RESUMO
Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.
Assuntos
Epistasia Genética , Estudo de Associação Genômica Ampla/métodos , Alelos , Inteligência Artificial , Estudo de Associação Genômica Ampla/estatística & dados numéricos , Humanos , Modelos Lineares , Modelos Genéticos , Epidemiologia Molecular , Penetrância , Análise de Componente Principal , Modelos de Riscos Proporcionais , Estatísticas não ParamétricasRESUMO
Type 2 diabetes is a common disorder associated with obesity. Lower plasma levels of adiponectin were associated with type 2 diabetes. Candidate regions on chromosomes 1 ( approximately 70 cM) and 14 ( approximately 30 cM) were evaluated for replication of suggestive linkage results for type 2 diabetes/impaired glucose homeostasis in an independent sample of Japanese Americans. Replication of independent linkage evidence for serum levels of adiponectin on chromosome 14 was also evaluated. We investigated 529 subjects from 175 sibships who were originally part of the Honolulu Heart Program. Analyses included nonparametric linkage and association using SAGE (Statistical Analysis for Genetic Epidemiology) and FBAT (family-based test of association) programs and Monte Carlo simulation of Fisher's exact test in SAS. For type 2 diabetes/impaired glucose metabolism, nominal linkage evidence (P < 0.02) followed-up by genotypic association (P = 0.016) was found with marker D14S297 at 31.8 cM; linkage analyses using only diabetes phenotype were also nominally significant at this marker (P < 0.02). Nominal evidence for genotypic association to adiponectin serum level phenotype (P = 0.04) was found with the marker D14S1032 at 23.2 cM. The present study was limited by relatively small sample size. Nevertheless, these results corroborate earlier studies, suggesting that further research is warranted in the candidate region approximately 30 cM on chromosome 14.
Assuntos
Adiponectina/sangue , Cromossomos Humanos Par 14 , Diabetes Mellitus Tipo 2/genética , Ligação Genética , Intolerância à Glucose/genética , Idoso de 80 Anos ou mais , Cromossomos Humanos Par 1 , Diabetes Mellitus Tipo 2/sangue , Marcadores Genéticos , Genótipo , Intolerância à Glucose/sangue , Havaí , Humanos , Japão/etnologia , Masculino , Repetições de Microssatélites , Método de Monte Carlo , FenótipoRESUMO
Knowledge of simulated genetic effects facilitates interpretation of methodological studies. Genetic interactions for common disorders are likely numerous and weak. Using the 200 replicates of the Genetic Analysis Workshop 16 (GAW16) Problem 3 simulated data, we compared the statistical power to detect weak gene-gene interactions using a haplotype-based test in the UNPHASED software with genotypic mixed model (GMM) and additive mixed model (AMM) mixed linear regression model in SAS. We assumed a candidate-gene approach where a single-nucleotide polymorphism (SNP) in one gene is fixed and multiple SNPs are at the second gene. We analyzed the quantitative low-density lipoprotein trait (heritability 0.7%), modulated by simulated interaction of rs4648068 from 4q24 and another gene on 8p22, where we analyzed seven SNPs. We generally observed low power calculated per SNP (