See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/322454442 Identification of Quantitative Trait Loci for Grain Yield and Other Traits in Tropical Maize Under High and Low Soil-Nitrogen Environments Article  in  Crop Science · January 2018 DOI: 10.2135/cropsci2017.02.0117 CITATIONS READS 2 233 8 authors, including: Priscilla F. Ribeiro B. 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Published online January 15, 2018 RESEARCH Identification of Quantitative Trait Loci for Grain Yield and Other Traits in Tropical Maize Under High and Low Soil-Nitrogen Environments P.F. Ribeiro, B.Badu-Apraku,* V.E. Gracen, E.Y. Danquah, A.L. Garcia-Oliveira, M.D. Asante, C. Afriyie-Debrah, and M. Gedil P.F. Ribeiro, M.D. Asante, C. Afriyie-Debrah, CSIR– Crops Research ABSTRACT Institute (CRI), P. O. Box 3785, Fumesua, Kumasi, Ghana; B. Badu- Low soil Nitrogen (low-N) is one of the most Apraku, A.L. Garcia-Oliveira, M. Gedil, International Institute of important abiotic stressors responsible for Tropical Agriculture, P.M.B. 5320, Ibadan, Nigeria; P.F. Riberio, V.E. significant yield losses in maize (Zea mays L.). Gracen, E.Y. Danquah, West Africa Centre for Crop Improvement, The development and commercialization of Univ. of Ghana, Legon, Ghana. Received 23 Feb. 2017. Accepted 2 Nov. low-N–tolerant genotypes can contribute to 2017. *Corresponding author (b.badu-apraku@cgiar.org). Assigned to improved food security in developing countries. Associate Editor Endang Septiningsih. However, selection for low-N tolerance is diffi- Abbreviations: ASI, anthesis–silking interval; CIM, composite inter- cult because it is a complex trait with strong val mapping; CSIR, Council for Scientific and Industrial Research; interaction between genotypes and environ- DTA, days to anthesis; DTS, days to silk; EHT, ear height; EPP, ears ments. Marker-assisted breeding holds great per plant; GEI, genotype by environment interaction; GY, grain yield; promise for improving such complex traits more HN, High Nitrogen; h2, Broad sense heritability; IITA, International efficiently and in less time, but requires markers Institute of Tropical Agriculture; LOD, logarithm of the odds; MAS, associated with the trait of interest. In this study, Marker assisted selection; P, plant aspect; PHT, Plant height; PVE, 150 BC2F1 families of CML 444 × CML 494 were phenotypic variance explained; QTL, Quantitative trait locus; QTLs, evaluated at two locations for two consecutive Quantitative trait loci; SG, stay-green; SGC, stay-green characteristic. seasons to identify SNP markers associated with quantitative trait loci (QTL) for yield and other agronomic traits under low- and high-N The increase in crop yield during the past century is environments. A total of 13 QTL were identified attributed to the selection of genotypes with higher yield with 158 SNP markers, of which nine and four potential and an increased amount of nutrients, particularly QTL were detected under low- and high-N envi- nitrogen (N) supplied during the growth cycle (Tuberosa et ronments, respectively. Five QTL one each for al., 2002). Available soil N is usually the critical factor limiting grain yield (qgy-1), days to silking (qdts-1) and plant growth. Therefore, N fertilizer is usually applied to maize anthesis- silking interval (qasi-6), and two for fields, resulting in marked increases in yield. Low N availability stay green characteristic (qsg-1 and qsg-4) were is a major cause of yield loss in maize in developing countries close to their adjacent markers, with an interval (Pingali and Pandey, 2001). This is because production is usually of 0.7 to 5.2 cM between them and explained at N-deficient conditions due to limited availability of fertil- phenotypic variance of 9 to 21%. These QTL izers or low purchasing power of farmers (Bänziger et al., 1997). would be invaluable for rapid introgression of Therefore, development of maize cultivars with tolerance to low genomic regions into maize populations using marker-assisted selection (MAS) approaches. N is the most effective and sustainable approach to mitigate the However, further validation of these QTL is problem of low N. needed before use in MAS. Progress in selecting for low N tolerance is limited by large genotype × season and genotype × location interactions. The Published in Crop Sci. 58:321–331 (2018). doi: 10.2135/cropsci2017.02.0117 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY license (https:// creativecommons.org/licenses/by/4.0/). crop science, vol. 58, january–february 2018 www.crops.org 321 efficiency of selection for yield in low N environments Field Experiments may be improved by selecting secondary traits with The 150 BC2F1 families, along with the parental lines and high correlations to grain yield under low N (Bänziger the F1 hybrids and the check (ENT 70), were evaluated and Lafitte, 1997; Badu-Apraku 2011, 2012). Selection under low- and high-N environments during the major indices based on these traits have been developed and have (April–July) and minor (September–December) rainy improved the selection efficiency under stress conditions seasons of 2014, at Fumesua (6°41′ N lat., 1°28′ W long.) significantly (Bänziger and Lafitte, 1997). The complexity and Ejura (7°23′ N lat., 1°21′ W long.) in Ghana. A 11 × 14 of measuring the secondary traits quickly and accurately, lattice design with two replications was used for the evalu- however, has limited their use in breeding programs ations at the two locations during the two planting seasons. (Monneveux and Ribaut, 2006). Single-row plots, each five meters long, spaced 0.75 m apart The introduction of molecular marker technology and with 0.5-m spacing between plants in each row were used the construction of saturated linkage maps have facilitated in all the environments. Three seeds were planted in each the detection of the genetic loci associated with complex hole and thinned to two plants per hill at two weeks after traits (Kang et al., 1998; Li et al., 1995; Song et al., 2001). emergence to give a population density of 53,333 plants Genetic linkage maps and quantitative trait loci (QTL) per hectare. The low-N plots received 30 kg N ha-1 while mapping technology have improved the efficiency of esti- the high-N plots received 90 kg N ha-1 applied in two mating the number of loci controlling genetic variation in splits at two and five weeks after planting. The low-N field a segregating population and the characterization of the had been previously depleted of N by growing maize and map positions in the genome (Xiao et al., 1996). In maize, removing all plant material. Soil analysis was performed genetic analysis of complex traits under abiotic stresses has at the Soil Research Institute, Kumasi, Ghana. The total focused mainly on drought tolerance (Agrama and Moussa, N in the soils was determined by Kjeldahl digestion and 1996; Ribaut et al., 1996: 1997; Tuberosa et al., 2002). Less colorimetric determination on Technicon AAII Autoanal- attention has been paid to understanding of the genetic yser (Bremner and Mulvaney, 1982). Information on the responses of segregating populations under soil nutrient soil properties of the experimental fields used in this study deficiencies, such as low phosphorus (Reiter et al., 1991) or is presented in Supplementary Table 1. Nutrient status, in low N (Agrama et al., 1999; Hirel et al., 2001). accordance with interpretation of analyzed soils by Landon The use of marker-assisted selection (MAS) could be a (1991), was generally low at both locations with N levels very effective strategy for breeding for tolerance to low N less than 0.2%. Fertilizers were applied to bring the total (Zhou, 2010). However, the effectiveness of MAS depends available N to 90 kg ha-1 for the high-N field and 30 kg ha-1 on the precise locations of the QTL and the identification for the low-N field when the soil N was less than the target of tightly-linked molecular markers, which are cost effec- level. Both low-N and high-N fields received 60 kg P ha-1 tive and easier to use. as single superphosphate (P205) and 60 kg K ha -1 as muriate The QTL identified in breeding populations could be of potash (K2O). The trials were kept weed-free with the used directly for crop improvement through MAS approaches application of both preemergence and postemergence herbi- (Würschum, 2012; Wang et al., 2012). The objective of this cides, primextra and paraquat each at five liters per hectare. study was to identify QTL associated with yield and yield Subsequently, hand weeding was used to supplement the related traits under low- and high-N environments. chemical weed control. MATERIALS AND METHODS Field Data Collection Data were recorded on both low- and high-N plots Mapping Population for days to 50% anthesis (DA) and silking (DS) as the The two parental lines used in the present study differed for number of days from planting to when 50% of the plants their responses to low N stress; CML 494 (highly suscep- in a plot had shed pollen and extruded silks, respectively. tible to low-N) and CML 444 (tolerant to low-N). These The anthesis–silking interval (ASI) was calculated as the parental lines were selected based on their performance in difference between DS and DA. Plant height (PHT) was multilocation trials conducted under low-N in Ghana. The measured as the distance from the base of the plant to F1 crosses were made between the inbreds at the CSIR- the height of the first tassel branch and ear height (EHT) Crops Research