A familial modeling framework for advancing precision medicine for children with neuropsychiatric disorders

Abstract

Children with Noonan syndrome experience cognitive challenges and symptoms of attention-deficit/hyperactivity disorder (ADHD), anxiety, and depression. Children also have differences in brain structure and function. All the above are attributed to the genetic changes that cause Noonan syndrome. Yet there is a great variability in terms of the level of a child's symptoms which makes it difficult for parents and clinicians to predict a child's outcome and plan treatment. Aside from the genetic changes causing Noonan syndrome, parents share genetic information and experiences with their children. Given this knowledge, the present study utilized parent cognitive and behavioural traits to predict a child's corresponding traits.

Results indicated that parent cognition predicted a child's cognition. A parent's level of depression, anxiety, and ADHD symptoms also predicted child outcomes in corresponding domains. Utilizing parent traits allowed for more precise prediction of corresponding child outcomes than when parent traits were not used. Parent cognition was also significantly associated with child's brain structure which was measured using magnetic resonance imaging. Since brain structure represents cumulative effects of brain development, this evidence suggests that a parent's cognition influences a child's brain development. Understanding how parent traits influence brain development will help tease apart the effects of shared factors such as genetics and environment, and unique factors such as Noonan syndrome genetic changes.

Current care for children with Noonan syndrome follows the same watchful, waiting approach used for other disorders like ADHD. While not typically included in diagnostic testing, measuring parent traits is a multidimensional, noninvasive method that can add information regarding expected outcomes that is useful for parents and clinicians. Predictive modeling (employing statistics to predict outcomes) using parent traits may also be useful clinically in other disorders and, with future research, this framework can help advance a precision medicine approach which involves individualized treatment utilizing information about each patient.