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This chapter describes the types of genetic variation and their possible consequences as well as various standards and their importance related to describing, interpreting, and reporting genetic variants and phenotypes. Some important points to consider when classifying variants are discussed, as well as general challenges and considerations to keep in mind when performing sequencing analysis.
To prevent those consequences from getting mixed, it is best to strictly separate and report each level individually DNA, RNA, and protein. In addition, the predicted consequences on the RNA and protein level can be given.
In general, current short-read high-throughput sequencing technologies cannot easily detect all different DNA variant types. To detect all variant types, either special analysis pipelines are required or long-read sequencing technologies need to be applied. The basic DNA sequence variant types identified are listed in Table 2. Deletions are variants where one or more nucleotides have been removed from the original DNA sequence. This is the next most common variant type. When a deletion spans one or more exons of a gene or more than nucleotides, it is referred to as a copy number variant CNV.
Insertions are the reverse of deletions and occur when one or more nucleotides are added to the original sequence. When the inserted sequence is a tandem copy of the original DNA sequence, it is called a duplication. Both duplications and deletions frequently occur where the DNA contains Table 2. Each type is explained by an example sequence, the original DNA sequence, and the changed DNA sequence, in which the variant occurred.
The nucleotides that are part of the variant have been highlighted in red. Deletion Insertion Duplication Deletion-insertion Inversion Structural variation One or more nucleotides have been removed. One or more nucleotides have been inserted. One or more nucleotides have been duplicated in tandem. One or more nucleotides have been removed and replaced by one or more other nucleotides, other than a substitution.
More than one nucleotide has been inverted into their reverse complement sequence. A variation where large parts of chromosomes have rearranged. When a duplication spans one or more exons of a gene or more than nucleotides, it is referred to as a CNV. Deletioneinsertions are a combination of a deletion and an insertion in the same location in the DNA excluding substitutions. One or more nucleotides are replaced by one or more other nucleotides. Inversions are variants where a stretch of DNA turns around inverts ; the inserted sequence is the exact reverse complement of the deleted sequence.
Inversions have a minimum length of two nucleotides; one-nucleotide inversions are classified as simple substitutions. Structural variation is a term for various large chromosomal changes such as translocations and transpositions. Note that these are usually not picked up by short-read sequencing methods and require additional tests to be detected. If the structural changes are large enough, they can be seen using optical mapping technologies or microscopy karyotyping.
In addition, variants may indirectly influence the RNA, altering its folding, stability, and degradation, and thereby its quantity in the cell. An exceptional case is RNA fusion transcripts where parts of two different genes get fused into one transcript. RNA fusion transcripts usually occur after a translocation or deletion removing the 30 end of a gene. Although effectively part of these categories, some variants affect protein translation and are treated separately, i.
Just like for RNA, variants may affect protein processing, translation initiation, translation termination, protein modification, protein folding, and proteineprotein interaction. In addition, variants may influence the stability and degradation of the protein molecule and thereby its quantity in the cell.
Variant consequences by location In the literature, rather than the genomic DNA change, variants are often described by their location in the gene or by the effect of the variant on the protein level. Such an effect can be deleterious in two ways: loss of function or gain of function. A single-nucleotide substitution in a crucial part of the gene results in a far more devastating effect than a deletion or insertion in, for instance, an intron.
Below a brief overview of the main categories, based on the basic genetic unit - a gene, and the RNA and protein it encodes. As such, variants in this region may render the gene dysfunctional. Furthermore, for many genes, the timing of expression when during development and in which tissue is the gene expressed is controlled by far distant sequences, such as locus control regions, enhancers, topologically associating domains, etc.
Variants in these sequences mainly influence translation initiation and affect translation levels. As with the promoter region, the annotation of relevant active sites within the 50 UTR is usually lacking, and functionally relevant variants cannot easily be distinguished from nonrelevant 50 UTR variants. Start codon Variants in the start codon that alter the ATG sequence, block translation initiation, and usually have serious consequences.
When the ATG is affected, initiation may move to another initiation site, either up- or downstream, and only when that site is in frame with the normal protein sequence, the translation product may be partially functional. The consequences of duplications involving the ATG motif are difficult to predict. Although they leave a normal sequence, at the same time they introduce a new competing upstream initiation site. The sequence surrounding the start codon, coined the Kozak sequence, also shows conservation and is sensitive to variants that can change the level of translation .
Protein-coding region The consequences of variants in the protein-coding region are, in general, more severe when a large segment of the protein is altered. When a deletion removes the entire gene or the start of a gene, no protein is made, and it depends on the activity of the copy on the other chromosome whether this loss can be compensated or not. Note that deletions on the X-chromosome in males cannot be compensated Genetic variation 13 since there is no second gene copy.
Furthermore, in many cases, missing one copy is often tolerated better than having one copy with an altered protein sequence disturbing normal cellular processes. Most variants in the protein-coding region lead to the production of an altered protein.
The resulting protein may not be functional at all, function only partially, or even give an additional or completely new function. The consequences of missense variants, which replace one amino acid for another amino acid, vary depending on the change in size, charge, and hydrophilicity of the affected amino acid, as well as its position relative to the functional domains of the protein. Nonsense variants replace an amino acid for a translation stop codon, therefore causing premature translation termination of the protein.
This usually has deleterious consequences. The same holds true for frameshift variants which not only truncate normal translation but, in addition, add a completely new C-terminal tail to the protein which can be of considerable size. This new protein tail may be either shorter or longer than the original and may have undesired functional consequences gain of function , interfering and disturbing normal cellular processes.
When nonsense or frameshift variants occur near the end of the protein and the length of the C-terminal tail is small, normal protein function may be unaffected. In-frame variants deletions, insertions, and duplications do not disturb the reading frame and may have less severe consequences. A well-known example is the DMD gene, where in-frame deletions or duplications, even when spanning many exons, cause a relatively mild phenotype compared to truncating variants nonsense or frameshift variants .
The effect of in-frame variants mainly depends on the function of the protein and the size of the segment of the protein affected. In general, variants affecting larger stretches have a more significant impact. Still, like the p. Phedel variant in the CFTR gene causing cystic fibrosis, even the deletion of a single amino acid may already have serious deleterious consequences . When DNA variants in the coding region do not lead to a predicted change in the amino acid sequence, they are referred to as silent or synonymous variants.
Splice region, splice sites, and introns After transcription, the RNA molecule undergoes a range of steps before the mature RNA is ready. The 50 end of the transcript is capped, a step which is important to protect the RNA from degradation. Most genes are spliced, a process whereby some parts of a gene the exons, mostly protein-coding are fused together after the removal of other sequences the introns.
Finally, many transcripts are processed at the 30 end by cleavage and the addition of a polyA tail, again protecting the RNA from degradation. The splicing process is rather complex and involves many sequences. While there is a clear and almost completely invariable DNA sequence motif spanning the first and last two nucleotides of the intron GT and AG, respectively, see Fig. Changes in the first and last two nucleotides of the intron nearly always result in a disruption of normal splicing.
On the 30 side, the splice acceptor site, especially variants creating a close-by AG dinucleotide cause problems . Some variant effect prediction tools consider a more cautious approach and extend the region that possibly affects splicing to the first and last eight nucleotides of the intron and including the first and last three nucleotides of the exon . The intron also contains the branch point, a small region close to the 30 end of the intron, containing a single strongly conserved adenine nucleotide.
The branch point initiates the formation of the loop structure lariat that is formed when the intron is spliced out. Finally, variants in intronic and exonic splice enhancer and silencer motifs ISE, ISS, ESE, and ESS also influence splicing but since their sequence is less conserved, their position is rarely known and their involvement is not considered.
Disruption of splicing can also occur through the creation of a new or activation of a cryptic splice site CSS. CSSs are normally dormant sites that are silenced suppressed by stronger, nearby canonical splice sites. Activation occurs when a sequence change strengthens the cryptic site or weakens the canonical site.
Upon activation of the CSS, the canonical splice site is no longer or not fully used and normal splicing is wholly or partially disrupted. Disruption of splicing has a range of different consequences on the RNA level.
Note that to correctly determine the effect of a variant on splicing, RNA analysis is essential see also Chapter 7. Variants affecting splicing frequently lead to multiple transcripts being produced, with the overall effect depending on the relative abundance of each of these transcripts. When a splice site is damaged, an exon might not be recognized at all deleted or splicing may shift to a new site in the exon.
The resulting deletion can be in-frame or out-of-frame. Out-of-frame deletions result in a frameshift and have a more devastating effect on the resulting protein than in-frame deletions. The insertion of intronic sequences. When a splice site is nonfunctional, an intron may not be removed at all inserted , splicing may shift to a new site in the intron thus elongating the exon, or a new exon pseudoexon may be inserted.
The inserted sequence may contain a translation stop codon or contain an open reading frame that fuses in-frame or out-of-frame with the remainder of the encoded protein sequence. Truncating insertions have a stronger negative effect on the resulting protein than in-frame insertions. Stop codon Changes in the stop codon that prevent the stop codon from being recognized lead to the elongation of the protein sequence.
The effect of the additional C-terminal tail on the function of the protein is difficult to predict. In general, a longer tail will have more serious consequences and most extensions negatively influence protein folding, function, and stability. Together, these directly or indirectly influence RNA stability, folding, transport, localization, and translation efficiency and consequently RNA and protein Standards on describing genetic variation 15 levels .
As the functional annotation of these elements except for the polyadenylation signal is largely lacking, variants in this region are rarely considered as having deleterious consequences. Other variation A specific type of diseases is caused by repeat expansions. In these disorders, a short repetitive sequence may increase in length to up to many kilobases.
When this sequence is translated e. When it is located in an intron or the UTR of a transcript, RNA processing splicing and stability are affected and transcription may be silenced e. Epigenetic variation, i. Methylation changes can cause disease by inappropriately silencing or activating gene expression .
Methylation cannot be measured by most sequencing protocols unless specific sample preparation steps are included. Also, RNA editing, the process where the RNA sequence is altered and becomes different from the genomic DNA template, is not detected by standard sequencing protocols but can be involved in disease .
Although disease through these mechanisms is rare, they should not be overlooked. Standards on describing genetic variation To implement DNA sequence variant analysis in a clinical setting, it is essential to apply universal standards. These standards are required to remove ambiguity, prevent false-negative or false-positive results, and ensure there is no misunderstanding of what has been found and what the associated consequences for the health of the individual were.
The standards required include naming genes, accepted reference sequences for the human genome and the encoded transcripts, the file formats to exchange sequence information, the description of sequence variants identified, the description of the phenotype of the individual studied, standards to classify the variants detected, and standards to store the information in gene variant and phenotype databases.
Even when there is a universal standard, this does not mean it is applied correctly. Other reports described the variant identified also incorrectly, but in a way such that it could be recognized and corrected. In both situations, however, incorrect variant descriptions cause inconsistencies and mismatches when comparing reports or searching databases and the literature. Querying external sources for variants identified is an essential part of variant interpretation and classification.
Gene variant databases contain data from the literature and from unpublished cases and provide detailed information on variants and phenotypes and the likelihood they are causally linked, i. Given the multiple steps in the process, to maximize efficacy and reduce the chance mistakes are made during variant interpretation, it is essential the same standards are used by everyone involved.
Although genes can be identified by their unique numerical ID that does not change, e. The HGNC is currently actively renaming genes which do not refer to their function e. Reference sequences As variants are defined as differences between the sample DNA sequence determined and a reference sequence, the main requirement for any variant description is to clearly define which reference sequence was used.
Reference sequences all have unique identifiers, referring to their respective entries in the reference sequence databases. A reference sequence identifier should be stable and the sequence it contains should not change over time. When the sequence does change, this is indicated by the addition of a version number in the identifier. For genomic variants detected by next-generation sequencing, the reference sequence will most likely be the human genome reference sequence. The first human genome reference sequence was published in Over many years, this reference sequence has improved and the latest version of the human genome is build Each genome build contains reference sequences per chromosome; chromosomes 1e22, X, Y, and the mitochondrial genome mtDNA , together representing an entire genome.
Describing variants After sequence analysis and variant calling, variants are generally stored in the Variant Call Format VCF file format, developed for the Genomes Project and since then adopted by many other large-scale sequencing projects . The VCF file does not contain information on the reference genome used. The file has a tabular format indicating the chromosome, a genomic position, the reference sequence at that position, the variant alternate sequence identified in the sample s and optionally various details on the sequencing quality, such as coverage, genotype quality, etc.
The HGVS nomenclature facilitates variant descriptions based on a genomic reference sequence g. Any reference sequence can be used, as long as the residues altered nucleotides, amino acids are located within the reference sequence. An overview of the main features of both formats can be found in Table 2. The Variant Call Format The VCF was developed for the Genomes Project  and has been designed to be machinereadable for faster processing of large genomic variant datasets.
It has become the most often used file format for storing and exchanging large-scale genomic variant data. It supports multiple samples within one file, rich annotation which can include mapping on transcripts and predicted protein change, and a method to indicate the absence of variants on a certain region gVCF.
Also structural variation can be stored within a VCF file. VCF files begin with a header section, in which metadata is stored. After the header, a single line defines the order of fields and the names of the samples that are stored in the file. Any type of variant, on any reference sequence.
Any genomic variant, excluding complex genomic rearrangements. It can contain transcript variant descriptions in HGVS format in its annotation. Requires specialized tools to compare variant sets. Variant annotations stored in the VCF file can be sample-dependent or sample-independent. Sample-dependent annotations include genotype, genotype quality, and read depth.
Examples of sample-independent annotations are gene symbol, mappings on transcripts, and protein change predictions. See Table 2. Also, users are encouraged to use the lowest coordinate for a variant, therefore shifting the variant as far 50 as possible.
Unfortunately, this is in contrast with the existing HGVS standard see below , which requires variants to be shifted as far 30 as possible. Also, it is common for variants around the same location to be merged into one line in VCF files. In this case, the ALT column will contain multiple values. In cases like these, the variant description differs quite significantly from the simplest form of describing the variant, and a simple comparison of variants is not possible.
The heterogeneity of variant descriptions in a VCF file is known to cause problems, even within individual diagnostic laboratories . It should be noted that none of these tools are perfect, partly also because for describing more complex variants, current HGVS recommendations are not unequivocal. In the VCF format, there are no strict rules regarding when to describe variants independently and when as one variant. As variant callers in use in NGS software pipelines rarely call deletioneinsertion events but instead prefer calling multiple consecutive variants, VCF files often contain variants found directly next to each other or in very close proximity to each other also see HGVS below.
The Human Genome Variation Society nomenclature Since the HGVS nomenclature was first described in , it has been widely adopted as the humanreadable standard for genetic variation. The HGVS nomenclature aims to remove ambiguity in variant descriptions to improve variant reporting in databases, literature, and genetic test reports. The nomenclature defines detailed rules for describing variants on DNA level genomic and transcript , RNA level, and protein level.
Recommendations include complex cases such as RNA fusion transcripts, chromosome translocations, and how to describe variants that have not been determined exactly down to the sequence level. The basic structure of an HGVS variant description is reference sequence : numbering scheme. Reference sequence identifiers should always include version numbers where available, and the numbering scheme indicates the type of reference sequence used e.
Indicator Usage Example g. Genomic, noncircular reference sequences. Counting starts at the first nucleotide. Genomic, circular reference sequences. Noncoding transcript reference sequences. Coding transcript reference sequences. Counting starts at the first nucleotide of the translation initiation codon ATG.
RNA reference sequences. Protein reference sequences. Counting starts at the first amino acid. The numbering schemes that are allowed depend on the reference sequence given. For an overview of the most common variant type descriptions, see Table 2. Note that we will not go into detail here, like how to describe variants relative to a coding DNA reference sequence in 50 or 30 UTRs, exons and introns, or on the protein level.
For this, please consult the HGVS variant nomenclature website. Table 2. Also, the most simple representation of the variant is chosen; in reality, the VCF file format can describe the same variant in many ways. To remove ambiguity in the description of variants in repeated sequences, the HGVS nomenclature uses the so-called 30 rule, defining that any variant should be described by its most 30 position possible.
If a stretch of nucleotides is shortened by one, the 30 rule states that the variant is described as if the last nucleotide has been deleted. In addition, HGVS nomenclature uses strict definitions per variant type as well as prioritization rules when several options would be possible. For instance, prioritization defines that a T to A change is described as a substitution and not as an inversion or a deletioneinsertion.
Unfortunately, the HGVS nomenclature guidelines are not used without error. Frequently observed errors include not applying the 30 rule and incorrectly describing duplications as insertions . Both partially derive from NGS pipelines where deletions and insertions are largely 50 aligned, and where duplications cannot be defined in the most commonly used file format. Fortunately, several computational tools exist that can help describe variants correctly: the hgvs software package  for direct integration into bioinformatics projects written in the Python programming language, and the online Mutalyzer  and VariantValidator  tools, which both can also be installed locally.
