RESUMO
Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.
Assuntos
Benchmarking , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Análise da Randomização Mendeliana/métodos , Humanos , Estudo de Associação Genômica Ampla/métodos , Fenótipo , Variação Genética , Causalidade , Polimorfismo de Nucleotídeo Único , Modelos GenéticosRESUMO
Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.
Assuntos
Estudo de Associação Genômica Ampla , Polimorfismo de Nucleotídeo Único , População Negra/genética , Predisposição Genética para Doença , Estruturas Genéticas , Estudo de Associação Genômica Ampla/métodos , Humanos , Herança Multifatorial/genética , Polimorfismo de Nucleotídeo Único/genéticaRESUMO
The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3%-198.0% accuracy gain for height and 19.5%-313.3% accuracy gain for body mass index (BMI) in terms of predictive R2 compared to existing PRS approaches. We also show that XPA and XPASS can achieve substantial improvement for construction of height PRSs in the African population, suggesting the generality of our framework across global populations.
Assuntos
Estatura/genética , Índice de Massa Corporal , Simulação por Computador , Modelos Genéticos , Herança Multifatorial/genética , África/etnologia , Povo Asiático/genética , População Negra/genética , China/etnologia , Bases de Dados Factuais , Feminino , Estudo de Associação Genômica Ampla , Humanos , Masculino , Análise de Componente Principal , Tamanho da Amostra , Reino UnidoRESUMO
MOTIVATION: The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet. RESULTS: To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/YangLabHKUST/PALM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Estudo de Associação Genômica Ampla , Software , Humanos , Fenótipo , Estudo de Associação Genômica Ampla/métodos , Algoritmos , Simulação por Computador , Polimorfismo de Nucleotídeo ÚnicoRESUMO
MOTIVATION: As increasing sample sizes from genome-wide association studies (GWASs), polygenic risk scores (PRSs) have shown great potential in personalized medicine with disease risk prediction, prevention and treatment. However, the PRS constructed using European samples becomes less accurate when it is applied to individuals from non-European populations. It is an urgent task to improve the accuracy of PRSs in under-represented populations, such as African populations and East Asian populations. RESULTS: In this article, we propose a cross-population and cross-phenotype (XPXP) method for construction of PRSs in under-represented populations. XPXP can construct accurate PRSs by leveraging biobank-scale datasets in European populations and multiple GWASs of genetically correlated phenotypes. XPXP also allows to incorporate population-specific and phenotype-specific effects, and thus further improves the accuracy of PRS. Through comprehensive simulation studies and real data analysis, we demonstrated that our XPXP outperformed existing PRS approaches. We showed that the height PRSs constructed by XPXP achieved 9% and 18% improvement over the runner-up method in terms of predicted R2 in East Asian and African populations, respectively. We also showed that XPXP substantially improved the stratification ability in identifying individuals at high genetic risk of type 2 diabetes. AVAILABILITY AND IMPLEMENTATION: The XPXP software and all analysis code are available at github.com/YangLabHKUST/XPXP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Diabetes Mellitus Tipo 2 , Humanos , Estudo de Associação Genômica Ampla , Polimorfismo de Nucleotídeo Único , Fenótipo , Herança MultifatorialRESUMO
OBJECTIVES: The Jing people are a recognized ethnic group in Guangxi, southwest China, who are the immigrants from Vietnam during the 16th century. They speak Vietnamese but with lots of language borrowings from Cantonese, Zhuang, and Mandarin. However, it's unclear if there is large-scale gene flow from surrounding populations into Jing people during their language change due to the very limited genetic information of this population. MATERIALS AND METHODS: We collected blood samples from 37 Jing and 3 Han Chinese individuals from Wanwei, Shanxin, and Wutou islands in Guangxi and genotyped about 600,000 genome-wide single nucleotide polymorphisms (SNPs). We used Principal Component Analysis (PCA), ADMIXTURE analysis, f statistics, qpWave and qpAdm to infer the population genetic structure and admixture. RESULTS: Our data revealed that the Jing people are genetically similar to the populations in southwest China and mainland Southeast Asia. But compared with Vietnamese, they show significant evidence of gene flow from surrounding East Asians. The admixture proportion is estimated to be around 35-42% in different Jing groups using southern Han Chinese as a proxy. The majority of the paternal lineages of Jing people are most likely from surrounding East Asians. DISCUSSION: We conclude that the formation and language change of present-day Jing people have involved genetic assimilation of surrounding East Asian populations. The language borrowing, in this case, is not only a cultural phenomenon but has involved demic diffusion.
Assuntos
Povo Asiático/genética , Fluxo Gênico/genética , Idioma , Antropologia Física , China/etnologia , Feminino , Genética Populacional , Humanos , Masculino , Análise de Componente Principal , Vietnã/etnologiaRESUMO
Intracerebral hemorrhage (ICH) is a severe stroke subtype with limited therapeutic options. Programmed cell death (PCD) is crucial for immunological balance, and includes necroptosis, pyroptosis, apoptosis, ferroptosis, and necrosis. However, the distinctions between these programmed cell death modalities after ICH remain to be further investigated. We used single-cell transcriptome (single-cell RNA sequencing) and spatial transcriptome (spatial RNA sequencing) techniques to investigate PCD-related gene expression trends in the rat brain following hemorrhagic stroke. Ferroptosis was the main PCD process after ICH, and primarily affected mature oligodendrocytes. Its onset occurred as early as 1 hour post-ICH, peaking at 24 hours post-ICH. Additionally, ferroptosis-related genes were distributed in the hippocampus and choroid plexus. We also elucidated a specific interaction between lipocalin-2 (LCN2)-positive microglia and oligodendrocytes that was mediated by the colony stimulating factor 1 (CSF1)/CSF1 receptor pathway, leading to ferroptosis induction in oligodendrocytes and subsequent neurological deficits. In conclusion, our study highlights ferroptosis as the primary PCD mechanism, emerging as early as 1 hour post-ICH. Early therapeutic intervention via the suppression of microglial LCN2 expression may alleviate ferroptosis-induced damage in oligodendrocytes and associated neurological deficits, thus offering a promising neuroprotective strategy following ICH.
Assuntos
Ferroptose , Oligodendroglia , Transcriptoma , Animais , Ferroptose/genética , Oligodendroglia/metabolismo , Ratos , Masculino , Substância Branca/metabolismo , Substância Branca/patologia , Ratos Sprague-Dawley , Acidente Vascular Cerebral/metabolismo , Acidente Vascular Cerebral/genética , Hemorragia Cerebral/metabolismo , Hemorragia Cerebral/patologia , Hemorragia Cerebral/genética , Apoptose/genética , Análise de Célula ÚnicaRESUMO
OBJECTIVE: To investigate the feasibility and accuracy of predicting locoregional recurrence (LR) in elderly patients with esophageal squamous cell cancer (ESCC) who underwent radical radiotherapy using a pairwise machine learning algorithm. METHODS: The 130 datasets enrolled were randomly divided into a training set and a testing set in a 7:3 ratio. Clinical factors were included and radiomics features were extracted from pretreatment CT scans using pyradiomics-based software, and a pairwise naive Bayes (NB) model was developed. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). To facilitate practical application, we attempted to construct an automated esophageal cancer diagnosis system based on trained models. RESULTS: To the follow-up date, 64 patients (49.23%) had experienced LR. Ten radiomics features and two clinical factors were selected for modeling. The model demonstrated good prediction performance, with area under the ROC curve of 0.903 (0.829-0.958) for the training cohort and 0.944 (0.849-1.000) for the testing cohort. The corresponding accuracies were 0.852 and 0.914, respectively. Calibration curves showed good agreement, and DCA curve confirmed the clinical validity of the model. The model accurately predicted LR in elderly patients, with a positive predictive value of 85.71% for the testing cohort. CONCLUSIONS: The pairwise NB model, based on pre-treatment enhanced chest CT-based radiomics and clinical factors, can accurately predict LR in elderly patients with ESCC. The esophageal cancer automated diagnostic system embedded with the pairwise NB model holds significant potential for application in clinical practice.
RESUMO
Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.
Assuntos
Estudo de Associação Genômica Ampla , Locos de Características Quantitativas , Desequilíbrio de Ligação , Herança Multifatorial/genética , Variação Genética , Polimorfismo de Nucleotídeo ÚnicoRESUMO
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.