Variation in patterns of human meiotic recombination

Reconstructing past changes in locus-specific recombination rates

BACKGROUND

Recombination rates vary at the level of the species, population and individual. Now recognized as a transient feature of the genome, recombination rates at a given locus can change markedly over time. Existing inferential methods, predominantly based on linkage disequilibrium patterns, return a long-term average estimate of past recombination rates. Such estimates can be misleading, but no analytical framework to infer recombination rates that have changed over time is currently available.

RESULTS

We apply coalescent modeling in conjunction with a suite of summary statistics to show that the recombination history of a locus can be reconstructed from a time series of genetic samples. More usefully, we describe a new method, based on n-tuple dataset subsampling, to infer past changes in recombination rate from DNA sequences taken at a single time point. This subsampling strategy can correctly assign simulated loci to constant, increasing and decreasing recombination models with an accuracy of 84%.

CONCLUSIONS

While providing an important stepping-stone to determining past recombination rates, n-tuple subsampling still exhibits a moderate error rate. Theoretical limitations indicated by coalescent theory suggest that highly accurate inference of past recombination rates will remain challenging. Nevertheless, we show for the first time that reconstructing historic recombination rates is possible in principle.

Basic statistical recipes for the emergence of biochemical discernment

An essential step towards understanding life would be to identify the very basic mechanisms responsible for the discerning behaviour of living biochemical systems, absent from randomly reacting chemical soups. One intuitively feels that this question goes beyond the particular nature of the biological molecules and should relate to general physical principles. The pre-eminent physicist Ludwig Boltzmann early envisioned life as a struggle for entropy, in concordance with the subsequent principle of self-organization out of equilibrium. Re-examination of elementary steady state biochemical systems from a statistical perspective supports this view and shows that sigmoidal responses arising from microstates elimination, are sufficient to explain innermost characteristics of life, including its capacity to convert random molecular interactions into accurate biological reactions. A primary operating strategy to achieve this goal is the introduction of time-irreversible transitions in molecular state conversion cycles by injection of free energy, which confers decisional capacity to single macromolecules. Selected examples from various fields of molecular biology such as enzymology and gene expression, are provided to show that these non-equilibrium steady state mechanisms remain important in contemporary biochemical systems. But in addition, information archiving allowed the emergence of the time-reversible counterparts of these mechanisms, mediated by evolutionary pre-organized macromolecular complexes capable of generating discernment in a non-dissipative manner.

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.

In eukaryotes genetic crossovers are responsible for generating genetic diversity and ensuring the proper segregation of chromosomes. Genetic crossovers are tightly clustered in hotspots. Although the existence of hotspots in humans is clearly proven, mechanisms of their formation and the regulation of meiotic recombination in general remain poorly understood. An additional complication in studies of meiotic recombination is the fact that the direct experimental mapping of human hotspots on a genome-wide scale is not feasible with current methods. The best available indirect methods compute the position of hotspots from patterns of historic associations between genetic markers in population samples. In this study we determined the positions of genetic crossovers in ten pedigrees of European origin and then compared the positions of crossovers with the hotspots computed from HapMap data. Importantly, we find that the population-averaged computed map is in close agreement with the observed distribution of genetic crossovers. We also find that cryptic hotspots that are not easily detected in the computed European map can be more effectively identified if other populations are included in the analysis. Our analysis shows that high-resolution recombination profiles are highly similar between distantly related populations and that by including computed hotspots from several populations we can predict nearly all crossovers.

Genetics of mammalian meiosis: regulation, dynamics and impact on fertility

Meiosis is an essential stage in gamete formation in all sexually reproducing organisms. Studies of mutations in model organisms and of human haplotype patterns are leading to a clearer understanding of how meiosis has adapted from yeast to humans, the genes that control the dynamics of chromosomes during meiosis, and how meiosis is tied to gametic success. Genetic disruptions and meiotic errors have important roles in infertility and the aetiology of developmental defects, especially aneuploidy. An understanding of the regulation of meiosis, coupled with advances in genomics, may ultimately allow us to diagnose the causes of meiosis-based infertilities, more wisely apply assisted reproductive technologies, and derive functional germ cells.