“QTG Seq: A paradigm shift for QTL fine mapping”

 

  Genomic resource-assisted crop improvement relies on the dissection of the genetic and molecular mechanisms underlying agronomic traits. Most agronomic traits vary quantitatively and are subject to complex genetic regulation. Detecting and characterizing quantitative trait genes (QTGs) has proved to be critical for genomics-assisted crop improvement.

Isolating QTGs is a successful application of map-based cloning. However, to reduce linkage drag while transferring derived genes to recipient genetic background, several generations of backcrossing are typically needed. Additionally, a large population must be screened for recombinants, and thorough field phenotyping is required. All of these steps add up to a time-consuming and expensive process for map-based cloning. An alternative method of genetic mapping, bulked segregant analysis (BSA), has been used to dissect the genetic basis of both qualitative and quantitative traits. Some of the advanced strategies of BSA are Mut-map2, Mut-map Gap, Mut-map+, QTL-seq and QTG-seq. For quantitative traits, quantitative trait locus sequencing (QTL-seq) was devised to map QTLs, but the mapping resolution is too low to identify candidate genes, especially in species with a large genome size. Therefore, a fine mapping strategy for rapid identification of candidate genes underlying quantitative traits is still needed.

To accelerate QTL fine-mapping, QTG sequencing (QTG-seq) was developed which involves three major steps. These are (1) QTL mapping, (2) QTL partitioning and (3) QTG mining. The process of identifying genomic regions associated with traits is known as QTL mapping. The conversion of quantitative traits into near-qualitative traits is known as QTL partitioning. The identification of candidate genes using softwares like dQTG seq1, QTG-miner3 and QTG-finder 2 is known as QTG mining. Using QTG-seq, plant-height QTL in maize, qPH7, was fine mapped to a 300-kb genomic interval4.

In summary, QTG- seq combines QTL partitioning to convert a quantitative trait into a near-qualitative trait, sequencing of bulked segregant pools from a large segregating population, and the use of a robust new algorithm for identifying candidate genes. With the rapid advancement of NGS technologies and a steep decrease in the cost of sequencing, it is expected that in near future, the sequencing depth would not be a matter of concern while estimating the overall cost of QTG-seq. Thus, QTG-seq provides an efficient method for QTL fine-mapping in the era of ‘‘big data’’.

References:

 1. LI, P., WEI, L.Q., PAN, Y.F. AND ZHANG, Y.M., 2022, dQTG. seq: A comprehensive R tool for detecting all types of QTLs using extreme phenotype individuals in bi-parental segregation populations. Comput. Struct. Biotechnol. J., 20: 2332-2337.

2. TAKAGI, H., TAMIRU, M., ABE, A., YOSHIDA, K., UEMURA, A., YAEGASHI, H., OBARA, T., OIKAWA, K., UTSUSHI, H., KANZAKI, E. AND MITSUOKA, C., 2015, MutMap accelerates breeding of a salt-tolerant rice cultivar. Nat. Biotechnol., 33(5): 445-449.

3. WANG, X., LI, J., HAN, L., LIANG, C., LI, J., SHANG, X., MIAO, X., LUO, Z., ZHU, W., LI, Z. AND LI, T., 2023, QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize. Nat. Commun., 14(1): 5232.

4. ZHANG, H., WANG, X., PAN, Q., LI, P., LIU, Y., LU, X., ZHONG, W., LI, M., HAN, L., LI, J. AND WANG, P., 2019, QTG-Seq accelerates QTL fine mapping through QTL partitioning and whole-genome sequencing of bulked segregant samples. Mol. Plant., 12(3): 426-437.