We propose an R package called pleior designed to analyze pleiotropy in genome-wide association studies (GWAS). Pleiotropy occurs when a single genetic variant influences multiple traits, offering insights into shared biological pathways and disease relationships. This package would enable users to identify, analyze, and visualize pleiotropic associations, addressing the question of how genetic variants contribute to multiple complex traits, such as Alzheimer’s disease and related conditions.

Understanding pleiotropy is crucial for uncovering shared biological mechanisms, explaining disease comorbidity, and identifying potential therapeutic targets. However, existing tools for pleiotropy analysis are often fragmented, focusing on specific aspects like detection or visualization, and may lack user-friendly interfaces or integration with functional annotations.

Why It Matters? Recent research studies suggests that pleiotropy is widespread in human genetics, with many genetic variants linked to multiple diseases or traits. For example, the SNP (rs814573) is know to be associated with Alzheimer’s disease, and other traits like myocardial infarction and tyrosine measurement. Understanding these connections could help explain why certain diseases co-occur (co-morbidity) to identify potential targets and responses of treatment/drug based on shared genetic predispositions with other known traits. pleior provides a unified platform to explore these relationships, making it easier to generate hypotheses and interpret results.

How can we systematically identify and interpret pleiotropic genetic variants across multiple complex traits to uncover shared biological mechanisms, disease relationships, and potential therapeutic targets?

This question is grounded in recent research highlighting the widespread nature of pleiotropy. For example, a 2019 study in Nature Genetics found that trait-associated loci cover over half the genome, with 90% overlapping multiple traits. Similarly, studies like FactorGo (2023) and PLACO (2020) emphasize the need for scalable, statistically robust methods to characterize pleiotropy across thousands of traits. pleior builds on these findings by providing a unified toolset to explore pleiotropy in a practical, accessible manner.