Loading…

Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis

Regional association analysis is a new statistical method which simultaneously considers all variants in a selected genome region. This method was created for the analysis of rare genetic variants, whose genotypes are determined by exome or genome sequencing. The gene is usually considered as a regi...

Full description

Saved in:
Bibliographic Details
Published in:Russian journal of genetics 2018-02, Vol.54 (2), p.250-258
Main Authors: Kirichenko, A. V., Zorkoltseva, I. V., Belonogova, N. M., Axenovich, T. I.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c240t-2da5e40466d6695d5d0ab1cd04fa590a90084bc4e23da26d90149e988c08b5753
container_end_page 258
container_issue 2
container_start_page 250
container_title Russian journal of genetics
container_volume 54
creator Kirichenko, A. V.
Zorkoltseva, I. V.
Belonogova, N. M.
Axenovich, T. I.
description Regional association analysis is a new statistical method which simultaneously considers all variants in a selected genome region. This method was created for the analysis of rare genetic variants, whose genotypes are determined by exome or genome sequencing. The gene is usually considered as a region. It was also proposed to use a regional analysis for testing of the association between a complex trait and a set of common variants genotyped by the panels developed for genome-wide association analysis. In this case, overlapping genome regions (sliding windows) are usually considered as a region. Since the size of such regions can be rather large, there is a risk of overestimation (inflation) of the test statistic and an increase in the type I error. In this work, the effect of the size of the region on the type I error was studied for traits with different heritability. The results of simulating experiments demonstrated that the physical size of the region but not the number of genetic variants in it is a limiting factor. The higher the trait heritability, the greater the type I error differs from the declared value. The analysis of a large number of real traits confirmed these conclusions. It is necessary to take into account these results during the interpretation of the results of regional association analysis conducted on large regions using common genetic variants.
doi_str_mv 10.1134/S1022795418010076
format article
fullrecord <record><control><sourceid>crossref_sprin</sourceid><recordid>TN_cdi_crossref_primary_10_1134_S1022795418010076</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1134_S1022795418010076</sourcerecordid><originalsourceid>FETCH-LOGICAL-c240t-2da5e40466d6695d5d0ab1cd04fa590a90084bc4e23da26d90149e988c08b5753</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBBIlMIHcPMPBNaO7cTHKIJSqRLiUThGTuxUqZq48oZD_h6HckPitDs7D62GkFsGd4yl4v6NAeeZloLlwAAydUYWTEGepKnS53GPdDLzl-QKcQ-zSKUL8rJFR31LV27w43R0OIPS970f6IcJnRlGpK0PP4LeJZ-ddfTV7To_mAMtEH3TmTEiWsTDhB1ek4vWHNDd_M4l2T4-vJdPyeZ5tS6LTdJwAWPCrZFOgFDKKqWllRZMzRoLojVSg9EAuagb4XhqDVdWAxPa6TxvIK9lJtMlYafcJnjE4NrqGLrehKliUM2dVH86iR5-8mDUDjsXqr3_CvFx_Mf0DT_tYrU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis</title><source>Springer Nature</source><creator>Kirichenko, A. V. ; Zorkoltseva, I. V. ; Belonogova, N. M. ; Axenovich, T. I.</creator><creatorcontrib>Kirichenko, A. V. ; Zorkoltseva, I. V. ; Belonogova, N. M. ; Axenovich, T. I.</creatorcontrib><description>Regional association analysis is a new statistical method which simultaneously considers all variants in a selected genome region. This method was created for the analysis of rare genetic variants, whose genotypes are determined by exome or genome sequencing. The gene is usually considered as a region. It was also proposed to use a regional analysis for testing of the association between a complex trait and a set of common variants genotyped by the panels developed for genome-wide association analysis. In this case, overlapping genome regions (sliding windows) are usually considered as a region. Since the size of such regions can be rather large, there is a risk of overestimation (inflation) of the test statistic and an increase in the type I error. In this work, the effect of the size of the region on the type I error was studied for traits with different heritability. The results of simulating experiments demonstrated that the physical size of the region but not the number of genetic variants in it is a limiting factor. The higher the trait heritability, the greater the type I error differs from the declared value. The analysis of a large number of real traits confirmed these conclusions. It is necessary to take into account these results during the interpretation of the results of regional association analysis conducted on large regions using common genetic variants.</description><identifier>ISSN: 1022-7954</identifier><identifier>EISSN: 1608-3369</identifier><identifier>DOI: 10.1134/S1022795418010076</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Animal Genetics and Genomics ; Biomedical and Life Sciences ; Biomedicine ; Human Genetics ; Methods ; Microbial Genetics and Genomics</subject><ispartof>Russian journal of genetics, 2018-02, Vol.54 (2), p.250-258</ispartof><rights>Pleiades Publishing, Inc. 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c240t-2da5e40466d6695d5d0ab1cd04fa590a90084bc4e23da26d90149e988c08b5753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Kirichenko, A. V.</creatorcontrib><creatorcontrib>Zorkoltseva, I. V.</creatorcontrib><creatorcontrib>Belonogova, N. M.</creatorcontrib><creatorcontrib>Axenovich, T. I.