The goal of this proposed research is to generate a genetic architecture of brain development.
We aim to achieve this goal in two solid steps. We first utilize our existing brain MR imaging measurements (traditional MRI volumetry, brain connectivity, resting state activity) of 235 individuals, 125 with autism and 110 typically developing subjects, to build a data structure of brain regions of interest that have at least 50% estimated heritability for typical brains, sorted first by size and then by components of structura-functional variability. In our next step, we interrogate whole-exome and whole-genome sequences and network analysis of molecular pathways by state-of-the-art molecular genetics methods in reference to this data structure. We expect that this detailed analysis of variability and extremes in brain imaging phenotypes in autism will result in the discovery of new genetic associations that reveal new biological pathways and advance our understanding of autism neuropathology and its severely impairing clinical manifestations. Our results will thus change imaging genetics in autism research.
Given the high prevalence of autism and the immense, lifelong adverse impact it has on affected individuals and their families, the present lack of any effective treatment or preventive intervention based on the biology of the disorder has produced an burgeoning, very expensive public health burden on the United States and worldwide horizon. This application seeks to break this long-standing barrier to gene discovery and public health benefit by plumbing the wealth of information found in abnormal brain variation and to employ the knowledge gained from this previously untapped source to identify genetic associations and molecular pathways that lead to autism and its effective treatment.
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|Maadooliat, Mehdi; Bansal, Naveen K; Upadhya, Jiblal et al. (2016) The Decay of Disease Association with Declining Linkage Disequilibrium: A Fine Mapping Theorem. Front Genet 7:217|
|Schrodi, Steven J (2016) The Use of Multiplicity Corrections, Order Statistics and Generalized Family-Wise Statistics with Application to Genome-Wide Studies. PLoS One 11:e0154472|
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