7 results on '"Statistical genomics"'
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2. Editorial: Integration of computational genomics into clinical pharmacogenomic tests: how bioinformatics may help primary care in precision medicine area.
- Author
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Tafazoli, Alireza, Abbaszadegan, Mohammad Reza, and Patrinos, George P.
- Subjects
INDIVIDUALIZED medicine ,PHARMACOGENOMICS ,GENOMICS ,PRIMARY care ,BIOINFORMATICS - Published
- 2023
- Full Text
- View/download PDF
3. Statistical genetics and polygenic risk score for precision medicine
- Author
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Konuma, Takahiro and Okada, Yukinori
- Published
- 2021
- Full Text
- View/download PDF
4. Méthodes pour l'inférence post-clustering appliquées à l'expression génique
- Author
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Hivert, Benjamin, Agniel, Denis, Thiébaut, Rodolphe, Hejblum, Boris P., Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Vaccine Research Institute (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Rand Corporation, CHU Bordeaux [Bordeaux], and Hivert, Benjamin
- Subjects
inférence sélective ,analyse circulaire ,géno- mique statistique ,selective inference ,statistical genomics ,double-dipping ,données de grande dimension ,high- dimensional data ,[MATH] Mathematics [math] ,[MATH]Mathematics [math] ,Classification non supervisée ,Clustering - Abstract
The analysis of RNA-seq gene expression data is often organised aroundtwo successive steps : i) clustering using all of the genes to group the observation units (pa-tients, cells, etc.) into separate and homogeneous subgroups ; then ii) differential analysisof individual genes using hypothesis testing to identify which genes, i.e. which variables,are differentially expressed between the subgroups. However, several subgroups construc-ted in i) can actually contain only units coming from the same homogeneous population :clustering will then artificially create differences between those spurious subgroups, lea-ding to false positives in ii). We propose two inference methods to take into account theinitial clustering step for differential analysis and thus guarantee an effective control of thetype I error. This first method is based on the concept of selective inference while the se-cond one use unimodality and multimodality to describe the separation between clusters.We evaluate the performance of both approaches in extensive numerical simulations aswell as in an application to a real, low dimensional dataset. Both proposed methods leadto valid p-values under their null hypothesis of no difference between subgroups in expres-sion at a selected gene independently of the clustering, while maintaining good statisticalpower. In high dimension, this type I error inflation can be overcome by the dilution of theclustering information, provided that the variables are independent. Yet, in the presenceof correlation (as for gene expression), spurious clusters appear, even though they are notseparable. An adaptation of the above methods to this high dimensional context is thusnecessary., L’analyse des données d’expression génique est souvent organisée autour de deux étapes successives : i) une classification non supervisée utilisant l’ensemble des gènes pour regrouper les unités d’observations (patients, échantillons ou cellules) en sous-groupes distincts et homogènes ; puis ii) l’analyse différentielle se faisant à l’aide de tests d’hypothèse visant à identifier quels gènes, c’est-à-dire quelles variables, sont différentiellement exprimés entre ces sous-groupes. Cependant, cette approche utilisant les même données lors des deux étapes ne permet pas de garantir un bon contrôle de l’erreur de type I à l’étape ii). Nous proposons deux méthodes d’inférence pour tenir compte de l’étape initiale de classification non supervisée lors de l’analyse différentielle et ainsi garantir un contrôle effectif de l’erreur de type I. La première méthode se base sur le concept d’inférence sélective tandis que la seconde repose sur une définition de la séparation de classes faisant uniquement intervenir les concepts d’unimodalité et de multimodalité. Nous avons évalué les performances des deux méthodes grâces à différentes simulations numériques, ainsi que dans une application sur un jeu de données réelles de faible dimension. Les méthodes proposées conduisent à des p-valeurs valides sous l’hypothèse nulle d’absence de différence entre les sous-groupes dans l’expression d’un gène sélectionné, indépendamment de la classification, tout en conservant une bonne puissance statistique. En grande dimension, cette inflation de l’erreur de type I peut-être contre-balancée par la dilution du signal utilisé pour la classification, à condition que les variables soient indépendantes. En revanche, en présence de corrélation (comme c’est le cas en pratique pour l’expression génique), des classes artificielles apparaissent alors que celles-ci ne sont pas séparables.Une adaptation des méthodes à ce contexte de grande dimension est donc nécessaire.
- Published
- 2022
5. Pharmacogenomic and Statistical Analysis.
- Author
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Bai H, Zhang X, and Bush WS
- Subjects
- Pharmacogenomic Testing, Phenotype, Pharmacogenetics methods, Research Design
- Abstract
Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
- Published
- 2023
- Full Text
- View/download PDF
6. Constructing a polygenic risk score for childhood obesity using functional data analysis.
- Author
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Craig SJC, Kenney AM, Lin J, Paul IM, Birch LL, Savage JS, Marini ME, Chiaromonte F, Reimherr ML, and Makova KD
- Abstract
Obesity is a highly heritable condition that affects increasing numbers of adults and, concerningly, of children. However, only a small fraction of its heritability has been attributed to specific genetic variants. These variants are traditionally ascertained from genome-wide association studies (GWAS), which utilize samples with tens or hundreds of thousands of individuals for whom a single summary measurement (e.g., BMI) is collected. An alternative approach is to focus on a smaller, more deeply characterized sample in conjunction with advanced statistical models that leverage longitudinal phenotypes. Novel functional data analysis (FDA) techniques are used to capitalize on longitudinal growth information from a cohort of children between birth and three years of age. In an ultra-high dimensional setting, hundreds of thousands of single nucleotide polymorphisms (SNPs) are screened, and selected SNPs are used to construct two polygenic risk scores (PRS) for childhood obesity using a weighting approach that incorporates the dynamic and joint nature of SNP effects. These scores are significantly higher in children with (vs. without) rapid infant weight gain-a predictor of obesity later in life. Using two independent cohorts, it is shown that the genetic variants identified in very young children are also informative in older children and in adults, consistent with early childhood obesity being predictive of obesity later in life. In contrast, PRSs based on SNPs identified by adult obesity GWAS are not predictive of weight gain in the cohort of young children. This provides an example of a successful application of FDA to GWAS. This application is complemented with simulations establishing that a deeply characterized sample can be just as, if not more, effective than a comparable study with a cross-sectional response. Overall, it is demonstrated that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes; and shows how FDA approaches can be used as an alternative to the traditional GWAS., Competing Interests: Declarations of interest none
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- 2023
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7. Genomic structure predicts metabolite dynamics in microbial communities.
- Author
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Gowda, Karna, Ping, Derek, Mani, Madhav, and Kuehn, Seppe
- Subjects
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BACTERIAL communities , *BIOGEOCHEMICAL cycles , *BIOPHYSICS , *MICROBIAL communities , *GENE expression , *GENE mapping - Abstract
The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism. [Display omitted] • Metabolite fluxes in microbial communities are predictable from individual genotypes • A diverse collection of 79 bacterial isolates was sequenced and phenotyped • Gene presence and absence predict metabolic phenotypes of isolates via regression • A consumer-resource model predicts community metabolite fluxes from phenotypes The presence or absence of specific genes within communities of wild bacterial isolates is sufficient to predict community-level metabolite dynamics without detailed knowledge of pathway regulation or complex ecological processes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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