Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (11): 2243-2253.doi: 10.3864/j.issn.0578-1752.2024.11.015

• ANIMAL SCIENCE·VETERINARY SCIENCE • Previous Articles     Next Articles

The Efficiency of Haplotype-Based Genomic Selection Using Genotyping by Target Sequencing in Pigs

LIU YanLing1(), QIU Ao1(), ZHANG ZiPeng1, WANG Xue1, DU HeHe1, LUO WenXue2, WANG GuiJiang2, WEI Xia3, SHI WenYing3, DING XiangDong1()   

  1. 1 College of Animal Science and Technology, China Agricultural University/National Engineering Laboratory of Animal Breeding/ Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Beijing 100193
    2 Hebei Animal Husbandry Extension Station, Shijiazhuang 050061
    3 Zhangjiakou Dahaoheshan New Agricultural Development Co., Ltd., Zhangjiakou 076250, Hebei
  • Received:2023-12-12 Accepted:2024-03-01 Online:2024-06-01 Published:2024-06-07
  • Contact: DING XiangDong

RichHTML

13

PDF

480

Abstract:

【Objective】This study explored the efficiency of haplotype-based genomic selection using genotyping by target sequencing technology in order to provide the useful experience for molecular breeding of pigs in China.【Method】The growth traits records of 1 267 Large White pigs and the reproduction trait records of 800 Large White pigs were collected and genotyped by using the porcine 50K liquid-phase SNP panel (liquid-phase 50K) based on the Genotyping by Target Sequencing (GBTS) as well as its target resequencing data. Three strategies for haplotype block partition, including fixed SNP number, fixed length, and target block, were proposed to construct haplotypes, and the corresponding haplotype-based genomic prediction were compared with single SNP genomic prediction. The target block partition strategy was mainly based on target resequencing data obtained by the GBTS, and one haplotype block contained one target site in liquid-phase 50K and its upstream and downstream SNPs (mSNP) within 400 bp from target resequencing data. In this study, after phasing haplotype using Beagle 5.1 within each haplotype block, the different haplotypes were recoded as haplotype alleles, and then haplotype allele matrix was constructed using haplotype dosage model. A single-step GBLUP model (ssGBLUP) was then used to estimate genomic breeding values for three traits of days at 100 kg body weight (AGE), backfat thickness at 100 kg (BF), and total number of born (TNB). A two-trait animal model was implemented for two growth traits of AGE and BF, and repeatability model for TNB. Younger validation population and five-fold cross validation were carried out to assess the efficiency of genomic prediction. The correlation coefficients between estimated breeding values and genomic estimated breeding values were measured as the accuracy of genomic prediction, and the regression coefficients of genomic estimated breeding values on estimated breeding values were measured unbiasedness, respectively.【Result】The results from the younger validation population showed that the target resequencing data yielded lower accuracies of genomic selection on three traits than liquid-phase 50K SNP, even its number of SNP after genotype quality control was increased from 42 302 in liquid phase 50K to 88 105. The haplotype blocks partitioned by target block strategy contained 2.08 SNPs and 5.67 haplotype alleles on average. The accuracies of haplotype-based genomic selection based on all three haplotype block partition strategies were improved, and the target block gained the largest improvement, yielding 4.80%, 1.98% and 6.04% higher accuracies on AGE, BF and TNB than liquid- phase 50K, respectively. In addition, the target block strategy generated the lowest bias in most scenarios as well. The five-fold cross-validation obtained similar results as younger validation population did, target block gained advantages over both single SNP and other haplotype block partition methods. The fixed length of 400 bp block partition strategy performed comparable with target block, while it was time demanding. Although fixed 2 and 5 SNP haplotype block partition methods performed better than single SNP method, they were worse than target block.【Conclusion】Due to the short length, the linkage disequilibrium between most of the target sites in liquid-phase 50K SNPs and their mSNPs in same blocks are strong, resulting in target block strategy obtained higher accuracy in haplotype-based genomic selection than single SNP and other haplotype block partition strategies, which could make use of the technique advantage of GBTS, and further broaden the application of liquid-phase chip.

Key words: pig, liquid chip, genomic selection, haplotype, genotyping by target sequencing

Table 1

Data statistics of growth traits and reproductive traits"

达百公斤日龄
Age at 100 kg weight		 达百公斤背膘厚
Backfat thickness at 100 kg weight		 总产仔数
Total born number
表型数据量Number of phenotype records 1267 1267 2898
测定年份Year 2019-2021 2019-2021 2016-2021
系谱记录数Number of pedigree animals 42198

Fig. 1

Flowchart of haplotype matrix construction"

Table 2

Statistics of haplotype number and haplotype allele from different haplotype block partition methods"

统计量
Statistic		 2 SNPs/
block		 5 SNPs/
block		 400 bp/
block		 靶向block
Targeting block
单倍型等位基因数Number of haplotype alleles 153367 210348 238449 240015
单倍型区块数Number of haplotype blocks 44053 17621 42473 42302
每个单倍型区块的平均SNP数
The average number of SNPS per haplotype block		 2 5 2.07 2.08
每个单倍型区块的平均单倍型等位基因数
The average number of haplotype alleles per haplotype block		 3.48 11.94 5.61 5.67
每个单倍型区块的最大SNP数
The maximum number of SNPS per haplotype block		 2 5 7 7
每个单倍型区块的最小SNP数
The minimum number of SNPs per haplotype block		 2 5 1 1
SNP大于等于2的单倍型区块数
The number of haplotype blocks with more than or equal to two SNPs		 44052 17621 21066 21089

Fig. 2

Averaged LD (r2) of adjacent markers in 50K liquid-phase chip, target resequencing data and different kinds of haplotype blocks"

Table 3

The accuracy and unbiasedness of genomic selection on growth and reproduction traits based on SNP and haplotype in younger validation population"

SNP/单倍型
SNP/Haplotype		 单倍型等位基因数或SNP标记数
The number of haplotype alleles or SNP markers		 AGE BF TNB
准确性
Accuracy		 无偏性
Unbiasedness		 准确性Accuracy 无偏性
Unbiasedness		 准确性Accuracy 无偏性Unbiasedness
液相50K SNP
Liquid phase 50K SNP		 42302 0.562 1.139 0.607 1.020 0.596 0.744
靶向重测序mSNP
Target sequencing mSNP		 88105 0.554 1.172 0.599 1.011 0.578 0.745
2 SNPs/block 153367 0.563 1.150 0.608 1.039 0.612 0.736
5 SNPs/block 210348 0.557 1.153 0.604 1.107 0.606 0.743
400 bp/block 238449 0.589 1.134 0.619 1.010 0.632 0.745
靶向block
Target block		 240015 0.589 1.134 0.619 1.010 0.633 0.745

Table 4

The accuracy and unbiasedness of genomic selection on growth and reproduction traits based on SNP and haplotype in five-fold cross validation"

SNP/单倍型
SNP/Haplotype		 AGE BF TNB
准确性
Accuracy		 无偏性
Unbiasedness		 准确性
Accuracy		 无偏性
Unbiasedness		 准确性
Accuracy		 无偏性
Unbiasedness
液相50K SNP
Liquid phase 50K SNP		 0.785±0.004 0.735±0.09 0.736±0.008 0.688±0.11 0.740±0.010 0.929±0.12
靶向重测序mSNP
Target sequencing mSNP		 0.780±0.003 0.756±0.09 0.730±0.007 0.704±0.12 0.725±0.012 0.903±0.11
2 SNPs/block 0.792±0.003 0.744±0.08 0.746±0.007 0.700±0.12 0.751±0.012 0.895±0.11
5 SNPs/block 0.787±0.004 0.747±0.09 0.740±0.007 0.710±0.12 0.746±0.011 0.884±0.11
400 bp/block 0.796±0.003 0.739±0.09 0.748±0.007 0.992±0.48 0.756±0.012 0.897±0.11
靶向 block
Target block		 0.796±0.003 0.739±0.09 0.748±0.007 0.992±0.48 0.756±0.012 0.897±0.11
[1]
ZHANG Z, ZHANG Q, DING X D. Advances in genomic selection in domestic animals. Chinese Science Bulletin, 2011, 56(25): 2655-2663.
[2]
HAYES B J, BOWMAN P J, CHAMBERLAIN A J, GODDARD M E. Invited review: Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 2009, 92(2): 433-443.

doi: 10.3168/jds.2008-1646 pmid: 19164653
[3]
张金鑫, 唐韶青, 宋海亮, 高虹, 蒋尧, 江一凡, 弥世荣, 孟庆利, 于凡, 肖炜, 云鹏, 张勤, 丁向东. 北京地区大白猪基因组联合育种研究. 中国农业科学, 2019, 52(12): 2161-2170. doi: 10.3864/j.issn.0578-1752.2019.12.013.
ZHANG J X, TANG S Q, SONG H L, GAO H, JIANG Y, JIANG Y F, MI S R, MENG Q L, YU F, XIAO W, YUN P, ZHANG Q, DING X D. Joint genomic selection of Yorkshire in Beijing. Scientia Agricultura Sinica, 2019, 52(12): 2161-2170. doi: 10.3864/j.issn.0578-1752.2019.12.013. (in Chinese)
[4]
周子文, 付璐, 孟庆利, 周海深, 张勤, 丁向东. 利用后裔测定验证大白猪基因组选择实施效果研究. 畜牧兽医学报, 2020, 51(10): 2367-2377.

doi: 10.11843/j.issn.0366-6964.2020.10.005
ZHOU Z W, FU L, MENG Q L, ZHOU H S, ZHANG Q, DING X D. Using progeny testing to evaluate the efficiency of genomic selection in large white pigs. Chinese Journal of Animal and Veterinary Sciences, 2020, 51(10): 2367-2377. (in Chinese)
[5]
SONG H L, ZHANG Q, DING X D. The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs. Journal of Animal Science and Biotechnology, 2020, 11(1): 88.
[6]
SONG H L, ZHANG Q, MISZTAL I, DING X D. Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single-step genomic reaction norm model. Journal of Animal Breeding and Genetics, 2020, 137(6): 523-534.
[7]
BHAT J A, ALI S, SALGOTRA R K, MIR Z A, DUTTA S, JADON V, TYAGI A, MUSHTAQ M, JAIN N, SINGH P K, SINGH G P, PRABHU K V. Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Frontiers in Genetics, 2016, 7: 221.
[8]
MACLEOD I M, BOWMAN P J, VANDER JAGT C J, HAILE- MARIAM M, KEMPER K E, CHAMBERLAIN A J, SCHROOTEN C, HAYES B J, GODDARD M E. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics, 2016, 17(1): 144.
[9]
SOLBERG T R, SONESSON A K, WOOLLIAMS J A, MEUWISSEN T H E. Genomic selection using different marker types and densities. Journal of Animal Science, 2008, 86(10): 2447-2454.

doi: 10.2527/jas.2007-0010 pmid: 18407980
[10]
MAHER B. Personal genomes: the case of the missing heritability. Nature, 2008, 456: 18-21.
[11]
WON S, PARK J E, SON J H, LEE S H, PARK B H, PARK M, PARK W C, CHAI H H, KIM H, LEE J, LIM D. Genomic prediction accuracy using haplotypes defined by size and hierarchical clustering based on linkage disequilibrium. Frontiers in Genetics, 2020, 11: 134.

doi: 10.3389/fgene.2020.00134 pmid: 32211021
[12]
BOICHARD D, GUILLAUME F, BAUR A, CROISEAU P, ROSSIGNOL M N, BOSCHER M Y, DRUET T, GENESTOUT L, COLLEAU J J, JOURNAUX L, DUCROCQ V, FRITZ S. Genomic selection in French dairy cattle. Animal Production Science, 2012, 52(3): 115.
[13]
MUCHA A, WIERZBICKI H, KAMIŃSKI S, OLEŃSKI K, HERING D. High-frequency marker haplotypes in the genomic selection of dairy cattle. Journal of Applied Genetics, 2019, 60(2): 179-186.

doi: 10.1007/s13353-019-00489-9 pmid: 30877657
[14]
FEITOSA F L B, PEREIRA A S C, AMORIM S T, PERIPOLLI E, DE OLIVEIRA SILVA R M, BRAZ C U, FERRINHO A M, SCHENKEL F S, BRITO L F, ESPIGOLAN R, DE ALBUQUERQUE L G, BALDI F. Comparison between haplotype-based and individual SNP-based genomic predictions for beef fatty acid profile in Nelore cattle. Journal of Animal Breeding and Genetics, 2020, 137(5): 468-476.

doi: 10.1111/jbg.12463 pmid: 31867831
[15]
徐云碧, 杨泉女, 郑洪建, 许彦芬, 桑志勤, 郭子锋, 彭海, 张丛, 蓝昊发, 王蕴波, 吴坤生, 陶家军, 张嘉楠. 靶向测序基因型检测(GBTS)技术及其应用. 中国农业科学, 2020, 53(15): 2983-3004. doi: 10.3864/j.issn.0578-1752.2020.15.001.
XU Y B, YANG Q N, ZHENG H J, XU Y F, SANG Z Q, GUO Z F, PENG H, ZHANG C, LAN H F, WANG Y B, WU K S, TAO J J, ZHANG J N. Genotyping by target sequencing (GBTS) and its applications. Scientia Agricultura Sinica, 2020, 53(15): 2983-3004. doi: 10.3864/j.issn.0578-1752.2020.15.001. (in Chinese)
[16]
BURRIDGE A J, WILKINSON P A, WINFIELD M O, BARKER G L A, ALLEN A M, COGHILL J A, WATERFALL C, EDWARDS K J. Conversion of array-based single nucleotide polymorphic markers for use in targeted genotyping by sequencing in hexaploid wheat (Triticum aestivum). Plant Biotechnology Journal, 2018, 16(4): 867-876.

doi: 10.1111/pbi.12834 pmid: 28913866
[17]
GUO Z F, WANG H W, TAO J J, REN Y H, XU C, WU K S, ZOU C, ZHANG J N, XU Y B. Development of multiple SNP marker panels affordable to breeders through genotyping by target sequencing (GBTS) in maize. Molecular Breeding, 2019, 39(3): 37.
[18]
BOLGER A M, LOHSE M, USADEL B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 2014, 30(15): 2114-2120.

doi: 10.1093/bioinformatics/btu170 pmid: 24695404
[19]
LI H, DURBIN R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009, 25(14): 1754-1760.

doi: 10.1093/bioinformatics/btp324 pmid: 19451168
[20]
LI H, HANDSAKER B, WYSOKER A, FENNELL T, RUAN J, HOMER N, MARTH G, ABECASIS G, DURBIN R, SUBGROUP 1 G P D P. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 2009, 25(16): 2078-2079.

doi: 10.1093/bioinformatics/btp352 pmid: 19505943
[21]
VAN DER AUWERA G A, CARNEIRO M O, HARTL C, POPLIN R, DEL ANGEL G, LEVY-MOONSHINE A, JORDAN T, SHAKIR K, ROAZEN D, THIBAULT J, BANKS E, GARIMELLA K V, ALTSHULER D, GABRIEL S, DEPRISTO M A. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Current Protocols in Bioinformatics, 2013, 43(1110): 11.10.1-11.10.33.
[22]
BROWNING B L, BROWNING S R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. The American Journal of Human Genetics, 2009, 84(2): 210-223.
[23]
LEGARRA A, AGUILAR I, MISZTAL I. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 2009, 92(9): 4656-4663.

doi: 10.3168/jds.2009-2061 pmid: 19700729
[24]
CHRISTENSEN O F, LUND M S. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 2010, 42(1): 2.
[25]
VANRADEN P M. Efficient methods to compute genomic predictions. Journal of Dairy Science, 2008, 91(11): 4414-4423.

doi: 10.3168/jds.2007-0980 pmid: 18946147
[26]
UTSUNOMIYA Y T, MILANESI M, UTSUNOMIYA A T H, AJMONE-MARSAN P, GARCIA J F. GHap: An R package for genome-wide haplotyping. Bioinformatics, 2016, 32(18): 2861-2862.

doi: 10.1093/bioinformatics/btw356 pmid: 27283951
[27]
SONG H, ZHANG J, JIANG Y, GAO H, TANG S, MI S, YU F, MENG Q, XIAO W, ZHANG Q, DING X. Genomic prediction for growth and reproduction traits in pig using an admixed reference population. Journal of Animal Science, 2017, 95(8): 3415-3424.

doi: 10.2527/jas.2017.1656 pmid: 28805914
[28]
MEUWISSEN T H E, GODDARD M E. Prediction of identity by descent probabilities from marker-haplotypes. Genetics Selection Evolution, 2001, 33(6): 605-634.

pmid: 11742632
[29]
DA Y, LIANG Z X, PRAKAPENKA D. Multifactorial methods integrating haplotype and epistasis effects for genomic estimation and prediction of quantitative traits. Frontiers in Genetics, 2022, 13: 922369.
[30]
WERNER C R, GAYNOR R C, GORJANC G, HICKEY J M, KOX T, ABBADI A, LECKBAND G, SNOWDON R J, STAHL A. How population structure impacts genomic selection accuracy in cross- validation: implications for practical breeding. Frontiers in Plant Science, 2020, 11: 592977.
[31]
HESS M, DRUET T, HESS A, GARRICK D. Fixed-length haplotypes can improve genomic prediction accuracy in an admixed dairy cattle population. Genetics Selection Evolution, 2017, 49(1): 54.

doi: 10.1186/s12711-017-0329-y pmid: 28673233
[32]
HAILE A, HILALI M, HASSEN H, LOBO R N B, RISCHKOWSKY B. Estimates of genetic parameters and genetic trends for growth, reproduction, milk production and milk composition traits of Awassi sheep. Animal, 2019, 13(2): 240-247.

doi: 10.1017/S1751731118001374 pmid: 29954467
[33]
LIANG Z X, TAN C, PRAKAPENKA D, MA L, DA Y. Haplotype analysis of genomic prediction using structural and functional genomic information for seven human phenotypes. Frontiers in Genetics, 2020, 11: 588907.
[34]
BIAN C, PRAKAPENKA D, TAN C, YANG R F, ZHU D, GUO X L, LIU D W, CAI G Y, LI Y L, LIANG Z X, WU Z F, DA Y, HU X X. Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs. Genetics Selection Evolution, 2021, 53(1): 78.

doi: 10.1186/s12711-021-00661-y pmid: 34620094
[35]
CALUS M P, MEUWISSEN T H, WINDIG J J, KNOL E F, SCHROOTEN C, VEREIJKEN A L, VEERKAMP R F. Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values. Genetics Selection Evolution, 2009, 41(1): 11.
[36]
邱奥, 王雪, 孟庆利, 张勤, 丁向东. 3款猪50K SNP芯片基因型填充效果研究. 中国畜牧杂志, 2021, 57(S1): 33-38.
QIU A, WANG X, MENG Q L, ZHANG Q, DING X D. Study on genotype imputation of three pig 50K SNP chips. Chinese Journal of Animal Science, 2021, 57(S1): 33-38. (in Chinese)
[1] WANG YongSheng, NIU Li, WANG ChangJie, MA LiHua, LIAN XiaoXiao, MENG YaXiong, MA XiaoLe, YAO LiRong, ZHANG Hong, YANG Ke, LI BaoChun, WANG HuaJun, SI ErJing, WANG JunCheng. Genome-Wide Association Study and Candidate Gene Identification for Thousand Grain Weight in Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 499-514.
[2] ZHOU Qi, ZHANG ShiHao, ZHANG Liang, PAN Yu, ZHANG LiJuan, TU Zhi, PAN HongMei, LONG Xi. Prediction of the Potential Habitat Suitability of Luopanshan Pigs in Chongqing Based on the Optimized MaxEnt Model [J]. Scientia Agricultura Sinica, 2026, 59(1): 205-219.
[3] WANG JiYing, LI JingXuan, WANG YanPing, GUO JianFeng, LIN HaiChao, ZHAO XueYan. Weighted Gene Co-Expression Network Analysis Reveals Potential Candidate Genes Affecting Fat Deposition in Pigs [J]. Scientia Agricultura Sinica, 2025, 58(9): 1845-1855.
[4] JIN YiDan, HE NiQing, CHENG ZhaoPing, LIN ShaoJun, HUANG FengHuang, BAI KangCheng, ZHANG Tao, WANG WenXiao, YU MinXiang, YANG DeWei. Screening and Identification of Pigm-1 Interaction Proteins for Disease Resistance of Rice Blast [J]. Scientia Agricultura Sinica, 2025, 58(6): 1043-1051.
[5] LIU Jing, ZHAO ShiLin, YANG XiaoTing, WEI YiXuan, LI JiaPeng, ZHAO Yan. Study on the Origin Traceability of Beijing Black Pig Based on Stable Isotope Technology [J]. Scientia Agricultura Sinica, 2025, 58(23): 5071-5080.
[6] GAO Rong, LI HengYu, CHEN LiJuan, MA HuiLing. Physiological Effects of 5-AzaC on Alleviating Salt‑Alkali Stress in Alfalfa and Its Impact on the Expression of DNA Methylation Enzyme Genes [J]. Scientia Agricultura Sinica, 2025, 58(21): 4482-4496.
[7] GAO XiaoPing, PAN HongMei, GUO ZongYi, ZHANG JunJie, LIN Yan, ZHANG Liang. Post-Freezing Quality and Targeted Lipidomics Analysis of Rongchang Pig Spermatozoa with Different Freezing Tolerance [J]. Scientia Agricultura Sinica, 2025, 58(2): 387-400.
[8] LI Ming, CHENG YuKun, BAI Bin, LEI Bin, GENG HongWei. Genome-Wide Association Study on Spike Architecture Traits and Elite Haplotype Mining in Winter Wheat [J]. Scientia Agricultura Sinica, 2025, 58(18): 3583-3597.
[9] ZHUANG RunJie, LIU HuiMing, WANG ShiYu, LÜ WanPing, WEN YongXian. Genomic Selection Method Based on G2PSE Stacking Ensemble [J]. Scientia Agricultura Sinica, 2025, 58(15): 2960-2979.
[10] ZHANG HuaPeng, ZHANG QingZe, HE Fan, QI MengFan, FU BinBin, LI QingChun, LI MengXun, MA LiPeng, LIU Yi, HUANG Tao. Cloning and Identification of Differentially Expressed lncRNAs in Follicles of Meishan Pigs and Duroc Pigs with Their Correlation Analysis with miRNAs [J]. Scientia Agricultura Sinica, 2024, 57(9): 1807-1819.
[11] ZHAO ZhenJian, WANG Kai, CHEN Dong, SHEN Qi, YU Yang, CUI ShengDi, WANG JunGe, CHEN ZiYang, YU ShiXin, CHEN JiaMiao, WANG XiangFeng, TANG GuoQing. Integrated Aanalysis of Genome and DNA Methylation for Screening Key Genes Related to Pork Quality Traits [J]. Scientia Agricultura Sinica, 2024, 57(7): 1394-1406.
[12] LIU ZhuoLin, LIU HongYun. The Potential and Mechanisms of Apigenin to Relieve Heat Stress and Hypoxia in Dairy Cows Based on Network Pharmacology and Molecular Docking [J]. Scientia Agricultura Sinica, 2024, 57(5): 1010-1022.
[13] ZHOU YuanQing, DONG HongMin, ZHU ZhiPing, WANG Yue, LI NanXi. Review on Carbon Footprint Assessment of Pig Farming System [J]. Scientia Agricultura Sinica, 2024, 57(2): 379-389.
[14] SHANG Hang, CHENG YuKun, REN Yi, GENG HongWei. Genome-Wide Association Analysis of Starch Gelatinization Traits in Winter Wheat [J]. Scientia Agricultura Sinica, 2024, 57(18): 3507-3521.
[15] ZHANG ZiHui, ZHANG YanFei, LI Long, LI ChaoNan, WANG JingYi, YANG DeLong, MAO XinGuo, JING RuiLian. Wheat Enolase Gene TaENO1-5B Involved in Regulating Plant Height and Grain Number Per Spike in Multiple Environments [J]. Scientia Agricultura Sinica, 2024, 57(14): 2717-2731.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd