Mortality associated with acute lung injury (ALI) remains substantial, with recent estimates of 35-45% similar to those obtained decades ago. Although evidence for sex-related differences in ALI mortality remains equivocal, death rates differ markedly for age, with more than 3-fold increased mortality in older versus younger patients. Strains of mice also show large differences in ALI mortality. To tease out genetic factors affecting mortality, we established a mouse model of differential hyperoxic ALI (HALI) survival. Separate genetic analyses of backcross and F2 populations generated from sensitive C57BL/6J (B) and resistant 129X1/SvJ (X1) progenitor strains identified two quantitative trait loci (QTLs; Shali1 and Shali2) with strong, equal but opposite, within-strain effects on survival. Congenic lines confirmed these opposing QTL effects, but also retained the low penetrance seen in the 6-12 week X1 control strain. Sorting mice into distinct age groups revealed that 'age at exposure' inversely correlated with survival time and explained reduced penetrance of the resistance trait. While B mice were already sensitive by 6 weeks old, X1 mice maintained significant resistance up to 3-4 weeks longer. Reanalysis of F2 data gave analogous age-related findings, and also supported sex-specific linkage for Shali1 and Shali2. Importantly, we have demonstrated in congenic mice that these age effects on survival correspond with B alleles for Shali1 (6-week old mice more sensitive) and Shali2 (10-week old mice more resistant) placed on the X1 background. Further studies revealed significant sex-specific survival differences in subcongenics for both QTLs. Accounting for age and sex markedly improved penetrance of both QTLs, thereby reducing trait variability, refining Shali1 to <8.5Mb, and supporting several sub-QTLs within the Shali2 interval. Together, these congenics will allow age- and sex-specific studies to interrogate myriad subphenotypes affected during ALI development and progression and identify intermediary injury biomarkers that can predict outcome.