Institute, (Fumesua, Ghana) during the as the distance to the node bearing the upper ear. Plant major cropping season of 2013. The F1s were backcrossed aspect (PA) was recorded on a scale of 1 to 5 based on to CML 494 during the minor cropping season of 2013 in the plant type, where 1 = excellent and 5 = poor. Husk Kwadaso, Ghana to obtain the BC1F1s. This was followed cover was scored on a scale of 1 to 5, where 1 = husks by another cycle of backcrossing of BC1F1s to CML 494 in tightly arranged and extended beyond the ear tip, and 5 Fumesua to obtain 150 BC2F1 families. = ear tips exposed. EASP was based on a scale of 1 to 5, where 1 = clean, uniform, large and well-filled ears, and 322 www.crops.org crop science, vol. 58, january–february 2018 5 = ears with undesirable features. In addition, stay-green at independence LOD (logarithm of the odds) values greater characteristic (SG) was recorded at 70 d after planting on than 6.0 and threshold values that ranged from 2.0 to 20 with a scale of 1 to 10, where 1 = almost all leaves still green an interval of 1.0. A regression mapping algorithm was used and 10 = virtually all leaves dead (Badu-Apraku et al., to order the markers and Haldane’s mapping function was 2015). Number of ears per plant (EPP) was computed by used to transform estimates of recombination frequency to dividing the total number of ears harvested per plot by map distances in centimorgans (cM). The linkage groups the number of plants in a plot at harvest. Harvested ears from JointMap were rearranged into chromosomes according from each plot were shelled to determine the grain weight to their order on the reference map. and the percentage grain moisture for the low N experi- Quantitative trait loci mapping was done in R/qtl ments. Grain yield (GY) in kg ha-1 was adjusted to 15% using a single–QTL model. Furthermore, composite moisture and computed from the shelled grain weight. interval mapping (CIM) was used to define QTL peak In the high-N plots, grain yield was computed based on position and to estimate effects of the mapped loci and their 80% (800 g grain kg-1 ear weight) shelling percentage and constributions to the phenotypic variances. The thresholds adjusted to 15% moisture content. of the QTL (LOD scores) were obtained at p = 0.05 by 1000 random permutations of the trait values. In addition, Data Analysis epistatic gene interactions for grain yield and other agro- Phenotypic data were analyzed using SAS 9.0 (SAS Insti- nomic traits were determined under both low- and high-N tute, 2011) with the GLM procedure. Pearson correlation environments using QTL Network v2.1 (Yang et al., 2008). coefficients were calculated between the traits, using the adjusted means of the BC2F1 families. Repeatability of the RESULTS traits (Falconer and Mackay, 1996) under low- and high- N conditions were computed on genotypic-mean basis Evaluation of BC2F1 Population using the following formula: In all environments, the target traits measured in the 2 BC2F1 population followed normal distribution (Fig. 1 and s R = g 2). The combined analysis of variance showed significant 2 s 2 ge s2 mean squares of genotypes, environments and genotype s g + + e e re by environment interaction (GEI) for GY, SG and EPP across low N environments. The few exceptions included where s2g is the genotypic variance, s 2 ge is the genotype the mean squares of genotypes for ASI and GEI for DTA × environment, and s2e is the residual variance, e is the (days to anthesis), DTS (days to silk), ASI, EHT and PHT number of environments, and re is the number of repli- across low-N conditions which did not reach signifi- cates per environment. cant levels (Table 1). Similarly, significant mean squares were observed for genotypes, environments and GEI of Single Nucleotide Polymorphism Genotyping all measured traits across high-N environments except and Construction of Genetic Linkage Map and the genotype mean squares for EHT, and the GEI mean Quantitative Trait Loci Analysis squares for DTA, ASI, EHT, PHT, EPP and SG (Table 2). A total of 153 freeze-dried leaf (two-week–old) samples The repeatability estimates of the traits ranged from consisting of 150 BC2F1, two parental lines and the F1 8% for ears per plant to 48% for days to silking under low hybrid were sent to LGC Genomics for SNP genotyping. N, and 32% for ear height to 72% for plant height under Details on the principle and procedure of the DNA assays high-N environments. High repeatability estimates (i.e., are available at http://www.lgcgroup.com/our-science/ ≥ 0.60) were recorded for most of the traits under high-N genomics-solutions/#.WKgsBRrLfIU. The parental lines environments. A total of 23 significant correlations were were genotyped with a set of 1250 SNP markers, for which