Polygenic risk scores (PRS) have demonstrated predictive validity across a range of cohorts and diseases, but quantifying their clinical utility remains a challenge. As PRS can be derived from a single biological sample and remains stable throughout life, we explore the potential of PRS to optimize existing screening programs. Via an integrated modelling approach, we quantify the potential clinical benefits arising from a knowledge of PRS across seven diseases with existing screening programs (abdominal aortic aneurysm, breast cancer, colorectal cancer, coronary artery disease, hypertension, prostate cancer, and type 2 diabetes). We identify individuals at high genetic risk (PRS OR>2) and very high genetic risk (PRS OR>3) and estimate the optimal screening ages for these genetically high-risk individuals, based on the equivalent risk to population-level risk at recommended screening ages. We then leverage published data on differential mortality and other outcomes, with and without screening-based interventions, to assess the potential benefits of tailoring screening age based on genetic risk. Very high risk individuals reach the risk level associated with usual starting screening age on average 10.8 years earlier, high risk individuals 8.9 years earlier and reduced risk individuals (OR<0.5) 16.8 years later. During this time, case enrichment (the ratio of the percentage of cases in the high PRS risk group and in the total population) in the high risk group is between 1.7 and 3.0, depending on the disease. Across all seven diseases, appropriate interventions following PRS-guided screening could reduce premature deaths in high-risk individuals by 23.3%. Knowledge of genetic risk, measured using PRS, has the potential to deliver substantial public health benefits when aggregated across conditions, and could reduce premature mortality by tailoring existing screening programs.

We assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits that has been made available on the individuals in UKB, using a unified pipeline for PRS evaluation. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 76 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in a separate cohort (100,000 Genomes Project). The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit.

Background and Aims A cardiovascular disease polygenic risk score (CVD-PRS) can stratify individuals into different categories of cardiovascular risk, but whether the addition of a CVD-PRS to clinical risk scores improves the identification of individuals at increased risk in a real-world clinical setting is unknown.Methods The Genetics and the Vascular Health Check Study (GENVASC) was embedded within the UK National Health Service Health Check (NHSHC) programme which invites individuals between 40-74 years of age without known CVD to attend an assessment in a UK general practice where CVD risk factors are measured and a CVD risk score (QRISK2) is calculated. Between 2012-2020, 44,141 individuals (55.7% females, 15.8% non-white) who attended an NHSHC in 147 participating practices across two counties in England were recruited and followed. When 195 individuals (cases) had suffered a major CVD event (CVD death, myocardial infarction or acute coronary syndrome, coronary revascularisation, stroke), 396 propensity-matched controls with a similar risk profile were identified, and a nested case-control genetic study undertaken to see if the addition of a CVD-PRS to QRISK2 in the form of an integrated risk tool (IRT) combined with QRISK2 would have identified more individuals at the time of their NHSHC as at high risk (QRISK2 10-year CVD risk of >= 10%), compared with QRISK2 alone.Results The distribution of the standardised CVD-PRS was significantly different in cases compared with controls (cases mean score .32; controls, -.18, P = 8.28x10-9). QRISK2 identified 61.5% (95% confidence interval [CI]: 54.3%-68.4%) of individuals who subsequently developed a major CVD event as being at high risk at their NHSHC, while the combination of QRISK2 and IRT identified 68.7% (95% CI: 61.7%-75.2%), a relative increase of 11.7% (P = 1x10-4). The odds ratio (OR) of being up-classified was 2.41 (95% CI: 1.03-5.64, P = .031) for cases compared with controls. In individuals aged 40-54 years, QRISK2 identified 26.0% (95% CI: 16.5%-37.6%) of those who developed a major CVD event, while the combination of QRISK2 and IRT identified 38.4% (95% CI: 27.2%-50.5%), indicating a stronger relative increase of 47.7% in the younger age group (P = .001). The combination of QRISK2 and IRT increased the proportion of additional cases identified similarly in women as in men, and in non-white ethnicities compared with white ethnicity. The findings were similar when the CVD-PRS was added to the atherosclerotic cardiovascular disease pooled cohort equations (ASCVD-PCE) or SCORE2 clinical scores.Conclusions In a clinical setting, the addition of genetic information to clinical risk assessment significantly improved the identification of individuals who went on to have a major CVD event as being at high risk, especially among younger individuals. The findings provide important real-world evidence of the potential value of implementing a CVD-PRS into health systems. Structured Graphical Abstract GENVASC design and key findings: The GENVASC study was set up to evaluate whether the addition of a polygenic risk score for cardiovascular disease (CVD-PRS) to clinical risk assessment at an NHS Health Check would increase the number of individuals who went to have a major CVD event as being at high risk at their health check. 44 141 individuals free of CVD were recruited at their NHS Health Check and when 195 CVD events had occurred during follow-up, a retrospective case-control study was undertaken to see how many additional individuals would have been identified as high risk at their health check by the addition of the CVD-PRS in the form of an integrated risk tool (IRT) to assessment by the clinical risk score (QRISK2) alone.

Previous studies have highlighted how African genomes have been shaped by a complex series of historical events. Despite this, genome-wide data have only been obtained from a small proportion of present-day ethnolinguistic groups. By analyzing new autosomal genetic variation data of 1333 individuals from over 150 ethnic groups from Cameroon, Republic of the Congo, Ghana, Nigeria, and Sudan, we demonstrate a previously underappreciated fine-scale level of genetic structure within these countries, for example, correlating with historical polities in western Cameroon. By comparing genetic variation patterns among populations, we infer that many northern Cameroonian and Sudanese groups share genetic links with multiple geographically disparate populations, likely resulting from long-distance migrations. In Ghana and Nigeria, we infer signatures of intermixing dated to over 2000 years ago, corresponding to reports of environmental transformations possibly related to climate change. We also infer recent intermixing signals in multiple African populations, including Congolese, that likely relate to the expansions of Bantu language–speaking peoples.

Introduction: We have previously shown that combining a polygenic risk score (PRS) for cardiovascular disease (CVD), with standard clinical risk calculators such as QRISK®2, results in improved CVD risk prediction via an integrated risk tool (CVD-IRT). Research Question: The objective of this study was to explore the implementation of CVD-IRT within routine practice in the UK National Health Service (NHS), through participant and healthcare provider (HCP) surveys and interviews. Methods: The Healthcare Evaluation of Absolute Risk Testing Study (HEART) (NCT05294419), a prospective, single-arm pragmatic trial, enrolled 836 participants undergoing health checks across 12 NHS general practices. QRISK2 and CVD-IRT scores were returned to participants via HCPs. The primary outcome of the study was feasibility of CVD-IRT implementation. This was assessed by a mixed methods approach (quantitative and qualitative methods). Results: After the results were reported and discussed, 520 surveys were completed by participants and 824 surveys were completed by HCPs. Subsequently, 21 participants were interviewed and 13 HCPs attended focus groups or interviews to explore their experiences. 23 HCPs completed a final questionnaire (34.8% physician, 21.7% research nurse, 43.5% other). For 90.7% of reports, HCPs indicated that the CVD-IRT could be incorporated into routine primary care in a straightforward manner. 80% of HCPs agreed that having these tests could lead to better health outcomes for patients, and 68.4% believed that the CVD-IRT could help them manage their patients through a shared decision making process covering lifestyle and treatment options. Participants found the report personally useful (98.5%) and easy to understand (94.3%); agreed that genetic measures are important to identify the risks of developing CVD (85.2%); thought that the test should be widely made available (interview summaries); and would recommend to friends and family (86.8%). Conclusion: The implementation of the CVD-IRT into routine health-checks was recommendable, feasible and well received by both HCPs and participants, who felt that the information was useful, could support clinical decisions, and easy to understand.

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

Introduction: We have previously shown that combining a polygenic risk score (PRS) for cardiovascular disease (CVD), a numerical summary of an individual’s genetic predisposition to CVD, with standard clinical risk calculators such as ASCVD-PCE and QRISK results in improved estimates of CVD risk. Implementation of such a cardiovascular integrated risk tool (CVD IRT) into real world clinical practice is a key focus for further study. Hypothesis: We assessed the hypothesis that a CVD IRT can be incorporated into routine primary care. Methods: The Healthcare Evaluation of Absolute Risk Testing Study (NCT05294419) is a prospective trial recruiting up to 1,000 healthy participants undergoing health checks across 12 UK NHS general practices. Both QRISK2 and CVD IRT scores were generated and returned to clinicians, who then communicated the results to participants. The primary outcome of this study is operational success as well as feedback from health care providers (HCPs) and participants. The study also measures the impact of the CVD IRT on clinical decision making. Results: These are interim analyses. As of April 2022, 624 eligible participants (62% female, mean age 55) have been recruited. A total of 371 CVD IRT reports have been generated, with 100% of blood samples generating scores that were all returned within the designated time frame. Among the primary care HCPs, 89% (8/9) agreed that the incorporation of CVD IRT into routine care could be done in a straightforward manner. Among the participants who have completed a survey to date, 93% (125/135) would likely or very likely recommend the CVD IRT to friends and family. Average QRISK2 (6.3%) and CVD IRT (6.6%) risk scores did not differ significantly, but there were broad changes in risk among individual patients, with 5% (19/371) of patients crossing above the risk threshold to treat according to NICE guidelines (10-year risk ≥ 10%) as well as 3% (11/371) of patients reclassified as very high risk (10-year risk ≥ 20%). Conclusions: The rollout of an integrated risk tool combining polygenic risk into a standardized CVD risk calculator within primary care is feasible and well accepted by clinicians and participants. The CVD IRT results suggest clinically actionable changes in a substantial proportion of this population.

Aims The aim of the study was to assess the real-world feasibility, acceptability, and impact of an integrated risk tool for cardiovascular disease (CVD IRT, combining the standard QRISK (R) 2 risk algorithm with a polygenic risk score), implemented within routine primary practice in the UK National Health Service.Methods and results The Healthcare Evaluation of Absolute Risk Testing Study (NCT05294419) evaluated participants undergoing primary care health checks. Both QRISK2 and CVD IRT scores were returned to the healthcare providers (HCPs), who then communicated the results to participants. The primary outcome of the study was feasibility of CVD IRT implementation. Secondary outcomes included changes in CVD risk (QRISK2 vs. CVD IRT) and impact of the CVD IRT on clinical decision-making. A total of 832 eligible participants (median age 55 years, 62% females, 97.5% White ethnicity) were enrolled across 12 UK primary care practices. Cardiovascular disease IRT scores were obtained on 100% of the blood samples. Healthcare providers stated that the CVD IRT could be incorporated into routine primary care in a straightforward manner in 90.7% of reports. Participants stated they were 'likely' or 'very likely' to recommend the use of this test to their family or friends in 86.9% of reports. Participants stated that the test was personally useful (98.8%) and that the results were easy to understand (94.6%). When CVD IRT exceeded QRISK2, HCPs planned changes in management for 108/388 (27.8%) of participants and 47% (62/132) of participants with absolute risk score changes of >2%.Conclusion Amongst HCPs and participants who agreed to the trial of genetic data for refinement of clinical risk prediction in primary care, we observed that CVD IRT implementation was feasible and well accepted. The CVD IRT results were associated with planned changes in prevention strategies.

We present and assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits being made available on the individuals in UKB. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 81 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in other cohorts. The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit. ### Competing Interest Statement Peter Donnelly and Gil McVean are partners in Peptide Groove LLP. All other authors declare no competing interests. ### Funding Statement This work was funded by Genomics plc. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: UK Biobank data use (Project Application Number 9659) was approved by the UK Biobank according to their established access procedures, and legal and ethical approval is covered by the Research Tissue Bank approval obtained from the UK Biobank's governing Research Ethics Committee (REC 16/NW/0274), as recommended by the National Research Ethics Service. Our use of 100,000 Genomes Project data was approved by Genomics England's Access Review Committee under application reference AR88. Legal and ethical approval for our use of dbGaP cohort data is provided by the Western Institutional Review Board (Study Number 1264897, IRB Tracking Number 20192201). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The polygenic risk scores computed for each individual in UK Biobank have been made available to all approved UK Biobank researchers through the data showcase mechanism. The new GWAS data have been made available via .

Risk-guided intervention in the US, informed by the pooled cohort equations (PCE) risk calculator, is the accepted practice for prevention of atherosclerotic cardiovascular disease (ASCVD). We recently showed that a polygenic risk score (PRS, summarising common genetic risk) can be integrated with PCE into an integrated risk tool (IRT) and we validated its utility across multiple contexts including sex, age, genetic ancestry and self-identified ethnicity (Weale et al 2021). The addition of PRS is always beneficial, but its contribution varies by context. When meta-analysed across multiple cohorts, the PRS is more predictive in men (odds ratio per standard deviation, ORsd=1.57) than women (ORsd=1.46). Across ethnicities, the group with the lowest effect size is African American (ORsd=1.14 in men, ORsd=1.11 in women), a result that predominantly reflects a lack of available training samples. The availability of a CVD IRT has practical clinical implications. Because the IRT substantially spreads the risk scores in a given patient stratum, the inclusion of genetic factors can result in some individuals crossing actionable risk thresholds in both directions. Using UK Biobank, a large UK population cohort aged 45-64 with extensive medical and genetic information, we show that the rates of up/down classification are 7.8%/4.2% in younger men (40-54yo), 1.9%/11.6% in older men (55-69yo), 1.0%/0.2% in younger women, and 6.5%/6.6% in older women. This implies a tendency to under-treat in younger and over-treat in older individuals under current guidelines. Model discrimination (Harrell’s C, a measure of area-under-the-curve for survival data) is also greatest in the younger (C=0.751/0.732 in men/women) vs older (C=0.666/0.697 in men/women) age group. Given the increased up-classification and discrimination in the younger group, the addition of genetics provides an opportunity for earlier intervention in those most at risk.

Genetic risk factors contribute to cardiovascular disease (CVD) risk and are imperfectly captured by family history. The development of integrated risk tools (IRT) that integrate polygenic risk scores (PRS) with conventional risk factors enables improved risk assessment. However, IRTs rely on accurate estimation of the genetic component across millions of genetic variants, either typed or imputed. To support two real-world implementations of CVD IRTs, we present analytical validation data for three biosample types (whole blood, saliva and buccal swab samples) and two genetic assay technologies (a custom-designed genotyping array (845,608 markers) and low pass whole genome sequencing (lpWGS) with 1.8x average read depth, both combined with computational imputation) across 48 ethnically-diverse donors. IRT measurements (range 4%-23%) were performed on the 10-year CVD risk percentage scale, assuming a non-genetic (pooled cohort equations) risk component of 10%. We find measurement errors are below 1% across the three biosample types, so that an IRT of 4% will at most be measured as 3% or 5%. Array and lpWGS showed different bias-variance trade offs. The array technology was more reproducible, with a standard deviation on the percentage scale of 0.09 (0.072-0.129 95% CI) for blood samples compared with 0.30 (0.201-0.617 95% CI) for lpWGS. In contrast, the array technology showed somewhat larger bias when compared with high depth sequencing data (mean absolute error on the percentage scale of 0.49 (0.217-0.755 95% CI) for blood samples against 0.30 (0.008-0.597 95% CI) for lpWGS, partially reflecting the sequencing basis of the reference data. Pass rate was high for both technologies and all three biosample types. In conclusion, both genotyping array and lpWGS approaches were appropriate and performed well for the purpose of assessing the genetic component of integrated cardiovascular disease risk using either blood, saliva or buccal samples.

