Exon Expression Analysis for Neuropsychiatric Disease Causality
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Solution Overview
Problem
Current methods are inadequate for identifying the causality of rare genomic variants in complex neuropsychiatric disorders like Autism Spectrum Disorder (ASD) due to the rarity and uniqueness of mutations, making it challenging to correlate genetic alterations with disease phenotypes.
Innovation Solution
A method involving the identification of exons with high expression levels greater than the 75th percentile, comparison to healthy controls to detect rare or de novo mutations, calculation of mutation burden, and correlation analysis to determine if an inverse relationship exists between exon expression and mutation burden, indicating disease causality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If genome scanning experiments are conducted to identify genetic variants, then the number of detected variants increases, but the ability to determine disease causality decreases due to the rarity and uniqueness of mutations
Solution Approach 1:
The patent applies local quality by focusing analysis on specific exons with high expression levels in relevant tissues rather than treating all genomic variants equally. By identifying exons that are highly expressed in disease-relevant tissues and prioritizing mutations in these regions, the method enhances causality determination for rare variants while maintaining comprehensive screening capability.
Solution Approach 2:
Instead of assessing all detected variants for causality regardless of expression level, the patent inverts the approach by first identifying high-expression exons and then evaluating mutations within these specific regions. This inversion allows rare variants in functionally important regions to be prioritized for causality assessment, improving measurement precision without reducing the quantity of variants detected.
2Measurement precision
If rare or de novo mutations are identified in exons, then disease causality can be determined, but the complexity of analysis increases due to the need for expression level comparison and correlation analysis
Solution Approach 1:
The patent segments the complex analysis into distinct manageable steps: (1) identifying high-expression exons in disease-relevant tissues, (2) detecting rare or de novo mutations in these exons, (3) calculating mutation burden, and (4) determining correlation between expression and mutation burden. This segmentation reduces overall analysis complexity by breaking down the complex causality determination into sequential, manageable operations.
Solution Approach 2:
The patent introduces expression level data as an intermediary factor that mediates between mutation detection and causality determination. By comparing mutation burden against expression levels, the method creates a standardized framework that simplifies the complex task of determining causality for rare variants, reducing analytical complexity through the use of a measurable intermediate parameter.
3Device complexity
If expression level threshold of 75th percentile is applied to identify critical exons, then the number of candidate genes decreases, but the sensitivity for detecting disease-related mutations may be reduced
Solution Approach 1:
The patent applies parameter changes by using the 75th percentile expression level threshold as a dynamic cutoff that adapts to the specific tissue and disease context. This parameter-based approach allows the method to maintain an optimal balance between reducing candidate gene complexity and preserving detection sensitivity by adjusting the threshold based on biological relevance rather than using a fixed arbitrary value.
Data Source
AI summary
A method of identifying a gene or genomic mutation that is linked to causality of a neuropsychiatric disorder is provided. The method comprises identifying exons which exhibit an expression level that is at least within the 75th percentile of exon expression levels within a nucleic acid-containing sample from a mammal having a neuropsychiatric disorder; comparing the sequence of each identified exon to the sequence of a corresponding exon from a healthy control to identify rare or de novo sequence mutations within the identified exon; calculating the burden of rare or de novo mutations within the exon; and determining the correlation between expression level of the identified exon and burden of de novo or rare mutations in the exon, wherein an inverse correlation indicates that the exon gene is linked to causality of the neuropsychiatric disorder.