Multivariate eQTL mapping uncovers functional variation on the X-chromosome associated with complex disease traits

Area of Science:

• Genomics
• Human Genetics
• Molecular Biology

Background:

• Investigating genetic polymorphisms and X-chromosome gene expression is crucial for understanding trait-associated variants found in genome-wide association studies (GWAS).
• Functional follow-up studies are often limited by a lack of knowledge regarding X-chromosome genetic associations.

Purpose of the Study:

• To identify single nucleotide polymorphisms (SNPs) associated with gene expression variation on the X-chromosome (eSNPs).
• To explore the link between identified eSNPs and known disease or trait-associated variants.

Main Methods:

• Utilized a multivariate approach to analyze associations between SNPs and exon expression levels.
• Examined data from 356 Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) from the Geuvadis RNA sequencing project.
• Identified eSNPs on the X-chromosome using a false discovery rate (FDR) of 5% and linkage disequilibrium (LD) thresholds.

Main Results:

• Discovered 548 independent X-chromosome eSNPs.
• Found 35 eSNPs in high LD (r² > 0.8) with previously identified GWAS variants.
• Identified a strong eSNP (rs35975601) associated with F8A1 expression, in LD with a type 1 diabetes risk variant.
• Observed eSNPs for DNASE1L1 in LD with variants linked to bilirubin levels, type 1 diabetes, and schizophrenia.

Conclusions:

• Multivariate exon-level analysis is more powerful than univariate gene-level analysis for detecting expression quantitative trait loci (eQTLs).
• The identified X-chromosome eSNPs provide potential molecular mechanisms linking gene expression to human trait variation and disease risk.
• These findings enhance our understanding of X-chromosome genetic architecture and its role in complex traits.