Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects.
Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects.
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DOI：
10.1186/1471-2156-14-103
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发表时间：
2013-10-29
期刊：
影响因子：
2.9
通讯作者：
Reecy JM
中科院分区：
文献类型：
--
作者：
Cheng Y;Rachagani S;Cánovas A;Mayes MS;Tait RG Jr;Dekkers JC;Reecy JM
身体组成的变化，如身体脂肪的积累，可能是许多慢性人类疾病的症状;因此，已经努力研究身体组成的遗传机制。例如，一些数量性状基因座（QTL）已被发现使用全基因组关联研究，这将最终导致与组织性状相关的因果突变的发现。虽然已经在小鼠中确定了一些身体组成QTL，但有限的研究集中在涉及这些性状的印记和互作效应上。以前，我们发现肌肉生长抑制素基因型，正反交，和性别与许多染色体区域相互作用，影响生长性状。在这里，我们报告的肌肉，脂肪，和形态表型QTL（pQTL），翻译和转录QTL（tQTL）和表达QTL（eQTL）的应用与加性，显性，印迹和互作效应的QTL模型的识别。使用来自肌生长抑制素缺失的C57 BL/6和M16 i小鼠系的1000只小鼠的F2群体，在染色体6、9、10、11和18上发现了6个印记的pQTL。我们还发现了两个IGF 1和两个Atp 2a 2 eQTL，这可能是重要的反式调节元件。检测到与Myostatin互作、正反交和性别互作的pQTL、tQTL和eQTL。结合加性和显性效应，这些变异解释了本研究中大量的表型变异。我们的研究表明，印记和互作效应是身体组成性状遗传模型的重要组成部分。此外，整合eQTL和传统的QTL定位可能有助于解释更多的表型变异比单独，从而揭示更多的组织性状是如何调节的分子细节。
Shifts in body composition, such as accumulation of body fat, can be a symptom of many chronic human diseases; hence, efforts have been made to investigate the genetic mechanisms that underlie body composition. For example, a few quantitative trait loci (QTL) have been discovered using genome-wide association studies, which will eventually lead to the discovery of causal mutations that are associated with tissue traits. Although some body composition QTL have been identified in mice, limited research has been focused on the imprinting and interaction effects that are involved in these traits. Previously, we found that Myostatin genotype, reciprocal cross, and sex interacted with numerous chromosomal regions to affect growth traits. Here, we report on the identification of muscle, adipose, and morphometric phenotypic QTL (pQTL), translation and transcription QTL (tQTL) and expression QTL (eQTL) by applying a QTL model with additive, dominance, imprinting, and interaction effects. Using an F2 population of 1000 mice derived from the Myostatin-null C57BL/6 and M16i mouse lines, six imprinted pQTL were discovered on chromosomes 6, 9, 10, 11, and 18. We also identified two IGF1 and two Atp2a2 eQTL, which could be important trans-regulatory elements. pQTL, tQTL and eQTL that interacted with Myostatin, reciprocal cross, and sex were detected as well. Combining with the additive and dominance effect, these variants accounted for a large amount of phenotypic variation in this study. Our study indicates that both imprinting and interaction effects are important components of the genetic model of body composition traits. Furthermore, the integration of eQTL and traditional QTL mapping may help to explain more phenotypic variation than either alone, thereby uncovering more molecular details of how tissue traits are regulated.
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DOI：
10.1123/ijsnem.11.s1.s44
发表时间：
2001-12-01
影响因子：
2.5
作者：
Chakravarthy, MV;Booth, FW;Spangenburg, EE
通讯作者：
Spangenburg, EE
影响因子：
30.8
作者：
Bystrykh, L;Weersing, E;de Haan, G
通讯作者：
de Haan, G
影响因子：
4.1
作者：
Ding, VDH;Qureshi, SA;Zhang, BB
通讯作者：
Zhang, BB
DOI：
10.1073/pnas.140216397
发表时间：
2000-07-05
影响因子：
11.1
作者：
de Koning, DJ;Rattink, AP;Groenen, MAM
通讯作者：
Groenen, MAM
影响因子：
64.8
作者：
Cheung, VG;Spielman, RS;Burdick, JT
通讯作者：
Burdick, JT