Gene Prioritization Research Articles

Monoallelic loss-of-function variants in GSK3B lead to autism and developmental delay.

De novo variants adjacent to the canonical splicing sites or in the well-defined splicing-related regions are more likely to impair splicing but remain under-investigated in autism spectrum disorder (ASD). By analyzing large, recent ASD genome sequencing cohorts, we find a significant burden of de novo potential splicing-disrupting variants (PSDVs) in 5048 probands compared to 4090 unaffected siblings. We identified 55 genes with recurrent de novo PSDVs that were highly intolerant to variation. Forty-six of these genes have not been strongly implicated in ASD or other neurodevelopmental disorders previously, including GSK3B. Through international, multicenter collaborations, we assembled genotype and phenotype data for 15 individuals with GSK3B variants and identified common phenotypes including developmental delay, ASD, sleeping disturbance, and aggressive behavior. Using available single-cell transcriptomic data, we show that GSK3B is enriched in dorsal progenitors and intermediate forms of excitatory neurons in the developing brain. We showed that Gsk3b knockdown in mouse excitatory neurons interferes with dendrite arborization and spine maturation which could not be rescued by de novo missense variants identified from affected individuals. In summary, our findings suggest that PSDVs may play an important role in the genetic etiology of ASD and allow for the prioritization of new ASD candidate genes. Importantly, we show that genetic variation resulting in GSK3B loss-of-function can lead to a neurodevelopmental disorder with core features of ASD and developmental delay.

Association of genetically predicted human metabolomic gene expression with incident heart failure

Abstract Background Heart failure (HF) represents a significant challenge both for patients and healthcare systems worldwide. Despite ongoing efforts, therapeutic options are limited. The underlying pathophysiology involves intricate changes in cardiac metabolism that strongly influence the clinical phenotype, thus potentially offering novel options for therapeutic targeting. Systematic prioritisation of metabolic genes regarding their phenotypic impact on HF is outstanding. Purpose We investigate the association of genetically predicted metabolic gene expression with the onset of HF within UK Biobank (UKB). We followed up on identified candidate genes using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) models of cardiomyocyte hypertrophy. Methods Utilising genomic data of 484,372 UKB participants and left ventricle-specific expression quantitative trait loci (eQTL) information from Genotype-Tissue Expression (GTEx) project, we predicted genetically regulated gene expression using established methods (PrediXcan) for all metabolic genes. Subsequently, we employed per-gene Cox proportional hazards (COX-PH) models to assess the association of genetically predicted metabolic gene expression with incident HF. We adjusted models for clinical risk using established risk models (Pooled Cohort Equations to Prevent HF (PCP-HF)). To validate our findings in vitro, we subjected hiPSC-CMs to a sympathomimetic stimulus via treatment with endothelin-1 (ET-1; 10 nM). We evaluate alterations in expression via real-time quantitative polymerase chain reaction (RT-qPCR) and establish methods for assessing hypertrophic remodelling. Results COX-PH models revealed the predicted expression of 104/1515 metabolic genes to be independently associated with the onset of HF. Subsequent refinement with a meta-analysis of commonly dysregulated genes in end-stage HF narrowed our findings to 46 candidate genes, of which several were known for their role in HF pathophysiology. We further traced nine genes for validation in hiPSC-CM models and showed significant dysregulation of several genes following the treatment with ET-1. Flow cytometry measured hiPSC-CM hypertrophy following sympathomimetic stimulation (p=0.036). Extracellular flux analysis revealed canonical changes following treatment with ET-1. Conclusions The study revealed several metabolic genes associated with incident HF, thus confirming previous findings and highlighting novel, putative therapeutic target genes. We confirmed transcriptional changes in an in vitro model. Ongoing work includes further validation via silencing candidate genes in hiPSC-CMs and subjection to the established phenotypic in vitro models.

PONYTA: prioritization of phenotype-related genes from mouse KO events using PU learning on a biological network.

Transcriptome data from gene knock-out (KO) experiments in mice provide crucial insights into the intricate interactions between genotype and phenotype. Differentially expressed gene (DEG) analysis and network propagation (NP) are well-established methods for analyzing transcriptome data. To determine genes related to phenotype changes from a KO experiment, we need to choose a cutoff value for the corresponding criterion based on the specific method. Using a rigorous cutoff value for DEG analysis and NP is likely to select mostly positive genes related to the phenotype, but many will be rejected as false negatives. On the other hand, using a loose cutoff value for either method is prone to include a number of genes that are not phenotype-related, which are false positives. Thus, the research problem at hand is how to deal with the trade-off between false negatives and false positives. We propose a novel framework called PONYTA for gene prioritization via positive-unlabeled (PU) learning on biological networks. Beginning with the selection of true phenotype-related genes using a rigorous cutoff value for DEG analysis and NP, we address the issue of handling false negatives by rescuing them through PU learning. Evaluations on transcriptome data from multiple studies show that our approach has superior gene prioritization ability compared to benchmark models. Therefore, PONYTA effectively prioritizes genes related to phenotypes derived from gene KO events and guides in vitro and in vivo gene KO experiments for increased efficiency. The source code of PONYTA is available at https://github.com/Jun-Hyeong-Kim/PONYTA.

Identifying pathogenic variants in rare pediatric neurological diseases using exome sequencing.

Variant annotations are crucial for efficient identification of pathogenic variants. In this study, we retrospectively analyzed the utility of four annotation tools (allele frequency, ClinVar, SpliceAI, and Phenomatcher) in identifying 271 pathogenic single nucleotide and small insertion/deletion variants (SNVs/small indels). Although variant filtering based on allele frequency is essential for narrowing down on candidate variants, we found that 13 de novo pathogenic variants in autosomal dominant or X-linked dominant genes are registered in gnomADv4.0 or 54KJPN, with an allele frequency of less than 0.001%, suggesting that very rare variants in large cohort data can be pathogenic de novo variants. Notably, 38.4% candidate SNVs/small indels are registered in the ClinVar database as pathogenic or likely pathogenic, which highlights the significance of this database. SpliceAI can detect candidate variants affecting RNA splicing, leading to the identification of four variants located 11 to 50bp away from the exon-intron boundary. Prioritization of candidate genes by proband phenotype using the PhenoMatcher module revealed that approximately 95% of the candidate genes had a maximum PhenoMatch score ≥ 0.6, suggesting the utility of phenotype-based variant prioritization. Our results suggest that a combination of multiple annotation tools and appropriate evaluation can improve the diagnosis of rare diseases.

Human-augmented large language model-driven selection of glutathione peroxidase 4 as a candidate blood transcriptional biomarker for circulating erythroid cells

The identification of optimal candidate genes from large-scale blood transcriptomic data is crucial for developing targeted assays to monitor immune responses. Here, we introduce a novel, optimized large language model (LLM)-based approach for prioritizing candidate biomarkers from blood transcriptional modules. Focusing on module M14.51 from the BloodGen3 repertoire, we implemented a multi-step LLM-driven workflow. Initial high-throughput screening used GPT-4, Claude 3, and Claude 3.5 Sonnet to score and rank the module’s constituent genes across six criteria. Top candidates then underwent high-resolution scoring using Consensus GPT, with concurrent manual fact-checking and, when needed, iterative refinement of the scores based on user feedback. Qualitative assessment of literature-based narratives and analysis of reference transcriptome data further refined the selection process. This novel multi-tiered approach consistently identified Glutathione Peroxidase 4 (GPX4) as the top candidate gene for module M14.51. GPX4’s role in oxidative stress regulation, its potential as a future drug target, and its expression pattern across diverse cell types supported its selection. The incorporation of reference transcriptome data further validated GPX4 as the most suitable candidate for this module. This study presents an advanced LLM-driven workflow with a novel optimized scoring strategy for candidate gene prioritization, incorporating human-in-the-loop augmentation. The approach identified GPX4 as a key gene in the erythroid cell-associated module M14.51, suggesting its potential utility for biomarker discovery and targeted assay development. By combining AI-driven literature analysis with iterative human expert validation, this method leverages the strengths of both artificial and human intelligence, potentially contributing to the development of biologically relevant and clinically informative targeted assays. Further validation studies are needed to confirm the broader applicability of this human-augmented AI approach.

Comprehensive Evaluation of the Genetic Basis of Keratoconus: New Perspectives for Clinical Translation.

Keratoconus (KC) is a corneal disorder with complex etiology, apparently involving both genetic and environmental factors, characterized by progressive thinning and protrusion of the cornea. We aimed to identify novel genetic regions associated with KC susceptibility, elucidate relevant genes for disease development, and explore the translational implications for therapeutic intervention and risk assessment. We conducted a genome-wide association study (GWAS) that integrated previously published data with newly generated genotyping data from an independent European cohort. To evaluate the clinical translation of our results, we performed functional annotation, gene prioritization, polygenic risk score (PRS), and drug repositioning analyses. We identified two novel genetic loci associated with KC, with rs2806689 and rs807037 emerging as lead variants (P = 1.71E-08, odds ratio [OR] = 0.88; P = 1.93E-08, OR = 1.16, respectively). Most importantly, we identified 315 candidate genes influenced by confirmed KC-associated variants. Among these, MINK1 was found to play a pivotal role in KC pathogenesis through the WNT signaling pathway. Moreover, we developed a PRS model that successfully differentiated KC patients from controls (P = 7.61E-16; area under the curve = 0.713). This model has the potential to identify individuals at high risk for developing KC, which could be instrumental in early diagnosis and management. Additionally, our drug repositioning analysis identified acetylcysteine as a potential treatment option for KC, opening up new avenues for therapeutic intervention. Our study provides valuable insights into the genetic and molecular basis of KC, offering new targets for therapy and highlighting the clinical utility of PRS models in predicting disease risk.

Multiomic analysis of genes related to oil traits in legumes provide insights into lipid metabolism and oil richness in soybean

Soybean (Glycine max) and common bean (Phaseolus vulgaris) diverged approximately 19 million years ago. While these species share a whole-genome duplication (WGD), the Glycine lineage experienced a second, independent WGD. Despite the significance of these WGDs, their impact on gene families related to oil-traits remains poorly understood. Here, we report an in-depth investigation of oil-related gene families in soybean, common bean, and twenty-eight other legume species. We adopted a systematic approach that included a total of 605 RNAseq samples for transcriptome and co-expression analyse s, identification of orthologous groups, gene duplication modes and evolutionary rates, and family expansions and contractions. We curated a list of oil candidate genes and found that 91.5% of the families containing these genes expanded in soybean in comparison to common bean. Notably, we observed an expansion of triacylglycerol (TAG) biosynthesis (∼3:1) and an erosion of TAG degradation (∼1.4:1) families in soybean in comparison to common bean. In addition, TAG degradation genes were two-fold more expressed in common bean than in soybean, suggesting that oil degradation is also important for the sharply contrasting seed oil contents in these species. We found 17 transcription factor hub genes that are likely regulators of lipid metabolism. Finally, we inferred expanded and contracted families and correlated these patterns with oil content found in different legume species. In summary, our results do not only shed light on the evolution of oil metabolism genes in soybean, but also present multifactorial evidence supporting the prioritization of promising candidate genes that, if experimentally validated, could accelerate the development of high-oil soybean varieties.

493P Gene prioritization for enhancing molecular diagnosis in rare muscle diseases of singletons

493P Gene prioritization for enhancing molecular diagnosis in rare muscle diseases of singletons

Systematic prioritization of functional variants and effector genes underlying colorectal cancer risk

Genome-wide association studies of colorectal cancer (CRC) have identified 170 autosomal risk loci. However, for most of these, the functional variants and their target genes are unknown. Here, we perform statistical fine-mapping incorporating tissue-specific epigenetic annotations and massively parallel reporter assays to systematically prioritize functional variants for each CRC risk locus. We identify plausible causal variants for the 170 risk loci, with a single variant for 40. We link these variants to 208 target genes by analyzing colon-specific quantitative trait loci and implementing the activity-by-contact model, which integrates epigenomic features and Micro-C data, to predict enhancer–gene connections. By deciphering CRC risk loci, we identify direct links between risk variants and target genes, providing further insight into the molecular basis of CRC susceptibility and highlighting potential pharmaceutical targets for prevention and treatment.

Whole-exome sequencing to identify causative variants in juvenile sudden cardiac death

BackgroundJuvenile sudden cardiac death (SCD) remains unexplained in approximately 40% of cases, leading to a significant emotional burden for the victims’ families and society. Comprehensive investigations are essential to uncover its elusive causes and enable cascade family screening. This study aimed to enhance the identification of likely causative variants in juvenile SCD cases (age ≤ 50 years), particularly when autopsy findings are inconclusive.ResultsAutopsy revealed diagnostic structural abnormalities in 46%, non-diagnostic findings in 23%, and structurally normal hearts in 31% of cases. Whole-exome sequencing (WES), refined through a customized virtual gene panel was used to identify variants. These variants were then evaluated using a multidisciplinary approach and a structured variant prioritization scheme. Our extended approach identified likely causative variants in 69% of cases, outperforming the diagnostic yields of both the cardio panel and standard susceptibility gene analysis (50% and 16%, respectively). The extended cardio panel achieved an 80% diagnostic yield in cases with structurally normal hearts, demonstrating its efficacy in challenging scenarios. Notably, half of the positive cases harboured a single variant, while the remainder had two or more variants.ConclusionThis study highlights the efficacy of a multidisciplinary approach employing WES and a tailored virtual gene panel to elucidate the aetiology of juvenile SCD. The findings support the expansion of genetic testing using tailored gene panels and prioritization schemes as part of routine autopsy evaluations to improve the identification of causative variants and potentially facilitate early diagnosis in first-degree relatives.

