Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another . For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects . Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of “missing heritability” in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects. a priori or suspected. Here we propose to leverage the fact that most traits are genetically controlled to investigate the influence of the social environment. To do so, we study associations between genotypes of one individual and phenotype of another individual . Importantly, SGE can be studied even when the traits that mediate the influence of the social environment are not known. For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) explains up to 29% of the variation in anxiety, wound healing, immune function, and body weight. Hence our study uncovers an unexpectedly large influence of the social environment. Additionally, we show that ignoring SGE can severely bias estimates of direct genetic effects , which has important implications for the study of the genetic basis of complex traits.Daily interactions between individuals can influence their health both in positive and negative ways. Often the mechanisms mediating social effects are unknown, so current approaches to study social effects are limited to a few phenotypes for which the mediating mechanisms are known  Social interactions contribute to health and disease (e.g. peer smoking increases one’s risk of taking up smoking). So far, quantifying social effects has required a clear hypothesis about the mechanisms mediating the influence of the social environment . For many phenotypes however, such hypotheses do not exist. Therefore we propose an alternative strategy to study social effects: we investigate effects on an individual's phenotype that arise from genotypes of social partners . SGEIf SGE do contribute to such traits, they are a promising approach to quantify effects of the social environment. Additionally, they provide an anchor to investigate causal paths and dissect the mechanisms underlying social effects. Finally, in studies of direct genetic effects carried out by the broad community, SGE may be used to account for social environmental effects.Our study aimed at quantifying the contribution of SGE to multiple biomedical traits. We uncover unexpectedly large social genetic effects on multiple organismal and molecular phenotypes.To investigate whether SGE explain variation in biomedical traits, we considered two experiments in laboratory mice involving complementary genetic designs, and assessed both organismal and gene expression traits.We first carried out an experiment with two inbred strains. We chose C57BL/6J (B6) and DBA/2J (D2), the progenitor strains of the largest mouse reference population, the BxD recombinant inbred panel. We co-hIn this design with two inbred strains there are three potential genetic sources of phenotypic variation: differences in the strain of the focal (i.e. phenotyped) mice (DGE), differences in the strain of their cage mates (SGE) and an interaction between the two, whereby the effect of the strain of the cage mate depends on the strain of the focal mouse  provided−3, Q = 0.36), showing that SGE are not limited to behaviors.  and “regulation of dopamine metabolic process”  as the most significant Gene Ontology (GO) terms in D2 and B6 mice respectively  mice–24. ThisSimulations showed that DGE and SGE were unbiased . Of the Among the organismal phenotypes most significantly and strongly affected by SGE in this dataset were measures of lymphocyte activation .In addition, rate of wound healing, the measure most significantly and strongly associated with SGE in the experiment with two inbred strains, was also significantly affected by SGE in the outbred dataset (P = 1.6 10Three measures of body weight (collected on weeks 6 and 7) also figured among the traits significantly affected by SGE. Other measures of body weight, collected at weeks 9 and 10, showed no SGE . The twoFinally, a subset of measures of anxiety, blood biochemistry, and lung function were also affected by the genotypes of cage mates.−5 corresponding to a Q value of 64%, In contrast, there was no statistical evidence for SGE on gene expression levels in the hippocampus . In the outbred dataset, cage mates are genetically more similar to each other than average . As a reUsing two complementary genetic designs–one using two mouse inbred strains and one using outbred mice—we estimated the contribution of social genetic effects to a variety of organismal phenotypes and gene expression traits. The experiment with two inbred strains was designed to investigate SGE and focused on behaviours (anxiety and helplessness), as there is strong evidence that behaviours are socially affected–33. To tSome phenotypes were measured in both experiments with similar protocols. Wound area, the measure of wound healing, was collected in both experiments using the exact same protocol. It is significantly affected by SGE in both experiments. The protocols for measuring body and adrenal gland weight are fairly simple thus reducing technical variation between experiments, and body weight was measured at about the same time point (around 50 days of age) in both experiments. There was strong evidence for SGE on body weight in the outbred dataset but no evidence in the experiment with two inbred strains. No SGE on adrenal gland weight were detected in either dataset. Finally, a partially overlapping set of measures of unconditioned anxiety was significantly affected by SGE in both experiments. While reviewing results from the two experiments in parallel is informative, positive results in one experiment are not strictly expected to replicate in the other. Indeed, although the variants that give rise to SGE in the experiment with two inbred strains also segregate in the outbred population (the two strains used in our experiment were among the eight founders of the outbred population), they have recombined with many additional variants from the six other founders. Moreover, the housing conditions were very different in the experiment with inbred strains and outbred experiment . Therefore, one should not expect the overall contribution of SGE be the same in the two experiments. Rather, combining the two experiments provides a first hint at the generalizability of our results. Published studies of SGE provide additional information on this matter, and suggest that SGE may contribute to variation in body weight across species[That social genetic effects contribute to variation in anxiety probably does not come as a surprise but their contribution to wound healing maybe more so. This result is however supported by significant p-values in both experiments of our study and large effect sizes (18 and 6%). When interpreting this result, it is important to bear in mind that social effects on wound healing can  be mediated by traits of cage mates that are different from wound healing. For example, social effects on wound healing could be mediated by social grooming, which could either mechanically disrupt the healing process or chemically enhance it. Any traa priori be affected by social effects and the mechanisms at play are rarely known, SGE offer an attractive alternative to investigate social effects. First, as we have shown, they can be used to quantify social effects, effectively providing a lower bound estimate of social effects (as only the genetic component is captured). Second, SGE can be used to test whether a particular trait of social partners has an effect on a phenotype of interest. Establishing a causal relationship between two phenotypes is always difficult because of the risk of reverse causation and independent action of hidden confounders on both traits; SGE provide an anchor to test causality.Because any phenotype may Independent of their relevance for studying social effects, we