Search Results (110)

Growth traits, as core economic indicators in pig breeding, are closely associated with production costs, rearing duration, and final carcass quality and have thus consistently been a major focus of genetic improvement. This study aimed to identify candidate genes affecting Age to 120 kg live weight (AGE120), Backfat thickness at 120 kg (BF120), and Loin muscle depth at 120 kg (LMD120) in pigs. Ear tissue samples were collected from 3364 healthy adult pigs (including 558 boars and 2805 sows) from three breeds: Large White, Landrace, and Duroc. Genotyping was performed using an 80 K functional site array, and quality-controlled SNP (Single-Nucleotide Polymorphism) loci were subjected to genotype imputation, resulting in 15,447,611 loci obtained. Genome-wide association studies (GWASs) for Age to 120 kg live weight, Back fat thickness at 120 kg, and Loin muscle depth at 120 kg were conducted using a mixed linear model in Genome-wide Complex Trait Analysis (GCTA). Genes located within 500 kb upstream and downstream of significant GWAS loci were extracted using the biomaRt package in R. Furthermore, colocalization analysis was performed using expression Quantitative Trait Locus (eQTL) data of 34 tissues from the PigGTEx database to identify genes that share the same causal variant as the GWAS signals. Through integrated GWAS and eQTL colocalization analysis, in addition to five previously reported genes associated with pig growth traits (TAF11, ZC3HAV1L, ANKS1A, USP20, and TBC1D1), a set of novel, high-confidence candidate genes was identified: ZNF215, UBE2Z, HOXB7, SARDH, ADAMTSL2, ATP6V0A4, RPL10A, PGM2, and RELL1. These findings enrich our understanding of the genetic architecture underlying growth traits in pigs at heavy body weights and provide an important foundation for subsequent functional validation and molecular breeding applications. Full article

Genome and exome sequencing have become central to diagnosing rare hereditary diseases, but each test returns thousands of variants that a clinical scientist must review by hand to find the one responsible for the patient’s condition. This manual interpretation is the main bottleneck in clinical genomics. To reduce it, we developed DiagAI, a machine-learning system that ranks the variants found in a patient and returns a short list of the most likely causal candidates. DiagAI combines three sources of evidence: a pathogenicity score from the Universal Pathogenicity Predictor (UP2), a model we trained to estimate how damaging a variant is on the five-tier scale of the American College of Medical Genetics and Genomics (ACMG); a phenotype-matching score from PhenoGenius, which weighs how well a gene’s known clinical features match the patient’s symptoms (encoded as Human Phenotype Ontology, or HPO, terms); and expert rules covering inheritance pattern and sequencing quality. We evaluated DiagAI on 966 exomes from adults investigated for kidney disease of unknown cause, of which 196 had a confirmed genetic diagnosis. We first tested UP2 on its own by ranking 62 confirmed disease-causing missense variants that were absent from its training data: UP2 placed the causal variant within the top 100 candidates in 87% of cases, compared with 61% for the widely used tool REVEL. Across the 196 diagnosed exomes, the full DiagAI shortlist contained the causal variant in 94.9% of cases when the patient’s symptoms were provided and in 90.8% when they were not, with a typical shortlist of about 10 variants. When symptoms were provided, the single top-ranked variant was the correct diagnosis in 74% of cases, versus 42% without symptoms, exceeding the performance of the established tools Exomiser and AI-MARRVEL on the same cohort. DiagAI produces compact, accurate shortlists that can reduce the manual interpretation workload as diagnostic sequencing volumes continue to grow. Full article

Career growth path planning is still dominated by statistical association models that summarize historical transitions but do not explicitly represent the causal mechanisms linking capability development, project exposure, policy support, performance improvement, and promotion outcomes. This study develops a reproducible simulation benchmark for evaluating whether prompt-structured priors, when coupled with dual validation, can help assemble intervention-ready career causal graphs. A structural causal model (SCM) first generated 20,000 synthetic career trajectories with known ground-truth dependencies among ten variables, including education, experience, training hours, certification, project exposure, performance, and promotion. Four prompt families-zero-shot, few-shot, Chain-of-Thought (CoT), and CoT plus schema constraints-were instantiated through a controlled prompt-response emulator so that prompt structure could be studied independently of vendor-specific model drift. The emulator gradients should therefore be read as literature-informed design assumptions about structured prompting rather than as empirical measurements from any named production LLM. Candidate edges were subsequently refined by data validation and expert-proxy domain rules. In the main 30-run benchmark, the best prompt-only setting (CoT plus schema) achieved an F1-score of 0.842, while the proposed hybrid method achieved an F1-score of 0.959 and an intervention-effect mean absolute error of 0.0046. Run-wise confidence intervals and approximate significance checks further indicated that the hybrid workflow materially outperformed the prompt-only variants under the benchmark protocol. A public employee-promotion dataset (N= 54,808) was further used as an external plausibility anchor, where KPI attainment, awards, previous ratings, training score, and length of service were all positively associated with promotion. The results indicate that prompt-structured priors can be useful as a transparent proposal-and-validation mechanism, but not as a substitute for direct validation on real LLMs, matched comparisons with standard causal-discovery baselines, or real HR deployment settings. Accordingly, the central aim is a domain-specific methodological benchmark for testing prompt-structured proposal mechanisms in career-growth causal modeling, rather than a claim of standalone LLM causal discovery or a universal benchmark for every causal-discovery setting. Full article

Background/Objectives: Adenomyosis is a common estrogen-dependent gynecological condition with a largely undefined genetic architecture. Ciliary dysfunction has been implicated in its pathogenesis, positioning genes governing ciliary structure and motility as biologically plausible candidates for investigation. The DNAH5 gene encodes a critical component of the outer dynein arms within the ciliary axoneme, and pathogenic variants are among the most common causes of primary ciliary dyskinesia. This study aimed to systematically determine the frequency of a novel synonymous DNAH5 variant, NM_001369.3:c.9258C>T, p.(Leu3086=), in a large, histopathologically confirmed sporadic adenomyosis cohort from Turkiye, and to evaluate its occurrence relative to population-level reference data. Methods: A total of 121 women with histopathologically confirmed adenomyosis following hysterectomy were enrolled. Sanger sequencing was performed under stringent quality control conditions, including primer specificity verification by NCBI BLAST and UCSC In Silico PCR. Variant frequency was compared against gnomAD v4.0 and an in-house Turkish exome database (NGS Cloud; ~30,000 sequences) using Fisher’s exact test. In silico splice site analysis was performed using SpliceAI, and variant classification followed ACMG/AMP guidelines. Results: The variant was detected in 63 of 121 patients (52.1%; 95% CI: 43.1–61.0%), exclusively in the heterozygous state; no homozygous carriers were identified. The variant was absent from both gnomAD v4.0 across all populations and the NGS Cloud Turkish exome database (MAF: 0.0000), yielding a frequency difference (p < 2.2 × 10⁻¹⁶). SpliceAI analysis predicted no significant splice site impact (all delta scores < 0.1). The variant was classified as a variant of uncertain significance (VUS; BP7, PM2_supporting). Conclusions: This study identifies a difference in the frequency of a novel synonymous DNAH5 variant between a histopathologically confirmed adenomyosis cohort from Turkiye and population-level reference datasets, in which the variant was absent. Given the unphenotyped nature of the reference dataset, these findings are hypothesis-generating and do not establish a causal genetic association. Replication in independent cohorts and functional studies are warranted to elucidate the biological significance of this variant in adenomyosis susceptibility. Full article