PSIV-5 Prioritizing candidate genes for seminal traits and residual feed intake in cattle: Integrating QTLome Analysis and RNAseq Datasets

Abstract The QTLome represents the collection of QTLs (quantitative trait loci) documented for each trait within a specific species, detailing the position, effect, and mode of inheritance of each locus. Despite the abundance of known QTLs for productive traits in cattle, only a few genes have been identified as selection markers. This study aimed to prioritize genes within validated QTLs associated with crucial traits in cattle by integrating this information with RNAseq data. Initially, bovine QTLs (n = 161,730) were sourced from the AnimalQTLdb database (version 44), and both graphical and quantitative analyses were conducted using R software. Only annotations within the UMD 3.1 genome were considered, with overlapping QTLs from the same scientific report grouped together. Following quality control measures, the number of QTLs was reduced to 37,146. Validated QTLs were determined by identifying overlapping annotations from various studies or those within a distance of < 1 Mb for the same phenotypic trait, resulting in a total of 3,856 validated QTLs. For gene prioritization, validated QTLs were selected based on semen traits and residual feed intake (RFI). Transcriptomes corresponding to these traits were sourced from NCBI (PRJNA516089 and PRJEB7696). Sequence filtering was performed using the fastp program, and transcript counts were obtained with SALMON software. Sequences were aligned to the Ensembl UMD 3.1 reference genome (version 94), and differential expression analysis was conducted using the R package DESeq2, considering only the group factor (high/low) for each trait (fertility/RFI). Integration of QTLs and transcriptomes utilized the methodology of the cageminer R package, which prioritizes candidate genes within validated QTLs, identifies those grouped in co-expression modules enriched by reference genes, and correlates with the study phenotype (biserial r > 0.2). Reference genes were obtained from Ignatieva et al. (2021 PMID 34290736) and Chen et al. (2021; PMID 33664767). Among the 18 validated QTLs for semen traits, four candidate genes (TSPAN6, QSER1, ADRB2, AKAP4) were identified. Except for ADRB2, these genes exhibited significant differential expression (P < 0.05) in contrasting groups of Holstein cattle. They are associated with semen traits and perform functions such as regulation of spermatogenesis, development, activation, and cell growth. Regarding RFI, 127 validated QTLs yielded 205 prioritized genes, of which 123 showed significant differential expression (P < 0.05) in divergent groups of the Nelore breed. These genes are involved in the immune response and the polymerization and organization of actin filaments, processes that have been reported as biologically relevant to RFI. The implemented strategy was efficient for gene prioritization through the integration of QTLs and RNAseq.

Rank Aggregation Methods and Tools in Genomic Data Analysis

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. These encompass a broad range of approaches, from distributional and heuristic methods to Bayesian and stochastic optimization algorithms. By examining these techniques, we aim to equip researchers with the background knowledge needed to navigate the intricacies of RA in genomics data integration effectively. We review the practical applications to highlight the relevance and impact of RA methods in advancing genomics research. As the field continues to evolve, we identify open problems and suggest future directions to enhance the effectiveness of rank aggregation in genomics, by addressing the challenges related to data heterogeneity, single-cell omics and spatial transcriptomics data, and the development of clear and consistent evaluation methods. In summary, RA stands as a powerful tool in genomics research, which can offer deeper insights and more comprehensive data integration solutions.

Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes

Progressive supranuclear palsy (PSP), a rare Parkinsonian disorder, is characterized by problems with movement, balance, and cognition. PSP differs from Alzheimer’s disease (AD) and other diseases, displaying abnormal microtubule-associated protein tau by both neuronal and glial cell pathologies. Genetic contributors may mediate these differences; however, the genetics of PSP remain underexplored. Here we conduct the largest genome-wide association study (GWAS) of PSP which includes 2779 cases (2595 neuropathologically-confirmed) and 5584 controls and identify six independent PSP susceptibility loci with genome-wide significant (P < 5 × 10−8) associations, including five known (MAPT, MOBP, STX6, RUNX2, SLCO1A2) and one novel locus (C4A). Integration with cell type-specific epigenomic annotations reveal an oligodendrocytic signature that might distinguish PSP from AD and Parkinson’s disease in subsequent studies. Candidate PSP risk gene prioritization using expression quantitative trait loci (eQTLs) identifies oligodendrocyte-specific effects on gene expression in half of the genome-wide significant loci, and an association with C4A expression in brain tissue, which may be driven by increased C4A copy number. Finally, histological studies demonstrate tau aggregates in oligodendrocytes that colocalize with C4 (complement) deposition. Integrating GWAS with functional studies, epigenomic and eQTL analyses, we identify potential causal roles for variation in MOBP, STX6, RUNX2, SLCO1A2, and C4A in PSP pathogenesis.

Prioritization of candidate genes for major QTLs governing yield traits employing integrated multi-omics approach in rice (Oryza sativa L.).

Rapidly identifying candidate genes underlying major QTLs is crucial for improving rice (Oryza sativa L.). In this study, we developed a workflow to rapidly prioritize candidate genes underpinning 99 major QTLs governing yield component traits. This workflow integrates multiomics databases, including sequence variation, gene expression, gene ontology, co-expression analysis, and protein-protein interaction. We predicted 206 candidate genes for 99 reported QTLs governing ten economically important yield-contributing traits using this approach. Among these, transcription factors belonging to families of MADS-box, WRKY, helix-loop-helix, TCP, MYB, GRAS, auxin response factor, and nuclear transcription factor Y subunit were promising. Validation of key prioritized candidate genes in contrasting rice genotypes for sequence variation and differential expression identified Leucine-Rich Repeat family protein (LOC_Os03g28270) and cytochrome P450 (LOC_Os02g57290) as candidate genes for the major QTLs GL1 and pl2.1, which govern grain length and panicle length, respectively. In conclusion, this study demonstrates that our workflow can significantly narrow down a large number of annotated genes in a QTL to a very small number of the most probable candidates, achieving approximately a 21-fold reduction. These candidate genes have potential implications for enhancing rice yield.

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease

Phenotype-driven gene prioritization is fundamental to diagnosing rare genetic disorders. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models (LLMs) promise a streamlined text-to-gene solution. In this study, we evaluated five LLMs, including two generative pre-trained transformers (GPT) series and three Llama2 series, assessing their performance across task completeness, gene prediction accuracy, and adherence to required output structures. We conducted experiments, exploring various combinations of models, prompts, phenotypic input types, and task difficulty levels. Our findings revealed that the best-performed LLM, GPT-4, achieved an average accuracy of 17.0% in identifying diagnosed genes within the top 50 predictions, which still falls behind traditional tools. However, accuracy increased with the model size. Consistent results were observed over time, as shown in the dataset curated after 2023. Advanced techniques such as retrieval-augmented generation (RAG) and few-shot learning did not improve the accuracy. Sophisticated prompts were more likely to enhance task completeness, especially in smaller models. Conversely, complicated prompts tended to decrease output structure compliance rate. LLMs also achieved better-than-random prediction accuracy with free-text input, though performance was slightly lower than with standardized concept input. Bias analysis showed that highly cited genes, such as BRCA1, TP53, and PTEN, are more likely to be predicted. Our study provides valuable insights into integrating LLMs with genomic analysis, contributing to the ongoing discussion on their utilization in clinical workflows.

Exome Sequencing of a Blastomycosis Case–Control Cohort From Manitoba and Northwestern Ontario, Canada

ABSTRACTBackgroundBlastomycosis is a pulmonary disease caused by Blastomyces spp., a group of pathogenic dimorphic fungi endemic to a number of geographic regions, specifically Manitoba and northwestern Ontario, Canada. Immunosuppression is a major risk factor affecting disease susceptibility, yet host immunity is not well understood. Genetic immunodeficiencies can also influence disease, with variants in IL6, GATA2 and VDBP shown to influence susceptibility. Additional genetic factors in disease susceptibility and severity remain undetected. Our study seeks to identify potential genetic risk factors in a blastomycosis case–control cohort from Manitoba and northwestern Ontario, Canada.MethodsExomes from 18 blastomycosis cases and 9 controls were sequenced, variants were identified and filtered for accuracy and quality. We performed candidate gene prioritisation and variant aggregation to identify genetic associations and explored the full exome dataset.ResultsNinety‐nine genetic variants in 42 candidate genes were identified in the exome dataset. No variants associated with susceptibility were identified in a single‐variant analysis although two non‐synonymous variants in TYK2 were enriched among cases suggesting a possible role in susceptibility. Gene‐based association analysis found variants in TLR1 enriched in controls (p = 0.024) suggesting a possible protective effect. Gene cluster analysis identified genetic variants in genes of chromatin remodelling, proteasome and intraflagellar transport significantly enriched in cases (false discovery rates &lt; 14%).ConclusionsThe findings in this study show novel associations with blastomycosis susceptibility. A better understanding of host immunity and genetic predisposition to Blastomyces infection can help to inform clinical practice for improved outcomes.

Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization

Identifying causal genes underlying genome-wide association studies (GWAS) is a fundamental problem in human genetics. Although colocalisation with gene expression quantitative trait loci (eQTLs) is often used to prioritise GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analysed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalogue datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalisation methods (coloc.susie, coloc.abf and CLPP) and five Mendelian randomisation (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalisation methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalisation method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalising QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritising novel targets requires triangulation of evidence from multiple sources.

Genetic Foundations of Nellore Traits: A Gene Prioritization and Functional Analyses of Genome-Wide Association Study Results.

The main goal of this study was to pinpoint functional candidate genes associated with multiple economically important traits in Nellore cattle. After quality control, 1830 genomic regions sourced from 52 scientific peer-reviewed publications were used in this study. From these, a total of 8569 positional candidate genes were annotated for reproduction, 11,195 for carcass, 5239 for growth, and 3483 for morphological traits, and used in an over-representation analysis. The significant genes (adjusted p-values < 0.05) identified in the over-representation analysis underwent prioritization analyses, and enrichment analysis of the prioritized over-represented candidate genes was performed. The prioritized candidate genes were GFRA4, RFWD3, SERTAD2, KIZ, REM2, and ANKRD34B for reproduction; RFWD3, TMEM120A, MIEF2, FOXRED2, DUSP29, CARHSP1, OBI1, JOSD1, NOP58, and LOXL1-AS1 for the carcass; ANKRD34B and JOSD1 for growth traits; and no genes were prioritized for morphological traits. The functional analysis pinpointed the following genes: KIZ (plays a crucial role in spindle organization, which is essential in forming a robust mitotic centrosome), DUSP29 (involved in muscle cell differentiation), and JOSD1 (involved in protein deubiquitination, thereby improving growth). The enrichment of the functional candidate genes identified in this study highlights that these genes play an important role in the expression of reproduction, carcass, and growth traits in Nellore cattle.

Novel Loss-of-Function SYCP2 Variants in Infertile Males Upgrade the Gene-Disease Clinical Validity Classification for SYCP2 and Male Infertility to Strong.

Male infertility affects approximately 7% of the male population, and about 15% of these cases are predicted to have a genetic etiology. One gene implicated in autosomal dominant male infertility, SYCP2, encodes a protein critical for the synapsis of homologous chromosomes during meiosis I, resulting in impaired spermatogenesis. However, the clinical validity of the gene-disease pair was previously categorized as on the border of limited and moderate due to few reported cases. This study investigates the genetic cause of infertility for three unrelated Chinese patients with oligoasthenozoospermia. Whole exome sequencing (WES) and subsequent Sanger sequencing revealed novel heterozygous loss-of-function (LOF) variants in SYCP2 (c.89dup, c.946_947del, and c.4378_4379del). These cases, combined with the previously reported cases, provide strong genetic evidence supporting an autosomal dominant inheritance pattern. The experimental evidence also demonstrates a critical role for SYCP2 in spermatogenesis. Collectively, this updated assessment of the genetic and experimental evidence upgrades the gene-disease association strength of SYCP2 and autosomal dominant male infertility from on the border of limited and moderate to strong. The reclassification improves SYCP2 variant interpretation and qualifies it for the inclusion on diagnostic male infertility gene panels and prioritization in whole exome or genome studies for related phenotypes. These findings therefore improve the clinical interpretation of SYCP2 LOF variants.

Positive-Unlabelled learning for identifying new candidate Dietary Restriction-related genes among ageing-related genes

Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, the existing ML approach naively labels genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence. This hinders the reliability of the negative examples (non-DR-related genes) and the method’s ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms (p<0.05) the existing state-of-the-art approach in three predictive accuracy metrics with up to ∼40% lower computational cost in the best case, and we identify 4 new promising DR-related genes (PRKAB1, PRKAB2, IRS2, PRKAG1), all with evidence from the existing literature supporting their potential DR-related role.