Pleiotropic Associations Research Articles

DNA methylation biomarkers and myopia: a multi-omics study integrating GWAS, mQTL and eQTL data.

This study aimed to identify DNA methylation biomarkers associated with myopia using summary-data-based Mendelian randomization (SMR). A systematic search of the PubMed, Web of Science, Cochrane Library, and Embase databases was conducted up to March 27, 2024. SMR analyses were performed to integrate genome-wide association study (GWAS) with methylation quantitative trait loci (mQTL) and expression quantitative trait loci (eQTL) studies. The heterogeneity in the dependent instrument (HEIDI) test was utilized to distinguish pleiotropic associations from linkage disequilibrium. The systematic review identified 26 DNA methylation biomarkers in five studies, with no overlap observed among those identified by different studies. After integrating GWAS with multi-omics data of mQTL and eQTL, six genes were significantly associated with myopia: PRMT6 (cg00944433 and cg15468180), SH3YL1 (cg03299269, cg11361895, and cg13354988), ZKSCAN4 (cg01192291), GATS (cg17830204), NPAT (cg04826772), and UBE2I (cg03545757 and cg08025960). We identified six methylation biomarkers associated with the risk of myopia that may be helpful to elucidate the etiology mechanisms of myopia. Further experimental validation studies are required to corroborate these findings.

A phenome-wide association study of polygenic scores for selected childhood cancer: Results from the UK Biobank

The aim of this study was to scan phenotypes in adulthood associated with polygenic risk scores (PRS) for childhood cancers with well-articulated genetic architectures: acute lymphoblastic leukemia (ALL), Ewing sarcoma, and neuroblastoma to examine genetic pleiotropy. Furthermore, we aimed to determine which single nucleotide polymorphisms (SNPs) could drive associations. Per-SNP summary statistics were extracted for PRS calculation. Participants with white British ancestry were exclusively included for analyses. SNPs were queried from the UK biobank genotype imputation data. Records from the cancer registry, death registry, and inpatient diagnoses were abstracted for phenome-wide scans. Firth logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) alongside corresponding p-values, adjusting for age at recruitment and sex. A total of 244,332 unrelated white British participants were included. We observed a significant association between ALL-PRS and ALL (OR: 1.20e+24, 95% CI: 9.08e+14-1.60e+33). In addition, we observed a significant association between high-risk neuroblastoma PRS and nonrheumatic aortic valve disorders (OR: 43.9, 95% CI: 7.42-260). There were no significant phenotype associations with Ewing sarcoma and neuroblastoma PRS. Regarding individual SNPs, rs17607816 increased the risk of ALL (OR: 6.40, 95% CI: 3.26-12.57). For high-risk neuroblastoma, rs80059929 elevated the risk of atrioventricular block (OR: 3.04, 95% CI: 1.85-4.99). Our findings suggest that individuals with genetic susceptibility to ALL may face a lifelong risk for developing ALL, along with a genetic pleiotropic association between high-risk neuroblastoma and circulatory diseases.

Potential of Plant Stem Cells as Helpful Agents for Skin Disorders—A Narrative Review

Recently, cellular senescence has been of great interest due to its pleiotropic nature and association with both physiological (e.g., aging) and pathological conditions. Excessive accumulation of reactive oxygen species (ROS) can induce inflammation, which accelerates skin aging (also premature aging) and may cause several dermatoses. It has been postulated that plant-derived antioxidants, especially plant stem cells, may prevent cell damage by preserving stemness and reducing cellular senescence by ROS targeting. Therefore, this paper aims to review and summarize recent developments and innovative techniques associated with plant-derived stem cells in relation to skin senescence. This review also presents the possible pathways involved in this process. Particular attention was paid to the potential applications of plant stem cells as senostatics/senomorphics produced by modern plant biotechnology methods. Furthermore, the advantages, limitations, and future directions of this technology are also discussed. This knowledge allows the development of personalized strategies to create a healthy balance in skin care. Plant stem cells could be a more feasible and practical approach to combating the adverse effects of skin senescence processes.

Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 2$$\\end{document} traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value <2.2×10−16\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ < 2.2\ imes {10}^{-16}$$\\end{document}). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.

MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test.

Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of Arabidopsis thaliana shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.

Shared genetic aetiology of respiratory diseases: a genome-wide multitraits association analysis

ObjectiveThis study aims to explore the common genetic basis between respiratory diseases and to identify shared molecular and biological mechanisms.MethodsThis genome-wide pleiotropic association study uses multiple statistical methods to systematically...

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants.

