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Mixture modelling as an exploratory framework for genotype-trait associations.

ABSTRACT: We propose a mixture modelling framework for both identifying and exploring the nature of genotype-trait associations. This framework extends the classical mixed effects modelling approach for this setting by incorporating a Gaussian mixture distribution for random genotype effects. The primary advantages of this paradigm over existing approaches include that the mixture modelling framework addresses the degrees-of-freedom challenge that is inherent in application of the usual fixed effects analysis of covariance, relaxes the restrictive single normal distribution assumption of the classical mixed effects models and offers an exploratory framework for discovery of underlying structure across multiple genetic loci. An application to data arising from a study of antiretroviral-associated dyslipidaemia in human immunodeficiency virus infection is presented. Extensive simulations studies are also implemented to investigate the performance of this approach.

SUBMITTER: Au K

PROVIDER: S-EPMC3285383 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Mixture modelling as an exploratory framework for genotype-trait associations.

Au Kinman K Lin Rongheng R Foulkes Andrea S AS

Journal of the Royal Statistical Society. Series C, Applied statistics 20110501 3

We propose a mixture modelling framework for both identifying and exploring the nature of genotype-trait associations. This framework extends the classical mixed effects modelling approach for this setting by incorporating a Gaussian mixture distribution for random genotype effects. The primary advantages of this paradigm over existing approaches include that the mixture modelling framework addresses the degrees-of-freedom challenge that is inherent in application of the usual fixed effects anal ...[more]

PMID: 22368304

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Project description:Background & aimsGenome-wide association studies have greatly increased our understanding of intestinal disease. However, little is known about how genetic variations result in phenotypic changes. Some polymorphisms have been shown to modulate quantifiable phenotypic traits; these are called quantitative trait loci. Quantitative trait loci that affect levels of gene expression are called expression quantitative trait loci (eQTL), which can provide insight into the biological relevance of data from genome-wide association studies. We performed a comprehensive eQTL scan of intestinal tissue.MethodsTotal RNA was extracted from ileal biopsy specimens and genomic DNA was obtained from whole-blood samples from the same cohort of individuals. Cis- and trans-eQTL analyses were performed using a custom software pipeline for samples from 173 subjects. The analyses determined the expression levels of 19,047 unique autosomal genes listed in the US National Center for Biotechnology Information database and more than 580,000 variants from the Single Nucleotide Polymorphism database.ResultsThe presence of more than 15,000 cis- and trans-eQTL was detected with statistical significance. eQTL associated with the same expression trait were in high linkage disequilibrium. Comparative analysis with previous eQTL studies showed that 30% to 40% of genes identified as eQTL in monocytes, liver tissue, lymphoblastoid cell lines, T cells, and fibroblasts are also eQTL in ileal tissue. Conversely, most of the significant eQTL have not been previously identified and could be tissue specific. These are involved in many cell functions, including division and antigen processing and presentation. Our analysis confirmed that previously published cis-eQTL are single nucleotide polymorphisms associated with inflammatory bowel disease: rs2298428/UBE2L3, rs1050152/SLC22A4, and SLC22A5. We identified many new associations between inflammatory bowel disease susceptibility loci and gene expression.ConclusionseQTL analysis of intestinal tissue supports findings that some eQTL remain stable across cell types, whereas others are specific to the sampled location. Our findings confirm and expand the number of known genotypes associated with expression and could help elucidate mechanisms of intestinal disease.

Project description:Cropping system diversification through annual intercropping provides a pathway for agricultural production with reduced inputs of fertilizer and pesticides. While several studies have shown that intercrop performance depends on the genotypes used, the available evidence has not been synthesized in an overarching analysis. Here, we review the effects of genotypes in cereal/legume intercropping systems, showing how genotype choice affects mixture performance. Furthermore, we discuss the mechanisms underlying the interactions between genotype and cropping system (i.e., sole cropping vs. intercropping). Data from 69 articles fulfilling inclusion criteria were analyzed, out of which 35 articles reported land equivalent ratio (LER), yielding 262 LER data points to be extracted. The mean and median LER were 1.26 and 1.24, respectively. The extracted genotype × cropping system interaction effects on yield were reported in 71% out of 69 publications. Out of this, genotype × cropping system interaction effects were significant in 75%, of the studies, whereas 25% reported non-significant interactions. The remaining studies did not report the effects of genotype × cropping system. Phenological and morphological traits, such as differences in days to maturity, plant height, or growth habit, explained variations in the performance of mixtures with different genotypes. However, the relevant genotype traits were not described sufficiently in most of the studies to allow for a detailed analysis. A tendency toward higher intercropping performance with short cereal genotypes was observed. The results show the importance of genotype selection for better in cereal/legume intercropping. This study highlights the hitherto unrevealed aspects of genotype evaluation for intercropping systems that need to be tackled. Future research on genotype effects in intercropping should consider phenology, root growth, and soil nutrient and water acquisition timing, as well as the effects of weeds and diseases, to improve our understanding of how genotype combination and breeding may help to optimize intercropping systems.

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Mixture modelling as an exploratory framework for genotype-trait associations.

Publications

Mixture modelling as an exploratory framework for genotype-trait associations.

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