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Zhang C, Yang H, Xu Q, Liu M, Chao X, Chen J, Zhou B, Liu Y. Comprehensive Genome and Transcriptome Analysis Identifies SLCO3A1 Associated with Aggressive Behavior in Pigs. Biomolecules 2023; 13:1381. [PMID: 37759782 PMCID: PMC10526945 DOI: 10.3390/biom13091381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Copy number variation (CNV) represents a significant reservoir of genetic diversity within the genome and exhibits a strong association with economically valuable traits in livestock. The manifestation of aggressive behavior in pigs has detrimental effects on production efficiency, immune competency, and meat quality. Nevertheless, the impact of CNV on the aggressive behavior of pigs remains elusive. In this investigation, we employed an integrated analysis of genome and transcriptome data to investigate the interplay between CNV, gene expression changes, and indicators of aggressive behavior in weaned pigs. Specifically, a subset of pigs comprising the most aggressive pigs (MAP, n = 12) and the least aggressive pigs (LAP, n = 11) was purposefully selected from a herd of 500 weaned pigs following a mixing procedure based on their composite aggressive score (CAS). Subsequently, we thoroughly analyzed copy number variation regions (CNVRs) across the entire genome using next-generation sequencing techniques, ultimately revealing the presence of 6869 CNVRs. Using genome-wide association study (GWAS) analysis and evaluating variance-stabilizing transformation (VST) values, we successfully identified distinct CNVRs that distinguished the MAP and LAP counterparts. Among the prioritized CNVRs, CNVR-4962 (designated as the top-ranked p-value and VST value, No. 1) was located within the Solute Carrier Organic Anion Transporter Family Member 3A1 (SLCO3A1) gene. The results of our analyses indicated a significantly higher (p < 0.05) copy number of SLCO3A1 in the MAP compared to the LAP. Furthermore, this increased copy number exhibited a positive correlation with the CAS of the pigs (p < 0.05). Furthermore, we integrated genomic data with transcriptomic data from the temporal lobe to facilitate the examination of expression quantitative trait loci (eQTL). Importantly, these observations were consistent with the mRNA expression pattern of SLCO3A1 in the temporal lobe of both MAP and LAP (p < 0.05). Consequently, our findings strongly suggest that CNVs affecting SLCO3A1 may influence gene expression through a dosage effect. These results highlight the potential of SLCO3A1 as a candidate gene associated with aggressive traits in pig breeding programs.
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Affiliation(s)
| | | | | | | | | | | | - Bo Zhou
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Z.); (H.Y.); (Q.X.); (M.L.); (X.C.); (J.C.)
| | - Yang Liu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Z.); (H.Y.); (Q.X.); (M.L.); (X.C.); (J.C.)
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2
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Ding R, Zhuang Z, Qiu Y, Wang X, Wu J, Zhou S, Ruan D, Xu C, Hong L, Gu T, Zheng E, Cai G, Huang W, Wu Z, Yang J. A composite strategy of genome-wide association study and copy number variation analysis for carcass traits in a Duroc pig population. BMC Genomics 2022; 23:590. [PMID: 35964005 PMCID: PMC9375371 DOI: 10.1186/s12864-022-08804-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Carcass traits are important in pig breeding programs for improving pork production. Understanding the genetic variants underlies complex phenotypes can help explain trait variation in pigs. In this study, we integrated a weighted single-step genome-wide association study (wssGWAS) and copy number variation (CNV) analyses to map genetic variations and genes associated with loin muscle area (LMA), loin muscle depth (LMD) and lean meat percentage (LMP) in Duroc pigs. RESULTS Firstly, we performed a genome-wide analysis for CNV detection using GeneSeek Porcine SNP50 Bead chip data of 3770 pigs. A total of 11,100 CNVs were detected, which were aggregated by overlapping 695 CNV regions (CNVRs). Next, we investigated CNVs of pigs from the same population by whole-genome resequencing. A genome-wide analysis of 21 pigs revealed 23,856 CNVRs that were further divided into three categories (851 gain, 22,279 loss, and 726 mixed), which covered 190.8 Mb (~ 8.42%) of the pig autosomal genome. Further, the identified CNVRs were used to determine an overall validation rate of 68.5% for the CNV detection accuracy of chip data. CNVR association analyses identified one CNVR associated with LMA, one with LMD and eight with LMP after applying stringent Bonferroni correction. The wssGWAS identified eight, six and five regions explaining more than 1% of the additive genetic variance for LMA, LMD and LMP, respectively. The CNVR analyses and wssGWAS identified five common regions, of which three regions were associated with LMA and two with LMP. Four genes (DOK7, ARAP1, ELMO2 and SLC13A3) were highlighted as promising candidates according to their function. CONCLUSIONS We determined an overall validation rate for the CNV detection accuracy of low-density chip data and constructed a genomic CNV map for Duroc pigs using resequencing, thereby proving a value genetic variation resource for pig genome research. Furthermore, our study utilized a composite genetic strategy for complex traits in pigs, which will contribute to the study for elucidating the genetic architecture that may be influenced and regulated by multiple forms of variations.
