High recombination rates and hotspots in a Plasmodium falciparum genetic cross

Using the universal P2/P8 primers, we were able to obtain the gene segments of chromo-helicase-DNA binding protein (CHD)-Z and CHD-W from ten species of ardeid birds including Chinese egret (Egretta eulophotes), little egret (E. garzetta), eastern reef egret (E. sacra), great egret (Ardea alba), grey heron (A. cinerea), Chinese pond-heron (Ardeola bacchus), cattle egret (Bubulcus ibis), black-crowned night-heron (Nycticorax nycticorax), cinnamon bittern (Ixobrychus cinnamomeus) and yellow bittern (I. sinensis). Based on conserved regions inside the P2/P8-derived sequences, we designed new PCR primers for sex identification in these ardeid species. Using agarose gel electrophoresis, the PCR products showed two bands for females (140 bp derived from CHD-W and the other 250 bp from CHD-ZW), whereas the males showed only the 250 bp band. The results indicated that our new primers could be used for accurate and convenient sex identification in ardeid species.National Natural Science Foundation of China[30970380, 40876077]; Fujian Natural Science Foundation of China[2008S0007, 2009J01195

How to make a sex chromosome

Sex chromosomes can evolve once recombination is halted between a homologous pair of chromosomes. Owing to detailed studies using key model systems, we have a nuanced understanding and a rich review literature of what happens to sex chromosomes once recombination is arrested. However, three broad questions remain unanswered. First, why do sex chromosomes stop recombining in the first place? Second, how is recombination halted? Finally, why does the spread of recombination suppression, and therefore the rate of sex chromosome divergence, vary so substantially across clades? In this review, we consider each of these three questions in turn to address fundamental questions in the field, summarize our current understanding, and highlight important areas for future work

Genetic Crossovers Are Predicted Accurately by the Computed Human Recombination Map

Hotspots of meiotic recombination can change rapidly over time. This instability and the reported high level of inter-individual variation in meiotic recombination puts in question the accuracy of the calculated hotspot map, which is based on the summation of past genetic crossovers. To estimate the accuracy of the computed recombination rate map, we have mapped genetic crossovers to a median resolution of 70 Kb in 10 CEPH pedigrees. We then compared the positions of crossovers with the hotspots computed from HapMap data and performed extensive computer simulations to compare the observed distributions of crossovers with the distributions expected from the calculated recombination rate maps. Here we show that a population-averaged hotspot map computed from linkage disequilibrium data predicts well present-day genetic crossovers. We find that computed hotspot maps accurately estimate both the strength and the position of meiotic hotspots. An in-depth examination of not-predicted crossovers shows that they are preferentially located in regions where hotspots are found in other populations. In summary, we find that by combining several computed population-specific maps we can capture the variation in individual hotspots to generate a hotspot map that can predict almost all present-day genetic crossovers

The potential of shifting recombination hotspots to increase genetic gain in livestock breeding

International audienceAbstractBackgroundThis study uses simulation to explore and quantify the potential effect of shifting recombination hotspots on genetic gain in livestock breeding programs.MethodsWe simulated three scenarios that differed in the locations of quantitative trait nucleotides (QTN) and recombination hotspots in the genome. In scenario 1, QTN were randomly distributed along the chromosomes and recombination was restricted to occur within specific genomic regions (i.e. recombination hotspots). In the other two scenarios, both QTN and recombination hotspots were located in specific regions, but differed in whether the QTN occurred outside of (scenario 2) or inside (scenario 3) recombination hotspots. We split each chromosome into 250, 500 or 1000 regions per chromosome of which 10% were recombination hotspots and/or contained QTN. The breeding program was run for 21 generations of selection, after which recombination hotspot regions were kept the same or were shifted to adjacent regions for a further 80 generations of selection. We evaluated the effect of shifting recombination hotspots on genetic gain, genetic variance and genic variance.ResultsOur results show that shifting recombination hotspots reduced the decline of genetic and genic variance by releasing standing allelic variation in the form of new allele combinations. This in turn resulted in larger increases in genetic gain. However, the benefit of shifting recombination hotspots for increased genetic gain was only observed when QTN were initially outside recombination hotspots. If QTN were initially inside recombination hotspots then shifting them decreased genetic gain.DiscussionShifting recombination hotspots to regions of the genome where recombination had not occurred for 21 generations of selection (i.e. recombination deserts) released more of the standing allelic variation available in each generation and thus increased genetic gain. However, whether and how much increase in genetic gain was achieved by shifting recombination hotspots depended on the distribution of QTN in the genome, the number of recombination hotspots and whether QTN were initially inside or outside recombination hotspots.ConclusionsOur findings show future scope for targeted modification of recombination hotspots e.g. through changes in zinc-finger motifs of the PRDM9 protein to increase genetic gain in production species