Book Description
Common genetic variability has failed to explain a large fraction of the heritability of complex disease. In contrast, rare genomic variation plays an important role in novel Mendelian phenotypes and complex traits, and is the most common type of genetic variation in populations (Tennessen et al, 2012). International large sequencing initiatives have demonstrated that individual genetic variability exceeds prior estimates (Fu et al, 2013), and complex traits are mediated through additive genetic variation (Hill WG, 2008). Identification of these rare variant profiles is relevant to pinpoint potential disease-related mechanisms in complex disease; however, due to the substantial number of possible targets in the genome and the variability between individuals, this task was not easily attainable until the advent of next-generation sequencing (NGS) and the subsequent ability to target any portion of the genome. These analyses require processing extensive amounts of NGS data using robust and accurate bioinformatics. To improve the ability to quantitatively characterize rare variability in complex phenotypes, I have participated in the development of bioinformatic pipelines that enable detection of rare substitutions, insertions and deletions from a few target genes in anonymous pools of DNA (Vallania, Ramos et al, 2010; Vallania, Ramos et al, 2012) and extended the capability of these pipelines to allow highly sensitive, specific and efficient quantification of rare variation from targeted capture of hundreds of candidate genes up to entire exomes in individually indexed ("bar-coded") NGS data (Ramos et al, 2012). I have demonstrated the utility of these algorithms through the identification of disease-causing genes in complex phenotypes such as fatal extreme microcephaly (Ramos et al, 2014) and congenital anomalies of the kidney or lower urinary tract (Chatterjee, Ramos et al, 2012). Additional types of rare epigenetic variation, such as DNA methylation, may further influence complex phenotypes and biological processes (Hernandez et al, 2012; Leung et al, 2012; Bell et al, 2011). However, outside of costly whole genome bisulfite sequencing, methods for large scale, targeted DNA methylation analysis (e.g. methylated DNA immunoprecipitation (MeDIP), reduced representation bisulfate sequencing (RRBS), methylation sensitive restriction enzyme (MSRE) digestion) have demonstrated conflicting results. To develop more robust bioinformatics for the quantification of rare epigenomic variation in complex traits, I have developed a novel genome-wide murine methylome hybridization capture array and adapted my previously established pipeline to enable highly sensitive methylation analysis. Using this array and bioinformatic pipeline, I have identified a discreet number of age and tissue-specific methylation changes in the mouse genome (manuscript in preparation), as well as, identification of novel tissue-specific DNA methylation patterns of genes involved in neurodevelopment (Hing, Ramos; et al.). Taken together, the goal of this study is to facilitate the quantification of different forms of rare variation, which will lead to novel insights in complex phenotypes and diseases.
