Differentially expressed genes in placenta have an important relationship with polygenic risk for mental disorders, such as, the schizophrenia. Lieberman group researchers hypothesized that the most significant associations found in GWAS could contribute to the development of mechanisms sensitive to environmental factors that are relatively common among patients, such as perinatal risk factors. Exposure to early in life complications represents an early environmental context, which influences the cumulative genomic risk for schizophrenia, derived from significant GWAS loci. The set of genes within these genomic loci is highly expressed in the placenta and exhibits differential enrichment in abnormal invasive placental states, suggesting that these genes interact with environmental factors that compromise placental and obstetric health. When the set of significant loci of GWAS was analyzed specifically by loci containing genes differentially expressed in the placenta and dynamically regulated in abnormal placental states, it was possible to derive a polygenic risk score (PRS), called PlacPRS. These results highlight the importance of assessing early environmental factors, such as obstetric complications, in addition to genetic risk, to investigate the joint effect on susceptibility to neurodevelopmental disorders. The results also point to the placenta as a crucial mediator of this interaction, probably to neurodevelopmental disorders in general, highlighting the need for more research on the physiology of the placenta in the context of brain development and genetic risk. The suggestion of a distinct and orthogonal biology for the placental component of genetic risk shows that genetic prediction can be increased by deconstructing genomic risk in discrete sub-compartments that represent alternatives for biological risk. A recent study applied a multiple polygenic scoring (MPS) approach, which explores the genetic correlations between the outcome trait and a multitude of characteristics, with no assumptions about the relationships between predictors. Using a suitable multiple regression model to deal with a large number of correlated predictors, the researchers tested how well these models predict results in a set of independent tests. They demonstrate that an MPS approach that combines GWAS data of various characteristics produces better phenotype prediction at the individual level than predictive models of a single score in independent test data, that is, they show that the polygenic variation associated with other traits than prognosis to be predicted, contributes to the prediction. The predictive power of a polygenic score is not only a function of the genetic correlation between the discovery and the characteristic of the result, but also of the statistical power present in the GWAS discovery on which it is based. The MPS approach exploits the fact that even GWAS with genetically distant traits can contribute with predictive power if their power is superior to GWAS with more proximal characteristics. The main reason for maximizing predictive power using the MPS approach is to predict individual phenotypes as accurately as possible. Thus, we believe that the integration of different polygenic risk scores and their genetic variants with placental data and epigenetic mechanisms associated with the mediation of the response to exposure to stress can contribute to the determination of important genomic areas for risk prediction models for phenotypes associated with trajectory of neurodevelopment. Our working hypothesis is that it is possible to build specific platforms and analysis models that will increase the predictive power for neurodevelopmental disorders.
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