Design of a low density SNP chip for genomic selection in layer chicken

Herry Florian, Hérault Frédéric, Varenne Amandine, Burlot Thierry, Le Roy Pascale,
Allais Sophie

Novogen, Mauguerand, 22800 Le Foeil ; PEGASE, INRA, Agrocampus Ouest, Domaine de
la Prise, 35590 Saint Gilles florian.herry@inra.fr,France
PEGASE, INRA, Agrocampus Ouest, Domaine de la Prise, 35590 Saint Gilles
freric.herault@inra.fr France

Novogen, Mauguerand, 22800 Le Foeil amandine.varenne@novogen-layers.com, France
Novogen, Mauguerand, 22800 Le Foeil thierry.burlot@novogen-layers.com France

PEGASE, INRA, Agrocampus Ouest, Domaine de la Prise, 35590 Saint Gilles pascale.le-
roy@inra.fr France

PEGASE, INRA, Agrocampus Ouest, Domaine de la Prise, 35590 Saint Gilles
sophie.allais@agrocampus-ouest.fr France

Corresponding author : florian.herry@inra.fr

The main goal of selection is to choose breeders of the next generation among a set of selection
candidates. In genomic selection, the choice of breeders rests on the use of information on DNA
polymorphisms, in particular SNP, in addition of performance measures. Since 2013, a
commercial high density genotyping chip (600 000 markers) for chicken allowed the
implementation of genomic selection in layer and broiler breeding. However, genotyping costs
with this chip still remain high for a routine use on a large number of selection candidates.
Consequently, the current aim of genomic selection is to develop, at a lower cost, low density
genotyping chips (LD chips). To do so, a set of SNP markers has to be selected to enable an
imputation (prediction) of missing genotypes on a high density chip (HD chip).

In this perspective, various simulation studies were conducted to choose the best strategy for
low density genotyping of laying hen lines. Different genotyping densities, several population
scenarii and two imputation programs (FImpute V2.2 and Beagle V4.1) were tested. Low
density genotyping chips were designed according to two strategies: a choice of SNP depending
on a clustering based on linkage disequilibrium threshold or a choice of SNP at regular intervals.
Three criteria of imputation accuracy were compared: genotyping error rate, allelic error rate
and correlation. It appears that genotyping error rate as a criterion is more discriminating than
the two others. FImpute software is preferred to Beagle software because FImpute is a better
compromise between imputation accuracy and calculation time. The study of different low
density genotyping chips shows that imputations improve with SNP density, when the LD
threshold increases and when SNP with low MAF (Minor Allele Frequency) are considered.
Population studies show that imputations are better when the size of the reference population
increases. The influence of kinship degree between reference population and target population,
and the impact on accuracy of genomic evaluations still remain to be deepened.

These studies will be extended to a second laying hens line to see if a shared low density
genotyping chip can be created for both lines.

Keywords: Imputation accuracy ; layer chickens ; SNP density ; reference population, genomic
evaluation
# DESIGN OF A LOW DENSITY SNP CHIP FOR GENOMIC SELECTION IN LAYER CHICKENS

Herry Florian¹,², Hérault Frédéric², Varenne Amandine¹, Burlot Thierry¹, Le Roy Pascale² & Allais Sophie²

¹NOVOGEN, 22800 Le Foeil, ²PEGASE, INRA, Agrocampus Ouest, 35590 Saint-Gilles

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## INTRODUCTION

Since 2013, a commercial high-density SNP chip of 600 000 SNP for chicken is available and enables the implementation of genomic selection in layers production. However, genotyping costs still remain high for a routine use on a large number of selection candidates. Combining genotyping on low-density SNP chip, at a lower cost, and genotype imputation should be considered on a large number of selection candidates. Thus, the definition of SNP panel is the milestone of this approach.

The main objective of this study is to choose the best strategy for low density genotyping of laying hen lines in order to optimize selection scheme.

## MATERIALS AND METHODS

**Study populations:** Study of two different lines of Rhode Island (RI) and Leghorn (L) from Novogen, genotyped on HD SNP chip (Table 1). After quality control, respectively 300 351 and 245 667 SNP are retained and distributed for Rhode Island and Leghorn lines on the genome.

<table><caption>Table 1: Study populations</caption><thead><tr><th>Line</th><th>Rhode Island</th><th>Leghorn</th></tr></thead><tbody><tr><td>Number</td><td>1027</td><td>1474</td></tr><tr><td>Sires</td><td>1027 ♂</td><td>561 ♂</td></tr><tr><td>Dams</td><td>0 ♀</td><td>913 ♀</td></tr><tr><td>Generation 0 (G0)</td><td>447</td><td>711</td></tr><tr><td>Generation 1 (G1)</td><td>580</td><td>763</td></tr></tbody></table>

**Imputation strategies:** Use of Fimpute (Sargolzaei et al., 2014) to impute G0 from G1. From low density SNP chips designed, study of the effect of:

* SNP density,

* LD threshold used to designed low density SNP chips,

* the type of chromosome (macro-chromosomes (1 to 5), intermediate chromosomes (6 to 10), micro-chromosomes (11 to 33) and sexual chromosome Z,

* MAF (Minor Allelic Frequency) of SNP,

* the methodology used to designed low-density SNP chips.

**Low density SNP chips:**

Simulation of low-density SNP chip according to two intra-chromosomes methodologies (Table 2):

* « Equidistant » methodology: Selection of SNP at regular intervals.

* « Linkage Disequilibrium » methodology: Selection of SNP according to the LD between SNP.

<table><caption>Table 2: Summary of SNP chips studied</caption><thead><tr><th rowspan="2">Methodology</th><th rowspan="2">SNP Chip</th><th colspan="2">Number of SNP</th></tr><tr><th>Rhode Island</th><th>Leghorn</th></tr></thead><tbody><tr><td rowspan="10">Equidistant</td><td>50Kequi</td><td>49636</td><td>50307</td></tr><tr><td>40Kequi</td><td>40160</td><td>39838</td></tr><tr><td>30Kequi</td><td>29970</td><td>30075</td></tr><tr><td>20Kequi</td><td>19910</td><td>19948</td></tr><tr><td>15Kequi</td><td>14963</td><td>14955</td></tr><tr><td>10Kequi</td><td>10001</td><td>9966</td></tr><tr><td>7.5Kequi</td><td>7527</td><td>7496</td></tr><tr><td>5Kequi</td><td>4991</td><td>4996</td></tr><tr><td>4Kequi</td><td>4023</td><td>4000</td></tr><tr><td>3Kequi</td><td>2992</td><td>3003</td></tr><tr><td>2Kequi</td><td>2013</td><td>2004</td></tr><tr><td rowspan="6">Linkage Disequilibrium</td><td>DL0.8</td><td>21717</td><td>18052</td></tr><tr><td>DL0.7</td><td>16615</td><td>13696</td></tr><tr><td>DL0.6</td><td>13214</td><td>10736</td></tr><tr><td>DL0.5</td><td>10711</td><td>8626</td></tr><tr><td>DL0.4</td><td>8521</td><td>6944</td></tr><tr><td>DL0.3</td><td>6875</td><td>5578</td></tr><tr><td></td><td>DL0.2</td><td>5371</td><td>4330</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Figure 2: Evolution of genotyping error rate as a function of the number of SNP on low density SNP chip</figcaption></figure>

**Choice of the methodology:**

* « Equidistant » methodology: Number of SNP proportional to the size of chromosome (Figure 4).

* « LD » methodology: Consideration of the particular structure of chicken species' LD and of the lower persistence of LD on micro-chromosomes than on macro-chromosomes.

→ Necessity of a greater number of SNP on micro-chromosome to cover the whole chromosome (Figure 4).

* « LD » methodology seems to be the most appropriated to get good imputations.

<figure><img src="image_4.png" /><figcaption>Figure 4: Evolution of the ratio Number of SNP/Chromosome size as a function of the type of chromosome.</figcaption></figure>

**Influence of Minor Allelic Frequencies (MAF):**

* Variability of genotyping error rate higher with equidistant methodology than with LD methodology (Figure 5).

* Lower genotyping error rates obtained with LD methodology, except for SNP with high MAF.

* Decrease of genotyping error rates for SNP high MAF with equidistant methodology.

* Valid for both lines.

<figure><img src="image_5.png" /><figcaption>Figure 3: Evolution of genotyping error rate as a function of LD threshold</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 5: Evolution of genotyping error rate according to the MAF for Rhode Island Line.</figcaption></figure>

* Consideration of the particular structure of chicken species' LD with LD methodology.

* Better results of imputation obtained with low density SNP chip based on LD.

* According to the MAF, lower genotyping error rates obtained with LD methodology, excepted for SNP with high MAF.

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Xth European Symposium on Poultry Genetics - from 26/06/2017 to 28/06/2017 - St-Malo

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INRA
SCIENCE & IMPACT

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GROUPE GRIMAUD
L'éclosion de la Vie

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vie agro santé
Ecole doctorale de Rennes