P = 6 × 10–61), higher nervous feelings (P = 1 × 10−46) and higher self-reported risk-taking (P = 3 × 10−38). We follow-up the risk-taking association, hypothesising that the association may be due to a genetic propensity for risk-taking leading to greater migration, urbanicity or drug-taking — reported environmental risk factors for schizophrenia, and all positively associated with risk-taking in these data. Next, to identify potential disorder or medication effects, we compare the PRS–trait associations in the general population to the trait values in 599 medicated and non-medicated individuals diagnosed with schizophrenia in the biobank. This analysis highlights, for example, levels of BMI, physical activity and risk-taking in cases in the opposite directions than expected from the PRS–trait associations in the general population. Our analyses offer simple yet potentially revealing insights into the possible causes of observed trait–disorder associations, which can complement approaches such as Mendelian Randomisation. While we urge caution in causal interpretations in PRS cross-trait studies that are highly powered to detect weak horizontal pleiotropy or population structure, we propose that well-designed polygenic score analyses have the potential to highlight modifiable risk factors that lie on the path between genetic risk and disorder.To characterise the trait-effects of increased genetic risk for schizophrenia, and highlight potential risk mediators, we test the association between schizophrenia polygenic risk scores (PRSs) and 529 behavioural traits  in the UK Biobank. Our primary analysis is performed on individuals aged 38–71 with no history of schizophrenia or related disorders, allowing us to report the effects of schizophrenia genetic risk in the sub-clinical general population. Higher schizophrenia PRSs were associated with a range of traits, including lower verbal-numerical reasoning ( The latter could be useful for highlighting potential mediators of disorder risk because their study should be less affected by the confounding effects of medication or disorder pathophysiology, which may have pre-clinical onset.While much is known about behavioural traits associated with schizophrenia diagnosis and medication3. The unidirectional nature of genetic effects on traits, however, provides a convenient ‘anchor’ to build more powerful causal inference from observational data4.Modification of lifestyle or behavioural risk factors has the potential of reducing disorder risk before the initiation of pathology. However, identifying such risk factors using clinical trials is typically infeasible and from standard observational data is highly challenging due to the complex network of traits that generates a myriad of non-causal associations9. Identifying overlap between genetic risk for a disorder and a trait can be useful both to characterise the trait–PRS associations and in prioritising putative disorder-modifying traits because genetic risk for traits that do modify disorder risk must form a sub-component of genetic risk for the disorder itself. Once genetic overlap has been established, the next step is to infer which of the following is most likely responsible: horizontal pleiotropy , vertical pleiotropy (the trait affects the disorder or vice versa) or that the observed overlap is an artefact of population structure or ascertainment10.Polygenic risk scores (PRSs), which correspond to a weak proxy of an individual’s genetic liability to a trait or disorder, can be used to infer genetic overlap between phenotypes via predicting one phenotype from the PRS of another13, while one study has also tested for associations between schizophrenia genetic risk and a large number of traits in the UK Biobank9. However, while these studies have used techniques such as Mendelian Randomisation9 and Genomic SEM13 to infer the causal relationships underlying the observed genetic overlap, they have not separated out non-diagnosed from diagnosed — medicated and non-medicated — samples, which may have compromised inferences made. Here we analyse these individuals separately. We utilise the large sub-cohort of the UK Biobank with no history of schizophrenia (or related disorders) in an exploratory study to test for the effects of schizophrenia genetic liability in the general population, and independently evaluate the large number of schizophrenia medicated and non-medicated cases; all of these individuals were collected and assayed as part of the UK Biobank in the same way. Contrasting the results across these groups provides a novel way of gaining insights that can help to distinguish between vertical and horizontal pleiotropy, as well as between disorder and medication effects. We also perform sensitivity testing to investigate potential confounding by population structure. To demonstrate how these results can motivate further interrogation of specific findings, we follow-up the positive association between schizophrenia PRS and risk-taking, investigating whether a greater propensity for risk-taking may expose individuals to modifiable risk factors for schizophrenia, such as illicit drug use.Several studies have tested for genetic overlap between schizophrenia and a small number of putative risk factors, such as cognitive, smoking and other psychiatric traits4.Our analyses should be considered an initial exploratory attempt to identify behaviours that may mediate risk of schizophrenia. We consider the simplicity of our approach to be a strength as an exploratory analysis, highlighting and contrasting effects in non-psychiatric, medicated and non-medicated cases in the same cohort in a direct and transparent way, making few assumptions. Analyses of this type could act as a complement to other more technical approaches, such as Genomic SEM or Mendelian Randomisation, for investigating causality in trait–disorder associationsPRSice-214, with the schizophrenia GWAS summary statistics from the Psychiatric Genomics Consortium (PGC) as base data15 and the UK Biobank data on 307,823 individuals with no history of schizophrenia, bipolar disorder or major depressive disorder as target data  to control for population stratification. We set a highly conservative significance threshold of P < 1 × 10−7 based on PRS including SNPs at a threshold of PT < 0.05  across 529 target behavioural traits. This stringent threshold was used to minimise significant hits being due to chance. Schizophrenia PRS showed significant (P < 1 × 10−7) associations in 104 of the 529 behavioural traits in the screened sub-cohort of 307,823 individuals. Phenotypic correlations between these traits can be seen in Supplementary Figs. 15, based on the assumption that the effect size estimates of genome-wide significant variants should be less affected by population structure than non-significant variants; 77 of the 104 associated traits were nominally significant (P < 0.05) based on the PRS comprising only genome-wide significant variants. The results of all 104 significant associations are displayed in Supplementary Table We performed PRS analyses (see Methods) using 16, that most of the associations are in a direction that reflects ‘negative trait’ outcomes with higher genetic risk for schizophrenia; for example, positive correlations with nervous (P = 1 × 10−46) and guilty (5 × 10−30) feelings, and negative correlations with fluid intelligence (P = 2 × 10−53) and friendship satisfaction (P = 2 × 10−24). Since these analyses have been performed on a sample of individuals with no history of schizophrenia, then they should reflect the effects of genetic risk for schizophrenia in the general unaffected population. We test this claim further in the next sub-section.Figure The presence of individuals with schizophrenia in the UK Biobank allows us to contrast the PRS-by-trait trends observed in the undiagnosed general population with values of the corresponding traits in diagnosed individuals. There are 599 individuals in the full genotyped UK Biobank data with an ICD-10 diagnosis  of ‘schizophrenia, unspecified’. This is the largest category of official schizophrenia diagnosis in the UK Biobank. Of these individuals, 297 reported presently taking antipsychotic treatment during the baseline verbal interview, which is also when most traits tested here were measured; however, many traits were measured as part of the Mental Health Questionnaire performed approximately 8 years after baseline interviews and so there will be greater misclassification of ongoing-treatment status in relation to these traits and correspondingly smaller estimated effects .The plots in Fig. In 13 of the 20 results shown in Fig. The fact that medicated cases generally have more extreme trait values than non-medicated cases could be explained by medicated cases having greater severity of disorder than non-medicated cases.P = 4.9 × 10−18; see Supplementary Table 17. There is increased self-reported risk-taking with higher schizophrenia PRS . Interestingly, dopamine agonist drugs have been found to increase risky behaviours in several studies on Parkinson’s disease patients19, and so these results, pertaining to antipsychotic-induced dopamine blockade, may reflect the reverse effect.However, some of the results show little difference between the groups or the reverse trend, which may be indicative of medication effects. For instance, while BMI reduces with increasing schizophrenia PRS Fig. , medicatPRS Fig. , but indP < 1 × 10−7, and Supplementary Table Supplementary Figs. The widespread significant and substantial differences observed in the behavioural traits between schizophrenia cases and the undiagnosed individuals Fig. –8: (1) sHere we follow-up one of our top-ranking associations that purporting to self-reported risk-taking (response to question ‘Would you describe yourself as someone who takes risks?’), since we consider this a plausible target for therapeutic intervention. We also perform this follow-up analysis to highlight the potential for our results in hypothesis-generation and to show how specific results can be further interrogated to gain greater insights into their underlying causes.23. Testing these pathways explicitly is underpowered here due to the relatively low power of a UK Biobank risk-taking PRS and the limited number of schizophrenia cases for such analyses, so instead we tested the phenotypic associations and those with the schizophrenia PRS. Migration patterns were characterised in three ways: Euclidean distance moved between birth and current residence, birth and current residence population density (and their difference) and time spent at current residence (see Methods). We also tested associations with several ‘control traits’, such as ‘leg pain on walking’, ‘breast fed’ and ‘month attended baseline assessment centre’ (see Methods) for comparison, since epidemiological testing on such large sample sizes are vulnerable to producing highly significant associations that are the consequence of small biases or statistical artefacts.We hypothesise that a part of the aetiology of schizophrenia may derive from a genetic propensity for risk-taking, resulting in greater exposure to drug-taking, migration or urbanicity — reported risk factors for schizophreniaP = 3 × 10−123; r2 = 0.001) and change in population density , and is negatively associated with time spent in current residence . Risk-taking is also positively associated with self-reported substance abuse . While these results are consistent with the proposed pathway . Schizophrenia PRS was positively associated with population density of current residence (P = 3 × 10−11), self-reported substance abuse (4 × 10−9), ever-smoked-cannabis (7 × 10−8) and change in population density from birth to current residence (P = 6 × 10−3), and negatively associated with time spent at current residence (P = 1 × 10−21) and distance travelled (5 × 10−4). However, each association is attenuated when Townsend deprivation index and education are controlled for, especially lifetime change in population density. This likely reflects a complex network of mediating, pleiotropic and causal relationships between the genetics of risk-taking, education, socio-economic status, migration and drug-taking patterns, which could generate cross-generational effects such as the association observed between schizophrenia PRS and birthplace population density. Focussed follow-up studies will be required to further unravel the proposed risk-taking–schizophrenia pathway in order to consider it a possible target for intervention.Next, we tested for the association between schizophrenia PRS and these traits in the undiagnosed sub-cohort  and cooked vegetable intake (P = 6.4 × 10−22). While the association between the GW-significant PRS and tea intake is reassuringly non-significant (P = 0.42), it remains a concern for the many similar analyses being conducted in the field9 that there is such a significant association between the best-fit PRS and a trait that is expected to play no causal role in schizophrenia. We exploit the engaging and memorable nature of this example to highlight the potential for confounding to generate PRS–trait associations, either in the form of horizontal pleiotropy or due to population structure. In Fig. While many of the associations in Fig. P = 6 × 10−4; maximum frequency of cannabis smoking), and also substance abuse (P = 0.05), but alcohol consumption (P = 3 × 10−106) and coffee consumption (P < 1 × 10−308) are negatively correlated with tea drinking. The best-fit schizophrenia PRS is positively associated with current smoking status (P = 2 × 10−37) and ever-taken-cannabis (P = 7 × 10−17), consistent with the literature27, but is negatively associated with beer intake (P = 2 × 10−11) and coffee intake (P = 3 × 10−12). Thus, any link with propensity to addiction is not straightforward and unlikely to be explained by the simple relationship in Fig. In Fig. 28. We repeated the analyses extending the number of PCs adjusted for from 15 to 40 PCs and observed little change in results , were significant at a stringent threshold. Most of these show that increased genetic risk of schizophrenia is associated with negative trait outcomes, such as lower friendship and family satisfaction and greater feelings of guilt and anxiety, reflecting known social cognition challenges in diagnosed and high-risk individualsWhile there is justified concern about generating false-positive associations when exploiting polygenic scores that include a large number of null variants, we observed high consistency between the results of best-fit PRS and PRS-based only on genome-wide significant SNPs Fig. , with be30.The unprecedented size of the UK Biobank as a deeply genotyped-phenotyped data set allowed us to perform a more comprehensive type of analytical comparison. The data were of sufficient size to both conduct a well-powered investigation of PRS–trait relationships at high resolution and to contrast those relationships with corresponding trait values of a sufficient number of individuals with schizophrenia Fig. . While t33. The PRS–trait associations reported here are therefore based on small and non-specific effects. The trait variance explained by the schizophrenia PRS has a maximum of only 0.04% 35, but is intended to clearly demonstrate the fallacy of assuming that highly significant PRS–trait associations only expose factors on a causal path to the outcome. For this reason, a large-scale systematic study of this type must be considered as exploratory, providing only broad aetiological insights and a screen of potentially interesting links between traits and disorder requiring further investigation. Certainly, none of the results from these analyses should be used to guide present clinical practice.Although many of the leading associations, and their directions, are consistent with the literature on schizophrenia aetiology, we highlight the need for more thorough investigation before strong interpretation of any specific association. We illustrate four simple associations Fig.  that cou23. However, this should be considered a hypothesis only, and confirmation of this and an assessment of the contribution of such a relationship to overall schizophrenia prevalence requires dedicated follow-up studies. It is interesting to note that gene–environment correlations of this type effectively inflate both pedigree-based and population-based heritability estimates, since they are regarded as a genetic contribution to the phenotype even if the critical causal agent is environmental.To exemplify the kind of follow-up analyses that can be performed based on the results of such exploratory studies, we further investigated the association of schizophrenia–PRS and self-reported risk-taking by calculating lifetime within-UK migration patterns and combining those with population density data from the UK National Office of Statistics. These analyses indicated that risk-taking genetics may be a sub-component of the genetic aetiology of schizophrenia due to mediation by migration and/or drug-takingBy mining one of the largest datasets with one of the most comprehensive and detailed behavioural phenotype information, we have arguably produced the most systematic interrogation of the link between genetic risk for schizophrenia and behaviour to date. We have generated a catalogue of 529 schizophrenia PRS–behavioural trait associations, 104 exceeding a stringent multiple testing threshold, demonstrating the influence of genetic risk of schizophrenia on behaviour and lifestyle in the general population. While the large-scale nature of the analyses makes them necessarily preliminary and exploratory, we have demonstrated several ways of gaining greater insights than those derived from the primary associations. We hope that these analytical strategies, and the array of PRS–trait associations generated here, will act as a useful starting point for follow-up investigations to further expose the network linking genetics, behavioural traits and schizophrenia, which may eventually provide targets of early intervention to reduce the risk of schizophrenia.15. These schizophrenia GWAS results are derived from a sample of up to 36,989 cases and 113,075 controls of European and East Asian ancestry, which yielded 108 independent loci harbouring genome-wide significant associations.The base (or discovery) data for this study were the genome-wide association study (GWAS) results from the PGC for schizophreniahttp://www.ukbiobank.ac.uk)37 and Mental Health Questionnaire. UK Biobank is a health resource for researchers that aims to improve the prevention, diagnosis and treatment of a range of illnesses. The recruitment process was coordinated around 22 centres in the UK (between 2007 and 2010)37. Individuals within travelling distance of these centres were identified using NHS patient registers (response rate = 5.47%)38. Invitations were sent using a stratified approach to ensure demographic parameters were in concordance with the general population. All participants provided written informed consent and the current study was ethically approved by the UK Biobank Ethics and Governance Council .In the present study we analysed target data on up to 307,823 participants , aged 38–73 years , involved in the UK Biobank baseline assessment . In the current study, SNPs were removed if they had missingness > 0.02 and MAF < 0.01. Samples were removed from the dataset if they had missingness > 0.01. We used a subset of European ancestry inferred individuals, defined using 4-means clustering applied to the first two PCs of the genotype data, because PRS are typically underpowered when applied to target samples of a different global ancestry to that of the base data using current PRS methods5. Confirming this to be the case here, we repeated the analysis of the top 20 PRS–trait associations . Samples were removed if they were discordant for sex. SNPs deviating from Hardy-Weinberg equilibrium (HWE) were removed at a threshold of P < 10−8. This QC process resulted in a data set of 560,173 SNPs and 386,192 samples available for analysis.Blood samples from 488,366 UK Biobank participants were genotyped using the UK BiLEVE array or the UK Biobank axiom array. Further details on the genotyping and quality control (QC) can be found on the UK Biobank website . PRSs were generated using PRSice-2 software (www.prsice.info)14. PRSice calculates individual risk scores by calculating the sum of disease-associated alleles, weighted by the log odds ratio estimated in the discovery GWAS. SNPs were clumped to minimise their linkage disequilibrium (LD) using an r2 ≥ 0.1 threshold in sliding windows of 250 kb. PRS on schizophrenia were generated for individuals in the UK Biobank cohort based on the PGC discovery GWAS summary statistics, and then used to predict target phenotypes recorded the UK Biobank. We selected SNPs to calculate the polygenic scores at a P-value threshold of P < 0.05 based on the PGC’s analysis showing these explained the most case–control variance in 40 leave-one-out analyses13. Under a Bonferroni correction for multiple testing that conservatively assumes independence between PRS thresholds and among the base and target traits, we use a significance threshold of 0.05 / (529) = 9.4 × 10−5 for the most predictive PRS in our primary analyses, based on the 529 target traits tested. For further stringency, given the risk of detecting significant associations that are a result of subtle confounding in such large sample sizes, we only declare significance at P < 1 × 10−7. Details of the 104 phenotypes that show an association with the most predictive PRS at P < 1 × 10−7 in the target data are provided in Supplementary Table Summary statistics from the schizophrenia GWAS were downloaded from the PGC website , based on the 2011 UK Census data. The R packages sp and rgdal were then used to retrieve the appropriate population density metrics for each participant according to their location co-ordinates. Thus, the population density measures for birth place , current residence  and their difference , were derived for each participant and, again, corresponded to the local postcode area. Time spent at current residence was available directly as a UK Biobank variable . Substance abuse, defined as those with an ICD-10 hospital diagnosis or self-reported substance abuse , was also available.The UK Biobank data included birth and current residence (at baseline) location variables, under our application, defined using the British National Grid referencing system, corresponding to northerly and easterly positions, with a reference point close to the Isles of Scilly. We used these locations to characterise migration patterns of UK Biobank individuals within the UK. First, the Euclidean distance travelled between birth and current residence was calculated using the northerly/easterly co-ordinates of each; the distance is calculated to the nearest kilometre to prevent identification . Next, population density measures were derived by matching the co-ordinates of each participant to population density information held on national databases relating to each local authority district. Population density statistics and boundary data for local authority districts were downloaded from the office of national statistics ; birth weight known ; birth weight ; left hand grip ; leg pain on walking ; blood pressure device ID and month attended assessment centre. Each of these phenotypes were then tested for an association with the schizophrenia PRS. All of the analyses were adjusted for age, sex, Townsend deprivation index and educational attainment, as described in the main text and corresponding table captions.Further information on experimental design is available in the Supplementary material.Reporting summary.


