Genetic architecture of basal heart rate and its modulation by dietary salt in Drosophila melanogaster.

Heart rate (HR) is a complex quantitative phenotype that is influenced by both genetic and environmental factors, and it is a critical factor in the regulation of cardiac physiology. Despite the discovery of numerous genes associated with HR variation in humans and other organisms, the mechanisms by which these genes interact to affect HR changes in natural environments and their responses to environmental stressors remain incompletely understood. In this study, the genetic basis of basal HR and the response to salt stress were examined using the Drosophila Genetic Reference Panel (DGRP). The 154 DGRP lines exhibited moderate broad-sense heritability and substantial genetic variation in HR measurements during the third larval stage, with males and females analyzed separately, under both standard and 0.1% NaCl-containing medium conditions. The mixed-effects model revealed that the interaction between genotype and environment significantly influences HR. Genome-wide association analysis identified gene variants associated with critical biological processes such as ion transport, cardiac development, protein homeostasis, metabolism, and stress response. The identification of different genes under standard and saline (NaCl-supplemented) conditions suggests that environmental stress unlocks cryptic genetic variation. Candidate genes were tested using heart-specific GAL4 drivers in RNAi experiments, and a considerable number of these genes were found to significantly affect HR based on sex and the environment. These findings reveal that HR variation is strongly shaped by genotype-by-environment interactions and sex-specific genetic effects, with distinct sets of genes contributing under basal and stress conditions. The identification of environment-dependent genetic architectures suggests that salt stress can expose cryptic genetic variation influencing cardiac function. By integrating genome-wide association analyses with targeted RNAi-based functional validation, this study provides a framework for linking natural genetic variation to physiological phenotypes in a context-dependent manner.