show that ignoring SGE can lead to biased estimates of heritability (i.e. the collective effect of DGE). In our study (outbred dataset), DGE and SGE are correlated by design , and we show that this correlation leads to biased estimates of heritability if unaccounted for. Fitting cage effects, which has the primary goal of accounting for environmental effects shared by cage mates (e.g. noise levels), does not eliminate the bias. Our results are of interest to the broad genetics community as DGE and SGE are correlated in most if not all experimental designs traditionally used to estimate heritability in humans and model organisms, and SGE may thus have caused widespread bias. For example, in twin designs, MZ twins not only share 100% of their genotypes but they also share 100% of the genotypes of their sibling; DZ twins in comparison share both 50% of their genotypes and 50% of the genotypes of their sibling. Thus, SGE can contribute to increased concordance between MZ twins compared to DZ twins. If SGE are not modelled, heritability may be overestimated and appear “missing” when compared to genome-wide association results obtained from unrelated individuals. Note theffects” ) but wereffects” –41, willet al. investigated the correlation between genetics and urban setting [It is not the first time that unaccounted for gene-environment correlations are put forward as potential causes of bias (e.g. Conley  setting ). HoweveOur study sheds light on an important component of the genetic architecture of complex traits, one that lies outside the individual, in social partners. Social genetic effects have already been shown to play an important role in artificial selection of livestock and havead libitum access to food and water. Phenotypes were collected after six weeks of co-housing.Forty-five C57BL/6J (B6) and 45 DBA/2J (D2) about 3-weeks-old (±3 days) were shipped from The Jackson Laboratory. Mice were weaned on the day they were shipped and littermates were shipped together so as to minimise novel social interactions prior to experiments. Three D2 mice died during shipment and to maintain balance one B6 mouse was excluded. On arrival at UTHSC, mice were transferred to standard (75 square inches) mouse cages and allowed to recover overnight with their original littermates. Ears were punched for identification, and mice were transferred to new cages. Mice were co-housed in pairs to make up sixteen B6/D2 (BD) cages, 14 B6/B6 (BB) cages, and 13 D2/D2 (DD) cages assembled using mice from 15 B6 and 15 D2 litters . Littermates were never paired in BB and DD cages so cage mates were always unfamiliar with each other at the beginning of the experiment. Littermates were distributed across the three groups , and the cages ordered as successive BB, BD, and DD triplets on the shelves. Mice were housed in standard mouse cages with corncob bedding and a cardboard shelter on a fixed 12:12 hour light: dark cycle with All protocols were approved by the UTHSC Institutional Animal Care and Use Committee. Mice were phenotyped between 7am and 2pm, in a random order different for each test. The order in which tests were performed, as well as the measures collected in each test, are summarised in Elevated plus maze: We used an elevated plus maze (Columbus Instruments) with a surface ~30 cm above the floor that contained four 51-cm long, 11.5 cm-wide arms arranged at right angles. Closed arms had opaque walls 30 cm high, extending the length of the arm. Each mouse was placed in the center of the maze facing an open arm and allowed 5 minutes to explore the maze. Each cage mate was tested in turn and precaution was taken so that cage mates did not interact just after the first cage mate had finished the test and before the second one was taken to the maze.Activity chamber: We used six 17" X 17" Plexiglas activity chambers  with photobeams around the edge to measure movement. The beams were interfaced to a computer to record movement. Each activity chamber was located into a closed section of a cupboard, which had white light on. Each mouse was placed in the centre of the chamber and given 30 minutes to explore the activity chamber. Cage mates were tested at the same time.Tail suspension test: Adhesive tape was wrapped around each tail three quarters of the distance from the base, and used to attach a suspension hook. Animals were suspended for six minutes and video recorded from the side. Cage mates were tested at the same time.Forced swim test: Mice were placed in six cylinders  with water at about 25°C for 6 minutes and their behaviour recorded from the side. Upon completion mice were dried. Cage mates were tested at the same time.Body weight: Mice were weighed on the day after arrival at UTHSC and every week thereafter.Sacrifice, tissue collection, and gland weights: Mice were euthanized by injection of 250 mg/kg Avertin  followed by cardiac puncture and exsanguination under deep anaesthesia. The blood was collected in lithium heparin tubes, and an aliquot was taken in another lithium heparin tube for serotonin quantification. The aliquot was immediately snap frozen in liquid nitrogen and then stored at -80C.The brain was dissected and placed in an ice-cold coronal mouse brain matrix. A slab containing the prefrontal cortex (PFC) was removed, and the PFC taken, snap frozen and stored at -80°C. The part of the brain caudal to the amygdala was dissected, snap frozen and stored at -80°C. Then the midbrain was dissected by letting the brain soften on ice and removing the cerebellum. The midbrain was stored at -80°C and shipped on dry ice for analysis by high-pressure liquid chromatography.Both preputial glands were dissected and weighed together, as were both gonads. Right and left adrenal glands were weighed separately.-3M HCl containing sodium metabisulfite and ascorbic acid (antioxidants), and centrifuged at 22,000 g for 20min at 5°C. The supernatants were collected and filtered through a10-kDa membrane  by centrifugation at 7,000 g. Then, a 20 μl aliquot of sample was analyzed for serotonin by fluorometric detection[High-pressure liquid chromatography: Brain samples were weighed frozen. They were then crushed in 1,5 ml of 10detection. The amodetection. The ana−3 M HCl, followed by 2 centrifuge runs including one on Nanosep 10 kD. Quantification was carried out as for brain serotonin.Ten μl of blood were used for serotonin quantification. Serotonin was extracted by adding 400 μl of 10Size of ear hole: At sacrifice the ear that was clipped on arrival was cut and placed in formalin. It was then mounted on a glass slide, scanned, and the resulting image analysed with ImageJ. The scale was set once using the size of the slide. The hole was delimited using the freeform drawing tool.All collected phenotypes were quantitative. Individual phenotypes were normalized using the parametric Box-Cox power transformation, using ROn the Box-Cox transformed data for each phenotype, we tested for association with experimental covariates and regressed out significantly associated covariates that were unrelated to the measure of interest. The covariates that were accounted for are reported in Total RNA was extracted using a Qiagen Qiacube and RNeasy mini kit according to the manufacturers protocols and was loaded on a 96-well plate, with samples randomly allocated to wells, for shipment to the sequencing centre. The quantity and quality of the samples was assessed using Agilent high sensitivity R6K screen tape. One sample (B42) with high degradation was excluded. Two samples  had very low RNA integrity numbers , and one sample (B9) had low 28S/18S peak height ratio but they were included in library preparation and sequencing. All libraries were prepared according to the dUTP strand-specific protocol, multiplexed in one 85-plex and sequenced in 5 HiSeq 2500 rapid runs to obtain paired-end, 51 bp reads.We used SNPs and indels identified in D2 . Some of the affected genes were well-known immediate early genes. In our experiment, this effect was confounded with potential social genetic effects as B6 mice in BD cages were always sacrificed first and D2 mice second, while in BB cages some of the B6 mice were necessarily sacrificed first and others second, and the same applies for D2 mice in DD cages. To avoid possible false positive SGE, we took a conservative approach and excluded all genes for which the order of sacrifice within pair, in B6 mice from BB cages and D2 mice from DD cages, was associated with gene expression levels .We used a principal component analysis (PCA) to identify outlier samples. The first and second principal components explained 22% and 9% of variation in gene expression, and were used to define outliers . A totalIn addition to removing outlier samples, we used two approaches to adjust for observed and hidden covariates affecting the expression profiles. First, for significance tests of SGE on gene expression levels, we used PEER to account for hidden sources of variation; we ran proportion of selected read pairs that are exonic was significantly correlated with the first principal component . Therefore we regressed the effect of this covariate out of the expression of each gene, and used the residuals for variance partitioning and model selection.Second, for variance partitioning and model selection, we regressed out observed technical covariates only as removal of hidden factors can reduce the residual noise. We considered several technical covariates: initial RNA concentration , RIN, ratio between heights of 28S and 18S peaks, proportion of first reads mapping—as they should—to the strand opposite to the feature , insert size, yield, proportion of reads mapped to the (B6 or D2) genome, proportion of mapped reads that are of high quality (as defined above), proportion of high quality reads that are exonic, number of alignments with 0, 1, or 2 errors , whose phenotype is the phenotype of interest and the dependent variable in the model, and the focal individual’s social partners–here its cage mate (cm)–which may influence the phenotype of the focal individual through a number of unknown mediating phenotypes. No prior knowledge or measurement of these mediating phenotypes is required as they are captured through their genetic and environmental components .Conceptually we distinguish between the Genetic effects considered in this experiment include:direct genetic effects . This component is only relevant when both B6 and D2 focal mice are analysed together.social genetic effects .an interaction between DGE and SGE . This component is only relevant when both B6 and D2 focal mice are analysed together.Genetic effects were modelled as fixed effects.“Environmental”  variance may arise from:direct environmental effects social environmental effects cage effects Environmental effects were modelled as random effects.yf is the phenotype of interest of the focal animal f; AD,f and AS,cm respectively refer to additive DGE of the focal individual and additive SGE from the cage mate cm; AD,f : AS,cm refers to the interaction between AD,f and AS,cm; Z that indicates cage mates ; W that indicates cage assignment and c the vector of cage effects.The model of phenotypic variation that includes all of the genetic and environmental effects listed above  is the following:The joint distribution of the environmental random effects is defined as:The covariance The environmental covariance is:cov  may be negative as a result of Note that eD, eS, and c are not identifiable. Thus we instead used a single random environmental term, whose correlation structure is described below, as has been done in other studies of social genetic effects[When group size is constant, as is the case in this dataset (group size = 2), c effects:cov above we see that ρ ≤ 1.From the expressions of ρ ≤ 1 the environmental component could be rewritten as a sum of cage effects and iid residuals. This assumption is often made when group size is large  becauseModel selection and Variance partitioning below), we considered all mice (B6 and D2) as focal individuals. For all other analyses we analysed B6 focal mice and D2 focal mice separately. We did so because there was evidence that interactions exist between DGE and SGE , lme (nlme package), or gls , as appropriate. The best model was selected using the Akaike information criterion (AIC) calculated based on the maximum-likelihood (ML) fit using the AIC function in the R stats package. The results of this analysis are reported in To investigate whether interactions between DGE and SGE might contribute to phenotypic variation, we sought to identify the combination of genetic and environmental terms from {DGE, SGE, interaction between DGE and SGE, DEE, SEE, covariance between DEE and SEE, and cage effects} that best modelled phenotypic variation. All mice (B6 and D2) were included as focal individuals for this analysis. Constraints were imposed on the environmental correlation parameter βD is the regression coefficient of DGE, Xf the strain of f, and βD:S the regression coefficient of the interaction between DGE and SGE. Xf.Xcm is the product of the numerically encoded strains of f and cm, and therefore was 0 except for D2 focal mice with D2 cage mates.For All other results were obtained by analysing B6 and D2 focal mice separately using Model (1).All models were fitted using restricted maximum likelihood (REML). The proportion of phenotypic variance explained by each fixed effect in the model (Model (1) or Model (2)) was calculated as the ratio of the sample variance of that term  and Huang et al. (gene expression in the hippocampus), which were collected on Heterogeneous Stock (HS) mice. HS mice are descended from eight inbred progenitor strains through more than 50 generations of circular breeding with 24 families[We used the raw data from Valdar et al.  relationship matrix, and Agg is the pedigree-based relationship submatrix across genotyped individuals. The pedigree consisted of the genotyped individuals and their parents, some of them common across sibships. To warrant that both G(*) and A−1 refer to the same base population and to ensure that G(*) is a valid covariance matrix, G(*) was constructed as [a and b are constants (respectively 0.015 and 0.982) based on statistics of A and G so that average relationship and average inbreeding agree for both matrices. These constants correct for the random drift and the loss on heterozygosity across generations. Matrix M contains centered genotypes based on observed allelic frequencies p and q = 1 − p for each locus as in [H was obtained by inversion of H−1. Program pregsf90 ([http://nce.ads.uga.edu/wiki/doku.php) was used to construct H. Using this matrix, all estimates of genetic variance refer to the pedigree base populations, i.e. the parents of the animals in the study. Because this is only one generation back in time, we can assume that this base genetic variance is concordant with the genetic variance in the phenotyped population.We infered the pair-wise relationship coefficient of the 1,227 non-genotyped mice with all 3,149 mice using the single-step or H matrix–65, whicucted as , 67G(*)us as in . The matregsf90 , also called breeding value, of f. Z that indicates cage mates  and W that indicates cage assignment and c the column vector of cage effectsHere, Because the genetic setup is not binary (B6 / D2 in the experiment with inbred strains) but individuals are genetically unique and related to varying degrees, we modeled the components in Model (3) as random effects, while accounting for covariance between different effects. The joint distribution of all random effects is defined as:H1, H2, and H3 were calculated by dividing the (single-step) H matrix by, respectively, sampleVar(H), sampleVar(ZHZT), where sampleVar is the sample