Group Leader

Angela Hancock, PhD
Angela Hancock, PhD
Max Planck Research Group Leader
Phone:+49 221 5062-285

Molecular basis of adaptive evolution (Angela Hancock)

Molecular basis of adaptive evolution

Our group is broadly interested in understanding how populations adapt to their environments, with a particular focus on local adaptation to extreme environments and clines. We work at the interface of population genetics and quantitative genetics to dissect the genetic basis of quantitative traits and to reconstruct their histories in natural populations. Day-to-day work in the lab involves population genetic inference and modeling, trait mapping (GWAS and mapping in recombinant populations), functional molecular genetics and bioinformatics. Group members have diverse backgrounds and interests and tend to work closely to achieve ambitious goals.

African Arabidopsis Genomes Project

While Arabidopsis thaliana is often considered a weed, growing primarily in human-mediated environments, populations also grow naturally, particularly in alpine environments. While relict populations in Eurasia have largely been replaced by recent expansion of a weedy clade, populations in Africa appear to be native. Recently, our group showed that African A. thaliana populations harbor significant variation and best represent the early history of the A. thaliana species (Figure 1) (Durvasula, Fulgione, et al., 2017).

To follow up on these findings, we are sequencing an extensive collection of A. thaliana populations that we and collaborators have collected from Africa as well as regions that link Africa to Eurasia. This African Genomes Project will provide a resource for examining the demographic and adaptive history of Arabidopsis thaliana. Given the broad range of environments occupied by A. thaliana in Africa, this set of samples is provides us with opportunities to examine the genetic architecture of adaptive to extreme environments. The map below shows locations of previously sequenced (red) and new populations (blue).

Accessions included in the African Genomes Project were collected both in our own field expeditions and as part of collaborations with other research groups including:

 

Carlos Alonso Blanco (CSIC, Spain)

Christian Brochmann (University of Oslo, Norway)

Herculano Dinis (Parque Natural de Fogo, Cabo Verde)

Magdalena Julkowska (KAUST, Saudi Arabia)

Olivier Loudet (INRA, France)

Angela Moreno (INIDA, Cabo Verde)

Sileshi Nemomissa (Addis Ababa University, Ethiopia)

Xavier Pico (EBD/CSIC, Spain)

Fabrice Roux (CNRS/INRA, France)

Arnoldo Santos (Canary Islands, Instituto Canario de Investigaciones Agrarias)

Yuval Sapir (Tel Aviv University, Israel)

Habte Télia (Addis Ababa University, Ethiopia)

Mark Tester (KAUST, Saudi Arabia)

 

As well as groups involved in phenotyping subsets of these accessions:

 

Mark Aarts (University of Wageningen, Netherlands)

Olivier Loudet (INRA, France)

David Salt (University of Nottingham, UK)

Takashi Tsuchimatsu (University of Chiba, Japan)

Adaptive evolution in A. thaliana

Producing a comprehensive understanding of how adaptation progressed in specific cases is a daunting task because it requires knowledge of adaptive phenotypes, the genetic basis for phenotypic variation, and evidence that differences in this genetic basis equate to fitness differentials in the natural population. In most organisms, due to long life spans and inadequate genetic tools, this is currently not possible. However, ecologically interesting populations of well-studied model organisms can provide both the background knowledge and tools necessary to overcome this challenge.

Projects in the lab focus on identifying adaptive and functional genetic variants, modeling their evolutionary histories and testing for fitness differentials in simulated and natural environments. Approaches we use include population genetic analysis and modeling, GWAS, QTL mapping, molecular genetics and fieldwork.

Adaptation in the Cape Verde Islands

The Cape Verde Islands lie 650 km off the coast of Senegal. Plants in Cape Verde experience a long dry season with limited and highly variable rainfall. We have collected A. thaliana from across its distribution on the two Cape Verde Islands where it grows and are using these accessions together with Moroccan accessions and Canary Islands accessions to reconstruct the demographic and adaptive history of these plants. We use a combination of GWAS and mapping in recombinant populations to identify the loci underlying relevant traits and then testing these with CRISPR in relevant genetic backgrounds. The project aims to reconstruct broad-scale patterns of adaptive evolution as well as spatially and temporally varying selection within the islands.

Arabidopsis thaliana plant growing in the Cape Verde Islands Zoom Image
Arabidopsis thaliana plant growing in the Cape Verde Islands

Individual projects involve life history traits (flowering, seed dormancy), drought tolerance and avoidance, adaptation to edaphic factors, and the genetic basis of DNA methylation variation. This work is funded by our core funding from the Max Planck Society and the ERC grant CVI_ADAPT.

Adaptation to tropical alpine environments

Plants in tropical alpine environments are faced with stress from multiple environmental factors, including cold temperatures, high UV, low CO2 partial pressure and wind. The botanist Olav Hedberg famously described the alpine tropical environment as ‘summer every day and winter every night’. Because temperature varies diurnally rather than seasonally in this environment, it is an especially important selective pressure in this environment. 

We make use of repeated adaptive evolution across altitudinal gradients to identify the genetic basis of phenotypic convergence and to compare the genetic architecture across mountain ranges. We use both natural populations and recombinant populations to characterize the overall genetic architecture of the traits and to estimate their effect size distributions. Further, we will reconstruct the historical dynamics of these populations and the loci underlying trait variation.

This work is funded by the lab’s core funding from the Max Planck Society and DFG Grant

HA 8799/1-1.

 
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