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Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding

文献类型： 外文期刊

作者： Wang, Nan 1 ; Yuan, Yibing 2 ; Wang, Hui 2 ; Yu, Diansi 2 ; Liu, Yubo 2 ; Zhang, Ao 6 ; Gowda, Manje 7 ; Nair, Sudha K. 8 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Crop Sci, Beijing, Peoples R China

2.Int Maize & Wheat Improvement Ctr CIMMYT, Apdo Postal 6-641, Mexico City 06600, DF, Mexico

3.Sichuan Agr Univ, Maize Res Inst, Wenjiang, Sichuan, Peoples R China

4.Shanghai Acad Agr Sci, CIMMYT China Specialty Maize Res Ctr, Shanghai, Peoples R China

5.Shanghai Acad Agr Sci, Crop Breeding & Cultivat Res Inst, Shanghai, Peoples R China

6.Shenyang Agr Univ, Agron Coll, Shenyang, Liaoning, Peoples R China

7.Int Maize & Wheat Improvement Ctr CIMMYT, POB 1041, Nairobi 00621, Kenya

8.Int Crops Res Inst Semi Arid Trop, CIMMYT India, Patancheru 502324, Andhra Pradesh, India

期刊名称：SCIENTIFIC REPORTS （ 影响因子：4.379； 五年影响因子：5.133 ）

ISSN： 2045-2322

年卷期： 2020 年 10 卷 1 期

页码：

收录情况： SCI

摘要： Genotyping-by-Sequencing (GBS) is a low-cost, high-throughput genotyping method that relies on restriction enzymes to reduce genome complexity. GBS is being widely used for various genetic and breeding applications. In the present study, 2240 individuals from eight maize populations, including two association populations (AM), backcross first generation (BC1), BC1F2, F2, double haploid (DH), intermated B73xMo17 (IBM), and a recombinant inbred line (RIL) population, were genotyped using GBS. A total of 955,120 of raw data for SNPs was obtained for each individual, with an average genotyping error of 0.70%. The rate of missing genotypic data for these SNPs was related to the level of multiplex sequencing: similar to 25% missing data for 96-plex and similar to 55% for 384-plex. Imputation can greatly reduce the rate of missing genotypes to 12.65% and 3.72% for AM populations and bi-parental populations, respectively, although it increases total genotyping error. For analysis of genetic diversity and linkage mapping, unimputed data with a low rate of genotyping error is beneficial, whereas, for association mapping, imputed data would result in higher marker density and would improve map resolution. Because imputation does not influence the prediction accuracy, both unimputed and imputed data can be used for genomic prediction. In summary, GBS is a versatile and efficient SNP discovery approach for homozygous materials and can be effectively applied for various purposes in maize genetics and breeding.

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