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Affiliation(s)
- Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Guangdong Wens Breeding Swine Technology Co., Ltd, Guangdong, 527439, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Xingwang Wang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Linjun Hong
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Ting Gu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China
| | - Wen Huang
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China. .,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China.
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China. .,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, China.
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3
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Cortez DR, Lima FM, Reis-Cunha JL, Bartholomeu DC, Villacis RAR, Rogatto SR, Costa-Martins AG, Marchiano FS, do Carmo RA, da Silveira JF, Marini MM. Trypanosoma cruzi Genomic Variability: Array Comparative Genomic Hybridization Analysis of Clone and Parental Strain. Front Cell Infect Microbiol 2022; 12:760830. [PMID: 35402315 PMCID: PMC8992781 DOI: 10.3389/fcimb.2022.760830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/25/2022] [Indexed: 11/13/2022] Open
Abstract
Trypanosoma cruzi, the etiological agent of Chagas disease, exhibits extensive inter- and intrastrain genetic diversity. As we have previously described, there are some genetic differences between the parental G strain and its clone D11, which was isolated by the limiting dilution method and infection of cultured mammalian cells. Electrophoretic karyotyping and Southern blot hybridization of chromosomal bands with specific markers revealed chromosome length polymorphisms of small size with additional chromosomal bands in clone D11 and the maintenance of large syntenic groups. Both G strain and clone D11 belong to the T. cruzi lineage TcI. Here, we designed intraspecific array-based comparative genomic hybridization (aCGH) to identify chromosomal regions harboring copy-number variations between clone D11 and the G strain. DNA losses were more extensive than DNA gains in clone D11. Most alterations were flanked by repeated sequences from multigene families that could be involved in the duplication and deletion events. Several rearrangements were detected by chromoblot hybridization and confirmed by aCGH. We have integrated the information of genomic sequence data obtained by aCGH to the electrophoretic karyotype, allowing the reconstruction of possible recombination events that could have generated the karyotype of clone D11. These rearrangements may be explained by unequal crossing over between sister or homologous chromatids mediated by flanking repeated sequences and unequal homologous recombination via break-induced replication. The genomic changes detected by aCGH suggest the presence of a dynamic genome that responds to environmental stress by varying the number of gene copies and generating segmental aneuploidy.
