While genetic studies have provided insights into essential hypertension (EH, defined by high blood pressure ≥140/90 mmHg), investigation through epigenetics may address gaps in understanding its heritability. This study focused on African Brazilian populations in Vale do Ribeira River region, due to their high hypertension prevalence. We aimed to determine if DNA methylation is linked to hypertension susceptibility, through a genome-wide evaluation of 80 peripheral blood samples from normotensive (39) and hypertensive (41) individuals, with Infinium Methylation EPIC BeadChip platform. Data were analyzed using ChAMP package and cross-referenced with information from databases such as EWAS Atlas, GWAS catalog, GeneCards, literature, and tools such as VarElect and EWAS Toolkit. The comparison between hypertensive and normotensive revealed 190 differentially methylated CpG positions (DMPs) and 46 differentially methylated regions (DMRs), both with p-value ≤0.05. Among the DMPs, 27 were found to have a plausible role in blood pressure. Among the DMRs, those mapped to ABAT, BLCAP, CERS3, EIF4E, FMN1, GABBR1, HLA-DQB2, HOXA5, IL5RA, KCNH2, MIR487B, MIR539, MIR886, MKRN3, NUDT12, PON3, RNF39, RWDD3, and TSHBS1 were highlighted because of their lowest p-values, current literature, and/or VarElect prioritization. Our findings suggest that differences in methylation contribute to the high susceptibility to essential hypertension in these populations.

Loneliness and social isolation are serious yet underappreciated public health problems, with their genetic underpinnings remaining largely unknown. We aimed to explore the role of protein-coding variants in the manifestation of loneliness and social isolation.

We conducted the first exome-wide association analysis on loneliness and social isolation, utilizing 336,115 participants of white-British ancestry for loneliness and 346,115 for social isolation. Sensitivity analyses were performed to validate the genetic findings. We estimated the genetic burden heritability of loneliness and social isolation and provided biological insights into them.

We identified six novel risk genes (ANKRD12, RIPOR2, PTEN, ARL8B, NF1, and PIMREG) associated with loneliness and two (EDARADD and GIGYF1) with social isolation through analysis of rare coding variants. Brain-wide association analysis uncovered 47 associations between identified genes and brain structure phenotypes, many of which are critical for social processing and interaction. Phenome-wide association analysis established significant links between these genes and phenotypes across five categories, mostly blood biomarkers and cognitive measures.

The measurements of loneliness and social isolation in UK Biobank are brief for these multi-layer social factors, some relevant aspects may be missed.

Our study revealed 13 risk genes associated with loneliness and 6 with social isolation, with the majority being novel discoveries. These findings advance our understanding of the genetic basis of these two traits. The study provides a foundation for future studies aimed at exploring the functional mechanisms of these genes and their potential implications for public health interventions targeting loneliness and social isolation.

To identify genetic biomarkers that may be used in the diagnosis, prevention or management of different forms of periodontal disease.

Following protocol registration and PICOTS (patient, intervention, comparison, outcome, time, studies) questions, a systematic search of the literature was conducted (PudMed, Ovid), resulting in 1592 papers screened by two reviewers. Diagnostic data were extracted or calculated from included papers and compared with clinically determined diagnoses, disease progression and/or response to treatment.

A total of 607 articles met the inclusion criteria, including 10 reporting data on gingivitis and 597 on periodontitis. Only two papers reported diagnostic performance data, while for 41 articles on large candidate gene studies, diagnostic performance could be calculated from the reported data. No study using chair-side tests was identified. Low to moderate values for sensitivity, specificity, positive and negative predictive value and diagnostic accuracy were found.

No genetic diagnostic test of clinical value emerged for periodontal diagnosis, prevention or prediction of disease resolution. Thus, potential future applications of polygenic risk scores that encode susceptibility, as well as single-marker testing for monogenic or oligogenic forms of periodontal diseases, are discussed.

The impact of rare pathogenic variants on diabetic kidney disease (DKD) has not been investigated in detail. Previous studies have detected pathogenic variants in 22% of Caucasian patients with DKD; however, this proportion may vary depending on ethnicity and updates to the database. Therefore, we performed a whole-genome analysis of patients with DKD in type 2 diabetes mellitus in Japan, utilizing a recent database to investigate the prevalence of kidney-related pathogenic variants and describe the characteristics of these patients.

Whole-genome sequencing was performed, and variants were analyzed following the GATK Best Practices. We extracted data on 790 genes associated with Mendelian kidney and genitourinary diseases. Pathogenic variants were defined based on the American College of Medical Genetics criteria, including both heterozygous and homozygous variants classified as pathogenic or likely pathogenic.

Among 79 participants, heterozygous pathogenic variants were identified in 27 (34.1%), a higher prevalence than previously reported. No homozygous pathogenic variants were detected. The identified heterozygous pathogenic variants were roughly divided into 23.7% related to glomerulopathy, 36.8% related to tubulointerstitial disease, 10.5% related to cystic disease/ciliopathy, and 28.9% related to others. Diagnostic variants were found in 10 patients (12.7%) in seven genes (ABCC6, ALPL, ASXL1, BMPR2, GCM2, PAX2, and WT1), all associated with autosomal dominant congenital disease.

This study identified a considerable number of patients with DKD in Japan who carried kidney-related heterozygous pathogenic variants. These findings suggest potential ethnic differences and highlight the impact of database updates on variant detection.

