Risk variants and genetic architecture of ALS in sex- and population-specific subgroups
MND Association
Trinity College Dublin
Amyotrophic lateral sclerosis (ALS) is significantly heritable, but, despite considerable investment, remains enigmatic in its root causes for the majority of cases. Genomic studies of ALS such as genome-wide association studies have yielded relatively few definitively associated genetic loci and limited insight into the genetic architecture of the disease. A likely contributor to this is the restrictive models used in estimating the contribution of genetics to ALS risk. These models carry numerous assumptions that are violated in ALS, with imbalanced stratification of disease incidence between sexes and apparent non-uniformity of genetic determinants of disease across populations. Furthermore, most genomic studies in ALS have been restricted to the single phenotype of “classical” ALS, despite abundant evidence for causal or pleiotropic relationships between ALS and a number of other traits. Together, these assumptions restrict the extent to which ALS can be adequately explained from a genomic perspective and limit the biological insights that can be derived from genomic studies. The proposed research will, through collaboration with Prof. Jan Veldink at UMC Utrecht and the Project MinE Consortium, re-analyse the largest genomic datasets available for ALS, including genome-wide association study data for over 150,000 individuals and whole-genome sequence data for more than 10,000 individuals. This re-analysis will challenge these assumptions and genetically assess ALS risk from sex- and population-specific perspectives, as well as mapping out causal and non-causal genetic relationships with secondary neurological traits within these subgroups. Under a range of plausible models for which there is considerable support, this improves power to understand the genetic basis of ALS and its subtypes, which will be crucial for the future of patient categorisation in precision therapeutics and for disease prediction in unaffected individuals, as well as yielding novel insights into the biology of ALS and improving cellular and animal models of the disease.
2.1 Biological and endogenous factors