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Genome Wide Methodologies and Future Perspectives

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Genome Wide Methodologies and Future Perspectives

  1. 1. Genome Wide Methodologies and Future Perspectives Brian Krueger, PhD Duke University Center for Human Genome Variation
  2. 2. History of Genetic Linkage • Mendel’s Laws – Law of segregation • Each parent randomly passes one of two alleles to offspring – Law of Independent Assortment • Separate genes for separate traits are passed independently to offspring • Traits should appear in offspring in the ratio of 9:3:3:1 – Laws hold true for genes on different chromosomes or genes located far away from one another • Linkage – Bateson and Punnett quickly found traits that didn’t assort independently – Thomas Hunt Morgan and his student Alfred Sturtevant found that recombination frequency is a good predictor of distance between genes • Genes that are inherited together must be closer to one another – linked • Generated the first linkage maps – Serves as an important basis for understanding genetic association studies
  3. 3. Linkage Studies • Model Organisms – Fruit Flies, plants, etc – Extremely important for understanding human genetics – Fruit flies can produce new generations of 400+ offspring approximately every week! • Can very quickly understand the genetics of trait heritability • Familial Linkage Studies – Require multiple generations – Take decades to develop – Complicated by family participation • Association studies – Subtle difference between linkage studies – Try to apply knowledge of familial linkage to entire populations
  4. 4. Genome Wide Association Studies • GWA studies – Aim to find genetic variants that are associated with traits – Typically used to elucidate complex disease traits – Focus on SNPs, Indels, CNVs – Most often Case/Control Studies • SNP (Single Nucleotide Polymorphism) – Change in a single nucleotide position • Indel (Insertion/Deletion) – Describes the insertion or deletion of nucleotides • CNV (Copy number variations) – Large deletions or duplications of genetic material
  5. 5. GWA Study History • Human Genome Project (1990-2000) – Decade long international project to determine the complete human genome sequence – Provided the reference genome for future research on genome variation • Human HapMap (2002-2009) – Sequencing whole genomes is expensive – Needed a shortcut to understand how variation contributes to disease – Mapped millions of common known SNPs in 269 individuals – Theory that common SNPs are inherited and could be predictive of associated disease – Determine how SNPs from case/control studies associate with human disease
  6. 6. Defining Association • Variants are not always causal! – SNPs sometimes only serve as markers – Can play absolutely no role in the disease and even be located on different chromosomes from the gene actually responsible for the phenotype • Population stratification – Variants differ by population – Variants important markers of disease in one population or ethnicity may not be effective markers in another – For GWA studies to be effective predictors in multiple populations, large datasets for each ethnicity must be obtained
  7. 7. GWAS SNP Genotyping • Bead array genotyping – Uses a chip containing beads with covalently attached baits – Baits hybridized to fragmented DNA – Baits SPECIFIC for the DNA just upstream of a SNP – Base extension with fluorescently labeled bases allows interrogation of the SNP (each base has a different color!) – A single bead chip can assay millions of rs1372493 rs1372493 SNPs 16000 1.60 1.40 – Colorimetric output plotted 14000 12000 1.20 • Blue indicates homozygous for one version of the 10000 1 SNP - CC Intensity (B) 8000 0.80 • Purple is heterozygous - CA Norm R 6000 0.60 • Red homozygous for the other version of the SNP 4000 - AA 0.40 2000 0.20 0 0 2317 834 74 -2000 -0.20 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 0 0.20 0.40 0.60 0.80 1 Intensity (A) Norm Theta
  8. 8. GWAS SNP Genotyping and Validation • Realtime PCR – Use specific PCR probes to verify SNPs – Good for validating a handful of SNPs at a time • Mass Array – Use mass spec to find SNPs – Detected by looking at fragment weight differences – Good for detecting or validating a large number of SNPs rapidly • Sanger sequencing – Gold standard validation method – Can determine the SNP at its exact position – Very robust
  9. 9. GWA Study History • To this point in time, the power of most GWA studies was lacking – GWA not really genome wide – Looked at common variants across genome – Missed rare variants and not always descriptive of disease causation • Whole Genome Sequencing (WGS) – Actually assays the entire genome – Discovers all variants – Prohibitively costly before 2008 – Current cost of WGS ~$4000 • Thousand Genomes Project (2008-) – Facilitated by plummeting sequencing costs and technological advancements – Goal to fully sequence the genomes of 1000 healthy individuals to provide a true picture of genome wide variation
  10. 10. Second Generation Sequencing • Developed to increase throughput of Sanger sequencing • Can sequence many molecules in parallel – Does not require homogenous input – Sequenced as clusters • Sequencing by synthesis – Bases are added, signals scanned, and then washed – Cycle repeated (30-2000x)
