Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations

Key message

A novel high-density consensus wheat genetic map was obtained based on three related RIL populations, and the important chromosomal regions affecting yield and related traits were specified.

Abstract

A prerequisite for mapping quantitative trait locus (QTL) is to build a genetic linkage map. In this study, three recombinant inbred line populations (represented by WL, WY, and WJ) sharing one common parental line were used for map construction and subsequently for QTL detection of yield-related traits. PCR-based and diversity arrays technology markers were screened in the three populations. The integrated genetic map contains 1,127 marker loci, which span 2,976.75 cM for the whole genome, 985.93 cM for the A genome, 922.16 cM for the B genome, and 1,068.65 cM for the D genome. Phenotypic values were evaluated in four environments for populations WY and WJ, but three environments for population WL. Individual and combined phenotypic values across environments were used for QTL detection. A total of 165 putative additive QTL were identified, 22 of which showed significant additive-by-environment interaction effects. A total of 65 QTL (51.5 %) were stable across environments, and 23 of these (35.4 %) were common stable QTL that were identified in at least two populations. Notably, QTkw-5B.1, QTkw-6A.2, and QTkw-7B.1 were common major stable QTL in at least two populations, exhibiting 11.28–16.06, 5.64–18.69, and 6.76–21.16 % of the phenotypic variance, respectively. Genetic relationships between kernel dimensions and kernel weight and between yield components and yield were evaluated. Moreover, QTL or regions that commonly interact across genetic backgrounds were discussed by comparing the results of the present study with those of previous similar studies. The present study provides useful information for marker-assisted selection in breeding wheat varieties with high yield.     

QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance

Despite numerous published reports of quantitative trait loci (QTL) for drought-related traits, practical applications of such QTL in maize improvement are scarce. Identifying QTL of sizeable effects that express more or less uniformly in diverse genetic backgrounds across contrasting water regimes could significantly complement conventional breeding efforts to improve drought tolerance. We evaluated three tropical bi-parental populations under water-stress (WS) and well-watered (WW) regimes in Mexico, Kenya and Zimbabwe to identify genomic regions responsible for grain yield (GY) and anthesis-silking interval (ASI) across multiple environments and diverse genetic backgrounds. Across the three populations, on average, drought stress reduced GY by more than 50 % and increased ASI by 3.2 days. We identified a total of 83 and 62 QTL through individual environment analyses for GY and ASI, respectively. In each population, most QTL consistently showed up in each water regime. Across the three populations, the phenotypic variance explained by various individual QTL ranged from 2.6 to 17.8 % for GY and 1.7 to 17.8 % for ASI under WS environments and from 5 to 19.5 % for GY under WW environments. Meta-QTL (mQTL) analysis across the three populations and multiple environments identified seven genomic regions for GY and one for ASI, of which six mQTL on chr.1, 4, 5 and 10 for GY were constitutively expressed across WS and WW environments. One mQTL on chr.7 for GY and one on chr.3 for ASI were found to be ‘adaptive’ to WS conditions. High throughput assays were developed for SNPs that delimit the physical intervals of these mQTL. At most of the QTL, almost equal number of favorable alleles was donated by either of the parents within each cross, thereby demonstrating the potential of drought tolerant × drought tolerant crosses to identify QTL under contrasting water regimes.     

Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits

While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under-surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait Analysis (GCTA) was used to examine GxE variance contribution of 15 macronutrients and lifestyle to the total phenotypic variance of diabetes-related traits at the genome-wide level in a European American population. GCTA identified two key environmental factors making significant contributions to the GxE variance for diabetes-related traits: carbohydrate for fasting insulin (25.1% of total variance, P-nominal = 0.032) and homeostasis model assessment of insulin resistance (HOMA-IR) (24.2% of total variance, P-nominal = 0.035), n-6 polyunsaturated fatty acid (PUFA) for HOMA-β-cell-function (39.0% of total variance, P-nominal = 0.005). To demonstrate and support the results from GCTA, a GxE GWAS was conducted with each of the significant dietary factors and a control E factor (dietary protein), which contributed a non-significant GxE variance. We observed that GxE GWAS for the environmental factor contributing a significant GxE variance yielded more significant SNPs than the control factor. For each trait, we selected all significant SNPs produced from GxE GWAS, and conducted anew the GCTA to estimate the variance they contributed. We noted the variance contributed by these SNPs is higher than that of the control. In conclusion, we utilized a novel method that demonstrates the importance of genome-wide GxE interactions in explaining the variance of diabetes-related traits.     

Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments

Background

When rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (r_g) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and r_g between BW and its uniformity were influenced by a scale effect.

Results

Although heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (r_g = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low r_g (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The r_g between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively.

Conclusions

Genetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative r_g between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its r_g.     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

Multi-trait and multi-environment QTL analysis reveals the impact of seed colour on seed composition traits in <Emphasis Type="Italic">Brassica napus</Emphasis>

Brassica napus seed composition traits (fibre, protein, oil and fatty acid profiles), seed colour and yield-associated traits are regulated by a complex network of genetic factors. Although previous studies have attempted to dissect the underlying genetic basis for these traits, a more complete picture of the available quantitative trait loci (QTL) variation and any interaction between the different traits is required. In this study, QTL mapping for eleven seed composition traits, seed colour and a yield-related trait (TSW) was conducted in a spring-type canola-quality B. napus doubled haploid (DH) population from a cross between black-seeded (DH12075) and yellow-seeded (YN01-429) lines across five environments. A major QTL associated with fibre traits (acid detergent fibre, acid detergent lignin and neutral detergent fibre) and seed colour (whiteness index) was mapped on chromosome N9 across the five environments. Multi-trait analysis identified QTL which had pleiotropic effect for seed colour and other composition traits. Multi-environment analysis revealed genetic (QTL) × environment effects on most QTL. These findings provide a more detailed insight into the complex QTL networks controlling seed composition and yield-associated traits in canola-quality B. napus.     

Identification of quantitative trait loci (QTL) for fruit-quality traits and number of weeks of flowering in the cultivated strawberry

Fruit quality and repeat flowering are two major foci of several strawberry breeding programs. The identification of quantitative trait loci (QTL) and molecular markers linked to these traits could improve breeding efficiency. In this work, an F₁ population derived from the cross ‘Delmarvel’ × ‘Selva’ was used to develop a genetic linkage map for QTL analyses of fruit-quality traits and number of weeks of flowering. Some QTL for fruit-quality traits were identified on the same homoeologous groups found in previous studies, supporting trait association in multiple genetic backgrounds and utility in multiple breeding programs. None of the QTL for soluble solids colocated with a QTL for titratable acids, and, although the total soluble solid contents were significantly and positively correlated with titratable acids, the correlation coefficient value of 0.2452 and independence of QTL indicate that selection for high soluble solids can be practiced independently of selection for low acidity. One genomic region associated with the total number of weeks of flowering was identified quantitatively on LG IV-S-1. The most significant marker, FxaACAO2I8C-145S, explained 43.3 % of the phenotypic variation. The repeat-flowering trait, scored qualitatively, mapped to the same region as the QTL. Dominance of the repeat-flowering allele was demonstrated by the determination that the repeat-flowering parent was heterozygous. This genomic region appears to be the same region identified in multiple mapping populations and testing environments. Markers linked in multiple populations and testing environments to fruit-quality traits and repeat flowering should be tested widely for use in marker-assisted breeding.     

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

Key message

A comprehensive linkage atlas for seed yield in rapeseed.

Abstract

Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

     

Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes

Key message

A stable QTL that may be used in marker-assisted selection in wheat breeding programs was detected for yield, yield components and drought tolerance-related traits in spring wheat association mapping panel.

Abstract

Genome-wide association mapping has become a widespread method of quantitative trait locus (QTL) identification for many crop plants including wheat (Triticum aestivum L.). Its benefit over traditional bi-parental mapping approaches depends on the extent of linkage disequilibrium in the mapping population. The objectives of this study were to determine linkage disequilibrium decay rate and population structure in a spring wheat association mapping panel (n = 285–294) and to identify markers associated with yield and yield components, morphological, phenological, and drought tolerance-related traits. The study was conducted under fully irrigated and rain-fed conditions at Greeley, CO, USA and Melkassa, Ethiopia in 2010 and 2011 (five total environments). Genotypic data were generated using diversity array technology markers. Linkage disequilibrium decay rate extended over a longer genetic distance for the D genome (6.8 cM) than for the A and B genomes (1.7 and 2.0 cM, respectively). Seven subpopulations were identified with population structure analysis. A stable QTL was detected for grain yield on chromosome 2DS both under irrigated and rain-fed conditions. A multi-trait region significant for yield and yield components was found on chromosome 5B. Grain yield QTL on chromosome 1BS co-localized with harvest index QTL. Vegetation indices shared QTL with harvest index on chromosome 1AL and 5A. After validation in relevant genetic backgrounds and environments, QTL detected in this study for yield, yield components and drought tolerance-related traits may be used in marker-assisted selection in wheat breeding programs.     

