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Hujoel MLA, Sherman MA, Barton AR, Mukamel RE, Sankaran VG, Terao C, Loh PR. Influences of rare copy-number variation on human complex traits. Cell 2022; 185:4233-4248.e27. [PMID: 36306736 PMCID: PMC9800003 DOI: 10.1016/j.cell.2022.09.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/22/2022] [Accepted: 09/19/2022] [Indexed: 11/06/2022]
Abstract
The human genome contains hundreds of thousands of regions harboring copy-number variants (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs have been ascertainable from SNP-array data generated by large biobanks. We developed a computational approach leveraging haplotype sharing in biobank cohorts to more sensitively detect CNVs. Applied to UK Biobank, this approach accounted for approximately half of all rare gene inactivation events produced by genomic structural variation. This CNV call set enabled a detailed analysis of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (p < 5 × 10-8) likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling insights into dosage sensitivity of these genes and uncovering several gene-trait relationships. These results demonstrate the ability of haplotype-informed analysis to provide insights into the genetic basis of human complex traits.
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Affiliation(s)
- Margaux L A Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Maxwell A Sherman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alison R Barton
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
| | - Ronen E Mukamel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan; Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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2
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Balagué-Dobón L, Cáceres A, González JR. Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure. Brief Bioinform 2022; 23:6535682. [PMID: 35211719 PMCID: PMC8921734 DOI: 10.1093/bib/bbac043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 12/12/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.
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3
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Signatures of TSPAN8 variants associated with human metabolic regulation and diseases. iScience 2021; 24:102893. [PMID: 34401672 PMCID: PMC8355918 DOI: 10.1016/j.isci.2021.102893] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 06/18/2021] [Accepted: 07/20/2021] [Indexed: 02/08/2023] Open
Abstract
Here, with the example of common copy number variation (CNV) in the TSPAN8 gene, we present an important piece of work in the field of CNV detection, that is, CNV association with complex human traits such as 1H NMR metabolomic phenotypes and an example of functional characterization of CNVs among human induced pluripotent stem cells (HipSci). We report TSPAN8 exon 11 (ENSE00003720745) as a pleiotropic locus associated with metabolomic regulation and show that its biology is associated with several metabolic diseases such as type 2 diabetes (T2D) and cancer. Our results further demonstrate the power of multivariate association models over univariate methods and define metabolomic signatures for variants in TSPAN8.
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4
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Dennis J, Walker L, Tyrer J, Michailidou K, Easton DF. Detecting rare copy number variants from Illumina genotyping arrays with the CamCNV pipeline: Segmentation of z-scores improves detection and reliability. Genet Epidemiol 2021; 45:237-248. [PMID: 33020983 PMCID: PMC8005414 DOI: 10.1002/gepi.22367] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/03/2020] [Accepted: 09/22/2020] [Indexed: 01/21/2023]
Abstract
The intensities from genotyping array data can be used to detect copy number variants (CNVs) but a high level of noise in the data and overlap between different copy-number intensity distributions produces unreliable calls, particularly when only a few probes are covered by the CNV. We present a novel pipeline (CamCNV) with a series of steps to reduce noise and detect more reliably CNVs covering as few as three probes. The pipeline aims to detect rare CNVs (below 1% frequency) for association tests in large cohorts. The method uses the information from all samples to convert intensities to z-scores, thus adjusting for variance between probes. We tested the sensitivity of our pipeline by looking for known CNVs from the 1000 Genomes Project in our genotyping of 1000 Genomes samples. We also compared the CNV calls for 1661 pairs of genotyped replicate samples. At the chosen mean z-score cut-off, sensitivity to detect the 1000 Genomes CNVs was approximately 85% for deletions and 65% for duplications. From the replicates, we estimate the false discovery rate is controlled at ∼10% for deletions (falling to below 3% with more than five probes) and ∼28% for duplications. The pipeline demonstrates improved sensitivity when compared to calling with PennCNV, particularly for short deletions covering only a few probes. For each called CNV, the mean z-score is a useful metric for controlling the false discovery rate.
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Affiliation(s)
- Joe Dennis
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Logan Walker
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Jonathan Tyrer
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
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5
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Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019; 20:467-484. [PMID: 31068683 DOI: 10.1038/s41576-019-0127-1] [Citation(s) in RCA: 894] [Impact Index Per Article: 178.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.
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Affiliation(s)
- Vivian Tam
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Nikunj Patel
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Michelle Turcotte
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Yohan Bossé
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Québec City, Québec, Canada.,Department of Molecular Medicine, Laval University, Québec City, Quebec, Canada
| | - Guillaume Paré
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. .,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada. .,Inserm UMRS 954 N-GERE (Nutrition-Genetics-Environmental Risks), University of Lorraine, Faculty of Medicine, Nancy, France.
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6
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Abstract
Differences between genomes can be due to single nucleotide variants (SNPs), translocations, inversions and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 250 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease or phenotypic traits.While the link between SNPs and disease susceptibility has been well studied, to date there are still very few published CNV genome-wide association studies; probably owing to the fact that CNV analysis remains a slightly more complex task than SNP analysis (both in term of bioinformatics workflow and uncertainty in the CNV calling leading to high false positive rates and unknown false negative rates). This chapter aims at explaining computational methods for the analysis of CNVs, ranging from study design, data processing and quality control, up to genome-wide association study with clinical traits.
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Affiliation(s)
- Aurélien Macé
- Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zoltán Kutalik
- Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Chen W, Robertson AJ, Ganesamoorthy D, Coin LJM. sCNAphase: using haplotype resolved read depth to genotype somatic copy number alterations from low cellularity aneuploid tumors. Nucleic Acids Res 2017; 45:e34. [PMID: 27903916 PMCID: PMC5389684 DOI: 10.1093/nar/gkw1086] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 10/26/2016] [Indexed: 02/03/2023] Open
Abstract
Accurate identification of copy number alterations is an essential step in understanding the events driving tumor progression. While a variety of algorithms have been developed to use high-throughput sequencing data to profile copy number changes, no tool is able to reliably characterize ploidy and genotype absolute copy number from tumor samples that contain less than 40% tumor cells. To increase our power to resolve the copy number profile from low-cellularity tumor samples, we developed a novel approach that pre-phases heterozygote germline single nucleotide polymorphisms (SNPs) in order to replace the commonly used ‘B-allele frequency’ with a more powerful ‘parental-haplotype frequency’. We apply our tool—sCNAphase—to characterize the copy number and loss-of-heterozygosity profiles of four publicly available breast cancer cell-lines. Comparisons to previous spectral karyotyping and microarray studies revealed that sCNAphase reliably identified overall ploidy as well as the individual copy number mutations from each cell-line. Analysis of artificial cell-line mixtures demonstrated the capacity of this method to determine the level of tumor cellularity, consistently identify sCNAs and characterize ploidy in samples with as little as 10% tumor cells. This novel methodology has the potential to bring sCNA profiling to low-cellularity tumors, a form of cancer unable to be accurately studied by current methods.
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Affiliation(s)
- Wenhan Chen
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Alan J Robertson
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Devika Ganesamoorthy
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Lachlan J M Coin
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, 4072, Australia
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Genetic Candidate Variants in Two Multigenerational Families with Childhood Apraxia of Speech. PLoS One 2016; 11:e0153864. [PMID: 27120335 PMCID: PMC4847873 DOI: 10.1371/journal.pone.0153864] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 04/05/2016] [Indexed: 12/31/2022] Open
Abstract
Childhood apraxia of speech (CAS) is a severe and socially debilitating form of speech sound disorder with suspected genetic involvement, but the genetic etiology is not yet well understood. Very few known or putative causal genes have been identified to date, e.g., FOXP2 and BCL11A. Building a knowledge base of the genetic etiology of CAS will make it possible to identify infants at genetic risk and motivate the development of effective very early intervention programs. We investigated the genetic etiology of CAS in two large multigenerational families with familial CAS. Complementary genomic methods included Markov chain Monte Carlo linkage analysis, copy-number analysis, identity-by-descent sharing, and exome sequencing with variant filtering. No overlaps in regions with positive evidence of linkage between the two families were found. In one family, linkage analysis detected two chromosomal regions of interest, 5p15.1-p14.1, and 17p13.1-q11.1, inherited separately from the two founders. Single-point linkage analysis of selected variants identified CDH18 as a primary gene of interest and additionally, MYO10, NIPBL, GLP2R, NCOR1, FLCN, SMCR8, NEK8, and ANKRD12, possibly with additive effects. Linkage analysis in the second family detected five regions with LOD scores approaching the highest values possible in the family. A gene of interest was C4orf21 (ZGRF1) on 4q25-q28.2. Evidence for previously described causal copy-number variations and validated or suspected genes was not found. Results are consistent with a heterogeneous CAS etiology, as is expected in many neurogenic disorders. Future studies will investigate genome variants in these and other families with CAS.
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Walker LC, Wiggins GAR, Pearson JF. The Role of Constitutional Copy Number Variants in Breast Cancer. ACTA ACUST UNITED AC 2015; 4:407-23. [PMID: 27600231 PMCID: PMC4996380 DOI: 10.3390/microarrays4030407] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 08/26/2015] [Accepted: 09/01/2015] [Indexed: 01/16/2023]
Abstract
Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer in recent years has been dominated by the use of genome-wide technologies, such as single nucleotide polymorphism (SNP)-arrays, with a significant focus on single nucleotide variants. To date, these large datasets have been underutilised for generating genome-wide CNV profiles despite offering a massive resource for assessing the contribution of these structural variants to breast cancer risk. Technical challenges remain in determining the location and distribution of CNVs across the human genome due to the accuracy of computational prediction algorithms and resolution of the array data. Moreover, better methods are required for interpreting the functional effect of newly discovered CNVs. In this review, we explore current and future application of SNP array technology to assess rare and common CNVs in association with breast cancer risk in humans.
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Affiliation(s)
- Logan C Walker
- Mackenzie Cancer Research Group, Department of Pathology, University of Otago, Christchurch 8140, New Zealand.
| | - George A R Wiggins
- Mackenzie Cancer Research Group, Department of Pathology, University of Otago, Christchurch 8140, New Zealand.
| | - John F Pearson
- Biostatistics and Computational Biology Unit, University of Otago, Christchurch 8140, New Zealand.
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Estimating copy numbers of alleles from population-scale high-throughput sequencing data. BMC Bioinformatics 2015; 16 Suppl 1:S4. [PMID: 25707811 PMCID: PMC4331703 DOI: 10.1186/1471-2105-16-s1-s4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background With the recent development of microarray and high-throughput sequencing (HTS) technologies, a number of studies have revealed catalogs of copy number variants (CNVs) and their association with phenotypes and complex traits. In parallel, a number of approaches to predict CNV regions and genotypes are proposed for both microarray and HTS data. However, only a few approaches focus on haplotyping of CNV loci. Results We propose a novel approach to infer copy unit alleles and their numbers in each sample simultaneously from population-scale HTS data by variational Bayesian inference on a generative probabilistic model inspired by latent Dirichlet allocation, which is a well studied model for document classification problems. In simulation studies, we evaluated concordance between inferred and true copy unit alleles for lower-, middle-, and higher-copy number dataset, in which precision and recall were ≥ 0.9 for data with mean coverage ≥ 10× per copy unit. We also applied the approach to HTS data of 1123 samples at highly variable salivary amylase gene locus and a pseudogene locus, and confirmed consistency of the estimated alleles within samples belonging to a trio of CEPH/Utah pedigree 1463 with 11 offspring. Conclusions Our proposed approach enables detailed analysis of copy number variations, such as association study between copy unit alleles and phenotypes or biological features including human diseases.
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11
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Gamazon E, Cox N, Davis L. Structural architecture of SNP effects on complex traits. Am J Hum Genet 2014; 95:477-89. [PMID: 25307299 DOI: 10.1016/j.ajhg.2014.09.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Accepted: 09/16/2014] [Indexed: 12/16/2022] Open
Abstract
Despite the discovery of copy-number variation (CNV) across the genome nearly 10 years ago, current SNP-based analysis methodologies continue to collapse the homozygous (i.e., A/A), hemizygous (i.e., A/0), and duplicative (i.e., A/A/A) genotype states, treating the genotype variable as irreducible or unaltered by other colocalizing forms of genetic (e.g., structural) variation. Our understanding of common, genome-wide CNVs suggests that the canonical genotype construct might belie the enormous complexity of the genome. Here we present multiple analyses of several phenotypes and provide methods supporting a conceptual shift that embraces the structural dimension of genotype. We comprehensively investigate the impact of the structural dimension of genotype on (1) GWAS methods, (2) interpretation of rare LOF variants, (3) characterization of genomic architecture, and (4) implications for mapping loci involved in complex disease. Taken together, these results argue for the inclusion of a structural dimension and suggest that some portion of the "missing" heritability might be recovered through integration of the structural dimension of SNP effects on complex traits.
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Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, De T, Coin L, de Silva R, Cookson MR, Singleton AB, Hardy J, Ryten M, Weale ME. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci 2014; 17:1418-1428. [PMID: 25174004 PMCID: PMC4208299 DOI: 10.1038/nn.3801] [Citation(s) in RCA: 485] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Accepted: 07/30/2014] [Indexed: 12/12/2022]
Abstract
Germ-line genetic control of gene expression occurs via expression quantitative trait loci (eQTLs). We present a large, exon-specific eQTL data set covering ten human brain regions. We found that cis-eQTL signals (within 1 Mb of their target gene) were numerous, and many acted heterogeneously among regions and exons. Co-regulation analysis of shared eQTL signals produced well-defined modules of region-specific co-regulated genes, in contrast to standard coexpression analysis of the same samples. We report cis-eQTL signals for 23.1% of catalogued genome-wide association study hits for adult-onset neurological disorders. The data set is publicly available via public data repositories and via http://www.braineac.org/. Our study increases our understanding of the regulation of gene expression in the human brain and will be of value to others pursuing functional follow-up of disease-associated variants.
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Affiliation(s)
- Adaikalavan Ramasamy
- Department of Medical & Molecular Genetics, King’s College London, Guy’s Hospital, London, UK
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London (UCL) Institute of Neurology, London, UK
| | - Daniah Trabzuni
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London (UCL) Institute of Neurology, London, UK
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Sebastian Guelfi
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London (UCL) Institute of Neurology, London, UK
| | - Vibin Varghese
- Department of Medical & Molecular Genetics, King’s College London, Guy’s Hospital, London, UK
| | - Colin Smith
- Department of Neuropathology, MRC Sudden Death Brain Bank Project, University of Edinburgh, Edinburgh, UK
| | - Robert Walker
- Department of Neuropathology, MRC Sudden Death Brain Bank Project, University of Edinburgh, Edinburgh, UK
| | - Tisham De
- School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | | | | | - Lachlan Coin
- Institute of Molecular Bioscience, The University of Queensland, Brisbane St Lucia, Queensland, Australia
| | - Rohan de Silva
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London (UCL) Institute of Neurology, London, UK
| | - Mark R Cookson
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - John Hardy
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London (UCL) Institute of Neurology, London, UK
| | - Mina Ryten
- Department of Medical & Molecular Genetics, King’s College London, Guy’s Hospital, London, UK
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, University College London (UCL) Institute of Neurology, London, UK
| | - Michael E Weale
- Department of Medical & Molecular Genetics, King’s College London, Guy’s Hospital, London, UK
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13
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Lin YJ, Chen YT, Hsu SN, Peng CH, Tang CY, Yen TC, Hsieh WP. HaplotypeCN: copy number haplotype inference with Hidden Markov Model and localized haplotype clustering. PLoS One 2014; 9:e96841. [PMID: 24849202 PMCID: PMC4029584 DOI: 10.1371/journal.pone.0096841] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 04/11/2014] [Indexed: 11/18/2022] Open
Abstract
Copy number variation (CNV) has been reported to be associated with disease and various cancers. Hence, identifying the accurate position and the type of CNV is currently a critical issue. There are many tools targeting on detecting CNV regions, constructing haplotype phases on CNV regions, or estimating the numerical copy numbers. However, none of them can do all of the three tasks at the same time. This paper presents a method based on Hidden Markov Model to detect parent specific copy number change on both chromosomes with signals from SNP arrays. A haplotype tree is constructed with dynamic branch merging to model the transition of the copy number status of the two alleles assessed at each SNP locus. The emission models are constructed for the genotypes formed with the two haplotypes. The proposed method can provide the segmentation points of the CNV regions as well as the haplotype phasing for the allelic status on each chromosome. The estimated copy numbers are provided as fractional numbers, which can accommodate the somatic mutation in cancer specimens that usually consist of heterogeneous cell populations. The algorithm is evaluated on simulated data and the previously published regions of CNV of the 270 HapMap individuals. The results were compared with five popular methods: PennCNV, genoCN, COKGEN, QuantiSNP and cnvHap. The application on oral cancer samples demonstrates how the proposed method can facilitate clinical association studies. The proposed algorithm exhibits comparable sensitivity of the CNV regions to the best algorithm in our genome-wide study and demonstrates the highest detection rate in SNP dense regions. In addition, we provide better haplotype phasing accuracy than similar approaches. The clinical association carried out with our fractional estimate of copy numbers in the cancer samples provides better detection power than that with integer copy number states.
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Affiliation(s)
- Yen-Jen Lin
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Yu-Tin Chen
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Shu-Ni Hsu
- Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan
| | - Chien-Hua Peng
- Department of Resource Center for Clinical Research, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chuan-Yi Tang
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
- Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan
| | - Tzu-Chen Yen
- Head and Neck Oncology Group, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Wen-Ping Hsieh
- Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan
- * E-mail:
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14
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Shi J, Yang XR, Caporaso NE, Landi MT, Li P. VTET: a variable threshold exact test for identifying disease-associated copy number variations enriched in short genomic regions. Front Genet 2014; 5:53. [PMID: 24672538 PMCID: PMC3957064 DOI: 10.3389/fgene.2014.00053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2014] [Accepted: 02/27/2014] [Indexed: 11/13/2022] Open
Abstract
Copy number variations (CNVs) constitute a major source of genetic variations in human populations and have been reported to be associated with complex diseases. Methods have been developed for detecting CNVs and testing CNV associations in genome-wide association studies (GWAS) based on SNP arrays. Commonly used two-step testing procedures work well only for long CNVs while direct CNV association testing methods work only for recurrent CNVs. Assuming that short CNVs disrupting any part of a given genomic region increase disease risk, we developed a variable threshold exact test (VTET) for testing disease associations of CNVs randomly distributed in the genome using intensity data from SNP arrays. By extensive simulations, we found that VTET outperformed two-step testing procedures based on existing CNV calling algorithms for short CNVs and that the performance of VTET was robust to the length of the genomic region. In addition, VTET had a comparable performance with CNVtools for testing the association of recurrent CNVs. Thus, we expect VTET to be useful for testing disease associations of both recurrent and randomly distributed CNVs using existing GWAS data. We applied VTET to a lung cancer GWAS and identified a genome-wide significant region on chromosome 18q22.3 for lung squamous cell carcinoma.
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Affiliation(s)
- Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health Bethesda, MD, USA
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health Bethesda, MD, USA
| | - Neil E Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health Bethesda, MD, USA
| | - Maria T Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health Bethesda, MD, USA
| | - Peng Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health Bethesda, MD, USA
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Ho Jang G, Christie JD, Feng R. A method for calling copy number polymorphism using haplotypes. Front Genet 2013; 4:165. [PMID: 24069028 PMCID: PMC3780619 DOI: 10.3389/fgene.2013.00165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 08/07/2013] [Indexed: 12/15/2022] Open
Abstract
Single nucleotide polymorphism (SNP) and copy number variation (CNV) are both widespread characteristic of the human genome, but are often called separately on common genotyping platforms. To capture integrated SNP and CNV information, methods have been developed for calling allelic specific copy numbers or so called copy number polymorphism (CNP), using limited inter-marker correlation. In this paper, we proposed a haplotype-based maximum likelihood method to call CNP, which takes advantage of the valuable multi-locus linkage disequilibrium (LD) information in the population. We also developed a computationally efficient algorithm to estimate haplotype frequencies and optimize individual CNP calls iteratively, even at presence of missing data. Through simulations, we demonstrated our model is more sensitive and accurate in detecting various CNV regions, compared with commonly-used CNV calling methods including PennCNV, another hidden Markov model (HMM) using CNP, a scan statistic, segCNV, and cnvHap. Our method often performs better in the regions with higher LD, in longer CNV regions, and in common CNV than the opposite. We implemented our method on the genotypes of 90 HapMap CEU samples and 23 patients with acute lung injury (ALI). For each ALI patient the genotyping was performed twice. The CNPs from our method show good consistency and accuracy comparable to others.
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Affiliation(s)
- Gun Ho Jang
- Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Philadelphia, PA, USA
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16
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Age-related macular degeneration (AMD): Current concepts in pathogenesis and prospects for treatment. Tissue Eng Regen Med 2013. [DOI: 10.1007/s13770-012-0374-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Speed D, Hoggart C, Petrovski S, Tachmazidou I, Coffey A, Jorgensen A, Eleftherohorinou H, De Iorio M, Todaro M, De T, Smith D, Smith PE, Jackson M, Cooper P, Kellett M, Howell S, Newton M, Yerra R, Tan M, French C, Reuber M, Sills GE, Chadwick D, Pirmohamed M, Bentley D, Scheffer I, Berkovic S, Balding D, Palotie A, Marson A, O'Brien TJ, Johnson MR. A genome-wide association study and biological pathway analysis of epilepsy prognosis in a prospective cohort of newly treated epilepsy. Hum Mol Genet 2013; 23:247-58. [PMID: 23962720 DOI: 10.1093/hmg/ddt403] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
We present the analysis of a prospective multicentre study to investigate genetic effects on the prognosis of newly treated epilepsy. Patients with a new clinical diagnosis of epilepsy requiring medication were recruited and followed up prospectively. The clinical outcome was defined as freedom from seizures for a minimum of 12 months in accordance with the consensus statement from the International League Against Epilepsy (ILAE). Genetic effects on remission of seizures after starting treatment were analysed with and without adjustment for significant clinical prognostic factors, and the results from each cohort were combined using a fixed-effects meta-analysis. After quality control (QC), we analysed 889 newly treated epilepsy patients using 472 450 genotyped and 6.9 × 10(6) imputed single-nucleotide polymorphisms. Suggestive evidence for association (defined as Pmeta < 5.0 × 10(-7)) with remission of seizures after starting treatment was observed at three loci: 6p12.2 (rs492146, Pmeta = 2.1 × 10(-7), OR[G] = 0.57), 9p23 (rs72700966, Pmeta = 3.1 × 10(-7), OR[C] = 2.70) and 15q13.2 (rs143536437, Pmeta = 3.2 × 10(-7), OR[C] = 1.92). Genes of biological interest at these loci include PTPRD and ARHGAP11B (encoding functions implicated in neuronal development) and GSTA4 (a phase II biotransformation enzyme). Pathway analysis using two independent methods implicated a number of pathways in the prognosis of epilepsy, including KEGG categories 'calcium signaling pathway' and 'phosphatidylinositol signaling pathway'. Through a series of power curves, we conclude that it is unlikely any single common variant explains >4.4% of the variation in the outcome of newly treated epilepsy.
