Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework
MOTIVATION: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for...
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| creator | Biffi, Carlo de Marvao, Antonio Attard, Mark I Dawes, Timothy J W Whiffin, Nicola Bai, Wenjia Shi, Wenzhe Francis, Catherine Meyer, Hannah Buchan, Rachel Cook, Stuart A Rueckert, Daniel O'Regan, Declan P |
| description | MOTIVATION: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). RESULTS: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. AVAILABILITY: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work. |
| doi_str_mv | 10.48550/arxiv.1706.07355 |
| format | Article |
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Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). RESULTS: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. AVAILABILITY: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1706.07355</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Apexes ; Computer simulation ; Finite element method ; Gene expression ; Heart ; Image analysis ; Image resolution ; Magnetic resonance imaging ; Mapping ; Mathematical models ; Population (statistical) ; Quantitative Biology - Quantitative Methods ; Regression analysis ; Statistical analysis ; Statistics - Applications ; Three dimensional models ; Wall thickness</subject><ispartof>arXiv.org, 2017-09</ispartof><rights>2017. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.1093/bioinformatics/btx552$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.48550/arXiv.1706.07355$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Biffi, Carlo</creatorcontrib><creatorcontrib>de Marvao, Antonio</creatorcontrib><creatorcontrib>Attard, Mark I</creatorcontrib><creatorcontrib>Dawes, Timothy J W</creatorcontrib><creatorcontrib>Whiffin, Nicola</creatorcontrib><creatorcontrib>Bai, Wenjia</creatorcontrib><creatorcontrib>Shi, Wenzhe</creatorcontrib><creatorcontrib>Francis, Catherine</creatorcontrib><creatorcontrib>Meyer, Hannah</creatorcontrib><creatorcontrib>Buchan, Rachel</creatorcontrib><creatorcontrib>Cook, Stuart A</creatorcontrib><creatorcontrib>Rueckert, Daniel</creatorcontrib><creatorcontrib>O'Regan, Declan P</creatorcontrib><title>Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework</title><title>arXiv.org</title><description>MOTIVATION: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). RESULTS: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. AVAILABILITY: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.</description><subject>Apexes</subject><subject>Computer simulation</subject><subject>Finite element method</subject><subject>Gene expression</subject><subject>Heart</subject><subject>Image analysis</subject><subject>Image resolution</subject><subject>Magnetic resonance imaging</subject><subject>Mapping</subject><subject>Mathematical models</subject><subject>Population (statistical)</subject><subject>Quantitative Biology - Quantitative Methods</subject><subject>Regression analysis</subject><subject>Statistical analysis</subject><subject>Statistics - Applications</subject><subject>Three dimensional models</subject><subject>Wall thickness</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotz01PAjEUheHGxESC_ABXTuJ68Pa2nQ93hAiSoG7G9eRS7mBxPrAdUP-9CK7O5s1JHiFuJIx1Zgzck_92h7FMIRlDqoy5EANUSsaZRrwSoxC2AIBJisaogXgp3j1zvHYNt8F1LdXRlPzadQcKdl-TjxYNbVy7iefccu9seIgm0TOFEL217kDeUc_RzFPDX53_uBaXFdWBR_87FMXssZg-xcvX-WI6WcZkUMVrmRlWnEiuJGqkyiDksKIUkVlDZVY61bbChDNrwajKWkVKMlqZmyyRaihuz7cnbLnzriH_U_6hyxP6WNydi53vPvcc-nLb7f2RF0qENNE6R1DqF48wWbw</recordid><startdate>20170913</startdate><enddate>20170913</enddate><creator>Biffi, Carlo</creator><creator>de Marvao, Antonio</creator><creator>Attard, Mark I</creator><creator>Dawes, Timothy J W</creator><creator>Whiffin, Nicola</creator><creator>Bai, Wenjia</creator><creator>Shi, Wenzhe</creator><creator>Francis, Catherine</creator><creator>Meyer, Hannah</creator><creator>Buchan, Rachel</creator><creator>Cook, Stuart A</creator><creator>Rueckert, Daniel</creator><creator>O'Regan, Declan P</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>ALC</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20170913</creationdate><title>Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework</title><author>Biffi, Carlo ; de Marvao, Antonio ; Attard, Mark I ; Dawes, Timothy J W ; Whiffin, Nicola ; Bai, Wenjia ; Shi, Wenzhe ; Francis, Catherine ; Meyer, Hannah ; Buchan, Rachel ; Cook, Stuart A ; Rueckert, Daniel ; O'Regan, Declan P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a523-d185e3e61ef1242af52090ba722ee40f5b474cf26e8cc053fcc3a31e2c1958613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Apexes</topic><topic>Computer simulation</topic><topic>Finite element method</topic><topic>Gene expression</topic><topic>Heart</topic><topic>Image analysis</topic><topic>Image resolution</topic><topic>Magnetic resonance imaging</topic><topic>Mapping</topic><topic>Mathematical models</topic><topic>Population (statistical)</topic><topic>Quantitative Biology - Quantitative Methods</topic><topic>Regression analysis</topic><topic>Statistical analysis</topic><topic>Statistics - Applications</topic><topic>Three dimensional models</topic><topic>Wall thickness</topic><toplevel>online_resources</toplevel><creatorcontrib>Biffi, Carlo</creatorcontrib><creatorcontrib>de Marvao, Antonio</creatorcontrib><creatorcontrib>Attard, Mark I</creatorcontrib><creatorcontrib>Dawes, Timothy J W</creatorcontrib><creatorcontrib>Whiffin, Nicola</creatorcontrib><creatorcontrib>Bai, Wenjia</creatorcontrib><creatorcontrib>Shi, Wenzhe</creatorcontrib><creatorcontrib>Francis, Catherine</creatorcontrib><creatorcontrib>Meyer, Hannah</creatorcontrib><creatorcontrib>Buchan, Rachel</creatorcontrib><creatorcontrib>Cook, Stuart A</creatorcontrib><creatorcontrib>Rueckert, Daniel</creatorcontrib><creatorcontrib>O'Regan, Declan P</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Quantitative Biology</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Biffi, Carlo</au><au>de Marvao, Antonio</au><au>Attard, Mark I</au><au>Dawes, Timothy J W</au><au>Whiffin, Nicola</au><au>Bai, Wenjia</au><au>Shi, Wenzhe</au><au>Francis, Catherine</au><au>Meyer, Hannah</au><au>Buchan, Rachel</au><au>Cook, Stuart A</au><au>Rueckert, Daniel</au><au>O'Regan, Declan P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework</atitle><jtitle>arXiv.org</jtitle><date>2017-09-13</date><risdate>2017</risdate><eissn>2331-8422</eissn><abstract>MOTIVATION: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). RESULTS: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. AVAILABILITY: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.1706.07355</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Apexes Computer simulation Finite element method Gene expression Heart Image analysis Image resolution Magnetic resonance imaging Mapping Mathematical models Population (statistical) Quantitative Biology - Quantitative Methods Regression analysis Statistical analysis Statistics - Applications Three dimensional models Wall thickness |
| title | Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework |
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