Stabilizing selection on a polygenic trait reduces the trait's genetic variance by (i) generating correlations (linkage disequilibria) between opposite-effect alleles throughout the genome, and (ii) selecting against rare alleles at loci that affect the trait, eroding heterozygosity at these loci. Here, we show that the linkage disequilibria, which stabilizing selection generates on a rapid timescale, slow down the subsequent allele-frequency dynamics at individual loci, which proceed on a much longer timescale. Exploiting this separation of timescales, we obtain expressions for the expected per-generation change in minor-allele frequency at individual loci, as functions of the effect sizes at these loci, the strength of selection on the trait, its variance and heritability, and the linkage relations among loci. Using whole-genome simulations, we show that our expressions predict allele-frequency dynamics under stabilizing selection more accurately than the formulae that have previously been used for this purpose. Our results have implications for understanding the genetic architecture of complex traits.

Leveraging pleiotropy to improve genetic risk prediction across diseases.

Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded by enabling joint modeling, nonlinear effects, and integrative analysis. However, deep learning approaches remain underutilized in augmenting traditional GWAS frameworks, particularly in the presence of cryptic relatedness among sampled individuals. In this paper, we propose a deep neural network (DNN)-based machine learning method to assist genetic association testing in samples with related individuals. By approximating the phenotype–genotype relationships in classical association tests and combining approximations across multiple tests, the proposed method aims to improve predictive performance in the identification of associated variants. Simulation studies demonstrate that our approach effectively complements conventional statistical methods and generally achieves increased power for detecting genetic associations. We further apply the method to data from the Framingham Heart Study, illustrating how DNN-based machine learning can facilitate the identification of genome-wide SNPs associated with average systolic blood pressure.

Background/Objectives: Alzheimer’s disease (AD) is defined by amyloid-β plaques and tau neurofibrillary tangles and is typically associated with progressive cognitive decline. However, a substantial subset of individuals remains cognitively intact despite intermediate-to-high AD pathology, a phenomenon termed cognitive resilience. This review aims to synthesize genetic variants and biological pathways associated with preserved cognition in the presence of AD neuropathology. Methods: We performed a narrative thematic synthesis of human genetic studies (GWAS, sequencing, biomarker-informed cohorts) and extreme resilience case reports. Variants were prioritized by replication, mechanistic plausibility, and relevance to clinicopathologic dissociation, and were organized by shared biological pathways. When applicable, cognitive resilience was operationalized using residual-based approaches modeling cognitive performance after adjustment for neuropathological burden, age, sex, and education or cognitive reserve proxies reported by each cohort. Results: Recurrent resilience-associated variants include APOE ε2, APOE3-Christchurch, RELN-COLBOS, ATP8B1, RAB10, PLCG2, PICALM, CLU, FN1, and synapse-linked markers such as NPTX2. These variants converge on lipid metabolism, synaptic function and neuroplasticity, tau regulation and proteostasis, immune and inflammatory signaling, vascular/BBB resilience, and RNA regulation. Conclusions: Genetic determinants of cognitive resilience highlight mechanisms that preserve neural integrity independent of pathological load. Targeting resilience pathways may enable precision therapies designed to maintain cognitive function in AD.

