Post by Stephanie Williams
What's the science?
Amyloid lateral sclerosis (ALS) is a progressive, fatal disease that attacks nerve cells in the brain and spinal cord. Several studies have identified a handful of lifestyle and environmental factors that can contribute to ALS, but studies employing advanced genomic techniques to identify factors generally focus on single traits. It is still unclear which lifestyle factors and conditions can predispose individuals to develop ALS. This week in the Annals of Neurology, Bandres-Ciga and colleagues employ a data-driven approach to examine whether genetic risk for particular traits also share genetic risk for ALS, and whether any causal associations exist between these traits and ALS.
How did they do it?
The authors focused on two types of analysis in their study of two massive public datasets of genome-wide association study (GWAS) information. First, they used a method called Linkage disequilibrium score regression to see if polygenic risk factors that contribute to the phenotype of interest also contribute to the risk of developing ALS. Linkage disequilibrium refers to the correlation of nearby genetic variants such that the association of alleles is non-random in a population. Second, they used Mendelian randomization to assess whether there were causal links between the traits they identified and ALS. The authors identified single nucleotide polymorphisms (SNPs) associated with a trait, and extracted data for the identified SNPs for a large-scale GWAS of ALS. They used publicly available databases, which contained a wide range of traits, and tested traits to determine if they altered the risk of developing ALS. The authors also used several other types of analysis, such as multivariate analysis, genetic risk profiling and Bayesian colocalization, to further parse apart the variants involved in LDL cholesterol that contribute to risk for ALS.
What did they find?
Using disequilibrium score regression, the authors found eighteen traits that were genetically correlated with ALS. Nine of the traits were related to educational attainment and intelligence, and the authors found a negative correlation between education/intelligence traits and presence of ALS. Traits related to light physical activity showed a decreased risk for developing ALS, while traits related to moderate activity, like performing a job that involved walking or standing, were positively associated with ALS. This is an important distinction, which suggests that light rather than excessive exercise may exert a neuroprotective effect. The authors also found a correlation between exposure to tobacco or being around smokers and ALS. Results from Mendelian randomization suggested that LDL cholesterol and coronary heart disease was causally linked to ALS. Finally, the authors performed a Bayesian colocalization analysis and identified two SNPS related to increased LDL cholesterol that could be causally linked to ALS.
What's the impact?
The authors applied advanced genetic analytical techniques to genomic data of a large scale (spanning approximately 25 million individuals) in order to understand the traits associated with developing ALS. Based on these analyses, the authors were able to make recommendations to lower the risk of ALS, such as lowering blood cholesterol levels. Using a data-driven approach, the authors have generated a resource which can now be used for further explorations of risk factors for ALS.
Bandres-Ciga et al. Shared Polygenic Risk and Causal Inferences in Amyotrophic Lateral Sclerosis. Annals of Neurology (2019). Access the original scientific publication here.