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Affiliation(s)
- Danielle Rodrigues Cortez
- Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Fabio Mitsuo Lima
- Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
- Centro Universitário São Camilo, Biomedicina, São Paulo, Brazil
| | - João Luís Reis-Cunha
- Departamento de Parasitologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Silvia Regina Rogatto
- Department of Clinical Genetics, Institute of Regional Health Research, University of Southern Denmark, Vejle, Denmark
| | - André Guilherme Costa-Martins
- Department of Clinical and Toxicological Analyses, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Fernanda Sycko Marchiano
- Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Rafaela Andrade do Carmo
- Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Jose Franco da Silveira
- Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
- *Correspondence: Marjorie Mendes Marini, ; Jose Franco da Silveira,
| | - Marjorie Mendes Marini
- Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
- Centro Universitário São Camilo, Biomedicina, São Paulo, Brazil
- *Correspondence: Marjorie Mendes Marini, ; Jose Franco da Silveira,
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4
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Wang Z, Guo Y, Liu S, Meng Q. Genome-Wide Assessment Characteristics of Genes Overlapping Copy Number Variation Regions in Duroc Purebred Population. Front Genet 2021; 12:753748. [PMID: 34721540 PMCID: PMC8552909 DOI: 10.3389/fgene.2021.753748] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
Copy number variations (CNVs) are important structural variations that can cause significant phenotypic diversity. Reliable CNVs mapping can be achieved by identification of CNVs from different genetic backgrounds. Investigations on the characteristics of overlapping between CNV regions (CNVRs) and protein-coding genes (CNV genes) or miRNAs (CNV-miRNAs) can reveal the potential mechanisms of their regulation. In this study, we used 50 K SNP arrays to detect CNVs in Duroc purebred pig. A total number of 211 CNVRs were detected with a total length of 118.48 Mb, accounting for 5.23% of the autosomal genome sequence. Of these CNVRs, 32 were gains, 175 losses, and four contained both types (loss and gain within the same region). The CNVRs we detected were non-randomly distributed in the swine genome and were significantly enriched in the segmental duplication and gene density region. Additionally, these CNVRs were overlapping with 1,096 protein-coding genes (CNV-genes), and 39 miRNAs (CNV-miRNAs), respectively. The CNV-genes were enriched in terms of dosage-sensitive gene list. The expression of the CNV genes was significantly higher than that of the non-CNV genes in the adult Duroc prostate. Of all detected CNV genes, 22.99% genes were tissue-specific (TSI > 0.9). Strong negative selection had been underway in the CNV-genes as the ones that were located entirely within the loss CNVRs appeared to be evolving rapidly as determined by the median dN plus dS values. Non-CNV genes tended to be miRNA target than CNV-genes. Furthermore, CNV-miRNAs tended to target more genes compared to non-CNV-miRNAs, and a combination of two CNV-miRNAs preferentially synergistically regulated the same target genes. We also focused our efforts on examining CNV genes and CNV-miRNAs functions, which were also involved in the lipid metabolism, including DGAT1, DGAT2, MOGAT2, miR143, miR335, and miRLET7. Further molecular experiments and independent large studies are needed to confirm our findings.
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Affiliation(s)
- Zhipeng Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Yuanyuan Guo
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Shengwei Liu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.,Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Qingli Meng
- Beijing Breeding Swine Center, Beijing, China
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5
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Qiu Y, Ding R, Zhuang Z, Wu J, Yang M, Zhou S, Ye Y, Geng Q, Xu Z, Huang S, Cai G, Wu Z, Yang J. Genome-wide detection of CNV regions and their potential association with growth and fatness traits in Duroc pigs. BMC Genomics 2021; 22:332. [PMID: 33964879 PMCID: PMC8106131 DOI: 10.1186/s12864-021-07654-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 04/27/2021] [Indexed: 11/19/2022] Open
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. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07654-7.
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Affiliation(s)
- Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.,Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Ming Yang
- Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Yong Ye
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Qian Geng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Zheng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China
| | - Sixiu Huang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.,Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China. .,Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China. .,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China.
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China. .,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China.
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6
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Bovo S, Ribani A, Muñoz M, Alves E, Araujo JP, Bozzi R, Charneca R, Di Palma F, Etherington G, Fernandez AI, García F, García-Casco J, Karolyi D, Gallo M, Gvozdanović K, Martins JM, Mercat MJ, Núñez Y, Quintanilla R, Radović Č, Razmaite V, Riquet J, Savić R, Schiavo G, Škrlep M, Usai G, Utzeri VJ, Zimmer C, Ovilo C, Fontanesi L. Genome-wide detection of copy number variants in European autochthonous and commercial pig breeds by whole-genome sequencing of DNA pools identified breed-characterising copy number states. Anim Genet 2020; 51:541-556. [PMID: 32510676 DOI: 10.1111/age.12954] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2020] [Indexed: 02/06/2023]
Abstract
In this study, we identified copy number variants (CNVs) in 19 European autochthonous pig breeds and in two commercial breeds (Italian Large White and Italian Duroc) that represent important genetic resources for this species. The genome of 725 pigs was sequenced using a breed-specific DNA pooling approach (30-35 animals per pool) obtaining an average depth per pool of 42×. This approach maximised CNV discovery as well as the related copy number states characterising, on average, the analysed breeds. By mining more than 17.5 billion reads, we identified a total of 9592 CNVs (~683 CNVs per breed) and 3710 CNV regions (CNVRs; 1.15% of the reference pig genome), with an average of 77 CNVRs per breed that were considered as private. A few CNVRs were analysed in more detail, together with other information derived from sequencing data. For example, the CNVR encompassing the KIT gene was associated with coat colour phenotypes in the analysed breeds, confirming the role of the multiple copies in determining breed-specific coat colours. The CNVR covering the MSRB3 gene was associated with ear size in most breeds. The CNVRs affecting the ELOVL6 and ZNF622 genes were private features observed in the Lithuanian Indigenous Wattle and in the Turopolje pig breeds respectively. Overall, the genome variability unravelled here can explain part of the genetic diversity among breeds and might contribute to explain their origin, history and adaptation to a variety of production systems.
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Affiliation(s)
- S Bovo
- Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 46, Bologna, 40127, Italy
| | - A Ribani
- Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 46, Bologna, 40127, Italy
| | - M Muñoz
- Departamento Mejora Genética Animal, INIA, Crta. de la Coruña, km. 7,5, Madrid, 28040, Spain
| | - E Alves
- Departamento Mejora Genética Animal, INIA, Crta. de la Coruña, km. 7,5, Madrid, 28040, Spain
| | - J P Araujo
- Centro de Investigação de Montanha, Instituto Politécnico de Viana do Castelo, Escola Superior Agrária, Refóios do Lima, Ponte de Lima, 4990-706, Portugal
| | - R Bozzi
- DAGRI - Animal Science Section, Università di Firenze, Via delle Cascine 5, Firenze, 50144, Italy
| | - R Charneca
- MED - Mediterranean Institute for Agriculture, Environment and Development, Universidade de Évora, Pólo da Mitra, Apartado 94, Évora, 7006-554, Portugal
| | - F Di Palma
- Earlham Institute, Norwich Research Park, Colney Lane, Norwich, NR47UZ, UK
| | - G Etherington
- Earlham Institute, Norwich Research Park, Colney Lane, Norwich, NR47UZ, UK
| | - A I Fernandez
- Departamento Mejora Genética Animal, INIA, Crta. de la Coruña, km. 7,5, Madrid, 28040, Spain
| | - F García
- Departamento Mejora Genética Animal, INIA, Crta. de la Coruña, km. 7,5, Madrid, 28040, Spain
| | - J García-Casco
- Departamento Mejora Genética Animal, INIA, Crta. de la Coruña, km. 7,5, Madrid, 28040, Spain
| | - D Karolyi
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska c. 25, Zagreb, 10000, Croatia
| | - M Gallo
- Associazione Nazionale Allevatori Suini, Via Nizza 53, Roma, 00198, Italy
| | - K Gvozdanović
- Faculty of Agrobiotechnical Sciences Osijek, University of Osijek, Vladimira Preloga 1, Osijek, 31000, Croatia
| | - J M Martins
- MED - Mediterranean Institute for Agriculture, Environment and Development, Universidade de Évora, Pólo da Mitra, Apartado 94, Évora, 7006-554, Portugal
| | - M J Mercat
- IFIP Institut Du Porc, La Motte au Vicomte, BP 35104, Le Rheu Cedex, 35651, France
| | - Y Núñez
- Departamento Mejora Genética Animal, INIA, Crta. de la Coruña, km. 7,5, Madrid, 28040, Spain
| | - R Quintanilla
- Programa de Genética y Mejora Animal, IRTA, Torre Marimon, Caldes de Montbui, Barcelona, 08140, Spain
| | - Č Radović
- Department of Pig Breeding and Genetics, Institute for Animal Husbandry, Belgrade-Zemun, 11080, Serbia
| | - V Razmaite
