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Define Parametric Linkage Analysis and How to Determine a LOD Score

Define Parametric Linkage Analysis and How to Determine a LOD Score. James Polke, Institute of Neurology, London 12 th Jan 2009. Parametric Linkage Analysis.

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Define Parametric Linkage Analysis and How to Determine a LOD Score

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  1. Define Parametric Linkage Analysis and How to Determine a LOD Score James Polke, Institute of Neurology, London 12th Jan 2009

  2. Parametric Linkage Analysis • Linkage Analysis: A method to calculate the likelihood that loci are linked and to provide an estimate of how closely linked they are. OR: Linkage attempts to extract all available inheritance information from pedigrees and to test for co-inheritance of chromosome regions with a trait. • Parametric Linkage: Uses a model for a disease to ascertain how likely a genetic marker and disease allele are to be linked. e.g. Assumes an inheritance pattern (AR, AD) and penetrance and recombination fraction • Non-Parametic Linkage: Involves testing whether an inheritance pattern deviates form expectation under independent assortment. ???Add to this???

  3. Two Point Linkage Analysis • The simplest form of linkage analysis. Only considers 2 genetic markers: The disease locus (is the individual affected or unaffected?) and a genetic marker (RFLP, microsat, SNP etc.). The output is a plot of recombination fraction vs. LOD score. • Recombination fraction (θ): The fraction of meiotic events that show recombination between two markers. Range = 0 - 0.5. If markers are unlinked (or so far apart on a c’some to be effectively unlinked), there is a 50% chance that they will be co-inherited, θ = 0.5. If there is no recombination, θ=0. • The recombination fraction is also used to derive the GENETIC DISTANCE between markers (in cM), where 1 cM = 1% chance of recombination.

  4. LOD Scores • LOD = Logarithm of Odds Ratio • Odds ratio: Likelihood of result if linked (for a given θ) Likelihood of result if unlinked • To calculate a LOD score for a pedigree: • Identify an individual that sets the phase of the marker with disease • For each family member below that individual: Calculate the probability of their phenotype/genotype combination assuming linkage (for each θ) and assuming that they are no linkage.

  5. Pedigree etc.

  6. LOD Scores • Practically, pedigree information can be inputted into computer programs to calculate LOD scores (e.g. GeneHunter or others). Large and multiple pedigrees can be entered, disease model assumptions can be adjusted. • The MAXIMUM LOD score is derived from the output, and whether it passes the significance threshold: • Significance threshold for linkage is LOD>3.0 • LOD <-2 excludes linkage

  7. Deriving Significance Threshold • LOD >3.0 equates to 95% confidence (p<0.05) • Derived from a Bayesian calculation assuming that there's a 1/50 chance that two loci selected at random in the genome are linked: Final = (1/50) / (49/50000) = 0.049

  8. Support Interval • The support interval is the acceptable range of recombination fractions that is usually quoted for a LOD score (effectively a confidence interval). It is derived form the area under the curve described by 1 LOD unit below the peak: • Figure 13.7 • In the example above you would quote the maximum significant LOD score of 3.8 with the most likely recombination fraction of 0.23 and a support interval of 0.17 – 0.32.

  9. Multipoint Linkage • Considers several markers in a genomic region (and the disease locus). More powerful The result is a plot of genetic distance (in cM) vs. LOD:

  10. Multipoint Linkage • Though the genetic distance between markers can be derived from the disease pedigrees submitted to the software, a limited no. of meioses may contribute to the input, reducing accuracy. Therefore it is possible to use a known 'Marker Framework' (genetic map) derived from larger studies. E.g. CEPH (Centre d'Etude du Polymorphisme Humain) data.

  11. Limitations of LOD Sore Analysis • It's a powerful tool for estimating linkage over ~20 Mb segments, but with limitations • Computationally expensive • Locus Heterogeneity – Where a disease is in fact caused by mutations in more than one gene. Combining families with different genetic aetiologies will affect output. Some programs can take this into account and provide likelihoods of the given result on the assumption of different degrees of heterogeneity. • Meiotic mapping has limited resolution • Depends on assumptions: Mode of inheritance, penetrance etc.

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