GWAS vs NGS

James McKayGenetic Susceptibility Group

Genetics

Environment

Individual

PredictedPhenotype

Heritable

Non-heritable

What we expect in terms of effects of genetic variants in cancer susceptibilityWhat we expect in terms of effects of genetic variants in cancer susceptibilityPopulation frequency seems to impact on disease Severity of the consequence on the genes function

Genome wide association studies GWAS

Cases Controls

Agnostic approach -- no knowledge about the gene is needed

Test all common genetic variation across the genome

770,000 variants for common variants, each tested for differences between cases and controls

Assays to measure all common

genetic variation in human genome

Cases Controls-Test each one of the variants, tested for differences between cases and controls

Genome wide association studies Association in case-control groups

Cancer types with successful GWAS

Prostate cancerBreast cancerColorectal cancerLung cancerEsophageal cancer Ovarian cancerHead and NeckTesticular cancerBladder cancerThyroid cancerPancreatic cancer

MelanomaBasal cell carcinomaGliomaNeuroblastomaKidneyChronic lymphocytic leukemiaAcute lymphoblastic leukemiaFollicular lymphomaMyeloproliferative disordersHodgkin’s Lymphoma

Blue = carried out at IARC

-Log

10 (

p-va

lue)

Chromosome

6p21 MHC Region

5q31 IL13/IL4

GWAS Results Classical HL – 4 european studies

1200 ca 6713 generic control

K Urayama

MHC Region Associations

27 28 29 30 31 32 33

05

10

15

20

25

30

All classical HL

EBV-positive HL

EBV-negative HL

-Log

10 (

P v

alue

)

Position in MHC Region (MB)

HLA-DRA: rs2395185

MICB: rs2248462

Extended Class I Class I Class III Class II

27 28 29 30 31 32 33

05

10

15

20

25

30 HLA-DRA: rs6903608

HLA-DRA: rs6903608

27 28 29 30 31 32 33

05

10

15

20

25

30

HLA-A: rs2734986

HLA-A: rs6904029

HeterozygousHomozygous

EPILYMPH-GWASSCALE-GWASUK Studies-GWASNetherlands-GWASEPILYMPH-ReplicationUK Studies-Replication

NSHLMCHL

EBV- cHLEBV+ cHL

15-35 years36-90 years

MCHL vs NSHLEBV+ vs EBV- cHL

Log-additive

Study (P homogeneity=0.137)

Major subtypes (P homogeneity=0.144)

Tumor EBV status (P homogeneity=0.011)

Age-specific cHL (P homogeneity=0.155)

Case-case analysis

1757624261

18134139128764

493

1262330

958392

968788

330392

70201967232

862578

31671803396214

70207020

60556055

70207020

1262958

1.381.391.84

1.731.771.281.311.221.08

1.451.21

1.471.07

1.461.27

0.820.81

1.24-1.541.22-1.581.38-2.45

1.27-2.351.33-2.351.06-1.551.03-1.660.74-2.010.79-1.48

1.29-1.630.98-1.49

1.29-1.680.87-1.32

1.29-1.651.10-1.47

0.66-1.020.65-1.01

Chr 5: IL13rs20541 Ca Co OR 95%CI

1.0 1.5 2.0

OR

P=1.8x10-9

P=1.1x10-8

Results for IL13

Next step in GWAS

Very large sample sizesmeta-analysis lung cancer 14K ca 18K co

Are all SNPs equal?Bayesian approach, weight SNPs based

on different approaches – eQTL, medical literature

Many cancer loci are relevant to more than one cancer subtype – start with known loci decrease multiple testing burden

Very large sample sizesmeta-analysis lung cancer 14K ca 18K co

Are all SNPs equal?Bayesian approach, weight SNPs based

on different approaches – eQTL, medical literature

Many cancer loci are relevant to more than one cancer subtype – start with known loci decrease multiple testing burden

Limitations of GWAS

Small RR and many variants testedSample sizes in thousand samples

needed2nd cancers in Hodgkin’s Best et al Nat Med 2011

Only considers common genetic variants(and only ~ 80% of them)

Rare variants not assessed

Small RR and many variants testedSample sizes in thousand samples

needed2nd cancers in Hodgkin’s Best et al Nat Med 2011

Only considers common genetic variants(and only ~ 80% of them)

Rare variants not assessed

Next generation sequencing

Massive parallel sequencing

Now able to assay the entire sequence of an Individual

The seq first genome – $3 billion, 14+ labs

A single machine, $3000

Many applications other than DNA reseq

Now able to assay the entire sequence of an Individual

The seq first genome – $3 billion, 14+ labs

A single machine, $3000

Many applications other than DNA reseq

Review issue Exomes Genome Biology 2011

GWAS assays focus on common genetic variants, NGS givesIndividual seq hence common information on rare variants

GWAS assays focus on common genetic variants, NGS givesIndividual seq hence common information on rare variants

Families, trios, case control, tumour vs normal, Pooled/individual

Whole genome, target capture (exome, spec regions? Illumina SOLiD, PGM, 454…..

