Presence Of Strong Selection Research Articles

Scaling the discrete-time Wright-Fisher model to biobank-scale datasets.

The discrete-time Wright-Fisher (DTWF) model and its diffusion limit are central to population genetics. These models can describe the forward-in-time evolution of allele frequencies in a population resulting from genetic drift, mutation, and selection. Computing likelihoods under the diffusion process is feasible, but the diffusion approximation breaks down for large samples or in the presence of strong selection. Existing methods for computing likelihoods under the DTWF model do not scale to current exome sequencing sample sizes in the hundreds of thousands. Here, we present a scalable algorithm that approximates the DTWF model with provably bounded error. Our approach relies on two key observations about the DTWF model. The first is that transition probabilities under the model are approximately sparse. The second is that transition distributions for similar starting allele frequencies are extremely close as distributions. Together, these observations enable approximate matrix-vector multiplication in linear (as opposed to the usual quadratic) time. We prove similar properties for Hypergeometric distributions, enabling fast computation of likelihoods for subsamples of the population. We show theoretically and in practice that this approximation is highly accurate and can scale to population sizes in the tens of millions, paving the way for rigorous biobank-scale inference. Finally, we use our results to estimate the impact of larger samples on estimating selection coefficients for loss-of-function variants. We find that increasing sample sizes beyond existing large exome sequencing cohorts will provide essentially no additional information except for genes with the most extreme fitness effects.

A novel framework for inferring parameters of transmission from viral sequence data.

Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases.

Gene Conversion Facilitates Adaptive Evolution on Rugged Fitness Landscapes.

Gene conversion is a ubiquitous phenomenon that leads to the exchange of genetic information between homologous DNA regions and maintains coevolving multi-gene families in most prokaryotic and eukaryotic organisms. In this paper, we study its implications for the evolution of a single functional gene with a silenced duplicate, using two different models of evolution on rugged fitness landscapes. Our analytical and numerical results show that, by helping to circumvent valleys of low fitness, gene conversion with a passive duplicate gene can cause a significant speedup of adaptation, which depends nontrivially on the frequency of gene conversion and the structure of the landscape. We find that stochastic effects due to finite population sizes further increase the likelihood of exploiting this evolutionary pathway. A universal feature appearing in both deterministic and stochastic analysis of our models is the existence of an optimal gene conversion rate, which maximizes the speed of adaptation. Our results reveal the potential for duplicate genes to act as a "scratch paper" that frees evolution from being limited to strictly beneficial mutations in strongly selective environments.

Temporal genetic stability in natural populations of the waterflea Daphnia magna in response to strong selection pressure.

Studies monitoring changes in genetic diversity and composition through time allow a unique understanding of evolutionary dynamics and persistence of natural populations. However, such studies are often limited to species with short generation times that can be propagated in the laboratory or few exceptional cases in the wild. Species that produce dormant stages provide powerful models for the reconstruction of evolutionary dynamics in the natural environment. A remaining open question is to what extent dormant egg banks are an unbiased representation of populations and hence of the species' evolutionary potential, especially in the presence of strong environmental selection. We address this key question using the water flea Daphnia magna, which produces dormant stages that accumulate in biological archives over time. We assess temporal genetic stability in three biological archives, previously used in resurrection ecology studies showing adaptive evolutionary responses to rapid environmental change. We show that neutral genetic diversity does not decline with the age of the population and it is maintained in the presence of strong selection. In addition, by comparing temporal genetic stability in hatched and unhatched populations from the same biological archive, we show that dormant egg banks can be consulted to obtain a reliable measure of genetic diversity over time, at least in the multidecadal time frame studied here. The stability of neutral genetic diversity through time is likely mediated by the buffering effect of the resting egg bank.

Molecular Biology Techniques Q&A

Molecular Biology Techniques Q&A

Detecting and adjusting for small‐study effects in meta‐analysis

Publication bias and related types of small-study effects threaten the validity of systematic reviews. The existence of small-study effects has been demonstrated in empirical studies. Small-study effects are graphically diagnosed by inspection of the funnel plot. Though observed funnel plot asymmetry cannot be easily linked to a specific reason, tests based on funnel plot asymmetry have been proposed. Beyond a vast range of funnel plot tests, there exist several methods for adjusting treatment effect estimates for these biases. In this article, we consider the trim-and-fill method, the Copas selection model, and more recent regression-based approaches. The methods are exemplified using a meta-analysis from the literature and compared in a simulation study, based on binary response data. They are also applied to a large set of meta-analyses. Some fundamental differences between the approaches are discussed. An assumption common to the trim-and-fill method and the Copas selection model is that the small-study effect is caused by selection. The trim-and-fill method corresponds to an unknown implicit model generated by the symmetry assumption, whereas the Copas selection model is a parametric statistical model. However, it requires a sensitivity analysis. Regression-based approaches are easier to implement and not based on a specific selection model. Both simulations and applications suggest that in the presence of strong selection both the trim-and-fill method and the Copas selection model may not fully eliminate bias, while regression-based approaches seem to be a promising alternative.

Genetic variation at the alcohol dehydrogenase locus in natural populations of the seaweed fly, Coelopa frigida.

Samples of the seaweed fly, Coelopa frigida, from six populations widely spaced around the English coast were collected during 1974-76 and again in 1979-80. Gene frequencies at the Alcohol dehydrogenase (Adh) locus were found not to vary significantly between sites, nor over the five year period of their study. Genotype distributions are compared with Hardy-Weinberg expectations and show consistent, mostly significant, differences. These results are interpreted in the light of an association between the Adh locus and a chromosomal inversion polymorphism. It is argued that the remarkable geographical uniformity must indicate the presence of strong selection, probably taking the form of heterokaryotypic advantage.