The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
The main purpose of this article is to present several new statistical tests of neutrality of mutations against a class of alternative models, under which DNA polymorphisms tend to exhibit excesses of rare alleles or young mutations. Another purpose is to study the powers of existing and newly developed tests and to examine the detailed pattern of polymorphisms under population growth, genetic hitchhiking and background selection. It is found that the polymorphic patterns in a DNA sample under logistic population growth and genetic hitchhiking are very similar and that one of the newly developed tests, FS, is considerably more powerful than existing tests for rejecting the hypothesis of neutrality of mutations. Background selection gives rise to quite different polymorphic patterns than does logistic population growth or genetic hitchhiking, although all of them show excesses of rare alleles or young mutations. We show that Fu and Li's tests are among the most powerful tests against background selection. Implications of these results are discussed.
Mutations in the genealogy of the sequences in a random sample from a population can be classified as external and internal. External mutations are mutations that occurred in the external branches and internal mutations are mutations that occurred in the internal branches of the genealogy. Under the assumption of selective neutrality, the expected number of external mutations is equal to theta = 4Ne mu, where Ne is the effective population size and mu is the rate of mutation per gene per generation. Interestingly, this expectation is independent of the sample size. The number of external mutations is likely to deviate from its neutral expectation when there is selection while the number of internal mutations is less affected by the presence of selection. Statistical properties of the numbers of external mutations and of internal mutations are studied and their relationships to two commonly used estimates of theta are derived. From these properties, several new statistical tests based on a random sample of DNA sequences from the population are developed for testing the hypothesis that all mutations at a locus are neutral.
Inferring demographic history is an important task in population genetics. Many existing inference methods are based on pre-defined simplified population models, which are more suitable for hypothesis testing than for exploratory analysis. We developed a novel model-flexible method called stairway plot, which infers population size changes over time using SNP frequency spectra. This method is applicable for whole-genome sequences of hundreds of individuals. Using extensive simulation we demonstrated the usefulness of the method for inferring demographic history, especially recent population size changes. The method was applied to the whole genome sequence data of nine populations from the 1000 Genomes Project, and showed a pattern of human population fluctuations from 10 to 200 thousand years ago.
By comparing the low-, intermediate-, and high-frequency parts of the frequency spectrum, we gain information on the evolutionary forces that influence the pattern of polymorphism in population samples. We emphasize the high-frequency variants on which positive selection and negative (background) selection exhibit different effects. We propose a new estimator of u (the product of effective population size and neutral mutation rate), u L , which is sensitive to the changes in high-frequency variants. The new u L allows us to revise Fay and Wu's H-test by normalization. To complement the existing statistics (the H-test and Tajima's D-test), we propose a new test, E, which relies on the difference between u L and Watterson's u W . We show that this test is most powerful in detecting the recovery phase after the loss of genetic diversity, which includes the postselective sweep phase. The sensitivities of these tests to (or robustness against) background selection and demographic changes are also considered. Overall, D and H in combination can be most effective in detecting positive selection while being insensitive to other perturbations. We thus propose a joint test, referred to as the DH test. Simulations indicate that DH is indeed sensitive primarily to directional selection and no other driving forces.
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