Exome and genome sequencing for inborn errors of immunity

Abstract

The advent of next-generation sequencing (NGS) in 2010 has transformed medicine, particularly the growing field of inborn errors of immunity. NGS has facilitated the discovery of novel disease-causing genes and the genetic diagnosis of patients with monogenic inborn errors of immunity. Whole-exome sequencing (WES) is presently the most cost-effective approach for research and diagnostics, although whole-genome sequencing offers several advantages. The scientific or diagnostic challenge consists in selecting 1 or 2 candidate variants among thousands of NGS calls. Variant- and gene-level computational methods, as well as immunologic hypotheses, can help narrow down this genome-wide search. The key to success is a well-informed genetic hypothesis on 3 key aspects: mode of inheritance, clinical penetrance, and genetic heterogeneity of the condition. This determines the search strategy and selection criteria for candidate alleles. Subsequent functional validation of the disease-causing effect of the candidate variant is critical. Even the most up-to-date dry lab cannot clinch this validation without a seasoned wet lab. The multifariousness of variations entails an experimental rigor even greater than traditional Sanger sequencing-based approaches in order not to assign a condition to an irrelevant variant. Finding the needle in the haystack takes patience, prudence, and discernment.

Keywords: Next-generation sequencing; primary immunodeficiency; targeted sequencing; whole-exome sequencing; whole-genome sequencing.

Figure 1. Next generation sequencing: WES and WGS

Starting from the patient’s genomic DNA, short fragments are created by either sonication or by restriction enzymes. A shotgun library is made in which fragments of DNA are fused with adaptors. In WES the coding exome (or another genomic region of interest in targeted capture) is enriched by a “capture” step before sequencing. Ideally, each base or each coding region is then read at least 20 times to discriminate sequencing errors from true variants. After the sequencing cycles the reads are computationally mapped to a reference genome. Differences between the patient’s DNA sequence are compared to this reference and “called” as variants. Alterations in the patient’s DNA sequence and the reference genome are identified and “called” as variants. Various “calling” pipelines are available to call variants, e.g. the Genome Analysis Toolkit.

Figure 2

Flow diagram from the raw NGS dataset to validation of the mutation for FADD deficiency. Bolze et al. (42) investigated the WES dataset for members of a large, consanguineous, multiplex kindred in which biological features of auto-immune lymphoproliferative syndrome were found to be associated with severe bacterial and viral infections, hepatic encephalopathy and cardiac malformations. The AR genetic hypothesis set the choice of variant analysis algorithm and allowed the identification of a homozygous missense candidate variant in FADD, encoding the Fas-associated death domain protein (FADD). The variant was identified in all patients. The FADD variant was not found in ExAC, 1000 genomes or the in-house HGID database (MAF=0) and had a high CADD score (23.8) exceeding the FADD MSC (5.07). Connectome analysis predicted FADD to be a candidate novel PID gene as it directly interacts with FAS (p=0.0006). Functional analysis revealed an impaired type I IFN antiviral response in assessments of the antiviral effect of IFN-alpha in vesicular stomatitis virus infected cells. This accounted for the phenotype in vivo and validated the variant as the disease-causing mutation. For clarity, this flow shows only the steps performed to unravel FADD deficiency (in yellow). This flow diagram is applicable to all other genetic hypotheses (white).

Figure 3

Cumulative number of single-gene defects underlying PIDs described since 1980 until now with either conventional molecular techniques or NGS and their mode of inheritance. If inheritance can be either AR or AD for a given gene, the gene is counted in both categories. The initial description of genes indicated in red involved NGS. Many genes have both LOF and hypomorphic mutations – this is not considered separately. A number of genes harbor both GOF and LOF mutations: STAT1, STAT3, ZAP70. They are not considered separately except for STAT1 for which mutations with both AR and AD inheritance have been described. Blue: AR inheritance, Red: AD inheritance, Green: XR inheritance. Pale colors indicate genes discovered by NGS.