To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer. Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases. We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS. While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.

A pleiotropy scan to discover new susceptibility loci for pancreatic ductal adenocarcinoma.

Pleiotropic variants (i.e., genetic polymorphisms influencing more than one phenotype) are often associated with cancer risk. A scan of pleiotropic variants was successfully conducted ten years ago in relation to pancreatic ductal adenocarcinoma susceptibility. However, in the last decade, genetic association studies performed on several human traits have greatly increased the number of known pleiotropic variants. Based on the hypothesis that variants already associated with a least one trait have a higher probability of association with other traits, 61,052 variants reported to be associated by at least one genome wide association study (GWAS) with at least one human trait were tested in the present study consisting of two phases (discovery and validation), comprising a total of 16,055 pancreatic ductal adenocarcinoma (PDAC) cases and 212,149 controls. The meta-analysis of the two phases showed two loci (10q21.1-rs4948550 (P=6.52×10-5) and 7q36.3-rs288762 (P=3.03×10-5) potentially associated with PDAC risk. 10q21.1-rs4948550 shows a high degree of pleiotropy and it is also associated with colorectal cancer risk while 7q36.3-rs288762 is situated 28,558 base pairs upstream of the Sonic Hedgehog (SHH) gene, which is involved in the cell differentiation process and PDAC etiopathogenesis. In conclusion, none of the single nucleotide polymorphisms (SNPs) showed a formally statistically significant association after correction for multiple testing. However, given their pleiotropic nature and association with various human traits including colorectal cancer, the two SNPs showing the best associations with PDAC risk merit further investigation through fine mapping and ad hoc functional studies.

Revealing novel genomic insights and therapeutic targets for juvenile idiopathic arthritis through omics.

The genetic architecture of JIA remains only partially comprehended. There is a clear imperative for continued endeavours to uncover insights into the underlying causes of JIA. This study encompassed a comprehensive spectrum of endeavours, including conducting a JIA genome-wide association study (GWAS) meta-analysis that incorporated data from 4550 JIA cases and 18 446 controls. We employed in silico and genome-editing approaches to prioritizing target genes. To investigate pleiotropic effects, we conducted phenome-wide association studies. Cell-type enrichment analyses were performed by integrating bulk and single-cell sequencing data. Finally, we delved into potential druggable targets for JIA. Fourteen genome-wide significant non-HLA loci were identified, including four novel loci, each exhibiting pleiotropic associations with other autoimmune diseases or musculoskeletal traits. We uncovered strong genetic correlation between JIA and BMD traits at 52 genomic regions, including three GWAS loci for JIA. Candidate genes with immune functions were captured by in silico analyses at each novel locus, with additional findings identified through our experimental approach. Cell-type enrichment analysis revealed 21 specific immune cell types crucial for the affected organs in JIA, indicating their potential contribution to the disease. Finally, 24 known or candidate druggable target genes were prioritized. Our identification of four novel JIA-associated genes, CD247, RHOH, COLEC10 and IRF8, broadens the novel potential drug repositioning opportunities. We established a new genetic link between COLEC10, TNFRSF11B and JIA/BMD. Additionally, the identification of RHOH underscores its role in positive thymocyte selection, thereby illuminating a critical facet of JIA's underlying biological mechanisms.

Biological basis of extensive pleiotropy between blood traits and cancer risk.

The immune system has a central role in preventing carcinogenesis. Alteration of systemic immune cell levels may increase cancer risk. However, the extent to which common genetic variation influences blood traits and cancer risk remains largely undetermined. Here, we identify pleiotropic variants and predict their underlying molecular and cellular alterations. Multivariate Cox regression was used to evaluate associations between blood traits and cancer diagnosis in cases in the UK Biobank. Shared genetic variants were identified from the summary statistics of the genome-wide association studies of 27 blood traits and 27 cancer types and subtypes, applying the conditional/conjunctional false-discovery rate approach. Analysis of genomic positions, expression quantitative trait loci, enhancers, regulatory marks, functionally defined gene sets, and bulk- and single-cell expression profiles predicted the biological impact of pleiotropic variants. Plasma small RNAs were sequenced to assess association with cancer diagnosis. The study identified 4093 common genetic variants, involving 1248 gene loci, that contributed to blood-cancer pleiotropism. Genomic hotspots of pleiotropism include chromosomal regions 5p15-TERT and 6p21-HLA. Genes whose products are involved in regulating telomere length are found to be enriched in pleiotropic variants. Pleiotropic gene candidates are frequently linked to transcriptional programs that regulate hematopoiesis and define progenitor cell states of immune system development. Perturbation of the myeloid lineage is indicated by pleiotropic associations with defined master regulators and cell alterations. Eosinophil count is inversely associated with cancer risk. A high frequency of pleiotropic associations is also centered on the regulation of small noncoding Y-RNAs. Predicted pleiotropic Y-RNAs show specific regulatory marks and are overabundant in the normal tissue and blood of cancer patients. Analysis of plasma small RNAs in women who developed breast cancer indicates there is an overabundance of Y-RNA preceding neoplasm diagnosis. This study reveals extensive pleiotropism between blood traits and cancer risk. Pleiotropism is linked to factors and processes involved in hematopoietic development and immune system function, including components of the major histocompatibility complexes, and regulators of telomere length and myeloid lineage. Deregulation of Y-RNAs is also associated with pleiotropism. Overexpression of these elements might indicate increased cancer risk.

