A polygenic risk score added to a QRISK®2 cardiovascular disease risk calculator demonstrated robust clinical acceptance and clinical utility in the primary care setting.

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®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.

When a standard cardiovascular risk tool, as currently used in National Health Service Health Checks, was expanded to include genetic risk information, it was well accepted by both participants and healthcare providers and generated impactful changes in planned clinical decision-making.Most participants found the test useful and easy to understand, and healthcare providers found it straightforward to use in most cases.When risk was increased by the addition of genetic information, this influenced planned management decisions.

Analysis of subcellular RNA fractions demonstrates significant genetic regulation of gene expression in human brain post-transcriptionally.

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.

Dense sampling of ethnic groups within African countries reveals fine-scale genetic structure and extensive historical admixture.

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.

Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries.

The American College of Cardiology / American Heart Association pooled cohort equations tool (ASCVD-PCE) is currently recommended to assess 10-year risk for atherosclerotic cardiovascular disease (ASCVD). ASCVD-PCE does not currently include genetic risk factors. Polygenic risk scores (PRSs) have been shown to offer a powerful new approach to measuring genetic risk for common diseases, including ASCVD, and to enhance risk prediction when combined with ASCVD-PCE. Most work to date, including the assessment of tools, has focused on performance in individuals of European ancestries. Here we present evidence for the clinical validation of a new integrated risk tool (IRT), ASCVD-IRT, which combines ASCVD-PCE with PRS to predict 10-year risk of ASCVD across diverse ethnicity and ancestry groups. We demonstrate improved predictive performance of ASCVD-IRT over ASCVD-PCE, not only in individuals of self-reported White ethnicities (net reclassification improvement [NRI]; with 95% confidence interval = 2.7% [1.1 to 4.2]) but also Black / African American / Black Caribbean / Black African (NRI = 2.5% [0.6-4.3]) and South Asian (Indian, Bangladeshi or Pakistani) ethnicities (NRI = 8.7% [3.1 to 14.4]). NRI confidence intervals were wider and included zero for ethnicities with smaller sample sizes, including Hispanic (NRI = 7.5% [-1.4 to 16.5]), but PRS effect sizes in these ethnicities were significant and of comparable size to those seen in individuals of White ethnicities. Comparable results were obtained when individuals were analyzed by genetically inferred ancestry. Together, these results validate the performance of ASCVD-IRT in multiple ethnicities and ancestries, and favor their generalization to all ethnicities and ancestries.

Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction.

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.

Dystonia genes functionally converge in specific neurons and share neurobiology with psychiatric disorders.

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 systems-biology 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.

Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information.

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/.

Gene Variants at Loci Related to Blood Pressure Account for Variation in Response to Antihypertensive Drugs Between Black and White Individuals.

Selection of antihypertensive treatment according to self-defined ethnicity is recommended by some guidelines but might be better guided by individual genotype rather than ethnicity or race. We compared the extent to which variation in blood pressure response across different ethnicities may be explained by genetic factors: genetically defined ancestry and gene variants at loci known to be associated with blood pressure. We analyzed data from 5 trials in which genotyping had been performed (n=4696) and in which treatment responses to ß-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blocker, thiazide or thiazide-like diuretic and calcium channel blocker were available. Genetically defined ancestry for proportion of African ancestry was computed using the 1000 genomes population database as a reference. Differences in response to the thiazide diuretic hydrochlorothiazide, the ß-blockers atenolol and metoprolol, the angiotensin-converting enzyme inhibitor lisinopril, and the angiotensin receptor blocker candesartan were more closely associated to genetically defined ancestry than self-defined ethnicity in admixed subjects. A relatively small number of gene variants related to loci associated with drug-signaling pathways (KCNK3, SULT1C3, AMH, PDE3A, PLCE1, PRKAG2) with large effect size (-3.5 to +3.5 mm Hg difference in response per allele) and differing allele frequencies in black versus white individuals explained a large proportion of the difference in response to candesartan and hydrochlorothiazide between these groups. These findings suggest that a genomic precision medicine approach can be used to individualize antihypertensive treatment within and across populations without recourse to surrogates of genetic structure such as self-defined ethnicity.

Transcriptomic and genetic analyses reveal potential causal drivers for intractable partial epilepsy.

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.

The impact of donor and recipient common clinical and genetic variation on estimated glomerular filtration rate in a European renal transplant population.

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.

Phenome-wide association studies across large population cohorts support drug target validation.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Transethnic differences in GWAS signals: A simulation study.

Genome-wide association studies (GWASs) have allowed researchers to identify thousands of single nucleotide polymorphisms (SNPs) and other variants associated with particular complex traits. Previous studies have reported differences in the strength and even the direction of GWAS signals across different populations. These differences could be due to a combination of (1) lack of power, (2) allele frequency differences, (3) linkage disequilibrium (LD) differences, and (4) true differences in causal variant effect sizes. To determine whether properties (1)-(3) on their own might be sufficient to explain the patterns previously noted in strong GWAS signals, we simulated case-control data of European, Asian and African ancestry, applying realistic allele frequencies and LD from 1000 Genomes data but enforcing equal causal effect sizes across populations. Much of the observed differences in strong GWAS signals could indeed be accounted for by allele frequency and LD differences, enhanced by the Euro-centric SNP bias and lower SNP coverage found in older GWAS panels. While we cannot rule out a role for true transethnic effect size differences, our results suggest that strong causal effects may be largely shared among human populations, motivating the use of transethnic data for fine-mapping.

Long- and short-term outcomes in renal allografts with deceased donors: A large recipient and donor genome-wide association study.

