Background/objectives: Whole genome sequencing (WGS) has proven to be a powerful subtyping tool for foodborne pathogenic bacteria like L. monocytogenes. The interests of genome-scale analysis for national surveillance, outbreak detection or source tracking has been largely documented. The genomic data however can be exploited with many different bioinformatics methods like single nucleotide polymorphism (SNP), core-genome multi locus sequence typing (cgMLST), whole-genome multi locus sequence typing (wgMLST) or multi locus predicted protein sequence typing (MLPPST) on either core-genome (cgMLPPST) or pan-genome (wgMLPPST). Currently, there are little comparisons studies of these different analytical approaches. Our objective was to assess and compare different genomic methods that can be implemented in order to cluster isolates of L. monocytogenes.Methods: The clustering methods were evaluated on a collection of 207 L. monocytogenes genomes of food origin representative of the genetic diversity of the Anses collection. The trees were then compared using robust statistical analyses.Results: The backward comparability between conventional typing methods and genomic methods revealed a near-perfect concordance. The importance of selecting a proper reference when calling SNPs was highlighted, although distances between strains remained identical. The analysis also revealed that the topology of the phylogenetic trees between wgMLST and cgMLST were remarkably similar. The comparison between SNP and cgMLST or SNP and wgMLST approaches showed that the topologies of phylogenic trees were statistically similar with an almost equivalent clustering.Conclusion: Our study revealed high concordance between wgMLST, cgMLST, and SNP approaches which are all suitable for typing of L. monocytogenes. The comparable clustering is an important observation considering that the two approaches have been variously implemented among reference laboratories. Listeria monocytogenes (L. monocytogenes) is one out of 17 species belonging to the genus Listeria, a Gram-positive rod-shaped bacterium   EFSA, . PFGE waE) EFSA, . PFGE haL. monocytogenes. A number of studies have demonstrated the advantages of using WGS analysis for national surveillance, outbreak detection and source tracking of L. monocytogenes . Different cg or wgMLST schemes have been developed: in Germany -based subtyping has proven to be extremely powerful for L. monocytogenes isolated in France during the last 20 years.The rapid implementation of WGS by different laboratories and laboratory networks using different approaches to analyse their data makes necessary to assess the differences between clustering methods. The main aim of this study was to assess the concordance between cgMLST, wgMLST, SNP, cgMLPPST, and wgMLPPST approaches using a well-defined panel of food strains of L. monocytogenes of food origin isolated in France during the past 20 years. A panel of 207 L. monocytogenes strains from this study was selected to be statistically representative of the diversity of L. monocytogenes. It included strains isolated between 1989 and 2013, from various food matrixes and food processing environments. Out of the 207 strains, 127 isolates belonged to molecular serotype IIa, 25 to molecular serotype IIc, 17 to molecular serotype IIb, and 38 to molecular serotype IVb , with Dice's coefficient, tolerance and optimization set up at 1%; Henri et al., NC_003210, ST35, CC9, serotype 1/2a and molecular serotype IIa) and EGD  were included in the final set and used as reference for SNP calling. The complete list of the 209 genomes is available in Supplementary Table Previously, we have investigated by PFGE and conventional MLST the genetic diversity of approximately 2000 DNA extraction was performed using Easy-DNA™ gDNA Purification Kit from Invitrogen™ . The DNA concentrations were measured using the Qbit dsDNA BR Assay Kit from Invitrogen™.Libraries preparation and DNA sequencing were performed at the Welcome Trust Center for Human Genetics . Libraries were prepared by using the NEB library prep kits with in-house developed modifications. A sample of pooled libraries was loaded into Illumina HiSeq reagent cartridge with a standard flow cell. The 207 strains were subjected to pair-end sequencing. Insertion size of pair-end sequences ranged from 65 to 473 bp, with an average of 231 bp. The reads coverage ranged from 28 × to 442 ×, with an average of 213 × (Supplementary Table http://www.ebi.ac.uk/ena) under study accession no: PRJ 948.A biosample project was created as repository to store all raw sequence reads of this study with open access. The raw sequence data have been submitted to the European Nucleotide Archive . The wgMLST with the cgMLST schema is included in the commercial software [BioNumerics v7.5 ]. Alleles were called for both the wgMLST and cgMLST schemes using BioNumerics v7.5. Unless raw reads (fastq format) were available, assembly-based allele calling (fasta format) was completed. The contigs were assembled using SPAdes 3.5.0, plug-in of the BioNumerics software v7.5. Alleles were named if genes fulfill the following criteria: a start and stop codon were present, the DNA sequence met the 85% minimum homology cut-off, there were no ambiguous base calls in the allele sequence, and had less than 100 gaps in the sequence alignment. Dendrograms of wgMLST and cgMLST were created using the UPGMA algorithm with the allele calls considered categorical data.The wgMLST and cgMLST were performed at the Centers for Disease Control and Prevention, the USA (US-CDC) by the Enteric Diseases Laboratory Branch. The wgMLST scheme was developed from a set of over 200 annotated closed and high-quality draft genomes that represented the diversity of serotypes and lineages in www.genomicepidemiology.org) . Subsequently, a second SNP analysis was performed using the second reference genome: EGD .The paired-end reads were mapped to the reference genomes using Burrows–Wheeler Aligner (BWA)  a minimum distance of 10 bps between each SNP, (2) a minimum of 10x depth and 10% of the breadth coverage, (3) the mapping quality was above 30, (4) the SNP quality was higher than 20, and (5) all indels were excluded. For each genome, SNPs were concatenated to a single alignment corresponding to the positions of the reference genome.The concatenated SNPs (with either EGD or EGD-e as reference) were inferred with the multi-core architecture  was performed with MUSCLE version 3.8.31 Edgar, . The conBlastP, using 50 percent length and 50 percent similarity rules, was performed for each samples against pan-genomes previously defined  and cloud genes (genes that are rarely found). The wgMLPPST could be constructed by adding more weight either to cloud or shell genes. The trees were constructed using hierarchical clustering of the relative Manhattan distance according to the distance matrix  we assessed the concordance of the genomic clustering with conventional groups: lineages, molecular serotype, PFGE pulsotype and ST's. The results were reported in percentage of concordance.A phylogenic tree can be characterized with two properties: the topology and the branch lengths. The topology is the branching structure of the tree and it indicates patterns of relatedness among strains. The comparison of the tree topology and distance were performed using the R packages “ade4,” “ape,” “dendextend,” “phangorn,” and “phytools”  calculates the correlation between the cophenetic distance matrices of the two trees. The cor_cophenetic value ranges between −1 (perfect negative correlation) and 1 (perfect positive correlation). A value close to 0 (nil) indicates the absence of correlation for the two trees. The cophenetic and cor_cophenetic functions of dendextend and phangorn package were used to evaluate the clustering  index which assess the similarity between two clusters  CC13, CC193, CC31; (2) CC7, CC155, CC37, CC26, CC20, CC8, CC21, CC204, CC9, and seven singletons  ; and (3) to CC121 (lineage II) Figures . The inf CC13, CCA visual comparison of cgMLST- and wgMLST-inferred phylogenies showed that strains from lineage II were grouped similarly and correctly with both approaches. To make the comparative analysis of the clustering methods easier, the trees to be compared were plotted facing each other with the same strains being connected Figure . This daThe SNP trees were computed from concatenated SNPs identified from mapping raw reads to the reference genomes, EGD-e or EGD Figures . On averA visual comparison between the SNP analysis based on the EGD-e and EGD references showed that strains from lineage I are arranged in a similar way in the two trees, whereas strains from lineage II showed more variability. To assess the validity of these differences and remove artifacts, we performed a one to one plot with identical strains connected. To optimize matching, branches around nodes were also rotated Figure . We notiWe compared the phylogenic trees based on SNPs with cgMLST and wgMLST approaches, respectively. Figure . In bothThe cgMLPPST, as opposed to the allele-based cgMLST tree, was inferred based on the multiple alignment for each core genes found among the genomes included in the study . With more genomes, from diverse origin, and if our panel would include strains from lineage III and IV; the number of core genes would probably be lower than 2000 genes and should approach the 1748 genes used in the cgMLST scheme.  and numerous open-source SNP calling pipelines are available.Our results demonstrate with a strong statistical support that the SNP and genomic MLST approaches led to similar phylogenetic reconstruction. This provides microbiologists and epidemiologists working on cluster analysis of The main difference between the two approaches is that a database of loci and associated alleles is used to identify alleles for cg/wgMLST whereas one reference strain is used for SNP calling. An important benefit of the classification of isolates with cg/wgMLST is that it would be stable over time as new isolates are added, on the other hand it requires a careful curation of new alleles. An additional significant advantage is that the cg/wgMLST can provide a genome sequence type which could lead to a common nomenclature, provided that timely update of alleles databases between servers are adopted. A common nomenclature and a stable scheme should ease data portability and sharing making communication more effective. Allelic database management requires extensive curation  and a recent study has proposed some answers  for production and analysis of WGS data is of paramount importance in order to reach sound conclusions that will be confidently handled by the risk management authorities. In that perspective, the indexes we used in this study to compare clustering and topology will be valuable tools to set out SOP for WGS analysis in the field of microbiological food safety.L. monocytogenes. Moving into the genomic era, it is vital to keep a focus on enhancing the genomic technology, to produce “plug and play solutions” and to provide the technology to diagnostic laboratories responsible for outbreak detection and surveillance. Our results showed concordance between the phylogenetic clustering of L. monocytogenes by the genomic MLST and SNP approaches; they are statistically similar in term of tree topology and could be used in combination when facing complex epidemiological situations.The backwards comparability between the standard MLST methodology and the genomic MLST and SNP approaches were essentially perfect. Because genomic MLST or SNP approaches provide better resolution, WGS can replace PFGE as the new gold standard for epidemiological typing of CH was in charge of the whole project and participated in data production, data interpretation, and drafting the manuscript. PL contributed to the data production, data interpretation. HC contributed to the data production and drafting of the manuscript. NR participated to data production. RK participated to data production J-FM participated to the DNA extraction. AF participated to the genomic data production of SNP. FA participated to the design of the study and drafting the manuscript. SR participated in the design of the study. PG. participated in the drafting of the manuscript. LG participated to the study design and design the statistical analysis and contributed in drafting the manuscript. M-YM and RH participated in the design and coordination of the study and in drafting the manuscript. All authors read and approved the final 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.