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups.

Phenome-wide association studies (PheWASs) have been conducted on Asian populations, including Koreans, but many were based on chip or exome genotyping data. Such studies have limitations regarding whole genome-wide association analysis, making it crucial to have genome-to-phenome association information with the largest possible whole genome and matched phenome data to conduct further population-genome studies and develop health care services based on population genomics. Here, we present 4,157 whole genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest genomic resource of the Korean Genome Project. It encompasses most of the variants with allele frequency >0.001 in Koreans, indicating that it sufficiently covered most of the common and rare genetic variants with commonly measured phenotypes for Koreans. Korea4K provides 45,537,252 variants, and half of them were not present in Korea1K (1,094 samples). We also identified 1,356 new genotype-phenotype associations that were not found by the Korea1K dataset. Phenomics analyses further revealed 24 significant genetic correlations, 14 pleiotropic associations, and 127 causal relationships based on Mendelian randomization among 37 traits. In addition, the Korea4K imputation reference panel, the largest Korean variants reference to date, showed a superior imputation performance to Korea1K across all allele frequency categories. Collectively, Korea4K provides not only the largest Korean genome data but also corresponding health check-up parameters and novel genome-phenome associations. The large-scale pathological whole genome-wide omics data will become a powerful set for genome-phenome level association studies to discover causal markers for the prediction and diagnosis of health conditions in future studies.

Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection

Genetic pleiotropy refers to the simultaneous association of a gene with multiple phenotypes. It is widely distributed in the whole genome and can help to understand the common genetic mechanism of diseases or traits. In this study, a multivariate response best-subset selection (MRBSS) model based pleiotropic association analysis method is proposed. Different from the traditional genetic association model, the high-dimensional genotypic data are viewed as response variables while the multiple phenotypic data as predictor variables. Moreover, the response best-subset selection procedure is converted into an 0-1 integer optimization problem by introducing a separation parameter and a tuning parameter. Furthermore, the model parameters are estimated by using the curve search under the modified Bayesian information criterion. Simulation experiments show that the proposed method MRBSS remarkably reduces the computational time, obtains higher statistical power under most of the considered scenarios, and controls the type I error rate at a low level. The application studies in the datasets of maize yield traits and pig lipid traits further verifies the effectiveness.

The genetic relationships between brain structure and schizophrenia

Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer’s disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.

Identifying and ranking causal association between gut microbiota and neuroticism

BackgroundRecent studies had explored that gut microbiota was associated with personality traits through the gut-brain axis. Nevertheless, the fundamental causality remained obscure. MethodsMendelian randomization (MR) was used to investigate the causal relationship between gut microbiota, metabolites and neuroticism-related traits. Summary data-based Mendelian randomization (SMR) was utilized to identify genes associated pleiotropically with microbial taxa. ResultsMR analyses identified reliable causal evidence of 20 bacterial taxa related to neuroticism and its worry or depressed affect sub-cluster. A higher relative abundance of the Ruminococcus gauvreauii genus was associated with a higher risk of neuroticism [odds ratio (OR) = 1.04, 95% CI: 1.02–1.06, P = 2.85 × 10–3], depressed affect [OR = 1.04, 95% CI: 1.02–1.06, P = 5.50 × 10–3] and worry (OR = 1.04, 95% CI: 1.02–1.06, P = 7.81 × 10–4), respectively. We identified the probes tagging CPSF1, that showed pleiotropic association with Ruminococcus gauvreauii in brain or colon tissues. Finally, we provided evidence that Ruminococcus gauvreauii present causal association with the neurotransmitter such as glutamine and glutamate. ConclusionsThis study implied a causal relationship between the gut microbiome composition, metabolites and neuroticism, thus providing novel insights into the gut microbiota-mediated development mechanism of neuroticism.

