Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs
Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs
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DOI：
10.1534/genetics.117.200782
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发表时间：
2017-07-01
期刊：
影响因子：
3.3
通讯作者：
Bennewitz, Joern
中科院分区：
文献类型：
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作者：
Camarinha-Silva, Amelia;Maushammer, Maria;Bennewitz, Joern
本研究的目的是利用扩展的数量遗传学方法分析猪的胃肠道(GIT)微生物区系、宿主遗传学和复杂性状之间的相互作用。研究设计包括207头猪，在标准化条件下饲养和屠宰，并对日增重、采食量和饲料转化率进行表型分析。用标准的60K SNP芯片对猪进行基因分型。采用16S rRNA基因扩增片段测序技术分析GIT微生物区系组成。在调查的49个细菌属中，有8个具有显著的狭义寄主遗传力，范围在0.32~0.57之间。应用微生物混合线性模型估计每个复杂性状的微生物区系方差。日增重、饲料转化率和采食量的微生物变异对表型变异的解释分数分别为0.28、0.21和0.16。分别采用基因组最佳线性无偏预测(G-BLUP)和微生物最佳线性无偏预测(M-BLUP)方法对SNP数据和微生物区系组成进行预测。G-BLUP对日增重、饲料转化率和采食量的预测精度分别为0.35、0.23和0.20。M-BLUP的相应预测精度分别为0.41、0.33和0.33。因此，除了SNP数据外，微生物区系丰度也是复杂性状预测的信息来源。由于猪是一种非常适合于模拟人类消化道的动物，除了G-BLUP外，M-BLUP可能有助于预测人类对某些疾病的易感性，从而用于预防和个性化药物。
The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that were housed and slaughtered under standardized conditions, and phenotyped for daily gain, feed intake, and feed conversion rate. The pigs were genotyped with a standard 60 K SNP chip. The GIT microbiota composition was analyzed by 16S rRNA gene amplicon sequencing technology. Eight from 49 investigated bacteria genera showed a significant narrow sense host heritability, ranging from 0.32 to 0.57. Microbial mixed linear models were applied to estimate the microbiota variance for each complex trait. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota composition were used to predict the complex traits using genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods, respectively. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, in addition to SNP data, microbiota abundances are an informative source of complex trait predictions. Since the pig is a well-suited animal for modeling the human digestive tract, M-BLUP, in addition to G-BLUP, might be beneficial for predicting human predispositions to some diseases, and, consequently, for preventative and personalized medicine.