Mapping anorexia nervosa genes to clinical phenotypes

Abstract

Background Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes. Methods Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations. Results Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex. Conclusions Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.

Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study

“…First, we used hundreds of thousands of objectively measured weights, taken as part of the routine clinical practice in BioMe EHR data, to construct weight trajectories, yielding significant power. The extensive weight measurements made it possible to construct weight trajectories in a sample size of 21 487 for the first time, with a median of seven weight measurements per patient (IQR 5-10) spanning 8 years (5)(6)(7)(8)(9)(10). Next, we used the repeated weight measures collected at assessment centres in the UK Biobank for replication in a generally healthy population with a sample size of 59 561, with a median of two weight measures (2-2) per person spanning 9 years (6)(7)(8)(9)(10).…”

“…The extensive weight measurements made it possible to construct weight trajectories in a sample size of 21 487 for the first time, with a median of seven weight measurements per patient (IQR 5-10) spanning 8 years (5)(6)(7)(8)(9)(10). Next, we used the repeated weight measures collected at assessment centres in the UK Biobank for replication in a generally healthy population with a sample size of 59 561, with a median of two weight measures (2-2) per person spanning 9 years (6)(7)(8)(9)(10). Objective weight measures are less prone to subjective perception and self-sensitivity to weight than the self-reported weight changes in the UK Biobank.…”

“…However, given the low prevalence of anorexia nervosa (approximately 1%) 31 and our exclusion of individuals with any eating disorder diagnosis, we expect the impact of diagnostic contamination to be minimal. 9 Fifth, our analysis relied on PRS derived from a European anorexia nervosa GWAS, 16 reducing predictive accuracy in individuals with African and Hispanic Latino ancestry. Future GWAS that include diverse populations are crucial to accurately estimate the heritability of weight trajectories and the effect of anorexia nervosa PRS across ancestries.…”

“…Previous weight-related PheWAS examined body-mass index (BMI)-related genetic scores, [2][3][4][5] genetic variants, 6,7 or average BMI values, 8 but they have not yet explored longitudinal weight trajectories. Therefore, in this study, we aimed to use the multiple weight measures in the BioMe Biobank 9 to identify diseases associated with weight trajectories in a patient population. Gaining insights into the associations of weight trajectories with diseases could aid in monitoring disease progression and predicting prognosis.…”

Multiomic prioritisation of risk genes for anorexia nervosa

“…Anorexia TWAS data have previously been leveraged to reveal insights into the pathogenesis of the disorder (11)(12)(13), however, these studies did not specifically prioritise genes that could be involved in the disorder. In addition, extending TWAS to the use of protein and mRNA isoform weights has not previously been investigated for anorexia nervosa and could further identify novel risk genes.…”