Caenorhabditis elegans. We focused on the genomic regions that control metabolite levels by measuring fatty acid (FA) and amino acid (AA) composition in the RILs using targeted metabolomics. The genetically diverse RILs showed a large variation in their FA and AA levels with a heritability ranging from 32% to 82%. We detected strongly co-correlated metabolite clusters and 36 significant metabolite quantitative trait loci (mQTL). We focused on mQTL displaying highly significant linkage and heritability, including an mQTL for the FA C14:1 on Chromosome I, and another mQTL for the FA C18:2 on Chromosome IV. Using introgression lines (ILs), we were able to narrow down both mQTL to a 1.4-Mbp and a 3.6-Mbp region, respectively. RNAi-based screening focusing on the Chromosome I mQTL identified several candidate genes for the C14:1 mQTL, including lagr-1, Y87G2A.2, nhr-265, nhr-276, and nhr-81. Overall, this systems approach provides us with a powerful platform to study the genetic basis of C. elegans metabolism. Furthermore, it allows us to investigate interventions such as nutrients and stresses that maintain or disturb the regulatory network controlling metabolic homeostasis, and identify gene-by-environment interactions.Metabolic homeostasis is sustained by complex biological networks that respond to nutrient availability. Genetic and environmental factors may disrupt this equilibrium, leading to metabolic disorders, including obesity and type 2 diabetes. To identify the genetic factors controlling metabolism, we performed quantitative genetic analysis using a population of 199 recombinant inbred lines (RILs) in the nematode  Caenorhabditis elegans as a model organism to explore genetic variation affecting metabolic parameters. Considerable variation would validate C. elegans for further identification of complex gene by environment interactions correlated with metabolism.Energy homeostasis is maintained by biological networks that are affected by nutrient availability and keep functional balance at cellular and molecular levels . When thC. elegans is a versatile model organism for understanding complex genetic pathways underlying distinct phenotypes such as stress response, lifespan, host–pathogen interaction, and behavior  and CB4856 (Hawaii), which is suitable for studying genome-to-phenome relations  (C14:0-C30:0) and 19 amino acids (AAs) in a sample of approximately 500 worms . For instance, the two most abundant metabolites, C18:1 and alanine, showed a large absolute difference between the RILs with the lowest and highest abundance of 6.5- and 5.6-fold, respectively . In addition, metabolites present at lower concentrations, including C20:3 and methionine, showed a large absolute difference between the lowest and highest RIL of 10.5- and 11.1-fold, respectively. Transgression was observed for 18 metabolites, especially in FAs (FDR = 0.05) . Transgression analysis is a measure for the genetic complexity of a trait, for instance due to genetic interactions. It showcases that metabolite levels are affected by multiple polymorphic genes of which the alleles cause a balanced phenotype in the parental lines.To extract general principles about the complex genetics of metabolism, we measured the impact of genetic variation on metabolite levels in 199 RILs using our recently established sensitive MS platform (H2) of these metabolite traits, which is a measure of the genetic contribution to the variation in metabolite levels , ranging from 0.32 (tryptophan) to 0.69 (lysine) for the AAs . Together, our data show that both AAs and FAs have moderate to high heritability, increasing the chance to identify significant QTL. Specifically, the traits with nonsignificant transgression and high heritability, for instance the FA C14:1 (H2 = 0.82), are most likely associated with a single major locus explaining most of the metabolic variation.Next, we calculated the broad-sense heritability , including C18:3, C20:3, C20:4, and C20:5 forming a small cluster B. Strong10(p) > 3.7 as the significance threshold . We detected 36 significant mQTL for 26 metabolites , which shows that specific loci affecting metabolite trait variation can be identified. We observed 15 significant mQTL for eight different AAs. We found that four AAs  shared two broad mQTL peaks on Chromosome I (∼10.5 Mbp) and Chromosome IV (∼11.0 Mbp). These four AAs also displayed strong positive correlations between the trait levels in the RILs (10(p) = 18.8] was for C14:1, which mapped to a single locus on Chromosome I within a region of 12.2–14.7 Mbp. This metabolite trait also had the highest heritability (H2 = 0.82), without significant transgression.To identify loci that explain variation in metabolite levels, we performed QTL mapping on 56 metabolites measured in the 199 RILs A. We expthe RILs A. For thP > 0.01) . Second, we conducted a genome-wide two-marker scan for interactions. We identified 17 locus–locus interactions for six metabolites: C15:1, C16:1, C24:5, alanine, aspartic acid, and glutamine (FDR < 0.1) . Combining the single-marker and two-marker scan mQTL models, we calculated the amount of heritable variation explained by these mQTL. For some metabolites, such as isoleucine, no variation could be explained despite a high heritability. In contrast, for C14:1 ∼50% of the heritability was explained by mQTL. In total, for 28 metabolites part of the H2 could be explained by the identified mQTL . These findings suggest that many metabolite traits showed heritable variation and the genetic regulation of these metabolite traits was highly complex and likely involved multiple interactions between different genetic variants. This was supported by an analysis of the heritable variation in the parental strains versus the H2 in the RILs . Here we observed that most of the H2 was driven either by many additive loci with opposing effects or by genetic interactions such as epistasis.To gain an overview of the genetic complexity and its effect on the detection of QTL, we identified the overlap between metabolite traits with significant mQTL, transgressive segregation, and heritability C: 7/18 mNext, we decided to focus on two mQTL that displayed the most significant linkage and high heritability: the mQTL for C14:1 on Chromosome I and the one for C18:2 on Chromosome IV. For C14:1, 34% of variation could be explained by the genotype at the peak location on Chromosome I A,B, and Supplemental Fig. S3): (1) An N2 locus decreases the C14:1 abundance, and (2) a CB4856 locus increases the abundance. We found that the four CB4856-background ILs had lower C14:1 levels, confirming the first hypothesis (FDR < 0.05). Three out of four N2-background ILs had increased C14:1 levels, which confirmed the second hypothesis (FDR < 0.05). WN218 did not show a significant increase in the C14:1 abundance (We next used introgression lines (ILs) to independently validate the C14: 1 mQTL and narrow down the mQTL peak to a smaller region. These ILs contain a small genomic segment derived from one parental strain introgressed into the genetic background of the other parental strain C. For C1bundance D. By anaSupplemental Fig. S4). Here, three of the four CB4856-background ILs and one of the two N2-background ILs confirmed the mQTL for C18:2 (FDR < 0.05). Based on the minimum region of the confirming ILs, the mQTL could be confirmed to the region spanning 2.8–6.4 Mbp. Overall, these results independently confirmed the detection of the two mQTL in the RILs, demonstrating that this approach is an important step to make forward genetics possible for identifying complex genetics underlying metabolic regulation.For the FA C18:2, two QTL were identified: one on Chromosome II (lower abundance in N2 genotype) and one on Chromosome IV (higher abundance in N2 genotype) E,F. TogeSupplemental Table S8). Next, we analyzed C14:1 profiles of N2 worms with RNAi against a selection of 19 genes from all the candidates. The C14:1 amounts in RNAi-treated worms were compared to worms treated with control RNAi (Escherichia coli HT115 strain) . Of all 19 candidates tested, we found that worms fed with RNAi targeting lagr-1, Y87G2A.2, nhr-265, nhr-276, and nhr-81 contained a significantly higher amount of C14:1 compared to the control . The mammalian homolog of lagr-1 is CERS1, which encodes ceramide synthase 1 catalyzing the synthesis of C18 (dihydro)ceramide . Worms fed with RNAi against acox-1.1 or scrm-2 showed significantly decreased C15:1 amounts, suggesting a causal role of these two genes in the regulation of lipid abundance . The C26:0 concentration increased when acox-1.1 or acox-1.4 (isoforms of the same gene) was knocked down . This is fully in line with the suspected biochemical role of these proteins, since these are homologs of human ACOX1 encoding peroxisomal acyl-coenzyme A oxidase 1. This enzyme catalyzes the first reaction of the very-long-chain FA oxidation pathway in peroxisomes  involved in lipid metabolism or (2) transcription factors . These wceramide . It was staining . Y87G2A.staining . Althougoxisomes .SCD gene and the FA myristoleate C14:1  and CB4856 (Hawaii)  . This syC. elegans transgression has been reported for some quantitative morphological traits, such as body size, egg size, and fertility (C. elegans (affecting ∼6% of the genes) (H2 > 0.5)  falling beyond the parental phenotypes are often seen . For exaertility . For mete genes) . This ma2 > 0.5) . DespiteNext, we analyzed the metabolite–metabolite correlations of the same metabolite classes and found strong correlations between multiple pairs of metabolite traits. Because involvement of different types of AAs in different metabolic pathways is rather complex, strong correlations between different pairs of AAs imply shared biological properties. All three BCAAs—valine, leucine, and isoleucine—showed strong positive correlation, likely because they share chemical similarities and are catabolized using similar pathways . In addilagr-1, Y87G2A.2, nhr-265, nhr-276, and nhr-81 affected the levels of C14:1. Although lagr-1 and Y87G2A.2 indeed play a role in lipid metabolism, the other genes, encoding transcription factors, have not been studied previously for their involvement in the regulation of lipid metabolism. This could support further research into detecting the causal polymorphic genes underlying the mQTL for C14:1. Since the scope of this study was to investigate the overall genetic architecture of metabolites in C. elegans, we did not pursue the search for causal genetic variants or to addressing the phenotypic effect of parental alleles of these candidate genes. Altogether, we believe that this RIL population of C. elegans provides us with a powerful platform to study the genetic basis of metabolism. The systems approach with QTL analysis makes it possible to address important questions related to genetic architectures of quantitative traits, such as genetic actions or gene-by-gene interactions and with the environment. The RILs can thereby play an important role to dissect the mechanisms underlying the complex processes of metabolism in a natural and unbiased manner and allow us to identify factors important for gene-by-environment interactions. In addition, this systems approach will also enable researchers to explore further into additional interventions, such as dietary alterations and environmental stresses–associated metabolic changes.Independent confirmation of candidate genes by RNAi knockdown in Bristol N2 strain illustrated that Supplemental Table . For narrowing down the range of mQTL on Chromosomes I and IV, 14 ILs were used. Six of these ILs had an N2 genetic background: WN212, WN215, WN217, WN218, WN251, and WN252 .In total, 199 RILs were used . Approxind WN252 . Eight ond WN252 . The seqE. coli OP50 was obtained from the Caenorhabditis Genetics Center (CGC), RNAi bacterial clones are E. coli HT115 strains, including those to knockdown acox-1.1, acox-1.3, acox-1.4, gpdh-1, T27F6.6, ech-7, lagr-1, Y87G2A.2, scrm-2, nhr-82, nhr-265, nhr-81, nhr-165, nhr-217, nhr-77, nhr-174, nhr-169, nhr-276, and ces-2, which were obtained from the Ahringer library.E. coli OP50 lawn, 2000 eggs of each strain were then seeded onto NGM plates and cultured for 2.5 d, allowing development to young adults. For heritability analysis of FAs and AAs, we collected worms in triplicates from 51 RIL strains, in which the genome composition contains high recombination (together with N2 and CB4856). To narrow down the QTL peak for C14:1 on Chromosome I and the one for C18:2 on Chromosome IV, we prepared worm samples in four replicates for the ILs.Nematodes were cultured and maintained at 20°C on nematode growth media (NGM) agar plates. Culture conditions in all experiments were the same unless indicated otherwise. For metabolite profiling of 199 RIL strains, N2, and CB4856, age-synchronized worms were obtained by alkaline hypochlorite treatment of gravid adults grown on E. coli HT115  or RNAi clones mentioned in the previous section. After 2.5 d, young adult worms were collected and freeze-dried until FA extraction.To investigate candidate genes, we measured FA profiles of N2 worms with RNAi of candidate genes. RNAi experiments were performed from hatch in all cases, on NGMi plates (containing 2 mM IPTG and 25 mg/mL carbenicillin). Two thousand Bristol N2 eggs were seeded onto NGMi plates with a bacterial lawn of either 2) in a 10 µL final volume on ice. This reaction was incubated at 55°C for 10 min. The amplification reaction for each strain contained : 1× Ex Taq Buffer, 0.2 mM dNTPs, 1 U Ex Taq , 0.2 µM primer 1, 0.2 µM primer 2, and 5 µL of tagmentation material from the previous step in a 25 µL total volume. Each strain had a unique pair of indexed primers. We first made a master mix containing buffer, water, dNTPs, and Ex Taq and then aliquoted the appropriate volume of this mix into each well. We added the specific primer sets to each well and finally the tagmentation reaction. The amplification reaction was incubated in a thermocycler with the following conditions: 72°C for 3 min (1 cycle); 95°C for 30 sec (1 cycle); 95°C 10 sec, 62°C 30 sec, and 72°C 3 min (20 cycles); and 10°C hold. We combined 8 µL from each amplification reaction to generate a pool of libraries. A portion of the libraries was electrophoresed on a 2% agarose gel. DNA was excised and gel purified using Qiagen's gel purification kit . The libraries were sequenced on the Illumina HiSeq 2500 platform using a paired-end 100-bp reaction lane. Alignment, variant calling, and filtering were performed as previously . Following the ATL lysis step, 4 µL of 100 mg/mL RNAse was added to each sample and allowed to incubate for 2 min at room temperature. DNA concentration was determined using the Qubit dsDNA BR Assay Kit . For each strain, a total of 0.75 ng of DNA was combined with 2.5 µL transposome  diluted 35× in 1× Tris buffer  for two times of 2.5 min at frequency of 30 times/sec, followed by a tip sonication  for two times on ice water. Protein quantification was performed with BCA assay.Sample preparation for FA extraction was followed as mentioned in our previous study . A synchg for 1 min. The upper layer was transferred to an FA-free glass tube and evaporated at 30°C under a stream of nitrogen. FA residues were dissolved in 150 µL chloroform-methanol-water  solution containing 0.0025% aqueous ammonia and then transferred to a Gilson vial for ESI-MS analysis.Worm lysate (up to 150 µg protein) was transferred in a 4-mL FA-free glass vial, and 1 mL of freshly prepared 100% acetonitrile (ACN)/37% hydrochloric acid (HCl)  was added to the lysate, together with deuterium-labeled internal standards. FA samples were hydrolyzed by incubating at 90°C for 2 h. After the vials cooled down to room temperature, 2 mL of hexane was added to the samples and mixed by vortexing for 5 sec followed by a centrifugation step at 1000We added deuterium-labeled internal standards to each sample, including 17,17,18,18,18-D5-C18:0 , 3,3,5,5-D4-C24:0  and 3,3,5,5-D4-C26:0 ; we also ran additional five samples with increasing amount of a FA mixture  to make a five-point calibration curve. In these additional five samples, above-mentioned internal standards were also added .We used the same worm homogenate as mentioned and prepared for FA analysis. As described previously, AAs were extracted by transferring worm lysate (contains 50 µg of protein) to a 2-mL Eppendorf tube, and 1 mL of 80% ACN plus 20 µL of internal standard mixture was added to the lysate and homogenized by vortexing . Samples13C, 28 nmol L-ornithine-3,3,4,4,5,5-D6, 42 nmol L-isoleucine-D10, and 46 nmol DL-aspartic acid-2,3,3-D3 was added to each sample. For the calculation of AA concentrations, an additional sample was prepared by adding 50 µL standard mixture containing all AA with a concentration of 250 µmol/L to the internal standard  and analyzed together with the RIL samples.As standards, an internal standard mixture (Cambridge Isotope Laboratories) containing 68 nmol DL-alanine-2,3,3,3-D4, 44 nmol DL-glutamic acid-2,4,4-D3, 40 nmol L-leucine-5,5,5-D3, 28 nmol L-phenylalanine-ring-D5, 34 nmol L-valine-D8, 34 nmol L-methionine-(methyl-D3), 26 nmol L-tyrosine-ring-D4, 22 nmol L-tryptophan-(indole-D5), 46 nmol DL-serine-2,3,3-D3, 48 nmol proline-D7, 24 nmol L-arginine-2,3,3,4,4,5,5-D7, 28 nmol L-glutamine-2,3,3,4,4-D5, 32 nmol L-lysine-4,4,5,5-D4, 26 nmol L-citrulline-ureido-Supplemental Table S2).The 199 RILs were grown in five time-separated batches alongside with the parental strains N2 and CB4856. Metabolites were measured in five batches of FA and AA measurements. A subset of 51 RILs and the two parental strains were grown in three additional time-separated biological replicates and were measured in an additional batch of FA and AA measurements. In total, these experiments yielded 400 samples after quality control . More specifically, FA and AA measurements for N2 and CB4856 were repeated seven times; seven RILs were repeated five times, 44 RILs four times, 27 RILs two times, and 121 RILs one time. There are two reasons for these numbers. First, the experiment was conducted on the 199 RILs  and then replicated on a subset of 51 RILs . Second, for some samples in the experiment on the 199 RILs, we were not sure whether there was enough material. Therefore, these were repeated in another batch. However, after measuring FA and AA levels, the quality was found to be sufficient (and corresponding to the other sample); therefore, we found it most prudent to include both samples. For other samples, however, the initial sample did not provide enough material and had to be discarded. Details on FA and AA measurement batches and biological replicates are included in the raw data table (R is the fraction of metabolite i (one of 40 FAs or one of 19 AAs) of all the metabolites (either FAs or AAs) measured in sample x. M is the concentration of metabolite i of sample x.Because of reliability detection limits of the MS platform, FA measurements with a concentration below 0.03 nmol/mg of protein were removed from the analysis, as were AA concentrations below 0.4 nmol/mg of protein . In caseR is the is the fraction of metabolite i in sample x, of which the difference between the batch average and the total average is subtracted. For evaluation of the fold-differences in metabolite abundances, a batch correction on the untransformed metabolite abundances was conducted using the same formula with the absolute metabolite concentrations as input.The fractions were batch corrected byz-score byZ is the z-score of metabolite i of sample x, and µ is the mean for that metabolite and σ is the standard deviation. This transformation was used in the further analysis. Outliers were removed if the trait value exceeded µ ± 2*σ as measured per strain.Thereafter, the metabolite levels were expressed as a https://git.wur.nl/mark_sterken/Metabolomics) and as Supplemental Code. In the analysis, the tidyverse package was used for organizing the data (www.tidyverse.org); most plots were made using ggplot2  using custom written scripts . The cod ggplot2 .The traits were analyzed for correlation by calculating the Pearson correlation between metabolites (FAs and AAs independently) on the metabolite levels.