The forced migration of millions of Africans during the Atlantic Slave Trade led to the emergence of new genetic and linguistic identities, thereby providing a unique opportunity to study the mechanisms giving rise to human biological and cultural variation. Here we focus on the archipelago of São Tomé and Príncipe in the Gulf of Guinea, which hosted one of the earliest plantation societies relying exclusively on slave labor. We analyze the genetic variation in 25 individuals from three communities who speak distinct creole languages , using genomic data from expanded exomes in combination with a contextual dataset from Europe and Africa, including newly generated data from 28 Bantu speakers from Angola. Our findings show that while all islanders display mixed contributions from the Gulf of Guinea and Angola, the Angolares are characterized by extreme genetic differentiation and inbreeding, consistent with an admixed maroon isolate. In line with a more prominent Bantu contribution to their creole language, we additionally found that a previously reported high-frequency Y-chromosome haplotype in the Angolares has a likely Angolan origin, suggesting that their genetic, linguistic and social characteristics were influenced by a small group of dominant men who achieved disproportionate reproductive success. A fourth island, Fernando Pó (now Bioko), located only 32 km off the coast of Cameroon, had already been populated by the Bubi, an autochthonous Bantu-speaking group who had presumably reached its shores by canoe [During the first half of the 16th century, the archipelago of São Tomé and Príncipe, located in the Gulf of Guinea , became one of the first examples of the so-called “plantation complex”, which was soon to take over the New World [After a short homestead phase in which slaves from the African mainland were predominantly imported as domestic servants, the large-scale production of sugar soon required a substantial labor force, which was first acquired through trade with the Kingdom of Benin (in present-day Nigeria) and later with regions in modern-day Democratic Republic of Congo and Angola. In addition to the local work force, São Tomé and Príncipe also served as entrepôts for slaves meant for re-export to the Americas, in particular to the Spanish Main and Brazil ,4,5. WheThe cultural interactions that characterized the peopling of São Tomé and Príncipe led to the emergence of three distinct Portuguese-related creole languages: (1) Santome  is spoken by about 60,000 of São Tomé’s 180,000 inhabitants who identify themselves as Forros , as they mostly descend from slaves that were granted manumission throughout the history of the island ; (2) AngDespite the lack of mutual intelligibility, all these languages can be traced to a single Gulf of Guinea proto-creole that likely emerged in São Tomé during the short homestead period in the early 16th century . Due to The comparatively strong lexical Bantu contribution from especially Kimbundu in the language of the Angolares appears to be in line with their ethnic designation ,14,15. TWe have previously detected an unusually strong signal of genetic differentiation between the Angolares and the remaining populations of the island of São Tomé by using a set of just 15 autosomal microsatellite polymorphisms . HoweverHere, we reassess the genetic variation of the three speech communities of São Tomé and Príncipe  using genomic data from expanded exomes in combination with a contextual dataset from Europe and from major slave-trading zones in Africa, including new data from Angola. We found that, despite the strong levels of differentiation of the Angolares, the three groups share notable genetic similarities with respect to their Gulf of Guinea/Angolan ancestry ratios, suggesting that the Angolares are an admixed isolate. Based on the available genetic and linguistic evidence, we further propose that their origins trace back to a maroon community strongly influenced by the political and cultural dominance of one or several related men from Angola.We generated 53 expanded exomes from 9 Angolares (ANG), 8 Forros (FOR) and 8 Principenses (PRI) from São Tomé and Príncipe, as well as from 28 individuals belonging to five Angolan populations: 5 Ovimbundu (OVI), 5 Ganguela (GAN), 5 Nyaneka (NYK), 6 Himba (HIM) and 7 Kuvale (KUV). Samples from Forros and Angolares were obtained from Lungwa Santome and Lunga Ngola speakers whose four grandparents were born in villages where the two languages are still used as medium of everyday conversation. Although Lung’Ie is highly endangered, we could sample five individuals who still were active speakers and three additional individuals who only had a passive knowledge of the language. In all cases, all four grandparents were speakers of Lung’Ie and had been born on the island of Príncipe. Additional details on sampling procedures in São Tomé and Angola have been described elsewhere ,23,24 F. ® Rapid Capture Enrichment kit by Illumina, San Diego, CA, USA, following the protocol version #15037436 v01. Indexed samples were sequenced in two runs on an Illumina’s HiSeq 1500 System with 250 cycles in paired-end mode. A mean depth coverage of 21x (5-41x) was obtained for captured regions. The 53 newly generated exomes were compared with sequence data from 71 individuals with average genome coverage 13x, belonging to six reference populations from the 1000 Genomes Project [ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/ accessed on 21 October 2017), as well as with previously reported data from 12 Bubi (BBS) individuals (coverage 27x) from the island of Bioko [https://www.ebi.ac.uk/ena/browser/view/PRJEB26599 accessed on 31 July 2019)  . To avoihttp://broadinstitute.github.io/picard accessed on 15 December 2016). Variant discovery workflow was done with the Genome Analysis Tool Kit (GATK) v3.4.46, following GATK Best-Practices recommendations for exome sequencing [We performed a quality control check with FastQC (v0.10.1)  and applquencing ,33,34. Wp-value < 0.05 for at least two populations were also excluded. Overall, the filtering process yielded 149,501 autosomal SNPs. To improve the quality of the variant dataset, we further filtered the data with VCFtools (v0.1.13) , retainiAll individuals were checked for relatedness with the -relatedness2 option on VCFtools (v0.1.13) ,37. One Haplotype-based coancestry matrices, principal component analysis (PCA) and clustering dendrograms were obtained with fineSTRUCTURE/CHROMOPAINTER v.2 . PhasingTo estimate mutation emission and recombination scaling parameters used in the analyses relying on CHROMOPAINTER, we performed initial runs using 10 iterations of the Expectation-Maximization (EM) algorithm for a subset of five randomly selected chromosomes . The inferred parameters were first averaged by chromosome (weighted by their number of SNPs) and then by individuals. These parameters were then used in subsequent CHROMOPAINTER runs on all individuals and chromosomes. 2=0.2. We performed 20 independent ADMIXTURE runs for each K value from 2 to 5, applying a cross-validation (CV) procedure. The results were post-processed and plotted with the pong software [Genotype-based, unsupervised clustering analyses were performed by applying ADMIXTURE v1.3.0  to a linsoftware .Pairwise Fst values between populations were calculated with EIGENSOFT ,48 and vWe used PCA c and ADMFor PCA, we determined the centroids of the European, African, Gulf of Guinea and Angolan populations in PC1 + PC3. To obtain the European admixture proportions we projected each Forro and Principense individual onto the line connecting the European and African centroids and estimated the European contribution as one minus the Euclidian distance of each projection to the European centroid, divided by the Euclidian distance between the European and the African centroids. We repeated the same procedure to obtain the contributions from the Gulf of Guinea using the line connecting the Gulf of Guinea and Angolan centroids ,52. Sinc2), folded Site Frequency Spectra (SFS) and individual inbreeding coefficients (Fis). To control for uneven sample sizes in LD, SFS and Fis calculations, we downsampled the number of individuals to 5 . We repeated this process ten times, calculating each summary statistic in each replicate, and taking the average over replicates as the final estimate.We characterized genetic diversity using observed, individual per locus heterozygosities (Ho), runs of homozygosity (ROH), LD measured by the squared correlation of allele frequencies  and the total length of ROH (sROH).2) between pairs of SNPs in sliding windows of 1 Mb in each population using PLINK 1.9. To evaluate the LD decay, we binned the LD values between pairs of SNPs according to different genomic distance categories  and calculated the mean r2 value within each bin.We calculated LD  [We compared newly generated data from the island of Príncipe consisting of mitochondrial DNA (mtDNA) sequences of hypervariable regions (HVR) I and II, and Y-chromosome microsatellite haplotypes, with previously reported data from the island of São Tomé . MtDNA say 2020) . http://www.fluxus-engineering.com, accessed on 28 December 2020) using the median-joining algorithm alone (mtDNA) or in combination with a reduced-median algorithm (Y-chromosome) [Haplotype networks were built with the NETWORK 10.2 software  ,56. Moleomosome) .The TMRCA for a previously identified  Y-chromoUsing newly generated expanded exome data, we compared the genetic composition of three creole-speaking communities from São Tomé and Príncipe with five populations from Angola, as well as with available sequence data from Europe and from major slave-trading regions in Africa .In a haplotype-based principal component analysis (PCA) implemented by the fineSTRUCTURE algorithm , the AngConsideration of additional PCs  reveals While the position of the divergent Angolares cannot be determined in this genetic gradient, the Forros and Principenses lie between the Esan and Yoruba from Nigeria, and a group of Bantu-speakers from Angola that includes the Nyaneka, the Ganguela and the Ovimbundu c. This oIn contrast to the Forros and Principenses, the Bubi from the neighboring island of Bioko are grouped together with Bantu-speaking populations c. The BuTo better elucidate the relationship between the Angolares and other groups, we tried to reduce the impact of their genetic differentiation by further exploring haplotype sharing profiles generated by CHROMOPAINTER ,42. In tWe additionally used both PCA and ADMIXTURE to quantify the ancestral contributions of Europe (Iberians and CEU-Europeans), Gulf of Guinea/Nigeria (Esan and Yoruba) and Congo/Angola  to the genome of Forros and Principenses . Althougp = 0.09). Ancestry estimates based on the frequency of ADMIXTURE components were highly correlated with the PC-based results  in the Angolares, who otherwise retained a small number of equally frequent, molecularly divergent mtDNA lineages. This pattern contrasted with the high variability detected for the two uniparental markers in a sample of linguistically uncharacterized non-Angolar residents of São Tomé . Using nMoreover, we reassessed the provenance of the most common Angolar patrilineage by investigating its matching profile, using publicly available data from the Y- Chromosome Haplotype Reference Database (yhrd.org). We found that matches with Angola represent 39% (7/18) of all matches with African populations, although the Angolan sample size accounts for only 12% (309/2679) of the total sample size of populations in which at least one match was observed . These rWe also attempted to estimate the time to the most recent common ancestor (TMRCA) of this lineage. By using previously defined criteria , we firsThe emergence of new ethnic identities defined by distinct cultural and linguistic traits is one of the most remarkable outcomes of the forced displacement of millions of Africans during the Atlantic Slave Trade. In contrast to other instances of European conquest where local societies were subjected to colonial rule, the plantation complex that was installed in the New World, especially in the Caribbean, relied on the mass replacement of indigenous groups with Africans of different geographical origins . AlthougLocated right off the African coast, São Tomé and Príncipe anticipated some of these defining characteristics of Caribbean creole societies both in time and space . While mBy using 15 autosomal microsatellite polymorphisms together with mtDNA partial sequencing and Y-chromosome microsatellites, we have previously detected an unusually strong signal of genetic differentiation between the Angolares and a heterogeneous sample including non-Angolares inhabitants of the island of São Tomé . HoweverHere, we used newly generated genome-wide data to show that when the impact of genetic differentiation is reduced, the Angolares display mixed contributions from the Gulf of Guinea and Angola 58%:42%) and are genetically closer to the Forros and the Principenses than to any other African population . At the 2% and arLinguistic evidence offers additional insights into this scenario. The creoles spoken by the Forros (Lungwa Santome) and Angolares (Lunga Ngola) differ in the way in which Bantu-derived features are ingrained into different linguistic domains ,12. WhilAn important clue about this process is provided by the finding that the Angolares display a very high frequency of a single Y-chromosome microsatellite haplotype with a likely Angolan origin  Figure . SimilarA high status of prominent Angolan men provides the socio-linguistic context that could easily explain the emergence of Lunga Ngola through the partial relexification of a pre-existing creole under the influence of Kimbundu-speaking leaders. Moreover, the role of headmen, also known as “captains”, in the Angolares community has long been attested by historical sources ,8. As inThe effects of cultural transmission of male social dominance can be illustrated with a simple deterministic model, where the favored cultural phenotype (higher status) is associated with higher rates of polygyny among individuals inheriting the Y-chromosome from dominant males ,73. For Even when driven solely by males, social selection is expected to have a strong whole-genome impact. In their pioneer studies on the genetic structure of Native American tribes, James Neel and his colleagues ,75 have While the observed patterns of Y-chromosome and genome-wide variation of the Angolares can also be explained by neutral demographic factors such as strong bottlenecks and founder effects, neutrality and social selection are of course not mutually exclusive. Therefore, further work is needed to clarify the roles played by these evolutionary factors in shaping the genetic and non-genetic dimensions of human diversity in São Tomé and Príncipe. Our genome-wide results provide the empirical framework for these analyses.


The objective of this study was to perform a comparative analysis of allelic diversity to reveal population-genetic characteristics of animal breeds, namely Shami (SH), Holstein (HLS), and Aberdeen-Angus (A-A).n = 55, HLS) and bulls and cows of A-A breed bred at breeding enterprises in Russia  were collected. Genetic differences between the cattle groups were studied using 11 microsatellite markers. The genetic materials of SH breed animals represented by wool with hair follicles were collected from 39 SH heads in Syria. Also, genetic materials of HLS breed of American selection  and A-A (5.00). When analyzing the genetic equilibrium for individual locus in SH breed, a deviation from equilibrium at four loci was revealed: TGLA227, SPS115, TGLA122, and ETH225; at one locus in HLS breed: SPS115, for A-A breed: at two loci, i.e., TGLA122 and ETH225. When assessing the level of genetic consolidation, a deficiency of heterozygotes was observed in two of the three studied breeds: 4.8% for SH and 8.0% for A-A. A slight excess of heterozygotes was found in the HLS breed at the level of 0.2%. The average comparative measurement of genetic variation in different populations value for 11 loci for all breeds was 0.069, which indicates that 93.1% of the total variability is due to the intra-breed diversity, and only 6.9% is due to the differences between breeds. The analysis of the animals belonging to their breed has shown a 100% genetic consolidation and the compliance of individual animals with the respective breeds. The study of genetic distances, adjusted for small samples, revealed the smallest genetic distance between the SH breed and HLS breed, equaling 0.107. The A-A breed, which has its separate origin and has never been imported into the Syrian Arab Republic, adjoins this cluster as an independent branch. Microsatellites can be used as an essential criterion for assessing the population-genetic characteristics of groups of cattle of various breeds . Biodiversity is necessary for the survival of organisms, and this factor is becoming increasingly important and holds a significant priority in modern science relating to the breeding of farm animals ,2. The pThe search for candidate genes responsible for the level manifestation of economically advantageous quantitative traits as microsatellite alleles can be inherited linked to alleles of interest genes, is of great importance. The search for a locus of financially beneficial traits is carried out based on analysis of the length of microsatellite DNA fragments using special statistical programs and databases containing information on animal productivity -14. MicrThe knowledge of population genetic structure and variability is critical for creating efficient strategies to enhance the productivity, performance traits, conservation, and innovative management of farm animal genetic resources .Genetic markers allow one to obtain information on the characteristics of breeding material, assess the diversity of the gene pool farm animals, predict changes associated with breeding factors, identify potentially highly productive males at an early age, trying to make pairs for genotype selection for a heterogeneous effect in their offspring ,18. MoleBos taurus cattle, we conducted their comparative study along with the largest world breeds of dairy and meat production sectors, i.e., Holstein (HLS) and the Aberdeen-Angus (A-A) breeds . Molecular markers (microsatellites) were used in this study. DNA was purified using the S-Sorb DNA kit  according to the manufacturer‘s recommendation with some modifications. To study the allelic pool of Syrian cattle of the Shami (SH ) breed along with HLS and A-A breeds, 11 microsatellite locus have been used; these are TGLA227, BM2113, TGLA53, ETH10, SPS115, ETH225, BM1824, TGLA112, TGLA126, INRA23, and BM1818. The products of multiplex amplification were visualized using an ABI3130xl capillary laser analyzer , using the software Gene Mapper, v.4. The statistical processing was performed using the GenAlEx 6.0 [To characterize the studied cattle breed in Syria in the context of the global gene pool of The analysis of the genetic diversity about the total number of alleles per locus and the number of effective alleles (Ne) per microsatellite locus in the compared breeds are presented in B. taurus / Bos indicus, which causes such a high number of private alleles. It should be noted that, for the most part, the private alleles found in SIR_SH were found with a low incidence below or equal to 5% and do not significantly affect the population development. For significant over 5%, six alleles were found in Syrian cattle—at the TGLA53 locus: alleles 150 and 152 with incidences of 8.6% and 11.4%; SPS115: allele 250 with an incidence of 16.7%; TGLA122: allele 141 with an incidence of 7.7%; ETH225: alleles 136 and 138 with incidences of 6.4% and 30.8%. In the HLS breed three alleles—in locus TGLA53: alleles 176 and 188 with incidences of 13.6% and 10.0%; TGLA122: allele 183 with an incidence of 14.5%; and for A-A two alleles—in locus TGLA122: allele 155 with an incidence of 11.7%; and TGLA126: allele 113 with an incidence of 12.5%. The results of the analysis of preservation of genetic equilibrium at individual MS locus are summarized in The revealed number of private alleles, which was nine times greater, indicates a unique diversity of SH cattle breed in the Syrian allelic pool. The origin of studied regional populations probably involved the animals of the Oksh breed, which is assigned, judging by their origin, to hybrid cattle A comparison of the observed and the expected levels of heterozygosity makes it possible to evaluate the genetic diversity in a particular sample population breed ,22. It iAccording to the data in Fis values. The results of the analysis of F-statistics indicators are shown in Fis fixation index enables the determination of the relationship between individual animals and the entire breed in general and can be used to assess inbredness [Thus, in the Syrian cattle group, deficiency of heterozygotes was observed in 8 of 11 microsatellite locus and ranged from 3.4% in INRA23 up to 13.6% in SPS115. At locus BM2113, ETH10, and BM1818, the observed heterozygosity surpassed the expected value by 1.3%, 5.3%, and 1.9%, respectively. In the HLSs studied in 6 of the 11 studied locus, the deficiency of heterozygotes ranged from 0.1% at TGLA122 and BM1824 loci to 7.9 and 8.0% at ETH10 and SPS115 locus. In the studied animals of the A-A breed, the deficiency of heterozygotes was observed in 8 out of 11 MSs, which ranged, respectively, from 0.3% at ETH10 locus to 37.1% at ETH225 locus. Analysis for individual breeds  shows a Fis value greater than zero indicates a deficiency of heterozygotes in the studied group, and its negative value indicates their excessiveness. The obtained Fis values show the excess of heterozygotes at TGLA227, INRA23, and TGLA126 locus and its total average value for studied animals at the level of 5.8%. The Fit fixation index shows the relationship between individual animals and all studied animals in general. The Fit index and Fis make it possible to identify deviations from the theoretically expected incidence of heterozygotes in the studied group of animals. The calculation of the Fit value has shown a 12.2% deficiency of heterozygotes in studied breeds. The average Fst value for 11 locus for all the studied groups was 0.069, and this means that 93.1% of all variance is due to intra-breed differences and 6.9% due to the differentiation between breeds. Moreover, the fixation index The analysis of animals belonging to their own breed to the studied group using microsatellite markers has shown their 100% genetic consolidation. Thus, the studied animals are genetically consistent with their own breeds . The animals of each breed belong to the mentioned populations with 100.0% reliability. It should be noted that the SH and A-A breeds generate clusters overlapping more tightly, while the HLS breed forms its own quite isolated cluster. The genetic distances between the studied cattle breeds, calculated according to Nei , are preThe smallest genetic distances were found between the SH breed (Syria) and the HLS breed (USA) at the level of 0.107, which is graphically displayed as a combination of these groups in a shared cluster. On the other hand, the animals of the A-A breed, having different genetic characteristics, form a separate branch .The study reports a first genetic within breed diversity estimate of the SH cattle population through microsatellite markers recommended by the a Food and Agriculture Organization. Values observed in the present study indicate that the markers used are highly informative for the genetic characterization of SH cattle and give reliable information on genetic diversity and population structure. The study proved that the cattle breed in Syria was characterized by a high level of genetic, which is significantly higher than that in the HLS and A-A breed animals. The genetic distances, adjusted for small samples, the smallest genetic distance between the SH breed and the HLS breed. A-A breed, which has its separate origin and has never been imported into the Syrian Arab Republic, adjoins this cluster as an independent branch. It should be noted that the SH and A-A breeds generate clusters overlapping more tightly, while the HLS breed forms its own quite isolated cluster.Fis); observed heterozygosity (Ho); expected heterozygosity (He); Shami (SH); Holstein (HLS); Aberdeen-Angus (A-A); the deficiency or excess of average heterozygotes (Fit); comparative measurement of genetic variation in different populations (Fst); Syrian Shami (SIR-SH); microsatellites (MS); locus microsatellite (INRA23).Quantitative trait locus (QTL); total number of alleles (Na); number of effective alleles (Ne); fixation index (


Inherited retinal diseases (IRDs) are a diverse group of degenerative diseases of the retina that can lead to significant reduction in vision and blindness. Because of the considerable phenotypic overlap among IRDs, genetic testing is a critical step in obtaining a definitive diagnosis for affected individuals and enabling access to emerging gene therapy–based treatments and ongoing clinical studies. While advances in molecular diagnostic technologies have significantly improved the understanding of IRDs and identification of disease-causing variants, training in genetic diagnostics among ophthalmologists is limited. In this review, we will provide ophthalmologists with an overview of genetic testing for IRDs, including the types of available testing, variant interpretation, and genetic counseling. Additionally, we will discuss the clinical applications of genetic testing in the molecular diagnosis of IRDs through case studies. Inherited retinal diseases (IRDs) are a heterogenous group of visually debilitating diseases caused by pathogenic variation in proteins critical to retinal function. The majority of IRDs are characterized by retinal degeneration, which can lead to significant vision impairment and blindness –4. ColleWith an increased understanding of the human genome and the wide scope of genetic variants identified to be associated with IRDs (> 250 causative genes) , emerginThe American Academy of Ophthalmology Task Force on Genetic Testing and the European Reference Network for Rare Eye Diseases recommend genetic testing for all individuals with presumed or suspected IRDs for which a causative gene or genes have been identified , 13. HowHistorically, genetic testing has been ordered and interpreted by IRD specialists and ocular genetic counselors at large academic research centers . HoweverEarly and accurate diagnosis is necessary for individuals with IRDs to enable patient decision-making, to identify suitable clinical studies or treatment opportunities, and to improve patient outcomes. The objective of this review is to provide a general understanding of genetic testing and its clinical applications for IRDs through case studies.RPE65 mutation-associated retinal dystrophy, Luxturna® (voretigene neparvovec-rzyl) [Genetic testing is the analysis of an individual’s DNA to detect genetic changes or variants that could lead to disease . With thec-rzyl) , 22, andec-rzyl) –25, confec-rzyl) .When a person has been clinically diagnosed with a presumed IRD, diagnostic genetic testing is indicated. At this time, genetic testing should not be used to rule out an IRD, since a negative result from large panel genetic tests could reflect the limits of the panel’s design and not the comprehensive spectrum of potential genetic candidates . In manyPAX6 gene), systematic testing of single genes may be inefficient for diagnosing more complex IRDs that have a high degree of genetic heterogeneity, such as retinitis pigmentosa (> 100 identified causative genes) [Genetic tests can assess single genes or, more commonly, panels of genes associated with a group of genetic diseases . Previoue genes) , 19, 29.e genes) , 31. Nexe genes) –32. In te genes) , 31.In the processing of genetic test results, identified variants are analyzed to determine their potential association with an IRD phenotype. Not all identified variants cause disease . GeneticCHM gene and positive for 2 novel heterozygous mutations of the C2orf71 gene, namely, p.Arg1202Ter (nonsense mutation) and p.Ser134ArgfsTer47 (frameshift mutation). Mutations of the C2orf71 gene cause autosomal recessive retinitis pigmentosa; the 2 novel C2orf71 genetic variants found were considered likely positive. The individual was counseled regarding the hereditary pattern and the prognosis of his C2orf71-associated IRD and will be considered a candidate for future C2orf71 clinical trials.A 60-year-old healthy male with progressive night visual impairment and peripheral visual loss for many years and a diagnosis of choroideremia was referred for evaluation as a participant for a choroideremia natural history study. Family history was negative. Best-corrected visual acuity was 20/50 in each eye. Funduscopic findings showed areas of well-demarcated chorioretinal atrophy, with small islands of retained functional area, in the macula of each eye Fig. . The worPedigree analyses are an important complement to molecular assessment of IRDs , 37. TheRPGR gene on the X chromosome . Subsequent testing of his siblings showed a similar genotype. The genetic testing revealed that all 3 siblings actually had X-linked retinitis pigmentosa due to homozygous mutations in the RPGR gene. After 16 years, at the age of 23 years, the individual had a vision of 20/40 bilaterally with significant constricted visual fields and was enrolled into a clinical trial for RPGR gene therapy.A 7-year-old male suspected of difficulty with night vision was found to have 20/20 vision bilaterally with normal appearing maculas  should outline the benefits, limitations, and potential implications of genetic testing with affected individuals and their caregivers , 40. ForRPE65 gene (c.11 + 5G > A). The ordering provider incorrectly interpreted the positive test result as diagnostic of RPE65-related IRD, changed the diagnosis to Leber congenital amaurosis, and advised her that she would likely qualify for treatment with Luxturna.A 59-year-old female was referred for genetic counseling of her IRD. She noticed significant night blindness at age 17 and was diagnosed with retinitis pigmentosa at age 28. Further, at the age of 28, she was diagnosed with polycystic kidney disease and underwent a unilateral kidney transplant 2 days later. Genetic testing with a limited panel of 31 genes implicated in early onset retinal disease was ordered before referral for genetic counseling and indicated that she was positive for a heterozygous pathogenic variant in the NPHP1 gene (interpreted as pathogenic) in the homozygous state. The individual's retinal disease, kidney disease, and family history are consistent with a syndromic form of retinal dystrophy due to the NPHP1 gene. The presence of a pathogenic variant in the RPE65 gene indicates that she is additionally a carrier of recessively inherited RPE65-related retinal dystrophy, a state that does not qualify her for Luxturna gene therapy.During the genetic counseling appointment, the individual disclosed that she had 2 brothers who died of kidney disease in late childhood. Subsequent additional genetic testing revealed a complete deletion of the Post-test genetic counseling is also important for interpreting and discussing genetic test results in light of patient history , 37. AccBBS1 gene, p.Met390Arg (pathogenic) and p.Glu224Lys (likely pathogenic), resulting in a diagnosis of Bardet-Biedl syndrome. This condition is associated with multiple risks of other health conditions, requiring significant medical follow-up. The genetic counselor provided medical management guidelines established for Bardet–Biedl syndrome to the patient, the referring retina specialist, and the primary care physician. These include regular ophthalmic evaluations, body mass index calculations, diabetes testing, and lipid profiling.A female with progressive impairment of night vision from late childhood, legal blindness by age 16, and a diagnosis of severe nonsyndromic retinitis pigmentosa was referred for evaluation. Genetic testing showed that she was positive for 2 heterozygous mutations of the Barriers to genetic testing remain a challenge for individuals with IRDs and health care professionals. One significant challenge for the widespread implementation of genetic testing is the varied payment/reimbursement mechanisms across countries , 43. In Despite the complexity of genetic tests and their practical limitations, it is necessary to overcome these barriers to ensure that the best medical care is provided for individuals affected by IRDs. Some potential approaches to overcome these barriers and improve the patient’s experience include expanding education of health care professionals about IRDs and genetic testing options, creating tools to connect affected individuals with experts in IRDs , and impTo enhance genetic disease management, a decision tree algorithm may be useful for easy reference in clinical practice to help determine the indications for molecular diagnosis in individuals with IRDs Fig. . The AmeRetina specialists or ocular genetic counselors will review the individual’s clinical, ocular, and family history . In someIf testing reveals a variant of uncertain significance, additional clinical data may be collected or family members may be tested; however, this does not always help to elucidate the effect of the variant . VariantIf test results are negative for pathogenic variants, this could either indicate that the individual does not have a genetic cause of their disease  or, moreInherited retinal diseases present challenges in molecular diagnostics because of their high genetic heterogeneity, overlapping clinical presentations, and variability in inheritance patterns. Currently, ophthalmologists who come across individuals experiencing symptoms consistent with an IRD may benefit from the expertise of an ocular genetic counselor or IRD specialist. With the complexity of IRDs, collaboration with specialists may allow for improved decision-making regarding the most appropriate genetic test panel, pre- and post-test counseling, and accurate interpretation of the test findings in the context of the clinical diagnosis. While adequate genetic testing resources and specialty providers may be unavailable in many regions, improved understanding of the genetics within ophthalmic subspecialties and increased use of telemedicine-based options may help meet the needs of individuals to fully understand their diagnosis.


Plectropomus leopardus, Lacepède . To determine population structure, we genotyped 417 Great Barrier Reef coral trout from four populations sampled in 2 years (1996 and 2004) at nine microsatellite loci. To test for exploitation effects, we additionally genotyped 869 individuals from a single cohort (ages 3–5) across eight different reefs, including fished and control populations. Genetic structure differed substantially in the two sampled years, with only 1 year exhibiting isolation by distance. Thus, genetic drift likely plays a role in shaping population genetic structure in this species. Although we found no loss of genetic diversity associated with exploitation, our relatedness patterns show that pulse fishing likely affects population genetics. Additionally, genetic structure in the cohort samples likely reflected spatial variation in recruitment contributing to genetic structure at the population level. Overall, we show that fishing does impact coral reef fishes, highlighting the importance of repeated widespread sampling to accurately characterize the genetic structure of reef fishes, as well as the power of analysing cohorts to avoid the impacts of recruitment‐related genetic swamping. The high temporal and spatial variability in genetic structure, combined with possible selection effects, will make conservation/management of reef fish species complex.Many coral reef fishes are fished, often resulting in detrimental genetic effects; however, reef fishes often show unpredictable patterns of genetic variation, which potentially mask the effects of fishing. Our goals were to characterize spatial and temporal genetic variation and determine the effects of fishing on an exploited reef fish,  Additionally, Hogan et al. (S. partitus (bicolor damselfish). Similarly, Taylor and Hellberg  associated with aggressive harvesting. Losses of genetic diversity due to fishing pressure have also been reported for Gadus morhua . Such high dispersal potential is consistent with the lack of genetic structure reported in the same study  individuals and potentially those with adaptive phenotypes  from eight reefs that differ in fishing pressure and tested for the effects of exploitation on genetic diversity and structure. We found chaotic spatial and temporal genetic structure makes repeated and widespread sampling critical when characterizing genetic structure in species such as 22.1The GBR, located on the eastern coast of Australia, is the world's largest coral reef ecosystem Figure . As partTwo types of DNA analyses are included in this study, constituting two related, but separate analyses: 1) spatial/temporal genetic analyses and (2) cohort effects of fishing genetic analyses. The spatial/temporal samples include individuals obtained from four reefs, closed to fishing, sampled in 1996 and 2004 2SO4, 2 mM MgSO4, 0.1% Triton X‐100, 0.1 mg/ml bovine serum albumin, 200 µM of each dNTP, 200 nM of forward and reverse primers, 0.5 U of Taq polymerase (Bio Basic Canada Inc.) and 10 µl of diluted first‐round PCR product. Thermocycling conditions used for the ligation PCR were as follows: 94°C for 2 min, six cycles of 94°C for 30 s, 30 s at 60 and 72°C for 60 s, followed by 72°C for 5 min. The barcoded products were pooled, run on an agarose gel, and the bands were excised and cleaned using GenCatch Gel Extraction Kit (Epoch Life Science Inc.). The pooled amplicons were assessed on a 2100 Bioanalyzer (Agilent Technologies), diluted to 60 pM and sequenced using an Ion Torrent NGS personal genome machine (Thermo Fisher Scientific). We also sequenced negative controls from the first PCR.Multiplex PCRs of 16 microsatellite loci were performed in 7.0 µl reactions . We calculated individual heterozygosity, generated the mean heterozygosity across all genotyped loci and used a linear model with effects for population (N = 4) and year (1996 and 2004), and the interaction of the two terms using the ‘lme4’ v1.1–21 R package  with significance tested over 9999 permutations implemented in GenoDive v.3.04  to estimate allelic richness. To test for the effects of spatial region on genetic diversity, we used a linear mixed effects model in the ‘lme4’ v1.1–21 R package . We then used a linear mixed effects model with fixed effects of comparison type, cohort age, and the relative location of the reefs being compared , and locus as a random effect. We used a second linear mixed effects model to test for interaction effects with fixed effects for the type of comparison, cohort age, and type‐by‐age interactions, and random effects for locus. We then used likelihood ratio tests to assess significance of terms of interest as described above. For effects that were significant based on the results from the linear mixed effects model, we used the ‘lsmeans’ v2.30–0 R package .As individuals age within a cohort, the mean relatedness among individuals is expected to increase if selection acts to favour certain families . PL09 was removed from the cohort study due to low genotyping success in one of the eight populations.3.13.1.1We found that 14 of 72 tests for HWE deviations were significant Table , with alp = 0.74) effects were significant for heterozygosity among the four populations included in the study. We also found that population‐by‐year interactions were not significant (p = 0.34) However, exact tests of allele frequency distribution showed that Reef 18071  and Reef 21132  exhibited significant temporal change from 1996 to 2004.We measured individual‐level heterozygosity for the four populations in each year Figure . Our lik3.1.2FST values were 0.001 (p = 0.183) in 1996 and 0.004 (p = 0.009) in 2004. Although divergence in 1996 is low and not significant, there were some interesting patterns in the pairwise tests of genetic differentiation. In 1996, the pairwise FST values for the six population pairs ranged from −0.004 (p = 0.956) to 0.004  and one pairwise FST value between two adjacent reefs (Reefs 20142 and 18071) significantly differed from zero. Additionally, the exact test of allele frequency distributions showed significant differentiation between two adjacent reefs (14147 and 18071), but not in any other comparisons  to 0.008 (p = 0.016), with three population pairs showing significant FST values and one pair that approached significance ; however, the opposite was true in 2004 . These patterns of spatial population divergence are again, consistent with the chaotic genetic patchiness previously reported in coral reef fish populations  effects on allelic richness and age‐by‐status interaction (p = 0.093) and status (p = 0.083) effects that approached significance. As expected, allelic richness declined with age  in the entire cohort dataset than random expectation. Using two linear mixed models, we found that age effects and status‐by‐age interaction effects on pairwise relatedness were significant  within the Mesoamerican barrier reef system . Although we expected a decrease in heterozygosity associated with cohort age, we did not find that effect, perhaps due to the lower sensitivity of heterozygosity as a genetic diversity measure relative to allelic richness. We would expect changes in allele frequency distribution to be the most sensitive measure of genetic change, and we found that allele frequency shifts tended to be common, showing that cohorts were likely to undergo genetic changes as they aged.As a cohort ages, genetic divergence among reefs should increase as selection and random genetic change occur, independent of fishing pressure. However, fishing pressure should increase those effects as fishing can be selective and reduced population size enhances random genetic change  cohorts, with the direction of change depending upon the specific cohort and the age of the individuals. We also found that the direction of change in pairwise relatedness varied with cohort age. Although our observed changes in relatedness may be explained by changing selective pressures  resulted in no significant loss of genetic diversity  were also smaller than those from protected areas on the GBR, likely indicating that other underlying processes are responsible for driving the reported size differences. Furthermore, inconsistencies in the population response to fishing pressure have previously been reported in this species. For example, P. leopardus populations in Western Australia experienced no declines in abundance, despite being open to fishing, while populations in protected areas declined in abundance, indicating some level of resilience to fishing pressure as well as variability in population mortality schedules . Furthermore, the significant, but low, global FST values at each cohort age indicate that although spatial variation in recruitment likely contributed to cohort divergence, there was still genetic high similarity across the sampled populations. Clearly gene flow via larval dispersal is critical for replenishing diversity in fished reefs that experience the added stressors brought on by exploitation, highlighting the value of marine reserves in coral reef environments.Evolutionary processes contribute to effective conservation of fishes  in the Gulf of Mexico caught as by‐catch in fishing trawls were more related to each other than chance alone would predict, indicating that certain families are more susceptible to exploitation. In our study, both the Green and Blue (heavily fished) reefs experienced an increase in relatedness from ages 3 to 4, but not ages 3 to 5. Given that the protected and fished reefs showed similar patterns, it is unlikely to be selective effects related to fishing pressure. The lack of an effect on Blue reefs may be explained by the ongoing fishing pressure resulting in population adaptation to fishing‐related selection through social learning, which is thought to play a role in evading capture (discussed by Leigh et al., Fishing pressure has been shown to target specific families in a population, while others tend to avoid fishing mortality, thus inflating relatedness as populations are fished. For example, Saillant et al.  found thP. leopardus are top‐predators that are actively exploited both commercially and recreationally on the GBR. Here, we report spatial and temporal chaotic genetic structure, a pattern previously reported for other reef fishes, but not for P. leopardus. Using DNA from known‐aged fish, we tracked an individual cohort as it aged, allowing us to examine populations without the confounding effects of new recruits changing the genetic structure of the ‘populations’. Furthermore, we have shown that pulse fishing likely affects P. leopardus, likely through selection, and resilience to stressors is likely population‐specific, and may be unpredictable, as other studies on P. leopardus populations have shown (Mapstone et al., In general, coral reef fishes tend to be challenging for population genetic applications in conservation and management, given their larval dispersal and diverse life histories. However, reef fishes are experiencing multiple stressors, and it is critical that these iconic species are conserved for both economic and ecological purposes, and genetic analyses can provide valuable insights. In this study, we focused on a particularly problematic reef fish species – The authors report no conflict of interest associated with this work.Supplementary MaterialClick here for additional data file.


