Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes. Affecting around 0.5% of the human population4, schizophrenia is associated with increased mortality5 and a low rate of recovery of only 13.5%6. Moreover, schizophrenia has been associated with high rates of comorbid illnesses such as coronary heart disease, stroke, diabetes, respiratory diseases7 and a 15% rate of unnatural deaths, including suicide5.Schizophrenia is a complex mental disorder characterized by psychosis , social and emotional withdrawal, and cognitive deficits. Although the typical onset does not occur until adolescence or early adulthood, epidemiological and molecular evidence has consistently described the early neurodevelopmental nature of the disorder8, thus corroborating the major contribution of genetic variability to the development of the disorder. Although both rare copy number variants (CNVs)9 and disruptive mutations12 importantly contribute to the polygenic architecture of schizophrenia, genome-wide association studies (GWAS) have demonstrated that contribution from common genetic variation  account for up to a half of the genetic variance in liability13. Methods capturing the cumulative risk such as polygenic risk scores (PGS)14 or linkage disequilibrium (LD) score regression (LDSC)15 have demonstrated an uneven distribution of schizophrenia genetic heritability across the genome, with enrichment of synaptic or brain-related pathways18, even though predisposing loci are genome-wide distributed.Family, twin, and adoption studies have estimated schizophrenia heritability to be 65–80%19 and the early mortality associated with schizophrenia5, negative selection should result in the purge of deleterious alleles that contribute to the disorder. However, the persistence of common predisposing alleles and comparable prevalence rates of schizophrenia in human populations suggests an evolutionary paradox21. Some authors have attempted to explain this paradox by balanced selection, by which schizophrenia risk alleles could also provide advantages under certain environments22. In this sense, a recent study described that schizophrenia alleles are linked with creativity in the general population23, thus supporting classical theories that proposed schizophrenia vulnerability as a price to pay for the development of language and abstract or creative thinking25. Recent works, however, have shown strong evidence against a positive selection of predisposing loci28.Given the reduced fertility25. In this context, the study of human populations offers a unique opportunity to address recent natural selection (RNS)30 that has taken place since African populations initiated dispersion into Eurasia around 60,000–100,000 years ago33. A recent study has demonstrated that genetic predisposing variants to schizophrenia exert similar risks across East Asian and European populations, thus pointing to a shared genetic basis for schizophrenia regardless of the ethnic or cultural backgrounds34. However, at the same time, SNPs associated with schizophrenia display greater population differentiation than matched control SNPs29, and some are reportedly even population-specific36. This scenario suggests that recent population differentiation events could have shaped the allelic distribution of schizophrenia polygenic variation across populations.Most of the previous studies addressing the evolutionary nature of schizophrenia lack a clear dynamic perspective of the environment during human evolution. Classical theories stating schizophrenia as a by-product of human ancestral evolution may not be relevant to explain the recent evolution of schizophrenia predisposing allelesloci38, selective forces for other traits could have acted to differentiate human populations across schizophrenia predisposing alleles, thus generating uneven schizophrenia genetic risks across populations. For instance, the risk allele of SLC39A8, a genome-wide significant gene in schizophrenia39, could have arisen after the human expansion to Europe to ease adaptation to colder climates by reducing blood pressure and risk of hypertension40. Older environments, however, could have promoted different genetic adaptations. In this sense, the schizophrenia alleles that have emerged and rose their frequency due to polygenic adaptation after human divergence from Neanderthal 42 may not be adaptative throughout modern environments, and therefore subject to selective pressures in modern humans.Moreover, given the high comorbidity rates of schizophrenia with other mental and somatic conditions and the pleiotropic effects of its predisposing 29. Many of the available methods are based on FST statistical tests, which need an a priori classification of studied individuals in populations43. However, many methods do not account for the hierarchical population structure due to the unequal differentiation within populations, which may lead to detection of many false positives44. Moreover, classification in fixed population groups may be challenging when population ascertainment does not reveal differentiated clusters or individuals are misclassified46. Some individual-based methods performing genome-wide selection sweeps based on principal component analysis (PCA) have been recently developed to address these challenges48.The classical approach to discover RNS signals has consisted of seeking variants with large allele frequency differences between populationspcadapt48, a recently developed PCA-based method, we described a significant association between RNS signatures and schizophrenia. Second, by focusing on the derived alleles arisen during human evolution, we demonstrated a significant trend toward fixation of derived protective alleles. Third, we explored the biological and cell-type enrichments of the RNS markers associated with schizophrenia and observed less brain and neuronal specificity than across markers not subject to RNS. Finally, among the derived alleles arisen during recent evolution, we observed greater pleiotropy for protective than for risk alleles, thus suggesting that recent protective selection to schizophrenia is related to other phenotypes resulting from recent human adaptation to different environments.Here we aimed to perform a comprehensive approach to evaluate the effect of RNS on common genetic variation predisposing to schizophrenia Fig. . First, 31). Pcadapt48 was used to study RNS signals across human populations from 1000 Genomes Project data. Up to 3 principal components (PCs) were kept based on the proportion of variance explained . A marked accumulation of SNPs subject to RNS (pRNS < 0.05) was detected .RNS was characterized across the genome  across the most stringent schizophrenia GWAS thresholds  = 3.45 ; Fig. p-values, we selected the 5% of LD-independent SNPs with the lowest pRNS after LD-clumping based on RNS p-values  = 3.22 ; Fig. 51, a related brain disorder52 that may escape natural selection due to its late onset. LD-clumping was performed based on Alzheimer’s GWAS p-values. No significant enrichment between RNS and Alzheimer’s disease was observed  = 3.45 .51, 4.59) = 3.45 .51, 4.59) = 3.45 .51, 4.59pSCZ < 0.05; NSNPs = 41,079; LD-clumping based on pSCZ) and found a significant correlation . This result was confirmed by conducting 10,000 random permutations of the GWAS results with respect to the pRNS values and testing whether the observed correlation was significantly higher than the null distribution . We repeated this analysis using GWAS data from Alzheimer’s disease and observed no significant correlation with RNS  of schizophrenia across the RNS markers (h2SNP enrichment (CI 95%) = 1.31 (1.13 – 1.50); p = 1.19 × 10–3; Supplementary Data Heritability enrichment analyses using LDSC showed a significant SNP-based heritability enrichment (ment CI 9% = 1.31 SCZ) in an independent case–control cohort . After dividing the resultant schizophrenia predisposing variation into 20-quantiles (NSNPs = 3052) ranked by pRNS, we observed a reduction of the explained variance in the case–control cohort when moving to higher pRNS , distance to nearest gene, and gene density (see Methods). PGSSCZ prediction from the first quantile was found to be significantly higher than the distribution of 1000 predictions from matched SNP selections . Again, a similar reduction of the explained variance towards higher pRNS was found . Also, PGSSCZ prediction from the first quantile was found to be significantly higher than the distribution of 1000 predictions from the remaining SNPs  across the most stringent schizophrenia GWAS thresholds  = 2.76 ; Supplementary Data loci across East Asian-based RNS markers  = 1.65 ; Supplementary Data Following a similar methodology, we considered the 5% of LD-independent SNPs with the lowest p < 1 × 10–16).Moreover, we described a great overlap between European ancestry based and global RNS markers. Across the 46,872 common LD-independent SNPs in both European and all population RNS data, 1176 out of 2249 RNS markers were among the top 5% with highest probability to be RNS marker ). We calculated GWAS derived-ORSCZ referred to the derived allele and evaluated whether RNS markers associated with schizophrenia were enriched for derived alleles conferring risk or protection to the illness.In order to study whether RNS markers confer risk or protection to schizophrenia, we focused on the derived alleles arisen during human evolution . We used LD-independent SNPs with available information of the ancestral allele  in the 1000 Genomes database (N10(derived-ORSCZ) (CI95%) = − 0.052 ; p = 0.031 at PSCZ threshold = 5 × 10–10; Nprotective-SNPs = 7; Nrisk-SNPs = 1; Fig. 10(derived-ORSCZ) (CI95%) = 0.001 ; p = 0.759 at PSCZ threshold = 5 × 10–10; Nprotective-SNPs = 74; Nrisk-SNPs = 69; Fig. We described a significant enrichment of RNS markers for derived alleles with a protective effect on schizophrenia risk at the most stringent GWAS thresholds .Nine out of 13 RNS markers from schizophrenia GWS 55, we evaluated PGSSCZ performance in the independent CIBERSAM case–control sample  for RNS markers and the rest of non-selected SNPs, by considering variants with protective or derived risk alleles separately  and protective (NSNPs = 3168) SNPs subject to RNS, we then conducted PGSSCZ comparisons after using random permutations of 1000 SNPs for each subset of SNPs . The difference in explained variance between risk and protective variants was smaller across non-RNS SNPs . We observed similar MAF across protective (MAF(CI95%) = 0.366 ) and risk (MAF(CI95%) = 0.336 ) RNS markers, thus ruling out that the results could be inflated by higher MAF of risk SNPs.Additionally, as pleiotropy might limit effect sizeloci that were both subject to RNS and associated with schizophrenia. We used LDSC applied to specifically expressed genes (LDSC-SEG) to evaluate the schizophrenia heritability enrichments across RNS markers within 10 different tissues, 13 brain related tissues, and 3 brain cell-types and compared them to the LDSC-SEG enrichments across the rest of SNPs (Supplementary Data p = 0.029) and neuronal cell-type (FDR-p = 0.017), schizophrenia predisposing variation within RNS markers was found to be enriched beyond brain-related tissues and not enriched in any brain-related cell types  and not harboring (468 genes) RNS markers were tested for functional over-representation analysis (ORA) using the whole gene-set as background. Although no brain-relevant overrepresentation was found in any case, we observed an enrichment of loci subject to RNS in brain unrelated functional signatures  and that the derived alleles confer protection rather than risk to schizophrenia. Thus, our results are in line with recent studies reporting negative selection of schizophrenia predisposing alleles61. Pardiñas et al.26 have suggested a plausible explanation of the purported schizophrenia evolutionary paradox of persistent common variation and progressive removal of risk alleles through background selection62. By this mechanism, removal of rare haplotypes harboring deleterious mutations could reduce genetic diversity and allow common alleles with small deleterious effects to maintain a high frequency by drift62.Previous studies have tried to explain why genetic predisposing variation to schizophrenia persists in the population, despite the reproductive fitness reduction in affected individuals33, a reliable explanation for the persistence of common alleles conferring risk to schizophrenia may reside on fitness advantages provided during earlier stages of the human evolution. In this sense, a previous study focusing on the Sapiens-Neanderthal divergence described a pattern of positive selection of schizophrenia predisposing variation that could have contributed to the Homo sapiens success against other human species41. Moreover, schizophrenia loci were also described to be enriched in genomic regions that had experienced specific evolutionary acceleration during early human evolution (HARs) in comparison with other non-human primates63. Although these evidences reinforce the idea of schizophrenia predisposing variation acquisition as a price to pay for human abilities such as language development24 or even more adaptive immunological profiles64, the evolutionary pressures to retain schizophrenia risk alleles as an evolutionary advantage might have stopped acting before the dispersion of the Homo sapiens from Africa. In line with our results, studies exploring RNS patterns have not found a pattern of positive selection of schizophrenia predisposing alleles29. A recent study has proposed an evolutionary framework by which schizophrenia risk alleles increased their frequency with the development of the social brain and high-order cognitive functions, but after a turning point the trend was reversed and a negative pressure against schizophrenia alleles became the standard61. Interestingly, this turning point from positive to negative selection of predisposing alleles in schizophrenia could be shared by other psychiatric traits. In fact, a recent work observed a similar scenario for attention-deficit/hyperactivity disorder (ADHD): while introgressed Neanderthal alleles were enriched in ADHD risk variants, modern environments triggered the progressive elimination of ADHD-predisposing alleles65. This temporal perspective should also prevent readers from reaching conclusions from current evolutionary dynamics. While our approach explored RNS from around 100,000 years ago, recent methods have been used to assess modern selection from up to 100 generations (around 2000 years ago)67. In these recent studies, schizophrenia was also affected by even more recent negative selection67.Apparent contradictory results regarding the positive or negative selection of alleles that predispose to schizophrenia need to be integrated using a dynamic perspective. Since our study has focused only on genetic selection initiated after African populations started their dispersion into Eurasia around 60,000–100,000 years agoloci.Given the evidence here described about the relationship between predisposing variation to schizophrenia and RNS throughout human populations, we assessed whether these evolutionary dynamics were also present at the Asian or European intrapopulation level. While the association between RNS and the genetic predisposition to schizophrenia is replicated in European populations, this association appears to be absent among East Asian populations. This finding suggests that the observed relationship between schizophrenia and recent natural selection may be driven by selective pressures resulting from other traits, acting through specific environments that are unique to certain populations. However, the considerably smaller sample size of the Asian schizophrenia cohorts compared to the European samples could be the main cause of the lack of association between recent natural selection and schizophrenia predisposing loci subject to RNS, as compared to those not subject to RNS. While common schizophrenia predisposing variation not subject to RNS exhibit clear patterns of heritability enrichment in brain-related tissues and neuronal cell types, aligning with expectations from prior studies, schizophrenia predisposing variation under RNS display lower enrichments in neuronal and brain-related functions, along with significant heritability enrichments in other tissues not directly linked to the central nervous system, such as the kidney or liver  within the SLC39A8 gene has been described to be under positive selection of its derived risk T allele in Europeans39. This evolutionary event, however, has been reported to be driven by the migration of modern humans out of Africa and the consequent adaptation to colder climates by reducing blood pressure and the risk for hypertension40. Similarly, survival of the derived risk T allele of variant rs1150711 (C/T) of the ZNF323 gene could be the result from the ameliorated lung function provided by this allele to European populations59. These and similar examples of antagonistic pleiotropy, in which the allele that predisposes to schizophrenia confers adaptive advantage or protects against another condition, have been used to explain the evolutionary paradox of schizophrenia. However, our study suggests that, on a genome-wide scale, non-antagonistic pleiotropy could be contributing to the RNS enrichment across schizophrenia common variation. In this sense, recent positive selection of schizophrenia derived protective alleles could also be a likely by-product of the pleiotropic effect of some genetic variants favoring adaptation to the environment during modern evolution of human populations69.In fact, our results pointing to pleiotropic effects of schizophrenia 29 could therefore be the result of distinct evolutionary pressures related to other phenotypes, with predisposing variants having also an impact on schizophrenia risk. Although most of schizophrenia alleles have been described to exert similar effects across populations70, allele frequencies could vary across populations, thus leading to differences in individual genetic risk profiles. In this sense, greater polygenic scores for schizophrenia have been described in African populations71. However, reportedly greater polygenic scores and our finding of positive selection of schizophrenia derived protective alleles do not imply that African populations are at higher genetic risk for schizophrenia. Since GWAS have been performed mainly in cohorts of European ancestry, there is a limited portability for the estimation of polygenic risk scores across African cohorts using the available GWAS data71, and these inferences should be avoided.Differing genetic risks for schizophrenia described across distinct populations55. Consequently, the predictive performance of a set of variants will decrease as the level of pleiotropy increases. If there is an excess of pleiotropic variants among the RNS derived protective alleles compared with derived risk alleles, this might explain the higher predictive capacity of a PGS based on derived risk alleles.By evaluating the PGS predictions on schizophrenia within RNS with derived protective and risk alleles separately, we observed greater variance explained across similar numbers of risk than protective RNS markers Fig. D. These 49). In this sense, however, our sensitivity analyses considering population specific data lead to similar results in the case of European population, and therefore validating our findings, although this scenario was not replicated in East Asian populations. Therefore, the implications of our results suggesting protective selection by non-antagonistic pleiotropy may be limited, since environments shaping this evolutionary pattern could be absent in other populations. Nevertheless, this inconsistency between European and East Asian population specific analyses may be caused by a great difference in statistical power, and larger Asian cohorts should be used to rule out this possibility. Second, to have sufficient statistical power, our analysis is restricted to variants having a MAF higher than 5%. This filter could have removed many GWAS signals with remarkable RNS effects. For instance, a variant within SLC39A8, a schizophrenia-associated gene with well-described evolutionary properties, is absent from this study due to its low allele frequency. Third, along the lines of a previous related study72, we have used the top 5% markers with the highest probability of being subject to RNS for our main analyses in order to have enough statistical power. Finally, it is worth noting that while pcadapt has been employed in previous studies involving similar 1000 Genomes data47, PCA-based methods have been reported to have a susceptibility to false positives when applied to populations with pronounced stratification and a non-continuous principal component space. In this regard, the concurrence of selection outliers detected by alternative Fst methods served as validation for our identification of RNS markers using pcadapt.This study was subject to several limitations. First, we described RNS markers using whole 1000 Genomes data, while we used association data from mainly European-ancestry schizophrenia cohorts , we performed scans detection of RNS signatures using a principle components-based approach implemented in 73, across African, European, and Asiatic populations. American populations, who show greater genetic heterogeneity due to recent admixture events, were eliminated from the analysis, as suggested by the Pcadapt developers47. Moreover, given the elevated inbreeding coefficients for some of the 10,000 Genomes subpopulations74, only 1699 subjects that have been previously described as outbred and unrelated74 were finally considered. We retained common genetic variation (MAF > 0.05), biallelic, and overlapping with summary data from the schizophrenia GWAS used in this study, thus yielding a total of 5,554,437 SNPs.To study RNS signatures we used genetic data from the 1000 Genomes phase 3 sequencing database75, Pcadapt accounts for LD-genome structure  for the estimation of RNS probabilities for each of the 5,554,437 SNPs considered. The distribution of PC loadings was evaluated to ensure that selection signals correspond to regions subject to adaptation rather than to regions of lower recombination (high LD). We used Cattell’s rule to select the appropriate number of PCs (K = 3). Pcadapt test was then performed and derived p-values (pRNS) for every SNP were calculated to inform about their likelihood of being subject to RNS.Since LD can affect ascertainment of population structurepRNS; RNS markers) after LD-clumping based on schizophrenia GWAS p-values (PLINK v1.9 parameters-clump-r2 0.1-clump-kb 500) and refer to them as RNS markers. We prioritized the association with schizophrenia and the top 5% RNS markers were selected to have enough SNPs with which to perform the subsequent analyses. However, to ensure that the reported results were not biased by LD-clumping based on SCZ p-values, we also considered the top 5% RNS markers after LD-clumping based on pRNS and refer to them as LD-RNS markers.Throughout the subsequent analyses of the study, we consider the top 5% selected markers . Main analyses were performed using the whole summary statistics . Moreover, both ancestry-specific summary statistics for European  and East Asian subjects  were used for additional sensitivity analyses to explore association between RNS signals within European or East Asian populations and ancestry-specific predisposing variation to schizophrenia. As a specificity analysis for the detection of selection signals, we also used Alzheimer’s disease51 GWAS summary statistics in independent analyses. This disorder was selected based on its adequate statistical power comparable to that of schizophrenia, its described clinical similarities, and its later age at onset, after natural selection pressure has exerted its effect. Details regarding the summary statistics used are described in Supplementary data We used the latest summary statistics of the schizophrenia GWAS (PGC-SCZ3) conducted by the Psychiatric Genomics Consortium (PGC)Pcadapt in the analysis. 6,693,073 variants were used in the case of Alzheimer’s disease.The Major Histocompatibility Complex (MHC) region was excluded in all analyses and only biallelic SNP and those with an imputation quality score > 0.9 were considered. Overall, 5,554,437 variants from 1000 Genomes overlapping with schizophrenia summary data were converted to PLINK format and used as the input for 39. There is no overlap between PGC-SCZ2 and SCZ_CIBERSAM samples.We used a case–control sample including 1927 schizophrenia cases  and 1561 healthy controls (HC)  from CIBERSAM  as an independent target sample for PGS predictions (SCZ_CIBERSAM). All research was performed in accordance with relevant guidelines/regulations. Informed consent was obtained from all individual participants included in the study, and ethic boards/committees from the following involved Spanish hospitals approved the protocol: Clinical Research Ethics Committee of the Hospital Sant Joan de Reus, Research Ethics Committee of Asturias, Research Ethics Committee of Cantabria, Bioethics Commission of the University of Barcelona (CBUB), Galician Regional Research Ethics Committee, Scientific Research Ethics Committee of Hospital Gregorio Marañón and Research Ethics Committee of the Valencia’s clinical Hospital. All participants were genotyped as part of the Psychiatric Genomics Consortium (PGC), and passed quality control (QC filters) per PGC-SCZ2 criteriaSCZ) were calculated from PGC-SZ239 GWAS summary statistics as discovery sample and SCZ_CIBERSAM case–control cohort as target sample. Summary data from the latest schizophrenia GWAS49 was not used to estimate PGS because target sample was included as part the cohorts in the study. PGSSCZ were calculated using SNPs present in the 1000 genomes database for the studied populations. We included only biallelic variation, with imputation quality scores > 0.9, and excluded indels. 65PLINK 1.9 was used to calculate PGS across schizophrenia patients and healthy controls weighted by the logOR in the discovery sample. Standardized PGS were calculated, and significance was evaluated by logistic regression, using case–control status as dependent variable and sex, age, and the first 10 MDS ancestry components as covariates. We calculated explained variance attributable to PGS as the increase in Nagelkerke’s pseudo-R2 between a model with and without PGS variable.PGS for schizophrenia  and the whole genome variation to estimate predictions in the SCZ_CIBERSAM case–control cohort. The most significant P threshold (P < 0.2) was selected for the subsequent PGS analyses stratified by RNS. We then performed stratified PGS predictions to compare SNPs that were subject to RNS to those that were not. Summary SNP data with PSCZ < 0.2 was divided into 20 quantiles of increasing pRNS. PGS predictions were performed across the 20 SNP quantiles in the CIBERSAM case–control sample. Linear regression was performed to evaluate the change in the variance explained by PGS on case–control status in the CIBERSAM sample across the 20 quantiles. Nagelkerke’s pseudo-R2 from PGS predictions using the first quantile, corresponding to the 5% of SNPs with the highest probability of being subject to RNS, was compared against the distribution of pseudo-R2 obtained by selecting the same number of SNPs from the remaining 95% of SNPs. On account of the likely bias in prediction due to the non-random genome features of SNPs subject to RNS, selection of SNPs from the remaining 95% was carried out by identification of sets of randomly drawn SNPs that were matched to the top 5% selected SNP based on allele frequency, number of SNPs in LD, distance to nearest gene, and gene density using default parameters from SNPsnap77 (https://data.broadinstitute.org/mpg/snpsnap/). Since an earlier study by Price et al. suggested that recent selection signals could be partly explained as artifacts caused by long-range LD regions53 that can lead to inflated variance explanations from variants subject to RNS, we repeat the previously explained stratified PGS predictions after removing 24 described Long-Range LD Regions as a sensitivity analysis to assess whether the results hold.Initially, we used several P thresholds (SCZ < 1) or risk (derived-ORSCZ > 1) to schizophrenia. Across variants subject (RNS markers) and non-subject to RNS, independently, we compared 1000 predictions from protective and risk variants, using a similar number of SNPs (NSNPs = 1000), on the SCZ_CIBERSAM case–control sample. Wilcoxon test was used to compare the pseudo-R2 distributions of predictions for SNPs with a protective (derived-ORSCZ < 1) or risk (derived-ORSCZ > 1) derived allele across SNPs subject (RNS markers) and non-subject to RNS.Additionally, we also calculated stratified PGS after subsetting SNP summary data into those for which the derived allele that emerged during human evolution conferred protection (derived-ORh2SNP) estimates for the resulting genome partitions after dissections of schizophrenia summary genetic data based on RNS markers following the recommended procedure57 (https://github.com/bulik/ldsc/wiki/Partitioned-Heritability).We calculated SNP-based heritability (i)49 that belong to the top 5% with the highest likelihood (lowest pRNS) of being a RNS marker) and the rest of the SNPs (the remaining 95% of the SNPs from schizophrenia GWAS summary data).RNS markers (SNPs from schizophrenia GWAS (PGC-SCZ3) summary statisticsii)56, 13 brain-related tissues (Brain GTEx57), and 3 brain cell-type annotation files .Sub-annotations from the intersection between the described annotations (RNS markers and the rest of SNPs) and gene expression data from 10 whole tissuesFirst, we created per SNP annotation files (one per chromosome and desired annotation). Each file consisted of a row per SNP and a column for each sub-annotation . Annotation files were created for:ldsc v1.0.157, a command line tool for estimating heritability. We performed both heritability enrichment analyses across the described annotations (–h2) and one-sided t-tests to evaluate whether the cell-type enrichment in schizophrenia within a particular annotation was higher than the same cell-type enrichment in schizophrenia outside the target annotation (anti-target) but within a background annotation (using –h2-cts). Control background annotations from the original study were used57.We used 73. LD scores were computed for each annotation file using the recommended parameters: 1-cM window (–ld-wind-cm 1), restriction to Hapmap3 SNPs, and exclusion of the MHC region due to its high gene density and exceptional LD, as recommended by the developers15. The ‘–overlap-annot’ argument and 1000 Genomes phase 3—based frequency files (‘1000G_Phase3_frq’ files via –frqfile-chr argument) and LD weights (‘weights_hm3_no_hla’ files via-w-ld-chr argument) were used for LD score calculations.We ran LDSC using associated data files from phase 3 of the 1000 Genomes Projecthttps://data.broadinstitute.org/alkesgroup/LDSCORE/), as independent LD scores.Partitioned LDSC computes the proportion of SNP heritability associated with each annotation column while considering all the remaining annotations. This is performed by regression models using the estimated LD-scores jointly with other independent LD scores for baseline annotations to improve the model performance. We used the full baseline model v2.2, consistent of a full annotation column  and 158 independent functional annotations, available at LDSC repository  that contained at least one credible SNP from 249 regions with low numbers of expected causal SNPs (K < 3.5) were used49. Genes mapped only from RNS markers (22 genes) were compared against genes mapped by SNPs not subject to RNS (468 genes).We used FINEMAP results of the schizophrenia GWS http://www.webgestalt.org/)78. All mapped genes from schizophrenia GWS loci studied here in this study were used as a background list. Enrichment across gene sets from Gene Ontology (GO) cellular components (CC) and biological functions (BF) categories was evaluated. FDR by Benjamini–Hocheberg adjustment was used to evaluate enrichment significance across GO gene sets, and a minimum threshold of 5 genes overlapping with each gene set was considered.Functional overrepresentation of genes from each category was assessed by over-representation analysis (ORA) with WEB-based GEne SeT AnaLysis Toolkit . One-sample Kolmogorov–Smirnov test was performed to evaluate the overrepresentation of SNPs subject to RNS after principal components-based approach implemented in Pcadapt. Fisher’s exact tests were performed to study the enrichment for the RNS markers  or the LD-RNS markers  across the different schizophrenia GWAS thresholds . We also analyzed enrichment for the RNS markers for Alzheimer’s disease (NSNPs = 6263). Spearman correlation between P values from GWAS and RNS was performed . Correlations were confirmed by conducting 10,000 random permutations of the GWAS results with respect to the pRNS values and testing whether the observed correlation coefficients were significantly higher than the ones in the null distribution of the permuted datasets.Statistical analyses were performed using R  for each SNP and evaluated the overrepresentation of protective or derived risk alleles across schizophrenia GWAS thresholds: chi-square tests were performed to compare the proportion of derived risk alleles (derived-ORSCZ > 1) within and outside RNS markers (top 5% RNS loci) across schizophrenia GWAS thresholds . Additionally, one sample t-tests were performed to compare the average derived-ORSCZ within and outside RNS markers (top 5% RNS loci) to the neutrality across schizophrenia GWAS thresholds . Two sample t-tests were also performed to compare derived-ORSCZ within vs outside RNS markers.We calculated GWAS derived-ORloci subject to RNS with the derived protective or risk allele having the lead association with schizophrenia among all phenotypes included in GWAS Atlas (https://atlas.ctglab.nl/)79.We used Fisher's exact tests to evaluate the enrichment of schizophrenia GWS Supplementary Figures.Supplementary Information 1.


A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state. A challenge in diagnostics is integrating different data modalities to characterize physiological state. Here, the authors show, using the heart as a model system, that cross-modal autoencoders can integrate and translate modalities to improve diagnostics and identify associated genetic variants. In particular, such cross-modal representations provide an opportunity for a broad range of downstream tasks such as (1) prediction of clinical phenotypes for diagnostics; (2) imputation of missing modalities in biomedical data; and (3) identification of genetic variants associated with a given organ system. Using the heart as a model system, we here develop such an integrative framework and show its effectiveness in these three downstream tasks.Clinicians leverage measurements across many complementary diagnostic modalities to develop an integrated understanding of a patient’s physiological state. For example, heart function can be interrogated with a variety of modalities, such as electrocardiograms (ECGs) that provide myoelectric information , and cardiac magnetic resonance images (MRIs) that provide structural information . By utilizing measurements across both modalities, we can gain a more holistic view of cardiovascular state than with either modality alone. The recent availability of large-scale cross-modal measurements in biobanks3. Unlike multi-modal data integration approaches based on classical methods such as canonical correlation analysis (CCA)7 or non-negative matrix factorization9, our approach relies on a class of machine learning models called autoencoders. Autoencoders11 are a class of generative models that serve as a standard method for learning representations from unlabelled data. These models have been successfully applied in a variety of applications including computer vision14, chemistry15, and biology21. A line of recent works utilize autoencoders to learn joint representations of multi-modal data including natural images and captions in computer vision27, nuclear images and gene expression in biology20, and paired clinical measurements31. Indeed, autoencoders have been observed to perform competitively with other multi-modal integration methods including classical integration approaches using CCA7 and generative adversarial networks33. Unlike these prior works that focus primarily on improving a specific downstream task such as phenotype prediction or modality translation through multi-modal data integration, we develop a generalized representation that improves performance on several downstream applications. We demonstrate the utility of this representation on three important biomedical tasks: in addition to phenotype prediction and multi-modal data integration and translation, we show that our cross-modal representation yields a new framework for characterizing genotype–phenotype associations. While various prior works have conducted genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with cardiovascular diseases35, features measured on ECGs37, or features measured on cardiac MRI39, these GWAS approaches have relied on labelled data derived from individual modalities. Instead, our approach can identify SNPs that affect cardiac physiology in an unsupervised and general way. Namely, rather than merely identifying SNPs that affect a single phenotype such as the QT interval, our approach identifies SNPs that generally impact phenotypes present on ECGs or cardiac MRIs.Multi-modal data integration is a rich field with a variety of methods developed for specific applications. A survey of multi-modal approaches is presented in ref. 1, we develop a cross-modal autoencoder framework for building a representation of cardiovascular state . Importantly, while our model is trained on paired modalities, the model can be applied in settings where only one modality is present. Namely, we simply utilize the embedding given by the trained encoder for the single input modality. A description of our loss function, architecture, and training procedures is given in the “Methods” subsection “Cross-modal autoencoder architecture and training details” and Supplementary Fig. To build a cross-modal representation of the cardiovascular state, we utilize autoencoders to map paired cardiovascular data modalities, i.e. 38,686 paired median 12-lead ECGs and 50 frame videos of long-axis cardiac MRIs, from the UK Biobanksemi-supervised unimodal autoencoders, i.e., those trained to simultaneously autoencode and predict labels from the latent space. Overall, our cross-modal embeddings improve the representational power of inexpensive and prevalent ECGs for predicting clinical phenotypes by leveraging just a few MRI samples.We first demonstrate that supervised learning on cross-modal representations improves performance on phenotype prediction tasks. While our model is trained on ECG and MRI pairs, we consider the practically relevant setting in which only one modality (e.g. ECG) is available. In this case, we perform supervised learning on embeddings given by a single modality-specific encoder Fig. b. For ouk nearest neighbors (under cosine similarity) for 4752 test ECGs across embeddings from cross-modal autoencoders, unimodal autoencoders, and a baseline where ECGs and MRIs are randomly paired. Figure Cross-modal embeddings allow for matching cardiac MRI and ECG test samples. We begin by verifying that our training methodology provides a latent space in which corresponding ECG and MRI pairs are nearby. Hence, even in the absence of one of the modalities, the cross-modal autoencoder provides a representation that is characteristic of all available modalities. In Fig. Cross-modal representations improve phenotype prediction from a single modality. We now show that our learned cross-modal representations are more effective for downstream phenotype prediction than unimodal representations or supervised deep learning methods. In particular, we consider four groups of phenotype prediction tasks: prediction of (1) continuous valued MRI-derived phenotypes from ECG; 2) continuous valued ECG-derived phenotypes from MRI; (3) categorical physiological phenotypes from either ECG or MRI; and (4) continuous physiological phenotypes from either ECG or MRI , left ventricular systolic dysfunction (LVSD), and hypercholesterolemia. We utilize kernel regression to perform supervised learning from the cross-modal and unimodal embeddings; see Supplementary Fig. To further evaluate the results described above, in the “Methods” subsection “Models, data, and scaling law for phenotype prediction tasks” and Supplementary Fig. R2 performance across all 9 MRI derived phenotypes from Fig. Increasing the number of unlabelled samples improves the prediction of MRI-derived phenotypes from cross-modal ECG representations. We now analyze the relationship between the amount of labelled data for supervised learning, the amount of unlabelled data for cross-modal autoencoding, and the performance of supervised learning from cross-modal latent representations. Such an analysis is crucial for understanding the number of labelled and unlabelled data samples needed to build an effective cross-modal autoencoder for use in practice. In Fig. Our framework enables the translation of ECGs, an easy-to-acquire modality, to cardiac MRIs, a more expensive, difficult-to-acquire modality. To perform such translation, we simply provide test ECGs into our ECG-specific encoder and then apply the MRI-specific decoder to translate from ECGs to MRIs. We note that since the two data modalities capture complementary cardiac features , such translation is a nontrivial task. Nevertheless, we show that the translation of ECGs provided by a cross-modal autoencoder remarkably captures features present in MRIs, and we quantify the amount of such features captured via the translation.Cardiac MRIs generated from test ECGs capture MRI specific phenotypes. We begin by qualitatively analyzing the reconstructions and translations of 12-lead ECG and 50 frame cardiac MRI test pairs using our cross-modal autoencoder. In Fig. In order to quantify the effectiveness of the translations using the cross-modal autoencoder, we compare the predictions of the translation to a neural network directly trained to predict LVM and RVEDV on the original modality. In particular, in Fig. 40.Next, we analyze whether cross-modal embeddings can be used to identify genotype–phenotype associations related to the heart. As a first step, we verify that performing a GWAS on labels derived from cross-modal representations leads to the recovery of SNPs previously associated with common disease phenotypes. We then develop a method based on the cross-model latent space to perform GWAS in the absence of labelled phenotypes, i.e. an unsupervised GWAS. We demonstrate that our unsupervised GWAS approach applied to cross-modal embeddings recovers SNPs typically identified by performing GWAS on labelled data, as well as those found in more computationally demanding ECG-wide screens42. Similarly, performing a GWAS of right ventricular ejection fraction (RVEF) predicted from MRI cross-modal representations identifies lead SNPs corresponding to genes BAG3, HMGA2, and MLF1, which have all been previously associated with RVEF43. These results indicate that our learned representations are physiologically meaningful. Additional examples of ECG phenotypes derived from cross-modal embeddings are presented in Supplementary Fig. GWAS of phenotypes predicted from cross-modal representations recovers phenotype-specific SNPs. In order to verify that cross-modal representations capture genetic associations with respect to a specific phenotype, we perform a GWAS on single trait predictions based on these representations; see the “Methods” subsection “GWAS of phenotypes derived from cross-modal representations” for a description of performing such GWAS and a list of confounders considered. As an example, the Manhattan plot in Fig. p-value from a Multivariate Analysis of Variance (MANOVA). The statistics used for computing p-values using MANOVA are discussed in the “Methods” subsection “GWAS of phenotypes derived from cross-modal representations”. Importantly, prior to performing MANOVA across the sets, we need to account for potential genetic confounders (stratification), which are inherently reflected in the cross-modal embeddings. Without accounting for confounders, MANOVA is unable to recover the genetic signals from the latent embeddings  from appearing in word embeddings. We leverage INLP in the medical domain to ensure that confounders such as principal components of ancestry cannot be easily predicted from cross-modal embeddings, thereby ensuring that such features do not arise as confounders in the GWAS. This method iteratively removes dimensions from the latent space until the remaining embeddings cannot be used to predict any confounder . After removing latent space dimensions that are predictive of confounders using INLP, we utilize MANOVA on the lower dimensional embeddings to perform an unsupervised GWAS .Unsupervised GWAS of cross-modal representations leads to the recovery of SNPs associated with a given modality. While we have so far demonstrated that we can perform GWAS on one-dimensional traits using our cross-modal embedding, we note two limitations of this approach. The first limitation is that genetic variants are often pleiotropic, affecting many traits at the same time. Indeed, a variant can have a small effect on a pair of traits, and looking at one trait alone would not provide sufficient power for identifying this variant. The second limitation is that one-dimensional traits, even in aggregate, are an incomplete characterization of the information in a diagnostic modality. For example, in ECGs, we aim to not only identify variants that impact the measured phenotypes such as the QT interval but variants that affect the ECG in any way. In the following, we, therefore, develop an unsupervised GWAS methodology that provides a principled approach for automatically discovering genetic variants from rich diagnostic modalities without using any labelled phenotype measurements