Systems genetics is defined as the simultaneous assessment and analysis of multi-omics datasets. In the past few years, metabolomics has been established as a robust tool describing an important functional layer in this approach. The metabolome of a biological system represents an integrated state of genetic and environmental factors and has been referred to as a 'link between genotype and phenotype'. In this review, we summarize recent progresses in statistical analysis methods for metabolomics data in combination with other omics layers. We put a special focus on complex, multivariate statistical approaches as well as pathway-based and network-based analysis methods. Moreover, we outline current challenges and pitfalls of metabolomics-focused multi-omics analyses and discuss future steps for the field.