ANKH variants associated with ankylosing spondylitis: gender differences

The genetic basis of ankylosing spondylitis

PURPOSE OF REVIEW

Genetic factors provide over 90% of the overall susceptibility to ankylosing spondylitis, with about half of the genetic contribution attributed to HLA-B27 and other major histocompatibility complex genes. Recent studies have focused on non-major histocompatibility complex genes. This review is aimed at summarizing the status of major histocompatibility complex and non-major histocompatibility complex genes in ankylosing spondylitis susceptibility, and suggests areas for future studies.

RECENT FINDINGS

A recent meta-analysis of published scans of ankylosing spondylitis susceptibility has confirmed sites on chromosomes 3q, 6p (the major histocompatibility complex), 10q, 16q and 19q in ankylosing spondylitis susceptibility. Non-major histocompatibility complex candidate gene analyses have confirmed a role for the IL-1 gene complex. The search for other non-major histocompatibility complex candidate genes, however, has been complicated by inadequate power in most previous studies. Innovations in genetic methodologies will allow thorough genome wide linkage disequilibrium mapping studies in large cohorts of patients that will result in the dissection of the genetic susceptibility to ankylosing spondylitis.

SUMMARY

Nearly half of the susceptibility to ankylosing spondylitis is provided by major histocompatibility complex genes. Non-major histocompatibility complex genes, most notably the IL-1 gene complex, have been identified and novel technologies promise that a more thorough examination of the rest of the genome will soon elucidate the genetic basis of this disease.

LRTae: improving statistical power for genetic association with case/control data when phenotype and/or genotype misclassification errors are present

BACKGROUND

In the field of statistical genetics, phenotype and genotype misclassification errors can substantially reduce power to detect association with genetic case/control studies. Misclassification also can bias population frequency parameters such as genotype, haplotype, or multi-locus genotype frequencies. These problems are of particular concern in case/control designs because, short of repeated sampling, there is no way to detect misclassification errors. We developed a double-sampling procedure for case/control genetic association using a likelihood ratio test framework. Different approaches have been proposed to deal with misclassification errors. We have chosen the likelihood framework because of the ease with which misclassification probabilities may be incorporated into in the statistical framework and hypothesis testing. The statistic is called the Likelihood Ratio Test allowing for errors (LRTae) and is freely available via software download.

RESULTS

We applied our procedure to 10,000 replicates of simulated case/control data in which we introduced phenotype misclassification errors. The phenotype considered is Ankylosing Spondylitis (AS). The LRTae method power was always greater than LRTstd power for the significance levels considered (5%, 1%, 0.1%, 0.01%). Power gains for the LRTae method over the LRTstd method increased as the significance level became more stringent. Multi-locus genotype frequency estimates using LRTae method were more accurate than estimates using LRTstd method.

CONCLUSION

The LRTae method can be applied to single-locus genotypes, multi-locus genotypes, or multi-locus haplotypes in a case/control framework and can be more powerful to detect association in case/control studies when both genotype and/or phenotype errors are present. Furthermore, the LRTae method provides asymptotically unbiased estimates of case and control genotype frequencies, as well as estimates of phenotype and/or genotype misclassification rates.