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. 2021 Jun 4;11:11877. doi: 10.1038/s41598-021-91446-2

Meta-QTL analysis and identification of candidate genes for quality, abiotic and biotic stress in durum wheat

Jose Miguel Soriano 1,✉, Pasqualina Colasuonno 2, Ilaria Marcotuli 2,✉, Agata Gadaleta 2
PMCID: PMC8178383  PMID: 34088972

Abstract

The genetic improvement of durum wheat and enhancement of plant performance often depend on the identification of stable quantitative trait loci (QTL) and closely linked molecular markers. This is essential for better understanding the genetic basis of important agronomic traits and identifying an effective method for improving selection efficiency in breeding programmes. Meta-QTL analysis is a useful approach for dissecting the genetic basis of complex traits, providing broader allelic coverage and higher mapping resolution for the identification of putative molecular markers to be used in marker-assisted selection. In the present study, extensive QTL meta-analysis was conducted on 45 traits of durum wheat, including quality and biotic and abiotic stress-related traits. A total of 368 QTL distributed on all 14 chromosomes of genomes A and B were projected: 171 corresponded to quality-related traits, 127 to abiotic stress and 71 to biotic stress, of which 318 were grouped in 85 meta-QTL (MQTL), 24 remained as single QTL and 26 were not assigned to any MQTL. The number of MQTL per chromosome ranged from 4 in chromosomes 1A and 6A to 9 in chromosome 7B; chromosomes 3A and 7A showed the highest number of individual QTL (4), and chromosome 7B the highest number of undefined QTL (4). The recently published genome sequence of durum wheat was used to search for candidate genes within the MQTL peaks. This work will facilitate cloning and pyramiding of QTL to develop new cultivars with specific quantitative traits and speed up breeding programs.

Subject terms: Quantitative trait, Plant breeding

Introduction

Durum wheat is an important cereal crop grown in a wide range of agricultural regions. The Mediterranean basin represents more than half of the world’s durum wheat growing area, but it is also grown in the northern plains of the United States and Canada, the desert areas in the southeast United States and northern Mexico, and to a minor extent in other regions.

(International Grain Council, https://www.igc.int/en/default.aspx), all of which are characterized by low rainfall. Wheat is successful due to its wide adaptation to local environments and good processing propertiesin the Mediterranean, soil water availability is a limiting factor incereal crop productivity,and biotic and abiotic stress may strongly affect wheat quality)1.

Water scarcity, often associated with high temperatures during the grain filling period, severely affects durum wheat quality and yield2,3. At this stage of the crop cycle,lack of water and high temperaturesreduce photosynthesis and the source-to-sink transportation of photosynthates in the caryopsis, thereby affecting the formation of the seed proteome. In contrast, excess moisture improves the yield by increasing starch concentrations in the caryopsis and therefore reducing the protein content. A crucial factor in determining the quality of semolina is the seed protein content (and its composition)4–6. In addition, as reported in7, genotype × environment effects can also alter the composition of the reserve proteome.

Therefore, improving breeding programs aim tocombine the highest number of desirable traits in the same genotype. Combining all the most favourable alleles in one cultivar translates into advantages for the miller and the consumer. High-quality kernels produce good quality flour with a balanced protein profile that guarantees high quality doughs and therefore end products with adequate texture and structure that meet consumer requirements. Certain traits not only satisfy consumers but also have nutritional value. An example is the colour of semolina: consumers generally appreciatea yellow pigmentation, which alsoindicates a high level of carotenoid pigments in the kernel. Combining the highest number of genes involved in carotenoid trait expressionis thereforea tool for both improving the nutritional value of wheat and satisfying consumers8.

In 2019 nearly 16 million tons of pasta were produced worldwide. Italy is the greatest consumer, with near 24 kg of pasta consumed per person each year (https://internationalpasta.org/). There is increasing awareness of the importance of wheat-based products in a healthy diet, and producers are identifying and exploiting natural variations in bioactive compounds. However, in some cases natural variations in a trait may be limited in extent or be difficult to exploit, so that other approaches may be required, as in this case. The most important targets of this type of approach are currently minerals, resistant starch, antioxidant compounds, carotenoids, protein content and dietary fibre. As mentioned earlier, quality is directly linked to biotic and abiotic stress. In recent years many quantitative trait loci (QTL) studies have focused on these traits, such as fiber content QTL in Marcotuli et al.9, root and shoot morphological traits in Iannucci et al.10, and many others reviewed in Colasuonno et al.11. These studies identified hundreds of QTL in different mapping populations with different types of markers besides. To identify the genome regions most involved in trait variationand the major, stable QTLs affecting these traits, the QTL meta-analysis approach developed by Goffinet and Gerber12 can help narrow down QTL regions, identify candidate genes and tackle map-based cloning strategies.

This approach allows the integration of independent QTL studies in a consensus mapor reference genome of the species. QTL meta-analysis is a powerful tool for discovering genome regions most frequently implicated in trait variation and forreducing the QTL confidence intervals, thereby enhancing the detection of candidate genes for positional cloning13. To identify meta-QTL (MQTL) for their use in marker-assisted breeding, Loffler et al.14 defined three criteria: (1) the MQTL must have a small supporting interval, (2) include a high number of original QTL, and (3) those QTL must have a large effect on the phenotypic variance explained.

Many of the traits mentioned above and analysed in the present paper are polygenic traits, and associated QTL have been located on all the tetraploid wheat chromosomes.

Meta-QTL (MQTL) analysis is a good instrument for studying many traits at once and finding the consensus, robust QTL region through the use of data reported in multiple studies for the reliability of their location and effect across different genetic backgrounds and environments, as well as to refine QTL positions on a consensus map12.The recent sequencing of the ‘Svevo’ durum wheat genome has enabled the identification of consensus genomic regions, the study of relationships among candidate genes within QTL, and the identification of pleiotropic effects among them15.

There are many examples in which MQTL analysis has also been successfully used to detect consensus QTL regions in wheat: root-related traits13,16, pre-harvest sprouting tolerance17, ear emergence18,19, resistance against Fusarium head blight20–22, plant height23, grain dietary fiber content24, seed size and shape25, yield-contributing traits24,26–28, resistance to leaf rust29; pasta-making quality30; potassium use efficiency31; drought tolerance32; tan spot resistance33. The objective of the present study was to focus on MQTL analysis of durum wheat progenies using a highly saturated consensus map from Macaferri et al.15, taking into account a high number of traits in order to identify major regions and possible pleiotropic gene effects.

