ABSTRACT

Advances in marker-assisted breeding of soybean T. Vuong, University of Missouri, USA; and D. Walker, USDA-ARS and University of Illinois, USA

1 Introduction

2 Molecular marker types

3 Marker assays and genotyping platforms for marker-assisted selection

4 Marker-assisted selection in soybean breeding

5 Genomic selection

6 Conclusion and future trends

7 Acknowledgements

8 Where to look for further information

9 References

For centuries, plant breeding has had a significant impact on the improvement of crop productivity and quality worldwide. With extensive knowledge and practical experience in plant genetics, plant breeders have developed effective breeding methodologies and have made tremendous progress in phenotypic selection of superior genotypes. However, breeders using these methods also often encounter difficulties related to phenotyping procedures, which can be time-consuming, expensive, unreliable for traits with a low heritability and subject to genotype × environment (G × E) interactions (Francia et al., 2005). In recent decades, advances in molecular genetics and genomics have revolutionized breeding approaches for a number of crops, and DNA marker technologies offer great promise for increasing the precision and rate of progress in plant breeding through marker-assisted selection (MAS). The recent development of next-generation sequencing (NGS) technologies, coupled with reduced sequencing costs, facilitates the simultaneous discovery of large numbers of single nucleotide polymorphism (SNP) markers, even in crop species that are not major commodities. Many SNPs are located near or within genes or gene structural variants, revealing allelic variations in genes that affect traits of interest (Collard and Mackill, 2008). Such NGS-based DNA marker technologies, in combination with robust high-throughput genotyping assays, offer a wide

range of applications in modern molecular breeding. For example, analyses of genetic diversity can be conducted using whole-genome sequencing (WGS) data. Quantitative trait loci (QTL) can be identified using a genome-wide association study (GWAS) as an alternative mapping approach. Superior genotypes can be efficiently selected using accurate genomic-enabled prediction (GP) models for conducting genome-wide selection (GS) (Bhat et al., 2016; Crossa et al., 2017; Duhnen et al., 2017; Meuwissen et al., 2001).