# TAPS for Multimodal Epigenomic Profiling in Livestock
## A Comparison with ONT, WGBS, and EM-Seq

Céline Vandecasteele, Julie Demars, Sophie Leroux, Nathalie Iannuccelli, Carole Iampietro, Craig Marshall, Travis Sanders, Aaron Garnett, Martin Ranik, Kristina Giorda, et al.

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► To cite this version:

Céline Vandcasteele, Julie Demars, Sophie Leroux, Nathalie Iannuccelli, Carole Iampietro, et al. TAPS for Multimodal Epigenomic Profiling in Livestock A Comparison with ONT, WGBS, and EM-Seq. Plant and animal genome conference 32, Jan 2025, San Diego, United States. 2025. hal-04911060

HAL Id: hal-04911060

https://hal.inrae.fr/hal-04911060v1

Submitted on 24 Jan 2025

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# TAPS for Multimodal Epigenomic Profiling in Livestock
## A Comparison with ONT, WGBS, and EM-Seq

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Céline Vandecasteele¹, Julie Demars², Sophie Leroux², Nathalie Iannuccelli², Carole Iampietro¹, Craig Marshall³, Travis Sanders³, Aaron Garnett³, Martin Ranik³, Kristina Giorda³, Eduard Casas³, Thomas Harrison³, Doug Wendel³, Brian Kudlow³, Frédérique Pitel²

1 INRAE, US 1426, GeT-PlaGe, Genotoul, France Genomique, Université Fédérale de Toulouse, Castanet-Tolosan, France

2 GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet-Tolosan, France

3 Watchmaker Genomics, Boulder, Colorado, USA

## Introduction

Genetic and genomic selection advances have significantly optimized livestock performance over the past decades. However, genetics accounts for only a portion of the observed phenotypic variability, while much of this variability—often attributed to environmental factors—remains inaccessible to genetic approaches. One of the questions in breeding today is thus whether integrating epigenetic markers into prediction models could improve their accuracy or even whether epigenetics should be included in genetic evaluation models for breeding candidates. The availability of a large-scale epigenotyping tool is crucial for answering this question and eventually implementing epigenotyping in animal breeding. Here we evaluated CpG methylation detection using an advanced prototype of TAPS (TET-assisted pyridine borane sequencing) against existing data from Whole Genome Bisulfite Sequencing (WGBS), EM-seq (Enzymatic Methyl-seq), and ONT (Oxford Nanopore Technology) in two farm species: pigs (*Sus scrofa*) and quails (*Coturnix japonica*).

## Standard methods

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## TAPS

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* Reduces low-quality reads to enhance sequence economy.

* Speeds up analysis using standard mapping tools.

* Minimizes DNA damage relative to bisulfite for better recovery.

1 - developed at Ludwig Cancer Research by Benjamin Schuster-Böckler's lab

## Conclusion

This pilot study provides very encouraging results for the use of TAPS in epi/genotyping analyses in farm animals, with robust sequencing and mapping metrics and comparable CpG methylation profiles and SNP genotypes between all technologies.

## Available data sets / TAPS results

<table><thead><tr><th></th><th></th><th>WGBS</th><th>EM-Seq</th><th>ONT</th><th>MicroArray</th><th>TAPS</th></tr></thead><tbody><tr><td rowspan="2"><em>Sus scrofa</em></td><td>Sample 1</td><td>ND</td><td>148 Gb</td><td>192 Gb</td><td>650 k Array</td><td>287 Gb*</td></tr><tr><td>Sample 2</td><td>238 Gb</td><td>ND</td><td>152 Gb</td><td>650 k Array</td><td>269 Gb*</td></tr><tr><td rowspan="2"><em>Coturnix japonica</em></td><td>Sample 1</td><td>119 Gb</td><td>ND</td><td>55 Gb</td><td>ND</td><td>94 Gb*</td></tr><tr><td>Sample 2</td><td>283 Gb</td><td>90 Gb</td><td>65 Gb</td><td>ND</td><td>94 Gb*</td></tr></tbody></table>

*3 replicates

## Results

### Methylation analyses (Quail sample 2)

At the 10X threshold, the WGBS approach covers less than 75% of CpG sites, while EM-Seq covers around 90%, and both ONT and TAPS achieve nearly 100% coverage.

