Uncovering Missing Heritability in Rare Diseases

Abstract

The problem of 'missing heritability' affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The 'missing heritability' concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases-the majority of RDs are also facing the 'missing heritability' problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources.

Keywords: bioinformatics; genome sequencing; long/short read sequencing; missing heritability; rare disease; variant annotation; variant detection; variation databases.

Figure 1

ClinVar variome. Representation of ClinVar variant types (as of December 2018). About 13% were structural variants. The annotation of variants is according to sequence ontology [19].

Figure 2

Uncovering missing heritability. A spectrum of variants, beyond the SNVs (single nucleotide variants), contributes to human genetic conditions as either germline or somatic variations. In addition, different types of variants, such as large insertions (including mobile element insertions (MEI)), deletions, duplications, as well as translocations, inversions, repeat expansions and other complex changes may be the source of genetic modifiers with the capacity to alleviate or exacerbate the effect of the primary pathogenic variant, and thus contribute to phenotypic variability (severe-mild-none).

Figure 3

Populations represented in the gnomAD database. An example of various population exomes/genomes aggregated in the most comprehensive database, gnomAD (European populations are depicted in a spectrum of red colors).