Loxodonta cyclotis) numbers due to poaching and habitat reduction is driving the search for new tools to inform management and conservation. For dense rainforest species, basic ecological data on populations and threats can be challenging and expensive to collect, impeding conservation action in the field. As such, genetic monitoring is being increasingly implemented to complement or replace more burdensome field techniques. Single‐nucleotide polymorphisms (SNPs) are particularly cost‐effective and informative markers that can be used for a range of practical applications, including population census, assessment of human impact on social and genetic structure, and investigation of the illegal wildlife trade. SNP resources for elephants are scarce, but next‐generation sequencing provides the opportunity for rapid, inexpensive generation of SNP markers in nonmodel species. Here, we sourced forest elephant DNA from 23 samples collected from 10 locations within Gabon, Central Africa, and applied double‐digest restriction‐site‐associated DNA (ddRAD) sequencing to discover 31,851 tags containing SNPs that were reduced to a set of 1,365 high‐quality candidate SNP markers. A subset of 115 candidate SNPs was then selected for assay design and validation using 56 additional samples. Genotyping resulted in a high conversion rate (93%) and a low per allele error rate (0.07%). This study provides the first panel of 107 validated SNP markers for forest elephants. This resource presents great potential for new genetic tools to produce reliable data and underpin a step‐change in conservation policies for this elusive species.The continuing decline in forest elephant ( Blood, muscle, and skin samples were collected, as available, from 14 elephants immobilized for collaring operations in 2003  was selected for the library preparation stage. DNA was quantified using a Qubit Broad Range dsDNA Assay (ThermoFisher Scientific) according to the manufacturer's instructions and normalized to c. 7 ng/μl.SbfI and SphI, and then Illumina‐specific sequencing adaptors (P1 & P2) were ligated to fragment ends. The pooled samples were size selected (320–590 bp fragments) by gel electrophoresis, PCR amplified (15 cycles) and the resultant amplicons (ddRAD library) were purified and quantified. Combinatorial inline barcodes (five or seven bases long) included in the P1 and P2 adaptors allowed each sample replicate to be identified postsequencing. The ddRAD library was sequenced on the Illumina MiSeq Platform .A ddRAD library was constructed according to a modified protocol of the original Peterson et al.  methodol2.3process_radtags module (default parameters) of the stacks bioinformatics pipeline  to assemble and create a catalog of genetic loci contained in the data. The Stacks scripts export_sql.pl and populations and five filtering steps were used to retain all loci that fulfilled the following criteria:The individual data were then processed using the Contained exactly one SNP (in the concatenated forward and reverse reads) to remove physically linked markers and ensure availability of a constant sequence surrounding the target SNP to facilitate primer design;Contained exactly two alleles, as the presence of more than two alleles might represent repeat sequence found at multiple sites within the genome;Were present in the data for ≥10 elephants and had a read depth of ≥10 reads per individual to maximize the likelihood of the SNP being real;Were heterozygous in at least one individual but not in all individuals in the dataset; both the lack and apparent fixations of heterozygotes could be indicative of variation between repeat sequences found at more than one locus; andHad a minimum of 50 bases flanking sequence either side of the SNP to ensure that the sequence meets the requirements for the design of a genotyping probe assay  using NCBI's Basic Local Alignment Search Tool (BLAST) to investigate any repetition within the genome.In order to validate the results from the bioinformatics pipeline, two sets of SNPs were tested for validation using different approaches. The default parameters were used for all programs, unless otherwise specified. First, a random subset of 22 SNP loci was selected as candidates for assay design and ordered from LGC Genomics using the Kompetitive allele‐specific PCR (KASP) system to evaluate the conversion rate that is the proportion of successful assays that resulted in distinct genotyping clusters. They were run on a StepOne real‐time PCR machine (Applied Biosystems) on the DNA samples used to generate the library. PCR was carried out in 8 μl single‐locus reactions following thermal cycling conditions recommended in the KASP user guide , global FST, and FST in the three pairwise population combinations. Loci were then given an unweighted joint rank across all five categories, and the highest ranking 266 SNPs were chosen. Finally, loci were excluded that had zero or >1 BLAST matches against the L. africana genome using a discontiguous megablast of the 148 bases sequence containing the SNP. The cutoff e‐value was set at 10−10 with a minimum alignment length of 100 bp including the SNP site. Sequences with no matches based on these criteria were excluded on the basis that they could be from a different organism, while multiple matches revealed that the sequence was duplicated within the genome and therefore not suitable for assay design. The 30‐bp flanking sequences either side of the SNP were also independently searched against the savannah elephant genomic data  to minimize the chance of designing primers that may anneal at multiple sites. This step was added following validation of 22 probes from the pipeline (see above).Second, a genotyping panel was selected among the candidate SNP markers using a combination of measures of genetic diversity and divergence, in order to validate assay performance and select potentially informative markers with the aim to explore genetic variation among individuals and populations. The filtered matrix of sequencing genotype data at 1,365 loci was examined for “missingness” using PLINK  and observed heterozygosity (HO) were estimated for each population using the R package adegenet  even if they were not suitable for the ddRAD library construction. The stringent parameters used by LGC Genomics for automatic allele calling usually result in a high proportion of unassigned genotype calls  was used to match the sequences to scaffolds and determine if the SNPs were located within a gene locus, and in particular within a coding region. Pairwise linkage disequilibrium was tested for using the R package LDheatmap (default parameters)  were removed from the dataset at this stage due to having high levels of missing data in the matrix (>70%), leaving a dataset of 420 SNPs and 16 individuals with >60% of the loci genotyped. A list of 266 highest ranking SNPs was then selected according to the measures of genetic diversity and divergence (see above). A BLAST search of the whole sequence and of the flanking regions of the SNP against the Following genotyping of 74 samples, six SNPs  failed to provide distinct clusters in the signal intensity plot and were excluded from further analysis. When comparing the genotypes obtained from the KASP assays to the 19 ddRAD profiles, the proportion of missing data was higher in the ddRAD pipeline (23.0%) than in the LGC genotyping data (1.7%). The proportion of category 1 and category 2 mismatches was 1.40% and 0.15%, respectively. Only three loci yielded category 2 mismatches, of which one (CL_340) was rescored as the discrepancies were due to KASP scoring error caused by low‐quality plots, namely little separation between the heterozygous group and one of the homozygous groups. The two other loci (CL_3004 and CL_10172) were removed from consideration because of a high proportion of category 2 errors . This resulted in an estimated conversion rate of 93% (107 of 115).HO and HE per locus were 0.27 and 0.31, respectively. Mean overall FST was 0.015, suggesting low genetic differentiation, but ranged from 0.03 to 0.162 for 31 SNPs, indicating substantial differences in allele frequencies at these loci , as even though 73% of SNPs (78 of 107) needed to be rescored for at least one sample, only 16 were rescored for more than 5% of the samples (range: 0%–17.2%). The proportion of missing genotype data per locus was <15% for all except 13 loci  Table . Mean MAci Table . However3.1L. africana unplaced scaffolds (sequence similarity from 97% to 100%), of which 78 SNPs (71.6%) matched the same scaffold as one to five other SNPs suggesting that they could be linked (Table r2 > .3), but the two loci in each pair did not belong to the same scaffolds. In total, 50 sequences (46.7%) returned a match against a functional region of the L. africana genome, of which only seven SNPs occurred within the coding DNA sequence of the gene  Figure . All 107ed Table . Howeverne Table .4After quality filtering, we have generated a new genetic resource of 1,365 SNP loci which is available for further studies. As this is the first genome‐wide set of SNP markers generated for African elephants, it represents a major advance for the genetic study of this taxon.In this study, ddRAD was demonstrated to be effective for the rapid discovery of a large number of SNPs in the forest elephant. Due to double restriction digestion and precise size selection, ddRAD sequencing produces only the subset of fragments generated by cuts with both restriction enzymes and close to the target size. Therefore, ddRAD libraries are expected to provide less coverage than the original RAD method  of the loci containing exactly one SNP were removed from consideration because of the generally low read depth per individual at a locus, leading to a high rate of missing data among individuals. In retrospect, as the elephant genome is large, with a size between 3.1 and 4.01 Gb  were removed from consideration because they did not cluster as expected genotypes. Monomorphic results were observed in the cluster plots, whereas all three genotypes existed in the ddRAD data. This was likely due to sequence repeats that were not detected using the incomplete L. africana genomic data. Even though ddRAD sequencing is suitable for nonmodel organisms, these results highlighted the advantages of using genetic resources from a closely related species to detect sequence repeats. L. africana genomic data have also successfully been used to characterize SNP markers in the Bornean elephant (E. maximus borneensis)  removed almost 58% of loci from consideration. A similar issue has been raised by another study that reported that as many as 75% of potential SNPs were unsuitable for assay design , which is consistent with the species separation . Details for all SNPs and validated primers are found in Appendix  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Cynara cardunculus L. offers a natural source of phenolic compounds with the predominant molecule being chlorogenic acid. Chlorogenic acid is gaining interest due to its involvement in various biological properties such as, antibacterial, antifungal, antioxidant, hepatoprotective, and anticarcinogenic activities.Cynara cardunculus collection for new allelic variants in key genes involved in the chlorogenic acid biosynthesis pathway. The target genes encode p-coumaroyl ester 3′-hydroxylase (C3′H) and hydroxycinnamoyl-CoA: quinate hydroxycinnamoyl transferase (HQT), both participating in the synthesis of chlorogenic acid. Using high-resolution melting, the C3′H gene proved to be highly conserved with only 4 haplotypes while, for HQT, 17 haplotypes were identified de novo. The putative influence of the identified polymorphisms in C3′H and HQT proteins was further evaluated using bioinformatics tools. We could identify some polymorphisms that may lead to protein conformational changes. Chlorogenic acid content, antioxidant and antithrombin activities were also evaluated in Cc leaf extracts and an association analysis was performed to assess a putative correlation between these traits and the identified polymorphisms.In this work we screened a Cynara cardunculus biological properties with further confirmation by functional analysis.In this work we identified allelic variants with putative impact on C3′H and HQT proteins which are significantly associated with chlorogenic acid content and antioxidant activity. Further study of these alleles should be explored to assess putative relevance as genetic markers correlating with The online version of this article (doi:10.1186/s12864-017-3534-8) contains supplementary material, which is available to authorized users. Cynara cardunculus L. (Cc) has been known for its therapeutic activities and used in folk medicine since ancient times, renewed interest has arisen through recent studies on its chemical and biological characterization  were designed in order to amplify overlapping segments of DNA coding sequences using Primer 3 software (http://bioinfo.ut.ee/primer3-0.4.0/) . After data calibration and normalization, samples were grouped based on the shape of the normalized melting curves (profile) and the sensitivity level was adjusted to distinguish all genotype groups.DNA was extracted from 100 mg of young leaf tissue using the DNeasy Plant Mini kit  and quantified on a 1% agarose gel. High-resolution melting-specific primers for n et al. . PCR ampFrom each HRM profile, three DNA samples were amplified twice and sequenced to confirm the results obtained. PCR amplifications products were purified and sequenced by Beckman Coulter Genomics , with an ABI 3730xl sequencing platform. Sequences obtained were assembled and single nucleotide polymorphisms (SNPs) were identified in the different individuals. SNPs analyses that resulted in aminoacid (a.a) change allowed the identification of specific haplotypes. Based on previous background information of cultivated cardoon from Beja, Portugal, one individual (AC2) of this accession was chosen as reference.http://sniplay.cirad.fr/cgi-bin/home.cgi) − ions at m/z 353 and 515, respectively  (P < 0.05). The CGA content of this extract was 8.5-fold higher than the lowest one in the Y1 individual with 0.11% (w/w). CGA levels of the analysed Cc leaf phenolic extracts were slightly higher than the one earlier described for cultivated cardoon leaf extracts (0.01% w/w) [Regarding the CGA content Table , Cc leaf01% w/w) , includi01% w/w)  determinCc leaf phenolic-derived extracts was assessed through their scavenging capacity against DPPH free radicals. The inhibitory concentrations of Cc extracts and standard antioxidants needed to decrease by 50% (IC50) the initial concentration of DPPH were thus determined . Nevertheless, Cc leaf phenolic-derived extracts were less active relatively to the tested standard antioxidants, namely BHA (IC50 = 7.27 ± 0.13 μg/mL) and ascorbic acid (IC50 = 2.38 ± 0.09 μg/mL). The IC50 values of Cc extracts were also higher than CGA (IC50 = 5.24 ± 0.35 μg/mL). Comparing values in terms of molarity, CGA IC50 (0.014 ± 0.000 μM) is similar to the standard antioxidant ascorbic acid (0.013 ± 0.000 μM) and lower than BHA (0.040 ± 0.000 μM) showing an advantageous antioxidant potential for CGA present in Cc extracts. In order to compare the antioxidant efficiency of these extracts with data reported in the literature, the antioxidant activity index (AAI) was calculated taking the final concentration of DPPH into account (35.45 μg/mL). AAI of Cc leaf phenolic-derived extracts from the Q5 and J5 individuals  are in the same range as those obtained in previous studies using cardoon leaf extracts [Cc , for potential use as alternative antioxidants in the food industry. Previous experiments from our research team using nanofiltration resulted in total phenolic compounds concentration, as well on chlorogenic acid levels improving the antioxidant activity in C. cardunculus leaf extracts [Phenolic compounds present in medicinal plants are well-known for their antioxidant properties . Scavenged Table . The lowextracts  and highextracts . Even foextracts , a higheextracts .Cc leaf phenolic extracts from all 20 individuals (at 1 mg/mL) were tested for their thrombin inhibitory potential  (P > 0.05). Results from other studies using methanol extracts (in the same extract concentration) of Melia azedarach, Cyperus globulosus, Cinnamomum camphora, Ambrosia artemisiifolia, Paspalum dilatatum and Woodwardia radicans, revealed similar percentages of thrombin inhibition (≈40%). However, higher percentages of antithrombin activity (≈80–90%) were also described, namely with Callistemon lanceolatus, Lagerstroemia indica, Myrica cerifera, Polytrichum commune and Calocasia esculenta [Thrombin is an important enzyme in the blood coagulation process, being responsible for the conversion of soluble fibrinogen into stabilized insoluble fibrin. Its inhibition is important against many blood coagulation and platelet disorders . Consideal Table . The inhsculenta , 54.Interestingly, CGA standard (at 300 μg/mL), also showed thrombin inhibition activity (59.2%), in contrast to what was previously described by Bijak et al. , who repp-value ≤ 0.05  from the HQT gene showed significant associations with CGA content and antioxidant activity.For SNPs phenotype association analysis we used CGA content, antioxidant and antithrombin activities Table  of the 250 (P = 0.005 and r = −0.6). Antithrombin activity, however, was not related to any SNP or trait.To correlate trait significances, a Pearson correlation between results of CGA, antioxidant and antithrombin activities was further performed. This study revealed an inversely significant association with CGA content and ICCynara cardunculus genes, related to chlorogenic acid biosynthesis. A high-resolution melting approach was used to genotype single nucleotide polymorphisms with the objective of identifying putative variants in the corresponding enzymes. This method has been frequently used in clinical chemistry, patient genotyping and human pathology since 2003 [This work characterizes two nce 2003 , 89. Recnce 2003 , barley nce 2003 , grapevince 2003 , almond nce 2003 , olive [nce 2003 , pepper nce 2003 , sweet cnce 2003 , peach [nce 2003 , artichonce 2003 , durum wnce 2003  and ricence 2003 .Cynara cardunculus was used for HRM analysis. Considering the genetic variability of Cc, a diploid cross-pollinated species [A collection of 29 accessions comprising 127 individuals of  species , wheneveC3′H and HQT genes. Variability was observed by the heterozygosity level obtained. HRM allowed identification of 46 SNPs (confirmed by sequencing) in the coding sequences of C3′H and HQT genes. From these SNPs, 35 were classified as synonymous and, since they locate within exonic regions expected to be conserved in mature mRNA and consequently not interfering with mRNA splicing, they were no longer discussed within the present study.Considering the advantages of the HRM technique, such as the possibility of evaluating a large sample number , its simplicity, robustness and fast technique , 41, thiCynara, multiple statistical algorithms are used to infer phase genotypes. To reconstruct haplotypes of nonsynonymous SNPs, we used the SNiPlay pipeline that employs Phase or Gevalt algorithms. This analysis is based on identification of sets of alleles that are found together in multiple individuals and allowed, in our study, the definition of 21 haplotypes. The C3′H gene proved to be very conserved, with only 4 haplotypes, while for HQT, 17 haplotypes were identified. These results confirm HRM as good strategy to discover possible new allelic variants to genotype plants for assisted breeding.For diploid species, such as C3′H genomic sequence from Cynara cardunculus L. var. scolymus was reported by Moglia et al. [HQT cDNA sequences of artichoke and cultivated cardoon were first described by Comino et al. [C3′H and HQT coding sequences of artichoke, respectively. Although we could not identify these changes in our study, this work reports a number of novel SNPs in the C3′H and HQT gene sequences from different Cynara cardunculus individuals.The isolation and mapping of the a et al.  with a no et al.  and a G8o et al.  also ideC3′H and HQT genes were characterized using bioinformatics tools to assess their possible influence at the protein level. The C3′H protein belongs to the CYP98 family of plant cytochromes P450 (plant metabolism related) which has been labelled as the family of enzymes performing the meta-hydroxylation step in the phenylpropanoid pathway. This hydroxylation is essential not only for the synthesis of chlorogenic acid but it is also important for lignin biosynthesis [p-coumaroylquinic (first route) or/and p-coumaroylshikimic acids (second route) as substrates. C3′H from artichoke appears to have a lower affinity for quinate esters than shikimate esters [C3′H gene sequence analysis by bioinformatics tools revealed a putative endoplasmatic reticulum (ER) location for the C3′H protein. Phobius and SignalP tools revealed a signal peptide at the amino terminus of the protein. According to the Uniprot database, signal peptides are found in proteins that are targeted to ER and either destined to be secreted to the extracellular or periplasmic space, or to be retained in the ER lumen. Predotar confirmed the cellular ER localization of the C3′H protein although this has not been confirmed in the literature. The S194L and M198L alterations may account for changes in protein structure/function. The M198L can lead to an alteration of hydrophobicity and protein destabilization since there is evidence of an estimated loss of stability of about 1.4 kcal/mol for each leucine-to-methionine substitution at a buried site within a folded protein [The 11 nonsynonymous SNPs identified on ynthesis . The chle esters  and route esters , 66, 85. protein .p-coumaroyl-CoA or caffeoyl-CoA esters as an acyl donor, using quinic acid as an acceptor [HQT gene silencing resulted in a 98% reduction in the level of chlorogenic acid [Cynara cardunculus.HQT