A paternally imprinted QTL for mature body mass on mouse Chromosome 8

Body mass (BM) is a classic polygenic trait that has been extensively investigated to determine the underlying genetic architecture. Many previous studies looking at the genetic basis of variation in BM in murine animal models by quantitative trait loci (QTL) mapping have used crosses between two inbred lines. As a consequence it has not been possible to explore imprinting effects which have been shown to play an important role in the genetic basis of early growth with persistent effects throughout the growth curve. Here we use partially inbred mouse lines to identify QTL for mature BM by applying both Mendelian and Imprinting models. The analysis of an F₂ population (n ≈ 500) identified a number of QTL at 14, 16, and 18 weeks explaining in total 31.5%, 34.4%, and 30.5% of total phenotypic variation, respectively. On Chromosome 8 a QTL of large effect (14% of the total phenotypic variance at 14 weeks) was found to be explained by paternal imprinting. Although Chromosome 8 has not been previously associated with imprinting effects, features of candidate genes within the QTL confidence interval (CpG islands and direct clustered repeats) support the hypothesis that Insulin receptor substrate 2 may be associated with imprinting, but as yet is unidentified as being so.     

Quantitative Trait Loci Differentiating the Outbreeding Mimulus Guttatus from the Inbreeding M. Platycalyx

Theoretical predictions about the evolution of selfing depend on the genetic architecture of loci controlling selfing (monogenic vs. polygenic determination, large vs. small effect of alleles, dominance vs. recessiveness), and studies of such architecture are lacking. We inferred the genetic basis of mating system differences between the outbreeding Mimulus guttatus and the inbreeding M. platycalyx by quantitative trait locus (QTL) mapping using random amplified polymorphic DNA and isozyme markers. One to three QTL were detected for each of five mating system characters, and each QTL explained 7.6-28.6% of the phenotypic variance. Taken together, QTL accounted for up to 38% of the variation in mating system characters, and a large proportion of variation was unaccounted for. Inferred QTL often affected more than one trait, contributing to the genetic correlation between those traits. These results are consistent with the hypothesis that quantitative variation in plant mating system characters is primarily controlled by loci with small effect.     

Quantitative trait loci analysis of phenotypic traits and principal components of maize tassel inflorescence architecture

Maize tassel inflorescence architecture is relevant to efficient production of F₁ seed and yield performance of F₁ hybrids. The objectives of this study were to identify genetic relationships among seven measured tassel inflorescence architecture traits and six calculated traits in a maize backcross population derived from two lines with differing tassel architectures, and identify Quantitative Trait Loci (QTL) involved in the inheritance of those tassel inflorescence architecture traits. A Principal Component (PC) analysis was performed to examine relationships among correlated traits. Traits with high loadings for PC1 were branch number and branch number density, for PC2 were spikelet density on central spike and primary branch, and for PC3 were lengths of tassel and central spike. We detected 45 QTL for individual architecture traits and eight QTL for the three PCs. For control of inflorescence architecture, important QTL were found in bins 7.02 and 9.02. The interval phi034—ramosa1 (ral) in bin 7.02 was associated with six individual architecture trait QTL and explained the largest amount of phenotypic variation (17.3%) for PC1. Interval bnlg344–phi027 in bin 9.02 explained the largest amount of phenotypic variation (14.6%) for PC2. Inflorescence architecture QTL were detected in regions with candidate genes fasciated ear2, thick tassel dwarf1, and ral. However, the vast majority of QTL mapped to regions without known candidate genes, indicating positional cloning efforts will be necessary to identify these genes.     

Mapping single-locus and epistatic quantitative trait loci for plant architectural traits in chrysanthemum

Plant architecture is important for chrysanthemum cultivation and breeding. To determine the genetic basis of plant architectural traits in chrysanthemum, a population of 142 F1 plants derived from a cross between the creeping ground-cover chrysanthemum cultivar Yuhualuoying and the erect potted cultivar Aoyunhanxiao was used to detect quantitative trait loci (QTL) associated with plant height, plant width, inter-node length and flower neck length. The broad-sense heritability h _B ² for the four plant architectural traits ranged from 0.33 to 0.83, and transgressive segregation was observed. Single-locus QTL analysis revealed a total of five QTL, accounting for 6.0?C16.1% of the phenotypic variation. Additionally, 11 pairs of epistatic QTL were identified, explaining 3.5?C14.5% of the phenotypic variations. The majority of the interactions detected occurred between background loci. These results indicate that both additive and epistatic effects contribute to phenotypic variation in the plant architecture of chrysanthemum. It is expected that the identified markers associated with the additive QTL and epistatic QTL detected in this study will be of importance in future breeding programs to develop chrysanthemum cultivars exhibiting desirable plant architecture.     

Genetic and QTL analysis of maize tassel and ear inflorescence architecture

Maize (Zea mays L.) ear inflorescence architecture is directly relevant to grain yield components, and tassel architecture is relevant to hybrid seed production. The objectives of this study were to (1) determine heritabilities and correlations of a comprehensive set of tassel and ear inflorescence architecture traits in a set of (Illinois Low Protein×B73) B73 S₁ families, (2) identify chromosomal positions of QTL affecting tassel and ear architecture, and (3) identify possible candidate genes associated with these QTL. For tassel traits, the number of detected QTL ranged from one to five, and explained between 6.5 and 35.9% of phenotypic variation. For ear traits, the number of detected QTL ranged from one to nine and phenotypic variation explained by those QTL varied between 7.9 and 53.0%. We detected QTL for tassel architecture traits that required calculation of ratios from measured traits. Some of these calculated traits QTL were detected in regions that did not show QTL for the measured traits, suggesting that calculation of ratios may reveal developmentally relevant patterns of tassel architecture. We detected a QTL on chromosome 7 for tassel branch number near the gene ramosa1 (ra1), which is known to control tassel branch number, making ra1 a candidate gene for tassel branch number. We detected QTL for several traits on chromosomes 6, 8, and 9, where no inflorescence architecture genes have been mapped, thus providing initial information towards new gene discovery for control of inflorescence architecture.     

A large-sample QTL study in mice: III. Reproduction

Using lines of mice having undergone long-term selection for high and low growth, a large-sample (n 1000 F₂) experiment was conducted to gain further understanding of the genetic architecture of complex polygenic traits. Composite interval mapping on data from 10-week-old F₂ females (n=439) detected 15 quantitative trait loci (QTLs) on 5 chromosomes that influence reproduction traits characterized at day 16 of gestation. These QTL are broadly categorized into two groups: those where effects on the number of live fetuses (LF) were accompanied by parallel effects on the number of dead fetuses (DF), and those free of such undesirable effects. QTL for ovulation rate (OR) did not overlap with QTL for litter size, potentially indicating the importance of uterine capacity. Large dominance effects were identified for most QTL detected, and overdominance was also present. The QTL of largest effects were detected in regions of Chromosome 2, where large QTL effects for growth and fatness have also been found and where corroborating evidence from other studies exists. Considerable overlap between locations of QTL for reproductive traits and for growth traits corresponds well with the positive correlations usually observed among these sets of phenotypes. Some support for the relevance of QTL × genetic background interactions in reproduction was detected. Traits with low heritability demand considerably larger sample sizes to achieve effective power of QTL detection. This is unfortunate as traits with low heritability are among those that could most benefit from QTL-complemented breeding and selection strategies in food animal production.     

Quantitative trait loci for growth and body size in the nine‐spined stickleback Pungitius pungitius L.

Body size is an ecologically important trait shown to be genetically variable both within and among different animal populations as revealed by quantitative genetic studies. However, few studies have looked into underlying genetic architecture of body size variability in the wild using genetic mapping methods. With the aid of quantitative trait loci (QTL) analyses based on 226 microsatellite markers, we mapped body size and growth rate traits in the nine‐spined stickleback (Pungitius pungitius) using an F₂‐intercross (n = 283 offspring) between size‐divergent populations. In total, 17 QTL locations were detected. The proportion of phenotypic variation explained by individual body size‐related QTL ranged from 3% to 12% and those related to growth parameters and increments from 3% to 10%. Several of the detected QTL affected either early or late growth. These results provide a solid starting point for more in depth investigations of structure and function of genomic regions involved in determination of body size in this popular model of ecological and evolutionary research.     

Congruence in QTL for adventitious rooting in Pinus elliottii × Pinus caribaea hybrids resolves between and within-species effects

Targeting between-species effects for improvement in synthetic hybrid populations derived from outcrossing parental tree species may be one way to increase the efficacy and predictability of hybrid breeding. We present a comparative analysis of the quantitative trait loci (QTL) which resolved between from within-species effects for adventitious rooting in two populations of hybrids between Pinus elliottii and P. caribaea, an outbred F₁ (n=287) and an inbred-like F₂ family (n=357). Most small to moderate effect QTL (each explaining 2–5% of phenotypic variation, PV) were congruent (3 out of 4 QTL in each family) and therefore considered within-species effects as they segregated in both families. A single large effect QTL (40% PV) was detected uniquely in the F₂ family and assumed to be due to a between-species effect, resulting from a genetic locus with contrasting alleles in each parental species. Oligogenic as opposed to polygenic architecture was supported in both families (60% and 20% PV explained by 4 QTL in the F₂ and F₁ respectively). The importance of adventitious rooting for adaptation to survive water-logged environments was thought in part to explain oligogenic architecture of what is believed to be a complex trait controlled by many hundreds of genes.     

On the genetic architecture of rapidly adapting and convergent life history traits in guppies

The genetic basis of traits shapes and constrains how adaptation proceeds in nature; rapid adaptation can proceed using stores of polygenic standing genetic variation or hard selective sweeps, and increasing polygenicity fuels genetic redundancy, reducing gene re-use (genetic convergence). Guppy life history traits evolve rapidly and convergently among natural high- and low-predation environments in northern Trinidad. This system has been studied extensively at the phenotypic level, but little is known about the underlying genetic architecture. Here, we use four independent F2 QTL crosses to examine the genetic basis of seven (five female, two male) guppy life history phenotypes and discuss how these genetic architectures may facilitate or constrain rapid adaptation and convergence. We use RAD-sequencing data (16,539 SNPs) from 370 male and 267 female F2 individuals. We perform linkage mapping, estimates of genome-wide and per-chromosome heritability (multi-locus associations), and QTL mapping (single-locus associations). Our results are consistent with architectures of many loci of small-effect for male age and size at maturity and female interbrood period. Male trait associations are clustered on specific chromosomes, but female interbrood period exhibits a weak genome-wide signal suggesting a potentially highly polygenic component. Offspring weight and female size at maturity are also associated with a single significant QTL each. These results suggest rapid, repeatable phenotypic evolution of guppies may be facilitated by polygenic trait architectures, but subsequent genetic redundancy may limit gene re-use across populations, in agreement with an absence of strong signatures of genetic convergence from recent analyses of wild guppies.Subject terms: Evolutionary genetics, Quantitative trait     

Quantitative trait loci controlling plant architectural traits in cotton

Cotton plant architecture is an important characteristic influencing the suitability of specific cotton varieties in cultivation, fiber yield and quality. However, complex multigenic relationships and substantial genotype–environment interaction underlie plant architecture, and will hinder the efficient improvement of these traits in conventional cotton breeding programs. An enhanced understanding of the molecular-genetic regulation of plant morphological developmental can aid in the modification of agronomically relevant traits. In this study, an interspecific Gossypium hirsutum and Gossypium barbadense BC₁ population was used to identify QTL associated with plant architectural traits. Twenty-six single QTL were identified for seven plant architecture traits. The phenotypic variation explained by an individual QTL ranged from 9.56% to 44.57%. In addition, 11 epistatic QTL for fruit branch angle (FBA), plant height (PH), main-stem leaf size (MLS), and fruiting branch internode length (FBI) explained 2.28–15.34% of the phenotypic variation in these traits. The majority of the interactions (60%) occurred between markers linked to QTL influencing the same traits. The QTL detected in this study are expected to be valuable in future breeding programs to develop cultivars exhibiting desirable cotton architecture.     