Improvements in immunosuppression have modified short-term survival of deceased-donor allografts, but not their rate of long-term failure. Mismatches between donor and recipient HLA play an important role in the acute and chronic allogeneic immune response against the graft. Perfect matching at clinically relevant HLA loci does not obviate the need for immunosuppression, suggesting that additional genetic variation plays a critical role in both short- and long-term graft outcomes. By combining patient data and samples from supranational cohorts across the United Kingdom and European Union, we performed the first large-scale genome-wide association study analyzing both donor and recipient DNA in 2094 complete renal transplant-pairs with replication in 5866 complete pairs. We studied deceased-donor grafts allocated on the basis of preferential HLA matching, which provided some control for HLA genetic effects. No strong donor or recipient genetic effects contributing to long- or short-term allograft survival were found outside the HLA region. We discuss the implications for future research and clinical application.

The Genetic Legacy of Zoroastrianism in Iran and India: Insights into Population Structure, Gene Flow, and Selection.

Zoroastrianism is one of the oldest extant religions in the world, originating in Persia (present-day Iran) during the second millennium BCE. Historical records indicate that migrants from Persia brought Zoroastrianism to India, but there is debate over the timing of these migrations. Here we present genome-wide autosomal, Y chromosome, and mitochondrial DNA data from Iranian and Indian Zoroastrians and neighboring modern-day Indian and Iranian populations and conduct a comprehensive genome-wide genetic analysis in these groups. Using powerful haplotype-based techniques, we find that Zoroastrians in Iran and India have increased genetic homogeneity relative to other sampled groups in their respective countries, consistent with their current practices of endogamy. Despite this, we infer that Indian Zoroastrians (Parsis) intermixed with local groups sometime after their arrival in India, dating this mixture to 690-1390 CE and providing strong evidence that Iranian Zoroastrian ancestry was maintained primarily through the male line. By making use of the rich information in DNA from ancient human remains, we also highlight admixture in the ancestors of Iranian Zoroastrians dated to 570 BCE-746 CE, older than admixture seen in any other sampled Iranian group, consistent with a long-standing isolation of Zoroastrians from outside groups. Finally, we report results, and challenges, from a genome-wide scan to identify genomic regions showing signatures of positive selection in present-day Zoroastrians that might correlate to the prevalence of particular diseases among these communities.

Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease.

UK Biobank is among the world's largest repositories for phenotypic and genotypic information in individuals of European ancestry. We performed a genome-wide association study in UK Biobank testing ∼9 million DNA sequence variants for association with coronary artery disease (4,831 cases and 115,455 controls) and carried out meta-analysis with previously published results. We identified 15 new loci, bringing the total number of loci associated with coronary artery disease to 95 at the time of analysis. Phenome-wide association scanning showed that CCDC92 likely affects coronary artery disease through insulin resistance pathways, whereas experimental analysis suggests that ARHGEF26 influences the transendothelial migration of leukocytes.

An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

BACKGROUND: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). RESULTS: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. CONCLUSIONS: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis.

BACKGROUND: In frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition. We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products. RESULTS: Gene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses. CONCLUSIONS: This work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD-genes interactors as targets for further mechanistic characterization in hypothesis driven cell biology work.

Systematic assessment of the influence of complement gene polymorphisms on kidney transplant outcome.

The importance of the innate immune system, including complement, in causing transplant injury and augmenting adaptive immune responses is increasingly recognized. Therefore variability in graft outcome may in part be due to genetic polymorphism in genes encoding proteins of the immune system. This study assessed the relationship between single nucleotide polymorphisms (SNPs) in complement genes and outcome after transplantation. Analysis was performed on two patient cohorts of 650 and 520 transplant recipients. 505 tagged SNPs in 47 genes were typed in both donor and recipient. The relationships between SNPs and graft survival, serum creatinine, delayed graft function and acute rejection were analyzed. One recipient SNP in the gene encoding mannose binding lectin was associated with graft outcome after correction for analysis of multiple SNPs (p=6.41 × 10(-5)). When further correction was applied to account for analysis of the effect of SNPs in both donor and recipient this lost significance. Despite association p values of <0.001 no SNP was significantly associated with clinical phenotypes after Bonferroni correction. In conclusion, the variability seen in transplant outcome in this patient cohort cannot be explained by variation in complement genes. If causal genetic effects exist in these genes, they are too small to be detected by this study.

Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases.

OBJECTIVES: We assessed the current genetic evidence for the involvement of various cell types and tissue types in the etiology of neurodegenerative diseases, especially in relation to the neuroinflammatory hypothesis of neurodegenerative diseases. METHODS: We obtained large-scale genome-wide association study (GWAS) summary statistics from Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). We used multiple sclerosis (MS), an autoimmune disease of the central nervous system, as a positive control. We applied stratified LD score regression to determine if functional marks for cell type and tissue activity, and gene-set lists were enriched for genetic heritability. We compared our results to those from two gene-set enrichment methods (Ingenuity Pathway Analysis and enrichr). RESULTS: There were no significant heritability enrichments for annotations marking genes active within brain regions, but there were significant heritability enrichments for annotations marking genes active within cell types that form part of both the innate and adaptive immune systems. We found this for MS (as expected) and also for AD and PD. The strongest signals were from the adaptive immune system (e.g., T cells) for PD, and from both the adaptive (e.g., T cells) and innate (e.g., CD14: a marker for monocytes, and CD15: a marker for neutrophils) immune systems for AD. Annotations from the liver were also significant for AD. Pathway analysis provided complementary results. INTERPRETATION: For AD and PD, we found significant enrichment of heritability in annotations marking gene activity in immune cells.