[1] Zhang Z, Xu Z Q, Luo Y Y, et al. Whole genomic
prediction of growth and carcass traits in a Chinese
quality chicken population[J]. Journal of Animal
Science, 2017, 95(1): 72-80.
[2] Zhang Y, Zhuang Z, Liu Y, et al. Genomic prediction
based on preselected single-nucleotide polymorphisms from
genome-wide association study and imputed whole-genome
sequence data annotation for growth traits in duroc pigs[J].
Evolutionary Applications, 2024, 17(2): e13651.
[3] Pedrosa V B, Chen S Y, Gloria L S, et al. Machine
learning methods for genomic prediction of cow
behavioral traits measured by automatic milking
systems in north American holstein cattle[J]. Journal
of Dairy Science, 2024, 107(7): 4758-4771.
[4] 王元清 , 王泽昭 , 朱波 , 等 . 不同芯片密度对华西
牛重要经济性状基因组评估准确性的影响 [J]. 畜牧
兽医学报 , 2025, 56(2): 591-602.
[5] 王雪 , 司敬方 , 张毅 . 奶牛基因组预测方法的研
究进展 [J]. 中国畜牧杂志 , 2024, 60(11): 21-27.
[6] Van Den Berg I, Meuwissen T H E, MacLeod I M, et al.
Predicting the effect of reference population on the accuracy
of within, across, and multibreed genomic prediction[J].
Journal of Dairy Science, 2019, 102(4): 3155-3174.
[7] Teng J Yan, Ye S Pan, Gao N, et al. Incorporating
genomic annotation into single-step genomic prediction
with imputed whole-genome sequence data[J]. Journal
of Integrative Agriculture, 2022, 21(4): 1126-1136.
[8] Zhao C, Teng J, Zhang X, et al. Towards a cost￾effective implementation of genomic prediction based
on low coverage whole genome sequencing in dezhou
donkey[J]. Frontiers in Genetics, 2021, 12: 728764.
[9] Yin C, Zhou P, Wang Y, et al. Using genomic selection
to improve the accuracy of genomic prediction for multi￾populations in pigs[J]. Animals, 2024, 18(2): 101062.
[10] Ye H, Ji C, Liu X, et al. Improvement of the accuracy
of breeding value prediction for egg production traits
in Muscovy duck using low-coverage whole-genome
sequence data[J]. Poultry Science, 2025, 104(2): 104812.
[11] Zhou L, Zhu L, Ma F, et al. Assessing the impact
of different mixing strategies on genomic prediction
accuracy for beef cattle breeding values in multi-breed
genomic prediction[J]. Animals, 2025, 15(16): 2463.
[12] Wang X, Zhang Z, Du H, et al. Predictive ability
of multi-population genomic prediction methods of
phenotypes for reproduction traits in Chinese and austrian
pigs[J]. Genetics Selection Evolution, 2024, 56(1): 49.
[13] 郑扬勋 , 李涛 , 祁鑫 , 等 . 基于芯片和低深度测
序的大白猪繁殖性状多群体基因组选择研究 [J]. 中
国畜牧杂志 , 2025, 61(8): 85-90.
[14] Zhang T, Gao L, Zhou B, et al. Genomic selection
for early growth traits in inner mongolian cashmere
goats using ABLUP, GBLUP, and ssGBLUP methods.
[J]. Animals, 2025, 15(12): 1733.
[15] Afrazandeh M, Abdolahi-Arpanahi R, Abbasi M
A, et al. Comparison of different response variables
in genomic prediction using GBLUP and ssGBLUP
23 2026 年第 4 期
methods in iranian holstein cattle[J]. Journal of Dairy
Research, 2022, 89(2): 121-127.
[16] De Hollander C A, Chu T T, Marois D, et al. The
effect of preselection on the level of bias and accuracy in a
broiler breeder population, a simulation study[J]. Journal of
Animal Breeding and Genetics, 2025, 142(4): 392-407.
[17] Ajasa A A, Boison S A, Gjøen H M, et al. Accuracy
of genomic prediction using multiple atlantic salmon
populations[J]. Genetics Selection Evolution, 2024, 56(1): 38.
[18] Wang Z, Ma H, Li H, et al. Multi-trait genomic
predictions using GBLUP and Bayesian mixture prior
model in beef cattle[J]. Animal Research and One
Health, 2023, 1(1): 17-29.
[19] Wang X, Zhang Z, Du H, et al. Predictive ability
of multi-population genomic prediction methods of
phenotypes for reproduction traits in Chinese and Austrian
pigs[J]. Genetics Selection Evolution, 2024, 56(1): 49.
[20] 张锡飞 . 基于多性状一步法的杜洛克猪基因组选
择应用效果的比较研究 [D]. 雅安 : 四川农业大学 , 2025.
[21] Chen S, Zhou Y, Chen Y, et al. fastp: An ultrafast all-in-one FASTQ preprocessor[J]. Bioinformatics,
2018, 34(17): i884-i890.
[22] Li H, Durbin R. Fast and accurate short read
alignment with burrows-wheeler transform.[J].
Bioinformatics, 2009, 25(14): 1754-1760.
[23] Rhie A, McCarthy S A, Fedrigo O, et al. Towards
complete and error-free genome assemblies of all
vertebrate species[J]. Nature, 2021, 592(7856): 737-746.
[24] Li H, Handsaker B, Wysoker A, et al. The
sequence alignment/map format and SAMtools.[J].
Bioinformatics, 2009, 25(16): 2078-2079.
[25] Liu S, Huang S, Chen F, et al. Genomic Analyses
from Non-invasive Prenatal Testing Reveal Genetic
Associations, Patterns of Viral Infections, and Chinese
Population History[J]. Cell, 2018, 175(2): 347-359.e14.
[26] Davies R W, Kucka M, Su D, et al. Rapid genotype
imputation from sequence with reference panels[J].
Nature Genetics, 2021, 53(7): 1104-1111.
[27] Browning B L, Browning S R. Genotype Imputation
with Millions of Reference Samples[J]. The American
Journal of Human Genetics, 2016, 98(1): 116-126.
[28] Chang C C, Chow C C, Tellier L C, et al. Secondgeneration PLINK: Rising to the challenge of larger
and richer datasets.[J]. Gigascience, 2015, 4(1): s13742-
015-0047-0048.
[29] VanRaden P M. Efficient methods to compute
genomic predictions[J]. Journal of Dairy Science, 2008,
91(11): 4414-4423.
[30] 刘勃攻 , 杨杰 , 肖健 , 等 . 利用 BLUP 和 Bayes
方法对湘佳黑凤鸡 S1 系产蛋性状基因组育种值预
测效果的比较研究 [J]. 中国家禽 , 2025, 47(1): 1-7.
[31] 张德祥 , 徐振强 , 张燕 . 优质黄鸡矮小型母系
繁殖性能的遗传参数估计 [J]. 黑龙江畜牧兽医 ,
2016(23): 85-87.
[32] Huang X, Zhong Z, Zhang Z, et al. Genome-wide
association study reveals candidate genes associated
with egg-laying performance in wuhua yellow
chicken[J]. Poultry Science, 2025, 104(11): 105739.
[33] Yang J, Calus M P L, Wientjes Y C J, et al.
Incorporating information of causal variants in
genomic prediction using GBLUP or machine learning
models in a simulated livestock population[J]. Journal
of Animal Science and Biotechnology, 2025, 16(1): 118.
[34] Judge M M, Purfield D C, Sleator R D, et al. The impact
of multi-generational genotype imputation strategies on
imputation accuracy and subsequent genomic predictions1[J].
Journal of Animal Science, 2017, 95(4): 1489-1501.
[35] Speed D, Hemani G, Johnson M R, et al. Improved
heritability estimation from genome-wide SNPs.[J]. The
American Journal of Human Genetics, 2012, 91(6): 1011-1021.
[36] Song H, Zhang J, Zhang Q, et al. Using different
single-step strategies to improve the efficiency of
genomic prediction on body measurement traits in pig.
[J]. Frontiers in Genetics, 2019, 9: 730.
[37] Teng J, Zhai T, Zhang X, et al. Improving multipopulation genomic prediction accuracy using multitrait GBLUP models which incorporate global or
local genetic correlation information[J]. Briefings in
Bioinformatics, 2024, 25(4): bbae276.
[38] Tang X, Xiao S, Ding N, et al. Comparative study
of single-trait and multi-trait genomic prediction
models[J]. Animals, 2024, 14(20): 2961.
[39] Teng J, Zhai T, Zhang X, et al. Improving multitrait genomic prediction by incorporating local genetic
correlations[J]. Communications Biology, 2025, 8(1): 307.