  11. 11. 2nd Gen: Sequencing by Synthesis Overview Genomic Fragmented DNA Ligate Adaptors DNA Generate Clusters (On Flowcell or Beads) T T A T A T TA T A T T C C G G A G A G T T T T G G Repeat Hundreds of times on millions of clusters Detect Signals Add Bases
  12. 12. Flavors of Sequencing • Whole Genome Sequencing – Obtain whole blood or tissue sample – Create sequencing libraries of all DNA fragments • Whole Exome Sequencing – Utilizes a selection protocol – Attach complimentary RNA strands to beads – Fish out ONLY coding DNA sequences – Create sequencing libraries from enriched DNA – Reduces cost significantly • Custom Capture – Same protocol as Exome sequencing – Only target desired DNA sequences • Amplicon Sequencing – Use PCR to amplify target DNA – Sequence amplified DNA (Amplicon)
  13. 13. NGS Study Designs for Gene Discovery Multiplex families Case-control studies Trio sequencing of sporadic diseases
  14. 14. De novo Mutation Calling/Filtering Variant Individual variant Multi-sample calling calling variant calling Exome Variant Server 6500 exome Cross-checking sequenced individuals public databases Visual InspectionSanger sequencing confirmation
  15. 15. Detecting Copy Number Variants ERDS (Estimation by Read Depth with SNvs) Average read depth (RD) of every 2-kb window were calculated, followed by GC corrections. A paired Hidden Markov model was applied to infer copy numbers of every window by utilizing both RD information and heterozygosity information. homozygous heterozygous duplication deletion deletion Windows
  16. 16. Illumina • Uses a flow cell • Cluster generated on slide via bridge amplification • Sequencing by synthesis – Performed by flowing labeled bases over flow cell – 4 pictures taken (one for each base) – Cluster color determined at each cycle allows interrogation of sequence • Advantages – Low cost per base – Very high throughput • Limitations – High cost per experiment – Short read length (30-150bp) – Acquired a company that uses new tech to reach read lengths of 2-10Kb Schadt et al 2010 HMG
  17. 17. Ion Torrent • Emulsion PCR is used to generate clusters on a bead • Sequencing by synthesis – Pyrosequencing – Relies on release of pyrophosphate for detection – Instead of a visual cue, system senses the release of H+ as each base is flowed over the beads • Advantages – Short run time – Does not require modified bases – Longer read length (200bp) • Limitations – Low data output – High homopolymer error rate
  18. 18. Third Generation Sequencing • Defined as single molecule sequencing • Less complex sample prep • Much longer read length – SGS Short read length a huge disadvantage for de novo sequencing applications • Two categories – Sequencing by synthesis – Direct sequencing • Passing molecule through a nanopore • Using atomic force microscopy • Bleeding edge technology – Many technical hurdles – Currently very high error rates
  19. 19. Pacific Biosciences • Utilizes single molecule sequencing by synthesis • Extremely complex system – Each well contains a single DNA molecule and an immobilized polymerase – No reagent washing – Employs confocal microscopy to only detect fluorescence at the polymerase • Advantages – Very long read length (1-15kb) – Low complexity sample prep – Very fast data generation (real time) • Disadvantages – Prone to sequencing errors (~15% error rate) – Company on the verge of bankruptcy
  20. 20. Third/Second Generation Sequencing • Currently only one viable high throughput long read sequencing platform – PacBio system has a 15% error rate – Need long reads for many applications from de novo sequencing to haplotyping • Second generation sequencers high throughput and accurate – Short reads are hard to assemble and leave gaps in repetitive sequences • Can use both as a highly accurate and extremely powerful tool for de novo sequencing applications – Use PacBio assembly as a scaffold – Correct errors by aligning HiSeq reads on top – Effective error rate of 0.1% – Expensive but extremely fast and accurate compared to other methods Koren et al 2012 Nature Biotechnology
  21. 21. Future: Nanopore Sequencing • Leading candidate is Oxford Nanopore • Concept – Detect flow of electrons through the pore – Each base causes a detectable change in the current – Results in direct sequencing – Theoretically could be used to sequence RNA and protein too • Advantages – Long read length – Plug and play – Easily scalable • Disadvantages – No hard data yet Credit: John MacNeill/TechnologyReview – No specific release date
  22. 22. Future: Direct sequencing • Concept stage techniques – Significant technical hurdles to overcome – Mostly proof of concept experiments • IBM DNA Transistor Credit: IBM – Bases read as single stranded DNA passes through the transistor – Gold bands represent metal, gray bands are the dielectric • Atomic force microscopy sequencing – Use AFM tip to detect each base of single stranded DNA Credit: Lee et al US PAT 20040124084
