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Linkage analysis

Linkage analysis. Jan Hellemans. 6. Chapter. Aims. Interprete microsatellite results Add genotypes to pedigrees Create pedigree and genotype files Calculate and interprete LOD-scores Delineate linkage intervals Basic principles of linkage analysis Analyze other types of markers

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Linkage analysis

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  1. Linkage analysis Jan Hellemans 6 Chapter

  2. Aims Interprete microsatellite results Add genotypes to pedigrees Create pedigree and genotype files Calculate and interprete LOD-scores Delineate linkage intervals Basic principles of linkage analysis Analyze other types of markers Association studies Learn how to work with specific pedigree programs

  3. Starting linkage analysis

  4. Preparations • Clearly define the phenotype • If not specific enough than you may analyze different disorders that can map to different genomic loci • Find suitable families • larger is better • more patients is better • Collect genomic DNA from as much family members as possible • Determine the type of inheritance • Calculate the power to proove linkage with the available material (SLink – not part of this course)

  5. Linkage analysis types • Directed linkage analysis • Evaluate linkage at a specific locus such as a candidate gene • Common approach: evaluate an intragenic, 5’ and 3’ marker • Genome wide linkage analysis • Screen for linkage for markers spread across the entire genome • Microsatellites: ~400 markers spaced at about 10cM • SNP’s: 500k SNP array • Homozygosity mapping • Screen only affected individuals in inbred families • Select homozygous markers • Very efficient technology

  6. Exercise – Part 1 • 2 inbred families with a recessive disorder • With a homozygosity mapping based on 500k SNP arrays 2 candidate regions could be identified • Chromosome 4 • Patient 1 homozygous for • 6.052Mb - 14.488Mb • 21.008Mb – 37.477Mb • Patient 2 homozygous for • 11.186Mb – 37.219Mb • Task: find microsatellite markers to confirm linkage

  7. Find additional flanking markers • Find physical position of marker in NCBI > UniSTS • NCBI map viewer: http://www.ncbi.nlm.nih.gov/mapview/ • Go to Homo sapiens and to the right chromosome • Maps & options: show • DeCode, Généthon & Marshfield (genetic maps) • Genes • Set region: e.g. 2Mb up- and downstream of your marker • Click ‘Data as table view’ • Click on STS behind a marker to see its details • Select markers that • locate to only 1 genomic location • have a PCR product with an extended size rangeone size  not polymorphic

  8. Exercise – Part 1 > possible solution • Markers in 1st candidate region • D4S3017 (21.078Mb) • D4S3044 (25.189Mb) • D4S1618 (33.857Mb) • D4S3350 (33.857Mb) • D4S2988 (36.889Mb) • Markers in 2nd candidate region • D4S1582 (10.311Mb) • D4S2906 (12.321Mb) • D4S2944 (13.141Mb) • D4S1602 (14.059Mb) • D4S2960 (15.437Mb) •  Order primers & analyze them on all family members

  9. Analyzing microsatellite data

  10. Microsatellites > basics • Repeats of short sequences (e.g. 2bp)NNNNAC(AC)nACNNNN • Number of repeats is variable (instable sequence) • Number of repeats determines the allele • Number of repeats corresponds to specific length of PCR product: • allel 1: NNNNACACACACACNNNN (5*AC  18bp) • allel 2: NNNNACACACACACACNNNN (6*AC  20bp) • allel 3: NNNNACACACACACACACNNNN (7*AC  22bp) • ... • Determine length to know the allele (sequencer)

  11. Microsatellites > basics

  12. Microsatellites > determine size • Use internal size standard (other color) 220bp 230bp 225bp

  13. Microsatellites > heterozygotes 220bp 230bp 223bp 225bp

  14. Microsatellites > stutter peaks • Repeats are difficult to copy  polymerase slips • Some amplicons have 1 repeat lessa few even loose multiple repeats • Small repeats are more prone to slippage and show more pronounced stutter peaks • Largest product is the correct one • Distance between peaks = length of a repeat

  15. Microsatellites > stutter peaks allelic peak 1st stutter peak 2nd stutter peak

  16. Microsatellites > stutter peaks • Allelic peaks are the heighest • Stutter peaks are lower A1 A2

  17. Microsatellites > stutter peaks A1 A2

  18. Microsatellites > +A peaks • Taq polymerase tends to add an extra A at the 3’ end • Variable degree of products with or without this extra A • Do not confuse with stutter peaks (only 1bp difference) allelic peak allelic peak + A 1st stutter peak 1st stutter peak + A 2nd stutter peak 2nd stutter peak + A

  19. Microsatellites > complex plots (stutter & +A) A1 A2

  20. Microsatellites > mutliplex • Combine multiple markers in a single analysis ($$$) • Different size range • Multicolor • Commercial kits: e.g. 16 markers / lane

  21. Microsatellite plots examples

  22. Genotyping pedigrees

  23. Genotyping pedigrees • Screen one or multiple markers for some or all family members • For every marker: • Make a list of all occuring allele sizes • Due to technical variation on sizing the same allele can have a slightly different size in different measurements (-0.4bp _ +0.4bp). Give all alleles within this range the same allele number • Add the allele numbers to the pedigree at the corresponding individual/marker combination • Find the wright phase • Advanced software like GeneMapper can generate tables with allele numbers for every sample / marker • Advanced pedigree programs like Progeny can store genotype information for family members • Verify inheritance

  24. Exercise – Part 2 • Genotype 3 markers in all available individuals of 2 families • Pedigrees & microsatellite plots inExercisePart2-GenotypingData.pdf • Add allele numbers for the 3 markers to the pedigree • Interprete the genotyped pedigrees: linked?

  25. Family 1

  26. Family 2

  27. Exercise – Part 2 > Conclusions • D4S1582 • Mendelian error  can not be interpreted • D4S2944 • Linked • D4S3017 • Not-linked: unaffected individuals with the same genotype as a patient

  28. Calculate LOD scores

  29. EasyLinkage EasyLinkage = UI for linkage analysis http://genetik.charite.de/hoffmann/easyLINKAGE/index.html#start Bioinformatics. 2005 Feb 1;21(3):405-7 PMID: 15347576 Bioinformatics. 2005 Sep 1;21(17):3565-7 PMID: 16014370 Interface for many linkage analysis programs Input Pedigree file (linkage format) Genotype file(s) Marker information (already provided for popular markers) Settings

  30. Pedigree file Naming requirements for EasyLinkage:p_xxx.pro  e.g. p_SMMD.pro Format: Tab delimited text file 1 individual per row Columns: 1  family ID 2  person ID 3  father ID 4  mother ID 5  sex (1=male, 2=female, 0=unknown) 6  affection status (1=unaffected, 2=affected, 0=unknown) 7  DNA availability (optional, relevant for power calculations) 8  liability class (to be provided if multiple liability classes are used)

  31. Genotype files Person ID’s have to match exactly with those provided in the pedigree file Naming requirements for EasyLinkage:MarkerName_xxx.abi  e.g. D1S1609_SMMD.abi Format: Tab delimited text file 1 individual per row Columns (for microsatellite based analysis): 1  marker (same as in file name and matching a marker in an available marker set) 2  custom information (content doesn’t matter, but column must be present) 3  individual ID (match person ID in pedigree file) 4 & 5  genotypes for 2 alleles (unknown=0)

  32. Marker information Contains information on the chromosome and position of every marker Already available for a number of commercial SNP-arrays and for the microsatellite markers from Genethon Marshfield DeCode Custom marker sets can be created (see manual)

  33. EasyLinkage settings Choose a program: FastLink  Parametric, single-point SuperLink  Parametric, single-/multipoint SPLink  Nonparametric, single-point Genehunter  Nonpara-/parametric, single-/multipoint Genehunter Plus  Nonpara-/parametric, single-/multipoint Genehunter MOD  Nonpara-/parametric, single-/multipoint Genehunter Imprinting  Nonpara-/parametric, single-/multipoint GeneHunter TwoLocus  Parametric, two-locus, single-/multipoint Merlin  Nonpara-/parametric, single-/multipoint SimWalk  Nonparametric, single-/multipoint Allegro  Nonpara-/parametric, single-/multipoint & simulation, single-/multi-point PedCheck  Mendelian error check FastSLink  Simulation, single-/multi-point

  34. EasyLinkage settings Parametric <-> non-parametric Single point <-> multipoint Frequency of the disease allele Penetrance vectors (wt/wt, wt/mt, mt/mt) Standard dominant: 0 1 1 Standard recessive: 0 0 1 Reduced penetrance: replace 1 by penetrance (e.g. 0.9) Phenocopy: replace 0 by percentage of phenocopy (e.g. 0.1) Example: 0.01 0.9 0.991% chance to show a similar phenotype despite a normal genotype90% chance to show the phenotype when 1 mutant allele (dominant with incomplete penetrance)99% likelihood to present with the phenotype if both alleles are mutant

  35. Evaluate calculated LOD-scores Maximum LOD-scores can be seen in EasyLinkage Details about LOD-scores at different recombination fractions can be fount in text files generated by EasyLinkage  process in Excel (generate graphs, ...) Standard rules for LOD-scores >3  significant linkage 2<LOD<3  suggestive linkage -2<LOD<2  uninformative <-2  significant absence of linkage

  36. Interpreting LOD plots

  37. Exercise – Part 3 Generate one pedigree file containing all family members of both families (use Global ID’s) Generate a genotype file for each of the tested markers Run SuperLink analysis with the right settings Evaluate results

  38. Exercise – Part 3 > Results

  39. Strengthen the evidence • Analyze more family members • Analyze more families • Analyze flanking markers • Look for more informative markers that result in higher LOD-scores • A series of flanking markers allow for multipoint linkage analysis • A series of linked markers gives more confidence (subjective) • Flanking markers can also be used to fine-map the linkage interval

  40. NL NL NL ? ? L L ... candidate region L L L ? ? NL NL Determine the linkage interval

  41. Exercise 2: find the linkage interval

  42. Post linkage • Create a list of all the genes within the linkage interval • NCBI map viewer • UCSC (also for non-coding RNA’s) • Evaluate known gene functions for relevance to the investigated phenotype • Sequence genes • Start with those that seem the most relevant to the disorder • Start with the coding regions • Finding a mutation and proving its causality is the ultimate proof

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