We compared scenarios for genetic gain, rate of inbreeding, proportion of selected external sires, and the split-point rg that is the lowest value of rg for long-term cooperation. Between two equal-sized populations, cooperative GS breeding programs achieved a maximum increase of 19.3% in genetic gain and a maximum reduction of 24.4% in rate of inbreeding compared to independent GS breeding programs. The increase in genetic gain and the reduction in rate of inbreeding realized by GS breeding programs with cooperation were respectively at maximum 9.7% and 24.7% higher than those realized by PS breeding programs with cooperation. Secondly, cooperative GS breeding programs allowed a slightly lower split-point rg than cooperative PS breeding programs (0.85∼0.875 vs ≥ 0.9). Between two unequal-sized populations, cooperative GS breeding programs realized higher increase in genetic gain and showed greater probability for long-term cooperation than cooperative PS breeding programs. Secondly, cooperation using GS were more beneficial to the small population while also beneficial but much less to the large population. In summary, by cooperation in the presence of G × E, GS breeding programs realize larger improvements in terms of the genetic gain and rate of inbreeding, and have greater possibility of long-term cooperation than conventional PS breeding programs. Therefore, we recommend cooperative GS breeding programs in situations with mild to moderate G × E, depending on the sizes of two populations.Genotype × environment interaction (G × E) is of increasing importance for dairy cattle breeders due to international multiple-environment selection of animals as well as the differentiation of production environments within countries. This theoretical simulation study tested the hypothesis that genomic selection (GS) breeding programs realize larger genetic benefits by cooperation in the presence of G × E than conventional pedigree-based selection (PS) breeding programs. We simulated two breeding programs each with their own cattle population and environment. Two populations had either equal or unequal population sizes. Selection of sires was done either across environments (cooperative) or within their own environment (independent). Four scenarios, (GS/PS) × (cooperative/independent), were performed. The genetic correlation ( Environronments . An rg lountries . The rg  ∼ 0.74) . When G  ∼ 0.74) . Thereforg between environments. In the era before genomic selection (GS), rg was above 0.7 or 0.8. rg as low as 0.4 to 0.6, but only possible in the long term with rg as high as 0.8 to 0.9, which was called the split-point rg. The split-point rg is the lowest value of rg for long-term cooperation. In other words, if rg is lower than split-point rg then each breeding program selects animals from the external population only in the short-term, while if rg is higher than split-point rg then each breeding program selects animals from the external population in the short and also long term. Thus, when the split-point rg is lower, it indicates greater probability of long-term cooperation. It is evident that the level of G × E influences the decision of running cooperative or independent breeding programs. The above-mentioned studies using pedigree-based selection (PS) found improvement in genetic gain by cooperation but none of them investigated the impact of cooperation on the rate of inbreeding. While our study attempted to fill this gap of rate of inbreeding and investigated the mechanisms of benefiting from cooperation using GS in the presence of G × E.In many cases, different breeding programs are operated in different environments because of G × E. Moreover, cooperation of breeding programs, e.g., by using jointly each other’s sires, can result in larger genetic gain and lower rate of inbreeding. The cooperation discussed here refers to the combination of joint-evaluation and across-environment selection. Cooperation of breeding programs in the presence of G × E depends on the rg to a lower numeric value, i.e., has a greater probability of long-term cooperation. Inbreeding is expected to be reduced by the increased reference population that contains more genetic diversity and by the selection of external animals across environments with slightly different selection. In addition, at the split-point rg it is expected to observe the minimum rate of inbreeding. Because at the split-point rg it is an open breeding system with the greatest number of ancestors who have made long-term genetic contributions to the current generation among all levels of rg. The greatest number of ancestors results in a minimum rate of inbreeding, according to the relationship between long-term genetic contributions from a generation of ancestors and rate of inbreeding of the current population: ir is the long-term genetic contribution of ancestor i (rg of the selected trait expressed in two environments and the heritability (h2) of that trait.Genomic selection increases genetic gain and reduces rate of inbreeding compared to the conventional methods . We hypocestor i . In viewWe used stochastic simulation to estimate improvements in genetic gain and inbreeding and the probability of long-term cooperation made by two cooperative GS or PS breeding programs in the presence of G × E. For this purpose, we designed four scenarios each with two interacting breeding programs that resembled two dairy cattle breeding programs carried out in two different environments.t = 6…20. This scenario simulated two cooperative GS breeding programs. “Cooperation” in this study was defined as the situation that one population eventually selected sire(s) from the other population in generations t = 6…20 based on across-environment genetic evaluation.Both breeding programs adopted across-environment single-step genomic best linear unbiased prediction (ssGBLUP) for all animals and across-environment selection for sires in generations t = 6…20. This scenario simulated two independent GS breeding programs. It provided the reference to estimate the benefits of cooperation made by AG-AS compared to the independent GS breeding programs.Both breeding programs adopted within-environment ssGBLUP for all animals and within-environment selection for sires in generations t = 6…20. This scenario simulated two cooperative conventional breeding programs. It was compared to AG-AS for the capability of benefiting from cooperation in the presence of G × E either with PBLUP or ssGBLUP.Both breeding programs adopted across-environment pedigree-based BLUP (PBLUP) for all animals and across-environment selection for sires in generations t = 6…20. This scenario simulated two independent conventional breeding programs. It provided the reference to estimate the benefits of cooperation made by AP-AS compared to independent PS breeding programs.Both breeding programs adopted within-environment PBLUP for all animals and within-environment selection for sires in generations h2 ranging from 0.1 to 0.5. The rg between Trait 1 and Trait 2 varied from 0.5 to 0.9 representing different levels of G × E between E1 and E2. Phenotypes were only observed for females, which resembled milk production. Each scenario was replicated 50 times and each replicate was simulated for 20 discrete generations . Each generation took one unit of time that consisted of a series of related events, namely culling, update of reproductive cycles, selection, mating and sampling offspring.For all four scenarios, two breeding programs — having their own environment (E1/E2) and cattle population — had either equal (1000:1000) population sizes or unequal (400:1600) population sizes. The population size was determined by the number of selected reproductive females. The selected trait was controlled by 2,000 biallelic quantitative trait loci (QTL). Each breeding program aimed to improve the breeding goal trait in its own environment (Trait 1/Trait 2) and selected sires and dams by truncation selection on the EBV of its own environment. Trait 1 and Trait 2 in the base population had equal initial genetic means of 0, additive genetic variances  marker loci, which included 200 IBD markers equidistantly distributed within each chromosome and never involved in selection. Each base animal’s contribution to their descendant generations was traced and true inbreeding of descendants relative to the base population was inferred based on these 6,000 IBD markers.Simulation specifically for each scenario was initiated at generation ith base animal in each environment, the phenotype of the breeding goal trait, ip, was calculated as pi = ai + ei, where ia was that base animal’s true breeding value (TBV) and ie was the residual environmental value. In particular, TBV for the breeding goal trait (ia) as well as TBV for the “unexpressed” trait (aiu) that was only expressed in the other environment was simulated simultaneously, both of which were calculated as the sum of 2,000 QTL effects. The effects of each QTL on the two traits followed a bivariate normal distribution, ie was obtained by sampling from e∼N. As a result, the phenotypes of the breeding goal trait of base animals in each environment had a mean of 0 and a standard deviation of 1/h.Base animals were randomly appointed to E1 and E2, and the numbers of appointed males and females to either environment were determined by the population size. For the t = 1 were randomly selected from base animals within environments with the selected proportion of 50%. In generations t = 1…5, only selected sires were genotyped.Parents in generation t = 1…20, selected males were evenly and randomly mated with selected females. QTL transmitted from parents to offspring as per Mendelian rules without newly generated mutation after the founder population was established. QTL effects on both traits were the same as for base animals, but allele frequency at each QTL could change due to selection and drift. TBV for each trait for each animal born in generations t = 1…20 was the sum of two allelic effects over all QTL. The residual environmental values and phenotypes were obtained as for base animals. To maintain population sizes, each selected female produced three offspring with a probability of 0.5 for both male and female offspring.In generations t = 2…5, each breeding program was carried out independently in their own environment using within-environment truncation selection for both sexes based on within-environment PBLUP.In generations t = 6…20, four scenarios were carried out with different genetic evaluation strategies or different selection strategies of sires as above-mentioned. But dams were always selected within-environments irrespective of scenarios. All animals were genotyped prior to selection. AG-AS in full details. Except for the genetic evaluation strategies and the selection strategies of sires in generations t = 6…20, other all scenarios shared the same details as AG-AS.In generations t = 2…20. The models used in all scenarios can be summarized as follows:Genetic evaluation was executed by DMU version 6  in generiy was the vector of observations of ith trait, i = 1, 2; iβ was the vector of generation as the fixed effect in the ith environment, the number of levels of which increased by one every generation; ia was the vector of additive genetic effects of ith trait; and ie was the vector of residual effects of ith trait. iX and iZ were the incidence matrices connecting iβ and ia to iy, respectively. It was assumed that H was the matrix of additive genetic relationships among individuals in the pedigree if it was PBLUP, or the unified genetic relationship matrix of all genotyped and non-genotyped animals in the pedigree if it was ssGBLUP . The presented genetic gain was the average over 50 replicates. The unit of genetic gain was the genetic standard deviation of the breeding goal trait in the base population.Rate of inbreeding per generation was calculated for each replicate as ΔF = 1 − exp(bln(1−Ft), t), where ΔF was rate of inbreeding, Ft was the mean inbreeding coefficient at generation t, bln(1−tF), t was the regression coefficient of the natural logarithm of (1 − Ft) on generation (t = 10…20). Because the inbreeding coefficient at any generation t referred to the base population isTherefore,ln(1 − ΔF) is the regression coefficient of ln(1 − Ft) on generation t. Ft was derived based on the average inbreeding coefficient of those 6,000 fore-mentioned IBD loci. Inbreeding at each IBD locus was defined as the probability that two alleles at that locus for a randomly selected animal out of the population are IBD. The presented rate of inbreeding was the average over 50 replicates. In addition, the ancestors who had made long-term genetic contributions to the current generation were traced and counted to help interpret the occurrence or absence of the minimum rate of inbreeding.which deduces that AG-AS will be respectively calculated as [(ΔGAG−AS − ΔGWG−WS)/ΔGWG−WS] and [(ΔFAG−AS − ΔFWG−WS)/ΔFWG−WS] in percentage terms. While by AP-AS, they will be respectively [(ΔGAP−AS − ΔGWP−WS)/ΔGWP−WS] and [(ΔFAP−AS − ΔFWP−WS)/ΔFWP−WS] in percentage terms.We evaluated genetic gain and rate of inbreeding per generation instead of per unit of time. Because the biological risks of the negative influence of inbreeding, namely inbreeding depression and deleterious alleles, as well as its balancing processes, such as mutation, are more relevant in the context of generation being the time scale . AlthougSplit-point rg was determined for scenario AG-AS and AP-AS after computing the proportion of sires from the external environment in all sires selected by each environment in generations t = 6…20. The split-point rg is the lowest value of rg for long-term cooperation. In other words, above the split-point rg, one breeding program kept selecting sires from the external environment without decrease in the proportion of external sires till generation 20.The simulations were run using ADAM , a softwThe following sections presented findings for the situation that two environments had equal population sizes. These results were only presented for E1; results from E2 were similar to E1.AG-AS realized genetic gain ranging from 0.50 ± 0.04 to 0.79 ± 0.04, while AP-AS realized genetic gain between 0.31 ± 0.04 and 0.51 ± 0.04. It was almost always AG-AS > WG-WS > AP-AS > WP-WS in terms of the ranking of genetic gain. AG-AS increased up to 19.3% in genetic gain relative to WG-WS, while AP-AS increased up to 9.6% relative to WP-WS. AG-AS had significantly larger (P < 0.0001) increase in genetic gain than AP-AS given the same h2 and the same rg. At most AG-AS realized 9.7% higher increase than AP-AS at h2 = 0.1 and rg = 0.9. Also note that, at h2 = 0.3 and rg = 0.8, AG-AS obtained improvements in genetic gain with an increase of 1.2% and in rate of inbreeding with a reduction of 7.1% relative to WG-WS, without selecting any external sire in generations t = 10…20  for all levels of rg combined with h2 of 0.3. It turned out to be intermediate between AG-AS and WG-WS in terms of genetic gain and rate of inbreeding (not shown).Genomic selection breeding programs realized larger increase in genetic gain by cooperation than conventional PS breeding programs . Scenari = 10…20 . FollowiAG-AS realized rate of inbreeding ranging from 0.0068 ± 0.0016 to 0.0106 ± 0.0025, while AP-AS realized rate of inbreeding between 0.0088 ± 0.0027 and 0.0138 ± 0.0036. In most cases, it was AG-AS < WG-WS < AP-AS < WP-WS in terms of the numeric ranking of rate of inbreeding. Throughout all combinations of h2 and rg, AG-AS decreased the rate of inbreeding up to 24.4% relative to WG-WS, while AP-AS decreased the rate of inbreeding up to 16.1% relative to WP-WS. By comparison, AG-AS had significantly larger (P < 0.00001) reduction in rate of inbreeding than AP-AS given the same h2 and the same rg. At most, AG-AS realized 24.7% larger reduction in rate of inbreeding than AP-AS at h2 = 0.1 and rg = 0.875, where AG-AS favorably realized a reduction in inbreeding rate while AP-AS unfavorably realized an increase in inbreeding rate relative to their respective independent scenarios.Genomic selection breeding programs realized larger reduction in rate of inbreeding by cooperation than conventional PS breeding programs . ScenariAG-AS because it displayed the strongest capacity of increasing genetic gain and reducing rate of inbreeding. Within AG-AS genetic gain increased as rg increased independent of the heritability, with a few exceptions . It can be concluded that genomic selection decreases the split-point rg from 0.9 to 0.85–0.875 compared to pedigreed-based selection, because of gaining larger variances in EBV of domestic and external sires.Genomic selection breeding programs showed a slightly lower split-point erations . In compAG-AS ≥ WG-WS > AP-AS or WP-WS in terms of the ranking of the genetic gain for both E1 and E2. For E1 that had the small population size, AG-AS increased up to 51.7% in genetic gain relative to WG-WS, and AP-AS increased up to 22% relative to WP-WS. For E2 with the large population size, AG-AS increased up to 4.3% relative to WG-WS, and AP-AS increased up to 0.5% relative to WP-WS. AG-AS had significantly larger increase in the genetic gain than AP-AS given the same h2 and the same rg for both E1 (P = 0.0006) and E2 (P = 0.002), with the maximum difference between the increases realized by AG-AS and AP-AS being 29.7% for E1 and 4.8% for E2 both at rg = 0.9. These results revealed that cooperation was more beneficial for the small population in terms of genetic gain.Genomic selection breeding programs realized larger increase in genetic gain by cooperation than conventional PS breeding programs . It was rg between short-term cooperation and long-term cooperation than PS breeding programs, with 0.6∼0.7 vs. 0.7∼0.8 for the small population and 0.7∼0.8 vs. 0.8∼0.9 for the large population . Secondly, E1 displayed greater variation of inbreeding rates across all levels of rg than E2 by implementing either AG-AS or AP-AS. Then note the generally much higher rate of inbreeding in E2 than in E1 when using either WP-WS or AP-AS, while rather similar rates of inbreeding were observed in E2 and E1 when using either WG-WS or AG-AS. Also note the sharp increased rates of inbreeding at rg = 0.9 in E1 by AP-AS (0.0129) and AG-AS (0.0094).However, based on existing experimental design and results, we found no pattern that GS breeding programs realized larger reduction in rate of inbreeding by cooperation than conventional PS breeding programs . It showOur findings supported our hypothesis that GS breeding programs benefit more from cooperation regarding genetic gain and rate of inbreeding and have greater possibility of long-term cooperation in the presence of G × E than conventional PS breeding programs. Cooperation here referred to the combination of joint genetic evaluation and selection of sires across two environments. Otherwise no cooperation meant two populations remained independent by executing a completely separate breeding program within its own environment. GS increases accuracy therefore increasing genetic gain and decreasing rate of inbreeding. Cooperation with using external bulls from the other environment as candidates, increased the selection intensity thus increased genetic gain and also introduced more genetic diversity therefore decreased rate of inbreeding. Genomic selection increased the benefits of cooperation so that achieving larger increases in genetic gain, larger reductions in rate of inbreeding and a lower split-point genetic correlation. In short, genomic selection shows larger probability of long-term cooperation compared to conventional pedigree-based breeding programs in the presence of G × E. Hence, we recommend cooperative GS breeding programs with mild to moderate G × E depending on the sizes of two populations.AG-WS in which the cooperation included only joint genomic evaluation, but selection was within environment. Results showed that even such a “single-layer” cooperation strategy was also beneficial for genetic gain and rate of inbreeding, as reported by rg ≥ 0.8) between two equally sized populations from two different environments, cooperative GS breeding programs are superior with a greater ability of increasing genetic gain than cooperative conventional PS breeding programs, due to increased accuracy of both domestic and external sires and increased selection