1. Outline to SNP bioinformatics lecture
Brief introduction
SNPs in cell biology
SNP discovery
SNP assessment
SNP databases
SNPs in genome browsers

2. 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

3. Categories of SNPs Missense/Non-synonymous Nonsense
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

4. Categories of SNPs Synonymous Non-coding
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

5. Use to cell biologist Association studies Causative SNPs
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…

6. 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

7. SNP discovery resources
Polybayes
SNP discovery in redundant sequences
Polyphred
SNP discovery based on phred/phrap/consed
NovoSNP
Graphical identification of SNPs

8. Example: PolyPhred Detects heterozygotes from chromatograms
Runs together with phred/phrap/consed
Command line

9. SNP assessment Assess SNPs for functional effects
Non-synonymous SNPs
Conservation across species
Amino acid properties
Protein structure
Transmembrane regions, signal peptides etc.

10. SNP assessment resources
SIFT
PolyPhen
Pmut
SNPs3D
PANTHER PSEC
TopoSNP
MAPP
Etc

11. 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

13. SNP databases Maps of SNPs in human, mouse, etc Haplotype maps
Functional SNPs
Disease databases

14. SNP databases
dbSNP
F-SNP
HGVBase
PolyDoms
OMIN
Etc…

15. Example: dbSNP 50 million submissions 18 million clusters
7 million in genes
44 organisms
91 million SNPs submitted

16. 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

18. SNPs in genome browsers
Ensembl
UCSC

19. Example: UCSC

23. 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

24. Ng PC, Henikoff S.
Predicting the effects of amino acid substitutions on protein function.
Annu Rev Genomics Hum Genet. 2006;7: 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 Oct 15;164(8): 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 May;1020: Review.
The International HapMap Consortium.
A second generation human haplotype map of over 3.1 million SNPs.
Nature 449,