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: | Stephen Hsu |
| Institution: | Michigan State University |
| Department: | Physics and Astronomy |
| Country: | |
| Proposed Analysis: | This application is done in conjunction with an application to ADSP for the dataset NG00067, which requires ADNI credentials. Our goal is to use machine learning (ML) to understand and predict genetic cause and risk to Alzheimer's disease (AD). Our study is purely data informatic in nature and follows common ML-practices, including data cleaning and quality control of both samples and genotypes, pre-processing (such as correcting for covariates like age, sex and general population structure), training and parameter optimization, and evaluation using hold-out sets. Our primary goal is to increase the predictive power for disease status from only the genetic information. We use several different ML-algorithms to build predictors, such as compressed sensing (also known as LASSO), neural networks, and horseshoe Bayesian regression. The underlying genetic architectures are studied through dissecting and analyzing the trained predictors — which loci are important? how important are they (in particular the polygenetic importance beyond APOE)? what are the genetic correlations for these loci within and across different ancestries or other population groups? etc. — informing both fundamental disease research and future predictor algorithm designs. A priori, we will at least use the phenotypic characteristics sex, age/age of onset, race, ethnicity, AD status, and family history. We will also investigate whether a more informative case variable can be constructed as a function of the mentioned variables in conjunction with AD status comments. Other phenotypic characteristics may also be used in the continuous improvement of our predictor algorithms. All analysis will be performed on high-performance computing clusters at Michigan State University (MSU), where they will be stored under strict security, accessible only to PI and three other MSU staff (who also sign the Data Transfer Agreement), in accord with regulations. We will publish all scientific results in peer-reviewed journals and make developed general algorithms public. Published predictors will be made available, both to the public and returned to the NIAGADS. |
| Additional Investigators | |
| Investigator's Name: | Louis Lello |
| Proposed Analysis: | Our goal is to use machine learning (ML) to understand and predict genetic cause and risk to Alzheimer's disease (AD). Our study is purely data informatic in nature and follows common ML-practices, including data cleaning and quality control of both samples and genotypes, pre-processing (such as correcting for covariates like age, sex and general population structure), training and parameter optimization, and evaluation using hold-out sets. Our primary goal is to increase the predictive power for disease status from only the genetic information. We use several different ML-algorithms to build predictors, such as compressed sensing (also known as LASSO), neural networks, and horseshoe Bayesian regression. The underlying genetic architectures are studied through dissecting and analyzing the trained predictors — which loci are important? how important are they (in particular the polygenetic importance beyond APOE)? what are the genetic correlations for these loci within and across different ancestries or other population groups? etc. — informing both fundamental disease research and future predictor algorithm designs. A priori, we will at least use the phenotypic characteristics sex, age/age of onset, race, ethnicity, AD status, and family history. We will also investigate whether a more informative case variable can be constructed as a function of the mentioned variables in conjunction with AD status comments. Other phenotypic characteristics may also be used in the continuous improvement of our predictor algorithms. All analysis will be performed on high-performance computing clusters at Michigan State University (MSU), where they will be stored under strict security, accessible only to PI and three other MSU staff (who also sign the Data Transfer Agreement), in accord with regulations. We will publish all scientific results in peer-reviewed journals and make developed general algorithms public. Published predictors will be made available, both to the public and returned to the NIAGADS. |
| Investigator's Name: | Timothy Raben |
| Proposed Analysis: | Our goal is to use machine learning (ML) to understand and predict genetic cause and risk to Alzheimer's disease (AD). Our study is purely data informatic in nature and follows common ML-practices, including data cleaning and quality control of both samples and genotypes, pre-processing (such as correcting for covariates like age, sex and general population structure), training and parameter optimization, and evaluation using hold-out sets. Our primary goal is to increase the predictive power for disease status from only the genetic information. We use several different ML-algorithms to build predictors, such as compressed sensing (also known as LASSO), neural networks, and horseshoe Bayesian regression. The underlying genetic architectures are studied through dissecting and analyzing the trained predictors — which loci are important? how important are they (in particular the polygenetic importance beyond APOE)? what are the genetic correlations for these loci within and across different ancestries or other population groups? etc. — informing both fundamental disease research and future predictor algorithm designs. A priori, we will at least use the phenotypic characteristics sex, age/age of onset, race, ethnicity, AD status, and family history. We will also investigate whether a more informative case variable can be constructed as a function of the mentioned variables in conjunction with AD status comments. Other phenotypic characteristics may also be used in the continuous improvement of our predictor algorithms. All analysis will be performed on high-performance computing clusters at Michigan State University (MSU), where they will be stored under strict security, accessible only to PI and three other MSU staff (who also sign the Data Transfer Agreement), in accord with regulations. We will publish all scientific results in peer-reviewed journals and make developed general algorithms public. Published predictors will be made available, both to the public and returned to the NIAGADS. |
| Investigator's Name: | Erik Widen |
| Proposed Analysis: | Our goal is to use machine learning (ML) to understand and predict genetic cause and risk to Alzheimer's disease (AD). Our study is purely data informatic in nature and follows common ML-practices, including data cleaning and quality control of both samples and genotypes, pre-processing (such as correcting for covariates like age, sex and general population structure), training and parameter optimization, and evaluation using hold-out sets. Our primary goal is to increase the predictive power for disease status from only the genetic information. We use several different ML-algorithms to build predictors, such as compressed sensing (also known as LASSO), neural networks, and horseshoe Bayesian regression. The underlying genetic architectures are studied through dissecting and analyzing the trained predictors — which loci are important? how important are they (in particular the polygenetic importance beyond APOE)? what are the genetic correlations for these loci within and across different ancestries or other population groups? etc. — informing both fundamental disease research and future predictor algorithm designs. A priori, we will at least use the phenotypic characteristics sex, age/age of onset, race, ethnicity, AD status, and family history. We will also investigate whether a more informative case variable can be constructed as a function of the mentioned variables in conjunction with AD status comments. Other phenotypic characteristics may also be used in the continuous improvement of our predictor algorithms. All analysis will be performed on high-performance computing clusters at Michigan State University (MSU), where they will be stored under strict security, accessible only to PI and three other MSU staff (who also sign the Data Transfer Agreement), in accord with regulations. We will publish all scientific results in peer-reviewed journals and make developed general algorithms public. Published predictors will be made available, both to the public and returned to the NIAGADS. |
