Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast. Many statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each other’s effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term “polygenic epistasis” to distinguish these interactions from the more conventional two- or multi-locus interactions. Genetic association studies in humans and model organisms have identified a number of significant links between individual polymorphisms and phenotypic variability. A fundamental assumption of many of these studies is that an allele will have a similar effect in each member of the population, that is, that epistatic and other higher-order interactions across the genome can largely be ignored–4. PriorTcf7l2 and Cacna1c when expressed on different inbred strain backgrounds[Recently, we developed a SNP-ancestry interaction model and applied it to a human dataset, detecting only modest evidence of ancestry-specific genetic effects. There ickgrounds. Recentlckgrounds, the Colckgrounds,14, the ckgrounds, variousckgrounds, or the ckgrounds. The varTo address this problem, we incorporate a linear mixed model capable of accounting for the highly structured relatedness of constructed model organism into ourth generation intercross between the inbred LG/J and SM/J mouse strains[We first evaluated the statistical properties of our method on simulated phenotypes based on real recombinant inbred line data sets. The Gxθe strains, whose ae strains  for metallopeptidases, which play key roles in fertility and neo/perinatal lethality–23.Our observation of genomic locations where the effect size of a given polymorphism is driven by overall genomic context and ancestry highlights the importance of studying epistasis and other effects in model organisms. Studying these context-dependent effects in human cohorts has been notoriously difficult, but our results indicate that given the right design and analytical framework these effects can be highly significant, and has implications for our ability to build predictive models from genotype to phenotype and account for ancestry effects in population studies.Xi and a phenotype y, we can model this relationship as y = μ+βiXi+u+e, where μ is the mean phenotypic value, β is the effect size of the SNP, u is a random variable accounting for relatedness and e is a random variable accounting for all other sources of error in the study. We extend this analysis to account for interactions between a polymorphism and ancestry by adding terms to our model such that the relationship now becomesβGxθ are the weights of the Gxθ effect. Our Gxθ test is then an LRT test with a null of βGxθi = 0 and an alternate of βGxθi ≠ 0.Our objective is to determine whether a given SNP i has a different effect size as a function of changes in overall genetic background. As described in our previous work, this isTo account for multiple testing, we follow the best practices of the respective model organism communities, specifically we use the previously reported genome-wide significance threshold for the HMDP of 4.2E-6  or the AG or a SNP-ancestry interaction effect βGxθ where θ is the proportion of all SNPs arising from C57BL/6J  lines of the Hybrid Mouse Diversity Panel (HMDP). Random SNPs with a minor allele frequency (MAF) between 25% and 75% were selected from our panel and phenotypes constructed at 200 values of either a main SNP effect βL/6J see  mice forL/6J see .G or βGxθ independently did not affect the power of identifying an association for βGxθ or βG, respectively, indicating that these two terms are correctly being estimated independently of one another. We briefly explored incorporating a second genetic relationship matrix (GRM) accounting for population structure arising from descent from a single ancestor (the B6 mouse)[We observe that changes to either βB6 mouse), howeverTo evaluate what forms of interactions the test is powered to identify we examined a range of epistatic architectures, focusing specifically on our method’s ability to recover higher-order interactions, a type of epistasis which traditional models struggle to accommodate. For 100,000 simulations, a SNP was selected at random from SNPs with MAF between 25 and 75% and combined with 1–10 additional SNPs  and phenotypes generated for these SNP groupings followed by analysis by the Gxθ algorithm see . As the 2 cross between the sub-panel’s parental lines. In the case of the HMDP, one of the parental strains for each RI strain was the commonly studied strain C57BL/6J (B6). Using B6 as the common ancestral line to the RI panels, we calculated θs for each RI strain . Our first cohort is the HMDP, a set of 150+ commercially available inbred strains. NumerouI strain . As expe50 –F56 generation of an advanced intercross line (AIL) created by crossing the LG/J and SM/J inbred mouse strains [The second cohort consists of 1,063 animals from the F strains . Unlike GC equal to 0.978 and λGxθC equal to 1.045 after pooling the p values across all phenotypes. We observed 44 significant Gxθ loci across 9 phenotypes: E/A ratio, free fatty acid content in the blood, cardiac fibrosis, fractional shortening of the heart during contraction, heart rate, internal diameter of the left ventricle, left ventricular mass and left and right atrial weights , a secreted biomarker of cardiovascular disease[Top2b , which plays a cardioprotective role in response to stress[Rarb , a known regulator of inflammation with unknown function in the heart[Fibrosin , a major component of the fibrosis pathway[Lphn2, which has a role in the promotion of cellular adhesion in response to external stimuli.[By way of example, we focus on two important phenotypes from the HMDP panel. Cardiac fibrosis is a marker of cardiac dysfunction. Genes identified through the Gxθ screen  as potenr disease, Top2b  in blood after a 4 hour fast ), suggesting a role of Gxθ interactions in the regulation of mitochondrial dynamics. Genes in the treated cohort with significant associations were enriched for genes involved in post-transcriptional modifications to RNA including RNA splicing (P = 7.4E-3), highlighting the importance of alternative splicing to the response to catecholamine challenge[Phenotypic variability was larger in the catecholamine treated cohort of the heart failure HMDP study (average Coefficient of Variation in control was 22.6% vs 28.6% for the treated cohort), which cchallenge.To demonstrate that the method is able to replicate Gxθ results across cohorts, we examined the reproducibility of expression Gxθ QTLs in the treated and untreated RI lines of the HMDP. Of the 1,486 associations observed in the control data, we observe 305 (21%) with Gxθ interaction (P < .05) in the treated cohort (36 at FDR< 5%), indicating a strong replication  between the two cohorts despite differences in genetic background and the potentially reduced power in the treated cohort caused by the effects of the catecholamine drug. All significant associations may be found in S10 TableAfter population admixture, allelic frequencies will shift in the resulting population. This can be due to the effects of selection against deleterious allelic combinations (i.e. fitness epistasis), meiotic drive . Alternatively, allele frequencies may shift through random genetic drift. We hypothesize that Gxθ loci can be driven by fitness epistasis, or instances where allelic variation in one locus affects fitness at other loci across the genome. This hypothesis is in line with prior research–40 whichWe therefore conducted a test to search for individual loci with enriched or depleted B6 ancestry in the RI strains. As expected, the average B6 ancestry across all SNPs was 50.68% +/- 8.1%, which is indistinguishable from the ancestry by strain average of 50.63% +/- 6.7% . At the We subsequently performed a Wald test for ancestry frequency (θ) at each SNP i where DR of 5% . We suspEpc1, a transcription factor linked to DNA repair, muscle differentiation and cancer suppression[Nine loci were identified in which 5 or more SNPs were located together . These 9ppression,43. It iWhen all genes within 2 MB (the average Linkage Disequilibrium (LD) block size in the HMDP) of bounWe present a test which we call Gxθ, that leverages admixed populations such as inbred mouse strains to identify sites of “polygenic epistasis” where a SNP interacts with an unknown number of other loci summarized into a single genomic ancestry score, θ. One major advantage of this approach is that, unlike other epistasis-focused association approaches, it does not increase the number of tests when compared to a typical GWAS, so similar significance thresholds may be used.The existence of epistatic interactions and their role in human diseases and phenotypes have been known for many years, with notable examples of gene-gene interactions in Alzheimer’s disease, Bardet-Our method sought to circumvent this traditional pitfall in Epistasis GWAS by examining each SNP only once for an interaction with a global genomic ancestry to identify interactions or “polygenic epistasis”. Our model is able to identify much higher order interactions than can be observed using traditional SNP-SNP interaction models. As demonstrated through simulation, our model in fact increases in power as the complexity of the epistatic interactions increases, likely reaching peak power in the hypothetical scenario where every SNP interacts with the tested SNP to affect phenotypic expression. When applied to human populations, a different form of our test was able to find several significant associationsIn this study, we examined two populations of mice: the recombinant inbred lines of the Hybrid Mouse Diversity Panel and an ASeveral reasons may account for this difference. First, outbred mice have significantly lower power due to increased phenotypic variance caused by high rates of heterozygosity at alleles while inbred populations have increased power due to a lack of this heterozygosity. This can also be seen in our regular GWAS results,29,30. SFurthermore, we identify nine regions across the genome of the RI lines of the HMDP which show a strong selection for or against a particular parental allele. We propose that this is a result of an epistatic fitness interaction, where individual loci in one ancestral strain are genetically incompatible with alleles from another strain. We considered other alternatives explanations, some of which were explored in prior work. One altIn the future, our approach could be leveraged to study additional extant populations or newly created cohorts. For example, our tool could be used to examine whether genetic modifications such as knockouts have differential effects across the mice of a cross, and we are actively exploring its utility in this regard.The results of our study suggest that heterogenous SNP effects due to differing ancestries is pervasive in model organism populations. This observation is consistent with prior observations of epistatic interactions in inbred strains of mice as well as examples of ancestry interaction in human studies. HoweverModel organism data were drawn from previously reported studies,20,29. TKA), which would correct for relatedness in ancestry, into the algorithm.We created sets of simulated phenotypes based on the genotypes of the HDMP RI panel, which is an admixed population in which the B6 strain, on average, contributes 50% of each strain’s DNA. For each simulated phenotypes, we drew a SNP (75% > MAF > 25%) at random from the HMDP genotypes and created a phenotype based on β, the genetic effect size, ϕ the effect size of the interaction between global ancestry (θ) and the chosen SNP and a multivariate normal (mvn) derived from three variance terms: We simulated four distinct phenotypes for our analysisPhenotypes generated by including a SNP EffectPhenotypes generated by including a Gxθ Effectβ and βGxθ terms were queried by varying either β or βGxθ from 0 to 1  with 1,000 simulated phenotypes at each step .In each phenotype, 2 test statistic for the 2–11 interacting SNPs set to a large value of 20 to ensure a consistent and observable effect that could be recovered using either regular GWAS with pyLMM or our approach.Using the same panel and set of SNPs described above, we drew one SNP at random to be our test SNP and one to ten additional SNPs to be simulated epistatically interacting SNPs . For each simulation we created a composite SNP in which only strains with the minor (non-B6) allele in every one of the tests and interacting SNPs had that allele in the composite SNP. We used this composite SNP to generate a phenotype as described above with a χyk is the phenotype of individual k, μ is the mean phenotypic value, M is the number of markers, β are the weights on the SNPs, X is the m by n array of SNP genotypes, δ is the global weight of the ancestry effect, θ is the ancestries for all N individuals. βGxθ are the weights of the Gxθ effect and ε is the combined error term. We want to identify SNPs where βGxθi ≠ 0 as these are sites where Ancestry is interacting with our genotypes.The equation to determine the effects of a SNP and a SNP x Ancestry term on a phenotype can be written as:Motivated by our model above, we can write a new model for the effect of a single SNP i on a phenotypic trait as:www.biogps.org) which draws data from PubMed and other publicly available databases to help researchers understand the role of their genes of interest.GWAS thresholds were previously determined to be 4.1E-6 in the case of the HMDP or 8.06EAllelic imbalance significance was determined by performing a Wald test for ancestry frequency (θ) at each SNP i where All gene enrichments were performed using the GeneAnalytics platform, which uEthics approval and consent to participate: Not ApplicableConsent for publication: Not ApplicableS1 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 TableHomozygous differences are 1, heterozygous differences are 0.5.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(CSV)Click here for additional data file.S7 Table(CSV)Click here for additional data file.S8 Table(CSV)Click here for additional data file.S9 Table(CSV)Click here for additional data file.S10 Table(CSV)Click here for additional data file.


EphA2 is an important oncogenic protein and emerging drug target, but the oncogenic role and mechanism of ligand-independent phosphorylation of EphA2 at tyrosine 772 (pY772-EphA2) is unclear. In this study, we established nasopharyngeal carcinoma (NPC) cell lines with stable expression of exogenous EphA2 and EphA2-Y772A (phosphorylation inactivation) using endogenous EphA2-knockdown cells, and observed that pY772A EphA2 was responsible for EphA2-promoting NPC cell proliferation and anchorage-independent and in vivo growth in mice. Mechanistically, EphA2-Y772A mediated EphA2-activating Shp2/Erk-1/2 signaling pathway in the NPC cells, and Gab1 (Grb2-associated binder 1) and Grb2 (growth factor receptor-bound protein 2) were involved in pY772-EphA2 activating this signaling pathway. Our results further showed that Shp2/Erk-1/2 signaling mediated pY772-EphA2-promoting NPC cell proliferation and anchorage-independent growth. Moreover, we observed that EphA2 tyrosine kinase inhibitor ALW-II-41-27 inhibited pY772-EphA2 and EphA2-Y772A decreased the inhibitory effect of ALW-II-41-27 on NPC cell proliferation. Collectively, our results demonstrate that pY772-EphA2 is responsible for EphA2-dependent NPC cell growth in vitro and in vivo by activating Shp2/Erk-1/2 signaling pathway, and is a pharmacologic target of ALW-II-41-27, suggesting that pY772-EphA2 can serve as a therapeutic target in NPC and perhaps in other cancers. Radiotherapy is the major therapeutic modality used to treat NPC and NPC patients can be cured if the disease is diagnosed and treated at an early stage. However, most of NPC patients are diagnosed at advanced stages due to nonspecific symptoms and these patients respond poorly to radiotherapy, whose prognosis is dismal2. Therefore, it is imperative to elucidate the underlying mechanism of development and progression of NPC, which could offer novel therapeutic targets.Nasopharyngeal carcinoma (NPC) is a head and neck cancer that shows a distinct endemic distribution with a high prevalence in Southern China and Southeast Asia, and remains one of the leading lethal malignancies in these areas4. EphA2 signals from both the receptor  and the ligand  to form a communication system with critical and diverse roles in health and disease4. EphA2 activation by a ligand follows a classical pattern of RTK activation. When ligand Ephrin-A1 binds to EphA2, EphA2 occurs oligomer clustering and subsequent trans- and auto-phosphorylation of tyrosine residues in the juxtamembrane region and kinase domain5. This ligand- and tyrosine kinase-dependent EphA2 activation  inhibits cancer cell proliferation, adhesion, and motility and tumor angiogenesis9. In contrast, ligand- and tyrosine kinase-independent EphA2 activation  promotes cancer development and progression11. Previous studies have shown that Serine897 (S897) and Tyrosine 772 (Y772) of EphA2 are important phosphorylated residues6. Although numerous studies have demonstrated that ligand-independent S897 phosphorylation of EphA2 (pS897-EphA2) promotes cancer development and progression15, the role of ligand-independent Y772 phosphorylation of EphA2 (pY772-EphA2) in cancers is unclear.EphA2 is a member of the EphA family of receptor tyrosine kinases (RTKs) and possesses both tumor-promoting role in a ligand-independent manner and tumor -suppressing role in a ligand-dependent manner16. Shp2 encoded by PTPN11  gene is the first PTP to be identified as an oncogene18 and possesses an oncogenic role in the melanoma, leukemia, and lung and breast cancers22. Shp2 is implicated in the transduction of mitogenic, pro-survival, and pro-migratory signals from growth factor receptors23, and is required for the activation of Erk-1/2 signaling downstream of most RTKs26. EphA2 overexpression contributes to ErK-1/2 activation and cancer progression has been reported in many types of cancers28. A recent study indicates that EphA2 phosphorylates Shp2 and subsequently activates Erk-1/229. However, whether ligand-independent pY772-EphA2 mediates EphA2-activating Shp2/Erk-1/2 signaling is unknown.We recently used immunoprecipitation and mass spectrometry analysis (IP-MS) to search proteins interacted with EphA2 in NPC cells and found that Shp2, a protein tyrosine phosphatase (PTP), is an interactor of EphA2. PTPs play a crucial role in cancers30, possesses obvious in vitro and in vivo anti-tumor effects in lung cancer33, melanoma34, triple-negative breast cancer35, and intrahepatic cholangiocarcinoma36. As an EphA2 tyrosine kinase inhibitor, whether ALW-II-41-27 inhibits cancer progression by inhibiting pY772-EphA2 has not been explored.An ATP-competitive EphA2 tyrosine kinase inhibitor, ALW-II-41-27In the present study, we try to determine whether and how ligand-independent pY772-EphA2 promotes NPC growth, and tested whether pY772-EphA2 is a target of ALW-II-41-27. Our results demonstrate that pY772-EphA2 is responsible for EphA2-dependent NPC cell growth both in vitro and in vivo by activating the Shp2/Erk-1/2 signaling pathway, and that pY772-EphA2 is a pharmacologic target of ALW-II-41-27.37. To explore the functions of pY772-EphA2, we transfected plasmid expressing shRNA-resistant cDNA encoding EphA2 or EphA2-Y772A into 5-8F-shEphA2 and CNE2-shEphA2 cells, respectively, and established 5-8F and CNE2 cell lines with stable expression of exogenous EphA2 (EphA2-WT) or EphA2-Y772A (EphA2-YA). Western blotting showed that the established 5-8F and CNE2 cell lines expressed the equivalent levels of exogenous EphA2-WT and EphA2-YA, and Y772A mutation abolished the phosphorylation of EphA2 at Y772 (pY772-EphA2) but did not affect the phosphorylation of EphA2 at S897 (pS897-EphA2)  targeting EphA2 mRNA 3′-untranslated region, which were named as 5-8F-shEphA2 and CNE2-shEphA2, respectivelyA2) Fig. . Next, wA2) Fig. , indicatSoft agar colony formation assay showed that EphA2-WT dramatically increased the anchorage-independent growth of NPC cells, whereas EphA2-YA failed to do it as compared to endogenous EphA2 knockdown Fig. , indicatTo search proteins that interact with EphA2, EphA2 interactors were coimmunoprecipitated with anti-EphA2 antibody from NPC cell extracts, separated on SDS-polyacrylamide gel electrophoresis (PAGE), and stained with Coomassie blue Fig. . All pro26. The recent study has indicated that EphA2 activates Erk-1/2 by phosphorylating Y542/580 of Shp229. Our results showed that Shp2 interacted with EphA2 in the NPC cells  induces pS897-EphA2 in cancer cells by a ligand-independent mechanismIn summary, we found that ligand-independent pY772-EphA2 is responsible for EphA2-dependent NPC cell growth in vitro and in vivo by activating the Shp2/Erk-1/2 signaling pathway and pY772-EphA2 is a target of ALW-II-41-27 in the NPC cells Fig. . Our datThe antibodies against p-EphA2-Y772, p-EphA2 , p-Shp2 , p-Shp2 , p-Gab1, p-ERK-1/2(#4370), p-AKT, p-stat3 , Gab1(#3232), AKT (#4691), and Stat3 (#4904) were purchased from CST . Antibodies against Shp2 (ab187040) and Grb2(ab32111) were purchased from Abcam. Antibodies against EphA2 (sc-924) and Erk-2 (sc-154) were purchased from Santa Cruz. Horseradish peroxidase (HRP)-conjugated goat anti-rabbit (A24531) and anti-mouse IgG antibodies (A24512), and Protein G/A-Sepharose™ 4B (#82085) were purchased from ThermoFisher Scientific. SHP099 (S8278) and U0126 (S1102) were purchased from Selleck Chemicals. ALW-II-41-27 (HY-18007) was purchased from MedChemExpress. CCK-8 (C0038) was purchased from Beyotime, Inc. EdU incorporation detection kit (C10310-1) was purchased from RiboBio, Inc.37. pReceiver-M35-Shp2 and pReceiver-M13-ERK-2 expression plasmids were purchased from Genechem. pLKO.1 puro-Gab1-shRNA was a gift from Professor Hailing Cheng  and pSIH-H1-puro-Shp2-shRNA was a gift from Professor Shan Gao . The target for Gab1 and Shp2 shRNA is 5′-GTTACGCAGTGGCCGTTTAAC-3′ and 5′-GGGAAAGAAGCAGAGAAATTATTAACTGA-3′, respectively.Lentiviral vector GV341 expressing EphA2-Y772A cDNA was constructed by Genechem  and was confirmed by DNA sequencing. pBabepuro-EphA2 and -EphA2-S897A plasmids have been described previously by us37. Cells were cultured in RPMI-1640 medium supplemented with 10% FBS (Life Technologies) at 37 °C in 5% CO2. The cell lines were authenticated by short tandem repeat profiling prior to use and were routinely tested negative for mycoplasma contamination using 4,6-diamidino-2-phenylindole staining.5-8F and CNE2 NPC cell lines and 5-8F-shEphA2 and CNE2-shEphA2 NPC cell lines with stable knockdown of endogenous EphA2 by shRNA have been described previously by us42. Data of MS/MS were searched against Swiss-Prot database (Homo sapiens). Individual ions scores > 35 and unique peptide ≥ 1 indicate identity or extensive homology (P < 0.05) and were considered significant. All proteins deemed to be high confidence interactors of EphA2 were identified by at least two times of three replicates.Total proteins were extracted from NPC 5-8F cells using NP-40 lysis buffer and 1.2 mg of total proteins were incubated with 30 μl Protein A/G- Sepharose™ 4B for 4 h at 4 °C followed by centrifugation for 5 min at 4 °C. The clarified supernatants were immunoprecipitated with 2 μg anti-EphA2 antibody and 30 μl Protein A/G-Sepharose 4B overnight at 4 °C, and centrifuged for 5 min at 4 °C. The beads were washed three times with PBS at 4 °C and boiled in 2× SDS-PAGE loading buffer for 5 min to elute protein complexes. The elutants were separated on SDS-PAGE gel followed by Coomassie brilliant blue G250 staining and then all protein bands were excised from the gels, in-gel trypsin digested, and subjected to NanoLC-MS/MS analysis with the Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (ThermoFisher Scientific) to get MS/MS spectra as previously described5-8F-shEphA2 and CNE2-shEphA2 NPC cells were infected with the lentiviral vector GV341 expressing EphA2-Y772A cDNA following the manufacturer’s instruction, or transfected with pBabepuro-EphA2 plasmid using Lipofectamine 2000 (ThermoFisher Scientific). Cells were selected using puromycin (ThermoFisher Scientific) for 2 weeks, and 5-8F and CNE2 cell lines with the stable expression of exogenous EphA2 or EphA2-Y772A and their control cell lines were obtained.Nude male mice (BALB/c nu/nu) that were 4 weeks old were obtained from the Laboratory Animal Center of Central South University and maintained in pathogen-free conditions. All animal experimental procedures were performed in accordance with the Guide for the Care and Use of Laboratory Animals of Xiangya Hospital, Central South University, with the approval of the Institutional Animal Ethics Committee.6 cells resuspended in 200 μl of medium without serum were subcutaneously injected into the flanks of mice (n = 5 mice each). The mice were monitored daily for palpable tumor formation and tumor volume (in mm3) was measured by a vernier caliper every 3 days and calculated by using the modified ellipse formula (volume = length × width2/2). After 21 days, the mice were killed by cervical dislocation and their tumors were excised, weighed, and embedded in paraffin.The mice were randomly divided into the indicated groups before inoculation and 5 × 1037. Briefly, proteins were exacted from cells or tissues using RIPA lysis buffer. An equal amount of protein in each sample was subjected to SDS-PAGE separation, followed by blotting onto a PVDF membrane. After blocking, blots were incubated with primary antibody overnight at 4 °C, followed by incubation with HRP-conjugated secondary antibody for 1 h at room temperature. The signal was visualized with chemiluminescence detection reagent (Millipore).Western blotting was performed as described previously by usCo-IP was performed to detect protein interaction. In brief, whole cell lysates were incubated with indicated antibodies and Protein G/A-Sepharose 4B overnight at 4 °C. After five times wash with RIPA buffer, beads were boiled in 2× SDS-PAGE loading buffer for 5 min to elute protein complexes, followed by SDS-PAGE separation and immunoblotting with specific antibodies.43. The assay was performed three times in triplicate.Cell proliferation was measured using a CCK-8 kit as described previously by us43. The assay was performed three times in triplicate.Plate colony formation assay was performed to detect cell proliferation described previously by us43. The assay was performed three times in triplicate.EdU incorporation assay was performed to detect cell proliferation as described previously by us43. Cells were allowed to grow in the soft agar cultures for 12 days and colonies consisting of >50 cells were counted under the microscope. The assay was performed three times in triplicate.Soft agar colony formation assay was performed to detect cell anchorage-independent growth as described previously by us43. Briefly, tissue sections were incubated with anti-p-Shp2(Y542) antibody  or anti-p-Erk-1/2 antibody (1 : 400 dilution) overnight at 4 °C, and then incubated with biotinylated secondary antibody followed by avidin–biotin peroxidase complex (DAKO) at room temperature for 30 min. Finally, tissue sections were incubated with 3′, 3′-diaminobenzidine (Sigma-Aldrich) and counterstained with hematoxylin.IHC analysis was performed on the formalin-fixed and paraffin-embedded xenograft tissue sections as described previously by usStaining intensity was categorized as follows: absent staining as 0, weak as 1, moderate as 2, and strong as 3. The percentage of stained cells (examined in at least 500 cells) was categorized as no staining = 0, <30% of stained cells = 1, 30~60% = 2, and >60% = 3. The staining score (ranging from 0 to 6) for each tissue was calculated by adding the area score and the intensity score. A combined staining score of ≤3 was considered to be low expression and >3 was considered to be high expression.43. Briefly, cells were plated into chamber slides (Millipore) and fixed in 4% paraformaldehyde, permeabilized, and incubated with mouse anti-EphA2 antibody (1 : 300 dilution) and rabbit anti-Shp2 antibody (1 : 100 dilution), followed by incubation with DyLight® 488 anti-mouse IgG  and DyLight® 594 anti-Rabbit IgG . Images were captured using an inverted confocal fluorescent microscope (LEICA TCS SP8). Nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI).Immunofluorescent staining was performed as described previously by ust-test or χ2-test. All statistical tests were two-sided and p-values < 0.05 were considered statistically significant.Statistical analysis was performed using IBM SPSS statistical software package 22. Data are presented as means ± SD. Qualitative variables were compared by the Student’s Figure S1Figure S2Supplementary Figure legends


The antioxidant capacity of foods is essential to complement the body’s own endogenous antioxidant systems. The main antioxidant foods in the regular diet are those of plant origin. Although every kind of food has a different antioxidant capacity, thermal processing or cooking methods also play a role. In this work, the antioxidant capacity of 42 foods of vegetable origin was evaluated after in vitro digestion and fermentation. All foods were studied both raw and after different thermal processing methods, such as boiling, grilling roasting, frying, toasting and brewing. The cooking methods had an impact on the antioxidant capacity of the digested and fermented fractions, allowing the release and transformation of antioxidant compounds. In general, the fermented fraction accounted for up to 80–98% of the total antioxidant capacity. The most antioxidant foods were cocoa and legumes, which contributed to 20% of the daily antioxidant capacity intake. Finally, it was found that the antioxidant capacity of the studied foods was much higher than those reported by other authors since digestion–fermentation pretreatment allows for a higher extraction of antioxidant compounds and their transformation by the gut microbiota. According to nutritional epidemiological evidence, the consumption of fruit and vegetables, as well as other plant-derived foods, is associated with a protective effect against several noncommunicable diseases such as cardiovascular disease, diabetes 2, metabolic syndrome, cancer or inflammation, where oxidative stress plays an important role . PhytochOn the other hand, once ingested, foods undergo gastrointestinal digestion. However, due to the lack of enzymes to digest plant cell wall polysaccharides, some structures remain relatively intact upon reaching the large intestine. It is well known that fiber is the main energy source for gut microbes  and phenIn order to shed some light on this matter, this paper evaluates the antioxidant capacity of the most representative food items from the vegetable kingdom submitted to different cooking methods . In addition, these foods were submitted to in vitro digestion–fermentation to simulate the activity of the human gut microbiota for a more realistic estimation of the antioxidant capacity of foods. Finally, the contribution of the intake of plant foods to the daily antioxidant capacity of the Spanish diet was determined.Salivary alpha-amylase, pepsin, porcine bile acids (porcine bile extract), sodium dihydrogen phosphate, sodium sulfide, resazurin, tryptone and cysteine were obtained from Sigma-Aldrich . Pancreatin (from porcine pancreas) was purchased from Alpha Aesar . 2,2 Diphenyl-1-1-picrythydrazul hydrate 95% (DPPH), Trolox ((±)-6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid), 2,4,6-tri(2-pyridyl)-s-triazine (TPTZ), sodium acetate, iron (III) chloride hexahydrate, methanol, hydrochloric acid, sodium carbonate, gallic acid and the Folin–Ciocalteu® reagent were purchased from Sigma-Aldrich . A total of 42 plant foods  were stuThe samples were submitted to different culinary treatments: boiling, grilling, roasting, frying, and toasting. Brewing was also included as a culinary treatment (as stated above for coffees), although it involves a technological process to obtain some of the liquid foods studied (beer and red wine). In addition, for some samples, the raw food was also investigated as this is the common way to eat those foods. A total of 107 samples were obtained. Fruits, vegetables and tubers were cut in different sizes to achieve the same final texture after the different thermal treatments were applied.Extra virgin olive oil (EVOO) was used as a cooking medium for grilling and frying. Boiling was performed at 100 °C for 20 min at a water/food rate of 5:1. Grilling was performed at 220–250 °C for 3 min on each side at an oil/food rate of 0.5:1. Roasting was performed at 180 °C for 10 min. Fried foods were obtained at 180 °C for 8 min at an oil/food rate of 5:1. Toasting was performed in a Grunkel TS140H toaster at the fourth level for 3 min at 900 W following the manufacturer’s instructions. Cooking times and food/medium rates were acquired from Ramírez-Anaya et al. [The utensils used for sample preparation were the following: stainless steel spoons, forks, and knives; saucepan, frying pan, a portable oven (1500 W), fryer, and toaster. All these utensils were purchased from Centro Hogar Sánchez . Samples were homogenized and stored under a nitrogen atmosphere at −80 °C in order to avoid oxidation. All analyses were performed in duplicate.Plant foods, after proper cooking, were submitted to in vitro batch digestion–fermentation in order to mimic physiological processes in the human gut, according to a protocol previously described . For eacAfter in vitro gastrointestinal digestion and fermentation, two fractions were obtained: a digested fraction  and a fermented fraction (available for absorption at the large intestine). The antioxidant capacity was evaluated in the two fractions obtained after in vitro digestion and fermentation: the supernatant obtained after gastrointestinal digestion  and the supernatant obtained after fermentation . The sum of the two fractions accounts for the total antioxidant activity that each food could exert within the human body . Three dDPPH) assay. This method was conducted according to the procedure of Yen and Chen [Trolox equivalent antioxidant capacity against DPPH radicals  assay. The ferric reduction capacity of samples was assessed through the procedure described by Benzie and Strain [Trolox equivalent antioxidant capacity referred to reducing capacity . As a factor for ANOVA, we used the type of cooking , type of food  and type of sample . Statistical analysis was performed using raw vegetables and the mean of all food groups as the reference groups. Pearson’s correlation coefficient was calculated to show the linear relation between antioxidant capacity at p < 0.05. To obtain the significance between the different levels within the same group, Tukey’s test was performed. All statistical analyses were performed using Statgraphics Plus software, version 5.1. The statistical significance of the data was tested by one-way analysis of variance (ANOVA), followed by the Duncan test to compare the means that showed a significant variation (p < 0.05) were positive, with values around Rs = 0.80. We tested the potential physiological antioxidant capacity of plant foods after in vitro digestion–fermentation with three different methods . In general, a linear correlation was obtained by the Spearman method between the three methods . The sigIn the following sections, a deeper description of the results obtained by type of cooking, type of food and each group of plant foods will be reported.Cooking methods had an impact on the antioxidant capacity of the digested fraction, as will be explained in the following sections.p < 0.05) higher antioxidant capacity than boiled and toasted foods for the TEACDPPH assay (p < 0.05) as opposed to the previous method; however, raw foods were still more antioxidant than those cooked with the other methods  antioxidant than boiled, grilled and toasted foodstuffs, as in the other methods (Raw foods showed a significantly (PH assay  but lowePH assay A. In the methods B. For th methods C.p < 0.05): for the TEACDPPH method, fried foods were more antioxidant than boiled; for the Folin–Ciocalteu method, toasting resulted in higher antioxidant values than those of the other cooking methods; for the TEACFRAP method, raw foods showed higher antioxidant capacity than grilled foods. Comparing the mean antioxidant capacity of digested samples after different cooking treatments, the following significant differences were found  lower in boiled and toasted vegetables compared to raw foods (p < 0.05) lower antioxidant capacity in brews and toasted foods compared to raw foods  than that of raw foods  lower total antioxidant capacity for brewed and toasted foods  for the TEACDPPH method (p < 0.05) antioxidant capacity for nuts, tubers, alcoholic drinks, fruits and vegetables (FRAP), it was higher (p < 0.05) for coffee but lower for tubers, fruits and vegetables , the following significant differences were found: for the TEACDPPH method, fruits had a higher antioxidant capacity than cereals and vegetables, whereas legumes were more antioxidant than cereals, nuts, tubers and vegetables. In the case of the Folin–Ciocalteu method, cocoa and nuts had a higher mean than the rest of the foods (with no significant differences between them), and fruits were more antioxidant than cereals. Finally, for the TEACFRAP method, cocoa and coffee were more antioxidant than the other groups.When comparisons were made between different types of plant foods  than the other food groups (p < 0.05) higher antioxidant capacity for cocoa, legumes and tubers , were also analyzed separately. p < 0.05) measured with TEACDPPH was higher than that of the other cooking methods for both the digested and fermented fractions, resulting in a higher total antioxidant capacity (p < 0.05). Finally, the reducing capacity of cooked cereals (TEACFRAP) showed the same behavior as the DPPH method  than the other foods. However, for the fermented fraction and total antioxidant capacity, the following order was obtained: biscuits = breakfast cereals > bread = rice = pasta. Comparisons