1. Online Resources for Genetic Variation Study – Part One
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2. Online Resources for Genetic Variation Study – Part One
Yi-Bu Chen, Ph.D.
Bioinformatics Specialist
Norris Medical Library
University of Southern California
Dec. 6, 2007

3. Workshop Outline Overview of Bioinformatics Support Program at NML
Human Genetic Variation Overview
Main types of genetic variations
Basics of the single nucleotide polymorphisms (SNPs)
NCBI Genetic Variation Resources: dbSNP and OMIM
dbSNP overview
dbSNP search examples
OMIM overview
International HapMap Project
The HapMap project: overview and major findings
HapMap search examples
The Perlegen Genetic Variation Database
Genome Variation Server (SeattleSNPs)
Ensembl SNPs
Hands-on Search Question

4. Polymorphisms: How different are we?
Human vs. Chimp
~96% overall (~99% similar in terms of SNPs)
Human vs. Human
~99.9% similar with around 3.2 million single nucleotide differences (account for up to 90% of all genomic variations, total possible SNPs near 12 millions)
Adapted from a lecture slide by Jonathan Wren, NYU

5. Why do we care about genetic variations?
1. Genetic variations underlie phenotypic differences among different individuals
2. Genetic variations determine our predisposition to complex diseases and responses to drugs and environmental factors
3. Genetic variations reveal clues of ancestral human migration history

6. Main Types of Genetic Variations
A. Single nucleotide mutation
Resulting in single nucleotide polymorphisms (SNPs)
Accounts for up to 90% of human genetic variations
Majority of SNPs do NOT directly or significantly contribute to any phenotypes
B. Insertion or deletion of one or more nucleotide(s)
1. Tandem repeat polymorphisms
Tandem repeats are genomic regions consisting of variable length of sequence motifs repeating in tandem with variable copy number.
Used as genetic markers for DNA finger printing (forensic, parentage testing)
Many cause genetic diseases
Microsatelites (Short Tandem Repeats): repeat unit 1-6 bases long
Minisatelites: repeat unit bases long
2. Insertion/Deletion (INDEL or DIPS) polymorphisms
Often resulted from localized rearrangements between homologous tandem repeats.
C. Gross chromosomal aberration
Deletions, inversions, or translocation of large DNA fragments
Rare but often causing serious genetic diseases

7. How many variations are present in human genome?
SNPs appear once per kb interval or on average 1 per 300 bp. Considering the size of entire human genome (3.2 x109 bp), the total number of SNPs is well above 11 million. The high density and relatively easier assay make SNPs the ideal genomic markers.
In sillico estimation of potentially polymorphic variable number tandem repeats (VNTR) are over 100,000 across the human genome
The short insertion/deletions are very difficult to quantify and the number is likely to fall in between SNPs and VNTR.

8. Types of Single Base Substitutions
Transitions
Change of one purine (A,G) for another purine, or a pyrimidine (C,T) for another pyrimidine
Transversions
Change of a purine (A,G) for a pyrimidine (C,T), or vice versa.
The cytosine to thymine (C>T) transition accounts for approximately 2 out of every 3 SNPs in human genome.

9. SNP or Mutation? Call it a SNP IF Call it a mutation IF
the single base change occurs in a population at a frequency of 1% or higher.
Call it a mutation IF
the single base change occurs in less than 1% of a population.
A SNP is a polymorphic position where the point mutation has been fixed in the population.

10. From a Mutation to a SNP

11. SNPs Classification Intergenic region Gene region
SNPs can occur anywhere on a genome, they are classified based on their locations.
Intergenic region
Gene region
can be further classified as promoter region, and coding region (intronic, exonic, promoter region, UTR, etc.)

12. Coding Region SNPs Synonymous Non-Synonymous
Missense – amino acid change
Nonsense – changes amino acid to stop codon.
Geospiza Green Arrow™ tutorial by Sandra Porter, Ph.D.

13. The Consequences of SNPs
The phenotypic consequence of a SNP is significantly affected by the location where it occurs, as well as the nature of the mutation.
No consequence
Affect gene transcription quantitatively or qualitatively.
Affect gene translation quantitatively or qualitatively.
Change protein structure and functions.
Change gene regulation at different steps.

14. Simple/Complex Genetic Diseases and SNPs
Simple genetic diseases (Mendelian diseases) are often caused by mutations in a single gene.
-- e.g. Huntington’s, Cystic fibrosis, PKU, etc.
Many complex diseases are the result of mutations in multiple genes, the interactions among them as well as between the environmental factors.
-- e.g. cancers, heart diseases, Alzheimer's, diabetes, asthmas, etc.
Majority of SNPS may not directly cause any diseases.
SNPs are ideal genomic markers (dense and easy to assay) for locating disease loci in association studies.

16. Main Genetic Variation Resources
NCBI dbSNP
NCBI Online Mendelian Inheritance in Man (OMIM)
International HapMap Project
Perlegen
Genome Variation Server (Seattle SNPs)

17. Where to Find Bioinformatics Resources for Genetic Variation Studies?
OBRC: Online Bioinformatics Resources Collection (Univ. of Pittsburgh)
The most comprehensive annotated bioinformatics databases and software tools collection on the Web, with over 200 resources relevant to genetic variation studies.
HUGO Mutation Database Initiative

18. NCBI dbSNP Database: Overview
URL:
The NCBI’s Single Nucleotide Polymorphism database (dbSNP) is the largest and primary public-domain archive for simple genetic variation data.
The polymorphisms data in dbSNP includes:
Single-base nucleotide substitutions (SNPs)
Small-scale multi-base deletions or insertions variations (also called deletion insertion polymorphisms or DIPs or INDELs)
Microsatellite tandem repeat variations (also called short tandem repeats or STRs).

19. dbSNP Data Stats (build 128, Oct, 2007)

20. dbSNP Data Types The dbSNP contains two classes of records:
Submitted record
The original observations of sequence variation; submitted SNPs (SS) records started with ss (ss )
Computationally annotated record
Generated during the dbSNP "build" cycle by computation based the original submitted data, Reference SNP Clusters (ref SNP) start with rs (rs )

21. dbSNP Submitted Record
Provides information on the SNP and conditions under which it was collected.
Provides links to collection methods (assay technique), submitter information (contact data, individual submitter), and variation data (frequencies, genotypes). ss

22. SNPs records submitted
From Submitted Record to Reference SNP Cluster
SNP position mapped
to the reference genomic contigs
SNPs records submitted
by researchers
If the SNP position not unique, it will be assigned to the existing RefSNP cluster
If the SNP position is unique, a new RS# is assigned

23. Different Ways to Search SNPs in dbSNP
dbSNP Web site
Direct search of SS record; batch search; allow SNP record submission; NO search limits
Entrez SNP
Search limits options allows precise retrieval
Entrez Gene Record’s SNP Links Out Feature
Direct links to corresponding SNP records; access to genotype and linkage disequilibrium data
NCBI’s MapViewer
Visualize SNPs in the genomic context along with other types of genetic data.

24. Search SNPs from dbSNP Web Page
dbSNP Web site

25. Search SNPs from Entrez SNP Web Page
The dbSNP is a part of the Entrez integrated information retrieval system and may be searched using either qualifiers (aliases) or a combination search limits from 14 different categories.

26. Entrez SNP Search Limits
Organisms
Chromosome (including W and Z for non-mammals)
Chromosome Ranges
Map Weight (how many times in genome)
Function Class (coding non-synonymous; intron; etc.)
SNP Class (types of variations)
Method Class (methods for determining the variations)
Validation Status (if and how the data is validated)
Variation Alleles (using IUPAC- codes)
Annotation (Records with links to other NCBI database)
Heterozygosity (% of heterozygous genotype)
Success Rate (likelihood that the SNP is real)
Created Build ID
Updated Build ID

27. Search dbSNP: Example 1
Some mutations on human BRCA1 gene have been reported to be involved in the early onset of breast cancer.
Retrieve all validated non-synonymous coding reference SNPs for BRCA1 from dbSNP.
Hint: starting from the Entrez SNP:

28. Entrez SNP Search Results Example 1

29. dbSNP Ref SNP Record Example 1: Summery
This Ref SNP cluster contains multiple submitted SNP records from different groups

30. dbSNP Ref SNP Record Example 1: SNP position and the flank region

31. dbSNP Ref SNP Record Example 1: GeneView of an individual SNP
Because of alternative splicing, the very same SNP can locate in different region of the transcripts.

32. dbSNP Ref SNP Record Example 1: TableView of an individual SNP
Notice that the individual SNP is mapped to the same position on the reference genomic contig, but different positions on mRNAs and proteins due to alternative splicing.

33. dbSNP Ref SNP Record Example 1: Links to Various Annotated NCBI Databases
Link to the OMIM record where documented clinical and genetic data of this SNP can be found.
Warning: the lack of OMIM link does not necessary mean that this SNP is unrelated to any OMIM record.

34. dbSNP Ref SNP Record Example 1: Population Allele Frequency, Genotype and Heterozygosity Data
Link to the detailed population genotype data.
Data from National Cancer Institute.
Data from The NIH Polymorphism Discovery Resource
Data from Centre d'Etude du Polymorphisme Human (CEPH).
Data from the International HapMap Project.

35. dbSNP Ref SNP Record Example 1: GeneVeiw and SequenceView of ALL SNPs

36. dbSNP Ref SNP Record Example 1: Links to View SNPs on 3D Structure, Conserved Domains, and Multiple Sequence Alignment

37. Search dbSNP: Example 2
Mutations in Dopamine Receptor 5 (DRD5) gene have been observed in patients with various neurological disorders.
Find how many refSNP records have been reported for DRD5. Show all refSNPs in the context of a chromosome.
Hint: starting from the Entrez Gene:

38. Search dbSNP: SNP Links from Entrez Gene Record

39. Search dbSNP: SNP Display Using NCBI Map Viewer

40. Search dbSNP: Configure Map Viewer to Display other Relevant Data

41. SNPs Display in Map Viewer: Legend
Click on any column headings to see the refSNPs legend.

42. SNPs Display in Map Viewer: Legend

43. Online Mendelian Inheritance in Man (OMIM): A Brief Overview
URL:
OMIM is a human genetic disorders database built and curated using results from published studies.
Each OMIM record provides a summary of the current state of knowledge of the genetic basis of a disorder, which contains the following information:
description and clinical features of a disorder or a gene involved in genetic disorders;
biochemical and other features;
cytogenetics and mapping;
molecular and population genetics;
diagnosis and clinical management;
animal models for the disorder;
allelic variants.
OMIM is searchable via NCBI Entrez, and its records are cross-linked to other NCBI resources.

44. Online Mendelian Inheritance in Man Stats

45. OMIM: Allelic Variants
The OMIM database includes genetic disorders caused by various mutation/variation, from SNPs to large-scale chromosomal abnormalities.
The listed allelic variants are searchable through the "Allelic Variants" field.
Single nucleotide substitutions (SNPs);
small insertions and deletions (INDEL/DIPS);
frame shifts caused by these INDELs.
Allelic variants are represented by a 10-digit OMIM number, and can be searched in two ways:
Search for a gene or a disease, when retrieved, view its allelic variants.
Use the Limits to narrow your search to:
-- retrieve only records that contain allelic variant information;
-- search for particular terms within the allelic variants field.

46. Notes on OMIM Allelic Variants
For most genes, only selected mutations are included
Criteria for inclusion include: the first mutation to be discovered, high population frequency, distinctive phenotype, historic significance, unusual mechanism of mutation, unusual pathogenetic mechanism, and distinctive inheritance.
Most of the allelic variants represent disease-producing mutations, NOT polymorphisms.
A few polymorphisms are included, many of which show a positive statistical correlation with particular common disorders.
Few neutral polymorphisms are included in OMIM.
Some SNPs in the dbSNP records are not linked to the corresponding OMIM records.

47. Sequence variations view in UniProt Beta

48. Assessing Polymorphisms: Genotypes and Genotyping
Genotype: Each person has two copies of all chromosomes except the sex chromosomes. The set of alleles at a given locus forms the genotype.
Genotyping: the process of identifying what genotype a person has for any given locus (loci).
Whole-genome genotyping of all SNPs in a human genome? (11.8 million and counting)
Technologically daunting
Prohibitively expensive and time consuming

49. Assessing Polymorphisms: the Origin of Haplotype
Two ancestral chromosomes scrambled through recombination over many generations to yield different descendant chromosomes.
If a genetic variant marked by the X on the ancestral chromosome increases the risk of a particular disease, the two descendants who inherit that part of the ancestral chromosome will be at increased risk.
Adjacent to the variant marked by the X are many SNPs that can be used to identify the location of the variant.
Haplotype: A particular combination of alleles along a chromosome that tends to be inherited as a unit.

50. Assessing Polymorphisms: Linkage Disequilibrium, Haplotype Block, and Tag SNPs
Adapted from Nature 426, 6968: (2003)
Linkage Disequilibrium (LD): If two alleles tend to be inherited together more often than would be predicted, then the alleles are in linkage disequilibrium.
If most SNPs have highly significant correlation to one or more of neighbors, these correlations can be used to generate haplotypes, which represent excellent proxies for individual SNP.
Because haplotypes may be identified by a much small number of SNPs (tag SNPs), assessing polymorphisms via haplotypes dramatically reduces genotyping work.

51. Assessing Polymorphisms: Tag SNPs
Tag SNP: a representative SNP enabling to infer (or predict) other SNPs of its “neighborhood” (both distance and genealogically wise).
An r2 of 0.8 or greater is sufficient for tag SNP mapping to obtain a good coverage of untyped SNPs.
Tag SNPs allow genotyping of a lower number of marker SNPs with very small losses in power.
If LD between SNPs is low, almost every SNP might have to be genotyped to get all variation information. 51

52. ENCODE (Encyclopedia of DNA Elements)
Goals
Create a public genome-wide database of common human genetic variation in the context of geographic distribution
Provide such information to guide genetic studies of clinical phenotypes
Phase I (Oct. 2002)
One million common SNPs (every 5 kb across the genome) were genotyped in 269 DNA samples from four populations.
Common SNPs : Minor Allele Frequency ≥ 0.05
YRI : Yoruba in Nigeria (30 trios), CEU : Utah with European ancestry (30 trios), CHB : 45 Han Chinese, JPT: 44 Japanese
Phase II
An additional 4.6 million SNPs are genotyped.
ENCODE (Encyclopedia of DNA Elements)
Collection of ten regions, each 500kb in length.
Each 500 kb region was re-sequenced and all SNPs were genotyped.

53. HapMap Progress PHASE I – completed
1,000,000 SNPs successfully typed in all 269 HapMap samples
At least one common SNP every 5 kb across the genome
ENCODE variation reference resource available
PHASE II – data generation complete, about 4.6 million SNPs typed in total.
ENCODE-HAPMAP – A much more detailed variation resource
48 samples sequenced
All discovered SNPs (and any others in dbSNP) typed in all 270 HapMap samples
Current data set – average 1 SNP every 279 bp

54. HapMap Data Overview Available for bulk download:
Basic Data: genotypes of the 270 individual samples (frequencies of SNP alleles and genotypes in each population)
Recent data release (Full Data Set): January 11, 2007, NCBI B35 (includes both Phase I&II data, genotypes from Illumina 100k and 300k genotyping arrays and the Affymetrix nsSNPs)
Phase I: 600,000 common SNPs in 270 individuals
Phase II: 4-5 million SNPs in the same individuals
Available for bulk download:
All genotype data, haplotype phasing data (from PHASE)
Pedigree trio files
Raw LD data (D’, R2), recombination rates and hotspots
Allele and genotype frequencies
SNP assays and protocols
Allocated SNPs (dbSNP reference clusters chosen for genotyping)
Adapted from Alanna Morrison, Human Genetics Center, Feb lecture

55. Major Findings of the HapMap Project
Extensive Redundancy of SNP: over 90% of all SNPs on the map have highly statistically significant correlation to one or more neighbors.
Confirmed the generality of recombination hotspots and long segments of strong LD (Haplotype blocks), with the average length ranging from 7.3 (YRI) to 16.3 kb (CEU), and between 65-85% of human genome presented in such blocks.
Revealed limited haplotype diversity: while each haplotype block contains SNPs, on average only common haplotype blocks exist, which can be further identified by a smaller number of SNPs (tag SNPs).
The density of common SNPs can be reduced by 75–90% with essentially no loss of information. That is, the genotyping burden can be reduced from one common SNP every 500 bp to one SNP every 2 kb (YRI) to 5 kb (CEU and CHB/JPT).

56. What can you do from the HapMap Web Site?
Search for SNPs in a gene or any region of interest (ROI).
View patterns of LD in the ROI.
Select tagSNPs in the ROI.
Download information on the SNPs in ROI for genotype/haplotype data analysis and visualization in Haploview or other software.
Generate and retrieve customized subset data.
Download the entire data set in bulk.

57. Search HapMap: Example 1
SNPs in human BRCA1 gene have been reported to be involved in the early onset of breast cancer.
Find all available genotype and LD data for SNPs documented for BRCA1 in HapMap database.

58. Click “HapMap Genome Browser (B35 full data set)”
HapMap Search Example 1 Step 1: Open the Genome Browser with the Latest Full Data Set
Click “HapMap Genome Browser (B35 full data set)”

59. HapMap Search Example 1 Step 2: Specify the landmark/region of interests
Enter gene name “brca1” to specify the region of your interest
When there are multiple transcripts, click one of your choice

60. Examine the region for display using different scales
HapMap Search Example 1 Step 3: Examine and determine the desired region for display
The mRNA
Examine the region for display using different scales
Genotype frequency
Genotyped SNPs in the region, pie chart shows allelic frequencies (ref vs other)

61. HapMap Search Example 1 Step 4: display genotype data for each refSNP

62. HapMap Search Example 1 Step 5: Select the desired tracks for display
Select the desired analysis results for display
Click “Update Image” once the configuration is done

63. HapMap Search Example 1 Step 6: Configure the tag SNP Picker
Select the desired population
Select the desired tagging methods
Select r2 value to set desired stringency
Set MAF for the lowest threshold of alleles to be captured by the tagged SNPs
Specify SNPs to be included/excluded as tagged SNPs

64. HapMap Search Example 1 Step 7: Configure the LD Plot
Configure LD plot display
Select LD measurement and range
Select desired populations
Customize the color display for LD value

65. HapMap Search Example 1 Step 8: Tag SNPS and LD Plot
Genotyped SNPs in the region
LD plot shows LD between different pairs of SNPs
Tagged SNPs based on your criteria

66. HapMap Search Example 1 Step 9: Download various data and files
Click “Go”
The genotype data can be used for in depth LD and Haplotype analysis with the free Haploview program.
Select desired data or file for download

67. Haploview-- http://www.broad.mit.edu/mpg/haploview/

68. Haploview Screenshots

69. HapMap Data Extraction using HapMart Select desired population

70. HapMap Data Extraction using HapMart: Data filter and export

71. Perlegen Sciences
Found in 2000 with the mission of identifying clinically relevant patterns of genetic variation.
Over 1.6 millions common SNPs genotyped from 71 individuals from 3 American populations of European, African and Asian ancestry (about 1 SNP/1871 bp)
GWA studies on over 100,000 different human individual.
Re-sequenced the nuclear DNA genomes of 15 inbred laboratory mouse strains and generated genotype data.
Specialized Mouse Genome Brower allows users visualize the SNPs and LR-PCR primer pairs and access the SNP genotypes for the 15 strains

72. Perlegen Human Genotype Brower

73. Perlegen Human Genotype Brower

74. Hosting raw genotyping data for 4
Hosting raw genotyping data for 4.5 million human SNPs from HapMap, Perlegen, and other projects.
Generated SNPs data on candidate genes involved in cardiovascular diseases and inflammatory process.
Tools for searching, visualization and analysis of genotype data for association studies.
Merging SNP data sets from different populations.

75. Using Genome Variation Server
Select the search type to start the search
upload your genotype data for analysis
Detailed online tutorial

76. GVS Search Example: rs9939609 (FTO gene)
Step 1: select query type 1 2

77. GVS Search Example: rs9939609 (FTO gene)
Step 2: Select population(s)

78. GVS Search Example: rs9939609 (FTO gene)
Step 3: Configure parameters

79. GVS Search Example: rs9939609 (FTO gene)
Step 4: Display Results—Genotype data

80. GVS Search Example: rs9939609 (FTO gene)
Step 4: Display Results—Genotype data rs
SNP ID
Sample

81. GVS Search Example: rs9939609 (FTO gene)
Step 5: Display results—TagSNPs
TagSNPs Table Display

82. GVS Search Example: rs9939609 (FTO gene) TagSNPs Graphic Display
Step 5: Display results—TagSNPs
Bin
TagSNPs Graphic Display

83. GVS Search Example: rs9939609 (FTO gene)
Step 6: Display results—LD

84. GVS Search Example: rs9939609 (FTO gene)
Step 7: Display results—Summary

85. SNPs in Ensembl Most SNPs imported from dbSNP (rs……): For human also:
Most SNPs imported from dbSNP (rs……):
Imported data: alleles, flanking sequences, frequencies, ….
Calculated data: position, synonymous status, peptide shift, ….
For human also:
HGVbase
TSC
Affy GeneChip 100K and 500K Mapping Array
Ensembl-called SNPs (from Celera reads)
For mouse and rat also:
Sanger- and Ensembl-called SNPs

86. SNPs in Ensembl
MapView: SNP density on chromosome

87. SNPs in Ensembl
ContigView: SNPs in genomic context

88. SNPs in Ensembl
GeneSeqView: SNPs in genomic sequence

89. SNPs in Ensembl
TransView & ProtView: SNPs in transcript/ protein

90. SNPs in Ensembl
What SNPs does my gene contain? > GeneSNPView

91. SNPs in Ensembl Info about one specific SNP? > SNPView: SNP Report
Genotype and allele frequencies per population
Located in transcripts
SNP Context
Individual genotypes

94. User Question
A recent report (Frayling et al. Science 2007) found a common variant (rs , A>T) in the FTO gene (fat mass and obesity associated) is associated with body mass index and predisposes to obesity and diabetes.
The adults (16%) carrying homozygous risk allele A weighed 3 kg more and had 1.67 fold increased odds of obesity compared to those without the risk allele.
Use the HapMap and dbSNP to find the genotype data of this SNP in different populations.

95. Answer 1: Searching HapMap
Use the refSNP# (must starts with rs) as the landmark for the search
Click on the pie chart for detailed population genotype data

96. Answer 1: Searching HapMap
Population genotype data of the homozygous risk allele A
Retrieve detailed genotyping data

97. Answer 2: Searching NCBI’s dbSNP
Click on the rs record for detailed SNP data report

98. Answer 2: Searching NCBI’s dbSNP
Genotype data from Perlegen’s project with different population samples

99. Acknowledgement
In addition to those already stated, some slides of this workshop were adapted from the sources below:
Chattopadhyay A. and M.R. Tennant. “Genetic Variation Resources”. Lecture slides for 2007 NCBI Advanced Workshop for Bioinformatics Information Specialists.
Stein L. “Using HapMap.org: A tutorial”. Presentation slides as part of the Official HapMap Tutorial.
Overduin B. “Sequence Variation in Ensembl”. Lecture slides for “Ensembl Courses and Workshops”
Hopefully I have enticed you to take a closer look at these databases and all of the website and data I have shown will be part of the interactive tutorial later today.

100. Recommend Topics for the Second Part of “Online Resources for Genetic Variation Study”
Functional analysis of SNPs
Tools for SNP discovery and genotyping
Tools for TagSNPs selection
Tools for genome wide association study
Genetic association databases
Others??

101. Have questions or comments about this workshop? Please contact:
Please evaluate this workshop to help me improving future presentations:
Have questions or comments about this workshop? Please contact:
Yi-Bu Chen, Ph.D.
Bioinformatics Specialist
Norris Medical Library
University of Southern California