Camarinha-Silva Amelia, Maushammer Maria, Wellmann Robin, Vital Marius, Preuss Siegfried, Bennewitz Jörn
Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany.
Microbial Interactions and Processes Research Group, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany.
Genetics. 2017 Jul;206(3):1637-1644. doi: 10.1534/genetics.117.200782. Epub 2017 May 3.
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.
本研究的目的是使用扩展的数量遗传学方法分析猪胃肠道(GIT)微生物群、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,这些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了表型分析。使用标准的60K SNP芯片对猪进行基因分型。通过16S rRNA基因扩增子测序技术分析GIT微生物群组成。在49个被调查的细菌属中,有8个显示出显著的狭义宿主遗传力,范围从0.32到0.57。应用微生物混合线性模型来估计每个复杂性状的微生物群方差。微生物方差解释的表型方差比例,日增重、饲料转化率和采食量分别为0.28、0.21和0.16。分别使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,利用SNP数据和微生物群组成来预测复杂性状。G-BLUP对日增重、饲料转化率和采食量的预测准确率分别为0.35、0.23和0.20。M-BLUP的相应预测准确率为0.41、0.33和0.33。因此,除了SNP数据外,微生物群丰度也是复杂性状预测的一个信息来源。由于猪是模拟人类消化道的合适动物,除了G-BLUP之外,M-BLUP可能有助于预测人类对某些疾病的易感性,从而有助于预防医学和个性化医疗。