were also made between refined and whole-grain cereal products, but no significant differences were observed. On the other hand, when samples were compared, depending on the type of cereal-based food, they behaved similarly irrespective of the antioxidant assay A–C. For p < 0.05) than raw and grilled foods but only for the Folin–Ciocalteu method  than the other fruits but just for the TEACDPPH method  were analyzed. The digested fraction of olives and plum were more antioxidant . No statistically significant differences were observed for either the fermented fraction or total antioxidant capacity, although after-fermentation values were always much higher than those obtained for the digested fraction. The analysis of the effect of cooking techniques on vegetables was interesting since these types of foods can be eaten both raw or heat-treated. In the case of the digestion fraction, for the TEACDPPH A, Folin–DPPH B and TEAPH (FRAP C methodsp < 0.05) differences were found between most, regardless of the antioxidant assay used (p < 0.05) than the others. For the fermented fraction and total antioxidant capacity vegetables behaved differently depending on the antioxidant capacity method assessed, and no clear tendency was observed. Ten different vegetables were individually studied . Overall, the antioxidant capacity of the digested fractions across vegetables was similar and not significant , potentially due to the essential role that the gut microbiota plays in the release of antioxidant compounds from the undigested food matrix ,29. In fThe highest total antioxidant capacity was found in cocoa and the lowest in alcoholic drinks. Cocoa is rich in phenolic compounds, substances with a high antioxidant capacity , which eIn cereals, biscuits and breakfast cereals were the most antioxidant foods, potentially due to the generation of melanoidins during thermal processing ,32. In tThe antioxidant capacity of a given food could be of interest in regard to its shelf life; higher antioxidant capacity means lower oxidative degradation. However, much attention is drawn to human health. Therefore, it is interesting to calculate the contribution of each kind of food to the daily antioxidant intake in a regular diet. Accordingly, in vitro digestion and fermentation, cocoa and legumes stand out among all foods for their antioxidant capacity. In the group of fruits, olives and bananas were the most relevant, whereas lettuce and pepper were the most antioxidant foods in the vegetables group. In addition, based on the results included in this paper, the antioxidant capacity of plant foods has been underestimated in the last decades since the key role of the gastrointestinal system on the release and transformation of antioxidant molecules was not taken into consideration. Therefore, future studies should be conducted including this new approach to test the physiological transformation of foods to calculate their contribution to the daily antioxidant capacity intake. In conclusion, this study reinforces the concept that plant foods are a great source of antioxidant compounds for human beings. After


As an alternative, we aimed to identify associations among suggestive signals (5 × 10−8≤p<5×10−4) in increasingly powered GWAS efforts using chromatin accessibility and direct contact with gene promoters as biological constraints. We conducted retrospective analyses of three GIANT BMI GWAS efforts using ATAC-seq and promoter-focused Capture C data from human adipocytes and embryonic stem cell (ESC)-derived hypothalamic-like neurons. This approach, with its extremely low false-positive rate, identified 15 loci at p<5×10−5 in the 2010 GWAS, of which 13 achieved genome-wide significance by 2018, including at NAV1, MTIF3, and ADCY3. Eighty percent of constrained 2015 loci achieved genome-wide significance in 2018. We observed similar results in waist-to-hip ratio analyses. In conclusion, biological constraints on sub-significant GWAS signals can reveal potentially true-positive loci for further investigation in existing data sets without increasing sample size.To uncover novel significant association signals (p<5×10 For instance, in 2009, 54 near-independent genome-wide significant (GWS) signals associated with variation in height had been identified in a population of tens of thousands of individuals (Genome-wide association studies (GWAS) have been widely employed to identify genetic variants that are associated with risk for disease and physiologically relevant traits . GWAS waividuals , while bividuals . The numividuals . And yetividuals . These nividuals .−8 threshold in initial rounds, a strict bar that can only be met when larger cohort sizes are recruited. Various approaches have been previously utilized to identify sub-threshold signals that go on to achieve genome-wide significance. Sub-threshold signals have been selected for replication in a stage 2 GWAS due to their proximity to putative candidate genes to identify novel loci, as with the identification of the obesity locus NPC1 and replicated subsequently in a large meta-analysis  and waist-to-hip ratio adjusted for BMI (WHRadjBMI) as initial traits because they have both been the subject of particularly large GWAS efforts by the GIANT consortium. Additionally, both traits have implicated a large number of loci and therefore provide optimal statistical power for our purposes. With respect to variant-to-gene mapping, we utilized our existing 3D genomic data sets for adipocytes as well as hypothalamic neurons as previous studies have implicated these cell types in BMI-associated variants .2 > 0.8) for each of the 2010 suggestive SNPs, 5×10−8≤p<5×10−4. Here, we detected 26,343 proxy SNPs; however, some of these proxy signals were in LD with an already established genome-wide significant signal. Given the purpose of this analysis was to identify new signals that were not yet genome-wide significant, we eliminated all proxies that were in even very modest LD, with an already genome-wide significant signal using an r2 > 0.1. This filtering left a residual of 23,197 signals.We first applied our chromatin-based variant-to-gene mapping approach to predict BMI loci that would be subsequently reported as genome-wide significant in a 2015 GWAS  but were only suggestive in 2010 . Because the sentinel SNPs reported from a GWAS are not necessarily causal with regard to phenotype, but very likely in linkage disequilibrium (LD) with the responsible allele, we first identified proxy SNPs (rhttps://github.com/kundajelab/atac_dnase_pipelines). Peaks from all replicates were merged by bedtools (v2.25.0) provided peaks were present in at least two biological replicates. These resulted in 2,225,635 adipocyte peaks and 179,212 hypothalamic neuron peaks.We generated ATAC-seq and promoter-focused Capture C libraries from mesenchymal stem cell (MSC)-derived adipocytes and leveraged our existing comparable data from human ESC-derived hypothalamic neurons to query open chromatin maps for open sub-threshold SNPs that contact open gene promoters . The adiThe adipose capture C libraries had an average of 1.4 billion reads per adipose library, with an average of 41% valid read pairs and 89% capture efficiency. We then called significant interactions using the CHiCAGO pipeline and performed analyses at a 1-fragment resolution to identify short-distance interactions  and at 4-fragment resolution to identify long-distance interactions  .−8≤p<5×10−7, 5×10−8≤p<5×10−6, 5×10−8≤p<5×10−5, and 5×10−8≤p<5×10−4. Note that each successive bin is inclusive of the SNPs from the previous bin.We next identified physical contacts between any of the remaining proxy SNPs and gene promoters, with the additional constraint that both points of contact were within regions of open chromatin and that the SNP itself did not map to a gene promoter. This approach favors SNPs most likely to have a functional role in the regulation of genes relevant to a given trait and is therefore dependent on the cell type utilized to make such inferences. For BMI variant-to-gene mapping, we used data derived from MSC-derived adipocytes and human ESC-derived hypothalamic neurons because of the known roles of these cell types in BMI. To identify the point at which this approach is no longer viable due to noise, that is, at point of negligible return and excessive false positives, we stratified the suggestive regions into several bins: 5×102 threshold of >0.4.Upon identifying loci that passed these filters, we quantified those that had reached genome-wide significance by 2015. To avoid redundant inclusion of SNPs in LD with one another, we collapsed all biologically constrained SNPs into independent SNP clusters, designated here as separate independent ‘loci’. We defined such loci as the set of SNPs surviving the variant-to-gene mapping filter that were in LD with one another at a relatively relaxed r−8. Across all suggestive bins and cell types, the positive predictive value was low through 2015, and the proportion of constrained signals actually achieving genome-wide significance (GWS) did not differ significantly from the proportion of the unconstrained signals achieving GWS within the corresponding p-value bin , representing a nearly tripling in cohort size compared to 2010. Here, we observed that 45 of the 78 2010 biologically constrained loci achieved genome-wide significance by 2018 at the most relaxed p-value threshold .−8≤p<5×10−5 was particularly striking  of the constrained 2015 implicated loci reached GWS in 2018, while 59% (148/248) of the unconstrained 2015 loci reached GWS in 2018 . We observed that across all cell types with 5×10 in 2018 .−8≤p<5×10−6 threshold in both cell types, where 93% (26/28) of the surviving signals reached GWS by 2018 in both cell types. This appears to be principally due to the larger sample sizes at this bin size relative to 2010, which provided additional power to observe such differences. Considering the results of all BMI retrospective analyses through 2018, we found that this method positively identified sub-threshold BMI signals that went on to achieve GWS at a later date significantly more often than when we apply no biological constraint.The results from the analyses at these years present the greatest levels of significance observed thus far. Given the presence of many more loci in each bin, we observed significance at the 5×10−8≤p<5×10−4. One hundred and fifty-seven of these proxies survived our biological constraints, corresponding to 71 independent loci. Thirty-six of these ultimately achieved GWS by the 2018 GWAS  relative to 2018  . The threshold may be further relaxed (5×10−8<p<5×10−5) to identify additional loci: BMI: 13/15 vs WHRadjBMI: 13/20. Relaxing even further to p<5×10−4 yielded far more loci, but brought in more potential false positives (BMI: 44/78 vs WHRadjBMI: 36/71), although they could still become GWS at a future time point. Despite the larger rate of false positives at a threshold of 5×10−8<p<5×10−5 in WHRadjBMI relative to BMI, we still observe that the chromatin-constraint surviving sub-threshold WHRadjBMI signals went on to achieve GWS at a later date significantly more often than when we apply no constraint.Precision of the biologically constrained 2010 loci reaching genome-wide significance in 2018 for both traits through 5×10uals 83% . This reWe assessed whether there was any period (2010–2018) in which PPV for the set of SNPs that do not physically contact gene promoters or are not located within regions of open chromatin differed significantly from the unconstrained signals, that is, if there was any period in which such a negative control outperformed, the unconstrained signals. We found that there was no p-value bin, cell type or trait for which there was a difference between this negative set and the unconstrained set . The abs−8<p<5×10−5; however, expanding through to 5×10−8<p<5×10−4 resulted in relatively high proportions of false positives in most analyses, although always at a lower rate than