Integrative omics for health and disease

Abstract

Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

Figure 1. Identifying a causal variant to diagnose a patient with a rare disease

In Kremer et al. and Cummings et al., multi-omics approaches were used to aid in the diagnosis of patients with undiagnosed disease. Although exome and genome sequencing can be effective in identifying causal genetic variation between 20% and 50% of the time, depending on the mode of inheritance and phenotype, the majority of cases cannot be solved by these technologies alone. a,b | Using RNA sequencing (RNA-seq) data from patient tissue, these approaches were able to make a molecular diagnosis for many patients, identifying genes with aberrant expression, splicing or allele-specific expression, which would suggest a molecular mechanism for the disease progression. c | In some cases, functional validation, such as proteomics, can lend additional support to these diagnoses. Figure is adapted from REF. , Macmillan Publishers Limited.

Figure 2. From genome-wide association studies to mechanism

In a recent study, Claussnitzer and colleagues present a comprehensive approach to identifying a causal mechanism for an obesity-associated variant in the FTO gene. Part a shows an overview of the deciphered biological mechanisms and the numbered steps of the strategy referred to below. From the initial genome-wide association study (GWAS), the significant association of the FTO region with obesity is shown in the Manhattan plot (part b). First, the researchers established the relevant tissue or cell type (step 1) as well as the downstream target genes using regulatory genomics, including chromatin state information and chromosomal conformation (Hi-C) data. Here, they established the variant as an expression quantitative trait locus (eQTL) for the developmental genes iroquois homeobox 3 (IRX3) and IRX5 (step 2), where the risk allele shows increased expression of these genes but not others in the vicinity (part c). They demonstrate that expression of IRX3 and IRX5 is anti-correlated and correlated with genes involved in mitochondrial function and adipocyte size, respectively (part d). Next, they established the causal nucleotide variant (step 3) in an AT-rich interactive domain-containing protein 5B (ARID5B) motif (step 4) using CRISPR–Cas9 to show its molecular effects, including altered signatures of expression and phenotypic effects on the regulation of energy balance (step 5). Finally, they establish causality of the variant on an organismal level using mouse models (step 6). AKTIP, AKT interacting protein; CEU, Utah residents (CEPH) with northern and western European ancestry; CHD9, chromodomain helicase DNA binding protein 9; CRNDE, colorectal neoplasia differentially expressed; FXR, farnesoid X-activated receptor; LD, linkage disequilibrium; PGC1α, peroxisome proliferator-activated receptor-γ co-activator 1-α; PRDM16, PR domain zinc-finger protein 16; RBL2, RB transcriptional co-repressor like 2; RXR, retinoid X receptor; SNPs, single-nucleotide polymorphisms; TF, transcription factor; TSS, transcription start site; UCP1, mitochondrial brown fat uncoupling protein 1. Figure is adapted from The New England Journal of Medicine, Claussnitzer, M. et al., FTO obesity variant circuitry and adipocyte browning in humans, 373, 895–907, Copyright© (2015) Massachusetts Medical Society, REF. . Reprinted with permission from Massachusetts Medical Society.

Figure 3. Finding the relevant tissue

Although blood (part a) is often the most convenient tissue to assay owing to its availability and ease of procurement, it is often not the ideal tissue for observing a molecular phenotype for a given disease, which may primarily affect other tissues such as brain (part b) or lung (part c). In particular, its transcriptional landscape, including expression levels, splicing patterns and enhancer usage, may not be amenable to detecting differential uses of these patterns compared with a tissue that is more proximally affected by a disease, such as muscle tissue in muscular dystrophy.