It can be used on different sources of SNP data, including those with low SNP coverage such as exome sequencing, and is easily adaptable to genotype new inversions, either in humans or in other species. We present 20 human inversions that can be reliably and easily genotyped with scoreInvHap to discover their role in complex human traits, and illustrate a first genome-wide association study of experimentally-validated human inversions. scoreInvHap is implemented in R and it is freely available from Bioconductor.Polymorphic inversions contribute to adaptation and phenotypic variation. However, large multi-centric association studies of inversions remain challenging. We present  scoreInvHap, to call individual inversion genotypes from their haplotype similarity. We show that scoreInvHap has a high performance when analyzing heterogeneous sources of SNP data. Our current implementation contains 20 human inversions that can be readily genotyped in existing GWAS datasets. We exemplify the utility of scoreInvHap by running the first-genome wide association of experimentally validated inversions and a multi-centric inversion association study. All in all, scoreInvHap can substantially contribute to increase our knowledge of the role of chromosomal inversions in complex diseases by re-analyzing data from existing genetic association studies.Chromosomal inversions are structural variants consisting on an orientation change of a chromosome segment. Inversions have been linked to some phenotypic differences between individuals and to genetic divergence. However, their overall contribution to complex diseases is largely underdetermined as there are no high-throughput methods to call inversion-genotypes in large cohort studies. Here, we propose a new method,  Frequent polymorphic inversions contribute to adaptation and phenotypic variation ,2. Howeve.g. A/A, A/B, B/B) of divergent haplotype groups (A and B), supported by the suppression of recombination between inversion states  [Those inversions that have been successfully genotyped at large scale are either tagged by SNPs (e.g. inv-17q21.31) or their genotypes fully explain the clustering of individuals in the first principal components (PCs) of the SNPs within their breakpoints, such as inv-8p23.1  or inv-1rted: N) . This unrted: N) –8. Howevrted: N)  and forcrted: N) ,12. CurrscoreInvHap, a novel inversion-genotype classifier that enables the inclusion of inversions in regular GWASs. scoreInvHap compares how similar the SNPs of a new individual are to those in reference haplotype-genotypes, previously linked to reported experimental inversion-genotypes. Our current implementation enables the efficient and reliable genotyping of 20 human inversions in large cohorts. We studied the performance of the method on the inversion calling of inv-8p23.1 and inv-17q21.31 against two other methods (invClust and PFIDO) in a wide range of data types: whole genome sequencing, four SNP microarray studies and two exome datasets. We also evaluated the performance of scoreInvHap in inv-7p11.2 and inv-Xq13.2 [scoreInvHap can confidently call inversions with multiple haplotypes. We illustrate how scoreInvHap can be used to replicate previous associations of inv-8p23.1 and inv-17q21.31 with autism and schizophrenia, and to perform a genome-wide association study of 15 inversions on breast cancer.To tackle these problems, we developed scoreInvHap in any GWAS of European individuals, showing high prediction accuracy on experimental genotypes not used in the classifier , while PFIDO was unable to return a classification. These results can be explained by a low concordance between haplotype-genotypes and experimental inversion-genotypes. Nonetheless, we could not completely rule-out experimental error that penalized the methods’ accuracy  and found that the parallel version of t method .scoreInvHap to call inversion genotypes of inv-7p11.2 and inv-Xq13.2, the largest inversions with multiple haplotype-genotypes , for both inversions  and schizophrenia , using the exome data of UK10K studies [scoreInvHap is the only method that allows testing associations with inv-8p23.1 since inversion calling from such a low coverage data could not be performed with other methods  and inv-17q21.31 . However, we did not replicate the association with autism , 6p21.33  and 6q23.1 , which should be further investigated in larger association studies. These applications demonstrate that scoreInvHap is a robust genotyping tool of inversions, easy to use on already available GWAS data.Finally, to illustrate a first genome-wide association study of experimentally-validated human inversions, we tested the association between breast cancer and 15 inversions of scoreInvHap, a new bioinformatics tool to call inversions from SNP data. Its main advantage is the quick call of inversion genotypes from SNP data at the individual level with consistent genotype labeling. As a consequence, inversion genotyping is readily harmonized. Another important advantage is that the method allows the calling of inversion-genotypes using different sets of SNPs. As a result, inversions can be called on datasets with lower SNP coverage than the dataset used for the references as well as to call inversion-genotypes on individuals with missing SNP genotypes.We developed scoreInvHap classification. Consequently, scoreInvHap classification is readily comparable across different studies and genotyping techniques (from SNP array to exome data), allowing the harmonization of inversion calling in large meta-analyses. As the method classifies each individual separately, further gains in computational efficiency can be obtained from processing large datasets by batches allowing the genotyping of multiple inversions to be included in association studies.Previous bioinformatics methods relied on applying a dimensionality reduction technique to SNP data followed by clustering the individuals. Although these methods have been used to associate chromosomal inversions to phenotypic traits , they hascoreInvHap is the only method designed to genotype inversions with multiple haplotypes, whose abundance in the human genome is likely underestimated. We found inversions with multiple haplotypes on simulations under neutrality and in the inversions reported in invFEST and 1000 Genomes. This result suggests that the less common presence of only two haplotypes in inversions inv-8p23.1 and inv-17q21.31 could be due to the reported selection process that occurred in these regions [ regions ,11,21. I regions . For inv regions . Based o regions . Most ofscoreInvHap, nonetheless, also has limitations. In particular, its performance depends on the representativeness of the reference inversion-genotypes. For inversions inv-8p23.1 and inv-17q21.31 and European samples, we captured the haplotypic variability of the inversions using only one reference per inversion genotype. However, scoreInvHap needs to increase the number of experimental references for inversion calling in population samples with higher within haplotype variability, such as inv-8p23.1 in Africans. Further studies are needed to determine the accuracy of the method in inversions with larger genetic variability or populations with admixture.scoreInvHap, like other SNP based methods, is indirect: it does not detect the change of DNA orientation but relies on the haplotype structures generated by inversions. Therefore, it has some clear limitations against experimental methods to detect inversions, such as iPCR [scoreInvHap cannot detect small, recent or de novo inversions, as these inversions do not support different haplotype groups. In addition, scoreInvHap will produce wrong classifications for recurrent inversions, where the same haplotype can be found in standard and inverted chromosomes. Despite these limitations, scoreInvHap has the advantage of working with stringent conditions of SNP coverage and sample sizes.The inversion genotyping by  as iPCR , next ge as iPCR . In partscoreInvHap using SNP data in common formats, like PLINK, snpMatrix or vcf. Performing the genotyping of new inversions in large studies, in human and other species, can be achieved by creating their classifiers within scoreInvHap. To build a new classifier, the first step is to demonstrate that a reference sample of individuals can be clustered into haplotype-genotypes. The second step is to show that haplotype-genotypes are unambiguously labeled by experimentally inversion-genotypes. Finally, the reference haplotype-genotypes can be included in the program for genotyping the inversion in new individuals.All inversions in scoreInvHap inferences can be used to perform association studies, but additional analyses are needed to understand how the inversion affects the phenotype. One option is that the positional change caused by the inversion affects the regulation of nearby genes, leading to phenotypic differences between individuals. Another option is that the inversion captures the allele  that are the causal variants. Structural variants, such as deletions, copy number alterations or complex re-arrangements, can also be captured by the inversion status and produce the phenotype. Only further analyses can elucidate the mechanism linking an inversion to a phenotype.We showed how scoreInvHap can reliably perform inversion calling for large multi-centric studies with SNP genotype data. The method has been implemented for the calling of 20 human inversions which can be immediately included in any GWAS, to forward our understanding of the role of inversions in complex traits. The method is easily extended to other inversions, in humans and other species, as soon as experimental inversion genotypes become available.In summary, Inversions suppress recombination within the inverted segment when heterozygous. Therefore, for an ancient non-recurrent inversion, two divergent haplotype groups emerge for each inversion status . HaplotyscoreInvHap to new inversions. Each cluster of individuals was then identified as a reference group for a given haplotype-genotype to which new individuals are compared for inferring their own haplotype-genotypes. Consequently, at least one reference individual is needed for each haplotype-genotype. The haplotype-genotypes were then mapped to experimental inversion-genotypes to determine their inversion status. At this stage, we measured the degree of concordance between the haplotype and inversion genotypes by their percentage of agreement across individuals, accounting