Statistical properties of QTL linkage mapping in biparental genetic populations

文献类型： 外文期刊

第一作者： Li, H.

作者： Li, H.;Wang, J.;Li, H.;Wang, J.;Li, H.;Li, Z.;Hearne, S.;Baenziger, M.

作者机构：

关键词： confidence interval;false discovery rate;inclusive composite interval mapping;population size;statistical power

期刊名称：HEREDITY （ 影响因子：3.821； 五年影响因子：4.553 ）

ISSN： 0018-067X

年卷期： 2010 年 105 卷 3 期

页码：

收录情况： SCI

摘要： Quantitative trait gene or locus (QTL) mapping is routinely used in genetic analysis of complex traits. Especially in practical breeding programs, questions remain such as how large a population and what level of marker density are needed to detect QTLs that are useful to breeders, and how likely it is that the target QTL will be detected with the data set in hand. Some answers can be found in studies on conventional interval mapping (IM). However, it is not clear whether the conclusions obtained from IM are the same as those obtained using other methods. Inclusive composite interval mapping (ICIM) is a useful step forward that highlights the importance of model selection and interval testing in QTL linkage mapping. In this study, we investigate the statistical properties of ICIM compared with IM through simulation. Results indicate that IM is less responsive to marker density and population size (PS). The increase in marker density helps ICIM identify independent QTLs explaining >5% of phenotypic variance. When PS is >200, ICIM achieves unbiased estimations of QTL position and effect. For smaller PS, there is a tendency for the QTL to be located toward the center of the chromosome, with its effect overestimated. The use of dense markers makes linked QTL isolated by empty marker intervals and thus improves mapping efficiency. However, only large-sized populations can take advantage of densely distributed markers. These findings are different from those previously found in IM, indicating great improvements with ICIM. Heredity (2010) 105, 257-267; doi:10.1038/hdy.2010.56; published online 12 May 2010

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