Identification of Restless Legs Syndrome Genes by Mutational Load Analysis.

OBJECTIVE: Restless legs syndrome is a frequent neurological disorder with substantial burden on individual well-being and public health. Genetic risk loci have been identified, but the causatives genes at these loci are largely unknown, so that functional investigation and clinical translation of molecular research data are still inhibited. To identify putatively causative genes, we searched for highly significant mutational burden in candidate genes. METHODS: We analyzed 84 candidate genes in 4,649 patients and 4,982 controls by next generation sequencing using molecular inversion probes that targeted mainly coding regions. The burden of low-frequency and rare variants was assessed, and in addition, an algorithm (binomial performance deviation analysis) was established to estimate independently the sequence variation in the probe binding regions from the variation in sequencing depth. RESULTS: Highly significant results (considering the number of genes in the genome) of the conventional burden test and the binomial performance deviation analysis overlapped significantly. Fourteen genes were highly significant by one method and confirmed with Bonferroni-corrected significance by the other to show a differential burden of low-frequency and rare variants in restless legs syndrome. Nine of them (AAGAB, ATP2C1, CNTN4, COL6A6, CRBN, GLO1, NTNG1, STEAP4, VAV3) resided in the vicinity of known restless legs syndrome loci, whereas 5 (BBS7, CADM1, CREB5, NRG3, SUN1) have not previously been associated with restless legs syndrome. Burden test and binomial performance deviation analysis also converged significantly in fine-mapping potentially causative domains within these genes. INTERPRETATION: Differential burden with intragenic low-frequency variants reveals putatively causative genes in restless legs syndrome. ANN NEUROL 2020; 87:184-193.      «

OBJECTIVE: Restless legs syndrome is a frequent neurological disorder with substantial burden on individual well-being and public health. Genetic risk loci have been identified, but the causatives genes at these loci are largely unknown, so that functional investigation and clinical translation of molecular research data are still inhibited. To identify putatively causative genes, we searched for highly significant mutational burden in candidate genes. METHODS: We analyzed 84 candidate genes in...     »