复合性状的QTL定位模型构建

【目的】传统复合性状的QTL(quantitative trait locus)定位方法仅仅利用两个或几个构成性状的计算值作为表型值,未考虑复合性状的生物学内涵,从而影响定位的准确性。因此,发展适合于复合性状的QTL定位模型,对于深入解析控制复合性状的遗传结构,进而提高基因定位准确性显得越来越重要。【方法】针对全基因组重测序数据,构建了一个复合性状QTL定位模型(composite traits mapping model, CTM),利用CTM对复合性状进行分解,把分解后的组分以二元或多元正态分布形式整合到QTL作图的框架内。【结果】应用CTM分析杨树材积生长数据,可成功定位到大量与杨树材积生长相关的基因,并与传统方法进行了比较,定位出较多的显著位点,表现出较好的性能。计算机模拟试验表明,所构建的CTM模型在定位复合性状QTL中具有较高的效力,在达到一定的样本数量和遗传力条件下,CTM模型具有较强的效力,样本量和遗传力的增加都能够增加参数估计的精度。【结论】CTM模型有助于复合性状遗传结构的解析,促进林木分子标记辅助育种的开展。

Computational framework for mapping composite traits

Abstract: 【Objective】Composite traits are widely used in breeding. Currently, QTL(quantitative trait locus)mapping methods of composite traits only use a single phenotypic value, which is simply derived from a mathematical expression of two or more component traits. These approaches ignored the inherent biological mechanism of phenotype formation, which affects the precision of QTL mapping for composite traits.【Method】For the whole genome sequencing data, we present a new statistical infrastructure for QTL mapping that takes into account biological characteristics of composite traits. This method, termed composite traits mapping model(CTM), integrates different components within genetic mapping through mathematical relationships of composite traits.【Result】To validate the applicability of CTM, we applied it to analyze volume growth data of poplar and identified specific loci that were responsible for the volume growth. Compared with traditional methods for QTL mapping of composite traits, more significant loci were detected CTM exhibited a better performance. The computer simulation showed that CTM is a powerful model for mapping composite traits and the increase in sample size and heritability can increase the accuracy of parameter estimation. 【Conclusion】CTM is a useful tool for QTL mapping of composite traits, thus facilitating our understanding of the genetic architecture of composite traits.