# Cross-species use of neural networks trained on human and mouse sequences to predict chromatin regulation from pig sequences for genetics applications

Master 2 Bioinformatics for health (BIS), Rennes (France)

By Noémien MAILLARD

15th June 2023

**Laboratory:** INRAE OCCITANIE TOULOUSE

**Address:** 24 Chemin de Borde Rouge 31326 CASTANET-TOLOSAN FRANCE

**Teams:** GenEpi, MIAT

**Supervisors:** Julie DEMARS and Raphaël MOURAD
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# Contents

## Remerciements

## Introduction 1

Socio-economical context 1

Biological and genetic contexts 2

Artificial intelligence contribution 3

Project aim and global approach 4

## Materials and methods 4

Animals description 4

Sequencing information and preprocessed data 5

Files formats 6

Biological data 6

Data predictions 6

Neural network models 6

Bigwig preparation from predictions 7

Comparison of predictions against biological data 7

Genetic data analyses 8

Generation of variant file 8

Comparison and merging of back-cross variants 8

Phasing and variant annotation 8

DeepBind variant predictions analysis 9

## Results 10

Neural networks trained with human and mouse-related experiments predict

peaks from pig sequences 10

Genetic analysis of the whole dataset shortened the number of candidate

variants from 2429 to 1616 12

Technology bias 12

Variant exploration 14

First analysis of DeepBind predictions allow the selection of a subset of 58

variants of interest 15
<table><tr><td>Discussion and Conclusions</td><td>16</td></tr><tr><td>Annexes</td><td>19</td></tr><tr><td>References</td><td>21</td></tr></table>
Remerciements

Tout d'abord je tiens à remercier mes maîtres de stage, Julie DEMARS et Raphaël MOURAD pour m'avoir accueilli dans leur laboratoire et m'avoir permis de travailler sur ce sujet. Ils m'ont partagé leur passion, ont pris le temps de me transmettre leurs connaissances et ont su m'aiguiller tout au long de ce stage. Ils ont aussi su être à l'écoute quand c'était nécessaire, merci pour tout.

Ensuite j'aimerai remercier Katia Feve et Juliette Riquet qui, ayant travaillé sur le projet avant mon arrivé, se sont impliquées également et ont continué de suivre avec moi l'avancement du projet et m'ont aussi donné de précieux conseils. Pour cela, merci beaucoup.

Je souhaite aussi remercier les autres membres de l'équipe GenEpi qui m'ont partagé leurs savoirs et leur expérience.

Enfin, j'aimerai remercier l'ensemble des membres du laboratoire avec qui j'ai toujours eu du plaisir à discuter, professionnellement et personnellement. Merci à l'ensemble de l'unité GenPhySE.
# Introduction

## Socio-economical context

With a worldwide population estimated at around 9 billion in 2050, intake needs are expected to considerably increase. As shown in Figure 1, global meat consumption is estimated to be 557 million tonnes in 2050, including 143 million tonnes of pork meat. In 2020, Europe produced 24.2 million tonnes of pork meat with 2.276 million tonnes in France which represents 105% of its auto-supply and ranking France as the 3rd producing country in Europe¹.

<figure><img src="image_1.png" /><figcaption>**Figure 1: Global meat consumption, World, 1961 to 2050.** Expressed in tonnes of meat. Data from 1961-2013 is based on published FAO estimates; from 2013-2050 based on FAO projections. Projections are based on future population projections and the expected impacts of regional and national economic growth trends on meat consumption.</figcaption></figure>

To overcome this estimated increase in pig meat consumption, France, like other countries, has to improve its productivity. Moreover, livestock conditions and quality seals rely more and more important for consumers, so increasing the number of pigs on farms is not a solution. Some laboratories work in collaboration with farmers on food intake optimization. Another solution dependent on breeds used in farms is the selection of traits. As an example, we can imagine selecting animals on their capacity to grow faster. A good candidate phenotype is genetically heritable and easily transmitted. It is also easy to detect, either because physically perceptible or because easy to analyze (non-invasive sampling, technic easy to put in place).
## Biological and genetic contexts

In France, the French Institute of Research for Agriculture (INRA) initiated the PORQTL program in the 90s to better understand which agronomical important traits could be highly heritable and considered for the pig industry². The PORQTL program was an intercross between the Chinese pig breed, the Meishan (MS), and the most used pig breed in France, the Large White (LW). Its aim was to determine genomic regions, called QTL (Quantitative Trait Locus), associated with agronomical traits of interest. Indeed, those breeds are opposite and complementary for many phenotypes of economic interest. Among all identified QTLs in the PORQTL program, two were determined on chromosome 1 (SSC1) and are associated with carcass composition traits³.

