Clinical DNA Variant Interpretation: Theory and Practice

Front-Matter_2021_Clinical-DNA-Variant-Interpretation
Clinical DNA Variant Interpretation: Theory and Practice
Copyright_2021_Clinical-DNA-Variant-Interpretation
Copyright
Dedication_2021_Clinical-DNA-Variant-Interpretation
Dedication
Conxi Lázaro
Jordan Lerner-Ellis
Amanda Spurdle
Contributors_2021_Clinical-DNA-Variant-Interpretation
Contributors
Foreword--the-challenge-of-variant-interp_2021_Clinical-DNA-Variant-Interpre
Foreword: the challenge of variant interpretation
About-the-editors_2021_Clinical-DNA-Variant-Interpretation
About the editors
Chapter-1---Introduction--the-challenge-of-geno_2021_Clinical-DNA-Variant-In
1 . Introduction: the challenge of genomic DNA interpretation
Chapter-2---General-considerations--terminolo_2021_Clinical-DNA-Variant-Inte
2 . General considerations: terminology and standards
Introduction
Genetic variation
Types of DNA sequence changes
Types of RNA sequence changes
Types of protein sequence changes
Variant consequences by location
Promoter region
5′ untranslated region
Start codon
Protein-coding region
Splice region, splice sites, and introns
Stop codon
3′ untranslated region and the polyadenylation signal
Other variation
Standards on describing genetic variation
Gene symbols
Reference sequences
Describing variants
The Variant Call Format
The Human Genome Variation Society nomenclature
Variant classification
Functional classification
Clinical classification
Standards on reporting disorders and phenotypes
Challenges and considerations
Conclusions
References
Chapter-3---International-consensus-guidelines-for-c_2021_Clinical-DNA-Varia
3 . International consensus guidelines for constitutional sequence variant interpretation
Historical variant interpretation approaches
Current variant classification practices: the 2015 ACMG/AMP guideline for sequence variant interpretation
Background and scope
Variant classification terminology
Evidence criteria and application
Variant classification and case interpretation
Ongoing and future adaptations of the ACMG/AMP guidelines
Specifications from ClinGen: the clinical genome resource
Gene-specific versus general criteria
Qualitative versus quantitative/Bayesian approaches
Summary
References
Chapter-4---Quantitative-modeling--multifactori_2021_Clinical-DNA-Variant-In
4 . Quantitative modeling: multifactorial integration of data
Overview of quantitative modeling for variant interpretation
Derivation of likelihood ratios
Proportions of categorical data
Likelihood ratios for complex categorical data
Calibration of continuous variables
Combining likelihood ratios
Components of quantitative models
Prior probability of pathogenicity
Bioinformatic predictions
Cosegregation
Functional assays
Complete in vitro mismatch repair activity assay
TP53 assays
BRCA1/2 assays
Personal and family history
BRCA1/2
TP53
Tumor characteristics
MMR tumor characteristics
BRCA1/2 breast cancer histopathology
TP53 breast cancer histopathology
TP53 somatic/germ line ratio
Co-occurrence with a pathogenic variant
Population-based data
Population frequency
Healthy adult individuals
Case–control data
Caveats and considerations
References
Chapter-5---Clinical-and-genetic-evidence-and-_2021_Clinical-DNA-Variant-Int
5 . Clinical and genetic evidence and population evidence
Introduction
Phenotype description
Medical pedigree
Population genetic resources
Fitness—reproductive success
Hardy–Weinberg equilibrium
Population ethnic background
Prevalence of disease
Expected variant frequency
Ascertainment
Ascertainment bias
Ascertainment of “healthy” individuals
Ascertainment of individuals with disease
Matched controls in genetics studies
Population allele frequency
Allele frequency thresholds
MAF thresholds
Thresholds used for benign evidence criteria
Thresholds used for pathogenic evidence criteria
Population size
Family history
Inheritance patterns
Autosomal dominant (AD)
Autosomal recessive
X-linked recessive
X-linked dominant
Y-linked
Mitochondrial Inheritance
Inheritance analysis
Cosegregation
Cosegregation phenotyping
Cosegregation limitations
Molecular pathology
Hereditary cancer predisposition
Tumor first sequencing
Molecular pathology markers in hereditary colorectal cancers
Molecular pathology markers in hereditary breast and ovarian cancer
Molecular pathology markers in congenital disorders
Molecular pathology markers in newborn screening
Mosaicism
Somatic versus germ line mosaicism
Testing strategies in mosaicism
Identification of mosaicism using next-generation sequencing
Mosaic presentations
Example 1: mosaic neurofibromatosis
Example 2: mosaic polycystic kidney disease
Example 3: Li–Fraumeni syndrome
Conclusion
References
Further reading
Chapter-6---The-computational-approach-to-variant-int_2021_Clinical-DNA-Vari
6 . The computational approach to variant interpretation: principles, results, and applicability
Pathogenicity predictors for amino acid sequence variants
The molecular impact of amino acid variants: a biophysical view
Protein stability changes upon mutation
The effect of variants on protein interactions
The applicability of biophysical models
Bioinformatic pathogenicity predictors: principles and present situation
Development of a bioinformatic predictor
Training datasets
The discriminant features
The classifier
The validation process
The validation process
The performance of bioinformatic pathogenicity predictors
The variability of performance estimates
Future developments and challenges
Computational predictors for variants affecting splicing
RNA splicing factors
Mis-RNA splicing and disease
Bioinformatic approaches to predict variant effect on splicing
Future developments and challenges
Acknowledgments
References
Chapter-7---Functional-evidence--I--transcripts-_2021_Clinical-DNA-Variant-I
7 . Functional evidence (I) transcripts and RNA-splicing outline
Introduction
Splicing, alternative splicing events, and splicing isoforms: the splicing profile
“Reference” transcript
Spliceogenic variants overlap cis-acting determinants of alternative splicing: short sequence motifs and long-range sequenc ...
Trans-acting and epigenetic determinants of alternative splicing
Roles of alternative splicing
Alternative splicing profile is dynamic
Spliceogenic variants: alternative splicing informs on the prior probability of being pathogenic
Splicing analyses: determining the spliceogenic impact of a genetic variant
Conclusion
References
Chapter-8---Functional-evidence--II--protein-a_2021_Clinical-DNA-Variant-Int
8 . Functional evidence (II) protein and enzyme function
Historical background
The challenge of variants of uncertain significance
Assessment of variant pathogenicity
Prediction of variant effects: in silico tools
Functional assays
Validation and calibration
Example: BRCA1 and BRCA2
Example: DNA mismatch repair genes
Example: BLM
Example: RHO
Example: CFTR
High-throughput assays
In vivo assays
Conclusion
Conflict of interest statement
References
Chapter-9---Somatic-data-usage-for-classificati_2021_Clinical-DNA-Variant-In
9 . Somatic data usage for classification of germ line variants
Introduction
Data sources
Somatic data resources
Other databases with limited somatic data
Control database for comparison
Laboratory practices utilizing somatic data
Principles and rationale for utilizing somatic data for classifying germ line variants in cancer predisposition genes
Loss of heterozygosity, determining biallelic inactivation, and cancer hot spots
Loss of heterozygosity
Copy-neutral LOH
Determining biallelic inactivation
Mutational hot spots
RNA-seq tumor data
Tumor signatures
Germ line risk and variant pathogenicity informed from tumor signatures
Breast cancer
CHEK2 variants
Other considerations for integrating germ line and somatic data
Biomarker considerations (immunohistochemistry and hormone status)
Determining pathogenicity of alleles in genes with recessive and dominant phenotypes integrating population, somatic, and g ...
Recognizing clonal evolution and specific somatic mutations in the context of predisposition
Leukemia predisposition genes
Identifying candidate predisposition genes
References
Chapter-10---Pharmacogenetics-and-personali_2021_Clinical-DNA-Variant-Interp
10 . Pharmacogenetics and personalized medicine
Introduction to pharmacogenetics and personalized medicine
Variant nomenclature in pharmacogenetics
Star allele nomenclature
HLA nomenclature
Other pharmacogenetic nomenclatures
Technologies for pharmacogenetic testing
Databases/resources for pharmacogenetics
PharmGKB
PharmVar
Clinical guidelines and decision support tools in pharmacogenetics
Clinical guidelines from PGx consortia
Clinical annotation tools
Complete PGx annotation tools
CYP2D6 annotation software
Pharmacogenetics examples in clinical practice
Psychiatry: carbamazepine/oxcarbazepine and HLA-A/B
Cardiology: clopidogrel and CYP2C19
Oncology: fluoropyrimidines and DPYD
Gastroenterology: thiopurines and TPMT/NUDT15
Organ transplant: tacrolimus and CYP3A5
Pain relief: codeine and CYP2D6
Antiretroviral therapy: abacavir and HLA-B
Implementation of pharmacogenetic testing in clinical practice
Future perspectives of personalized medicine
References
Chapter-11---Data-sharing-and-gene-variant_2021_Clinical-DNA-Variant-Interpr
11 . Data sharing and gene variant databases
Introduction
General databases
Focused databases
HGMD
ClinVar and GV shared LOVD
ClinVar
Global Variome shared LOVD
Other databases
Final considerations
References
Internet resources
Chapter-12---Approaches-to-the-comprehensive-inter_2021_Clinical-DNA-Variant
12 . Approaches to the comprehensive interpretation of genome-scale sequencing
Clinical applications of GS
Diagnostics
Screening
Research applications of GS
Analysis of GS results for various applications
Variant annotation and filtration
Basic gene and variant-level data
Population frequency data
Publication data and phenotype associations
Inheritance patterns
Filtration approaches using available annotations
Criteria used for returning results of GS
Return of diagnostic findings in GS
Return of secondary and screening findings in GS
Findings related to risk for Mendelian disease risk
Predictive capacity for disease risk
Medical actionability
Age of the patient population
Patient preferences
Other types of findings
Carrier status for recessive disease
Pharmacogenetic variants
Variants with low penetrance that may be considered as risk factors
Conclusion
References
