Transcript Gareth Gerrard – Genetic Analysis of DBA

Genetic Analysis of DBA
Gareth Gerrard
Imperial Molecular Pathology / Centre for Haematology
Hammersmith Hospital
Molecular Diagnostics Begins With…
DNA, Codons and the Amino Acid Code
Genes: Exons, Introns & Splicing
DNA, RNA & Proteins (& Cake)
Amino acids
The Ribosome (80S)
60S (L) unit:
5S RNA,
28S RNA,
5.8S RNA +
~49 proteins
40S (S) unit:
18S RNA +
33 proteins
A cake making machine that uses mRNA as the recipe and amino acids as the
ingredients
DBA is a
ribosomopathy*
DBA
Mutations affecting
ribosomal protein
(RP) genes
*probably
Mutations affecting:
25-35%
RPL5, RPL11,
RPS26, RPS24,
RPS17, RPS10,
RPL35a,RPS7,
RPL26, RPL15
25%
RPS19
40-50% ??
Heterozygous,
autosomal
dominant
• ~80 RP genes in total
• 10 are known to be affected in
DBA
• GATA1 may also have a role
Leading to RP
haploinsufficiency
Types of Mutations in DBA – 1) Missense
Change in recipe – use salt instead of sugar
= cake no good!
Types of Mutations – 2) Nonsense
Change in recipe – leave out half of ingredients
= cake no good!
Types of Mutations – 3) Frameshift
Change in recipe – words become unreadable
= cake no good!
Types of Mutations – 4) Splice Site
Change in recipe – pages left out or go blank
= cake no good!
Types of Mutations – 5) Copy Number Variation (CNV)
Change in recipe – pages torn out
= cake no good!
Why Screen?
• Accurate diagnosis
• Donor selection for
allogeneic haematopoietic
stem cell transplantation
• Reproductive choices
• Linking genotype to
phenotype
10 Commonly Identified DBA associated RP Genes
Mutations are mostly SNVs and indels, but large deletions & insertion are also seen
RPS26
RPS24
RPS7
RPS17
RPL26
RPS35a
RPL11
RPS10
Unknown
RPL5
RPS19
= 7 genes in conventional molecular screen
Mutation Detection Technology – Sanger Sequencing
ABI 3130
ABI 3500xl
1 Sample / 1 Gene / day
5 Samples / 1 Gene / day
Standard
DBA
Screening
Pipeline
Measure & QC
Peripheral Blood
Extract DNA
RPS19
RPL5
RPL11
RPS24
RPS17
RPL35a
RPS7
Sanger Sequence
PCR target gene exons
Next Generation Sequencers
V1 - Pilot
Roche 454
Getting on a bit /
Expensive
Illumina MiSeq
Highest throughput
V2 - Current
Ion Torrent PGM
Fastest / most flexible
Why Next Generation Seq (NGS)?
