Statistical models are really important for understanding how genetics and the environment work together, known as genotype-environment interactions (GEIs).

These models help us see how different genetic traits behave in various environments. For example, researchers can use a type of model called a linear mixed model. This helps them study how one specific genotype performs in different climates. Here are some important things they can find out:

• Genotype-specific performance: Some types of plants or animals do better in dry conditions, while others grow best in wet environments.

• Heritability estimates: These models can tell us how likely it is that certain traits will be passed down to the next generation in different conditions.

By using math to describe these interactions, we can make smart predictions. For example, we might use a formula like this:

$y = \mu + G + E + GE + \epsilon$.

In this formula:

• $G$ stands for genotype (the genetic type),
• $E$ stands for environment (where it’s growing), and
• $GE$ shows how the genotype and environment interact.

Understanding these relationships helps us improve breeding strategies. This way, we can work towards creating plants or animals that thrive in their specific environments.