Genetic dissection of grain yield in bread wheat. I. QTL analysis

Grain yield forms one of the key economic drivers behind a successful wheat (Triticum aestivum L.) cropping enterprise and is consequently a major target for wheat breeding programmes. However, due to its complex nature, little is known regarding the genetic control of grain yield. A doubled-haploid population, comprising 182 individuals, produced from a cross between two cultivars ‘Trident’ and ‘Molineux’, was used to construct a linkage map based largely on microsatellite molecular makers. ‘Trident’ represents a lineage of wheat varieties from southern Australia that has achieved consistently high relative grain yield across a range of environments. In comparison, ‘Molineux’ would be rated as a variety with low to moderate grain yield. The doubled-haploid population was grown from 2002 to 2005 in replicated field experiments at a range of environments across the southern Australian wheat belt. In total, grain yield data were recorded for the population at 18 site-year combinations. Grain yield components were also measured at three of these environments. Many loci previously found to be involved in the control of plant height, rust resistance and ear-emergence were found to influence grain yield and grain yield components in this population. An additional nine QTL, apparently unrelated to these traits, were also associated with grain yield. A QTL associated with grain yield on chromosome 1B, with no significant relationship with plant height, ear-emergence or rust resistance, was detected (LOD ≥2) at eight of the 18 environments. The mean yield, across 18 environments, of individuals carrying the ‘Molineux’ allele at the 1B locus was 4.8% higher than the mean grain yield of those lines carrying the ‘Trident’ allele at this locus. Another QTL identified on chromosome 4D was also associated with overall gain yield at six of the 18 environments. Of the nine grain yield QTL not shown to be associated with plant height, phenology or rust resistance, two were located near QTL associated with grain yield components. A third QTL, associated with grain yield components at each of the environments used for testing, was located on chromosome 7D. However, this QTL was not associated with grain yield at any of the environments. The implications of these findings on marker-assisted selection for grain yield are discussed.     

苦荞种质主要农艺性状的变异及其对单株粒重的贡献研究

对180份苦荞种质资源的8个主要农艺性状进行了评价,并对单株粒重与其他7个农艺性状之间进行了相关性分析和通径分析。结果显示,主要农艺性状的变异系数为8.10%~39.40%;简单相关分析表明,单株粒重与单株粒数、主茎分支数呈极显著正相关;偏相关分析表明,单株粒重与单株粒数、千粒重呈极显著正相关;多元回归和通径分析均表明,单株粒数、千粒重是影响单株粒重的主要因素,其中单株粒数影响最大。系统聚类分析表明,180份苦荞种质资源可划分为5类,其中第Ⅲ类(包括47份供试材料)的综合农艺性状较好,主要表现为主茎分枝能力强、单株粒数多、单株粒重和单株米粒重高,可作为优异基因资源进一步开发利用,也为以增加单株米粒重与出米率为选育目标的苦荞薄壳育种提供依据。     

Molecular mapping of quantitative trait loci for yield and yield components in spring wheat (Triticum aestivum L.)

An F1 derived doubled haploid (DH) population of 402 lines from the adapted spring wheat cross Superb (high yielding)/BW278 (low yielding) was developed to identify quantitative trait loci (QTL) associated with yield and yield components. A subset of the population (186 lines) was evaluated in replicated field trials in 2001 and 2002 at six locations in Manitoba and Saskatchewan, Canada. Agronomic parameters, grain yield and yield components including 1,000 grain weight, harvest index, average seed weight spike(-1), seed number spike(-1) and spikes number m(-2) were measured. A genetic map was constructed with 268 microsatellite marker loci and included two morphological genes, reduced plant height, Rht-B1b, and the presence/absence of awns, B1. Composite interval mapping was conducted to estimate the location and effect of QTL associated with the evaluated traits. A total of 53 QTL were identified on 12 chromosomes for the 9 evaluated traits with the coefficient of determination ranging from 0.03 to 0.21 of the total variation. The increase in yield and yield components ranged from 4.5 to 17.1% over the population mean. The five grain yield QTL were detected on chromosomes 1A, 2D, 3B, and 5A and showed a combined increase of 34.4%, over the population mean. The alleles from Superb were associated with increased yield for four of the five QTL. This study identified potential chromosome segments for use in marker-assisted selection to improve yield and yield components in spring wheat.     

Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments

A large proportion of the worlds’ wheat growing regions suffers water and/or heat stress at some stage during the crop growth cycle. With few exceptions, there has been no utilisation of managed environments to screen mapping populations under repeatable abiotic stress conditions, such as the facilities developed by the International Wheat and Maize Improvement Centre (CIMMYT). Through careful management of irrigation and sowing date over three consecutive seasons, repeatable heat, drought and high yield potential conditions were imposed on the RAC875/Kukri doubled haploid population to identify genetic loci for grain yield, yield components and key morpho-physiological traits under these conditions. Two of the detected quantitative trait loci (QTL) were located on chromosome 3B and had a large effect on canopy temperature and grain yield, accounting for up to 22?% of the variance for these traits. The locus on chromosome arm 3BL was detected under all three treatments but had its largest effect under the heat stress conditions, with the RAC875 allele increasing grain yield by 131?kg?ha^?1 (or phenotypically, 7?% of treatment average). Only two of the eight yield QTL detected in the current study (including linkage groups 3A, 3D, 4D 5B and 7A) were previously detected in the RAC875/Kukri doubled haploid population; and there were also different yield components driving grain yield. A number of discussion points are raised to understand differences between the Mexican and southern Australian production environments and explain the lack of correlation between the datasets. The two key QTL detected on chromosome 3B in the present study are candidates for further genetic dissection and development of molecular markers.     

Genetic dissection of developmental behavior of grain weight in wheat under diverse temperature and water regimes

As a quantitatively inherited trait related to high yield potential, grain weight (GW) development in wheat is constrained by abiotic stresses such as limited water supply and high temperature. Data from a doubled haploid population, derived from a cross of (Hanxuan 10?×?Lumai 14), grown in four environments were used to explore the genetic basis of GW developmental behavior in unconditional and conditional quantitative trait locus (QTL) analyses using a mixed linear model. Thirty additive QTLs and 41 pairs of epistatic QTLs were detected, and were more frequently observed on chromosomes 1B, 2A, 2D, 4A, 4B and 7B. No single QTL was continually active during all stages or periods of grain growth. The QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the flowering to the seventh day after) formed a foundation for GW development. GW development at these stages can be used as an index for screening superior genotypes under diverse abiotic stresses in a wheat breeding program. One QTL, i.e. Qgw.cgb-6A.2, showed high adaptability for water-limited and heat-stress environments. Many A-QTLs interacted with more than one other QTL in the two genetic models, such as Qgw.cgb-4B.2 interacted with five QTLs, showing that the genetic architecture underlying GW development involves a collective expression of genes with additive and epistatic effects.     

QTLs for cell membrane stability and flag leaf area under drought stress in a wheat RIL population

A population of 206 recombinant inbred lines (RILs F₉–F₁₀) derived from wheat cross WL711/C306 was phenotyped for morpho-physiological traits such as flag leaf area (FLA), flag leaf length (FLL), flag leaf width (FLW), and cell membrane stability (CMS) under water deficit stress (WDS) environment. High yielding cultivar, WL711 had higher FLA than the medium yielding cultivar C306 across trials under both environments. Parent cultivar C306 maintained membrane integrity while WL711 showed higher membrane damage under WDS. The RIL population showed considerable variation, normal distribution and transgressive segregation for FLA, FLL, FLW and CMS under WDS. The genetic linkage map of WL711/C306 RIL population was constructed comprising of 346 markers. The total map distance was 4526.8 cM with an averaged interval of 12.9 cM between adjacent markers. Major consistent QTL for FLA, FLL, FLW, and CMS were identified on chromosomes 2DS and 3BS respectively in the WL711/C306 RIL population under WDS. The major QTL for FLA, qFLAWD.2D.1 which expressed in multiple environments and for CMS, qCMSWD.3B.3 and qCMSWD.3B.4, accounted for a large proportion of phenotypic variance (PV) with positive allele being contributed by C306, a drought resistant (DR) parent. QTL qFLAWD.2D.1 for FLA co-located with QTL for grain number (GN) and days to flowering (DTF) while QTL qCMSWD.3B.3 and qCMS.3B.4 co-located with QTL for grain yield and its components, days to flowering, canopy temperature and coleoptiles length as reported in our previous publications on the WL711/C306 population (Shukla et al. in Euphytica 203:449–467, 2015; Singh et al. in J Plant Biochem Biotechnol 24:324–330, 2015). Two candidate genes Ghd7 for grain yield and heading date and OsCDK4 for calcium dependent protein kinases were identified in the 2DS and 3BS QTL regions respectively on comparison with gene content of rice chromosomes 7 and 1 respectively. Hence, QTLs qFLAWD.2D.1 and qCMSWD.3B.3 are potential target regions for fine mapping and marker assisted selection for FLA and CMS respectively in wheat under water deficit environments.

     

Fine mapping a domestication-related QTL for spike-related traits in a synthetic wheat

QTL analysis using a BC5F2:3 mapping population derived from a cross between Am3, a synthetic hexaploid wheat as a donor parent, and Laizhou953, a Chinese winter wheat cultivar as a recurrent parent, showed that variation at the microsatellite locus Xgwm113 on chromosome 4B was associated with variation in grain number per spike (GN), spike length (SL), and spikelet number per spike (SPI). The Qgn.caas-4B, Qsl.caas-4B, and Qspi.caas-4B were responsible for 16.6%-35.6%, 18.0%-32.3%, and 23.7%-25.9% of the phenotypic variation present in two environments, respectively. Segregation for GN fit a Mendelian monogenic ratio. A subpopulation consisting of 497 plants was used to map the QTL to a 1.2?cM interval between Xgwm113 and Xgwm857. The three spike traits, GN, SL, and SPI, were correlated and were thus probably under the pleiotropic control of the QTL. The Am3 allele had a reduction effect on all three spike traits. Evidence for positive selective history on SSR locus Xgwm113 was supported using Ewens-Watterson's statistic test on a germplasm panel of wild and landrace entries, suggesting that this genomic region may contain genes under selection during wheat domestication.     

Identification of a QTL decreasing yield in barley linked to Mlo powdery mildew resistance

A molecular marker map, including Mlo mildew resistance, of the spring barley cross Derkado (Mlo-resistant) × B83-12/21/5 (Mlo-susceptible) was scanned for yield QTLs to determine whether the association of Mlo resistance with reduced yield was due to linkage or pleiotropy. Over the mapped portion of the genome of the cross, the QTL with the greatest effect upon yield was located within a 22 cM region between mlo and the simple sequence repeat HVM67 on chromosome 4(4H). The association of Mlo resistance with lower yield was therefore due to a repulsion linkage. Analysis of yield component characters revealed QTL alleles for reduced grain number and earlier heading date in the same region, also associated with Mlo resistance. Genotyping of a range of cultivars and sources of Mlo resistance with the HVM67 simple sequence repeat showed that the Derkado HVM67 allele was rare as it was found only in one other cultivar and four land-races or sources of disease resistance. Grannenlose Zweizeilige, the source, and Salome, the carrier of Mlo resistance in Derkado, have the same HVM67 genotype, although Salome was a mixture of two genotypes. The entire mlo-HVM67 chromosomal segment from Grannenlose Zweizeilige is therefore thought to have been transmitted to Derkado, possibly through joint selection for Mlo resistance and early heading. L92, synonym EP79, was another source of Mlo resistance with the same HVM67 allele as Derkado but recombination must have occurred during the breeding of Atem as it possesses a different HVM67 allele which is present in all the other Mlo sources and cultivars surveyed. Abbreviations: GN, grains per main stem ear; HD, heading date; MSTGW, thousand grain weight derived from GN and MSY; MSY, yield of grain on the main stem; PY, yield of grain from the whole plot; sCIM, simplified compound interval mapping; SIM, simple interval mapping; SPY, single plant yield; S-SAP, sequence-specific amplification polymorphism; TGW, thousand grain weight derived from bulk of plot seed; TN, number of fertile stems per plant.     

Epistasis for Three Grain Yield Components in Rice (Oryza Sativa L.)

The genetic basis for three grain yield components of rice, 1000 kernel weight (KW), grain number per panicle (GN), and grain weight per panicle (GWP), was investigated using restriction fragment length polymorphism markers and F(4) progeny testing from a cross between rice subspecies japonica (cultivar Lemont from USA) and indica (cv. Tequing from China). Following identification of 19 QTL affecting these traits, we investigated the role of epistasis in genetic control of these phenotypes. Among 63 markers distributed throughout the genome that appeared to be involved in 79 highly significant (P < 0.001) interactions, most (46 or 73%) did not appear to have ``main'''' effects on the relevant traits, but influenced the trait(s) predominantly through interactions. These results indicate that epistasis is an important genetic basis for complex traits such as yield components, especially traits of low heritability such as GN and GWP. The identification of epistatic loci is an important step toward resolution of discrepancies between quantitative trait loci mapping and classical genetic dogma, contributes to better understanding of the persistence of quantitative genetic variation in populations, and impels reconsideration of optimal mapping methodology and marker-assisted breeding strategies for improvement of complex traits.     

A haplotype inference algorithm for trios based on deterministic sampling

Background

The three-dimensional shape of grain, measured as grain length, width, and thickness (GL, GW, and GT), is one of the most important components of grain appearance in rice. Determining the genetic basis of variations in grain shape could facilitate efficient improvements in grain appearance. In this study, an F_7:8 recombinant inbred line population (RIL) derived from a cross between indica and japonica cultivars (Nanyangzhan and Chuan7) contrasting in grain size was used for quantitative trait locus (QTL) mapping. A genetic linkage map was constructed with 164 simple sequence repeat (SSR) markers. The major aim of this study was to detect a QTL for grain shape and to fine map a minor QTL, qGL7.

Results

Four QTLs for GL were detected on chromosomes 3 and 7, and 10 QTLs for GW and 9 QTLs for GT were identified on chromosomes 2, 3, 5, 7, 9 and 10, respectively. A total of 28 QTLs were identified, of which several are reported for the first time; four major QTLs and six minor QTLs for grain shape were also commonly detected in both years. The minor QTL, qGL7, exhibited pleiotropic effects on GL, GW, GT, 1000-grain weight (TGW), and spikelets per panicle (SPP) and was further validated in a near isogenic F₂ population (NIL-F₂). Finally, qGL7 was narrowed down to an interval between InDel marker RID711 and SSR marker RM6389, covering a 258-kb region in the Nipponbare genome, and cosegregated with InDel markers RID710 and RID76.

Conclusion

Materials with very different phenotypes were used to develop mapping populations to detect QTLs because of their complex genetic background. Progeny tests proved that the minor QTL, qGL7, could display a single mendelian characteristic. Therefore, we suggested that minor QTLs for traits with high heritability could be isolated using a map-based cloning strategy in a large NIL-F₂ population. In addition, combinations of different QTLs produced diverse grain shapes, which provide the ability to breed more varieties of rice to satisfy consumer preferences.     

Identifying loci influencing grain number by microsatellite screening in bread wheat (Triticum aestivum L.)

Grain number (GN) is one of three major yield-related components in wheat. We used the Chinese wheat mini core collection to undertake a genome-wide association analysis of grain number using 531 SSR markers randomly located on all 21 chromosomes. Grain numbers of all accessions were measured in four trials, i.e. two environments in four growing seasons. Association analysis based on a mixed linear model (MLM) revealed that 27 SSR loci were significantly associated with mean GN (MGN) estimated by the best linear unbiased predictor (BLUP) method. These included numerous breeder favorable alleles with strong positive effects at 23 loci. Significant or extremely significant differences were detected on MGN between varieties conveying favored allele and varieties with other alleles. Moreover, statistical simulation showed that the favored alleles have additive genetic effects. Although modern varieties combined larger numbers of favored alleles, the numbers of favored alleles were not significantly different from those in landraces, especially those alleles contributing mostly to the phenotypic variation. These results indicate that there is still considerable genetic potential for use of markers for genome selection of GN for high yield in wheat.     

