Utilising genomic association data for causal inference in anorexia nervosa.

Anorexia nervosa (AN) is a prevalent psychiatric disorder with high rates of mortality and limited treatment options. AN is a complex disorder, for which common variation contributes to disorder risk. To dissect the genetic architecture of AN, a variety of statistical methods can be applied. Many of these utilise genome-wide association study (GWAS) datasets to investigate biological mechanisms within disease progression in addition to broader associations between complex traits. GWAS for AN have revealed important biological insights, however, these have not translated into new pharmacotherapies. Here, we review the application of statistical methods that use GWAS, to investigate the relationship between genetic variation, biochemical compounds and complex traits to identify potential relationships which could advance our understanding of disease biology. We discuss genetic variant association data for AN, the application of gene-based and complex trait level correlation methods and approaches for establishing evidence of causality between complex traits and AN. These methods all contribute to the growing literature regarding the genetic influences of AN risk and demonstrate that statistical analysis utilising genetic data is a valuable tool to progress our understanding of this disease.