This study aimed to reveal the genetic variability ofmitochondrial DNA (mtDNA) displacement-loop (D-loop) region in 62 animalsbelonging to three native Turkish cattle breeds, namely Anatolian Black(AB), East Anatolian Red (EAR) and Zavot (ZAV), and to conduct phylogeneticrelationship analyses to obtain deeper information on their genetic originand breeding history by comparison of 6 taurine and 11 indicinebreeds, together with 66 polymorphic sites, a total of 31 haplotypes, ofwhich 15, 10 and 6 were detected in AB, EAR and ZAV, respectively. Meannucleotide and haplotype diversity were 0.01 and 0.891, respectively,whereas the genetic differentiation derived from Wright's  Therefore, the government initiated to import of globallywidespread high-production (cosmopolitan) cattle breeds such as HolsteinFriesian (HF), Brown Swiss (BS), Simmental (SIM), Jersey (JER), Hereford(HER) and Aberdeen Angus (ANG) from other countries between 1925 and 1970. Compared to native Turkish cattle breeds, farmers haveadopted cosmopolitan ones for their higher yield capacities . This preference has decreased effective population size and geneticdiversity among native Turkish cattle breeds . Genetic diversity, on the other hand, plays a crucial role in theadaptation process against changing environmental conditions such as climatechange, diseases, drought, forage and water scarcity. Indeed, today,priority is given to native livestock populations in conservation programmesacross the world since local breeds may carry unique genotype combinationsand genetic variants, which give opportunities to breeders to conductselection practices against these environmental challenges .In Türkiye, cattle farming is a part of daily life in rural areas to meetsociety's demand for milk and beef. Originated from Bos primigenius) diverged into two different genetic strains such as Bos taurus  and Bos indicus  . Although these strains differ in morphology, physiology, behaviour,and genetics, they could be easily distinguished by morphology in which Bos indicus isof hump phenotype, while Bos taurus is phenotypically humpless. mtDNA enablesscientists to assess genetic diversity and estimate the admixture level oftaurine and indicine in local cattle breeds since numerous sequence datadeposited in the GenBank could be used for phylogenetic relationshipanalyses. In this regard, this study aimed to reveal genetic diversity,haplogroup classification and maternal admixture level in three nativeTurkish cattle breeds via mtDNA D-loop region.Thanks to developing genotyping methods and statistical approaches, geneticdiversity could be easily estimated at different levels, such as from a single gene  to genome-wide in cattle. Of these, mtDNA shows cytoplasmic inheritance,meaning that approximately 16 340 base pairs (bp) length of geneticinformation is passed to the next generations only by the maternal line. Thisunique type of inheritance has allowed scientists to clarify cattleevolution, domestication and migration processes .Moreover, the mtDNA D-loop region has been analysed to reveal geneticdiversity in various cattle breeds since the mutation rate is highercompared to nuclear DNA . Via domestication, thewild auroch  and deposited into GenBank with accessionnumbers ON807611–ON807672.An 1138 bp fragment of mtDNA was amplified using primers CAP-F:52.3Most commonly preferred genetic diversity parameters such as nucleotidediversity  to explain the genetic origin and breedinghistory of native Turkish cattle breeds via phylogenetic relationshipanalyses. Neighbour-joining (NJ) and MJ analyses were utilised to visualisethe distribution of the Anatolian cattle samples into taurine, indicine andoutgroup clades. NJ tree analysis was conducted per individual and breedlevels based on the Jukes–Cantor method with 1000 bootstrap replicates viaMEGA 11 software . MJ analysis was performed by PopART1.7 software  with default parameters. DnaSP v.6 was used to perform haplotype classification to deepenknowledge of the genetic background of Anatolian cattle by comparison ofeach sequence with the haplotypes reported in 6 taurine and 11indicine cattle breeds simultaneously. The level of admixture among taurine,indicine and Anatolian cattle breeds was estimated based on the observedproportion of shared haplotypes. The detailed information on the unique andshared sequences between Anatolian and other cattle (taurine and indicine)breeds is summarised in File S3.A total of 125, 70 and 3 sequences belonging to cosmopolitan taurine, indicine  and outgroupspecies  were retrieved from GenBank database and summarised in File S2. The geographic origins of taurine samples were Argentina (ANG and HER),Israel , Canada , New Zealand (HF andJER) and USA (JER). In contrast, indicine samples originated from India, Myanmar,Iran, and Iraq. These sequences were combined with our 62 sequences andoptimised at 450 bp via the CLC Sequence Viewer 8 program were detected with a mean of 0.010 and0.891 nucleotide and haplotype diversity, respectively. The average NPS andNMS were 66 and 831, across all breeds. At the breed level, the highestgenetic diversity parameters were observed in AB, except for NPS. Althoughthe number of haplotypes was comparatively low, the highest NPS was detectedin ZAV due to higher sequence variations (Table 1).All detected haplotypes turned out to be breed-specific (File S1), of which three haplotypes belonging to the AB breed  were common in two geographiclocations known as Kütahya and Antalya. The highest and lowest number ofhaplotypes were detected in AB  assigned 31 haplotypes of native Turkish cattle into 6main clades  (Table 2) as well as severalsub-clades such as T1b, T1b1, T1c, T2b, T3d, T3h, T3o, T3p and T3q(File S1). Of these haplogroups, T1, T3 and Q were detected inall breeds, whereas haplogroup T2 was present in both AB and EAR. HaplogroupT and I2 were detected only in AB and ZAV, respectively. T3 was the mostcommon haplogroup (27 samples), while haplogroup T was detected in a samplefrom the AB breed.It is known that indicine cattle breeds possessing hump phenotype carry twomain haplogroups known as I1 and I2, according to mtDNA D-loop information. Surprisingly, haplogroup I2 was detected in ahaplotype  belonging to the ZAV breed, whichis phenotypically humpless. In order to deepen knowledge of haplogroup I2,all haplotypes were screened via MJ network analysis in which the haplotypebelonging to I2 differed from the nearest haplotypes (Hap_2and Hap_19) by 40 mutation variations .3.2Benefitting from 260 sequences  optimised at 450 bp length, the NJtree clearly separated outgroup, taurine and indicine sequences at bothindividual  and breed levels . At the individual level,AB and EAR samples were clustered with taurine samples, particularly HF, BS,and SIM breeds . However, four samples of ZAV  were assigned to the indicine clade located between Iranian andBhutanese samples . Similarly, NJ tree analysis clustered AB andEAR together close to HF, BS and SIM at the breed level . On theother hand, ZAV, which is still closer to taurine than indicine clade, wasgenetically different from native Turkish and cosmopolitan cattle breeds.The results of NJ tree analyses were confirmed by MJ analysis, in which allsamples of AB and EAR were connected with taurine haplotypes in at least onemutation event . However, Hap_26, detected in foursamples of ZAV, was assigned to the indicine group.All sequences belonging to native Turkish cattle breeds were compared tocosmopolitan taurine and indicine breeds to detect the status ofuniqueness and taurine and indicine admixture into Turkish cattle(File S3). A total of 15 (62.50 %), 6 (26.09 %) and 2(13.33 %) sequences were specific to AB, EAR and ZAV breed, respectively(Table 4). No shared haplotypes were detected between AB and HER breed,while sequence similarity between native Turkish and cosmopolitan taurinebreeds ranged from 9.56 % (HER) to 57.14 % (HF). Furthermore, no sharedindicine sequences were detected in AB and EAR breeds, whereas four sequencesof the ZAV breed were also present in three indicine breeds , indicating a low indicine admixture level (8.89 %) inZAV (Table 4).44.1Despite the low number of samples, a relatively high level of geneticdiversity was detected in native Turkish cattle breeds in which AB was ofthe highest nucleotide and haplotype diversity. Indeed, AB is the mostgeographically distributed and preferred among native breeds, while ZAV israised in the limited region of eastern Türkiye. As expected, nucleotidediversity was higher in native Turkish cattle breeds than in a previousstudy conducted on several cosmopolitan cattle breeds  . The long-term of artificial selection practicesto increase phenotypic performance, such as milk yield, could lead toreduced genetic diversity and the extension of genomic homozygosity in purecosmopolitan cattle breeds . Similarly, geneticdiversity parameters were higher in Anatolian cattle compared to severalIndonesian (Sragen Black) and Chinese  cattle breeds  due to geographic proximity of Türkiye todomestication centre. Naturally, genetic diversity is expected to decline asthe distance from the domestication centres increases, mainly because asmall percentage of the gene pool is distributed to other regions byhuman-mediated migration. High genetic diversity was also reported byDoğan et al. (2017), who revealed mtDNA diversity in all native Turkishcattle breeds  highlighted that T3 was the mainhaplogroup in European cattle. Results of higher genetic diversity, as wellas haplogroup distribution and archaeological evidence, support the ideathat taurine cattle were introduced to Europe from the domestication centrevia different routes, among which Anatolia is both a part of the FertileCrescent and has always been a trade route between Asia and Europecontinents throughout the history. Therefore, it is more likely that taurinecattle were introduced to several countries by human-mediated migration fromthe Middle East to Europe via the Anatolia route.4.2Bos taurus. Phylogeneticrelationships between native Turkish and other cattle breeds were alsoassessed by Özdemir and Dogru (2009) and Doğan et al. (2017). NativeTurkish cattle breeds were reported to be clearly distinct from cattlebreeds raised in India, Africa and Japan, while they were located close toEuropean cattle breeds such as HF, SIM, BS and HER . Indeed, in the present study, a high level of shared sequences wasdetected between native Turkish cattle and cosmopolitan breeds, particularlyHF (57.14 %). It is not surprising because with the introduction ofcosmopolitan cattle breeds to Türkiye in 1925, farmers, who adopted to raisehigh yielding breeds, initiated non-systematic crossbreeding practicesbetween native and cosmopolitan breeds in order to increase milk and beefyield. This kind of rearing may lead to genetic admixture and gene flowbetween native Turkish and European-origin cattle breeds. Xuan et al. (2010)also preferred mtDNA D-loop variation to reveal the phylogeneticrelationships in Romanian cattle breeds  by comparison with several taurine andindicine samples from GenBank. As detected in the current study, the authorsreported that Romanian cattle breeds clustered with taurine cladeparticularly close to HF, SIM and Grey cattle rather than indicine samples.All phylogenetic relationship analyses revealed three distinct clusters:taurine, indicine and outgroup, in which native Turkish cattle breeds wereassigned to the taurine clade. This finding confirms the genetic origin ofnative Turkish cattle breeds which originated from Indicine admixture (10 %) was previously reported in EAR breed via mtDNAdata by Edwards et al. (2007), while this paper is the first report todetect indicine admixture (8.89 %) in ZAV breed by mtDNA D-loop region.Hap_26, detected in four ZAV samples, was assigned to anindicine-specific haplogroup known as I2. Several controversial hypothesesare available about the genetic origin of ZAV in the literature. Historicalrecords indicate that ZAV, which is a crossbreed, was introduced to Ardahanand Kars provinces by human migration from Tsarist Russia between 1850–1900. Additionally, it is reported that ZAV wascrossed with Ukrainian Steppe, Simmental, Brown Swiss and East Anatolian Redduring both the same period and geographic locations . Here, we describe analternative explanation of the presence of I2 haplogroup and the geneticbackground of the ZAV breed.Although taurine and indicine cattle were domesticated at different timesand geographic locations, it is known that they were introduced to othercountries by human migration, resulting in crossbreeding events betweentaurine and indicine cattle in several geographic locations. Iran and Iraqare home to several indicine cattle breeds such as Sistani, Mazandarani, Taleshi,Najdi and Jenoubi . The presence of an indicine-specific haplogroup (I2) impliesthe possibility of crossbreeding events between ZAV and indicine cattlebreeds in the past. Indeed, with the geographic distribution of the ZAVbreed, previous studies on cattle evolution and migration routes  enable us to conclude that the crossbreeding possibility of ZAVand indicine Iranian cattle populations seems logical. This possibility mayallow the ZAV breed to become genetically different from the other nativeTurkish cattle breeds and possess haplogroup I2.5In this study, genetic variations and haplogroup distribution were assessedin three Anatolian cattle breeds via mtDNA D-loop region in which a highlevel of genetic diversity was observed. This finding supports the idea thatcattle populations geographically close to the domestication centre not onlyconserve more ancestral genetic variations but also they are a gene pool forthe other cattle breeds raised across the world. Conservation of highgenetic diversity will be helpful for animals to face environmentalchallenges and allow farmers to carry out different selection practices inthe future. Besides, phylogenetic relationship analyses revealed thatAnatolian cattle shared genetic similarities with cosmopolitan taurinebreeds at the mtDNA level due to originating from the same ancestor andcrossbreeding practices. However, indicine-specific haplogroup I2 wasdetected in ZAV for the first time, which should be further analysedtogether with Iranian and Iraqi cattle breeds to clarify gene flow andmigration. Besides, the farmers keep no pedigree records for ZAV breed,which prevents the scientists from estimating the effective population sizeand detecting unrelated animals for molecular studies. Moreover, pedigreerecords could be updated by farmers in order to achieve breeding andselection practices as well as to prevent genetic erosion in the ZAV breed.Unfortunately, mtDNA analyses in native Turkish cattle breeds are stillscarce. Moreover, present studies are limited by mtDNA D-loop regioncorresponding to a small part of mtDNA. Here, we highly recommend furtherstudies focusing on entire mitogenome diversity with a higher sample size toclarify not only genetic diversity in cattle breeds but also to enlightencattle domestication and migration process.10.5194/aab-66-31-2023-supplementThe supplement related to this article is available online at: https://doi.org/10.5194/aab-66-31-2023-supplement.


Schistosoma mekongi, a blood fluke that causes Asian zoonotic schistosomiasis, is distributed in communities along the Mekong River in Cambodia and Lao People’s Democratic Republic. Decades of employing numerous control measures including mass drug administration using praziquantel have resulted in a decline in the prevalence of schistosomiasis mekongi. This, however, led to a decrease in sensitivity of Kato–Katz stool microscopy considered as the gold standard in diagnosis. In order to develop a serological assay with high sensitivity and specificity which can replace Kato–Katz, recombinant S. mekongi thioredoxin peroxidase-1 protein (rSmekTPx-1) was expressed and produced. Diagnostic performance of the rSmekTPx-1 antigen through ELISA for detecting human schistosomiasis was compared with that of recombinant protein of S. japonicum TPx-1 (rSjTPx-1) using serum samples collected from endemic foci in Cambodia. The sensitivity and specificity of rSmekTPx-1 in ELISA were 89.3% and 93.3%, respectively, while those of rSjTPx-1 were 71.4% and 66.7%, respectively. In addition, a higher Kappa value of 0.82 calculated between rSmekTPx-1 antigen ELISA and Kato–Katz confirmed better agreement than between rSjTPx-1 antigen ELISA and Kato–Katz . These results suggest that ELISA with rSmekTPx-1 antigen can be a potential diagnostic method for detecting active human S. mekongi infection. Schistosoma [Schistosoma spp. and 800 million individuals at risk of infection [S. mansoni, S. haematobium, S. intercalatum, S. japonicum and S. mekongi [S. japonicum and S. mekongi are the two species causing Asian zoonotic schistosomiasis. S. japonicum is endemic in the Philippines, the People’s Republic of China and parts of Indonesia [S. mekongi is found in Cambodia and Lao People’s Democratic Republic (Lao PDR) [Schistosomiasis, also known as bilharzia, is a parasitic disease caused by blood-dwelling flukeworms or flatworms, belonging to the class Trematoda and genus istosoma . This renfection ,3. Human mekongi . S. japondonesia ,6. On thLao PDR) . It has Lao PDR) .S. mekongi infection is based on the simple and low-cost Kato–Katz technique which detects schistosome eggs in stool samples and has been recommended as the “gold standard” by the World Health Organization (WHO) [S. mekongi infection in Lao PDR and Cambodia.Schistosomiasis mekongi was discovered in Lao PDR and Cambodia fifty years ago, and until now, the disease is still a public health concern for residents in the endemic area ,8,9. Theon (WHO) . The Katon (WHO) . Howeveron (WHO) . As suchon (WHO) ,13,14. Ion (WHO) ,16. TherS. japonicum infection in humans and animals in the locations where the disease has been nearly eliminated [It has been reported that ELISA with recombinant antigen showed higher sensitivity than that of the Kato–Katz technique for detecting human schistosomiasis ,18,19. Siminated ,20,21,22S. japonicum (SjTPx-1), has been identified as a good antigen in ELISA for detecting human [S. mekongi was cloned and the recombinant protein was expressed. The recombinant protein was evaluated for its diagnostic performance as an antigen in ELISA for detecting S. mekongi infection in humans.Thioredoxin peroxidase-1, which is expressed in the tegument and as the excretory/secretory products of adult worms and larvae of ng human ,24 and ang human ,25. In tOpisthorchis viverrini and 5 serum samples from Japanese patients infected with Paragonimus westermani.Archived serum samples collected from schistosomiasis mekongi patients and individuals in the disease non-endemic area in Cambodia by National Center for Parasitology, Entomology and Malaria Control, Ministry of Health in Cambodia, were used. Twenty-eight serum samples were collected from endemic areas in Kratie province, with patients whose infections were confirmed by the Kato–Katz technique. Thirty endemic negative serum samples were collected from individuals in Phnom Penn . Thirty S. mekongi has not yet been available. The sequence information of the gene coding for TPx-1 of S. mekongi (SmekTPx-1) was retrieved from the S. mekongi genome project which is currently underway in collaboration with the Research Center for Zoonosis Control (CZC), Hokkaido University and Veterinary School of Rakuno Gakuen University, Japan (unpublished data). The coding sequence of TPx-1 of S. japonicum (SjTPx-1) was obtained from GenBank accession no. AB126036.2).So far, the whole genome information of S. mekongi adult worms [SmTPx-1 with BamHI and XhoI restriction enzyme sites (underlined) as follows: the forward (5′-GC GGA TCC ATG GTA CTT CC-3′) and reverse primers (5′-GC CTC GAG TTA GTG ATT AGT TTT AAT TC-3′), respectively. The PCR product was cloned into pCR 2.1-TOPO vector . The identity of the cloned sequence with the sequence in the draft genome was confirmed by sequencing using an ABI Prism 3100 Genetic Analyzer . The coding sequence was inserted into pET28 vector  and the plasmid was transformed into Escherichia coli Rosetta (DE3) (Novagen) and was grown in LB medium  supplemented with 50 µg/mL of kanamycin. Induction of the expression of the recombinant proteins with 0.5 mM isopropyl-thio-β-D-galactoside (IPTG) was done in the SOB medium  and then maintained at 37 °C for 3 h. Using nickel nitrilotriacetic acid (Ni-NTA) agarose , the recombinant protein was recovered and purified, followed by elution and dialysis with 20 mM Tris, pH 8.0, according to the manufacturer’s instructions. The quality of the protein was then evaluated using 12% polyacrylamide gel electrophoresis (SDS-PAGE) while the quantity was measured using a bicinchoninic acid (BCA) assay kit . The recombinant proteins were stored in aliquots at −80 °C until used.The recombinant protein of SjTPx-1 was expressed as previously described in . For explt worms  using TRIndirect ELISA was performed as described in our previous studies ,23. In bhttps://www.medcalc.org/calc/diagnostic_test.php (accessed on 12 August 2022). An agreement between the ELISA and the Kato–Katz technique for detecting the patients was estimated as a Kappa value [The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the recombinant antigens were calculated using the online software MedCalc at pa value .S. mansoni TPx-1 (GenBank accession no. AF121199.1) (The coding sequence of SmekTPx-1 was successfully amplified and sequenced. The 555 bp of the sequence coded a protein comprising 184 amino acids. SmekTPx-1 showed 94.8% identity with the coding sequence of SjTPx-1 (GenBank accession no. AB126036.2). SmekTPx-1 showed 81.8% identity with the coding sequence of 21199.1) .The coding sequence of recombinant antigen SmekTPx-1 was successfully expressed as a 6xHis-tag fusion protein and was assessed by SDS-PAGE. The SDS-PAGE and Western blotting experiments confirmed rSmekTPx-1 with the expected molecular weight of 22 kDa including the 6x-His tag  for detecting the infection in mice. The rSmekcatB showed 91.7% sensitivity and 100% specificity against the mice infection; however, the antigen was not evaluated for its potential against S. mekongi infections in humans [Several recombinant antigens have been evaluated for their diagnostic potential for human and animal schistosomiasis ,23,29. Rcificity . Anotherfections . Severalfections . In contantigens ,33. An icificity . The poiechnique . Nonetheechnique ,37. Sangn humans .S. japonicum, including SjTPx-1, SjTPx-2, SjTPx-3 and SjPrx-4 [S. mansoni [S. japonicum has been reported to be a potential antigen with high sensitivity and specificity in ELISA [S. mekongi was cloned and the recombinant protein was expressed for evaluation of its diagnostic potential as an antigen in ELISA. The amino acid sequence of S. mekongi TPx-1 showed high identity of 93.5% with S. japonicum TPx-1, suggesting a close relation between the two species according to the previous studies [TPxs are members of the peroxidase family, important for antioxidant defense in schistosome parasites . TPxs pr SjPrx-4 ,41. Thre mansoni ,42. Amonin ELISA ,22,24. Tin ELISA . In the  studies ,44,45,46S. mekongi infection [S. mekongi (SmekSEA) has been used in ELISA for the diagnosis of patients with 100% sensitivity and specificity [S. mansoni showed 94.5% sensitivity in ELISA for diagnosis of S. mekongi infection in humans [O. viverrini TPx-1 should be compared in order to modify rSmekTPx-1 by removing the peptide sequence with high identity between these parasite species which may trigger the cross-reaction.The rSmekTPx-1 antigen showed good diagnostic performance in ELISA with high sensitivity, specificity, PPV, NPV and Kappa value against the Kato–Katz technique . Althougnfection . The solcificity . Howevercificity ,49. Nickn humans . Howevern humans . In addin humans . In thisSmekTPx-1, the gene coding for S. mekongi TPx-1, was cloned and the recombinant protein, rSmekTPx-1, was successfully expressed. The rSmekTPx-1 antigen showed higher sensitivity and specificity as compared to those of the rSjTPx-1 antigen in ELISA with a panel of serum samples. A good agreement between ELISA with rSmekTPx-1 antigen and Kato–Katz suggested that the ELISA can detect an active infection, which requires treatment with praziquantel. The results suggested that rSmekTPx-1 can be a potential antigen in ELISA for diagnosis of schistosomiasis mekongi in the regions where the disease is still endemic. Field evaluation should be done to determine the performance of the rSmekTPx-1 ELISA in areas with low prevalence in the future study.In the present study,


Malus orientalis Uglitzk.) is rich in valuable traits, such as later blooming, adaptation to a wider array of habitats, and capacity for longer storage of the apples, that should be considered important to the genetic makeup of the domestic apple. Here, the morphological diversity of this species was evaluated. There were significant differences among the accessions studied as revealed by the recorded traits. Ripening date ranged from late April to mid‐May. Fruit skin ground color showed strong diversity, including light cream, cream, yellow, light green, and green‐white. Also, fruit skin over color was highly variable, including white, cream, yellow, light green, and green‐white. Fruit weight ranged from 3.45 to 16.74 g. Principal component analysis (PCA) showed 13 PCs which contributed to 83.30% of total variance and fruit‐related characters were the most effective traits for separating and identifying the studied accessions. The Ward dendrogram reflected the similarities and dissimilarities among the accessions based on the qualitative and quantitative variables measured. A high phenotypic diversity within the collected material of M. orientalis was indicated. The diversity existing in the indigenous wild M. orientalis could further add new genetic information in the global gene pool of Malus species. The present study confirmed the necessity of preserving this unique genetic resource and continuing its study no matter the fact that in practice.Oriental apple ( Malus orientalis Uglitzk. was shown that could further add new genetic information in the global gene pool of Malus species. The present study confirmed the necessity of preserving this unique genetic resource and continuing its study no matter the fact that in practice.The diversity existing in the indigenous wild M. orientalis could further add new genetic information in the global gene pool of Malus species. The present study confirmed the necessity of preserving this unique genetic resource and continuing its study no matter the fact that in practice.A high phenotypic diversity within the wild collected material of  The lowest CV belonged to leaf length (11.42%), fruit stalk diameter (11.46%), leaf shape index (12.23%), and seed length (12.83%) , in agreement with Khadivi et al.  who obse90.91%, i%) Table  who obse2. Petiole length varied from 15.65 to 35.62 mm, and petiole width ranged from 1.15 to 2.19 mm , while weeping (6), open 13), and semi‐erect (3) habits were also observed. Tree growth vigor, tree height, branch flexibility, trunk diameter, and leaf density were predominantly moderate. Branching and branch density were predominantly high. Canopy density was low (2 accessions), moderate (21), and high (22). Tendency to form suckers was low (15), moderate (16), and high (14). Leaf shape was predominantly lanceolate (30), while oblong (4) and ovate (11) shapes were also observed. Leaf base shape was round in 15 and triangular in 30 accessions. Leaf serration depth was low (25) and moderate (20). Petiole color was light green (31), green (10), and pink (4) , and 8 accessions in mid‐May. Fruit yield was low (3 accessions), moderate (22), and high (20). Oblate fruit shape was predominant (27 accessions), while flat (6) and globular (12) were also observed. Fruit apex shape was predominantly flat (43). Fruit symmetry was present only in 10 accessions, while fruit was asymmetric in 35 accessions. Fruit skin ground color showed strong diversity, including light cream (3 accessions), cream (2), yellow (24), light green (15), and green‐white (1). Also, fruit skin over color was highly variable, including white (3 accessions), cream (5), yellow (10), light green (26), and green‐white (1). Color of spots on fruit skin was white (16 accessions), yellow (8), and green (21). Fruit taste was predominantly sour‐sweet (34 accessions), and fruit quality was high in most of the accessions (27) Table  reportedr et al.  reportede et al.  recordedi et al.  reportedM. orientalis accessions from the Isfahan province, Iran.One to five seeds were observed per fruit and were low in 39 and moderate in six accessions. Seed size was medium in the majority of accessions (28). The range of seed‐related characters was as follows: seed length, 4.15–7.30 mm; seed width, 2.63–4.36 mm; and seed thickness, 1.05–2.46 mm. Khadivi et al.  reportedM. orientalis accessions from the Isfahan province, Iran. The PC1 explained 15.83% of total variance and was represented by fruit length (0.93), fruit width (0.89), fruit weight (0.88), endocarp diameter (0.73), mesocarp diameter (0.78), seed size (0.73), seed length (0.68), and seed thickness (0.77) with positive correlations. The PC2 explained 9.26% of total variance and was constituted by branching (0.92), branch density (0.86), trunk diameter (0.56), canopy density (0.76), leaf density (0.69), and fruit yield (0.62) with positive correlations. The PC3 accounted for 7.68% of total variability and was represented by leaf shape (0.90), leaf base shape (0.93), leaf width (−0.72), leaf shape index (0.59), and leaf area (−0.56). These characters were the most effective traits for separating and identifying the studied accessions. The PCA has been widely used to analyze the phenotypic diversity of apple  was used to identify the patterns of variability among the accessions studied. For PCA, components with eigenvalues more than 1.00 were retained to uphold reliability of the final output. Thus, 13 PCs were observed which contributed 83.30% of total variance and the first three PCs explained 32.76% of the variance Table  reportedThe scatter plot prepared according to the PC1 and PC2  reflected the relationship among the accessions in terms of phenotypic characteristics. The plot distributed accessions into four sides and showed significant differences for some traits Figure . ClusterM. orientalis collections from different countries . For instance, several wild accessions of M. orientalis with resistance to apple scab  and powdery mildew (Podosphaera leucotricha) , apple scab, and cedar‐apple rust (Gymnosporangium juniperi‐virginianae) have been revealed (Volk et al., 4M. orientalis with the goal of enhanced germplasm representation in gene banks. A high phenotypic diversity within the collected material of M. orientalis was indicated. The diversity existing in the indigenous wild M. orientalis could further add new genetic information in the global gene pool of Malus species. The present study confirmed the necessity of preserving this unique genetic resource and continuing its study no matter the fact that in practice. The conservation of a wide range of genetic variability and progress in long‐term breeding programs should be the main goal because greater genetic diversity would be desirable for future development of innovative and market‐driven cultivars.The current plant expedition targeted wild The authors declare no conflict of interest.


Before about 1990, insofar as diagnostic and other medical tests were subject to regulatory oversight, it was chiefly to ensure that they met appropriate standards of analytic and clinical validity. Over the course of the 1990s, however, regulatory reformers in the United States began to argue that genetic tests, specifically, should also be assessed to determine whether or not they actually benefit those undergoing testing—whether they possess “clinical utility”, as they put it. The present paper asks why this shift in regulatory focus occurred specifically in relation to genetic tests, and why clinical utility became a key object of assessment. It answers these questions by situating concerns about genetic tests in the longer history of medical genetics. Looking back to the 1970s and medical geneticists' efforts to distance themselves from their earlier association with eugenics, it shows that they adopted a particular framing of the dangers of genetic testing which would inform their response to the proliferation of new genetic tests and the growth of commercial testing in the 1990s. In a series of policy committees convened over the course of that decade, medical geneticists called for regulatory measures to be implemented to ensure that genetic tests were only introduced into medical practice if they had been shown to be beneficial to those tested. The paper follows the deliberations of those committees to show in detail how geneticists worked within this framing to accommodate new technical capacities and regulatory opportunities. In the course of these deliberations, they adopted the idea of clinical utility to signify the need for evidence of benefit specifically to those tested. The paper concludes with some observations regarding how this framing of genetic tests relates to current understandings of “genetic exceptionalism” and to more recent articulations of clinical utility. •Calls to regulate genetic tests on grounds of “clinical utility” first emerged in the 1990s.•They were informed by a framing of the risks of genetic testing that dated back to the 1970s.•That framing stressed the need for tests to benefit the individuals undergoing testing.•Calls for formal regulation reflected geneticists' declining control over genetic testing.•More recent versions of “clinical utility” neglect the need for testing to benefit those tested. Other than that, regulatory agencies generally assumed that medical practitioners were best left to decide for themselves, on the basis of their own knowledge and experience, whether and when to use such tests. Over the course of the 1990s, however, American medical geneticists began to argue that genetic tests, specifically, should also be assessed to determine whether or not they actually benefit those undergoing testing—whether they possess what came, in the course of these debates, to be called “clinical utility”. While these efforts had only limited impact on the statutory regulation of genetic tests, the idea of assessing their clinical utility has been widely adopted in the formulation of professional clinical guidelines, as well as by health technology assessment bodies charged with deciding whether new genetic tests are worth implementing in practice. The turn to utility as a key assessment criterion thus represents a significant shift in ideas about the extent of medical competence and the role of regulation in relation to genetic testing.Building Genetic Medicine  has been understood from the 1950s to be a recessive single-gene disorder. Individuals who inherit two defective copies of the gene—one from each parent—develop the disease; while those who inherit only a single defective copy are unaffected carriers. This knowledge offered limited opportunities for medical intervention. In families where medical geneticists were able to map the inheritance of the disease, they could often calculate the probability that any given individual within that family was a carrier, and so could advise that individual on the risks of having an affected child. Given the rarity of CF mutations in the general population, however, most cases were diagnosed before any family history of the disease became evident. Consequently, efforts to mitigate the effects of CF generally focused on improving treatment options for children already diagnosed with the condition, rather than on its genetic aspects , the US National Academy of Sciences established a Committee for the Study of Inborn Errors of Metabolism—a large class of usually single gene disorders, many of which become apparent quite early in life. Prompted by questions about the effectiveness of newborn screening for phenylketonuria, which had been rolled out—often on a mandatory basis—in a growing number of American states since the mid-1960s, as well as by controversies about screening for sickle-cell anaemia, Tay-Sachs disease, and birth defects such as Down syndrome, the Committee was charged to report on “the problems and difficulties [arising from these initiatives] and give some procedural guidance, in order to minimise the shortcomings and maximize the effectiveness of future genetic screening programs”  of bearing a child with cystic fibrosis” , p. 70. Genetic counselling would help those who underwent cystic fibrosis carrier testing to make sense of their results. But the sheer volume of testing that would result from population-wide CF carrier screening would far exceed the capacity of existing genetic services to provide such counselling. As early as 1983, when the development of cystic fibrosis carrier tests first began to look like a realistic prospect, medical geneticists had warned that “If a test becomes available to identify these carriers, the demand for genetic screening and counselling could quickly become overwhelming” , p. 5. WGiven these concerns, in November 1989 the American Society of Human Genetics (ASHG) adopted a statement that while “it will be appropriate to begin large-scale population screening in the foreseeable future”, it should not be rolled out until “the test detects a larger proportion of CF carriers and more information is available regarding the issues surrounding the screening process” . By the 1.2While professional self-restraint was sufficient to stall the introduction of cystic fibrosis carrier screening, medical geneticists also began exploring other, more formal means of regulating genetic tests. Besides calling for deferral of population screening, the ASHG's 1989 statement on CF carrier testing also declared “an immediate need for centralized quality control of laboratories conducting these tests” . In so dWall Street Journal and other US media outlets ran a series of reports about laboratories returning unacceptably high rates of inaccurate or misleading results for cervical smears and other medical tests. In 1988, Congress responded by passing the Clinical Laboratory Improvement Amendments (CLIA88), which aimed to strengthen and extend powers vested in the US Public Health Service, acting through the Centers for Disease Control (CDC), to impose quality assurance procedures on laboratories offering medical testing services , along with more stringent staffing requirements to ensure that laboratories possessed the technical expertise to conduct tests accurately. As we have seen, however, clinical geneticists took the view that accurate test results could still be harmful if incorrectly interpreted or understood. Their submissions to the consultation reflected this. As Thomas Caskey and Herbert Lubs of the ASHG observed, genetic tests have “unique features which require interpretation of data on a highly individualized basis for a specific family or patient. Detailed information about a specific patient or family, and a complex, computerized program is often required for such interpretation”. Consequently, they urged, laboratories undertaking genetic tests should be required to employ staff with recognised competence in medical genetics, so that test results could be accompanied by a proper interpretation of those results; while proficiency testing of participating laboratories should examine not just the analytical accuracy of test results but also the quality of the interpretations that accompanied them —part of the National Academy of Sciences—which in May 1990 set about establishing “a panel of experts to evaluate issues in the development, application, and use of tests for genetic disorders”. In proposing this enquiry, the IOM was moved by many of the same concerns that medical geneticists had raised about cystic fibrosis carrier screening. Where medical genetics had previously dealt primarily with individuals “known to be at risk because of their family history”, the IOM anticipated that developments in molecular genetics would soon make it possible “to detect abnormal genes directly, opening the floodgates for predictive genetic screening for the masses”. Among the diseases the IOM anticipated would be brought within the scope of predictive genetic testing were not just cystic fibrosis, but “schizophrenia, alcoholism, and certain cancers”—common health problems, with a far higher incidence than the rare monogenic conditions and birth defects that had previously occupied medical geneticists. “If these tests become more widespread,” the IOM noted, “it is doubtful that there will be enough health care professionals prepared to provide even the most basic counselling required to assist patient understanding” .The IOM did not initially include laboratory regulation among the topics to be considered by the Committee. But when the prospective members—mostly clinical and molecular geneticists, plus a number of lawyers, ethicists, and members of patient and civil society organisations—were canvassed on their views, it became apparent that they regarded “laboratory quality assurance, including the clouded regulatory climate” as a “focal issue” . The ComCLIA88 was a late addition to the Committee's agenda. But the IOM had another set of regulatory provisions in its sights from the start. Among the topics initially proposed for consideration was “the ability of the Federal [sic.] Drug Administration (FDA) to guide the evaluation of the efficacy, sensitivity and reliability of tests given the unique complexity of genetic disease” . This opThe US Food and Drug Administration had long been empowered to regulate the marketing of diagnostic tests, but exercised those powers with a notably light touch. FDA confined its regulatory oversight to tests sold as kits and to commercially-marketed reagents, leaving tests sold as laboratory services to be regulated under CLIA. And even where test kits were concerned, FDA sought as far as possible to work cooperatively with manufacturers rather than impose strict standards , p. 1812Manufacturers seeking approval for a diagnostic test were normally expected to provide evidence that it accurately measured what it claimed to measure, that the results were “clinically significant”, and that the numbers of false positive and false negative results it generated were within acceptable limits. They were not usually expected to provide information about how a test should be used , p. 775.This was precisely what medical geneticists doubted: most practitioners, they feared, were simply not equipped to interpret and communicate the results of genetic tests in ways which did not endanger their patients. The IOM Committee therefore recommended that FDA's premarket approval procedures for genetic tests should be enhanced to minimise that risk. Manufacturers should be required to indicate “the intended and potential use(s) of the test ”, and to provide relevant data “for each intended use”. Additionally, manufacturers should provide a “description to be given to health care providers and to patients regarding the objectives of the test and the interpretations of negative or positive findings”  and Department of Energy (DOE) budget for research into the ethical, legal and social implications (ELSI) of human genome science. Following publication of the IOM Committee's report, the NIH-DOE ELSI Working Group decided to set up its own Task Force on Genetic Testing to conduct further investigations and “when necessary, make recommendations to ensure the development of safe and effective genetic tests” , ch. 1. The successive committees charted a trajectory through the orbits of government policy-making, from the academic detachment of the IOM committee to SACGT in the office of the Secretary of Health and Human Services. At the same time, the balance of interests represented in the committees shifted to include a larger contingent of stakeholders and policy experts , p. 40. On the one hand, the Task Force and the SACGT were more sanguine than the IOM committee about the ability of non-geneticists to make safe use of genetic tests. “Even when aware that a problem that concerns them might have a genetic origin, they [patients] are more likely to seek the care of the specialist who manages the problem when it becomes overt than the care of a geneticist”, noted the Task Force. “Consequently, non-genetic specialists, as well as primary care providers, become the gateway to genetic testing.” But far from being feared, this should be welcomed. “With proper training and adequate knowledge of test validity, disease and mutation frequencies in the ethnic groups to whom they provide care, primary care providers and other non-genetic specialists can and should be the ones to offer predictive genetic tests to at-risk individuals”. Measures would need to be put in place to ensure that practitioners received the necessary training, including not just technical knowledge of genetics and genetic tests, but also a practical appreciation of “the means of communicating genetic concepts and risks to patients”. But the Task Force noted that such measures were already being implemented by a range of professional and policy bodies, and simply urged that these measures be encouraged and supported , ch. 4.On the other hand, the committee reports reflected growing concern at the accelerating commercialisation of genetic tests and testing. “Until the 1980s”, the Task Force observed, “most genetic and cytogenetic testing was performed in the laboratories of non-profit organisations, most of them in academic medical centers. These labs were often directed by the same professionals who cared for patients. In the last decade, genetic testing has been commercialized. As a result, providers who were close to patients and families at risk of illness might not have as much influence on testing policy as they once did” , ch. 1. As the committees' anxieties shifted to focus on the expanding commercial sector, so they looked increasingly to external regulatory agencies to mitigate those anxieties. This included repeating the IOM committee's calls to strengthen oversight of genetic testing laboratories under CLIA88: laboratories should be required, among other things, to return genetic test results “in a form that is understandable to the non-geneticist health care provider”, and to employ staff with adequate training and experience in genetics to provide such interpretations , ch. 3. and clinical utility” of genetic tests, and particularly screening tests  framework in 2000 . SubsequNeither the Task Force nor the SACGT specified just what kind of evidence would serve to demonstrate the clinical utility of genetic tests. Even the expectation that it include evidence of benefit to those tested, implicit in the committees' overall framing of the risks attending genetic testing, was rarely stated overtly. As the idea of assessing genetic tests for clinical utility was adopted beyond the confines of the policy committees, so it escaped that distinctive framing and began to acquire different connotations. Most notably, the emphasis on assessing harms and benefits to the individuals undergoing testing was progressively weakened. Thus CDC's EGAPP initiative called for “evidence that test results can change patient management decisions and improve net health outcomes ” , p. 6. IMeanwhile other organisations, including professional bodies and healthcare providers, have enacted more pervasive regulation of genetic tests through informal governance arrangements such as clinical practice guidelines and health technology assessment procedures . While mFaced with these changing conditions of practice, as well as the growth of direct-to-consumer genetic tests of little or no medical value, some social scientists have begun to argue that the concept of “utility” should be expanded to include the public good arising from research conducted on the genetic data accumulated by such means . This neSteve Sturdy: Conceptualization, Investigation, Methodology, Writing - original draft, Writing - review & editing.


Genomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs.1 or F2 stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F1 intercross (F1i) families and keeping the total number of progeny of F1i families constant, we observed a decrease in genetic gain and increase in genetic diversity, whereas increasing the number of progeny per F1i family while keeping a constant number of F1i families increased the rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F2 family phenotypes to the training population increased the accuracy of genomic breeding values (GEBVs) and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at FThe online version contains supplementary material available at 10.1007/s00122-022-04071-6. Lens culinaris ssp. culinaris), chickpea (Cicer arietinum), faba bean (Vicia faba), field pea (Pisum sativum), lupin (Lupinus) and mungbean (Vigna radiata) are grain legumes that are mainly produced for human consumption. These crops have been commercially grown in Australia since the 1970s, in rotation with cereals due to their ability to fix atmospheric nitrogen and management of weeds. Pulse crops are now mainstream in Australia’s cropping rotations and provide a valuable and sustainable source of protein (https://www.pulseaus.com.au). Australian pulses are in demand around the world for their quality, versatility and nutritional attributes . Most pulses produced in Australia are exported to countries around the world, holding an enviable reputation for quality in global markets. Pulse breeding in Australia aims to increase high-quality grain production through improving regional adaptation, yield potential, disease resistance, and tolerance to harsh environments (GRDC Pulses including lentil (nts GRDC .Genomic selection (GS) is an efficient selection method for improving quantitative traits in plant and livestock breeding by using high-density genome-wide markers often obtained by genotyping-by-sequencing  family number and size on genetic gain and genetic diversity loss per unit of time; and preserving genetic diversity while maximizing genetic gain.The objectives of this study were to investigate strategies for implementing GS in a lentil breeding program using a stochastic computer simulation. Using phenotypic selection  as a benchmark, we investigated: cycling parents back to the germplasm pool from different stages of the breeding cycle; testing the effects of F2 selfing in PS or intercrossing in GS, bulk-up (F3–F5 in PS or F3–F6 in GS), preliminary yield trial (PYT), Stage 1 trials, and Stage 2 trials   were generated from 150 inbred lines by random mating, where each inbred line was used up to 4 times. Three hundred F1 families were randomly selected with 20 F2 seeds per family. These F2 seeds were bulked up within F1 families until F5 (family-bulkup). Thirty per cent of single F5 plants were dropped based on low seed yield, and 800 lines of the remaining 70% were then randomly selected for the PYT stage. These selection pressures mimicked selection applied during bulk-up for a variety of breeder’s preferred traits with some impact on yield. In the PYT, lines were tested in a partial replication trial for disease resistance, grain weight, and grain yield, of which 400 lines were further selected based on phenotypic performance. In Stage 1, 400 lines were tested in a trial with 2 replicates at two environments and assessed for disease resistance, grain weight and grain yield, with the top-performing 200 lines being selected based on phenotypic performance. In Stage 2, 200 lines were tested in a trial with 2 replicates at two environments and assessed for disease resistance, grain weight, and grain yield, and 80 lines were selected based on phenotypic performance and used as the parents of the next crossing cycle.This simulation modelled a lentil breeding program with two main pathways phenotypic selection (PS) and genomic selection (GS). It consisted of 6 main steps: primary crosses, F1 intercrosses (F1i) were selected based on the optimal haploid value (OHV) . All possible crosses were simulated from 300 F1s and their OHVs were calculated. Two hundred F1is with the highest mean OHVs were selected. Each F1 intercross produced 10 seeds resulting in 2000 F1is in total. These 2000 F1is were simulated in silico for selfing to F8 through single seed descent (SSD) where 20 seeds were used at the F8 stage. The 200 F1is with the highest mean OHVs plus the standard deviation were selected for selfing. Twenty F2 seeds were harvested from each F1i; therefore, a total of 4000 seeds were available for the bulk-up step with two options: (a) single seed descent (SSD) with each F1i seed selfed to F6 (4 years in the field), (b) accelerated single seed descend (aSSD) with each F1i seed selfed to F6 in a glasshouse (1.5 years) to reduce the time for selfing. At the end of the bulk-up stage, 4000 lines were genotyped and based on GEBVs, 800 were selected for evaluation at the next stage (PYT). PYT along with subsequent stages i.e. Stage 1 and Stage 2 had the same trial settings as the phenotypic selection with the only difference being that genomic selection was applied at PYT and Stage 2.In the GS pathway, 300 primary crosses and 200 F6, F2 and F1 were compared to investigate the effect of shortening the breeding cycle on the rate of genetic gain.Parents of each new breeding cycle were selected from a germplasm pool. The germplasm pool consisted of 1568 lentil inbred lines that were used as the reference set in this study (details given below). Eighty inbred lines were selected from each breeding cycle and added to the germplasm pool. A hundred fifty parents initiating the primary crosses were selected from the germplasm pool, based on phenotypic performance (in PS) or GEBVs (in GS). The new inbred lines that were added to the germplasm pool were selected from Stage 2 in PS. In GS, three parental selection time points from FTranscriptome genotyping-by-sequencing (GBS) data from 1,568 lentil inbred lines (the base population) genotyped at Agriculture Victoria with 88,376 single nucleotide polymorphisms (SNPs) formed the base generation for the simulations. Missing genotypes were imputed using LinkImpute  interactions) and 0.4 for grain yield (a high level of G × E interaction)  of an individual  across the three most recent breeding cycles. GEBVs of individuals for traits under study were calculated based on the marker effects and their genotypic profiles. Bayesian ridge regression was implemented using R package BGLR (de los Campos and Pérez with Eq. :3\documeThe simulation was conducted for eight breeding cycles with 50 replicates for each scenario (defined below) and 150 parents at the beginning of breeding cycle 1 were resampled from the base population in each replicate. PS was conducted at breeding cycles 1–8 and GS was divergently conducted at breeding cycles 4–8, using the end of phenotypic cycle 3 as the starting point. Selection was conducted using a selection index  and the average genomic relationship of parental lines that were used to generate primary crosses at the beginning of each breeding cycle. An aggregate TBV variance was also calculated as the sum of TBV variance of disease resistance, grain weight and grain yield weighted by their selection index weights. The average genomic relationship was expressed as the mean off-diagonal of the genomic relationship matrix (GRM). The genomic relationship matrix was derived using the method proposed by VanRaden . Higher The prediction accuracy of GEBVs was defined as the correlation between GEBVs and TBV. When evaluating the prediction accuracy of GEBVs, phenotypes of individuals to be ranked were not used in estimating marker effects, which means that GEBVs were calculated using marker effects estimated at the previous breeding cycle.3-F6, and different stages, i.e. Stage 2 (STG2), F6, F2 or F1 for cycling parents into crossing.Scenarios for shortening generation intervals with phenotypic selection and genomic selection were investigated Table . GS scen1i family size on genetic gain and genetic diversity were investigated using scenarios defined by a different number of F1i families, i.e. 100, 200 and 400 to generate a final constant number of 4000 seeds. Other scenarios with a different number of seeds per F1i family, i.e. 20, 40 or 80 to make 4000, 8000 or 16000 seeds from 200 F1i families were also simulated.In the genomic selection scenario with accelerated single seed descent (aSSD), the effects of F2, the selection candidates become further remote from the training population due to a reduced cycle time. Therefore, a choice of phenotyping 50 F2 families was also included to add phenotypes to the training population more quickly, where the average phenotypic performance and marker genotypic values across the individuals per F2 family were used as the equivalent of one individual in the training population.The training population consisted of inbred lines chosen from the three most recent breeding cycles, 400 inbred lines per breeding cycle randomly chosen from Stage 2. In the accelerated scenarios that selected parents at Fλ1 is a penalty applied to the average relationship of selected individuals, λ2 is a penalty applied to λ1 = 1 and λ2 = 10–6 were compared with a scenario without penalties in aSSD where parents were selected from F2. The genetic algorithm was applied with a population size of 2000, with 1000 iterations, a mutation rate of 0.001 and 20% highest fitness subsets randomly crossed each with one point crossover. Penalties on λ1 and λ2 and parameters used in the genetic algorithm were defined by choosing from a range of arbitrary values with which the optimal output of genetic diversity and genetic gain were achieved.To maintain more diversity in the GS scenario, restrictions on the average relationship of the 150 selected parents and the number of fixed alleles within those parents were implemented with a genetic algorithm Holland . The fitsing Eq. , and \do2. A clear trend for increased genetic gain with decreased cycle time was observed from GS scenarios. GS increased genetic gain per year by 40% in SSD-STG2 scenario and 98% in aSSD-STG2 (with a family bulking-up in F3–F6) over PS. If parents were selected from F6 (at the end of bulking-up), GS increased genetic gain per year by 90% with SSD-F6 and 165% with aSSD-F6 over PS. No difference in genetic gain was observed between SSD and aSSD if parents were selected from F2 or F1 because SSD and aSSD were selected before bulk-up and with the same composition of the reference population. The additional genetic gain achieved in GS with SSD or aSSD with parents selected from F2 and F1 stage was 393% and 547%, respectively. Details of genetic gain achieved in each trait are given in Suppl Figure 1.All GS scenarios led to a higher rate of genetic gain when compared to PS  also increased TBV variance reduction, percentage of alleles fixed and genomic relationship than a normal bulk-up from F3 to F6 in GS (SSD).When selecting parents from STG2, GS scenarios led to a higher percentage of alleles fixed and higher genomic relationship than PS. Furthermore, GS with aSSD led to a higher percentage of alleles fixed and genomic relationship as compared to GS with SSD, while there was no