Conjunctional False Discovery Rate Research Articles

Unveiling the shared genetic architecture between testosterone and polycystic ovary syndrome

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

Unraveling the shared genetics of common epilepsies and general cognitive ability

PurposeCognitive impairment is prevalent among individuals with epilepsy, and increasing evidence indicates that genetic factors can underlie this relationship. However, the extent to which epilepsy subtypes differ in their genetic relationship with cognitive function, and information about the specific genetic variants involved remain largely unknown. MethodsWe investigated the genetic relationship between epilepsies and general cognitive ability (COG) using complementary statistical tools, including linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR). We analyzed genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes ‘all epilepsy’, focal epilepsies and genetic generalized epilepsies (GGE), as well as specific subtypes. We functionally annotated the identified loci using several biological resources and validated the results in independent samples. ResultsUsing MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than ‘all epilepsy’, GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k – 2.9k variants). The other epilepsy phenotypes were insufficiently powered for MiXeR analysis. We quantified extensive genetic overlap between COG and epilepsy types, but with varying negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and ‘all epilepsy’, and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), ‘all epilepsy’ (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 3.62 × 10–7; ‘all epilepsy’: p = 2.58 × 10–3). ConclusionOur study further dissects the substantial genetic basis shared between epilepsies and COG and identifies novel shared loci. An improved understanding of the genetic relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

Identification of novel genomic loci for anxiety symptoms and extensive genetic overlap with psychiatric disorders.

Anxiety disorders are prevalent and anxiety symptoms (ANX) co-occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders. We included a genome-wide association study of ANX (meta-analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders. Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k-11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MD , BIP , SCZ , ADHD , and ASD . Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci. Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets.

Shared genetic architecture of cortical thickness alterations in major depressive disorder and schizophrenia

BackgroundMajor depressive disorder (MDD) and schizophrenia (SCZ) are heritable brain disorders characterized by alterations in cortical thickness. However, the shared genetic basis for cortical thickness changes in these disorders remains unclear. MethodsWe conducted a systematic literature search on cortical thickness in MDD and SCZ through PubMed and Web of Science. A coordinate-based meta-analysis was performed to identify cortical thickness changes. Additionally, utilizing summary statistics from the largest genome-wide association studies for depression (Ncase = 268,615, Ncontrol = 667,123) and SCZ (Ncase = 53,386, Ncontrol = 77,258), we explored shared genomic loci using conjunctional false discovery rate (conjFDR) analysis. Transcriptome-neuroimaging association analysis was then employed to identify shared genes associated with cortical thickness alterations, and enrichment analysis was finally carried out to elucidate the biological significance of these genes. ResultsOur search yielded 34 MDD (Ncase = 1621, Ncontrol = 1507) and 19 SCZ (Ncase = 1170, Ncontrol = 1043) neuroimaging studies for cortical thickness meta-analysis. Specific alterations in the left supplementary motor area were observed in MDD, while SCZ exhibited widespread reductions in various brain regions, particularly in the frontal and temporal areas. The conjFDR approach identified 357 genomic loci jointly associated with MDD and SCZ. Within these loci, 55 genes were found to be associated with cortical thickness alterations in both disorders. Enrichment analysis revealed their involvement in nervous system development, apoptosis, and cell communication. ConclusionThis study revealed the shared genetic architecture underlying cortical thickness alterations in MDD and SCZ, providing insights into common neurobiological pathways. The identified genes and pathways may serve as potential transdiagnostic markers, informing precision medicine approaches in psychiatric care.

Identification of novel genetic loci associated with major depressive disorder and the hippocampus in a European population using the condFDR method

To identify additional loci associated with depression and the hippocampus (HIP) through genome-wide association study. The depression-related genome-wide association study (GWAS) meta summary data was downloaded from the official website of the Psychiatric Genomics Consortium, which had involved 170 756 cases and 329 443 controls. The left and right hippocampal volume GWAS data sets were downloaded from the UK Biobank, which involved 33 224 participants. The conditional false discovery rate (condFDR) was used to identify novel genetic loci for depression and left and right hippocampal volumes, and a conjunctional false discovery rate (conjFDR) was used to evaluate the enrichment of pleiotropic loci between depression and left and right hippocampal volumes. Respectively, 7, 13, and 12 new loci have been associated with depression, left hippocampal volume and right hippocampal volume, with a significant threshold of condFDR < 0.01. A site of rs1267073 locus was found to be shared by the depression and right hippocampal volume with a threshold of conjFDR < 0.01. Above findings have provided more insights into the genetic mechanisms underlying the volume of hippocampus and the risk for depression. The results may also provide evidence for future clinical trials for treating depression.

Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology.

Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences. We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples. The epilepsies (2.4 k-2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples. Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases

BackgroundDespite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD.MethodsBased on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets.ResultsCompared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15–1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure.ConclusionsThis study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

Genetic overlap between schizophrenia and cognitive performance

Schizophrenia (SCZ), a highly heritable mental disorder, is characterized by cognitive impairment, yet the extent of the shared genetic basis between schizophrenia and cognitive performance (CP) remains poorly understood. Therefore, we aimed to explore the polygenic overlap between SCZ and CP. Specifically, the bivariate causal mixture model (MiXeR) was employed to estimate the extent of genetic overlap between SCZ (n = 130,644) and CP (n = 257,841), and conjunctional false discovery rate (conjFDR) approach was used to identify shared genetic loci. Subsequently, functional annotation and enrichment analysis were carried out on the identified genomic loci. The MiXeR analyses revealed that 9.6 K genetic variants are associated with SCZ and 10.9 K genetic variants for CP, of which 9.5 K variants are shared between these two traits (Dice coefficient = 92.8%). By employing conjFDR, 236 loci were identified jointly associated with SCZ and CP, of which 139 were novel for the two traits. Within these shared loci, 60 exhibited consistent effect directions, while 176 had opposite effect directions. Functional annotation analysis indicated that the shared genetic loci were mainly located in intronic and intergenic regions, and were found to be involved in relevant biological processes such as nervous system development, multicellular organism development, and generation of neurons. Together, our findings provide insights into the shared genetic architecture between SCZ and CP, suggesting common pathways and mechanisms contributing to both traits.

Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets.

Patients with severe mental disorders such as bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) show a substantial reduction in life expectancy, increased incidence of comorbid medical conditions commonly observed with advanced age and alterations of aging hallmarks. While severe mental disorders are heritable, the extent to which genetic predisposition might contribute to accelerated cellular aging is not known. We used bivariate causal mixture models to quantify the trait-specific and shared architecture of mental disorders and 2 aging hallmarks (leukocyte telomere length [LTL] and mitochondrial DNA copy number), and the conjunctional false discovery rate methodto detect shared genetic loci. We integrated gene expression data from brain regions from GTEx and used different tools to functionally annotate identified loci and investigate their druggability. Aging hallmarks showed low polygenicity compared with severe mental disorders. We observed a significant negative global genetic correlation between MDD and LTL (rg = -0.14, p = 6.5E-10), and no significant results for other severe mental disorders or for mtDNA-cn. However, conditional QQ plots and bivariate causal mixture models pointed to significant pleiotropy among all severe mental disorders and aging hallmarks. We identified genetic variants significantly shared between LTL and BD (n = 17), SCZ (n = 55) or MDD (n = 19), or mtDNA-cn and BD (n = 4), SCZ (n = 12) or MDD (n = 1), with mixed direction of effects. The exonic rs7909129 variant in the SORCS3 gene, encoding a member of the retromer complex involved in protein trafficking and intracellular/intercellular signaling, was associated with shorter LTL and increased predisposition to all severe mental disorders. Genetic variants underlying risk of SCZ or MDD and shorter LTL modulate expression of several druggable genes in different brain regions. Genistein, a phytoestrogen with anti-inflammatory and antioxidant effects, was an upstream regulator of 2 genes modulated by variants associated with risk of MDD and shorter LTL. While our results suggest that shared heritability might play a limited role in contributing to accelerated cellular aging in severe mental disorders, we identified shared genetic determinants and prioritized different druggable targets and compounds.

