The genetic view of disease etiology has historically focused on finding a causal variant for a given phenotype. This approach has worked well for diseases that are ostensibly monogenic, such as cystic fibrosis ¹ or Huntington’s disease ² . Within a pedigree, the segregation of genetic variants with a given phenotype was originally studied using linkage analysis ³ . Though instrumental for finding associations of simple genetic factors with disease, early linkage studies typically needed further experiments in fine mapping disease loci in order to find a candidate protein-altering variant within a gene.

Inherited or acquired protein-coding variants represent the majority of disease-causing variants, accounting for upwards of 60% of all known causative genomic variation ^{4, 5} . Exome sequencing (ES) is the targeted sequencing of nearly every protein-coding region of the genome ^{6, 7} . Typically, either a hybridization capture or multiplex primer-based amplification is used to generate libraries of exonic sequences that can be mapped to the reference genome to find variants. Given the abundance of knowledge on protein-coding genes compared with other regions of the genome, ES leverages well-sequenced and mapped regions of the genome with in silico predictions of protein function. The field of genetics was shifted from multi-step loci discovery and subsequent resequencing to testing nearly every protein-coding gene simultaneously.

The number of diseases and syndromes that are explained by a single variant, or even a single altered gene, becomes smaller and smaller as the field of genetic research progresses. Indeed, even diseases easily defined as monogenic are now being studied in terms of genetic modifiers of severity and age at onset ^{8– 10} . Monogenic or “familial” forms of a heterogenous disease often account for small proportions of a total disease population ³ . Taking as an example the genetic etiology of amyotrophic lateral sclerosis (ALS), a multi-step model has been proposed to incorporate risk from genetic variants and environmental exposure ¹¹ . In this model, an apparently monogenic variant would account for several or all “steps” that are necessary to instigate disease. However, the same disease might be acquired through several variants of lower penetrance or through a combination of genetics and environmental factors.

In this review, we will outline the recent successes and applications of ES and subsequent gene discovery in disease research. We aim to demonstrate the utility and efficacy of ES while giving a perspective on the future of the study of genetic disease etiology, specifically focusing on upcoming techniques and technologies. While ES has been instrumental in broadening our knowledge of disease genetics, the lessons learned from ES studies will bridge the gap into genome sequencing (GS), long-read sequencing (LRS), and beyond.

Genetic studies are dependent on the accuracy of diagnosis, the pleiotropy of variants of a given gene, and the prior association of variants with an outcome. Determining a specific diagnosis can be challenging because several diseases and disorders can have similar or overlapping symptoms ²¹ . Pleiotropic genes, whose variants can result in a variety of diseases, can also lead to lower diagnostic efficacy ²² . Therefore, genetically heterogenous diseases with many known genetic causes benefit more from an unbiased screening of all genes, as in ES or GS ²³ .

ES is now used as a rapid and effective means to diagnose or aid in the diagnosis of disease. ES can be employed at a prenatal period to detect fetal abnormalities ^{24– 26} and postnatally following a phenotypic observation ^{27, 28} . A significant benefit of ES comes from the ability to determine whether genetic abnormalities have been inherited from the parents or whether a variant has occurred spontaneously during gametogenesis or in gestation ( de novo or genetic mosaicism, respectively) ²⁹ . This screening could help to inform current medical intervention or to act as a basis for genetic counselling. While the utility of genetic aid in diagnosis is not limited to ES (GS and panel sequencing are also used), the cost, speed, and ease of interpretation of ES maintain it as a preferred method ³⁰ . As a result of the combined knowledge generated from association studies and further functional validation of variants, the ability to generate diagnoses from ES data will increase. For example, given an uncertain phenotype in a newborn, success rates to provide a diagnosis through ES continue to rise, between about 50 and 80% of all cases depending on phenotypic severity and the range of diagnoses ^{24, 28} . The success rate of clinical ES appears to lower for older patients with adult-onset phenotypes, between about 25 and 50% ^{31, 32} ; the difference in success rate because of age could be due to several factors ranging from probands being presymptomatic until later in life ³² to the amount of research performed on different diseases.

The application of ES is dependent on the direction in which the technology is used. Applying ES on a patient with non-specific symptoms or a novel disorder might not be as informative as for a patient with a well-characterized disease with many uniquely associated genes. The average ES results of a patient can generate more than 20,000 variants, of which a median of 21 would be predicted as loss of function ³³ . Determining the genetic cause of a rare disease proves difficult as observed variants in an individual may be either coincidental or, if truly causal, private to a family. Interpretation of these variants is critical to filtering out common variants and prioritizing candidate variants.

Whether testing during the pre- or post-natal period or testing relatives of a proband for potentially associated variants, there is always the risk of incidental genetics findings. Variants are sought after clinically for an involvement in the phenotype or disease of interest, but as ES allows the discovery of potentially all coding variants, this can result in the discovery of variants that are not related to the clinical testing ³⁴ . Additionally, incidental issues with relatedness testing can arise ³² and could affect both clinical efficacy and personal aspects. As ES data become accessible from open-science initiatives and data sharing across consortia, reanalysis of data should also be undertaken following qualified guidelines (for example, from the Canadian College of Medical Geneticists) ³⁵ . New techniques and analysis paradigms will continue to be generated, and ES data should be reanalyzed with the newest information in order to best utilize the data generated ³⁶ . As ES is also expanded into industry settings, ethical care and legal preventions must be taken to avoid the improper dissemination of incidental findings to patients and customers alike.

Genetic variants that are not present in the genomes of the parents of the proband arise de novo. ES of both unaffected parents and the affected individual, known as “trio sequencing”, has been successful to find genes in which these de novo variants are associated with disease ^{21, 37– 39} . Although certain specific disease-related variants tend to arise de novo, such as the p.P525L variant in FUS associated with ALS ^{40, 41} , an unbiased approach to finding de novo variants is required in most cases.

A recent innovation in the detection of de novo variants has been the “pooled parent” approach ⁴² . Because detection of de novo variants necessitates knowing the genetic status of both parents of a proband, sequencing can be more costly than simply screening a proband with ES. By fully sequencing the proband with ES, detailed and high-fidelity information about variants is acquired; as parents are used only to filter out non- de novo variants, less information is required on their genetic status. Therefore, in this method, the parents of all sequenced probands are pooled into a single ES sample and used to test for the presence of any candidate de novo alleles in the parent generation. The impetus for this approach was to use the lower cost of the singleton approach (that is, screening only the proband for likely causal variants) while using a population of parent genomes to increase diagnostic yield. However, the necessity of collecting and sequencing parental DNA applies to both traditional trio sequencing and pooled-parent sequencing, which is often a difficult task in late-onset disease research. The study also uses the gnomAD public database ⁴³ to filter variants that are observed in a significant proportion of the general population and likely not a de novo cause of disease ⁴² . However, as not every ethnicity or population is represented in gnomAD, it is essential to sequence both parents of a given proband in the pooled-parent technique, as the absence of a variant in gnomAD is not adequate to conclude that a de novo variant has occurred.

The pooled-parent approach to ES demonstrates that an established technique can be refined. Although the same conclusions are reached after applying traditional or pooled-parent trio sequencing, a larger diagnostic yield can be achieved, enabling lower-cost and more efficient processing of genetic analysis.

Many exome variants have a low allelic frequency in the general population. If a variant alters the coding sequence of a protein (that is, if the variant is non-synonymous or induces a premature termination of the protein), the effect on the translated protein may be significant. In genes intolerant to loss-of-function variants, protein-truncating variants are often associated with diseases and functional consequences ⁴⁴ . If a disease is associated with a single penetrant variant or with several variants in the same gene, less effort is required to find the association. However, diseases can be due to a combination of alleles with incomplete penetrance, often across several genes ⁴⁵ ; a clear association may be difficult to determine in this case. This situation is especially difficult when the variants are observed at a low frequency, as the power to detect an association is higher when variants are common ^{45, 46} .

Standard statistical genetic techniques, such as the chi-squared test, Fisher’s exact test, or logistic regression, are often not able to detect associations of a very low frequency variant with a disease phenotype and this is simply because of lack of observations in a sample cohort. However, by aggregating these variants within a given genomic window or functional region (for example, a gene), variants that might individually cause or generate risk for a disease are used to generate a “gene-based association” signal. Such statistical tests, such as the sequence kernel association test (SKAT) ⁴⁷ , combined multivariate and collapsing (CMC) ⁴⁸ , or custom enrichment algorithms ⁴⁹ , combine the signal from individual variants of a given interval into a single score that can be tested against a null model (generally the same score in unaffected controls).

In a study of underlying genetic risk factors for generalized epilepsy, May et al. used multiple collapsing statistical methods to test variants within gene sets, namely CMC and SKAT (optimal) ⁵⁰ . Although epilepsies tend to have high heritability ⁵¹ , few genetic associations have been found. As with other complex disorders, it may be that several concurrent variants are required to increase risk or that variants are rare and perhaps unique to each phenotypic subset. Even if the study by May et al. did not find a single gene that had an enrichment of variants in epilepsy cases, the authors observed a significant association of variants in genes that encode for GABAA receptors ³⁷ . Only by grouping several genes together into a “gene set” were the authors able to find an association signal; this suggests that any or all of the genes within the set might be associated with the phenotype but that individual variants within genes are too rare or not sufficiently penetrant to generate associations ⁵⁰ .

Although this example highlights the success of collapsing statistics to study rare variants, caution must be stressed in terms of limitations. In order to reach the required power both to observe enough rare variation and to detect a substantial difference between cases and controls, considerable sample sizes are required ⁴⁴ . Furthermore, these sample sizes depend on phenotypic severity and the relative risk to be detected ⁵² . In addition, such findings are of limited value for individual diagnosis and predictive testing.

The major downside to ES is the unanalyzed portion of the genome. Increasingly, regulatory, intronic, and intergenic regions are being considered for relevance in biology and disease ^{53– 56} . Although there exist some ES kits that enrich for regions outside the strict definition of the exome, a considerable amount of genomic variation is simply not targeted by standard ES. Typically, ES does not target regions of the genome outside of coding exons, although variants within 200 base pairs (bp) of the coding region can be informative variants ⁵³ .

Disease genetics research and genetic diagnosis will likely move away from ES once cost and ease of analysis for GS are acceptable. A similar number of variants in coding regions are captured by both ES and GS, and generally the variants observed are of equal or higher sequencing quality ⁵⁷ . Included in GS results are non-coding variants, those outside of gene-coding regions. However, despite significant effort to categorize the effect of non-coding variants, the effect of non-coding variants generally is not as well known as that of protein-altering exonic variants ⁴ . Nonetheless, GS and other extra-exonic technologies are becoming more widely used in genetic disease research.

Burden studies using ES have focused mainly on the amount of potentially deleterious variants across samples. However, much of the variation in the genome is not apparently deleterious, and although generally there is not a singular cause of disease, these variants might be enriched under certain phenotypes. Although non-coding variants generally do not alter protein structure or function, they may be critical to splicing, regulation, isoform usage, or other functions ^{53, 54, 56, 58} . Variants altering the coding sequence of an RNA/DNA-binding protein may affect the affinity of the protein to bind its targets ⁵⁹ . Conversely, if the target of one of these binding proteins is altered, a similar loss of affinity might occur ⁶⁰ .

An example of the efficacy of GS to study non-coding variation outside the exome is shown in CTCF/cohesin-binding site variants ⁵⁵ . Somatic variants occur throughout the genome as a function of DNA replication and repair ⁶¹ ; however, the signature and location of these variants may be informative to disease genetic etiology. No single variant in these binding sites was causal per se for colorectal cancers. However, when the focus was on variants across the genome that fall within regions where CTCF and cohesin bind to DNA, a striking number of somatic variants were observed at specific points of the binding sites. This finding was further used to differentiate between subsets of these tumors: the aggregation of somatic variants within CTCF-binding sites was observed only in microsatellite-stable tumors, whereas microsatellite-instable and POLE variant-related tumors showed no such enrichment. This finding could be generalized to any transcription factor– or DNA–binding sites ⁶⁰ but would require GS, as ES would not sequence many of the binding sites.

Many copy number variation (CNV) callers have been created for ES data to varying degrees of accuracy and usage ⁶² . A recent refinement of ES CNV calling showed that information from multiple callers and quality-control metrics could increase ES CNV accuracy substantially ⁶³ . However, CNV detection will suffer from the limited target regions of ES ⁶⁴ . Furthermore, more sophisticated tools accurately resolve small CNV across the genome, using deviations from GS coverage uniformity ⁶⁵ or using reads that align to a given motif sequence ⁶⁴ .

The shortcomings of ES to find repetitive pathogenic variants can be seen in the identification of repeat expansions. A striking example is the recent discovery of a novel genetic cause of glutamine deficiency ⁶⁶ . Three unrelated patients were screened using traditional ES in order to find protein-altering variants in the glutaminase ( GLS) gene. As any identified variants were also observed in the unaffected parents, GS was used to test for additional variation near the gene. A trinucleotide repeat expansion in the 5′ untranslated region of GLS was identified as a disease-causing variant; patients either carried a non-synonymous GLS variant in trans with the expansion or carried two expanded alleles. The expanded repeat resulted in decreased expression of GLS, possibly through modified chromatin ⁶⁶ . This finding relied on another recent advancement in repeat detection, the software Expansion Hunter ⁶⁴ , which allows local discovery and estimation of repeat sequence deviation using observed repeats within sequencing reads.

This example highlights the added benefit of studying the genome outside the exome. A variant of incomplete segregation or penetrance was observed, and had the study been conducted prior to the ability to perform GS, this might have been the conclusion of the study. However, by analyzing outside the regions targeted in ES, a non-coding repeat variant was able to explain the disease in these patients and explain the inability of the missense variant to cause disease alone ⁶⁶ .

There are genomic regions that owing to technical limitations are not studied and that could have implications for clinical genetics ⁶⁷ . These regions could be categorized as difficult to sequence because of nucleotide composition and as possible to sequence but difficult to confidently place in the genome because of sequence complexity ⁶⁷ . Both ES and GS rely on the speed and ease of generating fragmented genomic sequences that can be aligned back to the reference genome. However, these genomic regions that are difficult to sequence or align create major problems in fully assessing genetic risk, as they are not included in association studies or clinical genomics ⁶⁸ . Furthermore, genotyping errors can result in ES if large structural variants (SVs) are not detected by short-read sequencing ⁶⁹ . SVs themselves are important with regard to disease etiology: ES and GS have difficulty resolving both simple SV and complex SV (multiple genomic breakpoints) and these variants can have marked effects on individual disease progression ⁷⁰ .

The genetic etiology of an unknown number of diseases might lie in these genomic regions that are difficult to examine. GS and ES can estimate the probable number of repeats in a repeat expansion but this is by inference of the number of reads with a repetitive sequence and the composition of the region of interest ⁶⁴ . Because the repeat expansion can exceed the number of bases in a short read (typically between 50 and 150 bp), the repeat is not directly observed. LRS allows direct study of high-molecular-weight DNA samples ⁷¹ either through recording current changes induced by the passage of a DNA molecule through a channel (Oxford Nanopore Technologies, Oxford, UK) ⁷² or through imaging of an anchored polymerase and fluorescent nucleotide additions (PacBio, Menlo Park, CA, USA) ⁷³ . Both technologies allow the sequencing of several kilobase-long genomic fragments ^{72, 73} .

A more recent example of the utility of LRS of an intronic repeat expansion is that of the gene sterile alpha motif domain-containing protein 12 ( SAMD12) in familial cortical myoclonic tremor with epilepsy (FCMTE) ^{74, 75} . Several loci associated with FCMTE have been reported which were found through classic linkage studies and ES ⁷⁶ . Despite the existence of several genes in each locus, no causal variants had been discovered for one of the loci (FCMTE1), even after having applied ES ⁷⁶ . Using nanopore sequencing, two independent groups were able to describe an expanded repeat and insertion of a separate motif within the SAMD12 gene that associated with FCMTE1 ^{74, 75} . This repeat expansion would not be found using ES, as it was intronic and not covered by conventional ES. CNV detection methods in GS may not have observed the expansion, as repetitive reads did not align well to the reference and were problematic to test using polymerase chain reaction (PCR) ⁷⁴ .

Although LRS has helped our understanding of regions that are difficult to test using conventional ES or GS, it has been used mainly in a targeted manner ^{77– 79} . The throughput of this technology does not allow deep, high-fidelity sequencing of the entire human genome, and repeat discovery methods using long-read genome sequencing are lacking. However, as technologies become more refined in the future, high-depth and high-fidelity LRS may become the new standard for ES or GS analyses.

A plethora of ES data has been generated in the past decade. New technologies and sequencing methods will eventually supplant ES, but the data it generated will continue to be useful in disease research. With the shift to more collaborative and open science, smaller-scale ES studies will contribute to large-scale consortium ES studies, using very large cohort sizes to detect very rare variants. The success of this paradigm shift has been seen in undertakings such as the Simons Simplex Collection ⁸⁰ , the ALS exome collaboration ⁸¹ , or the UK Biobank ⁸² .

Despite its shortcomings, ES will continue to be used in disease research and its applications. Its ease of generation and interpretation allow rapid analysis. Until GS can be performed and analyzed for equal or lesser cost than ES and unless significant progress is made in understanding non-exonic variants, ES will continue to be used to study disease. Clinical screening of potential genetic causes of disease can be easily performed using ES. As our knowledge of the exome remains incomplete, we will continue to study the protein-coding regions. However, much variation outside of the exome must account for genetic causes of disease and therefore research must strive to understand the entire genome.

ALS, amyotrophic lateral sclerosis; CMC, combined multivariate and collapsing; CNV, copy number variant; ES, exome sequencing; FCMTE, familial cortical myoclonic tremor with epilepsy; GLS, glutaminase; GS, genome sequencing; LRS, long-read sequencing; SAMD12, sterile alpha motif domain-containing protein 12; SKAT, sequence kernel association test; SV, structural variant