Array CGH-based detection of CNV regions and their potential association with reproduction and other economic traits in Holsteins.
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ABSTRACT:
SUBMITTER: Liu M
PROVIDER: S-EPMC6407259 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
Publications
Array CGH-based detection of CNV regions and their potential association with reproduction and other economic traits in Holsteins.
Liu Mei M Fang Lingzhao L Liu Shuli S Pan Michael G MG Seroussi Eyal E Cole John B JB Ma Li L Chen Hong H Liu George E GE
BMC genomics 20190307 1
<h4>Background</h4>Copy number variations (CNVs) are structural variants consisting of large-scale insertions and deletions of genomic fragments. Exploring CNVs and estimating their effects on phenotypes are useful for genome selection but remain challenging in the livestock.<h4>Results</h4>We identified 1043 CNV regions (CNVRs) from array comparative genomic hybridization (CGH) data of 47 Holstein bulls. Using a probe-based CNV association approach, we detected 87 CNVRs significantly (Bonferron ...[more]
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Project description:BackgroundMilk production is an economically important sector of global agriculture. Much attention has been paid to the identification of quantitative trait loci (QTL) associated with milk, fat, and protein yield and the genetic and molecular mechanisms underlying them. Copy number variation (CNV) is an emerging class of variants which may be associated with complex traits.ResultsIn this study, we performed a genome-wide association between CNVs and milk production traits in 26,362 Holstein bulls and cows. A total of 99 candidate CNVs were identified using Illumina BovineSNP50 array data, and association tests for each production trait were performed using a linear regression analysis with PCA correlation. A total of 34 CNVs on 22 chromosomes were significantly associated with at least one milk production trait after false discovery rate (FDR) correction. Some of those CNVs were located within or near known QTL for milk production traits. We further investigated the relationship between associated CNVs with neighboring SNPs. For all 82 combinations of traits and CNVs (less than 400 kb in length), we found 17 cases where CNVs directly overlapped with tag SNPs and 40 cases where CNVs were adjacent to tag SNPs. In 5 cases, CNVs located were in strong linkage disequilibrium with tag SNPs, either within or adjacent to the same haplotype block. There were an additional 20 cases where CNVs did not have a significant association with SNPs, suggesting that the effects of those CNVs were probably not captured by tag SNPs.ConclusionWe conclude that combining CNV with SNP analyses reveals more genetic variations underlying milk production traits than those revealed by SNPs alone.
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Project description:Genomic imbalances are a major cause of constitutional and acquired disorders. Therefore, aneuploidy screening has become the cornerstone of preimplantation, prenatal and postnatal genetic diagnosis, as well as a routine aspect of the diagnostic workup of many acquired disorders. Recently, array comparative genomic hybridization (array CGH) has been introduced as a rapid and high-resolution method for the detection of both benign and disease-causing genomic copy-number variations. Until now, array CGH has been performed using a significant quantity of DNA derived from a pool of cells. Here, we present an array CGH method that accurately detects chromosomal imbalances from a single lymphoblast, fibroblast and blastomere within a single day. Trisomy 13, 18, 21 and monosomy X, as well as normal ploidy levels of all other chromosomes, were accurately determined from single fibroblasts. Moreover, we showed that a segmental deletion as small as 34 Mb could be detected. Finally, we demonstrated the possibility to detect aneuploidies in single blastomeres derived from preimplantation embryos. This technique offers new possibilities for genetic analysis of single cells in general and opens the route towards aneuploidy screening and detection of unbalanced translocations in preimplantation embryos in particular.
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Project description:Copy number variation (CNV) is an important type of genetic variation contributing to phenotypic differences among mammals. Up to now, GWAS analysis using CNV called by array CGH is lacking in livestock like Holstein cattle. The objectives of this work are to identify CNVs using high-density aCGH data and explore functional CNVs which are associated with complex traits by GWAS method in Holstein cattle. In this study, we reported a systematic CNV association analysis of CNVs and 39 complex production traits in Holsteins. This research identified 1043 CNV regions (CNVRs) by array CGH data in 47 Holstein bulls. Using a genome-wide association analysis (GWAS) approach, we identified 79 significant CNVRs associated with at least one complex traits after false discovery rate (FDR) correction. Notably, 24 CNVRs were markedly related to daughter pregnancy rate (DPR). This study observed the pleiotropy phenomenon of 39 CNV loci which can simultaneously regulate at least 2 complex traits. In summary, the significant CNVs identified in this research could be utilized additional molecular markers for genetic improvement programs in Holsteins.