is a hydroxycinnamoyl transferase implicated in CGA production that can use either acceptor . A stronnic acid . The PreC3′H and HQT genes  from the HQT gene showed significant associations with CGA content and antioxidant activity. As previously explained, the S218F mutation leads to an alteration of hydrophobicity (S to F residues) in a solvent-exposed protein zone and also leads to a loss in a serine phosphorylation site resulting in a putative regulatory level alteration likely to affect protein function. The S218F alteration present in haplotypes B, C, M and Q is associated both with CGA content and antioxidant activity . Despite the protein conformation predictions explained above, it seems that the existence of the S218F alteration on these HQT haplotypes could increase CGA content. Since biological properties of plants are due to their content in different metabolites, alterations in HQT gene can influence, positively the final CGA content and thus the level of the antioxidant activity. This result was confirmed by the Pearson correlation, which revealed an inversely significant correlation between CGA content and IC50 meaning that CGA content improved the plant antioxidant activity. The D322N alteration present in haplotypes D and P could cause protein destabilization and changes at the regulatory level. This mutation leads to a change from a negative (D residue) to a polar uncharged residue (N) and influences the 319 and 323 phosphorylation sites as well as 333 ubiquitination causing loss of these regulatory elements. This alteration was significantly associated with antioxidant activity (IC50). Heterozygous individual Y1 (haplotype D and P) yielded the highest IC50 value. However, this association was not correlated with the CGA content.Association analyses are becoming increasingly important to exploit the natural diversity of genes related to a particular phenotype. The SNPs present in the ty Table . As we oNevertheless, it should be highlighted that these putative associations could represent false positives and, since the association analysis power is highly dependent on the number of genotypes employed , 28, a lAntithrombin activity was not related to any SNP or trait.C. cardunculus genome. Our work provides, for the first time, detailed information about the natural allelic variants of C3′H and HQT genes involved in C. cardunculus phenylpropanoid pathway.In order to develop a targeted breeding strategy, directed at improving the content of the relevant plant metabolites, it is important to have a better knowledge of the Cc leaf phenolic-derived extracts, identified two hydroxycinnamic acids, CGA and 1,5-di-O-caffeoylquinic acid, as well as two flavones, luteolin 7-O-glucoside and luteolin acetyl-hexoside. The differences obtained in the biological activities characterized here could be related to different synergisms of the total phenolic content including flavonoids of Cc extracts. The phenolic content of a plant depends on a number of intrinsic (genetic) and extrinsic  factors. The genotype proved to be a major determinant of variation in Cc polyphenol content and profile [Cc individuals from diverse geographic locations. Usually, antioxidant activity has been correlated with total phenolic content [Cc leaf extracts, we found an evident correlation between CGA content and antioxidant activity meaning that this compound could have a significant impact on overall antioxidant power. Thus further studies on quantification, isolation and characterisation of Cc compounds responsible for specific biological activities are highly recommended.The qualitative analysis in all the 20  profile , 27, 67. content . AlthougCc genetics, trait selection and breeding applications. Association analysis allowed identification of interesting haplotypes, such as C, M and Q haplotypes, which present the S218F alteration on the HQT sequence, associated with higher chlorogenic acid content and improved antioxidant activity. Further, the SNPs detected herein will be interesting for future association studies with other traits. This study opens new lines for research, specifically for validation of the identified SNPs by functional analysis and correlation to particular phenotypes.In the context of the molecular diversity of target genes, the association results shown in this study may provide markers that are useful for


A total of 18 loci surpassing the genome-wide suggestive significance level were detected for 4 traits: fat content in dorsal muscle (MFdo), fat content in abdominal muscle (MFab), abdominal fat weight (AbFW), and AbFW as a percentage of eviscerated weight (AbFP). Among them, one SNP (carp089419) affecting both AbFW and AbFP reached the genome-wide significance level. Ten of those loci were harbored in or near known genes. Furthermore, relative expressions of 5 genes related to MFdo were compared using dorsal muscle samples with high and low phenotypic values. The results showed that 4 genes were differentially expressed between the high and low phenotypic groups. These genes are, therefore, prospective candidate genes for muscle fat content: ankyrin repeat domain 10a (ankrd10a), tetratricopeptide repeat, ankyrin repeat and coiled-coil containing 2 (tanc2), and four jointed box 1 (fjx1) and choline kinase alpha (chka). These results offer valuable insights into the complex genetic basis of fat metabolism and deposition.Muscle fat content is an important phenotypic trait in fish, as it affects the nutritional, technical and sensory qualities of flesh. To identify loci and candidate genes associated with muscle fat content and abdominal fat traits, we performed a genome-wide association study (GWAS) using the common carp 250 K SNP assay in a common carp F Cyprinus carpio) is one of the most widely cultured fish species in the world. In 2012, the world production of common carp exceeded 4.16 million tons and, in China, the yield reached more than 3.17 million tons . The species is important in the global aquaculture industry, and it is also a traditional food in China. In addition, it serves as a model species for many research areas, e.g. toxicology, ecology, physiology, nutrition and evolution. Significant progress has been made in developing common carp genomic tools in recent years. A greater amount of genomic resources is available for common carp research, such as a medium-density [www.carpbase.org, laying the foundation for future studies on the genetic architectures of economic traits in common carp and related species.Common carp  to detect potential SNPs and candidate genes. These results will eventually help develop breeding strategies for improving flesh quality in common carp.From the above, the genetic architecture of fish flesh quality trait is still unclear. In the past ten years, GWAS has proven to be a powerful tool for identifying genetic variants associated with complex diseases and traits . Many stThis study was approved by the Animal Care and Use Committee of Heilongjiang River Fisheries Research Institute of Chinese Academy of Fishery Sciences. The methods were taken in accordance with approved guidelines. Before the muscle and blood samples were collected, all fishes were euthanized by being immersed in MS222 solution.2 family containing 220 individuals was used in this study. This family was generated by intercrossing two F1 parents originated from a mass cross among 60 brooders, including 40 female and 20 male fishes. All fishes were cultured in the Hulan Aquaculture Experimental Station of Heilongjiang River Fisheries Research Institute, Harbin, China. After hatching, groups of fry were selected for rearing in a pond for 6 months. A total of 220 progenies were randomly collected and used for genotyping. After anesthesia with MS222, growth related traits were measured and blood samples (200–500μl) were collected from the caudal peduncle. The removable adipose tissues depositing in abdominal cavity and surrounding the viscera were weighed as AbFW. Then eviscerated weight (EW) was weighed, and the percentage of AbFW to EW was calculated as AbFP. Dorsal and abdominal muscle samples (~50 g) from each fish were taken and kept at -80°C for DNA extraction and analysis of fat content, respectively. Total lipids were extracted from the dorsal (MFdo) and abdominal (MFab) muscles of each fish and determined in duplicate according to Folch et al. [A full-sib Fh et al. . The meaGenomic DNA was isolated from whole blood using a QIAamp DNA Blood Mini Kit  following manufacturer's protocol. DNA was quantified using spectroscopy by Nanodrop 8000 (Thermo Scientific), and the integrity of DNA was checked by 1.5% agarose gel electrophoresis stained with ethidium bromide. DNA concentration was diluted to 50 ng/ul and the quality of genomic DNA met the requirement for the Illumina Infinium SNP genotyping platform.2 offspring as well as their parents and grandparents.We have developed a common carp 250 K SNP array using Affymetrix Axiom genotyping technology . Genotyphttps://www.cog-genomics.org/plink2) [Quality control was carried out using PLINK v1.9 (/plink2) . Sampleshttp://www.r-project.org/).The descriptive statistics of the traits were completed using SPSS 13.0. All traits  deviated from normality, and Box-Cox transformation was applied using R software  . To imprP-value was decided based on Bonferroni-correction, we used the simpleM method [P-values were 0.05/8,992 = 5.56×10−6  = 5.25) and 1/8,992 = 1.11×10−4  = 3.95), respectively.The genome-wide significant/suggestive threshold M method  to correhttp://cran.r-project.org/package=qqman).The Manhattan plot and Quantile-quantile (Q-Q) plots were made by qqman package available on the Comprehensive R Archive Network  . Tissue samples selected from individuals at the extremes of the phenotypic rankings as high (n = 10) and low (n = 10), as MFdo . Wu’s methods  were ref3.ut.ee) .The descriptive statistics of the phenotypic measurements of muscle fat content and abdominal fat traits used for the present GWAS studies are given in We assessed the SNP quality and excluded 56,869 SNPs with a call rate that is < 95%, 105,737 SNPs with MAF that is < 5%, and 1,576 SNPs with non-Mendelian segregation patterns. The SNP located on 50 chromosomes and those not assigned in the chromosomes and linkage maps were also excluded. Therefore, a set of 85,818 SNPs was included for GWAS . The disP < 1.11× 10−4) level for the four traits. Only one SNP (carp089419) surpassed the genome-wide significance (P < 5.56 × 10−6) level for both AbFW and AbFP . Four SNPs  were located within the genes for ankyrin repeat domain 10a (ankrd10a), four jointed box 1 (fjx1), choline kinase alpha (chka) and ADAM metallopeptidase domain 8a (adam8a), respectively. For MFab, 2 suggestive SNPs were identified on LGs. The SNP (carp077510), located at 7.11 Mb on LG9, was identified in the zinc finger AN1-type domain 5a (zfand5a) gene. The other SNP (carp153837) was located at 0.56Mb on LG23.The SNPs associated with muscle fat content and abdominal fat traits