Copy Number Variation Accuracy in Genome-Wide Association Studies

Neuropsychiatric disorders are complex conditions with poorly defined neurobiological bases. In recent years, there have been significant advances in our understanding of the genetic architecture of these conditions and the genetic loci involved. This review article describes historical attempts to identify susceptibility genes for neuropsychiatric disorders, recent progress through genome-wide association studies, copy number variation analyses and exome sequencing, and how these insights can inform the neuroscientific investigation of these conditions.

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

BMC Genomics ◽

10.1186/s12864-021-07654-7 ◽

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.

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Current analysis platforms and methods for detecting copy number variation

Physiological Genomics ◽

10.1152/physiolgenomics.00082.2012 ◽

2013 ◽

Vol 45 (1) ◽

pp. 1-16 ◽

Cited By ~ 45

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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