Figure 5.

Overview of methods. An overview of our hidden Markov model’s application to Asian genotype data. Genotype data was gathered from SNP array assays of Korean and Mongolian family pedigrees. These pedigrees were split into family quartets and input into our algorithm. The hidden Markov model considers each SNP position as belonging to one of four transmission states. The transmission state at a position depends on the previous transmission state (pink arrows: the state is most likely to remain unchanged) and the family quartet’s genotypes (orange arrows: genotypes are expected to be consistent with the transmission state). The Viterbi algorithm determined the most likely sequence of transmission states of paternal and maternal alleles to the two children. At state changes, the algorithm scanned up and down to find the region where recombination may have occurred. The returned recombination prediction intervals were used to construct a genetic map, to analyse hotspot usage and to find the effect of maternal age on recombination rate.

Bleazard et al. BMC Genetics 2013 14:19   doi:10.1186/1471-2156-14-19
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