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. 2022 Jul 5;49(6):346–357. doi: 10.1159/000524849

Preliminary Study of Genome-Wide Association Identified Novel Susceptibility Genes for Hemorheological Indexes in a Chinese Population

Yuxiao Sun a,b,c,d, Zhaoyun Cheng a,b,c, Zhiping Guo a,b,c,d, Guoyou Dai a,b,c,d, Yongqiang Li a,b,c, Yan Chen a,b,c, Ruigang Xie a,b,c, Xianqing Wang a,b,c, Mingxia Cui b,c, Guoqing Lu a,b,c, Aifeng Wang b,c, Chuanyu Gao a,b,c,d,*
PMCID: PMC9768296  PMID: 36654975

Abstract

Background

Genome-wide association studies for various hemorheological characteristics have not been reported. We aimed to identify genetic loci associated with hemorheological indexes in a cohort of healthy Chinese Han individuals.

Methods

Genotyping was performed using Applied Biosystems Axiom™ Precision Medicine Diversity Array in 838 individuals, and 6,423,076 single nucleotide polymorphisms were available for genotyping. The relations were examined in an additive genetic model using mixed linear regression and combined with identical by descent matrix.

Results

We identified 38 genetic loci (p < 5 × 10<sup>−6</sup>) related to hemorheological traits. In which, LOC102724502-OLIG2 rs28371438 was related to the levels of nd30 (p = 8.58 × 10<sup>−07</sup>), nd300 (p = 1.89 × 10<sup>−06</sup>), erythrocyte rigidity (p = 1.29 × 10<sup>−06</sup>), assigned viscosity (p = 6.20 × 10<sup>−08</sup>) and whole blood high cut relative (p = 7.30 × 10<sup>−08</sup>). The association of STK32B rs4689231 for nd30 (p = 3.85 × 10<sup>−06</sup>) and nd300 (p = 2.94 × 10<sup>−06</sup>) and GTSCR1-LINC01541 rs11661911 for erythrocyte rigidity (p = 9.93 × 10<sup>−09</sup>) and whole blood high cut relative (p = 2.09 × 10<sup>−07</sup>) was found. USP25-MIR99AHG rs1297329 was associated with erythrocyte rigidity (p = 1.81 × 10<sup>−06</sup>) and erythrocyte deformation (p = 1.14 × 10<sup>−06</sup>). Moreover, the association of TMEM232-SLC25A46 rs3985087 and LINC00470-METTL4 rs9966987 for fibrinogen (p = 1.31 × 10<sup>−06</sup> and p = 4.29 × 10<sup>−07</sup>) and plasma viscosity (p = 1.01 × 10<sup>−06</sup> and p = 4.59 × 10<sup>−07</sup>) was found.

Conclusion

These findings may represent biological candidates for hemorheological indexes and contribute to hemorheological study.

Keywords: Genome-wide association studies, Hemorheological traits, Healthy Chinese Han population, Applied Biosystems Axiom™ Precision Medicine Diversity Array, Gene

Introduction

Most metabolic diseases are always accompanied by disorders of blood rheology, such as increased blood and plasma viscosity, decreased red blood cell deformability, and increased cell aggregation [1, 2]. Hemorheology, the study of deformation and blood flow, has more focused on the deformation and aggregation of erythrocytes since erythrocytes are the main components in blood [3, 4, 5]. Blood viscosity and erythrocytes deformability are the main factors for maintaining and regulating microcirculation. Blood and plasma viscosity are risk factors for atherosclerosis; and erythrocytes rheological changes, such as erythrocytes rigidity, have been observed in patients with hypertension, diabetes mellitus, and obesity [6, 7, 8]. Caprari et al. [1] reported that the hemorheological characteristics of SCA subjects showed high blood viscosity, increased erythrocytes aggregation, and decreased erythrocytes deformability. Hemorheological variations of the erythrocyte behavior and blood plasma can help in the clinical diagnosis [9].

