Adaptive evolution: evaluating empirical support for theoretical predictions

Olson‐Manning, Carrie F.; Wagner, Maggie R.; Mitchell-Olds, Thomas

doi:10.1038/nrg3322

Cited by 186 publications

(180 citation statements)

References 116 publications

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“…HA alleles with high antigenic weights but only slight increases in frequency turned out to be different from the prevailing antigenic strains but did not show the potential to rise to predominance in the viral population. This is in line with expectations from population genetics, which posits that most allelic diversity with altered fitness is usually present at low levels in a population and driven to extinction, with only a few alleles rising to predominance, with chances of fixation increasing along with their rise in frequencies (61). It is the combined consideration of allele epidemiological dynamics and estimates of their phenotype impact in terms of antigenicity which allowed us to predict future predominant alleles with altered antigenicity.…”

Section: Discussionsupporting

confidence: 80%

Computational Prediction of Vaccine Strains for Human Influenza A (H3N2) Viruses

Steinbrück

Klingen

McHardy

2014

J Virol

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Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. IMPORTANCEHuman influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.

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Section: Discussionsupporting

confidence: 80%

Computational Prediction of Vaccine Strains for Human Influenza A (H3N2) Viruses

Steinbrück

Klingen

McHardy

2014

J Virol

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“…That combinations of GSL alleles impact fitness in the field raises the possibility that epistatic selection played a role in shaping natural variation in GSL profiles (38). A possible signature of such coevolving genes is linkage disequilibrium (LD) (39).…”

Section: Significancementioning

confidence: 99%

Coselected genes determine adaptive variation in herbivore resistance throughout the native range of Arabidopsis thaliana

Brachi

Meyer

Villoutreix

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

122

176

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The "mustard oil bomb" is a major defense mechanism in the Brassicaceae, which includes crops such as canola and the model plant Arabidopsis thaliana. These plants produce and store blends of amino acid-derived secondary metabolites called glucosinolates. Upon tissue rupture by natural enemies, the myrosinase enzyme hydrolyses glucosinolates, releasing defense molecules. Brassicaceae display extensive variation in the mixture of glucosinolates that they produce. To investigate the genetics underlying natural variation in glucosinolate profiles, we conducted a large genomewide association study of 22 methionine-derived glucosinolates using A. thaliana accessions from across Europe. We found that 36% of among accession variation in overall glucosinolate profile was explained by genetic differentiation at only three known loci from the glucosinolate pathway. Glucosinolate-related SNPs were up to 490-fold enriched in the extreme tail of the genome-wide F ST scan, indicating strong selection on loci controlling this pathway. Glucosinolate profiles displayed a striking longitudinal gradient with alkenyl and hydroxyalkenyl glucosinolates enriched in the West. We detected a significant contribution of glucosinolate loci toward general herbivore resistance and lifetime fitness in common garden experiments conducted in France, where accessions are enriched in hydroxyalkenyls. In addition to demonstrating the adaptive value of glucosinolate profile variation, we also detected long-distance linkage disequilibrium at two underlying loci, GS-OH and GS-ELONG. Locally cooccurring alleles at these loci display epistatic effects on herbivore resistance and fitness in ecologically realistic conditions. Together, our results suggest that natural selection has favored a locally adaptive configuration of physically unlinked loci in Western Europe.Arabidopsis thaliana | glucosinolates | genome-wide association mapping | linkage disequilibrium | adaptation

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“…At some point, the environment changes such that previously deleterious and/or neutral alleles become favourable, and provide the raw material for adaptation to the new phenotypic optimum. Selection on SGV can be more effective than selection on new mutations because the adaptive alleles are already present in the population when the environment changes, and because newly beneficial alleles are at a higher initial frequency than a new mutation, making them less likely to be lost by genetic drift [20,63,68]. However, the equilibrium frequency of SGV depends critically on the population size and mutation rate [65,69], which are likely to differ among species (see below).…”

Section: Standing Genetic Variationmentioning

confidence: 99%

Factors influencing the effect size distribution of adaptive substitutions

et al. 2016

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The distribution of effect sizes of adaptive substitutions has been central to evolutionary biology since the modern synthesis. Early theory proposed that because large-effect mutations have negative pleiotropic consequences, only small-effect mutations contribute to adaptation. More recent theory suggested instead that large-effect mutations could be favoured when populations are far from their adaptive peak. Here we suggest that the distributions of effect sizes are expected to differ among study systems, reflecting the wide variation in evolutionary forces and ecological conditions experienced in nature. These include selection, mutation, genetic drift, gene flow, and other factors such as the degree of pleiotropy, the distance to the phenotypic optimum, whether the optimum is stable or moving, and whether new mutation or standing genetic variation provides the source of adaptive alleles. Our goal is to review how these factors might affect the distribution of effect sizes and to identify new research directions. Until more theory and empirical work is available, we feel that it is premature to make broad generalizations about the effect size distribution of adaptive substitutions important in nature.

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Adaptive evolution: evaluating empirical support for theoretical predictions

Cited by 186 publications

References 116 publications

Computational Prediction of Vaccine Strains for Human Influenza A (H3N2) Viruses

Computational Prediction of Vaccine Strains for Human Influenza A (H3N2) Viruses

Coselected genes determine adaptive variation in herbivore resistance throughout the native range of Arabidopsis thaliana

Factors influencing the effect size distribution of adaptive substitutions

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