Questions remain regarding whether genetic influences on early life psychopathology overlap with cognition and show developmental variation.Using data from 9,421 individuals aged 8–21 from the Philadelphia Neurodevelopmental Cohort, factors of psychopathology were generated using a bifactor model of item-level data from a psychiatric interview. Five orthogonal factors were generated: anxious-misery (mood and anxiety), externalizing (attention deficit hyperactivity and conduct disorder), fear (phobias), psychosis-spectrum, and a general factor. Genetic analyses were conducted on a subsample of 4,662 individuals of European American ancestry. A genetic relatedness matrix was used to estimate heritability of these factors, and genetic correlations with executive function, episodic memory, complex reasoning, social cognition, motor speed, and general cognitive ability. Gene × Age analyses determined whether genetic influences on these factors show developmental variation.2h = 0.46, p = 1 × 10−6), but not anxious-misery , fear , psychosis-spectrum , or general psychopathology . Externalizing showed genetic overlap with face memory , verbal reasoning , spatial reasoning , motor speed , verbal knowledge , and general cognitive ability (g). Gene × Age analyses revealed decreasing genetic variance  and increasing environmental variance  on externalizing.Externalizing was heritable (Cognitive impairment may be a useful endophenotype of externalizing psychopathology and, therefore, help elucidate its pathophysiological underpinnings. Decreasing genetic variance suggests that gene discovery efforts may be more fruitful in children than adolescents or young adults. Psychiatric symptoms in early life are associated with poor cognition . For exaEarly life psychopathology is also underpinned by genes with evidence of substantial genetic effects on childhood and adolescent psychopathology ,10, as wGaps in knowledge also exist regarding whether genetic influences on early life psychopathology show developmental variation. A recent review reported static and dynamic effects on internalizing and externalizing symptoms between infancy and early adulthood , with ceWe used data from the Philadelphia Neurodevelopmental Cohort (PNC) , a largehttps://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v3.p2).PNC is a population-based sample from the greater Philadelphia area, comprising 9,421 individuals aged 8–21. The study has been described in detail  = 3.6), 50.3% were male (g) as the first component of principal component analysis (PCA) of accuracy scores. We also derived a general composite score for speed (gs) as the first component of PCA of reaction times. To minimize the impact of missing data, multivariate imputation by chained equation (MICE) [mice package in R [Participants completed the Penn computerized neurocognitive battery ,46, whicn (MICE)  was usedage in R . Imputatage in R . SubsequPsychiatric symptoms were ascertained using a computerized, structured interview (GOASSESS) ,39,50, aWe applied a confirmatory bifactor model  in MplusSamples were genotyped on one of four Illumina arrays: HumanHap550, HumanHap610, OmniExpress, or Human1M. Genotyped data were imputed at the Broad Institute  using IMr2 > 0.1) using PLINK [Empirical relatedness quantifies the proportion of alleles that are identical by descent between individuals. Empirical relatedness was calculated for all pairs of individuals using genotype data . Brieflyng PLINK . Relatedng PLINK (up to 50ng PLINK . EmpiricR  was used2pσ) of a trait is decomposed into genetic (2gσ) and environmental (2eσ) components. Environmental variance incapsulates all variance that is not genetic, including error. Narrow-sense heritability (2h) is the proportion of phenotypic variance accounted for by additive genetic variance (2h = 2gσ/2pσ). To determine whether heritability (2h) was significantly greater than 0, likelihood of the polygenic model was compared to that of a model with 2h constrained to 0. Under a bivariate polygenic model, phenotypic covariance between two traits is decomposed into genetic and environmental components to determine the extent to which traits are influenced by shared genetic effects. Since genetic correlations between traits are only meaningful if traits are heritable, we estimated heritability of all traits. Bivariate polygenic analyses were then applied to significantly heritable pairs of traits to estimate genetic (gρ), environmental (eρ), and phenotypic (p)ρ correlations. The genetic correlation (gρ) denotes the correlation between latent additive genetic factors influencing both traits. The environmental correlation (eρ) denotes the correlation between nongenetic factors influencing both traits. To determine whether genetic (gρ) and environmental (eρ) correlations were significantly different from 0, likelihood of the bivariate polygenic model was compared to that of a model where the parameter of interest was constrained to 0.SOLAR implements linear mixed-effects models, which decompose the overall variance of a quantitative trait ,59. Trad2g.σ This linear function contains a free parameter, γ, reflecting change in 2gσ unit of the environmental variable (age in years). A nonzero value of γ implies a heritable response to the environment, and therefore, a G × E interaction. This G × E interaction tests for fluctuations (with age) in action of genetic factors and a significant G × E interaction suggests a change in magnitude of effect of specific genetic factors (with age). A second consequence of G × E is that the trait exhibits imperfect pleiotropy with itself, that is, the genetic correlation between the trait measured under one environment and the trait measured under another environment is less than 1. This phenomenon can be examined in cross-sectional studies where individuals are tested under a single environment (timepoint), provided relatedness between individuals is known [λ, reflecting the rate of decay in genetic correlation (gρ) as difference in the environmental variable increases. A nonzero value of λ implies imperfect pleiotropy, and therefore, a G × E interaction. This G × E interaction tests for variation in genetic factors influencing the trait (at different ages) and a significant G × E interaction suggests a change (with age) in the genetic factors themselves. G × E interaction models were fitted to heritable traits, with age in years as the continuous environmental variable that is, Gene × Age interactions. See Table S3 for more information.A polygenic model can be extended to examine Gene × Environment (G × E) interactions –38. One is known . To test2, sex, and their interactions as covariates. To adjust for multiple testing, false discovery rate (FDR) was set at 5% [All models included age, ageet at 5% . Rank-baIn addition to the bifactor model described above, we applied a confirmatory hierarchical model b in Mplu2 =h 0.21, p = 0.040) and externalizing psychopathology , but only externalizing survived FDR correction (2h range = 0.21–0.72) and reaction time (2h range = 0.23–0.38) measures were also heritable. Subsequent bivariate and G × A analyses were run on significantly heritable traits (after FDR correction).Significant heritability estimates were observed for general (rrection . As repopρ = −0.042 to −0.127) and some reaction time measures , verbal reasoning , spatial processing , general verbal knowledge , g , and sensorimotor speed  suggesting that genetic factors underlying externalizing overlap with lower accuracy and slower reaction times.Externalizing showed significant negative phenotypic correlations with most accuracy measures . Decay in genetic correlation did not reach statistical significance , such that we did not find evidence for change in genetic factors, that is, novel genetic influences. Data presented in Significant decrease in genetic variance was observed on externalizing  remained heritable, but general psychopathology  anxious-misery  and fear  were also significantly heritable, although fear did not survive FDR correction . General psychopathology and anxious-misery showed significant phenotypic correlations with cognition (Table S6), but only externalizing showed significant genetic correlations with verbal reasoning , general verbal knowledge , and gg , after FDR correction. Again, significant decrease in genetic variance  and increase in environmental variance  were observed for externalizing  Change in genetic variance on general psychopathology  and anxious-misery  was not significant. Data presented in Figure S4 were generated using Formula 5 in Table S3.Univariate, bivariate, and G × A analyses conducted on psychopathology factors derived from the hierarchical model generated similar results. Externalizing  throughout childhood and early adulthood. Our finding of heritable general psychopathology, anxious-misery, externalizing, and fear are in line with previous evidence –15. We dIn contrast, we found externalizing psychopathology to be significantly and moderately heritable, with genetic factors explaining 46–58% of the variance. Equally substantial genetic influences on externalizing between ages 8 and 26 have been reported in twin and adoption studies –15. A veh2 = 0.15–0.77) for anxiety and depression [h2 = 0.23–0.72) [Importantly, we found significant heritability estimates for general psychopathology and anxious-misery factors generated from a hierarchical, but not bifactor, model. This finding highlights the utility of bifactor models, in which all factors  are orthogonal ,71, therpression ,19, and 23–0.72) .g). These findings are in line with previous evidence of genetic overlap between ADHD and general and executive cognitive functions [Second, we found negative genetic correlations between externalizing and face memory, verbal reasoning, spatial processing, sensorimotor speed, verbal knowledge, and general cognitive ability , and increase in environmental variance  on externalizing factors derived from bifactor and hierarchical models, respectively. Future studies that are able to combine longitudinal, self- and parent-report symptom data will help elucidate these age-associated effects further. Conversely, we previously reported increasing genetic and environmental variance on general cognitive ability in this sample [Finally, we found static and dynamic genetic influences on externalizing psychopathology between childhood and early adulthood, in line with previous evidence –15,82. Ws sample . Thus, wThis study has limitations. First, our analyses were restricted to European American individuals and future studies should include other populations. Second, our data were cross-sectional and longitudinal studies with repeated assessments of identical measures and individuals are needed to fully establish age-related changes in genetic factors. Third, lower heritability estimates for general psychopathology and anxious-misery meant less power to detect genetic correlations with cognition and G × A interactions. Finally, although we used a large sample, and comprehensive assessments of cognition and psychopathology, our findings require replication.


