Large-scale in silico mapping of complex quantitative traits in inbred mice.
Large-scale in silico mapping of complex quantitative traits in inbred mice.
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在近交小鼠中复杂定量性状的大规模硅映射。
DOI：
10.1371/journal.pone.0000651
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发表时间：
2007-07-25
期刊：
影响因子：
3.7
通讯作者：
You, Ming
中科院分区：
文献类型：
--
作者：
Liu, Pengyuan;Vikis, Haris;Lu, Yan;Wang, Daolong;You, Ming
了解常见疾病的遗传基础和疾病相关的数量性状将有助于诊断和治疗的发展。通过快速有效地整合定义明确的小鼠基因组和表型组数据资源，可以加快基因发现的进程。我们在这里描述了一种通过全基因组关联（GWA）扫描具有广泛遗传变异的近交系小鼠的计算机基因发现策略。我们从对173个小鼠表型的调查中确定了937个数量性状基因座（QTL），这些表型包括人类疾病（动脉粥样硬化、心血管疾病、癌症和肥胖）以及行为、血液学、免疫学、代谢和神经学性状的模型。67%的QTL被精确定位在0.5Mb以下的基因组区域，定位精度比经典连锁分析提高了近40倍。这有助于更有效地识别疾病背后的基因。我们已经确定了两个QTL基因，Adam12和Cdh2，作为致动脉粥样硬化饮食诱导的肥胖症的因果遗传变异。我们的研究结果表明，小鼠GWA分析有可能解决多个紧密连锁的QTL，并实现单基因解析。这些高分辨率的QTL数据可以作为研究界定位克隆和基因鉴定的主要资源。
Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with ∼40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.
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