Examination of Genetic Variants Revealed from a Rat Model of Brain Ischemia in Patients with Ischemic Stroke: A Pilot Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Subjects
2.2. Selection and Genotyping of Markers
2.3. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Campbell, B.C.V.; De Silva, D.A.; MacLeod, M.R.; Coutts, S.B.; Schwamm, L.H.; Davis, S.M.; Donnan, G.A. Ischaemic stroke. Nat. Rev. Dis. Prim. 2019, 5, 1–22. [Google Scholar] [CrossRef]
- Chauhan, G.; Debette, S. Genetic Risk Factors for Ischemic and Hemorrhagic Stroke. Curr. Cardiol. Rep. 2016, 18, 124. [Google Scholar] [CrossRef] [Green Version]
- Malik, R.; AFGen Consortium; Chauhan, G.; Traylor, M.; Sargurupremraj, M.; Okada, Y.; Mishra, A.; Rutten-Jacobs, L.; Giese, A.-K.; van der Laan, S.W.; et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 2018, 50, 524–537. [Google Scholar] [CrossRef] [Green Version]
- Lindgren, A.; Maguire, J. Stroke Recovery Genetics. Stroke 2016, 47, 2427–2434. [Google Scholar] [CrossRef] [PubMed]
- Söderholm, M.; Pedersen, A.; Lorentzen, E.; Stanne, T.M.; Bevan, S.; Olsson, M.; Cole, J.W.; Fernandez-Cadenas, I.; Hankey, G.J.; Jimenez-Conde, J.; et al. Genome-wide association meta-analysis of functional outcome after ischemic stroke. Neurology 2019, 92, e1271–e1283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mola-Caminal, M.; Carrera, C.; Soriano-Tárraga, C.; Giralt-Steinhauer, E.; Díaz-Navarro, R.M.; Tur, S.; Jiménez, C.; Medina-Dols, A.; Cullell, N.; Torres-Aguila, N.P.; et al. PATJ Low Frequency Variants Are Associated With Worse Ischemic Stroke Functional Outcome. Circ. Res. 2019, 124, 114–120. [Google Scholar] [CrossRef]
- Ibanez, L.; Heitsch, L.; Carrera, C.; Farias, F.H.G.; Dhar, R.; Budde, J.; Bergmann, K.; Bradley, J.; Harari, O.; Phuah, C.-L.; et al. Multi-ancestry genetic study in 5,876 patients identifies an association between excitotoxic genes and early outcomes after acute ischemic stroke. medRxiv Prepr. Serv. Heal. Sci. 2020. [Google Scholar] [CrossRef]
- Gallagher, M.D.; Chen-Plotkin, A.S. The Post-GWAS Era: From Association to Function. Am. J. Hum. Genet. 2018, 102, 717–730. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nicholls, H.L.; John, C.R.; Watson, D.; Munroe, P.B.; Barnes, M.R.; Cabrera, C.P. Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci. Front. Genet. 2020, 11. [Google Scholar] [CrossRef] [PubMed]
- Zheng, Q.; Ma, Y.; Chen, S.; Che, Q.; Chen, D. The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease. Front. Genet. 2020, 11, 320. [Google Scholar] [CrossRef] [Green Version]
- Dergunova, L.V.; Filippenkov, I.B.; Stavchansky, V.V.; Denisova, A.E.; Yuzhakov, V.V.; Mozerov, S.A.; Gubsky, L.V.; Limborska, S.A. Genome-wide transcriptome analysis using RNA-Seq reveals a large number of differentially expressed genes in a transient MCAO rat model. BMC Genom. 2018, 19, 655. [Google Scholar] [CrossRef] [Green Version]