Objectives: This study investigated a variant, RPGR NM_001034853.2 c.1307G>A, p.[Gly436Asp, p?], in a large Irish pedigree with severe X-Linked Retinitis Pigmentosa (XLRP). The effect of the variant on RNA splicing was interrogated using in vitro functional analysis to provide evidence of disease causality. Methods: Three related individuals presenting with XLRP underwent target-capture sequencing, together with confirmatory Sanger sequencing and cascade analyses, to identify candidate variants. In silico investigations were undertaken using SpliceAI (version 1.3.1) and Alamut Visual software (version 2.13), among others. Functional analyses using in vitro midigene splice assays employing gateway expression vectors were undertaken. Variant and wildtype RNA were amplified by RT-PCR to investigate effects on splicing. RPGR c.1307G>A was subsequently reclassified using ACMG/AMP and ClinGen SVI recommendations. Results: Midigene investigation confirmed a cryptic acceptor site is being utilised together with the cryptic branchpoint motif to excise intron 10 and 90 bases of exon 11, leading to a frameshift and the creation of a premature stop codon. No functional RPGR transcript is predicted to remain. Given evidence of aberrant splicing, the variant classification was upgraded to pathogenic. Conclusions: RPGR c.1307G>A leads to creation of a cryptic branchpoint within an exon, resulting in protein truncation with deleterious effect(s). To the best of our knowledge, this is the first variant that leads to creation of a cryptic branchpoint within an exon associated with any IRD. The results illustrate the importance of investigating the functional consequences of both coding and non-coding variants with a predicted impact on splicing to understand their pathogenicity. Full article

Background/Objectives: Advances in whole-genome sequencing (WGS) technology have led to the widespread adoption of WGS for investigating genetic diseases and traits in domestic animals. This has created a need for improved methods for prioritizing candidate causal variants. One way variants are prioritized is using variant annotators that predict variant effects based on their proximity to genomic features and effect on amino acid sequence. However, validation of variant annotators for domestic animal genomes is lacking. Methods: In this study, we calculated the agreement of three popular variant annotators, Ensembl Variant Effect Predictor (Ensembl-VEP), SnpEff, and ANNOVAR, across >58 million variants identified in 1065 horse genomes. Results: Comparisons showed that agreement across all three variant annotators was >90% when terminology was standardized. Terminology standardization was the most important factor affecting agreement, as agreement dropped to 0–67% when terminology was not standardized across variant annotators. Genomic context was also a major factor, as exonic, and specifically loss-of-function, variants showed lower agreement rates than intergenic variants. In addition to annotation agreement, differences in computational resource requirements were identified. ANNOVAR required ~28× more memory and ~1.5× more time than the next best tool. Conclusions: These results demonstrate that tool selection for annotating variants should not be based on a single metric; rather, a study’s needs and available computational resources should be considered when selecting the appropriate variant annotators(s) along with the standardization of terminology across annotators. These findings are a resource for guiding decisions on the use of variant annotators in domestic animals and suggest areas for improvement in the standardization of variant prioritization. Full article

Identifying causal genetic variants and candidate genes underlying complex traits remains a central challenge in animal breeding and genetics. Genome-wide association studies (GWAS) are widely used for this purpose. However, their reliance on marginal variant effects and sensitivity to linkage disequilibrium (LD) can lead to redundant and less accurate identification of variants or genes of biological relevance. Here, we propose SNP prioritization (GWAS-based and fine-mapping-based) strategies within a unified framework, designed to improve the selection of more informative variants and candidate genes by explicitly modeling LD structure and genetic architectures of three pig teat-related traits (total teat number, teat symmetry, and teat adequacy). While GWAS prioritization favored variants with strong marginal effects, fine-mapping substantially improved joint explanatory performance and prediction accuracy. For total teat number, the best-performing fine-mapping-derived SNP subset achieved a mean PCC of 0.6599 across 10-fold cross-validation, compared with 0.3755 for GWAS-based prioritization. Similarly, for teat adequacy, the highest mean AUC increased from 0.7012 (GWAS) to 0.8547 (fine-mapping). Moreover, fine-mapping-derived SNP sets identified more coherent and trait-specific biological pathways and functionally relevant candidate genes. Taken together, our findings demonstrate that fine-mapping provides a more accurate and biologically meaningful framework for SNP and candidate gene prioritization, supporting its integration into genetic analysis and breeding applications. Full article

Pangenomics has become an important framework for representing genetic diversity beyond a single linear reference genome. In agricultural species, it improves access to structural variants (SVs), copy number variations (CNVs), presence/absence variations (PAVs), and non-reference regulatory or coding sequences that may contribute to domestication, adaptation, and breeding traits. This review summarizes recent progress in long-read sequencing, telomere-to-telomere (T2T) assembly, and graph-based genome analysis, with emphasis on both livestock and crop systems. We first define the conceptual boundary between pangenome representations and reference-based variant catalogs. We then compare three major technical routes: variant integration, reference-guided iterative graph construction, and reference-free graph construction. Their performance is evaluated in terms of accuracy, scalability, coordinate consistency, reference bias, computational demand, annotation transfer, and suitability for downstream breeding questions. We further discuss how pangenome resources support hidden variant discovery, QTL and GWAS interpretation, environmental adaptation analysis, and multi-omics-based candidate prioritization. Importantly, we highlight unresolved limitations, including graph complexity, pipeline-dependent SV calls, incomplete functional annotation, weak cross-study comparability, and the difficulty of distinguishing causal variants from linked or neutral variation. This review therefore treats pangenome studies as connected but non-equivalent evidence: resource-building studies establish representational breadth, method papers define technical feasibility, and trait-focused studies provide varying levels of biological support. Apparent inconsistencies among studies are interpreted as signals of differences in sampling, genome complexity, validation depth, and graph construction strategy rather than as simple disagreements. Full article

Background/Objectives: Atrial fibrillation (AF) is a complex polygenic disorder; its genetic architecture remains challenging to fully elucidate. Methods: In this study, we leveraged the extensive genetic overlap between AF and a spectrum of cardiometabolic and behavioral factors—collectively defined by Life’s Essential 8 (LE8)—to advance our understanding of its etiology. Results: We first estimated significant genetic correlations between AF and all LE8 components (r_g: −0.11 to 0.19) using LD score regression. We then applied conditional false discovery rate analysis and detected 970 pleiotropic loci associated with AF and at least one LE8 trait. Subsequent colocalization analysis identified 179 loci harboring shared causal variants between AF and one or more LE8 components, which were further refined into 137 distinct colocalized regions. Through region-based annotation and functional predictors, we finally prioritized 164 candidate genes from these colocalized loci, including 40 novel genes. These candidate genes were enriched in pathways related to heart development and regulation of cardiac contraction, and were also enriched among molecular targets of otological agents. Among all LE8 components, blood pressure demonstrated the most extensive shared genetic architecture with AF, supported by the strongest genetic correlation, highest pleiotropic enrichment, and the greatest number of colocalized loci with AF. Polygenic risk scores constructed from these colocalized loci demonstrated significant associations not only for AF but also for arrhythmia and heart failure. Conclusions: Our findings establish a genetic pleiotropy-informed framework that enhances the discovery of novel risk loci of AF and advances our understanding of the shared genetic architecture and potential biological mechanisms between AF and LE8 components. Full article

Background: Although emerging evidence indicates that major depressive disorder (MDD) raises the risk of developing ovarian cancer (OC) and worsens survival, the biological mechanisms underlying this relationship remain unclear. This study explores the MDD-OC association using single-cell transcriptomics and genetic approaches. Methods: Using single-cell RNA-seq profiles of peripheral blood from MDD and OC patients, we compared shifts in immune cell subsets and selected the consistently expanded CD8⁺ effector memory (CD8_EM) T cells population for follow-up, validated using flow cytometry. We integrated expression quantitative trait loci (eQTL) data from CD8_EM T cell-specific genes with OC genome-wide association study (GWAS) summary statistics through two-sample Mendelian randomization (MR). In vitro experiments were additionally conducted to assess CLSTN3’s role in OC cell proliferation. Results: Among the 554 differentially expressed genes (DEGs) identified in CD8_EM T cells, MR showed a nominal association between CLSTN3 and ovarian cancer risk (OR 1.21, 95% CI 1.03–1.43), though this did not withstand correction for multiple comparisons. Colocalization analysis confirmed that CLSTN3 expression, regulated by the genetic variant rs3759416, shares a causal variant with the OC GWAS signal (PPH4 = 99.99%). Functionally, siRNA-mediated CLSTN3 silencing in HOC7 cells significantly reduced cell viability (CCK-8 assay). Conclusions: By focusing on CD8_EM T cells shared by MDD and ovarian cancer, we identified CLSTN3 as a candidate molecule through nominated by the convergence of genetic, transcriptomic, and functional evidence. These findings provide preliminary insights into the connection between depression and OC, though further validation is warranted. Full article

