characteristics of the variant and checks for consistency. These include the variant class (SNP, indel, microsatellite, etc.), how it maps to the genomic sequence (single base, between two bases or a range of bases), the variant’s function (splice site, non-synonymous, etc.), validation status, map weight (a quality measure for the alignment between the var- iant’s ﬂanking sequence and the genomic sequence) and the allele represented in the current genome assembly. The error checking includes several types of error checking and annotation: data format inconsistencies, missing data, disag- reement between expected and observed allele lengths, poor quality alignments of ﬂanking sequences, alignment of a vari- ant to multiple genomic locations, inconsistency in functional classiﬁcation and disagreement between the genomic alleles as identiﬁed by dbSNP and the reference assembly. Consider- ing all errors and other annotations together, 5% of the dbSNP build for human warrants additional scrutiny. Genotyping array SNPs. The growth of common genotyping platforms is making genome-wide association studies more accessible, but integration of the genotyping results with other genomic features may be challenging for some users. We have added tracks for the large, ﬁxed SNP sets to assist in the visualization and analysis of these data. Currently, we support the Affymetrix GeneChip Human Mapping 500K Array Set and the Illumina HumanHap300 BeadChip genotyping platforms, which are available in the ‘SNP Arrays’ track in the Genome Browser. This is built in the ﬂexible composite track format which simpliﬁes the process of adding new platforms as they are released; we intend to support the Affymetrix ‘Million-SNP’ chip when it is released in early 2007. Linkage disequilibrium. LD describes the association of alle- les at separate loci in the genome, usually on the same chro- mosome. It is useful for understanding the associations between genetic variants throughout the genome, and can be helpful in selecting SNPs for genotyping. LD measures the difference between the observed allele frequency for a pair of alleles—one at each of two loci—as compared to the expected joint allele frequency, which is the product of the two individual allele frequencies. When LD is low, the two loci tend to be inherited independently because recomb- ination decouples them. Regions of high LD reﬂect lack of recent recombination between the two loci within the popula- tion, causing the inheritance of their alleles to be linked. Three pairwise measures of LD are commonly used. Between two alleles, r2 is the square of the correlation coef- ﬁcient, D0 is the covariance normalized to 1, and the LOD score is the log odds score (3). These values are calculated separately for three HapMap populations—the Yoruba people in Ibadan, Nigeria (YRI), the European samples from the Centre d’Etude du Polymorphisme Humain (CEU), and the combined populations of Japanese from Tokyo and Han Chinese from Beijing (JPT + CHB) (4). Calculation of LD from diploid data ﬁrst requires breaking each diploid genotype into its underlying genotypes and assigning each haploid genotype to one of the two chromo- somes in the individual, often called phasing. This process of deﬁning the phase of the mutations determines which alleles are paired on the same chromosome. Inferring the Nucleic Acids Research, 2007, Vol. 35, Database issue D717 phasing between heterozygous genotypes from two nearby loci in the same individual is complicated and addressed else- where (5). Given the diploid genotypes A/a and B/b at two loci for one individual, probabilistic methods are used to infer which of the four possible combinations—AB/ab, Ab/ aB, aB/Ab and ab/AB—are most likely at these loci for that individual. All genotype data at all loci from the population is used together in this process. The result is that all diploid genotypes for each individual are then partitioned into two sets representing the individual’s two inherited chromosomes. This phased data then forms the basis for the calculation of LD as described above. Although not currently displayed in the Browser, this phased data also forms the basis for partitioning of the SNPs into commonly co-inherited sets, or haplotype blocks. A unique feature of this resource is its data compression, which is an internal engineering trick used to decrease the time of data retrieval and image rendering in the Genome Browser. First, LD values were precomputed and binned, allowing storage of an intensity index in a single byte rather than as a real number. Second, the data were stored in a single denormalized table for each population such that all LD values downstream of a given SNP were stored in a single record; this requires fewer table joins and disk hits, allowing tens of megabases of LD data to be drawn within a few seconds. To build the current ‘HapMap LD’ track, Haploview (3) was used to infer phasing and calculate LD values for pairs of genotypes separated by 250 kb or less from HapMap release 20 (http://www.hapmap.org/genotypes/2006-01/non- redundant). This approach infers phase using a standard EM algorithm with a partition–ligation approach for blocks with >10 markers. The dataset is currently being updated to use the results from another phasing algorithm, PHASE (6,7), as it typically produces lower error rates in several measures used to evaluate phasing methods (5). An example of the HapMap LD track display is available in Figure 1. As LD data describes the relationship of a pair of alleles, the display must also reﬂect this complexity. To understand this plot, consider a pair of SNPs at two points on a chromosome, then follow the lines up from those points until they meet. This point deﬁnes the top of a trapezoid, whose color represents the amount of LD observed between the two SNPs. Tajima’s D0. Tajima’s D0 (8) is one of many classic tests of the neutral model. It is a statistic used to compare two measures of nucleotide diversity under the assumptions that all poly- morphisms are selectively neutral and the population has con- stant size. The neutral model expects correlation between the expected number of polymorphic sites ( s) and the average number of nucleotide differences (p). Tajima’s D0 is the normalized difference between these two estimates (8): p s D0 ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ Varðp sÞ The theoretical distribution of Tajima’s D0 (95% conﬁdence interval between 2 and +2) assumes that polymorphism ascertainment is independent of allele frequency. High values of Tajima’s D0 suggest an excess of common variation in a