Fusarium head blight (FHB) is a devastating disease in cereals around the world. Because it is quantitatively inherited and technically difficult to reproduce, breeding to increase resistance in wheat germplasm is difficult and slow. Genomic selection (GS) is a form of marker-assisted selection (MAS) that simultaneously estimates all locus, haplotype, or marker effects across the entire genome to calculate genomic estimated breeding values (GEBVs). Since its inception, there have been many studies that demonstrate the utility of GS approaches to breeding for disease resistance in crops. In this study, the Uniform Northern (NUS) and Uniform Southern (SUS) soft red winter wheat scab nurseries  were evaluated as possible training populations (TP) to predict FHB traits in breeding lines of the UK (University of Kentucky) wheat breeding program. DON was best predicted by the SUS; Fusarium damaged kernels (FDK), FHB rating, and two indices, DSK index and DK index were best predicted by NUS. The highest prediction accuracies were obtained when the NUS and SUS were combined, reaching up to 0.5 for almost all traits except FHB rating. Highest prediction accuracies were obtained with bigger TP sizes (300–400) and there were not significant effects of TP optimization method for all traits, although at small TP size, the PEVmean algorithm worked better than other methods. To select for lines with tolerance to DON accumulation, a primary breeding target for many breeders, we compared selection based on DON BLUES with selection based on DON GEBVs, DSK GEBVs, and DK GEBVs. At selection intensities (SI) of 30–40%, DSK index showed the best performance with a 4–6% increase over direct selection for DON. Our results confirm the usefulness of regional nurseries as a source of lines to predict GEBVs for local breeding programs, and shows that an index that includes DON, together with FDK and FHB rating could be an excellent choice to identify lines with low DON content and an overall improved FHB resistance. Triticum aestivum L.) worldwide, which leads to significant losses in grain yield and quality. FHB is particularly aggressive in regions with cropping systems in rotation with maize and high humidity and moisture through heading and maturity. It is primarily caused by Fusarium graminearum Schwabe, which infects spikes of wheat leading to the discoloration and deterioration of grain, and the contamination with mycotoxins, mainly deoxynivalenol  is one of the most devastating diseases of bread wheat  has produced some interesting results. Some studies have predicted GEBVs under a cross validation scheme (Genomic selection (GS) is a form of MAS that simultaneously estimates all locus, haplotype or marker effects across the entire genome to calculate genomic estimated breeding values (GEBVs) . Since in scheme , while on scheme .https://scabusa.org/publications#pubs_uniform-reports). These nurseries are evaluated in multiple locations every year, and several FHB traits are recorded at every location. Training the GS model with lines belonging to these data sets would enable breeders to rely on multilocation and multiyear data and allow them to predict GEBVs for lines of local programs based on a wider range of germplasm evaluated in many locations. While these studies have contributed information on the implementation of GS in a wheat breeding program, little information is found about building the training population with lines coming from regional scab nurseries. These nurseries provide multiyear data sets comprising breeding lines nominated yearly to be included in the Uniform Northern and Uniform Southern soft wheat scab nurseries . The experiment was planted in a randomized complete block design with two blocks. Two checks, a resistant line (KY02C-3005-25) and a susceptible cultivar (Pioneer Brand 2555) were planted across the experiment. Sixty-six (66) of the total 306 lines that advanced in the breeding program based on grain yield, agronomic and disease profile, were also grown in Lexington, KY during the 2017–2018 growing season in the FHB nursery, under the same protocol explained before.Fusarium graminearum –infected corn (Zea mays L.) Inoculum comprised 27 isolates taken from scabby wheat seeds collected over the years 2007–2010 from multiple locations across Kentucky  whose aim is to develop control measures against FHB.Each nursery cooperator submits his or her breeding materials for evaluation and conducts an inoculated FHB trial at this or her location following the protocols developed by the U.S. Wheat and Barley Scab Initiative  to 9 (≥ 90% of FHB blighted spikelets). Heading date (HD) was recorded when 50% of the spikes in a row had emerged from the flag leaf sheath . Plant height (cm) was measured from the soil surface to the top of the spike, excluding awns (data not shown). Lines were manually harvested using a sickle, mechanically threshed and cleaned. After cleaning, a grain sample of approximately 15 g from each row was further cleaned by hand and evaluated for Fusarium damaged kernels (FDK). The percentage of FDK was estimated by visually comparing samples with known levels of FDK ranging from 10 to 90%. The same sample (15 g) was subsequently sent to the University of Minnesota DON testing laboratory for DON analysis. DON concentration was determined by gas chromatography with mass spectrometry  Two indiDSK index was created combining FHB rating, FDK percentage and DON content with the formula: FHB*0.2 + FDK* 0.3 + DON*0.5;DK index was obtained combining FDK percentage and DON content with the formula: FDK*0.4+DON*0.6.Both indices were created to emphasize the importance of kernels traits  for breeding against FHB. The NUS and SUS data were obtained for every genotype, location, year combination. Lines were planted in a 1.2 m row spaced 30 cm with two blocks. A common check cultivar (Ernie) was planted in the NUS and SUS across years and locations. Historical data consisted of entry mean data for FHB rating, FDK and DON concentration for each combination of genotype/location/year.The following linear mixed model was utilized for the analysis of the FHB traits for which individual row-level was available:lk was phenotypic observation of the lth genotype at the kth block, Bk was effect of the block, Gl was the effect of the genotype, and ϵkl represented the residual term. The overall mean and the genotypic effects were considered fixed and block term random. Best Linear Unbiased Estimators (BLUEs) were derived from the model above. For the historical data of the NUS and SUS nurseries, a single value of each line-environment combination was available for the different traits . Therefore, the following linear mixed model was used for this data:where μ was the mean, Yijl was phenotypic observation of the lth genotype at the ith year in the jth location, Yi was the effect of the year, Lj was the effect of the location, Gl was the effect of the genotype and YGil and LGjl were the interaction terms year by genotype and location by genotype respectively. Eijl represented the residual term. The overall mean and the genotypic effects were considered fixed and all the remaining terms random. The model above is the one from which BLUEs were derived.where μ was the mean, Y4:5 or F4:6 lines that were collected by sampling a minimum of eight 7–10 day old seedlings. Genotyping by sequencing (GBS)   using thng (GBS)  was condGEBVs for FHB rating, FDK, DON, DSK, and DK were estimated using ridge regression best linear unbiased prediction (RR-BLUP)  with theu2) and for the residual term was e~N . The estimates of u were obtained from the mixed.solve function using the package RR-BLUP in R , u is a vector of random marker effects, X and Z are the design matrices for fixed and random effects, respectively, and e is a vector representing residual terms. The variance–covariance structure associated with the random term was u~N  (described below). Under these combinations, the NUS and SUS were used separately as the source of lines for the TP. A second strategy was to combine the lines of the NUS and SUS as training population. Data from the two nurseries comes from the same years in which they have environments in common (15) but no common lines were used from the two nurseries (with the exception of the moderately resistant check cultivar Ernie). For this approach, we evaluated four TP sizes , and three different optimization methods: Random, Two tails and PEV. The validating populations (VP) were created by selecting 50 genotypes randomly from the total 306 breeding lines for each validating population, creating a total of 20 validation sets.As was described