Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits. Genetic signals from quantitative traits could be a challenge to finemap. Flashfm uses summary-level data in a Bayesian framework to favour shared causal variants and capitalises on information between traits, providing an accurate and efficient joint fine-mapping tool for up to six traits. Among these findings are many examples of pleiotropy, where a gene affects several phenotypes2. This could be due to a shared variant affecting a pathway involved in multiple related phenotypes. Identifying the causal mutations which underlie such findings is key to facilitating translation into new therapeutic targets or elucidating new biological insights. However, this is a complex task. Lead SNPs  are often correlated (are in high linkage disequilibrium—LD) with many other variants in the genome. In addition, lead variants are not necessarily causal and may be detected due to LD with the causal SNP(s). Statistical fine-mapping is therefore needed to refine sets of potential causal variants. The fewer the number of variants identified as potentially causal, the easier it will be to perform downstream functional validation experiments. Here, we focus on three fine-mapping challenges: multiple trait analysis, missing trait values, and related individuals in a cohort.Genome-wide association studies (GWAS) have been extremely successful in identifying genetic variants that are associated with a wide spectrum of diseases and related traits5. These BFs can be calculated for different combinations of causal SNPs and, for a specified prior probability, posterior probabilities (PP) for each causal variant model are easily calculated, modelling the pattern of association within a region. Models may then be prioritised by PPs.Bayesian approaches are common in fine-mapping, and use a Bayes’ factor (BF) to summarise the evidence of association, either for each SNP under the assumption of a single causal variant, or for each combination of SNPs in the more flexible multiple causal variant setting. In the more general multiple causal variant setting, the BF for a model compares the evidence for a model consisting of a particular set of SNPs that could be causal for a trait to the null model of no causal SNPs4, FINEMAP5, CAVIARBF6 SuSiE7) assume the specified sample size N relates to independent individuals, whilst the effective sample size after adjustment for relatedness via a linear mixed model, is <N. Such inflated N will suggest more certainty than actually present in the sample.Current fine-mapping methods do not allow missing trait data, so that a portion of the data is disregarded to avoid any missing measurements. Also, fine-mapping methods that make use of GWAS summary statistics  or enrichment-based annotations that consider GWAS data from other traits (DAP-G). PolyFUN13 leverages functional annotations to specify prior probabilities for existing fine-mapping methods. The CaVEMaN14 method estimates the probability that the lead SNP for an expression trait is causal for that association, and could assist in SNP prioritisation. KnockoffZoom15 localizes causal variants at multiple resolutions by testing if a phenotype is independent of all SNPs in a LD block, conditional on the others; it requires individual-level data from unrelated individuals.Some methods use SNP annotations to improve fine-mapping resolution. A potential caveat of such approaches is that they depend on the completeness of the annotation tool. This is not an issue for some annotations that are intrinsic to the DNA itself, such as DNA topologyJointly fine-mapping multiple traits could give an improvement in fine-mapping accuracy and resolution, analogous to the power increase for multi-trait GWAS, but this is computationally challenging due to the many possible combinations of models  between traits, which is not an issue for multi-trait GWAS that involve testing only one SNP for association with multiple traits. For this reason, few methods exist for fine-mapping with more than two traits.16 and mcoloc17 make the simplifying assumption of at most one causal variant for each trait. HyPrcoloc ignores trait correlations and is only able to incorporate trait correlations by adjusting the prior configuration probabilities; the authors show that ignoring this adjustment can reduce power to detect a cluster of colocalised traits. Correlation between traits is not considered by mcoloc, as it requires that all traits are measured in distinct datasets of unrelated individuals. An approach that allows multiple causal variants, eCAVIAR18, requires that the traits are measured from independent studies. This is because eCAVIAR is designed to assess if there are shared causal variants between a quantitative trait from a GWAS and expression quantitative trait loci (eQTL), which are often available in independent studies.When multiple traits have signals in the same region, colocalization is often used to evaluate how likely the traits share a causal variant. In some methods colocalization includes the fine-mapping step of identifying potential shared causal variants. HyPrcoloc19. Although this greatly reduces the number of models to consider, this does not allow traits to have different sets of causal variants. Although multi-trait fine-mapping is motivated by traits having shared causal variants, the possibility of a single shared causal variant and additional trait-specific causal variants cannot be ignored, as well as the fact that causal variants may actually differ for all traits. An approach that does not make this assumption is  which can be used to fine-map multiple related diseases with shared controls. This approach is made computationally tractable by showing that the joint Bayes’ factor (BF) for M diseases with shared controls is a function of the individual disease BFs, model complexity and sample sizes.One approach for jointly fine-mapping signals for quantitative traits from the same study is to limit the combinations of models by assuming all causal variants are shared between traits and allowing heterogeneity in effects, as in fastPAINTOR22; within this sample 2907/6407 are at least second degree related. In this dataset, we see several cases where flashfm improves on single-trait fine-mapping, distinguishing between two models with similar levels of support under single-trait fine-mapping and generally producing higher resolution solutions. In particular, the groups of likely causal variants constructed under flashfm are subsets of those from independent fine-mapping, resulting in finer resolution.In this work, we introduce a method for general quantitative multi-trait fine-mapping that solves the issues outlined above, allowing for related individuals and missing trait measurements: flashfm (flexible and shared information fine-mapping). Through extensive simulations we demonstrate that flashfm improves fine-mapping accuracy (mean PP increase of 0.15 for correct model) and resolution  over single-trait fine-mapping, when there is at least one shared causal variant. We also show that flashfm gives higher prioritisation to causal SNPs than fastPAINTOR, most notably when traits have multiple causal variants. We subsequently apply flashfm to genetic association signals for 33 cardiometabolic traits measured in 6407 participants from Uganda - the largest GWAS of a single population from Africa20. However, flashfm addresses different statistical challenges than MFM, as we now have multiple quantitative traits measured on the same individuals, so we need to account for correlation between the traits. In addition, the statistical modelling differs between the methods, as MFM uses a multinomial logistic framework, whereas flashfm is in a multivariate regression framework.Flashfm uses a similar Bayesian framework (same prior probabilities) to MFM23. Flashfm does not require an assumption of exchangeable effect sizes when modelling shared genetic architecture across traits. It uses GWAS summary statistics from each trait to fit the joint models, allowing for multiple causal variants for each trait, with no restrictions on shared causal variants between traits. Flashfm shares information between traits by up-weighting joint models (combinations of fine-mapping models across traits) that have a shared causal variant. For each trait, flashfm outputs the top SNP models and the model posterior probability (PP), adjusted for information from the other traits. It also provides the marginal posterior probability (MPP) that a SNP contributes to any model. A schematic diagram of flashfm shows the link with single-trait fine-mapping  which are calculated from the pre-specified prior probability and the BF that is calculated from the data. For multi-trait fine-mapping, we first find the PP for joint models then marginalise the joint PPs to get trait-specific PPs that are adjusted for the other traits; when there are two traits, the multi-trait adjusted PP for model 24. If the traits are independent, then the joint ABF of M traits, denoted ABFM, is the product of the marginal ABFs. As the traits are correlated the joint ABF is not a simple expression, and we derive the difference, using the log-scale: DM varies for each model configuration. For M traits, CM is a M × M matrix with element  equal to Cov/Var(trait i), and CM. CM is constant and depends on the trait covariance matrix, whereas M computationally feasible: To generate posterior support for fine-mapping models, flashfm needs to calculate the ABF (approximate Bayes’ factor) for all possible model combinations across SNPs and traits. We find expressions of the log(ABF) for each of the joint and marginal models by using the approximation based on the Bayesian information criterion (BIC)DM includes additional terms that account for the individuals that do not have measurements for all traits, using a combinatorial argument. The joint BF is first expressed as the BF for the multiple traits (at a particular model for each trait) on the portion of the sample that have no missing data, then an additional BF is added for each combination of traits with data available, careful to include each individual in only one term  and Mj (trait 2), with marginal prior probabilities pi and pj, we denote the joint model as configuration Cij and set the prior probability as 20. It requires setting a target odds (TO) of the odds for traits not having a shared causal variant compared to having a shared causal variant; setting TO = 1 coincides with a 50/50 chance of shared causal variant(s) and is the setting that we use. When κ = 1, there is no weight for joint models with shared causal variants and the flashfm PP for each model for a given trait is the same as that from single-trait fine-mapping, which we also refer to as independent fine-mapping, as it does not make use of data from other traits. The term τij is a correction factor that anchors the prior probabilities so that the prior probability of traits having particular model sizes is consistent for different values of κ; identical to MFM20.The prior probability for the joint models includes a term κ that gives more weight to joint models that have a shared causal variant between the traits. For models M5 could be input to flashfm. In the