Biology subjects

Torsten, P.

Publications and source records attributed to Torsten, P..

2 recordsLinked to original sources

bioRxiv2021

Newly developed MAGIC population allows identification of strong associations and candidate genes for anthocyanin pigmentation in eggplant

MAGIC populations facilitate the genetic dissection of complex quantitative traits in plants and are valuable breeding materials. We report the development of the first eggplant MAGIC population (S3MEGGIC; 8-way), constituted by 420 S3 individuals developed from the intercrossing of seven cultivated eggplant (Solanum melongena) and one wild relative (S. incanum) parents. The S3MEGGIC recombinant population was genotyped with the eggplant 5k probes SPET platform and phenotyped for anthocyanins presence in vegetative plant tissues (PA) and fruit epidermis (FA), and for the light-sensitive anthocyanic pigmentation under the calyx (PUC). The 7,724 filtered high-confidence SNPs confirmed a low residual heterozygosity (6.87%) and a lack of genetic structure in the S3MEGGIC population, including no differentiation among subpopulations carrying cultivated or wild cytoplasm. Inference of haplotype blocks of the nuclear genome revealed an unbalanced representation of founder genomes, suggesting cryptic selection in favour or against specific parental genomes. GWAS analysis for PA, FA and PUC detected strong associations with two MYB genes similar to MYB113 involved in the anthocyanin biosynthesis pathway and with a COP1 gene, which encodes for a photo-regulatory protein and may be responsible for the PUC phenotype. Evidence was found of a duplication of an ancestral MYB113 gene with a translocation from chromosome 10 to chromosome 1. Parental genotypes for the three genes were in agreement with the candidate genes identification performed in the S3MEGGIC population. Our new eggplant MAGIC population is the largest recombinant population in eggplant and is a powerful tool for eggplant genetics and breeding studies.

genetics↗

bioRxiv2020

Analysis of variance when both input and output sets are high-dimensional

MotivationModern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. ResultsWe propose and evaluate two methods for analysis variance when both input and output sets are high-dimensional. Our approach uses random effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. We used simulations to assess the bias and variance of each of the methods, and to compare it with that of the Partial Least Squares (PLS)-an approach commonly used in multivariate-high-dimensional regressions. The MC-ANOVA method gave nearly unbiased estimates in all the simulation scenarios considered. Estimates produced by Eigen-ANOVA and PLS had noticeable biases. Finally, we demonstrate insight that can be obtained with the of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken genomes and to the assessment of inter-dependencies between gene expression, methylation and copy-number-variants in data from breast cancer tumors. AvailabilityThe Supplementary data includes an R-implementation of each of the proposed methods as well as the scripts used in simulations and in the real-data analyses. Contactgustavoc@msu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

genomics↗