Aims and objectives of the workshop David Moore. Aims Classification of variants is subjective and NEQAS results suggest this is not a major problem To.

Slides:



Advertisements
Similar presentations
CQ Deng, PhD PPD Development Research Triangle Park, NC 27560
Advertisements

Implications of Consanguinity for Routine Diagnostic Testing and Development of Specialist Services Teresa Lamb Clinical Scientist Leeds DNA Laboratory.
Data analytics for better patient genetics
Familial adenomatous polyposis
Clinical background: Patient RM, YoB 1966 Her family were originally referred because of a history of breast and ovarian cancer. BRCA testing found a missense.
SCID Review Discussion. Decision Matrix Key Questions 1.This is the overarching question for the evidence review: Is there direct evidence that screening.
VUS: The clinician’s view Mary Porteous On behalf of Scottish Clinical Geneticists.
PREDetector : Prokaryotic Regulatory Element Detector Samuel Hiard 1, Sébastien Rigali 2, Séverine Colson 2, Raphaël Marée 1 and Louis Wehenkel 1 1 Bioinformatics.
Decision Criteria and Process Advisory Committee on Heritable Disorders in Newborns and Children February 26-27, 2009.
Pathogenicity of sequence variants interpretation pilot EQA David Moore.
Predicting the Function of Single Nucleotide Polymorphisms Corey Harada Advisor: Eleazar Eskin.
Protein Modules An Introduction to Bioinformatics.
PolyPhen and SIFT: Tools for predicting functional effects of SNPs Epi 244 Spring 2009 Sam S. Oh.
Sample size calculations
Type 2 Diabetes With type 2 diabetes, your body either resists the effects of insulin — a hormone that regulates the movement of sugar into your cells.
Putting it all together: Finding the cystic fibrosis gene Cystic fibrosis (CF) is a genetic disorder that is relatively common in some ethnic groups A.
Presented by Karen Xu. Introduction Cancer is commonly referred to as the “disease of the genes” Cancer may be favored by genetic predisposition, but.
Making Sense of DNA and protein sequence analysis tools (course #2) Dave Baumler Genome Center of Wisconsin,
An informatics approach to analyzing the incidentalome J.Berg et al. Genetics in Medicine Presented by Li Changjian.
Novel splice-site mutations as the cause of FAP-related cancer in two families K Sweet, B McIlhatton, V McConnell, W Logan and C Graham Regional Molecular.
Chapter 13 Carrier Screening. Introduction Carrier screening involves testing of individuals for heterozygosity for genes that would produce significant.
01/03/2013UK NEQAS UV Participants Meeting 2013 in a quality perspective.
Yvonne Wallis UKNEQAS for Molecular Genetics Unclassified Sequence Variants Participants Meeting Edinburgh
Locally Agreed Guidelines May Reduce Inappropriate Preoperative Echocardiography Requests Dr Sheila Carey Anaesthetic SpR Northern Deanery.
EVIDENCE ABOUT DIAGNOSTIC TESTS Min H. Huang, PT, PhD, NCS.
Bioinformatics. Sequence information Mapping information Phenotypic information Literature Prediction programs -Gene prediction -Promotor prediction -Functional.
From Genome-Wide Association Studies to Medicine Florian Schmitzberger - CS 374 – 4/28/2009 Stanford University Biomedical Informatics
Variant Prioritization in Disease Studies. 1. Remove common SNPs Credit: goldenhelix.com.
Models of Molecular Evolution III Level 3 Molecular Evolution and Bioinformatics Jim Provan Page and Holmes: Sections 7.5 – 7.8.
Molecular Genetics in the Von Willebrand disease Ghasem Rastegarlari.
Can view other patients. Also search Pubmed, google, etc.
Pre-mRNA secondary structures influence exon recognition Michael Hiller Bioinformatics Group University of Freiburg, Germany.
Motif Search and RNA Structure Prediction Lesson 9.
COURSE OF BIOINFORMATICS Exam_30/01/2014 A.
Variant Classification and Reclassification. Introduction This slide presentation covers several topics pertaining to Variant classification, reclassification.
How do we interpret the variants?. Overview How do we prioritize the filtered variants? What filters can be used to identify the causative variants? What.
The Royal College of Emergency Medicine The Royal College of Emergency Medicine Clinical Audits Initial management of the fitting child Clinical Audit.
ResultsIntroduction Atrial Fibrillation (AF) affects 1.2% 1 of the population and 10% of those over the age of 75 2 It is the commonest arrhythmia in primary.
Considerations for multi-omics data integration Michael Tress CNIO,
A2 unit 4 Clinical Psychology 4) Content Reliability of the diagnosis of mental disorders Validity of the diagnosis of mental disorders Cultural issues.
Integrated sequence analysis pipeline provides one-stop solution for identifying disease-causing mutations Cougar Hao Hu, MPIMG.
Interpreting exomes and genomes: a beginner’s guide
Monogenic Disorders Genetic Counselling
Complex disease and long-range regulation: Interpreting the GWAS using a Dual Colour Transgenesis Strategy in Zebrafish.
Development of a next-generation sequencing gene panel for neurogenetic disorders J E Martindale, R Crookes, L Crooks, N J Beauchamp, A Dalton Sheffield.
Frances Bond West Midlands Regional Genetics Laboratory 12/04/10
Interpretation Next Generation Sequencing (Bench Clinic)
A2 unit 4 Clinical Psychology
Regulatory perspective
Kirsty Russell Trainee Clinical Scientist (Bioinformatics – Genomics)
Algorithm for the diagnosis of CF starting with the CFTR DNA test.
Type 2 Diabetes With type 2 diabetes, your body either resists the effects of insulin — a hormone that regulates the movement of sugar into your cells.
Annotation of Sequence Variants in Cancer Samples
VWF sequence variants: innocent until proven guilty
Annotation of Sequence Variants in Cancer Samples
A Clinical Grade Sequencing-Based Assay for CEBPA Mutation Testing
By Stitziel, Tseng, Pervouchine, Goddeau, Kasif, Liang
Daniel C. Koboldt, David E. Larson, Lori S. Sullivan, Sara J
The Genetic Basis for Cancer Treatment Decisions
ClinGen Gene Curation: Segregation Analysis
DNA and the Genome Key Area 6a & b Mutations.
Assessing for Cognitive Impairment
Variant Triaging and ESMO Guidelines
Genetics made easy: demystifying genetic testing
DNA and the Genome Key Area 6a & b Mutations.
Figure 1 Dominant and recessive missense and nonsense variants in neurofilament light (NEFL)‏ Dominant and recessive missense and nonsense variants in.
Evaluating the Effect of Unclassified Variants Identified in MMR Genes Using Phenotypic Features, Bioinformatics Prediction, and RNA Assays  Lucia Pérez-Cabornero,
Genotype–Phenotype Correlation in Recessive Dystrophic Epidermolysis Bullosa: When Missense Doesn't Make Sense  Vesarat Wessagowit, Soo-Chan Kim, Se Woong.
Patient carrying two probably damaging missense variants in HCFC1 and ATRX: one causative mutation and one modifier variant? (A) Family tree of patient.
Presentation transcript:

Aims and objectives of the workshop David Moore

Aims Classification of variants is subjective and NEQAS results suggest this is not a major problem To agree and recommend: 1.Which class system to use 2.Which variants to report 3.Specific report wording for the different variant classes 4.Appropriate follow-up testing for different variant classes 5.What evidence to include in reports 6.Interpretation of frequency data 7.Interpretation of splice scores

1.Which classification system to use –5 class 5 path, 4 likely path, 3UV, 2 unlikely path, 1 not path –4 class 4 path, 3 likely path, 2 unlikely path, 1 not path –3 class 3 path, 2 unknown, 1 not path –A different system?

2. What classes of variants to report –Yes: classes 3, 4, 5 Clinically relevant or potential to be. –?: class 2 Unlikely to be pathogenic Available on request? –No: class 1 Not pathogenic

3. Specific report wording for the different variant classes Class 5 –Predicted to be pathogenic, this result therefore confirms the diagnosis Class 4 –Likely pathogenic, consistent with the diagnosis Class 3 –Uncertain pathogenicity, does not confirm or exclude diagnosis –Unsure about the pathogenicity and offer further work before offering further diagnostic or carrier testing. Class 2 –Unlikely to be pathogenic, diagnosis not confirmed molecularly. –No evidence suggesting pathogenicity but not at a high enough frequency to say it’s not pathogenic? Class 1 –Not pathogenic. –“Commom” polymorphism. –No evidence suggesting pathogenicity and at “high” frequency.

4. Appropriate follow-up testing for different variant classes Class 5 –Prenatal and predictive testing offered. Class 4 –Should further work be offered prior to either prenatal or predictive testing? If so, how much until deemed appropriate for PST/PND? –Unlikely that any routine work available to diagnostic labs (bar RNA) will lead to definitively calling it a 5. –Should class 4 variants be treated as an almost 5 class and anything that labs are unhappy to do further tests on be classed as a 3? –Even if there is literature, the majority of missense variants are probably still class 4’s, unless there is functional evidence? –If it is a recessive disorder would you offer PND to a family where the affected individual had: Class 5 and class 4 variant 2 class 4 variants

4. Appropriate follow-up testing for different variant classes Class 3 –No to PST/PND –Segregation analysis and any other further work available is warranted –Is it always warranted, if evidence is weak, should you offer follow up? Class 2 or 1 –No further work required? –If thought that there is a possibility of a class 2 being upgraded, then should it be a class 3?

5. What evidence to include in reports Should labs list all of the lines of evidence used to classify variants or just refer to Alamut and any relevant references? –This variant is likely to be pathogenic. The amino acid is highly conserved across mammals and down to zebrafish, Alamut software (1) supports it as being pathogenic, the amino acid change itself doesn’t have a large Grantham score but is from a basic to a polar uncharged in a specific functional domain of the protein (EF-hand binding site) it is in the vicinity of reported known pathogenic variants and has been reported by Waldmuller et al (2) Or –This variant is likely to be pathogenic (1,2) – (1) Assessment of pathogenicity carried out using bioinformatics software Alamut v2.2 which includes tools; AlignGVGD, SIFT, mutationTASTER polyphen and interrogation of the swissprot variation database and utilises splicing algorithms: SpliceSiteFinder-like, MaxEntScan, NNSplice and GeneSplicer. (2) Waldmuller et al in the Eur J Heart Fail 2011, vol13, p Should they only list evidence when justifying the reason for follow up studies? i.e. class 3 variants.

6. Interpretation of frequency data Frequency to class a variant as a 1, issues- –Frequency of the disease- known? –Population specific –“normal” population studied ESP uses individuals affected with heart, lung and blood disorders –Modifying effect? c.131C>T (p.Ser44Leu) leads to early onset HSP if in-trans with pathogenic variant. If freq of variant is “significantly higher” than that of disease, then can it be a 1? –What figure?

7. Interpretation of splice scores Splicing- –+/- 1, 2 (invariant sites): class 5? –Other bases- drop of over 10% in at least 3 programs- class 4? –Some exceptions (CFTR exon 10). –Degree of nt conservation an unreliable indication (esp for variants in the coding regions) CFTR c.1584G>A (p.Glu528Glu)