Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine

Charlotte Brault,Thierry Simonneau,Aude Coupel-Ledru,Timothée Flutre,Agnès Doligez,Julien Chiquet,Le Cunff,Patrice This,J-L Jannink

doi:10.1093/g3journal/jkab248

Abstract

Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

Highlights

Viticulture is facing two major challenges, i.e., coping with climate change and decreasing inputs such as pesticides, while maintaining high yield and quality
We considered markers selected by both MIM and Elastic Net (EN).Marginal false discovery rate (mFDR) as highly reliable ones for three reasons: (1) markers selected by both MIM and EN were considered as reliable ones, because most markers selected by LASSO were selected by EN, whereas MIM marker selection was quite different; (2) simulations showed that MIM and mFDR methods led to a very low FPR; (3) these methods belong to different method classes (IM vs penalized regression)
Rather than decoupling genomic prediction from the identification of major quantitative trait loci (QTL), we argue for the need to pursue both goals jointly

Summary

Introduction

Viticulture is facing two major challenges, i.e., coping with climate change and decreasing inputs such as pesticides, while maintaining high yield and quality. Quantitative trait loci (QTL) detection in bi-parental populations led to the identification of major genes for traits with a simple genetic architecture such as resistance to downy and powdery mildews, berry color, seedlessness, and Muscat flavor (Fischer et al 2004; Welter et al 2007; FournierLevel et al 2009; Emanuelli et al 2010; Mejıa et al 2011; Schwander et al 2012). Based on these results, most breeding efforts in grapevine use MAS to improve disease resistance.

Objectives

Methods

Results

Discussion

Conclusion