Heterogeneity in the longitudinal courses of global functioning in children at familial risk of major psychiatric disorders: Association with trauma and familial characteristics.

OBJECTIVES: The extent to which heterogeneity in childhood risk trajectories may underlie later heterogeneity in schizophrenia (SZ), bipolar disorder (BP), and major depressive disorder (MDD) remains a chief question. Answers may optimally be found by studying the longitudinal trajectories of children born to an affected parent. We aimed to differentiate trajectories of global functioning and their sensitive periods from the age of 6 to 17 years in children at familial risk (FHRs). METHODS: First, a latent class mixed model analysis (LCMM) was applied to yearly ratings of the Children's Global Assessment Scale (CGAS) from the age of 6 to 17 years in 170 FHRs born to a parent affected by DSM-IV SZ (N = 37), BP (N = 82) or MDD (N = 51). Then, we compared the obtained Classes or trajectories of FHRs in terms of sex, parental diagnosis, IQ, child clinical status, childhood trauma, polygenic risk score (PRS), and outcome in transition to illness. RESULTS: The LCMM on yearly CGAS trajectories identified a 4-class solution showing markedly different childhood and adolescence dynamic courses and temporal vulnerability windows marked by a functioning decline and a degree of specificity in parental diagnosis. Moreover, IQ, trauma exposure, PRS level, and timing of later transition to illness differentiated the trajectories. Almost half (46%) of the FHRs exhibited a good and stable global functioning trajectory. CONCLUSIONS: FHRs of major psychiatric disorders show heterogeneous functional decline during development associated with parental diagnosis, polygenic risk loading, and childhood trauma.

Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits.

Genetic correlations between human traits and disorders such as schizophrenia (SZ) and bipolar disorder (BD) diagnoses are well established. Improved prediction of individual traits has been obtained by combining predictors of multiple genetically correlated traits derived from summary statistics produced by genome-wide association studies, compared with single trait predictors. We extend this idea to penalized regression on summary statistics in Multivariate Lassosum, expressing regression coefficients for the multiple traits on single nucleotide polymorphisms (SNPs) as correlated random effects, similarly to multi-trait summary statistic best linear unbiased predictors (MT-SBLUPs). We also allow the SNP contributions to genetic covariance and heritability to depend on genomic annotations. We conducted simulations with two dichotomous traits having polygenic architecture similar to SZ and BD, using genotypes from 29,330 subjects from the CARTaGENE cohort. Multivariate Lassosum produced polygenic risk scores (PRSs) more strongly correlated with the true genetic risk predictor and had better discrimination power between affected and non-affected subjects than previously published sparse multi-trait (PANPRS) and univariate (Lassosum, sparse LDpred2, and the standard clumping and thresholding) methods in most simulation settings. Application of Multivariate Lassosum to predict SZ, BD, and related psychiatric traits in the Eastern Quebec SZ and BD kindred study revealed associations with every trait stronger than those obtained with univariate sparse PRSs, particularly when heritability and genetic covariance depended on genomic annotations. Multivariate Lassosum thus appears promising to improve prediction of genetically correlated traits with summary statistics for a selected subset of SNPs.