without a chromatin-constraint. As a control, we analyzed classifications of a negative control set in which loci failed to survive the variant-to-gene mapping filter for each cell type and suggestive threshold. There was no instance across trait, cell type, or p-value bin in which the specificity of the negative control outperformed the variant-to-gene mapping filter  that were profiled. By combining the loci identified in either adipose or hypothalamic neurons, sensitivity was increased, though the increment was modest. Specificity, however, was consistently >80%, and often >90%, indicating few false positives. The precision was particularly high through 5×10g filter . All togIn this study, we used our recently described variant-to-gene structural mapping approach  to conduWe assessed the extent to which this method can be applied to ‘salvage’ sub-threshold loci for possible consideration. We used BMI and WHR adjusted for BMI because of the relatively large number of loci identified in GWAS efforts conducted in large cohorts that continued to increase over time. The number of SNPs surviving these filters for each cell type and trait at each successively relaxed statistical cutoff are described in −4 showed a much clearer trend for both traits: roughly 20% of the surviving 2010 signals achieved GWS by 2015 and nearly 50% achieved GWS by 2018. Despite positive predictive values consistently greater than 80% for loci with 5×10−8≤p<5×10−6, it remains to be seen how many of the remaining surviving SNPs with less significant p-values will achieve GWS by the publication of the next GWAS in the future for each corresponding trait.Given the fairly modest increase in sample size between 2010  and 2015  for BMI, it was not particularly surprising that majority of the surviving 2010 BMI signals did not achieve GWS until 2018 . Only six of the 15 surviving signals achieved GWS by 2015, but 13 eventually achieved GWS by 2018. Reaching deeper into the 2010 suggestive signals through p<5×10−8≤p<5×10−5, only 65% of the surviving 2010 WHRadjBMI achieved GWS by the subsequent 2018 study. At the next, more restrictive, threshold, 5×10−8≤p<5×10−6, we found that nearly all of the surviving loci for both traits of the same years reached GWS by their respective 2018 study, although the number of loci within this threshold is small . Thus, our findings suggest that this strategy is capable of identifying loci that will achieve GWS at 5×10−8≤p<5×10−6. Additional loci can be identified at 5×10−8≤p<5×10−5, at the expense of a degree of false positives. Although as noted above, such false-positive signals could possibly go on to be GWS in a future study.In this study, we never identified a particular sub-threshold bin where no constraint was as precise as this chromatin-based constraint. We did, however, find that the precision observed for loci in data sets between 2010 and 2018 varied by trait. While the positive predictive value was nearly 90% for 2010 BMI constrained loci reaching GWS by 2018 at 5×10−8≤p<5×10−6, suggest that true negatives are largely properly identified without the generation of large amounts of false positives. The low false-positive rate did come at a cost, however. Sensitivity was extremely low at all thresholds, often below 20%  and centrifuged immediately at 550 × g for 10 min at 4°C. The nuclei were resuspended in the transposition reaction mix  on ice and then incubated for 45 min at 37°C. The transposed DNA was then purified using the MinElute Kit (Qiagen), eluted with 10.5 μl elution buffer (EB), frozen, and sent to the Center for Spatial and Functional Genomics at CHOP. The transposed DNA was PCR amplified using Nextera primers for 12 cycles to generate each library. The PCR was subsequently cleaned up using AMPureXP beads (Agencourt), and libraries were paired-end sequenced on an Illumina HiSeq 4000 (100 bp read length) and the Illumina NovaSeq platform. Open chromatin regions were called using the ENCODE ATAC-seq pipeline (https://www.encodeproject.org/atac-seq/), selecting the resulting IDR conservative peaks . We define a genomic region open if it has 1 bp overlap with an ATAC-seq peak.Tn5 transposase transposition  and purification of the Tn5 transposase derived DNA from 100,000 cells of each investigated cell type. The samples were then shipped to the Center of Spatial and Functional Genomics at CHOP where the ATAC-seq process was completed. Live cells were harvested via trypsinization, followed by a series of wash steps. One hundred thousand cells from each sample were pelleted at 550 × g for 5 min at 4°C. The cell pellet was then resuspended in 50 μl cold lysis buffer . Five hundred and forty microliters of 37% formaldehyde was added and incubated for 10 min at RT on a platform rocker. The reaction was quenched by adding 1.5 ml 1 M cold glycine (4°C) for a total volume of 12 ml. Fixed cells were centrifuged at 1000 rpm for 5 min at 4°C and supernatant removed. The cell pellets were washed in 10 ml cold PBS (4°C) followed by centrifugation as above. Supernatant was removed and cell pellets were resuspended in 5 ml of cold lysis buffer . Resuspended cells were incubated for 20 min on ice, centrifuged as above, and the lysis buffer removed. Finally, cell pellets were resuspended in 1 ml fresh lysis buffer, transferred to 1.5 ml Eppendorf tubes, and snap frozen (ethanol/dry ice or liquid nitrogen). Cells were stored at −80°C until they were thawed for 3C library generation.The protocol used for cell fixation was similar to previous methods . Cells wCustom capture baits were designed using an Agilent SureSelect library design targeting both ends of DpnII restriction fragments encompassing promoters  of all human coding genes, noncoding RNA, antisense RNA, snRNA, miRNA, snoRNA, and lincRNA transcripts, totaling 36,691 RNA-baited fragments. The library was designed using scripts generously provided by Dr. Hughes , utilizing the RefSeq, lincRNA transcripts, and sno/miRNA tracks in the hg19 assembly downloaded from the UCSC Table Browser on 16 September 2015. The capture library design covered 95% of all coding RNA promoters and 88% of RNA types described above. The missing 5% of coding genes that could not be designed were either duplicated genes or contained highly repetitive DNA in their promoter regions.2O using a QSonica Q800R to an average DNA fragment size of 350 bp. QSonica settings used were 60% amplitude, 30 s on, 30 s off, 2 min intervals, for a total of five intervals at 4°C. After shearing, DNA was purified using AMPureXP beads (Agencourt). Sample concentration was checked via Qubit fluorometer and DNA size was assessed on a Bioanalyzer 2100 using a 1000 DNA Chip. Agilent SureSelect XT Library Prep Kit (Agilent) was used to repair DNA ends and for adaptor ligation following the standard protocol. Excess adaptors were removed using AMPureXP beads. Size and concentration were checked again before hybridization. One microgram of adaptor ligated library was used as input for the SureSelect XT capture kit using their standard protocol and our custom-designed Capture C library. The quantity and quality of the captured library were assessed by Qubit fluorometer and Bioanalyser using a high-sensitivity DNA Chip. Each SureSelect XT library was initially sequenced on one lane HiSeq 4000 paired-end sequencing (100 bp read length) for QC. All six Capture C promoter interactome libraries were then sequenced three at a time on an S2 flow cells on an Illumina NovaSeq, generating ~1.6 billion paired-end reads per sample (50 bp read length).The isolated DNA from the 3C libraries was quantified using a Qubit fluorometer (Life Technologies), and 10 μg of each library was sheared in dHQuality control of the raw fastq files was performed with FastQC. Paired-end reads were pre-processed with the HiCUP pipeline , with bohttps://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files  consortium ta_files . We ideniPA v3.3  to find We next conducted a variant-to-gene mapping for each cell type within each of the suggestive bins to identify the gene promoters that are in contact with these SNPs. Promoter Capture C libraries are utilized to identify the genes with which these variants have interactions. Significant interactions were called using the CHiCAGO pipeline  utilizin2 > 0.1 and remove any variant within the suggestive region that is found to be a proxy of these genome-wide significant variants. This provides a filtered set of SNPs that are independent of any SNP that is significant at a genome-wide scale.We next removed SNPs within the suggestive bins that were in linkage disequilibrium with any SNP that was significant genome wide. This was done to determine an entirely novel set of variant interacting genes that are entirely independent of those that are already known. To accomplish this, we removed all variants that were found to be in LD with a variant that is significant at a genome level. Thus, we identified all proxies of genome-wide significant variants at rr2 >0.8) with an SNP that is genome-wide significant in a future study. The implicated genes derived from our variant-to-gene mapping approach were frequently different from the locus ‘names’ traditionally used in publications reporting GWAS findings, typically based on the nearest gene. In contrast, our variant-to-gene mapping used the integration of ATAC-seq and promoter-focused Capture C data to identify the gene promotor(s) physically in contact with a genomic region  harboring an associated variant. However, in order to assess the 2015 loci that were identifiable in 2010 with this method, for consistency’s sake, we annotated the implicated 2010 loci with the published locus ‘name’ annotation from the respective 2015 GWAS report. However, unlike the 2015 efforts, the 2018 studies did not annotate the reported loci with an arbitrary gene notation; thus, we simply labeled the nearest gene to the 2018 sentinel SNP in which the surviving signals were in tight LD. The gene promoters in contact with each surviving signal are also available in Interacting SNP-gene promoter pairs were identified by the variant-to-gene mapping pipeline described above for each GWAS across all suggestive zones for each cell type. Of these SNP-gene promoter pairs, we identify those that are in tight LD .In the case of all remaining comparisons, retrospective annotation was performed differently. Rather than trying to provide a locus name to the set of SNPs that were identified to be in tight LD with a genome-wide significant SNP in a later year, we elected to identify the surviving SNP, the gene promoters it physically contacts, and its best proxy that reached GWS (as defined by both r2 > 0.4) to one another as a locus. We extend this one additional time to include the proxies (r2 > 0.4) of each previously identified proxy SNP as a component of each locus. This allows SNPs that failed to be identified as a proxy of the lead SNP to be included within the locus via the transitive property, but only via one iteration of this process. The resulting loci identified are independent from one another, which prevents double counting of loci. We count these loci for each cell type across each suggestive bin, and we also identify the number of distinct loci regardless of cell type. We then identify the set of true positives as a locus that is in tight LD with a genome-wide significant SNP in a future GWAS. Those failing to meet these criteria are deemed false positives. We also identified loci of all genome-wide significant independent suggestive SNPs (r2 < 0.4) for each retrospective analysis and identified true-positive, false-positive, false-negative, and true-negative counts for this set using the same metrics as the constrained data sets. We used a beta-binomial model and used Monte Carlo approximation over 100,000 iterations to identify the posterior probability that loci identified by our chromatin-based constraint more often achieve GWS than loci with no constraint. We also assessed significance of the positive