Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat

Abstract

Background

Wheat is a cool seasoned crop requiring low temperature during grain filling duration and therefore increased temperature causes significant yield reduction. A set of 125 spring wheat genotypes from International Maize and Wheat Improvement Centre (CIMMYT-Mexico) was evaluated for phenological and yield related traits at three locations in Pakistan under normal sowing time and late sowing time for expose to prolonged high temperature. With the help of genome-wide association study using genotyping-by-sequencing, marker trait associations (MTAs) were observed separately for the traits under normal and late sown conditions.

Results

Significant reduction ranging from 9 to 74% was observed in all traits under high temperature. Especially 30, 25, 41 and 66% reduction was observed for days to heading (DH), plant height (PH), spikes per plant (SPP) and yield respectively. We identified 55,954 single nucleotide polymorphisms (SNPs) using genotyping by sequencing of these 125 hexaploid spring wheat genotypes and conducted genome-wide association studies (GWAS) for days to heading (DH), grain filled duration (GFD), plant height (PH), spikes per plant (SPP), grain number per spike (GNS), thousand kernel weight (TKW) and grain yield per plot (GY). Genomic regions identified through GWAS explained up to 13% of the phenotypic variance, on average. A total of 139 marker-trait associations (MTAs) across three wheat genomes (56 on genome A, 55 on B and 28 on D) were identified for all the seven traits studied. For days to heading, 20; grain filled duration, 21; plant height, 23; spikes per plant, 13; grain numbers per spike, 8; thousand kernel weight, 21 and for grain yield, 33 MTAs were detected under normal and late sown conditions.

Conclusions

This study identifies the essential resource of genetics research and underpins the chromosomal regions of seven agronomic traits under normal and high temperature. Significant relationship was observed between the number of favored alleles and trait observations. Fourteen protein coding genes with their respective annotations have been searched with the sequence of seven MTAs which were identified in this study. These findings will be helpful in the development of a breeder friendly platform for the selection of high yielding wheat lines at high temperature areas.

Electronic supplementary material

The online version of this article (10.1186/s12870-019-1754-6) contains supplementary material, which is available to authorized users.

Background

As the world population will be increased to 9 billion at the end of twenty-first century, it is predicted that the food demand, especially for wheat, will be increased by 50% by 2030 and 70% by 2050 [1, 2]. On the other hand, the mean temperature in South Asia will increase by 4 °C until 2050 or by end of this century [3]. Significant wheat yield losses of 32 to 39% worldwide [4] and 40% in less developed-irrigated wheat growing areas [5] have been reported. During grain filling duration, high temperature (> 30 °C) also occurs in 40% of the temperate zones that grow 36 million ha of wheat [6].

Around 6% decline in global wheat production [7] and 3–4% in indo-gigantic planes [8] has been assessed for each degree rise in temperature during the reproductive stage of the crop. High temperature decreases chlorophyll content and photosynthetic capacity of leaves [9], decreases the grain number per spike due to floret abortion, and lowers the grain weight [10]. Grain filling rate and ultimately the yield are highly affected as the temperature increases above 30 °C during anthesis [11]. Stone and Nicolas [12] reported that exposure of 32 °C for 4 days reduced yield by 23%. Losses due to heat stress are phenomenal and compelling wheat researchers to focus more on heat stress as compared to other abiotic stresses such as drought, metal or radiation etc.

To cope with high temperature in wheat during reproductive stage is desired by the breeders. Traits affected by high temperature are investigated to develop heat tolerant wheat cultivars. It is therefore essential to grow wheat lines under normal and heat stressed conditions to examine the variability. Using conventional wheat breeding approaches, valuable traits related to stress tolerance have been incorporated. However, it is time consuming due to limited field screening methods and laborious due to low probability of combining coveted alleles [13]. Progeny screening can be more precise and genetic gains can be accelerated if the conventional approaches are aided by molecular based techniques.

To dissect the genetic basis of the quantitative traits associated with heat tolerance, loci for grain filling duration and heat tolerance were mapped [14, 15] with few simple sequence repeats (SSRs). Other quantitative trait loci (QTL) for heat tolerance using SSRs were then identified [16, 17]. These studies were based on low marker density using SSRs. QTL mapping identifies genomic regions with low resolution; it hinders their use in diverse germplasm [18].

In order to obtain better genomic dissection of complex quantitative traits like heat tolerance, a high-throughput and robust genotyping approach is in demand [19]. Most recently Diversity Arrays Technology (DArT) markers were used to map agronomic traits in heat prone areas [20]. Genotyping with SSRs and DArT is cost-ineffective, laborious and less accuracy of prediction [19]. Genotyping-by-sequencing (GBS) is a high-throughput, cost effective, next generation sequence-based technology [21] which detects single nucleotide polymorphisms (SNP). GBS markers allow researchers to obtain high prediction accuracy as compared to previously used array of hybridization-based markers [22].

Based on such high density SNP markers, genome-wide association study (GWAS) can inspect large gene pools representative of diverse breeding reservoirs. GWAS is the most suitable approach to locate robust QTLs that show effect in both normal and stressed conditions. Recently GWAS has been effectively used to map QTLs for grain weight, drought stress, floret fertility, grain architecture and starch granule size [23–27].

Our objective was to use a high-density (GBS) platform through GWAS to identify chromosomal regions potentially associated with days to heading (DH), grain filling duration (GFD), plant height (PH), spikes per plant (SPP), grain number per spike (GNS), thousand kernel weight (TKW) and grain yield (GY) under normal and late sown conditions. Information from this scientific work will help wheat breeders for genetic improvement in the development of heat tolerant lines.

Results

Phenotypic assessment

Description of seven studied trait at three locations under two treatments (NS: Normal and LS: Late sown) indicates significant (p-value < 0.01) effect on 125 genotypes (Table 1). For each trait, the difference recorded between NS and LS condition across all the environments was above the critical difference i.e. least significant difference (LSD at p-value 0.05). It was confirmed that the late sown trials faced high temperature from anthesis to physiological maturity that affects each studied trait. Yield losses due to high temperature ranged from 66 to 76% on average at three locations. Plant height was reduced by 25, 30 and 20% on average at Islamabad (ISD), Sargodha (SGD) and Bahawalpur (BWP) respectively. Average days to heading were decreased from 126 to 88 at ISD, 123 to 81 at SGD, 108 to 78 at BWP. Grain filling duration was decreased by 9 to 34%. Mean square of genotypes was also separately calculated for each location and treatment as a one-way source of variation to observe the inherent potential of the lines under study. Mean square of genotypes, locations and treatments for all studied traits were found to be significant (p-value < 0.001) with the degree of precision (CV%) ranging from 6.5 to 18.52 (Table 2). Interaction mean squares for all the traits except GFD were significant.

Table 1.

Description of studied traits at three locations (Islamabad: ISD, Sargodha: SGD and Bahawalpur: BWP) along with over all mean, mean squares, percent reduction due to late sown and heritability

Trait	Location	Treatment	Mean	SE	Min.	Max.	Overall	Mean	MS	h2
DH	ISD	Normal	126	0.39	110	133	Normal	119	19.7***	0.86
		Late	88	0.18	84	93	Late	82	8.65ns	0.71
	SGD	Normal	123	0.24	118	130	LSD	0.5
		Late	81	0.09	78	82	Reduction%	ISD	SGD	BWP
	BWP	Normal	108	0.31	97	113	Mean	30	34	28
		Late	78	0.44	64	86	Min-Max	24–30	34–37	24–34
GFD	ISD	Normal	34	0.31	26	42	Normal	35	9.82ns	0.74
		Late	27	0.35	18	38	Late	28	13.39ns	0.76
	SGD	Normal	37	0.2	31	42	LSD	0.43
		Late	33	0.17	29	38	Reduction%	ISD	SGD	BWP
	BWP	Normal	35	0.32	24	45	Mean	22	9	34
		Late	23	0.4	14	35	Min-Max	9–31	06–10	22–42
PH (cm)	ISD	Normal	93.2	0.56	75.7	109.3	Normal	87.9	47.83***	0.83
		Late	69.53	0.61	51	90.7	Late	65.7	93.68***	0.88
	SGD	Normal	85.77	0.55	72	101.5	LSD	0.78
		Late	59.74	0.69	40	80	Reduction%	ISD	SGD	BWP
	BWP	Normal	84.75	0.46	72	96	Mean	25	30	20
		Late	67.83	0.69	48.67	88.67	Min-Max	17–33	21–44	8–32
SPP	ISD	Normal	8	0.14	5	16	Normal	9	2.79***	0.82
		Late	5	0.08	3	8	Late	5	2.03ns	0.78
	SGD	Normal	10	0.15	7	14	LSD	0.17
		Late	5	0.11	2	8	Reduction%	ISD	SGD	BWP
	BWP	Normal	8	0.08	6	11	Mean	41	51	52
		Late	4	0.15	1	8	Min-Max	40–50	43–71	27–83
GNS	ISD	Normal	53	0.81	24	74	Normal	59	152.8***	0.86
		Late	41	0.92	10	60	Late	41	100.8**	0.81
	SGD	Normal	59	1.01	30	85	LSD	1.04
		Late	41	0.7	24	60	Reduction%	ISD	SGD	BWP
	BWP	Normal	64	0.84	40	87	Mean	23	31	34
		Late	42	0.77	22	62	Min-Max	19–58	20–29	29–45
TKW (g)	ISD	Normal	42.26	0.52	26.07	54	Normal	40.6	46.78**	0.81
		Late	33.2	0.62	14.87	48.31	Late	28.8	43.68***	0.85
	SGD	Normal	41.99	0.5	19	51.87	LSD	0.64
		Late	27.77	0.41	17	44.36	Reduction%	ISD	SGD	BWP
	BWP	Normal	37.65	0.6	22.32	54.55	Mean	21	34	32
		Late	25.46	0.41	14.79	41.2	Min-Max	11–43	11–14	24–34
GY (Kg/plot)	ISD	Normal	0.16	0.01	0.01	0.34	Normal	0.14	0.0037ns	0.72
		Late	0.05	0.002	0.01	0.17	Late	0.03	100.8**	0.81
	SGD	Normal	0.15	0.01	0.02	0.39	LSD	0.006
		Late	0.04	0.002	0.001	0.13	Reduction%	ISD	SGD	BWP
	BWP	Normal	0.11	0.01	0.02	0.56	Mean	66	76	74
		Late	0.03	0.002	0.002	0.13	Min-Max	17–49	68–95.6	77–98

Days to heading: DH, grain filled duration: GFD, plant height: PH, spikes per plant: SPP, grain number per spike: GNS, thousand kernel weight: TKW and grain yield per plot: GY

*p-value <0.05, **p-value < 0.01, ***p-value < 0.001

Table 2.

Mean squares of studied traits from two-way analysis of variance

SOV	df	DH	GFD	PH	SPP	GNS	TKW	GY
Genotype (G)	124	7.25E+ 05**	15.55***	96.47***	2.84***	186.94***	74.03***	1.2***
Location (L)	2	5.78E+ 08***	2088.15***	4677.76***	198.97***	2158.05***	2389.6***	3.57***
Treat (T)	1	1.05E+ 10***	11,097.63***	92,463.63***	3555.58***	58,132.81***	26,224.08***	71.81***
G X L	248	3.08E+ 05 ns	13.82***	34.32 ns	2***	90.25***	34.37***	0.09***
G X T	124	6.21E+ 05*	7.63 ns	45.034**	1.98**	66.67*	16.43 ns	0.08***
L X T	2	1.74E+ 08***	1181.97***	1395.99***	53.16***	1353.50***	423.86***	1.09***
Error	248	4.62E+ 05	9.07	29.71	1.341	52.41	20.04	0.05
CV%		6.5	9.54	7.1	16.94	14.15	12.89	18.52

DH: days to heading, GFD: grain filled duration, PH: plant height, SPP: spikes per plant, GNS: grain number per spike, TKW: thousand kernel weight and GY: grain yield. *p-value < 0.05, **p-value < 0.01, ***p-value < 0.001

In NS conditions, GY was positively correlated with SPP (r = 0.24) and GNS was correlated with TKW (r = 0.26), DH (r = 0.21) and PH (r = 0.23); whereas GFD was significantly negative correlated with DH (r = − 0.56) and positive with SPP (r = 0.20) at p-value < 0.05 (Fig. 1). In LS conditions, GY was significantly negative correlated with DH (r = − 0.28) and positively correlated with GFD (r = 0.18), PH (r = 0.31), SPP (0.41) and GNS (r = 0.25); whereas DH was significantly negative correlated with GFD (r = − 0.73) and PH (r = − 0.25) at p-value < 0.05.

Fig. 1.

Pearson’s coefficient (r) of correlation among traits studied under normal sown (upper right diagonal) and late sown (lower left diagonal) conditions with traits arranged according to first principal component (FPC). The absolute value of 0.189 and 0.184 was found significant for normal sown and late sown trials respectively. DH: days to heading, GFD: grain filled duration, PH: plant height, SPP: spikes per plant, GNS: grain number per spike, TKW: thousand kernel weight and GY: grain yield

Biplots were constructed for NS and LS conditions separately. The two principal components explained 45.27% variability in NS and 53.09% variability in LS conditions. Traits TKW, GNS, PH, GY, SPP occupied the same section in the biplot which indicates similar pattern of variability among these traits. These traits were found correlated to each other. On the other hand GFD and DH were opposite to each other in two dimensional biplot spread indicates their highly negative correlation. Genotypes with serial number 6, 81, 86, 97 and 39 performed well in normal sown conditions while 111, 120, 121, 110 performed poorly under NS conditions. Under LS conditions, genotypes with serial numbers 87, 120, 38 and 34 were found efficient with respect to GY, GNS, PH and SPP traits (Fig. 2). Genotype with serial number 34 was (waxwing*2/Heilo), the released wheat variety “Lemu”, genotype with serial number 38 was developed from Kachu#1/Kiritati//Kachu while the genotype with serial number 87 has well known synthetic wheat in its pedigree. Genotype with serial number 120 was the line having Pastor, Kakuna, Milan, Kauz in its pedigree, further detail has been given in Additional file 1: Table S1. Genotypes with serial numbers 104, 88 and 113 showed the lowest DH and the highest GFD under LS condition.

Fig. 2.

Biplot of studied traits with 125 genotypes under normal sown conditions (a) and under late sown conditions (b). PC1 and PC2 are the principal components along x-axis and y-axis respectively. Numbers are genotypes serial number as in Additional file 1: Table S1. Trait legends are same as in Table 1

To check the overall performance of each genotype in NS and LS conditions, squared cosine values were calculated from principal component analysis (PCA). In Fig. 3, red colored rectangles indicate significant squared cosines of genotypes at F1 (PC1) and F2 (PC2). Genotypes with significant scores in LS conditions performed best with respect to all seven traits. These genotypes can be the genetic resource in the development of heat tolerant cultivars.

Fig. 3.

Heat map of squared cosine values of 125 lines in normal and late sown (NS, LS) conditions through factorial analysis along with F1 and F2 on the basis of seven traits

Marker distribution, principal component analysis (PCA), agglomerative hierarchical clustering (AHC) and linkage disequilibrium (LD)

In total 87,096 SNPs were identified but 55,954 GBS markers were filtered with MAF > 0.05, average polymorphism information content (PIC) and diversity was 0.28 and 0.32, respectively. Out of these 55,954 markers, 35% were from A genome, 41% from B genome and 24% from D (Table 3). Chromosome 2B has the highest number of markers (4248), while 4D has the lowest (1180). Average physical distance between two adjacent SNP loci (SNP-density) along each chromosomal length can be found in Additional file 2: Figure S2.

Table 3.

Genome-wide details of 55,954 markers along with polymorphism information contents and marker diversity

	Chromosome	Markers	Diversity	PIC
Genome A	1	2388	0.34	0.29
	2	3185	0.34	0.29
	3	2714	0.32	0.28
	4	2684	0.33	0.28
	5	2473	0.32	0.28
	6	2352	0.32	0.27
	7	3728	0.32	0.27
		19,524a	0.33b	0.28b
Genome B	1	2908	0.32	0.27
	2	4248	0.33	0.28
	3	3728	0.32	0.27
	4	1906	0.32	0.28
	5	3504	0.32	0.28
	6	3354	0.32	0.27
	7	3511	0.31	0.26
		23,159	0.32	0.27
Genome D	1	1686	0.33	0.29
	2	2353	0.32	0.28
	3	1992	0.31	0.27
	4	1180	0.33	0.29
	5	1811	0.33	0.29
	6	1777	0.32	0.28
	7	2472	0.32	0.28
		13,271	0.32	0.28
Total		55,954	0.32§	0.28c

PIC 0.6 ≥ 0.5 = 1561 markers; 0.7 ≥ 0.6 PIC = 1260 markers and ≥ 0.7 PIC = 213 markers

^aTotal Value; ^bMean value of sub-genome; ^cMean value across the whole genome A, B and D

Five sub-groups (Fig. 4a) were expressed by 125 genotypes after the distribution along two principal components (PCs) and this finding was further validated through AHC analysis which confirmed five distinct clusters. Sub-group 3, 5 and 2 were distinctly spread in two dimensional space (Fig. 4a). Range of dissimilarity index was 0.05 to 0.25 which elucidated five clusters at cutoff value of 0.12 (Fig. 4b). Between-class variance decomposition for the optimal classification was 0.022 (15.51%). Range of distance between each class centroid was 0.178 to 0.406. Clusters 1 to 5 contained 43, 17, 14, 22 and 29 genotypes respectively. Each genotype with its corresponding score of five PCs and its designated cluster can be seen in Additional file 1: Table S1.

Fig.4.

A panel of 125 lines classified through principal component analysis (a) and Agglomerative Hierarchical Clustering (b) using 55,954 markers

Linkage disequilibrium was calculated from 3,154,450 pairs using 100 marker sliding window operation out of which 16% of the pairs were with zero LD and 12% of the pairs were found in significant (p-value < 0.05) LD. Out of these significant pairs, inter-and intra-chromosomal LD was calculated for 3838 and 320,510 pairs respectively. Out of 320,510, there were 162,362 (51%) intra-chromosomal pairs which showed > 0.1 LD with an average of 0.293 within 16.70 M base pair (Mb) throughout the whole genome. There were 504 inter-chromosomal pairs with > 0.1 r² LD with an average of 0.151. Intra-chromosomal LD ranged from 0.1 at chromosome 4D within 38.84 Mb pair distance to 0.24 r² at chromosome 1D within 13.33 Mb pair distance (Fig. 5a). Average LD on A, B and D-genome was 0.177, 0.17 and 0.168 with in the average 14.62, 14.58 and 20.19 Mb pairs distance respectively. Inter-chromosomal LD was noted between 0.06 to 0.08 r² (Fig. 5b). The extent of (threshold LD > 0.1 R²) LD-decay was observed at 25 Mb pair distance (Fig. 5c). A total of 162,866 pairs were identified having > 0.1 r² LD (Fig. 5d).

Fig. 5.

a Intra chromosomal LD with Mega base (Mb) pair distance annexed with chromosome number at x-axis and inter chromosomal LD (b) with number of pairs in significant (p-value < 0.05) LD; c LD-decay plot against distance base pair with LOWESS smoothing curve; d Frequency distribution of LD (R^2) shows number of base pairs in significant (p-value < 0.05)

Marker-trait associations (MTAs)

A total of 139 MTAs were identified out of which 69 were identified in NS experiments and 70 were found under LS conditions. Among them, 56, 55 and 28 were on genome A, B and D, respectively (Table 4). Marker S4A_610095520 was found to be associated with two (DH, GFD) traits in LS conditions; S7B_700803008 was linked with SPP in NS and GY in LS conditions and marker S6A_453869891 was identified for TKW in both NS and LS conditions (Table 4).

