Research And Application Of Genetic Variation Detection Method Based On BioNano Map

Structural variants(SVs) are collectively account for a significant fraction of genetic polymorphism and diseases. Whole genome analysis of variation is becoming possible with improved biotechnology, and this is anticipated to have profound implications for biology and medicine. Ideally, one would like to record a sampled genome at the nucleotide level, but this goal remains beyond our reach in spite of the fact that we now have a finished reference copy for several species. To address these challenges, we applied a high-throughput,cost-effective genome BioNano optical mapping technology to comprehensively discover genome-wide SVs using long single molecules(>150 kb) in a global fashion.Optical mapping is well developed for small(e.g. microbial) genomes, and recent advances have enabled optical mapping of mammalian-sized genomes as well. This development, however, raises important new computational and statistical questions. The availability of reference genomes has been instrumental in the development of methods based on optical mapping to detect within species variation, by serving as the basis for comparison with a sampled genome. Reference copies also open up other, less obvious, possibilities that impact the understanding and statistical analysis of optical mapping data. In this thesis we explore some such possibilities, particularly in the context of large genomes. In particular, we address parameter estimation in optical map models,and the use of optical map data to detect copy number alterations with the Hidden Markov model. In all, the copy number analysis described here is a fast,simple tool that effectively complements assembly-based analysis of optical map data.High-molecular weight DNA was extracted from the YH cell line, Structural variation was classified as a significant discrepancy between the consensus maps and the hg19 insilico map. Utilizing nanochannel-based genome mappingtechnology, we obtained 698 insertions/deletions and 19 inversions larger than 1kb. Excluding the 62 SVs that overlap with N-base gaps in the reference assembly hg19, 636 non-gap SVs remained. Overall, 604 out of 636(95%)variants were supported by experimental orthogonal methods or historical evidence in public databases. At the same time, we detected the copy number polymorphism with the use of Hidden Markov model, fitting the state to the genomic copy number changes. we can say the effect is very significant. Our study highlights genome mapping technology as a comprehensive and cost-effective method for detecting structural variation. This research will provide a new research direction for the future.