Evolutionary Contributions to Solving the ��Matrilineal Puzzle��

Matriliny has long been debated by anthropologists positing either its primitive or its puzzling nature. More recently, evolutionary anthropologists have attempted to recast matriliny as an adaptive solution to modern social and ecological environments, tying together much of what was known to be associated with matriliny. This paper briefly reviews the major anthropological currents in studies of matriliny and discusses the contribution of evolutionary anthropology to this body of literature. It discusses the utility of an evolutionary framework in the context of the first independent test of Holden et al.'s 2003 model of matriliny as daughter-biased investment. It finds that historical daughter-biased transmission of land among the Mosuo is consistent with the model, whereas current income transmission is not. In both cases, resources had equivalent impacts on male and female reproduction, a result which predicts daughter-biased resource transmission given any nonzero level of paternity uncertainty. However, whereas land was transmitted traditionally to daughters, income today is invested in both sexes. Possible reasons for this discrepancy are discussed.     

From Genotype �� Environment Interaction to Gene �� Environment Interaction

Historically in plant breeding a large number of statistical models has been developed and used for studying genotype × environment interaction. These models have helped plant breeders to assess the stability of economically important traits and to predict the performance of newly developed genotypes evaluated under varying environmental conditions. In the last decade, the use of relatively low numbers of markers has facilitated the mapping of chromosome regions associated with phenotypic variability (e.g., QTL mapping) and, to a lesser extent, revealed the differetial response of these chromosome regions across environments (i.e., QTL × environment interaction). QTL technology has been useful for marker-assisted selection of simple traits; however, it has not been efficient for predicting complex traits affected by a large number of loci. Recently the appearance of cheap, abundant markers has made it possible to saturate the genome with high density markers and use marker information to predict genomic breeding values, thus increasing the precision of genetic value prediction over that achieved with the traditional use of pedigree information. Genomic data also allow assessing chromosome regions through marker effects and studying the pattern of covariablity of marker effects across differential environmental conditions. In this review, we outline the most important models for assessing genotype × environment interaction, QTL × environment interaction, and marker effect (gene) × environment interaction. Since analyzing genetic and genomic data is one of the most challenging statistical problems researchers currently face, different models from different areas of statistical research must be attempted in order to make significant progress in understanding genetic effects and their interaction with environment.