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Affiliation(s)
- Doug Speed
- UCL Genetics Institute, University College London WC1E 6BT, UK
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Valsesia A, Macé A, Jacquemont S, Beckmann JS, Kutalik Z. The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation. Front Genet 2013; 4:92. [PMID: 23750167 PMCID: PMC3667386 DOI: 10.3389/fgene.2013.00092] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2013] [Accepted: 05/04/2013] [Indexed: 02/03/2023] Open
Abstract
Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.
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Affiliation(s)
- Armand Valsesia
- Genetics Core, Nestlé Institute of Health Sciences Lausanne, Switzerland
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Coin LJM, Cao D, Ren J, Zuo X, Sun L, Yang S, Zhang X, Cui Y, Li Y, Jin X, Wang J. An exome sequencing pipeline for identifying and genotyping common CNVs associated with disease with application to psoriasis. Bioinformatics 2013; 28:i370-i374. [PMID: 22962454 PMCID: PMC3436806 DOI: 10.1093/bioinformatics/bts379] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Motivation: Despite the prevalence of copy number variation (CNV) in the human genome, only a handful of confirmed associations have been reported between common CNVs and complex disease. This may be partially attributed to the difficulty in accurately genotyping CNVs in large cohorts using array-based technologies. Exome sequencing is now widely being applied to case–control cohorts and presents an exciting opportunity to look for common CNVs associated with disease. Results: We developed ExoCNVTest: an exome sequencing analysis pipeline to identify disease-associated CNVs and to generate absolute copy number genotypes at putatively associated loci. Our method re-discovered the LCE3B_LCE3C CNV association with psoriasis (P-value = 5 × 10e−6) while controlling inflation of test statistics (λ < 1). ExoCNVTest-derived absolute CNV genotypes were 97.4% concordant with PCR-derived genotypes at this locus. Availability and implementation: ExoCNVTest has been implemented in Java and R and is freely available from www1.imperial.ac.uk/medicine/people/l.coin/. Contact:wangj@genomics.org.cn or Lachlan.J.M.Coin@genomics.org.cn
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Walters RG, Coin LJM, Ruokonen A, de Smith AJ, El-Sayed Moustafa JS, Jacquemont S, Elliott P, Esko T, Hartikainen AL, Laitinen J, Männik K, Martinet D, Meyre D, Nauck M, Schurmann C, Sladek R, Thorleifsson G, Thorsteinsdóttir U, Valsesia A, Waeber G, Zufferey F, Balkau B, Pattou F, Metspalu A, Völzke H, Vollenweider P, Stefansson K, Järvelin MR, Beckmann JS, Froguel P, Blakemore AIF. Rare genomic structural variants in complex disease: lessons from the replication of associations with obesity. PLoS One 2013; 8:e58048. [PMID: 23554873 PMCID: PMC3595275 DOI: 10.1371/journal.pone.0058048] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Accepted: 01/30/2013] [Indexed: 01/19/2023] Open
Abstract
The limited ability of common variants to account for the genetic contribution to complex disease has prompted searches for rare variants of large effect, to partly explain the ‘missing heritability’. Analyses of genome-wide genotyping data have identified genomic structural variants (GSVs) as a source of such rare causal variants. Recent studies have reported multiple GSV loci associated with risk of obesity. We attempted to replicate these associations by similar analysis of two familial-obesity case-control cohorts and a population cohort, and detected GSVs at 11 out of 18 loci, at frequencies similar to those previously reported. Based on their reported frequencies and effect sizes (OR≥25), we had sufficient statistical power to detect the large majority (80%) of genuine associations at these loci. However, only one obesity association was replicated. Deletion of a 220 kb region on chromosome 16p11.2 has a carrier population frequency of 2×10−4 (95% confidence interval [9.6×10−5–3.1×10−4]); accounts overall for 0.5% [0.19%–0.82%] of severe childhood obesity cases (P = 3.8×10−10; odds ratio = 25.0 [9.9–60.6]); and results in a mean body mass index (BMI) increase of 5.8 kg.m−2 [1.8–10.3] in adults from the general population. We also attempted replication using BMI as a quantitative trait in our population cohort; associations with BMI at or near nominal significance were detected at two further loci near KIF2B and within FOXP2, but these did not survive correction for multiple testing. These findings emphasise several issues of importance when conducting rare GSV association, including the need for careful cohort selection and replication strategy, accurate GSV identification, and appropriate correction for multiple testing and/or control of false discovery rate. Moreover, they highlight the potential difficulty in replicating rare CNV associations across different populations. Nevertheless, we show that such studies are potentially valuable for the identification of variants making an appreciable contribution to complex disease.
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Affiliation(s)
- Robin G. Walters
- Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, United Kingdom
| | - Lachlan J. M. Coin
- Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Aimo Ruokonen
- Institute of Diagnostics, Clinical Chemistry, University of Oulu, Oulu, Finland
- Oulu University Hospital, Oulu, Finland
| | - Adam J. de Smith
- Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
| | | | - Sebastien Jacquemont
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- MRC Health Protection Agency (HPA) Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Tõnu Esko
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Anna-Liisa Hartikainen
- Institute of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, Oulu, Finland
| | | | - Katrin Männik
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- The Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Danielle Martinet
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - David Meyre
- CNRS 8199-Institute of Biology, Pasteur Institute, Lille, France
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, Ernst-Moritz-Arndt-University, Greifswald, Germany
| | - Claudia Schurmann
- Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University, Greifswald, Germany
| | - Rob Sladek
- McGill University and Genome Quebec Innovation Centre, Montreal, Canada
- Department of Medicine and Human Genetics, McGill University, Montreal, Canada
| | | | - Unnur Thorsteinsdóttir
- deCODE Genetics, Reykjavík, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Armand Valsesia
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Gerard Waeber
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Flore Zufferey
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Beverley Balkau
- INSERM, CESP Centre for Research in Epidemiology and Population Health, U1018, Villejuif, France
- University Paris Sud 11, UMRS 1018, Villejuif, France
| | - François Pattou
- INSERM U859, Lille, France
- Université Lille Nord de France, Centre Hospitalier Universitaire Lille, Lille, France
| | - Andres Metspalu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Henry Völzke
- Institute for Community Medicine, Ernst-Moritz-Arndt-University, Greifswald, Germany
| | - Peter Vollenweider
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Kári Stefansson
- deCODE Genetics, Reykjavík, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- MRC Health Protection Agency (HPA) Centre for Environment and Health, Imperial College London, London, United Kingdom
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Department of Lifecourse and Services, National Institute for Health and Welfare, Oulu, Finland
| | - Jacques S. Beckmann
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
| | - Philippe Froguel
- Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
- CNRS 8199-Institute of Biology, Pasteur Institute, Lille, France
- * E-mail: (AIFB); (PF)
| | - Alexandra I. F. Blakemore
- Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
- Section of Investigative Medicine, Imperial College London, London, United Kingdom
- * E-mail: (AIFB); (PF)
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Bellos E, Johnson MR, Coin LJM. cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data. Genome Biol 2012; 13:R120. [PMID: 23259578 PMCID: PMC4056371 DOI: 10.1186/gb-2012-13-12-r120] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2012] [Accepted: 12/22/2012] [Indexed: 02/08/2023] Open
Abstract
Recent advances in sequencing technologies provide the means for identifying copy number variation (CNV) at an unprecedented resolution. A single next-generation sequencing experiment offers several features that can be used to detect CNV, yet current methods do not incorporate all available signatures into a unified model. cnvHiTSeq is an integrative probabilistic method for CNV discovery and genotyping that jointly analyzes multiple features at the population level. By combining evidence from complementary sources, cnvHiTSeq achieves high genotyping accuracy and a substantial improvement in CNV detection sensitivity over existing methods, while maintaining a low false discovery rate. cnvHiTSeq is available at http://sourceforge.net/projects/cnvhitseq.
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Costelloe SJ, El-Sayed Moustafa JS, Drenos F, Palmen J, Li Q, Qiao L, Whiting S, Thomas M, Kivimaki M, Kumari M, Hingorani AD, Tzoulaki I, Järvelin MR, Marjo-Riitta J, Ruokonen A, Aimo R, Hartikainen AL, Pouta A, Walters RG, Blakemore AIF, Humphries SE, Coin LJM, Talmud PJ. Gene-targeted analysis of copy number variants identifies 3 novel associations with coronary heart disease traits. CIRCULATION. CARDIOVASCULAR GENETICS 2012; 5:555-60. [PMID: 22972876 DOI: 10.1161/circgenetics.111.961037] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Copy number variants (CNVs) are a major form of genomic variation, which may be implicated in complex disease phenotypes. However, investigation of the role of CNVs in coronary heart disease (CHD) traits has been limited. METHODS AND RESULTS We examined the use of the cnvHap algorithm for CNV detection, using data for 2500 men from the Second Northwick Park Heart Study (NPHS-II). An Illumina custom chip, including 722 single-nucleotide polymorphisms covering 76 coronary heart disease-trait genes, was used. Common CNVs were significantly associated (at P<0.05, after correction) with coronary heart disease phenotypes in 5 genes. Novel associations of CNVs in toll-like receptor-4 with apolipoprotein AI were replicated (P<0.05) in the Whitehall II cohort (4887 subjects), whereas newly described associations of CNVs in sterol regulatory element-binding protein with apolipoprotein AI and associations of interleukin-6 signal transducer with apolipoprotein B were replicated in the data from 3546 subjects from the North Finnish Birth Cohort 1966 (P<0.05). CONCLUSIONS This study supports the use of CNV detection algorithms such as cnvHap as potential tools for the identification of novel CNVs, some of which show significant association and replication with coronary heart disease risk phenotypes. However, the functional basis for these associations requires further substantiation.
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Affiliation(s)
- Seán J Costelloe
- Center for Cardiovascular Genetics, Institute of Cardiovascular Science, The Royal Free London NHS Foundation Trust, Pond St, London, UK.
