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Both HASH and HapCut algorithms obtain much more accurate haplotypes than the Greedy algorithm.
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When there are enough DNA sequence fragments that cover two or more consecutive variant loci, SIH builds longer and more accurate haplotype blocks than haplotype inference does [ 12].
Extensive experiments show that this model is more accurate in haplotype reconstruction than other models.
However, we have found the consensus haplotypes to be more accurate than the haplotypes obtained from the Viterbi algorithm.
We treat the more accurate family-based haplotypes as phase-known haplotypes, and use Haplous to compare them with the estimated phase-predicted haplotypes of the same HapMap samples, using Haplous.
Extensive simulation experiments show that H-BOP is generally faster and more accurate in assembling haplotypes than a recent state-of-art algorithm ReFHap.
F1 animals inherit their haplotypes from the LL and FL animals; therefore, including them allows an increase in sample size up to 20, and thus a more accurate inference of haplotype diversity.
Measurements of LD in recent years become more accurate as data of haplotypes accumulate.
Including multiple markers per haplotype and IBD information, with a moderate marker density generally yields more accurate results than models that include haplotypes based on two marker alleles but not relations between haplotypes, or models that include marker alleles instead of haplotypes [ 3].
The longer the shared haplotype, the more accurate the match [ 11].
While the inclusion of additional SNPs further reduces group sizes, and hence statistical power, the more accurate functional characterization gained by haplotype analysis may sometimes offset this loss of power.
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