BACKGROUND The detection of regions that affect quantitative traits (QTL), to implement selection assisted by molecular information, remains of particular interest in dairy sheep for which genetic gain is constrained by the high costs of large-scale phenotype and pedigree recording. QTL detection based on the combination of linkage disequilibrium and linkage analysis (LDLA) is the most suitable approach in family-structured populations. The main issue in performing LDLA mapping is the handling of the identity-by-descent (IBD) probability matrix. Here, we propose the use of principal component analysis (PCA) to perform LDLA mapping for milk traits in Sarda dairy sheep. METHODS A resource population of 3731 ewes belonging to 161 sire families and genotyped with the OvineSNP50 Beadchip was used to map genomic regions that affect five milk traits. The paternally and maternally inherited gametes of genotyped individuals were reconstructed and IBD probabilities between them were defined both at each SNP position and at the genome level. A QTL detection model fitting fixed effects of principal components that summarize IBD probabilities was tested at each SNP position. Genome-wide (GW) significance thresholds were determined by within-trait permutations. RESULTS PCA resulted in substantial dimensionality reduction, in fact 137 and 32 (on average) principal components were able to capture 99% of the IBD variation at the locus and genome levels, respectively. Overall, 2563 positions exceeded the 0.05 GW significance threshold for at least one trait, which clustered into 75 QTL regions most of which affected more than one trait. The strongest signal was obtained for protein content on Ovis aries (OAR) chromosome 6 and overlapped with the region that harbours the casein gene cluster. Additional interesting positions were identified on OAR4 for fat content and on OAR11 for the three yield traits. CONCLUSIONS PCA is a good strategy to summarize IBD probabilities. A large number of regions associated to milk traits were identified. The outputs provided by the proposed method are useful for the selection of candidate genes, which need to be further investigated to identify causative mutations or markers in strong LD with them for application in selection programs assisted by molecular information.

中文翻译：

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通过使用OvineSNP50 Beadchip及其主要成分进行组合连锁和连锁不平衡分析，绘制出影响Sarda绵羊乳特性的基因组区域图。

背景技术对影响定量性状（QTL）的区域的检测，以实施由分子信息辅助的选择，在奶牛中尤其受到关注，对于奶牛而言，其遗传增益受到大规模表型和谱系记录的高额费用的限制。基于连锁不平衡和连锁分析（LDLA）相结合的QTL检测是最适合家庭结构人群的方法。执行LDLA映射的主要问题是按血统身份（IBD）概率矩阵的处理。在这里，我们建议使用主成分分析（PCA）对萨达（Sarda）奶羊的牛奶性状进行LDLA映射。方法使用OvineSNP50 Beadchip基因分型的161个父本家族的3731头母羊，对影响5个乳性状的基因组区域进行定位。重建了基因型个体的父本和母本遗传的配子，并在每个SNP位置和基因组水平上定义了它们之间的IBD概率。在每个SNP位置测试了一个QTL检测模型，该模型拟合了总结IBD概率的主要成分的固定效应。全基因组（GW）重要性阈值由特征内排列确定。结果PCA导致尺寸大幅降低，实际上137和32个（平均）主要成分能够分别在基因座和基因组水平上捕获99％的IBD变异。总体而言，至少有2个性状的2563个位置超过了0.05 GW的显着性阈值，这些位置聚集在75个QTL区中，其中大多数影响一种以上的性状。获得了最强的信号，表明卵（OAR aries）（OAR）6号染色体上的蛋白质含量较高，并且与包含酪蛋白基因簇的区域重叠。在OAR4的脂肪含量和在OAR11的三种产量性状上也发现了其他有趣的位置。结论PCA是总结IBD概率的好策略。确定了与牛奶性状相关的大量区域。提出的方法提供的输出可用于选择候选基因，需要进一步研究以鉴定强LD中的致病性突变或标记，并将其应用于分子信息辅助的选择程序中。在OAR4的脂肪含量和在OAR11的三种产量性状上也发现了其他有趣的位置。结论PCA是总结IBD概率的好策略。确定了与牛奶性状相关的大量区域。提出的方法提供的输出可用于选择候选基因，需要进一步研究以鉴定强LD中的致病性突变或标记，并将其应用于分子信息辅助的选择程序。在OAR4的脂肪含量和在OAR11的三种产量性状上也发现了其他有趣的位置。结论PCA是总结IBD概率的好策略。确定了与牛奶性状相关的大量区域。提出的方法提供的输出可用于选择候选基因，需要进一步研究以鉴定强LD中的致病性突变或标记，并将其应用于分子信息辅助的选择程序。