Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.T HE rapid increase in availability of fine-scale genetic marker maps has led to the intensive use of QTL mapping in the genetic study of quan-

No abstract

Background: Worldwide feralization of crop species into agricultural weeds threatens global food security. Weedy rice is a feral form of rice that infests paddies worldwide and aggressively outcompetes cultivated varieties. Despite increasing attention in recent years, a comprehensive understanding of the origins of weedy crop relatives and how a universal feralization process acts at the genomic and molecular level to allow the rapid adaptation to weediness are still yet to be explored. Results: We use whole-genome sequencing to examine the origin and adaptation of 524 global weedy rice samples representing all major regions of rice cultivation. Weed populations have evolved multiple times from cultivated rice, and a strikingly high proportion of contemporary Asian weed strains can be traced to a few Green Revolution cultivars that were widely grown in the late twentieth century. Latin American weedy rice stands out in having originated through extensive hybridization. Selection scans indicate that most genomic regions underlying weedy adaptations do not overlap with domestication targets of selection, suggesting that feralization occurs largely through changes at loci unrelated to domestication. Conclusions: This is the first investigation to provide detailed genomic characterizations of weedy rice on a global scale, and the results reveal diverse genetic mechanisms underlying worldwide convergent rice feralization.

No abstract

The inheritance and genetic linkage analysis for seed dormancy and preharvest sprouting (PHS) resistance were carried out in an F8 recombinant inbred lines (RILs) derived from the cross between "CN19055" (white-grained, PHS-resistant) with locally adapted Australian cultivar "Annuello" (white-grained, PHS-susceptible). Seed dormancy was assessed as germination index (GI7) while assessment for preharvest sprouting resistance was based on whole head assay (sprouting index, SI) and visibly sprouted seeds (VI). Segregation analysis of the F2, F3 data from the glasshouse and the RIL population in 2004 and 2005 field data sets indicated that seed dormancy and PHS resistance in CN19055 is controlled by at least two genes. Heritabilities for GI7 and VI were high and moderate for SI. The most accurate method for assessing PHS resistance was achieved using VI and GI7 while SI exhibited large genotype by environment interaction. Two quantitative trait loci (QTLs) QPhs.dpivic.4A.1 and QPhs.dpivic.4A.2 were identified. On pooled data across four environments, the major QTL, QPhs.dpivic.4A.2, explained 45% of phenotypic variation for GI7, 43% for VI and 20% for SI, respectively. On the other hand, QPhs.dpivic.4A.1 which accounted for 31% of the phenotypic variation in GI7 in 2004 Horsham field trial, was not stable across environments. Physical mapping of two SSR markers, Xgwm937 and Xgwm894 linked to the major QTL for PHS resistance, using Chinese Spring deletions lines for chromosome 4AS and 4AL revealed that the markers were located in the deletion bins 4AL-12 and 4AL-13. The newly identified SSR markers (Xgwm937/Xgwm894) showed strong association with seed dormancy and PHS resistance in a range of wheat lines reputed to possess PHS resistance. The results suggest that Xgwm937/Xgwm894 could be used in marker-assisted selection (MAS) for incorporating preharvest sprouting resistance into elite wheat cultivars susceptible to PHS.