- Ma, R.; Xie, Q.; Li, Y.; Chen, Z.; Ren, M.; Chen, H.; Li, H.; Li, J.; Wang, J. Animal models of cerebral ischemia: A review. Biomed. Pharmacother. 2020, 131, 110686. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Cai, Y. Obtaining Human Ischemic Stroke Gene Expression Biomarkers from Animal Models: A Cross-species Validation Study. Sci. Rep. 2016, 6, 29693. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Brubaker, D.K.; Lauffenburger, D.A. Translating preclinical models to humans. Science 2020, 367, 742–743. [Google Scholar] [CrossRef]
- Khvorykh, G.; Khrunin, A.; Filippenkov, I.; Stavchansky, V.; Dergunova, L.; Limborska, S. A Workflow for Selection of Single Nucleotide Polymorphic Markers for Studying of Genetics of Ischemic Stroke Outcomes. Genes 2021, 12, 328. [Google Scholar] [CrossRef]
- Pasterkamp, G.; Van Der Laan, S.W.; Haitjema, S.; Asl, H.F.; Siemelink, M.A.; Bezemer, T.; Van Setten, J.; Dichgans, M.; Malik, R.; Worrall, B.B.; et al. Human Validation of Genes Associated With a Murine Atherosclerotic Phenotype. Arter. Thromb. Vasc. Biol. 2016, 36, 1240–1246. [Google Scholar] [CrossRef] [Green Version]
- Shetova, I.M.; Timofeev, D.I.; A Shamalov, N.; A Bondarenko, E.; A Slominskiĭ, P.; A Limborskaia, S.; Skvortsova, V.I. The association between the DNA marker rs1842993 and risk for cardioembolic stroke in the Slavic population. Zhurnal Nevrol. i psikhiatrii im. S.S. Korsakova 2012, 112. [Google Scholar]
- Adams, H.P., Jr.; Bendixen, B.H.; Kappelle, L.J.; Biller, J.; Love, B.B.; Gordon, D.L.; Marsh, E.E., 3rd. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993, 24, 35–41. [Google Scholar] [CrossRef] [Green Version]
- Brott, T.; Adams, H.P.; Olinger, C.P.; Marler, J.R.; Barsan, W.G.; Biller, J.; Spilker, J.; Holleran, R.; Eberle, R.; Hertzberg, V. Measurements of acute cerebral infarction: A clinical examination scale. Stroke 1989, 20, 864–870. [Google Scholar] [CrossRef] [Green Version]
- van Swieten, J.C.; Koudstaal, P.J.; Visser, M.C.; Schouten, H.J.; van Gijn, J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke 1988, 19, 604–607. [Google Scholar] [CrossRef] [Green Version]
- Milligan, B.G. Total DNA isolation. In Molecular Genetic Analysis of Populations; Hoelzel, A., Ed.; Oxford University Press: London, UK, 1998; pp. 29–60. [Google Scholar]
- Roy-O’Reilly, M.M.; McCullough, M.L.D. Age and Sex Are Critical Factors in Ischemic Stroke Pathology. Endocrinology 2018, 159, 3120–3131. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, L.; Zhang, W.; Li, Q. AssocTests: An R Package for Genetic Association Studies. J. Stat. Softw. 2020, 94, 1–26. [Google Scholar] [CrossRef]
- Barrett, J.; Fry, B.; Maller, J.; Daly, M.J. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics 2004, 21, 263–265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kongsawasdi, S.; Klaphajone, J.; Wivatvongvana, P.; Watcharasaksilp, K. Prognostic Factors of Functional Outcome Assessed by Using the Modified Rankin Scale in Subacute Ischemic Stroke. J. Clin. Med. Res. 2019, 11, 375–382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sciacchitano, S.; Lavra, L.; Morgante, A.; Ulivieri, A.; Magi, F.; De Francesco, G.P.; Bellotti, C.; Salehi, L.B.; Ricci, A. Galectin-3: One Molecule for an Alphabet of Diseases, from A to Z. Int. J. Mol. Sci. 2018, 19, 379. [Google Scholar] [CrossRef] [Green Version]
- Gao, Z.; Liu, Z.; Wang, R.; Zheng, Y.; Li, H.; Yang, L. Galectin-3 Is a Potential Mediator for Atherosclerosis. J. Immunol. Res. 2020, 2020, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Shen, Y.-F.; Yu, W.-H.; Dong, X.-Q.; Du, Q.; Yang, D.-B.; Wu, G.-Q.; Zhang, Z.-Y.; Wang, H.; Jiang, L. The change of plasma galectin-3 concentrations after traumatic brain injury. Clin. Chim. Acta 2016, 456, 75–80. [Google Scholar] [CrossRef]
- Wang, Y.; He, S.; Liu, X.; Li, Z.; Zhu, L.; Xiao, G.; Du, X.; Du, H.; Zhang, W.; Zhang, Y.; et al. Galectin-3 Mediated Inflammatory Response Contributes to Neurological Recovery by QiShenYiQi in Subacute Stroke Model. Front. Pharmacol. 2021, 12. [Google Scholar] [CrossRef]
- Ward, L.D.; Kellis, M. HaploReg v4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2015, 44, D877–D881. [Google Scholar] [CrossRef]
- Boyle, A.P.; Hong, E.L.; Hariharan, M.; Cheng, Y.; Schaub, M.A.; Kasowski, M.; Karczewski, K.J.; Park, J.; Hitz, B.C.; Weng, S.; et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012, 22, 1790–1797. [Google Scholar] [CrossRef] [Green Version]
- Bottazzi, B.; Garlanda, C.; Teixeira, M.M. Editorial: The Role of Pentraxins: From Inflammation, Tissue Repair and Immunity to Biomarkers. Front. Immunol. 2019, 10, 2817. [Google Scholar] [CrossRef] [Green Version]
- Latini, R.; Maggioni, A.P.; Peri, G.; Gonzini, L.; Lucci, D.; Mocarelli, P.; Vago, L.; Pasqualini, F.; Signorini, S.; Soldateschi, D.; et al. Prognostic Significance of the Long Pentraxin PTX3 in Acute Myocardial Infarction. Circulation 2004, 110, 2349–2354. [Google Scholar] [CrossRef]
- Ryu, W.-S.; Kim, C.K.; Kim, B.J.; Kim, C.; Lee, S.-H.; Yoon, B.-W. Pentraxin 3: A novel and independent prognostic marker in ischemic stroke. Atherosclerosis 2012, 220, 581–586. [Google Scholar] [CrossRef] [PubMed]
- Rodriguez-Grande, B.; Varghese, L.; Molina-Holgado, F.; Rajkovic, O.; Garlanda, C.; Denes, A.; Pinteaux, E. Pentraxin 3 mediates neurogenesis and angiogenesis after cerebral ischaemia. J. Neuroinflammation 2015, 12, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Oggioni, M.; Mercurio, D.; Minuta, D.; Fumagalli, S.; Popiolek-Barczyk, K.; Sironi, M.; Ciechanowska, A.; Ippati, S.; De Blasio, D.; Perego, C.; et al. Long pentraxin PTX3 is upregulated systemically and centrally after experimental neurotrauma, but its depletion leaves unaltered sensorimotor deficits or histopathology. Sci. Rep. 2021, 11, 1–17. [Google Scholar] [CrossRef]
- Rodriguez-Grande, B.; Swana, M.; Nguyen, L.; Englezou, P.; Maysami, S.; Allan, S.; Rothwell, N.J.; Garlanda, C.; Denes, A.; Pinteaux, E. The Acute-Phase Protein PTX3 is an Essential Mediator of Glial Scar Formation and Resolution of Brain Edema after Ischemic Injury. Br. J. Pharmacol. 2013, 34, 480–488. [Google Scholar] [CrossRef]
- El Melegy, E.K.; Badr, E.A.; ElKersh, A.M.; EL Shafey, W.H.; Fareed, W.A. Pentraxin 3 genotyping in relation to serum levels of pentraxin 3 in patients with acute ST-segment elevation myocardial infarction. Clin. Trials Regul. Sci. Cardiol. 2016, 13, 6–13. [Google Scholar] [CrossRef] [Green Version]
- Barbati, E.; Specchia, C.; Villella, M.; Rossi, M.L.; Barlera, S.; Bottazzi, B.; Crociati, L.; D’Arienzo, C.; Fanelli, R.; Garlanda, C.; et al. Influence of Pentraxin 3 (PTX3) Genetic Variants on Myocardial Infarction Risk and PTX3 Plasma Levels. PLoS ONE 2012, 7, e53030. [Google Scholar] [CrossRef] [PubMed]
- Seshadri, S.; Beiser, A.; Kelly-Hayes, M.; Kase, C.S.; Au, R.; Kannel, W.B.; Wolf, P.A. The Lifetime Risk of Stroke. Stroke 2006, 37, 345–350. [Google Scholar] [CrossRef] [Green Version]
- Benjamin, E.J.; Blaha, M.J.; Chiuve, S.E.; Cushman, M.; Das, S.R.; Deo, R.; de Ferranti, S.D.; Floyd, J.; Fornage, M.; Gillespie, C.; et al. Heart disease and stroke statistics—2017 update: A report from the American heart association. Circulation 2017, 135, e146–e603. [Google Scholar] [CrossRef]
rs | chr | Position (hg 19) | Alleles | Gene | Primer and Probe Sequences | Ta *, °C |
---|---|---|---|---|---|---|
rs62278647 | 3 | 157149133 | A/T | PTX3 | 5’-(FAM)AAACAGACTGATACATCCA(BHQ1)-3’ 5’-(VIC)AAACAGACAGATACATCCA(BHQ2)-3’ 5’-CAGTCTCAGGTAGTATC-3’ 5’-CTGTGAAGATGGAGAA-3’ | 55 |
rs2316710 | 3 | 157150180 | C/A | PTX3 | 5’-(FAM)TTTATGAACCTGGAATACA(BHQ1)-3’ 5’-(VIC)TTTATGACCCTGGAATACA(BHQ2)-3’ 5’-TGAGCTACTGAATGAA-3’ 5’-GCCTTTAAGAGAAAATATAG-3’ | 54 |
rs7634847 | 3 | 157159145 | C/T | PTX3 | 5’-(FAM)TTTAATCCTTATGCCAACCA(BHQ1)-3’ 5’-(VIC)TTTAATCCTCATGCCAACC(BHQ2)-3’ 5’-ACTGCTTATCAGCTATGTA-3’ 5’-TTGGGCATTCACTATGTA-3’ | 58 |
rs1877822 | 17 | 63165077 | A/G | RGS9 | 5’-(FAM)ACTCTGCTACCTCAGTT(BHQ1)-3’ 5’-(VIC)ACTCTGCTGCCTCAGT(BHQ2)-3’ 5’-GGTCTGACTGCTACATA-3’ 5’-GAGGAGGAAGAACATTTC-3’ | 58 |
rs74063268 | 12 | 13352358 | T/C | EMP1 | 5’-(FAM)TCTTCAACGTGTCTCTCT(BHQ1)-3’ 5’-(VIC)TCTTCAACGCGTCTCTC(BHQ2)-3’ 5’-CAGGTAGTGCCAGAC-3’ 5’-TGCCTCATCCACAAG-3’ | 58 |
rs2569192 | 5 | 140015208 | C/G | CD14 | 5’-(FAM)CTTTCTAGCAACCCTAGT(BHQ1)-3’ 5’-(VIC)CTTTCTACCAACCCTAGT(BHQ2)-3’ 5’-CAGCTTGATTCAACAA-3’ 5’-AGGGACTTGTCTTTG-3’ | 55 |
rs66782529 | 14 | 55587867 | C/T | LGALS3 | 5’-(FAM)AGTTATTTGACTCTCTGTGAC(BHQ1)-3’ 5’-(VIC)AGTTATTTGATTCTCTGTGACT(BHQ2)-3’ 5’-CTGACACTTACCAGTTG-3’ 5’-GCTCACAACAAACCTATA-3’ | 58 |
rs1009977 | 14 | 55603002 | T/G | LGALS3 | 5’-(FAM) ACTGCCAAATATTTTATTTG(BHQ1)-3’ 5’-(VIC) CTGCCAAAGATTTTATTTG(BHQ2)-3’ 5’-GCATCTTAGACCCAAA-3’ 5’-GCTGTTGGAGCTAAG-3’ | 55 |
rs1491961 | 3 | 46250348 | T/C | CCR1 | 5’-(FAM)TGGCACCCTATGCCC(BHQ1)-3’ 5’-(VIC)TGGCACCCCATGCC(BHQ2)-3’ 5’-CTGACATGGGTGCTCAC-3’ 5’-CCTTCATGCTGGAGGTTC-3’ | 62 |
SNP | Gene | Genotype | Study 1 * | p | Study 2 * | p | Study 3 * | p | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
mRS 0–2 | mRS 3–6 | mRS 0–3 | mRS 4–6 | ΔmRS > 0 | ΔmRS < 0 | ΔmRS = 0 | ||||||
rs62278647 | PTX3 | TT | 0.20 (23) | 0.25 (53) | 0.2375 | 0.22 (39) | 0.24 (37) | 0.6600 | 0.23 (35) | 0.22 (15) | 0.24 (26) | 0.5626 |
AT | 0.67 (78) | 0.58 (123) | 0.63 (110) | 0.59 (91) | 0.64 (98) | 0.55 (37) | 0.61 (66) | |||||
AA | 0.13 (15) | 0.17 (37) | 0.14 (25) | 0.17 (27) | 0.14 (21) | 0.22 (15) | 0.15 (16) | |||||
rs2316710 | PTX3 | CC | 0.10 (12) | 0.16 (34) | 0.3577 | 0.12 (21) | 0.16 (25) | 0.3881 | 0.11 (17) | 0.21 (14) | 0.14 (15) | 0.3980 |
CA | 0.59 (69) | 0.55 (116) | 0.56 (97) | 0.57 (88) | 0.59 (91) | 0.52 (34) | 0.56 (60) | |||||
AA | 0.30 (35) | 0.29 (62) | 0.32 (56) | 0.27 (41) | 0.30 (46) | 0.27 (18) | 0.31 (33) | |||||
rs7634847 | PTX3 | CC | 0.41 (48) | 0.40 (85) | 0.5256 | 0.42 (73) | 0.39 (60) | 0.5311 | 0.39 (60) | 0.42 (28) | 0.42 (45) | 0.4598 |
CT | 0.51 (59) | 0.48 (103) | 0.49 (86) | 0.49 (76) | 0.53 (82) | 0.43 (29) | 0.47 (51) | |||||
TT | 0.08 (9) | 0.12 (25) | 0.09 (15) | 0.12 (19) | 0.08 (12) | 0.15 (10) | 0.11 (12) | |||||
rs1877822 | RGS9 | AA | 0.65 (75) | 0.67 (144) | 0.6443 | 0.65 (113) | 0.68 (106) | 0.7086 | 0.65 (100) | 0.61 (41) | 0.72 (78) | 0.3930 |
GA | 0.32 (37) | 0.31 (66) | 0.33 (57) | 0.30 (46) | 0.33 (51) | 0.34 (23) | 0.27 (29) | |||||
GG | 0.03 (4) | 0.02 (4) | 0.03 (5) | 0.02 (3) | 0.03 (4) | 0.04 (3) | 0.01 (1) | |||||
rs74063268 | EMP1 | TT | 0.87 (101) | 0.83 (178) | 0.6348 | 0.85 (149) | 0.84 (130) | 0.9496 | 0.85 (131) | 0.84 (56) | 0.85 (92) | 0.7112 |
CT | 0.12 (14) | 0.15 (33) | 0.14 (24) | 0.15 (23) | 0.14 (21) | 0.15 (10) | 0.15 (16) | |||||
CC | 0.01 (1) | 0.01 (3) | 0.01 (2) | 0.01 (2) | 0.02 (3) | 0.01 (1) | 0.00 (0) | |||||
rs2569192 | CD14 | CC | 0.45 (52) | 0.47 (99) | 0.5947 | 0.45 (77) | 0.48 (74) | 0.5365 | 0.45 (69) | 0.44 (29) | 0.50 (53) | 0.8523 |
CG | 0.50 (58) | 0.45 (96) | 0.50 (86) | 0.44 (68) | 0.49 (75) | 0.47 (31) | 0.45 (48) | |||||
GG | 0.05 (6) | 0.08 (1)6 | 0.06 (10) | 0.08 (12) | 0.06 (10) | 0.09 (6) | 0.06 (6) | |||||
rs66782529 | LGALS3 | CC | 0.46 (53) | 0.51 (109) | 0.6104 | 0.44 (77) | 0.55 (85) | 0.1469 | 0.45 (70) | 0.61 (41) | 0.47 (51) | 0.0488 |
TC | 0.45 (52) | 0.39 (84) | 0.46 (80) | 0.36 (56) | 0.44 (67) | 0.27 (18) | 0.47 (51) | |||||
TT | 0.09 (11) | 0.09 (20) | 0.10 (17) | 0.09 (14) | 0.11 (17) | 0.12 (8) | 0.06 (6) | |||||
rs1009977 | LGALS3 | TT | 0.40 (46) | 0.36 (78) | 0.4536 | 0.33 (58) | 0.43 (66) | 0.2069 | 0.35 (55) | 0.46 (31) | 0.35 (38) | 0.5467 |
GT | 0.42 (49) | 0.49 (105) | 0.50 (88) | 0.43 (66) | 0.48 (74) | 0.39 (26) | 0.50 (54) | |||||
GG | 0.18 (21) | 0.14 (31) | 0.17 (29) | 0.15 (23) | 0.17 (26) | 0.15 (10) | 0.15 (16) | |||||
rs1491961 | CCR1 | CC | 0.41 (48) | 0.46 (98) | 0.3249 | 0.42 (73) | 0.47 (73) | 0.0773 | 0.44 (68) | 0.46 (31) | 0.44 (47) | 0.9413 |
CT | 0.51 (59) | 0.43 (92) | 0.51 (89) | 0.40 (62) | 0.47 (73) | 0.42 (28) | 0.46 (50) | |||||
TT | 0.08 (9) | 0.11 (24) | 0.07 (13) | 0.13 (20) | 0.09 (14) | 0.12 (8) | 0.10 (11) |
SNP | Gene | Genotypes | Patients * | Controls * | Chi-Square ** | p ** |
---|---|---|---|---|---|---|
rs62278647 | PTX3 | TT | 0.23 (76) | 0.28 (63) | 20.9166 | 0.000029 † |
AT | 0.61 (201) | 0.42 (94) | ||||
AA | 0.16 (53) | 0.29 (65) | ||||
rs2316710 | PTX3 | CC | 0.14 (47) | 0.25 (56) | 11.99 | 0.0025 † |
CA | 0.56 (185) | 0.45 (99) | ||||
AA | 0.29 (97) | 0.30 (67) | ||||
rs7634847 | PTX3 | CC | 0.40 (133) | 0.41 (92) | 5.2685 | 0.0718 |
CT | 0.49 (162) | 0.42 (93) | ||||
TT | 0.11 (35) | 0.17 (37) | ||||
rs1877822 | RGS9 | AA | 0.66 (220) | 0.63 (140) | 1.08678 | 0.5808 |
GA | 0.31 (103) | 0.33 (74) | ||||
GG | 0.02 (8) | 0.04 (8) | ||||
rs74063268 | EMP1 | TT | 0.85 (280) | 0.82 (181) | 2.22535 | 0.3287 |
CT | 0.14 (47) | 0.18 (40) | ||||
CC | 0.01 (4) | 0.00 (1) | ||||
rs2569192 | CD14 | CC | 0.46 (151) | 0.50 (110) | 0.698198 | 0.7053 |
CG | 0.47 (155) | 0.45 (99) | ||||
GG | 0.07 (22) | 0.06 (13) | ||||
rs66782529 | LGALS3 | CC | 0.49 (162) | 0.47 (104) | 0.474517 | 0.7888 |
TC | 0.41 (136) | 0.44 (98) | ||||
TT | 0.10 (32) | 0.09 (20) | ||||
rs1009977 | LGALS3 | TT | 0.37 (124) | 0.38 (85) | 0.26155 | 0.8774 |
GT | 0.47 (154) | 0.47 (105) | ||||
GG | 0.16 (53) | 0.14 (32) | ||||
rs1491961 | CCR1 | CC | 0.44 (146) | 0.52 (115) | 4.97237 | 0.0832 |
CT | 0.46 (152) | 0.42 (92) | ||||
TT | 0.10 (33) | 0.06 (13) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Khrunin, A.V.; Khvorykh, G.V.; Rozhkova, A.V.; Koltsova, E.A.; Petrova, E.A.; Kimelfeld, E.I.; Limborska, S.A. Examination of Genetic Variants Revealed from a Rat Model of Brain Ischemia in Patients with Ischemic Stroke: A Pilot Study. Genes 2021, 12, 1938. https://doi.org/10.3390/genes12121938
Khrunin AV, Khvorykh GV, Rozhkova AV, Koltsova EA, Petrova EA, Kimelfeld EI, Limborska SA. Examination of Genetic Variants Revealed from a Rat Model of Brain Ischemia in Patients with Ischemic Stroke: A Pilot Study. Genes. 2021; 12(12):1938. https://doi.org/10.3390/genes12121938
Chicago/Turabian StyleKhrunin, Andrey V., Gennady V. Khvorykh, Alexandra V. Rozhkova, Evgeniya A. Koltsova, Elizaveta A. Petrova, Ekaterina I. Kimelfeld, and Svetlana A. Limborska. 2021. "Examination of Genetic Variants Revealed from a Rat Model of Brain Ischemia in Patients with Ischemic Stroke: A Pilot Study" Genes 12, no. 12: 1938. https://doi.org/10.3390/genes12121938