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基于基因集富集和蛋白质-蛋白质相互作用网络的 GWAS 结合调控 SNP 鉴定与韩牛胴体性状相关的候选基因和途径。

A Gene-Set Enrichment and Protein-Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle.

机构信息

Animal Genomics and Bioinformatics Division, National Institute of Animal Science, RDA, Wanju 55365, Korea.

Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea.

出版信息

Genes (Basel). 2020 Mar 16;11(3):316. doi: 10.3390/genes11030316.

Abstract

Non-synonymous SNPs and protein coding SNPs within the promoter region of genes (regulatory SNPs) might have a significant effect on carcass traits. Imputed sequence level data of 10,215 Hanwoo bulls, annotated and filtered to include only regulatory SNPs (450,062 SNPs), were used in a genome-wide association study (GWAS) to identify loci associated with backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). A total of 15, 176, and 1 SNPs were found to be significantly associated ( < 1.11 × 10) with BFT, CWT, and EMA, respectively. The significant loci were BTA4 (CWT), BTA6 (CWT), BTA14 (CWT and EMA), and BTA19 (BFT). BayesR estimated that 1.1%1.9% of the SNPs contributed to more than 0.01% of the phenotypic variance. So, the GWAS was complemented by a gene-set enrichment (GSEA) and protein-protein interaction network (PPIN) analysis in identifying the pathways affecting carcass traits. At < 0.005 (2,261 SNPs), 25 GO and 18 KEGG categories, including calcium signaling, cell proliferation, and folate biosynthesis, were found to be enriched through GSEA. The PPIN analysis showed enrichment for 81 candidate genes involved in various pathways, including the PI3K-AKT, calcium, and FoxO signaling pathways. Our finding provides insight into the effects of regulatory SNPs on carcass traits.

摘要

非 synonymous SNPs 和基因启动子区域的蛋白质编码 SNPs(调控 SNPs)可能对胴体性状有显著影响。使用了 10215 头韩牛公牛的序列水平数据进行全基因组关联研究(GWAS),这些数据经过注释和过滤,只包括调控 SNPs(450062 个 SNPs),与背膘厚(BFT)、胴体重(CWT)、眼肌面积(EMA)和大理石花纹评分(MS)相关的位点进行了关联分析。总共发现了 15、176 和 1 个 SNP 与 BFT、CWT 和 EMA 分别显著相关( < 1.11 × 10)。显著的位点是 BTA4(CWT)、BTA6(CWT)、BTA14(CWT 和 EMA)和 BTA19(BFT)。BayesR 估计,1.1%1.9%的 SNPs 对超过 0.01%的表型方差有贡献。因此,通过基因集富集(GSEA)和蛋白质-蛋白质相互作用网络(PPIN)分析补充了 GWAS,以确定影响胴体性状的途径。在 < 0.005(2261 个 SNP)水平上,通过 GSEA 发现了 25 个 GO 和 18 个 KEGG 类别,包括钙信号、细胞增殖和叶酸生物合成,富集显著。PPIN 分析显示,涉及各种途径的 81 个候选基因富集,包括 PI3K-AKT、钙和 FoxO 信号通路。我们的研究结果为调控 SNPs 对胴体性状的影响提供了深入了解。

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