Abstract

The malaria-protective β-globin polymorphisms, sickle-cell (βS) and β0-thalassaemia, are canonical examples of human adaptation to infectious disease. Occurring on distinct genetic backgrounds, they vary markedly in their patterns of linked genetic variation at the population level, suggesting different evolutionary histories. βS is associated with five classical restriction fragment length polymorphism haplotypes that exhibit remarkable specificity in their geographical distributions; by contrast, β0-thalassaemia mutations are found on haplotypes whose distributions overlap considerably. Here, we explore why these two polymorphisms display contrasting spatial haplotypic distributions, despite having malaria as a common selective pressure. We present a meta-population genetic model, incorporating individual-based processes, which tracks the evolution of β-globin polymorphisms on different haplotypic backgrounds. Our simulations reveal that, depending on the rate of mutation, a large population size and/or high population growth rate are required for both the βS- and the β0-thalassaemia-like patterns. However, whilst the βS-like pattern is more likely when population subdivision is high, migration low and long-distance migration absent, the opposite is true for β0-thalassaemia. Including gene conversion has little effect on the overall probability of each pattern; however, when inter-haplotype fitness variation exists, gene conversion is more likely to have contributed to the diversity of haplotypes actually present in the population. Our findings highlight how the contrasting spatial haplotype patterns exhibited by βS and β0-thalassaemia may provide important indications as to the evolution of these adaptive alleles and the demographic history of the populations in which they have evolved.

Highlights

  • The mutations responsible for sickle-cell disease and β0-thalassaemia represent two unequivocal examples of balanced polymorphisms in the human genome (Hedrick, 2011; Roberts and Williams, 2003; Taylor et al, 2012)

  • Selective sweep, in which a single adaptive allele sweeps rapidly through a population, resulting in the predominance of a single haplotype associated with the adaptive allele in the population, and (ii) a soft selective sweep, whereby ancestral genetic variation around the adaptive site is partially preserved owing to multiple alleles at the site being selected (Messer and Petrov, 2013; Ralph and Coop, 2010)

  • If the population was too small and/or too highly structured, the probability of the β0-thalassaemia-like pattern remained low (b0.20) even at the highest tested mutation rate. This was due to there being insufficient genetic variation or population movement to facilitate overlap in the haplotypes' distributions (Fig. 1C, E, respectively)

Read more

Summary

Introduction

The mutations responsible for sickle-cell disease and β0-thalassaemia represent two unequivocal examples of balanced polymorphisms in the human genome (Hedrick, 2011; Roberts and Williams, 2003; Taylor et al, 2012). In the context of this study, we define a haplotype as a set of DNA variations, including the variant under selection, that are located on a single chromosome and, by virtue of their close proximity, are inherited together. Both the sickle-cell mutation (βS) and β0-thalassaemia appear at first glance to be examples of soft selective sweeps. Glu6-Val), is associated with five “classical” restriction fragment length polymorphism (RFLP) haplotypes (Table 1) (Flint et al, 1998) The latter results from any mutation that completely eliminates the production of protein from the β-globin gene (Weatherall and Clegg, 2001a). One-hundred and fifty-eight such mutations are currently reported (http://www.globin.bx.psu.edu/cgibin/hbvar/query_vars, accessed 29 June 2015; Patrinos et al, 2004), and many of these can be found on more than one genetic background (Table 2) (Trabuchet et al, 1991; Weatherall and Clegg, 2001a)

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call