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Browsing by Author "Langefeld, Carl D."

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    Allele-specific methylation in the FADS genomic region in DNA from human saliva, CD4+ cells, and total leukocytes
    Rahbar, Elaheh; Waits, Charlotte M. K.; Kirby, Edward H.; Miller, Leslie R.; Ainsworth, Hannah C.; Cui, Tao; Sergeant, Susan; Howard, Timothy D.; Langefeld, Carl D.; Chilton, Floyd H. (2018-04-06)
    Background Genetic variants within the fatty acid desaturase (FADS) gene cluster (human Chr11) are important regulators of long-chain (LC) polyunsaturated fatty acid (PUFA) biosynthesis in the liver and consequently have been associated with circulating LC-PUFA levels. More recently, epigenetic modifications such as DNA methylation, particularly within the FADS cluster, have been shown to affect LC-PUFA levels. Our lab previously demonstrated strong associations of allele-specific methylation (ASM) between a single nucleotide polymorphism (SNP) rs174537 and CpG sites across the FADS region in human liver tissues. Given that epigenetic signatures are tissue-specific, we aimed to evaluate the methylation status and ASM associations between rs174537 and DNA methylation obtained from human saliva, CD4+ cells and total leukocytes derived from whole blood. The goals were to (1) determine if DNA methylation from these peripheral samples would display similar ASM trends as previously observed in human liver tissues and (2) evaluate the associations between DNA methylation and circulating LC-PUFAs. Results DNA methylation at six CpG sites spanning FADS1 and FADS2 promoter regions and a putative FADS enhancer region were determined in two Caucasian cohorts of healthy volunteers: leukocytes in cohort 1 (n = 89, median age = 43, 35% male) and saliva and CD4+ cells in cohort 2 (n = 32, median age = 41, 41% male). Significant ASM between rs174537 and DNA methylation at three CpG sites located in the FADS2 promoter region (i.e., chr11:61594865, chr11:61594876, chr11:61594907) and one CpG site in the putative enhancer region (chr11:61587979) were observed with leukocytes. In CD4+ cells, significant ASM was observed at CpG sites chr11:61594876 and chr11:61584894. Genotype at rs174537 was significantly associated with DNA methylation from leukocytes. Similar trends were observed with CD4+ cells, but not with saliva. DNA methylation from leukocytes and CD4+ cells also significantly correlated with circulating omega-6 LC-PUFAs. Conclusions We observed significant ASM between rs174537 and DNA methylation at key regulatory regions in the FADS region from leukocyte and CD4+ cells. DNA methylation from leukocytes also correlated with circulating omega-6 LC-PUFAs. These results support the use of peripheral whole blood samples, with leukocytes showing the most promise for future nutrigenomic studies evaluating epigenetic modifications affecting LC-PUFA biosynthesis in humans.
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    Comparative analysis of methods for detecting interacting loci
    Chen, Li; Yu, Guoqiang; Langefeld, Carl D.; Miller, David J.; Guy, Richard T.; Raghuram, Jayaram; Yuan, Xiguo; Herrington, David M.; Wang, Yue (Biomed Central, 2011-07-05)
    Background: Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. Results: We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs. Conclusion: This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulationtool-bmc-ms9169818735220977/downloads/list.