The G/T mutation of rs80659072 was identified to be highly associated with polydactyly in our resequencing and was validated in random samples from an expanded population. Thus, we confirmed that LMBR1 was the causative gene of polydactyly in the Beijing fatty chicken by using GWAS with restriction-site associated DNA based markers and resequencing.Polydactyly, a common heritable limb malformation in vertebrates, is characterized by supernumerary digits. In chickens, basic characteristics and rough dominant genes have been explored in past decades; however, the elaborate pattern of inheritance and the determinant gene remain obscure. In this study, different types of polydactylism were classified by the numbers and the shapes of toes, including the newly defined subtypes of B’ and G, for the Beijing fatty chicken, a native breed of chicken from China. Through experiments on hybridization, we demonstrated a complete dominant inheritance of polydactyly instead of an incomplete penetrance or genetic modification of the previous conjecture. In particular, by using the F2 population of the five-digit purebred line of Beijing fatty chicken backcrossed to Shiqiza chicken and by using restriction-site associated DNA based markers, we performed a genome-wide association study on the trait of polydactyly. Furthermore, whole genome resequencing strategy was applied to sweep SNPs across the whole genome. An outlier-based F Polydactyly (Po) is a common heritable limb malformation in numerous vertebrates and is characterized by supernumerary digits –4. In ch2 population of the cross between Silkies and broilers [It is generally thought that the mutation of Po in fowls is caused by an incomplete penetrant autosomal-dominant gene –9. By usbroilers . Furtherbroilers , 15.The Beijing fatty chicken (BF) is an excellent Chinese indigenous breed with some distinctive features such as combs that are covered by heavy feathers, feathered yellow legs and polydactyly. According to statistics, there is an incidence of Po ranging from 25% to 31% in the natural population of BF . As a veThe spectrum of genetic variants across the whole genome can be applied to various population genetics studies. As a complementary procedure of whole genome resequencing, reduced-representation genome sequencing has turned out to be widely suitable for plants and animals ,18. CompTo perform genetic analysis and gene mapping, we established a purebred polydactylous BF line over the past decade. In this study, GWAS using RAD-based markers was applied to a testcross of our purebred polydactylous BF purebred line to normal Shiqiza (SZ) that was bred for three generations in hopes of finding the determined gene of Po. To validate the SNPs found by RAD and to scan all of the mutations linked to the markers associated with the trait of polydactyly, whole genome re-sequencing was performed.In Europe, Po was considered to be originally from Silkie and Houdan chickens and present in various crossbred stocks. In 1943, Warren described and classified 5 major types of polydactylism of the feet [All subtypes of the trait of Po described above were found with similar incidence in our pilot experiments with purebred and crossbred birds. Furthermore, several new variants were found and were named type G, A’ and B’ according to their respective morphologies. As Results of reciprocal mating between homogenous polydactylous BF and SZ / Yellow Dwarf (YD) are shown in 0) and 4 of their sons, were selected to be male parents. These roosters were mated to the daughters of G0, and their progeny were then classified as the first purebred generation (G1). The number of toes of G1 was recorded and is shown in Nine typical type B (as described by Warren) roosters, a group which consisted of 5 individuals with the highest penetrance in the pilot  and a total of 767,810 high confidence SNPs were identified. As shown in All 112 samples, consisting of 48 cases and 64 controls, were successfully genotyped. 35,578 independent SNP markers were included in multidimensional scaling (MDS) analysis. The first and second MDS components were used as covariates in the logistic model which we mentioned in the methods section. Meanwhile, a mixed linear model in a GCTA package  was appl−10) in the intron 22 of the partitioning defective 3 homolog gene (PARD3); this gene is known to be essential for asymmetric cell division and polarized growth [−10) at 76 kb downstream and 430 kb upstream of, respectively, the sonic hedgehog gene (SHH) and the limb region 1 homolog (LMBR1) [The most significant SNP was located at 1340.88 kb (P = 1.83 × 10 (LMBR1) –25, two A total of ~200 Gb raw reads were generated by sequencing six samples, with a theoretical 30-fold coverage for each sample compared with the expected genome size. 90% of the whole genome assembly was covered at least 20-fold depth. 9,896,366 high confidence SNPs were found, and 6,079 of these found SNPs were identified to be opposite homozygous genotypes between polydactylous and normal samples. Being opposite homozygotes indicates that the genotype at a specific allele is homozygous and in the same intra-population but different inter-population, the number of which should be generally positively correlated with the length of the chromosome. However, 2,831 opposite homozygotes were from chromosome 2, approximately two times of the quantity in chromosome 1, although the latter had a longer physical length. The same situation occurred to the InDels. 693 of 1,322,701 InDels were opposite homozygotes and 355 came from chromosome 2. It was interesting that, although consisting of the most opposite homozygous SNPs and InDels, chromosome 2 was not the densest when compared with other chromosomes. 1,057 opposite homozygous SNPs and 100 opposite homozygous InDels were found in chromosome 9, which gave it the highest density among all the chromosomes.st) [st value exceeded the 99.9% level of the empirical distribution gathered in the region that ranged from 8.3 Mb to 8.7 Mb, where LMBR1 was located. Various sliding window-size and step-size resulted in similar areas.The signatures of artificial selection can be reflected in allele frequencies in a long range and is known as the hitch-hiking effect . This efst) . Thus, tst. Given former studies on chicken polydactyly, LMBR1 deserves complete attention. 79 opposite homozygous SNPs were located in the region of LMBR1; one of these SNPs, rs80659072, was strongly associated with the trait of Po in Silkie chickens [It was worth noting that the gene region of LMBR1  precisely matched the overlap region of a GWAS significant locus and outlier Fchickens –25. In pTo further validate the SNPs that were identified by resequencing, Sanger sequencing was applied to some of the promising candidate opposite homozygous SNPs in 138 randomly selected samples. As As far back as 1920, Po was demonstrated to be autosomal dominant to the normal four-digit trait in fowls  and the Former efforts in Po gene mapping in exploited Silkie or normal BF chickens had resulted in a relatively low incidence of Po, which always led to lack of positive cases for sufficient statistical power. In addition, types other than B might be inherited in a more complex manner  and, thust outlier method ruled out chromosome 9 because of a relatively even distribution of opposite homozygous SNPs. Comprehensive implication of RAD and whole genome scanning can be a conventional procedure of gene mapping.Our work provided a new integrated strategy for gene mapping. First, RAD was proven to be an effective tool for genotyping and population genetic studies –33. In tPrevious studies in mice and humans have shown that LMBR1 and SHH play a critical role in Po –38. IntrIn conclusion, a highly purebred BF line with 100% penetrance of Po was obtained and served as an ideal model for the study of Po in fowls. By observing the process of pure breeding and cross breeding, we provided evidence that chicken polydactyly was a complete dominant trait. With the help of an integrated strategy of RAD-seq and resequencing, we further confirmed that this dominant trait was highly associated with the genotype of rs80659072 in the BF line.First, a pilot experiment including the pure breeding of polydactylous BF and the crossbreeding of BF and SZ was performed. Observation lasted for five generations, and all types of toe-characteristics from both feet were recorded. A reciprocal cross between BF and SZ / YD was then performed. Ten typical type B polydactylous BF roosters from the pilot experiment were selected, and each rooster was mated to 3 normal BF hens, 3 SZ hens and 3 YD hens. Meanwhile, 10 SZ roosters and 10 YD roosters were mated to polydactylous BF hens at the same mating ratio.1) to SZ was applied to confirm the five-digit progeny’s genotypes. Roosters and hens from G1 with a penetrance of above 90% and 80%, respectively, were selected to mate, and generated 882 polydactylous progeny (G2) which were more than 95% typical type B. Since then, the pure bred polydactylous BF population have been reproductively isolated by mating roosters and hens with top penetrance. All the birds were bred indoors at the research base in Animal Husbandry and Veterinary Research Institute, Shanghai Academy of Agricultural Science, Shanghai, China, under standardized conditions.Due to the high penetrance of Po in their progeny, 5 polydactylous BF roosters and 4 of their sons  were sampled. The number of digits on their feet was recorded. The pedigree of all individuals and their digit numbers are shown in Supporting Information, All animal experiments followed the guidelines established by the ethics committee for the Care and Use of Laboratory Animals in Shanghai Jiao Tong University. The protocol was approved and the permit number is 2008–0713). A three-generation design was used by crossing male purebred polydactyly BF and female SZ. The FGallus gallus  with BWA v0.7.12 [The next generation sequencing reads were aligned to the reference genome of  v0.7.12  in order v0.7.12  and SAMt v0.7.12 . The SAMGWAS was performed with PLINK by using a logistic model with the first and second MDS components as covariates. The significant values were adjusted with a Bonferroni correction. A mixed linear model was applied to the same subset of SNPs with a GCTA package.st between the polydactylous and normal population was calculated with in-house Perl scripts by using a sliding-window approach.DNA from six BF males , all presumed to be homozygous for each phenotype, were extracted. Six sequencing libraries were generated and sequenced for each sample with the standard NextSeq 500 protocol. Quality control was performed with our in-house Perl script; the adaptors were trimmed and reads with average quality above 20 were retained. Trimmed reads which passed QC were aligned to the galGal4 genome with BWA v0.7.12. Picard 1.107 was performed to remove PCR duplicates, and GATK was then used to call high quality SNPs and InDels. F5’-CAGTGTAGCCCATCTCACCT-3’; R: 5’ -ACATGACAGCACAATGGAGGA-3’. The PCR products were sequenced by ABI 3730.Blood was sampled from individuals that were randomly selected from the natural population of BF. Genomic DNA was extracted following the standard phenol/chloroform method. PCR steps were 94°C, 5 min for 1 cycle, 94°C, 30 sec, 60°C, 30 sec, 72°C, and 30 sec for 35 cycles. The primers of rs80659072 were: F: S1 Fig(TIF)Click here for additional data file.S2 FigEach bar stands for a chromosome, whose length is proportional to its physical length (Mbp) in assembly of Gal4. The red tiles stand for the SNPs density in a 20000 bp region.(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableColumn 1 shows the roosters’ IDs. Column 2 shows the hens’ IDs. Columns 3 and 4 represent the number of wild-type and polydactyly progeny, respectively. Column 5 is the percentage of polydactyly progeny. Each group of cross is summarized in “sum” raws.(XLSX)Click here for additional data file.S3 Table44 / 45 / 55 stand for, respectively, the count of progeny with different digit numbers on both feet.(XLSX)Click here for additional data file.S4 Table56 / 66 / 45 / 55 stand for, respectively, the count of progeny with different digit numbers on both feet. A stands for type A of polydactylism.(XLSX)Click here for additional data file.S5 TableColumn 1 shows the sample IDs. Columns 2 and 3 show the ID of sample’s dam and sire. The last column is the phenotype of the sample.(XLSX)Click here for additional data file.


