Parametric and Nonparametric Linkage Analysis


Parametric or model‐based linkage analysis assumes that models describing both the trait and genetic marker loci are known without error, although sensitivity analysis approaches allow one to account for uncertainty in the trait model. Nonparametric, model‐free or weakly parametric linkage methods make fewer assumptions about the trait model. Family‐based linkage analysis in general is most powerful to detect genetic variants that have large effects on risk of a disease or variation of a quantitative trait. These variants tend to be rare in the population for any disease which is likely to have undergone negative selection over evolutionary time. Linkage studies are more powerful than genome‐wide association studies (GWAS) to detect genes with rare, high penetrance risk variants, whereas GWAS are more powerful to detect common risk variants which tend to have small individual effects on risk. Thus, both linkage and association analysis strategies are useful in the era of whole‐genome sequencing. Both parametric and nonparametric linkage methods are useful for detecting regions of the genome harbouring high penetrance risk variants which are of particular interest for precision medicine.

Key Concepts

  • Linkage analysis evaluates the likelihood that a specific allele or haplotype of alleles co‐segregates with a disease or trait in a family or group of families.
  • Linkage analysis searches for evidence of a violation of Mendel's Law of Independent Assortment of alleles at two loci to the offspring.
  • Linkage is due to the fact that long stretches of DNA on a specific chromosome are transmitted together to an offspring.
  • Genetic loci on different chromosomes are not linked and do segregate independently to offspring.
  • Genetic loci on the same chromosome that are close together show some evidence of linkage, but loci that are far apart on the same chromosome may not show evidence of linkage due to the fact that chiasmata occur at the four‐strand stage of meiosis, which results in recombination between the chromatids of the parental pair of chromosomes.
  • Linkage is a measure of the probability of recombination between two loci (such as a risk allele for a disease and a genetic variant somewhere in the genome) given the observed genotypes of the parents and offspring.
  • In parametric linkage, a model is assumed that specifies the mode of inheritance, allele frequency and penetrance of all genotypes for the disease locus (probability of a given value of a quantitative trait) plus the mode of inheritance, allele frequency and probability of observing each laboratory phenotype given genotype for the genotyped marker loci (usually codominant and 100% for modern single‐nucleotide polymorphism and DNA sequencing genotypes).
  • In nonparametric linkage, no specific assumptions are made about the trait model, but the genotyped marker locus model must be fully described as above for parametric linkage.
  • The power of these two approaches depends on many different factors, but both depend on the observation of large numbers of meioses; so, large families with multiple affected individuals (or a wide range of quantitative trait values) in which all individuals have both phenotype and genotype data are most powerful.
  • Linkage analyses are particularly useful for detecting genetic variants with large effects on risk of disease or large effects on the variance of a quantitative trait, which are of particular utility for precision medicine.

Keywords: linkage; genetics; power; type 1 error; heterogeneity; model based; bias

Figure 1. Example of extended pedigrees exhibiting linkage between a disease locus (D) and four marker loci in a small region of a chromosome. The diagram illustrates the scenario where common variants can be linked along a haplotype within families even though a different disease allele (and different linked haplotype) is present in each family. Here, we assume that the disease causing gene, D, is located between the second and third marker locus on the diagram and that the actual disease variants, D1, D2 and D3 in the first, second and third families, respectively, are rare, have not been genotyped and are highly penetrant. In this example, we also assume that only carriers of a risk allele are affected with the disease. Markers A, B, C and D are assumed to have common minor allele frequency (MAF) and have been genotyped. All four markers are also located very close to each other on the same chromosome, so that it is unlikely any crossing over will occur within families in this small number of observed meioses. Looking within each family, it is clear that all markers are linked to the disease as in each family the black haplotype co‐segregates with the disease phenotype. Parametric linkage evaluates the likelihood of linkage at a particular recombination fraction between the unknown disease gene and the marker loci based on co‐segregation patterns within the family and on the assumed genetic models for the disease and marker loci. Affected‐pair nonparametric linkage evaluates whether affected relative pairs share more alleles identical by descent than expected based on their relationship.
Figure 2. Example of one form of nonparametric linkage analysis of a quantitative trait in sibling pairs. Regression of the proportion of marker alleles shared identical by descent (i.b.d.) versus the squared difference in the quantitative phenotypes for each sibling pair is shown here. Sibs 1 and 2 have phenotypes y1 and y2. If there is no linkage between the marker locus and a gene that affects variation of the quantitative trait, then the regression line should be flat, and the regression coefficient should be zero. If linkage is present, then the regression coefficient should be significantly different from zero as sibling pairs that share a higher proportion of alleles i.b.d. at a linked marker locus should also share a higher proportion of alleles i.b.d. at the trait locus and thus should have more similar quantitative phenotypes than would be observed in pairs who exhibit lower proportions of alleles shared i.b.d.


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Further Reading

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Wijsman EM (2012) The role of large pedigrees in an era of high‐throughput sequencing. Human Genetics 131 (10): 1555–1563. DOI: 10.1007/s00439-012-1190-2. Epub 20 June 2012.

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Bailey‐Wilson, Joan E(Jun 2018) Parametric and Nonparametric Linkage Analysis. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0005403.pub2]