Neuroimaging in complex polygenic disorders

2018-05-07Virtually all non-Mendelian complex diseases are triggered by the additive effect of polygenicity and environmental factors. As the past decade has seen an explosion in GWA studies identifying genetic variants associated with disease risk, the next great challenge for researchers is to put a functional description on the mechanism driving these associations. This dissertation describes several imaging genetics studies with the aim of understanding how genetic risk variants influence multiple sclerosis and coronary artery disease, two hallmark complex polygenic disorders. Additionally, we describe several methods for quantifying “genetic connectivity” across traits and measurements. ❧ Multiple sclerosis is a neurodegenerative demyelinating disease with an unknown pathogenesis. The disease is highly heterogeneous with a highly complex genetic architecture. Examining the overlap between genetic loci that affect MS risk and regional brain structure may reveal mechanisms that influence lesion development in the brain. Based on recent genomic screens for common variants associated with MS, we examined pleiotropic overlap between summary statistics from the most recent MS case-control ImmunoChip study and GWAS summary statistics for seven subcortical brain volumes (nucleus accumbens, amygdala, caudate nucleus, globus pallidus, hippocampus, putamen, thalamus), and intracranial volume. We used continuous inflation analysis (CIA) to test profiles of overlap. We found significant evidence of overlap between variants in MS risk genes and the volumes of the hippocampus and thalamus. In follow up analyses of these structures, we used pleiotropy-informed conditional FDR and identified 24 novel variants associated with both MS risk and regional brain volumes. This discovery of gene variants associated with both MS risk and hippocampal and thalamic volumes in the brain may narrow the search for causal pathways mediating their effect on the human brain. ❧ The mechanism of how single nucleotide polymorphisms (SNPs) confer disease risk is not completely understood. MRI is utilized clinically and in research as a diagnostic tool in the identification of the hallmark white-matter lesions. We extracted gene expression maps of MS risk genes from the Allen Brain Atlas and overlaid them with results from Rinker et al., 2014 & 2015 where we showed an effect of several MS susceptibility alleles and polygenic risk score (PRS) on white-matter integrity in healthy young adults, and replicated these results in healthy aging adults. ❧ The degree to which shared genetic factors influence CAD and brain morphology will give important insight to the pathogenesis of CAD. Here, using SNP Effect Concordance Analysis (SECA) we combined summary statistics from the CARDIoGRAM GWAS with those from Hibar et al. 2015 GWAS of brain volumes, we tested for (1) global genetic pleiotropy (2) genetic concordance between genetic variants associated with CAD and brain volume. ❧ MRI and DTI measures of brain volume, cortical thickness and white matter (WM) integrity are commonly used in imaging genetics studies, but the genetic relationship between these measures is not well understood. Here we use structural equation models (SEM) in a twin design to test the genetic correlation between these common imaging measures. MRI and DTI data from 442 participants (mean age: 23.5 years +/- 2.1 SD; 151 women; 98 MZ pairs, 123 DZ pairs) were analyzed using standardized ENIGMA protocols. We found significant genetic associations between measure of the integrity (fractional anisotropy, or FA) of several WM tracts and subcortical volume ROIs, notably the thalamus and pallidum. Correlation was low between cortical thickness or volume and DTI measures from the WM. Total cortical surface area was, however, highly correlated with FA in several WM regions and all of the subcortical volume regions. These results may be useful for future studies assessing specific genetic associations, and offer insight into the genetics underlying common imaging measures. ❧ Finally we describe future plans to extend this work