Staphylococcus aureus. Four combinations  showed SN, SNP + TET showed PS and Indifferent effect (ID) were observed for SNP + NEO against Pseudomonas aeruginosa. SN was observed for SNP + CEF, SNP + GEN, SNP + NEO, SNP + TET and SNP + MUP showed ID, SNP + VAN showed PS against Escherichia coli. In addition, we elucidated the possible mechanism involved in the pharmacodynamic interaction between SNP-topical antibiotics by increased ROS level, membrane damage following protein release, K+ leakage and biofilm inhibition. Thus, our findings support that conjugation of the SNP with topical antibiotics have great potential in the topical formulation when treating complex resistant bacterial infections and where there is a need of more concentration to kill pathogenic bacteria.In order to exploit the potential benefits of antimicrobial combination therapy, we need a better understanding of the circumstances under which pharmacodynamic interactions expected. In this study, Pharmacodynamic interactions between silver nanoparticle (SNP) and topical antibiotics such as Cefazolin (CEF), Mupirocin (MUP), Gentamycin (GEN), Neomycin (NEO), Tetracycline (TET), Vancomycin (VAN) were investigated using the MIC test, Combination assay followed by Fractional Inhibitory concentration Index and Agar well diffusion method. SNP + MUP, SNP + NEO, SNP + VAN combinations showed Synergism (SN) and SNP + CEF, SNP + GEN, SNP + TET showed Partial synergism (PS) against  Pharmacodynamic interactions are those where the effects of one drug are changed by the presence of another drug at its site of action. An interaction is said to occur when the effects of one drug are altered by the co-administration of another drug, herbal medicine, food, drink or other environmental chemical agentsThe use of Silver nanoparticles can be exploited in various fields, particularly medical and pharmaceutical due to their low toxicity to human cells, high thermal stability and low volatility+ leakage and biofilm inhibition could allow for a more careful application of antibiotics that maintain the clinical capability and does not sacrifice the future usefulness of these drugs. To the best of the authors’ knowledge, this is the first report on the possible pharmacodynamic interaction of several topical antibiotics and SNPs.The present study aims to evaluate any pharmacodynamic interactions such as synergistic, additive, antagonistic effect of SNPs when conjugate with the commonly used topical deliverable antibiotics such as Cefazolin (CEF), Mupirocin (MUP), Gentamycin (GEN), Neomycin (NEO), Tetracycline (TET), Vancomycin (VAN) against major causes of wound, burn bacterial infections. In order to exploit the potential benefits of antimicrobial combination therapy, we need a better understanding of the circumstances under which pharmacodynamic interactions expected. In this study, we elucidated pharmacodynamic interactions by increased ROS level, membrane damage following protein release, KPhytophthora infestans. The results of the SNP synthesis and characterization were described in our previous studyThe current study utilizes the SNP synthesized biologically using potato plant pathogenic fungus Staphylococcus aureus ATCC 25922, Pseudomonas aeruginosa ATCC 25619, Escherichia coli ATCC 10536 was evaluated by MIC test  and Gentamycin, Tetracycline activity were also found to be similar level, 1.25 μg/ml against S. aureus ATCC 25922. Mupirocin, Vancomycin found to be more susceptible than other antibiotics tested and neomycin showed no activity against P. aeruginosa ATCC 25619. Cefazolin and Gentamycin showed similar activity, 0.3125 μg/ml followed by Tetracycline and Vancomycin, 0.625 μg/ml against E. coli ATCC 10536 but this strain showed resistant against Mupirocin.Antibacterial susceptibility pattern of SNP and various topical antibiotics against major causes of wound, burn bacterial infection such as MIC test . The MICS. aureus, P. aeruginosa, E. coli were shown in the S. aureus; SNP + MUP, SNP + NEO, SNP + VAN showed synergistic (SN) effect and partially synergism (PS) were observed with SNP + CEF, SNP + GEN and SNP + TET. Interestingly, four combinations  showed synergistic efficacy and one SNP + TET showed PS but the FICI of SNP + NEO were found to be 1.75 μg/ml showed Indifferent (ID) efficacy against P. aeruginosa. Similarly, four combinations such as SNP + CEF, SNP + GEN, SNP + TET, SNP + NEO showed SN efficacy, one combination (SNP + VAN) showed PS and SNP + MUP showed an ID effect against E. coli.Combination assay followed by FIC Index was measured to determine the pharmacodynamic interactions such as synergistic, partial synergistic and antagonistic or additive effects of SNP combined with topical antibiotics. Fractional Inhibitory concentration Index of SNPs and topical antibiotics against S. aureus and ZOI comparison was depicted in the S. aureus and highest ZOI 33, 32 and 31 mm were observed for the SNP + MUP, SNP + VAN and SNP + CEF respectively. In a case of individual or combinations treated against P. aeruginosa, Neomycin (concentration 2.5 μg/ml) alone showed no activity. However, SNP + NEO combination showed 21 mm ZOI and highest ZOI 35, 32 and 31 mm were observed for the SNP + MUP, SNP + GEN and SNP + VAN respectively. The inhibitory effect of individual substance and combination against P. aeruginosa were shown in the E. coli also showed resistance against Mupirocin (concentration 0.625 μg/ml) but the combination SNP + MUP showed 26 mm ZOI and other combinations such as SNP + NEO, SNP + GEN showed ZOI about 36, 33 mm respectively. The results of the individual substance and their combinations against E. coli were depicted in the Pharmacodynamic interactions of SNP combined with topical antibiotics were also evaluated by agar well plate method, Zone of inhibition (ZOI) of SNP or antibiotics alone and the combination were measured and compared with the control. Measurement of ROS generation was utilized to elucidate the pharmacodynamic interaction in this study since the elevated ROS formation leads to the imbalance redox system and can cause oxidative stress, the results of the comparative ROS formation assay were depicted in the + release was carried out to confirm bacterial membrane damage by individual substances and combination tested in this study. All individual substances, including SNP showed significant levels of intracellular K+ generation as compared to the negative and positive control against the strains S. aureus and P. aeruginosa  for the stain E. coli.Measurement of intracellular Kruginosa . But theE. coli followed by 40.33 ± 5.033 for P. aeruginosa and 34.33 ± 9.71 against the strain S. aureus. The inhibitory effect of combinations with antibiotics on the biofilm formation was compared with the SNP alone, the results depicted that all combinations tested showed significant antibiofilm activity (P < 0.05) except the SNP combined with Neomycin (SNP + NEO) when compared to the SNP alone. Interestingly, the equal inhibitory effect was observed for SNP + CEF and SNP + MUP combinations against the strain P. aeruginosa.Antibiofilm assay was performed for SNP alone and combined with topical deliverable antibiotics against three bacterial strains . The resP. aeruginosa cells were observed by SDS-PAGE assay. Lane 1, 10 was loaded with marker, 2 were loaded with SNP and 3, 5, 7, 9, 12 were loaded with individaul antibiotics, lane 4, 6, 8, 11, 13 were loaded with combinations. The results were visualized after SDS-PAGE and staining with coomassie as in P. aeruginosa cells.Membrane damage following intracellular protein caused by SNP, individual antibiotics and combination on P. aeruginosa cells were treated with SNP or combination and control for 4 hrs and visualized in the fluorescence microscopy. P. aeruginosa cells with a damaged membrane showing red color fluorescence that considered to be dead, whereas cells with an intact membrane showing green color fluorescence. The results of LIVE/DEAD BacLight bacterial viability were shown in the The discovery rate of new antibiotics is in decline, while antibiotic resistance in pathogens is rapidly increasing. Drug combinations offer potential strategies for controlling the evolution of drug resistanceS. aureus, P. aeruginosa, E. coli. Shrivastava et al.E. coli showed resistance against SNPS. aureus, Neomycin found to be not effective against P. aeruginosa and Mupiracin also not showed efficacy against E. coli strain. The previous studies stated that, antibacterial activities of few antibiotics were increased in the presence of SNPE. coliThis study elucidates for the first time, pharmacodynamic interactions of SNP – topically deliverable antibiotics against major causes of wound, burn bacterial infections. To examine susceptibility patterns of the SNP, topical antibiotics alone and conjugation with SNP, MIC test was performed against The current study evaluated the entire pharmacodynamic interaction between SNP and topical antibiotics, results of combined assay followed by FIC Index and agar well diffusion assay showed that various degrees of pharmacodynamic interactions such as SN, PS and ID against a common cause of infections in wound, burns. Interestingly, antagonistic (AN) effect was not observed among the combinations against all tested bacterial strains in this study. However, no report was found in the literature showing the antagonistic effect of SNP with antiinfectives. Our study results suggested that the SNP could be conjugated with an anti-infective agents having less or no efficacy, an agents need more concentration to kill pathogenic bacteria and to target both gram-positive/negative resistant bacterial strains causing complex infections.et al.et al.et al.S. aureus, P. aeruginosa, E. coli.In order to elucidate the pharmacodynamic interactions observed in this study, we conducted the comparative ROS formation assay, Comparative measurement of intracellular potassium release, anti-biofilm assay, SDS-PAGE and Microscopic comparison of live/dead cells. Kohanski M. A. et al. have shown that alginate, a polysaccharide of P. aeruginosa biolfim matrix has an important role in P. aeruginosa resistanceBiofilms are microbial communities consisting of cells attached to biotic or abiotic surfaces, embedded in an exopolymeric matrix. These structures are well known for their remarkable resistance to diverse chemical, physical, and biological antimicrobial agents and are one of the main causes of persistent infectionsThis study presented pharmacodynamic interaction between SNP and topical antibiotics such as synergism, partial synergism and indifferent effect against the major cause of wound infections. Moreover, we elucidated the possible mechanism involved in the pharmacodynamic interaction caused by increased ROS level, membrane damage following protein release, K+ leakage and biofilm inhibition. Thus, our findings support that conjugation of the SNP with topical antibiotics have great potential in the topical formulation when treating complex resistance, bacterial infections and where there is a need of more concentration to kill pathogenic bacteria.Staphylococcus aureus ATCC 25922, Pseudomonas aeruginosa ATCC 25619, Escherichia coli ATCC 10536 were obtained from the American Type Culture Collection. LB medium purchased from Oxoid, UK was used for the bacterial cell growth at 37 °C and growth was monitored by measuring the optical density at 620 nm.All antibiotics used in this study  were purchased from Sigma Chem. Co., USA. Phytophthora infestans. Method of synthesis and characterization of the SNP is described in our previous studyThe current study utilizes the SNP synthesized biologically using potato plant pathogenic fungus 6 ml−1; 10 ml each of the serially diluted solutions of the compounds was then added to the bacterial cells. The MIC was defined as the lowest concentration of the agent/drug inhibiting visible growth after overnight incubation at 37 °C.Susceptibility tests with SNPs, Cefazolin, Mupirocin, Gentamycin, Neomycin, Tetracycline, Vancomycin were carried out in 96-well microtitre plates using a standard twofold broth micro dilution method in Mueller Hinton (MH) broth (HiMedia), referring Clinical and Laboratory Standards Institute guidelinesThe MICs of each antibiotic substance alone or in combination were determined by a broth microdilution method in accordance with CLSI standards using cation-adjusted MH broth, modified for a broth microdilution checkerboard procedureTo determine the effectiveness of test substances for synergistic, antagonistic or additive effects, FIC index was measured, using the following formulas6 ml−1 density were treated with each substance or combination so that the final concentration was the MIC or FIC, respectively. Pharmacodynamic interaction, such as synergism, additive, indifferent and antagonistic effect were defined as comparing the zone of inhibition surroundings of well containing topical antibiotic, antibiotic + SNPs and control.Pharmacodynamic interaction was confirmed by agar well plate diffusion method6 ml−1 were treated with each substance or combination so that the final concentration was the MIC or FIC, respectively. Samples were incubated for 1 h at 37 °C. The negative and positive controls were maintained with untreated and 70% ethanol (50 μL/mL) treated cells, respectively. To detect the ROS formation, we used the fluorescent reporter dye 2,7-dichlorodihydrofluorescein diacetate  at a concentration of 5 mM and used a spectrofluorometer (Shimadzu) to measure excitation at a wavelength of 490 nm and emission at 515 nm. The level of reactive oxygen species increase was calculated using the equation: Bacterial cells at a density of 1 × 10Experiments were performed in triplicate and means ± SD were calculatedThe assessment of the potassium ions present in medium was carried out using a luminescence spectrometer (Perkin-Elmer) at an excitation wavelength of 346 nm and an emission wavelength of 505 nm. The cells in exponential phase were obtained, the potassium sensitive probe  was added and treated with a substance or combination followed by incubation at 37 °C for 4 h. The negative and positive controls were maintained with untreated and 70% ethanol (50 μL/mL) treated cells, respectively. The cells were measured following separation of cellular debris by centrifugation at 4000 rpm.6 ml−1 were added in the 96- well polystyrene sterile flat bottom tissue culture plates and incubated for 18 hrs. 10 μl SNP alone or combination so that the final concentration was the MIC or FIC, respectively, and incubated at 37 °C for 6 hrs. The tissue culture plates were washed with sterile phosphate-buffered saline for the removal of floating bacteria and sessile adherent bacteria were fixed using 2% sodium acetate, 0.1% crystal violet stain. Tissue culture plates were washed with deionized water and 95% ethanol was added to the wells after drying. The absorbance of stained adherent bacteria was recorded at 595 nm ELISA reader (Molecular Devices). The % of antibiofilm activity was calculated using the equation: 190 μl bacterial cells at a density of 1 × 10P. aeruginosa cells were observed by SDS-PAGE assay according to the methodThe pharmacodynamic effect of SNP and combination on P. aeruginosa cells were treated with SNP or combination and control for 4 h. Later, the cells were washed, resuspended in sterile phosphate-buffered saline and stained with LIVE/DEAD BacLight bacterial viability kit as per manufacturer’s instructions.The pharmacodynamic effect of the SNP or combination and control was also observed microscopically using Live/dead assay kit (Thermo Fisher Scientific). Three independent experiments were carried out and the results are represented as mean ± SD. The Student’s T test was applied to calculate the statistical significance of the experimental data.How to cite this article: Thirumurugan, G. et al. Elucidating pharmacodynamic interaction of silver nanoparticle - topical deliverable antibiotics. Sci. Rep.6, 29982; doi: 10.1038/srep29982 (2016).


Quality control (QC) analysis is an important component in maize breeding and seed systems. Genotyping by next-generation sequencing (GBS) is an emerging method of SNP genotyping, which is being increasingly adopted for discovery applications, but its suitability for QC analysis has not been explored. The objectives of our study were 1) to evaluate the level of genetic purity and identity among two to nine seed sources of 16 inbred lines using 191 Kompetitive Allele Specific PCR (KASP) and 257,268 GBS markers, and 2) compare the correlation between the KASP-based low and the GBS-based high marker density on QC analysis.Genetic purity within each seed source varied from 49 to 100 % for KASP and from 74 to 100 % for GBS. All except one of the inbred lines obtained from CIMMYT showed 98 to 100 % homogeneity irrespective of the marker type. On the contrary, only 16 and 21 % of the samples obtained from EIAR and partners showed ≥95 % purity for KASP and GBS, respectively. The genetic distance among multiple sources of the same line designation varied from 0.000 to 0.295 for KASP and from 0.004 to 0.230 for GBS. Five lines from CIMMYT showed ≤ 0.05 distance among multiple sources of the same line designation; the remaining eleven inbred lines, including two from CIMMYT and nine from Ethiopia showed higher than expected genetic distances for two or more seed sources. The correlation between the 191 KASP and 257,268 GBS markers was 0.88 for purity and 0.93 for identity. A reduction in the number of GBS markers to 1,343 decreased the correlation coefficient only by 0.03.Our results clearly showed high discrepancy both in genetic purity and identity by the origin of the seed sources (institutions) irrespective of the type of genotyping platform and number of markers used for analyses. Although there were some numerical differences between KASP and GBS, the overall conclusions reached from both methods was basically similar, which clearly suggests that smaller subset of preselected and high quality markers are sufficient for QC analysis that can easily be done using low marker density genotyping platforms, such as KASP. Results from this study would be highly relevant for plant breeders and seed system specialists.The online version of this article (doi:10.1186/s12864-015-2180-2) contains supplementary material, which is available to authorized users. Zea mays L.) is a staple food for more than 300 million people and is commonly grown by small-scale and resource-poor farmers in rural areas [http://faostat3.fao.org). During the same period, (i) maize yield in the country doubled from 1.6 t ha−1 in 2000 to 3.2 t ha−1 in 2013; and (ii) after eight years of erratic production, grain yield showed a rapid increase since 2007 [In sub Saharan Africa (SSA), maize . After nearly four decades, the breeding program released its first top cross hybrid, BH140, in 1988  SubsequeInitial adoption of hybrids by resource poor farmers was very slow for a number of reasons, including (i) high cost of hybrid seed relative to OPVs  (ii) limited or no access to improved hybrid seed in some regions; (iii) inadequate knowledge on agronomic management; (iv) insufficient seed companies and seed regulations in the country; (v) inadequate seed production infrastructure; and (vi) high cost of fertilizers . The demInbred lines are assumed to be genetically pure and possess all the genetic qualities that a breeder has selected for. Small changes in allele frequencies may occur during seed regeneration and maintenance breeding, and possible contamination with seeds or pollen of other samples , 6 Signihttp://www.biotech.cornell.edu/brc/genomic-diversity-facility/services/gbs-project-design-and-optimization). To our knowledge, however, the correlation between GBS and KASP markers for QC analyses is not known. The objectives of our study were to 1) evaluate the level of genetic purity within each inbred line and understand genetic identity among different seed sources of the same line designation; and 2) compare the correlation between the KASP-based low density and the GBS-based high density information for QC analysis.Single nucleotide polymorphic (SNP) markers have emerged as powerful tools for many genetic applications, including germplasm characterization , QC analysis , quantitative trait loci (QTL) mapping, allele mining, marker-assisted backcrossing, marker-assisted recurrent selection, and genomic selection. SNP data can be obtained using one of the numerous uniplex or multiplex SNP genotyping platforms that combine a variety of chemistries, detection methods, and reaction formats. Kompetitive Allele Specific PCR (KASP) is a uniplex SNP genotyping platform, and has developed into a global benchmark technology. CIMMYT uses a subset of 100 to 200 SNPs for routine QC genotyping using the KASP platform at LGC Genomics in the UK. The subset of SNPs for QC genotyping were selected out of the 1536 Illumina GoldenGate random chip using the following criteria: (1) ease of scoring with unambiguous separation of the two homozygous and heterozygous genotypes; (2) a minor allele frequency (MAF) and polymorphism information content (PIC) of at least 0.20 and 0.25, respectively; (3) good distribution across chromosomes based on physical position, and (4) good polymorphism across a wide range of mapping populations. The genotyping cost for KASP depends on the number of data points (1 data point = 1 sample genotyped by 1 SNP) and data turnaround, which is 4 to 6 weeks for normal turnaround and 2 to 3 weeks for rapid turnaround. The current KASP genotyping cost for normal and rapid data turnaround ranges from US$0.064 to US$0.242 and from US$0.100 to US$0.360, respectively . Genotyphttp://www.lgcgroup.com) and the Genomic Diversity facility at Cornell University (http://www.biotech.cornell.edu/brc/genomic-diversity-facility). Samples were genotyped with 200 SNPs , were used in this study , Cornell University using an algorithm that searches for the closest neighbor in small SNP windows across the maize database [Eight datasets were used for all statistical analyses. Dataset 1 and dataset 2 consisted of the 191 KASP SNPs and the 257,268 GBS markers after filtering using a MAF of 0.05, respectively. Six additional input files were later created for correlation analyses: (a) dataset 3 consisted of a subset of 100 out of 191 SNPs routinely used by CIMMYT for QC genotyping; and (b) data set 4 to dataset 8 were created from dataset 2 using a MAF of 0.10, 0.20, 0.30, 0.40 and 0.50. For QC analysis, the best SNPs would be those which amplify the two alleles equally . The proportion of heterogeneity  in each sample was calculated from all datasets using TASSEL version 4.3.2. Genetic purity was calculated from all datasets in Excel as the difference between 100-h, where h refers to heterogeneity in percentage obtained from TASSEL. For all eight datasets, genetic distance was calculated between each pair of samples using the identity by descent (IBS) method implemented in TASSEL. Dendrograms were constructed from the genetic distance matrices of both dataset 1 and dataset 2 using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm implemented in molecular evolutionary genetics analysis (MEGA), version 6 . Mantel 4 or later generations, but previously, lines were often derived at earlier generations. An inbred line may be considered pure or homogenous if the proportion of heterozygous or heterogeneous loci does not exceed 5 % [We first computed the proportion of homozygous SNPs on each of the 80 samples as an estimate of genetic purity or homogeneity. The results were highly variable across samples, with homogeneity varying from 49 to 100 % for KASP and from 74 to 100 % for GBS . They are now developing inbred lines after four or later generations of selfing and more recently released hybrids developed using inbred lines with higher genetic purity. This will assist to maintain homogeneous parental inbred lines in the next generation hybrids in Ethiopia.The use of parental inbred lines with high heterogeneity in breeding programs can have multifold negative effects, including the use of wrong donors in new breeding starts for line development, hybrid formation as well as genetic and molecular studies. Genetic purity of a given parental line also has major impact on the production and distribution of certified seed to farmers. Failure to undertake regular quality control analysis could result in generation and dissemination of incorrect products to the end user. Maize breeders commonly exchange seed of the most widely used inbred lines; therefore, unexpected level of genetic heterogeneity in a given seed lot or high mismatch across different seed sources of a given inbred line can quickly spread across different programs with consequent negative effects. Obtaining seed from a reliable source and undertaking routine quality control, will be useful for minimizing errors associated with purity and identity. Our results showed that a subset of 100 to 200 SNP (KASP) markers would be sufficient for routine QC analysis. In cases where there is no reference genotype data for inbred lines that will be used for comparison purposes, it is recommended to grow out multiple sources of different sources of the same line designation in nurseries, generate SNP data, compare SNP data with field notes, and discard those sources that show deviation from expectation in terms of purity and identity. CIMMYT has carried out this exercise to resolve genetic purity and identity issues from 280 seed sources involving 40 inbred lines and strongly recommends this regular activity for other breeders . The information presented in this paper would be highly useful for maize breeders that are involved in new pedigree start, developing populations for QTL mapping and marker-assisted breeding, and the seed companies.Our results clearly showed high discrepancy both in genetic purity and identity by the origin of the seed sources (institutions) irrespective of the type of genotyping platform and number of markers used for analyses. Overall, most seed sources from CIMMYT were considered genetically pure, which was not the case for the majority of samples originated from EIAR. One of the reasons for such discrepancy in genetic purity and identity was the level of inbreeding prior to releasing the parental lines for hybrid formation. Although there were some differences between KASP and GBS results, the overall conclusions reached from both methods was basically similar, which clearly suggests that smaller subset of preselected high quality markers are sufficient for QC analysis that can easily be done using low marker density genotyping platforms, such as KASP. GBS data would be highly useful for establishing reference marker database at the time of releasing an inbred parental line for used in heterotic grouping and planning hybrid combinations and new pedigree starts. Results from this study would be highly relevant for plant breeders and seed system specialists.


Discovering disease-associated non-synonymous SNPs (nsSNPs) altering PTM sites can help to estimate the various PTM candidates involved in diseases, therefore, an integrated analysis between SNPs, PTMs and diseases is necessary. However, only a few types of PTMs affected by nsSNPs have been studied without considering disease-association until now. In this study, we developed a new database called PTM-SNP which contains a comprehensive collection of human nsSNPs that affect PTM sites, together with disease information. Total 179,325 PTM-SNPs were collected by aligning missense SNPs and stop-gain SNPs on PTM sites (position 0) or their flanking region (position -7 to 7). Disease-associated SNPs from GWAS catalogs were also matched with detected PTM-SNP to find disease associated PTM-SNPs. Our result shows PTM-SNPs are highly associated with diseases, compared with other nsSNP sites and functional classes including near gene, intron and so on. PTM-SNP can provide an insight about discovering important PTMs involved in the diseases easily through the web site. PTM-SNP is freely available at  Since the advance of next-generation sequencing (NGS) technologies, the number of single nucleotide polymorphisms (SNPs) keeps increasing precipitously and it becomes crucial issue to discovering functional implication of SNPs on the biological process and complex diseases. Especially, non-synonymous SNPs (nsSNPs), including missense SNPs that provoke amino acid mutation and stop-gain SNPs that terminate the peptide synthesis by generating stop codon, are thought to play key roles in causing diseases by changing the protein structure and functions. It is estimated that nsSNPs cover more than half of the disease-causing mutation in the Human Gene Mutation Database .In this regard, several efforts have been made to elucidate the effect of nsSNPs on diseases recently. For example, CanPredict  and DisePost-translational modification plays essential roles in the most biological pathways and disruption of PTM sites is known as the major cause of diseases . RadivojHere, we built a comprehensive PTM-SNP database containing more than 50 kinds of experimentally validated PTMs and predicted PTMs. While previous studies considered phosphorylation, PTM-SNP can examine multiple PTMs simultaneously. We also matched PTM-SNPs with various disease-associated SNPs based on GWAS to find disease-associated PTM-SNPs. The statistical significance values of the disease-association from GWAS are provided for accurate consideration of disease-association. We found that PTM-SNPs are highly correlated with diseases, compared with other SNPs in functional units. In addition, the result of the case study about PTM-SNP on type 2 diabetes shows our PTM-SNP provides an insight into unveiling important PTMs involved in causing diseases and finding novel disease markers. The web interface of PTM-SNP make easier to access our data and be conjugated as useful evidences in biomedical studies like drug development or predicting diagnosis and drug response.We collected total 517,466 human nsSNPs from the NCBI dbSNP database Build 135 . From 51Human PTM site data were retrieved from dbPTM3 , which iPTM-SNPs were detected by aligning the SNP sequence with the PTM site sequence because not all sequences in UniProt and RefSeq are perfectly identical . Each residue in the SNP sequence is matched with the PTM site sequence by sliding one-by-one Figure . The PTMWe also extracted and integrated PTM-SNPs from the public resource, PhosSNP 1.0. PhosSNP includes phosphorylation-associated nsSNP from computationally translating mRNA sequences. However, about 18% of SNPs in PhosSNP have not yet been defined as nsSNP by dbSNP Build 135, therefor, were removed in the integrating process.-5  [From GWASs using SNP arrays and NGS techniques, huge numbers of statistically significant disease-associated SNPs have been identified recently. National Human Genome Research Institute (NHGRI) GWAS catalog have been provided 8,771 trait-associated SNPs with p-values < 1.0 × 102, 2013) . The gen2, 2013) . To find-5 were identified from quality controlled (QC) 409,656 SNPs, based on Cochran-Armitage trend test statistics using PLINK 1.07 [-4, and minor allele frequency (MAF) < 1%. Linkage disequilibrium pruning was leaved out to preserve the PTM-SNP candidates. T2D PTM-SNPs were detected by mapping T2D-associated SNPs and PTM-SNPs. Significantly associated T2D PTM-SNPs were further studied to interpret disease mechanisms with various biological information resources. Known disease genes are mainly collected from OMIM [Furthermore, we analyzed type 2 diabetes (T2D) associated SNPs that were identified in our previous study  by usingINK 1.07 . QC is arom OMIM , GAD, anrom OMIM . Drug tarom OMIM , PharmGKrom OMIM , and KEGPTM-SNP were collected by integrating dbPTM-dbSNP matching and PhosSNP 1.0. Totally, distinct 179,325 PTM-SNPs were identified, consisting of 133,266 PTM-SNPs from dbPTM-dbSNP matching and 64,035 PTM-SNPs from PhosSNP. All PTM-SNPs were categorized into 3 groups based on the SNP position; PTM-SNPs on PTM site (position 0), PTM-SNPs flanking PTM sites (position -7 to 7), and stop-gain PTM-SNPs , and weight of a Val66Met PTM-SNP (rs6265) located on BDNF are discovered from NHGRI GWAS catalogs and WTCCC T2D dataset analysis. The association between obesity and diabetes is discovered with rs6265 due to GWASs on BMI, weight, and T2D identified G allele as a risk allele and A allele as a non-risk allele. Adjacent site Thr62 of BDNF is predicted as a phosphorylation site and kinases for Thr62 are predicted as ARAF, RAF1, MAP3K2, and MAP3K4 by GPS2.0 and filtrated by STRING. RAF1 is known as a T2D gene and a member of insulin signaling pathway.PTMs play crucial roles in biological mechanisms, and PTM-SNPs may influence PTM network regulation and have effects on associated diseases. From disease-associated PTM-SNPs, we can find disease-associated PTM networks regulated by PTM-SNPs that may provide insight into the mechanisms of disease. Our case studies show the possibility of using PTM-SNPs, combined with various types of biological information including known disease genes, drug targets, protein-protein interaction networks, and pathway networks, to identify mechanisms underlying disease. Novel disease-candidate genes can be found from disease-associated PTM networks assembled from the relations of PTM enzymes and PTM substrate proteins.Collected PTM-SNPs and disease-associated PTM-SNPs are provided at the PTM-SNP website. The integrated PTM-SNP search interface allows users to search PTM-SNPs by dbSNP rs ID, RefSeq accession (AC), UniProt ID, or disease terms using the same search interface. Search results are provided with a PTM-SNP report, protein-level report or disease-level report, depend on the kind of the input search terms. When user input a dbSNP rs ID, the PTM-SNP report is displayed with the general information about the PTM-SNP including dbSNP rs ID, RefSeq AC, UniProt ID, residue of SNP, sequence location of PTM binding site, residue of PTM binding site, relative sequence location of PTM-SNP from PTM binding site, PTM name, the type of PTM-SNP, and the disease-association of the PTM-SNP including disease name and p-value if available. All columns can be easily sorted by clicking up/down arrows. In addition to the PTM-SNP information, the protein-level report provides all other PTM-SNPs that have been identified in the same substrate protein when user input a protein identifier such as RefSeq AC or UniProt ID. The disease-term search interface provides the disease-level report including