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: | Brittany Lasseigne |
| Institution: | University of Alabama at Birmingham |
| Department: | Cell, Developmental and Integrative Biology |
| Country: | |
| Proposed Analysis: | Genomic instability (GIN) is a hallmark of aging (López-Otín et al., 2013). As a type of GIN, Chromosomal Instability (CIN) has been described in various degenerative diseases associated with age (Niccoli et al., 2012). CIN is hypothesized to be a key player in Alzheimer disease’s (AD) neuropathology, as it mediates neuronal cell loss, a primary symptom of AD dementia (Yurov et al., 2019). While CIN has been implicated in neurodegenerative diseases, its role in AD etiology and progression, as well as its utility as a therapeutic marker, remains to be determined (Cummings et al., 2019; Hou et al., 2017; Yurov et al., 2019). CIN can be measured through total functional aneuploidy and copy number variation (CNV), describing either whole chromosome differences or amplifications and deletions within chromosomes. We have developed an R package – CINmetrics – that calculates six different chromosomal instability scores and allows direct comparisons between the scores (Oza et al., 2021). CINmetrics enables the analysis of AD CIN profiles with the following 6 scores: number of bases with alterations, number of breakpoints, TAI (Baumbusch et al., 2013), mod TAI, FGA (Chin et al., 2007), and copy number aberration (CNA). We will first apply our CIN methodology to public human microarray data to evaluate CIN in AD and different AD metrics with respect to age and sex. We will assess associations between chromosomal instability scores and age or sex by a Wilcox test. Using the same microarray data used for our CINmetrics tool application, we will call CNVs using established pipelines and R packages (Pique-Regi et al., 2010; Wang et al., 2007; Zhou et al., 2021). CNVs will be evaluated between conditions with respect to age and sex. We will also generate our own sequencing data (DNA microarray and long-read Nanopore) from 3xTg-AD mouse hippocampus across 2 time points to evaluate disease progression and compare findings from our public data analyses to findings from our own pre- and post-symptomatic mouse sequencing analyses. This work is funded by NHGRI (project number: 3R00HG009678-04S1). |
| Additional Investigators | |
| Investigator's Name: | Timothy Howton |
| Proposed Analysis: | Genomic instability (GIN) is a hallmark of aging (López-Otín et al., 2013). As a type of GIN, Chromosomal Instability (CIN) has been described in various degenerative diseases associated with age (Niccoli et al., 2012). CIN is hypothesized to be a key player in Alzheimer disease’s (AD) neuropathology, as it mediates neuronal cell loss, a primary symptom of AD dementia (Yurov et al., 2019). While CIN has been implicated in neurodegenerative diseases, its role in AD etiology and progression, as well as its utility as a therapeutic marker, remains to be determined (Cummings et al., 2019; Hou et al., 2017; Yurov et al., 2019). CIN can be measured through total functional aneuploidy and copy number variation (CNV), describing either whole chromosome differences or amplifications and deletions within chromosomes. We have developed an R package – CINmetrics – that calculates six different chromosomal instability scores and allows direct comparisons between the scores (Oza et al., 2021). CINmetrics enables the analysis of AD CIN profiles with the following 6 scores: number of bases with alterations, number of breakpoints, TAI (Baumbusch et al., 2013), mod TAI, FGA (Chin et al., 2007), and copy number aberration (CNA). We will first apply our CIN methodology to public human microarray data to evaluate CIN in AD and different AD metrics with respect to age and sex. We will assess associations between chromosomal instability scores and age or sex by a Wilcox test. Using the same microarray data used for our CINmetrics tool application, we will call CNVs using established pipelines and R packages (Pique-Regi et al., 2010; Wang et al., 2007; Zhou et al., 2021). CNVs will be evaluated between conditions with respect to age and sex. We will also generate our own sequencing data (DNA microarray and long-read Nanopore) from 3xTg-AD mouse hippocampus across 2 time points to evaluate disease progression and compare findings from our public data analyses to findings from our own pre- and post-symptomatic mouse sequencing analyses. This work is funded by NHGRI (project number: 3R00HG009678-04S1). |
| Investigator's Name: | Tabea Soelter |
| Proposed Analysis: | Genomic instability (GIN) is a hallmark of aging (López-Otín et al., 2013). As a type of GIN, Chromosomal Instability (CIN) has been described in various degenerative diseases associated with age (Niccoli et al., 2012). CIN is hypothesized to be a key player in Alzheimer disease’s (AD) neuropathology, as it mediates neuronal cell loss, a primary symptom of AD dementia (Yurov et al., 2019). While CIN has been implicated in neurodegenerative diseases, its role in AD etiology and progression, as well as its utility as a therapeutic marker, remains to be determined (Cummings et al., 2019; Hou et al., 2017; Yurov et al., 2019). CIN can be measured through total functional aneuploidy and copy number variation (CNV), describing either whole chromosome differences or amplifications and deletions within chromosomes. We have developed an R package – CINmetrics – that calculates six different chromosomal instability scores and allows direct comparisons between the scores (Oza et al., 2021). CINmetrics enables the analysis of AD CIN profiles with the following 6 scores: number of bases with alterations, number of breakpoints, TAI (Baumbusch et al., 2013), mod TAI, FGA (Chin et al., 2007), and copy number aberration (CNA). We will first apply our CIN methodology to public human microarray data to evaluate CIN in AD and different AD metrics with respect to age and sex. We will assess associations between chromosomal instability scores and age or sex by a Wilcox test. Using the same microarray data used for our CINmetrics tool application, we will call CNVs using established pipelines and R packages (Pique-Regi et al., 2010; Wang et al., 2007; Zhou et al., 2021). CNVs will be evaluated between conditions with respect to age and sex. We will also generate our own sequencing data (DNA microarray and long-read Nanopore) from 3xTg-AD mouse hippocampus across 2 time points to evaluate disease progression and compare findings from our public data analyses to findings from our own pre- and post-symptomatic mouse sequencing analyses. This work is funded by NHGRI (project number: 3R00HG009678-04S1). |
