Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population
Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of co...
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| description | Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D. |
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The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0187644</identifier><identifier>PMID: 29099854</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Alleles ; Bioinformatics ; Biology and Life Sciences ; Consortia ; Datasets ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus, Type 2 - genetics ; Female ; Genetic Predisposition to Disease ; Genetics ; Genomes ; Health risk assessment ; Health risks ; Humans ; Hyperglycemia ; Insulin ; Insulin resistance ; Laboratories ; Life sciences ; Male ; Medicine and Health Sciences ; Models, Theoretical ; Physical Sciences ; Polymorphism, Single Nucleotide ; Predictions ; Principal components analysis ; Research and Analysis Methods ; Risk ; Risk factors ; Schizophrenia ; Single nucleotide polymorphisms ; Single-nucleotide polymorphism ; Studies ; Sugar ; Type 2 diabetes ; United Kingdom</subject><ispartof>PloS one, 2017-11, Vol.12 (11), p.e0187644-e0187644</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Lei, Huang. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Lei, Huang 2017 Lei, Huang</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-5bfeb3e0265924e7292abbea5b4429dba939c38d76384a73e5b6091ab39bb0933</citedby><cites>FETCH-LOGICAL-c692t-5bfeb3e0265924e7292abbea5b4429dba939c38d76384a73e5b6091ab39bb0933</cites><orcidid>0000-0003-2674-2830</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/PMC5669465/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5669465/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23849,27907,27908,53774,53776,79351,79352</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29099854$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Baroni, Marco Giorgio</contributor><creatorcontrib>Lei, Xiaoyun</creatorcontrib><creatorcontrib>Huang, Shi</creatorcontrib><title>Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D.</description><subject>Adult</subject><subject>Alleles</subject><subject>Bioinformatics</subject><subject>Biology and Life Sciences</subject><subject>Consortia</subject><subject>Datasets</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Female</subject><subject>Genetic Predisposition to Disease</subject><subject>Genetics</subject><subject>Genomes</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Humans</subject><subject>Hyperglycemia</subject><subject>Insulin</subject><subject>Insulin resistance</subject><subject>Laboratories</subject><subject>Life sciences</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Models, Theoretical</subject><subject>Physical 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One</addtitle><date>2017-11-03</date><risdate>2017</risdate><volume>12</volume><issue>11</issue><spage>e0187644</spage><epage>e0187644</epage><pages>e0187644-e0187644</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29099854</pmid><doi>10.1371/journal.pone.0187644</doi><tpages>e0187644</tpages><orcidid>https://orcid.org/0000-0003-2674-2830</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Alleles Bioinformatics Biology and Life Sciences Consortia Datasets Diabetes Diabetes mellitus Diabetes Mellitus, Type 2 - genetics Female Genetic Predisposition to Disease Genetics Genomes Health risk assessment Health risks Humans Hyperglycemia Insulin Insulin resistance Laboratories Life sciences Male Medicine and Health Sciences Models, Theoretical Physical Sciences Polymorphism, Single Nucleotide Predictions Principal components analysis Research and Analysis Methods Risk Risk factors Schizophrenia Single nucleotide polymorphisms Single-nucleotide polymorphism Studies Sugar Type 2 diabetes United Kingdom |
| title | Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population |
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