Linkage Disequilibrium Mapping - LD Mapping

Linkage disequilibrium mapping

Linkage disequilibrium (LD) is the non-random association of alleles at two loci, not necessarily on the same chromosome; while linkage is non-random association of alleles at two loci due to limited recombination between the loci. Linkage of necessity involves loci on the same chromosome. This presentation compares the traditional/conventional QTL mapping and LD mapping, followed by a detailed description and analysis of the latter.

Practical LD mapping

Linkage disequilibrium mapping (LD) or association mapping is a method for QTL (Quantitative Trait Locus) detection widely applied in human genetics. It essentially consists of finding marker-trait associations in genetically diverse populations. LD mapping has recently gained attention in plant genetics. An important asset of association mapping is that there is no need to develop specific crosses as it can take advantage of the use of existing diverse collections of genotypes. In addition, association mapping can target a broader and more relevant genetic spectrum for plant breeders than conventional QTL mapping does. However, significant marker-trait association may or may not be the consequence of physical linkage between markers and QTLs (false positives or ‘spurious’ associations). A major cause of false positives is the genetic correlation between individuals stemming from the genetic relatedness. Several strategies have been proposed to account for genetic relatedness, either by structuring the population and imposing the groupings in the statistical model (Pritchard et al. 2000; Kraakman et al. 2004) or by using estimates of genetic relatedness between individuals, coancestry information, in a mixed model (Yu et al. 2006; Malosetti et al. 2007). An interesting intermediate approach is that based on principal component analysis ideas proposed by Patterson et al. (2006). The different methods have been implemented in GenStat procedures.
This practical uses GenStat to perform association mapping based on different methods to account for genetic relatedness. To illustrate the methods a barley association panel is used as an example (Comadran et al. 2009).

Training Materials - Mixed Model QTL Detection

This material introduces the different aspects of QTL detection with examples. It follows a didactical structure, first introducing basic concepts and gradually incorporating more advanced topics. Each section of the material has a short introduction followed by a number of questions around an example data set. The questions increase in complexity towards the end of the particular section. This is a hands-on training resource, where an example data set is presented and typical research questions are proposed to be solved by the researcher/trainee. An answer sheet with a short discussion of the different questions is included, making it possible to use the material for self-study.

The material has six examples/exercises: Exercise 1 – a simulated data set based on a 1 QTL model, to illustrate the basic principles of QTL detection using segregating populations (marker-based detection, simple interval mapping); Exercise 2 – a simulated data set where several QTLs are included in the model, to illustrate further developments in QTL mapping using segregating populations (eg: use of cofactors in composite interval mapping, QTL model selection, etc); Exercise 3 – a real data set where the principles learnt in exercises 1 and 2 are applied to a real situation; Exercise 4 – a real data set of several field trials, where the principles of field data analysis are discussed (mixed models for field experiments such as RCBD, Alpha design, spatial design, etc), estimation of important genetic parameters such as h2, genetic variance components, as well as the estimation of adjusted means; Exercise 5 – a real multi-environment data set, where phenotypic genotype by environment interaction (GxE) analysis is discussed as a first step of a QTL analysis. In this exercise, molecular marker information is included to discuss the principles of QTL mapping in multiple environments for detection of QTL and QTLxE; Exercise 6 – a real association mapping panel is used to illustrate the principles and statistical methods/models for linkage disequilibrium mapping.

These materials are downloadable below, together with the example dataset and relevant references.

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