Ancestral polymorphisms are defined as variants that arose by mutation prior to the speciation event that generated the species in which they segregate. Their presence may complicate the interpretation of molecular data and lead to incorrect phylogenetic inferences. They may also be used to identify regions of the genome that are under balancing selection. It is thus important to take into account the contribution of ancestral polymorphisms to variability within species and divergence between species. Here, we extend and improve a method for estimation of the proportion of ancestral polymorphisms within a species, and apply it to a dataset of 33 X-linked and 34 autosomal protein-coding genes for which sequence polymorphism data are available in both Drosophila pseudoobscura and Drosophila miranda, using Drosophila affinis as an outgroup. We show that a substantial proportion of both X-linked and autosomal synonymous variants in these two species are ancestral, and that a small number of additional genes with unusually high sequence diversity seem to have an excess of ancestral polymorphisms, suggestive of balancing selection.

We measure genetic variation in lifespan and fecundity at two food levels in 34 core lines of the Drosophila Genetic Reference Panel collection. Lines were significantly different from each other in lifespan and fecundity at both restricted and full food. There was a strong food-by-line interaction for the slope of age-specific mortality, fecundity and proportion of fertilized eggs, indicating the presence of genetic variation for the strength of the dietary restriction effect, likely to represent standing genetic variation in a natural population from which the lines used have originated. No trade-off between fecundity and lifespan manifested in life-history variation among inbred lines. Our data partially corroborate the recent proposition that availability of nutrient-free water eliminates the apparent dietary restriction at least in some conditions. Although flies on full food with water added had lifespan slightly higher than those without a water source, it was still significantly lower than that in flies on restricted food, with no indication of interaction. We fully corroborate the recently discovered effect of addition of essential amino acids to the medium: addition of 1·5 mM methionine to restricted food significantly increased fecundity without a measurable decrease in lifespan; addition of each of 10 essential amino acids increased fecundity and decreased females lifespan to the levels observed on full food, again with no evidence of line-by-food interactions. We propose a mechanistic hypothesis explaining the observed data, based on the assumption that food consumption by flies is adjusted according to flies’ saturation in water and methionine.

DNA topoisomerases are specialized nuclear enzymes that perform topological modifications on double-stranded DNA (dsDNA) and hence are essential for DNA metabolism such as replication, transcription, recombination, condensation and segregation. In a genetic screen, we identified a temperature-sensitive mutant allele of topoisomerase 2 that exhibits conditional synthetic lethality with a chk1 knockout strain. The mutant allele of topoisomerase 2 is defective in chromosome segregation at a non-permissive temperature and there was increase in chromosome segregation defects in the double mutant of top2–10 and chk1 delete at a non-permissive temperature. More importantly, topoisomearse 2 mutant cells mildly delay the mitotic progression at non-permissive temperature that is mediated by checkpoint protein kinase Chk1. Additionally, top2–10 mutant cells also activate the Chk1 at a non-permissive temperature and this activation of Chk1 takes place at the time of mitosis. Interestingly, top2–10 mutant cells retain their viability at a non-permissive temperature if the cells are not allowed to enter into mitosis. Taking together our results, we speculate that in the top2–10 mutant, the segregation of entangled chromatids during mitosis could result in delaying the mitotic progression through the activation of Chk1 kinase.

The covariance of heterozygosity serves as a measure of linkage disequilibrium (LD) between genes at two loci, although one that does not have as much information as a parameter such as r2. However, it may be extended to blocks of loci (single nucleotide polymorphisms, SNPs) along a chromosome. This has two advantages when searching for significant associations between different chromosomal regions. Calculations for a data set such as Hapmap are complicated by the large number of pairs of loci (SNPs) that need to be considered. For example, a search for significant associations between SNPs on different chromosomes involves around 1012 calculations for a single population. Furthermore, this may not be an efficient way of detecting associations since r2 values calculated from neighbouring pairs will not be independent of each other. The covariance of heterozygosity provides an average measure of association between blocks of any size, and reduces the number of calculations by a factor of b2, where b is the block size. Unlike the calculation of r2, the covariance of heterozygosity uses just diploid data and is not biased by sample size. Calculations using a block size of 50 have been used to look for associations in the Hapmap data set between regions within and between chromosomes. Within chromosomes, a signal is detected up to around 10 cM. No obviously significant associations have been detected between regions on different chromosomes, although there is a low level of association consistent with departures from random mating.

Tolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits, which could not be obtained using the previously implemented statistical methods. Genetic variance in tolerance was estimated as genetic variance in regression slopes of host performance along an increasing pathogen burden level. Random regressions combined with covariance functions allowed genetic variance for host performance to be estimated at any point along the pathogen burden trajectory, providing a novel means to analyse infection-induced changes in genetic variation of host performance. Yet, the results implied that decreasing family size as well as a non-zero environmental or genetic correlation between initial host performance before infection and pathogen burden led to biased estimates for tolerance genetic variance. In both cases, genetic correlation between tolerance slope and host performance in a pathogen-free environment became artificially negative, implying a genetic trade-off when it did not exist. Moreover, recording a normally distributed pathogen burden as a threshold trait is not a realistic way of obtaining unbiased estimates for tolerance genetic variance. The results show that random regressions are suitable for the genetic analysis of tolerance, given suitable data structure collected either under field or experimental conditions.

A new estimation-based Bayesian variable selection approach is presented for genetic analysis of complex traits based on linear or logistic regression. By assigning a mixture of uniform priors (MU) to genetic effects, the approach provides an intuitive way of specifying hyperparameters controlling the selection of multiple influential loci. It aims at avoiding the difficulty of interpreting assumptions made in the specifications of priors. The method is compared in two real datasets with two other approaches, stochastic search variable selection (SSVS) and a re-formulation of Bayes B utilizing indicator variables and adaptive Student's t-distributions (IAt). The Markov Chain Monte Carlo (MCMC) sampling performance of the three methods is evaluated using the publicly available software OpenBUGS (model scripts are provided in the Supplementary material). The sensitivity of MU to the specification of hyperparameters is assessed in one of the data examples.