Capsicum baccatum (PRH1- a PM resistant line) and Capsicum annuum (Saengryeg- a PM susceptible line), were resequenced to develop SNP markers. A total of 6,213,009 and 6,840,889 SNPs for PRH1 and Saengryeg respectively have been discovered. Among the SNPs, majority were classified as homozygous type SNPs, particularly in the resistant line. Moreover, the SNPs were differentially distributed among the chromosomes in both the resistant and susceptible lines. In total, 4,887,031 polymorphic SNP loci were identified between the two lines and 306,871 high-resolution melting (HRM) marker primer sets were designed. In order to understand the SNPs associated with the vital genes involved in diseases resistance and stress associated processes, chromosome-wise gene ontology analysis was performed. The results revealed the occurrence that SNPs related to diseases resistance genes were predominantly distributed in chromosome 4. In addition, 6281 SNPs associated with 46 resistance genes were identified. Among the lines, PRH1 consisted of maximum number of polymorphic SNPs related to NBS-LRR genes. The SNP markers were validated using HRM assay in 45 F4 populations and correlated with the phenotypic disease index.The present study deals with genome wide identification of single-nucleotide polymorphism (SNP) markers related to powdery mildew (PM) resistance in two pepper varieties.  The most common cultivated pepper species are Capsicum annuum, Capsicum frutescens, Capsicum chinense, Capsicum pubescens, and Capsicum baccatum5. Though pepper consists of several potential economic values, fungi, bacteria and viruses cause heavy losses in pepper fruit production. Powdery mildew (PM) is the most common devastating fungal disease in pepper and is caused by Leveillulataurici. In an agricultural setting, this disease could be controlled using agrochemicals or genetic resistance lines. The selection of good PM resistance varieties through traditional breeding potentially requires more than 10 years. Hence, molecular marker-assisted breeding is the current plant breeding method of choice, and the most frequently used markers include single-nucleotide polymorphisms (SNPs). DNA-based molecular markers are employed in plant breeding for genetic diversity and genome association analyses9. Over the last decade, major innovations in sequencing technologies and bioinformatics have been achieved, prompting a transition from classical conservation genetics to conservation genomics13. Rapid innovations in genome sequencing platforms, such as next generation sequencing (NGS), provide numerous opportunities for transcriptome assembly, functional annotation of genes, and identification of molecular markers15. New software tools in NGS technology enable the cost effective identification, confirmation, and evaluation of genetic markers on a large scale.Chili pepper is an economically important horticultural crop in Solanaceae family that also includes potato, tomato, eggplant, petunia and tobacco. The Solanaceae family includes more than 3,000 varied species with the similar numbers of chromosomes (n = 12) but significantly different genomic sizes. Peppers have been used as a vegetable, condiment, spice, medicine, coloring agent and source of vitamins6. NGS technologies have identified genome-wide SNPs in several crops, such as bean16, barley17, cassava18, cabbage19, grape20 and maize21. In pepper, several thousand genetic markers, especially SNPs have been discovered28. Recently, Kim et al.29 sequenced and assembled the pepper genome (Capsicum annuum cv. CM334) at a genomic size of 3.48 Gb. This reference genome will provide the opportunity to improve quality, cultivation, and disease resistance in Capsicum species. The aim of this research is to discover SNP variants for future marker-assisted breeding studies related to PM resistance using Capsicum annuum cv. CM334 as a reference for data mining. Thus, in the present study resequencing of two pepper varieties, Capsicum baccatum (PRH1- PM resistant line) and Capsicum annuum (Saengryeg - PM susceptible line), using the HiSeq. 4000 Illumina platform and the genome wide identification of SNPs have been implementedSNPs have been accepted as potential selection markers in genome-wide studies given the high density of markers near loci of interestA summary of the sequencing, sequence preprocessing, and alignment to the read mapping were presented in Table Genome-wide SNPs were identified using an improved BWA-SAMtools workflow. The high-quality filtered reads of PRH1 and Saengryeg were mapped to the reference genome. A total of 6,213,009 and 6,840,889 SNPs were identified for both pepper varieties. Based on the SNP ratio to the read map, SNPs were classified into homozygous, heterozygous and other types. Among the identified SNPs, 88.59% homozygous, 3.65% heterozygous, and 7.76% other types of SNPs were determined in PRH1. Likewise, in Saengryeg, 95.04% homozygous, 1.91% heterozygous, and 3.05% other type SNPs were identified. The occurrence of low percentage of heterozygous SNPs in both lines was due to the relatively low sequence depth and rigid SNP calling requirement. Capsicum consists of 12 chromosomes, and the SNPs are distributed evenly across all chromosomes. Our further analysis revealed that the number of SNPs differed in chromosome 1 to 12 for the two pepper varieties  according to their position in the pepper genome sequence. Further genic SNPs were sub-classified as intron and coding DNA sequences (CDS). A total of 6,213,009 and 6,804,889 genome-wide SNPs were discovered for PRH1 and Saengryeg, respectively. Of the discovered SNPs, 5,781,951 (93.06%) and 6,695,385 (93.39%) of intergenic SNPs were recognized for PRH1 and Saengryeg, respectively. Further, these SNPs were classified into homozygous, heterozygous and other type depending upon the ratio to read map. In addition, 82.28% and 93.58% of homozygous type SNPs were identified in the intergenic region for PRH1 and Saengryeg, respectively. We discovered that the number of SNPs in intron was greater than that of CDSs in the genic regions. Most of the SNPs were located in the intergenic regions and were classified as homozygous type Table . These sIn order to gain deeper insight into the SNPs associated with the genes involved in disease resistance and stress tolerance process, chromosome-wide functional annotation of polymorphic SNPs were performed. The distribution of SNP markers were analyzed in each chromosomes and the functional characterization of genes with higher polymorphic SNPs have been carried out. Overall, the majority of the genes with high polymorphic SNPs widely involved in carbohydrate metabolism, transcription regulation, ion binding, nucleotide binding, protein transport, fatty acid metabolism, receptors, photosynthesis, post-translational modifications, stress response, regulatory elements, proteolysis, secondary metabolism, biosynthesis, diseases resistance, and others. However, in each chromosome the genes with various functions displayed the major proportion Fig. . For insIn total, 6281 SNPs associated with 46 pathogen resistance genes with nucleotide binding site-leucine rich repeat (NBS-LRR) motif were identified in the introns and coding regions of the genes  displayed high resistance scale of 1, whereas the C. annuum variety (Saengryeg) exhibited resistance score of 5. However, among the 45 individuals in F4 population, 11 exhibited the resistance score of 1 followed by 22 plants resulted in the moderate disease resistance level of 3 and 12 plants displayed the severity with the index of 5. Further, to validate the identified SNP markers, HRM assay in both the parental types along with the F4 population of 45 progenies has been performed. Among the 36 HRM primers employed, 19 primers significantly distinguished the resistant and susceptible progenies in the F4 population. The HRM primers employed in this study have been listed in the Table In order to assess the disease resistance indexes, the parental types and the F9. SNP markers are highly polymorphic, co-dominant, precise, reproducible, high-throughput, economical and informative28. Moreover, the discovery of genome-wide SNPs aids in the improvement of marker assisted selection, particularly for the identification of traits associated with disease resistance. In this study, a complete genome resequencing of two pepper varieties with contrasting powdery mildew (PM) tolerance ability, PRH1 (PM resistance) and Saengryeg (PM susceptible), has been examined for the identification of SNP markers associated with powdery mildew resistance. The available whole genome sequence information of Capsicum annuum cv CM334 has been utilized as the reference genome to enable the comparison between the C. annum and C. baccatum lines used in this study.In general, a primary requisite of genotyping of all the individuals in a population is necessary for trait mapping in traditional approaches of breeding, which is a highly expensive, labor intensive and time consuming process. Moreover, the occurrence of mere levels of variations or polymorphism also acts as a vital challenge during molecular marker discovery. In order to address these difficulties, next generation sequencing (NGS) strategies have been widely applied in genomics based on breeding of important agricultural and horticultural crops. Recent advancements in NGS technology have facilitated the routine use of high-throughput, low-cost markers for plant breeding programs. New software tools enable the discovery, validation, and assessment of genetic markers on a large scale. Among different marker systems, SNPs are the most important and attractive DNA-based molecular markers used for genetic diversity and genome association analyses and comparative genetics in plant breedingC. baccatum is well-known for fruit quality, disease resistance, and high contents of valuable secondary metabolites29. Therefore, the interspecific breeding of peppers results in progenies with high fruit quality and disease resistance. The C. baccatum variety used in this study displayed resistance to powdery mildew and anthracnose diseases. Hence, the whole genome re-sequencing (WGRS) based on discovery of SNPs in the variable pepper varieties could enhance the understanding of SNPs associated with disease resistance. The resequencing and SNP discovery resulted in the identification of 6,213,009 SNPs for PRH1 and 6,840,889 SNPs for Saengryeg. The SNPs identified in the present study were higher than the SNPs discovered by Nimmakayala et al. in C.annuum and C.baccatum varieties using genotyping by sequencing approach30. The report suggested the collective identification of 36,621 potential SNP markers linked to various genomic regions in in C. annuum and C. baccatum that can be utilized for the genome wide association studies in pepper varieties30. Moreover, the identified SNPs in the present study have been majorly categorized into homozygous type with 88.59% and 95.04% for PRH1 and Saengryeg, respectively. This suggests that the sequence of reference genome could be generated from homozygous loci. Further, the chromosomal distribution of SNPs in the pepper genome revealed that a total of 10.92% of homozygous SNPs were located on chromosome 1, and 16.96% of the homozygous SNPs were located on chromosome 10 for PRH1 and Saengryeg, respectively.In