In sexual populations, selection operates neither on the whole genome, which is repeatedly taken apart and reassembled by recombination, nor on individual alleles that are tightly linked to the chromosomal neighborhood. The resulting interference between linked alleles reduces the efficiency of selection and distorts patterns of genetic diversity. Inference of evolutionary history from diversity shaped by linked selection requires an understanding of these patterns. Here, we present a simple but powerful scaling analysis identifying the unit of selection as the genomic "linkage block" with a characteristic length, ξ b , determined in a self-consistent manner by the condition that the rate of recombination within the block is comparable to the fitness differences between different alleles of the block. We find that an asexual model with the strength of selection tuned to that of the linkage block provides an excellent description of genetic diversity and the site frequency spectra compared with computer simulations. This linkage block approximation is accurate for the entire spectrum of strength of selection and is particularly powerful in scenarios with many weakly selected loci. The latter limit allows us to characterize coalescence, genetic diversity, and the speed of adaptation in the infinitesimal model of quantitative genetics.Hill-Robertson interference | genealogy | Bolthausen-Sznitman coalescent I n asexual populations, different genomes compete for survival, and the fate of most new mutations depends more on the total fitness of the genome they reside in than on their own contribution to fitness. As a result, beneficial mutations on one genetic background can be lost to competition with other backgrounds, an effect known as "clonal interference" (1-3); likewise, deleterious mutations in very fit genomes can fix. This interference is reduced by recombination and disappears when recombination is rapid enough such that selection can act independently on different loci. Many eukaryotes recombine their genetic material by crossing-over of homologous chromosomes. As a result, distant loci evolve independently but nearby tightly linked loci remain coupled. Such interference, known as Hill-Robertson interference, reduces the efficacy of selection (4, 5) and reduces levels of neutral variation. Neutral diversity is indeed correlated with local recombination rates in several species, suggesting that linked selection is an important evolutionary force (6, 7). One typically distinguishes background selection against deleterious mutations (8, 9) from sweeping beneficial mutations, which lead to hitchhiking (10, 11). Both of these processes reduce diversity at linked loci and probably contribute to the observed correlation (12). Another piece of evidence for the importance of linked selection comes from the weak correlation between levels of genetic diversity and the population size (13). Whereas classic neutral models predict that diversity should increase linearly with the population size (14), in models dominated ...

Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by major histocompatibility complex molecules on the surface of virally infected cells and generate an anti-viral response that can kill the infected cells. Virus variants whose protein fragments are not efficiently presented on infected cells or whose fragments are presented but not recognized by CTLs therefore have a competitive advantage and spread rapidly through the population. We present a method that allows a more robust estimation of these escape rates from serially sampled sequence data. The proposed method accounts for competition between multiple escapes by explicitly modeling the accumulation of escape mutations and the stochastic effects of rare multiple mutants. Applying our method to serially sampled HIV sequence data, we estimate rates of HIV escape that are substantially larger than those previously reported. The method can be extended to complex escapes that require compensatory mutations. We expect our method to be applicable in other contexts such as cancer evolution where time series data is also available.

Humans typically consider altruism a moral good and condition their social behavior on the moral reputations of others. Indirect reciprocity explains how social norms and reputations support cooperation: individuals cooperate with others who are considered good. Indirect reciprocity works when an institution monitors and publicly broadcasts moral reputations. Here we develop a theory of adherence to public monitoring in societies where individuals are, at first, independently responsible for evaluating the reputations of their peers. Using a mathematical model, we show that adherence to an institution of moral assessment can evolve and promote cooperation under four different social norms, including norms that previous studies found to perform poorly. We determine how an institution’s size and its degree of tolerance towards anti-social behavior affect the rate of cooperation. Public monitoring serves to eliminate disagreements about reputations, which increases cooperation and payoffs, so that adherence evolves by social contagion and remains robust against displacement.

Reputations provide a powerful mechanism to sustain cooperation, as individuals cooperate with those of good social standing. But how should someone’s reputation be updated as we observe their social behavior, and when will a population converge on a shared norm for judging behavior? Here, we develop a mathematical model of cooperation conditioned on reputations, for a population that is stratified into groups. Each group may subscribe to a different social norm for assessing reputations and so norms compete as individuals choose to move from one group to another. We show that a group initially comprising a minority of the population may nonetheless overtake the entire population—especially if it adopts the Stern Judging norm, which assigns a bad reputation to individuals who cooperate with those of bad standing. When individuals do not change group membership, stratifying reputation information into groups tends to destabilize cooperation, unless individuals are strongly insular and favor in-group social interactions. We discuss the implications of our results for the structure of information flow in a population and for the evolution of social norms of judgment.

Humans usually consider altruism a moral good, and they condition their social behavior on the moral reputations of others. Indirect reciprocity explains how social norms and moral reputations can collectively support large-scale cooperation: members of the society cooperate who others who are considered good. But the theory of indirect reciprocity does not explain how the requisite institutions that monitor and broadcast moral reputations themselves evolve. Here we study the emergence of public monitoring in societies where individuals are, at first, independently responsible for evaluating the moral reputations of their peers. We show that public institutions of moral assessment that promote cooperation can evolve under all simple social norms, depending upon the institution's tolerance to occasional antisocial behavior. Public monitoring serves to eliminate disagreements about reputations in the population, which in turn increases cooperation and individual payoffs -- and so the tendency to adhere to a public institution can evolve by social contagion. Moreover, the resulting public institution is then robust to invasion or collapse. We also show how institutions can be designed to dramatically increase cooperation rates, even for social norms that previous studies found to perform poorly. Our results help explain why societies tend to elect centralized institutions to provide top-down moral governance of their individual behavior.