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Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species, implies there is standing natural genetic variation for photosynthesis offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not been explored yet is that the variation probably involves thousands of genes, each contributing only little to photosynthesis, making them hard to identify without the proper phenotyping and genetic tools. This is changing though, and increasingly more studies report on quantitative trait loci (QTLs) for photosynthetic phenotypes. So far, hardly any of these QTLs are used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever are the underlying causal genes identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine their physiological function, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.

Drought sensitivity of potato leads to a reduction in total tuber yield and marketable yield. An investigation of drought effects on tuber yield attributes will facilitate our understanding of how to reduce such huge yield losses. We have evaluated tuber yield, tuber size distribution and marketable yield of a set of 103 European commercial potato cultivars under irrigated and non-irrigated conditions in the field. The multi-year results from two locations, Connantre, France (2013-2015) and Nieuw-Namen in Zeeland, The Netherlands (2013-2014), were analysed. We used Normal and Gamma Distribution models to describe the tuber size distribution of tuber fresh weight and tuber number, respectively. The interactions among parameters of tuber size distribution and total/marketable tuber yield traits were analysed using correlation matrices and biplots. Finally, we used a 14K Infinium SNP marker array to find associations between the parameters or traits and genetic loci on the potato genome. Late foliage maturity facilitated a wider spread of tuber size distribution in favour of larger-sized tubers. Drought effects on total yield were representative of their impact on marketable yield, however, absolute values of total tuber number may not be indicative of marketable number of tubers. We found significant marker-trait associations between a region on chromosome 3 and the spread of tuber number distribution, size class with maximum tuber number and marketable fractions of tuber number and tuber weight. These findings will contribute to improvement and selection for drought tolerance in potato. Keywords Drought Á Association panel Á Marketable tubers Á Size Á Modelling Á Yield Abbreviations TBW Tuber fresh weight TBN Tuber number TBW MX Maximum tuber fresh weight observed among size classes TBW mcs Average tuber size of the class at which TBW MX occurs TBW spread Wideness or spread of tuber size distribution of tuber fresh weight TBN ms Tuber number mean size Electronic supplementary material The online version of this article (

The Green Revolution has resulted in major improvements in crop productivity, but left photosynthesis largely unimproved. Despite ample variation of photosynthetic performance in crops and their wild relatives, the photosynthetic capacity of elite breeding lines remains well below its theoretical maximum. As yield is often the primary selective trait, current plant breeding approaches result in photosynthetic trade-offs that prevent positive selection for photosynthetic performance itself. Currently, genetic variation for photosynthetic performance is seldomly validated at the genetic level, and as a result these photosynthetic trade-offs remain poorly understood. Here we reveal the physiological nature of a photosynthetic trade-off caused by the NAD(P)H dehydrogenase (NDH) complex. The use of an Arabidopsis thaliana cybrid panel revealed how a natural allele of the chloroplastic gene NAD(P)H-QUINONE OXIDOREDUCTASE SUBUNIT 6 - a subunit of the NDH complex - results in a faster recovery of photosystem II efficiency after a transition from high to low irradiances. This improvement is due to a reduction in NDH activity. Under low-light conditions this reduction in NDH activity has a neutral effect on biomass, while under highly fluctuating light conditions, including high irradiances, more NDH activity is favoured. This shows that while allelic variation in one gene can have beneficial effects on one aspect of photosynthesis, it can, depending on environmental conditions, have negative effects on other aspects of photosynthesis. As environmental conditions are hardly ever stable in agricultural systems, understanding photosynthetic trade-offs allows us to explore shifting photosynthetic performance closer to the theoretical maximum.

27 28 29 30 2Introductory paragraph: 31 Assessing the impact of variation in chloroplast and mitochondrial DNA (collectively termed the 32 plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from 33 nuclear derived variation (the nucleotype). Haploid inducer lines can be used as efficient plasmotype 34 donors to generate new plasmotype-nucleotype combinations (cybrids) (Ravi et al., 2014). We generated 35 a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively 36 phenotyped these lines for 1859 phenotypes under stable and fluctuating conditions. We show that 37 natural variation in the plasmotype results in additive as well as epistatic effects across all phenotypic 38 categories. Plasmotypes which induce more additive phenotypic changes also cause more significant 39 epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average 40 epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype 41 effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful 42 environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of 43 multilevel Nucleotype X Plasmotype X Environment interactions and, as such, the plasmotype is likely 44 to serve as a reservoir of variation which is only exposed under certain conditions. The production of 45 cybrids using haploid inducers is a quick and precise method for assessing the phenotypic effects of 46 natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype-47 plasmotype combinations to both improve our understanding of natural variation in nucleotype 48 plasmotype interactions and identify favourable combinations to improve plant performance. 49Chloroplasts and mitochondria play essential roles in metabolism, cellular homeostasis and 50 environmental sensing (Petrillo et al., 2014; Chan et al., 2016). Their genomes contain only a limited set 51 of genes whose functioning requires tight coordination with the nucleus through signaling pathways that 52 modulate nuclear and organellar gene expression (Petrillo et al., 2014; Kleine and Leister, 2016). 53 Plasmotype variation can be strongly additive, such as in the case of chloroplast encoded herbicide 54 tolerance (Flood et al., 2016), or can manifest in complex cytonuclear interactions as non-additive, non-55 linear effects (epistasis), such as found for secondary metabolites (Joseph et al., 2013). The phenotypic 56 consequences of epistasis can be detected when a plasmotype causes phenotypic effects in 57 combination with some, but not all nuclear backgrounds. Recent studies suggest that cytonuclear 58 epistasis is the main route through which variation in the plasmotype is expressed (Zeyl et al., 2005; 59 3 Montooth et al. , 2010; Joseph et al., 2013; Joseph et al., 2013; Tang et al., 2014; Roux et al., 2016; 60 Mossman et al., 2019) an...

In the past decades, genomic prediction has had a large impact on plant breeding. Given the current advances of high-throughput phenotyping and sequencing technologies, it is increasingly common to observe a large number of traits, in addition to the target trait of interest. This raises the important question whether these additional or “secondary” traits can be used to improve genomic prediction for the target trait. With only a small number of secondary traits, this is known to be the case, given sufficiently high heritabilities and genetic correlations. Here we focus on the more challenging situation with a large number of secondary traits, which is increasingly common since the arrival of high-throughput phenotyping. In this case, secondary traits are usually incorporated through additional relatedness matrices. This approach is however infeasible when secondary traits are not measured on the test set, and cannot distinguish between genetic and non-genetic correlations. An alternative direction is to extend the classical selection indices using penalized regression. So far, penalized selection indices have not been applied in a genomic prediction setting, and require plot-level data in order to reliably estimate genetic correlations. Here we aim to overcome these limitations, using two novel approaches. Our first approach relies on a dimension reduction of the secondary traits, using either penalized regression or random forests (LS-BLUP/RF-BLUP). We then compute the bivariate GBLUP with the dimension reduction as secondary trait. For simulated data (with available plot-level data), we also use bivariate GBLUP with the penalized selection index as secondary trait (SI-BLUP). In our second approach (GM-BLUP), we follow existing multi-kernel methods but replace secondary traits by their genomic predictions, with the advantage that genomic prediction is also possible when secondary traits are only measured on the training set. For most of our simulated data, SI-BLUP was most accurate, often closely followed by RF-BLUP or LS-BLUP. In real datasets, involving metabolites in Arabidopsis and transcriptomics in maize, no method could substantially improve over univariate prediction when secondary traits were only available on the training set. LS-BLUP and RF-BLUP were most accurate when secondary traits were available also for the test set.