Flowering time is an important agronomic trait, attributed by multiple genes, gene-gene interactions and environmental factors. Population stratification and polygenic effects might confound genetic effects of the causal loci underlying this complex trait. We proposed a two-step approach for detecting epistasis interactions underlying rice flowering time by accounting population structure and polygenic effects. Simulation studies showed that the approach used in this study performs better than classical and PC-linear approaches in terms of powers and false discovery rates in the case of population stratification and polygenic effects. Whole genome epistasis analyses identified 589 putative genetic interactions for flowering time. Eighteen of these interactions are located within 10 kilobases of regions of known protein-protein interactions. Thirty-seven SNPs near to twenty-five genes involve in rice or/and Arabidopsis (orthologue) flowering pathway. Bioinformatics analysis showed that 66.55% pairwise genes of the identified interactions (392 out of the 589 interactions) have similarity in various genomic features. Moreover, significant numbers of detected epistatic genes have high expression in different floral tissues. Our findings highlight the importance of epistasis analysis by controlling population stratification and polygenic effect and provided novel insights into the genetic architecture of rice flowering which could assist breeding programmes.

中文翻译：

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通过控制群体分层和多基因效应鉴定水稻开花时间的上位基因座。

开花时间是一个重要的农艺性状，受多基因、基因间相互作用和环境因素的影响。种群分层和多基因效应可能会混淆这一复杂性状背后因果位点的遗传效应。我们提出了一种两步方法，通过考虑种群结构和多基因效应来检测水稻开花时间背后的上位相互作用。模拟研究表明，在群体分层和多基因效应的情况下，本研究中使用的方法在功效和错误发现率方面优于经典方法和 PC 线性方法。全基因组上位性分析确定了 589 个与开花时间相关的推定遗传相互作用。其中 18 个相互作用位于已知蛋白质-蛋白质相互作用区域的 10 KB 范围内。接近25个基因的37个SNP涉及水稻或/和拟南芥（直系同源）开花途径。生物信息学分析表明，所识别的相互作用中有 66.55% 的成对基因（589 个相互作用中的 392 个）在各种基因组特征上具有相似性。此外，大量检测到的上位基因在不同的花组织中高表达。我们的研究结果强调了通过控制种群分层和多基因效应进行上位分析的重要性，并为水稻开花的遗传结构提供了新的见解，这有助于育种计划。