Genotype × diet interactions in mice predisposed to mammary cancer. I. Body weight and fat

High dietary fat intake and obesity may increase susceptibility to certain forms of cancer. To study the interactions of dietary fat, obesity, and metastatic mammary cancer, we created a population of F₂ mice cosegregating obesity QTL and the MMTV-PyMT transgene. We fed the F₂ mice either a very-high-fat or a matched-control-fat diet and measured growth, body composition, age at mammary tumor onset, tumor number and severity, and formation of pulmonary metastases. SNP genotyping across the genome facilitated analyses of QTL and QTL × diet interaction effects. Here we describe development of the F₂ population (n = 615) which resulted from a cross between the polygenic obesity model M16i and FVB/NJ-TgN (MMTV-PyMT)^634Mul, effects of diet on growth and body composition, and QTL and QTL × diet and/or gender interaction effects for growth and obesity-related phenotypes. We identified 38 QTL for body composition traits that were significant at the genome-wide 0.05 level, likely representing nine distinct loci after accounting for pleiotropic effects. QTL × diet and/or gender interactions were present at 15 of these QTL, indicating that such interactions play a significant role in defining the genetic architecture of complex traits such as body weight and obesity.     

Phenotypic Plasticity Contributes to Maize Adaptation and Heterosis

Plant phenotypic plasticity describes altered phenotypic performance of an individual when grown in different environments. Exploring genetic architecture underlying plant plasticity variation may help mitigate the detrimental effects of a rapidly changing climate on agriculture, but little research has been done in this area to date. In the present study, we established a population of 976 maize F₁ hybrids by crossing 488 diverse inbred lines with two elite testers. Genome-wide association study identified hundreds of quantitative trait loci associated with phenotypic plasticity variation across diverse F₁ hybrids, the majority of which contributed very little variance, in accordance with the polygenic nature of these traits. We identified several quantitative trait locus regions that may have been selected during the tropical-temperate adaptation process. We also observed heterosis in terms of phenotypic plasticity, in addition to the traditional genetic value differences measured between hybrid and inbred lines, and the pattern of which was affected by genetic background. Our results demonstrate a landscape of phenotypic plasticity in maize, which will aid in the understanding of its genetic architecture, its contribution to adaptation and heterosis, and how it may be exploited for future maize breeding in a rapidly changing environment.     

Mapping species differences for adventitious rooting in a <Emphasis Type="Italic">Corymbia torelliana</Emphasis> × <Emphasis Type="Italic">Corymbia citriodora</Emphasis> subspecies <Emphasis Type="Italic">variegata</Emphasis> hybrid

Quantitative trait loci (QTL) detection was carried out for adventitious rooting and associated propagation traits in a second-generation outbred Corymbia torelliana × Corymbia citriodora subspecies variegata hybrid family (n = 186). The parental species of this cross are divergent in their capacity to develop roots adventitiously on stem cuttings and their propensity to form lignotubers. For the ten traits studied, there was one or two QTL detected, with some QTL explaining large amounts of phenotypic variation (e.g. 66% for one QTL for percentage rooting), suggesting that major effects influence rooting in this cross. Collocation of QTL for many strongly genetically correlated rooting traits to a single region on linkage group 12 suggested pleiotropy. A three locus model was most parsimonious for linkage group 12, however, as differences in QTL position and lower genetic correlations suggested separate loci for each of the traits of shoot production and root initiation. Species differences were thought to be the major source of phenotypic variation for some rooting rate and root quality traits because of the major QTL effects and up to 59-fold larger homospecific deviations (attributed to species differences) relative to heterospecific deviations (attributed to standing variation within species) evident at some QTL for these traits. A large homospecific/heterospecific ratio at major QTL suggested that the gene action evident in one cross may be indicative of gene action more broadly in hybrids between these species for some traits.     

A large-sample QTL study in mice: II. Body composition

Using lines of mice having undergone long-term selection for high and low growth, a large-sample (n = ~1,000 F₂) experiment was conducted to gain further understanding of the genetic architecture of complex polygenic traits. Composite interval mapping on data from male F₂ mice (n = 552) detected 50 QTL on 15 chromosomes impacting weights of various organ and adipose subcomponents of growth, including heart, liver, kidney, spleen, testis, and subcutaneous and epididymal fat depots. Nearly all aggregate growth QTL could be interpreted in terms of the organ and fat subcomponents measured. More than 25% of QTL detected map to MMU2, accentuating the relevance of this chromosome to growth and fatness in the context of this cross. Regions of MMU7, 15, and 17 also emerged as important obesity hot-spots. Average degrees of directional dominance are close to additivity, matching expectations for body composition traits. A strong QTL congruency is evident among heart, liver, kidney, and spleen weights. Liver and testis are organs whose genetic architectures are, respectively, most and least aligned with that for aggregate body weight. In this study, growth and body weight are interpreted in terms of organ subcomponents underlying the macro aggregate traits, and anchored on the corresponding genomic locations. Present address (Joao L. Rocha): Sygen International, 2929 Seventh Street, Berkeley, California 94710, USA.     

Deciphering the genomic architecture of the stickleback brain with a novel multilocus gene‐mapping approach

Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small‐effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine‐spined sticklebacks (Pungitius pungitius) with the aid of novel multilocus QTL‐mapping approaches based on a de‐biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL‐mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single‐locus analyses of an F₂ interpopulation cross with 239 individuals and 15 198, fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multilocus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multilocus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1–6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small‐effect loci. Apart from suggesting moderately heritable (h² ≈ 0.15–0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL‐mapping approach developed here has distinctive advantages over the traditional QTL‐mapping methods in analyses of dense marker panels.     

Genomic architecture of habitat‐related divergence and signature of directional selection in the body shapes of Gnathopogon fishes

Evolution of ecomorphologically relevant traits such as body shapes is important to colonize and persist in a novel environment. Habitat‐related adaptive divergence of these traits is therefore common among animals. We studied the genomic architecture of habitat‐related divergence in the body shape of Gnathopogon fishes, a novel example of lake–stream ecomorphological divergence, and tested for the action of directional selection on body shape differentiation. Compared to stream‐dwelling Gnathopogon elongatus, the sister species Gnathopogon caerulescens, exclusively inhabiting a large ancient lake, had an elongated body, increased proportion of the caudal region and small head, which would be advantageous in the limnetic environment. Using an F₂ interspecific cross between the two Gnathopogon species (195 individuals), quantitative trait locus (QTL) analysis with geometric morphometric quantification of body shape and restriction‐site associated DNA sequencing‐derived markers (1622 loci) identified 26 significant QTLs associated with the interspecific differences of body shape‐related traits. These QTLs had small to moderate effects, supporting polygenic inheritance of the body shape‐related traits. Each QTL was mostly located on different genomic regions, while colocalized QTLs were detected for some ecomorphologically relevant traits that are proxy of body and caudal peduncle depths, suggesting different degree of modularity among traits. The directions of the body shape QTLs were mostly consistent with the interspecific difference, and QTL sign test suggested a genetic signature of directional selection in the body shape divergence. Thus, we successfully elucidated the genomic architecture underlying the adaptive changes of the quantitative and complex morphological trait in a novel system.     

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.     

Two reproductive traits show contrasting genetic architectures in Plantago lanceolata

In many species, temperature‐sensitive phenotypic plasticity (i.e., an individual's phenotypic response to temperature) displays a positive correlation with latitude, a pattern presumed to reflect local adaptation. This geographical pattern raises two general questions: (a) Do a few large‐effect genes contribute to latitudinal variation in a trait? (b) Is the thermal plasticity of different traits regulated pleiotropically? To address the questions, we crossed individuals of Plantago lanceolata derived from northern and southern European populations. Individuals naturally exhibited high and low thermal plasticity in floral reflectance and flowering time. We grew parents and offspring in controlled cool‐ and warm‐temperature environments, mimicking what plants would encounter in nature. We obtained genetic markers via genotype‐by‐sequencing, produced the first recombination map for this ecologically important nonmodel species, and performed quantitative trait locus (QTL) mapping of thermal plasticity and single‐environment values for both traits. We identified a large‐effect QTL that largely explained the reflectance plasticity differences between northern and southern populations. We identified multiple smaller‐effect QTLs affecting aspects of flowering time, one of which affected flowering time plasticity. The results indicate that the genetic architecture of thermal plasticity in flowering is more complex than for reflectance. One flowering time QTL showed strong cytonuclear interactions under cool temperatures. Reflectance and flowering plasticity QTLs did not colocalize, suggesting little pleiotropic genetic control and freedom for independent trait evolution. Such genetic information about the architecture of plasticity is environmentally important because it informs us about the potential for plasticity to offset negative effects of climate change.     

Quantitative Trait Loci Affecting Response to Crowding Stress in an F2 Generation of Rainbow Trout Produced Through Phenotypic Selection

Selective breeding programs for salmonids typically aim to improve traits associated with growth and disease resistance. It has been established that stressors common to production environments can adversely affect these and other traits which are important to producers and consumers. Previously, we employed phenotypic selection to create families that exhibit high or low plasma cortisol concentrations in response to crowding stress. Subsequent crosses of high × low phenotypes founded a multigenerational breeding scheme with the aim of dissecting the genetic basis for variation underlying stress response through the identification of quantitative trait loci (QTL). Multiple methods of QTL analyses differing in their assumptions of homozygosity of the causal alleles in the grandparental generation yielded similar results in the F₁ generation, and the analysis of two stress response phenotype measurement indexes were highly correlated. In the current study, we conducted a genome scan with microsatellites to detect QTL in the F₂ generation of two families created through phenotypic selection and having larger numbers of offspring than families screened in the previous generation. Seven suggestive and three significant QTL were detected, seven of which were not previously detected in the National Center for Cool and Cold Water Aquaculture germplasm, bringing the total number of chromosomes containing significant and suggestive stress response QTL to 4 and 15, respectively. One significant QTL which peaks at 7 cM on chromosome Omy12 spans 12 cM and explains 25 % of the phenotypic variance in family 2008052 particularly warrants further investigation. Five QTL with significant parent-of-origin effects were detected in family 2008052, including two QTL on Omy12. The 95 % confidence intervals for the remaining QTL we detected were broad, requiring validation and fine mapping with other genotyping approaches and mapping strategies. These results will facilitate identification of potential casual alleles that can be employed in strategies aimed at better understanding the genetic and physiological basis of stress responses to crowding in rainbow trout aquaculture production.     

Study of tree architecture of apple (<Emphasis Type="Italic">Malus</Emphasis> × <Emphasis Type="Italic">domestica</Emphasis> Borkh.) by QTL analysis of growth traits

Apple tree architecture is naturally very diverse, but for fruit production, certain tree habits are more desirable than others. Here we describe the results of a QTL analysis performed to study the genetic control of growth traits in apple. This was carried out on the progeny of a cross between two apple cultivars of contrasting tree architectures. “Telamon” has a columnar tree form and “Braeburn” has a more standard, “normal” growth habit. The growth traits were measured on the F ₁ seedlings of the Telamon × Braeburn population for two consecutive years of growth on own roots and for the first year of growth on M9 rootstock. QTL analysis was carried out using either the Kruskal–Wallis method or the Multiple QTL Method. For all but one growth characteristic, significant QTLs were detected. A major cluster of QTLs was located in the Co gene region of “Telamon”, confirming the major influence of the Co gene on tree architecture, although this influence changed as the plant material aged and was generally more pronounced for rootstock-grown plants. Additional QTL results suggest the occurrence of genes with pleiotropic effects on tree architecture. The observed QTL instability over different years and for different root systems indicates that the genetic control of tree architecture is largely influenced by environmental factors and probably changes as the tree matures. Finally, a major influence of the root system on all the traits determining tree architecture was clearly demonstrated.