A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression
BACKGROUND Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre‐symptomatic risk assessment but also for building personalized therapeutic strategies. METHODS We implemented a novel simulative deep learning model to chromosome 19 genetic data fr...
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| Veröffentlicht in: | Alzheimer's & dementia 2023-12, Vol.19 (12), p.5690-5699 |
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| creator | Bae, Jinhyeong Logan, Paige E. Acri, Dominic J. Bharthur, Apoorva Nho, Kwangsik Saykin, Andrew J. Risacher, Shannon L. Nudelman, Kelly Polsinelli, Angelina J. Pentchev, Valentin Kim, Jungsu Hammers, Dustin B. Apostolova, Liana G. |
| description | BACKGROUND
Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre‐symptomatic risk assessment but also for building personalized therapeutic strategies.
METHODS
We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD‐risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.
RESULTS
Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD‐risk SNPs were significant predictors of AD progression.
DISCUSSION
The model successfully estimated the contribution of AD‐risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine. |
| doi_str_mv | 10.1002/alz.13319 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10770299</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2834000422</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3769-d79134fe7fc41f509372a38fd0601ff0bbb0c0a2a38a13d4966e5eff01c77a7e3</originalsourceid><addsrcrecordid>eNp1kcuOFCEUhonROBdd-AKGnc6iZw5FVdGsTGfiLemoibpxQ2g4dKMU1ED1mJmH8Jml7ZmOLlxB-D--Q_gJecbgnAE0FzrcnjPOmXxAjlnXNbOuEfLhYd_DETkp5TtAC3PWPSZHXLQg-zkck18LWvywDXry10gt4kgD6hx9XNMhWQw0Ofr5wyfq44RZm8mnWGiK1GxyGlJJA1ImqUuZjhmtr0C9uQi3G_QD5heFWl9QF6TZlx9UR0uznrDstPfJmNM6YylV_YQ8cjoUfHq3npKvb15_uXw3W358-_5ysZwZLno5s0Iy3joUzrTMdSC5aDSfOws9MOdgtVqBAb0704zbVvY9dlgDZoTQAvkpebX3jtvVgNZgnLIOasx-0PlGJe3Vv0n0G7VO14qBENBIWQ0v7ww5XW2xTGrwxWAIOmLaFtXMeQv1x5umomd71ORUSkZ3mMNA7QpUtUD1p8DKPv_7YQfyvrEKXOyBnz7gzf9NarH8tlf-BnybqG0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2834000422</pqid></control><display><type>article</type><title>A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Bae, Jinhyeong ; Logan, Paige E. ; Acri, Dominic J. ; Bharthur, Apoorva ; Nho, Kwangsik ; Saykin, Andrew J. ; Risacher, Shannon L. ; Nudelman, Kelly ; Polsinelli, Angelina J. ; Pentchev, Valentin ; Kim, Jungsu ; Hammers, Dustin B. ; Apostolova, Liana G.</creator><creatorcontrib>Bae, Jinhyeong ; Logan, Paige E. ; Acri, Dominic J. ; Bharthur, Apoorva ; Nho, Kwangsik ; Saykin, Andrew J. ; Risacher, Shannon L. ; Nudelman, Kelly ; Polsinelli, Angelina J. ; Pentchev, Valentin ; Kim, Jungsu ; Hammers, Dustin B. ; Apostolova, Liana G. ; Alzheimer's Disease Neuroimaging Initiative ; for the Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><description>BACKGROUND
Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre‐symptomatic risk assessment but also for building personalized therapeutic strategies.
METHODS
We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD‐risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.
RESULTS
Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD‐risk SNPs were significant predictors of AD progression.
DISCUSSION
The model successfully estimated the contribution of AD‐risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.</description><identifier>ISSN: 1552-5260</identifier><identifier>ISSN: 1552-5279</identifier><identifier>EISSN: 1552-5279</identifier><identifier>DOI: 10.1002/alz.13319</identifier><identifier>PMID: 37409680</identifier><language>eng</language><publisher>United States</publisher><subject>Alzheimer Disease - genetics ; Alzheimer's disease ; Chromosomes, Human, Pair 19 ; Deep Learning ; Disease Progression ; genetics ; Humans ; Magnetic Resonance Imaging - methods ; Neuroimaging - methods ; Polymorphism, Single Nucleotide - genetics</subject><ispartof>Alzheimer's & dementia, 2023-12, Vol.19 (12), p.5690-5699</ispartof><rights>2023 The Authors. published by Wiley Periodicals LLC on behalf of Alzheimer's Association.</rights><rights>2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3769-d79134fe7fc41f509372a38fd0601ff0bbb0c0a2a38a13d4966e5eff01c77a7e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Falz.13319$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Falz.13319$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37409680$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bae, Jinhyeong</creatorcontrib><creatorcontrib>Logan, Paige E.</creatorcontrib><creatorcontrib>Acri, Dominic J.</creatorcontrib><creatorcontrib>Bharthur, Apoorva</creatorcontrib><creatorcontrib>Nho, Kwangsik</creatorcontrib><creatorcontrib>Saykin, Andrew J.</creatorcontrib><creatorcontrib>Risacher, Shannon L.</creatorcontrib><creatorcontrib>Nudelman, Kelly</creatorcontrib><creatorcontrib>Polsinelli, Angelina J.</creatorcontrib><creatorcontrib>Pentchev, Valentin</creatorcontrib><creatorcontrib>Kim, Jungsu</creatorcontrib><creatorcontrib>Hammers, Dustin B.</creatorcontrib><creatorcontrib>Apostolova, Liana G.</creatorcontrib><creatorcontrib>Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><creatorcontrib>for the Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><title>A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression</title><title>Alzheimer's & dementia</title><addtitle>Alzheimers Dement</addtitle><description>BACKGROUND
Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre‐symptomatic risk assessment but also for building personalized therapeutic strategies.
METHODS
We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD‐risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.
RESULTS
Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD‐risk SNPs were significant predictors of AD progression.
DISCUSSION
The model successfully estimated the contribution of AD‐risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.</description><subject>Alzheimer Disease - genetics</subject><subject>Alzheimer's disease</subject><subject>Chromosomes, Human, Pair 19</subject><subject>Deep Learning</subject><subject>Disease Progression</subject><subject>genetics</subject><subject>Humans</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Neuroimaging - methods</subject><subject>Polymorphism, Single Nucleotide - genetics</subject><issn>1552-5260</issn><issn>1552-5279</issn><issn>1552-5279</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNp1kcuOFCEUhonROBdd-AKGnc6iZw5FVdGsTGfiLemoibpxQ2g4dKMU1ED1mJmH8Jml7ZmOLlxB-D--Q_gJecbgnAE0FzrcnjPOmXxAjlnXNbOuEfLhYd_DETkp5TtAC3PWPSZHXLQg-zkck18LWvywDXry10gt4kgD6hx9XNMhWQw0Ofr5wyfq44RZm8mnWGiK1GxyGlJJA1ImqUuZjhmtr0C9uQi3G_QD5heFWl9QF6TZlx9UR0uznrDstPfJmNM6YylV_YQ8cjoUfHq3npKvb15_uXw3W358-_5ysZwZLno5s0Iy3joUzrTMdSC5aDSfOws9MOdgtVqBAb0704zbVvY9dlgDZoTQAvkpebX3jtvVgNZgnLIOasx-0PlGJe3Vv0n0G7VO14qBENBIWQ0v7ww5XW2xTGrwxWAIOmLaFtXMeQv1x5umomd71ORUSkZ3mMNA7QpUtUD1p8DKPv_7YQfyvrEKXOyBnz7gzf9NarH8tlf-BnybqG0</recordid><startdate>202312</startdate><enddate>202312</enddate><creator>Bae, Jinhyeong</creator><creator>Logan, Paige E.</creator><creator>Acri, Dominic J.</creator><creator>Bharthur, Apoorva</creator><creator>Nho, Kwangsik</creator><creator>Saykin, Andrew J.</creator><creator>Risacher, Shannon L.</creator><creator>Nudelman, Kelly</creator><creator>Polsinelli, Angelina J.</creator><creator>Pentchev, Valentin</creator><creator>Kim, Jungsu</creator><creator>Hammers, Dustin B.</creator><creator>Apostolova, Liana G.</creator><scope>24P</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>202312</creationdate><title>A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression</title><author>Bae, Jinhyeong ; Logan, Paige E. ; Acri, Dominic J. ; Bharthur, Apoorva ; Nho, Kwangsik ; Saykin, Andrew J. ; Risacher, Shannon L. ; Nudelman, Kelly ; Polsinelli, Angelina J. ; Pentchev, Valentin ; Kim, Jungsu ; Hammers, Dustin B. ; Apostolova, Liana G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3769-d79134fe7fc41f509372a38fd0601ff0bbb0c0a2a38a13d4966e5eff01c77a7e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Alzheimer Disease - genetics</topic><topic>Alzheimer's disease</topic><topic>Chromosomes, Human, Pair 19</topic><topic>Deep Learning</topic><topic>Disease Progression</topic><topic>genetics</topic><topic>Humans</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Neuroimaging - methods</topic><topic>Polymorphism, Single Nucleotide - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bae, Jinhyeong</creatorcontrib><creatorcontrib>Logan, Paige E.</creatorcontrib><creatorcontrib>Acri, Dominic J.</creatorcontrib><creatorcontrib>Bharthur, Apoorva</creatorcontrib><creatorcontrib>Nho, Kwangsik</creatorcontrib><creatorcontrib>Saykin, Andrew J.</creatorcontrib><creatorcontrib>Risacher, Shannon L.</creatorcontrib><creatorcontrib>Nudelman, Kelly</creatorcontrib><creatorcontrib>Polsinelli, Angelina J.</creatorcontrib><creatorcontrib>Pentchev, Valentin</creatorcontrib><creatorcontrib>Kim, Jungsu</creatorcontrib><creatorcontrib>Hammers, Dustin B.</creatorcontrib><creatorcontrib>Apostolova, Liana G.</creatorcontrib><creatorcontrib>Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><creatorcontrib>for the Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Alzheimer's & dementia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bae, Jinhyeong</au><au>Logan, Paige E.</au><au>Acri, Dominic J.</au><au>Bharthur, Apoorva</au><au>Nho, Kwangsik</au><au>Saykin, Andrew J.</au><au>Risacher, Shannon L.</au><au>Nudelman, Kelly</au><au>Polsinelli, Angelina J.</au><au>Pentchev, Valentin</au><au>Kim, Jungsu</au><au>Hammers, Dustin B.</au><au>Apostolova, Liana G.</au><aucorp>Alzheimer's Disease Neuroimaging Initiative</aucorp><aucorp>for the Alzheimer's Disease Neuroimaging Initiative</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression</atitle><jtitle>Alzheimer's & dementia</jtitle><addtitle>Alzheimers Dement</addtitle><date>2023-12</date><risdate>2023</risdate><volume>19</volume><issue>12</issue><spage>5690</spage><epage>5699</epage><pages>5690-5699</pages><issn>1552-5260</issn><issn>1552-5279</issn><eissn>1552-5279</eissn><abstract>BACKGROUND
Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre‐symptomatic risk assessment but also for building personalized therapeutic strategies.
METHODS
We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD‐risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.
RESULTS
Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD‐risk SNPs were significant predictors of AD progression.
DISCUSSION
The model successfully estimated the contribution of AD‐risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.</abstract><cop>United States</cop><pmid>37409680</pmid><doi>10.1002/alz.13319</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Alzheimer's & dementia, 2023-12, Vol.19 (12), p.5690-5699 |
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| language | eng |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Alzheimer Disease - genetics Alzheimer's disease Chromosomes, Human, Pair 19 Deep Learning Disease Progression genetics Humans Magnetic Resonance Imaging - methods Neuroimaging - methods Polymorphism, Single Nucleotide - genetics |
| title | A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression |
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