</creatorcontrib><title>Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis</title><title>Russian journal of genetics</title><addtitle>Russ J Genet</addtitle><description>Regional association analysis is a new statistical method which simultaneously considers all variants in a selected genome region. This method was created for the analysis of rare genetic variants, whose genotypes are determined by exome or genome sequencing. The gene is usually considered as a region. It was also proposed to use a regional analysis for testing of the association between a complex trait and a set of common variants genotyped by the panels developed for genome-wide association analysis. In this case, overlapping genome regions (sliding windows) are usually considered as a region. Since the size of such regions can be rather large, there is a risk of overestimation (inflation) of the test statistic and an increase in the type I error. In this work, the effect of the size of the region on the type I error was studied for traits with different heritability. The results of simulating experiments demonstrated that the physical size of the region but not the number of genetic variants in it is a limiting factor. The higher the trait heritability, the greater the type I error differs from the declared value. The analysis of a large number of real traits confirmed these conclusions. It is necessary to take into account these results during the interpretation of the results of regional association analysis conducted on large regions using common genetic variants.</description><subject>Animal Genetics and Genomics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Human Genetics</subject><subject>Methods</subject><subject>Microbial Genetics and Genomics</subject><issn>1022-7954</issn><issn>1608-3369</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9UMtOwzAQtBBIlMIHcPMPBNaO7cTHKIJSqRLiUThGTuxUqZq48oZD_h6HckPitDs7D62GkFsGd4yl4v6NAeeZloLlwAAydUYWTEGepKnS53GPdDLzl-QKcQ-zSKUL8rJFR31LV27w43R0OIPS970f6IcJnRlGpK0PP4LeJZ-ddfTV7To_mAMtEH3TmTEiWsTDhB1ek4vWHNDd_M4l2T4-vJdPyeZ5tS6LTdJwAWPCrZFOgFDKKqWllRZMzRoLojVSg9EAuagb4XhqDVdWAxPa6TxvIK9lJtMlYafcJnjE4NrqGLrehKliUM2dVH86iR5-8mDUDjsXqr3_CvFx_Mf0DT_tYrU</recordid><startdate>20180201</startdate><enddate>20180201</enddate><creator>Kirichenko, A. V.</creator><creator>Zorkoltseva, I. V.</creator><creator>Belonogova, N. M.</creator><creator>Axenovich, T. I.</creator><general>Pleiades Publishing</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20180201</creationdate><title>Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis</title><author>Kirichenko, A. V. ; Zorkoltseva, I. V. ; Belonogova, N. M. ; Axenovich, T. I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c240t-2da5e40466d6695d5d0ab1cd04fa590a90084bc4e23da26d90149e988c08b5753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Animal Genetics and Genomics</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Human Genetics</topic><topic>Methods</topic><topic>Microbial Genetics and Genomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kirichenko, A. V.</creatorcontrib><creatorcontrib>Zorkoltseva, I. V.</creatorcontrib><creatorcontrib>Belonogova, N. M.</creatorcontrib><creatorcontrib>Axenovich, T. I.</creatorcontrib><collection>CrossRef</collection><jtitle>Russian journal of genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kirichenko, A. V.</au><au>Zorkoltseva, I. V.</au><au>Belonogova, N. M.</au><au>Axenovich, T. I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis</atitle><jtitle>Russian journal of genetics</jtitle><stitle>Russ J Genet</stitle><date>2018-02-01</date><risdate>2018</risdate><volume>54</volume><issue>2</issue><spage>250</spage><epage>258</epage><pages>250-258</pages><issn>1022-7954</issn><eissn>1608-3369</eissn><abstract>Regional association analysis is a new statistical method which simultaneously considers all variants in a selected genome region. This method was created for the analysis of rare genetic variants, whose genotypes are determined by exome or genome sequencing. The gene is usually considered as a region. It was also proposed to use a regional analysis for testing of the association between a complex trait and a set of common variants genotyped by the panels developed for genome-wide association analysis. In this case, overlapping genome regions (sliding windows) are usually considered as a region. Since the size of such regions can be rather large, there is a risk of overestimation (inflation) of the test statistic and an increase in the type I error. In this work, the effect of the size of the region on the type I error was studied for traits with different heritability. The results of simulating experiments demonstrated that the physical size of the region but not the number of genetic variants in it is a limiting factor. The higher the trait heritability, the greater the type I error differs from the declared value. The analysis of a large number of real traits confirmed these conclusions. It is necessary to take into account these results during the interpretation of the results of regional association analysis conducted on large regions using common genetic variants.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.1134/S1022795418010076</doi><tpages>9</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1022-7954
ispartof Russian journal of genetics, 2018-02, Vol.54 (2), p.250-258
issn 1022-7954
1608-3369
language eng
recordid cdi_crossref_primary_10_1134_S1022795418010076
source Springer Nature
subjects Animal Genetics and Genomics
Biomedical and Life Sciences
Biomedicine
Human Genetics
Methods
Microbial Genetics and Genomics
title Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T18%3A13%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Use%20of%20Genotypes%20of%20Common%20Variants%20for%20Genome-Wide%20Regional%20Association%20Analysis&rft.jtitle=Russian%20journal%20of%20genetics&rft.au=Kirichenko,%20A.%20V.&rft.date=2018-02-01&rft.volume=54&rft.issue=2&rft.spage=250&rft.epage=258&rft.pages=250-258&rft.issn=1022-7954&rft.eissn=1608-3369&rft_id=info:doi/10.1134/S1022795418010076&rft_dat=%3Ccrossref_sprin%3E10_1134_S1022795418010076%3C/crossref_sprin%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c240t-2da5e40466d6695d5d0ab1cd04fa590a90084bc4e23da26d90149e988c08b5753%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true