Reproductive traits are economically important in the pig industry, and it is critical to explore their underlying genetic architecture. Hence, four reproductive traits, including litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), and litter weight at weaning (LWW), were examined. Through a genome-wide association study in a Duroc pig herd, several candidate single-nucleotide polymorphisms (SNPs) and genes were found potentially associated with the traits of interest. These findings help to understand the genetic basis of porcine reproductive traits and could be applied in pig breeding programs. TXN2, KCNA1, ENSSSCG00000003546, ZDHHC18, MAP2K6, BICC1, FAM135B, EPHB2, SEMA4D, ST3GAL1, KCTD3, FAM110A, TMEM132D, TBX3, and FAM110A, were identified and might compose the underlying genetic architecture of porcine reproductive traits. These findings help to understand the genetic basis of porcine reproductive traits and provide important information for molecular breeding in pigs. In the pig industry, reproductive traits constantly influence the production efficiency. To identify markers and candidate genes underlying porcine reproductive traits, a genome-wide association study (GWAS) was performed in a Duroc pig population. In total, 1067 pigs were genotyped using single-nucleotide polymorphism (SNP) chips, and four reproductive traits, including litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), and litter weight at weaning (LWW), were examined. The results showed that 20 potential SNPs reached the level of suggestive significance and were associated with these traits of interest. Several important candidate genes, including  Though genetic gain for these traits has been obtained with traditional breeding strategies, the slow genetic improvement has increased the need for molecular breeding, such as genomic selection [Animal reproductive traits are economically important but are mostly sex-specific , and most of them are complex and present low heritability. Hence, the genetic improvement on these traits is especially difficult compared to other complex traits in livestock breeding practices. In the pig industry, litter traits are extremely important economic traits for pig production, as they are directly related to production efficiency. Improving the litter size is the main breeding goal and has been intensively selected in breeding programs for decades in many well-organized breeding systems, such as at the Canadian Center for Swine Improvement  affecting the traits under consideration . Hence, In the past decade, GWAS was widely used to dissect the genetic architecture of growth ,5, reproThe main objective of this study was to perform a GWAS to identify potentially important single-nucleotide polymorphism (SNPs) or QTL regions associated with four reproductive traits, including litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), and litter weight at weaning (LWW) in a Duroc pig population. Subsequently, the potential function of significant chromosomal regions was analyzed in detail. Animal care and experiments were conducted according to the Regulations for the Administration of Affairs Concerning Experimental Animals  and were approved by the Animal Care and Use Committee of the South China Agricultural University, Guangzhou, Guangdong, China (permit number: SCAU#2013-10).The population used in the present study was normally maintained in a breeding herd in Fujian, China. Four reproductive traits, including LSB, LWB, LSW, and LWW, were recorded for all sows in this herd. LSB and LWB were measured within 24 hours after delivery, and LSW and LWW were recorded 24 days (the day of weaning) after delivery. At present, information for 4539 Duroc pigs with 15,662 farrowing records has been collected for a period of 10 years (2008 to 2017). A multi-traits animal model was used to estimate covariance components for calculating heritability and genetic correlation. The models used were as follows:Estimated breeding values (EBVs) of all pigs and the reliabilities of EBVs were imputed using animal model best linear unbiased prediction  and obta260/280 ratios of DNA samples were determined with NanoDrop 2000 (Thermo Scientific). The samples with A260/280 ratio between 1.7 and 2.0 were genotyped using either the Illumina PorcineSNP60 BeadChip  or the GeneSeek GGP-Porcine chip . In the present study, a total of 1067 Duroc pigs (81 boars and 986 sows) were genotyped for further GWAS analysis. Genomic DNA was extracted from pig ear tissue using the TaKaRa MiniBEST Universal Genomic DNA Extraction Kit (Ver 4.0). The Ap-value < 1.00e-6. Following the quality control, 32,147 SNPs were retained for further analysis. In order to check whether population stratification existed in the Duroc pig herd, the genomic kinship between all pairs of individuals was calculated using SNPs ion autosomes. For phenotypes, descriptive statistical analyses were performed in R  to checkThe association between each SNP marker and the phenotypes was tested with a single-marker regression mixed linear model by GEMMA software . The sta−6, and the genome-wide suggestive significance threshold was 1/9266 = 1.08 × 10−4. To confirm the thresholds for the genome-wide significance and suggestive significance, effectively independent tests based on the independent markers and linkage disequilibrium (LD) block  were calculated as in . A totalSus scrofa 10.2 reference genome assembly. The SNP-containing or nearest annotated genes for each potential SNP were obtained from the Ensembl release 89 (http://may2017.archive.ensembl.org/index.html) and taken as candidate genes. Candidate genes were identified according to their physical positions and functions based on the p-value <0.05. Genes that were less than 1 Mb away from the potential SNPs were selected and identified as the functional genes. Further Gene Ontology (GO) terms annotation was conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Version 6.8 , then thDescriptive statistics of DEBVs for the traits of LSB, LWB, LWW, and LSW analyzed in this study are presented in There were 38,544 SNPs before frequency and genotyping pruning. Through quality control, 130 and 6267 SNPs were excluded from our dataset due to HWE testing and MAF, respectively. Finally, 32,147 SNPs from 1067 animals were retained for further analysis. BICC1 gene on chromosome 14. Additionally, the other five potential SNPs were located on chromosome 5, 6, and 12. For LWB, two SNPs located between 5.66 Mb and 5.68 Mb on chromosome 4, two SNPs located between 68.26 Mb and 74.78 Mb on chromosome 6, and a SNPs located on chromosome 14 at 1.18 Mb reached suggestive significance. For both LWW and LSW, SNP rs328230332 located in the intron of FAM110A on chromosome 17 was simultaneously associated with these two traits. Additionally, there were other four and two potential SNPs associated with LWW and LSW, respectively. By extending 1 Mb downstream and upstream of the potential SNPs, 146, 62, 58, and 63 functional genes were identified for LSB, LWB, LSW, and LWW, respectively. For LSB, the functional genes were enriched in “GO: 0061436, establishment of skin barrier”, “GO: 0045606, positive regulation of epidermal cell differentiation”, and some other epidermal growth-associated GO terms. For LWB, the functional genes were involved in organismal defense- and immunity-associated GO terms, including “GO: 0042742, defense response to bacterium” and “GO: 0045087, innate immune response”. Furthermore, the functional genes of LSW were enriched in “GO: 0042742, defense response to bacterium”, “GO: 0045087, innate immune response”, and “GO: 003511,~ embryonic forelimb morphogenesis”. Besides, the functional genes of LWW were involved in “GO: 0061436, establishment of skin barrier”, “GO: 0043552, positive regulation of phosphatidylinositol 3-kinase activity”, and “GO: 0034644, cellular response to UV” . FAM110A has been reported to be significantly associated with young-onset hypertension in Han Chinese population of Taiwan [FAM110A is located in genomic regions associated with dairy production in sheep. SNP rs80979042 and rs80825112 on chromosome 14 were associated with LSB and are located within the intron of Bicaudal C Homolog 1 (BICC1) gene. BICC1 has been identified by GWAS as a candidate gene associated with major depressive disorder in humans [BICC1 provokes renal and pancreatic cysts and the visceral left-right patterning of ectopic Wnt/β-catenin signaling during visceral left-right patterning [BICC1 has been reported to be associated with prenatal development of skeletal muscles and to be expresses differently during myogenesis in Pietrain and Duroc pigs [EPHB2, which belongs to the Eph family, the largest known tyrosine kinase receptor (RTK) family [EPHB2 is activated by its ligand EphrinB1, which results in protein phosphorylation in cells. In embryonic development and postnatal life processes, EPHB2 is expressed in a tissue-specific and time-specific manner [EPHB2 is involved in regulating postnatal myogenesis through inhibiting satellite cell formation in mice [SNP rs328230332 was associated with both LWW and LSW. This SNP is located in the intron of the f Taiwan . In liven humans . BICC1 ptterning . In pigsroc pigs . Two SNPc manner  and also in mice .KCNA1, a member of potassium channels family, has been involved in the maturation of the mouse auditory forebrain [ZDHHC18 is the component of Hippo pathway, influencing skeletal muscle feed arteries in rat [FAM135B, associated with the traits of LWB, has been related to mid-test metabolic weight in U.S. beef cattle [SEMA4D differential expression has been reported in the skeletal muscle of pigs with distinct growth and fatness profiles [Additionally, several other candidate genes were identified to be potentially associated with these traits of interest. orebrain . ZDHHC18s in rat . FAM135Bf cattle . SEMA4D profiles . For theprofiles . There wprofiles ,32, whicTaking the DEBV as “dependent variable” could improve the power of GWAS. Generally, trait phenotype is the dependent variable in most GWAS. However, in this study, DEBVs obtained from on-farm genetic evaluation were employed, because they are more suitable than the original phenotypes. This is mainly because: (1) Systematic environmental effects are excluded from DEBVs; (2) More phenotypic records from relatives were combined to evaluate the individual under consideration; (3) Some trait phenotypes can be recorded only in one gender; and (4) They do not contain information from relatives. Recently, many GWAS have used DEBVs as “phenotype”, especially for livestock populations ,34,35.In this study, we have not identified significant SNPs associated with the four reproduction traits examined. We speculate that the relatively small number of SNPs reaching the suggestive significance level is partly due to the small size of the research population used in the present study. Additionally, the substructure within this population further decreased the effective population size and, hence, affected the power of GWAS. In a population with limited size, only large or moderate QTLs can be detected. Hence, research populations must be enlarged in our future studies to confirm the findings from the present study. TXN2, KCNA1, ENSSSCG00000003546, ZDHHC18, MAP2K6, BICC1, FAM135B, EPHB2, SEMA4D, ST3GAL1, KCTD3, FAM110A, TMEM132D, TBX3, and FAM110A might be important candidate genes that compose the underlying genetic architecture of porcine reproductive traits. These findings help to understand the genetic basis of porcine reproductive traits and could be potentially applied in pig breeding programs. Through a genome-wide association study of four reproductive traits in a Duroc pig herd, we detected 20 SNPs that were potentially associated with these traits of interest.


