Shared Genes, Opposing Effects: Hidden Genetic Link Between Heart Failure and Osteoporosis Revealed

Heart failure and osteoporosis have long traveled together through the epidemiological literature. Patients with weakened hearts are disproportionately diagnosed with weakened bones, and clinicians have repeatedly observed that the two age-related disorders cluster in the same individuals. What has remained stubbornly unclear is whether this association reflects shared lifestyle factors, the side effects of treatment, or something deeper written into the genome. A new integrative multi-omics study published in Scientific Reports now argues that the connection is, at least in part, genetic — and that the relationship between the two diseases is more subtle, and more paradoxical, than a simple shared-risk model would predict.

The research team, led by Ruikang Liu, Chiyun Sun and Yonghao Li of Beijing University of Chinese Medicine and the China Academy of Chinese Medical Sciences, together with colleagues at several Chinese hospitals, assembled genome-wide association study (GWAS) summary statistics for heart failure and osteoporosis and subjected them to a battery of complementary statistical tools. Rather than asking whether the two diseases share one or two risk genes, the investigators probed the architecture of the entire genetic landscape: how many variants influence both traits, whether those variants push risk in the same or opposite directions, which genes the signals point to, and where in the body those genes are active.

The first and perhaps most striking finding concerns polygenic overlap. Using MiXeR, a bivariate causal mixture model that estimates the number of genetic variants with non-null effects on each trait, the team calculated that approximately 447 ± 114 variants influence both heart failure and osteoporosis. That shared set corresponds to roughly 52 percent of the estimated non-null variant count for osteoporosis and about 21 percent of that for heart failure. In other words, a substantial fraction of the genetic determinants of bone fragility are also determinants of cardiac dysfunction — a degree of overlap that far exceeds what most single-gene approaches would ever reveal.

Yet here the story takes an unexpected turn. Despite this extensive overlap, the genome-wide genetic correlation between the two diseases was weakly negative, at rg = −0.091 (standard error 0.024, P = 1.07 × 10⁻⁴). Moreover, when the researchers examined the correlation of effect sizes within the shared non-null component, they again found a negative value, rho_beta = −0.295. The interpretation is counterintuitive but biologically plausible: many of the same variants are involved in both conditions, but they frequently act in opposite directions. A variant that raises the risk of heart failure may, on average, lower the risk of osteoporosis, and vice versa. The diseases share their genetic machinery even as that machinery is tuned in opposing ways.

To move from global statistics to specific genomic locations, the team applied conditional and conjunctional false discovery rate (cond/conjFDR) analysis, a framework that borrows statistical strength across related traits to detect associations that neither dataset could confirm alone. This approach identified five pleiotropic loci — genomic regions harboring variants that influence both diseases — and, notably, three novel loci associated with heart failure that had not previously been flagged. The method’s power lies in its ability to elevate weak, sub-threshold signals to significance when they are corroborated across traits, effectively squeezing more discovery out of existing GWAS resources.

The next layer of analysis asked which genes, rather than which markers, the associations implicate. Through transcriptome-wide association studies (TWAS) conducted with the MetaXcan framework, the researchers imputed the genetic regulation of gene expression across tissues and tested whether genetically predicted expression levels associate with disease risk. They then cross-referenced these results with Bayesian colocalization methods — eCAVIAR and fastenloc — which test whether a GWAS signal and an expression quantitative trait locus (eQTL) signal in the same region are driven by the same causal variant rather than by two distinct variants in linkage disequilibrium. The consistent winner of this triangulation was HOXC6, a homeobox transcription factor gene prioritized as a shared transcriptomic candidate across multiple tissues. HOX genes are master regulators of developmental patterning, and their involvement in both a cardiovascular and a skeletal disorder hints at developmental programs that echo into adult disease susceptibility.

Perhaps the most novel component of the study is its spatial dimension. Using gsMap — genetically informed spatial mapping of cells for complex traits — the team projected human disease-associated genetic signals onto a reference atlas of single-cell spatial transcriptomics from E16.5 mouse embryos. This technique asks not just which genes are implicated, but in which anatomical niches their expression is concentrated. The results were strikingly compartmentalized: heart failure-associated genetic signals were enriched in embryonic muscle regions, consistent with the cardiac muscle as the affected tissue, while osteoporosis-associated signals concentrated in cartilage and cartilage-primordium regions, the developmental precursors of the skeletal system. Within this spatial framework, GRB10 emerged as a shared candidate gene. GRB10 is an adaptor protein in insulin-like growth factor signaling, a pathway with well-documented roles in growth, metabolism and tissue size regulation — making its dual appearance in heart and bone contexts a compelling lead for future work.

The authors are careful to frame these results as hypothesis-generating rather than definitive. The analyses rely on summary statistics and reference atlases, and the spatial mapping uses an embryonic mouse reference to interpret human adult disease, an approach that assumes developmental expression patterns illuminate disease-relevant biology. The negative genetic correlation also complicates any simple clinical translation: shared variants acting in opposite directions mean that a therapeutic strategy targeting a shared gene could, in principle, benefit one organ while harming the other. Precisely for that reason, the identification of candidates such as HOXC6 and GRB10 matters — they define concrete molecular entry points whose directionality and tissue context can now be tested experimentally in cellular and animal models.

What the study delivers is a new conceptual map of the heart–bone axis. It quantifies, for the first time at this resolution, just how extensively the genetic architectures of heart failure and osteoporosis interpenetrate, while simultaneously demonstrating that overlap does not equal concordance. The five pleiotropic loci, the three novel heart failure loci, the multi-tissue prioritization of HOXC6 and the spatially resolved nomination of GRB10 together generate a set of testable predictions about regulatory links between cardiovascular and skeletal traits. As the population ages and the clinical burden of both diseases grows, understanding the shared — and antagonistic — genetic wiring beneath them may prove essential for designing interventions that strengthen the heart without weakening the skeleton, and vice versa.

Subject of Research: Shared genetic architecture and spatial transcriptomic correlations between heart failure and osteoporosis

Article Title: Integrating multi-omics analysis reveals shared genetic architecture and spatial correlations between heart failure and osteoporosis

Article References: Integrating multi-omics analysis reveals shared genetic architecture and spatial correlations between heart failure and osteoporosis. (n.d.). https://doi.org/10.1038/s41598-026-74146-7

Image Credits: AI Generated

DOI: 10.1038/s41598-026-74146-7

Keywords: heart failure, osteoporosis, GWAS, polygenic overlap, MiXeR, TWAS, colocalization, HOXC6, GRB10, spatial transcriptomics, genetic correlation, pleiotropy