A genome-wide association analysis of loss of ambulation in dystrophinopathy patients suggests multiple candidate modifiers of disease severity.

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.

KELVIN: a software package for rigorous measurement of statistical evidence in human genetics.

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.