The latter two tools provide a website interface for verifying variants one by one, a batch interface to verify a file with variants, and online Application Programming Interfaces APIs , online interfaces allowing software to communicate with these online tools. Although the HGVS nomenclature is comprehensive on having one valid description for each variant, not all areas have yet been covered in great detail. Although describing this change as one variant seems obvious, variant callers in use in NGS software pipelines often choose for the latter and define two single variants.
When data are then shared without allelic information, it is no longer clear whether these two variants are in cis or trans, and they can no longer be merged into one variant. In addition, when the consequences of such variants on protein level are reported, serious errors may occur . When encountering two closely spaced variants, it is recommended to check whether they are on the same allele. If so, check for the combination in external sources like population frequency databases and gene variant databases.
Variant classification To get to a clinical classification of a variant, i. The available knowledge has two major components: all observations of the variant in individuals with or without the associated phenotype and the interpretation of the predicted consequences of the variant for the function of the gene functional, or molecular classification. To discriminate between the effect of a variant on the function of a gene and its consequences for the individual carrying the variant, the HGVS recommends to clearly separate the functional classification from the clinical classification.
Functional classification Functional classification of a certain variant can only be done in an animal model or by performing a functional assay, where the function of the gene with the variant is compared with that of the wild-type Variant classification 21 form of the gene. A very simple, semifunctional assay which is mostly neglected is the analysis of an RNA sample from the patient.
Actual functional assays are often difficult and costly to be performed. Firstly, a clear idea of the function of a gene is required. Secondly, cell types must be available where the gene is expressed and the consequences of variants can be measured. For several relatively common diseases, functional assays have been developed, e. Although functional assays cannot give direct evidence regarding the consequences of a variant in a patient, they do aid in providing evidence for a weighted clinical classification.
For functional classifications, there is currently no standard that is broadly followed among different areas of research. Assays measuring the function of genes affected by certain variants commonly use relative efficiency, indicated in a percentage relative to the wild-type gene [28,30]. Clinical classification Genome diagnostic laboratories and researchers have broadly accepted the use of a standard, 5-tier scheme for classifying variants .
Although this system standardized the naming of the different variant classifications, it did not cover what evidence would be required to get a variant classified in each category. The recommendations clearly fulfilled a need and they were quickly adopted, greatly improving comparability of classifications made by laboratories worldwide see Chapter 3. Although a one-on-one relationship between a functional and a clinical classification seems obvious, there are many exceptions.
It is clear that when a variant does not alter the function of a gene, the health of the individual will not be affected, either. The opposite, however, is not always true. In pharmacogenomics, variants are cataloged that increase or decrease enzyme function, thereby affecting the level in which an individual is able to metabolize chemicals medicine and therefore how effective a certain drug dosage is or whether the drug is effective at all.
A variant increasing enzyme activity, e. Another reason for discordance between functional and clinical classifications of a variant is penetrance. A variant may be clearly affecting the function of a gene, but may cause disease only in a subset of the individuals in which the change has been found.
One example are variants in the BRCA1 or BRCA2 gene, each increasing the risk to develop breast cancer before a certain age, yet some with a much higher risk than others, e. ArgGln variant . A recent study  shows that when clearly pathogenic variants with a reduced penetrance are classified by different labs, some will classify them as Class 5 Pathogenic while others classify them as Class 3 VUS.
Another example are the many nondisease phenotypes including eye color, the ability to taste bitter, or blood group types. While any variant in the ABO gene would be classified as benign Class 1 , the variant is clearly of medical relevance when the individual needs a blood transfusion. Another problem is the gray zone between a disease and a trait, where the same variant for a disease would be clinically classified as a Class 5 but for a trait as a Class 1.
Dominant, recessive, maternal, and paternal are used to indicate the mode of inheritance. Benign dominant and benign recessive are used to indicate associations with nondisease phenotypes. Standards on reporting disorders and phenotypes Describing and classifying a variant is only relevant in the context of a certain phenotype disease.
As such, standards for describing phenotypes are equally important as standards for describing genetic variants. A clear description of the characteristic features observed in the individual investigated following a standardized ontology is crucial for elucidating geneephenotype relationships, genee panel development, and recruiting patients for clinical trials.
Most unresolved genetic diseases remaining nowadays derive from rare to ultrarare cases with only a few patients known worldwide. A critical component to establish causative diseaseegene links is always to identify more cases where Challenges and considerations 23 variants in a gene give a similar phenotype.
HPO was developed specifically to facilitate automated phenotype matching. For this, a nested tree structure was defined where deeper terms give an increasing level of detail on each specific phenotypic feature. HPO is actively updated and community efforts have been initiated to translate HPO terms into different languages, an important step to further increase its value.
One element of the very successful GeneMatcher initiative , built to identify patients with similar geneephenotype properties, is the use of HPO-based phenotype matching. Finally, HPO allows phenotype matching across species, facilitating correlations between human disease and observations in animal models e. Another important standard is provided by OMIM Online Mendelian Inheritance of Man , providing standardized disease names and the description of the main disease features observed.
While HPO defines individually identifiable phenotypic features, OMIM focuses on disorders diseases , in which these features are found in specific combinations. Several tools are available for searching and collecting HPO terms, suggesting disorders that match the given terms.
Examples are Phenomizer  and PhenoTips , both web-based systems. When the underlying disorder is unknown, HPO terms can still be used for identifying genes for gene-panelbased exome data analysis like with PanelApp , and for matching phenotypes when variants identified in patients are also found in external databases.
Challenges and considerations Although NGS has successfully been implemented into the clinical workflow and the technology has since continuously been improved, challenges remain, and there are important caveats to consider. There are both technical and biological reasons for false-negative and false-positive results.
It is important to keep these in mind when analyzing NGS data. The most apparent difference between analyzing data from NGS studies and techniques such as Sanger sequencing is the sheer size of the region in the genome covered. However, there is a major difference in the way results are presented, which is often overlooked.
With Sanger sequencing, not having a proper sequence returned would simply mean a failed analysis. There are several reasons why the sequencing pipeline software may generate a false-negative result. The most common reason for a false-negative result is the lack of coverage. It is good practice to prevent false positives by only selecting good quality variant calls for analysis, by putting a threshold on the minimum number of reads required to report a certain variant.
During this step, it is often overlooked that the number of expected reads on the sex chromosomes in males is only half of that in females, requiring flexible thresholds to prevent false negatives. A lack of coverage can also be the consequence of how the sequencing was performed, the quality of the sample, an incomplete or incorrect reference sequence, or reads mapping to multiple regions in the genome.
Reads are often discarded when they map to multiple regions in the genome, like the telomeres and centromeres, large repetitive sequences, and segmental duplications. Finally, variants not present in all cells mosaic variants , including heteroplastic mitochondrial variants, are hard to detect because either the tissue sample sequenced does not harbor the variant, or the allele fraction may be too low.
When a variant is only represented in a small subset of reads, it is considered likely a sequencing error and ignored or called with only low confidence . Variants that are too large to be contained in one sequencing read are also often missed. Large CNVs, like whole exon or gene deletions and duplication, can be observed by, respectively, a drop or a rise in the coverage in the affected region, but they are often left undetected by common variant calling methods as the reads spanning the variant breakpoints do not align to the reference genome well and end up discarded, or are not present in the sample analyzed as can be the case with whole-exome sequencing.
Specialized tools have become available that specifically detect such large changes [49,50]. When using long-range single-molecule sequencing techniques, it is much easier to detect CNVs. False-positive results can occur due to reads aligning to pseudogenes or duplicated regions such as the pseudoautosomal regions PARs on the X and Y chromosomes.
It is not uncommon for variants to be detected on the Y chromosome in female individuals, or heterozygous variants to be detected on the X-chromosome for male individuals. Besides rare genetic disorders, a far more common cause is sequencing reads aligning to the wrong chromosome in these PARs. The same holds for pseudogenes; variants detected in a gene could very well be variants belonging to the homologous region in the pseudogene. Variants derived from gene conversion, where the sequence of a pseudogene gets copied to the normal gene, are especially problematic since all variant reads will map to the pseudogene.
In such cases, designing gene-specific primers and confirming variants by Sanger sequencing is essential to rule out false positives . Another source for error is nonnormalized variant calling. Due to variants not being normalized before annotation is loaded, a common variant with a high frequency in the population might not get annotated as such when it has been described differently.
Also note that NGS analysis pipelines often split deletioneinsertion events into multiple variants, possibly causing similar problems. Conclusions In this chapter, we introduced genetic variation and how NGS analyses measure it. References 25 We listed the relevant standards for describing, interpreting, and reporting genetic variants and phenotypes, the relationship and differences between these standards, explained their importance and current status of needed improvement, and mentioned some caveats to keep in mind when describing, classifying, and reporting variants and phenotypes.
Finally, we explained which technical and biological factors can lead to false-negative and falsepositive results and suggest some solutions to these problems. References  Scally A. The mutation rate in human evolution and demographic inference.
Curr Opin Genet Dev ; 36e Rate of de novo mutations and the importance of father-s age to disease risk. Nature ; e5. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat Genet ;46 8 e Genetic variants in mRNA untranslated regions. Br J Haematol ; 2 e Entries in the Leiden Duchenne muscular dystrophy mutation database: an overview of mutation types and paradoxical cases that confirm the reading-frame rule.
Muscle Nerve ;34 2 e Identification of SNPs in the cystic fibrosis interactome influencing pulmonary progression in cystic fibrosis. Eur J Hum Genet ;21 4 e Splicing mutations in human genetic disorders: examples, detection, and confirmation. J Appl Genet ;59 3 e Bioinformatics ;26 16 e Hum Mutat Epigenetics and human disease. Cold Spring Harb Perspect Biol The role of RNA editing in cancer development and metabolic disorders.
Front Endocrinol ;9. HGVS nomenclature in practice: an example from the United Kingdom national external quality assessment scheme. Hum Mutat ;37 6 e8. Nucleic Acids Res ;47 D1 :De Nucleic Acids Res Ensembl Locus reference genomic: reference sequences for the reporting of clinically relevant sequence variants.
The variant call format and VCFtools. Bioinformatics ;27 15 : e8. HGVS recommendations for the description of sequence variants: update. Hum Mutat ;37 6 e9. Dutch genome diagnostic laboratories accelerated and improved variant interpretation and increased accuracy by sharing data. Comparing variant call files for performance benchmarking of next-generation sequencing variant calling pipelines. Cold Spring Harbor Labs J Unified representation of genetic variants.
Bioinformatics Hum Mutat ;39 12 e VariantValidator: accurate validation, mapping, and formatting of sequence variation descriptions. Hum Mutat ;39 1 e8. Improving sequence variant descriptions in mutation databases and literature using the Mutalyzer sequence variation nomenclature checker.
Hum Mutat ;29 1 :6e Haplosaurus computes protein haplotypes for use in precision drug design. Nat Commun ;9 1. A guide for functional analysis of BRCA1 variants of uncertain significance. Hum Mutat ;33 11 e A functional assayebased procedure to classify mismatch repair gene variants in Lynch syndrome. Genet Med ;21 7 e Functional analysis of genetic variants in the high-risk breast cancer susceptibility gene PALB2.
Nat Commun ;10 1 Pharmacogenetics: a general review on progress to date. Br Med Bull ; 1 e Full-gene haplotypes refine CYP2D6 metabolizer phenotype inferences. Int J Leg Med ; 4 e Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat ;29 11 e Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.
Genet Med ;17 5 e Genet Med ;19 10 e Genet Med ;20 11 e Genet Med ;20 3 e9. Hum Mutat ;40 6 e The BRCA1 c. J Med Genet ;55 1 e J Med Genet ;56 6 e LOVD v. Hum Mutat ;32 5 e Expansion of the human phenotype ontology HPO knowledge base and resources. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet ;85 4 e PhenoTips: patient phenotyping software for clinical and research use. Hum Mutat ;34 8 e PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels.
Nat Genet ;51 11 e5. Development and validation of targeted next-generation sequencing panels for detection of germline variants in inherited diseases. Arch Pathol Lab Med ; 6 e The clinical implementation of copy number detection in the age of next-generation sequencing. Expert Rev Mol Diagn ;18 10 e Free-access copy-number variant detection tools for targeted next-generation sequencing data.
Mutat Res n. Harrison1, Tina F. Pesaran2, Jessica L. A small number of individuals with a distinct and severe phenotype were typically among the first selected for testing, many of whom were members of families that had participated in linkage studies to help identify the specific gene being interrogated. Thus the a priori risk to identify the disease-causing germ line variant was high, leading researchers to safely assume in most cases that an identified variant was pathogenic.
Missense variants were soon recognized as being especially problematic , and Cotton et al. When genetic testing moved from research settings to being offered as a fee-based service, the population of individuals undergoing testing broadened to include patients with less-specific phenotypes and unaffected individuals with a family history of disease.
Testing a larger and lower-risk population led to the identification of novel variants with less certain pathogenic status, placing greater importance on having a thoughtful process by which to classify variants. As the population of individuals undergoing testing evolved, so too did the terminology used to describe the impact of identified variants. However, some disease-causing variants are present at high frequencies in specific populations, muddying these definitions .