Furthermore, homozygous SNP is considered a dummy variable, and the SNPs are updated  if necessary. Importantly, we validated this algorithm as a proof of concept: two patients with lung cancer were identified as the maximum risk cases from 57 Chinese individuals. Our logistic model‐based DRP algorithm is apparently more intuitive and self‐evident than the algorithms explored by commercial companies, and it may facilitate DRP commercialization in the era of personalized medicine.Human diseases are usually linked to multiloci genetic alterations, including single‐nucleotide polymorphisms (SNPs). Methods to use these SNPs for disease risk prediction (DRP) are of clinical interest. DRP algorithms explored by commercial companies to date have tended to be complex and led to controversial prediction results. Here, we present a general approach for establishing a logistic model‐based DRP algorithm, in which multiple SNP risk factors from different publications are directly used. In particular, the coefficient β of each SNP is set as the natural logarithm of the reported odds ratio, and the constant coefficient β DRPdisease risk predictionGWASgenome‐wide association studyORodds ratioSNPsingle‐nucleotide polymorphismGenome‐wide association studies (GWASs) are increasingly uncovering the effect of genetic alterations in most of the common diseases Retrospectively, the algorithms of three DRP companies  have notable differences in principle  and heterozygous SNPs were collected. SNPs with an OR <1.15 were omitted. Then, the frequency of each SNP in Chinese individuals and all individuals was extracted from the National Center for Biotechnology Information database by searching each SNP ID  of each patient, accompanied with the information of only sex, age, smoking and clinical symptoms, was transferred by the hospital to us for DNA extraction and SNP genotyping, with the data being analyzed anonymously.Genomic DNA was extracted from peripheral blood by using a QIAamp DNA Blood Mini Kit  in accordance with the manufacturer's instructions. The primers for SNP genotyping were designed using MassArray Assay Design v4.0 according to the sequence information flanking the SNP, were synthesized by Invitrogen and were validated by MassArray Analyzer Compact. The PCR, removal of free dNTP and sequencing by MassArray Analyzer Compact  were performed according to the manufacturer's instructions.microorigin software using the ANOVA algorithm at a significance level of 0.05.Each sample was resequenced independently, and all of the SNP sequencing results were consistent between independent repeated experiments. Risk calculation was performed using Microsoft Excel . A statistical analysis was performed in the i and xi are the coefficient and the logic value of each risk factor, respectively. Then, the disease risk P is expressed as follows:Logistic regression has been widely used for risk factor identification and evaluation, and is generally presented as follows:In a research study, researchers usually use a majority of their experimental data to estimate the coefficients in Eqns ,2 by usiIn general, only one or a few novel SNP risk factors related to a disease are identified in a specific GWAS, in which the coefficients are determined and a specific logistic model is generated as described above. If the disease is linked to only these SNP risk factors, the logistic model generated from this GWAS is suitable for DRP directly. However, most human diseases are usually linked to a large number of SNP risk factors  of each SNP and the average population disease risk (P0) are usually available in public databases; the coefficient (βi) of each SNP has been already determined through Eqn 0 can be calculated using Eqn Next, β0 reflects the contribution of the unidentified and/or unselected SNP risk factors, as well as of the non‐SNP risk factors, including the traditional risk factors . Importantly, if new critical SNPs are identified and need to be incorporated into our model, β0 can be recalculated according to Eqn Apparently, βP for each subject is collectively determined by the sum of the product of the variance of each βi and the logic value xi /ln(ORheterozygous).Equations https://www.ncbi.nlm.nih.gov/snp/docs/RefSNP_about/). Of these, about 71 600 SNPs have been found to be associated with human diseases and traits, as reported in the NHGRI‐EBI GWAS Catalog database https://www.ebi.ac.uk/gwas/). Lung cancer is considered a partially inherited complex disease Thus far, about 400 000 reference SNPs have been deposited for human genomes  and was significantly higher than that of the remaining 37 men (P = 0.000015). In addition, the average lifetime risk for the 38 men and the 10 women was 8.9% and 5.5%, respectively (Table P > 0.05).To demonstrate the effectiveness of our logistic algorithm, we collected blood samples of 48 Chinese subjects (38 men and 10 women), genotyped the eight selected SNPs  the SNP risk factor markers, with or without the OR value for the homozygous forms, were all suitable for risk prediction; and (d) accordingly, the non‐SNP risk factors  could also be included in our logistic model for DRP. Therefore, our study is of interest for the commercialization of DRP, given that similar logistic model‐based algorithms can be established for common complex diseases  that have been reported to be linked quantitatively to multiple SNP risk factors Our logistic algorithm had several advantages: (a) the coefficient β in the risk calculation equation directly reflected the contribution of each SNP and was simply determined by the OR value; (b) the SNP risk factor markers and the OR value were adjustable and/or promptly updated if necessary, such that βThe authors declare no conflict of interest.XF designed the research and established the model. GL performed genotyping experiments. XF and CL analyzed the data and wrote the manuscript.Data S1. DRP algorithms of commercial companies.Click here for additional data file.Table S1. SNP profiling results of 48 individuals.Click here for additional data file. Click here for additional data file.


Recently, the use of whole-genome sequencing (WGS) has been popular for these analyses, and the identification of single-nucleotide polymorphisms (SNPs) between isolates is the most basic analysis performed following WGS. The performance of SNP-calling methods therefore has a significant effect on the accuracy of downstream analyses, such as phylogenetic tree inference. In particular, when closely related isolates are analyzed, e.g. in outbreak investigations, some SNP callers tend to detect a high number of false-positive SNPs compared with the limited number of true SNPs among isolates. However, the performances of various SNP callers in such a situation have not been validated sufficiently. Here, we show the results of realistic benchmarks of commonly used SNP callers, revealing that some of them exhibit markedly low accuracy when target isolates are closely related. As an alternative, we developed a novel pipeline BactSNP, which utilizes both assembly and mapping information and is capable of highly accurate and sensitive SNP calling in a single step. BactSNP is also able to call SNPs among isolates when the reference genome is a draft one or even when the user does not input the reference genome. BactSNP is available at  This study represents realistic benchmarks to reveal that some of those methods exhibit low-accuracy results. As an alternative method, we developed a novel pipeline, BactSNP, which can detect SNPs both accurately and sensitively. BactSNP provides every researcher, even those lacking well-trained bioinformatic skills, a user-friendly tool to identify SNPs highly accurately, and will accelerate microbial genomic research.While many studies on the intra-species genetic diversity of bacteria have been conducted, this research area is at a turning point in terms of molecular typing technologies. Whole-genome sequencing (WGS) is now often used instead of conventional methods such as multi-locus sequence typing (MLST) or pulsed-field gel electrophoresis (PFGE). WGS enables the differentiation of the genetic features of isolates, even when conventional methods cannot identify differences owing to the exceedingly high similarity between isolates . WGS is In such WGS-based studies, the most basic analysis consists of identifying single-nucleotide polymorphisms (SNPs) among target isolates; these are variants that may explain phenotypes and provide the basis for phylogeny inference or other downstream analyses. Various SNP callers, including SAMtools , GATK 55, FreebaIn spite of the importance of SNP calling and the increasing number of SNP callers, the accuracy of SNP-calling methods has not been validated sufficiently. In particular, when researchers focus on closely related isolates, the accumulation of false-positive SNPs between each isolate and the reference isolate easily leads to a high number of false-positive SNPs compared with the limited number of true SNPs among isolates. To our knowledge, there have been no studies that have benchmarked the sensitivity and accuracy of SNP calling methods among bacterial isolates.de novo assembles the input reads and uses the alignment information between the assembled contigs and the reference genome to avoid false positives. Our benchmarks demonstrate that BactSNP achieves highly sensitive and accurate SNP calling.Here, we describe realistic benchmarks of these tools in calling SNPs among closely related isolates and reveal that some of them often exhibit low accuracy. In addition, we present a novel pipeline BactSNP. Though BactSNP, like the above-mentioned pipelines, also uses mapping information, it simultaneously When researchers call SNPs among isolates, they generally use a known reference genome, and SNPs are called at positions along this genome. We simulated this situation as shown in Staphylococcus aureus, Neisseria meningitidis and Escherichia coli were downloaded from the NCBI database [First, the complete genomes of database , and paidatabase  so that Simulated substitutions were moved to random positions in regions where nucmer  generateThe advantage of this benchmark is that variants between the reference and the target isolates are not simulated but real, complicated ones, except for the substitutions introduced against the root to simulate each target isolate's sequence. These complicated variants make SNP calling realistically difficult, therefore this benchmark can precisely evaluate the accuracy of each tool.First, we validated the above-mentioned general SNP callers, i.e., Cortex, Freebayes, GATK, SAMtools, and VarScan, which are not dedicated to calling SNPs among bacterial isolates  and S1.Surprisingly, with the exception of Cortex, they exhibited low positive predictive values (PPVs), even though their results were filtered following the respective manuals. It is worth noting that these tools exhibited low PPVs (<90) even when the identity between the reference and the target isolates was relatively high. Cortex showed a PPV ≥90 for all cases, but its sensitivity was relatively low, especially in the case of low identity. This benchmark tends to overestimate sensitivity because SNPs among isolates were simulated only in regions where SNP calling should be easy , indicating that the sensitivity of Cortex in real analyses would be even lower.To overcome this overestimation problem, as an alternative index of sensitivity, we calculated the