QTLNetwork-MP: integrative mapping of additive, epistatic, and G × E effects for complex traits in multiparent advanced generation intercross populations
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
Abstract Quantitative trait locus (QTL) mapping is a powerful approach to uncover the genetic architecture of complex traits. Traditional biparental populations, though widely used, offer limited genetic diversity and resolution, making them less effective for robust genetic architecture dissection. In contrast, multiparent advanced generation intercross (MAGIC) populations possess more allelic diversity, enabling the detection of intricate gene interactions including dominance and epistasis in QTL mapping. However, QTL mapping in MAGIC populations encounters some great challenges, particularly in inferring parental origin of QTL and estimating their effects unbiasedly. We tackled these challenges by developing a novel QTL mapping method for pure-line MAGIC populations, accompanied by the software QTLNetwork-MP. This method extends the mixed linear model framework by integrating a Markov chain-based algorithm and orthogonal QTL effect decomposition. This allows for the estimation of QTL effects, including additive and additive–additive epistasis as well as environmental interactions for MAGIC populations derived from four-way or eight-way crosses. Simulation studies show that QTLNetwork-MP achieves high statistical power and well-controlled false discovery rates across varying heritability, population size, and parent number. We applied QTLNetwork-MP to map QTLs for seed size in an eight-parent recombinant inbred line MAGIC cowpea population, identifying three significant add
Abstract
Abstract Quantitative trait locus (QTL) mapping is a powerful approach to uncover the genetic architecture of complex traits. Traditional biparental populations, though widely used, offer limited genetic diversity and resolution, making them less effective for robust genetic architecture dissection. In contrast, multiparent advanced generation intercross (MAGIC) populations possess more allelic diversity, enabling the detection of intricate gene interactions including dominance and epistasis in QTL mapping. However, QTL mapping in MAGIC populations encounters some great challenges, particularly in inferring parental origin of QTL and estimating their effects unbiasedly. We tackled these challenges by developing a novel QTL mapping method for pure-line MAGIC populations, accompanied by the software QTLNetwork-MP. This method extends the mixed linear model framework by integrating a Markov chain-based algorithm and orthogonal QTL effect decomposition. This allows for the estimation of QTL effects, including additive and additive–additive epistasis as well as environmental interactions for MAGIC populations derived from four-way or eight-way crosses. Simulation studies show that QTLNetwork-MP achieves high statistical power and well-controlled false discovery rates across varying heritability, population size, and parent number. We applied QTLNetwork-MP to map QTLs for seed size in an eight-parent recombinant inbred line MAGIC cowpea population, identifying three significant additive QTLs and accurately estimating their genetic effects, validating the value of the method in practical application. QTLNetwork-MP is a novel statistical framework for mapping quantitative trait locus (QTL), specifically designed for the pure-line multiparent advanced generation intercross (MAGIC) populations. The method utilizes a mixed linear model, consisting of orthogonal parameters of QTL effects with coefficients inferred from Markov chain algorithm, and enables the simultaneous estimation of effects in additive, additive-by-additive epistasis, as well as their interaction effects with environment. Intensive simulations and real data analysis on an eight-parent cowpea MAGIC population demonstrated the effectiveness of the new method in terms of statistical power and accuracy in estimation of QTL parameters. QTLNetwork-MP is a robust and powerful tool for uncovering complex genetic architecture of complex traits in MAGIC populations. teaser