Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 12 de 12
Filtrar
1.
Biostatistics ; 2024 Mar 08.
Artigo em Inglês | MEDLINE | ID: mdl-38459704

RESUMO

Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.

2.
Genome Biol ; 25(1): 28, 2024 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-38254214

RESUMO

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.


Assuntos
Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Locos de Características Quantitativas , Transcriptoma , Análise de Sequência de RNA
3.
Nat Commun ; 14(1): 6317, 2023 10 09.
Artigo em Inglês | MEDLINE | ID: mdl-37813843

RESUMO

Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.


Assuntos
Diabetes Mellitus Tipo 2 , Regulação da Expressão Gênica , Humanos , Alelos , Diferenciação Celular/genética , Simulação por Computador , Diabetes Mellitus Tipo 2/metabolismo , Análise de Célula Única/métodos , Análise de Sequência de RNA/métodos , Perfilação da Expressão Gênica/métodos
4.
Genet Epidemiol ; 46(2): 122-138, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-35043453

RESUMO

Physical inactivity (PA) is an important risk factor for a wide range of diseases. Previous genome-wide association studies (GWAS), based on self-reported data or a small number of phenotypes derived from accelerometry, have identified a limited number of genetic loci associated with habitual PA and provided evidence for involvement of central nervous system in mediating genetic effects. In this study, we derived 27 PA phenotypes from wrist accelerometry data obtained from 88,411 UK Biobank study participants. Single-variant association analysis based on mixed-effects models and transcriptome-wide association studies (TWAS) together identified 5 novel loci that were not detected by previous studies of PA, sleep duration and self-reported chronotype. For both novel and previously known loci, we discovered associations with novel phenotypes including active-to-sedentary transition probability, light-intensity PA, activity during different times of the day and proxy phenotypes to sleep and circadian patterns. Follow-up studies including TWAS, colocalization, tissue-specific heritability enrichment, gene-set enrichment and genetic correlation analyses indicated the role of the blood and immune system in modulating the genetic effects and a secondary role of the digestive and endocrine systems. Our findings provided important insights into the genetic architecture of PA and its underlying mechanisms.


Assuntos
Estudo de Associação Genômica Ampla , Modelos Genéticos , Acelerometria , Exercício Físico/fisiologia , Loci Gênicos , Predisposição Genética para Doença , Humanos
5.
PLoS Genet ; 18(1): e1009666, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-35061661

RESUMO

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.


Assuntos
Perfilação da Expressão Gênica/métodos , Células-Tronco Pluripotentes Induzidas/citologia , Miócitos Cardíacos/citologia , Análise de Célula Única/métodos , Técnicas de Cultura de Células , Diferenciação Celular , Linhagem Celular , Linhagem da Célula , Regulação da Expressão Gênica , Humanos , Células-Tronco Pluripotentes Induzidas/química , Miócitos Cardíacos/química , RNA-Seq
6.
Int J Epidemiol ; 50(4): 1335-1349, 2021 08 30.
Artigo em Inglês | MEDLINE | ID: mdl-33393617

RESUMO

BACKGROUND: Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. METHODS: We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). RESULTS: Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. CONCLUSION: The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.


Assuntos
Diabetes Mellitus Tipo 2 , Análise da Randomização Mendeliana , Biomarcadores , Causalidade , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/genética , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo Único
7.
Circulation ; 143(9): 895-906, 2021 03 02.
Artigo em Inglês | MEDLINE | ID: mdl-33249881

RESUMO

BACKGROUND: Recent clinical guidelines support intensive blood pressure treatment targets. However, observational data suggest that excessive diastolic blood pressure (DBP) lowering might increase the risk of myocardial infarction (MI), reflecting a J- or U-shaped relationship. METHODS: We analyzed 47 407 participants from 5 cohorts (median age, 60 years). First, to corroborate previous observational analyses, we used traditional statistical methods to test the shape of association between DBP and cardiovascular disease (CVD). Second, we created polygenic risk scores of DBP and systolic blood pressure and generated linear Mendelian randomization (MR) estimates for the effect of DBP on CVD. Third, using novel nonlinear MR approaches, we evaluated for nonlinearity in the genetic relationship between DBP and CVD events. Comprehensive MR interrogation of DBP required us to also model systolic blood pressure, given that the 2 are strongly correlated. RESULTS: Traditional observational analysis of our cohorts suggested a J-shaped association between DBP and MI. By contrast, linear MR analyses demonstrated an adverse effect of increasing DBP increments on CVD outcomes, including MI (MI hazard ratio, 1.07 per unit mm Hg increase in DBP; P<0.001). Furthermore, nonlinear MR analyses found no evidence for a J-shaped relationship; instead confirming that MI risk decreases consistently per unit decrease in DBP, even among individuals with low values of baseline DBP. CONCLUSIONS: In this analysis of the genetic effect of DBP, we found no evidence for a nonlinear J- or U-shaped relationship between DBP and adverse CVD outcomes; including MI.


