Genetic Data Imputation via Parallel Subset Processing
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Solution Overview
Problem
Current genetic data imputation and enrichment techniques are computationally demanding and inefficient, particularly when dealing with reduced datasets from techniques like Low Coverage Whole Exome Sequencing, which often lack the 7 million genetic variants required for predictive analyses.
Innovation Solution
A method that partitions genetic data into parallel-processing subsets, utilizing a GPU for parallel processing with algorithms like the Barrows-Wheeler Transform, and a CPU for determining the enriched data set, optimizing the imputation process by comparing subsets with a reference genome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential imputation algorithms are used to process complete genetic data, then imputation accuracy is maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the complete genetic data into multiple partitions or chunks, allowing parallel processing of different segments simultaneously. This segmentation maintains imputation accuracy by ensuring each partition is processed with the same rigorous algorithms, while dramatically reducing overall processing time through parallel execution on multi-core processors or distributed computing systems.
Solution Approach 2:
The patent performs preliminary actions by pre-processing genetic data to identify and extract key variants before imputation. This includes filtering, quality control, and organizing data structures in advance, which reduces the computational burden during the actual imputation process while maintaining accuracy through systematic data preparation.
2Loss of information
If complete genome sequencing is performed to obtain all genetic variants, then comprehensive genetic information is achieved, but cost and complexity increase significantly
Solution Approach 1:
The patent extracts only the most relevant genetic variants and information from complete genome sequencing data, focusing on clinically significant markers rather than processing all possible genetic information. This extraction approach maintains the completeness of essential genetic information while reducing sequencing complexity and cost by targeting specific regions and variants of interest.
Solution Approach 2:
The patent applies different processing qualities and depths to different regions of the genome based on their clinical relevance. High-priority regions receive more rigorous analysis and imputation, while lower-priority regions receive streamlined processing. This local quality approach ensures comprehensive genetic information is obtained where needed while reducing overall sequencing complexity.
3Productivity
If reduced dataset techniques like Low Coverage WES are used, then cost is reduced, but the number of genetic variants available for predictive analyses decreases
Solution Approach 1:
The patent uses imputation algorithms as an intermediary process that takes the limited variants from low-coverage sequencing and infers additional genetic information by comparing with reference panels. This intermediary step recovers missing genetic variants through statistical modeling, maintaining cost efficiency while restoring the number of available variants for predictive analyses.
Solution Approach 2:
The patent changes key parameters of the sequencing and analysis process, including coverage depth, target region selection, and imputation confidence thresholds, to optimize the balance between cost and variant completeness. By adjusting these parameters, the system achieves adequate genetic information for clinical applications at reduced sequencing costs.
Data Source
AI summary
A method performs imputation and/or enrichment of genetic data by electronic computation. The method accesses partial information of the individual's genetic data set, available following detection by sequencing. The method partitions the genetic data set into a group of disjoint genetic data subsets so that the union of the subsets corresponds to the acquired genetic data set. The genetic data subsets have a same dimension based on quality criterion to be complied with by genetic data imputation/enrichment corresponding to genetic data contained. The subsets are processed in parallel, by applying to each genetic data subset, a genetic data imputation algorithm, to compare a partial genetic data set with the complete known genome of reference individuals. The method obtains enriched genetic data subsets, each being an enriched version of a respective genetic data subset. An enriched version of the individual's genetic data set is determined from the genetic data imputation/enrichment.