Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four healthcare systems

Abstract

OBJECTIVE: Individuals at high risk for schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health consequences of a high genetic risk of schizophrenia are poorly understood, despite being relevant to treatment decisions. METHOD:We used electronic health records for 106,160 patients from four healthcare systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites.RESULTS: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS=1.55 [95% confidence interval (CI), 1.4-1.7], p=4.48 x 10 -16 ) and patients in the highest risk decile of the PRS distribution had up to 4.6-fold increased odds of schizophrenia compared to those in the bottom decile (95% CI, 2.9-7.3, p=1.37 x 10 -10 ). PRSs were also positively associated with a range of other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. CONCLUSIONS:We demonstrate that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in healthcare settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. Our results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in healthcare systems.

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

“…These disease-specific enrichments in neurons, including the MDN transcriptomes, and the a priori functional importance of the MDN both for psychosis and body weight, feeding, and metabolism [5,6], would provide strong rationale to explore the genomic risk architectures of SCZ and BMI in cell-specific manner. However, as mentioned above, charting sequences carrying risk variants for schizophrenia and BMI on the linear genome is largely non-informative from the perspective of cross-disorder comparison [18,19], with very few single nucleotide polymorphisms (SNPs) implicated in both conditions [70]. To re-examine and further confirm this observation, we surveyed schizophrenia GWAS summary statistics involving 105,318 subjects combined from the Psychiatric Genomics Consortium and CLOZUK [56] and counted 12/139 (< 5%) SCZ risk loci harboring one or more of the 289 risk SNPs for the 339,224 subjects BMI GWAS [48], with > 80% of SCZ risk loci separated by > 1 Mb linear genome sequence from the nearest BMI index SNP (Additional file 2: Table S4; Additional file 3: Figure S3).…”

“…10, SCZ and BMI risk sequences from domains in chrs. 2,7,16,17,19,20, and 22 are interconnected with 16p11.2 neurodevelopmental risk sequences often affected by micro-deletions and -duplications associated with obesity or underweight phenotypes, micro-and macrocephaly in conjunction with symptoms on the autism and psychosis spectrum [87,94] (Fig. 3e).…”

“…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].…”

Physical activity offsets genetic risk for incident depression assessed via electronic health records in a biobank cohort study

“…Detailed information about genotyping, quality control (QC), imputation, and population assignment procedures for the Partners Biobank is available elsewhere (Zheutlin et al, 2019). Briefly, DNA samples for each participant were genotyped on one of three Illumina arrays (MEGA, MEGA EX , and MEG BeadChip); arrays were merged and only genetic variants (i.e., single-nucleotide polymorphisms [SNPs]) on all three were retained.…”

“…We selected a two-year window to capture depression episodes over a clinically relevant period of time that was relatively proximal to survey completion. To reduce the likelihood of outcome misclassification, individuals with only one code (N = 371) were removed for analysis as in previous studies (Zheutlin et al, 2019) (resulting N = 10,016). For sensitivity analyses, we also created a variable reflecting evidence of clinically significant depression before the study window (i.e., at any point in the system before the year before the survey).…”