Genome-wide analysis reveals extensive genetic overlap between childhood phenotypes and later-life type 2 diabetes

Observational studies have indicated a potential influence of childhood phenotypes on the later development of type 2 diabetes (T2D). However, the underlying biological mechanisms remain unclear. In this study, we conducted a comprehensive genome-wide analysis to investigate the shared genetic architecture and genetic loci between nine childhood phenotypes (N = 4202–620,26) and later-life T2D (N = 80,154) using genetic correlation, mendelian randomization (MR), and conjunctional false discovery rate (conjFDR) statistical frameworks. Our findings demonstrated substantial genetic correlations and pleiotropic enrichment between childhood obesity, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and later-life T2D. Childhood obesity exhibited a significant association with increased later-life T2D risk through 10 mediators, 6 of which were adulthood obesity-related phenotypes. Additionally, we identified 69, 83, 3, 5, 10, 5, 3, and 7 loci shared between childhood obesity, BMI, SBP, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoprotein A-I (ApoA-I), apolipoprotein B (ApoB), and T2D at conjFDR <0.05, with the majority of these loci being novel discoveries. Overall, our study reveals extensive genetic overlap between childhood obesity-related phenotypes and T2D with concordant effect directions, shedding new light on variants and phenotypes with lifelong effects.

Dissecting the shared genetic basis of Alzheimer’s disease and hippocampal volumes

AbstractBackgroundHippocampal atrophy is an important pathophysiological event in Alzheimer’s disease (AD) etiopathology. Still the mechanistic link between these volumetric abnormalities and AD remains poorly understood. In this study, we estimated polygenic overlap with mixed effect directions between AD and bilateral hippocampal volumes, and identified commonly shared genetic variants between two traits, involved in several synaptic and immunomodulatory genes.MethodFor this study we used GWAS summary statistics on AD (1,11,326 cases and 6,77,663 controls) (Bellenguezet et al., 2022) and hippocampal volumes (33,224 populations controls) (Smith et al., 2021). First, we applied Bivariate causal mixture model (MiXeR version1.3, Frei et al., 2021) to quantify the polygenic overlap between AD and bilateral hippocampal volumes. For each AD‐volume pair, MiXeR estimated the shared polygenic architecture between two traits, irrespective of the direction of individual variants effects.To explore the MiXeR estimated shared genetic basis at individual variant level, we used the conjunctional false discovery rate (conjFDR) analysis (Andreassen et al., 2013) on the association of AD with bilateral hippocampal volumes, and vice versa. The conjFDR analysis is built on an empirical Bayesian statistical framework. Four regions (MHC, 8p23.1, MAPT, APOE) with complex LD patterns were excluded from the analyses because complex correlations in regions with intricate LD might bias the estimation.ResultMiXeR univariate analysis estimated the number of AD risk variants to be around 343 genetic variants, and the number of variants for bilateral hippocampal volumes between 901 and 942 variants. MiXeR bivariate analysis estimated that the number of shared variants between AD and bilateral hippocampal volumes was around 75 and 96 variants (see Figure 1). At conjFDR &lt; 0.05, we identified 10 common loci shared by AD and left hippocampal volume,6 lead SNPs had consistent effect directions, while 4 loci have opposite effect directions according to their Z value. 8 common loci shared by AD and right hippocampal volume, 5 lead SNPs have consistent effect directions, 3 have opposite effect directions. (see Figure 2 and Table 1)ConclusionThis study provides evidence of a shared genetic architecture between AD and bilateral hippocampal volumes. The identification of common loci helps to understand the role of hippocampus in AD pathology.

Integration of multiple-omics data to reveal the shared genetic architecture of educational attainment, intelligence, cognitive performance, and Alzheimer's disease.

Growing evidence suggests the effect of educational attainment (EA) on Alzheimer's disease (AD), but less is known about the shared genetic architecture between them. Here, leveraging genome-wide association studies (GWAS) for AD (N = 21,982/41,944), EA (N = 1,131,881), cognitive performance (N = 257,828), and intelligence (N = 78,308), we investigated their causal association with the linkage disequilibrium score (LDSC) and Mendelian randomization and their shared loci with the conjunctional false discovery rate (conjFDR), transcriptome-wide association studies (TWAS), and colocalization. We observed significant genetic correlations of EA (rg = -0.22, p = 5.07E-05), cognitive performance (rg = -0.27, p = 2.44E-05), and intelligence (rg = -0.30, p = 3.00E-04) with AD, and a causal relationship between EA and AD (OR = 0.74, 95% CI: 0.58-0.94, p = 0.013). We identified 13 shared loci at conjFDR <0.01, of which five were novel, and prioritized three causal genes. These findings inform early prevention strategies for AD.

