Jun 14, 2013

De novo inference of stratification and local admixture in sequencing studies

BMC Bioinformatics
Yu Zhang

Abstract

Analysis of population structures and genome local ancestry has become increasingly important in population and disease genetics. With the advance of next generation sequencing technologies, complete genetic variants in individuals' genomes are quickly generated, providing unprecedented opportunities for learning population evolution histories and identifying local genetic signatures at the SNP resolution. The successes of those studies critically rely on accurate and powerful computational tools that can fully utilize the sequencing information. Although many algorithms have been developed for population structure inference and admixture mapping, many of them only work for independent SNPs in genotype or haplotype format, and require a large panel of reference individuals. In this paper, we propose a novel probabilistic method for detecting population structure and local admixture. The method takes input of sequencing data, genotype data and haplotype data. The method characterizes the dependence of genetic variants via haplotype segmentation, such that all variants detected in a sequencing study can be fully utilized for inference. The method further utilizes a infinite-state Bayesian Markov model to perform de novo stratific...Continue Reading

  • References23
  • Citations1

References

  • References23
  • Citations1

Citations

Mentioned in this Paper

Biologic Segmentation
Genome
Genetics, Population
Computer Programs and Programming
Genomics
Sequencing
Massively-Parallel Sequencing
High-Throughput Nucleotide Sequencing
Bayesian Prediction
Markov Chains

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