A genome-wide association study of brain lesion distribution in multiple sclerosis(20/02/13)Summary: This research group tested whether genetic variation is associated with multiple sclerosis lesion topology by a genome-wide association study (GWAS). The researchers tested this hypothesis by analysing the distribution of multiple sclerosis lesions and used that measure as a trait in a GWAS.

They used voxel-level 3T magnetic resonance imaging T1 weighted scans to reconstruct the 3D topology of lesions in 284 subjects with multiple sclerosis and tested if this was a heritable phenotype. They computed the first ten principal components in order to focus on lesion distribution and carried out GWAS on each of these.

The researchers found 31 significant associations with component eight, which represents variation of lesion topology in the population. The majority can be linked to genes related to immune cell function and to myelin and neural growth.

The results show how quantitative traits derived from brain MRI can be used as dependent variables in a GWAS. In the future, the integration of imaging and genetic data sets is likely to become a mainstream tool for understanding the complex biological processes of MS.

Abstract

Brain magnetic resonance imaging is widely used as a diagnostic and monitoring tool in multiple sclerosis and provides a non-invasive, sensitive and reproducible way to track the disease.

Topological characteristics relating to the distribution and shape of lesions are recognized as important neuroradiological markers in the diagnosis of multiple sclerosis, although these have been much less well characterized quantitatively than have traditional measures such as T(2) hyperintense or T(1) hypointense lesion volumes.

Here, we used voxel-level 3 T magnetic resonance imaging T(1)-weighted scans to reconstruct the 3D topology of lesions in 284 subjects with multiple sclerosis and tested whether this is a heritable phenotype. To this end, we extracted the genotypes from a published genome-wide association study on these same individuals and searched for genetic associations with lesion load, shape and topological distribution. Lesion probability maps were created to identify frequently affected areas and to assess the overall distribution of T(1) lesions in the subject population as a whole. We then developed an original algorithm to cluster adjacent lesional voxels (cluxels) in each subject and tested whether cluxel topology was significantly associated with any single-nucleotide polymorphism in our data set. To focus on patterns of lesion distribution, we computed the first 10 principal components.

Although principal component 1 correlated with lesion load, none of the remaining orthogonal components correlated with any other known variable. We then conducted genome-wide association studies on each of these and found 31 significant associations (false discovery rate <0.01) with principal component 8, which represents a mode of variation of lesion topology in the population. The majority of the loci can be linked to genes related to immune cell function and to myelin and neural growth; some (SYK, MYT1L, TRAPPC9, SLITKR6 and RIC3) have been previously associated with the distribution of white matter lesions in multiple sclerosis. Finally, we used a bioinformatics approach to identify a network of 48 interacting proteins showing genetic associations (P < 0.01) with cluxel topology in multiple sclerosis. This network also contains proteins expressed in immune cells and is enriched in molecules expressed in the central nervous system that contribute to neural development and regeneration. Our results show how quantitative traits derived from brain magnetic resonance images of patients with multiple sclerosis can be used as dependent variables in a genome-wide association study. With the widespread availability of powerful computing and the availability of genotyped populations, integration of imaging and genetic data sets is likely to become a mainstream tool for understanding the complex biological processes of multiple sclerosis and other brain disorders. Authors: Gourraud PA, Sdika M, Khankhanian P Source: Brain. 2013 Feb 13. & Pubmed PMID: 23412934 (20/02/13)