Association mapping in tetraploid potato

The results of a four year project within the Centre for BioSystems Genomics (www.cbsg.nl), entitled “Association mapping and family genotyping in potato” are described in this thesis. This project was intended to investigate whether a recently emerged methodology, association mapping, could provide the means to improve potato breeding efficiency. In an attempt to answer this research question a set of potato cultivars representative for the commercial potato germplasm was selected. In total 240 cultivars and progenitor clones were chosen. In a later stage this set was expanded with 190 recent breeds contributed by five participating breeding companies which resulted in a total of 430 genotypes. In a pilot experiment, the results of which are reported in Chapter 2, a subset of 220 of the abovementioned 240 cultivars and progenitor clones was used. Phenotypic data was retrieved through contributions of the participating breeding companies and represented summary statistics of recent observations for a number of traits across years and locations, calculated following company specific procedures. With AFLP marker data, in the form of normalised log-transformed band intensities, obtained from five well-known primer combinations, the extent of linkage disequilibrium (LD), using the r2 statistic, was estimated. Population structure within the set of 220 cultivars was analysed by deploying a clustering approach. No apparent, nor statistically supported population structure was revealed and the LD seemed to decay below the threshold of 0.1 at a genetic distance of about 3cM with this set of marker data. Furthermore, marker-trait associations were investigated by fitting single marker regression models for phenotypic traits on marker band intensities with and without correction for population structure. Population structure correction was performed in a straightforward way by incorporating a design matrix into the model assuming that each breeding company represented a different breeding germplasm pool. The potential of association mapping in tetraploid potato has been demonstrated in this pilot experiment, because existing phenotypic data, a modest number of AFLP markers, and a relatively straightforward statistical analysis allowed identification of interesting associations for a number of agro-morphological and quality traits. These promising results encouraged us to engage into an encompassing genome-wide association mapping study in potato. Two association mapping panels were compiled. One panel comprising 205 genotypes, all of which were also present in the set used for the pilot experiment, and another panel containing in total 299 genotypes including the entire set of 190 recent breeds together with a series of standard cultivars, about 100 of which are in common with the first panel. Phenotypic data for the association panel with 205 genotypes were obtained in a field trial performed in 2006 in Wageningen at two locations with two replicates. We will refer to this set as the “2006 field trial”. Phenotypic data for the other panel with 299 genotypes was contributed by the five participating breeding companies and consisted of multi-year-multi-location data obtained during generations of clonal selection. The 2006 data were nicely balanced, because the trial was designed in that way. The historical breeding dataset was highly unbalanced. Analysis of these two differing phenotypic datasets was performed to deliver insight in variance components for the genotypic main effects and the genotype by environment interaction (GEI), besides estimated genotype main effects across environments. Both phenotypic datasets were analysed separately within a mixed model framework including terms for GEI. In Chapter 3 we describe both phenotypic datasets by comparing variance components, heritabilities (=repeatabilities), intra-dataset relationships and inter-dataset relationships. Broader aspects related to phenotypic datasets and their analysis are discussed as well. To retrieve information about hidden population structure and genetic relatedness, and to estimate the extent of LD in potato germplasm, we used marker information generated with 41 AFLP primer combinations and 53 microsatellite loci on a collection of 430 genotypes. These 430 genotypes contain all genotypes present in the two association mapping panels introduced before plus a few extra genotypes to increase potato germplasm coverage. Two methods were used: a Bayesian approach and a distance-based clustering approach. Chapter 4 describes the results of this exercise. Both strategies revealed a weak level of structure in our material. Groups were detected which complied with criteria such as their intended market segment, as well as groups differing in their year of first registration on a national list. Linkage disequilibrium, using the r2 statistic, appeared to decay below the threshold of 0.1 across linkage groups at a genetic distance of about 5cM on average. The results described in Chapter 4 are promising for association mapping research in potato. The odds are reasonable that useful marker-trait associations can be detected and that the potential mapping resolution will suffice for detection of QTL in an association mapping context. In Chapter 5 a comprehensive genome-wide association mapping study is presented. The adjusted genotypic means obtained from two association mapping panels as a result of phenotypic analysis performed in Chapter 3 were combined with marker information in two association mapping models. Marker information consisted of normalised log-transformed band intensities of 41 AFLP primer combinations and allele dosage information from 53 microsatellites. A baseline model without correction for population structure and a more advanced model with correction for population structure and genetic relatedness were applied. Population structure and genetic relatedness were estimated using available marker information. Interesting QTL could be identified for 19 agro-morphological and quality traits. The observed QTL partly confirm previous studies e.g. for tuber shape and frying colour, but also new QTL have been detected e.g. for after baking darkening and enzymatic browning. In the final chapter, the general discussion, results of preceding chapters are evaluated and their implications for research as well as breeding are discussed. <br/

High-Density Genotypes of Inbred Mouse Strains: Improved Power and Precision of Association Mapping.

