RESUMEN
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.
Asunto(s)
Asma/genética , Estudio de Asociación del Genoma Completo , Hispánicos o Latinos/genética , Polimorfismo de Nucleótido Simple , Adolescente , Adulto , Asma/etnología , Niño , Cromosomas Humanos Par 17/genética , Femenino , Predisposición Genética a la Enfermedad , Humanos , Desequilibrio de Ligamiento , Modelos Logísticos , Masculino , Persona de Mediana Edad , Puerto Rico/epidemiología , Adulto JovenRESUMEN
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.