Schizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology. We used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology. g = −0.07; 95% C.I., −0.03,0.12; P = .002) and BMI . We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 (BDNF); rs8192675 (SLC2A2); rs3800229 (FOXO3); rs17514846 (FURIN)) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways. We found evidence for weak genome-wide negative correlation of schizophrenia with T2D (rLDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders. Traditionally, the increased cardiometabolic risk observed in schizophrenia has been attributed solely to sociodemographic and lifestyle factors, and the adverse effects of antipsychotic medication.3 However, cardiometabolic dysfunction is detectable in antipsychotic-naïve young-adults with first-episode psychosis, suggesting that lifestyle factors/medication may not be the full explanation.4 For example, schizophrenia and cardiometabolic disorders share similar associations with elevated concentrations of circulating inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6), both cross-sectionally6 and longitudinally.8 Mendelian randomization (MR) studies have shown that genetically-predicted levels of IL-6 and CRP could be causally linked with cardiometabolic disorders9 and schizophrenia,10 and that inflammation could be a common cause for comorbid insulin resistance and schizophrenia.11 Therefore, it is possible that schizophrenia, cardiometabolic, and inflammation-related traits could share pathophysiologic mechanisms, including a common genetic basis.People with schizophrenia die on average 10–15 years sooner than the general population,12 to estimate whole-genome correlation between schizophrenia and cardiometabolic traits, with recent studies consistently reporting evidence of partial negative genetic correlation between schizophrenia and body mass index (BMI).14 However, there is limited evidence for other cardiometabolic and inflammation-related traits,12 and the LDSC approach may have limitations. First, there may be a distinction in how common and less common variants are involved in the shared genetic architecture between traits.15 This potential difference cannot be captured with LDSC, and so genetic correlation analysis which takes into account the relative frequency of variants is required. Second, LDSC estimates may be biased towards the null when opposing mechanisms exist (eg regions of positive and negative correlation nullifying each other when averaged16). This may be expected in a relatively heterogeneous condition like schizophrenia.17 For example, a recent study found negligible genetic similarity between schizophrenia and cardiometabolic disorders using LDSC, but evidence in support of a causal relationship when using the same summary GWAS data in MR analysis.18 Therefore, more fine-grained locus level genetic correlation analysis is required to identify genomic regions of interest. Third, while LDSC can provide evidence of overall genomic similarity between traits, it cannot provide information with which to consider biological plausibility, or infer potential causality.Previous studies have predominantly used Linkage Disequilibrium (LD) score regression analysis (LDSC)20 We aimed to identify specific putative biological pathways underpinning the comorbidity and address key limitations of previous approaches. First, in addition to LDSC to estimate genome-wide correlation of schizophrenia with cardiometabolic and inflammation-related traits, we used genetic covariance analyser (GNOVA15), a recent methodological extension of LDSC, to estimate genetic correlation after stratifying variants by minor allele frequency (MAF). Second, we used Heritability Estimation from Summary Statistics (ρ-HESS16) to identify positive or negative regions of locus-level genetic correlation that otherwise may be masked by LDSC. Finally, we interrogated genomic regions showing evidence of locus-level correlation for the possibility of common-causal variants in those regions by estimating colocalization between clusters of traits, using the novel hypothesis prioritization multi-trait colocalization (HyPrColoc21) method.Therefore, we used a range of complementary genomic approaches in a stepwise manner to rigorously examine the potential for a common genomic basis for schizophrenia and a number of cardiometabolic and inflammation-related traits previously reported to be associated with it.22). We used publicly available summary GWAS data for twelve cardiometabolic and inflammation-related traits (fasting plasma glucose (FPG), two-hour glucose, fasting insulin (FI), homeostatic model assessment for insulin resistance (HOMA-IR), glycated haemoglobin (HbA1C), type 2 diabetes (T2D), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), body mass index (BMI), coronary artery disease (CAD), and CRP) from large-scale consortia  , and genome-wide correlations (rg) between all trait-pairs were estimated using LDSC32 and an LD reference panel from the 1000 Genomes Project's Phase 3 European (1kG CEU) sample. Quality control (QC) steps on each GWAS dataset prior to analysis were: (1) filtering SNPs that were not included within the HapMap3 reference panel or had MAF <5% within the 1kG CEU reference sample; (2) filtering SNPs within the major histocompatibility complex (MHC) due to the complex LD structure within the region33; filtering out poorly imputed SNPs (INFO > 0.9). Bonferroni adjustment was applied to reduce the risk of type I statistical error due to multiple comparisons. The original alpha level (0.05) was divided by the number of included cardiometabolic and inflammation-related traits which were paired with schizophrenia (12), leaving a Bonferroni-adjusted threshold of P < .004 which was used to define strong evidence of genome-wide genetic correlation.The genome-wide proportion of trait variability explained by shared genetic variation . QC methods were the same as above. MAF quartiles were defined by the method authors15 and calculated using genotyping data from the 1kG CEU reference sample. MAF cut-offs for each quartile were as follows: Q1 = 0.05–0.11; Q2 = 0.11–0.22; Q3 = 0.22–0.35; Q4 = 0.35–0.50. As per the LDSC analysis described above, the original alpha level (0.05) was divided by the number of included cardiometabolic and inflammation-related traits (12), leaving a Bonferroni-adjusted threshold of P < .004 which was used to define strong evidence MAF-stratified genetic correlation. For each trait pair and for each quartile, we report the number of SNPs included in each quartile, correlation estimate, and P-value.MAF-stratified genetic correlations between schizophrenia and other traits were estimated using GNOVA.16 which may have biased “averaged” correlation estimates (eg from LDSC or GNOVA) toward the null. We used ρ-HESS16 to estimate partitioned heritability and genetic correlations within predefined34 genomic LD-blocks based on European participants, allowing for greater resolution of the correlation within each LD block. We assumed no sample overlap between data from different consortia as recommended.16 Where no single nucleotide polymorphisms (SNPs) were available for analysis within a particular LD block, that LD block was excluded from analysis. As per the analyses described above, the original alpha level (0.05) divided by the number of traits taken through for locus-level correlation analysis, divided by the number of LD blocks tested between pairs of traits. Therefore, a Bonferroni-adjusted threshold of between P = 3.93 × 10−6 and 3.38 × 10−6 depending on the number of LD blocks per trait pair were used to define strong evidence of locus level genetic correlation.Next, we explored locus level correlation between schizophrenia and traits with at least nominal evidence of either whole-genome or MAF-stratified genetic correlation. We accepted a less-stringent significance threshold to select traits for locus level correlation analysis to allow for an examination of opposing mechanisms,correlation vs causation in genomic regions with evidence of locus-level correlation, we used HyPrColoc.21 HyPrColoc estimates the posterior probability of colocalization across multiple traits at a single causal variant by enumerating putative causal configurations. In doing so, HyPrColoc can identify distinct clusters of traits which colocalize at independent putative causal variants within the genomic region of interest. Colocalization analysis does not estimate the direction of association between traits. To conduct this stage of analysis, we identified the lead SNP for schizophrenia within each LD-block showing Bonferroni-significant evidence of locus-level correlation with cardiometabolic and inflammation-related traits  and regional and alignment threshold settings (0.5 for both). To provide greater resolution, allow for a consideration of biological plausibility, triangulate evidence between methods, and examine for a distinction between d traits . For eachttps://gtexportal.org/home/) was conducted to examine for relevant tissue-specific gene expression of colocalized variants at a Bonferroni-corrected P-value threshold based on the number of colocalized SNPs.To test the strength of evidence for colocalization and also cluster stability, we repeated the colocalization analysis over: (1) increasingly stringent prior 2 settings, ; and (2) increasingly stringent regional and alignment threshold settings . To visualise cluster stability across the permutations, heatmaps were drawn based on a similarity matrix between clusters. Where we found evidence for potential colocalization, stacked regional association plots were drawn to visually inspect putative candidate SNPs, their strength of association within each putative colocalized trait, and the LD structure in the genomic region. Finally, a manual search of the GTEx portal  and T2D . In hierarchical clustering, two clusters were formed; schizophrenia in the first, and all other included traits in the second ; TG ; CAD ; in the second-lowest MAF-quartile between schizophrenia and LDL ; and in the highest MAF-quartile between schizophrenia and both BMI  and T2D   .P-value ranges for the lead schizophrenia SNP of between P = 1.11 × 10−18 to P = 8.80 × 10−03. A genome-wide significant SNP for schizophrenia was contained in 24 regions  >0.80) between schizophrenia, cardiometabolic, and inflammation-related traits at seven loci, which included missense , intronic , and synonymous (rs3814883) variants, and one intergenic variant (rs12782894) (coloc = 0.54–0.79) at three loci, including intronic  and synonymous (rs2239647) variants  were stable only at the recommended prior settings and regional/alignment thresholds.We found the strongest evidence for colocalization  . We founvariants . Of the  . Across trait pairs, we found evidence of multiple regions of positive and negative correlation with schizophrenia, indicating the presence of opposing mechanisms. This may explain the weaker evidence for correlation we found in LDSC and GNOVA, and the results of previous LDSC researchcoloc > 0.80), four  relate to pathways involving brain derived neurotrophic factor (BDNF). BDNF is a major member of the neurotrophin family and has been linked with a range of clinical features of schizophrenia43; is involved in regulation of cardiometabolic function44; and is associated with cardiometabolic indices in people with schizophrenia.45We found ten loci indicating evidence of colocalization between traits at the default prior configuration. Many of these were stable over increasingly stringent settings in sensitivity analysis, suggesting robust evidence for colocalisation, and most are expressed in relevant neurological and cardiometabolic-related tissues. Several loci exhibited stronger evidence for colocalization after one weaker trait was dropped at more stringent thresholds. Of the seven loci returning the strongest evidence of colocalization  is a missense SNP in the SLC2A2, which encodes the facilitated glucose transporter GLUT2. GLUT2 regulates entry of glucose into the pancreatic β-cell, thus initiating the cascade of events leading to insulin secretion. GLUT2 is also highly expressed in both the liver, where it is involved in the regulation of both glucose uptake and output, and the hypothalamus where it regulates synaptic activity and neurotransmitter release.50 Variants in SLC2A2 impair GLUT2 expression and are strongly associated with T2D.51 Rs8192675 is associated with increased diabetic symptomatology but may also be associated with favourable T2D treatment response.52 Impaired GLUT2 expression is associated with lower levels of BDNF,53 and conversely, higher levels of BDNF are associated with a protective effect on GLUT2 in pancreatic β-cells, reducing T2D risk.54Second, rs8192675 is located in an intronic region of FOXO3, which regulates diverse cellular processes, eg adult stem cell homeostasis55 and immuno-metabolic processes.56FOXO3 is associated with brain development and intracranial volume57 and is associated with poor cognition in schizophrenia.58 Interestingly, FOXO3 is implicated as a potential therapeutic target for obesity,59 and mediates the inhibitory actions of insulin in diverse pathways including cell metabolism and survival.60FOXO3 signalling can be disrupted by BDNF, mediated by the phosphatidylinositol 3-kinase (PI3K)/Akt pathway.61 The PI3K/Akt pathway has roles in insulin sensitivity, neuronal development, dopamine regulation, and the immune system,62 and has been implicated as a putative mechanism linking schizophrenia and T2D.63Third, rs3800229 lies in an intron of FURIN, which encodes a protease that processes latent precursor proteins into their biologically active products. FURIN is expressed in neuroendocrine, liver, gut, and brain tissues. A recent GWAS found a significant association between rs17514846 and CAD,64 and rs17514846 has been shown to regulate FURIN expression in monocytes, which modulates their migration and proliferation in atherosclerotic plaques.65 Furthermore, rs17514846 is in high-LD with rs4702, a genome-wide significant variant for schizophrenia66 which lies in the 3′ untranslated region of FURIN leading to reduced gene expression and impaired BDNF secretion.67Fourth, the rs17514846 variant lies in an intron of SLC39A8 which encodes a protein responsible for metal ion transport and homeostasis. Rs13107325 has been associated with weight gain,29 lipid dysfunction,68 changes in brain volume,69 and brain metal homeostasis, the latter of which may influence schizophrenia risk.70 Finally, two variants, rs3814883 in TAOK2 and rs11191514 in CNNM2, are each associated with schizophrenia,72 and both are associated with increased risks of cardiometabolic and cardiovascular disorders.73Outside of BDNF-related pathways, there is biological plausibility for additional colocalized variants. One of these is rs13107325, a missense SNP in The main strengths of our work include the triangulation of evidence from several complementary independent genomic analysis methods that refine genetic correlation estimates to putative common-causal SNPs, permitting a distinction between correlation and causation. In turn, this can inform future basic research, and highlight potential pathways which might be investigated for therapeutic potential for both schizophrenia and its associated cardiometabolic comorbidity. Our findings show a consistent pattern across complimentary methods which are able to address the limitations of previous research. We used stringent Bonferroni correction to reduce the risk of type I statistical error in our work, and combined frequentist analysis with Bayesian colocalization analysis, fostering increased confidence in our findings.16 owing to the fact that GWAS between analysed trait pairs were conducted by different consortia. Some degree of sample overlap may have been present and this may increase the risk of bias. HyPrColoc assumes the presence of at most one causal SNP in the region, a limitation common to colocalisation methods. Yet, HyPrColoc estimates may only become unreliable when the secondary causal variants explain a similar amount of trait variation as the primary shared variant.21 We could only include one inflammatory marker, CRP, since large-scale GWAS of other inflammatory biomarkers are scarce. Despite CRP being a generalized marker of inflammation, future replication of our work with a larger set of upstream inflammatory markers may help to test specific inflammatory pathways. Future research may also consider other mental disorders, eg depression; which is genetically correlated with schizophrenia74 and is also observationally associated with cardiometabolic disorders.75 Finally, some level of similarity in genetic architecture might be expected between any set of complex disease traits; however, our results show a consistent pattern across a number of analysis methods, suggesting that chance associations are unlikely to fully explain our results.The main limitations of this study are as follows: We considered traits for further analysis based upon a nominal threshold since correlation estimates, which are either (1) averaged across the whole genome (LDSC); or, (2) averaged across MAF quartiles (GNOVA), may have been biased toward the null where opposing mechanisms exist, and, our locus-level correlation analyses suggested this to be the case for schizophrenia with all included traits. Following a priori determined stringent Bonferroni correction, some findings, particularly from MAF-stratified analysis did not reach the evidential threshold. Future better powered analyses may help to confirm our findings. Future work may also consider using other methods to control for the risk of type I statistical error. For our MAF-stratified analysis, due to limitations in current GWAS power, we could only include SNPs with MAF >5% in our lowest MAF-quartile, a limitation common to methods using GWAS data. Such variants are therefore best described as a lower-frequency tranche of common genetic variation. Since we included GWAS data from mostly European samples, it is unclear whether the findings apply to non-European samples. As per the HESS method authors, we assumed no sample overlap for locus-level correlation analysisIn conclusion, we present evidence indicating a partial shared genetic basis for schizophrenia, cardiometabolic, and inflammation-related traits, suggesting that the commonly observed comorbidity between these conditions may be partly heritable. Our results imply that LDSC may downwardly bias genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. This may be due to the presence of opposing mechanisms, and because the shared genetic aetiology may be confined to relatively lower-frequency common genetic variants. The majority of loci showing evidence for colocalization are biologically plausible, with a number implicating pathways involved in regulation of BDNF. Our results highlight putative pathophysiological mechanisms that could underly the comorbidity, which may form the basis for future basic and therapeutics research, both for schizophrenia and its associated cardiometabolic comorbidity. Future research should also seek to examine the interaction between genetic and environmental factors for the associations of schizophrenia and cardiometabolic disorders.sgac001_suppl_Supplementary_DataClick here for additional data file.


