Abstract
<jats:p>The gene-sex association quality control (QC) metric was developed during the era of early small-sized genome-wide association studies and typically filter variants based on controls alone. While this practice had minimal impact in early studies, in contemporary large-scale settings they can introduce systematic bias by disproportionately discarding variants with pronounced sex differences in allele frequencies (AF), potentially removing real gene-sex interaction signals. To address this limitation, we introduce Sex-Prevalence Adjusted Allelic Difference Estimates (SPADE) metric, an X chromosome-inclusive QC framework that not only preserves potentially informative variants exhibiting gene-sex interaction but also enables a rapid and exploratory scan for such interaction. SPADE adjusts for sex-specific disease prevalence, maintains correct type I error control, and reduces the risk of falsely excluding variants compared to existing QC approaches. Extensive simulations across diverse disease architectures demonstrated that SPADE remained well calibrated, whereas the controls-only approach exhibited massively inflated type I error when variants were associated with disease. We further developed an open-source command-line software implementation to facilitate its application in large-scale genetic studies. Applying SPADE to an autism spectrum disorder (ASD) case-control cohort (6,873 cases and 8,981 controls) comprising of the Autism Speaks MSSNG, Simons Simplex Collection (SSC), and Simons Powering Autism Research (SPARK) datasets, we demonstrate that SPADE is well calibrated relative to a controls-only approach. Notably, the complementary exploratory gene-sex interaction scan implicates a sex antagonistic region encompassing RBMX2, SLC25A14, and BCORL1 (lead SNP rs150885581: A>G, p = 6.19 * 10 ^ -9), providing candidate genes for future functional investigation.</jats:p>