Perilla crop (Perilla frutescens L.) have attracted interest as health foods in East Asia. This crop has been traditionally cultivated and used for a long time as a folk plant, especially in Korea. In our study, the 22 SSR markers and eight morphological traits were used to assess the genetic diversity and population structure, to select a core collection of 400 Perilla accessions conserved in the RDA-Genebank of South Korea. A total of 173 alleles were detected and the number of alleles per locus ranged from 4 to 15 (average = 7.9). Gene diversity and polymorphic information content ranged from 0.138 to 0.868 (average = 0.567) and 0.134 to 0.853 (average = 0.522), respectively. The 400 accessions were not clearly distinguished geographically by STRUCTURE and UPGMA analyses. A core collection (44 accessions) was selected from the entire collection by using PowerCore. The core collection accounted for 11.0% of the entire Perilla collection, including 100% of the number of alleles maintained in the whole collection and with similar or greater Shannon–Weaver and Nei diversity indices than the whole collection. The core collection selected by SSR markers was evenly distributed in three clusters on a scatter plot by eight morphological traits. The first core collection of Perilla accessions was constructed, and it maintained allelic richness. Further modification of the core collection is expected with the continuous addition of new accessions of the two cultivated types of Perilla crop and their weedy types.The leaves and seed oil of  These PGRs are utilized to provide new plant varieties with better resistance, adaptation, and nutritive qualities. Conservation and collection of PGRs is necessary to maintain higher genetic diversity for food security and use in the future2. Moreover, the conservation and collection of PGRs in most crops has been made possible by the establishment of genebanks in countries around the world. The fundamental role of a genebank is to increase genetic diversity and prevent genetic erosion by collecting, conserving, and documenting PGRs. In South Korea, the Genebank of the National Agrobiodiversity Center (NAAS) of the Rural Development Administration (RDA-Genebank) plays an important role in supporting genetic resource conservation and utilization. The RDA-Genebank currently preserves a total of 266,649 genetic resources for 3,083 species, including food, horticulture, industrial, and forage crops (http://genebank.rda.go.kr). Although a large number of PGRs have been collected in genebanks, the large sample size and lack of adequate information about population structure and genetic diversity of these collections hinders the successful utilization of the genetic potential for PGRs3. Efficient management and utilization of the existing collections could be greatly enhanced by reducing the number of accessions from large collections4. Frankel (1984) suggested the concept of a core collection, which is a subset of accessions with minimum redundancy and maximum genetic diversity from an entire collection. Various studies have proposed subsampling proportions ranging from 5 to 30% for a core collection8. A core collection is commonly established to conserve phenotypic and genetic diversity and enable the selection of candidate alleles associated with important agronomic traits9. Development of a core collections has been promoted as the most important activity in conservation and utilization of PGRs since 19962. Therefore, core collections have already been reported for many crops, such as rice10, soybean11, and wheat12. The development of core collections was based at first on passport data, geographical distribution, and phenotypic data15. Subsequently, the advance of molecular markers, which are an efficient means of confirming genetic diversity, has allowed the development of more powerful core collections in many crop species, either alone16 or in combination with phenotypic data17. Among various molecular markers, simple sequence repeats (SSRs) are often selected for genetic studies, such as for genetic diversity and the development of core collections, because of their advantages such as high reproducibility, polymorphism, abundance, and codominance19. Although there are a number of useful methods for selection of a core collection, the power core method for the development of a core collection utilizes the advanced M (maximization) strategy implemented through a modified heuristic algorithm by Kim et al. (2007). Using this program, core collections have been developed in various crops, such as rice21, soybean11, barnyard millet22, ragi23, and cassava24.Plant genetic resources (PGRs) mean individuals or populations of wild species, genetic stocks, and cultivars, which are maintained as types of plants, seeds, and tissuesPerilla crop  is an annual self-fertilizing species of the Lamiaceae family. This species has been traditionally cultivated and used for a long time as a folk plant, especially in East Asian countries such as Korea, Japan, and China. It is divided into two different varieties (or cultivated types), P. frutescens var. frutescens and var. crispa, based on morphology and use in East Asia. For example, P. frutescens var. frutescens, which is called “dlggae” in Korea, “egoma” in Japan, and “ren” in China, is tall with a large seed size (more than 2 mm), green leaf and stem color, and non-wrinkled leaf and used as an oil and leafy vegetable crop26. In contrast, P. frutescens var. crispa, which is called “cha-jo-ki” in Korea, “shiso” in Japan, and “zisu” in China, is short with a small seed size (less than 2 mm), red or green leaves and stems, and wrinkly or non-wrinkly leaves and is used as a vegetable and herbal medicine crop26.P. frutescens var. frutescens has become a cash crop in South Korea. The leaves of var. frutescens are abundant in vitamin B and C, and it is a favored salad vegetable eaten with meat and used for pickles. Furthermore, Perilla seed oil of cultivated P. frutescens var. frutescens has a high content of polyunsaturated fatty acids, such as linoleic acid (C18:2) and α-linolenic acid (C18:3), which comprise approximately 80% of Perilla seed oil. The Perilla seed oil, similarly to soybean, rapeseed, corn, and sesame seed oils, has been used for foods such as cooking oils28. Recently, the leaves and seed oil have attracted interest as health foods in South Korea; therefore, the cultivation area of P. frutescens var. frutescens has increased greatly. Although approximately thirty commercial varieties have been registered in Korea (http://www.seed.go.kr), there have been few efforts to develop new cultivars for Perilla crop. Most farmers still cultivate landraces of their own regions29. The RDA-Genebank in South Korea maintains 2,368 genetic resources of cultivated and weedy types of Perilla crop, which are available to researchers and farmers (http://www.genebank.go.kr/).Perilla resources and for more efficient breeding programs, the morphological and genetic characteristics of the collected resources should be defined before use. Although many Perilla accessions in the RDA-Genebank have already been evaluated for phenotypic characteristics and diversity in the field, these characteristics are easily affected by environmental effects and evaluations are time and labor-intensive30. To overcome these limitations, molecular marker systems could be used to mitigate against the environmental effects and provide a better explanation of the variation than phenotype-based evaluation31. However, there has been a lack of information about genetic diversity and relationships at the molecular level for the RDA-Genebank accessions. Most of the Perilla accessions of the RDA-Genebank have been used rarely or not at all in Perilla crop breeding programs. Moreover, there are no reports of a core collection of Perilla based on DNA molecular markers.To maximize the utilization of Perilla germplasm collection of the RDA-Genebank using 22 Perilla SSRs. This allowed us to 1) examine the level of genetic diversity and the population structure within accessions of cultivated P. frutescens var. frutescens of the RDA-Genebank and 2) select a Perilla core collection that represents the entire collection without redundancy. This core group will provide useful information for efficient conservation and the utilization of genetic resources as well as for the selection of useful genetic resources for Perilla breeding programs.Therefore, in our study, we performed population structure and genetic diversity analyses of 400 accessions of the P. frutescens var. frutescens  was 56.1%  among the 173 alleles, whereas intermediate-frequency alleles (frequency of 0.05–0.5) and abundant alleles (frequency > 0.5) represented 36.4%  and 7.5% , respectively, of the 173 alleles  and southern  regions of South Korea and foreign or unknown , our study compared the number of alleles and the GDI of the three groups  to clearly determine the number of subgroups in interpreting the actual K values. The highest value of ∆K for the 400 accessions of cultivated P. frutescens var. frutescens was found at K = 2  based on a threshold of 0.8 . Group IV , VI , VIII , IX , and X  each included only one accession. Meanwhile, Group I contained 33 accessions . Group II consisted of 325 accessions . Group V and VII included four accessions  and six accessions , respectively . In addition, Group II, which contained 81.8% of the 400 accessions, was further subdivided into ten sub-clusters with a genetic similarity of 51.8% . The first sub-cluster contained 261 accessions, which divided again into eight sub-groups with a genetic similarity of 56.9% . The second sub-cluster consisted of 13 accessions . The third sub-cluster  and fifth sub-cluster  each contained five accessions. The fourth sub-cluster consisted of 12 accessions . The sixth sub-cluster contained 16 accessions . The seventh sub-cluster (2 accessions from the southern region) and eighth sub-cluster  each consisted of two accessions. The ninth sub-cluster included eight accessions . Finally, the tenth sub-cluster of Group II contained only one accession, collected from the central region .To confirm the genetic relationships by distance-based analysis among the 400 accessions of cultivated P. frutescens var. frutescens using PowerCore software20. For the core collection, 44 accessions were selected. The core collection contained 16 representatives from the central region, 21 from the southern region, and 7 foreign or unknown  and Nei calculation  of the 372 Perilla accessions, 86 accessions showed dark brown, 212 accessions showed brown, 24 accessions showed gray, 37 accessions showed white, the remaining 13 accessions showed mixed colors. For seed hardness (QL8) of the 372 Perilla accessions, 305 accessions showed soft seeds and the remaining 67 accessions showed hard seeds (Supplement Table 3).In the survey of color of leaf surface (QL1), 59 accessions showed light green color, 207 showed green, and 104 showed deep green. The remaining 2 accessions showed light purple color. For color of reverse side leaf (QL2), 144 accessions showed light green color, 203 accessions showed green, and 24 showed deep green. One accession only showed light purple. For stem color (QL3), 58 accessions showed light green, 207 accessions showed green, and 105 accessions showed deep green. Only 2 accessions had light purple. For leaf shape (QL4), 161 accessions showed lanceolate shape, 146 accessions showed heart shape, and 65 accessions showed oblong shape. For degree of pubescence (QL5), 90 accessions showed slightly pubescent, 222 accessions showed normal pubescent, and 58 accessions showed heavily pubescent. The remaining 2 accessions showed more heavily pubescent. In the case of flowering time (QL6), 179 accessions showed intermediate flowering (flowering days from August 15 to September 5) and 193 accessions showed late flowering (flowering days from September 6 after September 25). In the case of the two seed characteristicsP. frutescens var. frutescens  of the core collection could not be investigated for morphological characteristics Table , but the35. With the development of molecular biology, DNA molecular marker technology provides useful information for the analysis of genetic diversity, genetic relationships, population structure, and core collections in the germplasm of many crop species24.Information about the genetic diversity of PGRs, which provide useful alleles associated with plant development and improvement, is very important for both the conservation and the utilization of germplasm that has been collected in a genebankPerilla crop, much analysis has been performed on genetic diversity, genetic relationships, and population structure using amplified fragment length polymorphisms (AFLP)29, random amplification of polymorphic DNAs (RAPD)36, and SSR markers40. Unfortunately, and in contrast with other major crop species, other molecular marker technologies in Perilla species have not yet been developed. Among these marker systems, as already explained in the Introduction, SSR marker technology is highly polymorphic and reproducible, generally co-dominant and abundant in the plant genome, and it has provided useful information for the analysis of genetic diversity, genetic relationships, population structure, etc. in the germplasm of Perilla species44. Recently, SSR primer sets have been developed for Perilla crop by many researchers46 and used successfully for the analysis of genetic diversity, genetic relationships, population structure, and association mapping among the accessions of cultivated and weedy types of Perilla crop48. Therefore, this study used SSR markers to identify genetic diversity and relationships, population structure, and a core collection of RDA-Genebank Perilla germplasm. The GD and PIC values determined in this study of 0.567 and 0.522, respectively  or quantitative traits loci (QTL) analysis, because these markers were developed by using the results of transcriptome analysis for Perilla crop52.Meanwhile, polymorphism of loci can be considered high, medium, or low with GDI > 0.5, GDI < 0.5 and > 0.25, or GDI < 0.25, respectively, according to a report by Vaiman et al. 1994). The population in the current study consisting of the 400 accessions of cultivated 994. The Perilla crop, South Korea is assumed to be the secondary center of biodiversity of Perilla crop because of extensive cultivation, various uses, and high morphological and genetic diversity as well as the existence of weedy types50. Recently, cultivated P. frutescens var. frutescens of Perilla crop has become a cash crop in South Korea, and the cultivation area has expanded significantly. To maximize the use of genetic resources of cultivated P. frutescens var. frutescens of Perilla crop preserved in the RDA-Genebank, the genetic characteristics of the collected resources should be analyzed for efficient conservation and utilization in South Korea. Therefore, this study compared the average allele numbers and GDI values among the central  and southern region  accessions of South Korea and foreign or unknown  accessions 29.Although numerous PGRs are currently conserved in genebanks around the world, the large amount of PGRs makes their accessibility and application difficult55. PowerCore in this study captured 100% of the alleles with a sampling percentage of 11%, based on 22 SSR markers throughout the collection  by Yonezawa et al. (1995)7. Sh.W. and Nei diversity indices were used for validation of the core collection, and the averages of the Sh.W. index (1.306) and Nei index (0.639) of the Perilla core collection were higher than those of the entire initial collection , indicating increased genetic diversity of the core collection. This may be because of the removal of genetic redundancy in the core collection compared with the entire initial collection. It is obvious that this core collection is an exact representation of the diversity of the entire collection  were obtained from the RDA-Genebank of the Republic of Korea (http://genebank.rda.go.kr). The accessions of Perilla germplasm preserved by the RDA-Genebank of the Republic of Korea have been collected for decades with the approval of farmers for the Korean native Perilla accessions. The IT number and location information for these materials is shown in Supplementary Table 1. South Korea is geographically divided into central and southern regions . The central region includes Gangwon-do, Gyeonggi-do, and Chungcheong-do; while the southern region includes Gyeongsang-do, Jeolla-do, and Jeju-do. A total of 359 accessions were collected from the central and southern regions of South Korea, whereas the remaining 41 accessions were either collected from foreign countries (20 accessions) or the collection region is unknown (21 accessions) (Supplementary Table 1). In the central region, 148 accessions were collected from Gangwon-do (46 accessions), Gyeonggi-do (53 accessions), and Chungcheong-do (49 accessions); while in the southern region, 211 accessions were collected from Gyeongsang-do (125 accessions), Jeolla-do (85 accessions), and Jeju-do (1 accession). The accessions used in this study were selected from the RDA-Genebank as breeding materials for the development of leaf vegetable cultivars of cultivated var. frutescens. Total DNA was extracted from the young leaf tissue of individual representative plants of each accession according to Plant DNAzol Reagent protocols . Our studies were complied with local and national regulations and following Kangwon National University  and National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA  regulations.A total of 400 accessions of cultivated P. frutescens var. frutescens, 20 seeds of each accession were sown in a nursery bed in early May 2020, and kept in a glass house for a month. After that, seven seedlings of each accession were then transplanted to the fields of the experimental farm of Kangwon National University, Chuncheon, Gangwon-do in early June 2020. We examined eight morphological characteristics related to leaf and seed quality and quantity, namely color of leaf surface (QL1), color of reverse side leaf (QL2), stem color (QL3), leaf shape (QL4), degree of pubescence (QL5), flowering time (QL6), seed color (QL7), and seed hardness (QL8) at the appropriate growth stages. These morphological traits, as shown in Supplementary Table 2, were selected based on a previous report by Lee and Ohnishi (2001). In our study, 28 accessions were not used for measurement because of failure of plant growth in the field. Therefore, in this study, only 372 accessions out of the 400 accessions of cultivated P. frutescens var. frutescens were evaluated for morphological characteristics (Supplementary Table 1).For evaluation of morphological variation among the 400 accessions of cultivated Taq polymerase . The PCR profile consisted of initial denaturation at 95 °C for 3 min, followed by 36 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 1 min 30 s, with a final extension step of 5 min at 72 °C. After PCR, DNA electrophoresis analysis was performed with a QIAxcel advanced system  according to the protocol described in the QIAxcel DNA Handbook. The samples were run in the QIAxcel advanced electrophoresis system, and sample separation was performed over 15 min. Gel images were obtained as the results, and the quantification analysis was performed with QIAxcel software. The results were displayed as gel images and electropherograms acquired from the QIAxcel advanced system software.SSR amplification was performed in a total volume of 20 μL containing 20 ng genomic DNA, 1 × polymerase chain reaction (PCR) buffer, 0.2 mM dNTPs, 0.5 μM forward and reverse primers, and 1 unit of 57 was used to confirm information on the number of alleles, allele frequency, and genetic diversity index (GDI), such as major allele frequency (MAF), gene diversity (GD), and polymorphic information content (PIC). Genetic similarities (GS) were calculated for each pair of accessions using the Dice similarity index58. To illustrate the genetic relationships of the total accessions, a similarity matrix was used to construct an unweighted pair group method with arithmetic mean (UPGMA) dendrogram by the application of SAHN-Clustering from NTSYS-pc V2.159. Principal component analysis (PCA) was performed to detect differences between and within accessions of P. frutescens var. frutescens. The PCA analysis was performed using NTSYS-pc V2.159. Population structure was investigated for the 400 accessions of cultivated P. frutescens var. frutescens using STRUCTURE 2.2 software60. Five independent runs with K values ranging from one to ten were performed with 100,000 cycles for both burn-in and the run length. The delta K statistic, based on the rate of change in the log probability of data between K values32, was calculated with STRUCTURE HARVESTER. For extraction of a core collection, this study used PowerCore software, which uses the advanced M strategy implemented through the modified heuristic algorithm for the core collection as described in the user’s manual20.The fragments amplified for each SSR primer set were scored as present (1) or absent (0). Power Marker version 3.25Supplementary Information.


