Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease

Abstract

De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD.

Functional annotation of the human PTSD methylome identifies tissue-specific epigenetic variation across subcortical brain regions

“…Univariate analysis of our dataset identified a handful of genes with significant DMCs, a caveat noted in other postmortem methylation studies 44 . Previous work from our group and others have successfully applied "Joint Analysis" to transcriptomic and other high dimensional genomic data to identify significant biological signals where power maybe lacking 30,31,45,46 . Therefore, we employed a Markov random field model joint analysis to take advantage of the regional colocalization of sub nuclei of the amygdala and subregions of the hippocampus and integrate univariate summary statistics from differential methylation analysis with topology information from these regions in order to improve our power in detecting biologically significant DMCs.…”

“…In fact, we were only able to find a limited number of DMCs (and associated genes) between PTSD and controls in each brain region after the Benjamini-Hochberg correction (Figure 2). The Markov Random Field (MRF) model has been applied to both genome-wide association studies and bulk RNA-seq studies to model biological dependencies/networks in genomic and transcriptomic data [30][31][32] . In these previous Figure 2 | Univariate analysis reveals regional and sex-specific differences in CpG methylation between PTSD cases and controls (A) Two-sided Manhattan plot for PTSD case-control differential methylation using all samples included in the study.…”

Statistical methods for assessing the effects of de novo variants on birth defects

“…Compared to DAWN, N-DATA is a model that does not require summary statistics results from other methods such as TADA [ 55 ]. It directly incorporates PPI information into the prior risk gene status based on the Poisson mixture distribution.…”

Whole Exome Sequencing Uncovers the Genetic Complexity of Bicuspid Aortic Valve in Families with Early Onset Complications

“…We first identified rare gene variants known to cause congenital heart disease (CHD) in the EBAV cohort. The list of candidate genes included 29 HTAD genes that are on current clinical sequencing panels, as well as 12 BAV genes and 190 CHD genes that have strong cumulative evidence to cause BAV or related congenital malformations from human or animal model data (Supplemental Data) (10,11).…”