Outline to SNP bioinformatics lecture
• Brief introduction
• SNPs in cell biology
• SNP discovery
• SNP assessment
• SNP databases
• SNPs in genome browsers
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
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
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
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…
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
SNP discovery resources
• Polybayes – SNP discovery in redundant sequences
• Polyphred– SNP discovery based on phred/phrap/consed
• NovoSNP– Graphical identification of SNPs
Example: PolyPhred
• Detects heterozygotes from chromatograms
• Runs together with phred/phrap/consed
• Command line
SNP assessment
• Assess SNPs for functional effects– Non-synonymous SNPs
• Conservation across species
• Amino acid properties
• Protein structure
• Transmembrane regions, signal peptides etc.
SNP assessment resources
• SIFT• PolyPhen• Pmut• SNPs3D• PANTHER PSEC• TopoSNP• MAPP• Etc
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
SNP databases
• Maps of SNPs in human, mouse, etc
• Haplotype maps
• Functional SNPs
• Disease databases
SNP databases
• dbSNP
• F-SNP
• HGVBase
• PolyDoms
• OMIN
• Etc…
Example: dbSNP
• 50 million submissions
• 18 million clusters
• 7 million in genes
• 44 organisms
• 91 million SNPs submitted
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
SNPs in genome browsers
• Ensembl
• UCSC
Example: UCSC
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
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.
Recommended
