Abstract
OBJECTIVE: Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic basis of diseases by examining millions of genetic variants across the genome. Rheumatoid arthritis (RA) and multiple sclerosis (MS) are chronic autoimmune diseases characterized by immune system dysregulation and inflammation. Investigating the genetic correlation between RA and MS can provide insights into shared genetic factors, potential mechanisms, and pathways underlying these complex disorders. The objective of this study was to compare different statistical methods to estimate the genetic correlation between RA and MS using GWAS summary statistics. MATERIALS AND METHODS: To estimate single nucleotide polymorphism (SNP) heritability and genetic correlation, we utilized two popular methods: Linkage Disequilibrium Score Regression (LDSC) and Linkage Disequilibrium Adjusted Kinship (LDAK) models. RESULTS: Our analysis revealed a significant, moderate, positive correlation between RA and MS using both LDSC and LDAK (LSDC(MS-RA)=0.448, LDAK(MS-RA)=0.387, Spearman(MS-RA)=0.0262, p<0.001). Additionally, there were notable differences in heritability estimates between the two methods and the traits. The LDAK model demonstrated higher heritability estimates for the RA-MS relationship (h(2) (MS-RA) =0.314) compared to the LDSC (h(2) (MS-RA) =0.138). CONCLUSION: There is a significant positive genetic correlation between RA and MS, indicating a shared genetic component. Differential heritability estimates from LDAK and LDSC highlight the importance of the method. Genetic overlap informs common pathways and potential therapeutic targets. These findings contribute to the evidence of a moderately positive genetic correlation, emphasizing the need for further research and personalized approaches to managing autoimmune diseases.