Today’s guest post was contributed by Nele Haelterman, assistant professor at Baylor College of Medicine’s molecular and human genetics department, who combines team science, genetics, and neuroscience to study the mechanisms that drive joint pain. Nele is passionate about science communication and advocacy: she runs a blog for early career scientists (ecrLife) and promotes open, reproducible science (reproducibility 4 everyone). You can follow Nele on LinkedIn.

Genome-wide association studies (GWAS) have helped researchers untangle the complex genetic basis of numerous complex traits and common diseases. By comparing the genomes of large groups of people (ranging from a few hundred to more than a million individuals) with or without a given genetic trait, GWAS identifies common genetic variants that co-segregate with the phenotype. This approach has identified thousands of genetic associations across diverse genetic traits and diseases, providing insights into the mechanisms that underlie human biology and disease risk. However, despite millions of genotyped variants and very large sample sizes, standard GWAS analyses typically only explain a fraction of the expected genetic contribution.

Part of this missing heritability stems from the fact that traditional GWAS assesses variants independently, while many genetic traits are complex, and are shaped by the combined, interactive effect of multiple variants. Hence, scientists have been developing different types of approaches to study how variants may interact with each other to result in a given phenotype. One such approach is kernel-based association testing, which applies statistical modeling to assess the combined impact of many GWAS variants at once, enabling the discovery of genetic effects that are additive, nonlinear, interactive, or functionally informed. Researchers have also started integrating external datasets, such as transcriptomic or structural data, into kernel-based analysis to further increase its analytical power. However, as genomic and multi-omic datasets continue to expand in scale and complexity, there is a growing need for robust, scalable, and reproducible tools that streamline such integrated, kernel-based analyses.

New research led by Dr. David Enoma and Dr. Jingni He, published in G3: Genes|Genomes|Genetics now introduces Multi-omics Kernel-based Association (MOKA), a streamlined, automated pipeline for integrated variant aggregation and prioritization studies that combines variant-level functional weights, obtained from external datasets, with kernel-based annotation.

MOKA, which is open-source and available on github, allows researchers to define relevant external datasets and leverage them to compute variants’ biologically-informed functional weights. Such datasets can include imaging-derived annotations, evolutionary conservation scores, transcription factor occupancy, neural network predictions, or others that may inform a variant’s functional consequence. Next, the pipeline combines these variant-level functional weights with genotypes in a kernel-based association model to jointly evaluate the contribution of multiple variants within a gene region while accounting for population structure and relatedness. Additionally, the platform enables automated downstream interpretation and analysis through visualization, disease database validation, Gene Ontology enrichment, and KEGG pathway analysis, allowing researchers to identify biological processes and functional themes among significantly associated genes. Lastly, the pipeline includes an external validation step, during which obtained results are compared to curated knowledge bases such as DisGeNET and quantify the proportion of associated genes that have been replicated in independent datasets.

In summary, MOKA offers a one-stop-shop for GWAS scientists embarking on robust, multiomics-informed genetic discovery journeys.

References

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