Genome-wide Mendelian randomization identifies drugs associated with body height

Mendelian randomization (MR) has been used to identify drug targets in many conditions. Height is a classic complex trait affected by genetic and early-life environmental factors. No systematic screening has been conducted to identify drugs that interact with height. We investigated the causal relationship between genes and height, and systematically screened for interactive drugs that may promote or delay growth.

We performed MR using summary statistics from the Genetic Investigation of ANthropometric Traits consortium (N=253,288), the UK Biobank (N=461,950), and the BioBank Japan Project (N=159,095). Gene expression-single-nucleotide polymorphism associations represented by cis-expression quantitative trait loci data were obtained from the Genotype-Tissue Expression study and were used as genetic instruments. We performed annotation and enrichment analyses of the genes. Interactive drugs were identified through drug-gene interactions.

Of the 27,094 genes screened, 209 had causal associations with height, including genes associated with height and short stature phenotypes (AMZ1, GNA12, NPPC, UQCC1, and ZBTB38), genes associated with height in a few studies (ANKIB1, CEP250, DCAF16, HIST1H4E, and HLA-C), and genes without previous evidence (BTN2A2 and RBMS1P1). Enrichment analysis showed that transcriptional regulation by RUNX1 was the most enriched pathway. Interactive drugs were identified, including amoxicillin, atenolol, infliximab, colchicine, propionyl-L-carnitine, BMN-111, and tamoxifen, which were known to have a positive effect on height. We also identified drugs that had a negative effect on height, including antineoplastic drugs, corticosteroids, and antiepileptic drugs. Moreover, many interactive drugs have not been previously reported to be associated with height.

Our results suggest that many genes have causal effects on height. By interrogating drug-gene interactions, interactive drugs have been identified as having both positive and negative effects on growth, which would help make clinical decisions.