Abstract
The major determinant of disease severity in Duchenne muscular dystrophy (DMD) or milder Becker muscular dystrophy (BMD) is whether the dystrophin gene (DMD) mutation truncates the mRNA reading frame or allows expression of a partially functional protein. However, even in the complete absence of dystrophin, variability in disease severity is observed, and candidate gene studies have implicated several genes as modifiers. Here we present the largest genome-wide search to date for loci influencing severity in N = 419 DMD patients. Availability of subjects for such studies is quite limited, leading to modest sample sizes, which present a challenge for GWAS design. We have therefore taken special steps to minimize heterogeneity within our dataset at the DMD locus itself, taking a novel approach to mutation classification to effectively exclude the possibility of residual dystrophin expression, and utilized statistical methods that are well adapted to smaller sample sizes, including the use of a novel linear regression-like residual for time to ambulatory loss and the application of evidential statistics for the GWAS approach. Finally, we applied an unbiased in silico pipeline, utilizing functional genomic datasets to explore the potential impact of the best supported SNPs. In all, we obtained eight SNPs (out of 1,385,356 total) with posterior probability of trait-marker association (PPLD) ≥ 0.4, representing six distinct loci. Our analysis prioritized likely non-coding SNP regulatory effects on six genes (ETAA1, PARD6G, GALNTL6, MAN1A1, ADAMTS19, and NCALD), each with plausibility as a DMD modifier. These results support both recurrent and potentially new pathways for intervention in the dystrophinopathies.