Literature DB >> 35675840

Progress in the genetics of restless legs syndrome: the path ahead in the era of whole-genome sequencing.

Fulya Akçimen1,2, Patrick A Dion2,3, Guy A Rouleau1,2,3.   

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Year:  2022        PMID: 35675840      PMCID: PMC9366632          DOI: 10.1093/sleep/zsac136

Source DB:  PubMed          Journal:  Sleep        ISSN: 0161-8105            Impact factor:   6.313


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Restless legs syndrome (RLS) is a common neurological disorder characterized by an urge to move the legs, particularly in the evening and at night [1]. Epidemiological studies have shown that 5%–15% of European and North American populations suffer from RLS, whereas the prevalence is lower in most Asian populations, ranging from 1% to 3% [2]. Because of its high prevalence, RLS leads to an important social and economic impact by disrupting sleep, affecting work productivity and the quality of life. Twin and familial aggregation studies have suggested that genetic factors contribute up to 70% of the risk of developing RLS [3, 4]. Linkage studies in multiplex families have identified several large genomic regions [5, 6], but to date, no definitive causal variant or gene has been identified based on this work. Genome-wide association studies (GWAS) have robustly identified many predisposing loci. A recent meta-analysis identified 22 RLS predisposing loci in individuals of European ancestry [6-8]. However, these variants only explain approximately 20% of the heritability [7]. Several small-scale hypothesis-based candidate gene studies were performed for RLS, none of which have been replicated in additional populations [5]. In this issue of SLEEP, Schormair et al. [9] analyzed previously reported RLS associations for replication in the International EU-RLS-GENE Consortium GWAS dataset that includes 6228 RLS cases and 10992 controls of European ancestry [7, 9]. They showed that candidate variants and genes identified in these small-scale hypothesis-based candidate gene studies were not validated in the EU-RLS-GENE dataset. Because the Schormair et al. study was sufficiently powered to detect these reported associations, the authors concluded that these previous reports were likely to be false-positive, and that future studies with larger sample sizes as well as stringent significance thresholds are needed to obtain reliable associations in RLS. This study confirms that small association studies are not suitable to identify common variants in RLS [10]. This negative study highlights the fact that the currently known variants account for only 20% of the RLS genetic risk, and so a large fraction of the heritability remains to be discovered [7]. Where is this missing heritability? One source may be intermediate frequency variants with moderate effect sizes. Another may be variants that remain difficult to find using technology used to date, such as repeat sequences and insertion/deletion variants. Third, the variants identified in the GWAS studies are likely not the biologically causative variants responsible for the signal. Finding these biologically active variants will allow a more precise measure of the contribution of these loci to the risk of developing RLS. The decreasing cost of next-generation sequencing will allow the generation of whole-genome sequencing data on large cohorts, enabling the search for all these possible sources for the missing heritability. Emerging technologies, such as long-read sequencing will also facilitate the detection of disease-related structural variants, such as large insertions, deletions, and repeat expansions. Most large-scale RLS genetic studies included mainly patients of European ancestry [7, 8]. The genetic factors predisposing to RLS in other populations need to be studied. Some may be like those found in the European descent population, but surely other genetic factors are at play in these other population. Finally, molecular studies aimed at understanding the biology of RLS, and the roles of the known loci in disease pathogenesis will define pathways critical to the disease. This knowledge can be used to better focus genetic studies and identify or validate new factors predisposing to RLS. Taken together, although 15 years of genome-wide efforts identified several risk loci, whole-genome sequencing analysis, the inclusion of diverse populations, and biological studies will be essential to unravel the genetic mechanisms leading to RLS.
  10 in total

1.  Family study of restless legs syndrome in Quebec, Canada: clinical characterization of 671 familial cases.

Authors:  Lan Xiong; Jacques Montplaisir; Alex Desautels; Amina Barhdadi; Gustavo Turecki; Anastasia Levchenko; Pascale Thibodeau; Marie-Pierre Dubé; Claudia Gaspar; Guy A Rouleau
Journal:  Arch Neurol       Date:  2010-05

2.  Canadian restless legs syndrome twin study.

Authors:  L Xiong; K Jang; J Montplaisir; A Levchenko; P Thibodeau; C Gaspar; G Turecki; G A Rouleau
Journal:  Neurology       Date:  2007-05-08       Impact factor: 9.910

3.  Power versus phenotyping precision of genome-wide association studies on sleep traits.

Authors:  Konrad Oexle
Journal:  Sleep       Date:  2018-11-01       Impact factor: 5.849

Review 4.  Genetics of restless legs syndrome: An update.

