A computerized procedure to construct integrated genetic maps is presented. The computer program (JOINMAP) can handle raw data from F,s, backcrosses and recombinant inbred lines, as well as listed pairwise recombination frequencies. The procedure is useful for combining linkage data that have been collected in different experiments; the result is a mathematical alignment of the distinct genetic maps. Data from single experiments can be dealt with as well. In view of the fast growing amount of linkage information for molecular markers, which is often being generated by different research groups, integrated maps provide useful information on the map position of genes and DNA markers.The procedure performs a sequential build-up of the map and, at each step, a numerical search for the best fitting order of markers. Weighted least squares is used for the estimation of map distances.

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Associations between markers and complex quantitative traits were investigated in a collection of 146 modern two-row spring barley cultivars, representing the current commercial germ plasm in Europe. Using 236 AFLP markers, associations between markers were found for markers as far apart as 10 cM. Subsequently, for the 146 cultivars the complex traits mean yield, adaptability (Finlay-Wilkinson slope), and stability (deviations from regression) were estimated from the analysis of variety trial data. Regression of those traits on individual marker data disclosed marker-trait associations for mean yield and yield stability. Support for identified associations was obtained from association profiles, i.e., from plots of P-values against chromosome positions. In addition, many of the associated markers were located in regions where earlier QTL were found for yield and yield components. To study the oligogenic genetic base of the traits in more detail, multiple linear regression of the traits on markers was carried out, using stepwise selection. By this procedure, 18-20 markers that accounted for 40-58% of the variation were selected. Our results indicate that association mapping approaches can be a viable alternative to classical QTL approaches based on crosses between inbred lines, especially for complex traits with costly measurements.T HE genetic dissection of complex traits still prepleiotropic effects on a number of performance traits in barley, but Cattivelli et al. (2002) concluded that sents a challenge. The oligo/polygenic character little is known about the regulatory mechanisms controlof complex traits, combined with interactions between ling stress responses, mainly because all stress responses loci, makes the task a priori difficult and intricate. In involve many genes. addition, environmental factors trigger and modify geneThe polygenic basis of complex traits has conseactions and thereby further complicate the analysis.quences for the application of quantitative trait locus Yield is the classical example of a complex trait. Yield (QTL) mapping methodology, as many markers that fluctuations in relation to environmental factors are are associated with the trait need to be identified. Typioften described in terms of adaptability and stability.cally, for QTL mapping, a cross between two inbred The latter can be considered to constitute complex traits lines is made and the cosegregation of alleles of mapped on their own. Parameters quantifying adaptability and marker loci and phenotypic traits allows the identificastability require observations across a range of environtion of linked markers. For complex traits with GE interments for their estimation. The parameters are typically action, this approach implies large-scale testing of spedefined in terms of linear and quadratic functions of cial mapping populations across a range of environments. the genotype by environment (GE) interaction (Lin et

SummaryA computerized procedure to construct integrated genetic maps is presented. The computer program (JOINMAP) can handle raw data from F,s, backcrosses and recombinant inbred lines, as well as listed pairwise recombination frequencies. The procedure is useful for combining linkage data that have been collected in different experiments; the result is a mathematical alignment of the distinct genetic maps. Data from single experiments can be dealt with as well. In view of the fast growing amount of linkage information for molecular markers, which is often being generated by different research groups, integrated maps provide useful information on the map position of genes and DNA markers.The procedure performs a sequential build-up of the map and, at each step, a numerical search for the best fitting order of markers. Weighted least squares is used for the estimation of map distances.

During meiosis, homologous chromosomes (homologs) undergo recombinational interactions, which can yield crossovers (COs) or noncrossovers. COs exhibit interference; they are more evenly spaced along the chromosomes than would be expected if they were placed randomly. The protein complexes involved in recombination can be visualized as immunofluorescent foci. We have analyzed the distribution of such foci along meiotic prophase chromosomes of the mouse to find out when interference is imposed and whether interference manifests itself at a constant level during meiosis. We observed strong interference among MLH1 foci, which mark CO positions in pachytene. Additionally, we detected substantial interference well before this point, in late zygotene, among MSH4 foci, and similarly, among replication protein A (RPA) foci. MSH4 foci and RPA foci both mark interhomolog recombinational interactions, most of which do not yield COs in the mouse. Furthermore, this zygotene interference did not depend on SYCP1, which is a transverse filament protein of mouse synaptonemal complexes. Interference is thus not specific to COs but may occur in other situations in which the spatial distribution of events has to be controlled. Differences between the distributions of MSH4͞RPA foci and MLH1 foci along synaptonemal complexes might suggest that CO interference occurs in two successive steps.crossing-over ͉ immunofluorescence ͉ meiosis M eiosis consists of two divisions, meiosis I and II, by which a diploid cell produces four haploid daughters. Reduction in ploidy occurs at meiosis I, when homologous chromosomes (homologs) disjoin. This event is prepared during meiotic prophase, when homologs recognize each other and form stable pairs (bivalents) that can line up in the metaphase I spindle. In most eukaryotes, including mouse and yeast, both the recognition of homologs and the formation of stable bivalents depend on recombinational interactions between homologs (reviewed in ref. 1). For this process, the meiotic prophase cell actively induces DNA double-strand breaks (DSBs) and repairs them by homologous recombination, using preferably a nonsister chromatid of the homolog as template (2). In species such as yeast and mouse, most interhomolog recombinational interactions are not resolved as reciprocal exchanges [crossovers (COs)] and probably serve homolog recognition and alignment (3, 4). A small proportion, however, yields COs, which become cytologically visible as chiasmata and are essential for the stable connection of homologs. COs are not randomly distributed among and along bivalents; every bivalent forms at least one CO (obligate CO), and, if multiple COs occur, they are more evenly spaced along the bivalent than would be expected if they were randomly placed. This phenomenon was originally detected genetically by the finding that the frequency of double recombinants involving a pair of adjacent or nearby intervals was lower than the frequency expected from recombinant frequencies for each of those intervals (reviewed in refs. 5 ...