Migraine is associated with a range of symptoms and comorbid disorders and has a strong genetic basis, but the currently identified risk loci only explain a small portion of the heritability, often termed the "missing heritability". We aimed to investigate if machine learning could exploit the combination of genetic data and general clinical features to identify individuals at risk for new-onset migraine.

This study was a population-based cohort study of adults from the second and third Trøndelag Health Study (HUNT2 and HUNT3). Migraine was captured in a validated questionnaire and based on modified criteria of the International Classification of Headache Disorders (ICHD) and participants underwent genome-wide genotyping. The primary outcome was new-onset migraine defined as a change in disease status from headache-free in HUNT2 to migraine in HUNT3. The migraine risk variants identified in the largest GWAS meta-analysis of migraine were used to identify genetic input features for the models. The general clinical features included demographics, selected comorbidities, medication and stimulant use and non-headache symptoms as predictive factors. Several standard machine learning architectures were constructed, trained, optimized and scored with area under the receiver operating characteristics curve (AUC). The best model during training and validation was used on unseen test sets. Different methods for model explainability were employed.

A total of 12,995 individuals were included in the predictive modelling (491 new-onset cases). A total of 108 genetic variants and 67 general clinical variables were included in the models. The top performing decision-tree classifier achieved a test set AUC of 0.56 when using only genotypic data, 0.68 when using only clinical data and 0.72 when using both genetic and clinical data. Combining the genotype only and clinical data only models resulted in a lower predictivity with an AUC of 0.67. The most important clinical features were age, marital status and work situation as well as several genetic variants.

The combination of genotype and routine demographic and non-headache clinical data correctly predict the new onset of migraine in approximately 2 out of 3 cases, supporting that there are important genotypic-phenotypic interactions partaking in the new-onset of migraine.

Resulting from a combination of genetic and environmental factors, type 2 diabetes is highly heterogeneous in manifestation and disease progression, with the only common feature being chronic hyperglycaemia. In spite of vigorous efforts to elucidate the pathogenetic origins and natural course of the disease, there is still a lack of biomarkers and tools for prevention, disease stratification and treatment. Genome-wide association studies have reported over 1200 variants associated with type 2 diabetes, and the decreased cost of generating genetic data has facilitated the development of polygenic scores for estimating an individual's genetic disease risk based on combining effects from most-or all-genetic variants. In this review, we summarise the current knowledge on type 2 diabetes-related polygenic scores in different ancestries and outline their possible clinical role. We explore the potential applicability of type 2 diabetes polygenic scores to quantify genetic liability for prediction, screening and risk stratification. Given that most genetic risk loci are determined from populations of European origin while other ancestries are under-represented, we also discuss the challenges around their global applicability. To date, the potential for clinical utility of polygenic scores for type 2 diabetes is limited, with such scores outperformed by clinical measures. In the future, rather than predicting risk of type 2 diabetes, the value of polygenic scores may be in stratification of the severity of disease (risk for comorbidities) and treatment response, in addition to aiding in dissecting the pathophysiological mechanisms involved.

Huge genetic diversity is evident among the diverse goat breeds in terms of production, reproduction, adaptability, growth, disease resistance and thermo-tolerance. This diversity is an outcome of both natural and artificial selection acting on the caprine genome over the years. A fine characterization of whole genome variation is now possible by employing Next Generation Sequencing (NGS) technologies. To explore underlying genetics, genome-wide analysis of genetic markers is the best resolution. The study strived to capture variation in terms of CNV/CNVRs among 11 Indian goat breeds. In this study, the first ever resequencing-based CNV/CNVR distribution of Indigenous goat breeds was delineated, providing a sizable addition to the prior caprine CNVRs reported. Different diversity metrics were analyzed using identified CNVR. Principal component analysis (PCA) showed separate clustering of Kanniadu (KAN) and Jharkhand Black (JB) from other breeds under the study, indicating their unique genetic profile as the former breeds were sampled from institutional farms. The admixture analysis and introgression revealed by f3 statistics suggested distinct genetic structuring of JB, KAN and TEL(Tellicherry) as compared to the rest of the studied populations. Apart from this, we also identified 32 selection signatures through VST (Variance-stabilizing transformation) method and key genes such as ZBTB7C, BHLHE22, AGT were found elucidating the genetic architecture of hot and cold adaptation in Indian goats. Information generated hereby in the form of 32,711 autosomal CNVRs and the custom scripts ( https://github.com/kkokay07/Climate-Variables-Analysis.git , https://github.com/chau-mau/SelectCNVR.git and https://github.com/chau-mau/CNVrecaller.git ) will be of relevance in further studies on copy number based genetics.

Polycystic ovary syndrome (PCOS) is a very common endocrine, metabolic and reproductive disorder. The underlying pathophysiology is not yet fully understood and both genetic and environmental factors contribute to its development. We aimed to explore clinical and genetic aspects of familial clustering in PCOS, shedding light on its reproductive and metabolic consequences in both male and female first-degree relatives of the affected women.

Searching the electronic database of PubMed up to October 2023, we synthesized findings from available prospective and retrospective studies and review articles, investigating the familial clustering of PCOS and incorporating data on its metabolic consequences and genetic associations.

There is a significant clustering of reproductive and metabolic abnormalities in first-degree relatives of women with PCOS. Genetic studies, including genome-wide association studies (GWAS), reveal a complex molecular etiology, emphasizing polygenic architecture. This is supported by the identification of two distinct PCOS subtypes, termed "reproductive" and "metabolic" which exhibit differential genetic underpinnings.