Seq ACGTACGTACGAGCT……ACGTACGTACGTACGT75 – 150 – 250 bp

Mapping

Variant calling

Variant consequence

Sboner et al Genome Biology 2011

An example of a NGS workflow

Variant calling, heterozygote calls, 50% of reads should be wild type allele, C (ie in the reference)50% of read should be variant ie T30 reads / base seems to be solution in terms of accuracy/cost effectiveness

NGS data, many many short sequence reads

~3 million SNPs

15 – 20,000Coding SNPs

5,000 – 7,000Coding SNPs

200 -500 Nonsynon + trun SNPs

50 – 100 Functional SNPs

Target exomes

Silent, Synonymous

Previously identifed

Functional – truncatingIn silico predictions

Variant filtering

Families, trios, case control, tumour vs normal, Pooled/individual

Whole genome, target capture (exome, spec regions? Illumina SOLiD, PGM, 454…..

Seq ACGTACGTACGAGCT……ACGTACGTACGTACGT75 – 150 – 250 bp

Mapping

Variant calling

Variant consequence

Ahhh, yes, tricky, we might have to form a working group and get back to you on that one

Sboner et al Genome Biology 2011

An example of a NGS workflow

After Qc filtering

50-100 variants per individual that are in Genes and appear functional

How do we differentiate true from false?

Bin variants across genes? Test for association? (need @ least 3K ca 3kco)

50-100 variants per individual that are in Genes and appear functional

How do we differentiate true from false?

Bin variants across genes? Test for association? (need @ least 3K ca 3kco)

NPC pedigree Sarawak Malaysia

11 cases for which we have genomic DNA

Exome sequencing underway

Triage variants in pedigree, interesting variant should segregating in cases

Validation in remaining individuals + additional pedigrees, (Allan Hildesheim US NCI)

Genes following two hit models

(Knudson’s hypothesis)

NGS quite successful in recessive diseases (two mutations, a rare event)

Many inherited tumours have no normal alleles, one inherited, the second (wildtype) then deleted somatically, RB, TP53, VHL, BRCA1/2, APC, PTEN

NGS quite successful in recessive diseases (two mutations, a rare event)

Many inherited tumours have no normal alleles, one inherited, the second (wildtype) then deleted somatically, RB, TP53, VHL, BRCA1/2, APC, PTEN

chrA chrB

BRCA1BRCA1

Exomes seq

Seq

Exomes seq

Seq

Genomic DNA Somatic Tissue events

Catalog mutation events in consistutional DNA

And somatic events

Identify genes for which there isCo-occurence of events, consistent with two hithypothes

Identify genes for which there isCo-occurence of events, consistent with two hithypothes

chrA (inherited events)50 by chance?

chrB (somatic events)500 by chance?

Exome seqCNV

1.3 times per genome

IARC biorep has close to 500 lung cancer cases with a blood sample and snap frozen tumour 30 LC have a first degree relatives with lung cancer

IARC biorep has close to 500 lung cancer cases with a blood sample and snap frozen tumour 30 LC have a first degree relatives with lung cancer

IARC biorepos approx contains lung cancers

blood and frozen tumour

Two stage designExome sequencing

Normal/tumour 30 fh+

470 for replication

Two stage designExome sequencing

Normal/tumour 30 fh+

470 for replication

I’ll stop there, Thanks

Next generation sequencing

Massive parallel sequencing

Assay single cell and single position

Say chr 3 1 - 50 (from a single cell)Diploid:chr1 ACGTACGTACGAGACGTACGTACGTACGTchr2 ACGTACGTACGAAACGTACGTACGTACGT

Not a single cell (although its being worked on), but sample a individuals In parallel, massive billions of reads,

Assay single cell and single position

Say chr 3 1 - 50 (from a single cell)Diploid:chr1 ACGTACGTACGAGACGTACGTACGTACGTchr2 ACGTACGTACGAAACGTACGTACGTACGT

Not a single cell (although its being worked on), but sample a individuals In parallel, massive billions of reads,