INRA, UMR598, Génétique Animale, IFR140 GFAS, F-35000 Rennes, France.
BMC Genomics. 2009 Dec 2;10:575. doi: 10.1186/1471-2164-10-575.
Although many QTL for various traits have been mapped in livestock, location confidence intervals remain wide that makes difficult the identification of causative mutations. The aim of this study was to test the contribution of microarray data to QTL detection in livestock species. Three different but complementary approaches are proposed to improve characterization of a chicken QTL region for abdominal fatness (AF) previously detected on chromosome 5 (GGA5).
Hepatic transcriptome profiles for 45 offspring of a sire known to be heterozygous for the distal GGA5 AF QTL were obtained using a 20 K chicken oligochip. mRNA levels of 660 genes were correlated with the AF trait. The first approach was to dissect the AF phenotype by identifying animal subgroups according to their 660 transcript profiles. Linkage analysis using some of these subgroups revealed another QTL in the middle of GGA5 and increased the significance of the distal GGA5 AF QTL, thereby refining its localization. The second approach targeted the genes correlated with the AF trait and regulated by the GGA5 AF QTL region. Five of the 660 genes were considered as being controlled either by the AF QTL mutation itself or by a mutation close to it; one having a function related to lipid metabolism (HMGCS1). In addition, a QTL analysis with a multiple trait model combining this 5 gene-set and AF allowed us to refine the QTL region. The third approach was to use these 5 transcriptome profiles to predict the paternal Q versus q AF QTL mutation for each recombinant offspring and then refine the localization of the QTL from 31 cM (100 genes) at a most probable location confidence interval of 7 cM (12 genes) after determining the recombination breakpoints, an interval consistent with the reductions obtained by the two other approaches.
The results showed the feasibility and efficacy of the three strategies used, the first revealing a QTL undetected using the whole population, the second providing functional information about a QTL region through genes related to the trait and controlled by this region (HMGCS1), the third could drastically refine a QTL region.
尽管在牲畜中已经定位了许多与各种性状相关的 QTL，但置信区间仍然很宽，这使得鉴定因果突变变得困难。本研究的目的是测试微阵列数据在检测牲畜种 QTL 方面的贡献。提出了三种不同但互补的方法来改进先前在第 5 号染色体 (GGA5) 上检测到的腹部肥胖 (AF) 的鸡 QTL 区域的特征。
使用 20 K 鸡寡核苷酸芯片获得了已知为 GGA5 远端 AF QTL 杂合的 45 个后代的肝转录组图谱。660 个基因的 mRNA 水平与 AF 性状相关。第一种方法是通过根据其 660 个转录谱识别动物亚组来剖析 AF 表型。使用其中一些亚组进行连锁分析揭示了 GGA5 中部的另一个 QTL，并增加了 GGA5 远端 AF QTL 的显著性，从而细化了其定位。第二种方法针对与 AF 性状相关且受 GGA5 AF QTL 区域调节的基因。在 660 个基因中，有 5 个被认为受 AF QTL 突变本身或其附近的突变控制；其中一个与脂质代谢（HMGCS1）有关。此外，使用包含这 5 个基因集和 AF 的多性状模型进行 QTL 分析，使我们能够细化 QTL 区域。第三种方法是使用这 5 个转录组图谱来预测每个重组后代的父本 Q 与 q AF QTL 突变，然后在确定重组断点后，将 QTL 定位从 31 cM（100 个基因）细化到最可能的置信区间 7 cM（12 个基因），这与前两种方法获得的结果一致。
结果表明了三种策略的可行性和有效性，第一种策略揭示了使用整个群体未检测到的 QTL，第二种策略通过与性状相关且受该区域控制的基因提供了 QTL 区域的功能信息（HMGCS1），第三种策略可以大大细化 QTL 区域。