•
•
•
•
Very high throughput (fast)
Can look at all 80+ RP genes at once
Can multiplex many samples at once
Potential to pick up allele-loss deletions &
insertions (CNV)
• Cost effective per-gene / per-sample
• Once identified, family members can be
screened by conventional sequencing
RP Gene loci used for V1 Gene Capture
Small
SA
S2
S3
S3A
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
S15
S15A
S16
S17
S18
S19
S20
S21
S23
S24
S25
S26
S27
S27A
S28
S29
S30
RPSA
RPS2
RPS3
RPS3A
RPS4X
RPS4Y
RPS5
RPS6
RPS7
RPS8
RPS9
RPS10
RPS11
RPS12
RPS13
RPS14
RPS15
RPS15A
RPS16
RPS17
RPS18
RPS19
RPS20
RPS21
RPS23
RPS24
RPS25
RPS26
RPS27
RPS27A
RPS28
RPS29
FAU
Location (for capture)
chr3:39448180-39453929
chr16:2012061-2014861
ch11:75110530-75133324
chr4:152020725-152025804
chrX:71475529-71497150
chrY:2709527-2734997
chr19:58898636-58906170
chr9:19375713-19380252
chr2:3622795-3628509
chr1:45240923-45244451
chr19:54704610-54752862
chr6:34385231-34393902
chr19:49999634-50002944
chr6:133135580-133138703
chr11:17095936-17099334
chr5:149822753-149829319
chr19:1438363-1440492
chr16:18792617-18801656
chr19:39923852-39926618
chr15:82821158-82824972
chr6:33239787-33244287
chr19:42363988-42375482
chr8:56979854-56987069
chr20:60962105-60963576
chr5:81569177-81574396
chr10:79793518-79816570
chr11:118886422-118889401
chr12:56435637-56438116
chr1:153963235-153964626
chr2:55459039-55462989
chr19:8386384-8387809
chr14:50043390-50053094
chr11:64888100-64889945
Large Subunit
L3
RPL3
L4
RPL4
L5
RPL5
L6
RPL6
L7
RPL7
L7A
RPL7A
L8
RPL8
L9
RPL9
L10
RPL10
L10A
RPL10A
L11
RPL11
L12
RPL12
L13
RPL13
L13A
RPL13A
L14
RPL14
L15
RPL15
L17
RPL17
L18
RPL18
L18A
RPL18A
L19
RPL19
L21
RPL21
L22
RPL22
L23
RPL23
L23A
RPL23A
L24
RPL24
L26
RPL26
L27
RPL27
L27A
RPL27A
L28
RPL28
L29
RPL29
L30
RPL30
L31
RPL31
L32
RPL32
chr22:39708887-39716394
chr15:66790801-66797221
chr1:93297597-93307481
chr12:112842994-112856642
chr8:74202506-74208024
chr9:136215069-136218281
chr8:146015150-146017972
chr4:39455744-39460568
chrX:153618315-153637504
chr6:35436185-35438562
chr1:24018269-24022915
chr9:130209953-130213684
chr16:89627056-89630950
chr19:49990811-49995565
chr3:40498783-40506549
chr3:23958036-23965183
chr18:47014858-47018906
chr19:49118585-49122793
chr19:17970730-17974962
chr17:37356536-37360980
chr13:27825446-27830828
chr1:6241329-6269449
chr17:37004118-37010064
chr17:27046411-27051377
chr3:101399935-101405626
chr17:8280838-8286568
chr17:41150446-41154956
chr11:8703958-8736306
chr19:55897300-55903449
chr3:52027644-52029958
chr8:99037079-99058697
chr2:101618177-101640494
chr3:12875984-12883087
L34
L35
L35A
L36
L36A
L37
L37A
L38
L39
L40
L41
LP0
LP1
LP2
RPL34
RPL35
RPL35A
RPL36
RPL36A
RPL37
RPL37A
RPL38
RPL39
UBA52
RPL41
RPLP0
RPLP1
RPLP2
chr4:109541722-109551568
chr9:127620159-127624260
chr3:197676858-197683481
chr19:5690272-5691674
chrX:100645812-100651105
chr5:40825364-40835437
chr2:217362912-217443903
chr17:72199721-72206676
chrX:118920467-118925606
chr19:18682614-18688269
chr12:56510370-56511727
chr12:120634489-120639038
chr15:69745123-69748172
chr11:809647-812880
Latest Version adds
GATA1, but loses
RPS17
http://
ribosome.med.miyazaki-u.ac.jp
NGS Workflow – v1
3µg Genomic DNA
20 probands
Target Enrichment
Fragment DNA:
Covaris e220
Hybridise and capture
Ribosomal Protein Gene DNA
including exons, introns, &
regulatory regions
Total Time = 2 weeks
Data analysis
Sanger seq validation
High-throughput
Sequencing
Library quant, pool, clean up and
cluster generation
DBA – NGS v1 – Results from Initial 20 Samples
Gene
n= (17)
RPL5
5(4)
British Journal of Haematology, 2013, 162,530–536
RPS26
3
RPL11
2
RPS17
2(1)
RPS7
1
RPS10
1
RPS24
1
RPS19
0
Tot Mut
15(13)
NoMut
2
%
29.4%
17.6%
11.8%
11.8%
5.9%
5.9%
5.9%
0.0%
88.2%
11.8%
Type
3(2) SG/2 FSD
SG/FSI/SL
FSD/FSI
2(1) SG
SSD
SG
SG
SG= Stop Gain SNV (Nonsense); FSD= Frame-shift Deletion; FSI= Frame-shift Insertion;
SL= Start Loss SNV (Missense); SSD= Splice Site Defect
DBA – NGS – v2 Workflow: Days 1 - 3
20ng
gDNA
Allows screening of 16 samples for 80+ Genes per run
AmpliSeq
Library Prep
(1-2 days)
qPCR quant
& pool
KAPA Quant Kit
Day 1
Template
& Enrich
OneTouch2 & ES
Day 2
PGM
Sequence
2 x 8 barcode
Day 3
DBA NGS – Day 4: Analysis...
Variant Caller
TSv3.6.2
DON’T PANIC!
VCF Files
VEP
IGV
SHOW ME THE KITTEHS
Ensembl v72
Virtualbox 4.2
NextGene / SeqNext
Ion Reporter
v1.6
CONDEL / Mutation Assessor
Human Splicing Finder v2.4.1
MolDiag team for
Sanger
validation &
reporting
DBA – NGS - Analysis
DBA Mutation - IGV PileUp showing RPS26 Nonsense
TTC (Phenylalanine) -> TAA (STOP)
DBA-NGS v2 – Initial Results
DBA-HALO
Barcode
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Results
Gene
RPL15
RPS26
RPL13A
RPS7
RPL29
RPS19
RPL7
RPL15
RPL17
RPS10
Consequence
Stop-Gain
splice donor variant
missense_variant
missense_variant
inframe_insertion
frameshift_insertion
splice_region_variant
missense_variant
Exon
Base
Codon
LOVD?
LOF?
dbSNP
MAF
Sanger Valid?
4 3:23960737G>A
p120W>* No
Yes(?)
n/a
n/a
Yes
1 n.30+1G>AINTRON=1/2
Yes
Yes
rs148622862
n/a
Yes
7 c.481C>A p.Ala161Asp
No
?
rs150697570
n/a
6 c.562T>C p.133L>S No
???
n/a
n/a
Yes
4 c.386_391dupCCAAGG
p.Ala129_Lys130dup
n/a
???
rs141201675
n/a
*
4 c.199-200_insG 67 No
???
n/a
n/a
*
1+8 c.107+8A>GINTRON=1/3
n/a
???
rs74460527 0.0096 *
5 c.466T>G p.141S>A n/a
??Splicing n/a
n/a
*
splice_region_variant,5_prime_UTR_variant
1 c.87G>A Exon1/6 (5'UTR)
n/a
Stop-Gain
c.337C>T p.113R>* Yes
???
Yes
rs140522052
<1%
rs267607022
*
Yes - c
3 definite hits (1 novel); 2 very likely; 5 interesting
Only 1 DBA had no mutation (9); 10-13 non-affected family members
Summary
• Screening for mutations in DBA is now an established
technology
• We now use NGS technology to screen all 80+
Ribosomal protein genes
• Family members screened by conventional sequencing
(for known mutation)
• Will introduce screening for CNV in near future
Thank You!
IPML Hammersmith
Letizia Foroni
Kikkeri Naresh
MRD
Pierre Foskett
Thet Myint
Faisal Abdillah
Mol Diag
Mikel Valganon
Alex Foong
Natalie Killeen
Sarmad Toma
R&D
Mary Alikian
George Nteliopoulos
Students
Clinical Team
Aysha Patel
Sakuntala Ale
Robin Ferrari
Josu de la Fuente
Anastasios Karadimitris
Deena Iskander
ACHS CGL
Tim Aitman
Michael Müller
Dalia Kasperaviciute
Laurence Game
Jane Apperley
David Marin
Dragana Milojkovic
Jiri Pavlu
John Goldman