We used the GWAS summary statistics for our 21 traits and 37 publicly available traits [50]. Coloc revealed that eQTLs for 176 eGenes were colocalized with 47 traits (Fig. 5a; Table S7; Fig. S18 and S19), corresponding to 354 gene-trait pairs. The SMR suggested that the abundance of 17 cis-regulated genes mediated the association between genetic variants and 15 agronomic traits, resulting in 44 tissue-gene-trait pairs (Fig. 5b). We discovered 29 tissue-gene-trait pairs that were shared by both Coloc and SMR results. In liver tissue, we found that eQTLs of NADSYN1 (NAD synthetase 1) were colocalized with GWAS signals of stature. The eQTLs of NDUFS3 (NADH: ubiquinone oxidoreductase core subunit S3) were associated with PH and rib eye area. The eQTLs of LTF (lactoferrin) were associated with somatic cell score. The eQTLs of KIFC2 (kinesin family member C2) were detected colocalized with milk fat (Fig. S20). In adipose tissue, GRAMD1C (GRAM domain containing 1C) is a cholesterol transfer gene. We found the eQTLs of GRAMD1C were colocalized with the signals of rear thigh circumference (Fig. 5c). The eQTLs of TMTC2 (transmembrane O-mannosyltransferase targeting cadherins 2) and ZNF613 (zinc finger protein 613) were associated with body conformation traits. In muscle tissue, the eQTLs of TIGAR (TP53-induced glycolysis and apoptosis regulator) were colocalized with rump width, stature, strength, body depth, sire calving ease, and productive life (Fig. 5d). The eQTLs of NDUFS3 and L3HYPDH (trans-L-3-hydroxyproline dehydratase) were associated with daily weight gain and rib eye area, respectively. In general, the use of GWAS, eQTL mapping, TWAS, colocalization, and SMR analysis improved our ability to identify potentially causal genes and comprehend the genetic basis of complex traits in cattle (Fig. 6).
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We used the GWAS summary statistics for our 21 traits and 37 publicly available traits [50]. Coloc revealed that eQTLs for 176 eGenes were colocalized with 47 traits (Fig. 5a; Table S7; Fig. S18 and S19), corresponding to 354 gene-trait pairs. The SMR suggested that the abundance of 17 cis-regulated genes mediated the association between genetic variants and 15 agronomic traits, resulting in 44 tissue-gene-trait pairs (Fig. 5b). We discovered 29 tissue-gene-trait pairs that were shared by both Coloc and SMR results. In liver tissue, we found that eQTLs of NADSYN1 (NAD synthetase 1) were colocalized with GWAS signals of stature. The eQTLs of NDUFS3 (NADH: ubiquinone oxidoreductase core subunit S3) were associated with PH and rib eye area. The eQTLs of LTF (lactoferrin) were associated with somatic cell score. The eQTLs of KIFC2 (kinesin family member C2) were detected colocalized with milk fat (Fig. S20). In adipose tissue, GRAMD1C (GRAM domain containing 1C) is a cholesterol transfer gene. We found the eQTLs of GRAMD1C were colocalized with the signals of rear thigh circumference (Fig. 5c). The eQTLs of TMTC2 (transmembrane O-mannosyltransferase targeting cadherins 2) and ZNF613 (zinc finger protein 613) were associated with body conformation traits. In muscle tissue, the eQTLs of TIGAR (TP53-induced glycolysis and apoptosis regulator) were colocalized with rump width, stature, strength, body depth, sire calving ease, and productive life (Fig. 5d). The eQTLs of NDUFS3 and L3HYPDH (trans-L-3-hydroxyproline dehydratase) were associated with daily weight gain and rib eye area, respectively. In general, the use of GWAS, eQTL mapping, TWAS, colocalization, and SMR analysis improved our ability to identify potentially causal genes and comprehend the genetic basis of complex traits in cattle (Fig. 6).
我们利用了21种特征和37种公开可用特征的GWAS摘要统计数据。Coloc结果显示，有176种eGenes的eQTLs与47种特征共定位(Fig. 5a; Table S7; Fig. S18 and S19)，对应354个基因-特征对。SMR结果建议，17种顺式调控基因的丰度介导了遗传变异和15种农艺特性之间的关联，产生了44个组织-基因-特征对(Fig. 5b)。我们发现在Coloc和SMR结果中共享了29个组织-基因-特征对。在肝脏组织中，我们发现NADSYN1(烟酰胺腺嘌呤二核苷酸合酶1)的eQTLs与身材的GWAS信号共定位。NDUFS3(NADH: ubiquinone氧化还原酶核心亚基S3)的eQTLs与PH和肋眼面积有关。LTF(乳铁蛋白)的eQTLs与体细胞得分有关。我们检测到KIFC2(肌动蛋白家族成员C2)的eQTLs与乳脂肪共定位(Fig. S20)。在脂肪组织中，我们发现GRAMD1C(含GRAM结构域的1C)是一个胆固醇转移基因。我们发现GRAMD1C的eQTLs与后大腿围度的信号共定位(Fig. 5c)。TMTC2(跨膜O-甘露醇转移酶靶向钙粘蛋白2)和ZNF613(锌指蛋白613)的eQTLs与体型特征有关。在肌肉组织中，TIGAR(TP53诱导的糖酵解和凋亡调控器)的eQTLs与臀部宽度、身高、力量、体深、公牛分娩易度和生产寿命共定位(Fig. 5d)。NDUFS3和L3HYPDH(跨L-3-羟脯氨酸脱水酶)的eQTLs分别与日增重和肋眼面积有关。总的来说，GWAS、eQTL绘图、TWAS、共定位和SMR分析的使用提高了我们识别潜在致因基因和理解牛复杂特性遗传基础的能力(Fig. 6)。
Main points:
- Conducted a comprehensive GWAS analysis for 21 agronomic traits in Simmental beef cattle
- Identified numerous association signals for beef agronomic traits using GWAS
- Discovered cis-eQTLs and trans-eQTLs in three tissues (longissimus dorsi muscle, backfat, and liver)
- Built a synergistic network by integrating transcriptomics with agronomic traits
- Observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping
- Identified candidate genes for economic traits through colocalization and Mendelian randomization analyses
- Provides valuable resources for genomics and breeding in beef cattle industry.
主要内容：
- 对西门塔尔肉牛的21种农艺性状进行了全面的GWAS分析
- 使用GWAS为肉牛农艺性状鉴定了众多关联信号
- 在三种组织（肋部长肌，背膘和肝脏）中发现了顺式eQTLs和反式eQTLs
- 通过整合转录组学与农艺性状，构建了一个协同网络
- 使用多组织eQTL绘图，顺式eQTL发现的平均改进了43.5%
- 通过共定位和孟德尔随机化分析，确定了经济性状的候选基因
- 为肉牛产业的基因组学和育种提供了宝贵的资源。
This study focuses on the genetic mechanisms underlying agronomic traits in Simmental beef cattle. The specific roles of the longissimus dorsi muscle, backfat, and liver in these mechanisms are still unknown. The researchers hypothesized that a genome-wide association study (GWAS) strategy combined with transcriptomic analysis could help identify candidate genes and causal variants related to these traits. They conducted a comprehensive GWAS analysis for 21 agronomic traits using whole-genome variants in Simmental beef cattle. They also performed expression quantitative trait loci (eQTL) mapping in three tissues (longissimus dorsi muscle, backfat, and liver) and integrated the GWAS results with the eQTL data to prioritize genes and variants affecting agronomic traits. The study provides valuable insights into the genetic architecture of these traits in cattle.
这项研究关注西门塔尔肉牛农艺性状的遗传机制。肋部长肌、背膘和肝脏在这些机制中的具体作用仍然未知。研究人员假设结合全基因组关联研究（GWAS）策略和转录组学分析，可以帮助识别与这些性状相关的候选基因和致因变异。他们对西门塔尔肉牛的21种农艺性状进行了全面的GWAS分析，并使用全基因组变异。他们还在三种组织（肋部长肌，背膘和肝脏）中进行了表达量性状位点（eQTL）绘图，并将GWAS结果与eQTL数据进行了整合，以确定影响农艺性状的基因和变异。该研究为理解牛的这些性状的遗传结构提供了宝贵的洞察。