Identification and validation of genomic regions that affect shoot fly resistance in sorghum [<Emphasis Type="Italic">Sorghum bicolor</Emphasis> (L.) Moench]

Shoot fly is one of the most important pests affecting the sorghum production. The identification of quantitative trait loci (QTL) affecting shoot fly resistance enables to understand the underlying genetic mechanisms and genetic basis of complex interactions among the component traits. The aim of the present study was to detect QTL for shoot fly resistance and the associated traits using a population of 210 RILs of the cross 27B (susceptible) × IS2122 (resistant). RIL population was phenotyped in eight environments for shoot fly resistance (deadheart percentage), and in three environments for the component traits, such as glossiness, seedling vigor and trichome density. Linkage map was constructed with 149 marker loci comprising 127 genomic-microsatellite, 21 genic-microsatellite and one morphological marker. QTL analysis was performed by using MQM approach. 25 QTL (five each for leaf glossiness and seedling vigor, 10 for deadhearts, two for adaxial trichome density and three for abaxial trichome density) were detected in individual and across environments. The LOD and R ² (%) values of QTL ranged from 2.44 to 24.1 and 4.3 to 44.1%, respectively. For most of the QTLs, the resistant parent, IS2122 contributed alleles for resistance; while at two QTL regions, the susceptible parent 27B also contributed for resistance traits. Three genomic regions affected multiple traits, suggesting the phenomenon of pleiotrophy or tight linkage. Stable QTL were identified for the traits across different environments, and genetic backgrounds by comparing the QTL in the study with previously reported QTL in sorghum. For majority of the QTLs, possible candidate genes were identified. The QTLs identified will enable marker assisted breeding for shoot fly resistance in sorghum.     

Genetic Architecture of Ear Fasciation in Maize (Zea mays) under QTL Scrutiny

Maize ear fasciation

Knowledge of the genes affecting maize ear inflorescence may lead to better grain yield modeling. Maize ear fasciation, defined as abnormal flattened ears with high kernel row number, is a quantitative trait widely present in Portuguese maize landraces.

Material and Methods

Using a segregating population derived from an ear fasciation contrasting cross (consisting of 149 F_2:3 families) we established a two location field trial using a complete randomized block design. Correlations and heritabilities for several ear fasciation-related traits and yield were determined. Quantitative Trait Loci (QTL) involved in the inheritance of those traits were identified and candidate genes for these QTL proposed.

Results and Discussion

Ear fasciation broad-sense heritability was 0.73. Highly significant correlations were found between ear fasciation and some ear and cob diameters and row number traits. For the 23 yield and ear fasciation-related traits, 65 QTL were identified, out of which 11 were detected in both environments, while for the three principal components, five to six QTL were detected per environment. Detected QTL were distributed across 17 genomic regions and explained individually, 8.7% to 22.4% of the individual traits or principal components phenotypic variance. Several candidate genes for these QTL regions were proposed, such as bearded-ear1, branched silkless1, compact plant1, ramosa2, ramosa3, tasselseed4 and terminal ear1. However, many QTL mapped to regions without known candidate genes, indicating potential chromosomal regions not yet targeted for maize ear traits selection.

Conclusions

Portuguese maize germplasm represents a valuable source of genes or allelic variants for yield improvement and elucidation of the genetic basis of ear fasciation traits. Future studies should focus on fine mapping of the identified genomic regions with the aim of map-based cloning.     

Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture

A previous genetic map containing 117 microsatellite loci and 400 F(2) plants was used for quantitative trait loci (QTL) mapping in tropical maize. QTL were characterized in a population of 400 F(2:3) lines, derived from selfing the F(2) plants, and were evaluated with two replications in five environments. QTL determinations were made from the mean of these five environments. Grain yield (GY), plant height (PH), ear height (EH) and grain moisture (GM) were measured. Variance components for genotypes (G), environments (E) and GxE interaction were highly significant for all traits. Heritability was 0.69 for GY, 0.66 for PH, 0.67 for EH and 0.23 for GM. Using composite interval mapping (CIM), a total of 13 distinct QTLs were identified: four for GY, four for PH and five for EH. No QTL was detected for GM. The QTL explained 32.73 % of the phenotypic variance of GY, 24.76 % of PH and 20.91 % of EH. The 13 QTLs displayed mostly partial dominance or overdominance gene action and mapped to chromosomes 1, 2, 7, 8 and 9. Most QTL alleles conferring high values for the traits came from line L-14-4B. Mapping analysis identified genomic regions associated with two or more traits in a manner that was consistent with correlation among traits, supporting either pleiotropy or tight linkage among QTL. The low number of QTLs found, can be due to the great variation that exists among tropical environments.     

Simultaneous improvement and genetic dissection of grain yield and its related traits in a backbone parent of hybrid rice (Oryza sativa L.) using selective introgression

Three populations with a total of 125 BC2F3:4 introgression lines (ILs) selected for high yields from three BC2F2 populations were used for genetic dissection of rice yield and its related traits. The progeny testing in replicated phenotyping across two environments and genotyping with 140 polymorphic simple sequence repeat markers allowed the identification of 21 promising ILs that had significantly higher yields than the recurrent parent Shuhui527 (SH527). A total of 94 quantitative trait loci (QTL) were identified using the selective introgression method based on Chi-squared (χ ²) and multi-locus probability tests and the RSTEP-LRT method based on stepwise regression. These QTL were mostly mapped to 12 clusters on seven rice chromosomes. Several important properties of the QTL affecting grain yield (GY) and its related traits were revealed. The first one was the presence of strong and frequent non-random associations between or among QTL that affect low-heritability traits (GY and spikelet number per panicle, SN) in the ILs with high trait values. Second, beneficial alleles at 88.9 % GY and 75 % SN QTL for increased productivity were from the donors, suggesting that direct phenotypic selection for high yield in our introgression breeding program was a powerful way to transfer beneficial alleles at many loci from the donors into SH527. Third, most QTL were in clusters with large effects on multiple traits, which should be the focal points in further investigations and marker-assisted selection in rice. The majority of the QTL identified were expressed only in one of the environments, suggesting that differential expression of QTL in different environments is the primary genetic basis of genotype × environment interaction. Finally, a large variation in both the direction and magnitude of QTL effects was detected for different donor alleles at seven QTL in the same genetic background and environments. This finding suggests the possible presence of functional diversity among the donor alleles at these loci. The promising ILs and QTL identified provide valuable materials and genetic information for further improving the yield potential of SH527, which is a backbone restorer of hybrid rice in China.     

Quantitative trait loci mapping of yield and related traits using a high-density genetic map of maize

Improving grain yield is the ultimate goal of the maize-breeding programs. In this study, analyses of conditional and unconditional quantitative trait locus (QTL) and epistatic interactions were used to elucidate the genetic architecture of yield and its related traits. A total of 15 traits of a recombinant inbred line population, including yield per plant (YPP), seven ear-related traits, and seven kernel-related traits, were measured in six different environments. Based on the genetic linkage map constructed using 2091 bins as markers, 56 main-effect QTLs for these traits were identified. These QTLs were distributed across eight genomic regions (bin 1.06, bin 4.02/4.05/4.08, bin 5.04, bin 7.04, bin 8.08, and bin 9.04), within the marker intervals of 85.45–6260.66 kb, and the phenotypic variance explained ranging from 5.69 to 11.56 %. One gene (GRMZM2G168229) encoding SBP-box domain protein was located in the small interval of qKRN4-3 and may be involved in patterning of kernel row number. Seventeen conditional QTLs identified for YPP were conditioned on its related traits and explained 6.18–23.15 % of the phenotypic variance. Conditional mapping analysis revealed that qYPP4-1, qYPP6-1, and qYPP8-1 are partially influenced by YPP-related traits at the individual QTL level. Digenic epistatic analysis identified 12 digenic interactions involving 22 loci across the whole genome. In addition, conditional digenic epistatic analysis identified 14 digenic interactions involving 21 loci. This study provides valuable information for understanding the genetic relationship between YPP and related traits and constitutes the first step toward the cloning of the relevant genes.     