difference in TBV variance decrease in PS versus GS as well as between GS with aSSD and GS with SSD  from F3 to F6 did not affect the level of genetic diversity loss. Within GS-aSSD scenario, the reduction in TBV variance was nearly two-fold when parents were selected from F2 as compared to the reduction observed when parents were selected from F6. Furthermore, when parents were selected from F1, the reduction in TBV variance was nearly three-fold of the reduction observed when parents were selected from F2. Selecting parents from F1 and F2 doubled and tripled the percentage of alleles fixed respectively when compared to selecting parents from F6 in GS-aSSD. Selecting parents from F2 and/or F1 resulted in a similar level of genomic relationship increase of 160% of the level observed when selecting parents from F6 in GS with aSSD.As observed in the aggregate genetic gain, when parents were selected from F2 and F1, the generation interval was reduced to 1 or 0.5 years, respectively, and phenotypes for estimating marker effects in breeding cycles 4 to 8 all came from breeding cycle 3. As a result, the accuracy of GEBVs decreased from breeding cycle 4 to breeding cycle 8. When parents were selected from F6, phenotypes generated at breeding cycle 4 were available for estimating marker effects at the end of breeding cycle 6; therefore, the accuracy of GEBVs increased from breeding cycle 7. When using the same training population, for example, at breeding cycle 4, the accuracy of GEBVs was the highest when parents were selected from F2 and the lowest when parents were selected from STG2.The accuracy of GEBVs for grain yield was lower than that for disease resistance and grain weight, as grain yield had low heritability and a high level of G × E interactions Fig. . The acc1i individuals) generated from a varying number of F1i families  and a varying number of seeds  per F1i family from a fixed number of F1i families i.e. 200. For a constant number (N = 4000) of F1i individuals, increasing the number of F1i families and decreasing the number of seeds within family led to lower aggregated genetic gain and less genomic relationship per year  and having more progeny helps sort out the best GEBVs.Two scenarios were investigated: a constant (fixed) number of 4000 seeds (F1i families (N = 200), increasing the number of seeds from each F1i led to higher aggregated genetic gain and higher loss of genetic diversity per year only when parents were selected at F2 , adding additional phenotypes obtained from F2 families to the training population significantly increased genetic gain for each trait . The magnitude of genetic diversity loss per unit of time was reduced in terms of TBV variance reduction, percentage of fixed alleles and the average relationship among parents; however, the genetic gain was also reduced, compared to the scenarios without applying the genetic diversity preservation strategy , the aggregated TBV variance loss reduced from 27 to 19, the percentage of alleles fixed reduced from 1.06 to 0.86%, the genomic relationship reduced from 0.117 to 0.045, and genetic gain reduced from 0.35 to 0.15, after applying the diversity preservation strategy. Note that the genetic gain in the F2 scenario with diversity preservation was still higher than that of Stage 2 parental selection.A genetic diversity preservation strategy that restricted the number of alleles fixed as well as the average relationship between selected parents was applied to two scenarios where parents were selected from Stage 2 and Fegy Fig. . In the 5+), and field testing for target traits. Field evaluation of breeding progeny usually starts in a single environment with partial replication and eventually moves to multiple environments with full replication later in the breeding cycle (Stages 1 & 2). This information is used in making selections at each of the stages in the breeding cycle. Finally, a subset of best performing breeding lines is selected for final evaluation in National Variety Trials (NVT) for commercialization. The major difference between a conventional phenotypic-based selection and a genomic-assisted selection pipeline is that in PS, breeding selections are made based on phenotypic measurements and in GS cycle, genomic predictions are used for making selections. One breeding cycle is defined as the time taken from crossing to the selection of parents for the next crossing cycle. In the conventional phenotypic scenario of this study, parents for the next breeding cycle are selected based on phenotypic performance in Stage 2. When parents are selected at Stage 2 that amounts to at least 8 years. With the availability of high-density genomic data, the genetic potential of any genotyped plant or line can be accurately estimated with the genomic selection at any stage of the breeding cycle.A typical lentil breeding program is comprised of several steps: cross hybridization, seed bulking and generation advance through selfing . OHV was first proposed by Daetwyler et al.  in a glasshouse (aSSD), the time needed for the bulk-up step was reduced to one and half years from 4 years. The generation interval was reduced from 8.5 to 6 years if parents were selected from Stage 2 and from 5 to 2.5 years if parents were selected from F6. When selecting parents from the end of the bulk-up step (F6), F2 or F1, the generation interval was significantly shortened to 5, 1 or 0.5 years, respectively.The breeder equation stipulates that genetic change = (genetic standard deviation × selection intensity × selection accuracy)/cycle time. The majority of increases in genetic gain in genomic selection scenarios can be achieved by decreasing cycle time, either by accelerating the generation of inbred lines [field single seed descent (SSD) versus glasshouse accelerated SSD (aSSD)] or by selecting parents earlier from FPinus radiata) was reduced from 17 to 9 years. The genetic gain per year was increased by 21–103% for a trait with low heritability and 22–177% for a trait with high heritability (Li and Dungey Triticum aestivum), durum wheat (T. durum), barley (Hordeum vulgare), 4 generations for chickpea (Cicer arietinum), pea (Pisum sativum), and canola (Brassica napus). Generation interval could be further reduced if the speed breeding and genomic selection are applied together   and when parents selected from STG2, F6, F2 and F1 in GS with single-seed-descent (SSD) in F3–F6.Suppl Figure 1. The genetic gain per year achieved in disease resistance, grain weight, grain yield and the aggregated gain when parents selected from STG2 in PS with F2 family bulking-up from F3 to F5 (PS_STG2) and when parents selected from STG2, F6, F2 and F1 in GS with single-seed-descent (SSD) in F3–F6.Suppl Figure 2. Reduction in TBV variance per year achieved in disease resistance, grain weight, grain yield and the aggregated gain when parents selected from STG2 in PS with F2 family bulking-up from F3 to F5 (PS) and in GS with single-seed-descent (GS).Suppl Figure 3. Mean annual aggregated genetic gain when parents selected from STG2 in PS with FSupplementary file1 (TIFF 149 kb)Supplementary file2 (TIFF 153 kb)Supplementary file3 (TIFF 75 kb)Below is the link to the electronic supplementary material.


Centrocercus urophasianus), providing a new method to identify centers of genetic differentiation and combining multiple methods to help inform management and conservation strategies for this and other such species. Our objectives were to (1) describe large-scale patterns of population genetic structure and gene flow and (2) to characterize genetic subpopulation centers across the range of Greater Sage-grouse. Samples from 2,134 individuals were genotyped at 15 microsatellite loci. Using standard STRUCTURE and spatial principal components analyses, we found evidence for four or six areas of large-scale genetic differentiation and, following our novel method, 12 subpopulation centers of differentiation. Gene flow was greater, and differentiation reduced in areas of contiguous habitat . As expected, areas of fragmented habitat such as in Utah (with 6 subpopulation centers) exhibited the greatest genetic differentiation and lowest effective migration. The subpopulation centers defined here could be monitored to maintain genetic diversity and connectivity with other subpopulation centers. Many areas outside subpopulation centers are contact zones where different genetic groups converge and could be priorities for maintaining overall connectivity. Our novel method and process of leveraging multiple different analyses to find common genetic patterns provides a path forward to characterizing genetic structure in wide-ranging, continuously distributed species.Characterizing genetic structure across a species’ range is relevant for management and conservation as it can be used to define population boundaries and quantify connectivity. Wide-ranging species residing in continuously distributed habitat pose substantial challenges for the characterization of genetic structure as many analytical methods used are less effective when isolation by distance is an underlying biological pattern. Here, we illustrate strategies for overcoming these challenges using a species of significant conservation concern, the Greater Sage-grouse ( Many wildlife species are comprised of multiple spatially disjunct subgroups and the amount and juxtaposition of this genetic structure across the landscape is shaped by a species’ social behavior, their ability to disperse, and the spatial heterogeneity of the landscape , 2. MoveImprovements in molecular technology and laboratory methods over the last decade have facilitated the collection and analysis of large-scale genetic data sets , 11–13 eCentrocercus urophasianus; sage-grouse hereafter). Although sage-grouse occupy sagebrush habitat covering much of western North America, their range has been reduced by 56% since pre-European settlement  in R  upper left defined by population 1 and 2). After the second round, each population was divided into another two subpopulations . Four more rounds of analysis are represented in order by the [C-F].(JPG)Click here for additional data file.S7 FigEffective migration rates are shown on a log scale where the zero value indicates the mean effective migration rate, positive values indicate greater than average effective migration (orange) and negative values indicate those less than average (blue). These maps represent the average of models run with a deme count of 500 and 1,000. State names are represented by the following abbreviations California (CA), Colorado (CO), Idaho (ID), Montana (MT), Nevada (NV), North Dakota (ND), Oregon (OR), South Dakota (SD), Utah (UT), Washington (WA), and Wyoming (WY).(TIF)Click here for additional data file.S1 TableThe primary round of analysis included 2,091 individuals. Subpopulation center nomenclature is shown across the top row of the table and the round of SIBP analysis is in the first column. After seven rounds of analysis, no samples remained within ≥ 0.70 population cluster membership kriged isoclines.(XLSX)Click here for additional data file.S2 TableThe centers are represented by both a number and a subpopulation center name as defined in (XLSX)Click here for additional data file.