To better characterize the effects of those QTLs and identified causal mutations that affected carcass composition traits, Sanchez et al.⁴ designed a recombinant back-cross (BC) progeny test approach. They showed that some growth, carcass, and meat quality traits were related to the SSC1 and confirmed previous observations. However, they also notice that less important effects on SSC1 have been obtained. Interestingly, they observed favorable effects from the MS allele that seemed to explain a better backfat thickness and a higher growth rate. This result was in the opposite way of what was observed for all other QTLs for carcass traits with always favorable effects of LW alleles. In the continuity of this study, Riquet et al.⁵ developed the same back-cross progeny test strategy with LW and MS individuals but in favor of the MS allele. Thereby, they reduced the SSC1 QTL interval from a 30 cM to 8 cM. Additional functional and genetic analyses focused on the region of interest allowed to shorten the region to 300 kb length on SSC1.

For the last two decades, many association and liaison studies led to the production of much genetic data and thus precise the relationship between genotype and phenotype. This phenomenon is observed in farm animals but also in human genetic data. Although many genomic regions have been associated with a broad range of phenotypes, these approaches present bottlenecks⁶. Identifying the causal mutation involved in the variability of the phenotype and better understanding the underlying molecular mechanism still remain challenging. Indeed most variants are located outside of coding regions and mostly in regulated regions leading to difficulties in the association between genotype-phenotype. So to go further and associate variants with phenotypes, global consortia have been created, like ENCODE
(https://www.encodeproject.org/) and GTEx (https://www.gtexportal.org/) for human data or FAANG<sup>7</sup> and farmGTEx<sup>8</sup> for farm animals-related data. ENCODE and FAANG aim to process, analyze, and annotate gene regulation data while GTEx and farmGTEx aim to process, analyze and annotate gene expression data. All this work allows free access to data and could greatly help the annotation of variants and the evaluation of their functional impact. Although international initiatives are ongoing for farm animals, the amount of annotated data would never reach those available for humans and mice. To overcome this issue, using artificial intelligence (AI) trained on human and mouse data seems to be a good solution to predict annotations in mammals due to their close phylogeny.

## Artificial intelligence contribution

Since the end of the '90s, the development of machine learning (ML), commonly called AI, has never stopped growing. This discipline consists of the use of diverse statistical methods to predict output, given a formatted input. This discipline is often split into two categories: supervised and unsupervised algorithms. Supervised algorithms consist of a first learning step to prepare predictions while unsupervised algorithms do not. Those approaches, and particularly neural networks (NN), have also been adapted to the analysis of genetic data because they are able to catch information a human eye cannot<sup>9</sup>.

The common principle of those NN is to make predictions for a central sequence, given a neighboring environment, then repeat the operation along the sequence of interest. For this, convolutional neural networks (CNN) have been used first. The CNN principle is to catch local patterns invariant in translation. Then, dilated CNN (dCNN) has been proposed, consisting of the expansion of the area of the convolution kernel and a larger local search<sup>10</sup>. In 2017, Vaswani et al.<sup>11</sup> proposed another solution to get self-attention from words: Transformers. Unlike convolutional methods, transformers do not look for patterns but assign scores on words according to neighboring words. It is this new type of attention layer that made the success of GPT models.

In genomics, NN have first been developed to predict biological functions like promoter activity or splice sites by recognition of local patterns<sup>12,13</sup>. More complexity in NN architecture allows more complex predictions from sequences and multiple outputs as did by Liu et al.<sup>14</sup> algorithm, DeepGenGrep. From one sequence, this NN can predict three genome signals:
translational initiation sites, polyadenylation sites, and splice sites. Other NN more focused
on gene regulation have been developed to predict binding sites of proteins onto the DNA/RNA
molecule or histone marks such as DeepBind¹⁵ and Enformer¹⁶. DeepBind is a NN constructed
on a classic CNN layer followed by rectification, pooling, and a dense neural network layer¹⁵.
It has been designed to predict DNA/protein and RNA/protein interactions and trained from
ChIP-seq, Protein Binding Microarray (PBM), RNAcompete, and HT-SELEX data. Contrary
to DeepBind, Enformer is an advanced NN with more complex architecture¹⁶. It consists of 7
CNN layers followed by 11 Transformer layers closed by two heads each trained to predict
human or mouse outputs. Enformer is designed to make predictions related to chromatin
regulation by previous training with DNase-seq, ATAC-seq, ChIP-seq, and CAGE experiments.

**Project aim and global approach**

In the context of the PORQTL program, the QTL located on SSC1 and affected carcass traits
composition in pigs contains 4 genes and more than 2400 variants that could all be potential
causal mutations. To sort and prioritize those variants for their potential functional impact on
the phenotype, I used neural networks (DeepBind and Enformer) trained on human and mouse
data to predict chromatin regulation from pig sequences and the likely effects of variants.

My work consisted first in showing the capacity of cross-species application of those NN for
protein binding ability and histone marks by comparing predictions to pig ChIP-seq biological
data generated in the frame of the FAANG project (https://data.faang.org/). Then I prepared
a variant dataset from the experimental design to reduce the number of candidate variants.
Finally, DeepBind and Enformer have been run on the sequence of 300 kb with the integration
of variants of interest. For this last step, I aimed to propose a reduced number of variants of
interest for biological validation.

**Materials and methods**

**Animals description**

All animals in this project are part of a larger study: the PORQTL program. The dataset used
in this project contains 12 founders (6 LW and 6 MS) and 13 BC (from 1st to 7th generation)
originating from one BC family (Figure 2).
<figure><img src="image_2.png" /><figcaption>**Figure 2: Detailed pedigree in dataset.** The 15 individuals used in my internship are framed in green. Squares are males, rounds are females. Blue and yellow subsquares indicate the proportion of Large White (LW) and Meishan (MS) haplotypes. All framed BC are heterozygous for the QTL of interest based on progeny testing. Light green filled squares are individuals genotyped in amplicons and dark green frames are individuals genotyped in WGS.</figcaption></figure>

## Sequencing information and preprocessed data

As previously explained, BC progeny-testing detailed by Riquet et al.⁵ has been pursued until the 7th generation. Some BC of generations 1 to 4 have been sequenced for the QTL by amplicons. This amplicons method consists of PCR amplification of the 300 kb region of interest by around 10kb length overlapping amplicons. In addition, Whole Genome Sequencing (WGS) data have also been obtained by short reads sequencing by the same platform and primo-analyzed. This work has been done by Katia Fève in the frame of her internship.

At the beginning of my internship, I had files (format.vcf or .gvcf) including a list of variants for the 13 animals sequenced by amplicons and for the 14 animals sequences by WGS. Some of those animals are included in the pedigree described in Figure 2.
## Files formats

**gvcf.** File format starting with multiple lines with information about previous algorithms used followed by 'CHROM', 'POS', 'ID', 'REF', 'ALT', 'INFO' and 'FORMAT' columns, then each column is an individual. For each position, each individual has information related to his genotype (alleles composition of the genotype, alleles depths, etc).

**vcf.** Identical to the gvcf file format but excluding positions with no 'ALT' allele so this format stores only positions with alternative alleles.

**bed.** Tabular file format with 3 mandatory columns (no header) corresponding to 'chromosome', 'start position', and 'end position'. This light file format is used in Integrative Genome Viewer (IGV)¹⁷ to see ChIP peaks vertices.

**bigwig.** Binary version of Wig format. This file format contains mandatory 'chromosome', 'start position', 'end position', 'score', and 'strand' columns and can have optional informative columns. This file format has been used in IGV to see ChIP peaks and each variation along chromosomes.

**csv/tsv.** Common file formats representing tables with values separated by coma (csv) or tabulations (tsv). Predictions outputs are csv or tsv format.

## Biological data

Biological data of the FAANG project have been extracted from 3 databases: UCDAVIS (to access files, precede accession number with "https://farm.cse.ucdavis.edu/~ckern/FAANG Project/Pig_2017/DeepTools/"), NCBI GEO and VizFaDa (viz.faang.org). All biological data with their accession numbers used are presented in Annex 1.

## Data predictions

### Neural network models

Both models have been first used to predict peaks from the pig reference sequence (Sscrofa 11.1). The outputs, in tsv (DeepBind) or csv (Enformer) format, consisting of a bin for each line and an experimental condition for each column. A condition (or experiment) is the combination of an organism (Homo sapiens, Mus musculus), a method (ChIP-seq, PBM, ATAC-seq...), and a tissue or cell line. Enformer conditions include also precisions like sex, age, or specifications
on tissue (for example, sigmoid colon). For validation of predictions, CTCF and histone marks have been selected. For the variant impact predictions, conditions related to the liver, adipose, and muscle have been kept from both NN. To work and extract experiments of interest, a homemade Python script has been written.

The DeepBind model has been trained with 12 terabases of sequences of PBM, ChIP-seq, RNAcompete, and HT-SELEX experimental data representing 538 unique transcription factors (TF) and 194 RNA binding proteins (RBP). DeepBind predicts protein binding score for 927 experiments. Enformer model has been trained with 34,021 human sequences and 29,295 mouse sequences from ChIP-seq, ATAC-seq, DNAse-seq, and CAGE experiments. All those experiments bring together 2,131 TF, 1,860 histone marks, 684 DNAse-seq or ATAC-seq, and 638 CAGE tracks. During the project, only the 5,313 humans related predictions have been used from Enformer.

## Bigwig preparation from predictions

The principle is the same for DeepBind and Enformer predictions. To extract predictions and make them exploitable, results from csv or tsv files have been transformed into bigwig format using GenomicRanges (GRanges)¹⁸ and rtracklayer¹⁹ R libraries.

## Comparison of predictions against biological data

**IGV.** Bed and bigwig files from the FAANG project are loaded on IGV to visualize peaks. The region of interest is set to chr1:270,380,000-270,680,000 bp and all peaks observed with bigwig files are autoscaled.

**DeepBind comparison.** The two samples' bed files have been merged with the bedops toolkit²⁰ for each tissue. Then the number of peaks predicted by DeepBind (score greater than 0) have been compared to observed merged bed files with the findOverlaps method of the GRanges¹⁸ package. Then overlaps are represented as confusion matrices. DeepBind predictions are qualitative: it gives a probability to observe a peak. A score equal to 0 means a probability of 50% to observe a peak (the probability can be calculated by processing the score into a sigmoid function).

**Enformer correlations.** For each condition (CTCF or histone mark associated with tissue), Pearson's correlations have been calculated between each possible observed/predicted data
couple. For each tissue, 2 CTCF files of observed data are compared to 2, 4, and 5 predictions for the liver, lung, and spleen respectively. For histone marks, 2 colon, 4 liver, 3 muscle, and 2 stomach observed data are compared to 2 to 8 predictions. Enformer predictions are quantitative. It predicts a peak intensity for each bin.

# Genetic data analyses

## Generation of variant file

12 gvcf files from founders and 2 gvcf files from 6th and 7th generation BC progeny, all sequenced by WGS, were first limited to the region of interest (chromosome 1 from 270,382,778 to 270,675,029 bp) using vcftools²¹. Then, those output gvcf files have been merged with gatk CombineGVCFs (https://gatk.broadinstitute.org/) to obtain one gvcf file. From this file, a vcf format file has been produced with gatk GenotypeGVCFs (https://gatk.broadinstitute.org/).

The vcf file obtained has been merged with another vcf file containing genotypes, determined by amplicons, of the same two BC and 11 other BC (Figure 2). For this, bcftools merge²² has been used with the forcing option because of the common individuals of both files.

## Comparison and merging of back-cross variants

For BC sequenced by WGS and amplicons, variants have been manipulated within an R script from vcf file containing all individuals. To consider the highest number of variants for animals that had genotypes with both technologies, several steps and filters have been applied and implemented in a homemade Python/bash script (Figure 3). Once the final list of variants was obtained for those 2 BC animals, a merged file was built with the other 12 animals from the pedigree.

## Phasing and variant annotation

To identify the origin of the MS haplotype expected to be carried out by all BC animals originating from the same MS female founder (Figure 2), several phasing algorithms have been used based on their specificity for population or family-based haplotype phasing. While Beagle 4.0²³ and Shapeit v2.17²⁴ are more dedicated to population genetics, Yapp 0.3.1 (https://github.com/BertrandServin/yapp) and phase v2.1.1²⁵ are more suitable for familial genetics. Because of the unsatisfying results, haplotypes have finally been phased by a
homemade semi-automatic approach. VEP²⁶ was used with default parameters to get a vcf with annotations and diagrams to represent it.

<figure><img src="image_3.png" /><figcaption>**Figure 3: Flowchart of script developed to merge back-cross genotyped in amplicons and WGS.**
The main input file is the vcf format file. A file containing positions to check (variants with genotype only in one technology or in both but different) and another file containing positions in biased amplicons are also given. The output is the same vcf input file but with no duplicated back-cross and the most probable allele for each variant.</figcaption></figure>

## DeepBind variant predictions analysis

The variant impact score is calculated for each variant and for each experiment predicted by DeepBind following the equation:

$$ \text{impact}_{\text{score}(alt)} = (\text{pred}_{\text{score}(alt)} - \text{pred}_{\text{score}(ref)}) $$
with the score calculated for each allele by DeepBind.

The impact score is included in [-∞, +∞] interval. A positive impact score means that the predicted score of the alternative allele is greater than the predicted score of the reference allele, so the probability of having a peak is increased. On the other hand, the probability of having a peak is decreased if the score is negative.

DeepBind output containing impact scores is a vcf table with each column as a condition. Conditions are filtered to keep only predictions related to adipose, liver, and muscle, namely 20 ChIP-seq predictions. Two analyses have been performed: (i) each variant is overlapped on each experiment with the findOverlaps function from the GRanges¹⁸ library considering a peak when the prediction score is greater than 0. Peak overlaps are counted for each variant. A threshold is set to keep variants observed in more than 6 experiments. (ii) for each experiment, the 50 best absolute scores are extracted to obtain 1000 scores. The intersection of variants overlapping peaks on more than 6 experiments and the best scores is done to extract variants of interest.

# Results

## Neural networks trained with human and mouse-related experiments predict peaks from pig sequences

The project stands on the hypothesis that, because of phylogenetic proximity, genomes and mechanisms of regulation are conserved between human, mouse, and pig species. If it is right, NN trained to predict proteins/DNA interactions (like insulators, TF), and histone marks on human and mouse sequences should be able to predict those phenomena from pig sequences.

To verify this hypothesis, NN has been used with the sequence of reference of the pig genome (Sscrofa 11.1). An example of a comparison between observed biological data extracted from FAANG and predicted data from DeepBind and Enformer is shown in Figure 4. CTCF binding is similar between observed and predicted data and we can denote slightly better predictions with Enformer as pointed out by the peak on the left (arrow) not predicted by DeepBind. For histone marks, we can see that the predictions fit very closely to observations even for marks with more noise, like H3K4me1. The small differences we can see on this mark are probably due to pig sequence specificities.
<figure><img src="image_4.png" /></figure>

**Figure 4: Comparison of observed and predicted data.** Representative data showing observed in red (bigwig and bed) and predicted data in turquoise or green (bigwig formatted). DeepBind predictions are in turquoise and Enformer peaks are in green. CTCF observed data and Enformer predictions are related to lung, DeepBind predictions are not associated with any tissue. Observed and predicted histone marks are related to muscle.

To consolidate those observations on the whole dataset, predicted data have been compared to observations by confusion matrices and Pearson's correlations according to DeepBind or Enformer predictions. Figure 5A shows confusion matrices of DeepBind predictions (one experiment only is associated with CTCF) compared to observed data of the liver, lung, and spleen. While 66% to 83% of observed peaks seem predicted, predictions show also many false positive peaks. In fact, those predicted peaks do probably exist but are too small to be detected in observations and so their detection would be dependent on pipeline analysis parameters.

**Figure 5: Correlations between observed and predicted data.** (A) CTCF DeepBind predictions are compared to observed data. Liver and spleen peaks are the merging of two samples and the lung consists of only one sample. (B) CTCF mean correlations for each tissue. 2 replicates of observed data are compared to 2, 4, and 5 predictions for liver, lung, and spleen respectively. (C) Histone marks mean correlations for each mark for each tissue. For each tissue 2, 4, 3, and 2 replicates of observed data for the colon, liver, muscle, and stomach respectively are compared to predictions (variable from 2 to 8).

<figure><img src="image_5.png" /></figure>
Figure 5B shows CTCF correlations between Enformer predictions and observations. The correlations are quite strong, as we could expect and the variability is clearly due to noise in one of the two observed samples (data not shown). Histone marks also show strong correlations (Figure 5C). In general, the variability can also be explained by a difference in noise or peak intensity between observations and predictions. Unlike proteins that bind to DNA, histone marks do not show clear peaks (except for H3K4me3) because of their biological purpose. In addition, correlation values seemed to vary between tissues for H3K27ac and H3K27me3 with correlations in the stomach either strongest or lower for H3K27me3 and H3K27ac, respectively, and the opposite for correlations in muscle.

Predictions on the sequence of reference of the pig from NN trained on human and mouse species are sensibly close to observed data. Although the variability is partially due to noise and peak intensity, peaks observed are also predicted. Those results are well enough to extrapolate on conditions for which there is no biological data and to evaluate the variant impact on chromatin regulation.

## Genetic analysis of the whole dataset shortened the number of candidate variants from 2429 to 1616

### Technology bias

Before predicting the potential impact of variants on chromatin regulation, I had to prepare the full dataset. This step included merging the 2 BC which are genotyped by WGS and amplicons to keep variants we can trust, and keep positions we are more interested in.

Among the total 7654 initial variants, 4059 variants had the same genotype with both technologies (sameGT), 880 had no genotype neither in amplicons nor in WGS (noGT), 1743 had genotype only in amplicons (GT amp only), 549 had genotype only in WGS (GT WGS only) and 423 had different genotypes between WGS and amplicons (GTdiff) (Figure 6). The two first categories can be kept as they are, however, we had to determine what genotype to keep for other categories, especially for the GTdiff category.

At first, genotype depths were compared between WGS and amplicons, according to their observation only in one technology or in both but with different genotypes. Amplicons have read depth higher than WGS (Figures 7A and 7B). Standard thresholds of read depth of 30
<figure><img src="image_6.png" /><figcaption>**Figure 6:** Description of vcf dataset for the 2 BC genotyped in WGS and amplicons. 7654 variants are referenced in the starting vcf. 5 subcategories are segregated according to the genotype and its source (amplicons or WGS). sameGT: variants with the same genotype in WGS and amplicons, noGT: variants without genotype in amplicons neither in WGS, GT_amp_only: variants with genotype only in amplicons, GT_WGS_only: variants with genotype only in WGS, GTdiff: variants with genotype different between WGS and amplicons callings</figcaption></figure>

and 15 for amplicons and WGS, respectively seemed a good compromise to prune ambiguous genotypes while keeping enough data. However, many variants still show different genotypes (Figure 7C top right corner). At this stage, the hypothesis is those variants are mainly homozygous in amplicons technology, because of its related bias, leading sometimes to the amplification of one allele over the other.

<figure><img src="image_7.png" /><figcaption>**Figure 7:** Distribution of variants to consider to merge back-cross. Number of variants related to variant depths regarding genotype in (A) amplicons or (B) WGS only. (C) Depth of amplicon genotypes related to WGS genotypes for variants with different genotypes between technologies (GTdiff).</figcaption></figure>

To test this assumption, we showed that most of the remaining variants that showed different
genotypes between both technologies are clustered in three regions corresponding to three different amplicons (arrows on Figure 8A). The percentage of heterozygosity significantly dropped for variants from amplicons 7, 16, and 22 compared to the same variants genotyped from WGS (Figure 8B) suggesting that different genotypes are homozygous in amplicons, and heterozygous in WGS. In conclusion, this confirms the amplicons-related bias hypothesis and BC genotypes have been correctly rebuilt.

<figure><img src="image_8.png" /><figcaption>**Figure 8: Impact on heterozygosity of variants with different genotypes in both technologies.** (A) WGS depth of variants with different genotypes in amplicons and WGS which pass 14 and 29 thresholds along the region of interest. (B) Percentages of heterozygosity in amplicons (blue) and WGS (red) for each amplicon related to the number of variants in each amplicon range (bars).</figcaption></figure>

Variant exploration

The whole dataset included 25 animals (6MS, 6LW, and 13 BC) and 7654 variants before any filtering. Missing genotypes have been imputed and haplotypes have been phased to identify the common MS haplotype carried out by all BC from the pedigree (Figure 9A). A first filtering step pruning multiallelic variants, indels, and low informative genotypes removed 536 variants. A second filtering removing 550 variants that showed Mendelian incompatibility between the MS founder and BC offspring has been applied. A third filter was based on the experimental design and progeny-testing showing that all BC were heterozygous for the QTL located on SSC1 suggesting the causal mutation is expected to be heterozygous in all BC. This assumption resulted in a final dataset of 1616 variants that are all considered candidate mutations. The functional consequences of the remaining variants have been then estimated using Variant Effect Predictor (VEP). Most of the variants are non-coding as shown in Figure 9B suggesting an impact on chromatin regulation.
<figure><img src="image_9.png" /><figcaption>**Figure 9:** From input vcf to annotated variants. (A) From input vcf, variants genotyped by WGS and amplicons technologies have been analyzed to produce merged backcross we can trust. Variants are then phased and imputed before filtering on heterozygosity and estimating how they are distributed in annotations terms. (B) Annotation using VEP leads to the observation that 97% of kept variants in the QTL are non-coding (mainly intronic, intergenic).</figcaption></figure>

First analysis of DeepBind predictions allow the selection of a subset of
58 variants of interest

<figure><img src="image_10.png" /><figcaption>**Figure 10:** Variant prioritization. On one hand, variants overlapping peaks of predictions are extracted given a threshold. On the other hand, the 50 best scores are extracted for each prediction. The intersection of the two analyses leads to a subset of 58 variants of interest.</figcaption></figure>

To sort and prioritize variants among the 1616 remaining, based on their potential functional impact on chromatin regulation, a focus has been done on the 20 ChIP experimental predictions (related to different DNA binding proteins) associated with to liver, adipose, or muscle. For each of the 20 DNA binding factors, 2 analyses are realized: (i) the overlap between variant
locations and predicted peaks and (ii) the evaluation of best impact scores between both alleles.

As shown in Figure 10 (top), the number of variants overlapping more than 6 predictions shows a break in the distribution curve, so a threshold to keep those variants seemed to be a good threshold as the first subset of variants. Using this threshold, 231 variants are kept. In parallel, the distribution of best impact scores shows that 472 variants are observed. The intersection of variants overlapping peaks in more than 6 predictions and observed in best impact scores leads to the extraction of 58 variants. Those variant subsets are showed in IGV and associated with overlaps on predicted peaks (Figure 11).

<figure><img src="image_11.png" /><figcaption>**Figure 11: IGV plot of variants of interest on the region of interest.** The first track represents the 231 variants that overlap peaks on more than 6 predictions. The second track is the 472 variants observed in the best scores of the 20 predictions. The third track is the 58 variants observed in more than 6 predictions and the best scores. The fourth track is the two variants among the previous track which are observed in best scores of 8 experiments. The other tracks correspond to the DNA binding proteins onto the two variants of the fourth track overlap as surrounded.</figcaption></figure>

# Discussion and Conclusions

Despite the ever-increasing amount of data available to improve the annotation of farm animal genomes, much more studies especially chromatin regulation experiments are needed to have the most complete overview<sup>7,27</sup>. Indeed, the lack of full annotation limits the possibilities in the estimation of the impact of variants on phenotypes. Linking genotype to phenotype requires therefore in-depth knowledge of gene regulation, given that 75 percent of genetic
variants are located in the non-coding sequences<sup>28</sup>. In contrast, human and murine data related to gene regulation mechanisms are highly available and annotated. The current developments of deep learning methods applied to genetics and genomics bring new opportunities for livestock genomics through cross-species approaches<sup>29,30</sup>.

Here, we applied this strategy to a region of chromosome 1 of the pig genome that is involved in growth and adiposity<sup>5</sup>. With more than 66% of truly predicted peaks by DeepBind and Pearson's correlations between 0.5 and 0.8 for Enformer, the predictions can be considered good enough to offer an alternative and compensate for the lack of pig-annotated data. The potential functional effect of variants has then been predicted in order to sort and prioritize them for reducing the number of variants of interest. Among the 1616 candidate variants located within the genomic interval, only 231 overlapped likely important predicted regulation elements. Combining this information with the functional impact of each variant led to a subset of 58 variants of interest.

The reduction of the number of candidate variants is an important point but this work is also a proof-of-concept of the cross-species use of neural networks. Given this, some limits are known and can lead to an improvement in the results:

(i) Some variants, because of poor quality scores, have been filtered out during the variants calling or with so low depth and we cannot exclude that they could be associated with the phenotype;

(ii) Although predictions are estimated well, it has been validated on 1 prediction of 1 DNA-binding protein (CTCF) for a limited portion of the pig genome around our QTL. Then, extrapolation on conditions of interest is based on this consideration only;

(iii) The predictions are related to human and mouse genomes so pig species-specific motifs would probably be ignored in the estimation of the binding sites, which can explain part of the variability between observations and predictions<sup>31</sup>;

(iv) During the impact prediction step, scores have been calculated independently between the pig reference sequence (Sus Scrofa 11.1) and the alternative allele from LW or MS breed, so the haplotypes are not considered;

(v) A limitation of the use of NN is the need for some computational resources, particularly Enformer, and the need for particular expertise to use it properly. Furthermore, because of their architectures, DeepBind and Enformer do not predict with the same accuracy. DeepBind predicts a probability of observing a peak while Enformer, for each bin, predicts the number
of reads. The size of bins (200 bp for DeepBind and 128 bp for Enformer) leads to higher resolution from Enformer predictions and the use of transformers allows it to take into account a broader genomic environment. However, it is also a good reason to hope for some improvement in our conclusions from Enformer predictions.

To conclude and go further, the biological implication in chromatin regulation of DNA binding proteins and histone marks, predicted as of interest, will have to be considered. For proteins, the presence or absence of binding motifs can be used in conjunction with their biological role to show if they bind directly on the DNA or in a complex. If the research of remaining predictions is not conclusive, because we don't know how many are responsible for the phenotype nor the biological process they are implicated in, the use of other neural networks specialized in other mechanisms like alternative splicing can be applied to our sequence³². At last, when the number of variants will be small enough, biological validation will be necessary to associate predicted implications with experimental observation.
# Annexes

**Annexe 1: Data from FAANG project used to compare predictions.** The first table contains CTCF-related data information, the second table contains data related to H3K4 histone marks, and the third table contains data related to H3K27 histone marks.