Chapter-13---Phenotype-evaluation-and-clinical-context-_2021_Clinical-DNA-Va
13 . Phenotype evaluation and clinical context: application of case-level data in genomic variant interpretation
Introduction
Genetic testing in clinical practice
History of clinical genetics services
The role of the clinical geneticist
The purposes of genetic consultations and genomic testing
The evolving knowledgebase underpinning clinical diagnostic testing
Technological advances
Understanding the genomic architecture of disease
Large-scale data generation
Evolution in clinical diagnostic variant interpretation
Historic empirical disease-based interpretation
International coordination in variant data sharing
Emergence of international frameworks
Application of clinical and phenotypic information to variant interpretation and classification
Sources of clinical data
Contribution of the patient under investigation
Cases from clinical networks
Publicly available clinical evidence
Scientific literature
Repositories of variant information and locus-specific databases
Phenotype assessment
Incorporation of clinical data in variant interpretation
Reliability and robustness of phenotypic data under evaluation
Completeness of the available information and active inclusion/exclusion of clinical features
Presence of other valid explanations for the clinical features observed in the proband
Absence of classical high-sensitivity features in the proband(s)
Specificity of the observed phenotypic feature(s) for the genetic form of disease
Genetic heterogeneity: number of genes associated with the genetic form of the disease
Frequency information in genes with rare variation in the general population
Composition of the type of established pathogenic variation within a gene
Frequency of variation observed in cases with disease compared to the control population
Mode of inheritance and segregation of disease
Management of the patient based on the genomic data
Genomic findings of uncertain significance
The “negative” genetic result: when no causative variants are found
Management for a pathogenic variant
Individualized risk estimation
General risk estimates
Contextualizing risk estimation based on pattern of disease and family history
Hypomorphic variants
Moderate risk genes
Other genetic factors
Oligogenic modifier variants
Polygenic modifiers
Nongenetic factors
Individualizing patient management based on genomic information
Conclusions
References
Chapter-14---Inherited-cardiomyopathi_2021_Clinical-DNA-Variant-Interpretati
14 . Inherited cardiomyopathies
Introduction
Inherited heart diseases
Inherited cardiomyopathies
Hypertrophic cardiomyopathy
Dilated cardiomyopathy
Arrhythmogenic cardiomyopathy
Other cardiomyopathies
Restrictive cardiomyopathy
The role of genetic testing in cardiomyopathies
Value of genetic testing in cardiomyopathies
Identification of at-risk relatives and targeting of clinical screening
Emerging gene-directed treatments
Common issues in interpreting cardiomyopathy variants
Incomplete penetrance, age- and sex-related penetrance, and additional genetic variants
Case 1: Lack of segregation in family
Incomplete phenotype information or variable expression
Case 2: variable expression
Insufficient evidence for variant pathogenicity
Case 3: insufficient variant information
Future directions
Improved phenotyping, experimental evidence, and functional data for genetic variants
Tackling secondary findings of cardiac variants
Increased genetic screening of cardiac patients
Summary
Acknowledgment
References
Chapter-15---Phenylketonuria_2021_Clinical-DNA-Variant-Interpretation
15 . Phenylketonuria
Introduction
History of phenylketonuria
Clinical features
Clinical symptoms
Newborn screening
Diagnosis
Classification of PKU
Incidence of PKU
Genetic counseling
Management
Maternal PKU
Evolution of genotyping
Practical genotype–phenotype correlation
Case 1
Case 2
Case 3
References
Chapter-16---Hearing-loss_2021_Clinical-DNA-Variant-Interpretation
16 . Hearing loss
Introduction
Genetic tests for hearing loss
Disease sections: practical examples that highlight the main challenges of the molecular diagnosis of hearing loss
Apparent non-syndromic hearing loss
Large families with more than one gene involved
The importance of molecular karyotyping in the analysis of hearing loss
Cases negative for known deafness genes: what to do?
Conclusions
References
Chapter-17---Familial-hypercholesterole_2021_Clinical-DNA-Variant-Interpreta
17 . Familial hypercholesterolemia
Variant interpretation in FH
Functional studies
LDLR
APOB
PCSK9
Cosegregation
In silico prediction algorithms
Laboratory genetic testing for FH
Cases presentations
Case A
Presentation of the case
Laboratory results
Variant interpretation
Case B
Presentation of the case
Laboratory results
Variant interpretation
Case C
Presentation of the case
Laboratory results
Variant interpretation
Case D
Presentation of the case
Laboratory results
Variant interpretation
Case E
Presentation of the case
Laboratory results
Variant interpretation
Case F
Presentation of the case
Laboratory results
Variant interpretation
Case G
Presentation of the case
Laboratory results
Variant interpretation
Case H
Presentation of the case
Laboratory results
Variant interpretation
Main final conclusion
References
Chapter-18---Classification-of-genetic-variants-_2021_Clinical-DNA-Variant-I
18 . Classification of genetic variants in hereditary cancer genes
Introduction
BRCA1/2-associated hereditary breast and ovarian cancer syndrome
ATM-associated susceptibility to breast cancer
Lynch syndrome
BRCA2 c.9976A﹥T p.(Lys3326Ter)
Presentation of the case
Variant information: BRCA2 c.9976A﹥T p.(Lys3326Ter)
Pathogenicity assessment of the variant
Population data
BRCA1/BRCA2 allele frequency thresholds
Allele frequency
Population frequencies
Coverage of exon
Computational and predictive data
Functional data
Assay—Homology-directed repair assay
Experimental data from Mesman et al. [43]
Segregation data
Cosegregation analysis
Data from Wu et al. [44]
De novo data
Allelic data
Other database
Other data
Other data not considered in ACMG/AMP classification
Case–control analysis
Data from Meeks et al. [46]
Summary of evidence and final classification (Box 18.12)
Biological and clinical interpretation
BRCA2 c.9117G﹥A
Presentation of the case
Pathogenicity assessment of the variant
Population data
BRCA1/BRCA2 allele frequency thresholds
Allele frequency
Population frequencies
Coverage of exon
Case–control data
Case–control association study—Momozawa et al. [50]
Computational and predictive data
Splice predictors
Functional data
Assay 1—Patient mRNA splicing assay
Experimental data from Colombo et al. [52]—Results extracted from Table 2
Assay 2—Construct-based assay
Experimental data from Acedo et al. [51]
Segregation data
De novo data
Allelic data
Other database
Other data
Other data not considered in ACMG/AMP classification
Multifactorial data from Lindor et al. [56]—Results extracted from Table 6
Summary of evidence and final classification (Box 18.23)
Biological and clinical interpretation
ATM c.9007_9034del
Presentation of the case
Pathogenicity assessment of the variant
Population data
Allele frequency
Population frequencies
Coverage of exon
Computational and predictive
Splice predictors
Functional data
Assays article 1
Carranza et al. [58]
Assays article 2
Fievet et al. [59]
Segregation data
De novo data
Allelic data
Other database
Other data
Summary of evidence and final classification (Box 18.34)
Biological and clinical interpretation
MLH1 c.2041G﹥A
Presentation of the case
Pathogenicity assessment of the variant
Population data
Allele frequency
Coverage of exon
Summary of evidence
Computational and predictive data
Splice predictors
Protein predictors
Functional data
Segregation data
De novo data
Allelic data
Other database
Other data
Other data not considered in ACMG/AMP classification
Summary of evidence and final classification (Box 18.46)
Biological and clinical interpretation
References
Chapter-19---RASopathies_2021_Clinical-DNA-Variant-Interpretation
19 . RASopathies
Introduction
Classification of variants associated with a RASopathy
General evidence criteria
Gene-specific evidence criteria
Case-level evidence criteria
Case examples
Noonan syndrome (Table 19.2)
Cardio-facio-cutaneous Syndrome (Table 19.3)
Costello syndrome (Table 19.4)
Unknown RASopathy diagnosis (Tables 19.5 and 19.6)
Summary
References
Chapter-20---Summary-and-conclusions_2021_Clinical-DNA-Variant-Interpretatio
20 . Summary and conclusions
Future directions
Index_2021_Clinical-DNA-Variant-Interpretation
Index
A
B
C
D
E
F
G
H
I
L
M
N
O
P
Q
R
S
T
U
V
W