There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Simon McAdams |
| Institution: | Cambridge Quantum Computing Ltd |
| Department: | Quantum Machine Learning |
| Country: | |
| Proposed Analysis: | Title: Modelling Epistatic Interactions using Quantum Computing Proposed Analysis: This project aims to explore quantum computing approaches to the epistatic interaction problem, i.e. understanding the role of single nucleotide polymorphism (SNP) interactions in disease traits, such as mild cognitive impairment and Alzheimer’s disease. ADNI Dataset Analysis: The project will start by using genetic data from ADNI using a reduced version of Whole Genome Sequencing containing 25k variants (mostly pathogenic non-synonymous SNPs) to run PLINK v1.9 epistatic calculation between cognitively normal and Alzheimer’s subjects in reasonable amount of time (2-3 hrs). This data reduction makes the use of PLINK for creating classical baselines and tests more convenient. Nevertheless, further reduction may be required for the development of a quantum baseline. An additional suitable test dataset will be prepared, which should be sufficiently small but still retains key properties of the larger ADNI dataset. If the comparison between the classical and quantum baselines leads to satisfactory results, the project will aim to extend to running the method using all homozygous minor allele carrier variants (~7.7M) and then full epistasis on 3.2 billion variants. |
| Additional Investigators | |
| Investigator's Name: | Mattia Fiorentini |
| Proposed Analysis: | Title: Modelling Epistatic Interactions using Quantum Computing Proposed Analysis: This project aims to explore quantum computing approaches to the epistatic interaction problem, i.e. understanding the role of single nucleotide polymorphism (SNP) interactions in disease traits, such as mild cognitive impairment and Alzheimer’s disease. ADNI Dataset Analysis: The project will start by using genetic data from ADNI using a reduced version of Whole Genome Sequencing containing 25k variants (mostly pathogenic non-synonymous SNPs) to run PLINK v1.9 epistatic calculation between cognitively normal and Alzheimer’s subjects in reasonable amount of time (2-3 hrs). This data reduction makes the use of PLINK for creating classical baselines and tests more convenient. Nevertheless, further reduction may be required for the development of a quantum baseline. An additional suitable test dataset will be prepared, which should be sufficiently small but still retains key properties of the larger ADNI dataset. If the comparison between the classical and quantum baselines leads to satisfactory results, the project will aim to extend to running the method using all homozygous minor allele carrier variants (~7.7M) and then full epistasis on 3.2 billion variants. |
| Investigator's Name: | Matthias Rosenkranz |
| Proposed Analysis: | Title: Modelling Epistatic Interactions using Quantum Computing Proposed Analysis: This project aims to explore quantum computing approaches to the epistatic interaction problem, i.e. understanding the role of single nucleotide polymorphism (SNP) interactions in disease traits, such as mild cognitive impairment and Alzheimer’s disease. ADNI Dataset Analysis: The project will start by using genetic data from ADNI using a reduced version of Whole Genome Sequencing containing 25k variants (mostly pathogenic non-synonymous SNPs) to run PLINK v1.9 epistatic calculation between cognitively normal and Alzheimer’s subjects in reasonable amount of time (2-3 hrs). This data reduction makes the use of PLINK for creating classical baselines and tests more convenient. Nevertheless, further reduction may be required for the development of a quantum baseline. An additional suitable test dataset will be prepared, which should be sufficiently small but still retains key properties of the larger ADNI dataset. If the comparison between the classical and quantum baselines leads to satisfactory results, the project will aim to extend to running the method using all homozygous minor allele carrier variants (~7.7M) and then full epistasis on 3.2 billion variants. |
| Investigator's Name: | Gergana Velikova |
| Proposed Analysis: | Title: Modelling Epistatic Interactions using Quantum Computing Proposed Analysis: This project aims to explore quantum computing approaches to the epistatic interaction problem, i.e. understanding the role of single nucleotide polymorphism (SNP) interactions in disease traits, such as mild cognitive impairment and Alzheimer’s disease. ADNI Dataset Analysis: The project will start by using genetic data from ADNI using a reduced version of Whole Genome Sequencing containing 25k variants (mostly pathogenic non-synonymous SNPs) to run PLINK v1.9 epistatic calculation between cognitively normal and Alzheimer’s subjects in reasonable amount of time (2-3 hrs). This data reduction makes the use of PLINK for creating classical baselines and tests more convenient. Nevertheless, further reduction may be required for the development of a quantum baseline. An additional suitable test dataset will be prepared, which should be sufficiently small but still retains key properties of the larger ADNI dataset. If the comparison between the classical and quantum baselines leads to satisfactory results, the project will aim to extend to running the method using all homozygous minor allele carrier variants (~7.7M) and then full epistasis on 3.2 billion variants. |
| Investigator's Name: | Marcello Benedetti |
| Proposed Analysis: | Title: Modelling Epistatic Interactions using Quantum Computing Proposed Analysis: This project aims to explore quantum computing approaches to the epistatic interaction problem, i.e. understanding the role of single nucleotide polymorphism (SNP) interactions in disease traits, such as mild cognitive impairment and Alzheimer’s disease. ADNI Dataset Analysis: The project will start by using genetic data from ADNI using a reduced version of Whole Genome Sequencing containing 25k variants (mostly pathogenic non-synonymous SNPs) to run PLINK v1.9 epistatic calculation between cognitively normal and Alzheimer’s subjects in reasonable amount of time (2-3 hrs). This data reduction makes the use of PLINK for creating classical baselines and tests more convenient. Nevertheless, further reduction may be required for the development of a quantum baseline. An additional suitable test dataset will be prepared, which should be sufficiently small but still retains key properties of the larger ADNI dataset. If the comparison between the classical and quantum baselines leads to satisfactory results, the project will aim to extend to running the method using all homozygous minor allele carrier variants (~7.7M) and then full epistasis on 3.2 billion variants. |
| Investigator's Name: | Sam Duffield |
| Proposed Analysis: | Title: Modelling Epistatic Interactions using Quantum Computing Proposed Analysis: This project aims to explore quantum computing approaches to the epistatic interaction problem, i.e. understanding the role of single nucleotide polymorphism (SNP) interactions in disease traits, such as mild cognitive impairment and Alzheimer’s disease. ADNI Dataset Analysis: The project will start by using genetic data from ADNI using a reduced version of Whole Genome Sequencing containing 25k variants (mostly pathogenic non-synonymous SNPs) to run PLINK v1.9 epistatic calculation between cognitively normal and Alzheimer’s subjects in reasonable amount of time (2-3 hrs). This data reduction makes the use of PLINK for creating classical baselines and tests more convenient. Nevertheless, further reduction may be required for the development of a quantum baseline. An additional suitable test dataset will be prepared, which should be sufficiently small but still retains key properties of the larger ADNI dataset. If the comparison between the classical and quantum baselines leads to satisfactory results, the project will aim to extend to running the method using all homozygous minor allele carrier variants (~7.7M) and then full epistasis on 3.2 billion variants. |