or turning to manual, time-intensive interpretability strategies such as saliency maps or direct visualization of ECGs and MRIs. Our approach is as follows: (1) for each SNP, we identify those individuals in the latent space that are either homozygous reference, heterozygous, or homozygous alternate; (2) we then ask whether these distributions are separable, and quantify the level of separation among these groups via a 45, TTN47, SCN10A50, SLC35F151, and KCNQ153 are consistent with those identified by prior work. In Supplementary Fig. λGC values to verify that the corresponding p-values are not inflated after removing the effect of confounders via INLP. In Supplementary Fig. 54, USP34 previously associated with cardiovascular disease55, and NRG1 previously associated with systolic blood pressure56. We note that the cross-modal MRI GWAS identifies fewer lead SNPs than the cross-modal ECG GWAS, which could be because MRIs are more strongly associated with the confounders and thus removing confounders via INLP may also remove genetic signal. Indeed, confounders such as age and sex are much more easily predicted from cross-modal MRI embeddings than cross-modal ECG, as is showcased in Fig. In Fig. Clustering SNPs in the cross-modal latent space identifies SNPs with similar phenotypic impact. An additional benefit of our cross-modal approach for genetic discovery is that we can cluster SNPs in the latent space to group those with similar phenotypic effects. In particular, we perform hierarchical clustering based on the direction from the mean embedding of the homozygous reference group to the mean embedding of the heterozygous and homozygous alternate groups for any given SNP see Fig. d. In FigIn this work, we developed a cross-modal autoencoder framework for integrating data across multiple modalities to learn holistic representations of the physiological state. Using the heart as a model system, we integrated cardiac MRI and ECG data to showcase the benefit of cross-modal representations via the following three applications: (1) improving prediction of phenotypes from a single modality; (2) enabling imputation of hard-to-acquire modalities like MRIs from easy-to-acquire ECGs; and (3) identifying genotype associations with general cardiovascular phenotypes. In particular, we showed that cross-modal representations improve the prediction of cardiovascular phenotypes from ECGs alone. This setting is of practical importance given the abundance of ECG data over more difficult-to-acquire modalities such as MRI. Interestingly, we observed that increasing the number of unlabelled ECG and MRI pairs was more beneficial than increasing the number of labelled MRI data. We also demonstrated that cross-modal autoencoders enable imputing cardiac MRIs from ECGs. Importantly, we showed that the MRI-derived phenotypes are conserved in the translation. We also showed that the cross-modal representations can be used to perform GWAS. Notably, such an analysis not only recovers known phenotype-specific SNPs but can also be used to perform unsupervised GWAS to identify SNPs that generally affect the cardiovascular system. The proposed unsupervised GWAS method provides an effective and efficient approach to genetic discovery as it has the same computational cost as performing a single GWAS and, in contrast to existing methods for GWAS, it does not require any labelled phenotype data.57. It would therefore be important to re-train or update our model on a more diverse population and perform a careful analysis of how well our model generalizes to underrepresented cohorts in future work before translating this method to hospital settings. For deployment in such settings, it is critical to account for potential confounding factors. For example, in our dataset, LDL was negatively correlated with the incidence of hypercholesterolemia, which is presumably due to these individuals taking lipid-lowering medications. While we showed how our cross-modal embedding of ECG and cardiac MRI data can be used to improve the prediction of clinical phenotypes such as LVH, LVSD, and hypercholesterolemia, our framework is general and only requires a few labelled samples to be applicable to other clinical phenotypes. As such, an interesting direction for future research is to understand the extent to which related neurological phenotypes can be predicted from cross-modal ECG and MRI embeddings.The reliable performance boost in phenotype prediction from a cross-modal embedding further highlights its applicability to aid in diagnostics. An interesting future application of our framework is to determine the extent to which cross-modal ECG embeddings can be translated to a hospital setting and, in conjunction with other biomarkers, improve the prediction of specific cardiovascular diseases. However, we acknowledge that a current limitation of our work is that UK Biobank samples are limited in their diversity with individuals primarily falling between the ages of 40–69. In addition, the UK Biobank is known to contain racial and socioeconomic biases, which can lead to problematic inequities in terms of healthcareOur unsupervised GWAS was able to leverage information across ECGs and cardiac MRIs to capture a wide range of SNPs that had an impact on the cardiovascular system. Subsets of these SNPs were previously found by traditional supervised approaches on individual phenotypes. Given that our approach is cross-modal, we also identified SNPs that had not been found by previous GWAS approaches. This framework for performing unsupervised GWAS in cross-modal representations also opens important avenues for future work. Investigating the differences in the identified SNPs between unsupervised and traditional GWAS is an interesting direction for future work and requires careful consideration of potential confounders. In particular, since cross-modal autoencoders learn representations from modalities directly, confounders are typically embedded in the representations. Indeed, we for example observed that MRI cross-modal embeddings can predict sex and age effectively. To minimize the effect of such confounders when performing genetic analyses, we were stringent in adjusting the latent space such that one could no longer predict confounders effectively from the learned representations. Developing more causally-grounded methods for confounder removal from a cross-modal latent space is an important open problem. Moreover, via a simple clustering of cross-modal embeddings, our framework allows for grouping SNPs by phenotypic effect without the need of labelled phenotypes. Since our framework can be used to integrate any number of data modalities, an exciting direction of future work is to use such modalities in other organs to better characterize the effect of SNPs with similar signatures in an unsupervised manner. Such identification requires reliable translation from the cross-modal latent space into different modalities. While we showed that our framework is capable of translating from easy-to-collect ECGs to more difficult-to-collect MRIs while preserving relevant features, an interesting direction of future work is to understand how far such translations can be pushed.With the rise of Biobanks around the world, our cross-modal integration framework opens an important avenue to integrate multiple modalities to build better representations of patient physiological state and thereby have an important impact on diagnostics and genomics. While we demonstrated the effectiveness of our cross-modal autoencoder framework on the cardiovascular system, our framework is broadly applicable to other organ systems.Our research complies with all relevant ethical regulations. Access was provided under UK Biobank application #7089. Analysis was approved by the Broad Institute institutional review board.58. Labels for the sex of individuals were provided by UK Biobank. In particular, of the samples used for training and evaluating autoencoders that had cardiac MRI available, 21,066 individuals were labelled by UK Biobank as genetic sex of male and 23,577 individuals were labelled as genetic sex of female.All analyses were performed on the UK Biobank, a prospective cohort of over 500,000 healthy adults that were aged 40–69 during enrollment, which took place from 2006 to 2010. At the time of our analysis the UK Biobank had released cardiovascular magnetic resonance imaging for over 44,644 participants, 38,686 of whom also had a 12-lead 10-second resting ECG acquired on the same day. The ECG and MRI data for an individual are collected in the same assessment center. While different MRI views were obtained, we only considered the 4-chamber long axis view with balanced steady-state free-precession cines, containing 50 frames throughout the cardiac cycle. The ECG data also spanned a single cardiac cycle, as we used the 1.2-s median waveforms (600 voltages) derived from the full 10-s ECG. All voltages were transformed to millivolts, and all MRI pixels were normalized to have mean 0 and standard deviation 1 for each individual. The MRIs were cropped to the smallest bounding box which contained all cardiac tissues in all 50 frames as determined by the semantic segmentation in59. In particular, we used a base architecture of densely connected parallel convolutional blocks61 with 1d convolutional layers for ECGs and 2d convolutional layers for MRIs. For modality-specific modals, we optimized over the width, depth, activation functions, regularization and normalization strategies to achieve minimum reconstruction error for a given maximum overall capacity of 10 million parameters and a 256 dimensional latent space. Since optimization occurs for each modality independently, encoding, decoding, and pairing are distinct tasks and can be trained asynchronously and distributed across machines. We note that simpler architectures such as those from ref. 62 are also usable in our framework, but we observed that the optimized models showed improvements in convergence speed, reconstruction, and latent space utility for downstream tasks  a representation loss LContrast, which is used to ensure that the representations for modalities corresponding to the same sample are embedded nearby in the latent space. We now provide a formal definition of these loss functions:λ is a hyperparameter to balance the losses, temp is a trainable temperature scalar as in ref. 64, and given a batch size m] such that ∣Iℓ∣ = b for The training loss, b = 4) and used λ = 0.1. All models were optimized with the Adam optimizer65 and a learning rate of 1e−3 for unimodal autoencoder training and 2e−5 for cross-modal fine-tuning. The learning rate was decayed by a factor of 2 after each epoch without an improvement in validation loss and after 3 decays optimization was terminated. Samples were split into a training set of 27,160 individuals, a validation set of 6780, and a test set of 4746. To avoid any data leakage the entire test set was made of samples with MRI phenotypes labelled in39, ensuring a fair comparison with models trained on downstream prediction tasks. The MRIs were normalized per individual to have mean zero and standard deviation 1. All ECG readings were converted to millivolts before training.In our experiments, we used a batch size of 4 samples (66 and tensorflow (version 2.9.1)67 for training cross-modal autoencoders.We used numpy (version 1.22.4)We compared phenotype prediction from cross-modal embeddings to training supervised models with the same encoder architecture as described in the section “Cross-modal autoencoder architecture and training details”. In particular, we trained these supervised models for phenotype prediction by adding a last layer and updating the weights via a logcosh loss for continuous tasks and cross entropy loss for categorical tasks. We also used the same optimization procedures for the hyper-parameters and the same stopping criteria as described in the section “Cross-modal autoencoder architecture and training details”.The following standard abbreviations were used for MRI-derived phenotypes. Left ventricular mass was denoted LVM, left ventricular end diastolic volume was denoted LVEDV, left ventricular