predictive values by calculating an empirical distribution from 10,000 iterations of a randomly sampled population of the unconstrained suggestive signals within each p-value threshold bin. At each iteration, we randomly selected N loci, where N is the corresponding number of loci surviving our chromatin-constraints from either cell type, and quantified the positive predictive value from this sampling. We then plotted the location where the positive predictive values fell for each cell type to determine whether these different significantly from the random population.To assess the predictive power of this method, we identified the positive predictive value of each set of salvaged SNPs across each retrospective analysis. To accurately quantify the number of loci that are identifiable using salvaged suggestive SNPs, we group the surviving variants based on their LD  as well as the randomly sampled data. For both of these data sets, we identified these metrics across 10,000 randomly sampled populations where we randomly selected N loci, where N is the corresponding number of loci surviving our chromatin-constraints from either cell type at the corresponding p-value threshold.https://github.com/rkweku/SubThresholdProjectScripts.All source code available on Github:  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The study is well done and is likely to significantly impact our understanding gene associations in large GWAS datasets. Review critique was thoughtfully and thoroughly addressed. Notably, statistical correction for multiple testing of GWAS data requires increasingly large sample sizes to establish potential associations. This retrospective study used chromatin accessibility and direct contact with gene promoters as biological constraints. The application of such constraints on otherwise sub-significant GWAS signals was shown to reveal potentially true-positive loci without the requirement to increase sample size.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Clifford Rosen as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: David Meyre (Reviewer #2).Thank you for submitting your article "Biological constraints on GWAS SNPs at suggestive significance thresholds reveals true BMI loci" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that substantial revisions are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:The authors sought to determine whether SNP's located in chromatin that physically contacts GWAS hits provide evidence that boost test scores from suggestive to significant. If successful, this approach might obviate the need for larger, more expensive GWAS studies. The strategy is to first identify relevant open chromatin sites, focusing on genes that were suggestive in early studies. Then in parallel ask which suggestive loci in these early studies became significant in later and larger studies. They claim that positive results support the argument that epigenetic evidence would have boosted scores and precluded the need for larger and more expensive studies. The manuscript has potential, but work is needed. Critical evidence is not provided, or provided in a way that makes critical reading a challenge.Essential revisions:1) This is not the first study to demonstrate that biological annotations combined to more relax thresholds of statistical associations from GWAS “rescue” true associations in obesity field. As an illustration, Meyre et al., 2009, rescued a modest stage 1 GWAS association signal for extreme obesity in the NPC1 gene using a candidate gene strategy, and the association was replicated in stage 2. The GWAS obesity hit in NPC1 was recently confirmed as a genome-wide significant signal for BMI in a large meta-analysis . Wang et al. (Diabetes 2019) demonstrated a strong enrichment in positive associations with BMI for SNPs located in/near syndromic obesity genes. The authors may like to discuss these and other reports in literature.2) A strengths and limitation section would add a lot in the Discussion.3) The biological criteria (chromatin accessibility / direct contact with gene promoters) used in this study are very original, but to be more exhaustive additional biological criteria may have been used to select more SNPs . This may be acknowledged as a limitation of the study.4) Tissues targeted in the study  are extremely relevant in the context of genetic susceptibility to obesity. However, genetic association studies have also highlighted the important role of other tissues in energy balance . Having not explored all tissues potentially relevant for obesity may be acknowledged as another limitation of the study.5) While Speliotes et al., 2010 and Yengo et al., 2018 GWAS for BMI have been performed in populations of European ancestry, the Locke et al., 2015 study included a multi-ethnic population. Did the authors analyze the GWAS summary statistics in the European population in the Locke et al., 2015 study. If yes, they may provide more details on what they did in the Materials and methods section. If no, I think using the multi-ethnic GWAS summary statistics may add some heterogeneity, and I recommend to focus the analysis in the European population in the Locke et al., 2015 study.6) Organization of data and analysis. Curiously, the epigenetic data, evidence and analysis of the Capture-C and ATAC-seq data is in the Materials and methods rather than the Results. The combination of methods and data are somewhat incomplete perhaps because of their unusual location in the manuscript. These are key to the overall study and should be in the Results section. As I read the manuscript, my curiosity grew about the nature of the evidence for connecting flanking and regulatory SNPs with the previously reported target SNPs – what is the source of the data, what is the nature of the evidence, – only to learn that they are in the Materials and methods, albeit incompletely. What is the evidence for open chromatin at their targets and in the sentinel genes that were suggestive in early GWAS studies?7) Study design, analysis, and presentation. A flow chart would help readers understand the sequence of tasks and help the authors with the organization of the results.8) Bottom-line. The key question is what is the benefit of the proposed method? What's missing – a simple statement about how many suggestive loci became significant later, and how many didn't, in the regular course of work, and in parallel how many epigenetic hits at suggestive loci become significant later, and how many didn't? That is, what's the quantitative benefit of the new assay. Remarkably they don't show the numbers for this seemingly simple and central question. Surprisingly, this point is not even in the Abstract; the 4th and 5th sentences, which address the key results, do not make these points clearly.9) Chromatin evidence. The evidence is based on two cell lines – MSC-derived adipocytes and ESC-derived hypothalamic-like neurons. Both are relevant cell types in vivo. But beyond that functional connection, no other rationale is provided, and no discussion is provided to critically evaluate the reliability of the evidence. Chromatin states are dynamic properties of cells both in vivo and in vitro. Physiological conditions and disease state can impact these profiles. How stable are these profiles in vitro, and how consistent are these features with their in vivo counter-parts? How do these profiles vary among individuals in health or disease? What about single-cell heterogeneity? Presumably, these factors would contribute noise in the assays, creating false positives, false negatives. All of this is fine, in principle; every approach has limitations. But remarkably little consideration to these issues, either in study design or in discussion of the results and analysis. These issues need careful, thorough and critical consideration.10) The authors should discuss how the model might work. The authors are correctly concerned about linkage disequilibrium, with an emphasis on independent evidence (subsection “Variant-to-gene mapping pipeline”). But no data are formally presented; if these SNPs are independent (no LD), what is the argument that these data can be combined rather than additive? Are positive results for this variant-to-gene mapping simply a reflection of additive effects? Or do they argue that SNP-interaction (epistasis) is involved? The text is unclear about these issues.11) Approach. These are important questions for any proposed method:A) The authors use a frequentist approach. Perhaps they justify that approach versus Bayes, which the logic of their approach nicely fits.B) The authors should be able to give clear basic statistics – number/percentage of hits that validate in general, and then when applying their approach, essentially they need to give the false positive and false negative rates, i.e. how many does their approach put forward that end up being negative, and how many do they miss with their approach (presumably loci with non-epigenetic mechanisms would obviously be missed). Obviously, they need to be clear that a comparable threshold is being applied so that readers can assess the relative performance of the proposed and standard methods. Importantly, are they preserving the type I error rate with their new method, and what is their power? Unfortunately, the Abstract, Results and Discussion are not clear on these points – these are the essence of the paper. Essential revisions:1) This is not the first study to demonstrate that biological annotations combined to more relax thresholds of statistical associations from GWAS “rescue” true associations in obesity field. As an illustration, Meyre et al., 2009, rescued a modest stage 1 GWAS association signal for extreme obesity in the NPC1 gene using a candidate gene strategy, and the association was replicated in stage 2. The GWAS obesity hit in NPC1 was recently confirmed as a genome-wide significant signal for BMI in a large meta-analysis . Wang et al. (Diabetes 2019) demonstrated a strong enrichment in positive associations with BMI for SNPs located in/near syndromic obesity genes. The authors may like to discuss these and other reports in literature.The reviewer raises a good point and speaks to the feasibility of the uniform genome wide approach we have employed. In response to these points, we now refer to previous work in this context of using biological annotations to salvage sub-threshold signals. We’ve taken the suggestion of including the key example of the Meyre et al., 2009 study as an introduction to this idea of sub-threshold SNP mining, along with citing the Turcot et al. paper which replicated this association. We have also added an additional study by Wang et al., 2016, as it relates greatly to this paper in its use of epigenomic data to mine biologically relevant sub-threshold signals from GWAS.2) A strengths and limitation section would add a lot in the Discussion.We agree that a strengths and limitation section is an excellent addition to the manuscript. While we previously included a number of points regarding the limitations of this study, we recognize that a more complete breakdown is warranted. We have added a far more comprehensive discussion concerning the classification rates, noting the relatively poor sensitivity while also highlighting the high precision and specificity. We believe this trade-off is an acceptable outcome given the positive predictions went on to largely achieve genome-wide significance. As per comment #4, we have also noted in our limitations the lack of a more diverse set of cell types, although adipocytes and hypothalamic neurons are prime candidate, it is clear there are other relevant cell types that could be used in such a study; indeed they could be the subject of a future study in this regard.3) The biological criteria (chromatin accessibility / direct contact with gene promoters) used in this study are very original, but to be more exhaustive additional biological criteria may have been used to select more SNPs . This may be