Integrated QTL mapping, gene expression and nucleotide variation analyses to investigate complex quantitative traits: a case study with the soybean–<i>Phytophthora sojae</i> interaction

Ronne, Maxime de; Labbé, Caroline; Lebreton, Amandine; Sonah, Humira; Deshmukh, Rupesh; Jean, Martine; Belzile, François; O’Donoughue, Louise S.; Bélanger, Richard R.

doi:10.1111/pbi.13301

Cited by 19 publications

(14 citation statements)

References 12 publications

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“…Although QTL mapping and genomic prediction studies for quantitative traits are considered valuable (de Ronne et al 2020;Haile et al 2020;Lyra et al 2020) and are being applied for key traits in white lupin (Annicchiarico et al 2020;Annicchiarico et al 2019), the method encounters various limitations (Wang et al 2018;Xu et al 2017). Genomic prediction models could effectively be applied to less related populations (Hao et al 2019), but optimal predictions are achieved with related training populations (Akdemir and Isidro-Sánchez 2019; Albrecht et al 2011;Berro et al 2019).…”

Section: Discussionmentioning

confidence: 99%

A High-Throughput Phenotyping Tool to Identify Field-Relevant Anthracnose Resistance in White Lupin

et al. 2021

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The seed- and air-borne pathogen Colletotrichum lupini, the causal agent of lupin anthracnose, is the most important disease in white lupin (Lupinus albus) worldwide and can cause total yield loss. The aims of this study were to establish a reliable high-throughput phenotyping tool to identify anthracnose resistance in white lupin germplasm and to evaluate a genomic prediction model, accounting previously reported resistance QTLs, on a set of independent lupin genotypes. Phenotyping under controlled conditions, performing stem inoculation on seedlings, showed to be applicable for high-throughput and its disease score strongly correlated with field plot disease assessments (r = 0.95, p<0.0001) and yield (r = -0.64, p=0.035). Traditional 1-row field disease phenotyping showed no significant correlation with field plot disease assessments (r = 0.31, p=0.34) and yield (r = -0.45, p=0.17). Genomically-predicted resistance values showed no correlation with values observed under controlled or field conditions, and the parental lines of the RIL population used for constructing the prediction model exhibited a resistance pattern opposite to that displayed in the original (Australian) environment used for model construction. Differing environmental conditions, inoculation procedures or population structure may account for this result. Phenotyping a diverse set of 40 white lupin accessions under controlled conditions revealed eight accessions with improved resistance to anthracnose. The standardized area under the disease progress curves (sAUDPC) ranged from 2.1 to 2.8 compared to the susceptible reference accession with a sAUDPC of 3.85. These accessions can be incorporated into white lupin breeding programs. In conclusion, our data supports stem inoculation-based disease phenotyping under controlled conditions as a time-effective approach to identify field-relevant resistance which can now be applied to further identify sources of resistance and their underlying genetics.

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Section: Discussionmentioning

confidence: 99%

A High-Throughput Phenotyping Tool to Identify Field-Relevant Anthracnose Resistance in White Lupin

et al. 2021

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“…In this study, we have used an analytical approach combining phenotyping of the resistance through a single corrected variable, the CDW value, an inoculation procedure with the simultaneous presence of different P. sojae isolates covering the spectrum of commercially deployed Rps genes (Lebreton et al., 2018; de Ronne et al., 2020) and genotyping of the soybean population based on WGS data. This has led to the identification of a single major QTL confirmed by several powerful GWAS statistical models.…”

Section: Discussionmentioning

confidence: 99%

“…Phenotypes were measured at 14 d postinoculation (dpi) for both control and infected plants as per the methods of de Ronne et al. (2020). Briefly, each harvested plant was placed in a paper envelope and dried in a freeze dryer (Labconco), and corrected dry weight (CDW) was calculated as previously described (Stewart & Robertson, 2012) where:

\begin{equation*} \mathrm{CDW}=\frac{\text{Mean}\hspace*{0.2424242424242424em}\text{dry}\hspace*{0.2424242424242424em}\text{wt.}\mathrm{of}P.\textit{sojae}-\text{inoculated}\hspace*{0.2424242424242424em}\text{plants}}{\text{Mean}\hspace*{0.2424242424242424em}\text{dry}\hspace*{0.2424242424242424em}\text{wt.

