found to decline by 35% in Spielman et al. [s: Pearson’s r = −0.127, P = 0.082; πt: Pearson’s r = −0.13, p = 0.109) between π and IUCN red list categories, suggesting a weak tendency for genetic diversity to be lower in threat categories with higher extinction risk. Reduction of genetic diversity in plants on the way to extinction might be a gradual and slow process, in which the decline tends to appear far from distinguishable in the preliminary stages. The fuzzy patterns might be caused by confounding factors. By further examining the patterns of four traits  associated with the five IUCN red list categories in our dataset, we found a clear shift in elevated extinction risk from wide to narrow geographic range, but no similar pattern was detected for the three other traits  with moderate extinction risk, significant differences were detected between CR and LC at the two extremes of the categories in the multiple comparison, suggesting the influence of π on the threat categories ranked by IUCN. CR categorized taxa presented 39% (52%) lower mean πn et al. . To examr traits . Thus, geH estimates [It is worth noting that a number of limitations might arise in our data compilation and deduction. First, we assumed that the mutation rate ratios among different markers are constant across plant species. However, this assumption would be violated to some extent because mutation rates vary across species, genomic regions and through evolutionary time. Second, slight differences among several versions of basic equations for calculating stimates ,36,37,38stimates ,23. SomeeH) [eH estimates were separately recorded at the population (sH) or species (tH) levels, whereas all π values were recorded at the species level. The sH estimates were averaged from at least two populations for each species, subspecies or variety, whereas the tH estimates were calculated for all pooled samples. Only the estimates with polymorphic loci were chosen if both estimates of all loci (including monomorphic loci) and polymorphic loci were reported in the same paper, and only the estimates with more represented populations or loci were retained if there was more than one value assessed by the same marker for the same taxon.Genetic diversity data on seed plants were collected through a literature survey. Papers published before May 2013 that provided expected heterozygosity (He) ,36,37 eseH values were transformed to eH/(1 − eH) prior to the correlation analysis. For numeric conversion from eH to π, regression analyses between these two values were performed for each taxon. The hypothetic equation was a simple linear function without intercept, π = eb H/(1 − eH). If the numbers of the same taxa for the markers were too few and the deduced coefficients were not significant, regression analyses via other markers were used for the eH-π conversion, i.e. π = 1 H1b/(1 − 1H) = 2 H2b/(1-2H). Polyploid taxa were excluded for the regression analyses. Subsequently, all the eH values were converted to π at the population and species levels according to the regression equations. All the πs and πt values were gathered and prioritized in the sequential order, sequence > SSR > Allozyme > RAPD > AFLP > ISSR, so that only one value is retained for each taxon. Cultivated taxa  were then removed for further analysis. Each species was also verified and classified in various taxonomic databases, and angiosperm species followed the APG III version (2009) [Pairwise correlation analyses were performed among the estimates derived by AFLP, Allozyme, ISSR, RAPD, SSR and nucleotide sequences for the same taxa available. The species-level estimates were preferentially included over the population-level estimates. All the n (2009) .http://plants.usda.gov), pertinent floras or other botanical literature. The threat category information was from the IUCN Red List of Threatened Species version 2013.1 [For each taxon, data were collected for the important life history traits breeding system  and geographic range , as well as for the threat category . For geographic range, “narrow” was equivalent to “endemic” and “narrow” used in Hamrick and Godt , whereasn 2013.1 . Mean π The present study established a uniform π criterion and developed a comprehensive genetic diversity database across a wide taxonomic spectrum of seed plants and all commonly used genetic markers, thus providing a meaningful benchmark reference for future studies with evolutionary biological and conservation concern. However, questions still remain wide open on the way to solve the old riddle about genetic diversity. The compiled large-scale comprehensive plant genetic diversity database in this study will serve as a solid reference resource for future work. Further discrimination between population-level and species-level with detailed trait hierarchies should be considered in future attempts.


