Dataset Information

The refinement of genetic predictors of multiple sclerosis.

ABSTRACT: A recent genome wide association study (GWAS) demonstrated that more than 100 genetic variants influence the risk of multiple sclerosis (MS). We investigated what proportion of the general population can be considered at high genetic risk of MS, whether genetic information can be used to predict disease development and how the recently found genetic associations have influenced these estimates. We used summary statistics from GWAS in MS to estimate the distribution of risk within a large simulated general population. We profiled MS associated loci in 70 MS patients and 79 healthy controls (HC) and assessed their position within the distribution of risk in the simulated population. The predictive performance of a weighted genetic risk score (wGRS) on disease status was investigated using receiver operating characteristic statistics. When all known variants were considered, 40.8% of the general population was predicted to be at reduced risk, 49% at average, 6.9% at elevated and 3.3% at high risk of MS. Fifty percent of MS patients were at either reduced or average risk of disease. However, they showed a significantly higher wGRS than HC (median 13.47 vs 12.46, p?=?4.08×10(-10)). The predictive performance of the model including all currently known MS associations (area under the curve?=?79.7%, 95%CI?=?72.4%-87.0%) was higher than that of models considering previously known associations. Despite this, considering the relatively low prevalence of MS, the positive predictive value was below 1%. The increasing number of known associated genetic variants is improving our ability to predict the development of MS. This is still unlikely to be clinically useful but a more complete understanding of the complexity underlying MS aetiology and the inclusion of environmental risk factors will aid future attempts of disease prediction.

SUBMITTER: Disanto G

PROVIDER: S-EPMC4008598 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The refinement of genetic predictors of multiple sclerosis.

Disanto Giulio G Dobson Ruth R Pakpoor Julia J Elangovan Ramyiadarsini I RI Adiutori Rocco R Kuhle Jens J Giovannoni Gavin G

PloS one 20140502 5

A recent genome wide association study (GWAS) demonstrated that more than 100 genetic variants influence the risk of multiple sclerosis (MS). We investigated what proportion of the general population can be considered at high genetic risk of MS, whether genetic information can be used to predict disease development and how the recently found genetic associations have influenced these estimates. We used summary statistics from GWAS in MS to estimate the distribution of risk within a large simulat ...[more]

PMID: 24794218

Similar Datasets

Project description:BackgroundGenomic sequence data cannot be fully appreciated in isolation. Comparative genomics--the practice of comparing genomic sequences from different species--plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program.ResultsWe tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly; but significantly, this occurred at the same time that the overall alignment length decreased--through the removal of gaps--by approximately 200 gapped regions representing roughly 1,300 gaps.ConclusionWe have implemented a genetic algorithm in parallel mode to optimize multiple genomic sequence alignments initially generated by various alignment tools. Benchmarking experiments showed that the refinement algorithm improved genomic sequence alignments within a reasonable period of time.

Project description:Data regarding effectiveness and safety of ocrelizumab in the post-marking setting are lacking. The aim of our study was to provide effectiveness and safety data of ocrelizumab treatment in patients with relapsing-remitting (RR-) and progressive multiple sclerosis (PMS) and to evaluate clinical and immunological predictors of early treatment response. In this single-center prospective observational study, we investigated effectiveness outcomes (time-to-confirmed disability worsening, time-to-first relapse, time-to-first evidence of MRI activity and time-to-first evidence of disease activity), clinical and immunological predictors of early treatment response, and incidence of adverse events (AEs). One hundred and fifty-three subjects were included (93 RRMS; 84 females). Median follow-up was 1.9 (1.3-2.7). At 2-year follow-up (FU), disability worsening-free survival were 90.5%, 64.7%, and 68.8% for RRMS, primary-progressive MS (PPMS), and secondary-progressive MS (SPMS) patients, respectively. At 2-year FU, 67.1%, 72.7%, and 81.3% of patients with RRMS, PPMS, and SPMS were free of MRI activity, with NEDA-3 percentages of 62.1%, 54.6%, and 55.1%, respectively. Lower baseline EDSS was independently associated with a reduced risk of disability worsening (HR(95%CI) = 1.45(1.05-2.00), p = 0.024) and previous treatment exposure was independently associated with increased probability of radiological activity (HR = 2.53(1.05-6.10), p = 0.039). At 6-month FU, CD8 + cell decrease was less pronounced in patients with inflammatory activity (p = 0.022). Six patients (3.9%) discontinued ocrelizumab due to severe AEs. Our findings suggest that ocrelizumab is an effective treatment in real-world patients with RRMS and PMS, with a manageable safety profile. Better outcomes were observed in treatment-naïve patients and in patients with a low baseline disability level. Depletion of CD8 + cells could underlie early therapeutic effects of ocrelizumab.

Dataset Information

The refinement of genetic predictors of multiple sclerosis.

Publications

The refinement of genetic predictors of multiple sclerosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets