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1.
Science ; 366(6463): 351-356, 2019 10 18.
Artigo em Inglês | MEDLINE | ID: mdl-31601707

RESUMO

Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.

3.
Lancet Respir Med ; 7(6): 509-522, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31036433

RESUMO

BACKGROUND: Childhood-onset and adult-onset asthma differ with respect to severity and comorbidities. Whether they also differ with respect to genetic risk factors has not been previously investigated in large samples. The goals of this study were to identify shared and distinct genetic risk loci for childhood-onset and adult-onset asthma, and to identify the genes that might mediate the effects of associated variation. METHODS: We did genome-wide and transcriptome-wide studies, using data from the UK Biobank, in individuals with asthma, including adults with childhood-onset asthma (onset before 12 years of age), adults with adult-onset asthma (onset between 26 and 65 years of age), and adults without asthma (controls; aged older than 38 years). We did genome-wide association studies (GWAS) for childhood-onset asthma and adult-onset asthma each compared with shared controls, and for age of asthma onset in all asthma cases, with a genome-wide significance threshold of p<5 × 10-8. Enrichment studies determined the tissues in which genes at GWAS loci were most highly expressed, and PrediXcan, a transcriptome-wide gene-based test, was used to identify candidate risk genes. FINDINGS: Of 376 358 British white individuals from the UK Biobank, we included 37 846 with self-reports of doctor-diagnosed asthma: 9433 adults with childhood-onset asthma; 21 564 adults with adult-onset asthma; and an additional 6849 young adults with asthma with onset between 12 and 25 years of age. For the first and second GWAS analyses, 318 237 individuals older than 38 years without asthma were used as controls. We detected 61 independent asthma loci: 23 were childhood-onset specific, one was adult-onset specific, and 37 were shared. 19 loci were associated with age of asthma onset. The most significant asthma-associated locus was at 17q12 (odds ratio 1·406, 95% CI 1·365-1·448; p=1·45 × 10-111) in the childhood-onset GWAS. Genes at the childhood onset-specific loci were most highly expressed in skin, blood, and small intestine; genes at the adult onset-specific loci were most highly expressed in lung, blood, small intestine, and spleen. PrediXcan identified 113 unique candidate genes at 22 of the 61 GWAS loci. Single-nucleotide polymorphism-based heritability estimates were more than three times larger for childhood-onset asthma (0·327) than for adult-onset disease (0·098). The onset of disease in childhood was associated with additional genes with relatively large effect sizes, with the largest odds ratio observed at the FLG locus at 1q21.3 (1·970, 95% CI 1·823-2·129). INTERPRETATION: Genetic risk factors for adult-onset asthma are largely a subset of the genetic risk for childhood-onset asthma but with overall smaller effects, suggesting a greater role for non-genetic risk factors in adult-onset asthma. Combined with gene expression and tissue enrichment patterns, we suggest that the establishment of disease in children is driven more by dysregulated allergy and epithelial barrier function genes, whereas the cause of adult-onset asthma is more lung-centred and environmentally determined, but with immune-mediated mechanisms driving disease progression in both children and adults. FUNDING: US National Institutes of Health.

4.
Genet Epidemiol ; 43(6): 596-608, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-30950127

RESUMO

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

5.
Nat Genet ; 51(4): 592-599, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30926968

RESUMO

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn's disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.


Assuntos
Predisposição Genética para Doença/genética , Transcriptoma/genética , Doença de Crohn/genética , Variação Genética/genética , Estudo de Associação Genômica Ampla/métodos , Humanos , Lipoproteínas LDL/genética , Locos de Características Quantitativas/genética , Esquizofrenia/genética
6.
Nat Genet ; 51(4): 659-674, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30911161

RESUMO

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.


Assuntos
Encéfalo/fisiopatologia , Expressão Gênica/genética , Esquizofrenia/genética , Estudos de Casos e Controles , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla/métodos , Genótipo , Humanos , Polimorfismo de Nucleotídeo Único/genética , Locos de Características Quantitativas/genética , Risco , Transcriptoma/genética
7.
Hum Mol Genet ; 28(7): 1212-1224, 2019 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-30624610

RESUMO

Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.


Assuntos
Previsões/métodos , Metaboloma/genética , Metaboloma/fisiologia , Adulto , Idoso , Pressão Sanguínea , Índice de Massa Corporal , Mapeamento Cromossômico/métodos , Grupos Étnicos/genética , Grupo com Ancestrais do Continente Europeu/genética , Feminino , Estudos de Associação Genética/métodos , Estudo de Associação Genômica Ampla/métodos , Humanos , Masculino , Pessoa de Meia-Idade , Herança Multifatorial/genética , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Transcriptoma/genética
8.
PLoS Genet ; 15(1): e1007889, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30668570

RESUMO

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations. Here we propose an efficient statistical method (MultiXcan) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes. MultiXcan integrates evidence across multiple panels using multivariate regression, which naturally takes into account the correlation structure. We apply our method to simulated and real traits from the UK Biobank and show that, in realistic settings, we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed a summary result-based extension called S-MultiXcan, which we show yields highly concordant results with the individual level version when LD is well matched. Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension. Results from our analysis as well as software and necessary resources to apply our method are publicly available.


Assuntos
Estudo de Associação Genômica Ampla/estatística & dados numéricos , Locos de Características Quantitativas/genética , Transcriptoma/genética , Expressão Gênica/genética , Humanos , Polimorfismo de Nucleotídeo Único/genética , Software/estatística & dados numéricos
9.
Bioinformatics ; 2018 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-30395166

RESUMO

Summary: Large biobanks, such as UK Biobank with half a million participants, are changing the scale and availability of genotypic and phenotypic data for researchers to ask fundamental questions about the biology of health and disease. The breadth of the UK Biobank data is enabling discoveries at an unprecedented pace. However, this size and complexity pose new challenges to investigators who need to keep the accruing data up to date, comply with potential consent changes, and efficiently and reproducibly extract subsets of the data to answer specific scientific questions. Here we propose a tool called ukbREST designed for the UK Biobank study (easily extensible to other biobanks), which allows authorized users to efficiently retrieve phenotypic and genetic data. It exposes a REST API that makes data highly accessible inside a private and secure network, allowing the data specification in a human readable text format easily shareable with other researchers. These characteristics make ukbREST an important tool to make biobank's valuable data more readily accessible to the research community and facilitate reproducibility of the analysis, a key aspect of science. Availability: It is implemented in Python using the Flask-RESTful framework for the API, and it is under the MIT license. It works with PostgreSQL and a Docker image is available for easy deployment. The source code and documentation is available in Github: https://github.com/hakyimlab/ukbrest.

10.
PLoS Genet ; 14(8): e1007586, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-30096133

RESUMO

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 > 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop.

11.
Cancer Res ; 78(18): 5419-5430, 2018 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-30054336

RESUMO

Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P < 2.2 × 10-6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10-7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P < 1.47 × 10-3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis.Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419-30. ©2018 AACR.

12.
Nat Commun ; 9(1): 1825, 2018 05 08.
Artigo em Inglês | MEDLINE | ID: mdl-29739930

RESUMO

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

13.
Nat Genet ; 50(1): 151-158, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29229983

RESUMO

The excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable splicing events from short-read RNA-seq data and finds events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both to detect differential splicing between sample groups and to map splicing quantitative trait loci (sQTLs). Compared with contemporary methods, our approach identified 1.4-2.1 times more sQTLs, many of which helped us ascribe molecular effects to disease-associated variants. Transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at a 5% false discovery rate by an average of 2.1-fold compared with that detected through the use of gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available online.

14.
Hum Genet ; 136(11-12): 1497-1498, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28975356

RESUMO

The article "A multi-stage genome-wide association study of uterine fibroids in African Americans", written by Jacklyn N. Hellwege, was originally published Online First without open access. After publication in volume 136, issue 10, page 1363-1373 the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to

15.
PLoS Genet ; 13(9): e1006727, 2017 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-28957356

RESUMO

Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified TP53INP2 (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10-7, Bonferroni threshold = 4.33×10-6). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of TP53INP2 expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between TP53INP2 and ER-negative breast cancer was significant (p = 5.07x10-3). Expression of HP (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.


Assuntos
Neoplasias da Mama/genética , Receptor alfa de Estrogênio/genética , Haptoglobinas/genética , Proteínas Nucleares/genética , Neoplasias da Mama/patologia , Feminino , Regulação Neoplásica da Expressão Gênica , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo Único
16.
Hum Genet ; 136(10): 1363-1373, 2017 10.
Artigo em Inglês | MEDLINE | ID: mdl-28836065

RESUMO

Uterine fibroids are benign tumors of the uterus affecting up to 77% of women by menopause. They are the leading indication for hysterectomy, and account for $34 billion annually in the United States. Race/ethnicity and age are the strongest known risk factors. African American (AA) women have higher prevalence, earlier onset, and larger and more numerous fibroids than European American women. We conducted a multi-stage genome-wide association study (GWAS) of fibroid risk among AA women followed by in silico genetically predicted gene expression profiling of top hits. In Stage 1, cases and controls were confirmed by pelvic imaging, genotyped and imputed to 1000 Genomes. Stage 2 used self-reported fibroid and GWAS data from 23andMe, Inc. and the Black Women's Health Study. Associations with fibroid risk were modeled using logistic regression adjusted for principal components, followed by meta-analysis of results. We observed a significant association among 3399 AA cases and 4764 AA controls at rs739187 (risk-allele frequency = 0.27) in CYTH4 (OR (95% confidence interval) = 1.23 (1.16-1.30), p value = 7.82 × 10-9). Evaluation of the genetic association results with MetaXcan identified lower predicted gene expression of CYTH4 in thyroid tissue as significantly associated with fibroid risk (p value = 5.86 × 10-8). In this first multi-stage GWAS for fibroids among AA women, we identified a novel risk locus for fibroids within CYTH4 that impacts gene expression in thyroid and has potential biological relevance for fibroids.


