Meta-analysis methods for genome-wide association studies and beyond
Key Points Meta-analysis of genome-wide association studies has contributed to the discovery of most of the recently identified genetic risk factors for complex diseases. Common meta-analytical approaches have been successfully applied; however, novel methods have been proposed that may have some ad...
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| Veröffentlicht in: | Nature reviews. Genetics 2013-06, Vol.14 (6), p.379-389 |
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| creator | Evangelou, Evangelos Ioannidis, John P. A. |
| description | Key Points
Meta-analysis of genome-wide association studies has contributed to the discovery of most of the recently identified genetic risk factors for complex diseases.
Common meta-analytical approaches have been successfully applied; however, novel methods have been proposed that may have some advantages and disadvantages.
Heterogeneity in meta-analysis can be introduced from various sources and should not be disregarded. Several methods have been proposed that may optimize power in the presence of heterogeneity from known or unknown sources.
Next-generation sequence data will boost the study of rare variants; however, larger sample sizes are required. Several techniques have been developed for the meta-analysis of rare variants. Tools other than
P
values may be useful for inference.
Scientists will benefit from publicly available data sets and collaboration between consortia that will facilitate a wide range of methodological and applied research.
The authors review statistical methods for meta-analysis of genome-wide association studies (GWASs) and extensions of these methods to complex data. They discuss how low-frequency variants can be incorporated into meta-analyses as next-generation sequencing data become more commonly used in GWASs.
Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia. |
| doi_str_mv | 10.1038/nrg3472 |
| format | Article |
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Meta-analysis of genome-wide association studies has contributed to the discovery of most of the recently identified genetic risk factors for complex diseases.
Common meta-analytical approaches have been successfully applied; however, novel methods have been proposed that may have some advantages and disadvantages.
Heterogeneity in meta-analysis can be introduced from various sources and should not be disregarded. Several methods have been proposed that may optimize power in the presence of heterogeneity from known or unknown sources.
Next-generation sequence data will boost the study of rare variants; however, larger sample sizes are required. Several techniques have been developed for the meta-analysis of rare variants. Tools other than
P
values may be useful for inference.
Scientists will benefit from publicly available data sets and collaboration between consortia that will facilitate a wide range of methodological and applied research.
The authors review statistical methods for meta-analysis of genome-wide association studies (GWASs) and extensions of these methods to complex data. They discuss how low-frequency variants can be incorporated into meta-analyses as next-generation sequencing data become more commonly used in GWASs.
Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.</description><identifier>ISSN: 1471-0056</identifier><identifier>EISSN: 1471-0064</identifier><identifier>DOI: 10.1038/nrg3472</identifier><identifier>PMID: 23657481</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>Agriculture ; Animal Genetics and Genomics ; Bayes Theorem ; Biomedicine ; Cancer Research ; Consortia ; Data Interpretation, Statistical ; Epidemiology ; Gene Function ; Gene loci ; Genetic Heterogeneity ; Genetic susceptibility ; Genetics ; Genome-wide association studies ; Genome-Wide Association Study - methods ; Genomes ; Health risk assessment ; Heritability ; Human Genetics ; Humans ; Hypotheses ; Meta-analysis ; Meta-Analysis as Topic ; Methods ; Phenotype ; review-article ; Software ; Statistical analysis</subject><ispartof>Nature reviews. Genetics, 2013-06, Vol.14 (6), p.379-389</ispartof><rights>Springer Nature Limited 2013</rights><rights>COPYRIGHT 2013 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Jun 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c573t-cea502e3dd9d40dcf2467e260d718615b1bc8e0bdf2b6d1cd936f149b84d27c63</citedby><cites>FETCH-LOGICAL-c573t-cea502e3dd9d40dcf2467e260d718615b1bc8e0bdf2b6d1cd936f149b84d27c63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/nrg3472$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/nrg3472$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23657481$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Evangelou, Evangelos</creatorcontrib><creatorcontrib>Ioannidis, John P. A.</creatorcontrib><title>Meta-analysis methods for genome-wide association studies and beyond</title><title>Nature reviews. Genetics</title><addtitle>Nat Rev Genet</addtitle><addtitle>Nat Rev Genet</addtitle><description>Key Points
Meta-analysis of genome-wide association studies has contributed to the discovery of most of the recently identified genetic risk factors for complex diseases.
Common meta-analytical approaches have been successfully applied; however, novel methods have been proposed that may have some advantages and disadvantages.
Heterogeneity in meta-analysis can be introduced from various sources and should not be disregarded. Several methods have been proposed that may optimize power in the presence of heterogeneity from known or unknown sources.
Next-generation sequence data will boost the study of rare variants; however, larger sample sizes are required. Several techniques have been developed for the meta-analysis of rare variants. Tools other than
P
values may be useful for inference.
Scientists will benefit from publicly available data sets and collaboration between consortia that will facilitate a wide range of methodological and applied research.
The authors review statistical methods for meta-analysis of genome-wide association studies (GWASs) and extensions of these methods to complex data. They discuss how low-frequency variants can be incorporated into meta-analyses as next-generation sequencing data become more commonly used in GWASs.
Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.</description><subject>Agriculture</subject><subject>Animal Genetics and Genomics</subject><subject>Bayes Theorem</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Consortia</subject><subject>Data Interpretation, Statistical</subject><subject>Epidemiology</subject><subject>Gene Function</subject><subject>Gene loci</subject><subject>Genetic Heterogeneity</subject><subject>Genetic susceptibility</subject><subject>Genetics</subject><subject>Genome-wide association studies</subject><subject>Genome-Wide Association Study - methods</subject><subject>Genomes</subject><subject>Health risk assessment</subject><subject>Heritability</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Meta-analysis</subject><subject>Meta-Analysis as Topic</subject><subject>Methods</subject><subject>Phenotype</subject><subject>review-article</subject><subject>Software</subject><subject>Statistical analysis</subject><issn>1471-0056</issn><issn>1471-0064</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqN0ktv1DAQAGALgWhZEP8ARULicUjxK3ZyrMqrUhESj3Pk2JOsq8RuPY5g_z1ZupTdigPywZb9zVgeDyFPGT1hVNRvQhqE1PweOWZSs5JSJe_frit1RB4hXlLKFNPiITniQlVa1uyYvP0E2ZQmmHGDHosJ8jo6LPqYigFCnKD84R0UBjFab7KPocA8Ow9YmOCKDjYxuMfkQW9GhCe7eUW-v3_37exjefH5w_nZ6UVpKy1yacFUlINwrnGSOttzqTRwRZ1mtWJVxzpbA-1czzvlmHWNUD2TTVdLx7VVYkVe3eS9SvF6Bszt5NHCOJoAccaWiUo1Qqha_w8VVMp68Svy_A69jHNaKvJbcS15I_fUYEZofehjTsZuk7anQnAutdTba0_-oZbhYPI2Buj9sn8Q8PogYDEZfubBzIjt-dcvh_bFnl2DGfMa4zhvfwUP4csbaFNETNC3V8lPJm1aRtttv7S7flnks93b524Cd-v-NMjfOuJyFAZIe8W5k-sX8urDtQ</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>Evangelou, Evangelos</creator><creator>Ioannidis, John P. A.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><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>ISR</scope><scope>3V.</scope><scope>7QP</scope><scope>7QR</scope><scope>7RV</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20130601</creationdate><title>Meta-analysis methods for genome-wide association studies and beyond</title><author>Evangelou, Evangelos ; Ioannidis, John P. A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c573t-cea502e3dd9d40dcf2467e260d718615b1bc8e0bdf2b6d1cd936f149b84d27c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Agriculture</topic><topic>Animal Genetics and Genomics</topic><topic>Bayes Theorem</topic><topic>Biomedicine</topic><topic>Cancer Research</topic><topic>Consortia</topic><topic>Data Interpretation, Statistical</topic><topic>Epidemiology</topic><topic>Gene Function</topic><topic>Gene loci</topic><topic>Genetic Heterogeneity</topic><topic>Genetic susceptibility</topic><topic>Genetics</topic><topic>Genome-wide association studies</topic><topic>Genome-Wide Association Study - methods</topic><topic>Genomes</topic><topic>Health risk assessment</topic><topic>Heritability</topic><topic>Human Genetics</topic><topic>Humans</topic><topic>Hypotheses</topic><topic>Meta-analysis</topic><topic>Meta-Analysis as Topic</topic><topic>Methods</topic><topic>Phenotype</topic><topic>review-article</topic><topic>Software</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Evangelou, Evangelos</creatorcontrib><creatorcontrib>Ioannidis, John P. 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Genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Evangelou, Evangelos</au><au>Ioannidis, John P. A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Meta-analysis methods for genome-wide association studies and beyond</atitle><jtitle>Nature reviews. Genetics</jtitle><stitle>Nat Rev Genet</stitle><addtitle>Nat Rev Genet</addtitle><date>2013-06-01</date><risdate>2013</risdate><volume>14</volume><issue>6</issue><spage>379</spage><epage>389</epage><pages>379-389</pages><issn>1471-0056</issn><eissn>1471-0064</eissn><abstract>Key Points
Meta-analysis of genome-wide association studies has contributed to the discovery of most of the recently identified genetic risk factors for complex diseases.
Common meta-analytical approaches have been successfully applied; however, novel methods have been proposed that may have some advantages and disadvantages.
Heterogeneity in meta-analysis can be introduced from various sources and should not be disregarded. Several methods have been proposed that may optimize power in the presence of heterogeneity from known or unknown sources.
Next-generation sequence data will boost the study of rare variants; however, larger sample sizes are required. Several techniques have been developed for the meta-analysis of rare variants. Tools other than
P
values may be useful for inference.
Scientists will benefit from publicly available data sets and collaboration between consortia that will facilitate a wide range of methodological and applied research.
The authors review statistical methods for meta-analysis of genome-wide association studies (GWASs) and extensions of these methods to complex data. They discuss how low-frequency variants can be incorporated into meta-analyses as next-generation sequencing data become more commonly used in GWASs.
Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>23657481</pmid><doi>10.1038/nrg3472</doi><tpages>11</tpages></addata></record> |
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| subjects | Agriculture Animal Genetics and Genomics Bayes Theorem Biomedicine Cancer Research Consortia Data Interpretation, Statistical Epidemiology Gene Function Gene loci Genetic Heterogeneity Genetic susceptibility Genetics Genome-wide association studies Genome-Wide Association Study - methods Genomes Health risk assessment Heritability Human Genetics Humans Hypotheses Meta-analysis Meta-Analysis as Topic Methods Phenotype review-article Software Statistical analysis |
| title | Meta-analysis methods for genome-wide association studies and beyond |
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