are presented in ankrd10a gene, was also associated with MFdo. Three SNPs  were located within the genes for intermediate filament family orphan 2 (iffo2), karyopherin subunit alpha 4 (kpna4) and chloride channel 3 (clcn3), respectively. Eight loci associated with AbFP were identified on 3 LGs and 5 scaffolds. Only carp089419 surpassed the genome-wide significance level, which is also related to AbFW. Besides, four SNPs  were found to be associated with both AbFW and AbFP. Two SNPs (carp128746 and carp128748), harbored within a 1.24 Kb region on scaffold000000695, were both identified in the intron of the clcn3 gene. In addition, carp196106, located at 4.81 Mb on LG34, which is harbored in the downstream of apolipoprotein E receptor 2 (apoer2).One genome-wide significance and 5 genome-wide suggestive significance SNPs were related to AbFW. Four SNPs were located within known genes. Notably, carp139438, locating in the ankrd10a, tanc2, fjx1, chka and adam8a) containing or in proximity to SNPs associated with MFdo were further validated by Q-PCR in subsets of eight individuals with lowest or highest trait phenotypic values. Significant differential expression (P < 0.05 or <0.001) between the low and high individuals was showed for 4  of the 5  and 17 suggestive SNPs (P < 1.11× 10−4) associated with 4 traits  were identified. Moreover, with the aid of the common carp genome and gene annotation information, 10 possible candidate genes were screened. However, the sample size was still relatively limited in the current study, which may be the reason why genome-wide significance was not achieved on MFdo and MFab. In addition, a larger number of SNPs may be from genes that have not been annotated, or located on scaffolds. Therefore, further annotations on current common carp assembly version are necessary.GWAS have been offering increased opportunities for detecting susceptibility loci for complex traits. Many published GWAS with high-density SNP markers are based on study designs with the samples of unrelated individuals . It is ganalysis . Recentlanalysis . Here, wankrd10a, tanc2, fjx1, chka and adam8a) are shown to be potentially associated with MFdo, and they were selected for measurement of mRNA expression. Four  of 5 genes were differentially expressed in individuals with high and low MFdo  of intramuscular adipose tissue in Japanese Black cattle [zfand5) were identified. The zfand5 was down-regulated in the obese group compared with the lean group of dogs [Muscle fat is an important determinative factor of flesh quality influencing the juiciness, drop, texture and flavor of meat . As revelow MFdo , some ar balance . This ge balance . In addiogenesis . Polymordiabetes . The tank cattle . Interesk cattle . Two SNP of dogs .ankrd10a, iffo2, kpna4 and clcn3) containing or near SNPs associated with AbFW were identified. Three genes, iffo2, kpna4 and clcn3, were found to be associated with both AbFW and AbFP. In addition, apoer2 was also related to AbFP. The gene ankrd10a has been detected as a candidate gene for abdominal fat yield on GGA3 in chicken [iffo2 has been associated with residual body weight gain in cattle [apoer2  is a member of the low density lipoprotein receptor (LDLR) gene family, which is not only involved in the lipid metabolism, but also plays an important role in endocytosis and signal transduction [lrp8 gene can influence plasma cholesterol levels as well as the size and composition of LDL particles [lrp8 is also involved in other physiologically processes, e.g. lipid-raft sorting, endothelial cell migration and fetal growth [et al. reported that lrp8 gene was a new member of eggshell matrix protein and significantly associated with eggshell traits [In addition to muscle fat content, other fat traits, and especially abdominal fat, are important selection goal in animal breeding, e.g. chicken , and pig chicken . The genn cattle . The aposduction . Polymorarticles . Furtherl growth ,52. In al traits .2 common carp population. Among them, one SNP (carp139438), and 4 SNPs  for AbFW were also found to be involved in MFdo and AbFP, respectively, indicating a pleiotropic effect. Based on the association signals and differential expression of harbored genes, 4 candidate genes for MFdo were identified. The results would contribute to further exploration into the potential mechanism of flesh quality traits and improve our understanding of the biology of fat metabolism and deposition in common carp.To our knowledge, this might be the earliest GWAS study on flesh quality traits in common carp. In summary, one genome-wide significant SNP and 17 suggestive SNPs for 4 traits were identified in a FS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(ZIP)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 FigMFdo, fat content in dorsal muscle; MFab, fat content in abdominal muscle; AbFW, abdominal fat weight; AbFP, percentage of AbFW to eviscerated weight.(TIF)Click here for additional data file.


Piglet uniformity (PU) and farrowing interval (FI) are important reproductive traits related to production and economic profits in the pig industry. However, the genetic architecture of the longitudinal trends of reproductive traits still remains elusive. Herein, we performed a genome-wide association study (GWAS) to detect potential genetic variation and candidate genes underlying the phenotypic records at different parities for PU and FI in a population of 884 Large White pigs. In total, 12 significant SNPs were detected on SSC1, 3, 4, 9, and 14, which collectively explained 1–1.79% of the phenotypic variance for PU from parity 1 to 4, and 2.58–4.11% for FI at different stages. Of these, seven SNPs were located within 16 QTL regions related to swine reproductive traits. One QTL region was associated with birth body weight (related to PU) and contained the peak SNP MARC0040730, and another was associated with plasma FSH concentration (related to FI) and contained the SNP MARC0031325. Finally, some positional candidate genes for PU and FI were identified because of their roles in prenatal skeletal muscle development, fetal energy substrate, pre-implantation, and the expression of mammary gland epithelium. Identification of novel variants and candidate genes will greatly advance our understanding of the genetic mechanisms of PU and FI, and suggest a specific opportunity for improving marker assisted selection or genomic selection in pigs. In the pig production industry, reproductive traits such as litter size, litter birth weight, and litter mortality play an important role in the development of production and economic profits. In the past few decades, litter size at birth has been treated as an important criterion to evaluate sow productivity . Among them, 1,416 QTL are associated with reproductive traits. Fifty QTL for PU have been detected across the majority of chromosomes, but there are still no QTL for FI. Although numerous QTL have been detected in domestic animals  were used in our study. We collected blood samples from the jugular vein using the standard procedure of the breeding program, which was approved by the Animal Welfare Committee of China Agricultural University (GB/T 17236–2008).A total of 884 Large White pigs from the nucleus pig breeding farm of Beijing Shunxin Agriculture Co., Ltd . The quality and quantity of the DNA samples were measured with a NanoDrop™ 2000 . All DNA samples were eligible for genotyping with a ratio of light absorption (A260/280) between 1.8 and 2.0, a concentration >50 ng/μL, and total volume <50 μL. Genotyping was conducted using the Porcine SNP80 BeadChip  which contained 68,528 SNPs across 18 autosomes and two sex chromosomes. Quality control of the genotype data was carried out using Plink software  of the SNP ; f is the regression coefficient of phenotypes on SNP genotypes; u is the vector of residual polygenetic effects with G is the genomic relationship matrix that was constructed through SNP markers . Genomic inflation factor λ was calculated to judge the extent of false positive signals with the estlambda function in the GenABEL packages in R.The Manhattan plots and quantile-quantile (QQ) plots were drawn by R packages . The annotations of genes were carried out using the NCBI database (https://www.ncbi.nlm.nih.gov/) based on the description of gene function and related literatures, simultaneously, gene ontology (GO) analysis was conducted using the DAVID Bioinformatics Resources (https://david.ncifcrf.gov) for PU and FI, respectively. The Fisher's exact test was used to assess the significance of the enriched terms  the identified significant SNPs were selected based on the swine genome assembly 10.2  and upstream (390 Kb) of this region, respectively. As for PU, however, relatively few significant SNPs generated haplotype blocks. Two significant SNPs were situated in an 18 Kb block on SSC3  of the region, and GTF2I gene located upstream (8 Kb) of the region.In our study of FI, we found five significant SNPs located on SSC14, and detected a haplotype block (132.8–132.97 Mb)  with biological functions such as prenatal skeletal muscle development, fetal energy substrate, pre-implantation, and the expression of mammary gland epithelium were selected as promising candidates for swine reproductive traits  the identified significant SNPs were obtained based on the swine genome assembly 10.2 Table . These gBoth PU and FI are important indices that can be used to evaluate pig reproductive ability and play crucial roles in production efficiency and economic profits. Thus, it is essential to understand the genetic mechanisms of reproductive performance for future pig breeding programs. GWAS provides an efficient way to detect potential genetic variation and candidate genes in domestic animals   (Rohrer et al., EMP2 gene. To summarize, our findings confirmed the importance of these two QTL.Five significant SNPs associated with PU are located on eight QTL regions for reproductive traits, including teat number (Ding et al., Our findings provide knowledge on genomic variation and candidate genes that are involved in the genetic mechanisms of PU and FI. The SNPs that are associated with additive genetic variability at different parities can be used as fundamental information in marker assisted selection or genomic selection, which will be helpful in improving pig breeding programs.https://figshare.com/articles/Genome-wide_Association_Study_of_Piglet_Uniformity_and_Farrowing_Interval/5594230.The genotype and phenotype data of the samples used in the present study are available from the FigShare Repository: CW and DS designed and supervised the study. YW contributed to genomic DNA extraction and conducted GWAS analysis, XD contributed to population construction and statistical analysis with help from CN, and ZT. KX, ZT, TY, and YP contributed to genomic DNA extraction, phenotypes collection, and samples collection. YW drafted the manuscript, which was critically a remarks by XD, DS, and CW. 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.


Plasmodium knowlesi is a significant cause of human malaria transmitted as a zoonosis from macaque reservoir hosts in South‐East Asia. Microsatellite genotyping has indicated that human infections in Malaysian Borneo are an admixture of two highly divergent sympatric parasite subpopulations that are, respectively, associated with long‐tailed macaques (Cluster 1) and pig‐tailed macaques (Cluster 2). Whole‐genome sequences of clinical isolates subsequently confirmed the separate clusters, although fewer of the less common Cluster 2 type were sequenced. Here, to analyse population structure and genomic divergence in subpopulation samples of comparable depth, genome sequences were generated from 21 new clinical infections identified as Cluster 2 by microsatellite analysis, yielding a cumulative sample size for this subpopulation similar to that for Cluster 1. Profound heterogeneity in the level of intercluster divergence was distributed across the genome, with long contiguous chromosomal blocks having high or low divergence. Different mitochondrial genome clades were associated with the two major subpopulations, but limited exchange of haplotypes from one to the other was evident, as was also the case for the maternally inherited apicoplast genome. These findings indicate deep divergence of the two sympatric P. knowlesi subpopulations, with introgression likely to have occurred recently. There is no evidence yet of specific adaptation at any introgressed locus, but the recombinant mosaic types offer enhanced diversity on which selection may operate in a currently changing landscape and human environment. Loci responsible for maintaining genetic isolation of the sympatric subpopulations need to be identified in the chromosomal regions showing fixed differences. Although long known as a malaria parasite of long‐tailed and pig‐tailed macaques that could potentially infect humans  on the mainland of South‐East Asia which includes Peninsular Malaysia , yielding results indicating that this has undergone long‐term population growth, with additional evidence of selection on particular loci , and all infections were confirmed to contain only P. knowlesi by nested PCR assays testing for all locally known malaria parasite species . All libraries were then normalized to 4 nm, and up to 12 samples were included on each sequencing run. Paired‐end whole‐genome sequencing was performed on pooled DNA libraries using MiSeq Chemistry version 3 reagents, on the MiSeq platform  with a read length of 300 bp. Raw data of short reads generated in FASTQ format were undergone for quality check using the trimmomatic software . Physical shearing of the genomic DNA into fragments having an average size of 550 bp was performed using a M220 Focused‐ultrasonicator . After denaturation at 95°C for 3 min, amplification of genomic DNA was performed with low number of PCR cycles  followed by a 72°C completion for 5 min. The quality of DNA libraries was assessed using the Agilent High Sensitivity DNA kit , while quantitation was performed using the KAPA Library Quantification Kit for IlluminaP. knowlesi strain H reference genome  using the burrows‐wheeler aligner software version 0.7 with the BWA‐MEM algorithm and default parameters . The average depth coverage was analysed by the bedtools version 2 package using the “genomeCoverageBed” command  was extracted from the resulting variant call format (VCF) file, and a list of unique SNP lists was generated by concatenating all variant positions from all isolates. Using these unique SNP positions, the mapping quality (mq) and base quality (bq) were checked for each isolate to remove positions with an excess of low‐quality reads with the requirement of the minimum read depth coverage at 10x. The ratio of read depth values at high‐quality  and low‐quality  thresholds were calculated for each isolate using customized Perl scripts, and any SNP positions with the ratio below 0.5 were discarded.The calling of high‐quality single nucleotide polymorphisms (SNPs) was performed using several steps, following procedures described previously , with the boundaries of subtelomeric regions defined as sequences adjacent to the first conserved protein‐coding gene  in the reference genome. The ne Table . After e2.4D index. Both indices were calculated using the same genome‐wide SNP data set in nonoverlapping window sizes of 10 kb and performed using the divstat software  among the sequences from the individual infection samples was calculated. The skewness in allele frequency distributions was estimated by Tajima's FST) between the two‐subpopulation clusters was computed with SNPs having minor allele frequencies (MAFs) above 0.1, and above 0.3, using customized R functions. An elevated FST threshold was set at the 90th percentile of the FST distributions for all SNPs. Average FST values were calculated in windows of 500 SNPs with sliding by 250 SNPs. The FST values for each window were tested for high‐ or low‐differentiated regions against the genome‐wide mean FST value.To estimate the divergence between the subpopulations, the genome‐wide distribution of the fixation index (FST distribution across the genome at two different MAFs (MAF above 0.1 and 0.3). For each MAF analysis, average FST values were calculated in windows of 200 SNPs (sliding by 100 SNPs), 500 SNPs (sliding by 250 SNPs) and 1,000 SNPs (sliding by 500 SNPs). Mean global FST values and window FST values were then converted into standard z‐scores in order to standardize the definition of outlier windows for different parameters. Regions of high‐ or low‐FST windows were observed and compared among the analyses that used different MAF parameters.Genomic regions with contrasting levels of intercluster divergence were determined empirically by examining the z‐scores < −0.5), intermediate divergence regions (IDR), and high divergence regions . To determine the contiguous extent of these regions in detail, adjacent outlier windows were merged to form larger adjoining regions. Peak and trough patterns of window z‐scores around the thresholds (z‐scores < −0.5 and z‐scores > 0.5) were taken into consideration in determining the junctions. Each candidate region was demarcated by first and last SNPs that fell within the merged windows, except for HDRs where SNPs with elevated FST values were used as start and endpoints.Genomic regions were categorized into low divergence regions  in all genomic regions were evaluated using divstat software. Test runs were performed in nonoverlapping window sizes of 10 kb for each subpopulation. Nonparametric Kruskal–Wallis tests were used to compare among the genomic regions as well as against the genome‐wide background.Patterns of polymorphisms  and 20 haplotypes from macaque isolates  in Kapit of Malaysian Borneo, and one human isolate from Thailand (AY598141). Three species, P. coatneyi (AB354575), P. cynomolgi (AB434919) and P. vivax (AY791551), that have close evolutionary relationships with P. knowlesi were included in the analysis as out‐groups. For the apicoplast genome of P. knowlesi, 30.6 kb of the DNA sequences that had clear alignment was extracted from the present whole‐genome data set as well as from previous data  was determined using the dnasp version 5 software , yielded a mean of 6.95 million high‐quality reads per sample, which were mapped against the P. knowlesi H strain version 2.0 reference genome sequence  than the Cluster 1 infections (π = 5.78 × 10−3). Furthermore, the Cluster 1 subpopulation demonstrated a homogenous pattern of sequence diversity across the 14 chromosomes , in contrast with Cluster 2 that showed heterogeneous levels of diversity across the chromosomes   to 4.38 × 10−3 (for chromosome 5), but all had a lower diversity than in Cluster 1 (Wilcoxon signed rank p < 10−16). In a majority of nonoverlapping 10‐kb windows genome‐wide, nucleotide diversity (π) indices were lower in Cluster 2  Figure . In Clus3.3FST = 0.25; Figure FST values was bimodal, one peak having values just above zero and a second peak having values at or approaching 1.0  showed complete fixation of alternative alleles (FST = 1.0). A large proportion of low FST values were removed when analysis focused on SNPs with overall allele frequencies of >0.3  to 0.40 for (chromosome 7).The genome‐wide variation in diversity in Cluster 2 suggested that there might be variation in levels of intercluster divergence. Analysing SNPs with overall minor allele frequencies above 10% , the mean genome‐wide fixation index indicated substantial divergence between the two subpopulations . Genomic regions were identified that contained contiguous windows defining low divergence regions (LDR with z‐score < −0.5) and high divergence regions (HDR with z‐score > 0.5). This revealed large genomic blocks of high or low divergence . Most of the highly differentiated regions were those in which nucleotide diversity was substantially lower in Cluster 2  in Cluster 2 across the genome Figure c was inv2 Figure .−3; Mann–Whitney p < 2.2 × 10−16), and not in Cluster 1 . Similarly, higher nucleotide diversity in LDRs compared to the rest of the genome was seen specifically within Cluster 2 (Mann–Whitney p = 2.2 × 10−16), and not in Cluster 1 (Mann–Whitney p = .77).Reduced nucleotide diversity in HDRs compared to the rest of the genome was specifically seen in Cluster 2 . Across all 10‐kb windows in the genome, there was a weak but highly significant correlation in the distribution of Tajima's D values in the two clusters . The allele frequency spectrum as summarized by Tajima's D index was less variable across the 14 chromosomes within the Cluster 1 subpopulation  compared to the Cluster 2 subpopulation  , suggesting it is likely to be under balancing selection in both. Interestingly, the ama1 apical membrane antigen gene that did not have a high value in Cluster 1 had an exceptionally high value in Cluster 2 here (D = 1.64). The csp circumsporozoite protein gene, that had the highest Tajima's D value of all genes in Cluster 1, did not have any detected nonrepeat sequence SNPs in Cluster 2. Thus, although an unbiased comparison cannot be straightforwardly performed, these examples indicate that there are some similarities as well as differences in the strength or targets of balancing selection on antigens in the two different parasite subpopulations.The mosaic pattern of genomic diversity in the Cluster 2 subpopulation suggests that a genome‐wide scan to identify individual genes with exceptionally high values of Tajima's 3.5−4, with higher values in samples from parasites in Cluster 1  than in Cluster 2 . The genealogical network of mitochondrial genomes contained 56 different haplotypes  of 7.9 × 10s Figure .−3, and this was higher among the Cluster 1 samples (π = 1.77 × 10−3) than Cluster 2 samples (π = 1.12 × 10−3). Two major lineages were seen, one of which consisted predominantly of Cluster 1 samples, and the other mainly of Cluster 2 samples  was 1.79 × 10s Figure , althoug4P. knowlesi has clearly revealed the genome‐wide patterns of divergence between the sympatric subpopulations, which illuminates aspects of their population history and is essential for understanding their adaptive potential. This provides the most informative overall analysis of population structure of P. knowlesi to date, extending the understanding of defined subpopulation clusters that were previously described (Assefa et al., P. knowlesi subpopulations in Malaysian Borneo, supporting the occurrence of independent zoonotic cycles associated with different macaque reservoir host species (Divis et al., This study analyses the largest ecological sample of sequences representing different subpopulations of a zoonotic eukaryotic parasite species. Whole‐genome sequencing of new samples from one of the major genetic subpopulations of Plasmodium are inherited together through the female parasite gamete in each transmission cycle (Lim & McFadden, P. knowlesi subpopulations. The mosaic pattern with adjacent large regions of alternating high and low diversity in the genome sequences of the Cluster 2 subpopulation, in contrast to the more consistent high diversity throughout the genome for the Cluster 1 subpopulation, suggests that introgression has probably occurred recently from Cluster 1 into the Cluster 2 population.The high differentiation between these two sympatric subpopulations indicates minimal or no ongoing gene flow occurring between them, and a large number of SNPs showed complete fixation of alternative alleles. However, the pattern of divergence was heterogeneous and bimodally distributed, with large regions of exceptionally high or low divergence interspersed throughout the genome. Reduced genetic diversity of the Cluster 2 subpopulation in highly diverged regions suggests there may have been an initial bottleneck in the formation of this subpopulation. The overall allele frequency spectra were negativly skewed for both subpopulations, signifying long‐term population growth, although this was more extreme for the Cluster 2 subpopulation. This gives a more detailed perspective than that previously obtained by analysis of mitochondrial genome sequences, which had already indicated a historical population expansion (Lee et al., P. knowlesi infections have been associated with a wide spectrum of disease (Cox‐Singh et al., Despite the differences at the genomic level, it is not yet known whether these two major sympatric subpopulations exhibit significant phenotypic differences, apart from the previously described association with different macaque reservoir host species (Divis et al., P. knowlesi infections in an area of Sabah state within Malaysian Borneo (Fornace et al., P. knowlesi subpopulations over time, and potential changes in their genetic composition. Sequencing of P. knowlesi genomes from natural macaque infections would be more challenging, given that these are usually coinfections together with other primate malaria parasite species (Lee et al., A recent study suggests a link between local deforestation and incidence of The genome‐wide mosaicism, showing bimodal levels of divergence as well as limited discordant occurrence of extra‐chromosomal genome lineages, indicate that introgression is likely to have occurred recently between these parasite subpopulations. The recombinant genomes that are now circulating offer a great diversity on which selection may operate, but there is no evidence yet of specific adaptation at introgressed loci. A recent re‐analysis of previously published data identified a common shared haplotype in a chromosomal region with low divergence between the subpopulations (Diez Benavente et al., Anopheles cracens (Amir, Sum, Lau, Vythilingam, & Fong, In contrast, it is likely that at least one of the chromosomal regions showing fixed differences between the clusters contains a locus responsible for maintaining genetic isolation of the sympatric subpopulations, potentially due to transmission in different mosquito vectors, as well as likely adaptation to the different reservoir macaque hosts. Parasites from these sympatric subpopulations have not yet been studied in laboratory infections or adapted to culture, which will be necessary to define phenotypes and enable experimental analyses of differences between them. Despite major technical challenges of such work, efforts should prove worthwhile, as they are likely to reveal parasite phenotypes not present in the old laboratory lines which were sampled from a different part of the parasite species range (Dankwa et al., Paired‐end short read genome sequence data for the new parasite infection isolates listed in Table P.C.S.D., B.S. and D.J.C. conceived and designed the study. K.A.K. and B.S. collected and prepared the samples. P.C.S.D. conducted the genome sequencing, bioinformatic S.N.P. calling and nucleotide data deposition. P.C.S.D., C.W.D. and D.J.C. performed data analysis and interpretation. P.C.S.D. and D.J.C. wrote the manuscript, with input from all authors. Click here for additional data file.


