Presentation by Nazneen Rahman at the GRC workshop held at the 2016 Genome Informatics meeting at Hinxton, UK

1. The Transforming Genetic
Medicine Initiative (TGMI)
£5.3M 4 year programme funded by
Wellcome

2. Designing, Developing and
Delivering Integrated Foundations
for Genetic Medicine

3. Nazneen Rahman
Paul Flicek Caroline Wright
Sian Ellard David Fitzpatrick
Ewan Birney
Fiona Cunningham
Graeme BlackHelen Firth
Gerton Lunter
Matthew Hurles
Patrick Chinnery
TGMI PIs

4. National and international collaborations

5. Transforming genetic medicine
Must ensure the wealth of existing medical
genetic knowledge informs our use of current
and future technology, if we are to do more
right and less wrong.
‘The past is never dead, it’s not even past.’ William Faulkner

6. GENOME PHENOME
Genomic medicine

7. GENOME PHENOME
TGMI is focussed on genes
GENE ‘MENDELIAN’
DISORDERS

8. Genetic medicine 1990-2010
GENE ‘MENDELIAN’
DISORDERS
Prior to NGS, genetic medicine was phenotype-driven.
Meticulous phenotyping used to decide which genes to test.

9. Genetic medicine 2020
GENE ‘MENDELIAN’
DISORDERS
With NGS, genetic medicine becomes genotype-driven and
can potentially be large-scale and routine.

10. Genetic medicine 2010-2016
GENE ‘MENDELIAN’
DISORDERS
With NGS, genetic medicine can be genotype-driven. But as
the processes are not well formed phenotyping often used
(often incorrectly) to decide which data is ‘relevant’.

11. TGMI aims to undertake conceptual,
foundational research to deliver
practical solutions to make genetic
medicine work

12. TGMI Aims
1. To provide robust, comprehensive information on
links between genes and human disease in a user-
friendly interface.
2. To develop standardised frameworks for consistent
clinical annotation and reporting of gene variation.
3. To develop approaches to deliver fast, automated,
high-throughput, large-scale variant interpretation.
4. To develop and validate flexible, multipurpose
analytical processes to maximise clinical and research
utilities of genetic testing.

13. GENES
Gene 1
Gene 20,000
For each gene ask qn:
Are germline mutations known to
‘cause’ a human disorder
YES – red (should not become
blue)
NO – blue (some will become
red)
All others – grey (further work
to classify to red or blue)
Gene Disease Map
DISEASES
Many complexities
at phenotype level.
‘Mendelian’
diseases

14. Why this is needed
Q: How many disease genes are there?
A: Depends who and how you ask.
OMIM: ‘genes phenotype-causing mutation’ = 3416
‘phenotype description, molecular basis known’ = 4482
BioMart: Ensembl Genes: + Swiss Prot IDs and OMIM
phenotype = 3268
Gene Cards: ‘disease genes’ = 9578

15. TGMI Aims
1. To provide robust, comprehensive information on
links between genes and human disease in a user-
friendly interface.
2. To develop standardised frameworks for consistent
clinical annotation and reporting of gene variation.
3. To develop approaches to deliver fast, automated,
high-throughput, large-scale variant interpretation.
4. To develop and validate flexible, multipurpose
analytical processes to maximise clinical and research
utilities of genetic testing.

16. TGMI – Aim 2
2.1 – Defining a Clinical Annotation Reference
System (CARS)
2.2 – Defining a Clinical Sequencing Notation
(CSN)
2.3 – Development and distribution of
conversion tools

17. Why this is needed
• In the clinic and research settings there is
huge variability in annotation of genetic
variation at every level (gene name, transcript
choice, variant annotation etc).
• This inevitably compromises data integration,
and clinical utility and fosters errors and
harms.

18. The CARS
• The Clinical Annotation Reference System
(CARS) encompasses the set of protein-coding
genes, the set of reference transcripts and
proteins corresponding to the genes, and a
Clinical Sequencing Notation (CSN) for
annotation of variation according to the
sequences.
• Defined against the reference human
genome.

19. TGMI gene set working criteria
• Has an HGNC ID
• Has an annotated start (which can be non-
methionine)
• Has an annotated stop
• Occurs on chromosomes 1-22, X, Y, or MT
• Has a gene and transcript biotype of “protein-
coding” from Ensembl (release 84)

20. The TGMI gene working set is
comprised of 18,885 genes

21. Clinical reference transcripts
1. Sequences must be based on the reference human
genome.
2. The system must allow flexible iteration without
compromising stability or clarity of sequence selection.
3. Reference transcripts must have durability, i.e. historical
sequences used for clinical reporting that are
subsequently superseded must remain available.
4. The reference transcript set should include as few
sequences as possible (one per gene for most genes) but
as many as required.
5. The reference transcript set must be easily available and
usable to encourage universal uptake.

22. CSN – Clinical Sequencing Notation
• Once transcript is selected, the observed variant
must be named according to its relative difference
from the reference.
• Fixed, standardised, automatic process for
annotation of sequence variation
• Consistent with historical HGVS guidelines

23. TGMI Aims
1. To provide robust, comprehensive information on
links between genes and human disease in a user-
friendly interface.
2. To develop standardised frameworks for consistent
clinical annotation and reporting of gene variation.
3. To develop approaches to deliver fast, automated,
high-throughput, large-scale variant interpretation.
4. To develop and validate flexible, multipurpose
analytical processes to maximise clinical and research
utilities of genetic testing.

24. Traditional interpretation process
1. Leveraging generic predictors, e.g. evolutionary
conservation, protein structural features, impact on
splicing etc to predict the functional consequences
of individual variants (done in lab).
2. Leveraging expert assessment of clinical impact
through disease and gene specific knowledge about
the phenotype, genetic architecture, genotype-
phenotype correlations, personal and family history
and variant segregation etc (done in clinic).

25. Interpretation requirements
1. High-throughput + large volume
2. Fast turnaround
3. Integrated into NGS pipelines
4. Integrated into clinical pipelines
5. Intelligible and usable by non-expert/patients

26. Variant Phenotype

27. Variant Phenotype
Frequency of phenotype
Mechanism of pathogenicity
Inheritance pattern
Attribution of gene for
phenotype
Penetrance of gene for
phenotype
Population variation
Variability of gene
Gene structure/function
Much useful information can be utilised and automated so
that the required manual curation can be focussed on the
~2-5% of variants where it is required.

28. TGMI Aims
1. To provide robust, comprehensive information on
links between genes and human disease in a user-
friendly interface.
2. To develop standardised frameworks for consistent
clinical annotation and reporting of gene variation.
3. To develop approaches to deliver fast, automated,
high-throughput, large-scale variant interpretation.
4. To develop and validate flexible, multipurpose
analytical processes to maximise clinical and research
utilities of genetic testing.

29. OpEx (Optimised Exome) pipeline
https://github.com/RahmanTeam/OpEx
www.icr.ac.uk/opex

30. OpEx pipeline
•Simple
•To clinical standards
•High-quality indel calling
http://icr.ac.uk/opex
https://github.com/RahmanTeam/OpEx
• Comparisons with ExAC
• Comparisons with clinical
exome pipelines

31. All input is welcome!
• The TGMI is keen to hear from and engage with anyone
interested in our aims. We are grateful for any input into
what is needed in genetic medicine, how those needs are
best met, and whether our solutions work.
• How to stay in touch:
– http://theTGMI.org
– info@theTGMI.org
– Weekly blog
– Twitter: @theTGMI