Genome wide association study of grain hardness and novel Puroindoline alleles in common wheat

doi:10.21203/rs.3.rs-1534277/v1

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Genome wide association study of grain hardness and novel Puroindoline alleles in common wheat

https://doi.org/10.21203/rs.3.rs-1534277/v1

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Grain hardness is a key determinant of wheat milling and end-use quality. Puroindoline genes (PINs) are the major genes responsible for wheat grain hardness, but other QTLs also contribute to this trait. Therefore, it is essential to identify loci associated with grain hardness and to the better describe the diversity of PINs in wheat. In this study, 287 wheat accessions representing 70 years of Shanxi Province wheat breeding were used to study grain hardness. Genome-wide association analysis (GWAS) was performed using the 15 K array, and the diversity of PIN alleles was investigated. Among the accessions there was a continuous distribution for grain hardness index (HI) with hard wheat being most common. GWAS identified nine significant marker-trait associations (MTAs), including that located on PINs, which explained 7.03–17.70% of phenotypic variation. Four MTAs on chromosome 2A, 2B, 5A, and 7A were novel loci. A total of 11 PIN alleles were detected, including a novel double-deletion allele of PIN genotype in Donghei1206. The most frequent genotypes were Pina-Dla/Pinb-Dlb (43.9%) and Pina-Dla/Pinb-Dip (18.8%), and both the frequency of Pina-D1a/Pinb-D1b and the HI value increased with breeding years. These results improve understanding of the genetics of grain hardness and help future molecular marker-assisted breeding for this important trait.

Grain hardness

PIN gene alleles

GWAS

MATs

Wheat (Triticum aestivum L.) is the most widely cultivated major food crop, providing staple food of approximately 40% of the world's population (Li et al., 2019). Grain hardness often correlates with yield traits, such as thousand-grain weight and bulk density, and is one of the key traits contributing to wheat processing quality (Li et al., 2020). Grain hardness is affected by certain physical and chemical properties of the seed, determines the flour yield and milling quality, and influences a wide range of end-use quality parameters. Because of the importance of this trait to wheat end-use quality, the genetic basis of kernel hardness has been studied extensively for several decades.

Wheat researchers have concluded that grain hardness differences between cultivars are caused by varying degrees of interaction between starch grains and the protein matrix within the endosperm (Tu and Li, 2020). Hard wheat kernels exhibit high resistance to crushing, as a result of strong adhesion between starch particles and the protein matrix. Flour from these hard grains is coarse-textured with more damaged starch and able to absorb more water than flour from soft grains. Hard wheat is best suited for making breads and noodles. In contrast, soft wheat is less resistant to crushing, and the flour has a finer texture with less damaged starch, making it better for cookies, cakes, and pastries. Thus, grain hardness has been an important breeding target, with various degrees of hardness needed for different end-uses. For example, soft and waxy pasta is preferred in Southern China and these products require soft wheat grains (Liu et al., 2003). People in the north of China prefer products that need stronger gluten flour and consequently wheat cultivars from the north have higher grain hardness (Chen et al., 2006a).

Many studies have demonstrated that grain hardness is mainly controlled by multiple genes and is less affected by environmental conditions and other grain characteristics (Tu and Li, 2020). In a landmark paper of Philip Greenwell and J. David Schofield (1986), discovered that the Mr 15 000 protein Friabilin, was found on the surface of water-washed starch granules. Subsequently, a series of biochemical separation and amino acid sequencing studies identified the two major proteins, puroindoline a and b (PINA and PINB, respectively), and a minor protein, grain softness protein-1(Gsp-1), composed of friabilin (Turner et al., 1999). The Pina-D1, Pinb-D1, and Gsp-D1 genes are linked at the Ha site on chromosome 5DS and are associated with the expression of grain softness (Tranquilli et al. 1999). A PIN-like gene nearly identical to the Pinb gene sequence was first discovered in 2008 (Wilkinson et al.,2008). Among those genes, Pina and Pinb genes play a key role in grain hardness. Studies have been focused on identifying allelic variations for Pin and the resulting phenotypes. Up to now, resulting in the identification of 26 alleles of Pina, 33 alleles of Pinb, and a few double null alleles, and in their wild type alleles (Pina-D1a and Pinb-D1a) result in softness of grains (Chen et al., 2006b, 2012, 2013; Kumar et al., 2015; Li et al., 2019; Tu and Li, 2020). Although most of alleles related to grain hardness are not widely used, a few have shown good utility for breeding. For example, Pinb-D1p is a rare allele but is found in the cultivar Jinmai 47(Pina-D1a/Pinb-D1p) which has been unusually successful and grown on 1.33ⅹ10⁷ hectares since its approval in 1995 (Li et al., 2019). Therefore, it is important to find other alleles with similar practical value.

In addition to the Puroindoline genes, grain hardness-related quantitative trait loci (QTLs) have been reported on all wheat chromosomes (Tu and Li, 2020). The most important locus is Ha on chromosome 5Ds, which can explain 60%~80% of the phenotypic variation for the trait (Sun et al., 2010; Wang et al.,2012; Li et al., 2013, 2016). Five QTLs for grain hardness were found using recombinant inbred lines (RILs), and the Pinb gene explained 76.8% of phenotypic variation while the other four QTLs explained only 2.8–6.5% (Sun et al., 2010). Two loci controlling soft wheat grain hardness, qkha.Orr4B and Qkha.OR4D explaining 20%~34% of the phenotypic variation, were found by using ultra-soft RILs (Wang et al., 2012). By using soft wheat RILs, a major QTL (Q.HI.scau-7D) was detected that accounted for ~ 30% of the phenotypic variation in kernel hardness (Li et al., 2013). Overall, these studies strengthen the idea that grain hardness was mainly controlled by PINs, but also other QTLs are also involved.

Linkage mapping studies have been used to identify genetic loci for grain hardness using a limited selection of parents. Populations selected for a genome-wide association study (GWAS) can avoid the limitations of bi-parental QTL studies. Only a few have used a GWAS to study the genetics of grain hardness (Chen et al., 2019; Muqaddasi et al., 2020; Navrotskyi et al., 2020; Aoun et al., 2021; Elhadi et al., 2021; Lou et al., 2021). Two studies used different materials and the 90K SNP array to do a GWAS for grain hardness and identified four (Lou et al., 2021) and nine (Navrotskyi et al., 2020) single-nucleotide polymorphisms (SNPs) associated with the trait. The phenotypic variation explanation rate (R²) ranged from 2.6 to 12.68% and the R² of SNPs on the PIN gene were the highest. The Pinb gene on chromosome 5D has been located in association analysis of 372 European wheat varieties in up to eight environments (Muqaddasi et al., 2020). These studies all located SNPs in Pinb gene with the highest R². Due to low phenotypic variation and low PIN gene locus diversity, some populations could not be associated with PIN gene or could be associated with SNPs in PIN gene without the highest R². For example, 400 lines developed by crossing and backcrossing the Japanese wheat cultivar Norin61 were used to identify 47 significant hardness-related MTAs explaining 8.3%~22.6% of the phenotypic variation, yet the R² of the MTA in the PIN gene was only 13% (Elhadi et al., 2021). Twenty SNPs were located using 172 advanced soft white winter wheat breeding lines in a GWAS (Aoun et al., 2021). Among these, QSKhard.wql-3A and QSKhard.wql-5A had the highest R². Similarly, Chen et al. (2019) selected 299 American hard winter wheats and located nine SNPs significantly related to grain hardness using the 90K SNP array. The R² of barc154-7D and wms130 on chromosome 7D was higher than that for the two SNPs located on the PIN gene. In addition to the Ha locus, grain hardness is also controlled by multiple genes. There are few studies on grain hardness using a GWAS approach and diverse wheat populations and, therefore, it is likely that other loci for this important trait have yet to be discovered.

Shanxi is located in the loess Plateau, accounting for more than 1/4 of the total area of the Loess Plateau. Shanxi Province has an ancient tradition of wheat cultivation going back more than 4000 years (Zheng et al., 2021). Within this environment over the last several millennia, Shanxi wheat has developed to representative for wheat research in semi-arid area. Shanxi has long been based on pasta, Pasta is prevalent in Shanxi and there are at least 300 kinds of pasta that have been invented in the province. The process of wheat breeding in this region can fully confirmed the theory of co-evolution of wheat quality resources and human environment. In the present study, Shanxi wheat germplasm and a 15K microarray are used for GWAS to assess the diversity of PIN alleles and search for novel loci for grain hardness. The experimental results provide a basis for molecular marker-assisted breeding for wheat hardness.

2.1 Plant materials

A diverse hexaploid wheat collection of 287 accessions from Shanxi Province, China was used (Table S1) The wheats in the collection have significant phenotypic differences, which were developed during various wheat breeding eras. Among the entries, 247 modern cultivars, including 127 irrigated wheat cultivars and 120 dryland cultivars, are 85% of the varieties bred in Shanxi Province since the founding of the People's Republic of China (PRC). And 40 landraces are Chinese wheat core collection from Shanxi Province (Hao et al., 2008).

2.2 Field experimentsThe 287 hexaploid wheats were planted in the Yaodu district in Shanxi province of China, at Linfen (36°08' N, 111°52' E, altitude 450 m) in 2019–2020 and 2020–2021. The trial in 2019–2020 was planted under natural conditions (E1) and in 2020–2021 two irrigation methods were used. These two water regimes were E2, irrigation once at the overwintering stage, and E3 irrigated at overwintering, jointing, and booting stages. The seed was sown in two 2 m rows per line spaced 0.3 m apart at 40 seeds per row. Field management practices were those commonly used in wheat production in the region.

2.3 Hardness measurement

The hardness index of the tested materials was measured with a wheat grain hardness index tester (Wuxi Suibang Science and Technology Co., LTD., JYDB 100 40) by the GuoBiao standard method (GB1351-2008). Before testing, wheat samples were placed under identical temperature and moisture conditions and the negative correlation between bulk density and moisture content was used to control the moisture content of test materials within 9% ~15%. For each sample, 25 g was placed into the hardness index tester and the HI value measured after automatic grinding 50s, repeated 3 times, and the average value was taken. The accessions with kernel HI < 40 were classified as soft wheat, while the accessions with kernel HI > 60 were classified as hard wheat. The accessions with HI between 40 and 60 were mixed wheat.

2.4 Phenotypic data analysis

The best linear unbiased predictions (BLUPS) for the three environments were used the results for GWAS. Broad-sense heritability (H²) was defined as H² = VG/(VG + VE), where VG and VE are the genetic and environment estimations, respectively (Zheng et al., 2020). Correlation analyses were performed using SPSS 2.0(BM SPSS Statistics; IBM Corp., Armonk, NY, United States).

2.5 Genome wide association analysis

GWAS for kernel hardness was performed using a mixed linear model (MLM) with a total of 9,793 high-quality SNPs (Zheng et al., 2021) with version 5 of the TASSEL software. The MLM model was used to make association analyses between grain hardness and SNP markers. GWAS was conducted with the BLUP, E1, E2 and E3 datasets. The models described above were compared by plotting expected versus. observed -log10(p-value) in the form of a quantile–quantile (QQ) plot. The best model was determined by checking how well the observed -log10(p-value) aligned with the expected. TASSEL MLM products were used to generate Manhattan plots using the R package. The linkage disequilibrium (LD) of each SNP marker was extended on each chromosome (Zheng et al., 2021). The extended region where the LD between nearby SNPs and the peak SNP decayed to r² = 0.2 was defined as the local LD-based QTL interval (Zheng et al., 2019). The physical positions of SNP markers were obtained from Chinese Spring reference genome sequences at the International Wheat Genome Sequencing Consortium website (IWGSC, http://www.wheatgenome.org/).

2.6 PIN genotyping

Genomic DNA of each wheat cultivar surveyed was extracted and preserved by Zheng et al. (2021). Genomic DNA was used as a PCR template for the identification of Pina and Pinb genes of soft and hardness wheat. Any mixed wheat cultivars were excluded from this study, as they possibly contained over one genotype for each cultivar (Chen et al., 2006b).

A stepwise approach was employed to characterize Pina and Pinb alleles (Chen et al., 2006b, 2012, 2013; Li et al., 2019). First, according to the method of Chen et al. (2013) potential null alleles of Pina and Pinb were identified and the known alleles of the PIN gene were verified. The test materials for which the allelic variation type of the PIN gene could not be identified were sent for sequencing. Novel deletion alleles of the Pina and/or Pinb genes were detected with a primer walking strategy (Li et al., 2019).

PCR amplifications were performed in a Thermocycler (BioRad, C1000 Touch) and were conducted in 10 µL reactions using 50 ng of genomic DNA, 0.15 µM primers, and 5 µL 2 × EasyTaq PCR SuperMix (TransGen Biotech). The cycling conditions were 94^〇C for 5 min following 33 cycles of 94^〇C for 50 s, 50^〇C to 65^〇C for 50 s, 72^〇C for 1 min (see Table S2 for primer-specific annealing temperatures and extension time), following a 10 min final extension time at 72^〇C. Each sequencing material was amplified four times, and 50µL PCR amplification products were sent to Biomed Genetic Technology co., Ltd. for forward or reverse sequencing to determine the type of allelic variation of the PIN gene.

2.7 Sequencing comparison of 145 resequenced samples

In the wheat union website (http://wheat.cau.edu.cn/WheatUnion/), resequencing results from 145 of the other Chinese cultivars were available (Hao et al., 2020) and using MEGA6 software the PIN gene sequences were compared.

3.1 Phenotypic data analysis

According to the international grading standard for determining grain hardness, the grain hardness distributions of 287 Shanxi wheat varieties were basically similar under three environments. In E1, E2, and E3 there were 211, 203, and 192 entries classified as hard wheats with mean HI values of 65.35, 63.87, 63.68, respectively. Mixed wheat was fewer, with 68, 79, and 92 identified in the three trials with mean HI values of 53.52, 54.26, 53.82, respectively. Soft wheats accounted for the fewest entries, with only seven, four, and three identified in the three environments, respectively (Fig. 1, Table S3).

Across environments the HI values ranged from 36.8 to 74.71 with average values of 61.88, 60.83, and 60.55, respectively, and the coefficients of variation ranging from 11.34–10.08%. The HI correlation coefficients among environments were highly significant and ranged from 0.889 to 0.934. The generalized heritability (H₂) of grain hardness in the three environments was 99.5%, indicating that the grain hardness of the tested materials was mainly influenced by heredity. In E2 and E3 the correlation coefficient was 0.934 and the generalized heritability (H₂) was 94.36%, indicating that water had little effect on grain hardness (Table S4).

3.2 Grain hardness loci identified by GWAS

Association analysis between phenotypic traits and SNP markers was performed using the MLM model. GWAS was conducted on the E1, E2, E3, and BLUP datasets and significant MTAs (-log10(p-value) = 4.0) were identified. For E1 five 5 MTAs were identified on chromosome 2B, 5D, and 7A were identified (Fig. 2b). For E2 five MTAs were identified on chromosomes 5A, 5D, and 7A (Fig. 2c). For E3 six MTAs were identified on chromosome 5A, 5D, and 7A (Fig. 2d). With the BLUP dataset, seven MTAs were identified on chromosomes 2A, 2B, 5D, and 7A (Fig. 2a).