the current endeavor,intersepecific breeding of sexually incompatible pepper species has been performed due to their potential traits. For instance, the et al.31. Furthermore, the location of SNPs plays a vital role, particularly SNPs should be located in intragenic regions to implicate the phenotypic traits. These SNPs are expected to be applied to marker assisted selection because they could be considered as functional markers. A total of 5,941,182 SNP loci have been detected between Saengryeg and PRH1. Of them, 30.63% SNPs were distributed in polymorphic loci. Potential polymorphic homozygous SNPs were filtered to discover breed-specific markers in both of the pepper varieties. HRM analysis has been applied to identify precise, cost-effective and efficient tool to detect sequence variations, such as SNPs32. This technique has been successfully implemented to identify SNPs that have been used for genotype discovery, genetic mapping and mutation scanning36. Among the discovered homozygous type polymorphic SNPs, 597,434 HRM marker primers were identified that potentially discriminate between two lines. Of them, 306,871 HRM primers were recommended for further experimental research related to PM-based melting patterns and amplification efficacy.In addition, the distribution of SNPs within the pepper genome illustrated the occurrence of higher percentage of SNPs in intergenic regions compared with genic regions. Likewise, several SNPs were identified in the intronic region than in CDSs. Similar results were also reported in tomato by Kim 37. The vital R genes associated with disease resistance were conserved among the related species such as pepper, tomato, and potato37. The current results revealed that each chromosome consisted of several SNPs associated with the genes involved in vital metabolic processes. However, chromosome 4 consisted of larger set of SNPs associated with disease resistance in comparison with other chromosomes. According to Grube et al.37, the diseases resistance gene loci located in the chromosome 4 of pepper could render resistance against fungal pathogens. Correspondingly, chromosome 4 could play a vital role in encompassing the genes required for disease resistance in pepper. Moreover, the chromosome 5–10 consisted of SNPs related to genes involved in ion and metal binding. The roles of ion/metal binding genes are inevitable particularly under stressed conditions in pepper plants. The uptake and transportation of nutrients and water from the environment to the plant is a complex as well an important process for the improvement of physiological functioning of plants in stress. Hence, the SNPs related to these genes could act as a vital marker under stress.The numerous amount of polymorphic SNPs identified in the genic region were functionally annotated in each chromosome to gain deeper insight into the SNPs associated with the genes involved in disease resistance. A comparative genetics study on the resistance genes in Solanaceae family has shed light on to the potential loci in different chromosomes linked with disease resistance38. Several reports suggested the importance of NBS-LRR proteins in the resistance against numerous diseases including powdery mildew in plants41. In the present study, polymorphic SNPs were identified in genes encoding for LRR receptor-like serine/threonine-protein kinase, F-box/LRR, TIR-NBS-LRR resistances protein, CC-NBS-LRR resistance protein, and TIR1 like protein, etc. Hence, the identification of SNPs associated with the disease resistance genes could aid in the enhancement of screening processes in the molecular breeding of pepper with powdery mildew resistance.Furthermore, higher number of polymorphic SNPs associated with disease resistance genes such as NBS-LRR were also identified in chromosome 4. Among the two varieties, the resistant PRH1 possessed higher distribution of polymorphic SNPs related to NBS-LRR genes. In plants, NBS-LRR is a large family of proteins encoded by the resistance genes and NBS-LRR proteins involved in the recognition of pathogens4 population derived from the C. annuum and C. baccatum varieties. The HRM primers were selected from all the chromosomes and evaluated in the parents and the population. Among the tested primers, 19 primers were able to distinguish the population and the results were correlated with the phenotypic disease evaluation scores for each individual. Overall, the polymorphic SNPs discovered in this study can be utilized for the identification of powdery mildew resistance and susceptible cultivars in pepper breeding. However, in future the present investigation will be extended to evaluate large populations with more number of HRM primers corresponding to important SNPs associated with powdery mildew resistance in pepper.The identified SNPs were validated using HRM primers in the parents and FC. annuum and C. baccatum varieties of pepper with different powdery mildew resistance property. The SNP information obtained from the current WGRS approach in pepper can be utilized for the genomics assisted breeding of Capsicum with powdery mildew resistance.In summary, the present endeavor reports the discovery of numerous SNP markers with potential applications in population genetics, molecular breeding, linkage mapping, and comparative genomics on gene-based association studies. For the first time, polymorphic SNPs were discovered from 42. Powdered samples were mixed with CTAB buffer and incubated at 65 °C for 10 minutes. Sample mixtures were cooled to room temperature, and chloroform was then added to the sample mixture. Chloroform sample mixtures were mixed thoroughly and centrifuged at 13,000 rpm for 5 minutes at 4 °C. The supernatant was transferred into a new tube, and an equal volume of absolute ethanol was added. The solution was centrifuged at 13,000 rpm for 5 minutes at 4 °C, and the supernatant was discarded. Then, 70% ethanol was added to the sample, which was then centrifuged at 13,000 rpm for 5 minutes at 4 °C. Once again, the supernatant was discarded, and precipitated DNA pellets were dried at room temperature. The precipitated DNA pellets were then used as a starting material for purification using the Sigma Genelute plant DNA isolation kit . The DNA quality was assessed by electrophoresing the DNA on 1% agarose gel. The concentration of the extracted DNA was estimated using a GE Healthcare Bio-Science NanoVue via assessment of a single absorbance peak at 260 nm, a 260/280 absorbance ratio of 1.8 to 2.0 and no evidence of substantial band shearing or contamination (either RNA or polysaccharide).Young leaves of PRH1 and Saengryeg were used for genomic DNA isolation. Briefly, 300 mg of leaves were ground into fine powder using liquid nitrogen. High-quality DNA was extracted using the cetyltrimethylammonium bromide (CTAB) extraction methodPurified whole genomic DNA was randomly sheared using a Covaris S2  to yield DNA fragments in the target range of 400 to 500 bp, and average molecular sizes were assessed using an Agilent Bioanalyzer 2100 . Subsequently, the resulted overhangs were converted to blunt ends using a TruSeq DNA Sample Preparation Kit v2  followed by a clean-up protocol using AMPure XP Beads . To enhance the ligation between the fragmented DNA and index adapters and to avoid self-ligation, the 3′ ends were adenylated. After adenylation, the index adapters were ligated to the fragmented genomic DNA, and the ligated products were purified using the AMPure XP Beads. The ligated products were size-selected on a 2% agarose gel followed by gel elution and column purification. The selected ligated DNA fragments with adapter sequences were enhanced through PCR using adapter-specific primers. Further, the DNA was re-isolated and the average molecular sizes of the libraries were evaluated using the Agilent Bioanalyzer 2100  to assess a sharp peak in the expected 500–600 bp range. Each library was loaded on the HiSeq. 4000 platform, and the high-throughput sequencing was performed to ensure that each sample met the 10-fold average sequencing depth.et al., 2010). The quality of bases from either end of Illumina reads commonly drop in, therefore either end of the reads were trimmed when the Phred quality score dropped below Q = 20 (or 0.05 probability of error). In addition, all 5′ and 3′ stretches of ambiguous ‘N’ nucleotides were also clipped. Trimming resulted in reads with a mean length of 101 bp across all samples, and a minimum length of 25 bp was applied during sequence trimming. These data were used for downstream analysis. The reference genome sequence of Capsicum annuum cv. CN334 was downloaded from Sol Genomic Network (SGN) at http://www.sgn.cornell.edu/.After sequencing, the raw reads were trimmed using the Solexa QA v.1.13 package  = 30, maximum differences in the seed (−k) = 1, number of threads (−t) = 16, maximum number of gap extensions (−e) = 50, mismatch penalty (−M) = 6, gap open penalty (−O) = 15, and gap extension penalty (−E) = 8. Mapped reads were extracted from the resulting BAM file using SAMtools 0.1.1644 for further analyses. The high mapping quality ensures reliable (unique) mapping of the reads, which is important for variant calling. Using the varFilter command, SNPs were called only for variable positions with a minimal mapping quality (−Q) of 30. The minimum and maximum of read depths were set as 3 and 100, respectively. An in-house script considering biallelic loci was used to select significant sites in the called SNP positions31. Depending on the ratio of SNP reads to mapped reads, variant types were classified into three categories: homozygous SNP (more than 90%), heterozygous SNP (morethan 40% and less than 60%), and other SNPs for the remaining types. The polymorphic SNPs between two samples with sufficient sequences on both sides of the SNP site, without structural variation were noted adjacent to the SNP site and selected for primer design. To design primers flanking the SNP, an in-house script and Primer3 (v2.3.5) software were used45. The parameters employed for the primer designing areas follows, primer length 18–24 bp, with 20 bp as the optimum; primer GC% = 20–80%, with the optimum value being 50%; primer Tm 55–65 °C, with 60 °C as the optimum; and product size range of 80–600 bp. After the designed primers were mapped to the genome sequence, only the primers that aligned were selected as candidates for SNP markers.To align the reads to the pepper reference genome, the Burrows-Wheeler Aligner (BWA 0.6.1-r104) programwww.geneontology.org) and Gene Ontology (UniProt) (www.uniprot.org/help/gene_ontology). The number of SNPs associated with each gene was identified manually.The functional annotations of polymorphic SNPs were determined using the information acquired from gene ontology consortium , 1× of SsoFastEvagreenSupermix , and 200 nM of forward and reverse primers. The reactions were performed in a fluorometric thermal cycler CFX96 real-time system , following program: 98 °C for 2 min, 45 cycles at 98 °C for 5 s and 60 °C for 10 s. The peaks obtained were normalized and analyzed for the difference in the melt curve.For the SNP validation, HRM primers were designed from each chromosome and evaluated in 46 FC. annuum - TF68 and C. baccatum – ARI are the close relatives of PRH1 and Saengryeg. The parents as well as the F4 populations were maintained in a polyvinyl house along with disease infected plants under a normal day light condition with night/day set temperatures of 27/15 °C and 60–70% RH. The experiment was performed in triplicates in random block design. The disease severity has been assessed in 1–5 scale  after two weeks.The HRM results were correlated with the physiological evaluation of disease resistance. For the infection of powdery mildew, the parental lines used in this study, Supplementary Information.Supplementary Dataset 2Supplementary Dataset 1


Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of  Drosophila melanogaster.The analysis of molecular phenotypes is expected to bridge the gap between genotypic and phenotypic variation, thereby facilitating both basic and health research , 2. Mostcis-regulatory modules (CRMs). Much experimental progress has been made in annotating Drosophila CRMs wPGP is afficient . In partfficient  for morew quantify the effect of how strongly the spatio-temporal expression pattern in the model solution for the mutated genotype deviates from the wild type solution, so that SNPs with stronger influence on gene expression lead to larger w values. The RMS score is not sensitive to the direction of solution change . The. Thew quTo detect any possible effects of purifying selection on segregating SNPs, we compare properties of mutations observed in the study population data to those we might have observed in data in which mutations were introduced randomly. If we see differences between the study population and datasets containing randomly introduced mutations, this provides some evidence of functional constraints. For this purpose we simulate multiple versions of the regulatory sequence for the four gap genes, each containing randomly mutated model TFBSs. We then run our model on these data.The analysis of the model TFBSs in the population genotypes reveals 90 SNPs . We perfD. melanogaster genome, according to the DGRP data [D. mel genome [D. mel genome is approximately 85bp), masked any short intron position which overlapped with UTR and CDS sequences, and masked out 8 base pairs on the intron edges . The resulting data set comprised 58041 polymorphic positions for 184 genotypes, and the site frequency spectrum (SFS) for this set was calculated and defined as the neutral spectrum  of 213 randomly mutated sequences, considered as analogs of the natural population (213 genotypes from the data). For each family, we keep the total number of SNPs within the model TFBSs equal to this number observed in the model TFBSs of the study population genotypes (90 SNPs) and simulate each such SNP according to a ‘neutral’ frequency spectrum , red. WeGRP data , 53. Thel genome , 55. Thel genome . In partspectrum , red.In the third mutation experiment, we simulate 100 families similarly to the second experiment except that each SNP is generated according to the spectrum derived from the 90 SNPs found in the model TFBSs of the study population , blue. Mhb, Kr, gt, and kni in the study population of 213 Drosophila lines , in the context of our model of gene expression. We study polymorphism observed in regulatory regions of the gap genes We also simulate genotypes containing various point mutations in the model TFBSs , in ordeApplying the model to the individual genotypes results in relatively little variation in expression patterns . The varΔv calculated for this SNP: the SNP has purely positive influence on expression (activation) if i and times t, and for gene a whose regulatory region contains this SNP, i.e. expression only increases in some or all spatial and temporal points when the SNP is inserted into the reference sequence; the SNP has purely negative influence (repression) if i and t and negative for others; and the SNP influence is evidently zero if i and t. SNPs from the study population distribute almost symmetrically between the pure-activation and pure-repression groups: there are 36 SNPs with purely positive influence, and 38 SNPs with purely negative. Among the rest of the SNPs, 13 have alternating sign and 3 exhibit no influence.When the presence of a SNP leads to increased expression comparing to the reference case, we can say that the SNP activates expression, and similarly we can define repression by a SNP. In the following two sections, we clarify the mechanisms of activation and repression caused by SNPs observed in the study population. We quantify the effect exerted on gap gene expression by a SNP by simulating the point mutation associated with this SNP in the regulatory regions of the gap genes and evaluating such genotype in the model . We defip = 0.001 by a resampling test; p = 0.001; We further analyze how the SNPs are distributed among the activating and repressing TFBSs and how this distribution relates to the SNP influence sign. Comparing the numbers of activating or repressing TFBSs containing SNPs from each of the influence sign groups, we find that it is more probable that a SNP with purely positive influence is located within a repressing binding site than within an activating site . We . We s exe values . The exiP analyzed separately for activating and repressing TFBSs and for each group of SNPs associated with a specific sign of influence on expression  between SNPs, or if such interactions are negligible.The SNPs with alternating sign of influence on expression show no preference in choosing between an activating or repressing TFBS  and tendCalculations reveal that the SNP influence may change its sign as a result of local interactions between the polymorphic site and other sites in the vicinity  w.r.t. the statistical weights q1 and q2 of these sites should be analyzed in this case to prove the possibility for sign alteration (p < 0.001). On the other hand, the overlaps from the cases 1 and 2 in which the sign alternating SNPs participate are as numerous as expected by chance (p = 0.577). Therefore, we conclude that the sign alteration mechanism from the case 3 is a distinctive feature of the population.The SNP influence may change sign also when the SNP occurs in the overlapping region   with corresponding perturbations δ1, δ2, …, δn. The activation level ZON and ZOFF are the relative probabilities of the states of the original (reference) genotype with the BTM bound and unbound, respectively, constituting the activation level E from Pn and Qn are polynomial functions of their arguments which contain all possible products of δi, with coefficients depending only on sites Si and their reduced versions We now use the analytical predictions of our model to explore how combinations of SNPs jointly influence gene expression. The key component of the model equations (Eqs  and 2)  2) in MeE . The modδi from the SNPs in a non-linear way. This expression also reveals that the magnitude of the effect that a set of SNPs has on the transcriptional activation is due not only to the TFBSs that physically interact with the mutated sites , but also a consequence of all binding sites in the regulatory sequence. This occurs because the thermodynamic approach describes a quasi-equilibrium situation, so that all sites participate in balancing the relative probabilities for site Si being free or bound. As a consequence, changes to these binding probabilities are modulated by the weights of other binding sites. Therefore, SNPs have non-local effects in the thermodynamic model of gene expression. In other words, the effect of one SNP on gene expression is conditional on the effects of all other SNPs in the model.The ΔvSNP measuring the perturbation which the selected SNP exerts on expression patterns  plane  contains 213 randomly mutated genotypes, and we consider 100 such families for each SFS type. The mean distance for the study population is decreased compared to the genotypes randomly mutated under the neutral SFS (p = 0.02) and not distinguishable from the distances for the genotypes randomly mutated under the population-derived SFS  controlled by the distribution of ΔEk. Changes of genotype lead to variation of ΔE, which then translates to variation of Δv. We calculate ΔE and Δv for all genotypes and compare the joint distribution of absolute values |ΔEk| for all genotypes and all values of k with the same joint distribution of |Δvk| , but this difference cannot explain the difference between the non-functional sequence and the model TFBSs  states of the site S, respectively; P is the log likelihood ratio score, or the PWM-score, of the site S quantifying the binding affinity of the site [n positions Si, its PWM-score P(S) is calculated as a sum of the inputs P(Si) from each position. We investigate how the change in binding affinities of the model TFBSs caused by SNPs in our natural population compares to that caused by randomly generated SNPs  lead to different distributions for the regulatory scores of the individual genotypes  the variation of this phenotype that results from SNPs observed in the natural population, to ii) the phenotypic variation that is predicted to result from randomly generated mutations. We use the regulatory scores from Eqs –6) as o as o6) aenotypes . The RMSρ = −0.15, p = 0.086 by the one-tailed permutation test; the Pearson correlation on the log-transformed data r = −0.20, p = 0.035 by the one-tailed permutation test; scatterplots are shown in First, we consider individual SNPs. The model predicts an almost exponential distribution for the SNP regulatory scores  test). In addition to the non-signed measure provided by the wPGP score, we analyze the signed scores for SNP influence provided by Δv and calculate the mean 〈Δv〉 of this score by averaging Δvk over all k. We calculate 〈Δv〉 for each unique SNP from the study population and each artificial SNP. This mean can be either negative or positive, representing a prevalence of either positive or negative perturbations in the expression pattern caused by a SNP. Similarly to the case with the wPGP score, the distribution of 〈Δv〉 for the population SNPs is statistically indistinguishable from that for the artificial SNPs , while the difference for the case of the population-derived spectrum is not significant (p = 0.34). This suggests the model perceives purifying selection acting at the level of the total number of mutations in TFBSs, but not at the level of specific positions of these mutations, at least for the given natural variation extent and for the chosen measure of SNP influence.In contrast to the case for individual SNPs, the distribution of the scores for the genotypes in our study population does significantly differ from the joint distribution for all simulated genotypes, in the case of families simulated under the neutral SFS . We statRMS score used instead of the wPGP score . This is another indication that the two measures can be associated with qualitatively different conclusions [Interestingly, we do not see the difference between the population and randomly mutated genotypes for both types of SFS in the case of the clusions , 32, 40.p-value for the difference between the distributions). We have also shown above that the population genotypes are not distinguishable from randomly generated genotypes in terms of individual SNP influence. These facts suggest that only strong enough perturbations should be analyzed when discriminating between the population and random mutations, because either the model or selection itself may not be sensitive to SNPs and their combinations having a weak effect on expression. Using the 2/3-quantile of the population score distribution (the dashed line in p = 0.01). This result provides evidence that the model is able to detect purifying selection affecting not only the total number of mutations per genotype, but also their positions within TFBSs, if mutations have strong enough effect on expression. The p-value corresponding to p = 0.05 level approximately at 62nd percentile, but starts to grow approximately after 80th percentile . As a consequence, the distribution of the mean 〈Δv〉 calculated for each genotype is also shifted to positive values, as compared to a symmetrical distribution for the randomly simulated genotypes ; however, as explained above, the magnitudes of such changes are rather low.We also analyze the signed scores ans 〈Δv〉 . Taking enotypes . This shenotypes , S15 FigAs there are large numbers of SNPs with putative regulatory effects, a fundamental challenge is to discern why some sets of sequence changes in CRMs lead to changes in gene expression, while others do not. While many CRMs are now well annotated –6, attemDrosophila gap gene system [As a step towards this ultimate goal, in this paper we annotated natural genomic variation in the context of a thermodynamic gene expression model and the e system . Our goaIt has been long posited that the majority of spontaneous mutations with phenotypic effects would cause only minimal perturbations, while relatively few would appear as ‘large effect.’ This notion is consistent with the inferences from QTL mapping and GWAS, but several biases make these sources of support somewhat non-robust . A direcE in the model has statistical origins, representing a probability for activation under the thermodynamic equilibrium, and incorporates a summation over different states of the regulatory region [The thermodynamic approach to modeling transcriptional regulation of gene expression has the advantage of making it possible to predict the effects of even single-nucleotide genetic modifications on expression level. The transcriptional activation y region , 61. As It is the magnitude of the SNP effect that depends on the sequence non-locally, while the type, or sign, of this effect on the spatio-temporal expression pattern  is determined via interplay of the polymorphic TFBS and its local vicinity. On the other hand, the chain of such local interactions, like those discussed in our paper, can be indefinitely long and involve many TFBSs, so that parameters of even distant binding sites may theoretically condition the sign of the SNP effect, thus blurring the difference between locality and non-locality. Another source of non-local conditions for the effect sign are the indirect interactions (via TFs) between TFBSs, which are not accounted for by our model and not analyzed in the paper.Analytical derivation from the model predicts that transcriptional activation level responds to a SNP non-linearly, while numerical calculations on our population SNP data demonstrate that this non-linearity is flattened out to become an essentially additive effect for each SNP. How is it that this occurs? This question is central to quantitative genetics, as it is not generally clear how predominantly additive variation could originate from predominantly non-linear gene networks. For instance, Hill et al.  hypothesThe different levels of non-linearity in response to SNPs exhibited by the transcriptional activation and ultimate protein concentrations can be linked with a more general finding that gap gene expression demonstrates precision at the RNA level despite highly stochastic transcriptional activity. That finding was explained by straightforward spatio-temporal averaging, as a mechanism reducing variation in transition between the two levels . In the Our work demonstrates two types of evidence for the existence of purifying selection acting on the gap gene regulatory network. First, TFBSs of genes in the network are significantly less polymorphic than other sequence segments. A significant difference in mutation rate between TFBSs lying inside and outside the DNase I accessibility regions might in principle explain this difference , but ourSelection acts on whole organism phenotypes, but the outcome of that selection is accomplished through underlying molecular processes. Which molecular phenotypes are the primary evolutionary targets? Models of TFBS evolution have been used to infer a variety of evolutionary forces acting on the binding sites , 57, 63.wPGP scores of the population genotypes for the two parameter sets show a high correlation (Spearman’s ρ = 0.92). Therefore, our results demonstrate stability with respect to the choice of parameter values in the model.It was demonstrated previously that a reliable analysis of models similar to our model should be based on the ensemble approach, involving analysis of the modeling results for many parameter sets of similar quality . The reswPGP scores Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 TableThe SNPs are arranged in the order of increasing genomic position. The first six columns provide the following information (from left to right): SNP index, gene, chromosome, genomic position (dm3 / BDGP5), reference and alternative nucleotides of the SNP, and SNP frequency in the study population. The next five columns present the following information about the model TFBSs containing these SNPs: TF, starting and ending positions of the binding site, reference sequence of the binding site, and its PWM-score. The table is divided into four parts, separately for each gap gene.(PDF)Click here for additional data file.S1 FigThe figure shows expression patterns (model solutions) at 9 time points for 213 individual genotypes and for the reference genotype, together with the observed expression patterns. The time points include the mid cleavage cycle 13 and eight time classes T1–T8 in cleavage cycle 14A, as described in Methods.(PDF)Click here for additional data file.S2 Figp-values were estimated from these panels: p = 0.001 both for the positive and for the negative sign groups, and p = 0.45 is the minimal value of the two possible one-sided values for the group of alternating sign.(A) Results are shown for 1000 resampling experiments. For each experiment, a set of positions was randomly sampled within the model TFBSs, and each position was randomly assigned to one of the three groups of putative sign of SNP influence: 36 positions for the group of positive sign, 38 for negative sign, and 13 for alternating sign. The regulatory roles (activator or repressor) of all TFBSs containing these positions were recorded and their total number was counted for each sign group. These numbers are shown in the panel. (B)–(D) For the sampled positions from each experiment and each sign group, the ratio of the total number of activating TFBSs to the total number of repressing TFBSs was calculated. The distribution of 1000 ratio values is shown for each sign group, together with the population ratio values (red lines). The one-tailed (PDF)Click here for additional data file.S3 FigPs is the PWM score of binding site s containing a SNP, and The probability density function is shown for (PDF)Click here for additional data file.S4 Figp < 0.001), while the frequency of their appearance in TFBSs overlapping with other TFBSs of the same type is not distinguished from randomly sampled positions .(A–C) For each group of SNP influence sign (columns), the panels present the population values (red lines) for the number of SNPs appearing in the overlapping regions of multiple TFBSs as a fraction of the total number of SNPs in the group. These values are compared with the distributions of the same fraction, but for positions randomly sampled from the model TFBSs. We simulate 1000 sets of such random positions, and for each set we count the fraction of positions appearing in the overlapping region of multiple TFBSs. The total number of positions in each set equals to the number of the population SNPs from a given sign group . (D–F) The same as in (A–C) but for the fraction of positions appearing in overlapping TFBSs of the same regulatory type (mechanisms 1 and 2 from (PDF)Click here for additional data file.S5 FigThe vertical axis shows the difference (PDF)Click here for additional data file.S6 FigThe scores are normalized by the maximal values. Only non-zero scores are considered.(PDF)Click here for additional data file.S7 Figwb from p-value for the comparison of the two distributions according to the bootstrap chi-square test: p = 0.27. (B) Comparison of the median wb for the population SNPs (red) and the medians for 1000 sets randomly sampled from the all-mutations distribution (histogram); each sampled set has the same length as the data set. The one-tailed p-value: p = 0.09. (C) The same as in B, but for the means instead of medians (p = 0.003).(A) The distribution chart for the binding score (PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 FigPDF stands for the probability density function. Long tails are not included in the scope of the figure.(PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.S12 FigwPGP score distribution for the population genotypes Click here for additional data file.S13 Figp < 0.0001).PDF stands for the probability density function. The difference between each pair of the distributions is statistically significant based on the bootstrap KS test ((PDF)Click here for additional data file.S14 FigΔv〉 were calculated for each SNP inside each genotype in a family, and the ratio was then computed for this family. The one-tailed p-values: p < 0.01 for the neutral SFS and p = 0.04 for the population-derived SFS.The distributions are shown for the neutral SFS (red) and population-derived SFS (blue). The means 〈(PDF)Click here for additional data file.S15 Fig(PDF)Click here for additional data file.S16 FigThe spatial profiles of protein concentrations are shown at the end of cleavage cycle 14A for 213 individual genotypes and for the reference genotype, together with the observed expression patterns.(PDF)Click here for additional data file.S17 FigwPGP score distributions for the 213 polymorphic genotypes from our study population (black), for 100 families of 213 artificial genotypes (pooled together) simulated under the neutral SFS (red), and for 100 families simulated under the population-derived SFS (blue). The dashed line marks the 2/3-quantile of the population score distribution, used for analysis in panel C. (B) The distributions of the family mean of the log-transformed wPGP scores for the two types of families. The black line marks the population value. p-values of the difference between the population and the randomly mutated genotypes: p = 0.01 for the neutral SFS and p = 0.57 for the population-derived SFS. (C) The distribution of the median wPGP scores calculated only for genotypes of strong effect on expression, i.e. genotypes with the score exceeding the value marked by the dashed line in panel A. The distribution corresponds to the genotypes randomly mutated under the population-derived SFS. p-value of the difference between the population and the randomly mutated genotypes: p < 0.01 for the population-derived SFS.(A) The log-transformed (PDF)Click here for additional data file.


Listeria monocytogenes found in clinical, food, and environmental samples. Each year, more genomes of other foodborne pathogens such as Escherichia coli, Campylobacter jejuni, and Salmonella enterica are being sequenced. Comparing thousands of genomes across an entire species requires a fast method with coarse resolution; however, capturing the fine details of highly related isolates requires a computationally heavy and sophisticated algorithm. Most L. monocytogenes investigations employing WGS depend on being able to identify an outbreak clade whose inter-genomic distances are less than an empirically determined threshold. When the difference between a few single nucleotide polymorphisms (SNPs) can help distinguish between genomes that are likely outbreak-associated and those that are less likely to be associated, we require a fine-resolution method. To achieve this level of resolution, we have developed Lyve-SET, a high-quality SNP pipeline. We evaluated Lyve-SET by retrospectively investigating 12 outbreak data sets along with four other SNP pipelines that have been used in outbreak investigation or similar scenarios. To compare these pipelines, several distance and phylogeny-based comparison methods were applied, which collectively showed that multiple pipelines were able to identify most outbreak clusters and strains. Currently in the US PulseNet system, whole genome multi-locus sequence typing (wgMLST) is the preferred primary method for foodborne WGS cluster detection and outbreak investigation due to its ability to name standardized genomic profiles, its central database, and its ability to be run in a graphical user interface. However, creating a functional wgMLST scheme requires extended up-front development and subject-matter expertise. When a scheme does not exist or when the highest resolution is needed, SNP analysis is used. Using three Listeria outbreak data sets, we demonstrated the concordance between Lyve-SET SNP typing and wgMLST.Modern epidemiology of foodborne bacterial pathogens in industrialized countries relies increasingly on whole genome sequencing (WGS) techniques. As opposed to profiling techniques such as pulsed-field gel electrophoresis, WGS requires a variety of computational methods. Since 2013, United States agencies responsible for food safety including the CDC, FDA, and USDA, have been performing whole-genome sequencing (WGS) on all Availability: Lyve-SET can be found at https://github.com/lskatz/Lyve-SET. Modern outbreak investigation is enhanced with molecular subtyping evidence. These lines of evidence have been, but are not limited to: pulsed-field gel electrophoresis (PFGE), multiple-locus variable number tandem repeat analysis (MLVA), and multi-locus sequence typing  and single nucleotide polymorphisms (SNPs). In the wgMLST method for a single genome, as in conventional MLST  are extracted from raw reads supplied for the genomes being analyzed  has participated in an interagency collaboration to routinely sequence and analyze all clinical and food-related Listeria monocytogenes isolates in the US with the eventual goal to replace PFGE  of The Centers for Disease Control and Prevention (CDC) briefly described an initial SNP-based workflow called the SNP Extraction Tool (SET). The initial version of SET was used for the Haiti cholera outbreak of 2010 . Each of the aforementioned pipelines produces output that can be used for interpreting the relationship between genomes in various forms such as distance matrices, MSAs, and dendrograms; however, they have different underlying algorithms and output formats. For example, each SNP pipeline uses a different read mapper and SNP caller and might produce a different format to describe their SNP calls. In comparing wgMLST and SNP workflows which are wholly different algorithms, one SNP might be located in an intergenic region, yielding zero allelic differences by wgMLST; on the other hand many SNPs might be located on a single gene, yielding the collapse of multiple SNPs into a single allelic difference.A reasonable approach to pipeline comparison, therefore, might be at the phylogenetic level. A classic comparison method is the Robinson-Foulds metric, sometimes called the symmetric difference metric, where the number of internal branches that exist in one tree but not the other are counted .Lyve-SET is a high quality SNP (hqSNP) pipeline, designed to remove lower-quality SNPs from its analysis and increase phylogenetic signal. Lyve-SET has its origins in the original SET algorithm described in Katz et al. . Major cWith the default workflow, there is a well-defined audit trail such that it is clear how Lyve-SET was initialized Table  and fromread_cleaner option, reads are cleaned with CG-Pipeline . If the slope of coverage is >3 reads per bp or <−3 reads per bp, then the region is masked, and SNPs will not be called at the particular locus in the particular genome.The second step is mapping reads of each genome against the reference assembly by SMALT using the mpileup2cns method of VarScan v2.3.7 to find and detect SNPs using the launch_varscan.pl script . Briefly, NCBI creates high-quality genome assemblies. The MinHash algorithm is applied to each genome, and a Jaccard distance is calculated between each pair of genomes. Then, NCBI creates a tree based on the Jaccard distances. Closely related genomes are refined into subclades using SNPs found among these related assemblies, and a subtree is created with FastME  tree was constructed based on all loci among all the genomes.wgMLST analysis was performed using tools in the graphical user interface of BioNumerics 7.5 . Briefly, alleles were identified by both an assembly-free k-mer based approach using raw reads and assembly-based BLAST approach based on SPAdes v3.5.0 assembled genomes using the wgMLST Three categories of pipeline comparisons were performed: the Sn and Sp of outbreak isolates included in the target outbreak clade, tests of tree topology, and tests of variant positions and distances.If an isolate fell into the same well-supported clade as outbreak isolates , it was counted as a positive. Otherwise, it was counted as a negative. Positives that retrospectively agree with the outbreak investigation were counted as TP; otherwise, FN. The target well-supported clade is defined as a having a confidence value >70% and being as complementary as possible for Sn and Sp for each tree. Sn was calculated as TP/(TP+FN). Sp was calculated as TN/(TN+FP). For the following statistical scripts, Perl v5.16.1 and R v3.3.0 were used.5 random trees using the APE package in R  indicates that the query tree is more closely related to the Lyve-SET tree topology than would be expected by chance. Given that low-confidence nodes would not be considered during an outbreak investigation, we removed low-confidence nodes (bootstrap support <70%), potentially creating multifurcating trees, before performing the Kendall-Colijn test. From this transformation, 47 out of 63 trees became multifurcating for this comparison. Only one Lyve-SET tree, the three wgMLST trees, and 12 RealPhy trees remained binary. The Robinson-Foulds test does not tolerate multifurcation; therefore low-confidence nodes were not removed for those tests. Unless otherwise indicated, all trees were midpoint-rooted. All statistical scripts used in this study are available at https://github.com/lskatz/Lyve-SET-paper.To compare trees, we implemented the Kendall-Colijn and Robinson-Foulds tests  and 1410MLJBP-1 . However, according to the Kendall-Colijn test for topology (when λ = 0), at least one tree per pipeline yields a p-value > 0.05.In general, all SNP pipelines in the comparison ascribe outbreak isolates to the outbreak clade with 100% Sn Table . The oneL. monocytogenes, S. enterica, and C. jejuni; SNVPhyl correlates with >0.8 for S. enterica, E. coli and C. jejuni. Only SNVPhyl has a high correlation with Lyve-SET distances for E. coli outbreaks (R2 = 0.92). Overall except for C. jejuni outbreaks (R2 = 0.89), kSNP has low correlation with Lyve-SET . For many of the organism-specific outbreaks tested, viewing a correlation between outbreak-only isolates was difficult because, the range of Lyve-SET SNPs is very low  examining whether individual pipelines could identify outbreak-associated isolates consistent with previous investigation, and (2) whether two trees were significantly similar to each other using the Kendall-Colijn and Robinson-Foulds metrics. We have identified several methods for comparing genome distances. Most notably, the regression analysis has helped identify whether genome distances correlate well between two pipelines and if so, it supplies an equation (y = mx+b) that describes how much distance in one pipeline is in another pipeline.SNP analysis for epidemiological investigations is becoming more common and is a powerful technique  Initiative at the Centers for Disease Control and Prevention. SNVPhyl development was funded by the Public Health Agency of Canada (PHAC), the Canadian Federal Government Genomics Research and Development Initiative (GRDI) Interdepartmental Shared Priority Project on Food and Water Safety, and Genome BC. The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of 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.


Ascites syndrome is the most severe manifestation of pulmonary hypertension in fast-growing broilers. The disease can be attributed to increased body weights of birds, where the higher metabolic load is not matched by sufficient oxygen supply to the cells and tissues. Although there are environmental components, the disease exhibits moderate to high heritability. The current study uses high throughput whole genome resequencing (WGR) to identify genes and chromosomal regions associated with ascites.CPQ gene on chromosome 2. The association was confirmed by genotyping a large collection of DNAs from phenotyped birds from three distinct broiler lines using SNPs in intron 6 and exon 8 of the CPQ gene. By combining the genotype data for these two SNP loci, we identified three different alleles segregating in the three broiler lines. Particular genotypes could be associated with resistance to ascites. We further determined that particular genotypes most associated with resistance overexpress CPQ mRNA in three tissues which might explain the role of these alleles in contributing to resistance.The WGR data identified the CPQ gene has been associated with pulmonary hypertension in genome-wide association studies in humans. Therefore, ascites investigations in broilers are likely to provide insights into some forms of hypertension in humans.Our findings indicate CPQ is an important determinant of pulmonary hypertension syndrome leading to ascites in broilers. We identified particular SNPs that can be used for marker-assisted selection of broilers for resistance to the disease. Our findings validate WGR as a highly efficient approach to map determinants contributing to complex phenotypic or disease-related traits. The  Idiopathic pulmonary arterial hypertension (IPAH), also known as ascites in poultry, is a metabolic disorder attributed to rapid growth in modern broilers. Broilers (meat-type chickens) are selected for rapid growth and increased muscle mass. Since the 1950s, selection has yielded an improved growth rate of about 5% per year . The faipost-mortem, selection of broilers against ascites susceptibility can be difficult. Hence, in current breeding programs, information from siblings and relatives are used for genetic selection of the broiler lines against ascites [Ascites is one of the health traits that is of concern in selective breeding and management practices for broilers. The goals are to minimize economic losses from ascites-induced mortality, reduce hypoxemic affects on meat quality, and improve overall animal well-being . Evaluat ascites .CPQ) gene specifically to conferring resistance to ascites in a gender-biased manner.Development of genetic markers for selection for resistance to ascites is essential for more effective broiler breeding programs. Although several independent studies have indicated ascites to be a polygenic trait , 22–29, 2 inhalation, or cervical dislocation.All animal procedures were approved by the University of Arkansas Institutional Animal Care and Use Committee under protocol 12039 and 15040, including specific review and approval of the mortality aspects of this research. All birds were observed daily and any moribund birds were immediately euthanized. Ascites does not generally result in the development of outward signs of discomfort, nor does it appear to prevent birds from eating or drinking. Death from ascites is typically the result of the accumulation of fluid in the abdominal cavity which over time exerts pressure on the air sacs and in turn results in congestive heart failure. Daily handling and palpation of the abdominal region could be successful at identifying late stage progression of ascites. However, daily handling