MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data.

BACKGROUND: Combining multiple independent tests, when all test the same hypothesis and in the same direction, has been the subject of several approaches. Besides the inappropriate (in this case) Bonferroni procedure, the Fisher's method has been widely used, in particular in population genetics. This last method has nevertheless been challenged by the SGM (symmetry around the geometric mean) and Stouffer's Z-transformed methods that are less sensitive to asymmetry and deviations from uniformity of the distribution of the partial P-values. Performances of these different procedures were never compared on proportional data such as those currently used in population genetics. RESULTS: We present new software that implements a more recent method, the generalised binomial procedure, which tests for the deviation of the observed proportion of P-values lying under a chosen threshold from the expected proportion of such P-values under the null hypothesis. The respective performances of all available procedures were evaluated using simulated data under the null hypothesis with standard P-values distribution (differentiation tests). All procedures more or less behaved consistently with approximately 5% significant tests at alpha = 0.05. Then, linkage disequilibrium tests with increasing signal strength (rate of clonal reproduction), known to generate highly non-standard P-value distributions are undertaken and finally real population genetics data are analysed. In these cases, all procedures appear, more or less equally, very conservative, though SGM seems slightly more conservative. CONCLUSION: Based on our results and those discussed in the literature we conclude that the generalised binomial and Stouffer's Z procedures should be preferred and Z when the number of tests is very small. The more conservative SGM might still be appropriate for meta-analyses when a strong publication bias in favour of significant results is expected to inflate type 2 error.

Worldwide variation in life-span sexual dimorphism and sex-specific environmental mortality rates.

In all human populations mean life span of women generally exceeds that of men, but the extent of this sexual dimorphism varies across different regions of the world. Our purpose here is to study, using global demographic and environmental data, the general tendency of this variation and local deviations from it. We used data on male and female life history traits and environmental conditions for 227 countries and autonomous territories; for each country or territory the life-span dimorphism was defined as the difference between mean life spans of women and men. The general tendency is an increase of life-span dimorphism with increasing average male-female life span; this tendency can be explained using a demographic model based on the Makeham-Gompertz equation. Roughly, the life-span dimorphism increases with the average life span because of an increase in the duration of expressing sex- and age-dependent mortality described by the second (exponential) term of the Makeham-Gompertz equation. Thus we investigated the differences in male and female environmental mortality described by the first term of the Makeham-Gompertz equation fitted to the data. The general pattern that resulted was an increase in male mortality at the highest and lowest latitudes. One plausible explanation is that specific factors tied to extreme latitudes influence males more strongly than females. In particular, alcohol consumption increases with increasing latitude and, on the contrary, infection pressures increase with decreasing latitude. This finding agrees with other observations, such as an increase in male mortality excess in Europe and Christian countries and an increase in female mortality excess in Asia and Muslim countries. An increase in the excess of female mortality may also be due to increased maternal mortality caused by an increase in fertility. However, this relation is not linear: In regions with the highest fertility (e.g., in Africa) the excess of female mortality is smaller than in regions with relatively lower fertility (e.g., in Asia). A possible explanation of this phenomenon is an evolutionary adaptation of women to the pressures of extremely high fertility by means of some reduction of their maternal mortality.