A note on the rationale for estimating genealogical coancestry from molecular markers

Background

Genetic relatedness or similarity between individuals is a key concept in population, quantitative and conservation genetics. When the pedigree of a population is available and assuming a founder population from which the genealogical records start, genetic relatedness between individuals can be estimated by the coancestry coefficient. If pedigree data is lacking or incomplete, estimation of the genetic similarity between individuals relies on molecular markers, using either molecular coancestry or molecular covariance. Some relationships between genealogical and molecular coancestries and covariances have already been described in the literature.

Methods

We show how the expected values of the empirical measures of similarity based on molecular marker data are functions of the genealogical coancestry. From these formulas, it is easy to derive estimators of genealogical coancestry from molecular data. We include variation of allelic frequencies in the estimators.

Results

The estimators are illustrated with simulated examples and with a real dataset from dairy cattle. In general, estimators are accurate and only slightly biased. From the real data set, estimators based on covariances are more compatible with genealogical coancestries than those based on molecular coancestries. A frequently used estimator based on the average of estimated coancestries produced inflated coancestries and numerical instability. The consequences of unknown gene frequencies in the founder population are briefly discussed, along with alternatives to overcome this limitation.

Conclusions

Estimators of genealogical coancestry based on molecular data are easy to derive. Estimators based on molecular covariance are more accurate than those based on identity by state. A correction considering the random distribution of allelic frequencies improves accuracy of these estimators, especially for populations with very strong drift.     

ROS signaling: the new wave?

Reactive oxygen species (ROS) play a multitude of signaling roles in different organisms from bacteria to mammalian cells. They were initially thought to be toxic byproducts of aerobic metabolism, but have now been acknowledged as central players in the complex signaling network of cells. In this review, we will attempt to address several key questions related to the use of ROS as signaling molecules in cells, including the dynamics and specificity of ROS signaling, networking of ROS with other signaling pathways, ROS signaling within and across different cells, ROS waves and the evolution of the ROS gene network.     

WordCluster: detecting clusters of DNA words and genomic elements

Background  

Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds.     

Season effect on genitalia and epididymal sperm from Iberian red deer, roe deer and Cantabrian chamois

Seasonality deeply affects the physiology and behavior of many species, and must be taken into account when biological resource banks (BRBs) are established. We have studied the effect of seasonality on many reproductive parameters of free-ranging Iberian red deer, roe deer and Cantabrian chamois, living in Spain. Testicles from hunted animals were collected and sent to our laboratory at different times during the year. We recorded the weight and volume of testis, the weight of the epididymis and its separate parts (caput, corpus, and cauda), the weight of the sperm sample collected from the cauda epididymis, and several sperm parameters (sperm concentration, spermatozoa recovered, motility, HOS test reactivity, acrosomal status, and viability). We studied the data according to several periods, defined accordingly to each species. For red deer, we defined rut (mid-September to mid-October), post-rut (mid-October to mid-December), and non-breeding season (February). For roe deer, they were pre-rut (June), rut (July), post-rut (first fortnight of August), and non-breeding season (September). For chamois: non-breeding season (June to mid-September) and breeding season (October-November). The rut/breeding season yielded significantly higher numbers for almost all parameters. However, in the case of red deer, sperm quality was higher in the post-rut. For roe deer, testicular weight was similar in the pre-rut and in the rut, and sperm quality did not differ significantly between these two periods, although we noticed higher values in the rut. In the case of chamois, sperm quality did not differ significantly from the breeding season, but data distribution suggested that in the non-breeding season there are less males with sperm of good quality. On the whole, we find these results of interest for BRB planning. The best season to collect sperm in this species would be the breeding season. However, post-rut in red deer, pre-rut in roe deer, and non-breeding season in chamois could be used too, because of the acceptable sperm quality, despite the lower quantity salvaged. More in-depth research needs to be carried out on the quality of sperm salvaged at different times of the year in order to confirm these findings.     

The S phase checkpoint: when the crowd meets at the fork

Accumulation of unrepaired DNA lesions is the biggest threat to genomic stability. DNA damage checkpoints create windows of time that allow the cell to repair assaults on DNA in each phase of the cell cycle. When DNA lesions arise in S phase, however, the checkpoint machinery must work to coordinate DNA replication and repair processes. In fact some upstream components of the DNA damage checkpoint play parallel roles in maintaining the continuity of DNA replication and signaling to downstream components.     

GDNF family receptor alpha1 phenotype of spermatogonial stem cells in immature mouse testes

Spermatogonial stem cells (SSCs) are essential for spermatogenesis, and these adult tissue stem cells balance self-renewal and differentiation to meet the biological demand of the testis. The developmental dynamics of SSCs are controlled, in part, by factors in the stem cell niche, which is located on the basement membrane of seminiferous tubules situated among Sertoli cells. Sertoli cells produce glial cell line-derived neurotrophic factor (GDNF), and disruption of GDNF expression results in spermatogenic defects and infertility. The GDNF signals through a receptor complex that includes GDNF family receptor alpha1 (GFRA1), which is thought to be expressed by SSCs. However, expression of GFRA1 on SSCs has not been confirmed by in vivo functional assay, which is the only method that allows definitive identification of SSCs. Therefore, we fractionated mouse pup testis cells based on GFRA1 expression using magnetic activated cell sorting. The sorted and depleted fractions of GFRA1 were characterized for germ cell markers by immunocytochemistry and for stem cell activity by germ cell transplantation. The GFRA1-positive cell fraction coeluted with other markers of SSCs, including ITGA6 and CD9, and was significantly depleted of KIT-positive cells. The transplantation results confirmed that a subpopulation of SSCs expresses GFRA1, but also that the stem cell pool is heterogeneous with respect to the level of GFRA1 expression. Interestingly, POU5F1-positive cells were enriched nearly 15-fold in the GFRA1-selected fraction, possibly suggesting heterogeneity of developmental potential within the stem cell pool.