Software for the calculation of genetic linkage maps in experimental populations

JoinMap® 4, Software for the calculation of genetic linkage maps in experimental populations

Maize silage is a significant energy source for animal production operations, and the efficiency of the conversion of forage into animal mass is an important consideration when selecting cultivars for use as feed. Fiber and lignin are negatively correlated with digestibility of feed, so the development of forage with reduced levels of these cell-wall components (CWCs) is desirable. While variability for fiber and lignin is present in maize germplasm, traditional selection has focused on the yield of the ear rather than the forage quality of the whole plant, and little information is available concerning the genetics of fiber and lignin. The objectives of this study were to map quantitative trait loci (QTLs) for fiber and lignin in the maize stalk and compare them with QTLs from other populations. Stalk samples were harvested from 191 recombinant inbred lines (RILs) of B73 (an inbred line with low-to-intermediate levels of CWCs) x De811 (an inbred line with high levels of CWCs) at two locations in 1998 and one in 1999 and assayed for neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL). The QTLs were detected on nine chromosomes, mostly clustered in concordance with the high genetic correlations between NDF and ADF. Adjustment of NDF for ADF and ADF for ADL revealed that most of the variability for CWCs in this population is in ADF. Many of the QTLs detected in this study have also been detected in other populations, and several are linked to candidate genes for cellulose or starch biosynthesis. The genetic information obtained in this study should be useful to breeding efforts aimed at improving the quality of maize silage.

Quantitative trait loci for cell-wall components in recombinant inbred lines of maize (Zea mays L.) I: stalk tissue.

https://www.cell.com/trends/plant-science/pdf/S1360-1385(00)01797-0.pdf

Phenotypic plasticity for plant development, function and life history.

A very general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci (QTLs) and markers] in segregating generations obtained from line crosses. The method exploits two features, (a) the use of additional parental and F1 data, which fixes the joint QTL effects and the environmental error, and (b) the use of markers as cofactors, which reduces the genetic background noise. As a result, a significant increase of QTL detection power is achieved in comparison with conventional QTL mapping. The core of the method is the completion of any missing genotypic (QTL and marker) observations, which is embedded in a general and simple expectation maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. The method is described in detail for the analysis of an F2 generation. Because of the generality of the approach, it is easily applicable to other generations, such as backcross progenies and recombinant inbred lines. An example is presented in which multiple QTLs for plant height in tomato are mapped in an F2 progeny, using additional data from the parents and their F1 progeny.

https://www.genetics.org/content/genetics/136/4/1447.full.pdf

High Resolution of Quantitative Traits Into Multiple Loci via Interval Mapping

The development of linkage maps with large numbers of molecular markers has stimulated the search for methods to map genes involved in quantitative traits (QTLs). A promising method, proposed by Lander and Botstein (1989), employs pairs of neighbouring markers to obtain maximum linkage information about the presence of a QTL within the enclosed chromosomal segment. In this paper the accuracy of this method was investigated by computer simulation. The results show that there is a reasonable probability of detecting QTLs that explain at least 5% of the total variance. For this purpose a minimum population of 200 backcross or F2 individuals is necessary. Both the number of individuals and the relative size of the genotypic effect of the QTL are important factors determining the mapping precision. On the average, a QTL with 5% or 10% explained variance is mapped on an interval of 40 or 20 centiMorgans, respectively. Of course, QTLs with a larger genotypic effect will be located more precisely. It must be noted, however, that the interval length is rather variable.

Accuracy of mapping quantitative trait loci in autogamous species.

The interval mapping method is widely used for the genetic mapping of quantitative trait loci (QTLs), though true resolution of quantitative variation into QTLs is hampered with this method. Separation of QTLs is troublesome, because single-QTL is models are fitted. Further, genotype-by-environment interaction, which is of great importance in many quantitative traits, can only be approached by separately analyzing the data collected in multiple environments. Here, we demonstrate for the first time a novel analytic approach (MQM mapping) that accommodates both the mapping of multiple QTLs and genotype-by-environment interaction. MQM mapping is compared to interval mapping in the mapping of QTLs for flowering time in Arabidopsis thaliana under various photoperiod and vernalization conditions.

/pdf/genotype-by-environment-interaction-in-genetic-mapping-of-3lx9se10as.pdf

Genotype-by-environment interaction in genetic mapping of multiple quantitative trait loci.

This paper describes a novel combination of techniques for the construction of dense genetic linkage maps. The construction of such maps is hampered by the occurrence of even small proportions of typing errors. Simulated annealing is used to obtain the best map according to the optimality criterion: the likelihood or the total number of recombination events. Spatial sampling of markers is used to obtain a framework map. The construction of a framework map is essential if the steps used for simulated annealing are required to be simple. For missing-data imputation the Gibbs sampler is used. Map construction using simulated annealing and missing-data imputation are used in an iterative way. In order to obtain some measure of precision of the genetic linkage map obtained, the Metropolis-Hastings algorithm is used to obtain posterior intervals for the positions of markers. The process of map construction is embedded in a framework of pre-mapping and post-mapping diagnostics. The techniques described are illustrated using a practical application.

Constructing dense genetic linkage maps

Segregation of 692 polymorphic AFLPTM (amplified fragment length polymorphism) fragments was determined in an F2 of the interspecific cross A. roylei x A. cepa. Two different enzyme combinations were used, PstI/MseIand EcoRI/MseI; in the latter one extra selective nucleotide was added to the MseI primer. The map based on A. cepa markers consisted of eight linkage groups with 262 markers covering 694 cM of the expected 800 cM. The map based on A. roylei markers comprised 15 linkage groups with 243 markers and had a length of 626 cM. The two maps were not integrated, and 25% of the markers remained unlinked. One of the alliinase genes and a SCAR marker linked to the disease resistance gene to downy mildew are present on this map. Of the AFLP markers, 50—80% were polymorphic between A. cepa and A. roylei; the level of polymorphic markers between different A. cepa accessions was4-8%.

J.W. van Ooijen

Papers

Accuracy of mapping quantitative trait loci in autogamous species.

Accuracy of mapping quantitative trait loci in autogamous species.

Genotype-by-environment interaction in genetic mapping of multiple quantitative trait loci.

Constructing dense genetic linkage maps

A genetic map of an interspecific cross in Allium based on amplified fragment length polymorphism (AFLPTM) markers