Many economically important traits in plants and animals, as well as human complex diseases, are complex. Association analysis method based on mixed linear model can accurately predict the genetic architecture of complex traits and effectively uncovers their genetic mechanisms. Our research group has proposed a mixed linear model approach for genome-wide association analysis and developed a GPU-based software, named QTXNetwork. It consists of three functional modules: QTL analysis (QTLNetwork), genome-wide association analysis (QTS) and multi-omics association analysis (QTT).
is based on full-QTL model to map and visualize the genetic architecture underlying complex traits for experimental populations derived from a cross between two inbred lines. It can simultaneously map quantitative trait loci (QTL) with individual effects, epistasis and QTL–environment interaction. Currently, it is able to handle data from F2, backcross, recombinant inbred lines and double-haploid populations, as well as populations from specific mating designs (immortalized F2 and BCnFn populations).
software is based on the generalized multifactor downscaling (GMDR) method and provides a practical solution to the problem of detecting multifactor gene (G×G) and gene-environment (G×E) interactions underlying complex traits. GMDR software can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates.
is a software for complex trait association analysis based on histological genotypes. It is available in and with switch selection via container profiles.
analyses the association between transcriptome and phenotype, the relationship between expression variation and transcript abundance. Typically, several hundred QTTs are associated with a single expression at the same time.