FitTetra 2.0-improved genotype calling for tetraploids with multiple population and parental data support

BackgroundGenetic studies in tetraploids are lagging behind in comparison with studies of diploids as the complex genetics of tetraploids require much more elaborated computational methodologies. Recent advancements in development of molecular techniques and computational tools facilitate new methods for automated, high-throughput genotype calling in tetraploid species. We report on the upgrade of the widely-used fitTetra software aiming to improve its accuracy, which to date is hampered by technical artefacts in the data.ResultsOur upgrade of the fitTetra package is designed for a more accurate modelling of complex collections of samples. The package fits a mixture model where some parameters of the model are estimated separately for each sub-collection. When a full-sib family is analyzed, we use parental genotypes to predict the expected segregation in terms of allele dosages in the offspring. More accurate modelling and use of parental data increases the accuracy of dosage calling. We tested the package on data obtained with an Affymetrix Axiom 60k array and compared its performance with the original version and the recently published ClusterCall tool, showing that at least 20% more SNPs could be called with our updated.ConclusionOur updated software package shows clearly improved performance in genotype calling accuracy. Estimation of mixing proportions of the underlying dosage distributions is separated for full-sib families (where mixture proportions can be estimated from the parental dosages and inheritance model) and unstructured populations (where they are based on the assumption of Hardy-Weinberg equilibrium). Additionally, as the distributions of signal ratios of the dosage classes can be assumed to be the same for all populations, including parental data for some subpopulations helps to improve fitting other populations as well. The R package fitTetra 2.0 is freely available under the GNU Public License as Additional file with this article.</p

Crop growth models for the -omics era: the EU-SPICY project

The prediction of phenotypic responses from genetic and environmental information is an area of active research in genetics, physiology and statistics. Rapidly increasing amounts of phenotypic information become available as a consequence of high throughput phenotyping techniques, while more and cheaper genotypic data follow from the development of new genotyping platforms. , A wide array of -omics data can be generated linking genotype and phenotype. Continuous monitoring of environmental conditions has become an accessible option. This wealth of data requires a drastic rethinking of the traditional quantitative genetic approach to modeling phenotypic variation in terms of genetic and environmental differences. Where in the past a single phenotypic trait was partitioned in a genetic and environmental component by analysis of variance techniques, nowadays we desire to model multiple, interrelated and often time dependent, phenotypic traits as a function of genes (QTLs) and environmental inputs, while we would like to include transcription information as well. The EU project 'Smart tools for Prediction and Improvement of Crop Yield' (KBBE-2008-211347), or SPICY, aims at the development of genotype-to-phenotype models that fully integrate genetic, genomic, physiological and environmental information to achieve accurate phenotypic predictions across a wide variety of genetic and environmental configurations. Pepper (Capsicum annuum) is chosen as the model crop, because of the availability of genetically characterized populations and of generic models for continuous crop growth and greenhouse production. In the presentation the objectives and structure of SPICY as well as its philosophy will be discussed

QualitySNP: a pipeline for detecting single nucleotide polymorphisms and insertions/deletions in EST data from diploid and polyploid species

BACKGROUND: Single nucleotide polymorphisms (SNPs) are important tools in studying complex genetic traits and genome evolution. Computational strategies for SNP discovery make use of the large number of sequences present in public databases (in most cases as expressed sequence tags (ESTs)) and are considered to be faster and more cost-effective than experimental procedures. A major challenge in computational SNP discovery is distinguishing allelic variation from sequence variation between paralogous sequences, in addition to recognizing sequencing errors. For the majority of the public EST sequences, trace or quality files are lacking which makes detection of reliable SNPs even more difficult because it has to rely on sequence comparisons only. RESULTS: We have developed a new algorithm to detect reliable SNPs and insertions/deletions (indels) in EST data, both with and without quality files. Implemented in a pipeline called QualitySNP, it uses three filters for the identification of reliable SNPs. Filter 1 screens for all potential SNPs and identifies variation between or within genotypes. Filter 2 is the core filter that uses a haplotype-based strategy to detect reliable SNPs. Clusters with potential paralogs as well as false SNPs caused by sequencing errors are identified. Filter 3 screens SNPs by calculating a confidence score, based upon sequence redundancy and quality. Non-synonymous SNPs are subsequently identified by detecting open reading frames of consensus sequences (contigs) with SNPs. The pipeline includes a data storage and retrieval system for haplotypes, SNPs and alignments. QualitySNP's versatility is demonstrated by the identification of SNPs in EST datasets from potato, chicken and humans. CONCLUSION: QualitySNP is an efficient tool for SNP detection, storage and retrieval in diploid as well as polyploid species. It is available for running on Linux or UNIX systems. The program, test data, and user manual are available at and as Additional files

Large-scale identification of polymorphic microsatellites using an in silico approach

