A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.     

Randomized unit root processes for modelling and forecasting financial time series: Theory and applications

This paper considers the problems of statistically analysing the levels of financial time series rather than their differences, which are often equivalent to returns and which are traditionally analysed in econometric modelling. This focus on differences is a consequence of the inherent nonstationarity of the levels, and hence analysing the latter requires introducing an alternative framework for modelling nonstationary behaviour. We do this by considering randomized unit root processes, arguing that these can have a natural interpretation in the financial context. The paper thus develops methods for testing for randomized unit roots and for modelling such processes. It then applies these techniques to various financial time series, so as to ascertain their potential usefulness, particularly for forecasting.     

Constitutional 11p15 abnormalities, including heritable imprinting center mutations, cause nonsyndromic Wilms tumor

Constitutional abnormalities at the imprinted 11p15 growth regulatory region cause syndromes characterized by disordered growth, some of which include a risk of Wilms tumor. We explored their possible contribution to nonsyndromic Wilms tumor and identified constitutional 11p15 abnormalities in genomic lymphocyte DNA from 13 of 437 individuals (3%) with sporadic Wilms tumor without features of growth disorders, including 12% of bilateral cases (P = 0.001) and in one familial Wilms tumor pedigree. No abnormality was detected in 220 controls (P = 0.006). Abnormalities identified included H19 DMR epimutations, uniparental disomy 11p15 and H19 DMR imprinting center mutations (one microinsertion and one microdeletion), thus identifying microinsertion as a new class of imprinting center mutation. Our data identify constitutional 11p15 defects as one of the most common known causes of Wilms tumor, provide mechanistic insights into imprinting disruption and reveal clinically important epigenotype-phenotype associations. The impact on clinical management dictates that constitutional 11p15 analysis should be considered in all individuals with Wilms tumor.     

Adaptation varies through space and time in a coevolving host-parasitoid interaction

One of the central challenges of evolutionary biology is to understand how coevolution organizes biodiversity over complex geographic landscapes. Most species are collections of genetically differentiated populations, and these populations have the potential to become adapted to their local environments in different ways. The geographic mosaic theory of coevolution incorporates this idea by proposing that spatial variation in natural selection and gene flow across a landscape can shape local coevolutionary dynamics. These effects may be particularly strong when populations differ across productivity gradients, where gene flow will often be asymmetric among populations. Conclusive empirical tests of this theory have been particularly difficult to perform because they require knowledge of patterns of gene flow, historical population relationships and local selection pressures. We have tested these predictions empirically using a model community of bacteria and bacteriophage (viral parasitoids of bacteria). We show that gene flow across a spatially structured landscape alters coevolution of parasitoids and their hosts and that the resulting patterns of adaptation can fluctuate in both space and time.