variance of the corresponding covariance matrix: suppose that we have a vector M, the sample variance of M is calculated asTr denotes the trace, n is the sample size, and P=I−11′n , 76.As a result of this scaling, I1, I2, and I3 were calculated by dividing the identity matrix I by, respectively, sampleVar(I) = 1, sampleVar(ZIZT), so that Similarly, I4 was calculated by dividing the identity matrix I by sampleVar(WIWT).See For each trait, Eq  was usedStandard errors on variance components are estimated as the square root of the corresponding diagonal entries of the inverse of the Fisher information, which is the negative expected value of the Hessian.Phenotypes were simulated based on the genotypes and the cage relationships of the full set of 2,448 mice. Phenotypes were drawn from model (3). We note We present several sets of simulations:one based on the estimated contributions of all parameters averaged over all organismal phenotypes: C2 = 22% one based on the estimated contribution of all parameters averaged over the six phenotypes with highest SGE: C2 = 51% 2 = 22%) enotypes enotypes enotypes enotypes enotypes enotypes 500 simulations were drawn for each combination of parameters.2 distribution with 2 degrees of freedom, which is conservative. Q values[Statistical significance was assessed using restricted likelihood ratio tests, comparing the full model in Eq  to a nul Q values were use Q values.Gene expression data from the experiment with inbred strains are available from ArrayExpress E-MTAB-5276. Phenotype data for the same experiment are provided as S1 Note(DOCX)Click here for additional data file.S1 Fig(A) When jointly analyzing B6 and D2 focal mice for , DGE, SG(TIF)Click here for additional data file.S2 FigThe contribution to phenotypic variance of DGE, SGE, and their interaction is shown. All organismal phenotypes are shown (see (TIF)Click here for additional data file.S3 FigBox plots for the six anxiety-related phenotypes that are significantly affected by SGE in D2 mice Click here for additional data file.S4 Fig(EPS)Click here for additional data file.S5 FigComparison of the contribution of DGE (A) and SGE (B) to organismal phenotypes, estimated either using genotype and pedigree data (see (EPS)Click here for additional data file.S6 Figst and 9th deciles of the distribution. (A) Phenotypes simulated with varying levels DGE, SGE, correlation between DGE and SGE, DEE, SEE, correlation between DEE and SEE, and cage effects (top to bottom panels). (B) Phenotypes simulated based on the estimated contributions of all parameters averaged over all organismal phenotypes. (C) Phenotypes simulated based on the estimated contributions of all parameters averaged over the six organismal phenotypes with strongest SGE. The boxplots in blue correspond to results from the full model with DGE, SGE SEE and cage effects, those in green correspond to the model with DGE and cage effects only, and those in red correspond to the model with DGE only.DGE estimates were evaluated using simulated phenotypes: the difference between estimated and simulated DGE is shown on the y-axis. Each boxplot summarizes the results from 500 simulations, and the whiskers extend to the 1(EPS)Click here for additional data file.S7 Figst and 9th deciles of the distribution. (A) Phenotypes simulated with varying levels DGE, SGE, correlation between DGE and SGE, DEE, SEE, correlation between DEE and SEE, and cage effects (top to bottom panels). (B) Phenotypes simulated based on the estimated contributions of all parameters averaged over all organismal phenotypes. (C) Phenotypes simulated based on the estimated contributions of all parameters averaged over the six organismal phenotypes with strongest SGE. Estimates were obtained from the full model with DGE, SGE, and cage effects.SGE estimates were evaluated using simulated phenotypes: the difference between estimated and simulated SGE is shown on the y-axis. Each boxplot summarizes the results from 500 simulations, and the whiskers extend to the 1(EPS)Click here for additional data file.S8 Fig(EPS)Click here for additional data file.S9 Fig(A) Variance explained by the principal components. (B) Scatterplot of the first and second principal components. Samples that were excluded from all analyses (outliers) are highlighted.(EPS)Click here for additional data file.S10 FigThe quality of the quantification is evaluated based on the correlation between probes that target the same gene (orange line) and probes that target different genes (blue line). The empirical cumulative function of these correlations is shown, so a smaller area under the curve is indicative of greater correlations.et al.26: expression values from pairs of probes that map to the same gene are more highly correlated than expression values from pairs of probes that map to different genes.(A) Raw data from Huang et al.26: same-gene probes are only as correlated as pairs of probes mapping to different genes, suggesting a loss of biological signal.(B) Pre-processed data from Huang (C) Data pre-processed by us: same-gene probes are more highly correlated than different-gene probes, suggesting biological signal is retained.(EPS)Click here for additional data file.S11 FigThe residuals from a model with covariates fitted as fixed effects only (y axis) were plotted as a function of group size (x axis). Only the six organismal phenotypes with strongest SGE are shown.(EPS)Click here for additional data file.S12 FigComparison of the contribution of DGE (A) and SGE (B) to organimal phenotypes, estimated either using all mice (x axis) or only those mice that were in groups of size 4, 5 or 6 (y axis).(EPS)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLS)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLS)Click here for additional data file.


Background: Genetic factors impact alcohol use behaviors and these factors may become increasingly evident during emerging adulthood. Examination of the effects of individual variants as well as aggregate genetic variation can clarify mechanisms underlying risk.Methods: We conducted genome-wide association studies (GWAS) in an ethnically diverse sample of college students for three quantitative outcomes including typical monthly alcohol consumption, alcohol problems, and maximum number of drinks in 24 h. Heritability based on common genetic variants (h2SNP) was assessed. We also evaluated whether risk variants in aggregate were associated with alcohol use outcomes in an independent sample of young adults.Results: Two genome-wide significant markers were observed: rs11201929 in GRID1 for maximum drinks in 24 h, with supportive evidence across all ancestry groups; and rs73317305 in SAMD12 , tested only in the African ancestry group. The h2SNP estimate was 0.19 (SE = 0.11) for consumption, and was non-significant for other outcomes. Genome-wide polygenic scores were significantly associated with alcohol outcomes in an independent sample.Conclusions: These results robustly identify genetic risk for alcohol use outcomes at the variant level and in aggregate. We confirm prior evidence that genetic variation in GRID1 impacts alcohol use, and identify novel loci of interest for multiple alcohol outcomes in emerging adults. These findings indicate that genetic variation influencing normative and problematic alcohol use is, to some extent, convergent across ancestry groups. Studying college populations represents a promising avenue by which to obtain large, diverse samples for gene identification. Alcohol use phenotypes are genetically influenced, with heritability for alcohol use disorder typically estimated at around 50% in twin and adoption studies  have been conducted for diagnostic alcohol outcomes  of individual small effect. Methods that examine the aggregate effects of common variants represent an important complement to analyses based on single markers  and support the results