Linkage analysis

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Linkage analysis
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• Jan Hellemans

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Finding causal mutations

• 2 opposing strategies
• sequence then select
• select then sequence
• Sequencing
• traditional Sanger sequencing only possible after
  selection
• Massively parallel sequencing possible prior to
  or after selection
• RNA sequencing
• exome sequencing
• genome sequencing

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Finding causal mutations

• Selection
• positional (prior to sequencing)
• linkage analysis
• GWAS
• structural variations (e.g. microdeletions)
• functional (prior to after sequencing)
• candidate genes selected based on known function
  or involvement in related disorders
• filtering of variants based on functional
  predictions
• overlap (after sequencing)
• looking for genes / variants that occur in
  multiple independent patients
• mostly a combination is used

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exome sequencing
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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

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Starting linkage analysis
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Preparations

• Clearly define the phenotype
• If not specific enough than you may analyze
  different disorders that can map to different
  genomic loci
• LOD scores are additive
• 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 prove linkage with the
  available material (SLink not part of this
  course)

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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 markeroften microsattelites
• Genome wide linkage analysis
• Screen for linkage for markers spread across the
  entire genome
• Microsatellites 400 markers spaced at about
  10cM
• SNPs 500k SNP array
• Homozygosity mapping
• Screen only affected individuals in inbred
  families
• Select homozygous markers (typically SNP markers)
• Very efficient technology
• Fine mapping
• Some linked markers are known, but the borders of
  the linkage interval still need to be defined

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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

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Find additional flanking markers

• Find physical position of marker in NCBI gt UniSTS
  
• NCBI map viewer http//www.ncbi.nlm.nih.gov/mapvi
  ew/
• Go to Homo sapiens and to the wright 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

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http//www.ncbi.nlm.nih.gov/projects/mapview
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http//www.ncbi.nlm.nih.gov/projects/mapview
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http//www.ncbi.nlm.nih.gov/projects/mapview
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Exercise Part 1 gt 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

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Analyzing microsatellite data
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Microsatellites gt 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 (5AC ? 18bp)
• allel 2 NNNNACACACACACACNNNN (6AC ? 20bp)
• allel 3 NNNNACACACACACACACNNNN (7AC ? 22bp)
• ...
• Determine length to know the allele (sequencer)

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Microsatellites gt basics
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Microsatellites gt determine size

• Use internal size standard (other color)

230bp
220bp
225bp
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Microsatellites gt heterozygotes
230bp
220bp
225bp
223bp
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Microsatellites gt 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

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Microsatellites gt stutter peaks
allelic peak
1st stutter peak
2nd stutter peak
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Microsatellites gt stutter peaks

• Allelic peaks are the heighest
• Stutter peaks are lower

A1
A2
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Microsatellites gt stutter peaks
A1
A2
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Microsatellites gt 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
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Microsatellites gt complex plots (stutter A)
A1
A2
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Microsatellites gt mutliplex

• Combine multiple markers in a single analysis
  ()
• Different size range
• Multicolor
• Commercial kits e.g. 16 markers / lane

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Microsatellite plots examples
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Genotyping pedigrees
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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

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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?

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Family 1
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Family 2
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Exercise Part 2 gt Conclusions

• D4S1582
• Mendelian error ? can not be interpreted
• D4S2944
• Linked
• D4S3017
• Not-linked unaffected individuals with the same
  genotype as a patient

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Calculate LOD scores
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EasyLinkage

• EasyLinkage UI for linkage analysis
• http//genetik.charite.de/hoffmann/easyLINKAGE/ind
  ex.htmlstart
• Bioinformatics. 2005 Feb 121(3)405-7 PMID
  15347576
• Bioinformatics. 2005 Sep 121(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

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Pedigree file

• Naming requirements for EasyLinkagep_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 (1male, 2female, 0unknown)
• 6 ? affection status (1unaffected, 2affected,
  0unknown)
• 7 ? DNA availability (optional, relevant for
  power calculations)
• 8 ? liability class (to be provided if multiple
  liability classes are used)

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Genotype files

• Person IDs have to match exactly with those
  provided in the pedigree file
• Naming requirements for EasyLinkageMarkerName_xx
  x.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 doesnt matter,
  but column must be present)
• 3 ? individual ID (match person ID in pedigree
  file)
• 4 5 ? genotypes for 2 alleles (unknown0)

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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)

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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

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EasyLinkage settings

• Parametric lt-gt non-parametric
• Single point lt-gt 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

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Evaluate calculated LOD-scores

• Maximum LOD-scores can be seen in EasyLinkage
• Details about LOD-scores at different
  recombination fractions can be found in text
  files generated by EasyLinkage ? process in Excel
  (generate graphs, ...)
• Standard rules for LOD-scores
• gt3 ? significant linkage
• 2ltLODlt3 ? suggestive linkage
• -2ltLODlt2 ? uninformative
• lt-2 ? significant absence of linkage

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Interpreting LOD plots
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Exercise Part 3

• Generate one pedigree file containing all family
  members of both families (use Global IDs)
• Generate a genotype file for each of the tested
  markers
• Run SuperLink analysis with the right settings
• Evaluate results

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Exercise Part 3 gt Results
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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 allows 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

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Determine the linkage interval
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Exercise 2 find the linkage interval
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Post linkage

• Create a list of all the genes within the linkage
  interval
• NCBI map viewer
• UCSC (also for non-coding RNAs)
• 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
• Screen the entire region with capture sequencing
• Finding a mutation and proving its causality is
  the ultimate proof