Multi-parent multi-environment QTL analysis: an illustration with the EU-NAM Flint population

Vincent Garin,Fred Van Eeuwijk,Marcos Malosetti

doi:10.1007/s00122-020-03621-0

Vincent Garin, Fred Van Eeuwijk + Show 1 more

Open Access

https://doi.org/10.1007/s00122-020-03621-0

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Abstract

Key messageMulti-parent populations multi-environment QTL experiments data should be analysed jointly to estimate the QTL effect variation within the population and between environments.Commonly, QTL detection in multi-parent populations (MPPs) data measured in multiple environments (ME) is done by analyzing genotypic values ‘averaged’ across environments. This method ignores the environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects, but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyse MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariance. Using data from the EU-NAM Flint population, we show that these methods estimate the QTLxE effects and that they can improve the quality of the QTL detection. Those methods also have a larger inference power. For example, they can be extended to integrate environmental indices like temperature or precipitation to better understand the mechanisms behind the QTLxE effects. Therefore, our methodology allows the exploitation of the full MPP-ME data potential: to estimate QTL effect variation (a) within the MPP between sub-populations due to different genetic backgrounds and (b) between environments.

Highlights

The use of multi-parent populations (MPPs) becomes progressively a regular practice in plant genetics and plant breeding
We focused on examples showing significant and observable QTLxE interactions coming from the EU-nested association mapping (NAM) Flint population tested in two environments
The significance of the QTL genetic effect along the genome should, be taken with caution because it is based on a conditional Wald test that can change given the order of the tested parameters (Butler et al 2009)

Summary

Introduction

The use of multi-parent populations (MPPs) becomes progressively a regular practice in plant genetics and plant breeding. Different statistical approaches have been proposed to detect QTLs in MPPs composed of biparental crosses (Jourjon et al 2005), Communicated by Laurence Moreau. Researchers have developed statistical procedures to detect QTLs taking the genotype by environment (GxE) interactions into consideration (Boer et al 2007; Korte et al 2012). Several MPPs have been tested in multiple environments (MPP-ME) (Buckler et al 2009; Giraud et al 2014; Saade et al 2016), but only few studies have proposed a proper MPP GxE QTL detection methodology (Piepho and Pillen 2004; Verbyla et al 2014). The authors performed separate analyses in each environment (e.g. Saade et al 2016)

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