Today's paper is a review for genetic diagnosis of paediatric rare diseases.
The title is "Paediatric genomics: diagnosing rare disease in children." published in 2018, Nature review genetics.
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doi: 10.1038/nrg.2017.116.
Rare diseases in children and genetic diagnosis - part 1 [Today's paper]
1. Today’s paper
Paediatric genomics: diagnosing rare
disease in children
Caroline F. Wright1
, David R. FitzPatrick2
and Helen V. Firth3,4
Abstract | The majority of rare diseases affect children, most of whom have an underlying genetic
cause for their condition. However, making a molecular diagnosis with current technologies and
knowledge is often still a challenge. Paediatric genomics is an immature but rapidly evolving field
that tackles this issue by incorporating next-generation sequencing technologies, especially
whole-exome sequencing and whole-genome sequencing, into research and clinical workflows.
This complex multidisciplinary approach, coupled with the increasing availability of population
genetic variation data, has already resulted in an increased discovery rate of causative genes and
TRANSLATIONAL GENETICS
REVIEWS
§ From genetic tests to genomic tests
§ Integrating and interpreting data
§ Considerations for clinical application
§ Future perspectives
2. Backgrounds
Rare disease
occur in fewer than
1
2,000
7,000 rare diseases defined
80 % have a genetic cause
1/3 die before the age of 5
Accurate diagnosis of
rare disease
§ Access to information of disease
care and treatments
§ Enables access to disease-
specific support groups
§ Enables accurate family
determination for the future
5. Genetic testing
Modern genetic testing
Next generation sequencing
(NGS)
Massively parallel sequencing
à Whole exome / genome
§ Identification of sequence of
multiple genes simultaneously
§ high sensitivity of SNVs and
indels identification
§ Useful for detecting CNVs and
other structural variants
* SNV : Single nucleotide variation
* Indel: Insertion and deletion
* CNV : Copy number variation
7. Diagnostic tests
Targeted approaches Non-targeted test
§ Whole single gene
§ Sequencing panel : 2 - 2,000
disease-specific genes
§ Cheaper and less off-target
§ Limited genes
§ Missed diagnoses
§ WES or WGS based diagnosis
§ High sensitivity and large amount
of data
§ Logistical, ethical issues
§ Expensive
Which one is better?
11. Variant filtering
~ 20,000 variants per individual
Few candidate varaints
§ Trio-based approach
§ Functional effect prediction
(VEP, SNPeff, etc)
§ Transcript selection method
§ Large-scale population data
(ExAC, gnomAD)
12. Variant 1: chr1-4-A-T
Variant 2: chr1-27-T-C
Variant 3: chr1-63-C-G
: Uncertain significance
: Pathogenic
: Likely pathogenic
§ Gene – disease association
§ Databases for pathogenic variants (HGMD)
§ Databases for population genome (gnomAD, ClinVar)
§ Clinical features (HPO)
§ ACMG-AMP guidelines
§ Phenotype-capture platforms (DECIPHER)
Assigning pathogenicity
13. Data sharing
§ Sheer volume of data
§ Ethical challenges
DECIPHER / Phenome Central
à Minimized number of variants shared
GA4GH Matchmaker Exchange
à Increased scope of search
American College of Medical Genetics
and Genomics
à Statements for data sharing for
improving genetic health care
15. From genetic tests to genomic tests
1. Modern genetic tests apply NGS
technologies to improve sensitivity and
the amount of data
2. There are targeted approaches and non-
targeted approaches for genomic tests
3. Trio-based analysis is a useful approach
for better diagnosis
Summary
Integrating and interpreting data
1. Variant calling and annotation are crucial
steps, but the results depend on multiple
features.
2. Variant filtering step can provide candidate
variants using many resources
3. Pathogenicity of variants can be assigned
through multiple processes
4. Data sharing is challenging because of the
data volume and ethical issues
