Detecting genetic variation in developmental instability by artificial selection on fluctuating asymmetry

Abstract

Abstract Fluctuating asymmetry (FA) is frequently used as a measure of developmental instability (DI). Assuming a genetic basis to DI, many have argued that FA may be a good indicator of genetic quality to potential mates and to human managers of populations. Unfortunately FA is a poor indicator of DI, making it very difficult to verify this assertion. A recent review of the literature suggests that previous studies of the inheritance of FA and DI using half-sib covariances and parent–offspring regression have been unable to put meaningful limits on the heritability of FA and DI because of the extremely low power of the experiments performed. In this study, we consider the power of artificial selection on FA as an alternative approach to studying the inheritance of FA and DI. Using simulations, we investigate the efficacy of selection for both increased and decreased FA for detecting genetic variation. We find that selection for increased FA has much more power to detect the presence of genetic variation than does selection for decreased FA. These results hold when realistic sample sizes are employed. Artificial selection for increased FA is currently the most powerful approach for the detection of genetic variation in DI.