GWAS vs NGS

download GWAS  vs  NGS

of 25

  • date post

    15-Jan-2016
  • Category

    Documents

  • view

    127
  • download

    0

Embed Size (px)

description

GWAS vs NGS. James McKay Genetic Susceptibility Group. Genetics. Individual. Predicted Phenotype. Non-heritable. Heritable. Environment. What we expect in terms of effects of genetic variants in cancer susceptibility. Population frequency seems to impact on disease - PowerPoint PPT Presentation

Transcript of GWAS vs NGS

  • GWAS vs NGSJames McKayGenetic Susceptibility Group

  • Genetics

    EnvironmentIndividualPredictedPhenotypeHeritableNon-heritable

  • What 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 GWASCasesControlsAgnostic 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

  • CasesControls

    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 GWASProstate cancerBreast cancerColorectal cancerLung cancerEsophageal cancer Ovarian cancerHead and NeckTesticular cancerBladder cancerThyroid cancerPancreatic cancer MelanomaBasal cell carcinomaGliomaNeuroblastomaKidneyChronic lymphocytic leukemiaAcute lymphoblastic leukemiaFollicular lymphomaMyeloproliferative disordersHodgkins Lymphoma

    Blue = carried out at IARC

  • -Log10 (p-value)Chromosome6p21 MHC Region5q31 IL13/IL4GWAS Results Classical HL 4 european studies1200 ca 6713 generic controlK Urayama

  • MHC Region AssociationsAll classical HLEBV-positive HLEBV-negative HL-Log10 (P value)Position in MHC Region (MB)HLA-DRA: rs2395185MICB: rs2248462 Extended Class I Class I Class III Class IIHLA-DRA: rs6903608HLA-DRA: rs6903608HLA-A: rs2734986HLA-A: rs6904029

  • ORP=1.8x10-9P=1.1x10-8Results 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

  • Limitations of GWAS

    Small RR and many variants testedSample sizes in thousand samples needed2nd cancers in Hodgkins Best et al Nat Med 2011

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

    Rare variants not assessed

  • Next generation sequencingMassive 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

    Review issue Exomes Genome Biology 2011

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

  • Families, trios, case control, tumour vs normal, Pooled/individualWhole genome, target capture (exome, spec regions? Illumina SOLiD, PGM, 454..Seq ACGTACGTACGAGCTACGTACGTACGTACGT75 150 250 bp

    Mapping

    Variant calling

    Variant consequenceSboner et al Genome Biology 2011An 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 SNPs15 20,000Coding SNPs5,000 7,000Coding SNPs200 -500 Nonsynon + trun SNPs50 100 Functional SNPsTarget exomesSilent, Synonymous Previously identifedFunctional truncatingIn silico predictionsVariant filtering

  • Families, trios, case control, tumour vs normal, Pooled/individualWhole genome, target capture (exome, spec regions? Illumina SOLiD, PGM, 454..Seq ACGTACGTACGAGCTACGTACGTACGTACGT75 150 250 bp

    Mapping

    Variant calling

    Variant consequenceAhhh, yes, tricky, we might have to form a working group and get back to you on that oneSboner et al Genome Biology 2011An 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)

  • NPC pedigree Sarawak Malaysia11 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 (Knudsons 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

    chrAchrBBRCA1BRCA1

  • Exomes seq

    Seq

    Genomic DNASomatic Tissue eventsCatalog mutation events in consistutional DNAAnd somatic events

    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 seqCNV1.3 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 biorepos approx contains lung cancersblood and frozen tumour

    Two stage designExome sequencingNormal/tumour 30 fh+

    470 for replication

  • Ill stop there, Thanks

  • Next generation sequencingMassive 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,

    *********Supplementary Figure 2 Results based on combined dataNot stat significant replicationOR, although EBV- only (EBV status mostly from UKYork, do we have EBV?)

    *