Genome-Wide Composite Interval Mapping Reveal Closely Linked Quantitative Genes Related to OJIP Test Parameters under Chilling Stress Condition in Barley

Abstract

Abiotic stressors such as cold temperatures have intensified due to climate change. Cold stress is a crucial factor that restricts the growth of barley in tropical and subtropical regions. Fast chlorophyll a-fluorescence induction (OJIP test) parameters are also used as biological markers for screening cultivars tolerant to abiotic stresses. Therefore, an experiment was conducted over three growing seasons in the research farm of Gonbad Kavous University to identify closely linked quantitative trait loci (QTLs) controlling OJIP test parameters under chilling stress, in the Iranian barley RILs population. For this study, the genome-wide composite interval mapping method was utilized to identify closely linked QTLs associated with OJIP test parameters under chilling stress conditions. Combined analysis of variance showed that the lines had significant differences (p < 0.05) in terms of OJIP test parameters, indicating genetic diversity among the lines. Also, Pearson correlation coefficients showed that grain yield had a significant positive correlation (p < 0.05) with Fm, Fv, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSo, REo/CSm, TRo/CSo and TRo/CSm parameters under chilling stress conditions. Cluster analysis using the WARD method identified 50 lines tolerant to chilling stress. A total of 48 stable and closely linked QTLs related to 12 OJIP test parameters were identified on seven barley chromosomes under chilling stress conditions.

1. Introduction

In recent years, with the densification of climate change, various abiotic stresses such as cold have increased. This is one of the most significant factors limiting the growth and development of cereals worldwide [1]. The tolerance of different cereals to cold stress varies. Cereals from temperate regions, such as wheat and barley, exhibit greater tolerance to cold stress compared to cereals from tropical regions such as rice and corn [2]. In general, the damages caused by cold stress depend on the origin of the plant (temperate or tropical), plant species, growth stage (vegetative or reproductive), plant organ (shoot or root), stress intensity and stress duration [2]. Cold stress is defined at three levels: freezing (below zero °C), chilling stress (above zero °C to the growth limit) and suboptimal (range of growth to optimum) [3]. However, in tropical and subtropical regions, chilling stress is more common than freezing stress. On the other hand, the damage caused by chilling stress compared to suboptimal cold stress is greater in cereals, especially barley. Cereals are sensitive to chilling stress in both vegetative and reproductive stages. However, in the reproductive stage, plants show more sensitivity to chilling stress [4]. During the reproductive stage, stress can cause flower shedding, pollen grain infertility and ovule abortion, resulting in lost fertilization and reduced seed filling [5]. This ultimately leads to a decrease in both the quality and quantity of the product, as fewer materials reach the grain due to an imbalance of source and sink [2]. Cold stress also leads to the accumulation of ROSs [6]. Additionally, it reduces plant resistance against certain biotic stresses, particularly viruses [7].

Generally, cereals use acclimation mechanisms to reduce the damage caused by chilling stress. Plants respond to this stress by accumulating specific proteins, modifying membranes, regulating signaling pathways, osmotic regulation and inducing endogenous hormones [2]. These acclimation mechanisms in wild plants are considered the primary strategy for dealing with chilling stress. However, in cultivated germplasms, agronomic improvement and plant breeding methods such as treating the farm with phosphate fertilizer and improving sensitive cultivars are used to improve the chilling tolerance [8]. Today conservation agriculture is recognized as the most important sustainable agriculture strategy for dealing with chilling stress conditions. It is essential to consider practices such as direct cultivation, crop rotation and crop residue management in order to efficiently manage chilling stress [9]. In addition to physiological cold stress tolerance mechanisms, the genomic aspect of the plant’s defense system against cold stress is also important. In this regard, several genes such as FR-H1, FR-H2 and FR-H3 have been identified in response to cold stress in barley [10,11]. Moreover, the VRN-H1, VRN-H2 and PPD-H1 genes were associated with vernalization [10]. Also, different gene families, such as WRKY and CBF as transcription factors binding to gene promoters in the pathway of tolerance to abiotic stresses such as cold, increase the activity of defense genes in barley [12,13,14,15,16]. Furthermore, various microRNAs are effective in regulating the expression of defense genes under cold stress conditions in barley [17,18].

One of the first and most important responses of plants to abiotic stresses is the decrease in photosynthesis, which is caused by the disruption of PSII activity [19]. This disruption is due to the accumulation of ROSs and the decrease in plant tissue water [20]. When plants are subjected to abiotic stress, the stomata on their leaves close, leading to a decrease in gas exchange. As a result, the absorption of light energy disrupts the electron transfer chain, causing the RCs to close and ultimately decreasing the quantum yield of PSII [21]. However, when evaluating the plant’s response to abiotic stresses, PSII is more important than PSI. This is because, in the photosynthetic electron transfer chain, PSII is more sensitive to abiotic stresses than PSI. One of the most significant reasons for this is the presence of the water-splitting complex in PSII [22]. The decrease in photosynthetic activity under stress conditions is related to factors such as an increase in the levels of the phytohormone ABA, which acts as a stress hormone. This hormone triggers the closure of stomata in the leaves [23]. Today, measuring the stability of photosynthetic activity is utilized as a quick, accurate and non-destructive tool to screen cultivars that are tolerant to abiotic stresses [24]. Energy from sunlight is absorbed by leaf chlorophyll molecules, then transferred along the electron transfer chain and consumed during the process of photosynthesis (photochemistry). However, any excess energy is either reflected as heat (non-photochemical) or red light (chlorophyll fluorescence) [25]. These last three processes are competitive, and as a result, measuring chlorophyll fluorescence can help investigate the photochemical and non-photochemical efficiency [26]. Chlorophyll fluorescence specifically occurs in PSII [27]. To measure OJIP test parameters, the Kautsky curve [28] and OJIP analysis [29] are used and different parameters have been defined for it. Fo and Fm are among the most important parameters of the OJIP test. When the RCs are completely open and the light intensity is at a level that creates light saturation, a large part of the light energy is consumed by photochemical activities. Finally, only a small part of it is reflected as minimum fluorescence. Then, with the saturation of light radiation, Q_A becomes reduced. Due to the continuity of PSII chemical reactions, the RCs are closed. The result of this process will be maximum fluorescence [21,30]. Another important parameter for evaluating chlorophyll fluorescence in stressed plants is the quantum efficiency of PSII. This parameter represents the rate at which abiotic stress disrupts electron transfer to advance photochemical reactions in different stages of PSI and PSII. In general, abiotic stresses, such as chilling stress, damage PSII and other components of electron transfer, leading to severe inhibition or decrease in photosynthetic electron transfer. Consequently, a greater portion of light energy will be wasted as heat and fluorescence. Therefore, the storage of electron transfer products including ATP and NADPH in light-dependent reactions in photosynthesis decreases. As a result, the quantum yield of PSII also decreases [21,30]. In general, OJIP test parameters are formed from ABS, ET and TR sections, through RC and CS [31]. Consequently, the OJIP test can be used to evaluate the tolerance or sensitivity of the plant to abiotic stresses [29].

In recent decades, with the development of marker techniques, QTL mapping [32,33] and association analysis [34] methods have been used to identify QTLs and MTAs related to abiotic stress tolerance traits, respectively. So far, various studies have been conducted to identify QTLs and MTAs controlling OJIP test parameters on barley under other abiotic stress conditions, such as drought and salinity [31]. However, very limited studies have been conducted using the mentioned techniques on barley under chilling stress conditions. For example, in a study, the effect of chilling and freezing stresses on 96 spring barley cultivars was evaluated and under these conditions, the values of two OJIP test parameters including Fv/Fm and Fv/Fo were measured. To identify gene regions controlling these parameters, the GWAS technique and MLM were used. Accordingly, three gene regions related to the mentioned parameters were tracked on chromosomes 1H, 3H and 6H [35].

Based on the previous considerations, the aim of this study was to evaluate key OJIP test parameters under chilling stress conditions in barley. Additionally, other objectives were to identify barley lines tolerant to chilling stress conditions and trace stable and closely linked QTLs associated with OJIP test parameters. Finally, the ultimate goal of this research was to identify important gene regions related to chilling tolerance in Iranian barley. To date, there have been no reports on the study of important gene regions related to OJIP test parameters under chilling stress conditions in barley. Therefore, this paper presented new information on this area. Overall, the findings of this research were deemed accurate and reliable, as the experiment was conducted in three growing seasons.

2. Results

2.1. Evaluation of OJIP Test Parameters (Quantitative Data)

2.1.1. Descriptive Statistics

Comparing the values of 12 OJIP test parameters in two parental cultivars (Badia and Kavir) indicated that chilling stress altered the parameter values in 2018/2019, 2019/2020 and 2020/2021. Accordingly, the values of parameters like ABS/RC in Badia (a sensitive cultivar) were higher than in Kavir (a tolerant cultivar) under chilling stress conditions. Conversely, the values of Fm, Fv, Fv/Fo, TRo/RC, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSo, REo/CSm, TRo/CSo and TRo/CSm parameters in Badia were lower than in Kavir under chilling stress condition. Moreover, the values of Pearson’s skewness and kurtosis coefficients under chilling stress conditions showed that all parameters had a normal distribution during 2018/2019, 2019/2020 and 2020/2021. Overall, the intensity of chilling stress in 2020/2021 was higher than in 2018/2019 and 2019/2020. Therefore, the mean values of Fm, Fv, Fv/Fo, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSo, REo/CSm, TRo/CSo and TRo/CSm parameters in 2020/2021 were lower than in 2018/2019 and 2019/2020. Finally, for all parameters, values both lower and higher than those of parents were observed under chilling stress conditions, with the latter attributed to transgressive segregation (

Table 1. Descriptive statistics of OJIP test parameters (quantitative data) under chilling stress in barley.

OJIP Test Parameters	Years	Badia	Kavir	Min.	Max.	Q1	Q2	Q3	Mean	SEM	Skewness	Kurtosis
Fm	2018/2019	495	559	484	570	511.5	536	550.5	531.01	2.31	−0.19	−1.11
	2019/2020	498	557	488	570	508.50	528	547	528.27	2.22	0.08	−1.15
	2020/2021	396	521	389	533	418	442	471.50	447.71	3.38	0.45	−0.73
Fv	2018/2019	416	476	409	485	425	449	467.5	447.26	2.26	−0.05	−1.25
	2019/2020	423	474	416	485	435.50	449	469.50	451.01	2.00	−0.02	−1.14
	2020/2021	342	439	331	450	349.50	370	399.50	375.23	3.06	0.63	−0.53
Fv/Fo	2018/2019	4.15	8.84	4.08	8.90	6.10	6.92	7.97	6.92	0.12	−0.25	−0.79
	2019/2020	4.55	8.82	4.48	8.90	6.35	7.11	7.99	7.11	0.10	−0.27	−0.68
	2020/2021	4.22	5.86	4.16	5.99	4.84	5.03	5.23	5.04	0.03	0.18	0.30
ABS/RC	2018/2019	1.29	0.35	0.29	1.37	0.56	0.80	1.04	0.81	0.03	0.09	−1.05
	2019/2020	1.21	0.37	0.30	1.30	0.52	0.71	0.91	0.74	0.03	0.36	−0.80
	2020/2021	1.02	0.64	0.57	1.09	0.77	0.84	0.90	0.84	0.01	0.01	−0.22
TRo/RC	2018/2019	0.31	0.95	0.26	1.10	0.44	0.63	0.83	0.65	0.02	0.20	−1.05
	2019/2020	0.28	0.93	0.23	1.10	0.44	0.63	0.82	0.64	0.02	0.14	−0.98
	2020/2021	0.53	0.86	0.48	0.91	0.64	0.70	0.75	0.70	0.01	−0.04	−0.16
ABS/CSm	2018/2019	485	564	479	570	503.5	523	542	523.57	2.50	0.17	−1.09
	2019/2020	484	563	478	570	503	529	551	526.47	2.58	−0.07	−1.27
	2020/2021	396	525	389	533	418	442	471.50	447.71	3.38	0.45	−0.73
ETo/CSo	2018/2019	49.45	66.12	47.44	70.53	60.08	63.43	67.13	63.10	0.52	−0.74	0.01
	2019/2020	50.73	65.33	47.44	70.53	60.08	63.43	67.13	63.10	0.52	−0.74	0.01
	2020/2021	49.21	62.14	46.27	66.01	52.91	56.80	59.97	56.80	0.45	0.04	−0.76
ETo/CSm	2018/2019	363	411	355	416	377	390	404	390.69	1.65	−0.08	−1.07
	2019/2020	359	410	350	416	375.50	385	402	386.54	1.67	−0.04	−0.88
	2020/2021	291	398	284	404	316	335	364	340.96	2.83	0.33	−0.89
REo/CSo	2018/2019	29.34	47.16	27.69	50.64	41.44	45.37	48.44	44.24	0.55	−1.07	0.40
	2019/2020	32.76	47.38	29.57	50.62	41.21	45.15	48.20	44.13	0.52	−1.02	0.31
	2020/2021	33.91	41.88	30.40	45.98	37.10	39.30	41.84	39.23	0.35	−0.45	−0.43
REo/CSm	2018/2019	196	291	188	300	259	273	288	269.45	2.58	−1.43	1.99
	2019/2020	199	290	190	300	265	276	291.50	272.95	2.44	−1.67	2.85
	2020/2021	191	285	182	287	222.50	237.39	252	237.39	2.10	0.03	−0.15
TRo/CSo	2018/2019	59.56	74.83	55.87	77.65	66.01	70.42	74.33	69.67	0.55	−0.63	−0.67
	2019/2020	60.43	73.99	55.06	77.64	64.59	70.43	74.76	69.37	0.60	−0.48	−0.89
	2020/2021	58.19	70.48	53.25	75.69	58.74	62.90	66.84	62.91	0.53	0.30	−0.75
TRo/CSm	2018/2019	419	479	412	485	429	446	466	447.66	2.10	0.17	−1.08
	2019/2020	423	477	415	485	433	457	472	452.11	2.14	−0.23	−1.27
	2020/2021	340	441	331	450	349.50	370	399.50	375.32	3.08	0.64	−0.52

SEM: standard error of the mean, Q1: first quartile, Q2: second quartile and Q3: third quartile.

).

2.1.2. ANOVA

Combined analysis of variance of the experiments showed significant differences (p < 0.01) between years for all parameters. Therefore, the level of cold stress was different in the three experimental years. Also, significant differences (p < 0.01) were observed among the lines for all parameters. This indicates the existence of genetic diversity among the lines for all parameters. The year × line interaction also shows significant differences (p < 0.01) for all parameters. This suggests differences in the trend of changes in lines in the experimental years for the parameters (Table S1). Therefore, due to the significance of the lyear × line interaction, separate ANOVA was performed in the three experimental years. Accordingly, the results of ANOVA in 2018/2019, 2019/2020, and 2020/2021 showed that the lines had significant differences (p < 0.01) for all parameters. In 2018/2019, there were no significant differences among the blocks for Fv, Fv/Fo, ABS/RC, ABS/CSm, REo/CSm, and TRo/CSo parameters, but the blocks showed significant differences at 5% probability level for Fm and ETo/CSo parameters and at a 1% level for TRo/RC, ETo/CSm, REo/CSo, and TRo/CSm parameters (Table S2). Also, in 2019/2020, the blocks did not have significant differences for the Fm, Fv, ABS/RC, TRo/RC, ABS/CSm, ETo/CSo, ETo/CSm, and REo/CSm parameters, but the Fv/Fo, REo/CSo, and TRo/CSm parameters showed differences among the blocks at a 1% level, and TRo/CSo parameter showed a significant difference among the blocks at a 5% level (Table S3). In 2020/2021, there were significant differences among the blocks for Fv/Fo and ABS/RC parameters at the 1% and 5% levels, respectively. However, the blocks did not differ for the rest of the parameters (Table S4). Overall, despite the lack of significant differences among the blocks in the three experimental years for some parameters, the relative efficiency of the randomized complete block design was higher than the completely randomized design.

2.1.3. Correlation

Pearson correlation coefficients in 2018/2019, 2019/2020, and 2020/2021 are shown (Figures S1–S3). However, Pearson correlation coefficients using the average data of the three experimental years showed that grain yield had a significant positive correlation (p < 0.05) with the Fm, Fv, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSo, REo/CSm, TRo/CSo, and TRo/CSm parameters (Figure 1). Overall, one of the important economic indicators of plant tolerance to stress is maintaining grain yield. Therefore, parameters correlated with grain yield under these conditions are introduced as important parameters in screening cold-stress-tolerant cultivars, and their increase indicates the plant’s tolerance to cold stress.

2.1.4. Cluster Analysis

Cluster analysis and class centroids in 2018/2019, 2019/2020 and 2020/2021 are shown (Figures S4–S6 and Tables S5–S7). However, cluster analysis using the average data of the three experimental years showed that the lines were divided into three clusters including tolerant (50 lines), semi-sensitive (39 lines), and sensitive (14 lines) (Figure 2). The MANOVA was carried out between groups and the difference between groups was significant (p < 0.05). Also, the tolerant group had the highest values of grain yield, Fm, Fv, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSm, TRo/CSo, and TRo/CSm, and also the lowest value of the ABS/RC parameter. However, the sensitive group had the highest value of ABS/RC and showed the lowest values of grain yield, Fv/Fo, and ETo/CSm (Table S8). Overall, the lowest values of grain yield, Fm, Fv, Fv/Fo, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSm, TRo/CSo, and TRo/CSm in sensitive and semi-sensitive lines indicated a decrease in PSII activity.

2.2. Linkage Groups

Barley chromosomes were identified using 217 SSR markers and 11 different dominant markers including RAPD, ISSR, SCoT, CBDP, iPBS, EST, IRAP, TE, ISJ, and ISSR-iPBS combined, and iPBS-iPBS combined was used to saturate the linkage groups. Accordingly, the present genetic map covered 1045 cM of the genome, including seven barley chromosomes, so that the H1 to H7 chromosomes covered 133.3, 120, 169.7, 161.3, 142.9, 144.9, and 172.9 cM of the barley genome, respectively. Also, the average distance between the two markers was 1.45 cM, and as a result, this genetic map was close to saturation and very suitable for QTL mapping (Figure 3).

2.3. QTL Mapping

A total of 48 stable and closely linked QTLs were detected on seven barley chromosomes in relation to 12 OJIP test parameters including Fm, Fv, Fv/Fo, ABS/RC, TRo/RC, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSo, REo/CSm, TRo/CSo and TRo/CSm (

Table 2. The stable and closely linked QTLs controlling OJIP test parameters under chilling stress in barley.

