Genotype Sequence Segmentation: Handling Constraints and Noise
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Fine-Scale Recombination Mapping of High-Throughput Sequence Data
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Transforming Genomes Using MOD Files with Applications
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Numerous methods exist for inferring the ancestry mosaic of an admixed individual based on its genotypes and those of its ancestors. These methods rely on bialleic SNPs obtained from genotype calling algorithms, which classify each marker as belonging to one of four states (reference allele, alternate allele, heterozygous, or no call) based on probe hybridization intensity signals. We demonstrate that this conversion of probe intensities to discrete genotypes can lead to a loss of information and introduce errors via incorrect genotype calls. We propose a method that directly infers ancestry from probe intensities by minimizing the intensity difference between a target individual and one or more of its ancestors. We demonstrate our method on mice from the developing Collaborative Cross (CC) genetic reference population, which are admixtures of a common set of eight ancestors. Our samples were genotyped using a 7.8K-marker Illumina Infinium platform called the Mouse Universal Genotyping Array (MUGA). We compare our reconstructions with a standard genotype-based method and validate our results using DNA sequencing data. Our algorithm is able to use information not captured by genotype calls and avoid errors due to incorrect calls.