Natural Variations in SLG7 Regulate Grain Shape in Rice

Rice (Oryza sativa) grain shape, which is controlled by quantitative trait loci (QTL), has a strong effect on yield production and quality. However, the molecular basis for grain development remains largely unknown. In this study, we identified a novel QTL, Slender grain on chromosome 7 (SLG7), that is responsible for grain shape, using backcross introgression lines derived from 9311 and Azucena. The SLG7 allele from Azucena produces longer and thinner grains, although it has no influence on grain weight and yield production. SLG7 encodes a protein homologous to LONGIFOLIA 1 and LONGIFOLIA 2, both of which increase organ length in Arabidopsis. SLG7 is constitutively expressed in various tissues in rice, and the SLG7 protein is located in plasma membrane. Morphological and cellular analyses suggested that SLG7 produces slender grains by longitudinally increasing cell length, while transversely decreasing cell width, which is independent from cell division. Our findings show that the functions of SLG7 family members are conserved across monocots and dicots and that the SLG7 allele could be applied in breeding to modify rice grain appearance.     

Genetic Analysis of Carbon Isotope Discrimination and its Relation to Yield in a Wheat Doubled Haploid Population

Carbon isotope discrimination (Δ¹³C) is considered a useful indicator for indirect selection of grain yield (GY) in cereals. Therefore, it is important to evaluate the genetic variation in Δ¹³C and its relationship with GY. A doubled haploid (DH) population derived from a cross of two common wheat varieties, Hanxuan 10 (H10) and Lumai 14 (L14), was phenotyped for Δ¹³C in the flag leaf, GY and yield associated traits in two trials contrasted by water availability, specifically, rain‐fed and irrigated. Quantitative trait loci (QTLs) were identified by single locus and two locus QTL analyses. QTLs for Δ¹³C were located on chromosomes 1A, 2B, 3B, 5A, 7A and 7B, and QTLs for other traits on all chromosomes except 1A, 4D, 5A, 5B and 6D. The population selected for high Δ¹³C had an increased frequency of QTL for high Δ¹³C, GY and number of spikes per plant (NSP) when grown under rain‐fed conditions and only for high Δ¹³C and NSP when grown under irrigated conditions, which was consistent with agronomic performance of the corresponding trait values in the high Δ¹³C progeny; that is, significantly greater than that in the low Δ¹³C. Therefore, selection for Δ¹³C was beneficial in increasing grain yield in rain‐fed environments.     

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice (<i>Oryza sativa</i> L.)

Grain size traits are critical agronomic traits which directly determine grain yield, but the genetic bases of these traits are still not well understood. In this study, a total of 154 chromosome segment substitution lines (CSSLs) population derived from a cross between a japonica variety Koshihikari and an indica variety Nona Bokra was used to investigate grain length (GL), grain width (GW), length-width ratio (LWR), grain perimeter (GP), grain area (GA), and thousand grain weight (TGW) under four environments. QTL mapping analysis of six grain size traits was performed by QTL IciMapping 4.2 with an inclusive composite interval mapping (ICIM) model. A total of 64 QTLs were identified for these traits, which mapped to chromosomes 1, 2, 3, 4, 6, 7, 8, 10, 11, and 12 and accounted for 1.6%–27.1% of the total phenotypic variations. Among these QTLs, thirty-six loci were novel and seven QTLs were identified under four environments. One locus containing the known grain size gene, qGL3/GL3.1/OsPPKL1, also have been found. Moreover, five pairs of digenic epistatic interactions were identified except for GL and GP. These findings will facilitate fine mapping of the candidate gene and QTL pyramiding to genetically improve grain yield in rice.     

Genetic dissection of grain yield in bread wheat. II. QTL-by-environment interaction

The grain yield of wheat is influenced by genotype, environment and genotype-by-environment interaction. A mapping population consisting of 182 doubled haploid progeny derived from a cross between the southern Australian varieties ‘Trident’ and ‘Molineux’, was used to characterise the interaction of previously mapped grain yield quantitative trait locus (QTL) with specific environmental covariables. Environments (17) used for grain yield assessment were characterised for latitude, rainfall, various temperature-based variables and stripe rust infection severity. The number of days in the growing season in which the maximum temperature exceeded 30°C was identified as the variable with the largest effect on site mean grain yield. However, the greatest QTL-by-environmental covariable interactions were observed with the severity of stripe rust infection. The rust resistance allele at the Lr37/Sr38/Yr17 locus had the greatest positive effect on grain yield when an environment experienced a combination of high-stripe rust infection and cool days. The grain yield QTL, QGyld.agt-4D, showed a very similar QTL-by-environment covariable interaction pattern to the Lr37/Sr38/Yr17 locus, suggesting a possible role in rust resistance or tolerance. Another putative grain yield per se QTL, QGyld.agt-1B, displayed interactions with the quantity of winter and spring rainfall, the number of days in which the maximum temperature exceeded 30°C, and the number of days with a minimum temperature below 10°C. However, no cross-over interaction effect was observed for this locus, and the ‘Molineux’ allele remained associated with higher grain yield in response to all environmental covariables. The results presented here confirm that QGyld.agt-1B may be a prime candidate for marker-assisted selection for improved grain yield and wide adaptation in wheat. The benefit of analysing the interaction of QTL and environmental covariables, such as employed here, is discussed.     