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**Coverage of CpG sites across technologies**

The distribution of CpG site coverage and the percentage of CpG sites covered at a minimum depth of coverage

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Pearson correlation matrix of CpG methylation level per-site across technologies (Filtering data: 10x < depth < 100x). Barplots above the matrix show the number of CpG sites covered by 10 or more reads and their percentage compared to the reference genome. Average CpG coverage by technology before depth filtering is shown on the right.

### SNP analyses (pigs)

Filtering variants post genotyping with stringent criteria
1 - Keep only variants present on 650K for SSC1 to SSC18, n=524,117 variants

2 - Remove positions corresponding to CpG identified with rastair, n=453,192 variants

3 - Keep only bi allelic SNPs, n=350,011 variants

4 - Keep only SNPs QUAL>300 & <1300, n=155,285 variants

5 - Keep only SNPs with meanDP >15 across samples, n=129,211 variants

<table><caption>AB72734224</caption><thead><tr><th>TAPS</th><th>Homoz Ref</th><th>Hetero</th><th>Homoz Alt</th></tr></thead><tbody><tr><td>Homoz Ref</td><td>67871</td><td>1009</td><td>232</td></tr><tr><td>Hetero</td><td>52</td><td>55790</td><td>2810</td></tr><tr><td>Homoz Alt</td><td>1</td><td>20</td><td>1140</td></tr></tbody></table>

<table><caption>AB72734224</caption><thead><tr><th>TAPS</th><th>650K</th><th>Homoz Ref</th><th>Hetero</th><th>Homoz Alt</th></tr></thead><tbody><tr><td>Homoz Ref</td><td>68358</td><td>714</td><td>128</td></tr><tr><td>Hetero</td><td>192</td><td>55570</td><td>2992</td></tr><tr><td>Homoz Alt</td><td>1</td><td>20</td><td>1143</td></tr></tbody></table>

<table><caption>AB72734233</caption><thead><tr><th>TAPS</th><th>ONT</th><th>Homoz Ref</th><th>Hetero</th><th>Homoz Alt</th></tr></thead><tbody><tr><td>Homoz Ref</td><td>27802</td><td>1767</td><td>98</td></tr><tr><td>Hetero</td><td>81</td><td>96344</td><td>2217</td></tr><tr><td>Homoz Alt</td><td>1</td><td>78</td><td>537</td></tr></tbody></table>

<table><caption>AB72734233</caption><thead><tr><th>TAPS</th><th>650K</th><th>Homoz Ref</th><th>Hetero</th><th>Homoz Alt</th></tr></thead><tbody><tr><td>Homoz Ref</td><td>28173</td><td>1480</td><td>43</td></tr><tr><td>Hetero</td><td>1186</td><td>96645</td><td>2669</td></tr><tr><td>Homoz Alt</td><td>3</td><td>77</td><td>537</td></tr></tbody></table>

**Genotypes comparisons**

The excess of heterozygous in TAPS libraries can be explained by *de novo* SNV-driven CpGs

<table><thead><tr><th></th><th>Replicates</th><th>ONT vs. TAPS</th><th>650K vs. TAPS</th></tr></thead><tbody><tr><td>AB72734224_A</td><td>0.9889</td><td>0.9680</td><td>0.9686</td></tr><tr><td>AB72734224_B</td><td>0.9867</td><td>0.9678</td><td>0.9684</td></tr><tr><td>AB72734224_C</td><td>0.9870</td><td>0.9663</td><td>0.9670</td></tr><tr><td>AB72734233_A</td><td>0.9773</td><td>0.9671</td><td>0.9576</td></tr><tr><td>AB72734233_B</td><td>0.9884</td><td>0.9681</td><td>0.9585</td></tr><tr><td>AB72734233_C</td><td>0.9807</td><td>0.9709</td><td>0.9614</td></tr></tbody></table>

**Genotypes correlations between**

**TAPS libraries replicates and across technologies**

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**IGV visualization of concordant and discordant genotypes**

→ A custom script was developed to resolve variant calling complexities associated with *de novo* SNV-driven CpGs. Contact Support@watchmakergenomics.com for details.

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