sgpl-diagram HIGH THROUGHPUT Design of field trials & phenotyping experiments User defined output & r eporting Decision Support Quantitative genetics Heritability Diversity analysis & imputation Phenotypic analysis Heritability Analysis of genotype by envi r onment interactions (GxE) Mixed model QTL analysis & genomic p r ediciton (G W AS) Genetic map construction BIG DATA VISUALIZATION INTEROPERABILITY APIs
Problem solving across large collections of high dimensional data (genomic and phenotypic)
The statistical genetic pipeline API provides a standard interface to access the tools and algorithms to serve analysis to their applications.
Designed to allow smooth information exchange of data between the statistical algorithms, visualization tools, databases and applications.
A wide variety of experimental designs that allow precise estimation of genotypic effects and contrasts while correcting for the peculiarities of disturbing noise factors, including well known classical designs as well as the latest developments in partially replicated designs.
Unique feature to design series of experiments for a common set of genotypes.
Unique feature to measure how much variation in a trait within a population is due to genetic variation.
As a preliminary to QTL analysis and genomic prediction, traditional quantitative genetic analyses can be performed on all types of breeding populations that deliver estimates for important statistical genetic parameters as repeatabilities, heritabilities, and genetic and environmental variances, covariances, and correlations. Furthermore, quantitative genetic analysis of various special mating designs produces estimates related to combining ability and epistasis. All these analyses can be generalized across multiple trials.
Exploration and quantification of genetic diversity and distances using sequence, molecular marker and phenotypic data. Preparation for genotype-to-phenotype modelling.
To locate quantitative trait loci on chromosomes, a genetic map needs to be available. Genetic maps can be constructed from recombination frequencies as observed and estimated on segregating populations. A powerful map construction algorithm is available that efficiently produces genetic maps for biparental and multiparental populations. Furthermore, flexible facilities for map integration of multiple genetic maps and of genetic maps with physical maps are offered.
Extensive suite of methods to deal with the major problem in plant breeding and evolutionary biology, genotype by environment interaction, where genotypic differences are conditional on the environment, and achieving the breeders’ main objective, selection of the best genotypes, becomes complicated. Bilinear = Flexible and popular class of models for GxE, including classic models like the Finlay-Wilkinson model and the AMMI model (Additive Main effects and Multiplicative Interactions model). Furthermore, the GGE model (Genotypic main effects plus GxE model) is part of this family. All these models are closely related to principal component models and the model parameters can be visualized in biplots.
A powerful mixed model based approach identifies quantitative trait loci (QTLs) for whichever kind of breeding population, mating type, ploidy level, number of traits and environments. In this approach first Hidden Markov Model technology calculates identity by descent (IBD) relations between founders, parents and offspring. Next, these IBD relations are translated into design vectors and matrices in mixed models. These mixed models take into account the relations between genotypes, environments, and traits. In addition, heterogeneity of variance at multiple levels is taken into account to guarantee identification of QTLs under acceptable type I and II errors. When prediction of phenotypic performance is the primary goal of the statistical analysis, mixed model genomic prediction methodology that generalizes multi-QTL models and uses all DNA variation for prediction is available for the same situations as described for QTL mapping.
To help the breeder take decisions in the selection process, the results of QTL analyses and genomic prediction need to be followed up with various procedures for decision support.
Standard and bespoke output and reporting options.