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Freitas SDS, Rezende SM, de Oliveira LC, Prezotti ANL, Renni MS, Corsini CA, Amorim MVDA, Matosinho CGR, Carvalho MRS, Chaves DG. Genetic variants of VWF gene in type 2 von Willebrand disease. Haemophilia 2019; 25:e78-e85. [PMID: 30817071 DOI: 10.1111/hae.13714] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 12/31/2018] [Accepted: 01/28/2019] [Indexed: 02/06/2023]
Abstract
INTRODUCTION von Willebrand disease (VWD) is the most common inherited bleeding disorder. Few studies have explored the molecular basis of type 2 VWD. AIM This study aimed to identify variants associated with type 2 VWD. METHODS We collected clinical and laboratory data, as well as response to desmopressin and bleeding assessment tool (BAT) score in patients diagnosed with type 2 VWD. We sequenced exons 17, 18, 20 and 28 of the VWF gene. RESULTS We identified 19 different variants in 40 unrelated patients (47.5%). Most of the variants (84.2%) were found in exon 28. A total of 10/19 variants (52.6%) were identified as "likely causative" in 17/40 patients (42.5%), according to the ISTH-SSC and EAHAD VWF gene mutations databases. Nine variants were initially identified as potentially benign. However, through analyses in silico, four of these variants were reclassified as "likely pathogenic" (Ile1380Val, Asn1435Ser, Ser1486Leu and Tyr1584Cys). Response to desmopressin was associated with three variants: Met740Ile, Arg1597Gln and Tyr1584Cys. Major bleeding was associated with variants related to VWD subtypes 2B and 2M. CONCLUSION In conclusion, we identified 19 variants, of which 14 are "likely pathogenic" and therefore associated with VWD. We suggest a possible association of pathogenic variants with major bleeding, response to desmopressin and BAT score ≥10, although this requires further confirmation.
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Affiliation(s)
- Samuel da Silva Freitas
- Fundação HEMOMINAS, Minas Gerais, Brazil.,Faculty of Medicine, Universidade Federal de Minas Gerais, Minas Gerais, Brazil
| | | | - Luciana Correa de Oliveira
- Hospital das Clínicas, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil
| | | | - Marília Sande Renni
- Instituto de Hematologia Arthur de Siqueira Cavalcanti (HEMORIO), Rio de Janeiro, Brazil
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2
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Abstract
While genome-wide association studies have been very successful in identifying associations of common genetic variants with many different traits, the rarer frequency spectrum of the genome has not yet been comprehensively explored. Technological developments increasingly lift restrictions to access rare genetic variation. Dense reference panels enable improved genotype imputation for rarer variants in studies using DNA microarrays. Moreover, the decreasing cost of next generation sequencing makes whole exome and genome sequencing increasingly affordable for large samples. Large-scale efforts based on sequencing, such as ExAC, 100,000 Genomes, and TopMed, are likely to significantly advance this field.The main challenge in evaluating complex trait associations of rare variants is statistical power. The choice of population should be considered carefully because allele frequencies and linkage disequilibrium structure differ between populations. Genetically isolated populations can have favorable genomic characteristics for the study of rare variants.One strategy to increase power is to assess the combined effect of multiple rare variants within a region, known as aggregate testing. A range of methods have been developed for this. Model performance depends on the genetic architecture of the region of interest.
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Affiliation(s)
- Karoline Kuchenbaecker
- Wellcome Trust Sanger Institute, Cambridge, UK. .,University College London, London, UK.
| | - Emil Vincent Rosenbaum Appel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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Greene D, Richardson S, Turro E, Turro E. A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases. Am J Hum Genet 2017; 101:104-114. [PMID: 28669401 PMCID: PMC5501869 DOI: 10.1016/j.ajhg.2017.05.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 05/22/2017] [Indexed: 11/26/2022] Open
Abstract
We present a rapid and powerful inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison. Under a baseline model, disease risk is fixed across all individuals in a study. Under an association model, disease risk depends on a latent bipartition of rare variants into pathogenic and non-pathogenic variants, the number of pathogenic alleles that each individual carries, and the mode of inheritance. A parameter indicating presence of an association and the parameters representing the pathogenicity of each variant and the mode of inheritance can be inferred in a Bayesian framework. Variant-specific prior information derived from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integrated into the inference. Association models can be fitted to different subsets of variants in a locus and compared using a model selection procedure. This procedure can improve inference if only a particular class of variants confers disease risk and can suggest particular disease etiologies related to that class. We show that our method, called BeviMed, is more powerful and informative than existing rare variant association methods in the context of dominant and recessive disorders. The high computational efficiency of our algorithm makes it feasible to test for associations in the large non-coding fraction of the genome. We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare diseases. We show that it can identify multiple loci involved in rare diseases, while correctly inferring the modes of inheritance, the likely pathogenic variants, and the variant classes responsible.
