HaploSeq HMM Deconvolution for Genetic Mixture Analysis
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Solution Overview
Problem
Current methods face challenges in accurately and cost-effectively deconvoluting mixture samples containing genetic material from different sources, such as maternal plasma, cancer patients, and transplantation recipients, due to the complexity of distinguishing between fetal, tumor, or donor DNA from maternal or normal DNA, which is essential for non-invasive prenatal testing, cancer detection, and transplantation monitoring.
Innovation Solution
The use of HaploSeq for determining parental or germline haplotypes, combined with Hidden Markov Model (HMM)-based analysis, allows for minimal sequencing depth by enumerating allele fractions from nearby bases on the same haplotype, reducing variance and enabling accurate deconvolution of mixture samples, including fetal genome determination, tumor-associated mutations, and donor contamination quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sequencing methods are used to deconvolute mixture samples, then sufficient sequencing depth is required to achieve accurate genetic content determination, but this increases cost and reduces efficiency
Solution Approach 1:
The patent performs preliminary haplotype determination from parental samples before analyzing the mixture sample. By pre-establishing parental haplotype information, the system reduces the sequencing depth needed for the actual mixture deconvolution, as the preliminary haplotype data serves as a reference that guides the analysis of the complex mixture sample.
Solution Approach 2:
The patent introduces haplotype information as an intermediary element that mediates between the raw mixture sample data and the final genetic content determination. The haplotype data acts as a bridge that simplifies the deconvolution process by providing structured reference information that reduces the computational and sequencing complexity of directly analyzing the mixture sample.
2Measurement precision
If high sequencing depth is used to achieve accurate deconvolution of mixture samples, then measurement accuracy improves, but cost increases significantly
Solution Approach 1:
The system performs preliminary haplotype sequencing and analysis on parental samples before the actual fetal genome determination. This preliminary action creates a reference framework that reduces the sequencing depth and cost required for the definitive fetal genome analysis, as the pre-established haplotype information guides the interpretation of the mixture sample data.
Solution Approach 2:
The patent segments the deconvolution process into distinct phases: preliminary haplotype determination from parental samples, followed by application of this haplotype information to the mixture sample analysis. This segmentation allows the more expensive high-accuracy sequencing to be focused only where absolutely necessary, while leveraging lower-cost preliminary analysis for the reference framework.
3Ease of manufacture
If minimal sequencing depth is used to reduce cost, then cost-effectiveness improves, but the ability to accurately distinguish fetal DNA from maternal DNA deteriorates
Solution Approach 1:
The patent performs preliminary haplotype characterization of parental DNA before analyzing the fetal-maternal mixture. This preliminary action creates a reference map of parental haplotypes that enables accurate fetal-maternal distinction even with minimal sequencing depth in the actual test, because the pre-established haplotype information serves as a template for interpreting the mixture data.
Solution Approach 2:
The patent uses pre-determined parental haplotype information as an intermediary that enables accurate fetal-maternal DNA distinction without requiring high sequencing depth. The haplotype reference acts as a mediator that translates minimal sequencing data into accurate genetic content determination by providing the structural framework for interpretation.
Data Source
AI summary
The present disclosure relates to methods to deconvolute a mixture sample of genetic material from different origins or sources. The disclosed methods can be used in various applications, including, the non-invasive determination of a fetal genome, a fetal -ome (e.g. exome). or other targeted fetal locus from cell-free nucleic acids in maternal plasma or other body fluids; the determination of cancer-associated mutations from cell-free nucleic acids in a body fluid sample that contains a mixture of nucleic acids from normal cells and tumor cells; and quantification of donor cell contamination using a body fluid from a transplantation recipient to monitor and/or predict the outcome of a transplantation procedure.