μ ± 2*σ, where the parent with the lowest μ determines the low threshold and the parent with the highest μ determines the high threshold.Transgressive segregation was calculated as in The significance of transgression was determined by permutation, where the trait values were randomized over the strain designations. Subsequently, the same test as described above was executed. The permutation was repeated 1000 times for each trait, where after the obtained values were used as the by-chance distribution; the FDR = 0.05 threshold was taken as the 50th highest value.n ≥ 3) and for metabolites that were consistently detected (more than 100 observations). Using an ANOVA explaining the metabolite variation over the strains, the broad-sense heritability was calculated asH2 is the broad-sense heritability, Vstrain is the variation explained by strain, and Vres is the residual variation. The significance of the heritability was calculated by permutation, where the trait values were randomly assigned to strains. Over these permutated values, the variance captured by strain and the residual variance were calculated. This procedure was repeated 1000 times for each trait. The obtained values were used as the by-chance distribution, and an FDR = 0.05 was taken as the 50th highest value.The heritability of the metabolite levels was calculated over a subset of RIL strains for which repeated measurements were conducted  the heritability was calculated by ANOVA, usingh2 is the heritability, Vparent is the variation explained by the parental genotypes, and Vres is the residual variation. The factor 0.5 corrects for the overestimation of the additive variation in inbred strains . The metabolite levels were fitted in a single-marker model,Z is the z-score averaged over all strain replicates for metabolite i (one of 39 FAs or one of 19 AAs that were reliably measured in >100 strains) of RIL j (n = 199). This was explained over the genotype (either CB4856 or N2) on marker location x  of RIL j.QTL were mapped using custom scripts in R  of RIL j (n = 199). This was explained over the genotype (either CB4856 or N2) on marker location x1, x2, …, xn  of RIL j, which were identified in the single-marker mapping (maximum number of markers was three).For nine metabolites, multiple mQTL were identified with the single-marker model; to verify that these mQTL were not caused by linkage and were indeed independent, the mQTL peaks were fitted to an additive multiple marker model,Z is the z-score averaged over all strain replicates for metabolite i (one of 39 FAs or one of 19 AAs that were reliably measured in more than 100 strains) of RIL j (n = 199). This was explained over the genotype (either CB4856 or N2) on marker location x1, x2, and the interaction effect between x1 and x2  of RIL j. For the traits where a significant interaction was found , a full ANOVA model containing all the additive and interaction terms was used to determine the amount of variation explained by the additive and interaction terms. The effect sizes were calculated by a full linear model.A full two-locus interaction model was fitted for all metabolites,z-scores per metabolite were randomly distributed over the genotypes. This permutated data set was thereafter used in the same single-marker model for QTL mapping. This procedure was repeated for 100 randomized data sets. From these randomized mappings, an FDR was determined based on multiple testing under dependency  was taken. The q-value was set at 0.05. In this way, a −log10(p) > 3.7 was found. The eQTL confidence interval was determined by a 1.5 drop in the −log10(p) value as measured from the eQTL peak.In order to account for multiple testing in the single-marker mapping, the genome-wide significance threshold was determined via permutation. Here the pendency ,FDSRDS≤σ = 1, μ = 0). The simulated peak size was set correspondingly . Based on the set permutation threshold [−log10(p) > 3.7], the number of correctly detected QTL, the number of false positives, and the number of undetected QTL were counted. From the simulation, we also inferred the precision of the mapping by evaluating the effect size estimation and the QTL location [based on a −log10(p) drop of 1.5]. The detailed results of the analysis can be found in Supplemental Table S5.The statistical power in the single-marker mapping at the FDR threshold was determined by simulation. Using the genetic map of the 199 strains used in this study, QTL were simulated for each marker location. Per location, 10 QTL were simulated, explaining 5%–80% of the variation (in increments of 5%). In order to simulate technical noise, we introduced random variation based on a standard normal distribution (The significance threshold in the interaction model was determined on the marker–marker interaction with markers that were more than 50 markers apart (or located on different chromosomes). The p.adjust function in R was used to calculate the FDR.t-test, explicitly testing the hypothesis formed from the RIL data. Therefore, for C14:1 the CB4856-background ILs were tested for lower trait levels compared to the genetic background strain, and the N2-background ILs were tested for higher trait levels compared to the genetic background strain. For C18:2 the CB4856-background ILs were tested for higher trait levels compared to the genetic background strain, and the N2-background ILs were tested for lower trait levels compared to the genetic background strain. The significances were adjusted for multiple testing using the Benjamini and Hochberg correction, as implemented in the p.adjust function in “R” . Specifically, the lipid metabolism pathways in C. elegans were obtained . This list was expanded by adding likely lipid metabolism genes identified in a comparative study on lipid metabolism genes in C. elegans (C. elegans with an ontology term containing the word “transcription factor” were downloaded from WormBase (WS258)  .https://www.ncbi.nlm.nih.gov/sra) under accession number SRP154243. The MS data are available at the NIH Common Fund's Metabolomics Data Repository and Coordinating Center  website, the Metabolomics Workbench, http://www.metabolomicsworkbench.org, where they have been assigned Project ID PR000676. The data can be accessed directly via their Project DOI: 10.21228/M8968S (https://doi.org/10.21228/M8968S).The sequencing data for genotyping of the introgression lines have been submitted to the NCBI Sequence Read Archive (SRA;


Simulation of genetic variants data is frequently required for the evaluation of statistical methods in the fields of human and animal genetics. Although a number of high-quality genetic simulators have been developed, many of them require advanced knowledge in population genetics or in computation to be used effectively. In addition, generating simulated data in the context of family-based studies demands sophisticated methods and advanced computer programming.R package, sim1000G, which simulates variants in genomic regions among unrelated individuals or among families. The only input needed is a raw phased Variant Call Format (VCF) file. Haplotypes are extracted to compute linkage disequilibrium (LD) in the simulated genomic regions and for the generation of new genotype data among unrelated individuals. The covariance across variants is used to preserve the LD structure of the original population. Pedigrees of arbitrary sizes are generated by modeling recombination events with sim1000G. To illustrate the application of sim1000G, various scenarios are presented assuming unrelated individuals from a single population or two distinct populations, or alternatively for three-generation pedigree data. Sim1000G can capture allele frequency diversity, short and long-range linkage disequilibrium (LD) patterns and subtle population differences in LD structure without the need of any tuning parameters.To address these issues, we propose a new user-friendly and integrated Sim1000G fills a gap in the vast area of genetic variants simulators by its simplicity and independence from external tools. Currently, it is one of the few simulation packages completely integrated into R and able to simulate multiple genetic variants among unrelated individuals and within families. Its implementation will facilitate the application and development of computational methods for association studies with both rare and common variants.The online version of this article (10.1186/s12859-019-2611-1) contains supplementary material, which is available to authorized users. With the emergence of next-generation sequencing (NGS) technologies, the amount of genetic data generated every year grows exponentially. Developing new methods for analyzing these data is an area of active research, not only in human populations but also in plant and animal species. It is a common practice to generate large number of simulated datasets for validation and comparison of novel bioinformatics tools and statistical methods.Genetic Epidemiology” and found out that out of 36 articles that included simulated genetic data, only 8 of them used genetic simulators or simulators already catalogued in the Genetic Simulation Resources page of NIH [Simulation programs are a key component of genetic and genomic research, useful for improving our understanding of the mechanisms underlying complex biological processes . Unlike e of NIH . Many reHapGen2 [simuPOP [simuRARE [While many approaches have been introduced for simulations of genetic variants data  such as HapGen2 , simuPOP[simuPOP  and simusimuRARE , many ofsim1000G, which is user-friendly, completely integrated into R and fits various simulation purposes. Implementing sim1000G in R also facilitates the integration of analytic methods for genetic association tests, many of them being also available in R. Sim1000G has been designed to simulate existing genetic variants with a wide range of MAFs under different study designs including independent individuals and pedigree data. It was not developed to simulate sequence data nor novel genetic variants. Other types of genetic variants such as MNPs, indels, CNVs, functional annotations can be simulated if they are included in the input VCF file, as long as they are biallelic. The impact of evolutionary processes  and the impact of demographic models (e.g. admixture populations) was not considered either although a easy way to simulate admixed populations is given as illustration (see Results section). The value of sim1000G is also its ability to create benchmark genetic variants data sets that can be used subsequently for assessing genetic association methodologies. Compared to most genetic simulators, the integration of our genetic simulator into R allows a much simpler workflow and minimal set-up time for the users of the package. The capacities of sim1000G allow the