EPD Genomic Model for Bovine Congestive Heart Failure Risk Prediction
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Solution Overview
Problem
Bovine Congestive Heart Failure (BCHF) is a significant cause of death in cattle, leading to substantial economic losses, and existing genetic screening measures are inadequate to effectively reduce the incidence of this heritable condition in future generations.
Innovation Solution
A method using genomic sequencing and a genotype-phenotype library, combined with a genomic best linear unbiased prediction (BLUP) model, to generate expected progeny differences (EPDs) that predict the risk of BCHF, allowing for selective breeding to reduce the inherited risk factors in bovine populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional genetic screening measures are used, then breeding decisions can be made, but the accuracy of predicting BCHF risk is insufficient
Solution Approach 1:
The patent introduces Expected Progeny Differences (EPDs) as an intermediary metric that translates complex genomic data into a single predictive value for BCHF risk. The EPD system acts as a mediator between the complex genotype-phenotype relationships and practical breeding decisions, providing an accurate yet simplified output that resolves the contradiction between measurement precision and operational simplicity
Solution Approach 2:
The patent transforms multiple genetic parameters (genomic sequences, SNP data, phenotypic observations) into a single standardized parameter (EPD value) that quantifies BCHF risk. This parameter transformation enables accurate risk prediction while simplifying the complexity of underlying genetic mechanisms into an actionable metric for breeders
2Measurement precision
If genomic sequencing and BLUP models are implemented, then prediction accuracy improves, but the complexity of the screening system increases
Solution Approach 1:
The patent segments the complex genomic analysis process into distinct functional modules: genomic data collection, phenotype recording, BLUP model computation, and EPD calculation. This segmentation allows the complex system to be managed through standardized interfaces and protocols, reducing operational complexity while maintaining high prediction accuracy through systematic data processing
Solution Approach 2:
The patent creates a universal EPD calculation framework that can process multiple types of genomic data (SNP arrays, whole genome sequencing) and phenotypic data through a single standardized model. This multi-functional system resolves the contradiction by providing accurate predictions across different data types without requiring separate complex systems for each data source
Data Source
AI summary
The disclosure relates to quantitative trait loci associated with BCHF. The disclosure further teaches methods of generating EPDs using a genotype-phenotype library and associated algorithms with individual genomic information. Methods of using the EPDs for breeding selection to reduce inherited risk factors for BCHF in a bovine population as well management of individuals who are at risk for BCHF are also included.