Insights from multi-omics integration in complex disease primary tissues

Abstract

Genome-wide association studies (GWAS) have revealed the genetic basis of complex diseases. Integrative studies investigating multi-omics data of disease-relevant primary tissues are needed to refine these insights.By highlighting recent integrative multi-omics studies in relevant tissues of four distinct complex diseases (type 2 diabetes, osteoarthritis, Alzheimer’s disease, and systemic lupus erythematosus), we outline the usefulness of this approach across complex disease types.Multi-omics approaches have extended our biological understanding (e.g., functional interpretation of GWAS signals, construction of new molecular maps) and revealed potential clinically relevant insights (e.g., patient stratification, biomarker identification). Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer’s disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations. Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer’s disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations. Complex diseases are driven by a combination of multiple environmental and genetic factors. Due to their high prevalence (e.g., osteoarthritis: 40% over the age of 70 years [1.Cui A. et al.Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies.EClinicalMedicine. 2020; 29–30100587Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar]; diabetes: 6.28% of the world population [2.Khan M.A.B. et al.Epidemiology of type 2 diabetes - global burden of disease and forecasted trends.J. Epidemiol. Glob. Health. 2020; 10: 107-111Crossref PubMed Google Scholar]), complex diseases represent a substantial burden for public health systems [3.Vos T. et al.Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019.Lancet. 2020; 396: 1204-1222Abstract Full Text Full Text PDF PubMed Scopus (4846) Google Scholar]. In the context of an aging population, this burden is predicted to increase in the future, underlining the importance of developing effective and personalized treatment methods, including discovery of novel drug targets (especially for drugs that have been approved in another context, referred to as drug repurposing), the identification of biomarkers, and improved patient stratification [4.Zeggini E. et al.Translational genomics and precision medicine: moving from the lab to the clinic.Science. 2019; 365: 1409-1413Crossref PubMed Scopus (86) Google Scholar]. GWAS have identified genetic risk loci implicated in complex diseases and have provided much-needed insights into their complex genetic architecture [5.Buniello A. et al.The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.Nucleic Acids Res. 2019; 47: D1005-D1012Crossref PubMed Scopus (1935) Google Scholar]. However, translating genetic findings into clinical applications remains challenging across complex diseases. Issues include the strong linkage disequilibrium between variants on risk haplotypes (the actual causal variant of a risk locus often remains elusive) or the identification of effector genes of risk variants, particularly for variants in noncoding regions (see Glossary). Multi-omics data of human primary tissues provide molecular profiles of disease-relevant cell types, thus revealing insights beyond those derived from genetic studies. This molecular information will contribute to overcoming current challenges in translational efforts of complex diseases (Figure 1, Key figure). Briefly, omics data can be integrated with GWAS results to identify target genes of risk variants using causal inference (e.g., Mendelian randomization [6.Hemani G. et al.The MR-Base platform supports systematic causal inference across the human phenome.eLife. 2018; 7e34408Crossref PubMed Scopus (1872) Google Scholar] or colocalization approaches [7.Giambartolomei C. et al.Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS Genet. 2014; 10e1004383Crossref PubMed Scopus (1036) Google Scholar,8.Giambartolomei C. et al.A Bayesian framework for multiple trait colocalization from summary association statistics.Bioinformatics. 2018; 34: 2538-2545Crossref PubMed Scopus (113) Google Scholar]). Furthermore, omics data can improve risk variant characterization, especially for those residing in noncoding sequence. Indeed, computational intersections of GWAS with datasets generated using functional genomics techniques [e.g., chromatin immunoprecipitation followed by sequencing (ChIP-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), etc.] have found that for some complex traits, risk variants tend to reside and are enriched within regulatory sequence [9.Boer C.G. et al.Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.Cell. 2021; 184: Full Text Full Text PDF PubMed Scopus Google et al.A genome-wide association with individuals new risk loci for Alzheimer’s Genet. 2021; PubMed Scopus Google A. et variant on in human and their for 2020; PubMed Scopus Google Scholar]. studies have been by recent in that molecular across such as on chromatin and or Furthermore, of genetic regulatory across human 2020; PubMed Google et integrated of in the human PubMed Scopus Google et analysis of human PubMed Scopus Google and the A. et al.The Scopus Google Scholar] have molecular thus of molecular datasets of disease-relevant tissues across multi-omics et approaches to PubMed Scopus Google Scholar] and provide insights into the between risk factors and data for omics have generated omics data as for for functional studies of GWAS in the a of of genetic variants on and of across from of individuals of genetic regulatory across human 2020; PubMed Google in with a to functional in human and on of the and analysis of functional in of the human by the PubMed Scopus Google to the current chromatin and and data of in the human and 2020; PubMed Scopus Google Scholar]. and human data of human tissues or cell types cell types from thus in and et analysis of human PubMed Scopus Google is an that to of human tissues single-cell A. et al.The Scopus Google Scholar]. recent and a single-cell for human cell types of tissues or a single-cell of Scholar]. data data from more of are that provide is a platform for genetic and data relevant for et al.The to multi-omics PubMed Scopus Google Scholar]. datasets for is a 2 data are including omics and data from human et the regulatory of human 2021; Full Text Full Text PDF PubMed Scopus Google Scholar] and the et chromatin cell and regulatory of diabetes Genet. 2021; PubMed Scopus Google the is an that data et al.The a for data on Alzheimer’s disease and Genet. 2020; Google Scholar]. have generated omics data as for for functional studies of GWAS in the a of of genetic variants on and of across from of individuals of genetic regulatory across human 2020; PubMed Google Scholar]. in with a to functional in human and on of the and analysis of functional in of the human by the PubMed Scopus Google to the current chromatin and and data of in the human and 2020; PubMed Scopus Google Scholar]. and human data of human tissues or cell types cell types from thus in and et analysis of human PubMed Scopus Google Scholar]. is an that to of human tissues single-cell A. et al.The Scopus Google Scholar]. recent and a single-cell for human cell types of tissues or a single-cell of Scholar]. data data from more of Furthermore, are that provide is a platform for genetic and data relevant for et al.The to multi-omics PubMed Scopus Google Scholar]. datasets for is a 2 data are including omics and data from human et the regulatory of human 2021; Full Text Full Text PDF PubMed Scopus Google Scholar] and the et chromatin cell and regulatory of diabetes Genet. 2021; PubMed Scopus Google Scholar]. the is an that data et al.The a for data on Alzheimer’s disease and Genet. 2020; Google Scholar]. 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