linkage analysis n.
Skip this Video
Download Presentation
Linkage analysis

Loading in 2 Seconds...

play fullscreen
1 / 56

Linkage analysis - PowerPoint PPT Presentation

  • Uploaded on

Linkage analysis. Jan Hellemans. 6. 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

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Linkage analysis' - adrian-porter

Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
linkage analysis

Linkage analysis

Jan Hellemans


finding causal mutations
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
finding causal mutations1
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

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

  • 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)
linkage analysis types
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
    • SNP’s: 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
exercise part 1
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
find additional flanking markers
Find additional flanking markers
  • Find physical position of marker in NCBI > UniSTS
  • NCBI map viewer:
  • 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
exercise part 1 possible solution
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
microsatellites basics
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)
microsatellites determine size
Microsatellites > determine size
  • Use internal size standard (other color)




microsatellites stutter peaks
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
microsatellites stutter peaks1
Microsatellites > stutter peaks

allelic peak

1st stutter peak

2nd stutter peak

microsatellites stutter peaks2
Microsatellites > stutter peaks
  • Allelic peaks are the heighest
  • Stutter peaks are lower



microsatellites a peaks
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

microsatellites mutliplex
Microsatellites > mutliplex
  • Combine multiple markers in a single analysis ($$$)
    • Different size range
    • Multicolor
    • Commercial kits: e.g. 16 markers / lane
genotyping pedigrees1
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
exercise part 2
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?
exercise part 2 conclusions
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

EasyLinkage = UI for linkage analysis

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


Pedigree file (linkage format)

Genotype file(s)

Marker information (already provided for popular markers)


pedigree file
Pedigree file

Naming requirements for  e.g.


Tab delimited text file

1 individual per row


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)

genotype files
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


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)

marker information
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




Custom marker sets can be created (see manual)

easylinkage settings
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

easylinkage settings1
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

evaluate calculated lod scores
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

>3  significant linkage

2<LOD<3  suggestive linkage

-2<LOD<2  uninformative

<-2  significant absence of linkage

exercise part 3
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

strengthen the evidence
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
determine the linkage interval


















Determine the linkage interval
post linkage
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
    • Screen the entire region with capture sequencing
  • Finding a mutation and proving its causality is the ultimate proof