Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster

Abstract

Senescence, the decline in survivorship and fertility with increasing age, is a near-universal property of organisms. Senescence and limited lifespan are thought to arise because weak natural selection late in life allows the accumulation of mutations with deleterious late-age effects that are either neutral (the mutation accumulation hypothesis) or beneficial (the antagonistic pleiotropy hypothesis) early in life. Analyses of Drosophila spontaneous mutations, patterns of segregating variation and covariation, and lines selected for late-age fertility have implicated both classes of mutation in the evolution of aging, but neither their relative contributions nor the properties of individual loci that cause aging in nature are known. To begin to dissect the multiple genetic causes of quantitative variation in lifespan, we have conducted a genome-wide screen for quantitative trait loci (QTLs) affecting lifespan that segregate among a panel of recombinant inbred lines using a dense molecular marker map. Five autosomal QTLs were mapped by composite interval mapping and by sequential multiple marker analysis. The QTLs had large sex-specific effects on lifespan and age-specific effects on survivorship and mortality and mapped to the same regions as candidate genes with fertility, cellular aging, stress resistance and male-specific effects. Late age-of-onset QTL effects are consistent with the mutation accumulation hypothesis for the evolution of senescence, and sex-specific QTL effects suggest a novel mechanism for maintaining genetic variation for lifespan.

Figure 1

(A) The solid bars depict variation in mean female lifespan, and the open bars depict variation in mean male lifespan, among the Oregon and 2b RI lines. The solid and open symbols mark the mean lifespans of Oregon (triangles) and 2b (diamonds) females and males, respectively. The variation among RI lines for both sexes was highly significant by ANOVA (females: F_{97, 2129} = 6.568, P < 0.0001; males: F_{97, 2194} = 9.264, P < 0.0001). The estimates of among-line (σ_L²) and within-line (σ_E²) variance components were σ_LF² = 92.166 and σ_EF² = 376.117 for females and σ_LM² = 80.700 and σ_EM² = 228.383 for males. (B) There is highly significant sex × line (S × L) variation for lifespan among the RI lines (F_{97, 4323} = 6.111, P < 0.0001; σ_S×L² = 67.023), exhibited as crossing of reaction norms when the mean male and mean female longevity of each line are connected. The among-line variance component from ANOVA of lifespan including the fixed effect of sex (σ_LP²) was 26.180. The variance component estimate of the genetic correlation between the sexes (18) = r_G = σ_LP²/(σ_LF²×σ_LM²)^½ = 0.304. (C) The genetic variance in survival of the RI lines increases with age. The genetic variance, V_G, was estimated by the among-line variance in survival (σ_L²) at each time point for females (dashed line) and males (solid line). The coefficient of genetic variation, CV_G = 100(V_G)^½/x̄, where x̄ is the mean survival at each time point, is plotted against time.

Figure 2

Plot of LR statistics from composite interval mapping (above the abscissa) and F statistics from multiple regression of lifespan on marker genotype (below the abscissa) against recombination rate on the first (A), second (B), and third (C) chromosomes for males (solid lines) and females (dotted lines). Marker positions are indicated on the abscissa by ⧫. The horizontal lines above the x axis mark the Bonferroni-corrected LR critical value for experimentwise α = 0.05, and the horizontal lines below the x axis mark the empirical F statistics for experimentwise α = 0.05 obtained from permutation tests of the final model for males (solid lines) and females (dashed lines). The F statistic for each nonsignificant marker was computed from a model that accounted for the effects of all significant markers. The F statistic for each significant marker was computed similarly, but markers closely linked to the significant marker under consideration were not included in the regression model. The abrupt declines in values of the F statistics flanking markers of the significant regions are artefacts of this procedure.