The quantitative genetic basis of polyandry in the parasitoid wasp, Nasonia vitripennis

Understanding the evolution of female multiple mating (polyandry) is crucial for understanding sexual selection and sexual conflict. Despite this interest, little is known about its genetic basis or whether genetics influences the evolutionary origin or maintenance of polyandry. Here, we explore the quantitative genetic basis of polyandry in the parasitoid wasp Nasonia vitripennis, a species in which female re-mating has been observed to evolve in the laboratory. We performed a quantitative genetic experiment on a recently collected population of wasps. We found low heritabilities of female polyandry (re-mating frequency after 18 h), low heritability of courtship duration and a slightly higher heritability of copulation duration. However, the coefficients of additive genetic variance for these traits were all reasonably large (CV(A)>7.0). We also found considerable dam effects for all traits after controlling for common environment, suggesting either dominance or maternal effects. Our work adds to the evidence that nonadditive genetic effects may influence the evolution of mating behaviour in Nasonia vitripennis, and the evolution of polyandry more generally.     

A unified approach for quantifying, testing and correcting population stratification in case-control association studies

The HapMap project has given case-control association studies a unique opportunity to uncover the genetic basis of complex diseases. However, persistent issues in such studies remain the proper quantification of, testing for, and correction for population stratification (PS). In this paper, we present the first unified paradigm that addresses all three fundamental issues within one statistical framework. Our unified approach makes use of an omnibus quantity (delta), which can be estimated in a case-control study from suitable null loci. We show how this estimated value can be used to quantify PS, to statistically test for PS, and to correct for PS, all in the context of case-control studies. Moreover, we provide guidelines for interpreting values of delta in association studies (e.g., at alpha = 0.05, a delta of size 0.416 is small, a delta of size 0.653 is medium, and a delta of size 1.115 is large). A novel feature of our testing procedure is its ability to test for either strictly any PS or only 'practically important' PS. We also performed simulations to compare our correction procedure with Genomic Control (GC). Our results show that, unlike GC, it maintains good Type I error rates and power across all levels of PS.     

Native arthropods on exotic sand dune flowers: consideration of sample size and number for investigating rare species and sparse communities

When studying arthropod visitors of flowers, the sampling unit (individual flowers, groups of flowers, areas of plants, timed visits, etc.) depends upon the aim of the study and the sampling method employed. In this study, arthropods using flowers of the ice plant, Carpobrotus edulis, were recorded on the sand dunes at New Brighton in the south island of New Zealand. Of 3600 flowers, only 10% contained invertebrates and only 478 specimens were recorded in total. Of 32 arthropod species observed on this exotic plant, we consider at least 20 to be native to New Zealand and five species are probably New Zealand endemics. Based on an occupation rate of individual flowers of 10%, a binomial model indicated that a sample of 100 flowers would have <0.003% chance of containing no specimens, and 96% chance that 5–16 flowers would contain some animals. Species accumulation models (e.g. bootstrap, Chao and rarefaction) and models examining the likelihood of recording rare species indicated that after examining 2000 flowers, 80% of arthropod species would be recorded, and that only the rarest species in our study would fall below an 80% statistical power of detection. The results suggest that for this flower–invertebrate system, a scheme that involved 20 independent samples, each consisting of 100 flowers, would provide a good chance of (1) avoiding totally empty samples (2) collecting a high proportion of the total species present and (3) recording all but the very rarest species that occur in this system.     

The development of foraging microhabitat preferences in meerkats

Animals of many species tend to target their foraging attemptstoward particular microhabitats within their habitat. Althoughthese preferences are critical determinants of the foragingniche and have important ecological and evolutionary implications,we know little about how they develop. Here, we use detailedlongitudinal data from meerkats (Suricata suricatta) to examinehow individual learning and the use of social information affectthe development of foraging microhabitat preferences. Despiteliving in an open, arid environment, adult meerkats frequentlyforaged at the base of vegetation. Young pups seldom did so,but their foraging microhabitat choices became increasinglyadult-like as they grew older. Learning about profitable microhabitatsmay have been promoted in part by positive reinforcement fromprey capture. Foraging may also have become increasingly targetedtoward suitable locations as pups grew older because they spentmore time searching before embarking on foraging bouts. Thedevelopment of microhabitat preferences might also have beeninfluenced by social cues. Foraging in close proximity to adultsmay increase the probability that pups would dig in similarmicrohabitats. Also, pups often dug in holes created by olderindividuals, whereas adults never dug in existing holes. Foragingin existing holes was no more profitable to pups than creatingtheir own foraging hole but could provide pups with importantinformational benefits. The integration of personal and socialinformation is likely to be a common feature in the developmentof the foraging niche in generalist species.     

Effect of population stratification on case-control association studies. I. Elevation in false positive rates and comparison to confounding risk ratios (a simulation study)

OBJECTIVES: This is the first of two articles discussing the effect of population stratification on the type I error rate (i.e., false positive rate). This paper focuses on the confounding risk ratio (CRR). It is accepted that population stratification (PS) can produce false positive results in case-control genetic association. However, which values of population parameters lead to an increase in type I error rate is unknown. Some believe PS does not represent a serious concern, whereas others believe that PS may contribute to contradictory findings in genetic association. We used computer simulations to estimate the effect of PS on type I error rate over a wide range of disease frequencies and marker allele frequencies, and we compared the observed type I error rate to the magnitude of the confounding risk ratio. METHODS: We simulated two populations and mixed them to produce a combined population, specifying 160 different combinations of input parameters (disease prevalences and marker allele frequencies in the two populations). From the combined populations, we selected 5000 case-control datasets, each with either 50, 100, or 300 cases and controls, and determined the type I error rate. In all simulations, the marker allele and disease were independent (i.e., no association). RESULTS: The type I error rate is not substantially affected by changes in the disease prevalence per se. We found that the CRR provides a relatively poor indicator of the magnitude of the increase in type I error rate. We also derived a simple mathematical quantity, Delta, that is highly correlated with the type I error rate. In the companion article (part II, in this issue), we extend this work to multiple subpopulations and unequal sampling proportions. CONCLUSION: Based on these results, realistic combinations of disease prevalences and marker allele frequencies can substantially increase the probability of finding false evidence of marker disease associations. Furthermore, the CRR does not indicate when this will occur.