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Korbolina EE, Bryzgalov LO, Ustrokhanova DZ, Postovalov SN, Poverin DV, Damarov IS, Merkulova TI. A Panel of rSNPs Demonstrating Allelic Asymmetry in Both ChIP-seq and RNA-seq Data and the Search for Their Phenotypic Outcomes through Analysis of DEGs. Int J Mol Sci 2021; 22:ijms22147240. [PMID: 34298860 PMCID: PMC8303726 DOI: 10.3390/ijms22147240] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/24/2021] [Accepted: 06/30/2021] [Indexed: 12/12/2022] Open
Abstract
Currently, the detection of the allele asymmetry of gene expression from RNA-seq data or the transcription factor binding from ChIP-seq data is one of the approaches used to identify the functional genetic variants that can affect gene expression (regulatory SNPs or rSNPs). In this study, we searched for rSNPs using the data for human pulmonary arterial endothelial cells (PAECs) available from the Sequence Read Archive (SRA). Allele-asymmetric binding and expression events are analyzed in paired ChIP-seq data for H3K4me3 mark and RNA-seq data obtained for 19 individuals. Two statistical approaches, weighted z-scores and predicted probabilities, were used to improve the efficiency of finding rSNPs. In total, we identified 14,266 rSNPs associated with both allele-specific binding and expression. Among them, 645 rSNPs were associated with GWAS phenotypes; 4746 rSNPs were reported as eQTLs by GTEx, and 11,536 rSNPs were located in 374 candidate transcription factor binding motifs. Additionally, we searched for the rSNPs associated with gene expression using an SRA RNA-seq dataset for 281 clinically annotated human postmortem brain samples and detected eQTLs for 2505 rSNPs. Based on these results, we conducted Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and constructed the protein-protein interaction networks to represent the top-ranked biological processes with a possible contribution to the phenotypic outcome.
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Affiliation(s)
- Elena E. Korbolina
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Science, 10 LavrentyevaProspekt, 630090 Novosibirsk, Russia; (L.O.B.); (I.S.D.); (T.I.M.)
- Correspondence:
| | - Leonid O. Bryzgalov
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Science, 10 LavrentyevaProspekt, 630090 Novosibirsk, Russia; (L.O.B.); (I.S.D.); (T.I.M.)
- VECTOR-BEST, PO BOX 492, 630117 Novosibirsk, Russia
| | - Diana Z. Ustrokhanova
- Department of Information Biology, The Novosibirsk State University, 1 Pirogovast, 630090 Novosibirsk, Russia;
| | - Sergey N. Postovalov
- Department of Theoretical and Applied Informatics, The Novosibirsk State Technical University, 630073 Novosibirsk, Russia; (S.N.P.); (D.V.P.)
| | - Dmitry V. Poverin
- Department of Theoretical and Applied Informatics, The Novosibirsk State Technical University, 630073 Novosibirsk, Russia; (S.N.P.); (D.V.P.)
| | - Igor S. Damarov
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Science, 10 LavrentyevaProspekt, 630090 Novosibirsk, Russia; (L.O.B.); (I.S.D.); (T.I.M.)
| | - Tatiana I. Merkulova
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Science, 10 LavrentyevaProspekt, 630090 Novosibirsk, Russia; (L.O.B.); (I.S.D.); (T.I.M.)
- Department of Information Biology, The Novosibirsk State University, 1 Pirogovast, 630090 Novosibirsk, Russia;
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Zhang J, Yang J, Zhang L, Luo J, Zhao H, Zhang J, Wen C. A new SNP genotyping technology Target SNP-seq and its application in genetic analysis of cucumber varieties. Sci Rep 2020; 10:5623. [PMID: 32221398 PMCID: PMC7101363 DOI: 10.1038/s41598-020-62518-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 03/11/2020] [Indexed: 01/18/2023] Open
Abstract
To facilitate the utility of SNP-based genotyping, we developed a new method called target SNP-seq which combines the advantages of multiplex PCR amplification and high throughput sequencing. Compared with KASP, Microarrays, GBS and other SNP genotyping methods, target SNP-seq is flexible both in SNPs and samples, yields high accuracy, especially when genotyping genome wide perfect SNPs with high polymorphism and conserved flanking sequences, and is cost-effective, requiring 3 days and $7 for per DNA sample to genotype hundreds of SNP loci. The present study established a DNA fingerprint of 261 cucumber varieties by target SNP-seq with 163 perfect SNPs from 4,612,350 SNPs based on 182 cucumber resequencing datasets. Four distinct subpopulations were found in 261 Chinese cucumber varieties: the north China type, the south China type, the Europe type, and the Xishuangbanna type. The north China type and Xishuangbanna type harbored lower genetic diversity, indicating greater risk of genetic erosion in these two subpopulations. Furthermore, a core set of 24 SNPs was able to distinguish 99% of the 261 cucumber varieties. 29 core cucumber backbone varieties in China were identified. Therefore, target SNP-seq provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties. The high efficiency and low cost of target SNP-seq is more competitive than the current SNP genotyping methods, and it has excellent application prospects in genetic research, as well as in promoting plant breeding processes in the near future.
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Affiliation(s)
- Jian Zhang
- Beijing Vegetable Research Center, Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Jingjing Yang
- Beijing Vegetable Research Center, Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Like Zhang
- National Agricultural Technology Extension and Service Center, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Jiang Luo
- Beijing Vegetable Research Center, Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Hong Zhao
- Beijing Vegetable Research Center, Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Jianan Zhang
- Molbreeding Biotechnology Company, Shijiazhuang, 050000, China
| | - Changlong Wen
- Beijing Vegetable Research Center, Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China.
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China.
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