Skip to main content
padlock icon - secure page this page is secure

Free Content Mixed-effects Logistic Approach for Association Following Linkage Scan for Complex Disorders

Download Article:

You have access to the full text article on a website external to Ingenta Connect.

Please click here to view this article on Wiley Online Library.

You may be required to register and activate access on Wiley Online Library before you can obtain the full text. If you have any queries please visit Wiley Online Library

Summary

An association study to identify possible causal single nucleotide polymorphisms following linkage scanning is a popular approach for the genetic dissection of complex disorders. However, in association studies cases and controls are assumed to be independent, i.e., genetically unrelated. Choosing a single affected individual per family is statistically inefficient and leads to a loss of power. On the other hand, because of the relatedness of family members, using affected family members and unrelated normal controls directly leads to false-positive results in association studies. In this paper we propose a new approach using mixed-model logistic regression, in which associations are performed using family members and unrelated controls. Thus, the important genetic information can be obtained from family members while retaining high statistical power. To examine the properties of this new approach we developed an efficient algorithm, to simulate environmental risk factors and the genotypes at both the disease locus and a marker locus with and without linkage disequilibrium (LD) in families. Extensive simulation studies showed that our approach can effectively control the type-I error probability. Our approach is better than family-based designs such as TDT, because it allows the use of unrelated cases and controls and uses all of the affected members for whom DNA samples are possibly already available. Our approach also allows the inclusion of covariates such as age and smoking status. Power analysis showed that our method has higher statistical power than recent likelihood ratio-based methods when environmental factors contribute to disease susceptibility, which is true for most complex human disorders. Our method can be further extended to accommodate more complex pedigree structures.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Association study; mixed model; pedigree

Document Type: Research Article

Publication date: March 1, 2007

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more