The Turner Laboratory is interested in understanding the genetic etiology of neurodevelopmental disorders to ultimately lead to better therapeutics and improvement of quality of life. We utilize both computational and experimental approaches in our lab to detect variants, perform statistical tests, and functionally assess potential variant effects.

Genomic approaches

To the right (on computer) or below (on mobile device) you will see a video with a schematic depicting the human genome. The different technologies we use in the laboratory are shown. As can be seen newer technologies (i.e., whole-genome sequencing) are allowing for better assessment of variation in the human genome. Note: inaccessible are regions that are not accessible by most genomic technologies.

Statistical Modeling

 To the right (on computer) or below (on mobile device) you will see a video showing a method we developed in collaboration with the laboratory of Dr. Rachel Karchin to assess clustering of missense variants. This method is called CLUstering by Mutation Position (CLUMP) and is one example of the types of novel statistical approaches we utilize to assess genomic data. Beyond development of the method itself, we have been able to show its utility in demonstrating that mutations involved in autosomal dominant disorders are more clustered than those in autosomal recessive disorders. We have also utilized this method to detect significant clustering of de novo missense mutations in certain proteins within individuals with neurodevelopmental disorders. 


Functional assessment 

 We have used a variety of functional approaches to assess protein-coding variation (shown at the right are the results of overexpression experiments in rat hippocampal neurons) and are now actively developing in vitro approaches to assess noncoding variation in the genome. We are also collaborating with Dr. Len Pennacchio at the Lawrence Berkeley National Laboratory to model some of these noncoding variants in vivo