- Animal Science Institute, Lithuanian University of Health Sciences, R. Žebenkos 12, Baisogala, 82317, Lithuania
| | - J Riquet
- GenPhySE, INRA, Université de Toulouse, Chemin de Borde-Rouge 24, Auzeville Tolosane, Castanet Tolosan, 31326, France
| | - R Savić
- Faculty of Agriculture, University of Belgrade, Nemanjina 6, Belgrade-Zemun, 11080, Serbia
| | - G Schiavo
- Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 46, Bologna, 40127, Italy
| | - M Škrlep
- Kmetijski Inštitut Slovenije, Hacquetova 17, Ljubljana, SI-1000, Slovenia
| | - G Usai
- AGRIS SARDEGNA, Loc. Bonassai, Sassari, 07100, Italy
| | - V J Utzeri
- Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 46, Bologna, 40127, Italy
| | - C Zimmer
- Bäuerliche Erzeugergemeinschaft Schwäbisch Hall, Haller Str. 20, Wolpertshausen, 74549, Germany
| | - C Ovilo
- Departamento Mejora Genética Animal, INIA, Crta. de la Coruña, km. 7,5, Madrid, 28040, Spain
| | - L Fontanesi
- Division of Animal Sciences, Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 46, Bologna, 40127, Italy
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Alshawaqfeh M, Al Kawam A, Serpedin E, Datta A. Robust Recurrent CNV Detection in the Presence of Inter-Subject Variability. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1056-1067. [PMID: 30387737 DOI: 10.1109/tcbb.2018.2878560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The study of recurrent copy number variations (CNVs) plays an important role in understanding the onset and evolution of complex diseases such as cancer. Array-based comparative genomic hybridization (aCGH) is a widely used microarray based technology for identifying CNVs. However, due to high noise levels and inter-sample variability, detecting recurrent CNVs from aCGH data remains a challenging topic. This paper proposes a novel method for identification of the recurrent CNVs. In the proposed method, the noisy aCGH data is modeled as the superposition of three matrices: a full-rank matrix of weighted piece-wise generating signals accounting for the clean aCGH data, a Gaussian noise matrix to model the inherent experimentation errors and other sources of error, and a sparse matrix to capture the sparse inter-sample (sample-specific) variations. We demonstrated the ability of our method to separate accurately recurrent CNVs from sample-specific variations and noise in both simulated (artificial) data and real data. The proposed method produced more accurate results than current state-of-the-art methods used in recurrent CNV detection and exhibited robustness to noise and sample-specific variations.
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Zheng X, Zhao P, Yang K, Ning C, Wang H, Zhou L, Liu J. CNV analysis of Meishan pig by next-generation sequencing and effects of AHR gene CNV on pig reproductive traits. J Anim Sci Biotechnol 2020; 11:42. [PMID: 32337028 PMCID: PMC7171861 DOI: 10.1186/s40104-020-00442-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Accepted: 02/27/2020] [Indexed: 12/17/2022] Open
Abstract
Background Reproductive performance of livestock is an economically important aspect of global food production. The Chinese Meishan pig is a prolific breed, with an average of three to five more piglets per litter than European breeds; however, the genetic basis for this difference is not well understood. Results In this study, we investigated copy number variations (CNVs) of 32 Meishan pigs and 29 Duroc pigs by next-generation sequencing. A genome-wide analysis of 61 pigs revealed 12,668 copy number variable regions (CNVRs) that were further divided into three categories based on copy number (CN) of the whole population, i.e., gain (n = 7,638), and loss (n = 5,030) CNVRs. We then compared Meishan and Duroc pigs and identified 17.17 Mb of 6,387 CNVRs that only existing in Meishan pigs CNVRs that overlapped the reproduction-related gene encoding the aryl hydrocarbon receptor (AHR) gene. We found that normal AHR CN was more frequent than CN loss in four different pig breeds. An association analysis showed that AHR CN had a positive effect on litter size (P < 0.05) and that a higher CN was associated with higher total number born (P < 0.05), number born alive (P < 0.05), number of weaned piglets, and birth weight. Conclusions The present study provides comprehensive CNVRs for Meishan and Duroc pigs through large-scale population resequencing. Our results provide a supplement for the high-resolution map of copy number variation in the porcine genome and valuable information for the investigation of genomic structural variation underlying traits of interest in pig. In addition, the association results provide evidence for AHR as a candidate gene associated with reproductive traits that can be used as a genetic marker in pig breeding programs.
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Affiliation(s)
- Xianrui Zheng
- 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Pengju Zhao
- 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Kaijie Yang
- 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Chao Ning
- 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Haifei Wang
- 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China.,2Department of Animal Genetics, Breeding and Reproduction and Molecular Design, College of Animal Science and Technology, Yangzhou University, Yangzhou, 225009 China
| | - Lei Zhou
- 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Jianfeng Liu
- 1National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
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Keel BN, Nonneman DJ, Lindholm-Perry AK, Oliver WT, Rohrer GA. A Survey of Copy Number Variation in the Porcine Genome Detected From Whole-Genome Sequence. Front Genet 2019; 10:737. [PMID: 31475038 PMCID: PMC6707380 DOI: 10.3389/fgene.2019.00737] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 07/12/2019] [Indexed: 12/11/2022] Open
Abstract
Copy number variations (CNVs) are gains and losses of large regions of genomic sequence between individuals of a species. Although CNVs have been associated with various phenotypic traits in humans and other species, the extent to which CNVs impact phenotypic variation remains unclear. In swine, as well as many other species, relatively little is understood about the frequency of CNV in the genome, sizes, locations, and other chromosomal properties. In this work, we identified and characterized CNV by utilizing whole-genome sequence from 240 members of an intensely phenotyped experimental swine herd at the U.S. Meat Animal Research Center (USMARC). These animals included all 24 of the purebred founding boars (12 Duroc and 12 Landrace), 48 of the founding Yorkshire-Landrace composite sows, 109 composite animals from generations 4 through 9, 29 composite animals from generation 15, and 30 purebred industry boars (15 Landrace and 15 Yorkshire) used as sires in generations 10 through 15. Using a combination of split reads, paired-end mapping, and read depth approaches, we identified a total of 3,538 copy number variable regions (CNVRs), including 1,820 novel CNVRs not reported in previous studies. The CNVRs covered 0.94% of the porcine genome and overlapped 1,401 genes. Gene ontology analysis identified that CNV-overlapped genes were enriched for functions related to organism development. Additionally, CNVRs overlapped with many known quantitative trait loci (QTL). In particular, analysis of QTL previously identified in the USMARC herd showed that CNVRs were most overlapped with reproductive traits, such as age of puberty and ovulation rate, and CNVRs were significantly enriched for reproductive QTL.
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Affiliation(s)
- Brittney N Keel
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
| | - Dan J Nonneman
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
| | | | - William T Oliver
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
| | - Gary A Rohrer
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States
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Cai H, Chen P, Chen J, Cai J, Song Y, Han G. WaveDec: A Wavelet Approach to Identify Both Shared and Individual Patterns of Copy-Number Variations. IEEE Trans Biomed Eng 2019; 65:353-364. [PMID: 29346103 DOI: 10.1109/tbme.2017.2769677] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Copy-number variations (CNVs) are associated with complex diseases and particular tumor types. Array-based comparative genomic hybridization (aCGH) is a common approach for the detection of CNVs. Traditional CNV detection methods for multiple aCGH samples mainly use batch samples to find common variations, not accounting for the individual characteristics of each sample. Accurately differentiating both the commonly shared and the individual CNV patterns is pivotal to identify cell populations, or to distinguish cell growth (as in cancer) from invasion of new cells. Our preliminary results have now demonstrated that both the shared and individual CNV patterns have distinctive characteristics after wavelet transform. METHODS To exploit these characteristics, we propose to formulate a quadratic data-separation problem within the wavelet space to discriminate the shared and individual CNVs from raw data. We have elaborated a numerical solution and shown that the solution can be obtained by solving decoupled subproblems. By this approach, computational costs can be limited, enabling efficient application in the analysis of large sequencing datasets. RESULTS The advantages of our proposed method, called WaveDec, have been demonstrated by comparison with popular CNV-detection methods using synthetic and empirical aCGH data. The performance of WaveDec was further validated by experiments with single-cell-sequencing data. CONCLUSION WaveDec can successfully differentiate shared and individual patterns, and performs well even in data contaminated with high levels of noise. SIGNIFICANCE Both the shared and individual patterns can be uniquely characterized as well as effectively decomposed within the wavelet space.