Cardiovascular disease (CVD) is the leading cause of death in the United States (US), with familial hypercholesterolemia (FH) being a major inherited and genetic risk factor for premature CVD and atherosclerosis. Genetic testing has helped patients and providers confirm the presence of known pathogenic and likely pathogenic variations in FH-associated genes. Key organizations, such as the Centers for Disease Control and Prevention (CDC), American Heart Association (AHA), FH Foundation, and National Lipid Association (NLA), have recognized the clinical utility of FH genetic testing. However, FH genetic testing is underutilized in clinical practice in the US for reasons that are underexplored through the lens of implementation science. In this commentary, we discuss seven key implementation challenges that must be overcome to strengthen the clinical adoption of FH genetic testing in the US. These implementation challenges center on evidence of cost-effectiveness, navigating patient and provider preferences and concerns, gender and ethnic diversity and representation in genetic testing, and establishing clinical consensus around FH genetic testing based on the latest and most relevant research findings. Overcoming these implementation challenges is imperative to the mission of reducing CVD risk in the US. Cardiovascular disease (CVD) is a long-standing and leading cause of death in the United States (US) . FamiliaFH is inherited in a Mendelian and an autosomal dominant fashion. Genetic testing  can confirm variations in FH-associated genes/biomarkers in individuals. These genes/biomarkers are apolipoprotein B (ApoB), low density lipoprotein receptor (LDLR) and proprotein convertase subtilisin/kexin type 9 (PCSK9), lipase A (LIPA), signal-transducing adaptor protein 1 (STAP1), and apolipoprotein E (ApoE). Variations in LDLR are the most common, followed by variations in ApoB then PCSK9 (70-95% of FH cases) ,7,8,9. AThe clinical utility of FH genetic testing is generally recognized by key organizations such as the Centers for Disease Control and Prevention (CDC), the American Heart Association (AHA), the FH Foundation, and the National Lipid Association (NLA) ,12,13,14In this commentary we discuss seven key implementation challenges that must be overcome to strengthen the clinical adoption of FH genetic testing in the US. These are, as discussed in order in the text: (1) Addressing the cost-effectiveness and affordability of genetic testing; (2) identifying and addressing patient and provider concerns about discriminatory insurance underwriting; (3) navigating patient and provider preferences and concerns about ordering and receiving FH genetic test results; (4) ensuring gender and ethnic diversity and representation across populations screened for FH; (5) updating and clarifying FH genetic test indications based on the presence of certain FH phenotypes in relevant clinical guidelines; (6) determining best practices for communicating results from the reclassification of FH variants of unknown significance (VUS) and variants with conflicting evidence of pathogenicity at the patient-provider level; and (7) establishing a clearer pathogenicity assessment of FH variants to more accurately assess CVD risk in FH populations with various phenotypes.The affordability of genetic/genomic testing programs varies based on the geographic location of the program . Thus, the first key challenge is determining the cost-effectiveness of population-based FH genetic/genomic testing programs in the US health care market. After reviewing recent literature, we found that only one study published between the years 2016–2018 measured the cost-effectiveness of implementing its FH genetic testing program [Health care policies and insurance coverages for genetic testing vary considerably within the US and internationally . MoreoveIn a study reported by Setia et al., the participants received free genetic testing, which Setia et al. explained may have influenced or led to the observed 100% genetic test uptake among the participants. Outside the research settings, free genetic testing is unlikely or uncommon in non-socialized health care systems like that of the US . InsuranThe second key challenge is identifying and addressing patient and provider concerns about discriminatory insurance underwriting, insurance claim denial for FH genetic testing, or unanticipated social stigmas that may accompany genetic testing . An openn = 173, average of 23 years of experience) [The third key challenge is navigating patient and provider preferences and behaviors as barriers and facilitators to FH genetic testing. The American College of Cardiology reported that only 25% of primary care providers, 24% of cardiologists, and 15% of cardiovascular team members order genetic testing to diagnose FH . Insteaderience) . Implemeerience) . Similarerience) . It is iThe fourth key challenge is ensuring gender and ethnic diversity in FH genetic testing programs. Gender and ethnic diversity are needed to better characterize the penetrance of FH variations among and across ethnic populations and the general US population. Khera et al. reported slightly less LDLR variation penetrance among their more ethnically diverse study cohort , as compared to most studies involving mainly Caucasian populations with relatively higher LDLR variation penetrance ,34. KullThe fifth key challenge is updating and clarifying FH genetic test indications based on FH phenotypes listed in relevant clinical guidelines. A recent review of 10 clinical guidelines by Migliara et al. outlined and summarized key indications for FH genetic testing  . The guin = 78) [n = 148; detection rate of 56.8%) [In children, however, genetic testing is indicated if cholesterol concentrations are >230 mg/dL or are