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Shi J, Li P. An integrative segmentation method for detecting germline copy number variations in SNP arrays. Genet Epidemiol 2012; 36:373-83. [PMID: 22539397 DOI: 10.1002/gepi.21631] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Germline copy number variations (CNVs) are a major source of genetic variation in humans. In large-scale studies of complex diseases, CNVs are usually detected from data generated by single nucleotide polymorphism (SNP) genotyping arrays. In this paper, we develop an integrative segmentation method, SegCNV, for detecting CNVs integrating both log R ratio (LRR) and B allele frequency (BAF). Based on simulation studies, SegCNV had modestly better power to detect deletions and substantially better power to detect duplications compared with circular binary segmentation (CBS) that relies purely on LRRs; and it had better power to detect deletions and a comparable performance to detect duplications compared with PennCNV and QuantiSNP. In two Hapmap subjects with deep sequence data available as a gold standard, SegCNV detected more true short deletions than PennCNV and QuantiSNP. For 21 short duplications validated experimentally in the AGRE dataset, SegCNV, QuantiSNP, and PennCNV detected all of them while CBS detected only three. SegCNV is much faster than the HMM-based (where HMM is hidden Markov model) methods, taking only several seconds to analyze genome-wide data for one subject.
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Affiliation(s)
- Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20854, USA.
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Marenne G, Real FX, Rothman N, Rodríguez-Santiago B, Pérez-Jurado L, Kogevinas M, García-Closas M, Silverman DT, Chanock SJ, Génin E, Malats N. 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; 13:326. [PMID: 22817656 PMCID: PMC3425254 DOI: 10.1186/1471-2164-13-326] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2011] [Accepted: 07/20/2012] [Indexed: 12/15/2022] Open
Abstract
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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Affiliation(s)
- Gaëlle Marenne
- Spanish National Cancer Research Center (CNIO), Madrid, E-28029, Spain
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Tuo J, Grob S, Zhang K, Chan CC. Genetics of immunological and inflammatory components in age-related macular degeneration. Ocul Immunol Inflamm 2012; 20:27-36. [PMID: 22324898 DOI: 10.3109/09273948.2011.628432] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Age-related macular degeneration (AMD), affecting 30 to 50 million elder individuals worldwide, is a disease affecting the macular retina and choroid that can lead to irreversible central vision loss and blindness. Recent findings support a role for immunologic processes in AMD pathogenesis, including generation of inflammatory related molecules in the Bruch's membrane, recruitment of macrophages, complement activation, microglial activation and accumulation in the macular lesions. Pro-inflammatory effects of chronic inflammation and oxidative stress can result in abnormal retinal pigment epithelium, photoreceptor atrophy and choroidal neovascularization. The associations of immunological and inflammatory genes, in particular the genes related to innate immunity with AMD support the involvement of various immunological pathways in the AMD pathogenesis. We review the literature on the involvements of inflammatory genes in AMD, highlight recent genetic discoveries, and discuss the potential application of such knowledge in the management of patients with AMD.
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Affiliation(s)
- Jingsheng Tuo
- Immunopathology Section, Laboratory of Immunology, National Eye Institute, National Institutes of Health, Bethesda, MD 20892-1857, USA
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26
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Valsesia A, Stevenson BJ, Waterworth D, Mooser V, Vollenweider P, Waeber G, Jongeneel CV, Beckmann JS, Kutalik Z, Bergmann S. Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort. BMC Genomics 2012; 13:241. [PMID: 22702538 PMCID: PMC3464625 DOI: 10.1186/1471-2164-13-241] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2011] [Accepted: 06/15/2012] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets. RESULTS Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs. CONCLUSION Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.
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Affiliation(s)
- Armand Valsesia
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
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27
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Kumasaka N, Fujisawa H, Hosono N, Okada Y, Takahashi A, Nakamura Y, Kubo M, Kamatani N. PlatinumCNV: a Bayesian Gaussian mixture model for genotyping copy number polymorphisms using SNP array signal intensity data. Genet Epidemiol 2012; 35:831-44. [PMID: 22125222 DOI: 10.1002/gepi.20633] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We present a statistical model for allele-specific patterns of copy number polymorphisms (CNPs) in commercial single nucleotide polymorphism (SNP) array data. This model is based on the observation that fluorescent signal intensities tend to cluster into clouds of similar allele-specific copy number (ASCN) genotypes at each SNP locus. To capture the tendency of this clustering to be made vague by instrumental errors, our model allows for cluster memberships to overlap each other, according to a Bayesian Gaussian mixture model (GMM). This approach is flexible, allowing for both absolute scale differences and X/Y scale imbalances of fluorescent signal intensities. The resulting model is also robust toward unobserved ASCN genotypes, which can be problematic for ordinary GMMs. We illustrated the utility of the model by applying it to commercial SNP array intensity data obtained from the Illumina HumanHap 610K platform. We retrieved more than 4,000 allele-specific CNPs, though 99% of them showed rather simple allele-specific CNP patterns with only a single aneuploid haplotype among the normal haplotypes. The genotyping accuracy was assessed by two approaches, quantitative PCR and replicated subjects. The results of both of these approaches demonstrated mean genotyping error rates of 1%. We demonstrated a preliminary genome-wide association study of three hematological traits. The result exhibited that it could form the foundation for new, more effective statistical methods for the mapping of both disease genes and quantitative trait loci with genome-wide CNPs. The methods described in this work are implemented in a software package, PlatinumCNV, available on the Internet.
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Affiliation(s)
- Natsuhiko Kumasaka
- Research Group for Medical Informatics, Center for Genomic Medicine, RIKEN, Shirokane-dai, Minato-ku,Tokyo, Japan.
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28
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El-Sayed Moustafa JS, Eleftherohorinou H, de Smith AJ, Andersson-Assarsson JC, Alves AC, Hadjigeorgiou E, Walters RG, Asher JE, Bottolo L, Buxton JL, Sladek R, Meyre D, Dina C, Visvikis-Siest S, Jacobson P, Sjöström L, Carlsson LMS, Walley A, Falchi M, Froguel P, Blakemore AIF, Coin LJM. Novel association approach for variable number tandem repeats (VNTRs) identifies DOCK5 as a susceptibility gene for severe obesity. Hum Mol Genet 2012; 21:3727-38. [PMID: 22595969 DOI: 10.1093/hmg/dds187] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Variable number tandem repeats (VNTRs) constitute a relatively under-examined class of genomic variants in the context of complex disease because of their sequence complexity and the challenges in assaying them. Recent large-scale genome-wide copy number variant mapping and association efforts have highlighted the need for improved methodology for association studies using these complex polymorphisms. Here we describe the in-depth investigation of a complex region on chromosome 8p21.2 encompassing the dedicator of cytokinesis 5 (DOCK5) gene. The region includes two VNTRs of complex sequence composition which flank a common 3975 bp deletion, all three of which were genotyped by polymerase chain reaction and fragment analysis in a total of 2744 subjects. We have developed a novel VNTR association method named VNTRtest, suitable for association analysis of multi-allelic loci with binary and quantitative outcomes, and have used this approach to show significant association of the DOCK5 VNTRs with childhood and adult severe obesity (P(empirical)= 8.9 × 10(-8) and P= 3.1 × 10(-3), respectively) which we estimate explains ~0.8% of the phenotypic variance. We also identified an independent association between the 3975 base pair (bp) deletion and obesity, explaining a further 0.46% of the variance (P(combined)= 1.6 × 10(-3)). Evidence for association between DOCK5 transcript levels and the 3975 bp deletion (P= 0.027) and both VNTRs (P(empirical)= 0.015) was also identified in adipose tissue from a Swedish family sample, providing support for a functional effect of the DOCK5 deletion and VNTRs. These findings highlight the potential role of DOCK5 in human obesity and illustrate a novel approach for analysis of the contribution of VNTRs to disease susceptibility through association studies.
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Affiliation(s)
- Julia S El-Sayed Moustafa
- Department of Genomics of Common Disease, School of Public Health Inperial College, London, W12 ONN, UK
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29
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Xue F, Li S, Luan J, Yuan Z, Luben RN, Khaw KT, Wareham NJ, Loos RJF, Zhao JH. A latent variable partial least squares path modeling approach to regional association and polygenic effect with applications to a human obesity study. PLoS One 2012; 7:e31927. [PMID: 22384102 PMCID: PMC3288051 DOI: 10.1371/journal.pone.0031927] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2011] [Accepted: 01/18/2012] [Indexed: 01/10/2023] Open
Abstract
Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a range of effect and sample sizes. When applied to 12 candidate regions in data (N = 2,417) from the European Prospective Investigation of Cancer (EPIC)-Norfolk study, a region in FTO was found to have stronger association (rs7204609∼rs9939881 at the first intron P = 4.29×10(-7)) than single SNP analysis (all with P>10(-4)) and a latent quantitative phenotype was obtained using a subset sample of EPIC-Norfolk (N = 12,559). We believe our method is appropriate for assessment of regional association and polygenic effect on a single or multiple traits.
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Affiliation(s)
- Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China
- MRC Epidemiology Unit and Institute of Metabolic Science, Cambridge, United Kingdom
| | - Shengxu Li
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Jian'an Luan
- MRC Epidemiology Unit and Institute of Metabolic Science, Cambridge, United Kingdom
| | - Zhongshang Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China
| | - Robert N. Luben
- Strangeways Research Laboratory, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas J. Wareham
- MRC Epidemiology Unit and Institute of Metabolic Science, Cambridge, United Kingdom
| | - Ruth J. F. Loos
- MRC Epidemiology Unit and Institute of Metabolic Science, Cambridge, United Kingdom
| | - Jing Hua Zhao
- MRC Epidemiology Unit and Institute of Metabolic Science, Cambridge, United Kingdom
- * E-mail:
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30
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Pandey P, Harbinder S. The Caenorhabditis elegans D2-like dopamine receptor DOP-2 physically interacts with GPA-14, a Gαi subunit. J Mol Signal 2012; 7:3. [PMID: 22280843 PMCID: PMC3297496 DOI: 10.1186/1750-2187-7-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2011] [Accepted: 01/26/2012] [Indexed: 01/11/2023] Open
Abstract
Dopaminergic inputs are sensed on the cell surface by the seven-transmembrane dopamine receptors that belong to a superfamily of G-protein-coupled receptors (GPCRs). Dopamine receptors are classified as D1-like or D2-like receptors based on their homology and pharmacological profiles. In addition to well established G-protein coupled mechanism of dopamine receptors in mammalian system they can also interact with other signaling pathways. In C. elegans four dopamine receptors (dop-1, dop-2, dop-3 and dop-4) have been reported and they have been implicated in a wide array of behavioral and physiological processes. We performed this study to assign the signaling pathway for DOP-2, a D2-like dopamine receptor using a split-ubiquitin based yeast two-hybrid screening of a C. elegans cDNA library with a novel dop-2 variant (DOP-2XL) as bait. Our yeast two-hybrid screening resulted in identification of gpa-14, as one of the positively interacting partners. gpa-14 is a Gα coding sequence and shows expression overlap with dop-2 in C. elegans ADE deirid neurons. In-vitro pull down assays demonstrated physical coupling between dopamine receptor DOP-2XL and GPA-14. Further, we sought to determine the DOP-2 region necessary for GPA-14 coupling. We generated truncated DOP-2XL constructs and performed pair-wise yeast two-hybrid assay with GPA-14 followed by in-vitro interaction studies and here we report that the third intracellular loop is the key domain responsible for DOP-2 and GPA-14 coupling. Our results show that the extra-long C. elegans D2-like receptor is coupled to gpa-14 that has no mammalian homolog but shows close similarity to inhibitory G-proteins. Supplementing earlier investigations, our results demonstrate the importance of an invertebrate D2-like receptor's third intracellular loop in its G-protein interaction.
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Affiliation(s)
- Pratima Pandey
- Department of Biological Sciences, Delaware State University, Dover, DE 19901, USA.
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31
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Lou H, Li S, Yang Y, Kang L, Zhang X, Jin W, Wu B, Jin L, Xu S. A map of copy number variations in Chinese populations. PLoS One 2011; 6:e27341. [PMID: 22087296 PMCID: PMC3210162 DOI: 10.1371/journal.pone.0027341] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2011] [Accepted: 10/14/2011] [Indexed: 12/02/2022] Open
Abstract
It has been shown that the human genome contains extensive copy number variations (CNVs). Investigating the medical and evolutionary impacts of CNVs requires the knowledge of locations, sizes and frequency distribution of them within and between populations. However, CNV study of Chinese minorities, which harbor the majority of genetic diversity of Chinese populations, has been underrepresented considering the same efforts in other populations. Here we constructed, to our knowledge, a first CNV map in seven Chinese populations representing the major linguistic groups in China with 1,440 CNV regions identified using Affymetrix SNP 6.0 Array. Considerable differences in distributions of CNV regions between populations and substantial population structures were observed. We showed that ∼35% of CNV regions identified in minority ethnic groups are not shared by Han Chinese population, indicating that the contribution of the minorities to genetic architecture of Chinese population could not be ignored. We further identified highly differentiated CNV regions between populations. For example, a common deletion in Dong and Zhuang (44.4% and 50%), which overlaps two keratin-associated protein genes contributing to the structure of hair fibers, was not observed in Han Chinese. Interestingly, the most differentiated CNV deletion between HapMap CEU and YRI containing CCL3L1 gene reported in previous studies was also the highest differentiated regions between Tibetan and other populations. Besides, by jointly analyzing CNVs and SNPs, we found a CNV region containing gene CTDSPL were in almost perfect linkage disequilibrium between flanking SNPs in Tibetan while not in other populations except HapMap CHD. Furthermore, we found the SNP taggability of CNVs in Chinese populations was much lower than that in European populations. Our results suggest the necessity of a full characterization of CNVs in Chinese populations, and the CNV map we constructed serves as a useful resource in further evolutionary and medical studies.
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Affiliation(s)
- Haiyi Lou
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Shilin Li
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yajun Yang
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Longli Kang
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xin Zhang
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Wenfei Jin
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Bailin Wu
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
- Children's Hospital Boston, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Li Jin
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Shuhua Xu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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32
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Jacquemont S, Reymond A, Zufferey F, Harewood L, Walters RG, Kutalik Z, Martinet D, Shen Y, Valsesia A, Beckmann ND, Thorleifsson G, Belfiore M, Bouquillon S, Campion D, De Leeuw N, De Vries BBA, Esko T, Fernandez BA, Fernández-Aranda F, Fernández-Real JM, Gratacòs M, Guilmatre A, Hoyer J, Jarvelin MR, Kooy FR, Kurg A, Le Caignec C, Männik K, Platt OS, Sanlaville D, Van Haelst MM, Villatoro Gomez S, Walha F, Wu BL, Yu Y, Aboura A, Addor MC, Alembik Y, Antonarakis SE, Arveiler B, Barth M, Bednarek N, Béna F, Bergmann S, Beri M, Bernardini L, Blaumeiser B, Bonneau D, Bottani A, Boute O, Brunner HG, Cailley D, Callier P, Chiesa J, Chrast J, Coin L, Coutton C, Cuisset JM, Cuvellier JC, David A, De Freminville B, Delobel B, Delrue MA, Demeer B, Descamps D, Didelot G, Dieterich K, Disciglio V, Doco-Fenzy M, Drunat S, Duban-Bedu B, Dubourg C, El-Sayed Moustafa JS, Elliott P, Faas BHW, Faivre L, Faudet A, Fellmann F, Ferrarini A, Fisher R, Flori E, Forer L, Gaillard D, Gerard M, Gieger C, Gimelli S, Gimelli G, Grabe HJ, Guichet A, Guillin O, Hartikainen AL, Heron D, Hippolyte L, Holder M, Homuth G, Isidor B, Jaillard S, Jaros Z, Jiménez-Murcia S, Joly Helas G, Jonveaux P, Kaksonen S, Keren B, Kloss-Brandstätter A, Knoers NVAM, Koolen DA, Kroisel PM, Kronenberg F, Labalme A, Landais E, Lapi E, Layet V, Legallic S, Leheup B, Leube B, Lewis S, Lucas J, Macdermot KD, Magnusson P, Marshall CR, Mathieu-Dramard M, Mccarthy MI, Meitinger T, Antonietta Mencarelli M, Merla G, Moerman A, Mooser V, Morice-Picard F, Mucciolo M, Nauck M, Coumba Ndiaye N, Nordgren A, Pasquier L, Petit F, Pfundt R, Plessis G, Rajcan-Separovic E, Paolo Ramelli G, Rauch A, Ravazzolo R, Reis A, Renieri A, Richart C, Ried JS, Rieubland C, Roberts W, Roetzer KM, Rooryck C, Rossi M, Saemundsen E, Satre V, Schurmann C, Sigurdsson E, Stavropoulos DJ, Stefansson H, Tengström C, Thorsteinsdóttir U, Tinahones FJ, Touraine R, Vallée L, Van Binsbergen E, Van Der Aa N, Vincent-Delorme C, Visvikis-Siest S, Vollenweider P, Völzke H, Vulto-Van Silfhout AT, Waeber G, Wallgren-Pettersson C, Witwicki RM, Zwolinksi S, Andrieux J, Estivill X, Gusella JF, Gustafsson O, Metspalu A, Scherer SW, Stefansson K, Blakemore AIF, Beckmann JS, Froguel P. Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. Nature 2011; 478:97-102. [PMID: 21881559 PMCID: PMC3637175 DOI: 10.1038/nature10406] [Citation(s) in RCA: 309] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2011] [Accepted: 07/29/2011] [Indexed: 12/25/2022]
Abstract
Both obesity and being underweight have been associated with increased mortality. Underweight, defined as a body mass index (BMI) ≤ 18.5 kg per m(2) in adults and ≤ -2 standard deviations from the mean in children, is the main sign of a series of heterogeneous clinical conditions including failure to thrive, feeding and eating disorder and/or anorexia nervosa. In contrast to obesity, few genetic variants underlying these clinical conditions have been reported. We previously showed that hemizygosity of a ∼600-kilobase (kb) region on the short arm of chromosome 16 causes a highly penetrant form of obesity that is often associated with hyperphagia and intellectual disabilities. Here we show that the corresponding reciprocal duplication is associated with being underweight. We identified 138 duplication carriers (including 132 novel cases and 108 unrelated carriers) from individuals clinically referred for developmental or intellectual disabilities (DD/ID) or psychiatric disorders, or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight and BMI. Half of the boys younger than five years are underweight with a probable diagnosis of failure to thrive, whereas adult duplication carriers have an 8.3-fold increased risk of being clinically underweight. We observe a trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive eating behaviours and a significant reduction in head circumference. Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus. The phenotypes correlate with changes in transcript levels for genes mapping within the duplication but not in flanking regions. The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance.
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Affiliation(s)
| | - Alexandre Reymond
- Centre de génomique intégrative
Université de Lausanne1015 Lausanne,CH
| | - Flore Zufferey
- Service de génétique médicale
CHU Vaudois1011 Lausanne,CH
| | - Louise Harewood
- Centre de génomique intégrative
Université de Lausanne1015 Lausanne,CH
| | - Robin G. Walters
- Department of Genomics of Common Disease
Imperial College LondonHammersmith hospital, London W12 0NN,GB
| | - Zoltán Kutalik
- Department of Medical Genetics
University of LausanneCH
- SIB, Swiss Institute of Bioinformatics
Swiss Institute of BioinformaticsQuartier Sorge - Batiment Genopode 1015 Lausanne Switzerland,CH
| | | | - Yiping Shen
- Laboratory Medicine
Children's Hospital BostonBoston, Massachusetts 02115,US
- Center for Human Genetic Research
Massachusetts General HospitalBoston, Massachusetts 02114,US
| | - Armand Valsesia
- Department of Medical Genetics
University of LausanneCH
- SIB, Swiss Institute of Bioinformatics
Swiss Institute of BioinformaticsQuartier Sorge - Batiment Genopode 1015 Lausanne Switzerland,CH
- Ludwig Institute for Cancer Research
Université de Lausanne1015 Lausanne,CH
| | | | | | - Marco Belfiore
- Service de génétique médicale
CHU Vaudois1011 Lausanne,CH
| | - Sonia Bouquillon
- Laboratoire de Génétique Médicale
Hôpital Jeanne de FlandreCHRU Lille59037 Lille Cedex,FR
| | - Dominique Campion
- Génétique médicale et fonctionnelle du cancer et des maladies neuropsychiatriques
INSERM : U614Université de RouenUFR de Medecine et de Pharmacie 22, Boulevard Gambetta 76183 Rouen cedex,FR
- Estonian Genome and Medicine
University of Tartu51010 Tartu,EE
| | - Nicole De Leeuw
- Department of human genetics
Radboud University Nijmegen Medical CentreNijmegen Centre for Molecular Life SciencesInstitute for Genetic and Metabolic Disorders6500 HB Nijmegen,NL
| | - Bert B. A. De Vries
- Department of human genetics
Radboud University Nijmegen Medical CentreNijmegen Centre for Molecular Life SciencesInstitute for Genetic and Metabolic Disorders6500 HB Nijmegen,NL
| | - Tõnu Esko
- Estonian Genome and Medicine
University of Tartu51010 Tartu,EE
- Institute of Molecular and Cell Biology
University of Tartu51010 Tartu,EE
| | - Bridget A. Fernandez
- Disciplines of Genetics and Medicine
Memorial University of NewfoundlandSt. John's Newfoundland,CA
| | - Fernando Fernández-Aranda
- IDIBELL, Department of Psychiatry
University Hospital of BellvitgeCIBERobn Fisiopatología de la Obesidad y Nutrición08907 Barcelona,ES
| | - José Manuel Fernández-Real
- Section of Diabetes, Endocrinology and Nutrition