Assuntos
Pressão Sanguínea/fisiologia , Doenças Cardiovasculares/patologia , Idoso , Doenças Cardiovasculares/genética , Bases de Dados Factuais , Feminino , Genótipo , Humanos , Masculino , Análise da Randomização Mendeliana , Pessoa de Meia-Idade , Razão de Chances , Modelos de Riscos Proporcionais , Fatores de Risco
8.
Nat Genet ; 52(6): 572-581, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32424353

RESUMO

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1-3. To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 × 10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate < 0.05). Five loci showed associations (P < 0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.


Assuntos
Neoplasias da Mama/genética , Estudo de Associação Genômica Ampla , Proteína BRCA1/genética , Neoplasias da Mama/patologia , Estudos de Casos e Controles , Feminino , Predisposição Genética para Doença , Humanos , Desequilíbrio de Ligação , Mutação , Neoplasias de Mama Triplo Negativas/genética , Neoplasias de Mama Triplo Negativas/patologia
9.
Kidney Int ; 98(3): 708-716, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32454124

RESUMO

Blood pressure and kidney function have a bidirectional relation. Hypertension has long been considered as a risk factor for kidney function decline. However, whether intensive blood pressure control could promote kidney health has been uncertain. The kidney is known to have a major role in affecting blood pressure through sodium extraction and regulating electrolyte balance. This bidirectional relation makes causal inference between these two traits difficult. Therefore, to examine the causal relations between these two traits, we performed two-sample Mendelian randomization analyses using summary statistics of large-scale genome-wide association studies. We selected genetic instruments more likely to be specific for kidney function using meta-analyses of complementary kidney function biomarkers (glomerular filtration rate estimated from serum creatinine [eGFRcr], and blood urea nitrogen from the CKDGen Consortium). Systolic and diastolic blood pressure summary statistics were from the International Consortium for Blood Pressure and UK Biobank. Significant evidence supported the causal effects of higher kidney function on lower blood pressure. Based on the mode-based Mendelian randomization method, the effect estimates for one standard deviation (SD) higher in log-transformed eGFRcr was -0.17 SD unit (95 % confidence interval: -0.09 to -0.24) in systolic blood pressure and -0.15 SD unit (95% confidence interval: -0.07 to -0.22) in diastolic blood pressure. In contrast, the causal effects of blood pressure on kidney function were not statistically significant. Thus, our results support causal effects of higher kidney function on lower blood pressure and suggest preventing kidney function decline can reduce the public health burden of hypertension.


Assuntos
Hipertensão , Análise da Randomização Mendeliana , Pressão Sanguínea , Estudo de Associação Genômica Ampla , Humanos , Hipertensão/epidemiologia , Hipertensão/genética , Rim
10.
Nat Commun ; 10(1): 1941, 2019 04 26.
Artigo em Inglês | MEDLINE | ID: mdl-31028273

RESUMO

Mendelian randomization (MR) has emerged as a major tool for the investigation of causal relationship among traits, utilizing results from large-scale genome-wide association studies. Bias due to horizontal pleiotropy, however, remains a major concern. We propose a novel approach for robust and efficient MR analysis using large number of genetic instruments, based on a novel spike-detection algorithm under a normal-mixture model for underlying effect-size distributions. Simulations show that the new method, MRMix, provides nearly unbiased or/and less biased estimates of causal effects compared to alternative methods and can achieve higher efficiency than comparably robust estimators. Application of MRMix to publicly available datasets leads to notable observations, including identification of causal effects of BMI and age-at-menarche on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI on the risk of major depressive disorder.


Assuntos
Algoritmos , Neoplasias da Mama/genética , Doença da Artéria Coronariana/genética , Transtorno Depressivo Maior/genética , Genoma Humano , Análise da Randomização Mendeliana/estatística & dados numéricos , Fatores Etários , Índice de Massa Corporal , Neoplasias da Mama/sangue , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/etiologia , HDL-Colesterol/sangue , Doença da Artéria Coronariana/sangue , Doença da Artéria Coronariana/diagnóstico , Doença da Artéria Coronariana/etiologia , Conjuntos de Dados como Assunto , Transtorno Depressivo Maior/sangue , Transtorno Depressivo Maior/diagnóstico , Transtorno Depressivo Maior/etiologia , Feminino , Estudo de Associação Genômica Ampla , Humanos , Menarca/sangue , Menarca/genética , Característica Quantitativa Herdável , Fatores de Risco , Triglicerídeos/sangue
11.
PLoS Genet ; 14(10): e1007549, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30289880

RESUMO

Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N = 188,577), psychiatric diseases (Ncase = 33,332, Ncontrol = 27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.


Assuntos
Estudos de Associação Genética/métodos , Pleiotropia Genética/genética , Estudo de Associação Genômica Ampla/métodos , Padrões de Herança/genética , Algoritmos , Predisposição Genética para Doença/genética , Humanos , Modelos Genéticos , Polimorfismo de Nucleotídeo Único
12.
Nat Genet ; 50(9): 1318-1326, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30104760

RESUMO

We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.


Assuntos
Predisposição Genética para Doença/genética , Genoma/genética , Estudo de Associação Genômica Ampla/métodos , Humanos , Funções Verossimilhança , Desequilíbrio de Ligação/genética , Transtornos Mentais/genética , Modelos Genéticos , Herança Multifatorial/genética , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Fatores de Risco
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...