Metrosideros polymorpha, a unique example of the incipient radiation of woody species, to examine how an ecological divergence continues in the face of gene flow. We analyzed the whole genomes of 70 plants collected throughout the island of Hawaii, which is the youngest island with the highest altitude in the archipelago and encompasses a wide range of environments. The continuous M. polymorpha forest stands on the island of Hawaii were differentiated into three genetic clusters, each of which grows in a distinctive environment and includes substantial genetic and phenotypic diversity. The three genetic clusters showed signatures of selection in genomic regions encompassing genes relevant to environmental adaptations, including genes associated with light utilization, oxidative stress, and leaf senescence, which are likely associated with the ecological differentiation of the species. Our demographic modeling suggested that the glaberrima cluster in wet environments maintained a relatively large population size and two clusters split: polymorpha in the subalpine zone and incana in dry and hot conditions. This ecological divergence possibly began before the species colonized the island of Hawaii. Interestingly, the three clusters recovered genetic connectivity coincidentally with a recent population bottleneck, in line with the weak reproductive isolation observed in the species. This study highlights that the degree of genetic differentiation between ecologically-diverged populations can vary depending on the strength of natural selection in the very early phases of speciation.Ecological divergence in a species provides a valuable opportunity to study the early stages of speciation. We focused on  Metrosideros polymorpha, an endemic woody species in the Hawaiian Islands, is a good system to study developing genetic barriers in a species, because it colonized the diverse environments and diversified the morphology for a relatively short period of time. We analyzed the genomes of 70 M. polymorpha plants from a broad range of environments on the island of Hawaii to infer the current and past genetic barriers among them. Currently, M. polymorpha plants growing in different environments have substantially different genomes, especially at the genomic regions with genes putatively controlling physiology to fit in distinct environment. However, in its history, they had hybridized with one another, possibly because plants formerly growing in different environments came into close contact due to the climate changes. It is suggested that genetic barriers can easily strengthen or weaken depending on environments splitting the ecology of a species before reproductive isolation becomes complete.Knowledge about how genetic barriers are formed between populations in distinct environments is valuable to understand the processes of speciation and conserve biodiversity.  Adaptation to distinct resources or environments often causes genetic differentiation in a species . The genMetrosideros polymorpha Gaud. (Myrtaceae), which is an endemic and dominant species in the native forests in the Hawaiian Islands, represents a unique example of incipient adaptive radiation of a woody species. After colonizing the Hawaiian Islands 3.1–3.9 million years ago [ears ago ,6, the sears ago ,8. The mears ago ,9–12. Moears ago ,12,13. Hears ago . Despiteears ago ,16, the M. polymorpha is not evident. Metrosideros polymorpha plants are classified into different varieties based on morphological characteristics and habitat environments [M. polymorpha are comprised of not only plants with representative morphologies of particular varieties but also plants with intermediate morphologies. This suggests that hybridization between varieties is common in the species and reproductive isolation between varieties is likely to be incomplete [Metrosideros species/varieties across islands showed the genetic distances between the same varieties in different islands is larger than those between different varieties within islands despite its phenotypic similarity, suggesting that the radiation occurred repeatedly in each island [M. polymorpha or more complex demographic history should be considered.The genetic differentiation between ecologically-divergent populations in ronments  and generonments . Howevercomplete . Moreoveh island ,20. TherM. polymorpha maintained the ecological divergence on the island of Hawaii. As the island of Hawaii is the largest, youngest island with highest altitude in the archipelago, it encompasses the most variety of climates associated with the variation in altitude and substrate age compared to other islands [M. polymorpha populations on the island is a suitable system to study the most recent and extensive adaptive evolution of the species and gain insight into demographic and selective forces sustaining an ecological divergence. Previously, population genomic analyses on the island of Hawaii focused on nine populations along an altitudinal transect [newelii [M. polymorpha on the island and identified the primary evolutionary units that are genetically differentiated along environmental gradients across the island. Secondly, we employed multiple methodologies for selection scan and identified candidate genes with relevant functions for environmental adaptation. Lastly, we used coalescent modeling to infer the demographic history of M. polymorpha. We discuss how ecological divergence has been maintained in this species despite the presence of gene flow.This study aimed to establish how  islands . Therefotransect  and the [newelii . With th[newelii , we inveM. polymorpha plants from 55 sites on the island of Hawaii with a 29.6-fold mean sequencing coverage  . In the principal component analysis (PCA), the PC1 and PC2 scores showed that the 70 plants were differentiated into three groups, which mostly corresponded to the three common varieties found on the island of Hawaii: var. lymorpha . Var. neDMIXTURE  supporteTURE [K) . Furtherat K = 5 .M. polymorpha plants. Environmental distances accounted for 7.1% (p < 0.001) of the genetic variation and 3.2% (p < 0.001) while controlling for geographic distances (p < 0.001), but after controlling the confounded fractions, the proportion decreased into 0.7% . As a result, both geographic and environmental distances significantly accounted for the genetic variation of the 70 istances . The propolymorpha and var. nuda was primarily distributed in subalpine zones, where cloud does not occur frequently (Cloud frq in incana was characterized by high mean air temperature and high vapor pressure deficit (VPD in glaberrima and var. newelii occurred in wet environments in mature forests on old substrate (Lava age in Scatter plot of the first two components of RDA (RDA1 and 2) suggested that the three primary genetic clusters identified in PCA and ADMIXTURE were differentiated according to the environment of habitat . The clut VPD in , indicatM. polymorpha populations were mainly differentiated according to environmental differences on the island of Hawaii. Hereafter, we focus on the three genetic clusters according to the plot of their PC1 and 2 scores (K = 3 (glaberrima and var. newelii), incana (including var. incana), and polymorpha (including var. polymorpha and var. nuda) genetic clusters.In sum, 2 scores , RDA1 an2 scores , and thes  exceeded 100 kb (π: 0.00522 ± 0.00367) was observed in the glaberrima cluster than the other two genetic clusters , and absolute genetic distance (DXY). As statistical power to detect selection varies among methodologies [To search for candidate genomic regions under selection, we here employed approaches using extended haplotype homozygosity, sweep scan and dologies , combiniFST calculated for non-overlapping 20 kb windows. Genome-wide FST was 0.103 ± 0.091, 0.099 ± 0.09, 0.132 ± 0.116 between the glaberrima and incana, the glaberrima and polymorpha, and the incana and polymorpha cluster, respectively. We identified outlier FST at 17, 11, and 40 windows between the glaberrima and incana, the glaberrima and polymorpha, and the incana and polymorpha clusters, respectively  and NTL9 encoding a membrane-associated NAC transcription factor (Mpol2_06G0152800)  .D windows in the polymorpha cluster  and three regions in the polymorpha clusters (P1–P3). The P1 and P2 regions significantly overlapped with the bottom 5% π windows and RAiSD outliers, and all the P1, P2, and P3 regions were among the lower 5% Tajima’s  cluster . Among tFST outlier windows found above, different clusters had different alleles at more than half SNPs embedded in a window. The most prominent signature was found in a window with large FST between the incana and polymorpha cluster, which included a gene putatively functioning in the RNA-directed DNA methylation pathway (Mpol2_07G0105400), membrane-associated kinase regulator (Mpol2_07G0105600), and one gene with unknown function (Mpol2_07G0105500)  .−3, the effective population size (Ne) of the three clusters corresponded very well and the cross-coalescent rates (CCRs) stabilized around one, strongly suggesting they were not separated populations   analysisulations . The subulations . The genradually . Then, attleneck , suggested again . By usinneration  and a generation , three g−9 per site per generation [N0) than the incana and polymorpha clusters , which harvests sunlight [WRKY28 (Mpol2_01G0166100) is likely to respond to oxidative stress as its expression is increased under oxidative stress in A. thaliana [JUNGBRUNNEN1 (Mpol2_01G0168500), whose overexpression delays leaf senescence in A. thaliana [Other candidate genes were found in the P1 region, which showed a signature of selective sweep in the polymorpha cluster . Becausesunlight ,41, and thaliana –47. In tthaliana , making thaliana , could bthaliana . This cothaliana .The nine EHH regions  could beFST outliers  or alleM. polymorpha adapted to the diverse environments on the Hawaiian Islands is a long-standing question. A scenario in which multiple, ecologically-diverged M. polymorpha lineages independently colonized each island was suggested because of similar ecological and morphological adaptations on different islands. However, genetic differentiation between varieties within islands was negligible compared to that between islands [M. polymorpha occurred on each island. Our data suggest a new scenario in which these two scenarios may not be mutually exclusive.How  islands ,20, suggMetrosideros species are monophyletic and most closely related to a species from southern Polynesia [Ne found around 5 Mya was associated with the introduction into the Hawaiian Archipelago through long-distance dispersal. Considering that long-distance dispersal does not always result in a temporal reduction in Ne [Previous phylogenic studies suggested that all the Hawaiian olynesia ,6. Thus,on in Ne ,55, the on in Ne ,57, and/on in Ne .Arabidopsis, the split was estimated to start 4.9–5.1 Mya in fastsimcoal  in the elevational range of the species distribution could lead to high genetic diversity through increased hybridization between other genetic clusters or varieties. As a previous implication based on microsatellite polymorphism [polymorpha and var. nuda) is not found on Kauai Island, and it is rare on Oahu Island [incana) does not exist on Kauai Island and remains in small populations on Oahu Island [Among the three clusters, the archipelago-wide distribution of var. del M22; . Var. glmorphism , this lau Island , where tu Island , where fu Island , it is pNe [Metrosideros polymorpha, however, remarkably increased the Ne since the first immigration to the archipelago . After the genomic DNA was fragmented into approximately 350 bp fragments using a sonicator , sequencing libraries were synthesized using NEBNext Ultra II DNA Library Prep Kit for Illumina  or a TruSeq DNA PCR-Free High Throughput Library Prep Kit  following the manufacturer’s protocols. Each DNA library was tagged with distinctive barcode sequences and equimolarly pooled into a tube. Of the 70 samples, 30 were sequenced in four lanes in HiSeq2500 (Illumina) with the paired-end 125 bp chemistry, while the remaining were sequenced in four lanes in HiSeq X Ten (Illumina) with the paired-end 150 bp chemistry. Raw sequence data are deposited in the DNA Data Bank of Japan (accession: DRA011715). We obtained an average of 10.3 G bases (range: 2.95–34.4 Gb) per sample.M. polymorpha reference genome sequence [http://broadinstitute.github.io/picard/). We obtained alignments with an average of 29.6-fold (range: 9.5–52.5 fold) coverage.After trimming adapter sequences and low-quality bases using Trimmomatic (ver. 0.33) , the clesequence  using thsequence . For twoM. polymorpha gene annotation [Nucleotide polymorphisms against the reference sequence were called for each sample using GATK HplotypeCaller (ver. 3.6) . The resnotation . A totalM. polymorpha plants was assessed using ADMIXTURE (ver. 1.3) [K = 1–10) with 10 replicated runs per K. We used 57,196 four-fold degenerate SNPs, in which the minor allele frequency was larger than 0.05, LD between any sites within 50 bp did not exceed 0.5, and less than 20% missing data was allowed per SNP. In the PCA, we used 16,972 four-fold degenerate SNPs, which were retained after removing SNPs with missing genotypes from the SNP dataset used in ADMIXTURE.The population structure among the 70 er. 1.3) , principhttp://climate.geography.hawaii.edu) [https://pubs.usgs.gov/of/2007/1089/) [dbmem function in adespatial package (ver. 0.3.14) [ordiR2step function in vegan package (ver. 2.5.7) [varpart function in vegan package based on the full and partial RDA models. Statistical significance of explanatory variables in each model was examined by a permutation test (999 permutations).In order to evaluate the effect of geographic and environmental distances on the genetic differentiation, we modeled the genetic variation among the 70 plants with geographic (GEO) and environmental (ENV) distance matrices and conducted RDA to partition the variation. Genetic variation was represented by the genotypes at the same SNPs used for PCA. The environmental distances were estimated from the scaled 12 environment variables, including substrate age, mean annual air temperature, annual precipitation, cloud frequency, net radiation, albedo, Penman-Monteith potential evapotranspiration, soil evaporation, transpiration, vegetation height, and vapor pressure deficit. These data were obtained from the Geography Department of the University of Hawaii (aii.edu)  and the 7/1089/) . The geo 0.3.14) . In orde. 2.5.7) . In the In the subsequent analyses, we focused on 40 samples with >90% ancestry in the population clustering, assuming three ancestries  between SNP genotypes at a maximum of 100 kb apart. The ROH, i.e., the stretches of homozygous genotypes, and Tajima’s D in 20-kb nonoverlapping sliding windows were calculated for each genetic cluster using VCFtools [π) in 20-kb sliding windows using pixy [LD decay was estimated using PopLDdecay , in whicVCFtools . Nucleoting pixy .FST, and diversity and divergence parameters such as π, Tajima’s D, and DXY.Genomic regions with a signature of selection were searched based on haplotype homozygosity estimates, sweep statistics, local deviation of M. polymorpha genetic clusters, we calculated cross-population extended haplotype homozygosity (xp-EHH) around SNPs with more than four minor alleles in each pair using the R package rehh (ver. 3.1.0) [−6. Around each outlier SNP, genomic regions showing EHH in one genetic cluster against the other was identified using calc_furcation function in rehh [If the frequency of a beneficial allele increases rapidly, the haplotype homozygosity around the allele becomes more extended than expected with normal recombination. Therefore, significantly different haplotype homozygosity between genetic clusters indicates positive selection affecting one genetic cluster. For each pair of three . 3.1.0) . As this. 3.1.0)  and Shap. 3.1.0) . SNPs we in rehh  and consThe signatures of selective sweep were searched using RAiSD (ver. 2.0) . RAiSD cFST outlier scan. We characterized each 20-kb window with an FST vector, which is composed of three pairwise FST values and embedded within a three-dimensional numerical space. The local deviation of each window was measured based on k-nearest neighbor (kNN) method [FST between the gaberrima and incana, glaberrima and polymorpha, and inacna and polymorpha clusters, respectively) and windows with less than 20 such SNPs were removed. Pairwise FST [k windows, where k = 6993 was determined based on the diagnostic plot provided by Pfeifer et al. [FST, which is the difference between an FST vector and the medoid of the all pairwise FST vectors [Local deviation of genetic differentiation from genome-wide one was evaluated by ) method . To exclwise FST  were calwise FST , the degr et al. . Windows vectors , were reπ), Tajima’s D, and the absolute genetic differentiation between clusters (DXY) in 20-kb sliding windows using pixy [π and Tajima’s D; top 5% for DXY).We further computed nucleotide diversity within clusters . This calculation led a range of mutation rate of 2.9–9.5 × 10−9 and 8.8–28.5 × 10−9 for a generation time of 10 and 30 years, respectively.The historical change in the effective population size and gene flow was first inferred using a model-free approach. The coalescent times of haplotypes within and across genetic clusters were computed using MSMC (ver. 2.1.1)  with eig20 years . It is r20 years , while a Populus . These u Populus . Briefly reports ,94–96, t reports , and theN0, N1, N2, NA, and NB), time points of population divergence (T1 and T2), a time point when migration rates changed (TC) and migration rates between genetic clusters . The models M1–12 hypothesized that the divergence among the three genetic clusters occurred relatively recently, thus the search range for T2 was set to be less than 15,000 generations before present . This corresponds to the scenario that the three genetic clusters diverged after the colonization of the island of Hawaii. On the other hand, the models M13–24 hypothesized that the divergence had occurred before the colonization of the island and the migration rates between genetic clusters changed after that, thus the search range for T2 and TC was set as >15,000 and <15,000 generations, respectively. We simplified the models by assuming constant effective population sizes and unidirectional migration rates to reduce computational effort. Using fastsimcoal2 (ver. 2.6) [−9 per site per generation [We then hypothesized 24 demographic models to estimate demographic parameters using SFS . The parer. 2.6) , the parer. 2.6) . For theneration .S1 TableSample identifications, localities (including GPS coordination), leaf trait data, and variety classification are shown.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 Figglabrrrima, var. incana, var. polymorpha, var. newelii, var. nuda, and unclassified respectively. Letters in parenthesis indicate volcanos where samples were collected . Samples used for the coalescent simulations and selection scans are indicated with an asterisk.G, I, P, NW, ND, and Un indicate var. (EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 FigHorizontal dashed lines indicate the thresholds for outlier SNPs (Mean + 20 SD).(TIFF)Click here for additional data file.S4 FigFManhattan plot of STbetween (A) the glaberrima and incana clusters, (B) the glaberrima and polymorpha clusters, and (C) the incana and polymorpha clusters. Significantly deviated FST from the genome-wide distributions were indicated in red.(TIF)Click here for additional data file.S5 Figπ, Tajima’s D, FST, and DXY around the three genes overlapped with an outlier 20-kb window were shown.Allele frequency of non-reference allele (frq(ALT)), (EPS)Click here for additional data file.S6 Fig(A) Observed and (B) expected two dimensional SFSs for each pair of the three genetic clusters. (C) Residuals between the expected and the observed SFS. (D) Histograms of residuals.(EPS)Click here for additional data file.S7 Fig(EPS)Click here for additional data file.


Astroniumfraxinifolium in an area degraded during the construction of ahydroelectric dam. We mapped, sampled, sexed, and genotyped 386 individuals inthe regenerant population (RP), as well as 128 adult trees located along twohighways adjacent to the degraded area; one in Mato Grosso do Sul State (MS) andother in São Paulo State (SP). Parentage analyses was carried out for 370individuals of the RP population, using as putative parents 348 individuals fromRP and all 128 individuals sampled in MS and SP. Based on parentage analysis andeight microsatellite loci, our analyses revealed that for individuals of the RPwith an identified father (pollen donor), 1.1% of the pollen was dispersed up to532 m, while for those with an identified mother (seed donor), 0.5% of seedswere dispersed up to 4,782 m. However, a large proportion of pollen (76.5%) andseeds (57%) immigrated from trees outside the sampled populations. Pollen andseeds were dispersed through a pattern of isolation by distance. Geneticdiversity was significantly similar between adults of both highway populationsand individuals from RP, with significant levels of inbreeding detected only inRP. Our results demonstrate that the nearest trees contributed pollen and seedsfor the recovery of the degraded area, indicating reproductive spatial isolationamong the sampled populations due to the damming of the river. Such results helpto understand the process of regeneration for A.fraxinifolium in regenerant populations to informstrategies for conservation and environmental recovery with this species.Gene flow studies provide information on gene exchange between populations, whichis essential for developing genetic conservation strategies. Such analysesenable a better understanding of the life history and seed and pollen dispersalmechanisms of plant species. In this study, we investigate pollen and seed flowin a regenerant population of the pioneer species  Human interference in environments can trigger changes in biodiversity and disruptecosystem processes in tropical forests , especiaHowever, many tropical tree species are resilient to the effects of spatialpopulation isolation and respond to genetic pressures through mechanisms oflong-distance gene flow, long life span, and flexible mating systems . Gene flFew have assessed the ecological genetic effects of Hydropower Plant (HPP) damconstruction or rivers as barriers to pollen and seed dispersal among treepopulations –25. SuchAstronium fraxinifolium Schott. (Anacardiaceae),emerged naturally in several of these inhospitable sites. This species has beenpreviously reported as occurring naturally in totally degraded habitats, especiallyalong road margins or in forest fragments  5–296m) .A. fraxinifolium have different populationdynamics coupled with the dispersion method, particularly for seeds. Therefore,there is a clear need for further in-depth studies in the region to verify theexistence of nearby genetically structured populations. These results can alsobe attributed to the small number of markers analyzed herein when compared tohigh-throughput methods such as single nucleotide polymorphisms (SNP) that canincrease the resolution of such analyses.The population structure analysis based on STRUCTURE and PCA analysis showed thatthe main genetically related clusters are MS and RP, suggesting the existence ofat least two genetic clusters. The STRUCTURE results suggest that MS and RP aremore genetic similar than SP and RP populations. However, our results alsodemonstrate that there are different genetic clusters for which it is impossibleto determine the genetic origin of the regenerant individuals in the RP.Furthermore, a large proportion of the pollen and seed donors to the RP were notidentified. This suggests that there are considerable numbers of individuals ofthe species in the landscape that were not sampled due to restricted access or that the populations ofFinally, we suggest that management of areas affected by dam construction must bedone with caution, particularly in terms of understanding the genetic mechanismsof species. For species with a dioecious sexual system, for example, seeddispersal is an important factor in the genetic dynamics of populations.Therefore, priority must be given to the conservation of all forest fragmentsand remnants that can contribute significantly to genetic variability and ensurethe maintenance of the species into the future.S1 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


STAT3) oncogene is a transcription factor with a central role in head and neck cancer. Hypopharyngeal cells (HCs) exposed to acidic bile present aberrant activation of STAT3, possibly contributing to its oncogenic effect. We hypothesized that STAT3 contributes substantially to the bile reflux‐induced molecular oncogenic profile, which can be suppressed by STAT3 silencing or pharmacological inhibition. To explore our hypothesis, we targeted the STAT3 pathway, by knocking down STAT3 (STAT3 siRNA), and inhibiting STAT3 phosphorylation (Nifuroxazide) or dimerization , in acidic bile (pH 4.0)‐exposed human HCs. Immunofluorescence, luciferase assay, Western blot, enzyme‐linked immunosorbent assay and qPCR analyses revealed that STAT3 knockdown or pharmacologic inhibition significantly suppressed acidic bile‐induced STAT3 activation and its transcriptional activity, Bcl‐2 overexpression, transcriptional activation of IL6, TNF‐α, BCL2, EGFR, STAT3, RELA(p65), REL and WNT5A, and cell survival. Our novel findings document the important role of STAT3 in bile reflux‐related molecular oncogenic events, which can be dramatically prevented by STAT3 silencing. STA‐21, SI3‐201 or Nifuroxazide effectively inhibited STAT3 and cancer‐related inflammatory phenotype, encouraging their single or combined application in preventive or therapeutic strategies of bile reflux‐related hypopharyngeal carcinogenesis.The signal transducer and activator of transcription 3 ( STAT3 pharmacologic inhibition consisted of blocking (i) the upstream extracellular receptor JAK2, which affects STAT3 phosphorylation; or (ii) the SH2 domain of phosphorylated STAT3, which blocks its dimerization and DNA binding, and therefore preventing the subsequent target‐gene transcription.To investigate whether STAT3 actually contributes to early oncogenic molecular events, previously linked to bile‐induced hypopharyngeal carcinogenesis,22.12, as previously described.®). pH at 4.0 was adjusted by 1 M HCl (using a pH meter). ‘BA’ or acidic bile at pH 4.0, the cut‐off of reflux diseaseNormal human hypopharyngeal cells  were cultured, in Human Hypopharyngeal Normal Cell Culture Media (Celprogen Inc.), at 37°C in humidified air and 5% CO®).Experimental and control groups were cultured in parallel. All the experiments were independently repeated three times. Cells were harvested immediately after the last treatment by 0.05% trypsin‐EDTA ; sc‐29493, Santa Cruz Biotechnology Inc.) and Control siRNA, as a negative control  were used. STAT3 and Control siRNAs were diluted to a final concentration of 5 nM in serum‐free culture medium , including HiPerFect®Transfection Reagent , according to the manufacturers’ instructions, and incubated for 10 min at room temperature. Cells were mixed with transfected complexes, seeded at 1.5 × 105 cells/well of six‐well plates and incubated under normal growth conditions (at 37°C and 5% CO2) (To knockdown  5% CO2) .Sixteen hours after transfection, media were changed with complete growth medium and 4 h later were treated with ‘BA’ for 7 min. The media were removed and replaced with serum‐free medium until the next day, we repeated the 7‐min treatment two times with an interval time 6 h. After 12 h, cells were treated for 7‐min and immediately after this last treatment media were removed, the cells were washed once with PBS and harvested either (i) for total protein isolation, using M‐PER reagent , or (iii) for total RNA isolation using RNA mini kit (Qiagen). Assays were carried out according to the manufacturer's instructions and performed in triplicate. All experiments were independently repeated two times.2.3® 2000 (Invitrogen™), according to manufacturer's procedure.Luciferase assay was performed to measure the transcriptional activity of STAT3 dimers (homodimers or heterodimers) in HCs exposed to (a) BA with knockdown of STAT3 or (b) BA with pharmacologic inhibitors of STAT3, Nifuroxazide, S3I‐201 or STA‐21, compared to BA alone and controls, as described in 2.4®; Thermo Fisher Scientific) and treated with BA, with or without pharmacologic inhibitors of STAT3, Nifuroxazide, SI3‐201 or STA‐21, acid at pH 4.0 alone. Primary antibodies were used, including anti‐p‐STAT3 (Tyr705)  or anti‐phospho‐NF‐κB . Secondary antibodies were also used, including anti‐rabbit or anti‐mouse DyLight®488 . Prolong Gold Mountant with diamidino‐phenylindole  was also used for nuclear staining (blue colour).Immunofluorescence (IF) assay was performed for p‐STAT3 (Tyr705) and p‐NF‐κB (p65 S536), as previously describedZeiss Confocal microscope and imaging software by Zen were used to examine stained slides and captured images, respectively .2.5STAT3 expression, and to determine the effect of STAT3 knockdown or its pharmacologic inhibition on BA‐induced STAT3, NF‐κB and Bcl‐2 protein levels. We used primary antibodies for p‐STAT3 (Tyr 705) (clone B‐7), STAT3 (clone F‐2), p‐NF‐κB (p65 Antibody 27. Ser 536), bcl2 (Clone N‐19), Histone 1 (AE‐4) and β‐actin (C4) (Santa Cruz Biotechnology Inc.). Protein levels obtained by Western blot analysis were quantified by the Gel imaging system (Bio‐Rad) in each nuclear or cytoplasmic cellular compartment . Protein levels obtained by ELISA were quantified by Gen5™ software reading the absorbance values using a microplate reader . Assays were carried out according to the manufacturer's instructions and performed in triplicates and repeated two times, independently.Western blot analysis and a direct enzyme‐linked immunosorbent assay (ELISA) were performed, as previously described2.6EGFR, TNF‐α, IL6, STAT3, RELA(p65), REL, BCL2 and WNT5A, previously associated with HNSCChGAPDH) .Quantitative real‐time polymerase chain reaction (qPCR) analysis  was performed, as previously described2.7® Luminescent Cell Viability Assay (Promega Corp.) was used as described in Cell Titer‐Glo2.8t test analysis  were used to demonstrate the differential expression  for each analysed gene, protein expression and cell viability between different experimental and control groups.GraphPad Prism 7 software and multiple 33.1STAT3 knockdown prevented the BA‐induced activation of STAT3, as indicated by weak nuclear straining of p‐STAT3 (Tyr705) in treated HCs compared to BA alone  relative to BA alone  assay revealed that e Figure . IF alsoe Figure . Westerns Figure . Quantife Figure . IF, Wess Figure ,B,C‐a. Ss Figure , accompan Figure ‐b,c.3.2Immunofluorescence (IF) assay presented that BA induced an intense nuclear staining of p‐STAT3 (Tyr705), as it was expected.IF assay also revealed that application of Nifuroxazide, SI3‐201 or STA‐21, diminished the BA‐induced activation of NF‐κB, as shown by a less intense nuclear staining of p‐NF‐κB (p65 S536) relative to BA alone Figure . HoweverIn order to confirm the above IF data, and further determine if targeting JAK/STAT3 phosphorylation can affect the acidic bile‐induced STAT3 activation, Bcl‐2 overexpression and NF‐κB activation, we performed Western blot analysis. Our analysis revealed that Nifuroxazide inhibited the activation of STAT3 and Bcl‐2 overexpression, caused by acidic bile. This was shown by significantly reduced nuclear levels of p‐STAT3(Tyr705) and cytoplasmic levels of Bcl‐2 in HCs exposed to BA plus Nifuroxazide compared to BA alone Figure ,b. Howev3.3STAT3 knockdown, compared to BA alone .Our analysis revealed a significant reduction of STAT3‐reporter luciferase activity in BA‐treated HCs with STAT3. Specifically, our analysis showed that BA‐treated HCs presented significantly higher STAT3‐transcriptional activity, compared to controls , STAT3, REL, WNT5A and EGFR, compared to BA alone , oncogenic STAT3 and EGFR, as well as of cell proliferation or tumour‐promoting factor WNT5A,qPCR analyses showed that application of Nifuroxazide prevented the BA‐induced mRNA profile of antiapoptotic gene α, RELAp6, oncogenTNF‐α, IL6, BCL2, RELA(p65), STAT3, EGFR and WNT5A, as well as of cell proliferation or tumour‐promoting factors, REL,Application of SI3‐201 similarly prevented the BA‐induced mRNA profile of 2, RELAp6, STAT3, IL6, TNF‐α, BCL2, RELA(p65), STAT3 and EGFR , STAT3 and EGFR, compared to BA.Finally, as presented in Figure and EGFR . Targeti3.6STAT3 knockdown or its pharmacologic inhibition on BA‐induced cell survival rates. Our analysis revealed that silencing STAT3 . Blocking STAT3 by STA‐21 presented similar to STAT3 knocking down changes in cell survival of BA‐treated HCs , and STAT3,REL, EGFR, WNT5A and REL,STAT3 also reduced BA‐induced cell survival rates. These findings strongly support the hypothesis that STAT3 plays an important role in bile reflux‐related oncogenic molecular events in HCs.Our novel findings conclude that silencing of Our findings from the pharmacologic inhibition of STAT3, using Nifuroxazide,Notably, both SI3‐201 and STA‐21 appeared to produce a relatively more pronounce suppressive effect on BA‐induced mRNA phenotype than Nifuroxazide  and EGFR  levels, our findings strongly support a role of STAT3 in promoting the transcriptional activation of NF‐κB. NF‐κB inhibition, using BAY 11‐7082, had previously supported the role of NF‐κB in BA‐induced activation of STAT3, among other factors.Given the central role of STAT3 pathway in HNSCC, despite its complexity, our data provide supportive evidence of STAT3 inhibition as a preventive and therapeutic approach in hypopharyngeal cancer. STAT3 is indeed a demanding target not only because of the complexity of its activating signal but also because of its resistance to standard treatment in HNSCC.5STAT3 gene which induced a strong suppression of total p‐STAT3 and Bcl‐2 protein levels, and a dramatic reduction of transcriptional levels of cancer‐related cytokines, genes and cell survival. Using three inhibitors, each one targeting a different step of STAT3 upstream signalling, such as Nifuroxazide, S3I‐201 or STA‐21, we extracted novel findings supporting the constitutive activation of STAT3 under the acidic bile effect. This constitutive activation does not appear to be exclusively depended on JAK/STAT3. We also show that all three STAT3 inhibitors, with STA‐21 having the most profound effect, can effectively suppress the continuous production of cancer‐related molecules, IL6, TNF, RELA(p65), EGFR, BCL2 and STAT3, previously associated with hypopharyngeal cancer, caused by acidic bile. These findings document the important role of STAT3 in hypopharyngeal carcinogenesis associated with bile reflux, and also encourage the single or combined application of Nifuroxazide, S3I‐201 or STA‐21, in clinical studies for preventive or therapeutic approaches.Our novel data strongly support that STAT3 contributes substantially to bile reflux‐related molecular oncogenic events in HCs. This view was documented by silencing The authors whose names are listed in this article certify that they have no affiliations with or involvement in any organization or entity with any financial or non‐financial interest in the subject matter or materials discussed in this manuscript.Dimitra P Vageli: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology ; Project administration ; Resources ; Software (lead); Supervision (lead); Validation ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Panagiotis G. Doukas: Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Athanasios Siametis: Formal analysis ; Methodology ; Software ; Visualization ; Writing‐review & editing . Benjamin L. Judson: Data curation ; Funding acquisition (lead); Project administration ; Resources ; Validation ; Writing‐review & editing .Supinfo S1Click here for additional data file.


Botrytis cinerea is a necrotrophic plant pathogen with high genetic variability that can infect more than 200 plant species worldwide. A panel of 52 amplicons were sequenced for 82 isolates collected from four Michigan vineyards representing 2 years of collection and varying fungicide resistance. A panel of nine microsatellite markers previously described was also tested across 74 isolates from the same population. A microsatellite and SNP marker analysis of B. cinerea populations was performed to assess the genetic diversity and population structure of Michigan vineyards, and the results from both marker types were compared. Both methods were able to detect population structure associated with resistance to the individual fungicides thiabendazole and boscalid, and multiple fungicide resistance (MFR). Microsatellites were also able to differentiate population structure associated with another fungicide, fluopyram, while SNPs were able to additionally differentiate structure based on year. For both methods, AMOVA results were similar, with microsatellite results explaining a smaller portion of the variation compared with the SNP results. The SNP-based markers presented here were able to successfully differentiate population structure similar to microsatellite results. These SNP markers represent new tools to discriminate B. cinerea isolates within closely related populations using multiple targeted sequences.As sequencing costs continue to decrease, new tools are being developed for assessing pathogen diversity and population structure. Traditional marker types, such as microsatellites, are often more cost effective than single-nucleotide polymorphism (SNP) panels when working with small numbers of individuals, but may not allow for fine scale evaluation of low or moderate structure in populations.  Botrytis cinerea is a necrotrophic pathogenic fungus that infects hundreds of plant species  and gray mold (postharvest), is the most common  classes are available to control bunch rot, including quinone outside inhibitors (QoIs), benzimidazole, phenylpyrroles, succinate dehydrogenase inhibitors (SDHIs), anilinopyrimidines (APs), dicarboximides, and hydroxyanilides . However by wind  and the  by wind  that all by wind . In grap by wind  and in t by wind  and Cali by wind . An incr by wind . Fungici by wind .Botrytis is highly genetically diverse with more than 30 species that differ in morphology, ecology, biology, and host range . Commonly, the presence/absence of TEs is used for the determination of Botrytis sp. population diversity  or sequencing-based markers and microsatellite markers to describe population structure and diversity . In one rrelated . Similarrrelated . Howeverrrelated . Yet mosBotrytis . A study markers .Botrytis sp. can exhibit differences in fungicide resistance profile, it is critical to understand the pathogen population structure in different environments. This will allow development of better disease management schemes. There is no information about B. cinerea population structure and genetic diversity in Michigan grapevine. Therefore, this is the first study to investigate the genetic diversity of the B. cinerea population in Michigan grapes in combination with fungicide sensitivity phenotypic characteristics. Our objectives were to study the population structure of MI isolates of B. cinerea related to year, location, fungicide resistance, and to compare the use of microsatellite and amplicon-based sequencing SNP strategies to quantify genetic diversity and population structure.Because different B. cinerea isolates were collected from four Michigan vineyards, three southwest and one northwest Michigan locations (B. cinerea isolates recovered from a Vitis interspecific hybrid (cv. Vignoles), and “Southwest” Michigan vineyards 1 and 2 are also hybrids recovered from cultivars Vignoles and Aurora, respectively. Finally, the “Northwest” Michigan samples were recovered from symptomatic Vitis vinifera (cv. Reisling).A total of 82 ocations . The “We3, 10 g of agar, and 400 ml of distilled water) at room temperature. All isolates were previously evaluated for fungicide resistance against eight fungicides with seven different chemical classes , sdhB, cytochrome b (cytb), and keto-reductase (erg27), mating type (Mat1-1 and Mat1-2), transposable element (Flipper), virulence (Nep1 and mrr1), and species identification , 1 μl of 10 μM forward primer, 1 μl of 10 μM reverse primer, 1 μl of DNA template (50 ng/μl), and 9.5 μl of sterile double-deionized water (sddH2O). Reaction conditions were 95°C for 2 min followed by 30 cycles of denaturation at 95°C for 30 s, annealing at 60°C for 30 s, and extension at 72°C for 1 min, then a final extension at 72°C for 5 min. The PCR products were separated by electrophoresis at 65 V for 1.5 h on ethidium bromide stained (10 μg/ml) 1.5% agarose gel, then visualized under the UV light for validation. All primer sets successfully produced clear bands within the expected size range for the 15 isolates. Designed primers were aligned to the reference genome B05.10 (ASM83294v1 http://fungi.ensembl.org/Botrytis_cinerea/Info/Index) using Geneious Prime v 2020.2.1 to determine the expected SNP locations.Candidate genes were selected to screen for resistance-associated mutations in β-tubulin (Botrytis target genes. Grouping was based on the following parameters: (1) expected amplicon size, with at least 30-bp difference between each amplicon for clear validation by gel electrophoresis and (2) the same annealing temperature. Since the presence of multiple oligonucleotides in one PCR reaction could alter the efficiency of amplification, several combinations of primer concentrations (0.2–0.4 μM) were tested in parallel in a single-plex and in multiplex formats using the cycling conditions mentioned above. Amplicons were separated by electrophoresis at 50 V for 2 h on ethidium bromide-stained (10 μg/ml) 4% agarose gel and visualized under the UV light for validation of band presence.Primers (microsatellite and candidate genes) were grouped in sets of five, six, or eight-plex for simultaneous amplification of Botrytis target regions on all 82 isolates. For each multiplex PCR reaction, each isolate was amplified using QIAGEN Multiplex PCR Kit (Qiagen) as follows: 5 μl of 2× QIAGEN Multiplex PCR master mix (Qiagen), 1 μl of 10× primer mix, 2 μl of DNA template (50 ng/μl), and 2 μl of sddH2O. Reaction conditions were 95°C for 2 min followed by 30 cycles of denaturation at 95°C for 30 s, annealing temperature varied between 50 and 60°C (based on primer design) for 30 s, and extension at 72°C for 1 min and, then, a final extension at 72°C for 5 min. For sample DNA pooling, the multiplex PCR products were grouped by isolates as follows: for each isolate, 5 μl from each multiplex group was transferred to new PCR plates to end up having a total of 15 μl of DNA per isolate, representing three multiplex groups (5 μl each). DNA was diluted 1:10 using sddH2O in PCR plates for quantification using Qubit dsDNA HS Assay Kit (ThermoFisher Scientific) following the manufacture procedure. Concentrations were adjusted to 2–10 ng/μl of DNA and submitted to the Research Technology Support Facility  for sequencing using a 250-bp paired-end MiSeq.After primer validation, multiplex groups  were used to amplify B. cinerea reference genome  for 30 s, extension at 72°C for 30 s, followed by a final extension at 72°C for 5 min. One microliter of PCR product was added to 0.3 μl of GeneScan 500 LIZ-labeled size standard and 9.7 μl of Hi-Di formamide . Amplicons were denatured by incubation at 95°C for 5 min and immediately placed on ice. Fragment analysis was conducted on an Applied Biosystems 3730xl 96-capillary DNA Analyzer. Geneious Prime v.11.0.3 (Poppr (v 2.8.6) implemented within R  were calculated for the population based on location, year, and fungicide resistance using distance .Botrytis genome, unaligned reads were able to be aligned or were determined to be Botrytis but not in the nuclear reference genome . SNP distribution across the genome ranged from three SNPs on chromosome 11 to 101 SNPs on chromosome 1 with a total of 496 SNPs detected post filtering used for subsequent analyses  difference was detected between isolates collected in 2014 and those collected in 2018  was explained by the populations , overall G’ST was 0.154. Both the genetic distance tree and AMOVA showed no significant differences based on location among isolates (ulations . When grulations .exp = 0.61–0.64), but rbarD varied widely with slightly negative values for Southwest 2 and West  and a large positive value (0.15) for Southwest 1 (p = 0.069).Alleles identified in the population ranged from four to seven, with an average of 5.89 alleles per locus and a total of 53 alleles across all nine markers . After cthwest 1 . Howeverp = 0.037 and 4.4% at p = 0.012, respectively; p = 0.001) was explained (p = 0.026).When grouped by fungicide resistance , no significant variability was detected between resistant and sensitive populations for cyprodinil, iprodione, or fenhexamid. MFR was grouped into categories designated 3 (0–3), 4, and 5 (5–7) because insufficient numbers of isolates had resistance to 0, 1, 2, 6, or 7 fungicides. AMOVA results indicated significant variability in the population was explained when grouped by MFR (0–7) or MFR categories .Both SNP and microsatellite markers were able to identify significant population differentiation and genetic diversity in tellites  and 7. Btellites . For insB. cinerea using isolates collected from Michigan vineyards. B. cinerea is a globally distributed pathogen with a broad host range and widespread fungicide resistance. Thousands of studies across the globe have used morphological, genic, microsatellite, and other PCR-based markers to characterize genetic diversity and population structure of Botrytis spp. The pathogen is genetically diverse with closely related morphologically similar species that can be found on the same host or tissue complicate diversity studies  and may not require large (>400) numbers of SNPs for accurately differentiating populations  can be selected to improve detection.Microsatellites have historically been used to assess genetic diversity and population structure because of their widespread accessibility, requiring no prior known sequence information, highly specialized equipment, or software to analyze. As sequencing technologies have become more affordable, SNP-based assessment of genetic diversity and population structure has become more prevalent . Using sulations . This coBotrytis isolates were collected in 2014 and 2018. When comparing genetic diversity between the two marker systems, the 496 SNPs were only able to distinguish an additional two to three MLGs for the locations with the highest number (Southwest 1 and Southwest 2), but with the two lower MLG locations (West and Northwest), more than six additional MLGs were detected. While both types of markers were able to differentiate population structure at the multiple fungicide resistance level, thiabendazole and boscalid resistance, neither marker system was able to detect significant differentiation based on location similar to other Botrytis studies . This is similar to other studies showing that fungicide resistance may be a driving factor in Botrytis population structure in agricultural systems .The datasets generated for this study can be found in the NCBI SRA repository (RN conceived of experiment, analyzed the data, and contributed to writing. JD collected and analyzed the data and contributed to writing. NA and SS collected the data and contributed to the experimental design and writing. TM and SA contributed to the experiment design and writing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.