University Hospital of GironaBiomedical Research Institute "Dr Josep Trueta"CIBERobn Fisiopatología de la Obesidad y Nutrición17007 Girona,ES
| | - Mònica Gratacòs
- CRG-UPF, Center for Genomic Regulation
CIBER de Epidemiología y Salud Pública (CIBERESP)C/ Dr. Aiguader, 88 08003 Barcelona, Catalonia, Spain,ES
| | - Audrey Guilmatre
- Génétique médicale et fonctionnelle du cancer et des maladies neuropsychiatriques
INSERM : U614Université de RouenUFR de Medecine et de Pharmacie 22, Boulevard Gambetta 76183 Rouen cedex,FR
- Estonian Genome and Medicine
University of Tartu51010 Tartu,EE
| | - Juliane Hoyer
- Institute of Human Genetics
Friedrich-Alexander University Erlangen-Nuremberg91054 Erlangen,DE
| | - Marjo-Riitta Jarvelin
- Department of child and adolescent health
National Institute for Health and WelfareUniversity of OuluInstitute of Health Sciences and Biocenter OuluBox 310, 90101 Oulu,FI
| | - Frank R. Kooy
- Department of Medical Genetics
University Hospital Antwerp2650 Edegem,BE
| | - Ants Kurg
- Institute of Molecular and Cell Biology
University of Tartu51010 Tartu,EE
| | - Cédric Le Caignec
- Service d'ORL et de Chirurgie Cervicofaciale
INSERM : U587Hôpital d'Enfants Armand-TrousseauUniversité Pierre et Marie Curie - Paris 6Paris,FR
| | - Katrin Männik
- Institute of Molecular and Cell Biology
University of Tartu51010 Tartu,EE
| | - Orah S. Platt
- Laboratory Medicine
Children's Hospital BostonBoston, Massachusetts 02115,US
| | - Damien Sanlaville
- Service de cytogénétique constitutionnelle
Hospices Civils de LyonCHU de LyonCentre Neuroscience et Recherche69000 Lyon,FR
| | - Mieke M. Van Haelst
- Department of Genomics of Common Disease
Imperial College LondonHammersmith hospital, London W12 0NN,GB
- Department of Medical Genetics
University Medical Center Utrecht3584 EA Utrecht,NL
| | - Sergi Villatoro Gomez
- CRG-UPF, Center for Genomic Regulation
CIBER de Epidemiología y Salud Pública (CIBERESP)C/ Dr. Aiguader, 88 08003 Barcelona, Catalonia, Spain,ES
| | - Faida Walha
- Centre de génomique intégrative
Université de Lausanne1015 Lausanne,CH
| | - Bai-Lin Wu
- Laboratory Medicine
Children's Hospital BostonBoston, Massachusetts 02115,US
- Institutes of Biomedical Science
Fudan UniversityChildren's Hospital200032 Shanghai,CN
| | - Yongguo Yu
- Laboratory Medicine
Children's Hospital BostonBoston, Massachusetts 02115,US
- Shanghai Children's Medical Center
Shanghai Children's Medical Center200127 Shanghai,CN
| | - Azzedine Aboura
- Département de génétique
Assistance publique - Hôpitaux de Paris (AP-HP)Hôpital Robert DebréUniversité Paris VII - Paris Diderot48, boulevard Sérurier 75935 Paris cedex 19,FR
| | | | - Yves Alembik
- Service de cytogénétique
CHU StrasbourgHôpital de Hautepierre1 Av Moliere 67098 Strasbourg Cedex,FR
| | | | - Benoît Arveiler
- MRGM, Maladies Rares - Génétique et Métabolisme
Hôpital PellegrinService de Génétique Médicale du CHU de BordeauxUniversité Victor Segalen - Bordeaux II : EA4576146 rue Léo-Saignat - 33076 Bordeaux Cedex,FR
- Service de génétique médicale
CHU BordeauxGroupe hospitalier PellegrinUniversité de BordeauxBordeaux,FR
| | - Magalie Barth
- Service de génétique [Angers]
CHU AngersUniversité d'Angersrue Larrey, 49100 Angers,FR
| | - Nathalie Bednarek
- URCA, Université de Reims Champagne-Ardenne
Ministère de l'Enseignement Supérieur et de la Recherche Scientifique9 boulevard Paix - 51097 Reims cedex,FR
| | - Frédérique Béna
- Génétique médicale
Hôpitaux Universitaires de Genève1205 Geneva,CH
| | - Sven Bergmann
- Department of Medical Genetics
University of LausanneCH
- SIB, Swiss Institute of Bioinformatics
Swiss Institute of BioinformaticsQuartier Sorge - Batiment Genopode 1015 Lausanne Switzerland,CH
- Department of Molecular Genetics
Weizmann Institute of ScienceRehovot,IL
| | - Mylène Beri
- Laboratoire de Génétique
CHU NancyVandoeuvre les Nancy,FR
| | - Laura Bernardini
- Mendel Laboratory
IRCCS Casa Sollievo della Sofferenza Hospital71013 San Giovanni Rotondo,IT
| | - Bettina Blaumeiser
- Department of Medical Genetics
University Hospital Antwerp2650 Edegem,BE
| | - Dominique Bonneau
- Service de génétique [Angers]
CHU AngersUniversité d'Angersrue Larrey, 49100 Angers,FR
| | - Armand Bottani
- Génétique médicale
Hôpitaux Universitaires de Genève1205 Geneva,CH
| | - Odile Boute
- Service de Génétique clinique
Hôpital Jeanne de FlandreCHRU Lille2 avenue Oscar Lambret, 59000 Lille,FR
| | - Han G. Brunner
- Department of human genetics
Radboud University Nijmegen Medical CentreNijmegen Centre for Molecular Life SciencesInstitute for Genetic and Metabolic Disorders6500 HB Nijmegen,NL
| | - Dorothée Cailley
- Service de génétique médicale
CHU BordeauxGroupe hospitalier PellegrinUniversité de BordeauxBordeaux,FR
| | | | - Jean Chiesa
- Laboratoire de Cytogénétique
CHU Nîmes30029 Nimes,FR
| | - Jacqueline Chrast
- Centre de génomique intégrative
Université de Lausanne1015 Lausanne,CH
| | - Lachlan Coin
- Department of Genomics of Common Disease
Imperial College LondonHammersmith hospital, London W12 0NN,GB
| | - Charles Coutton
- Département de génétique et procréation
CHU GrenobleUniversité Joseph Fourier - Grenoble Ifaculté de médecine-pharmacieDomaine de la Merci, 38706 Grenoble,FR
- AGIM, AGeing and IMagery, CNRS FRE3405
Université Joseph Fourier - Grenoble IEcole Pratique des Hautes EtudesCNRS : UMR5525Faculté de médecine de Grenoble, 38700 La Tronche,FR
- Laboratoire de biochimie et génétique moléculaire
CHU Grenoble38043 Grenoble,FR
| | - Jean-Marie Cuisset
- Service de Neuropédiatrie
CHRU LilleHôpital Roger Salengro59037 Lille,FR
| | | | - Albert David
- Service d'ORL et de Chirurgie Cervicofaciale
INSERM : U587Hôpital d'Enfants Armand-TrousseauUniversité Pierre et Marie Curie - Paris 6Paris,FR
| | | | - Bruno Delobel
- Centre de Génétique Chromosomique
GHICLHôpital Saint Vincent de PaulBoulevard de Belfort B.P. 387 59020 LILLE CEDEX,FR
| | - Marie-Ange Delrue
- MRGM, Maladies Rares - Génétique et Métabolisme
Hôpital PellegrinService de Génétique Médicale du CHU de BordeauxUniversité Victor Segalen - Bordeaux II : EA4576146 rue Léo-Saignat - 33076 Bordeaux Cedex,FR
- Service de génétique médicale
CHU BordeauxGroupe hospitalier PellegrinUniversité de BordeauxBordeaux,FR
| | - Bénédicte Demeer
- Service de génétique médicale
CHU AMIENSPlace Victor Pauchet, 80054 Amiens Cedex 1,FR
| | - Dominique Descamps
- Centre hospitalier de Béthune
Centre hospitalier de Béthune62408 Bethune,FR
| | - Gérard Didelot
- Centre de génomique intégrative
Université de Lausanne1015 Lausanne,CH
| | | | - Vittoria Disciglio
- Department of Biotechnology
Università degli studi di SienaMedical Genetics53100 Siena,IT
| | - Martine Doco-Fenzy
- Service de Génétique
CHU ReimsHôpital Maison BlancheIFR 5351092 Reims,FR
| | - Séverine Drunat
- Département de génétique
Assistance publique - Hôpitaux de Paris (AP-HP)Hôpital Robert DebréUniversité Paris VII - Paris Diderot48, boulevard Sérurier 75935 Paris cedex 19,FR
| | - Bénédicte Duban-Bedu
- Centre de Génétique Chromosomique
GHICLHôpital Saint Vincent de PaulBoulevard de Belfort B.P. 387 59020 LILLE CEDEX,FR
| | - Christèle Dubourg
- IGDR, Institut de Génétique et Développement de Rennes
CNRS : UMR6061Université de Rennes 1IFR140Faculté de Médecine - CS 34317 2 Av du Professeur Léon Bernard 35043 RENNES CEDEX,FR
| | | | - Paul Elliott
- Department of Epidemiology and Public Health
Imperial College LondonSt Mary's Campus, Norfolk Place, London W2 1PG,GB
| | - Brigitte H. W. Faas
- Department of human genetics
Radboud University Nijmegen Medical CentreNijmegen Centre for Molecular Life SciencesInstitute for Genetic and Metabolic Disorders6500 HB Nijmegen,NL
- Department of Human Genetics, Radboud University Medical Centre, PO Box 9101, 6500 HB Nijmegen
Department of Human Genetics, Radboud University Medical Centre, PO Box 9101, 6500 HB NijmegenNL
| | - Laurence Faivre
- Department of Experimental Cardiology
Heart Failure Research Center (HFRC)Academic Medical Center (AMC)Meibergdreef 9, PO Box 22660, 1100 DD Amsterdam,NL
| | - Anne Faudet
- Département de Génétique Cytogénétique et Embryologie
Assistance publique - Hôpitaux de Paris (AP-HP)Hôpital Pitié-SalpêtrièreUniversité Paris VI - Pierre et Marie Curie47-83, boulevard de l'Hôpital 75651 PARIS Cedex 13,FR
| | | | | | - Richard Fisher
- Institute of human genetics
International Centre for LifeNewcastle Upon Tyne NE1 4EP,GB
| | - Elisabeth Flori
- Service de cytogénétique
CHU StrasbourgHôpital de Hautepierre1 Av Moliere 67098 Strasbourg Cedex,FR
| | - Lukas Forer
- Division of genetic epidemiology
Innsbruck Medical UniversityDepartment of Medical GeneticsMolecular and Clinical Pharmacology6020 Innsbruck,AT
| | - Dominique Gaillard
- Service de Génétique
CHU ReimsHôpital Maison BlancheIFR 5351092 Reims,FR
| | - Marion Gerard
- Département de génétique
Assistance publique - Hôpitaux de Paris (AP-HP)Hôpital Robert DebréUniversité Paris VII - Paris Diderot48, boulevard Sérurier 75935 Paris cedex 19,FR
| | - Christian Gieger
- Institute of Experimental Medicine
Academy of Sciences of the Czech RepublicVídeÅ�ská 1083 142 20 Prague,CZ
| | - Stefania Gimelli
- Génétique médicale
Hôpitaux Universitaires de Genève1205 Geneva,CH
- Department of Obstetrics and Gynecology
Institute of Clinical MedicineUniversity of Oulu90570 Oulu,FI
| | - Giorgio Gimelli
- Laboratorio di citogenetica
G. Gaslini Institute16147 Genova,IT
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy
Ernst-Moritz-Arndt University Greifswald17475 Greifswald and D-18437 Stralsund,DE
| | - Agnès Guichet
- Service de génétique [Angers]
CHU AngersUniversité d'Angersrue Larrey, 49100 Angers,FR
| | - Olivier Guillin
- Génétique médicale et fonctionnelle du cancer et des maladies neuropsychiatriques
INSERM : U614Université de RouenUFR de Medecine et de Pharmacie 22, Boulevard Gambetta 76183 Rouen cedex,FR
| | - Anna-Liisa Hartikainen
- Department of Obstetrics and Gynecology
Institute of Clinical MedicineUniversity of Oulu90570 Oulu,FI
| | - Délphine Heron
- Département de Génétique Cytogénétique et Embryologie
Assistance publique - Hôpitaux de Paris (AP-HP)Hôpital Pitié-SalpêtrièreUniversité Paris VI - Pierre et Marie Curie47-83, boulevard de l'Hôpital 75651 PARIS Cedex 13,FR
| | | | - Muriel Holder
- Service de Génétique clinique
Hôpital Jeanne de FlandreCHRU Lille2 avenue Oscar Lambret, 59000 Lille,FR
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics
Ernst-Moritz-Arndt University GreifswaldD-17487 Greifswald,DE
| | - Bertrand Isidor
- Service d'ORL et de Chirurgie Cervicofaciale
INSERM : U587Hôpital d'Enfants Armand-TrousseauUniversité Pierre et Marie Curie - Paris 6Paris,FR
| | - Sylvie Jaillard
- IGDR, Institut de Génétique et Développement de Rennes
CNRS : UMR6061Université de Rennes 1IFR140Faculté de Médecine - CS 34317 2 Av du Professeur Léon Bernard 35043 RENNES CEDEX,FR
| | - Zdenek Jaros
- Abteilung für Kinder und Jugendheilkunde
Landesklinikum Waldviertel Zwettl3910 Zwettl,AT
| | - Susana Jiménez-Murcia
- IDIBELL, Department of Psychiatry
University Hospital of BellvitgeCIBERobn Fisiopatología de la Obesidad y Nutrición08907 Barcelona,ES
| | | | | | - Satu Kaksonen
- The Habilitation Unit of Folkhalsan
The Habilitation Unit of FolkhalsanFolkhalsan, SF 00250 Helsinki,FI
| | - Boris Keren
- Département de Génétique Cytogénétique et Embryologie
Assistance publique - Hôpitaux de Paris (AP-HP)Hôpital Pitié-SalpêtrièreUniversité Paris VI - Pierre et Marie Curie47-83, boulevard de l'Hôpital 75651 PARIS Cedex 13,FR
| | - Anita Kloss-Brandstätter
- Division of genetic epidemiology
Innsbruck Medical UniversityDepartment of Medical GeneticsMolecular and Clinical Pharmacology6020 Innsbruck,AT
| | - Nine V. A. M. Knoers
- Department of Medical Genetics
University Medical Center Utrecht3584 EA Utrecht,NL
| | - David A. Koolen
- Department of human genetics
Radboud University Nijmegen Medical CentreNijmegen Centre for Molecular Life SciencesInstitute for Genetic and Metabolic Disorders6500 HB Nijmegen,NL
| | | | - Florian Kronenberg
- Division of genetic epidemiology
Innsbruck Medical UniversityDepartment of Medical GeneticsMolecular and Clinical Pharmacology6020 Innsbruck,AT
| | - Audrey Labalme
- Service de cytogénétique constitutionnelle
Hospices Civils de LyonCHU de LyonCentre Neuroscience et Recherche69000 Lyon,FR
| | - Emilie Landais
- Service de Génétique
CHU ReimsHôpital Maison BlancheIFR 5351092 Reims,FR
| | - Elisabetta Lapi
- Medical Genetics Unit
Children's Hospital Anna Meyer50139 Firenze,IT
| | - Valérie Layet
- Unité de Cytogénétique et Génétique Médicale
Hôpital Gustave FlaubertGroupe Hospitalier du Havre76600 Le Havre,FR
| | - Solenn Legallic
- Génétique médicale et fonctionnelle du cancer et des maladies neuropsychiatriques
INSERM : U614Université de RouenUFR de Medecine et de Pharmacie 22, Boulevard Gambetta 76183 Rouen cedex,FR
| | - Bruno Leheup
- Service de médecine infantile III et génétique clinique
CHU NancyUniversité Henri Poincaré - Nancy IPRES de l'université de Lorraine54511 Vandoeuvre les Nancy,FR
| | - Barbara Leube
- Institute of Human Genetics and Anthropology
Heinrich-Heine University Hospital DuesseldorfD-40001 Duesseldorf,DE
| | - Suzanne Lewis
- Department of Medical Genetics
University of British ColumbiaChild and Family Research InstituteVancouver V6H 3N1,CA
| | - Josette Lucas
- IGDR, Institut de Génétique et Développement de Rennes
CNRS : UMR6061Université de Rennes 1IFR140Faculté de Médecine - CS 34317 2 Av du Professeur Léon Bernard 35043 RENNES CEDEX,FR
| | - Kay D. Macdermot
- North West Thames Regional Genetics Service
Northwick Park & St Marks HospitalHarrow HA1 3UJ,GB
| | - Pall Magnusson
- Child and Adolescent Psychiatry
Landspitali University HospitalIS-105 Reykjavík,IS
| | - Christian R. Marshall
- The Centre for Applied Genomics and Program in Genetics and Genomic Biology
The Hospital for Sick ChildrenToronto, Ontario, M5G 1L7,CA
| | | | - Mark I. Mccarthy
- OCDEM, Oxford Centre for Diabetes, Endocrinology and Metabolism
University of OxfordChurchill Hospital Oxford OX3 7LJ,GB
- Wellcome Trust Centre for Human Genetics
University of OxfordOxford,GB
| | - Thomas Meitinger
- Institute of Human Genetics
HelmholtzZentrum MünchenTechnische Universität München (TUM)German Research Center for Environmental Health85764 Neuherberg,DE
| | | | - Giuseppe Merla
- Medical Genetics Unit
IRCCS Casa Sollievo della Sofferenza Hospital71013 San Giovanni Rotondo,IT
| | - Alexandre Moerman
- Service de Génétique clinique
Hôpital Jeanne de FlandreCHRU Lille2 avenue Oscar Lambret, 59000 Lille,FR
| | - Vincent Mooser
- Genetics, GlaxoSmithKline R&D
GlaxoSmithKline720 Swedeland Road, King of Prussia, Pennsylvania 19406,US
| | - Fanny Morice-Picard
- MRGM, Maladies Rares - Génétique et Métabolisme
Hôpital PellegrinService de Génétique Médicale du CHU de BordeauxUniversité Victor Segalen - Bordeaux II : EA4576146 rue Léo-Saignat - 33076 Bordeaux Cedex,FR
- Service de génétique médicale
CHU BordeauxGroupe hospitalier PellegrinUniversité de BordeauxBordeaux,FR
| | - Mafalda Mucciolo
- Department of Biotechnology
Università degli studi di SienaMedical Genetics53100 Siena,IT
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine
Ernst-Moritz-Arndt University GreifswaldD-17475 Greifswald,DE
| | - Ndeye Coumba Ndiaye
- Génétique cardiovasculaire
Université Henri Poincaré - Nancy I : EA437354000 Nancy,FR
| | - Ann Nordgren
- Department of Molecular Medicine and Surgery
Karolinska InstitutetSE
| | - Laurent Pasquier
- IGDR, Institut de Génétique et Développement de Rennes
CNRS : UMR6061Université de Rennes 1IFR140Faculté de Médecine - CS 34317 2 Av du Professeur Léon Bernard 35043 RENNES CEDEX,FR
| | - Florence Petit
- Service de Génétique clinique
Hôpital Jeanne de FlandreCHRU Lille2 avenue Oscar Lambret, 59000 Lille,FR
| | - Rolph Pfundt
- Department of human genetics
Radboud University Nijmegen Medical CentreNijmegen Centre for Molecular Life SciencesInstitute for Genetic and Metabolic Disorders6500 HB Nijmegen,NL
| | - Ghislaine Plessis
- Service de génétique
CHU CaenHôpital ClémenceauAvenue Georges Clémenceau, Caen,FR
| | - Evica Rajcan-Separovic
- Department of Pathology
University of British ColumbiaChild and Family Research InstituteVancouver, British Columbia V5Z 4H4,CA
| | | | - Anita Rauch
- Institute of Medical Genetics
University of Zurich8603 Schwerzenbach,CH
| | - Roberto Ravazzolo
- Department of pediatrics and CEBR
University of GenovaG. Gaslini Institute16126 Genova,IT
| | - Andre Reis
- Institute of Human Genetics
Friedrich-Alexander University Erlangen-Nuremberg91054 Erlangen,DE
| | - Alessandra Renieri
- Department of Biotechnology
Università degli studi di SienaMedical Genetics53100 Siena,IT
| | - Cristobal Richart
- Department of Internal Medicine
University Hospital Juan XXIIIUniversitat Rovira y VirgiliCiber Fisiopatologia Obesidad y Nutricion (CIBEROBN)Instituto Salud Carlos III43005 Tarragona,ES
| | - Janina S. Ried
- Institute of Experimental Medicine
Academy of Sciences of the Czech RepublicVídeÅ�ská 1083 142 20 Prague,CZ
| | - Claudine Rieubland
- Division of Human Genetics
University of BernDepartment of Paediatrics, Inselspital3010 Bern,CH
| | - Wendy Roberts
- Autism Research Unit
The Hospital for Sick Children and Bloorview Kids RehabilitationUniversity of TorontoToronto, Ontario, M5G 1Z8,CA
| | | | - Caroline Rooryck
- MRGM, Maladies Rares - Génétique et Métabolisme
Hôpital PellegrinService de Génétique Médicale du CHU de BordeauxUniversité Victor Segalen - Bordeaux II : EA4576146 rue Léo-Saignat - 33076 Bordeaux Cedex,FR
- Service de génétique médicale
CHU BordeauxGroupe hospitalier PellegrinUniversité de BordeauxBordeaux,FR
| | - Massimiliano Rossi
- Service de cytogénétique constitutionnelle
Hospices Civils de LyonCHU de LyonCentre Neuroscience et Recherche69000 Lyon,FR
| | | | - Véronique Satre
- Département de génétique et procréation
CHU GrenobleUniversité Joseph Fourier - Grenoble Ifaculté de médecine-pharmacieDomaine de la Merci, 38706 Grenoble,FR
- AGIM, AGeing and IMagery, CNRS FRE3405
Université Joseph Fourier - Grenoble IEcole Pratique des Hautes EtudesCNRS : UMR5525Faculté de médecine de Grenoble, 38700 La Tronche,FR
| | - Claudia Schurmann
- Interfaculty Institute for Genetics and Functional Genomics
Ernst-Moritz-Arndt University GreifswaldD-17487 Greifswald,DE
| | - Engilbert Sigurdsson
- University of Iceland
University of IcelandDepartment of Electrical and Computer Engineering, University of Iceland, Hjardarhaga 2-6, 107 Reykjavik, Iceland;,IS
| | - Dimitri J. Stavropoulos
- Department of Pediatric Laboratory Medicine
Hospital for Sick ChildrenToronto, Ontario M5G 1X8,CA
| | | | - Carola Tengström
- Genetic Services
Rinnekoti Research FoundationKumputie 1, SF-02980 Espoo,FI
| | | | - Francisco J. Tinahones
- Department of Endocrinology and Nutrition
Clinic Hospital of Virgen de la VictoriaCiber Fisiopatologia y Nutricion (CIBEROBN)Instituto Salud Carlos III29010 Malaga,ES
| | - Renaud Touraine
- Service de génétique
CHU Saint-EtienneHôpital nord42055 St Etienne,FR
| | - Louis Vallée
- Service de Neuropédiatrie
CHRU LilleHôpital Roger Salengro59037 Lille,FR
| | - Ellen Van Binsbergen
- Department of Medical Genetics
University Medical Center Utrecht3584 EA Utrecht,NL
| | | | - Catherine Vincent-Delorme
- Centre de Maladies Rares
Anomalies du Développement Nord de FranceCH Arras - CHRU Lille59000 Arras,FR
| | - Sophie Visvikis-Siest
- Génétique cardiovasculaire
Université Henri Poincaré - Nancy I : EA437354000 Nancy,FR
| | - Peter Vollenweider
- Department of Internal Medicine
Centre Hospitalier Universitaire Vaudois1011 Lausanne,CH
| | - Henry Völzke
- Institute for Community Medicine
Ernst-Moritz-Arndt University GreifswaldD-17475 Greifswald,DE
| | - Anneke T. Vulto-Van Silfhout
- Department of human genetics
Radboud University Nijmegen Medical CentreNijmegen Centre for Molecular Life SciencesInstitute for Genetic and Metabolic Disorders6500 HB Nijmegen,NL
| | - Gérard Waeber
- Department of Internal Medicine
Centre Hospitalier Universitaire Vaudois1011 Lausanne,CH
| | - Carina Wallgren-Pettersson
- Department of Medical Genetics
University of HelsinskiFolkhälsan Insitute of GeneticsHaartman Institute00251 Helsinki,FI
| | | | - Simon Zwolinksi
- Institute of human genetics
International Centre for LifeNewcastle Upon Tyne NE1 4EP,GB
| | - Joris Andrieux
- Laboratoire de Génétique Médicale
Hôpital Jeanne de FlandreCHRU Lille59037 Lille Cedex,FR
| | - Xavier Estivill
- CRG-UPF, Center for Genomic Regulation
CIBER de Epidemiología y Salud Pública (CIBERESP)C/ Dr. Aiguader, 88 08003 Barcelona, Catalonia, Spain,ES
| | - James F. Gusella
- Center for Human Genetic Research
Massachusetts General HospitalBoston, Massachusetts 02114,US
| | | | - Andres Metspalu
- Estonian Genome and Medicine
University of Tartu51010 Tartu,EE
- Institute of Molecular and Cell Biology
University of Tartu51010 Tartu,EE
| | - Stephen W. Scherer
- The Centre for Applied Genomics
The Hospital for Sick ChildrenMcLaughlin CentreDepartment of Molecular GeneticsUniversity of TorontoToronto, Ontario, Canada M5G 1L7,CA
| | | | - Alexandra I. F. Blakemore
- Department of Genomics of Common Disease
Imperial College LondonHammersmith hospital, London W12 0NN,GB
| | - Jacques S. Beckmann
- Service de génétique médicale
CHU Vaudois1011 Lausanne,CH
- Department of Medical Genetics
University of LausanneCH
| | - Philippe Froguel
- Department of Genomics of Common Disease
Imperial College LondonHammersmith hospital, London W12 0NN,GB
- IBLI, Institut de biologie de Lille - IBL
Institut Pasteur de LilleCNRS : UMR8090Université Lille I - Sciences et technologiesUniversité Lille II - Droit et santéInstitut de Biologie de Lille 1 Rue du Professeur Calmette - 447 59021 LILLE CEDEX,FR
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Trewick AL, Moustafa JSES, de Smith AJ, Froguel P, Greve G, Njølstad PR, Coin LJM, Blakemore AIF. Accurate single-nucleotide polymorphism allele assignment in trisomic or duplicated regions by using a single base-extension assay with MALDI-TOF mass spectrometry. Clin Chem 2011; 57:1188-95. [PMID: 21677093 DOI: 10.1373/clinchem.2010.159558] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND The accurate assignment of alleles embedded within trisomic or duplicated regions is an essential prerequisite for assessing the combined effects of single-nucleotide polymorphisms (SNPs) and genomic copy number. Such an integrated analysis is challenging because heterozygotes for such a SNP may be one of 2 genotypes-AAB or ABB. Established methods for SNP genotyping, however, can have difficulty discriminating between the 2 heterozygous trisomic genotypes. We developed a method for assigning heterozygous trisomic genotypes that uses the ratio of the height of the 2 allele peaks obtained by mass spectrometry after a single-base extension assay. METHODS Eighteen COL6A2 (collagen, type VI, alpha 2) SNPs were analyzed in euploid and trisomic individuals by means of a multiplexed single-base extension assay that generated allele-specific oligonucleotides of differing M(r) values for detection by MALDI-TOF mass spectrometry. Reference data (mean and SD) for the allele peak height ratios were determined from heterozygous euploid samples. The heterozygous trisomic genotypes were assigned by calculating the z score for each trisomic allele peak height ratio and by considering the sign (+/-) of the z score. RESULTS Heterozygous trisomic genotypes were assigned in 96.1% (range, 89.9%-100%) of the samples for each SNP analyzed. The genotypes obtained were reproduced in 95 (97.5%) of 97 loci retested in a second assay. Subsequently, the origin of nondisjunction was determined in 108 (82%) of 132 family trios with a Down syndrome child. CONCLUSIONS This approach enabled reliable genotyping of heterozygous trisomic samples and the determination of the origin of nondisjunction in Down syndrome family trios.
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Affiliation(s)
- Anne L Trewick
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
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Inferring haplotypes of copy number variations from high-throughput data with uncertainty. G3-GENES GENOMES GENETICS 2011; 1:35-42. [PMID: 22384316 PMCID: PMC3276117 DOI: 10.1534/g3.111.000174] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2010] [Accepted: 03/14/2011] [Indexed: 11/18/2022]
Abstract
Accurate information on haplotypes and diplotypes (haplotype pairs) is required for population-genetic analyses; however, microarrays do not provide data on a haplotype or diplotype at a copy number variation (CNV) locus; they only provide data on the total number of copies over a diplotype or an unphased sequence genotype (e.g., AAB, unlike AB of single nucleotide polymorphism). Moreover, such copy numbers or genotypes are often incorrectly determined when microarray signal intensities derived from different copy numbers or genotypes are not clearly separated due to noise. Here we report an algorithm to infer CNV haplotypes and individuals' diplotypes at multiple loci from noisy microarray data, utilizing the probability that a signal intensity may be derived from different underlying copy numbers or genotypes. Performing simulation studies based on known diplotypes and an error model obtained from real microarray data, we demonstrate that this probabilistic approach succeeds in accurate inference (error rate: 1-2%) from noisy data, whereas previous deterministic approaches failed (error rate: 12-18%). Applying this algorithm to real microarray data, we estimated haplotype frequencies and diplotypes in 1486 CNV regions for 100 individuals. Our algorithm will facilitate accurate population-genetic analyses and powerful disease association studies of CNVs.
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Eleftherohorinou H, Andersson-Assarsson JC, Walters RG, El-Sayed Moustafa JS, Coin L, Jacobson P, Carlsson LMS, Blakemore AIF, Froguel P, Walley AJ, Falchi M. famCNV: copy number variant association for quantitative traits in families. Bioinformatics 2011; 27:1873-5. [PMID: 21546396 DOI: 10.1093/bioinformatics/btr264] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED A program package to enable genome-wide association of copy number variants (CNVs) with quantitative phenotypes in families of arbitrary size and complexity. Intensity signals that act as proxies for the number of copies are modeled in a variance component framework and association with traits is assessed through formal likelihood testing. AVAILABILITY AND IMPLEMENTATION The Java package is made available at www.imperial.ac.uk/medicine/people/m.falchi/. CONTACT m.falchi@imperial.ac.uk.
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Affiliation(s)
- Hariklia Eleftherohorinou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Hospital, London, UK
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Wineinger NE, Pajewski NM, Tiwari HK. A Method to Assess Linkage Disequilibrium between CNVs and SNPs Inside Copy Number Variable Regions. Front Genet 2011; 2:17. [PMID: 21660233 PMCID: PMC3109359 DOI: 10.3389/fgene.2011.00017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2011] [Accepted: 03/31/2011] [Indexed: 11/23/2022] Open
Abstract
Since the discovery of the ubiquitous contribution of copy number variation to genetic variability, researchers have commonly used metrics such as r2 to quantify linkage disequilibrium (LD) between copy number variants (CNVs) and single nucleotide polymorphisms (SNPs). However, these reports have been restricted to SNPs outside copy number variable regions (CNVR) as current methods have not been adapted to account for SNPs displaying variable copy number. We show that traditional LD metrics inappropriately quantify SNP/CNV covariance when SNPs lie within CNVR. We derive a new method for measuring LD that solves this issue, and defaults to traditional metrics otherwise. Finally, we present a procedure to estimate CNV–SNP allele frequencies from unphased CNV–SNP genotypes. Our method allows researchers to include all SNPs in SNP/CNV LD measurements, regardless of copy number.
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Affiliation(s)
- Nathan E Wineinger
- Department of Biostatistics, University of Alabama at Birmingham Birmingham, AL, USA
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Handsaker RE, Korn JM, Nemesh J, McCarroll SA. Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nat Genet 2011; 43:269-76. [PMID: 21317889 PMCID: PMC5094049 DOI: 10.1038/ng.768] [Citation(s) in RCA: 242] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2010] [Accepted: 01/20/2011] [Indexed: 11/09/2022]
Abstract
Accurate and complete analysis of genome variation in large populations will be required to understand the role of genome variation in complex disease. We present an analytical framework for characterizing genome deletion polymorphism in populations using sequence data that are distributed across hundreds or thousands of genomes. Our approach uses population-level concepts to reinterpret the technical features of sequence data that often reflect structural variation. In the 1000 Genomes Project pilot, this approach identified deletion polymorphism across 168 genomes (sequenced at 4 × average coverage) with sensitivity and specificity unmatched by other algorithms. We also describe a way to determine the allelic state or genotype of each deletion polymorphism in each genome; the 1000 Genomes Project used this approach to type 13,826 deletion polymorphisms (48-995,664 bp) at high accuracy in populations. These methods offer a way to relate genome structural polymorphism to complex disease in populations.
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Affiliation(s)
- Robert E Handsaker
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
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Abstract
The elucidation of several genetic etiologies of both monogenic and polygenic type 2 diabetes (T2D) has revealed several key regulators of glucose homeostasis and insulin secretion in humans. Genome-wide association studies (GWAS) have been instrumental in most of these recent discoveries. The T2D susceptibility genes identified so far are mainly involved in pancreatic beta-cell maturation or function. However, common DNA variants in those genes only explain approximately 10% of T2D heritability. The resequencing of whole exomes and whole genomes with next-generation technologies should identify additional genetic changes that contribute to the monogenic forms of diabetes and possibly provide novel clues to the genetic architecture of common adult T2D.
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