Recently, the identification of various susceptibility loci and genes related to hematological traits in diverse ethnic groups may provide important insights into the hematology [10]. Christiansen et al. [11] have demonstrated that ABO locus was related to the increased platelet aggregation in patients with stable coronary artery disease. Krause et al. [12] reported that rs17114036, a common noncoding polymorphism at 1p32.2, is located in the endothelial enhancer dynamically regulated by hemodynamics. Seiki et al. [13] displayed the association of ABO, PDGFRA-KIT, USP49-MED20-BSYL-CCND3, C6orf182-CD164, TERT, and TMPRSS6 variants with erythrocyte traits in Japanese population. Qayyum et al. [14] demonstrated that six single nucleotide polymorphisms (SNPs) were associated with platelet aggregation (p < 5 × 10−8). So far, although a number of loci associated with quantitative hematological traits have been discovered [15], genome-wide association studies (GWASs) for various hemorheological characteristics have not been reported. Therefore, it is necessary to determine the genetic variation related to hemorheological traits. Here, we performed a genome-wide association study to identify imputed genetic loci associated with hemorheological indexes in a cohort of healthy Chinese Han individuals by the Applied Biosystems AxiomTM Precision Medicine Diversity Array Chip.

Material and Method

Study Cohorts

A total of 838 participants (413 women and 425 men) visited the Health Care Center of Henan Provincial People's Hospital for annual checkup. All individuals were healthy Chinese Han population. Subjects with tumors, known disease, or pregnant women were excluded. To evaluate genetic association, 14 hemorheological indexes were examined (Table 1), including nd1 (mPas), nd30 (mPas), nd300 (mPas), relative index of whole blood hyposectomy, erythrocyte aggregation index, erythrocyte rigidity index, erythrocyte deformation index, assigned viscosity (mPas), fibrinogen (g/L), whole blood high cut reduction viscosity, whole blood high cut relative index, whole blood low cut reduction viscosity, plasma viscosity (mPas), and hematocrit (L/L). Demographic and hemorheology data were obtained from questionnaires or medical records. Written informed consent was obtained from all of the participating cohort. The protocols were approved by the Institutional Research Ethics Committee of Henan Provincial People's Hospital and complied with the Declaration of Helsinki.

Table 1.

Characteristics of samples used in the GWAS

Characteristics N/median (IQR)
n 838
Age, years 44.00 (36.00–50.00)
Female, n (%) 413 (49.3)
BMI, kg/m2 23.53 (21.22–25.95)
nd1, mPas 20.51 (18.38–22.91)
nd30, mPas 5.43 (4.86–6.08)
nd300, mPas 4.14 (3.69–4.60)
Relative index of whole blood hyposectomy 14.85 (13.13–16.48)
Erythrocyte aggregation index 4.92 (4.60–5.29)
Erythrocyte rigidity index 4.59 (4.02–5.23)
Erythrocyte deformation index 0.82 (0.77–0.87)
Assigned viscosity, mPas 3.39 (3.03–3.78)
Fibrinogen, g/L 3.09 (2.97–3.15)
Whole blood high cut reduction viscosity 6.31 (5.56–7.22)
Whole blood high cut relative index 2.98 (2.67–3.33)
Whole blood low cut reduction viscosity 44.87 (41.02–48.39)
Plasma viscosity, mPas 1.41 (1.36–1.43)
Hematocrit, L/L 0.42 (0.40–0.46)
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GWAS, genome-wide association study; IQR, interquartile range; BMI, body mass index.

GWAS Genotyping and Genotype Imputation

A peripheral blood sample (5 mL) of each participant was collected in EDTA-coated tubes, and genomic DNA was purified using the GoldMag DNA Isolation Kit (GoldMag Co., Ltd., Xi'an, China). A total of 796,288 loci were available for GWAS analysis based on the following: (1) sample calling rate >0.95, marker calling rate >0.90, and Hardy-Weinberg equilibrium >5 × 10−06 for quality control and (2) removing indels, copy number variation, duplication, and loci from sex chromosome. Genome-wide genotyping of the subjects was carried out using the Applied Biosystems AxiomTM Precision Medicine Diversity Array on the GeneTitanTM Multi-Channel Instrument (Thermo Fisher, CA, USA). Genotype clustering was conducted using Axiom Analysis Suite 6.0 software. Genome-wide data were imputed from the third phase of 1,000 genomes haplotype reference panels through IMPUTE2 software, and loci with minor allele frequency <1%, the correlation coefficient (r2) linkage disequilibrium <0.5, and non-biallelic were deleted. Taking into account the uncertainty of imputation, the association analysis was performed by Gold Helix SNP & Variation Suite 8.7 software. After quality control and imputation, 6,423,076 loci were included for final analysis.

Data Analysis

Continuous variables were evaluated for normality using the Kolmogorov-Smirnov test. Continuous variables with non-normal distribution as median with interquartile range were compared using the Mann-Whitney U test. Relations of SNPs to hemodynamic phenotypes were examined in the additive genetic model using mixed linear regression adjusting with age and gender by the PLINK software and combined with identical by descent matrix. The levels of hemodynamic indexes were normalized using rank-based inverse normal transformations. Manhattan plots and quantile-quantile plots were conducted by −log10 (p value) using R-package version 3.32. Locus regional plots were constructed by LocusZoom 1.1 software. The values of p < 5 × 10−8 means that the genetic polymorphism is genome-wide significantly associated with hemorheological indexes. The values of p < 5 × 10−6 suggests a suggestively significant genome-wide association with hemorheological indexes.

Bioinformatics Analysis

GWAS catalog (https://www.ebi.ac.uk/gwas/) and Clinvar (https://www.ncbi.nlm.nih.gov/clinvar/) were used to see if some of the SNPs were already associated with clinical phenotypes and if these relate somehow to the parameters. HaploReg v4.1 database (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php) is a database used to predict the potential functions of selected SNPs.

Results

The Manhattan plot (Fig. 1) displayed the chromosome location of significantly associated loci with hemodynamic indexes, including nd1 (1 loci), nd30 (2 loci), nd300 (3 loci), relative index of whole blood hyposectomy (5 loci), erythrocyte aggregation index (4 loci), erythrocyte rigidity index (3 loci), erythrocyte deformation index (2 loci), assigned viscosity (1 loci), fibrinogen (4 loci), whole blood high cut reduction viscosity (5 loci), whole blood high cut relative index (3 loci), whole blood low cut reduction viscosity (6 loci), plasma viscosity (6 loci), and hematocrit (1 loci). A quantile-quantile plot for hemodynamic index levels is shown in Figure 2, and the distribution of p values for the association tests showed no evidence of systematic bias.

Fig. 1.

Fig. 1

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a–n Manhattan plot for loci associated with levels of hemorheological indexes.

Fig. 2.

Fig. 2

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a–n QQ plot for levels of hemorheological indexes. QQ, quantile-quantile.

Table 2 displays the details of 38 loci with p values <5 × 10−6 for the levels of hemodynamic indexes. Of the 63 identified loci, LOC730100 rs117580912 (p = 9.56 × 10−07) was associated with the nd1 level (online suppl. Fig. 1; see www.karger.com/doi/10.1159/000524849 for all online suppl. material). The significant association with nd30 concentration was for rs4689231 in STK32B (p = 3.85 × 10−06) and rs28371438 in LOC102724502-OLIG2 (p = 8.58 × 10−07, online suppl. Fig. 2). Three SNPs (STK32B rs4689231: p = 2.94 × 10−06, CCSER2-LINC01519 rs115646937: p = 3.90 × 10−06 and LOC102724502-OLIG2 rs28371438: p = 1.89 × 10−06, respectively) were considered significant markers for nd300 level (online suppl. Fig. 3). Rs34469348 in LSAMP (p = 4.87 × 10−06), rs9361295 in MEI4 (p = 2.44 × 10−06), rs2410367 in TUSC3-MSR1 (p = 4.24 × 10−07), rs10977588 in PTPRD (p = 2.88 × 10−06), and rs745439 in TSHZ3-THEG5 (p = 3.14 × 10−06) were associated with the level of the relative index of whole blood hyposectomy (online suppl. Fig. 4). The significant association with the concentration of erythrocyte aggregation index was for SFMBT2 rs11255044 (p = 4.83 × 10−07), LINC01493-LRRC4C rs67538811 (p = 5.21 × 10−07), NELL1-ANO5 rs12420881 (p = 2.21 × 10−06) and C12orf42 rs56670740 (p = 1.53 × 10−06, online suppl. Fig. 5). Three genome-level significant SNPs (p value of 9.93 × 10−09 for rs11661911 in GTSCR1-LINC01541, 1.81 × 10−06 for rs1297329 in USP25-MIR99AHG and 1.29 × 10−06 for rs28371438 in LOC102724502-OLIG2) associated with erythrocyte rigidity index level were identified (online suppl. Fig. 6). We also found that TMEM232 rs2900050 (p = 3.72 × 10−06) and USP25-MIR99AHG rs1297329 (p = 1.14 × 10−06) were significant loci for the circulating level of erythrocyte deformation index (online suppl. Fig. 7). Rs28371438 in LOC102724502-OLIG2 (p = 6.20 × 10−08, online suppl. Fig. 8) was a significant marker related to the level of assigned viscosity. Four loci were associated with fibrinogen concentration, including rs78314456 in CNIH3 (p = 1.95 × 10−06), rs3985087 in TMEM232-SLC25A46 (p = 1.31 × 10−06), rs34247085 in LINC00917-FENDRR (p = 1.80 × 10−06), and rs9966987 in LINC00470-METTL4 (p = 4.29 × 10−07, online suppl. Fig. 9). For whole blood high cut reduction viscosity, the significant association was with rs144907988 in ADGRA3-GBA3 (p = 1.74 × 10−06), rs6827644 in C4orf22 (p = 3.18 × 10−06), rs1030490 in IRX1-LINC02114 (p = 2.53 × 10−06), rs11911466 in LOC102724502-OLIG2 (p = 7.37 × 10−07), and rs6519816 in PNPLA5-PNPLA3 (p = 3.37 × 10−06, online suppl. Fig. 10). Rs11990599 in LOC101927657-FAM84B (p = 3.11 × 10−06), rs11661911 in GTSCR1-LINC01541 (p = 2.09 × 10−07), and rs28371438 in LOC102724502-OLIG2 (p = 7.30 × 10−08, online suppl. Fig. 11) were related to the level of whole blood high cut relative index. Six SNPs also achieved significant association with the level of whole blood low cut reduction viscosity, with p values of 4.56 × 10−07, 4.41 × 10−06, 1.90 × 10−06, 2.05 × 10−06, 2.09 × 10−06, and 3.02 × 10−06 for SZT2-PTPRF rs2842173, MIR4272-SUCLG2 rs74525620, MIR5186-AADACL2 rs73872706, ITGA1-ITGA2 rs246509, LINC01526-IBTK rs117016320, and PTPRD rs10977641, respectively (online suppl. Fig. 12). Rs3985087 in TMEM232-SLC25A46 (p = 1.01 × 10−06), rs118096558 in RASGEF1C (p = 1.06 × 10−06), rs11794922 in ADAMTSL1 (p = 4.96 × 10−06), rs76126204 in LINC02231 (p = 8.66 × 10−07), rs9966987 in LINC00470-METTL4 (p = 4.59 × 10−07), and rs140700208 in LINC01625-LOC100132735 (p = 1.06 × 10−06) showed suggestive associations with the level of plasma viscosity (online suppl. Fig. 13). Moreover, the significant association of hematocrit was with rs78577937 in the intergenic region of the CCSER1-LNCPRESS2 gene (p = 1.43 × 10−06, online suppl. Fig. 14).

Table 2.