Shared genetic links between bladder cancer and obesity-related traits: A conjunctional false discovery rate study.

Bladder cancer (BCa) is a common cancer worldwide and is often linked with obesity-related comorbidities, but little is known about the underlying genetic mechanisms. To investigate these mechanisms, we used various quantitative tools, including conditional quantile-quantile (Q-Q) plots, conditional false discovery rate (cFDR), and conjunctional conditional false discovery rate (ccFDR), to explore the pleiotropic enrichment of risk loci between BCa and obesity-related traits. We also performed an expression quantitative trait locus (eQTL) analysis to assess the relationship between shared risk loci and gene expression. Finally, we conducted functional annotation using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis. Our findings indicated that there was successive enrichment for a range of obesity-related traits, including body fat percentage, body mass index, fasting insulin, type 2 diabetes mellitus, fasting glucose, high-density lipoprotein cholesterol, total triglycerides, and waist-to-hip ratio. Using the tools mentioned above, we identified 18 significant SNPs and 18 closely related genes (cFDR<0.01) under the condition of 8 obesity-related traits. The SNPs included rs143004880, rs73301337, rs10798572, rs11594929, rs17019138, rs2877, rs149795948, rs142509736, rs12727575, rs1571277, rs12131828, rs635634, rs76895963, rs118081211, rs7044247, rs138895564, rs4135275, and rs148023060. Additionally, we identified 15 novel loci using ccFDR, including rs143004880, rs73301337, rs10798572, rs11594929, rs17019138, rs2877, rs142509736, rs1571277, rs635634, rs76895963, rs12131828, rs118081211, rs7044247, rs138895564, and rs4135275. Of the 2 significant loci that modify gene expression, rs12131828 and rs635634 were identified. The functional annotation indicated that the conditional risk genes mainly participated in the regulation of gene silencing. Our study provided evidence of pleiotropic enrichment between BCa and 8 obesity-related traits, and we identified potential genetic mechanisms underlying this relationship. These findings may help in developing targeted clinical treatments for BCa.

Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors.

Schizophrenia is associated with increased risk of cardiovascular disease (CVD), although there is variation in risk among individuals. There are indications of shared genetic etiology between schizophrenia and CVD, but the nature of the overlap remains unclear. The aim of this study was to fill this gap in knowledge. Overlapping genetic architectures between schizophrenia and CVD risk factors were assessed by analyzing recent genome-wide association study (GWAS) results. The bivariate causal mixture model (MiXeR) was applied to estimate the number of shared variants and the conjunctional false discovery rate (conjFDR) approach was used to pinpoint specific shared loci. Extensive genetic overlap was found between schizophrenia and CVD risk factors, particularly smoking initiation (N=8.6K variants) and body mass index (BMI) (N=8.1K variants). Several specific shared loci were detected between schizophrenia and BMI (N=304), waist-to-hip ratio (N=193), smoking initiation (N=293), systolic (N=294) and diastolic (N=259) blood pressure, type 2 diabetes (N=147), lipids (N=471), and coronary artery disease (N=35). The schizophrenia risk loci shared with smoking initiation had mainly concordant effect directions, and the risk loci shared with BMI had mainly opposite effect directions. The overlapping loci with lipids, blood pressure, waist-to-hip ratio, type 2 diabetes, and coronary artery disease had mixed effect directions. Functional analyses implicated mapped genes that are expressed in brain tissue and immune cells. These findings indicate a genetic propensity to smoking and a reduced genetic risk of obesity among individuals with schizophrenia. The bidirectional effects of the shared loci with the other CVD risk factors may imply differences in genetic liability to CVD across schizophrenia subgroups, possibly underlying the variation in CVD comorbidity.