disease-associated PTM-SNPs with disease name. Researchers can search the disease-associated PTM-SNPs easily using PTM-SNP web interface Figure .In this study, we performed the investigation to associate PTMs, SNPs and disease using PTM-SNPs and disease-associated GWAS studies. PTM-SNP is a database based on dbSNP build 135, dbPTM3, and public GWAS databases of disease-associated SNPs that influence PTMs. In total, more than 50 kinds of PTMs were considered. PTM sites involved in signalling pathways such as phosphorylation, ubiquitination, and acetylation tended to have a greater proportion of PTM-SNPs than other PTM sites. Genes that contain disease-associated PTM-SNPs have been collected and matched against disease genes and drug targets to elucidate the effects of the SNPs and out analysis showed that PTM-SNP has higher disease-associations as compared with non-PTM-SNPs. We were also able to identify some of the mechanisms of disease-associated SNPs using PTM-SNP data. Researchers can match their own GWAS datasets with the PTM-SNP database to find PTM-related effects of the disease-associated SNPs easily by using the PTM-SNP website. We expect that provides an insight into unveiling important PTMs involved in causing diseases and finding novel disease markers.The authors declare that they have no competing interests.YK and CK designed and implemented the proposed database and wrote the manuscript. BM constructed web interface of PTM-SNP. GSY designed and directed this study, and reviewed the manuscript. All authors worked on and approved the final manuscript.Statistics of whole PTM site in PTM-SNP based on their PTM typesClick here for fileStatistics of whole trait or disease-associated PTM-SNPs from GWAS catalogsClick here for file


These mQTLs include reported obesity, lipid and type 2 diabetes loci, e.g. ADCY3/POMC, APOA5, CETP, FADS2, GCKR, SORT1 and LEPR. Significant mQTLs were overrepresented in intergenic regions meanwhile underrepresented in promoter regions and CpG islands. We further identified 635 SNPs in significant cis-mQTLs associated with expression of 86 genes in adipose tissue including CHRNA5, G6PC2, GPX7, RPL27A, THNSL2 and ZFP57. SNPs in significant mQTLs were also associated with body mass index (BMI), lipid traits and glucose and insulin levels in our study cohort and public available consortia data. Importantly, the Causal Inference Test (CIT) demonstrates how genetic variants mediate their effects on metabolic traits , hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)) via altered DNA methylation in human adipose tissue. This study identifies genome-wide interactions between genetic and epigenetic variation in both cis and trans positions influencing gene expression in adipose tissue and in vivo (dys)metabolic traits associated with the development of obesity and diabetes.Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men, where 592,794 single nucleotide polymorphisms (SNPs) were related to DNA methylation of 477,891 CpG sites, covering 99% of RefSeq genes. SNPs in significant mQTLs were further related to gene expression in adipose tissue and obesity related traits. We found 101,911 SNP-CpG pairs (mQTLs) in  Genetic factors contribute to the risk of complex metabolic diseases such as obesity and type 2 diabetes. Although genome-wide association studies (GWAS) have identified numerous genetic loci influencing the risk of developing obesity and type 2 diabetes, only a few of these loci have been linked to the molecular mechanisms contributing to the phenotype outcome . MoreoveThe phenomenon of epigenetic modifications are of interest to study for their possible involvement in phenotype transmission and predisposition to complex human diseases, including obesity and type 2 diabetes . Epigenecis and trans effects of genetic variation on DNA methylation covering most genes and regions in the human genome. Identified mQTLs were followed-up and related to gene expression in adipose tissue. Additionally, since the adipose tissue contributes to whole body energy homeostasis by glucose uptake, triglyceride storage and adipokine secretion, we investigated if the identified SNPs in significant mQTLs affect metabolic traits that are associated with increased risk of obesity and type 2 diabetes in the studied cohort. We further used a causal inference test (CIT) [The aim of the present study was therefore to perform a genome-wide mQTL analysis in human adipose tissue, investigating both st (CIT)  to modelcis and trans positions affecting the genome-wide DNA methylation pattern in human adipose tissue as well as numerous metabolic traits. Identified mQTLs cover known lipid, obesity and diabetes loci. Our study highlights that interaction analysis between genetic and epigenetic variation in a tissue of relevance for metabolic diseases may give new insights to biological processes affecting disease susceptibility.The present study provides the first detailed map of genetic loci in both Fig 1). While most previous mQTL studies have been limited to analysis of ~0.1% of human CpG sites [cis- and trans-mQTL analysis covering DNA methylation of most genes and genomic regions in human adipose tissue of 119 Scandinavian men (Table 1). Here, we pairwise associated genotype data of 592,794 common SNPs (MAF>0.05) with DNA methylation of 477,891 CpG sites throughout the human genome using a linear regression model including sub-cohort, age and BMI as covariates.To examine and map underlying genetic control of DNA methylation patterns in human adipose tissue, we performed a genome-wide mQTL analysis  showing significant associations between genotype and the degree of DNA methylation after correction for multiple testing (seeTable 2and S1 Table). Of these 15,208 significant CpG sites, 10,064 were annotated to 5,589 unique genes . The most significant mQTL among these 447 cis-mQTLs is presented in Previously, we reported that approximately 50% of type 2 diabetes associated SNPs identified by GWAS either introduce or remove a CpG site, a so called CpG-SNP. These CpG-SNPs were further associated with differential DNA methylation of the CpG-SNP site in human pancreatic islets . Among t−16) of SNPs in significant cis-mQTL located close to the CpG site , with a median distance between SNPs and CpG sites of significant cis-mQTLs of 29.6 kb. Moreover, the strongest association signals were found for SNPs located close to a CpG site .When a distance analysis was performed, we found an overrepresentation . Among unique CpG sites of significant trans-mQTLs, 366 CpG sites were annotated to 375 unique genes (Table 2and S2 Table) and 230 CpG sites were annotated to intergenic regions. The most and least significant trans-mQTLs are shown in In the ting see, correspcis- and trans-mQTLs is shown in  We found an overrepresentation of CpG sites in significant cis-mQTLs on chromosome 6, 7, 8, 13 and 21 together with an underrepresentation on chromosomes 1, 2, 3, 11, 12, 14, 15, 17, 18, 19, 20 and X when compared to the chromosomal distribution of all analyzed CpG sites . The highest deviation from expectation of CpGs in significant cis-mQTLs was observed on chromosome 6 , where the highly polymorphic HLA region is located, a genomic region linked to numerous autoimmune diseases [trans-mQTLs were overrepresented on chromosomes 6 and Y while underrepresented on chromosomes 9 and 14 .DNA methylation in proximal promoter and/or enhancer regions is generally thought to have silencing effects on gene transcription, meanwhile DNA methylation in the gene body might stimulate transcriptional elongation and contribute to alternative splicing events . Giving diseases ,24. CpG st exon, gene body, 3’UTR and intergenic regions [cis-mQTLs were overrepresented in the intergenic regions and gene body, while significantly underrepresented in the TSS1500, TSS200, 5’UTR, 1st exon and 3'UTR . Among significant trans-mQTLs, we found an overrepresentation of CpGs in the intergenic region and underrepresentation in TSS1500 and gene body .Furthermore, the Infinium HumanMethylation450 BeadChip estimates DNA methylation in several genomic features and the analyzed CpG sites have been annotated based on their genomic location in relation to the nearest gene including genomic regions TSS1500 and TSS200 (1500–201 and 200–0 bases upstream of transcription start site (TSS), respectively), 5’UTR (untraslated region), 1 regions . In the cis-mQTLs, we found an overrepresentation in the open sea, northern- and southern shores as well as in southern shelf . Moreover, an underrepresentation was found in CpG islands . CpGs in significant trans-mQTLs showed overrepresentation in the open sea and underrepresentation in northern shore as well as southern shelf .The analyzed CpG sites have also been annotated based on their relation to CpG islands, including the following regions: CpG islands, northern and southern shores, northern and southern shelves and open sea . For CpGcis- and trans- mQTL analyses in human adipose tissue. Using WebGestalt [cismQTLs (S3 Table), including Metabolic pathways (Padj = 6.3x10-15) and Pathways in Cancer (Padj = 7.3x10-42) were found among the most enriched KEGG pathways (S3 Table). Moreover, 25 KEGG pathways were enriched among 375 genes annotated to significant trans mQTLs (S3 Table).Next, we performed a KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway analysis to identify cellular components or biological pathways which show enrichment among genes identified in bGestalt , we iden−5) [cis-mQTL analysis and when taking proxy SNPs into account , which constitutes 1.17% of cis-mQTL SNPs and 10.8% of GWAS catalog SNPs. Representative mQTLs for some of these loci are shown in Fig 4A–4J. These mQTLs include POMC and LEPR, which encode proopiomelanocortin and the leptin receptor, respectively. Mutations in both these genes have been associated with early onset obesity [GIPR (encoding gastric inhibitory polypeptide receptor), PARP4 (encoding poly(ADP-ribosyl)transferase-like 1 protein), CEPT (encoding cholesteryl ester transfer protein), APOA5 (encoding apolipoprotein A5), SORT1 (encoding sortilin 1), GCKR (encoding glucokinase regulator), FADS2 (encoding fatty acid desaturase 2), ACADS (encoding acyl-CoA dehydrogenase) and GRB10 (encoding growth factor receptor bound protein 10). SNPs in these loci have previously been associated with BMI, T2D and/or obesity- and lipid-related traits [Numerous SNPs associated with obesity, type 2 diabetes and related traits have previously been identified by GWAS . However−5) . Out of  obesity . We alsod traits –36.trans-mQTLs, we found 4 SNPs overlapping with reported obesity loci in the GWAS catalog (S5 Table).Of SNPs in significant cis-mQTL analysis, with mRNA expression of genes within 500kb (cis-distance). In the eQTL analysis of significant cis-mQTL SNPs, we identified 926 SNP-mRNA transcript pairs showing significant associations between genotypes and mRNA expression levels after correction for multiple testing . CHRNA5 encodes a nicotinic acetylcholine receptor subunit and SNPs in this locus have been associated with body weight in relation to tobacco use [G6PC2 encodes glucose-6-phosphatase catalytic subunit 2 and SNPs in this locus have been associated with glycemic traits [GPX7 encodes glutathione peroxidase 7 a protein involved in glutathione metabolism. RPL27A encodes Ribosomal protein L27A, which has been linked to human obesity [THNSL2 encodes threonine synthase like 2 and SNPs in this locus have been associated with obesity [ZFP57 encodes a zink finger protein and DNA methylation