medRxivGenetic & Genomic MedicinePreprint
Household income does not affect the pleiotropy of schizophrenia genetic liability with mental and physical health outcomes
Manuela R. Kouakou, Brenda Cabrera‐MendozaiD, Gita A. PathakiD, Tyrone D. CannoniD, Renato A PolimantiiD
Abstract
Background and HypothesisIndividuals with schizophrenia (SCZ) suffer from comorbidities that substantially reduce their life expectancy. Socioeconomic inequalities could contribute to many of the negative health outcomes associated with SCZ. Study DesignWe investigated genome-wide datasets related to SCZ (52,017 cases and 75,889 controls) from the Psychiatric Genomics Consortium, household income (HI; N=361,687) from UK Biobank, and 2,202 medical endpoints assessed in up to 342,499 FinnGen participants. A phenome-wide genetic correlation analysis of SCZ and HI was performed, also assessing whether SCZ genetic correlations were influenced by HI effect on SCZ. Additionally, SCZ and HI direct effects on medical endpoints were estimated using multivariable Mendelian randomization (MR). Study ResultsSCZ and HI showed overlapping genetic correlations with 70 traits (p<2.89x10-5), including mental health, substance use, gastrointestinal illnesses, reproductive outcomes, liver diseases, respiratory problems, and musculoskeletal phenotypes. SCZ genetic correlations with these traits were not affected by HI effect on SCZ. Considering Bonferroni multiple testing correction (p<7.14x10-4), MR analysis indicated that SCZ and HI may affect medical abortion (SCZ odds ratio, OR=1.07; HI OR=0.78), panic disorder (SCZ OR=1.20; HI OR=0.60), personality disorders (SCZ OR=1.31; HI OR=0.67), substance use (SCZ OR=1.2; HI OR=0.68), and adjustment disorders (SCZ OR=1.18; HI OR=0.78). Multivariable MR analysis confirmed that SCZ effects on these outcomes were independent of HI. ConclusionsThe effect of SCZ genetic liability on mental and physical health may not be strongly affected by socioeconomic differences. This suggests that SCZ-specific strategies are needed to reduce negative health outcomes affecting patients and high-risk individuals.
- organism: human
- assay: GWAS summary statistics
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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