In a study published in in February

In a study published in in February 2017, investigators from the Infant Brain Imaging Study (IBIS) described promising findings in screening children for autism spectrum disorders (ASDs). Using raas inhibitor magnetic resonance imaging (MRI) to assess cortical development and brain volume, investigators were able to predict in infants as young as 6-12 months of age at risk for ASD—that is, with an ASD-affected sibling—which children would develop ASD by 24 months of age. While this study requires further validation in a larger cohort—15 of 106 high-risk subjects ultimately developed ASD—it speaks to the vast unmet medical need of biomarkers for neurodevelopmental and psychiatric disorders. This need is especially striking given evidence that early intervention may be critical for correcting an array of mental illnesses. For instance, with particular regard to ASDs, a long-term follow-up of the parent-mediated social communication therapy for young children with autism (PACT) controlled trial, published in in November 2016 showed that autistic children receiving therapy between 2-4 years of age showed clinical improvement up to six years after the therapy had ended. The global burden of mental illness is staggering, with recent data published in in February 2016 suggesting that psychiatric disorders are the leading cause of years lost to disability. These data are simply estimates, though, largely confounded by how mental illnesses are classified and diagnosed. At present, the approved diagnoses of all psychiatric disorders—from schizophrenia and major depressive disorder (MDD) to obsessive-compulsive disorder and ASDs—are arrived at through reporting of mental and behavioral symptoms by patients or caregivers to mental health professionals. Many disorders catalogued in the Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases describe a spectrum of symptoms. For example, for a diagnosis of MDD, a patient must display at least five of nine symptoms in the DSM. It is therefore feasible that two patients, both with MDD, share only one common symptom. Cultural and social norms and stigmas can further complicate patient and caregiver reporting of symptoms or how these symptoms are interpreted by mental health professionals. Co-morbidities with other psychiatric disorders are also not uncommon and contribute to a dizzying heterogeneity in possible diagnoses. Clinical biomarkers could help transcend these limitations.
There is a wealth of evidence from both epidemiological studies () and animal models () that environmental exposures during early mammalian development may alter developmental trajectories in a manner that results in altered disease susceptibility and stress responsiveness in later life. This phenomenology is termed ‘developmental programming.’ Developmental programming is of key interest because it has the potential to expand phenotypic diversity within a population in a manner that is not entirely accounted for by the underlying genetic diversity. Furthermore, there are many indicators that such factors may contribute to increased risk of human cardio-metabolic disease. Yet, despite convincing evidence at the phenotypic and epidemiological level, the molecular underpinnings of this phenomenon have remained enigmatic. The last decade has seen the emergence of genome-wide association studies (GWAS) aimed at addressing the genetic foundation of common human traits and multifactorial disease. Whilst GWAS have been highly successful in delineating many common genetic variants that are associated with particular traits, a surprising outcome is that, collectively, the common variants associated with a given trait only explain a relatively small proportion of its heritability. This intriguing conundrum of ‘missing heritability’ has led to much speculation as to its origin (). This article will discuss recent work that suggests that developmental programming may act at genomic regions not profiled within the context of GWAS to contribute to phenotypic diversity through epigenetic mechanisms. Such regions of the genome are overlooked in the context of large-scale genomic studies as current sequencing and computational technologies are unable to resolve their repetitive sequence architecture to sufficient accuracy.