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Genetic research into atrial fibrillation (AF) and myocardial infarction (MI) has predominantly focused on comparing afflicted individuals with their healthy counterparts. However, this approach lacks granularity, thus overlooking subtleties within patient populations. In this study, we explore the distinction between AF and MI patients who experience only a single disease event and those experiencing recurrent events. Integrating hospital records, questionnaire data, clinical measurements, and genetic data from more than 500,000 HUNT and United Kingdom Biobank participants, we compare both clinical and genetic characteristics between the two groups using genome-wide association studies (GWAS) meta-analyses, phenome-wide association studies (PheWAS) analyses, and gene co-expression networks. We found that the two groups of patients differ in both clinical characteristics and genetic risks. More specifically, recurrent AF patients are significantly younger and have better baseline health, in terms of reduced cholesterol and blood pressure, than single AF patients. Also, the results of the GWAS meta-analysis indicate that recurrent AF patients seem to be at greater genetic risk for recurrent events. The PheWAS and gene co-expression network analyses highlight differences in the functions associated with the sets of single nucleotide polymorphisms (SNPs) and genes for the two groups. However, for MI patients, we found that those experiencing single events are significantly younger and have better baseline health than those with recurrent MI, yet they exhibit higher genetic risk. The GWAS meta-analysis mostly identifies genetic regions uniquely associated with single MI, and the PheWAS analysis and gene co-expression networks support the genetic differences between the single MI and recurrent MI groups. In conclusion, this work has identified novel genetic regions uniquely associated with single MI and related PheWAS analyses, as well as gene co-expression networks that support the genetic differences between the patient subgroups of single and recurrent occurrence for both MI and AF.

Aims/hypothesis: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. Methods: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. Results: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10−9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. Conclusions/interpretation: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. Data availability: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal (https://t2d.hugeamp.org/downloads.html) and through the GWAS catalog (https://www.ebi.ac.uk/gwas/, accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog (https://www.pgscatalog.org, publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445). [ABSTRACT FROM AUTHOR]

Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration. Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS). Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS. Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted. [ABSTRACT FROM AUTHOR]

Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration.Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS).Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS.Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted.

Introduction: Dementia is a multifactorial disease with Alzheimer's disease (AD) and vascular dementia (VaD) pathologies making the largest contributions. Yet, most genome-wide association studies (GWAS) focus on AD., Methods: We conducted a GWAS of all-cause dementia (ACD) and examined the genetic overlap with VaD. Our dataset includes 800,597 individuals, with 46,902 and 8702 cases of ACD and VaD, respectively. Known AD loci for ACD and VaD were replicated. Bioinformatic analyses prioritized genes that are likely functionally relevant and shared with closely related traits and risk factors., Results: For ACD, novel loci identified were associated with energy transport (SEMA4D), neuronal excitability (ANO3), amyloid deposition in the brain (RBFOX1), and magnetic resonance imaging markers of small vessel disease (SVD; HBEGF). Novel VaD loci were associated with hypertension, diabetes, and neuron maintenance (SPRY2, FOXA2, AJAP1, and PSMA3)., Discussion: Our study identified genetic risks underlying ACD, demonstrating overlap with neurodegenerative processes, vascular risk factors, and cerebral SVD., Highlights: We conducted the largest genome-wide association study of all-cause dementia (ACD) and vascular dementia (VaD). Known genetic variants associated with AD were replicated for ACD and VaD. Functional analyses identified novel loci for ACD and VaD. Genetic risks of ACD overlapped with neurodegeneration, vascular risk factors, and cerebral small vessel disease., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum. Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers). As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism–based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD. Our work underlines the value of redefining the framework for research across traditional diagnostic categories. [ABSTRACT FROM AUTHOR]

Genetic research into atrial fibrillation (AF) and myocardial infarction (MI) has predominantly focused on comparing afflicted individuals with their healthy counterparts. However, this approach lacks granularity, thus overlooking subtleties within patient populations. In this study, we explore the distinction between AF and MI patients who experience only a single disease event and those experiencing recurrent events. Integrating hospital records, questionnaire data, clinical measurements, and genetic data from more than 500,000 HUNT and United Kingdom Biobank participants, we compare both clinical and genetic characteristics between the two groups using genome-wide association studies (GWAS) meta-analyses, phenome-wide association studies (PheWAS) analyses, and gene co-expression networks. We found that the two groups of patients differ in both clinical characteristics and genetic risks. More specifically, recurrent AF patients are significantly younger and have better baseline health, in terms of reduced cholesterol and blood pressure, than single AF patients. Also, the results of the GWAS meta-analysis indicate that recurrent AF patients seem to be at greater genetic risk for recurrent events. The PheWAS and gene co-expression network analyses highlight differences in the functions associated with the sets of single nucleotide polymorphisms (SNPs) and genes for the two groups. However, for MI patients, we found that those experiencing single events are significantly younger and have better baseline health than those with recurrent MI, yet they exhibit higher genetic risk. The GWAS meta-analysis mostly identifies genetic regions uniquely associated with single MI, and the PheWAS analysis and gene co-expression networks support the genetic differences between the single MI and recurrent MI groups. In conclusion, this work has identified novel genetic regions uniquely associated with single MI and related PheWAS analyses, as well as gene co-expression networks that support the genetic differences between the patient subgroups of single and recurrent occurrence for both MI and AF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Hall, Skogholt, Surakka, Dalen and Almaas.)

Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9. By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2. This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events.

