SNP Effects on Yield and Agronomic Traits in an International Winter Wheat Collection Grown in Western Siberia

Abstract

The extension of genetic diversity is the basis for yield and adaptability improvements of winter wheat varieties under climate fluctuations. In the present study, an international collection consisting of 96 winter bread wheat accessions from Russia, Germany, Finland, Kazakhstan, Bulgaria, Turkey, the USA, and the international programme (Turkey–CIMMYT–ICARDA) was analysed under the conditions of Western Siberia during three growing seasons. Yield and yield-related traits were recorded following standard agronomy practices. Genotyping of the germplasm panel was conducted using 55 KASP markers at the Institute of Plant Biology and Biotechnology (Kazakhstan). The yield had a high correlation with the number of fertile tillers per unit area (0.68), which indicates significant yield reduction in wheat accessions from different origins that are not adaptive to the conditions of Western Siberia. The main stable QTLs associated with yield-related traits during two growing seasons, ippb_ta_1147 (1A), ippb_ta_107 (4A), ippb_ta_239 (5D), and ippb_ta_283 (6A), can be used in MAS for the improvement of yield and related traits. The outperforming genotypes Zhiva, Zolushka, Doneko, Line K 18918, Line 2293; CO13D1299, KS13DH0030-32, Gondvana//HBK0935-29-15/KS90W077-2-2/VBF0589-1… are recommended to be included in hybridisation programmes and represent promising sources for breeding high-yielding and climate-resilient winter wheat.

1. Introduction

Winter wheat is the main cereal crop that meets the growing global demand for food. In the context of climate change, Western Siberia, due to the increased thermal suitability of the high-latitude region, is considered to be a promising area for winter wheat cultivation, which possess a higher yield compared to spring wheat [1,2]. For the development of high-yielding winter wheat varieties, it is crucial to select suitable donor genotypes from the wheat accessions stored in gene banks. This process depends critically on the density of information on the individual characteristics of the accessions [3,4]. Competitive allele-specific PCR (KASP) markers play a significant role in improving different target agronomic traits and promote genetic studies, such as genetic diversity analysis, genome-wide association studies, and MAS, in wheat [5,6,7,8]. A total of 70 KASP assays were validated for reliability in a panel of 300 diverse wheat cultivars and proved their ability to accelerate the characterisation of crossing parents and advanced lines for MAS [9].

QTLs with pleiotropic effects on yield-related traits have significant potential in breeding practice. For instance, pleiotropic cluster QTL (QSc/Sl.cib-5A and QSc/Sl.cib-6A) simultaneously affects plant height, thousand kernel weight, and kernel length [10]. Based on genome-wide association studies for 285 regional wheat lines, Zhao et al. [11] found that SNPs AX-111917292 and AX-109124553 on chromosome 5D were associated with wheat water absorption and thousand kernel weight. KASP markers associated with grain yield traits are important for the development of winter wheat yield potential improvement. In the study by Yang et al. [12], 266 recombinant inbred lines were genotyped using the wheat 660 K SNP array, and yield traits-friendly KASP markers were developed for marker-assisted selection. KASP markers for the unique sequences of the QYld.osu-1BS allele were developed in a population of 260 doubled haploid lines derived from the cross of winter wheat varieties Duster and Billings. They are aimed at breeding novel varieties with increased grain yield [13]. The genome-wide association study on a panel of 236 bread wheat varieties that were phenotyped in three experiments at two locations in Florida suggested that utilising MAS can enhance grain yield and harvest index of bread wheat [14]. Despite the economic efficiency of high-throughput genotyping [15], phenotyping and genotyping of individual accessions from genetic resource collections—including for low-heritability traits—alongside implementing diverse selection strategies remain crucial for enhancing winter wheat yield [16,17]. The analyses of genetic diversity in winter wheat germplasm suggested that wheat germplasm from other countries should be adopted to create new wheat germplasm with increased genetic diversity [18,19]. The evaluation of the effects of gene alleles on the phenotypic manifestation of agronomic traits allows for the determination of priority traits when selecting breeding material in specific soil and climatic conditions [20,21]. Genotyping of the winter wheat accession collection and analysis of the effects of SNPs in identified genes on yield and agronomic traits enabled the determination of genetic sources of high adaptability and productivity and increased the efficiency of assessment of genetic diversity and breeding value of winter wheat germplasm in Western Siberia.

Currently, only a few studies have used GWAS to identify SNPs associated with agronomic traits in Western Siberian wheat varieties [22,23]; therefore, the purpose of this study was (1) to analyse the effects of SNPs on the yield and its components and (2) to select genetic sources from the winter wheat collection of different origins for breeding programmes.

2. Materials and Methods

2.1. Field Trials and Phenotyping

In this study, an international collection of 96 winter bread wheat accessions from Russia, Germany, Finland, Kazakhstan, Bulgaria, Turkey, the USA, and the international TCI programme (Turkey–CIMMYT–ICARDA) was analysed in the southern forest-steppe of Western Siberia (Supplementary Table S1). The plant material was grown in the field without fertilisation, chemical plant protection products, and irrigation. The experiments were conducted over three growing seasons (2022, 2023, and 2024), with the accessions planted in the field following black fallow. The accessions were distributed in plots, each measuring 1 m² and spaced 0.15 m apart, with a sowing rate of 500 seeds per plot. The plots were arranged in a randomised design in three replicates. The standard included the medium-maturity winter bread wheat variety Omskaya 4. Yield and yield-related traits were recorded following standard agronomy practices. The yield components were recorded following methodological guidelines for exploring the wheat collection [24] on 20 individual plants per replicate in three replicates each year, resulting in a total of 180 plants per accession over the three-year period.

The Omsk region is located at GPS coordinates 55.0246° N and 73.3105° E. The experimental field’s soil type is thin meadow-chernozem, with an organic matter content of 3.5% to 4%. The region has a sharply continental climate, with an average annual precipitation of approximately 300–380 mm and a sum of effective temperatures ranging from 2000 °C to 2100 °C. The factors limiting wheat grain yield include insufficient rainfall, the threat of soil erosion by wind, and low phosphorus content. The average duration of the winter period is 164 days, with soil freezing to depths of 165–220 cm and snow cover varying between 20 and 25 cm.

Weather conditions varied during the vegetative periods of this study. The precipitation regime was categorised using Selyaninov’s Hydrothermic Coefficient (HTC): HTC > 1—wet season, HTC < 1—drought season. In the summer of 2022, there was a drought; the HTC was 0.91. Precipitation fluctuated: 6 mm was registered in May, representing 35% of the long-term average for that month. The temperature regime corresponded to the long-term average. The weather conditions in 2023 were characterised as extremely dry (HTC = 0.63). In May, there was insufficient rainfall (12 mm), whereas precipitation exceeded the long-term average in August and early September (46 mm). The summer of 2024 was characterised as excessively humid, which exceeded the long-term average by 200 mm; the HTC was registered at 1.49. In the winter period of 2021–2022, there was a lower snow depth (20.1 cm) compared to 2022–2023 (24.9 cm), while the average daily temperatures did not differ significantly. In the winter period of 2023–2024, the snow depth was less than that in the 2022–2023 season by 10 mm.

2.2. DNA Extraction and SNP Genotyping

Research at the Institute of Plant Biology and Biotechnology (IPBB, Kazakhstan) mapped more than 100 QTLs of economically valuable traits of spring bread wheat as a result of a genome-wide association study (GWAS) [25,26]. Quantitative trait loci mapping was performed as previously described by Amalova et al. [27]. To validate stably detected QTLs, KASP markers were developed. Fifty-five of these markers, associated with genes of the main agronomic traits (including the number of days to heading and maturity, yield, grain quality, and resistance to fungal diseases), were used to genotype 96 winter wheat accessions (Supplementary Table S2). Detailed information on these KASP markers was provided in a previous study [28].

Genomic DNA was extracted in triplicate from young leaves using the modified Delaporta et al. protocol [29]. The extracted DNA was purified using commercial kits (Qiagen, Hilden, Germany). DNA concentration and quality were assessed by spectrophotometry (BioRad, Hercules, CA, USA). The composition of the isolated DNA was determined according to the optical density (OD260/OD280). The DNA concentration for each sample was adjusted to 50 ng/μL.

2.3. Statistical Analysis

The yield and yield components differences were evaluated using a two-way analysis of variance (ANOVA) and Pearson correlation. The significance of differences (p < 0.05) between mean values of accessions groups (SNP position—A/G) was determined by the Student’s t-test with Bonferroni correction. The analysis used R-statistics Open 4.4.2. URL: http://www.r-project.org (accessed on 7 July 2025).

The broad-sense heritability (H²) was calculated using the following formula:

$$H^2={\displaystyle \frac{{{\sigma }^2}_G}{{{\sigma }^2}_G+\frac{{{\sigma }^2}_{GxE}}{n_E}+\frac{{{\sigma }_e}^2}{n_E}}},$$

where σ²_G, σ²_GxE, and σ²_e are the mean square deviations of the genotype, genotype/environment interaction, and residual variance, respectively; n_E is the number of growing seasons.

3. Results

3.1. Field Experiment and Phenotyping

The highest yield occurred in 2023 (

Table 1. Agronomic performance, heritability, and analysis of variance (ANOVA) of international collection genotypes, 2022–2024.

Trait	2022		2023		2024		ANOVA
	Means *	H2 **	Means	H2	Means	H2	G, %	E, %
Spike length, cm	8.00 ± 0.82	0.56	8.30 ± 0.85	0.59	9.20 ± 0.54	0.62	72	28
No. of fertile tillers per m2	89.8 ± 2.90	0.58	101 ± 4.00	0.58	66.5 ± 1.80	0.61	73	27
No. of grains per spike, units	29.5 ± 3.01	0.58	32.8 ± 3.35	0.58	40.6 ± 2.39	0.64	49	51
Grain weight per spike, g	1.00 ± 0.10	0.58	1.20 ± 0.13	0.56	1.50 ± 0.87	0.63	61	39
Grain weight per plant, g	1.70 ± 0.12	0.56	1.90 ± 0.19	0.56	2.40 ± 0.24	0.66	82	18
Thousand kernel weight, g	33.2 ± 3.39	0.55	37.7 ± 3.84	0.56	38.1 ± 2.24	0.62	90	10
Yield, g/m2	173 ± 17.6	0.56	315 ± 32.2	0.54	215 ± 22.2	0.60	68	32

* Means ± standard error of the means; ** H²—broad-sense heritability; G—genotype, E—environment.

), which can be explained by better plant survival under winter conditions and high fertile tiller density (101 units).

The better moisture conditions in 2024 were not favourable for yield (215 g/m²) due to the effects of frosts in May up to −15 °C that provoked the smallest number of tillers per unit area (66.5). Depending on vegetative period, the number of grains per spike ranged from 29.5 to 40.6 units, and the grain weight per plant ranged from 1.70 to 2.40 g, respectively. The thousand kernel weight showed low variability (33.2–38.1 g) under the conditions of Western Siberia. All studied traits had moderate broad-sense heritability ranging from 0.54 to 0.66. The results of a two-way analysis of variance (ANOVA) showed significant differences among the 96 genotypes for NFT (73%), GWP (82%), and TKW (90%). The environment had a highly significant influence on NGS (51%), GWS (39%), and yield (32%).

Phenotyping evaluation of the winter wheat collection during the three years revealed significant interannual variation in yield-related traits (Figure 1), particularly for fertile tiller density (66.5–101 units) and yield (173–315 g/m²). In 2022, there was a severe spring drought, which greatly affected yield (173 g/m²) and other yield-related traits. In 2023, there was optimal plant survival under winter conditions, while 2024 was characterised by severe frosts in May.

Table 2. Pairwise correlation coefficients (r) between yield and yield-related traits of winter bread wheat accessions tested in Omsk, 2022–2024.

Traits	SL	NFT	NGS	GWS	GWP	TKW	Yield
SL	1.00
NFT	−0.35 ***	1.00
NGS	0.11 *	0.07	1.00
GWS	0.18 **	−0.05	0.67 ***	1.00
GWP	0.04	0.04	0.13 *	0.42 ***	1.00
TKW	0.20 ***	−0.06	−0.12 *	0.41 ***	0.65 ***	1.00
Yield	−0.16 **	0.68 ***	0.13*	0.23 ***	0.18 **	0.15 **	1.00

*, **, and *** correlation coefficients are significant at p < 0.05, p < 0.01, and p < 0.001, respectively.

presents trait correlations across the 2022–2024 vegetative periods.

Yield showed a high correlation with the number of fertile tillers per unit area (0.68) but only a weak association with the number of grains per spike (0.13) and grain weight per plant (0.18). This indicates a significant yield reduction in wheat accessions from different ecological groups that are not adapted to the conditions of Western Siberia. Negative correlation coefficients between the number of fertile tillers per unit area and spike length (−0.35) as well as grain weight per spike (−0.05) may indicate reduced spike productivity when the number of fertile tillers per square metre increases. Grain weight per plant had a positive effect on both grain weight per spike and thousand kernel weight (r = 0.42 and r = 0.65, respectively).