and palpation would be stressful on the birds, and would not be effective in identifying birds in early stage progression of ascites. At the end of the experiments all remaining birds were euthanized by either COth generation of the REL line were used for the WGR data. Parents from 17th generation were mated by artificial insemination of females using pooled semen from males. Eggs were pedigree tracked by hen, immediately wing-banded and randomly assigned to cages in the hypobaric chamber. The chicks belonged to the 18th generation of the REL line. The hypobaric challenge was for six weeks at simulated 2,744 m  above sea level as described previously [The birds used in this study represent one research line (REL) and two commercial elite lines (Lines Y and Z). Birds from the 18eviously . Daily meviously , 32, 33.eviously , 32. At eviously , 32. LinAt four days of age, 10 μl of blood was extracted via wing vein lancet puncture. DNA was isolated from blood samples using our rapid protocol . SelecteDNAs submitted for library construction consisted of pools of equal weights of 10 DNAs. Eight pools were generated representing both genders and ascites resistant and susceptibile phenotypes, with two different pools for each gender-phenotype. Pools (100μl) of 40 ng/μl DNA were submitted for bar-coded library construction followed by 2*125 paired-end reads on Illumina HiSeq 2500 to generate at least 65 Gigabases (Gb) per pool. Library construction and sequencing were performed by the Research Technology Support Facility at Michigan State University .Gallus_gallus-5 assembly (downloaded from NCBI) in a templated alignment using NGen 14  using the default parameters. Separate templated alignments were generated for each gender. The templated assembly was then processed using ArrayStar 14 (Lasergene Suite 14) to tabulate SNPs relative to the reference genome and the frequency of each SNP in each DNA pool. The SNP dataset  was exported for individual chromosomes and then imported into Microsoft Excel  for further filtering and analysis. SNPs were filtered out with: low depth of coverage (<9) or missing frequencies from one or more replicate pools to reduce false discoveries from low coverage regions. The difference in average SNP frequencies for the two pools for each phenotype was then calculated and was plotted by chromosome position to identify clusters of SNPs with markedly different frequencies between the resistant and susceptible phenotype pools.The NGS sequences (adapters removed) as FASTQ files were aligned to the reference Gallus_gallus-5 assembly. (Accession ID: GCF_000002315.4) obtained from NCBI.All genome positions and SNP ID (rs entries) correspond to those from Gallus_gallus-5 assembly were manipulated and annotated in SeqBuilder software (DNASTAR Lasergene Suite 14). PCR primers and probes were designed using Primer3  using custom primer size (19-30bp), GC content (40–60%) and Tm range (60–65°C). Primers for reverse transcriptase PCR assays targeted 3’ exons separated by a large intron  so as to amplify only cDNAs and eliminate any genomic DNA amplification. Primer and probe sequences were checked for uniqueness by BLAT using the UCSC genome browser. Primers and probes were synthesized by Integrated DNA Technologies . Probes incorporated a Zen modification quenched with Iowa Black. Primer and probe sequences designed and used in this study are listed in Specific genomic sequences from the Targeted SNPs were genotyped by TaqMan 5’-exonuclease assays using qPCR as described previously . PrimersGenotype calls were verified for select samples by purifying the product using RapidTip®  and then quantified by Hoechst 33258 fluorescence . Cleaned samples were submitted for capillary sequencing . Sequence ab1 files were aligned using SeqMan Pro 14 (DNASTAR Lasergene Suite 14) for editing and scoring sequence data.Tissue samples were collected from birds with specific allelic patterns at six weeks of age immediately at necropsy. These tissue samples included total heart (both auricles and ventricles), lungs and liver. All freshly harvested specimens for RNA extraction were immediately submerged in of RNAlater solution  at 10:0.5 (v:v) ratio of RNAlater to tissue (thinly sectioned). The suspension was mixed well, equilibrated at 4°C for four to five hours and then stored at -20°C until used. During RNA extraction, the tissues were allowed to thaw before homogenization.Total RNA from tissue homogenates were extracted using Trizol Reagent  according to the manufacturer’s instructions. RNAs were quantified using a Nanovue  and RNA integrity was assessed by agarose gel electrophoresis. Expression levels were determined using single-step RT-qPCR employing Pyrophage® Exo-minus . Total RNA (500 ng) was denatured at 65°C for 10 mins, and then added to a mastermix consisting of 1x Pyrophage® 3173 PCR buffer, 1x Evagreen dye , 0.5 μM dNTPs, 2 μM each of specific forward and reverse primers , 0.5 μl Genotype and haplotype frequencies were calculated for ascites-resistant and ascites-susceptible phenotypic groups in each line. Expected genotype counts for each phenotype were computed from the genotypic frequency observed for the respective populations. Chi-square values comparing expected to observed counts were then computed using the chitest function in Microsoft Excel to derive P-values and then modified by a simple Bonferroni adjustment based on the number of tests . GenotypGallus_gallus-5 reference genome, and SNP data filtered for high-quality SNPs with good read depth (for details see https://www.ncbi.nlm.nih.gov/gap/phegeni). The peak from 127.62–127.75 Mbp appeared to be associated with the most relevant gene. The peak in males and females encompasses the 3’ end of the gene for carboxypeptidase Q . Most importantly the CPQ gene has been associated with hypertension, blood pressure, and heart rate, in human GWAS studies. The peak we observed for this region begins in intron 4 and extends 43 kbp downstream of the final exon, exon 8. In females, we did not observe any additional gene regions that exhibited consistent and significant skew for further analysis.Eight pools of equal DNA quantities were constructed for two replicates for each ascites phenotype (resistant vs. susceptible) and each gender. Illumina libraries were constructed with bar-coding, and paired-end sequenced using Illumina Hiseq 2500 to generate FASTQ 2x125 datasets of ≥65 Gb per DNA pool. The reads were mapped to the CPQ gene are intronic; five SNPs affect coding sequence including three missense SNPs, and four SNPs are in the 3’UTR of the gene (rs80617053)and a A/C SNP at 127,670,534 bp (ID: rs80617053) (rs738850243).We, therefore, developed exonuclease assays for further genotyping for this region. Majority of the SNPs within the the gene . For the0617053) . From re0617053) . The exo0617053)  at 12770The exonuclease assays were applied to a collection of DNAs from three different broiler lines phenotyped for ascites in hypobaric challenges; the REL line, and two commercial broiler lines, Line Y and Line Z. For the intron-6 SNPs, we genotyped 964 DNAs for REL line birds, 188 DNAs for commercial elite line Y, and 185 DNAs for commercial elite line Z. In the REL line, 66% of all birds and 72% of males homozygous for the non-reference SNP (C/C) were resistant  SNP genotypes indicated the homozygous non-reference genotype (C/C) to be significantly associated with the resistant phenotype in all the three lines of REL, Y and Z. Considering all birds with the non-reference genotype (C/C), 69% individuals in REL, 89% in line Y and 90% in line Z were resistant  with the exon-8 SNP (rs738850243). We identified three haplotypes: T-A-G (reference haplotype), C-C-C (non-reference haplotype), and C-C-G (recombinant haplotype), with potentially six different genotypes: genotype G1 (homozygous reference): T-A-G/ T-A-G; genotype G2 (homozygous non-reference): C-C-C/ C-C-C; genotype G3 (homozygous recombinant): C-C-G/ C-C-G; genotype G4 (heterozygous recombinant): C-C-G/ C-C-C; genotype G5 (heterozygous): T-A-G/ C-C-C; and genotype G6 (heterozygous recombinant): T-A-G/ C-C-G. Interestingly, there was no evidence for the “recombinant” T-A-C haplotype in any of the three populations.Based on the genotype data we constructed haplotypes combining the two intron-6 SNPs , 76% of males (P = 0.051), and 69% (P = 0.027) of females with G2 were resistant. In LineCPQ gene expression in unchallenged, healthy REL line birds. We focused on expression in heart, lung, and liver, as these are major organs affected in ascites. Our results indicate that the individuals with homozygous non-reference genotype/ haplotype (G2) exhibited the highest level of expression in all the three tissues with a six-fold increase in the lung (P = 0.001), 4.7 folds increase in each of heart (P = 0.001), and liver (P = 0.007) as compared to those with the reference genotype/ haplotype G1  and exon-8 SNP (rs738850243). The associations are most pronounced in males. Generally, increased ascites resistance is not seen in heterozygotes arguing that the reference allele is dominant over the non-reference allele. The exon 8 SNP would result in a lysine to asparagine substitution at residue 444 of the encoded mature protein. Based on structural data from related proteins in NCBI, the K444N substitution would be on a surface exposed region of the enzyme and not near the active site. Therefore, if this substitution is mediating the QTL effect, it would have to be through affecting some protein-protein interaction, perhaps affecting CPQ activity on a circulating peptide hormone involved in blood pressure homeostasis. Our gene expression data argue that the non-reference genotypes express higher levels of CPQ mRNA than the reference genotypes in the three tissues tested; heart, lung, and liver .Our genotyping data on different broiler lines demonstrate consistent, strong association of the homozygous non-reference genotypes with resistance for both the intron-6 SNP locus  has the highest expression in three different tissues . GenotypCPQ gene and our new WGR SNP data do not support the region around 70.8 Mbp. There were only three SNPs  in the 60k SNP panel located within the 134 kbp region. Two of these SNPs showed little to no difference in frequency between phenotypes and the other is monomorphic in the WGR data for the REL samples. Thus, the WGR approach is an unbiased, robust and efficient method to identify genomic regions contributing to complex traits, such as pulmonary hypertension or other diseases. In this context, it is worth noting that neither of the regions we had identified on Gga9 [CPQ, where there is a region of skewed SNP frequencies between phenotypes (unpublished). This further validates our WGR (NGS) dataset in identifying QTL regions and reducing false discoveries from our previous GWAS.Prior to this study, we had used multiple 60k SNP panels for GWAS in the REL line, to identify a region on Gga2 around 70.8 Mbp as a candidate for association with ascites . However on Gga9 , 31 or o on Gga9  was confPulmonary hypertension or ascites is a metabolic disorder and is genetically correlated with body weight . Males hCPQ gene is also associated with pulmonary hypertension in broilers. Similarly, broilers have been shown to form plexiform arteriopathies analogous to those in some forms of human pulmonary hypertension [CPQ with hypertension in chickens and the possible contribution of relative expression levels for some alleles may inform biomedical investigations on the role of CPQ in human hypertension. Fast growing broilers with an increased metabolic rate induce an increased secretion of thyroxine, which is deiodinated to triiodothyronine in the liver and kidney [Although CPQ has been associated with hypertension and blood pressure in humans, the precise role of the CPQ protein in mediating these bodily functions are still unclear. Our study indicates that the rtension . Our curd kidney . Increasd kidney . This inCPQ gene is the most prominent candidate gene for ascites identified to date, and could be employed in marker assisted selection for increased ascites resistance. The correlation of altered mRNA expression levels for some alleles with resistance to ascites could also relate to the association of CPQ with human hypertension and development of new therapies. Understanding the gene network and downstream protein-protein interactions of the gene is necessary to fully comprehend the importance of the gene in disease-associated phenotypes. Additionally, the WGR approach should be easily adapted to other complex traits and diseases with genetic components in different agricultural species.The S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableFor each region, the table lists the location, the Maximum SNP frequency difference between resistant and susceptible, the calculated P-value (see text), the gene found in the region, and the function of that gene.(DOCX)Click here for additional data file.