Ytournel, Florence (2008) Déséquilibre de liaison et cartographie de QTL en population sélectionnée. PhD thesis Génétique animale, UFR Génétique, elevage et Reproduction, AgroParistech 2008AGPT0004 p.219.

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Abstract

Linkage disequilibrium (LD) is due to non random associations between alleles at two loci. It has

become a classical tool to fine map loci implied in quantitative trait (Quantitative Trait Loci, QTL)

determinism, through identification of the maxima of LD between alleles of a marker locus (or a group

of marker loci) and a locus involved in the variability of a quantitative trait. The creation and intensity

of LD evolves according to the evolutionary forces affecting the population. Among these forces,

random drift and selection are particularly present in livestock populations. This PhD thesis aimed to

study the influence of selection on the structure of LD around a QTL, as well as its impact on the

precision on the fine mapping of QTL.

A software has been developed to simulate the evolution of populations. Starting from a population in

linkage equilibrium, LD due to evolutionary forces is created over historical generations. QTL

detection is applied to the next generations where the pedigree is known. The main experimental

designs applied in livestock populations are implemented in the software. All data used for the further

analysis of this work were obtained from this simulation program.

We first analyzed LD in the neighbourhood of the QTL by recording the location of the marker in

maximum LD with the QTL. LD was estimated with two classical measures: D’ and χ²’. Molecular

information was either provided by single markers or by haplotypes composed of two or four markers.

The concentration of the loci in maximum LD around the QTL first increased over the generations,

before reducing. For populations of 100 or 200 individuals observed after 100 generations, the 95%

confidence interval of the position in maximum LD with the QTL is 5 to 7 cM for markers separated

by 1 cM. Selection augments the length of this segment of 0 to 4 cM, mainly because of the loss of the

genetic variability in the co-selected region located around the QTL. This implies that selection

reduces the efficiency of fine mapping when methods using LD are employed.

The influence of selection on the accuracy of fine mapping methods has been studied in a second step.

One of the most classical methods in animal genetics estimates the probabilities of Identity By

Descent (IBD) of pairs of loci to use LD. We investigated the quality of the estimation of these

probabilities in relation to the real IBD status of the QTL. IBD probabilities estimated between QTL

being truly IBD were lower when the populations were selected, with an increase of the frequencies of

the probabilities comprised between 0.5 and 0.8. In all the simulated designs, the probabilities of non

IBD QTL remain close to 0. But it has not been proved possible to define a rule to determine a

threshold allowing for grouping 95% of the IBD QTL with an error rate lower than 5%. With such a

threshold, a limited number of clusters of haplotypes could be established, such that the computing

difficulties when fine mapping the QTL with methods using the estimation of the variance components

as classically done could be reduced.

The accuracy for fine mapping purposes of five methods that use different kinds of molecular

information and include the pedigree information or not was compared. The optimal method differs

depending on the simulated data: the linear regression on a single marker provided the most accurate

results when multi-allelic markers are used, while the method of analysis of the variance components

using 4-loci haplotypes was the most precise with bi-allelic markers. Selection affected the fine

mapping accuracy but does not influence the ranking of the methods.

To sum up, selection influences both the LD structure in the QTL neighbourhood and the fine

mapping accuracy by increasing the distance between the QTL and (a) the locus in maximum LD with

it and (b) the QTL and its estimated position.

Table of content

INTRODUCTION GENERALE - 15

PREMIERE PARTIE : REVUE BIBLIOGRAPHIQUE - 19

Partie 1.1. Déséquilibre de liaison dans les populations animales - 21

Introduction - 23

I. Définition et propriétés - 24

II. Mesures du déséquilibre de liaison entre locus - 26

II.A. Mesures du LD entre deux locus - 26

II.B. Mesures du LD avec plus de deux locus - 29

III. Forces évolutives et déséquilibre de liaison - 29

III.A. Mutation - 29

III.B. Migration et mélange de populations - 30

III.C. Dérive génétique - 31

III.D. Sélection - 32

IV. Déséquilibre de liaison dans des populations animales - 33

Partie 1.2. Cartographie fine de QTL - 39

Introduction - 42

I. Principes et facteurs influençant la résolution de la cartographie - 43

I.A. Principe général - 43

I.B. Facteurs de précision de la cartographie - 46

II. Dissection chromosomique - 54

III. Méthodes statistiques de cartographie fine - 59

III.A Méthodes utilisant exclusivement le LD - 59

III.B Méthodes combinant LD et analyse de liaison - 63

Conclusion - 66

DEUXIEME PARTIE : EVOLUTION DE LA STRUCTURE DU

DESEQUILIBRE DE LIAISON SOUS L’INFLUENCE DE LA SELECTION73

Partie 2.1. Développement d’un simulateur : « Linkage Disequilibrium with several

options » (LDSO) - 77

Introduction - 79

I. Article - 80

II. Justification du choix de simulation des haplotypes - 88

III. Dispositifs expérimentaux simulables - 90

Conclusion - 94

12

Partie 2.2. Structure du déséquilibre de liaison dans des populations sélectionnées - 95

Introduction - 97

I. Article - 98

II. Résultats complémentaires sur les locus en LD maximum avec le QTL et discussion

finale - 124

III. Conclusion - 128

TROISIEME PARTIE : INFLUENCE DE LA SELECTION SUR LES

METHODES DE CARTOGRAPHIE FINE - 131

Partie 3.1 : Influence de la sélection sur les probabilités d’IBD - 133

Introduction - 135

I. Article - 136

II. Lien entre l’IBD et le LD - 147

II.A. Critère de comparaison - 147

II.B. Résultats - 147

III. Conclusion - 148

Partie 3.2 : Robustesse des méthodes de cartographie fine à la sélection - 151

Introduction - 153

I. Matériel et méthodes - 154

I.A. Populations simulées - 154

I.B. Cartes génétiques simulées - 155

I.C. Méthodes de cartographie fine - 155

I.D. Evaluation de la précision de cartographie des méthodes - 160

I.E. Evaluation de la concordance entre LD maximum et position putative du QTL

161

II. Résultats - 161

II.A. Comparaison des méthodes - 161

II.B. Sensibilité des méthodes à la sélection - 163

II.C. Concordance LD – cartographie du QTL - 164

III. Discussion - 168

IV. Conclusion - 171

QUATRIEME PARTIE : DISCUSSION GENERALE ET PERSPECTIVES.. 173

Introduction - 175

I. Réalisme des simulations - 176

I.A. Adéquation aux populations réelles - 176

I.B. Adéquation aux données génétiques réelles - 178

II. Effets de la sélection - 179

II.A. LD et lien LD - IBD - 179

II.B. Cartographie - 182

Conclusion générale - 185

Références bibliographiques - 189

13

Glossaire - 199

ANNEXES - 201

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