We present an expanded data set of 50 unlinked autosomal noncoding regions, resequenced in samples of Hausa from Cameroon, Italians, and Chinese. We use these data to make inferences about human demographic history by using a technique that combines multiple aspects of genetic data, including levels of polymorphism, the allele frequency spectrum, and linkage disequilibrium. We explore an extensive range of demographic parameters and demonstrate that our method of combining multiple aspects of the data results in a significant reduction of the compatible parameter space. In agreement with previous reports, we find that the Hausa data are compatible with demographic equilibrium as well as a set of recent population expansion models. In contrast to the Hausa, when multiple aspects of the data are considered jointly, the non-Africans depart from an equilibrium model of constant population size and are compatible with a range of simple bottleneck models, including a 50 -90% reduction in effective population size occurring some time after the appearance of modern humans in Africa 160,000 -120,000 years ago.bottlenecks ͉ combining P values ͉ human demographic inference ͉ population growth E lucidating how and when populations change in size is an important element in reconstructing evolutionary history because these changes often reflect crucial events in the history of a species, such as range expansions, environmental changes, and mixture between groups (1). In addition, making inferences based on population variation data typically requires the specification of a demographic model. Such applications include detecting the signature of natural selection or estimating recombination rates from patterns of linkage disequilibrium (LD) (2-5). Finally, better knowledge of demographic histories in human populations is particularly important for whole-genome, LD-based association studies (6, 7).Motivated by the excess of rare variants observed in mitochondrial DNA data, attention initially focused on models of ancient population growth and on the idea that population expansions may have accompanied the dispersal out of Africa or the emergence of new tool technology in the Upper Paleolithic (8-13). However, the accumulation of nuclear sequence variation surveys showed that this simple growth model was consistent with the observed frequency spectrum only for a subset of the loci (14-16). Likewise, LD surveys revealed marked differences in the rate of LD decay in African populations compared with that in non-African populations (17)(18)(19). These results together with the higher levels of sequence variation in African populations compared with that in non-African populations led to the proposal that population size reduction, such as bottlenecks, account for patterns of variation and LD in non-African populations (15,18,19). This bottleneck was hypothesized to correspond with the dispersal of modern humans out of Africa (18).However, the investigation of formal bottleneck models has typically used a single aspect of genetic var...
A maximum-likelihood method for demographic inference is applied to data sets consisting of the frequency spectrum of unlinked single-nucleotide polymorphisms (SNPs). We use simulation analyses to explore the effect of sample size and number of polymorphic sites on both the power to reject the null hypothesis of constant population size and the properties of two-and three-dimensional maximumlikelihood estimators (MLEs). Large amounts of data are required to produce accurate demographic inferences, particularly for scenarios of recent growth. Properties of the MLEs are highly dependent upon the demographic scenario, as estimates improve with a more ancient time of growth onset and smaller degree of growth. Severe episodes of growth lead to an upward bias in the estimates of the current population size, and that bias increases with the magnitude of growth. One data set of African origin supports a model of mild, ancient growth, and another is compatible with both constant population size and a variety of growth scenarios, rejecting greater than fivefold growth beginning Ͼ36,000 years ago. Analysis of a data set of European origin indicates a bottlenecked population history, with an 85% population reduction occurring 000,03ف years ago. P ATTERNS of genetic variation in contemporaryto be statistically independent of each other and the data are completely characterized by the number of populations can be used to make inferences about past population size changes. Ideally, likelihood methpolymorphic sites and the frequency spectrum. That is, we can represent the full data by m ϭ (m 0 , m 1 , m 2 , m nϪ1 ), ods using the full data would be applied to make such inferences. For the case of DNA sequence polymorphism where m 0 is the number of sites monomorphic in the sample, and, for i Ͼ 0, m i is the number of polymorphic and where no recombination occurs between the varisites in which the derived allele is present i times in the able sites, methods are available for carrying out such sample of n chromosomes. We assume all polymorphic inferences (
Recently researchers have made efforts to reconceptualize digital inequality into discrete levels. These levels reflect access to and diffusion of technologies, proficiency in Internet usage, and propensity to take advantage of the opportunities afforded by information and communication technologies for assistance in daily life. We assess the utility of this approach for studying digital inequality across rural, suburban, and urban counties. Based on data from a 2005 nationally representative random sample telephone survey of 2,185 adults, the results provide mixed support for using this approach to studying digital inequality. In particular, we find that rural residents use Internet technologies less for assistance in helping with economics and other daily activities when compared with individuals from suburban and urban areas; however, our results suggest that this relationship is the product of the slow diffusion of advanced technologies to rural areas. The implications of these findings for understanding this under-theorized form of inequality are discussed, and we make contributions to this literature through empirically addressing issues of digital capital.
Recent research suggests that Internet usage can positively influence social capital in rural communities by fostering avenues for voluntary participation and creating social networks. Most of this research has examined whether Internet use is associated with participation in local organizations and social networks but not the means by which residents use the technology to learn about local activities. To address this gap in the literature, the authors use a mixed-methods approach in an isolated rural region of the western United States to evaluate how residents use their connections to maintain local social networks and learn about local community events and organizations. The authors show that Internet usage can play an important role in building social capital in rural communities, thus extending the systemic model of rural voluntary participation and community attachment. Implications for rural community development are addressed.
Although attention has been given to how broadband access is related to economic development in rural areas, scant consideration has been given to how it may be associated with voluntary participation. This issue is important in that numerous studies have shown how much more vital community participation is in rural areas as compared to suburban and urban places. Drawing on three diverse data sets, we examine the influence of broadband access on community participation. In addition, we explore whether broadband access exerts its influence through, in conjunction with, or independent of social networks. The results suggest that broadband access and social network size have independent effects on volunteering in rural places.
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