Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we perfor...
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Autor*in:	Shuai Liu [verfasserIn] Weiming Gong [verfasserIn] Lu Liu [verfasserIn] Ran Yan [verfasserIn] Shukang Wang [verfasserIn] Zhongshang Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis

Übergeordnetes Werk:	In: International Journal of Molecular Sciences - MDPI AG, 2003, 23(2022), 21, p 13555
Übergeordnetes Werk:	volume:23 ; year:2022 ; number:21, p 13555

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3390/ijms232113555

Katalog-ID:	DOAJ086458817

Internformat


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520			\|a Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.
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allfields	10.3390/ijms232113555 doi (DE-627)DOAJ086458817 (DE-599)DOAJ5ba32dcb702244beb3897edecc20a615 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Shuai Liu verfasserin aut Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA. juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis Biology (General) Chemistry Weiming Gong verfasserin aut Lu Liu verfasserin aut Ran Yan verfasserin aut Shukang Wang verfasserin aut Zhongshang Yuan verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 13555 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 13555 https://doi.org/10.3390/ijms232113555 kostenfrei https://doaj.org/article/5ba32dcb702244beb3897edecc20a615 kostenfrei https://www.mdpi.com/1422-0067/23/21/13555 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 13555
spelling	10.3390/ijms232113555 doi (DE-627)DOAJ086458817 (DE-599)DOAJ5ba32dcb702244beb3897edecc20a615 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Shuai Liu verfasserin aut Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA. juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis Biology (General) Chemistry Weiming Gong verfasserin aut Lu Liu verfasserin aut Ran Yan verfasserin aut Shukang Wang verfasserin aut Zhongshang Yuan verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 13555 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 13555 https://doi.org/10.3390/ijms232113555 kostenfrei https://doaj.org/article/5ba32dcb702244beb3897edecc20a615 kostenfrei https://www.mdpi.com/1422-0067/23/21/13555 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 13555
allfields_unstemmed	10.3390/ijms232113555 doi (DE-627)DOAJ086458817 (DE-599)DOAJ5ba32dcb702244beb3897edecc20a615 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Shuai Liu verfasserin aut Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA. juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis Biology (General) Chemistry Weiming Gong verfasserin aut Lu Liu verfasserin aut Ran Yan verfasserin aut Shukang Wang verfasserin aut Zhongshang Yuan verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 13555 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 13555 https://doi.org/10.3390/ijms232113555 kostenfrei https://doaj.org/article/5ba32dcb702244beb3897edecc20a615 kostenfrei https://www.mdpi.com/1422-0067/23/21/13555 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 13555
allfieldsGer	10.3390/ijms232113555 doi (DE-627)DOAJ086458817 (DE-599)DOAJ5ba32dcb702244beb3897edecc20a615 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Shuai Liu verfasserin aut Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA. juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis Biology (General) Chemistry Weiming Gong verfasserin aut Lu Liu verfasserin aut Ran Yan verfasserin aut Shukang Wang verfasserin aut Zhongshang Yuan verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 13555 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 13555 https://doi.org/10.3390/ijms232113555 kostenfrei https://doaj.org/article/5ba32dcb702244beb3897edecc20a615 kostenfrei https://www.mdpi.com/1422-0067/23/21/13555 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 13555
allfieldsSound	10.3390/ijms232113555 doi (DE-627)DOAJ086458817 (DE-599)DOAJ5ba32dcb702244beb3897edecc20a615 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Shuai Liu verfasserin aut Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA. juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis Biology (General) Chemistry Weiming Gong verfasserin aut Lu Liu verfasserin aut Ran Yan verfasserin aut Shukang Wang verfasserin aut Zhongshang Yuan verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 13555 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 13555 https://doi.org/10.3390/ijms232113555 kostenfrei https://doaj.org/article/5ba32dcb702244beb3897edecc20a615 kostenfrei https://www.mdpi.com/1422-0067/23/21/13555 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 13555
language	English
source	In International Journal of Molecular Sciences 23(2022), 21, p 13555 volume:23 year:2022 number:21, p 13555
sourceStr	In International Journal of Molecular Sciences 23(2022), 21, p 13555 volume:23 year:2022 number:21, p 13555
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis Biology (General) Chemistry
isfreeaccess_bool	true
container_title	International Journal of Molecular Sciences
authorswithroles_txt_mv	Shuai Liu @@aut@@ Weiming Gong @@aut@@ Lu Liu @@aut@@ Ran Yan @@aut@@ Shukang Wang @@aut@@ Zhongshang Yuan @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	316340715
id	DOAJ086458817
language_de	englisch
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callnumber-first	Q - Science
author	Shuai Liu
spellingShingle	Shuai Liu misc QH301-705.5 misc QD1-999 misc juvenile idiopathic arthritis misc transcriptome-wide association study misc gene-based association analysis misc enrichment analysis misc Biology (General) misc Chemistry Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis
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topic_title	QH301-705.5 QD1-999 Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis juvenile idiopathic arthritis transcriptome-wide association study gene-based association analysis enrichment analysis
topic	misc QH301-705.5 misc QD1-999 misc juvenile idiopathic arthritis misc transcriptome-wide association study misc gene-based association analysis misc enrichment analysis misc Biology (General) misc Chemistry
topic_unstemmed	misc QH301-705.5 misc QD1-999 misc juvenile idiopathic arthritis misc transcriptome-wide association study misc gene-based association analysis misc enrichment analysis misc Biology (General) misc Chemistry
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title	Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis
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title_full	Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis
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title_sort	integrative analysis of transcriptome-wide association study and gene-based association analysis identifies in silico candidate genes associated with juvenile idiopathic arthritis
callnumber	QH301-705.5
title_auth	Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis
abstract	Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.
abstractGer	Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.
abstract_unstemmed	Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.
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title_short	Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis
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Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (<i<P</i<<sub<FUSION</sub< = 5.12 × 10<sup<−6</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.94 × 10<sup<−7</sup< for whole blood), IL-6R (<i<P</i<<sub<FUSION</sub< = 8.63 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 2.74 × 10<sup<−6</sup< for cells EBV-transformed lymphocytes), and Fas (<i<P</i<<sub<FUSION</sub< = 5.21 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.08 × 10<sup<−6</sup< for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (<i<P</i<<sub<FUSION</sub< = 2.10 × 10<sup<−7</sup<, <i<P</i<<sub<eMAGMA</sub< = 3.93 × 10<sup<−8</sup< for Liver), LAT (<i<P</i<<sub<FUSION</sub< = 1.53 × 10<sup<−4</sup<, <i<P</i<<sub<eMAGMA</sub< = 4.62 × 10<sup<−7</sup< for Artery Aorta), and MAGI3 (<i<P</i<<sub<FUSION</sub< = 1.30 × 10<sup<−5</sup<, <i<P</i<<sub<eMAGMA</sub< = 1.73 × 10<sup<−7</sup< for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. 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Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis

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