Clinicians should be aware of increased reproductive and metabolic dysfunction both in female and male first-degree relatives of PCOS probands. Current challenges include refining genetic risk scores and understanding the impact of PCOS genetic factors on diverse outcomes, necessitating a sex-specific approach in research and clinical practice. Future directions should address causality, improve diagnostic capability, and unravel the long-term consequences in both genders, emphasizing the importance of proactive clinical assessment in PCOS probands and their families.

Motile cilia dysfunction was reported to lead to scoliosis-like phenotypes in zebrafish models. There is still a lack of population-based study supporting the role of cilia motility associated genes in the etiology of idiopathic scoliosis (IS).

To investigate the molecular mechanism underlying the relationship between cilia motility associated genes and the development of adolescent idiopathic scoliosis (AIS).

Population-based genetic study.

A cohort of 56 female AIS patients and 30 age-matched nonscoliotic controls were included for tissue expression analysis. 28 patients with lower CCDC40 expression were selected for the exon sequencing. The novel variation was replicated in an independent cohort of 1326 AIS patients and 954 healthy controls. Exogenous versions of WT or mutant human CCDC40 mRNAs were expressed in zebrafish and the phenotype of body axis curvature was observed.

CCDC40 was found significantly down-expressed in AIS patients as compared with the nonscoliotic controls. A novel coding variant rs185157579 (c.1459G>A) was found significantly associated with AIS, with the mutant allele A adding to the risk of AIS by 2.44 folds. Zebrafish embryo injected with CCDC40 mRNAs containing mutant c.1459G>A presented significantly higher incidence of scoliosis-like phenotype than the wild group.

The mutation c.1459G>A in the exon 10 of CCDC40 may lead to body axis curvature of zebrafish by impacting mRNA expression. The underlying molecular mechanism is worthy of further investigation.

Our findings shed a new light on the etiopathogenesis of AIS. The downstream signaling of CCDC40 may be candidate for potential drug targets to prevent the development of AIS. Moreover, the novel variation can be used as a genetic marker of polygenic risk score predicting the risk of AIS.

The Saanen breeds are often used as terminal sires for hybridization and play an important role in the global dairy food industry. However, there is still a lack of genomics information on the Saanen dairy goats. Whole-genome sequencing offers a promising approach to identify genetic markers associated with economic traits and discover new candidate genes. This can effectively utilize genetic resources to accelerate breeding processes and improve lactation performance in Saanen dairy goats. In this study, we present the genomes of 298 Saanen dairy goats. Through rigorous sequencing and quality control, we achieved an average sequencing depth of 14.6X, with 92.3% of high-quality (Q30 > 90%) data and an average mapping ratio of 99.9%, indicating reliable results. By comparing our data to a reference genome of Saanen dairy goats, we identified14.59 million single nucleotide polymorphisms (SNPs) and 1.34 million insertions-deletions (InDels). This dataset significantly contributes to enriching public databases in dairy goats and provides valuable resources for studying genetic diversity, improving breeds, and developing new varieties.

There is a pressing need to establish objective measures to improve diagnosis, prediction, prevention and treatment of bipolar disorder (BD). Multimodal artificial intelligence (AI) tools could provide these means by incorporating various layers of data orthogonally related to BD, including genomics and other omics, environmental exposures, imaging measures, electronic health records, cognition, sensing devices and clinical variables. These rapidly evolving AI models hold promise to capture the multidimensional complexity of BD and delineate clinically relevant developmental trajectories that could guide clinical care and therapeutic strategies. In this review, we describe the potential of mapping developmental trajectories underlying BD, outline how novel multimodal models could improve the prediction of BD and related outcomes, and discuss specific clinical use cases and key ethical and practical challenges regarding the development and potential implementation of these multimodal AI solutions to advance precision medicine approaches in BD.

The intestinal microbiome influences health and disease. Its composition is affected by host genetics and environmental exposures. Understanding host genetic effects is critical but challenging in humans, due to the difficulty of detecting, mapping and interpreting them. To address this, we analysed host genetic effects in four cohorts of outbred laboratory rats exposed to distinct but controlled environments. We found that polygenic host genetic effects were consistent across environments. We identified three replicated microbiome-associated loci. One involved a sialyltransferase gene and Paraprevotella and we found a similar association, between ST6GAL1 and Paraprevotella , in a human cohort. Given Paraprevotella 's known immunity-potentiating functions, this suggests ST6GAL1 's effects on IgA nephropathy and COVID-19 breakthrough infections may be mediated by Paraprevotella . Moreover, we found evidence of indirect genetic effects on microbiome phenotypes, which substantially increased their total genetic variance. Finally, we identified a novel mechanism whereby indirect genetic effects can contribute to "missing heritability".

The relationship between brain asymmetry and inattention, and their heritability is not well understood. Utilizing advanced neuroimaging, we examined brain asymmetry with data from the Adolescent Brain Cognitive Development (ABCD; n = 8943; 9-10 y) and the Human Connectome Project (HCP) cohorts (n = 1033; 5-100 y). Data-driven metrics from resting-state fMRI and morphometrics revealed reproducible and stable brain asymmetry patterns across the lifespan. In children, high levels of inattention were highly heritable (61%) and linked to reduced leftward asymmetry of functional connectivity in the dorsal posterior superior temporal sulcus (dpSTS), a region interconnected with a left-lateralized language network. However, reduced dpSTS asymmetry had low heritability (16%) and was associated with lower cognitive performance suggesting that non-genetic factors, such as those mediating cognitive performance, might underlie its association with dpSTS asymmetry. Interventions that enhance cognition might help optimize brain function and reduce inattention.