Uplift modeling optimizes intervention-based campaigns by identifying customers whose behavior changes exclusively due to specific treatments, moving beyond standard baseline risk predictions. However, in real-world deployments, algorithms that maximize traditional causal ranking metrics (e.g., the Qini coefficient) often fail to be optimal in practice. The inherent variance of Conditional Average Treatment Effect (CATE) estimators exposes critical trade-offs between expected economic value, algorithmic stability, and policy interpretability. To address this gap, this study proposes a stability-aware, value-driven computational framework for selecting an uplift policy. The pipeline evaluates multiple causal and non-causal algorithmic families, including traditional baselines, multimodel approaches, and transformed-outcome variants, within a repeated-run validation protocol. Candidate policies are assessed primarily through incremental revenue and target-set stability, whereas a post hoc surrogate tree distillation step is used to translate the selected policy into interpretable rule-based customer segments. An empirical evaluation of the publicly available Telco Customer Churn dataset under two distinct regimes (a causally controlled semisynthetic scenario and an observational proxy scenario) reveals that the highest-yielding causal policy frequently suffers from severe targeting instability, inducing a clear risk–return trade-off. Furthermore, uplift models outperform traditional baselines in the causally controlled regime, whereas traditional baselines remain economically superior in the confounded proxy settings. Overall, this study establishes that jointly assessing economic utility, algorithmic stability, and transparent segmentation is essential for deploying robust and defensible causal machine learning in production environments. Full article

Major depressive disorder (MDD) is associated with altered membrane lipids, but the causal species remain uncertain. Using two-sample Mendelian randomization (MR) on lipidomic GWAS data and the latest MDD meta-analysis (~400,000 cases; 1.5 million controls), we identified 49 lipid species linked to MDD risk, notably enriched for phosphatidylcholines. Protective lipids were enriched for long-chain polyunsaturated fatty acids (20:3–20:5), whereas shorter-chain or less unsaturated species, particularly 18:2-containing lipids, increased risk. These associations were also observed in a subset of clinically assessed MDD cases. Colocalization supported shared causal variants between many lipid traits and MDD, prominently at the FADS1/2 locus and additional loci, suggesting multiple entry points into lipid metabolism that differ partly from bipolar disorder. MR-implicated lipid shifts overlapped with cortisol-induced changes in a human cell stress model and were often reversed by co-treatment with St. John’s wort extract (Ze 117). Cholesteryl ester 20:3 emerged as a robust candidate marker, showing protective MR effects in two cohorts, colocalizing genetic support, normalization by Ze 117, and an inverse correlation with depressive symptom severity in a non-clinical sample. Together, these results define a depression-associated lipidomic signature centered on polyunsaturated fatty acid metabolism with biomarker and therapeutic potential. Full article

Background/Objectives: Glomerular diseases (GD) possess strong polygenic susceptibility, yet exact causal genes remain unclear because most variants identified by genome-wide association studies (GWAS) reside in non-coding regions. While transcriptome-wide association studies (TWAS) effectively decode complex traits, cross-tissue profiling for GD remains largely unexplored. Therefore, this study employs an integrative cross-tissue TWAS and Mendelian randomization framework to systematically identify and validate novel GD susceptibility genes. Methods: We conducted a systematic cross-tissue TWAS integrating Genotype-Tissue Expression (GTEx) v8 eQTL data across 49 tissues. Candidate genes were nominated using five complementary frameworks (sparse canonical correlation analysis (sCCA), functional summary-based imputation (FUSION), fine-mapping of causal gene sets (FOCUS), summary-data-based Mendelian randomization (SMR), and multi-marker analysis of genomic annotation (MAGMA)). Findings were refined via Mendelian randomization (MR), pathway enrichment, protein interaction networks, and druggability profiling. Results: We identified 21 candidate susceptibility genes for GD, with 10 genes (AGER, C6orf48, CSNK2B, CYP21A2, HLA-DRB1, HSD17B8, LST1, MICB, PRRT1, TCF19) strongly supported by MR analysis. Notably, five of these MR-prioritized genes (C6orf48, CSNK2B, HSD17B8, LST1, and PRRT1) were previously unreported. Functionally, these prioritized genes are primarily involved in immune modulation, inflammation, and steroid metabolism. Furthermore, five genes (AGER, CSNK2B, CYP21A2, HLA-DRB1 and MICB) were identified as potentially druggable targets. Conclusions: This first systematic cross-tissue TWAS of GD prioritizes a set of genetically supported susceptibility genes. By uncovering novel drivers and druggable proteins, this study advances the mechanistic understanding of GD and provides a foundation for future therapeutic development and precision nephrology. Full article

Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), are increasingly recognized as conditions with a complex, multisystemic origin. ASD frequently co-occurs with other neurological conditions, such as epilepsy. We report a female patient, born to unrelated healthy parents, presenting with a complex clinical phenotype characterized by ASD level 1 with fluent speech, borderline intellectual functioning (BIF), coordination disorder, and epilepsy. Trio-based whole-exome sequencing (WES) revealed a de novo variant in the USP24 gene (c.3155G>T; p.Ser1052Ile), classified as likely pathogenic according to ACMG criteria (PS2, PM2, PP2, BP4). USP24 has previously been associated with Parkinson’s disease and has recently emerged as a candidate risk gene for ASD. In addition, WES detected two variants of uncertain significance (VUS), both inherited from the clinically unaffected father: c.388G>C (p.Gly130Arg) in NRXN2 and c.6395C>A (p.Ser2132Tyr) in LRP2. Although neither gene shows a fully penetrant causal relationship with the observed phenotype, both have been implicated in neurodevelopmental disorders. Array-CGH analysis did not reveal pathogenic copy number variants; however, the presence of additional genetic contributors not detectable by WES cannot be excluded. Overall, the de novo USP24 variant likely represents the primary genetic driver of the phenotype, while the potential contribution of the inherited NRXN2 and LRP2 variants remains plausible. This case underscores the complexity of the genetic architecture underlying NDDs and supports a model involving cumulative effects of multiple variants rather than a strictly multigenic interaction. Full article

Soil salinity remains a major constraint to rice productivity, particularly during early developmental stages when plants are highly sensitive to osmotic and ionic stress. In this study, we evaluated 201 genetically diverse rice genotypes from the 3K Rice Diversity Panel to investigate stage-specific mechanisms of salinity tolerance and develop machine learning-based predictive models for rapid phenotypic screening. Morphological and physiological traits were measured under control and saline conditions at germination and early seedling stages to derive Stress Tolerance Indices (STIs). The average membership function value (AMFV), calculated from multi-trait STI profiles, effectively captured variation in salinity responses and enabled classification of genotypes into five tolerance categories. Genome-wide association analysis using high-density SNP markers identified 36 significant marker–trait associations, including potentially novel SNPs on chromosomes 1 and 12. Several loci co-localized with candidate genes (LTR1, LGF1, OsCPS4, OsNCX7, and OsNHX4), while functional SNPs within genes (OsDRP2C, RLCK168, and OsMed37_2) and non-synonymous variants (qSVII11.1 and qSNaK3.1) further supported their candidacy in salinity tolerance. Mining favourable SNPs of causal genes identified superior multilocus combinations consistent with STI-based phenotypic patterns, with genotype 91-382 emerging as the strongest performer, exhibiting enhanced Na⁺ exclusion, K⁺ retention, and biomass resilience across developmental stages. To address multicollinearity among STI traits, we applied cross-validated LASSO (germination) and Elastic Net (early seedling) models, achieving high predictive accuracy and revealing a developmental shift from biomass-driven tolerance at germination to ion-regulatory processes at the seedling stage. Independent validation showed strong agreement between predicted and observed AMFVs. By integrating physiological indices, GWAS-derived SNP signals, and regularized machine learning approaches, this study provides a robust framework for identifying elite donors and accelerating breeding for salt-tolerant rice. Full article