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Affiliation(s)
| | | | | | - Ernest Turro
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0XY, UK; NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK; Medical Research Council Biostatistics Unit, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
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4
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A fast algorithm for Bayesian multi-locus model in genome-wide association studies. Mol Genet Genomics 2017; 292:923-934. [DOI: 10.1007/s00438-017-1322-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 04/18/2017] [Indexed: 12/27/2022]
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Pagliari MT, Baronciani L, Stufano F, Garcia-Oya I, Cozzi G, Franchi F, Peyvandi F. von Willebrand disease type 1 mutation p.Arg1379Cys and the variant p.Ala1377Val synergistically determine a 2M phenotype in four Italian patients. Haemophilia 2016; 22:e502-e511. [DOI: 10.1111/hae.13084] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2016] [Indexed: 11/26/2022]
Affiliation(s)
- M. T. Pagliari
- Angelo Bianchi Bonomi Hemophilia and Thrombosis Center; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and Fondazione Luigi Villa; Milan Italy
| | - L. Baronciani
- Angelo Bianchi Bonomi Hemophilia and Thrombosis Center; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and Fondazione Luigi Villa; Milan Italy
| | - F. Stufano
- Angelo Bianchi Bonomi Hemophilia and Thrombosis Center; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and Fondazione Luigi Villa; Milan Italy
| | - I. Garcia-Oya
- Angelo Bianchi Bonomi Hemophilia and Thrombosis Center; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and Fondazione Luigi Villa; Milan Italy
| | - G. Cozzi
- Angelo Bianchi Bonomi Hemophilia and Thrombosis Center; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and Fondazione Luigi Villa; Milan Italy
| | - F. Franchi
- Department of Pathophysiology and Transplantation; Università degli Studi di Milano; Milan Italy
| | - F. Peyvandi
- Angelo Bianchi Bonomi Hemophilia and Thrombosis Center; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and Fondazione Luigi Villa; Milan Italy
- Department of Pathophysiology and Transplantation; Università degli Studi di Milano; Milan Italy
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Larson NB, McDonnell S, Albright LC, Teerlink C, Stanford J, Ostrander EA, Isaacs WB, Xu J, Cooney KA, Lange E, Schleutker J, Carpten JD, Powell I, Bailey-Wilson J, Cussenot O, Cancel-Tassin G, Giles G, MacInnis R, Maier C, Whittemore AS, Hsieh CL, Wiklund F, Catolona WJ, Foulkes W, Mandal D, Eeles R, Kote-Jarai Z, Ackerman MJ, Olson TM, Klein CJ, Thibodeau SN, Schaid DJ. Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies. Genet Epidemiol 2016; 40:461-9. [PMID: 27312771 PMCID: PMC5063501 DOI: 10.1002/gepi.21983] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 04/22/2016] [Accepted: 04/27/2016] [Indexed: 12/27/2022]
Abstract
Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.
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Affiliation(s)
- Nicholas B. Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Shannon McDonnell
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Lisa Cannon Albright
- Dept. Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT
| | - Craig Teerlink
- Dept. Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT
| | | | | | | | - Jianfeng Xu
- NorthShore University Health System Research Institute, Chicago, IL
| | - Kathleen A. Cooney
- Depts. of Internal Medicine and Urology, University of Michigan Medical School, Ann Arbor, MI
| | - Ethan Lange
- Dept. of Genetics, University of North Carolina, Chapel Hill, NC
| | - Johanna Schleutker
- Dept. of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Finland
| | - John D. Carpten
- Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, AZ
| | | | - Joan Bailey-Wilson
- Statistical Genetics Section, National Human Genome Research Institute, Bethesda, MD
| | | | | | - Graham Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Robert MacInnis
- Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | | | | | - Chih-Lin Hsieh
- Dept. of Urology, University of Southern California, Los Angeles, CA
| | - Fredrik Wiklund
- Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - William Foulkes
- Depts. Of Oncology and Human Genetics, Montreal General Hospital, Montreal QC, Canada
| | - Diptasri Mandal
- Dept. of Genetics, LSU Health Sciences Center, New Orleans, LA
| | - Rosalind Eeles
- Genetics and Epidemiology, Institute of Cancer Research, Sutton Surrey, UK
| | - Zsofia Kote-Jarai
- Genetics and Epidemiology, Institute of Cancer Research, Sutton Surrey, UK
| | | | - Timothy M. Olson
- Dept. of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Daniel J. Schaid
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
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The effect of phenotypic outliers and non-normality on rare-variant association testing. Eur J Hum Genet 2016; 24:1188-94. [PMID: 26733287 DOI: 10.1038/ejhg.2015.270] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 11/03/2015] [Accepted: 11/15/2015] [Indexed: 02/07/2023] Open
Abstract
Rare-variant association studies (RVAS) have made important contributions to human complex trait genetics. These studies rely on specialized statistical methods for analyzing rare-variant associations, both individually and in aggregate. We investigated the impact that phenotypic outliers and non-normality have on the performance of rare-variant association testing procedures. Ignoring outliers or non-normality can significantly inflate Type I error rates. We found that rank-based inverse normal transformation (INT) and trait winsorisation were both effective at maintaining Type I error control without sacrificing power in the presence of outliers. INT was the optimal method for non-normally distributed traits. For RVAS of quantitative traits with outliers or non-normality, we recommend using INT to transform phenotypic values before association testing.
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8
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Cheng Y, Dai JY, Kooperberg C. Group association test using a hidden Markov model. Biostatistics 2015; 17:221-34. [PMID: 26420797 DOI: 10.1093/biostatistics/kxv035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 08/25/2015] [Indexed: 11/13/2022] Open
Abstract
In the genomic era, group association tests are of great interest. Due to the overwhelming number of individual genomic features, the power of testing for association of a single genomic feature at a time is often very small, as are the effect sizes for most features. Many methods have been proposed to test association of a trait with a group of features within a functional unit as a whole, e.g. all SNPs in a gene, yet few of these methods account for the fact that generally a substantial proportion of the features are not associated with the trait. In this paper, we propose to model the association for each feature in the group as a mixture of features with no association and features with non-zero associations to explicitly account for the possibility that a fraction of features may not be associated with the trait while other features in the group are. The feature-level associations are first estimated by generalized linear models; the sequence of these estimated associations is then modeled by a hidden Markov chain. To test for global association, we develop a modified likelihood ratio test based on a log-likelihood function that ignores higher order dependency plus a penalty term. We derive the asymptotic distribution of the likelihood ratio test under the null hypothesis. Furthermore, we obtain the posterior probability of association for each feature, which provides evidence of feature-level association and is useful for potential follow-up studies. In simulations and data application, we show that our proposed method performs well when compared with existing group association tests especially when there are only few features associated with the outcome.
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Affiliation(s)
- Yichen Cheng
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - James Y Dai
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
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Peng B. Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools. Genet Epidemiol 2015; 39:45-52. [PMID: 25395236 PMCID: PMC6432799 DOI: 10.1002/gepi.21867] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Revised: 09/14/2014] [Accepted: 09/26/2014] [Indexed: 12/31/2022]
Abstract
Computer simulations have been widely used to validate and evaluate the power of statistical methods for genetic epidemiological studies. Although a large number of simulation methods and software packages have been developed for genome-wide association studies, methodological and bioinformatics challenges have limited their applications in simulating datasets for whole-genome and whole-exome sequencing studies. With the development of more sophisticated statistical methods that make fuller use of available data and our knowledge of the human genome, there is a pressing need for genetic simulators that capture more features of empirical data (e.g., multiallele variants, indels, use of the Variant Call Format) and the human genome (e.g., functional annotations of genetic variants). This article introduces Variant Simulation Tools (VST), a module of Variant Tools for the simulation of genetic variants for sequencing-based genetic epidemiological studies. Although multiple simulation engines are provided, the core of VST is a novel forward-time simulation engine that simulates real nucleotide sequences of the human genome using DNA mutation models, fine-scale recombination maps, and a selection model based on amino acid changes of translated protein sequences. The design of VST allows users to easily create and distribute simulation methods and simulated datasets for a variety of applications and encourages fair comparison between statistical methods through the use of existing or reproduced simulated datasets.
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Affiliation(s)
- Bo Peng
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1401, Houston, TX, 77030
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10
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Lee S, Abecasis G, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 2014; 95:5-23. [PMID: 24995866 DOI: 10.1016/j.ajhg.2014.06.009] [Citation(s) in RCA: 658] [Impact Index Per Article: 65.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Indexed: 12/30/2022] Open
Abstract
Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions.
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