Saxifraga granulata populations (max. distance 61 km) in Luxembourg and neighboring Germany using RAPD markers and a common garden experiment. We assessed (1) the relationships between plant fitness, quantitative genetic variation, molecular genetic variation, and population size; and (2) the relative importance of genetic drift and selection in shaping genetic variation. Molecular genetic diversity was high but did not correlate with population size, habitat conditions, or plant performance. Genetic differentiation was low (FST = 0.079 ± 0.135), and there was no isolation by distance. Longevity, clonality, and the long‐lived seed bank of S. granulata may have prevented strong genetic erosion and genetic differentiation among populations. However, genetic distinctness increased with decreasing genetic diversity indicating that random genetic drift occurred in the studied populations. Quantitative and molecular genetic variations were correlated, and their differentiation (QST vs. FST) among S. granulata populations was similar, suggesting that mainly random processes have shaped the quantitative genetic differentiation among populations. However, pairwise quantitative genetic distances increased with geographic and climatic distances, even when adjusted for molecular genetic distances, indicating diversifying selection. Our results indicate that long‐lived clonal species may be buffered at least temporarily against the negative effects of fragmentation. The relationship between quantitative genetic and geographic distance may be a more sensitive indicator of selection than QST–FST differences.Formerly common plant species are expected to be particularly susceptible to recent habitat fragmentation. We studied the population genetics of 19 recently fragmented  Saxifraga granulata. Molecular genetic diversity of populations was high and did not correlate with population size, habitat conditions, or plant performance. Quantitative and molecular genetic variations were correlated, and their differentiation (QST vs. FST) among populations was similar. The results show that in fragmented species, strong genetic drift and the consequent loss of genetic diversity may be prevented by polyploidy, longevity, and a long‐lived seed bank and that the relationship between quantitative genetic and geographic distance may be a more sensitive indicator of selection than QST−FST differences.We found low overall genetic differentiation between populations of the formerly common grassland species  TEMP explained a further 17.5% of the variance and was highly correlated with the maximum temperature of the warmest month (r = .99), annual mean temperature (r = .94), and minimum temperature of the coldest month (r = .86). Based on the two principle components PRECIP and TEMP, we calculated pairwise climatic distances between populations as Euclidian distances.In May and June 2002, we selected 15 sites in Luxembourg and four additional ones in the neighboring state of Rheinland‐Pfalz in Germany for a study of the genetic structure of the populations  the largest diameter, the width of the longest leaf and counted the number of flowers. There were on average 14 families per population and 5.7 plants per family.In September 2009, we placed two batches of 15 seeds per mother plant in separate Petri dishes on moist filter paper and stratified them in a growth chamber at 4°C for 4 weeks. The temperature was raised to 20°C at the end of October, and the seeds were put under a 12 h day/12 h night light regime. The position of the Petri dishes was randomized every 3–4 days. Seed germination was recorded every 2 weeks, and 3–10 seedlings per mother plant (hereafter referred to as a seed family) of a minimum size of 1 cm were selected at random and planted into soaked peat pellets (“Jiffy pots”). The plants were placed on trays under fluorescent tubes . Early survival of plants was recorded for 2 months. In March 2010, we measured for each plant ; 0.5 μl dNTP's (10 mM), 2.5 μl PCR Buffer with (NH4)2SO4 ; 5 μl Primer (5 μM); 0.3 μl Taq DNA Polymerase ; and 0.125 μl BSA (20 mg/mL). The volumes were held in polycarbonate microtitre plates and covered by adhesive sealing sheets. The plates were then incubated in a thermocycler  programmed with the following settings: Denaturation of the DNA at 94°C for 2 min, followed by 44 repetitive cycles consisting of denaturation for 45 s at 94°C, annealing for 2 min 30 s at 36°C, and extension for 2 min at 72°C followed by a final extension phase of 5 min at 72°C. The samples were kept at 4°C until analysis. Amplified DNA fragments were separated by electrophoresis on precast ReadyAgarose™ 1.0% Agarose gels with ethidium bromide in 1× TBE buffer (Bio‐Rad Laboratories) in an electrical field . The gels were put under UV light and photographed using the Bio Doc system (Bio‐Rad Laboratories).The frozen dried leaf material was ground , and DNA extracted using the DNeasy® Plant Mini Kit (QIAGEN). We carried out amplifications in 25 μl volumes containing 5 μl of template DNA (5 ng DNA/μl), 8.575 μl ddHIn a first series of amplifications 60 10‐base primers  were screened in a random sequence and tested for reproducibility of the amplified fragment profile using four replicates of a single DNA extract. The first seven primers yielding good‐quality reproducible patterns  were selected for the RAPD analysis of 250 sampled plants Table . We onlyWe identified markers under divergent or balancing selection with the program BAYESCAN 2.1 with the false discovery rate set to 0.05 . FIS was calculated using the approximate Bayesian computation for F‐statistics (ABC4F) for dominant data  at the 5% level, and (2) Nei's gene diversity (expected heterozygosity HeN) according to the method of Lynch and Milligan  using GenAlex version 6.501  and related it to the genetic diversity of the populations to test whether genetic drift might have simultaneously resulted in increased distinctness of populations and reduced genetic diversity , heritability (h2) and evolvability , between families within populations (Vfam), and between individuals within families (Verror) for each trait by restricted maximum likelihood with the lmer and VarCorr functions of the R‐package lme4 v.1.1–30  was calculated as h2=Vfam/2*θ/Vfam+Verror, and the evolvability (genetic coefficient of variation) as CVgenetic=√Vfam/2*θ/mean, where θ is a measure of the kinship of the plants  of each trait in a population and its heritability (h2) and its population mean value. We estimated the overall quantitative genetic variability as the mean evolvability over all traits and studied the relation between mean evolvability and expected heterozygosity, PPL, mean heritability of the quantitative traits, and population size with simple regressions. We also analyzed the relationship between population mean trait values and heritability.We calculated mean CVFST) and pairwise geographic distances between populations with the permutation test available in the R‐package lmPerm  in the 19 populations was 92.8% and varied among the populations from 71.2% to 100% (Table HeN) per population using the FIS estimated by ABC4F (FIS [f] = 0.643 ± 0.04) was 0.345 and varied from 0.287 in the very small population Lallange to 0.378 in the large population Niedercorn  bands were observed, and each individual had a unique band pattern. Considering the error rate of 6.6%, three pairs of individuals, which differed in less than five bands, were considered as putative clones, and one of each pair was removed at random. The distance between members of the same putative clone ranged from 0.01 to 0.93 m. We removed two bands that had been identified as putative non‐neutral loci  by the program BAYESCAN 2.1 and obtained a final matrix of 247 unique genotypes and 52 neutral loci for our study populations. The mean proportion of polymorphic loci (p < .001), while variation among individuals within populations accounted for 89%. FST estimated by AFLP‐SURV (assuming FIS = 0.643) was 0.079 ± 0.1348. The mean FST between a population and all other populations was negatively related to its genetic diversity, i.e., the lower the molecular genetic diversity of a population was, the more distinct was it .There was no significant relationship between the various measures of performance in the common garden and the size of the population of origin or its molecular genetic diversity Table . The meagenetic) was significantly larger than zero for plant diameter , leaf width , and number of flowers  of the individual traits per population were strongly correlated , and mean evolvability and mean heritability averaged over all studied traits were also strongly correlated .Mean quantitative genetic diversity within populations estimated as evolvability  in a population, as well as between heritability and HeN . To relate population means of the quantitative traits with their genetic variation, we related trait means to the standard deviation of genetic variability instead of evolvability  to avoid spurious correlations. Population means and the standard deviation of genetic variability of plant diameter and leaf width .There was a strong positive relationship between the mean evolvability over all traits and h Figure , but noth Figure . None ofQST was similar to the molecular differentiation (FST) for all three quantitative traits studied  indicating the absence of an isolation by distance pattern. In contrast, the pairwise quantitative genetic distances increased with geographic distances . This relationship remained strong if corrected for the effect of molecular distance (p < .001). Adding the climatic distance between populations in addition to molecular distance had a strong effect (p = .008) and explained a large part of the effect of geographical distance on quantitative genetic distance, but geographical distance still had an effect (p = .04). These results suggest that the variation in quantitative traits is not determined by random processes alone, but is also a result of local adaptation to different climates and local environmental factors.The quantitative genetic differentiation among populations estimated as d Figure . The pai55.1HeN of the populations of S. granulata (0.35) as estimated by our study was high in comparison to the mean HeN found in other RAPD studies . Considering that genetic diversities estimated by dominant markers are on average about only a third of that estimated by microsatellite marker studies  estimated by microsatellite markers  and was much lower than the mean ΦST found in studies of species with a similar life history using dominant markers  may also have contributed to the low variation among populations, as FST in S. granulata was relatively low, we found also signs of genetic drift, as the mean genetic distance between a population and all other populations (distinctness) was negatively related to its genetic diversity, indicating that the higher the loss of genetic diversity through drift was, the more distinct became a population. A similar relationship has been found in Vaccinium stamineum  and population size.Quantitative genetic diversity as measured by mean evolvability or heritability of the studied traits was not affected by population size, suggesting that genetic drift has not decreased the adaptive potential of small populations of d et al. , who fouh2) of each trait and averaged over all studied traits in a population, which is in contrast to the general conclusions of reviews by Houlé ; data curation (lead); formal analysis ; investigation ; methodology ; writing – original draft (lead). Guy Colling: Conceptualization ; formal analysis ; methodology ; supervision ; writing – review and editing . Sylvie Hermant: Data curation (supporting); investigation ; methodology (supporting). Diethart Matthies: Conceptualization ; formal analysis (lead); methodology ; supervision (lead); writing – review and editing (lead).None declared.https://doi.org/10.5061/dryad.b8gtht7g5].This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at [


