Review and simulation of homoplasy and collision in AFLP

In this paper we give a short review of the problems of homoplasy and collision in AFLP, and describe a software tool that we developed to illustrate these problems. AFLP is a DNA fingerprinting technique, producing profiles of bands, the result of the separation of DNA fragments by length on a gel or microcapillary system. The profiles are usually interpreted as binary band absence/presence patterns. We focus on two major problems: (1) Within a profile two or more fragments of the same length but of different genomic origin may have been selected, colliding into a single band. This collision problem, akin to the birthday problem, may be surprisingly large. (2) In a pair of profiles two equally long fragments of different genomic origin may have been selected, appearing as identical bands in the two profiles. This is called homoplasy. Both problems are quantified by modeling AFLP as a random sampling technique of fragment lengths. AFLP may be used in phylogenetic studies to estimate the pairwise genetic similarity of individuals. Similarity coefficients like Dice and Jaccard coefficients overestimate the true genetic similarity because of homoplasy, with increasing bias for higher numbers of bands per profile. Corrected estimators are described, which do not suffer from bias. The ideas are illustrated using a new software tool. Data from studies on Arabidopsis and tomato serve as examples. Finally, we make some recommendations with respect to the use of AFLP

The use of general and specific combining abilities in a context of gene expression relevant to plant breeding

Many common traits are believed to be a composite reflection of multiple genetic and environmental factors. Recent advances suggest that subtle variations in the regulation of gene expression may contribute to quantitative traits. The nature of sequence variation affecting the regulation of gene expression either in cis (that is, affecting the expression of only one of the two alleles in a heterozygous diploid) or in trans (that is, affecting the expression of both alleles in a heterozygous diploid) is a key and usually unknown feature for the breeders. If the change in expression acts entirely in cis, then the structural gene can be treated as a candidate gene and a potential target for marker-assisted selection. Therefore, gene surveys for cis-regulatory variation are a first step in identifying potential targets for marker-assisted breeding. Here, we discuss in detail the ¿genome-wide analysis of allele-specific expression differences¿ (GASED) approach. The GASED approach was developed to screen for cis-regulatory variation on a genome-wide scale. In GASED, mRNA abundance is treated as if it were a quantitative phenotypic response variable, whose genetic between-F1 hybrid variance is partitioned into additive and non-additive components. In plant breeding, this partitioning of the genetic variance is well known in the context of estimation of general and specific combining abilities for diallel crossing schemes. We demonstrate the GASED method using Arabidopsis thaliana data. The method can be used to screen for cis-regulatory variation in any crop species for which diallel crossing schemes are appropriate and genomic tools are available

Optimal time scaling for plant growth analysis

In field trials the development of plants is regularly scored on a visual scale. Plots of the data show strongly curved relationships with time. We investigate optimal scaling of the time axis in order to get linear curves and apply it to decay data of potato plants

A comparison of population types used for QTL mapping in Arabidopsis thaliana

In Arabidopsis, a variety of mapping populations have been used for the detection of quantitative trait loci (QTLs) responsible for natural variation. In this study, we presentan overview of the advantages and disadvantages of the different types of populations used. To do this, we compare the results of both experimental and natural populations for the commonly analysed trait flowering time. It is expected that genome wide association (GWA) mapping will be an increasingly important tool for QTL mapping because of the high allelic richness and mapping resolution in natural populations. In Arabidopsis, GWA mapping becomes ever more facilitated by the increasing availability of re-sequenced genomes of many accessions. However, specifically designed mapping populations such as recombinant inbred lines and near isogenic lines will remain important. The high QTL detection power of such experimental populations can identify spurious GWA associations, and their unique genomic structure is superior for investigating the role of low-frequency alleles. Future QTL studies will therefore benefit from a combined approach of GWA and classical linkage analysis

Phenotypic analyses of multi-environment data for two diverse tetraploid potato collections: comparing an academic panel with an industrial panel

Phenotypic analyses of two different association panels of tetraploid potato cultivars are presented. Association panels are sets of variously related genotypes assembled for association analysis purposes. The aims of this research were to inspect, analyse and compare two phenotypic data sets, a first step in association mapping analysis. A first panel of 205 contemporary and historical cultivars, selected to represent the commercial potato germplasm pool, was evaluated in two trials in 2006, one on sandy soil and the other on clay soil, both with two replications. It was called the academic panel. Data for the second panel with 299 genotypes were compiled from contributions from five breeding companies and included 66 locations and 18 years. Each of the participating breeding companies contributed data from their clonal selection programmes for 38 advanced breeding clones and a series of standard cultivars. It was called the industrial panel. Variance components for genotypic main effects and genotype-by-environment interactions were calculated, and estimates for the random genotypic main effects were produced. The genotypic main effects for 19 agro-morphological and quality traits were used to study trait by trait correlations within each panel. In addition, for the genotypes shared by both panels, the correlation of genetic main effects between the panels was investigated. The heritability of all traits was high and no large differences were observed between panels. Coefficients of trait variation were highly correlated (r¿=¿0.9) for both panels and trait by trait correlations in both panels showed highly similar patterns. These results demonstrate that a single-year balanced field trial as well as using breeders’ records yields robust phenotypic information that can be used in a genome-wide association study. Issues related to data management and definition of traits are discussed

Genetic research in a public-private research consortium: prospects for indirect use of Elige breeding germplasm in academic research

The creation of a public¿private research partnership between plant breeding industry and academia can be beneficial for all parties involved. Academic partners benefit from the material contributions by industry and a practically relevant research focus, while industry benefits from increased insights and methodology tailored to a relevant set of data. However, plant breeding industry is highly competitive and there are obvious limits to the data and material partners are willing and able to share. This will usually include current and historic released cultivated materials, but will very often not include the elite germplasm used in-house to create new cultivars. Especially for crops where hybrid cultivars dominate the market, parental lines of hybrid cultivars are considered core assets that are never provided to outside parties. However, this limitation often does not apply to DNA or genetic fingerprints of these parental lines. We developed a procedure to take advantage of elite breeding materials for the creation of new promising research populations, through indirect selection of parents. The procedure starts with the identification of a number of traits for further study based on the presence of marker-trait associations and a priori knowledge within the participating companies about promising traits for quality improvement. Next, regression-based multi-QTL models are fitted to hybrid cultivar data to identify QTLs. Fingerprint data of parental lines of a limited number of specific hybrids are then used to predict parental phenotypes using the multi-QTL model fitted on hybrid data. The specific hybrids spanned the whole of the sensory space adequately. Finally, a choice of parental lines is made based on the QTL model predictions and new promising line combinations are identified. Breeding industry is then asked to create and provide progeny of these line combinations for further research. This approach will be illustrated with a case study in tomato

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance¿covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities