A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo-devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.

Cell types are the basic building blocks of multicellular organisms and are extensively diversified in animals. Despite recent advances in characterizing cell types, classification schemes remain ambiguous. We propose an evolutionary definition of a cell type that allows cell types to be delineated and compared within and between species. Key to cell type identity are evolutionary changes in the 'core regulatory complex' (CoRC) of transcription factors, that make emergent sister cell types distinct, enable their independent evolution and regulate cell type-specific traits termed apomeres. We discuss the distinction between developmental and evolutionary lineages, and present a roadmap for future research.

BackgroundDespite evidence that genetic factors contribute to gestational length and preterm birth, robust associations with genetic variants have not been identified. We hypothesized that analyzing larger data sets with gestational length information by genomewide association would reveal trait-influencing variants.MethodsWe performed a genomewide association study in a discovery data set of 43,568 women of European ancestry from 23andMe, Inc., for gestational length as a continuous trait and for term or preterm (<37 weeks) birth as a dichotomous outcome. We used three Nordic data sets (8,643 women) for replication of 14 genomic loci achieving either genomewide (P < 5×10-8) or suggestive association (P < 1×10-6).ResultsIn the discovery stage, for gestational length, four loci (EBF1, EEFSEC, AGTR2 and WNT4) achieved genomewide significance, all of which were replicated in the Nordic data sets. Functional analysis of the WNT4 locus indicated the likely causative variant alters the binding of ESR1. ADCY5 and RAP2C, which had suggestive significance in the discovery stage, were significantly replicated and achieved genomewide significance in joint analysis. Common variants in EBF1, EEFSEC and AGTR2 were also associated with preterm birth with genomewide significance. Analysis of mother-infant dyads indicated that these findings likely resulted from maternal genome actions.ConclusionsOur study is the first to identify maternal genetic variants robustly associated with gestational length and preterm birth. Roles of these loci in uterine development, maternal nutrition, and vascular control support their mechanistic involvement and create opportunities to investigate new risk factors for prevention of preterm birth.

The concept of morphological integration describes the pattern and the amount of correlation between morphological traits. Integration is relevant in evolutionary biology as it imposes constraint on the variation that is exposed to selection, and is at the same time often based on heritable genetic correlations. Several measures have been proposed to assess the amount of integration, many using the distribution of eigenvalues of the correlation matrix. In this paper, we analyze the properties of eigenvalue variance as a much applied measure. We show that eigenvalue variance scales linearly with the square of the mean correlation and propose the standard deviation of the eigenvalues as a suitable alternative that scales linearly with the correlation. We furthermore develop a relative measure that is independent of the number of traits and can thus be readily compared across datasets. We apply this measure to examples of phenotypic correlation matrices and compare our measure to several other methods. The relative standard deviation of the eigenvalues gives similar results as the mean absolute correlation (W.P. Cane, Evol Int J Org Evol 47:844-854, 1993) but is only identical to this measure if the correlation matrix is homogenous. For heterogeneous correlation matrices the mean absolute correlation is consistently smaller than the relative standard deviation of eigenvalues and may thus underestimate integration. Unequal allocation of variance due to variation among correlation coefficients is captured by the relative standard deviation of eigenvalues. We thus suggest that this measure is a better reflection of the overall morphological integration than the average correlation.