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