What Can Cause Restless Leg Syndrome? Genetic Study Offers Clues

Targets for drug development, the casual association between restless leg syndrome (RLS) and relevant comorbidities and risk factors, and disease risk prediction were identified in a genome-wide study published in Nature Genetics.

Patients with RLS often receive delayed diagnosis and have inadequate therapeutic options. Researchers conducted a genome-wide meta-analysis to advance risk prediction in RLS, using machine learning approaches.  

The researchers evaluated a total of 3 genome-wide association studies (GWAS) for RLS that included patients with RLS and control participants of European ancestry. Sex-stratified GWAS and a genetic investigation of the X chromosome were conducted, in addition to Mendelian randomization analyses to identify risk factors.

Meta-analysis of the GWAS included 116,647 patients with physician-diagnosed RLS who were matched with 1,546,466 control participants. A total of 161 RLS risk loci were identified (P <5×10^-8) from 9,196,648 variants.

Sex-specific meta-analysis indicated high genetic correlation between the sexes (r_g=0.96 [SE, 0.018]), with 58 single-nucleotide polymorphisms (SNPs) in 50 risk loci among men and 155 SNPs in 130 loci among women.

Pooled meta-analyses of the X-chromosome showed a high genetic correlation between the sexes (r_g=0.926 [SE, 0.071]), as well as 3 independent risk loci for RLS on the X-chromosome.

To advance drug repurposing and improve patient care, 13 potential candidate genes were identified against the druggable genome. Specifically, GRIA1 and GRIA4 provided genetic evidence of an association between RLS and glutamate receptor function.

Mendelian randomization analyses indicated that RLS had a significant effect on type 2 diabetes (P <.05), with an effect estimate of 0.99 (SE, 0.06; P =1.5×10^-68).

The researchers used machine learning approaches combining genetic and nongenetic information to predict disease risk (area under the curve [AUC], 0.82-0.91).

Limitations of the analysis included the lack of longitudinal data and high-quality RLS phenotyping and the lack of generalizability to populations other than those of European ancestry.

“[O]ur study marks a substantial advance in deciphering the genetic basis of RLS and paves the way for improving treatment and prevention strategies,” the researchers concluded.