Ahmadi Nourollah
CIRAD, UMR AGAP Institut, Montpellier, France.
AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
Methods Mol Biol. 2022;2467:1-44. doi: 10.1007/978-1-0716-2205-6_1.
Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.
作为本书的总体介绍,本章回顾了基因定位和基于分子标记的预测的核心概念。它概述了复杂性状变异定位方法以及基于QTL的人类疾病风险和动植物育种值预测方法的原理和发展历程。基于连锁和连锁不平衡的QTL定位方法的原理在最简单的单标记方法背景下进行了描述。分析了方法的发展演变与其解释基因型-表型关系复杂性能力的相关性。介绍了利用大量无关个体群体进行QTL定位研究得出的复杂性状遗传结构的主要特征。综述了将标记-QTL关联数据整合到多基因风险评分中以捕捉个体对复杂疾病易感性的方法。总结了在动植物育种计划中使用表型和系谱数据基于最佳线性混合模型预测育种值的原理,并介绍了从BLUP过渡到标记-QTL BLUP的方法。讨论了影响使用分子数据实现额外遗传进展的因素及其优化规则。