Parkinson disease (PD) is known as a common progressive neurodegenerative disease which is clinically diagnosed by the manifestation of numerous motor and nonmotor symptoms. PD is a genetically heterogeneous disorder with both familial and sporadic forms. To date, researches in the field of Parkinsonism have identified 23 genes or loci linked to rare monogenic familial forms of PD with Mendelian inheritance. Biochemical studies revealed that the products of these genes usually play key roles in the proper protein and mitochondrial quality control processes, as well as synaptic transmission and vesicular recycling pathways within neurons. Despite this, large number of patients affected with PD typically tends to show sporadic forms of disease with lack of a clear family history. Recent genome-wide association studies (GWAS) meta-analyses on the large sporadic PD case-control samples from European populations have identified over 12 genetic risk factors. However, the genetic etiology that underlies pathogenesis of PD is also discussed, since it remains unidentified in 40% of all PD-affected cases. Nowadays, with the emergence of new genetic techniques, international PD genomics consortiums and public online resources such as PDGene, there are many hopes that future large-scale genetics projects provide further insights into the genetic etiology of PD and improve diagnostic accuracy and therapeutic clinical trial designs. [ABSTRACT FROM AUTHOR]

Background: Obesity is heritable. It predisposes to many diseases. The objectives of this study were to create a compendium of genes relevant to feeding behavior (FB) and/or body weight (BW) regulation; to construct and to analyze networks formed by associations between genes/proteins; and to identify the most significant genes, biological processes/pathways, and tissues/organs involved in BW regulation. Results: The compendium of genes controlling FB or BW includes 578 human genes. Candidate genes were identified from various sources, including previously published original research and review articles, GWAS meta-analyses, and OMIM (Online Mendelian Inheritance in Man). All genes were ranked according to knowledge about their biological role in body weight regulation and classified according to expression patterns or functional characteristics. Substantial and overrepresented numbers of genes from the compendium encoded cell surface receptors, signaling molecules (hormones, neuropeptides, cytokines), transcription factors, signal transduction proteins, cilium and BBSome components, and lipid binding proteins or were present in the brain-specific list of tissue-enriched genes identified with TSEA tool. We identified 27 pathways from KEGG, REACTOME and BIOCARTA whose genes were overrepresented in the compendium. Networks formed by physical interactions or homological relationships between proteins or interactions between proteins involved in biochemical/signaling pathways were reconstructed and analyzed. Subnetworks and clusters identified by the MCODE tool included genes/proteins associated with cilium morphogenesis, signal transduction proteins (particularly, G protein-coupled receptors, kinases or proteins involved in response to insulin stimulus) and transcription regulation (particularly nuclear receptors). We ranked GWAS genes according to the number of neighbors in three networks and revealed 22 GWAS genes involved in the brain-specific PPI network. On the base of the most reliable PPIs functioning in the brain tissue, new regulatory schemes interpreting relevance to BW regulation are proposed for three GWAS genes (ETV5, LRP1B, and NDUFS3). Conclusions: A compendium comprising 578 human genes controlling FB or BW was designed, and the most significant functional groups of genes, biological processes/pathways, and tissues/organs involved in BW regulation were revealed. We ranked genes from the GWAS meta-analysis set according to the number and quality of associations in the networks and then according to their involvement in the brain-specific PPI network and proposed new regulatory schemes involving three GWAS genes (ETV5, LRP1B, and NDUFS3) in BW regulation. The compendium is expected to be useful for pathology risk estimation and for design of new pharmacological approaches in the treatment of human obesity. [ABSTRACT FROM AUTHOR]

Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9. By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2. This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events. [ABSTRACT FROM AUTHOR]

Recently, large scale genome-wide association study (GWAS) meta-analyses have boosted the number of known signals for some traits into the tens and hundreds. Typically, however, variants are only analysed one-at-a-time. This complicates the ability of fine-mapping to identify a small set of SNPs for further functional follow-up. We describe a new and scalable algorithm, joint analysis of marginal summary statistics (JAM), for the re-analysis of published marginal summary statistics under joint multi-SNP models. The correlation is accounted for according to estimates from a reference dataset, and models and SNPs that best explain the complete joint pattern of marginal effects are highlighted via an integrated Bayesian penalized regression framework. We provide both enumerated and Reversible Jump MCMC implementations of JAM and present some comparisons of performance. In a series of realistic simulation studies, JAM demonstrated identical performance to various alternatives designed for single region settings. In multi-region settings, where the only multivariate alternative involves stepwise selection, JAM offered greater power and specificity. We also present an application to real published results from MAGIC (meta-analysis of glucose and insulin related traits consortium) - a GWAS meta-analysis of more than 15,000 people. We re-analysed several genomic regions that produced multiple significant signals with glucose levels 2 hr after oral stimulation. Through joint multivariate modelling, JAM was able to formally rule out many SNPs, and for one gene, ADCY5, suggests that an additional SNP, which transpired to be more biologically plausible, should be followed up with equal priority to the reported index., (© 2016 The Authors Genetic Epidemiology Published by Wiley Periodicals, Inc.)

Genome-wide association studies are proven tools for finding disease genes, but it is often necessary to combine many cohorts into a meta-analysis to detect statistically significant genetic effects. Often the component studies are performed by different investigators on different populations, using different chips with minimal SNPs overlap. In some cases, raw data are not available for imputation so that only the genotyped single nucleotide polymorphisms (SNPs) results can be used in meta-analysis. Even when SNP sets are comparable, different cohorts may have peak association signals at different SNPs within the same gene due to population differences in linkage disequilibrium or environmental interactions. We hypothesize that the power to detect statistical signals in these situations will improve by using a method that simultaneously meta-analyzes and smooths the signal over nearby markers. In this study, we propose regionally smoothed meta-analysis methods and compare their performance on real and simulated data., (© 2015 WILEY PERIODICALS, INC.)