Identification of the major genes involved in multiple sclerosis (MS) is now possible as a result of the rapid progress in delineating the landscape of genetic variation across the human species. Our overall objective is to characterize the complete repertoire of genes that predispose to MS and modulate its presentation. MS is a complex polygenic disorder in which biologic heterogeneity (i.e. different underlying causes in different individuals) and locus heterogeneity (different genes in different individuals) is likely to exist, thus a careful delineation of subphenotypes that have a hereditable component will be essential to fully dissect the MS-prone genotype. We organized the International Multiple Sclerosis Genetics Consortium (TJMSGC) with the purpose of bringing together teams from multiple academic institutions with the requisite expertise and commitment towards this goal. The availability of a truly unique clinical dataset, combined with state-of-the-art approaches to gene identification, will be used to identify new MS loci and to characterize disease-specific variations responsible for currently known linkage signals at 6p21, 17q21, and 19ql3. In the first aim, we will address the issue of heterogeneity in MS by focusing on the phenotypic characterization of 2 large independent MS cohorts, including a newly prospectively collected dataset. Clinical and laboratory data such as age and site of disease onset, accumulation of disability, lesion distribution, and progression will be carefully recorded and incorporated into the analysis of genomic data.
In specific aim 2, we will carry out a high-density whole genome linkage screen with over 5,500 SNPs in 1000 affected MS sibling pairs to localize regions harboring MS genes.
In specific aim 3, we will perform a comprehensive association analysis to identify the causal variation(s) within the 4 Mb MHC locus at 6p21. This locus represents the strongest and most consistent genetic factor identified in MS. Finally, in specific aim 4, we will utilize haplotype-based association studies to identify the causal variations within the 19ql3 and 17q21 loci.
For specific aims 3 and 4, a screening set of 1000 MS trios, and a replication set of 1500 trios, will be employed. Key to the success of these studies is the availability of a large and informative dataset, the standardization of rigorous and consistent methods to collect relevant clinical data as stratifying variables for genetic analyses, and the application of efficient methods for genotyping and statistical analysis. Strong collaborative ties between skillful teams, access to a formidable DNA collection, a superb research environment and substantial preliminary data, all indicate that this project has a high chance for success. The resulting knowledge, obtained using methods that are not biased by any preexisting hypothesis about the underlying cause of MS, could revolutionize our understanding of this disease and our capacity to intervene therapeutically in a meaningful fashion.
Pröbstel, Anne-Katrin; Hauser, Stephen L (2018) Multiple Sclerosis: B Cells Take Center Stage. J Neuroophthalmol 38:251-258 |
Jia, Xiaoming; Madireddy, Lohith; Caillier, Stacy et al. (2018) Genome sequencing uncovers phenocopies in primary progressive multiple sclerosis. Ann Neurol 84:51-63 |
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Greenfield, Ariele L; Hauser, Stephen L (2018) B-cell Therapy for Multiple Sclerosis: Entering an era. Ann Neurol 83:13-26 |
International Multiple Sclerosis Genetics Consortium. Electronic address: chris.cotsapas@yale.edu; International Multiple Sclerosis Genetics Consortium (2018) Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk. Cell 175:1679-1687.e7 |
Gelfand, Jeffrey M; Cree, Bruce A C; Hauser, Stephen L (2017) Ocrelizumab and Other CD20+ B-Cell-Depleting Therapies in Multiple Sclerosis. Neurotherapeutics 14:835-841 |
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International Multiple Sclerosis Genetics Consortium. Electronic address: cotsapas@broadinstitute.org; International Multiple Sclerosis Genetics Consortium (2016) NR1H3 p.Arg415Gln Is Not Associated to Multiple Sclerosis Risk. Neuron 92:333-335 |
Keshavan, Anisha; Paul, Friedemann; Beyer, Mona K et al. (2016) Power estimation for non-standardized multisite studies. Neuroimage 134:281-294 |
Papinutto, Nico; Schlaeger, Regina; Panara, Valentina et al. (2015) Age, gender and normalization covariates for spinal cord gray matter and total cross-sectional areas at cervical and thoracic levels: A 2D phase sensitive inversion recovery imaging study. PLoS One 10:e0118576 |
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