Identification of Genome-wide Copy Number Variations and a Family-based Association Study of Avellino Corneal Dystrophy

Bae, Joon Seol; Cheong, Hyun Sub; Chun, Ji Yong; Park, Tae Joon; Kim, Ji On; Kim, Eun Mi; Park, Miey; Kim, Dong Joon; Lee, Eun Ju; Kim, Eung Kweon; Lee, Jong‐Young; Shin, Hyoung Doo

doi:10.1016/j.ophtha.2009.11.021

Cited by 3 publications

(7 citation statements)

References 29 publications

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“…Previous studies that have used the Joint-model have not paid attention to this possible bias in their association tests [Bae, et al 2010; Yang, et al 2009]. Our results here show that none of possible intuitive predictions for relative CNV call rates in parents versus offspring is always correct.…”

Section: Discussioncontrasting

confidence: 62%

“…Inference about different CNV frequencies depends on the relative call rates both in the presence and in the absence of true underlying CNVs. In addition to use of unrelated cases and controls, published association studies of CNVs span designs ranging from use of cases from trio samples or small multiplex families compared to unrelated controls [Bucan et al, ; Pinto et al, ; Salyakina et al, ], through to designs where cases and controls are from the same families [Bae et al, ; Pamphlett et al, ; Walsh et al, ; Yang et al, ]. In all these designs, CNV calls are compared between cases and controls for finding an association.…”

Section: Introductionmentioning

confidence: 99%

“…A comparison of CNV calls in an unbalanced design requires a calling method that results in similar call rates in parents vs. offspring in the absence of true differences because any difference could be inferred as association of CNVs and disease. However, despite use of calling algorithms for such comparisons within‐family samples [Bae et al, ; Yang et al, ], possible differences in relative call rates in this context have not yet been discussed or evaluated.…”

Section: Introductionmentioning

confidence: 99%

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Inheritance Model Introduces Differential Bias in CNV Calls Between Parents and Offspring

Kim

Millard

et al. 2012

Genetic Epidemiology

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Copy Number Variation (CNV) is increasingly implicated in disease pathogenesis. CNVs are often identified by statistical models applied to data from single nucleotide polymorphism (SNP) 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 versus 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. First, 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-offspring in one multi-marker 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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Section: Discussioncontrasting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inheritance Model Introduces Differential Bias in CNV Calls Between Parents and Offspring

Kim

Millard

et al. 2012

Genetic Epidemiology

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“…CNV can contribute to disease susceptibility by influencing the gene expression level [11]. CNV has been revealed to be associated with complex human diseases including autism, inflammatory autoimmune disorders, lung cancer, osteoporosis, subarachnoid aneurysmal hemorrhage, sporadic amyotrophic lateral sclerosis, avellino corneal dystrophy, and schizophrenia [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. Recent studies have focused on how CNV in the human genome affects various inherited phenotypes, including disease susceptibility [22], [23].…”

Section: Introductionmentioning

confidence: 99%

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

et al. 2011

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BackgroundUnlike Caucasian populations, genetic factors contributing to the risk of type 2 diabetes mellitus (T2DM) are not well studied in Asian populations. In light of this, and the fact that copy number variation (CNV) is emerging as a new way to understand human genomic variation, the objective of this study was to identify type 2 diabetes–associated CNV in a Korean cohort.Methodology/Principal FindingsUsing the Illumina HumanHap300 BeadChip (317,503 markers), genome-wide genotyping was performed to obtain signal and allelic intensities from 275 patients with type 2 diabetes mellitus (T2DM) and 496 nondiabetic subjects (Total n = 771). To increase the sensitivity of CNV identification, we incorporated multiple factors using PennCNV, a program that is based on the hidden Markov model (HMM). To assess the genetic effect of CNV on T2DM, a multivariate logistic regression model controlling for age and gender was used. We identified a total of 7,478 CNVs (average of 9.7 CNVs per individual) and 2,554 CNV regions (CNVRs; 164 common CNVRs for frequency>1%) in this study. Although we failed to demonstrate robust associations between CNVs and the risk of T2DM, our results revealed a putative association between several CNVRs including chr15:45994758–45999227 (P = 8.6E-04, Pcorr = 0.01) and the risk of T2DM. The identified CNVs in this study were validated using overlapping analysis with the Database of Genomic Variants (DGV; 71.7% overlap), and quantitative PCR (qPCR). The identified variations, which encompassed functional genes, were significantly enriched in the cellular part, in the membrane-bound organelle, in the development process, in cell communication, in signal transduction, and in biological regulation.Conclusion/SignificanceWe expect that the methods and findings in this study will contribute in particular to genome studies of Asian populations.

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“…To date, the identification of CNV highlights the importance of human genome diversity. As the genetic effects of some CNV regions have been implicated in the risk of several human complex diseases including autism, inflammatory autoimmune disorders, lung cancer, osteoporosis, subarachnoid aneurysmal hemorrhage, sporadic amyotrophic lateral sclerosis, avellino corneal dystrophy, and schizophrenia (Bae et al., 2010a, 2008; Blauw et al., 2008; de Cid et al., 2009; Diskin et al., 2009; Sebat et al., 2007; Wang et al., 2009; Weiss et al., 2008; Yang et al., 2008), recent studies have focused on how CNVs affect various inherited phenotypes, including disease susceptibility (Zhang et al., 2006). As CNVs can contribute to various complex human diseases, we hypothesized that CNVs can predict the risk of alcoholism in a Korean population.…”

mentioning

confidence: 99%

The Genetic Effect of Copy Number Variations on the Risk of Alcoholism in a Korean Population

Bae

Jung

Lee

et al. 2011

Alcoholism Clin & Exp Res

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Identification of Genome-wide Copy Number Variations and a Family-based Association Study of Avellino Corneal Dystrophy

Cited by 3 publications

References 29 publications

Inheritance Model Introduces Differential Bias in CNV Calls Between Parents and Offspring

Inheritance Model Introduces Differential Bias in CNV Calls Between Parents and Offspring

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

The Genetic Effect of Copy Number Variations on the Risk of Alcoholism in a Korean Population

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