for the cases where more than one haplotype group was found in a single inverted status.We studied 59 inversions reported in the 503 European individuals of the 1000 Genomes projects. For each inversion, we perform an MDS analysis for all SNPs within the inverted region and studied whether the clustering conformed to a model where haplotype-genotypes could be unambiguously defined. We, therefore, selected the inversions that followed any of the patterns illustrated in scoreInvHap for inversions that could be consistently mapped to haplotypes. Therefore, scoreInvHap is suitable for those inversions for which the clustering pattern presents no haplotype sharing between the inverted and standard status, and where individuals can be reliably classified into haplotype-genotype groups. We considered that both conditions were fulfilled when clusters followed at least one of the inversion-haplotype mappings in We developed scoreInvHap computes a similarity score between a subject’s SNP genotypes in the inverted region and the haplotype-genotypes that map to experimentally validated inversion-genotypes. Note that the mapping is at the level of SNP and haplotype genotypes and not on individual chromosomes. As such, no phasing is needed for the inferences.scoreInvHap then classifies a new individual into the reference haplotype-genotypes for which their link to inversion-genotypes has been established. The classification is based on similarity scores between the SNP genotypes of the individual and the SNPs in each haplotype-genotype reference group. To compute the score, we first build the classifier from the frequency of each SNP i in each reference haplotype-genotype k made of Mk reference individuals,fki is the frequency of the i-th SNP genotype x = {0,1,2} in the haplotype-genotype reference group k. The frequency is the ratio between the number of reference individuals (nk) in k with SNP genotype xi and Mk. The score of a subject S, with L (L ⊆ N) SNP genotypes in the inverted segment , s = {0,1,2}, in the haplotype-genotype reference group k is defined asρi2 is the maximum linkage disequilibrium between the SNP i and the haplotype groups in the reference individuals. For inversions with two haplotypes, ρi2 corresponds to the linkage disequilibrium R2 between SNPi and the inversion-genotypes. For inversions with three haplotypes , we compute the R2 between SNPi and each haplotype-genotype. For instance for haplotype A the three haplotype-genotype are given by RR: {BB, BC, CC}, RH: {AB, AC} and HH: {AA}. We use these three haplotype-genotypes to compute the R2 between the haplotype group A and SNPi in the reference individuals. ρi2 is then, the highest R2 across A, B and C.S is, therefore, the genotype for which the score is maximum, that is arg where J is the total amount of haplotype-genotypes; that is, 3 haplotype-genotypes for 2 haplotype groups, 6 for 3 groups, 10 for 4, and so on  is the probability that the individual S has genotype t.For imputed data, the score is computed asscoreInvHap in an R package that supports snpMatrix or VCF formats, two standard Bioconductor classes for SNP data. The stable version is available in Bioconductor (https://bioconductor.org/packages/release/bioc/html/scoreInvHap.html) while the development version can be installed from the GitHub repository . scoreInvHap requires the SNP genotypes of an individual in the inversion region. The allele frequencies of SNPs in each genotype reference and the ρ2 between the SNPs and the validated inversion genotypes are built in the classifier and included in the package for the 20 inversions described in imputeInversion, a wrapper to impute SNP array data to use scoreInvHap. This tool is available from the GitHub repository .We implemented scoreInvHap, invClust and PFIDO under different conditions, we re-sampled the original dataset under different scenarios. Four scenarios run 200 times each, with different SNPs coverage  for the three methods and six scenarios with different number of individuals  for invClust and PFIDO. To evaluate scoreInvHap performance under different number of individuals, subsets for the references varied from 1 to 5 individuals per haplotype-genotype group. Full scoreInvHap performance was tested with a leave-one-out classification approach, classifying one individual with experimental inversion-genotype and using the remaining individuals as references.We used the SNP data (MAF > 5%) of 503 European individuals of 1000 Genomes phase 3 . To testWe analyzed autism cohorts from the Autism Genome Project (AGP)  and the scoreInvHap to clustering methods: one of schizophrenia cases (UK10K_NEURO_ABERDEEN) and another of autism cases (UK10K_NEURO_ASD_GALLAGHER). Both datasets are deposited in the European Genome-phenome Archive (EGA) under study accession codes EGAD00001000433 and EGAD00001000436. To select European individuals, we performed a genome-wide PCA of the merge between the UK10K neurodevelopment datasets and two control GWAS datasets: British Birth Cohort (BBC) and National Blood Service (NBD). We discarded subjects outside the central PCA cluster, likewise AGP.We obtained exome data from the UK10K Neurodevelopment datasets. We analyzed two datasets to compare −7) and mutation rates (2.3×10−7). In all simulations, the entire simulated region was 2Mb while the inversion comprised 800Kb. Stop frequency was set at 0.4 for the first three inversions and to 0.2 for the forth.We generated four different inversions using invertFREGENE . We usedscoreInvHap in SNP arrays, imputed data and exome data using the inversion-genotype references included in the package. We discarded SNPs with call rate lower than 0.9. We ran invClust using the first two multidimensional scaling components of the SNPs in the inverted regions. We ran PFIDO with the default values of SNPs and subject call rate filtering (0.9). We forced the model to return 3 groups and set all the other parameters to default.We run peddy [peddy, we created two datasets: the first one was the merger between controls and autism cohorts and the other was the merger between controls and schizophrenia. In both cases, we included the 68,689 SNPs that were common between the SNP arrays and the exome data. We applied scoreInvHap on each dataset. As cases and controls cohorts belong to different studies, we tested whether the differences in inversion frequencies were not statistically significant (chi-squared test) between the two control cohorts, and among the ten cases cohorts. We used SNPassoc for association testing between disease status and inversion genotypes in the joint dataset across all cohorts, adjusting for the joint genome-wide PCs.We tested the associations between autism spectrum disorder and schizophrenia, and inversions inv-8p23.1 and inv17q21.31 in ten UK exome studies of the UK10K project . We usedpeddy : 5,529 cpeddy [SHAPEIT as phasing algorithm. We removed SNPs with an imputation R2 smaller than 0.4. We called inversion genotypes with scoreInvHap in the 15 inversions having at least 4 SNPs with high quality imputation. We used SNPassoc for association testing between disease status and inversion genotypes, adjusting for age and the joint genome-wide PCs.We tested the association between inversions and breast cancer on the Cancer Markers of Susceptibility (CGEMS) study ,19, avaipeddy : 1,061 cpeddy . We seleS1 Text(PDF)Click here for additional data file.S1 Dataset(CSV)Click here for additional data file.S1 FigColors indicate the inversion status of the individuals . (A) First two MDS components of a simulated inversion showing 6 clusters that map to the inversion genotypes, where standard homozygous support two haplotype groups (case B in (PNG)Click here for additional data file.S2 FigscoreInvHap vs the number of reference individuals (Mk) in each haplotype-genotype. We selected inversion references using the same number of individuals for each inversion genotype (i.e. MNN = MNI = MII) and computed the accuracy of classifying the other individuals with experimental inversion-genotypes. (B) Accuracy of invClust and PFIDO vs sample size. Each boxplot is the summary of 200 subsamples without replacement of the same size.(A) Accuracy of (PNG)Click here for additional data file.S3 FigIndividuals were colored based on experimental inversion genotypes reported in invFEST. Clusters are clearly differentiated with one standard homozygous close to the heterozygous cluster and one heterozygous individual in the inverted homozygous, suggesting experimental error.(JPG)Click here for additional data file.S4 FigEUR is the frequency in the European individuals of the 1000 Genomes Project. Error bars include the 95% confidence interval of the estimated frequencies.(JPG)Click here for additional data file.S5 FigEUR is the frequency in the European individuals of the 1000 Genomes Project. Error bars include the 95% confidence interval of the estimated frequencies.(JPG)Click here for additional data file.S6 FigEUR is the frequency in the European individuals of the 1000 Genomes Project. Error bars include the 95% confidence interval of the estimated frequencies.(JPG)Click here for additional data file.S7 FigEUR is the frequency in the European individuals of the 1000 Genomes Project. Error bars include the 95% confidence interval of the estimated frequencies.(JPG)Click here for additional data file.S8 FigscoreInvHap: green, PFIDO: blue, invClust: red. Dark colors are frequencies in the Aberdeen dataset and light colors are frequencies in the Gallagher dataset.EUR is the frequency in the European individuals of the 1000 Genomes Project. Error bars include the 95% confidence interval of the estimated frequencies. (JPG)Click here for additional data file.S9 Fig200 random sets of SNPs were selected at each SNP coverage from the original dataset.(JPG)Click here for additional data file.S10 FigEUR is the frequency in the European individuals of the 1000 Genomes Project. Error bars include the 95% confidence interval of the estimated frequencies.(JPG)Click here for additional data file.S11 FigEUR is the frequency in the European individuals of the 1000 Genomes Project. Error bars include the 95% confidence interval of the estimated frequencies.(JPG)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableTable contains the mean and SD runtime in seconds of 10 independent calls.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


Maternal methylmercury (MeHg) exposure may be associated with immune response during pregnancy.PUFA), and immune markers  at 28 weeks’ gestation. Linear regression examined associations between MeHg exposure and immune markers with and without adjustment for PUFA.In the high fish‐eating Seychelles Child Development Study Nutrition Cohort 2, we examined the association between maternal MeHg, polyunsaturated fatty acids  concentrations decreased. MeHg was not associated with total Th2 cytokines but was associated with a decrease in IL‐4 and IL‐10. MeHg was positively associated with TARC and VEGF‐D and negatively associated with CRP. There was a significant interaction between MeHg and the n‐6:n‐3 ratio, with MeHg associated with a larger decrease in Th1:Th2 at higher n‐6:n‐3 PUFA ratios. The n‐3 PUFA were associated with lower CRP, IL‐4 and higher IFN‐γ. The n‐6 PUFA were associated with higher IL‐1β, IL‐2, TNF‐α, IL‐4, IL‐10, CRP and IL‐6.In all models, as MeHg concentrations increased, the Th1:Th2 ratio, total Th1 and individual Th1 (PUFA may mitigate any immunosuppressive associations of MeHg. The n‐3 and n‐6 PUFA were associated with suppressive and stimulatory immune responses, respectively. Overall, the associations were of small magnitude, and further research is required to determine the clinical significance.Maternal MeHg was associated with markers of immune function at 28 weeks’ gestation. A significant interaction between MeHg and the n‐6:n‐3 ratio on the Th1:Th2 ratio suggests that the n‐3  All samples were stored at −80°C and maintained at this temperature throughout their shipment to Ulster University for storage and analysis.At 28 weeks of gestation, nonfasting maternal blood samples were collected by antecubital venepuncture into evacuated serum and EDTA tubes and were immediately processed by centrifuging at 700 2.3Maternal whole‐blood samples were shipped to the University of Rochester for total Hg (THg) analysis using a PSA Millennium Merlin System . Our limit of detection for THg in blood is ~0.01 ng/mL, depending on sample volume2.4Serum samples were analysed at Ulster for a panel of inflammatory markers by Meso Scale Discovery (MSD) multiplex assay . These assays yield greater sensitivity and larger dynamic range and require less sample volume than traditional methods for immunoassays.2.5Maternal serum samples were used to determine total PUFA analysis at Ulster University, using a method adapted from that of Folch et al2.62). Data on prepregnancy BMI were unavailable, and we adjusted for postnatal BMI as data from our NC1 showed it to strongly correlate (r = 0.93) with maternal BMI prepregnancy. Gestational age (weeks) at blood collection was calculated using the gestational age at delivery and date of blood collection.Our models adjusted for variables known to impact immune response as covariates in the regression models: child sex,2.7From our final database of mothers (n = 1473) with complete inflammatory marker data, we removed two mothers with extremely high concentrations of multiple inflammatory markers indicating possible illness and two additional mothers with a gestational age at blood collection (18 and 40 weeks) very far from the average of 28 weeks. Undetectable inflammatory marker data are common in multiplex assays; however, the MSD platform is extremely sensitive, and as such, ≥80% of concentrations were above the LLOD for most markers. For undetectable values, we inputted LLOD/√2 values as a replacement value.We fit a series of linear regression models to determine the association of immune markers with MeHg exposure in pregnancy, and the potential influence of PUFA on this relationship. Model assumptions, including linearity, constant variance, normality of residuals and presence of outlying influential observations, were checked graphically using standard methods.3Table 3.1P < 0.003), for example, a 0.5% decrease, with or without adjustment for PUFA. Maternal blood MeHg was associated with a significant decrease in Th1 for all three main‐effects models. In covariate‐adjusted models, each 1 μg/L increase in maternal blood Hg was associated with a 0.7% decrease in Th1 (P < 0.001); with or without adjustment for PUFA. There were no significant associations between maternal blood MeHg, n‐3 PUFA and Th2 in any of the models after adjusting for covariates. N‐6 PUFA was associated with a significant 17.9% increase in Th2 (P = 0.013).Covariate‐adjusted associations between maternal blood MeHg, PUFA and the inflammatory markers  from fitting Models 1‐3 are presented in Table P = 0.02, 2‐df test; Figure P = 0.005). The interactions between MeHg and tertiles of the n‐6:n‐3 ratio were not significant for total Th1 nor total Th2.In the interaction model, the association between MeHg and Th1:Th2 differed by n‐6:n‐3 tertiles .4This is one of the first studies to examine associations between maternal MeHg exposure and immune responses in pregnancy, in a large human population, and accounting for PUFA status. At 28 weeks’ gestation, we observed that increasing maternal MeHg levels were associated with a small magnitude decrease in the Th1:Th2 ratio along with a decrease in individual and total Th1 cytokines. There was no association between MeHg and total Th2 cytokines. Our data also suggest an interaction between MeHg exposure and the balance of n‐6 to n‐3 PUFA status in relation to these immune marker associations, with MeHg being associated with a decrease in Th1:Th2 but only at higher n‐6:n‐3 PUFA ratios. This association was not apparent with the lower ratios of n‐6:n‐3; therefore, it is plausible that the n‐6 PUFA are having stimulatory actions on Th1 while a higher concentration of n‐3 PUFA may be important to mitigate this association.An appropriate balance between maternal Th1 and Th2 cytokines is believed to be important for the continuous normal development of pregnancy and for optimal foetal outcomes. In this study, we observed an immunosuppressive association of MeHg exposure at 28 weeks’ gestation with the Th1 cytokines IL‐1β, IL‐2 and TNF‐α. This association, which was small in magnitude, was not modified by PUFA status. A recent meta‐analysis identified an association of anti‐TNF‐α therapies with an increased risk of preterm birth, spontaneous abortion and low birthweight.A lower Th1:Th2 immune response in the second trimester compared to the first trimester is supportive for physiologic pregnancy.In this high fish‐eating cohort, adjustment for PUFA did not alter the associations between MeHg and the inflammatory markers with the exception of IL‐4, which was no longer significant after controlling for PUFA status. We observed, however, associations between both n‐3 and n‐6 PUFA with a number of the quantified immune markers, which suggest that PUFA is directly associated with the immune response at this stage of pregnancy. We observed that n‐6 PUFA appeared to enhance both the Th1 and the Th2 response and a higher n‐6:n‐3 ratio was observed to be associated with higher concentrations of IL‐1β, IL‐2, IL‐4 and CRP, supporting a more proinflammatory response. In contrast, n‐3 PUFA, as expected, was associated with lower concentrations of IL‐4 and CRP suggesting a regulatory role of n‐3 PUFA at this stage of pregnancy. This finding confirms previous results from our cohort showing an inverse relationship between n‐3 PUFA and the proinflammatory marker CRPThere may be an optimal balance of n‐6:n‐3 PUFA for IL‐4, as suggested by our finding that n‐6 PUFA was associated with higher and n‐3 PUFA associated with lower concentrations of this cytokine. IL‐4 is detectable at the maternal‐foetal interface throughout pregnancy having a role in placental formation, modulation of trophoblast invasion and differentiation, inducing placental angiogenesis and inhibiting proinflammatory cytokines. Furthermore, an interaction between MeHg exposure and the balance of n‐6 to n‐3 PUFA status in relation to the immune response was apparent, with MeHg being associated with a decrease in Th1:Th2 but only at higher n‐6:n‐3 PUFA ratios. Given that the association was not significant at lower ratios of n‐6:n‐3 PUFA, it may be that the n‐3 PUFA are having a regulatory effect on the MeHg immunosuppression of Th1 cytokines. Achieving an optimal balance between n‐6:n‐3 PUFA during pregnancy in controlling inflammatory responses via cytokine signalling may therefore be important for a successful pregnancy. Given the opposing associations of MeHg and n‐6 PUFA on Th1 markers, it may be plausible that n‐6 PUFA also play an important role in stimulating immune responses during pregnancy.The n‐3 PUFA were associated with higher concentrations of IFN‐γ, a cytokine important in innate immunity and in pregnancy where in the first trimester it enhances blood flow to the implantation site and aids placental development. MeHg in this study was not associated with IFN‐γ or IL‐6 which is contrary to what others have previously reported.2 was shown to stimulate VEGF‐D and IL‐6 release, which could in turn promote inflammation at the blood‐brain barrier.To investigate in greater depth the potential immunomodulatory mechanisms of MeHg and potential regulation of inflammation by PUFA, we characterized additional chemokines and angiogenesis markers within this cohort. We measured TARC, a chemokine important in Th2‐cell recruitment in pregnancy. The positive association observed between maternal MeHg and TARC is interesting given that we did not see a similar positive association between MeHg and Th2 cytokines. One possible explanation for this is that elevated concentrations of TARC in the maternal circulation act to recruit Th2 cells predominantly at the materno‐foetal interface.This study has several strengths. This large cohort had high fish consumption, with concurrent high intake of n‐3 PUFA and relatively high MeHg exposure (10 times higher than in the USA);5In this exploratory study, we found associations of maternal MeHg with markers of maternal immune function at 28 weeks’ gestation. Additionally, the n‐3 and n‐6 PUFA were found to have associations, which might indicate suppressive and stimulatory immune responses, respectively. The associations with PUFA were largely independent of MeHg, albeit a significant interaction between MeHg and the n‐6:n‐3 ratio on the Th1:Th2 ratio suggests that the n‐3 PUFA could potentially mitigate the immunosuppressive associations of MeHg observed. Overall, the associations were of small magnitude and of unclear significance given normal immune changes during pregnancy. Further research on these associations is indicated.


These approaches consistently pinpointed 89 ‘selective sweeps’, out of which 30 selection loci were identified on D-genome. These key selections co-localized with important functional genes of adaptive traits such as TaElf3-D1 (1D) for earliness per se (Eps), TaCKX-D1 (3D), TaGS1a (6D) and TaGS-D1 (7D) for grain size, weight and morphology, TaCwi-D1 (5D) influencing drought tolerance, and Vrn-D3 (7D) for vernalization. Furthermore, 55 SNPs and 23 haplotypes of agronomic and physiological importance such as grain yield, relative water content and thousand grain weight in SYN-DER, were among the top 5% of divergent selections contributed by synthetic hexaploid wheats. These divergent selections associated with improved agronomic performance carry new alleles that have been introduced to wheat. Our results demonstrated that GWAS and selection sweep analyses are powerful approaches for investigating favorable introgressions under strong selection pressure and the use of crop-wild hybridization to assist the improvement of wheat yield and productivity under moisture limiting environments.Crop-wild introgressions have long been exploited without knowing the favorable recombination points. Synthetic hexaploid wheats are one of the most exploited genetic resources for bread wheat improvement. However, despite some QTL with major effects, much less is known about genome-wide patterns of introgressions and their effects on phenotypes. We used two genome-wide association approaches: SNP-GWAS and haplotype-GWAS to identify SNPs and haplotypes associated with productivity under water-limited conditions in a synthetic-derived wheat (SYN-DER) population. Haplotype-GWAS further enriched and identified 20 more genomic regions associated with drought adaptability that did not overlap with SNP-GWAS. Since GWAS is biased to the phenotypes in the study and may fail to detect important genetic diversity during breeding, we used five complementary analytical approaches ( Genetic variation in food crops has been successfully exploited through conventional breeding over the last century, resulting in 0.8–1.2% annual genetic yield gain in five major food crops including wheat , is a source of genetic diversity and favorable alleles for tolerance to biotic and abiotic stresses  through GWAS could help to identify alleles or haplotypes that may not exist in crop cultivars but could be incorporated into the existing elite gene pool  to identify marker-trait associations under divergent selection in SYN-DERs./38//N 72°51/21//E, 498m asl), Pakistan and the National Agriculture Research Center (NARC), Islamabad , Pakistan. At each location/year, field trials were conducted in two water regimes i.e., well-watered (WW) and water-limited (WL) conditions. Chakwal is a rain-fed, semi-arid area lies at the beginning of the Potohar plateau. The deep, well-drained soil comprises moderately fine textured particles. It is slightly calcareous, non-saline, with pH 7.6 and an electrical conductivity of 0.32 dS/m . Among the modern bread wheat cultivars, 32 were recurrent bread wheat parents used in the development of SYN-DER. These SYN-DERs were developed by crossing primary SHWs with advanced lines and elite cultivars of Pakistan and CIMMYT (see pedigrees in Table S1). More than 800 SYN-DERs were developed initially at the National Agriculture Research Center (NARC), Pakistan. This was reduced to 171 following re-selection for better agronomic characteristics. Field trials were conducted at two locations during the 2014-15 and 2015-16 cropping seasons at the Barani Agriculture Research Institute (BARI), Chakwal , GYWL (GY in water-limited treatment), and GYDIFF (GY for difference in well-watered and water-limited treatments).Phenotypic traits recorded at each location included chlorophyll content index (CCI), total chlorophyll (Chl), canopy temperature (CT), number of grains per spike (GS), grain yield (GY), plant height (PH), proline, relative water contents (RWC), shoot dry weight (SDW), shoot fresh weight (SFW), spike length (SL), superoxide dismutase (SOD), sugar contents (Sugar), thousand grain weight (TGW), and tillers per plant (TP). Specific suffixes were provided to each trait according to the water treatment A chlorophyll meter of model CCM200 plus was used to measure chlorophyll content index. Similarly, canopy temperature was measured using an infrared thermometer IRT206 at booting stage (Z45) . For this purpose, 0.5 g of fresh leaf tissues were taken with 10 mL of ethanol and were heated at 80° in for one hour with continuous shaking. One mL phenol (18%) was added to the 0.5 ml of pre-heated extract and incubated for one hour. After this, 2.5 mL of concentrated H2S04 was added and vortexed. The extract was used to measure absorbance at 420 nm by a UV spectrophotometer (Biochem-2100) and the concentration of soluble sugar was measured using a standard glucose curve.For biochemical analysis, fresh leaf samples were collected at the onset of flowering stage (Z60)  . BrieflyProline was measured following protocol of The concentration of superoxide dismutase (SOD) in leaf was measured with following protocol. First 0.5 g of fresh wheat leaf samples were ground in a chilled mortar and pestle with 10 mL of chilled phosphate buffer (50 mM) in an ice bath. The mixture was centrifuged at 13,000 RPM at 4° for 25 min and was filtered in four layers of cheesecloth. The supernatant obtained was used to assay SOD activity . The extRelative water content (RWC) was measured as per method devised by Bar and http://www.illumina.com), based on the data from all the genotypes.Five viable seeds of each genotype were planted in 5 cm diameter pots. DNA was extracted per the CIMMYT Molecular Genetics Manual  ranging from 1 to 10 was evaluated using 100,000 burn-in iterations followed by 500,000 recorded Markov-Chain iterations. Robustness (sampling variance) of inferred population structure was estimated by carrying out 10 independent runs for each k. The optimum number of subpopulations was determined utilizing ADHOC statistics Δk based on the rate of change in log probability of data between successive k  was formed by organization of all population entries into clusters. An assumed number of subpopulation  were included in LD decay plots and stacked bar plots.Genome-wide linkage disequilibrium (LD) was evaluated across A, B and D genomes. Using TASSEL v.5.0, the LD parameter rr2 value  was inve markers . An r2 bi.e., well-watered and water-limited for each trait. ANOVA was used to test the statistical significance of different sources of variation for each of the nine traits. In the ANOVA model, phenotypic effect was partitioned into overall mean, treatment effect, replication  within environment (year and location combination) effect, genotypic effect, environment effect, genotype by environment effect, genotype by treatment effect, and random error effect. Let ylijk be the observed value of a trait of interest for the ith accession in the kth replication under the jth environment  and the lth treatment. The linear model used in ANOVA is therefore,l = 1, 2, ..., L (L = 2 for well-watered and water-limited treatments), i = 1, 2, ..., n (n = 203), j = 1, 2, ..., e (e = 4 with two locations and two years), k = 1, 2, ..., r (r = 2), is overall mean of the whole population, Rk/j is the kth replication effect in the jth environment, Gi is genotypic effect of the ith accession, Ej is environmental effect of the jth environment, GEij is interaction effect between the ith accession and the jth environment, GDil is interaction effect between the ith accession and the lth treatment, and εlijk is random error effect which was assumed to be normally distributed with a mean of zero, and variance The phenotypic data were collected from the two locations over two consecutive years under well-watered and water-limited conditions. The data were averaged for both years x locations leading to two datasets yijk for the ith accession in the kth replication in the jth environment . The mixed model used for BLUP was therefore,i = 1, 2, ..., n (n = 203), j = 1, 2, ..., e (e = 4 with two locations and two years), k = 1, 2, ..., r (r = 2), k/jR, iG, jE, and ijGE were the same as the description above. Except for, all the effects were viewed as random effects following the normal distributions The BLUP of genotypic value for each accession under each water treatment was used as the phenotype for all subsequent comparisons. BLUPs were calculated as follows: the observed value of trait was defined as vs. observed P values from MTAs were also produced and deviations from the expectation demonstrated that statistical analysis might cause spurious associations. Bonferroni corrections were applied, and a P-value of 10−5 was defined as the threshold for significant MTAs. SNPs with P-values in the range of 10−3-10−4 for one trait and P-values <10−5 for another trait were also reported. The LD decay distance at r2 = 0.1 was used as the support interval to avoid multiple significance within one LD block.For marker-trait associations (MTAs), a mixed linear regression (MLM) model controlling both population structure (Q matrix) and kinship matrix (K matrix) was applied in TASSEL Standalone v.5.0 . Quantilhttp://www.broadinstitute.org/haploview/haploview). The package defined haplotype blocks based on 4 gametes, and provided the number of haplo-groups and their genetic length (bp) for each block, as well as the number of tag SNPs based on solid spine of linkage disequilibrium (LD) (Extend spine if D′>0.8). In brief, this meant that the first and the last marker in a block were in strong LD with the intermediate markers that were not necessarily in LD with each other. All the haplotype blocks consisting of two haplo-groups were removed to rule out perfect LD and similarity to SNP polymorphism. Haplotype frequency was calculated using a custom Perl script. If a haplotype block is only observed in one group (SYN-DER or BW) but not in the other group, it is considered to be a group-specific block. If a haplotype block was simultaneously detected in both groups, the haplotype block was considered to be a common block. Haplotype-GWAS was performed using the linear regression procedure implemented in PLINK (−5 was defined to declare haplo-groups associated with phenotypes.Genome-wide haplotype blocks were constructed using PLINK with the default parameters as used by Haploview 4.2 software package (mth marker locus between the bread wheat (BW) set and the SYN-DER set were tested by Student’s t-test:f1 and f2 were the allele frequencies of a specific marker locus in the BW and SYN-DER sets, respectively, and n1 and n2 were the sample sizes in the BW and SYN-DER sets, respectively. The population-specific alleles were determined for each subpopulation based on zero allele frequency in one subpopulation and non-zero in another subpopulation; and different allele frequencies between two subpopulations at significance level P < 0.001.Five statistical methods/parameters were used to detect the loci under selection. (1) Difference in allele frequencies for the Fst was calculated for individual SNPs byhttps://vcftools.github.io/index.html) were used to calculate Fst with a sliding window of 100 kb and a step size of 10 kb  =n2n1+n2 . VCFtoolith individual, where x was the number of the alleles and M was the number of marker loci, the genetic relationship matrix A for each pair of 240 accessions was calculated byfm is the allele frequency for the mth marker locus. Second, the principle component analysis (PCA) was conducted on the A matrix  A search for loci under selection using genome-wide association of eigenvectors was implemented by EigenGWAS (SYN-DER/πBW) were calculated using VARISCAN version 2.0 with a sliding window of 100 kb and a step size of 10 kb.Further, (4) Tajima’s D, and (5) nucleotide diversity (π) relative to the founder population , Islamabad, Pakistan and Barani Agriculture Research Institute (BARI), Chakwal, Pakistan. The remaining experimentation does not require any ethical statement.A total of 19,676 SNPs was retained after quality control . SNP marPCA with high quality SNP markers clearly separated bread wheat cultivars from SYN-DER in various sub-clusters . The fir2r at P ≤ 0.001 from all pairs of SNPs along each chromosome. Pairs of loci in significant LD on the sub-genome level was 45.55%, 47.47% and 35.85% on A, B and D genomes, respectively . All the agronomic traits including GY, GS, PH, TP and TGW were significantly reduced by 19.4% (TGW) to 62.8% (GY) under WL conditions. However, CT, SOD, Sugar, Chl and Proline were significantly increased by 10.3% (CT) to 22.3% (SOD). In the WW environment, very high heritability (0.79 – 0.92) was observed for almost all physiological traits. In diversity panel, SYN-DER showed high performance on an average for all traits, for example GY in WL conditions was 16.7% higher in SYN-DER as compared to BW and can be visualized in violin distribution plots . In the WW environment, GY was positively correlated with SL (r = 0.29), TP (r = 0.61), GS (r = 0.40), and TGW (r = 0.74). Among biochemical traits, Chl was positively correlated with Sugar (r = 0.37), while proline was positively correlated with GS (r = 0.43) and SL (r = 0.28) . Other biochemical components had moderate to low correlation with grain yield and yield components. In the WL environment, GY was positively correlated with TGW (r = 0.66), GS (r = 0.72) and SL (r = 0.53), while SDW was negatively correlated with PH (r = -0.14) and Chl was negatively correlated with CTspike (r = -0.16) .WW, three with GYWL, and one with GYDIFF, of these four were located within selective sweep regions. Six SNPs were associated with GSWW, two with GSWL, and one with GSDIFF. Of these, two were distributed on chromosomes 1A and 6D that were under selection (Table S3). For TGW, an important yield component trait, three SNPs were associated with TGWWW, six with TGWWL, and 10 with TGWDIFF. Among these, six SNPs were under selective sweeps, two of which were located on the D genome . Three important loci were associated with phenotypic traits under both WW and WL conditions including SNP IWB20120 on chromosome 1A which was associated with sugar contents, IWB2303 on chromosome 1D linked to RWC and IWB8919 on chromosome 5B associated with TGW  conditions . Three of the TGW associated haplotypes were under divergent selection, while two haplotypes were closely linked to vernalization (Vrn-A1 and Vrn-B1 genes) response on chromosomes 5A and 5B.In haplotype-GWAS, 127 haplotypes were associated with phenotypes under different water regimes. Of these, 71 were identified in WW, 43 in WL and 20 in ‘Diff’  and IWB61578 (5D) were only associated with GYWL. These SNPs gave a yield advantage of 46.2, 26.5 and 28.2 gm-2 in water-limited conditions. Each of the three haplotype blocks consisted of three haplo-groups and favored haplo-groups significantly increased GYWL by 39.7 to 87.2 gm-2. Similarly, the favored SNP alleles gave an advantage of 2.9–6.3% for RWCWL. Three haplotype blocks; Hap2A-RWC1, Hap3B-RWC1 and Hap7A-RWC1 were associated with RWCWL and consisted of 3, 6, and 4 haplo-groups, respectively . These 89 loci or divergent selections were present on all 21 chromosomes (Table S5). The number of loci under divergent selection differed among the three genomes, and the most loci were located on the D-genome (32), followed by the B- (29) and A-genomes (28). Similarly, EigenGWAS was plotted against SNP-GWAS and haplotype-GWAS to highlight GY and RWC associated loci under divergent selection .Genome-wide divergent selection was investigated in SYN-DER by comparing allele frequencies in the two panels. Five different approaches were used to provide complementary information (Ae. tauschii) reference genome sequence has recently been published  are presented for 45 phenotype datasets. SNP-GWAS identified a relatively high number of MTAs (181) compared to haplotype-GWAS (128). We used physical positions of SNPs based on Chinese Spring reference genome sequence version 1.0 (https://urgi.versailles.inra.fr/download/iwgsc/IWGSC_RefSeq_Assemblies/v1.0/), while all the previous studies used genetic maps based on linkage mapping population. Therefore, it is hard to compare the results with previous findings. GWAS was performed on 15 agronomic and physiological traits under two water regimes (WW and WL) and their difference (DIFF) leading to 45 phenotypic datasets in total. We further calculated LD between all SNPs associated with phenotypes and excluded those SNPs with WL on chromosome 5D (393.05 Mb) is a novel MTA because it is under strong divergent selection (-log(p)=81.9) and the favorable allele gave a yield advantage of 76.2 gm-2. This MTA was also associated with SODWL. Two other MTAs for GYWL on chromosomes 2A and 4A are likely to be MQTL13 and MQTL32 (WL MTA on chromosome 2A (553.2 Mb) is within the vicinity of grain weight gene TaCwi-A1 (TaCwi-4A and TaMoc1 (7A) were co-localized with GYWL and CCIWL MTAs. TaMoc1 is an important gene associated with yield-related traits and is involved in regulating axillary meristem initiation and growth, while TaCwi-4A is an important drought related gene only expressed in anthers and is involved in pollen sterility under drought stress. The association of these genes directly with GYWL in SYN-DER indicated the potential usefulness of this MTA because the minor allele frequency is higher (0.25), while the TaCwi-4A favorable allele is highly fixed during modern wheat breeding  of this haplotypes block showed significant differences for GY and a third haplo-group ‘AT’ remained non-significant. In haplotype block Hap-1B-GY-1, there was no significant difference between GYWL of two haplo-groups ‘ATG’ and ‘GTA’, while the ‘ACG’ variant had a significantly different effect on GY , the haplotype associated with TGW is within the proximity of Vrn-B1. These two genes are homeologous to VERNELIZATION1 (Vrn1) gene, a key regulator of flowering time in cereals that have pleiotropic effects on agronomic traits like plant height and root traits , which is the Ppd-A1a photoperiod insensitive allele in durum wheat. This allele is different from Ppd-A1a allele in bread wheat and has higher transcript levels  and Vrn-D3 (7D at 70.8 Mb) for earliness per se (Eps) and flowering time (TaCKX6-D1 (3D at 137.4 Mb), TaCwi-D1 (5D at 557.3 Mb) and TaGS-D1 (7D at 6.7 Mb) . They showed that this new model outperformed six other prediction models for various traits. Furthermore, the prediction accuracies of extended GS models outperformed classical models using phenotype data from dry seasons, implying that this approach is particularly suitable for the improvement of drought-resilience in future crop cultivars. Because of their increased information content compared to bi-allelic SNP markers, fitting haplotypes with statistically significant trait associations to phenotypes as fixed effects in GS models could further improve prediction accuracies (The benefit of using synthetic wheats or other crop-wild introgressions is that new useful haplotypes could be generated due to the differential recombination rates and patterns (Our analytical approaches successfully identified the divergent selections in SYN-DER, dissected the complex architecture of quantitative traits under drought stress and identified the favorable SNP and haplotypes underpinning traits of breeding interest through crop-wild introgressions.


The deep sea precious corals Pleurocorallium elatius and P. konojoi are distributed in Japanese waters and have distinct morphological features: (1) the terminal branches of the colony form of P. elatius are very fine, while those of P. konojoi are blunt and rounded, (2) the autozooids of P. elatius are arranged in approximately four rows, while those of P. konojoi are clustered in groups. However, previous genetic analysis using mtDNA and nuclear DNA did not indicate monophyly. Therefore, it is important to clarify their species status to allow for their conservation.Precious corals known as coralliid corals  play an important role in increasing the biodiversity of the deep sea. Currently, these corals are highly threatened because of overfishing that has been brought on by an increased demand and elevated prices for them.Corallium japonicum and 27 of P. konojoi) from around the Ryukyu Islands and Shikoku Island, which are geographically separated by approximately 1,300 km. We used a multiplexed inter-simple sequence repeat (ISSR) genotyping by sequencing (MIG-seq) and obtained 223 SNPs with which to perform STRUCTURE analysis and principle coordinate analysis (PCoA). In addition, two relatively polymorphic mtDNA regions were sequenced and compared.We collected a total of 87 samples .This study indicated that  Precious corals belong to family Coralliidae  play an important role in increasing the biodiversity of the deep sea by providing a complex habitat structure, suitable for other species to live in Hemicorallium cannot be delineated using mtDNA   has beensogorgia  and reved-method . AdditioHeliopora were successfully delineated using MIG-seq (Multiplexed inter simple sequence repeat (ISSR) genotyping by sequencing (MIG-seq) is an easy, cost-effective, novel method to obtain a moderate number of single nucleotide polymorphisms (SNPs) of non-model organisms using polymerase chain reaction (PCR) and high-throughput technology. The number of available SNPs from MIG-seq analysis is generally less than those using other techniques such as RAD-seq . However MIG-seq , implyinP. elatius and P. konojoi are distributed in Japanese waters at depths of 115–330 m  terminal branches of the colony form of P. elatius are fine, while those of P. konojoi are blunt and rounded, (2) autozooids of P. elatius are arranged in approximately four rows, while those of P. konojoi are clustered in groups  were collected from depths of 100 to 330 m using either a traditional coral net, underwater remotely operated vehicles (ROV), or submarines off Ryukyu, Kyushu, and Shikoku, Japan .Two mtDNA regions were sequenced for MEGA 7.0  was consMEGA 7.0  was usedP. elatius and 60 P. konojoi individuals collected from the Ryukyu Islands to Shikoku Island, geographically separated by approximately 1,300 km  with a fastq-quality-filter setting of –Q 33 –q 30 –p 40. We removed adapter sequences for the Illmina MiSeq run from both the 5’ end (GTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC) and the 3′end (CAGAGATCGGAAGAGCGTCGTGTAGGGAAAGAC) using Cutadapt version 1.13 (m)′ = 3, ‘maximum distance allowed between stacks (M)′ = 1, ‘maximum distance allowed to align secondary reads to primary stacks (N)′ = 1, and the deleveraging (d) and removal (r) algorithms enabled. Secondly, we used the C-stacks with the option ‘number of mismatches allowed between sample loci when build the catalog (n)′ = 4, followed by the S-stacks. To confirm the consistency of the results using different sets of SNPs with different amounts of missing data, we first created three different SNP sets using population software implemented in Stacks v. 1.4 by restricting the data analysis to different criteria: (i) the minimum percentage of individuals required to process a locus across all data (r) was set at 70% and restricting data analysis to a single SNP per locus, (ii) r was set at 50% and a single SNP per locus was used, and (iii) r was set at 50% and all SNPs per locus were used. Confirming that all the results were consistent irrespective of the data sets, we only showed the results using (i) r, 70%, and single SNP per locus. For all of the above analyses , we set the following parameters: the minimum number of populations that a locus must be present in to process a locus (p)′ = 1, the minimum minor allele frequency required to process a nucleotide site at a locus (min_maf) = 0.01, the maximum observed heterozygosity required to process a nucleotide site at a locus (max_obs_het) = 0.9. BayeScan v 2.0 was used to detect possible SNPs under natural selection assuming two morphological species with a default setting.To eliminate low-quality reads and primer sequences from the raw data, we used the FASTX-toolkit version 0.0.14   were 200,000. We estimated lrtri K  were obtained and concatenated to reconstruct the phylogenetic tree  was 0.453 and nucleotide diversity (π) was 0.00047. The haplotype network analysis revealed P. elatius and P. konojoi shared a major haplotype  from 25 individuals  r = 0.5 and only a single SNP per locus resulted in 762 SNPs, and (iii) r was set at 0.5 and all SNPs per locus resulted in 2251 SNPs. All data sets indicated consistent STRUCTURE and PCoA results, regardless of the different ratio of missing data and possible linkage disequilibrium  ilibrium .BayeScan indicated that all the loci were neutral . Among the 223 SNPs examined, 12 SNPs indicated species-specific alleles (fixed substitution between the species) that could be observed.P. elatius and P. konojoi are genetically distinct. Calculation of lrtri K of the STRUCTURE results indicated that K = 2 best explained the data with a mean likelihood = − 5, 666.970 and mean similarity score among 10 independent runs = 0.999. Clear genetic differences corresponded with the morphological differences without any genetically admixed individuals . Further application of MIG-seq analysis for a wider range of octocoral or other taxa would aid in verifying the robustness of MIG-seq analysis on species delimitation.This study indicated that 10.7717/peerj.7769/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.7769/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.7769/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.7769/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.7769/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.7769/supp-6Supplemental Information 6Click here for additional data file.


Meloidogyne incognita in Arabidopsis thaliana, which suggests that the genetic architecture underlying host susceptibility could be much simpler than previously thought. Here, we report that, by using a relaxed stringency approach in a GWA analysis, we could identify 15 additional loci linked to quantitative variation in the reproductive success of M. incognita in Arabidopsis. To test the robustness of our analysis, we functionally characterized six genes located in a QTL with the lowest acceptable statistical support and smallest effect size. This led us to identify ETHYLENE RESPONSE FACTOR 6 (ERF6) as a novel susceptibility gene for M. incognita in Arabidopsis. ERF6 functions as a transcriptional activator and suppressor of genes in response to various abiotic stresses independent of ethylene signalling. However, whole‐transcriptome analysis of nematode‐infected roots of the Arabidopsis erf6‐1 knockout mutant line showed that allelic variation at this locus may regulate the conversion of aminocyclopropane‐1‐carboxylate (ACC) into ethylene by altering the expression of 1‐aminocyclopropane‐1‐carboxylate oxidase 3 (ACO3). Our data further suggest that tolerance to abiotic stress mediated by ERF6 forms a novel layer of control in the susceptibility of Arabidopsis to M. incognita.Root‐knot nematodes transform vascular host cells into permanent feeding structures to selectively withdraw their nutrients from host plants during the course of several weeks. The susceptibility of host plants to root‐knot nematode infections is thought to be a complex trait involving many genetic loci. However, genome‐wide association (GWA) analysis has so far revealed only four quantitative trait loci (QTLs) linked to the reproductive success of the root‐knot nematode  Meloidogyne incognita is ranked as one of the most rapidly spreading biological threats of agricultural productivity, occurring in more than 143 countries worldwide  of M. incognita might be a highly polygenic trait in plants. However, it should be noted that many genes may be regulated in response to the massive cellular changes induced by feeding nematodes, but may not necessarily be required for the susceptibility of plants to nematode infections. Nonetheless, allelic variation in genes that do enable initiation, expansion and maintenance of giant cells may translate into quantitative variation in susceptibility to nematode infections. If this holds true, gain‐ and loss‐of‐function alleles of these so‐called susceptibility genes may be used in the future to make crops more resilient to nematode infections  in Arabidopsis  = 5, the accumulated effect sizes of the alleles in these four loci could account for only 50% of the heritable variation in susceptibility of Arabidopsis to M. incognita. We also noted that reducing this threshold to the level of −log10(P) = 4 would have explained all of the heritable variation in susceptibility of Arabidopsis to M. incognita. However, relaxing the stringency of our GWA approach would also have increased the risk of pursuing false positives. Here, we describe how we challenged this higher risk of false discovery under relaxed criteria by functionally characterizing six genes located at QTL13, which, in terms of statistical support and effect size, had a relatively high chance of being a false positive. This led us to identify ETHYLENE RESPONSE FACTOR 6 (ERF6) as a regulator of susceptibility to M. incognita in Arabidopsis. The transcription factor ERF6 has not been associated previously with susceptibility to M. incognita, but its role as an activator and repressor of abiotic stress response genes may shed new light onto the role of tolerance to abiotic stress in feeding site formation.Previously, we have used a genome‐wide association (GWA) mapping approach to assess whether quantitative variation in susceptibility to 10(P) = 5 to −log10(P) = 4 revealed 28 additional single nucleotide polymorphisms (SNPs), resulting in a total number of 36 SNPs significantly associated with the number of egg masses of M. incognita per plant : four SNPs around position Chr1:28.18Mb, six SNPs around position Chr5:6.26Mb and two SNPs around position Chr5:22.68Mb  = 4 as a threshold for significance, we could map all of the allelic variation (in 19 QTLs) underlying the heritable phenotypic variation in the reproductive rate of M. incognita in our population of Arabidopsis lines.Stepwise lowering of the threshold for significant associations in our original analysis from −log8Mb Fig. , we aggr8Mb Fig. . It shou10(P) = 4] and within the subset with the smallest effect size (3.83 egg masses). SNP marker Chr4.145290 is located at genomic position 9765013 of chromosome 4 inside the fifth predicted intron of gene model At4G17505 . With the average LD decline in Arabidopsis (±10 kb) in mind, we identified in total six predicted genes that could be linked to SNP marker Chr4.145290, and which could therefore contribute to the observed variation in the number of egg masses of M. incognita per plant at 6 weeks post‐inoculation.The cost associated with the lowering of the threshold for significance is an increase in the false discovery rate to 0.55. To challenge this high risk of false positive SNPs, we investigated QTL13 marked by only one SNP (Chr4.145290) with significance on the threshold value  and with the smallest effect size of all SNPs meeting this criterion. SNP Chr4.145290, marking QTL13, is located within the predicted coding sequence AtG17505 (DUF239). However, we found no further evidence to conclude that allelic variation in DUF239 contributes to the variation in susceptibility of Arabidopsis to M. incognita.As the false discovery rate increases by lowering the threshold of significance for association mapping, we challenged the risk of pursuing false positives by further investigating QTL13. The association of susceptibility with QTL13 was based on a single SNP with a significance on the threshold value  and the reproductive success of M. incognita ; SALK_059419C with T‐DNA insert in At4G174505 (duf239‐1); SALK_087258C with T‐DNA insert in At4G147510 (uch3‐1); SALK_129052C with T‐DNA insert in At4G17520 (hln‐1); and SALK_104999C with T‐DNA insert in At4G17530 (rab1c‐1). The T‐DNA insert in all mutant lines interrupted the (predicted) open reading frames in the coding sequences of the genes. The T‐DNA insertion lines were all generated in the background of Col‐0 (N60000), which was used as wild‐type Arabidopsis throughout this study.The following homozygous Arabidopsis T‐DNA insertion mutant lines were obtained from the Nottingham oC, 35 cycles of 30 s at 94 oC, 1.5 min at 60 oC and 1 min at 72 oC, and a final incubation of 10 min at 72 oC. The PCR amplification products were analysed by agarose gel electrophoresis.The presence and homozygosity of the T‐DNA insert in the mutant lines were checked by PCR on genomic DNA isolated from leaf material of 12 seedlings  was amplified as a reference for gene constitutive expression . cDNA samples were used as template in qPCR with the Absolute SYBR Green Fluorescein mix . cDNA matching  Pfaffl, .M. incognita were obtained by treating tomato roots infected with M. incognita  with 0.05% v/v NaOCl for 3 min. Roots were rinsed with tap water and the eggs were collected on a 25‐μm sieve. Next, the eggs were incubated in a solution of 2.4 mm NaN3 for 20 min whilst shaking. Thereafter, the eggs were rinsed with sterile tap water and incubated on a 25‐μm sieve in a solution of 1.5 mg/mL gentamycin and 0.05 mg/mL nystatin in the dark at room temperature. Hatched juveniles were collected after 4 days and surface sterilized using 0.16 mm HgCl2, 0.49 mm NaN3 and 0.002% v/v Triton X‐100 for 10 min. After surface sterilization, the juveniles were rinsed three times with sterile tap water and transferred to 0.7% Gelrite solution .Eggs of m NaOCl and 1% HCl in tap water) for 5 h and transferred to a six‐well cell culture plate containing Murashige and Skoog (MS) medium . The six‐well plates were first incubated in the dark at 4 ºC for 3 days. Thereafter, the seeds were allowed to germinate at 21 ºC under 16 h light/8 h dark conditions in a growth cabinet for a further 7 days. Individual 1‐week‐old seedlings were subsequently transferred to separate wells in a new six‐well plate containing MS medium. The seedlings were incubated for a further 7 days at 21 ºC under a 16 h light and 8 h dark regime. Next, individual seedlings were inoculated with 180 infective J2s of M. incognita per plant and incubated at 24 ºC in the dark. The number of egg masses per plant was counted at 6 weeks post‐inoculation by visually inspecting the roots with a dissection microscope. Unless indicated otherwise, the differences in counts per plants were statistically analysed using two‐way analysis of variance (ANOVA) and post‐hoc Tukey’s honestly significant difference (HSD) test in R . Each Arabidopsis genotype was tested in at least three independent experiments with 10 replicates per experiment. Both genotype and experiment were used as factors to test for significance.To test the susceptibility of Arabidopsis seedlings, seeds were vapour sterilized (with 7.1 m NaOCl and 1% HCl in tap water) for 5 h and transferred to a six‐well cell culture plate containing MS medium . The six‐well plates were first incubated in the dark at 4 ºC for 3 days. Thereafter, the seeds were allowed to germinate at 21 ºC under 16 h light/8 h dark conditions in a growth cabinet for 7 days. Next, four seedlings were transferred to 12‐cm2 plates containing MS medium, which were placed vertically in a growth cabinet for 7 days at 21 ºC under a 16 h light and 8 h dark regime. Next, individual seedlings were inoculated with 180 infective J2s of M. incognita per plant and incubated horizontally at 24 ºC in the dark. Samples of 12 whole‐root systems were collected at the time of inoculation, at 7 dpi with M. incognita, and at 7 days after mock inoculation. The samples were stored at −80 ºC until further use.To collect roots of infected and non‐infected Arabidopsis seedlings for gene expression analyses with microarrays and qRT‐PCR, seeds were vapour sterilized (with 7.1 post‐hoc Tukey’s HSD test in R (P < 0.05).Arabidopsis seedlings were allowed to germinate and grow for 14 days on MS medium as described above for the nematode bioassays. To determine the total root length and number of root tips of the seedlings, the complete plants were transferred from the media onto a plastic tray with water. Next, the leaves of the seedlings were removed and the roots were spread out over the surface of the tray. The total root length was measured using the WinRHIZO package for Arabidopsis . The number of root tips was counted manually based on the scan made using WinRHIZO. Differences in the total root length per seedling in centimetres and the number of root tips were statistically analysed with a two‐way ANOVA and R2 (from the Pearson correlation) between marker pairs in R .GWA mapping was performed as described previously  and the residual variance (VE) using REML. These were used to calculate the narrow‐sense heritability as:The narrow‐sense heritability was estimated using EMMA  = 6.1 to −log10(P) = 4.0 in steps of 0.1. For each set of SNPs, an additive ANOVA model was constructed to determine the phenotypic variation captured by the set of SNPs. As a measure of model complexity vs. explanatory power, the Bayesian information criterion (BIC) was calculated using the BIC function in R.where erf6‐1, duf239‐1, uch3‐1, rab1c‐1 and Col‐0 wild‐type. The frozen root systems were homogenized using TissueLyser  twice for 30 s. Total RNA was extracted from 100 mg of the homogenate with the Maxwell Plant RNA kit  using the Maxwell 16 Robot (Promega Corporation), according to the manufacturer’s protocol. A DNAse treatment was included in the Maxwell Robot processing. The amount of total RNA per sample was determined by an ND‐1000 spectrophotometer .Expression analysis for the gene of interest was performed on the stored root samples produced during the nematode infection study. Whole‐root systems were cut from aerial parts of the seedlings and snap frozen in liquid nitrogen. Total RNA was isolated from whole roots of 12 14‐day‐old plants of Arabidopsis thaliana elongation factor 1α was amplified as a reference for constitutive expression using the primers indicated in Table post‐hoc Tukey’s HSD test in R (P < 0.05).For qRT‐PCR, first‐strand cDNA was synthesized from total RNA using a Superscript III First‐Strand synthesis system (Invitrogen) according to the manufacturer’s protocol. Samples were analysed by qPCR using Absolute SYBR Green Fluorescein mix (Thermo Fisher Scientific). cDNA matching Arabidopsis V4 Gene Expression Microarray . The probes were re‐blasted against TAIR11 using the BLASTN function of the command line blast tool . The default settings were used and the top‐hit was used as probe annotation. Probes with multiple hits were censored  and erf6‐1 at 7 dpi. All microarray data were deposited in the ArrayExpress database at EMBL‐EBI (www.ebi.ac.uk/arrayexpress) under accession number E‐MTAB‐6711.For microarray analysis, approximately 825 ng of total RNA of each sample was used for gene expression analysis on an 2‐transformed for further analysis.After scanning the microarrays, the spot intensities were not background corrected prior to analysis  of spot i  was explained over treatment (T). Afterwards, the significances obtained were corrected for multiple testing using the false discovery rate procedure in P adjust, obtaining q values  and the number of egg masses per plant of Meloidogyne incognita in Arabidopsis at 6 weeks post‐inoculation. The broken horizontal line indicates the threshold for significance in the genome‐wide association (GWA) mapping set at –log10(P) = 4. Green dots indicate the positions of 36 significantly associated SNPs, nine of which are overlapping and are visualized by the darker green colour of the dot. The rectangles represent the five chromosomes of Arabidopsis.Click here for additional data file.Fig. S2  Linkage between 36 single nucleotide polymorphisms (SNPs) in the Arabidopsis genome significantly associated with the number of egg masses of Meloidogyne incognita per plant. The linkage was calculated using the R2 method and expressed as the level of correlation in shades of purple. Presented here is a pairwise comparison between all 36 SNPs as indicated by their positional marker. The grey rectangles present the five chromosomes of Arabidopsis.Click here for additional data file.Fig. S3  Confirmation of T‐DNA insert in lines erf6‐1, duf239‐1, uch3‐1 and rab1c‐1 and the presence of actin in wild‐type Arabidopsis Col‐0 with polymerase chain reaction (PCR). (A) Allele‐specific PCRs on genomic DNA isolated from each Arabidopsis mutant line. PCR amplification products using primer combinations for only the wild‐type gene allele (P1) and for the wild‐type allele harbouring a T‐DNA insert (P2). PCR amplification product using primer combination specific for actin in genomic DNA isolated from wild‐type Arabidopsis Col‐0. (B–E) Relative gene expression of the genes harbouring the T‐DNA insert in the mutant lines when compared with the wild‐type Arabidopsis Col‐0 using quantitative reverse transcription‐polymerase chain reaction (RT‐PCR) on 14‐day‐old seedlings. (B) Relative gene expression of DUF239 in duf239‐1. (C) Relative gene expression of ERF6 in erf6‐1. (D) Relative gene expression of UCH‐3 in uch3‐1. (E) Relative gene expression of Rab1C in rab1c‐1. Data in (B–E) were generated with three independent biological replicates with three technical replicates each.Click here for additional data file.Fig. S4  Total root length and number of root tips of wild‐type Arabidopsis plants and erf6‐1 mutant line. (A) Number of root tips per plant of 14‐day‐old seedlings. (B) Total root length of 14‐day‐old seedlings. Data were analysed with analysis of variance (ANOVA) and post‐hoc Tukey’s honestly significant difference (HSD) test for significant differences .Click here for additional data file.Table S1  Percentage of heritable variation explained for different significance thresholds of –log10(P) with corresponding numbers of significantly associated single nucleotide polymorphisms (SNPs).Click here for additional data file.Table S2  Primers used for quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR) to assess gene expression levels and T‐DNA insert in homozygous Arabidopsis lines. Identifier indicates the gene target with PCR with matching forward and reverse primers. Primer efficiency is expressed as a percentage of the product amplification.Click here for additional data file.Table S3  Correspondence between quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR) and microarray data of gene expression levels in wild‐type Arabidopsis seedlings at 7 days post‐inoculation with Meloidogyne incognita and in mock‐inoculated seedlings.Click here for additional data file.Table S4  Genes differentially regulated between nematode‐infected roots of the erf6‐1 mutant and wild‐type Arabidopsis seedlings at 7 days post‐inoculation with Meloidogyne incognita.Click here for additional data file.


Populus nigra is a major tree species of ecological and economic importance for which several initiatives have been set up to create genomic resources. In order to access the large number of Single Nucleotide Polymorphisms (SNPs) typically needed to carry out a genome scan, the present study aimed at evaluating RNA sequencing as a tool to discover and type SNPs in genes within natural populations of P. nigra.P. nigra transcriptomic data. The accuracy of the resulting RNAseq-based SNP calling and typing has been evaluated by (i) comparing their position and alleles to those previously reported in candidate genes, (ii) assessing their genotyping accuracy with respect to a previously available SNP chip and (iii) evaluating their inter-annual repeatability. We found that a combination of several callers yields a good compromise between the number of variants type and the accuracy of genotyping. We further used the resulting genotypic data to carry out basic genetic analyses whose results confirm the quality of the RNAseq-based SNP dataset.We have devised a bioinformatics pipeline to call and type SNPs from RNAseq reads and applied it to P. nigra. These results open prospects towards the use of this technology for quantitative and population genomics studies.We demonstrated the potential and accuracy of RNAseq as an efficient way to genotype SNPs in The online version of this article (10.1186/s12864-018-5239-z) contains supplementary material, which is available to authorized users. Populus nigra is a major tree species from Eurasian riparian ecosystems and one of the 3 main parental species used in poplar breeding programs to develop highly productive interspecific cultivated hybrids. For these reasons, several initiatives have recently been set up to create genomic resources within this species as tools to improve conservation and breeding strategies  on 5 genes. (XLSX 157 kb)Position of SNPs on et al. 3, on 5 genAdditional file 2Table S1: Total number of SNPs detected with four different callers and applying different filters. Table S2: Comparison of number of SNPs detected with RNAseq data, identical positions with the SNP chip and genotyping accuracy using 7 calling modalities times 3 options for missing values. (XLSX 12 kb)Number of SNPs detected by different callers or combinations of callers and using different filters. Additional file 3Figure S2: Distribution of genotyping accuracy of RNAseq data computed from a comparison with genotyping from a previously available SNP array [Figure S3: Variation of the total SNP number and identical positions found with the chip data using 7 calling modalities times 3 options for missing values. Figure S4: Positions of SNPs discovered and genotyped with RNAseq across 12 Populus nigra individuals and along two genes. Figure S5: Graphical representation of the enrichment in GO terms  for the genes covered by at least 5 SNPs. (PDF 434 kb)NP array  for the