Super‐variants identification for brain connectivity
Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of...
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| Veröffentlicht in: | Human brain mapping 2021-04, Vol.42 (5), p.1304-1312 |
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| creator | Li, Ting Hu, Jianchang Wang, Shiying Zhang, Heping |
| description | Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. Similar to but different from the classic concept of gene, a super‐variant is a combination of alleles in multiple loci but contributing loci can be anywhere in the genome. We hypothesize that the super‐variants are easier to detect and more reliable to reproduce in their associations with brain connectivity. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants. Specifically, we investigate a discovery set with 16,421 subjects and a verification set with 2,882 subjects, where they are formed according to release date, and the verification set is used to validate the genetic associations from the discovery phase. We identified 12 replicable super‐variants on Chromosomes 1, 3, 7, 8, 9, 10, 12, 15, 16, 18, and 19. These verified super‐variants contain single nucleotide polymorphisms that locate in 14 genes which have been reported to have association with brain structure and function, and/or neurodevelopmental and neurodegenerative disorders in the literature. We also identified novel loci in genes RSPO2 and TMEM74 which may be upregulated in brain issues. These findings demonstrate the validity of the super‐variants and its capability of unifying existing results as well as discovering novel and replicable results.
The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants. |
| doi_str_mv | 10.1002/hbm.25294 |
| format | Article |
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The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.25294</identifier><identifier>PMID: 33236465</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Adult ; Biomarkers ; Brain ; Brain - anatomy & histology ; Brain - diagnostic imaging ; Brain - physiology ; brian connectivity ; Chromosomes ; Connectome - methods ; Databases, Factual ; Datasets as Topic ; Functional anatomy ; Genes ; Genetic Association Studies - methods ; Genetic diversity ; Genetic effects ; Genetic variance ; Genomes ; GWAS ; Humans ; Loci ; Nerve Net - anatomy & histology ; Nerve Net - diagnostic imaging ; Nerve Net - physiology ; Neural networks ; Neurodegenerative diseases ; Neurodevelopmental disorders ; Nucleotides ; Phenotypes ; Polymorphism, Single Nucleotide ; Single-nucleotide polymorphism ; Structure-function relationships ; UK Biobank ; Verification</subject><ispartof>Human brain mapping, 2021-04, Vol.42 (5), p.1304-1312</ispartof><rights>2020 The Authors. published by Wiley Periodicals LLC.</rights><rights>2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4434-281f6ebc79eab69c30e94cc37e21944182cf2c0218eef618b586007e1ae78ff43</citedby><cites>FETCH-LOGICAL-c4434-281f6ebc79eab69c30e94cc37e21944182cf2c0218eef618b586007e1ae78ff43</cites><orcidid>0000-0002-0688-4076 ; 0000-0002-3880-8609</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7927294/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7927294/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1417,11562,27924,27925,45574,45575,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33236465$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Ting</creatorcontrib><creatorcontrib>Hu, Jianchang</creatorcontrib><creatorcontrib>Wang, Shiying</creatorcontrib><creatorcontrib>Zhang, Heping</creatorcontrib><title>Super‐variants identification for brain connectivity</title><title>Human brain mapping</title><addtitle>Hum Brain Mapp</addtitle><description>Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. Similar to but different from the classic concept of gene, a super‐variant is a combination of alleles in multiple loci but contributing loci can be anywhere in the genome. We hypothesize that the super‐variants are easier to detect and more reliable to reproduce in their associations with brain connectivity. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants. Specifically, we investigate a discovery set with 16,421 subjects and a verification set with 2,882 subjects, where they are formed according to release date, and the verification set is used to validate the genetic associations from the discovery phase. We identified 12 replicable super‐variants on Chromosomes 1, 3, 7, 8, 9, 10, 12, 15, 16, 18, and 19. These verified super‐variants contain single nucleotide polymorphisms that locate in 14 genes which have been reported to have association with brain structure and function, and/or neurodevelopmental and neurodegenerative disorders in the literature. We also identified novel loci in genes RSPO2 and TMEM74 which may be upregulated in brain issues. These findings demonstrate the validity of the super‐variants and its capability of unifying existing results as well as discovering novel and replicable results.
The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants.</description><subject>Adult</subject><subject>Biomarkers</subject><subject>Brain</subject><subject>Brain - anatomy & histology</subject><subject>Brain - diagnostic imaging</subject><subject>Brain - physiology</subject><subject>brian connectivity</subject><subject>Chromosomes</subject><subject>Connectome - methods</subject><subject>Databases, Factual</subject><subject>Datasets as Topic</subject><subject>Functional anatomy</subject><subject>Genes</subject><subject>Genetic Association Studies - methods</subject><subject>Genetic diversity</subject><subject>Genetic effects</subject><subject>Genetic variance</subject><subject>Genomes</subject><subject>GWAS</subject><subject>Humans</subject><subject>Loci</subject><subject>Nerve Net - anatomy & histology</subject><subject>Nerve Net - diagnostic imaging</subject><subject>Nerve Net - physiology</subject><subject>Neural networks</subject><subject>Neurodegenerative diseases</subject><subject>Neurodevelopmental disorders</subject><subject>Nucleotides</subject><subject>Phenotypes</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Single-nucleotide polymorphism</subject><subject>Structure-function relationships</subject><subject>UK Biobank</subject><subject>Verification</subject><issn>1065-9471</issn><issn>1097-0193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp1kc1OGzEURq2qqATooi9QReoGFhP8N_Z4UwlQW5BALIC15THXxdHETu2ZoOx4hD4jT4JDIKJIXflKPjr67v0Q-kLwhGBMD-_a2YTWVPEPaESwkhUmin1czaKuFJdkG-3kPMWYkBqTT2ibMcoEF_UIiathDunx4e_CJG9Cn8f-FkLvnbem9zGMXUzjNhkfxjaGALb3C98v99CWM12Gzy_vLrr5-eP65LQ6v_x1dnJ0XlnOGa9oQ5yA1koFphXKMgyKW8skUKI4Jw21jlpMSQPgBGnauhEYSyAGZOMcZ7vo-9o7H9oZ3NoSLZlOz5OfmbTU0Xj970_wd_p3XGipqCwHKYL9F0GKfwbIvZ75bKHrTIA4ZE25KDEkr2VBv71Dp3FIoaxXKFUzhgVfUQdryqaYcwK3CUOwXrWhSxv6uY3Cfn2bfkO-nr8Ah2vg3new_L9Jnx5frJVP4pKU5w</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Li, Ting</creator><creator>Hu, Jianchang</creator><creator>Wang, Shiying</creator><creator>Zhang, Heping</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</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>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0688-4076</orcidid><orcidid>https://orcid.org/0000-0002-3880-8609</orcidid></search><sort><creationdate>20210401</creationdate><title>Super‐variants identification for brain connectivity</title><author>Li, Ting ; Hu, Jianchang ; Wang, Shiying ; Zhang, Heping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4434-281f6ebc79eab69c30e94cc37e21944182cf2c0218eef618b586007e1ae78ff43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Biomarkers</topic><topic>Brain</topic><topic>Brain - anatomy & histology</topic><topic>Brain - diagnostic imaging</topic><topic>Brain - physiology</topic><topic>brian connectivity</topic><topic>Chromosomes</topic><topic>Connectome - methods</topic><topic>Databases, Factual</topic><topic>Datasets as Topic</topic><topic>Functional anatomy</topic><topic>Genes</topic><topic>Genetic Association Studies - methods</topic><topic>Genetic diversity</topic><topic>Genetic effects</topic><topic>Genetic variance</topic><topic>Genomes</topic><topic>GWAS</topic><topic>Humans</topic><topic>Loci</topic><topic>Nerve Net - anatomy & histology</topic><topic>Nerve Net - diagnostic imaging</topic><topic>Nerve Net - physiology</topic><topic>Neural networks</topic><topic>Neurodegenerative diseases</topic><topic>Neurodevelopmental disorders</topic><topic>Nucleotides</topic><topic>Phenotypes</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Single-nucleotide polymorphism</topic><topic>Structure-function relationships</topic><topic>UK Biobank</topic><topic>Verification</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Ting</creatorcontrib><creatorcontrib>Hu, Jianchang</creatorcontrib><creatorcontrib>Wang, Shiying</creatorcontrib><creatorcontrib>Zhang, Heping</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human brain mapping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Ting</au><au>Hu, Jianchang</au><au>Wang, Shiying</au><au>Zhang, Heping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Super‐variants identification for brain connectivity</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum Brain Mapp</addtitle><date>2021-04-01</date><risdate>2021</risdate><volume>42</volume><issue>5</issue><spage>1304</spage><epage>1312</epage><pages>1304-1312</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. Similar to but different from the classic concept of gene, a super‐variant is a combination of alleles in multiple loci but contributing loci can be anywhere in the genome. We hypothesize that the super‐variants are easier to detect and more reliable to reproduce in their associations with brain connectivity. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants. Specifically, we investigate a discovery set with 16,421 subjects and a verification set with 2,882 subjects, where they are formed according to release date, and the verification set is used to validate the genetic associations from the discovery phase. We identified 12 replicable super‐variants on Chromosomes 1, 3, 7, 8, 9, 10, 12, 15, 16, 18, and 19. These verified super‐variants contain single nucleotide polymorphisms that locate in 14 genes which have been reported to have association with brain structure and function, and/or neurodevelopmental and neurodegenerative disorders in the literature. We also identified novel loci in genes RSPO2 and TMEM74 which may be upregulated in brain issues. These findings demonstrate the validity of the super‐variants and its capability of unifying existing results as well as discovering novel and replicable results.
The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>33236465</pmid><doi>10.1002/hbm.25294</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-0688-4076</orcidid><orcidid>https://orcid.org/0000-0002-3880-8609</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Biomarkers Brain Brain - anatomy & histology Brain - diagnostic imaging Brain - physiology brian connectivity Chromosomes Connectome - methods Databases, Factual Datasets as Topic Functional anatomy Genes Genetic Association Studies - methods Genetic diversity Genetic effects Genetic variance Genomes GWAS Humans Loci Nerve Net - anatomy & histology Nerve Net - diagnostic imaging Nerve Net - physiology Neural networks Neurodegenerative diseases Neurodevelopmental disorders Nucleotides Phenotypes Polymorphism, Single Nucleotide Single-nucleotide polymorphism Structure-function relationships UK Biobank Verification |
| title | Super‐variants identification for brain connectivity |
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