detected under each environment (Supplementary Table 2). KASP assays (Semagn et al., 2013), were designed at LGC The grain yield (GY) showed consistently significant and Genomics Facility in London, UK. Theoretically, the 150 highly positive correlations with ASI, PHT, EHT and EPP, BC2F1 families used in the study had an eighth of the CML whereas negative correlation was found with DTS under 444 genome in the genetic background of CML 494 with both low- and high-N environments. Similarly, the trait the expected genotypic frequency of 0.75 and 0.25 per EHT had significant negative correlations under low- and marker locus for the allele of CML 494 in homozygous and high-N environments. The associations of PHT with DTA, heterozygous conditions, respectively. Segregation of marker DTS and ASI were negative under both low- and high-N loci was evaluated with a Chi-squared test. Markers that had environments. Similarly, EHT had significant and negative insufficient linkage data were excluded and the final linkage associations with DTA and DTS under both environments. map was constructed with 158 SNP markers using JoinMap4 In contrast, significant and negative correlation was observed (Van Ooijen, 2006). Markers were assigned to linkage groups between EHT and ASI under high-N environments. crop science, vol. 58, january–february 2018 www.crops.org 323 Fig. 1. Frequency distribution of eight traits in BC2F1 population under high-N environment. Fig. 2. Frequency distribution of eight traits in BC2F1 population under low-N environment. Genetic Linkage Map Construction under high-N environment on chromosome 10 flanked Linkage analysis was performed on 150 BC2F1 families by PZA01292_1 and PZB0049_1 markers at interval of genotyped with 158 SNP markers. A linkage map was 29.0 cM with LOD of 3.15. In contrast, two QTL were then constructed that corresponded to the ten chromo- mapped for GY on Chromosomes 1 (qgy-1) and 10 (qgy- somes with length of 622.7cM and an average marker 10–2) under low-N environment. Of the QTL, the major interval of 3.9 cM (Supplementary Table 3). QTL, qgy-1 accounted for 21% of PVE and was located between markers PZA02487_1 and PZB02058_1 with Quantitative Trait Loci Identification marker interval of 0.7cM. The QTL, qgy-10–2 with PVE A total of 13 QTL for all the traits were detected under of 8% had a LOD score of 4.12. Interestingly, the QTL, both low- and high-N environments with the phenotypic qgy-10–2 and qgy-10–1 were flanked by the same markers, variance explained (PVE) ranging from 5 to 31% (Table but their peak positions were different on the Chromo- 3; Fig. 3). Of these QTL, four and nine were identified some 10. QTL qgy-10–1 had a marker interval of 29.0 cM under high- and low-N environments, respectively. For while qgy-10–2 had a marker interval of 0.7 cM. GY, one QTL (qgy-10–1) with PVE of 10% was detected 324 www.crops.org crop science, vol. 58, january–february 2018 Table 1. Mean squares of BC2F1 population evaluated across low-N environments§. Source DF GY DTA DTS ASI EHT PHT SG EPP Envt 2 176548648.9** 55.54** 1805.59** 1314.29** 6653.92** 2933.90** 430.73** 30.93** Blk (Rep×Envt) 78 552478.4** 30.94** 44.28** 4.50** 297.79** 1309.32** 0.67** 0.04** Rep (Envt) 3 3745158.4** 675.63** 762.35** 13.30** 178.09ns 1968.28** 0.19** 0.25** Entry 153 272850.2** 17.07** 22.62** 2.65NS† 117.4936** 415.11** 0.26** 0.04** Envt (Entry) 306 255538.1** 8.45NS 12.27NS 2.52NS 76.89NS 277.826NS 0.22* 0.04* Error 381 192721.80 9.94 12.50 2.23 72.84 255.93 0.18 0.02 h2‡ 16 48 47 37 35 34 14 8 * Significant at p = 0.05 ** Significant at p = 0.01 † NS, not significant ‡ h2, Broad sense heritability § ASI, anthesis silking interval; DF, Degree of freedom; DTA, days to anthesis; DTS, days to silk; EHT, ear height; EPP, number of ears per plant; GY, Grain yield; PHT, plant height; SG, Stay green characteristic. Table 2. Mean squares of BC2F1 population evaluated across high N environments§. Source DF GY DTS DTA ASI EHT PHT EPP SG Envt 2 189200830.2** 2230.58** 144.66** 1406.34** 25285.19** 59359.99** 7.64** 199.25** Blk(Rep×Envt) 78 1024333.8** 34.79** 26.71** 2.19** 789.05** 1379.33** 0.06** 0.76** Rep(Envt) 3 9677253.8** 321.13** 358.14** 15.47** 2943.70** 6490.33** 1.17** 3.48** Entry 153 756050.8** 18.37** 16.66** 1.52* 558.86NS† 434.72** 0.06** 0.29* Envt(Entry) 306 448258.3* 11.18NS 8.79NS 1.39NS 499.14NS 212.62NS 0.04NS 0.22NS Error 380 397162.8 10.22 7.75 1.22 462.62 238.98 0.04 0.23 h2‡ 46 62 69 52 32 72 61 53 * Significant at p = 0.05 ** Significant at p = 0.01 † NS, not significant ‡ h2, Broad sense heritability § ASI, anthesis silking interval; DF, Degree of freedom; DTA, days to anthesis; DTS, days to silk; EHT, ear height; EPP, number of ears per plant; GY, Grain yield; PHT, plant height; SG, Stay green characteristic. Table 3. QTL identified based on BC2F1 population from CML 444 × CML 494 across two nitrogen (N) environments. Trait† N level QTL Chromosome Markers Marker Interval Position‡ Add§ LOD¶ R2# GY HN qgy-10–1 10 PZA01292_1- PZB0049_1 29 18.2 310.13 3.15 10 LN qgy-1 1 PZA02487_1- PZB02058_1 0.7 58.5 -10.4 3.6 21 qgy-10–2 10 PZA01292_1- PZB0049_1 29 10.3 −52.8 4.12 8 DTS LN qdts-1 1 PHM13191_6- PZB02058_1 0.7 59.3 2.34 3.1 10.3 HN qdts-5 5 PZA00980_1- PZ202792_25 9.2 51.2 -1.53 2.8 8 qdts-10 10 PZA01292_1-PZB0049_1 29 1.3 -2.24 3.62 31 SG HN qsg-8 8 PZA02748_3-PZA01079_1 17.8 25.3 3.27 3.3 12 LN qsg-1 1 PZA24787_1-PHM1100_21 2.8 58.5 1.55 3.8 9 qsg-4 4 PHM3587_6-PHM3963_33 5.2 3.8 0.56 4.13 18 ASI LN qasi-6 6 PZB00414_2-PHM15251_3 4.3 15.2 0.26 4.1 12 qasi-10 10 PZA01292_1-PZB0049_1 29 5.8 1.2 2.8 5 EPP LN qepp-1 1 PHM174_13-PHM1100_21 7.7 58.5 0.2 2.7 7 PHT LN qpht-1 1 PHM16533_31-PHM13094_8 31.9 128.9 9.06 3.2 9.6 † ASI, anthesis silking interval; DTS, days to silk; EPP, number of ears per plant; GY, Grain yield; HN, high N; LN, low N; PHT, plant height; SG, Stay green characteristic. ‡ Position of peak marker in centiMorgans. § Add = Additive effect;- and + sign indicate favorable alleles came from CML494 and CML444, respectively. ¶ LOD = log10 of odds ratio. # R2 = Percentage of phenotypic variation explained by QTL. Similarly, three QTL for DTS were identified under marker interval of 0.7cM under low-N environment. On both environments, with QTL qdts-1 accounting for the other hand, two QTL qdts-5 and qdts-10 accounted 10.3% of PVE. This QTL was located on Chromosome for 8% and 31% of PVE, and were mapped on Chromo- 1 (PHM13191_6 and PZB02058_1) with LOD of 3.1 and somes 5 and 10, respectively under high N environments. crop science, vol. 58, january–february 2018 www.crops.org 325 Fig. 3. Linkage map showing QTL on Chromosomes 1, 4, 5, 6, 8, and 10 for six traits (GY, ASI, DTS, SG, EPP and PHT). QTL qdts-5 was flanked by markers PZA00980_1 and Epistatic Interactions PZ202792_25 at a marker interval of 9.2 and had a LOD A total of sixteen digenic (QTL×QTL; QQ) interactions score of 2.8. The QTL qdts-10 with LOD 3.62 was flanked involving 22 loci were detected for the studied traits in the by the same markers that flanked QTL qgy-10–2 and qdts- present investigation (Table 4). Significant epistatic inter- 10–1 (PZA01292_1-and PZB0049_1). actions (P ≤ 0.05) were observed for all the traits under The QTL qasi-6 and qasi-10 for ASI accounted for both low- and high-N except for ASI and EPP which 12% and 5% of PVE and were mapped on Chromosomes showed epistasis only under low- and high-N conditions, 6 and 10, respectively under low-N environments. The respectively (Table 4). Interestingly, none of these epistatic markers flanked QTL, qgy-10–1 and qgy-10–2 for GY, loci contained significant main effect QTL (interaction qdts-10 for DTS, and the QTL qasi-10 detected for ASI on between two QTL with additive effects). All the interac- Chromosome 10. tions were observed either between a QTL with additive Three SG QTL, QTL qsg-8 located on Chromosome effect and a locus without significant additive effect 1 and two QTL, qsg-1 and qsg-4 located on Chromosomes (AN or NA) or interactions between two loci with only 1 and 4, were found under high- and low-N environ- epistatic effects (NN). These epistatic QTL explained 0.14 ments, respectively. to 4.42% of the phenotypic variation for the studied traits. QTL qsg-8, qsg-1 and qsg-4 with PVE of 12%, 9% The PVE explained by the epistatic QTL were lower than and 18%, were flanked by markers PZA02748_3 and the main effects QTL for all the measured traits. PZA01079_1 at 17.8 cM, PZA24787_1 and PHM11000_21 at 2.8 cM, and PHM3587_6 and PHM3963_33 at 5.2 cM, DISCUSSION respectively. One QTL each for EPP (qepp-1) accounted Low N is one of the major constraints militating against for 7% of PVE with a LOD score of 2.7, and PHT (qpht-1) the achievement of the full yield potential of maize in accounted for 9.6% of PVE with an LOD score of 3.2 sub-Saharan Africa. In depth understanding of grain were detected on Chromosome 1 between the marker yield and its related traits will be beneficial for the devel- interval of PHM174_13 and PHM1100_21 at 7.7cM and opment of low N stress resilient cultivars. Precise and PHM16533_31 and PHM13094 at 31.9 cM, respectively. consistent phenotyping of such complex traits is very 326 www.crops.org crop science, vol. 58, january–february 2018 Table 4. Epistatic (QTL×QTL) interactions for grain yield and its contributing traits under high and low nitrogen in BC2F1 maize population by QTL Network v2.1. Traits§¶ N Level Chri Marker intervali Pi Chrj Marker interval P AA h 2 j j (aa)(%)‡ Interaction ‡ P-value