Transition from Background Selection to Associative Overdominance Promotes Diversity in Regions of Low Recombination.

Linked selection is a major driver of genetic diversity. Selection against deleterious mutations removes linked neutral diversity (background selection [BGS]) [1], creating a positive correlation between recombination rates and genetic diversity. Purifying selection against recessive variants, however, can also lead to associative overdominance (AOD) [2, 3], due to an apparent heterozygote advantage at linked neutral loci that opposes the loss of neutral diversity by BGS. Zhao and Charlesworth [3] identified the conditions under which AOD should dominate over BGS in a single-locus model and suggested that the effect of AOD could become stronger if multiple linked deleterious variants co-segregate. We present a model describing how and under which conditions multi-locus dynamics can amplify the effects of AOD. We derive the conditions for a transition from BGS to AOD due to pseudo-overdominance [4], i.e., a form of balancing selection that maintains complementary deleterious haplotypes that mask the effect of recessive deleterious mutations. Simulations confirm these findings and show that multi-locus AOD can increase diversity in low-recombination regions much more strongly than previously appreciated. While BGS is known to drive genome-wide diversity in humans [5], the observation of a resurgence of genetic diversity in regions of very low recombination is indicative of AOD. We identify 22 such regions in the human genome consistent with multi-locus AOD. Our results demonstrate that AOD may play an important role in the evolution of low-recombination regions of many species.

Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences.

Disentangling the effect on genomic diversity of natural selection from that of demography is notoriously difficult, but necessary to properly reconstruct the history of species. Here, we use high-quality human genomic data to show that purifying selection at linked sites (i.e. background selection, BGS) and GC-biased gene conversion (gBGC) together affect as much as 95% of the variants of our genome. We find that the magnitude and relative importance of BGS and gBGC are largely determined by variation in recombination rate and base composition. Importantly, synonymous sites and non-transcribed regions are also affected, albeit to different degrees. Their use for demographic inference can lead to strong biases. However, by conditioning on genomic regions with recombination rates above 1.5 cM/Mb and mutation types (C↔G, A↔T), we identify a set of SNPs that is mostly unaffected by BGS or gBGC, and that avoids these biases in the reconstruction of human history.

Assigning function to natural allelic variation via dynamic modeling of gene network induction.

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be "personalized" according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

Recombination, meiotic expression and human codon usage.

Synonymous codon usage (SCU) varies widely among human genes. In particular, genes involved in different functional categories display a distinct codon usage, which was interpreted as evidence that SCU is adaptively constrained to optimize translation efficiency in distinct cellular states. We demonstrate here that SCU is not driven by constraints on tRNA abundance, but by large-scale variation in GC-content, caused by meiotic recombination, via the non-adaptive process of GC-biased gene conversion (gBGC). Expression in meiotic cells is associated with a strong decrease in recombination within genes. Differences in SCU among functional categories reflect differences in levels of meiotic transcription, which is linked to variation in recombination and therefore in gBGC. Overall, the gBGC model explains 70% of the variance in SCU among genes. We argue that the strong heterogeneity of SCU induced by gBGC in mammalian genomes precludes any optimization of the tRNA pool to the demand in codon usage.

SENCA: A Multilayered Codon Model to Study the Origins and Dynamics of Codon Usage.

Gene sequences are the target of evolution operating at different levels, including the nucleotide, codon, and amino acid levels. Disentangling the impact of those different levels on gene sequences requires developing a probabilistic model with three layers. Here we present SENCA (site evolution of nucleotides, codons, and amino acids), a codon substitution model that separately describes 1) nucleotide processes which apply on all sites of a sequence such as the mutational bias, 2) preferences between synonymous codons, and 3) preferences among amino acids. We argue that most synonymous substitutions are not neutral and that SENCA provides more accurate estimates of selection compared with more classical codon sequence models. We study the forces that drive the genomic content evolution, intraspecifically in the core genome of 21 prokaryotes and interspecifically for five Enterobacteria. We retrieve the existence of a universal mutational bias toward AT, and that taking into account selection on synonymous codon usage has consequences on the measurement of selection on nonsynonymous substitutions. We also confirm that codon usage bias is mostly driven by selection on preferred codons. We propose new summary statistics to measure the relative importance of the different evolutionary processes acting on sequences.

Bio++: efficient extensible libraries and tools for computational molecular evolution.

Efficient algorithms and programs for the analysis of the ever-growing amount of biological sequence data are strongly needed in the genomics era. The pace at which new data and methodologies are generated calls for the use of pre-existing, optimized-yet extensible-code, typically distributed as libraries or packages. This motivated the Bio++ project, aiming at developing a set of C++ libraries for sequence analysis, phylogenetics, population genetics, and molecular evolution. The main attractiveness of Bio++ is the extensibility and reusability of its components through its object-oriented design, without compromising the computer-efficiency of the underlying methods. We present here the second major release of the libraries, which provides an extended set of classes and methods. These extensions notably provide built-in access to sequence databases and new data structures for handling and manipulating sequences from the omics era, such as multiple genome alignments and sequencing reads libraries. More complex models of sequence evolution, such as mixture models and generic n-tuples alphabets, are also included.