These genetic associations of LV volume provide insights into brain morphology.The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes ( An increase in the volume of the brain lateral ventricles is a sign of normal aging, but can also be associated with neurological and psychiatric disorders. Here, Vojinovic et al. identify seven genetic loci in a GWA study for ventricular volume in 23,500 individuals and find correlation with thalamus volume. The enlargement of lateral ventricles has also been suggested in various complex neurological disorders such as Alzheimer’s disease, vascular dementia, and Parkinson’s disease8 as well as psychiatric disorders such as schizophrenia and bipolar disorder11. Furthermore, ventricular enlargement has been associated with poor cognitive functioning and cerebral small vessel disease pathology14. Even though it might be intuitive to interpret ventricular expansion primarily as an indicator of brain shrinkage after the onset of the disorder, recent studies have provided evidence against this notion16. The size of lateral ventricles is influenced by genetic factors with heritability estimated to be 54%, on average16, but changing with age, from 32–35% in childhood to about 75% in late middle and older age16. Even though the size of surrounding gray matter structures is also heritable19, ventricular volume is reported to be genetically independent of other brain regions surrounding the ventricles20. Similarly, ventricular enlargement in schizophrenia does not appear to be linked to volume reduction in the surrounding structures15.The volume of lateral ventricles increases in normal agingO-Methyltransferase (COMT) and Neuregulin 1 (NRG1) genes as associated with larger lateral ventricular volume in patients with the first episode of non-affective psychosis22. However, a comprehensive investigation of the genetic determinants of lateral ventricular volume is lacking.Elucidating the genetic contribution to inter-individual variation in lateral ventricular volume can thus provide important insights and better understanding of the complex genetic architecture of brain structures and related neurological and psychiatric disorders. Candidate gene studies have identified single-nucleotide polymorphisms (SNPs) mapping to Catechol-23. We identify 7 genetic loci associated with lateral ventricular volume and report genome-wide overlap with thalamus volume.Here, we perform a genome-wide association (GWA) meta-analysis of 23,533 middle-aged to elderly individuals from population-based cohorts participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium in order to identify common genetic variants that influence lateral ventricle volume. We apply a commonly used two-stage GWA design followed by a joint analysis approach that combines information across the stages and provides greater powerThe overview of study design is illustrated in Supplementary Fig. N = 11,396) are illustrated in Supplementary Fig. λ = 1.04)  with the same direction of effect at Bonferroni adjusted significance . Subsequently, the results from all individual studies were combined in the stage 3 GWA meta-analysis . The quantile–quantile plot showed again adequate control of population stratification or relatedness   and African-American (AA) ancestry were included, all significant associations were mainly driven by EA samples  . Additionally, three out of seven lead variants  showed pleiotropic association  with other traits according to the PhenoScanner database  but not in women .To capture gender-based differences, sex-stratified GWA analysis was performed . None of the 15,660,719 variants that were tested for heterogeneity between men and women reached genome-wide significance threshold  analysis using the Genome-wide Complex Trait Analysis (GCTA) identified no other additional variants, after conditioning on the lead variant at the locus 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, or 22q13.1.n = 6). In brain tissue, the alternate allele of this SNP was associated with higher expression of TRIOBP suggesting that higher expression was associated with smaller lateral ventricles   using LD score regression, and this was higher in women 0.19 (SE = 0.04) than in men 0.15 (SE = 0.05). The seven lead variants explained 1.5% of total variance in lateral ventricular volume. Partitioning of heritability based on functional annotation using LD score regression, revealed significant enrichment of SNPs within 500 bp of highly active enhancers, where 17% of SNPs accounted for 54% of the heritability  and H3K36me3 (associated with transcribed regions))25 and DNaseI hypersensitivity sites (DHS) than a permuted background  method revealed that SNPs associated with lateral ventricular volume at und Fig. .Fig. 3FuTRIOBP at the locus 22q13.1  and MRPS16 at the locus 10q22.2  were associated with lateral ventricular volume.Integration of functional data from the Genotype-Tissues Expression (GTEx) project using the MetaXcan method revealed two significant associations between genetically predicted expression in brain tissue and lateral ventricular volume . Genes of the “regulation of cytoskeleton organization” pathway have previously been implicated in various neurological or cardiovascular diseases . This finding was also confirmed at the phenotype level , intracranial volume , height , and mean pallidum , whereas no significant genetic overlap was found with neurological diseases, psychiatric diseases, or personality traits.Additionally, we examined the genetic overlap between lateral ventricular volume and other traits Table . We founp-value = 9.59 × 10−3) and lateral ventricular volume (Supplementary Table We next examined the association of genetic risk scores (GRS) for Alzheimer’s disease, Parkinson’s disease, schizophrenia, bipolar disorder, cerebral small vessel disease, and tau-related pathology, including tau and phosphorylated tau levels in cerebrospinal fluid, amyotrophic lateral sclerosis (ALS), and progressive supranuclear palsy (PSP), using the lead SNPs from the largest published GWA study and lateral ventricular volume  score of 21.5 and is located in an enhancer region  vasculature38. Disruption of S1P regulation results in defective angiogenesis in GM, hemorrhage, and enlarged ventricles38.In our data, the significant variants of 7p22.3 region had the highest probability of being regulatory based on the RegulomeDB score (1b). The lead variant at 7p22.3 was in an active chromatin state and was associated with differential expression of 39. Further, the alternate allele of the lead SNP at 22q13.1 in TRIOBP is associated with higher expression of the same gene in basal ganglia and brain cortex, and the same allele is associated with smaller lateral ventricular volume. Interestingly, predicted expression of this gene in cerebral cortex was significantly associated with lateral ventricular volume, suggesting a causal functional role of the gene. The same analysis revealed significant association of the expression of MRPS16 in frontal cortex with lateral ventricular volume. This gene was previously related to agenesis/hypoplasia of corpus callosum and enlarged ventricles40.The other identified locus, 16q24.2, has previously been connected with small vessel disease and white-matter lesions formationC11orf53 and ARHGAP20, whereas the 10p12.31 region encompasses MLLT10 which has been linked to various leukemias, ovarian cancer, and meningioma42. The effect size of this variant on lateral ventricular volume was correlated with mean cohort age, with the effect being near zero at younger age and larger at older ages.Finally, the lead intergenic SNP at 11q23.1 maps between PARK2), frontotemporal dementia (MAPT), neurofibromatosis 2 (NF2), tuberous sclerosis (TSC1)  pathway. Genes