Dissecting the Genetic Basis of Extremely Large Grain Shape in Rice Cultivar ＇JZ1560＇

Rice grain shape,grain length(GL),width(GW),thickness(GT)and length-to-width ratio(LWR),are usually controlled by multiple quantitative trait locus(QTL).To elucidate the genetic basis of extremely large grain shape,QTL analysis was performed using an F2 population derived from a cross between a japonica cultivar ’JZ1560’(extremely large grain)and a contrasting indica cultivar ’FAZ1’(small grain).A total number of 24 QTLs were detected on seven different chromosomes.QTLs for GL,GW,GT and LWR explained 11.6%,95.62%,91.5%and 89.9%of total phenotypic variation,respectively.Many QTLs pleiotropically controlled different grain traits,contributing complex traits correlation.GW2 and qSW5/GW5,which have been cloned previously to control GW,showed similar chromosomal locations with qGW2-I/qGT2-I/qLWR2-2 and qGW5-2/qLWR5-l and should be the right candidate genes.Plants pyramiding GW2 and qSW5/GW5 showed a significant increase in GW compared with those carrying one of the two major QTLs.Furthermore,no significant QTL interaction was observed between GW2 and qSW5/GW5.These results suggested that GW2 and qSW5/GW5 might work in independent pathways to regulate grain traits.’JZ1560’ alleles underlying all QTLs contributed an increase in GW and GT and the accumulation of additive effects generates the extremely large grain shape in ’JZ1560’.     

Mapping QTL controlling yield and yield components in a spring barley (Hordeum vulgare L.) cross using marker regression

An RFLP map constructed from 99 doubled haploid lines of a cross between two spring barley varieties (Blenheim × Kym) was used to localize quantitative trait loci (QTL) controlling grain yield and yield components by marker regression and single-marker analysis. Trials were conducted over three years. Genotype-by-year interaction was detected for plant grain weight and ear grain weight so they were analysed separately for each year. None was detected for thousand-grain weight and ear grain number so data were pooled over years. A total of eleven QTL were detected for plant grain weight over two years and fourteen for ear grain weight over three years. Seven QTL were detected for plot yield. The locus with the largest effect was on chromosome 2(2H)L and accounted for 19% of the variation in the progeny. Eight QTL were detected for thousand-grain weight and five for ear grain number. Many of the QTL detected were in comparable positions in each year. Yield and yield components were only partly correlated. Comparisons based on common RFLP markers showed that some QTL were found in positions similar to those identified in other studies. For a number of QTL the identification of linked markers provided suitable opportunities for marker-assisted selection and improvement of barley and reference markers with which to analyse the homoeologous chromosome regions of wheat and other cereals.     

Identification of eight chromosomes and a microsatellite marker on 1AS associated with QTL for grain weight in bread wheat

