Shared Genetic Loci Between Body Mass Index and Major Psychiatric Disorders

Abstract

IMPORTANCE People with major psychiatric disorders (MPDs) have a 10-to 20-year shorter life span than the rest of the population, and this difference is mainly due to comorbid cardiovascular diseases. Genome-wide association studies have identified common variants involved in schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) and body mass index (BMI), a key cardiometabolic risk factor. However, genetic variants jointly influencing MPD and BMI remain largely unknown. OBJECTIVE To assess the extent of the overlap between the genetic architectures of MPDs and BMI and identify genetic loci shared between them. DESIGN, SETTING, AND PARTICIPANTS Using a conditional false discovery rate statistical framework, independent genome-wide association study data on individuals with SCZ (n = 82 315), BIP (n = 51 710), MD (n = 480 359), and BMI (n = 795 640) were analyzed. The UK Biobank cohort (n = 29 740) was excluded from the MD data set to avoid sample overlap. Data were collected from August 2017 to May 2018, and analysis began July 2018. MAIN OUTCOMES AND MEASURESThe primary outcomes were a list of genetic loci shared between BMI and MPDs and their functional pathways.RESULTS Genome-wide association study data from 1 380 284 participants were analyzed, and the genetic correlation between BMI and MPDs varied (SCZ: r for genetic = −0.11, P = 2.1 × 10 −10 ; BIP: r for genetic = −0.06, P = .0103; MD: r for genetic = 0.12, P = 6.7 × 10 −10 ). Overall, 63, 17, and 32 loci shared between BMI and SCZ, BIP, and MD, respectively, were analyzed at conjunctional false discovery rate less than 0.01. Of the shared loci, 34% (73 of 213) in SCZ, 52% (36 of 69) in BIP, and 57% (56 of 99) in MD had risk alleles associated with higher BMI (conjunctional false discovery rate <0.05), while the rest had opposite directions of associations. Functional analyses indicated that the overlapping loci are involved in several pathways including neurodevelopment, neurotransmitter signaling, and intracellular processes, and the loci with concordant and opposite association directions pointed mostly to different pathways. CONCLUSIONS AND RELEVANCEIn this genome-wide association study, extensive polygenic overlap between BMI and SCZ, BIP, and MD were found, and 111 shared genetic loci were identified, implicating novel functional mechanisms. There was mixture of association directions in SCZ and BMI, albeit with a preponderance of discordant ones.

A chromosomal connectome for psychiatric and metabolic risk variants in adult dopaminergic neurons

“…This apparent functional convergence of the genetic risk architectures of cognitive [4] and metabolic [7,8] disorders within a specific cell type-the MDN-is of clinical relevance, given that metabolic sequelae, including excess body mass index [9], impaired glucose homeostasis [10] and dyslipidemias [11,12] (as well as their co-occurrence, clinically termed "metabolic syndrome" [13]), significantly contribute to medical comorbidities and early mortality, with 15-20-year gaps in life expectancy in subjects diagnosed with schizophrenia as compared to healthy controls [14][15][16]. However, cell typespecific cross-disorder exploration of the genomic risk architectures of schizophrenia and excess BMI and other metabolic traits is challenging [17] as these conditions show, on a genome-wide scale, only very limited or even discordant overlap based on cross-disorder correlation methods including LD score regression or correlation of polygenic risk scoring [18][19][20].…”

Phenotypically independent profiles relevant to mental health are genetically correlated

“…In addition to substantial polygenicity, previous findings have documented genetic overlap between disorders [8][9][10][11] , even in the absence of genetic correlations as recently demonstrated for schizophrenia and educational attainment 12,13 . Adding to the complexity, psychiatric disorders also overlap with multiple complex traits, such as BMI 14 and cardio-metabolic diseases 15 . Taken together, the landscape of current psychiatric genetics suggests highly complex patterns of associations and unclear specificity for many common psychiatric disorders.…”

Metabolic and Addiction Indices in Patients on Opioid Agonist Medication-Assisted Treatment: A Comparison of Buprenorphine and Methadone

“…The above metabolic restraint by insulin is, however, rendered inefficient by sweet and fatty 'junk' food 46,47,50 that is avidly consumed by opioid addicts 43,51,52 attributable to the exaggerated opioidergic activity enhancing the hedonic appeal of the unhealthy diets 21 . Therefore, a common result in vulnerable individuals could be a feedforward loop whereby high caloric content palatable food produces additional deterioration in the regulatory mechanisms prompting unhealthy eating patterns and opioid consumption to the extent that a bona fide MetS, OUD and comorbid conditions 53 may ensue.…”