Novel Candidate Genes Detection Using Bayesian Network-Based Genome-Wide Association Study of Latent Traits in F2 Chicken Population.

IF 1.9 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Siavash Manzoori, Rasoul Vaez Torshizi, Ali Akbar Masoudi, Mehdi Momen
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引用次数: 0

Abstract

In chickens, economically important traits are commonly controlled by multiple genes and are often correlated. The genetic mechanisms underlying the correlated phenotypes likely involve pleiotropy or linkage disequilibrium, which is not handled properly in single-trait genome-wide association studies (GWAS). We employed factor analytical models to estimate the value of latent traits to reduce the dimensionality of the adjusted phenotypes. The dataset included phenotypes from 369 F2 chickens, categorised into six observable classes, namely body weight (BW), feed intake (FI), feed efficiency (FE), immunity (IMU), blood metabolites (BMB), and carcass (CC) traits. All birds were genotyped using a 60K SNP Beadchip. A Bayesian network (BN) algorithm was used to discern the recursive causal relationships among the inferred latent traits. Multi-Trait (MT) and Structural Equation Model (SEM) were applied for association analysis. Several candidate genes were detected across six phenotypic classes, namely the IPMK gene for BW and FI, and, the MTERF2 gene for BW and FE. The rs14565514 SNP, close to genes IPMK, UBE2D1, and CISD1, was recognised as a pleiotropic marker by both models. The NRG3 gene, located on chromosome 6, was associated with FI. CRISP2, RHAG, CYP2AC1, and CENPQ genes, located on chromosome 3, were detected for BMB through both MT- and SEM-GWAS. In general, the results indicated that the SEM-GWAS is superior to MT-GWAS due to considering the causal relationships among the traits, correcting the effects of the traits on each other, and also leading to the identification of pleiotropic SNP markers.

基于贝叶斯网络的新型候选基因检测F2鸡群体潜在性状的全基因组关联研究。
在鸡中,经济上重要的性状通常由多个基因控制,而且往往是相关的。相关表型的遗传机制可能涉及多效性或连锁不平衡,这在单性状全基因组关联研究(GWAS)中没有得到适当的处理。我们采用因子分析模型来估计潜在性状的价值,以降低调整后表型的维度。该数据集包括369只F2鸡的表型,分为6类,即体重(BW)、采食量(FI)、饲料效率(FE)、免疫力(IMU)、血液代谢物(BMB)和胴体(CC)性状。所有鸟类使用60K SNP珠芯片进行基因分型。采用贝叶斯网络(BN)算法来识别推断出的潜在性状之间的递归因果关系。采用多性状(MT)和结构方程模型(SEM)进行关联分析。在6个表型类别中检测到几个候选基因,即BW和FI的IPMK基因,以及BW和FE的MTERF2基因。rs14565514 SNP靠近IPMK、UBE2D1和CISD1基因,被两种模型识别为多效性标记。NRG3基因位于6号染色体上,与FI有关。位于3号染色体上的CRISP2、RHAG、CYP2AC1和CENPQ基因通过MT-和SEM-GWAS检测BMB。综上所述,SEM-GWAS考虑了性状间的因果关系,纠正了性状间的相互影响,并导致了多效SNP标记的鉴定,因此优于MT-GWAS。
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来源期刊
Journal of Animal Breeding and Genetics
Journal of Animal Breeding and Genetics 农林科学-奶制品与动物科学
CiteScore
5.20
自引率
3.80%
发文量
58
审稿时长
12-24 weeks
期刊介绍: The Journal of Animal Breeding and Genetics publishes original articles by international scientists on genomic selection, and any other topic related to breeding programmes, selection, quantitative genetic, genomics, diversity and evolution of domestic animals. Researchers, teachers, and the animal breeding industry will find the reports of interest. Book reviews appear in many issues.
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