In total, nine MTAs with a phenotypic variation explanation rate (R²) ranging from 7.03–17.70% were identified across chromosomes 2A (1), 2B (1), 5D (2), 5A (1), and 7A (4) (Fig. 2, Table 1). Three MTAs (5D_3609894, 7A_93084120, and 7A_514977827) were significantly associated with HI in all four datasets with maximum values for the explanation of phenotypic variation of 17.7%, 8.83%, and 8.11%, respectively. Among these, 5D_3609894 corresponded to the Pinb gene of the Ha locus, indicating the accuracy of the association analysis. The other six MTAs (2A_521944695, 2B_169782527, 5A_311126149, 5D_7029654, 7A_92745962, and 7A_101617032) showed significant associations in two fewer datasets. These MTAs were on chromosomes 2A, 2B, 5A, 5D, and 7A, and explained 7.49%, 8.96%, 7.09%, 8.05%, 7.47%, and 7.59% of phenotypic variation, respectively. According to the Zheng et al. (2021) analysis of LD attenuation distance, the MTAs 7A_92745962, 7A_101617032, and 7A_93084120 were the same QTL and explained 7.03%~8.83% of the phenotypic variation. 7A_93084120 had the largest R² as measured from the E3 dataset. The loci that significantly associated with 2A_521944695, 2B_169782527, 5A_311126149, and 7A_514977827 have not been reported previously and thus are novel grain hardness loci (Table 1).

Table 1

MTAs significantly associated with grain hardness in 287 wheat cultivars
Chromosome	Position (Mb)	Marker	Dataset	p-value	-log10p	R2(%)
5D	3.61	5D_3609894	BLUP	5.66E-05	9.53	17.29%
			E3	2.79E-05	8.96	16.37%
			E2	2.95E-10	7.91	14.37%
			E1	1.09E-09	9.65	17.70%
	7.03	5D_7029654	BLUP	1.23E-08	4.25	7.47%
	7.03	5D_7029654	E1	2.24E-10	4.55	8.05%
5A	311.13	5A_311126149	E3	8.91E-05	4.05	7.09%
5A	311.13	5A_311126149	E2	9.29E-05	4.03	7.08%
7A	92.75-101.62	7A_93084120	BLUP	2.69E-05	4.57	7.92%
			E3	9.99E-06	5.00	8.83%
			E2	1.53E-05	4.82	8.51%
			E1	3.38E-05	4.47	7.80%
		7A_92745962	E3	5.49E-05	4.26	7.47%
		7A_92745962	E2	7.62E-05	4.12	7.23%
		7A_101617032	BLUP	4.08E-05	4.39	7.59%
		7A_101617032	E3	9.57E-05	4.02	7.03%
	514.98	7A_514977827	BLUP	8.17E-05	4.09	7.05%
			E3	2.46E-05	4.61	8.11%
			E2	4.77E-05	4.32	7.60%
			E1	5.52E-05	4.26	7.42%
2A	521.94	2A_521944695	BLUP	4.66E-05	4.33	7.49%
2B	169.78	2B_169782527	BLUP	1.87E-05	4.73	8.96%
2B	169.78	2B_169782527	E1	6.66E-05	4.18	7.76%

3.3 Diversity of PINs in 145 resequenced materials

The 145 resequenced materials represent wheats that have been widely grown in China and the parents most often used by breeders in China, including 100 modern Chinese cultivars, 25 Chinese landraces, and 20 elite cultivars introduced from other national breeding programs (Hao et al., 2020). Sequence alignment was conducted for the Pina and Pinb gene sequences and single SNPs were found in the promoter region of Pina gene and in the Pinb gene sequence. The SNP in the Pinb gene sequence occurred at 3609894 bp on chromosome 5D and belonging to Pinb-D1b genotype mutation. This mutation was found in 57 materials, including NanDai2419, ZhongNong28, and other excellent imported cultivars. All the other materials were wild type, indicating that the PIN gene was highly conserved in Chinese wheat.

3.4 Puroindoline-D1 allelic diversity

Among the 217 hard wheats and seven soft wheats classified based on HI values from the three environments, we identified PIN gene allelic variation using known molecular markers (Table S2) and sequencing methods. Among the 224 accessions, six Pina alleles (Table S1) were detected, including the wild type allele Pina-D1a, a mutant allele Pina-D1n, and four deletion alleles (Pina-D1b, Pina-D1s, Pina-D1r, and Pina-D1u). Pina-D1a was observed in 199 accessions (87.34%), Pina-D1b in eight cultivars (3.93%), Pina-D1r in two landraces (0.87%), and Pina-D1s in three landraces (1.31%). The remaining known alleles of Pina, including Pina-D1n and Pina-D1u, were rare. Five known variants of Pinb (Table S1) were observed in the germplasm, of which Pinb-D1a, Pinb-D1b, and Pinb-D1p were common with allelic frequencies of 6.99%, 61.14%, and 25.33%, respectively. In total, 12 alleles of the PIN gene were found, ten in cultivars, and only six in the landraces, showing that PIN gene diversity was higher in cultivars than in landraces.

3.5 Puroindoline-D1 genotype diversity

The relationship between different PIN gene haplotypes and grain hardness was identified. Among the 224 accessions, combinations of Pina-D1 and Pinb-D1 alleles produced 11 genotypes (Table S1). Among these there were 16 wild-type hard wheats and one double-deletion of Pin gene hard wheat. In the landraces, there were four PIN gene haplotypes, among which Pina-D1s/Pinb-D1a and Pina-D1r/Pinb-D1a were only found in local cultivars, and Pina-D1a/Pinb-D1p was the main variant. In the cultivars, there were nine PIN gene haplotypes among which seven (Pina-D1a/Pinb-D1a, Pina-D1a/Pinb-D1d, Pina-D1a/Pinb-D1aa, Pina-D1a/Pinb-D1q, Pina-D1b/Pinb-D1a, Pina-d1n/Pinb-D1a, and Pina-D1u/Pinb-D1a) were absent in landraces. Also in the cultivars, Pina-D1a/Pinb-D1b was the main variant, followed by Pina-D1a/Pinb-D1p. The diversity of PIN genotype combinations within cultivars was higher than that within landraces.

The average HI of Shanxi wheats has increased from 59.87 in the 1940s to 67.16 currently (Fig. 3). The frequency of Pina-D1a/Pinb-D1b among Shanxi wheats from the 1940s has increased from 7.5–75%, and the proportion of Pina-D1a/Pinb-D1p has decreased from a high of 55% in the 1940’s (Fig. 3). It is likely that the frequency of the Pina-D1a/Pinb-D1b allele increased because through selection by wheat breeders, displacing the Pina-D1a/Pinb-D1p allele.

3.6 MTAs effect analysis within the wild PIN background

Within 23 test materials with the wild-type PIN genotype, we examined the effects of MTAs identified via GWAS that were significantly associated with grain hardness. The analysis revealed that a combination of markers 2B_169782527, 7A_93084120, and 2A_521944695 differentiated the soft wheat group from the hard wheat group (Table 2). Except Lvhan133, Yunhei28, Jinchun3, and Jintai1310, the hard wheat group carried the GG allele of 2B_169782527, the AA allele of 7A_93084120, and the AA allele of 2A_521944695. Except for Jinmai33, all of the soft wheat group carried the AA allele of 2B_169782527, the CC allele of 7A_93084120, and the GG allele of 2A_521944695. Therefore, these three marker loci are associated with significant effects on grain hardness.

Table 2

Alleles of the most significant MTAs per QTL in wheat variety with wild genotype (color Table online)
Cultivar(lines)	HI	Phenotype	2B_169782527	7A_93084120	2A_521944695	5A_311126149	5D_7029654	7A_514977827
Lvhan133	62.75	Hard	AA	CC	GG	CC	AA	CC
Linyou145	62.02	Hard	AA	AA	GG	CC	GG	CC
Yunhei28	64.87	Hard	AA	CC	GG	CC	GG	CC
Jinchun3	65.80	Hard	AA	CC	GG	CC	AA	CC
Chang6359	65.18	Hard	GG	CC	AA	CC	GG	CC
Chang7016	64.33	Hard	AA	AA	GG	CC	GG	CC
Jintai9923	65.19	Hard	AA	AA	GG	CC	GG	CC
Jintai182	63.95	Hard	GG	CC	AA	CC	AA	CC
Jintai1310	62.89	Hard	AA	CC	GG	CC	GG	CC
Jintai141	65.61	Hard	GG	CC	GG	CC	AA	CC
Yunhei161	64.93	Hard	GG	CC	GG	CC	GA	CC
Niuzhijia2	65.25	Hard	GG	AA	AA	GG	GG	TT
Xianmai	63.19	Hard	AA	AA	AA	GG	GG	TT
Benmai	62.00	Hard	GG	AA	AA	GG	GG	TT
Hongxiaomai	64.61	Hard	--	AA	AA	GG	GG	TT
Xin6160	66.79	Hard	AA	CC	AA	CC	GG	CC
Jinmai33	43.76	Soft	GG	CC	GG	CC	AA	CC
88 − 10γ	44.39	Soft	AA	CC	GG	CC	AA	CC
Jinmai67	43.62	Soft	AA	CC	GG	CC	GG	CC
Jinchun15	40.46	Soft	AA	CC	GG	CC	GG	TT
Chang4853	43.92	Soft	AA	CC	GG	CC	GG	CC
Datongxiaomai	40.27	Soft	AA	CC	GG	GC	GG	TT
Jinmai6	40.78	Soft	AA	CC	GG	CC	AA	TT
Note: The light-gray filled cells indicate haplotypes associated with grain hardness.

