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Whole and targeted sequencing of human genomes is a promising, increasinglyfeasible tool for discovering genetic contributions to risk of complexdiseases. A key step is calling an individual's genotype from the multiplealigned short read sequences of his DNA, each of which is subject to nucleotideread error. Current methods are designed to call genotypes separately at eachlocus from the sequence data of unrelated individuals. Here we proposelikelihood-based methods that improve calling accuracy by exploiting twofeatures of sequence data. The first is the linkage disequilibrium (LD) betweennearby SNPs. The second is the Mendelian pedigree information available whenrelated individuals are sequenced. In both cases the likelihood involves theprobabilities of read variant counts given genotypes, summed over theunobserved genotypes. Parameters governing the prior genotype distribution andthe read error rates can be estimated either from the sequence data itself orfrom external reference data. We use simulations and synthetic read data basedon the 1000 Genomes Project to evaluate the performance of the proposedmethods. An R-program to apply the methods to small families is freelyavailable at http://med.stanford.edu/epidemiology/PHGC/.

Improving sequence-based genotype calls with linkage disequilibrium and pedigree information