Summary-data based Mendelian randomization identifies gene expression regulatory polymorphisms associated with bovine paratuberculosis by modulation of the nuclear factor Kappa β (NF-κß)-mediated inflammatory response

Genome-wide association studies (GWAS) have identified host genetic variants associated with paratuberculosis (PTB) susceptibility. Most of the GWAS-identified SNPs are in non-coding regions. Connecting these non-coding variants and downstream affected genes is a challenge and, up to date, only a few functional mutations or expression quantitative loci (cis-eQTLs) associated with PTB susceptibility have been identified. In the current study, the associations between imputed whole-genome sequence genotypes and whole RNA-Sequencing data from peripheral blood (PB) and ileocecal valve (ICV) samples of Spanish Holstein cows (N = 16) were analyzed with TensorQTL. This approach allowed the identification of 88 and 37 cis-eQTLs regulating the expression levels of 90 and 37 genes in PB and ICV samples, respectively (False discorey rate, FDR ≤ 0.05). Next, we applied summary-based data Mendelian randomization (SMR) to integrate the cis-eQTL dataset with GWAS data obtained from a cohort of 813 culled cattle that were classified according to the presence or absence of PTB-associated histopathological lesions in gut tissues. After multiple testing corrections (FDR ≤ 0.05), we identified two novel cis-eQTLs affecting the expression of the early growth response factor 4 (EGR4) and the bovine neuroblastoma breakpoint family member 6-like protein isoform 2 (MGC134040) that showed pleiotropic associations with the presence of multifocal and diffuse lesions in gut tissues; P = 0.002 and P = 0.017, respectively. While EGR4 acts as a brake on T-cell proliferation and cytokine production through interaction with the nuclear factor Kappa β (NF-κß), MGC134040 is a target gene of NF-κß. Our findings provide a better understanding of the genetic factors influencing PTB outcomes, confirm that the multifocal lesions are localized/confined lesions that have different underlying host genetics than the diffuse lesions, and highlight regulatory SNPs and regulated-gene targets to design future functional studies.

Genetic overlap for ten cardiovascular diseases: A comprehensive gene-centric pleiotropic association analysis and Mendelian randomization study

Recent studies suggest that pleiotropic effects may explain the genetic architecture of cardiovascular diseases (CVDs). We conducted a comprehensive gene-centric pleiotropic association analysis for ten CVDs using genome-wide association study (GWAS) summary statistics to identify pleiotropic genes and pathways that may underlie multiple CVDs. We found shared genetic mechanisms underlying the pathophysiology of CVDs, with over two-thirds of the diseases exhibiting common genes and single-nucleotide polymorphisms (SNPs). Significant positive genetic correlations were observed in more than half of paired CVDs. Additionally, we investigated the pleiotropic genes shared between different CVDs, as well as their functional pathways and distribution in different tissues. Moreover, six hub genes, including ALDH2, XPO1, HSPA1L, ESR2, WDR12, and RAB1A, as well as 26 targeted potential drugs, were identified. Our study provides further evidence for the pleiotropic effects of genetic variants on CVDs and highlights the importance of considering pleiotropy in genetic association studies.

Exome-Wide Association Study Identified Clusters of Pleiotropic Genetic Associations with Alzheimer's Disease and Thirteen Cardiovascular Traits.

Alzheimer's disease (AD) and cardiovascular traits might share underlying causes. We sought to identify clusters of cardiovascular traits that share genetic factors with AD. We conducted a univariate exome-wide association study and pair-wise pleiotropic analysis focused on AD and 16 cardiovascular traits-6 diseases and 10 cardio-metabolic risk factors-for 188,260 UK biobank participants. Our analysis pinpointed nine genetic markers in the APOE gene region and four loci mapped to the CDK11, OBP2B, TPM1, and SMARCA4 genes, which demonstrated associations with AD at p ≤ 5 × 10-4 and pleiotropic associations at p ≤ 5 × 10-8. Using hierarchical cluster analysis, we grouped the phenotypes from these pleiotropic associations into seven clusters. Lipids were divided into three clusters: low-density lipoprotein and total cholesterol, high-density lipoprotein cholesterol, and triglycerides. This split might differentiate the lipid-related mechanisms of AD. The clustering of body mass index (BMI) with weight but not height indicates that weight defines BMI-AD pleiotropy. The remaining two clusters included (i) coronary heart disease and myocardial infarction; and (ii) hypertension, diabetes mellitus (DM), systolic and diastolic blood pressure. We found that all AD protective alleles were associated with larger weight and higher DM risk. Three of the four (75%) clusters of traits, which were significantly correlated with AD, demonstrated antagonistic genetic heterogeneity, characterized by different directions of the genetic associations and trait correlations. Our findings suggest that shared genetic factors between AD and cardiovascular traits mostly affect them in an antagonistic manner.

Genome-wide association study of antisocial personality disorder diagnostic criteria provides evidence for shared risk factors across disorders.

While progress has been made in determining the genetic basis of antisocial behaviour, little progress has been made for antisocial personality disorder (ASPD), a condition that often co-occurs with other psychiatric conditions including substance use disorders, attention deficit hyperactivity disorder (ADHD), and anxiety disorders. This study aims to improve the understanding of the genetic risk for ASPD and its relationship with other disorders and traits. We conducted a genome-wide association study (GWAS) of the number of ASPD diagnostic criteria data from 3217 alcohol-dependent participants recruited in the UK (UCL, N = 644) and the USA (Yale-Penn, N = 2573). We identified rs9806493, a chromosome 15 variant, that showed a genome-wide significant association ( Z -score = -5.501, P = 3.77 × 10 -8 ) with ASPD criteria. rs9806493 is an eQTL for SLCO3A1 (Solute Carrier Organic Anion Transporter Family Member 3A1), a ubiquitously expressed gene with strong expression in brain regions that include the anterior cingulate and frontal cortices. Polygenic risk score analysis identified positive correlations between ASPD and smoking, ADHD, depression traits, and posttraumatic stress disorder. Negative correlations were observed between ASPD PRS and alcohol intake frequency, reproductive traits, and level of educational attainment. This study provides evidence for an association between ASPD risk and SLCO3A1 and provides insight into the genetic architecture and pleiotropic associations of ASPD.

Single and multi‐trait genome‐wide association studies identify genomic regions associated with phenological traits in cowpea

AbstractUnderstanding the genetic control of traits linked to phenology is essential to address strategies and efforts in the development of resilient cultivars capable of adapting to climate change. In this study, we performed genome‐wide association studies on a cowpea [Vigna unguiculata (L.) Walp] mini‐core collection to dissect the genetic architecture and identify genomic regions associated with days to flowering, days from flowering to pod maturity, and days to pod maturity. Phenotypic data were collected in 2 years and used to test associations with 41,533 single‐nucleotide polymorphism markers by single and multi‐trait analysis. In addition, we searched for candidate genes putatively involved in phenological traits within genome‐associated regions. The single‐trait approach identified 28 significant marker‐trait associations for the three traits, while the multi‐trait identified 18 significant associations, both evidencing pleiotropic associations. Five of these markers were associated with phenological traits reported in previous studies. Candidate genes were identified, including genes involved in phosphorylation and encoding the maternal effect embryo arrest 60, known to be associated with pod maturity and flowering, respectively. Functional validation of these genes will further improve our understanding of the variation of phenological traits in cowpea and other related crops.

Prevailing Antagonistic Risks in Pleiotropic Associations with Alzheimer's Disease and Diabetes.

The lack of efficient preventive interventions against Alzheimer's disease (AD) calls for identifying efficient modifiable risk factors for AD. As diabetes shares many pathological processes with AD, including accumulation of amyloid plaques and neurofibrillary tangles, insulin resistance, and impaired glucose metabolism, diabetes is thought to be a potentially modifiable risk factor for AD. Mounting evidence suggests that links between AD and diabetes may be more complex than previously believed. To examine the pleiotropic architecture of AD and diabetes mellitus (DM). Univariate and pleiotropic analyses were performed following the discovery-replication strategy using individual-level data from 10 large-scale studies. We report a potentially novel pleiotropic NOTCH2 gene, with a minor allele of rs5025718 associated with increased risks of both AD and DM. We confirm previously identified antagonistic associations of the same variants with the risks of AD and DM in the HLA and APOE gene clusters. We show multiple antagonistic associations of the same variants with AD and DM in the HLA cluster, which were not explained by the lead SNP in this cluster. Although the ɛ2 and ɛ4 alleles played a major role in the antagonistic associations with AD and DM in the APOE cluster, we identified non-overlapping SNPs in this cluster, which were adversely and beneficially associated with AD and DM independently of the ɛ2 and ɛ4 alleles. This study emphasizes differences and similarities in the heterogeneous genetic architectures of AD and DM, which may differentiate the pathogenic mechanisms of these diseases.