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Quantitative Phenotype Scan Statistic (QPSS) reveals rare variant associations with Alzheimer’s disease endophenotypes
Yuriko Katsumata
University of Kentucky
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0188-8094
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Current sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants. Scan statistic approaches have previously been adapted to genetic sequencing data. Unlike currently-employed association tests, scan-statistic-based approaches can both localize clusters of disease-related variants and, subsequently, examine the phenotype association within the resulting cluster. In this study, we present a novel Quantitative Phenotype Scan Statistic (QPSS) that extends an approach for dichotomous phenotypes to continuous outcomes in order to identify genomic regions where rare quantitative-phenotype-associated variants cluster.
Results: We demonstrate the performance and practicality of QPSS with extensive simulations and an application to a whole-genome sequencing (WGS) study of cerebrospinal fluid (CSF) biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Using QPSS, we identify regions of rare variant enrichment associated with levels of AD-related proteins, CSF Aβ1-42 and p-tau181P.
Conclusions: QPSS is implemented under the assumption that causal variants within a window have the same direction of effect. Typical self-contained tests employ a null hypothesis of no association between the target variant set and the phenotype. Therefore, an advantage of the proposed competitive test is that it is possible to refine a known region of interest to localize disease-associated clusters. The definition of clusters can be easily adapted based on variant function or annotation.
Keywords
QPSS, WGS, ADNI, Localization
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CURRENT STATUS: Under Review
Version 2
Posted 19 May, 2020
No community comments so far
Submission checks complete
On 13 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 27 Apr, 2020
Reviewer #2 agreed
On 02 Feb, 2020
Review #1 received
Received 28 Jan, 2020
Reviewer #1 agreed
On 16 Jan, 2020
Editor assigned
On 16 Jan, 2020
Reviewers invited
Invitations sent on 16 Jan, 2020
Submission checks complete
On 14 Jan, 2020
Editor invited
On 14 Jan, 2020
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Subject Areas
Medical Genetics
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This preprint is under consideration at BMC Medical Genetics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Software
Quantitative Phenotype Scan Statistic (QPSS) reveals rare variant associations with Alzheimer’s disease endophenotypes
Yuriko Katsumata
University of Kentucky
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0188-8094
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 2
Posted 19 May, 2020
No community comments so far
Submission checks complete
On 13 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 27 Apr, 2020
Reviewer #2 agreed
On 02 Feb, 2020
Review #1 received
Received 28 Jan, 2020
Reviewer #1 agreed
On 16 Jan, 2020
Editor assigned
On 16 Jan, 2020
Reviewers invited
Invitations sent on 16 Jan, 2020
Submission checks complete
On 14 Jan, 2020
Editor invited
On 14 Jan, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Genetics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Current sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants. Scan statistic approaches have previously been adapted to genetic sequencing data. Unlike currently-employed association tests, scan-statistic-based approaches can both localize clusters of disease-related variants and, subsequently, examine the phenotype association within the resulting cluster. In this study, we present a novel Quantitative Phenotype Scan Statistic (QPSS) that extends an approach for dichotomous phenotypes to continuous outcomes in order to identify genomic regions where rare quantitative-phenotype-associated variants cluster.
Results: We demonstrate the performance and practicality of QPSS with extensive simulations and an application to a whole-genome sequencing (WGS) study of cerebrospinal fluid (CSF) biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Using QPSS, we identify regions of rare variant enrichment associated with levels of AD-related proteins, CSF Aβ1-42 and p-tau181P.
Conclusions: QPSS is implemented under the assumption that causal variants within a window have the same direction of effect. Typical self-contained tests employ a null hypothesis of no association between the target variant set and the phenotype. Therefore, an advantage of the proposed competitive test is that it is possible to refine a known region of interest to localize disease-associated clusters. The definition of clusters can be easily adapted based on variant function or annotation.
Figure 1
Figure 2
Figure 3
Figure 4
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.