Standardized subsets of the HGDP-CEPH Human Genome Diversity Cell Line Panel, accounting for atypical and duplicated samples and pairs of close relatives

Population structure and eigenanalysis

Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general "phase change" phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.

A worldwide survey of haplotype variation and linkage disequilibrium in the human genome

Recent genomic surveys have produced high-resolution haplotype information, but only in a small number of human populations. We report haplotype structure across 12 Mb of DNA sequence in 927 individuals representing 52 populations. The geographic distribution of haplotypes reflects human history, with a loss of haplotype diversity as distance increases from Africa. Although the extent of linkage disequilibrium (LD) varies markedly across populations, considerable sharing of haplotype structure exists, and inferred recombination hotspot locations generally match across groups. The four samples in the International HapMap Project contain the majority of common haplotypes found in most populations: averaging across populations, 83% of common 20-kb haplotypes in a population are also common in the most similar HapMap sample. Consequently, although the portability of tag SNPs based on the HapMap is reduced in low-LD Africans, the HapMap will be helpful for the design of genome-wide association mapping studies in nearly all human populations.