in the >95th percentile for age and gender . Geneticn = 78) . Pang etf 56.8%) . n = 344) [n = 229) [n = 79). Thus, clinical guidelines should undergo timely revisions for updates based on a systematic review of relevant findings that challenge the cut-offs for when genetic testing could be indicated based on phenotypic data.Clinical guidelines recommend genetic testing in adults with Dutch Lipid Clinical Network (DLCN) criteria scores >5. This recommendation was challenged by Séguro et al. who showed that, among a study cohort of individuals aged 44 ± 18 years, a DLCN score >8 is strongly correlated with the presence of a FH variant n = 344) ,38. Abul4 ,38. Abn = 229) . Lastly,The sixth key challenge is determining best practices for communicating results from the reclassification of FH VUS and variants with conflicting evidence of pathogenicity at the patient-provider level. To date, there is an unclear pathogenicity assessment for nearly one-third of FH variants in the ClinVar database; of over 3000 variants listed, there are more than 500 VUS and more than 400 variants with conflicting interpretations of pathogenicity. Illustrating this issue are both Amor-Salamanca et al. and Séguro et al., who each discovered VUS among their study populations. Their discoveries suggest a possible need for perpetual reanalysis of VUS and reconfirmation of FH variant significance over time ,39. TsaiIn further consideration of the fact that nearly one-third of FH variants in the ClinVar database have an unclear pathogenicity assessment, the seventh key challenge is establishing a clearer pathogenicity assessment of FH variants to more accurately assess CVD risk across various FH phenotypes. This is clinically important because studies show that either genetic testing or lipid screening alone do not properly identify all FH cases ,37. For In summary, the implementation of FH genetic/genomic testing as part of FH screening is recommended by the CDC, AHA, FH Foundation, and NLA in the US for CVD prevention. FH is also a key topic of discussion among health scientists at institutions such as the National Academies of Science and Engineering, and the Medicine Board on Health Sciences Policy and the Roundtable on Genomics and Precision Health . We high


Avena strigosa Schreb.), one of the four cultivated species of the genus Avena, could be considered as another alternative crop. In gene banks 865 germplasm samples of this species have been preserved that have not been thoroughly investigated so far. The results of phenotyping (36 traits), isoenzymatic (12 systems) and genetic (8 pairs of Sequence-related amplified polymorphism markers) variation were used to obtain the complete description of 56 accessions diversity originated from different parts of world.Sand oat (Breeded and weedy forms represented similar pool of morphological traits that indicated a short-term and extensive breeding process, albeit all accessions which we classified as cultivated were characterized by better grain and green mass parameters compared to the weedy ones. Isoenzymes showed relationships with geographical origin, which was not possible to detect by SRAP markers. There was no similarity between morphological and biochemical results. The polymorphism level of SRAP markers was lower than indicated by the available literature data for other species, however it may result from the analysis of pooled samples of accessions with a high internal variability. The extensive type of breeding and its relatively short duration was also reflected in the population structure results. Joint analysis revealed that a secondary centre of diversity is being created in South America and that it has its genealogy from the Iberian Peninsula.Despite the relatively large representation of this species is in various gene banks, it is highly probable that the vast majority of stored worldwide accessions are duplicates, and the protected gene pool is relatively narrow. Sand oat meets all the requirements for an alternative crop species, but further studies are needed to identify the genotypes/populations with the most favourable distribution of utility and quality parameters. Avena strigosa Schreb.). It is one of the four cultivated species of the genus Avena besides A. sativa L., A. byzantina and A. abyssinica. From the economical point of view, the most important is common oat (A. sativa) cultivated for grain while other species are marginal. Sand oat (A. strigosa) is currently grown only in South America as a winter season cover crop and as forage and pasture in the Southwestern U.S. [Ustilago avenae (Pers.) Rostr., Puccinia graminis f. sp. avenae Erikss. & Henning and Puccinia coronata f. sp. avenae P. Syd. & Syd [Buchloe dactyloides (Nutt.) Englem. provided more information than RAPD, ISSR and SSR markers [Cucumis melo L, Cucurbita maxima Duchesne, Cucurbita moschata Duchesne, Cucurbita pepo L., Solanum tuberosum L. and Citrus and its relative species [A. strigosa genetic diversity analysis.Global climate change implicates search for alternative crops that will be more stable in yielding in conditions of increased temperature and drought stress, will also be less resource-dependent, resistant to pathogens and pests, and will be rich in nutrients. One of these alternative crops may become sand oat  germplasm variation. The objective was to obtain the complete description of diversity of accessions originated from different parts of world.In this study morphological traits, isozymes and SRAP (Sequence-related amplified polymorphism) markers, were used in integrative research of sand oat . Within each accession from one to four varieties were recorded. Of 56 accessions 16 belonged to one botanical variety, 30 to two, nine to three and one to four botanical varieties. This indicated the internal heterogeneity of the material ) and var. intermedia (PL 51742(39)). Five accessions were a mixture of var. strigosa and gilva and one additionally contained individuals of var. melanocarpa. Three botanical varieties: strigosa, gilva and intermedia were identified in Portuguese accessions. The same three varieties was found within French accessions. They differed from each other in composition although, as indicated passport data, all three were derived from one accession VIR 2172 gathered by N.I. Vavilov in 1919 and preserved in N.I. Vavilov Institute of Plant Genetic Resources in St. Petersburg. The accessions obtained from The United Kingdom contained four botanical varieties strigosa, gilva, melanocarpa and intermedia but within accession compositions were diverse. Among 16 Polish accessions as many as seven was homogeneous var. strigosa and one intermedia. The mixture of var. strigosa and var. gilva was identified in seven accessions and in one more var. melanocarpa was admixed. South American accessions originated from Brazil and Uruguay were composed of two botanical varieties i.e. strigosa and gilva whereas Chilean ones contained addition of var. melanocarpa individuals. For more details see Table The accessions collected in Spain were botanically the most diverse and contained five out of seven botanical varieties identified in the set. Two accessions were pure var. strigosa and two intermedia. Only one accession i.e. PL 51744(41) had grains with white lemma.More than half of the analysed accessions were mixtures of grains with grey and brown lemma (55%). In six accessions beside grey and brown lemma an admixture of grains with black lemma was observed. Fourteen accessions have only grains with grey lemma. Among them, ten were homogenous variety In general, South American plants had short awns and high weight of thousand grains, the Spanish accessions had short and narrow flag leaves while the Polish ones were short with short upper internodes and had long rachillas and glumes. Cultivated accessions and breeding or research materials were higher and had longer upper internodes than weedy ones. They also had high mean number of spikelets, short rachillas and glumes. Weedy plants were short and have short upper internodes.(39) and 87.9 for PL 51199(8). A ratio of glumes length (1.04–1.19) and a ratio of lower glume length to spikelet length (0.88–0.99) were almost uniform within the set of accessions. The range of qualitative traits diversity was similar to the quantitative ones. The highest variation was observed for rigidity of flag leaf (0.32) while the lowest was for leaf-blades twist (0.05).The morphological diversity of tested set of sand oat was at the moderate-low level Table . Among qThe diversity index of the morphological traits was calculated for groups based on the major geographical regions i.e. West Europe, East Europe and South America. Additionally, two minor groups were separated i.e. the Iberian Peninsula and Poland Fig. a. The mo(19) originated from Spain was distinctive from the above-mentioned groups , that origin remains unknown, as the only one did not match to any of four groups.Principal Coordinate Analysis revealed that 86.12% of variability was explained by the first three coordinates. The scatter plot of the first two coordinates confirmed the earlier observation of limited informativeness of isoenzymatic assays Fig. a. Most o(4); PL 51746(43) and PL 51747(44)) could not be assigned to clusters based on 70% membership threshold, meaning that they were considered to have admixed parentage . As many as 324 fragments appeared with very low frequency (p < 0.05) and were treated as unique. Finally, only 16 fragments were monomorphic in tested set of sand oat accessions. The PIC was in the 0.26–0.41 range (Me3/Em4 and Me4/Em7 respectively) with mean 0.33  and PL 51579(17)) were assigned to the first group. South American accessions were distributed into both groups as follows: the Brazilian sand oats were in the first group, the Chilean ones were placed in the second, while the Uruguayan accessions were split in two groups. Polish accessions also were scattered. Three French accessions were placed in the first group, but PL 51584(21) was distinctive from the other two.PCoA analysis was performed using Dice distance matrix to graphically summarize the genetic diversity among 56 sand oat accessions. The first three coordinates accounted for 36.1% of total variance. PCoA plot was made using first two coordinates Fig. a. The ac(53) and PL 51759(55)) belonged to the first one. Sand oats from West Europe were in the first cluster except two from Germany (PL 51738(35) and PL 51739(36)) whereas the East European ones were placed in the second group except PL 51499(9) and PL 51579(17). Polish accessions were evenly distributed in both groups. All of the above exceptions to the general grouping pattern applied to materials influenced by breeding.Bayesian analysis was conducted to determine the genetic structure among sand oat accessions Fig. b,c. The (5), PL 51578(16), PL 51748(45) and PL 51752(48)) and some others were predicted to have their origin from at least two sources of diversity. The presence of the third group perfectly reflect botanical diversity of three French accessions i.e. PL 51584(21) classified as var. strigosa placed in the first group, PL 51105(5) a mixture of var. strigosa and intermedia was assigned into third group, while PL 51749(46) the most botanically diverse  had certain level of admixture between the group one (0.336) and three (0.643). Within the groups of accessions originated from Iberian Peninsula and East Europe the level of admixture was the lowest, whereas the highest level was observed in the West European and South American group  of total variance respectively. The first coordinate was strongly negatively correlated with both latitude and longitude. The projection of accessions on bi-plot of Coord. 