Genetic polymorphism in C3 is associated with progression in chronic kidney disease (CKD) patients with IgA nephropathy but not in other causes of CKD.

OBJECTIVES: The R102G variant in complement 3 (C3) results in two allotypic variants: C3 fast (C3F) and C3 slow (C3S). C3F presents at increased frequency in patients with chronic kidney disease (CKD), our aim is to explore its role in CKD progression and mortality.
METHODS: Delta (Δ) eGFR for 2038 patients in the Salford Kidney Study (SKS) was calculated by linear regression; those with ≤-3ml/min/1.73m2/yr were defined as rapid progressors (RP) and those with ΔeGFR between -0.5 and +1ml/min/1.73m2/yr, labelled stable CKD patients (SP).A group of 454 volunteers was used as a control group. In addition, all biopsy-proven glomerulonephritis (GN) patients were studied regardless of their ΔeGFR. R102G was analysed by real-time PCR, and genotypic and allelic frequencies were compared between RP and SP along with the healthy control group.
RESULTS: There were 255 SP and 259 RP in the final cohort. Median ΔeGFR was 0.07 vs. -4.7 ml/min/1.73m2/yr in SP vs. RP. C3F allele frequency was found to be significantly higher in our CKD cohort (25.7%) compared with the healthy control group (20.6%); p = 0.008.However, there was no significant difference in C3F allele frequency between the RP and SP groups. In a subgroup analysis of 37 patients with IgA nephropathy in the CKD cohort (21 RP and 16 SP), there was a significantly higher frequency of C3F in RP 40.5% vs. 9.4% in SP; p = 0.003. In the GN group, Cox regression showed an association between C3F and progression only in those with IgA nephropathy (n = 114);HR = 1.9 (95% CI 1.1-3.1; p = 0.018) for individuals heterozygous for the C3F variant, increased further for individuals homozygous for the variant, HR = 2.8 (95% CI 1.2-6.2; p = 0.014).
CONCLUSION: The C3 variant R102G is associated with progression of CKD in patients with IgA nephropathy.

Introduction

Chronic kidney disease (CKD) is a worldwide health concern due to the high morbidity and mortality associated with CKD progression and end stage renal disease (ESRD) [1,2]. Multiple risk factors play a role in CKD progression [3,4]. Better predictive tools to characterise patients`future risk of progression could help in delivering targeted treatment to high-risk patients. Therefore, there has been increased attention in elucidating the genetic determinants of CKD progression given patients with the same phenotypic risk profile progress at different rates.

Several genome-wide association studies (GWAS) identified numerous genetic loci associated with CKD [5-8]. Unfortunately, the lack of clinical and biochemical characteristics of the populations involved in these large GWAS and the absence of longitudinal data makes it difficult to determine if these genetic loci are associated with CKD progression or specific causes of CKD. These important issues can be addressed by targeted studies considering a limited number of genetic polymorphisms, but in a well-known CKD cohort with sufficient period of follow-up [9,10].

Although the complement system has an important role in our innate immune system, it is involved in the pathogenesis of most glomerular and tubular kidney diseases [11].The third human complement component, C3, plays a pivotal role in the complement system cascade, such that a polymorphism in its genetic coding can affect the activity of the complement system resulting in more activation, inflammation and tissue destruction [12,13].An extensively studied variant inC3 is R102G (rs2230199), with three polymorphic variants: homozygote C3SS (slow), homozygote C3FF (fast), and heterozygote C3FS. The fast and slow description for C3 activity was known even before the discovery of this single nucleotide polymorphism (SNP); the fast or slow labelling refers to the speed of movement of C3 on the agarose gel during protein electrophoresis [14].The R102G polymorphism is determined by a change in one nucleotide where cytosine is replaced by guanine at position 364 in the exon 3 of the β chain in the C3 gene on chromosome 19.This results in the replacement of a positively charged arginine amino acid in the C3S allele by a neutral glycine amino acid in the C3F allele [15]. The neutral glycine amino acid in the C3F allele decreases its ability to bind to complement factor H, an important complement regulator protein, leading to less regulation and more activation of the complement system [12].The frequency of the C3F allele differs in different ethnicities; it is 20% in Caucasian, 5% in blacks and 1% in Asian populations [16].

The C3F variant has been associated with an increased risk for age related macular degeneration [17],rheumatoid arthritis and Crohn’s disease[18].In kidney diseases, C3F has been found to be more prevalent in CKD[19] especially in glomerulonephritis (GN), such as; membranoproliferative GN type II (MPGN II) [20], IgA nephropathy (IgAN)[21] and systemic vasculitis[22].Hence we have designed our study, aiming to investigate the association between the C3 variant R102G and CKD progression and all-cause mortality in all-cause CKD, as well as in different specific CKD causes, in a large, well characterised non-dialysis CKD cohort (stages 3–5) with a long period of follow-up.

Materials and methods

Study population

Patients were ascertained through the Salford Kidney Study (SKS), a large non-dialysis CKD cohort that has recruited and followed-up CKD patients referred to Salford renal service prospectively since 2002 [23-25].The participants are ≥ 18 years with estimated glomerular filtration rate (eGFR) <60 ml/min/1.73m2 and have not started renal replacement therapy (RRT).Data recorded at baseline includes demographics (age, sex, weight, height, CKD aetiology, smoking status, alcohol history and functional status), comorbid conditions, medications and laboratory results. Blood samples including whole blood, serum and plasma are collected and stored at -80°C for future research, with most recruited patients having extracted DNA that is also stored. All patients are followed up annually until reaching an SKS endpoint which includes: a) RRT commencement b) death c) lost to follow-up or discharge from clinic and d) unable to participate, or withdrawal of consent. The study complies with the declaration of Helsinki and ethical approval has been obtained from the regional ethical committee (current REC reference 15/NW/0818).

Study 1 included 514 CKD patients (255 stable CKD patients and 259 rapid progressors) from patients recruited into the Salford kidney study (SKS) and 454 ethnically matched individuals from a bank of anonymized, healthy, unrelated individuals through the regional molecular genetics laboratory of Manchester Royal Infirmary hospital.

As most of the literature has emphasized the role of the complement system in GN[26] and because an increase of the C3F allele frequency has been found in various GN types[20-22], we designed the second part of the study in order to increase the number of GN patients.

Hence, study 2 included 269 GN patients recruited into the SKS.

Patient selection

Study 1:the total number of patients who were recruited into SKS from October 2002 to December 2016 was 3115.The eGFR was calculated, for patients who had a complete data set, from creatinine values obtained during outpatient’s visits by the CKD Epidemiology Collaboration (CKD-EPI) formula[27]. To ensure an adequate follow-up period to enable determination of the rate of CKD progression, all patients with <2 years follow-up or <4 available eGFR values were excluded. The delta (Δ) eGFR (±ml/min/1.73m2/yr) was calculated for remaining patients by linear regression. We defined rapid progressors (RP) as patients with ΔeGFR ≤-3ml/min/1.73m2/yr and stable CKD patients (SP)as patients with ΔeGFR between -0.5 and +1ml/min/1.73m2/yr. As it is well-known that acute kidney injury (AKI) episodes can leave some irreversible effects and can contribute to CKD progression [28,29], it was also important to exclude the patients who had AKI during their follow-up period. The resultant cohort of RP and SP was further refined by two independent researchers reviewing patients`eGFR-time graphs in the hospital electronic records such that those with AKI episodes contribute to the fast decline in eGFR or treatment effects contribute to improving or stability of eGFR were excluded. The final cohort was then derived from the above patients who had available DNA samples for analysis (Fig 1).

Fig 1

Flowchart of patient recruitment to study 1.

Study 2: All biopsy proven GN patients in the SKS, regardless of their ΔeGFR, and with available DNA samples totalled 269 GN patients. These included 114 IgAN, 50 focal segmental glomerulosclerosis (FSGS), 59 membranous nephropathy, and 46 other types of GN including minimal change disease, post streptococcus GN, MPGN. (Fig 2)

Fig 2

Flowchart of patient recruitment to study 2.

Data gathering and study outcomes

Demographic data and laboratory results were collected from electronic patient records (EPR) and the SKS data base at baseline. Two main outcomes were studied:

CKD progression: As competing risks can lead to inaccurate survival analysis with Cox regression, we used composite end point [30]. CKD progression was defined as ESRD (reaching RRT or eGFR<10 ml/min/1.73m2) or ≥40% eGFR decline. This is a validated end point for CKD progression used in many trials [31-33].

All-cause mortality.

Genotyping of the R102G SNP

The participants were genotyped for the R102G C3 polymorphism (rs2230199) by a validated TaqMan SNP Genotyping Assay. SNP genotyping was performed by the Applied Biosystem Step OneTM Real-Time PCR system according to the manufacturer’s recommendations (Thermo Fisher Scientific). The assay mix (including unlabelled PCR primers, and FAM and VIC dye-labelled TaqMan MGB probes) was designed by Thermo Fisher Scientific. The reaction system utilised 20 ng of genomic DNA, 5 μl of TaqMan Universal PCR Master Mix, and 0.5 μl 20× Assay Mix and was adjusted with water for a total volume of 10 μl in each well. Alleles were scored using Applied Biosystem Step OneTM Real-Time PCR software (version 2.1).