Background: It is widely accepted that cleft lip with or without cleft palate (CL/P) results from the complex interplay between multiple genetic and environmental factors. However, a robust investigation of these gene-environment (GxE) interactions at a genome-wide level is still lacking for isolated CL/P.Materials and Methods: We used our R-package Haplin to perform a genome-wide search for GxE effects in isolated CL/P. From a previously published GWAS, genotypes and information on maternal periconceptional cigarette smoking, alcohol intake, and vitamin use were available on 1908 isolated CL/P triads of predominantly European or Asian ancestry. A GxE effect is present if the relative risk estimates for gene-effects in the offspring are different across exposure strata. We tested this using the relative risk ratio (RRR). Besides analyzing all ethnicities combined , separate analyses were conducted on Europeans and Asians to investigate ethnicity-specific effects. To control for multiple testing, q-values were calculated from the p-values.Results: We identified significant GxVitamin interactions with three SNPs in “Estrogen-related receptor gamma” (ESRRG) in the pooled analysis. The RRRs  were 0.56 (0.45–0.69) with rs1339221 (q = 0.011), 0.57 (0.46–0.70) with rs11117745 (q = 0.011), and 0.62 (0.50–0.76) with rs2099557 (q = 0.037). The associations were stronger when these SNPs were analyzed as haplotypes composed of two-SNP and three-SNP combinations. The strongest effect was with the “t-t-t” haplotype of the rs1339221-rs11117745-rs2099557 combination [RRR = 0.50 (0.40–0.64)], suggesting that the effects observed with the other SNP combinations, including those in the single-SNP analyses, were mainly driven by this haplotype. Although there were potential GxVitamin effects with rs17734557 and rs1316471 and GxAlcohol effects with rs9653456 and rs921876 in the European sample, respectively, none of the SNPs was located in or near genes with strong links to orofacial clefts. GxAlcohol and GxSmoke effects were not assessed in the Asian sample because of a lack of observations for these exposures.Discussion/Conclusion: We identified significant interactions between vitamin use and variants in ESRRG in the pooled analysis. These GxE effects are novel and warrant further investigations to elucidate their roles in orofacial clefting. If validated, they could provide prospects for exploring the impact of estrogens and vitamins on clefting, with potential translational applications. With a prevalence of 3.4–22.9 per 10,000 live births  have now been published on orofacial clefts  under study accession ID phs000094.v1.p1. Besides genotypes, information was also available on maternal cigarette smoking, alcohol intake, and vitamin use in the periconceptional period . Interviews and questionnaires were used to assess maternal exposures and the data were coded as simple yes/no responses for cigarette smoking, any reported alcohol consumption, and any use of vitamin supplements. For a detailed description of these maternal exposures, see the recent work on isolated CPO by Wu et al.  algorithm , Europeans (n = 825), and all participants combined irrespective of their ethnicity . The different sets of analyses are illustrated in Figure If an offspring was homozygous for the risk allele at a locus, we assumed a multiplicative dose-response model. In other words, the relative risk with two copies of the risk allele was assumed to be simply RR × RR. For each maternal exposure , we performed separate analyses on Asians n = ,024, Eurq-values. This was done using the haplinSlide function in Haplin, as described in our previous works on candidate genes for clefts  plots . This plot provides a visual display of linkage disequilibrium between the lead SNP and other SNPs, along with their positions. It also provides the position of the closest genes and the recombination rates in the region. We generated the plot by modifying an R-script available at https://neo4j.het.io/). For example, to explore possible links between a given disease, compounds and gene nodes, Hetionet uses the following databases: Disease Ontology  or Bgee  under study accession number phs000094.v1.p1.Ethics approvals for the International Cleft Consortium were obtained from the respective institutional review boards of the participating sites. The consortium was formed in 2007 and each participating institution approved research protocols for the recruitment of case-parent triads from 13 individual sites. All participants have granted their written informed consents. The participating sites included institutions in the US ; University of Pittsburgh), Denmark (University of Southern Denmark), Norway (University of Bergen), China , Korea (Yonsei University), Taiwan , and Singapore . For more details on the recruitment sites, the research approvals and protocols, see the online “Supplementary Note” of the original publication  in the QQ-plots are expected to fall along the straight diagonal line representing the null distribution, within the 95% confidence band (gray area). Conversely, –log10  would fall above this line, outside the confidence band and in the upper right-hand corner of the plot. Several SNPs were significantly associated with isolated CL/P in the GxVitamin analyses of the pooled sample . These SNPs are in strong linkage disequilibrium (LD) with one another (R2 = 0.77 and D' = 1.00). A third SNP, rs2099557 (q = 0.037), is also located in ESRRG but is in weaker LD with the above two SNPs . A regional plot centered around the lead SNP in ESRRG (rs1339221) was generated to visualize the strength of the association signals and regional information around that SNP . RRRs and 95% CIs were 0.56 (0.45–0.69) with the variant allele at rs1339221 (q = 0.011), 0.57 (0.46–0.70) with the variant allele at rs11117745 (q = 0.011), and 0.62 (0.50–0.76) with the variant allele at rs2099557 (q = 0.037)  above, suggesting that the observed effects with SNP combinations (i) and (ii), as well as those observed in the single-SNP analyses, are likely to be driven by this haplotype.Next, we considered haplotypes of the top three SNPs in RG Table  and analRG Table . The patrs Table . The RRsin silico analyses using Hetionet revealed that the cleft lip phenotype is influenced by vitamins A and D through a network of genes connected to ESRRG , few of the genes in the network have previously been linked to orofacial clefts. Figure ESRRG is expressed in three major tissues/organs and that the cleft lip phenotype is localized to the embryo, head, and telencephalon (the most highly developed and anterior part of the forebrain). ESRRG regulates or interacts with various genes that are regulated by the levels of vitamin A and D. Note that several genes are up- or down-regulated in the telencephalon  and CPO (~10%) . ERRγ itself does not appear to be important for skeletal development, but it is a sex-dependent negative regulator of postnatal bone formation  signaling pathway. Compared to PDGFA, PDGFC has a well-substantiated role in palatogenesis  exhibit a complete cleft of the secondary palate  would be interested in replicating these findings.ESRRG and vitamin use in the pooled analysis. Our in silico analyses revealed an intricate network of genes linking cleft lip, ESRRG and two vitamins in particular: vitamins A and D. These GxE effects are novel and warrant further investigations to unravel the potential interplay between vitamins and ESRRG variants. If confirmed in other cleft samples, they could provide prospects for exploring the impact of estrogens and vitamins on orofacial clefts, with potential translational applications.To summarize, we identified significant interactions between variants in AJ, HG, RL, ØH, MG, and JR: Conception of the work, study design, data analysis and interpretation, manuscript preparation and final manuscript approval; HG, MG, JR, and ØH: Statistical modeling and software design; AJ, HG, and RL: Funding acquisition.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.


Cyprinus carpio) being one of the most valuable species of the group. The use of modern selective breeding methods in carp is at a formative stage, implying a large scope for genetic improvement of key production traits. In the current study, a population of 1,425 carp juveniles, originating from a partial factorial cross between 40 sires and 20 dams, was used for investigating the potential of genomic selection (GS) for juvenile growth, an exemplar polygenic production trait. RAD sequencing was used to identify and genotype SNP markers for subsequent parentage assignment, construction of a medium density genetic map , genome-wide association study (GWAS), and testing of GS. A moderate heritability was estimated for body length of carp at 120 days (as a proxy of juvenile growth) of 0.33 (s.e. 0.05). No genome-wide significant QTL was identified using a single marker GWAS approach. Genomic prediction of breeding values outperformed pedigree-based prediction, resulting in 18% improvement in prediction accuracy. The impact of reduced SNP densities on prediction accuracy was tested by varying minor allele frequency (MAF) thresholds, with no drop in prediction accuracy until the MAF threshold is set <0.3 . These results point to the potential for GS to improve economically important traits in common carp breeding programs.Cyprinids are the most important group of farmed fish globally in terms of production volume, with common carp ( Carps have the highest global production volume of all aquaculture fish  and are Traditional pedigree-based selective breeding incorporating best linear unbiased predictor (BLUP) methodology  has greaThe effectiveness of GS at deriving more accurate breeding values than traditional pedigree-based selection has been demonstrated using simulated and empirical data in both livestock and aquaculture . EmpiricRestriction-site-associated DNA sequencing (RAD-seq) is a reduced representation of high-throughput sequencing technique for the concurrent detection and genotyping of SNP markers . RAD-seqA population of Amur Mirror Carp was created at the University of South Bohemia, Czech Republic in May 2014 using artificial insemination  involvinGenomic DNA was extracted from fin samples using the REALPure genomic DNA extraction kit (Durviz S.L.) and treated with RNase. Each sample was quantified by spectrophotometry (Nanodrop), and its quality was assessed by agarose gel electrophoresis, before being diluted to a concentration of 20 ng/μL [measured by Qubit Fluorometer (Invitrogen)] in 5 mmol/L Tris, pH 8.5.SbfI (recognizing the CCTGCA| GG motif) high fidelity restriction enzyme . The reactions  were then heat inactivated at 65°C for 20 min. Individual-specific P1 adapters, each with a unique 5 bp barcode, were ligated to the SbfI-digested DNA at 20°C for 60 min by adding 1.8/0.6 μL 100 nmol/L P1 adapter, 0.45/0.15 μL 100 mmol/L rATP (Promega), 0.75/0.25 μL 10× Reaction Buffer 2 (NEB), 0.36/0.12 μL T4 ligase , and reaction volumes made up to 45/15 μL with nuclease-free water for each parental/offspring sample. Following heat inactivation at 65°C for 20 min, the ligation reactions were slowly cooled to room temperature (over 1 h) then combined in appropriate multiplex pools. Shearing and initial size selection (300–600 bp) by agarose gel separation were followed by gel purification, end repair, dA overhang addition, P2  paired-end adapter ligation, library amplification, as described in the original RAD protocol  were discarded.Reads missing the restriction site, with ambiguous barcodes and PCR duplicates, were identified and discarded using the Stacks software 1.4 . The rem951615.2  using bo951615.2 . The ali951615.2 . A minimDue to the partial factorial crosses performed in this experiment, the family structure of the offspring was unknown at the time of sampling. Parentage assignment was performed using the RAD-seq-derived SNP data and the R/hsphase  softwareP < 0.001) were excluded. Linkage groups were formed using a minimum LOD threshold value of 18 in the “SeparateChromosomes” module, allowing a maximum distance between consecutive SNPs of 50 cM. Marker order within each linkage group was performed using the “OrderMarkers” module using the SexAverage option. Map distances were calculated in centiMorgans (cM) using the Kosambi mapping function.Linkage map construction was performed using Lep-Map v2 . SNPs wiHeritability estimates of weight and length were performed using both the pedigree-based relationship matrix and the genomic relationship matrix. Variance components was estimated using AIREMLF90  with theN with A corresponding to the pedigree-based relationship matrix or the genomic relationship matrix G  and σe2 the residual variance.where y is the vector of recorded phenotypes, b vector of the fixed effects , X the incidence matrix relating phenotypes with the fixed effects, Z the incidence matrix relating phenotypes with the random animal effects, u the vector of random animal effects ∼matrix G , σg2 theHeritability was estimated using the following formula:Bivariate models with the same fixed and random effects as in Equation (1) were used in order to estimate genetic correlations between the phenotypes of weight and length.N), where N represents the number of QC-filtered SNPs.To test the association between individual SNPs and growth (only length records were utilized), GWAS was performed using R/gaston . The mixThe accuracy of prediction of genomic breeding values (GEBVs) was calculated and benchmarked against the accuracy of prediction for EBVs using traditional pedigree-based best linear unbiased prediction (BLUP) . GEBVs wn = 972 individuals) and validation sets (n = 242). The fivefold cross-validation procedure was repeated 10 times in order to reduce random sampling effects. The accuracy of the estimated breeding values was approximated as:A fivefold cross-validation was performed in order to test prediction accuracy. The data set was randomly split into sequential non-overlapping training , 0.2 , 0.3 , 0.4 , and 0.45 (530 SNPs). Bias in the form of the regression coefficient of the phenotypic trait on (G)EBV was estimated for both PBLUP and all tested scenarios of GBLUP.where y is the vector of recorded phenotypes and Supplementary Table S1) was 16.3 g (SD 4.6) and the mean SL was 77 mm (SD 7.1). The Pearson correlation coefficient between length and weight was r = 0.93 .The mean weight of the genotyped carp juveniles after approximately 4 months of growth (Supplementary Table S2).Animals with fewer than 25% missing SNP genotypes were retained for downstream analysis . The total number of raw reads passing the QC filters was 6.89 (SD 1.33) M for the parental samples and 3.68 (SD 1.28) M for the offspring. The mean number of RAD loci identified was 57,983  and 57,235  for parents and offspring, respectively, with mean coverage of 60 (SD 10) X and 29 (SD 8) X, respectively. A total of 22,756 putative SNPs were identified, of which 20,039 SNPs passed QC filters and were retained for downstream analysis  ranging from 1 to 21 animals per family with a mean size of 6 (SD 4). The individual dam contribution to the population ranged from 9 to 99 animals with a mean of 61 (SD 23), while the sire contribution ranged from 7 to 53 animals with a mean of 30 (SD 12).Table 1) that were grouped into 50 linkage groups (in accordance with the expected karyotype). The length of the consensus linkage map was 3,944 cM (Supplementary Table S3). The number of SNPs per chromosome ranged from 157 to 350 , while linkage group length ranged between 67 and 106 cM .The linkage map constructed using the aforementioned families consisted of 12,311 SNPs (The estimated heritabilities for weight and length were 0.26 (SE 0.05) and 0.33 (SE 0.05), respectively, and were consistent between the pedigree and genomic models. Genetic correlation between body weight and SL was 0.94 (SE 0.02), and as such only length data were used for downstream analyses.Figure 2), indicating that juvenile growth is likely to be a polygenic trait. Using the cross-validation approach, breeding value prediction accuracy was estimated to be 0.60 (SE 0.03) for PBLUP, as opposed to 0.71 (SE 0.03) with GBLUP. Accuracies obtained through GBLUP using the various reduced SNP densities ranged from 0.66 to 0.71 . GBLUP using only SNPs with a minimum MAF of 0.45 (530 SNPs) had approximately a 10% accuracy improvement compared to PBLUP. SNP densities of a minimum MAF of 0.3 had practically the same prediction accuracies as the full data set . Estimated bias of (G)EBVs ranged between 0.78 and 1.02. The scenario using SNPs with minimum MAF of 0.45 was found to produce the most biased GEBVs (Table 2).Genome-wide association study and GS were performed using only the SL data. GWAS did not identify SNPs surpassing the genome-wide significant threshold  genotype data, especially considering the large genome size (∼1.8 Gb) of common carp. Similar results were recorded for Atlantic salmon where similar prediction accuracies were obtained from 5K SNPs as for 112K SNPs . Both coThe results from the current study demonstrated that the use of SNP markers generated via RAD-seq is an efficient approach for investigating and potentially improving a polygenic trait in a common carp breeding population. These SNPs enabled pedigree assignment, genetic parameter estimation, GWAS, and GS within a single experiment. GS resulted in improved prediction accuracy versus pedigree approaches even when only relatively sparse marker information was utilized.MK, MP, and RH conceived the study and contributed to designing the experimental structure. MK and MP shared on establishing and on-growing the experimental stock, PIT tagging, phenotype data recording, and fin clipping the fish. CP carried out the DNA extractions, RAD library preparation, and sequence data processing. CP and RH carried out parentage assignment and the quantitative genetic analyses. All authors contributed to drafting 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.


Sickle cell anemia causes severe complications and premature death. Five common β-globin gene cluster haplotypes are each associated with characteristic fetal hemoglobin (HbF) levels. As HbF is the major modulator of disease severity, classifying patients according to haplotype is useful. The first method of haplotype classification used restriction fragment length polymorphisms (RFLPs) to detect single nucleotide polymorphisms (SNPs) in the β-globin gene cluster. This is labor intensive, and error prone.We used genome-wide SNP data imputed to the 1000 Genomes reference panel to obtain phased data distinguishing parental alleles.We successfully haplotyped 813 sickle cell anemia patients previously classified by RFLPs with a concordance >98%. Four SNPs  marking four different restriction enzyme sites unequivocally defined most haplotypes. We were able to assign a haplotype to 86% of samples that were either partially or misclassified using RFLPs.Phased data using only four SNPs allowed unequivocal assignment of a haplotype that was not always possible using a larger number of RFLPs. Given the availability of genome-wide SNP data, our method is rapid and does not require high computational resources. HBB) cluster are associated with different levels of HbF . , 6. 5, 6Haplotypes among 55 Southwestern Province patients classified using the RFLP method included 39 BEN/BEN, 11 CAR/CAR, 2 BEN/SEN, 1 BEN/CAR, 1 SEN/SEN and one unknown. The distribution of haplotypes for these subjects derived using the SNP method was 48 BEN/BEN, 3 BEN/UNKNOWN, 2 BEN/CAM, and one AI/AI  between 4-SNP haplotypes derived from a phasing algorithm using GWAS data with 5-SNP haplotypes determined using restriction analysis. Haplotype assignment in a sickle cell anemia iPSC library also showed high concordance and demonstrated the efficiency of SNP-based method to classify samples that failed RFLP classification. The discordance between SNP-based and RFLP ascertainment most likely resulted from errors in the RFLP classification that is sensitive to the presence of other SNPs in the restriction sites and the vagaries of restriction enzyme analysis and Southern blotting that was used for haplotype analysis in the CSSCD.HBG2), rs3834466 (Hinc2 5′ to HBE1), and rs549964658 (5′ to HBD) we had 100% concordance.In 30 Saudi East patients where the AI haplotype was ascertained by genotyping rs7482144  and upstream of OR51B4 (3′ olfactory gene cluster) in both patient groups. The SNP genotypes of the 11 CAR/CAR that we reclassified as BEN/BEN or BEN heterozygous had the same SNP genotype of BEN/BEN patients that were classified as such with both methods. The genotype data, average HbF, and the imputation quality score of rs968857 suggest that the high discordance in Southwestern Saudi patients is due to RFLP errors.To investigate the discordance in Southwestern Province patients, we examined the genotype data of the A limitation of our method is the dependency on the availability of GWAS data for many SNPs in the β-globin gene region. However, many large patient cohorts have been genotyped using genome-wide SNP arrays. In these patients, haplotype information might be useful as a covariate in a genetic risk analysis. RFLP analysis might be suitable for a small number of patients but requires optimization of all of the individual assays. The main advantage of our haplotype determination method is the rapid classification and high accuracy. This method can also be used for whole genome sequence data classification after SNP calling and phasing. Moreover, it is not sensitive to SNPs that alter the restriction enzyme recognition sequence that can lead to error using RFLPs.Phased data using only four SNPs allowed unequivocal assignment of a β-globin gene cluster haplotype that was not always possible using a larger number of RFLPs, and was also more accurate. With the availability of genome-wide SNP data our method is rapid and does not require high computational resources.


We have developed a new 50k Illumina Infinium iSelect genotyping array for barley, a cereal crop species of major international importance. The majority of SNPs on the array have been extracted from variants called in exome capture data of a wide range of European barley germplasm. We used the recently published barley pseudomolecule assembly to map the exome capture data, which allowed us to generate markers with accurate physical positions and detailed gene annotation. Markers from an existing and widely used barley 9k Infinium iSelect array were carried over onto the 50k chip for backward compatibility. The array design featured 49,267 SNP markers that converted into 44,040 working assays, of which 43,461 were scorable in GenomeStudio. Of the working assays, 6,251 are from the 9k iSelect platform. We validated the SNPs by comparing the genotype calls from the new array to legacy datasets. Rates of agreement averaged 98.1 and 93.9% respectively for the legacy 9k iSelect SNP set (Comadran et al.,  Hordeum vulgare L.) is a cereal crop of major importance, ranked fourth grain crop in the world by the UN's Food and Agriculture Organization in terms of production volume (http://faostat.fao.org). Its major uses are as animal feed, for brewing and distilling, and to a minor extent, human nutrition.Barley  are today's marker of choice due to their abundance, relative ease of discovery in high-throughput sequencing data .High throughput genotyping in barley was first introduced in 2006 with the development of two Illumina GoldenGate assays  sequence data  × Morex (Mo) cross . DNA quality was assessed using Nanodrop 2000  with a requirement of >1.8 for 260/280 and 260/230 ratios. DNA was quantified using Picogreen . A total of 300 ng of lyophilised DNA per sample was sent to Geneseek  for Illumina HTS processing and HiScan chip imaging . Genotyping at TraitGenetics was performed in house using the same technology and scanner type. SNP alleles were called using GenomeStudio Genotyping Module v2.0.2 . A total of 792 samples from various sources were genotyped, of which 148 overlapped with the 170 exome captured samples used for variant discovery (section SNP discovery). Agreement rates were then computed for these 148 samples between their GenomeStudio calls and the genotypes called by the variant calling pipeline in the exome capture data, using the approach detailed in section 1.7 of the Supplementary Data. In addition, DNA of 136 FThe 6,951 markers from the 9k chip were mapped onto the 2017 pseudomolecules genome assembly to supplement their existing genetic map positions with physical positions. Command line BLASTN version 2.6.0+  spring 2-rowed types; (ii) spring 6-rowed types; (iii) winter 2-rowed types; (iv) winter 6-rowed types and (v) older European (mainly spring) types. From these groups, we used available background information, including pedigree, breeder and year of release, to select 148 representative accessions. We then added to these 148 lines another 22 old UK and Northern European landrace accessions without prior 9k iSelect SNP information, which made for a total of 170 lines (Table http://support.illumina.com/array/array_software/assay_design_tool.html) to prioritise candidates for inclusion on the array. In total, 295,642 SNPs passed the ADT criteria and a shortlist of SNPs to be included on the array was made on the basis of a trade-off between maximal sample representation and gene coverage. First, designable SNPs were filtered by including only those with ≤20% missing samples and a design score of ≥0.6, and removing duplicates based on association with genes (according to SnpEff annotation). This left 16,957 SNPs, representing single genes, ensuring maximum possible genic representation. Further SNPs were then added taking account of the relationship between the genetic and physical map in barley . After strict processing see  a total The genetic map positions of the 9k SNPs were plotted on a Morex × Barke RIL population against the calculated physical positions of these SNPs . These legacy markers were designed to keep the sequence orientation and allele calling identical to the 9k array to ensure direct comparability of the genotype data.n = 43,048) were then screened for potential overlaps with those from the 9k set using standalone command line BLASTN  were mappable to a position on the pseudomolecules. Of these, 4,500 73.8%) were validated by a corresponding SNP in the exome capture data. The physical positions for all SNPs on the chip are available in Supplementary Table .8% were The final set of 49,267 SNPs in the array design converted into 44,040 working assays on the chip itself (= 89.4% conversion rate). Of these, 98.6%  were scorable in GenomeStudio, while 579 were eliminated due to insufficient cluster cut-off or other scoring problems. For the 6,951 SNP markers carried forward from the 9k array for design, 700 did not produce a working assay and were hence excluded, leaving 6,251 working legacy 9k markers on the 50k chip . However, barley is an inbreeding crop and the lack of heterozygous alleles can frequently lead to miscalls. Manual cluster curation by visual verification was therefore carried out, enabling optimum cluster coverage for each SNP according to the allelic distribution within GenomeStudio. Following adjustment of the cluster file, the genotyped individuals were rescored, generating mean agreement rates of 98.1 and 93.9% for 9k and the original EC SNPs respectively  scored with the manually adjusted TraitGenetics cluster file fared substantially better at 98.5 and 93.3% for 9k and EXCAP SNPs respectively. This indicates that there is some variation in the cluster distribution generated by individual Illumina scanners. The implication of this is that sharing adjusted cluster files is a good start but might require further adjustments to the local equipment.To assess technical variation across equipment and sites, manual cluster adjustment was also carried out at TraitGenetics  on an independent set of 2,535 accessions, varieties and segregating material. We then scored the 148 samples genotyped at the James Hutton Institute, also with the manually adjusted TraitGenetics cluster file. This resulted in a decrease in mean agreement rates to 94.7 and 84.0% for 9k and EC respectively  RILs, along with the two parental lines, resulted in data for 44,040 SNPs in the population. Of these, 29,413 SNPs were either monomorphic or had scoring issues . The segregation patterns of the remaining 14,627 SNPs were analysed, and co-segregating SNPs were excluded from the dataset to increase computational efficiency of subsequent linkage mapping, leaving 1,157 SNPs with unique segregation patterns. The genotypic data indicated that four of the RILs were duplicates and two others had excessive levels of heterozygosity, suggesting recent outcrossing. These six lines were excluded and the remaining 130 RILs used to develop a genetic linkage map using JoinMap 4.0  and the percentage of polymorphic markers (97.3 vs. 94.7% respectively), although the latter figures need to be interpreted with caution as they are not based on the same set of lines in each case, and polymorphism rates will obviously vary with the types of samples assessed. The performance statistics are comparable to those reported in other recent crop genotyping platforms  and meaningful coverage across chromosomes. The pericentromeric regions of barley chromosomes are effectively devoid of recombination and hence any markers located in these regions are likely to be in linkage disequilibrium and consequently of little use to breeders. A two-step approach was used that first selected a single SNP per available gene, and then added to this a second set of SNPs that were preferentially drawn from the recombination-rich distal parts of chromosomes. This provides a SNP set that is both broad and even in terms of gene coverage, but also focused on SNPs that are as informative as possible.Use of the 50k chip on the bi-parental GP × Mo RIL population (Liu et al., The completeness and quality of the segregation data facilitated construction of a map with the majority of individual recombination events delineated by flanking SNP markers. Given the coverage of the genome of the chip, some of the 11 gaps >5 cM could be ascribed to regions of monomorphism due to identity-by-state, despite the genetic distance between the UK two-rowed and US six-rowed parental lines, or to regions of high recombination rate, in particular on the short arm of 5H flanking the presumed NOR region (Dubcovsky and Dvorák, ari-e.GP locus was well populated, with 43 new SNPs. This indicates that this is not a region of monomorphism as previously postulated (Liu et al., HvDEP1 (Wendt et al., Importantly, the SNP coverage of the genome represented on the 50k chip improved the coverage of the genetic map significantly compared to previous GBS work (Liu et al., The genetic map derived from the GP × Mo RIL population validated the strategy developed here and the quality of the information used to construct the 50k chip, as well as the quality of the barley physical map (Mascher et al., MB carried out the variant calling and downstream analysis of the exome capture data. PR carried out the benchmarking of the GATK components. MG and JP selected 9k markers for inclusion on the 50k chip and generated data for the chip validation. JR selected lines for inclusion in the data analysis and with PH and LR generated the strategy for inclusion of markers on the chip. MM genotyped the set of lines used for validation and generated the adjusted cluster file for GenomeStudio. LR carried out the stratification of markers across chromosomes, the comparison of physical vs. genetic positions of the 9k legacy SNPs, and the genetic mapping in the GPxMo RIL population. PS generated the SNP marker database and web interface. WT curated the set of lines used for validation. RW coordinated and supervised the work. All authors contributed to drafting the manuscript and/or helped revise the manuscript.MG and JP are affiliated with TraitGenetics GmbH, a commercial provider of genotyping services offering the platform described here. This does not alter the authors' adherence to all editorial policies on sharing data and materials. No conflicting interests are declared by any of the other authors.