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Understanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of the use of local ancestry on high-dimensional omics analyses, including, most prominently, expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored. Here, we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches. Applying our method to National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that the use of local ancestry can improve eQTL mapping in admixed and multiethnic populations, respectively. We estimate the trait variance explained by ancestry by using local admixture relatedness between individuals. By using simulations of diverse genetic architectures and degrees of confounding, we show improved accuracy in estimating heritability when accounting for local ancestry similarity. Furthermore, we characterize the sparse versus polygenic components of gene expression in admixed individuals. Our study has important methodological implications for genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.
Copyright © 2019 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
Enhancers and promoters both regulate gene expression by recruiting transcription factors (TFs); however, the degree to which enhancer promoter activity is due to differences in their sequences or to genomic context is the subject of ongoing debate. We examined this question by analyzing the sequences of thousands of transcribed enhancers and promoters from hundreds of cellular contexts previously identified by cap analysis of gene expression. Support vector machine classifiers trained on counts of all possible 6-bp-long sequences (6-mers) were able to accurately distinguish promoters from enhancers and distinguish their breadth of activity across tissues. Classifiers trained to predict enhancer activity also performed well when applied to promoter prediction tasks, but promoter-trained classifiers performed poorly on enhancers. This suggests that the learned sequence patterns predictive of enhancer activity generalize to promoters, but not vice versa. Our classifiers also indicate that there are functionally relevant differences in enhancer and promoter GC content beyond the influence of CpG islands. Furthermore, sequences characteristic of broad promoter or broad enhancer activity matched different TFs, with predicted ETS- and RFX-binding sites indicative of promoters, and AP-1 sites indicative of enhancers. Finally, we evaluated the ability of our models to distinguish enhancers and promoters defined by histone modifications. Separating these classes was substantially more difficult, and this difference may contribute to ongoing debates about the similarity of enhancers and promoters. In summary, our results suggest that high-confidence transcribed enhancers and promoters can largely be distinguished based on biologically relevant sequence properties.
Copyright © 2019 by the Genetics Society of America.
BACKGROUND - Recent GWAS studies have identified more than 300 SNPs associated with variation in blood pressure. We investigated whether a genetic risk score constructed from these variants is associated with burden of coronary heart disease.
METHODS - From 2010-2014, 4,809 individuals admitted to coronary angiography in Capital Region of Copenhagen were genotyped. We calculated hypertension GRS comprised of GWAS identified SNPs associated with blood pressure. We performed logistic regression analyses to estimate the risk of hypertension and prevalent CHD. We also assessed the severity of CHD associated with the GRS. The analyses were performed using GRS quartiles. We used the Inter99 cohort to validate our results and to investigate for possible pleiotropy for the GRS with other CHD risk factors.
RESULTS - In COGEN, adjusted odds ratios comparing the 2nd, 3rd and 4th cumulative GRS quartiles with the reference were 1.12(95% CI 0.95-1.33), 1.35(95% CI 1.14-1.59) and 1.29(95% CI 1.09-1.53) respectively, for prevalent CHD. The adjusted multinomial logistic regression showed that 3rd and 4th GRS quartiles were associated with increased odds of developing two(OR 1.33, 95% CI 1.04-1.71 and OR 1.36, 95% CI 1.06-1.75, respectively) and three coronary vessel disease(OR 1.77, 95% CI 1.36-2.30 and OR 1.65, 95% CI 1.26-2.15, respectively). Similar results for incident CHD were observed in the Inter99 cohort. The hypertension GRS did not associate with type 2 diabetes, smoking, BMI or hyperlipidemia.
CONCLUSION - Hypertension GRS quartiles were associated with an increased risk of hypertension, prevalent CHD, and burden of coronary vessel disease in a dose-response pattern. We showed no evidence for pleiotropy with other risk factors for CHD.
DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study ( = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of < 7.94 × 10. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely , and . We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. SIGNIFICANCE: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.
©2018 American Association for Cancer Research.
Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.
Copyright © 2018 the Author(s). Published by PNAS.
BACKGROUND - Accurately predicting the impact of rare nonsynonymous variants on disease risk is an important goal in precision medicine. Variants in the cardiac sodium channel (protein Na1.5; voltage-dependent cardiac Na+ channel) are associated with multiple arrhythmia disorders, including Brugada syndrome and long QT syndrome. Rare variants also occur in ≈1% of unaffected individuals. We hypothesized that in vitro electrophysiological functional parameters explain a statistically significant portion of the variability in disease penetrance.