and mutations in this locus are associated with transient neonatal diabetes [It is well established that mRNA expression is regulated by both genetic variation and DNA methylation independently ,37. Howeting see. These cacco use . G6PC2 ec traits . GPX7 en obesity . THNSL2  obesity . Additiodiabetes .trans-mQTL analysis were also followed-up and related to mRNA expression of all analyzed genes. In the eQTL analysis of significant trans-mQTL SNPs, we identified 270 significant associations between genotypes and mRNA expression levels after correction for multiple testing .The 2,735 unique SNPs identified in the ting see, consistcis-mQTLs, we found 62 SNPs associated with BMI, 185 with waist-hip ratio (WHR), 77 with fasting glucose, 62 with fasting insulin, 91 with homeostasis model of beta-cell function (HOMA-B), 49 with HOMA-IR, 146 with HbA1c, 85 with total cholesterol, 84 with triglycerides, 197 with HDL, 67 with LDL in both our study cohort and consortia data with the same direction of allele effects and with P≤0.05 (S8 Table). Several of these SNPs show genome-wide significance in GIANT, MAGIC or GLGC. Representative associations between genotype and some metabolic traits as well as DNA methylation in the 119 Scandinavian men are shown in  The SNPs presented in Fig 6do also show genome-wide significance with respective trait in GLGC  and MAGIC , respectively . These data show the effect of interactions between common genetic variation and DNA methylation on gene expression and metabolic outcome .Of identified cis-mQTL SNPS were previously found to be associated with C-reactive protein (CRP) levels  levels .cis- and trans- mQTLs are annotated to candidate genes for adipose-related traits. Out of the 157 loci previously implicated in lipid biology in GLGC consortium [Additionally, some of the identified nsortium , 48 . Among the genes annotated to these SNP-CpG pairs, CDK2AP1, HLA-DMA, MCM6, TCF19, CAMK1D and NEIL2 were found. None of the cis-mQTLs showed a reactive relationship between a SNP and a metabolic phenotype.We proceeded to evaluate the potential causality relationships between genotypes (G), DNA methylation (M) and phenotypic traits (P) using the CIT . The poset al. restricted their mQTL analysis to SNPs located within 100 kb from analyzed CpG sites [et al. included women and the two studies used different bioinformatic and statistical approaches, which may affect the possibility to replicate the results. Nevertheless, among our significant cis-mQTLs, we found that 5,468 CpG sites also stand under genetic control of SNPs in the study by Grundberg et al., and out of these 2,118 (38.6%) were associated with the same SNP in both their and our study [To validate whether the results of mQTL analysis hold in an independent cohort, we also looked for overlap with a recent study also showing associations between genetic variation and DNA methylation in human adipose tissue . While bpG sites  and therur study .cis-mQTLs in adipose tissue, 39,386 were also found in pancreatic islets . Moreover, 1,852 significant trans-mQTLs overlapped between the two different tissues .Additionally, we recently performed an mQTL analysis in human pancreatic islets . Here, wcis and trans mQTLs in human adipose tissue . Interestingly, we discovered that 237 CpG sites among our significant cis-mQTLs and 7 CpG sites among our significant trans-mQTLs are also differentially methylated in adipose tissue from subjects with type 2 diabetes (S14 Table), suggesting that DNA methylation may mediate the genetic impact of type 2 diabetes.We have previously identified CpG sites that are differentially methylated in adipose tissue from subjects with type 2 diabetes compared with non-diabetic controls . Howevercis and trans-mQTLs in human adipose tissue . We found that 33,058 CpG sites previously identified as associated with BMI overlapped with 577 cis and 19 trans significant mQTLs in current study (S15 Table). Moreover, out of 711 CpG sites associated with HbA1c, 25 and 1 CpG site overlapped with significant cis and trans mQTLs respectively . Out of those 66,329 mQTLs, 63,714 (96%) overlapped with the analysis of all 4 cohorts.Since the subjects in the four sub-cohorts included in this study differ in age and BMI, we performed a sub-analysis only including cohorts #1 and #2 as these subjects are phenotypically similar. Here, we detected 66,329 mQTLs in trans-mQTL analysis, we identified 3,243 SNP-CpG pairs showing significant associations between genotypes and the degree of DNA methylation in adipose tissue after correction for multiple testing, corresponding to 1,865 unique SNPs and 538 unique CpG sites (S17 Table). Out of those 3,243 mQTLs, 2,919 (90%) were previously identified in the analysis of all 4 cohorts.In the cis mQTLs corresponding to 51,435 unique SNPs and 15,267 unique CpG sites. Out of those, 99,661 (97.2%) were also identified in the analysis where BMI was included as a covariate (S18 Table). In trans, we discovered 5,435 significant mQTLs, corresponding to 608 unique CpG sites and 2,765 unique SNPs, where 5,272 (97%) were also identified in the main mQTL analysis (S19 Table).In order to validate whether BMI as a covariate has a significant effect on a number of discovered mQTLs, we performed a mQTL analysis of all 4 study cohorts without BMI as a covariate. Overall, we detected 102,467 significant S20 Table), which are annotated to 173 genes.We finally tested the direct association between DNA methylation and gene expression in human adipose tissue by performing a linear regression between individual mRNA transcripts and DNA methylation of CpG sites in cis (500 kb up- and 100 kb downstream of respective gene) including age, BMI and study cohort as covariates. We found significant associations between DNA methylation and mRNA expression for 546 combinations (FDR<5%), consisting of 473 unique CpG sites and 194 unique mRNA transcripts (S20 Table).In addition, we found that 262 CpG sites among our significant cis-mQTLs and 13 among our significant trans-mQTLs overlapped with methylation sites associated with mRNA expression in adipose tissue . However, none of them was located on chromosome 6 [Multiple SNPs identified through GWAS associate with complex metabolic disease including obesity and type 2 diabetes ,43,49–52ariation –20,61, a regions –19 or ciractions , we can  regions . It may  regions –64. Inte regions ,24. Numemosome 6 .ANKRD31 (ankyrin repeat domain 31), HDL levels and annotated to CELSR2  as well as fasting plasma glucose levels and annotated to ARAP1 (ankyrin repeat and PH domain 1).Genetic association studies have improved our understanding of the biological basis of metabolic disease . NeverthNDUFB6, support this hypothesis [NDUFB6 expression and decreased glucose uptake, young carriers had low degree of methylation in the SNP-CpG site together with increased skeletal muscle NDUFB6 expression and no effect on glucose uptake. Together, this study demonstrates a clear interaction between genetic, epigenetic and non-genetic factors. Additionally, genetic variation may carry inheritance of epigenetic variation and thereby have an impact on the heritability of human diseases and may explain some of the missing heritability of human complex diseases. Furthermore, we also found that several SNPs associated with DNA methylation in adipose tissue overlapped directly or via proxy SNPs to previously reported disease loci of obesity related traits, including CETP and FADS2, which are both known to be associated with total cholesterol, LDL, HDL and triglyceride levels [Given that SNPs affect DNA methylation and that DNA methylation is a dynamic process that may change in response to environmental factors and affects phenotype transmission ,69, it mpothesis . Here, we levels  These daMCM6. This is an MCM (minichromosome maintenance) complex gene that previously has been shown to affect total cholesterol levels [TCF19, which has been associated with type 1 diabetes through GWAS [CAMK1D, which has been associated with type 2 diabetes [cis-mQTL SNP-CpG pairs were found to have independent effects on the analyzed phenotypes, the independence cannot be concluded due to some limitations in our analysis. First, only a few phenotypes were considered in the course of the analysis, and it might require other phenotypes to discover all cause–effect relationships between SNPs, methylation and metabolic phenotype. Second, as CIT only considers one SNP and one CpG site at a time, more complex interactions involving several SNPs and or CpGs can be missed, which suggests that more sophisticated analytical methods should be developed. An additional drawback of our study is the small sample size relative to the number of statistical comparisons. As the number of analyzed SNP–CpG pairs is in the order of 1011, only the strongest interaction effects can be detected by means of our mQTL analysis. To reduce the number of type 2 errors during multiple testing procedures, we implemented a modified Bonferroni correction method, which took into account a linkage disequilibrium dependency between analyzed SNPs. Additionally, we performed eQTL and CIT analysis only on the data that was shown to be significant in the mQTL analysis, thus again significantly reducing number of performed statistical tests. While eQTL analyses have been used to identify causal genetic variants for metabolic disease [In order to provide further insights into mechanisms of genetic and epigenetic interaction and its impact on regulation of metabolic phenotypes, we used the CIT ,70. We dl levels . Among ough GWAS , and CAMdiabetes  This sup disease , here weInterestingly, SNPs throughout the genome may introduce or delete CpG sites and thereby affect the possibility for DNA methylation to take place . These scis-mQTLs in a study by Grundberg et al. [et al. performed mQTL analyses in human adipose tissue using the Illumina 450K array for DNA methylation and thereby comparable, divergence in cis boundary, sex and correction methods for multiple testing may explain some of the different results between the studies. It should also be noted that 39,386 of our significant cis-mQTLs in human adipose tissue were previously also identified in human pancreatic islets [Furthermore, we were able to replicate numerous of our unique CpG sites of significant g et al.  confirmic islets . While tcis- and trans-mQTL analysis in human adipose tissue covering most genes and genomic regions. Our study demonstrates that interactions between genetic and epigenetic variation influences gene expression, molecular phenotypes and metabolic traits related to complex diseases in humans. We also provide details on potential causal relationships between genetic and epigenetic variation on metabolic phenotypes. Thus, DNA methylation variation may be of high importance in genetic association studies and may improve our understanding of molecular pathways in the context of complex human metabolic diseases.We provide for the first time a combined genome-wide Table 1. The cohort includes subjects from four sub-cohorts, all previously described [ All study participants underwent a physical examination including measurements of BMI, waist and WHR. Moreover, blood sampling for analysis of lipids, glucose and insulin were done during the fasting state. Written informed consent was obtained from all participants and the research protocols were approved by the local human research ethics committees: Dnr 13/2006 (Lund University), Dnr 461/2006 (Lund University), KA 03129gm (Köpenhavns AMT). While three of sub-cohorts