Gruen Lab Projects

NeuroStack allows you to easily run neuroimaging workflows using AWS cloud computing. It is designed to set up the AWS infrastructure needed to perform neuroimaging analysis at scale and is freely available to the research community. Large, and frequently accessed neuroimaging datasets are increasingly being hosted on AWS, and researchers are interested in moving their neuroimaging analysis workflows to the cloud. However, processing brain imaging data on AWS differs substantially from researchers’ typical workflows and requires a steep learning curve. NeuroStack facilitates researchers' migration to AWS by building a pipeline of AWS infrastructure optimized for neuroimaging workflows, so that researchers can immediately begin analyzing their data in the cloud. NeuroStack was built in collaboration with the University of Massachusetts Medical School.

To identify rare and common variants that contribute to Reading Disability, Math Disability, Attention Deficit Hyperactivity Disorder, and their co-occurence. The available data for RD, MD, and ADHD support the hypothesis that some genes affect multiple phenotypes (genetic pleiotropy), but the magnitude of any single gene’s effect may not be the same for each disorder. That is, DCDC2 may have a greater influence on reading ability so that it is detected in fairly broad linkage screens of RD, but its effect on MD is only noted with targeted genotyping of candidate genes. The finding of a molecular basis for co-occurence also supports the multiple deficit model of Pennington. We hypothesize (1) that rare variants are causal in families in which RD segregates by apparent Mendelian transmission, and (2) that common variants have pleiotropic effects across neurocognitive domains that include reading/language, math, and attention/executive function.

Our human genetic studies have recently uncovered a synergistic genetic interaction between two risk elements within the DYX2 locus: (1) READ1 (‘regulatory element associated with dyslexia 1’) within intron 2 of DCDC2 and (2) a risk haplotype within KIAA0319, another known dyslexia risk gene in the same locus as DCDC2. Using shift assays, mass spectrometry, and chromatin immunoprecipitation techniques, we determined that the potent transcription factor ETV6 specifically binds the READ1 element. Ongoing studies aim to determine the biological implications of READ1 and its possible regulatory capabilities, within DYX2 and throughout the genome. Future studies are planned to study the biological effects of READ1 alleles in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) that can be differentiated into neural progenitor cells and terminally differentiated neural cells.

In addition to traditional cognitive assessments, we also perform genetic association with non-invasive brain imaging phenotypes, using magnetic resonance imaging (MRI). We use several MRI protocols, including structural (T2 and diffusion weighted imaging), functional, structural connectivity (fractional anisotropy), and functional connectivity. Subjects for these studies are recruited from the Pediatric Imaging NeuroGenetics (PING) Study, as well as the GRaD Study. The goal of these imaging-genetics studies is to connect risk variants from our neurobehavioral genetic studies to the biological phenotypes observed with high-resolution structural and functional imaging.

The GRaD Study is a multi-center case/control study of the genetics of dyslexia in Hispanic-American and African-American children. Each child receives an extensive battery of standardized reading, language, IQ, attention, and motivation assessments. We collect DNA from every child in preparation for a genome-wide association study (GWAS) to identify genetic markers informative in understudied populations in the U.S. and Canada. To date we have enrolled over 1100 children from recruitment and testing centers in New Haven, Boston, Toronto, Denver, Boulder, Albuquerque, and Baltimore.