that are part of this pathway have previously been implicated in various neurological diseases such as Parkinson’s disease  is mapped to FAT1 which encodes atypical cadherins. Mutation in this gene causes a defect in cranial neural tube closure in a mouse model and an increase in radial precursor proliferation in the cortex48. However, the SNP-based heritability estimates were slightly higher in females. This may be explained by the differences in sample size in male and female-specific analyses implying that there is lower precision.Previous studies showed significant sex-specific differences in lateral ventricular volumeAPOE alleles and found no association between the APOE ɛ4  or APOE ɛ2  and lateral ventricular volume in our study population.We estimated that 20% of genetic variance in lateral ventricular volume could be explained by common genetic variants, suggesting that common variants represent a substantial fraction of overall genetic component of variance. Moreover, the most statistically significant effect occurred in the regions of highly active enhancers and histone marks, suggesting their involvement in gene expression. Using the LD score regression method, we found a significant negative genetic correlation between lateral ventricular volume and thalamus volume. However, these may not be independent events, but inverse reflections of the same biology. Even though not strictly significant, we also observed trends for genetic correlations with other brain volumetric measures. Furthermore, no genome-wide overlap was found between lateral ventricular volume and various neurological or psychiatric diseases. Given that enlargement of lateral ventricles has been suggested in Alzheimer’s disease, we examined the association of 16.As we identified loci underlying lateral ventricular volume at the genome-wide level, but also genes and common pathways, our results provide various insights into the genetic contribution to lateral ventricular volume variability and a better understanding of the complex genetic architecture of brain structures. The genes with variants that we found to be associated with lateral ventricular volume are relevant to neurological aging given the characteristics of the study population which is relatively free from the disease as participants with stroke, traumatic brain injury and dementia at the time of magnetic resonance imaging (MRI) were excluded. This is in line with the previously published work of Pfefferbaum et al. who showed that the stability of lateral ventricles is genetically determined, whereas other factors such as normal aging or trauma and disease play a role in its change49. Even though our study does not provide a definite statement regarding the relationship between lateral ventricular volume and neurological or psychiatric disorders, it lays the foundation for future studies which should disentangle whether lateral ventricular volume is genetically related or unrelated to various neurological and psychiatric disorders . Novel insights may be revealed by improving the power of the studies, studying homogeneous samples with harmonized phenotype assessment methods along with evaluation of common and rare variants.However, while studying genetic overlap of lateral ventricular volume and various neurological or psychiatric disorders at multiple levels , we found evidence that some single genetic variants have pleiotropic effect on lateral ventricular volume and biochemical markers for a neurological disease (AD) or meningioma . Given the disparity in sample size, it is difficult to distinguish whether any inconsistency in results between the two groups stems from true genetic differences or from differential power to detect genetic effects. Indeed, this is also exemplified by the plots of the To conclude, we identified genetic associations of lateral ventricular volume with variants mapping to 7 loci and implicating several pathways, including pathway related to tau pathology, cytoskeleton organization, and S1P signaling. These data provide new insights into understanding brain morphology.p-value < 5 × 10−8) were subsequently evaluated in an independent sample of 12,137 participants of mainly European ancestry from 14 studies (stage 2). Finally, we performed a meta-analysis of all stage 1 and stage 2 studies (stage 3).The overview of study design is illustrated in Supplementary Fig. 50. A detailed description of participating studies can be found in Supplementary Note All participating studies are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium2. The assessment of consistency of lateral ventricular volume on volumetric scale across time and different versions of software , revealed high intraclass correlation (ICC > 0.98) in a subset of participants from the Rotterdam Study. Participants with dementia at the time of MRI, traumatic brain injury, prior or current stroke or intracranial tumors were excluded.Each study performed MRI and estimated the volume of the lateral ventricles and intracranial volume (ICV). The field strength of scanners ranged from 0.35 to 3 T. Information on scanner manufacturers and measurement methods is provided in Supplementary Data 51, IMPUTE52, BEAGLE53), each study performed genotype imputations using mostly the 1000 Genome phase 1 v3 reference panel.Information on genotyping platforms, quality control procedures and imputations methods for each participating study are provided in Supplementary Data 2 if significant, population stratification, familial relationship (family-based studies) or study site (multi-site studies). Population stratification was controlled for by including principal components derived from genome-wide genotype data. Study-specific details on covariates and software used are provided in Supplementary Data 54. Variants with low imputation quality r2 < 0.3 or minor allele count (MAC) ≤ 6 were filtered out. The association results of participating studies were combined using a fixed-effect sample-size weighted Z-score meta-analysis in METAL because of the difference in measurement methods of lateral ventricular volume55. Genomic control was applied to account for small amounts of population stratification or unaccounted relatedness. After the meta-analysis, variants with information in less than half the total sample size were excluded. Meta-analyses were performed separately for each of the stages. In the stage 1 meta-analysis, a p-value < 5 × 10−8 was considered significant. Variants that surpassed the threshold were evaluated in the stage 2 meta-analysis. In order to model linkage disequilibrium (LD) between those variants, we first calculated the number of independent tests using the eigenvalues of a correlation matrix using the Matrix Spectral Decomposition (matSpDlite) software56. Subsequently, a Bonferroni correction was applied for the effective number of independent tests (0.05/10 independent SNPs = 5 × 10−3). Additionally, all analyses were stratified by sex. Following the same QC steps as for overall analyses, the sex-stratified association results of participating studies were combined using a fixed-effect sample-size weighted Z-score meta-analysis in METAL while applying genomic control55. The variants were assessed only if test statistics (Z-score) were heterogeneous between males and females  and if the association in a sex-combined analysis did not reach genome-wide significance threshold57.Each participating study performed the GWA analysis of total lateral ventricular volume under an additive model using variant allele dosage as predictors and natural logarithm of the total lateral ventricular volume as the dependent variable. Transformation of the lateral ventricular volume was applied to obtain approximately normal distribution  GWA analysis using Genome-wide Complex Trait Analysis (GCTA), version 1.26.059, RegulomeDB v1.160, and Combined Annotation Dependent Depletion (CADD) tools61. To determine whether they have an effect on gene expression, we used GTEx data62. For the lead variants, we explored 5 chromatin marks assayed in 127 epigenomes  of RoadMap data63. To search for pleiotropic associations between our lead variants and their proxies (r2 > 0.7) with other traits, we used the PhenoScanner database designed to facilitate the cross-referencing of genetic variants with many phenotypes9. The association results with genome-wide significance at 5 × 10−8 were extracted.To annotate genome-wide significant variants with regulatory information, we used HaploReg v4.164. The total proportion of variance in lateral ventricular volume was calculated by adding up the proportions of variance in lateral ventricular volume explained by each lead association signal.The proportion of variance in lateral ventricular volume explained by each lead variant was calculated using Pearson’s phi coefficient squared as explained in Draisma et al.65. Partitioned heritability analysis determines enrichment of heritability in SNPs partitioned into 24 functional classes as reported in Finucane et al.65. To avoid bias, an additional 500 bp window was included around the variants included in the functional classes. Only the HapMap3 variants were included as these seem to be well-imputed across cohorts.SNP-based heritability and partitioned heritability analyses were performed using LD score regression following the previously described method66. The method provides fold enrichment (FE) statistics at various GWA p-value thresholds after taking into account LD, minor allele frequency, and local gene density66. The FE statistics were calculated at eight GWA p-value thresholds (0.1 to 1 × 10−8). The associations were tested for various regulatory elements including DNase-I hypersensitivity sites, histone modifications, chromatin states and transcription factor binding sites in over 1000 cell and tissue-specific annotations66. The significance threshold calculated based on the number of annotations used was set at 4.97 × 10−5.We performed functional enrichment analysis using regulatory regions from the ENCODE and Roadmap projects using GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction (GARFIELD) method67. This method is an extension of PrediXcan method modified to use summary statistic data from meta-analysis67. Based on a total number of genes tested, the Bonferroni-corrected significance threshold was set to 0.05/12,379 = 4 × 10−6.To integrate functional data in the context of our meta-analysis results, we used the MetaXcan method, which evaluated the association between lateral ventricular volume and brain-specific gene-expression levels predicted by genetic variants using the data from GTEx project68. LD structure was computed based on the 1000 Genomes phase 3 population. Variants within 10 kb of the 5′ and 3′ untranslated regions were included in this analysis in order to maintain regulatory variants68. Subsequently, the gene-based scores were used to perform gene-set enrichment analysis using VEGAS2pathway69. VEGAS2Pathway approach accounts for LD between variants within a gene, and between neighboring genes, gene size, and pathway size69. It uses computationally predicted Gene Ontology pathways and curated gene-sets from the MSigDB, PANTHER, and pathway commons databases69. The pathway-based significance threshold was set to the p-value = 1 × 10−5 while taking into account the multiple testing of correlated pathways (0.05/5000 independent tests)69.The gene-based test statistics were computed using VEGAS2 software which tests for enrichment of multiple single variants within the genes while accounting for LD structure70. Summary-level GWA study results for white matter hyperintensities were obtained from the CHARGE consortium71 and the analyses were performed using the ldsc tool (https://github.com/bulik/ldsc).We used the LD score regression method to estimate genetic correlations between lateral ventricular volume and various traits including anthropometric traits, brain volumes, neurological and psychiatric diseases and personality traits. The analyses were performed using a centralized database of summary-level GWA study results and a web interface for LD score regression, the LD-hubZ-statistics using the previously published formula72. The allele associated with an increased risk in corresponding traits was considered to be the effect allele. The weighted GRS was constructed as the sum of products of effect sizes as weights and respective allele dosages from 1000 Genome imputed data of Rotterdam Study using R software version 3.2.5 (https://www.R-project.org). Variants with low imputation quality (r2 < 0.3) were excluded. Subsequently, the GRS was tested for association with lateral ventricular volume in three cohorts of Rotterdam Study while adjusting for age, sex, total intracranial volume, age2 and population stratification. The significance threshold for genetic risk score association was set to p-value = 5 × 10−3 (0.05/10) based on the number of genetic risk scores tested.We generated genetic risk scores (GRS) for Alzheimer’s disease, amyotrophic lateral sclerosis (ALS), Parkinson’s disease, bipolar disorder, schizophrenia, white matter lesions and tau-related phenotypes. The tau-related phenotypes, including tau and phosphorylated tau levels in cerebrospinal fluid, and progressive supranuclear palsy (PSP), were studied in relatively small sample and are therefore not appropriate for LD score regression. We extracted the lead genome-wide significantly associated SNPs and their effect estimates from the largest published GWA studies (Supplementary Data Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10


In this study, we apply the principles of Mendelian randomization to systematically evaluate transcriptome-wide associations between gene expression (across 48 different tissue types) and 395 complex traits. Our findings indicate that variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. Moreover, detailed investigations of our results highlight tissue-specific associations, drug validation opportunities, insight into the likely causal pathways for trait-associated variants and also implicate putative associations at loci yet to be implicated in disease susceptibility. Similar evaluations can be conducted at  Gene expression and how genetic variants can influence gene expression are tissue-specific processes with important implications for phenotypes. Here, Richardson et al. use eQTL data from GTEx and the eQTLGen project in a two-sample SMR + HEIDI framework for causal inference of gene expression associations with complex trait. Moreover, endeavours leveraging eQTL data derived from different tissue types can help to further ascertain the biological and clinical relevance of variants associated with complex traits6. In particular, these efforts are important when investigating tissue specificity, the phenomenon whereby a gene’s function is restricted to particular tissue types7.Advancements in high-throughput sequencing technologies present an unparalleled opportunity to investigate the molecular determinants of complex disease. This has facilitated the identification of genetic variants that influence gene expression, known as expression quantitative trait loci (eQTL). Recent studies have demonstrated the benefit of using eQTL data to help understand the underlying mechanisms of findings from genome-wide association studies (GWAS)10. This approach harnesses eQTL as instrumental variables (IVs) to investigate whether genetic variants at a locus influence both gene expression and complex trait