Most complex traits evolved in the ancestors of all modern humans and have been under negative or balancing selection to maintain the distribution of phenotypes observed today. Yet all large studies mapping genomes to complex traits occur in populations that have experienced the Out-of-Africa bottleneck. Does this bottleneck affect the way we characterise complex traits? We demonstrate using the 1000 Genomes dataset and hypothetical complex traits that genetic drift can strongly affect the joint distribution of effect size and SNP frequency, and that the bias can be positive or negative depending on subtle details. Characterisations that rely on this distribution therefore conflate genetic drift and selection. We provide a model to identify the underlying selection parameter in the presence of drift, and demonstrate that a simple sensitivity analysis may be enough to validate existing characterisations. We conclude that biobanks characterising more worldwide diversity would benefit studies of complex traits. Understanding complex traits is one of the most important questions facing genetics as we progress into the Biobank era. The number of Single Nucleotide Polymorphisms (SNPs) that influence complex traits may vary from tens to thousands in human and non-human species , 2. The S that describes this shape, and which is often linked to selection. S = 0 means that effect size and SNP frequency are unrelated. S < 0 means that rare SNPs have larger effect, and is expected if large effect SNPs are driven to low frequency by negative or balancing selection. Conversely, S > 0 implies that common SNPs have a larger effect, and is expected if selection increases the frequency of large effect SNPs via positive selection.There is much interest in describing the genetic architecture  of complGenetic drift  is the ptarget population of Europeans, and any other non-African population have a rather different genetic architecture to the evolving population, proxied by Africans, in which selection predominantly occurred. As a consequence, S cannot be understood as a direct quantification of selection, and indeed the value obtained depends on many things including any Minor Allele Frequency (MAF) thresholding performed in quality control. Models of genetic architecture that do not correct for drift are a useful description of the data, but further work is needed for inference about selection.We use a simulation approach to examine whether the out-of-Africa bottleneck should change the interpretation of parameters in the genetic architecture of complex traits. We find that inference in a h2 = 0.5 throughout. We vary the SNP frequency relationship S; recall that S < 0 implies ‘negative selection’ on the trait, and therefore high frequency SNPs can only have a small effect on the trait, whilst rare SNPs are permitted to have larger effect sizes.To assess the effect of genetic drift on genetic architecture we need a large sample of individuals from around the world, which is not currently available. To address this we resample data from the 1000 Genomes dataset  using HAf and effect size β, for each SNP i of the form:evolving population are treated in the generative model for complex traits. Because less information is available about real rare variation, little is known about how, in reality, these affect complex traits. One reasonable assumption is that the effect size follows the model described above for all frequencies fi (the default unbounded effect simulation). However, this leads to rare SNPs having unbounded effect size. An alternative reasonable assumption is that bounded effect simulation) (‘Methods’).To generate genetic variability in each of our populations we follow  by assumh2 = 0.5 with the specified MAF threshold, from which we compute an observed heritability h2. We also report values computed with ‘GCTB using “--bayes S”’ [Heritability is the proportion of variance attributable to genetic variation, and therefore depends critically on assumptions about transferability of environmental variation. Our simulation assumes a constant value for environmental variability, determined to be that required in Africans to give ayes S”’ .target population by excluding those beneath some MAF threshold. These need not be the same SNPs that were rare in the evolving population.Finally, in real data analysis, it is necessary to exclude SNPs that are very rare in the S. However, we observe that thresholding critically impacts the inferred heritability. If no thresholding is performed, the inferred h2 is significantly larger than simulated, whilst if thresholding is strict, the inferred h2 may be smaller. It is not that this heritability is ‘wrongly estimated’, but is a property of a trait realised in a specific population due to different genetic variation leading to different phenotypic variation.The resulting heritability for simulated complex traits in African and other populations is given in Fig. This is a direct consequence of genetic drift changing allele frequencies independently of SNP effect size Fig. . Low freIt is important to emphasise that these heritability changes are a consequence of the total genetic variation changing as a consequence of genetic drift. Similarly, environmental variation for real phenotypes varies due to factors including lifestyle, societal organisation, and so on. We report these heritability results to emphasise how important assumptions are in modelling. Of course, it is possible to scale the environmental variation with the genetic variation to ensure a desired heritability. Changing the environmental variation added to phenotypes will not affect the inference that follows.S. For this we implemented Eq. We then asked whether the relationship between SNP frequency and effect size has been distorted by genetic drift, by estimating the selection coefficient We call this the ‘simple model’ as it does not account for genetic drift. This relationship is typically a prior that affects effect size estimates; for our model this is a likelihood for the observed effect size, which we assume given. These would be taken from GWAS, but in simulations effect sizes are treated as known. This eliminates the estimation error that often dominates genetic architecture studies.S, like h2, is biased by genetic drift, but this depends critically on how the phenotype is truly formed. In the unbounded effect simulation where no MAF thresholding is performed (threshold = 0.0001 excludes only SNPs absent in Africa), the inferred S is larger in magnitude than the simulated S. Conversely, in the bounded effect simulation S can be below the true value, and for large thresholds tends towards the prior mean of 0, due to a lack of variation in the data. There is a transition around minor-allele-frequency of 0.05 where the biases cancel out. However, there is significant variability in the inferred S, due to the random nature of genetic drift and the sensitivity of the inference to the most extreme causal SNPs.Figure S. The choice of environmental variation does not effect S as it is simply adding different amounts of noise to the phenotype. This is therefore a rather different sort of bias.Unlike for heritability, it is not clear how a simulation should be updated to maintain a desired S are recovered for a range of thresholds  which induced considerable bias in every other population. MAF thresholding is therefore a potential sensitivity analysis tool for the interpretation of S.Critically however, the choice of MAF thresholding does not affect inference in the population that experienced the selection; in our simulations this is Africa (AFR). In this population, accurate estimates of S are both natural consequences of genetic drift. To model genetic drift and hence recover the pre-drift values  we allow for genetic drift in a ‘drift model’  than Figs. N = 1000). We demonstrate two cases where the drift model works well; when the Balding–Nichols model holds  has led to a small fraction (around 5%) of SNPs with high differentiation, from Neanderthals for all non-Africans cite , and Denevolving population prior to the out-of-Africa event that led to the peopling of Eurasia and beyond. This bottleneck led to considerable genetic drift in all non-Africans, which can bias inference of selection where these are used as a target population.Selection occurred on most complex traits in the evolution of modern humans; that is, most selection will have acted on the S can be positive or negative, sensitively to details of complex traits that are not currently well understood: the true value of S is more extreme in drifted populations if the effect size of extremely rare SNPs is appropriately modelled by the bulk of the distribution. However, it is smaller if the effect size remains bounded. From an ‘extreme value’ perspective  may matter. It is likely that semi-parametric models, which are not sensitive to the distribution of effect sizes in the bulk of the SNPs, will be more robust to these issues, and potentially restricting inference to common SNPs in both Europeans and Africans will aid robustness.We hypothesise similar issues surrounding model-misspecification of the