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  • Wegelius, Asko; Pankakoski, Maiju; Tomppo, Liisa; Lehto, Ulriika; Lonnqvist, Jouko; Suvisaari, Jaana; Paunio, Tiina; Hennah, William (2015)
    Pre- and perinatal environmental factors have been shown to increase schizophrenia risk particularly when combined with genetic liability. The investigation of specific gene environment interactions in the etiology of psychiatric disorders has gained momentum. We used multivariate GEE regression modeling to investigate the interaction between genes of the DISCI pathway and birth weight, in relation to schizophrenia susceptibility in a Finnish schizophrenia family cohort. The study sample consisted of 457 subjects with both genotype and birth weight information. Gender and place of birth were adjusted for in the models. We found a significant interaction between birth weight and two NDE1 markers in relation to increased schizophrenia risk: a four SNP haplotype spanning NDE1 (b = 1.26, SE= 0.5, p = 0.012) and one of its constituent SNPs rs4781678 (b = 1.33, SE = 0.51, p = 0.010). Specifically, high birth weight (> 4000 g) was associated with increased schizophrenia risk among subjects homozygous for the previously identified risk alleles. The study was based on a family study sample with high genetic loading for schizophrenia and thus our findings cannot directly be generalized as representing the general population. Our results suggest that the functions mediated by NDE1 during the early stages of neurodevelopment are susceptible to the additional disruptive effects of pre- and perinatal environmental factors associated with high birth weight, augmenting schizophrenia susceptibility. (C) 2015 The Authors. Published by Elsevier Ireland Ltd.
  • Gyllenberg, David; McKeague, Ian W.; Sourander, Andre; Brown, Alan S. (2020)
    Objectives Few interactions between risk factors for schizophrenia have been replicated, but fitting all such interactions is difficult due to high-dimensionality. Our aims are to examine significant main and interaction effects for schizophrenia and the performance of our approach using simulated data. Methods We apply the machine learning technique elastic net to a high-dimensional logistic regression model to produce a sparse set of predictors, and then assess the significance of odds ratios (OR) with Bonferroni-corrected p-values and confidence intervals (CI). We introduce a simulation model that resembles a Finnish nested case-control study of schizophrenia which uses national registers to identify cases (n = 1,468) and controls (n = 2,975). The predictors include nine sociodemographic factors and all interactions (31 predictors). Results In the simulation, interactions with OR = 3 and prevalence = 4% were identified with = 80% power. None of the studied interactions were significantly associated with schizophrenia, but main effects of parental psychosis (OR = 5.2, CI 2.9-9.7; p <.001), urbanicity (1.3, 1.1-1.7; p = .001), and paternal age >= 35 (1.3, 1.004-1.6; p = .04) were significant. Conclusions We have provided an analytic pipeline for data-driven identification of main and interaction effects in case-control data. We identified highly replicated main effects for schizophrenia, but no interactions.