called-sites ratio which is the ratio of sites where the nucleotide was determined unambiguously in all isolates to the genome size. In the case where a phylogenetic tree is inferred with complete deletion , the called-sites ratio determines the number of SNPs used in the phylogenetic analysis. This index revealed that SAMtools would be less sensitive in real analyses.The common feature of the low-accuracy general SNP callers is that they use mapping information. In order to reveal the reason for these low-accuracy results, we checked how reads were mapped around the false-positive SNPs. The majority of false-positive SNPs turned out to be located in ‘soft-clip regions’ where many reads were soft-clipped  , Fig. S2PPVs occasionally did not show monotonic decline with identity, and this is assumed to be an effect of SVs and repetitive regions where SNP calling is difficult. Identity was calculated using substitutions in one-to-one alignment regions, but the frequency of SVs and repetitive regions does not necessarily increase monotonically, and one SV or repetitive region sometimes causes a large number of dense false positives.SNP callers often mask regions where it is difficult to call SNPs correctly. Importantly, the larger the masked region, the easier it becomes to achieve high PPVs. In order to compare PPVs among tools that mask some regions in different ways, we introduced ‘Common-region PPV’, i.e. PPV calculated only in regions where all tools determined alleles for all isolates without masking  and S5. We also carried out another supplementary benchmark in which variants between the reference and the target isolates were simulated by TreeToReads  . These pipelines filter ambiguous SNPs with low coverage depth or low allele frequency, and thus these filters are considered to be effective against false positives called by VarScan and GATK (Supplementary Notes). CFSAN still called a relatively large number of false positives, though it additionally filters high-density SNPs; therefore, further filtering procedures may be required for VarScan.In contrast, Snippy exhibited low PPVs though it also filtered SNPs with low coverage depth or allele frequency, indicating that the internal variant caller, Freebayes, called many false positives that were not filtered by these criteria.de novo assembled internally.Although PHEnix exhibited high PPVs, it often called a few false positives. NASP did not call any false positives in most cases; however, it exhibited lower sensitivities than PHEnix. Regarding usability, when the reference genome was a draft one, the vcf2fasta command of PHEnix, which converts the VCF files of multiple isolates into a multi-FASTA file to obtain SNPs among them, did not work. We developed a novel assembly-based pipeline, BactSNP, and verified whether it settled these problems. BactSNP also uses the mapping information secondarily, but it is mainly based on the alignment information between the reference genome and the contigs of the target isolates which are de novo assembled by Platanus [allelen and indeld represent the number of alleles aligned at the site and the distance from the nearest indel to the site, respectively. At each site in the reference genome, the corresponding allele is determined as the aligned allele if the site satisfies both of the following conditions:BactSNP uses reads of each isolate and a reference genome as input. First, the reads are Platanus  for eachallelen=1(1)indeld >5 bp(2)allc and allelec represent the coverage depth of all reads mapped to the site and that of the reads supporting the allele of the pseudogenome at the site, respectively. The corresponding allele of the pseudogenome is masked if the reference site does not satisfy either of the following conditions:In addition to assembling the reads, they are mapped to the reference genome by BWA-MEM  and duplallelec ≥10(3)allelec/allc≥0.9(4)Lastly, SNPs among isolates are determined by using the pseudogenomes generated in one-to-one manner.BactSNP works well, even when the reference genome is a draft one. A detailed description of the algorithm is provided in the Supplementary Notes.We evaluated BactSNP using the above-mentioned benchmark . While PN. meningitidis [In order to validate the performance of these SNP callers in real data analysis, we applied them to the read data sequenced in a comparative genomic study on ngitidis . We downOur benchmark revealed that some SNP callers detect a large number of false positives. In contrast, some dedicated pipelines including NASP, PHEnix, and our novel pipeline BactSNP achieved both highly accurate and sensitive SNP calling, regardless of the identity between the reference and target isolates. Therefore, these pipelines are powerful even when a reliable and well-annotated reference genome closely related to the target isolates is not available or when a subset of target isolates is not closely related to the others and the user cannot therefore use a reference genome close to all of them.BactSNP did not call even one false positive, while PHEnix often called some false positives. The sensitivity of BactSNP was slightly lower than that of PHEnix, but it exceeded that of NASP in all cases involving various species and identities.de novo assembles one of the target isolates, which the user can specify, and uses it as the reference genome automatically. This function is useful when the user only needs the phylogeny of the target isolates and does not require the SNP position to be well annotated in the reference genome. In this case, the assembled reference genome, which is exceedingly closely related to the other target isolates in outbreak studies, would enable highly sensitive SNP calling, and the user does not need to consider the proper reference genome. BactSNP creates a TSV file containing SNP information and an alignment FASTA file containing the constructed pseudogenomes of target isolates in a single step. The alignment FASTA file can be input to Gubbins [As for usability, the filtering parameters of BactSNP are well optimized and set as the default, whereas the user is required to consider the parameters or even the internal mapper and variant caller in the other pipelines. Unlike PHEnix, BactSNP is also usable when the reference genome is a draft one. In addition, BactSNP can be used even when the user does not specify a reference genome; in this case, it  Gubbins  to prediBactSNP is expected to enable every researcher, even those who do not have proficient bioinformatic skills, to obtain accurate SNP information easily and to aid and accelerate microbial genomic research.https://github.com/IEkAdN/BactSNP and simulated correct SNPs and reads in the benchmarks are available at http://platanus.bio.titech.ac.jp/bactsnp.BactSNP is available at Click here for additional data file.


GDF9 gene polymorphisms and their association with reproductive traits in chicken using DNA sequencing. A total of 279 Dongxiang blue-shelled (DX) chickens and 232 Luhua (LH) chickens were used for validation. We detected 15 single nucleotide polymorphisms (SNPs): nine SNPs were previously unreported in chicken, two were missense mutations, and only three exhibited significant associations with reproductive traits. G.17156387C>T was significantly associated with age at first egg (AFE) and weight of first egg (WFE) in both breeds. Birds carrying the CC genotype exhibited higher AFE and WFE values than those with the TT genotype. The SNP g.17156427A>G exhibited an association with egg weight at 300 days of age (EWTA) in DX but not in LH chickens. The SNP g.17156703A>C affected the AFE and EN  in DX chickens. In addition, certain diplotypes significantly affected AFE, BWTA (body weight at 300 days of age), and EN in both breeds. RT-PCR results showed that the GDF9 gene was highly expressed in stroma with cortical follicles (STR) and prehierarchal follicles. These results provided further evidence that the GDF9 gene is involved in determining reproductive traits in chicken.The aim of the study was to investigate IGF2) in swine [GDF8) in sheep [DGAT1) in cattle [In the modern poultry industry, reproductive traits of laying hens, such as age at first egg, egg number, and egg weight, are major factors of economic concern –4. Studiin swine , growth in sheep , and dian cattle , 14.GDF9), involved in growth and differentiation and secreted by oocytes, belongs to the transforming growth factor β superfamily and plays a critical role in ovarian follicular development and ovulation rate in mammals [ GDF9 regulated hyaluronic acid synthesis, gonadotrophin-stimulated progesterone production, cumulus expansion, and the maintenance of an optimal oocyte microenvironment through synergistic action along with BMP15 (bone morphogenetic protein 15) [ GDF9 gene is exclusively expressed in oocytes within the ovary of most animals [ GDF9 polymorphisms are associated with animal reproductive performance. Dag et al. [ GDF9 causing a Val→Met substitution was significantly associated with litter size in sheep. The FecGE allele of a novel GDF9 polymorphism significantly increased ovulation rate and prolificacy in sheep [ GDF9 polymorphisms have also shown significant correlations with high prolificacy in goats [ GDF9 on reproductive performance have been conducted in mammals, and there are limited data regarding its role in the reproductive system of poultry.Growth differentiation factor 9  –18. Besi animals –23. Recein sheep . Additioin goats , the numin goats , and spein goats . However GDF9 gene was involved in the reproductive traits of laying hens. Single nucleotide polymorphisms (SNPs) are DNA sequence polymorphisms resulting from variation of a single nucleotide at the genomic level, including insertions, deletions, transversions, and transitions. SNPs comprise the latest generation of genetic markers and are often applied in animal breeding programs owing to their advantages such as high frequency, stability, and ease of genotyping [ GDF9 SNPs via pooled DNA sequencing and investigated these SNPs association with reproductive traits in Luhua chicken and Dongxiang blue-shelled chicken. Furthermore, we analyzed expression levels of GDF9 mRNA in different chicken tissues and different breeds using quantitative real-time PCR.Thus, we hypothesized that thenotyping , 28, 29.Two chicken populations, Luhua chickens LH, N = 232) and Dongxiang blue-shelled chickens , were used to assess the relationships between screened SNPs and reproductive traits. LH chickens are commercial egg-layers originating from the American Plymouth Rock and exhibit high egg productivity , 30. DX  and Dong GDF9 gene polymorphisms and their association with reproductive traits in chickens was based on pooled DNA sequencing. Five primer pairs . Firstly, a pooled DNA sample containing 80 ng DNA from 60 chickens (30 birds from each breed) was constructed to identify SNPs. SNPs in PCR products were identified on the basis of multiple peaks at the same base position in sequencing results. Next, primer pairs producing SNP-containing PCR products were amplified using DNA from all birds (232 LH chickens and 279 DX chickens). Finally, eliminating low frequency SNPs (< 1%), we further investigated the relationship between these SNPs and reproductive traits in LH chickens and DX chickens.Discovery ofs P1–P5,  for amplμL, including 12 μL of distilled H2O, 25 μL of 2× Taq PCR Master Mix , 4 μL of each primer (10 nmol/L), and 80 ng DNA template. PCR cycling conditions were as follows: 95°C for 5 min followed by 35 cycles of 95°C for 40 s, 51–59°C  in a volume of 50 Six 30-week-old birds were randomly selected from among the DX and LH egg-laying hens and then euthanized by cervical dislocation to collect the following ovarian tissues: stroma with cortical follicles < 1 mm in diameter (STR), prehierarchical follicles including white follicles , yellowish follicles  and small yellow follicles , and hierarchical follicles F1–F6 . All samples were submerged in RNAlater  overnight at 4°C and then stored at −80°C .