are intervention studies [This study includes a total of 119 Scandinavian men without known disease. Their characteristics are presented in escribed ,48,75–77 studies . Only bahttp://pngu.mgh.harvard.edu/purcell/plink/) [Genotyping was performed in DNA extracted from blood of the 121 Scandinavian men using Illumina HumanOmniExpress BeadChip, which is a genome-wide array covering 731,412 SNPs, together with the iScan system . Genomic DNA was extracted from blood using the Gentra Puregene Blood Kit . Genotypes were called using GenomeStudio® software (Illumina). All subjects passed call rate threshold of > 98%. Sex discrepancy between reported sex and genotypic sex based on X-chromosome heterozygosity was detected for two subjects and these subjects were excluded from subsequent analyses. No subjects were found to be population outliers based on a population stratification test. SNPs were excluded if missing calls > 5%, Hardy-Weinberg Equilibrium p-value < 0.001 and minor allele frequency < 0.05. Overall, 592,794 SNPs for 119 subjects passed quality control and were used for subsequent analyses. All genotype data were analyzed using Plink software (/plink/) .Genome-wide DNA methylation profiling was performed in genomic DNA extracted using Qiagen DNA extraction kits (Qiagen) from adipose tissue from 119 Scandinavian men using the Infinium HumanMethylation450 BeadChip (Illumina). The DNA methylation array targets 485,577 probes across the genome, covering 99% of RefSeq genes and 96% of CpG islands. Genomic DNA (500 ng) from adipose tissue was bisulfite treated using the EZ DNA methylation kit . DNA methylation analysis of bisulfite treated DNA was carried out with Infinium® assay following the standard Infinium HD Assay Methylation Protocol Guide (Part #15019519). BeadChips were scanned with Illumina iScan and raw data was imported to the GenomeStudio Methylation module software for calculation of methylation scores represented as methylation β-values. In sample quality control, all samples passed GenomeStudio quality control steps for bisulfite conversion efficiency, staining, hybridization, extension and specificity.Individual probes with a mean Illumina detection p-value > 0.01 were considered not detected and subsequently excluded from further analysis. Non-CpG methylation probes and SNP-probes included on the array were also filtered out. After these quality control steps and after filtering DNA methylation data, 477,891 CpG sites remained for all included samples. Before further analysis, the DNA methylation data was exported from GenomeStudio and subsequently analyzed using Lumi package from Bioconductor . Extract package . To corr package  was usedGenome-wide mRNA expression profiling using the whole-transcript GeneChip® Human Gene 1.0 ST Array  following the Affymetrix standard protocol was performed in RNA extracted from the subcutaneous adipose tissue biopsies of 118 out of 119 Scandinavian men using miRNeasy kit followed by the RNeasy MiniElute Cleanup Kit (Qiagen) or using the RNeasy Lipid Tissue Mini Kit (Qiagen). The array data was background corrected, quantile normalized and summarized with robust multichip average (RMA) procedure using oligo package  from BioAssociations between SNPs and DNA methylation of CpG sites were modeled as a linear relationship using DNA methylation levels as a dependent variable, SNP genotypes encoded as 0, 1 or 2 according to number of minor alleles. Due to the fact that both BMI and age can affect DNA methylation and, therefore, the association between SNP and DNA methylation, age, BMI and the sub-cohort were included as covariates. Calculations of associations were performed using the MatrixEQTL library for R programming language .cis) and distant (trans) mQTLs a distance less or equal to 500 kb between a SNP and CpG site was used to define cis-mQTLs. All remaining SNP-CpG pairs were considered trans-mQTLs. In total we found 283,290,917,454 CpG–SNP pairs in the dataset, where 112,842,462 pairs were defined to be located in cis and 283,178,074,992 in trans. The cis- and trans-mQTL analyses were performed separately. In order to correct for multiple testing, p-value significance threshold was set, accounting for number of tests performed as well as the dependency of linkage disequilibrium (LD) between SNPs. LD-based SNP pruning was used to take into account the linkage dependency of SNPs that are run against the same quantitative trait locus in the mQTL analysis by calculating the number of independent tests based on r2<0.9 for the SNPs. In the cis-analysis, LD based pruning of SNPs within a distance of 500 kb from a CpG site was performed by pairwise-tagging (r2<0.9) and the total sum of all tag SNPs connected to each CpG site was used as correction value when correcting for multiple testing. LD calculations were performed using R trio package [trans-analysis was calculated as the total number of analyzed CpG sites multiplied by the number of tag SNPs in the whole dataset (pairwise-tagging r2<0.9) and subtracted by the correction value for the cis-analysis. Significance threshold was set to p<0.05 after correction for multiple testing. All SNPs connected to each CpG site after LD-based pruning were summed and the remaining number of 104,023,091 SNP-CpG pairs was used as correction value for multiple testing in cis. This resulted in a significance threshold of 0.05/104,023,091 = 4.8x10-10 in cis. In the trans-mQTL analysis, after LD-based pruning, 211,781,637,483 SNP-CpG pairs remain and this number was used as correction value for multiple testing. This resulted in a significance threshold of 0.05/211,781,637,483 = 2.3x10-13 in trans.To distinguish between local . Significant SNPs identified in the trans-mQTL were related to mRNA expression transcripts of all analyzed genes. In total, 1,164,807 SNP-mRNA transcript combinations were found for significant cis-mQTLs, and 78,710,565 SNP-mRNA transcript combinations were found for significant trans-mQTLs. Correction value for multiple testing in the eQTL analysis was then calculated in similar way as for the mQTL analysis taking LD-based SNP pruning (r2<0.9) into account. In the eQTL analysis of significant cis-mQTL SNPs, the number of LD pruned SNPs (r2<0.9) to each mRNA transcript within 500 kb were summed up and used as the correction value for multiple testing. After LD-based pruning, 934,021 SNP-mRNA transcripts remain. This resulted in a significance threshold of 0.05/934,021 = 5.4x10-8 in cis. In the eQTL analysis of significant trans-mQTL SNPs, the correction value for multiple testing was calculated as the number of all trans-mQTL SNPs pruned for LD (r2<0.9) multiplied by total number of analyzed mRNA transcripts giving a remaining number of 33,326,082 SNP-mRNA transcripts. This resulted in a significance threshold of 0.05/33,326,082 = 1.5x10-9 in trans.The relationship between SNPs found to be significantly associated with DNA methylation in the mQTL analysis and mRNA expression was tested in 118 of the men included in the study using a linear regression model with mRNA expression as a dependent variable, SNP genotypes encoded as 0, 1 or 2 according to number of minor alleles, and age, BMI and sub-cohort as covariates. Significant SNPs identified in the The impact of identified SNPs in significant mQTLs on the following phenotypes; BMI, WHR, cholesterol, triglycerides, HDL, LDL, fasting glucose, fasting insulin, HOMA-B, HOMA-IR and HbA1c, was tested in 119 Scandinavian men included in this study. Associations between identified SNPs in the significant mQTLs and metabolic phenotypes were modeled as a linear relationship using metabolic phenotypes as the dependent variable, SNP genotypes encoded as 0, 1 or 2 according to number of minor alleles, and age and sub-cohort included as covariates in all the analyses. BMI was also included as a covariate when analyzing associations between SNPs and fasting glucose, fasting insulin, HOMA IR, HOMA-B and HbA1c. Traits for fasting insulin, HOMA-B and HOMA-IR have been naturally log transformed in the study cohort before analyses. Identified mQTL SNPs showing association to a metabolic phenotype in our study cohort (p<0.05), were also looked-up in public available GWAS data from the GIANT consortium ,44, MAGI−5). To increase reference coverage for overlap between datasets of identified mQTL SNPs and identified SNPs reported in GWAS catalog, a SNP annotation and proxy (SNAP) search [2>0.8 and distance limit of 500 kb from the query SNP.The catalog of published GWAS data was used to search for SNPs reported to be associated with obesity, type 2 diabetes and related metabolic traits (p<10) search  was perfThe CIT was used to test if DNA methylation is a mediator between genotype variation and a phenotypic trait . The cauData were analyzed using linear regression models, Pearson chi-squared test or Fisher's exact test. All statistical calculations were performed using R programming language . ResultsS1 Tablecis-mQTL SNP-CpG pairs, including chromosomal location and relation to CpG islands and gene regions. Sheet b: SNP-CpG pairs where SNP is located in either C or G of the CpG site, so called CpG-SNPs. Sheet c: Additional annotation data for SNPs present in sheet a, based on HumanOmniExpress-12v1_J_Gene_Annotation_build37 (Illumina). Sheet d: Additional annotation data for CpGs present in sheet a, based on Infinium HumanMethylation 450 BeadChip [Sheet a: Identified BeadChip  and probBeadChip .(XLSX)Click here for additional data file.S2 Tabletrans-mQTL SNP-CpG pairs, including statistical results of associations and chromosomal location and relation to CpG islands and gene regions. Sheet b: Additional annotation data for SNPs present in sheet a, based on HumanOmniExpress-12v1_J_Gene_Annotation_build37 (Illumina). Sheet c: Additional annotation data for CpGs present in sheet a, based on Infinium HumanMethylation 450 BeadChip [Sheet a: Identified BeadChip  and probBeadChip (XLSX)Click here for additional data file.S3 TableKEGG pathways enriched among genes annotated to CpG sites from significant cis-mQTLs. Sheet b: KEGG pathways enriched among genes annotated to CpG sites from significant trans-mQTLs.Sheet a: (XLSX)Click here for additional data file.S4 TableSNPs that are found directly in the catalog are marked with grey, and ones that are found to be in LD with GWAS catalog SNPs with white. LD proxy analysis performed using SNAP  .(XLSX)Click here for additional data file.S5 TableSNPs that are found directly in the catalog are marked with grey, and ones that are found to be in LD with GWAS catalog SNPs with white. LD proxy analysis performed using SNAP  .(XLSX)Click here for additional data file.S6 Tablecis SNP-mRNA transcript pairs, including statistical results of associations and gene assignment for mRNA transcripts. Only pairs with p-value < 0.05 after multiple testing corrections are included. Sheet b: Additional annotation data for SNPs present in sheet a, based on HumanOmniExpress-12v1_J_Gene_Annotation_build37 (Illumina). Sheet c: Additional annotation data for probesets present in sheet a. Annotations are based on NetAffx transcript cluster data for HuGene-1_0-st array (Affymetrix).Sheet a: Identified (XLSX)Click here for additional data file.S7 Tabletrans SNP-mRNA transcript pairs, including statistical results of associations and gene assignment for mRNA transcripts. Sheet b: Additional annotation data for SNPs present in sheet a. Sheet c: Additional annotation data for probesets present in sheet a. Annotations are based on NetAffx transcript cluster data for HuGene-1_0-st array (Affymetrix).Sheet a: Identified (XLSX)Click here for additional data file.S8 Tablecis-mQTL SNPs that are identified in MAGIC, GIANT, or GLGC consortia (p<0.05) overlapping with SNPs that show association with metabolic phenotypes in the study cohort (p<0.05). Sheet a: SNPs associated with BMI in study cohort and GIANT consortium [Excel table representing nsortium . Sheet bnsortium . Sheet cnsortium . Sheet dnsortium . Sheet ensortium . Sheet fnsortium . Sheet gnsortium . Sheet hnsortium . Sheet insortium . Sheet jnsortium . Sheet knsortium .