ejection fraction was denoted LVEF, left ventricular end systolic volume was denoted LVESV, left ventricular stroke volume was denoted LVSV, right ventricular ejection fraction was denoted RVEF, right ventricular end systolic volume was denoted RVEDV, and right ventricular end diastolic volume was denoted RVEDV.For all phenotype prediction tasks, we only considered data that was held out during cross-modal autoencoder training. This is crucial since otherwise the autoencoder would automatically utilize both MRI and ECG data for all phenotype predictions and thus naturally perform better than prediction from any individual modality. Since we were limited by the availability of labelled data for MRI derived phenotypes, we held out all data for which there was an available MRI derived phenotype from the autoencoder training and validation set. In particular, even though we may have phenotypes such as age or sex for all individuals, we only measured performance on phenotypes for the held out test set to ensure fair comparison with other models. Naturally, using all available labelled samples for predicting sex or age would have boosted performance, but would have given an unfair advantage to our method. This left us with 4218 samples containing MRI derived phenotypes. For MRI derived phenotype prediction, we split these into 3163 samples for training, 527 for validation, and 528 for test. Only 4120 of these samples had corresponding ECG derived phenotypes, and so we used 3083 of these for training, 516 for validation, and 521 for test. For categorical general phenotypes, we used the same splits as those for MRI-derived phenotypes. For continuous valued general phenotypes, we considered only the subset of the 4218 samples that had labels available. In particular, we used 3158 samples for training, 527 for validation, and 527 for testing.68; (2) linear regression ; and (3) logistic regression. We considered the NTK since it was shown to have superior performance on supervised learning problems70. For the prediction of MRI derived phenotypes, ECG derived phenotypes, or continuous general phenotypes, we measured performance using R2, and we compared the performance of the NTK and linear regression. For the prediction of categorical phenotypes, we measured performance using the area under the receiver operator characteristic curve (AUROC), and we compared the performance of the NTK and logistic regression. We utilized EigenPro71 to solve kernel ridge-less regression and linear regression. We used the validation splits to select the early-stopping point for the EigenPro iteration. Similar results can be obtained with ℓ2-regularized kernel and linear regression using the Scikit-learn implementation72, but require the more computationally demanding step of fine-tuning of the regularization parameter based on validation performance. For classification tasks, we used the implementation of ℓ2-regularized logistic regression from ref. 72, and we applied the following weighting on the loss to account for class imbalances: if there were n total samples of which r had label 1, then we weighted the loss for these samples by For phenotype prediction from latent space embeddings, we considered the performance of three models (1) kernel regression with the Neural Tangent Kernel (NTK)v), the amount of unlabelled data for cross-modal autoencoding (denoted by u), and the performance of supervised learning from cross-modal latent representations (denoted by r). We used linear regression to map from r for the 54 samples considered in Fig. R2 = 0.983. Hence for these tasks, we were able to reliably predict the boost in performance from supervised models on cross-modal embeddings when varying the number of unlabelled ECG–MRI pairs and labelled MRIs. Note that the coefficient of We now describe our scaling law analysis used to determine the relationship between the amount of labelled data for supervised learning  and LVEF to derive thresholds for left ventricular systolic dysfunction (LVSD). To provide a binarized label for LVH, we first normalized all LVM measurements by dividing by body surface area (derived using the Mosteller method). We then stratified by sex and set the LVH label to be 1 if the normalized LVM was greater than 72 if the sex was male, respectively 1 if the normalized LVM was greater than 55 if the sex was female66, sklearn (version 0.24.2)72, scipy (version 1.7.1)74, and pytorch (version 1.9.11) with gpu support (cudatoolkit version 11.1.74)75 for training predictive models. We used pandas (version 1.3.3)77 for loading data and matplotlib (version 3.4.2)78 for generating plots.We used numpy (version 1.21.2)N = 36,645), MRI-derived phenotypes (e.g. RVEF N = 4788) and general demographics . These simple models endow these GWAS with much greater statistical power, since phenotypes can be predicted for the whole cohort, not just those with labels, as described in ref. 43. For example, GWAS of the less than 5000 MRI phenotypes returned by Petersen et al.39 yield no genome wide significant hits, while inferences from ridge-regression yield dozens of plausible sites. These sites are confirmed by GWAS of traits computed from semantic segmentation described in ref. 43. Models were fit with 80% of the available labels and evaluated on the remaining 20% and then inferred on the entire cohort.We found multi-pathway genetic signals in the cross-modal latent spaces by analyzing the inferences of the kernel regression models described above. Specifically, we trained ridge-regression models to use modality-specific cross-modal embeddings to predict ECG phenotypes . At a high-level, the algorithm involves iterating the following steps until the R2 from step 1 is below a pre-selected threshold (we used R2 < 0.001).Step 1: Use linear regression to learn a mapping from cross-modal latent embeddings to confounders.Step 2: Use the singular value decomposition to construct a projection matrix that projects onto the directions of the cross-modal space that are least useful for confounder prediction, i.e. the nullspace of the predictor from step 1.Step 3: Multiply the cross-modal embeddings by the projection matrix found in step 2.To remove the effect of confounders, we utilized the idea of iterative nullspace projection from Ravfogel et al.n individuals and let m confounders for the n individuals. To correct for confounders, we do the following:Step 1: Learn the regression coefficients w = UΣVT given by the SVD, where d−m rows of VT into a matrix Step 2: Let xi)( with Step 3: Replace each Mathematically, these steps are implemented as follows. Let R2 of the predictor w is lower than a fixed threshold.Repeat the above steps until the p-values reported for unsupervised GWAS are from Pillai’s trace test statistic from the MANOVA computation. The Python statsmodels80 package was used to perform MANOVA.The 72 clustering package was used to derive the clusters and dendrograms.Agglomerative clustering with Ward’s method, which minimizes the total within-cluster variance, was applied to the matrix of SNP vectors. The python sklearnFurther information on research design is available in the Supplementary InformationSupplementary TablesPeer review fileDescription to Additional Supplementary InformationSupplementary Video S1Supplementary Video S2Supplementary Video S3Supplementary Video S4Supplementary Video S5Reporting Summary


COL4A4, TRIM27, ETV1 and CUBN. TRIM27 is part of the extended MHC locus. All loci with the exception of ETV1 were replicated in the Geisinger MyCode cohort. The previous GWAS of hematuria reported COL4A3-COL4A4 variants and HLA-B*0801 within MHC, which is in linkage disequilibrium with the TRIM27 variant (D′ = 0.59). TRIM27 is highly expressed in the tubules. Additional loci included a coding sequence variant in CUBN , p = 3.29E−8, OR = 2.09, 95% CI = 1.61–2.72). Overall, GWAS for the composite trait of hematuria and albuminuria identified 4 loci, 2 of which were not previously identified in a GWAS of hematuria.Our GWAS of hematuria in the UK Biobank identified 6 loci, some of which overlap with loci for albuminuria suggesting pleiotropy. Since clinical syndromes are often defined by combinations of traits, generating a combined phenotype can improve power to detect loci influencing multiple characteristics. Thus the composite trait of hematuria and albuminuria was chosen to enrich for glomerular pathologies. Cases had both hematuria defined by ICD codes and albuminuria defined as uACR > 3 mg/mmol. Controls had neither an ICD code for hematuria nor an uACR > 3 mg/mmol. 2429 cases and 343,509 controls from the UK Biobank were included. eGFR was lower in cases compared to controls, with the exception of the comparison in females using CKD-EPI after age adjustment. Variants at 4 loci met genome-wide significance with the following nearest genes:  However, there has been less focus on this trait compared to albuminuria and eGFR. Nonetheless, reports show that hematuria is associated with a higher risk of kidney failure in the Israeli, Korean populations and CRIC cohort5. This in turn informs diagnostic possibilities, prognoses and management. Hematuria can be identified as being more likely to be glomerular when there is accompanying albuminuria, hypertension and/or reduced kidney function (eGFR7). When hematuria and albuminuria co-occur, the diagnostic possibilities still remain long but IgA nephropathy has traditionally been taught as the commonest primary glomerulonephritides in the adult population8.A clinical decision point in hematuria comes from determining its origin which can be glomerular, tubular, interstitial or urinary tract13. The specific variants driving the association from these reports were different, however, owing to methodologic differences including differing minor allele frequency thresholds13. To address study limitations, we recently reported a GWAS with an exclusive focus on hematuria enriched for glomerular causes using phecode 593 in the white British subset of the UK Biobank14. Cases were found to have higher systolic blood pressure, higher urinary albumin:creatinine (uACR) and lower eGFR compared to controls, supporting the successful enrichment for glomerular pathologies14. Regarding methodology, we performed imputation using a reference dataset with higher resolution at associated loci on chromosome 2 and applied conditional regional analysis to identify independent variants driving the statistical association. As a result, we identified 3 independent variants in COL4A4-COL4A3 but also a locus at MHC, both of which previous studies have implicated in Alport syndrome and IgA nephropathy, respectively17. Four other loci were also discovered and replicated.In previous genome wide association studies (GWAS) of kidney traits that included hematuria using the UK Biobank and deCODE, overlapping signals were identified namely on chromosome 2 where the collagen IV genes responsible for Alport syndrome reside18. Though there are few multi-trait GWAS to date, a recent study evaluated coronary artery disease (CAD) and peripheral artery disease (PAD), which share similar risk factors, frequently co-occur and are genetically correlated19. Multi-trait GWAS was performed on PAD, CAD and different combinations of risk factors including BMI, smoking, type 2 diabetes and cholesterol using data from the Million Veteran Program, CARDIoGRAMplusC4D, GIANT, GLCG and the UK Biobank19. This analysis resulted in 25 novel pleiotropic loci, and co-localization with single-tissue eQTLs identified candidate causal genes at 14 of the signals. Thus, we take an analogous approach to hematuria and report the results of a GWAS for the combined phenotype of hematuria and albuminuria in the UK Biobank. Given access to individual-level genotypes and phenotypic data in the UK Biobank cohort, we create our combined phenotype using individual-level data to maximize statistical power.Although most GWAS analyze one trait at a time, generating a combined phenotype can improve power to detect loci influencing multiple traits. Multivariate methods, such as multi-trait analysis of GWAS (MTAG), exist to combine summary-level genetic variant data. However, the latter can result in decreased power  and carries assumptionsAll experiments were performed in accordance with the Declaration of Helsinki. The study was approved by the Toronto General Hospital Research Ethics Board (21-5361.0) and by the Montreal Heart Institute Research Ethics Board (2022-3104). The UK Biobank is guided by the Ethics Advisory Committee to conduct research that abides by ethical principles involving human studies (Ethics (ukbiobank.ac.uk)). The UK Biobank has approval from the North West Multi-centre Research Ethics Committee as a Research Tissue Bank (RTB) approval. Researchers with approved access operate under the RTB approval and do not require separate ethical approval. For the replication cohort, Geisinger MyCode, all patients provided written informed consent. The Geisinger institutional review board approved the study.20.GWAS was performed using UK Biobank data to identify variants associated with the combined traits of hematuria and albuminuria in white British subjects from the UK Biobank. The white British subgroup has been identified by the UK Biobank based on both self-report as ‘white British’ and similar genetic ancestry based on principal components analysis of genotypes. The first four principal components of genetic ancestry computed in the white British subset were derived in cases and controls, which were then included in the model as covariates to adjust for population stratification. Using the first four principal components has been shown to adequately control genomic inflation in the UK Biobank white British subset for binary traits using a linear mixed model to account for case–control imbalance21. The UK Biobank performed array genotyping at the Affymetrix Research Services Laboratory. We used the UK Biobank centrally imputed data21 that used the Haplotype Reference Consortium (HRC) data, consisting of primarily European genetic ancestry individuals, along with the merged UK10K and the 1000 Genomes project phase 3 reference panels23. When a genetic variant was present in both panels, the HRC imputation was used. The UK Biobank’s quality control procedure for samples and variants (either on the genotype array or imputed) have been described in detail21. In brief, for array variants, Affymetrix applied a custom genotype calling pipeline and quality control filtering, and the UK Biobank applied further standard filtering including testing for batch effects, plate effects, departures from Hardy–Weinberg equilibrium, sex effects, array effects, and discordance across control replicates. A variant that failed at least one quality control metric was assigned missing genotype calls. Pre-filtering of variants was conducted before phasing and imputation. Additionally, the UK Biobank provides the imputation R2 (referred to as information measure in the UK Biobank documentation) and the minor allele frequency for post-GWAS filtering. The UK Biobank notes that an information measure of 0.3 in around 150,000 samples corresponds to an effective sample size of around 45,000. We present R2 and minor allele frequency for all significant results.This research has been conducted using the UK Biobank Resource under Application numbers 48839 (M.B.) and 66222 (S.A.G.T). The UK Biobank is a prospective cohort involving approximately 500,000 UK adults between the ages of 40–69 years at the time of recruitment, in whom genetic and phenotypic data are collectedhttps://imputation.biodatacatalyst.nhlbi.nih.gov/#!) to impute genetic variants in the chr2:226000000-228000000 region from the UK Biobank array data in batches of 20,000 individuals at a time24. The batches were subsequently merged. The panel includes 97,256 reference samples of diverse genetic ancestries and 308,107,085 genetic variants (distributed throughout the autosomes and chromosome X). Previous work has shown that imputing the UK Biobank to the TOPMed panel can help identify rare variants that are not present on other commonly-used reference panels in the white British subset24.At the time of analysis, the TOPMed-imputed UK Biobank data was not yet available through the UK Biobank Research Analysis Platform. We thus used the Trans-Omics for Precision Medicine Program (TOPMed) imputation server, version r2 21. The number of individuals included in the HLA reference panel varies depending on the HLA allele but ranges from 808 to 9120 . Imputation was conducted using HLA*IMP:02 with modified settings using a set of genetically diverse reference datasets25. The imputation procedure and quality control steps have been described26. In brief, for each locus, only the reference individuals which had lab-based HLA types for that locus, and only the SNPs that were polymorphic and were typed in at least 98% of that set of individuals were included.The https://github.com/PheWAS/PheWAS/blob/master/inst/doc/PheWAS-package.pdf) was used to map ICD9 and ICD10 codes  to phecode 593 “hematuria”27.The R package PheWAS (Composite cases had the following ICD9 or ICD10 codes: “hematuria”: 599.7 (ICD9), R31 (ICD10), “hematuria unspecified”: 599.70 (ICD9), R31.9 (ICD10) and “gross hematuria”: 599.71 (ICD9), R31.0 (ICD10) as well as an uACR ratio > 3 mg/mmol. Exclusion codes  were: any genitourinary phecodes . ICD9 and ICD10 codes underlying each phecode in this range were extracted to get the full list.Controls did not have an ICD 9/10 code for hematuria and they had an uACR ≤ 3 mg/mmol.21. Individuals marked as having urinary albumin < 6.7 mg/L  were assigned as having an urinary albumin value of 6.7 mg/L ). The assay manufacturer for urine creatinine was Beckman Coulter, UK with enzymatic method and an analytical range from 88–44,200 µmol/L (https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/urine_assay.pdf). Estimated glomerular filtration rate (eGFR) was calculated in two ways, using the CKD-EPI equation or as 100/cystatin C (mg/L) (https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/serum_biochemistry.pdf)29.uACR values were computed as /(creatinine in μmol/L/1000) from urine samples collected at baseline, where the numerator is UK Biobank field 30,500 Individuals were categorized as taking an angiotensin-converting enzyme inhibitor (ACEi) or an angiotensin receptor blocker (ARB) if they had at least one code corresponding to an ACEi/ARB medication from field 20,003  at the baseline visit. The specific ACEi/ARB medications from field 20,003 used are listed in Supplementary Table p value) of the trait of interest on composite case–control status. For each trait of interest, we also constructed the same regression model but this time with age as an additional predictor to obtain the age-adjusted estimates. The models were constructed in females and males separately. To quantify whether a trait of interest differs significantly between the sexes, for each trait we also reported the sex-trait interaction term from a logistic model with the composite phenotype as the outcome and the following predictors: the trait of interest, age, sex and the interaction term for the trait of interest and sex. Values for the traits of interest were derived from the baseline visit.For the descriptive tables, all regression models were conducted in R (version 4.0.5). For each trait of interest  we created a linear regression model with the trait as the outcome and the composite trait as the predictor to obtain the non-age-adjusted regression estimates  release of the UK Biobank whole-exome sequencing dataset to replicate protein coding results30.SAIGE (v. 0.44.5) was used to test variants for association with the composite phenotype in the white British subset of the UK Biobank, where the effect allele is the non-reference alleleVariables31. LocusZoom plots were generated32.Sex, birth year, and the first four genetic ancestry principal components computed on the white British subset were the covariates used in the analyses, except for the sex-stratified analyses on chromosome X and for the analyses on chromosome Y, for which sex was not included as a covariate. Related individuals were included given SAIGE uses a genetic relationship matrix to properly account for kinship. QR transformation (composition of the covariate matrix) of the covariate matrix was performed by default on non-binary covariates, such as birth year and principal components, to solve the linear least squares problemConditional analyses at each the top loci were performed using the HRC/UK10K-imputed UK Biobank on the most significant variant at the locus using the condition flag within SAIGE.33. The summary statistics used for eGFR were the European-ancestry summary statistics from the meta-analysis conducted by Stanzick et al. 2021 which included European-ancestry individuals from the UK Biobank34. Other than the summary statistics for eGFR, all other summary statistics were derived exclusively from UK Biobank participants. The fat mass  and fat-free mass  summary statistics (measured from impedance) are made publicly available from the Neale lab from their round 2 single-variant association analyses testing the inverse rank-normal transformed trait in the white British UK Biobank subset. Phenotypic correlations were estimated by computing the Pearson correlation for the trait values in the UK Biobank white British subset.We used genome-wide association summary statistics to estimate pairwise global genetic correlation between our in-house composite trait, ICD-based hematuria (phecode 593), continuous uACR, publicly available creatinine-based eGFR, and two impedance measurements (whole body fat mass and whole body fat-free mass) using LD score regression based on common HapMap autosomal SNPs and phase 3 of the 1000 Genomes Project European super-population as the linkage disequilibrium reference35. 149,492 participants of European genetic ancestry have whole-exome sequencing data and 147,813 also have TOPMed-imputed genotypes.To replicate findings in the UK Biobank analysis, we used the European genetic ancestry subset of the Geisinger MyCode 175 k freeze cohort with exome sequencing and TOPMed-imputed genotypes from array data as an independent replication cohort. Geisinger MyCode is a health system-based cohort comprised of patients receiving care from Geisinger Health, encompassing central and northeast Pennsylvania, USACOL4A336. In brief, cases had both dipstick hematuria  and albuminuria  was created for the European-only subset using an LD-pruned subset of genetic variants in accordance with SAIGE recommendations . Step 1 of SAIGE was performed using the sparse GRM with the same 639,020 variants. Cases and controls were defined in a prior MyCode paper on Step 2 of SAIGE (i.e. the association testing step) was then performed on both the whole-exome sequencing data and the TOPMed-imputated genotypes. For the TOPMed imputation, the following filters were used: MAF > 0.0001, INFO > 0.5, and AMPC >  = 0.95 . Current age, sex, and the first ten principal components of genetic ancestry were included as covariates.COL4A4 causal for Alport syndrome, TRIM27 which is part of the extended MHC locus, CUBN previously reported to be associated with albuminuria, and a novel locus in the intron of ETV1. Three loci excluding ETV1 were replicated in the Geisinger MyCode cohort. Though TRIM27 is highly expressed in the tubules, the identified variant is in linkage disequilibrium with a hematuria association we previously reported, HLA-B*0801. Clinically, our study guides clinicians as to possible etiologies for the combined trait of hematuria with albuminuria.In this study, we evaluated genetic loci associated with the composite traits of hematuria and albuminuria. In 2429 cases from the UK Biobank, 4 loci met genome-wide significance with the nearest genes: p < 1E−5). Within the analysis subset with information on hematuria and albuminuria, the rate of glomerular hematuria was 4%  and for albuminuria (defined as an uACR ratio > 3 mg/mmol) was 11%  . Before and after adjustment for age, systolic and diastolic blood pressures were statistically significantly higher in cases than controls in both sexes. eGFR measured by CKD-EPI and cystatin C was lower in cases compared to controls, with the exception of the