Browsing by Author "Chakraborty, Arindom"

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    New aQTL SNPs for the CYP2D6 Identified by a Novel Mediation Analysis of Genome-Wide SNP Arrays, Gene Expression Arrays, and CYP2D6 Activity
    (Hindawi, 2013) Jiang, Guanglong; Chakraborty, Arindom; Wang, Zhiping; Boustani, Malaz; Liu, Yunlong; Skaar, Todd; Li, Lang; Medical and Molecular Genetics, School of Medicine
    Background: The genome-wide association studies (GWAS) have been successful during the last few years. A key challenge is that the interpretation of the results is not straightforward, especially for transacting SNPs. Integration of transcriptome data into GWAS may provide clues elucidating the mechanisms by which a genetic variant leads to a disease. Methods: Here, we developed a novel mediation analysis approach to identify new expression quantitative trait loci (eQTL) driving CYP2D6 activity by combining genotype, gene expression, and enzyme activity data. Results: 389,573 and 1,214,416 SNP-transcript-CYP2D6 activity trios are found strongly associated (P < 10⁻⁵, FDR = 16.6% and 11.7%) for two different genotype platforms, namely, Affymetrix and Illumina, respectively. The majority of eQTLs are trans-SNPs. A single polymorphism leads to widespread downstream changes in the expression of distant genes by affecting major regulators or transcription factors (TFs), which would be visible as an eQTL hotspot and can lead to large and consistent biological effects. Overlapped eQTL hotspots with the mediators lead to the discovery of 64 TFs. Conclusions: Our mediation analysis is a powerful approach in identifying the trans-QTL-phenotype associations. It improves our understanding of the functional genetic variations for the liver metabolism mechanisms.
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    Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis
    (Springer Nature, 2013) Chakraborty, Arindom; Jiang, Guanglong; Boustani, Malaz; Liu, Yunlong; Skaar, Todd; Li, Lang; Medical and Molecular Genetics, School of Medicine
    Background: Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with complex human diseases, clinical conditions and traits. Genetic mapping of expression quantitative trait loci (eQTLs) is providing us with novel functional effects of thousands of single nucleotide polymorphisms (SNPs). In a classical quantitative trail loci (QTL) mapping problem multiple tests are done to assess whether one trait is associated with a number of loci. In contrast to QTL studies, thousands of traits are measured alongwith thousands of gene expressions in an eQTL study. For such a study, a huge number of tests have to be performed (~10(6)). This extreme multiplicity gives rise to many computational and statistical problems. In this paper we have tried to address these issues using two closely related inferential approaches: an empirical Bayes method that bears the Bayesian flavor without having much a priori knowledge and the frequentist method of false discovery rates. A three-component t-mixture model has been used for the parametric empirical Bayes (PEB) method. Inferences have been obtained using Expectation/Conditional Maximization Either (ECME) algorithm. A simulation study has also been performed and has been compared with a nonparametric empirical Bayes (NPEB) alternative. Results: The results show that PEB has an edge over NPEB. The proposed methodology has been applied to human liver cohort (LHC) data. Our method enables to discover more significant SNPs with FDR<10% compared to the previous study done by Yang et al. (Genome Research, 2010). Conclusions: In contrast to previously available methods based on p-values, the empirical Bayes method uses local false discovery rate (lfdr) as the threshold. This method controls false positive rate.