Genetic architecture of a selection response in Arabidopsis thaliana

Quantitative trait locus (QTL) mapping has become an established and effective method for studying the genetic architecture of complex traits. In this report, we use a QTL mapping approach in combination with data from a large selection experiment in Arabidopsis thaliana to explore a response to selection of experimental populations with differentiated genetic backgrounds. Experimental populations with genetic backgrounds derived from ecotypes Landsberg and Niederzenz were exposed to multiple generations of fertility and viability selection. This selection resulted in phenotypic shifts in a number of life-history and fitness-related characters including early development time, flowering time, dry biomass, longevity, and fruit production. Quantitative trait loci were mapped for these traits and their positions were compared to previously characterized allele frequency changes in the experimental populations (Ungerer et al. 2003). Quantitative trait locus positions largely colocalized with genomic regions under strong and consistent selection in populations with differentiated genetic backgrounds, suggesting that alleles for these traits were selected similarly in differentiated genetic backgrounds. However, one QTL region exhibited a more variable response; being positively selected on one genetic background but apparently neutral in another. This study demonstrates how QTL mapping approaches can be combined with map-based population genetic data to study how selection acts on standing genetic variation in populations.     

Meta-analysis of quantitative trait loci for grain yield and component traits under reproductive-stage drought stress in an upland rice population

A recombinant inbred population developed from a cross between high-yielding lowland rice (Oryza sativa L.) subspecies indica cv. IR64 and upland tropical rice subspecies japonica cv. Cabacu was used to identify quantitative trait loci (QTLs) for grain yield (GY) and component traits under reproductive-stage drought stress. One hundred fifty-four lines were grown in field trials in Indonesia under aerobic conditions by giving surface irrigation to field capacity every 4 days. Water stress was imposed for a period of 15 days during pre-flowering by withholding irrigation at 65 days after seeding. Leaf rolling was scored at the end of the stress period and eight agronomic traits were evaluated after recovery. The population was also evaluated for root pulling force, and a total of 201 single nucleotide polymorphism markers were used to construct the molecular genetic linkage map and QTL mapping. A QTL for GY under drought stress was identified in a region close to the sd1 locus on chromosome 1. QTL meta-analysis across diverse populations showed that this QTL was conserved across genetic backgrounds and co-localized with QTLs for leaf rolling and osmotic adjustment (OA). A QTL for percent seed set and grains per panicle under drought stress was identified on chromosome 8 in the same region as a QTL for OA previously identified in three different populations.     

QTL Analysis and Effect of the fin Locus on Tropical Adaptation in an Inter-Gene Pool Common Bean Population

Common bean architecture is in part determined by the determinacy gene fin which can affect yield potential and adaptation to various environments and has many known effects in temperate regions. Tropical adaptation, meanwhile, requires adaptation to rainy and dry seasons where heat and drought stress are major problems. The goal of this research was to determine the effect of the fin gene on plants grown under heat, drought and non-stress conditions in the tropics and to identify quantitative trait loci (QTL) for architectural, phenological and yield traits related to fin in an inter-genepool population derived from a cross between an indeterminate Mesoamerican genotype with erect architecture (A55) and a determinate Andean genotype with heat tolerance (G122). The population was evaluated in four experiments conducted in a tropical location across two rainy seasons and across dry season drought stress and dry season irrigated treatments. A total of 71 SSR loci and 245 AFLP, RAPD, seed protein or phenotypic markers were integrated together into a genetic map with a total distance of 982.8 cM. A total of 36 QTL were identified based on the evaluation of six phenotypic variables differentiating the parents. The A55 parent was high yielding under all conditions; while G122 was confirmed to be resistant to high temperatures, but not to drought. Some QTL for yield were associated with erect growth alleles inherited from the indeterminate parent and were located on linkage group b01 near the fin locus while other QTL for seed weight were found across the genome.