The rapid increase in demand for cancer genetic testing in Australia led to the establishment of private Familial Cancer Clinics (FCCs) as alternatives to public sector FCCs. Australian studies conducted in the public sector have shown high patient satisfaction with genetic counselling. No study has investigated patient satisfaction with genetic counselling in the private sector in Australia. Our aim was to assess patient satisfaction with genetic counselling for familial cancer within the private healthcare sector of Western Australia. Questionnaires were given to all eligible patients after their first genetic counselling appointment, consisting of the 12-item Satisfaction with Genetic Counselling Scale and an added question regarding the perceived value for the financial cost. Outcomes assessed included instrumental satisfaction, affective satisfaction, procedural satisfaction and perceived value for financial cost. Participants scored the representative questions from one to four (unsatisfied - highly satisfied). Two hundred and twenty patients were given the questionnaire, 75 questionnaires were returned (response rate 34%), and 73 were appropriately completed and analysed. Overall, seventy (96%) participants were highly satisfied with the genetic counsellor’s explanation; seventy-four (98%) were highly satisfied/satisfied with the reassurance provided. Sixty-eight participants (93%) were highly satisfied/satisfied with the help received. Seventy-two (99%) participants had their expectations met and sixty-nine (95%) participants were highly satisfied with the service. Sixty-eight (93%) participants were highly satisfied/satisfied with the cost of private genetic counselling. Sixty-one (83.6%) proceeded to genetic testing. Private genetic counselling was considered highly satisfactory, and the cost considered acceptable by most participants. The aim of cancer genetic counselling is to identify individuals at increased risk of cancer and provide information and support regarding their risks and risk management options, in order to reduce morbidity and mortality . While demand for Familial Cancer Clinic (FCC) services has been high for more than a decade, in 2013 the so-called “Angelina Jolie Effect” led to significantly increased and sustained interest in, and demand for, FCC services . As a consequence, the demand for FCC services considerably outweighed the resources that were available to meet the demand . In part, this situation prompted the establishment of alternatives to traditional public sector FCC services in Australia.In Australia, genetic counselling for familial/hereditary cancer is primarily available through FCCs that operate within the public healthcare sector. Public FCCs have stringent eligibility criteria for genetic counselling and genetic testing, meaning that services are not available to everyone who wishes to access them. For those who are eligible, costs are covered by Medicare and/or the FCC/hospital, however there can be significant waiting times for appointments and test results. Private FCCs are now operating in most Australian states. In recognition of the increasing trend towards private genetic counselling in Australia, practice guidelines have been developed . In early 2015, genetic counselling and testing for familial cancer became available in the private healthcare sector of Western Australia (WA). This represented a significant change to the provision of familial cancer genetic counselling in the state, which was previously available exclusively through Genetic Services of Western Australia (GSWA) – a single, state-wide clinical genetics service provided by the public healthcare sector. WA patients who are eligible for public FCC services now have the option to be seen at GSWA or privately. Almost all services provided in the public sector at GSWA are paid for by Medicare and are free to patients. However, waiting times for genetic counselling appointments and testing can be significantly longer in the public system compared to the private sector. Hence patients may elect to see a genetic counsellor (GC) in private and self-pay for genetic counselling and testing to reduce waiting times. These costs are currently not reimbursed by health insurance funds in Australia. The importance of reduced waiting times is increasing, as treatment decisions may be affected by variant status. For example, targeted therapy with Poly-(ADP)-ribose polymerase inhibitors has been shown to improve progression-free survival in BRCA pathogenic variant-positive ovarian cancer in combination with, and following, chemotherapy for platinum sensitive disease and as a single agent in recurrent disease . The private FCC also provides WA patients who are not eligible for public FCC services with access to genetic counselling and testing. Access to the public FCC is determined by resource availability and does not consider patient preferences. In general, diagnostic genetic testing is only offered to individuals with a personal history of cancer and a >10% pre-test probability of carrying a likely/pathogenic variant according to genetic risk assessment instruments, such as The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Manchester Score for hereditary breast/ovarian cancer . Those who have no personal history of cancer but are at increased risk due to their family history are usually ineligible for public genetic testing, unless a likely/pathogenic variant has previously been identified in the family. Therefore, patients wishing to access genetic counselling/testing who did not meet eligibility criteria for the public FCC were left without an alternative, local option prior to 2015. These patients now have the opportunity to proceed with genetic testing in the private sector, following discussion of the advantages, limitations and implications of testing with a GC.Genetic testing can be accessed through some general practitioners or via companies that offer direct-to-consumer testing, but often these options may not provide the opportunity for adequate pre-test counselling to enable informed consent, and/or for individuals to discuss their results and implications with a GC. This could lead to false reassurance or undue anxiety especially if clinically significant findings are unanticipated . Concerns regarding direct-to-consumer testing include patients not fully comprehending the risk information they receive .There is an increasing focus being placed on patient-reported satisfaction with their medical care . As healthcare funding transitions to a value-based model, the ability to demonstrate the value of genetic counselling through outcomes research will be essential. A 2017 rapid systematic review of outcome studies in genetic counselling identified only five studies that had investigated patient satisfaction with genetic counselling or satisfaction with decision-making .Two of these five studies were conducted in non-cancer populations , two studies were in women at risk for breast cancer , and one study assessed low rates of African American participation in genetic counselling and testing for pathogenic variants in BRCA1/2 . Studies conducted in public FCCs in Australia have largely shown high patient satisfaction with genetic counselling including a 2005 study undertaken at GSWA in 122 participants, 24% of whom had attended the FCC . To our knowledge, no study has specifically addressed patient satisfaction with private genetic counselling for familial cancer in Australia. The objective of the current study was therefore to assess the satisfaction of patients with genetic counselling in the private healthcare sector of WA, in order to identify any unmet needs and to improve the delivery of care to these patients. ParticipantsParticipants were new patients who presented for genetic counselling and possibly testing for familial cancer at a solo private practice. Patients were eligible to participate if they were newly referred for genetic counselling because of a personal and/or family history suggestive of a hereditary cancer syndrome. Eligible patients were invited to participate in the study following their initial genetic counselling appointment. InstrumentationThe study design consisted of a questionnaire that assessed patient satisfaction via an amended version of the 12-item “Satisfaction with Genetic Counselling Scale” (SCS) . An additional question was included to assess participants perceived financial satisfaction – “Do you feel that private genetic counselling provided value for the financial cost involved?”. Participants were also given the opportunity to provide any general comments in an open text box. The patients were asked about their demographics, personal and/or family history of cancer, source of referral and whether they proceeded with genetic testing. The questionnaire  was completed by participants within one month of their initial genetic counselling appointment. The SCS comprised three sub-scales that reflect three dimensions of satisfaction: 1. Instrumental satisfaction: this component is the extent to which the respondent evaluates the health professional as having the required skills and provides appropriate treatment and reassurance.2. Affective satisfaction: relates to the evaluation of the health professional’s behavior toward the client as a person showing interest and care. 3. Procedural satisfaction: addresses the administrative components of the service.Participants were asked to evaluate the following: explanation of the condition, reassurance provided by the GC, listening skills, dedication, understanding, satisfaction with help received and whether the services matched the participant’s expectations. Participants gave each component a score from one - four, where a score of one is considered unsatisfactory and a score of four is considered highly satisfactory.There were also three individual items that specifically assessed general satisfaction (two items) and satisfaction with the information provided (one item). For each item, participants indicated their level of satisfaction using a Likert scale from one (low) to four (high). ProcedureBetween July 1, 2016, and July 31, 2019, all 220 patients attending for their first genetic counselling appointment were invited to participate in the study and provided a questionnaire. Genetic counselling was delivered at a one-hour ‘in person’ appointment in private consulting rooms in West Leederville, Perth, Western Australia, and if genetic testing was performed, a further follow-up appointment was arranged to discuss the results and associated implications. Genetic counselling was provided by a single GC (SOS). Genetic counselling and testing were paid for by the participants. Participants paid a standard charge for the genetic counselling of AU$225 (US$165). The cost of genetic testing varied depending on whether single gene, predictive or multigene panel testing was conducted; the majority of participants had testing of a multigene panel at a cost of US$250 but some patients paid up to US$875. The GC (SOS) consults within a holistic women’s health group private practice with specialists in gynaecologic oncology, obstetrics and gynaecology, menopause medicine, clinical psychology, clinical sexology, physiotherapy and exercise physiology. The practice is located in a commercial office building of medical consulting rooms in West Leederville, Perth, Western Australia. Additional staff include a practice manager, two part-time receptionists and two part-time nurses. Patients are referred to the GC by medical specialists from both public hospital medical oncology, gynecologic oncology, breast and colorectal surgery outpatient clinics, and private consulting rooms, general practitioners, or by self-referral. Patients attending the practice are predominantly married, Australian women of European descent, over age 50 years, and with a personal or family history of breast, ovarian and colorectal cancer.Genetic risk assessment was based on information relating to personal and family history of cancer, using risk scoring instruments such as BOADICEA. Written information about the study and the questionnaire was provided by reception staff. Informed consent was implicit in the completion and return of the questionnaire. Participants returned the questionnaires to the private practice in a self-addressed reply-paid envelope within one month. The questionnaires were stored in a locked cupboard of the offices of the Gynecological Cancer Research Group, St John of God Subiaco Hospital. Participants were not identifiable from the questionnaires. Data were extracted from questionnaires to an Excel spreadsheet and analysed by two investigators (CP and PC). The GC (SOS) was blinded to the study questionnaires and was not involved in data analysis. Ethical approval for the study was granted by The St John of God Healthcare Human Research Ethics Committee (Reference #976). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.Data AnalysisData were analysed using IBM SPSS Statistics for Windows, version 26 . Categorical variables were described using frequency and percent, with missing data noted. Continuous scale variables were described using mean and standard deviation. Open text responses were tabulated but did not undergo thematic analysis. This was a non-comparative, single arm study conducted over three years. The sample size was pragmatic. Participant CharacteristicsSeventy-five of 220 questionnaires were returned giving a response rate of 34%. Two of 75 questionnaires were incomplete and excluded from the analysis. No follow-up appointment occurred before participants received the survey. The participant characteristics are shown in Satisfaction with Genetic CounsellingOverall, 70 (96%) participants were highly satisfied with the genetic counsellor’s explanation; 64 (88%) were highly satisfied and seven (10%) were satisfied with the reassurance provided; 60 (82%) were highly satisfied and eight (11%) were satisfied with the help received; 72 (99%) participants had their expectations met and 69 (95%) participants were highly satisfied with the GC . Two parProcedural Satisfaction and ‘Value for Money’Other factors that affect participants’ satisfaction with the service include waiting time for the first appointment, length of time in the waiting room and treatment by other staff members. The majority of participants were highly satisfied with the service provided by other staff members and with the waiting times . Sixty-eOpen text responsesTwenty-five respondents provided open-text responses and most described genetic counselling. Quotes are provided in the online supplementary file. Overall satisfaction was high for all those quoted.To our knowledge this is the first study to assess patient satisfaction with private genetic counselling for familial cancer in an Australian setting. Overall, we found that participants were extremely satisfied with the referral process and their genetic counselling. The majority also perceived the cost of private genetic counselling to be good value. Studies of patient satisfaction with public genetic counselling services have shown similar results to the current study. This suggests that satisfaction with private genetic counselling is comparable to public genetic services. One study of 156, predominantly white, married, female patients who had received counselling from a cancer genetics professional at the Penn State Cancer Institute, Philadelphia, USA, found that 96.1 % of patients were satisfied, very satisfied or extremely satisfied with their genetic counselling . Our findings are consistent with those of another study that used the Satisfaction with Genetic Counselling Scale and demonstrated a mean score between 9.9-11.7 (maximum score of 12) for instrumental, affective and procedural satisfaction combined, and for the three individual items - satisfaction with the information, met expectations, and overall satisfaction with the counselling - between 3.7-3.9 (maximum 4) . In the current study the mean scores for satisfaction with the information, met expectations, and overall satisfaction with the counselling were 3.99, 3.99 and 3.95, respectively. Most participants were highly satisfied with the GC’s reassurance (mean score 3.85), listening skills (mean score 3.99), dedication (mean score 4.00) and understanding (mean score 3.96). Most participants were satisfied or highly satisfied with administrative procedures including the waiting time before the first appointment, length of time in waiting room and treatment by other staff members. Two scores of ‘one’ and 7 scores of ‘two’ related to aspects of procedural satisfaction, most commonly length of time prior to first contact, or to the patient’s health status and perception of their diagnosis and prognosis, suggesting that factors outside of the GC’s control may have influenced satisfaction. It was notable that most participants perceived private genetic counselling to be good value for money. Studies of patient-perceived value for money in genetic counselling are limited and this gap in knowledge represents an important area for future research. In women with ovarian cancer in whom genetic testing is recommended, the cost of testing was the most important factor, followed by probability of detecting a pathogenic variant, in 94 enrolled subjects recruited in a choice experiment survey of whom 68 (76.4%) presented for genetic counselling . As current testing costs are now lower than those at the time this study was undertaken and some private patients are now eligible for Medicare-funded genetic testing, it could be hypothesized that satisfaction/perceived value would now be even higher.Study Limitations The study was conducted at a private practice in Western Australia with a single GC and included few male participants. Further, the population was highly educated, of mainly European ancestry, and although socio-economic data were not collected, participants self-funded the costs of genetic counselling and testing. Hence our findings may not be applicable in other clinical settings. There may have been response bias as we were not able to assess satisfaction in non-responders. Further, we did not collect demographic data in non-responders, so we were unable to assess whether responders differed from non-responders in age, gender, ancestry and personal and/or family cancer history. Only satisfaction with pre-test counselling was investigated and so we cannot make inferences about patient satisfaction with post-test counselling. Perception of value for money is likely to depend on several factors including available alternatives, image of the practice, trust, service quality, costs and consumer/patient factors including socio-economic group, income, and background. Our aim was to assess perceived value of genetic counselling, which had a standard charge, but it is conceivable that the variable cost of genetic testing may have biased this outcome in patients who proceeded to genetic testing.In summary, this study shows a high level of self-reported patient satisfaction with a genetic counselling service at a private practice in Perth, Western Australia. Our findings support the value of this model of genetic counselling service delivery that is relatively new to Australia. Barriers to continuing to provide a high quality private genetic counselling service include low numbers of GCs in private practice and costs of genetic testing. Further research should investigate satisfaction with different models of private genetic counselling including telephone and video consultations for patients in regional and remote locations, and assessment of post-test counselling. Qualitative interviewing of patients may provide insight into any reported dissatisfaction, leading to implementation of changes that increase satisfaction with private genetic counselling further.List of AbbreviationsBOADICEA - Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm BRCA - Breast Cancer GeneFCC - Familial Cancer ClinicGC - Genetic CounsellorGSWA - Genetic Services of Western AustraliaSCS - Satisfaction with Genetic Counselling ScaleWA - Western AustraliaCP: data acquisition, data analysis and interpretation, writing the first draft of the manuscript and final approval of the version to be published. Accountable for all aspects of the work and for the accuracy or integrity of the work. SOS: conception and design of the work, critical review of the manuscript and final approval of the version to be published. Accountable for all aspects of the work and for the accuracy or integrity of the work. NP: conception and design of the work, critical review of the manuscript and final approval of the version to be published. Accountable for all aspects of the work and for the accuracy or integrity of the work. JT: conception and design of the work; critical review of the manuscript and final approval of the version to be published. Accountable for all aspects of the work and for the accuracy or integrity of the work. PC: conception and design of the work; analysis and interpretation of data; drafting the manuscript and final approval of the version to be published. Accountable for all aspects of the work and for the accuracy or integrity of the work. Charmi N. Perera and Paul A. Cohen confirm that they had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All of the authors gave final approval of this version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.


Streptopelia turtur, is a widespread Palearctic species. Due to a long-term population decline, it is listed as vulnerable by the IUCN. Population genetics studies are important to the management of threatened species. Previous research based on mitochondrial DNA cytochrome-b of European Turtle Doves sampled in Western and Southern Europe showed a lack of genetic structure of this species. The present study aimed to identify the possible genetic divergence in the European Turtle Dove. A total of 258 birds collected from Spain, Ukraine, and Morocco were examined using mitochondrial DNA cytochrome-b and D-loop sequence analysis. The high genetic diversity was evaluated in both loci analysed. Various population genetic analyses displayed genetic differences between Turtle Doves from Morocco and Ukraine, and certain Spanish samples. The results of this study will be vital for effective conservation and sustainable management of this vulnerable species.The European Turtle Dove, Streptopelia turtur, a long-distance migrant wintering in Africa, is a widespread Palearctic species. This species is classified as vulnerable and is undergoing a long-term demographic decline. The results of the previous study  cytochrome-b (cytb) sequences of birds from Western and Southern Europe) indicated that the species was not genetically structured. We analysed the mtDNA cytb and D-loop of 258 birds collected from Morocco, Spain, and Ukraine. High genetic variability, expressed by haplotype diversity and nucleotide diversity, was revealed in both cytb  and the D-loop . SAMOVA and principal coordinates analysis revealed the birds belonged to two genetically distinct groups. One group included birds collected in Spain, while birds sampled in Morocco and Ukraine formed another group. Furthermore, significant genetic differentiation was identified between Turtle Doves from Morocco and Ukraine, and certain Spanish samples. The present results indicate that specific management and conservation plans relevant for the species in various regions should be applied. However, further nuclear DNA research and new studies (particularly in Eastern Europe) are necessary for the decisive results on genetic structure of this species.The European Turtle Dove,  Streptopelia turtur , is a widespread Palearctic species [Streptopelia turtur turtur, is listed in Appendix II of the Convention on Migratory Species.The European Turtle Dove,  species ,2. The E species . It is a species . Several species . Turtle  species . Since 2 species . In Euro species . The nomColumba livia, was extensively examined [In this context, it is essential to study the population genetic structure of the Turtle Dove. Yet only one study analysing genetic population structure of this species has been conducted . Previouexamined ,12,13,14examined .cytb) sequences and nuclear SNP analysis of Turtle Doves, though limited to Western and Southern Europe, suggested that the species is panmictic across Europe [Results from the earlier research based on the cytochrome-b , in Eastern Spain , in Central Spain  in Southern Spain  and at the Strait of Gibraltar coast of Spain . Birds migrating and breeding in Morocco were sampled in Southwestern Morocco , while in central Morocco—only local breeding birds were sampled . Migrating birds were sampled in Central Ukraine (n = 42).Samples from 258 Turtle Doves were used for genetic analysis. Blood samples were taken from the brachial vein of live individuals in their breeding sites in Morocco in 2018–2019. Tissue samples were taken from the heart or liver of legally hunted individuals in Spain, Ukraine, and Morocco in 2017–2020. Birds in Spain  were collected from six sites: in the Balearic Islands . PCR products were sequenced directly using the same forward and reverse primers as for amplification. The Big-Dye® Terminator v. 3.1 Cycle Sequencing Kit and the 3500 Genetic Analyser  were used for performing sequencing reactions. The D-loop and cytb sequences generated in the present study were deposited in GenBank under accession numbers MW438351–MW438608 and MW438609–MW438866, respectively.Genomic DNA was extracted following the universal and rapid salt-extraction method  and elutcytb . The totcytb sequences obtained in our study were compared with 94 sequences  obtained by Calderón et al. [cytb sequences were aligned using the MUSCLE algorithm implemented into the MEGA7 [cytb, respectively. The bootstrap phylogeny test was performed with 1000 replicates. DnaSP v 6 was used for calculation of some intraspecific genetic variation measurements, the number of haplotypes (h), the number of variable sites (S), parsimony informative sites, haplotype diversity (Hd), and nucleotide diversity (π) [The n et al.  which inhe MEGA7 . The beghe MEGA7 . The selsity (π) .ST with Arlequin v. 3.5.2.2 [ST was tested by 10,000 permutations at the 95% confidence level.Genetic differentiation for the Turtle Dove sample pairs was evaluated using Φ 3.5.2.2 . The staCT) and was significant (p < 0.05). Principal coordinates analysis (PCoA) based on Nei’s [We assessed population structure using spatial analysis of molecular variance with SAMOVA v. 2.0 . We choson Nei’s  distanceon Nei’s .D-loop analysis was conducted on samples from 258 Turtle Doves collected in nine sites in different regions of Spain, Morocco, and Ukraine. In 371-bp sequences, we obtained 44 variable sites, of which 28 were parsimony informative. Eighty haplotypes were identified. The mean frequency of haplotypes was 3.2. Of 80 haplotypes, 48 (60.0%) were singletons. The frequency of 27 haplotypes ranged from 2 to 7, the frequency of four haplotypes ranged from 11 to 23, and the most common haplotype (A1) was confirmed in 52 individuals. The high bootstrapping support value (86) was given to divide haplotypes into two clusters a. Twentycytb haplotypes from the analysis of 258 Turtle Doves. The cytb sequences generated in the present study were compared with sequences obtained by Calderón et al. [cytb analysis included 352 sequences and identified 93 haplotypes. Of 892 nucleotide positions, 76 were polymorphic, 36 of which were parsimony informative sites. The mean frequency of haplotypes was 3.8. Of the 93 haplotypes, 66 were present in a single individual (71.0%), 22 haplotypes were represented by 2–7 individuals, and the five most common haplotypes  were represented by 13, 21, 50, 60, and 71 individuals, respectively. The three most frequent haplotypes comprised 51.4% of the total sample. As in the case of D-loop, cytb haplotypes clustered into two groups  and the D-loop (Hd = 0.937 ± 0.009). More than twice, the D-loop showed higher total nucleotide diversity (π = 0.01502 ± 0.00034) than cytb (π = 0.00628 ± 0.00014).We identified 68 n et al. . The fino groups b: 30 haplogroups . Cytb ancytb results, the detected pairwise ΦST values showed a genetic divergence between birds collected in Spain and birds collected in Morocco and Ukraine  and cytb  data. At both loci, Morocco and Ukraine samples formed one group, while Spanish samples formed another group. The principal coordinates analysis (PCoA) demonstrated a close relationship between Turtle Doves sampled in Ukraine and Morocco  were used [cytb analysis, with samples analysed from 95 birds collected in eight countries of Western and Southern Europe  [S. t. arenicola), based on cytb and D-loop sequences, were examined. The obtained genetic differences between certain Turtle Dove populations have revealed the necessity of appropriate management and conservation measures for this threatened species.Examination of population genetics in Columbidae species is mainly based on microsatellite markers and maternal mtDNA sequence analysis. Microsatellite markers have been used to determine the genetic diversity, gene flow, and relationships between different populations or lineages ,13,29,30ere used ,32,33. TColumba janthina (π = 0.0009–0.0057 [Hemiphaga novaeseelandiae (π = 0.00142 ± 0.00036) [Columba palumbus (π = 0.04113 ± 0.00150), which is an abundant and widespread Palearctic species [A high genetic variability expressed in the number of polymorphic sites (S), the number of haplotypes (h), the haplotype diversity (Hd), and the nucleotide diversity (π) were assessed at both studied mtDNA loci . Based o9–0.0057 , π = 0.09–0.0057 ) and the0.00036) . Both sp species . The Eurcytb (cytb of the Turtle Dove has been previously suggested, and the median-joining network demonstrated that they were differentiated by six mutational steps [S. t. arenicola in Morocco, was identified in birds of the nominal subspecies migrating via Ukraine. It is possible that Turtle Doves of the nominal subspecies breeding in Eastern Europe mix with S. t. arenicola on the wintering grounds [COI were also shown for the Eurasian Collared Dove Streptopelia decaocto [Streptopelia t. turtur and Streptopelia t. arenicola differ morphologically, significant genetic differences between them were not observed within mtDNA, which indicated the recent evolutionary origin, as it was earlier suggested for different subspecies of the Dunlin Calidris alpina [This study identified two evolutionary lineages in both analysed mtDNA regions: the D-loop and cytb . The exial steps . The rat grounds . Notablydecaocto . The gendecaocto . Althougs alpina .cytb sequence analysis showed no evidence of population genetic structure of Turtle Doves sampled in Western and Southern Europe [Genetic analysis based on mtDNA sequencing data is increasingly used to designate conservation priorities and flyways of bird species ,36,37. Tcytb sequences, high genetic variability was shown for the Turtle Dove, a vulnerable species experiencing a long-term population decline. The genetic differences among birds sampled in Morocco, Ukraine, and Spain were observed. For conclusive results on the Turtle Dove’s population genetic structure, further research covering a representative sample from different regions of Europe and the use of nuclear DNA markers are necessary.Based on mtDNA D-loop and


