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

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

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

Multi-environment QTL mixed models for drought stress adaptation in wheat

Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance-covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m(-2) was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environment-specific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m(-2)) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m(-2)). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes

Evaluation of sweetpotato (Ipomoea batatas (L.) Lam.) germplasm from north-eastern Uganda through a Farmer Participatory Approach

Ugandan farmers grow many landrace sweetpotato varieties, but some of these are relatively low yielding and susceptible to pests. The objective of the present research was to involve farmers in a large-scale assessment of Ugandan farmers’ varieties to rapidly identify those with superior yield performance, pest resistance and consumer acceptance. One hundred sixty distinct farmers’ varieties collected from Lira, Soroti, Katakwi, Kumi and Pallisa Districts of North-eastern Uganda were evaluated in on-station trials. Trials were conducted at two sites (Serere Agricultural and Animal Research Institute and Arapai Agricultural College) in Soroti District in the second rainy season of 1999. Twenty-five farmers from surrounding areas participated in trial harvest at each site. At harvest, fresh storage root yield, foliage yield, and dry matter content were determined by researchers. Farmers observed a number of characteristics and rated each entry with respect to the following variables: general impression, dry matter content, pests, and defects. A strong positive correlation was observed between farmers’ general impression and yield and harvest index in the trials. Farmers selected 10 superior varieties from each trial for further multi-environment, on-station and on-farm trials. Coincidentally, nine of the selected varieties were common to both sites

The simultaneous analysis of genotype by environment interaction for a number [of] traits using three-way multiplicative modelling

Datatheorie voor analyse van individuele verschille