scholarly journals Using Family Data as a Verification Standard to Evaluate Copy Number Variation Calling Strategies for Genetic Association Studies

2012 ◽  
Vol 36 (3) ◽  
pp. 253-262 ◽  
Author(s):  
Xiaojing Zheng ◽  
John R. Shaffer ◽  
Caitlin P. McHugh ◽  
Cathy C. Laurie ◽  
Bjarke Feenstra ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Genetic Association ◽  
Copy Number ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Family Data ◽  
Number Variation ◽  
Copy Number Variation Calling

scholarly journals Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e34262 ◽  
Author(s):  
Patrick Breheny ◽  
Prabhakar Chalise ◽  
Anthony Batzler ◽  
Liewei Wang ◽  
Brooke L. Fridley
Keyword(s):  
Copy Number Variation ◽  
Genetic Association ◽  
Copy Number ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Number Variation

COPY NUMBER VARIATION IN METABOLIC SYNDROME

2008 ◽  
Vol 31 (4) ◽  
pp. 15
Author(s):  
M Lanktree ◽  
R A Hegele
Keyword(s):  
Metabolic Syndrome ◽  
Copy Number Variation ◽  
Copy Number ◽  
Late Onset ◽  
Association Studies ◽  
Partial Lipodystrophy ◽  
Increased Risk ◽  
Number Variation ◽  
Familial Partial Lipodystrophy ◽  
Obesity Hypertension

Metabolic syndrome (MetS) is defined by the presence of abdominal obesity, hypertension, dysglycemia, and dyslipidemia. Many mutations have been discovered that cause rare monogenic components of metabolic syndrome, and association studies have linked common variants with increased risk of MetS and its components. Despite successes in identifying genetic contributors to metabolic syndrome, unexplained heritability exists and copy number variation (CNV) could be responsible for a portion of this variation. As observed with single nucleotide changes, it is likely that both rare and common CNVs will contribute to MetS disease susceptibility. Recent efforts to map CNVs in control populations have given insight into their size, frequency and distribution. However, despite being observed in controls, the reported CNVs could still modulate susceptibility for late-onset complex traitsor produce subtle metabolic phenotypes. Here we examine the overlap between CNVs found in control datasets and genes with functional hypotheses or evidence of previous association to MetS. Secondly, we present the results and methodology of a search for a rare CNV in a high-penetrance Mendelian disorder, namely familial partial lipodystrophy. As methods to identify CNVs increase in precision and accuracy, the prospect of identifying their role in both rare Mendelian and common complex diseases is exciting.

scholarly journals Genome-wide patterns of copy number variation in the diversified chicken genomes using next-generation sequencing

2014 ◽  
Author(s):  
Guoqiang Yi ◽  
Lujiang Qu ◽  
Jianfeng Liu ◽  
Yiyuan Yan ◽  
Guiyun Xu ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Association Studies ◽  
False Negative ◽  
Gene Copy ◽  
Comparative Genomic ◽  
Mineral Density ◽  
Validation Rate ◽  
Genome Wide ◽  
Number Variation

Copy number variation (CNV) is important and widespread in the genome, and is a major cause of disease and phenotypic diversity. Herein, we perform genome-wide CNV analysis in 12 diversified chicken genomes based on whole genome sequencing. A total of 9,025 CNV regions (CNVRs) covering 100.1 Mb and representing 9.6% of the chicken genome are identified, ranging in size from 1.1 to 268.8 kb with an average of 11.1 kb. Sequencing-based predictions are confirmed at high validation rate by two independent approaches, including array comparative genomic hybridization (aCGH) and quantitative PCR (qPCR). The Pearson?s correlation values between sequencing and aCGH results range from 0.395 to 0.740, and qPCR experiments reveal a positive validation rate of 91.71% and a false negative rate of 22.43%. In total, 2,188 predicted CNVRs (24.2%) span 2,182 RefSeq genes (36.8%) associated with specific biological functions. Besides two previously accepted copy number variable genesEDN3andPRLR, we also find some promising genes with potential in phenotypic variants.FZD6andLIMS1, two genes related to diseases susceptibility and resistance are covered by CNVRs. Highly duplicatedSOCS2may lead to higher bone mineral density. Entire or partial duplication of some genes likePOPDC3andLBFABPmay have great economic importance in poultry breeding. Our results based on extensive genetic diversity provide the first individualized chicken CNV map and genome-wide gene copy number estimates and warrant future CNV association studies for important traits of chickens.

scholarly journals Genome-wide detection of CNV regions and their potential association with growth and fatness traits in Duroc pigs

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yibin Qiu ◽  
Rongrong Ding ◽  
Zhanwei Zhuang ◽  
Jie Wu ◽  
Ming Yang ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Complex Traits ◽  
Copy Number ◽  
Association Studies ◽  
Average Daily Gain ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Genome Wide ◽  
Number Variation ◽  
Genetic Mechanisms

Abstract Background In the process of pig breeding, the average daily gain (ADG), days to 100 kg (AGE), and backfat thickness (BFT) are directly related to growth rate and fatness. However, the genetic mechanisms involved are not well understood. Copy number variation (CNV), an important source of genetic diversity, can affect a variety of complex traits and diseases and has gradually been thrust into the limelight. In this study, we reported the genome-wide CNVs of Duroc pigs using SNP genotyping data from 6627 animals. We also performed a copy number variation region (CNVR)-based genome-wide association studies (GWAS) for growth and fatness traits in two Duroc populations. Results Our study identified 953 nonredundant CNVRs in U.S. and Canadian Duroc pigs, covering 246.89 Mb (~ 10.90%) of the pig autosomal genome. Of these, 802 CNVRs were in U.S. Duroc pigs with 499 CNVRs were in Canadian Duroc pigs, indicating 348 CNVRs were shared by the two populations. Experimentally, 77.8% of nine randomly selected CNVRs were validated through quantitative PCR (qPCR). We also identified 35 CNVRs with significant association with growth and fatness traits using CNVR-based GWAS. Ten of these CNVRs were associated with both ADG and AGE traits in U.S. Duroc pigs. Notably, four CNVRs showed significant associations with ADG, AGE, and BFT, indicating that these CNVRs may play a pleiotropic role in regulating pig growth and fat deposition. In Canadian Duroc pigs, nine CNVRs were significantly associated with both ADG and AGE traits. Further bioinformatic analysis identified a subset of potential candidate genes, including PDGFA, GPER1, PNPLA2 and BSCL2. Conclusions The present study provides a necessary supplement to the CNV map of the Duroc genome through large-scale population genotyping. In addition, the CNVR-based GWAS results provide a meaningful way to elucidate the genetic mechanisms underlying complex traits. The identified CNVRs can be used as molecular markers for genetic improvement in the molecular-guided breeding of modern commercial pigs.

scholarly journals Current analysis platforms and methods for detecting copy number variation

2013 ◽  
Vol 45 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Wenli Li ◽  
Michael Olivier
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genomic Structural Variation ◽  
Disease Phenotypes ◽  
Human Genomes ◽  
Genome Wide ◽  
Number Variation ◽  
Genomic Regions

Copy number variation (CNV), generated through duplication or deletion events that affect one or more loci, is widespread in the human genomes and is often associated with functional consequences that may include changes in gene expression levels or fusion of genes. Genome-wide association studies indicate that some disease phenotypes and physiological pathways might be impacted by CNV in a small number of characterized genomic regions. However, the pervasiveness and full impact of such variation remains unclear. Suitable analytic methods are needed to thoroughly mine human genomes for genomic structural variation, and to explore the interplay between observed CNV and disease phenotypes, but many medical researchers are unfamiliar with the features and nuances of recently developed technologies for detecting CNV. In this article, we evaluate a suite of commonly used and recently developed approaches to uncovering genome-wide CNVs and discuss the relative merits of each.

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