ArticleBMC bioinformatics2013
Copy number variation genotyping using family information.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Copy number variation analysis identifies MIR9-3 and MIR1299 as novel miRNA candidate genes for CAKUT.Pediatric nephrology (Berlin, Germany) · 2024Article
- Copy number determination of the gene for the human pancreatic polypeptide receptor NPY4R using read depth analysis and droplet digital PCR.BMC biotechnology · 2019Article
- Copy number of pancreatic polypeptide receptor gene NPY4R correlates with body mass index and waist circumference.PloS one · 2018Article
- Joint detection of copy number variations in parent-offspring trios.Bioinformatics (Oxford, England) · 2016Article
- Comparison of sequencing based CNV discovery methods using monozygotic twin quartets.PloS one · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundIn recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies.
resultsTo help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments.
conclusionsIn conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.
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Registered trials
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