SNP genotype and hyperspectral reflectance data from: Ensembles of genomic and hyperspectral imaging-based prediction enable selection for reduced deoxynivalenol content in wheat grains

Breeding for low deoxynivalenol (DON) mycotoxin content in wheat is challenging due to the complexity of the trait and phenotyping limitations. Since phenomic prediction relies on non-additive effects and genomic prediction on additive effects, their complementarity can improve selection accuracy. In this study DON-infected wheat kernels were imaged using a hyperspectral camera to generate reflectance values across the spectrum of visible and near infrared light that were used in phenomic predictions. Five Bayesian generalized linear regression models and two machine learning models were trained using phenomic and genomic predictions from advanced soft winter wheat breeding lines evaluated in 2021 and 2022. Across all training sets and models, phenomic predictions using wavebands in the visible light spectrum (400-700 nm) had higher predictive ability than genomic predictions or phenomic predictions using the full waveband range (400-1000 nm). Forward prediction was peformed using model..., , , # SNP genotype and hyperspectral reflectance data from: Ensembles of genomic and hyperspectral imaging-based prediction enable selection for reduced deoxynivalenol content in wheat grains

[https://doi.org/10.5061/dryad.d2547d8bx](https://doi.org/10.5061/dryad.d2547d8bx)

## Description of the SNP data and file structure

Eric Olson, Michigan State University, [[e-mail]](mailto:[e-mail])

File name: AMAT_IMPUTED_NUMERIC_3117ind_15456snp_0.05MAF_0.70coverage_0.10het.csv

This is a .csv file of genotypic data used in genomic prediction of the mycotoxin DON in the publication \"Ensembles of genomic and hyperspectral imaging-based predictions enable selection for reduced deoxynivalenol content in wheat grains\".

SNPs were developed using a double digest RAD seq method. SNPs were initially filtered for 70% coverage on 3,117 individuals, 10% heterozygocity (as F4-derived and inbred lines were genotyped) and 5% minor allele frequency. Nucleotides were then coverted to numeric format...,

由于目标性状的复杂性与表型鉴定技术的局限性，培育脱氧雪腐镰刀菌烯醇（deoxynivalenol, DON）毒素含量低的小麦品种极具挑战。表型组预测（phenomic prediction）依赖非加性效应，基因组预测（genomic prediction）则基于加性效应，二者的互补性可显著提升选择精度。本研究利用高光谱相机（hyperspectral camera）对感染DON的小麦籽粒进行成像，获取可见光与近红外光谱范围内的反射率数据，并将其用于表型组预测。本研究基于2021和2022年评估的优良软冬小麦育种品系的表型组与基因组预测数据，训练了5个贝叶斯广义线性回归模型（Bayesian generalized linear regression models）与2个机器学习模型（machine learning models）。在所有训练集与模型中，使用可见光波段（400-700 nm）的表型组预测模型，其预测能力均优于基因组预测模型，以及使用全波段范围（400-1000 nm）的表型组预测模型。本研究采用相关模型开展正向预测……，相关单核苷酸多态性（Single Nucleotide Polymorphism, SNP）基因型与高光谱反射率数据来源：《Ensembles of genomic and hyperspectral imaging-based predictions enable selection for reduced deoxynivalenol content in wheat grains》，DOI: https://doi.org/10.5061/dryad.d2547d8bx

## SNP数据与文件结构说明
埃里克·奥尔森（Eric Olson），密歇根州立大学，邮箱：[[e-mail]](mailto:"[e-mail]")
文件名：AMAT_IMPUTED_NUMERIC_3117ind_15456snp_0.05MAF_0.70coverage_0.10het.csv
本文件为发表于论文《Ensembles of genomic and hyperspectral imaging-based predictions enable selection for reduced deoxynivalenol content in wheat grains》中，用于真菌毒素DON基因组预测的基因型数据CSV文件。
SNP通过双酶切RAD测序（double digest RAD seq）技术开发。SNP位点初始过滤条件为：覆盖3117个个体的测序覆盖度不低于70%、杂合率不超过10%（所用材料为F4衍生自交系）以及次要等位基因频率不低于5%。随后将核苷酸序列转换为数值格式……