Grand Challenges in Evolutionary and Population Genetics: The Importance of Integrating Epigenetics, Genomics, Modeling and Experimentation

TLDR: This essay will lay out my personal interpretation of what some of the biggest opportunities and challenges are for evolutionary and population genetics over the next decade and how genomics, epigenetics, bioinformatics, experiments and modeling must effectively combine.

Abstract: This is a time of explosive growth in the fields of evolutionary and population genetics, with whole genome sequencing and bioinformatics driving a transformative paradigm shift (Morozova and Marra, 2008). At the same time, advances in epigenetics are thoroughly transforming our understanding of evolutionary processes and their implications for populations, species and communities (Callinan and Feinberg, 2006). These revolutionary changes present tremendous opportunities and challenges to our field (Table 1). In this essay, I will lay out my personal interpretation of what some of the biggest opportunities and challenges are for evolutionary and population genetics over the next decade. I believe that for our field to take full advantage of these tremendous opportunities, we must effectively combine genomics, epigenetics, bioinformatics, experiments and modeling (Figure 1). Genomic pipelines are rapidly producing intractably large volumes of data (e.g., Griffiths-Jones et al., 2008), often without sufficient forethought about what the data will be used for, or how it will be curated, archived, and analyzed. We would be well served by thinking carefully in advance about hypotheses, what data would be best suited to address them, what experiments could be designed to evaluate and validate results, and how powerful modeling approaches could be coupled with experimentation and data mining to generalize experimental results and explore their implications across scales of biological organization from nucleotides to ecosystems.