Research article

A luteinizing hormone receptor intronic variant is significantly associated with decreased risk of Alzheimer's disease in males carrying an apolipoprotein E epsilon4 allele

Ryan J Haasl , M. Reza Ahmadi , Sivan Vadakkadath Meethal , Carey E Gleason , Sterling C Johnson , Richard L Bowen , Sanjay Asthana and Craig S Atwood

BMC Medical Genetics 2008, 9:37doi:10.1186/1471-2350-9-37

Published:	25 April 2008

Abstract (provisional)

Background

Genetic and biochemical studies support the apolipoprotein E (APOE) epsilon4 allele as a major risk factor for late-onset Alzheimer's disease (AD), though ~50% of AD patients do not carry the allele. APOE transports cholesterol for luteinizing hormone (LH)-regulated steroidogenesis, and both LH and neurosteroids have been implicated in the etiology of AD.

Results

Since polymorphisms of genes encoding LH beta-subunit (LHB) and its receptor (LHCGR) have not been tested for their association with AD, we scored AD and age-matched control samples for APOE genotype and 14 polymorphisms of LHB and LHCGR. Thirteen gene-gene interactions between the loci of LHB, LHCGR, and APOE were associated with AD. The most strongly supported of these interactions was between an LHCGR intronic polymorphism (rs4073366; lhcgr2) and APOE in males, which was detected using all three interaction analyses: linkage disequilibrium, multi-dimensionality reduction, and logistic regression. While the APOE epsilon4 allele carried significant risk of AD in males [p=0.007, odds ratio (OR) = 3.08(95%confidence interval: 1.37, 6.91)], epsilon4-positive males carrying 1 or 2 C-alleles at lhcgr2 exhibited significantly decreased risk of AD [OR = 0.06(0.01, 0.38); p=0.003].

Conclusions

These data suggest that the lhcgr2 C-allele or a closely linked locus greatly reduces the risk of AD in males carrying an APOE epsilon4 allele. The reversal of risk embodied in this interaction powerfully supports the importance of considering the role gene-gene interactions play in the etiology of complex biological diseases and demonstrates the importance of using multiple analytic methods to detect well-supported gene-gene interactions.