Bruno Goffinet

Maximum-likelihood models for mapping genetic markers showing segregation distortion. 2. F2 populations

A maximum-likelihood approach is used in order to estimate recombination fractions between markers showing segregation distortion in backcross populations. It is assumed that the distortions are induced by viability differences between gametes or zygotes due to one or more selected genes. We show that Baileys (1949) estimate stays consistent and efficient under more general assumptions than those defined by its author. This estimate should therefore be used instead of the classical maximum-likelihood estimate. The question of detection of linkage is also discussed. We show that the order of markers on linkage groups may be affected by segregation distortion.

Evaluation of genetic distances between pepper inbred lines for cultivar protection purposes: comparison of AFLP, RAPD and phenotypic data

Summary We evaluated concordance of AFLP and RAPD markers for estimating genetic distances of 47 pepper inbred lines belonging to five varietal types. It enabled us to see the efficiency of these markers for identification, estimation of distances between varieties and variety discrimination. Genetic distance and multidimensional scaling results showed a general agreement between AFLP and RAPD markers. Based on pattern scores, dendrograms were produced by the UPGMA method. Phenetic trees based on molecular data were consistent with the classification of variety group. The precision of the estimation of the genetic distance was given. The molecular genetic distances were correlated with distances based on a set of discriminating agronomic traits measured for identification and distinctiveness tests. The relationship between molecular and morphological distances appeared to be triangular. These results and their implications in the cultivar protection purposes of pepper hybrids are discussed.

Genetic diversity of Ralstonia solanacearum as assessed by PCR-RFLP of the hrp gene region, AFLP and 16S rRNA sequence analysis, and identification of an African subdivision.

The genetic diversity among strains in a worldwide collection of Ralstonia solanacearum, causal agent of bacterial wilt, was assessed by using three different molecular methods. PCR-RFLP analysis of the hrp gene region was extended from previous studies to include additional strains and showed that five amplicons were produced not only with all R. solanacearum strains but also with strains of the closely related bacteria Pseudomonas syzygii and the blood disease bacterium (BDB). However, the three bacterial taxa could be discriminated by specific restriction profiles. The PCR-RFLP clustering, which agreed with the biovar classification and the geographical origin of strains, was confirmed by AFLP. Moreover, AFLP permitted very fine discrimination between different isolates and was able to differentiate strains that were not distinguishable by PCR-RFLP. AFLP and PCR-RFLP analyses confirmed the results of previous investigations which split the species into two divisions, but revealed a further subdivision. This observation was further supported by 16S rRNA sequence data, which grouped biovar 1 strains originating from the southern part of Africa.

MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments

BackgroundIntegration of multiple results from Quantitative Trait Loci (QTL) studies is a key point to understand the genetic determinism of complex traits. Up to now many efforts have been made by public database developers to facilitate the storage, compilation and visualization of multiple QTL mapping experiment results. However, studying the congruency between these results still remains a complex task. Presently, the few computational and statistical frameworks to do so are mainly based on empirical methods (e.g. consensus genetic maps are generally built by iterative projection).ResultsIn this article, we present a new computational and statistical package, called MetaQTL, for carrying out whole-genome meta-analysis of QTL mapping experiments. Contrary to existing methods, MetaQTL offers a complete statistical process to establish a consensus model for both the marker and the QTL positions on the whole genome. First, MetaQTL implements a new statistical approach to merge multiple distinct genetic maps into a single consensus map which is optimal in terms of weighted least squares and can be used to investigate recombination rate heterogeneity between studies. Secondly, assuming that QTL can be projected on the consensus map, MetaQTL offers a new clustering approach based on a Gaussian mixture model to decide how many QTL underly the distribution of the observed QTL.ConclusionWe demonstrate using simulations that the usual model choice criteria from mixture model literature perform relatively well in this context. As expected, simulations also show that this new clustering algorithm leads to a reduction in the length of the confidence interval of QTL location provided that across studies there are enough observed QTL for each underlying true QTL location. The usefulness of our approach is illustrated on published QTL detection results of flowering time in maize. Finally, MetaQTL is freely available at http://bioinformatics.org/mqtl.

Alternative models for QTL detection in livestock. I. General introduction

In a series of papers, alternative models for QTL detection in livestock are proposed and their properties evaluated using simulations. This first paper describes the basic model used, applied to independent half-sib families, with marker phenotypes measured for a two or three generation pedigree and quantitative trait phenotypes measured only for the last generation. Hypotheses are given and the formulae for calculating the likelihood are fully described. Different alternatives to this basic model were studied, including variation in the performance modelling and consideration of full-sib families. Their main features are discussed here and their influence on the result illustrated by means of a numerical example

A method to measure genetic distance between allogamous populations of alfalfa (Medicago sativa) using RAPD molecular markers

Alfalfa (Medicago sativa L.) is a forage legume of world-wide importance whose both allogamous and autotetraploid nature maximizes the genetic diversity within natural and cultivated populations. This genetic diversity makes difficult the discrimination between two related populations. We analyzed this genetic diversity by screening DNA from individual plants of eight cultivated and natural populations of M. sativa and M.  falcata using the RAPD method. A high level of genetic variation was found within and between populations. Using five primers, 64 intense bands were scored as present or absent across all populations. Most of the loci were revealed to be highly polymorphic whereas very few population-specific polymorphisms were identified. From these observations, we adopted a method based on the Roger’s genetic distance between populations using the observed frequency of bands to discriminate populations pairwise. Except for one case, the between-population distances were all significantly different from zero. We have also determined the minimal number of bands and individuals required to test for the significance of between-population distances.

COMPARING POWER OF DIFFERENT METHODS FOR QTL DETECTION

We compared the powers of two methods for detection of quantitative trait loci (QTL) using genetic markers, in the simple case of an interval between two codominant markers and a backcross population. The first method is the interval mapping approach, based on the use of likelihood ratio tests performed in many positions within the interval considered and the second is the classical analysis of variance (ANOVA) testing only on the positions of the two markers. For both approaches we took into account the correlation between tests performed at different markers or positions in the interval. Appropriate thresholds and powers of tests were then calculated using analytical formulations. Simulations were also done to check the validity of the approximations used to calculate the power of the interval mapping test. Results show that the interval mapping test is slightly more powerful (about 5%) than ANOVA for small intervals (less than 20 cM) and that, for quite large effects of the QTL, the advantage of interval mapping increases as the distance between markers increases. It is more than 30% for intervals of about 70 cM.

More about quantitative trait locus mapping with diallel designs

We present a general regression-based method for mapping quantitative trait loci (QTL) by combining different populations derived from diallel designs. The model expresses, at any map position, the phenotypic value of each individual as a function of the specific-mean of the population to which the individual belongs, the additive and dominance effects of the alleles carried by the parents of that population and the probabilities of QTL genotypes conditional on those of neighbouring markers. Standard linear model procedures (ordinary or iteratively reweighted least-squares) are used for estimation and test of the parameters.

Linkage disequilibrium fine mapping of quantitative trait loci: A simulation study

Recently, the use of linkage disequilibrium (LD) to locate genes which affect quantitative traits (QTL) has received an increasing interest, but the plausibility of fine mapping using linkage disequilibrium techniques for QTL has not been well studied. The main objectives of this work were to (1) measure the extent and pattern of LD between a putative QTL and nearby markers in finite populations and (2) investigate the usefulness of LD in fine mapping QTL in simulated populations using a dense map of multiallelic or biallelic marker loci. The test of association between a marker and QTL and the power of the test were calculated based on single-marker regression analysis. The results show the presence of substantial linkage disequilibrium with closely linked marker loci after 100 to 200 generations of random mating. Although the power to test the association with a frequent QTL of large effect was satisfactory, the power was low for the QTL with a small effect and/or low frequency. More powerful, multi-locus methods may be required to map low frequent QTL with small genetic effects, as well as combining both linkage and linkage disequilibrium information. The results also showed that multiallelic markers are more useful than biallelic markers to detect linkage disequilibrium and association at an equal distance.

SELECTIVE GENOTYPING FOR LOCATION AND ESTIMATION OF THE EFFECT OF A QUANTITATIVE TRAIT LOCUS

Usually, experiments designed for mapping quantitative trait loci (QTL) involve the genotyping of all individuals of a segregating population but are not very efficient with populations of feasible sizes. The use of selective genotyping, i.e., genotyping only a selected sample of the population (the high and low phenotypic tails), can considerably increase the efficiency of an experiment, provided the size of the population phenotyped is increased. In this paper, we examine the consequences of the use of this method for backcross and similar populations. New formulas, derived from likelihood functions, are proposed to estimate easily, without numerical maximization of the likelihood function, the effect of a QTL on the selected trait and on other traits of interest. This estimation procedure is shown to be reliable and quite robust to nonnormality, at least when the selection rate is not too low. The effect of selection on the efficiency of QTL detection for a trait other than the trait selected on is also considered: we show that selective genotyping never results in a loss of accuracy compared to random selection. Formulas to estimate QTL effects in more complex segregating populations could be easily derived in the same way as those proposed here.