µg total RNA using the PrimeScript RT Reagent Kit Perfect Real-Time (TaKaRa), according to the manufacturer's protocol. Real-time PCR primers were designed using the partial mRNA sequence of the GDF9 gene (Genbank accession number: NM_206988.2) without SNP sites using Primer Premier 5.0 . The β-actin gene (Genbank accession number: NM_205518.1) was used as the endogenous control for normalization, and the primer sequences were as follows: forward primer (5′–GAGAAATTGTGCGTGACATCA–3′) and reverse primer (5′–CCTGAACCTCTCATTGCCA–3′). A total volume of 15 μL in each reaction included 7.5 μL of 2× SYBR Green SuperMix , 0.6 μL each of 10 μM forward and reverse primers, 1 μL of cDNA, and 5.3 μL of RNase-free water. Real-time PCR reactions were performed in triplicate on a CFX96 qPCR system (Bio-Rad). The cycling conditions were as follows: 95°C for 30s, followed by 40 cycles of 95°C for 5s, a variable annealing temperature  for 30s, and 72°C for 10s. The melting curve was determined and analyzed at 65-95°C. The amplification efficiencies of target genes ranged from 95% to 105%.Total RNA was extracted from tissue samples with TRIzol RNA extraction reagent . The concentration of total RNA was measured, its purity was validated with spectrophotometry at 260/280 nm, and its integrity was evaluated by 2% agarose gel electrophoresis. The first strand of cDNA was synthesized from 1  GDF9 polymorphisms and reproductive traits and expression levels were evaluated using SAS 9.0 . The relationship between polymorphisms and reproductive traits in each population was modeled as follows: Yij = µ + Gi + Bj + (GB)ij + eij, where Yij is the observation value for the trait; µ is the population mean of the trait; Gi is the fixed effect of the genotype or diplotype; Bj is the fixed effect of the breed; (GB)ij is the interaction effect of the genotype or diplotype based on breed; and eij is the residual error. The quantification of relative mRNA expression was performed using the 2-ΔΔCt method. Statistical significance was determined by the general linear model and further analyzed using Tukey's multiple comparison test. Results are presented as least square means ± SEM, and differences were considered significant at P < 0.05.Associations between GDF9 gene is located on chromosome 13 and comprises two exons and one intron. We amplified and sequenced all exons, and parts of the intron and untranslated regions (UTR) of chicken GDF9 gene ; all other pairs of SNPs showed low LD values .The chickenDF9 gene . A totalDF9 gene  and inclP < 0.05)  . Meanwhi < 0.05) . TherefoP = 0.03), and there was an interaction (P = 0.008) of genotype and breed where the genetic effect was associated with EWTA in DX but not LH chickens. Homozygous laying hens presenting the GG genotype have shown a significant increase in EWTA (56.92 g) compared to birds with the AA (50.27 g) and AG (51.56 g) genotypes in DX chickens (P < 0.05) ( GDF9 SNP to be assessed was g.17156703A>C in exon 1 which showed significant effects on AFE (0.01) (P = 0.02) and EN (P = 0.04). The polymorphism had a significant genetic effect on AFE and EN in DX hens (P < 0.05); birds with the AA genotype had lower AFE and higher EN values compared to those with other genotypes  . The finE (0.01) . Some in Figures . Meanwhi Figures .P < 0.05), whereas AFE was greater in DX than in LH chickens (P < 0.05).A total of 19 diplotypes were identified in the present study, and eight diplotypes with frequencies more than 0.02 were selected from each breed to further evaluate associations with chicken reproductive traits . The res GDF9 mRNA was most abundant in the STR, and all prehierarchical follicles  exhibited higher expression of the GDF9 gene than the hierarchical follicles (F1–F6). Moreover, GDF9 mRNA expression was significantly higher in the STR, SYF, and F6 of LH chickens than in the corresponding tissues of DX chickens (P < 0.05)  . GDF9 is a candidate gene involving reproductive performance and plays a critical role in animal reproductive biology [ GDF9 polymorphisms and reproductive traits have focused on swine, sheep, and goats. Zhang et al. [ GDF9 gene. Hanrahan et al. [ GDF9 coding region through direct sequencing of Cambridge and Belclare sheep DNA, three of which were nonsense mutations and four of which were G>A mutations. Dong et al. [ GDF9, comprising C183A, C719T, A959C, and G1189A. Aside from C183A, these SNPs all resulted in a change in the encoded amino acid. In the present study, we sequenced the complete coding regions of the chicken GDF9 gene and detected 15 SNPs including three located in the promoter region, one in exon 1, two in exon 2, and nine in the 3′ UTR. Among these, nine SNPs were previously unreported in chicken; two coding sequence SNPs were missense mutations, resulting in the amino acids substitutions of Ile→Leu and Ile→Val, respectively. These results significantly broaden our current understanding of GDF9 polymorphisms underlying the reproductive systems of laying hens.Numerous studies have shown that biology –43. Howeg et al.  found 12n et al.  reportedg et al.  detected GDF9 have been reported to cause significant negative effects inducing infertility, ovulation impairment, and susceptibility to premature ovarian failure in some mammalian species [ GDF9 promoter region could influence transcriptional efficiency and subsequently alter the gene expression level. In the case of the SNP g.17156703A>C, it caused the amino acid substitution of Ile→Leu, which may subsequently result in a change of function in the GDF9 protein.Several different types of mutations and polymorphisms in species , 42, 45. species , 26, 46, species . In this species . The two GDF9 polymorphisms exert relatively stronger effects in DX than in LH chickens. Compared with LH chickens, DX chickens possess enormous potential for selective breeding because of their low productivity [In our study, BWFE, WFE, BWTA, EWTA, and EN were greater in LH than in DX chickens, whereas AFE was greater in DX than in LH chickens. This provided valuable evidence for differences in reproductive traits between the two breeds. We performed further association analyses and found that three SNPs affected several reproductive traits in DX hens, whereas only g.17156387C>T was significantly associated with two reproductive traits (AFE and WFE) in LH chickens. These results indicate thatuctivity –32. Thus GDF9 polymorphisms were associated litter size in goat [ GDF9 gene with respect to reproductive traits. In addition, we found that laying hens with an earlier AFE had a higher EN. These results are consistent with a negative correlation between egg number and age at first egg often observed in quantitative genetics studies [Interestingly, no SNPs were associated with BWTA in either breeds or with EN in LH chickens, which are major traits of laying hens , but the in goat , ovulati in goat , and sup in goat . Several in goat , 51. In  GDF9 gene is expressed in human, rodent, and ruminant oocytes and is an essential factor that regulates development of ovarian follicles [ GDF9 differs across species. For example, GDF9 expression was first reported in the primordial follicles of possum, hamsters, sheep, and cattle [ GDF9 mRNA was found to be expressed in the STR, WF, YF, SYF, and all hierarchical follicles in chickens. These data indicate that GDF9 also plays an important role in ovary function and follicle development in chicken. Chicken GDF9 mRNA was most abundant in the STR and was relatively highly expressed in the WF and YF, consistent with previous studies in mammals, implying that GDF9 is responsible for early development and differentiation of the ovarian follicles [ GDF9 mainly affected the prehierarchal follicles in chicken. Yan et al. [ GDF9 is one of the important conditions for maintaining the growth of a large number of ovarian follicles. Intriguingly, GDF9 expression was higher in the STR, SYF, and F6 of LH chickens than in DX counterparts. These results imply that the elevated expression of GDF9 may be related to high reproductive performance in LH chickens.Theollicles , 52, 53.d cattle –21 and id cattle , 23. In ollicles , 43, 54.n et al.  showed t GDG9 gene. Among these, nine SNPs were previously unreported in chicken; two (g.17156703A>C and g.17158286A>G) were missense mutations, resulting in the amino acids substitutions of Ile→Leu and Ile→Val, respectively; and three showed significant associations with partial reproductive traits. G.17156387C>T was significantly associated with AFE and WFE in both breeds. Birds carrying the CC genotype exhibited higher AFE and WFE values than those with the TT genotype. The SNP g.17156427A>G exhibited an association with EWTA in DX chickens but not LH chickens. This SNP g.17156703A>C had significant genetic effects on AFE and EN in DX hens; birds with the AA genotype had lower AFE and higher EN values compared to those with other genotypes. In addition, certain diplotypes significantly affected AFE, BWTA, and EN in both breeds. GDF9 was found to be highly expressed in the STR and all prehierarchal follicles, and its expression was increased in the STR, SYF, and F6 of LH chickens compared to the corresponding tissues of DX chickens. These results broadened our current understanding of the chicken GDF9 gene underlying reproductive systems and indicated that the GDF9 gene was involved in determining reproductive traits in chicken, but further studies are necessary for functional validation.In this study, we detected 15 SNPs in the chicken


