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Selection Of Genetically Diverse Recombinant Inbreds With An Ordered Gene Evolutionary Algorithm

机译:用有序基因进化算法选择遗传多样性重组近铬

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Recombinant inbreds are created by crossing two genetically distinct inbred lines and then inbreeding the resulting progeny multiple times. They are used to estimate associations of genes by co-inheritance of alleles from the two parent inbred types in the recombinant inbreds derived from the cross in a process called genetic mapping. Typically the recombinant inbred lines used in a genetic mapping study are relatively well studied and so they are natural choices for microarray, proteomic, and metabolomic studies. These are quite costly and so typically use fewer individuals than are used in most genetic mapping studies. An evolutionary algorithm for selecting a subset of a collection of recombinant inbred lines with maximum genetic diversity in their mapping characters is described. The evolutionary algorithm is an ordered-gene algorithm with the first k genes in the ordered selection taken to be the subset. Ordered genes are a convenient representation for subset selection. It is found that the problem is not difficult and that in a well mixed mapping population of recombinant inbreds the marginal increase in diversity obtained by evolutionary optimization is small but significant. In order to better understand the problem, synthetic data are also examined and suggest that the problem is easy in general, not only in the specific biological cases used. Recombinant inbreds are created by crossing two genetically distinct inbred lines and then inbreeding the resulting progeny multiple times.
机译:通过交叉两种遗传明显的自交系,然后多次近亲繁殖的后代来产生重组近交。它们用于通过在称为遗传映射的过程中衍生自交叉的重组血统中的两个母线血统中的等级中的等位基因的共同遗传来估计基因的缔合。通常,在遗传映射研究中使用的重组近交系相对较好地研究,因此它们是微阵列,蛋白质组学和代谢组研究的自然选择。这些是非常昂贵的,因此通常使用比在大多数遗传映射研究中使用的人更少。描述了一种用于选择具有最大遗传多样性的重组自交系集合的进化算法。进化算法是一种有序基因算法,其有序选择中的第一k基因被拍摄为子集。订购基因是子集选择的方便表示。结果发现,问题并不困难,并且在重组近代的良好混合映射群中,通过进化优化获得的多样性的边际增加小而且很大。为了更好地理解问题,还检查了合成数据,并表明问题很容易,不仅在使用的特定生物学案件中。通过交叉两种遗传明显的自交系,然后多次近亲繁殖的后代来产生重组近交。

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