Study of linkage disequilibrium in genetic resources: how to understand population history and exploit LD for quantitative genetics
Linkage disequilibrium — the nonrandom association of alleles at different loci — reflects the population genetic forces that structure a genome. Evolutionary biologists and geneticists are increasingly exploiting linkage disequilibrium to understand past evolutionary and demographic events. Estimation of linkage disequilibrium, together with knowledge of the structure of genetic diversity, is also crucial to design and perform association genetics studies in any plant species, in order to identify genes of interest. In heterozygous and orphan plants, such knowledge is not easy to obtain and appropriate methods need to be developed.
The Linkage disequilibrium project aims t o develop statistical methods and to make these and other approaches available via a platform open to the scientific community, to enable the production of the required knowledge on LD estimation and inference of population histories. This platform will then be used for the development of association genetics studies and genome-wide estimation of LD in many highly heterozygous species or orphan species, particularly those analyzed by the ARCAD community.
More information:
Rationale
The study of the genetic determinism of quantitative agronomical traits and the development of molecular markers are especially pertinent for perennial species in order to speed up breeding processes. Classical QTL approaches for these species are costly and time-consuming in the generation of trait-specific progenies.
An innovative alternative is based on association studies in non-related genetic resources using linkage disequilibrium in natural populations for the identification of the genes underlying quantitative variations.
However, the application of current methods to a large list of species is not simple, due to the impact of several parameters inherent to plant species. The level of resolution and the power of the analyses greatly depend on the genetic structure of populations analyzed and on the level of LD in the populations. The estimation of LD itself depends on the type of markers used and is difficult in structured populations and for heterozygous models (estimation of haplotype).
On the other hand, linkage disequilibrium — the non-random association of alleles at different loci — is a sensitive indicator of the population genetic forces that structure a genome. Evolutionary biologists and geneticists are increasingly exploiting linkage disequilibrium to understand past evolutionary and demographic events and the joint evolution of linked sets of genes.
To answer these questions, theoretical advances are required and these would greatly benefit from comparative studies on plant models with very different characteristics: annual versus perennial plants, autogamous versus allogamous plants, homozygous versus heterozygous models.
Objectives
We will develop statistical methods and a platform open to the ARCAD scientific community to enable the production of the required knowledge on LD estimation, inference of population histories in association genetics for Mediterranean and tropical heterozygous plant models.
The scientific objectives of the project are to:
- develop bioinformatics tools for handling data produced by high-throughput analysis of genetic resources,
- develop bio-statistical methods and concepts to be applied to the different aspects of the analysis, from the reconstruction of haplotypes, to the estimation of LD in structured and non structured population, to the inference of species history from the estimation of LD extent.
Actions planned
WP1: Statistical development of methods for haplotype reconstruction
WP2: LD estimation in relation with population structure
WP3: Inference of population history by the analysis of LD
Project team
| SURNAME, Name | Institution | Research unit | ||
| SEGUIN, Marc | Project co-leader | CIRAD | AGAP | mac.seguin[at]cirad.f |
| THIS, Patrice | Project co-leader | INRA | AGAP | patrice.this[at]supagro.inra.fr |