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Abstract:
Aim. To identify novel candidate genes and gene sets for diabetes. Methods. We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-basedMendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis resultswere further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. Results. SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 x 10(-8)), MRPL33 (p value = 1.24 x 10(-7)), and FADS1 (p value = 2.39 x 10(-7)). Gene set analysis identified HUANG_FOXA2_TARGETS_ UP (false discovery rate = 0.047) associatedwith fasting glucose. Conclusion. Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases.
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BIOMED RESEARCH INTERNATIONAL
ISSN: 2314-6133
Year: 2017
Volume: 2017
2 . 5 8 3
JCR@2017
3 . 4 1 1
JCR@2020
ESI Discipline: BIOLOGY & BIOCHEMISTRY;
ESI HC Threshold:157
JCR Journal Grade:3
CAS Journal Grade:3
Cited Count:
WoS CC Cited Count: 6
SCOPUS Cited Count: 2
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 6
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