GY High 1 PHM4752_14- P3M5293_11 68.2 6 PHM15251_5- PZ202436_1 14.0 176.6 1.17 NN† 0.000 1 PZA03200_2- PZB01403_1 92.9 9 PHM11946_19- PHM13183_12 1.0 -251.0 1.49 NN 0.001 5 PHM16854_3- ae1_7 9.4 5 PZA02068_1- PHM563_9 57.6 209.2 1.17 NN 0.001 8 PHM934_19- PZA02748_3 27.6 8 PHM15278_6- PHM4560_54 81.6 -355.8 1.06 N*N 0.000 Low 2 PHM13648_11- PHM4425_25 72.7 5 ae1_7- PZA01327_1 45.8 -220.4 0.73 NN 0.000 4 P3M3963_33- PHM3587_6 5.0 6 PHM15251_5- PZ202436_1 14.0 87.5 0.84 N*N* 0.009 8 PHM2749_10- PZA01079_1 1.0 10 PZA00444_1- PZB01301_5 32.0 -165.1 0.72 N*N 0.047 DTS High 1 PHM1438_34- PZB01227_6 80.4 6 PHM12904_7- PHM5529_4 25.7 -1.304 2.11 NN 0.000 Low 1 PHM13191_6- PHM174_13 62.4 1 PHM13094–8- csu1171_2 114.8 2.140 2.61 AN* 0.000 SG High 4 PHM3155_14- P3M14618_14 16.8 5 ae1_7- PZA01327_1 45.8 0.073 0.19 NN 0.012 5 ae1_7- PZA01327_1 45.8 8 PHM2749_10- PZA01079_1 17.0 -0.109 0.14 NA 0.044 Low 6 PZA02247_1- PZB00414_2 15.8 10 PHM4066_11- PHM15331_16 47.9 0.103 0.29 N*N 0.022 ASI Low 1 Blb1_2- PHM1438_34 73.4 2 PHM13648_11- PHM4425_25 72.7 0.576 1.27 NN 0.003 EPP High 1 PZA03200_2- PZB01403_1 92.9 10 PHM1752_36- PHM4066_11 36.7 -0.139 4.42 NN 0.000 PHT High 6 PHM12904_7- PHM5529_4 26.7 10 P3M2770_19- PZA00866_2 14.0 12.917 3.41 NN 0.000 Low 1 csu1138_4- PHM5306_16 1.0 10 PZA00866_2- PZA01292_1 18.0 -17.485 3.58 NN* 0.000 †Without significant additive QTL for this trait but with significant additive QTL for other traits in the present study ‡ h2(aa)(%) represents percentage of phenotypic variance explained by individual epistatic effects of the mapped QTL. § Types of epistatic (QQ) interaction: NN interaction between two loci with epistatic effects only whereas NA/AN represents interactions between a QTL with additive effects and a locus without significant additive effects or vice versa, respectively. ¶ ASI, anthesis silking interval; DTS, days to silk; EPP, number of ears per plant;GY, Grain yield; PHT, plant height; SG, Stay green characteristic. difficult due to highly fluctuating environmental and soil 2002). In the present study, significant phenotypic corre- conditions. Selection and release of new varieties based lations were observed between GY and other measured on inconsistent phenotypic data often leads to failure in traits (Supplementary Table 2). This finding is in agree- adoption by farmers. Thus, integration of genomics tools ment with the results of other researchers (Bolaños and with conventional breeding would facilitate the develop- Edmeades, 1996; Ribaut et al., 1997; Zheng et al., 2009; ment of improved cultivars with high yield under low-N Lu et al., 2011; Ifie, 2013; Mafouasson, 2014). conditions. The target traits measured in the present study We constructed a linkage map corresponding to followed normal distribution suggesting the suitability of 10 chromosomes of maize using 158 SNP markers that the BC2F1 population for QTL mapping (Fig. 1 and 2). spanned 622.7 cM in length. The results revealed that the We found significant environmental variation for GY and availability of limited number of polymorphic markers for other measured traits indicating differences in the test envi- the BC F population resulted in relatively large inter- ronments. Several researchers have previously reported 2 1vals between markers at some chromosomes, suggesting variations in response of maize to environmental stresses that some QTL may have remained undetected in the (Betrán et al. 2003; Badu-Apraku et al., 2007; Worku corresponding regions (Li et al., 2007). However, with et al., 2007; Derera et al., 2008). The highly significant markers spaced about 10 to 15 cM apart, it was possible GEI observed for only GY indicated that the measured to identify markers associated with the trait of interest traits of most individual families responded similarly in (Bernardo, 2008). Although the length of the linkage the research environments. This result is in agreement map constructed in the present study was shorter than with the findings of Makumbi et al., (2011), who found that of earlier researchers who used similar SNP markers significant GEI for GY under low N conditions. The high (Almeida et al., 2014; Zaidi et al., 2015), it was longer repeatability estimates recorded for most measured traits than that reported by Šimić et al. (2009). The differences under high N environments indicated that the expression between the results of this study and other studies could of these traits was consistent. Although the heritability be attributed to the type and size of the mapping popula- estimates were lower for GY, other agronomic traits had tion and the number of markers used. substantially higher heritability estimates indicating their Quantitative trait loci analysis resulted in the identi- potential to aid in indirect selection for increased GY fication of 13 QTL for six different traits under low- and under these environments. This result is consistent with high-N (4 QTL) environments. Some QTL for different the findings of Ifie (2013) and Mafouasson (2014). Besides traits overlapped in some specific genomic regions. For heritability, the strong correlation of the secondary traits instance, interval PZA01292_1 through PZB0049 at with GY is an important attribute that would enable their Chromosome 10 harbored overlapping QTL for GY, routine integration in breeding programs (Bänziger et al., DTS and ASI. These QTL may have pleiotropic effects crop science, vol. 58, january–february 2018 www.crops.org 327 explaining the correlation observed among these traits. this is a new QTL associated with PHT in maize. Plant Similar overlapping genomic regions for GY and ASI on height was also shown to be correlated with yield; hence, Chromosome 10 were reported by Ribaut et al. (1997) it is an important trait for selection for improved yield. and Malosetti et al. (2008). This explains the strong corre- Overall, the favourable alleles at QTL qgy-10–1 for GY, lation of ASI with GY across a broad range of germplasm, qdts-1 for DTS, qsg-1, qsg-4 and qsg-8 for SG, qasi-6 and suggesting the possibility of a cluster of tightly-linked loci qasi-10 for ASI, qepp-1 for EPP, and qpht-1 for PHT, were controlling low-N tolerance through coordinated expres- contributed by the inbred CML 444, while the favourable sion of these traits. Higher heritability was recorded for alleles at QTL qgy-1 and qgy-10–2 for GY and qdts-5 and ASI and DTS than for GY for both low- and high-N qdts-10 for DTS were contributed by the inbred CML 494. environments. Thus, the understanding of the genetic It is noteworthy that QTL for GY, ASI, EPP and basis of ASI and DTS would aid in designing efficient PHT detected in the present study have also been previ- marker-based breeding strategies for enhanced selection ously reported by other researchers (Table 5). However, for GY under low-N environments. Some earlier studies our results differ substantially from earlier reports in many have reported QTL for grain yield and its related traits on respects in terms of QTL positions and their contribu- Chromosome 10 under optimal and water stress condi- tions in trait expression. Another notable aspect of our tions (Li et al., 2010; Zheng et al., 2009). study is the detection of epistatic QTL under low- and Similarly, the co-location of QTL for GY, SG and high-N environments, although their contributions EPP on Chromosome 1 confirmed the physiological were limited. The maximum epistatic interactions were relationship and strong correlation among these traits. detected for GY under both high- and low-N condi- Close linkage between GY and EPP has been reported tions, contributing from 0.72% to 1.49% of the variance, in numerous classical studies (Agrama and Moussa, 1996; indicating the complex nature of GY and its contributing Ifie, 2013; Mafouasson, 2014). The mapping of the traits in traits. In the present study, all the observed interactions the same region could indicate that this region is a hotspot were either between a QTL with main effect and a locus for yield-related traits and introgression of this region into without significant effect or interactions between two loci maize genotypes will lead to varieties with improved yield. with only epistatic effects. These results are consistent In maize, QTL for GY have been reported previously on with the findings of Yan et al. (2006), who also detected Chromosome 1 under low N (Table 5). Correspondingly, epistatic QTL for GY and its contributing traits in maize, a QTL for EPP has also been reported on Chromosome 1 suggesting that many QTL are affecting trait expressions under low-N and drought-stress conditions (Ribaut et al., indirectly through interactions with other loci. 1997). The identification of common QTL under drought and low-N conditions has important implications for maize breeding, because maize yield would be expected CONCLUSIONS to suffer due to the insufficient N supply in drought-prone A total of 13 QTL were identified on a linkage map areas, located particularly in developing countries. In spanning a total length of 622.7 cM with marker density maize, it has been observed that selection for tolerance to of 3.9 cM. The colocalization of QTL for GY and other midseason drought stress is crucial for yield enhancement agronomic traits is a good indication of their strong asso- under N deficiency (Bänziger et al., 2002; Badu-Apraku ciations. The identification of QTL for yield-related et al., 2013). traits that improve crop growth and performance, espe- The quest for stress