Joint mapping of quantitative trait loci for multiple binary characters
Joint mapping of quantitative trait loci for multiple binary characters
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DOI：
10.1534/genetics.103.019406
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发表时间：
2005-02-01
期刊：
影响因子：
3.3
通讯作者：
Xu, SZ
中科院分区：
文献类型：
--
作者：
Xu, CW;Li, ZK;Xu, SZ
多个数量性状的联合定位，通过确定多效效应和紧密连锁，为遗传作图提供了新的思路。联合作图还可以提高QTL检测的统计功效。然而，这样的联合映射程序还没有提供离散性状。大多数抗病性状都是以一个或多个离散性状来衡量的。这些离散的特征通常是相关的。多个二元疾病性状的联合作图可以提供探索多效性效应和增加检测疾病位点的统计功效的机会。我们开发了一个最大似然映射多个二元性状的方法。我们假设一组多元的正常疾病负债，每一个有助于一个疾病性状的表型方差。基础负债通过一些基础阈值与二元表型相关联。新的方法实际上映射的多变量正常负债的变化轨迹。因此，我们能够利用现有的方法联合定位的数量性状。我们将多变量负债视为缺失值，以便可以在这里应用期望最大化（EM）算法。我们还扩展的方法，联合映射离散和连续性状。使用模拟数据证明了该方法的效率。我们还将新方法应用于一组真实的数据，并检测了几个负责稻瘟病抗性的位点。
Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice.