Crop

Maize:

1. Gao J, Wang S, Zhou Z, et al. Linkage mapping and genome-wide association reveal candidate genes conferring thermotolerance of seed-set in maize. J Exp Bot. 2019;70(18):4849-4864.
2. Guo Z, Wang H, Tao J, et al. Development of multiple SNP marker panels affordable to breeders through genotyping by target sequencing (GBTS) in maize. Mol Breeding. 2019;39(37).
3. Liu HJ, Jian L, Xu J, et al. High-Throughput CRISPR/Cas9 Mutagenesis Streamlines Trait Gene Identification in Maize. Plant Cell. 2020;32(5):1397-1413. DOI: 10.1105/tpc.19.00934
4. Wen J, Shen Y, Xing Y, et al. QTL Mapping of Fusarium Ear Rot Resistance in Maize. Plant Dis. 2021;105(3):558-565.
5. Guo Z, Yang Q, Huang F, et al. Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip. Plant Commun. 2021;2(6):100230.
6. Han L, Jiang C, Zhang W, et al. Morphological Characterization and Transcriptome Analysis of New Dwarf and Narrow-Leaf (dnl2) Mutant in Maize. Int J Mol Sci. 2022;23(2):795.
7. Zhang X, Wang M, Zhang C, et al. Genetic dissection of QTLs for starch content in four maize DH populations. Front Plant Sci. 2022;13:950664.
8. Huang J, Li Y, Ma Y, et al. The rhizospheric microbiome becomes more diverse with maize domestication and genetic improvement. J Integr Agric. 2022;4:1188-1202.
9. Liu R, Cui Y, Kong L, et al. Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines. Genes (Basel). 2023;14(5):1044.
10. Gao J, Feng P, Zhang J, et al. Enhancing maize's nitrogen-fixing potential through ZmSBT3, a gene suppressing mucilage secretion. J Integr Plant Biol. 2023;65(12):2645-2659.
11. Yu G, Cui Y, Jiao Y, et al. Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance. Crop J. 2023;11(2):490-498.
12. Luo P, Wang H, Ni Z, et al. Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design. Crop J. 2023;11(6):1884-92.
13. Xu F, Liu S, Zhao A, et al. iFLAS: positive-unlabeled learning facilitates full-length transcriptome-based identification and functional exploration of alternatively spliced isoforms in maize. New Phytol. 2024;241(6):2606-2620.

Soybean:

1. Liu Y, Liu S, Zhang Z, et al. GenoBaits Soy40K: a highly flexible and low-cost SNP array for soybean studies. Sci China Life Sci. 2022;65(9):1898-1901. DOI: 10.1007/s11427-022-2130-8
2. Chen X, Liu C, Guo P, et al. Differential SW16.1 allelic effects and genetic backgrounds contributed to increased seed weight after soybean domestication. J Integr Plant Biol. 2023;65(7):1734-1752.
3. Yang Q, Zhang J, Shi X, et al. Development of SNP marker panels for genotyping by target sequencing (GBTS) and its application in soybean. Mol Breed. 2023;43(4):26.

Rice:

1. Li, X., Zheng, H., Wu, W. et al. QTL Mapping and Candidate Gene Analysis for Alkali Tolerance in Japonica Rice at the bud Stage Based on Linkage Mapping and Genome-Wide Association Study. Rice (N Y). 2020;13(1):48.
2. Hussain I, Ali S, Liu W, et al. Identification of Heterotic Groups and Patterns Based on Genotypic and Phenotypic Characteristics Among Rice Accessions of Diverse Origins. Front Genet. 2022;13:811124.
3. Lei L, Cao L, Ding G, et al. OsBBX11 on qSTS4 links to salt tolerance at the seeding stage in Oryza sativa L. ssp. Japonica. Front Plant Sci. 2023;14:1139961.
4. Ashfaq M, Rasheed A, Zhu R, et al. Genome-Wide Association Mapping for Yield and Yield-Related Traits in Rice (Oryza Sativa L.) Using SNPs Markers. Genes (Basel). 2023;14(5):1089.
5. Li S, Xu S, Zheng J, et al. Joint QTL Mapping and Transcriptome Sequencing Analysis Reveal Candidate Genes for Salinity Tolerance in Oryza sativa L. ssp. Japonica Seedlings. Int J Mol Sci. 2023;24(24):17591.

Wheat:

1. Hou J, Liu Y, Hao C, et al. Starch Metabolism in Wheat: Gene Variation and Association Analysis Reveal Additive Effects on Kernel Weight. Front Plant Sci. 2020;11:562008.
2. Shaukat M, Sun M, Ali M, et al. Genetic Gain for Grain Micronutrients and Their Association with Phenology in Historical Wheat Cultivars Released between 1911 and 2016 in Pakistan. Agronomy. 2021; 11(6):1247.
3. Guo H, Du Q, Xie Y, et al. Identification of Rice Blast Loss-of-Function Mutant Alleles in the Wheat Genome as a New Strategy for Wheat Blast Resistance Breeding. Front Genet. 2021;12:623419.
4. Qiu D, Huang J, Guo G, et al. The Pm5e Gene Has No Negative Effect on Wheat Agronomic Performance: Evidence From Newly Established Near-Isogenic Lines. Front Plant Sci. 2022;13:918559.
5. Qiao L, Li H, Wang J, et al. Analysis of Genetic Regions Related to Field Grain Number per Spike From Chinese Wheat Founder Parent Linfen 5064. Front Plant Sci. 2022;12:808136. Published 2022 Jan 5.
6. Zheng X, Qiao L, Liu Y, et al. Genome-Wide Association Study of Grain Number in Common Wheat From Shanxi Under Different Water Regimes. Front Plant Sci. 2022;12:806295.
7. Huang S, Zhang Y, Ren H, et al. Epistatic interaction effect between chromosome 1BL (Yr29) and a novel locus on 2AL facilitating resistance to stripe rust in Chinese wheat Changwu 357-9. Theor Appl Genet. 2022;135(7):2501-2513.
8. Wang J, Yang C, Zhao W, et al. Genome-wide association study of grain hardness and novel Puroindoline alleles in common wheat. Mol Breed. 2022;42(7):40.
9. Zhou C, Xiong H, Fu M, et al. Genetic mapping and identification of Rht8-B1 that regulates plant height in wheat. BMC Plant Biol. 2023;23(1):333.
10. Li JC, Li JJ, Zhao L, et al. Rapid identification of Psathyrostachys huashanica Keng chromosomes in wheat background based on ND-FISH and SNP array methods. J Integr Agric. 2023;22(10):2934-48.
11. Li J, Zhao L, Lü B, et al. Development and characterization of a novel common wheat–Mexico Rye T1DL·1RS translocation line with stripe rust and powdery mildew resistance. J Integr Agric.
12. Xiang M, Liu S, Wang X, et al. Development of breeder chip for gene detection and molecular-assisted selection by target sequencing in wheat. Mol Breed. 2023;43(2):13.
13. Xiong H, Guo H, Fu M, et al. A large-scale whole-exome sequencing mutant resource for functional genomics in wheat. Plant Biotechnol J. 2023;21(10):2047-2056.
14. Hu J, Gebremariam TG, Zhang P, et al. Resistance to Powdery Mildew Is Conferred by Different Genetic Loci at the Adult-Plant and Seedling Stages in Winter Wheat Line Tianmin 668. Plant Dis. 2023;107(7):2133-2143.
15. Niu F, Liu Z, Zhang F, et al. Identification and validation of major-effect quantitative trait locus QMS-5B associated with male sterility in photo-thermo-sensitive genic male sterile wheat. Theor Appl Genet. 2023;136(12):257.
16. Zhou J, Li W, Yang Y, et al. A promising QTL QSns.sau-MC-3D.1 likely superior to WAPO1 for the number of spikelets per spike of wheat shows no adverse effects on yield-related traits. Theor Appl Genet. 2023;136(9):181.
17. Zhao R, Liu B, Wan W, et al. Mapping and characterization of a novel adult-plant leaf rust resistance gene LrYang16G216 via bulked segregant analysis and conventional linkage method [published correction appears in Theor Appl Genet. 2023 Mar 23;136(4):84.
18. Qiu Y, Han Z, Liu N, et al. Effects of Aegilops longissima chromosome 1Sl on wheat bread-making quality in two types of translocation lines. Theor Appl Genet. 2024;137(2)
19. Xu X, Su Y, Yang J, et al. A novel QTL conferring Fusarium crown rot resistance on chromosome 2A in a wheat EMS mutant. Theor Appl Genet. 2024;137(2):49.
20. Li Y, Hu J, Lin H, et al. Mapping QTLs for adult-plant resistance to powdery mildew and stripe rust using a recombinant inbred line population derived from cross Qingxinmai × 041133. Front Plant Sci. 2024;15:1397274.
21. Ren H, Zhang X, Zhang Y, et al. Identification of Two Novel QTL for Fusarium Head Blight Resistance in German Wheat Cultivar Centrum. Plant Dis. 2024;108(8):2462-2471.
22. Liu S, Xiang M, Wang X, et al. Development and application of the GenoBaits WheatSNP16K array to accelerate wheat genetic research and breeding. Plant Commun. 2025;6(1):101138.
23. Li Y, Hu J, Lin H, et al. Mapping QTLs for adult-plant resistance to powdery mildew and stripe rust using a recombinant inbred line population derived from cross Qingxinmai × 041133. Front Plant Sci. 2024;15:1397274.

Peanut:

1. Lu Q, Hong Y, Li S, et al. Genome-wide identification of microsatellite markers from cultivated peanut (Arachis hypogaea L.). BMC Genomics. 2019;20(1):799.
2. Chen X, Lu Q, Liu H, et al. Sequencing of Cultivated Peanut, Arachis hypogaea, Yields Insights into Genome Evolution and Oil Improvement. Mol Plant. 2019;12(7):920-934.
3. Sun Z, Zheng Z, Qi F, et al. Development and evaluation of the utility of GenoBaits Peanut 40K for a peanut MAGIC population. Mol Breed. 2023;43(10):72.
4. Huai D, Zhi C, Wu J, et al. Unveiling the molecular regulatory mechanisms underlying sucrose accumulation and oil reduction in peanut kernels through genetic mapping and transcriptome analysis. Plant Physiol Biochem. 2024;208:108448.

Cotton:

1. Chen H, Han Z, Ma Q, et al. Identification of elite fiber quality loci in upland cotton based on the genotyping-by-target-sequencing technology. Front Plant Sci. 2022;13:1027806.
2. Si Z, Jin S, Li J, et al. The design, validation, and utility of the “ZJU CottonSNP40K” liquid chip through genotyping by target sequencing. Ind Crops Prod. 2022;188:115629.

Vegetable & Fruit

Broccoli:

1. Shen Y, Wang J, Shaw RK, et al. Development of GBTS and KASP Panels for Genetic Diversity, Population Structure, and Fingerprinting of a Large Collection of Broccoli (Brassica oleracea L. var. italica) in China. Front Plant Sci. 2021;12:655254.

Cucumber:

1. Yang J, Zhang J, Han R, et al. Target SSR-Seq: A Novel SSR Genotyping Technology Associate With Perfect SSRs in Genetic Analysis of Cucumber Varieties. Front Plant Sci. 2019;10:531.
2. Zhang J, Yang J, Zhang L, et al. A new SNP genotyping technology Target SNP-seq and its application in genetic analysis of cucumber varieties. Sci Rep. 2021 Apr 7;11(1):8010.

Eggplant:

1. Liu W, Qian Z, Zhang J, et al. Impact of fruit shape selection on genetic structure and diversity uncovered from genome-wide perfect SNPs genotyping in eggplant. Mol Breeding. 2019;39(140).

Pepper:

1. Du H, Yang J, Chen B, et al. Target sequencing reveals genetic diversity, population structure, core-SNP markers, and fruit shape-associated loci in pepper varieties. BMC Plant Biol. 2019;19(1):578.
2. Li Z, Jia Z, Li J, et al. Development of a 45K pepper GBTS liquid-phase gene chip and its application in genome-wide association studies. Front Plant Sci. 2024;15:1405190. Published 2024 Jun 25.

Melon:

1. Yu Q, Li S, Su X, et al. Melon2K array: A versatile 2K liquid SNP chip for melon genetics and breeding. Horticultural Plant Journal, 2024;2468-0141.

Grape:

1. Yang B, Wu W, Lv J, Li J, Xu Y, et al. Identification of sex determination locus and development of marker combination in Vitis based on genotyping by target sequencing. Fruit Res. 2023;3:31.

Litchi:

1. Zhang L, Wang P, Li F, et al. Litchi40K v1.0: a cost-effective, flexible, and versatile liquid SNP chip for genetic analysis and digitalization of germplasm resources in litchi. Hortic Res. 2025;12(5):uhaf038.

Livestock

Sheep:

1. Guo Y, Bai F, Wang J, et al. Design and characterization of a high-resolution multiple-SNP capture array by target sequencing for sheep. J Anim Sci. 2023;101:skac383.

Bovine:

1. Chen Y, Guo Y, Ge F, et al. Developing a liquid capture chip to accelerate the genetic progress of cattle. Anim Res One Health. 2024;2(2):204-16.
2. Zhang M, Xu L, Lu H, et al. Genomic prediction based on a joint reference population for the Xinjiang Brown cattle. Front Genet. 2024;15:1394636.
3. Wang H, Wu H, Zhang W, et al. Development and validation of a 5K low-density SNP chip for Hainan cattle. BMC Genomics. 2024;25(1):873.

Porcine:

1. Zhang Z, Xing S, Qiu A, et al. The development of a porcine 50K SNP panel using genotyping by target sequencing and its application. J Integr Agric. 2023;2095-3119.
2. Wang X, Shi S, Wang G, et al. Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs. J Anim Sci Biotechnol. 2022;13(1):60

Chicken:

1. Liu Y, Shan Y, Tu Y, Zhang M, Ji G, Ju X, Shi S, Fan C, Li Y, Shu J. Designing and evaluating a cost-effective single nucleotide polymorphism liquid array for Chinese native chickens. Anim Res One Health. 2023.

Donkey:

1. Liu Z, Wang T, Shi X, et al. Identification of LTBP2 gene polymorphisms and their association with thoracolumbar vertebrae number, body size, and carcass traits in Dezhou donkeys. Front Genet. 2022;13:969959.
2. Shi X, Li Y, Wang T, et al. Association of HOXC8 Genetic Polymorphisms with Multi-Vertebral Number and Carcass Weight in Dezhou Donkey. Genes (Basel). 2022;13(11):2175.
3. Wang X, Wang T, Liang H, et al. A novel SNP in NKX1-2 gene is associated with carcass traits in Dezhou donkey. BMC Genom Data. 2023;24(1):41.
4. Liu S, Su J, Yang Q, et al. Genome-wide analyses based on a novel donkey 40K liquid chip reveal the gene responsible for coat color diversity in Chinese Dezhou donkey. Anim Genet. 2024;55(1):140-146.

Aquaculture

1. Liu J, Peng W, Yu F, et al. Genomic selection applications can improve the environmental performance of aquatics: A case study on the heat tolerance of abalone. Evol Appl. 2022;15(6):992-1001.
2. Wu Y, Peng W, Wang Y, Huang Z, Feng Y, Han Z, Luo X, You W, Ke C. Identification and dimorphic expression of sex-related genes in Pacific abalone (Haliotis discus hannai). Aquaculture.
3. Wang J, Miao L, Chen B, et al. Development and evaluation of liquid SNP array for large yellow croaker (Larimichthys crocea). Aquaculture. 2023;563(2):739021.

How to start a genotyping project

Starting a genotyping and sequencing project is simple! Just fill out the form below with your email and inquiry details. Our team will reach out to you promptly to provide a tailored service plan and pricing details. We invite breeders to collaborate with us and discover how our technology can enhance your projects!