scholarly journals Two are better than one: combining landscape genomics and common gardens for detecting local adaptation in forest trees

2014 ◽  
Vol 23 (19) ◽  
pp. 4671-4673 ◽  
Author(s):  
Olivier Lepais ◽  
Cecile F. E. Bacles
Keyword(s):  
Local Adaptation ◽  
Forest Trees ◽  
Landscape Genomics ◽  
Common Gardens ◽  
Better Than

scholarly journals Simulating local adaptation to climate of forest trees with a Physio‐Demo‐Genetics model

2014 ◽  
Vol 7 (4) ◽  
pp. 453-467 ◽  
Author(s):  
Sylvie Oddou‐Muratorio ◽  
Hendrik Davi
Keyword(s):  
Local Adaptation ◽  
Forest Trees

scholarly journals Climate-driven divergent selection in a foundation tree species: QST-FST evidence from multiple common gardens

2021 ◽  
Author(s):  
Hillary Cooper ◽  
Gerard Allan ◽  
Lela Andrews ◽  
Rebecca Best ◽  
Kevin Grady ◽  
...  
Keyword(s):  
Local Adaptation ◽  
Tree Species ◽  
Divergent Selection ◽  
Population Variation ◽  
Population Divergence ◽  
Phenotypic Traits ◽  
Local Conditions ◽  
Basal Diameter ◽  
Common Gardens ◽  
Fremont Cottonwood

Widespread tree species span large climatic gradients that often lead to high levels of local adaptation and phenotypic divergence across their range. To evaluate the relative roles of selection and drift in driving divergence in phenotypic traits, we compared molecular and quantitative genetic variation in Populus fremontii (Fremont cottonwood), using data from > 9000 SNPs and genotypes from 16 populations reciprocally planted in three common gardens that span the species’ climatic range. We present three major findings: 1) There is significant within- and among-population variation in functional traits expressed in each of the common gardens. 2) There is evidence from all three gardens that population divergence in leaf phenology and specific leaf area has been driven by divergent selection (QST > FST). In contrast, QST-FST comparisons for performance traits like height and basal diameter were highly dependent on growing environment, indicating divergent, stabilizing, or no selection across the three gardens. We show this is likely due to local adaptation of source populations to contrasting growing environments. 3) Climate is a primary selective force driving trait divergence, where the traits showing the strongest correlations with a genotype’s provenance climate also had the highest QST values. We conclude that climatic gradients have contributed to significant phenotypic differences and local adaptation in Fremont cottonwood. These results are important because as climate is changing much more rapidly, traits such as phenology that are finely tuned to local conditions may now be subject to intense selection or quickly become maladaptive.

scholarly journals Disease swamps molecular signatures of genetic-environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations

2019 ◽  
Author(s):  
Alexandra K. Fraik ◽  
Mark J. Margres ◽  
Brendan Epstein ◽  
Soraia Barbosa ◽  
Menna Jones ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Local Adaptation ◽  
Environmental Variables ◽  
Abiotic Factors ◽  
Rapid Onset ◽  
Tasmanian Devil ◽  
Landscape Genomics ◽  
Abiotic Environment ◽  
Candidate Loci ◽  
Sarcophilus Harrisii

AbstractLandscape genomics studies focus on identifying candidate genes under selection via spatial variation in abiotic environmental variables, but rarely by biotic factors such as disease. The Tasmanian devil (Sarcophilus harrisii) is found only on the environmentally heterogeneous island of Tasmania and is threatened with extinction by a nearly 100% fatal, transmissible cancer, devil facial tumor disease (DFTD). Devils persist in regions of long-term infection despite epidemiological model predictions of species’ extinction, suggesting possible adaptation to DFTD. Here, we test the extent to which spatial variation and genetic diversity are associated with the abiotic environment and/or by DFTD. We employ genetic-environment association (GEAs) analyses using a RAD-capture panel consisting of 6,886 SNPs from 3,286 individuals sampled pre- and post-disease arrival. Pre-disease, we find significant correlations of allele frequencies with environmental variables, including 349 unique loci linked to 64 genes, suggesting local adaptation to abiotic environment. Following DFTD arrival, however, we detected few of the pre-disease candidate loci, but instead frequencies of candidate loci linked to 14 genes correlated with disease prevalence. Loss of apparent signal of abiotic local adaptation following disease arrival suggests swamping by the strong selection imposed by DFTD. Further support for this result comes from the fact that post-disease candidate loci are in linkage disequilibrium with genes putatively involved in immune response, tumor suppression and apoptosis. This suggests the strength GEA associations of loci with the abiotic environment are swamped resulting from the rapid onset of a biotic selective pressure.

scholarly journals Regarding the F-word: the effects of data Filtering on inferred genotype-environment associations

2020 ◽  
Author(s):  
Collin W Ahrens ◽  
Rebecca Jordan ◽  
Jason Bragg ◽  
Peter A Harrison ◽  
Tara Hopley ◽  
...  
Keyword(s):  
Missing Data ◽  
Allele Frequency ◽  
Minor Allele Frequency ◽  
Management Strategies ◽  
Minor Allele ◽  
Sequencing Data ◽  
System Availability ◽  
Landscape Genomics ◽  
Genotype By Sequencing ◽  
Better Than

AbstractGenotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results. These effects could be amplified in downstream predictions, including management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for adaptation to environment. Using empirical and simulated datasets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the dataset, lessening the power to detect adaptive variants (i.e. simulated true positives) with strong and weak strength of selections. Regardless, strength of selection was a good predictor for GEA detection, but even SNPs under strong selection went undetected. We further show that filtering can significantly impact the predictions of adaptive capacity of species in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending largely on the study system, availability of genomic resources, and desired objectives of the study.

scholarly journals Genomic footprints of local adaptation along elevation gradients associate with present phenotypic variation in teosintes

2019 ◽  
Author(s):  
M-A. Fustier ◽  
N.E. Martínez-Ainsworth ◽  
A. Venon ◽  
H. Corti ◽  
A. Rousselet ◽  
...  
Keyword(s):  
Climate Change ◽  
Gene Flow ◽  
Local Adaptation ◽  
Phenotypic Variation ◽  
Allele Frequencies ◽  
Maize Breeding ◽  
Elevation Gradients ◽  
Common Gardens ◽  
Molecular Determinants ◽  
Set Up

AbstractLocal adaptation across species range is widespread. Yet, much has to be discovered on its environmental drivers, the underlying functional traits and their molecular determinants. Because elevation gradients display continuous environmental changes at a short geographical scale, they provide an exceptional opportunity to investigate these questions. Here, we used two common gardens to phenotype 1664 plants from 11 populations of annual teosintes. These populations were sampled across two elevation gradients in Mexico. Our results point to a syndrome of adaptation to altitude with the production of offspring that flowered earlier, produced less tillers, and larger, longer and heavier grains with increasing elevation. We genotyped these plants for 178 outlier single nucleotide polymorphisms (SNPs), which had been chosen because they displayed excess of allele differentiation and/or correlation with environmental variables in six populations with contrasted altitudes. A high proportion of outlier SNPs associated with the phenotypic variation of at least one trait. We tested phenotypic pairwise correlations between traits, and found that the higher the correlation, the greater the number of common associated SNPs. In addition, allele frequencies at 87 of the outlier SNPs correlated with an environmental component best summarized by altitudinal variation on a broad sample of 28 populations. Chromosomal inversions were enriched for both phenotypically-associated and environmentally-correlated SNPs. Altogether, our results are consistent with the set-up of an altitudinal syndrome promoted by local adaptation of teosinte populations in the face of gene flow. We showed that pleiotropy is pervasive and potentially has constrained the evolution of traits. Finally, we recovered variants underlying phenotypic variation at adaptive traits. Because elevation mimics climate change through space, these variants may be relevant for future maize breeding.Author summaryAcross their native range, species encounter a diversity of habitats promoting local adaptation of geographically distributed populations. While local adaptation is widespread, much has yet to be discovered about the conditions of its emergence, the targeted traits, their molecular determinants and the underlying ecological drivers. Here we employed a reverse ecology approach, combining phenotypes and genotypes, to mine the determinants of local adaptation of teosinte populations distributed along two steep altitudinal gradients in Mexico. Evaluation of 11 populations in two common gardens located at mid-elevation pointed to the set-up of an altitudinal syndrome, in spite of gene flow. We scanned genomes to identify loci with allele frequencies shifts along elevation. Interestingly, variation at these loci was commonly associated to variation of phenotypes. Because elevation mimics climate change through space, these variants may be relevant for future maize breeding.

