When confronted with the same chronic illnesses or pathogen invasions, why do some individuals exhibit greater resistance while others are more susceptible? Could these differences stem from genetic variations? If so, where are these genes located? A recent study may provide some insights into these questions.

On June 19, 2023, Cell Genomics published an online research paper titled “Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes”. This study developed a method called OPERA that efficiently integrates and analyzes large-scale genomic and multi-omics data from human populations. This method is aimed at mapping human complex disease-related genes and genomic functional regions at a finer resolution, ultimately enhancing our understanding of the genetic regulatory mechanisms involved in complex diseases.

Human behaviours, physiological traits, and susceptibility to diseases, among other complex traits, are all affected by numerous genetic and environmental factors. These traits include height, body mass index, and susceptibility to chronic diseases (such as diabetes, mental illness, and cancers) or pathogen infections (such as the SARS-CoV-2).

Mapping genetic loci for complex traits is a highly challenging task, mainly because each locus's individual contribution to the trait is typically small. Therefore, to determine whether a genetic locus affects a trait, individuals in a cohort are grouped based on the presence of a specific genetic variant, and the mean values between groups are then compared. In such experiments, the larger the cohort, the more accurate the mean value estimation, making it more likely to detect subtle differences between groups. Thus, the key factors in this experimental design are cohort size and coverage of genetic variants on the genome.

The development of high-throughput sequencing and molecular marker detection technologies has made it possible to perform genome-wide genetic loci detection in large cohorts, thereby promoting the development of genome-wide association studies (GWAS). GWAS scans the genomes of a large cohort in an unbiased manner, searching for genetic variants associated with complex traits. With the continuous increase in GWAS cohort size, tens of thousands of genetic loci for complex traits have been located. However, the genes corresponding to most of these GWAS loci are still unclear, greatly hindering our understanding of the related molecular mechanisms and making it difficult to translate GWAS results into biological mechanisms and clinical applications.

Molecular trait loci (xQTL) are genetic loci affecting molecular phenotypes (such as gene expression, protein abundance, or epigenetic modifications). Integrating xQTL data with GWAS results helps to gain a deeper understanding of the molecular mechanisms linking genetic variations to complex traits.

The OPERA method developed in this study is a significant breakthrough in the field, as it can integrate xQTL data and GWAS data from multiple omics levels, determining which molecular phenotypes share potential genetic regulatory mechanisms with traits, and thus genetically associate numerous molecular phenotypes with complex traits. This simultaneous integration of multiple omics xQTL and GWAS data not only overcomes computational bottlenecks but also increases the statistical testing power, linking many previously unknown GWAS loci to molecular phenotypes genetically, taking an essential step in revealing the biological mechanisms behind GWAS loci.

Another advantage of the OPERA method is that it does not require raw genotype, trait phenotype, or omics data; it only needs to analyze the summary statistics of GWAS and xQTL studies. This feature increases data analysis flexibility and strengthens data privacy and security protection.

Using OPERA to analyze GWAS data for 50 complex traits and xQTL data for seven omics levels, researchers detected 17,619 molecular phenotypes genetically associated with complex traits and found that nearly 50% of complex trait GWAS loci could be genetically associated with at least one molecular phenotype. This discovery deepens our understanding of how molecular phenotypes mediate genetic effects and influence complex traits. Particularly, GWAS signals that share genetic control with multiple molecular phenotypes, such as the MSMB locus signal for prostate cancer, provide essential theoretical and data support for future research to reveal the genetic regulatory mechanisms of complex traits. This study takes an important step from GWAS signals to complex trait genes, and the discovery of these new complex disease genes will provide many new targets for developing new treatment plans, helping to develop more targeted and effective treatments for various diseases and conditions.

Additionally, the study envisions future research directions, suggesting that more in-depth molecular phenotype research is needed, along with larger cohort sizes and consideration of spatiotemporal effects, to construct a more comprehensive genetic association map between molecular phenotypes and GWAS signals. This will continue to deepen our understanding of the relationship between genetic variations and complex traits, providing more possibilities for future disease prevention and treatment.

The first author of this study is Dr. Yang Wu, who obtained his Ph.D. from the University of Queensland in Australia and recently joined Sichuan University as a principal investigator. Dr. Jian Zeng, a Senior Research Fellow at the University of Queensland, and Professor Jian Yang of Westlake University are the co-corresponding authors of this paper.

Link：

https://www.cell.com/cell-genomics/fulltext/S2666-979X(23)00119-2