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Genova F, Longeri M, Lyons LA, Bagnato A, Strillacci MG. First genome-wide CNV mapping in FELIS CATUS using next generation sequencing data. BMC Genomics 2018; 19:895. [PMID: 30526495 PMCID: PMC6288940 DOI: 10.1186/s12864-018-5297-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 11/21/2018] [Indexed: 01/09/2023] Open
Abstract
Background Copy Number Variations (CNVs) have becoming very significant variants, representing a major source of genomic variation. CNVs involvement in phenotypic expression and different diseases has been widely demonstrated in humans as well as in many domestic animals. However, genome wide investigation on these structural variations is still missing in Felis catus. The present work is the first CNV mapping from a large data set of Next Generation Sequencing (NGS) data in the domestic cat, performed within the 99 Lives Consortium. Results Reads have been mapped on the reference assembly_6.2 by Maverix Biomics. CNV detection with cn.MOPS and CNVnator detected 592 CNVs. These CNVs were used to obtain 154 CNV Regions (CNVRs) with BedTools, including 62 singletons. CNVRs covered 0.26% of the total cat genome with 129 losses, 19 gains and 6 complexes. Cluster Analysis and Principal Component Analysis of the detected CNVRs showed that breeds tend to cluster together as well as cats sharing the same geographical origins. The 46 genes identified within the CNVRs were annotated. Conclusion This study has improved the genomic characterization of 14 cat breeds and has provided CNVs information that can be used for studies of traits in cats. It can be considered a sound starting point for genomic CNVs identification in this species. Electronic supplementary material The online version of this article (10.1186/s12864-018-5297-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- F Genova
- Department of Veterinary Medicine, University of Milan, 20122, Milan, Italy
| | - M Longeri
- Department of Veterinary Medicine, University of Milan, 20122, Milan, Italy
| | - L A Lyons
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA
| | - A Bagnato
- Department of Veterinary Medicine, University of Milan, 20122, Milan, Italy
| | | | - M G Strillacci
- Department of Veterinary Medicine, University of Milan, 20122, Milan, Italy.
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Stachowiak M, Szczerbal I, Switonski M. Genetics of Adiposity in Large Animal Models for Human Obesity-Studies on Pigs and Dogs. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2016; 140:233-70. [PMID: 27288831 DOI: 10.1016/bs.pmbts.2016.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The role of domestic mammals in the development of human biomedical sciences has been widely documented. Among these model species the pig and dog are of special importance. Both are useful for studies on the etiology of human obesity. Genome sequences of both species are known and advanced genetic tools [eg, microarray SNP for genome wide association studies (GWAS), next generation sequencing (NGS), etc.] are commonly used in such studies. In the domestic pig the accumulation of adipose tissue is an important trait, which influences meat quality and fattening efficiency. Numerous quantitative trait loci (QTLs) for pig fatness traits were identified, while gene polymorphisms associated with these traits were also described. The situation is different in dog population. Generally, excessive accumulation of adipose tissue is considered, similar to humans, as a complex disease. However, research on the genetic background of canine obesity is still in its infancy. Between-breed differences in terms of adipose tissue accumulation are well known in both animal species. In this review we show recent advances of studies on adipose tissue accumulation in pigs and dogs, and their potential importance for studies on human obesity.
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Affiliation(s)
- M Stachowiak
- Department of Genetics, Animal Breeding, Poznań University of Life Sciences, Poznań, Poland
| | - I Szczerbal
- Department of Genetics, Animal Breeding, Poznań University of Life Sciences, Poznań, Poland
| | - M Switonski
- Department of Genetics, Animal Breeding, Poznań University of Life Sciences, Poznań, Poland.
