Here we show that the Drosophila homologue of Lissencephaly-1, DLis-1, acts together with Bicaudal-D (Bic-D), Egalitarian (Egl), dynein and microtubules to determine oocyte identity. DLis-1 is further required for nurse-cell-to-oocyte transport during oocyte growth, and for the positioning of the nucleus in the oocyte. Immunostaining of DLis-1 protein reveals a cortical localization that is independent of microtubules. DLis-1 may function in this position as a cortical anchor for the other nuclear-localization factors. DLis-1 and Bic-D are further required for nuclear localization in the developing nervous system, indicating that homologues of Bic-D, dynein and Egl-like proteins may also be involved in vertebrate neural migration and that their absence may cause a Miller-Dieker-like lissencephaly.

BackgroundGenomic predictions can be applied early in life without impacting selection candidates. This is especially useful for meat quality traits in sheep. Carcass and novel meat quality traits were predicted in a multi-breed sheep population that included Merino, Border Leicester, Polled Dorset and White Suffolk sheep and their crosses.MethodsPrediction of breeding values by best linear unbiased prediction (BLUP) based on pedigree information was compared to prediction based on genomic BLUP (GBLUP) and a Bayesian prediction method (BayesR). Cross-validation of predictions across sire families was used to evaluate the accuracy of predictions based on the correlation of predicted and observed values and the regression of observed on predicted values was used to evaluate bias of methods. Accuracies and regression coefficients were calculated using either phenotypes or adjusted phenotypes as observed variables.Results and conclusionsGenomic methods increased the accuracy of predicted breeding values to on average 0.2 across traits (range 0.07 to 0.31), compared to an average accuracy of 0.09 for pedigree-based BLUP. However, for some traits with smaller reference population size, there was no increase in accuracy or it was small. No clear differences in accuracy were observed between GBLUP and BayesR. The regression of phenotypes on breeding values was close to 1 for all methods, indicating little bias, except for GBLUP and adjusted phenotypes (regression = 0.78). Accuracies calculated with adjusted (for fixed effects) phenotypes were less variable than accuracies based on unadjusted phenotypes, indicating that fixed effects influence the latter. Increasing the reference population size increased accuracy, indicating that adding more records will be beneficial. For the Merino, Polled Dorset and White Suffolk breeds, accuracies were greater than for the Border Leicester breed due to the smaller sample size and limited across-breed prediction. BayesR detected only a few large marker effects but one region on chromosome 6 was associated with large effects for several traits. Cross-validation produced very similar variability of accuracy and regression coefficients for BLUP, GBLUP and BayesR, showing that this variability is not a property of genomic methods alone. Our results show that genomic selection for novel difficult-to-measure traits is a feasible strategy to achieve increased genetic gain.

In single-step genomic evaluation using best linear unbiased prediction (ssGBLUP), genomic predictions are calculated with a relationship matrix that combines pedigree and genomic information. For missing pedigrees, unknown selection processes, or inclusion of several populations, a BLUP model can include unknown-parent groups (UPG) in the animal effect. For ssGBLUP, UPG equations also involve contributions from genomic relationships. When those contributions are ignored, UPG solutions and genetic predictions can be biased. Options to eliminate or reduce such bias are presented. First, mixed model equations can be modified to include contributions to UPG elements from genomic relationships (greater software complexity). Second, UPG can be implemented as separate effects (higher cost of computing and data processing). Third, contributions can be ignored when they are relatively small, but they may be small only after refinements to UPG definitions. Fourth, contributions may approximately cancel out when genomic and pedigree relationships are constructed for compatibility; however, different construction steps are required for unknown parents from the same or different populations. Finally, an additional polygenic effect that also includes UPG can be added to the model.

Meiosis is a highly specialized cell division that requires significant reorganization of the canonical cell-cycle machinery and the use of meiosis-specific cell-cycle regulators. The anaphase-promoting complex (APC) and a conserved APC adaptor, Cdc20 (also known as Fzy), are required for anaphase progression in mitotic cells. The APC has also been implicated in meiosis, although it is not yet understood how it mediates these non-canonical divisions. Cortex (Cort) is a diverged Fzy homologue that is expressed in the female germline of Drosophila, where it functions with the Cdk1-interacting protein Cks30A to drive anaphase in meiosis II. Here, we show that Cort functions together with the canonical mitotic APC adaptor Fzy to target the three mitotic cyclins (A, B and B3) for destruction in the egg and drive anaphase progression in both meiotic divisions. In addition to controlling cyclin destruction globally in the egg, Cort and Fzy appear to both be required for the local destruction of cyclin B on spindles. We find that cyclin B associates with spindle microtubules throughout meiosis I and meiosis II, and dissociates from the meiotic spindle in anaphase II. Fzy and Cort are required for this loss of cyclin B from the meiotic spindle. Our results lead to a model in which the germline-specific APC Cort cooperates with the more general APC Fzy , both locally on the meiotic spindle and globally in the egg cytoplasm, to target cyclins for destruction and drive progression through the two meiotic divisions.