<p>Abstract</p> <p>Background</p> <p>Simple Sequence Repeat (SSR) or microsatellite markers are valuable for genetic research. Experimental methods to develop SSR markers are laborious, time consuming and expensive. <it>In silico </it>approaches have become a practicable and relatively inexpensive alternative during the last decade, although testing putative SSR markers still is time consuming and expensive. In many species only a relatively small percentage of SSR markers turn out to be polymorphic. This is particularly true for markers derived from expressed sequence tags (ESTs). In EST databases a large redundancy of sequences is present, which may contain information on length-polymorphisms in the SSR they contain, and whether they have been derived from heterozygotes or from different genotypes. Up to now, although a number of programs have been developed to identify SSRs in EST sequences, no software can detect putatively polymorphic SSRs.</p> <p>Results</p> <p>We have developed PolySSR, a new pipeline to identify polymorphic SSRs rather than just SSRs. Sequence information is obtained from public EST databases derived from heterozygous individuals and/or at least two different genotypes. The pipeline includes PCR-primer design for the putatively polymorphic SSR markers, taking into account Single Nucleotide Polymorphisms (SNPs) in the flanking regions, thereby improving the success rate of the potential markers. A large number of polymorphic SSRs were identified using publicly available EST sequences of potato, tomato, rice, <it>Arabidopsis</it>, <it>Brassica </it>and chicken.</p> <p>The SSRs obtained were divided into long and short based on the number of times the motif was repeated. Surprisingly, the frequency of polymorphic SSRs was much higher in the short SSRs.</p> <p>Conclusion</p> <p>PolySSR is a very effective tool to identify polymorphic SSRs. Using PolySSR, several hundred putative markers were developed and stored in a searchable database. Validation experiments showed that almost all markers that were indicated as putatively polymorphic by polySSR were indeed polymorphic. This greatly improves the efficiency of marker development, especially in species where there are low levels of polymorphism, like tomato. When combined with the new sequencing technologies PolySSR will have a big impact on the development of polymorphic SSRs in any species.</p> <p>PolySSR and the polymorphic SSR marker database are available from <url>http://www.bioinformatics.nl/tools/polyssr/</url>.</p

Variation In Aggressiveness And Aflp Among Alternaria Solani Isolates From Indonesia

Alternaria solani is a necrotroph fungus that causes three-phased diseases in tomato. Management of the pathogen by using resistant cultivars requires knowledge on the aggressiveness and genetic diversity of the fungus. The aims of this study were to isolate A. solani from major tomato and potato producing areas in Indonesia and to study their aggressiveness and genetic variability. Twenty two A. solani isolates were recovered from early blighted tomato and potato in Central and West Java. A. alternata was also isolated from tomato leaves in West Java and North Sumatra, indicating that early blight in Indonesia may be caused by more than one Alternaria species. Resistance tests of four tomato genotypes to selected A. solani isolates revealed that local isolates were more aggressive in inciting early blight and stem lesion than an imported isolate from USA. This implies that introduced breeding materials must be tested to local isolates to obtain effective resistance genes. Cluster analysis based on amplified fragment length polymorphism (AFLP) obtained from EcoRI+AG and MseI+C primer amplification separated 28 local and Taiwan isolates from the US isolate, which was coincided with aggressiveness separation between the local isolates and the US isolate. Three clusters of AFLP genotypes which did not associate with geographic origin were observed among tropical isolates. The low genetic diversity among the Indonesian isolates suggests clonal population structure with wide distribution. Successful local tomato breeding requires the availability of local A. solani collection with well-characterized aggressiveness level and molecular diversity to obtain effective resistance genes

Habitual intake of flavonoid subclasses and risk of colorectal cancer in two large prospective cohorts

Background: Flavonoids inhibit the growth of colon cancer cells in vitro. In a secondary analysis of a randomized controlled trial, the Polyp Prevention Trial, a higher intake of one sub-class, flavonols, was significantly associated with reduced risk of recurrent advanced adenoma. Most previous prospective studies on colorectal cancer evaluated only a limited number of flavonoid sub-classes and intake ranges, yielding inconsistent results.  Objective: To examine whether higher habitual dietary intakes of flavonoid subclasses (flavonols, flavones, flavanones, flavan-3-ols and anthocyanins) are associated with lower risk of colorectal cancer.  Design: Using data from validated food frequency questionnaires administered every four years and an updated flavonoid food composition database flavonoid intakes were calculated for 42,478 male participants from the Health Professionals Follow-up Study and for 76,364 female participants from the Nurses’ Health Study.  Results: During up to 26 years of follow-up, 2,519 colorectal cancer cases (1,061 in men, 1,458 in women) were documented. Intakes of flavonoid subclasses were not associated with risk of colorectal cancer in either cohort. Pooled multivariable adjusted relative risks (95% confidence interval) comparing the highest with the lowest quintile were 1.04 (0.91, 1.18) for flavonols; 1.01 (0.89, 1.15) for flavones; 0.96 (0.84, 1.10) for flavanones; 1.07 (0.95, 1.21) for flavan-3-ols; and 0.98 (0.81, 1.19) for anthocyanins (all p-values for heterogeneity by sex >0.19). In subsite analyses, flavonoid intake was also not associated with colon or rectal cancer risk.  Conclusion: Our findings do not support the hypothesis that a higher habitual intake of any flavonoid sub-class decreases the risk of colorectal cancer