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Xie J, Li R, Li S, Ran X, Wang J, Jiang J, Zhao P. Identification of Copy Number Variations in Xiang and Kele Pigs. PLoS One 2016; 11:e0148565. [PMID: 26840413 PMCID: PMC4740446 DOI: 10.1371/journal.pone.0148565] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 01/19/2016] [Indexed: 12/24/2022] Open
Abstract
Xiang and Kele pigs are two well-known local Chinese pig breeds that possess rich genetic resources and have enormous economic and scientific value. We performed a comprehensive genomic analysis of the copy number variations (CNVs) in these breeds. CNVs are one of the most important forms of genomic variation and have profound effects on phenotypic variation. In this study, PorcineSNP60 genotyping data from 98 Xiang pigs and 22 Kele pigs were used to identify CNVs. In total, 172 candidate CNV regions (CNVRs) were identified, ranging from 3.19 kb to 8175.26 kb and covering 80.41 Mb of the pig genome. Approximately 56.40% (97/172) of the CNVRs overlapped with those identified in seven previous studies, and 43.60% (75/172) of the identified CNVRs were novel. Of the identified CNVRs, 82 (47 gain, 33 loss, and two gain-loss events that covered 4.58 Mb of the pig genome) were found only in a Xiang population with a large litter size. In contrast, 13 CNVRs (8 gain and 5 loss events) were unique to a Xiang population with small litter sizes, and 30 CNVRs (14 loss and 16 gain events) were unique to Kele pigs. The CNVRs span approximately 660 annotated Sus scrofa genes that are significantly enriched for specific biological functions, such as sensory perception, cognition, reproduction, ATP biosynthetic processes, and neurological processes. Many CNVR-associated genes, particularly the genes involved in reproductive traits, differed between the Xiang populations with large and small litter sizes, and these genes warrant further investigation due to their importance in determining the reproductive performance of Xiang pigs. Our results provide meaningful information about genomic variation, which may be useful in future assessments of the associations between CNVs and important phenotypes in Xiang and Kele pigs to ultimately help protect these rare breeds.
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Affiliation(s)
- Jian Xie
- Institute of Agro-Bioengineering and College of Life Sciences, Guizhou University, Guiyang, China
| | - Rongrong Li
- Institute of Agro-Bioengineering and College of Life Sciences, Guizhou University, Guiyang, China
| | - Sheng Li
- Institute of Agro-Bioengineering and College of Life Sciences, Guizhou University, Guiyang, China
| | - Xueqin Ran
- College of animal Science, Guizhou University, Guiyang, China
- * E-mail: (XQR); (JFW)
| | - Jiafu Wang
- Institute of Agro-Bioengineering and College of Life Sciences, Guizhou University, Guiyang, China
- * E-mail: (XQR); (JFW)
| | - Jicai Jiang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Pengju Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing, China
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Gao X. Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations. BMC Bioinformatics 2015; 16:407. [PMID: 26652207 PMCID: PMC4676147 DOI: 10.1186/s12859-015-0835-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 11/23/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Copy number variation (CNV) analysis has become one of the most important research areas for understanding complex disease. With increasing resolution of array-based comparative genomic hybridization (aCGH) arrays, more and more raw copy number data are collected for multiple arrays. It is natural to realize the co-existence of both recurrent and individual-specific CNVs, together with the possible data contamination during the data generation process. Therefore, there is a great need for an efficient and robust statistical model for simultaneous recovery of both recurrent and individual-specific CNVs. RESULT We develop a penalized weighted low-rank approximation method (WPLA) for robust recovery of recurrent CNVs. In particular, we formulate multiple aCGH arrays into a realization of a hidden low-rank matrix with some random noises and let an additional weight matrix account for those individual-specific effects. Thus, we do not restrict the random noise to be normally distributed, or even homogeneous. We show its performance through three real datasets and twelve synthetic datasets from different types of recurrent CNV regions associated with either normal random errors or heavily contaminated errors. CONCLUSION Our numerical experiments have demonstrated that the WPLA can successfully recover the recurrent CNV patterns from raw data under different scenarios. Compared with two other recent methods, it performs the best regarding its ability to simultaneously detect both recurrent and individual-specific CNVs under normal random errors. More importantly, the WPLA is the only method which can effectively recover the recurrent CNVs region when the data is heavily contaminated.
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Affiliation(s)
- Xiaoli Gao
- Department of Mathematics and Statistics, University of North Carolina at Greensboro, 1400 Spring Garden St, Greensoboro, NC, USA.
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