Genome-wide epistatic expression quantitative trait loci discovery in four human tissues reveals the importance of local chromosomal interactions governing gene expression

Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel

BACKGROUND

Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle genetic interactions contributing to disease risk. This challenge may be further exacerbated by the curse of dimensionality when considering large-scale pairwise genetic combinations with limited samples. Overcoming these limitations could transform biomedicine by providing deeper insights into disease mechanisms, moving beyond black-box models and single-locus analyses, and enabling a more comprehensive understanding of cross-disease patterns.

RESULTS

We developed Ge-SAND (Genomic Embedding Self-Attention Neurodynamic Decoder), an explainable deep learning-driven framework designed to uncover complex genetic interactions at scales exceeding 106 in parallel for accurate disease risk prediction. Ge-SAND leverages genotype and genomic positional information to identify both intra- and interchromosomal interactions associated with disease phenotypes, providing comprehensive insights into pathogenic mechanisms crucial for disease risk prediction. Applied to simulated datasets and UK Biobank cohorts for Crohn's disease, schizophrenia, and Alzheimer's disease, Ge-SAND achieved up to a 20% improvement in AUC-ROC compared to mainstream methods. Beyond its predictive accuracy, through self-attention-based interaction networks, Ge-SAND provided insights into large-scale genotype relationships and revealed genetic mechanisms underlying these complex diseases. For instance, Ge-SAND identified potential genetic interaction pairs, including novel relationships such as ISOC1 and HOMER2, potentially implicating the brain-gut axis in Crohn's and Alzheimer's diseases.

CONCLUSION

Ge-SAND is a novel deep-learning approach designed to address the challenges of capturing large-scale genetic interactions. By integrating disease risk prediction with interpretable insights into genetic mechanisms, Ge-SAND offers a valuable tool for advancing genomic research and precision medicine.

Hippocampal stimulation promotes intracellular Tip60 dynamics with concomitant genome reorganization and synaptic gene activation

Genomic reorganizations mediating the engagement of target genes to transcription factories (TFs), characterized as specialized nuclear subcompartments enriched in hyperphosphorylated RNA polymerase II (RNAPII) and transcriptional regulators, act as an important layer of control in coordinating efficient gene transcription. However, their presence in hippocampal neurons and potential role in activity-dependent coregulation of genes within the brain remains unclear. Here, we investigate whether the well-characterized role for the histone acetyltransferase (HAT) Tip60 in mediating epigenetic control of inducible neuroplasticity genes involves TF associated chromatin reorganization in the hippocampus. We show that Tip60 shuttles into the nucleus following extracellular stimulation of rat hippocampal neurons with concomitant enhancement of Tip60 binding and activation of specific synaptic plasticity genes. Multicolor three-dimensional (3D) DNA fluorescent in situ hybridization (DNA-FISH) reveals that hippocampal stimulation mobilizes these same synaptic plasticity genes and Tip60 to RNAPII-rich TFs. Our data support a model by which external hippocampal stimulation promotes intracellular Tip60 HAT dynamics with concomitant TF associated genome reorganization to initiate Tip60mediated synaptic gene activation.

Exploring hepsin functional genetic variation association with disease specific protein expression in bipolar disorder: Applications of a proteomic informed genomic approach

In a prior discovery study, increased levels of serum Growth Differentiation Factor 15 (GDF15), Hepsin (HPN), and Matrix Metalloproteinase-7 (MMP7) were observed in bipolar depressed patients vs controls. This exploratory post-hoc analysis applied a proteomic-informed genomic research strategy to study the potential functional role of these proteins in bipolar disorder (BP). Utilizing the Genotype-Tissue Expression (GTEx) database to identify cis-acting blood expression quantitative trait loci (cis-eQTLs), five eQTL variants from the HPN gene were analyzed for association with BP cases using genotype data of cases from the discovery study (n = 58) versus healthy controls (n = 777). After adjusting for relevant covariates, we analyzed the relationship between these 5 cis-eQTLs and HPN serum level in the BP cases. All 5 cis-eQTL minor alleles were significantly more frequent in BP cases vs controls [(rs62122114, OR = 1.6, p = 0.02), (rs67003112, OR = 1.6, p = 0.02), (rs4997929, OR = 1.7, p = 0.01), (rs12610663, OR = 1.7, p = 0.01), (rs62122148, OR = 1.7, P = 0.01)]. The minor allele (A) in rs62122114 was significantly associated with increased serum HPN level in BP cases (Beta = 0.12, P = 0.049). However, this same minor allele was associated with reduced gene expression in GTEx controls. These exploratory analyses suggest that genetic variation in/near the gene encoding for hepsin protein may influence risk of bipolar disorder. This genetic variation, at least for the rs62122114-A allele, may have functional impact (i.e. differential expression) as evidenced by serum HPN protein expression. Although limited by small sample size, this study highlights the merits of proteomic informed functional genomic studies as a tool to investigate with greater precision the genetic risk of bipolar disorder and secondary relationships to protein expression recognizing, and encouraging in subsequent studies, high likelihood of epigenetic modification of genetic disease risk.

Nuclear organization and 3D chromatin architecture in cognition and neuropsychiatric disorders

The current view of neuroplasticity depicts the changes in the strength and number of synaptic connections as the main physical substrate for behavioral adaptation to new experiences in a changing environment. Although transcriptional regulation is known to play a role in these synaptic changes, the specific contribution of activity-induced changes to both the structure of the nucleus and the organization of the genome remains insufficiently characterized. Increasing evidence indicates that plasticity-related genes may work in coordination and share architectural and transcriptional machinery within discrete genomic foci. Here we review the molecular and cellular mechanisms through which neuronal nuclei structurally adapt to stimuli and discuss how the perturbation of these mechanisms can trigger behavioral malfunction.

Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.