variation. Furthermore, this framework has advantages over alternative transcriptome-wide approaches by incorporating techniques in genetic colocalization12. This helps to mitigate the likelihood of spurious findings attributed to two separate but correlated variants at a locus, one responsible for influencing gene expression and the other affecting the associated complex trait. As such, associations supported by evidence of genetic colocalization are more likely to be driven by a shared genetic factor. Crucially, we note that genetic colocalization is necessary, but not sufficient, for causality. This is because the genetic effect may influence the associated trait due to mediated changes in gene expression, or it may operate on both through independent biological pathways13.An important challenge in molecular epidemiology is assessing how associations between gene expression and complex traits depend upon the tissue analysed. We previously proposed an analytical pipeline to detect associations between tissue-specific gene expression and complex traits by applying the principles of Mendelian randomization (MR)14 (v7), as well as whole blood-derived data from the eQTLGen project15 . With this putative causal map of tissue-dependent associations, we undertake several extensive analyses. Firstly, we evaluate the relationship between gene expression across many tissues and pleiotropy; the phenomenon whereby a gene influences variation in multiple traits16. Next, we undertake a series of transcriptome and phenome-wide analyses to uncover tissue-dependent associations. Findings such as these can help to develop insight into the underlying regulatory mechanisms that reside along the causal pathway from a genetic variant to its associated complex trait. Moreover, they can help uncover pleiotropic effects that may be confined to separate tissue types.In this study, we apply our framework to comprehensively evaluate the association between the transcription of 32,116 protein-coding, RNA- and pseudo genes, and 395 complex traits. To assess the importance of tissue dependency for these associations, we use gene expression from 48 tissue types using data from the genotype-tissue expression (GTEx) consortium18. Finally, we explore the tissue dependency of associations between selected genetic variants and blood pressure traits. Our findings suggest that integrating tissue-specific eQTL data can help prioritize likely functional genes and tissues responsible for GWAS signals.We also demonstrate that phenome-wide evaluations of target genes have translatable value. For example, they can help predict whether therapeutic intervention will result in potential on-target side effects, as well as propose scope for drug repurposing. This is particularly attractive as previous findings suggest that support from genetic association studies can improve efficacy and safety rates for drug validation efforts n = 80–491, Supplementary Table n = 31,684). Full summary statistics for 395 complex traits were obtained from large-scale GWAS  method (v0.710)20.We pooled together eQTL data from the GTEx consortium (v7) for 48 tissue types (r2 < 0.001) using tissue-specific data from GTEx. For example, only 285 of the 7865 genes with an eQTL using thyroid-derived gene expression have more than a single instrument. This caveat meant that we decided to undertake all analyses in our study using the top eQTL for each gene and applied the HEIDI method to reduce the likelihood of false-positive findings. Furthermore, knowing that our analyses were confined to using the top eQTL only, we also decided to apply a lenient P value threshold of P < 1.0 × 10−04 (based on linear regression coefficients) to define lead eQTL as IVs in our analysis. This was to include as many cross-tissue comparisons in our study as possible where results have all been derived using the same single IV approach. All findings can be visualized and downloaded using our web application located at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/. A schematic of our study analysis can be found in Fig. This approach was chosen over alternatives due to the vast majority of genes only having a single independent eQTL that can be used as an IV in an MR framework (based on P < 5.0 × 10−08) and filtered for evidence of genetic colocalization . In total, 39,586 MR associations were robust to multiple testing and genetic colocalization based on these criteria. Estimating F-statistics for these instruments used in these analyses suggested that eQTL with weaker evidence of association may reduce instrument strength as expected, although all instruments had an F-statistic > 10  is simply a heuristic for highlighting associations worthy of follow-up21. Investigations of results can therefore apply more (or less) stringent lead eQTL and HEIDI thresholds by filtering associations downloadable from the web application.Each analysis undertaken was adjusted for conventional genome-wide corrections . This analysis was adjusted for minor allele frequencies, linkage disequilibrium (LD) score, and distance to gene expression probe for lead eQTL, given that these genomic properties may influence the number of associated traits for a given SNP. In a subsequent analysis, we clustered eQTL effects based on their associated genes. Overall, there was a positive correlation between the number of traits that each gene was associated with and the number of different tissue groups that these associations were detected across .We identified strong evidence of a positive relationship between the number of associated traits for each lead eQTL and the number of tissues they were detected in  and 17 of these survived HEIDI filtering  may likely act via changes to gene expression in thyroid tissue. Figure TPO, NR3C2, and PDE8B), as they were only identified in thyroid tissue after correcting for the number of tissues evaluated , which reflects the tissue dependency of associations for TPO.We evaluated the association for each of these genetic effects on thyroid disease in all other available tissue types. Although we report these genetic effects based on their corresponding gene symbols, it should be noted that they are based on the MR effect estimates using lead eQTL. We found that in particular two of these associations appeared to be highly tissue specific . Instead findings such as this may be attributed to different eQTL used as IVs for the same gene but within a different tissue type (as is the case for FBN2). As such, they may elucidate tissue-dependent regulatory mechanisms that can help explain associations at pleiotropic loci23.Investigating phenome-wide associations for genes of interest can also yield insight into tissue-dependent effects. As an example, we evaluated genes in our atlas associated with two traits with a substantial heritable component within the UK Biobank study; diastolic blood pressure (DBP) and forced vital capacity (FVC). We found that 24. A well-established example of this is the impact of HMG-coenzyme A reductase (HMG-CoA) inhibition using statins, which is known to reduce low-density lipoprotein (LDL) cholesterol levels. However, this is known to also potentially result in increased bodyweight and risk of diabetes25.Exploring our associations in a phenome-wide manner may also be valuable for other purposes, such as helping validate whether genes may be viable drug targetsHMGCR (the gene responsible for HMG-CoA) using data derived from whole blood supports these findings. We observed strong positive associations between the lead eQTL for this gene and high LDL and total cholesterol levels (Supplementary Data P = 1.87 × 10−15), although the association with self-reported diabetes did not survive phenome-wide corrections (MR P = 0.001). Nonetheless, these findings help support the notion that MR analyses can help mimic the findings of randomized control trials26 and identify potential on-target side effects of therapeutic intervention27. We note however that the tissue analysed may play an important part in such analyses, particularly with respect to the sensitivity of genetic colocalization. Notably, associations with lipid traits using whole blood-derived data did not survive HEIDI corrections, although stronger evidence of colocalization was detected using skeletal muscle tissue .Undertaking a phenome-wide evaluation of CYP19A1 expression using data derived from whole blood .In terms of targets which are less well established in the literature, our findings highlighted several potential adverse effects by conducting a similar analysis for ACHE, which is a target for drugs used to treat cognitive decline in Alzheimer’s patients, such as galantamine and donepezil30. The causal pathway targeted by these drugs would likely be expected to inhibit ACHE expression in brain tissue. However, conducting a phenome-wide evaluation for this gene in other tissues (such as artery aorta) indicates that its transcription is associated with higher blood pressure . Harnessing all available tissue-dependent results from our atlas allowed us to evaluate associations between nearby genes for which this SNP is an eQTL. Doing so identified only one association signal that survived multiple comparisons, which was CDH13 using eQTL data derived from the aorta  based on analyses undertaken using data from the UK Biobank study . However, by integrating tissue-specific eQTL data, along with the reduced burden on multiple testing, our analysis provided evidence suggesting that this may be a trait-associated locus yet to be reported by previous studies (Supplementary Data ATP13A3 expression derived from heart tissue (MR P = 3.0 × 10−06), which again may help yield mechanistic insight into the causal pathway from genetic variant to phenotype. Furthermore, this is a putative illustration that the nearest gene to a trait-associated SNP is not always the causative one32, as the nearest gene to rs1706003 provided weak evidence of association . Locus zoom plots for the results highlighted in this section can be found in Supplementary Figs. This approach may also prove useful in identifying trait-associated variants yet to be discovered by GWAS. For instance, rs1706003 is a SNP associated with blood pressure that may be overlooked based on conventional GWAS corrections . Specifically, an instrument which is associated with the transcription of a large number of genes (across a diverse range of tissue types) is likely to be more prone to horizontal pleiotropy and therefore invalidate the underlying assumptions of MR.In our results, we have demonstrated that phenome-wide evaluations of genes can help elucidate tissue-dependent associations. As an example of this, we show that 39. Moreover, this is particularly crucial given the costs of drug development40, but also timely given that the highest number of new drugs were approved in 201841. As a proof of concept, we undertook a phenome-wide scan of HMGCR which is targeted by statins to reduce elevated cholesterol levels. We identified strong associations with cholesterol traits, as well as findings which reflect reported on-target effects of statins (namely changes in bodyweight and risk of diabetes25). So although GWAS datasets typically investigate disease incidence as opposed to disease progression or treatment, evaluations such as these may still be useful for therapeutic validation24. We also note that the ideal tissue type for a specific hypothesis may not always be feasible based on current datasets . Moreover, the ideal tissue  may not always be well documented in the literature.Phenome-wide evaluations of our findings also have the potential to assist in drug target prioritization. This supports emerging evidence concerning the benefit in using findings from genetic association studies to support therapeutic validationCYP19A1 suggested that inhibiting this target may result in lower BMD. This finding supports a side effect previously reported for the anticancer drug anastrozole which targets this gene29. The therapeutic benefit of statins on lower risk of coronary heart disease has been found to outweigh the adverse side effects on diabetes risk42. Uncovering potential side effects for other drug targets should motivate future endeavours to evaluate whether the benefits of therapeutic intervention outweigh the possible drawbacks. Similar evaluations may also help highlight the potential drug repurposing and repositioning opportunities. We provide an example of this suggesting that targeting ACHE  may help lower blood pressure levels. There are likely many other potential associations from our analyses which may highlight the potential drug repurposing/repositioning opportunities.Our results can also be used to flag on-target effects which are less well established in pharmacogenetics. For instance, our evaluation of 43. The examples we have showcased in this regard involve SNPs associated with blood pressure, where we prioritize CDH13 and ATP13A3 as genes which may be responsible for these effects. CDH13 is a regulator of vascular wall remodelling and angiogenesis44, and ATP13A3 has recently been implicated in pulmonary arterial hypertension susceptibility through rare loss of function analyses46. However, although there are likely many instances where integrating tissue-specific eQTL data can help pinpoint genes responsible for GWAS associations, this may not always be possible due to the complexities of coexpression and widely expressed genes47. Moreover, we emphasize that integrating gene expression data to help highlight potential genes underlying GWAS hits should only be considered as evidence of prioritizing likely candidates which functional analyses can investigate in detail.In the final series of analyses in our study, we propose that integrating tissue-specific eQTL data into GWAS analyses may help highlight genes responsible for association signals. Our approach therefore supports the notion of triangulation in epidemiology, whereby many lines of evidence are needed to support robust conclusions 48. Although the current sample sizes have meant that the analyses in this study have been restricted to using lead eQTLs only, future efforts will benefit from leveraging multiple valid instruments within a MR framework. This will also facilitate the application of various sensitivity analyses that can be undertaken for MR analyses, such as leave-one out analyses and the MR-Egger approach49. Nonetheless, techniques in genetic colocalization will likely continue to play an important role in discerning whether associations are detected due to shared causal variants. We also note that the inference of colocalization methods may be limited when evaluating associations at loci of dense LD (such as the HLA region of the genome).Endeavours which continue to generate increasingly large-scale tissue-specific molecular datasets will facilitate data mining opportunities across the human transcriptomeFurthermore, the approach used in our study  is unable to robustly rule out that findings may be influenced by molecular horizontal pleiotropy. This is the process whereby a genetic variant influences gene expression and a complex trait via two independent biological pathways. It may also be possible that a variant may influence complex trait variation via a posttranscriptional mechanism, which should be worthwhile evaluating in future studies once tissue-specific protein QTL data becomes accessible in large sample sizes. Moreover, we note that cross-tissue inference of our findings has the caveat of differing sample sizes in GTEx for different tissues. It is therefore important to take into account the sample size for each tissue type when interpreting findings, as this has an influence on the power to detect lead eQTLs for genes  were comparable to those detected using a transcriptome-wide association study (TWAS) by querying findings from the TWAS hub51. However, other findings such as the association between TPO and thyroid disease were not identified using this alternative method. We therefore propose although the analysis used in this study is comparable to that of TWAS, both approaches may prove useful in detecting evidence of association which the other may overlook.When evaluating associations in our results, it is important to remember that they are based on SNP effect sizes which are often relatively modest52, enloc53, or eCAVIAR11, in our analyses may have detected evidence for different association signals. Whilst the HEIDI method is not prone to some of the caveats of these approaches (such as sensitivity to prior distributions), it may not always be capable of detecting heterogeneity under certain circumstances. Finally, evidence from the literature suggests that the expression for an increasingly large proportion of protein-coding genes is influenced by multiple independent variants54. Future study designs should therefore take this into account as tissue-specific datasets increase in scale . Only 48 of the 53 tissues available from GTEx v7 were analysed as each of the remaining 5 had fewer than 50 samples. As anticipated, there was a strong positive correlation between the number of unique genes eligible for analysis in a single tissue type compared with the sample size of that tissue (Supplementary Table http://www.eqtlgen.org). GWAS summary statistics were obtained from the Neale Lab analyses of UK Biobank data and consortia who have made their results publicly available . This is to reduce the likelihood of associations attributed to horizontal pleiotropy to which trans-effects are more prone. The summary statistics from GWAS analysed in our study are typically restricted to disease incidence as opposed to disease progression. Furthermore, these effect estimates do not involve analysis of repeated measures.We conducted analyses using the summary-data-based MR (SMR) method (v0.710). A reference panel of European individuals from the 1000 genomes project (phase 3) was used to compute LD estimation for all analysesr2 < 0.001), only the lead eQTL based on observed P values was used as an IV. This approach was also applied when analysing data from the eQTLGen consortium despite the larger sample sizes, for consistency when comparing associations between dataset. We defined eQTL based on a lenient P value threshold of P < 1 × 10−04, maximizing the number of possible genes analysed across tissues but also allowing readers to filter out associations should they wish to apply a more stringent threshold. To assess instrument strength based on this lenient threshold we calculated F-statistics as proposed by Bowden et al.74:γj is the SNP-exposure association and σXj is the standard deviation for the SNP-exposure association for variant j.Consequently, only lead eQTLs for each gene were used as IVs given that very few genes could be robustly instrumented with multiple independent SNPs in the GTEx dataset. In the few instances where genes from GTEx could be instrumented using multiple independent instruments . A strict lead eQTL threshold of P < 5 × 10−08 was also applied to assemble this curated set of results. However, it is also possible that genomic properties  may influence the number of traits which multitissue eQTLs are associated with. Therefore, we adjusted our analysis for minor allele frequencies, LD score, and distance to gene expression probe for lead eQTL. Furthermore, associations detected using eQTLGen whole blood-derived data were removed from this analysis to reduce any bias which may be attributed to the large sample size of this dataset. Pearson’s r2 was calculated to compare the correlation between the number of associations identified with the number of tissues they were detected across for each eQTL clustering by their associated gene.An analysis of variance model was applied to investigate the association between the number of traits and number of tissue types detected for all lead eQTL in our curated results . The R package ‘shiny’ v1.1 was used to develop the web application. The R packages ‘manhattanly’ v0.2 and ‘highcharter’ v0.5 were used to generate interactive plots. Figures in this manuscript were generated using ‘ggplot2’ v2.2.1.By default, our web application displays multiple testing comparisons based on Bonferroni correction for the number of tests undertaken in the search query. Subsequently, HEIDI corrections are applied based on the number of associations which survived multiple testing in this look upFurther information on research design is available in the Dataset 1Dataset 2Dataset 3Dataset 4Dataset 5Dataset 6Dataset 7Dataset 8Dataset 9Dataset 10Dataset 11Supplementary InformationReporting Summary


Philasterides dicentrarchi, a parasite causing scuticociliatosis that leads to substantial economic losses in the aquaculture industry. A noticeable characteristic of the parasite is that it causes visual signs that can be used for disentangling resistance and endurance. Our results showed the existence of genetic variation for both traits  and for the composite trait resilience (heritability = 0.15). The genetic correlation between resistance and resilience was very high (0.90) indicating that both are at a large extent the same trait, but no significant genetic correlation was found between resistance and endurance. A total of 18,125 SNPs obtained from 2b-RAD sequencing enabled genome-wide association analyses for detecting QTLs controlling the three traits. A candidate QTL region on linkage group 19 that explains 33% of the additive genetic variance was identified for resilience. The region contains relevant genes related to immune response and defence mechanisms. Although no significant associations were found for resistance, the pattern of association was the same as for resilience. For endurance, one significant association was found on linkage group 2. The accuracy of genomic breeding values was also explored for resilience, showing that it increased by 12% when compared with the accuracy of pedigree-based breeding values. To our knowledge, this is the first study in turbot disentangling the genetic basis of resistance and endurance to scuticociliatosis.Selective breeding for improving host responses to infectious pathogens is a promising option for disease control. In fact, disease resilience, the ability of a host to survive or cope with infectious challenge, has become a highly desirable breeding goal. However, resilience is a complex trait composed of two different host defence mechanisms, namely resistance (the ability of a host to avoid becoming infected or diseased) and endurance (the ability of an infected host to survive the infection). While both could be targeted for genetic improvement, it is currently unknown how they contribute to survival, as reliable estimates of genetic parameters for both traits obtained simultaneously are scarce. A difficulty lies in obtaining endurance phenotypes for genetic analyses. In this study, we present the results from an innovative challenge test carried out in turbot whose design allowed disentangling the genetic basis of resistance and endurance to  Infectious diseases represent a major threat to farmed animal populations. In addition to their impact on health and welfare of the affected animals and the associated production losses, they can also have implications for food security and human health . In receThe existence of genetic variability is a prerequisite for obtaining genetic responses through selection but very few studies have been designed to estimate the genetic basis of both resistance and endurance. In fact, most studies investigating host genetics for infectious diseases have focused on host resistance , as enduThe consequences of infectious diseases on animal production seems to be most problematic in the conditions of aquaculture settings. The confinement in tanks or cages, where many individuals share a common environment, highly facilitates the transmission of infections. Indeed, the economic impact of diseases on aquaculture, entails losses of more than six billion US $ per year . In seleScophthalmus maximus) is the main flatfish produced worldwide due to its high commercial value. This has prompted its intensive farming during the last decades and a fast development of genomic resources for the species , fine flounder , European sea bass (Dicentrarchus labrax) and kelp grouper (Epinephelus bruneus) among others . Given that the statistical models for genomic analyses of infectivity are not fully developed yet, here we focus on the two first components . We also considered the composite of both traits; i.e., resilience. This is the trait most commonly recorded in aquaculture species to address disease resistance in fish.Philasterides dicentrarchi causes systemic infection in turbot, invading internal organs such as brain, gills, liver, and intestine that generally results in the death of the host  facilities. The CETGA’s broodstock represent a population of Atlantic origin  were challenged  with c origin  that wasc origin . Due to P. dicentrarchi used in trial 1 (see below). In addition, each tank contained 20 recipient fish  from four different families (five fish per family). These four families constituted a recipient family combination. In each trial, the total number of shedder families and family combinations were four and nine, respectively. Each of the four shedder families infected all nine family combinations (4 × 9 = 36 tanks) by cohabitation. Each recipient family was represented in eight tanks and in two different family combinations. A summary of numbers of fish and families used in each trial is given in The detailed experimental design is given in Fish were inspected twice a day for visual signs and mortality, and dates of first detection of visual signs or death were collected. Weight of fish was recorded at the start and at the end of the challenge (or at the time of death). The average weight (g) of fish was 32.9 (SD = 9.5) and 88.5 (SD = 27.3) in trials 1 and 2, respectively. Individual fish were identified using elastomers (families) and fin clipping . Fin tissue samples were collected for DNA extraction and genotyping at GeneAqua facilities.The data analysed corresponded to the 1,440 recipient fish challenged by cohabitation. These fish belonged to 36 full-sib families  created from 23 sires and 23 dams that were unrelated. These families included 12 paternal half-sib families  and 11 maternal half-sib families . Both trials were connected through two male and three female parents.p < 0.001) were removed. Also, for tags containing multiple polymorphisms only one SNP was retained. After quality control, a total of 18,125 SNPs were retained. The assembly of the turbot genome  from the 36 full-sib families and their 46 parents. Genotypes were obtained using a 2b-RAD sequencing approach as described in t genome  has beent genome  and 97% t genome .The analysis of the genetic and environmental components of resistance, endurance, and resilience was carried out using survival analysis methodology. Survival analysis is optimal to analyse time-to-event variables because it allows the use of information from incomplete records (censored data), it takes into account factors that determine the risk of the event of interest over time and relies on functions that adequately represent survival processes. Those individuals that survived to the end of the experiment were considered as censored data for resilience; those that survived without showing any visual sign were considered as censored for resistance, and those that had shown signs but survived to the end of the experiment were considered as censored for endurance.A non-parametric Kaplan–Meier (KM) estimator of the survival function was obtained for each recipient family and for each trial in order to assess the existence of variation between family and trial groups for the three traits analysed.Significance of systematic effects on survival was determined by likelihood ratio tests using the Cox partial likelihood of the data under the complete  and reduced models  using the following modelh(t) is the risk of onset of visual signs (resistance) or death (resilience and endurance) at time t; h0(t) is the baseline function at time t (Cox model) stratified by trial, b is the vector of systematic effects including the weight at the start of the experiment  and the tank , and X is the incidence matrix relating risk at time t to the corresponding level of systematic effects. Note that sources of variation within tanks come from a combination of the environmental conditions of each tank, shedder family, recipient family combination and trial. Overall goodness of fit was assessed by the statistic where The general model fitted to estimate the variance components of the disease traits was a proportional hazards mixed model of the formh(t), h0(t), X and b are as in (1), u is a vector of animal genetic effects, and Z is the design matrix for u. Animal genetic effects were assumed to be distributed as a N . The heritability in the original scale was estimated following h2 = p)), where p is the proportion of uncensored data.where S(t)) against log t, where S(t) is the survival function. A straight line is expected if the Weibull model fits well.The previous mixed model was solved both with semi-parametric Cox and with parametric Weibull approaches. The use of the Cox model does not require specification of the distribution of the independent variable, but it has a larger computational burden and it is more affected by the data structure than Weibull models. The hypothesis of a Weibull distribution was graphically tested by regressing log  and the start of the experiment divided by the number of days. Pairwise correlations between resilience, resistance and endurance and between these traits and growth rate were estimated fitting bivariate animal models for each pair of traits of the following formy is the vector of phenotypic records for a pair of traits, b is the vector of fixed effects (weight and tank effects for both traits), u and e are vectors of random animal genetic and residual effects, respectively, and X, Z are the design matrices for the corresponding effects. Here, var(u) = oG ⊗ G, where ⊗ denotes the Kronecker product, Go is the 2 × 2 additive genetic (co)variance matrix between the two traits and G is the genomic relationship matrix computed as in e) = Ro⊗I, where Ro is the (co)variance matrix between residual terms for each trait and I is the identity matrix. Bayesian estimates for (co)variance components in Go and Ro were obtained via Gibbs sampling using the software GIBBS1F90 (where IBBS1F90 . The numu) was AGr/h, where r is the correlation between the observed and predicted phenotype, and h is the square root of the heritability  with the aim of detecting QTL affecting the disease traits, testing one SNP at a time. The assumed model wasy, X, b, Z, and u are the same as in GBLUP, w is the vector containing the SNP allelic dosage for each fish in y and α is the SNP substitution effect that was included as a fixed effect.where G matrix computed with all SNPs. Both GWAS and RHA were performed using the software DISSECT  assuming the same mixed linear model was performed. For that, a reduced genomic relationship matrix constructed from the SNPs covering a specific putative QTL region previously identified by GWAS was used instead of the  DISSECT . Signifi DISSECT . Adjuste Myriads .1 , but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of CETGA.Phenotypic distributions for resilience, resistance and endurance for the two trials are presented in The differences between trials justified the stratification of the baseline hazard by trial for the three traits in the survival analysis, particularly for resilience and resistance. Kaplan–Meier survival curves  providedR2 from the linear regression analysis was high (>0.84), the graphs showed deviations from linearity. This result justifies the use of both Cox and Weibull models for the estimation of genetic parameters. The estimates of the Weibull parameters  for resilience, resistance and endurance for trial 1  were, respectively, 2.23 (4.77) and -9.12 (-23.37), 1.95 (2.90) and -7.65 (-14.28), and 1.23 (1.61) and -3.04 (-3.46). The fact that slopes were >1 implies that the hazards increased overtime, as expected. The different estimates of ρ and λ obtained for both trials again justified the fit of different baseline hazards for each trial.The hypothesis of a Weibull distribution was graphically tested . AlthougM2 indicated that the proportion of variation explained by the model for the three traits decreased by about 5% when the initial weight was excluded from the model. This proportion decreased up to 15% for endurance and to 35% for both resilience and resistance when tank was the effect excluded. Differences in the relative risk of death or of showing visual signs across weight categories were not significantly different, although a general trend was observed. The larger the initial weight the lower was the risk  indicating that both are, at a large extent, the same trait. The genetic correlation between resilience and endurance, although lower, was also high (0.77) and much higher than the phenotypic correlation . It is w2r) of 0.51, and showed an average effect on resilience of 4.66 (SD = 0.42) days. Their minor allele frequencies ranged from 0.15 to 0.39 (p) values was very similar to that obtained for resilience. For endurance, one significant association was detected on LG2 . GWAS revealed 16 significant SNPs associated with resilience (FDR < 0.05). Thirteen of them were located on LG19 and the remaining three were located on LG7, LG16, and LG23 . Twelve  to 0.39 . The regd on LG2 .Functional enrichment of the candidate region at LG19 using the turbot transcriptome as a reference revealed 32 genes with functions related to the immune or defence system. These functions were associated with (i) tissue regeneration; (ii) response to wounding; (iii) leukotriene production involved in inflammatory response ; (iv) acQuantifying the host genetic contribution to epidemic risk and severity remains a long-standing challenge in infectious disease research. In this study, survival analysis and linear models were used to disentangle the genetic basis of resistance and endurance to disease, using scuticociliatosis in turbot as a model and taking benefit from both pedigree and genomic information. The results showed the existence of genetic variation for both traits and also for resilience, the composite trait that includes both resistance and endurance. Estimates of heritability were 0.26, 0.12, and 0.15 for resistance, endurance and resilience, respectively. The genetic correlation between resilience and resistance and that between resilience and endurance were high. However, there was no evidence of the existence of a genetic correlation between resistance and endurance.To the best of our knowledge, only few studies have attempted to formally decompose genetic variation in survival in fish populations . Using aIn this study, we distinguish resistance and endurance as the two different genetic host traits affecting host survival to infectious challenge. A much more common approach is to distinguish between host resistance and tolerance as the two alternative host defence mechanisms to infection, where resistance refers to the propensity of a host to prevent infection or limit its extent, and tolerance refers to the propensity of the host to reduce fitness loss  under infection . Based oEstimates of heritability tended to be higher under the Weibull than under the Cox model, especially for endurance . The fact that the graphical test of the Weibull assumption  showed dOur estimates of both phenotypic and genetic variances were larger for resistance than for endurance, in agreement with previous studies in fish . This maP. dicentrarchi was addressed by P. dicentrarchi by using high-throughput SNP genotyping and a more refined map, and have analysed a larger amount of families. Also, both studies differ in the via of infection used in the challenge tests (injection vs. cohabitation) and thus the traits analysed may not be necessarily the same. Although the GWAS for resistance had not enough power to detect SNPs significantly associated to this trait (the number of data available was lower for resistance than for resilience), the pattern of -log(p) values in the Manhattan plot was very similar to that for resilience, supporting the hypothesis that both traits are, to a large extent, the same. The pattern found for endurance was however different, which may suggest that this trait is controlled by different genes.The availability of genomic information obtained from RAD-sequencing for 36 families allowed us to identify a candidate QTL region for resilience on LG19 that explains 33% of the additive genetic variance. A preliminary study to identify QTLs for survival to P. dicentrarchi , the PLEK gene (involved in the regulation of response to stress) and the TRIM16 gene . Validation of the candidate region identified here in other genetic backgrounds is required, but it may be an appropriate target for MAS, and for functional studies designed to investigate the underlying causative genes. An alternative approach for improving disease traits would be to perform genomic selection  is fundamental in order to determine optimal disease control strategies. Here, genetic correlations between resilience and its two components (resistance and endurance) were positive and high, indicating that selection on resilience will lead to improvement in both defence mechanisms. However, when dealing with infectious diseases, ignoring the impact of selection on disease transmission  can lead to undesired outcomes. Individuals with greater endurance to infection will live longer when infected and therefore may have more chances of infecting others. The negative side-effects are exacerbated if the hypothesis that tolerance and hence also endurance mechanisms play a role in the maintenance of the asymptomatic superspreader state is true , as seleThis study was carried out in accordance with the recommendations of the ethical regulations and with the approvement of the Regional Government of Xunta de Galicia (registered under the code ES150730055401/ 16/PROD.VET.047ROD.01).AD-W, OA, SC, and BV contributed to the conception and design of the study. SC carried out the experiments. MS, MC, and AF performed the statistical analyses. FM, AM, MH, CF, and PM developed the genomic tools and carried out the data mining. MS and BV wrote the first draft of the manuscript. All authors contributed to the discussion of results and the edition of the manuscript revision.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer CP declared a past co-authorship with one of the authors PM to the handling editor.