Human genome-wide association studies have identified thousands of loci associated with disease phenotypes. Genome-wide association studies also have become feasible using rodent models and these have some important advantages over human studies, including controlled environment, access to tissues for molecular profiling, reproducible genotypes, and a wide array of techniques for experimental validation. Association mapping with common mouse inbred strains generally requires 100 or more strains to achieve sufficient power and mapping resolution; in contrast, sample sizes for human studies typically are one or more orders of magnitude greater than this. To enable well-powered studies in mice, we have generated high-density genotypes for ∼175 inbred strains of mice using the Mouse Diversity Array. These new data increase marker density by 1.9-fold, have reduced missing data rates, and provide more accurate identification of heterozygous regions compared with previous genotype data. We report the discovery of new loci from previously reported association mapping studies using the new genotype data. The data are freely available for download, and Web-based tools provide easy access for association mapping and viewing of the underlying intensity data for individual loci

A framework for gene mapping in wheat demonstrated using the Yr7 yellow rust resistance gene

We used three approaches to map the yellow rust resistance gene Yr7 and identify associated SNPs in wheat. First, we used a traditional QTL mapping approach using a double haploid (DH) population and mapped Yr7 to a low-recombination region of chromosome 2B. To fine map the QTL, we then used an association mapping panel. Both populations were SNP array genotyped allowing alignment of QTL and genome-wide association scans based on common segregating SNPs. Analysis of the association panel spanning the QTL interval, narrowed the interval down to a single haplotype block. Finally, we used mapping-by-sequencing of resistant and susceptible DH bulks to identify a candidate gene in the interval showing high homology to a previously suggested Yr7 candidate and to populate the Yr7 interval with a higher density of polymorphisms. We highlight the power of combining mapping-by-sequencing, delivering a complete list of gene-based segregating polymorphisms in the interval with the high recombination, low LD precision of the association mapping panel. Our mapping-by-sequencing methodology is applicable to any trait and our results validate the approach in wheat, where with a near complete reference genome sequence, we are able to define a small interval containing the causative gene

Probabilistic Surfel Fusion for Dense LiDAR Mapping

With the recent development of high-end LiDARs, more and more systems are able to continuously map the environment while moving and producing spatially redundant information. However, none of the previous approaches were able to effectively exploit this redundancy in a dense LiDAR mapping problem. In this paper, we present a new approach for dense LiDAR mapping using probabilistic surfel fusion. The proposed system is capable of reconstructing a high-quality dense surface element (surfel) map from spatially redundant multiple views. This is achieved by a proposed probabilistic surfel fusion along with a geometry considered data association. The proposed surfel data association method considers surface resolution as well as high measurement uncertainty along its beam direction which enables the mapping system to be able to control surface resolution without introducing spatial digitization. The proposed fusion method successfully suppresses the map noise level by considering measurement noise caused by laser beam incident angle and depth distance in a Bayesian filtering framework. Experimental results with simulated and real data for the dense surfel mapping prove the ability of the proposed method to accurately find the canonical form of the environment without further post-processing.Comment: Accepted in Multiview Relationships in 3D Data 2017 (IEEE International Conference on Computer Vision Workshops

Association Mapping for Common Bunt Resistance in Wheat

Common bunt, caused by Tilletia caries and T. foetida, is a fungal disease of wheat world wide. Infection, occurring via seed borne teliospores, is generally controlled by the application of seed treatments prior to sowing. Farming systems like organic agriculture with a very limited range of organic seed treatments available rely heavily on common bunt resistance genes within wheat. In the framework of the BIOBREED project an association study in winter wheat was conducted, aiming at the identification of genetic loci linked to resistance towards common bunt in wheat. 152 European wheat cultivars were phenotyped for their resistance reaction for the two consecutive years 2011/12 at Agrologica research station at Mariager. Infection was scored as percent infected ears. The scorings were log-transformed to fit a disease scoring scale ranging from 1 to 9. The association analysis was performed for each year separately as well as for the mean scoring of the two years. The wheat cultivars were genotyped with DArT markers, yielding 1832 polymorphic loci. The association analysis was conducted using the computer program Genstat, with the ASReml module. Minimun allele frequency for the association analysis was set to 0.07. 13 out of the total of1832 marker in our study were linked to common bunt resistance in wheat (-log10(P) >3). These marker are located on 8 out of the 21 wheat chromosomes. Comparisons of these findings with other published results are difficult since only very little is known about the chromosomal location of common bunt resistance genes/QTL in wheat. Chromosome 2B was previously reported to carry gene(s) for common bunt resistance. Findings of our analysis are in accordance with this: 4 of the linked marker resided on this chromosome. Further, another two linked marker were found on chromosome 2D, another chromosome previously reported to carry common bunt resistance genes. Our study shows the possibilities of finding makers linked to common bunt resistance in wheat, and of using these markers for marker assisted selection of wheat cultivars tailored for the needs of organic agriculture

Efficient network-guided multi-locus association mapping with graph cuts

As an increasing number of genome-wide association studies reveal the limitations of attempting to explain phenotypic heritability by single genetic loci, there is growing interest for associating complex phenotypes with sets of genetic loci. While several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci, or do not scale to genome-wide settings. We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype, while being connected in an underlying network. Our approach is based on a minimum cut reformulation of the problem of selecting features under sparsity and connectivity constraints that can be solved exactly and rapidly. SConES outperforms state-of-the-art competitors in terms of runtime, scales to hundreds of thousands of genetic loci, and exhibits higher power in detecting causal SNPs in simulation studies than existing methods. On flowering time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci that enable accurate phenotype prediction and that are supported by the literature. Matlab code for SConES is available at http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/Comment: 20 pages, 6 figures, accepted at ISMB (International Conference on Intelligent Systems for Molecular Biology) 201

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and Automation Letter