Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes

Annual Review of Ecology Evolution and Systematics, Journal Year: 2020, Volume and Issue: 51(1), P. 245 - 269

Published: Aug. 10, 2020

Signals of local adaptation have been found in many plants and animals, highlighting the heterogeneity distribution adaptive genetic variation throughout species ranges. In coming decades, global climate change is expected to induce shifts selective pressures that shape this variation. These changes will likely result varying degrees maladaptation spatial reshuffling underlying distributions alleles. There a growing interest using population genomic data help predict future disruptions locally gene-environment associations. One motivation behind such work better understand how effects changing on populations’ short-term fitness could vary spatially across Here we review current use disruption climates. After assessing goals motivationsunderlying approach, main steps associated statistical methods currently explore our understanding limits potential genomics (mal)adaptation.

Language: Английский

---

LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies

Molecular Biology and Evolution, Journal Year: 2019, Volume and Issue: 36(4), P. 852 - 860

Published: Jan. 9, 2019

Gene-environment association (GEA) studies are essential to understand the past and ongoing adaptations of organisms their environment, but those complicated by confounding due unobserved demographic factors. Although problem has recently received considerable attention, proposed approaches do not scale with high-dimensionality genomic data. Here, we present a new estimation method for latent factor mixed models (LFMMs) implemented in an upgraded version corresponding computer program. We developed least-squares approach confounder that provides unique framework several categories data, restricted genotypes. The speed algorithm is order faster than existing GEA then our previous LFMM In addition, outperforms other fast based on principal component or surrogate variable analysis. illustrate program use analyses 1000 Genomes Project data set, leading findings adaptation humans DNA methylation profiles providing insights how tobacco consumption could affect patients rheumatoid arthritis. Software availability: available R package lfmm at https://bcm-uga.github.io/lfmm/.

Language: Английский

---

Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation

Evolutionary Applications, Journal Year: 2017, Volume and Issue: 11(7), P. 1035 - 1052

Published: Oct. 30, 2017

Abstract Identifying and monitoring locally adaptive genetic variation can have direct utility for conserving species at risk, especially when management may include actions such as translocations restoration, rescue, or assisted gene flow. However, genomic studies of local adaptation require careful planning to be successful, in some cases not a worthwhile use resources. Here, we offer an framework help conservation biologists managers decide genomics is likely effective detecting adaptation, how plan assessment address objectives. Studies using tools will inform many cases, including applications flow identifying units. In others, assessing diversity, inbreeding, demographics selectively neutral markers most useful. And assessed more efficiently alternative approaches common garden experiments. identify key considerations variation, provide road map successful collaborations with experts issues study design data analysis, guidelines interpreting results from assessments programs actions.

Language: Английский

---

Controlling false discoveries in genome scans for selection

Molecular Ecology, Journal Year: 2015, Volume and Issue: 25(2), P. 454 - 469

Published: Dec. 15, 2015

Abstract Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on models, these genomewide testing procedures attracted considerable attention tools identify loci potentially targeted by natural selection. An important issue with PD EA is that incorrect model specification can generate large numbers false‐positive associations. Spurious may indeed arise when shared demographic history, patterns isolation distance, cryptic relatedness or genetic background are ignored. Recent works widely focused improvements test corrections for those confounding effects. Despite significant algorithmic improvements, there still a number open questions how check false discoveries under control implement corrections, combine from multiple genome scan methods. This tutorial study provides detailed answer questions. It clarifies the relationships between traditional based allele frequency unified framework their underlying tests. We demonstrate techniques developed in area studies, such inflation factors linear mixed benefit provide guidelines good practice while conducting landscape applications. Finally, we highlight combination several well‐calibrated increase power reject neutrality, improving our ability infer data sets.

Language: Английский

---

ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity

New Phytologist, Journal Year: 2019, Volume and Issue: 222(4), P. 1757 - 1765

Published: Jan. 30, 2019

Improving our understanding of species ranges under rapid climate change requires application knowledge the tolerance and adaptive capacity populations to changing environmental conditions. Here, we describe an emerging modelling approach, ΔTraitSDM, which attempts achieve this by explaining distribution based on phenotypic plasticity local adaptation fitness-related traits measured across large geographical gradients. The collection intraspecific trait data in common gardens spanning broad clines has promoted development these new models - first trees but now rapidly expanding other organisms. We review, explain harmonize main findings from generation that, including variation over scales, are able provide insights into future ranges. Overall, ΔTraitSDM predictions generally deliver a less alarming message than previous climates, indicating that should help, considerable degree, some plant persist change. ΔTraitSDMs offers perspective analyse single multiple traits, with rationale (co)variation consequently fitness can significantly gradients climates.

Language: Английский

---