comparison in females using CKD-EPI after age adjustment. As expected given our case definition, uACR was significantly higher in cases compared to controls in both sexes. Cases and controls showed evidence of overlapping distributions in terms of principal component-space  composite cases and 343,509  controls. The descriptive characteristics of the study population are presented in Table Male cases had both significantly higher BMI and whole body fat mass compared to male controls, after adjustment for age , whole-exome sequencing association results were consistent with the TOPMed-imputed results.We tested a total of 50,178,126 variants after imputation with HRC and UK10K with a minor allele count (MAC) > 20 located on either the autosomes or chromosome X for association with our composite trait. On the Y chromosome, 273 variants with a MAC > 20 on the UK Biobank genotyping array were tested for association. For mitochondrial variants, 239 on the array that passed the frequency threshold were tested for association for both sexes. There was no evidence for test statistics inflation , which is intergenic and the closest protein-coding gene is IRS1. We further investigated this region using a denser set of imputed genetic variants using the TOPMed imputation reference panel. The most significant variant in this region was COL4A4 p.Ser969Ter; rs35138315  in cases and MAF = 0.00022 in controls), which is the same top variant that we previously identified in our hematuria GWAS14. No variants in this region remained genome-wide significant after conditioning on the COL4A4 p.Ser969Ter variant. Effect size estimates for the chromosome 2 variant in Table The strongest association was observed for rs71431010 on chromosome 2 (MAF = 0.00059 (A) in cases and MAF = 0.00037 in controls, COL4A4 p.Ser969Ter and CUBN p.Ala2914Glu) using the whole-exome sequencing data from the UK Biobank , p = 9.64E−9, OR = 0.80, 95% CI = 0.74–0.87) within the extended MHC locus, where both HLA and non-HLA genes exist. This variant is in linkage disequilibrium (LD) with the HLA-B*0801 locus reported in the previous hematuria GWAS. Interestingly, TRIM27 displays expression in the proximal tubule, distal convoluted tubule and collecting duct  MAF = 0.014 (A), p = 3.29E−8, OR = 2.09, 95% CI = 1.61–2.72). The top variant at the CUBN locus in our composite GWAS (rs45551835) is one of the CUBN variants identified by Casanova et al. as being associated with uACR and it is in linkage disequilibrium (LD) (D′ = 1) with the other two CUBN variants reported by the group  in the UK Biobank based on high-coverage LD information from the European super-population of Phase 3 of the 1000 Genomes Project as implemented in the LDpair tool of the LDlink suite of web-based applications  , p value is < 1E−3) are shown in Supplementary Table ETV1, which has overall low expression in the kidney, appears to have the highest expression in the loop of Henle based on a single cell sequencing atlas (https://humphreyslab.com/SingleCell/displaycharts.php), the Human Protein Atlas and GTex v8  and the composite (p < 8.88E−31). The ETV1 intronic variant is significantly associated with just the composite (p < 1.28E−8) and has not been previously reported. The CUBN variant is predominantly associated with uACR (p < 1.22E−99). As another approach to dissect the associations for the two traits (hematuria and uACR), for each of the four top variants, we ran a logistic regression model in an unrelated UK Biobank analysis subset with the variant as the outcome and hematuria and uACR jointly as predictors as well as covariates sex, birth year and the first four principal components . For the p.Ser969Ter COL4A4 variant, hematuria was significant (p < 2E−16), and uACR was not significant (p = 0.067). For the last two models (TRIM27 and ETV1 variants), hematuria was significant (p < 3.4E−6), but not albuminuria (p > 0.1).Comparison of effect sizes and site p < .88E−31. We examined the phenotypic correlation of these 3 traits to one another as well as their correlation with other traits using participants from the white British UK Biobank  and albuminuria 41. The association with nearest gene TRIM27 is within the MHC locus, which has been implicated in IgA nephropathy and steroid sensitive nephrotic syndrome studies46. The top TRIM27 variant is in LD with HLA-B*0801 that was reported in our previous hematuria GWAS using the same dataset14. We identified one novel association in an intron of ETV1, but ETV1 has low expression in the kidney, and was not replicated in the Geisinger MyCode cohort.Here we present a dedicated GWAS for the composite traits of hematuria and albuminuria in the white British subset of the UK Biobank. We identified four loci as genome-wide significantly associated. For the loci closest to 48. In this study, we took advantage of phecodes to define hematuria that is enriched for glomerular causes that has occurred over the time period during which ICD codes have been used in UK National Health Service databases, which is typically over decades. Alternatively, hematuria can be defined by urine dipsticks and/or microscopy which was not available in the UK Biobank. In our previous work, we demonstrated that the majority of genetic associations identified for hematuria in the UK Biobank were confirmed in GWAS using urinalysis data from deCODE14. Our current work with the Geisinger MyCode cohort also confirms this approach14. We note that there is considerable range in the hematuria rates among cohorts: 4% in the UK Biobank (phecode-based), 22% in Geisinger (dipstick-based), and 49% (mild/moderate/severe) in deCODE (dipstick-based). The variability is likely in at least part due to the different sensitivity and specificity of each phenotyping approach.The UK Biobank is linked to electronic health records (EHR) to facilitate GWAS using International Classification of Diseases (ICD) codes. Alternatively, phecodes are a high-throughput tool that aggregates ICD codes and are designed to rapidly define phenotypes using EHR data that can also be used for GWAS. Previous studies have been performed to confirm the precision of phecodes to define phenotypes, though not for hematuria49.By contrast, the uACR comes from a single cross-sectional measure obtained at UK Biobank baseline examination. Combining phenotypes offers multiple advantages including phenotype precision, efficiency in detecting genetic variants missed by univariate screening and identifying pleiotropic effects for associated lociCOL4A4, implicated in a monogenic disorder called Alport syndrome, reported to follow autosomal dominant and recessive inheritance patterns54. For example, rs35138315 (COL4A4 p.Ser969Ter) has been reported in GWAS of albuminuria (UK Biobank), GWAS of hematuria (UK Biobank) and in typical forms of autosomal recessive Alport syndrome13. Furthermore, we identify three additional loci/variants that were not discovered in our previous GWAS of the single trait hematuria in the UK Biobank. These include variants in or closest to TRIM27, CUBN and ETV1, though the latter was not replicated. Possible reasons for the lack of replication of the ETV1 variant could be lack of power and/or different phenotyping approaches. The variant closest to TRIM27 is intergenic and is in LD with an association with HLA-B*0801, that we previously reported in a hematuria GWAS in the white British subset of the UK Biobank. TRIM27 itself is expressed along the tubular segments , with known mechanisms driving this association7.Though hematuria and albuminuria had a marginal genetic correlation  remained the same, with just slightly different effect sizes and p values  will provide greater resolution at associated loci, though LD may still affect the ability to identify the specific variants driving the statistical association. Using a composite trait decreases case count so replication of novel loci will have to be in cohorts that are adequately powered. In our previous GWAS for the single trait of hematuria, we used deCODE  for replication but the cohort size is significantly smaller than the UK Biobank. In general, however, even with replication GWAS signals are associations and cannot be said to be causative without additional supportive evidence including tissue expression and importantly functional studies. Finally, we used the first four principal components of genetic ancestry which has been shown to adequately control genomic inflation in the UK Biobank white British subset for binary traits using a linear mixed model to account for case–control imbalanceCOL4A4 and within the extended MHC locus, the latter of which is in LD with a nearby signal in HLA-B from the hematuria GWAS. Additionally, a variant in CUBN is also identified and replicated. The power of analyzing the combined trait includes identifying pleiotropic effects and uncovering novel statistical associations.We extend our previous hematuria GWAS by evaluating the genetic associations for the combined trait of hematuria and uACR in the white British subset of the UK Biobank and in the Geisinger MyCode cohort. Analyses identified a variant in Supplementary Information.Supplementary Table 2.


There is increasing recognition of the need to study genotype by environment (G×E) interactions in farm animals. This interest is currently driven mainly by the desire to breed robust animals with high resilience and reduced environmental sensitivity , or by tBhatia et al. the authors performed a comprehensive investigation of the genetic basis of pelvic organ prolapse, a serious welfare and economic issue plaguing sow production. They found a moderate heritability of pelvic organ prolapse and six genomic regions, which together explain up to 9% of genetic variance of the trait in the studied population. Different incidence and heritability of pelvic organ prolapse reported in other cohorts provide a possible indication of G×E interaction influencing its manifestation, which warrants further investigation. As further discussed by the authors, more precise description of the prolapse condition is required for better understanding of the background of the trait.In the study of Freitas et al. examined the best environmental descriptors based on seven climatic variables obtained from public weather station information as well as critical periods to evaluate heat tolerance and its genetic relationship to a range of reproduction, growth, and body composition traits in Large White pigs. They found evidence for G×E interactions for most of the studied performance traits. The authors further demonstrated that heat tolerance is heritable and genetically correlated with different performance traits, indicating that genetic improvement of heat tolerance in Large White pigs is feasible. Notwithstanding the observation that public weather station data show reasonable correlation with on-farm weather records, the authors recommend validation of their findings using in-barn recorded environmental data.It is not only important to thoroughly characterize the phenotypic trait, but also to accurately record and define the environmental conditions that will be investigated in terms of their interaction with the genotype. There is a steadily growing number of routinely measured parameters, e.g., from smart technologies, that can be harnessed for these purposes. Hu et al. investigated copy number variation (CNV) in three ecotypes of Tibetan sheep kept in three different environments differing primarily in altitude, feed availability and temperature. Because single nucleotide polymorphisms (SNPs) are ideal genetic markers for genome-wide association studies, other types of genetic variation are currently largely neglected. However, due to their higher functional potential and mechanisms linking their emergence with cellular responses to  environment, CNVs  using mRNA-Seq data. This approach , originally developed in humans for high-throughput screening of G×E interactions  and methodological advances  will bring new possibilities for accurate and fine-grained characterization of the environment, phenotypic response and genome, and will provide a tremendous boost to research in this important area.