Human Genetic Variation

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Human Genetic Variation
SNPs
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Human Genetic Identity

• 99.9 identical
• 3,196,800,000 nucleotides identical
• 3,200,000 nucleotides different

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Human Genetic Variation

• Single base differences in genomes between any
  two individuals 2-5 million
• Amino acid differences in proteomes between
  between any two individuals about 100,000
• Inheritance vs. Alterations

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Relevance Variation Bears Difference

• Physiological and anatomical differences based on
  molecular differences
• Exception Trauma, environmental impacts
• Genetics Inherited contribution to phenotypic
  variation

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Variation

• Macro
• Chromosome numbers
• Segmental duplications, rearrangements, and
  deletions
• Medium
• Sequence Repeats
• Transposable Elements
• Short Deletions, Sequence and Tandem Repeats
• Micro
• Single Nucleotide Polymorphisms (SNPs)
• Single Nucleotide Insertions and Deletions
  (Indels)

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Relevance Important applications

• Identify inherited contribution to
• Disease risk
• Reactions to environmental triggers
• Reactions to treatments
• Cognitive abilities
• Requirements
• Forensics (incl. WTC)
• Ontology Phylogeny
• Evolution

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SNPs in Biomedicine

• Medical Conditions
• Diagnosis and treatment
• Gene therapy
• Pharmacogenomics
• Individualized drugs
• Less drug side effects
• Faster clinical trials

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Most common diseases are caused by a combination
of genes and environment
Stroke
Manic-depression
Myocardial Infarction
Breast cancer
Hypertension
Diabetes
High Cholesterol
Obesity
Schizophrenia
Inflammatory Bowel Disease
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Traditional Approach

• Linkage or recombinatorial mapping
• Successful for single gene disorders
• Little success for common complex traits, such as
  heart disease, diabetes, asthma, mental disorders

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Mendelian disease genetics
genotype
disease state
Linkage analysis powerful because genetic risk
factors are highly penetrant
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Complex Disease Genetics
phenotype1

other genes
phenotype2
disease state
genotype
phenotype3
phenotype4
phenotype5
environment
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CHALLENGE
Find genetic susceptibility factors for common
disease, drug and pathogen response

• To understand the fundamental basis of disease
• To identify at-risk populations
• To identify targets for chemical
  intervention/drug development
• To predict outcomes and treatment efficacy

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Complex Disease Genetics

• Despite significant genetic component,
  traditional approaches (e.g., linkage analysis)
  have little or no power because the genetic risk
  factors are individually too modest/incompletely
  penetrant
• If traditional approaches arent succeeding -
  where do we turn?

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Raw Genome Data
Biological variation vs. sequence variation
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• Human Genetic Variation

Most abundant type SNPs-Single Nucleotide
Polymorphisms GATTTAGATCGCGATAGAG GATTTAGATCTCGAT
AGAG

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SNPs in Overlapping Genomic Sequences
Overlapping BACs from library
50 of overlaps contain polymorphisms
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SNPs What they are (and arent )

• Single base pair variations among allelic
  sequences
• Least abundant allele has frequency gt 1
• Not all single base pair differences are SNPs
• Nor are all point mutations

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Types of SNPs

• Causative SNPs
• coding SNPS (non/-synonymous)
• non-coding SNPs
• (Read Making Sense of Nonsense NRG 5/02)
• Linked SNPs
• usually intra- and intergenic SNPs

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Occurrence

• Ca. 1/1300 bp in genomic DNA from two equivalent
  chromosomes
• Ca. 1/300 bp in whole populations
• In intergenic regions, introns exons
• Functionally constrained DNA less diverse
• 50 of CDS SNPs synonymous
• Frequency varies by variation type

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TSC SNPdb

• A/G C/T 33
• A/C A/T C/G G/T 8
• A/C/G A/C/T A/G/T C/G/T 0.006
• A/C/G/T 0.002

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• Transitions c?t g?a

Transversions c?a g?t c?g g?c t?a a?t
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SNPs - Identification

• Sequence comparison
• PCR
• Microarrays
• Databases

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In Silico SNP Identification