<table><thead><tr><th>Extraction database</th><th>Protein</th><th>Tissue</th><th>Accession number</th><th>File format</th></tr></thead><tbody><tr><td rowspan="6">UCDAVIS</td><td rowspan="12">CTCF</td><td rowspan="2">Liver</td><td>CTCF_Liver_P348.bw</td><td rowspan="6">bigwig</td></tr><tr><td>CTCF_Liver_P350.bw</td></tr><tr><td rowspan="2">Lung</td><td>CTCF_Lung_P348.bw</td></tr><tr><td>CTCF_Lung_P350.bw</td></tr><tr><td rowspan="2">Spleen</td><td>CTCF_Spleen_P348.bw</td></tr><tr><td>CTCF_Spleen_P350.bw</td></tr><tr><td rowspan="2">Liver</td><td>GSM4799683</td><td rowspan="6">bed</td></tr><tr><td>GSM4799684</td></tr><tr><td rowspan="2">Lung</td><td>GSM4799689</td></tr><tr><td>GSM4799690</td></tr><tr><td rowspan="2">Spleen</td><td>GSM4799701</td></tr><tr><td>GSM4799702</td></tr></tbody></table>
<table><thead><tr><th>Extraction database</th><th>Histone mark</th><th>Tissue</th><th>Accession number</th><th>File format</th></tr></thead><tbody><tr><td rowspan="15">VizFaDa</td><td rowspan="10">H3K4me1</td><td rowspan="2">Colon</td><td>ERX4019700</td><td rowspan="15">biwig</td></tr><tr><td>ERX4019725</td></tr><tr><td rowspan="4">Liver</td><td>ERX3212551</td></tr><tr><td>ERX3212556</td></tr><tr><td>ERX3212561</td></tr><tr><td>ERX3212566</td></tr><tr><td rowspan="3">Longissimus muscle</td><td>ERX3979844</td></tr><tr><td>ERX3979849</td></tr><tr><td>ERX3979859</td></tr><tr><td rowspan="2">Stomach</td><td>ERX4019720</td></tr><tr><td>ERX4019745</td></tr><tr><td rowspan="10">H3K4me3</td><td rowspan="2">Colon</td><td>ERX4019698</td><td rowspan="10"></td></tr><tr><td>ERX4019723</td></tr><tr><td rowspan="4">Liver</td><td>ERX3212552</td></tr><tr><td>ERX3212557</td></tr><tr><td>ERX3212562</td></tr><tr><td>ERX3212567</td></tr><tr><td rowspan="3">Longissimus muscle</td><td>ERX3979845</td></tr><tr><td>ERX3979850</td></tr><tr><td>ERX3979860</td></tr><tr><td rowspan="2">Stomach</td><td>ERX4019718</td></tr><tr><td>ERX4019743</td></tr></tbody></table>
<table><thead><tr><th>Extraction database</th><th>Histone mark</th><th>Tissue</th><th>Accession number</th><th>File format</th></tr></thead><tbody><tr><td rowspan="14">VizFaDa</td><td rowspan="14">H3K27ac</td><td rowspan="2">Colon</td><td>ERX4019699</td><td rowspan="14">biwig</td></tr><tr><td>ERX4019724</td></tr><tr><td rowspan="4">Liver</td><td>ERX3212554</td></tr><tr><td>ERX3212559</td></tr><tr><td>ERX3212564</td></tr><tr><td>ERX3212569</td></tr><tr><td rowspan="3">Longissimus muscle</td><td>ERX3979847</td></tr><tr><td>ERX3979852</td></tr><tr><td>ERX3979862</td></tr><tr><td rowspan="2">Stomach</td><td>ERX4019719</td></tr><tr><td>ERX4019744</td></tr><tr><td rowspan="14">H3K27me3</td><td rowspan="4">Colon</td><td>ERX4019701</td><td rowspan="14"></td></tr><tr><td>ERX4019726</td></tr><tr><td>ERX3212553</td></tr><tr><td>ERX3212558</td></tr><tr><td rowspan="3">Liver</td><td>ERX3212563</td></tr><tr><td>ERX3212568</td></tr><tr><td>ERX3979846</td></tr><tr><td rowspan="3">Longissimus muscle</td><td>ERX3979851</td></tr><tr><td>ERX3979861</td></tr><tr><td>ERX4019721</td></tr><tr><td rowspan="2">Stomach</td><td>ERX4019746</td></tr><tr><td></td></tr></tbody></table>

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Résumé

Le protocole PORQTL initié par l'INRA en 1991 a pour but d'identifier chez le porc les principaux loci associés à des caractères quantitatifs d'intérêt agronomique. L'une de ces région, sur le bras long du chromosome 1, a été associée à des caractères de poids et de gras et son intervalle a été réduit à 300 kb. Des travaux ont permis de dénombrer 2429 variants potentiellement associés à ces caractères. Les méthodes utilisées jusqu'alors n'ont pas permis d'en réduire le nombre mais l'utilisation de réseaux de neurones profonds semble être une alternative viable. Mon projet a pour but de faire la preuve de concept de l'utilisation inter-espèces des réseaux de neurones DeepBind et Enformer, entrainés avec des séquences humaines et murines, pour prédire l'impact potentiel de variants à partir de séquences de porc afin de proposer un ensemble réduit de variants candidats. Nos résultats préliminaires (i) valident l'exploitation translationnelle de ces méthodes d'apprentissage profond pour améliorer l'annotation des génomes des animaux d'élevage (ii) permettent de réduire à 58, dans la région d'intérêt du chromosome 1, la liste de variants candidats fonctionnels affectant potentiellement la régulation de la chromatine.

Summary

The PORQTL protocol, initiated by INRA in 1991, aims to determine in pigs the main
quantitative trait loci (QTL) associated with agronomical phenotypes. One of these QTLs,
on the long arm of chromosome 1, has been associated with carcass traits, and its interval has
been shortened to 300 kb. A total of 2,429 variants potentially associated with these traits
have been identified. The methods used to date have not been able to reduce the number of
variants, but the use of neural networks appears to be a promising alternative. The aim of
my internship is to prove the cross-species use of DeepBind and Enformer neural networks,
trained with human and murine sequences, to predict the potential impact of variants from pig
sequences in order to propose a reduced set of candidate variants. Our preliminary results (i)
validate the translational exploitation of these deep learning methods to improve the annotation
of farm animal genomes (ii) reduce the list (n=58) of functional candidate variants potentially
affecting chromatin regulation.