Linkage disequilibrium in wild mice.
Linkage disequilibrium in wild mice.
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DOI：
10.1371/journal.pgen.0030144
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发表时间：
2007-08
期刊：
影响因子：
4.5
通讯作者：
Nachman, Michael W.
中科院分区：
文献类型：
--
作者：
Laurie, Cathy C.;Nickerson, Deborah A.;Anderson, Amy D.;Weir, Bruce S.;Livingston, Robert J.;Dean, Matthew D.;Smith, Kimberly L.;Schadt, Eric E.;Nachman, Michael W.
实验室小鼠品系间的杂交为检测与人类疾病相关的复杂性状的数量性状基因座提供了一种强有力的方法。已经检测到数百个这样的基因座，但只有少数潜在的致病基因被确定。其主要困难是互交后代中广泛的连锁不平衡（LD）和用传统方法进行精细定位的过程缓慢。最近，新的方法已经引入，如与自交系和多代杂交的关联研究。这些方法对于间隔减少非常有用，但是通常不提供单基因分辨率，因为强LD延伸超过一到几兆碱基。在这里，我们调查的遗传结构的自然种群的小鼠在亚利桑那州，以确定其适用于精细规模的LD映射和关联研究。有三个主要发现：（1）亚利桑那小鼠具有高水平的遗传变异，其中包括实验室小鼠经典品系中存在的大部分序列变异;（2）它们显示出明显的地方近亲繁殖证据，但似乎缺乏整个研究区域的稳定种群结构;（3）LD以与人类种群相似的速度随距离衰减，这比实验室小鼠种群快得多。亚利桑那州小鼠的强关联主要限于相距小于100 kb的标记，这提供了在一个或几个基因水平上进行精细关联映射的可能性。虽然其他考虑因素，如样本量的要求和标记的发现，是严重的问题，在实施关联研究，遗传变异和LD的结果表明，野生小鼠可以提供一个有用的工具，用于识别基因，导致复杂性状的变化。连锁不平衡（LD）是指基因组中不同位点的变异体的非随机关联。近年来，LD在生物医学研究中引起了极大的兴趣，因为它在“关联研究”中的实用性，其中与疾病性状相关的DNA序列变异被用于鉴定易感基因。这种基因发现工具的分辨率取决于LD如何随着相关位点之间的距离而衰减。LD衰减的模式在人群中是众所周知的，其中它提供了一个或几个基因的量级的高分辨率。本文表明，野生家鼠（与实验室小鼠相反）的LD模式与人类非常相似。这一结果意味着野生小鼠（在实验室饲养）可以用于关联研究，以确定导致性状变异的基因。野生小鼠相关性研究可能会通过处理人类难以测量的特征（如对致癌物暴露的反应）以及通过筛选人类相关性以供随后用基因工程小鼠模型进行验证来补充人类相关性研究。
Crosses between laboratory strains of mice provide a powerful way of detecting quantitative trait loci for complex traits related to human disease. Hundreds of these loci have been detected, but only a small number of the underlying causative genes have been identified. The main difficulty is the extensive linkage disequilibrium (LD) in intercross progeny and the slow process of fine-scale mapping by traditional methods. Recently, new approaches have been introduced, such as association studies with inbred lines and multigenerational crosses. These approaches are very useful for interval reduction, but generally do not provide single-gene resolution because of strong LD extending over one to several megabases. Here, we investigate the genetic structure of a natural population of mice in Arizona to determine its suitability for fine-scale LD mapping and association studies. There are three main findings: (1) Arizona mice have a high level of genetic variation, which includes a large fraction of the sequence variation present in classical strains of laboratory mice; (2) they show clear evidence of local inbreeding but appear to lack stable population structure across the study area; and (3) LD decays with distance at a rate similar to human populations, which is considerably more rapid than in laboratory populations of mice. Strong associations in Arizona mice are limited primarily to markers less than 100 kb apart, which provides the possibility of fine-scale association mapping at the level of one or a few genes. Although other considerations, such as sample size requirements and marker discovery, are serious issues in the implementation of association studies, the genetic variation and LD results indicate that wild mice could provide a useful tool for identifying genes that cause variation in complex traits. Linkage disequilibrium (LD) refers to the nonrandom association of variants at different sites in the genome. In recent years, LD has been of great interest in biomedical research because of its utility in “association studies,” where DNA sequence variants associated with disease traits are used to identify susceptibility genes. The resolution of this gene-finding tool depends on how the LD decays with distance between the associated sites. The pattern of LD decay is well known in human populations, where it provides high resolution on the order of one or a few genes. This paper shows that the pattern of LD in wild house mice (in contrast to laboratory mice) is very similar to that in human populations. This result means that wild mice (reared in the laboratory) could be used in association studies to identify genes that cause trait variation. Wild mouse association studies might complement those in humans by dealing with traits that are difficult to measure in humans (such as response to carcinogen exposure) and by filtering human associations for subsequent validation with genetically engineered mouse models.
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