Medical Genomics

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Medical Genomics

• The Impact of Genome Data and New Technologies
  on Health Care

Stuart M. Brown Research Computing, NYU School of
Medicine
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A Genome Revolution in Biology and Medicine

• We are in the midst of a "Golden Era" of biology
• The Human Genome Project has produced a huge
  storehouse of data that will be used to change
  every aspect of biological research and medicine
• The revolution is mostly about treating biology
  as an information science, not about specific
  biochemical technologies.

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• I. The Human Genome Project
• II. Genomics
• - microarrays
• - SNP genotyping
• III. The medical and business applications

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The Human Genome Project
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Bold Words from Francis Collins

• The history of biology was forever altered a
  decade ago by the bold decision to launch a
  research program that would characterize in
  ultimate detail the complete set of genetic
  instructions of the human being.

Francis S. Collins Director of the National
Human Genome Research Institute N Engl J Med 1999
88242-65
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A review of some basic genetics
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DNA

• 4 bases (G, C, T, A)
• base pairs
• G--C
• T--A
• genes
• non-coding regions

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Decoding Genes
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• The human genome is the the complete DNA content
  of the 23 pairs of human chromosomes - 44
  autosomes plus two sex chromosomes
• - approximately 3.2 billion base pairs.

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Genome Projects

• Complete genomic sequences
• Dozens of microorganisms
• Yeast, C. elegans, Drosophila
• Mouse
• Human
• Comparative genomics
• All this data is enabling new kinds of research -
  for those with the computational skills to take
  advantage of it.

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How does genome sequencing technology work?

• Molecular biology of the Sanger method
• Manual Gels vs. ABI machines
• Sub-cloning of fragments - BAC, PAC, cosmid,
  plasmid, phage
• The need for computers to assemble the "reads"
  and manage the workflow

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• Automated sequencing machines,
• particularly those made by PE Applied
  Biosystems, use 4 colors, so they can read all 4
  bases at once.

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Subcloning

• DNA sequencers can only read small fragments of
  DNA 500-1000 bases long
• It is necessary to break the genome into small
  pieces .
• Individual chromosomes are cut into 1 million
  base chunks that are cloned into large vectors
  called BACs, PACs, and YACs.
• These pieces can then be further cut into
  sequenceable pieces (1000 bases) and cloned into
  plasmid or phage vectors.

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Raw Genome Data
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Lots of Sequence Data

• How to extract useful knowledge from all of this
  data?
• Need sophisticated computer tools
• Find the genes
• Figure out what they do (function)
• Diagnostic tests
• Medical treatments

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What is a Gene?

• For every 2 biologists, you get 3 definitions
• A DNA sequence that encodes a heritable
  trait.
• The unit of heredity
• Is it an abstract concept, or something you can
  isolate in a tube or print on your screen?
• Classic vs. modern understanding of molecular
  biology

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Classic Molecular Biology

• A gene is a DNA sequence at a particular locus on
  a chromosome that encodes a protein.
• The Central Dogma of Molecular Biology
• DNA gt RNA gt Protein
• A mutation changes the DNA sequence - leads to a
  change in protein sequence - or no protein.
• Alleles are slightly different DNA sequences of
  the same gene.

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Genome Confusion

• The sequence of a gene in the genome includes
• protein coding sequence
• introns and exons
• 5' and 3' untranslated regions on the mRNA
• promoter and 5' transcription factor binding
  sites
• enhancers??
• What about alternative splicing?
• Multiple cDNAs with different sequences (that
  produce different proteins) can be transcribed
  from the same genomic locus

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Finding genes in genome sequence is not easy

• About 1 of human DNA encodes functional genes.
• Genes are interspersed among long stretches of
  non-coding DNA.
• Repeats, pseudo-genes, and introns confound
  matters

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• The next step is obviously to locate all of the
  genes and describe their functions. This will
  probably take another 15-20 years!

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How Many Genes?

• The current estimate is 34,000 human genes.
• The same number as the mouse, only about 5 times
  more than yeast.
• Yet two different versions of the human genome
  (Celera vs. Ensembl/UCSC) show only about 50
  overlap between the genes that they have
  described.

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All the Genes?

• Any human cDNA can now be found in the genome by
  similarity searching with 99 certainty.
• However, the sequence still has many gaps
• unlikely to find a completely uninterrupted
  genomic segment for any gene
• still cant identify pseudogenes with certainty
• This will improve as more sequence data
  accumulates

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Data Mining Tools

• Scientists need to work with a lot of layers of
  information about the genome
• coding sequence of known genes and cDNAs
• genetic maps (known mutations and markers)
• gene expression
• cross species homology

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UCSC
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Ensembl at EBI/EMBL
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II. Genomics

• What is Genomics?
• An operational definition
• The application of high throughput automated
  technologies to biology.
• A philosophical definition
• A wholistic or systems approach to the study of
  information flow within a cell.

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Genome Sequencing created Genomics

• All genomics technologies depend on the data
  produced by genome sequencing
• Do molecular biology in a massively parallel
  fashion using robotics and automated data
  collection
• Build databases rather than ask questions about
  single genes or a single process

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Genomics Technologies

• Automated DNA sequencing
• Automated annotation of sequences
• DNA microarrays
• gene expression (measure RNA levels)
• SNP Genotyping
• Genome diagnostics (genetic testing)
• Proteomics
• Protein identification
• Protein-protein interactions

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DNA chip microarrays

• Put a large number (100K) of cDNA sequences or
  synthetic DNA oligomers onto a glass slide (or
  other substrate) in known locations on a grid.
• Label an RNA sample and hybridize
• Measure amounts of RNA bound to each square in
  the grid
• Make comparisons
• Cancerous vs. normal tissue
• Treated vs. untreated
• Time course
• Many applications in both basic and clinical
  research

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Goal of Microarray experiments

• Microarrays are a very good way of identifying a
  bunch of genes involved in a disease process
• Differences between cancer and normal tissue
• Tuberculosis infected vs resistant lung cells
• Mapping out a pathway
• Co-regulated genes
• Finding function for unknown genes
• Involved these processes

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Competing Microarray Technologies

• Affymetrix Gene chip system
• Uses 25 base oligos synthesized in place on a
  chip
• Can have as many as 20,000 genes on a chip
• Arrays get smaller every year (more genes)
• Chips are very expensive
• Proprietary system black box software, can
  only use their chips
• cDNA spotting technology
• Multiple vendors, or make your own
• Can buy chips, complete systems, or contract
  services (Incyte)
• Hundreds to a few thousands of genes per chip
• More sensitive, but less specific than Affymetrix
  system
• Oligonucleotides
• Nylon Filters

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cDNA spotted microarrays
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Direct Medical Applications

• Diagnosis
• Type of cancer
• Aggressive or benign?
• Monitor treatment outcome
• Is a treatment having the desired effect on the
  target tissue?

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Human Genetic Variation

• Every human has essentially the same set of genes
• But there are different forms of each gene --
  known as alleles
• blue vs. brown eyes
• genetic diseases such as cystic fibrosis or
  Huntingtons disease are caused by dysfunctional
  alleles

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• Alleles are created by mutations in the DNA
  sequence of one person - which are passed on to
  their descendants

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Effects of Mutations

• Mutations occur randomly throughout the DNA
• Most have no phenotypic effect (non-coding
  regions, equivalent codons, similar AAs)
• Some damage the function of a protein or
  regulatory element
• A very few provide an evolutionary advantage

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Human Alleles

• The OMIM (Online Mendelian Inheritance in Man)
  database at the NCBI tracks all human mutations
  with known pheontypes.
• It contains a total of about 2,000 genetic
  diseases and another 11,000 genetic loci with
  known phenotypes - but not necessarily known gene
  sequences
• It is designed for use by physicians
• can search by disease name
• contains summaries from clinical studies

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Clinical Manifestationsof Genetic Variation

• (All disease has a genetic component)
• Susceptibility vs. resistance
• Variations in disease severity or symptoms
• Reaction to drugs (pharmacogenetics)
• All of these traits can be traced back to
  particular genes (or sets of genes) but we don't
  know these associations yet.

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So Whats a SNP

• A mutation that causes a single base change is
  known as a Single Nucleotide Polymorphism (SNP)
• SNPs are very common in the human population (one
  SNP every 1250 bases)
• there are SNPs located near all genes
• they can be used as markers
• Most of these have no visible effect
• in regions between genes

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Genome Sequencing find SNPs
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SNP Genotyping

• SNPs are a form of mutation that can be used to
  measure genetic differences in a high-throughput
  fashion.
• A genomics approach to genetic testing
• Lots of room for bio-technology innovation
• Allele-specific PCR
• Site specific sequencing
• Genotyping microarray chips

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SNP Genotyping

• It is possible to measure many thousands of SNPs
  simultaneously in a small blood sample from a
  patient
• Can compare genotypes for SNP markers linked to
  virtually any trait
• A human genome can be characterized with a few
  thousand common SNP markers
• on a single chip
• a personal genetic profile

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Some Diseases Involve Many Genes

• There are a number of classic genetic diseases
  caused by mutations of a single gene
• Huntingtons, Cystic Fibrosis, Tay-Sachs, PKU,
  etc.
• There are also many diseases that are the result
  of the interactions of many genes
• asthma, heart disease, cancer
• Each of these genes may be considered to be a
  risk factor for the disease.
• Groups of genetic markers (SNPs) may be
  associated with disease risk without determining
  a mechanism.

