Functional genomics of the Drosophila melanogaster X chromosome and the role of DWnt5 during development

The collection of X chromosome insertions (PX) lethal lines, which was isolated from a screen for essential genes on the X chromosome, was characterized by means of cloning the insertion sites, mapping the sites within genomic DNA and determination of the associated reporter gene expresssion patterns. The established STS flanking the P element insertion sites were submitted to EMBL nucleotide databases and their in situ data together with the enhancer trap expression patterns have been deposited in the FlyView database. The characterized lines are now available to be used by the scientific community for a detailed analysis of the newly established lethal gene functions. One of the isolated genes on the X chromosome was the Drosophila gene Wnt5 (DWnt5). From two independent screens, one lethal and three homozygous viable alleles were recovered, allowing the identification of two distinct functions for DWnt5 in the fly. Observations on the developing nervous system of mutant embryos suggest that DWnt5 activity affects axon projection pattern. Elevated levels of DWNT5 activity in the midline cells of the central nervous system causes improper establishment and maintenance of the axonal pathways. Our analysis of the expression and mutant phenotype indicates that DWnt5 function in a process needed for proper organization of the nervous system. A second and novel function of DWnt5 is the control of the body size by regulation of the cell number rather than affecting the size of cells. Moreover, experimentally increased DWnt5 levels in a post-mitotic region of the eye imaginal disc causes abnormal cell cycle progression, resulting in additional ommatidia in the adult eye when compared to wild type. The increased cell number and the effects on the cell cycle after exposure to high DWNT5 levels is the result of a failure to downregulate cyclin B and therefore the unsuccessful establishment of a G1 arrest.

Enlarging a training set for genomic selction by imputation of un-genotyped animals in populations of varying genetic architecture

Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. ----- Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. ----- Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. ----- Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.