Fores-Martos, J. | bioRxiv Science

bioRxiv2026

A Genetic Atlas of Direct and Inverse Neuropsychiatric-Cancer Comorbidity

Direct and inverse comorbidities between neuropsychiatric disorders and cancer are increasingly recognised as important features of the nervous system-cancer relationship, yet the inherited genetic architecture underlying these patterns remains poorly understood. Here, we analysed pairwise genetic correlations across 35 diseases represented by 115 GWAS datasets, including 9 psychiatric disorders, 10 neurological diseases and 16 cancers, using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL), complemented by meta-analysis, subtype-resolved analyses, local covariance mapping and multi-omic benchmarking. Genetic correlations were predominantly positive and strongest within disease categories, whereas cancer-neurological pairs showed the weakest overall genetic affinity. Meta-analysis and subtype resolution uncovered associations obscured in aggregate analyses, including opposing correlations between familial and late-onset Alzheimers disease and lung cancer, revealing subtype-dependent neuro-oncological biology. Local analyses identified recurrent genomic loci where direct comorbidities are consistent with shared inflammatory, interferon, survival and tissue-remodelling programs, whereas inverse comorbidities suggest competing demands on apoptotic regulation, immune tone and stress-response calibration between neuronal and tumour-cell states. Together, these findings provide a genome-scale genetic framework for neuropsychiatric-cancer comorbidity and identify shared inherited biological programs as candidates for mechanistic investigation and therapeutic translation.

genomics↗

bioRxiv2020

A trans-omics comparison reveals common gene expression strategies in four model organisms and exposes similarities and differences between them

The ultimate goal of gene regulation should focus on the protein level. However, as mRNA is an obligate intermediary, and because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained following different strategies. By studying omics datasets for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CES) for functionally-related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the distantly related yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic datasets from the model prokaryote Escherichia coli as an external reference. We show that CES are also present in all the studied organisms and the differences in them between organisms can be used to establish their phylogenetic relationships. The phenogram based on 6VP has the expected topology for the phylogeny of these four organisms, but shows interesting branch length differences to DNA sequence-based trees. The analysis of the correlations among the six variables supports that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role in it. We propose that all living cells use CES for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CES have been modeled by evolution to adapt to the specific life circumstances of each organism. The obtained phenograms may reflect both evolutionary constraints in expression strategies, and lifestyle convergences.

genomics↗