3.2. Analysis of SNP Effects

Kazakhstan is the nearest region to the Omsk region, where the stable QTLs for yield-related traits, disease resistance, duration of vegetation, and grain quality were previously identified via GWAS analysis. Based on phenotyping and analysis of SNP effects in the Omsk region over the three growing seasons, 13 KASP markers reliably associated (p < 0.05) with yield and yield-related traits were detected (

Table 3. List of 13 KASP markers developed based on GWAS analysis, IPBB, Kazakhstan.

KASP-ID	Chr.	Position, cM	Trait	p-Value	MAF
ipbb_ta_114	1A	50.2	No. of fertile tillers	8.85 × 10−5	0.116
ipbb_ta_274	1B	99.1	Vitreousness	9.32 × 10−6	0.031
ipbb_ta_149	2B	76.8	Seed maturation time	6.66 × 10−5	0.063
ipbb_ta_289	2B	92.0	Plant height	7.23 × 10−6	0.389
ipbb_ta_259	3B	36.4	No. of grains per spike	5.81 × 10−6	0.340
ipbb_ta_260	3B	61.2	No. of grains per spike	4.62 × 10−6	0.348
ipbb_ta_261	4A	40.1	Grain protein content	6.02 × 10−6	0.063
ipbb_ta_107	4A	161.8	No. of fertile tillers	3.80 × 10−6	0.021
ipbb_ta_263	4B	85.3	Spike length	4.74 × 10−6	0.042
ipbb_ta_116	5A	53.5	Leaf/Stem rust resistance	3.09 × 10−4	0.365
ipbb_ta_239	5D	167.0	Seed maturation time	6.62 × 10−6	0.106
ipbb_ta_229	6A	0.88	Heading time	1.94 × 10−6	0.010
ipbb_ta_283	6A	56.3	Sedimentation value	6.13 × 10−6	0.281

and

Table 4. SNP markers with significant association identified in winter wheat accessions, tested in Omsk SAU, 2022–2024.

Trait	KASP-ID	Chr.	Allele *	Favourable Season	p-Value	R2, %	SNP Effect
SL, cm	ipbb_ta_149	2B	C/T	2022	0.01	6	0.64
				2023	0.18	2	0.35
				2024	0.28	2	0.27
	ipbb_ta_261	4A	A/G	2022	0.35	2	0.17
				2023	0.03	6	0.43
				2024	0.25	2	0.22
NFT, per m2	ipbb_ta_259	3B	C/T	2022	0.24	3	4.75
				2023	0.03	8	13.2
				2024	0.54	1	1.95
	ipbb_ta_107	4A	A/G	2022	0.02	6	24.4
				2023	0.29	2	15.3
				2024	0.47	1	5.67
	ipbb_ta_261	4A	A/G	2022	0.40	2	4.80
				2023	0.01	8	21.8
				2024	0.32	2	4.51
	ipbb_ta_263	4B	A/G	2022	0.003	8	23.0
				2023	0.12	2	16.8
				2024	0.87	1	0.96
	ipbb_ta_239 **	5D	A/G	2022	0.05	6	8.32
				2023	0.45	1	4.90
				2024	0.05	6	7.09
	ipbb_ta_229	6A	A/C	2022	0.04	5	27.5
				2023	0.18	2	26.2
				2024	0.48	1	7.47
	ipbb_ta_283 **	6A	C/T	2022	0.05	5	6.71
				2023	0.64	1	2.28
				2024	0.04	5	5.55
NGS, units	ipbb_ta_114 **	1A	A/G	2022	0.01	11	2.53
				2023	0.04	6	2.52
				2024	0.17	4	1.48
	ipbb_ta_260	3B	A/G	2022	0.04	7	1.82
				2023	0.87	1	0.19
				2024	0.57	1	0.58
	ipbb_ta_229	6A	A/C	2022	0.04	5	6.01
				2023	0.59	1	2.01
				2024	0.63	1	1.56
GWS, g	ipbb_ta_114	1A	A/G	2022	0.88	1	0.01
				2023	0.01	11	0.16
				2024	0.47	2	0.04
	ipbb_ta_289	2B	G/T	2022	0.05	6	0.10
				2023	0.37	2	0.05
				2024	0.64	1	0.02
	ipbb_ta_229	6A	A/C	2022	0.13	3	0.26
				2023	0.04	4	0.36
				2024	0.74	1	0.05
GWP, g	ipbb_ta_116	5A	C/T	2022	0,44	1	0.17
				2023	0,72	1	0.13
				2024	0.01	8	2.08
	ipbb_ta_239	5D	A/G	2022	0.81	1	0.10
				2023	0.02	8	1.33
				2024	0,64	1	0.23
TKW, g	ipbb_ta_289	2B	G/T	2022	0.03	8	1.73
				2023	0.23	3	1.22
				2024	0.18	4	3.14
	ipbb_ta_107	4A	A/G	2022	0.01	6	5.58
				2023	0.23	2	3.02
				2024	0.53	1	3.58
	ipbb_ta_116	5A	C/T	2022	0.77	1	0.26
				2023	0.95	1	0.07
				2024	0.02	7	5.80
Yield, g/m2	ipbb_ta_274	1B	A/G	2022	0.33	1	24.0
				2023	0.03	5	102.7
				2024	0.50	1	20.7
	ipbb_ta_259	3B	C/T	2022	0.40	2	9.98
				2023	0.01	9	58.5
				2024	0.44	2	11.2
	ipbb_ta_107 **	4A	A/G	2022	0.05	4	57.4
				2023	0.27	1	30.7
				2024	0.05	4	65.0