• Mine existing sequence resources
• Genomic sequences
• ESTs
• BAC-end sequences
• Cost-effective genome-wide SNP discovery by
  examining regions of redundant sequence coverage
• Rare alleles difficult to spot (below error rate)

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De Novo SNP Identification

• Select candidates
• Establish cell cultures
• Isolate DNA, digest, gel fractionate, clone
• Sequence, kill if repeats gt 50
• Blast against each other select gt99 id
• Run SNP Finding Program

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GATTTAGATCGCGATAGAGGATTTAGATCTCGATAGAG

• Rare Alleles
• ---o--------------------
• -----o------------------
• -------o----------------
• -----------o------------
• ---------------o--------
• -------------------o----
• Many

• Common Alleles
• ----o-------------------
• ----o-------------------
• ----o-------------------
• --------------------o---
• --------------------o---
• --------------------o---
• Few

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GATTTAGATCGCGATAGAGGATTTAGATCTCGATAGAG

• Rare Alleles
• ---o--------------------
• -----o------------------
• -------o----------------
• -----------o------------
• ---------------o--------
• -------------------o----
• Many

• Common Alleles
• ----o-------------------
• ----o-------------------
• ----o-------------------
• --------------------o---
• --------------------o---
• --------------------o---
• Few

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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 through selected trial
  populations
• Less drug side effects

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Drug Responses

• Absorption
• Distribution
• Activation
• Metabolism
• Excretion

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Patients

• Responders
• Non-responders
• Toxic responders

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Goals

• Right Drug
• Right Dose
• Right Patient

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SNPs vs. Haplotypes
GATTTAGATCGCGATAGAGGATTTAGATCTCGATAGAG
CGGGTATCGATTTAGATCGCGATAGAGTTGCCTACA CGAGTATCGATTT
AGATCTCGATAGAGTTGTCTACA
Many polymorphisms make a type
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• Responders
• tcgaggaacagggctcttaaaaatgctttatccgcttag
• tcgaggaacagggctcttaaaaatgctttctccgcttgg
• tagagcaacagggctctaaaaaatgctttctccgcttag
• Non-responders
• tagtgaaacagggctctgaaaactgctttatccgattcg
• tagtggaatagggctctgaaaactgctttatccgattgg
• tcgtggaacagggctctgaaaactgctttgtccgattgg

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• Responders
• -c-a-g--c--------t----a------a----c--a-
• -c-a-g--c--------t----a------c----c--g-
• -a-a-c--c--------a----a------c----c--a-
• Non-responders
• -a-t-a--c--------g----c------a----a--c-
• -a-t-g--t--------g----c------a----a--g-
• -c-t-g--c--------g----c------g----a--g-

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SNPFinder

• SNPFinder at the Cancer Gene Anatomy Project
  allows you to search for SNPs (single-nucleotide
  polymorphisms). You may upload your ABI or
  SCF-format chromatograms, which are basecalled,
  assembled and searched for SNPs. Sequences may
  optionally be assembled with UniGene data of your
  choosing. An account is required to use SNPFinder
  - it is free for academic users.

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Other Sites for Viewing SNPs

• UCSC Browser (http//genome.ucsc.edu)
• SNP Consortium (http//snp.cshl.org/)
• NCBIs dbSNP (http//www.ncbi.nlm.nih.gov/SNP/)

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Variation Data at NCBI

• Bioinformatics infrastructure
• Databases of sequence variation
• Haplotypes and variations as genome annotation
• Functional variants in genetic disease /
  pharmacogenomics
• Evolutionary Biology
• Forensic Biology
• mass casualty analysis

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dbSNP - variation and polymorphism
Variant position A/G
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Roles for variations in genome analysis
Physical Mappingenriched marker set Population
Structurehaplotype analysisevolutionary
studies Association Studiespopulation
stratificationmetabolic pathways Functional
Analysispharmacogenomicsprotein structure
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Variations mapped onto protein structures
3,868 variations (6 of coding region variations)
have been mapped to a 3D structure
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Haplotypes in MapViewer
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Frequency data by dbSNP population classes