In plant genetics, most modern association analyses are performed on panels that bring together individuals from several populations, including admixed individuals whose genomes comprise chromosomal regions from different populations. These panels can identify quantitative trait loci (QTLs) with population-specific effects and epistatic interactions between QTLs and polygenic backgrounds. However, analyzing a diverse panel constitutes a challenge for statistical analysis. The statistical model must account for possible interactions between a QTL and the panel structure while strictly controlling the detection error rate. Although models to detect population-specific QTLs have already been developed, they rely on prior information about the population structure. In practice, this prior information may be missing as many genome-wide association study (GWAS) panels exhibit complex population structures. The present study introduces 2 new models for detecting QTLs interacting with complex population structures. Both incorporate an interaction term between single nucleotide polymorphism/haplotype block and genetic background into conventional GWAS models. The proposed models were compared with state-of-the-art models through simulation studies that considered QTLs with different levels of interaction with their genetic backgrounds. Results showed that models matching simulation settings were most effective for detecting corresponding QTLs while the proposed models outperformed classical models in detecting QTLs interacting with polygenes. Additionally, when applied to a soybean dataset, one of our models identified putative associated QTLs that conventional models failed to detect. The new models, implemented in the RAINBOWR package available on CRAN, are expected to help uncover complex trait genetic architectures.

This study investigated copy number variations (CNVs) in 2112 pigs from five populations: Korean Duroc (DUC), Korean Native Pig (KNP), and their crossbred offspring (F1, F2, and WRH). CNVs were detected using PennCNV and QuantiSNP, with CNVRuler identifying 698 CNV regions (CNVRs), covering 109 Mb (4.83%) of the porcine genome. Comparison with previous CNV studies on swine revealed CNVR overlap rates ranging from 31.12% (French Yorkshire) to 81.27% (Xiang), and 9.06% newly identified CNVRs. DUC showed the most CNVRs (n = 384), followed by WRH (n = 225). Meanwhile, F1 and F2 exhibited far fewer CNVRs (five and seven, respectively). Functional enrichment analysis highlighted various genes overlapping with the CNVRs, including 1236 genes in DUC and 572 genes in WRH, linked to biological processes. The quantitative trait loci (QTLs), overlapping with CNVRs, exhibited particular overlapping with traits such as average daily gain (4.24% of QTLs in DUC, 4.51% of QTLs in WRH). In contrast, KNP, F1, and F2 populations exhibited a higher frequency of CNVRs containing QTLs overlapped with drip loss. These findings indicate that WRH may inherit growth traits from DUC. This study provides a better understanding of CNVs in the pigs, which can potentially be used in improving genetic merits of pig populations.

Long-read RNA sequencing (RNA-seq) is emerging as a powerful and versatile technology for studying human transcriptomes. By enabling the end-to-end sequencing of full-length transcripts, long-read RNA-seq opens up avenues for investigating various RNA species and features that cannot be reliably interrogated by standard short-read RNA-seq methods. In this review, we present an overview of long-read RNA-seq, delineating its strengths over short-read RNA-seq, as well as summarizing recent advances in experimental and computational approaches to boost the power of long-read-based transcriptomics. We describe a wide range of applications of long-read RNA-seq, and highlight its expanding role as a foundational technology for exploring transcriptome variations in human diseases.

A large proportion of genetic variations involved in complex diseases are rare and located within noncoding regions, making the interpretation of underlying biological mechanisms a daunting task. Although technical and methodological progress has been made to annotate the genome, current disease-rare-variant association tests incorporating such annotations suffer from two major limitations. First, they are generally restricted to case-control designs of unrelated individuals, which often require tens or hundreds of thousands of individuals to achieve sufficient power. Second, they were not evaluated with region-based annotations needed to interpret the causal regulatory mechanisms. In this work, we propose RetroFun-RVS, a new retrospective family-based score test, incorporating functional annotations. A critical feature of the proposed method is to aggregate genotypes to compare against rare variant-sharing expectations among affected family members. Through extensive simulations, we have demonstrated that RetroFun-RVS integrating networks based on 3D genome contacts as functional annotations reach greater power over the region-wide test, other strategies to include subregions and competing methods. Also, the proposed framework shows robustness to non-informative annotations, maintaining its power when causal variants are spread across regions. Asymptotic p-values are susceptible to Type I error inflation when the number of families with rare variants is small, and a bootstrap procedure is recommended in these instances. Application of RetroFun-RVS is illustrated on whole genome sequence in the Eastern Quebec Schizophrenia and Bipolar Disorder Kindred Study with networks constructed from 3D contacts and epigenetic data on neurons. In summary, the integration of functional annotations corresponding to regions or networks with transcriptional impacts in rare variant tests appears promising to highlight regulatory mechanisms involved in complex diseases.

Genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping methods provide valuable insights and opportunities for identifying functional gene underlying phenotype formation. However, the majority of GWAS risk loci and QTLs located in noncoding regions poses significant challenges in pinpointing the protein-coding genes associated with specific traits. Moreover, growing evidence suggests not all GWAS risk loci and QTLs are functional, emphasizing the critical need for prioritizing causal sites-a task of paramount importance for biologists. The accumulation of publicly available multiomics data provides an unprecedented opportunity to annotate and prioritize GWAS risk loci and QTLs. Therefore, we developed a comprehensive multiomics database encompassing four major agricultural species-pig, sheep, cattle, and chicken. This database integrates publicly accessible datasets, including 140 GWAS studies (covering 471 traits), 2625 QTL datasets (spanning 1235 traits), 86 Hi-C datasets (from eight cells/tissue types), 95 epigenomic datasets (from four cells/tissue types), and 769 transcription factor motifs. The database aims to link GWAS-QTL loci located in the noncoding regions to the target genes they regulate and prioritize functional and causal regulatory elements. Ultimately, it provides a valuable resource and potential validation targets for elucidating the genes and molecular pathways underlying economically important traits in agricultural animals.

Lung fibrosis, including idiopathic pulmonary fibrosis (IPF), is a complex and devastating disease characterised by the progressive scarring of lung tissue leading to compromised respiratory function. Aberrantly activated fibroblasts deposit extracellular matrix components into the surrounding lung tissue, impairing lung function and capacity for gas exchange. Both genetic and epigenetic factors have been found to play a role in the pathogenesis of lung fibrosis, with emerging evidence highlighting the interplay between these two regulatory mechanisms. This review provides an overview of the current understanding of the interplay between genetics and epigenetics in lung fibrosis. We discuss the genetic variants associated with susceptibility to lung fibrosis and explore how epigenetic modifications such as DNA methylation, histone modifications, and non-coding RNA expression contribute to disease. Insights from genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) are integrated to explore the molecular mechanisms underlying lung fibrosis pathogenesis. We also discuss the potential clinical implications of genetics and epigenetics in lung fibrosis, including the development of novel therapeutic targets. Overall, this review highlights the importance of considering both genetic and epigenetic factors in the understanding and management of lung fibrosis.

Rare genetic variants, characterized by their low frequency in a population, have emerged as essential components in the study of complex disease genetics. The biology of rare variants underscores their significance, as they can exert profound effects on phenotypic variation and disease susceptibility. Recent advancements in sequencing technologies have yielded the availability of large-scale sequencing data such as the UK Biobank whole-exome sequencing (WES) cohort empowered researchers to conduct rare variant association studies (RVASs). This review paper discusses the significance of rare variants, available methodologies, and applications. We provide an overview of RVASs, emphasizing their relevance in unraveling the genetic architecture of complex diseases with special focus on chronic pain and Arthritis. Additionally, we discuss the strengths and limitations of various rare variant association testing methods, outlining a typical pipeline for conducting rare variant association. This pipeline encompasses crucial steps such as quality control of WES data, rare variant annotation, and association testing. It serves as a comprehensive guide for researchers in the field of chronic pain diseases interested in rare variant association studies in large-scale sequencing datasets like the UK Biobank WES cohort. Lastly, we discuss how the identified variants can be further investigated through detailed experimental studies in animal models to elucidate their functional impact and underlying mechanisms.

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

The study aimed to investigate the causal relationship between serum 25-hydroxyvitamin D (25(OH)D) levels and epilepsy using Mendelian randomization (MR), thereby addressing confounding and reverse causality issues in observational studies.

We employed a two-sample bidirectional MR design utilizing summary-level data from the IEU OpenGWAS project. Serum 25(OH)D levels were analyzed using the publicly available dataset ebi-a-GCST90000618, which included 496,946 European samples and 68,960,93 SNPs. Data on epilepsy were obtained from ebi-a-GCST90018840, comprising 458,310 samples, including 4,382 epilepsy patients and 453,928 controls. To identify instrumental variables (IVs), we applied a significance threshold of P < 5e-8 for serum 25(OH)D levels as the exposure and P < 5e-6 for epilepsy as the exposure. IVs were required to demonstrate an r2 < 0.001 linkage disequilibrium and an F-statistic greater than 10. The MR analysis utilized five methods: inverse variance weighted (IVW), weighted median, MR-Egger, weighted mode, and simple mode, assessing causal relationships between serum 25(OH)D levels and epilepsy. Robustness checks included heterogeneity tests, leave-one-out sensitivity analyses, and assessments for horizontal pleiotropy.

Both directions of the MR analysis revealed no genetic correlation between serum 25(OH)D levels and epilepsy.

Our findings, supported by robust IV screening and consistent results across multiple MR methods, indicate a lack of causal relationship between serum 25(OH)D levels and epilepsy. These results suggest that while vitamin D plays a role in the nervous system, its relationship to epilepsy may not be direct, thus highlighting the need for further investigation in future studies.

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) has increased exponentially over the past three decades, in parallel with the global rise in obesity and type 2 diabetes. It is currently the most common cause of liver-related morbidity and mortality. Although obesity has been identified as a key factor in the increased prevalence of MASLD, individual differences in susceptibility are significantly influenced by genetic factors. PNPLA3 I148M (rs738409 C>G) is the variant with the greatest impact on the risk of developing progressive MASLD and likely other forms of steatotic liver disease. This variant is prevalent across the globe, with the risk allele (G) frequency exhibiting considerable variation. Here, we review the contribution of PNPLA3 I148M to global burden and regional differences in MASLD prevalence, focusing on recent evidence emerging from population-based sequencing studies and prevalence assessments. We calculated the population attributable fraction (PAF) as a means of quantifying the impact of the variant on MASLD. Furthermore, we employ quantitative trait locus (QTL) analysis to ascertain the associations between rs738409 and a range of phenotypic traits. This analysis suggests that these QTLs may underpin pleiotropic effects on extrahepatic traits. Finally, we outline potential avenues for further research and identify key areas for investigation in future studies.

This review explores the genetic basis of Tourette syndrome (TS), a complex neuropsychiatric disorder characterized by motor and vocal tics. Family, twin, and molecular genetic studies provide strong evidence for a genetic component in TS, with heritability estimates ranging from 50% to 80%. The genetic architecture of TS is complex, involving both common variants with small effects and rare variants with larger effects. Genetic studies have identified candidate genes and chromosomal regions associated with TS risk, implicating biological pathways related to neurodevelopment, neurotransmission, and synaptic function. The article also discusses the clinical implications of these findings and future research directions.

Infections with the liver fluke (Fasciola hepatica) cause economic losses in cattle production worldwide. Also, infections with rumen flukes (Calicophoron/Paramphistomum spp.) are gaining importance in grazing cattle in Europe. However, increasing resistance of helminth parasites against anthelmintics and limitations in treatment emphasize the need for alternative breeding approaches. This study included 1602 dairy cows kept on 29 farms with 2423 observations for F. hepatica and Calicophoron/Paramphistomum spp. egg counts per gram faeces (EPG). The EPGs were binary defined (infected: EPG > 0; non-infected: EPG = 0) and logarithmically transformed. The pedigree included 7939 cows. Genotypes (777 k) were available for 214 cows. A single-step GBLUP (ssGBLUP) model was applied to estimate genetic parameters for infection traits. Genomic breeding values from ssGBLUP were used in a single-step genome-wide association study (ssGWAS) to identify genetic variants associated with helminth infections. The heritability for liver fluke infections was up to 0.09, and up to 0.34 for rumen fluke infections. The genetic correlations between liver and rumen fluke infections ranged from 0.49 to 0.53, indicating that breeding for improved resilience to both helminth taxa is possible simultaneously. The ssGWAS revealed four SNPs for liver fluke infections on BTA 5, 13, 26 and 29, and 17 SNPs for rumen fluke infections on BTA 3 and 23. The SNPs for liver fluke infections were annotated to 12 potential candidate genes, most of which involved in liver fibrosis and immunity. The LRRC8B gene was found to be involved in host-rumen fluke interactions.

Familial mutations in myocilin cause vision loss in glaucoma due to misfolding and a toxic gain of function in a senescent cell type in the anterior eye. Here we characterize the cellular behavior and structure of the myocilin (myocilin A427T) mutant, of uncertain pathogenicity. Our characterization of A427T demonstrates that even mutations that minimally perturb myocilin structure and stability can present challenges for protein quality control clearance pathways. Namely, when expressed in an inducible immortalized trabecular meshwork cell line, inhibition of the proteasome reroutes wild-type myocilin, but not myocilin A427T, from endoplasmic reticulum associated degradation to lysosomal degradation. Yet, the crystal structure of the A427T myocilin olfactomedin domain shows modest perturbations largely confined to the mutation site. The previously unappreciated range of mutant myocilin behavior correlating with variable stability and structure provides a rationale for why it is challenging to predict causal pathogenicity of a given myocilin mutation, even in the presence of clinical data for members of an affected family. Comprehending the continuum of mutant myocilin behavior in the laboratory supports emerging efforts to use genetics to assess glaucoma risk in the clinic. In addition, the study supports a therapeutic strategy aimed at enhancing autophagic clearance of mutant myocilin.

This review explores the intricate landscape of neurodegenerative disease research, focusing on Amyotrophic Lateral Sclerosis (ALS) and the intersection of genetics and RNA biology to investigate the causative pathogenetic basis of this fatal disease. ALS is a severe neurodegenerative disease characterized by the progressive loss of motor neurons, leading to muscle weakness and paralysis. Despite significant research advances, the exact cause of ALS remains largely unknown. Thanks to the application of next-generation sequencing (NGS) approaches, it was possible to highlight the fundamental role of rare variants with large effect sizes and involvement of portions of non-coding RNA, providing valuable information on risk prediction, diagnosis, and treatment of age-related diseases, such as ALS. Genetic research has provided valuable insights into the pathophysiology of ALS, leading to the development of targeted therapies such as antisense oligonucleotides (ASOs). Regulatory agencies in several countries are evaluating the commercialization of Qalsody (Tofersen) for SOD1-associated ALS, highlighting the potential of gene-targeted therapies. Furthermore, the emerging significance of microRNAs (miRNAs) and long RNAs are of great interest. MiRNAs have emerged as promising biomarkers for diagnosing ALS and monitoring disease progression. Understanding the role of lncRNAs in the pathogenesis of ALS opens new avenues for therapeutic intervention. However, challenges remain in delivering RNA-based therapeutics to the central nervous system. Advances in genetic screening and personalized medicine hold promise for improving the management of ALS. Ongoing clinical trials use genomic approaches for patient stratification and drug targeting. Further research into the role of non-coding RNAs in the pathogenesis of ALS and their potential as therapeutic targets is crucial to the development of effective treatments for this devastating disease.