Downstream to Glc-TOR, the E2Fa signalling module regulates the transcription of heat shock factors through direct recruitment of E2Fa onto their promoter regions. Also, Glc epigenetically regulates the transcription of core HS signalling genes in a TOR-dependent manner. TOR acts in concert with p300/CREB HISTONE ACETYLTRANSFERASE1 (HAC1) and dictates the epigenetic landscape of HS loci to regulate thermotolerance. Arabidopsis plants defective in TOR and HAC1 exhibited reduced thermotolerance with a decrease in the expression of core HS signalling genes. Together, our findings reveal a mechanistic framework in which Glc-TOR signalling through different modules integrates stress and energy signalling to regulate thermotolerance.Global warming exhibits profound effects on plant fitness and productivity. To withstand stress, plants sacrifice their growth and activate protective stress responses for ensuring survival. However, the switch between growth and stress is largely elusive. In the past decade, the role of the target of rapamycin (TOR) linking energy and stress signalling is emerging. Here, we have identified an important role of Glucose (Glc)-TOR signalling in plant adaptation to heat stress (HS). Glc  TOR is r region .torRNAi lines into green, which may be due to constitutive decreasing translation in torRNAi lines  regulates the expression of thermotolerance-associated HSP genes. Also, plants overexpressing TOR showed increased thermotolerance . Howevertor35-7 RNAi, and TOR OE (G548) lines under the same experimental set-up. TOR OE survived better whereas tor35-7 exhibited more severe death phenotypes and defects in stress recovery under all the conditions  than Col-0 plants  MS media for 24 h, followed by 24-h Glc treatment . Following acclimation to Glc medium, seedlings were transferred to HS (37°C_3 h). The cDNA was prepared from RNA isolated from Glc- and HS-treated samples and was hybridised onto Affymetrix Arabidopsis whole genome ATH1 arrays. The extent and the overlap of Glc- and HS-regulated genes at the transcriptional level were analysed to determine the unique and overlapping set of genes, which are crucial targets of Glc-HS signalling crosstalk. We used four treatment conditions and designated the following terms for Glc-free (−Glc), Glc only (+Glc), HS only (−Glc/ + HS), Glc, and HS (+ Glc/ + HS). We considered the differential expression of all HS genes affected 1.5-fold or more in the presence of Glc.Out of 2,311 differentially regulated HS (−Glc/ + HS) genes, 1,124 (49%) genes were also found to be up and downregulated by Glc and HS  in the promoter of HSFA2, which encouraged us to check whether E2Fa could also regulate HSFA2 expression through direct binding to its promoter region. In addition to conventional E2F-binding (TTTCCCGCC) elements, E2F can bind on similar E2F-like motifs in order to activate the target gene promoters  under Glc sufficiency. Arabidopsis tor-es1 showed more than 50% reduction in HS gene expression under Glc + estradiol treatment than mock (DMSO)  . These rk (DMSO) . The rolk (DMSO) . We, thek (DMSO) . We nextficiency , 7. CollTOR and HAC1 in diurnal light-dark cycle transcriptome data obtained from public resources and identified that TOR expression overlaps with HAC1 expression throughout the diurnal cycle  were subjected to Glc-free and Glc-containing MS media for 24 h, followed by short-term-acquired thermotolerance (SAT) HS treatment. As expected, Col-0 and hac1 mutants treated without Glc could not survive the lethal HS. However, Col-0 plants supplemented with Glc showed improved thermotolerance   revealed greater perturbation in HS gene expression, following sugar and HS treatment  acetylation antibody coupled with qPCR. Intriguingly, Arabidopsis Col-0 plants showed increased accumulation of H3K acetylation at HS gene promoters upon sugar and HS treatment, whereas plants having a mutation in HAC1 showed reduced acetylation at these gene promoters  with hac1/5 obtained from public resources  showed that Glc-TOR signalling governs transcriptional reprogramming of HS genes , TOR OE , and e2fa  were obtained from ABRC at Ohio State University. The following lines were obtained from the published sources: tor35-7 RNAi [Col-0 background;  estimation was performed with seedlings treated with HS. Chl estimation was described by e2fa seedlings were used. For acetylation and H3K4me2 ChIP assay, 5-day-old tor-es1 seedlings were transferred to 0.5X MS medium containing 20-μM β-estradiol for 4 days. Following estradiol treatment, seedlings were starved for 24 h under a Glc-deficient medium. Following starvation, seedlings were subjected to 3 h of Glc treatment (167 mM). Briefly, 1-gm tissue of each sample was cross-linked with 1% formaldehyde to fix protein-DNA complexes. The samples were crushed in liquid N2 and homogenised in nuclei isolation and nuclei lysis buffers, followed by sonication. Sonication of chromatin was done in a 4°C water sonicator (DiagenodeBioruptor Plus). Sonicated samples were first precleared with Protein A Agarose beads (Millipore #16-157) and then mixed with antibodies. Serum containing anti-E2Fa antibodies was obtained from Prof. Lieven De Veylder, VIB Department of Plant Systems Biology, Ghent University  and high-light (HL) conditions, Arabidopsis seedlings were grown directly onto 0.5X MS medium containing without or with Glc (90 mM) for 7 days under LL and HL intensities and then transferred to HS. HS was applied as 1 h_37°C, 2 h_22°C, and 2.5 h_45°C, followed by recovery at 3–7 days_22°C under normal light  intensity. For thermotolerance of hac1 mutants, 5-day-old seedlings were grown initially in MS media, containing 1% sucrose (29 mM) and.8% Agar and then acclimatised to Glc-free and Glc-containing MS media for 24 h, followed by HS treatments. HS was applied as 1 h_37°C, 2 h_22°C, and 2.5 h_45°C, followed by recovery at 3–4 days_22°C.For quantitative real-time PCR (RT-qPCR) study in response to Glc, 5-day-old 0.5X MS-grown Arabidopsis seedlings were starved without Glc for 24 h in dark, followed by 3 h of Glc treatment in the light. For RT-qPCR study under HS, Arabidopsis seedlings were germinated and grown directly in 0.5X MS medium, containing sucrose (29 mM) for 7 days under standard 16-h-day/8-h-night conditions and subjected to HS at 37°C for 3 h. Total plant RNA was isolated using RNA easy Plant Mini Kit (Qiagen), and cDNA was prepared using 2 μg of total RNA with a high-capacity cDNA Reverse Transcription Kit (Applied Biosystems). All candidate gene primers were designed using a unique sequence within the genes, preferably 3′UTR. The normalization of genes was performed using 18S as a reference control. The fold-change for each candidate gene in different experimental conditions was determined using the quantitative ΔΔCT method. All primers used are listed in p-value less than.05 and false discovery rate (FDR) p-value.1, was used to analyse the DEGs. Microarray data have been submitted to Array Express with accession No. E-MTAB-6980. Three biological replicates were used in this microarray study.Whole-genome microarray analysis of 5-day-old Arabidopsis Col-0 seedlings treated without or with Glc (−/ + Glc) and HS was performed. To minimise the endogenous sugar background, 5-day-old Col-0 seedlings were first starved with 0.5X MS medium without sugar for 24 h in the dark, followed by 24-h Glc (167 mM) in the light and then subjected to HS. HS treatment was applied at 37°C for 3 h and 37°C for 3 h, followed by recovery at 22°C for 3 h. Harvested samples were outsourced for transcriptome profiling. The data were analysed using the Transcriptome Analysis Console  using default parameters. The differentially expressed genes (DEGs) in all conditions were compared to −Glc with fold change (FC) value (≥1.5). One-way between-subject ANOVA (unpaired) statistical test, with A. thaliana across various anatomical parts, developmental stages, and stress-related experiments were extracted using the mRNA-Seq dataset “AT_mRNASeq_ARABI_GL” from Genevestigator platform . Expression heat maps were generated using MeV_4_9_0.Gene expression data for TOR, RAPTOR1, and HAC1 in platform . The avaE-MTAB-6980.The original contributions presented in the study are publicly available. This data can be found here: Microarray data have been submitted to Array Express with accession no. MoS and AL conceived and designed the experiments and analysed the data. MoS performed most of the experiments. MoS and MJK performed the microarray experiment. MoS, BNS, MaS, and PA performed the Physiology and RT-qPCR experiments. MoS, SM, and GY performed the Bioinformatic analysis. MoS wrote the manuscript. MJK read the manuscript critically. AL supervised and complemented the writing. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Nothophaeocryptopus gaeumannii, occurs wherever Douglas-fir is found but disease damage is believed to be limited to the Coast Range and is of no concern outside the coastal fog zone . However, knowledge remains limited on the history and spatial distribution of Swiss Needle Cast (SNC) impacts in the Pacific Northwest (PNW). We reconstructed the history of SNC impacts on mature Douglas-fir trees based on tree ringwidth chronologies from the west slope of the Coast Range to the high Cascades of Oregon. Our findings show that SNC impacts on growth occur wherever Douglas-fir is found in western Oregon and is not limited to the coastal fog zone. The spatiotemporal patterns of growth impact from SNC disease were synchronous across the region, displayed periodicities of 25–30 years, strongly correlated with winter and summer temperatures and summer precipitation, and matched the patterns of enriched cellulosic stable carbon isotope indicative of physiological stress. While winter and summer temperature and summer precipitation influenced pathogen dynamics at all sites, the primary climatic factor of these three limiting factors varied spatially by location, topography, and elevation. In the 20th century, SNC impacts at low- to mid-elevations were least severe during the warm phase of the Pacific Decadal Oscillation  and most severe in 1984–1986, following the cool phase of the PDO (1945–1977). At high elevations on the west slope of the Cascade Mountains, SNC impacts were the greatest in the 1990s and 2000s, a period of warmer winter temperatures associated with climate change. Warmer winters will likely continue to increase SNC severity at higher elevations, north along the coast from northern Oregon to British Columbia, and inland where low winter temperatures currently limit growth of the pathogen. Surprisingly, tree-ring records of ancient Douglas-fir logs dated ~53K radioactive years B.P. from Eddyville, OR displayed 7.5- and 20-year periodicities of low growth, similar to those found in modern day coastal Douglas-fir tree-ring records which we interpret as being due to cyclic fluctuations in SNC severity. Our findings indicate that SNC has persisted for as long as its host, and as a result of changing climate, may become a significant forest health problem in areas of the PNW beyond the coastal fog zone.The fungal pathogen,  Pseudotsuga menziesii (Mirb.) Franco, P. menziesii var. glauca [Beissn.] Franco, and Pseudotsuga macrocarpa (Vasey) Mayr) . Tr. Tr17]. cal data . Growth cal data . The synN. gaeumannii, as seen under a scanning electron microscope . The logs, needles, and seed cones were encased in ancient landslide deposits at 26 m below the surface and were remarkably preserved. Radiocarbon dating estimates ages ~53K Before Present (BP) in the Marine Isotope Stage 3  period which was generally cold but with intermittent Dansgaard-Oeschger warm phases . Stem dicroscope . The twocroscope , indicatcroscope . The 20-We examined the spatial distribution of SNC impacts on mature Douglas-fir trees using TSIA of earlywood (EW) and latewood (LW) ringwidth chronologies from the west slope of the Coast Range to mid- and high-elevations on the west slope of the Cascade Mountains of Oregon . The EW To infer the climate relations with SNC, we used TSIA to classify each year into one of three disease states, SNC growth suppression, no suppression, and release. The dominant pattern of Douglas-fir growth at each site was a disease cycle with a primary periodicity of 25–30 years and secondary periodicity