Identification of quantitative trait loci underlying soybean (Glycine max [L.] Merr.) seed weight including main, epistatic and QTL × environment effects in different regions of Northeast China

Abstract

Seed weight (SW) is the important soybean (Glycine max [L.] Merr.), yield component and also affected the quality of soybean‐derived foods. The aim of this study was to identify the quantitative trait loci (QTL) underlying SW through 112 recombinant inbred lines (RILs) derived from the cross between “Zhongdou27” (G. max, designated by its bigger seed size, 21.9 g/100 seeds) and “Jiunong 20” (G. max, smaller seed size, 17.5 g/100 seeds). Phenotypic data were collected from this RIL population after it was grown in the sixteen tested environments. A total of eight QTL (QSW1‐1, QSW2‐1, QSW2‐2, QSW5‐1, QSW15‐1, QSW17‐1, QSW19‐1 and QSW20‐1) were identified, and they could explain 4.23%–14.65% of the phenotypic variation. Among these eight QTL, three QTL (QSW1‐1 located on the interval of Sat_159‐Satt603 of chromosome (Chr) 1 (LGD1a), QSW19‐1 located on the interval of Sat_340‐Satt523 of Chr 19 (LGL) and QSW20‐1 located on Sat_418‐Sat_105 of Chr 20 (LGI)) were newly identified and could explain 4.235%–10.08%, 8.45%–13.49% and 8.08%–10.18% of the phenotypic variation, respectively. Six of the eight identified QTL including QSW2‐2, QSW5‐1, QSW15‐1, QSW17‐1, QSW19‐1 and QSW20‐1 exhibited a significant additive (a) effect, while two QTL (QSW2‐1 and QSW19‐1) only displayed significant additive‐by‐environment (ae) effects. A total of four epistatic pairwise QTL for SW were identified in the different environments. These eight QTL and their genetic information obtained here were valuable for molecular marker‐assisted selection and the realization of a reasonable SW breeding programme in soybean.

Seed protein content and its relationships with agronomic traits in pigeonpea is controlled by both main and epistatic effects QTLs

“…The detection of at least two and up to seven major and several minor M-QTLs to condition SW in the crop indicates quantitative inheritance for the trait, consistent with findings in other legume crops 28,[51][52][53][54] . A genomic region on CcLG01 consistently showed highest PVE in three of the five mapping populations suggesting a common major genomic loci segregating in a wide range of genetic backgrounds, similar to reports in soybean 51 .…”

High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)

“…In "QTL Hotspot D", QTLs related to seed maturity [33] and seed oil content [52] have formerly reported. Seed oil and protein content in soybeans have reported a significant correlation with seed size and shape [53], as seed oil and protein content represents a major component of soybean seeds, representing 38-42% and 18-22%, respectively; hence, these traits are directly related to seed sizes and shapes in soybeans [13]. Both seed size and shape are important yield component traits [54] and it has been reported that days to flowering and maturity is directly correlated to yield in soybeans [55,56], signifying the potential probability of common genic factors for these traits and also showing the necessity to promote further study for these regions.…”

“…Specific soy-based food products made from soybean are also determined mainly by seed size and shape [9,10]. For example, for the production of fermented soybeans (natto) and sprouts, small-seeded cultivars are suitable, while for soymilk, green soybeans (edamame), boiled soybeans (nimame), and soybean curd (tofu), large-seeded varieties are used [11][12][13]. Additionally, these traits influence the germination ability and seedling vigor, and that, in turn, plays an essential role in determining the competitive strength of the seedlings for light, nutrient resources, and stress tolerance [14][15][16].…”

Identification of quantitative trait loci underlying soybean (Glycine max) 100‐seed weight under different levels of phosphorus fertilizer application

“…The highly consistent marker loci across environments implied that these loci could be feasibly used in the marker‐assisted selection (MAS) program for 100‐SW in soybean (Mian et al., 1996). To map stable QTLs, we have conducted QTL analysis of 100‐SW under 16 environments in different regions of northeast of China (Wu et al, 2018). QTLs detected in multiple environments were stable enough for application to MAS.…”