Unlocking Mendelian disease using exome sequencing

Christian Gilissen, Alexander Hoischen, Han G Brunner, Joris A Veltman, Christian Gilissen, Alexander Hoischen, Han G Brunner, Joris A Veltman

Abstract

Exome sequencing is revolutionizing Mendelian disease gene identification. This results in improved clinical diagnosis, more accurate genotype-phenotype correlations and new insights into the role of rare genomic variation in disease.

Figures

Figure 1
Figure 1
A timeline illustrating technological breakthroughs and hallmark publications for Mendelian disease gene identification. (a) The main historical events leading up to the introduction of whole exome sequencing (WES). The vast majority of all Mendelian disease genes known so far have been identified using conventional methods, including linkage analysis [6,57-59], homozygosity mapping [7], karyotyping [60] and copy number variation (CNV) detection [8,61,62]. Many studies following the initial descriptions have been based on technical achievements, such as the first human linkage map [63] or the first draft of the human genome [11,64]. The next generation sequencing (NGS) era was accelerated by the first commercial release of an NGS instrument [65], and using the same technology the first individual human genome was sequenced by NGS [66]. (b) The main exome sequencing events and landmark publications. More than 30 Mendelian disease genes have been identified by exome sequencing so far. Exome sequencing is now the tool of choice for Mendelian disease gene identification, starting with the proof of concept [67] and identification of the first recessive [14] and dominant disease genes [29]. It has been shown that linkage and homozygosity information can be retrieved directly from exome sequencing data, allowing the application for traditional mapping approaches [53,68]. Abbreviations: ID, intellectual disability; RFLP, restriction fragment length polymorphism; STS, sequence-tagged site; WGS, whole genome sequencing.
Figure 2
Figure 2
A representation of the relationship between the size of the mutational target and the frequency of disease for disorders caused by de novo mutations. Dashed lines separate different sizes of mutational target. Rounded rectangles represent examples of genes. Disease frequency categories range from extremely rare disorders (that is, only a few cases described) to disorders that occur more commonly within the population (such as intellectual disability, which has a frequency in the general population of more than 1%). Underneath each of these categories an example disorder is given. The lower part shows some of the implicated disease gene(s), ranging from a specific domain in a single gene, to single gene disorders, to multiple gene disorders, to disorders with extreme genetic heterogeneity. From left to right: SET binding protein 1 (SETBP1); dihydroorotate dehydrogenase (DHODH); NADH dehydrogenase (ubiquinone) Fe-S protein 1 (NDUFS1); acyl-CoA dehydrogenase family, member 9 (ACAD9); jumonji, AT rich interactive domain 1C (JARID1C); capicua homolog (CIC); deformed epidermal autoregulatory factor 1 (DEAF1); YY1 transcription factor (YY1); dynein, cytoplasmic 1, heavy chain 1 (DYNC1H1); member RAS oncogene family (RAB39B); synaptic Ras GTPase activating protein 1 (SYNGAP1).

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