Surrogate Marker Identification Using Secondary Tissue Expression
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Solution Overview
Problem
Current methods for identifying genes and biological pathways associated with traits, such as complex human diseases, are inefficient due to the complexity of diseases, heterogeneity in populations, and the difficulty in obtaining in vivo gene activity data from tissues like the brain, leading to high costs and limited success in drug discovery and diagnostics.
Innovation Solution
A computer system and method that uses secondary tissues to identify surrogate markers for gene activity in primary tissues, allowing for the monitoring of gene expression patterns to facilitate drug discovery and diagnostic purposes without directly measuring gene activity in inaccessible primary tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of gene activity in primary tissues (e.g., brain) is performed, then measurement precision is improved, but ease of operation deteriorates and cost increases
Solution Approach 1:
The patent uses secondary tissues as intermediary surrogates to measure gene activity that would otherwise require direct measurement in inaccessible primary tissues. The expression patterns in secondary tissues mediate the measurement process, allowing indirect but accurate assessment of target gene activity without the operational difficulties of accessing primary tissues directly
Solution Approach 2:
The patent creates a copy of the gene expression pattern information by measuring expression in secondary tissues that replicate or mirror the expression patterns found in primary tissues. This copying approach allows researchers to obtain measurement data from accessible tissues that reflect the state of inaccessible target tissues
2Reliability
If QTL mapping methodologies are used to identify genes associated with traits, then reliability is improved, but productivity deteriorates due to large genomic regions requiring extensive follow-up
Solution Approach 1:
The patent segments the large genomic regions identified by QTL mapping by using gene expression patterns as additional discriminatory criteria. This segmentation approach divides the search space from thousands of genes in a QTL region to a smaller subset of genes that exhibit characteristic expression patterns, thereby maintaining statistical reliability while improving identification speed
Solution Approach 2:
The patent adds another dimension to the gene identification process by incorporating gene expression pattern analysis alongside traditional QTL mapping. This dimensional addition allows researchers to filter and prioritize candidate genes based on expression characteristics, accelerating the identification process without sacrificing the statistical rigor of QTL associations
3Manufacturing precision
If high density marker maps and physical resequencing are performed on large genomic regions, then manufacturing precision is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent performs preliminary action by measuring gene expression patterns in secondary tissues before conducting detailed analysis of large genomic regions. This preliminary expression profiling allows researchers to prioritize and focus subsequent high-precision efforts on a smaller subset of candidate genes, reducing the overall time and resources required while maintaining identification precision
4Ease of operation
If gene expression patterns in secondary tissues are used as surrogate markers, then ease of operation is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent implements feedback by using gene expression patterns from secondary tissues to identify and validate surrogate markers, then using these validated surrogates to measure target gene activity. The feedback loop ensures that the surrogate markers are continuously refined and validated against the actual target gene expressions, maintaining measurement precision while preserving operational ease
Data Source
AI summary
Methods, computer program products and systems for identifying cellular constituents in a secondary tissue that serve as surrogate markers for a target gene expressed in a primary tissue of a species are provided. A classifier is constructed using cellular constituent abundances of cellular constituents in a first plurality of cellular constituents measured in the secondary tissue in a population. This population comprises a first and second subgroup. The classifier is based on a second plurality of cellular constituents that comprises all or a portion of the first plurality of cellular constituents. Abundance levels of each cellular constituent in the second plurality of cellular constituents varies between the first and second subgroup. All or portion of the population is classified into a plurality of subtypes using the classifier. Then, one or more cellular constituents that can discriminate members of the population between a first subtype and a second subtype in the plurality of subtypes are identified.