from twin and family studies. Heritabilities based on common markers using unrelated individuals have been reported including an alcohol problems score in Dutch adults  sample , 2012 , and 2013 , for a total N = 7,603. Of these, 98% provided a DNA sample. As the current analyses are based on the data capture after the Spring 2014 survey, data were available for 2–4 waves, depending on cohort. At wave 1 , the average (SD) age was 18.59 (0.61); at wave 2 , 18.99 (0.44); at wave 3 , 19.94 (0.60); and at wave 4 , 20.93 (0.69).Spit for Science is an ongoing longitudinal study of college students enrolled in a large, urban university in the Mid-Atlantic, as previously described , the lifetime maximum number of drinks consumed in a 24-h period (Maxdrinks), and an alcohol problems sum score (Symptoms). Abstainers were not administered the alcohol-related items and therefore were not included in any of the current phenotypic and genetic analyses. Participants were given the option of skipping questions therefore number of participants varies across constructs. When a participant had assessments from different waves, the highest score was used.Participants were asked to report on their drinking in the past month with items regarding how frequently they drank and how many drinks they typically consumed on a drinking day. These items were combined to create a single measure of grams of ethanol consumed in the last month, using a method previously described Dawson,  and repoIn waves 1–4, participants who reported having ever consumed alcohol were asked items related to DSM-5   using the Affymetrix BioBank array (653 k variants) which contains both common GWAS framework variants (296 k) for imputation and functional variants (357 k) including rare high impact exome variants (272 k), indels (18 k), eQTLs (16 k), and miscellaneous (51 k). QC excluded Off Target Variants found by SNPolisher, single nucleotide polymorphisms (SNPs) missing >5% of genotypes, samples missing >2% of genotypes, and SNPs missing >2% of genotypes after sample filtering, similar to the Psychiatric Genomics Consortium  using PLINK 1.9  phase 3 variants  found in common with the post QC filtered S4S genotypes were merged together. Regions with high LD were excluded ; Asian (N = 1223); Black/African American (N = 1464); Hispanic/Latino (N = 450); More than one race (N = 467); Native Hawaiian/Other Pacific Islander (N = 50); Unknown (N = 30); and White (N = 3763). Participants could also elect not to answer (N = 108). As noted previously, the sample of participants corresponds closely to the overall demographics of the university student population  without reference population outliers  was calculated. Each subject was then assigned to the 1KGP population with the minimum Mahalanobis distance and then collapsed into their respective super-population assignment. This empirically based ancestry has several advantages to self-identified race/ethnicity including reducing variance of the within group PCs and being able to include “Unknown,” “More than one race,” and small groups in the analysis without an increase in genomic inflation. There were five final ancestry groups: African descent (AFR), American descent (AMR), East Asian descent (EAS), European descent (EUR), and South Asian descent (SAS).For genetic analyses, S4S subjects were empirically assigned to 1KGP based ancestry super-populations. Briefly, using all 10 ancestry PCs, the Mahalanobis distance , minor allele frequency (MAF), and relatedness were performed within empirically assigned super-populations. Genome-wide IBD  were forced to be kept in the model while ancestry covariates were kept if they were retained in the best fitting model as measured by AIC. This approach, of only keeping PCs significant to a specific ancestry and phenotype, increases parsimony and minimizes risk of over-fitting. We have shown that there is no evidence of genomic inflation when this parsimonious approach is taken.p > 10−6) and sample size based MAFs. Instead of using a fixed MAF threshold for each group, the minimum observed minor allele count (MAC) was used. Previous research has shown a MAC of ~40 is robust for most association analyses performed in GWAS  for within super-population GWAS and meta-analyses was performed in R  using Bioconductor 3.2  were derived for each ancestry group. Within ancestry group, only unrelated individuals were included in the GRM as in the GWAS, resulting in the following sample sizes: AFR: N = 1339; AMR: N = 582; EAS: N = 557; EUR: N = 3018; SAS: N = 455. An ancestry group-specific MAF cutoff of 0.01 was applied. The same ancestry PCs used in the GWAS analyses were included in the heritability analyses.Genome-wide Complex Trait Analysis was used . Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee, Bristol University and Virginia Commonwealth University. Genetic data are available for N = 8,237 individuals who meet quality control filters.We used the Avon Longitudinal Study of Parents and Children  as a function of attrition and alcohol use initiation.ALSPAC participants were administered a self-report questionnaire that included alcohol use items at approximately age 20.75. These items included questions about frequency of alcohol use and average number of drinks per drinking day, from which the Consumption variable is derived; DSM-IV and AUDIT  and Laboratory Corporation of America . Samples were subjected to quality control filters as previously described  were calculated in ALSPAC using the S4S GWAS results. Prior to GPS derivation, we identified markers present in both the S4S and ALSPAC genetic data with MAF > 0.01 and INFO > 0.5. We pruned this list using the clumping function in PLINK 1.9  from the METAL meta-analyses as well as based on EUR-specific SNP regression coefficients , given the possibility that the latter might be more predictive of outcome in the European ALSPAC sample. In R (v3.2.3), we tested whether GPS were associated with alcohol outcomes , controlling for sex, using linear regression. Our analytic goal was to test for association rather than to try to predict outcome, given the sample sizes and expected genetic effect sizes . The sample consists of more women than men , though this distribution is only slightly different than that of the student population. Women reported lower levels of drinking for all outcomes (p < 0.01). Sex was included as a covariate in genetic analyses.Mean values for each variable are reported in Table h2SNP for each of the three phenotypes, separately by ancestry group. Results are provided in Table We used GCTA to calculate 1,000s of 1.0001 (Problems), 0.9997 (Consumption), and 0.9994 (Maxdrinks). Across the three phenotypes, FDR analysis showed 187 markers with q < 0.50. All markers with FDR q < 0.5 are listed in Supplementary Table p < 5 × 10−8) marker. However, in all but one of these bins the signal was limited to the AFR group. Using stricter genome-wide significance for samples of African ancestry (p < 1 × 10−8), only one AFR specific bin remained GWS.After applying filtering and meta-analysis, results were available for 16,511,702, 15,625,945, and 15,724,050 markers for Consumption, Problems, and Maxdrinks, respectively. For each phenotype, results for 33–35% of the markers were only testable (MAC > 40) in the AFR ancestry group due to frequency. The meta-analyses showed no evidence of genomic inflation with λp = 4.11 × 10−9, q = 0.06) and rs73317305 for Problems . rs11201929 is in the third intron of glutamate ionotropic receptor delta type 1 (GRID1) and was of sufficient frequency and quality to be tested in all five ancestry groups. Figure GRID1. The direction of effect was consistent across ancestries, though the strength of the association varied . The other GWS marker, rs73317305 (Problems), is in the second intron of sterile alpha motif domain containing 12 (SAMD12) on chromosome 8 and is rare in non-African populations , which does not meet our stringent genome-wide significance criterion for variants informative only in the AFR group (p < 1 × 10−8). This marker, along with 12 others that map to the same genomic region on chromosome 11, does not map to any known gene.For Consumption, the most strongly associated marker was rs76541530 (q < 0.50 and (2) p-value threshold based polygene scores. Importantly, only results based on equivalent phenotypes were examined . While several variants with q < 0.50 exhibited nominal associations (p < 0.05) in ALSPAC, we did not observe robust variant level replication after correction for multiple testing for any phenotype. Results are presented in Supplementary Table p < 0.05) for a wide (pthreshold 0.01 to 0.5) and narrow range (pthreshold 0.01) of thresholds, respectively. The Maxdrinks PRS was robustly associated (p < 0.005) across a wide range of thresholds (pthreshold 0.05–0.5) Further details are provided in Table For replication, we considered samples similar to S4S with respect to age and/or alcohol phenotypes in order to minimize lack of replication due to differences in ascertainment and phenotypes. ALSPAC met these criteria due to being a sample similar in age and the derived variables being nearly identical. Replication was attempted for (1) individual variants with FDR 0.50 and  p-value h2SNP 0.19, SE = 0.11). At the marker level, variation in a previously implicated (see below) gene, GRID1, surpasses stringent genome-wide significance criteria for association with MaxDrinks. Furthermore, polygenic scores derived from S4S for Consumption and Maxdrinks, and Problems to a lesser extent, are significantly associated with the equivalent outcomes in an independent and comparably-aged sample. These results provide empirical support for the influence of aggregate molecular variation on multiple alcohol outcomes.Using a population-based study of emerging adults at a diverse mid-Atlantic university, the Spit for Science sample, we present evidence of replicable aggregate genetic influences on three alcohol-related phenotypes: typical monthly consumption (Consumption), maximum drinks in 24 h (MaxDrinks), and an alcohol problems sum score (Problems). We find further support that Consumption is heritable (glutamate ionotropic receptor delta type subunit 1 gene (GRID1), which is involved in synaptic plasticity. GRID1 has been implicated in prior genetic studies of alcohol use outcomes: Chen et al.  variants in another sample assessed for comparable alcohol use phenotypes. Such failures to replicate may be due to population-specific effects  or false positive results. Many markers with q < 0.5 in the current analyses were assayed only in the AFR ancestry group due to low MAC in other groups. These results underscore the need for additional genetic analyses to be conducted in samples of non-European ancestry, not only to facilitate replication, but also to clarify potential differences in genetic risk factors across ancestries  predict those same outcomes in the ALSPAC sample. These results are consistent with the highly polygenic nature of alcohol phenotypes. Although GPS derived from relatively few markers  were not predictive of outcome, those that are more inclusive do capture meaningful genetic liability. Furthermore, this interpretation is consistent with the null results of our replication attempts for markers with q < 0.50. Thus, it is likely that many of the variants implicated at marginal thresholds are incrementally contributing to risk, though to a degree too small to be detected in isolation. We observe similar trends when GPS are derived from the European ancestry group only, though results are less robust . Nonetheless, this did not translate into significant h2SNP estimates within that ancestry group.Our estimate of the heritability of consumption was far lower than estimates  included in these analyses were rather small, resulting in large standard errors around parameter estimates for both individual variant analyses and aggregate tests . While the European and African descent groups were larger and more statistically powerful, substantial sample sizes are necessary to reliably detect low heritability using GCTA, and these results should be interpreted with caution.q < 0.50 were evaluated only in the AFR group due to low MAC in other groups. Given that most alcohol-related GWAS have been conducted on samples of European ancestry, these markers were largely unavailable for replication attempts in other samples. Had we calculated q-values based only on markers available in the EUR group, or only on those available across all groups, the list of markers for follow-up would have differed, potentially impacting the overall outcome of the SNP-based replication assessments.Many markers with p-values, as the tests are not independent and the appropriate correction approach is not immediately evident.GPS parameter estimates are quite modest, indicating that scores would account for little of the total variance in ALSPAC outcomes. In addition, we report uncorrected In spite of the above limitations, these analyses demonstrate that genetic factors are integrally involved in liability to alcohol use in college-aged students, at both individual variant and aggregate levels. Furthermore, the effects of many genetic variants are consistent across ethnicities, suggesting shared biological mechanisms. Critically, replication in an independent UK cohort indicates that the observed genetic effects are generalizable within a similarly aged cohort. These findings validate prior evidence of specific and general genetic effects on alcohol outcomes, and provide nascent support for novel loci that merit additional research.All Spit for Science protocols were approved by the VCU Institutional Review Board (IRB).The author contributions to the current research include study design , sample collection , phenotype analyses and QC , DNA sample processing and QC , genotype QC  statistical and bioinformatic analyses  and manuscript preparation .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.


Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value.A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity.We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci. Genome-wide association studies (GWAS) have been widely utilized over the past decade for studying the genetic origins of phenotypic traits, including a wide range of diseases. GWAS analysis compares the genome-wide genotype profiles of a large number of subjects with the corresponding phenotypic values. These studies aim to locate genetic loci where the genotype variation is highly correlated with the variation of the phenotype value –3. TheseTo address the limitations of typical GWAS analysis methods, vGWAS has emerged recently as an approach for detecting GxG and GxE loci –7. ThereThe number of vGWAS analysis methods have been steadily increasing in the literature in the past few years. These methods utilize statistical tests and regression models to locate loci that have statistically significant effects on the phenotype. For example, Levene’s (Brown-Forsythe) test is a single-pass statistic widely used in vGWAS analysis. The test calculates the phenotype’s variance for both values of the genotype and then assesses the significance of the difference between the two variances using a chi-distribution. Generalized linear models have also been proposed for vGWAS analysis. These methods aim to fit a linear regression model to uncover relations between the genotype profiles and the observed phenotype values. In these models, the genotypes represent the variables of the model, whereas their coefficients represent their contribution to the mean and variance changes . As oppoThese methods have been utilized to uncover GxG and GxE interacting loci in a number of studies. For example in a study about body mass, vGWAS identified a locus that is believed to interact with physical activity and the lifestyle of the individual . In anotThe proposed methods have been able to uncover biologically relevant information through detecting variance heterogeneity in different GWAS datasets. However, the development of vGWAS analysis models could still benefit from significant enhancements. A first step towards achieving that goal could be establishing reliable and consistent grounds for comparison between these tools. This could be achieved through simulating vGWAS loci. Simulation provides the researcher with three important features: 1) Control over the input genotypes, 2) Predictability of the output values, and 3) Consistency across different runs. Controlling the values of the input genotypes allows the researcher to simulate different genetic and evolutionary scenarios. The predictability of the output is particularly valuable for evaluating vGWAS methods as it provides a ground truth for comparison. And finally, consistency is achieved through eliminating the uncontrollable factors of randomness that are usually present in lab experiments. Therefore, simulation presents a reliable and inexpensive method for testing and evaluating vGWAS methods under different conditions.Several GWAS simulators have been developed in the past decade, however, none of these simulators account for variance heterogeneity effects. In this work we present a systematic procedure for simulating vGWAS data, as well as introducing the first simulation algorithm for quantitative vGWAS data. The algorithm could be applied to either haploid or diploid genotypes with different modes of dominance while incorporating the effects of multiple vGWAS loci using an additive model. We hope that the presented work will motivate the evaluation of current vGWAS methods and drive the development of new methods.This paper is organized as follows: we describe the genotype generation process and the subsequent mathematical framework for producing vGWAS phenotype values in the “The simulation of GWAS data enables researchers to investigate important aspects of genetic control . SimulatThe simulation process is divided into two steps. The first step of the simulation process focuses on producing a realistic population of genotypes while allowing control over several genetic and evolutionary parameters. The second step of the simulation process assigns or calculates phenotypic values for the simulated population. In this section, we describe both steps of the simulation process.The population generation process focuses on producing a realistic population of genotypes. Population generation is used to simulate different genetic properties and population structures, such as demographies, mutations, and recombination events. This level of control allows the researcher to engineer the population according to the genetic and evolutionary scenarios investigated. Several simulation methods have been developed to address this problem. These methods could be categories into three types, depending on the simulation strategy they use: resampling-based methods –15, backPhenotype assignment maps the genotype state of each simulated individual to a phenotype value. Phenotype values could be either quantitative or qualitative. Qualitative phenotypes are usually assigned in case/control studies where the phenotype could be one of only two values. Quantitative phenotypes on the other hand take a numerical value which could represent traits such as height, weight, or expression level of a certain gene.The individual has a haploid genome.The phenotype value is affected by the allelic value of a single genetic locus.In this section we formulate a method for simulating quantitative phenotypes. We discuss the steps for calculating the phenotype value for both haploid or diploid genomes using either an additive or dominant mode of inheritance in the diploid case. We extend our calculation method to incorporate effects from multiple vGWAS loci using an additive model for genetic control. To facilitate the phenotype value calculation we make the following two assumptions: y is the phenotype value, μ is the baseline mean value, g∈0,1 is the genotype state and α is the mean shift. ε represents the residual effects. In typical GWAS simulations, the general model used in calculating the quantitative phenotype value expresses the phenotype value as a function of the subject’s genotype state at the associated genetic loci in the presence of a normally distributed residual variance. The phenotype value calculation is performed according to Eq. ε is assumed to follow a normal distribution with a fixed variance ε is expressed as shown in Eq. σ is the baseline standard deviation, g is the genotype state, and ϕ is the standard deviation shift due to the minor allele. In typical GWAS models,  effects . Therefoentclass1pt{minimaThe phenotype calculation step uses the generated genotype profiles to calculate a phenotype value for each subject in the population. The genotype profiles are used to calculate the allele frequencies of each loci. The researcher then specifies the phenotype controlling loci and the effect size of each locus on the total phenotypic variation. In typical settings, the researcher would choose specific loci from the list of generated loci. Alternatively, the researcher could specify the number of phenotype controlling loci and an allele frequency range such that the simulator could pick appropriate loci from the list. The phenotype calculation process is often provided with parameters such as a baseline mean and the value of the total phenotype variation in order to control the range of possible phenotype values. This allows the generated phenotype values to resemble the values encountered in the researcher’s investigation. In this section, we show how these inputs could be used to calculate the parameters of Eq. A1 is the major frequency allele and A2 is the minor frequency allele. The probability of observing a major frequency allele is denoted by p. Similarly q is the probability of observing a minor frequency allele. Based on the two assumptions made earlier, each locus could have either one of two genotype values. The characteristics of each genotype can be summarized in Table VY is composed of the addition of the variance due to the mean shift, VM, the variance due to the variance shift between genotypes, VV, and the remaining residual variance VR. We translate to mathematical terms in Eq. According to the vGWAS model, the phenotype calculated using Eq. By comparing Eq. and, Consequently, Cμ, is calculated as Cμ=VM/VY. Similarly, the locus’s effect size on the total variance due to the variance shift, CV, is calculated as CV=VV/VY. Following these two terms, we can reach a formulation that expresses α, ϕ, and σ in terms of the total variance, the effect size of the locus, and the corresponding allele frequency, all of which are known at simulation time. These expressions are given in Eqs. ..Cμ, is cThe phenotype value for every individual in the population is calculated through substituting the above terms in Eq. Simulating diploid GWAS data is important for studying complex genetic traits especially in diploid organisms such as humans. We modify the formulation described in the previous subsection in order to calculate phenotype values that are based on a genotype that could take one of three values: major allele homozygous, minor allele homozygous, and heterozygous.In many genetic applications, the population would be engineered to only consist of homozygous individuals. However, we choose to account for heterozygous individuals to facilitate the investigation of a wide number of scenarios and genetic traits. We lay out the mathematical procedures needed to calculate the phenotype value for heterozygous individuals using two models of inheritance: 1) the co-dominance model and 2) the complete dominance model. In this section we start by describing the formulation of the co-dominance model before describing the extensions needed for using the complete dominance model. Co-dominance occurs when both alleles in the diploid are fully expressed. That is, the effects of both allele in the diploid are added together to produce the total effect of the genotype of the phenotype’s value.A1A1,A1A2,A2A2 respectively. Assuming the phenotype is affected by a single diploid genetic locus, we modify the phenotype calculation equation Eq.  to accouUsing the same procedure as in the haploid case, we express the parameters in Eq. Therefore, Substituting the values of these parameters in Eq. An allele shows dominance when it suppresses the effects of the recessive allele. Dominance could exist in three forms: complete dominance, incomplete dominance, and co-dominance. In this section we modify the formulation used in the co-dominance model to account for complete dominance. This type of dominance occurs when the dominant allele completely masks the effects of the recessive allele.A1 is the dominant major frequency allele and A2 is the recessive minor frequency allele. The probability of observing a genotype that includes the dominant is denoted by p. Similarly q is the probability of observing the genotype of two minor frequency alleles. q is calculated as the square of the probability of observing a minor allele  whereas 1 represents the recessive genotype (A2A2).Consequently, the genotype characteristics of this type of diploidy are modified as summarized in Table α, ϕ, and σ can all be calculated using Eqs. ..α, ϕ, anIn a more realistic scenario, the phenotype value is determined by the genotype state of multiple loci. We modify the phenotype calculation procedure to account for multi-loci effects, assuming an additive model. Linear interaction is only one form of multi-loci interaction. Our phenotype calculation process does not account for nonlinear effects, such as epistasis. However, extending the proposed process to account for these effects is a topic for future investigation.Extending the phenotype calculation to account for the multiple additive loci is accomplished through modifying Eq. 15Cμ and CV of length N, which represent the mean and variance effect sizes on the total variance for all additive loci. Using these values, and the allele frequencies vector, the parameters of Eq. According to this formulation, the researcher has to provide two vectors, Therefore, Consequently, the phenotype value of each individual in the population could be calculated through substituting these parameter values into Eq. To facilitate the implementation of vGWAS simulators, we translated the mathematical framework into an algorithm, presented in Algorithm 1. The algorithm uses genotype profiles as its input. These genotype profiles could be generated using population simulators or by using real data from genotyping experiments such as the 1000 Genome Project. The algorithm also requires some information about the loci to be simulated, including their number, minor allele frequency, and mean and variance effect size. The algorithm searches the inputted genotypes for loci that meet the specified criteria and then exploits the described mathematical framework to calculate the phenotype value. This algorithm is able to induce both mean and variance discrepancies based on one or more loci. The algorithm accounts for both haploid and diploid genotypes under co-dominance or complete dominance. The algorithm uses Eqs. In this section we describe the evaluation of our simulator through the generation of different genetic control scenarios. We conducted two sets of experiments; in the first set, we generated and visualized all the modes of variation supported by our tool to demonstrate its ability to mimic realistic patterns. In the second set of experiments, we used common GWAS and vGWAS approaches to recover the loci generated using our algorithm. Finally, we highlighted several applications in which our simulation process could be useful.The simulation algorithm was implemented as Python scripts and has been tested on a Unix platform. The simulation scripts were used to calculate quantitative phenotypes from genotype profiles generated using ms  and GENOOur first experiment aims to verify that the values of the generated phenotype are in accordance with the desired distribution. We generated a segment containing 10,000 genotypes for 2,000 samples using Hudson et al.’s ms  simulatoWe visualized the phenotype values of these six sets of results using box plots, as shown in Fig. X10−3. We then used our simulation algorithm to generate three loci, one on each chromosome. The first locus has a mean effect size of 5% and no variance effects. Similarly, the second locus has a variance effect size of 5% and no mean effects. The third locus has a total effect size of 6%, divided equally between mean and variance effects.To demonstrate the utility of our algorithm, we performed a second experiment which involved simulating genotypes based on realistic settings and then using common GWAS and vGWAS analysis tests to recover the simulated loci. The purpose of this experiment is to show that the GWAS and vGWAS signals simulated using our algorithm could be retrieved using common GWAS/vGWAS detection algorithms. To that purpose we used the GENOME  simulatoWe used Plink’s ‘assoc’ function which is a GWAS detection method. This method attempts to fit a linear regression at each genotype to determine if there is a relation between the varying genotype values and the corresponding phenotypes. Eq The Brown-Forsythe test which is a vGWAS detection method. This method single-pass test which measures the significance of the variance separation observed at one tested genotype at a time and ultimately produces a p-value statistic for that genotype. This test has been commonly used to check for variance heterogeneity in vGWAS experiments. We implemented this test using R’s Levene Test package .We implemented the DGLM association method described in  which isTo recover the simulated signals, we used three methods: Figure To further demonstrate the utility of our simulation algorithm, we developed several scenarios to show how simulation could help unveil important evolutionary and structural information in the genome. In the first experiment, we used GENOME  to generIn the second experiment, we used GENOME to generate two 25Mb segments containing 50,000 SNPs each. In the first segment, we simulated three loci each having a 3% variance effect and no mean effects. We placed the loci within 2000 bases of each other to resemble loci occurring in the same gene. In the second segment, we simulated only one locus having a 3% variance effect and no mean effects located in the same region as loci of the first segment. We present the p-value patterns of this experiment in Fig. In this work we presented a mathematical framework for simulating quantitative vGWAS data. We translated the mathematical framework into a simulation algorithm, which, to the best of our knowledge, is the first vGWAS simulation algorithm in the literature. The presented algorithm generates quantitative vGWAS phenotype values using genotype profiles from common population simulators. The algorithm could be applied to either haploid or diploid genotypes with different modes of dominance while incorporating the effects of multiple GWAS and vGWAS loci using an additive model. To demonstrate the utility of our algorithm, we used common GWAS and vGWAS analysis methods to detect the loci generated using our algorithm. We hope that the presented work will motivate the further evaluation of current vGWAS methods and drive the development of new methods to address the limitations of current methods.