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  1. Article ; Online: Controlling for human population stratification in rare variant association studies

    Matthieu Bouaziz / Jimmy Mullaert / Benedetta Bigio / Yoann Seeleuthner / Jean-Laurent Casanova / Alexandre Alcais / Laurent Abel / Aurélie Cobat

    Scientific Reports, Vol 11, Iss 1, Pp 1-

    2021  Volume 14

    Abstract: Abstract Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these ... ...

    Abstract Abstract Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: SHIPS

    Matthieu Bouaziz / Caroline Paccard / Mickael Guedj / Christophe Ambroise

    PLoS ONE, Vol 7, Iss 10, p e

    Spectral Hierarchical clustering for the Inference of Population Structure in genetic studies.

    2012  Volume 45685

    Abstract: Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this ... ...

    Abstract Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2012-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Accounting for population stratification in practice

    Matthieu Bouaziz / Christophe Ambroise / Mickael Guedj

    PLoS ONE, Vol 6, Iss 12, p e

    a comparison of the main strategies dedicated to genome-wide association studies.

    2011  Volume 28845

    Abstract: Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic ... ...

    Abstract Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies.
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2011-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: IRF4 haploinsufficiency in a family with Whipple’s disease

    Antoine Guérin / Gaspard Kerner / Nico Marr / Janet G Markle / Florence Fenollar / Natalie Wong / Sabri Boughorbel / Danielle T Avery / Cindy S Ma / Salim Bougarn / Matthieu Bouaziz / Vivien Béziat / Erika Della Mina / Carmen Oleaga-Quintas / Tomi Lazarov / Lisa Worley / Tina Nguyen / Etienne Patin / Caroline Deswarte /
    Rubén Martinez-Barricarte / Soraya Boucherit / Xavier Ayral / Sophie Edouard / Stéphanie Boisson-Dupuis / Vimel Rattina / Benedetta Bigio / Guillaume Vogt / Frédéric Geissmann / Lluis Quintana-Murci / Damien Chaussabel / Stuart G Tangye / Didier Raoult / Laurent Abel / Jacinta Bustamante / Jean-Laurent Casanova

    eLife, Vol

    2018  Volume 7

    Abstract: Most humans are exposed to Tropheryma whipplei (Tw). Whipple’s disease (WD) strikes only a small minority of individuals infected with Tw (<0.01%), whereas asymptomatic chronic carriage is more common (<25%). We studied a multiplex kindred, containing ... ...

    Abstract Most humans are exposed to Tropheryma whipplei (Tw). Whipple’s disease (WD) strikes only a small minority of individuals infected with Tw (<0.01%), whereas asymptomatic chronic carriage is more common (<25%). We studied a multiplex kindred, containing four WD patients and five healthy Tw chronic carriers. We hypothesized that WD displays autosomal dominant (AD) inheritance, with age-dependent incomplete penetrance. We identified a single very rare non-synonymous mutation in the four patients: the private R98W variant of IRF4, a transcription factor involved in immunity. The five Tw carriers were younger, and also heterozygous for R98W. We found that R98W was loss-of-function, modified the transcriptome of heterozygous leukocytes following Tw stimulation, and was not dominant-negative. We also found that only six of the other 153 known non-synonymous IRF4 variants were loss-of-function. Finally, we found that IRF4 had evolved under purifying selection. AD IRF4 deficiency can underlie WD by haploinsufficiency, with age-dependent incomplete penetrance.
    Keywords Whipple's disease ; primary immunodeficiency ; IRF4 ; haploinsufficiency ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Subject code 610
    Language English
    Publishing date 2018-03-01T00:00:00Z
    Publisher eLife Sciences Publications Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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