acknowledged as a limitation of the study.p-value thresholds, so we have highlighted this point in our Discussion while presenting the possibility that additional criteria could be used in a future study to improve the quality of the predictions.We agree that more criteria and constraints could always be employed. Our intention was to observe if this unique, and relatively straightforward, combination of chromatin state and physical contacts between implicated proxy SNPs and gene promoters alone would suffice to salvage sub-threshold SNPs. We do not doubt that additional filters could improve predictions to a degree, especially at more relaxed 4) Tissues targeted in the study  are extremely relevant in the context of genetic susceptibility to obesity. However, genetic association studies have also highlighted the important role of other tissues in energy balance . Having not explored all tissues potentially relevant for obesity may be acknowledged as another limitation of the study.p-value thresholds, so we have highlighted this point in our Discussion while presenting the possibility that additional criteria could be used in a future study to improve the quality of the predictions.We agree that more criteria and constraints could always be employed. Our intention was to observe if this unique, and relatively straightforward, combination of chromatin state and physical contacts between implicated proxy SNPs and gene promoters alone would suffice to salvage sub-threshold SNPs. We do not doubt that additional filters could improve predictions to a degree, especially at more relaxed 5) While Speliotes et al., 2010 and Yengo et al., 2018 GWAS for BMI have been performed in populations of European ancestry, the Locke et al., 2015 study included a multi-ethnic population. Did the authors analyze the GWAS summary statistics in the European population in the Locke et al., 2015 study. If yes, they may provide more details on what they did in the Materials and methods section. If no, I think using the multi-ethnic GWAS summary statistics may add some heterogeneity, and I recommend to focus the analysis in the European population in the Locke et al., 2015 study.Thank you for catching this omission. We leveraged European ancestry results for all the studies analyzed. We have updated the Materials and methods accordingly to reflect this point.6) Organization of data and analysis. Curiously, the epigenetic data, evidence and analysis of the Capture-C and ATAC-seq data is in the Materials and methods rather than the Results. The combination of methods and data are somewhat incomplete perhaps because of their unusual location in the manuscript. These are key to the overall study and should be in the Results section. As I read the manuscript, my curiosity grew about the nature of the evidence for connecting flanking and regulatory SNPs with the previously reported target SNPs – what is the source of the data, what is the nature of the evidence, – only to learn that they are in the Materials and methods, albeit incompletely. What is the evidence for open chromatin at their targets and in the sentinel genes that were suggestive in early GWAS studies?Our intention with this form of presentation was to facilitate narrative flow and to dedicate largely the Results section to the process involving the retrospective analyses employed, while placing the processing of the epigenetic data in the Materials and methods section. However, given the reviewer’s comment, we agree that additional details should be provided in the Results section. We have therefore added additional information on these datasets in the Results section, while leaving the extensive details of how these data were generated and processed within the Materials and methods. The hypothalamic neuron related data were previously generated and analyzed in a separate study of ours – which we now cited. In addition, we include specific ATAC-seq peak numbers, as well as the interaction counts, for our three adipocyte libraries.7) Study design, analysis, and presentation. A flow chart would help readers understand the sequence of tasks and help the authors with the organization of the results.Given the complexity of these analyses, we strongly agree with this suggestion. To aid comprehension, we have now added a flowchart outlining the pipelines used . The flowchart we include uses the BMI 2010-2015 data to exemplify our approach, and includes numbers to aid description of each computational step and how many SNPs and loci occur at each step of these analyses.8) Bottom-line. The key question is what is the benefit of the proposed method? What's missing – a simple statement about how many suggestive loci became significant later, and how many didn't, in the regular course of work, and in parallel how many epigenetic hits at suggestive loci become significant later, and how many didn't? That is, what's the quantitative benefit of the new assay. Remarkably they don't show the numbers for this seemingly simple and central question. Surprisingly, this point is not even in the Abstract; the 4th and 5th sentences, which address the key results, do not make these points clearly.P-value bin given the variance in these numbers, potentially confusing or misleading the reader from the start. However, we do acknowledge that such numbers in text would be beneficial to keep the reader from consistently having to refer back to the figures and tables. Therefore, we have added such numbers at the end of each respective section using counts from within the consistent bin of 5 x 10-8 ≤ P < 5 x 10-5.We opted principally not to highlight these numbers outside of the various figures due to the many conditions that we were employed. This was to avoid the perception that we were somehow cherry-picking the best trait, year, and 9) Chromatin evidence. The evidence is based on two cell lines – MSC-derived adipocytes and ESC-derived hypothalamic-like neurons. Both are relevant cell types in vivo. But beyond that functional connection, no other rationale is provided, and no discussion is provided to critically evaluate the reliability of the evidence. Chromatin states are dynamic properties of cells both in vivo and in vitro. Physiological conditions and disease state can impact these profiles. How stable are these profiles in vitro, and how consistent are these features with their in vivo counter-parts? How do these profiles vary among individuals in health or disease? What about single-cell heterogeneity? Presumably, these factors would contribute noise in the assays, creating false positives, false negatives. All of this is fine, in principle; every approach has limitations. But remarkably little consideration to these issues, either in study design or in discussion of the results and analysis. These issues need careful, thorough and critical consideration.Our data are generated from three replicates and/or donors, and we focus on regions that are accessible across all replicates; indeed, we do note that there is high consistency between replicates. It should be noted that most chromatin differences across individuals are quantitative, not qualitative. A study by the Regev group  showed that open chromatin regions in T cells from >100 individuals were highly consistent, but that 15% of OCR varied quantitatively in a manner associated with common genetic variation. Combined with the additional constraint of insisting on only SNPs coinciding with open chromatin via ATAC-seq, our Capture C based approach avoids a high degree of false positives; however, it is challenging to determine the false negative rate, given a lack of reference data. However, given the statistical significance of our observations, we strongly believe our data should be shared with the complex disease genetics field. Furthermore, we ensure we only work with cells that are derived from healthy individuals, given we are focused on characterizing the susceptibility conferred by these loci before onset of disease, i.e. we are investigating the genetic build up to disease pathogenesis, or in other words “before the car crash”. The data generated here are consistent with the datasets we describe in recent papers we published in Nature Communications for lupus and bone mineral density. Finally, we have not conducted chromatin conformation capture in a single cell setting, an approach which is still in its infancy and does represent some drawbacks, including important multiple amplification steps which can itself a high degree of false positives. We have highlighted these points in the Discussion.10) The authors should discuss how the model might work. The authors are correctly concerned about linkage disequilibrium, with an emphasis on independent evidence (subsection “Variant-to-gene mapping pipeline”). But no data are formally presented; if these SNPs are independent (no LD), what is the argument that these data can be combined rather than additive? Are positive results for this variant-to-gene mapping simply a reflection of additive effects? Or do they argue that SNP-interaction (epistasis) is involved? The text is unclear about these issues.We are less clear on the issues raised by the reviewer in this regard. We are in fact not concerned about LD, rather we are leveraging it to integrate with ATAC-seq data in order to shortlist putative causal variants, which are further shortlisted by the further integration of Capture C data. We are treating all the independent BMI and WHR loci as independent signals, and making inferences as such. Our study does not address or observe any form of epistasis.11) Approach. These are important questions for any proposed method:A) The authors use a frequentist approach. Perhaps they justify that approach versus Bayes, which the logic of their approach nicely fits.We appreciate this suggestion. We have updated the statistical analysis with a Bayesian model to identify the probability that our constrained method outperforms no constraints. We note few changes to the results, though the situations where changes occurred resulted in >95% probability of our biological constraint outperforming no constraints. And overall, the changes do not affect our interpretation of these results.B) The authors should be able to give clear basic statistics – number/percentage of hits that validate in general, and then when applying their approach, essentially they need to give the false positive and false negative rates, i.e. how many does their approach put forward that end up being negative, and how many do they miss with their approach (presumably loci with non-epigenetic mechanisms would obviously be missed). Obviously, they need to be clear that a comparable threshold is being applied so that readers can assess the relative performance of the proposed and standard methods. Importantly, are they preserving the type I error rate with their new method, and what is their power? Unfortunately, the Abstract, Results and Discussion are not clear on these points – these are the essence of the paper.While we reported the sensitivity and specificity values in the previous version of the manuscript, we acknowledge that the false positive and false negative rates were not directly included. We did reference the general trend of these values, given their relationship to sensitivity and specificity, and the overall pattern of these rates across all analyses within the Results section entitled “Predictive power of negative control does not differ from the unconstrained set”, as well as in the Discussion; however, we acknowledge that such information relating to the preservation of the type I error rate was not included. To accommodate this comment, we have made our supplementary data file more comprehensive, which includes the classification metrics for each condition while also now including FPR and FNR. This file has been subsequently renamed Source data 1. Additionally, we now include a more direct discussion of these values and their trends within the Discussion section.