Table 4.

Marker-trait associations found for normal and late sown conditions keeping significant threshold p-value at 3.00 (−log10)

Trait		Marker	Chr	Pos (Mbs)	Allele	MAF	-log10(p)	Marker R2
Days to heading (DH)	NS	S2A_573947917	2A	573.95	A/G	0.314	3.51	0.15
		S2B_15783383	2B	15.78	A/T	0.253	3.38	0.12
		S3D_443442947	3D	443.44	G/T	0.079	3.33	0.13
		S5D_42549600	5D	42.55	G/A	0.365	3.56	0.15
		S7B_480230158	7B	480.23	T/C	0.057	3.06	0.1
		S7D_6002850	7D	6	C/T	0.48	3.34	0.15
	LS	S1D_416730006	1D	416.73	T/C	0.162	3.16	0.14
		S1D_417084381	1D	417.08	C/G	0.193	3.13	0.11
		S2A_43211220	2A	43.21	T/C	0.252	3.22	0.14
		S4A_610095520	4A	610.1	G/A	0.115	3.29	0.14
		S5B_586352552	5B	586.35	G/A	0.497	3.36	0.15
		S5D_44161505	5D	44.16	G/A	0.288	3.31	0.13
		S5D_552389328	5D	552.39	T/C	0.185	3.29	0.13
		S6A_71077435	6A	71.08	T/C	0.353	3.17	0.15
		S6B_296392848	6B	296.39	C/T	0.121	3.27	0.13
		S6B_557614637	6B	557.61	C/A	0.382	3.44	0.11
		S6B_700058360	6B	700.06	G/A	0.113	3.58	0.15
		S7A_3066534	7A	3.07	T/G	0.306	3.08	0.12
		S7A_321565067	7A	321.57	G/A	0.066	3.05	0.12
		S7A_571203933	7A	571.2	T/A	0.104	3.13	0.13
Grain filling duration (GFD)	NS	S1A_48746498	1A	48.75	T/G	0.215	3	0.13
		S2B_778750293	2B	778.75	G/T	0.155	3.37	0.14
		S3B_627922	3B	0.63	A/G	0.195	3.35	0.14
		S3B_792848261	3B	792.85	C/T	0.103	3.18	0.14
		S3B_819163184	3B	819.16	A/C	0.19	3.39	0.14
		S4D_441092051	4D	441.09	G/A	0.082	3.14	0.13
		S5B_498781903	5B	498.78	G/A	0.223	3.99	0.16
		S5D_82195139	5D	82.2	G/A	0.064	3.06	0.12
		S5D_193133124	5D	193.13	C/G	0.087	3.3	0.14
		S7A_17544272	7A	17.54	T/C	0.43	3.09	0.13
		S7B_603999358	7B	604	C/T	0.053	3.59	0.16
		S7D_28710742	7D	28.71	G/T	0.052	3.03	0.1
	LS	S1B_608238167	1B	608.24	G/A	0.141	3.18	0.15
		S1D_79799048	1D	79.8	A/T	0.068	4.03	0.13
		S2B_10582115	2B	10.58	A/C	0.127	3.07	0.13
		S2B_425762009	2B	425.76	G/T	0.279	3.33	0.14
		S4A_610095520	4A	610.1	G/A	0.115	3.24	0.14
		S4A_627995501	4A	628	G/A	0.274	3.4	0.14
		S5A_551669444	5A	551.67	G/A	0.17	3.13	0.13
		S6B_92246917	6B	92.25	A/T	0.456	3.58	0.14
		S6B_256489407	6B	256.49	G/A	0.08	3	0.13
Plant height (PH)	NS	S3B_824599939	3B	824.6	C/T	0.347	3.16	0.14
		S3D_12741374	3D	12.74	G/T	0.083	3.9	0.17
		S4A_710817792	4A	710.82	C/T	0.059	4.98	0.18
		S4B_95449920	4B	95.45	G/A	0.165	3.01	0.12
		S5A_14254129	5A	14.25	G/A	0.057	4.4	0.23
		S5A_426002537	5A	426	G/A	0.23	3.48	0.14
		S5A_432036515	5A	432.04	G/A	0.142	3.28	0.12
		S6A_212352003	6A	212.35	A/T	0.072	3.65	0.16
		S7A_163318525	7A	163.32	G/T	0.057	3.2	0.1
		S7A_618498297	7A	618.5	T/C	0.148	3.09	0.09
		S7A_643002715	7A	643	G/A	0.223	3.01	0.09
	LS	S1B_687868186	1B	687.87	A/T	0.49	3.82	0.14
		S2A_14756019	2A	14.76	A/G	0.055	3.52	0.13
		S2A_748204192	2A	748.2	A/C	0.358	3.3	0.12
		S2B_9131487	2B	9.13	T/G	0.054	3.03	0.11
		S2B_18494584	2B	18.49	A/T	0.088	3.07	0.11
		S2B_770220840	2B	770.22	G/C	0.446	4.21	0.16
		S3B_32471134	3B	32.47	A/G	0.487	3.16	0.12
		S3B_33202313	3B	33.2	A/G	0.29	3.51	0.13
		S3B_375363377	3B	375.36	G/A	0.051	3.86	0.14
		S5A_625839432	5A	625.84	C/T	0.055	3.58	0.14
		S6A_611557056	6A	611.56	C/T	0.253	3.37	0.1
		S7A_6229645	7A	6.23	G/A	0.089	3.68	0.14
Spikes per plan (SPP)	NS	S5A_593094332	5A	593.09	G/A	0.197	3.09	0.13
		S5B_62834860	5B	62.83	A/G	0.347	3.03	0.12
		S5B_411753477	5B	411.75	G/C	0.314	3.68	0.15
		S6A_611078199	6A	611.08	A/C	0.131	3.04	0.09
		S7A_695820062	7A	695.82	A/G	0.322	3.07	0.13
		S7B_687824893	7B	687.82	A/G	0.352	3.16	0.13
		S7B_700803008	7B	700.8	A/G	0.313	3.44	0.11
	LS	S1B_475270285	1B	475.27	G/T	0.195	3.43	0.14
		S2D_72213516	2D	72.21	G/A	0.131	3.17	0.12
		S2D_73405773	2D	73.41	C/T	0.128	3.12	0.12
		S2D_74859293	2D	74.86	C/T	0.135	3.65	0.12
		S4B_663622013	4B	663.62	C/T	0.155	4.06	0.14
		S7A_69855692	7A	69.86	G/C	0.12	3.28	0.12
Grain numbers per spike (GNS)	NS	S3A_730295762	3A	730.3	T/G	0.169	3.02	0.1
		S5D_503657305	5D	503.66	A/G	0.431	3.27	0.14
		S7B_687521301	7B	687.52	T/C	0.269	3.86	0.16
	LS	S2A_1050029	2A	1.05	A/G	0.412	3.81	0.16
		S2A_575379774	2A	575.38	C/T	0.142	3.18	0.12
		S2D_12870959	2D	12.87	C/G	0.234	3.02	0.13
		S6A_615812205	6A	615.81	T/G	0.132	3.2	0.12
		S6D_137788118	6D	137.79	G/A	0.13	4.35	0.19
Thousand kernel weight (TKW)	NS	S1B_526812249	1B	526.81	G/A	0.228	3.2	0.13
		S2A_752870462	2A	752.87	A/G	0.219	3.25	0.13
		S2B_235163611	2B	235.16	T/G	0.099	3.03	0.12
		S4A_733664972	4A	733.66	C/T	0.134	3.65	0.15
		S4A_737882127	4A	737.88	T/G	0.331	3.15	0.12
		S6A_453869891	6A	453.87	A/G	0.07	3.31	0.1
		S6B_47885327	6B	47.89	C/T	0.281	3.42	0.14
		S6B_48222896	6B	48.22	C/T	0.156	3.52	0.11
		S6B_48349083	6B	48.35	T/C	0.141	3.07	0.12
		S6B_51093265	6B	51.09	A/C	0.153	3.84	0.12
		S7A_646503003	7A	646.5	G/A	0.059	3.08	0.09
		S7A_718192191	7A	718.19	T/C	0.355	3.11	0.1
		S7A_732742920	7A	732.74	A/G	0.143	3.44	0.11
		S7A_735390023	7A	735.39	A/G	0.204	3.35	0.1
	LS	S2A_763944016	2A	763.94	G/A	0.352	3.09	0.13
		S3B_220267045	3B	220.27	G/A	0.202	3.43	0.11
		S3B_577609492	3B	577.61	A/G	0.165	3.07	0.1
		S5B_648308504	5B	648.31	C/A	0.22	3.13	0.13
		S6A_453869891	6A	453.87	A/G	0.07	3.07	0.1
		S6B_680699350	6B	680.7	C/T	0.223	4.33	0.18
		S7A_720839381	7A	720.84	T/C	0.299	3.06	0.1
Grain yield (GY)	NS	S1A_22415204	1A	22.42	G/T	0.232	3.04	0.13
		S1B_465548215	1B	465.55	A/C	0.169	3.1	0.13
		S1D_351086570	1D	351.09	C/T	0.421	3.29	0.14
		S2B_43278561	2B	43.28	T/C	0.076	3.54	0.12
		S2B_79870855	2B	79.87	G/A	0.107	3.68	0.12
		S2B_753442043	2B	753.44	G/C	0.439	3.18	0.13
		S3A_661975132	3A	661.98	C/A	0.129	3.02	0.12
		S3B_285767866	3B	285.77	C/T	0.247	3.09	0.14
		S3B_697598464	3B	697.6	C/A	0.18	3.4	0.14
		S5B_310279281	5B	310.28	C/A	0.084	3.03	0.12
		S6A_340738287	6A	340.74	T/C	0.326	3.2	0.13
		S6D_3422539	6D	3.42	G/A	0.138	4.36	0.19
		S6D_6070376	6D	6.07	T/A	0.089	3.46	0.14
		S7A_720744946	7A	720.74	G/A	0.233	3.02	0.14
		S7B_608086910	7B	608.09	C/A	0.168	3.1	0.1
		S7D_4215106	7D	4.22	G/A	0.238	3.19	0.14
	LS	S1A_14224631	1A	14.22	C/T	0.126	3.19	0.13
		S1B_49723852	1B	49.72	C/T	0.178	3.22	0.13
		S1B_164107163	1B	164.11	G/C	0.365	3.59	0.14
		S1B_592939183	1B	592.94	C/T	0.133	3.58	0.16
		S1D_250212446	1D	250.21	T/C	0.292	3	0.12
		S1D_450556479	1D	450.56	A/G	0.107	4.45	0.15
		S2A_488411487	2A	488.41	T/C	0.182	3.11	0.13
		S2A_748559027	2A	748.56	T/C	0.435	3.03	0.12
		S2D_21561794	2D	21.56	G/C	0.293	3.61	0.15
		S2D_22673899	2D	22.67	G/C	0.301	3.66	0.16
		S3A_21102523	3A	21.1	T/C	0.419	3.1	0.1
		S5A_129333455	5A	129.33	C/T	0.285	3.09	0.15
		S6D_471249189	6D	471.25	A/T	0.335	4.23	0.17
		S7A_268653916	7A	268.65	T/G	0.244	3.39	0.13
		S7A_511720607	7A	511.72	G/A	0.184	3.52	0.11
		S7A_512974235	7A	512.97	C/T	0.179	3.72	0.15
		S7B_700803008	7B	700.8	A/G	0.313	3.01	0.1

Six genomic regions were identified in NS and 14 were detected in LS conditions that affected days to heading. For GFD, 12 marker in NS and 11 markers in LS conditions were detected. The trait PH was found to be associated with 12 genomic regions in NS and 12 in LS conditions; SPP with seven regions in NS and six in LS conditions. Three markers were found associated with GNS in NS and five in LS conditions. Fourteen MTAs were found in NS and seven in LS conditions that affected TKW. Sixteen MTAs in NS and 17 MTAs in LS conditions were detected that control GY (Table 4). Manhattan plots for the studied traits are available as Additional files 3: Figures S3 to S9. A map based on physical base pair distance of 139 markers (associated with studied traits) was constructed and an 11 Mb (752 to 763 Mb) genomic region on chromosome 2A was associated with TKW; a very narrow distance of just 0.3 Mb (from 748.5 Mb to 748.2 Mb) was found associated with PH and GY under late sown conditions. A region spanning 10 Mb (465.5 to 475.2 Mb) on chromosome 1B is associated with GY, SPP and from 592.9 to 608.2 Mb (16 Mb) is linked with GY and GFD under LS conditions. A 27 Mb region on 4A was associated with PH and TKW in normal sown conditions. On chromosome 5D, a 2 Mb (42 to 44 Mb) region was detected that was associated with DH. A narrowed distance of 0.48 Mb (611.56 to 611.08 Mb) on chromosome 6A was linked with PH and SPP. Another noticeable 4 Mb region which extends from 47 to 51 Mb on 6B is responsible for controlling TKW in NS conditions. A genomic region (321.6 Mb to 735 Mb) on chromosome 7A was found to be of prime importance due to association with multiple agronomic traits including DH, TKW and GY traits (Fig. 6). Effect of favorable alleles was estimated for all seven traits studied under LS conditions. The highest allelic effect (61%) was observed for days to heading under LS conditions (Fig. 7). Genotypes (serial number 34, 109, 115, 120; Additional file 1: Table S1) with nine favorable alleles exhibited the highest (> 0.06 kg/plot) grain yield. Favorable alleles for grain yield explained 45% variability. Increase in grain yield was observed as the number of favored allele increased additively (R² = 0.45). Number of favored alleles in each genotype for seven traits under LS conditions is given in Additional file 1: Table S1.

