[Maximum likelihood analysis for mapping dynamic trait QTL in outbred population. I . Methodology].

Area of Science:

• Quantitative genetics
• Animal breeding
• Statistical genomics

Context:

• Dynamic traits, characterized by phenotypic values changing over time or with other quantitative factors, pose unique challenges for genetic analysis.
• Traditional quantitative trait loci (QTL) mapping methods often struggle to accurately model the complex genetic architecture of these time-dependent traits.
• Estimating breeding values for dynamic traits requires sophisticated statistical approaches, such as random regression test-day models.

Purpose:

• To develop a novel mathematical model for mapping dynamic trait QTL using Legendre polynomials to capture genetic effects over time.
• To implement Maximum Likelihood (ML) analysis via the Expectation-Maximization (EM) algorithm for parameter estimation, including QTL position and fixed genetic regression effects.
• To provide a more efficient and versatile method for dynamic trait QTL mapping in outbred populations compared to existing approaches.

Summary:

• A new mathematical model is presented for dynamic trait QTL mapping, utilizing Legendre polynomials to represent the genetic basis of traits that change over time.
• The model employs Maximum Likelihood analysis with the EM algorithm to estimate QTL positions and genetic regression effects, facilitating accurate genetic evaluation.
• This approach allows for dynamic trait sampling in disequilibrium and enables one-step QTL mapping in any resource population, offering significant advantages over previous methods.
• Theoretical considerations for incorporating dynamic trait genetic analysis into general QTL mapping frameworks are also discussed.

Impact:

• The developed method enhances the ability to map QTL for dynamic traits, providing a more accurate understanding of their genetic underpinnings.
• It offers a flexible and efficient one-step approach applicable to diverse outbred populations, streamlining genetic research.
• This advancement contributes to improved animal breeding strategies by enabling more precise selection for traits influenced by time or environmental factors.
• The study lays the groundwork for further theoretical developments in the genetic analysis of dynamic traits.