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

Genetic Association Studies in Restless Legs Syndrome: Risk Variants & Ethnic Differences

BACKGROUND

Genetic association studies have not produced consistent results in restless legs syndrome (RLS).

OBJECTIVES

To conduct a systematic review on genetic association studies in RLS to highlight the common gene variants and ethnic differences.

METHODOLOGY

We conducted Pubmed, Embase, and Cochrane search using terms "Genetic association studies" and "restless legs syndrome" for candidate gene-based studies. Out of the initial 43 studies, 18 case control studies (from 2012 to 2022) were included. Thirteen studies including 10794 Caucasian subjects (4984 RLS cases and 5810 controls) and five studies involving 2009 Asian subjects (796 RLS cases and 1213 controls) were tabulated and analyzed. In addition, three Genome-Wide Association Studies (GWAS) in Asians and Europeans/Caucasians were included for comparisons.

RESULTS

In the Asian population, gene variants in BST1, SNCA Rep1, IL1B, BTBD9, and MAP2K5/SKOR1 increased the risk of RLS (odds ratio range 1.2-2.8). In Caucasian populations, examples of variants that were associated with an increased risk of RLS (odds ratio range 1.1-1.9) include those in GABRR3 TOX3, ADH1B, HMOX1, GLO1, DCDC2C, BTBD9, SKOR1, and SETBP1. Based on the meta-analysis of GWAS studies, the rs9390170 variant in UTRN gene was identified to be a novel genetic marker for RLS in Asian cohorts, whereas rs113851554 in MEIS1 gene was a strong genetic factor among the >20 identified gene variants for RLS in Caucasian populations.

CONCLUSION

Our systemic review demonstrates that multiple genetic variants modulate risk of RLS in Caucasians (such as MEIS1 BTBD9, MAP2K5) and in Asians (such as BTBD9, MAP2K5, and UTRN).

Selection of genetic instruments in Mendelian randomisation studies of sleep traits

This review explores the criteria used for the selection of genetic instruments of sleep traits in the context of Mendelian randomisation studies. This work was motivated by the fact that instrument selection is the most important decision when designing a Mendelian randomisation study. As far as we are aware, no review has sought to address this to date, even though the number of these studies is growing rapidly. The review is divided into the following sections which are essential for genetic instrument selection: 1) Single-gene region vs polygenic analysis; 2) Polygenic analysis: biologically-vs statistically-driven approaches; 3) P-value; 4) Linkage disequilibrium clumping; 5) Sample overlap; 6) Type of exposure; 7) Total (R2) and average strength (F-statistic) metrics; 8) Number of single-nucleotide polymorphisms; 9) Minor allele frequency and palindromic variants; 10) Confounding. Our main aim is to discuss how instrumental choice impacts analysis and compare the strategies that Mendelian randomisation studies of sleep traits have used. We hope that our review will enable more researchers to take a more considered approach when selecting genetic instruments for sleep exposures.

Leveraging genetic discoveries for sleep to determine causal relationships with common complex traits

Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.