simulation of genotype data among unrelated individuals as well as within pedigrees of arbitrary sizes, a feature absent from most existing genetic simulators.To address some of the shortcomings of existing genetic simulators, we implemented a genetic variants simulator, sim1000G, this is achieved by reading a phased VCF file containing both rare and common genetic variants located on a specific chromosomal region and for a given population. A common source of VCF inputs is the phased variant calls from Phase III 1000 genomes sequencing data [sim1000G can be performed using the bcftools package [A realistic genetic simulator of genetic variants data should preserve the minor allele frequencies (MAF) distribution and the LD patterns observed in a single homogeneous population or in an admixed population. With ing data , where vR environment. There is an option in sim1000G for filtering genetic variants with respect to a minimum or maximum MAF and for the number of variants allowed, to speed-up computations. It is also possible to simulate only functional variants or specific classes of variants by providing a VCF file matching the required conditions.There is no current limit on the size of the simulated genomic region, i.e. up to a whole chromosome in length, but the number of genetic variants should not exceed the computational capability of the hapsim package in R [Hapsim provides a computationally efficient algorithm to generate large pools of haplotypes, which are then integrated into sim1000G. The computation time for this step is proportional to the square of the number of genetic variants and is the most time-consuming step when sim1000G is applied to regions with large number of variants. The size of the region in Mb does not affect the simulation speed.After reading through the VCF file, the haplotypes for a particular population are extracted and the correlation between each pair of genetic variants is estimated. The age in R  is used hapsim functionality. This step ensures that the simulated variants data capture both the allele frequency distribution, short and long-range LD structure from the genome as well as recombination hotspots. To enable higher computational efficiency, large pools of haplotypes are computed in batches. Each time a VCF file is read, a pool of 1000 haplotypes is automatically generated. Once this pool is exhausted, another pool of 1000 haplotypes is generated. The generation of unrelated individuals is performed with the function generateUnrelatedIndividuals in sim1000G.Simulating genetic variants for a new individual requires the generation of two simulated haplotypes for the genomic region considered using sim1000G can simulate genomic regions with a wide range of sizes and up to a full chromosome. Multiple chromosomes can also be simulated as long as the corresponding VCF files are provided. Examples of VCF files from across the genome are provided on the sim1000G github page.With this feature, The computer memory and computational burden grow in proportion to the square of number of variants considered. Simulations of up to 1000–2000 variants can be performed easily on a laptop computer while the generation of 4000 to 10,000 variants requires the use a workstation computer with sufficient memory.sim1000G is its ability to simulate genetic variants among family relatives. A set of functions in sim1000G allows the modeling of recombination events and offspring formation assuming diploid individuals and autosomal chromosomes. This functionality can be used to simulate pedigrees of arbitrary sizes together with realistic genetic variants datasets that preserve the LD structure of the genomic region considered.Since many genetic analyses use pedigree data including linkage or family-based association studies, an important feature of https://github.com/adimitromanolakis/geneticMap-GRCh37). We provided detailed genetic maps for all chromosomes obtained by re-mapping (lifting) the coordinates of the HapMap Phase II genetic map from build 35 to GRCh37. The original map was generated as part of the HapMap project [For this purpose, a detailed genetic map of the simulated region is needed. For human autosomal data, the genetic maps can be downloaded automatically from an online database on github  probabilities between pairs of relatives. Through its simulation model, sim1000G was evaluated using a laptop computer with a 2GHz processor and 4GB of RAM. Only one CPU core, out of the 4 available, was used for all timing reports. The number of simulated individuals varied from 100 to 8000 and the number of variants from 100 to 1600. Even when considering thousands of individuals, the entire simulation process with sim1000G was completed in less than 10 s. The simulation of an entire genomic region of size 1MBp with 400 variants on 4000 individuals was finished in less than 8 s  and an African subset . For each gene, we filtered out genetic variants with MAF < 2% and generated the corresponding VCF files for use with sim1000G and the genotypes were combined to create common sets of variants.In total, 1000 replicate datasets were generated for each gene, with a total of 2000 individuals in each replicate. To create distinct LD patterns and allele frequency distributions, each ethnic group was generated independently with yi was generated for each individual i, given its simulated genotypes Gi, j, where j ∈ {1,  … , J} denotes the genetic variant j from a standard logistic regression model:0 is a baseline parameter, si a population stratification term, assumed 0 for individuals from the European subset and 1 otherwise, b1 is the odds ratio of the disease risk between Africans and Europeans, and cj the effect size (i.e. log odds-ratio) for a specific variant j .An outcome Each simulated dataset included 3 causal genes, each with J = 10 causal genetic variants. The effect size of each variant was fixed in the range of log(1.5) to log(5). The number of individuals from African descent varied between 0 to 400 in order to simulate different magnitudes of population stratification effects.p-value under 2 scenarios: (a) with no covariates or (b) with the ethnic group as covariate as a way to adjust for population stratification.SKAT  was usedThe results from 16 different simulation scenarios are shown in Table Sim1000G can be used in this context.To assess the performance of genetic association tests with RVs under a case-control design, simulations of RV genotypes from cases and controls are necessary. sim1000G included: (a) The length n of the simulated region, where n corresponds to the number of variants with genetic variations; (b) The range of MAFs for the simulated variants.As an illustration, we evaluated the statistical power of several RV association tests  under d10MAF| [We simulated the phenotype data of individuals using a logistic regression model similar to eq. . We assu10MAF|  and thatsim1000G to the simuRareVaraints (SRV) script [simuPOP [To assess the quality of the generated data, we compared ) script , impleme[simuPOP  softwareWe first used SRV to simulate a population of DNA sequences with RVs forward in time, subject to mutation, natural selection and population expansion. The region lengths were 50 k bp, 100 k bp and 300 k bp, which correspond to 72, 147 and 442 genetic variants respectively, in the region having mutations in the population. A gamma distribution was used for the selection coefficient of the new mutants, assuming a complex bottleneck model for the European population . All othsim1000G vs. SRV. In Table sim1000G was more efficient and easier to implement than simuPOP. Indeed, the former only simulates genotype data for a pre-specified sample size while the latter requires for each simulation scenario to generate of a huge initial population from which the final set of individuals is extracted.In Table Family-based study designs allow the characterization of gene mutation effect on the disease risk by considering related individuals. A few methods have been developed for testing sets of genetic variants in family studies but only few approaches were proposed in the context of right-censored time-to-event data .i, i = 1, … , nf from a family f = 1, … , n, the risk for developing a disease is defined by the hazard function:A correlated frailty model can be used to test the association between a set of genetic variants and a survival outcome in family studies . In thisλ0(t) is a baseline hazard function, e.g. the Weibull hazard function β are the corresponding regression coefficients. The random effects b are normally distributed and correlated with each other. The covariance matrix Σ(σ) defines the dependence structure using the IBD matrix B, Σ(σ) = σ2B. The frailty parameter σ2 represents the familial correlation related to the SNP or region of SNPs included in the calculations of the IBD probabilities. The estimators of the model parameters  are found using a conditional maximum likelihood estimation that accounts for the selection bias coming from the sampling of families through affected probands [R package frailtypack [and where probands . The proiltypack .N = 100, N = 200 and N = 500. Using sim1000G, we generated families of three generations: parents, one or two children in the second generation and one or two children in the third generation . For generating the genotypes, we used the region from 1000 genomes Phase III sequencing data VCF files and a genetic map GRCh37 from the corresponding chromosome 4. The assumed MAF ranged from 0.02 to 0.1. The pedigree structures and mean IBD (mIBD = IBD1 + IBD2/2) were used to generate time-to-event data with the function simfam from the R package FamEvent [βsex to 0.5. The time of right-censoring was equal to an individual’s current age sampled from the normal distribution with variance 2.5 and mean fixed to 95, 75 and 55 for the first, second and third generation, respectively. Values for the Weibull parameters were chosen to obtain around 60% of censored cases in the samples (λ = 143 and ρ = 3.0).We performed simulation studies where the goal was to evaluate the type I error and the power of genetic association tests when the model is correctly specified. The number of families was FamEvent . We useds = 3, we assumed that they have equal effect on the disease risk, β1 = β2 = β3, this effect was fixed at 0.0, 0.5 or 1.0. In all the settings, we used model (2) for the estimation with the function frailtyPenal from the package frailtypack [σ2, was fixed at 0.0 under the null hypothesis (no association) and estimated under the alternative (association). The p-values were obtained using likelihood ratio test, in which, under the null the asymptotic distribution of the test statistic is the mixture of For generating the survival times, we used the model (2) with genotypes as covariates to modify the risk of disease. The number of variants was iltypack . In ordeβs. The power is low when the effect of genotypes is fixed to 0.5, which means that the test detects the genetic association only if the effect of genotypes on survival is strong. When the genotype effects are fixed to 1.0, for all sample sizes, the association test is very powerful. For datasets with 500 families, the test detects the association in all the replicates.The results of the simulation studies using 500 replicates are presented in Table R package sim1000G for easy generation of simulated genetic variants under realistic scenarios, mimicking the 1000 Genomes project or any other phased variant call VCF files. The capabilities of the package allow the simulation of genetic variants data in tens of thousands of individuals, generated either independently or within pedigrees of arbitrary sizes.We have developed the sim1000G provides a very efficient and compelling approach to simulation, completely integrated within the R environment. Besides, it avoids the need for complex scripts and is independent from external packages/softwares. It allows the generation of realistic genetic variants data, from 50 variants in European families to > 1000 variants in populations of independent individuals with mixed ethnicities. Sim1000G is able to perform these tasks under minimal computational burden, user interaction or set-up time. We have not yet implemented phenotype simulations as part of sim1000G to give more flexibility to the users to perform this task. A number of examples of phenotype simulations are however included in the Additional file Compared to other simulators designed to simulate existing genetic variants, sim1000G to perform analyses and simulations for various genetic problems. Our first application simulated unrelated individuals from a single population to compare the MAF distributions and patterns of LD of sim1000G to competing simulators. Our second example showed that sim1000G can be used to create admixed populations of unrelated individuals and such simulated data can be useful to assess methods that correct for population stratification, using either rare or common variants (or both). The third example, which simulated unrelated individuals from a single population, shows the interest of sim1000G to assess the power and type I error of various methods to detect genetic association with a set of RVs. Finally, our last application shows how sim1000G can be used to simulate sequence variants data in pedigrees and assess the power and type I error of various methods to detect genetic association in this setting.The applications described in this paper demonstrate the versatility of sim1000G should facilitate future applications and developments of computational methods for association tests with both rare and common variants, using either unrelated individuals or families. Many more simulation situations than those presented here, could benefit from a package such as sim1000G.Therefore, our implementation of sim1000G and the most recent version is also available on github, at: https://github.com/adimitromanolakis/sim1000G.Sim1000G is available for download on CRAN under the package name Project name: sim1000G.Project home page:https://github.com/adimitromanolakis/sim1000GOperating system(s): Platform independent.Programming language: R.Other requirements: R packages of stringr, readr and hapsim (available in CRAN).License: GNU GPL.Any restrictions to use by non-academics: No restrictions.Supplementary information: Available on the journal’s online website.Additional file 1:Supplementary materials. (PDF 216 kb)


Lameness represents a significant challenge for the dairy cattle industry, resulting in economic losses and reduced animal health and welfare. The existence of underlying genomic variation for lameness associated traits has the potential to improve selection strategies by using genomic markers. Therefore, the aim of this study was to identify genomic regions and potential candidate genes associated with lameness traits. Lameness related lesions and digital cushion thickness were studied using records collected by our research team, farm records, and a combination of both. Genome-wide analyses were performed to identify significant genomic effects, and a combination of single SNP association analysis and regional heritability mapping was used to identify associated genomic regions. Significant genomic effects were identified for several lameness related traits: Two genomic regions were identified on chromosome 3 associated with digital dermatitis and interdigital hyperplasia, one genomic region on chromosome 23 associated with interdigital hyperplasia, and one genomic region on chromosome 2 associated with sole haemorrhage. Candidate genes in those regions are mainly related to immune response and fibroblast proliferation. Quantitative trait loci (QTL) identified in this study could enlighten the understanding of lameness pathogenesis, providing an opportunity to improve health and welfare in dairy cattle with the addition of these regions into selection programs. Lameness is a complex trait defined as an abnormal stance or gait of the animal that results from disorders of the locomotor system. In dairy cattle, lameness is one of the most important health conditions together with impaired fertility and mastitis , and cauMany lameness cases are associated to various infectious and non-infectious diseases , resultiWhile reducing the incidence of lameness is one of the main objectives for the dairy cattle industry, current evaluations are based on observational scores such as claw health status data, lameness and mobility scores, conformational traits, and data collected by automated sensors . All thiFew studies have addressed traits associated with lameness using a genomic approach. The largest study  grouped The objective of the present study is to perform genome-wide analyses to identify regions and candidate genes, and understand the genetic basis of a wide range of lameness-related traits. This knowledge may inform genetic improvement schemes aiming to reduce prevalence of dairy cattle lameness. Digital cushion thickness (DCT) measurements are studied here for the first time from a genomic perspective.Ethical approval for the study was granted by the University of Liverpool Research Ethics Committee. ASPA regulated procedures were conducted under a Home Office License (Reference Number: PPL 70/8330).The study included a total of 554 Holstein–Friesian cows in lactation 0–8 from three different farms. Data sources were from a combination of previous research projects and routinely kept farm records. The recorded lameness-causing foot lesions were digital dermatitis, sole ulcer, white line disease, sole haemorrhage, and interdigital hyperplasia. Cases were defined following the ICAR Claw Health Atlas definitions .st research interval), and between October 2016 and August 2017 . A number of cows were also followed during the period between August 2017 and October 2017, but no DCT measurements were obtained . Lameness lesions were recorded by the veterinarian for 88 animals between December 2014 and January 2016, and for 337 animals between October 2016 and October 2017; 50 cows had records from both these two different time intervals. DCT measurements and recording of lameness-causing lesions were performed at three time points around animals’ calving: 3–4 weeks before the expected calving, the first week after calving, and approximately 8 weeks after calving. Seventy-nine animals only had lameness lesion records obtained from the farms’ records. Research and farm records were analysed both separately and combined. For the latter, animals were considered as affected when at least one of the available records (research or farm) indicated presence of the lesion. All these data are summarised in Farm phenotypic records for presence (1) or absence (0) of these lesions were extracted from the farm database for all these animals  from May 2006 to October 2017 using TotalVet software (Sum-IT). In addition, 475 cows were individually monitored for the same lesions by a research team led by an experienced veterinarian during three separate time intervals between December 2014 and October 2017. DCT measurements were taken using ultrasonography between December 2014 and January 2016  equipped with a linear probe 5–8 MHz. All measurements of DCT were undertaken at the midline, on the lateral claw of the hind left foot. To measure the DCT, the foot was cleaned and loose horn was removed with a hoof knife. Sole contact with the transducer was made using ultrasound gel  and a gel standoff . After freezing the image on the ultrasound monitor , measurements were taken to the nearest millimetre. The DCT was measured just cranial to the tuberculum flexorum of the pedal bone at the typical sole ulcer site. The distance from the inner margin of the sole (identified as a thin echogenic line) to the distal edge of the pedal bone (identified as a thick echogenic line) was assessed.Blood samples were collected from the tail vein of each cow in vacutainer tubes containing EDTA. Genomic DNA was extracted from buffy coat samples using the QIAamp DNA Blood MiniKit from Qiagen. Extracted DNA samples were quantified using a NanoDrop and stored at -20°C. Initially, 266 cows were genotyped using the Affymetrix Axiome bovine 54K SNP array. The Illumina BovineSNP50 bead chip containing 53,714 SNP was used to genotype the rest of the animals. Genotype data obtained from the Affymetrix array were converted to the Illumina chip format by selecting common SNPs using concordant strand assignment and identified allelic calls before further analyses were conducted.P-value of 0.05). The final genotype call rate after genotype quality control was 98.7%. Additional quality control of samples was performed by removing individuals with poor genotype quality . All these quality control procedures resulted in a final dataset of 549 animals genotyped for 34,658 SNPs with positions assigned according to the UMD 3.1 assembly.Quality control was performed using PLINK  in orderPrincipal component analyses of the genotyped animals were performed using GEMMA , replaciEstimates of the variance components for each individual trait were obtained by fitting the following model using REACTA :y represents the vector of phenotypes, W is an incidence matrix, α is the vector of associated fixed effects, Z is the design matrix for the vector u of random polygenic effects [distributed as a multivariate normal distribution MVN with G being the GRM and Vg the genetic variance of the trait], and ε represents the vector of residual errors [distributed as MVN with I being the identity matrix and Ve the residual variance]. The significance of the genomic (polygenic) effect (P = 0.05) was assessed using the likelihood ratio test statistic to compare a model that fits the effect against the base model that excludes it.where Fixed effects used in model (1) were tested previously using Wald tests in ASReml 4 , fittingIndividual SNP association analyses were performed in those traits with a significant genomic effect from model (1) using GEMMA . The linx represents the vector of genotypes (coded as 0/1/2) and β is the regression coefficient of the phenotype on the genotypes; all other effects are as described in model (1). The statistical significance of the regression coefficient was assessed using a Wald test. When determining the significant thresholds, a Bonferroni correction was performed for multiple testing due to the number of markers, but not for multiple traits. This resulted in a genome-wide significant threshold (P  =  0.05) defined at P  =  1.44E-6 [-log10(P)  =  5.84] and a suggestive threshold  defined at P  =  2.89E-5 [-log10(P)  =  4.54].where Despite including the polygenic effect in the model, genotyping errors or other artefacts such as cryptic population structure may inflate test statistics. Therefore, to account for any potential remaining inflation, the ratio of the median of the empirically observed distribution of the test statistic to the expected median (inflation factor λ) was used for correction, following the method described by Under the regional heritability mapping approach, the genome was divided into non-overlapping windows of 20 consecutive SNPs. The following model was used in REACTA :X and Z are the corresponding design matrices for the effects (i)u of the corresponding region i {distributed as MVN, with Vg(i) and (i)G being the genomic variance and the GRM corresponding to the SNPs in the thi region, respectively} and u(-i) of the genome (polygenic effect) excluding the region i {distributed as MVN with Vg(-i) and (-i)G being the genetic variance and the GRM corresponding to all SNPs other than those on the region i, respectively}.where P = 0.05) defined at P  =  2.89E-5 with Bonferroni correction for multiple regions [-log10(P)  =  4.54] and a suggestive threshold  defined at P  =  5.77E-4 [-log10(P)  =  3.24]. As with the genome-wide association analyses, a correction by the inflation factor λ was applied to account for any remaining inflation after fitting the polygenic effect in the model.The significance of the region effect was assessed using the likelihood ratio test statistic. A total of 1,733 regions were analysed, leading to a genome-wide significant threshold  based on the likelihood ratio test. Heritability estimates for the disease traits range from 0.129 (white line disease in combined records) to 0.516 , corresponding to genomic variances in the range of 0.008–0.067. Heritability for DCT at calving was moderate (0.228), corresponding to a genetic variance of 0.549.Two genome-wide significant SNPs  and 19 genome-wide suggestive SNPs were detected in the genome-wide association analyses , thus implying a positive effect of the minor allele against the disease. However, due to a possible Beavis effect , the proDCT at calving and sole ulcer in the combined records did not provide any significant or suggestive SNP despite showing a significant genomic effect in the previous analyses.A significant region for interdigital hyperplasia was detected based on farm records, and 10 suggestive regions were detected for other traits using the regional heritability mapping approach  affecting lameness related traits. Comparison of the results provided by individual SNP genome-wide association analyses and regional heritability mapping was performed to strengthen the evidence of the identified regions. QTLs were detected for digital dermatitis, interdigital hyperplasia, and sole haemorrhage.Three sources of data were used in this study in order to identify genomic regions for lameness related diseases. Although using only research-confirmed records is expected to provide more accurate phenotypes than using farm records, the number of observations available was smaller, thus reducing the power to detect significant genomic effects. Similarly, using farm records provided a larger number of records spanning animals’ whole lifetime; these records however are potentially less accurate, thus leading to a low detection power and increase the chances of introducing misclassification bias. Impact of phenotypic errors in genomic analyses has been discussed before both in the context of human  and animHeritability estimates for some of these traits have been previously estimated using pedigree data . Such esFPGT (fucose-1-phosphate guanylyl transferase) part of the L-fucose pathway, a key sugar in complex carbohydrates involved in cell-to-cell recognition, inflammation, and immune processes (TNNI3K (serine/threonine-protein kinase TNNI3K), also associated with inflammation mechanisms chanisms . Based oEDN1 (endothelin-1), a vasoconstrictor associated with several cardiovascular diseases and inflammatory and fibrotic processes , an oxidoreductase that has been associated previously with pulmonary fibrosis . This gene is as a sensor molecule involved in traumatic, vascular, and inflammatory pathologies in the central nervous system , Ethical approval for the study was granted by the University of Liverpool Research Ethics Committee. ASPA regulated procedures were conducted under a Home Office License (Reference Number: PPL 70/8330).ES-M analyzed the data and co-wrote the first draft of the manuscript. VB collected the data, performed laboratory work, analyzed the data, and co-wrote the first draft of the manuscript. RS provided funding and critically evaluated the manuscript. GO and GB secured funding, designed and supervised the study, and critically evaluated the manuscript.This study was funded by the Academy of Medical Sciences. VB acknowledges support from The Turkish Ministry of Education, and Ministry of Food, Agriculture, and Livestock. GO gratefully acknowledges support from the Wellcome Trust. GB and ES-M gratefully acknowledge funding from The Roslin Institute Strategic Programme (ISP) grants, and GB also the Rural and Environment Science and Analytical Services Division of the Scottish Government.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Identifying genetic variants that are associated with methylation variation—an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping—is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches. DNA methylation is a stable, covalent modification of cytosine residues that, in vertebrates, typically occurs at CpG dinucleotides. DNA methylation also functions as an important epigenetic regulatory mechanism, with known roles in genomic imprinting, X-inactivation, and suppression of transposable element activity , 2. DNA cis-acting SNPs located close to target CpG sites, where CpG methylation level is associated with the identity of physically linked alleles [cis-mQTL often colocalize with disease-associated loci and cis-expression QTL (cis-eQTL) [cis-mQTL is an important step towards understanding the genetic basis of gene regulatory variation and, ultimately, organism-level traits.Importantly, DNA methylation variation at individual CpG sites often has a strong genetic component –29. Fami alleles , 31–35.  alleles , 36. Furis-eQTL) , suggestArabidopsis thaliana [Most mQTL mapping studies thus far rely on DNA methylation data generated using array-based platforms –38. Howethaliana , 50. Impthaliana , 52). Eathaliana , 49, 53.thaliana , 38. To First, methylation data collected in sequencing studies are counts, not continuous representations like those produced by arrays. Specifically, methylation-level estimates at a given cytosine base are based on both the total read count at the site and the subset of those reads that are unconverted by sodium bisulfite (or other processes ). Previocis-acting genetic differences in DNA methylation are expected to lead to both (i) differential methylation by genotype across all three genotypes at a biallelic site and (ii) ASM in heterozygotes. These two types of evidence are only available in sequencing studies, since ASM is not generally detectable when DNA methylation is profiled using arrays. Notably, previous methods for detecting genotype-dependent ASM suggest that it is common across tissue types and species, is more often explained by cis-acting variants than trans-effects, and is enriched near genes that also display patterns of allele-specific expression [cis-regulatory effects on gene expression. There is strong precedent for such a combined strategy in other omics studies. For example, the methods implemented in TreCASE and WASP can integrate allele-specific expression information to greatly enhance the power of eQTL mapping [Second, sequencing-based techniques are capable of measuring DNA methylation levels in heterozygotes in an allele-specific fashion . When ASM estimates support differences in methylation levels between the two alleles carried by heterozygotes, they can be used to increase the power of mapping analysis. Indeed, assuming that additive genetic effects dominate, true pression –75. Thus mapping , 76–78,  mapping . Howeverwww.xzlab.org/software.html). IMAGE jointly accounts for both allele-specific methylation information from heterozygous individuals and non-allele-specific methylation information across all individuals, enabling powerful ASM-assisted mQTL mapping. In addition, IMAGE relies on an over-dispersed binomial mixed model to directly model count data, which naturally accounts for sample non-independence resulting from individual relatedness, population stratification, or batch effects that are commonly observed in sequencing studies [Here, we develop a new statistical method for mQTL mapping in bisulfite sequencing studies that both accounts for the count-based nature of the data and takes advantage of ASM analysis to improve power. We refer to our method as IMAGE (Integrative Methylation Association with GEnotypes), which is implemented as an open-source R package . We then applied IMAGE and five other approaches to analyze each SNP-CpG pair separately.We performed simulations to examine the effectiveness of IMAGE and compare it with other approaches. In each simulation, we started with real genotypes for ividuals  and examM values for analysis, following the previous literature [The five other approaches perform mQTL mapping using different information: (1) IMAGE-I, a special case of IMAGE, which uses only non-allele-specific, individual-level information across all individuals; (2) IMAGE-A, another special case of IMAGE, which uses only allele-specific information from heterozygous individuals; (3) MACAU , 57, whiterature , 57. We Overall, the simulation results show that IMAGE outperforms all other methods across all tested parameters , while the power of GEMMA is only 1.01%. When TR increases to 30, the power of IMAGE-I, MACAU, and BB becomes 15.25%, 15.32%, and 14.55%, respectively, while the power of GEMMA remains low, at 6.14%. The superior performance of count-based methods is consistent with previous observations [Careful examination of the relative performance of different methods in different scenarios yields additional insights. First, among the mQTL mapping methods, we found that count-based approaches  often outperform a normalized data-based approach (GEMMA). Such performance differences become more apparent when sample size rvations , 57, sugtrans-acting genetic background effects . Our simulations suggest that there are two important parameters that influence the relative power of ASM analysis and mQTL mapping. The first important parameter is background heritability, h2. Increased background heritability can reduce the performance of mQTL mapping methods, as increased confounding from polygenic effects of other SNPs likely increases the difficulty of identifying individual SNP associations [h2 = 0, the power of IMAGE-I, MACAU, GEMMA, and BB is 13.57%, 11.62%, 2.69%, and 13.88%, respectively. When h2 increases to 0.6, however, the power of IMAGE-I, MACAU, GEMMA, and BB reduces to 6.48%, 7.05%, 1.50%, and 5.92%, respectively. In contrast, ASM analysis relies on a model that explicitly accounts for the heritable component that arises from genetic background effects, and thus achieves relatively stable performance. For example, when h2 = 0, the power of IMAGE and IMAGE-A is 57.48% and 10.30%, respectively. When h2 increases to 0.6, the power of IMAGE and IMAGE-A actually increases to 63.07% and 23.09%, respectively. This observation is consistent with the fact that the two alleles modeled in ASM, for each individual, share an identical genetic background that becomes easier to control for as its contribution to DNA methylation increases . Thus, IMAGE-I outperforms IMAGE-A when background heritability is zero (h2 = 0), but performs worse when background heritability is moderate or high  often outperform mQTL mapping approaches that only use non-allele-specific data. This result holds even for IMAGE-A, even though it only models data for heterozygotes at nearby SNPs . The generally higher power of ASM analysis likely stems from the fact that ASM methods control for both environmental and ciations , 57. Forρ, which represents the relative contribution of shared/common environmental effects  and also influences the relative power of ASM vs mQTL. For mQTL methods, increasing ρ necessarily increases the contribution of common environmental noise shared between the two alleles. Common environmental noise is not explicitly accounted for by mQTL models, thus leading to a reduction in power. For example, when ρ = 0, IMAGE-I, MACAU, GEMMA, and BB detect 7.55%, 7.49%, 2.25%, and 6.79% of true effects, respectively. When ρ increases to 0.9, the power of IMAGE-I, MACAU, GEMMA, and BB reduces to 3.50%, 3.44%, 1.67%, and 3.57%, respectively. In contrast, ASM analysis explicitly accounts for both common and independent environmental background effects, again because it measures DNA methylation in the two alleles in the same individual. ASM methods thus achieve better, not worse, performance with higher values of ρ. For example, when ρ = 0, the power of IMAGE and IMAGE-A is 57.15% and 10.27%, respectively. When ρ increases to 0.9, the power of IMAGE and IMAGE-A becomes 84.15% and 67.55%, respectively. Consequently, while mQTL methods have similar power as ASM when ρ is small, ASM can outperform mQTL when ρ is large  associated SNP-CpG pairs based on IMAGE, 53.8% of them were identified in both subsets. In contrast, among the top 5% (n = 2511) associated SNP-CpG pairs based on IMAGE-I, MACAU, GEMMA, and BB, 35.84%, 35.12%, 33.92%, and 37.64% overlapped between the two subsets. The greater consistency of results from IMAGE thus provides convergent support for its increased power.To validate the mQTLs we identified, we randomly split the sample into two approximately equal-sized subsets  and examined the consistency of the SNP-CpG pairs detected in the two subsets. We removed IMAGE-A from this analysis as it requires at least five heterozygous individuals, which is no longer satisfied for many SNP-CpG pairs in each of the two subsets. For the remaining methods, we found that IMAGE detects more consistent SNP-CpG pairs between the two subsets than the other approaches  but underrepresented in CpG islands . The results are consistent with previous observations [Next, we assessed the set of detected SNP-CpG associations by performing functional enrichment analysis to compare our findings against published results Fig. c. Here, erion as  to annotrvations , 88, parrvations –91 that rvations . The resn = 240) were indeed identified as significant mCpG sites. For 95.70% of those we did not detect (n = 156), the non-disrupted CpG was also hypomethylated in our sample (< 10% methylation level), which would make it impossible to detect an mQTL . CpG sites disrupted by SNPs accounted for 3.72% of significant mCpG sites (compared to the 0.89% expected by chance), but only 0.43% of non-mCpG sites, in support of the accuracy of our mQTL mapping approach . In addition, as expected, the percentage of significant mCpG sites accounted for by CpG sites disrupted by SNPs gradually decreases with less stringent FDR cutoffs  associated SNP-CpG pairs based on IMAGE analysis, 53.8% of them are consistent between the two subsets, compared to 20.5–30.7% for the other four methods tested. Consistent with results from simulations and the baboon data, we also observed that our method could detect more associated SNP-CpG pairs with intermediate methylation levels, increasing MAF, increasing read depth, and increasing sample size  and were underrepresented in CpG islands   and shore regions . The higher frequency of mCpG sites in CpG island shelves and shores is consistent with previous studies [m = 242,784 in wolf vs m = 45,210 in baboon). The enrichment in open sea and underrepresentation of mCpG sites in CpG islands are robust regardless of whether we stratify sites based on mean methylation levels, although the shelf/shore results are noisier  . HoCpG site . HoweverCpG site . CertainAnother limitation of IMAGE is that type I error may not be well controlled when methylation background heritability is high . mQTL mapping has the benefit of using the entire sample, not just heterozygotes. In contrast, ASM has the benefit of internal control, since both alleles within each heterozygote experience the same genetic and environmental background.yi and ri as the methylated read count and total read count for the ith individual , for i = 1, ⋯, n. We denote the corresponding methylated and total read counts mapped to each of the two alleles of the ith individual as yil and ril, for l = 1 or 2. Thus, yi = yi1 + yi2 and ri = ri1 + ri2. Note that yil and ril are only observed in heterozygotes, so are treated as missing data in homozygotes (more details below). We then model the methylated read counts for each allele as a function of the total read counts for the same allele using a binomial model:πil is the true methylation level for the lth allele in the ith individual. We further model the logit-transformed methylation proportion πil as a function of allele genotype:μ is the intercept; xil is the lth allele type for the ith individual for the SNP of interest ; and β is the corresponding allele/genotype effect size. In addition to these fixed effects, we model three random effects to account for different sources of over-dispersion. Specifically, gi represents the genetic background/polygenic effect on DNA methylation for the ith individual and can be used to account for kinship or other population structure in the sample. We assume K is a known n by n genetic relatedness matrix that can be estimated either from genotype or pedigree data. ui represents individual-level environmental effects that we assume are independent across individuals but shared between the two alleles within the same individual. We assume eil represents the residual error and is used to account for independent noise that varies across both individuals and alleles . We assume K to ensure that the mean of the diagonal elements of K equals 1, or λ explained by the genetic effects from all SNPs other than the SNP of focus . Therefore, the background heritability is the usual heritability minus the genetic effect of x. Our primary goal is to test the null hypothesis that genotype is not associated with methylation levels, or equivalently, H0 : β = 0.To take advantage of both approaches, IMAGE independently models each CpG-SNP site pair. For each individual measured at a CpG-SNP pair, we denote yil and ril are not observed. For homozygotes, only the sums of the reads across both alleles, yi = yi1 + yi2 and ri = ri1 + ri2, are observed. Therefore, for homozygotes, we derive a model for yi and ri based on Eq.  = PP. We further assume that P(ri1| ri) follows a binomial distribution ri1~Bin, which reflects the assumption that both alleles are equally likely to be represented in the sequencing data. Even with these two assumptions, the probability P in Eq.  in Eq.  does not) in Eq.  using a H0 : β = 0 and obtaining a corresponding p value.The model defined in Eqs. , (2) ; an MAF bin centered on 0.3, which contains SNPs with an MAF between 0.25 and 0.35 ; and an MAF bin including 0.5 which contains SNPs with an MAF between 0.45 and 0.50 . To simulate SNP-CpG site pairs, given a combination of sample size and MAF bin, we randomly selected one SNP from the appropriate MAF bin and simulated methylation counts and total read counts based on the following procedure.We performed simulations to examine the effectiveness of our method and compare it with other approaches. To do so, we first randomly selected 150 individuals from the 1958 birth cohort study, which is a part of the control samples that were used in the Wellcome Trust Case Control Consortium Study (WTCCC) . We thenng GEMMA –85. We eNB to simulate the total read count ri for each individual. Here, TR is the mean parameter and ϕ is the dispersion parameter. We set TR= 10, 20, or 30, close to the median estimate across all CpG sites from the baboon data . We set ϕ= 3, which is close to the median estimates obtained from the baboon data . To obtain the total read count mapped to each of the two alleles, we further simulated a proportion parameter qi, which represents the proportion of reads mapped to one allele out of the two alleles. Specifically, qi was simulated from a beta distribution Beta, where we set the shape parameters a and b to both be 10, so that the simulated qi is symmetric around 0.5 and is within the range of  in 93.6% of cases. With ri and qi, we simulated the total read count mapped to one of the two alleles from ri1~Bin and set the total read count mapped to the other allele as ri2 = ri − ri1.For the total read counts, we first used a negative binomial distribution μ, which characterizes the baseline methylation level (interpretable as the mean methylation level within a given population); h2, which represents background heritability; σ2, which is the over-dispersion variance; ρ, which characterizes the proportion of common environmental variance  with respect to both the common environmental variance and the independent environmental variance that is independent between both individuals and alleles within individuals; and PVE, which represents genotype effect size in terms of proportion of phenotypic variance explained (PVE) by genotype. With these four parameters, we first simulated the genetic random effects g = T (an n-vector) across all individuals from a multivariate normal distribution with covariance h2  and ui together as a bivariate vector T from a bivariate normal distribution with a covariance Σ, where For the methylated read counts, we performed simulations using a combination of five parameters. These five parameters include the intercept cts g = g, ⋯, gnT β was set to zero and the background genetic effects, environmental effects, and an intercept (μ) were then summed together to yield the latent variable πil through logit(πil) = logit(π0) + gi + ui + eil for the lth allele in ith individual. For sites with true mQTL, we used logit(πil) = logit(π0) + xilβ + gi + ui + eil to yield the latent variable πil, where xil is the allele genotype for the lth allele in the ith individual. We randomly draw PVE in logit(πil), on average. We set PVE to be 5%, 10%, or 15% to represent different mean mQTL effect sizes, and we derive V (•) denotes the sample variance computed across individuals with x being a genotype vector of size n. Finally, we simulated the methylated read counts for each allele based on a binomial distribution with a rate parameter determined by the total read counts ri and the methylation proportion πil; that is, yil~Bin for the lth allele in ith individual. For heterozygotes, we retained the allele-level data  and . For homozygotes, we collapsed the allele-level data into individual-level data, yi = yi1 + yi2 and ri = ri1 + ri2.For sites where methylation level was not associated with genotype, the SNP effect n = 100, h2= 0.3, π0 = 0.5 , MAF = 0.3, ρ = 0, TR = 20, σ2= 0.7, and PVE = 0.1 for mQTL sites. We then varied one parameter at a time to generate different simulation scenarios to examine the influence of each parameter, following [π0 to be either 0.1, 0.5, or 0.9 to represent low, moderate, or high levels of DNA methylation. We varied h2= 0.0, 0.3, or 0.6 to represent no, medium, or high background heritability. We varied σ2= 0.3, 0.5, or 0.7 to represent different levels of over-dispersion. We varied ρ= 0, 0.3, or 0.9 to represent different levels of common environment influence. For each simulated combination of parameters, we performed 10 simulation replicates consisting of 10,000 CpG sites each. Among these sites, DNA methylation levels at 1000 of them were associated with the SNP genotype (β ≠ 0) while DNA methylation levels for the remaining 9000 were not (β = 0).Using the procedure described above, we first simulated data under a baseline simulation scenario of ollowing . Here, wPanu2.0) using BSseeker2 [We applied our method to a bisulfite sequencing data set from 69 wild baboons from the Amboseli ecosystem in Kenya , 45. TheSseeker2   using VCFtools [K in GEMMA, using this SNP data set.We performed SNP calling in the bisulfite sequencing data using CGmaptools, a SNP calling program specifically designed for bisulfite sequencing data. CGmaptools examines one individual at a time using the BayesWC SNP calling strategy . FollowiVCFtools . We thenr = 0.551; p value < 2.2 × 10−16; Additional file To validate the SNP genotype data, we compared the variants identified from the bisulfite sequencing data to a set of previously identified SNP variants in baboons . These pIn addition to genotyping, we used CGmaptools to obtain CpG-SNP pairs where the SNP and CpG site were profiled on the same sequencing read. The distance between the SNP-CpG site pairs ranges from 1 to 104 bp, with a median distance of 37 bp , and the median number of reads per allele was 13 in heterozygous individuals . To check the quality of DNA methylation estimates for these CpG sites, we examined their distribution across individuals. Similar to other RRBS data sets , we obseM values [yi1, yi2),  with the count data from the homozygotes , treated the two alleles of each heterozygote as two samples and treated each homozygote as one sample, permuted the sample label 10 times to create null permutations, and applied each method to analyze the permuted data. We note that an alternative permutation strategy would be to permute  along with covariates across individuals. In this strategy, the number of methylated reads for each allele  in heterozygotes could then be sampled from a binomial distribution with probability 0.5, conditional on yi and ri − yi respectively. This alternative strategy is not ideal for small sample sizes, but is likely to work well for large samples (approximately n > 150). Therefore, we have also implemented this alternative permutation strategy in the software and recommend users to explore both strategies and select one that performs the best for their data. Regardless of which permutation strategy one uses, the statistics from the permuted data allowed us to construct an empirical null distribution. With the empirical null distribution, we estimated the empirical false discovery rate (FDR) for different methods at different p value thresholds. We then compared the number of associations detected by different methods at a fixed FDR cutoff.We applied five different approaches (details in the “M values  and usedPanu2.0 directly from the UCSC Genome Browser, annotated the 2-kb region upstream and downstream of the CpG island boundaries as the shore, annotated the 2-kb regions upstream and downstream of the CpG shores as the CpG shelves, and annotated the remaining regions as open sea (Fig. Finally, following , we annosea Fig. e.canFam 3.1 [K with these SNPs using GEMMA.We also applied our method to analyze a second bisulfite sequencing data set, from 63 gray wolves from Yellowstone National Park in the USA , 94. ThenFam 3.1  as the rcanFam 3.1. We found that 17.9% of variants overlapped with known variants from Ensembl. Importantly, among overlapping variants, 99.1% of them have the same alternative allele as reported in Ensembl. In addition, the proportion of overlapping variants increases with increasing alternate allele frequency and reaches 41.3% when we focus on variants that have an alternate allele frequency > 0.5 in the RRBS data (Additional file To validate variants identified in the wolf data set, we compared the called variants from the bisulfite sequencing data to an existing SNV data base from the current Ensembl release for the dog genome We followed the same procedure described for baboons to extract methylation measurements on SNP-CpG pairs. In the wolves, the distance between SNP-CpG site in each pair ranges from 1 to 103 bp, with a median of 35 bp (mean = 38.41 bp; sd = 25.63 bp; Additional file canFam3.1 genome.We applied the same analysis procedure to analyze the wolf data as we did for the baboon data set. IMAGE-A was used to analyze 236,092 SNP-CpG pairs where the data set included at least 5 heterozygotes while the other methods were applied to all 279,223 SNP-CpG pairs. We used permutation to construct empirical null distributions for FDR control and controlled for the effects of sex and the top two methylation principal components in the same procedure described in the baboon data. Finally, we annotated CpG sites into island, shore, shelf, and open sea categories as described above, based on the Additional file 1: Supplementary text on IMAGE modeling and inference details.Additional file 2: Supplementary figures on the performance evaluation of IMAGE and on the quality control of the real data applications.Additional file 3: Supplementary tables on functional enrichment analyses and type I error control examination.Additional file 4: Review history.