TY - JOUR A1 - Tong, Hao A1 - Küken, Anika A1 - Razaghi-Moghadam, Zahra A1 - Nikoloski, Zoran T1 - Characterization of effects of genetic variants via genome-scale metabolic modelling JF - Cellular and molecular life sciences : CMLS N2 - Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism. KW - Single-nucleotide polymorphisms KW - Metabolic models KW - Genome-wide KW - association studies KW - Genomic selection Y1 - 2021 U6 - https://doi.org/10.1007/s00018-021-03844-4 SN - 1420-682X SN - 1420-9071 VL - 78 IS - 12 SP - 5123 EP - 5138 PB - Springer International Publishing AG CY - Cham ER - TY - JOUR A1 - Mbebi, Alain J. A1 - Tong, Hao A1 - Nikoloski, Zoran T1 - L-2,L-1-norm regularized multivariate regression model with applications to genomic prediction JF - Bioinformatics N2 - Motivation: Genomic selection (GS) is currently deemed the most effective approach to speed up breeding of agricultural varieties. It has been recognized that consideration of multiple traits in GS can improve accuracy of prediction for traits of low heritability. However, since GS forgoes statistical testing with the idea of improving predictions, it does not facilitate mechanistic understanding of the contribution of particular single nucleotide polymorphisms (SNP). Results: Here, we propose a L-2,L-1-norm regularized multivariate regression model and devise a fast and efficient iterative optimization algorithm, called L-2,L-1-joint, applicable in multi-trait GS. The usage of the L-2,L-1-norm facilitates variable selection in a penalized multivariate regression that considers the relation between individuals, when the number of SNPs is much larger than the number of individuals. The capacity for variable selection allows us to define master regulators that can be used in a multi-trait GS setting to dissect the genetic architecture of the analyzed traits. Our comparative analyses demonstrate that the proposed model is a favorable candidate compared to existing state-of-the-art approaches. Prediction and variable selection with datasets from Brassica napus, wheat and Arabidopsis thaliana diversity panels are conducted to further showcase the performance of the proposed model. Y1 - 2021 U6 - https://doi.org/10.1093/bioinformatics/btab212 SN - 1367-4803 SN - 1460-2059 VL - 37 IS - 18 SP - 2896 EP - 2904 PB - Oxford Univ. Press CY - Oxford ER -