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Pack, A. I.

Publications and source records attributed to Pack, A. I..

2 recordsLinked to original sources

Calculating the Effects of Autism Risk Gene Variants on Dysfunction of Biological Processes Identifies Clinically-Useful Information

Autism spectrum disorders (ASD) are neurodevelopmental conditions that are influenced by genetic factors and encompass a wide-range and severity of symptoms. The details of how genetic variation contributes to variable symptomatology are unclear, creating a major challenge for translating vast amounts of data into clinically-useful information. To determine if variation in ASD risk genes correlates with symptomatology differences among individuals with ASD, thus informing treatment, we developed an approach to calculate the likelihood of genetic dysfunction in Gene Ontology-defined biological processes that have significant overrepresentation of known risk genes. Using whole-exome sequence data from 2,381 individuals with ASD included in the Simons Simplex Collection, we identified likely damaging variants and conducted a clustering analysis to define subgroups based on scores reflecting genetic dysfunction in each process of interest to ASD etiology. Dysfunction in cognition-related genes distinguished a distinct subset of individuals with increased social deficits, lower IQs, and reduced adaptive behaviors when compared to individuals with no evidence of cognition-related gene dysfunction. In particular, a stop-gain variant in the pharmacogene encoding cycloxygenase-2 was associated with having an IQ<70 (i.e. intellectual disability), a key comorbidity in ASD. We expect that screening genes involved in cognition for deleterious variants in ASD cases may be useful for identifying clinically-informative factors that should be prioritized for functional follow-up. This has implications in designing more comprehensive genetic testing panels and may help provide the basis for more informed treatment in ASD.

genetics

Estimating sleep parameters using an accelerometer without sleep diary

Wrist worn raw-data accelerometers are used increasingly in large scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window), was compared against sleep diary in 3752 participants (range=60-82years) and polysomnography in sleep clinic patients (N=28) and in healthy good sleepers (N=22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.

epidemiology