Zhang YW - Search Results

1

IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.

Zhang YW, Han XL, Li M, Chen Y, Zhang YM. Zhang YW, et al. Comput Struct Biotechnol J. 2024 Dec 2;23:4357-4368. doi: 10.1016/j.csbj.2024.11.046. eCollection 2024 Dec. Comput Struct Biotechnol J. 2024. PMID: 39697676 Free PMC article.

2

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies.

Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM. Li M, et al. Among authors: zhang ym, zhang zc, zhang hq, zhang yw. Mol Plant. 2022 Apr 4;15(4):630-650. doi: 10.1016/j.molp.2022.02.012. Epub 2022 Feb 22. Mol Plant. 2022. PMID: 35202864 Free article.

3

QTL.gCIMapping.GUI v2.0: An R software for detecting small-effect and linked QTLs for quantitative traits in bi-parental segregation populations.

Zhang YW, Wen YJ, Dunwell JM, Zhang YM. Zhang YW, et al. Among authors: zhang ym. Comput Struct Biotechnol J. 2019 Dec 9;18:59-65. doi: 10.1016/j.csbj.2019.11.005. eCollection 2020. Comput Struct Biotechnol J. 2019. PMID: 31890145 Free PMC article.

4

IIIVmrMLM: The R and C++ tools associated with 3VmrMLM, a comprehensive GWAS method for dissecting quantitative traits.

Li M, Zhang YW, Xiang Y, Liu MH, Zhang YM. Li M, et al. Among authors: zhang ym, zhang yw. Mol Plant. 2022 Aug 1;15(8):1251-1253. doi: 10.1016/j.molp.2022.06.002. Epub 2022 Jun 8. Mol Plant. 2022. PMID: 35684963 Free article. No abstract available.

5

Identification of QTNs, QTN-by-environment interactions, and their candidate genes for grain size traits in main crop and ratoon rice.

Zhao Q, Shi XS, Wang T, Chen Y, Yang R, Mi J, Zhang YW, Zhang YM. Zhao Q, et al. Among authors: zhang ym, zhang yw. Front Plant Sci. 2023 Feb 2;14:1119218. doi: 10.3389/fpls.2023.1119218. eCollection 2023. Front Plant Sci. 2023. PMID: 36818826 Free PMC article.

6

Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies.

Ikram M, Han X, Zuo JF, Song J, Han CY, Zhang YW, Zhang YM. Ikram M, et al. Among authors: zhang ym, zhang yw. Genes (Basel). 2020 Jun 27;11(7):714. doi: 10.3390/genes11070714. Genes (Basel). 2020. PMID: 32604988 Free PMC article.

7

mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies.

Zhang YW, Tamba CL, Wen YJ, Li P, Ren WL, Ni YL, Gao J, Zhang YM. Zhang YW, et al. Among authors: zhang ym. Genomics Proteomics Bioinformatics. 2020 Aug;18(4):481-487. doi: 10.1016/j.gpb.2020.06.006. Epub 2020 Dec 18. Genomics Proteomics Bioinformatics. 2020. PMID: 33346083 Free PMC article.

8

An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2.

Wen YJ, Zhang YW, Zhang J, Feng JY, Dunwell JM, Zhang YM. Wen YJ, et al. Among authors: zhang ym, zhang yw, zhang j. Brief Bioinform. 2019 Sep 27;20(5):1913-1924. doi: 10.1093/bib/bby058. Brief Bioinform. 2019. PMID: 30032279 Free PMC article.

9

An evolutionary population structure model reveals pleiotropic effects of GmPDAT for traits related to seed size and oil content in soybean.

Liu JY, Zhang YW, Han X, Zuo JF, Zhang Z, Shang H, Song Q, Zhang YM. Liu JY, et al. Among authors: zhang ym, zhang yw, zhang z. J Exp Bot. 2020 Dec 31;71(22):6988-7002. doi: 10.1093/jxb/eraa426. J Exp Bot. 2020. PMID: 32926130

10

A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F₂ population.

Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM. Li P, et al. Among authors: zhang ym, zhang yw. Plant Commun. 2022 May 9;3(3):100319. doi: 10.1016/j.xplc.2022.100319. Epub 2022 Mar 25. Plant Commun. 2022. PMID: 35576159 Free PMC article.

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