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Genetic Variation

Genetic Variation

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Genetic Variation

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  1. GeneticVariation Dr. Lars Eijssen BiGCaTBioinformatics Image:

  2. Contents • Basics of geneticvariation • Technology to measurevariation • LinkingSNPs to traits • Functionaleffects of SNPs • SNPs in genome browsers

  3. 1. Basics of genetic variation

  4. This week we will focus on features of Genes related to variation

  5. Variations in Genes • In human beings, 99.9 percent of the bases are the same. • Remaining 0.1 percent makes a person unique. • Different attributes / characteristics / traits • how a person looks, • diseases he or she develops. • These variations can be: • Harmless (change in phenotype) • Harmful (diabetes, cancer, heart disease, Huntington's disease, and hemophilia ) • Latent (variations found in coding and regulatory regions, are not harmful on their own, and the change in each gene only becomes apparent under certain conditions e.g. susceptibility to lung cancer) • A SNP is called a mutationif a disadvantageous effect ondisease is proven

  6. Types of genetic variations

  7. Other variations in genes • Apart fromvariations in the sequence of the genes, other (inheritable) variationsoccur • These are calledepigeneticvariations • Will becovered in week 7 Image:

  8. Single Nucleotide Polymorphisms (SNP) • A SNP (single nucleotide polymorphism) is defined as a single base change in a DNA sequence that occurs in a significant proportion (more than 1 percent) of a large population • Currently, dbSNP at NCBI (build 132) has about 6.9 million human SNPs (4.5 million validated refSNPs)

  9. To refresh memory • The two copies of a gene we have (each of bothchromosomes) are calledalleles • In case of a SNP the nucleotide at a positioncanbe different between the alleles of anindividual • The combination of allelesone has, is called the genotype • The relatedtraitone has is called the phenotype • The combination of a number of SNPson has is called a haplotype • SNPsthat are close on the genome formhaplotypeblocks, as theyinherit as a groupdue to the mechanism of cross-over and recombination • Allelefrequency: the frequency of occurence of a specificallele • Penetrance: the number of peoplewith a certain genotype thatalsodevelop the associatedphenotype

  10. SNP facts • SNPs are found in • coding and (mostly) non-coding regions. • Occur with a very high frequency • about 1 in 1000 bases to 1 in 100 to 300 bases. • The abundance of SNPs and the ease with which they can be measured make these genetic variations significant. • SNPs close to a particular gene acts as a genetic marker for that gene. • SNPs in coding regions alter the protein structure made by that coding region: • Synonymous SNP: no protein structure alteration • Non-synonymous SNP: (yes) protein structure alteration

  11. SNPs may / may not alter protein structure

  12. SNPs act as gene markers

  13. More on markers • Several types of geneticpolymorphic markers canbeused to identifychromosomes, alleles, samples: • SNPs • SNPswith more than 2 alleles are of special interest as markers • Tandem repeats (microsatellites) • These canvary in number of repeats (repeatlength) • Longer variabele sequences of DNA • Satellites

  14. SNP Maps • Sequence genomes of a large number of people • Compare the base sequences to discover SNPs. • Generate a single map of the human genome containing all possible SNPs → SNP maps • For example Hapmap project

  15. SNP Maps (II)

  16. SNP Profiles • Genome of each individual contains distinct SNP pattern. • People can be grouped based on the SNP profile. • SNPs Profiles important for identifying response to Drug Therapy: • Correlations might emerge between certain SNP profiles and specific responses to treatment. • Personalized medicine

  17. SNP Profiles (II)

  18. Personalized medicine • Concept: use information about a patient's genome, epigenome, proteome, etc. to adapt medical care: • select between different medications • optimize drug dosage • provide a specific therapy • This omics-centered approach is not yet in widespread use clinically. • Important unanswered question: does personalized medicine really offer any significant advantages over the traditional combination of clinical approaches (medical history, family history, data from imaging, laboratory, and other tests)

  19. Personalized food • Same concept as personalized medicine: use omics information of an individual or specific group of individuals to “optimize” nutrition • Optimization on product level: • “You should eat more kiwi’s” • “You shouldn’t eat kiwi’s at all” • Optimization on molecular level: • “You should take extra vitamin B12” • “Taking extra vitamin B12 is dangerous for you” • Big business for food companies

  20. 2. Technology to measure variation

  21. Measuring SNPs • Sangersequencing • terminates the chainwithincorporation of a ddNT

  22. Measuring SNPs • SNPscanbemeasuredusingseveraltechnologies • Pyrosequencing • detectspyrophosphate (light) Images from: (left) and (right)

  23. Marker sets • A way to measuregeneticvariationon a more globalscale is the construction of genetic markers sets • Covering the whole genome or part of it • Often, microsatellite markers are usedforconstruction of these sets • Regions in whichchanges are observedcanbeexploredfurtherbysequencingorSNPs • Marker sets are alsoused in forensicsor in paternitydetermination

  24. Largescalemeasurement of SNPs • AffymetrixSNP chip • 500,000 or1M SNPs • Genome widestudies • Data analysis? • (SM Carr et al. 2008. Comp. Biochem. Physiol. D, 3:11)

  25. (after SM Carr et al. 2008. Comp. Biochem. Physiol. D, 3:11)

  26. Resequencing chips • Another type of chip allowssequencinggenesorgenomicregions of interest • Onecan design the chips dependingon the genes of interest • As suchonecanmeasure all knownmutationsrelated to a disease

  27. Sequencing the whole genome • NextGenerationSequecing (NGS) has made itpossible to sequence the whole genome of anorganism • In principle, all variationsbetweenindividualscanbedetermined • Methodological details willnotbecovered in thiscourse (several platforms available) • In any case: massiveamounts of dataare generated (Gbs per sample)

  28. Sequencing the whole genome • Data analysis is notthat easy • Aligning • Calling (‘peak’ calling) • Realchangesorsequencingerrors • Error file • Same issue with ‘regular’ sequencing, butthereonecanevaluatebyeyesight • Howmanyfoldcoverage is needed?

  29. 3. Linking SNPs to traits

  30. Traits • A trait is just a characteristic • Length, weights, eye color, sex, … • Traitscanbe discrete (sex, …) orcontinuous (weight, …) • Discrete = ‘quantitative’ • Continuous = ‘qualitative’

  31. Heritability • This term is oftenMISinterpreted • The heritability of a traitmeanshowmuch of itsvariationcanbeexplainedbygeneticvariation • …in the population in whichit is measured • Thus high heritability does notmeanthat the trait is geneticallydetermined in general

  32. Heritability • The more genetically uniform the population (e.g. inbredstrains) the LOWER the heritability of the trait All kinds of mice -geneticvariationcontributes to differencesin phenotype -environment does too Mickey clones -nogeneticvariation -all differences in phenotypemust bedue to environment Images: varioussources

  33. Heritability • Heritabilityestimates in yourpopulationcantellyouwhetheryoucanfindpossiblegenetic factors contributing to a phenotypeusing the population of choice

  34. Genome wideassociation studies • GWAS (‘association’) tries to link SNPs to traits (diseases) in a genome wideway • Makesuse of unrelatedindividuals • Sonofamilymembers • Tries to findwhichallelicvariants, correlatewith the phenotype of interest • If a complete haplotypegoestogetherwith the phenotype, this is consideredassociation • Toolssuch as HaploView1canassist 1

  35. Each color indicates a different haplotype in the studypopulation Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Control 1 Control 2 Control 3 Region of interest (determine in more detail, or check genesitcontains)


  37. Linkage studies • Linkagemakesuse of relatedindividuals • familymembers • Adantage is higher power as compared to GWAS • Butoneneedslargeenough families withenough (informative) ‘cross overs’ and preferablyseveralgenerations • Principle is the same as with GWAS, using markers orSNPs


  39. Computations • The linkagedisequilibrium(D or LD) indicated the deviation of a haplotype’sfrequencyfromitsexpectedfrequency • D’ is anadapted score, to correct for the influence of allelefrequences • Analternativeway of correcting is computing the correlationcoefficient (r) betweenloci, alsosometimesgiven as its square (r2) • more stringent than D’

  40. Computations • The LOD score (10log of the odds) indicates the likelihood of obtaining the data giventhat the loci are indeed linked, versus obtaining the data bychance • A score higherthan 3 (whichmeans a 1000:1 odds) is consideredevidence of linkage

  41. Limitations • A very large sample size is needed but also population uniformity • Trade-off • To most common diseases, many SNPs/genes contribute for a few percent each • Difficult to detect • Often many genes in haplotype blocks • Rare alleles make sampling even more difficult often discrepancy even between large studies

  42. Strategies • Populations to use to increase power: • Families (linkage more power) • Simplify by candidate gene approach • Scan globally, and scan regions of interest in more detail

  43. What’s more? • Always realisethat the SNPslinked to the phenotype are not (neccesarily) the functionalorcausingSNPs, they are just close enough to be markers • Now we onlydiscussedgeneticcontributions to a phenotype • Otheraspects to study: • Genesmodifying the effects ofothergenes (epistatis) • Gene-environmentinteractions • ADE models can help in estimatinggenetic and environmentalcontributions (heretwins are of interest) • Specificstudy of interactions is verydifficult • Even more possibilities • Even smaller effects Images: (left) and (right)

  44. 4. Functional effects of SNPs

  45. Functionaleffects of SNPs • Before we already mentioned coding versus non-coding SNPs and synonymousSNPs versus non-synonymous SNPs • When amino acid (AA) changes, is the change relevant? • Type of AA • Site of the change • Functional domain • Conservation in other species (furtherdiscussedin lectureonsequencealignment) • Truncatingmutation • When is a variation a mutation? • In fact functional proof is needed (functional testing protein, enzymes, protein-protein binding, cellular localization, make KO cells, substitute defect) (effects on protein function in lecture on protein structure) • Tools to predict effect on a protein: SIFT, Polyphen

  46. Functionaleffects of SNPs • Also non-coding SNPs may have an effect: • Effect on target sites of Transcription Factors (regulation of transcription) • Effect on target sites of miRNAs (regulation of transcript decay) • these topics will be discussed in week 7 • effect on splice donor or acceptor sites (regulation of – alternative – splicing) • Other…

  47. 5. SNPs in genome browsers

  48. SNP IDs • A standard ID for SNPs is the dbSNP ID • also called “rs number” • example: rs4986852 • Standardised, unique, stable • AnalternativefordiseaserelatedSNPs is the OMIM variation ID • example: 113705.0011 • Standardised, unique, stable • A finalpossibility is the • For non-codingSNPs: variation • Example: BRCA1, 2978G>A • For coding SNPs (also): mutation • Example: BRCA1, SER1040ASN • Easier to interpret, butnotstable

  49. Investigating SNPs in NCBI (Entrez SNP)

  50. Investigating SNPs in Ensembl Click on “Variation Table”