Genotyping with Graph Reference Structures
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Solution Overview
Problem
Traditional genotyping methods using next-generation sequencing (NGS) face limitations such as incorrect mapping of sequences absent or divergent from the reference genome, incomplete reference sequences, false variant calls due to focusing on single variant types, and inability to determine exact location or size of structural variants, as well as ignoring prior genetic variation information.
Innovation Solution
The use of genomic reference graphs structured as traversal graphs that represent diploid genotypes by connecting nodes with edges, allowing for the analysis of genetic variation across populations and identifying haploid genotypes by mapping NGS sequence reads, assigning scores, and selecting the highest-weighted path through the traversal graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If NGS sequence reads are mapped to a linear reference genome, then genotyping can be performed using standard approaches, but sequences absent or divergent from the reference genome cannot be correctly identified
Solution Approach 1:
The linear reference genome is segmented into a graph structure where multiple paths represent different haplotypes and variants. This allows the reference to accommodate sequences absent or divergent from the canonical linear reference by providing alternative paths through the graph, resolving the contradiction between standard genotyping accuracy and ability to handle divergent sequences.
Solution Approach 2:
The reference genome is transformed from a one-dimensional linear sequence into a multi-dimensional graph structure with nodes and edges representing sequences and variations. This dimensional change enables the reference to represent multiple haplotypes and divergent sequences simultaneously, improving both genotyping accuracy and adaptability to divergent sequences.
2Productivity
If reference sequences are used for mapping, then genotyping can be performed, but reference sequences are often incomplete in telomeric and pericentromeric regions
Solution Approach 1:
The graph reference structure serves multiple functions: it represents complete reference sequences, incorporates alternative haplotypes, and fills gaps in incomplete regions through multiple paths. This multi-functionality allows the reference to maintain high productivity for genotyping while compensating for incomplete reference sequences in telomeric and pericentromeric regions through alternative pathways in the graph.
3Ease of operation
If mapping methods focus on a single variant type, then analysis is simplified, but incorrect alignment occurs around indel polymorphisms
Solution Approach 1:
The graph reference structure dynamically adapts to different variant types through its multi-path architecture. When sequence reads are mapped, the graph structure allows flexible alignment across multiple paths representing different haplotypes and variants. This dynamic capability maintains analysis simplicity while improving alignment accuracy around indel polymorphisms by automatically selecting appropriate paths based on the read data.
4Productivity
If NGS mapping approaches are used, then genotyping can be performed at scale, but prior information about genetic variation within the species is ignored
Solution Approach 1:
The graph reference structure is pre-constructed by incorporating prior information about genetic variation within the species before actual genotyping analysis. Multiple paths representing known haplotypes and variants are established in advance, allowing the system to leverage prior genetic variation information during mapping while maintaining high productivity through efficient graph traversal algorithms.
Data Source
AI summary
Genomic references are structured as a reference graph that represents diploid genotypes in organisms. A path through a series of connected nodes and edges represents a genetic sequence. Genetic variation within a diploid organism is represented by multiple paths through the reference graph. The graph may be transformed into a traversal graph in which a path represents a diploid genotype. Genetic analysis using the traversal graph allows an organism's diploid genotype to be elucidated, e.g., by mapping sequence reads to the reference graph and scoring paths in the traversal graph based on the mapping to determine the path through the traversal graph that best fits the sequence reads.