Gaël Yvert

Bio: Gaël Yvert is an academic researcher from École normale supérieure de Lyon. The author has contributed to research in topics: Spinocerebellar ataxia & Quantitative trait locus. The author has an hindex of 23, co-authored 52 publications receiving 5450 citations. Previous affiliations of Gaël Yvert include French Institute of Health and Medical Research & Harvard University.

Genetic dissection of transcriptional regulation in budding yeast.

Abstract: To begin to understand the genetic architecture of natural variation in gene expression, we carried out genetic linkage analysis of genomewide expression patterns in a cross between a laboratory strain and a wild strain of Saccharomyces cerevisiae. Over 1500 genes were differentially expressed between the parent strains. Expression levels of 570 genes were linked to one or more different loci, with most expression levels showing complex inheritance patterns. The loci detected by linkage fell largely into two categories: cis-acting modulators of single genes and trans-acting modulators of many genes. We found eight such trans-acting loci, each affecting the expression of a group of 7 to 94 genes of related function.

Cloning of the gene for spinocerebellar ataxia 2 reveals a locus with high sensitivity to expanded CAG/glutamine repeats

Abstract: Two forms of the neurodegenerative disorder spinocerebellar ataxia are known to be caused by the expansion of a CAG (polyglutamine) trinucleotide repeat. By screening cDNA expression libraries, using an antibody specific for polyglutamine repeats, we identified six novel genes containing CAG stretches. One of them is mutated in patients with spinocerebellar ataxia linked to chromosome 12q (SCA2). This gene shows ubiquitous expression and encodes a protein of unknown function. Normal SCA2 alleles (17 to 29 CAG repeats) contain one to three CAAs in the repeat. Mutated alleles (37 to 50 repeats) appear particularly unstable, upon both paternal and maternal transmissions. The sequence of three of them revealed pure CAG stretches. The steep inverse correlation between age of onset and CAG number suggests a higher sensitivity to polyglutamine length than in the other polyglutamine expansion diseases.

Cloning of the SCA7 gene reveals a highly unstable CAG repeat expansion

Abstract: The gene for spinocerebellar ataxia 7 (SCA7) has been mapped to chromosome 3p12-13. By positional cloning, we have identified a new gene of unknown function containing a CAG repeat that is expanded in SCA7 patients. On mutated alleles, CAG repeat size is highly variable, ranging from 38 to 130 repeats, whereas on normal alleles it ranges from 7 to 17 repeats. Gonadal instability in SCA7 is greater than that observed in any of the seven known neuro-degenerative diseases caused by translated CAG repeat expansions, and is markedly associated with paternal transmissions. SCA7 is the first such disorder in which the degenerative process also affects the retina.

Trans -acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors

Abstract: Natural genetic variation can cause significant differences in gene expression, but little is known about the polymorphisms that affect gene regulation. We analyzed regulatory variation in a cross between laboratory and wild strains of Saccharomyces cerevisiae. Clustering and linkage analysis defined groups of coregulated genes and the loci involved in their regulation. Most expression differences mapped to trans-acting loci. Positional cloning and functional assays showed that polymorphisms in GPA1 and AMN1 affect expression of genes involved in pheromone response and daughter cell separation, respectively. We also asked whether particular classes of genes were more likely to contain trans-regulatory polymorphisms. Notably, transcription factors showed no enrichment, and trans-regulatory variation seems to be broadly dispersed across classes of genes with different molecular functions.

Molecular and Clinical Correlations in Spinocerebellar Ataxia 2: A Study of 32 Families

Abstract: Spinocerebellar ataxia 2 (SCA2) is caused by the ex-pansion of an unstable CAG repeat encoding a poly-glutamine tract. One hundred and eighty four indexpatients with autosomal dominant cerebellar ataxiatype I were screened for this mutation. We found ex-pansion in 109 patients from 30 families of differentgeographical origins (15%) and in two isolated caseswith no known family histories (2%). The SCA2chromosomes contained from 34 to 57 repeats andconsisted of a pure stretch of CAG, whereas all testednormal chromosomes (14–31 repeats), except one with14 repeats, were interrupted by 1–3 repeats of CAA. Asin other diseases caused by unstable mutations, astrong negative correlation was observed between theage at onset and the size of the CAG repeat ( r = –0.81).The frequency of several clinical signs such as myo-clonus, dystonia and myokymia increased with thenumber of CAG repeats whereas the frequency ofothers was related to disease duration. The CAG repeatwas highly unstable during transmission with vari-ations ranging from –8 to +12, and a mean increase of+2.2, but there was no significant difference accordingto the parental sex. This instability was confirmed bythe high degree of gonadal mosaicism observed insperm DNA of one patient.INTRODUCTIONAutosomal dominant cerebellar ataxias (ADCAs) are a clinicallyheterogeneous group of neurodegenerative disorders. Type IADCA, characterized by the variable association of cerebellarataxia with supranuclear ophthalmoplegia, optic atrophy, extra-pyramidal signs, dementia and amyotrophy (1,2), is geneticallyheterogeneous. Five different loci have been mapped: SCA1 on6p ( 3), SCA2 on 12q ( 4), SCA3/MJD (Machado–Joseph disease)on 14q ( 5,6), SCA4 on 16q ( 7) and SCA5 on 11cen ( 8). SCA1 ( 9)and SCA3/MJD ( 10) were the first to be identified. The mutationis the expansion of an unstable CAG repeat encoding apolyglutamine tract. Recently, Trottier et al. (11), using amonoclonal antibody which specifically recognizes proteins withpolyglutamine expansions, detected an abnormal 150 kDa proteinin SCA2 patients, suggesting that this disorder was caused bytranslated CAG expansions. Very recently, three independentgroups (12–14) identified the SCA2 gene which carries anexpanded CAG repeat in its coding sequence in patients,confirming this hypothesis. The gene encodes a 1312 amino acidprotein of unknown function (12,14).

Statistical significance for genomewide studies

Abstract: With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.

The sva package for removing batch effects and other unwanted variation in high-throughput experiments

Abstract: Summary: Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects—when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function. Availability: The R package sva is freely available from http://www.bioconductor.org. Contact: jleek@jhsph.edu Supplementary information:Supplementary data are available at Bioinformatics online.

Genetic effects on gene expression across human tissues.

Abstract: Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Capturing heterogeneity in gene expression studies by surrogate variable analysis.

Abstract: It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce “surrogate variable analysis” (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.