1 and Coord. 2 , grey, red, yellow and white which are fairly stable [In this paper the data from the description of plant phenotype  were analysed. Precise botanical identification is scarce in the germplasm databases as it requires from the curator to have expertise in botany. Tis kind of information, especially in case of self-pollinating plant species, can be the first indicator of internal accession diversity and can be used as a genetic integrity marker. Botanical differentiation at sub-species level has been identified earlier, for example for occum L. , 32, 33.y stable . It is py stable . The absy stable .A. strigosa in that region mainly as forage crop and, therefore, not paying special attention to grain yields [(48) and PL 51575(15) may be valuable genetic stocks for these purposes.South American accessions had a high 1000 grains weight, which may result from the use of n yields , 38. Then yields , 40. Morn yields . The pooA. strigosa based on SRAP molecular markers and morphology. For the analysis of 56 accessions preserved in the Polish collection of Avena, 12 enzymatic systems were used, which had been previously analysed and described by other authors [A. strigosa ecotypes were analysed, only two (MDH and EST - esterases) out of six applied systems showed polymorphism. However, research on 1005 accession of A. sterilis showed that out of 134 alleles, obtained by the use of 23 isoenzymatic systems, only 10 were not polymorphic and 54 alleles occurred in less than 10% of accessions [Since their discovery by Hunter and Markert in 1957, isoenzymes have played a key role in many fields of biology over several decades . However authors , 48. Sev authors . In Kubicessions . These mcessions –51.Cynodon dactylon (L.) Pers., and 53 Buchloe dactyloides genotypes polymorphism was found at the level over 90% [Festuca-Lolium complex all obtained fragments were polymorphous [A. strigosa is a self-pollinating species or it may prove that the genetic pool of this species is quite narrow. The values of PIC coefficient were consistent with literature data, however, they were lower than those obtained by using ISSR markers for eight sand oat accessions [In this study eight combinations of SRAP starter pairs were used, which produced a total of 589 fragments, 53% of which were polymorphic, and this value was significantly lower than indicated by the available literature data for other species. In the study of 16 Iraqi wheat cultivars using 28 primer pairs, over 87% of the fragments were polymorphic . 95% in over 90% . In the morphous . A lowermorphous . Howevercessions . This isA. strigosa has a long tradition of cultivation and was the main oat species till seventeenth century [The results of PCoA and model-based clustering agreed with existence of two major clusters, as well as presence of a secondary structure i.e. two sub-groups in the largest group. The general pattern of clustering is that the accessions from the Iberian Peninsula were placed in the second cluster except for two Portuguese ones (PL 51757 and PL 51759). These two accessions were classified as a breeding/research material thus they genetic makeup might be distinct from the native gene pool. It is also worth to consider whether a small sub-group, marked in the diagram as No 3, may reflect a gene pool characteristic for Great Britain. In that region  century . HoweverSouth American accessions showed the highest value of diversity coefficient and were assigned to three different groups. It seems to confirm the hypothesis about secondary diversity of sand oat in South America . DetaileThe majority of studies on the characterisation of genetic resources do not use the available statistical tools to integrate data from different types of analysis, i.e. phenotypic and genetic data. A joint analysis is essential in order to obtain a more reliable, complete description of genetic resources, which should result in their better utilization.Generalised Procrustes Analysis (GPA) allows using the most appropriate ordination method for each type of data i.e. MFA in phenotypic data and PCoA in biochemical and genetic data. The final configuration is the average of all data after their initial transformation. Based on these results, it can be concluded that a secondary centre of diversity is being created in South America and that it has its genealogy from the Iberian Peninsula. Materials from the Iberian Peninsula link the gene pool characteristic for South America and the one present in other regions of Europe. It is clear that the selection in these two regions took place independently and in different directions and its source should be considered both an anthropogenic and an environmental factor.(5), PL 51584(21) and PL 51749(46)) and three from Brazil (PL 51022(4), PL 51149(6) and PL 51730(28)). French accessions originate from a sample collected by N.I. Vavilov himself that is stored by N.I. Vavilov Research Institute of Plant Industry in St. Petersburg under the accession number VIR 2172. Each of them was obtained from a different donor institution i.e. PL 51105(5) was obtained from Genbank Department, Division of Genetics and Plant Breeding, Research Institute of Crop Production, Prague, Czech Republic; PL 51584(21) from Plant Genetics and Germplasm Institute, Beltsville, USA and PL 51749(46) from Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany. In each case, seeds, replicated outside the primary institution, were delivered to Poland. Differences between the three accessions were visible at each stages of the analysis, i.e. starting from the botanical composition and ending with