intensity.Genomic selection breeding programs realize larger improvement in genetic gain by cooperation than PS breeding programs  because AG-AS had significantly larger reduction in rate of inbreeding than AP-AS. This supportive proof is backed up by two underlying findings. First, GS breeding programs realized lower rate of inbreeding than conventional breeding programs either with cooperative breeding strategy or independent breeding strategy (rg ≥ 0.8) between two equally sized populations, cooperative GS breeding programs have greater capacity of slowing down rate of inbreeding than conventional cooperative PS breeding programs, because of more families selected and more genetic diversity introduced.Genomic selection breeding programs realizing larger improvement in rate of inbreeding by cooperation is directly supported by the finding that strategy , becausestrategy . SecondlAG-AS did not only always have the highest genetic gain and lowest rate of inbreeding than other strategies, irrespective of rg and h2. Meanwhile AG-AS showed two obvious patterns within itself. First, the genetic gain increased as rg increased due to more genetically related traits providing more valuable information and resulting in higher estimation accuracy. Second, the rate of inbreeding first increased then decreased as rg increased with the minimum rate of inbreeding appearing at rg of 0.85∼0.875 — the split-point rg for two equally sized populations, which was well explained by the number of ancestors of long-term contribution. The rate of inbreeding did not trend up all the way like the genetic gain, suggesting that the weaker G × E is not necessarily the better if the balance between inbreeding and genetic gain is desired.rg of AG-AS than that of AP-AS (0.85∼0.875 vs ≥0.9). Given a level of rg, a trade-off exists between the increased difference of genetic levels of two populations and the increased variation of EBVs (increased accuracy) of candidates from these two populations over time, as shown by rg and determines how long the cooperation would last  of bulls  enables cao.lu@mbg.au.dk.The datasets analyzed in this manuscript are not publicly available. Requests to access the datasets should be directed to AS and LC conceived this research, designed the scenarios and populations. LC, HL, JT, MK, and AS participated in the preliminary discussion of designed scenarios. LC performed the simulation and analyzed data with great help from HL. All authors involved in the interpretation of results. LC prepared the first draft. All authors contributed to revising the manuscript, especially HM and MH.JT was employed by company VikingGenetics. MK was employed by company SEGES, Denmark. The remaining 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.


Helianthus annuus L.) and its wild progenitor  to reconstruct phylogeographic relationships and population genetic/demographic patterns across sunflower. In common sunflower, south-north patterns in the distribution of nucleotide diversity and lineage splitting indicate a history of rapid postglacial range expansion from southern refugia. Cultivated sunflower accessions formed a clade, nested among wild populations from the Great Plains, confirming a single domestication event in central North America. Furthermore, cultivated accessions sorted by market type  rather than breeding pool, recapitulating the secondary development of oil-rich cultivars during its breeding history. Across sunflower, estimates of nucleotide diversity and effective population sizes suggest that cultivated sunflower underwent significant population bottlenecks following its establishment ~5000 years ago. The patterns inferred here corroborate those from previous studies of sunflower domestication, and provide a comprehensive overview of its evolutionary history.Patterns of genetic variation in crops are the result of selection and demographic changes that occurred during their domestication and improvement. In many cases, we have an incomplete picture of the origin of crops in the context of their wild progenitors, particularly with regard to the processes producing observed levels of standing genetic variation. Here, we analyzed sequence diversity in cultivated sunflower ( Helianthus annuus L.).Patterns of genetic variation in cultivated plants are the product of multiple processes that have occurred over their evolutionary histories. Gaining a comprehensive understanding of these patterns and the underlying processes requires reconstructing them within the broader context of the wild species from which they are derived. Such species-wide assessments can provide inferences into the ancestral lineages that gave rise to early domesticates and modern cultivars, and yield insights into the factors explaining the distribution of genetic diversity across gene pools. Such knowledge also has practical value, and can be applied to identify sources of novel alleles that should be preserved in germplasm collections and which may be of value in modern breeding programs ,2,3,4. HCommon sunflower is a widely distributed annual herb whose native geographic range is centered in the Great Plains region of the United States and Canada . SunflowThis historical account of the evolutionary history of sunflower serves as the basis of hypotheses concerning the expected population structure and levels of standing genetic variation across its gene pool. Previous studies have sought to infer these patterns in sunflower at various scales, and have shed light on certain aspects of its origin and subsequent evolution. For example, broad examinations across common and cultivated sunflower have provided strong evidence for a single domestication event ,14,15, aTo date, however, attempts to infer patterns of population divergence and population structure in common sunflower (particularly from a phylogeographic perspective) have been somewhat limited, and efforts to formally reconstruct the demographic history of sunflower domestication are lacking. As such, a number of important issues have not been addressed. For example, given that common sunflower populations currently span areas with widely varying climatic conditions during the Last Glacial Maximum , In this study, we sought to characterize the phylogeographic history of sunflower. We used genotyping-by-sequencing (GBS) to examine a panel of accessions that span the latitudinal range of common sunflower and the breadth of cultivated sunflower diversity to: (1) reconstruct the postglacial migration history of common sunflower; (2) identify patterns of genetic relatedness and structure across the various breeding pools of cultivated sunflower; and (3) gain insights into the history of demographic changes associated with domestication and subsequent improvement.Helianthus argophyllus and Helianthus petiolaris), to root phylogenies and polarize SNPs for the downstream analysis (see below). Seeds were sown in flats, and leaves were sampled from a single individual per cultivated sunflower line and up to 18 seedlings per wild sunflower accession. Genomic DNA was extracted from collected leaves using a modified CTAB protocol [We obtained seeds of cultivated and common sunflower from the USDA North Central Regional Plant Introduction Station  in high-output mode and set to produce 75 bp single-end reads. All library preparation and sequencing was performed at the Georgia Genomics and Bioinformatics Core .DNA extractions were prepared for sequencing following a two-enzyme GBS protocol  using thWe used iPyrad version 0.9.16  to proceH. annuus; 2 H. argophyllus, 3 H. petiolaris samples) at a minor allele frequency greater than 1% (MAF > 0.01). The sample coverage threshold in this dataset was chosen because it allowed for the retention of lower coverage, high mutation rate sites which are useful in resolving recent divergences [H. petiolaris and argophyllus . The reduced sample size and sample coverage thresholds of the “ingroup_dadi” dataset were chosen as they increased the number of segregating sites available for demographic analysis. Furthermore, the relatively low MAF and MAC thresholds of the “ingroup” datasets were chosen to allow for the inclusion of rare variants which provide greater resolution of genetic structure and demographic events [Phylogenetic analyses were conducted on the “phylogenomics” dataset composed of biallelic SNPs present in ≥ 50% of ingroup and outgroup samples , as well as the wild populations, separately. Differences in π among breeding pools were assessed by computing the 95% confidence intervals from 1000 bootstrap replicates of per-site estimates of π using the “boot” and “boot.ci” functions in the R [boot [Estimates of mean nucleotide diversity (π) across all sites were calculated with VCFtools to identify patterns in the distribution of genetic diversity across breeding pools within in the R  package  R [boot .ST and FIS) were estimated using all sites with the hierfstat R package [ade4 [ST [hierfstat R package [geosphere R package [6 Monte Carlo simulations. Clinal trends in π were investigated with linear regression, treating latitude and longitude as fixed effects using the “lm” function in R. AMOVA was conducted using the poppR package in R [We then estimated patterns of genetic diversity across wild sunflower populations. Global population genetic parameters  as implemented in the R package LEA [We estimated individual ancestry coefficients with fastSTRUCTURE 1.0  and ADMIkage LEA .We modeled the divergence history between wild and cultivated sunflower using the diffusion approximation approach implemented in δaδi 2.0.3 . We formeasySFS (https://github.com/isaacovercast/easySFS), sampling 24 and 30 haplotypes from the cultivated and wild lineages, respectively, to maximize the number of segregating SNPs for analysis [dadi_pipeline (https://github.com/dportik/dadi_pipeline), described in Portik et al. [dadi_pipeline was run using custom settings  and models were extrapolated to a grid size of 40, 50, 60 points and fitted with Nelder-Mead optimization. Maximum-likelihood parameter estimates from the best replicate run  for each model were used to calculate the Akaike information criterion (AIC) scores for model