…”

Section: Methodsmentioning

confidence: 99%

“…Over the years, the main approach to identify quantitative trait loci (QTL) contributing to PR has been the use of biparental mapping populations derived from crosses between parental lines contrasted in their resistance to P. sojae . This has led to the report of over 70 QTL for PR (Burnham et al., 2003; Han et al., 2008; Lee et al., 2013; Li et al., 2010; Mideros et al., 2007; Nguyen et al., 2012; de Ronne et al., 2020; Scott et al., 2019; Wang et al., 2010; Weng et al., 2007; Wu et al., 2011), and these are listed on SoyBase (https://www.soybase.org/search/qtllist_by_symbol.php; Grant et al., 2009). However, this strategy has limitations such as low resolution and the limited allelic diversity between the parents of the mapping population.…”

Section: Introductionmentioning

confidence: 99%

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Mapping of partial resistance to Phytophthora sojae in soybean PIs using whole‐genome sequencing reveals a major QTL

et al. 2021

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In the last decade, more than 70 quantitative trait loci (QTL) related to soybean [Glycine max (L.) Merr.] partial resistance (PR) against Phytophthora sojae have been identified by genome‐wide association studies (GWAS). However, most of them have either a minor effect on the resistance level or are specific to a single phenotypic variable or one isolate, thereby limiting their use in breeding programs. In this study, we have used an analytical approach combining (a) the phenotypic characterization of a diverse panel of 357 soybean accessions for resistance to P. sojae captured through a single variable, corrected dry weight; (b) a new hydroponic assay allowing the inoculation of a combination of P. sojae isolates covering the spectrum of commercially relevant Rps genes; and (c) exhaustive genotyping through whole‐genome resequencing (WGS). This led to the identification of a novel P. sojae resistance QTL with a relatively major effect compared with the previously reported QTL. The QTL interval, spanning ∼500 kb on chromosome (Chr) 15, does not colocalize with previously reported QTL for P. sojae resistance. Plants carrying the favorable allele at this QTL were 60% more resistant. Eight genes were found to reside in the linkage disequilibrium (LD) block containing the peak single‐nucleotide polymorphism (SNP) including Glyma.15G217100, which encodes a major latex protein (MLP)‐like protein, with a functional annotation related to pathogen resistance. Expression analysis of Glyma.15G217100 indicated that it was nearly eight times more highly expressed in a group of plant introductions (PIs) carrying the resistant (R) allele compared with those carrying the susceptible (S) allele within a short period after inoculation. These results offer new and valuable options to develop improved soybean cultivars with broad resistance to P. sojae through marker‐assisted selection.

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“…The copyright holder for this preprint this version posted August 6, 2021. ; https://doi.org/10.1101/2021.08.05.455283 doi: bioRxiv preprint In spite of the large volume of association and linkage mapping, genomic, and transcriptomic datasets, integrative approaches remain scarce Ronne et al, 2019), resulting in an incomplete picture of metabolic and regulatory genes determining soybean oil content. Here, we integrate large-scale datasets from various sources to define stable genomic regions and identify the most promising genes involved in oil-related traits therein.…”

Section: Introductionmentioning

confidence: 99%

Integrating omics approaches to discover and prioritize candidate genes involved in oil biosynthesis in soybean

Turquetti-Moraes

Moharana

Almeida‐Silva

et al. 2021

Preprint

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Soybean is one of the major sources of edible protein and oil. Oil content is a quantitative trait that is significantly determined by genetic and environmental factors. Over the past 30 years, a large volume of soybean genetic, genomic, and transcriptomic data have been accumulated. Nevertheless, integrative analyses of such data remain scarce, in spite of their importance for crop improvement. We hypothesized that the co-occurrence of genomic regions for oil-related traits in different studies may reveal more stable regions encompassing important genetic determinants of oil content and quality in soybean. We integrated publicly available data, obtained with distinct techniques, to discover and prioritize candidate genes involved in oil biosynthesis and regulation in soybean. We detected key fatty acid biosynthesis genes (e.g., BCCP and ACCase, FADs, KAS family proteins) and several transcripton factors, which are likely regulators of oil biosynthesis. In addition, we identified new candidates for seed oil accumulation and quality, such as Glyma.03G213300 and Glyma.19G160700, which encode a translocator protein and a histone acetyltransferase, respectively. Further, oil and protein genomic hotspots are strongly associated with breeding and not with domestication, suggesting that soybean domestication prioritized other traits. The genes identified here are promising targets for breeding programs and for the development of soybean lines with increased oil content and quality.

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Integrated QTL mapping, gene expression and nucleotide variation analyses to investigate complex quantitative traits: a case study with the soybean–Phytophthora sojae interaction

Cited by 19 publications

References 12 publications

A High-Throughput Phenotyping Tool to Identify Field-Relevant Anthracnose Resistance in White Lupin

A High-Throughput Phenotyping Tool to Identify Field-Relevant Anthracnose Resistance in White Lupin

Mapping of partial resistance to Phytophthora sojae in soybean PIs using whole‐genome sequencing reveals a major QTL

Integrating omics approaches to discover and prioritize candidate genes involved in oil biosynthesis in soybean

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