Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework. By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1, OAZ3, DNAJB8, TPPP2 and TNP1) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits. By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

中文翻译：

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结合GWAS和RNA-seq的系统生物学框架，以猪精子质量的分子基础阐明

动物育种中的遗传压力激发了育种者的兴趣，他们选择精子质量更高的精公猪来优化射精剂量和受精率。但是，精子质量的分子基础尚未完全了解。我们的目标是通过分析25种与精子相关的表型，并在系统生物学框架下整合全基因组关联研究（GWAS）和RNA-seq，从而确定与猪精子质量相关的候选基因，途径和DNA变体。通过GWAS，我们确定了12个定量性状位点（QTL），与头部和颈部异常，顶体异常和活动精子的百分比相关。候选基因包括CHD2，KATNAL2，SLC14A2和ABCA1。通过RNA-seq，我们鉴定了广泛的mRNA（例如PRM1，OAZ3，DNAJB8，TPPP2和TNP1）和miRNA（例如ssc-miR-30d，ssc-miR-34c，ssc-miR-30c-5p，ssc-miR-191，let-7家族成员和ssc-miR-425-5p）具有与精子生物学相关的功能。我们检测到精子性状和mRNA丰度之间有6128个显着的相关性（P值≤0.05）。通过表达（e）GWAS，我们鉴定了涉及基因IQCJ，ACTR2和HARS的三个反式表达QTL。利用GWAS和RNA-seq数据，我们建立了一个基因相互作用网络。我们认为，存在于GWAS和RNA-seq网络中的基因和相互作用具有较高的实际参与精子质量的可能性，并利用它们建立了一个强大的基因相互作用网络。另外，在最终的网络中，我们包括了具有与四个以上精液性状相关的RNA丰度的基因，以及与网络中的基因相互作用的miRNA。最终网络丰富了与配子生成和发育，减数分裂细胞周期，DNA修复或胚胎植入有关的基因。最后，我们根据GWAS，eGWAS和最终网络数据设计了一个73个SNP的小组，这些小组解释了5％（精子细胞浓度）和36％（颈部异常百分比）的精子性状表型变异。通过应用系统生物学方法，我们确定了可能影响精子质量的基因，并构建了一个SNP面板，该面板解释了我们研究中精液质量的表型差异的很大一部分，应该在其他猪群中进行测试以评估其与精子质量的相关性。养猪业。eGWAS和最终网络数据可解释5％（精子细胞浓度）至36％（颈部异常百分比）的精子性状表型变异。通过应用系统生物学方法，我们确定了可能影响精子质量的基因，并构建了一个SNP面板，该面板解释了我们研究中精液质量的表型差异的很大一部分，应该在其他猪群中进行测试以评估其与精子质量的相关性。养猪业。eGWAS和最终网络数据可解释5％（精子细胞浓度）至36％（颈部异常百分比）的精子性状表型变异。通过应用系统生物学方法，我们确定了可能影响精子质量的基因，并构建了一个SNP面板，该面板解释了我们研究中精液质量的表型差异的很大一部分，应该在其他猪群中进行测试以评估其与精子质量的相关性。养猪业。