Landscape genomics is an interdisciplinary field that merges population genetics, landscape ecology, and spatial statistics. The goal of landscape genomics is to understand how environmental factors affect the genetic makeup of populations. The field seeks to uncover the genetic basis of adaptation by linking specific genetic variants to ecological pressures. This can provide powerful insights into evolutionary processes in a real-world context.

Landscape genomics is broadly divided into two main conceptual frameworks, neutral and adaptive. Neutral landscape genomics, also known as landscape genetics, focuses on how the landscape influences demographic processes. It utilizes neutral genetic markers, which are not subject to natural selection. This approach examines how factors such as geographic distance or physical barriers limit gene flow. It also helps to understand population structure and history.

The goal of adaptive landscape genomics is to detect the signature of natural selection. This approach utilizes genome-wide data to identify specific genes that are strongly associated with environmental variables. By identifying these outlier loci, researchers can pinpoint the genetic basis of local adaptation. This process allows them to test for isolation by environment.

Two main types of analytical methods are used to achieve these goals. Distance-based analyses are often used in neutral studies. These methods work by correlating different matrices of data. For example, a matrix of genetic distances is compared to a matrix of geographic distances. This type of analysis determines whether genetic patterns are better explained by spatial separation or environmental differences. Correlative approaches are known as genotype-environment association methods. These are the primary tools of adaptive landscape genomics. The statistical models directly test for associations between individual genetic loci and particular environmental variables. A strong correlation suggests the locus may be under selection.

Landscape genomics has numerous practical applications. It is used to predict a species’ vulnerability to climate change. It helps to identify resilient populations for conservation and guide genetic rescue efforts. This field can also inform the selection of climate-adapted crops for agriculture. Detecting polygenic adaptation is statistically difficult because many genes of small effect control these traits. It is also hard to distinguish the signals of selection from complex demographic history. Moving from a statistical association to experimentally validating the function of a candidate gene is a major hurdle. Validation requires a substantial amount of time and resources.

References

Rajora, O. P. (2019). Population Genomics: Concepts, Approaches and Applications 1st ed. Springer Nature Switzerland AG.