Postoperative delirium is the most common postoperative complication in older individuals. Genome-wide association studies (GWAS) can provide insights into how genetic factors influence postoperative risk. We examined the genetic architecture of postoperative delirium after major surgery and its relationship with related cognitive conditions (delirium of any type and Alzheimer's disease, including the APOE ε4 allele). A case-control GWAS was performed in the UK Biobank to identify genetic variants associated with postoperative delirium, adjusted for age, sex, genetic chip, and the first 10 principal components. These results were then used in genetic correlation and polygenic risk score analyses to investigate shared genetic risk between postoperative delirium and a) delirium of all causes, and b) Alzheimer's disease. The GWAS (1,016 cases, 139,148 controls) identified seven Single Nucleotide Polymorphisms (SNPs) that mapped to four genes (APOE, TOMM40, APOC1, and PVRL2); p < 5 x 10-8. Five SNPs remained significant after excluding pre-existing dementia, and two after excluding subsequent dementia. The lead SNP was rs429358, a missense variant of APOE. Genetic correlation and polygenic risk score analyses revealed evidence of shared genetic architecture and risk between postoperative delirium and Alzheimer's disease (rho 0.68, 95% CI [0.46, 0.81]; p < 0.001). After adjustment for age and sex, the APOE ε4 isoform had a dose-response effect on risk (odds ratios for one and two copies: 1.75, 95% CI [1.53, 2.0], and 4.19, 95% CI [3.25, 5.41], respectively; p < 0.001). The main limitations of the study include the reliance upon clinical coding for outcome definition and limited statistical power to detect small or modest genetic effects. We identified genetic variants associated with increased risk of postoperative delirium. We also found evidence of shared genetic liability with Alzheimer's disease via APOE, complementing recent large-scale studies in all-cause delirium. If validated, the findings have potential clinical applications, including preoperative risk stratification and early identification of pre-clinical Alzheimer's disease risk.

Major depressive disorder (MDD), smoking, and drinking frequently co-occur, with evidence suggesting these relationships may differ by sex. However, the direction of causality and the extent of sex-specific associations remain unclear. We investigated sex-specific genetic relationships between MDD and substance use phenotypes using genome-wide association studies (GWAS) from the UK Biobank and publicly available sex-stratified GWAS for MDD and problematic alcohol use (PAU). Causal effects were assessed using bidirectional, sex-stratified Mendelian randomization (MR). We further applied multivariable MR (MVMR) to evaluate the influence of socioeconomic status (SES). Genetic correlation analyses indicated significant shared genetic architecture between MDD and all substance use traits in sex-combined GWAS. In sex-specific analyses, the correlation between cigarettes per day and MDD was significantly stronger in females, and drinks per week were correlated with MDD only in females. MR analyses showed that genetic liability to MDD increased the risk of smoking initiation and PAU in females, and was associated with reduced alcohol drinking frequency in males. In contrast, no tested substance use trait showed evidence of a causal effect on MDD in either sex. MVMR adjusting for SES attenuated the association between MDD and smoking initiation. The effect on PAU in females remained. In males, the negative association between MDD and drinking frequency became non-significant after SES adjustment. These findings reveal sex-specific genetic and causal relationships between smoking, drinking, and MDD, and highlight the role of SES as a potential confounder. Incorporating sex and socioeconomic context is critical when examining these associations.

Background/Objectives: Obesity and prediabetes are overlapping global epidemics. This systematic review synthesises evidence on gene-diet interactions in adults with obesity, prediabetes, or related cardiometabolic risks. It evaluates Mediterranean and DASH dietary patterns, macronutrient quality, and energy restriction across both single-variant and polygenic score approaches. Methods: PubMed was searched for English language papers published in the last 5 years (last run: 31 October 2025). Fewer than 200 studies were retained after excluding those lacking explicit statistical testing for gene-diet interactions or relevant endpoints. Results: Evidence supports restricting saturated fat and preserving carbohydrate quality as general baseline targets, with associations heterogeneous by genotype. Effect modification was observed: healthy dietary patterns were associated with lower risk in high polygenic-risk strata (OR~0.53) but little or no benefit in low-risk groups. TCF7L2 variants were associated with macronutrient thresholds (e.g., protein &gt; 18%, carbohydrate &lt; 48%) affecting visceral adiposity, while APOA2 variants showed genotype-dependent inflammation, including paradoxical increases in markers with higher dietary antioxidant capacity. Interpretation was limited by underpowered interaction tests, multiplicity, and uneven ancestry representation (e.g., unique SLC16A11 and CREBRF signals). Conclusions: While anti-inflammatory dietary substitutions improve biomarkers irrespective of some variants (e.g., TCF7L2), genotype-informed nutrition appears to yield the largest absolute risk reduction in high-risk populations. Clinical implementation should therefore combine baseline diet-quality guidance with targeted strategies for genotype-specific response patterns (e.g., APOA2 antioxidant heterogeneity and TCF7L2 carbohydrate thresholds), rather than rely on uniform recommendations alone. Future progress requires preregistered, genotype-stratified trials and locally trained polygenic scores to address ancestry-specific genetic architecture.

Multivariate genomic analysis elucidates the genetic architecture of shared components of burning mouth syndrome.

Genome-wide analyses to identify genomic regions associated with udder morphology traits in dairy sheep.

Protocol: Estimating cross-ancestry local genetic correlation using Logica.

Combined pathway-based biomarker discovery and ESR2 gene, polymorphism analysis of litter size prediction in sheep using a, multi model bioinformatics toolbox.

Autoencoders decode polyunsaturated fatty acid metabolism with strong genetic architecture in cancer risk.

Causal evidence linking chronic pain genetics to late-onset asthma via the nervous system.

Dissecting the genetic relationship between severe mental disorders and autoimmune diseases.

Baseline characteristics of the TRANSLATE2 cohort: A prospective study on rheumatoid arthritis-associated interstitial lung disease.

Pediatric neurological disorders comprise a highly heterogeneous group of conditions that together represent a substantial global public health burden. Many have a strong genetic basis and are associated with significant morbidity, premature mortality, and long-term disability, with far-reaching consequences for affected children, their families, and healthcare systems worldwide. Clinical heterogeneity is a hallmark of these disorders, as pathogenic variants in the same gene can give rise to diverse phenotypes with variable severity, age at onset, and disease course. In children, ongoing brain development and somatic growth further complicate diagnosis, often leading to nonspecific or atypical presentations that differ from classical adult neurological phenotypes. Advances in genetics and genomics have fundamentally transformed the understanding, diagnosis, and classification of pediatric neurological diseases. The widespread use of high-throughput sequencing, genome-wide association studies, and integrative bioinformatics approaches has enabled the rapid and precise identification of disease-associated genes, even in sporadic and complex conditions, facilitating earlier and more accurate diagnoses and highlighting the role of genetic background and gene–environment interactions in disease pathogenesis. Here we provide an overview of the genetic and genomic landscape of key pediatric neurological disorders with well-characterized molecular etiologies, including neuromuscular disorders, epilepsies, neurodevelopmental disorders, neurodegenerative diseases, and movement disorders. Current knowledge is synthesized with emphasis on clinical presentation, genetic architecture, and genotype–phenotype correlations. Gene-specific management strategies and emerging precision therapies are discussed for selected conditions, underscoring the central role of genetic diagnosis in guiding clinical decision-making and improving outcomes in affected children.

Atopic dermatitis (AD) is a chronic inflammatory skin disease with a complex and highly polygenic genetic architecture, in which immune-mediated mechanisms play a central role. Here, we integrated single-cell cis-expression quantitative trait loci from 14 immune cell types with AD GWAS summary statistics using a two-sample Mendelian Randomization (MR) framework to resolve cell-specific genetically mediated transcriptional effects. We identified 303 significant cell-specific gene–trait associations with limited overlaps across cell types. A multi-step prioritization strategy refined these findings to 35 genes across all 14 cell types. A comparison with whole blood cis-eQTLs revealed a limited concordance, suggesting an attenuation of cell-specific regulatory effects in bulk transcriptomic approaches. Intersecting single-cell and bulk evidence identified 22 high-confidence genes with a relatively independent mechanism of action. Integrative annotation implicated several immune-relevant and druggable genes, including IL2RA, with distinct cell-specific effects. Our findings demonstrate diverse mechanisms of risk genes for AD at the single-cell level that act across immune cell states and pathways, with implications for therapeutic interventions.

Background: Pigs are one of the most important livestock species for providing meat products in the world. Deciphering the genetic architecture of feed efficiency-related traits is beneficial to improve the genetic progress of these traits and save the total cost of pork production. However, the genetic architecture of feed efficiency-related traits remains unclear. Methods: To address this problem, we collected 1301 genotyped Yorkshire pigs with three feed efficiency-related traits, including days at 100 kg (DAYS_100), backfat thickness at 100 kg (BFT_100), and feed conversion ratio from 30 to 100 kg (FCR_30_100), to explore the genetic parameters and genetic basis of these traits. Results: The heritability of DAYS_100, BFT_100, and FCR_30_100 was 0.25 ± 0.04, 0.40 ± 0.05, and 0.23 ± 0.04, respectively. Additionally, BFT_100 and DAYS_100 had a weak negative genetic correlation (−0.01 ± 0.12), while trait FCR_30_100 showed a positive genetic correlation with DAYS_100 (0.51 ± 0.11) and BFT_100 (0.28 ± 0.12). A genome-wide association study identified 7, 5, and 4 SNPs independently associated with BFT_100, DAYS_100, and FCR_30_100, respectively. Further analysis found that the candidate gene ETV4 was significantly associated with DAYS_100 and the candidate gene ENSSSCG00000045751 was associated with FCR_30_100. The functional annotation of candidate genes was enriched in the bile acid metabolic process and protein ubiquitination terms. Conclusions: This study discovered 16 quantitative trait loci associated with feed efficiency-related traits, providing a comprehensive insight for understanding the genetic basis of feed efficiency-related traits in pigs. The candidate genes, such as ETV4 gene in DAYS_100, CAMK1D gene for BFT_100, and ENSSSCG00000045751 gene for FCR_30_100, could be used for further investigation.

Hybrid zones play a central role in evolutionary biology because they serve as natural laboratories for studying how traits and taxa diverge. Changes in gene regulation make important contributions to this process. However, the degree to which admixture shapes gene regulatory variation in hybrid populations remains poorly understood. Here, we combine genome-wide resequencing and DNA methylation data from 295 hybrid baboons-members of a single, intensively studied natural population-to investigate how admixture affects the genetic architecture of this important epigenetic mark. We find that local genetic ancestry frequently predicts DNA methylation levels and recapitulates differences between the parent species. By performing methylation quantitative trait locus mapping, we show that these differences predominantly arise due to evolved differences in allele frequencies. Thus, admixture in the hybrid population increases variance in DNA methylation, including by introducing genetic variants affecting DNA methylation that would otherwise be invariant. Finally, we integrate massively parallel reporter assay data to show that admixture-derived variation in DNA methylation alters enhancer activity and gene expression. Together, these results demonstrate how admixture can meaningfully alter the genetic architecture of gene regulatory traits in natural hybrid zones. They also suggest that the genetic architecture of DNA methylation is conserved across closely related primates, suggesting that genetic effects on gene regulation may remain stable over timescales that range into the millions of years.

Genomic selection (GS) is critical for accelerating genetic gain in modern plant breeding. Deep learning approaches offer powerful non-linear representation capabilities for modelling non-additive effects. However, their application in GS remains restricted, as high-dimensional, low-sample and noisy data hinder the identification of informative markers. The present study proposes DNAwhisper, a deep learning framework designed for multi-trait prediction and adaptive marker prioritisation. The framework integrates a cascaded architecture, GFIformer, employing shared network parameters across partitioned marker blocks to adaptively compress genetic features within a hierarchical pyramid. Pre-training on population genetic structure regularises feature learning to establish a generalisable latent representation. During trait modelling, importance scores for aggregated genomic regions at multi-resolutions are extracted from the distinct pyramid levels under trait-guided deep supervision, enhancing interpretability and supporting marker prioritisation. DNAwhisper was evaluated on maize, wheat, tomato and grape datasets for marker prioritisation and phenotypic prediction, achieving prediction accuracy approximately 3.0% to 10.0% higher than the baseline model. Furthermore, DNAwhisper identifies major QTLs (e.g., , ) and epistatic signals within the gibberellin metabolic pathway across maize flowering traits. This framework provides a new strategy for dissecting the genetic architecture of complex traits.