D718 Nucleic Acids Research, 2007, Vol. 35, Database issue Figure 1. SNPs, Linkage Disequilibrium (LD) and Recombination Display across a 1 Mb region of human chromosome 2 in the Genome Browser. Genes and their polymorphisms from dbSNP are shown in the first two tracks, where SNPs are colored by their function; red: non-synonymous or splice site, green: synonymous, blue: untranslated, black: intron. The HapMap SNPs track shows all of the polymorphic SNPs that were used to calculate LD, which is displayed in the HapMap LD track. Regions of high LD (red) tend to be separated by areas of high recombination rate and recombination hotspots. region, which can be consistent with balancing selection or population contraction. Negative values of Tajima’s D0, on the other hand, indicate an excess of rare variation, consistent with population growth or positive selection. Previous analyses have suggested that using the empiric distribution of Tajima’s D0 from a collection of regions across the genome provides advantages in assessing whether selection or demography might explain an observed deviation from expectation (9,10). It is important to note here that the observed allele frequency spectrum is dependent on the depth of coverage during SNP detection (ascertainment). If this coverage is uneven or if part of the frequency spectrum has been ignored, as is the case in the Perlegen dataset where a minor allele frequency cutoff was used, this can lead to ascertainment bias. Because of this bias toward common polymorphism in the Perlegen data set and the difﬁculty of modeling such bias, positive Tajima’s D0 values are difﬁcult to interpret. The ascertainment bias raises the mean of the distribution, so high values have reduced signiﬁcance. However, extreme negative values in extended regions can be useful in qualita- tively identifying interesting regions for full resequencing and more rigorous theoretical analysis of nucleotide diversity. As less biased datasets become available, this track will become more useful. The University of Calfornia Santa Cruz (UCSC) Tajima’s D0 track shows estimates from the three human populations in the Perlegen data set (11), which includes genotypes for 1 586 383 SNPs in 71 Americans of European, African and Asian ancestry. A separate track of SNPs that were used in this study is also available. Recombination rates and recombination hotspots. A better understanding of the genomic landscape of human recombi- nation rate variation would facilitate the efﬁcient design and analysis of disease association studies and greatly improve inferences from polymorphism data about selection and human demographic history. Recombination rate estimates also provide a new route to understanding the molecular mechanisms underlying human recombination. Observations from sperm studies (12) and patterns of genetic variation (13,14) show that recombination rates in the human genome vary extensively over kilobase scales and that much recombination is observed in recombination hotspots, providing an explanation for the apparent block-like structure of LD (15,16). Estimates of recombination rates and the locations of recombination hotspots were contributed by Gil McVean and Simon Myers (13) for the HapMap release 16a (17) and the Perlegen (11) datasets. They are available in the SNP Recombination tracks in the Genome Browser. Structural variants. During the ﬁnishing of the human genome, it became obvious that there were major differences between individuals, causing problems in reconciling the different sequences for a given region. Some of these prob- lems turned out to be caused by copy number polymorphism (CNP) and structural variation (SV) (18). These describe related types of variation, both of which are important in disease (19). Typically, CNPs refer to a single large region (>100 kb) being duplicated multiple times (20). SVs describe intermediate sized regions, larger than are included in dbSNP and smaller than CNPs, which complete the spectrum of observed indel sizes. Array-based comparative genomic hybridization [array CGH; (21)] can identify variants between 20 and 400 kb by hybridizing genomic DNA to arrays containing BAC clones (22,23). Representational oligonucleotide microarray analysis (ROMA) follows a similar approach, but uses oligonucleotide arrays and has found variants that range in size from several 100 bp up to 1.6 Mb (20). Paired-end sequencing of fosmid clones detects variants in approximately the same range, or 700 bp to 2 Mb (24). Using the diploid HapMap data to look for failures of Mendelian inheritance has shown deletions up to 1 Mb (25,26). Hybridizations of haploid sam- ples found deletions up to 8 kb (27). Further details about populations and individual genotypes are available in the ‘Structural Var’ track in the Genome Browser. A common database for these types of results is still in the planning phase at NCBI. We provide a uniﬁed view of these data gathered in a single ‘composite’ track (28), which groups together many datasets of the same type, allowing common control of the display while reducing track clutter and simplifying access. This approach also