Huigobio chenhsienensis (Cypriniformes: Gobionidae) is a small fish endemic to southern China. In this study, we used mitochondrial cytochrome b gene (Cytb), from wide-ranging samplings of H. chenhsienensis from the Ou River  to the Yangtze River Basin (the northernmost part of southern China) to explore genetic variations and the evolutionary history of H. chenhsienensis in southern China. In total, 66 haplotypes were identified from Cytb sequences of 142 H. chenhsienensis individuals, which could be divided into lineages A, B, and C with divergence times of ~4.24 Ma and ~3.03 Ma. Lineage A was distributed in the lower reaches of the Yangtze River, the Oujiang River, and the Jiao River, lineage B was distributed in the Qiantang River and the Cao’e River, whereas lineage C was restricted to the Poyang Lake drainage from the middle reaches of the Yangtze River. Lineage A could be subdivided into sub-lineages A-I, A-II, A-III, and A-IV, with divergence times of 1.30, 0.97, and 0.44 Ma. Lineage C could be subdivided into sub-lineages C-I and C-II, with a divergence time of 0.85 Ma. Our findings indicate that climate change during the Pliocene and Pleistocene eras, as well as the limited dispersal ability of H. chenhsienensis, have been major drivers for shaping the phylogeographical patterns of H. chenhsienensis.The freshwater gudgeon  Phylogeography focuses on the geographic distributions of inter- and intraspecific lineages, as well as the historical processes that have affected their evolution ,2. Freshh) as h ≥ 0.5 or h < 0.5, and high or low nucleotide polymorphism (π) as π ≥ 0.005 or π < 0.005. The Yangtze River is the northern boundary for freshwater fish distributed in southern China [h value and low π value) and type IV , as defined by Grant and Bowen [Grant and Bowen  classifirn China . Previound Bowen . The potnd Bowen . Howevernd Bowen .Based on the geographical distribution of lineages and the degree of genetic differentiation, Avise  summarizHuigobio chenhsienensis Fang 1938 is classified under the order Cypriniformes, family Gobionidae, subfamily Gobioninae, and genus Huigobio [H. chenhsienensis is small (standard length of less than 8 cm) and usually inhabits the upper reaches of streams, feeding mainly on benthic algae [H. chenhsienensis. In addition to H. chenhsienensis, the genus Huigobio comprises two other species: H. exilicauda Jiang and Zhang 2013 and H. heterocheilus Sun, Li, Tang and Zhao 2022. H. exilicauda is distributed in the Pearl River, whereas H. heterocheilus is found in the Xiang River, a tributary of Dongting Lake drainage in the middle reaches of the Yangtze River [Huigobio . It is aHuigobio ,21,22,23ic algae . The smab gene (Cytb) sequences were obtained from samples covering the distribution drainages of H. chenhsienensis. Based on analyses of phylogeny, genetic structure, and population history, we discuss the roles of river evolution and paleoclimatic events in the formation of phylogeographic patterns of H. chenhsienensis in southern China.In this study, mitochondrial cytochrome H. chenhsienensis were collected from the Ou River, Jiao River, Qiantang River, Cao’e River, and Yangtze River, using small set nets and gill nets by ourselves or with help from local fishermen. There are a total of 22 localities, with 1 to 18 specimens for each locality  version 7.0.26  [The obtained sequences were assembled in Sequencher v5.4  and supplemented with manual correction for incorrect base recognition of the chromatograms. Sequence alignment was performed with the default algorithm under MAFFT v7.427 . Amino aype data . Basic iPA, USA) .Cytb time tree of H. chenhsienensis was reconstructed in BEAST v2.6.6 software  [Huigobio for estimating the differentiation time of H. chenhsienensis, a mitochondrial genome time tree of Huigobio and its close relatives was thus developed to obtain the differentiation time of H. chenhsienensis and H. heterocheilus as the secondary calibration point by using two fossil species of Gobionidae with complete skeletons and corresponding stratigraphic age information   based onyuanensi , i.e., ceuciscus . This foeuciscus  and is †ocephala , i.e., cocephala . When thH. chenhsienensis, the median-joining network was constructed under NETWORK v10.2.0.0 . Combined with the geographical distribution and individual number of haplotypes, it was further modified in Adobe Illustrator.To test for the geographic correspondence of haplotypes across all populations of H. chenhsienensis and H. heterocheilus was 11.66 Ma (95% HDP: 13.04–10.44 Ma), which was obtained from the time tree of the mitochondrial genome presented above and used as a secondary calibration point to infer the Cytb time tree of H. chenhsienensis. The specific settings were as follows: normal distribution, m = 11.74, s = 0.66. Using the likelihood-ratio test in DAMBE v7.2.43, the result of the molecular clock test  did not support the molecular clock model [H. heterocheilus was selected as an outgroup. The Birth–Death model was selected as a tree prior. Two independent runs of 100 million generations, with sampling every 1000 generations, were performed. The remaining settings were the same as the time tree construction above.The interspecific divergence time between ck model , and theH. chenhsienensis was estimated, and the genetic differentiation coefficients, NST and GST, of the populations were calculated and compared with determination of the presence of a significant genetic structure using DnaSP v6.12.01 [ST and pairwise comparison ΦST values among basins were obtained in Arlequin v3.5.1 [H. chenhsienensis populations, the maximum ΦCT value and its corresponding grouping were obtained by simulating the groupings of different populations using SAMOVA v2.0 [Cytb mutation rate (1.30% substitutions per site per million years) obtained from the time tree of Cytb haplotypes of H. chenhsienensis was used in the reconstruction of the Bayesian skyline plot.The genetic diversity of v6.12.01 ,40. The n v3.5.1 . To deteOVA v2.0 . For eacOVA v2.0 ,44 and mOVA v2.0 . BayesiaOVA v2.0 , the RelOVA v2.0 . The CytH. chenhsienensis was performed by loading the BioGeoBEARS package version 0.2.1 [Cytb haplotypes time-calibrated tree of H. chenhsienensis as the input tree and the basins as the geographic distribution unit. Finally, the optimal model was selected according to Akaike information criterion (AIC) to reconstruct the ancestral area of H. chenhsienensis [The ancestral area reconstruction of on 0.2.1  in the Rienensis .http://www.gebco.net/, accessed on 10 November 2021). The maximum sea level decline during the glacial period of the Late Pleistocene was about −130 m [Topographic and bathymetric information on a 30 arc-second interval grid at a global scale, with accuracy in meters, was retrieved from the digital elevation model (DEM) of the General Bathymetric Charge of the Oceans 2021 Grid , with a GC content of 41.3%, containing 184 variable sites and 161 parsimony informative sites. The time-calibrated phylogeny (Cytb haplotypes of H. chenhsienensis could be divided into lineages A (29 haplotypes), B (18 haplotypes), and C (19 haplotypes); divergence times among them were 4.24 Ma and 3.03 Ma. The lineage A could be subdivided into four sub-lineages, namely, A-I, A-II, A-III, and A-IV, which diverged from each other at 1.30, 0.97, and 0.44 Ma. The lineage C consisted of two sub-lineages, C-I and C-II, with a divergence time of 0.85 Ma.A total of 66 haplotypes , haplotype diversity (h = 0.9500) and lowest in Cao’e River (h = 0.6667); nucleotide diversity (π = 0.0371) and lowest in Cao’e River (π = 0.0007).Among the distribution basins of iversity  was highiversity  was highST > GST: NST = 0.796, GST = 0.072) indicated a significant geographic structure for H. chenhsienensis. The total ΦST among basins was 0.627 (p = 0.000), and the pairwise comparison ΦST values among basins were relatively high and significant  was the largest.Comparison between two genetic differentiation coefficients . The current geographic distribution pattern was formed after three vicariant and three dispersal events , whereas the sub-lineage C-II was distributed in the Rao River (sampling site 14), Xin River (sampling site 15), and Xiu River (sampling site 16–19) of the Poyang Lake drainage. All 19 haplotypes from the two sub-lineages were private haplotypes for each river in the Poyang Lake drainage  may be responsible for the strong spatial genetic structure of H. chenhsienensis.The fixation coefficient of genetic differentiation, Nnormalis  and Hemiguttatus . An incrguttatus ,68,69,70H. chenhsienensis in the Yangtze River Basin showed high haplotype polymorphism (h) and high nucleotide polymorphism (π), which may be ascribed to the simultaneous occurrence of lineages A and C. However, there was only a single lineage (A or B) in the Ou River, Qiantang River, or Cao’e River, and the pattern of genetic diversity of H. chenhsienensis in each basin was observed as having high h values and low π values, which suggested that H. chenhsienensis populations in these basins may have suffered from genetic bottlenecks, followed by rapid population expansions [Fs values of lineages A and B, respectively, as well as the unimodal peak in mismatch distribution of the lineage B. Moreover, we detected that the population expansions of lineages A and B started at ~0.088 Ma or ~0.110 Ma, respectively, but the lineage C experienced population contraction from ~0.040 Ma to the present  and high nucleotide polymorphism (π); other drainage populations exhibited high h and low π. During the Late Pleistocene, lineages A and B experienced population expansion, whereas lineage C suffered from severe bottleneck effects. Based on the spatial subdivision of genetic structure for H. chenhsienensis, it is suggested that the four conservation geographic units  should be classified for management.In summary,