Selective Genotyping: A Rapid and Cost-Effective Method for QTL Detection Governing Economically Important Traits

Introduction

Quantitative trait loci (QTL) mapping is a foundational tool in understanding the genetic basis of complex traits in plant breeding. Two primary approaches for marker-trait association analysis are widely adopted: the marker-based approach and the trait-based approach. The marker-based method requires genotyping the entire mapping population (MP) and then assessing phenotypic differences among distinct genotypic classes. Although comprehensive, this method is often time-consuming and cost-intensive. In contrast, the trait-based approach, particularly selective genotyping (SG), involves genotyping only those individuals exhibiting extreme phenotypes. This significantly reduces the number of samples analyzed while retaining the ability to detect meaningful marker-trait associations.

Fig 1. Workflow of Selective Genotyping for QTL Detection in Plant Breeding

Selective genotyping is based on the concept that individuals at the upper and lower tails of a trait distribution are more likely to differ in alleles at loci influencing that trait. By comparing allele frequencies between these phenotypic extremes, researchers can identify markers linked to the trait of interest. This strategy is statistically powerful and resource-efficient, especially when genotyping costs are higher than phenotyping costs. SG is applicable in both biparental and association mapping populations and is well-suited for traits controlled by one or a few major QTLs.

SG offers a rapid and economical alternative for QTL mapping, particularly in populations with large sample sizes. A notable extension of this strategy is QTL-seq, which combines bulked segregant analysis (BSA) with next-generation sequencing. In a study conducted by Takagi et al. (2013), QTL-seq was applied to a population of 241 recombinant inbred lines (RILs) and successfully identified a major QTL for rice blast resistance (caused by Magnaporthe oryzae) on chromosome 6 within the 2.39–4.39 Mb region. This result exemplifies the potential of SG-based strategies in accelerating gene discovery for important agronomic traits.

A comparative study by evaluated through effectiveness of SG versus the entire genotyping strategy (EGS) for detecting QTLs controlling flowering time in Lablab purpureus (dolichos bean). The study identified two SSR markers LPD 25 and LPD 190—as being significantly associated with flowering time in both SG and EGS. The detection of identical QTLs by both methods demonstrates that SG can offer comparable statistical power to full-population genotyping, while being significantly more economical and efficient.

Breeding programs typically involve numerous crosses and generate large numbers of progeny, in which favorable alleles from diverse genetic backgrounds are segregating. Genotyping every individual in such populations is logistically challenging and cost-prohibitive. SG provides an effective alternative by concentrating genotyping efforts on the most informative individuals—those with extreme phenotypes. This not only facilitates efficient QTL detection but also integrates seamlessly with ongoing selection programs, making QTL discovery a by-product of breeding activities.

Moreover, SG is particularly useful for identifying major QTLs with large effects. When used in conjunction with high-throughput phenotyping and genomic tools, it offers a pragmatic approach to dissect complex traits in various crops.

Conclusion

Selective genotyping is a powerful and efficient approach for QTL mapping that minimizes the need for extensive genotyping without compromising the precision of QTL detection. By focusing on phenotypic extremes, SG reduces costs and resources, making it highly suitable for large-scale breeding programs. The effectiveness of SG in identifying key genetic loci, coupled with its compatibility with advanced genomic techniques such as QTL-seq, underscores its potential to accelerate genetic improvement and trait dissection in crops.

References

Gallais, A., Moreau, L., & Charcosset, A. (2007). Detection of marker–QTL associations by studying change in marker frequencies with selection. Theoretical and Applied Genetics, 114, 669–680.

Sun, Y., Wang, J., Crouch, J. H., & Xu, Y. (2010). Efficiency of selective genotyping for genetic analysis of complex traits and potential applications in crop improvement. Molecular Breeding, 26, 493–511.

Basanagouda, G., Ramesh, S., Ranjitha, G. V., Kalpana, M. P., & Siddu, B. C. (2023). Selective genotyping for discovery of QTL controlling flowering time in dolichos bean (Lablab purpureus L.). Crop Breeding and Applied Biotechnology, 23(2).

Takagi, H., Abe, A., Yoshida, K., Kosugi, S., Takuno, S., & Innan, H. (2013). QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. The Plant Journal, 74(1), 174–183.