Prioritizing soybean resistance genes against fungal diseases by integrating GWAS and gene coexpression networks

# Prioritizing soybean resistance genes against fungal diseases by integrating GWAS and gene coexpression networks
### Fabricio Almeida-Silva <span class="citation">@almeidasilvaf</span>
### UENF / Brazil
### October 19, 2021

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## Introduction

<br />

Annual loss of billions of dollars .cit[(Osman et al., 2015)]
]

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## Introduction

<br />

Annual loss of billions of dollars .cit[(Osman et al., 2015)].

**Phytopathogenic fungi** -  yield loss due to:

1. Leaf damage

2. Root rot

3. Seed damage

4. Death

.footnote[Source: Crop Protection Network | Chiotta *et al.*, 2016 | Daren Mueller | Elevagro | Agrolink]

]

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## The research problem

<br />

GWAS can identify .bgb[causative SNPs] associated with traits, but not .bgb[causative genes].

Current methods lead to high false-positive and false-negative rates.

<br />

![](https://github.com/almeidasilvaf/GCN_GWAS_fungi/blob/main/figs/gwas_problem_2.png?raw=true)

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## A network-based solution

.bgb[Gene coexpression networks]: Nodes represent genes and edges represent their correlation coefficients.

In large-scale analyses, we can detect .bgp[coexpression modules] &rarr; functionally similar genes.

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## The rationale: guilt-by-association

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## Goal

<br />
.center[.font120[Identify high-confidence candidate genes involved in resistance to fungal diseases by integrating GWAS and coexpression networks]]

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## Methods

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## {cageminer}'s algorithm

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## Data overview

<br />

A species must be represented by:
- transcriptome samples
- GWAS-derived SNPs
]

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## Data overview

.footnote[Source: Crop Protection Network | Chiotta *et al.*, 2016 | Daren Mueller | Elevagro | Agrolink]

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## Prioritized candidate genes

<br />

- *Cadophora gregata:* **11**

- *Fusarium graminearum:* **59**

- *Fusarium virguliforme:* **191**

- *Macrophomina phaseolina:* **8**

- *Phakopsora pachyrhizi:* **3**

Highly .bgp[species-specific] response.

]

.pull-right[
![](https://github.com/almeidasilvaf/GCN_GWAS_fungi/blob/main/figs/venn_diagram_candidates.png?raw=true)
]

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## A network of processes

<br />
.pull-left[
Both well-known and novel candidates.

Most candidates likely involved in .bgp[defense signaling].

Hidden treasure? 8% of the candidates encode proteins of unknown function.

]

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## Promising targets for genetic engineering

<br />
.pull-left[
Candidates were scored and ranked with:

where:

`$$\kappa = 2 \text{ if the gene is a transcription factor}$$`

`$$\kappa = 2 \text{ if the gene is a hub}$$`

`$$\kappa = 3 \text{ if the gene is a hub and a transcription factor}$$`

]

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## Potential accessions in the USDA germplasm

<br />

**Goal:** .bgb[largest] number of .bgb[resistance SNPs] and .bgr[smallest] number of .bgr[susceptibility SNPs].

<br />

GG = 2

AG = 1

AA = 0
]
]

GG = 0

AG = 1

AA = 2 
]
]

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## Potential accessions in <br /> the USDA germplasm

<br />
.pull-left[
.font110[Main findings:]

- There is still room for alelle pyramiding
- Best accessions can be improved through MAS-based breeding or genetic engineering

]

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## Software development

R/Bioconductor package for:
- expression data preprocessing
- coexpression/regulatory network inference
- functional analyses

<br/ >
.brand-charcoal[.font130[.bold[cageminer:]]]

R/Bioconductor package for:
- candidate gene prioritization by integrating GWAS and gene coexpression networks

]

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## A web app to facilitate data reuse

Users can explore the coexpression network we inferred at https://soyfungigcn.venanciogroup.uenf.br.

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## Conclusions

.pull-left[
We found high-confidence candidates that can be used to increase soybean resistance to fungal diseases through:

- Gene editing

- Plant transformation

]

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background-image: url(https://www.agroscope.admin.ch/agroscope/en/home/topics/plant-production/plant-breeding/ackerpflanzen/selection_soja/_jcr_content/par/columncontrols/items/1/column/image/image.imagespooler.jpg/1473888418017/selection_soja_fleur.jpg)
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## Conclusions

.pull-left[
We found high-confidence candidates that can be used to increase soybean resistance to fungal diseases through:

- Gene editing

- Plant transformation

We found promising accessions in the USDA germplasm that can be used in:

- Breeding programs

- CRISPR/Cas-mediated editing to insert missing alleles.

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background-image: url(https://github.com/almeidasilvaf/GCN_GWAS_fungi/blob/main/figs/package_logos.png?raw=true)
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## Conclusions

.pull-left[
We found high-confidence candidates that can be used to increase soybean resistance to fungal diseases through:

- Gene editing

- Plant transformation

We found promising accessions in the USDA germplasm that can be used in:

- Breeding programs

- CRISPR/Cas-mediated editing to insert missing alleles.

We have developed 2 packages and a web app that can serve as a valuable resource.

]

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## Contact: