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In spite of long-held beliefs that traits related to reproductive success tend to become fixed by evolution with little or no genetic variation, there is now considerable evidence that the natural variation o f fertility within populations is genetically influenced and that a portion of that influence is related to the motivational precursors to fertility. We conduct a two-stage analysis to examine these inferences in a time-ordered multivariate context. First, using data from the National Longitudinal Survey of Youth, 1979, and LISREL analysis, we develop a structural equation model in which five hypothesized motivational precursors to fertility, measured in 1979-1982, predict both a child-timing and a child-number outcome, measured in 2002. Second, having chosen two time-ordered sequences of six variables from the SEM to represent our phenotypic models, we use Mx to conduct both univariate and multivariate behavioral genetic analyses with the selected variables. Our results indicate that one or more genes acting within a gene network have additive effects that operate through child-number desires to affect both the timing of the next child born and the final number of children born, that one or more genes acting through a separate network may have additive effects operating through gender role attitudes to produce downstream effects on the two fertility outcomes, and that no genetic variance is associated with either child-timing intentions or educational intentions.

The article discusses the issue of admixture between anatomically modern and archaic human populations. It focuses on the hypothesis that the growing anatomically modern population acquired locally adapted genetic variants from archaic forms of the genus 'Homo'.

Variation in DNA sequences specific to the nonrecombinant part of the human Y chromosome is particularly interesting from an anthropological point of view, because the variation relates to remote paternal […]

I propose a new estimator for jointly estimating two-gene and four-gene coefficients of relatedness between individuals from an outbreeding population with data on codominant genetic markers and compare it, by Monte Carlo simulations, to previous ones in precision and accuracy for different distributions of population allele frequencies, numbers of alleles per locus, actual relationships, sample sizes, and proportions of relatives included in samples. In contrast to several previous estimators, the new estimator is well behaved and applies to any number of alleles per locus and any allele frequency distribution. The estimates for two- and four-gene coefficients of relatedness from the new estimator are unbiased irrespective of the sample size and have sampling variances decreasing consistently with an increasing number of alleles per locus to the minimum asymptotic values determined by the variation in identity-by-descent among loci per se, regardless of the actual relationship. The new estimator is also robust for small sample sizes and for unknown relatives being included in samples for estimating allele frequencies. Compared to previous estimators, the new one is generally advantageous, especially for highly polymorphic loci and/ or small sample sizes.

The subdivision of a species into local populations causes its response to selection to change, even if selection is uniform across space. Population structure increases the frequency of homozygotes and therefore makes selection on homozygous effects more effective. However, population subdivision can increase the probability of competition among relatives, which may reduce the efficacy of selection. As a result, the response to selection can be either increased or decreased in a subdivided population relative to an undivided one, depending on the dominance coefficient [F.sub.ST] and whether selection is hard or soft. Realistic levels of population structure tend to reduce the mean frequency of deleterious alleles. The mutation load tends to be decreased in a subdivided population for recessive alleles, as does the expected inbreeding depression. The magnitude of the effects of population subdivision tends to be greatest in species with hard selection rather than soft selection. Population structure can play an important role in determining the mean fitness of populations at equilibrium between mutation and selection.

In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequency alleles drift to fixation and no longer contribute to polymorphism, while linkage disequilibrium is broken down by recombination. As a result, loci chosen without independent evidence of recent selection are not expected to exhibit either of these features, even if they have been affected by numerous sweeps in their genealogical history. How then can we explain the patterns ill the data? One possibility is population structure, with unequal sampling from different subpopulations. Alternatively, positive selection may not operate as is commonly modeled. In particular, the rate of fixation of advantageous mutations may have increased in the recent past.

The infinitesimal model which hypothesizes that an infinite quantity of unconnected loci without epistasis influences the quantitative characteristic formed the basis of an analysis of the alterations evident for the genetic variance of a quantitative characteristic in a self-fertilizing population under repeated cycles of truncation selection. Application of the model provides recurrence relations of genetic variance for the succeeding generations with respect to purely additive gene action, pure dominance without additive influence and the instance of additive influence and dominance.

The natural Wright-Fisher model, without recombination and population subdivision and with effective population size, Ne and mutation rate per locus per generation, mu, provides a new estimator of the essential parameter theta having the value 4Ne-mu from DNA polymorphism data. The minimum variance of all possible unbiased estimators of the parameter is only marginally smaller than the new estimator. The variance of any currently used estimator is much bigger than the new estimator's variance.

Luca Cavalli-Sforza, a pioneer of genetic archaeology, gives details of geographical distribution of fifty two populations in the human genome. The genetic aspects of these populations passed on from years, are also examined.

Evolutionary developmental biology has focused largely on (macroevolution and the) analyses of the phylogenetic patterns of developmental events. Studies in this field have been concerned with the identification of primitive (conserved) and derived genetic mechanisms, and with the analysis of patterns of divergence of gene expression, genetic regulation, and morphogenesis (in different taxa). In order to understand the evolution of development it is also necessary to understand (it at the microevolutionary level. This can be done by examining) how the fundamental mechanisms of evolutionary change: mutation, selection, and drift, affect the genetic mechanisms of development. Such an understanding depends, in part, on developing methods for studying the association between natural genetic variation and variation in developmental processes. We have begun to develop theoretical methods for studying the population genetics of developmental mechanisms, and have been using these methods to study how developmental systems respond to selection. Our results show that selection on the phenotype can result in non-intuitive changes at the level of the genotype. Our results help to explain the variable and inconsistent correlation between specific genes and the heritable properties of complex traits, and suggest methods by which such correlations can be predicted.