Two approaches have been routinely used for marker-trait association analysis. These are (1) marker-based approach and (2) trait-based approach. Marker-based approach relies on testing the average phenotypic difference among mapping population(MP) individulas classified into distinct marker genotypes . Trait based approach relies on comparing marker allele frequencies amongst MP individuals with extreme phenotypes .The first approach is based on genotyping the entire MP which is more expensive and time consuming . The second approach relies on genotyping only individuals from high and low tails of the phenotypic distribution of MP referred as selective genotyping (SG)1.

SG provides a cost efficient alternative to analyze the entire MP for genetic mapping and it can be practiced in both biparental and association mapping populations by comparing marker allele frequencies between the two extreme tails. The another approach for rapid identification and mapping of QTLs is QTL-Seq which is an extension of BSA(Bulk segregant analysis) and relies on whole genome resequencing of DNA bulks from extreme phenotypic groups4.

Using a mapping population with 241 RILs Takagi et al.(2013) detected the major QTL between 2.39 and 4.39 Mb on chromosome 6 for rice blast resistance caused by M.oryzae fallowing QTL-seq4.

Basanagouda et al.(2023) evaluated the effectiveness of SG as compared to entire MP genotyping strategy (EGS) to detect QTL controlling flowering time(FT). Their results suggest that alleles at two SSR markers (LPD 25 and LPD 190) are linked to QTL controlling FT in both SGS and EGS which provides adequate evidencefor comparable statistical power of SGS relative to EGS for detection of QTL controlling FT3.

Compared with conventional QTL mapping, SG is cost-effective when the ratio of genotyping to phenotyping costs is higher than one1. Generally, a lot of crosses and selections are involved in breeding programs every year, resulting in many progenies or lines in which multiple favorable alleles from different genetic resources are present. SG provides an excellent choice for breeders to explore these material for QTL detection underlying the variation of targeted traits, making QTL identifcation a co-product of breeding programs.

References:

1. GALLAIS, A., MOREAU, L. AND CHARCOSSET, A., 2007, Detection of marker–QTL associations by studying change in marker frequencies with selection. Theor. Appl. Genet., 114:669–6.

2. SUN, Y., WANG, J., CROUCH, J. H. AND XU, Y., 2010, Efficiency of selective genotyping for genetic analysis of complex traits and potential applications in crop improvement. Mol. Breeding., 26:493–511.

 3. BASANAGOUDA,G., RAMESH,S., RANJITHA,G. V., KALPANA,M.P., AND SIDDU,B.C., 2023, Selective genotyping for discovery of QTL controlling flowering time in dolichos bean (Lablab purpureus L.). Crop Breeding and App. Biotechnology., 23(2). 4. TAKAGI, H., ABE, A., YOSHIDA, K., KOSUGI, S., TAKUNO, S. AND 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.