Laboratories also took to developing their own terminology for variant classification, leading to multiple terms being used to describe the same variant. Just as the terminology used to categorize variants might differ among laboratories, in the absence of unifying standards, so too did the degree of certainty or amount of evidence required to classify a variant as disease-causing or harmless. However, these guidelines did not define what degree of certainty was required to classify a variant as disease-causing or harmless, how much weight should be assigned to different types of evidence, or how to combine different pieces of evidence to arrive at a classification.
The first effort toward combining evidence types into a multifactorial likelihood model was published in by Goldgar et al. A major step forward in unifying variant classification terminology occurred in with the publication of a 5-tier classification system developed by the IARC Unclassified Genetic Variants Working Group . Developed within a framework of hereditary cancer susceptibility testing, this system not only proposed unifying terminology for variant classification, but also defined probabilities of pathogenicity necessary to achieve each level of classification.
Slight shifts from the previous degrees of certainty proposed by Goldgar et al. InSiGHT International Society for Gastrointestinal Hereditary Tumors also applied quantitative strategies to classify variants in the mismatch repair genes associated with Lynch syndrome along with a qualitative system able to be utilized in the absence of data supporting a quantitative, multifactorial analysis . Although gene-specific quantitative approaches to variant classification are ideal, the expertise and amount of data necessary for their development are rate-limiting factors for thousands of other more rare and newly described genetic disorders.
This chapter will provide an overview of current variant classification practices and ongoing efforts to enable gene-specific, data-driven classification strategies. Due to sequencing technology evolution increasing the number of variants requiring interpretation and variability in variant interpretation between laboratories, ACMG partnered with the Association for Molecular Pathology AMP to revise and publish the guideline .
This updated guideline recommends specific standard terminology to classifying sequence variants and provides a process for determining the appropriate classification term. Additionally, the guideline is not intended for variants in candidate genes or genes that have no known association to human disease. The guideline does not specify pathogenicity confidence for pathogenic or benign classifications; however, as seen in Table 3.
Each criterion is assigned a direction, either pathogenic P or benign B , and a relative strength: very strong VS , strong S , moderate M , supporting P , or stand-alone A. Combination of the direction and relative strength creates an 32 Chapter 3 International consensus guidelines Table 3.
The evidence used to classify the variants in the calibration reference set must be independent of the component being calibrated. For example, functional assay evidence cannot be used in the classification of variants that were included in a calibration set for deriving a functional assay LR. Additionally, the calibration set cannot include variants that were classified based on evidence that was not measured by the component being calibrated.
For example, a variant that is pathogenic due to a splicing aberration cannot be included in the reference set for the calibration of an assay for protein function only. It is possible to apply an LR for a given evidence type to variants included in the reference sets, using a jackknife estimation approach; the distributions informing the LR estimation is recalculated by excluding one variant at a time, and the LR derived is then applied to the variant that was excluded.
There are multiple approaches for LR derivation depending on the variables categorical vs. Ascertainment of the reference dataset and method of derivation can also determine whether an LR can be applicable to any variant or is dataset-specific. The approaches are described in more detail in this section and a decision tree for the different approaches is shown in Fig.
For all of these approaches the predictors used in the model must be statistically significant in order to be used in the model. Proportions of categorical data When the observational data are categorical, a straightforward method of LR estimation using proportions of pathogenic variant carriers PVC and noncarriers NC can be utilized.
LR estimates can be further stratified to account for confounding using the ManteleHaenszel approach  or strata-specific LRs can be used; for example, by age group 10 being measured in the observational data used as the reference, there are many possibilities for categorizing the data. There could be a multitude of possible categories that could end up with very small numbers; thus proportional LR derivation is impractical.
In this situation logistic regression can be used to fit a model to the dataset and then identify the best predictors statistically significantly associated with pathogenic variant carrier status to use . The P is calculated based on the reference dataset used because differences in ascertainment of data affect the weight of clinical parameters as predictors of pathogenicity .
It is for this reason that LRs estimated through this approach are dataset-specific and can only be applied to variants identified within that dataset. Additional data from independent datasets allow rederivation and reapplication of LRs to more variants based on evidence pertinent to each dataset. Calibration of continuous variables When the data is a numeric continuous variable it is calibrated against a reference set of variants classified with confidence using other types of data.
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