3.7 Novel allele of Puroindoline-D1

Donghei1206 had a double deletion of the PIN gene and greater grain hardness with a HI of 68.56. Using the primer step method, a series of primers were used to explore the double deletion sites of the Donghei1206 PIN gene with amplification of Chinese spring DNA as a control. The eight pairs of primers from PinA-11 to PinA-16 and PinB-3 to PinB-4 did not generate corresponding PCR amplification products. PCR products were obtained after the amplification of other primers (Fig. 4). Donghei1206 had a ~ 4500bp deletion in the Pina coding region and a ~ 1500bp deletion in the Pinb coding region. This novel double deletion of the Pina and Pinb alleles was identified in Donghei1206 via comparison with step-primer results from various PIN gene deletion mutations (Table S5).

4.1 Relationship between breeding for grain hardness and consumer preferences

The pasta quality characteristics preferred by various groups of consumers subtly affect the selection of wheat grain quality traits. The present study found most Shanxi was hard wheat with an average HI score of about 60. Others have shown that grain hardness is related to noodle color and smoothness and that a HI value of 60–70 is needed to ensure high quality noodles and steamed bread (Chen et al., 2006a; Zhao et al., 2009). Thus, wheat breeding in the Shanxi region has been influenced by local dietary preferences for noodles. Such preferences were also evident in the breeding results from in the late 1940s. Landraces were the best adapted to local agronomic and cultural requirements. Before the 1940s, the PIN genotypes of landraces were mainly Pina-D1a/Pinb-D1p. In the early 1950s, Chinese wheat was improved mainly through the introduction of cultivars such as NanDai2419, ZhongNong28 and Quality. The introduction of these cultivars led to the rapid selection of new alleles (such as Pinb-D1b allele) by breeders (Hao et al., 2020). Studies have shown that wheat cultivars with Pinb-D1b have excellent processing quality of flour, noodles, and steamed bread (Chen et al., 2006b; Liu et al., 2003). Therefore, since grain hardness was required for better quality, the Pina-D1a/Pinb-D1b genotype combination was strongly selected by the local wheat breeders, resulting in a decrease in the proportion of Pina-D1a/Pinb-D1p genotypes in wheats grown in the Shanxi region.

Breeding and selection have led to the introgression of haplotypes from introduced germplasm into local cultivars and promoted the recombination of the two gene pools, creating recombinants. Hence, the allelic diversity of the PIN gene in the modern wheat cultivars grown in this region was higher than that in landraces. This also confirmed the results of Li and Hao et al. (2022,2020): some important haplotypes of modern cultivars have higher diversity than local cultivars. Also, in resequencing data from the 145 materials originating from various regions of China, Pinb-D1b allelic variation occurred in only 57 materials. In contrast, the 287 experimental materials from Shanxi province had 11 kinds of allelic variation, indicating rich diversity of wheat grain hardness genotypes and greater PIN allele diversity compared to cultivars from other parts of China.

4.2 New allele and new loci

The PIN gene plays an important role in grain hardness and every new allele found is of interest to the wheat breeding community (Chen et al., 2019; Muqaddasi et al., 2020; Tu and Li, 2020; Lou et al., 2021). Using the primer stepwise method in the present study, Donghei1206 was shown to have double deletion alleles of ~ 4.5Kb Pina-null and ~ 1.5Kb Pinb-null for the PIN gene. Such alleles are rare in Chinese germplasm resources. Only two double deletion alleles of the PIN gene, ~ 33Kb and ~ 29Kb, has been reported previously (Chen et al., 2013; Li et al., 2019). It is uncertain of these deletions represent either the same or different mutations. By comparing the results of the stepwise method, Pina-null and Pinb-null on Donghei1206 differ from the previously described double deletions. Donghei1206 has a pedigree of (Heixiaomai76// Tai633 / Zi6)/Wheike962. The grain HI of Heixiaomai76 is 66.41 and its genotype is Pina-D1a/Pinb-D1b (Table S1), which does not have a gene deletion. Zi6 and Wheike962 are descendants of a wide hybridization between Thinopyrum intermedium and wheat. The double deletion of the PIN gene in Donghei1206 may have been derived from either Zi6 or Wheike962, and this hypothesis could be tested by using step primers in future experiments.

Previous studies have shown that wheat grain hardness is not only controlled by the main effect genes (Ha) on chromosome 5DS but is also affected by minor genes on other chromosomes (Tu and Li, 2020). We located 5D_3609894 at the Ha site PIN gene on chromosome 5D and eight MTAs (5D_7029654, 5A_311126149, 7A_93084120, 7A_92745962, 7A_101617032, 7A_514977827, 2A_521944695, and 2B_169782527) associated with grain hardness on other chromosomes. With populations of offspring from crosses between high-quality and non-adapted bread wheats, Kumar et al. (2019) located the wsnp_Ex_c42653_49180603 and Excalibur_c42993_561 markers for grain hardness on chromosome 7A. These markers coincide with the physical location of 7A_93084120 in the present study and all three markers likely represent the same locus. Similarly, a QTL for grain hardness associated with SSR markers Xbarc130, Xcfd18, Xgwm190, gpw326, and gwm190 was previously found at the same physical location in different populations and this locus likely corresponds to the site the on chromosome 5D (5D_7029654) identified in the present study (Sun et al.,2010; Tu and Li, 2020). The loci for grain hardness on chromosomes 2A(521.94Mb), 2B(169.78Mb), 5A(311.13Mb), and 7A(514.98Mb) have not been previously reported and are likely novel.

Based on the comparison results of six significant haplotype loci in 27 samples with the wild-type PIN gene, the combination markers of 2B_169782527, 7A_93084120, and 2A_521944695 greatly influenced grain hardness. Further development of markers can be used to breed hard and soft wheat in the future. For example, when the PIN gene is the wild-type background, the haplotype composed of the G allele of 2B_169782527, the A allele of 7A_93084120, and the A allele of 2A_521944695 could be selected to enhance grain hardness. And for soft wheat breeding, the haplotype with the A allele of 2B_169782527, the G allele of 7A_93084120, and G allele of 2A_521944695 could be selected.

Author contribution statement

Jun-You Wang: Investigation, Data curation, Writing-original draft. Chen-Kang Yang: Investigation, Data curation, Visualization. Ying Wang: Investigation, Resources, Data curation. Wen-Jia Zhao: Investigation, Formal analysis. Bang-Bang Wu: Validation, Software. Jia-Jia Zhao and Ling Qiao: Validation. Jun Zheng: Conceptualization, Methodology, Project administration, Writing-original draft. Xing-Wei Zheng: Resources, Supervision, Funding acquisition.

Funding

This work was supported by Shanxi key laboratory of crop genetics and molecular improvement [KFJJ2019-02], the Key Research Laboratory of Shanxi Province [201803D421021], the State Key Laboratory of Sustainable Dryland Agriculture (in preparation), Shanxi Agricultural University [No. 202002-1].

Data availability

Not applicable.

Declarations

Ethics declarations

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Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Aoun M, Carter AH, Ward BP, Morris CF (2021) Genome-wide association mapping of the ‘super-soft’kernel texture in white winter wheat. Theor Appl Genet 134(8):2547–2559
Chen F, Chen DS, Qian SH (2006) Influence of puroindoline gene on milling performance, steamed bread and noodle qualities in spring wheat. Acta Agron Sinica 32:980–986 (in Chinese)
Chen F, He ZH, Xia XC, Xia LQ, Zhang XY, Lillemo M, Morris CF (2006) Molecular and biochemical characterization of puroindoline a and b alleles in Chinese landraces and historical cultivars. Theoretical and Applied Genetics, 112(3), 400–409
Chen F, Zhang FY, Xia XC, Dong ZD, Cui DQ (2012) Distribution of puroindoline alleles in bread wheat cultivars of the Yellow and Huai valley of China and discovery of a novel puroindoline a allele without PINA protein. Mol Breeding 29(2):371–378
Chen F, Li H, Cui D (2013) Discovery, distribution and diversity of Puroindoline-D1 genes in bread wheat from five countries (Triticum aestivum L.). BMC Plant Biol 13(1):1–13
Chen JH, Zhang FY, Zhao CJ, Lv GG, Sun CW, Pan YB, Guo XY, Chen F (2019) Genome wide association study of six quality traits reveals the association of the TaRPP13L1 gene with flour colour in Chinese bread wheat. Plant Biotechnol J 17(11):2106–2122
Elhadi GMI, Kamal NM, Gorafi YSA, Yamasaki Y, Ban Y, Kato K, Tahir ISA, Ishii T, Tanaka h, Tsujimoto H (2021) Novel Loci for Kernel Hardness Appeared as a Response to Heat and Combined Heat-Drought Conditions in Wheat Harboring Aegilops tauschii Diversity. Agronomy 11(6):1061
Greenwell P, Schofield JD (1986) A Starch granule protein associated with endosperm softness in wheat. Cereal Chem 63:379–380
Hao CY, Dong YC, Wang LF, You GX, Zhang HN, Gai HM, Jia JZ, Zhang XY (2008) The construction and genetic diversity analysis of the core species of wheat in our country. Chin Sci Bull 53(8):908–915
Hao CY, Jiao CZ, Hou J, Li T, Liu HX, Wang YQ, Zheng J, Liu H, Bi ZH, Xu FF, Zhao J, Ma L, Wang YM, Majeed U, Liu X, Appels R, Maccaferri M, Tuberosa R, Lu HF, Zhang XY (2020) Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol Plant 13(12):1733–1751
Kumar R, Arora S, Singh K, Garg M (2015) Puroindoline allelic diversity in Indian wheat germplasm and identification of new allelic variants. Breeding science, 65(4), 319–326
Kumar A, Mantovani EE, Simsek S, Jain S, Elias EM, Mergoum M (2019) Genome wide genetic dissection of wheat quality and yield related traits and their relationship with grain shape and size traits in an elite × non-adapted bread wheat cross.PLoS One, 14(9), e0221826
Liu JJ, He ZH, Zhao ZD, Pena RJ, Rajaram S (2003) Wheat quality traits and quality parameters of cooked dry white Chinese noodles. Euphytica 131(2):147–154
Li HM, Liang H, Tang ZX, Zhang HQ, Yan BJ, Ren ZL (2013) QTL Analysis for grain pentosans and hardness index in a Chinese 1RS. 1BL × non-1RS. 1BL wheat cross. Plant Mol biology Report 31(2):477–484
Li CL, Bai GH, Chao SM, Carver B, Wang ZH (2016) Single nucleotide polymorphisms linked to quantitative trait loci for grain quality traits in wheat. Crop J 4(1):1–11
Li XY, Li Y, Zhang M, Yu XF, Hu R, Chang JL, Yang GX, Wang YS, He GY (2019) Diversity of Puroindoline genes and their association with kernel hardness in Chinese wheat cultivars and landraces. Mol Breeding 39(4):1–13
Li AL, Hao CY, Wang ZY, Geng SF, Jia ML, Wang F, Han X, Kong XC, Yin LJ, Tao S, Deng ZY, Liao RY, Sun GL, Wang K, Ye XG, Jiao CZ, Lu HF, Zhou Y, Liu DG, Fu XD, Zhang XY, Mao L (2022) Wheat breeding history reveals synergistic selection of pleiotropic genomic sites for plant architecture and grain yield. Molecular Plant
Lou HY, Zhang RQ, Liu YT, Guo DD, Zhai SS, Chen AY, Zhang YF, Xie CJ, You MS, Peng HR, Liang RQ, Ni ZF, Sun QX, Li BY (2021) Genome-wide association study of six quality-related traits in common wheat (Triticum aestivum L.) under two sowing conditions. Theor Appl Genet 134(1):399–418
Muqaddasi QH, Brassac J, Ebmeyer E, Kollers S, Korzun V, Argillier O, Stiewe G, Plieske J, Ganal MW, Röder MS (2020) Prospects of GWAS and predictive breeding for European winter wheat’s grain protein content, grain starch content, and grain hardness. Sci Rep 10(1):1–17
Navrotskyi S, Belamkar V, Baenziger PS, Rose DJ (2020) Insights into the genetic architecture of bran friability and water retention capacity, two important traits for whole grain end-use quality in winter wheat. Genes 11(8):838
Sun X, Marza F, Ma H, Carver BF, Bai G (2010) Mapping quantitative trait loci for quality factors in an inter-class cross of US and Chinese wheat. Theor Appl Genet 120(5):1041–1051
Turner M, Mukai Y, Leroy P, Charef B, Appels R, Rahman S (1999) The Ha locus of wheat: Identification of a polymorphic region for tracing grain hardness in crosses. Genome 42(6):1242–1250
Tranquilli G, Lijavetzky D, Muzzi G, Dubcovsky J (1999) Genetic and physical characterization of grain texture-related loci in diploid wheat. Mol Gen Genet 262(4–5):846–850
Tu M, Li Y (2020) Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat. Plants 9(12):1631
Wilkinson M, Wan Y, Tosi P, Leverington M, Snape J, Mitchell RA, Shewry PR (2008) Identification and genetic mapping of variant forms of puroindoline b expressed in developing wheat grain. J Cereal Sci 48(3):722–728
Wang G, Leonard JM, Ross AS, Peterson CJ, Zemetra RS, Campbell G, Riera-Lizarazu K, O (2012) Identification of genetic factors controlling kernel hardness and related traits in a recombinant inbred population derived from a soft × ‘extra-soft’ wheat (Triticum aestivum L.) cross. Theor Appl Genet 124(1):207–221
Zhao WH (2009) The relationship between wheat hardness a quality of wheat [J]. https://doi.org/ 10.3969/j.issn.1007-6395.2009.04.004. Chinese Food Processing
Zheng XW, Qiao L, Liu Y, Wei N, Zhao J, Wu BB, Yang B, Wang j, Zheng J (2021) Genome-Wide Association Study of Grain Number in Common Wheat From Shanxi Under Different Water Regimes. Front Plant Sci 12:806295–806295
Zheng XW, Liu C, Qiao L, Zhao JJ, Han R, Wang XL, Ge C, Zhang WY, Zhang SW, Qiao L, Zheng J, Hao CY (2020) The MYB transcription factor TaPHR3-A1 is involved in phosphate signaling and governs yield-related traits in bread wheat. J Exp Bot 71(19):5808–5822
Zheng XW, Wen XJ, Qiao L, Zhao JJ, Zhang XJ, Li X, Zhang SW, Yang ZJ, Chang ZJ, Chen JL, Zheng J 2019. A novel QTL QTrl.saw-2D.2 associated with the total root length identified by linkage and association analyses in wheat (Triticum aestivum L.).Planta, 250(1),129–143

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Genome wide association study of grain hardness and novel Puroindoline alleles in common wheat

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Plant materials

2.4 Phenotypic data analysis

2.5 Genome wide association analysis

2.6 PIN genotyping

2.7 Sequencing comparison of 145 resequenced samples

3. Results

3.1 Phenotypic data analysis

3.2 Grain hardness loci identified by GWAS

3.3 Diversity of PINs in 145 resequenced materials

3.4 Puroindoline-D1 allelic diversity

3.5 Puroindoline-D1 genotype diversity

3.6 MTAs effect analysis within the wild PIN background

3.7 Novel allele of Puroindoline-D1

4. Discussion

4.1 Relationship between breeding for grain hardness and consumer preferences

4.2 New allele and new loci

Declarations

References

Supplementary Files

Status:

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