2019-02-16 | GSE122478 | GEO
Project description:Array comparative genomic hybridization (aCGH) is a powerful tool for the molecular elucidation and diagnosis of disorders resulting from genomic copy-number variation (CNV). However, intragenic deletions or duplications--those including genomic intervals of a size smaller than a gene--have remained beyond the detection limit of most clinical aCGH analyses. Increasing array probe number improves genomic resolution, although higher cost may limit implementation, and enhanced detection of benign CNV can confound clinical interpretation. We designed an array with exonic coverage of selected disease and candidate genes and used it clinically to identify losses or gains throughout the genome involving at least one exon and as small as several hundred base pairs in size. In some patients, the detected copy-number change occurs within a gene known to be causative of the observed clinical phenotype, demonstrating the ability of this array to detect clinically relevant CNVs with subkilobase resolution. In summary, we demonstrate the utility of a custom-designed, exon-targeted oligonucleotide array to detect intragenic copy-number changes in patients with various clinical phenotypes.
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Project description:BackgroundThe advent of genomic information and the reduction in the cost of genotyping have led to the use of genomic information to estimate genomic inbreeding as an alternative to pedigree inbreeding. Using genomic measures, effects of genomic inbreeding on production and fertility traits have been observed. However, there have been limited studies on the specific genomic regions causing the observed negative association with the trait of interest. Our aim was to identify unique run of homozygosity (ROH) genotypes present within a given genomic window that display negative associations with production and fertility traits and to quantify the effects of these identified ROH genotypes.MethodsIn total, 50,575 genotypes based on a 50K single nucleotide polymorphism (SNP) array and 259,871 pedigree records were available. Of these 50,575 genotypes, 46,430 cows with phenotypic records for production and fertility traits and having a first calving date between 2008 and 2018 were available. Unique ROH genotypes identified using a sliding-window approach were fitted into an animal mixed model as fixed effects to determine their effect on production and fertility traits.ResultsIn total, 133 and 34 unique ROH genotypes with unfavorable effects were identified for production and fertility traits, respectively, at a 1% genome-wise false discovery rate. Most of these ROH regions were located on bovine chromosomes 8, 13, 14 and 19 for both production and fertility traits. For production traits, the average of all the unfavorably identified unique ROH genotypes effects were estimated to decrease milk yield by 247.30 kg, fat yield by 11.46 kg and protein yield by 8.11 kg. Similarly, for fertility traits, an average 4.81-day extension in first service to conception, a 0.16 increase in number of services, and a - 0.07 incidence in 56-day non-return rate were observed. Furthermore, a ROH region located on bovine chromosome 19 was identified that, when homozygous, had a negative effect on production traits. Signatures of selection proximate to this region have implicated GH1 as a potential candidate gene, which encodes the growth hormone that binds the growth hormone receptor. This observed negative effect could be a consequence of unfavorable alleles in linkage disequilibrium with favorable alleles.ConclusionsROH genotypes with unfavorable effects on production and fertility traits were identified within and across multiple traits on most chromosomes. These identified ROH genotypes could be included in mate selection programs to minimize their frequency in future generations.
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Project description:BackgroundArray-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.ResultsWe show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays).ConclusionWe have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at http://bioinfo.curie.fr/projects/manor and available from the Bioconductor site http://www.bioconductor.org. It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.
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Project description:BackgroundMicroarray-based comparative genomic hybridization (aCGH) is a powerful diagnostic tool for the detection of DNA copy number gains and losses associated with chromosome abnormalities, many of which are below the resolution of conventional chromosome analysis. It has been presumed that whole-genome oligonucleotide (oligo) arrays identify more clinically significant copy-number abnormalities than whole-genome bacterial artificial chromosome (BAC) arrays, yet this has not been systematically studied in a clinical diagnostic setting.ResultsTo determine the difference in detection rate between similarly designed BAC and oligo arrays, we developed whole-genome BAC and oligonucleotide microarrays and validated them in a side-by-side comparison of 466 consecutive clinical specimens submitted to our laboratory for aCGH. Of the 466 cases studied, 67 (14.3%) had a copy-number imbalance of potential clinical significance detectable by the whole-genome BAC array, and 73 (15.6%) had a copy-number imbalance of potential clinical significance detectable by the whole-genome oligo array. However, because both platforms identified copy number variants of unclear clinical significance, we designed a systematic method for the interpretation of copy number alterations and tested an additional 3,443 cases by BAC array and 3,096 cases by oligo array. Of those cases tested on the BAC array, 17.6% were found to have a copy-number abnormality of potential clinical significance, whereas the detection rate increased to 22.5% for the cases tested by oligo array. In addition, we validated the oligo array for detection of mosaicism and found that it could routinely detect mosaicism at levels of 30% and greater.ConclusionsAlthough BAC arrays have faster turnaround times, the increased detection rate of oligo arrays makes them attractive for clinical cytogenetic testing.
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