Linkage analysis of a cluster-based quantitative phenotype constructed from pulmonary function test data in 27 multigenerational families with multiple asthmatic members

Functional Fcgamma receptor polymorphisms are associated with human allergy

Objective

IgG Fc receptors (FcγRs) play important roles in immune responses. It is not clear whether FcγR receptors play a role in human asthma and allergy. The aim of current study was to investigate whether functional single nucleotide polymorphisms (SNPs) of FcγR genes (FCGR) are associated with human asthma and allergy.

Methods

Functional SNPs of FCGR2A (FcγRIIA-131His>Arg, rs1801274), FCGR2B (FcγRIIB-187Ile>Thr, rs1050501), FCGR2C (FcγRIIC-13Gln>Stop, rs10917661), FCGR3A (FcγRIIIA-158Val>Phe, rs396991), and FCGR3B variants (FcγRIIIB NA1 and NA2) were genotyped in an asthma family cohort including 370 atopy positive, 239 atopy negative, and 169 asthma positive subjects. The genotype and phenotype data (asthma, bronchial hyper-responsiveness, and atopy) of subjects were analyzed using family-based association tests (FBAT) and logistic regression adjusted for age and sex.

Result

The FcγRIIA-131His>Arg SNP is significantly associated with atopy in a family-based association test (P = 0.00287) and in a logistic regression analysis (P = 0.0269, OR 0.732, 95% CI: 0.555–0.965). The FcγRIIA-131His (or rs1801274-A) allele capable of binding human IgG2 has a protective role against atopy. In addition, the rare FcγRIIB-187Thr (or rs1050501-C) allele defective for the receptor-mediated inhibitory signals is a risk factor for atopy (P = 0.0031, OR 1.758, 95% CI: 1.209–2.556) and IgE production (P<0.001). However, variants of activating FcγRIIIA (rs396991), and FcγRIIIB (NA1 and NA2), and FcγRIIC (rs10917661) are not associated with asthma, BHR, and atopy (P>0.05).

Conclusions

FcγRIIA and FcγRIIB functional polymorphisms may have a role in the pathogenesis of allergy.

Variable selection in canonical discriminant analysis for family studies

In family studies, canonical discriminant analysis can be used to find linear combinations of phenotypes that exhibit high ratios of between-family to within-family variabilities. But with large numbers of phenotypes, canonical discriminant analysis may overfit. To estimate the predicted ratios associated with the coefficients obtained from canonical discriminant analysis, two methods are developed; one is based on bias correction and the other based on cross-validation. Because the cross-validation is computationally intensive, an approximation to the cross-validation is also developed. Furthermore, these methods can be applied to perform variable selection in canonical discriminant analysis. The proposed methods are illustrated with simulation studies and applications to two real examples.