Development of molecular inversion probes for soybean progeny genomic selection genotyping

Haichuan Wang, Benjamin Campbell, Mary Happ, Samantha McConaughy, Aaron Lorenz, Keenan Amundsen, Qijian Song, Vincent Pantalone, David Hyten

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Increasing rate of genetic gain for key agronomic traits through genomic selection requires the development of new molecular methods to run genome-wide single-nucleotide polymorphisms (SNPs). The main limitation of current methods is the cost is too high to screen breeding populations. Molecular inversion probes (MIPs) are a targeted genotyping-by-sequencing (GBS) method that could be used for soybean [Glycine max (L.) Merr.] that is both cost-effective, high-throughput, and provides high data quality to screen breeder's germplasm for genomic selection. A 1K MIP SNP set was developed for soybean with uniformly distributed markers across the genome. The SNPs were selected to maximize the number of informative markers in germplasm being tested in soybean breeding programs located in the northern-central and middle-southern regions of the United States. The 1K SNP MIP set was tested on diverse germplasm and a recombinant inbred line (RIL) population. Targeted sequencing with MIPs obtained an 85% enrichment for the targeted SNPs. The MIP genotyping accuracy was 93% overall, whereas homozygous call accuracy was 98% with <10% missing data. The accuracy of MIPs combined with its low per-sample cost makes it a powerful tool to enable genomic selection within soybean breeding programs.

Original languageEnglish (US)
Article numbere20270
JournalPlant Genome
Volume16
Issue number1
DOIs
StatePublished - Mar 2023

Bibliographical note

Funding Information:
This work was supported by Nebraska Soybean Board project #1723, Development of Next‐Generation Sequencing Application for Improving Soybean and the North Central Soybean Research Program project: Increasing the Rate of Genetic Gain for Yield in Soybean Breeding Programs. This work was completed using the Holland Computing Center of the University of Nebraska, which receives support from the Nebraska Research Initiative. This material is based upon work supported by the National Science Foundation Postdoctoral Research Fellowship in Biology (Grant No. IOS‐1710790). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper ( http://www.msi.umn.edu ).

Funding Information:
This work was supported by Nebraska Soybean Board project #1723, Development of Next-Generation Sequencing Application for Improving Soybean and the North Central Soybean Research Program project: Increasing the Rate of Genetic Gain for Yield in Soybean Breeding Programs. This work was completed using the Holland Computing Center of the University of Nebraska, which receives support from the Nebraska Research Initiative. This material is based upon work supported by the National Science Foundation Postdoctoral Research Fellowship in Biology (Grant No. IOS-1710790). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper (http://www.msi.umn.edu).

Publisher Copyright:
© 2022 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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