Conditional QTL Analysis and Fine Mapping for Thousand-Kernel Weight in Common Wheat

Abstract

To elucidate the genetic basis of thousand-kernel weight (TKW) related to fundamental traits such as kernel length (KL), kernel width (KW), and kernel diameter ratio (KDR) at the individual quantitative trait loci (QTL) level, both unconditional QTL analysis and conditional QTL analysis for TKW were analyzed using a recombinant inbred line (RIL) population, along with a simplified physical map. A total of 37 unconditional QTLs and 34 conditional QTLs were identified. Six QTLs exhibited independent effects from individual traits (KL, KW, or KDR), while 18 QTLs showed common influences from two or three of these traits simultaneously. Additionally, 26 pairs of epistatically interacting QTLs involving 16 loci were detected. Subsequently, fine mapping of the stable and major-effect QTL QTkw1B was carried out using the derived near-isogenic lines (NILs), ultimately locating it within the interval of 698.15-700.19 Mb on chromosome 1B of the KN9204 genome. The conditional QTL analysis and genetic effect analysis based on NILs both indicated that the increase in TKW was primarily contributed by kernel length. The QTL identified in the present study through the combination of conditional and unconditional QTL mapping could increase the understanding of the genetic interrelationships between TKW and kernel size traits at the individual QTL level and provide a theoretical basis for subsequent candidate gene mining.

Keywords: conditional QTL; fine mapping; thousand-kernel weight; unconditional QTL; wheat.

Figure 1

Frequency distribution and correlation analysis of TKW data in the KJ-RIL population in various environments. *** represents the significance level of p < 0.001.

Figure 2

Chromosomal locations of conditional and unconditional QTL for kernel traits. The map markers are listed on the right side of the corresponding chromosomes. Physical locations of markers are indicated on the left side of the chromosomes. The combinations of letters and numbers at the top of each image represent the wheat different chromosomes.

Figure 3

Fine mapping of QTkw1B. (a) Primary fine mapping of QTkw1B in 2020-2021. (b) Further fine mapping of QTkw1B in 2023-2024. On the left, genotype diagrams of different NILs in the target region are shown. The white bars represent the NIL-KN9204 genotype, which is consistent with the genotype of KN9204. The black bars represent the NIL-J411 genotype, which is consistent with the genotype of J411. The figure on the right shows the TKW values under different environments. The white columns indicate that the target segment genotypes of NILs are consistent with those of KN9204, while the black columns indicate that the target segment genotypes of NILs are consistent with those of J411. The mean value of TKW (±SD) is shown in each histogram. ANOVA analysis plus the LSD test was used for multiple comparison, and a shared letter within groups indicated no significant differences in TKW between NILs at the level of p < 0.05.

Figure 4

Genetic effect analysis of QTkw1B on wheat kernel and yield-related traits based on NIL pairs. E1, Ludong University experimental field (2020–2021); E2, Pulagu experimental field, Yantai (2020–2021). PH: plant height; SL: spike length; KNPS: kernel number per spike; SNPS: spikelet number per spike; SN: spike number; KYPP: kernel yield per plant; KL: kernel length; KW: kernel width; TKW: thousand-kernel weight. ** represents the significance level of p < 0.01. * represents the significance level of p < 0.05. ns represents no significance.