Meta-Qtest: meta-analysis of quadratic test for rare variants
Abstract Background In genome-wide association studies (GWASs), meta-analysis has been widely used to improve statistical power by combining the results of different studies. Meta-analysis can detect phenotype associated variants that are failed to be detected in single studies. Especially, in biome...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12920-019-0516-5 |
| _version_ | 1819176485150261248 |
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| author | Jieun Ka Jaehoon Lee Yongkang Kim Bermseok Oh T2D-GENES Consortium Taesung Park |
| author_facet | Jieun Ka Jaehoon Lee Yongkang Kim Bermseok Oh T2D-GENES Consortium Taesung Park |
| author_sort | Jieun Ka |
| collection | DOAJ |
| description | Abstract Background In genome-wide association studies (GWASs), meta-analysis has been widely used to improve statistical power by combining the results of different studies. Meta-analysis can detect phenotype associated variants that are failed to be detected in single studies. Especially, in biomedical sciences, meta-analysis is often necessary not only for improving statistical power, but also for reducing unavoidable limitation in data collection. As next-generation sequencing (NGS) technology has been developed, meta-analysis of rare variants is proceeding briskly along with meta-analysis of common variants in GWASs. However, meta-analysis on a single variant that is commonly used in common variant association test is improper for rare variants. A sparse signal of rare variant undermines the association signal and its large number causes multiple testing problem. To over-come these problems, we propose a meta-analysis method at the gene-level rather than variant level. Results Among many methods that have been developed, we used the unified quadratic tests (Q-tests); Q-test is more powerful than or as powerful as other tests such as Sequence Kernel Association Tests (SKAT). Since there are three different versions of Q-test (QTest1, QTest2, QTest3), each assumes different relationships among multiple rare variants, we extended them into meta-study accordingly. For meta-analysis, we consider two types of approaches, the one is to combine regression coefficients and the other is to combine test statistics from each single study. We extend the Q-test for meta-analysis, proposing Meta Quadratic Test (Meta-Qtest). Meta Q-test avoids the limitations of MetaSKAT. It does not only consider genetic heterogeneity among studies as MetaSKAT but also reflects diverse real situations; since we extend three different Q-tests into meta-analysis respectively, flexible Meta Q-test suggests way to deal with gene-level rare variant meta-analysis efficiently From the results of real data analysis of blood pressure trait, our meta-analysis could successfully discovered genes, KCNA5 and CABIN1 that are already well known for relevance with hypertension disease and they are not detected in MetaSKAT. Conclusion As exemplified by an application to T2D Genes projects data set, Meta-Qtest more effectively identified genes associated with hypertension disease than MetaSKAT did. |
| first_indexed | 2024-12-22T21:11:30Z |
| format | Article |
| id | doaj.art-7dbb790d1a6f49d4851875d7be4ea8f7 |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-12-22T21:11:30Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-7dbb790d1a6f49d4851875d7be4ea8f72022-12-21T18:12:31ZengBMCBMC Medical Genomics1755-87942019-07-0112S511510.1186/s12920-019-0516-5Meta-Qtest: meta-analysis of quadratic test for rare variantsJieun Ka0Jaehoon Lee1Yongkang Kim2Bermseok Oh3T2D-GENES ConsortiumTaesung Park4Department of Statistics, Seoul National UniversityDepartment of Statistics, Seoul National UniversityDepartment of Statistics, Seoul National UniversityDepartment of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee UniversityDepartment of Statistics, Seoul National UniversityAbstract Background In genome-wide association studies (GWASs), meta-analysis has been widely used to improve statistical power by combining the results of different studies. Meta-analysis can detect phenotype associated variants that are failed to be detected in single studies. Especially, in biomedical sciences, meta-analysis is often necessary not only for improving statistical power, but also for reducing unavoidable limitation in data collection. As next-generation sequencing (NGS) technology has been developed, meta-analysis of rare variants is proceeding briskly along with meta-analysis of common variants in GWASs. However, meta-analysis on a single variant that is commonly used in common variant association test is improper for rare variants. A sparse signal of rare variant undermines the association signal and its large number causes multiple testing problem. To over-come these problems, we propose a meta-analysis method at the gene-level rather than variant level. Results Among many methods that have been developed, we used the unified quadratic tests (Q-tests); Q-test is more powerful than or as powerful as other tests such as Sequence Kernel Association Tests (SKAT). Since there are three different versions of Q-test (QTest1, QTest2, QTest3), each assumes different relationships among multiple rare variants, we extended them into meta-study accordingly. For meta-analysis, we consider two types of approaches, the one is to combine regression coefficients and the other is to combine test statistics from each single study. We extend the Q-test for meta-analysis, proposing Meta Quadratic Test (Meta-Qtest). Meta Q-test avoids the limitations of MetaSKAT. It does not only consider genetic heterogeneity among studies as MetaSKAT but also reflects diverse real situations; since we extend three different Q-tests into meta-analysis respectively, flexible Meta Q-test suggests way to deal with gene-level rare variant meta-analysis efficiently From the results of real data analysis of blood pressure trait, our meta-analysis could successfully discovered genes, KCNA5 and CABIN1 that are already well known for relevance with hypertension disease and they are not detected in MetaSKAT. Conclusion As exemplified by an application to T2D Genes projects data set, Meta-Qtest more effectively identified genes associated with hypertension disease than MetaSKAT did.http://link.springer.com/article/10.1186/s12920-019-0516-5Meta-analysisRare variant analysisExome sequencingMeta-Qtest |
| spellingShingle | Jieun Ka Jaehoon Lee Yongkang Kim Bermseok Oh T2D-GENES Consortium Taesung Park Meta-Qtest: meta-analysis of quadratic test for rare variants BMC Medical Genomics Meta-analysis Rare variant analysis Exome sequencing Meta-Qtest |
| title | Meta-Qtest: meta-analysis of quadratic test for rare variants |
| title_full | Meta-Qtest: meta-analysis of quadratic test for rare variants |
| title_fullStr | Meta-Qtest: meta-analysis of quadratic test for rare variants |
| title_full_unstemmed | Meta-Qtest: meta-analysis of quadratic test for rare variants |
| title_short | Meta-Qtest: meta-analysis of quadratic test for rare variants |
| title_sort | meta qtest meta analysis of quadratic test for rare variants |
| topic | Meta-analysis Rare variant analysis Exome sequencing Meta-Qtest |
| url | http://link.springer.com/article/10.1186/s12920-019-0516-5 |
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