Aggregation of multiple sclerosis genetic risk variants in multiple and single case families
Abstract
Objective:
Multiple sclerosis (MS) is a multifactorial neurologic disease characterized by modest but tractable heritability. Genome-wide association studies have identified and/or validated multiple polymorphisms in approximately 16 genes associated with susceptibility. We aimed at investigating the aggregation of genetic MS risk markers in individuals by comparing multiple- and single-case families.
Methods:
A weighted log-additive integrative approach termed MS genetic burden (MSGB) was used to account for the well-established genetic variants from previous association studies and meta-analyses. The corresponding genetic burden and its transmission was analyzed in 1,213 independent MS families (810 sporadic and 403 multicase families).
Results:
MSGB analysis demonstrated a higher aggregation of susceptibility variants in multicase compared to sporadic MS families. In addition, the aggregation of non-major histocompatibility complex single nucleotide polymorphisms depended neither on gender nor on the presence or absence of HLA-DRB1*15:01 alleles. Interestingly, although a greater MSGB in siblings of MS patients was associated with an increased risk of MS (odds ratio, 2.1; p = 0.001), receiver operating characteristic curves of MSGB differences between probands and sibs (area under the receiver operator curves, 0.57 [95% confidence interval, 0.53–0.61]) show that case–control status prediction of MS cannot be achieved with the currently available genetic data.
Interpretation:
The primary interest in the MSGB concept resides in its capacity to integrate cumulative genetic contributions to MS risk. This analysis underlines the high variability of family load with known common variants. This novel approach can be extended to other genetically complex diseases. Despite the emphasis on assembling large case–control datasets, multigenerational, multiaffected families remain an invaluable resource for advancing the understanding of the genetic architecture of complex traits. Ann Neurol 2011;69:65–74