case of two traits, the trait-adjusted PP for model γI of trait 1 is calculated usingDM for M = 2 traits at models 4, that requires either the SNP correlation matrix and allele frequencies or the SNP matrix (Methods). So, flashfm requires the same information needed for single-trait fine-mapping, as well as the trait covariance matrix and results from single-trait fine-mapping.This means that rather than calculating the joint BF for each model, the joint PP could be directly calculated from the model PPs from single-trait fine-mapping; e.g. the model PPs from the *.config file of FINEMAP25 or BOLT-LMM26 and using the summary statistics to approximate the effective sample size Ne for each trait - Ne is approximated at each SNP and the median of the Ne is taken as the effective sample size for the trait; the Neff function in flashfm does this calculation . These SNP groups could be viewed in a similar way to the credible sets that are constructed for single-trait fine-mapping. As flashfm leverages information from the other traits, the per SNP PPs tend to concentrate on fewer SNPs, so the resulting SNP groups tend to be smaller than those from independent fine-mapping.Extensive simulations of two quantitative traits with varying sample size, proportion of missing trait measurements, and trait correlation suggest the same general conclusion. Flashfm improves precision over independent fine-mapping in terms of higher accuracy, as flashfm gives higher levels of evidence (larger posterior probability) for the correct model Fig. , and fin1, C1, and D1, such that one trait had two causal variants A1+D1, and the other trait either had two causal variants A1+C1, one of which was common, or a single distinct causal variant C1  and 0.957 (flashfm) and for N = 5000 the null model PPs are 0.966 for both methods. The median difference in SNP group sizes between those constructed from flashfm and those based on independent fine-mapping is zero, and at a given sample size, both methods give the same probabilities that the SNP groups contain the true causal variants: 0.984 , 0.991 , 0.996 .To demonstrate that flashfm is well-calibrated, we considered simulations of two traits such that trait 1 has causal variants A1+D1.In all settings, for groups C and D, there were negligible differences in group sizes between those constructed from flashfm and those based on independent fine-mapping; groups C and D were not shared between traits so do not gain from shared information. For all settings there is a negligible difference in probability that the SNP group contains the causal variant used in the simulation  between single and multi-trait fine-mapping. We provide detailed resolution results for group A Table . From th1+D1 and trait 2 has causal variant C1), there are negligible differences between flashfm and independent fine-mapping; the model PPs are nearly indistinguishable  due to the borrowed information between the traits. Also, flashfm tends to result in fewer SNPs in group A with a median size reduction of 32% for N ≥ 3000  of missing data from the trait with causal variants A+D, flashfm showed an average gain of 0.20 in the level of PP support for A+D over that of independent fine-mapping  between two traits (A+D and A+C) we found that the PP remained similar within each method and for trait A+D flashfm gave a median PP increase of 0.14 over independent fine-mapping Fig. . Comparehttps://github.com/jennasimit/flashfm/blob/master/R/jamexpanded.corX.R), varying the number of SNPs in a region and varying the number of traits in simulated data with 100 replications in each setting. All simulations were done within a region containing CTLA4 , and then expanding these models to include tagged SNPs by interchanging tag SNPs with their tagged SNPs within each model. As the prior probabilities depend on the number of SNPs in a region, the region of 1000 SNPs tended to run faster (4 traits median 168 s) than the smaller regions . Although there are more tagged SNPs to consider among models in the 1000-SNP region, the PPs are also more concentrated among these models relative to the large number of SNPs with low evidence of association, meaning fewer models are carried forward at cumulative PP 0.99 for consideration in flashfm.Within a given region size, as expected, the time increases with the number of traits. However, there was not an observed increase in time as the region size increases. The region that we continuously reduced initially contained 1231 SNPs and was previously defined for fine-mapping of autoimmune diseaseshttps://github.com/jennasimit/flashfm/blob/master/R/jamexpanded.corX.R).We provide a wrap-around function that runs single-trait fine-mapping, runs flashfm, constructs SNP groups for both methods, and provides summary results at the SNP and SNP group levels, FLASHFMwithJAM ; the traits each have two causal variants, with one shared (Methods). Rankings of the causal variants (using MPP) are compared between the flashfm results using the estimated (non-shifted) trait correlations and those that use trait correlations that are shifted upwards/downwards by 0.1 or 0.2 from the correlation estimate. This region is difficult to fine-map (Methods) and gives a worst case scenario.There is robustness in the results of flashfm, even when the input trait correlation is shifted upwards/downwards by 0.2. For all correlation shifts, the median rankings of causal variants are identical to those based on the correlation estimate - trait 1: 4.5 (E), 1 (G); trait 2: 5.5(E), 2.5 (H). For a more thorough assessment, we examine the probabilities that the rankings match between the shifted and non-shifted input correlation. As rankings that are higher in the shifted analysis are not a negative consequence, we also consider the probability that the shifted analysis ranks are at least as high as those from the original analysis. Our results suggest that flashfm is robust to both positive and negative shifts from the estimated trait correlation Table . The pro19. Both methods use GWAS summary statistics and the SNP correlation matrix to jointly fine-map multiple traits, allowing for multiple causal variants. Flashfm outputs the marginal posterior probabilities (MPP) that a SNP is a causal variant for each trait, whereas fastPAINTOR outputs the MPP that a SNP is causal within the set of traits. Flashfm also outputs the posterior probabilities for multi-SNP models for each trait, whereas fastPAINTOR does not indicate which SNPs are likely joint causal variants, appearing in a model together. For this reason, rather than assessing accuracy in model selection, our comparisons focus on the mean MPPs of the causal variants for each trait and their median rankings, as well as the probability that different proportions of causal variants appear among the top 5 or 10 ranked SNPs for each method. In the IL2RA region we simulated three traits with causal variants A+D, A+C+E, and I and, in the CTLA4 region, two traits with causal variants E+H and E+G  and 0.745 (fastPAINTOR). However, for all causal variants, flashfm tends to have a higher median ranking than fastPAINTOR  and 0.543 (fastPAINTOR); for both A+D, 0.897 (flashfm) and 0.670 (fastPAINTOR). These probabilities have consistently higher differences for rankings among the top 5, with flashfm consistently higher than fastPAINTOR for two or three causal variants in the top 5  than fastPAINTOR, and has a higher probability of ranking all causal variants among the top 5 (or 10) SNPs. At shared causal variants, the fastPAINTOR MPPs are similar to those from flashfm in the 3-trait IL2RA simulations, and at some non-shared causal variants flashfm has higher MPPs  and 5.5 (trait 2), compared to 8.25 (fastPAINTOR). At all sample sizes, the probabilities of at least one causal variant among the top 10, is higher for flashfm than fastPAINTOR, and the flashfm probabilities of both causal variants in the top 10 are twice that of fastPAINTOR  and 0.457 (fastPAINTOR), and for trait 2 they are 0.783 (flashfm) and 0.353 (fastPAINTOR). Similar patterns are seen for top 5 ranking probabilities, with flashfm also having noticeably higher probabilities of at least 1 causal variant in the top 5, compared to fastPAINTOR  with at least 2 of the traits, we constructed 56 regions for fine-mapping  for these regions. The remaining 4/56 regions had concordant SNP groups between the two methods, but flashfm had a slightly lower PP than FINEMAP (median decrease 0.05%). In 36% of the regions (20/56), flashfm gave a finer resolution than FINEMAP5, based on the total number of SNPs in the SNP groups of the top model (for each trait) from each method; the median reduction in the total number of SNPs for each trait was 31% with a maximum of 91%. In 88% of the regions (49/56), flashfm had higher confidence than FINEMAP for the top model with a median PP increase of 18% (mean increase 29%); in half of the regions (28/56) flashfm had a noticeable gain with an average PP increase of 52%.Within the 56 regions the total number of potential causal variants from the top models of FINEMAP was 1147, whereas flashfm reduced this total by 20% to 914 variants , which shows association to multiple lipid traits: LDL, TC, TG and HDL , and FINEMAP selects its top SNP rs75627662 as its best candidate. The second strongest SNP in the region for HDL is rs7412 (r2 with rs75627662 is 0.56) and flashfm levers information on LDL and TC to select this SNP as its best candidate for HDL. rs75627662 is an intergenic SNP upstream of APOE, while rs7412 encodes a missense variant . Homozygous carriers for this variant have the condition Type III hyperlipoproteinemia32 (OMIM 617347), which is characterised by hyperlipidaemia and affected individuals are susceptible to severe coronary artery disease. ApoE is a 34 kDa glycoprotein, initially noted as a component of plasma VLDL and HDL. rs7412 is known to have an effect on the efficacy of Atorvastatin, a statin medication used to prevent cardiovascular disease in those at high risk ; Atorvastatin decreases LDL and triglycerides in the blood and increases HDL. These observations highlight the complex interrelation among LDL, HDL and triglycerides and their regulation and the value of using approaches that capture information from multiple traits.HDL shows a much weaker association than the other traits . GWAS summary statistics could be used to approximate the effective sample size for each trait. The input of flashfm makes it readily applicable to meta-analysis results from multiple traits, and the large sample sizes do not increase computational time as only summary-level data are needed.4 to obtain single-trait fine-mapping results in the flashfm R package. When using flashfm for the Uganda GWAS of cardiometabolic traits, we used single-trait fine-mapping results from both expanded JAM and FINEMAP5, which generally resulted in concordant results.Flashfm is flexible to accept output from any single-trait fine-mapping approach and, for convenience, we include an expanded version of JAM34 are predictive of trait correlation within an independent sample from the same population35.Trait correlation could be influenced by both genetic and environmental correlations, and genetic correlation among traits may be used in place of trait correlation. This is especially recommended when there are GWAS summary statistics from a cohort that contains related individuals, as the GWAS summary statistics already account for relatedness, provided that a mixed linear model approach had been used. Sodini et al. have shown that, for 17 UK Biobank traits, the genetic correlations calculated from LD score regression24 to derive an expression for the joint BF, showing that the log(BF) of a joint model for M traits may be expressed as a sum of the marginal log(BF) and a term that depends on the GWAS summary statistics, sample sizes, trait covariance matrix, and LD; GWAS summary statistics are used to approximate the joint SNP effects. As our derivation provides a direct relationship between the joint and marginal BFs and does not disregard trait correlations, LD and joint SNP effects, it should give similar results to multivariate analyses with individual-level genotype data, provided that the same Bayesian framework is used. In its joint fine-mapping, flashfm uses a prior probability that upweights models having a shared causal variant between traits, sharing information between the traits, resulting in improved resolution when traits share causal variants. As flashfm makes use of GWAS summary statistics, it is easily scalable to large biobank-style datasets, whereas individual-level data approaches are not scalable. When there is access to individual-level genotype data, we recommend using this data to calculate the SNP correlation matrix and running a single-trait fine-mapping method, such as JAM or FINEMAP, as input to flashfm. For smaller sample sizes , users may run the required single-trait fine-mapping with the raw genotype matrix, using the JAMexpanded.multi function of flashfm, followed by flashfm.In developing flashfm, we use the BIC approximation for BFsBoth flashfm and JAM are developed under the assumption of conditional normality and homogeneity of variance. As with any statistical method, departures from these assumptions could potentially produce misleading results. When using summary GWAS data it can be difficult to confirm that modelling assumptions hold, so we urge users to check that these assumptions were tested when the original data were analysed.Although we find a quick approximation to the joint BF that is based on the marginal BFs, for computational efficiency, we avoid storing these joint BFs in flashfm, and instead use these to approximate the joint PPs , which are used to find the trait-adjusted PPs for each trait. Flashfm outputs posterior probabilities for multi-SNP models for each trait, adjusted for information from the other traits, as well as marginal PPs (MPPs) for each SNP under each trait. SNPs with the highest MPPs have more evidence for being causal and the model PPs indicate which combinations of SNPs have evidence of being joint causal variants. We use the MPPs and PPs, together with LD, to construct SNP groups such that SNPs in the same group are in LD and rarely appear in a model together. These groupings allow us to further summarise the SNP-level PP results to SNP group level, which simplifies interpretation and assessing fine-mapping resolution.As flashfm depends on GWAS summary statistics and model PPs from single-trait fine-mapping, we advise that sample size is assessed in the same way as for a GWAS. When selecting traits to jointly fine-map signals in a region with flashfm, we advise that traits should have a minimum p-value of 1E-6 in the region. Otherwise, fine-mapping will either favour the null model or, if the null model has prior probability of 0, spread the PP over many models. Provided that there is sufficient power to detect a signal in the GWAS and there is a shared causal variant, flashfm will tend to construct SNP groups that are subsets of those from single-trait fine-mapping. In addition, when traits have multiple causal variants, with partial sharing of causal variants, flashfm shows improvements in prioritising causal variants over another multi-trait fine-mapping method, fastPAINTOR.29. When a reference panel is the source of the LD matrix for fine-mapping, it must be based on samples of the same ancestry. Benner et al. also show that the size of the reference sample must scale with the GWAS sample size; for a GWAS sample size of 10,000, a reference panel of 1,000 samples is sufficient to estimate LD, whereas a panel of around 10,000 is needed for a GWAS sample size of 50,000.As with all existing fine-mapping methods that use summary statistics, inaccurate LD information could reduce the accuracy of the method, either missing causal variants, or inflating evidence for non-causal variants29. LD matrices based on 337,000 British ancestry UK Biobank36 samples are freely available for download at https://alkesgroup.broadinstitute.org/UKBB_LD13; LD matrices for additional ancestry groups  within UK Biobank are available for download by the Pan-UKB team at https://pan.ukbb.broadinstitute.org. 2020. Another source for African ancestry LD is the AFR superpopulation of 1000 Genomes37 that consists of 1,418 samples of African ancestry from both Africa and the United States; data are available for download from http://grch37.ensembl.org/Homo_sapiens/Tools/DataSlicer. As African Americans reflect admixture of people of West and Central-West African descent38, the AFR superpopulation of 1000 Genomes or the African ancestry cohort from UK Biobank would be an appropriate source of LD for either African or African American samples.With the growing availability of biobanks, there are more potential sources for large reference panels and LDstore assists in this as a tool for efficient estimation, storage, and sharing of LD informationOur simulation studies demonstrated that flashfm tends to give greater improvement in resolution over single-trait fine-mapping when the traits have a common causal variant and a moderate/low correlation between them, though even with highly correlated traits, there is some resolution gain over single-trait fine-mapping. Likewise, the greatest refinement in resolution within the Uganda data tended to be for sets of traits that included at least one trait with a correlation below 0.9 and single-trait fine-mapping top trait models with common SNP groups, which were refined by flashfm. Replication in another data set and downstream functional validation experiments are needed to confirm that the SNPs excluded by flashfm are indeed unlikely to be causal variants. These results promote the joint fine-mapping of traits that are relevant to certain disorders (and have genetic association in the same regions), but not usually considered together; flashfm could not only refine the sets of potential causal variants for the traits, but also reveal new shared causal variants between them.39. A multi-ancestry version of flashfm is in progress as a crucial extension to this software, that will allow for multiple reference panels from the different ancestries.There are clear advantages to multi-trait fine-mapping with flashfm, and the inclusion of diverse ancestries could lead to further improvements in resolution; where ancestries have common signals, the differences in LD between the ancestries could help pinpoint the causal variant(s)4 facilitates fine-mapping from marginal summary statistics. JAM requires a thinned reference panel such that the genotype matrix does not contain SNPs in high LD and then infers joint LD-adjusted multi-SNP models, highlighting the best SNPs, and combinations of SNPs, via a Bayesian sparse regression framework. Included in the flashfm software, we developed expanded versions of JAM, JAMexpanded.multi (https://github.com/jennasimit/flashfm/blob/master/R/prep.R) and JAMexpandedCor.multi. (https://github.com/jennasimit/flashfm/blob/master/R/jamexpanded.corX.R) that accounts for all SNPs that were thinned out. Each joint multi-SNP model is expanded by considering all the possible models formed by all the combinations of SNPs in the JAM model, in the same manner as the fine-mapping approach GUESSFM3 https://github.com/chr1swallace/GUESSFM/blob/master/R/groups.R). SNPs with marginal posterior probability of inclusion > 0.001 were grouped such that SNPs in the same group are in LD - high r2 - and rarely selected together in models (model selection correlation (rmodel) should be negative); both rmodel and r2 are used so that our SNP grouping is informed by both model posteriors and LD. This algorithm was used in MFM20 to construct SNP groups based on the single-disease fine-mapping posterior probabilities and these same groups were used to summarise the fine-mapping results from both single-disease and multi-disease (MFM) fine-mapping; using the same groups for both methods allows a convenient comparison of results between the two methods.Rather than reporting results in terms of SNPs, we construct SNP groups using an algorithm based on the group.multi function in GUESSFM (https://github.com/jennasimit/flashfm/blob/master/R/group.multi.R).As this grouping algorithm makes use of the model posteriors, we extended this grouping algorithm such that the groups are constructed independently for single-trait and multi-trait (flashfm) fine-mapping. That is, SNP groups are constructed for both methods using the same LD information, but groups may differ due to differences in the model PPs between the methods. Since the groups are constructed independently, the new algorithm incorporates a mapping between the group labels of the two sets of groups such that if a flashfm group overlaps a single-trait fine-mapping (STFM) group, the flashfm group label takes the name of the STFM group label. Sometimes a single STFM group overlaps multiple flashfm groups due to the higher precision of flashfm, which could reduce a SFTM group or split it into smaller groups. To account for this, if the STFM group has label A, each flashfm group that intersects A is given label A.1, A.2, etc. to denote that these flashfm groups were from the same larger group in STFM. This algorithm is available as the makeSNPgroups2 function in flashfm (r2 in the IL2RA region (345 SNPs in chromosome 10p-6030000-6220000 (GRCh37/hg19)). This region was selected as it has been previously shown to exhibit a tagging behaviour for causal variants; when there are two causal variants (A1 = rs61839660 and D1 = rs62626317), sometimes a different variant (B1 = rs2104286), that is correlated with both causal variants, is detected as a single causal variant20; in this region this tagging behaviour was also observed for two causal variants, A1 = rs61839660 and C1 = rs11594656, jointly tagged by J1 = rs706779. For this region, we generated a population of 100,000 individuals based on the CEU 1000 Genomes Phase 3 data37 using HapGen240. Details of the LD between A1, B1, C1, D1, and J1 and their MAFs are given in Supplementary Data Simulations were carried out under a realistic scenario that mimics the MAF and N individuals was selected from the population of 100,000. Causal variants for each trait were selected for a certain disease model, such that trait 1 has two causal variants (A1 and D1) and trait 2 either has two causal variants (A1 and C1); shared A1 with trait 1) or a single distinct causal variant (C1 = rs11594656). Various values for the SNP effects were selected and a multiplicative model was assumed throughout. At each parameter configuration there are 300 replications.For each replication, a random sample of M traits, the measurement for trait k of individual j, ykj, is obtained fromxij is the number of non-reference alleles of variant i for individual j (i.e. genotype score), βik is the effect of causal variant i for trait k, mk is the number of causal variants for trait k, and εkj is the kth element of the jth multivariate Normal distributed error variable with mean 0 and covariance Σ, which is the covariance matrix of the M traits.For 1+D1 is represented by A+D; model A+D consists of all 2-SNP models where one SNP is from group A and the other is from group D. Group labels were assigned according to the variants contained within them, listed as in the beginning of this section for A, B, C, D, and J; if the variant is not captured by a group, then the group is given the label of the variant if it contains a variant that is in LD (r2 > 0.7) with the group variant.For each replication, Expanded JAM was used for single-trait fine-mapping (SFTM) and used as input into flashfm. We used the makeSNPgroups2 function of flashfm to construct two sets of SNP groups, one set based on SFTM results and one set based on those of flashfm. Results from each method were then summarised based on their coinciding SNP groups, such that a 2-SNP model given by rs61839660 + rs62626317 = AAccuracy of the fine-mapping approaches was evaluated by comparing the mean group model PPs between the two methods. Precision was assessed by comparing the SNP group sizes (number of SNPs in each group) of the groups containing the causal variants for each method; within a replication, for each causal SNP group, the size of the flashfm group was subtracted from the JAM group and the median over the replications was used as a summary. To account for the possibility of a method giving a smaller group size, but not containing the causal variant, we also considered the coverage for each causal variant, defined as the proportion of simulations in which each causal variant is captured by a SNP group. In low power scenarios  the methods may select the correct SNP groups with low PP, and as in a real analysis such SNP groups would not be followed up, we do not consider the SNP group size and coverage for a SNP group if its marginal PP < 0.1 in a replication; all replications are included in the summaries of the model PPs.r2 in the CTLA4 region (1231 SNPs in chromosome 2q-204446258-204816382 (GRCh37/hg19)). This region was chosen for its noted difficulty in fine-mapping of multiple autoimmune diseases and we selected causal variants based on previous findings for CTLA420. In particular, causal variants were selected from the SNPs rs1980422, rs231775, and rs3087243, which we denote E, H, and G  causal variants have rank 5 or less and likewise, 10 or less.As fastPAINTOR provides the marginal PP (MPP) of a SNP being a causal variant, and does not output the PPs for joint SNP model configurations, we focus on the MPPs of causal variants and their rankings under each method. For each setting, under flashfm and fastPAINTOR, we compare the following:We have profiled the running time of flashfm, varying the number of traits and the number of SNPs in a locus. This was done with the large CTLA4 region described above, reducing this region to 1000, 500, and 250 SNPs. We simulated 2, 3, and 4 traits within each region. These analyses were run on Intel Skylake 2.6 GHz CPU.N = 5000. For 300 replications, we run flashfm using the estimated trait correlation as input, and also with this estimate shifted by −0.2, −0.1, 0.1, and 0.2. For each shift, we calculate the probability that the ranks from the shifted estimate match those of the original analysis, as well as the probability that these ranks from the misspecified correlation match or are higher than the original analysis.We assess the robustness of flashfm to misspecification of the input trait correlation by simulating two traits in the CTLA4 region, using the same settings as in the multi-trait methods comparison and 41 the authors show that a recombination rate of 0.1 corresponds to an LD of 0.4 for European ancestry population, meanwhile for African populations the same level of linkage disequilibrium corresponds to a recombination rate of 0.05. After investigating the dynamics between the recombination rate and the LD, we found that our data required a tighter criterion between 0.03 and 0.05 cM. In order to be conservative and align with the literature, we selected a criterion of ±0.05 cM to define our fine mapping regions  distance. In particular, for European ancestry populations, fine-mapping regions are often constructed using a boundary of ±0.1 cM around an association signal. When we applied this criterion to the Ugandan cohort, the constructed genome regions were excessively wide. In Park5 and ii) JAM4. FINEMAP is a Bayesian stochastic search algorithm based on summary statistics from GWAS. As there are related samples, the effective sample size, as approximated from the GWAS summary statistics, is used for the number of measurements for each trait; this is easily calculated using the Neff function in flashfm (https://github.com/jennasimit/flashfm/blob/master/R/Neffective.R).Input to flashfm includes single trait fine-mapping results (model posterior probabilities) and single-SNP effect estimates for each trait from the GWAS. We consider two single trait fine-mapping algorithms that use GWAS summary statistics and a reference panel: i) FINEMAPBesides single-trait fine-mapping results, flashfm also needs the single-SNP effect estimates, and a reference (or in-sample) genotype matrix or its covariance matrix. If GWAS summary statistics are available, but not single-trait fine-mapping results, then expanded JAM could be applied to provide input to flashfm. Only SNPs that exist in both the GWAS and the reference panel are carried forward for fine-mapping and this is automated within the flashfm software. Rather than fitting each joint model between traits, we derived an approximation to the joint Bayes’ factor (BF) that depends on the marginal BFs, single-SNP effect estimates for each trait, trait covariance matrix, and a reference panel. Based on the single-trait fine-mapping results, flashfm selects the top models from each trait, which are then carried forward for consideration in joint models between the traits. Preferably, models are selected for each trait based on the cumulative posterior probability , or a maximum number of models may be selected instead.With exceptions to be clarified on a case-by-case basis, we consider a cpp of 0.99 in our analysis of the Uganda data. For ease of interpretation, as discussed in the SNP Groups section of Methods, main results are presented in terms of SNP groups, rather than individual SNPs; SNPs are grouped such that SNPs within each group are in high LD and rarely appear together in a model. Also, the average LD between groups is lower than 0.6.43. In general, FINEMAP and JAM agree on the top model, though there are 6 regions for which JAM could not converge. For this reason, we focus on the more complete FINEMAP results (Supplementary Data For comparison purposes, in addition to results from single-trait fine-mapping from FINEMAP (and JAM in Supplementary Data Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


SMOX gene expression. We then did a Mendelian randomization analysis by testing the association between the SMOX genetic instrument and neuroblastoma, gastric, lung, breast, prostate, and colorectal cancers using GWAS summary statistics. GWAS of spermidine/spermine ratio identified SMOX locus (P = 1.34 × 10–49) explaining 32% of the variance. The lead SNP rs1741315 was also associated with SMOX gene expression in newborns (P = 8.48 × 10–28) and adults (P = 2.748 × 10–8) explaining 37% and 6% of the variance, respectively. Genetically determined SMOX activity was not associated with neuroblastoma, gastric, lung, breast, prostate nor colorectal cancer (P > 0.05). A PheWAS of rs1741315 did not reveal any relevant associations. Common genetic variation in the SMOX gene was strongly associated with SMOX activity in newborns, and less strongly in adults. Genetic down-regulation of SMOX was not significantly associated with lower odds of neuroblastoma, gastric, lung, breast, prostate and colorectal cancer. These results may inform studies of SMOX inhibition as a target for chemoprevention.Spermine oxidase (SMOX) catalyzes the oxidation of spermine to spermidine. Observational studies have reported SMOX as a source of reactive oxygen species associated with cancer, implying that inhibition of SMOX could be a target for chemoprevention. Here we test causality of SMOX levels with cancer risk using a Mendelian randomization analysis. We performed a GWAS of spermidine/spermine ratio to identify genetic variants associated with regulation of SMOX activity. Replication analysis was performed in two datasets of  Already in the 1960s, ornithine decarboxylase (ODC), the first rate-limiting enzyme in polyamine biosynthesis, was demonstrated to be at high levels in human cancer specimens5. Moreover, a genetic variant in ODC, regulating its enzymatic activity, confirmed the role of this enzyme in human colon cancer risk7, and ODC levels have been shown to be elevated in human skin, breast and prostate cancer specimens10. Other polyamine metabolic enzymes have not yet been reported to be associated with tumorigenesis in humans.Although polyamines are essential for normal cell growth and developmentSMOX), a member of the mammalian polyamine catabolic pathway, encodes an enzyme with cytoplasmic and nuclear expression in most tissues11, that catalyzes the oxidation of spermine to spermidine with the production of hydrogen peroxide (H2O2) and 3-aminopropanal (3-AP)13. SMOX has been reported as a source of induced reactive oxygen species (ROS) associated with neuroblastoma, gastric, lung, breast, prostate and colon cancers20, implying that inhibition of SMOX could be a target for chemoprevention3. However, so far, studies of SMOX inhibition have been observational in their nature with no direct inference on causation20. Genome-wide association studies (GWAS) of SMOX activity are also lacking. A GWAS could potentially detect genetic determinants of SMOX activity that might serve as instruments to assess the causality of SMOX on cancer using a Mendelian randomization framework21.Spermine oxidase  to query possible deleterious effect of altered SMOX activity through all disease categories and to test possible pleiotropic effects.23 (SSI-IHPS cohort). In addition, dried blood spot samples of 262 newborns of Danish ancestry were included for expression quantitative trait locus (eQTL) analysis (PSYCH-twin cohort). These 262 newborns were taken at random from each of 262 monozygotic twin pairs discordant for later diagnosis of psychiatric disorders as previously described24. Of these 262 newborns, 235 were included for methylation quantitative trait loci (mQTL) analysis. Furthermore, 671 whole blood samples taken from adults of the Estonian Genome Center University of Tartu (EGCUT) cohort25 were also used for eQTL (N = 508)27 and mQTL  lookups. The Danish Scientific Ethics Committee, the Danish Data Protection Agency and the Danish Neonatal Screening Biobank Steering Committee approved the PSYCH-twins study as well as the GWAS of metabolites in newborns. Usage of EGCUT RNA-seq dataset was approved by Estonian Committee on Bioethics and Human Research, protocol nr. 1.1–12/624, data extraction nr. N26.The GWAS of spermidine/spermine ratio reflecting SMOX activity, was based on dried blood spot samples taken during routine newborn screening from 534 individuals of Danish ancestry, recruited in a matched case–control study of infantile hypertrophic pyloric stenosis (IHPS), as previously described28 with 79,194 cases and 61,112 controls; the BCAC consortium (breast cancer)29 with 62,533 cases and 60,976 controls; the TRICL consortium (lung cancer)30 with 29,266 cases and 56,450 controls, and the North American-based Children’s Oncology Group (neuroblastoma)31 with 2,101 cases and 4,202 controls. GWAS summary statistics for gastric and colorectal cancers relied on population-based cohorts: UK Biobank with 5,693 colorectal cancer cases and 386,740 controls32, and the BioBank Japan Project with 6,563 gastric cancer cases and 195,745 controls33. Analyses were restricted to individuals of European ancestry, except for gastric cancer due to the lack of publicly available European gastric cancer GWAS data.The study also included GWAS summary statistics from four cancer-specific consortia cohorts: The ELLIPSE consortium (prostate cancer)Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.23. In our previous study23, we quantified 148 of the 408 metabolites, from which spermidine and spermine measurements were also used for this study. Briefly, since all samples were run sequentially, the only batch effect we noticed was plate effect, for which we corrected this by dividing the raw concentration values of all samples by a plate-specific correction factor23. We also did metabolite filtering by selecting the 148 metabolites that were above the limit of detection (LOD) in at least 80% of the samples and had a coefficient of variation of the replicates below 25%23. In this study we performed genome-wide association scans of all these 148 metabolites and their biologically relevant ratios (e.g. direct substrate and product of an enzyme), including spermidine to spermine ratio, which had the most significant genome-wide hit of all the metabolites and ratios tested. Our GWAS p-value threshold, correcting for the 148 metabolites tested, is P < 5 × 10–8/148 = 3.4 × 10–10. For computing time issues, the GWAS analysis was first implemented in PLINK34 and then repeated in R35 only for the genome-wide significant variants. The PLINK34 and R35 analyses were done using the following linear model: spermidine/spermine inverse normally transformed concentration ratios ~ SNP + sex + YOB + GA + Parity + IHPS. YOB pertains to year of birth, GA as gestational age in weeks, Parity as number of completed pregnancies, and IHPS as case/control status. Of note, the GWAS analysis was also repeated in R35 in order to obtain the variance explained (R2) of the genome-wide significant variants and to allow YOB to be coded as a factor. As described before22, the SSI-IHPS cohort was comprised of Danish ancestry and unrelated individuals of which DNA samples were array-genotyped with the Illumina Multi-Ethnic Global_v2_A2 array, and after genotyping QC, unobserved genotypes were imputed from the Haplotype Reference Consortium panel36. Altogether, 6,846,507 SNPs with minor allele frequency >  = 1% and imputation info score >  = 0.8 were used in the analysis. Being unrelated and of Danish ancestry was determined from the genome-wide SNP array data as previously described22, using the kinship and principal component analysis, respectively. The GWAS summary statistics of spermidine/spermine ratio of the SSI-IHPS newborn cohort is available here: danishnationalbiobank.com/gwas.The metabolites spermidine and spermine were quantified in whole blood of dried blood spots using the AbsoluteIDQ® p400 Kit  37, log2 transformed, gene expression values were centered and scaled, and genetic PCs were regressed out26. In addition, in order to remove non-genetic variance from the data, up to 20 gene expression-based PCs which were not associated with genetics were regressed out from the residuals of the expression data. Finally, inverse normal transformation was applied on the residuals from the previous step. In order to detect a possible age-dependent eQTL effect, the adult cohort (EGCUT) was stratified into different age groups . Formal interaction analysis was also performed, in R35, between donor age at blood draw and rs1741315 genotype on SMOX expression.To replicate the genetic association with SMOX activity at the gene expression level, eQTL analysis was performed in whole blood from two additional data sets of 262 newborns (a subset of the PSYCH-twin cohort) and 508 adults (a subset of the EGCUT cohort) that had previously been RNA-sequenced and GWAS-genotyped24 also had genome-wide methylation array data from the Infinium HumanMethylation450 BeadChip (450 k) array, as previously described24. For this study, all samples were filtered by Call Rate > 0.99 (for detection P < 0.01 and bead count > 2) and median methylation and unmethylation intensity signal > 2000 to ensure proper sample measurement quality. All probes (CpGs) were filtered by Call Frequency > 0.99 (for detection P < 0.01 and bead count > 2) to ensure sufficient probe representation across most samples. The data was normalized with the noob background dye correction method after sample filtering38, implemented in the R35 package minfi. For all the 480 CpG sites within 1 Mb of the SMOX gene, we tested their methylation fraction against the lead (most significant) SMOX SNP. The analysis was implemented in R35 as the following linear model: CpG methylation fraction ~ lead SMOX SNP + sex + YOB + GA + BW (birth weight). An association was deemed significant if P-value < 0.05/480 = 1.04 × 10–4 to correct for the number of CpGs tested. Furthermore, 305 individuals from the adult EGCUT cohort also had genome-wide methylation array data from the Infinium HumanMethylation450 BeadChip (450 k) array. Briefly, methylation data was normalized according to the GoDMC (http://www.godmc.org.uk/) pipeline39 using functional normalization method as implemented in the R package meffil40. In step 1, we adjusted normalized methylation proportion for age, sex, predicted cell counts, predicted smoking and genetic PCs. Predicted smoking was performed as the methylation fraction of known CpG sites that are well correlated with smoking status41. In step 2, PCA was applied on the residuals from the previous step, and top non-genetic PCs were regressed out. To be able to estimate the SNPxAge effect on methylation, inverse normally transformed methylation proportions were regressed against covariates from step 1 and methylation PCs from step 2.Moreover, 235 out of 262 individuals from the newborn PSYCH-twin cohortSMOX) being genome-wide significantly (P < 5 × 10–8) associated with spermidine/spermine ratio was used as the genetic instrument in the Mendelian randomization analyses. Variance explained by the genetic determinant of SMOX activity was calculated as the adjusted R2 of lm function in R35 implemented as spermidine/spermine inverse normally transformed concentrations ratio ~ SNP, or normalized and inverse normally transformed gene expression value of SMOX ~ SNP. The Wald ratio method42, implemented in the R35 package MendelianRandomization (v0.3.0) was used to test whether the association between SMOX genotype and incident cancers could be caused by differences in SMOX activity (in the SSI-IHPS newborn cohort) or SMOX gene expression (in the EGCUT adult cohort). To confirm the results of the MR Wald ratio test, we also used another implementation of this method from the TwoSampleMR (v0.5.6) R35 package. The primary analysis measured the association between genetically determined SMOX activity and the risk of neuroblastoma, gastric, lung, breast, prostate and colorectal cancers. The PhenoScanner bioinformatic tool43, a curated database of publicly available results from large-scale genetic association studies, was used for the PheWAS analysis and detect possible concurrent risk factors of the investigated cancer types (pleiotropy). The PhenoScanner database was queried from the MendelianRandomization R23 package. In our main analyses we tested six key outcomes, namely neuroblastoma, gastric, lung, breast, prostate and colorectal cancers. A P-value of less than 0.05/6 (number of cancers tested) = 0.008 was considered statistically significant. Of note, Table The lead SNP located on chromosome 20q12 (in cis with 44 platform was used with default settings (accessed 29 April 2021). Briefly, genome-wide significant hits of GWAS of reticulocyte count45, lymphocyte count45 and hemoglobin concentration45 were used as genetic instruments (clumping was used to prune SNPS for LD) of those exposures to test their causal role on the six cancers tested.To study the possible causal effects of reticulocyte count, lymphocyte count, and hemoglobin concentration on the cancers tested, MR-BaseAll methods were carried out in accordance with relevant guidelines and regulations.The Danish Scientific Ethics Committee, the Danish Data Protection Agency and the Danish Neonatal Screening Biobank Steering Committee approved the PSYCH-twins study as well as the GWAS of metabolites in newborns. The Danish Scientific Ethics Committee granted exemption from obtaining informed consent from participants as this research project was based on genotyping samples from biobank material (H-4-2013-055). Usage of EGCUT RNA-seq dataset was approved by Estonian Committee on Bioethics and Human Research, protocol nr. 1.1-12/624, data extraction nr. N26. Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.All authors consent this study for publication.33.A total of 534 newborn participants (SSI-IHPS cohort) were included in the GWAS of spermidine/spermine ratio to find the genetic instrument of SMOX activity (Methods). An additional 262 newborns (PSYCH-twin cohort) and 508 adults (EGCUT cohort) were included to confirm the genetic instrument of SMOX activity at the gene expression level (Table –8/148 = 3.4 × 10–10)  being an intronic variant in the SMOX gene  explaining 37% of the variance  explaining 6% of the variance (Table https://www.eqtlgen.org/), which the EGCUT cohort is a part of, rs1741315 is also associated with SMOX gene expression (eQTL P = 3.27 × 10–310). Of note, rs1741315 was array-genotyped in all cohorts, in contrast to being just imputed. We stratified the adult cohort (EGCUT) into different age groups  in order to detect a possible age-dependent eQTL effect in early vs. late adulthood . Zooming in on CpGs at the SMOX locus, we found rs1741315 to be associated with the methylation proportion of the CpG cg07472708 (mQTL P = 4.63 × 10–5) explaining 0.5% of the variance in methylation proportion for this CpG (Table SMOX intron as rs1741315 and is under a region of transcription factor binding site for the transcription factor GATA-1 (Methods). It should be noted that the 50-mer probe containing cg07472708 overlaps with rs1741317 (37 bp apart from the C nucleotide of cg07472708), a high LD (r2 = 0.996) SNP with our lead rs1741315. This could lead to a technical artifact from poorer measurement conditions in those individuals carrying the minor allele, so the results from mQTL analysis should be interpreted with caution.To assess effects of rs1741315 on SMOX activity at the gene expression level, we performed eQTL analyses in two additional cohorts. In a newborn cohort of 262 individuals (PSYCH-twin)ce Table . In the d Figure , but foun Figure . Due to pG Table , but foupG Table . The CpG47. To test if this assumption holds, we checked the association of the SMOX genetic instrument rs1741315 against the PhenoScanner database of genotype–phenotype associations43 at P < 1 × 10–5 (to correct for the 2998 traits/diseases tested in the database). With rs1741315 ‘A’ allele being the effect allele, we detected reticulocyte count , lymphocyte count  and hemoglobin concentration  as traits associated with this variant45. One potential way to test and adjust for pleiotropic effects through associations with these blood traits would be by multivariable Mendelian randomization analysis48. However, such analysis is only possible for multi-SNP genetic instruments, which we did not have. Moreover, running MR using genetics instruments for reticulocyte count45, lymphocyte count45, and hemoglobin concentration45 as exposures for cancer, provided no evidence of these potential confounders being causal for the cancers tested   or adult cancers .We conducted the first GWAS of spermidine/spermine ratio to identify genetic variants regulating spermine oxidase (SMOX) activity. Variants in the 18, and it has been hypothesized that SMOX inhibition could be an effective target for chemoprevention3. Our results do not indicate that genetically driven variation in SMOX activity plays a major role in cancer risk. Several factors and limitations of our study might explain this discrepancy between the observational epidemiological evidence18 and our Mendelian randomization results.Observational studies have reported elevated SMOX levels in gastric, lung, breast, prostate, and colorectal cancersSMOX expression is genetically regulated over the life course might violate an important MR assumption of monotonicity or homogeneity49. However, we also used the estimate of SMOX activity from the EGCUT adult cohort for the MR analysis and found similar results as when using the estimate of SMOX activity from the newborn cohort , which could have biased our MR estimates. However, the top correlated variants of rs1741315 in Europeans are also the same as in the Japanese population  than the on Table , which mFinally, SMOX activity and expression levels were measured in blood in this study, compared to cancer tissue cells in the observational studies.21 none of which are concerns of Mendelian randomization studies21. Furthermore, using spermidine to spermine ratio to measure SMOX activity was here validated by the orthogonal measurement of SMOX gene expression and its genetic regulation. In addition, the fact that our genetic instrument explained a sizeable proportion of the variance of SMOX activity together with the fact that we used summary statistics from the largest meta-analyses of primary GWAS of these cancer types to date33 are factors enhancing the statistical power to detect causal effects50.Our study had important strengths. Several previous studies which examined SMOX activity and cancer risk were susceptible to recall bias, confounding and reverse causationIn conclusion, lifelong genetic exposure to low levels of SMOX was not associated with lower cancer risk. However, this finding cannot be assumed to represent a short-term strong drug inhibition of SMOX, thus establishing causality would require a randomized clinical trial. To better interpret the complexity of the relationship between SMOX activity and cancer, future studies should effectively distinguish newborn versus adulthood SMOX activity and short versus long exposures. This study is the first to use Mendelian randomization to assess the possible benefits of SMOX inhibitors on cancer risk, paving the way for other polyamine catabolic pathway enzymes to be tested with the same methodology.Supplementary Information 1.Supplementary Information 2.


The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort  demonstrated significant heritability for a subset of traits (0.17–0.53) and highlighted 218 genome-wide significant loci  associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation. The human face is a highly variable trait composed of distinct features, each influenced by genetic and environmental factors. The strong genetic component is primarily evidenced by the facial similarity between identical twins and the clear facial resemblances within families. Over the past decade, a powerful methodological toolbox of computational and statistical genetics has been developed to study the genetic architecture of complex traits. However, these methods typically require one-dimensional phenotypic measurements (e.g. width of the nose or spacing between the eyes) that fail to accurately describe 3D facial shape. In this paper, we learn from 3D facial data itself, a series of relevant traits that are guided by the facial similarity observed between sibling pairs. Importantly, while preserving the structural convolution of the face, these traits also fit the requirements as input to the well-established statistical tools. In doing so, we have identified many genetic loci that are associated with a wide range of facial features. Some of these regions contained genes related to embryonic facial development, and craniofacial malformations. An improved understanding of the genetic basis of facial shape can have several important applications, for example in developmental biology, medical genetics and forensic sciences. Systematic characterization of facial morphology is important in a variety of domains such as anthropology, medicine, and genetics ,2. It haTo address the current limitations, we propose to prioritize, in a supervised and data-driven manner, specific facial traits of interest within the multidimensional facial space, in order to reduce the complexity of 3D facial shape into genetically informed and therefore biologically relevant facial traits. In this context, families are potentially informative, as the similarities among family members clearly indicate a heritable component of facial shape ,19. FollFacial variation was studied at multiple levels of scale by subdividing facial shape into 63 hierarchical segments. First, homologous spatially dense quasi-landmark configurations n = 7,160) were established by the mapping of a generic template mesh onto the images 60 were e. SubsequSince siblings are likely to share facial features due to close kinship, biologically relevant traits can be extracted from phenotypically similar sibling pairs. We aimed to identify these pairs and derive their overlapping facial features through a biometric identification setup, which essentially involves the comparison of facial shape between siblings in the context of multiple facial comparisons with unrelated individuals . To thisMA,fullface = 36.81%; MA,combined = 44.32%; The ability of the different similarity measures to identify siblings was tested in a biometric identification task. In a one-to-many setup, faces were matched against a gallery of candidates for individual and combined facial segments. The rank-k% identification rate then indicates the proportion of times the true sibling was present in the top-k% matches as determined by the similarity score . TherefoSiblings could be matched with varying levels of accuracy depending on the similarity measure and facial features  being tested Figs  and S1. A total of 1,048 traits were extracted across all segments, each of them representing a particular facial feature shared by a specific pair of siblings. Visual representations of the sib-shared traits are available online . The 1,0Sib-shared traits can be described as vectors within the multidimensional shape space, extending from the global mean shape to the average facial shape of both siblings . Moving US < 3.6 x 10−3; pFDRUK < 2.2 x 10−3), with significant SNP-based heritability estimates ranging from 0.17 to 0.42 in the US cohort and 0.24 to 0.53 in the UK cohort  using GCTA ,33. SignK cohort . Higher K cohort . Low SNP−8), of which 2,749 SNPs at 38 loci had p-values lower than the study-wide threshold adjusted for the effective number of independent tests  on all 1,048 sib-shared traits in the US and UK cohorts separately and meta-analyzed the resulting p-values using inverse variance-weighting ,34 Figs and S6. A total of 548 sib-shared traits  were as−6), yet none surpassed the strict study-wide significance level adjusted for the number of independent tests (p < 1.22 x 10−8)  using a gene-based testing approach in a subset of the US cohort (PITT) with exome-wide data available  . A total x 10−8) . Of thes x 10−8) .We performed gene ontology analysis using GREAT  to studyWe next sought to identify tissues and cell-types enriched for active regulatory regions near the 218 genome-wide significant lead SNPs. We used ChIP–seq for histone H3 on lysine 27 (H3K27ac), a mark of active regulatory elements, from diverse cell types as described previously . H3K27acWe explored both phenotypic and genetic correlations among the sib-shared traits using GCTA  20]. Hi. Hi20]. −6; We also investigated the genetic overlap between our derived facial traits and publicly available non-facial traits and diseases n = 38;  using crOver the past decade, a versatile toolbox of computational and statistical genetics methods and accompanying open-access software has been developed to investigate the genetic architecture of complex phenotypes. Examples include, but are not limited to, genome-wide association scans followed by fine mapping , rare vaWe set up a biometric identification experiment leading to the identification of phenotypically similar sibling pairs from which their overlapping facial traits were derived, each representing global or local aspects of facial morphology that were informative for accurately matching the siblings. Individuals from additional cohorts, independent of their genetic background, can then be scored on the sib-shared traits by measuring the angle between the new individual and the vector of the sib-shared trait, in a common coordinate system. Simultaneously, complex 3D facial structures are converted into univariate descriptors, allowing for the application of contemporary genomic tools. Although traditional univariate, anthropometric measures will remain a valuable resource because of their widespread use and simplicity, they fail to capture the full morphological complexity of 3D facial shape. In contrast, sib-shared traits defined in this work may affect multiple parts in the face at once, thereby preserving its multivariate and multipartite nature. Alternative strategies exist to reduce complex shape to univariate traits such as PCA, where each individual PC constitutes a unique facial trait. However, PCA does not necessarily imply biological relevance, whereas the use of traditional epidemiological approaches (e.g. family-based designs) to drive facial phenotyping facilitates a focus on facial traits that are genetically determined ,43.In the biometric identification task, the MA as similarity measure correctly identified the greatest number of siblings from among their peers. Note that, although we use self-reported kinship, the presence of half-sibs or unrelated pairs will have little to no effect as they most likely will not be selected in the matching experiment. Two main observations were made: angular measurements outperformed distance measurements; and Mahalanobis metrics were more discriminative than Euclidean metrics. These results are in line with previous research that demonstrated the role of the angle between vectors, encoded as deviations from a global average, in the perception of facial similarity rather than physical distances ,32,44. MFacial similarity between siblings was studied in the context of different global-to-local facial segments, determined by the structural variations in the EURO reference space. The EURO cohort was chosen as a reference because of its larger sample size in comparison with the SIB cohort, yielding a more robust segmentation of the face of which existing variations are more accurately described following PCA in combination with parallel analysis . FurtherAlthough matching at the global, full-face level performed better than any individual local matcher, the highest identification rate was achieved through the integration of information across segments. Specifically, almost half of the sibling pairs (44.32%) could be matched within the top-1% when segment-wise MA-corrected features were combined. The improvement of multibiometric systems over unibiometric systems is well-known as they consolidate multiple sources of evidence compensating for the limitations in performance of individual matchers . In otheIn previous work, we used biometric authentication methods to establish multiple ‘face-to-DNA’ classifiers, each predicting DNA-encoded aspects  from facial shape in order to match given faces against a DNA profile . SimilarInterestingly and affirmative of the proposed methodology, facial regions that were more often shared between siblings  coincide with regions of high heritability as described independently in previous work . SimilarTBX15 [PAX3 [RUNX2 [SOX9 [PAX1 [−8), though the effect was located near the area surrounding the nostrils . The nearest gene, AXIN2, plays a critical role in craniofacial and axial skeleton development of mice [AXIN2 in blood (GTEx), and overlaps with promoter and enhancer epigenetic marks in many cell types [−8). Previous GWAS also identified associations of the same region with aspects of nasal morphology [ROR2, in which mutations were found to cause a severe skeletal dysplasia known as Robinow syndrome-1 (OMIM 268310). Multiple dysmorphic facial features have been described in patients with Robinow syndrome-1 including frontal bossing, hypertelorism, long philtrum, triangular mouth and a broad nose [Ror2 has been shown to play a crucial role at multiple sites during organogenesis, providing a developmental basis for the distinct clinical features and anomalies described for Robinow syndrome [In a genome-wide meta-analysis, we observed 38 study-wide significant loci associated with normal-range facial morphology in individuals of European descent. An additional 180 loci surpassed the conventional genome-wide threshold of significance. Given that association signals close to this threshold are likely to be enriched for genuine signals , we repoTBX15 ,25,60, P15 [PAX3 ,25,60, R3 [RUNX2 ,17,25,60X2 [SOX9 , PAX1 [9X9 [PAX1 ,15,25 am of mice ,63. The ll types . An assooad nose , consistsyndrome ,67.−11) with down-turning corners of the mouth . Among the genes within 500kb of the lead SNP rs6695444, CACNA1E is a potentially relevant candidate gene. Mutations in the CACNA1E cause developmental and epileptic encephalopathy , a severe and genetically heterogeneous neurodevelopmental disorder characterized by characterized by refractory infantile-onset seizures, severe hypotonia, and profound developmental impairment [CACNA1E is less clear. In the study of Helbig et al. [CACNA1E mutations, but no other dysmorphic facial features could be observed. Among the novel genome-wide significant loci, some harbor genes known to play a role in craniofacial development or malformations. For example, we observed a signal near FOXE1  associated with nose  and chin  morphology, and a peak spanning EPHA3  showed an association in the buccal region . FOXE1 and EPHA3 are both orofacial clefting candidate genes [−8). The lead SNP rs6136885 is an intronic variant of the RIN2 gene. Mutations in this gene were found to cause RIN2 syndrome, formerly known as macrocephaly, alopecia, cutis laxa and scoliosis (MACS) syndrome (OMIM 613075), which is a rare connective tissue disorder characterized by multiple facial and skeletal anomalies [RAD21 showed an association  with variation in the tip of the nose . Mutations in RAD21 result in Cornelia de Lange syndrome (OMIM 614701), a developmental disorder characterized by mild intellectual disability and several facial dysmorphisms [In addition to the replicated findings, we also identified a total of 109 loci (1 study-wide) that did not overlap with the results of prior GWAS on normal-range facial variation. The 1q25.3 region showed a study-wide significant association  and 5 x 10−4 (UK)). However, there still is great overlap in the heritability range between traits that did or did not lead to significant findings when testing for genotype-phenotype associations  significant loci associated with normal-range facial morphology. Not only do we provide additional support for numerous previously reported loci, we also identify 109 new genome-wide significant signals (1 study-wide), some of them harboring promising candidate genes as implicated from human craniofacial malformations. Moreover, our analyses indicate a preferential activity of the novel loci in embryonic craniofacial tissue compared with CNCCs, suggesting their action in further differentiated cell-types of the face including osteoblasts and chondrocytes, further complemented by their involvement in limb development as evidenced by gene ontology analysis. A number of follow-up analyses were conducted on the derived set of univariate sib-shared features, such as the analysis of low-frequency variants and genetic correlations using GCTA and LDSC  Texts. HEthical approval was obtained at each recruitment site and all participants gave their written informed consent prior to participation. For individuals under 18 years of age, written consent was obtained from a parent or legal guardian. For the SIB sample, the following ethics approval was obtained: Ethics Committee Research UZ/KU Leuven (S56392: ML10285). For the PITT sample, the following local ethics approvals were obtained: Pittsburgh, PA (PITT IRB #PRO09060553 and #RB0405013); Seattle, WA (Seattle Children’s IRB #12107); Houston, TX ; and Iowa City, IA (University of Iowa Human Subjects Office IRB #200912764 and #200710721). For the PSU sample, the following local ethics approvals were obtained: Urbana-Champaign, IL (PSU IRB #13103); New York, NY (PSU IRB #45727); Cincinnati, OH (UC IRB #2015–3073); Twinsburg, OH (PSU IRB #2503); State College, PA (PSU IRB #44929 and #4320); Austin, TX (PSU IRB #44929); and San Antonio, TX (PSU IRB #1278). For the IUPUI sample, the following local ethics approval was obtained: Indianapolis, IN and Twinsburg, OH (IUPUI IRB #1409306349). For the UK sample, ethical approval for the study  was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Consent for biological samples has been collected in accordance with the Human Tissue Act (2004).Our study included one family-based (SIB) and one population-based (EURO) cohort. The basic demographic features of both cohorts are provided in The EURO cohort  included 3D facial images and genotype data of four independent samples, three originating from the US and one from the UK, each composed of unrelated individuals of European ancestry  S1 Tabl. The US http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/). Here, 3D facial images, genotype data and self-reported information on age, sex, height, and weight were available for 3,566 unrelated adolescents .The UK dataset was derived from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK-based birth cohort study ,82. A toPITT participants were genotyped on the Illumina OmniExpress + Exome v1.2 array, plus 4,322 investigator-chosen SNPs included to capture variation in specific regions of interest based on previous studies of the genetics of facial variation. For the PSU sample, participants were either genotyped on the Illumina Human Hp200c1 BeadChip or on the 23andMe v3 and v4 arrays. Participants from the IUPUI sample were genotyped on the Illumina’s Infinium Multi-Ethnic Global-8 v1 array. Standard data cleaning and quality assurance procedures were performed based on the GRCh37 (hg19) genome assembly using PLINK 1.9 . Specifi−6), ultimately resulting in 7,417,619 SNPs for analysis [Genotypes in the PITT, PSU and IUPUI samples, separately, were imputed to the 1000 Genomes Project Phase 3 reference panel . First, analysis .European individuals in the US cohort were selected using principal component analysis (PCA) of approximately 450,000 SNPs, after excluding from the imputed data all indels, multi-allelic SNPs, and SNPs with low MAF (≤ 0.1), and SNPs in linkage disequilibrium  from the 1000G Phase 3 dataset. A k-nearest neighbor algorithm was used to assign a 1000G population label to each US participant, and those with the 1000G European label of CEU, TSI, FIN, GBR, and IBS were selected for analysis only .−7). Individuals were excluded on the basis of gender mismatches, minimal or excessive heterozygosity, disproportionate levels of individual missingness (removed if > 3%), and insufficient sample replication (removed if IBD < 0.8). Only individuals of European descent, compared to the HapMap II dataset by way of multidimensional scaling analysis, were kept for imputation. SHAPEIT2 [For the UK dataset, genotype information was obtained directly from the ALSPAC database. Because restrictions are in place against merging the ALSPAC genotypes with any other genotypes, these were held separately during the analysis. UK participants were genotyped on the Illumina HumanHap550 quad chip platform by Sample Logistics and Genotyping Facilities at the Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America), supported by 23andMe. Genotypes were subjected to standard quality control methods. SNPs were evaluated for minor allele frequency (removed if < 0.01), call rate (removed if < 0.95), and deviation from Hardy-Weinberg genotype proportions  by generalized Procrustes analysis (GPA). This was done for original and reflected configurations combined, with the latter constructed by changing the sign of the x-coordinate . The aveA single EURO cohort was obtained by combining and GPA-aligning the US and UK datasets. We used this large cohort  as a ref2, height, weight, facial size and camera system [For the three datasets  separately, symmetrized facial shapes were first adjusted for the confounders of sex, age, agea system  in a para system , in a hia system . Per segIndividual faces can be described as single points or vectors within a multidimensional space, whose dimensions characterize facial variation ,32 S4 F. Image sThe ability of the similarity measures introduced above to match siblings was tested using a biometric identification setup for individual and combined segments. The multidimensional PC scores constituted the facial features of each participant for each facial segment. Shape information was combined by concatenating the Euclidean or Mahalanobis-corrected feature sets of multiple segments (segments 1–63) into a single feature vector per participant, similar to feature level fusion in multibiometric systems , after wk. High identification rates and a steep slope of the curve at higher ranks  indicated better performance [In a one-to-many setup, the identification task aimed to identify the one true sibling among a gallery of faces. The gallery was built from the combination of the corresponding sibling and all non-relatives present in the SIB cohort. When multiple (>2) siblings per family existed, the matching experiment was repeated for each pair provided that all other relatives were omitted. Per segment, a total of 546 experiments  were performed with gallery sizes varying from 417 to 423. In essence, similarity between the query image and all possible candidates in the gallery was measured and ranked in decreasing order from the most likely to least likely candidate. A final score was assigned per experiment reflecting the position of the true sibling in the sorted gallery list. The performance was assessed via cumulative match characteristic (CMC) curves, which plots the cumulative rank-k% identification rate for different values of formance .Per segment, sibling pairs were selected if they matched close to perfect (within the top-1%) in the identification task using MA, as this measure performed best in the sibling matching, and when they scored within the lowest 2.5 percentile for the measured similarity. The second constraint was imposed to differentiate between the matching on similarity and distinctiveness   as the lComplex shape transformations, encoded by the sib-shared traits, were converted into simple univariate phenotypes so that well-established toolboxes could be used to assess genetic contributions to the traits. This was achieved by scoring unrelated individuals along the vectors that make up the sib-shared traits. Scores were generated by computing the MA between the trait vector and all EURO individuals, as this measure best captured facial similarity in the matching of siblings, and represented the variation of the sib-shared traits in the EURO cohort . Individ2SNP) of the sib-shared traits was estimated using GREML implemented in the GCTA package [−3 and p < 2.2 x 10−3 in the US and UK cohorts, respectively.For the US and UK datasets separately, SNP-based heritability , corresponding to an adjustment for the number of independent phenotypes estimated from the eigenvalues of the phenotypic correlation matrix [We used the conventional threshold of p < 5 x 10n matrix .Because of the large number of signals considered, we used the relatively automated peak detection criterion based on genomic position solely. SNPs that reached the genome-wide threshold were grouped using a 1Mb window. For each region, the lead SNP was defined as the SNP with the lowest p-value for any of the derived traits, resulting in a total of 221 peaks. These peaks were followed up with examination of patterns of linkage disequilibrium (LD) , leadingGenes 500 kb up- and downstream of the lead SNPs were identified using the Table Browser of the UCSC Genome Browser . We firsChromatin activity in the vicinity of the lead SNPs was quantified using H3K27ac ChIP-seq signals from approximately 100 different cell and tissue types, including human CNCCs –103, mes−5 and p < 1.00 x 10−5 in the US and UK cohorts, respectively.We estimated the Pearson’s phenotypic correlation coefficients among the sib-shared traits. To estimate the genetic correlation between sib-shared traits that is tagged by SNPs, we conducted a bivariate GREML analysis ,109 of cS1 FigCumulative match characteristic curves of (A) the full-face (segment 1) and combined segments for the different similarity measures and (B) individual local matchers and combined segments using the Mahalanobis angle. The diagonal line represents random performance. ED, Euclidean distance; EA, Euclidean angle; MD, Mahalanobis distance; MA, Mahalanobis angle.(TIF)Click here for additional data file.S2 FigRank-k and rank-k% identification rates for varying gallery sizes based on the full-face matching of siblings using the Mahalanobis angle. The experiment was repeated 1,000 times, with mean identification rates represented by the solid (rank-k) and dashed (rank-k%) lines, and the minimum and maximum performance indicated by the shaded area. For rank-k% matchings, results are plotted for gallery sizes of 100 and above. In case of rank-1% identification, results are valid only for multiples of 100 as ranks cannot have non-integer values, explaining the decreasing/increasing pattern observed.(TIF)Click here for additional data file.S3 FigCumulative match characteristic curves of individual local matchers for the three sex-based groups . Facial similarity was determined using the Mahalanobis angle. The diagonal line represents random performance.(TIF)Click here for additional data file.S4 Fig(A) Illustration of a multi-dimensional facial space using PCA, and (B) supervised scoring of individuals onto a specific shape direction coding for the sib-shared trait, depicted by the blue nasal shape. Positive scores indicate the presence of facial features similar to those shared by siblings, while negative scores correspond to features opposite to the sibling pair (left y-axis). The score distribution for all EURO participants is plotted on top of the histogram (right y-axis).(TIF)Click here for additional data file.S5 Fig2SNP) of the 1,048 sib-shared traits was estimated using GCTA and average values per segment are plotted on top of each node. The structure of the rosette plot corresponds to the polar dendrogram displaying the facial segments in Mean phenotypic variance explained by commons SNPs in the US cohort and UK cohort. SNP-based heritability (h(TIF)Click here for additional data file.S6 Fig−8) and the dashed horizontal line represents the study-wide threshold (p < 1.55 x 10−10).Combined Manhattan plot of the sib-shared traits, highlighting the novel and overlapping loci in red and blue, respectively. Per SNP, the lowest meta-analysis p-value across all 1,048 traits is plotted. The solid horizontal line represents the genome-wide significance threshold (p < 5 x 10(TIF)Click here for additional data file.S7 Fig(A) Number of genome-wide significant loci that showed an association with one (or multiple) of the sib-shared traits, defined within a particular segment. (B) Proportion of sib-shared traits defined per facial segment (%) that showed an association with at least one of the 218 genome-wide significant loci. The structure of the rosette plot corresponds to the polar dendrogram displaying the facial segments in (TIF)Click here for additional data file.S8 FigTop 15 gene ontology enrichment of biological process GO terms, human phenotypes and mouse phenotypes. Plotted is the binomial test FDR (blue) and binomial enrichment (orange).(TIF)Click here for additional data file.S9 Figp, phenotypic correlation; Rg, genetic correlation.Relationship between genetic correlation p-values (x-axis) and the phenotypic correlation (y-axis) in the US and UK cohort. Pairwise correlations between traits that were derived from the same family were excluded. R(TIF)Click here for additional data file.S10 FigPairwise correlations between facial and non-facial traits that reached nominal significance (p < 0.05), computed using cross-trait LDSC. Facial traits are sorted per quadrant, corresponding to the polar dendrogram displaying the facial segments in (TIF)Click here for additional data file.S11 FigHierarchical facial segmentation and number of significant principal components determined by parallel analysis in the (A) EURO  and (B) SIB (n = 424) cohort. (C) Number of significant components retained by parallel analysis in varying, randomly generated subsets of the EURO cohort. (D) Cumulative match characteristic curves for full-face matchings (segment 1) of siblings based on the Mahalanobis angle in a EURO-based (solid line) and SIB-based (dashed line) reference space. In the ‘SIB70’ space (dotted line), the number of dimensions is equal to the original EURO reference space. ED, Euclidean distance; EA, Euclidean angle; MD, Mahalanobis distance; MA, Mahalanobis angle.(TIF)Click here for additional data file.S12 FigThe amount of variation explained by the sib-shared traits expressed as percentage for each facial segment. The structure of the rosette plot corresponds to the polar dendrogram displaying the facial segments in (TIF)Click here for additional data file.S13 FigShown are the boxplots of the distribution of H3K27ac ChIP-seq signals in 20 kb regions around the (A) 218 lead SNPs, (B) 100 overlapping SNPs and (C) 118 non-overlapping SNPs in various adult, embryonic and in vitro–derived cell types. Overlap was determined with reference to the study of White et al. , who uti(TIF)Click here for additional data file.S14 FigLink between SNP-based heritability and (A) study-wide (‘SW’) and (B) genome-wide (‘GW’) significance of sib-shared traits in the GWAS meta-analysis. Traits that didn’t reach statistical significance in the GWAS are coded as ‘0’; traits that were associated with at least one of the identified loci are coded as ‘> = 1’. The two-sample t-test p-value is plotted on top of each panel, with significant values indicated in bold.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableR1, rank-1% identification rate; R10, rank-10% identification rate; R20, rank-20% identification rate; ED, Euclidean distance; EA, Euclidean angle; MD, Mahalanobis distance; MA, Mahalanobis angle.(XLSX)Click here for additional data file.S3 Table2SNP, SNP-based heritability; SE, standard error.Significant heritability estimates are indicated in bold. h(XLSX)Click here for additional data file.S4 TableThe column ’Best Trait’ represents the index  of the sib-shared trait in which the lowest meta p-value  was found, together with the corresponding segment and quadrant in which the original trait was defined (’Best Segment’ and ’Best Quadrant’). MAF, minor allele frequency; SE, standard error.(XLSX)Click here for additional data file.S5 TableNumVar, number of variants tested per gene.(XLSX)Click here for additional data file.S6 TablePairwise genetic correlations in the US cohort (lower/left triangle) and UK cohort (upper/right triangle). Standard errors are displayed between brackets.(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 TableRg, genetic correlation; SE, standard error.(XLSX)Click here for additional data file.S9 TableStatistical significance is indicated in bold.(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.