scholarly journals CartograTree: Enabling Landscape Genomics for Forest Trees

2016 ◽  
Author(s):  
Nic Herndon ◽  
Emily S Grau ◽  
Iman Batra ◽  
Steven A Demurjian Jr. ◽  
Hans A Vasquez-Gross ◽  
...  
Keyword(s):  
Population Structure ◽  
Economic Value ◽  
Genomic Data ◽  
Environmental Data ◽  
Geospatial Data ◽  
Data Sets ◽  
Forest Trees ◽  
Analytic Framework ◽  
Landscape Genomics ◽  
The World

Forest trees cover just over 30% of the earth's surface and are studied by researchers around the world for both their conservation and economic value. With the onset of high throughput technologies, tremendous phenotypic and genomic data sets have been generated for hundreds of species. These long-lived and immobile individuals serve as ideal models to assess population structure and adaptation to environment. Despite the availability of comprehensive data, researchers are challenged to integrate genotype, phenotype, and environment in one place. Towards this goal, CartograTree was designed and implemented as a repository and analytic framework for genomic, phenotypic, and environmental data for forest trees. One of key components, the integration of geospatial data, allows the display of environmental layers and acquisition of environmental metrics relative to the positions of georeferenced individuals.

scholarly journals Lokale Anpassung bei Waldbaumarten: genetische Prozesse und Bedeutung im Klimawandel

2016 ◽  
Vol 167 (6) ◽  
pp. 333-340
Author(s):  
Christian Rellstab ◽  
Andrea R. Pluess ◽  
Felix Gugerli
Keyword(s):  
Local Adaptation ◽  
Environmental Gradients ◽  
Management Strategies ◽  
Environmental Parameters ◽  
Forest Trees ◽  
Local Conditions ◽  
Genetic Changes ◽  
Association Analyses ◽  
Temperature And Precipitation ◽  
Technological Advances

Local adaptation in forest trees: genetic processes and relevance under climate change Forest trees will have to adapt to future climatic changes, a process that will comprise genetic changes as a key component. Owing to technological advances it is now possible to identify the signature of natural selection and local adaptation in the genome. Environmental association analyses aim at associating adaptive genetic patterns with environmental parameters describing the local habitat. On the basis of such studies – including own investigations using oak and beech in Switzerland –, we show that forest trees are genetically differentiated along various environmental gradients, especially temperature and precipitation. Numerous genes could be found that presumably play a role in the adaptation to such environmental factors. Based on these findings, one could identify trees or stands that are adapted to future local conditions, and respective seed material could be considered in silviculture. Because such approaches are still in their infancy and because genome-environment interactions are complex, management strategies should focus on the preservation of (adaptive) genetic diversity, natural regeneration, and connectivity among stands. This would set the basis for the local adaptation of forest stands to altered environmental conditions by natural processes.

scholarly journals CartograTree: Enabling landscape genomics for forest trees

2016 ◽  
Author(s):  
Nic Herndon ◽  
Emily S Grau ◽  
Iman Batra ◽  
Steven A Demurjian Jr. ◽  
Hans A Vasquez-Gross ◽  
...  
Keyword(s):  
Economic Value ◽  
Environmental Data ◽  
Geospatial Data ◽  
Data Sets ◽  
Forest Trees ◽  
Analytic Framework ◽  
Landscape Genomics ◽  
Data Limitations ◽  
New Development ◽  
The World

Forest trees cover just over 30% of the earth's surface and are studied by researchers around the world for both their conservation and economic value. With the onset of high throughput technologies, tremendous phenotypic and genomic data sets have been generated for hundreds of species. These long-lived and immobile individuals serve as ideal models to assess population structure and adaptation to environment. Despite the availability of comprehensive data, researchers are challenged to integrate genotype, phenotype, and environment in one place. Towards this goal, CartograTree was designed and implemented as an open repository and open-source analytic framework for genomic, phenotypic, and environmental data for forest trees. One of its key components, the integration of geospatial data, allows the display of environmental layers and acquisition of environmental metrics relative to the positions of georeferenced individuals. Currently, CartograTree uses the Google Maps API to load environmental data. Limitations inherent to this API are driving new development with a focus on functionality to provide efficient queries of numerous environmental metrics.

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