of ~4 years ,7,8,10 tcan=−0.98) and June-July dewpoint de icit (rcan=−0.98)  . The mulcan=−0.91) and February-April temperature (rcan=0.88)  . The mulWinter temperature, summer temperature and precipitation are the key limiting factors of pathogen abundance at all sites but, of these three climate factors, the primary limiting factor varies by site conditions and location . Summer th century, the PNW has experienced high climatic variability including a strong warm phase of the PDO (1925–1946), followed by a strong cool phase (1947–1978). We found an inverse relation between PDO and SNC effects on tree-ring width, i.e., reduced effects during the warm phase and increased effects during the cool phase (In the 20ol phase .The period from 1917 to 1940 was exceptionally warm and dry and the drought of the 1930s was the second most severe drought of the last 250 years [N. gaeumannii. This long history of SNC predates forest management practices and improves our understanding of the climate factors affecting the causal fungus. SNC impacts on Douglas-fir growth as seen in tree-rings display 6 to 30 year periodicities throughout the life of the tree. The higher frequencies in the disease cycle represent a lagged growth response to SNC caused by the infection of only the newly emerged needles at time of sporulation, followed by colonization of the needle over several years which is unique to N. gaeumannii. With warmer winters, SNC impacts are increasing in mature closed-canopy Douglas-fir stands on the east slopes of the Coast Range and in the high Cascades. Temperatures will likely continue to increase due to climate change, and consequently, SNC is expected to intensify in frequency and magnitude at higher elevations, north along the coast from northern Oregon to British Columbia, and inland where current winter temperatures limit fungal growth.SNC impacts occur wherever Douglas-fir is found and are synchronous across western Oregon, indicating that SNC is in luenced by regional climate. SNC impacts in the PNW date back to ~53K radioactive years BP as evidenced by the cyclical patterns of low growth in the master chronologies of ancient Douglas-fir that match the modern SNC disease cycles at coastal sites, and supported by the presence in the ancient needles of putative pseudothecia of


We found that gNIS at small sample sizes (14% of population) can provide accurate population diversity measures, but slightly underestimated population inbreeding coefficients. Accurate measures of internal relatedness required at least 33% of the population to be sampled. Accurate geographic and genetic spatial autocorrelation analysis requires between 28% and 51% of the population to be sampled. We show that gNIS at low sample sizes can provide a powerful tool to aid conservation decision‐making and provide recommendations for researchers looking to apply these techniques to free‐ranging systems.Effective conservation requires accurate data on population genetic diversity, inbreeding, and genetic structure. Increasingly, scientists are adopting genetic non‐invasive sampling (gNIS) as a cost‐effective population‐wide genetic monitoring approach. gNIS has, however, known limitations which may impact the accuracy of downstream genetic analyses. Here, using high‐quality single nucleotide polymorphism (SNP) data from blood/tissue sampling of a free‐ranging koala population ( Genetic non‐invasive sampling is a powerful tool for monitoring free‐ranging populations. Here we test the accuracy of using non‐invasively sampled DNA for downstream individual and population level analysis. We find that non‐invasive genetic sampling can provide accurate genetic measures, depending on the questions being investigated. Koalas are listed as “vulnerable” by the IUCN and by national law in the northern parts of their range  koala monitoring study that was part of a rail infrastructure development project in southeast Queensland, Australia . During the project, the study area was extensively surveyed, and all identified koalas were captured for veterinary examination and the attachment of tracking devices. Full protocols are available in the project technical report by Hanger et al.  or tissue samples collected during ear tag attachment (stored in 70% ethanol). DNA was extracted using the DNeasy Blood and Tissue Kit (QIAGEN), following the manufacturer's protocol, and DNA extracts were stored at −80°C.2.2The SNP dataset used in this study is the same dataset of 8649 SNPs for 430 individuals used in Schultz et al. . SNP genAll genetic data sets used in this study were filtered using the dartR package  DNA quality is lower than when sampling tissue or blood and (2) an unknown proportion of the population is sampled. To test the accuracy of using scat sampling for genetic monitoring of koala populations, we first subsampled our complete blood/tissue SNP dataset and simulated lower call‐rates to replicate genotyping results that one might expect from degraded DNA found in scats. Second, we investigated how the proportion of the population that is sampled influences the accuracy of genetic measures when using non‐invasive sampling.2.3.1We simulated datasets to reflect SNP data that may be obtained using non‐invasive sampling. To do this, we first randomly subsampled 1300 SNPs from our complete blood/tissue dataset. We selected this number firstly because 1300 loci have been successfully sequenced from experimentally aged koala scats  to investigate the effects of population sampling intensity. Specifically, we randomly subsampled between 40 and 420 individuals, in intervals of 20. To mirror spatially explicit sampling approaches used in field projects , Shannon's information index (I), inbreeding coefficient (FIS)  is calculated as:He is expected heterozygosity and Ho is observed heterozygosity. All genetic measures were analyzed in the R Statistical Environment v3.4.3  due to normally distributed data.Internal relatedness was calculated as:33.1He as 0.284 ± 0.002, I as 0.441 ± 0.002, and FIS as 0.125. Average internal relatedness (IR) was estimated as 0.12 ± 0.004 (range −0.33–0.43).Our complete blood/tissue SNP dataset consisted of 430 individual koalas from the same population, with 8649 SNP loci prior to filtering, and 6615 loci post‐filtering. From this dataset we estimated population p < .05 derived from bootstrapping). We did not find evidence of sex‐biased dispersal in this population   Figure b.3.2He, I, and FIS, we found that precision was not strongly affected by the number of individuals sampled, with large sample sizes having very similar distributions to small sample sizes. At the individual level, we found that simulated and observed IR values still displayed a positive correlation, with an average correlation of r = .69  for the 250‐m distance class, and 0.04  for the 500‐m distance class. Errors for the 250‐m distance class fell below 0.04 from 180 koalas (42% of population) onwards, and for the 500‐m class below 0.02 from 220 koalas (51% of population) onwards.When we assessed the sample sizes required for accurate genetic measures using the simulated datasets, we found that increasing sample size had little effect on improving accuracy or precision for both diversity indices. Similarly, population inbreeding coefficients were underestimated consistently Figure  at all sWe also found variation in population genetic measures due only to degradation of differentially informative SNPs across replicates , and precise but underestimated inbreeding coefficients (FIS). For internal relatedness, precision was achieved when 140 individuals were sampled (33% of population), while accuracy was not strongly influenced by sample size and showed moderate to strong correlations with true IR values (average = 0.69). For accurate spatial autocorrelation estimates using non‐invasive data, which can be used to assess inbreeding risk in free‐ranging populations, 28% of the population required sampling to find positive fine‐scale genetic structure (matching that found in the complete blood/tissue dataset) at 250 m, and 51% of the population at 500 m.We found that simulated genetic samples, even with the reduced call rates and SNP panel typical of non‐invasive sampling, can provide useful and informative genetic estimates for free‐ranging populations. By sampling more than 60 koalas (14% of population), we achieved consistent but slightly overestimated diversity measures  can provide reasonably accurate diversity and inbreeding coefficients suggests that practitioners may be able to design studies with less sampling intensity than perhaps anticipated. While this may increase the cost‐effectiveness of gNIS of populations, it is important to acknowledge that these sample sizes are based on a single population of koalas, and may not translate across other species and study systems. It is therefore important for practitioners to engage with their own study systems to determine sufficient sampling intensities.FIS was consistently underestimated, we found that inbreeding coefficients were very precise in their underestimation. Inbreeding estimates in conservation are often a baseline measure used to inform management decisions, particularly interventions such as genetic rescue or facilitating increased connectivity , genetic rescue decisions are made using a wide range of data, not only inbreeding coefficients  or 220 koalas  are sampled. Accurate spatial autocorrelation measures therefore require higher sampling effort than genetic diversity measures but may help to inform inbreeding risk where population inbreeding coefficient is not sufficient to identify whether intervention is required.It is important to note some of the limitations to our study. Here we have tested some of the more common factors associated with gNIS—namely reduced SNP panel, reduced call rates, and variable population sampling size. However, there may be other impacts on genotype accuracy caused by degraded DNA from non‐invasive samples. Null alleles, where heterozygous loci are incorrectly read as homozygous due to allelic dropout, is one example of this, and the degree of allelic dropout can vary between samples  and genetic  conservation interventions ; Formal analysis ; Investigation (lead); Methodology (supporting); Project administration (supporting); Writing – original draft (lead); Writing – review & editing (lead). Kasha Strickland: Formal analysis (lead); Investigation ; Methodology (lead); Software (lead); Validation (lead); Writing – original draft (supporting); Writing – review & editing . Romane H. Cristescu: Investigation ; Methodology ; Resources (supporting); Supervision (supporting); Writing – review & editing . Jonathan Hanger: Data curation (lead); Project administration ; Resources ; Writing – review & editing . Deidre de Villiers: Data curation (lead); Project administration ; Resources ; Writing – review & editing . Céline H. Frère: Conceptualization (lead); Funding acquisition (lead); Investigation ; Project administration (supporting); Resources ; Supervision (lead); Validation ; Writing – original draft ; Writing – review & editing .Appendix S1Click here for additional data file.