Understanding the causal genetic architecture of complex phenotypes will fuel future research into disease mechanisms and potential therapies. Here, we illustrate the power of a novel framework: it detects, starting from summary statistics, and across the entire genome, sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. The approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform existing methods in false discovery rate control, statistical power and various fine-mapping criteria. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. Massively parallel reporter assays and CRISPR-Cas9 experiments have confirmed the functionality of the putative causal variants our method points to. Finally, we retrospectively analyzed summary statistics from 67 large-scale GWAS for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

Osteonecrosis (ON) is a debilitating orthopedic condition characterized by bone cell death due to impaired blood supply, leading to structural changes and disability. Osteoporosis (OP), a systemic skeletal disease, results in reduced bone density and quality, making bones fragile and prone to fractures. Although distinct, OP and ON share several risk factors such as corticosteroid use and smoking. This study aims to investigate the causal relationships between OP, bone mineral density (BMD), and ON using a bidirectional two-sample Mendelian randomization (MR) approach.

This study utilized genome-wide association study (GWAS) data for OP from the FinnGen database, and BMD data for the lumbar spine and femoral neck from the Genetic Factors for Osteoporosis (GEFOS) consortium. ON data were also obtained from the FinnGen database. All participants were of European descent. Genetic instruments were selected based on genome-wide significance, linkage disequilibrium, and strength (F-statistic). Bidirectional MR analysis was performed using inverse-variance weighted (IVW), MR-Egger regression, and weighted median methods to assess causality. Sensitivity analyses, including Cochran's Q test and MR-PRESSO, were conducted to evaluate heterogeneity and pleiotropy.

MR analysis demonstrated a positive causal effect of OP on ON using the IVW method, with an odds ratio (OR) of 1.223 (95% CI: 1.026-1.459, P = 0.025). The weighted median method also confirmed this result with an OR (95% CI) 1.290 (1.021-1.630), P = 0.033. No significant causal effects were found between BMD (lumbar spine and femoral neck) and ON. Furthermore, ON did not exhibit a causal effect on OP or BMD. Sensitivity analyses confirmed the robustness of the results, showing no evidence of heterogeneity or pleiotropy.

This study provides evidence of a unidirectional causal relationship between OP and ON, suggesting that individuals with a genetic predisposition to OP have an increased risk of developing ON. These findings highlight the importance of early OP detection and management to potentially reduce ON incidence. The lack of a significant causal relationship between BMD and ON indicates that factors other than bone density, such as vascular health, may play a crucial role in ON development. Future research should explore these mechanisms further to inform clinical interventions.

Type 2 diabetes (T2D) etiology is highly complex due to its multiple roots of origin. Polygenic risk scores (PRS) based on genome-wide association studies (GWAS) can partially explain T2D risk. Asian Indian people have up to six times higher risk of developing T2D than European people, and underlying causes of this disparity are unknown.

We have performed targeted sequencing of ten T2D GWAS/candidate regions using endogamous Punjabi Sikh families and replication studies using unrelated Sikh people and families from three other Indian endogamous ethnic groups (EEGs).

We detect rare and ultra-rare variants (RVs) in KCNJ11-ABCC8 and HNF4A (MODY genes) cosegregated with late-onset T2D. We also identify RV enrichment in two new genes, SLC38A11 and ANPEP, associated with T2D. Gene-burden analysis reveals the highest RV burden contributed by HNF4A (p = 0.0003), followed by KCNJ11/ABCC8 (p = 0.0061) and SLC38A11 (p = 0.03). Some RVs detected in Sikh people are also found in Agarwals from Jaipur, both from Northern India, but were monomorphic in other two EEGs from South Indian people. Despite carrying a high burden of T2D and RVs, most families have a significantly lower burden of PRS. Functional studies show that an intronic regulatory variant (RV) in ABCC8 affects the binding of Pax4 and NF-kB transcription factors, influencing downstream gene regulation.

The high burden of T2D in these families may stem from the enrichment of noncoding RVs in a small number of major known genes (including MODY genes) with oligogenic inheritance alongside RVs from genes associated with polygenic susceptibility. These findings highlight the need to conduct deeper evaluations of families from non-European ancestries to identify potential novel therapeutics and implement preventative strategies.

The JAK-STAT pathway is central to cytokine signaling and controls normal physiology and disease. Aberrant activation via mutations that change amino acids in proteins of the pathway can result in diseases. While disease-centric databases like COSMIC catalog mutations in cancer, their prevalence in healthy populations remains underexplored. We systematically studied such mutations in the JAK-STAT genes by comparing COSMIC and the population-focused All of Us database. Our analysis revealed frequent mutations in all JAK and STAT domains, particularly among white females. We further identified three categories: Mutations uniquely found in All of Us that were associated with cancer in the literature but could not be found in COSMIC, underscoring COSMIC's limitations. Mutations unique to COSMIC underline their potential as drivers of cancer due to their absence in the general population. Mutations present in both databases, e.g., JAK2Val617Phe/V617F - widely recognized as a cancer driver in hematopoietic cells, but without disease associations in All of Us, raising the possibility that combinatorial SNPs might be responsible for disease development. These findings illustrate the complementarity of both databases for understanding mutation impacts and underscore the need for multi-mutation analyses to uncover genetic factors underlying complex diseases and advance personalized medicine.

Genome-wide association studies have identified several hundred susceptibility single nucleotide variants for coronary artery disease (CAD). Despite single nucleotide variant-based genome-wide association studies improving our understanding of the genetics of CAD, the contribution of structural variants (SVs) to the risk of CAD remains largely unclear.