genetics. The results showed that we are dealing with a loss of genetic integrity which is resulting from a genetic drift caused by the multiplication of an insufficient number of seeds provided by the donor institution to create a duplicate in another facility. A comparison of internal variation of these accessions with their analogues in three gene banks, and reference of all of them to the original accession would reveal exactly what processes caused the differentiation of these samples and when the major changes were introduced. Taking into account all obtained results, it should be excluded that at any time contamination with foreign seeds has occurred. In the present situation it is necessary to consider whether it is more sensible to maintain the status quo or to combine them into one accession which will better reflect the initial variability.Based on passport data, two groups of accessions were selected, which potentially represent a multiplication of one original sample. This applies to three accessions from France (PL 51105(4)), and Wielopole (PL 51149(6) and PL 51730(28)). Only in case of one of them we have an accession number from a gene bank in the USA. The origin of remaining two is unknown. All three accessions have the same botanical composition i.e. are a mixture of var. strigosa and gilva. However, the results of all analyses indicate similarity between two accessions received from the station in Wielopole and distinctiveness of the accession from Borowo. In this case we can state that accessions PL 51149(6) and PL 51730(28) are duplicates which arisen as a result of double materials submission to the gene bank in a few years interval both originate from CIav 4639 preserved by USDA-ARS National Small Grains Collection, Aberdeen, USA. Due to the lack of data on the origin of the third sample and its certain distinctiveness manifested at all levels of the performed analysis, it is impossible to state unequivocally whether it is a duplicate that has lost its genetic integrity, or whether it is a distinct accession.Three Brazilian accessions have the same cultivar name in the passports i.e. Saia. This is an old Brazilian cultivar introduced to the State of Rio Grande do Sul in the early 1940’s, bred through selection from local population , 59. AccA. strigosa should be subjected to a sustainable breeding process. Due to low yield, i.e. two times lower than in common oat and significantly smaller seeds, currently this species has little chance of being returned to cultivation. It seems reasonable to promote its usefulness for cultivation in extensive organic farms to produce functional food. Sand oat has many features that are seen as valuable in this type of farming. Based on unpublished studies (Podyma unpublished), it can be concluded that it is suitable for cultivation on very weak, acidic soils and in mountain conditions. It tolerates weed infestation and agrotechnical deficiencies much better than A. sativa. It can be cultivated without chemical protection against fungal diseases such as powdery mildew or crown rust. Sand oat can be an important complementary grain crop because it has a higher protein content (16.0% air-dry matter) than oats (10.5%). A higher content of beta-glucan and polyphenols in dehusked grains in comparison to common oat was found, that proves high pro-health value of the product. This species does not seem to have the potential to replace commonly cultivated cereals such as wheat, barley or even rye. A balanced breeding programme that would improve the profitability of the crop but would not significantly reduce genetic variability would be advisable.To be successful as a crop in today’s commercialised world it is essential that The information on comprehensive, multi-level research on sand oat is missing. Despite the relatively large representation of this species in various gene banks, it is highly probable that the vast majority of stored worldwide accessions are duplicates, and the protected gene pool is relatively narrow. Considering that this species is not known to occur in the wild-state and its spread and survival is inextricably linked to humankind, the verification of existing genetic resources as well as the acquisition of new samples from areas where it is still cultivated, conserved on farms or occurs in the common oat fields is a necessary activity for the sustainable use of this species in agriculture. Further research on sand oat, carried out by our team, focuses on the evaluation of agronomic and quality characteristics in organic farming. We hope that in the near future we will be able to promote sand oat for a new, healthy, trendy, alternative crop.A. strigosa.Majority of weedy accessions diversity was reflected in cultivated forms or breeding materials and it is a derivative of relatively brief and extensive breeding of A. strigosa diversity is being created in South America and it originates from populations from the Iberian Peninsula.The second centre of A. strigosa meets all the requirements for alternative crop species, but further studies are needed to identify the genotypes/populations with the most favourable distribution of utility and quality parametersFifty-six sand oat accessions representing diverse origin were obtained from long term storage of National Centre for Plant Genetic Resources, Radzików, Poland Table . The accAvena L. according to Rodionova et al. [A. strigosa botanical varieties is included in Table Botanical varieties were identified based on intra-specific taxonomic systems of genus a et al. . The claMorphological evaluation was carried out in experimental fields of the Plant Breeding and Acclimatization Institute – National Research Institute in 1980’s. Thirty-six traits Table  were obsTwelve isoenzymatic systems were tested Table . Isozyme2, 1.2 mM of each dNTP and 0.2 μM of each primer. The PCR amplification was performed using Verity 96 Thermal Cycler (Applied Biotechnology) under following temperature profile: 3 min. at 94 °C followed by five cycles each one including 1 min at 94 °C, 1 min at 35 °C and 1 min at 72 °C, followed by 40 cycles of 1 min at 94 °C, 1 min at 50 °C and 1 min at 72 °C and the final extension for 10 min at 72 °C. Four forward Me primers were labelled at the 5′ end with one of fluorochromes . The amplified fragments were analysed using capillary sequencer Genetic Analyser 3130XL. The 36 cm capillary array field with NanoPOP7 (Nimagen) was used. The length of fragments was assessed against the GeneScan 1200 LIZ Size Standard (Applied Biosystem). Each PCR reaction and fragment analysis were performed in three independent replicates. Only repeatable fragments were scored.DNA was extracted from leaf tissue of two-weeks old seedlings. Each accession was represented by bulk sample composed of 12 randomly chosen individuals. The tissue was lyophilized and homogenised in bead mill MM301 (Retch). The total DNA was extracted using Genomic Mini AX Plant (A & A Biotechnology). Sixty-four SRAP primers combination were initially tested . Out of SRAP and isoenzymatic fragments were scored and coded as 0/1 matrices, where 0 indicated absence and 1 presence of fragment. Genetic distance was calculated based on Dice formula. The resulting matrices were used in Principal Coordinate Analysis (PCoA). Multiple Factor Analysis (MFA) was performed to simultaneous analysis of qualitative and quantitative morphological traits. Generalized Procrustes Analysis (GPA) was used to reduce the scale effect and to obtained consensus configuration of morphological, izoenzymatic and genetic results. The GPA analysis unifies individual systems or geometric representations on a plane by transforming them through iterative algebraic steps i.e. rotation, translation, reflection and scaling. The transformations must meet two assumptions, i.e. to maintain a relative distance between the elements of each system and to minimize the sum of squares between points which, in different systems, correspond to the same elements . The Peai is the ith allele of the jth marker, n is the number of alleles of the jth marker and p is an allele frequency.The marker informativeness was determined by the Polymorphic Information Content (PIC) coefficient using the following formula:H′ is standardized relative diversity index, n is the number of classes per trait/marker, pi is the proportion of the total number of entries in the ith class. The variation coefficient defined as:σ is the standard deviation and μ is arithmetic mean, was used to measure the dispersion of quantitative traits. The unbiased genetic diversity coefficient was calculated as follows:n is the sample size and pi is the frequency of the ith trait/marker.The Shannon-Weaver (H′) index was used as a diversity index and was calculated as follows:The isoenzymatic and genetic data were subjected to a hierarchical analysis of molecular variance (AMOVA) described by Excoffier et al. .All analyses were performed using the Microsoft Excel 2016, XLSTAT Ecology  and GenAlEx 6.501 .The Bayesian model-based analysis of population structure was performed with the most commonly used software STRUCTURE 2.3.4 . The num


Populations of fishes are increasingly threatened by over‐exploitation, pollution, habitat destruction, and climate change. In order to better understand the factors that can explain the amount of genetic diversity in wild populations of fishes, we collected estimates of genetic diversity  along with habitat associations, conservation status, and life‐history information for 463 fish species. We ran a series of phylogenetic generalized least squares models to determine which factors influence genetic diversity in fishes after accounting for shared evolutionary history among related taxa. We found that marine fishes had significantly higher genetic diversity than freshwater fishes with marine fishes averaging 11.3 more alleles per locus than their freshwater counterparts. However, contrary to our expectations, genetic diversity was not found to be lower in threatened versus not‐threatened fishes. Finally, we found that both age at maturity and fecundity were negatively related to genetic variation in both marine and freshwater fishes. Our results demonstrate that both life‐history characteristics and habitat play a role in shaping patterns of genetic diversity in fishes and should be considered when prioritizing species for conservation. In sum total, we ended up with 463 studies, each corresponding to a distinct species  to filter out medical journals unrelated to genetic diversity in fishes. We focused on microsatellite loci because 1. far fewer studies to date have used alternative nuclear markers to measure genetic diversity in fishes  and 2. we wanted to make comparisons across similar marker types to avoid confounding factors . To expedite data entry, we cross‐referenced our search results against a similar dataset amassed for all vertebrates . Because the number of microsatellite alleles identified per locus is positively related to the number of individuals sampled  and these species were categorized into a third category (“mixed”). Next, we determined species’ conservation need using IUCN Red List designations for each species .We next supplemented our genetic data with taxonomic, habitat, and life‐history data for each species. First, phylogenetic relationships were approximated using taxonomic classifications  from Nelson, Grande, and Wilson . Althouges IUCN, . Based o2.2We used three sets of statistical models to understand how genetic diversity varied in fishes that (a) occupied different habitats, (b) were characterized by differing levels of conservation need, and (c) had different life‐history characteristics. For each model set, we considered the effects of our predictor variables  on both heterozygosity and the rarefied mean number of alleles for each species. We analyzed our genetic, taxonomic, habitat, and life‐history data simultaneously using Phylogenetic Generalized Least Squares (PGLS) regressions using the NLME package in R . Next, we analyzed the effect of conservation status  on genetic diversity for fishes within each major habitat type . PGLS models assessing the relationship between conservation status and genetic diversity were run separately for marine and freshwater fishes using the model Yi = ß1 * conservation statusi + εi. Finally, we considered the effects of two life‐history traits—age at maturity and fecundity—on genetic diversity. For these life‐history models, we analyzed each life‐history parameter separately, and ran separate models for the two habitat groups . Analyzing all factors together  resulted in overdispersion. The general model used for analyzing the relationship between life‐history variables and genetic diversity was iY = ß1 * life historyi + iε, where life historyi represents each of our life‐history variables of interest for the ith species. For each model, the error term iε can be thought of as ε ~ N, where V is the covariance matrix containing estimates of shared evolutionary history between pairs of species and σ2 is the standard deviation .For our first set of PGLS models, we assessed the relationship between habitat and genetic diversity by comparing freshwater and marine fishes using the model Pennell, . All of Pennell, , and all3N = 426 in Osteichthyes, N = 32 in Chondrichthyes, and N = 5 in Petromyzontida), 19 orders, and 46 families of fishes. Marine and freshwater species were represented equally with 215 and 204 species, respectively, whereas mixed species represented a much smaller proportion of our dataset  and more alleles per locus  relative to freshwater species  and mordata deficient or extinct; Table Of the species included in our dataset, 283 had been assessed by the IUCN; we identified 194 not‐threatened and 69 threatened species . We found that as age at maturity and fecundity increased in marine fishes, genetic diversity decreased. For marine species, age at maturity was negatively related to the rarefied mean number of alleles , although this trend was not significant for heterozygosity . Fecundity was negatively related to both heterozygosity  and mean rarefied number of alleles . We found similar patterns for freshwater fishes  with the exception of heterozygosity .4eN) is often positively correlated with census population size, larger population sizes in marine fishes should generally translate to larger eN and therefore higher genetic diversity relative to freshwater species  eN can be orders of magnitude smaller than census population sizes for some marine fishes  the ratio between We predicted that species listed as threatened by the IUCN should have lower genetic diversity than non‐threatened species. However, we found no significant difference in genetic diversity between conservation categories in either marine or freshwater habitats, as coefficient estimates of both mean heterozygosity and rarefied mean number of alleles did not differ significantly between not‐threatened and threatened groups of fishes in either freshwater or marine environments Figure . NeverthWe also found that minimum age at maturity was negatively related to genetic diversity in fishes Figure . The obseN, NeN, and therefore genetic diversity. Given that highly fecund marine species typically experience high variance in reproductive success , as increasing evidence suggests that population‐level processes affect different classes of genetic loci differently . Finally, while our study utilized a taxonomic correction to account for trait similarities due to shared evolutionary history, advances in our understanding of phylogenetic relatedness among fishes will improve as genomic data continues to emerge for new species. Based on the trends we uncovered, we expect that future studies can capitalize on well‐resolved phylogenies and thereby remove additional noise from the analysis; such studies may find stronger and perhaps different drivers of genetic diversity in fishes.Future studies examining the factors that drive patterns of genetic diversity in fishes should investigate alternative markers and measures of genetic diversity (4.2IUCN conservation rankings are the preeminent worldwide conservation ranking system used to categorize species based on risk of extinction. However, our results suggest that IUCN conservation status is a poor indicator of genetic diversity in fishes. Instead, we found that incorporating species‐specific habitat and life‐history information can improve our ability to discern fish species that exhibit reduced genetic diversity. As anthropogenic impacts continue to occur, rapid habitat alteration will present species with novel challenges. While we examined putatively neutral genetic markers, several studies have highlighted positive correlations between neutral markers and fitness or fitness‐related traits, suggesting that increased genome‐wide genetic diversity may also increase viability for populations facing environmental change (Chapman et al., None declared.ASM, JRW, and MRC conceived the questions and objectives associated with the study. ASM and JRW compiled and analyzed the data. All authors contributed to the writing of the manuscript.Data utilized in this study are available in Supporting information Appendix  Click here for additional data file. Click here for additional data file. Click here for additional data file.