Candidate Gene Selection via GWA NAM eQTL Analysis
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Solution Overview
Problem
Current methods for identifying genes associated with traits in humans and crops are limited by the broad regions identified through linkage analysis, requiring further refinement and are often based on known gene functions, which can be incomplete or misleading.
Innovation Solution
The use of a combination of genome-wide association (GWA) analysis, nested association mapping (NAM), and expression QTL (eQTL) analysis to select and validate candidate markers, along with novel regression models like single marker regression (SMR) and multiple marker regression (MMR), to prioritize markers associated with specific traits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If linkage analysis is used to identify genes associated with traits, then broad regions can be identified, but the regions are too broad to identify specific candidate genes
Solution Approach 1:
The patent segments the broad linkage regions into smaller, more manageable intervals by introducing multiple QTLs and analyzing their individual effects. This segmentation allows researchers to focus on specific genomic intervals rather than broad regions, thereby improving gene identification precision while maintaining comprehensive coverage through the combined analysis of multiple segmented regions.
Solution Approach 2:
The patent applies local quality by performing detailed analysis at specific genomic intervals where QTLs are detected. Rather than treating the entire genome uniformly, the method focuses computational and analytical resources on local regions with detected QTLs, using local regression models and cofactor markers to precisely identify candidate genes within these specific intervals.
2Measurement precision
If multiple QTL analysis is performed to improve mapping resolution, then gene identification precision improves, but the complexity of the analysis increases
Solution Approach 1:
The patent applies preliminary action by first detecting QTLs using initial analysis, then using these detected QTLs as cofactors in subsequent regression models. This stepwise approach simplifies the overall complexity by breaking down the multiple QTL analysis into manageable stages: initial QTL detection, followed by refined analysis using detected QTLs as known cofactors, thereby improving mapping resolution without overwhelming computational complexity.
Solution Approach 2:
The patent uses detected QTLs as intermediaries or cofactors that mediate between the initial linkage analysis and the final gene identification. These QTLs serve as intermediate markers that simplify the complex relationships between multiple genetic loci and the trait of interest, making the overall analysis more tractable while maintaining high mapping resolution.
3Ease of manufacture
If candidate genes are selected based on known gene functions, then the selection process is simplified, but the knowledge base is limited and may be incomplete or misleading
Solution Approach 1:
The patent replaces the traditional mechanical approach of selecting candidate genes based on known gene functions with a statistical and computational approach. Instead of relying on existing knowledge bases and manual selection based on gene annotations, the method uses regression models, LOD scores, and statistical associations between markers and traits to objectively identify candidate genes, thereby eliminating the limitations of incomplete or biased knowledge bases while maintaining selection simplicity through automated computational pipelines.
Data Source
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AI summary
Provided herein are methods for evaluating associations between candidate genes and a trait of interest in a population. The methods include a combination of genome-wide association analysis and one or both of nested association mapping (NAM), and expression QTL analysis (eQTL). Markers are selected or prioritized if they are shown to be positively-correlated with a trait of interest using GWA and a combination of one or both of NAM and eQTL. Also provided are models for evaluating the association between a candidate marker and a trait in a nested population of organisms. These methods include single marker regression and multiple marker regression models. Markers identified using the methods of the invention can be used in marker assisted breeding and selection, as genetic markers for constructing linkage maps, for gene discovery, for identifying genes contributing to a trait of interest, and for generating transgenic organisms having a desired trait.