Fig. 6.

A Physical map showing significantly associated markers for all the studied traits. Colored shapes indicating markers significantly associated with traits under late sown conditions

Fig. 7.

Effect of favorable alleles estimated for traits studied under late sown conditions

Seven MATs were selected on the basis of > 0.3 MAF and their sequences were searched using Basic Local Alignment Search Tool (BLAST) through the link (https://plants.ensembl.org /Multi/Tools/Blast?db = core). With these seven sequences, 144 hits were found. Fourteen gene hits with greater than 90% sequence similarity were selected with their respective annotations (Table 5). Our BLAST results can be accessed with the link: https://plants.ensembl.org/Multi/Tools/Blast/Ticket?tl=MSwRDAw72lU8IpJV.

Table 5.

Gene hits of marker-trait association (MTAs) regions identified with basic local alignment search tool (BLAST) and their annotation with high confidence by IWGSC assembly BLASTX (NCBI BLAST)

Trait	Loci	Gene hit	protein	aE-value	b%ID	Protein annotation
GNS (LS)	S2A_1050029	TraesCS2B02G020100	681aa	3.90E-15	97	Phenylalanine ammonia-lyase; L-Aspartase-like
PH (LS)	S2A_748204192	TraesCS2D02G532600	776aa	1.90E-06	95	Tetratricopepetide-like helical domain superfamily
		TraesCS2D02G532100	772aa	1.90E-06	95	Pentatricopeptide repeat
DH (LS)	S5B_586352552	TraesCS5B02G410800	928aa	8.80E-15	100	Eukaryotic translation initiation factor Winged helix DNA-binding domain superfamily Proteasome component (PCI) domain
		TraesCS5A02G406000	923aa	8.80E-15	100
		TraesCS5D02G416000	927aa	3.10E-14	97
		TraesCS5B02G219100	928aa	5.80E-14	94
		TraesCS5A02G220000	928aa	5.80E-14	94
		TraesCS5D02G228100	928aa	5.80E-14	94
GNS (NS)	S5D_503657305	TraesCS5D02G459800	252aa	3.60E-12	92	helix-loop-helix (bHLH) domain
GY (NS)	S6A_340738287	TraesCS1A02G176100	599aa	1.00E-10	96	Coiled-coils
DH (LS)	S7A_3066534	TraesCS7A02G023900	333aa	2.80E-07	100	PTHR 44519
		TraesCS4A02G466400	381aa	3.30E-17	97	F-box-like domain superfamily
DH (NS)	S7D_6002850	TraesCS7D02G013900	220aa	3.10E-04	100	Tansmembrane helices; Coild-coils

Trait legends are same as in Table 1; a. E-value: Expect value; b. %ID: Similarity percentage by which sequences are related

Discussion

Source to sink ratio is disturbed when heat stress affects the photosynthetic apparatus due to alteration in carbon assimilatory processes [28] which causes reduction in yield and yield related agronomic traits. Hence the present study was designed to evaluate the heat resistance potential of 125 spring wheat genotypes under three different locations. In this detailed phenotypic experiment, the traits were examined under two sowing conditions (NS and LS) that provided the copious opportunity to observe the difference exhibited by a trait under normal and heat-stressed (late sown) conditions. Traits studied in different locations were averaged to understand the stability across the environment as performed by Ogbonnaya et al. [20]. In this way their combined contribution under overall environmental variation in the detection of associated genomic regions has been highlighted under normal and heat-stressed conditions. Thus our study showed the MTAs for normal and late sown conditions separately, so that actively participating genomic regions controlling the traits under heat stress can be emphasized.

After reviewing the previous literature, only those traits were selected for evaluation and eventually genome-wide mapped which were reportedly affected by the heat stress [16, 17]. Significant reduction in all studied traits was observed due to heat stress. Except DH and PH, all traits were affected by genotype x environment interaction and their ranges show that the traits are quantitatively inherited, as has been in prior investigation [17] also showed such trends. Heritability estimates for the traits DH, SPP and GNS were lower in late sown than normal sown trials same as reported by Tiwari et al. [17] while GFD, PH, TKW and GY have higher heritability in late sown than normal sown conditions as shown by Paliwal et al. [16], similar heritability estimates for TKW have been reported by Pinto et al. [29].

Two-way analysis of variance showed acceptable ranges for CV% indicated that the design of the experiment is appropriate, similar trends in CV % have been shown by Ogbonnaya et al. [20]. Under heat-stressed conditions, GY is significantly affected by PH, GFD, SPP and GNS in a positive manner while PH significantly affected DH, SPP and TKW. Under heat stressed conditions DH is inversely related with SPP, GFD and GY as well while GFD if reduced that can cause ultimate reduction in the grain yield. Similar trends have also been reported by Paliwal et al. [16]. Early maturing wheat lines have maximum GFD that can cause an increase in GY.

The biplot was constructed to see the expression of genotypes in normal and heat-stressed conditions with respect to the studied traits. The biplot distinctly which genotypes performed well under normal conditions, under heat-stressed and under both. It has also been observed that the first principal component in LS conditions explains higher variability (30.14%) as than NS conditions (24.52%), and the same trend was followed by second principal component in NS and LS conditions. Variation among the genotypes has been well explained under heat-stressed conditions as compared to under normal conditions. Genotypes with good height were high yielding and those genotypes having high number of spikes per plant (SPP) have been found with high GNS under heat-stressed conditions and delayed heading caused shrinkage in GFD.

As proven by the LSD value for each trait, the effect of heat stress was significant, so the MTAs detected in both normal and heat-stressed conditions were underlined. The main focus of the study was to identify the genomic regions under normal and heat-stressed conditions so the heat susceptibility indices for the traits as calculated and mapped by some of the previous studies [16, 17] was not the objective of this study. Mapping for heat susceptibility indices of the traits cannot detect those genomic regions specifically expressed under heat stress, so the main interest was to identify the MTAs under heat stress and to pinpoint the involvement of different chromosomal regions due to normal and heat-stressed conditions.

In the present trait DH was associated with six markers in normal and 14 markers on 8 different chromosomes in heat-stressed conditions. Two markers on 1D and two markers on 5D along with three markers on 6B and three markers on 7A were identified as controlling the DH in LS conditions. Four markers, one on each of 2A, 4A, 5B and 6A were also identified meanwhile. Fifteen percent variation in DH has been explained under heat stressed conditions by the marker (S6A_71077435) on 6A; Ogbonnaya et al. [20] also reported a region on 6A with 7.48 R² for DH. Using functional markers VRN-A1, VRN-B1 and VRN-D1 on chromosomes 5A, 5B and 5D Ogbonnaya et al. [20] explained the variation in DH. In the present study, four markers (one on 5B, three on 5D) explained 15% variation in DH.

Grain filling duration was controlled by the regions on some multiple chromosomes (Table 4) in NS and LS conditions. Above 12% variation has been explained by most of the regions in both sowing conditions. Paliwal et al. [16] reported the distance between two markers (Xgwm935-Xgwm1273) on chromosome 2B long arm linked with GFD, later on Tiwari et al. [17] identified other QTLs for GFD were identified and most recently Ogbonnaya et al. [20] detected a region on 1B explaining 4.5% of GFD. Present study identified some GFD operating regions on 2B, 5B, 5D and 7D which validates previous [16, 17] findings and also underlined some additional regions on chromosomes 3B and 4A. A region on chromosome 1B that explained 15% variation in GFD under heat-stressed conditions also corroborate with the findings of Ogbonnaya et al. [20].

Using DArTseq, plant height was mapped by Bellucci et al. [30] on chromosome 2A and 6A explaining 6 to 7% variation. Ogbonnaya et al. [20] reported a reported an MTA for PH on 5A with 4.85 R². We report a 6 Mb region on 5A (426 to 732 Mb) associates with PH and explains 12 to 14% variation along with another locus on the same (5A) chromosome with the 23% R². Under heat stress, there is another region of 1 Mb (32 Mb to 33 Mb) on chromosome 3B that is associated with PH. In European winter wheat, using SSR, MTA on 6A for PH was reported [31, 32]. In our study a genomic region on chromosome 6A at 212 Mb is linked with PH in NS conditions but a region on the same (6A) chromosome ~ 400 Mb away (at 611 Mb) has also been detected controlling plant height under heat stress explaining 16 and 10% variation respectively. Spikes per plant was associated with genetic regions on 5A, 5B, 6A, 7A, 7B in NS conditions but in LS conditions the trait variation has been explained up to 14% with regions on 1B, 4B and 7A. A two Mb spanned genetic region on 2D (72-74 Mb) explained 12% variation in SPP under heat stress. Number of spikes per plant is a trait of prime importance for yield [33], so the information given by these MTAs can be used to predict the number of spikes under heat stress. For grain number per spike, in addition to the previously reported [20] region on 7B, we report MTAs for GNS on 3A and 5D under NS and on 2A, 2D, 6A and 6D under LS conditions with explained R² from 10 to 19%.

Thousand kernel weight is reportedly governed by the regions on 5A, 6A [18], 1A, 1B, 2D, 5A, 6A [20]. Paliwal et al. [16] detected MTAs for TKW on 2B, 7B and 7D. Tiwari et al. (2013) also showed the MTAs for TKW in normal and heat-stressed conditions on 2B, 6B and 2B, 6A, 7D respectively. In the present research a 4 Mb region on chromosome 6B uncovered by four GBS markers explained 12 to 14% variation in TKW, not only these results validating the previous findings as well as revealing the function of additional genomic region on 6B in NS conditions. But in heat-stressed conditions, a chromosomal location on 3B revealed by three GBS markers explained 10 to 13% variation in TKW. An MTA for TKW on 2B identified in this study is in agreement with the findings of Paliwal et al. [16] and Tiwari et al. [17]. Another set of three markers on 7A in the region from 718 to 735 Mb explained 10 to 11% variation in TKW.

Grain yield is a complex quantitative trait and has MTAs spread over different chromosomes; in both NS and LS conditions, two MTAs were detected on chromosome 1A that explained 13% variation. Chromosome 1B has been reported [20] responsible for 7.55% explained variation while in our study we identified three markers on 1B which explained 13 to 16% variation in GY under heat-stress conditions. In NS condition, two markers on 2B explained 12 to 13% variation; Tiwari et al. [17] and Paliwal et al. [16] also reported QTLs on 2B for GY. Moreover, some MTAs on 3B, 5B, 6A, 6D, 7A, 7B and 7D have also been detected in this study for GY.

As argued by Paliwal et al. [16] in the heat stress studies, a chromosomal region on 2B is of prime importance. In the present study, 11 MTAs for different traits have been detected on 2B. Using reference genome, GBS-markers associated with the traits of interest were ordered on the basis of physical mega base pair distance and have been reported in this study in the form of a physical map as guided by Poland et al. [22].

Conclusion

To assess the thermo tolerance of the wheat germplasm, there is a dire need to highlight those regions involved in the adaptation to high temperature. This can be done by finding the association of genomic regions with the help of next generation SNP genotyping (such as GBS) so that the detected sequences can be utilized for the development of high throughput user friendly markers which may be used in marker assisted or genomic selection. Our study contributed in the effort of finding the genomic regions expressed during high temperature. Seven chromosomal regions with minor allele frequencies greater than 0.3 have been identified and their sequence was searched with BLAST which showed 14 gene hits with > 90% similarity. The genotypes with high number of favorable alleles detected in this study can be utilized in wheat breeding programs.

Material and methods

Material

Germplasm was comprised of 125 spring wheat genotypes (Additional file 1: Table S1) obtained from International Maize and Wheat Improvement Centre (CIMMYT). Field trials were conducted at three different locations in Pakistan; i) National Agriculture Research Center (NARC), Islamabad; ii) University of Sargodha, Sargodha and iii) Regional Agriculture Research Institute (RARI) Bahawalpur. Geographical coordinates for the experimental location at Islamabad (ISD) were 33o40’34″N 73o7’54″E, Sargodha (SGD) 32o7’50″N 72o40’50″E and Bahawalpur (BWP) 29o23’11″N 71o39’12″E. At each location, one complete set of germplasm was sown at the optimum time (during third week of November) considered as normal and the second complete set was sown late (during third week of December) to uncover the genotype response to high temperature during anthesis. The meteorological data has been presented in the Additional file 4: Figure S10.

Phenotyping

Alpha lattice design with two replications was followed for all the experiments. Plots of two meter two rows with 30 cm row spacing were specified for each entry. Each trait under study was recorded separately for normal and late sown trials. Days to heading (DH) which is the period from sowing to the appearance of heads was noted when more than 50% of the plants of each genotype displayed heads at Zadoks stage 59 [34]. Grain filling duration (GFD) was recorded as the phase between heading and physiological maturity; the stage from sowing date to the time when green color of more than 50% of the spikes disappeared at Zadoks stage 89 [34], which is referred to as physiological maturity. After reaching physiological maturity, plant height (PH) in centimeters was measured with meter rule from the base of the plant up to the top of the spike excluding the awn length. Number of spikes per plants (SPP) was also estimated by taking the average of the number of spikes of five randomly sampled plants of each entry. Grain number per spike (GNS) was calculated, as it was the average of kernels in the main stem spikes of each of ten randomly selected plants from each entry. Grains of all spikes of ten randomly selected plants of each entry were bulked separately. Thousand kernel weight (TKW) was measured in grams by counting thousand grains randomly from each bulk using electrical weighing balance. Grain yield in kilograms for each entry was recorded after threshing whole 2 meter two rows plot.

GBS-SNP genotyping

All 125 lines were genotyped using genotyping-by-sequencing (GBS). DNA extraction kit from Prima Scientific (Bangkok, Thailand) was used to extract genomic DNA. GBS was performed following the Mascher et al. [35] protocol using two-enzyme (MspI-PstI) approach. GBS-SNPs were identified from sequence tags by aligning the sequence reads to the reference genome of Chinese Spring (CS) wheat. A pipeline of TASSEL 5 software was used for SNP calling against the whole wheat (CS) genome assembly (WGA 0.4, International Wheat Genome Sequencing Consortium). SNPs were called by setting minimum/maximum minor allele frequencies (MAF, 0.02 and 0.5) and minimum locus coverage was set at 0.2. Identified SNPs were named as “chromosome number_ physical position” i.e. 6D_13778811. The SNP markers with > 10% missing data and > 20% heterozygosity were not considered for further analysis. The genotypic panel of 55,954 SNP marker used in the study has average minor allele frequency 0.197; proportion of overall heterozygosity was 0.107 while the average missing proportion was 0.052.

Statistical analysis

By using the software Meta-R, best linear unbiased estimates (BLUEs) for three locations were calculated from restricted maximum likelihood (REML) analysis. The REML analysis was performed on results achieved in the alpha lattice experiments. Variance components were estimated with the software Statistix 8.1. Heritability (h²) was calculated for normal and late sown trials using the formula, h² = σ²g / σ²p; where σ²g (σ²g = [(σ²genotypes- σ²error)/replicates]) is genotypic mean square and σ²p (σ²p = σ²g + σ²error) is phenotypic mean square. Biplot construction and correlation analysis for the traits in normal and late sown trials was performed using XLSTATS 2010 and the package “Corrplot” in R-software respectively [36].

To understand the genetic stratification of 125 genotypes, principal component analysis (PCA) was performed using their marker data and distance square matrix was obtained using built-in relatedness tab through TASSEL. This distance square matrix was used to explore the clusters in the panel with Ward’s method of agglomerative hierarchical clustering (AHC).

Linkage disequilibrium (LD) analysis was perform to obtain LD estimates expressed as r2 on sliding window of 100 markers by treating heterozygous calls as missing. To observe the LD decay, intra-chromosomal r2 was plotted against base pair distance using R-package ggplot2. Locally weighted scatter-plot smoother (LOWESS) function was used to draw a trend line for the detection of a threshold r2 below which relationship of any two pairs is to be considered not due to physical linkage. Marker pairs in significant (p-value < 0.05) LD were further observed with respect to inter and intra-chromosomal aspect and pairs with < 0.1 r² were identified.

Polymorphism information content (PIC) and minor allele frequency (MAF) were calculated using the software Trait Analysis by Association, Evolution and Linkage (TASSEL 5.0) [37]. For the examined traits, calculated BLUEs were used for marker-trait association (MTAs). Genome-wide association study (GWAS) was performed using an optimally compressed mixed linear model (MLM) along with variance component estimation with P3D (population parameters previously determined) as implemented in TASSEL by Zhang et al. [38]. A kinship matrix (K) and five principal components (PC) as covariate were used [39] to run MLM (K + PCA) in TASSEL. The equation fitted in TASSEL 5.0 was: y = Xβ + Zμ + e, where y is the vector of observation for phenotypic values; β is an unknown vector with fixed effects including genetic marker and PC and μ is the unknown vector of random additive genetic effects for lines. To avoid the false positives, kinship matrix (K) was used in the model as an additive genetic effect for lines; X and Z are known matrices; and e is the vector which is unobserved of random residual. In this model μ and e are the vectors which are assumed to be normally distributed. False discovery rate (FDR) method was used to estimate the significance between marker and trait association [40] keeping a q-value cutoff of 0.05.

Abbreviations

AHC

Agglomerative hierarchal clustering

BLUEs

Best linear unbiased estimations

BWP

Bahawalpur

DArT

Diversity array technology

DH

Days to heading

FDR

Falls discovery rate

GBS

Genotyping-by- sequencing

GFD

Grain filled duration

GNS

Grain number per spike

GWAS

Genome-wide association study

GY

Grain yield

ISD

Islamabad

LD

Linkage disequilibrium

LS

Late sown

MAF

Minor/minimum allele frequency

Mb

Mega base

MTA

Marker-trait association

NARC

National agriculture research center

NS

Normal sown

PCA

Principal component analysis

PH

Plant height

PIC

Polymorphism information contents

QTL

Quantitative trait locus

RARI

Regional agriculture research institute

REML

Restricted maximum likelihood

SGD

Sargodha

SNP

Single nucleotide polymorphism

SPP

Spikes per plant

TKW

Thousand kernel weight

WGA

Whole genome assembly

Authors’ contributions

MJ performed the experiment and wrote the paper. AA performed the multivariate data analysis. AG² performed genotyping-by-sequencing. AG³ performed the field planting and sample collection of wheat lines. AAN collected the field data. AMHI formulated the univariate statistical analysis. NHN and NAY contributed in revisions. AMK planned the experiment. All the authors have read and approved the final manuscript.

Ethics approval and consent to participate

We declare that these experiments within the ethical standards and legislations in Pakistan.

Consent for publication

National and international guidelines were followed during the collection of plant material.

Competing interests

The authors declare that they have no competing interests.

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