ASP-Based Programs of Best Linear Unbiased Prediction-Estimated Breeding Values in Breeding Stock

In order to improve the breeding effect of livestock, the data were read from an Excel file with Active Server Page （ASP） programs, and the breeding values of breeding stock were calculated by best linear unbiased prediction （BLUP） method.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

Identification of main effect and epistatic QTLs controlling initial flowering date in cultivated peanut(Arachis hypogaea L.)

Initial flowering date(IFD)is closely related to mature period of peanut pods.In present study,a population of recombinant inbred lines(RIL)derived from the cross between Silihong(female parent)and Jinonghei 3(male parent)was used to map QTLs associated with IFD.The RIL population and its two parental cultivars were planted in two locations of Hebei Province,China from 2015 to 2018(eight environments).Based on a high-density genetic linkage map(including 2996 SNP and 330 SSR markers)previously constructed in our laboratory,QTLs were analyzed using phenotypic data and the best linear unbiased prediction(BLUP)value of initial flowering date by inclusive composite interval mapping(ICIM)method.Interaction effects between every two QTLs and between individual QTL and environment were also analyzed.In cultivated peanut,IFD was affected by genotypic factor and environments simultaneously,and its broad sense heritability(h2)was estimated as 86.8%。Using the IFD phenotypic data from the eight environments,a total of 19 QTLs for IFD were detected,and the phenotypic variation explained(PVE)by each QTL ranged from 1.15 to 21.82%.Especially,five of them were also detected by the BLUP value of IFD.In addition,12 additive QTLs and 35 pairs of epistatic QTLs(62 loci involved)were identifed by the joint analysis of IFD across eight environments.Three QTLs(qIFDB04.1,qIFDB07.1 and qIFDB08.1)located on chromosome B04,B07 and B08 were identified as main-effect QTL for IFD,which had the most potential to be used in peanut breeding.This study would be helpful for the early-maturity and adaptability breeding in cultivated peanut.

Kriging Model Averaging Based on Leave-One-Out Cross-Validation Method

In recent years,Kriging model has gained wide popularity in various fields such as space geology,econometrics,and computer experiments.As a result,research on this model has proliferated.In this paper,the authors propose a model averaging estimation based on the best linear unbiased prediction of Kriging model and the leave-one-out cross-validation method,with consideration for the model uncertainty.The authors present a weight selection criterion for the model averaging estimation and provide two theoretical justifications for the proposed method.First,the estimated weight based on the proposed criterion is asymptotically optimal in achieving the lowest possible prediction risk.Second,the proposed method asymptotically assigns all weights to the correctly specified models when the candidate model set includes these models.The effectiveness of the proposed method is verified through numerical analyses.