FD = 2 and CW 1010 with FD = 10). The mapping population was evaluated in three replications at two locations . FD levels showed low to moderate correlations with WH (0.22–0.57). Assessing dormancy in winter is more reliable than in the fall in southern regions with warm winters. The mapping population was genotyped using Genotyping-by-sequencing (GBS). Single dose allele SNPs (SDA) were used for constructing linkage maps. The parental map (CW 1010) consisted of 32 linkage groups spanning 2127.5 cM with 1377 markers and an average marker density of 1.5 cM/SNP. The maternal map (3010) had 32 linkage groups spanning 2788.4 cM with 1837 SDA SNPs with an average marker density of 1.5 cM/SNP. Forty-five significant (P < 0.05) QTLs for FD and 35 QTLs for WH were detected on both male and female linkage maps. More than 75% (22/28) of the dormancy QTL detected from the 3010 parent did not share genomic regions with WH QTLs and more than 70% (12/17) dormancy QTLs detected from CW 1010 parent were localized in different genomic regions than WH QTLs. These results suggest that the two traits have independent inheritance and therefore can be improved separately in breeding programs.Understanding key adaptation traits is crucial to developing new cultivars with broad adaptations. The main objective of this research is to understand the genetic basis of winter hardiness (WH) and fall dormancy (FD) in alfalfa and the association between the two traits. QTL analysis was conducted in a pseudo-testcross F1 population developed from two cultivars contrasting in FD (3010 with  Medicago sativa L.) is a perennial cool-season forage legume grown worldwide for hay, pasture and silage  of alfalfa cultivars is assigned based on fall regrowth height, after clipping, to 11 groups ranging from FD 1 to FD 11 with lower numbers indicating more dormant  genes such as cas15 and other cold related genes are also potential genetic factors controlling WH without affecting dormancy , rose (Rosa hybrida), and alfalfa  even in species with no prior genome assemblies. The GBS method developed by Elshire et al.  compriseSinorhizobium meliloti and Rhizobium leguminosarum), and grown in the greenhouse. In order to confirm the true hybrids, 24 simple sequence repeat (SSR) markers were screened for polymorphism between the parents. These markers were developed from M. truncatula  winter-hardy alfalfa cultivar  with a tetraploid non-dormant (FD = 10) winter susceptible cultivar . The cross was made in the greenhouse using hand pollination in isolation under 18 hr. light and 6 hr. dark. About 384 F1 seeds were harvested, scarified, inoculated with rhizobium strain N-dure  markers were identified using genotyping-by-sequencing of the parents and progeny. DNA of each progeny and parents was extracted using CTAB method with some modifications  and treated with PCI and CIA as described earlier. The supernatant was pipetted into 1.5 ml tubes and 1/10 volume of 3 M Na-acetate pH 5.2 (stored at 4°C) and 2.5 volumes of absolute ethanol was added, left for 10 min and centrifuged for 20 min at 13,000 rpm. The supernatant was discarded and the pellet was washed with 70% ethanol, then air dried. The DNA was dissolved into 50–100 μl of sterile 10 mM Tris-Cl (pH 8.0). High quality DNA was ensured by quantification in Qubit® 3.0 Fluorometer  and running DNA samples in 1% agarose gel.The DNA solution was treated with 4 μl (10 mg/ml) of RNase A, followed by 5.0 μl (20 mg/ml) proteinase K, and incubated at 37°C in a water bath for 30 min after each addition. Sterile Millipore grade HApeKI and both common as well as barcode adapters were ligated. The step was followed by pooling the libraries (multiplexing) and cleaning up with Qiagen PCR  cleanup kit using the protocol provided with the kit. Moreover, the steps were followed by simple PCR using Kapa Library Amplification Readymix  and two PCR primers. Finally, PCR products were purified using QIAquick PCR purification kit . Both libraries were submitted to Georgia Genomics and Bioinformatics Core (GGBC), UGA, for removing short fragments by solid phase reversible immobilization (SPRI), cleanup, and sequencing. Sequencing was performed on an Illumina Next Seq (150 Cycles) 75 PE High Output flow cell with four lanes. The raw sequence data was processed using two pipelines; GBS SNP Calling Reference Optional Pipeline (GBS-SNP-CROP) version 2.0 . The library was constructed using the protocol described in Li et al. . The DNASRP150116 and it can be accessed at https://www.ncbi.nlm.nih.gov/sra/SRP150116.The GBS-SNP-CROP was a useful tool for de novo SNP calling. The raw reads were parsed and trimmed for quality using Trimmomatic software version 0.36 . The SNPs that were present in more than 30% progenies but have segregation ratios other than 1:1 were considered in segregation distortion  or CW 1010 (AAx AB) were screened as single dose allele (SDA) markers  for querying the consensus tags of SNPs with M. i et al. .Alfalfa dormancy data was collected as regrowth height after clipping in the fall and winter. In the fall, canopy height data was taken at 4 weeks after clipping the plants on 21st September according to NAAIC protocol  for both locations, JPC and BVL, a winter data set collected in the first week of January . WH was evaluated on the F1 population and the two parents on a scale of 0–5 according to NAAIC protocol  was considered random . A QTL was declared significant when the peak LOD value exceeded a conservative LOD threshold of 3.0. In the case of more than one peak, multiple QTLs were declared if LOD values between the peaks falls below 3.0 for more than one contiguous segment for at least one dataset analyzed  algorithm on Windows QTL Cartographer version 2.5 . The model and parameters used for CIM analysis were as described  SNPs segregating in the maternal parent 3010 and 794 SNPs segregating in the paternal parent CW 1010. Among these, 423 SDA SNPs from 3010 to 220 SNPs from CW 1010 were filtered as high-quality SDA after chi-square test (α = 0.05) for the segregation ratio of 1:1 (AB: AA). The SNPs obtained from this pipeline were identified with suffix MRG referring “Mock reference genome” followed by SNPs physical position with reference to MRG created using the reads of two parents.A total of 100 Gb raw reads were generated using Illumina NextSeq High Output Flowcell  amounting to one billion usable paired-reads. Using the GBS-SNP-CROP pipeline for Using the Tassel UNEAK pipeline, 500 million high-quality R1 reads were identified and processed using default parameter settings. A total of 65101 biallelic SNPs were identified. After filtering for missing data (<30%), 34122 (52.4%) SNPs were retained. Additionally, we removed 1625 loci with missing marker information in either parent retaining 32497 SNPs. Therefore, about 50% of the raw SNPs obtained from the UNEAK pipeline were filtered out in the initial screening because of missing data. Among the 32497 SNPs obtained from UNEAK, 4925 SNPs were single dose SNPs for 3010 parent and 2121 SNPs were identified as single dose SNPs for CW 1010 based on chi-square (α = 0.05) test for segregation ratio (1:1).After merging the GBS SDA-SNP obtained from both Tassel UNEAK and GBS-SNP-CROP pipelines for each parent, we generated a total of 5348 SNPs for the maternal parent 3010 and 2340 SDA for the paternal parent CW 1010 (File S1). Further screening of the SDA loci on JoinMap 5.0M. truncatula as described in Figure SNPs from both parents were assembled into 32 linkage groups using LOD for independence of 12 or above in the 3010 parent and LOD of 10 or above in CW 1010. Based on the SNP physical positions determined from BLAST analysis, each four linkage groups or haplotypes of each parent were assigned to a corresponding chromosome of Thirty-two linkage groups of the maternal parent 3010 consisting of 1837 SDA SNPs were assembled in a linkage map spanning about 2788.4 cM with an average marker density of 1.5 cM/SNP Table . The numThe 32 linkage groups of CW 1010 spanned 2127.5 cM with 1377 mapped markers Table . The aveM. truncatula reference genome  SNPs from the parent CW 1010 exhibited similarity with the M. truncatula genome with sequence identity of 85 to 100% using the same cut-off value as for 3010 SNPs. CW 1010 SNPs and M. truncatula genome similarity were obtained within a range of 0.5 Kb–56.3 MB.BLAST analysis showed that alfalfa genetic loci mapped in this study were syntenic with  Figures , 2. FromM. truncatula clearly displayed the grouping of markers on the 32 LG groups to the corresponding eight Medicago truncatula chromosomes  ranged from 0.37 for fall 2015 at BVL to 0.73 of winter 2016 dormancy of JPC environment with six out of the eight regression models having coefficient of determinations R2 > 0.50. The regression coefficients of the winter rating were higher than fall ratings at both sites.Eight regression equations were generated to estimate FD of the mapping population at two locations in fall 2015 (FD/2015), fall 2016 (FD/2016), winter 2016 (WD/2016) and winter 2017 (WD/2017). The regression models suggested that the relationship between standard FDR and canopy regrowth height of alfalfa checks was strong and positive. The regression coefficients (P ≤ 0.01) and significant G x E for FD ratings in most dates except for FD/2015 data. Because of the significant G x E, the LS means for each trait were estimated separately for each location  and fall 2016 exhibited the least R2 (0.59). The LS means estimated for traits and parents revealed the presence of transgressive segregation on both sides of the parents for both FD and WH traits (Table There were significant differences between the genotypes (ts Table . The FD P ≤ 0.01) for WH and significant G x E. Because of the significant G x E, the LS means were estimated for each location in addition to across locations  QTLs for FD and 35 QTLs for WH on both male and female linkage maps  was detected on homolog 1A and was located at 90.6–92.9 cM. The same homolog harbors another QTL (dorm2) with a LOD value of 6.2 and a peak at the interval 98.2–104 cM  in the WISFAL-6 cultivar reported previously . Homologs of this chromosome also harbor QTL dorm3, dorm5, dorm6, and dorm13 (Tables P ≤ 0.05) QTLs for dormancy (Tables Another two stable dormancy QTLs from 3010 were detected on chromosome 7A (3 Tables . Two othR2 = 0.13) was detected on homolog 8D at the interval 44.6–46.3 cM  followed by QTL wh2 on homolog 7A (0.12) and wh3 on homolog 1A (0.11)  Table . The wh11) Table . Similar1) Table . With th1) Table –8.R2 (0.14) followed by ws2, ws3, and ws4 (R2 = 0.10). However, contrary to ws1 and ws2, the ws3 and ws4 QTLs had positive effects (+) on WH  QTLs for WH were detected for the winter susceptible parent CW 1010 and were coded as  Tables . Major sWH Table . Most ofWH Table .Among the seven stable dormancy QTLs detected in the 3010 the dorm1 and dorm2 on homolog 1A shared the same genomic location as WH stable QTLs wh1 and wh3, respectively Figure . SimilarIn the CW 1010 parent, the stable QTLs ndorm1 and winter hardiness QTL ws5 shared the same genomic location on homolog 8D   Tables . The fal) Tables , which lr = 0.92, p < 0.01) (Tables The non-dormant parent CW 1010 exhibited slightly lower dormancy level 4.7–7.9), than it supposed to be in most of the years. The 3010 parent mostly exhibited the expected dormancy level between 2.3and 6.4 Table . Dormanc.7–7.9, tr) observed between dormancy and WH irrespective of the time of assessment is a clear indication of the weak association between the two traits   in outcrossing polyploid species and using the maps for linkage analysis of quantitative traits has been a common practice for decades  and 3010  genetic maps. Even though previous studies reported QTL mapping of dormancy in alfalfa, these maps were based on parents relatively close in dormancy and constructed with traditional markers  and most likely genetically associated leading breeders to use one trait as surrogate to select for the other  of the dormancy QTL detected for the 3010 parent did not share genomic regions with winter hardiness QTLs (Tables  of the dLA: contributed to the data collection, analysis, and writing the manuscript. OL: contributed to the winter hardiness data collection. JM: contributed to the phenotypic data collections at the two locations. AM: contributed to the experimental design of the study and writing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Neisseria meningitidis, including the currently widely applied cgMLST approach and different SNP-based methods. In addition, the impact of the use of different tools for detection and filtering of recombinant regions and of different reference genomes were tested. Our benchmarking analysis included both assessment of technical performance of the pipelines and functional comparison of the generated genetic distance matrices and phylogenetic trees. It was carried out using replicate sequencing datasets of high- and low-coverage, consisting mainly of isolates belonging to the clonal complex 269. We demonstrated that cgMLST and some of the SNP-based subtyping workflows showed very good performance characteristics and highly similar genetic distance matrices and phylogenetic trees with isolates belonging to the same clonal complex. However, only two of the tested workflows demonstrated reproducible results for a group of more closely related isolates. Additionally, results of the SNP-based subtyping workflows were to some level dependent on the reference genome used. Interestingly, the use of recombination-filtering software generally reduced the similarity between the gene-by-gene and SNP-based methodologies for subtyping of N. meningitidis. Our study, where N. meningitidis was taken as an example, clearly highlights the need for more benchmarking comparative studies to eventually contribute to a justified use of a specific WGS data analysis workflow within an international public health laboratory context.Whole genome sequencing is increasingly recognized as the most informative approach for characterization of bacterial isolates. Success of the routine use of this technology in public health laboratories depends on the availability of well-characterized and verified data analysis methods. However, multiple subtyping workflows are now often being used for a single organism, and differences between them are not always well described. Moreover, methodologies for comparison of subtyping workflows, and assessment of their performance are only beginning to emerge. Current work focuses on the detailed comparison of WGS-based subtyping workflows and evaluation of their suitability for the organism and the research context in question. We evaluated the performance of pipelines used for subtyping of  Whole genome sequencing (WGS) is becoming increasingly recognized in a public health context as a single-shot method to determine species , serotypde novo assembled sequences, or by counting short nucleotide sequences, kmers, in the raw or assembled data samples , also termed gene-by-gene approaches, and methods based on single nucleotide polymorphism (SNP) detection . The gen samples . Establi samples . An impo samples . In SNP- samples . Such re samples  and Gubb samples . cgMLST Despite the growing amount of comparative studies , it is cNeisseria meningitidis, the causative agent of meningococcal disease. This organism is marked by substantial homologous gene transfer, with the rates of nucleotide changes due to recombination exceeding those due to point mutation  evaluate the technical performance of the individual subtyping workflows, and recombination filtering tools and (b) to compare the output obtained with the different data analysis approaches.In this study we performed a comparison between the existing WGS workflows for subtyping of mutation . In the ngitidis   269. Technical performance characteristics of the workflows were firstly assessed by calculating the performance metrics used for evaluation of classical subtyping methods, more specifically epidemiologic concordance, discriminatory power and reproducibility metrics . SecondlN. meningitidis, chosen from the collection of 2002 to 2016 of the Belgian National Reference Centre (NRC) for Neisseria  and large dataset 2 (LD2) originating from the pool of 69-isolates libraries, and small dataset 1 (SD1) and small dataset 2 (SD2) originating from the pool of 24-isolates libraries . Hereto,Prior to running the workflows, the quality of the sequencing data was analyzed with FastQC 0.11.4 , using MPacbio sequencing was carried out on a PacBio RS II instrument (Pacific Biosciences) at the Earlham Institute  using DNA of the isolate S13BD00117 obtained as described above. Raw sequencing data was pre-processed using SMRT analysis portal 2.3.0 (Pacific Biosciences), which yielded 47033 high-quality reads (320507566 bp) with a mean subread length of 6814 bp and N50 of 9216 bp. Processed reads were assembled with SMRT analysis 2.3.0, yielding two contigs, one of 2277000 bp, with a coverage of ∼125X and overlapping ends, and one of 14819 bp with a coverage of ∼5X. The small contig was discarded based on the coverage filter. Assembly was finished using minimus2 and fixstart functions from Circulator 1.4.0  for circ2 0.2.2, yielding 199278 high quality reads (1107136137 bp) with an average read length of 5555 bp and N50 of 7936 for the barcode of interest. A hybrid de novo assembly was carried out with SPAdes 3.11.1  flowcell (Oxford Nanopore Technologies) and using a LSK108 library prep kit (Oxford Nanopore Technologies) and a NBD103 barcode kit (Oxford Nanopore Technologies). Basecalling was performed with Albacore 2.0.1 (Oxford Nanopore Technologies) and demultiplexing and adapter trimming was carried out with Porechops 3.11.1  using Ils 3.11.1  error coThe two assemblies displayed 24 SNPs and 51 gaps with a maximal size of 52 bp  between 3, a tool that determines similarity between sequencing samples and/or assemblies based on kmer content. The collection of reference genomes used in the analysis consisted of 14 reference genomes, and was created by selecting one reference per clonal complex from the 73 high-quality N. meningitidis assemblies available on NCBI. Isolates were attributed to the most closely related assembly if they demonstrated at least 85% similarity to it.Groups of closely related isolates that can be subtyped using the same reference genome were identified using KmerIDThe workflows evaluated in this study, have been summarized in de novo assembled using SPAdes. The contigs are aligned against the cgMLST database for sequence typing using blastn. The best hit for each cluster is determined by the method for allele scoring as described by The tested implementation of the cgMLST approach is described in In the current study, alleles identified by the pipeline for each of the 1605 tested genes and for each isolate were saved in an allele matrix. The matrix was filtered to remove genes for which no allele was called in more than 10% of the isolates and used for the downstream analyses.The Panseq pipeline allows to identify the core and accessory genome among a collection of genomic sequences, find SNPs within the core genome and determine the presence/absence of genomic regions within the accessory genome . The rawN. meningitidis  or branch length (λ = 1). To estimate significance values, background distribution of 105 random trees was created using the tree function from APE package in R 3.4.2 , was used as the cluster of epidemiologically related isolates during evaluation of epidemiologic concordance, and as the group of closely related isolates with small inter-isolate distances during SNP matrix comparison tests.The obtained results showed that while all of the workflows retrieved the same groups of genetically similar isolates, the relations between these groups, and the corresponding genetic distances differed between the workflows. In the subsequent analyses, the sequencing data from the two LDs and the two SDs were used to assess the performance of the pipelines and their sensitivity toward variation of input sequencing data and to compare the output between the pipelines . In addi∗ isolates to a separate clade with all datasets, yielding a concordance of 100%. As expected, allele/SNP matrix sizes differed considerably between the pipelines, with the SMALTPpl, Parsnp and PanseqSNP detecting the largest number of polymorphisms, followed by kSNP- and LyveSET-based workflows, PanseqBin and cgMLST  and cgMLST produced highly correlated distance matrices with B:NT cc-269 isolate subsets from the replicate datasets . These observations were largely confirmed by the output of the linear regression analysis , while cgMLST, PanseqSNP, Parsnp, kSNPCon and LyveSETStr demonstrated less correlated output (r2 < 95.6 for SDs or LDs). Linear regression analysis showed similar results, although the decline of Parsnp and kSNPCon performance with the closely related isolates was much less pronounced . According to the same test, ClonalframeML had no large effect on the stability of the workflows for B:NT cc-269 isolates, and even improved the output slightly for B:NT:P1.5,2∗ isolates  with both B:NT cc-269 LDs and B:NT cc-269 SDs . The pipelines that do not use recombination filtering showed a variable performance depending on the datasets, but the KC distances between phylogenetic trees returned by PanseqBin and PanseqSNP were among the largest, while those returned by cgMLST, kSNPRead, LyveSETRel, and SMALTPpl – among the smallest with both LDs and SDs .Similarity of the phylogenetic trees was measured using the Kendall-Colijn (KC) test for phylogenetic tree topology (λ = 0) . All pip∗ and B:NT:P1.14 ST2693 isolates, respectively. The differences between the trees produced with replicate datasets were mainly in the location of the remaining B:NT:P1.14 ST269 isolates, as well as the positions of the more closely related isolates within the two stably recurring clades. Noticeably, SMALTPpl and kSNPRead produced identical arrangement of the B:NT:P1.5,2∗ isolates between the datasets, and placed the three confirmed epidemiologically related isolates together with all datasets, which was not the case for the other workflows. This observation is concordant with the combined results of Mantel test and linear regression analysis, according to which the two pipelines returned both highly correlated, and linear pairwise distances between the B:NT:P1.5,2∗ subsets of the two SDs and the two LDs . In practice, rapid generation of a high-quality closely related PacBio assembly might not be possible and/or financially justified, and the analysis might have to be performed using an often more distinct publicly available reference. Alternatively, a closely related reference genome of a potentially lower quality could be generated using Nanopore sequencing technology, which is more accessible for smaller laboratories. In the current section, we have tested whether the source of the reference genome, and its relatedness to the isolates affected the performance of one mapping-based (SMALTPpl) and one assembly based (Parsnp) pipeline that showed a good performance in the previous tests. Also the combination of the two workflows with the more stable recombination-filtering tool, ClonalframeML, was analyzed (ParsnpCl and SMALTPplCl). Therefore, a second reference genome was generated for the S13BD00117 isolate using MinION and Illumina sequencing data, and a third high-quality reference genome of a rare isolate, B:NT:P1.19,15 also belonging to ST269 . These results were confirmed by the linear regression analysis, with the only exception being slightly more deviating slope values observed with the NC017515.1 genome for ClonalframeML-based pipelines  and linear regression analysis . cgMLST demonstrated the highest correlation with SMALTPpl (r2 > 97.3 for LDs and SDs), kSNP-based pipelines (r2 > 96.8 for LDs and SDs), and LyveSETRel (r2 > 96.6 for LDs and SDs).The Mantel test performed using B:NT cc-269 subsets of LDs and SDs illustrated that between each-other, the tested pipelines produced more correlated genetic distances according to the used recombination-filtering strategy, i.e., pipelines in which recombination filtering was omitted (including cgMLST), pipelines containing ClonalframeML, and pipelines containing Gubbins tended to form three separate clusters . Lyveset-based pipelines, Parsnp and kSNPCon in some cases also co-clustered with SMALTPpl and kSNPRead but not for both all datasets (99.7 ≤ r2 ≤ 91.9), while cgMLST and PanseqSNP produced more distinct output (93.8 ≤ r2 ≤ 63.4). Results of a linear regression analysis largely confirmed the output of the Mantel test , while SMALTPpl showed somewhat larger differences between reference genomes, with hybrid Nanopore and NC017515.1 producing more correlated output to each other (r2 > 98.6) than to Pacbio (r2 > 95.7). Thereby, the output obtained using the Pacbio reference genome was more correlated to that of the other tested pipelines, followed by the hybrid Nanopore and NC017515.1 genome for SMALTPpl  and Parsnp pipelines . With B:NT:P1.5,2∗, the output of SMALTPpl obtained using hybrid Nanopore and Pacbio was relatively similar between each other (r2 > 96.6), and to that of kSNPRead (r2 > 97.6), and highly different from that generated with NC017515.1 (r2 > 71.4)  , S7B.∗ isolates with all datasets and in case of SMALTPpl with all three reference genomes.With B:NT cc-269 SDs, SMALTPpl, LyveSET-based, kSNP-based Parsnp and PanseqSNP pipelines produced topologically nearly identical phylogenetic trees  . For theN. meningitidis isolates. But compared to Pacbio, the Oxford Nanopore MinION sequencer is small, highly affordable and utilizes a relatively simple library preparation which makes the technology more accessible for the use in smaller public health laboratories. Besides, we showed that a reference genome of lower, but still sufficient quality can still be obtained if multiple genomes are sequenced on a single MinION flow cell if hybrid assembly with Illumina short reads is carried out, which could result in lower sequencing costs.As elaborated above, among all of the tested pipelines, the best performance with closely related isolates was shown by SMALTPpl and kSNPRead. While kSNPRead is a reference-free approach, SMALTPpl requires a reference genome to run. The tool showed equally stable performance with all three reference genomes tested, but similarity of its output to that of the other pipelines was dependent on the relatedness level of the reference genome used. Moreover, the availability of a closely related reference genome is necessary if a more detailed characterization of isolates is envisaged, for instance in case of comparative genomic analysis. Generation of a closed reference genome requires the availability of long-read sequencing data, which currently can be obtained using two technologies: Pacbio and Nanopore. In this study, both approaches were tested, evaluating the quality of the obtained reference genome and its suitability for the high-resolution subtyping. Pacbio sequencing data generated using a single SMART cell appeared to be sufficient to create a closed high-quality reference using long-read data only. MinION sequencing data had a lower coverage, among others because two isolates were multiplexed on a single cell, and showed a higher error rate. A closed reference genome could still be obtained from MinION reads using hybrid assembly with Illumina sequencing data. The generated reference genome aligned along the entire length to the Pacbio reference, showing only a limited number of SNP differences and gaps. Despite the slightly lower quality of the MinION reference, the two reference genomes showed sufficiently similar performance characteristics, and facilitated calculation of linear SNP distance matrices and phylogenetic trees for both Parsnp and SMALTPpl, indicating that both of them are equally useful for subtyping and detailed characterization of N. meningitidis, the use of recombination filtering software resulted in more distinct genetic distances and phylogenetic tree topologies compared to the SNP-based workflows without recombination filtering and to cgMLST. Parsnp combined with Gubbins even resulted in some cases in a misplacement of isolates from otherwise stably recurring isolate clusters. These observations could be explained by the fact that (a) the SNP-based subtyping workflows and cgMLST do not reflect the correct clonal genealogy of N. meningitidis, (b) as N. meningitidis mainly evolves by recombination, there is a less well pronounced clonal geneaology, and filtering out recombination leaves insufficient signal for robust reconstruction of the population structure and (c) the recombination filtering tools did not perform correctly, because of not being fine-tuned to the high levels of recombination in the genome of N. meningitidis. In addition, pipelines using Gubbins showed an increased sensitivity to inter-run variation of the sequencing data compared to the pipelines in which recombination filtering step was omitted. For ClonalframeML, which performs a more relaxed SNP filtering, the output was less stable for the closely related isolates, showing a higher sensitivity for the inter-run variation and coverage of the input data, and reference genome compared to the original workflow. Notably, among the tested pipelines, the most similar output to the ClonalframeML and Gubbins workflows was demonstrated by the LyveSET-based workflows. The LyveSET pipeline allows to perform extensive SNP quality filtering, including a SNP pruning step, i.e., removal of SNPs that are located too closely to each other on the chromosome. SNP pruning is used to mask polymorphisms from regions that show a too high SNP frequency, discarding among others a fraction of polymorphisms arising from recombination events, which could explain the observed similarities to ClonalframeML. Taken together, our observations suggest that caution should be associated with using a recombination filtering step or extensive SNP pruning for subtyping of N. meningitidis. However, both of the tested recombination filtering tools, and the LyveSET pipeline are undoubtfully useful for finding recombinant regions in case of comparative genomic studies of N. meningitidis, or for subtyping of species with a different, more clonal population structure. Besides, the applied tools could possibly be fine-tuned for a more accurate performance with N. meningitidis.It has been shown previously that SNP-based subtyping approaches that do not use recombination filtering can generate phylogenetic trees that are similar to the real clonal genealogy  and thusCurrent work focuses on the evaluation of WGS-based workflows using different approaches. The established methodology, relying on the calculation of performance metrics, permits to relatively quickly describe the different characteristics of the workflows. A newer approach, proposed by PRJNA549235.The datasets generated for this study can be found in the NCBI Sequence Read Archive under Accession Number N. meningitidis samples to be used in this study, and provided specialist feedback on the obtained results on Neisseria relatedness. AS, NR, KM, SB, and SD participated in the interpretation of the results. AS and SD wrote the draft of the manuscript. All authors read and approved the submitted version.AS, KM, and SD conceived and designed this study. SD supervised the project. AS performed all bioinformatics analysis. WM and SB collected and isolated DNA of 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.


Anastrepha ludens is such a pest, and its incursions into Texas and California represent major threats to the agricultural systems of those regions. We developed a draft genome assembly for A. ludens, conducted range‐wide population genomics using restriction site‐associated DNA sequencing, and then developed and demonstrated a panel of highly differentiated diagnostic SNPs for source determination of intercepted flies in this system. Using 2,081 genomewide SNPs, we identified four populations across the range of A. ludens, corresponding to western Mexico, eastern Mexico/Texas, Guatemala/Belize/Honduras, and Costa Rica/Panama, with some intergradation present between clusters, particularly in Central America. From this population genomics framework, we developed a diagnostic panel of 28 highly differentiated SNPs that were able to recreate the genomewide population structure in this species. We demonstrated this panel on a set of test specimens, including specimens intercepted as part of regular trapping surveillance in Texas and California, and we were able to predict populations of origin for these specimens. This methodology presents a highly applied use of genomic techniques and can be implemented in any group of recurrently invading pests.Recurrently invading pests provide unique challenges for pest management, but also present opportunities to utilize genomics to understand invasion dynamics and inform regulatory management through pathway analysis. In the southern United States, the Mexican fruit fly  InvasivPopulation genetics provides unique evolutionary perspective on invasive systems Lee, . PopulatFST‐based, contribution to principal components, etc.), which can potentially affect the relative success of assignment tests  and Mediterranean fruit fly Ceratitis capitata (Wiedemann)) . Given the cost of eradication and establishment‐prevention measures and additional impacts of subsequent quarantines, regulatory agencies have high demand for population genetic tools for geographic source determination, both to respond to individual invasions and to identify trends in invasion route over time  (Diptera: Tephritidae) is one of the most serious fruit fly pests in the tropical Americas  and mango (Anacardiaceae: Mangifera indica L.), although it has been recorded from >30 other fruits . In contrast, Tapachula 7 and Family 10 are genetic sexing strains, which both harbor a mutation for black pupae, which is made sex‐linked through a stable autosomal translocation with the Y chromosome .The Mexican fruit fly A. ludens. We generate genomewide SNP markers with double‐digest restriction site‐associated DNA sequencing  and larvae extracted from host fruit material. Specimens were preserved in 95% ethanol or frozen at −80°C, and species identifications were based on morphology and conducted by DBT. The majority of our sampling are flies from the same traps that were previously investigated using sequencing methods by Ruiz‐Arce et al. .Wild e et al. , and we We followed the approach of Peterson et al.  to prepa2.2To provide a reference assembly for mapping reads from the ddRAD data, we generated libraries that were optimized to be assembled using the ALLPATHS‐LG assembler  and the alternative ancestry prior. For location prior analyses, and to avoid populations with low sample sizes, we grouped individuals by medium‐sized political regions  , and xvaldapc was used to perform cross‐validation to assess the optimal number of principal components to retain in the discriminant analysis . Like with STRUCTURE, we conducted DAPC with and without inclusion of the rearing individuals.We also used discriminant analysis of principal components .We calculated basic population genetic statistics  and pairwise population differentiation using GenoDive v2.0b27 . We calculated Weir and Cockerham's FST ; this led to seven groups, corresponding to (a) western Mexico and (b) Texas/eastern Mexico, as differentiated by STRUCTURE, (c) Guatemala, (d) Belize, (e) Honduras, (f) Costa Rica, and (g) Panama. We then manually filtered the ddRAD loci to a subset of putative assays based on their locus‐specific FST and position on reference scaffolds (to avoid selecting loci on the same scaffold). DAPC was used, as above, to ensure that this subset resulted in similar overall population structure and cluster separation, as compared to the full dataset. We then used samtools v1.8 . We created positive controls for all assays by synthesizing gBlock synthetic Gene Fragments  representing either the reference or alternate alleles for each SNP. To create these positive controls, we extracted sequences from the reference genome  which spanned the allele‐specific and locus‐specific primers of the rhAmp assays plus 3 bp and 10 bp on the 5' and 3' ends, respectively, of these fragments (to provide padding between assays as recommended by Integrated DNA Technologies). We then concatenated these fragments into total lengths of up to 1,000 bp (each containing positive control sequences for 7–11 assays). These fragments were synthesized for both the reference and alternate alleles for each SNP, and a heterozygote positive control was created by mixing the reference and alternate at equal molarity. For each of the positive control types , we combined all individual gBlock fragments, resulting in a single positive control sample per control type for all assays . We used three dilutions  of these positive controls to mimic potential variability in DNA quality/quantity in intercepted samples.All sample DNA was normalized to 10 ng/µl, and we prepared sample master mixes containing 3.5 µl 2× rhAmp genotyping master mix, 0.35 µl 20× Fluidigm GT Sample loading reagent, 0.175 µl 40× rhAmp reporter mix, 0.03 µl ROX reference dye , 0.945 µl dH2O, and 2 µl of either the DNA sample or the diluted gBlock . Assay master mixes contained 2.5 µl 2× Fluidigm assay loading reagent and 2.5 µl 20× rhAmp SNP assay, and we followed manufacturer recommendations to use the Fluidigm Integrated Fluidic Circuit (IFC) Controller HX to prime and load the IFC cartridge. Thermal cycling and imaging were conducted using the Fluidigm Biomark as follows: 95°C for 10 min and 40 cycles of  with imaging conducted at the end of each cycle.2.6We used the Fluidigm SNP Genotyping Analysis software  to process data from the Biomark and call genotypes. Genotypes were called at cycle 40 using the default confidence threshold of 65, NTC normalization, and K‐means clustering. The final genotyping was done in triplicate per each sample and control (on a single IFC cartridge) and included samples to validate the assays  and test specimens with unknown origins and genotypes; we used different genotyping rules for validation versus test individuals. For validation individuals, we required two of the triplicates to agree on the ddRAD genotype to be considered correct validation, and two correct triplicates with high confidence (>95%) overruled one incorrect genotype. We only allowed a single triplicate to be considered a correct validation of the genotype when the other two triplicates did not result in a genotype call. For test individuals, we generally required triplicate genotypes to match in order to call a genotype for the specimen; if a specimen displayed any mismatch between triplicates, it was coded as missing data. The only exception to this rule was if triplicates failed ; in these cases, we called a genotype for the specimen if the confidence of the called genotype(s) was >95% or if separation between genotypes across the entire assay was very distinct, but the confidence call was between 80% and 95%.A. ludens to predict the geographic origin of intercepted specimens and assign a confidence score to that prediction based on the fit of these supplemental individuals to the initial DAPC model. Our final dataset for this analysis consisted of ddRAD genotypes for all individuals in the main, filtered dataset, combined with Biomark genotypes for the test specimens.We used DAPC to analyze the dataset resulting from the diagnostic panel. The main DAPC workflow was identical to that of the main dataset; however, the test specimens with unknown origins were not included in the main analysis and model construction, but instead were treated as supplemental individuals  versus de novo ddRAD data analysis methods which can be confounded by contamination and repetitive genomic sequences when not anchored to a reference.To generate a reference genome for read mapping of ddRAD data, after filtering, 39.9 Gb of fragment library (~61.9× coverage) and 17.9 Gb of mate‐pair library data (~27.8× coverage) were used with ALLPATHS‐LG, which resulted in an assembly containing 152,464 contigs >1 kb (N50 = 6.1 kb) placed onto 44,974 scaffolds (N50 = 43 kb). The estimated genome size for this species, based on Kmer abundance of these data, is 644.7 Mb, and our assembly is 683.0 Mb in length with 20% of the genome as scaffold gaps. Overall the contiguity of this assembly was marginal, with most protein coding genes likely to span contigs and scaffolds. However, this is sufficient to use as a reference for ddRAD data mapping, as it allows higher confidence in generating catalog loci . Analyses using default parameters and those with alternative ancestry prior and a specified α resulted in virtually identical results. Using a location prior identified the same main clusters, but tended to increase admixture in all individuals, and analyses including rearing strains consistently identified those individuals as a unique cluster . Given the similarity of these analyses, we focused on and present the results of the analyses with default parameters, excluding the rearing strains , ΔK, and the Puechmaille statistics, provided in Figures K. At the broadest level, K = 3 is well supported by a clear peak in values of ΔK and a broad plateau in the values of Ln Pr(X|K). The Puechmaille statistics supported K = 5, 6, and 7, and Ln Pr(X|K) showed a second, finer plateau at around K = 7 and a peak at K = 10. Geographically, the K = 3 clusters corresponded to (a) west Mexico (west of the Mexican Plateau), (b) Texas and east Mexico, and (c) Costa Rica and Panama, with populations in Guatemala, Belize, and Honduras being intermediate to the latter two clusters . The other three clusters predicted by find.clusters matched those predicted when rearing strains were excluded, although with better separation in the latter, and corresponded to west Mexico, east Mexico and Texas, and Costa Rica/Panama  to 0.096 (west Mexico versus Costa Rica/Panama) . PairwQ < 0.4), only one state (Querétaro) contains individuals from both the west Mexico cluster and the east Mexico/Texas cluster, although the individuals belonging to each cluster were from separate collection localities). Descriptive population genetic statistics were quite similar when considering the regional divisions, and all individual measurements were relatively moderate. Observed heterozygosity and gene diversity ranged from 0.147 and 0.138, respectively, in Costa Rica and Panama to 0.157 and 0.149, respectively, in west Mexico ; however, GIS was negative, as in the wild populations, indicating that inbreeding associated with mass rearing is not substantially increasing relatedness between individuals  to 0.266 (San Luis Potosí versus Tapachula 7) . Although these finer‐scale boundaries are biologically arbitrary, they allow us to delineate localities while accommodating the fact that many of the sampling localities had small sample sizes . The 96 individuals we used for genotyping validation and assay testing included two sets of individuals from the ddRAD dataset which represented the fewest number of individuals with as many ddRAD genotypes as possible  = 8,064 potential genotypes), 6,138 of all potential genotypes (76.1%) resulted in a genotype call, and the average confidence score for successful genotypes was 97.3%. For the ddRAD validation individuals, there were 1,353 genotypes to validate ; 1,041 of those were successfully validated (76.9%), 212 failed to amplify , and 100 resulted in failed validations   . We then used ddRAD to assess the genomic population structure across A. ludens’ range and designed and validated a set of cost‐effective SNP genotyping assays for source determination of intercepted specimens. From our population genomic dataset of 2,081 SNPs, we strategically subsampled markers to arrive at a panel of 28 SNPs capable of recreating the same broad population structure as the genomewide dataset. We validated this panel by genotyping specimens included in the ddRAD dataset and then genotyping a set of real‐world test specimens that were intercepted as part of regular surveillance efforts in California and Texas. This is the first genomic‐scale assay for source determination in a pest tephritid. It will serve as a proof of concept for other recurrently invading pest species and be used as a tool to support decision making in the management of A. ludens.Here, we used a combination of genomic strategies to assess population structure and develop pathway analysis tools in 4.1FST between main clusters = 0.096; Table A. ludens’ range, corresponding to the Isthmus of Tehuantepec (the narrowest point of Mexico). However, this pattern was driven by the distribution of low‐frequency haplotypes, and the most prevalent haplotype  was geographically widespread. While we do observe some differentiation between the northern and southern parts of the distribution (comparing Mexico to Costa Rica/Panama), similar to that identified by Ruiz‐Arce et al. ; these e et al. . We inclA. ludens. Historically, northeastern Mexico was considered the ancestral range of the species, based on high levels of infestation in sapote (Casimiroa spp.: Rutaceae), a native genus to Mexico and Central America, and a diverse assemblage of parasitoids compared to other regions  in Figure We included rearing strains of s Figure a. The Wi4.2A. ludens and predicting the geographic source of 40 test specimens. These test specimens included both real‐world unknown specimens captured in regular monitoring efforts in Texas and California, and specimens with questionable origins as determined with mtDNA in Ruiz‐Arce et al.  are often used to assess the success of a subset of loci for assigning individuals to particular populations. However, these methods have also been criticized for “high‐grading bias” stemming from the indiscriminate use of individuals for both constructing the model and evaluating its success  being called as homozygotes with the rhAMP assay. When using a microfluidic genotyping platform, this is indicative of poor or inconsistent DNA quality/quantity, as it is less likely that a full set of template DNA molecules end up in each microfluidic reaction chamber . Although higher success would obviously be preferred in such a regulatory context, we feel that this level of validation is satisfactory given the inherent stochasticity of missing data when using both ddRAD and the Fluidigm Biomark. Including more loci and filtering loci based on genotype validation success  would potentially increase validation success rates if that is important to regulatory agencies.Broccanello et al.  had highThe cost of this approach is entirely dependent on the scale of the genotyping in question. A back‐of‐the‐envelope calculation of the cost of this panel of 28 loci run in triplicate on the BioMark platform, for a diagnostic laboratory that would be conducting hundreds to thousands of these assays (at current market price), is approximately $13 USD per individual (without DNA extraction or the cost of technician/scientist time). In a direct comparison, Broccanello et al.  demonstrNone declared. Click here for additional data file. Click here for additional data file. Click here for additional data file.