BACKGROUND:There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment. METHODS:Using the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals. RESULTS:The novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7-7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%-15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6-19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period. CONCLUSIONS:An IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

Summary-level coronary artery disease (CAD) and ischaemic stroke (IS) GWAS data generated by Genomics plc as presented in: Weale M. et al. Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries. American Journal of Cardiology (in press). If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com NOTES ----------------------------- These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “PRS training” in the Supplementary Materials of the published paper. “PRS training” included 187,150 randomly sampled individuals from the White British unrelated (WBU) UK Biobank subset. CAD and IS case phenotypes were defined as described in the “UK Biobank phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events. All analyses included age-at-assessment, sex, genotyping chip, and 10 principal components as covariates. We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION ----------------------------- cpra: Variant ID in ‘CPRA’ format. Position reflects position in b37. chrom: Chromosome pos: Position in base pairs (b37, 1-based) alt: Alternative allele (effect allele) beta: Effect size (log odds ratio) standard_error: Standard error of beta minus_log10_p: Minus log(base 10) of P-value ref: Reference allele (non-effect allele) ncase: Number of cases ncontrol: Number of controls

AbstractBackgroundThere is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment.MethodsThis research has been conducted using the UK Biobank (UKB) resource. We developed our own polygenic risk score (PRS) for coronary artery disease (CAD), using novel and established methods to combine published genomewide association study (GWAS) data with data from 114,196 UK Biobank individuals, also leveraging a large resource of other GWAS datasets along with functional information, to aid in the identification of causal variants, and thence define weights for > 8M genetic variants. We utilised a further 60,000 UKB individuals to develop an integrated risk tool (IRT) that combined our PRS with established risk tools (either the American Heart Association/American College of Cardiology’s pooled cohort equations (PCE) or the UK’s QRISK3) which was then tested in an additional, independent, set of 212,563 UKB individuals. We evaluated prediction performance in individuals of European ancestry, both as a whole and stratified by age and sex.FindingsThe novel CAD PRS showed superior predictive power for CAD events, compared to other published PRSs. As an individual risk factor, it has similar predictive power to each of systolic blood pressure, HDL cholesterol, and LDL cholesterol, but is more predictive than total cholesterol and smoking history. Our novel CAD PRS is largely uncorrelated with PCE, QRISK3, and family history, and, when combined with PCE into an integrated risk tool, had superior predictive accuracy. In individuals reclassified as high risk, CAD event rates were markedly and significantly higher compared to those reclassified as low risk. Overall, 9.7% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, in contrast to 3.7% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.7% (95% CI 4.4−7.0), but when individuals were stratified into four age-by-sex subgroups the improvement was larger for all subgroups (range 7.7%−17.3%), with best performance in younger middle-aged men aged 40–54yo (17.3%, 95% CI 13.0–21.5). Broadly similar results were found using a different risk tool (QRISK3), and also for cardiovascular disease events defined more broadly.InterpretationAn integrated risk tool that includes polygenic risk outperforms current, clinical risk stratification tools, and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person’s polygenic risk.FundingGenomics plc

Dystonia is a neurological disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and postures, often occurring in absence of any structural brain abnormality. Psychiatric comorbidities, including anxiety, depression, obsessive-compulsive disorder and schizophrenia, are frequent in patients with dystonia. While mutations in a fast-growing number of genes have been linked to Mendelian forms of dystonia, the cellular, anatomical, and molecular basis remains unknown for most genetic forms of dystonia, as does its genetic and biological relationship to neuropsychiatric disorders. Here we applied an unbiased systemsbiology approach to explore the cellular specificity of all currently known dystonia-associated genes, predict their functional relationships, and test whether dystonia and neuropsychiatric disorders share a genetic relationship. To determine the cellular specificity of dystonia-associated genes in the brain, single-nuclear transcriptomic data derived from mouse brain was used together with expression-weighted cell-type enrichment. To identify functional relationships among dystonia-associated genes, we determined the enrichment of these genes in co-expression networks constructed from 10 human brain regions. Stratified linkage-disequilibrium score regression was used to test whether co-expression modules enriched for dystonia-associated genes significantly contribute to the heritability of anxiety, major depressive disorder, obsessive-compulsive disorder, schizophrenia, and Parkinson's disease. Dystonia-associated genes were significantly enriched in adult nigral dopaminergic neurons and striatal medium spiny neurons. Furthermore, 4 of 220 gene co-expression modules tested were significantly enriched for the dystonia-associated genes. The identified modules were derived from the substantia nigra, putamen, frontal cortex, and white matter, and were all significantly enriched for genes associated with synaptic function. Finally, we demonstrate significant enrichments of the heritability of major depressive disorder, obsessive-compulsive disorder and schizophrenia within the putamen and white matter modules, and a significant enrichment of the heritability of Parkinson's disease within the substantia nigra module. In conclusion, multiple dystonia-associated genes interact and contribute to pathogenesis likely through dysregulation of synaptic signalling in striatal medium spiny neurons, adult nigral dopaminergic neurons and frontal cortical neurons. Furthermore, the enrichment of the heritability of psychiatric disorders in the co-expression modules enriched for dystonia-associated genes indicates that psychiatric symptoms associated with dystonia are likely to be intrinsic to its pathophysiology.

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/ .

Genetic variation across the human leukocyte antigen loci is known to influence renal-transplant outcome. However, the impact of genetic variation beyond the human leukocyte antigen loci is less clear. We tested the association of common genetic variation and clinical characteristics, from both the donor and recipient, with posttransplant eGFR at different time-points, out to 5 years posttransplantation. We conducted GWAS meta-analyses across 10 844 donors and recipients from five European ancestry cohorts. We also analyzed the impact of polygenic risk scores (PRS), calculated using genetic variants associated with nontransplant eGFR, on posttransplant eGFR. PRS calculated using the recipient genotype alone, as well as combined donor and recipient genotypes were significantly associated with eGFR at 1-year posttransplant. Thirty-two percent of the variability in eGFR at 1-year posttransplant was explained by our model containing clinical covariates (including weights for death/graft-failure), principal components and combined donor-recipient PRS, with 0.3% contributed by the PRS. No individual genetic variant was significantly associated with eGFR posttransplant in the GWAS. This is the first study to examine PRS, composed of variants that impact kidney function in the general population, in a posttransplant context. Despite PRS being a significant predictor of eGFR posttransplant, the effect size of common genetic factors is limited compared to clinical variables.

Mesial temporal lobe epilepsy with hippocampal sclerosis represents the most common epilepsy syndrome in adult patients with medically intractable partial epilepsy. Mesial temporal lobe epilepsy is usually regarded as a polygenic and complex disorder, still poorly understood but probably caused and perpetuated by dysregulation of numerous biological networks and cellular functions. The study of gene expression changes by single nucleotide polymorphisms in regulatory elements (expression quantitative trait loci, eQTLs) has been shown to be a powerful complementary approach to the detection and understanding of risk loci by genome-wide association studies. We performed a whole (gene and exon-level) transcriptome analysis on cortical tissue samples (Brodmann areas 20 and 21) from 86 patients with mesial temporal lobe epilepsy with hippocampal sclerosis and 75 neurologically healthy controls. Genome-wide genotyping data from the same individuals (patients and controls) were analysed and paired with the transcriptome data. We report potential epilepsy-risk eQTLs, some of which are specific to tissue from patients with mesial temporal lobe epilepsy with hippocampal sclerosis. We also found large transcriptional and splicing deregulation in mesial temporal lobe epilepsy with hippocampal sclerosis tissue as well as gene networks involving neuronal and glial mechanisms that provide new insights into the cause and maintenance of the seizures. These data (available via the ‘Seizubraineac’ web-tool resource, www.seizubraineac.org) will facilitate the identification of new therapeutic targets and biomarkers as well as genetic risk variants that could influence epilepsy and pharmacoresistance.