complex trait. For example, if the distribution of effects is not normal, if the variance does not fit the assumed model, or if typical non-ancestral variants have a biased effect . In such a misspecified model, details such as the prior on the noise can affect inference; for example, the scale of the variation in effect size  can be obtained. Whilst our implementation lacks the SNP selection component of established tools, our model can be directly used by performing SNP selection within other software, or software could be updated to allow more appropriate models. Fst, as it would be inconsistent to infer Fst on a trait-by-trait basis for the same SNP set. However, it is the case that Fst varies considerably between SNP sets and the Fst we observed across populations was low, which may be due to the relatively high frequency imposed on this during SNP selection.Our model uses relatively little information and is not likely to reconstruct true allele frequencies from the past; it instead learns ancestral SNP frequencies that make the Complex Trait effect size distribution most plausible. It also does not implement inference of Fst between Africans and Eurasians is high at ∼0.2 [Fst from the ancestor of all humans is not completely clear. Diversity within Africa is extremely high (again ∼0.2−0.3) [Genome-Wide,  at ∼0.2 ; within 0.2−0.3) . As largOn Complex Traits whose variation is dominated by relatively few SNPs, it will be hard to separate genetic drift and selection. This leads to two independent avenues of further research. The first is to increase diversity of large-scale population studies and especially African ancestry, to access the genetic diversity that was lost in the Out-of-Africa bottleneck. The second is to develop multi-ethnic models of genetic architecture to account for population structure.We use the 1000 Genomes Project data for simulation analyses. The latest release is phase 3, containing 84.4 million variants for 2504 individuals. Population groups in this data are African (AFR), European (EUR), South Asian (SAS), East Asian (EAS) and American (AMR) .1000 Genomes data (genome wide) were pruned based on linkage disequilibrium. Variant pruning was done using PLINK 1.9 , 27 withN causal SNPs at random, and simulating effects from our model following [fi are taken as the African SNP frequencies.We generate a random complex trait by selecting ollowing : \documeevolved population to set the environmental variation N = 1000 and the phenotype of an individual k is sampled from their (binary) genome N causal SNPs were considered by our algorithm. For Fig. N = 4000.Individual level data are required for running GCTB, and for the computation of heritability and genetic variance under genetic drift. Then the genetic variation itability \documenWe created a novel MCMC algorithm in Stan   defined in terms of shape and scale parameters with expectation α/(α + β). Therefore fi has expectation Here, Normal distributions are specified via  and the Beta distribution is specified as Fst is known  [When own Fig.  this is nk/1.9/)  using ‘-target population, except for those that have reached fixation, which are omitted as they have zero probability under the likelihood.Unless otherwise stated, all SNPs are considered without thresholding in the Rhat statistic [Rhat(S) < 1.2. Typically, each chain either converges rapidly to the correct mode (Rhat < 1.02 in 78% of replicates) or one or more chains become stuck in a poor local optima with S > 0 leading to Rhat ≥ 1.5.For Fig. tatistic  using thdefault simulation, all SNPs with frequency > 0 in Africans are considered. For the bounded effect simulation, only SNPs with frequency > 0.01 in Africans are considered for sampling.The difference between these models is created solely by the selection of SNPs to be included in the simulation. For the bounded effect simulation is therefore bounded at S = −1 and The nomenclature arises from the consequences of this thresholding. The variance of SNPs in the Fst values and demonstrating performance under the assumed model. We choose a value of S and Fst and then simulate data from the ‘drift model’ with a specified L .We created a simulation model that could characterise our model rapidly without going through the 1000 Genomes data, hence providing a simulation that could generate a range of simulated We also threshold MAF to 0.0001, i.e. in the inference model, any frequency less than 0.0001 is treated as 0.0001.Supplementary Material


The breeding values of animals in the validation groups (n = 392–774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their deregressed EBVs (dEBVs) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG, and Yorkshire-BFT scenarios, and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD, and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT, and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG, and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of back-fat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) in Canadian Duroc, Landrace, and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total of 41,304, 48,580, and 49,102 single-nucleotide polymorphisms remained for Duroc ( In the pork industry, genetics has a pivotal role in improving the economically important traits, such as meat quality, growth, and reproductive performance . The genn = 1,363) of Duroc pigs. The single-step GBLUP (ssGBLUP) method was considerably more accurate than GBLUP and BayesR for growth traits in a larger population  of Yorkshire breed  in the validation group . The accre breed . The difre breed . Therefo1. Similar to GBLUP method, the infinitesimal model is assumed in ssGBLUP method. The pivotal difference between ssGBLUP and GBLUP methods is applying a blended genetic relationship matrix (using genomic and pedigree relationship matrices) in the ssGBLUP method , back-fat thickness (BFT), and loin muscle depth (LMD) traits in Canadian swine populations.The hogs used in this study were cared for according to the Canadian Council on Animal Care  guidelinIn this study, we used genotypic and phenotypic data of BFT, ADG, and LMD in three swine breeds of Duroc, Landrace, and Yorkshire. The BFT and LMD were measured by ultrasonic machine (B mode), and ADG was calculated using Eq. 1:2.These phenotypes were collected from 2010 to 2019 and adjusted to the weight of 120 kg . The aven = 4,615–5,875) and validation  groups were genotyped with Illumina 60K  or Affymetrix 50K  panels  were used for imputation of the missing genotypes with FImpute 2.2 software  as pseudophenotypes according to the approach proposed by Cy is the vector of dEBVs (reference population) as pseudophenotypes, μ is the overall mean effect, Z is the incidence matrix relating phenotypes (dEBVs) to GEBVs, g is the vector of GEBVs, and e is the vector of random residual effects. It was assumed that G is the genomic relationship matrix, W is a diagonal matrix of residual weights, and In Model 2, The genomic relationship matrix was constructed as Z is the allele frequency adjusted genotype matrix with 0–2jp(for AA genotype), 1–2jp (for AB genotype), and 2–2jp (for BB genotype) elements, and dimension of the number of individuals by the number of markers. jp is the minor allele frequency for j-th marker.where aireml” procedure (in SNP1101 software) implemented in GBLUP model (Model 2) are reported in The estimated variance components obtained from the “The ssGBLUP analysis  was perfy, μ, X, b, W, u, and e were explained in Model 1, Z is the incidence matrix relating phenotypes to GEBVs, and g is the vector of GEBVs. It was assumed that the variance of genomic effects  and pedigree information (A). In the inverse of H matrix, A22 is the pedigree-based relationship matrix for genotyped animals, and τ and ω are the scaling factors, which both were set equal to one as the default option in AIREMLF90 1.61 software (G (α) and A (β) matrices in the inverse of H matrix were set equal to 0.95 and 0.05, respectively, which were defined as where software . The bledictions . The estIn BayesC framework, the genomic evaluation was performed by implementing Model 4 and MCMC process in GS3 2.0 software  with theCy is the vector of pseudophenotypes as defined in Model 2, μ is the overall mean, n is the number of SNPs, iZ is the vector of genotype covariates, αi is the allele substitution effect for SNPi, δi is an indicator for whether the SNPi has effect (1) or not (0), and e is the vector of random residual effects. The residuals were weighed based on the reliabilities of dEBVs as defined by Legarra et al. in GS3 software  of the validation group and their dEBVs after performance test. The standard errors of prediction accuracies were calculated using Eq. 4:lm” and “summary” functions in R 4.0.2 software (The regression coefficients of dEBVs (after performance test in January 2019) on predicted breeding values (before performance test in January 2019) were calculated to evaluate the bias of predictions . The regsoftware .The accuracy improvements were calculated using Eq. 5:Two criteria of accuracy and regression coefficient of dEBV on GEBV were used to compare the predictive ability of different genomic prediction methods. Additionally, a fixed model equation (Model 5) was employed to detect the significant differences of the prediction accuracies obtained from different methods.y is the accuracy of prediction for the trait, μ is the overall mean, M is the fixed effect of prediction method i, B is the fixed effect of breed j, and e is the random residual effect for i-th method and j-th breed. The computational times of different scenarios with the same number of threads (n = 80) and memories (202 GB) were calculated using the default option of Slurm 20.02.3 software3 in Compute Canada server (Niagara cluster4). In this study, the computing time included the construction of genomic relationship matrix and its inverse, variance components estimation, and solving models.where n = 50,060) and maximum  numbers of phenotyped animals were observed for Duroc-ADG and Yorkshrie-ADG scenarios. The Landrace-BFT (14.27 mm), Duroc-ADG (736.72 g/day), and Duroc-LMD (72.61 mm) had the highest averages. The coefficients of variation ranged from 18.78 (Duroc) to 23.07 (Landrace), from 8.34 (Landrace) to 8.65 (Yorkshire), and from 8.25 (Duroc) to 8.83 (Yorkshire) for BFT, ADG, and LMD, respectively.The standard deviation, minimum and maximum values, and the coefficient of variation for each trait and breed are given in After quality control, removing SNPs on sex chromosomes, and imputation, the total numbers of 41,304, 48,580, and 49,102 SNPs remained for genomic evaluation of Duroc, Landrace, and Yorkshire breeds, respectively. Finding an accurate and unbiased genomic prediction method can be a lucrative strategy for genetic improvement of key traits in livestock species . The preThe average computational times for GBLUP, ssGBLUP, BayesC, and BayesCπ were 00:00:33, 00:18:47, 15:16:37, and 15:40:02 h, respectively. The computational times of GBLUP, ssGBLUP, BayesC, and BayesCπ ranged from 00:00:27 h (Yorkshire-BFT and Landrace-BFT) to 00:00:43 h (Duroc-LMD), 00:09:54 h (Duroc-LMD) to 00:31:43 h (Landrace-BFT), 14:21:19 h (Yorkshire-ADG) to 16:21:31 h (Duroc-ADG), and 14:16:28 h (Yorkshire-BFT) to 16:22:40 h (Duroc-LMD), respectively . The corP > 0.05) different from each other based on Tukey test implemented in Model 5 (P < 0.05) lower than those obtained from genomic methods except for BayesC in LMD trait (P < 0.05) different among breeds except for Yorkshire-Duroc (for BFT) and Yorkshire-Landrace (for ADG and LMD).The accuracies ranged from 13.9% (Duroc-PA) to 52.7% (Landrace-ssGBLUP) for BFT, from 5.7% (Duroc-PA) to 34.5% (Landrace-ssGBLUP) for ADG, and from 3.7% (Duroc-PA) to 25.1% (Landrace-BayesCπ) for LMD. The accuracy improvements over PA ranged from 35.6% (Yorkshire-BayesC) to 204.6% (Duroc-BayesCπ) for BFT, from 62.1% (Landrace-BayesC) to 314.3% (Duroc-ssGBLUP) for ADG, and from 6.4% (Landrace-BayesC) to 284.5% (Yorkshire-GBLUP) for LMD . The pre Model 5 . HoweverMD trait . Moreoven = 2,489). Higher prediction accuracies obtained from ssGBLUP for BFT in Yorkshire and Landrace breeds compared to Duroc breed might be due to the similarity of these breeds as maternal lines as well as using the larger population size (pedigree and phenotype data) of Yorkshire-BFT  and Landrace-BFT  in comparison with Duroc-BFT . For BFT, the prediction accuracy superiority of ssGBLUP method over the other genomic prediction methods such as GBLUP was confirmed by previous studies in American Yorkshire breed  and Yorkshire (44.7%) breeds . The ssGre breed , 2019a. re breed .P > 0.05) different across breeds. In a previous genomic prediction study on a small population size of Canadian Duroc breed , The regression coefficients higher and lower than 1 indicate overestimation and underestimation, respectively. The ssGBLUP method showed higher prediction accuracies in Landrace-BFT and Yorkshire-BFT scenarios, but BayesCπ was the least biased method in these scenarios (1.06 for Landrace-BFT and 1.12 for Yorkshire-BFT). In Duroc-BFT scenario, BayesCπ (42.4%) and GBLUP (1.11) were the most accurate and least biased methods, respectively. The higher prediction accuracy of BayesCπ for Duroc-BFT scenario might be due to a low number of major-effect SNPs underlying genetic variation of BFT in Duroc breed . Howevern = 1,022) of Duroc breed in the reference group. The implementation of genomic multitrait models using ADG, FCR, and RFI traits might be beneficial for improvement of prediction accuracy for ADG in Duroc breed through implementing information from genetically correlated traits  (Our results indicated that ssGBLUP was the most accurate method (34.5%) for prediction of ADG in Landrace breed ; howeverd traits . Additiod traits . By comp= 1,363) . Althougn = 8,756) and Landrace  pigs , BayesCπ (25.1%), and GBLUP (21.6%) methods, respectively . However54) pigs , althoug54) pigs . In Duro54) pigs  compared54) pigs  and York54) pigs . Our res54) pigs .The prediction accuracy is also affected by response variables through correlating predicted breeding values and response variables  after performance test. The reliability of dEBV as a response variable is highly dependent on the population size of phenotyped animals . In DuroP > 0.05) different in all scenarios. High prediction accuracies obtained from ssGBLUP method in aforementioned scenarios might be due to the blended pedigree and genotypic data used for prediction of GEBVs  among prediction accuracies obtained from these genomic methods in each scenario. These results can be beneficial for implementing the suggested genomic prediction methods for improvement of BFT, LMD, and ADG in swine breeding companies.The accuracies of traditional BLUP, GBLUP, BayesC, ssGBLUP, and BayesCπ methods in a moderate genotyped size of Canadian swine populations were evaluated to compare their predictive abilities. In most scenarios, ssGBLUP and BayesCπ methods demonstrated the highest prediction accuracies and unbiasedness, respectively, although there were no significant differences (The datasets presented in this article are not readily available because the data are not publicly available due to privacy. Requests to access the datasets should be directed to YM.Ethical review and approval was not required for the animal study because the animals in this study were raised in commercial farms and only data was provided to this study without any changes to the routine production and thus no animal ethics were needed. Animals were also cared for according to the Canadian Council on Animal Care guidelines.YM, BS, and MJ conceived and designed this experiment. MJ prepared the imputed genotype files. SS wrote the first draft of the manuscript and analyzed the data under supervision of YM, BS, MS, and MJ. SS done visualizations. All authors have read and agreed to the published version of the manuscript.MS is employed at Select Sires Inc. This organization did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of MS’s salary. The remaining 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.


Cannabis samples quantified for terpene and cannabinoid content and genotyped for over 100,000 single nucleotide polymorphisms indicated that Sativa- and Indica-labelled samples were genetically indistinct on a genome-wide scale. Instead, we found that Cannabis labelling was associated with variation in a small number of terpenes whose concentrations are controlled by genetic variation at tandem arrays of terpene synthase genes.Analysis of over 100  Cannabis samples for terpene and cannabinoid content and genotyping them for over 100,000 single nucleotide polymorphisms, this study finds that Cannabis labelling is associated with genetic variants in terpene synthase genes.By quantifying over 100  Cannabis has been consumed for its psychoactive properties for over 2,500 years, and its estimated global market value is US$340 billion3. Because it is a widely used drug that is increasingly being legalized for medicinal and recreational use, it is critical that Cannabis’s genetic and chemical variation be accurately quantified and communicated. The vernacular labels Sativa and Indica (not to be confused with the taxonomic names C. sativa sativa L. and C. sativa indica Lam.) are routinely assigned to Cannabis cultivars by breeders, retailers and users to describe a cultivar’s morphology, aromas and/or psychoactive effects4. However, it is unclear whether these labels capture meaningful information about Cannabis genetic and chemical variation.Cannabis genomics research has thus far largely focused on the characterization of genes underlying the production of the cannabinoids cannabidiol (CBD) and tetrahydrocannabinol (THC)8. However, Cannabis produces hundreds of aromatic terpenes that drive consumer preference and are frequently associated with Sativa and Indica labels9. In addition, there is evidence to suggest that a cultivar’s terpene profile affects its psychoactive properties11. To date, various terpene synthase genes have been identified in Cannabis; however, the genetic control of terpene variation across Cannabis cultivars remains largely unexplored15.Cannabis that were previously quantified for 40 terpenes and cannabinoids using gas chromatography–mass spectrometry (GC–MS)16 . Furthermore, the overall genetic structure (captured by including the first ten PCs of the genomic data in a linear model) explained only 37% of the variance in labelling  were often as genetically and chemically distant from each other as pairs of samples with different names  of the genomic data showed no clear clustering according to sample labels Fig. . Even thing Fig. . Sativa–R2 = 0.049, P = 7.5 × 10−5; PC2: R2 = 0.24, P = 3.7 × 10−19). Including the first ten PCs from the terpene and cannabinoid profiles in a linear model accounted for only 41% of the variance in labelling  of the variance in labelling. Since the overall patterns of genetic and chemical relatedness could not fully account for the labels applied to Cannabis samples, we aimed to determine which individual chemicals were the strongest predictors of Sativa–Indica labelling.Similar to the PCA of the genome-wide SNP data, the PCA of the terpene and cannabinoid profiles provided poor separation of samples according to their Sativa–Indica labels Fig. . Neverthing Fig. . The paiP < 0.01  and farnesene , which impart tea-like and fruity aromas, respectively23. This is consistent with descriptions of Sativa cultivars as having a ‘sweet’ or ‘herbal’ aroma9. The strongest correlation was between Indica content and myrcene, whose concentration explained 21.2% of the variation in labelling , γ-eudesmol  and β-eudesmol . Hillig27 found that these three sesquiterpenes were associated with plants from Afghanistan, which is considered the region of origin for Indica cultivars.Of the 40 measured terpenes and cannabinoids, 12 (30%) were correlated with the Sativa–Indica scale at Cannabis have suggested that the distinction between Sativa and Indica is best explained by differences in the concentrations of specific monoterpenes and sesquiterpenes30. In addition, the contrasting aromas that have been associated with Sativa  and Indica  were key discriminators in a sensory evaluation of Cannabis cultivars and mediated customers’ perceptions of potency and quality9. As a previous study suggested31, we hypothesize that Cannabis growers and breeders have been assigning labels to cultivars primarily on the basis of aroma profiles and purported effects, rather than genetic ancestry or overall chemical similarity. The primary differences between cultivars labelled as Sativa and Indica may thus be driven by a small set of genomic regions controlling the concentrations of a small number of contrasting aromas. To examine this, we conducted a genome-wide association study (GWAS) of the 40 chemicals examined here . These observations suggest that myrcene synthesis is mediated by genetic variants at two independent terpene synthase gene clusters on chromosome 5. The other three sesquiterpenes  strongly associated with Sativa–Indica labelling are correlated with each other  , HempFlax (n = 205) and Dutch ‘coffee shops’ either directly or indirectly through the TRIMBOS Institute (n = 55). Samples labelled as ‘Hemp’ were excluded from the analysis. We retained and analysed 297 samples that were classified along a five-point scale according to ancestries reported by the sources: ‘Sativa’ (100% Sativa), ‘Hybrid-Sativa’ , ‘Hybrid’ , ‘Hybrid-Indica’  and ‘Indica’ (100% Indica). These five groups were encoded as 1 (100% Sativa) to 5 (100% Indica) for the statistical analyses described below.The samples come from a previous study of 460 16. Briefly, for each sample, 500 mg of ground homogenized dried flower material was mixed with 40 ml of ethanol, agitated for 10 minutes and centrifuged. The supernatant was collected, and the process was repeated twice more on the pellet. An internal standard consisting of 200 μl of 1% solution of 1-octanol was added to the combined supernatant, the volume was adjusted to 100 ml with ethanol and the combined sample was centrifuged again. The combined sample was analysed using an Agilent GC 6890 series (Agilent Technologies) equipped with a 7683 autosampler and a flame ionizing detector. The instrument was equipped with a DB-5 column . Peaks from the sample chromatograms were manually integrated, and the peak area was recorded with correction for the internal standard peak area. Peak identification was conducted by analysing selected samples using GC–MS and then comparing compounds’ mass spectra and retention times with authentic standards and literature reports as described in ref. 16. Compounds without authentic standards are marked with an asterisk in the figures to indicate that they were tentative identifications. Peak areas of monoterpenes, sesquiterpenes and cannabinoids were quantified  using calibrated standards of β-pinene, α-humulene and CBD, respectively. We re-assessed the compound identifications in Hazekamp et al.16, and in certain cases we renamed compounds on the basis of the inability to distinguish stereoisomers using a DB-5 column. For example, in the case of the compound listed by Hazekamp et al.16 as ‘(−)-linalool’, we renamed this to ‘linalool’. There are also two compounds that could not be reliably identified; they are listed as ‘unidentified compounds’  . The DNA sequence data are available as NCBI BioProject PRJNA713792. Calling of SNPs was performed in TASSEL (v.5.0)35 by aligning to the CBDRx reference genome8. SNP calling was performed before the implementation of the new chromosome numbering of the CBDRx genome in April 2020. Chromosomes were recoded for analyses to reflect the new chromosome numbering system. We used VCFtools (v.0.1.15)36 to retain only bi-allelic SNPs and samples with <70% missing data, which resulted in 155 remaining samples and 284,988 SNPs. Genotype imputation was performed using LinkImputeR37 with a minor allele frequency threshold of 0.01, a minimum read depth for masking of 20 and the number of masked genotypes set to 5,000. We chose to impute with a minimum read count of 2 and a maximum missingness threshold of 70%, which resulted in an imputation accuracy of 92.88%. After imputation, 149 samples remained. An additional 12 samples were removed because they had no phenotype data. This resulted in a final set of 137 samples with both genetic and chemical data. The SNP data were filtered using PLINK (v.1.90)38 to exclude SNPs with a minor allele frequency less than 0.05 and SNPs with excess heterozygosity resulting in Hardy–Weinberg P values less than 1 × 10−5. The final SNP dataset used for GWAS consisted of 116,296 SNPs from 137 samples. For PCA, 1,257 unanchored SNPs were removed, and the remaining 115,039 SNPs were LD-pruned using PLINK (command: –indep-pairwise 10 3 0.5), resulting in 80,939 SNPs.Whole-genome DNA was extracted using a NucleoSpin 96 Plant II kit (Machery-Nagel) and quantified using the QuantiFluor dsDNA System and the GloMax-Multi + Microplate Multimode Reader with Instinct (Promega). Genotyping-by-sequencing libraries were prepared using the restriction enzyme ApeKI32 by first reducing the chemical matrix to the 137 samples with both chemical and genetic datasets. PCA was performed on the scaled genetic and chemical data using the prcomp function in R. To calculate the variance in labelling explained by the chemical and genetic data, linear models including the top ten PCs from the genetic data, the chemical data and both the chemical and genetic datasets together were performed. Pearson correlations between chemical concentration and the 1-to-5 Sativa–Indica scale were performed with the cor.test function in R. A Bonferroni correction was applied to the P values from the correlation test between chemical concentration and the Sativa–Indica scale.The chemical distance between cultivars was calculated as the Euclidean distance using the ‘dist’ function in R from the matrix of metabolomic data—that is, 40 terpenes and cannabinoids quantified across 297 samples. The genetic similarity between samples was calculated as an inverse identity-by-state matrix generated in PLINK. The correlations between the matrices were computed using a Mantel test in R39 accounting for relatedness using a kinship matrix created in TASSEL (v.5.0)35. The MLMM incorporates significant SNPs as cofactors using stepwise regression (maxsteps = 10), and the optimal model was chosen on the basis of the extended Bayesian information criterion. We also present the first step of the MLMM, which is equivalent to an MLM where relatedness is accounted for but no SNPs are included as cofactors. Using the simpleM40 package in R, the effective number of independent tests (Meff) was generated, and the threshold for significance was then calculated using −log10(α/Meff), where α = 0.05. Quantile–quantile and Manhattan plots were created using the qq function in R. Genomic regions with significant GWAS hits were explored, and the physical locations of genes within these regions were retrieved using annotations from the CBDRx reference genome8 in Geneious Prime (v.2020.1.2). The GWAS results and LD regions of interest were visualized using code adapted from ref. 41.We performed GWAS for 40 terpene and cannabinoid phenotypes, using both normalized and non-normalized data. Normalizing was conducted to generate values for a chemical concentration in a sample relative to the total abundance of its chemical class  in that sample. Thus, a sample’s myrcene content was divided by the total concentration of all monoterpenes in that sample to generate a normalized value for myrcene. GWAS was performed using an MMLMFurther information on research design is available in the Supplementary InformationSupplementary Figs. 1 and 2.Reporting SummarySupplementary TablesCannabis samples. The asterisks denote chemicals with tentative identifications. Supplementary Table 2: Significant SNPs from the MLMM GWAS for myrcene and three sesquiterpenes. Only SNPs identified as significantly (P < 6.69 × 10−7) associated with a trait according to the MLMM GWAS are shown. The genomic coordinates and annotations, P value, R2 value and nearby candidate genes are shown. Supplementary Table 3: A list of compound names identified by Hazekamp et al.16, a list of authentic standards used for compound identification and a list of compound names based on re-analysis of the methods used in Hazekamp et al.16.Supplementary Table 1: Chemical concentrations and labels across 297


Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals.We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases.https://github.com/shuangsong0110/OWAS.The R package OWAS that implements our method is available at Bioinformatics online. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic associations , which h regions .Most existing literature assumes that annotated SNPs are more likely to be causal and are enriched for heritability. For example, In this article, we are particularly interested in understanding the role of chromatin accessibility in human complex diseases. Chromatin accessibility is the degree to which nuclear macromolecules are able to physically contact chromatinized DNA , which leverages OWAS is a segment-based association approach, in which openness of a genomic segment is predicted for each personal genome, and subsequently tested for the association with the phenotype of interest . ImportaX denote the genotype matrix of n individuals (rows) and m SNPs (columns), and let ijX be the genotype of the j-th SNP of the i-th individual. The segment-level openness score s in individual i isjw is the predicted openness effect for the j-th SNP, and Ωs indicates the set of SNPs in segment s. To study the phenotypic association of each openness score, we posit the following linear phenotype model:s is called the effect size for segment s, and the error ϵ follows the normal distribution. The OWAS Z-score is simplyLet X is provided, sO can be directly calculated with Z-scores can be derived by fitting the linear regression model in Z-score as:jz is the z-score for the j-th SNP in GWAS summary statistics; j and s. The derivation of When individual-level genotype data jw) of each SNP with deltaSVM (http://www.beerlab.org/deltasvm/). A list of the cell types is provided in We derive the predicted openness effect  matrix, which is estimated from 1000 Genomes Project European samples on chromosome 22. The sample size n was fixed at 10 000. The number of SNPs is 141 123. We set k = 1, 2, 3, which contain 0.5%, 1.5% and 8% of the total number of SNPs, respectively.We first simulated GWAS P-values and estimating the heritability explained by specific proportions of prioritized segments. The simulations were repeated for 100 times.As a comparison, we used an unweighted model regarding all SNPs in each segment equally. We compared the performance of OWAS with the unweighted model, by ranking the segments with the derived We tested the enrichment of OWAS genes in KEGG pathways .FUSION , hypertension (HT) and RA. The cell types selected for each trait is provided in As a comparison, we also prioritized genes/SNPs using FUSION, FUSION-open and FUMA-CADD (Section 2.6). The UK Biobank (UKB) summary statistics were used as the discovery cohort, and all the methods were evaluated with the WTCCC datasets . With thz-scores from a genetic model with heritability enriched in SNPs with high openness effects (Section 2.4). The heritability of genomic segments detected by OWAS is significantly greater than that of the unweighted method , and used the study by n = 100 184) as the replication cohort. For each trait, we used a two-stage process. We first binned the SNPs by their GWAS P-values in the discovery cohort. Then, in each bin, we compared the replication rate of prioritized SNPs (i.e. SNPs harbored by significant segments in OWAS) to that of the other SNPs. OWAS prioritized SNPs had a greater replication rate, and the trend was consistent across different traits and different P-value bins , we took the largest meta-analysis available as the discovery cohort and the UKB studies as the replication cohort. For high cholesterol (HC), we took UKB studies as the discovery cohort due to their large sample sizes  and CD19+ B cells (P = 7.0e–06), and these two cell types are both associated with autoimmune diseases (P = 3.4e–05) for cell-type-specific gene expression relative to 78 other tissues in the Gene Atlas (P = 2.5e–04) tissue for PrCa, and the prostate came second (P = 2.5e–03).We identified genes tagged by significant OWAS segments, and examined their tissue specificity and functional enrichment. In particular, OWAS identified 398, 313 and 211 genes with 4895, 1635 and 706 segments associated with RA, HC and PrCa, respectively. We performed SNPsea analysis . OWAS sediseases . SimilarWe further investigated overlaps between OWAS segments and predicted chromatin states from the 15-state ChromHMM model . While Oion test .PHTF1, HIPK1, PTPN22 and RSBN1 have been reported in GWAS of RA. Although not directly mapped to RA, AP4B1 and BCL2L15 are located in RA-associated locus 1p13.2, and were reported to have interactions with enhancers  to its substrate chordin in Xenopus   to show how OWAS can provide novel insights into the disease etiology. We mapped the significant segments OWAS identified to the closest genes, which we simply refer to as the OWAS genes. For RA, 398 genes were identified by OWAS at the 0.05 significance level after the Bonferroni correction, among which 205 were outside HLA regions. The large number of OWAS genes located in HLA regions (48.49%) validates the significant role of HLA regions in the immune system. In fact, strong associations between HLA regions and autoimmune diseases have been well documented in the literature , thus wenhancers . LAMA3 h Xenopus . OLFML3 pathways , which us . The advantages of OWAS in replication rate and heritability enrichment in various traits are retained in all four settings, compared to existing approaches . Thus, ORecently, there is a growing interest in understanding roles of non-coding variations in complex human diseases, empowered by the huge amount of data generated from epigenomic profiling efforts and the large scale GWAS of various phenotypes. Here, we develop a computational approach, OWAS, which integrates chromatin accessibility information in association tests. Our approach leverages predicted openness scores trained with machine learning methods, overcoming the difficulties in acquiring individual-specific openness measurement in large cohorts. We also derive a mathematical expression to compute OWAS results without individual-level genotype data, which broadens the scope of its application.We recognize that recent progress in caQTLs (Our method has several limitations. First, the results are influenced by the prediction accuracy of the machine learning methods. Nevertheless, with increased sample sizes and improvements in methodologies, we expect the prediction models to be further improved over the time. Second, when used with GWAS summary statistics, the method requires LD information estimated from a reference panel as input. Therefore, a reference cohort that accurately matches the target cohort is of great significance. Third, although incorporating chromatin accessibility information leads to interesting biological interpretations, we emphasize that the method alone cannot infer causality. Fourth, our method relies on pre-trained cell-type-specific sequence models, and its power could be limited by data availability of relevant cell types. A recent study (In conclusion, we have proposed a new segment-based computational framework, OWAS, which leverages and aggregates the prediction of chromosome accessibility in personal genomes to prioritize GWAS signals. In extensive simulations and real data analyses, OWAS identifies genes/segments that explain more heritability than TWAS methods and have high replication rates in independent cohorts. Our method requires only GWAS summary statistics and a reference LD panel, which guarantees its general applicability without any privacy concerns. As sample sizes of GWAS studies continue to grow, directly using summary statistics also helps maintain the computational burden at a constant level and increases the power of the methods. Furthermore, the significant associations identified by OWAS can lead to interesting biological interpretations, as exemplified in real data analyses of RA and ATH.btab514_Supplementary_DataClick here for additional data file.


Genetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.The online version contains supplementary material available at 10.1186/s13059-021-02403-1. The success of the human genome project has led to a paradigm shift in the complex trait analysis that focuses on the genome-wide association studies (GWAS) . GWAS haCurrent state-of-the-art whole-genome methods for estimating GxE include genotype-covariate interaction genomic restricted maximum likelihood (GREML) and random regression GREML . RecentlTo reduce the computational limitation of GREML, linkage disequilibrium score regression (LDSC) was introduced to estimate SNP-based heritability and genetic correlation . LDSC isIn this study, we propose a novel approach to estimate the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics (GxEsum) for a large-scale biobank dataset, correctly accounting for genotype-environment correlation and scale effects. In simulated and real data analyses, we show that the computational efficiency of the proposed approach is substantially higher than RNM, an existing GREML-based method, while the estimates are reasonably accurate and precise. Because of this computational advantage, GxEsum may be an efficient tool to estimate GxE that can be applied to large-scale data across multiple complex traits.ℓj is the LD score at the variant j that can be estimated from a reference panel (please see the “Methods” section for a full derivation of this equation). The jth SNP and the environmental covariate (E). The outcome trait is pre-adjusted for confounders and the main effects of E, and then a regression model with the main and interaction effects is run by SNP-by-SNP. If chi-square statistics from GWAS are regressed on LD scores, non-genetic interaction effects  and the computational efficiency of GxEsum and GREML-based GxE model .Finally, we show how the genetic effects of a complex trait  are modulated by environment  by using the proposed method.For a continuous trait, under the null (no GxE), whether or not there were confounding effects (RxE and G-E and R-E correlations), the type I error rate of GxEsum was not significantly inflated Table . Note thIn the simulation with non-zero interactions, estimated GxE g1) was not remarkably different from the true values whether there were significant G-E and R-E correlations or not  was slightly biased especially when using a large G-E or R-E correlation  traits  of GxEsum and RNM estimates. The SE of GxEsum was obtained from the LDSC software (using a jackknife method). The SE of RNM for the GxE component can be obtained from the information matrix  or from While the precision of GxEsum is competitive with that of RNM, the computational efficiency is dramatically different between the two methods  that were modulated by an environment such as age, alcohol intake frequency, neuroticism scores, or physical activity. The significant GxE was observed from the analyses using neuroticism scores. On the other hand, we did not find any significant GxE when using age, alcohol intake frequency, and physical activity after Bonferroni correction  Table . The GxEBecause not all variables were without missing, we imputed missing phenotypes using the mean value for each variable in the analyses, in order to maximize the sample size. In this real data analysis, there was no remarkable difference in the results whether using phenotypic imputation or not although some variables improved their significance, e.g., NEU kurtosis − 1) from Eq. . There are some issues that the measure of the environmental variable may not be standardized across study cohorts, and the environmental variable maybe even unavailable in some cohorts. However, these issues can be remedied when the information of exposome that is the standardized measure of all exposures for individuals, complemented to the genome, is available.There is a GxE method that can use GWAS summary statistics, i.e., VarExp, which is recently published. While VarExp benefits computationally from using GWAS summary statistics, it needs to invert the correlation matrix between SNPs, which prevents from using a large number of SNPs . FurtherLike RNM, GxEsum can fit environmental exposures such that the genetic effects of a trait can be modeled as a nonlinear function of a continuous environmental gradient. The potential modifier of the genetic effects is not limited to environmental exposures but can be extended to novel variables from multi-omics data such as gene expression, protein expression, and methylation data , 34. PolIn the analysis of binary disease traits, estimates on the liability scale, transformed from those on the observed scale using Robertson transformation, should be interpreted with caution. Biased estimates on the liability scale are likely due to the violation of the normality assumption that is essentially required for the Robertson transformation, i.e., large interaction effects can cause a non-normal phenotypic distribution. It is also known that if the transformation involves substantial non-additive effects, it can give biased estimates on the liability scale , 26, 37.There are a number of limitations to our study. First, like RNM, GxEsum does not determine the causal direction between variables, which can be provided from previous studies or other epidemiologic methods, e.g., Mendelian randomization, as prior information. Second, we only modeled the first order of random regression coefficients with a single environmental variable, and there may be significant additional effects when modeling a higher-order interaction or multiple environmental variables. It is possible to extend the GxEsum model to fit additional quadratic and polynomial terms or multiple environmental variables simultaneously. However, assessing the performance of these advanced models is a formidable task, requiring further study. Third, the estimation for the main genetic effects can be biased when there are large G-E and/or R-E correlations. Because of such correlations, the main genetic effects are over-adjusted when the phenotypes of the main trait are adjusted for the environmental variable in the model. Therefore, a careful interpretation of the estimated main genetic effects is required when using GxEsum. Fourth, we did not investigate the performance of GxEsum for ascertained case-control studies in which cases are over-sampled. A further study is required to extend the method to non-random case-control samples so that it can be applied to consortium data with multiple case-control studies. Lastly, when using the same sample size, the precision of GxEsum is not better than GREML-based GxE methods, implying that the former is only useful when using a large sample size that the latter cannot handle.Despite these caveats, GxEsum can be a useful tool to estimate whole-genome GxE as it can achieve a higher precision  from a larger sample size, compared to existing GxE methods. Especially when the scale of available resources increases, GxEsum may be a unique method that can be applied to large-scale data across multiple complex traits and diseases in the context of GxE.y is the N vector of phenotypic observations; b is a vector of fixed effects; g is the N individual genetic effects, which can be decomposed into the first and second order of genetic random regression coefficients, g0 and g1; τ is the residual effects, decomposed into the first and second order of residual random regression coefficients, τ0 and τ1; and E is the N vector of environmental variable. Note that E can be also any covariate variable .Following Ni et al. , RNM cany) are pre-adjusted for the main genetic effects (g0), environmental or covariate variable (E), and other fixed effects (b), the model can be rewritten as:X is an N x M standardized genotype matrix for M SNPs, and β1 is an M vector of SNP interaction effects modulated by the environment . It is noted that τ0 is the residual effects that are consistent across the environment whereas τ1 captures the heterogeneous residual effects across the environment .Assuming that the phenotypes , and the conditional expectation of Using the law of total variance, Noting that the least-square estimate of Therefore, mentclass2pt{minimj and other variants can be expressed as a function of LD scores, i.e.:jth variant and the other (M-1) variants, and ℓj = 1+jth SNP.According to Bulik-Sullivan et al. , the proX1, X2 and E), each with an N vector, useful equations are:According to the central moment theory of standard normal distribution of three independent random variables , the term (EXj)′(EX)(EX)′(EXj) can be expressed as a function of LD scores as:Therefore, assuming that Thus, a part in Eq.  can be rN, in Eq.  is non-normal, the general form of the fourth central moment term can be expressed as kurtosis∙N where kurtosis is the kurtosis of E. And only the intercept part of the Eq.  To validate the proposed model in general, we used comprehensive phenotypic simulations that were based on real genotype data  has been distributed through European Genome-phenome Archive. A stringent quality control process for individuals was set as follows: (1) who were reported as non-white British, (2) who were having mismatched gender between the reported and the inferred by the genotypic data, (3) who were having missing rate over 0.05, and (4) who were having putative sex chromosome aneuploidy. In addition, only HapMap3 SNPs were used which were passed from the stringent quality controls for SNPs. The filter for SNPs is set as follows: (1) which were having INFO score less than 0.6, (2) which were having a MAF less than 1%, (3) which were having Hardy-Weinberg Equilibrium (HWE) P-value less than 1E−4, and (4) one of which from the duplicated SNPs. From those passing the tough procedures, we additionally excluded one of pair of samples who were having a genomic relationship higher than 0.05. After quality control, 288,837 individuals and 1,133,273 SNPs remained. We estimated LD scores using the genotypic data of UK Biobank after these quality control processes.Among the trait phenotypes available in the UK biobank, we arbitrarily selected BMI (a quantitative trait) and hypertension and type 2 diabetes (binary disease traits) and tested if the genetic effects of the complex traits were significantly modulated by an environmental variable, i.e., NEU, ALC, PA, or age (for testing BMI); BMI, WHR, or BFP (for hypertension); and BMI, diastolic BP, or systolic BP (for type 2 diabetes). The number of cases for hypertension and type 2 diabetes was 134,499  and 11,694 , respectively. The phenotypes of the main trait were adjusted for potential confounders such as age, gender, year of birth, assessment center, Townsend Deprivation Index, genetic batch, household income, educational qualification , the firIn GWAS, we used a linear model for quantitative traits as well as for binary responses. The use of a linear model applied to binary responses is because it has been reported that a logistic regression may generate biased estimates in some instances , and ourAdditional file 1. Supplementary materials. It includes all supplementary notes, figures and tables.Additional file 2. Review history.