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High-Resolution Genetic Mapping of Complex Traits from a Combined Analysis of F2 and Advanced Intercross Mice

机译:从F2和先进的交叉小鼠的组合分析复杂性状的高分辨率遗传图谱。

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摘要

Genetic influences on anxiety disorders are well documented; however, the specific genes underlying these disorders remain largely unknown. To identify quantitative trait loci (QTL) for conditioned fear and open field behavior, we used an F2 intercross (n = 490) and a 34th-generation advanced intercross line (AIL) (n = 687) from the LG/J and SM/J inbred mouse strains. The F2 provided strong support for several QTL, but within wide chromosomal regions. The AIL yielded much narrower QTL, but the results were less statistically significant, despite the larger number of mice. Simultaneous analysis of the F2 and AIL provided strong support for QTL and within much narrower regions. We used a linear mixed-model approach, implemented in the program QTLRel, to correct for possible confounding due to familial relatedness. Because we recorded the full pedigree, we were able to empirically compare two ways of accounting for relatedness: using the pedigree to estimate kinship coefficients and using genetic marker estimates of “realized relatedness.” QTL mapping using the marker-based estimates yielded more support for QTL, but only when we excluded the chromosome being scanned from the marker-based relatedness estimates. We used a forward model selection procedure to assess evidence for multiple QTL on the same chromosome. Overall, we identified 12 significant loci for behaviors in the open field and 12 significant loci for conditioned fear behaviors. Our approach implements multiple advances to integrated analysis of F2 and AILs that provide both power and precision, while maintaining the advantages of using only two inbred strains to map QTL.
机译:遗传因素对焦虑症的影响已有充分文献记载。然而,导致这些疾病的具体基因在很大程度上仍然未知。为了确定条件性恐惧和旷野行为的数量性状基因座(QTL),我们使用了LG / J和SM /的F2交叉(n = 490)和第34代高级交叉(AIL)(n = 687) J自交系小鼠品系。 F2为多个QTL提供了强大的支持,但是在宽广的染色体区域内。尽管小鼠数量较多,AIL产生的QTL窄得多,但结果在统计学上不那么显着。对F2和AIL的同时分析为QTL和狭窄地区提供了有力的支持。我们使用了在程序QTLRel中实现的线性混合模型方法,以纠正由于家族相关性而可能造成的混淆。因为我们记录了完整的血统书,所以我们能够凭经验比较两种相关性的计算方法:使用血统书估计亲属系数,并使用遗传标记估计“已实现的相关性”。使用基于标记的估计值进行QTL定位可为QTL提供更多支持,但前提是我们仅从基于标记的相关性估计值中排除了正在扫描的染色体。我们使用正向模型选择程序来评估同一染色体上多个QTL的证据。总体而言,我们确定了12个重要的基因座,用于在开放领域中的行为和12个重要的基因座,用于条件性恐惧行为。我们的方法在F2和AIL的集成分析方面实现了多项进步,同时提供了功能和精度,同时保持了仅使用两个自交系绘制QTL的优势。

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