OJIP Test Parameters	Years	QTL	Chr.	Pos. (cM)	LOD	Flanking Markers	Ϭg2	R2 (%)	AE a	DPE b
Fm	2018/2019	qFm-6H	6H	67.65	9.97	HvSMEi846-Bmag0867	151.66	14.09	12.32	Kavir
		qFm-7H	7H	33.25	2.93	UMB101	30.51	2.83	5.52	Kavir
	2019/2020	qFm-4H	4H	55.70	4.14	EBmac0906-OPB-04-B	37.03	6.93	6.09	Kavir
		qFm-5H	5H	13.56	6.57	OPB-17-D-UMB710	57.85	10.82	7.61	Kavir
		qFm-6H	6H	67.65	3.60	HvSMEi846-Bmag0867	26.82	10.01	5.18	Kavir
		qFm-7H	7H	33.25	8.44	UMB101	42.89	8.02	6.55	Kavir
	2020/2021	qFm-4H	4H	55.70	6.24	EBmac0906-OPB-04-B	308.84	8.91	17.57	Kavir
		qFm-5H	5H	13.56	8.48	OPB-17-D-UMB710	315.11	10.09	17.75	Kavir
Fv	2018/2019	qFv-3H	3H	130.25	4.98	GBMS022	14.84	2.82	3.85	Kavir
		qFv-4Ha	4H	117.75	7.13	ISSR13-1-ISJ15-C	33.55	6.37	5.79	Kavir
		qFv-4Hb	4H	151.10	4.06	ET15-32-C-OPB-19-D	10.56	2.01	3.25	Kavir
		qFv-5H	5H	59.98	2.63	Bmac0163	7.14	1.36	2.67	Kavir
		qFv-6Ha	6H	65.94	7.12	HvSMEi846-Bmag0867	30.67	5.83	5.54	Kavir
		qFv-6Hb	6H	74.81	6.52	HVM65-EBmac0874	22.74	4.32	4.77	Kavir
		qFv-7Hb	7H	63.72	6.69	GBMS0111	13.46	2.56	−3.67	Badia
	2019/2020	qFv-3H	3H	130.25	8.33	GBMS022	31.58	4.91	5.62	Kavir
		qFv-4Ha	4H	117.75	3.79	ISSR13-1-ISJ15-C	30.07	4.67	5.48	Kavir
		qFv-5H	5H	59.98	5.75	Bmac0163	23.75	3.69	4.87	Kavir
		qFv-6Ha	6H	65.94	5.94	HvSMEi846-Bmag0867	44.08	6.85	6.64	Kavir
		qFv-7Ha	7H	33.25	6.68	UMB101	18.27	2.84	4.27	Kavir
		qFv-7Hb	7H	63.72	3.58	GBMS0111	12.86	2.00	−3.59	Badia
		qFv-7Hc	7H	165.22	3.37	ISJ10-A-SCoT4-D	28.41	4.42	−5.33	Badia
	2020/2021	qFv-4Hb	4H	151.10	2.78	ET15-32-C-OPB-19-D	29.11	1.31	5.40	Kavir
		qFv-6Ha	6H	66.79	4.30	HvSMEi846-Bmag0867	157.75	7.08	12.56	Kavir
		qFv-6Hb	6H	74.81	6.17	HVM65-EBmac0874	112.45	5.05	10.60	Kavir
		qFv-7Ha	7H	33.25	7.12	UMB101	106.53	4.78	10.32	Kavir
		qFv-7Hb	7H	63.72	2.67	GBMS0111	41.60	1.87	−6.45	Badia
		qFv-7Hc	7H	165.22	4.51	ISJ10-A-SCoT4-D	67.09	3.01	−8.19	Badia
Fv/Fo	2018/2019	qFv/Fo-5H	5H	26.08	4.84	ISJ17-C	0.38	19.98	−0.62	Badia
	2019/2020	qFv/Fo-5H	5H	26.08	4.73	ISJ17-C	0.32	19.57	−0.57	Badia
	2020/2021	qFv/Fo-5H	5H	26.08	2.84	ISJ17-C	0.04	12.27	−0.19	Badia
ABS/RC	2018/2019	qABS/RC-3H	3H	43.21	3.04	HVM33	0.01	12.77	−0.11	Kavir
	2019/2020	qABS/RC-3H	3H	43.21	4.17	HVM33	0.01	13.10	−0.10	Kavir
	2020/2021	qABS/RC-3H	3H	43.21	2.67	HVM33	0.01	11.31	−0.05	Kavir
TRo/RC	2018/2019	qTRo/RC-3H	3H	43.21	2.69	HVM33	0.07	11.40	−0.08	Badia
	2020/2021	qTRo/RC-3H	3H	43.21	3.30	HVM33	0.01	10.61	−0.04	Badia
ABS/CSm	2018/2019	qABS/CSm-6Ha	6H	67.65	5.83	HvSMEi846-Bmag0867	82.60	7.91	9.09	Kavir
		qABS/CSm-7H	7H	165.22	6.78	ISJ10-A-SCoT4-D	89.98	8.62	−9.49	Badia
	2019/2020	qABS/CSm-6Ha	6H	67.65	11.23	HvSMEi846-Bmag0867	103.87	8.19	10.19	Kavir
		qABS/CSm-6Hb	6H	74.81	5.52	HVM65-EBmac0874	33.88	2.67	5.82	Kavir
		qABS/CSm-7H	7H	165.22	2.89	ISJ10-A-SCoT4-D	22.37	1.76	−4.73	Badia
	2020/2021	qABS/CSm-6Hb	6H	74.81	2.85	HVM65-EBmac0874	98.87	2.85	9.94	Kavir
ETo/CSo	2018/2019	qETo/CSo-1H	1H	125.07	4.32	ISSR16-2	7.89	7.26	2.81	Kavir
		qETo/CSo-2H	2H	101.71	3.13	ISSR30iPBS2076-4	5.30	4.88	2.30	Kavir
		qETo/CSo-3H	3H	139.38	5.74	GBM1405-GBM1288	20.32	18.69	−4.51	Badia
		qETo/CSo-5H	5H	26.08	3.67	ISJ17-C	6.38	5.87	2.53	Kavir
		qETo/CSo-7H	7H	126.86	3.19	ISJ15-D-SCoT5-B	9.29	8.55	3.05	Kavir
	2019/2020	qETo/CSo-1H	1H	125.07	4.32	ISSR16-2	7.89	7.26	2.81	Kavir
		qETo/CSo-2H	2H	101.71	3.13	ISSR30iPBS2076-4	5.30	4.88	2.30	Kavir
		qETo/CSo-3H	3H	139.38	5.74	GBM1405-GBM1288	20.32	18.69	−4.51	Badia
		qETo/CSo-5H	5H	26.08	3.67	ISJ17-C	6.38	5.87	2.53	Kavir
		qETo/CSo-7H	7H	126.86	3.19	ISJ15-D-SCoT5-B	9.29	8.55	3.05	Kavir
	2020/2021	qETo/CSo-1H	1H	125.07	3.12	ISSR16-2	2.19	3.64	1.48	Kavir
		qETo/CSo-3H	3H	139.38	5.74	GBM1405-GBM1288	6.92	11.51	−2.63	Badia
		qETo/CSo-5H	5H	26.08	5.46	ISJ17-C	3.41	5.67	1.85	Kavir
		qETo/CSo-7H	7H	126.86	3.09	ISJ15-D-SCoT5-B	1.97	3.27	1.40	Kavir
ETo/CSm	2018/2019	qETo/CSm-2H	2H	101.71	2.88	ISSR30iPBS2076-4	14.53	4.31	3.81	Kavir
		qETo/CSm-3H	3H	130.25	4.22	GBMS022	26.77	7.94	5.17	Kavir
		qETo/CSm-4H	4H	93.90	3.45	GBM1324-EBmacc0009	39.90	11.84	6.32	Kavir
		qETo/CSm-5Ha	5H	59.98	2.96	Bmac0163	18.73	10.56	4.33	Kavir
		qETo/CSm-5Hb	5H	121.08	3.65	EBmac0970	21.93	6.50	4.68	Kavir
		qETo/CSm-6H	6H	50.37	4.03	iPBS2391-C	19.35	5.74	−4.40	Badia
	2019/2020	qETo/CSm-2H	2H	101.71	4.85	ISSR30iPBS2076-4	28.21	8.22	5.31	Kavir
		qETo/CSm-4H	4H	93.90	3.63	GBM1324-EBmacc0009	49.49	14.41	7.03	Kavir
		qETo/CSm-5Ha	5H	59.98	5.16	Bmac0163	37.34	10.87	6.11	Kavir
		qETo/CSm-5Hb	5H	121.08	4.15	EBmac0970	30.38	8.85	5.51	Kavir
		qETo/CSm-6H	6H	50.37	3.79	iPBS2391-C	21.94	6.39	−4.68	Badia
	2020/2021	qETo/CSm-3H	3H	130.25	5.67	GBMS022	77.99	7.99	8.83	Kavir
		qETo/CSm-4H	4H	93.90	3.03	GBM1324-EBmacc0009	56.36	10.78	7.51	Kavir
		qETo/CSm-5Ha	5H	59.98	3.97	Bmac0163	54.75	10.61	7.40	Kavir
		qETo/CSm-5Hb	5H	121.08	3.62	EBmac0970	45.19	4.63	6.72	Kavir
		qETo/CSm-6H	6H	50.37	4.78	iPBS2391-C	48.17	4.94	−6.94	Badia
REo/CSo	2018/2019	qREo/CSo-6Ha	6H	37.33	2.57	ISSR16-3	7.29	10.08	2.70	Kavir
		qREo/CSo-6Hb	6H	50.37	2.78	iPBS2391-C	9.44	13.06	−3.07	Badia
	2019/2020	qREo/CSo-6Ha	6H	37.33	2.58	ISSR16-3	7.26	10.06	2.69	Kavir
		qREo/CSo-6Hb	6H	50.37	2.85	iPBS2391-C	9.60	13.29	−3.10	Badia
	2020/2021	qREo/CSo-6Hb	6H	50.37	3.71	iPBS2391-C	3.49	14.14	−1.87	Badia
REo/CSm	2018/2019	qREo/CSm-3H	3H	129.98	2.75	ISJ13-B	204.43	12.48	−14.30	Badia
		qREo/CSm-4H	4H	66.38	3.31	UMB404	197.61	12.07	14.06	Kavir
	2019/2020	qREo/CSm-3H	3H	129.98	2.91	ISJ13-B	216.57	13.24	−14.72	Badia
		qREo/CSm-4H	4H	66.38	3.13	UMB404	185.60	11.35	13.62	Kavir
TRo/CSo	2018/2019	qTRo/CSo-3Ha	3H	56.37	2.77	GBM1450-HvSMEi843	6.65	10.12	2.58	Kavir
		qTRo/CSo-3Hb	3H	138.43	2.98	GBM1405-GBM1288	11.59	12.41	−3.40	Badia
		qTRo/CSo-5H	5H	51.64	3.17	scssr02306-GBMS115	6.96	10.46	2.64	Kavir
	2019/2020	qTRo/CSo-3Ha	3H	56.37	3.18	GBM1450-HvSMEi843	15.21	14.63	3.90	Kavir
		qTRo/CSo-5H	5H	51.64	5.50	scssr02306-GBMS115	12.31	11.84	3.51	Kavir
	2020/2021	qTRo/CSo-3Hb	3H	138.43	3.17	GBM1405-GBM1288	6.35	10.95	−2.52	Badia
		qTRo/CSo-5H	5H	51.64	4.97	scssr02306-GBMS115	5.80	10.34	2.41	Kavir
TRo/CSm	2018/2019	qTRo/CSm-1H	1H	26.18	3.87	Bmag0211	8.30	1.26	2.88	Kavir
		qTRo/CSm-3Ha	3H	15.88	3.94	GBM1382-iPBS2389-C	16.67	2.54	4.08	Kavir
		qTRo/CSm-3Hb	3H	17.94	4.58	iPBS2230-A	17.34	2.64	4.16	Kavir
		qTRo/CSm-3Hc	3H	130.25	3.52	GBMS022	12.15	1.85	3.49	Kavir
		qTRo/CSm-4H	4H	117.75	2.92	ISSR13-1-ISJ15-C	15.55	2.37	3.94	Kavir
		qTRo/CSm-5H	5H	59.98	5.10	Bmac0163	15.50	2.36	3.94	Kavir
		qTRo/CSm-6Ha	6H	65.94	4.01	HvSMEi846-Bmag0867	19.71	10.01	4.44	Kavir
		qTRo/CSm-6Hb	6H	74.81	2.73	HVM65-EBmac0874	9.47	1.44	3.08	Kavir
	2018/2019	qTRo/CSm-7Ha	7H	33.25	7.91	UMB101	23.38	3.56	4.84	Kavir
		qTRo/CSm-7Hb	7H	114.55	4.35	ISJ8-C	9.13	1.39	3.02	Kavir
		qTRo/CSm-7Hc	7H	165.22	8.36	ISJ10-A-SCoT4-D	36.67	5.59	−6.06	Badia
	2019/2020	qTRo/CSm-1H	1H	26.18	5.44	Bmag0211	10.86	1.48	3.30	Kavir
		qTRo/CSm-3Hb	3H	17.94	5.37	iPBS2230-A	39.47	5.39	6.28	Kavir
		qTRo/CSm-3Hc	3H	130.25	9.81	GBMS022	28.06	3.83	5.30	Kavir
		qTRo/CSm-4H	4H	117.75	4.55	ISSR13-1-ISJ15-C	26.11	3.57	5.11	Kavir
		qTRo/CSm-5H	5H	59.98	4.50	Bmac0163	17.52	2.40	4.19	Kavir
		qTRo/CSm-6Ha	6H	65.94	6.70	HvSMEi846-Bmag0867	29.02	10.97	5.39	Kavir
		qTRo/CSm-7Ha	7H	33.25	4.64	UMB101	11.95	1.63	3.46	Kavir
TRo/CSm	2019/2020	qTRo/CSm-7Hc	7H	165.22	3.08	ISJ10-A-SCoT4-D	9.85	1.35	−3.14	Badia
	2020/2021	qTRo/CSm-3Ha	3H	15.88	5.65	GBM1382-iPBS2389-C	105.21	7.86	10.26	Kavir
		qTRo/CSm-6Ha	6H	65.94	7.27	HvSMEi846-Bmag0867	171.04	12.78	13.08	Kavir
		qTRo/CSm-6Hb	6H	74.81	5.65	HVM65-EBmac0874	90.58	6.77	9.52	Kavir
		qTRo/CSm-7Ha	7H	33.25	8.45	UMB101	111.19	8.31	10.54	Kavir
		qTRo/CSm-7Hb	7H	114.55	3.34	ISJ8-C	44.14	3.30	6.64	Kavir
		qTRo/CSm-7Hc	7H	165.22	3.58	ISJ10-A-SCoT4-D	88.27	6.59	−9.40	Badia

The major QTLs were marked with bold. a: Additive effects, b: direction of phenotypic effect.

and Figure 3).

2.3.1. Fm Parameter

Four QTLs including qFm-4H (EBmac0906-OPB-04-B region, 55.70 cM), qFm-5H (OPB-17-D-UMB710 region, 13.56 cM), qFm-6H (HvSMEi846-Bmag0867 region, 67.65 cM) and qFm-7H (UMB101 region, 33.25 cM) were found to be related to the Fm parameter (Figure 3) and were inherited from the direction of the Kavir parent. The coefficient of determination for qFm-5H in 2019/2020 and 2020/2021 was 10.82% and 10.09%, respectively. Similarly, the coefficient of determination for qFm-6H in 2018/2019 and 2019/2020 was 14.09% and 10.01%, respectively. Therefore, qFm-5H and qFm-6H were considered major QTLs (Table 2).

2.3.2. Fv Parameter

Nine QTLs including qFv-3H (GBMS022 region, 130.25 cM), qFv-4Ha (ISSR13-1-ISJ15-c region, 117.75 cM), qFv-4Hb (ET15-32-C-OPB-19-D region, 151.10 cM), qFv-5H (Bmac0163 region, 59.98 cM), qFv-6Ha (HvSMEi846-Bmag0867 region, 65.94 cM), qFv-6Hb (HVM65-EBmac0874 region, 74.81 cM), qFv-7Ha (UMB101 region, 33.25 cM), qFv-7Hb (GBMS0111 region, 63.72 cM) and qFv-7Hc (ISJ10-A-SCoT4-D region, 165.22 cM) were controlled the Fv parameter (Figure 3). qFv-3H, qFv-4Ha, qFv-4Hb, qFv-5H, qFv-6Ha, qFv-6Hb and qFv-7Ha were in the direction of the Kavir parent. However, qFv-7Hb and qFv-7Hc were in the direction of the Badia parent (Table 2).

2.3.3. Fv/Fo Parameter

qFv/Fo-5H (ISJ17-C region, 26.08 cM) was related to the Fv/Fo parameter (Figure 3) and was in the direction of the Badia parent. qFv/Fo-5H was major and explained 19.98, 19.57 and 12.27 percent of the phenotypic variation in 2018/2019, 2019/2020 and 2020/2021, respectively (Table 2).

2.3.4. ABS/RC Parameter

qABS/RC-3H (HVM33 region, 43.21 cM) was tracked in relation to the ABS/RC parameter (Figure 3) and was in the direction of the Kavir parent. The coefficient of determination of qABS/RC-3H in 2018/2019, 2019/2020 and 2020/2021 was 12.77%, 13.10% and 11.31%, respectively. Thus, qABS/RC-3H was considered as a major QTL (Table 2).

2.3.5. TRo/RC Parameter

qTRo/RC-3H (HVM33 region, 43.21 cM) was identified in relation to the TRo/RC parameter (Figure 3) and was in the direction of the Badia parent. qTRo/RC-3H had R² of 11.40% and 10.61% in 2018/2019 and 2020/2021, respectively. Therefore, qTRo/RC-3H was detected as a major QTL (Table 2).

2.3.6. ABS/CSm Parameter

Three QTLs including qABS/CSm-6Ha (HvSMEi846-Bmag0867 region, 67.65 cM), qABS/CSm-6Hb (HVM65-EBmac0874 region, 74.81 cM) and qABS/CSm-7H (ISJ10-A-SCoT4-D region, 165.22 cM) were tracked in relation to the ABS/CSm parameter (Figure 3). qABS/CSm-6Ha and qABS/CSm-6Hb were located in the direction of Kavir. However, qABS/CSm-7H was in the direction of the Badia parent (Table 2).

2.3.7. ETo/CSo Parameter

Five QTLs including qETo/CSo-1H (ISSR16-2 region, 125.07 cM), qETo/CSo-2H (ISSR30iPBS2076-4 region, 101.71 cM), qETo/CSo-3H (GBM1405-GBM1288 region, 139.38 cM), qETo/CSo-5H (ISJ17-Cregion, 26.08 cM) and qETo/CSo-7H (ISJ15-D-SCoT5-B region, 126.86 cM) were related to the ETo/CSo parameter (Figure 3). qETo/CSo-3H was in the direction of the Badia parent and the rest of the QTLs related to the ETo/CSo parameter were in the direction of the Kavir parent. Also, the coefficient of determination of qETo/CSo-3H in 2018/2019, 2019/2020 and 2020/2021 was 18.69%, 18.69% and 11.51%, respectively. Thus, qETo/CSo-3H was introduced as a major QTL (Table 2).

2.3.8. ETo/CSm Parameter

Six QTLs including qETo/CSm-2H (ISSR30iPBS2076-4 region, 101.71 cM), qETo/CSm-3H (GBMS022 region, 130.25 cM), qETo/CSm-4H (GBM1324-EBmacc0009 region, 93.90 cM), qETo/CSm-5Ha (Bmac0163 region, 59.98 cM), qETo/CSm-5Hb (EBmac0970 region, 121.08 cM) and qETo/CSm-6H (iPBS2391-C region, 50.37 cM) were related to the ETo/CSm parameter (Figure 3). qETo/CSm-6H was in the direction of Badia and the rest of the QTLs were in the direction of Kavir. qETo/CSm-4H and qETo/CSm-5Ha were major; because qETo/CSm-4H had a coefficient of determination of 11.84%, 14.41% and 10.78% in 2018/2019, 2019/2020 and 2020/2021, respectively. Also, qETo/CSm-5Ha was 10.56%, 10.87% and 10.61% in 2018/2019, 2019/2020 and 2020/2021, respectively (Table 2).

2.3.9. REo/CSo Parameter

Two QTLs including qREo/CSo-6Ha (ISSR16-3 region, 37.33 cM) and qREo/CSo-6Hb (iPBS2391-C region, 50.37 cM) were tracked for the REo/CSo parameter (Figure 3). qREo/CSo-6Ha and qREo/CSo-6Hb were major and were in the direction of the Kavir and Badia parents, respectively. qREo/CSo-6Ha explained 10.08 and 10.06 percent of the phenotypic variation in 2018/2019 and 2019/2020, respectively. Also, qREo/CSo-6Hb explained 13.06, 13.29 and 14.14 percent of the existing variation in 2018/2019, 2019/2020 and 2020/2021, respectively (Table 2).

2.3.10. REo/CSm Parameter

Two QTLs including qREo/CSm-3H (ISJ13-B region, 129.98 cM) and qREo/CSm-4H (UMB404 region, 66.38 cM) were identified for the REo/CSm parameter (Figure 3). qREo/CSm-3H and qREo/CSm-4H were in the direction of the Badia and Kavir parents, respectively. Moreover, theses QTLs are considered as major. The coefficient of determination of qREo/CSm-3H in 2018/2019 and 2019/2020 was 12.48% and 13.24%, respectively. Also, in 2018/2019 and 2019/2020, qREo/CSm-4H had a coefficient of determination of 12.07% and 11.35%, respectively (Table 2).

2.3.11. TRo/CSo Parameter

Three QTLs including qTRo/CSo-3Ha (GBM1450-HvSMEi843 region, 56.37 cM), qTRo/CSo-3Hb (GBM1405-GBM1288 region, 138.43 cM) and qTRo/CSo-5H (scssr02306-GBMS115 region, 51.64 cM) were controlled the TRo/CSo parameter (Figure 3). qTRo/CSo-3Ha and qTRo/CSo-5H were in the direction of the Kavir parent. However, qTRo/CSo-3Hb was in the direction of the Badia parent. qTRo/CSo-3Ha had a coefficient of determination of 10.12% and 14.63% in 2018/2019 and 2019/2020, respectively. Also, the coefficient of determination of qTRo/CSo-3Hb in 2018/2019 and 2020/2021 was 12.41% and 10.95%, respectively. Finally, the coefficient of determination of qTRo/CSo-5H in 2018/2019, 2019/2020 and 2020/2021 was 10.46%, 11.84% and 10.34%, respectively. Therefore, these three QTLs were identified as major (Table 2).

2.3.12. TRo/CSm Parameter

Eleven QTLs including qTRo/CSm-1H (Bmag0211 region, 26.18 cM), qTRo/CSm-3Ha (GBM1382-iPBS2389-C region, 15.88 cM), qTRo/CSm-3Hb (iPBS2230-A region, 17.94 cM), qTRo/CSm-3Hc (GBMS022 region, 130.25 cM), qTRo/CSm-4H (ISSR13-1-ISJ15-C region, 117.75 cM), qTRo/CSm-5H (Bmac0163 region, 59.98 cM), qTRo/CSm-6Ha (HvSMEi846-Bmag0867 region, 65.94 cM), qTRo/CSm-6Hb (HVM65-EBmac0874 region, 74.81 cM), qTRo/CSm-7Ha (UMB101 region, 33.25 cM), qTRo/CSm-7Hb (ISJ8-C region, 114.55 cM) and qTRo/CSm-7Hc (ISJ10-A-SCoT4-D region, 165.22 cM) were detected for the TRo/CSm parameter (Figure 3). qTRo/CSm-7Hc was in the direction of the Badia parent and the rest of the QTLs were in the direction of the Kavir parent. qTRo/CSm-6Ha was major, because it explained 10.01, 10.97 and 12.78 percent of the phenotypic variation in 2018/2019, 2019/2020 and 2020/2021, respectively (Table 2).

3. Discussion

The present population exhibits transgressive segregation across all parameters, a result of complementary gene action [36], as the studied population consisted of RILs. However, impure populations, likely experience this phenomenon due to other factors such as non-additive gene effects (epistasis or overdominance). Additionally, linkage is also a contributing factor to this phenomenon [37]. Overall, this phenomenon is utilized to enhance tolerance to various stresses like cold, heat, salinity, and drought. Therefore, to increase the probability of this phenomenon occurring in artificial populations, breeding methods such as SSD are implemented [38]. With this goal, the SSD method was used to prepare the current population. Thus, a total of 50 lines were found to be tolerant to chilling stress. Therefore, these lines have a genetic background of tolerance to chilling stress. It is suggested that, after additional studies and regional experiments, they should be used as tolerant cultivars.

Generally, researchers use comparisons of their findings with others’ results to confirm the changes in OJIP test parameters under abiotic stress conditions, as well as to confirm QTL mapping results. However, this study is the first to report changes in OJIP test parameters under chilling stress conditions in barley, and no report has been provided on mapping these parameters under the mentioned conditions so far. As a result, the findings of the present study were compared with the results of other researchers in similar stresses such as drought and salinity on barley. Accordingly, the Fm, Fv, Fv/Fo, TRo/RC, ABS/CSm, ETo/CSo, ETo/CSm, REo/CSo, REo/CSm, TRo/CSo and TRo/CSm parameters in the tolerant cultivar were higher than the sensitive cultivar under drought and salinity stress conditions. However, the ABS/RC parameter in the tolerant cultivar was lower than those in the sensitive cultivar under drought and salinity stress conditions [31]. Therefore, the recent results confirmed the findings of the present study, as drought, salinity and cold stresses are effective factors in increasing osmotic stress. Under osmotic stress, water potential decreases and leaf turgor is lost [39]. Therefore, considering the similarity of the damages caused by the three mentioned stresses and the defense mechanism of plants against them, it can be expected that these stresses would elicit similar reactions in terms of changes in OJIP test parameters. Moreover, from a physiological perspective, it has been determined that cultivars and lines sensitive to chilling stress were faced with a decrease in the Fm parameter under chilling stress conditions. This is because the application of environmental stresses leads to a reduction in the Q_A pool size. Also, the activity of the water-splitting enzyme complex decreases, and finally, changes occur in the electron transport chain inside or around PSII [40]. Another important damage of chilling stress is the increase in ABS/RC parameter, which reduces RC and consequently disrupts the balance of active to inactive RC [41]. In general, with the decrease in indices, such as energy absorption, electron transfer, and energy trapped in each CS, the efficiency of PSII decreases.

In the present study, 12 important gene regions were identified on six barley chromosomes. Accordingly, 1, 3, 1, 2, 3 and 2 important gene regions were identified on chromosomes 2H, 3H, 4H, 5H, 6H and 7H, respectively (Table 2 and Figure 3). In each of the important gene regions, several QTLs overlapped, and this overlap occurred due to reasons such as the pleiotropic effects of a single gene or the close linkage of multiple small genes [42]. To distinguish the cause of QTLs overlap between the two mentioned reasons, association analysis can be used [43]. A comparison of the present findings with the results of other researchers shows that a QTL controlling Area is present on chromosome 3H (128 cM), between the HVM27 and GBM1405 markers, under drought stress conditions [31]. However, in the current map, this region was saturated with four markers including EBmac0708, Bmac0029, ISH13-B and GBMS022, where three QTLs were detected: qFv-3H, qETo/CSm-3H and qTRo/CSm-3Hc. Furthermore, they mapped a QTL affecting ABS/RC on chromosome 4H (116 cM) was mapped between the ISSR30iPBS2076-5 and ISSR13-1 markers under drought stress conditions [31]. In this study, two QTLs, qFv-4Ha and TRo/CSm-4H were identified in this gene region. Another QTL related to the Area parameter was found on chromosome 6H (62 cM), between the Bmag0867 and ISSR31-1, under drought stress conditions [31]. In the present study, this region was saturated with the HvSMEi846 marker and four QTLs were identified in this region: qFm-6H, qFv-6Ha, qABS/CSm-6Ha and qTRo/CSm-6Ha. Additionally, in this study, four QTLs related to Fo, Fm, Fv, ABS/CSo and TRo/CSo were detected on chromosome 2H (102 cM) under salinity, within the interval between the CBDP6-C and ISSR30iPBS2076-4 markers [31]. In this study, this region was saturated with the Bmag0113e marker and two QTLs qETo/CSo-2H and qETo/CSm-2H were identified. In total, the remaining important gene regions under various abiotic stress conditions have not been previously reported. As a result, the other eight regions including HVM33 (3H, 43.21 cM, containing the qABS/RC-3H, qTRo/RC-3H), GBM1405-GBM1288 (3H, 138.43 cM, contains the qETo/CSo-3H, qTRo/CSo-3Hb), ISJ17-C (5H, 26.08 cM, containing the qFv/Fo-5H, qETo/CSo-5H), Bmac0163 (5H, 59.98 cM, containing the qFv-5H, qETo/CSm-5Ha, qTRo/CSm-5H), iPBS2391-C (6H, 50.37 cM, containing the qETo/CSm-6H, qREo/CSo-6Hb), HVM65-EBmac0874 (6H, 74.81 cM, containing the qFv-6Hb, qABS/CSm-6Hb, qTRo/CSm-6Hb), UMB101 (7H, 33.25 cM, containing the qFm-7H, qFv-7Ha, qTRo/CSm-7Ha) and ISJ10-A-SCoT4-D (7H, 165.22 cM, containing the qFv-7Hc, qABS/CSm-7H, qTRo/CSm-7Hc) are likely novel (Table 2 and Figure 3).

In the current study, a total of 16 major QTLs including qFm-5H, qFm-6H, qFv/Fo-5H, qABS/RC-3H, qTRo/RC-3H, qETo/CSo-3H, qETo/CSm-4H, qETo/CSm-5Ha, qREo/CSo-6Ha, qREo/CSo-6Hb, qREo/CSm-3H, qREo/CSm-4H, qTRo/CSo-3Ha, qTRo/CSo-3Hb, qTRo/CSo-5H and qTRo/CSm-6Ha were mapped on chromosomes 3H, 4H, 5H and 6H (Table 2). Finally, although these major and stable QTLs have been evaluated and repeated in several growing seasons, it is recommended to confirm the results by tracing QTLs with different parents in other regions under chilling stress. Furthermore, following the QTL mapping technique, map-based gene cloning can be pursued [44]. Despite the practical value of the latter technique, it has many problems such as high cost and being time-consuming. This technique involves chromosome walking, where researchers encounter large amounts of repetitive DNA in the genome. As a solution, the chromosome landing technique has been proposed. This method is more practical than map-based gene cloning because it relies on identifying markers very close to the target gene. In this approach, the physical distance between the marker and the target gene should be less than the average size of the added DNA [45,46].

Finally, the flanking markers associated with these QTLs can be used to identify chilling-tolerant cultivars in MAS projects. Overall, molecular plant breeding methods are shorter in time than classical methods but require a larger budget. Accordingly, the time required to breed a cultivar in classical and molecular plant breeding methods is 10–15 and 6–7 years, respectively. Also, in terms of cost, the classical plant breeding method has low to medium cost. However, molecular plant breeding has a very high cost [47]. In general, measuring the chlorophyll fluorescence is faster than other methods of measuring the photosynthesis efficiency, such as chlorophyll a and b amounts. Nevertheless, the classical plant breeding method with phenotyping by chlorophyll a fluorescence also requires time; however, molecular plant breeding with phenotyping by chlorophyll a fluorescence will be more accurate and faster. In total, the use of markers in MAS includes the steps of QTL mapping, validation of marker efficiency, and possibly marker conversion [48]. As a result, the present study was the first and most important step in achieving this goal.

4. Materials and Methods

4.1. Plant Materials and Experimental Conditions

The plant materials used in the current experiment consisted of 103 eighth-generation barley lines resulting from the crossing of the Kavir (tolerant to chilling stress) and Badia (sensitive to chilling stress) cultivars. The Kavir and Badia cultivars were licensed by SPII and ICARDA, respectively. To maintain the entire population and maximize the probability of transgressive segregation, the population was evaluated using the SSD method. The SSD method is similar to the bulk method. However, in this method, only one seed is harvested from each plant in each segregating generation (F₂ to F₅), and then these seeds are mixed and planted in the next year. Overall, the goal of the SSD method is to prevent natural selection and maintain maximum diversity during the segregating generations. Thus, the probability of the natural elimination of desirable plants, especially in traits with low heritability such as yield, decreases. This method, like the bulk method, is simple and inexpensive, and due to the lack of selection in segregating generations, several generations can be advanced in one year in the greenhouse [49]. The plant materials for this experiment were planted in an alpha lattice design with three replications at the research farm of Gonbad Kavous University during the 2018/2019, 2019/2020 and 2020/2021 growing seasons. The typical planting date for the region is 13 December, with plants reaching the flowering stage in early April. However, in this study, the planting date was adjusted earlier to synchronize the chilling stress with the reproductive stage of the plant. Chilling stress ranges from 0 to 15 °C [50], while the optimum temperature for the grain-filling stage in barley is 25 °C [51]. Taking into account this information and the trend of monthly average temperature in the Gonbad Kavous region (Figure 4), the planting date of 7 October was chosen, leading and the plants to enter the reproductive phase in late February. Therefore, during the flowering stage of the plants, they were exposed to temperatures within the chilling stress range (Figure 4). The planting arrangement involved, each line or cultivar being planted in 2 m rows, with a row spacing of 20 cm and a density of 270 plants/m². The nutritional requirements of the plants were determined based on soil test (Table S9). To ensure the effectiveness of chilling stress conditions, grain yield (g/m²) was also measured. Overall, all stages of planting, maintenance and harvesting were conducted following the international protocols.

4.2. Measurement of OJIP Test Parameters

From each line or cultivar, 20 plants were randomly selected and the values of the flag leaves’ OJIP test parameters were measured using a fluorometer (Handy PEA; Hansatech Instruments, King’s Lynn, Norfolk, UK) during the anthesis halfway (anthers occurring halfway to the tip and base of the ear) according to GS65 (Zadok’s growth scale) [52] under chilling stress condition. Accordingly, between 11:00 and 13:00, before measuring chlorophyll fluorescence, the leaves were adapted to dark conditions for 30 min with a special leaf clip. Then, the fast chlorophyll fluorescence induction kinetics were measured with saturating flash intensity of 3000 µmol/m²/s for 1 s [53]. Finally, the values of chlorophyll fluorescence were measured and the 12 parameters (

Table 3. The OJIP test parameters were evaluated under chilling stress in barley.

OJIP Test Parameters	Description	References
Fm	The maximal fluorescence level/final step of chlorophyll a fluorescence transient	[54,56]
Fv	The maximal variable fluorescence	[54]
Fv/Fo	The maximum efficiency of water-splitting complex	[54,55]
ABS/RC	Absorption flux per reaction center (RC) corresponding directly to its apparent antenna size	[54,55,56]
TRo/RC	Trapping flux leading to quinone A (QA) reduction per RC at t = 0	[54,55,56]
ABS/CSm	Absorption of energy per excited cross-section (CS) approximated by Fm	[54]
ETo/CSo	Electron transport flux from QA to quinone B (QB) per CS	[55]
ETo/CSm	Electron flux transported by PSII of a photosynthesizing sample CS approximated by Fm	[54]
REo/CSo	Electron transport flux until photosystem I (PSI) acceptors per CS	[55]
REo/CSm	The flux of electrons from QA to final PSI acceptors per CS of PSII at t = m	[31]
TRo/CSo	Maximum trapped exciton flux per CS	[55]
TRo/CSm	Excitation energy flux trapped by photosystem II (PSII) of a photosynthesizing sample CS approximated by Fm	[54]

) were calculated [31,54,55,56] according to the OJIP test algorithm [29].

4.3. Statistical Analyses for Phenotypic Data

Due to the non-significance of incomplete blocks, the experiments were analyzed as a randomized complete block design. Accordingly, combined analysis of variance of the experiments and separate ANOVA in each year were performed using SAS software 9.4. Furthermore, descriptive statistics of phenotypic data including minimum, maximum, mean, and standard error of the mean and quartiles were estimated. In order to evaluate the frequency distribution of the data and ensure the normal distribution of the data, two statistical tests including Pearson’s skewness and kurtosis coefficients were used. Additionally, the Pearson correlation coefficient was calculated. Moreover, cluster analysis was conducted using the WARD method, and MANOVA was used to ensure the cutting point of the dendrogram and to establish the actual number of groups. Descriptive statistics and the mentioned tests were calculated using SPSS software 27.0.1. Finally, a heatmap of the Pearson correlation coefficient matrix and cluster analysis were created using XLSTAT software 2019.2.2.59614.

4.4. Measurement of Genotypic Data

Random flag leaf samples were taken during the flowering stage and their genomic DNA was extracted using the Doyle and Doyle method [57]. Subsequently, the quality and quantity of DNA samples were assessed using horizontal electrophoresis (agarose gel) and spectrophotometry methods. The linkage map in this study was constructed based on the maps by Taliei et al. (2024) [33] and Sabouri et al. (2023) [32]. Accordingly, co-dominant markers (SSR) were utilized as anchors, while dominant markers were employed to saturate the map. The dominant markers in this study included RAPD, ISSR, SCoT, CBDP, iPBS, EST, IRAP, TE, ISJ, ISSR-iPBS combined and iPBS-iPBS combined. The co-dominant markers were carefully selected to ensure representation on both the short and long arms of all chromosomes for chromosome identification [58]. Genome DNA amplification was achieved using PCR with a thermocycler device (BIO-RAD, USA). The PCR thermal cycles were programmed as a touchdown method [32]. The PCR solution was prepared in a volume of 10 µL including 2.5 µL of DNA, 0.48 µL of MgCl₂ (50 mM), 0.6 µL of dNTP (10 mM), 0.75 µL of forward primer (10 pmol), 0.75 µL of reverse primer (10 pmol), 1 µL of PCR buffer, 0.12 µL of Taq DNA polymerase enzyme (5 U/mL) and 3.8 µL of sterile distilled water. Subsequently, the PCR products were separated using vertical electrophoresis with a 6% polyacrylamide gel, followed by staining and visualization of the bands using the silver nitrate method [59].

4.5. Preparation of Linkage Map

The results of vertical electrophoresis were used to create the linkage map. Accordingly, scores of 1 (band) and 2 (no band) in SSR markers were utilized to construct the genetic matrix. For dominant markers, scores of 1 (band) and 3 (no band) were assigned when the Kavir parent was amplified, and scores of 2 (band) and 4 (no band) were assigned when the Badia parent was amplified. The genotypes of individuals were verified using Mendelian ratios (1:1) and analyzed with the χ² test and SPSS software. Finally, linkage groups were then established using Map Manager QTX software (https://gaow.github.io/genetic-analysis-software/m/map-manager-qtx/) [60]. Moreover, marker distances were calculated in cM units based on the Kosambi map function [61].

4.6. Identification of Closely Linked QTLs

In order to track closely linked QTLs, estimate their additive effects, and determine their contribution to explaining the phenotypic variance (coefficient of determination or R² statistic) of each OJIP test parameter under chilling stress conditions, the GCIM method with R software QTL.gCIMapping.GUI v2.0 (https://pubmed.ncbi.nlm.nih.gov/31890145/) [62]. In the LOD plot, the highest peak values indicate the highest probability of QTL presence. Additionally, red peaks on the graph signify significant QTLs. The LOD threshold was determined using a permutation test method at an α = 0.05 level with 1000 reshufflings [63]. QTLs with an R² of more than 10% were classified as major [32]. QTLs were named by adding the prefix “q” followed by the OJIP test parameters and then, the chromosome number. If multiple parameters were located on the same chromosome, small English letters were used for differentiation.

5. Conclusions

In the current study, a total of 50 lines tolerant to chilling stress were detected. Moreover, we examined the genetic basis of chilling stress through the perspective of QTLs that control OJIP test parameters. We identified a total of 12 significant gene regions and 16 major QTLs at the genomic level. These recent findings highlight the considerable genetic potential of plant materials in terms of chilling stress tolerance. Once validated in other populations, these findings could be utilized in breeding programs aimed at developing chilling-sensitive cultivars. Finally, flanking markers are utilized to screen chilling-resistant cultivars in MAS projects.