Search

Children with Autism Spectrum Disorder (ASD) exhibit a wide diversity in type, number, and severity of social deficits as well as communicative and cognitive difficulties. It is a challenge to categorize the phenotypes of a particular ASD patient with their unique genetic variants. There is a need for a better understanding of the connections between genotype information and the phenotypes to sort out the heterogeneity of ASD. In this study, single nucleotide polymorphism (SNP) and phenotype data obtained from a simplex ASD sample are combined using a PheWAS-inspired approach to construct a phenotype-phenotype network. The network is clustered, yielding groups of etiologically related phenotypes. These clusters are analyzed to identify relevant genes associated with each set of phenotypes. The results identified multiple discriminant SNPs associated with varied phenotype clusters such as ASD aberrant behavior (self-injury, compulsiveness and hyperactivity), as well as IQ and language skills. Overall, these SNPs were linked to 22 significant genes. An extensive literature search revealed that eight of these are known to have strong evidence of association with ASD. The others have been linked to related disorders such as mental conditions, cognition, and social functioning.Clinical relevance- This study further informs on connections between certain groups of ASD phenotypes and their unique genetic variants. Such insight regarding the heterogeneity of ASD would support clinicians to advance more tailored interventions and improve outcomes for ASD patients.

Feature selection provides a useful method for reducing the size of large data sets while maintaining integrity, thereby improving the accuracy of neural networks and other classifiers. However, running multiple feature selection models and their accompanying classifiers can make interpreting results difficult. To this end, we present a data-driven methodology called Meta-Best that not only returns a single feature set related to a classification target, but also returns an optimal size and ranks the features by importance within the set. This proposed methodology is tested on six distinct targets from the well-known REGARDS dataset: Deceased, Self-Reported Diabetes, Light Alcohol Abuse Risk, Regular NSAID Use, Current Smoker, and Self-Reported Stroke. This methodology is shown to improve the classification rate of neural networks by 0.056 using the ROC Area Under Curve metric compared to a control test with no feature selection.

Clinicians need better tools to assess severity, prognosis, and recovery from mild Traumatic Brain Injury (mTBI), which can cause long term impairment. To enable better mTBI outcome prediction, an initial step is to analyze the trajectory of recovery metrics over time. This study provides an assessment of recovery trajectories of mTBI while incorporating heterogeneity of individual responses. We analyze the trajectories over multiple discrete time points from baseline to 6 months post injury using a combination of neurocognitive and postural stability assessments and serum biomarkers. The data, obtained from FITBIR, consists of concussed subjects and a matched control group, to allow for comparison in prognostic assessment. Outcomes derived from this exploratory analysis will aid future studies in developing a mTBI recovery timeline model.Clinical relevance- This study further informs clinicians as to the recovery trajectory of clinical measures and biomarkers after mTBI to support return to play decisions. GFAP biomarker and measures related to balance, memory, orientation, and concentration were significantly different than controls early after mTBI.

Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.