Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. II. Spring and fall cold-hardiness

Jermstad, K. D.; Bassoni, Daniel L.; Wheeler, Nicholas C.; Anekonda, Thimmappa S.; Aitken, Sally N.; Adams, W. T.; Neale, David B.

doi:10.1007/s001220000506

Cited by 77 publications

(56 citation statements)

References 14 publications

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“…These results suggest that common genetic loci affected trait variation and that alleles had the same functional effects across treatments with the same temperature (in CL vs CS), whereas differences existed in the causal genetic loci or functional effects of alleles across treatments with different temperatures (in most comparisons involving WL). This significant temperature effect is in agreement with results of research on reproductive traits in other species (Jermstad et al, 2003;Hedhly et al, 2005;Schmuths et al, 2006). Also consistent with the finding that GEI was due to differential sensitivity to temperature, all but one QTL Â E interaction was in response to temperature.…”

Section: Discussionsupporting

confidence: 91%

The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across simulated seasonal settings

Edwards

Weinig

2010

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Within organisms, groups of traits with different functions are frequently modular, such that variation among modules is independent and variation within modules is tightly integrated, or correlated. Here, we investigated patterns of trait integration and modularity in Brassica rapa in response to three simulated seasonal temperature/photoperiod conditions. The goals of this research were to use trait correlations to understand patterns of trait integration and modularity within and among floral, vegetative and phenological traits of B. rapa in each of three treatments, to examine the QTL architecture underlying patterns of trait integration and modularity, and to quantify how variation in temperature and photoperiod affects the correlation structure and QTL architecture of traits. All floral organs of B. rapa were strongly correlated, and contrary to expectations, floral and vegetative traits were also correlated. Extensive QTL co-localization suggests that covariation of these traits is likely due to pleiotropy, although physically linked loci that independently affect individual traits cannot be ruled out. Across treatments, the structure of genotypic and QTL correlations was generally conserved. Any observed variation in genetic architecture arose from genotype Â environment interactions (GEIs) and attendant QTL Â E in response to temperature but not photoperiod.

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Section: Discussionsupporting

confidence: 91%

The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across simulated seasonal settings

Edwards

Weinig

2010

Heredity

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“…However, differences among the traits can also be observed. The higher number of QTL detected for the timing of bud burst and in comparison to growth traits was already reported in forest trees (Weng et al 2002;Jermstad et al 2003). One possible explanation is that timing of bud burst is under a moderate to strong genetic control, often showing high heritability values (Howe et al 2003).…”

Section: Discussionmentioning

confidence: 73%

Comparison of Quantitative Trait Loci for Adaptive Traits Between Oak and Chestnut Based on an Expressed Sequence Tag Consensus Map

et al. 2006

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A comparative genetic and QTL mapping was performed between Quercus robur L. and Castanea sativa Mill., two major forest tree species belonging to the Fagaceae family. Oak EST-derived markers (STSs) were used to align the 12 linkage groups of the two species. Fifty-one and 45 STSs were mapped in oak and chestnut, respectively. These STSs, added to SSR markers previously mapped in both species, provided a total number of 55 orthologous molecular markers for comparative mapping within the Fagaceae family. Homeologous genomic regions identified between oak and chestnut allowed us to compare QTL positions for three important adaptive traits. Colocation of the QTL controlling the timing of bud burst was significant between the two species. However, conservation of QTL for height growth was not supported by statistical tests. No QTL for carbon isotope discrimination was conserved between the two species. Putative candidate genes for bud burst can be identified on the basis of colocations between EST-derived markers and QTL.T HE genetic basis and evolution of adaptive traits that evolve in response to selection are still largely unknown. Because of the prominent neo-Darwinian view that pointed out the major role of mutations with small effects (infinitesimal model) (Fisher 1930), the study of the genetic basis of adaptation has received little attention until recently (Orr and Coyne 1992). At the same time, Barton and Turelli (1989) reviewed theories and experimental results on evolutionary quantitative genetics. Most of the quantitative traits can evolve in response to selection because the additive variance represents a significant part of their phenotypic variance. Nevertheless, the number of loci involved, the magnitude of their effects, the type of gene action (additivity, dominance, epistasis, and pleiotropy), and the existence of genotypeby-environment interaction effect remain unknown for many traits of adaptive significance. In particular, the number of their underlying loci and the magnitude of the allelic effects are key factors of the evolution of adaptive traits. Orr and Coyne (1992) showed that Fisher's model was incomplete and that mutations with large effects were sometimes involved in adaptation. Indeed, on the basis of Orr's (1998) recent theoretical work, adaptation seems to involve many loci of small and moderate effect but also a few loci of large effect, giving rise to an L-shaped distribution of factors fixed during adaptive evolution. Moreover, from an evolutionary point of view, in a complex organism, adaptation would occur mainly with mutations of intermediate effects that permit it to achieve an appropriate tradeoff between an acceptable probability of fixation and an acceptable probability to be favorable (Orr 2000).Quantitative trait locus (QTL) studies were expected to provide new insights into fundamental questions regarding the genetic basis of quantitative traits and adaptation (Mitchell-Olds 1995). Despite some biases in QTL analysis, such as the underestimation of the number ...

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“…Adaptation to both the local patch (the non-climatic environment of each sub-population) and climate was simulated using a multilocus system, reflecting the important role of polygenic traits in determining an individual's fitness response to its environment [43,44]. Diploid individuals each have 20 loci, 10 of which code for adaptation to the patch-specific environmental conditions while the other half code for adaptation to climate.…”

Section: (A) Genetic Architecturementioning

confidence: 99%

Between migration load and evolutionary rescue: dispersal, adaptation and the response of spatially structured populations to environmental change

et al. 2014

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The evolutionary potential of populations is mainly determined by population size and available genetic variance. However, the adaptability of spatially structured populations may also be affected by dispersal: positively by spreading beneficial mutations across sub-populations, but negatively by moving locally adapted alleles between demes. We develop an individual-based, two-patch, allelic model to investigate the balance between these opposing effects on a population's evolutionary response to rapid climate change. Individual fitness is controlled by two polygenic traits coding for local adaptation either to the environment or to climate. Under conditions of selection that favour the evolution of a generalist phenotype (i.e. weak divergent selection between patches) dispersal has an overall positive effect on the persistence of the population. However, when selection favours locally adapted specialists, the beneficial effects of dispersal outweigh the associated increase in maladaptation for a narrow range of parameter space only (intermediate selection strength and low linkage among loci), where the spread of beneficial climate alleles is not strongly hampered by selection against non-specialists. Given that local selection across heterogeneous and fragmented landscapes is common, the complex effect of dispersal that we describe will play an important role in determining the evolutionary dynamics of many species under rapidly changing climate.

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Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. II. Spring and fall cold-hardiness

Cited by 77 publications

References 14 publications

The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across simulated seasonal settings

The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across simulated seasonal settings

Comparison of Quantitative Trait Loci for Adaptive Traits Between Oak and Chestnut Based on an Expressed Sequence Tag Consensus Map

Between migration load and evolutionary rescue: dispersal, adaptation and the response of spatially structured populations to environmental change

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