MARS: leveraging allelic heterogeneity to increase power of association testing

MARSweb: a fully automated web service for set-based association testing

BACKGROUND

Despite the successes in GWAS, there is still a large gap between the known heritability and the part explained by the SNPs identified by GWAS. Set-based analysis is one of the approaches that has tried to identify associations between multiple variants in a locus a trait, leveraging allelic heterogeneity to increase power in association testing. MARS is a set-based analysis method that integrates likelihood ratio test with a recently developed fine mapping technique to accurately account for causal status of variants in a risk locus. Unfortunately, due to its complex running process, time complexity, and the requirement of high-performance computing resources, it is not widely used.

RESULTS

To address these issues, we proposed a fully automated web-based analysis service, MARSweb. By providing a web service, we minimized the effort required for initial configuration. Additionally, users can perform analyses by simply uploading their data without needing to familiarize themselves with intricate analysis procedures. Furthermore, it facilitates easier interpretation of results by integrating advanced visualization tools. We confirmed the performance of MARSweb by detecting eGenes and performing pathway analysis of the genes using a Yeast Dataset.

CONCLUSIONS

MARSweb is a web-based analysis service that fully automates set-based analysis. It offers an intuitive user interface, making complex analyses more accessible while significantly reducing processing time for enhanced efficiency. MARSweb is available for use at http://cblab.dongguk.edu/MARSweb and its source code is available at https://github.com/DGU-CBLAB/MARSweb .

Graph pangenome captures missing heritability and empowers tomato breeding

Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits^1,2. The solution to this problem is to identify all causal genetic variants and to measure their individual contributions^3,4. Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used for genome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding.

A precise catalogue of more than 19 million variants from 838 tomato genomes, including 32 new reference-level genome assemblies, advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding.

Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.