Genome-wide analysis of anorexia nervosa and major psychiatric disorders and related traits reveals genetic overlap and identifies novel risk loci for anorexia nervosa

Anorexia nervosa (AN) is a heritable eating disorder (50–60%) with an array of commonly comorbid psychiatric disorders and related traits. Although significant genetic correlations between AN and psychiatric disorders and related traits have been reported, their shared genetic architecture is largely understudied. We investigated the shared genetic architecture of AN and schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), mood instability (Mood), neuroticism (NEUR), and intelligence (INT). We applied the conditional false discovery rate (FDR) method to identify novel risk loci for AN, and conjunctional FDR to identify loci shared between AN and related phenotypes, to summarize statistics from relevant genome-wide association studies (GWAS). Individual GWAS samples varied from 72,517 to 420,879 participants. Using conditional FDR we identified 58 novel AN loci. Furthermore, we identified 38 unique loci shared between AN and major psychiatric disorders (SCZ, BIP, and MD) and 45 between AN and psychological traits (Mood, NEUR, and INT). In line with genetic correlations, the majority of shared loci showed concordant effect directions. Functional analyses revealed that the shared loci are involved in 65 unique pathways, several of which overlapped across analyses, including the “signal by MST1” pathway involved in Hippo signaling. In conclusion, we demonstrated genetic overlap between AN and major psychiatric disorders and related traits, and identified novel risk loci for AN by leveraging this overlap. Our results indicate that some shared characteristics between AN and related disorders and traits may have genetic underpinnings.

Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders

C-reactive protein (CRP) tends to be elevated in individuals with psychiatric disorders. Recent findings have suggested a protective effect of the genetic liability to elevated CRP on schizophrenia risk and a causative effect on depression despite weak genetic correlations, while causal relationships with bipolar disorder were inconclusive. We investigated the shared genetic underpinnings of psychiatric disorders and variation in CRP levels. Genome-wide association studies for CRP (n = 575,531), bipolar disorder (n = 413,466), depression (n = 480,359), and schizophrenia (n = 130,644) were used in causal mixture models to compare CRP with psychiatric disorders based on polygenicity, discoverability, and genome-wide genetic overlap. The conjunctional false discovery rate method was used to identify specific shared genetic loci. Shared variants were mapped to putative causal genes, which were tested for overrepresentation among gene ontology gene-sets. CRP was six to ten times less polygenic (n = 1400 vs 8600–14,500 variants) and had a discoverability one to two orders of magnitude higher than psychiatric disorders. Most CRP-associated variants were overlapping with psychiatric disorders. We identified 401 genetic loci jointly associated with CRP and psychiatric disorders with mixed effect directions. Gene-set enrichment analyses identified predominantly CNS-related gene sets for CRP and each of depression and schizophrenia, and basic cellular processes for CRP and bipolar disorder. In conclusion, CRP has a markedly different genetic architecture to psychiatric disorders, but the majority of CRP associated variants are also implicated in psychiatric disorders. Shared genetic loci implicated CNS-related processes to a greater extent than immune processes, which may have implications for how we conceptualise causal relationships between CRP and psychiatric disorders.

Genetic Overlap Between Global Cortical Brain Structure, C-Reactive Protein, and White Blood Cell Counts

BackgroundFor many brain disorders, a subset of patients jointly exhibit alterations in cortical brain structure and elevated levels of circulating immune markers. This may be driven in part by shared genetic architecture. Therefore, we investigated the phenotypic and genetic associations linking global cortical surface area and thickness with blood immune markers (i.e., white blood cell counts and plasma C-reactive protein levels). MethodsLinear regression was used to assess phenotypic associations in 30,823 UK Biobank participants. Genome-wide and local genetic correlations were assessed using linkage disequilibrium score regression and local analysis of covariance annotation. The number of shared trait-influencing genetic variants was estimated using MiXeR. Shared genetic architecture was assessed using a conjunctional false discovery rate framework, and mapped genes were included in gene-set enrichment analyses. ResultsCortical structure and blood immune markers exhibited predominantly inverse phenotypic associations. There were modest genome-wide genetic correlations, the strongest of which were for C-reactive protein levels (rg_surface_area = −0.13, false discovery rate–corrected p = 4.17 × 10−3; rg_thickness = −0.13, false discovery rate–corrected p = 4.00 × 10−2). Meanwhile, local genetic correlations showed a mosaic of positive and negative associations. White blood cells shared on average 46.24% and 38.64% of trait-influencing genetic variants with surface area and thickness, respectively. Additionally, surface area shared 55 unique loci with the blood immune markers while thickness shared 15. Overall, monocyte count exhibited the largest genetic overlap with cortical brain structure. A series of gene enrichment analyses implicated neuronal-, astrocytic-, and schizophrenia-associated genes. ConclusionsThe findings indicate shared genetic underpinnings for cortical brain structure and blood immune markers, with implications for neurodevelopment and understanding the etiology of brain-related disorders.

Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system

BackgroundNeuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system. MethodsWe analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets. ResultsMiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons. ConclusionsDespite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

Leveraging genetic overlap between irritability and psychiatric disorders to identify genetic variants of major psychiatric disorders

Irritability is a heritable core mental trait associated with several psychiatric illnesses. However, the genomic basis of irritability is unclear. Therefore, this study aimed to 1) identify the genetic variants associated with irritability and investigate the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability; 2) explore the relationships between irritability and various traits, including psychiatric disorders; and 3) identify additional and shared genetic variants for irritability and psychiatric disorders. We conducted a genome-wide association study (GWAS) using 379,506 European samples (105,975 cases and 273,531 controls) from the UK Biobank. We utilized various post-GWAS analyses, including linkage disequilibrium score regression, the bivariate causal mixture model (MiXeR), and conditional and conjunctional false discovery rate approaches. This GWAS identified 15 independent loci associated with irritability; the total SNP heritability estimate was 4.19%. Genetic correlations with psychiatric disorders were most pronounced for major depressive disorder (MDD) and bipolar II disorder (BD II). MiXeR analysis revealed polygenic overlap with schizophrenia (SCZ), bipolar I disorder (BD I), and MDD. Conditional false discovery rate analyses identified additional loci associated with SCZ (number [n] of additional SNPs = 105), BD I (n = 54), MDD (n = 107), and irritability (n = 157). Conjunctional false discovery rate analyses identified 85, 41, and 198 shared loci between irritability and SCZ, BD I, and MDD, respectively. Multiple genetic loci were associated with irritability and three main psychiatric disorders. Given that irritability is a cross-disorder trait, these findings may help to elucidate the genomics of psychiatric disorders.

Metabolic Overlap between Alzheimer's Disease and Metabolic Syndrome Identifies the PVRL2 Gene as a New Modulator of Diabetic Dyslipidemia.

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) share metabolic alterations such as abnormal insulin and lipid metabolism and have some common genetic factors such as APOE genotype. Taking this into account, we hypothesized that we could identify common genetic factors involved in the development of diabetes and cardiovascular diseases. We first genotyped 48 single nucleotide polymorphisms (SNPs) previously associated with AD in a cohort composed of 330 patients with cognitive impairment (CI) to assess their association with plasma lipids. Second, we conducted pleiotropy-informed conjunctional false discovery rate (FDR) analysis designed to identify shared variants between AD and plasma lipid levels. Finally, we used the SNPs to be found associated with lipid parameters and AD to search for associations with lipoprotein parameters in 281 patients with cardiometabolic risk. Five SNPs were significantly associated with lower levels of cholesterol transported in remnant lipoprotein particles (RLPc) in subjects with CI; among these SNPs was the rs73572039 variant in PVRL2. Stratified QQ-plots were conducted on GWAS designed for AD and triglycerides (TG). The cross-trait analysis resulted in a total of 22 independent genomic loci associated with both AD and TG levels with a conjFDR < 0.05. Among these loci, two pleiotropic variants were located in PVRL2 (rs12978931 and rs11667640). The three SNPs in PVRL2 were significantly associated with RLPc, TG, and number of circulating VLDL and HDL particles in subjects with cardiometabolic risk. We have identified three variants in PVRL2 that predispose individuals to AD that also influence the lipid profile that confers cardiovascular risk in T2DM subjects. PVRL2 is a potential new modulating factor of atherogenic dyslipidemia.