Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials

Crop Science

Kolb, Frederic L.^4¹  Ward, Brian P.^1²  Griffey, Carl A.^7³  Tyagi, Priyanka^3⁴  Van Sanford, David A.^5⁵  Sneller, Clay H.^6⁶  Brown-Guedira, Gina^2⁷ 

[1] Dep. of Crop Sciences, Univ. of Illinois Urbana, IL 61801^4;Dep. of Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, VA 24061 current address, Dep. of Crop and Soil Sciences, North Carolina State Univ., Raleigh, NC 27695^1;Dep. of Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, VA 24061^7;Dep. of Crop and Soil Sciences, North Carolina State Univ., Raleigh, NC 27695^3;Dep. of Plant and Soil Sciences, Univ. of Kentucky, Lexington, KY 40546^5;Ohio Agricultural Research and Development Center, Ohio State Univ., Wooster, Ohio 44691^6;USDA-ARS Plant Science Research Unit, Raleigh, NC 27695^2

关键词: AR1;    first-order autoregressive residual structure;    BLUP;    best linear unbiased prediction;    CV;    cross validation;    EMMA;    efficient mixed model association;    FLSG;    flag leaf stay green;    GBLUP;    genomic best linear unbiased prediction;    GEBV;    genomic estimated breeding value;    GEI;    genotype × environment interaction;    GGE;    genotype + genotype × environment;    GRM;    genomic relationship matrix;    GS;    genomic selection;    GSQM;    grains per square meter;    GW;    grain weight;    HD;    heading date;    HT;    plant height;    LD;    linkage disequilibrium;    MAT;    physiological maturity date;    MEI;    marker × environment interaction;    NDVI;    normalized difference vegetation index at Zadok’s growth stage 25;    NIR;    near-infrared;    PROT;    whole-grain protein content;    PYT;    preliminary yield test;    QTL;    quantitative trait locus;    SNP;    single nucleotide polymorphism;    SPH;    seeds per head;    SSQM;    spikes per square meter;    STARCH;    whole-grain starch content;    TKW;    thousand-kernel weight;    TP;    training population;    TWT;    test weight;    VP;    validation population;    YLD;    grain yield;   

DOI  :  10.2135/cropsci2018.03.0189

学科分类：农业科学（综合）

来源: Crop Science

【 摘 要 】

The majority of studies evaluating genomic selection (GS) for plant breeding have used single-trait, single-site models that ignore genotype × environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study’s goal was to test GS methods for prediction in scenarios that simulate early-generation yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across-environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low-heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.

【 授权许可】

CC BY