Assuntos
Afro-Americanos/genética , Moléculas de Adesão Celular , Regulação Neoplásica da Expressão Gênica , Frequência do Gene , Fatores de Troca do Nucleotídeo Guanina , Leiomioma , Proteínas de Neoplasias , Neoplasias Uterinas , Adulto , Alelos , Moléculas de Adesão Celular/biossíntese , Moléculas de Adesão Celular/genética , Feminino , Loci Gênicos , Estudo de Associação Genômica Ampla , Fatores de Troca do Nucleotídeo Guanina/biossíntese , Fatores de Troca do Nucleotídeo Guanina/genética , Humanos , Leiomioma/genética , Leiomioma/metabolismo , Pessoa de Meia-Idade , Proteínas de Neoplasias/biossíntese , Proteínas de Neoplasias/genética , Fatores de Risco , Neoplasias Uterinas/genética , Neoplasias Uterinas/metabolismo
17.
Diabetes ; 66(7): 2019-2032, 2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-28341696

RESUMO

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.


Assuntos
Diabetes Mellitus Tipo 2/genética , Grupo com Ancestrais do Continente Europeu/genética , Jejum/metabolismo , Resistência à Insulina/genética , Insulina/metabolismo , Proteínas Proto-Oncogênicas c-akt/genética , Afro-Americanos/genética , Alelos , Grupo com Ancestrais do Continente Asiático/genética , Estudos de Casos e Controles , Diabetes Mellitus Tipo 2/metabolismo , Finlândia , Frequência do Gene , Predisposição Genética para Doença , Genótipo , Hispano-Americanos/genética , Humanos , Razão de Chances
18.
PLoS Genet ; 12(11): e1006423, 2016 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-27835642

RESUMO

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).


Assuntos
Regulação da Expressão Gênica/genética , Modelos Genéticos , Especificidade de Órgãos/genética , Característica Quantitativa Herdável , Teorema de Bayes , Genótipo , Humanos , Herança Multifatorial/genética , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Tamanho da Amostra
19.
Am J Hum Genet ; 98(4): 697-708, 2016 Apr 07.
Artigo em Inglês | MEDLINE | ID: mdl-27040689

RESUMO

Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.


Assuntos
Regulação da Expressão Gênica , Genótipo , Locos de Características Quantitativas , Transcriptoma , Humanos , Fenótipo , Projetos Piloto , Reprodutibilidade dos Testes
20.
Cancer Biol Ther ; 17(1): 91-103, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26574927

RESUMO

We previously investigated MET and its oncogenic mutants relevant to lung cancer in C. elegans. The inactive orthlogues of the receptor tyrosine kinase Eph and MET, namely vab-1 and RB2088 respectively, the temperature sensitive constitutively active form of KRAS, SD551 (let-60; GA89) and the inactive c-CBL equivalent mutants in sli-1 (PS2728, PS1258, and MT13032) when subjected to chronic exposure of nicotine resulted in a significant loss in egg-laying capacity and fertility. While the vab-1 mutant revealed increased circular motion in response to nicotine, the other mutant strains failed to show any effect. Overall locomotion speed increased with increasing nicotine concentration in all tested mutant strains except in the vab-1 mutants. Moreover, chronic nicotine exposure, in general, upregulated kinases and phosphatases. Taken together, these studies provide evidence in support of C. elegans as initial in vivo model to study nicotine and its effects on oncogenic mutations identified in humans.


Assuntos
Proteínas de Caenorhabditis elegans/genética , Caenorhabditis elegans/genética , Proteínas de Ciclo Celular/genética , Neoplasias/genética , Nicotina/toxicidade , Receptores Proteína Tirosina Quinases/genética , Sequência de Aminoácidos/genética , Animais , Caenorhabditis elegans/efeitos dos fármacos , Proteínas de Caenorhabditis elegans/biossíntese , Proteínas de Ciclo Celular/biossíntese , Fertilidade/genética , Humanos , Locomoção/efeitos dos fármacos , Locomoção/genética , Mutação , Neoplasias/induzido quimicamente , Neoplasias/patologia , Proteínas Proto-Oncogênicas c-met/biossíntese , Proteínas Proto-Oncogênicas c-met/genética , Proteínas ras/biossíntese
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