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DNA Diagnostic Testing

• Hereditary diseases - potential parents,
  pre-natal, late onset diseases
• Genes that predisposes to disease (risk factors)
• Genotyping of infectious agents (bacterial
  viral)
• Measure the type and stage of cancer tumors
• Forensics - using DNA testing to establish
  identity

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III. The Medical and Business Applications of
Genomics
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Implications for Biomedicine

• Physicians will use genetic information to
  diagnose and treat disease.
• Virtually all medical conditions have a genetic
  component.
• Faster drug development research
• Individualized drugs
• Gene therapy
• All Biologists will use gene sequence information
  in their daily work

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Pharmacogenomics

• The use of DNA sequence information to measure
  and predict the reaction of individuals to drugs
• Personalized drugs
• Faster clinical trials
• Less drug side effects

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People React Differently to Drugs

• Side effects
• Effectiveness
• There are genes that control these reactions
• SNP markers can be used to identify these genes

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Make Genetic Profiles

• Identify populations of people who show specific
  responses to a drug
• Scan these populations with a large number of SNP
  markers.
• Find markers linked to drug response phenotypes.

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Use the Profiles

• Genetic profiles of new patients can then be used
  to prescribe drugs more effectively avoid
  adverse reactions.
• Sell a drug with a gene test
• Can also speed clinical trials by testing on
  those who are likely to respond well.

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Toxicogenomics

• There are a number of common pathways for drug
  toxicity (or environmental tox.)
• It is possible to compile genomic signatures
  (gene expression data) for these pathways.
• Candidate drug molecules can be screened in cell
  culture or in animals for induction of these
  toxicity pathways.

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Genomics supports Biotechnology

• Biotechnology is based on developing new drugs
• Some Biotech companies produce and sell these
  drugs (Amgen, Genentech), while others partner
  with big pharmaceutical companies (sell
  intellectual property)
• Genomics is a way of using information to find
  new drugs faster and more cheaply.

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Tools vs. Targets

• Genomics/Bioinformatics companies can sell
  information and software (tools) or the results
  of genome analysis (targets)
• Tools are bought by big Biotech or Pharma
  companies to aid their own research.
• Targets are proteins that have already been
  identified as playing a role in disease
• ready for drug development

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Tools can be Software or Technology

• Database, data analysis, data mining, and
  interface software is essential
• Machines and reagents for genomics experiments
  (GeneChips, gene testing machines)
• Some tools will have a mainstream application in
  medicine (diagnostic tests)
• a much wider market

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Impact on Bioinformatics

• Genomics produces high-throughput, high-quality
  data, and bioinformatics provides the analysis
  and interpretation of these massive data sets.
• It is impossible to separate genomics laboratory
  technologies from the computational tools
  required for data analysis.
• Investment in genomics lab technology must
  include funding for bioinformatics support

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Planning for a Genomics Revolution

• Bioinformatics support must be integral in the
  planning process for the development of new
  genomics research facilities.
• Genome Project sequencing centers have more staff
  and more spent on data analysis than on the
  sequencing itself.
• Microarray facilities will be even more skewed
  toward data analysis
• It is an information-intensive business!

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Long Term Implications

• A "periodic table for biology" will lead to an
  explosion of research and discoveries - we will
  finally have the tools to start making systematic
  analyses of biological processes (quantitative
  biology).
• Understanding the genome will lead to the
  ability to change it - to modify the
  characteristics of organisms and people in a wide
  variety of ways

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Genomics Education

• Genomics scientists need basic training in both
  Molecular Biology and Computing
• Specific training in the use of automated
  laboratory equipment, the analysis of large
  datasets, and bioinformatics algorithms
• Particularly important for the training of
  medical doctors - at least a familiarity with the
  technology

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Genomics in Medical Education

• The explosion of information about the new
  genetics will create a huge problem in health
  education. Most physicians in practice have had
  not a single hour of education in genetics and
  are going to be severely challenged to pick up
  this new technology and run with it."
• Francis Collins

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Stuart M. Brown, Ph.D.stuart.brown_at_med.nyu.eduww
w.med.nyu/rcr
Bioinformatics A Biologist's Guide to
Biocomputing and the Internet