(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 TableSheet a: SNPs associated with BMI in study cohort and GIANT consortium . Sheet b(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.S14 Tablecis-mQTL CpG sites that are reported in Nilsson et al. [trans-mQTL CpG sites that are reported in Nilsson et al. [Sheet a: Identified n et al. . Sheet bn et al. . Sheet cn et al. . Sheet d(XLSX)Click here for additional data file.S15 Table(XLSX)Click here for additional data file.S16 Tablecis-mQTL SNP-CpG pairs, including chromosomal location and relation to CpG islands and gene regions. Sheet b: SNP-CpG pairs where SNP is located in either C or G of the CpG site, so called CpG-SNPs. Sheet c: Additional annotation data for SNPs present in sheet a, based on HumanOmniExpress-12v1_J_Gene_Annotation_build37 (Illumina). Sheet d: Additional annotation data for CpGs present in sheet a, based on Infinium HumanMethylation 450 BeadChip [Sheet a: Identified BeadChip  and probBeadChip .(XLSX)Click here for additional data file.S17 Tabletrans-mQTL SNP-CpG pairs, including statistical results of associations and chromosomal location and relation to CpG islands and gene regions. Sheet b: Additional annotation data for SNPs present in sheet a, based on HumanOmniExpress-12v1_J_Gene_Annotation_build37 (Illumina). Sheet c: Additional annotation data for CpGs present in sheet a, based on Infinium HumanMethylation 450 BeadChip [Sheet a: Identified BeadChip  and probBeadChip .(XLSX)Click here for additional data file.S18 Tablecis-mQTL SNP-CpG pairs, including chromosomal location and relation to CpG islands and gene regions. Sheet b: SNP-CpG pairs where SNP is located in either C or G of the CpG site, so called CpG-SNPs. Sheet c: Additional annotation data for SNPs present in sheet a, based on HumanOmniExpress-12v1_J_Gene_Annotation_build37 (Illumina). Sheet d: Additional annotation data for CpGs present in sheet a, based on Infinium HumanMethylation 450 BeadChip [Sheet a: Identified BeadChip  and probBeadChip .(XLSX)Click here for additional data file.S19 Tabletrans-mQTL SNP-CpG pairs, including statistical results of associations and chromosomal location and relation to CpG islands and gene regions. Sheet b: Additional annotation data for SNPs present in sheet a, based on HumanOmniExpress-12v1_J_Gene_Annotation_build37 (Illumina). Sheet c: Additional annotation data for CpGs present in sheet a, based on Infinium HumanMethylation 450 BeadChip [Sheet a: Identified BeadChip  and probBeadChip .(XLSX)Click here for additional data file.S20 Table(XLSX)Click here for additional data file.S21 Table(XLSX)Click here for additional data file.


Argopecten irradians irradians (Lamarck) and the southern bay scallop Argopecten irradians concentricus (Say) were introduced into China in the 1980s and 1990s, and are now major aquaculture molluscs in China. Here, we report the transcriptome sequencing of the two subspecies and the subsequent association analysis on candidate gene on the trait of heat tolerance. In total, RNA from six tissues of 67 and 42 individuals of northern and southern bay scallops, respectively, were used and 55.5 and 34.9 million raw reads were generated, respectively. There were 82,267 unigenes produced in total, of which 32,595 were annotated. Altogether, 32,206 and 23,312 high-quality SNPs were identified for northern and southern bay scallops, respectively. For case-control analysis, two intercrossed populations were heat stress treated, and both heat-susceptible and heat-resistant individuals were collected. According to annotation and SNP allele frequency analysis, 476 unigenes were selected, and 399 pairs of primers were designed. Genotyping was conducted using the high-resolution melting method, and Fisher’s exact test was performed for allele frequency comparison between the heat-susceptible and heat-resistant groups. SNP all-53308-760 T/C showed a significant difference in allele frequency between the heat-susceptible and heat-resistant groups. Notably, considerable difference in allele frequency at this locus was also observed between the sequenced natural populations. These results suggest that SNP all-53308-760 T/C may be related to the heat tolerance of the bay scallop. Moreover, quantitative expression analysis revealed that the expression level of all-53308 was negatively correlated with heat tolerance of the bay scallop.The northern bay scallop  Argopecten irradians, is naturally distributed along the eastern coast of the United States and the Gulf of Mexico, and it includes four subspecies: A. i. irradians (Lamarck), A. i. concentricus (Say), A. i. amplicostatus  and A. i. taylorae (Petuch). The northern bay scallop A. i. irradians was first introduced to China in 1982 A. i. concentricus was introduced from Florida and North Carolina in 1991 http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html), it is generally assumed that the northern and southern bay scallops differ genetically in heat tolerance. Thus, intercrossed individuals are desirable for the genetic dissection of the heat tolerance of the bay scallop.The bay scallop, HSP70) increased substantially under high temperature exposure Chlamys farreri Jones et Preston), one SNP in HSP22 associated with molecular chaperone activity and heat tolerance was revealed Many studies suggest that heat shock proteins, antioxidant enzymes and the ubiquitin-proteasome system are very important for plants and animals to adapt to temperature change by keeping the proteins at homeostasis and eliminating reactive oxygen species (ROSs) Association analysis has become popular in the past several years for trait dissection and has yielded very promising results in human disease research In this study, transcriptome sequencing was conducted for both northern and southern bay scallops, providing useful resources including unigene sequences and plentiful SNPs for further research. Moreover, hundreds of SNPs were validated, and a candidate gene association analysis was conducted in intercrossed materials with one SNP identified been closely related to the heat tolerance of the bay scallop. Quantitative expression analysis further proved that the expression level of all-53308 was negatively correlated with the heat tolerance of the bay scallop.Argopecten irradians used in this study was a marine mollusc species and was brought from the farm and acclimated in the aquarium at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS). All of the experiments were conducted according to local and national regulations. No specific permissions were required for the collection of bay scallops and the experiments described. All of the field studies were conducted at the culture station of IOCAS in Jiaonan, Qingdao and did not involve any endangered or protected species.The bay scallop Sixty-seven northern bay scallop individuals from three different aquaculture populations  and 42 southern bay scallop individuals from Zhanjiang, Guangdong Province were used to make two pools for transcriptome sequencing . For eacFirst, the RNA integrity was confirmed using an Agilent 2100 Bioanalyzer, and poly(A)+ RNA was enriched using a NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB), followed by quantification using a Qubit Fluorometer (Invitrogen). For library preparation, 100 ng of poly(A)+ RNA was fragmented in 1× fragmentation buffer (Affymetrix) at 80°C for 4 min, purified and concentrated. Double-stranded cDNA was synthesized and end repaired, and a single ‘A’ nucleotide was added to the 3′ end of the blunt fragment before the index adapter was ligated (Illumina TruSeq RNA Sample Preparation Kit). Finally, the DNA fragment was enriched by PCR amplification and quantified by quantitative real-time PCR (KAPA Library Quantification Kit) before sequencing started on the Illumina Genome Analyzer II platform.de novo assembly of short reads was conducted using the Trinity software Raw reads with an average quality score less than 15 or that contained too many N (>5%) were removed, and any low sequencing quality bases at the 3′ end were trimmed. The The general unigene sequences were blasted against the NCBI non-redundant (Nr) protein database and the Swiss-prot database using blastx with an E-value of 1E-5. In addition, both Gene Ontology analysis P value was <0.01 and there was at least a two-fold change in RPKM between the two libraries. For enrichment analysis, a hypergeometric test was performed, followed by a Bonferroni adjustment. A significant enrichment was determined when the adjusted P value was <0.01.The gene expression level was measured as reads per kilo-base per million reads (RPKM) according to Mortazavi et al. To ensure the comparability of SNPs identified from the two subspecies, the general unigene sequences were used as reference for SNP identification. SNP calling was implemented for the two subspecies of bay scallops by four steps: (1) map clean reads back to the reference using the SOAP2 package with, at most, two mismatches allowed; (2) construct the consensus for each subspecies using the SOAPsnp package; (3) compare the consensus between the subspecies and call out both heterozygous and homologous SNPs; and (4) discard SOAPsnp calls if (a) the genotype score <30, (b) the sequencing depth ≤10 or ≥100, (c) for heterozygous locus, a sequencing depth of the minor allele <4 or the minor allele frequency (MAF) <5% and (d) more than 5 SNPs exist in the adjacent 50 bp. The SNPs from the two subspecies were classified into four categories : (1) theAfter filtration, 60 SNPs were randomly selected to validate the SNP prediction. Small amplicons, each containing one selected SNP, were obtained for eight individuals and genotyped using the high-resolution melting (HRM) method To use intercrossed material for the association analysis, two populations were developed as follows: in 2011, northern bay scallops were crossed with southern bay scallops in a 20♀×20♂ manner according to Zheng et al. Unigenes annotated as heat shock proteins, proteins involved in antioxidant activity  and ubiquitin proteasome system , together with unigenes containing allelic imbalanced SNPs, were selected as candidate genes for association analysis on heat tolerance. The following two types of SNPs located in these candidate genes could not be distinguished by the HRM method directly and were filtered out: (1) SNPs with a second SNP detected in the flanking 20 bp and (2) transversion between A and T and between G and C. After filtration, primers were designed with one SNP contained in each amplicon that ranged between 40 bp and 100 bp in length. For the amplification of the target SNPs, the DNA was diluted to 10–20 ng/µL, and 1 µL was used as template. PCR was performed in a 10 µL reaction volume at 94°C for 5 min, following 45 cycles of 94°C for 30 s, 55°C for 30 s and 72°C for 30 s using a PCR premix . The primer pairs were pre-detected using polyacrylamide gel electrophoresis, and only those that produced a unique and bright electrophoretic band that was consistent with the expected size were selected for genotyping in ZZ96. The SNPs that showed polymorphism in ZZ96 were further genotyped in ZN96.Genotyping was conducted using a HRM method according to Wittwer et al. −ΔΔCT method Forty northern bay scallops were acclimated for one week at 23.5°C in a 100-L tank with aerated, filtered seawater in the laboratory. After acclimation, 20 individuals were subjected to two hours of heat stress at 30°C, and the 20 individuals that did not receive treatment were used as a control. Both the adductor muscle and gill were sampled for genomic DNA and RNA isolation, respectively. The genotype of each heat tolerance related SNPs for each individual was determined by HRM analysis. Then, individuals of each genotype were selected, and the expression pattern of the gene containing heat tolerance related SNP was investigated. Quantitative real-time PCR was conducted in a 20 µL reaction volume containing 0.4 µL of gene-specific primers (10 µM), 0.4 µL ROXII reference dye, 8.8 µL of diluted cDNA that was reverse transcribed from 1 µg of total RNA from gill tissue and 10 µL of TaKaRa Ex Taq™ SYBR premix. The mixture was subjected to the following conditions using an Applied Biosystems 7500 fast system: one cycle of 95°C for 30 s; 40 cycles of 95°C for 3 s and 60°C for 30 s; and one cycle of 95°C for 15 s, 60°C for 1 min and 95°C for 15 s. The primers used in this study are presented in −ΔCT, where ΔCT represents the difference of the CT value between two alleles. Then, the relative expression of each allele in the allele-specific quantitative real-time PCR was normalized according to the coefficient determined above.For allele-specific quantitative expression analysis, two allele-specific primers were designed according to Ye et al. P value was <0.01. The statistical significance of the differences in heat tolerance among populations and the expression level between alleles under different conditions were investigated using Student’s t-test, with a P value <0.05 considered statistically significant and a P value <0.01 considered highly significant. An association analysis was conducted using Fisher’s exact test in PLINK For the gene enrichment analysis, the results of the Gene Ontology and KEGG pathway analysis were further subjected to the hypergeometric test followed by Bonferroni adjustment de novo assembly was conducted using clean reads from each subspecies and combined reads of both. The assembly using clean reads from the northern and southern subspecies produced 60,037 and 43,996 unigenes with an N50 of 654 bp and 497 bp, respectively. In addition, the assembly using combined clean reads from the two subspecies produced 71,909 unigenes with an N50 of 750 bp  showed homology to at least one protein or domain in the Nr and Swiss-Prot databases, and 14,725 (∼17.9%) were assigned to at least one GO term. Furthermore, 9,270 unigenes were assigned pathway annotation when the KEGG pathway mapping was conducted .P<0.05), with one mucin-19-like gene up-regulated and three collagen genes down-regulated in the southern bay scallops. The GO enrichment analysis was also implemented, and enrichments in the peptidase inhibitor activity, structural molecule activity and ferric iron binding activity were observed (P<0.05).In total, 112 unigenes were down-regulated, and 43 were up-regulated in southern bay scallops . Among tSNP calling of the two subspecies of bay scallop was carried out by mapping filtered reads to the reference sequences, and SNPs were detected using SOAPsnp as described. The SOAPsnp calls were further filtered, generating a total of 32,206 and 23,312 high-quality SNPs for northern and southern bay scallops, respectively. Further, the SNPs of the two subspecies were compared and classified into four categories : 22,077 Among the 8,803 SNPs shared by the two subspecies, a subset of 60 SNPs were chosen to validate the SNP prediction. Sixty pairs of primers were designed, and 36 were well amplified by pre-detection on PAGE gel prior to the HRM analysis  and 746 unigenes annotated as proteins involved in the ubiquitin proteasome system. These 1,723 unigenes, together with 241 unigenes containing allelic-imbalanced SNPs, were selected as heat tolerance candidate genes. Because the PCR amplicon-based HRM analysis would be adopted for genotyping, the SNP loci located in these genes were further screened, and 476 unigenes remained. Among these candidate genes, 308 unigenes were determined according to annotation information, and the other 168 unigenes contained imbalanced SNPs. Overall, 399 pairs of primers were designed, of which 298 amplified well  and TT (P<0.05) genotypes  and the sampling conditions for the two subspecies.In this study, RNA-seq using pooled RNA samples from multi-individuals was conducted for both northern and southern bay scallops using the Illumina Genome Analyzer II platform. The In total, 32,206 and 23,312 high-quality SNPs were identified in northern and southern bay scallops, respectively. This result indicates that the two aquaculture stocks of bay scallop in China still possess high genetic diversity after nearly 30 years of farming. This diversity may benefit from the group breeding and spawning practices used in the industry, which incorporate hundreds to thousands of bay scallops to supply eggs and sperm The SNPs of the two subspecies were further classified into four categories, i.e., northern subspecies-specific, southern subspecies-specific, northern and southern subspecies shared and inter-specific. Sixty pairs of primers were designed for the SNP prediction validation from the shared SNPs. Among the 36 pairs of primers that were well amplified, 31 were polymorphic in eight scallop individuals that were collected at random. It was determined through this verification that over 86% of the predicted SNPs were correct. Furthermore, it was proposed that more loci would have been polymorphic if more individuals had been used. These predicted SNPs, including the 329 that were validated , could be valuable marker resources for linkage mapping and association mapping in future research.Enrichment analysis was conducted for both northern- and southern-specific SNPs. Significant enrichment was detected in oxidative phosphorylation and proteasome pathways and in protein binding, endopeptidase activity and proton-transporting ATPase activity. This result implies that the genomic structural divergence in the genes related to energy metabolism and defense may be involved in the adaptation to the different temperatures for the bay scallops.According to the chronic heat stress experiment, the ZZ and ZN populations were more heat tolerant compared with the NN population. It is suspected that the involvement of the germplasm from the southern bay scallop is responsible for the significantly improved heat tolerance of the two intercrossed populations. This finding is consistent with the recognition that the southern bay scallop is naturally distributed in a region of relatively higher temperature and should thus be more heat tolerant than the northern bay scallop.The allele frequencies derived from RNA-seq may not precisely reflect the authentic allele frequencies in the population due to allele-specific expression Consequently, in this study, unigenes that annotated as heat shock proteins, proteins that were involved in antioxidant activity and ubiquitin-proteasome system, together with unigenes that contained allelic imbalanced SNPs, were selected as candidate genes for the association analysis on heat tolerance. In total, 22 SNPs were detected to be related to the heat tolerance of the bay scallop in at least one association population. Among the unigenes involved, 18 had a blastx hit in the Nr database. Eight of these genes were related to protein homeostasis and the elimination of ROSs, and the others were involved in the molecular function of transcription activity, ion channel activity and signal transduction. SNP all-53308-760 T/C was cross-validated to be closely related to the heat tolerance of the bay scallop in the two association populations. Notably, the two sequenced natural populations showed a considerable difference in T allele frequency. It is implied that the SNP all-53308-760 T/C may have been subjected to natural selection for temperature adaptation and that the higher frequency of T allele in the southern subspecies is the consequence of local adaptation. However, it is unclear whether there is also a significant difference in allele frequency at this locus in the wild populations of the two subspecies along the eastern cost of America.It was conjectured that SNP all-53308-760 T/C or a haplotype containing this locus might indirectly influence the heat tolerance of the bay scallop by altering gene expression. Quantitative expression analysis was conducted, and a significant difference in expression was detected between individuals with different genotypes under both control and heat stress conditions. This difference may indicate the existence of a different expression pattern between the T and C alleles. Thus, allele-specific quantitative real-time PCR was further implemented in heterozygote individuals, in which the expression pattern of a particular allele was determined. A striking difference in expression level was observed between the C and T alleles, and the normalized ratio of expression level was over four to one. In combination with the association analysis, we propose that the gene expression level of all-53308 was negatively correlated with the heat tolerance of the bay scallop.The quantitative expression analysis also revealed a different expression pattern of the T allele between the TT and TC genotype individuals. It is speculated that the C allele is in high linkage disequilibrium or on the same haplotype with another variation that regulates gene expression of all-53308 in trans and most likely does so in a dominant manner. Though further study is required to address the role of all-53308 in the heat tolerance of bay scallops, our results demonstrate that SNP all-53308-760 T/C may serve as a marker for breeding practice.Figure S1Principles for the classification of SNPs between the subspecies.(TIF)Click here for additional data file.Figure S2Relative expression of all-53308 in individuals with different genotypes under control conditions.(TIF)Click here for additional data file.Figure S3The normalized ratio of the expression level between alleles under control conditions.(TIF)Click here for additional data file.Table S1Annotation information.(XLSX)Click here for additional data file.Table S2Differentially expressed genes and annotations.(XLSX)Click here for additional data file.Table S3List of inter-specific SNPs.(XLSX)Click here for additional data file.Table S4List of shared SNPs.(XLSX)Click here for additional data file.Table S5List of northern bay scallop-specific SNPs.(XLSX)Click here for additional data file.Table S6List of southern bay scallop-specific SNPs.(XLSX)Click here for additional data file.Table S7List of primer pairs that amplify well.(XLSX)Click here for additional data file.Table S8Imbalanced SNPs between the subspecies.(XLSX)Click here for additional data file.Table S9KEGG enrichment analysis information of subspecies-specific SNPs.(XLSX)Click here for additional data file.Table S10GO enrichment analysis information of subspecies-specific SNPs.(XLSX)Click here for additional data file.Table S11Heat tolerance of the ZZ, ZN and NN populations.(XLSX)Click here for additional data file.