Authors:  Félix J Jiménez-Jiménez; Hortensia Alonso-Navarro; Elena García-Martín; José A G Agúndez
Journal:  Sleep Med Rev       Date:  2017-08-31       Impact factor: 11.609

Review 5.  Epidemiology of restless legs syndrome: a synthesis of the literature.

Authors:  Maurice M Ohayon; Ruth O'Hara; Michael V Vitiello
Journal:  Sleep Med Rev       Date:  2011-07-26       Impact factor: 11.609

6.  Reassessment of candidate gene studies for idiopathic restless legs syndrome in a large genome-wide association study dataset of European ancestry.

Authors:  Barbara Schormair; Chen Zhao; Aaro V Salminen; Konrad Oexle; Juliane Winkelmann
Journal:  Sleep       Date:  2022-08-11       Impact factor: 6.313

Review 7.  Restless legs syndrome: diagnostic criteria, special considerations, and epidemiology. A report from the restless legs syndrome diagnosis and epidemiology workshop at the National Institutes of Health.

Authors:  Richard P Allen; Daniel Picchietti; Wayne A Hening; Claudia Trenkwalder; Arthur S Walters; Jacques Montplaisi
Journal:  Sleep Med       Date:  2003-03       Impact factor: 3.492

8.  Large genome-wide association study identifies three novel risk variants for restless legs syndrome.

Authors:  Maria Didriksen; Muhammad Sulaman Nawaz; Joseph Dowsett; Steven Bell; Christian Erikstrup; Ole B Pedersen; Erik Sørensen; Poul J Jennum; Kristoffer S Burgdorf; Brendan Burchell; Adam S Butterworth; Nicole Soranzo; David B Rye; Lynn Marie Trotti; Prabhjyot Saini; Lilja Stefansdottir; Sigurdur H Magnusson; Gudmar Thorleifsson; Thordur Sigmundsson; Albert P Sigurdsson; Katja Van Den Hurk; Franke Quee; Michael W T Tanck; Willem H Ouwehand; David J Roberts; Eric J Earley; Michael P Busch; Alan E Mast; Grier P Page; John Danesh; Emanuele Di Angelantonio; Hreinn Stefansson; Henrik Ullum; Kari Stefansson
Journal:  Commun Biol       Date:  2020-11-25

Review 9.  Identification of novel risk loci for restless legs syndrome in genome-wide association studies in individuals of European ancestry: a meta-analysis.

Authors:  Barbara Schormair; Chen Zhao; Steven Bell; Erik Tilch; Aaro V Salminen; Benno Pütz; Yves Dauvilliers; Ambra Stefani; Birgit Högl; Werner Poewe; David Kemlink; Karel Sonka; Cornelius G Bachmann; Walter Paulus; Claudia Trenkwalder; Wolfgang H Oertel; Magdolna Hornyak; Maris Teder-Laving; Andres Metspalu; Georgios M Hadjigeorgiou; Olli Polo; Ingo Fietze; Owen A Ross; Zbigniew Wszolek; Adam S Butterworth; Nicole Soranzo; Willem H Ouwehand; David J Roberts; John Danesh; Richard P Allen; Christopher J Earley; William G Ondo; Lan Xiong; Jacques Montplaisir; Ziv Gan-Or; Markus Perola; Pavel Vodicka; Christian Dina; Andre Franke; Lukas Tittmann; Alexandre F R Stewart; Svati H Shah; Christian Gieger; Annette Peters; Guy A Rouleau; Klaus Berger; Konrad Oexle; Emanuele Di Angelantonio; David A Hinds; Bertram Müller-Myhsok; Juliane Winkelmann
Journal:  Lancet Neurol       Date:  2017-11       Impact factor: 59.935

  10 in total

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