tolerance, high yield and good cially under low-N environments, will certainly assist quality is unending for crop breeders, so the desirable breeders in rapid introgression of these genomic regions crop production characteristics of functional stay-green into desired elite germplasm. Five QTL, one each for genotypes make them very attractive. Beavis et al. (1994), GY (qgy-1), DTS (qdts-1) and ASI (qasi-6), and two for SG identified three and five QTL for SG in an F4 and a top- (qsg-1 and qsg-4) were close to their adjacent markers with cross maize population generated from B73_Mo17, while an interval of 0.7 to 5.2cM between them. These QTL Zheng et al. (2009) detected 14 QTL in an F2 population. with PVE of 9 to 21% suggested that the markers were In the present study, only three QTL for SG, including linked with the genes controlling the traits and could be one QTL on Chromosome 8 and two QTL on Chro- used for MAS. However, other QTL identified for these mosomes 1 and 4, were identified under high and low N, traits were far ( ≥ 10 cM) from their linked markers, indi- respectively. Wang et al. (2012) also identified QTL for cating that there will be the need for further fine mapping SG on Chromosomes 1 and 4, indicating the important of these chromosomal regions to narrow down the marker role of these loci for improving SG trait in maize. A QTL interval. The detection of several epistatic interactions for PHT (qpht-1) with PVE of 9.6% was detected on Chro- for the measured traits, especially GY in both high- and mosome 1 in the present study. No QTL for PHT has ever low-N conditions, indicated the complex nature of yield been reported on Chromosome 1 (Table 5), indicating that and its contributing traits. Finally, the validation of these 328 www.crops.org crop science, vol. 58, january–february 2018 Table 5. Comparison of QTL for ASI, PHT, GY and EPP for two N levels with those of other studies. Trait Mapping population Chromosome Marker type N level QTL position Authors ASI† F2:3 1,3,10 RFLP High 75, 39, 63 Ribaut and Ragot, 2007 1,3,4,6,7,8,10 Low 1.08, 3.05, 4.08, 6.05, 7.04, 8.02, 8.06, 10.03 RIL 3, 6, 7, 8 high 3.06, 3.07, 6.01, 7.02, 8.02, 8.06 Liu et al., 2012 6, 7, 8 SSR Low 6.01,7.02,8.03 BC2F1 6 SNP High 4.4 Present study 10 Low 29 PHT‡ F2:3 3,5,9 RFLP High 48.6, 85.7, 21.1 Agrama et al., 1999 2,3,5,9 Low 51.4, 57.1,58.9,137.7,32.6 F2:3 4,6,7,8,9 RFLP High 59,120,69,90,60 Ribaut and Ragot, 2007 BC2F1 1 SNP Low 31.9 Present study GY§ F2:3 1,4,5,9,10, RFLP High 131.4,33.6, 8.5, 122.7, 74.8 Agrama et al. 1999 1,2,7,9,10 Low 46.9,90.6, 110.8, 59.6, 120.7, 69.4 F2:3 1,3,10 RFLP High 95,39,63 Ribaut et al.,2007 1,2,3.4,8,9 Low 67,18,101,53,188,128,136,64 BC2F1 10 SNP High 18.1 Present study EPP¶ F2:3 1,4,6,9 RFLP High 196.4,55.3,30,122.7 Agrama et al., 1999 1,3.6,9 Low 94.5,144.3,35.6,102.1 BC2F1 1 SNP Low 7.7 Present study † ASI, anthesis silking interval. ‡ PHT, plant height. § GY, Grain yield. ¶ EPP, number of ears per plant. QTL in another mapping population would be necessary environments. Crop Science. 52(5):2050–2062. doi:10.2135/ cropsci2011.12.0629 before their use in MAS. Badu-Apraku, B., M.A.B. Fakorede, and A.F. Lum. 2007. Evalu- Therefore, in a follow up study, fine mapping of the ation of experimental varieties from recurrent selection for identified QTL will be performed with a larger popula- Striga resistance in two extra-early maize populations in the tion size and a saturated map (GBS or DArT-seq). savannas of West and Central Africa. Exp. Agric. 43:183–200. doi:10.1017/S0014479706004601 Acknowledgments Badu-Apraku, B., Fakorede, M. A. B., Oyekunle, M. and Akin- The authors are grateful to the Alliance for Green Revolu- wale, R. O. 2015. Genetic gains in grain yield under nitrogen tion in Africa (AGRA) for funding support. We thank staff of stress following three decades of breeding for drought toler- West Africa Centre for Crop Improvement (WACCI), Univer- ance and Striga resistance in early maturing maize. J. Agric. sity of Ghana, CSIR-Crop Research Institute (CRI), the Bill Sci. doi:10.1017/S0021859615000593. and Melinda Gates Foundation (BMGF) for funding support Badu-Apraku, B., Oyekunle, M., Akinwale, R. O., and Lum, A. through the Drought Tolerant Maize for Africa (DTMA) Proj- F. 2011. Combining ability of early-maturing white maize ect, the International Institute of Tropical Agriculture (IITA) inbreds under stress and non-stress environments. Agronomy BioScience Department especially Mr. Inaana Okechukwu, the Journal 103: 544–557. Badu-Apraku, B., M. Oyekunle, A. Menkir, K. Obeng-Antwi, International centre for maize and wheat improvement (CIM- and C.G. Yallou. 2013. Comparative performance of early MYT), and Ing. 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