above, the effect of the TP size on the predictive ability of the genomic selection model was assessed using two  and four  different population sizes. For each population size, we implemented three approaches for comparison of training population selection:For this method, a random sample of genotypes was selected as training population for each TP size, varying the source of lines from which the random sample came. The same random sample was used for all validating populations.To implement this method, fully described by This approach utilized a training population optimization algorithm for each VP that minimized the mean prediction error variance . The PEVWe first investigated the predictive ability of the genomic selection model for each of the three traits and two indices calculating the Pearson correlation between the phenotypic values (BLUES) and the GEBVs (predicted) across 100 iterations of cross validation. A random sampling cross validation was conducted, training the model with the NUS, SUS, the combination of both nurseries (NUS+SUS) and the set of KY lines. The cross validation randomly assigned 80% of the total lines to the TP and the other 20% lines to the VP.The assessment of the DON content provided by the U Minnesota DON testing laboratory (phenotypic data) and the correlation with GEBVs obtained with GS was performed in this way: for each one of the twenty validating populations a ranking of the lines from lowest to highest DON was made based on BLUEs, and another ranking was made based on the GEBVs for DON content, DSK index and DK index obtained with GS. Different selection intensities were chosen: 20, 30, and 40%. Afterwards we calculated at the different selection intensities the percentage of lines with lowest DON levels that would have been also selected using only the GEBVs for DON, DSK, and DK. This approach was done for the 306 lines evaluated in 2017 and the 66 lines evaluated in both years (2017–2018).The scatter plot of the first two principal components  to 32% . As an average among traits, the two tails optimization method showed the highest increase: 18% when the TP size increased from 100 to 200 individuals. The random method showed a 10% increase and the PEV optimization method showed a 1% increase. But it is important to mention that at TP=100, PEVmean showed the highest prediction accuracy for all traits. When the SUS lines were the TP source, the TP size had a positive effect only for DON when increased from 100 to 200 for the three optimization methods. We found a positive effect with Random and PEV optimization methods when predicting FDK and a positive effect with Two Tails and PEV optimization methods when predicting FHB rating. On average, by optimization method, PEVmean showed an increase of 26% in prediction accuracy when TP size increased from 100 to 200. Two tails showed an 8% increase and Random did not change when TP size went from 100 to 200.No significant differences in prediction accuracy were found among TP optimization methods by trait. Despite this result, choosing lines at random showed the highest increase from 100 to 400 TP size; this was especially due to the high increment for DON and FHB rating with prediction accuracies of 0.23–0.24 with a TP size 100 vs. 0.35–0.43 with a size of 400 individuals in the TP. When we looked at averages by TP size, both indices, DSK and DK showed the highest prediction accuracies at the four TP sizes, ranging from 0.41 to 0.49 for DSK and ranging from 0.41 to 0.47 for DK. FHB rating showed the lowest prediction accuracies at the four TP sizes ranging from 0.24 to 0.34. Finally, at the smallest TP size, TP=100, PEVmean showed the highest prediction accuracies for the two traits (FHB rating and DON) and two indices.We analyzed the impact of selection based on predicted breeding values for a critical trait, DON content on the 306 breeding lines evaluated in 2017  is the possibility of breeding for quantitative traits with better outcomes than with marker assisted selection (MAS), because in contrast to MAS, the use of whole-genome prediction models generally has greater power to capture small-effect loci that would be missed by MAS because of limited power for declaring significant marker effects .The ability to improve FHB resistance through genomic selection has been studied and reviewed in recent years . A big qOverall, results from our study show that it is possible to use historical scab nurseries as TP to predict FHB traits. The study also showed encouraging results regarding the use of GEBVs for indices as indirect selection criteria for low DON genotypes. FHB traits are complex, highly polygenic and their expression is under a great environmental influence, which makes the phenotyping more difficult and costly.The model’s predictive ability, with cross validation, showed moderate prediction accuracies for the different traits, varying from 0.27 to 0.49 for FHB rating, 0.46 to 0.6 for FDK, 0.49 to 0.63 for DON. These results are in agreement with studies that used cross validation in wheat; for FHB rating,  found a The two indices created to predict levels of scab resistance yielded moderate prediction accuracies: 0.49 to 0.64 for DSK and 0.51 to 0.64 for DK. Septoria tritici blotch the same study found a prediction accuracy of 0.5 at the same TP size. Training population size can have a critical effect on prediction accuracies and it is a major issue for breeders as it relates to the genotyping and phenotyping efforts and the costs associated with them. In this study, we obtained the numerically highest prediction accuracies with a TP size of 400 although there was not a statistically significant difference between the TP of size 400 and the TP size of 300 for any trait (Optimization methods to select the training population have also received attention from breeders and scientists because the lines selected to train the model are critical in obtaining GEBVs accurate enough to be used in the breeding program. We did not find significant differences among any of the three optimization methods; rather we found the highest prediction accuracies at TP size 400 for the three optimization methods. We did observe that at TP100, higher prediction accuracies were obtained with PEV compared to Random and Two-Tails for all traits, showing that at the lowest TP size, combining lines from the two nurseries and using the PEV method allows one to achieve prediction accuracies similar to the ones at TP400. The importance of relatedness between TP and VP has been extensively discussed in the literature, and higher prediction accuracies are always associated with closer relationships between individuals in the TP and VP. In studies where cross validation is performed within populations of sibs or half sib, positive and moderate to high prediction accuracies have been found . When thWhen breeding for FHB resistance, all traits are of critical importance and increasing FHB resistance in the germplasm implies improving all of them. Some authors have shown that measuring incidence, severity and FDK in the field, because of the high correlation with DON content, allows selection for these traits with easier evaluation and data recording compared with DON . Our stuOur study validates the use of DON related indices in applying GS for low DON (This study involved a complex scheme for GS, that included forward GS, historical data sets building the TP, three optimization methods, multiple TP sizes and the evaluation of three traits and two indices to improve and hasten breeding for FHB resistance. While these results are encouraging, we conclude that the relatedness between TP and VP becomes a critical issue if one wants to exceed a prediction accuracy of 0.5. In spite of this concern we are optimistic that the use of regional nurseries to predict scab traits will be useful from a breeder’s standpoint and allow one to predict scab resistance prior to actual phenotypic evaluation. For a resource intensive breeding target like scab resistance, this is a huge consideration.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.VV and DS conceived and designed the experiments. VV performed the experiments. JL generated hapmaps and assisted with R coding. YD perform DON analysis. VV and DS analyzed the data. VV wrote the paper. DS edited it. All authors contributed to the article and approved the submitted version.This work was funded by a grant from the U.S. Department of Agriculture, through the US Wheat and Barley Scab Initiative under agreement no. 59-0206-9-054.The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.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.


While still under discussion, particularly whether the genus is polytypic or monotypic, this broad classification reflects putative geographical origins of each group and putative chemotype and pharmacologic effect.The taxonomic classification of Cannabis genus has been delineated through three main types: Here we describe a thorough investigation of cannabis accessions using a set of 23 highly informative and polymorphic SNP (Single Nucleotide Polymorphism) markers associated with important traits such as cannabinoid and terpenoid expression as well as fibre and resin production. The assay offers insight into cannabis population structure, phylogenetic relationship, population genetics and correlation to secondary metabolite concentrations. We demonstrate the utility of the assay for rapid, repeatable and cost-efficient genotyping of commercial and industrial cannabis accessions for use in product traceability, breeding programs, regulatory compliance and consumer education.We identified 5 clusters in the sample set, including industrial hemp (K5) and resin hemp, which likely underwent a bottleneck to stabilize cannabidiolic acid (CBDA) accumulation . Tetrahydrocannabinolic acid (THCA) resin (Type I) makes up the other three clusters with terpinolene , myrcene/pinene (K1) and myrcene/limonene/linalool , which also putatively harbour an active version of the cannabichrometic acid Synthase gene (CBCAS).Cannabis sativa Annotation Release 100 allows for hypothesis testing with regards to secondary metabolite production. Genetic markers related to secondary metabolite production will be important in many sectors of the cannabis marketplace. For example, markers related to THC production will be important for adaptable and compliant large-scale seed production under the new US Domestic Hemp Production Program.The final chemical compositions of cannabis products have key traits related to their genetic identities. Our analyses in the context of the NCBI  This approach which prevails today in the USDA interim regulations, employs a THC concentration of 0.3% as the threshold separating hemp and drug-type cultivars, with concentrations below 0.3% defined as hemp , and a wide diversity of secondary metabolites found predominantly as terpenoids and cannabinoids , 2) indica (short and highly branched plant with broader leaves) and 3) ruderalis . While still under debate, particularly whether the genus is polytypic or monotypic, this broad classification reflects the putative geographical origins of each group  The recent legalization of drug type cannabis for commercial production and recreational use in Canada, several US States, and other countries worldwide, as well a hemp under the US 2018 Farm Bill has created renewed scientific interest in developing a robust empirical classification system for cannabis. To that end, a particular focus has been placed on secondary metabolite expression with a clear separation based on CBD (cannabidiol):THC ratios. Differences in CBD:THC ratios delineate three class types: type-I (ratio < 0.5), type-II (ratio 0.5–3.0) and type-III (ratio > 3.0) , resin hemp (type-II and type-III) and THC drug-type (type-I) cannabis. The sampling strategy did not follow any particular selection criteria as samples were randomly chosen from several licenced cultivators who graciously agreed to have their accessions analysed in this study  following the manufacturer’s instructions. After the addition of 90 μL Lysis buffer PN, samples were incubated at 65 °C for > 10 min. The samples were then centrifuged at 2500 x g for 10 min to pellet the debris. 50 μL of the supernatant in this tube, referred to as the lysate, was then transferred to a clean 1.5 ml microcentrifuge tube with 120 μL Binding buffer PN and 10 μL Sbeadex® particle suspension and incubated at room temperature for 4 min. The tube was then brought into contact with a magnet for roughly 1 min until magnetic particles form a pellet. The supernatant was then discarded, and the pellet was then subjected to three consecutive wash steps. The washed beads were then eluted with 70 μL Elution buffer PN and incubated at 55 °C for 3 min prior to bringing the tubes in contact with the magnet. 50 μL of the eluate was then transferred to a new tube which contain high purity plant DNA.2 in an optimised buffer solution.Twenty-three optimized assay mixes, each specific to single nucleotide polymorphisms (SNP) previously identified as associated with phylogeny and chemotypic expression were screened in the sample set Henry . These aThe genotypes were generated using an Eco RT , a CFX 96  and an Intelliqube array tape platform  with multiple blind replicates across platforms to ensure cross system repeatability. Genotypes were called using the Kluster Caller software and manually verified using the SNPviewer software .Cannabis sativa Annotation Release 100 (Jenkins and Orsburn https://blast.ncbi.nlm.nih.gov) accessed October 31, 2019. The location of the 10-chromosome map as well as the putative functional gene in which the 23 SNP are found were recorded.Given the recent release of the 10-chromosome map of the cannabis genome  of genotypes with individuals as rows and markers as columns. As the total dataset of 681 plant DNA samples contained some missing data, we culled all missing data out and undertook the following analyses on 420 samples with complete genotype information across all markers. Metadata, including individual and population names, were separated from the genotype data and imported into the flexible statistical environment of R  were also incorporated into the genind object to allow for population level calculations to shed light on the stability of claimed variety names and to assess the level of genetic diversity within and between these hypothesized groups. Clonal lines were identified using mlg and mlg.id functions, which determines the number and identity of mutilocus genotypes. Basic population genetics metrics, particularly expected heterozygosity was calculated for each population and individual using the poppr function.Briefly, the bitwise.dist function and was visualized using a phylogenetic tree using the nj function. Principal component analyses (PCA) were undertaken to provide an independent line of evidence of the genetic affinities between accessions using the dudi.pca function. Broad signals of population genetic structure were investigated using discriminant analyses of principal components  as well as for the inferred clusters were calculated using the pairwise.fst function. Diversity indices for varieties representing putative seed lines for which at least three individuals were available in the dataset were also assessed using the locus_ table function where variety names were used as population indicator.To shed light on the underlying relationships between our diverse sample set, a dissimilarity matrix or Hamming distance between multilocus genotypes was calculated using the read.table function. The boxplot function was used to plot the top cannabinoid and terpenes expressed in each cluster.A subset of 120 samples from Nevada were also analyzed by various LC / MS combination at 9 cannabinoid and 17 terpenes, following the methods described by Orser et al. . Since tThe 23 SNP panel used in this study was selected to represent a broad coverage of the cannabis genome and individual SNPs were found to be located on all cannabis linkage groups with the exception of chromosome 8 Table . As suchSW6 and VSSL_BtBD markers were fixed for one allele in all CBD expressing varieties (fibre and resin-types), while being fixed for other allele or heterozygote in all THC expressing varieties. In addition, the SVIP14 locus was also strongly associated with cannabinoid expression data  was clearly distinct from all drug-type cannabis accessions, including high CBD resin expressing accessions. Interestingly resin (drug)-type cannabis consisted of four main genetic clusters, K1 and 3  which can be considered having a C. s. indica phenotype and perceived effect, while K4  contain mainly accessions of equatorial or C. s. sativa designation. K2  consisted mostly of the high CBD resin-hemp from the United States  was found to be assigned to both K2 and K5 in a 40:60 proportion skewed towards the father’s origin  between an autoflowering male “gin Fig. . Other pgin Fig. , 2, 3.” betweenUnidentified” and “Hindu Kush”, “GGC” and “Purple God”, “Atomical Haze” and “Tangerine Dream” and “SFVOG”, “Gorilla Glue” and “Holy Grail”, “Agent Orange” and “Girl Scout Cookies”, “UK Cheese” and “Jamaican Ten Speed”, “Chem 91” and “Colorado Sunset”, “Jet Fuel” and “Louis VIII”, “Blackberry Cream” and “Slime Dawg MillaNaire”, “Tangerine Dream” and “Violator Kush”, “Original Amnesia” and “Sour Tangie”, “Billy Crystal” and “Blueberry Kush”, “5th Dimension” and “Gorilla Glue”, “Garlic” and “Gelato Breath”, “Blue Dream” with two “Blue Hash Plant” samples, seven samples including five labelled “Pink Kush”, one mislabelled “Atomical Haze” and one “LA Lights”, seven unlabelled Resin Hemp from Nevada, including one labeled “Cherry Wine”, as well as three Resin Hemp samples labelled “Alamo”, “Adam” and “Shore”.In total, 361 multilocus genotypes  were identified in the 420 cannabis samples. Fourteen pairs of known labelled clones were confirmed using the 23 SNP assay. Mislabelled accessions with identical multilocus genotypes were frequently detected as follows: “X59”  in at least one of the 71 populations/seed lines  also consistently expressed p-cymene more so than other resin-type accessions  draft genome has a haploid genomic sequence of over 876 Mb – 1000 Mb  is the ancestral group and originated in Europe about 19.7 M years ago. A combination of genetic drift and selection then likely contributed to the observed differentiation between fiber and resin cultivars  was found to contribute to the separation between resin and fiber types which may play a role in fiber quality, given its putative function of breaking down the major constituent of cell walls. Such a marker may provide a possible avenue for the development of multi-purpose resin/fiber cultivars.C. sativa genome were associated with the differentiation between terpene groups in the resin accessions sampled here. Two markers in particular showed strong differentiation between terpinolene dominant (“sativa”) and the other myrcene and limonene dominant accessions (“indica”), in particular VSSL_digi2, located in an O-glucosyltransferase Rumi analogue involved in ribosome biogenesis and SVIP16 a protein kinase possibly involved in developmental and defense-related processes.Integrative analyses revealed a co-expression network of genes involved in the biosynthesis of both cannabinoids and terpenoids from common precursors  for each samples seed stock. Heat map indicated P-value of test with pink boxes indication significant deviation from HWE. Supplementary Figure S2. Locus specific deviation from Hardy-Weinberg Equilibrium (HWE) for each inferred clusters. Heat map indicated P-value of test with pink boxes indication significant deviation from HWE.Supplementary Table S1. Seed stock specific population genetic metrics. Supplementary Table S2. Locus specific statistics. Supplementary Table S3. Table indicating the origin of each sample analysed in the study.


All prediction models are made publicly available at predictdb.org.The integration of transcriptomic studies and genome‐wide association studies (GWAS) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA‐sequencing samples from 948 post‐mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine‐mapping ( These mechanisms help elucidate the function of loci identified in genome‐wide association studies (GWAS) by identifying potential causal genes that link genetic variation with complex traits  Project  between distinct causal variants can lead to noncausal associations between predicted expression and complex traits . This data arises from 17,382 RNA‐seq samples from 54 tissues of 948 post‐mortem subjects, aligned to the GRCh38 assembly. Primary and extended results generated by consortium members are available on the Google Cloud Platform storage accessible via the GTEx Portal (see URLs).Eight hundred and ninety‐nine whole‐genome sequencing samples were analyzed, 68 of them at an average coverage of 30x on HiSeq200, and the rest on HiSeqX. Eight hundred and sixty‐six GTEx donors' samples were included in the downstream variant call files, after excluding one each from 30 duplicate samples and 3 donors. Among these, 838 subjects with RNA‐seq data were included for QTL mapping and analysis.N < 150 per tissue; 30 factors for 150 ≤ N < 250; 45 factors for 250 ≤ N < 350; and 60 factors for N ≥ 350. Expression phenotypes were adjusted for unwanted variation using covariates such as gender, sequencing platform, and PCR protocol, the top five principal components from genotype data, and said PEER factors. Finally, fastQTL  results published in Aguet et al.  < 0.05 in these samples were included. We used 49 tissues with sample sizes ranging from 65 (Kidney Cortex) to 602 .This ancestry restriction mitigated problems due to LD mismatch when integrating with most publicly available GWAS summary statistics, which are conducted on predominantly European populations. Prediction models in other ancestries are important, and we are currently dedicating substantial effort to creating and analyzing such models. However, non‐European models are beyond the scope of this paper.We only generated models for genes annotated in GENCODE v26 as protein‐coding, long noncoding RNA (lncRNA), or pseudogenes.2.3We fitted an Elastic Net model for each gene–tissue pair with available adjusted expression data. We restricted the set of variants to those present in the HapMap 3 CEU track  of a variant affecting gene expression to select explanatory variables, without restricting to variants in the HapMap CEU track. For every gene, we used all variants in the gene's cis‐window with MAF > 0.01 and PIP > 0.01. Since dap‐g groups variants in clusters according to LD, we kept the top variant (by PIP) per cluster to avert variable redundancy. Since we reasoned that more probable variants should bear more impact in the model's outcome, we multiplied each variant's penalty term in the Elastic Net regularization by a factor of 1 − PIP. We used the same thresholds from the previous subsections (ρ > .1 and p < .05) to select models with acceptable prediction performance.We also trained models via the Elastic Net algorithm using fine‐mapping information to refine the list of variants to be used as explanatory variables and lent more weight to variants with higher chances of affecting expression phenotypes. To this aim, we used We refer to these models as DAPGW‐M.2.6mashr; Urbut et al., mashr on the same set of variants from DAPGW‐M models. We kept models only for eGenes and effect sizes only for variants with PIP > 0.01 (from dap‐g) at each gene–tissue pair. Unfortunately, there is no natural prediction performance measure in this scenario as cross‐validation was not performed.Finally, we explored an entirely different algorithm to determine the prediction models. We executed multivariate adaptive shrinkage in R  expression study for independent validation of prediction performance. We obtained GEUVADIS expression data and sample information from the European Bioinformatics Institute web portal at For each one of the four previous model training schemes  we predicted expression through PrediXcan  and keeping only variants with matching alleles. Then, GTEx variants with missing summary statistics for any GWAS were imputed with the best linear unbiased prediction method, a standard in the field  using 49 tissues, for a total of 17,052  tuples. We integrated with t et al. .When analyzing the versatility of the models and GWAS preprocessing schemes, we used GWAS studies not belonging to the rapid GWAS study. This was decided because the rapid GWAS project has a common, homogeneous variant set that could dominate comparisons.2.9SEs) of the estimated AUC were estimated by a bootstrap approach using 2,000 replicates, as implemented by Robin et al. . To reduce LD misspecification problems, most apparent when applying summary statistics‐based versions of PrediXcan on GWAS of European populations, we used only European samples.We restricted the analysis to genes that are annotated as protein‐coding, lncRNA, and pseudogenes in GENCODE version 26  and prediction performance p < .05 were kept. We will refer to this family as the EN‐M models.The first strategy used the Elastic Net  and cross‐validated prediction performance threshold (p < .05) to accept models. We will refer to this family as the CTIMP‐M family. We verified that this method's performance is not significantly improved by using all available GTEx variants, as explained in the Supporting Information Materials.The second strategy used CTIMP . Only those models achieving good enough cross‐validated prediction performance (p < .05) and correlation (ρ > .1) were kept. We will refer to this family as DAPGW‐M (dap‐g weighted). As discussed later, the cross‐validated prediction performance of this approach cannot be fairly compared to EN‐M and CTIMP‐M because the preselection of fine‐mapped variants is based on the same underlying data.The third strategy used the PIP of a variant being causal for gene expression as estimated by the Bayesian fine‐mapping method mashr  and 686,241 from MASHR‐M (143% more than EN‐M). Please note that given the different criteria used to accept a model as valid, simple counts of available models should not be considered a measure of performance.We show the distribution of cross‐validated prediction performances in Figure 3.2Next, we sought to validate the models' predictions in an independent RNA‐seq data set. We analyzed data from the GEUVADIS project  for genes present in all four methods on the LCL tissue. We observed that all four families achieved similar levels of performance, with MASHR‐M, DAPGW‐M, and CTIMP‐M faring slightly but consistently better than EN‐M. Mean correlations were .028 (SE = 0.006), .027 (SE = 0.006), and .018 (SE = 0.006) points larger for MASHR‐M, DAPGW‐M, and CTIMP‐M, respectively compared to EN‐M.To compare prediction performances, we used Spearman's rank correlation coefficient n = 115 individuals, much lower than the 341 available in GEUVADIS.We attribute the smaller performance differences to low power, since GTEx LCL tissue has a sample size of 3.3Next, we assessed whether any of these models perform better at identifying causal genes. We considered the number and proportion of colocalized genes among the significant ones as measures of association quality.p‐value below the Bonferroni‐corrected threshold (0.05/number of gene–tissue pairs) within each trait.We used the four families of models to correlate predicted expression with 87 phenotypes through 49 tissues using the summary version of PrediXcan. Results of applying the EN‐M models to GWAS summary statistics, harmonized and imputed to GRCh38  that a trait shares causal variants with a gene's expression (or an intron's splicing quantification), within a GWAS region and the overlapping gene's cis‐window. We say that a gene–tissue pair is “colocalized” with a trait if it achieves an enloc rcp > 0.5. Note that rcp ≤ 0.5 should not be interpreted as a false association; rather, it only means that there is not enough evidence of colocalization. See Section We used We say that a gene–tissue pair that is both significant and colocalized is a “prioritized” detection or candidate. To simplify the interpretation of results across multiple tissues, we count the number of unique genes among the prioritized gene–tissue pairs for each trait.SD] = 44.4), 25.5 (SD = 40.3), 36.7 (SD = 57.8), 36.6 (SD = 57.0) genes were identified with EN‐M, CTIMP‐M, DAPGW‐M, and MASHR‐M, respectively. We display the numbers of detections for each trait in Figure SE = 0.013), 8.88% (SE = 0.013), 9.01% (SE = 0.013), 11.7% (SE = 0.013) of identified genes with EN‐M, CTIMP‐M, DAPGW‐M, and MASHR‐M, respectively, were colocalized. In general, we observed that associations obtained through both DAPGW‐M and MASHR‐M models tend to agree .We found that MASHR‐M typically yields more candidate genes. On average 28.3  and 0.0682 (SE = 0.0287), respectively, providing evidence that MASHR‐M models are better equipped for detecting known genes and reinforcing our choice of MASHR‐M as the best option.When considering the AUC for the combined OMIM and rare‐variant‐based silver standards, we computed the point estimate and estimated the 3.5The prediction models' usefulness depends on the availability of their variants in the GWAS of interest. Publicly available GWAS use different sequencing and genotyping techniques, based on different genotype imputation panels and human genome release versions so that the lists of available variants vary wildly across traits. Thus, a GWAS might lack particular variants from a