Results

QTL distribution and projection

A total of 41QTL studies for quality, abiotic and biotic stress reported inColasuonno et al.11 were analysed, including 36 different traits (Table 1). The studies involved 34 different mapping populations, including 53 different parental accessions (Table 2). QTL projection was carried out using only QTL having the same flanking markers in the consensus map. A total of 368 QTL distributed on all 14 chromosomes (genomes A and B) were projected: 171 corresponded to quality-related traits; 127 to abiotic stress, and 71 to biotic stress.

Table 1.

Traits for biotic stress, abiotic stress and quality reported in the QTL meta-analysis.

Trait Description
Biotic stress
CP Clavicepspurpurea resistance
FHB Fusarium head blight resistance
LR Leaf rust resistance
LS Loose smut resistance
PM Powdery mildew resistance
SBCMV Soil-borne cereal mosaic virus resistance
SR Stem rust resistance
STB Septoriatritici blotch resistance
YR Yellow rust resistance
Abiotic stress
CC Chlorophyll content
CIR Carbon isotope ratio
CL Coleoptile length
DB Dry biomass
FLRI Flag leaf rolling index
NDVI NDVI
OP Osmotic potential
PDL Length of the ear peduncle
RRT Root related traits
SPAD Chlorophyll content
Quality
AX Arabinoxylan
BG β-glucan
Fb Flour yellow colour
GCaC Grain calcium concentration
GCuC Grain copper concentration
GFeC Grain iron concentration
GKC Grain potassium concentration
GMgC Grain magnesium concentration
GMnC Grain mangnese concentration
GPC Grain protein content
GSC Grain sulphur concentration
GSeC Grain selenium concentration
GseY Grain selenium yield
GZnC Grain zinc concentration
PGC Phosphorus grain concentration
SV SDS-sedimentation volume
YPC Yellow pigment content
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Table 2.

Mapping populations used in the study and related references, including the years when experiments were done and the number of environments (Env).

References Cross Type Size Trait N QTL Years Env
50 Langdon × G18-16 RIL 156 CIR, OP, CC, FLRI 6, 9, 7, 9 2004 2
51 Kofa × Svevo RIL 247 PDL, SPAD, NDVI 4, 3, 5 2004, 2005 8
52 Omrabi5 × Belikh2 RIL 114 CL, RRT 5, 1 2009 2
53 Colosseo × Lloyd RIL 176 RRT 28 – 1
53 Meridiano × Caludio RIL 181 RRT 32 – 1
10 Simeto × MolliseColli RIL 136 RRT 18 – 1
54 Strongfield × Blackbird DH 85 FHB 2 – 1
55 LDN × LDN-Dic7A RIL 118 FHB 1 2004, 2005 3
56 Colosseo × Lloyd RIL 176 LR 1 2006, 2007 1
57 Meridiano × Claudio RIL 181 SBCMV 1 2007, 2008 1
58 DS × Td161 BC 134 FHB 1 2006, 2008 2
58 Floradur × Td161 BC 129 FHB 3 2006, 2008 2
58 Helidur × Td161 BC 126 FHB 1 2006, 2008 2
59 Kristal × Sebatel RIL 85 SR 7 2008–2010 2
60 Simeto × Levante RIL 180 SBCMV 7 2008, 2009 1
61 BGRC3487 × 2 * DT735 RIL 160 FHB 2 2008–2010 2
62 Cirillo × Neodur RIL 146 SBCMV 2 2008 1
63 Wollaroi × Bansi RIL 92 YR 2 2007–2009 1
64 Gerizim × Helidur RIL 103 FHB 1 2008, 2009 2
65 Langdon × G18-16 RIL 157 PM 4 – 1
66 Latino × MG5323 RIL 110 LR 3 – 1
67 Ben × PI41025 RIL 200 FHB 3 2010–2012 1
68 Sumai-3 × Saragolla RIL 135 FHB 11 2012, 2013 2
69 Karur × DBC-480 RIL 111 FHB 1 2013–2015 1
70 Strongfield × Blackbird DH 90 LS 2 2011, 2012 1
71 Kofa × W9262-260D3 DH 155 YR 1 2013 1
72 Joppa × 10Ae564 RIL 205 FHB 3 2015, 2016 2
73 Rusty × PI 192051-1 RIL 180 LR 5 2017 2
74 Ben × Tunisian 108 BIL 171 FHB 3 2010, 2011 2
75 Greenshank × AC Avonlea DH 132 CP 4 – 2
76 UC1113 × Kofa BP 93 YPC 4 2003–2006 2
50 Langdon × G18-16 RIL 152 GCaC, GCuC, GFeC, GKC, GMgC, GMnC, GPC, GSC, GZnC, PGC 5, 10, 10, 8, 2, 2, 4, 5, 6, 3 2004 2
77 DT695 × Strongfield DH 185 GPC 6 2002, 2003, 2005 3
78 Latino × Primadur BP 121 YPC 4 2006, 2008 3
79 UC1113 × Kofa RIL 93 GPC, SV 8, 10 2006, 2007 3
80 UC1113 × Kofa BP 93 F, YPC 7, 6 2006, 2007 3
81 Svevo × Ciccio BP 120 YPC 7 2006, 2007 2
82 Duilio × Avonlea RIL 134 BG 2 2014 2
83 Langdon × G18-16 RIL 152 GSeC, GSeY 9, 6 2005, 2007 2
8 Colosseo × Lloyd BP 176 YPC 9 – –
8 Kofa × Svevo BP 249 YPC 9 – –
8 Meridiano × Claudio BP 181 YPC 6 – –
84 Svevo × Y12-3 RIL 208 GPC 12 2014, 2015 3
4 Saragolla × 02-5B-318 RIL 135 GPC 4 2015–2017 1
61 Pelissier × Strongfield DH 162 SV 6 2008–2010 2
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Differences in the number of projected QTL were observed not only among all the seven homoeologous groups, but also among individual chromosomes within a homoeologous group (Fig. 1).The number of projected QTL per genome was 144 (39%) and 244 (61%) for genomes A and B, respectively.The number of QTL per chromosome ranged from 11 in chromosome 1A to 40 in chromosomes 2B and 7B, with an average of 26 QTL per chromosome.

Figure 1.

Figure 1

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QTL distribution along durum wheat genome chromosomes A and B. Colour code: green: abiotic stress QTL; orange: biotic stress QTL; blue: quality QTL. Black bars within chromosomes represent marker density.

The means of the proportion of phenotypic variance explained (PVE) by the original QTL showed a similar pattern among the traits, with 63%, 53% and 48% of the QTL showing a PVE < 0.10, for abiotic stress, biotic stress and quality respectively (Fig. 2).

Figure 2.

Figure 2

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Phenotypic variance explained by original QTL. Colour code: green: abiotic stress QTL; orange: biotic stress QTL; blue: quality QTL.

When the confidence interval (CI) was not reported in the original studies, it was calculated as the distance between the flanking markers. The CIs in the projected QTL were estimated at 95% using the empirical formula proposed by Guo et al. (2006). Comparison between CIs in original and projected QTL (Fig. 3) revealed clear differences for abiotic stress and quality traits. Most of the projected QTL for these traits showed lower CIs, with respective mean values of 35 cM and 18 cM for original and projected abiotic stress CIs and of 28 cM and 14 cMfor original and projected quality traits. In the case of biotic stress traits, instead, the original QTL showed lower CIs (mean 13 cM) than the projected QTL (mean 17 cM). For abiotic stress, 69% of the original QTL had CIs greater than 20 cM, whereas 73% of the projected QTL had CIs lower than 20 cM. For biotic stress traits, 79% and 65% of the original and projected QTL yielded CI values lower than 20 cM, respectively. Lastly, for quality traits, 54% of the original QTL had CIs greater than 20 cM, whereas 85% of the projected QTL yielded CIs lower than 20 cM.

Figure 3.

Figure 3

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Comparison of confidence intervals for original and projected QTL and their correlation for the different traits.

QTL meta-analysis

Of the 368 QTL projected onto the consensus map of Maccaferri et al. (2015), 318 were grouped in 85 meta-QTL (MQTL) (Table 3) and 24 remained as single QTL not overlapping with MQTL. The remaining 26 QTLwere not assigned to any MQTL either, because their CI overlapped with different MQTL or because the predicted QTL peaks were not included within any MQTL. They were not considered as single QTL, as their CI overlapped with MQTL.

Table 3.

Characterization of MQTL.

MQTL Peak N QTL Traits CI left (cM) CI right (cM) Left closest marker Position (bp) Right closest marker Position (bp)
durumMQTL1A.1 3.5 3 FHB, YPC, GPC 2.1 5.0 BS00064204_51 3,574,024 wsnp_Ex_c2868_5293485 7,130,925
durumMQTL1A.2 46.1 4 LR, SR, CC, YPC 41.2 50.9 RAC875_c17283_453 48,029,342 Tdurum_contig48416_335 362,986,005
durumMQTL1A.3 92.7 2 AX, SV 90.1 95.4 RAC875_c16149_298 509,524,234 wsnp_Ex_c3258_6004611 521,653,795
durumMQTL1A.4 142.0 2 LR, YPC 139.3 144.6 wsnp_Ex_c3201_5910659 571,925,048 CAP12_rep_c5332_341 576,427,845
durumMQTL1B.1 16.9 4 RRT 16.5 17.4 wPt-5006 16,362,423 wPt-0655 17,484,592
durumMQTL1B.2 29.3 2 LR, YPC 28.2 30.5 TA002065-1430 52,576,257 Kukri_c5861_360 65,241,426
durumMQTL1B.3 55.3 10 FHB, YPC, RRT, GPC, CL, AX, SV 52.9 57.8 BS00069723_51 449,298,414 RAC875_c92464_53 478,463,076
durumMQTL1B.4 95.3 2 RRT, GPC 92.5 98.1 wsnp_Ex_rep_c71376_70138381 585,368,279 BS00064162_51 594,840,328
durumMQTL1B.5 117.6 5 GseC, FHB, YPC, GPC 116.2 119.0 Kukri_rep_c97349_140 626,782,208 BS00089790_51 633,135,607
durumMQTL1B.6 151.3 2 GseY, YPC 149.8 152.7 D_GBUVHFX01AHO3C_336 661,200,398 IAAV6011 664,610,714
durumMQTL2A.1 8.2 4 FHB, SBCMV, BG, RRT 4.9 11.6 RFL_Contig4030_493 4,105,954 D_contig79877_194 10,965,955
durumMQTL2A.2 39 4 LR, BG, FHB, NDVI 36.1 42.3 Kukri_c27040_309 29,530,680 Ku_c23118_149 34,130,605
durumMQTL2A.3 50.84 3 NDVI, GPC, SPAD 48.8 52.9 Tdurum_contig32692_271 38,336,059 Tdurum_contig46797_585 43,936,608
durumMQTL2A.4 104.6 6 YPC, SR, NDVI 103.1 106.1 gwm95 156,761,990 Kukri_c52614_291 192,760,487
durumMQTL2A.5 126.1 4 FLRI, YPC, GZnC 123.6 128.5 Tdurum_contig42540_843 603,597,766 Tdurum_contig101781_53 608,746,813
durumMQTL2A.6 135.2 4 GPC, GZnC, LR, YPC, GFeC 133.5 136.9 BobWhite_c34273_67 644,481,819 wsnp_JD_c514_781859 671,466,946
durumMQTL2B.1 10.8 5 RRT, SBCMV, BG, SPAD, NVDI 10.4 11.2 BS00081871_51 11,131,675 BS00085748_51 9,712,138
durumMQTL2B.2 59.0 2 GseY, GPC 55.4 62.7 Tdurum_contig74936_133 79,053,860 Tdurum_contig59780_988 99,231,827
durumMQTL2B.3 91.0 2 NDVI, BG 87.5 94.6 RAC875_c5080_915 201,176,819 RFL_Contig3353_125 404,189,154
durumMQTL2B.4 102.9 2 LR, GFeC 100.5 105.3 Ra_c106383_270 446,141,376 RAC875_c992_370 493,056,835
durumMQTL2B.5 115.0 3 GFeC, SV, YPC 112.0 118.0 Excalibur_c84741_99 537,614,290 wsnp_Ex_c114_229879 570,335,910
durumMQTL2B.6 126.7 4 RRT, GPC, NDVI, LR 124.4 128.9 GENE-1352_214 603,649,910 IAAV5675 633,838,200
durumMQTL2B.7 147.5 11 GseY, LR, FLRI, RRT, GFeC 145.8 149.1 BobWhite_c27184_148 697,868,929 Tdurum_contig17826_338 714,626,523
durumMQTL2B.8 183.3 5 NDVI, SPAD, PDL, PM 183.2 183.5 BS00065302_51 778,068,615 BS00083998_51 778,539,953
durumMQTL3A.1 61.8 5 FHB, DB, NDVI, SBCMV 58.4 65.1 Tdurum_contig43850_140 117,216,598 BS00063531_51 282,116,044
durumMQTL3A.2 83.7 4 LR, PDL, GFeC, STB 80.4 87.1 Tdurum_contig59531_914 532,975,275 wsnp_Ku_c5378_9559013 560,465,228
durumMQTL3A.3 123.5 2 GSeC, AX 117.7 129.4 Kukri_c25064_120 649,262,262 RAC875_c22641_993 739,903,219
durumMQTL3A.4 150.2 5 RRT 148.9 151.6 Tdurum_contig42495_389 705,563,307 wsnp_Ex_c9377_15572157 707,002,492
durumMQTL3B.1 6.3 7 SPAD, NDVI, RRT, LR, PDL 5.5 7.2 Kukri_rep_c88385_226 6,221,552 wsnp_Ex_c40595_47620787 6,886,922
durumMQTL3B.2 45.7 2 FHB 42.7 48.8 BobWhite_c62702_587 54,867,872 GENE-1900_115 67,253,287
durumMQTL3B.3 67.0 2 SBCMV, YPC 63.9 70.1 RFL_Contig3857_548 133,186,432 RAC875_c79844_323 167,491,237
durumMQTL3B.4 91.7 5 SR, SV 89.2 94.2 BobWhite_rep_c61884_158 516,937,853 Kukri_c15521_2027 553,129,195
durumMQTL3B.5 159.9 5 RRT, YR, CL, RRT 157.7 162.1 BS00063624_51 772,236,110 Kukri_c3243_1016 776,834,461
durumMQTL3B.6 196.6 3 YPC 195.2 198.1 RAC875_c111148_585 817,039,063 wsnp_BE444579B_Ta_2_2 818,195,973
durumMQTL4A.1 44.0 3 GPC, CIR, LR 40.3 47.7 IACX62 46,560,597 wsnp_Ex_c30989_39836034 88,066,155
durumMQTL4A.2 71.6 2 LR, GCuC 67.8 75.4 wsnp_BF484585A_Td_2_1 572,071,933 TA005643-0627 583,594,297
durumMQTL4A.3 92.7 5 GPC, YPC, GCuC 90.2 95.2 Ku_c6779_1381 604,660,434 Tdurum_contig61343_177 608,153,268
durumMQTL4A.4 112.4 2 SR, GCuC 108.7 116.1 wsnp_Ex_c41313_48161689 622,734,003 BobWhite_c10610_149 635,190,258
durumMQTL4A.5 154.5 3 SR, CC 151.2 157.9 Kukri_c13761_379 702,329,076 wPt-9196 707,410,962
durumMQTL4B.1 11.6 2 RRT, CL 10.5 12.6 Tdurum_contig29961_68 11,083,105 wsnp_Ex_c10347_16946522 12,042,854
durumMQTL4B.2 17.1 4 RRT, CL 15.5 18.7 GENE-4933_489 13,403,076 Tdurum_contig68677_480 18,074,456
durumMQTL4B.3 21.7 3 FHB, RRT 20.7 22.7 Kukri_c34633_69 20,795,117 BS00022431_51 23,204,984
durumMQTL4B.4 30.3 2 GPC, NDVI 28.0 32.7 Tdurum_contig75738_113 26,056,520 IACX47 30,112,862
durumMQTL4B.5 54.6 6 GMnC, GPC, AX, SV, GMnC 51.5 57.8 TA006298-0500 383,231,914 wsnp_Ex_c16083_24512551 453,294,222
durumMQTL4B.6 64.4 3 GCuC, RRT, GseY 62.9 65.9 Tdurum_contig24612_209 504,883,147 Kukri_c322_1394 524,075,645
durumMQTL4B.7 82.8 2 RRT 81.0 84.6 wsnp_Ex_c23638_32875196 607,834,125 RAC875_c14455_1148 621,516,171
