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1.
Nat Med ; 25(6): 911-919, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31160820

RESUMO

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.


Assuntos
Doenças Raras/genética , Ceramidase Ácida/genética , Estudos de Casos e Controles , Criança , Pré-Escolar , Estudos de Coortes , Feminino , Variação Genética , Humanos , Masculino , Modelos Genéticos , Mutação , Oxirredutases atuantes sobre Doadores de Grupo CH-CH/genética , Canais de Potássio/genética , RNA/sangue , RNA/genética , Processamento de RNA/genética , Doenças Raras/sangue , Análise de Sequência de RNA , Sequenciamento Completo do Exoma
2.
Am J Epidemiol ; 186(7): 771-777, 2017 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-28978191

RESUMO

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.


Assuntos
Doença/etiologia , Interação Gene-Ambiente , Estudo de Associação Genômica Ampla/métodos , Animais , Doença/genética , Genômica , Humanos , Modelos Animais , Análise de Sequência de RNA
3.
Nature ; 550(7675): 239-243, 2017 10 11.
Artigo em Inglês | MEDLINE | ID: mdl-29022581

RESUMO

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.


Assuntos
Perfilação da Expressão Gênica , Variação Genética/genética , Especificidade de Órgãos/genética , Teorema de Bayes , Feminino , Genoma Humano/genética , Genômica , Genótipo , Humanos , Masculino , Modelos Genéticos , Análise de Sequência de RNA
4.
Bioinformatics ; 33(24): 3895-3901, 2017 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-28961785

RESUMO

Motivation: Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies. Results: We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types. Availability and implementation: FIRE scores for genome-wide SNVs in hg19/GRCh37 are available for download at https://sites.google.com/site/fireregulatoryvariation/. Contact: nilah@stanford.edu. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Regulação da Expressão Gênica , Variação Genética , Software , Genômica , Humanos , Locos de Características Quantitativas
5.
Nat Methods ; 14(7): 699-702, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-28530654

RESUMO

Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on associations between environmental variables and allele-specific expression. Combining whole-blood RNA-seq with extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared with only four using standard GxE interaction testing. EAGLE provides new opportunities for researchers to identify GxE interactions using functional genomic data.


Assuntos
Alelos , Epigênese Genética , Regulação da Expressão Gênica , Variação Genética , Adulto , Estudos de Coortes , Feminino , Humanos , Masculino , Modelos Genéticos , Locos de Características Quantitativas
6.
Nat Genet ; 49(5): 700-707, 2017 May.
Artigo em Inglês | MEDLINE | ID: mdl-28394350

RESUMO

Genetic studies of complex traits have mainly identified associations with noncoding variants. To further determine the contribution of regulatory variation, we combined whole-genome and transcriptome data for 624 individuals from Sardinia to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 new QTLs. We identified high-frequency QTLs and found evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.


Assuntos
Perfilação da Expressão Gênica/métodos , Variação Genética , Estudo de Associação Genômica Ampla/métodos , Locos de Características Quantitativas/genética , Processamento Alternativo , Mapeamento Cromossômico , Saúde da Família , Feminino , Predisposição Genética para Doença/genética , Genética Populacional , Genótipo , Humanos , Itália , Masculino , Polimorfismo de Nucleotídeo Único , Sítio de Iniciação de Transcrição
7.
Nat Genet ; 49(5): 692-699, 2017 May.
Artigo em Inglês | MEDLINE | ID: mdl-28369037

RESUMO

Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.


Assuntos
Regulação da Expressão Gênica , Variação Genética , Genoma Humano/genética , Locos de Características Quantitativas/genética , Análise de Sequência de DNA/métodos , Algoritmos , Mapeamento Cromossômico , Estudo de Associação Genômica Ampla/métodos , Humanos , Mutação INDEL , Modelos Lineares , Polimorfismo de Nucleotídeo Único
8.
Genome Res ; 26(6): 768-77, 2016 06.
Artigo em Inglês | MEDLINE | ID: mdl-27197214

RESUMO

The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.


Assuntos
Cromossomos Humanos X/genética , Transcriptoma , Feminino , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Predisposição Genética para Doença , Genoma Humano , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Caracteres Sexuais
9.
Am J Hum Genet ; 98(1): 216-24, 2016 Jan 07.
Artigo em Inglês | MEDLINE | ID: mdl-26749306

RESUMO

Methods for multiple-testing correction in local expression quantitative trait locus (cis-eQTL) studies are a trade-off between statistical power and computational efficiency. Bonferroni correction, though computationally trivial, is overly conservative and fails to account for linkage disequilibrium between variants. Permutation-based methods are more powerful, though computationally far more intensive. We present an alternative correction method called eigenMT, which runs over 500 times faster than permutations and has adjusted p values that closely approximate empirical ones. To achieve this speed while also maintaining the accuracy of permutation-based methods, we estimate the effective number of independent variants tested for association with a particular gene, termed Meff, by using the eigenvalue decomposition of the genotype correlation matrix. We employ a regularized estimator of the correlation matrix to ensure Meff is robust and yields adjusted p values that closely approximate p values from permutations. Finally, using a common genotype matrix, we show that eigenMT can be applied with even greater efficiency to studies across tissues or conditions. Our method provides a simpler, more efficient approach to multiple-testing correction than existing methods and fits within existing pipelines for eQTL discovery.


Assuntos
Desequilíbrio de Ligação , Locos de Características Quantitativas , Humanos
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