Reviews and feature article
Exome and genome sequencing for inborn errors of immunity

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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.

Section snippets

Generating NGS data

NGS, also known as deep sequencing, massive parallel sequencing, or second-generation sequencing, is a sequencing method in which hundreds of millions of small DNA fragments are sequenced in parallel (Fig 1). NGS can be used to sequence entire genomes (WGS) or a targeted panel of genes, ranging from a small number of genes (eg, all genes known to cause PIDs, hereafter referred to as a gene panel) to the whole exome (WES).7, 8, 9, 10 The technical process of NGS is summarized in Fig 1, and the

Filtering and selecting appropriate variants

NGS identifies between 20,000 and 50,000 high-quality variants per exome, depending on the kit used and some of the criteria for data processing.11, 12, 22 The variants/calls are analyzed and selected according to criteria both at the variant level (allele frequency [AF], variant annotation, and potential functional effect) and the gene level (gene expression, gene function, and gene population genetics). An allele with an AF of greater than 1% is regarded as common; the remainder are rare or

Testing a genetic hypothesis

The experimenter who does not know what he is looking for will not understand what he sees. This is true in human genetics, particularly for unbiased, genome-wide, so-called “hypothesis-generating” approaches based on NGS. These approaches are unbiased from a physiologic point of view and might generate immunologic hypotheses, but first, they must be designed and interpreted in light of a genetic hypothesis. A thorough knowledge of the clinical and cellular phenotype, its prevalence in the

Validating genetic findings experimentally

Experimental validation of the causal relationship between genotype and phenotype is crucial. NGS can easily lead to a false-positive result being associated with disease if the genetic hypothesis is flawed, population genetics is neglected, or experimental validation is insufficient (eg, the recent report of a presumed novel disease-causing mutation in 7 patients with multiple sclerosis from 2 multiplex families).44, 45 Only in rare cases of phenotypic and genetic homogeneity can a strong

Discoveries of PID-causing mutations by using NGS

The introduction of NGS in research settings has resulted in an exponential increase in the number of disease-causing genotypes identified for PIDs. AR disorders remain 4 times more common than AD disorders because they are easier to identify by using classic molecular methods and NGS (Fig 3 and Table III). Since 2010, the number of AD conditions identified has been steadily increasing because NGS has made it possible to decipher disease-causing mutations in small pedigrees and in multiple

The thin line between diagnostic and research settings

NGS is increasingly being used for the molecular diagnosis of PIDs.83 Limited resources have invited some to question the benefits of providing a molecular diagnosis to patients with PIDs.84 However, a molecular diagnosis is a definitive diagnosis. Second, in case of a strong genotype-phenotype correlation, molecular diagnosis offers prognostic information. Third, genetic analysis allows for identifying potentially fatal PIDs before onset of symptoms, enabling timely intervention (eg,

Conclusion

The advent of NGS has revolutionized gene detection for both research and diagnostic purposes. NGS-based gene panel sequencing, WES, and WGS are most useful in the field of PIDs. WES is presently the most cost-effective approach for PID research and diagnosis, but WGS provides more uniform coverage. The scientific or diagnostic challenge is the selection of 1 or 2 candidate variants among thousands of NGS calls, a task truly resembling finding a needle in a haystack. We stress the importance of

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    I.M. is funded by a KOF mandate of the KU Leuven and by the Jeffrey Modell Foundation. B.B. is funded by a Research Mandate of the FWO Vlaanderen. X.B. is funded by a research grant from the Research Council of the Catholic University of Leuven. The Laboratory of Human Genetics of Infectious Diseases (J.-L.C., A.B., B.B., and Y.I.) was supported in part by grants from the Institut National de la Santé et de la Recherche Médicale (INSERM), University Paris Descartes, the Rockefeller University, the St. Giles Foundation, the European Research Council (grant no. ERC-2010-AdG-268777; to L.A.), the French National Research Agency (ANR) under the “Investments for the future” program (grant no. ANR-10-IAHU-01), and the National Institute of Allergy and Infectious Diseases (grant no. R37AI095983).

    Disclosure of potential conflict of interest: I. Meyts has received travel support from Octapharma and Gilead Sciences and has received payment from the Jeffrey Modell Foundation. B. Bosch has received a grant from Research Foundation Flanders (FWO). A. Bolze is employed by Helix and owns stock in Illumina. L. Abel has received research support from the European Research Council and the French National Research Agency. J.-L. Casanova has received grants from Biogen Idec. The rest of the authors declare that they have no relevant conflicts of interest.

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