Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Abstract

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Keywords: GWAS; complex diseases; genetic variations; genomic medicine; molecular profiling; multi omics; polygenic risk score (PRS); precision medicine.

Figure 1.

Construction and applications of a polygenic risk score (PRS). Cases and controls for a disease (A) are collected, and a GWAS is performed (B). The effect estimates are extracted from the GWAS and used, in combination with information on linkage disequilibrium, to construct weights for the PRS (C). PRSs are computed in an independent cohort, in which a majority of the participants will have intermediate PRSs, and a small fraction will have high vs. low PRSs respectively (D). The distribution of PRSs can be compared between cases and controls (E), or the disease incidence rate can be compared between participants with high, intermediate, or low PRSs (F) to evaluate the performance of the PRSs.

Figure 2.

The effect size for genetic variants of different allele frequencies. The figure shows the effect on BMI in kg/m² per copy of the minor allele for individual SNPs (blue circles), or for the burden of rare alleles (purple circles), of different allele frequencies. The blue circles represent the effect sizes from a genome-wide association study (GWAS), and the purple circles represent rare coding variants from gene-based tests performed in over 600,000 samples with whole-exome sequencing (WES) data. The grey area indicates the effect sizes for which the study was underpowered to detect any effects for different allele frequencies. The figure is adapted from Akbari et al [32]. Reprinted with permission from AAAS.

Figure 3.

The “palette” model of the multifactorial etiology of a complex disease. The figure is adapted from the figure by McCarthy [70] licensed under CC BY 4.0 [139], where the model was proposed for type 2 diabetes. The different colors to the left represent four tentative disease pathways or pathophysiological mechanisms. Each of the five individuals (A-F) have different contributions of the colors representing the various pathways that can contribute to disease. The contribution of each pathway is illustrated by an “X”, and individuals with the same disease can have very different underlying pathophysiological mechanisms, as illustrated by the difference in colors of the individuals to the right.