We leveraged SVs detected from high-coverage whole genome sequencing data in a diverse group of participants from the National Heart Lung and Blood Institute's Trans-Omics for Precision Medicine program. Single variant tests were performed on 58 706 SVs in a study sample of 11 556 CAD cases and 42 907 controls. Additionally, aggregate tests using sliding windows were performed to examine rare SVs. One genome-wide significant association was identified for a common biallelic intergenic duplication on chromosome 6q21 (P=1.54E-09, odds ratio=1.34). The sliding window-based aggregate tests found 1 region on chromosome 17q25.3, overlapping USP36, to be significantly associated with coronary artery disease (P=1.03E-10). USP36 is highly expressed in arterial and adipose tissues while broadly affecting several cardiometabolic traits.

Our results suggest that SVs, both common and rare, may influence the risk of coronary artery disease.

The NC_000006.12: g.34887814C>G variant in TAF11 was identified as a potential functional variant in a Chinese pedigree including two non-syndromic cleft lip only (NSCLO) cases. Applying Chromatin Immunoprecipitation (ChIP), Electrophoretic mobility shift and super-shift assays, we found that the mutant G allele recruited more STAT1 and STAT3, and increased the expression of TAF11. RNA sequencing, GO and KEGG pathway enrichment, ChIP and dual-luciferase reporter assays revealed that TAF11 downregulated CDH1 and CTNND1 in the cell adhesion pathway by binding to their promoter regions and inhibiting transcriptional activities. Alcian blue staining, time-lapse photography, whole-mount in situ hybridization, phospho-Histone H3 immunofluorescence and TUNEL assays indicated that TAF11 and taf11 overexpression (TAF11OE and taf11OE, respectively) contributed to disturbed migration of cranial neural crest cells and abnormal craniofacial development, as well as increased death and deformity rates in zebrafish. In conclusion, a functionally relevant TAF11 variant, affecting cell migration via modulating CDH1 and CTNND1, was associated with etiology of NSCLO.

Parental substance abuse increases the risk of neurological and psychiatric disorders in offsprings. However, its underlying mechanism remains elusive. Our previous study demonstrated that long-term exposure to methamphetamine (Meth), a psychostimulant drug with high addiction potential, remarkably alters the gut microbiome and metabolites in male mice, which contribute to Meth-induced anxiety-like behaviors. The current study aimed to investigate whether gut microbiota and metabolism serve as potential peripheral targets for transgenerational mental problems by paternal Meth exposure. We found that paternal Meth exposure induced depression-like behaviors both in the first (F1) and the second (F2) generations of male mice. Further, the depletion of gut bacteria through antibiotic treatments normalized the depression-like behaviors to normal levels in both F1 and F2 male mice. Then, alterations in gut bacterial composition were observed in both F1 and F2 male mice. Specifically, Eubacterium_ruminantium_group, Enterorhabdus, Alloprevotella, and Parabacteroides were the commonly affected bacterial taxa in F1 and F2 male mice. In addition, the results of alterations in gut metabolism showed that LPC 14:1-SN1 emerged as the consistently altered metabolite in the colons of F1 and F2 male mice. Taken together, our findings provide the first evidence that paternal Meth exposure enhances depression-like behaviors in F1 and F2 male mice, potentially mediated by the gut microbiome and metabolism.

India harbors the world's largest cattle population, encompassing over 50 distinct Bos indicus breeds. This rich genetic diversity underscores the inadequacy of a single reference genome to fully capture the genomic landscape of Indian cattle. To comprehensively characterize the genomic variation within Bos indicus and, specifically, dairy breeds, we aim to identify non-reference sequences and construct a comprehensive pangenome.

Five representative genomes of prominent dairy breeds, including Gir, Kankrej, Tharparkar, Sahiwal, and Red Sindhi, were sequenced using 10X Genomics 'linked-read' technology. Assemblies generated from these linked-reads ranged from 2.70 Gb to 2.77 Gb, comparable to the Bos indicus Brahman reference genome. A pangenome of Bos indicus cattle was constructed by comparing the newly assembled genomes with the reference using alignment and graph-based methods, revealing 8 Mb and 17.7 Mb of novel sequence respectively. A confident set of 6,844 Non-reference Unique Insertions (NUIs) spanning 7.57 Mb was identified through both methods, representing the pangenome of Indian Bos indicus breeds. Comparative analysis with previously published pangenomes unveiled 2.8 Mb (37%) commonality with the Chinese indicine pangenome and only 1% commonality with the Bos taurus pangenome. Among these, 2,312 NUIs encompassing ~ 2 Mb, were commonly found in 98 samples of the 5 breeds and designated as Bos indicus Common Insertions (BICIs) in the population. Furthermore, 926 BICIs were identified within 682 protein-coding genes, 54 long non-coding RNAs (lncRNA), and 18 pseudogenes. These protein-coding genes were enriched for functions such as chemical synaptic transmission, cell junction organization, cell-cell adhesion, and cell morphogenesis. The protein-coding genes were found in various prominent quantitative trait locus (QTL) regions, suggesting potential roles of BICIs in traits related to milk production, reproduction, exterior, health, meat, and carcass. Notably, 63.21% of the bases within the BICIs call set contained interspersed repeats, predominantly Long Interspersed Nuclear Elements (LINEs). Additionally, 70.28% of BICIs are shared with other domesticated and wild species, highlighting their evolutionary significance.

This is the first report unveiling a robust set of NUIs defining the pangenome of Bos indicus breeds of India. The analyses contribute valuable insights into the genomic landscape of desi cattle breeds.