A meta-analysis of genome-wide association studies of asthma in Puerto Ricans.

Puerto Ricans are disproportionately affected with asthma in the USA. In this study, we aim to identify genetic variants that confer susceptibility to asthma in Puerto Ricans.We conducted a meta-analysis of genome-wide association studies (GWAS) of asthma in Puerto Ricans, including participants from: the Genetics of Asthma in Latino Americans (GALA) I-II, the Hartford-Puerto Rico Study and the Hispanic Community Health Study. Moreover, we examined whether susceptibility loci identified in previous meta-analyses of GWAS are associated with asthma in Puerto Ricans.The only locus to achieve genome-wide significance was chromosome 17q21, as evidenced by our top single nucleotide polymorphism (SNP), rs907092 (OR 0.71, p=1.2×10-12) at IKZF3 Similar to results in non-Puerto Ricans, SNPs in genes in the same linkage disequilibrium block as IKZF3 (e.g. ZPBP2, ORMDL3 and GSDMB) were significantly associated with asthma in Puerto Ricans. With regard to results from a meta-analysis in Europeans, we replicated findings for rs2305480 at GSDMB, but not for SNPs in any other genes. On the other hand, we replicated results from a meta-analysis of North American populations for SNPs at IL1RL1, TSLP and GSDMB but not for IL33Our findings suggest that common variants on chromosome 17q21 have the greatest effects on asthma in Puerto Ricans.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models.

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs.

Replication of a genome-wide association study of birth weight in preterm neonates.

OBJECTIVE: To examine associations between rs9883204 in ADCY5 and rs900400 near LEKR1 and CCNL1 with birth weight in a preterm population. Both markers were associated with birth weight in a term population in a recent genome-wide association study of Freathy et al. STUDY DESIGN: A meta-analysis of mother and infant samples was performed for associations of rs900400 and rs9883204 with birth weight in 393 families from the US, 265 families from Argentina, and 735 mother-infant pairs from Denmark. Z-scores adjusted for infant sex and gestational age were generated for each population separately and regressed on allele counts. Association evidence was combined across sites by inverse-variance weighted meta-analysis. RESULTS: Each additional C allele of rs900400 (LEKR1/CCNL1) in infants was marginally associated with a 0.069 SD lower birth weight (95% CI, -0.159 to 0.022; P = .068). This result was slightly more pronounced after adjusting for smoking (P = .036). No significant associations were identified with rs9883204 or in maternal samples. CONCLUSIONS: These results indicate the potential importance of this marker on birth weight regardless of gestational age.