Biomedical subjects

Pierre-Antoine Gourraud

Publications and source records attributed to Pierre-Antoine Gourraud.

5 recordsLinked to original sources

PubMed2006

A new classification of HLA-DRB1 alleles differentiates predisposing and protective alleles for rheumatoid arthritis structural severity.

OBJECTIVE: A new classification of HLA-DRB1 alleles supporting the shared epitope hypothesis of rheumatoid arthritis (RA) susceptibility was recently introduced. We investigated the relevance of this classification in terms of the structural severity of RA. METHODS: The study group comprised 144 patients who were included in a prospective longitudinal cohort of French Caucasoid patients with early RA. Progression of the total radiographic damage score (Sharp/van der Heijde method) was used to quantify the structural severity of RA after 4 years of followup. HLA-DRB1 typing and subtyping were performed by polymerase chain reaction, using a panel of sequence-specific oligonucleotide probes. HLA-DRB1 alleles were classified according to the above-mentioned new system. The association between the HLA-DRB1 allele groups (S1, S2, S3P, S3D, and X) and the structural severity of RA was analyzed with nonparametric statistical tests. RESULTS: The presence of S2 alleles (HLA-DRB1*0401 and HLA-DRB1*1303) was associated with severe forms of RA (P = 0.004); a significant dose effect was observed (P = 0.01). The presence of S3D alleles (HLA-DRB1*11001, HLA-DRB1*1104, HLA-DRB1*12, and HLA-DRB1*16) was associated with benign forms of RA (P < 0.0001), and a significant dose effect was observed (P < 0.01). CONCLUSION: The studied classification of HLA-DRB1 alleles is relevant in terms of RA outcomes. Compared with a previously described classification system, this system differentiates predisposing (S2) and protective (S3D) alleles for RA structural severity, which, respectively, correspond to KRRAA and DRRAA amino acid patterns at position 70-74 of the third hypervariable region of the HLA-DRbeta chain.

Alleles↗

PubMed2005

Strategies in analysis of the genetic component of multifactorial diseases; biostatistical aspects.

Complex polygenic and multifactorial diseases remain a challenge for human geneticists. Here we aim to remind basic definitions of multifactorial diseases and the genetic related concepts underlying classical methods. Knowledge on pathophysiological process and the genetic information available conditions the design of study. The choice of methodology, between candidate gene approach and genome scan approach, between linkage and association studies, is the most important step. Both methods, linkage analysis and association studies are usually considered as complementary approaches for a given disease. For this reason, in this article, we present the most important classical methodologies in genetic epidemiology of complex disorders. References and examples are given to illustrate.

Biometry↗

PubMed2005

Inferred HLA haplotype information for donors from hematopoietic stem cells donor registries.

Human leukocyte antigen (HLA) matching remains a key issue in the outcome of transplantation. In hematopoietic stem cell transplantation with unrelated donors, the matching for compatible donors is based on the HLA phenotype information. In familial transplantation, the matching is achieved at the haplotype level because donor and recipient share the block-transmitted major histocompatibility complex region. We present a statistical method based on the HLA haplotype inference to refine the HLA information available in an unrelated situation. We implement a systematic statistical inference of the haplotype combinations at the individual level. It computes the most likely haplotype pair given the phenotype and its probability. The method is validated on 301 phase-known phenotypes from CEPH families (Centre d'Etude du Polymorphisme Humain). The method is further applied to 85,933 HLA-A B DR typed unrelated donors from the French Registry of hematopoietic stem cells donors (France Greffe de Moelle). The average value of prediction probability is 0.761 (SD 0.199) ranging from 0.26 to 1. Correlations between phenotype characteristics and predictions are also given. Homozygosity (OR = 2.08; [2.02-2.14] p <10(-3)) and linkage disequilibrium (p <10(-3)) are the major factors influencing the quality of prediction. Limits and relevance of the method are related to limits of haplotype estimation. Relevance of the method is discussed in the context of HLA matching refinement.

Algorithms↗

PubMed2005

Introduction to statistical analysis of population data in immunogenetics.

We review the most classical questions addressed by the analysis of population data in immunogenetics. Basic genetics' definitions are reminded. Questions related to the population data itself (structure, missing values nomenclature, sampling) are developed first, and secondly, the population genetics questions (relevance of genetic parameters (phenotype, genotype allele and haplotype frequencies; genetic distances; linkage disequilibrium measures), methods and practical computing) are illustrated by immunogenetics polymorphisms. This article gives the essential of population immunogenetics on examples and key references. We underline the importance of population dimension in statistical analysis: structure of linkage disequilibrium and genetic diversity between populations may affect the power of the study or the interpretation of correlations between markers, genes and diseases, making population genetics both theoretical and very practical.

Data Interpretation, Statistical↗

PubMed2004

Handling missing values in population data: consequences for maximum likelihood estimation of haplotype frequencies.

Haplotype frequency estimation in population data is an important problem in genetics and different methods including expectation maximisation (EM) methods have been proposed. The statistical properties of EM methods have been extensively assessed for data sets with no missing values. When numerous markers and/or individuals are tested, however, it is likely that some genotypes will be missing. Thus, it is of interest to investigate the behaviour of the method in the presence of incomplete genotype observations. We propose an extension of the EM method to handle missing genotypes, and we compare it with commonly used methods (such as ignoring individuals with incomplete genotype information or treating a missing allele as any other allele). Simulations were performed, starting from data sets of haematopoietic stem cell donors genotyped at three HLA loci. We deleted some data to create incomplete genotype observations in various proportions. We then compared the haplotype frequencies obtained on these incomplete data sets using the different methods to those obtained on the complete data. We found that the method proposed here provides better estimations, both qualitatively and quantitatively, but increases the computation time required. We discuss the influence of missing values on the algorithm's efficiency and the advantages and disadvantages of deleting incomplete genotypes. We propose guidelines for missing data handling in routine analysis.

Computational Biology↗