durumMQTL4B.8 98.14 6 YPC, GCuC, RRT 95.8 100.5 Tdurum_contig8322_966 646,421,893 wsnp_Ex_c14138_22066009 652,716,927
durumMQTL5A.1 48.6 3 YPC, RRT 47.1 50.0 D_GA8KES402GAVSF_317 111,907,960 Kukri_c25407_645 331,277,629
durumMQTL5A.2 61.6 4 GseC, YPC, AX 58.6 64.6 Tdurum_contig5481_369 395,919,866 BS00022110_51 401,330,652
durumMQTL5A.3 96.1 2 SBCMV, SR 89.1 103.1 wsnp_BE443745A_Ta_2_1 439,542,927 BobWhite_rep_c50888_306 468,004,808
durumMQTL5A.4 131.1 3 GCaC, CIR, RRT 128.7 133.5 CAP7_c4800_276 527,044,675 Tdurum_contig60421_74 529,441,492
durumMQTL5A.5 143.2 3 GseY, OP, GPC 139.9 146.6 BobWhite_c40643_370 537,480,079 Excalibur_c26671_57 553,019,889
durumMQTL5A.6 175.6 3 FHB 174.9 176.3 Excalibur_c4083_874 607,909,980 Ku_c24141_700 610,522,247
durumMQTL5B.1 9.9 2 NDVI, SPAD 5.9 13.8 wsnp_Ku_c10586_17464696 9,787,752 Tdurum_contig92396_380 17,218,406
durumMQTL5B.2 42.0 3 GPC, CP, FHB 40.3 43.8 Excalibur_c57167_475 84,059,870 Excalibur_c15262_2304 327,019,780
durumMQTL5B.3 69.3 2 Fb 64.4 74.2 Tdurum_contig28754_218 439,553,088 Kukri_c10296_1512 475,596,832
durumMQTL5B.4 87.1 4 FLRI, PM, GCaC, SR 81.5 92.7 GENE-3437_68 489,178,563 Tdurum_contig53926_455 514,395,841
durumMQTL5B.5 122.2 3 GCaC, GPC, YPC 118.0 126.4 wsnp_Ex_c13485_21225504 559,774,294 wsnp_Ra_c39562_47242455 576,849,094
durumMQTL5B.6 139.0 3 GMgC, CC, GCaC 135.0 143.1 Excalibur_rep_c88310_1394 588,418,255 RFL_Contig3835_475 604,697,415
durumMQTL5B.7 159.3 5 YPC, CIR, OP 157.8 160.8 Tdurum_contig56335_223 643,149,387 IACX3775 649,698,450
durumMQTL6A.1 2.3 2 NDVI 0.0 4.9 BobWhite_c43135_397 1,819,265 Tdurum_contig41990_1324 7,436,293
durumMQTL6A.2 53.7 6 YPC, CL, LR, RRT, SR 52.4 55.0 Excalibur_c33110_52 323,649,274 wsnp_Ex_c35545_43677480 443,168,317
durumMQTL6A.3 81.3 6 YPC, SV, CIR 79.6 83.1 BS00023893_51 552,510,396 BS00065082_51 553,838,425
durumMQTL6A.4 123.6 6 RRT, NDVI, AX 123.4 123.8 BobWhite_c24258_496 602,232,048 RAC875_c27781_591 602,503,159
durumMQTL6B.1 18.1 5 GKC, RRT, YPC, GKC 15.8 20.3 Excalibur_c72517_251 13,113,351 Kukri_rep_c103034_636 17,165,695
durumMQTL6B.2 61.0 5 GKC, OP, FHB, NDVI 56.8 65.3 Tdurum_contig48689_514 126,247,427 BS00073879_51 146,626,621
durumMQTL6B.3 77.5 5 RRT, GKC, YPC, FHB, RRT 76.0 78.9 IACX4889 442,381,268 BS00089580_51 454,883,952
durumMQTL6B.4 90.8 2 RRT 86.9 94.7 wsnp_JG_c1834_901723 537,655,953 Tdurum_contig44825_307 588,937,432
durumMQTL6B.5 104.7 2 LS, NDVI 103.4 105.9 TA004372-0730 621,526,724 BS00011523_51 633,371,119
durumMQTL6B.6 127.2 3 PGC 126.5 128.0 BS00109717_51 662,889,085 Tdurum_contig45914_283 663,681,523
durumMQTL7A.1 61.6 5 LR, SPAD, PDL, NDVI 60.4 62.7 Tdurum_contig31137_373 61,412,931 RAC875_c10701_435 65,969,594
durumMQTL7A.2 102.3 2 FHB, YPC 98.3 106.3 BobWhite_c48548_106 131,332,420 Tdurum_contig51089_1066 163,557,572
durumMQTL7A.3 114.7 2 RRT 109.1 120.3 RFL_Contig5676_748 200,976,557 BS00069163_51 511,685,823
durumMQTL7A.4 145.4 4 YPC, Fb, SR, RRT 142.8 147.9 BS00044234_51 631,404,872 BS00022202_51 641,161,271
durumMQTL7A.5 175.7 3 YPC, PGC 173.7 177.7 wPt-5558 682,897,955 Tdurum_contig31699_276 691,003,050
durumMQTL7A.6 179.6 2 AX, YPC 179.5 179.8 Tdurum_contig31699_276 691,003,050 Tdurum_contig31699_276 691,003,050
durumMQTL7B.1 2.3 5 RRT, GPC, FHB 0.1 4.6 Ex_c21249_1111 886,966 Tdurum_contig49737_462 5,096,321
durumMQTL7B.2 29.7 4 FHB, CIR, GseY 27.6 31.9 RAC875_c10672_440 87,960,765 wsnp_Ex_c36325_44308589 53,938,632
durumMQTL7B.3 52.9 4 RRT, YPC 51.5 54.3 BS00000170_51 103,606,730 Excalibur_c1694_899 105,323,515
durumMQTL7B.4 75.4 2 GseC, Fb 72.7 78.1 Kukri_c9353_642 255,076,815 RAC875_c22594_125 388,539,117
durumMQTL7B.5 89.2 2 GSC, AX 84.8 93.6 CAP8_c949_312 437,505,136 Kukri_rep_c71356_236 512,177,803
durumMQTL7B.6 116.4 3 LR, YPC, YR 113.8 119.0 RAC875_c18043_411 578,959,591 Excalibur_c58742_144 593,689,787
durumMQTL7B.7 137.0 3 GSeC, SR 134.9 139.2 wsnp_Ex_c10307_16890310 630,702,498 Kukri_c31628_571 641,717,556
durumMQTL7B.8 172.8 3 YPC, RRT 170.7 174.8 wsnp_Ex_rep_c101269_86663549 684,341,861 wsnp_Ex_c2365_4431185 687,868,244
durumMQTL7B.9 206.53 7 YPC, LR, Fb, SR, GPC 206.5 206.6 RAC875_rep_c106035_443 715,557,101 Tdurum_contig28601_486 716,329,509
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The number of MQTL per chromosome ranged from four in chromosomes 1A and 6A to 9 in chromosome 7B. Chromosomes 3A and 7A showed the highest number of individual QTL (4), chromosome 7B the highest number of undefined QTL (4). The number of QTL per MQTL ranged from 2 in 26 MQTL to 11 in the durumMQTL2B.7.As 41 MQTL (47%) derived from the clustering of QTL from threeor more different studies on different parental lines, they will be more stable across environments. The number of traits involved in each MQTL ranged from 1 in twelveMQTL to 7 in the MQTL durum MQTL1B.3. Six MQTL involved 5or more different traits (Table 3). The CI of the MQTL ranged from 0.1 to 14 cM, with an average of 4.9 cM. This isa significant reduction from the original QTL, whichranged from 0.4 to 108.1 cM, with an average of 25.5 cM.

The three criteria proposed by Löffler et al.14 were used toidentify the most promising MQTL for marker-assisted selection and candidate gene analysis: (1) small MQTL supportintervals, (2) large number of initial QTL and (3) high PVE values of the original QTL. A total of 17 MQTL were selected using the following criteria: a number of QTL per MQTL equal to or greater than 5, with a CI equal toor lower than the average (4.9), and a mean PVE value for the original QTL in the MQTL equal to or greater than 0.10 (Table 4).Only MQTL with a physical distance of less than 5 Mb were subsequently selected for candidate gene (CG) identification.

Table 4.

Selected MQTL.

MQTL QTL CI (cM) Distance between flanking markers (Mb) PVE original QTL
durumMQTL2B.1 5 0.8 1.4 0.41
durumMQTL2B.8 5 0.4 0.5 0.10
durumMQTL3A.4 5 2.6 1.4 0.11
durumMQTL3B.1 7 1.7 0.7 0.11
durumMQTL3B.5 5 4.4 4.6 0.13
durumMQTL6A.3 6 3.5 1.3 0.10
durumMQTL6A.4 6 0.4 0.3 0.10
durumMQTL6B.1 5 4.5 4.1 0.10
durumMQTL7A.1 5 2.3 4.6 0.22
durumMQTL7B.9 7 0.1 0.8 0.19
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Candidate genes and in silico gene expression analysis

Candidate genes (CG) for investigating and estimating relative gene expression levels were identified within the MQTL regions reported in Table 4. The flanking markers of the CI were launched against the genome browser for both ‘Svevo’ (durum wheat)34 and ‘Chinese spring’ (bread wheat) (https://iwgs.org/) reference genomes. Excluding transposable elements, atotal of 436 and 326 gene modelswere detected for ‘Svevo’ and ‘Chinese Spring’ respectively (Additional file 1). Differentially expressed genes (DEG) upregulated under abiotic and biotic stress conditions (Table 5) and expressed in the grain tissues for quality CGs were subsequently analysed using the RNAseq data available at http://www.wheat-expression.com/35.

Table 5.

Number of genes detected for each MQTL.

MQTL Number of genes
DURUM wheat Bread wheat
durumMQTL2B.1 29 42
durumMQTL2B.8 8 11
durumMQTL3A.4 24 32
durumMQTL3B.1 20 22
durumMQTL3B.5 111 45
durumMQTL6A.3 20 8
durumMQTL6A.4 16 4
durumMQTL6B.1 107 69
durumMQTL7A.1 104 69
durumMQTL7B.9 17 24
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Thebread wheat gene models were analysed using the RNAseq experiments available at www.wheat-expression.com35,36. In particular, the study focused on identifying expression genes involved in biotic and abiotic stress, in different tissues and developmental phases (Fig. 4).

Figure 4.

Figure 4

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Expressed genes involved in biotic and abiotic stress, in different tissues and developmental phases for each MQTL.

A total of 36 CGs upregulated under biotic and abiotic stress were found in seven MQTL. MQTL3B.1 and MQTL7B.9 in ‘Svevo’ and ‘Chinese spring’ did not yieldhomologous gene models, and no upregulated gene models were found for MQTL6A.4 (Fig. 4).

The genes most expressed during biotic stress conditions with respect to control conditionswithout stress were: (1) for expression analyses using pathogens associated molecular patterns (PAMP), F-box plant-like protein (7A.1), amino acid permease (3A.4), HXXXD-type acyl-transferase family protein (7A.1) and NAC domain-containing protein (3A.4); (2) for powdery mildew infection, CDT1-like protein and embryogenesis transmembrane protein (2A.1);( 3) for infection with Fusarium pseudograminearum, homeobox-leucine zipper family protein G (6A.3), protease inhibitor/seed storage/lipid transfer family protein (6B.1) and 3-ketoacyl-CoA synthase (7A.1); and (4) for infection with Zymoseptoriatritici, cytochrome P450-like protein (2B.1) and 3-ketoacyl-CoA synthase (7A.1) during stress.

Expression analysis under abiotic stress included: phosphorous starvation, drought stress, heat stress, combined drought and heat, addition of PEG 6000 to simulate drought and cold stress. No upregulated genes were found for cold stress. The most expressed genes identified associated to phosphorus starvation conditions were NBS-LRR disease resistance proteins (2B.1 and 7A.1), receptor kinase 1 (2B.1), embryogenesis transmembrane protein-like (2B.1) and 3-ketoacyl-CoA synthase (6B.1).

Phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (3B.5) was differentially expressed under combined drought and heat and under those single conditions, whereas a thioredoxin (2B.8) was also expressed under combined drought and heat stress. Lastly, three genes were the most upregulated when simulating drought stress using PEG:CDT1-like protein a, chloroplastic (2B.1), embryogenesis transmembrane protein-like (2B.1) and mitochondrial transcription termination factor (6B.1).

According to the plant tissue where the genes were upregulated, the spikes showed the higher number of transcripts with expression levels higher than 1tpm (7), whereas the lower number was found in grain (3). From below ground to the top of the plant, the most expressed gene models were: (1) roots: protease inhibitor/seed storage/lipid transfer family protein (6B.1), leucine-rich repeat receptor kinase (3B.5), soluble inorganic pyrophosphatase (2B.8), CDT1-like protein (2B.1), HXXXD-type acyl-transferase family protein (7A.1), electron transport complex subunit B G (7A.1) and phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (3B.5); (2) leaves: receptor kinase 1, Embryogenesis transmembrane protein-like, NBS-LRR-like resistance protein (2B.1), phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (3B.5) and NBS-LRR disease resistance protein (6B.1); 3) cytochrome P450-like protein (2B.1), 3-ketoacyl-CoA synthase (7A.1), phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (3B.5), soluble inorganic pyrophosphatase (2B.8), 3-ketoacyl-CoA synthase (7A.1), CDT1-like protein (2B.1), homeobox-leucine zipper family protein G (6A.3) and crossover junction endonuclease MUS81 (3A.4); and (4) grain: S-formylglutathione hydrolase (3B.5), soluble inorganic pyrophosphatase (2B.8) and glycosyl transferase (6B.1).

Three developmental phases were considered in expression analysis: seedling, vegetative, and reproductive. The most expressed genes in seedlings were protease inhibitor/seed storage/lipid transfer family protein (6B.1) and CDT1-like protein (2B.1), whereas during vegetative growth they were protease inhibitor/seed storage/lipid transfer family protein (6B.1), leucine-rich repeat receptor-like kinase (3B.5), CDT1-like protein (2B.1) and soluble inorganic pyrophosphatase (2B.8). Lastly, during the reproductive stage the most expressed gene models were receptor kinase 1, embryogenesis transmembrane protein (2B.1), phosphatidylinositol N-acetylglucosaminyl transferase subunit Y (3B.5), soluble inorganic pyrophosphatase (2B.8), cytochrome P450 protein (2B.1) and S-formylglutathione hydrolase (3B.5).

Gene expression in grains was analysed not only under biotic or abiotic stress conditions but also to detect candidate genes of importance in grain quality.

High expression levels (tpm > 2) were observed in grain for phospholipid-transporting ATPase, nascent polypeptide-associated complex subunit alpha-like protein, acetyltransferase component of pyruvate dehydrogenase complex, polyadenylate-binding protein-interacting protein 4, acyl-CoA N-acyltransferase with RING 2FFYVE 2FPHD-type zinc finger protein, mitochondrial inner membrane protease subunit 1, S-formylglutathione hydrolase and peroxidase (on 3B.5, 6A.3 and 6B.1).

When grain tissues ofthe endosperm, embryo, aleurone layer, seed coat and transfer cells were dissected, all the genes described above for the whole grain were strongly expressed in at least one of the different tissues. Other gene models that expressed over 2 tpm were: glycerol-3-phosphate dehydrogenase [NAD( +)] in the aleurone layer and seed coat, a 28S ribosomal S34 protein in the embryo, S-acyltransferase in the aleurone layer, a pimeloyl-[acyl-carrier protein] methyl ester esterase in the aleurone layer, glycosyltransferase in the endosperm, hydroxyproline-rich glycoprotein-like G in the aleurone layer and seed coat, histidine-containing phosphotransfer protein in the embryo, a general regulatory factor 1G in the embryo, aleurone layer and seed coat, S-adenosyl-L-methionine-dependent methyltransferase superfamily protein in the seed coat, an F-box in the aleurone layer, and phosphatidylinositol N-acetylglucosaminyl transferase subunit Y in the endosperm, embryo and seed coat.

Discussion

One of the main challenges of breeding programs is to increase crop yield. Crop productivity is highly affected by environmental constraints and diseases, so thatnew cultivars must incorporate new loci to cope with the different stresses affecting plant growth and yield. Breeders have another important challenge in the development of new cultivars: to improve grain quality for end products that meet industrial and consumer requirements.

In recent years numerous studies have been carried out to identify new loci controlling traits for abiotic and biotic stress tolerance and grain quality in bread and durum wheat. QTL meta-analysis has been carried out on most of the QTL identified in durum wheat for disease resistance, environmental tolerance and grain quality. This approach has been used extensively in plants since its development in 200437. It is especially useful in detecting major loci for quantitative traits and, by increasing map resolution, in identifying candidate genes controlling polygenic traits12.

This is the first study that provides an overview and comparison of genetic loci controlling multiple traits in durum wheat, including quality traits and biotic and abiotic traits. It adds new MQTL for durum grain traits: some of the MQTL were mapped with high precision and are relatively more robust and stable with major effects.

We report a total of 368 QTL distributed on all 14 chromosomes, of which 171 are related to quality traits, 127 to abiotic stress, and 71 to biotic stress, over a total of 34 mapping population. A total of 85 meta-QTL were identified, of which 15 meta-QTL were selected as the most promising for candidate gene selection.

The meta-analysis conducted in this study accurately compared genomic positions of individual QTL identified in different studies and refined the confidence intervals of the main genomic regions associated with different traits. The durum wheat consensus map15 preserved the marker order of individual maps, and confidence intervals were calculated to highlight differences between the original map position and its projection. For abiotic stress and quality traits, there was a reduction in the CI, whereas biotic stress traits showed an increase in the confidence interval. This may be due to the quantitative nature of the different traits; individual QTL for abiotic stress and quality showed lower PVE values, whereas those related to disease resistance yielded higher values (means of 0.11, 0.12 and 0.20 respectively). Biotic stress traits were controlled by a lower number of genes than traits related to abiotic stress or quality. Results reveal that the number of QTL per study was 25 for abiotic stress traits, 12 for quality related traits and 3 for biotic stress traits. Comparison of the reduction of CIs and number of genome regions involved in trait variation between this study and other studies carried out in durum wheat (quality)30, bread wheat (abiotic and biotic traits)13,29 and maize (yield)38 is reported in Additional file 3. Reduction of the CI and number of QTL after meta-analysis was 80% and 77% respectively, which is within the range among the different studies (from 60 to 88% for CI and from 65 to 90% for number of QTL).

The MQTL identified provide more closely linked markers due to the availability of a durum wheat consensus map15. Some of these are also linked to known major genes for other agronomically important traits, there by adding value to these MQTLas targets for marker assisted selection using the SNP markers flanking the MQTL, however an initial validation of the alleles reporting favourable effects should be addressed. According to the genome position of important agronomic genes reported in Liu et al.39, eleven MQTL were found to include 12 genes enhancing grain yield, quality, or plant development. DurumMQTL5A.5 and durumMQTL7B.9 included the vernalization genes Vrn-A1 and Vrn-B3 respectively. The incorporation of favourable alleles for this gene during breeding helps develop spring habit without cold requirements for flowering40, thus can be used as a strategy for introgressing important target traits from non-adapted pre-breeding materials combining the most favourable vernalization alleles. DurumMQTL4B.4 carries the dwarfing gene Rht-B1. Dwarfing genes were the basis of the green revolution, allowing an up to 35% increase in the yield of durum wheat41. Five durumMQTL, 2B.7, 4A.1, 7A.1, 7A.2 and 7A3, included genes involved in grain weight and size, the genes TaGS2-B1, TaCwi-A1, TaTEF-7A, TaGASR7-A1 and TaTGW-7A. Other genes affecting grain yield and quality were the TaSdr-A1 and TaALP-4A involved in preharvest sprouting tolerance and located in durumMQTL2A.4 and durumMQTL4A.5, respectively. Preharvest sprouting is an important limiting factor for grain yield in the major wheat production areas, especially when frequent rainfall occurs during harvest. Lastly, two genes involved in grain quality were found in durumMQTL1A.1 (Glu-A3) and durumMQTL7B.9 (Psy-B1). According to Subirà et al.42, the introgression of favorable alleles for HMW and LMW glutenin subunits led tothe improvement of pasta-making quality in modern durum wheat cultivars. The phytoene synthase gene Psy-B1 is involved in the biosynthesis of carotenoid pigments.

An interesting case of study was in the durumMQTL2B.1 where are co-located QTL for RRT (abiotic stress) and SBCMV (biotic stress). Looking at candidate gene reported in Fig. 4, NBS-LRR-like resistance genes were highly expressed in both abiotic and biotic stresses experiments, which may indicate a link between the two traits and a pleiotropic effect on root development and pathogen growth. This theory has been supported by Kochetov et al.43, which reported a differential expression of NBS-LRR-encoding genes detected in the root transcriptomes of two Solanumphureja.

The most promising MQTL arethe ones located on chromosome 1B (two MQTL), 2B (three MQTL), 3A (1 MQTL), 3B (two MQTL), 5B (1 MQTL), 6A (two MQTL), 6B (two MQTL), 7A (1 MQTL) and 7B (1 MQTL). These showed co-localized QTL for several grain traits, as found in earlier studies on bread wheat44–46, indicating that QTL are not randomly spread throughout the genome but cluster in specific genomic regions. The study of different MQTL has revealed how some traits are always associated, such as FHB, GPC and YPC (durumMQTL1B.3, durumMQTL1B.5 and durumMQTL6B.3) or RRT, SPAD and NVDI (durumMQTL2B.1, durumMQTL3B.1, durumMQTL6A.4, durumMQTL7A.1). This represents an important key for identifying and characterizing genes associated with the MQTL, with a pleiotropic effect on yield-related traits and quality traits.

To correlate between MQTL and previous QTL identified by GWAS, MQTL positions were compared with marker trait associations (MTA) reviewed by Colasuonno et al.11 for abiotic and biotic stress and quality traits. Of the 352 MTA, 58 were located within 33 durum MQTL. Of these, 37 MTA in 26 MQTL reported associations with one of the traits included in the MQTL (Additional file 2). The highest number of MTA per trait category corresponded to LR for biotic stress, NDVI for abiotic stress and YPC for grain quality. These MTA were distributed in 11 chromosomes. These results suggest that new bioinformatic tools are required to integrate association studies with QTL meta-analysis for better understanding the molecular bases of trait variation in crop species.

Conclusions

QTL meta-analysis can help validate QTL previously detected in different populations and unravel the most stable QTL for the most important wheat traits. This studyused QTL meta-analysis toacquirea comprehensive picture of the mainregions of the durum wheat genome involved in the control of multiple traits so as to identify QTL-enriched regions and candidate genes with possible pleiotropic effects.

The numerous markers within stable QTL and rich candidate gene regionscan helpelucidate the mechanism regulatingmany traits and speed up breeding programs for the production of top-quality cultivars.

Material and methods

Collection of QTL database and projection on a consensus map

A thorough bibliographic review was carried out on the literature reported in Colasuonno et al.11. QTL information on biparental durum wheat populations was retrieved from 41 independent studies, including a total of 36 different traits (Table 1) relating to quality (14), biotic stress (22) and abiotic stress (5).

Information on chromosome location, the most closely flanking markers, QTL position, logarithm of odds (LOD) values, confidence intervals (CIs) and phenotypic variance explained (PVE or r2) values are summarized in the review by Colasuonno et al.11.

To representall the QTL in one linkage map, the durum wheat consensus map developed by Maccaferri et al.15 was used for QTL projection, following the homothetic approach described by Chardon et al.37 as described in Colasuonno et al.11. The CIs for the projected QTL were estimated for a confidence interval of 95% using the empirical formula proposed by Guo et al.47.

QTL meta-analysis

QTL meta-analysis was conducted using BioMercator v.4.248, available at https://urgi.versailles.inra.fr/Tools/BioMercator-V4, adopting the approach developed by Veyrieras et al.49. Meta-analysis determines the best QTL model based on model choice criteria from the Akaike information criterion (AIC), a corrected AIC, a Bayesian information criterion (BIC) and the average weight of evidence (AWE). The best QTL model was selected when the lowest values of the model selection criteria were achieved in at least threemodels. Consensus QTL from the optimum model were regarded as MQTL.

Identification of candidate genes underlying the MQTL region and expression analysis

Gene models within MQTL were identified using the high-confidence genes reported for the durum wheat reference sequence34, available at https://wheat.pw.usda.gov/GG3/jbrowse_Durum_Svevo based on the positions of markers flanking the CI of the MQTL.

In silico expression analysis and the identification of upregulated gene models was carried out using the RNAseq data available at http://www.wheat-expression.com/35 using gene models, from ‘Chinese spring’, located within the markers flanking the MQTL (https://iwgs.org/). Homologous genes from ‘Svevo’ were subsequently identified in durum wheat.

Supplementary Information

Supplementary Information 1. (40KB, xlsx)
Supplementary Information 2. (11.7KB, xlsx)
Supplementary Information 3. (13.3KB, docx)

Author contributions

All authors contributed equally to the final manuscript.

Funding

This research was funded by the PRIMA 2019 “CEREALMED” project (Italy), PON-AIM Project AIM1812334 (Ministerodell’Istruzione, dell’Università e dellaRicerca, Italy) and project PID2019-109089RB-C31 (Ministerio de Ciencia e Innovación, Spain). JMS is supported by the CERCA programme/Generalitat de Catalunya (http://cerca.cat/).

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jose Miguel Soriano, Email: josemiguel.soriano@irta.cat.

Ilaria Marcotuli, Email: ilaria.marcotuli@uniba.it.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-021-91446-2.

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