testing [−9 substitutions/site/generation [6 bp.An unfolded 2D site-frequency spectrum was generated using the program analysis . Model fk et al. . dadi_pi testing  followin testing . Standar testing , which hneration , and an 9 reads were sequenced across 257 samples ; on average, 5.96 × 106 reads were sequenced per sample (range = 3.27 × 106–1.47 × 107 reads). Following quality filtering and processing, we assembled 1.08 × 106 loci; the number of SNPs recovered from these loci are listed in 1.53 × 10H. annuus indicates that primitive domesticated lines and improved cultivars harbor progressively less genetic diversity than their wild progenitor.There were notable differences in nucleotide diversity (π) among breeding pools A, with aST = 0.169) and inbreeding (FIS = 0.177) across and within populations of wild sunflower. FST between populations varied widely . Furthermore, clinal patterns in the distribution of diversity were observed, as indicated by significant declines in π with increasing latitude  and decreasing longitude . AMOVA found that most genetic variation is partitioned within samples , with relatively little variation explained by differences between samples , populations , and genetic clusters . The sum of these results suggest that genetic diversity is continuously distributed across the range of wild sunflower, with the highest levels of diversity being concentrated in populations located in the southeastern portion of the range.We observed moderate genetic differentiation  nested within the New Mexico-Central Great Plains grade A. Four NK , but consistently identified a distinct cluster of cultivated accessions while sorting wild samples into increasingly smaller, geographically defined clusters until K = 17, where all wild samples were sorted by their collecting locality. This fractal pattern of population clustering is consistent with IBD, so we present results from fastSTRUCTURE analyses for K = 2–6, which circumscribe landscape-level, geogenetic clusters  was consistently estimated to have ca. 50% membership in other clusters across all values of K, and samples from the Wyoming population were found to possess 30-40% admixed ancestry at K = 4 and 5  and, for both analyses, ancestry assignments at K > 3 were difficult to interpret. That being said, we present fastSTRUCTURE results for K = 2–3, which reveal subtle, biologically interpretable structure within the cultivated lines. At K = 2, a single Native American landrace accession (Hopi Dye) is inferred as its own cluster, with three other Native American landrace accessions  sharing some ancestry (< 20%) with the Hopi Dye cluster  rather than breeding pool ; shrubsulustris , Viburnutanoides , Viburnu complex , and theViburnum ); and trsacharum ,68, Carynd ovata ,70, Fagundifolia ,71, and  strobus ,73).Paleoecological studies provide a finer resolution to the approximate locations of refugial areas during the late Pleistocene, and suggest that many of the aforementioned species may have persisted in macrorefugia distributed along the Gulf Coast , with coK . As sucK B. Our fiPatterns of divergence and population structure in cultivated sunflower are complex, but largely reflective of its domestication and breeding history. Cultivated accessions form a strongly supported clade, nested among wild sunflower populations from the central Great Plains A, and esPatterns of phylogenetic and population structure outside of the early diverging landraces become apparent when the cultivars are coded by market type  rather than breeding pool  . The sore)) between wild and cultivated sunflower [Domestication and improvement have generated large differences in observed levels of genetic diversity in the wild and cultivated sunflower breeding pools. Unsurprisingly, exotic and elite lines were found to harbor ca. 60% and 50% of the nucleotide diversity (π) present in wild sunflower, respectively A. These unflower ,20,21,84Demographic reconstructions provide some insight into the timing and order of these changes in genetic diversity . For exaIn sunflower, moderate rates of gene flow from the wild into cultivated breeding pools do not appear to have strongly influenced current effective sizes , which m


Lactobacillus delbrueckii subsp. bulgaricus strain F17 and Leuconostoc lactis strain H52 on ‘Red Globe’ table grapes for the enhancement of shelf-life and improvement of grape quality characteristics during postharvest storage. Strains F17 and H52 were isolated from traditional fermented yak milk obtained in the Qinghai–Tibetan Plateau. Samples from untreated and treated grapes were analyzed for physicochemical, biochemical, and microbiological properties  for 20 days. The results demonstrated that supernatants from both strains significantly reduced weight loss, decay rate, aerobic mesophilic bacteria, and coliform bacteria counts; delayed maturity and senescence of table grapes; and reduced titratable acidity and total phenols. However, the supernatant of strain F17 was more effective and resulted in better sensory evaluations and had a significant inhibitory effect on yeast and molds by day 5. Meanwhile, the supernatant from strain H52 had a significant inhibitory effect on fungi over the whole storage period. In addition, the results of the Pearson correlation analysis suggested that weight loss, decay rate, total soluble solids content, and microorganisms were highly correlated with the sensory evaluation data and quality of postharvest grapes when treated with the products of strain F17. On the basis of these data and sensory organoleptic qualities, the supernatant containing products from strain F17 had the best potential as a biopreservative to improve the postharvest quality of ‘Red Globe’ table grapes.‘Red Globe’ table grapes are large, edible, seeded fruit with firm flesh that tastes good, but can have poor postharvest shelf-life. This study was conducted to explore the effects of products of  Fruits and vegetables are recommended as a regular part of the human diet because they contain antioxidants, dietary fiber, minerals, and vitamins, as well as other beneficial substances that help support good human health . NeverthTable grapes are not only delectable and nutritious, but also a source of polyphenols and resveratrol, with high economic and dietary value . Polyphe2) fumigation [2 fumigation can significantly inhibit most common pathogens that cause grape decay, such as gray mold and Rhizopus nigricans [2 is one key factor used to determine the quality of postharvest grapes, and the levels of residues are difficult to control during the fumigation treatment process. Once SO2 fumigation leads to improper levels of SO2 residues, these residues can both be potentially harmful to human health and give a sulfurous flavor to the table grapes [The quality of postharvest table grapes in China and abroad is widely attempted to be maintained using sulfur dioxide  by the U.S. Food and Drug Administration. Therefore, Lactobacillus delbrueckii subsp. bulgaricus strain F17 and Leuconostoc lactis strain H52, which were isolated from traditional fermented yak milk obtained in the Qinghai–Tibetan Plateau, were selected for this experiment. On the basis of previous studies in our laboratory, strains F17 and H52 possess favorable antioxidant activities. In addition, it was discovered that F17 produces a class IIa bacteriocin and H52 produces exopolysaccharides [bulgaricus is a lactic acid producing bacterium [Salmonella typhi, Staphylococcus aureus, Listeria monocytogenes, and Escherichia coli [As it is possible to inhibit microbial growth by introducing other competitive microorganisms that are beneficial to humans, some microorganisms with biological protective effects have been proposed as biological preservatives . Lactobaeriocins , and it charides ,19. Subsacterium ,21, and acterium . It was acterium ,19 such hia coli .Lactobacillus and their metabolites are widely used to control foodborne pathogens and spoilage organisms in meat products to ensure food quality and safety [Lactobacillus containing products as a potential biopreservative. Lactobacillus supernatant-treated grapes were compared to the untreated control grapes in terms of weight loss, decay rate, stem browning, pH, total soluble solids content (SSC), titratable acidity (TA), total phenols (TP), microbial counts, and sensory evaluation during storage at 0, 5, 10, 15, and 20 d at 25 °C. The overall effects of the supernatants of strains F17 and H52 were evaluated on the preservation of ‘Red Globe’ table grapes.Although d safety ,25,26,27Ripe ‘Red Globe’ table grapes were harvested from a local orchard in Yuzhong County, Gansu province, China. The grapes were immediately transported to the laboratory following collection. Grapes with defects such as decay and mechanical damage were discarded and grape berries of uniform size, shape, and color were selected for the experiment. A total of 600 grape berries were randomly divided into the control, and F17 and H52 supernatant-treatment groups. The F17 and H52 grape berry groups were immersed in a fermentation supernatant see  containi8 colony forming units (CFU)/mL using a UV/vis double beam U-2910 spectrophotometer . After that, the fermentation supernatant was separated from the bacteria by centrifugation at 8000× g for 15 min at 4 °C with a cryogenic high-speed Allegra 64R centrifuge . The bacterial precipitate was discarded and the supernatants containing the products of strains F17 and H52 were stored at 4 °C until use.The frozen strains F17 and H52 were removed from a –80 °C ultra-low temperature freezer  and thawed at room temperature, and then cultured in Man–Rogosa–Sharpe (MRS) broth  and incubated at 37 °C for 24 h. The bacteria were transferred to fresh MRS broth and incubated at 37 °C for 24 h, then the bacteria count of both F17 and H52 broths was adjusted to 1 × 10Weight loss of the table grapes in each group during storage was measured by monitoring the weight change of the grape berries after 0, 5, 10, 15, and 20 d of storage at 25 °C. Weight loss was calculated as the percentage loss of initial weight .During storage, decay incidence was monitored by determining the natural microorganisms associated with the grape berries, and the disease severity of each grape berry was evaluated according to the following empirical scale : ‘0’ = iThe browning grade was determined by visual scoring on a 0–4 scale according to the color change of the grape stem . The scoGrape juice was obtained using a food blender after the seeds were removed. The juice was then filtered through four layers of cheesecloth in preparation for measurement of total SSC and pH. Total SSC was measured by a hand-held refractometer  at 20 °C and the appropriate temperature correction adjustments were applied, and the results were expressed as %. The pH was determined by using a PB-10 pH meter . TA was determined by potentiometric titration with 0.1 N NaOH up to pH = 8.2 and expressed in % of tartaric acid . All expg for 10 min at 4 °C. The total phenol content in the table grape berry tissue supernatant was determined based on the Folin–Ciocalteu method [2CO3 (3.75 mL) was added followed by ultrapure water to a total volume of 25 mL. The reaction mixture in the brown volumetric flask was incubated in darkness for 2 h at room temperature. The absorbance of the reaction mixture was measured using a U-2910 spectrophotometer at 765 nm. A gallic acid standard  was used to generate a standard curve, and the total phenols were expressed as mg of gallic acid equivalents per gram of grape berry weight. The absorbance measurement of each sample was carried out in triplicate.Total phenols were measured according to the method described by Sánchez-González et al. . Table gu method . The supAerobic mesophilic bacteria (AMB), yeast and molds (YAMs), and coliform bacteria (CB) counts were evaluated in all groups throughout the entire grape storage period. Sterile physiological saline (225 mL) was added to each sample and control group (25 g of grape tissue/group). The microbes present on the surface of the table grapes were fully dissolved in the physiological saline with the aid of a constant temperature shaking incubator  at 60 rpm for 35 min at 25 °C. Serial dilutions of grape surface microbial fluid were plated on plate count agar (PCA)  to determine AMB, and potato dextrose agar (PDA)  and violet red bile agar  were used for enumeration of AMB, YAMs, and CB. PCA, PDA, and VRBA plates were incubated for 48 h at 30 °C, for 5 d at 25 °C, and for 1–2 d at 37 °C, respectively. Microbial counts were evaluated at 0, 5, 10, 15, and 20 d of sample storage at 25 °C. The results were expressed as log CFU/g grape berry weight, and each treatment of four replicates was analyzed in duplicate.Sensory evaluation of table grapes was based on the observed glossiness, color, odor, appearance, and overall acceptability parameters ,32, and p < 0.05.The results of physicochemical properties and microbial counts were expressed as the means ± standard deviation of four parallel samples. The statistical significance of the difference between the three groups was tested using analysis of variance (ANOVA). The Tukey test was applied for post hoc comparisons. Pearson correlation analysis was performed by selecting indicators to evaluate the extent of relationships between the groups. All of the above statistical analyses were completed using IBM SPSS Statistical Analysis Software v. 22.0.0.0  for Windows software, and the significance level was defined at Lactobacillus and Leuconostoc supernatant-treated grape berries throughout the storage period. Weight loss was significantly higher in the control grape berries than in the grape berries treated with supernatants from strains F17 and H52 from day 10 to 20 (p < 0.05).Lactobacillus and Leuconostoc supernatant-treated grape berries from 0 to 20 d is depicted in Lactobacillus and Leuconostoc supernatant-treated berries. In addition, the reduction of decay was more pronounced for the F17 supernatant-treated grape berries, which had the lowest decay rate throughout the experiment.The results of the control and the Lactobacillus and Leuconostoc supernatant-treated grapes when the storage time was extended. The control group had almost complete stem browning by the end of the storage period, and the Lactobacillus and Leuconostoc supernatant-treated grapes showed significant differences at 5, 10, and 20 d (p < 0.05) in comparison with the control grapes.Lactobacillus, and Leuconostoc supernatant-treated groups are listed in Lactobacillus and Leuconostoc supernatant-treated grapes increased gradually with increased storage time and the maximum value was reached at the end of storage, whereas the control grapes peaked at day 10 of storage and then progressively decreased. The total SSC of the strain H52 supernatant-treated grapes increased throughout storage, but decreased below the total SSC of the grapes treated with the strain H52 supernatant on day 20. The grape berries treated with the supernatant from strain F17 maintained a high level of total SSC throughout the storage period.Changes in SSC, TA, SSC/TA, and pH values of grapes in the control, Lactobacillus and Leuconostoc supernatant-treated grapes progressively increased and reached a maximum value at 20 d, while the value of SSC/TA in the control grapes increased through day 10 to 32.52 and then decreased at day 15, and slightly increased again at day 20. The SSC/TA from the strain F17 supernatant-treated grapes was always a somewhat lower value than the SSC/TA of the strain H52 supernatant-treated grapes with the SSC/TA at day 20 of F17 and H52 achieving 36.19 and 36.83, respectively. Lactobacillus and Leuconostoc supernatant-treated grapes showed the lowest pH values in comparison with the control grapes at all storage times.The TA of grapes in all groups gradually decreased over the entire storage period, while the largest TA decrease was observed in the control grapes, from 0.67 to 0.5, at Δ = 0.17 . In geneLactobacillus and Leuconostoc supernatant-treated grapes were significantly higher than that of the control grapes at all storage times (p < 0.05), and the Lactobacillus strain F17 supernatant-treated grapes had the highest total phenols.The effects of different treatments on total phenols are shown in Lactobacillus, and Leuconostoc supernatant-treated grapes are shown in p < 0.05).The results of AMB analysis for the untreated, Lactobacillus strain F17 and Leuconostoc strain H52 supernatant-treated grapes resulted in lower counts in comparison with the control grapes, and the Leuconostoc strain H52 supernatant-treated grapes showed still lower YAMs values with a significant difference compared with the other two groups at 10, 15, and 20 d storage periods (p < 0.05).The changes in the number of YAMs in all samples are presented in Leuconostoc strain H52 supernatant-treated grapes had the lowest number of CB counts among all groups during the entire storage period (p < 0.05). The number of CB from the strain H52 supernatant-treated table grapes was about 2.51 log CFU/g at 20 d; it was reduced by 0.27 log CFU/g in comparison with the control grapes. It should also be noted that the supernatant of strain F17 significantly inhibited CB during the storage periods of 5, 15, and 20 d (p < 0.05).The initial population of CB at time 0 on the table grapes was about 2.28 log CFU/g, as shown in Lactobacillus and Leuconostoc supernatant-treated table grapes was higher than the untreated table grapes. The strain F17 supernatant-treated table grapes had the highest sensory scores throughout postharvest storage and were significantly different than the other two groups.The sensory evaluation scores of all samples decreased with increasing storage time, and all samples reached the lowest value at 20 days . The senThe Pearson correlation analysis was used to verify the correlations between the experimental indicators, such as weight loss (WL), decay rate (DR), stem browning (SB), pH, total soluble solids content (SSC), titratable acidity (TA), total phenols (TP), microbial counts, and sensory evaluation (SE), and only the indicators of significant correlation are listed in Lactobacillus and Leuconostoc supernatant-treatment had a positive effect on maintaining the sensory quality of grapes. As previously shown in a study with strawberry fruit, Lactobacillus intact cells and cell lysates adhered naturally to the surface of the berries, resulting in a biofilm formation and enhanced water preservation [The grape berry skin acts as a barrier to protect against water loss and invasion of fungal pathogens, and it contributes to the control of gas exchange . Respiraervation .Lactobacillus supernatant-treated grape berries had a lower decay rate compared with the control berries, which may be a result of having products in the bacterial supernatants. In this regard, Dalié et al. [Lactobacillus produces antifungal low molecular weight compounds. Another possible reason for the observed protection may be the micro-acidic environment formed by the Lactobacillus supernatant-treatment.Decay rate was a key parameter for evaluation of commercial quality and postharvest shelf-life of grape berries. It has been reported that gray mold is considered the main pathogen causing postharvest decay of table grapes . The Lacé et al.  reportedLactobacillus and Leuconostoc supernatant-treated grapes showed lower stem browning than the untreated table grapes during the entire storage period. There are two possible reasons for this result. Desiccation is considered the main factor for stem browning, and weight loss of the untreated grapes in this study was significantly higher than that of Lactobacillus or Leuconostoc supernatant-treated grapes. The supernatants of strains F17 and H52 may have possessed favorable antioxidant capacity, which may inhibit the activity of oxidase, resulting in reduced stem browning.The grade of stem browning not only reflects the freshness of grapes, but also has a direct effect on the consumer’s intention to purchase the grapes. Moreover, browning and drying of stems may begin to occur even before the actual deterioration of fruit . Thus, sLactobacillus and Leuconostoc supernatant-treated grapes increased gradually over the length of storage, and reached a maximum value by the end of storage. This result was consistent with the report by Meng et al. [Total SSC, TA, SSC/TA, and pH are key indicators reflecting the organoleptic quality, maturity, and senescence of table grapes ,41. Totag et al.  showing Lactobacillus and Leuconostoc supernatant-treatments appear to reduce the table grape’s respiration. The reduction in TA correlates with the loss of water in the control grapes. The SSC/TA ratio value is related to the maturity of grape berries and may be used as an indicator to determine the postharvest senescence of table grapes [Lactobacillus and Leuconostoc supernatant-treated grapes, which reached maximum values at day 20, while the value of the SSC/TA ratio in the control grapes increased only until day 10 and then decreased by day 15. These observations may be interpreted as the supernatant-treatments can delay the onset of maturity and senescence of table grapes. pH has been used as an indicator that reflects the senescence of grapes [Lactobacillus and Leuconostoc supernatant-treatments may suppress the decomposition of organic acids by reducing the respiration of grapes.Organic acids, primarily tartaric acid, play a key role in grape organoleptic quality. Moreover, the change of the tartaric acid content is one of the indicators used to measure the grape’s physiological processes and fresh-keeping ability . The TA e grapes . We obsef grapes . It was f grapes . Howeverf grapes , or the Lactobacillus and Leuconostoc supernatant-treatments significantly increased total phenol production (p < 0.05) and the strain F17 supernatant-treated grapes exhibited the greatest retention of total phenols. A possible explanation for this result is that strains F17 and H52 possessed a favorable antioxidative capacity and had an antioxidant effect on the grapes.Polyphenol is an important indicator used to evaluate the internal postharvest quality of grapes, which reflects the antioxidant degree and quality of grapes . The chaLactobacillus and Leuconostoc supernatant-treated groups were lower than that in the control group when the storage period was 10, 15, and 20 d. Additionally, AMB counts showed a significant difference in all groups at a storage period of 10 and 15 d (p < 0.05). A likely reason for these observations is that the products of Lactobacillus and Leuconostoc, such as bacteriocins, extracellular polysaccharides, and lactic acid, inhibit aerobic mesophilic bacteria; however, inhibition mechanisms are unclear.The AMB counts for all groups increased from day 5 to day 15, but the AMB counts of the Lactobacillus and Leuconostoc supernatant-treated groups showed a lower number of YAMs counts in comparison with the control group, and can be explained by the production of antifungal low-molecular-weight-compounds, such as organic acids, reuterin, hydrogen peroxide, proteinaceous compounds, hydroxyl fatty acids, and phenolic compounds [The number of YAMs in the ompounds . The strompounds .Lactobacillus showed significant antibacterial activity against pathogens such as Cronobacter sakazakii, Escherichia coli, Listeria monocytogenes, Salmonella Typhimurium, Shigella sonnei, and Staphylococcus aureus [p < 0.05). Inhibition of CB may be a result of a bacteriocin, a product of strain F17 [It was reported that bacterial exopolysaccharides produced by s aureus . It shourain F17 .The sensory evaluation score of table grapes was based on the comprehensive evaluation of the glossiness, color, odor, overall appearance, and acceptability parameters, and reflects the consumers’ acceptance of postharvest table grapes. The strain F17 supernatant-treated table grapes had the highest sensory scores and were significantly different among the three groups. This demonstrated that the strain F17 supernatant-treatment significantly improved the sensory quality of postharvest table grapes. Therefore, from a business standpoint, the strain F17 supernatant-treatment was more suitable for table grape preservation.The weight loss of table grapes showed a significant positive correlation with decay rate, stem browning, and total SSC, and was consistent with the results of Champa et al. , who repLactobacillus delbrueckii subsp. bulgaricus strain F17 and Leuconostoc lactis strain H52 supernatants possessing probiotic properties were developed as potential biopreservatives for postharvest table grape preservation. Although, supernatants from both strains F17 and H52 significantly reduced weight loss, decay rate, and delayed maturity and senescence of table grapes, the supernatant from strain F17 was more effective and resulted in better grape sensory evaluations. For YAMs, strain F17 demonstrated significant initial inhibition in the first five days, while the supernatant of strain H52 showed a significantly greater growth inhibition during the entire storage period. In addition, the Pearson correlation analysis results suggested that weight loss, decay rate, stem browning, total SSC, and microorganisms were all highly correlated with the sensory evaluation and quality of postharvest grapes. On the basis of the overall characteristics and sensory quality of the treated grapes, the supernatant containing products of strain F17 was the overall best potential biopreservative for improving the postharvest quality of ‘Red Globe’ grapes.In this study,


HFE variant (c.845G>A :p.Cys282Tyr) which is classified as pathogenic/likely pathogenic for hereditary hemochromatosis. However, no action was taken by the individual.A 61‐year‐old woman underwent direct to consumer genetic testing and was found to be homozygous for the C282Y The individual took part in the Mayo Clinic Return of Actionable Variants Empiric (RAVE) study and the actionable finding was confirmed and results disclosed in person by a genetic counselor with subsequent referral to a hepatologist.Further testing revealed iron overload with an elevated ferritin level (560 ng/ml) and increased ferritin saturation (74%). Phlebotomy was initiated with subsequent normalization of the ferritin levels (252 ng/ml).This case highlights that actionable genetic results may not be acted on after direct to consumer testing and the need for effective genetic counseling after such testing. HFE variant on direct to consumer genetic testing but only took action when the result was returned by a genetic counselor as part of a subsequent research study. This case highlights that actionable genetic results may not be acted on after direct to consumer testing and the need for effective genetic counseling after such testing.We describe the case of a 61‐year‐old woman who was found to be homozygous for the C282Y  The Food and Drug Administration (FDA) has reclassified DTC genetic tests as class II medical devices rather than class III medical devices subjecting them to a less stringent approval process FDA, . ConsequThe Mayo Clinic Return of Actionable Variants Empiric (RAVE) study is a Mayo Clinic Institutional Review Board approved genomic medicine implementation study being conducted as part of the National Human Genome Research Institute (NHGRI)’s eMERGE network :c.845G>A :p.Cys282Tyr) which is listed in ClinVar as pathogenic/likely pathogenic for hereditary hemochromatosis (HH). The participant did not carry a diagnosis of HH, although she had previously undergone DTC‐GT which revealed homozygosity for the HFE variant and also had a family history of the disease (a sibling had been diagnosed with HH and was receiving therapy). After DTC‐GT, the participant did not appear to have engaged with her physician to review this result.A 61‐year‐old RAVE participant was found to be homozygous for the C282Y After a genetic counselor disclosed the results as part of the RAVE study, the participant was referred to a hepatologist. Testing was consistent with HH and hepatic iron overload Table . Weekly 3DTC genetic testing arguably empowers consumers by providing access to some of their genomic information. Nonetheless, this case raises an important question whether individuals will follow up on actionable findings. Typically DTC‐GT companies provide either online or printed material to help consumers understand test results and also to provide information about when to engage with a physician. However, the information presented may not be sufficient for helping patients navigate their genetic testing results. In a 2008 survey of members of the National Society of Genetic Counselors, 65% believed the information provided as part of DTC‐GT was insufficient (Hock et al., HFE C282Y homozygotes develop liver disease according to one study (Beutler, Felitti, Koziol, Ho, & Gelbart, Risk assessment using genetic information is complex since the role of genetics in disease is multifactorial and the estimates of penetrance for many variants are not reliable. Less than 10% of This inaction has been previously alluded to in the literature. Bloss et al.  demonstrAt present the onus remains on the consumer to place DTC‐GT results within their broader health goals and take action on the medically actionable results by pursuing follow up clinical interpretation or counseling. Within this context the role of physicians in helping patients follow up on DTC‐GT results is unclear. There is concern about the increasing workload of physicians in the primary care setting and that physicians may lack the training necessary to accurately counsel patients regarding DTC‐GT results (Covolo, Rubinelli, Ceretti, & Gelatti, In the case presented, it is reasonable to assume that the in‐person genetic counseling session played a crucial role in triggering the appropriate medical care. Additional influences might have been participation in the study and subsequent interactions with the study team and referral to a specialist. Regardless, this case highlights the need for genetic counseling following receipt of medically actionable variants from DTC‐GT. Given the shortage of genetic counselors, innovative models of counseling individuals for such results from DTC‐GT need to be considered including using educational videos and online chat‐bots. Educating physicians about actionable findings from DTC‐GT may help ensure appropriate follow up medical care.None declared.C.J.L. and I.J.K. conceived and designed the study. R.G., C.J.L., and I.J.K. analyzed the data. R.G. and I.J.K. drafted the manuscript. All of the authors reviewed the manuscript.4


