Allelic histories: positive selection on a HIV-resistance allele

Stumpf, Michael; Wilkinson‐Herbots, Hilde

doi:10.1016/j.tree.2004.02.001

Cited by 13 publications

(15 citation statements)

References 10 publications

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“…On the other hand, in the Moderate-uncertainty condition, the average performance of the collectives dropped below that of the solitaries after the environmental change (i.e. ξ t < 0 at a region t ∈ [42, 45]; Figure 3B). Also, the main effect of group size was negative in the post-change period (mean and the 95% Bayesian CI of ω 2 = -0.26 [-0.44, -0.11]; Table 1), suggesting that larger groups were more likely to get stuck in the out-dated option in the Moderate-uncertainty condition.…”

Section: Resultsmentioning

confidence: 99%

“…The diverse effects of social influence on the collective wisdom of a group has been drawing substantial attention 19;21;22;71;72 . The bulk of this literature, including many jury models and election models 45;73 , has focused primarily on the static estimation problem, where the ‘truth’ is fixed from the outset. However, in reality, there are many situations under which the state of the true value is changing over time so that monitoring and tracking the pattern of change is a crucial determinant of decision performance 74 .…”

Section: Discussionmentioning

confidence: 99%

“…From the viewpoint of the classic wisdom of crowds theory, increasing group size may increase collective accuracy 45–48 . The relative advantage of the collective over solitary individuals may also be highlighted by increased task difficulty, because there would be more room for the performance of difficult tasks to be improved compared to easier tasks in which high accuracy can be achieved by asocial learning only.…”

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confidence: 99%

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Social learning strategies regulate the wisdom and madness of interactive crowds

Toyokawa

Whalen

Laland

2018

Preprint

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Why groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model-ftting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity amongst individuals, with rates of copying increasing with group size, leading to high probabilities of herding amongst large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a longstanding puzzle in the literature.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Social learning strategies regulate the wisdom and madness of interactive crowds

Toyokawa

Whalen

Laland

2018

Preprint

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“…Because of the age of the mutation it has been argued that it is extremely unlikely to be a neutral mutation. As a result, CCR5-D32 has been hypothesized to have been under selection from the bubonic plague or smallpox with the low frequency being explained by intermittent temporal selection or balancing selection (for reviews see De Silva and Stumpf 2004;Stumpf and Wilkinson-Herbots 2004;Galvani and Novembre 2005). Recently, it has been demonstrated that homozygous individuals are completely resistant to HIV infection (heterozygous individuals exhibit lower infection rates and longer disease progressions) (McNicholl et al 1997).…”

Section: Resultsmentioning

confidence: 99%

Estimation of 2Nes From Temporal Allele Frequency Data

2008

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We develop a new method for estimating effective population sizes, N e , and selection coefficients, s, from time-series data of allele frequencies sampled from a single diallelic locus. The method is based on calculating transition probabilities, using a numerical solution of the diffusion process, and assuming independent binomial sampling from this diffusion process at each time point. We apply the method in two example applications. First, we estimate selection coefficients acting on the CCR5-D32 mutation on the basis of published samples of contemporary and ancient human DNA. We show that the data are compatible with the assumption of s ¼ 0, although moderate amounts of selection acting on this mutation cannot be excluded. In our second example, we estimate the selection coefficient acting on a mutation segregating in an experimental phage population. We show that the selection coefficient acting on this mutation is $0.43.

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“…Quantitative studies have concluded that heterozygous carriers of Δ32 in the past had a fitness advantage of at least 5% and possibly as high as 35% [2,3]. Bubonic plague was initially proposed as the selective agent [2], but subsequent analysis suggested that a disease like smallpox is a more plausible candidate ([6–8], with reviews in [9–11]).…”

Section: Introductionmentioning

confidence: 99%

The Geographic Spread of the CCR5 Δ32 HIV-Resistance Allele

2005

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The Δ32 mutation at the CCR5 locus is a well-studied example of natural selection acting in humans. The mutation is found principally in Europe and western Asia, with higher frequencies generally in the north. Homozygous carriers of the Δ32 mutation are resistant to HIV-1 infection because the mutation prevents functional expression of the CCR5 chemokine receptor normally used by HIV-1 to enter CD4+ T cells. HIV has emerged only recently, but population genetic data strongly suggest Δ32 has been under intense selection for much of its evolutionary history. To understand how selection and dispersal have interacted during the history of the Δ32 allele, we implemented a spatially explicit model of the spread of Δ32. The model includes the effects of sampling, which we show can give rise to local peaks in observed allele frequencies. In addition, we show that with modest gradients in selection intensity, the origin of the Δ32 allele may be relatively far from the current areas of highest allele frequency. The geographic distribution of the Δ32 allele is consistent with previous reports of a strong selective advantage (>10%) for Δ32 carriers and of dispersal over relatively long distances (>100 km/generation). When selection is assumed to be uniform across Europe and western Asia, we find support for a northern European origin and long-range dispersal consistent with the Viking-mediated dispersal of Δ32 proposed by G. Lucotte and G. Mercier. However, when we allow for gradients in selection intensity, we estimate the origin to be outside of northern Europe and selection intensities to be strongest in the northwest. Our results describe the evolutionary history of the Δ32 allele and establish a general methodology for studying the geographic distribution of selected alleles.

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Allelic histories: positive selection on a HIV-resistance allele

Cited by 13 publications

References 10 publications

Social learning strategies regulate the wisdom and madness of interactive crowds

Social learning strategies regulate the wisdom and madness of interactive crowds

Estimation of 2Nes From Temporal Allele Frequency Data

The Geographic Spread of the CCR5 Δ32 HIV-Resistance Allele

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