  23. 23. Sequencing Applications • Old techniques which used to take days or years to perform can now be completed in hours • Next generation sequencing has opened a new door for addressing very complicated genetic questions – Has huge potential to revolutionize human healthcare – Survey complex tumor types – Research into macro and micro community genomics – Reveal evolutionary history
  24. 24. De novo Sequencing• Human genome took 10 years to complete and cost $3 billion dollars – Done by laboriously cloning overlapping segments of the human genome into bacmid libraries and Sanger sequencing each one – Genome assembled using computers to line up over lapping sequences• Current estimate is around $4000 – Can be completed in a week – Companies like Complete Genomics say they have already sequenced thousands of human genomes• Future – Long read sequencers will make agricultural sequencing more viable – Whole genome sequencing for human diagnostics will become routine – Increasing the catalog of organismal genomes will improve our understanding of evolution and development
  25. 25. Genome Mutation Analysis • Previously done by completing complicated and time consuming familial linkage studies and targeted Sanger sequencing • Next generation sequencing can look at every gene at once – Can produce a genetic map of the complete genome – Used to detect genetic polymorphisms – See every possible mutation • Future – Whole genome sequence analysis – Targeted genome sequencing analysis using predetermined sequence selection arrays (ex: Exome Enrichment)
  26. 26. Pharmacogenetics • Very hot topic in the biotech and insurance industries • Use genetic typing to guess how a person might respond to different drug treatments • Currently relies on microarrays • NGS could provide significantly more information at more loci – Microarrays only look at a handful of polymorphisms – Current NGS approaches port the microarray technique to enrich pools for sequencing • Future – As the catalog of human genomes increases, it will be easier to calculate responses to treatment before drugs are administered Gauthier et al 2007 Cancer Cell
  27. 27. Epigenetics • Defined as heritable genetic information that is not coded in the DNA bases – DNA methylation – Histone modifications • Previous mechanisms for detecting these Chromatin or DNA modifications relied on targeted probing – ChIP-PCR – Bisulfite sequencing – Footprinting assays • Next generation sequencing changed everything – Whole genome methylation mapping (MAP-IT) – Whole genome histone modification and protein binding mapping (ChIP-Seq - acetylation, methylation, etc) • ENCODE project
  28. 28. ENCyclOpedia of Dna Elements (ENCODE) • International project – Follow up to the human genome project • Only 98% of the human genome codes for protein – Creating and maintaining DNA is biochemically expensive – What’s the other 98% of the genome doing? • ENCODE goals – Determine the functional elements of the human genome – Protein Coding – Non-Coding RNA – mRNA Expression – Regulatory protein binding sites – Histone modifications • Preliminary estimates show that 80% of human DNA is functional!
  29. 29. Transcriptome/Expression Analysis • Gene expression analysis is important for disease discovery and cancer diagnosis • Expression analysis first relied on Northern blotting followed by DNA microarrays – Both cases require a probe – Need to “know” what you are looking for – Low resolution screening • Next generation approaches screen the entire transcriptome (RNA-Seq) – Single base resolution of expression – Can see level of expression and also visualize mutations in expressed sequences • Future – Important for diagnosing/treating cancer and heritable diseases
  30. 30. Phenotypic Correlation • NGS data generates huge datasets with 85-99.9% base accuracy – Must determine which signals are real, and which are noise/errors – Most promising hits are validated by other assays (Sanger, qRT, Mass Spec) – How do we determine which hits to validate? • Currently have very small datasets, even in pharmacogenetics that have limited utility • Validated hits can be distractions See NYTimes Series on whole genome – Tumor diversity presents multiple escape Sequencing: http://nyti.ms/No4fgd routes during targeted treatment • Future – Require large validated datasets that are ethnically and geographically diverse
  31. 31. Metagenomics • Used to survey macro and micro environments – Microbial communities (Soil/Gut) – Tumors – Plant communities – Coral reef ecosystems • Previous techniques coupled mtDNA or ribosomal Sanger sequencing with BLAST analysis – Limited by number of sequenced species – Can determine who, but not what is going on • NGS approaches now being used to determine exactly what organisms are present and how they interact – Can get expression data and link it back to community groups – Survey community diversity
  32. 32. Data • Absolutely the largest roadblock for next generation sequencing • Terabytes of data are useless if we can’t efficiently analyze the data • How long should data be kept? – Depends on application • Human Diagnostic sequencing? • Research sequencing? • Where should data be kept and processed? – Local or Cloud (Amazon, etc)? – Cost of infrastructure vs cost of cloud service – Security issues • Future – Cloud based solutions will become more attractive

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