The present study in bread wheat was undertaken, firstly, to identify chromosomes carrying QTLs controlling 1000 grain weight (GW) and, secondly, to develop molecular marker(s) linked with this trait. Using the genotype Rye Selection111 (RS111), we carried out a monosomic analysis that suggested that 8 chromosomes (1A, 1D, 2B, 4B, 5B, 6B, 7A and 7D) carried QTLs controlling GW, with only 3 of these (1A, 2B, 7A) carrying alleles for high GW. To tag the QTLs present on these chromosomes, we crossed the genotype RS111 with high GW (56.83 g) with the genotype Chinese Spring (CS) with low GW (23.74 g) and obtained 100 RILs. These RILs showed normal distribution for GW. The parental genotypes were analysed with as many as 346 STMS primer pairs for detection of polymorphism. Of these, 267 primer pairs gave scorable amplification products, 63 of which detected polymorphism between the parents. Using each of these 63 primer pairs, we carried out bulked segregant analysis on RILs representing two extremes of the distribution. One primer pair (WMC333) showed an association of the marker locus Xwmc333 with grain weight. This was confirmed through selective genotyping, and the co-segregation data on molecular marker locus Xwmc333 and GW were analysed following a single marker linear regression approach. Significant regression suggested linkage between Xwmc333 and a QTL for GW. The results showed that the above QTL accounted for 15.09% of the variation for GW between the parents. The marker has been located on chromosome arm 1AS, and QTL was designated QGw1.ccsu-1A. Received: 15 September 1999 / Accepted: 9 November 1999     

Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions

Grain yield and grain weight of wheat are often decreased by water-limitation in the north-eastern cropping belt of Australia. Based on knowledge that CIMMYT lines are well-adapted in this region, a recombinant inbred line (RIL) population between two elite CIMMYT bread wheats (Seri M82 and Babax) was evaluated under water-limited environments. Fourteen productivity traits were evaluated in 192 progeny in up to eight trials. For three aggregations of the environments (all, high yield or low yield), multiple quantitative trait loci (QTL) were detected, each explaining <15% of variation. Co-location of multiple trait QTL was greatest on linkage groups 1B-a, 1D-b, 4A-a, 4D-a, 6A-a, 6B-a, 7A-a and an unassigned linkage group. Two putative QTL (LOD > 3) from Seri (6D-b and UA-d) increased grain yield and co-located with a suggestive (2 < LOD < 3) and a putative QTL for increased stem carbohydrate content (WSC), respectively; the latter QTL also co-located with a putative anthesis QTL for earlier flowering. Both QTL were detected only in high yield (>4t ha⁻¹) environments. A third increased grain yield QTL (7A-a) from Babax co-located with QTL for increased grain number. Six putative QTL increased grain weight and co-located with QTL for harvest index, grains per spike and spike number. Three putative QTL for increased grains per spike co-located with strong QTL for earlier flowering, increased grain weight and fewer spikes. A group of progeny that exceeded the mean grain yield and grain weight of commercial checks had an increased frequency of QTL for high WSC, large grain size, increased harvest index and greater height, but fewer stems, when compared to low yielding (20% less), low grain weight progeny. These findings were consistent with agronomic analyses of the germplasm and demonstrate that there should be opportunities to independently manipulate grain number and grain size which is typically difficult due to strong negative correlations.     

Characterisation of a novel quantitative trait locus, <Emphasis Type="Italic">GN4</Emphasis>-<Emphasis Type="Italic">1</Emphasis>, for grain number and yield in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Key message

A novel QTL for grain number, GN4-1, was identified and fine-mapped to an ~ 190-kb region on the long arm of rice chromosome 4.

Abstract

Rice grain yield is primarily determined by three components: number of panicles per plant, grain number per panicle and grain weight. Among these traits, grain number per panicle is the major contributor to grain yield formation and is a crucial trait for yield improvement. In this study, we identified a major quantitative trait locus (QTL) responsible for rice grain number on chromosome 4, designated GN4-1 (a QTL for Grain Number on chromosome 4), using advanced segregating populations derived from the crosses between an elite indica cultivar ‘Zhonghui 8006’ (ZH8006) and a japonica rice ‘Wuyunjing 8’ (WYJ8). GN4-1 was delimited to an ~ 190-kb region on chromosome 4. The genetic effect of GN4-1 was estimated using a pair of near-isogenic lines. The GN4-1 gene from WYJ8 promoted accumulation of cytokinins in the inflorescence and increased grain number per panicle by ~ 17%. More importantly, introduction of the WYJ8 GN4-1 gene into ZH8006 increased grain yield by ~ 14.3 and ~ 11.5% in the experimental plots in 2014 and 2015, respectively. In addition, GN4-1 promoted thickening of the culm and may enhance resistance to lodging. These results demonstrate that GN4-1 is a potentially valuable gene for improvement of yield and lodging resistance in rice breeding.