METHODS - From a comprehensive literature review, we quantified the number of carriers presenting with and without disease for 1712 reported variants. For 356 variants, data were also available for 5 Na1.5 electrophysiological parameters: peak current, late/persistent current, steady-state V1/2 of activation and inactivation, and recovery from inactivation.
RESULTS - We found that peak and late current significantly associate with Brugada syndrome (<0.001; ρ=-0.44; Spearman rank test) and long QT syndrome disease penetrance (<0.001; ρ=0.37). Steady-state V1/2 activation and recovery from inactivation associate significantly with Brugada syndrome and long QT syndrome penetrance, respectively. Continuous estimates of disease penetrance align with the current American College of Medical Genetics classification paradigm.
CONCLUSIONS - Na1.5 in vitro electrophysiological parameters are correlated with Brugada syndrome and long QT syndrome disease risk. Our data emphasize the value of in vitro electrophysiological characterization and incorporating counts of affected and unaffected carriers to aid variant classification. This quantitative analysis of the electrophysiological literature should aid the interpretation of Na1.5 variant electrophysiological abnormalities and help improve Na1.5 variant classification.
© 2018 American Heart Association, Inc.
Although type 2 diabetes (T2D) results from metabolic defects in insulin secretion and insulin sensitivity, most of the genetic risk loci identified to date relates to insulin secretion. We reported that T2D loci influencing insulin sensitivity may be identified through interactions with insulin secretion loci, thereby leading to T2D. Here, we hypothesize that joint testing of variant main effects and interaction effects with an insulin secretion locus increases power to identify genetic interactions leading to T2D. We tested this hypothesis with an intronic MTNR1B SNP, rs10830963, which is associated with acute insulin response to glucose, a dynamic measure of insulin secretion. rs10830963 was tested for interaction and joint (main + interaction) effects with genome-wide data in African Americans (2,452 cases and 3,772 controls) from five cohorts. Genome-wide genotype data (Affymetrix Human Genome 6.0 array) was imputed to a 1000 Genomes Project reference panel. T2D risk was modeled using logistic regression with rs10830963 dosage, age, sex, and principal component as predictors. Joint effects were captured using the Kraft two degrees of freedom test. Genome-wide significant (P < 5 × 10 ) interaction with MTNR1B and joint effects were detected for CMIP intronic SNP rs17197883 (P = 1.43 × 10 ; P = 4.70 × 10 ). CMIP variants have been nominally associated with T2D, fasting glucose, and adiponectin in individuals of East Asian ancestry, with high-density lipoprotein, and with waist-to-hip ratio adjusted for body mass index in Europeans. These data support the hypothesis that additional genetic factors contributing to T2D risk, including insulin sensitivity loci, can be identified through interactions with insulin secretion loci.
© 2018 WILEY PERIODICALS, INC.
Genomic maps of local ancestry identify ancestry transitions - points on a chromosome where recent recombination events in admixed individuals have joined two different ancestral haplotypes. These events bring together alleles that evolved within separate continential populations, providing a unique opportunity to evaluate the joint effect of these alleles on health outcomes. In this work, we evaluate the impact of genetic variants in the context of nearby local ancestry transitions within a sample of nearly 10,000 adults of African ancestry with traits derived from electronic health records. Genetic data was located using the Metabochip, and used to derive local ancestry. We develop a model that captures the effect of both single variants and local ancestry, and use it to identify examples where local ancestry transitions significantly interact with nearby variants to influence metabolic traits. In our most compelling example, we find that the minor allele of rs16890640 occuring on a European background with a downstream local ancestry transition to African ancestry results in significantly lower mean corpuscular hemoglobin and volume. This finding represents a new way of discovering genetic interactions, and is supported by molecular data that suggest changes to local ancestry may impact local chromatin looping.
BACKGROUND - Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.
RESULTS - In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5×10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8×10-6).
CONCLUSION - We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.
© 2017 WILEY PERIODICALS, INC.
Population stratification (PS) is a primary consideration in studies of genetic determinants of human traits. Failure to control for PS may lead to confounding, causing a study to fail for lack of significant results, or resources to be wasted following false-positive signals. Here, historical and current approaches for addressing PS when performing genetic association studies in human populations are reviewed. Methods for detecting the presence of PS, including global and local ancestry methods, are described. Also described are approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. Many traits are being examined for the first time in minority populations, which may inherently feature PS. © 2017 by John Wiley & Sons, Inc.
Copyright © 2017 John Wiley and Sons, Inc.