Genomics and bioinformatics of local adaptation: Studies on two non-model plants and a software for bioinformatics workflow management

It has been known for centuries that organisms from different geographic origins vary in fitness when transferred to another environment. Local adaptation – a situation where the local population fares best – is often observed. This phenomenon has a genetic basis but it is often not well understood. In this thesis, I examine the genetic basis of local adaptation using contemporary highthroughput sequencing and population genomic approaches and develop workflow management software to enable efficient computation of data from virtually any sequencing workflow.

I study the population genetic features of local adaptation in Scots pine, a tree species with wide distribution range spanning from westernmost Europe to Eastern Eurasia. Similar questions are also addressed in a perennial herb, Lyrate rockcress.

In Scots pine, a targeted sequence capture was implemented, and divergence-based and landscape genomics methods uncovered several variants contributing to local adaptation. We also identified a very large inversion, which is potentially under selection. Whole genome sequencing of Arabidopsis lyrata uncovered demographic history and post-glacial colonization patterns in Northern Europe. Computation in both studies is largely automated and parallelized by STAPLER – the workflow management software produced in this thesis.

The results highlight the benefits of allocating time in bioinformatics workflow design, the importance of developing novel methods to detect polygenic adaptation, and call for more frequent inclusion of analysis of structural variation in studies of local adaptation.