Outline to SNP bioinformatics lecture
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Outline to SNP bioinformatics lecture
• Brief introduction
• SNPs in cell biology
• SNP discovery
• SNP assessment
• SNP databases
• SNPs in genome browsers
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Single Nucleotide Polymorphisms
• Must be present in at least 1% of the population
• Most (90%) of the sequence variation between two genomes
• Two humans differ 0.1%• 1/300 bp in the human genome
– Lower in coding regions
• 10 million in the human genome
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Categories of SNPs
• Missense/Non-synonymous– Changes an amino acid– About half of the SNPs in coding sequence– Can alter function and or structure of the protein– Cause of most monogenetic diseases
• Hemochromatosis (HFE)• Cystic fibrosis (CFTR)• Hemophilia (F8)
• Nonsense– Introduces a stop codon– Same consequences as non-synonymous
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Categories of SNPs
• Synonymous– Does not alter the coding sequence– May alter splicing
• Non-coding– Can be located in promoter or regulatory
regions– Can impact the expression of the gene
• All SNPs can be used as markers
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Use to cell biologist
• Association studies– Use SNPs as markers to find regions associated with
phenotype
• Causative SNPs– Altered protein– Altered expression
• Regions of altered conservation between strains/species/individuals
• Evolutionary analyses• Etc…
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SNP discovery
• Discovery of SNPs usually from sequencing• Discovery is based on separating
sequencing errors from ’real’ differences and assessing the frequency in the sequenced population
• Separation of parologous sequences
• Validation, genotyping
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SNP discovery resources
• Polybayes – SNP discovery in redundant sequences
• Polyphred– SNP discovery based on phred/phrap/consed
• NovoSNP– Graphical identification of SNPs
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Example: PolyPhred
• Detects heterozygotes from chromatograms
• Runs together with phred/phrap/consed
• Command line
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SNP assessment
• Assess SNPs for functional effects– Non-synonymous SNPs
• Conservation across species
• Amino acid properties
• Protein structure
• Transmembrane regions, signal peptides etc.
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SNP assessment resources
• SIFT• PolyPhen• Pmut• SNPs3D• PANTHER PSEC• TopoSNP• MAPP• Etc
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Example: SIFT
• Sorting Intolerant From Tolerant
• Builds an alignment of similar sequences
• Calculates a score based on the aa in the alignment
• Takes the environment into account
• Takes the properties of the aa into account
• Does not use structure
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SNP databases
• Maps of SNPs in human, mouse, etc
• Haplotype maps
• Functional SNPs
• Disease databases
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SNP databases
• dbSNP
• F-SNP
• HGVBase
• PolyDoms
• OMIN
• Etc…
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Example: dbSNP
• 50 million submissions
• 18 million clusters
• 7 million in genes
• 44 organisms
• 91 million SNPs submitted
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dbSNP
• Search for SNPs, location, etc
• Information submitted on method, flanking sequence, alleles, population, sample size, validation etc
• Information computed on SNPs at same location including functional analysis, population diversity etc
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SNPs in genome browsers
• Ensembl
• UCSC
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Example: UCSC
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HapMap
• Aim: a haplotype map of the human genome describing common patterns of sequence variation
• A haplotype map is based on alleles of SNPs close together are inherited together
• HapMap will identify which SNPs are informative in mapping, reducing the number of SNPs to genotype by a magnitude
• Populations from Asia, Europe and Africa• 2nd generation map with over 3.1 million SNPs
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Ng PC, Henikoff S. Predicting the effects of amino acid substitutions on protein function.
Annu Rev Genomics Hum Genet. 2006;7:61-80. Review.
Bhatti P, Church DM, Rutter JL, Struewing JP, Sigurdson AJ.
Candidate single nucleotide polymorphism selection using publicly available tools: a guide for epidemiologists.
Am J Epidemiol. 2006 Oct 15;164(8):794-804. Epub 2006 Aug 21.
Clifford RJ, Edmonson MN, Nguyen C, Scherpbier T, Hu Y, Buetow KH.
Bioinformatics tools for single nucleotide polymorphism discovery and analysis.
Ann N Y Acad Sci. 2004 May;1020:101-9. Review.
The International HapMap Consortium.
A second generation human haplotype map of over 3.1 million SNPs.
Nature 449, 851-861. 2007.