Dataset Information

The Evolution of Genetics: Alzheimer's and Parkinson's Diseases.

ABSTRACT: Genetic discoveries underlie the majority of the current thinking in neurodegenerative disease. This work has been driven by the significant gains made in identifying causal mutations; however, the translation of genetic causes of disease into pathobiological understanding remains a challenge. The application of a second generation of genetics methods allows the dissection of moderate and mild genetic risk factors for disease. This requires new thinking in two key areas: what constitutes proof of pathogenicity, and how do we translate these findings to biological understanding. Here we describe the progress and ongoing evolution in genetics. We describe a view that rejects the tradition that genetic proof has to be absolute before functional characterization and centers on a multi-dimensional approach integrating genetics, reference data, and functional work. We also argue that these challenges cannot be efficiently met by traditional hypothesis-driven methods but that high content system-wide efforts are required.

SUBMITTER: Singleton A

PROVIDER: S-EPMC4936485 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Evolution of Genetics: Alzheimer's and Parkinson's Diseases.

Singleton Andrew A Hardy John J

Neuron 20160601 6

Genetic discoveries underlie the majority of the current thinking in neurodegenerative disease. This work has been driven by the significant gains made in identifying causal mutations; however, the translation of genetic causes of disease into pathobiological understanding remains a challenge. The application of a second generation of genetics methods allows the dissection of moderate and mild genetic risk factors for disease. This requires new thinking in two key areas: what constitutes proof o ...[more]

PMID: 27311081

Similar Datasets

Project description:Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences.In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine).This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.

Dataset Information

The Evolution of Genetics: Alzheimer's and Parkinson's Diseases.

Publications

The Evolution of Genetics: Alzheimer's and Parkinson's Diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets