Widespread epistasis regulates glucose homeostasis and gene expression.
Widespread epistasis regulates glucose homeostasis and gene expression.
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DOI：
10.1371/journal.pgen.1007025
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发表时间：
2017-09
期刊：
影响因子：
4.5
通讯作者：
Buchner DA
中科院分区：
文献类型：
--
作者：
Chen A;Liu Y;Williams SM;Morris N;Buchner DA
在复杂性状的调节中，加性与非加性相互作用的相对贡献仍然存在争议。这可能部分是因为传统上很难在复杂的多细胞生物中检测到大规模的上述性。我们假设使用小鼠染色体取代菌株检测相互作用会更容易，这些染色体取代菌株同时在受控的遗传背景下融合了许多基因的等位基因变异。在小鼠染色体取代菌株之间的一系列杂交后代中分析代谢性状和基因表达水平表明，染色体性关带层间骨质是这些复杂性状的主要特征。上的上科斯通常比仅由于添加效应而产生的可遗传效应比例更大。这些上任的相互作用通常导致特征值返回到父母CSS宿主菌株的水平。由于具有较大的上位效应，没有考虑到相互作用的分析始终低估了由于等位基因变异而导致的真实效应大小，或者无法检测到控制性状变化的基因座。这些研究表明，上皮相互作用是复杂性状的共同特征，因此识别这些相互作用是理解其遗传调节的关键。
大多数复杂的特征和疾病受多种遗传变异的综合影响来调节。但是，这些遗传变异是否独立影响复杂性状仍然存在争议，因此，可以简单地将每个变体的影响（添加性）加在一起，或者这些变体是否共同作用以影响性状变化，在这种情况下，多种变体的组合影响与每个单个变体的汇总影响（Epistisasis）都会有所不同。在这项研究中，我们在小鼠中发现，血糖水平和肝脏中基因表达的遗传调节主要由非加性相互作用控制，而体重则主要由添加剂相互作用控制。值得注意的是，肝脏中所有基因中近25％的表达水平受非加性相互作用的控制。非加性相互作用通常作用于对照小鼠中检测到的水平返回性状值，从而导致性状变化的减少。我们还证明，不考虑非加性相互作用会显着低估遗传变异对特定遗传背景的表型效应，这表明许多先前鉴定的风险基因座可能对个体子集中对疾病的敏感性产生更大的影响。这些研究强调了理解遗传变异方面相互作用以更好地了解疾病风险并个性化临床护理的重要性。
The relative contributions of additive versus non-additive interactions in the regulation of complex traits remains controversial. This may be in part because large-scale epistasis has traditionally been difficult to detect in complex, multi-cellular organisms. We hypothesized that it would be easier to detect interactions using mouse chromosome substitution strains that simultaneously incorporate allelic variation in many genes on a controlled genetic background. Analyzing metabolic traits and gene expression levels in the offspring of a series of crosses between mouse chromosome substitution strains demonstrated that inter-chromosomal epistasis was a dominant feature of these complex traits. Epistasis typically accounted for a larger proportion of the heritable effects than those due solely to additive effects. These epistatic interactions typically resulted in trait values returning to the levels of the parental CSS host strain. Due to the large epistatic effects, analyses that did not account for interactions consistently underestimated the true effect sizes due to allelic variation or failed to detect the loci controlling trait variation. These studies demonstrate that epistatic interactions are a common feature of complex traits and thus identifying these interactions is key to understanding their genetic regulation. Most complex traits and diseases are regulated by the combined influence of multiple genetic variants. However, it remains controversial whether these genetic variants independently influence complex traits, and therefore the impact of each variant could be simply added together (additivity), or whether the variants work together to influence trait variation, in which case the combined impact of multiple variants would differ from the summed impact of each individual variant (epistasis). In this study in mice, we discovered that the genetic regulation of blood sugar levels and gene expression in the liver were predominantly controlled by non-additive interactions, whereas body weight was predominantly controlled by additive interactions. Remarkably, the expression level of nearly 25% of all genes in the liver was controlled by non-additive interactions. The non-additive interactions typically acted to return trait values to the levels detected in control mice, thus contributing to a reduction in trait variation. We also demonstrated that not accounting for non-additive interactions significantly underestimated the phenotypic effect of a genetic variant on a particular genetic background, suggesting that many previously identified risk loci may have significantly larger effects on disease susceptibility in a subset of individuals. These studies highlight the importance of understanding interactions between genetic variants to better understand disease risk and personalize clinical care.
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影响因子：
2.1
作者：
Gonzalez PN;Pavlicev M;Mitteroecker P;Pardo-Manuel de Villena F;Spritz RA;Marcucio RS;Hallgrímsson B
通讯作者：
Hallgrímsson B
DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
64.5
作者：
Galarza-Muñoz G;Briggs FBS;Evsyukova I;Schott-Lerner G;Kennedy EM;Nyanhete T;Wang L;Bergamaschi L;Widen SG;Tomaras GD;Ko DC;Bradrick SS;Barcellos LF;Gregory SG;Garcia-Blanco MA
通讯作者：
Garcia-Blanco MA
影响因子：
30.8
作者：
Carlborg, Ö;Jacobsson, L;Andersson, L
通讯作者：
Andersson, L
影响因子：
4
作者：
Fu, Wenqing;O'Connor, Timothy D.;Akey, Joshua M.
通讯作者：
Akey, Joshua M.