RESUMEN
Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n ~408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability.
Asunto(s)
Epistasis Genética , Interacción Gen-Ambiente , Sitios Genéticos/fisiología , Estudio de Asociación del Genoma Completo/métodos , Sitios de Carácter Cuantitativo , Bancos de Muestras Biológicas , Composición Corporal/genética , Índice de Masa Corporal , Pesos y Medidas Corporales/estadística & datos numéricos , Epistasis Genética/fisiología , Femenino , Variación Genética , Humanos , Masculino , Modelos Genéticos , Variaciones Dependientes del Observador , Fenotipo , Polimorfismo de Nucleótido Simple , Programas Informáticos , Reino UnidoRESUMEN
Genetic studies have shown that obesity risk is heritable and that, of the many common variants now associated with body mass index, those in an intron of the fat mass and obesity-associated (FTO) gene have the largest effect. The size of the UK Biobank, and its joint measurement of genetic, anthropometric and lifestyle variables, offers an unprecedented opportunity to assess gene-by-environment interactions in a way that accounts for the dependence between different factors. We jointly examine the evidence for interactions between FTO (rs1421085) and various lifestyle and environmental factors. We report interactions between the FTO variant and each of: frequency of alcohol consumption (P=3.0 × 10(-4)); deviations from mean sleep duration (P=8.0 × 10(-4)); overall diet (P=5.0 × 10(-6)), including added salt (P=1.2 × 10(-3)); and physical activity (P=3.1 × 10(-4)).
Asunto(s)
Dioxigenasa FTO Dependiente de Alfa-Cetoglutarato/metabolismo , Índice de Masa Corporal , Variación Genética , Obesidad/genética , Consumo de Bebidas Alcohólicas , Dioxigenasa FTO Dependiente de Alfa-Cetoglutarato/genética , Bases de Datos Factuales , Encuestas sobre Dietas , Epigénesis Genética , Ejercicio Físico , Femenino , Genotipo , Humanos , Estilo de Vida , Masculino , Sueño , Reino UnidoRESUMEN
This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This article presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three biological sequence families which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution over sequence representations induced by the prior. By integrating out the posterior, our method compares favorably to leading binding predictors.