prediction model, so that the model cannot properly infer variation patterns as shown in Barbeira, Pividori et al. . Since m1.Harmonization only (no imputation): Simple harmonization of variants by lifting over genomic coordinates from the GWAS to match the GRCh38‐based GTEx prediction models, and then filtering for matching alleles (“Harmonization” for short)2.Harmonization and imputation of missing summary statistics (“Imputation” for short) on harmonized GWAS.We compared S‐PrediXcan results from MASHR‐M models on 69 publicly available GWAS with two preprocessing schemes:The 69 traits included in this analysis are those among the 87 traits not belonging to the Rapid GWAS project, to prevent the highly homogeneous Rapid GWAS datasets from dominating comparisons.We show in Figure Figure Figure Figure Therefore, we recommend to always perform variant harmonization due to its low complexity and time requirements, followed by summary‐statistics imputation if possible. For newer GWAS with modern sequencing and genotyping, summary‐statistics imputation may not be as critical depending on their intersection with model variants.4dap‐g) and cross‐tissue patterns (from mashr) improve the reliability of causal gene detection. These models (MASHR‐M) yield more detections when integrating GWAS and eQTL studies and show improved performance when validating results in a silver standard of known gene‐to‐trait associations (OMIM database). We make all prediction models and results publicly available.Through extensive analysis of different model training schemes, we conclude that using fine‐mapping information . EN‐M additionally features some “built‐in” redundancy: for a set of variants in LD among each other, they all tend to be included in a model with the effect spread between them.While our recommended MASHR‐M method offers several benefits compared to existing approaches, there is still room for improvement. Potential developments could rely on fine‐mapping methods that jointly incorporate cross‐tissue patterns or consensus between different fine‐mapping approaches. Also, epigenetic information has been shown to improve transcriptome prediction (Zhang et al., Our validation in silver standards, especially our difficulty interpreting the results from the rare‐variant‐based silver standard, also illustrates the need for well‐curated, large databases of known gene‐to‐phenotype associations to assess the performance of either new or improved methods.In conclusion, we present here a method for predicting the genetically regulated component of transcriptomic traits with a superior performance both in terms of prediction performance and gene–trait association detection.F. A. is an inventor on a patent application related to TensorQTL. S. E. C. is a cofounder, chief technology officer, and stock owner at Variant Bio. E. T. D. is chairman and member of the board of Hybridstat Ltd. B. E. E. is on the scientific advisory boards of Celsius Therapeutics and Freenome. G. G. receives research funds from IBM and Pharmacyclics, and is an inventor on patent applications related to MuTect, ABSOLUTE, MutSig, POLYSOLVER, and TensorQTL. S. B. M. is on the scientific advisory board of Prime Genomics Inc. D. G. M. is a cofounder with equity in Goldfinch Bio, and has received research support from AbbVie, Astellas, Biogen, BioMarin, Eisai, Merck, Pfizer, and Sanofi‐Genzyme. H. K. I. has received speaker honoraria from GSK and AbbVie. T. L. is a scientific advisory board member of Variant Bio with equity and Goldfinch Bio. P. F. is a member of the scientific advisory boards of Fabric Genomics Inc., and Eagle Genomes Ltd. P. G. F. is a partner of Bioinf2Bio. E. R. G. receives an honorarium from Circulation Research, the official journal of the American Heart Association, as a member of the Editorial Board, and has performed consulting for the City of Hope/Beckman Research Institute. R. D. has received research support from AstraZeneca and Goldfinch Bio, not related to this study.Supplementary InformationClick here for additional data file.


Understanding why individuals with severe mental illness  have increased risk of cardiometabolic disease , and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank  multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles. This is likely due to the well-established increased prevalence of cardiovascular and metabolic disorders compared to the general population. For example, obesity is up to 3.5-fold higher in those with SCZ2, type 2 diabetes is ~ twofold higher in those with MDD, BD or SCZ2, and cerebrovascular disease is increased by up to 3.3-fold in those with BD2. Understanding this increased risk and identifying individuals at highest risk of metabolic and cardiovascular disease are important priority areas for researchers and healthcare providers.Individuals with serious mental illness (such as schizophrenia (SCZ), major depressive disorder (MDD) and bipolar disorder (BD)) have a reduced life expectancy  has been attributed to social determinants and lifestyle factors  that co-exist with serious mental illness and effects of psychotropic medication3. Evidence of causal relationships between psychiatric and cardiometabolic traits have also been described5. However, the mechanisms involved have yet to be uncovered and therefore this knowledge has had no clinical impact.Genome-wide genetic correlations between psychiatric and cardiometabolic traits provide evidence for underlying common biology. Correlations have been described between depression and obesity (rg = 0.12) or cardiovascular disease (rg = 0.42)Here we tested whether a novel approach using multi-dimensional scaling (MDS) of genetic variation associated with psychiatric and cardiometabolic disorders could aid stratification of individuals into groups with differing cardiometabolic risk profiles.The demographic characteristics of the IMPROVE, UK Biobank subsets 1 (UKB1) and 2 (UKB2) are provided in Table Figure p = 0.0339), the clustering is not being driven by latitude  and lower fastest progression of cIMT . This is surprising given the (non-significant) higher rates of smoking in this group. Group 2 had (non-significantly) lower rates of T2D than the other groups (group 2: 25% vs groups 1 or 3: 28%). Similar groups were observed using T-distributed Stochastic Neighbour Embedding (tSNE) or principal component analyses , with the majority of individuals being consistently grouped together  with a minor allele frequency (MAF) > 1%, implicated in both SCZ and CMD (SCZ-CMD), plotting the first two multi-dimensional scaling components C1 and C2) demonstrated 3 groups of individuals   differences were observed in baseline SBP, SDP adjusted for blood-pressure medication, and frequency of hypertension and T2D Table. These eAs phenotypes and genetic loci for SCZ overlap with those for MDD and BD, it is perhaps unsurprising to see that the clusters include different proportions of individuals with MDD. To investigate whether these individuals were driving the clustering, the process was repeated in those without BD/MDD separately from those with these diagnoses (using SNPs with MAF > 10%). In those without mental illness, similar to the overall UKB2, there were there main groups, intermediate clusters accounting for 7.4% of the sample Fig. a. In tho7, there was no evidence of subgroups  per se, rather than SCZ in general that drives the clustering, the same procedure was followed using all SNPs in loci associated with SCZ in UKB2, with the same MAF and LD filtering being applied prior to MDS analysis. As shown in Supplementary Fig. This study provides proof of principle that, using the genetic overlap between SCZ and cardiometabolic disorders, subsets of European ancestry individuals with different metabolic profiles can be identified. These findings support the existence of mechanisms common to SCZ and blood pressure regulation.8. It is therefore both striking that the grouping was present in IMPROVE, and unsurprising that the blood pressure and hypertension differences between groups were more modest in UKB2 than those in IMPROVE.The discovery cohort IMPROVE deliberately recruited to identify genes and biomarkers associated with the risk of cardiovascular diseases, at a time when psychiatric disorders were typically excluded from non-psychiatric studies, therefore only a portion of the spectrum of psychiatric genetic burden is represented. In contrast, UKB1 and UKB2 are general population cohorts and therefore have a wider spectrum of both psychiatric and cardiometabolic disorder genetic burden, although it is recognised that the recruitment skews this distribution towards to the healthier segment of the populationIt is worth noting that similar groups were observed in the IMPROVE cohort, using three different methods and (where applicable) exploring a variety of parameter settings. This suggests that the grouping is robust. The metabolic profiles of the groups did not completely agree between the 3 cohorts, however the repeated observation of between-group differences in T2D and blood pressure/hypertension deserves further attention. If the method can be refined to better identify whether an individual is at increased risk of either hypertension or T2D would be of immense value. Even if the method is only robust in high CMD-risk populations , it could be of clinical importance.10.It is interesting that the analyses using BD and MDD genetic loci did not enable clustering of individuals in the same way as was observed for SCZ, particularly given that BD and SCZ demonstrate an overlap in genetic loci. There are several possible explanations for this, most notably the ability to identify genetic loci for each mental illness: SCZ is clinically a more severe phenotype with diagnostic criteria that are relatively specific (for example psychotic episodes). In comparison, MDD spans a wide spectrum severity, with phenotypic heterogeneity potentially diluting or obscuring some true genetic effects. Whilst BD can be considered an intermediate  diagnostic criteria for MDD and BD overlap to a large degree as both involve episodes of depression, meaning that there is potential for misdiagnosis and therefore dilution of genetic effects for either trait. Another explanation is that the mechanisms leading to CMD in SCZ differ from those in MDD or BD, with processes that are represented on the CardioMetabo and Immuno chips failing to capture some pathological mechanisms. With this in mind, the finding of different frequencies of MDD in the groups was not anticipated, as the MDD genetics did not achieve any form of grouping, and the overlap of MDD and SCZ genetics is modest. However, MDD is highly heterogeneous, therefore it would be of interest to further explore whether there are any differences between the MDD cases in each group, specifically whether any of the groups corresponds to the recently proposed atypical depression subtype10, however these methods assume that the entire genome influences both sets of traits. The small to moderate correlations could suggest that it is only a portion of the genome that has common effects. In contrast, the current study focuses on only the parts of the genome that have been implicated in both psychiatric and CMD. Whilst this study does not bring us any closer to understanding the mechanisms underlying the common pathological mechanisms, it does suggest that exploration of the SCZ-CMD loci could have clinical utility, irrespective of mechanistic understanding.Genetic correlation analyses have begun to explore the common biology and causal relationships between psychiatric and cardiometabolic diseases11 and growing efforts around the world to explore genetics of disease in non-European ancestry individuals, it will take time to gain full insight into the genetic architecture of diseases in these ancestry groups.One limitation is that these analyses were conducted in individuals of European ancestry and as SNPs were filtered by MAF and linkage disequilibrium, it is not possible to generalise them to other populations. Indeed, to apply current information from European ancestry individuals to additional ancestry groups has the potential to be misleading and is certainly incomplete. Whilst there is a recognised needAnother limitation is that the CardioMetabo and Immuno chips do not include all loci implicated in cardiometabolic disorders. Since these chips were described (2012 and 2011 respectively), many more loci involved in many more processes have been identified. However, as more and more samples are available for GWAS analyses, loci are being identified with smaller and smaller effect sizes. Therefore whilst not all possible information is captured by using the CardioMetabo and Immuno chips, the loci with the largest effects are represented.In conclusion, this study provides proof of concept that common biology underlying mental and physical illness is probable and can distinguish subsets of individuals with differing metabolic profiles, even if full understanding of mechanisms is lacking. Given that large-scale genotyping is not available to healthcare providers and the differences between groups are subtle, there is currently limited potential for translation of this into clinical practice. Further investigation with longitudinal datasets, particularly in high CVD risk populations, would define whether or not there is potential for clinical value in this method.The IMPROVE study has been described previously13. In short, 3700 individuals aged between 54–79 years with high CVD risk profiles  were recruited from seven centres in Finland, Sweden, the Netherlands, France and Italy. At baseline, individuals completed lifestyle and medical questionnaires and anthropometric measures taken. Blood was sampled for DNA extraction and clinical biochemistry and stored for further biochemical analyses. Detailed ultra-sound examination of the carotid intima-media thickness (cIMT) was conducted at baseline, 15 months and 30 months. Linear regression using all data points was used to calculate progression of cIMT. Mental illness was not assessed; however it is believed that if there is mental illness in this cohort it is likely to be subclinical. All participants provided written informed consent and the study was conducted in accordance with the Helsinki Declaration. Ethical approval was granted by the Regional Ethics Review Boards at Karolinska Institutet, Stockholm Sweden, the Groupe Hôpitalier Pitie-Salpetriere, Paris, France, the Comitato Etico delle Aziende Sanitarie della regione Umbria, Perugia, Italy, the Ospedale Niguarda Ca´Granda, Milano, Italy, the University Hospital Groningen, Groningen, the Netherlands, the Hospital District of Northern Savo, Kuopio, Finland and the University of Eastern Finland, Kuopio, Finland.14 and Immuno chips15, therefore cardiometabolic disorders (including immune and inflammatory components) were well represented. Standard quality control procedures were conducted, namely exclusion of SNPs for low call rate (< 95%) and deviation from Hardy–Weinberg Equilibrium (p < 1 × 10–6) and exclusion of samples for low call rate (< 95%), sex-mismatch, cryptic relatedness. Quality control was conducted on each chip separately, followed by a further round of quality control on the combined chip.The IMPROVE study was genotyped on the Illumina Cardio-MetaboThe UK Biobank (UKB) has been described previously17. Approximately 500,000 volunteers aged 39–73 years were recruited from 22 centres across the UK. At baseline, detailed questionnaires on sociodemographic factors, lifestyle factors and medical history were completed by all individuals. Measurements of anthropometric variables were recorded and blood samples were taken for DNA extraction. Subsequently (4–8 years after baseline), subsets of participants were invited for follow-up measurements and extensive imaging. All participants provided written informed consent and ethical approval was granted by the NHS national Research Ethics Service. This work was conducted under projects #6533 (Smith) and #1755 (Pell).Ultrasound measurement of cIMT was conducted in a pilot phase of ~ 2500 individuals (henceforth denoted as UKB1) followed by a subsequent phase including ~ 22,000 individuals (denoted UKB2) using the same recruitment and measurement protocol. cIMT measurements were generally consistent with the measurements available in IMPROVE. A mental health/thoughts and feelings questionnaire was also completed by a subset of participants, which enabled estimation of life history of MDD and BD. For both UKB1 and UKB2, 73% of participants completed the mental health questionnaire.18. Imputation was conducted using the Haplotype reference consortium and 1000 Genomes with standard pre- and post- imputation quality controls being applied by the UK Biobank team (further information is provided in18).Genome-wide genotyping was conducted and standard quality control procedures were applied by the UK Biobank team19, MDD20 and BD21 were identified. SNPs within these  loci which were present on the CardioMetabo and Immuno chips were selected15 . SNPs with MAF > 1% were included  were pruned by pairwise LD  using PLINK24. Individuals with similar genetic sequences are deemed more similar to each other than those with less similar genetic sequences. Clustering was also conducted using tSNE and PCA (Supplementary Methods).Clustering was performed using multi-dimensional scaling, implemented in PLINK, using default settings. Multidimensional scaling essentially measures similarity between individuals, in this case using the patterns of genetic variation as the assessment criteriaSubsequently in UKB1, SCZ-cardiometabolic SNPs only were used and individuals with > 1% missing genetic data were excluded prior to clustering. MDS analyses was conducted using either exactly the same SNPs as were used in IMPROVE (ie SCZ-CM SNPs after filtering for MAF and LD in IMPROVE) or SCZ-CM SNPs with filtering for MAF and LD being done in UKB1.Finally, in UKB2, Individuals with > 1% missing genetic data were excluded prior to clustering. MDS analysis was conducted on SCZ-CM SNPs with filtering for MAF and LD in UKB2, or on all SCZ SNPs after MAF filtering and pruning in UKB2.The first two MDS components (C1 and C2) were plotted for visual assessment.Choosing a negative control experiment is not straight forward, as current evidence suggests that most genetic variants are highly pleiotropic and that complex traits overlap with each other to a large degree. Despite some overlap with CMD or SCZ-related traits, SNPs in genetic loci associated with eye colour were used as a negative control experiment. The analysis was conducted in UKB2 with MAF > 10% filtering and pruning as described above.p < 0.05. No adjustment for multiple testing was applied, because these analyses are exploratory rather than definitive and secondly because most of the cardiometabolic phenotypes tested are interrelated and thus are not independent tests.In IMPROVE, Spearmans rank correlation coefficients were used to assess the relationship between the MDS components and latitude. For IMPROVE, UKB1 and UKB2, Differences between groups were assessed by Pearsons chi squared test for categorical values and Kruskal–Wallis test for continuous variables. All statistical analyses were conducted in Stata (version 11.0). The threshold for significance was set at Supplementary Information.