In modern scientific studies, it is often imperative to determine whether a set of phenotypes is affected by a single factor. If such an influence is identified, it becomes essential to discern whether this effect is contingent upon categories such as sex or age group, and importantly, to understand whether this dependence is rooted in purely non-environmental reasons. The exploration of such dependencies often involves studying pleiotropy, a phenomenon wherein a single genetic locus impacts multiple traits. This heightened interest in uncovering dependencies by pleiotropy is fueled by the growing accessibility of summary statistics from genome-wide association studies (GWAS) and the establishment of thoroughly phenotyped sample collections. This advancement enables a systematic and comprehensive exploration of the genetic connections among various traits and diseases. additive genetic correlation illuminates the genetic connection between two traits, providing valuable insights into the shared biological pathways and underlying causal relationships between them. In this paper, we present a novel method to analyze such dependencies by studying additive genetic correlations between pairs of traits under consideration. Subsequently, we employ matrix comparison techniques to discern and elucidate sex-specific or age-group-specific associations, contributing to a deeper understanding of the nuanced dependencies within the studied traits. Our proposed method is computationally handy and requires only GWAS summary statistics. We validate our method by applying it to the UK Biobank data and present the results.

翻译：在现代科学研究中，常需确定一组表型是否受单一因素影响。若发现此类影响，则需进一步判断该效应是否依赖于性别或年龄组等分类变量，且至关重要的是要理解这种依赖性是否源于纯粹的非环境因素。对此类依赖关系的探究常涉及多效性研究——即单个遗传位点影响多个性状的现象。随着全基因组关联研究（GWAS）汇总统计数据的日益可获取性提升，以及全面表型样本库的建立，通过多效性揭示依赖关系的研究兴趣愈发浓厚。这种进展使得系统性地探索不同性状及疾病间的遗传关联成为可能。加性遗传相关可阐明两个性状间的遗传联系，为揭示共享生物学通路及潜在因果关系提供重要线索。本文提出了一种新方法，通过分析目标性状对间的加性遗传相关性来研究此类依赖关系。随后运用矩阵比较技术识别并阐释性别特异性或年龄组特异性关联，从而深化对研究性状中细微依赖关系的理解。该方法计算简便，仅需GWAS汇总统计量即可实现。我们通过在UK Biobank数据中的应用验证了该方法的有效性，并展示了分析结果。