Human Genetics

, Volume 114, Issue 6, pp 588–593 | Cite as

Quasi-linkage: a confounding factor in linkage analysis of complex diseases?

  • Sinthuja Sivagnanasundaram
  • Karl W. Broman
  • Michelle Liu
  • Arturas Petronis
Original Investigation

Abstract

Human linkage analysis is based on the assumption that unlinked genomic loci, particularly loci located on non-homologous chromosomes, segregate independently during meiosis. An exception to this rule is the phenomenon of quasi-linkage (QL) that describes the non-random segregation of non-homologous chromosomes, which can undermine the basic concept of linkage. Molecular mechanisms of QL are not clear; however, observations in mice and plants suggest a possible affinity between non-homologous chromosomal regions containing repetitive or like sequences. QL has not been investigated in humans. As QL may generate false linkages in genome scans of complex diseases, we sought to determine whether genomic loci detected in such genome scans exhibit QL. A number of individual markers showing linkage to schizophrenia, asthma, multiple sclerosis, inflammatory bowel disease and type-1 diabetes were tested for QL in a pairwise linkage analysis against all other markers exhibiting evidence for linkage in each specific study. The Marshfield genotype dataset of eight CEPH families was used for this purpose. The best QL lod scores generated from the analysis were within the range of the “lukewarm” lod scores reported in the majority of linkage studies for complex disorders. In addition, we performed a genome-wide QL analysis on the Marshfield family database which detected eight QL lod scores >6. The replication of the best Marshfield QL scores was performed using the deCODE families and although none of the eight pairs demonstrated independent evidence for QL, three pairs generated maximal lod scores of 0.11, 0.3, and 1.51. In conclusion, although complex disease relevant markers did not produce high QL lod scores, the general phenomenon of QL in humans cannot be excluded and potentially can be a confounding factor in genetic studies of complex traits.

Notes

Acknowledgements

We thank Dr Andrew Paterson for his comments and suggestions. This work was supported by grants from the Ontario Mental Health Foundation, the National Alliance for Research in Schizoprhenia and Depression to A.P.. M.L. is supported by a Genome Canada grant.

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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Sinthuja Sivagnanasundaram
    • 1
  • Karl W. Broman
    • 2
  • Michelle Liu
    • 3
  • Arturas Petronis
    • 1
  1. 1.The Krembil Family Epigenetics Research LaboratoryCentre for Addiction and Mental HealthTorontoCanada
  2. 2.Department of BiostatisticsThe Johns Hopkins UniversityBaltimoreUSA
  3. 3.Program in Genetics and Genomic BiologyThe Hospital for Sick ChildrenTorontoCanada

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