Non-Mendelian segregation of markers, known as distorted segregation, is a common biological phenomenon. Although segregation distortion affects the estimation of map distances and the results of quantitative trait loci (QTL) mapping, the effects of distorted markers are often ignored in the construction of linkage maps and in QTL mapping. Recently, we have developed a multipoint method via a Hidden Markov chain method to reconstruct linkage maps in an F2 population that corrects for bias of map distances between distorted markers. In this article, the method is extended to cover backcross, doubled haploid and recombinant inbred line (RIL) populations. The results from simulated experiments show that: (1) the degree that two linked segregation distortion loci (SDL) affect the estimation of map distances increases as SDL heritability and interval length between adjacent markers increase, whereas sample size has little effect on the bias; (2) two linked SDL result in the underesti- mation of linkage distances for most cases, overestimation for an additive model with opposite additive effects, and unbiased estimation for an epistatic model with negative additive-by-additive effects; (3) the proposed method can obtain the unbiased estimation of linkage distance. This new method was applied to a rice RIL population with severely distorted segregation to reconstruct the linkage maps, and a bootstrap method was used to obtain 95% confidence intervals of map distances. The results from real data analysis further demonstrate the utility of our method, which provides a foundation for the inheritance analysis of quantitative and viability traits.
ZHU ChengSong WANG FuHua WANG JianFei LI GuangJun ZHANG HongSheng ZHANG YuanMing
