Rapid haplotype inference for nuclear families

The emergence of high throughput genotyping technologies has enabled rapid, low-cost assays of single nucleotide polymorphisms (SNPs) in large datasets of human subjects. These genotype data provide two unordered allele values at each queried genomic position, with each allele derived from the two homologous chromosomes in a diploid cell. However, genotype data do not identify which variant is present on each homologous chromosome.A haplotype is an assignment of each allele to the homologous chromosome it resides on, and the haplotypes of a set of individuals can be determined, with varying levels of accuracy, from their genotype data using haplotype inference or 'phasing' techniques. Haplotypes are essential for many important genetic applications, including: (1) imputation of genotypes at loci that were originally untyped in a set of samples [1-5], a technique that can uncover novel disease susceptibility loci when incorporated into a genome-wide association study; (2) studying the results of meiosis - within a single generation or averaged across many generations - providing the opportunity to build genetic maps [6], identify recombination hotspots [7], and identify genetic causes of recombination rate variation [8]; (3) studying parental transmission effects such as imprinting [9]; (4) identifying signatures of selection [10], and many others. Indeed, much research at the frontier of biological understanding, such as the allelic control of chromatin structure, will require accurate haplotype information.Genome scale haplotypes cannot be discovered using direct molecular means at present, so computational methods must be used to infer them. Algorithms for inferring haplotypes can be separated into three classes. One class of haplotyping algorithms applies to unrelated individuals, and techniques of this class use probabilistic constraints governed by mathematical models of population dynamics to infer haplotypes. Available algorithms [11,12] include PHASE [13], B