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首页> 外文期刊>Genetical Research >An algorithmic model for constructing a linkage and linkage disequilibrium map in outcrossing plant populations
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An algorithmic model for constructing a linkage and linkage disequilibrium map in outcrossing plant populations

机译：异种植物种群构建连锁和连锁不平衡图的算法模型

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摘要

A linkage-linkage disequilibrium map that describes the pattern and extent of linkage dis-equilibrium (LD) decay with genomic distance has now emerged as a viable tool to unravel the genetic structure of population differentiation and fine-map genes for complex traits. The prerequisite for constructing such a map is the simultaneous estimation of the linkage and LD between different loci. Here, we develop a computational algorithm for simultaneously estimating the recombination fraction and LD in a natural outcrossing population with multilocus marker data, which are often estimated separately in most molecular genetic studies. The algorithm is founded on a commonly used progeny test with open-pollinated offspring sampled from a natural population. The information about LD is reflected in the co-segregation of alleles at different loci among parents in the population. Open mating of parents will reveal the genetic linkage of alleles during meiosis. The algorithm was constructed within the polynomial-based mixture framework and implemented with the Expectation-Maximization (EM) algorithm. The by-product of the derivation of this algorithm is the estimation of outcrossing rate, a parameter useful to explore the genetic diversity of the population. We performed computer simulation to investigate the influences of different sampling strategies and different values of parameters on parameter estimation. By providing a number of testable hypotheses about population genetic parameters, this algorithmic model will open a broad gateway to understand the genetic structure and dynamics of an outcrossing population under natural selection.
机译：一种描述连锁不平衡（LD）衰变的模式和程度随基因组距离变化的连锁不平衡图谱，现已成为揭示种群分化的遗传结构和复杂性状的精细图谱基因的可行工具。构建此类图的前提是同时估计不同基因座之间的连锁和LD。在这里，我们开发了一种计算算法，可同时估算具有多基因座标记数据的自然杂交种群中的重组率和LD，这些基因通常在大多数分子遗传学研究中分别估算。该算法基于从自然种群中取样的开放授粉后代的常用后代测试。有关LD的信息反映在人群父母中不同基因座的等位基因的共同分离中。父母的开放交配将揭示减数分裂过程中等位基因的遗传联系。该算法是在基于多项式的混合框架内构造的，并使用Expectation-Maximization（EM）算法实现。该算法推导的副产品是异种率的估计，异种率是探索种群遗传多样性的有用参数。我们进行了计算机仿真，以研究不同采样策略和不同参数值对参数估计的影响。通过提供有关种群遗传参数的许多可检验的假设，该算法模型将为理解自然选择下异交种群的遗传结构和动力学打开广阔的大门。

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