Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations

Balkenhol, Niko; Dudaniec, Rachael Y.; Krutovsky, Konstantin V.; Johnson, Jeremy S.; Cairns, David M.; Segelbacher, Gernot; Selkoe, Kimberly A.; Heyden, Sophie von der; Wang, Ian; Selmoni, Oliver; Joost, Stéphane

doi:10.1007/13836_2017_2

Cited by 68 publications

(73 citation statements)

References 274 publications

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“…Landscape genomics is an emerging analytical framework that investigates how environmental and spatial processes structure the amount and distribution of neutral and adaptive genetic variation among populations (Balkenhol et al., 2017). Landscape genomics is sometimes conflated with genotype–environment association (GEA) analysis, which includes a wide variety of statistical approaches for identifying candidate adaptive loci that covary with environmental predictors (Rellstab, Gugerli, Eckert, Hancock, & Holderegger, 2015).…”

Section: Improving Downstream Computational Analysesmentioning

confidence: 99%

“…With this introduction to landscape genomics, ConGen participants worked on applications of GEA analysis, currently the most widely used landscape genomic technique (Balkenhol et al., 2017). The reasons for the popularity of GEA analyses are practical: They require no phenotypic data or prior genomic resources, do not require experimental approaches (such as reciprocal transplants) to demonstrate local adaptation, and are often more powerful than differentiation‐based outlier detection methods (De Mita et al., 2013; de Villemereuil, Frichot, Bazin, François, & Gaggiotti, 2014; Forester, Lasky, Wagner, & Urban, 2018; Lotterhos & Whitlock, 2015).…”

Section: Improving Downstream Computational Analysesmentioning

confidence: 99%

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Recent advances in conservation and population genomics data analysis

Hendricks

Anderson

Antão

et al. 2018

Evolutionary Applications

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New computational methods and next‐generation sequencing (NGS) approaches have enabled the use of thousands or hundreds of thousands of genetic markers to address previously intractable questions. The methods and massive marker sets present both new data analysis challenges and opportunities to visualize, understand, and apply population and conservation genomic data in novel ways. The large scale and complexity of NGS data also increases the expertise and effort required to thoroughly and thoughtfully analyze and interpret data. To aid in this endeavor, a recent workshop entitled “Population Genomic Data Analysis,” also known as “ConGen 2017,” was held at the University of Montana. The ConGen workshop brought 15 instructors together with knowledge in a wide range of topics including NGS data filtering, genome assembly, genomic monitoring of effective population size, migration modeling, detecting adaptive genomic variation, genomewide association analysis, inbreeding depression, and landscape genomics. Here, we summarize the major themes of the workshop and the important take‐home points that were offered to students throughout. We emphasize increasing participation by women in population and conservation genomics as a vital step for the advancement of science. Some important themes that emerged during the workshop included the need for data visualization and its importance in finding problematic data, the effects of data filtering choices on downstream population genomic analyses, the increasing availability of whole‐genome sequencing, and the new challenges it presents. Our goal here is to help motivate and educate a worldwide audience to improve population genomic data analysis and interpretation, and thereby advance the contribution of genomics to molecular ecology, evolutionary biology, and especially to the conservation of biodiversity.

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Section: Improving Downstream Computational Analysesmentioning

confidence: 99%

Section: Improving Downstream Computational Analysesmentioning

confidence: 99%

Recent advances in conservation and population genomics data analysis

Hendricks

Anderson

Antão

et al. 2018

Evolutionary Applications

View full text Add to dashboard Cite

show abstract

“…[73]; 0.270 in Picea glauca (Moench) Voss [74]). Genotyping by sequencing (GBS) methods such as RAD-seq, however, are becoming feasible for non-model forest tree species [75,76], and may provide more precise estimates of adaptive genetic variation in beech in the future.…”

Section: Potentially Adaptive Genetic Variationmentioning

confidence: 99%

Genetic Variation of European Beech Populations and Their Progeny from Northeast Germany to Southwest Switzerland

Müller

Cuervo-Alarcon

Gailing

et al. 2018

Forests

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Climate change can adversely affect the growth of European beech (Fagus sylvatica L.) across its entire distribution range. Therefore, knowledge of the adaptive potential of this species to changing climatic conditions is of foremost importance. Genetic diversity is the basis for adaptation to environmental stress, and the regeneration phase of forests is a key stage affecting genetic diversity. Nevertheless, little is known about the effect of climate change on the genetic diversity of adult trees compared to their progeny. Here, we present genetic diversity data for 24 beech populations ranging from northeast Germany to southwest Switzerland. Potentially adaptive genetic variation was studied using single nucleotide polymorphism (SNP) markers in candidate genes that are possibly involved in adaptive trait variation. In addition, more than 2000 adult trees and 3000 of their seedlings were genotyped with simple sequence repeat (SSR) markers to determine selectively neutral genetic diversity and differentiation among populations. All populations showed high SSR and SNP variation, and no differences in genetic diversity were found between adult trees and their offspring. The genetic differentiation between adults and seedlings within the same stands was also insignificant or very low. Therefore, we can conclude tentatively that the transfer of genetic variation among tree generations, currently, is not much affected by climate change, at least in the studied beech populations.

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“…Landscape genomics approaches allow for complementary assessment of how environmental and landscape features influence genetic connectivity (Manel & Holderegger, ), and environmental selection processes (Ahrens et al, ; Balkenhol et al, ). Recent advancements in statistical approaches such as environmental association analyses (EAAs) allow for the correlation between allele frequency and environmental variables to be tested (Coop, Witonsky, Di Rienzo, & Pritchard, ; Frichot, Schoville, Bouchard, & Francois, ; Gunther & Coop, ), and may identify selection on many genes of small effect (Rellstab, Gugerli, Eckert, Hancock, & Holderegger, ).…”

Section: Introductionmentioning

confidence: 99%

Detection of environmental and morphological adaptation despite high landscape genetic connectivity in a pest grasshopper (Phaulacridium vittatum)

2019

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Widespread species that exhibit both high gene flow and the capacity to occupy heterogeneous environments make excellent models for examining local selection processes along environmental gradients. Here we evaluate the influence of temperature and landscape variables on genetic connectivity and signatures of local adaptation in Phaulacridium vittatum, a widespread agricultural pest grasshopper, endemic to Australia. With sampling across a 900‐km latitudinal gradient, we genotyped 185 P. vittatum from 19 sites at 11,408 single nucleotide polymorphisms (SNPs) using ddRAD sequencing. Despite high gene flow across sites (pairwise FST = 0.0003–0.08), landscape genetic resistance modelling identified a positive nonlinear effect of mean annual temperature on genetic connectivity. Urban areas and water bodies had a greater influence on genetic distance among sites than pasture, agricultural areas and forest. Together, FST outlier tests and environmental association analysis (EAA) detected 242 unique SNPs under putative selection, with the highest numbers associated with latitude, mean annual temperature and body size. A combination of landscape genetic connectivity analysis together with EAA identified mean annual temperature as a key driver of both neutral gene flow and environmental selection processes. Gene annotation of putatively adaptive SNPs matched with gene functions for olfaction, metabolic detoxification and ultraviolet light shielding. Our results imply that this widespread agricultural pest has the potential to spread and adapt under shifting temperature regimes and land cover change.

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Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations

Cited by 68 publications

References 274 publications

Recent advances in conservation and population genomics data analysis

Recent advances in conservation and population genomics data analysis

Genetic Variation of European Beech Populations and Their Progeny from Northeast Germany to Southwest Switzerland

Detection of environmental and morphological adaptation despite high landscape genetic connectivity in a pest grasshopper (Phaulacridium vittatum)

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