Natural isolates of baculoviruses (as well as other dsDNA viruses) generally consist of homogenous or heterogenous populations of genotypes. The number and positions of single nucleotide polymorphisms (SNPs) from sequencing data are often used as suitable markers to study their genotypic composition. Identifying and assigning the specificities and frequencies of SNPs from high-throughput genome sequencing data can be very challenging, especially when comparing between several sequenced isolates or samples. In this study, the new tool “bacsnp”, written in R programming langue, was developed as a downstream process, enabling the detection of SNP specificities across several virus isolates. The basis of this analysis is the use of a common, closely related reference to which the sequencing reads of an isolate are mapped. Thereby, the specificities of SNPs are linked and their frequencies can be used to analyze the genetic composition across the sequenced isolate. Here, the downstream process and analysis of detected SNP positions is demonstrated on the example of three baculovirus isolates showing the fast and reliable detection of a mixed sequenced sample. Baculoviridae, [In recent years, sequencing of genomes of large dsDNA viruses has been facilitated by the availability of whole genome sequencing techniques that allow deciphering of genomes in relatively short time with up to thousands-fold read depth . One of viridae, ), which viridae, . More thviridae, . Since nOne applied technique to access genetic variation within sequenced baculovirus isolates or specimens is the detection of insertion and deletions (indels), as well as single nucleotide polymorphisms (SNP) ,4,5,6. IMore recent approaches used a combination of both methods, a determination of SNP specificities by consensus sequence alignments, and the use of those specific positions to quantify the presence of a certain isolate  or even Cydia pomonella granulovirus species (CpGV) (genus Betabaculovirus) [For that reason, this study focused on establishing an analysis method that (i) avoids creating a consensus sequence from the available data and yet (ii) allows the detection of previously known as well as unknown SNP positions. The only requirement is the choice of a reference sequence that should be closely related to the analyzed samples, e.g., from the same species. On the example of three isolates of the lovirus) , we descThree isolates of Cydia pomonella granulovirus (CpGV) were used: CpGV isolates CpGV-M (genome group A) and CpGV-S (genome group E)  were obtThe DNA of CpGV-M and CpGV-S was isolated according to laboratory standard protocol  and 100 The raw Illumina read data of CpGV-M, CpGV-S and CpGV-0006 were uploaded on a JKI Galaxy server and were processed in exactly the same manner by using the Galaxy server‘s workflow tool and assigning unique group identifier for each isolate . Raw reaCydia pomonella granulovirus species [For each isolate, the unpaired and paired reads were mapped in three independent steps against the reference sequence CpGV-M (GenBank accession no. KM217575) using BWA-MEM v0.8.0  Figure . To inve species .For the detection of SNPs, the nine BAM files of CpGV-M, CpGV-S and CpGV-0006 were processed commonly using MPileup v2.1.1  to call ƒ) should exceed ƒ > 0.05. The filtered SNP table was then conducted for specificity determination of SNP positions based on the alternative nucleotide. The assumption behind the de novo SNP specificity assignment is that a variable SNP position can possess three different frequencies (ƒ) for each isolate:(i)ƒ = 0, identical to the reference sequence (but detected variable in another isolate and therefore detected in the first place),(ii)ƒ = 1, entirely different to the reference sequence or(iii)ƒ < 1, both nucleotides, identical and different to the reference, were found.0 < To define the specificity of each SNP position, a de novo SNP calling approach was used that is provided by bacsnp package v0.1.0 developed in R programming language (v3.6.1) using RStudio (v1.2.5001) . The bac(i)A SNP position is assigned as being solely specific for one isolate, if all other isolates do not show alternative nucleotides in the same position, or(ii)is assigned as group specific if more than one isolate shows alternative nucleotides in this position.Based on these assumptions, SNP positions were assigned isolate or group (for more than one isolate) specific in R using the workflow provided by the bacsnp package under the following criteria:Since CpGV-0006 was known to be a mixture of CpGV-M and CpGV-S [For the detection of variable positions in sequenced isolates CpGV-M, CpGV-S and CpGV-0006, their quality filtered reads were mapped in two separate analyses against either CpGV-M or CpGV-E2 as a common reference. After filtering, most of the reads were still paired, while the unpaired reads were only between 3.8% and 4.3% . Based oƒref, and the three alternatives, ƒalt1, ƒalt2 and ƒalt3, were calculated on the example of the 284 SNP positions that were found when CpGV-M was used as reference  of the reference nucleotide varied between 0.087 ± 0.252 and 0.97 ± 0.092 for CpGV-S and CpGV-M, respectively, whereas it was 0.394 ± 0.199 for CpGV-0006  were approximating 1 − ƒref  explained about 99.7% (CpGV-S) to 99.8% (CpGV-M and CpGV-0006) of the SNPs found  between ƒalt2/alt3 = 0.002 and 0.003  explained 99.7% (CpGV-M) and 99.8% (CpGV-S and CpGV-0006), confirming to proceed with the SNP specificity determination with the first alternative.The mean frequency  grouped around three medians in the three analyzed isolates: (i) ƒS = 0 in CpGV-M  ƒn CpGV-S B and  ranged between ƒM = 0.136 and ƒM = 0.675 (ƒM+S) that varied between ƒ = 0.004 and ƒ = 0.962  the CpGV-M-specific SNP positions could only reflect its own intraspecific variation, and (iii) any large genetic variation was not detected (only 15 CpGV-M-specific SNP positions and relative frequencies ƒM < 0.675) (Interpretation of CpGV-M-specific SNPs in the three samples appears less obvious. Since the data of the re-sequenced CpGV-M sample was mapped against its own CpGV-M reference sequence (a previous consensus sequence) available at GenBank, only SNPs were expected that reflect either intraspecific variation within this isolate or sequencing errors in the reference sequence itself. From this self-mapping of CpGV-M reads, the relative frequencies of CpGV-M-specific SNP positions  A.ƒS = 1 (ƒM = 1)  B.ƒS = 0.678 explained variability in CpGV-0006, which was considered as the hidden portion of CpGV-M, was calculated to be ƒM = 1 − ƒS = 0.322. Based on SNP frequency patterns and quantitative SNP analysis, CpGV-M and CpGV-S were considered highly homogenous isolates with minor possible internal genotypic variation represented by the scattering CpGV-M  and CpGV-M (genome group A) in CpGV-0006 in a ratio of about 2:1  within their species, the independence of the reference sequence on the species level was underlined. Since the reference CpGV-M was part of the sequenced samples of CpGV-M and CpGV-0006, its presence cannot be interpreted intuitively from the analysis since its specific SNPs are missing in the plots. However, the proportion of CpGV-M in CpGV-0006 was determined by calculation as 32%, since it represented the missing part to the 68% that were explained by CpGV-S.In case of using CpGV-E2 as a mapping reference, which is neither part of CpGV-M, CpGV-S nor CpGV-006, both the CpGV-M- and CpGV-S-specific SNP positions were clearly visible and interpretable. Most importantly indeed, both analyses resulted in very similar calculated ratios of CpGV-M and CpGV-S in the CpGV-M, CpGV-S and CpGV-006, independently from the used reference.With the consensus sequence free method and newly developed bacsnp tool programmed in R, a rapid and easy-to-use downstream protocol is provided to evaluate the heterogeneity or homogeneity of sequenced baculovirus isolates. Furthermore, this method was developed to work for at least two to several isolates and allows cross-evaluating the presence of certain genotypes in each analyzed sample. The decision of whether a consensus sequence should be assembled from sequencing data of a given isolate is recommended to be done when the homogeneity and the absence of a mixture have been confirmed.