Mutational Spectrum of Causal Genes for Neurological/Neurodegenerative Diseases and Endometriosis Identified via High Resolution Genome Wide Copy Number Analysis

While single gene rare variants have been shown to play a significant role in Early-Onset Alzheimer’s Disease (EOAD), their role in Late-Onset (LOAD) has not been emphasised.
The gene discovery methodology we have developed at Population Bio allows for unbiased exploration of highly informative genomic variants in any cohort of interest. Our approach is based on ultra-high resolution copy number variant (CNV) analysis. We have invested heavily in such analysis on normal populations. These are used as comparators for cohorts of interest, such as LOAD. In our LOAD work, this analysis generated a list of CNVs which were either absent in the normal populations we studied or else present at significantly higher frequency in the LOAD cohort. Such CNVs are routinely annotated to determine if they overlie known genes and/or regulatory regions. As an example, we have discovered a deletion in 3% of our LOAD cases, which is present in <= 1% of normals. This deletion disrupts a transcription factor binding site in the intron of a gene, which, via GeneHancer, is known to control exon 1 of the gene. The gene in question is novel to LOAD, and is an important metabolic gene, with known biology. It is vital that we validate this finding by analysis of independent LOAD datasets. In addition, we wish to validate other genes discovered in the same manner
We have very deep experience of analyzing WGS/WES datasets. Our focus will be to pull out of the available WGS/WES datasets all the variants for the candidate genes of interest. Such variants, including SNVs, indels and CNVs (called using a variety of tools we have experience with) will be analyzed by reference to databases of normal individuals:
i.CNVs, by reference to our own internal database but also gnomad (https://gnomad.broadinstitute.org) CNV data and DGV (http://dgv.tcag.ca)
ii.SNVs/indels, by reference to gnomad
These analyses will allow us to determine whether there exists a mutational burden for our candidate genes of interest in independent LOAD cohorts, and will serve as validation/refutation.
The main phenotype of interest will be definitive diagnoses of LOAD, based on neuropathological and clinical cognitive analyses

Most of the common conditions that affect large numbers of the general population have a genetic basis. While progress has been rapid in the field of cancer, the same cannot be said for common, non-cancer, conditions, such as Late-Onset Alzheimer's Disease (LOAD). It is pretty clear now that not all cases of LOAD represent the same disease, in terms of what is the cause. Our approach has been to consider common diseases as collections of rare subgroups, each of which has a specific cause and which, in due course, will have a specific treatment. We have pioneered and implemented a method to rapidly uncover potentially causal genes in common disorders and will use the data generated from this study to strengthen our discoveries, by validating a set of novel candidate genes we have identified in LOAD
Our project will allow us to:
1.Define subsets of disease
2.Work with pharmaceutical companies to develop drugs that will specifically target each subset of disease. In some cases, disease progression may be halted by the therapies developed. In some cases, reversal and/or cure may be possible