* the favourable allele is highlighted in bold; ** SNPs showing association during two growing seasons.

).

For the spike length trait, two SNPs (ipbb_ta_149 and ipbb_ta_261) were found on chromosomes 2B and 4A, with phenotypic variation 2–6% (Table 4). The presence of the favourable allele reliably increased the spike length by 0.43–0.64 cm for one growing season. The favourable allele of locus ippb_ta_229 significantly enhanced the number of fertile tillers per unit area under the dry conditions in 2022 and 2023—27.5 and 26.2, respectively.

Loci ipbb_ta_239 and ipbb_ta_283 provided the formation of more fertile tillers per unit area during two growing seasons (average 7.7 and 6.1, respectively), while SNP ipbb_ta_239 was contained in the genotype of only 10% of the accessions. Three SNPs (ipbb_ta_114, ipbb_ta_260, ipbb_ta_229) were associated with an increased number of grains per spike (1.82–6.01 units), and locus ipbb_ta_114 explained the most phenotypic variation in this trait (6–11%).

For the grain weight per spike trait, no significant associations were identified for loci ippb_ta_114 and ippb_ta_229 (0.16–0.36 g), which were located on chromosomes 1A and 6A (Table 4). In 2024, the presence of SNP ippb_ta_116 was accompanied by a significant increase in grain weight per plant and thousand kernel weight (2.08 and 5.80 g, respectively). The locus ippb_ta_274 located on chromosome 1B determined a significant increase in yield in 2023 (102.7 g/m²), while locus ippb_ta_107 was associated with increasing yield by 57.4–65.0 g/m² for two growing seasons.

3.3. Outperforming Genotypes Revealed on Yield

No linear relationship was observed between total yield and the number of favourable alleles (

Table 5. Yield and its components of winter wheat accessions with the maximum number of favourable alleles, 2022–2024.

Variety, Line	SL, cm	NFT per m2	NGS, Units	GWS, g	GWP, g	TKW, g	Yield, g/m2	No. of Alleles
Russia
Zhiva	9.76 *	91.8	37.7 *	1.43 *	2.13 *	38.5 *	384 *	7
Donskaya Lira	8.10	115 *	30.9 *	1.17 *	2.33 *	37.9 *	367 *	7
Donna	8.15	94.5	31.0 *	1.18 *	1.96 *	37.9 *	351 *	8
Zolushka	9.02 *	119 *	34.1 *	1.30 *	5.01 *	37.9 *	344 *	8
Doneko	9.01 *	105 *	27.6	1.35 *	1.79	49.9 *	331 *	8
Vestnitsa	8.63 *	121 *	36.1 *	1.27 *	2.23 *	34.4	317 *	9
Line 2293 K 2-4	8.67 *	103 *	35.9 *	1.38 *	1.96 *	38.4 *	303	10
Line K 18918	7.89	139 *	40.3 *	1.23 *	1.96 *	30.7	297	12
Bulgaria
Darunok Podilla	8.64 *	97.5	38.5 *	1.59 *	2.54 *	40.7 *	328 *	7
Türkiye
Gelibolu	7.39	106 *	40.3 *	1.49 *	1.93	36.9 *	352 *	7
USA
CO13D1299	8.13	139 *	34.9 *	1.26 *	3.06 *	35.9 *	495 *	10
KS13DH0030-32	7.78	131 *	41.9 *	1.44 *	2.55 *	34.4	362 *	9
SY Wolf	7.28	133 *	32.8 *	1.13 *	1.65	34.0	350 *	8
TCI
WBLL1*2/Kuruku/5/Chuenmai 18…	7.62	115 *	34.0 *	1.09	2.02 *	31.9	389 *	8
OCW03S667T-2/KS020986~1	7.98	126 *	31.8 *	1.16 *	1.88	36.3 *	369 *	8
Gondvana//HBK0935-29-15/ KS90W077-2-2	8.61 *	98.2	35.5 *	1.38 *	2.08 *	38.6 *	312 *	7
Omskaya 4, St	8.28	96.8	29.1	1.06	1.77	32.9	288	8
LCD05	0.17	4.65	0.94	0.08	0.17	1.90	16.9

* reliable excess over standard.

). However, accessions possessing 7–12 favourable alleles exhibited higher yield (297–495 g/m²) compared to the standard variety Omskaya 4 (288 g/m²). The US variety CO13D1299 demonstrated significantly superior yield (495 g/m²), with 10 favourable alleles in its genotype, including low-frequency alleles (ippb_ta_239 and ippb_ta_283).

High-yielding varieties included Zhiva, Donskaya Lira, Donna, Zolushka, Doneko (Russia), Darunok Podilla (Bulgaria), Gelibolu (Türkiye), KS13DH0030-32, SYWolf (USA), WBLL1*2/Kuruku/5/Chuen-Mai 18…, OCW03S667T-2/KS020986~1 (TCI)—328–389 g/m². The accessions from the USA were also characterised by a large number of fertile tillers per square metre (131–139 pcs/m²), which was favourable for their yield.

The lines of the international TCI programme were obtained through step crossings; in particular, the pedigree of one line includes the Chinese variety of synthetic wheat, Chuenmai 18 (Table 5). Due to good stem growth (115–126 tillers per m²) and an enhanced number of grains per spike, two lines, WBLL1*2/Kuruku/5/Chuenmai 18… and OCW03S667T-2/KS020986~1, were not inferior in yield to the Omskaya 4 standard (369–389 g/m²).

4. Discussion

Phenotypic investigation of yield and yield-related traits was conducted from 2022 to 2024. Analysis of phenotypic variability of the studied traits indicated the strong effect of environmental conditions on these traits. In Western Siberia, due to harsh winter conditions, the number of fertile tillers per square metre and yield are accordingly dependent on the winter hardiness of genotypes. These traits were the most variable between 2022 and 2024 (66.5–101 tillers and 173–315 g/m², respectively). The highest heritability (average 0.60) was detected for the traits of NGS and GWP (Table 1). A total of 13 QTLs for seven traits associated with yield were identified in this research. Validation of QTLs for yield-related traits should provide cost-effective means of transferring traits of interest in breeding programmes to exploit the opportunities offered by the wider wheat gene pool [30,31].

In the studied population of winter wheat accessions, NFT was found to be closely correlated with yield (r = 0.68), as have other studies that determined it as the main trait, suggesting breeding efforts should focus on its increase [32]. The favourable alleles of SNPs ippb_ta_259 (3B), ippb_ta_107 (4A), ippb_ta_261 (4A), ippb_ta_263 (4B), and ippb_ta_229 (6A) had significant effects (R² = 5–8%) on NFT under drought conditions in 2022 and 2023. The frequency of occurrence of favourable alleles of these loci ranged from 0.656 to 0.990, with the exception of ippb_ta_263 (Table 3).

According to other sources, reliable correlations of yield are revealed with grain weight per spike and number of grains per square metre [33,34]. In this research, thousand kernel weight was determined by markers with small effects, corresponding to the report by Li et al. [35]. In the research by Gizaw et al. [36], eleven stable MTAs for yield on chromosomes 1A, 2B, 3A, 3B, 4B, 5A, 5B, 6A, 6B, and 7B were detected. Guan et al. [37] revealed stable QTLs for yield-related traits on chromosomes 4A and 4B. In this study, four blocks of SNPs associated with yield were revealed on chromosomes 2B, 3B, 4A, and 6A: ippb_ta_149; ippb_ta_289 (2B), ippb_ta_259; ippb_ta_260 (3B); ippb_ta_107, ippb_ta_261 (4A); and ippb_ta_229, ippb_ta_283 (6A). The significant SNP ippb_ta_107 located on chromosome 4A increased the yield by an average of 51 g/m² over the three years and was simultaneously linked to higher values of NFT and TKW (Table 4).

Of course, based on the experience of previous studies, SNPs can serve as valuable tools for genomics approaches and wheat improvement; however, the number of SNPs needs to be increased to achieve higher coverage of the chromosomes [38,39]. The Axiom Wheat 660K SNP array is reliable and cost-effective for identifying targeted QTLs of yield-related traits and allows for breeders to increase varietal yield in wheat breeding programmes [40,41].

Wheat yield potential depends on many factors, including its polygenic nature, the strong influence of the environment, and the complex relationship among yield component traits. Cao et al. [42] detected 58 QTL-rich clusters (QRC) to identify the major stable loci for wheat yield component traits. Pleiotropic QTLs for yield-related traits were identified using the Wheat 55K SNP Array in a genetic mapping study of 207 wheat recombinant inbred lines [43]. Some studies note that major quantitative agronomic traits of winter wheat are significantly affected by the accumulation of favourable alleles. Consequently, the agronomically favourable alleles identified in breeding programmes can be used to develop wheat cultivars with superior agronomic traits [44,45]. In this connection, SNPs that affected yield-related traits with small phenotypic effects should be accumulated in wheat genotypes to achieve an additive effect. The four loci with pleiotropic effects were identified: ippb_ta_114 located on chromosome 1A was associated with NGS and GWS; ippb_ta_116 (5A)—with GWP and TKW; ippb_ta_239 (5D)—with NFT and GWP; ippb_ta_229 (6A)—with NFT, NGS, and GWS (Table 4). These results were consistent with other reports [46,47]. Overall, these SNPs identified through GWAS analysis are recommended for strongly continental climate conditions to determine genetic sources of high productivity, thereby increasing the efficiency of assessing genetic diversity and breeding value of winter wheat germplasm.

The rare loci were identified within the studied winter wheat population, specifically ippb_ta_149, ippb_ta_239, and ippb_ta_263 (MAF = 0.042–0.106). Carriers of these loci are recommended for inclusion in hybridisation programmes to increase the proportion of favourable alleles: Zhiva, Line K 18918, Line 2293 K 2-4 (Russia); CO13D1299 (USA). The main stable QTLs (p < 0.05) associated with yield-related traits across two growing seasons—ippb_ta_114 (1A, 50.2 cM), ippb_ta_107 (4A, 161.8 cM), ippb_ta_239 (5D, 167.0 cM), and ippb_ta_283 (6A, 56.3 cM)—can be used in MAS (Table 4). The varieties and lines with the highest number of favourable alleles can serve as source material for improving yield-related traits in winter wheat breeding programmes: Zhiva, Zolushka, Doneko, Line 2293 K 2-4 (Russia); KS13DH0030-32 (USA), Gondvana//HBK0935-29-15/KS90W077-2-2/VBF0589-1… (TCI).

5. Conclusions

KASP genotyping combined with phenotypic data revealed that SNP loci associated with the number of fertile tillers per unit area were frequently present within the winter wheat collection and showed a close correlation with yield. SNPs ippb_ta_114 (1A, 50.2 cM), ippb_ta_107 (4A, 161.8 cM), ippb_ta_239 (5D, 167.0 cM), and ippb_ta_283 (6A, 56.3 cM) provided reliable haplotypes for improving wheat yield under the conditions of Western Siberia. Additionally, SNP loci ippb_ta_114 (1A), ippb_ta_116 (5A), ippb_ta_239 (5D), and ippb_ta_229 (6A) exhibited pleiotropic effects on yield and its components. The selected markers can serve as potential targets for MAS, aimed at improving yield and related traits. The high-performing genotypes Zhiva, Zolushka, Doneko, Line K 18918, Line 2293 K 2-4 (Russia); CO13D1299, KS13DH0030-32 (USA), Gondvana//HBK0935-29-15/KS90W077-2-2/VBF0589-1… (TCI) can serve as parental lines in developing high-yielding varieties of winter bread wheat.