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Kim S, Millard SP, Yu CE, Leong L, Radant A, Dobie D, Tsuang DW, Wijsman EM. Inheritance model introduces differential bias in CNV calls between parents and offspring. Genet Epidemiol 2012; 36:488-98. [PMID: 22628073 PMCID: PMC3678551 DOI: 10.1002/gepi.21643] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2012] [Revised: 04/06/2012] [Accepted: 04/24/2012] [Indexed: 11/10/2022]
Abstract
Copy Number Variation (CNV) is increasingly implicated in disease pathogenesis. CNVs are often identified by statistical models applied to data from single nucleotide polymorphism panels. Family information for samples provides additional information for CNV inference. Two modes of PennCNV (the Joint-call and Posterior-call), which are some of the most well-developed family-based CNV calling methods, use a "Joint-model" as a main component. This models all family members' CNV states together with Mendelian inheritance. Methods based on the Joint-model are used to infer CNV calls of cases and controls in a pedigree, which may be compared to each other to test an association. Although benefits from the Joint-model have been shown elsewhere, equality of call rates in parents and offspring has not been evaluated previously. This can affect downstream analyses in studies that compare CNV rates in cases vs. controls in pedigrees. In this paper, we show that the Joint-model can introduce different CNV call rates among family members in the absence of a true difference. We show that the Joint-model may analytically introduce differential CNV calls because of asymmetry of the model. We demonstrate these differential call rates using single-marker simulations. We show that call rates using the two modes of PennCNV also differ between parents and offspring in one multimarker simulated dataset and two real datasets. Our results advise need for caution in use of the Joint-model calls in CNV association studies with family-based datasets.
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Affiliation(s)
- Sulgi Kim
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington
| | - Steven P. Millard
- Mental Illness Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, Washington
| | - Chang-En Yu
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, Washington
- Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Lesley Leong
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle Washington
| | - Allen Radant
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle Washington
| | - Dorcas Dobie
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle Washington
| | - Debby W. Tsuang
- Mental Illness Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, Washington
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle Washington
| | - Ellen M. Wijsman
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
- Department of Genome Sciences, School of Medicine, University of Washington, Seattle, Washington
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Hong H, Shi L, Su Z, Ge W, Jones WD, Czika W, Miclaus K, Lambert CG, Vega SC, Zhang J, Ning B, Liu J, Green B, Xu L, Fang H, Perkins R, Lin SM, Jafari N, Park K, Ahn T, Chierici M, Furlanello C, Zhang L, Wolfinger RD, Goodsaid F, Tong W. Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples. Pharmacogenomics J 2010; 10:364-74. [PMID: 20368714 PMCID: PMC2928027 DOI: 10.1038/tpj.2010.24] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2009] [Accepted: 02/15/2010] [Indexed: 01/05/2023]
Abstract
The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results.
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Affiliation(s)
- H Hong
- Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
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Wineinger NE, Kennedy RE, Erickson SW, Wojczynski MK, Bruder CE, Tiwari HK. Statistical issues in the analysis of DNA Copy Number Variations. Int J Comput Biol Drug Des 2008; 1:368-95. [PMID: 19774103 PMCID: PMC2747762 DOI: 10.1504/ijcbdd.2008.022208] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Approaches to assess copy number variation have advanced rapidly and are being incorporated into genetic studies. While the technology exists for CNV genotyping, a further understanding and discussion of how to use the CNV data for association analyses is warranted. We present the options available for processing and analysing CNV data. We break these steps down into choice of genotyping platform, normalisation of the array data, calling algorithm, and statistical analysis.
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Affiliation(s)
- Nathan E. Wineinger
- Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA, Fax: 205-975-2540, E-mail:
| | - Richard E. Kennedy
- Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA, Fax: 205-975-2540, E-mail:
| | - Stephen W. Erickson
- Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA, Fax: 205-975-2540, E-mail:
| | - Mary K. Wojczynski
- Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA, Fax: 205-975-2540, E-mail:
| | - Carl E. Bruder
- Viral Biochemistry, Division of Drug Discovery, Southern Research Institute, Birmingham, Alabama 35205, USA, Fax: (205) 581-2097, E-mail:
| | - Hemant K. Tiwari
- Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA, Fax: 205-975-2541, E-mail:
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