Studies in Western cultures have shown that perfectionism is conceptualized by two-factor higher-order model including perfectionistic strivings and perfectionistic concerns. However, little is known about the construct of perfectionism in Eastern societies. Thus, we examined the two-factor higher-order model of perfectionism in Iranian general and clinical samples.We recruited a general population sample (n = 384) and patients with major depressive disorder, obsessive compulsive disorder, social anxiety disorder, and eating disorders (n = 152) from Tehran, Iran from September 2016 to December 2017. They completed the Clinical Perfectionism Questionnaire, Perfectionism Inventory, and Depression, Anxiety, Stress Scale-21.The two-factor higher-order model of perfectionism showed adequate fit with data for females from the general population and clinical sample. Data for males were only available from the general population, and the model showed adequate fit with the data first after removing the Rumination scale of the perfectionistic concerns. The perfectionistic strivings dimension showed no or negative association with depression, anxiety, and stress symptoms, but perfectionistic concerns dimension showed positive correlation with these indices in all samples for both males and females.The results support the two-factor higher-order model of perfectionism in samples of Iranian females from the general population and clinical sample. However, the results were different for males from the general population. In other words, the modified two-factor higher-order model showed acceptable fit with the data for males from the general population only after removing the Rumination scale from perfectionistic concerns. These differences among males and females were discussed. A number of theoreticians discuss about conceptualization of perfectionism. Sigmund Freud  considerFollowing Hamachek , some reThe multidimensional conceptualization of perfectionism encountered two primary criticisms. First, factor analytic studies using these measures consistently showed that these dimensions loaded on two higher-order factors named “Perfectionistic Strivings” (PS), and “Perfectionistic Concerns” (PC) rather than multiple factors –8. And tBased on the second criticism, Shafran et al.  argued aInterestingly, most of the research concerning the construct of perfectionism come almost completely from Western cultures and little is known about the construct of perfectionism among Eastern societies. To our knowledge, a few studies compared the Eastern and Western samples in terms of perfectionism. Smith et al.  have madThus, the aim of this cross-sectional study was to explore the higher order factor of perfectionism among Iranian females and males from the general population, and patients with psychological problems in clinical settings. Based on the evidence on two-factor higher-order model of perfectionism in Western cultures and findings of smith et al. , we predThe present study was part of a larger cross-sectional project investigating the etiological and maintaining mechanisms of perfectionism through structural equation modeling. The participants were recruited from the general population and clinics. The general population sample included 403 participants  in Tehran, Iran. They were selected via proportional quota sampling based on the last census data of Statistical Center of Iran . Proportional quota sampling is a type of non-random sampling. It sometimes referred to as a non-probability sampling method. Proportional quota sampling is usually utilized in surveys and opinion polls, where the total number of people to be surveyed is typically decided in advance. In this method, the sample was split between distinct subgroups or strata. Inclusion criteria were being between 18- and 50-years old, having completed high school, and living in Tehran for at least 6 previous years. Nineteen participants had skipped more than 10% of the items. Therefore, the data of 384 subjects  were analyzed.The clinical sample consisted of 152 patients with MDD , OCD , SAD , or EDs .N = 400 for the general population and N = 150 for the clinical sample.Monte Carlo Simulation Studies suggest that SEM models could be safely evaluated with small samples .They genThe research procedure was approved by Ethical Review Board of University of Social Welfare and Rehabilitation sciences. All participants provided written consent. In order to gather data from general population sample, five social workers selected participants according to quota sampling matrix from visitors in health centers, parks, and/or cultural houses of Tehran, Iran. Participants were asked to complete a battery of questionnaires.The clinical sample consisted of patients referred to the first author (R.M.) by psychiatrists or clinical psychologists for an evaluation using the Structured Clinical Interview for DSM-IV (SCID), who met the inclusion criteria and agreed to participate in the study. They were asked to complete a series of questionnaires and to return them to the researchers within one week.The CPQ  assessesHill et al.  developeThe DASS-21 is a self-report instrument consisted of three subscales that measure symptoms of depression , anxiety , and stress  over the past week. Participants were asked to answer the items using a 0  to 3 (apply to me very much) scale . The Per2/df-ratio less than 3, as well as Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI), Incremental Fit Index (IFI), Comparative Fit Index (CFI) with cut off ≥ 0.95 as acceptable ), and SRMR = 0.05)  and perfectionistic concerns higher order factor  showed adequate fit  = 246.62, p < 0.0001, χ2/df-ratio = 12.98, GFI = 0.90, AGFI = 0.75, IFI = 0.88, CFI = 0.88, RMSEA = 0.14, 90% CI , and SRMR = 0.11). Scrutinizing data indicated that the factor loading of the Rumination scale on perfectionistic concerns was not significant . Conducting CFA without Rumination scale resulted in relatively adequate fit  = 67.86, p < 0.0001, χ2/df-ratio = 5.22, GFI = 0.96, AGFI = 0.90, IFI = 0.94, CFI = 0.94, RMSEA = 0.09, 90% CI ). Given the evidence for the gender differences on rumination , SRMR = 0.06)  = 115.9, p < 0.001, χ2/df-ratio = 6.10, GFI = 0.89, AGFI = 0.77, IFI = 0.85, CFI = 0.85, RMSEA = 0.16, 90% CI , and SRMR = 0.11). Rumination scale was the source of problem as its factor loading on Perfectionistic Concerns was not significant . After removing the Rumination scale, model for males showed good fit with data  = 37.05, p < 0.001, χ2/df-ratio = 2.85, GFI = 0.95, AGFI = 0.89, IFI = 0.94, CFI = 0.94, RMSEA = 0.09, 90% CI ), and SRMR = 0.09  fit with data. These results are consistent with previous researches that support a two-factor higher-order model of perfectionism in various Western populations , 6–8. ThThe pattern of relationships of Perfectionistic Concerns and Perfectionistic Strivings dimensions with symptoms of depression, anxiety, and stress verified the two-factor higher-order model of perfectionism in Iran. Among females from the general population and clinical sample, the Perfectionistic Strivings showed no relationship with psychopathological indices. While, among males from the general population, the Perfectionistic Strivings correlated with lower level of depression, anxiety, and stress. This pattern of correlations is highly consistent with findings of previous studies on Western cultures , 14, 39.A notable strength of the study is the size of the samples from the general and clinical populations. However, the results should be interpreted with regard to its limitations. First, the study carried out among those between 20 and 50 years old and could not be generalizable to Iranian younger and older people. Second, in light of the lack of valid information about reliability and validity of Persian version of HMPS and FMPS, we could not use these measures in the present study. Lack for such instruments hindered us to assess the construct of perfectionism in a comprehensive manner in Iranian population. In addition, our results cannot be adequately compared with previous literature that used HMPS and FMPS. Therefore, future researches should concentrate on investigating psychometric properties of HMPS and FMPS and then replicate this study with those instruments. Third, the absence of measures of wellbeing or life satisfaction was another limitation of the study. Examining relationship of perfectionistic strivings and perfectionistic concerns with wellbeing, quality of life, or social adjustment indices would clarify more functional or dysfunctional nature of each dimension of perfectionism. Thus, future researches should investigate the relationship pattern of perfectionistic strivings and perfectionistic concerns with wellbeing, quality of life, or social adjustment indices. Lastly, we could not compare the higher-order model of perfectionism among males and females of clinical sample because of small sample size of males in clinical sample. Gender differences might be highly present and culturally different from one culture to another. Therefore, future studies should investigate potential gender differences in clinical groups.The main conclusion of this research is that perfectionism could be conceptualized by two-factor higher-order model including perfectionistic strivings and perfectionistic concerns among Iranian women of general population and clinical samples. However, the results indicated that among Iranian men of general population, Rumination might not be a genuine component of the perfectionistic concerns. Therefore, future research should explore the precise role of Rumination in the perfectionism. The results of the present study indirectly implied that parents and teachers should pay attention to the differences of positive and negative perfectionism and try their best to form positive perfectionism in the children. Also, current findings can help Iranian researchers and psychotherapists to assess and conceptualize negative and positive perfectionism astutely.Additional file 1. Clinical Perfectionism Questionnaire, Perfectionism Inventory, and Depression, Anxiety, Stress Scale-21.


Anthropogenic habitat fragmentation is often implicated as driving the current global extinction crisis, particularly in freshwater ecosystems. The genetic signal of recent population isolation can be confounded by the complex spatial arrangement of dendritic river systems. Consequently, many populations may presently be managed separately based on an incorrect assumption that they have evolved in isolation. Integrating landscape genomics data with models of connectivity that account for landscape structure, we show that the cumulative effects of multiple in‐stream barriers have contributed to the recent decline of a freshwater fish from the Murray–Darling Basin, Australia. In addition, individual‐based eco‐evolutionary simulations further demonstrate that contemporary inferences about population isolation are consistent with the 160‐year time frame since construction of in‐stream barriers began in the region. Our findings suggest that the impact of very recent fragmentation may be often underestimated for freshwater biodiversity. We argue that proactive conservation measures to reconnect many riverine populations are urgently needed. On the other hand, small‐bodied but ecologically important species often receive relatively little attention from conservation managers , a threatened small‐bodied fish (<80 mm) that recently experienced major demographic declines and local extinctions across the Murray–Darling Basin (MDB), Australia  and lower levels of genetic diversity. Additionally, we used forward genetic simulations to investigate whether high contemporary levels of genetic differentiation could have arisen in the relatively short time since the construction of in‐stream barriers began in the MDB. Our results demonstrate that recent anthropogenic habitat fragmentation has contributed to the loss of genetic diversity and population isolation observed. They also suggest that proactive conservation measures to restore connectivity  and increase evolutionary potential  are urgently required for this, and potentially many other poorly dispersing aquatic species.In this landscape genomics study, we examine the effects of recent habitat fragmentation on the southern pygmy perch , respectively. Sequencing libraries were prepared in‐house based on a double‐digest restriction site‐associated DNA (ddRAD) library protocol . Loci outside the neutral distribution at a false discovery rate (FDR) of 10% were considered outliers. Loci detected as outliers by either BayeScan or Arlequin were filtered. The remaining SNPs were examined for departure from expectations of Hardy–Weinberg equilibrium (HWE) using GenoDive 2.0b27  were completed to ensure consistency, and the most likely K was assessed by comparing the model complexity that maximized marginal likelihood across replicate runs.Pairwise 2.3FST and calculating the regression coefficient of determination (R2). This model was then compared with a model of IBD calculated using multiple matrix regression with randomization (MMRR) following the method of Wang  better explain population differentiation than isolation by distance (IBD), we used the StreamTree model of Kalinowski et al. . Genetic of Wang . Pairwis of Wang . Model sIn dendritic river systems, hierarchical network structure and spatial hydroclimatic variation can also drive patterns of genetic diversity of stream‐dwelling organisms . Population‐specific FST was estimated for each sampling site using the method of Weir and Hill  estimator implemented in NeEstimator 2.01  using SLiM 3.1  were simulated for each metapopulation size to examine the effect of increasing levels of fragmentation  was plotted against the number of fragments for each scenario for the three metapopulation models. Scripts used to perform the simulations and analyses are available at https://github.com/pygmyperch/SPP_SLiM.Simulation studies are becoming an increasingly important part of landscape genomics as a wide range of parameters can be explored for key evolutionary processes such as gene flow, genetic drift, mutation and selection . All pairwise FST estimates were significant (p < 0.003) except between immediately adjacent lower MDB sites MID and MUN   Table . Results3.3FST . Results indicated that local characteristics of the stream network better explain FST than the null hypothesis of IBD . Figure FST as estimated by the model  and the location of barriers marked with X. The StreamTree model was a good fit for the data and was significantly related to observed FST   , IBD was not significant in models across the whole basin, in contrast to models of stream hierarchy and barriers (see below). In addition, even when comparisons were limited to sites within catchments, the number of barriers still provided a better model than IBD  Figure , whereas) Figure . AlthougFollowing VIF analyses, 19 environmental variables from the five categories were retained. The first two PCs for temperature, flow and topographic variables scored eigenvalues > 1, while only one component each for the precipitation and human disturbance PCAs scored an eigenvalue > 1, so individual variables rather than PCs for these categories were retained. This resulted in a final list of six hydroclimatic PCs and five individual precipitation and disturbance variables Table .R2 = 0.170, β = 0.449 [0.336–0.562 95% CI], p < 0.0001) and the number of in‐stream barriers separating sites  were both good predictors of population differentiation, while there was no evidence for isolation by environment  . There was a sharp contrast between regions with average EH of 0.253 for sites in the more connected Lower Murray wetlands, compared to 0.143 for sites in the highly fragmented upper reaches  with the most isolated populations also harbouring the least genetic variation  could have evolved from a more connected system within the time since the construction of in‐stream barriers began ~160 generations ago , despite a previous genotype–environment association (GEA) study for the same species finding several hydroclimatic variables influenced putatively adaptive genetic variation at both regional and local scales  among previously homogeneous sites for populations up to Ne = 500, and just two barriers are required for Ne = 1,000. These findings are consistent with the hypothesis that anthropogenic habitat fragmentation has contributed to isolation of populations since European colonization of the region. Our estimates of local Ne sizes for N. australis were low, averaging approximately 200 for Lower Murray sites and just over 100 for sites in the upper reaches. While single sample Ne estimates should be interpreted cautiously , a small and sedentary species also found in the MDB . As the impacts of climate change intensify, proactive conservation management interventions, such as those already underway for N. australis, will be increasingly considered for other species inhabiting the MDB and fragmented freshwater ecosystems elsewhere in the world. Indeed, a recent study incorporating physiological and functional traits with species distribution models for 23 fish species predicted severe declines in taxonomic and functional diversity of MDB fish communities in the coming decades due to climate change (de Oliveira et al., An unprecedented severe and prolonged drought between 1997 and 2010 caused catastrophic loss of habitat and local extirpation for some Additionally, many species may be already depleted to the point where improved environmental conditions alone will not be sufficient to facilitate recovery. In this case, genetic rescue offers a potential solution for a broad range of threatened taxa (Ralls et al., There is a global biodiversity crisis unfolding in freshwater ecosystems with aquatic vertebrate populations declining by 80% over the last 50 years (Darwall et al., None declared.Supplementary MaterialClick here for additional data file.