Institute of Crop Sciences, The National Key Facility for Crop Gene Resources and Genetic Improvement, and CIMMYT China, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
J Integr Plant Biol. 2012 Apr;54(4):270-9. doi: 10.1111/j.1744-7909.2012.01110.x.
Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping (ICIM) is able to identify epistatic quantitative trait loci (QTLs) no matter whether the two interacting QTLs have any additive effects. In this article, we conducted a simulation study to evaluate detection power and false discovery rate (FDR) of ICIM epistatic mapping, by considering F2 and doubled haploid (DH) populations, different F2 segregation ratios and population sizes. Results indicated that estimations of QTL locations and effects were unbiased, and the detection power of epistatic mapping was largely affected by population size, heritability of epistasis, and the amount and distribution of genetic effects. When the same likelihood of odd (LOD) threshold was used, detection power of QTL was higher in F2 population than power in DH population; meanwhile FDR in F2 was also higher than that in DH. The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR. In simulated populations, ICIM achieved better mapping results than multiple interval mapping (MIM) in estimation of QTL positions and effect. At the end, we gave epistatic mapping results of ICIM in one actual population in rice (Oryza sativa L.).
上位性是一种常见的遗传现象，也是复杂性状变异的重要来源，它可以维持加性方差，从而保证在育种中获得长期的遗传增益。包容性复合区间作图（ICIM）能够识别上位性数量性状位点（QTL），无论两个相互作用的 QTL 是否具有任何加性效应。本文通过考虑 F2 和加倍单倍体（DH）群体、不同的 F2 分离比和群体大小，进行了模拟研究，以评估 ICIM 上位性作图的检测能力和假发现率（FDR）。结果表明，QTL 位置和效应的估计是无偏的，上位性作图的检测能力主要受群体大小、上位性的遗传力以及遗传效应的数量和分布的影响。当使用相同的奇数对数（LOD）阈值时，F2 群体中的 QTL 检测能力高于 DH 群体；同时，F2 中的 FDR 也高于 DH。标记密度从 10cM 增加到 5cM 导致了相似的检测能力，但 FDR 更高。在模拟群体中，ICIM 在 QTL 位置和效应的估计方面比多区间作图（MIM）取得了更好的作图结果。最后，我们给出了水稻（Oryza sativa L.）一个实际群体中 ICIM 的上位性作图结果。