Significant loci associated with hemorheology indexes from the GWAS cohorts

Characteristics SNP ID Chr Position REF/ALT SNP function RefGene MAF β SE ρ value GWAS catalog HaploReg
nd1 rs117580912 2 51,343,364 C/T ncRNA intronic LOC730100 0.005 0.011 0.039 9.56E–07 – Motifs changed
nd30 rs4689231 4 5,437,107 G/A Intronic STK32B 0.370 0.162 0.330 3.85E–06 – Motifs changed
rs28371438 21 32,986,517 C/T Intergenic LOC102724502;OLIG2 0.478 –0.231 0.331 8.58E–07 Height Motifs changed
nd300 rs4689231 4 5,437,107 G/A Intronic STK32B 0.370 0.200 1.107 2.94E–06 – Motifs changed
rs115646937 10 84,813,648 A/G Intergenic CCSER2;LINC01519 0.092 –0.031 1.107 3.90E–06 – Motifs changed
rs28371438 21 32,986,517 C/T Intergenic LOC102724502;OLIG2 0.478 –0.193 1.107 1.89E–06 Height Motifs changed
Relative index of whole blood hyposectomy rs34469348 3 115,815,531 G/T Intronic LSAMP 0.094 –0.359 0.140 4.87E–06 – Motifs changed
rs9361295 6 77,831,550 C/A Intronic MEI4 0.259 –0.388 0.141 2.44E–06 – Motifs changed
rs2410367 8 15,931,636 T/C Intergenic TUSC3;MSR1 0.222 –0.194 0.140 4.24E–07 – Motifs changed
rs10977588 9 9,218,157 C/T Intronic PTPRD 0.010 –0.266 0.139 2.88E–06 – Motifs changed
rs745439 19 31,537,088 C/T Intergenic TSHZ3;THEG5 0.386 –0.450 0.143 3.14E–06 – Motifs changed
Erythrocyte aggregation index rs11255044 10 7,221,420 G/C Intronic SFME5T2 0.033 –0.063 1.433 4.83E–07 – Motifs changed
rs67538811 11 39,184,039 G/A Intergenic LINC01493;LRRC4C 0.140 0.030 1.436 5.21E–07 – Motifs changed
rs12420881 11 21,664,423 C/A Intergenic NELLI;AN05 0.007 –0.006 1.433 2.21E–06 – Motifs changed
rs56670740 12 103,353,181 T/A Intronic C12orf42 0.247 –0.149 1.435 1.53E–06 – Enhancer histone marks, motifs changed
Erythrocyte rigidity index rs11661911 18 70,983,826 A/C Intergenic GTSCR1;LINC01541 0.135 –0.104 0.952 9.93E–09 – Motifs changed
rs1297329 21 15,939,669 G/A Intergenic USP25;MIR99AHG 0.013 –0.018 0.957 1.81E–06 – Motifs changed
rs28371438 21 32,986,517 C/T Intergenic LOC102724502;OLIG2 0.478 –0.220 0.958 1.29E–06 Height Motifs changed
Erythrocyte deformation index rs2900050 5 110,721,671 G/A Intronic TMEM232 0.008 0.092 0.998 3.72E–06 – Motifs changed, selected eQTL hits
rs1297329 21 15,939,669 G/A Intergenic USP25;MIR99AHG 0.013 0.057 0.996 1.14E–06 – Motifs changed
Assigned viscosity rs28371438 21 32,986,517 C/T Intergenic LOC102724502;OLIG2 0.479 –0.200 1.559 6.20E–08 Height Motifs changed
Fibrinogen rs78314456 1 224,640,188 C/T Intronic CNIH3 0.008 –0.024 0.407 1.95E–06 – Enhancer histone marks, DNAse, motifs changed
rs3985087 5 110,736,955 C/A Intergenic TMEM232;SLC25A46 0.025 –0.052 0.407 1.31E–06 Promoter histone marks, enhancer histone marks, DNAse, proteins bound, motifs changed, selected eQTL hits
rs34247085 16 86,472,977 G/A Intergenic LINC00917;FENDRR 0.008 0.033 0.408 1.80E–06 – Enhancer histone marks, motifs changed
rs9966987 18 1,918,849 C/A Intergenic LINC00470;METTL4 0.330 0.153 0.408 4.29E–07 – Enhancer histone marks, motifs changed
Whole blood high cut reduction viscosity rs144907988 4 22,589,091 C/T Intergenic ADGRA3;GBA3 0.002 –0.229 0.150 1.74E–06 – –
rs6827644 4 80,874,656 C/T Intronic C4orf22 0.066 –0.311 0.151 3.18E–06 – Motifs changed
rs1030490 5 4,574,832 T/G Intergenic IRX1;LINC02114 0.401 –0.037 0.156 2.53E–06 – Motifs changed
rs11911466 21 32,983,986 T/C Intergenic LOC102724502;OLIG2 0.416 –0.397 0.153 7.37E–07 – Enhancer histone marks, motifs changed
rs6519816 22 43,905,383 C/T Intergenic PNPLA5;PNPLA3 0.155 –0.165 0.151 3.37E–06 – Enhancer histone marks, motifs changed
Whole blood high cut relative index rs11990599 8 126,350,838 T/C Intergenic LOC101927657;FAM84B 0.120 –0.091 0.042 3.11E–06 – Motifs changed
rs11661911 18 70,983,826 A/C Intergenic GTSCR1;LINC01541 0.134 –0.105 0.043 2.09E–07 – Motifs changed
rs28371438 21 32,986,517 C/T Intergenic LOC102724502;OLIG2 0.479 –0.257 0.060 7.30E–08 Height Motifs changed
Whole blood low cut reduction viscosity rs2842173 1 43,493,328 T/C Intergenic SZT2;PTPRF 0.217 –0.280 0.159 4.56E–07 – Enhancer histone marks, selected eQTL hits
rs74525620 3 67,290,451 C/A Intergenic MIR4272;SUCLG2 0.003 –0.155 0.157 4.41E–06 Promoter histone marks, enhancer histone marks, motifs changed, selected eQTL hits
rs73872706 3 151,645,475 G/C Intergenic MIR5186;AADACL2 0.138 –0.056 0.157 1.90E–06 – Enhancer histone marks, motifs changed
rs246509 5 52,968,323 A/C Intergenic ITGA1;ITGA2 0.219 –0.242 0.159 2.05E–06 – Motifs changed, selected eQTL hits
rs117016320 6 81,831,622 T/A Intergenic LINC01526;IBTK 0.012 –0.142 0.157 2.09E–06 Promoter histone marks, enhancer histone marks, DNAse, proteins bound, motifs changed
rs10977641 9 9,291,862 A/G Intronic PTPRD 0.009 –0.140 0.157 3.02E–06 – Motifs changed
Plasma viscosity rs3985087 5 110,736,955 C/A Intergenic TMEM232;SLC25A46 0.025 –0.048 0.917 1.01E–06 Promoter histone marks, enhancer histone marks, DNAse, proteins bound, motifs changed, selected eQTL hits
rs118096558 5 180,197,694 G/A Intronic RASGEF1C 0.009 –0.016 0.917 1.06E–06 – Enhancer histone marks, motifs changed
rs11794922 9 18,789,607 T/G Intronic ADAMTSL1 0.002 –0.029 0.917 4.96E–06 – Enhancer histone marks, motifs changed
rs76126204 12 64,983,618 C/T ncRNA intronic LINC02231 0.030 0.076 0.919 8.66E–07 – Motifs changed
rs9966987 18 1,918,849 C/A Intergenic LINC00470;METTL4 0.269 0.093 0.917 4.59E–07 – Enhancer histone marks, motifs changed
rs140700208 6 139,497,581 T/C Intergenic LINC01625;LOC100132735 0.331 0.160 0.917 1.06E–06 Promoter histone marks, enhancer histone marks, DNAse, proteins bound, motifs changed
Hematocrit rs78577937 4 92,146,844 A/C Intergenic CCSER1;LNCPRESS2 0.168 –0.509 0.135 1.43E–06 – –
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GWAS, genome-wide association study; SNP, single nucleotide polymorphism; REF/ALT, reference/alternates; MAF, minor allele frequency.

Based on GWAScat database (Table 2), we found that rs28371438 was associated with height trait. The results of HaploReg v4.1 displayed that these SNPs were associated with the regulation of promoter and/or enhancer histones, DNAse, changed motifs, and selected eQTL hits.

Discussion

Hemorheology (also named blood rheology) is the study of the flow characteristics of blood and its elements. Hemorheology indicators, such as whole blood viscosity, plasma viscosity, erythrocyte aggregation, erythrocyte rigidity, erythrocyte deformation, fibrinogen, and hematocrit, play fundamental roles in maintaining microcirculation [16, 17]. In our study of 14 hemorheological traits, a total of 38 SNPs were significantly related to hemorheological traits (p < 5 × 10−6). In which, six SNPs, including rs28371438 for nd30, nd300, erythrocyte rigidity index, assigned viscosity and whole blood high cut relative index; rs4689231 of STK32B for nd30 and nd300; rs11661911 for erythrocyte rigidity index and whole blood high cut relative index; rs1297329 for erythrocyte rigidity index and erythrocyte deformation index; rs3985087 and rs9966987 for fibrinogen and plasma viscosity, were identified as multiple hematological markers. This is the first GWAS examining the genetic loci of hemorheological indexes in a normal Chinese Han population.

Blood viscosity and its major determinants (hematocrit and plasma viscosity) are related to increased risks of cardiovascular disease and cardiovascular-related premature mortality [16]. In our study, LOC730100 rs117580912 was associated with the nd1 level. LOC730100 on chromosome 2p16.3 was increased expression in glioma tissues and cell lines, and enhanced proliferation and invasion of glioma cells [18]. The significant association with nd30 and nd300 levels was for STK32B rs4689231 and LOC102724502-OLIG2 rs28371438. CCSER2-LINC01519 rs115646937 was also a significant marker for the nd300 level. The effects of overexpressed STK32B (chromosome 4p16.2) might be involved in relevant essential tremor pathways [19]. OLIG2, located on chromosome 21q22.11, is the key transcription factor that maintains the neural progenitor cells of the pMN domain [20]. CCSER2 on chromosome 10q23.1 was identified a reference gene, also called novel housekeeping gene [21].

Moreover, rs28371438 in LOC102724502-OLIG2, rs3985087 in TMEM232-SLC25A46, rs118096558 in RASGEF1C, rs11794922 in ADAMTSL1, rs76126204 in LINC02231, rs9966987 in LINC00470-METTL4, and rs140700208 in LINC01625-LOC100132735 showed suggestive associations with the level of plasma viscosity. TMEM232 associated with atopic dermatitis in the Chinese Han population [22] and SLC25A46 related to patients with Parkinson's disease and optic atrophy [23] are located on chromosome 5q22.1. ADAMTSL1 (chromosome 9p22.2-p22.1) protein was lower expressed in intracranial aneurysm tissue than in the control cerebral artery [24]. LINC00470 (chromosome 18p11.32) promoted the proliferation and invasion of glioma cell by LINC00470/miR-134/Myc/ABCC1 axis [25]. METTL4, located on chromosome 18p11.32, was identified as a candidate of N6-adenine methylase [26]. The function of long intergenic nonprotein coding RNA (LOC102724502, LINC01519, LINC02231, LINC01625, and LOC­100132735) and RASGEF1C needs further study.

Hematocrit and fibrinogen are important determinants of whole blood viscosity. McMullin et al. reported that red cell mass measurement along with hemoglobin and hematocrit cut-offs as a major diagnostic criterion for the diagnosis of polycythemia Vera [27]. In this study, the significant association of hematocrit was with rs78577937 in the intergenic region of CCSER1-LNCPRESS2. CCSER1 (chromosome 4q22.1), also known as FAM190A, was reported to be associated with type 1 diabetes [28]. LNCPRESS2 is a long intergenic nonprotein coding RNA, whose function needs further study. Fibrinogen is an acute phase protein with proinflammatory and anti-inflammatory properties. Its secretion in the liver is upregulated during inflammation [29]. Previous study has shown that elevated fibrinogen is associated with an increased risk of lung, colorectal, and breast cancers [30]. Four loci were associated with fibrinogen concentration, including CNIH3 rs78314456, TMEM232-SLC25A46 rs3985087, LINC00917-FENDRR rs34247085, and LINC00470-METTL4 rs9966987. Cornichon 3 (CNIH3, chromosome 1q42.12) enhanced the glutamate sensitivity, single-channel conductance, and calcium permeability of CP-AMPARs while decreasing their block by intracellular polyamines [31]. FENDRR (chromosome 16q24.1) is lower expressed in colorectal cancer tissue and cells, which is responsible for inhibiting of cell proliferation, migration, and invasion [32].

The aggregation of red blood cells may be enhanced during various pathophysiological processes, including circulatory and metabolic disorders, infection, blood pathology, and several other disease states [33]. Altered erythrocyte aggregation may be a factor that affects the clinical process and also an indicator for the development and prognosis of disease. We found that the significant association with the concentration of erythrocyte aggregation index was for SFMBT2 rs11255044, LINC01493-LRRC4C rs67538811, NELL1-ANO5 rs12420881, and C12orf42 rs56670740. SFMBT2, a circRNA located on chromosome 10p14, had an increased expression level in gastric cancer tissues and was associated with the proliferation of gastric cancer cells [34]. LRRC4C (chromosome 11p12) was a novel candidate susceptibility gene for pediatric central nervous system tumors [35]. NELL1 (chromosome 11p15.1) was associated with bone formation and osteoclast differentiation [36], and ANO5 (chromosome 11p15.1) was related to myopathy. Hypothetical gene C12orf42, located on chromosome 12q23.2-q23.3, was associated with T-lymphoblastic lymphoma [37].

Erythrocyte aggregation is reversible and shear-dependent (i.e., disperses at high shear and reforms at low shear), and the degree of erythrocyte aggregation is the main determinant of low-shear blood viscosity [33]. Our study displayed that LSAMP rs34469348, MEI4 rs9361295, TUSC3-MSR1 rs2410367, PTPRD rs10977588, and TSHZ3-THEG5 rs745439 were associated with the level of relative index of whole blood hyposectomy. Rs11990599 in LOC101927657-FAM84B, rs11661911 in GTSCR1-LINC01541, and rs28371438 in LOC102724502-OLIG2 were related to the level of whole blood high cut relative index. For whole blood high cut reduction viscosity, the significant association was with ADGRA3-GBA3 rs144907988, C4orf22 rs6827644, IRX1-LINC02114 rs1030490, LOC102724502-OLIG2 rs11911466, and PNPLA5-PNPLA3 rs6519816. Six SNPs also achieved the significant association with the level of whole blood low cut reduction viscosity for SZT2-PTPRF rs2842173, MIR4272-SUCLG2 rs74525620, MIR5186-AADACL2 rs73872706, ITGA1-ITGA2 rs246509, LINC01526-IBTK rs117016320, and PTPRD rs10977641, respectively.

The rigidity of erythrocyte is the main rheological characteristic of the blood of Sickle Cell Anemia patients and several pathologies [38]. Three genome-level significant SNPs (rs11661911 in GTSCR1-LINC01541, rs1297329 in USP25-MIR99AHG and rs28371438 in LOC102724502-OLIG2) associated with erythrocyte rigidity index level were identified. LINC01541 (chromosome 18q22.3) plays a key role in 17β-estradiol (17β-E2)-stimulated endometrial stromal cells [39]. USP25 (chromosome 21q21.1) suppresses the degradation of BCR-ABL protein in cells harboring the Philadelphia chromosome (Ph) in chronic myelogenous leukemia [40]. LncRNA host gene MIR99AHG (alias MONC) interfered with hematopoietic lineage decisions and enhanced the proliferation of immature erythroid progenitor cells in acute megakaryoblastic leukemia [41]. Erythrocyte deformation is determined mainly by the fluidity of the membrane and the viscosity of the cytoplasm, but extracellular factors may have an irreversible effect on the erythrocyte membrane [42]. Fornal et al. [43] reported a statistically significant correlation between left ventricular mass index, erythrocyte deformability, and aggregability. We also found that TMEM232 rs2900050 and USP25-MIR99AHG rs1297329 were significant loci for the circulating level of erythrocyte deformation index.

Several potential limitations of this study cannot be ignored. First, all subjects were recruited from the same hospital; therefore, there was selection bias. Second, all participants were only from populations of Chinese Han ancestry, suggesting our finding couldn't be generalized to other ethnic groups. Therefore, replication studies in other Chinese Han populations or other ethnic groups are required to confirm the association of the identified loci with hemorheological phenotypes. Third, the potential function of identified loci has not been assessed. Further functional analysis is required to reveal the biological mechanism behind the observed associations. Four, the most commonly accepted threshold of a genome-wide association study is p < 5 × 10−8. After consulting the literature on disease GWAS, we found that when the sample size is small, a relatively relaxed threshold will be selected [44, 45, 46]. Therefore, we chose a relatively relaxed threshold as the suggestive threshold for significant genome-wide association (p < 5 × 10−6). However, to the best of our knowledge, this is the first GWAS examining the genetic loci of hemorheological indexes in a normal Chinese Han population. The identification of susceptibility loci and genes related to hemorheological indexes may provide important insight into the regulation of hemorheological indexes.

Conclusion

In summary, we reported 38 suggestive loci associated with hemorheological indexes in the Chinese Han population. In particular, six SNPs (rs28371438 in LOC102724502-OLIG2, rs4689231 of STK32B, rs11661911 in GTSCR1-LINC01541, rs1297329 in USP25-MIR99AHG, rs3985087 in TMEM232-SLC25A46, rs9966987 in LINC00470-METTL4) were identified as multiple hematological markers. The results of our genome-wide association study may represent biological candidates for hemorheological indexes and contribute to hemorheological study.

Statement of Ethics

Written informed consent was obtained from all of the participating cohort. The protocols were approved by the Institutional Research Ethics Committee of Henan Provincial People's Hospital and complied with the Declaration of Helsinki.

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

Funding Sources

This work was supported by the National Key Research and Development Program comes from the Ministry of Science and Technology, PRC (Project number: 2018YFC0114502).

Author Contributions

Yuxiao Sun drafted the work or revised it critically for important content; Zhaoyun Cheng, Zhiping Guo, and Guoyou Dai performed the experiments; Yongqiang Li, Yan Chen, and Ruigang Xie analyzed the data; Xianqing Wang, Mingxia Cui, Guoqing Lu, and Aifeng Wang collected the samples and information. Chuanyu Gao conceived and designed the experiments. All the authors have read and approved the manuscript.

Data Availability Statement

All data generated or analyzed during this study are included in this article and its online supplementary material. Anyone who is interested in the information should contact the corresponding author.

Supplementary Material

Supplementary data

Click here for additional data file. (7.1MB, docx)

Funding Statement

This work was supported by the National Key Research and Development Program comes from the Ministry of Science and Technology, PRC (Project number: 2018YFC0114502).

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Associated Data

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Supplementary Materials

Supplementary data

Click here for additional data file. (7.1MB, docx)

Data Availability Statement

All data generated or analyzed during this study are included in this article and its online supplementary material. Anyone who is interested in the information should contact the corresponding author.

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