Back

A Genetic Atlas of Direct and Inverse Neuropsychiatric-Cancer Comorbidity

Flores-Rodero, M.; Fores-Martos, J.; Sanchez-Orti, J. V.; Martinez-Perez, S.; Winkler, F.; Valencia, A.; Tabares-Seisdedos, R.; Sanchez-Valle, J.

2026-07-11 genomics
10.64898/2026.07.10.737193 bioRxiv
Show abstract

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.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.