VERY LOW ADDITIVE GENETIC VARIANCE AND EVOLUTIONARY POTENTIAL IN MULTIPLE POPULATIONS OF TWO RAINFOREST DROSOPHILA SPECIES

Evolution ◽  
2006 ◽  
Vol 60 (5) ◽  
pp. 1104 ◽  
Author(s):  
Vanessa M. Kellermann ◽  
Belinda van Heerwaarden ◽  
Ary A. Hoffmann ◽  
Carla M. Sgrò
Keyword(s):  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Drosophila Species ◽  
Evolutionary Potential ◽  
Multiple Populations

VERY LOW ADDITIVE GENETIC VARIANCE AND EVOLUTIONARY POTENTIAL IN MULTIPLE POPULATIONS OF TWO RAINFOREST DROSOPHILA SPECIES

Evolution ◽  
2006 ◽  
Vol 60 (5) ◽  
pp. 1104-1108 ◽  
Author(s):  
Vanessa M. Killermann ◽  
Belinda Van Heerwaarden ◽  
Ary A. Hoffmann ◽  
Carla M. Sgro
Keyword(s):  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Drosophila Species ◽  
Evolutionary Potential ◽  
Multiple Populations

scholarly journals A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance

2018 ◽  
Author(s):  
Caroline E. Thomson ◽  
Isabel S. Winney ◽  
Oceane C. Salles ◽  
Benoit Pujol
Keyword(s):  
Animal Model ◽  
Genetic Variance ◽  
Genetic Relatedness ◽  
Additive Genetic Variance ◽  
Phenotypic Traits ◽  
Phenotypic Variance ◽  
Data Types ◽  
Evolutionary Potential ◽  
Recent Developments

AbstractNon-genetic influences on phenotypic traits can affect our interpretation of genetic variance and the evolutionary potential of populations to respond to selection, with consequences for our ability to predict the outcomes of selection. Long-term population surveys and experiments have shown that quantitative genetic estimates are influenced by nongenetic effects, including shared environmental effects, epigenetic effects, and social interactions. Recent developments to the “animal model” of quantitative genetics can now allow us to calculate precise individual-based measures of non-genetic phenotypic variance. These models can be applied to a much broader range of contexts and data types than used previously, with the potential to greatly expand our understanding of nongenetic effects on evolutionary potential. Here, we provide the first practical guide for researchers interested in distinguishing between genetic and nongenetic causes of phenotypic variation in the animal model. The methods use matrices describing individual similarity in nongenetic effects, analogous to the additive genetic relatedness matrix. In a simulation of various phenotypic traits, accounting for environmental, epigenetic, or cultural resemblance between individuals reduced estimates of additive genetic variance, changing the interpretation of evolutionary potential. These variances were estimable for both direct and parental nongenetic variances. Our tutorial outlines an easy way to account for these effects in both wild and experimental populations. These models have the potential to add to our understanding of the effects of genetic and nongenetic effects on evolutionary potential. This should be of interest both to those studying heritability, and those who wish to understand nongenetic variance.

scholarly journals Introduced populations of ragweed show as much evolutionary potential as native populations

2018 ◽  
Author(s):  
Brechann V. McGoey ◽  
John R. Stinchcombe
Keyword(s):  
Genetic Architecture ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Ambrosia Artemisiifolia ◽  
Empirical Literature ◽  
Ecological Problem ◽  
Adaptive Potential ◽  
Evolutionary Potential ◽  
Introduced Populations ◽  
Native Populations

AbstractInvasive species are a global economic and ecological problem. They also offer an opportunity to understand evolutionary processes in a colonizing context. The impacts of evolutionary factors, such as genetic variation, on the invasion process are increasingly appreciated but there remain gaps in the empirical literature. The adaptive potential of populations can be quantified using genetic variance-covariance matrices (G), which encapsulate the heritable genetic variance in a population. Here, we use a multivariate, Bayesian approach to assess the adaptive potential of introduced populations of ragweed, Ambrosia artemisiifolia, a serious allergen and agricultural weed. We compared several aspects of genetic architecture and the structure of G matrices between three native and three introduced populations, based on data collected in the field in a common garden experiment. We find moderate differences in the quantitative genetic architecture among populations, but we do not find that introduced populations suffer from a limited adaptive potential compared to native populations. Ragweed has an annual life history, is an obligate outcrosser, and produces billions of seeds and pollen grains per. These characteristics, combined with the significant additive genetic variance documented here, suggest ragweed will be able to respond quickly to selection pressures in both its native and introduced ranges.

The Contemporary Evolution of Fitness

2018 ◽  
Vol 49 (1) ◽  
pp. 457-476 ◽  
Author(s):  
Andrew P. Hendry ◽  
Daniel J. Schoen ◽  
Matthew E. Wolak ◽  
Jane M. Reid
Keyword(s):  
Genetic Variance ◽  
Evolutionary Dynamics ◽  
Additive Genetic Variance ◽  
Deleterious Mutations ◽  
Contemporary Evolution ◽  
Evolutionary Potential ◽  
Empirical Estimates ◽  
Evolutionary Rescue ◽  
Proportional Change ◽  
Mean Fitness

The rate of evolution of population mean fitness informs how selection acting in contemporary populations can counteract environmental change and genetic degradation (mutation, gene flow, drift, recombination). This rate influences population increases (e.g., range expansion), population stability (e.g., cryptic eco-evolutionary dynamics), and population recovery (i.e., evolutionary rescue). We review approaches for estimating such rates, especially in wild populations. We then review empirical estimates derived from two approaches: mutation accumulation (MA) and additive genetic variance in fitness (IAw). MA studies inform how selection counters genetic degradation arising from deleterious mutations, typically generating estimates of <1% per generation. IAw studies provide an integrated prediction of proportional change per generation, nearly always generating estimates of <20% and, more typically, <10%. Overall, considerable, but not unlimited, evolutionary potential exists in populations facing detrimental environmental or genetic change. However, further studies with diverse methods and species are required for more robust and general insights.

scholarly journals SELECTION FOR BLOOD PRESSURE LEVELS IN MICE

Genetics ◽  
1974 ◽  
Vol 76 (3) ◽  
pp. 537-549
Author(s):  
Gunther Schlager
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Population Bottleneck ◽  
Control Line ◽  
Selected Lines ◽  
High Line ◽  
Selection For ◽  
Random Control

ABSTRACT Response to two-way selection for systolic blood pressure was immediate and continuous for about eight generations. In the twelfth generation, the High males differed from the Low males by 38 mmHG; the females differed by 39 mmHg. There was little overlap between the two lines and they were statistically significant from each other and from the Random control line. There appeared to be no more additive genetic variance in the eleventh and twelfth generations. Causes for the cessation of response are explored. This is probably due to a combination of natural selection acting to reduce litter sizes in the Low line, a higher incidence of sudden deaths in the High line, and loss of favorable alleles as both selection lines went through a population bottleneck in the ninth generation.—In the eleventh generation, the selected lines were used to produce F1, F2, and backcross generations. A genetic analysis yielded significant additive and dominance components in the inheritance of systolic blood pressure.

scholarly journals Sapling and coppice biomass heritabilities and potential gains from Eucalyptus polybractea progeny trials

2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Beren Spencer ◽  
Richard Mazanec ◽  
Mark Gibberd ◽  
Ayalsew Zerihun
Keyword(s):  
Western Australia ◽  
Growth Rates ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Short Rotation ◽  
Sapling Growth ◽  
Genetic And Phenotypic Correlations ◽  
Breeding Selections ◽  
Cross Site

AbstractEucalyptus polybractea has been planted as a short-rotation coppice crop for bioenergy in Western Australia. Historical breeding selections were based on sapling biomass and despite a long history as a coppice crop, the genetic parameters of coppicing are unknown. Here, we assessed sapling biomass at ages 3 and 6 from three progeny trials across southern Australia. After the second sapling assessment, all trees were harvested. Coppice biomass was assessed 3.5 years later. Mortality following harvest was between 1 and 2%. Additive genetic variance for the 6-sapling estimate at one site was not significant. Sapling heritabilities were between 0.06 and 0.36 at 3 years, and 0.18 and 0.20 at 6 years. The heritability for the coppice biomass was between 0.07 and 0.17. Within-site genetic and phenotypic correlations were strong between all biomass assessments. Cross-site correlations were not different from unity. Selections based on net breeding values revealed positive gains in sapling and coppice biomass. Lower or negative gains were estimated if 3-year sapling selections were applied to the coppice assessments (−7.1% to 3.4%) with useful families culled. Positive gains were obtained if 6-year sapling selections were applied to the coppice assessment (6.4% to 9.3%) but these were lower than those obtained by applying coppice selections to the coppice assessment (8.4% to 14.8%). Removal of poor performing families and families that displayed fast sapling growth rates but under-performed as coppice will benefit potential coppice production. These results indicate that selections should be made using coppice data.

scholarly journals Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

2021 ◽  
Author(s):  
Valentin Hivert ◽  
Julia Sidorenko ◽  
Florian Rohart ◽  
Michael E. Goddard ◽  
Jian Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Large Sample ◽  
Human Complex
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