Author: Huihui Li

Imputed GbS derived SNPs for maize landrace accessions represented in the SeeD-maize GWAS panel: Imputation using Beagle v.4

Huihui Li (2014)

Obtain imputed SNP profiles using Beagle v.4 from genotyping-by-sequencing of the accession parents of the SeeD GWAS testcross panel.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS

Guo-Bo Chen awais rasheed Kai Sonder Cristian Zavala Espinosa Denise Costich Patrick Schnable Sarah Hearne Huihui Li (2019)

This dataset contains the genotypic data obtained using genotyping-by-sequencing (tGBS®) technology (Data2Bio LLC) and the passport data of a total of 1,143 maize accessions, which were collected from 20 countries, including 11 teosinte inbred lines, 764 landraces sampled from the maize collection of the CIMMYT germplasm bank (MGB), 290 CIMMYT elite maize lines (CMLs), and 78 popcorn lines from the USDA Ames inbred collection (Romay et al., 2013).

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding

Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023)

In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging. The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA