Publications by authors named "Gaëlle Marenne"

24 Publications

  • Page 1 of 1

The trans-ancestral genomic architecture of glycemic traits.

Nat Genet 2021 06 31;53(6):840-860. Epub 2021 May 31.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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http://dx.doi.org/10.1038/s41588-021-00852-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610958PMC
June 2021

Extension of SKAT to multi-category phenotypes through a geometrical interpretation.

Eur J Hum Genet 2021 May 14;29(5):736-744. Epub 2021 Jan 14.

CESP Inserm, U1018, UFR Médecine, Univ Paris-Sud, Université Paris-Saclay, Villejuif, France.

Rare genetic variants are expected to play an important role in disease and several statistical methods have been developed to test for disease association with rare variants, including variance-component tests. These tests however deal only with binary or continuous phenotypes and it is not possible to take advantage of a suspected heterogeneity between subgroups of patients. To address this issue, we extended the popular rare-variant association test SKAT to compare more than two groups of individuals. Simulations under different scenarios were performed that showed gain in power in presence of genetic heterogeneity and minor lack of power in absence of heterogeneity. An application on whole-exome sequencing data from patients with early- or late-onset moyamoya disease also illustrated the advantage of our SKAT extension. Genetic simulations and SKAT extension are implemented in the R package Ravages available on GitHub ( https://github.com/genostats/Ravages ).
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http://dx.doi.org/10.1038/s41431-020-00792-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110546PMC
May 2021

Exome Sequencing Identifies Genes and Gene Sets Contributing to Severe Childhood Obesity, Linking PHIP Variants to Repressed POMC Transcription.

Cell Metab 2020 06;31(6):1107-1119.e12

Wellcome Sanger Institute, Cambridge, UK; University of Cambridge MRC Epidemiology Unit, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK. Electronic address:

Obesity is genetically heterogeneous with monogenic and complex polygenic forms. Using exome and targeted sequencing in 2,737 severely obese cases and 6,704 controls, we identified three genes (PHIP, DGKI, and ZMYM4) with an excess burden of very rare predicted deleterious variants in cases. In cells, we found that nuclear PHIP (pleckstrin homology domain interacting protein) directly enhances transcription of pro-opiomelanocortin (POMC), a neuropeptide that suppresses appetite. Obesity-associated PHIP variants repressed POMC transcription. Our demonstration that PHIP is involved in human energy homeostasis through transcriptional regulation of central melanocortin signaling has potential diagnostic and therapeutic implications for patients with obesity and developmental delay. Additionally, we found an excess burden of predicted deleterious variants involving genes nearest to loci from obesity genome-wide association studies. Genes and gene sets influencing obesity with variable penetrance provide compelling evidence for a continuum of causality in the genetic architecture of obesity, and explain some of its missing heritability.
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http://dx.doi.org/10.1016/j.cmet.2020.05.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267775PMC
June 2020

Rare variant association testing for multicategory phenotype.

Genet Epidemiol 2019 09 13;43(6):646-656. Epub 2019 May 13.

Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France.

Genetic association studies have provided new insights into the genetic variability of human complex traits with a focus mainly on continuous or binary traits. Methods have been proposed to take into account disease heterogeneity between subgroups of patients when studying common variants but none was specifically designed for rare variants. Because rare variants are expected to have stronger effects and to be more heterogeneously distributed among cases than common ones, subgroup analyses might be particularly attractive in this context. To address this issue, we propose an extension of burden tests by using a multinomial regression model, which enables association tests between rare variants and multicategory phenotypes. We evaluated the type I error and the power of two burden tests, CAST and WSS, by simulating data under different scenarios. In the case of genetic heterogeneity between case subgroups, we showed an advantage of multinomial regression over logistic regression, which considers all the cases against the controls. We replicated these results on real data from Moyamoya disease where the burden tests performed better when cases were stratified according to age-of-onset. We implemented the functions for association tests in the R package "Ravages" available on Github.
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http://dx.doi.org/10.1002/gepi.22210DOI Listing
September 2019

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Nat Genet 2019 03 18;51(3):452-469. Epub 2019 Feb 18.

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.
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http://dx.doi.org/10.1038/s41588-018-0334-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6560635PMC
March 2019

Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Nat Genet 2018 05;50(5):766-767

Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.
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http://dx.doi.org/10.1038/s41588-018-0082-3DOI Listing
May 2018

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Nat Genet 2018 01 22;50(1):26-41. Epub 2017 Dec 22.

Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.
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http://dx.doi.org/10.1038/s41588-017-0011-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945951PMC
January 2018

Genetic aetiology of glycaemic traits: approaches and insights.

Hum Mol Genet 2017 10;26(R2):R172-R184

Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on recent advances in the genetic aetiology of glycaemic traits, and the resulting biological insights. We will provide a brief overview of results ranging from common, to low- and rare-frequency variant-trait association studies, studies leveraging the diversity across populations, and studies harnessing the power of genetic and genomic approaches to gain insights into the biological underpinnings of these traits.
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http://dx.doi.org/10.1093/hmg/ddx293DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5886471PMC
October 2017

Rare Variant Analysis of Human and Rodent Obesity Genes in Individuals with Severe Childhood Obesity.

Sci Rep 2017 06 29;7(1):4394. Epub 2017 Jun 29.

University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.

Obesity is a genetically heterogeneous disorder. Using targeted and whole-exome sequencing, we studied 32 human and 87 rodent obesity genes in 2,548 severely obese children and 1,117 controls. We identified 52 variants contributing to obesity in 2% of cases including multiple novel variants in GNAS, which were sometimes found with accelerated growth rather than short stature as described previously. Nominally significant associations were found for rare functional variants in BBS1, BBS9, GNAS, MKKS, CLOCK and ANGPTL6. The p.S284X variant in ANGPTL6 drives the association signal (rs201622589, MAF~0.1%, odds ratio = 10.13, p-value = 0.042) and results in complete loss of secretion in cells. Further analysis including additional case-control studies and population controls (N = 260,642) did not support association of this variant with obesity (odds ratio = 2.34, p-value = 2.59 × 10), highlighting the challenges of testing rare variant associations and the need for very large sample sizes. Further validation in cohorts with severe obesity and engineering the variants in model organisms will be needed to explore whether human variants in ANGPTL6 and other genes that lead to obesity when deleted in mice, do contribute to obesity. Such studies may yield druggable targets for weight loss therapies.
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http://dx.doi.org/10.1038/s41598-017-03054-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5491520PMC
June 2017

Rare and low-frequency coding variants alter human adult height.

Nature 2017 02 1;542(7640):186-190. Epub 2017 Feb 1.

Netherlands Comprehensive Cancer Organisation, Utrecht, 3501 DB, The Netherlands.

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.
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http://dx.doi.org/10.1038/nature21039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302847PMC
February 2017

A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease.

Sci Transl Med 2016 06;8(341):341ra76

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK.

Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.
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http://dx.doi.org/10.1126/scitranslmed.aad3744DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219001PMC
June 2016

Mosaic loss of chromosome Y is associated with common variation near TCL1A.

Nat Genet 2016 05 11;48(5):563-8. Epub 2016 Apr 11.

Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.

Mosaic loss of chromosome Y (mLOY) leading to gonosomal XY/XO commonly occurs during aging, particularly in smokers. We investigated whether mLOY was associated with non-hematological cancer in three prospective cohorts (8,679 cancer cases and 5,110 cancer-free controls) and genetic susceptibility to mLOY. Overall, mLOY was observed in 7% of men, and its prevalence increased with age (per-year odds ratio (OR) = 1.13, 95% confidence interval (CI) = 1.12-1.15; P < 2 × 10(-16)), reaching 18.7% among men over 80 years old. mLOY was associated with current smoking (OR = 2.35, 95% CI = 1.82-3.03; P = 5.55 × 10(-11)), but the association weakened with years after cessation. mLOY was not consistently associated with overall or specific cancer risk (for example, bladder, lung or prostate cancer) nor with cancer survival after diagnosis (multivariate-adjusted hazard ratio = 0.87, 95% CI = 0.73-1.04; P = 0.12). In a genome-wide association study, we observed the first example of a common susceptibility locus for genetic mosaicism, specifically mLOY, which maps to TCL1A at 14q32.13, marked by rs2887399 (OR = 1.55, 95% CI = 1.36-1.78; P = 1.37 × 10(-10)).
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http://dx.doi.org/10.1038/ng.3545DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848121PMC
May 2016

Next generation modeling in GWAS: comparing different genetic architectures.

Hum Genet 2014 Oct 17;133(10):1235-53. Epub 2014 Jun 17.

Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/MelchorFernándezAlmagro, 3, 28029, Madrid, Spain,

The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.
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http://dx.doi.org/10.1007/s00439-014-1461-1DOI Listing
October 2014

Whole genome prediction of bladder cancer risk with the Bayesian LASSO.

Genet Epidemiol 2014 Jul 5;38(5):467-76. Epub 2014 May 5.

Spanish National Cancer Research Center (CNIO), Madrid, Spain.

To build a predictive model for urothelial carcinoma of the bladder (UCB) risk combining both genomic and nongenomic data, 1,127 cases and 1,090 controls from the Spanish Bladder Cancer/EPICURO study were genotyped using the HumanHap 1M SNP array. After quality control filters, genotypes from 475,290 variants were available. Nongenomic information comprised age, gender, region, and smoking status. Three Bayesian threshold models were implemented including: (1) only genomic information, (2) only nongenomic data, and (3) both sources of information. The three models were applied to the whole population, to only nonsmokers, to male smokers, and to extreme phenotypes to potentiate the UCB genetic component. The area under the ROC curve allowed evaluating the predictive ability of each model in a 10-fold cross-validation scenario. Smoking status showed the highest predictive ability of UCB risk (AUCtest = 0.62). On the other hand, the AUC of all genetic variants was poorer (0.53). When the extreme phenotype approach was applied, the predictive ability of the genomic model improved 15%. This study represents a first attempt to build a predictive model for UCB risk combining both genomic and nongenomic data and applying state-of-the-art statistical approaches. However, the lack of genetic relatedness among individuals, the complexity of UCB etiology, as well as a relatively small statistical power, may explain the low predictive ability for UCB risk. The study confirms the difficulty of predicting complex diseases using genetic data, and suggests the limited translational potential of findings from this type of data into public health interventions.
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http://dx.doi.org/10.1002/gepi.21809DOI Listing
July 2014

Advantage of using allele-specific copy numbers when testing for association in regions with common copy number variants.

PLoS One 2013 10;8(9):e75350. Epub 2013 Sep 10.

Inserm UMR-S946, Univ. Paris Diderot, Institut Universitaire d'Hématologie, Paris, France ; Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.

Copy number variants (CNV) can be called from SNP-arrays; however, few studies have attempted to combine both CNV and SNP calls to test for association with complex diseases. Even when SNPs are located within CNVs, two separate association analyses are necessary, to compare the distribution of bi-allelic genotypes in cases and controls (referred to as SNP-only strategy) and the number of copies of a region (referred to as CNV-only strategy). However, when disease susceptibility is actually associated with allele specific copy-number states, the two strategies may not yield comparable results, raising a series of questions about the optimal analytical approach. We performed simulations of the performance of association testing under different scenarios that varied genotype frequencies and inheritance models. We show that the SNP-only strategy lacks power under most scenarios when the SNP is located within a CNV; frequently it is excluded from analysis as it does not pass quality control metrics either because of an increased rate of missing calls or a departure from fitness for Hardy-Weinberg proportion. The CNV-only strategy also lacks power because the association testing depends on the allele which copy number varies. The combined strategy performs well in most of the scenarios. Hence, we advocate the use of this combined strategy when testing for association with SNPs located within CNVs.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075350PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3769257PMC
February 2015

Genome-wide CNV analysis replicates the association between GSTM1 deletion and bladder cancer: a support for using continuous measurement from SNP-array data.

BMC Genomics 2012 Jul 20;13:326. Epub 2012 Jul 20.

Spanish National Cancer Research Center (CNIO), Madrid, E-28029, Spain.

Background: Structural variations such as copy number variants (CNV) influence the expression of different phenotypic traits. Algorithms to identify CNVs through SNP-array platforms are available. The ability to evaluate well-characterized CNVs such as GSTM1 (1p13.3) deletion provides an important opportunity to assess their performance.

Results: 773 cases and 759 controls from the SBC/EPICURO Study were genotyped in the GSTM1 region using TaqMan, Multiplex Ligation-dependent Probe Amplification (MLPA), and Illumina Infinium 1 M SNP-array platforms. CNV callings provided by TaqMan and MLPA were highly concordant and replicated the association between GSTM1 and bladder cancer. This was not the case when CNVs were called using Illumina 1 M data through available algorithms since no deletion was detected across the study samples. In contrast, when the Log R Ratio (LRR) was used as a continuous measure for the 5 probes contained in this locus, we were able to detect their association with bladder cancer using simple regression models or more sophisticated methods such as the ones implemented in the CNVtools package.

Conclusions: This study highlights an important limitation in the CNV calling from SNP-array data in regions of common aberrations and suggests that there may be added advantage for using LRR as a continuous measure in association tests rather than relying on calling algorithms.
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http://dx.doi.org/10.1186/1471-2164-13-326DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3425254PMC
July 2012

Detectable clonal mosaicism and its relationship to aging and cancer.

Nat Genet 2012 May 6;44(6):651-8. Epub 2012 May 6.

Division of Cancer Epidemiology and Genetics, National Cancer Institute (NCI), Rockville, Maryland, USA.

In an analysis of 31,717 cancer cases and 26,136 cancer-free controls from 13 genome-wide association studies, we observed large chromosomal abnormalities in a subset of clones in DNA obtained from blood or buccal samples. We observed mosaic abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of >2 Mb in size in autosomes of 517 individuals (0.89%), with abnormal cell proportions of between 7% and 95%. In cancer-free individuals, frequency increased with age, from 0.23% under 50 years to 1.91% between 75 and 79 years (P = 4.8 × 10(-8)). Mosaic abnormalities were more frequent in individuals with solid tumors (0.97% versus 0.74% in cancer-free individuals; odds ratio (OR) = 1.25; P = 0.016), with stronger association with cases who had DNA collected before diagnosis or treatment (OR = 1.45; P = 0.0005). Detectable mosaicism was also more common in individuals for whom DNA was collected at least 1 year before diagnosis with leukemia compared to cancer-free individuals (OR = 35.4; P = 3.8 × 10(-11)). These findings underscore the time-dependent nature of somatic events in the etiology of cancer and potentially other late-onset diseases.
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http://dx.doi.org/10.1038/ng.2270DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3372921PMC
May 2012

Assessment of copy number variation using the Illumina Infinium 1M SNP-array: a comparison of methodological approaches in the Spanish Bladder Cancer/EPICURO study.

Hum Mutat 2011 Feb 25;32(2):240-8. Epub 2011 Jan 25.

Centro Nacional de Investigaciones Oncológicas (CNIO) Madrid, Spain.

High-throughput single nucleotide polymorphism (SNP)-array technologies allow to investigate copy number variants (CNVs) in genome-wide scans and specific calling algorithms have been developed to determine CNV location and copy number. We report the results of a reliability analysis comparing data from 96 pairs of samples processed with CNVpartition, PennCNV, and QuantiSNP for Infinium Illumina Human 1Million probe chip data. We also performed a validity assessment with multiplex ligation-dependent probe amplification (MLPA) as a reference standard. The number of CNVs per individual varied according to the calling algorithm. Higher numbers of CNVs were detected in saliva than in blood DNA samples regardless of the algorithm used. All algorithms presented low agreement with mean Kappa Index (KI) <66. PennCNV was the most reliable algorithm (KI(w=) 98.96) when assessing the number of copies. The agreement observed in detecting CNV was higher in blood than in saliva samples. When comparing to MLPA, all algorithms identified poorly known copy aberrations (sensitivity = 0.19-0.28). In contrast, specificity was very high (0.97-0.99). Once a CNV was detected, the number of copies was truly assessed (sensitivity >0.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genome-wide scans. Further refinement is required to assess CNVs as risk factors in complex diseases.
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http://dx.doi.org/10.1002/humu.21398DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230937PMC
February 2011

Mosaic uniparental disomies and aneuploidies as large structural variants of the human genome.

Am J Hum Genet 2010 Jul;87(1):129-38

Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, E-08003 Barcelona, Spain.

Mosaicism is defined as the coexistence of cells with different genetic composition within an individual, caused by postzygotic somatic mutation. Although somatic mosaicism for chromosomal abnormalities is a well-established cause of developmental and somatic disorders and has also been detected in different tissues, its frequency and extent in the adult normal population are still unknown. We provide here a genome-wide survey of mosaic genomic variation obtained by analyzing Illumina 1M SNP array data from blood or buccal DNA samples of 1991 adult individuals from the Spanish Bladder Cancer/EPICURO genome-wide association study. We found mosaic abnormalities in autosomes in 1.7% of samples, including 23 segmental uniparental disomies, 8 complete trisomies, and 11 large (1.5-37 Mb) copy-number variants. Alterations were observed across the different autosomes with recurrent events in chromosomes 9 and 20. No case-control differences were found in the frequency of events or the percentage of cells affected, thus indicating that most rearrangements found are not central to the development of bladder cancer. However, five out of six events tested were detected in both blood and bladder tissue from the same individual, indicating an early developmental origin. The high cellular frequency of the anomalies detected and their presence in normal adult individuals suggest that this type of mosaicism is a widespread phenomenon in the human genome. Somatic mosaicism should be considered in the expanding repertoire of inter- and intraindividual genetic variation, some of which may cause somatic human diseases but also contribute to modifying inherited disorders and/or late-onset multifactorial traits.
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http://dx.doi.org/10.1016/j.ajhg.2010.06.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896781PMC
July 2010

Genetic susceptibility to distinct bladder cancer subphenotypes.

Eur Urol 2010 Feb 12;57(2):283-92. Epub 2009 Aug 12.

Spanish National Cancer Research Centre, Madrid, Spain.

Background: Clinical, pathologic, and molecular evidence indicate that bladder cancer is heterogeneous with pathologic/molecular features that define distinct subphenotypes with different prognoses. It is conceivable that specific patterns of genetic susceptibility are associated with particular subphenotypes.

Objective: To examine evidence for the contribution of germline genetic variation to bladder cancer heterogeneity.

Design, Setting, And Participants: The Spanish Bladder Cancer/EPICURO Study is a case-control study based in 18 hospitals located in five areas in Spain. Cases were patients with a newly diagnosed, histologically confirmed, urothelial cell carcinoma of the bladder from 1998 to 2001. Case diagnoses were reviewed and uniformly classified by pathologists following the World Health Organisation/International Society of Urological Pathology 1999 criteria. Controls were hospital-matched patients (n=1149).

Measurements: A total of 1526 candidate variants in 423 candidate genes were analysed. Three distinct subphenotypes were defined according to stage and grade: low-grade nonmuscle invasive (n=586), high-grade nonmuscle invasive (n=219), and muscle invasive (n=246). The association between each variant and subphenotype was assessed by polytomous risk models adjusting for potential confounders. Heterogeneity in genetic susceptibility among subphenotypes was also tested.

Results And Limitations: Two established bladder cancer susceptibility genotypes, NAT2 slow-acetylation and GSTM1-null, exhibited similar associations among the subphenotypes, as did VEGF-rs25648, which was previously identified in our study. Other variants conferred risks for specific tumour subphenotypes such as PMS2-rs6463524 and CD4-rs3213427 (respective heterogeneity p values of 0.006 and 0.004), which were associated with muscle-invasive tumours (per-allele odds ratios [95% confidence interval] of 0.56 [0.41-0.77] and 0.71 [0.57-0.88], respectively) but not with non-muscle-invasive tumours. Heterogeneity p values were not robust in multiple testing according to their false-discovery rate.

Conclusions: These exploratory analyses suggest that genetic susceptibility loci might be related to the molecular/pathologic diversity of bladder cancer. Validation through large-scale replication studies and the study of additional genes and single nucleotide polymorphisms are required.
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http://dx.doi.org/10.1016/j.eururo.2009.08.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3220186PMC
February 2010

Impaired performance of FDR-based strategies in whole-genome association studies when SNPs are excluded prior to the analysis.

Genet Epidemiol 2009 Jan;33(1):45-53

Inserm, UMR-S0535, Villejuif, France.

With recent advances in genomewide microarray technologies, whole-genome association (WGA) studies have aimed at identifying susceptibility genes for complex human diseases using hundreds of thousands of single nucleotide polymorphisms (SNPs) genotyped at the same time. In this context and to take into account multiple testing, false discovery rate (FDR)-based strategies are now used frequently. However, a critical aspect of these strAtegies is that they are applied to a collection or a family of hypotheses and, thus, critically depend on these precise hypotheses. We investigated how modifying the family of hypotheses to be tested affected the performance of FDR-based procedures in WGA studies. We showed that FDR-based procedures performed more poorly when excluding SNPs with high prior probability of being associated. Results of simulation studies mimicking WGA studies according to three scenarios are reported, and show the extent to which SNPs elimination (family contraction) prior to the analysis impairs the performance of FDR-based procedures. To illustrate this situation, we used the data from a recent WGA study on type-1 diabetes (Clayton et al. [2005] Nat. Genet. 37:1243-1246) and report the results obtained when excluding or not SNPs located inside the human leukocyte antigen region. Based on our findings, excluding markers with high prior probability of being associated cannot be recommended for the analysis of WGA data with FDR-based strategies.
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http://dx.doi.org/10.1002/gepi.20355DOI Listing
January 2009
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