Literature DB >> 34956240

Commentary: Long Non-Coding RNA Gene Polymorphisms and Their Expression Levels in Patients With Rheumatoid Arthritis.

Jorge Hernández-Bello1, Christian Johana Baños-Hernández1, José Francisco Muñoz-Valle1.   

Abstract

Entities:  

Keywords:  ANRIL; MALAT1; ZFAS1; lnc-DC; rheumatoid arthritis; single-nucleotide polymorphism

Mesh:

Substances:

Year:  2021        PMID: 34956240      PMCID: PMC8695717          DOI: 10.3389/fimmu.2021.801266

Source DB:  PubMed          Journal:  Front Immunol        ISSN: 1664-3224            Impact factor:   7.561


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Introduction

We read the paper by Zhang et al. (1) with interest. The authors report on a study that evaluated the association of four long noncoding RNA (lncRNA) (ANRIL, lnc-DC, MALAT1, ZFAS1) gene single-nucleotide polymorphisms (SNPs) with susceptibility to rheumatoid arthritis (RA) patients, as well as their expression levels. They concluded that ANRIL, lnc-DC, MALAT1, and ZFAS1 gene SNPs were not associated with RA susceptibility after false discovery rate (FDR) correction, while altered ANRIL, lnc-DC, MALAT1, and ZFAS1 levels in RA patients suggested that these lncRNAs might play a role in RA. After carefully reading, we identified some mistakes in the odds ratio (OR) calculations in “Genotypes and alleles frequencies of lncRNAs genes polymorphisms in RA patients and normal controls” [sic]. Likewise, there are inconsistencies in the genetic models.
Table 1

Correction: Genotypes and allele frequencies of lncRNA gene polymorphisms in RA patients and normal controls.

SNPAnalyze modelRA (N = 660) n (%)Control (N = 710) n (%)p valueOR (95% CI)
ANRIL
rs1412830GenotypesTT13 (1.97)3 (0.42) 0.008 4.66 (1.32–16.45)
CT119 (18.03)139 (19.58)0.550.92 (0.70–1.21)
CC528 (80.00)568 (80.00)Reference
AllelesT145 (10.98)145 (10.21)0.511.08 (0.85–1.38)
C1,175 (89.02)1,275 (89.79)Reference
Dominant modelTT+CT132 (20.00)142 (20.00)11.00 (0.76–1.30)
CC528 (80.00)568 (80.00)Reference
Recessive modelTT13 (1.97)3 (0.42) 0.007 4.73 (1.29–16.69)
CC+CT647 (98.03)707 (99.58)Reference
rs944796GenotypesGG11 (1.67)31 (4.37) 0.006 0.38 (0.19–0.78)
GC238 (36.06)230 (32.39)0.281.13 (0.90–1.41)
CC411 (62.27)449 (63.24)Reference
AllelesG260 (19.70)292 (20.56)0.570.95 (0.78–1.14)
C1,060 (80.30)1,128 (79.44)Reference
Dominant modelGG+GC249 (37.73)261 (36.76)0.711.04 (0.83–1.30)
CC411 (62.27)449 (63.24)Reference
Recessive modelGG11 (1.67)31 (4.37) 0.003 0.37 (0.18–0.74)
CC+GC649 (98.33)679 (95.63)Reference
rs61271866GenotypesAA25 (3.79)26 (3.66)0.981.00 (0.57–1.76)
TA185 (28.03)214 (30.140.390.9 (0.71–1.14)
TT450 (68.18)470 (66.20)Reference
AllelesA235 (17.80)266 (18.73)0.520.94 (0.77–1.14)
T1,085 (82.20)1,154 (81.27)Reference
Dominant modelAA+TA210 (31.82)240 (33.80)0.430.91 (0.72–1.14)
TT450 (68.18)470 (66.20)Reference
Recessive modelAA25 (3.79)26 (3.66)0.901.03 (0.59–1.81)
TT+TA635 (96.21)684 (96.34)Reference
rs2518723GenotypesTT111 (16.82)133 (18.73)0.260.83 (0.61–1.14)
CT326 (49.39)353 (49.72)0.530.92 (0.73–1.17)
CC223 (33.79)224 (31.55)Reference
AllelesT548 (41.52)619 (43.59)0.270.91 (0.78–1.06)
C772 (58.48)801 (56.41)Reference
Dominant modelTT+CT437 (66.21)486 (68.45)0.370.90 (0.72–1.13)
CC223 (33.79)224 (31.55)Reference
Recessive modelTT111 (16.82)133 (18.73)0.350.87 (0.66–1.15)
CC+CT549 (83.18)577 (81.27)Reference
rs3217992GenotypesTT160 (24.24)152 (21.41)0.111.27 (0.93–1.72)
CT338 (51.21)362 (50.99)0.341.13 (0.87–1.45)
CC162 (24.55)196 (27.61)Reference
AllelesT658 (49.85)666 (46.90)0.121.12 (0.96–1.30)
C662 (50.15)754 (53.10)Reference
Dominant modelTT+CT498 (75.45)514 (72.39)0.191.17 (0.92–1.49)
CC162 (24.55)196 (27.61)Reference
Recessive modelTT160 (24.24)152 (21.41)0.211.17 (0.91–1.51)
CC+CT500 (75.76)558 (78.59)Reference
Lnc-DC
rs7217280GenotypesAA3 (0.45)4 (0.56)0.740.78 (0.17–3.50)
GA52 (7.88)77 (10.85)0.0590.70 (0.48–1.01)
GG605 (91.67)629 (88.59)Reference
AllelesA58 (4.39)85 (5.99)0.060.72 (0.51–1.01)
G1,262 (95.61)1,335 (94.01)Reference
Dominant modelAA+GA55 (8.33)81 (11.41)0.0570.70 (0.49–1.01)
GG605 (91.67)629 (88.59)Reference
Recessive modelAA3 (0.45)4 (0.56)0.770.80 (0.18–3.61)
GG+GA657 (99.55)706 (99.44)Reference
rs10515177GenotypesGG4 (0.61)5 (0.70)0.790.83 (0.22–3.13)
AG94 (14.24)117 (16.48)0.240.84 (0.62–1.12)
AA562 (85.15)588 (82.82)Reference
AllelesG102 (7.73)127 (8.94)0.250.85 (0.65–1.11)
A1,218 (92.27)1,293 (91.06)Reference
Dominant modelGG+AG98 (14.85)122 (17.18)0.230.84 (0.62–1.12)
AA562 (85.15)588 (82.82)Reference
Recessive modelGG4 (0.61)5 (0.70)0.820.86 (0.23–3.21)
AA+AG656 (99.39)705 (99.30)Reference
MALAT1
rs619586GenotypesGG6 (0.91)4 (0.56)0.441.63 (0.46–5.83)
AG111 (16.82)113 (15.92)0.631.07 (0.80–1.42)
AA543 (82.27)593 (83.52)Reference
AllelesG123 (9.32)121 (8.52)0.461.10 (0.84–1.43)
A1,197 (90.681,299 (91.48)Reference
Dominant modelGG+AG117 (17.73)117 (16.48)0.531.09 (0.82–1.44)
AA543 (82.27)593 (83.25)Reference
Recessive modelGG6 (0.91)4 (0.56)0.451.61 (0.45–5.76)
AA+AG654 (99.09)706 (99.44)Reference
rs4102217GenotypesCC20 (3.03)13 (1.83)0.211.55 (0.76–3.16)
GC154 (23.33)205 (28.87) 0.02 0.76 (0.59–0.97)
GG486 (73.64492 (69.30)Reference
AllelesC194 (14.70)231 (16.27)0.250.88 (0.72–1.09)
G1,126 (85.30)1,189 (83.73)Reference
Dominant modelCC+GC174 (26.36)218 (30.70)0.070.80 (0.63–1.02)
GG486 (73.64)492 (69.30)Reference
Recessive modelCC20 (3.03)13 (1.83)0.141.67 (0.82–3.39)
GG+GC640 (96.97)697 (98.17)Reference
rs591291GenotypesTT124 (18.79)132 (18.59)0.550.91 (0.67–1.23)
CT298 (45.15)347 (48.87)0.130.83 (0.65–1.05)
CC238 (36.06)231 (32.53)Reference
AllelesT546 (41.36)611 (43.03)0.370.93 (0.80–1.08)
C774 (58.64)809 (56.97)Reference
Dominant modelTT+CT422 (63.94479 (67.46)0.160.85 (0.68–1.06)
CC238 (36.06)231 (32.53)Reference
Recessive modelTT124 (18.79)132 (18.59)0.921.01 (0.77–1.32)
CC+CT536 (81.21)578 (81.41)Reference
rs11227209GenotypesGG3 (0.45)3 (0.42)0.931.07 (0.21–5.33)
CG71 (10.76)79 (11.13)0.820.96 (0.68–1.35)
CC586 (88.79)628 (88.45)Reference
AllelesG77 (5.83)85 (5.99)0.860.97 (0.70–1.33)
C1,243 (94.17)1,335 (94.01)Reference
Dominant modelGG+CG74 (11.21)82 (11.55)0.840.96 (0.69–1.35)
CC586 (88.79)628 (88.45)Reference
Recessive modelGG3 (0.45)3 (0.42)0.921.07 (0.21–5.35)
CC+CG657 (99.55)707 (99.58)Reference
rs35138901GenotypesCC4 (0.61)2 (0.28)0.372.10 (0.38–11.54)
TC93 (14.09)115 (16.20)0.280.85 (0.63–1.14)
TT563 (85.30)593 (83.52)Reference
AllelesC101 (7.65)119 (8.38)0.480.90 (0.68–1.19)
T1,219 (92.35)1,301 (91.62)Reference
Dominant modelCC+TC97 (14.70)117 (16.480.360.87 (0.65–1.17)
TT563 (85.30)593 (83.52)Reference
Recessive modelCC4 (0.61)2 (0.28)0.362.15 (0.39–11.82)
TT+TC656 (99.39)708 (99.72)Reference
ZFAS1
rs237742GenotypesTT91 (13.79)104 (14.65)0.931.01 (0.72–1.40)
CT322 (48.79)320 (45.07)0.191.16 (0.92–1.46)
CC247 (37.42)286 (40.28)Reference
AllelesT504 (38.18)528 (37.18)0.581.04 (0.89–1.21)
C816 (61.82)892 (62.82)Reference
Dominant modelTT+CT413 (62.58)424 (59.72)0.271.12 (0.90–1.40)
CC247 (37.42)286 (40.28)Reference
Recessive modelTT91 (13.79)104 (14.65)0.640.93 (0.68–1.26)
CC+CT569 (86.21)606 (85.35)Reference
rs73116127GenotypesAA1 (0.15)3 (0.42)0.330.34 (0.03–3.35)
GA109 (16.52)133 (18.73)0.270.85 (0.64–1.13)
GG550 (83.33)574 (80.85)Reference
AllelesA111 (8.41)139 (9.79)0.210.84 (0.65–1.09)
G1,209 (91.59)1,281 (90.21)Reference
Dominant modelAA+GA110 (16.67)136 (19.15)0.230.84 (0.64–1.11)
GG550 (83.33)574 (80.85)Reference
Recessive modelAA1 (0.15)3 (0.42)0.350.35 (0.03–3.44)
GG+GA659 (99.85)707 (99.58)Reference
rs6125607GenotypesTT74 (11.21)48 (6.76) 0.004 1.75 (1.18–2.60)
CT277 (41.97)310 (43.66)0.871.01 (0.81–1.27)
CC309 (46.82)352 (49.58)Reference
AllelesT425 (32.20)406 (28.59) 0.04 1.18 (1.01–1.39)
C895 (67.80)1,014 (71.41)Reference
Dominant modelTT+CT351 (53.18)358 (50.42)0.301.11 (0.90–1.38)
CC309 (46.82)352 (49.58)Reference
Recessive modelTT74 (11.21)48 (6.76) 0.003 1.74 (1.19–2.54)
CC+CT586 (88.78)662 (93.23)Reference
rs6125608GenotypesGG9 (1.36)11 (1.55)0.700.84 (0.34–2.04)
AG125 (18.94)158 (22.25)0.120.81 (0.62–1.05)
AA526 (79.70)541 (76.20)Reference
AllelesG143 (10.83)180 (12.680.130.83 (0.66–1.05)
A1,177 (89.17)1,240 (87.32)Reference
Dominant modelGG+AG134 (20.30)169 (23.80)0.110.81 (0.63–1.05)
AA526 (79.70)541 (76.20)Reference
Recessive modelGG9 (1.36)11 (1.55)0.770.87 (0.36–2.13)
AA+AG651 (98.64)699 (98.45)Reference

Bold values denote statistical significance at the p < 0.05 level.

CI, confidence interval; lncRNA, long noncoding RNA; OR, odds ratio; RA, rheumatoid arthritis; SNP, single-nucleotide polymorphism.

ANRIL, antisense non-coding RNA in the INK4 locus); Lnc-DC, Lnc-RNA in dendritic cell; MALAT1, metastasis-associated lung adenocarcinoma transcript-1; zinc finger antisense 1.

Correction: Genotypes and allele frequencies of lncRNA gene polymorphisms in RA patients and normal controls. Bold values denote statistical significance at the p < 0.05 level. CI, confidence interval; lncRNA, long noncoding RNA; OR, odds ratio; RA, rheumatoid arthritis; SNP, single-nucleotide polymorphism. ANRIL, antisense non-coding RNA in the INK4 locus); Lnc-DC, Lnc-RNA in dendritic cell; MALAT1, metastasis-associated lung adenocarcinoma transcript-1; zinc finger antisense 1. In general, we find that the OR values were calculated and interpreted in an inappropriate way. This is very noticeable when analyzing the frequency of genotypes in cases and controls. For example, for rs1412830, the authors reported an OR = 0.214 (0.060–0.761), p = 0.017, for the TT genotype. That OR value suggests that this genotype is a protective or lower risk factor. However, the TT genotype has a higher prevalence in patients than that in controls (1.97% vs. 0.42%, respectively); therefore, the OR value should be >1. Regarding genetic models, the authors maintain the same mistake in the OR values. Taking rs1412830 as an example, we once again observed that there is a higher frequency of the TT genotype in patients than that in controls (1.97% vs. 0.42%, respectively). However, the authors reported an OR value = 0.211 (0.059–0.750), p = 0.016, but they describe it as a risk factor. We correctly calculate the OR values for your consideration ( ). We recommend that the authors (1) recalculate these data appropriately (2) in order to be able to rediscuss all their results. Also, we suggest that they corroborate the OR values in Table 3 (1), which we could not analyze due to lack of data.

Author Contributions

JH-B wrote this commentary and analyzed the data. CB-H and JM-V performed the analysis. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
  2 in total

1.  The confidence interval of allelic odds ratios under the Hardy-Weinberg disequilibrium.

Authors:  Yasunori Sato; Hideki Suganami; Chikuma Hamada; Isao Yoshimura; Hiromi Sakamoto; Teruhiko Yoshida; Kimio Yoshimura
Journal:  J Hum Genet       Date:  2006-08-18       Impact factor: 3.172

2.  Long Non-coding RNAs Genes Polymorphisms and Their Expression Levels in Patients With Rheumatoid Arthritis.

Authors:  Tian-Ping Zhang; Bang-Qiang Zhu; Sha-Sha Tao; Yin-Guang Fan; Xiao-Mei Li; Hai-Feng Pan; Dong-Qing Ye
Journal:  Front Immunol       Date:  2019-10-31       Impact factor: 7.561

  2 in total

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