Statistical analysis

Continuous data are expressed as the median and interquartile range (IQR), and categorical data as frequency and percentage. Comparisons between groups were undertaken with the Mann-Whitney U test, chi-squared test,Monte-Carlo test, or Fisher-Exact test as appropriate. The relationship between the C3 SNP and the incidence rate of renal events or death was investigated by univariate and multivariate Cox regression analyses and Kaplan–Meier survival curves. All risk factors for renal progression including: age, gender, smoking, diabetes, hypertension, albumin, haemoglobin, urinary protein:creatinine ratio, and baseline eGFR were tested in the univariate and multivariate Cox regression. The same factors were included in the univariate model for mortality with the addition of other risk factors including history of cancer, myocardial infarction, congestive cardiac failure (CCF) and C-reactive protein level (CRP) and those variables that showed significant association in the univariate model were included in the multivariate one. In the second part of the study, when the relationship between the C3 SNP and renal progression in different GN groups was investigated, active treatment with corticosteroids or immunosuppressive therapies was included in the univariate and multivariate Cox regression analyses.A p-value <0.05 was considered statistically significant throughout the analyses. All statistical analyses were performed using SPSS(Version 23) (IBM SPSS, Chicago, IL) licensed to the University of Manchester.

Results

Study 1 (CKD cohort)

Baseline characteristics

Baseline characteristics of the entire CKD cohort and comparison between RP and SP patients are summarized in Tables 1 and 2.

Table 1

Baseline clinical characteristics of the CKD cohort and comparison between rapid progressors and stable CKD patients.

	Total CKD (n = 514)	Stable patients (n = 255)	Rapid progressors (n = 259)	p-Value
Age (years)	62.8(50.5–73.7)	68.4 (57.4–76.5)	56 (45.6–69.3)	<0.001
Gender (male), n (%)	322 (62.6%)	187 (73.3%)	135 (52.1%)	<0.001
Ethnicity (Caucasian), n (%)	494 (96%)	250 (98%)	244 (94.2%)	0.025
Smoking, n (%)	323 (62.8%)	161 (63.1%)	162 (62.5%)	0.89
HTN, n (%)	495 (96.3%)	245 (96.1%)	250 (96.5%)	0.78
Systolic BP (mmHg)	138 (125–150)	136 (124–150)	138 (127–152)	0.11
Diastolic BP (mmHg)	76 (70–82)	75 (66–80)	78 (70–84)	<0.001
DM, n (%)	158 (30.7%)	91 (35.7%)	67 (25.9%)	0.016
MI, n (%)	46 (8.9%)	33 (12.9%)	13 (5%)	0.002
CCF, n (%)	21 (4%)	11 (4.3%)	10 (3.9%)	0.80
PVD, n (%)	32 (6.2%)	19 (7.5%)	13 (5%)	0.25
CVA, n (%)	20 (3.9%)	9 (3.5%)	11 (4.2%)	0.67
Tumor, n (%)	52 (10.1%)	34 (13.4%)	18 (6.9%)	0.016
CKD cause, n (%)
DM	101 (19.6%)	51 (20%)	50 (19.3%)	0.84
HTN	55 (10.7%)	34 (13.3%)	21 (8.1%)	0.06
RVD	32 (6.2%)	20 (7.8%)	12 (4.6%)	0.13
IgA nephropathy	37 (7.2%)	16 (6.3%)	21 (8.1%)	0.34
FSGS	16 (3.1%)	5 (2%)	11 (4.2%)	0.14
Membranous GN	12 (2.3%)	5 (2%)	7 (2.7%)	0.58
Other GN &vasculitis	33 (6.4%)	17 (6.7%)	16 (6.2%)	0.68
ADPKD	57 (11.1%)	2 (0.8%)	55 (21.2%)	<0.001*
Pyelonephritis and interstitial nephritis	67 (13%)	42 (16.5%)	25 (9.7%)	0.021
Unknown	60 (11.6%)	38 (14.9%)	22 (8.5%)	0.023
Others	44 (8.5%)	25 (9.8%)	19 (7.3%)	0.28

HTN-hypertension, BP-blood pressure, mmHg-millimeter of mercury, DM-diabetes mellitus, MI-myocardial infarction, CCF-congestive cardiac failure, PVD-peripheral vascular disease, CVA-cerebrovascular accident, CKD-chronic kidney disease, RVD-renovascular disease, FSGS- focal segmental glomerular sclerosis, GN-glomerulonephritis, ADPKD-autosomal dominant polycystic kidney disease.

Continuous variables are expressed as median (interquartile range) and p-Value by Man-Whitney U test.

Categorical variables are expressed as number (%) and p-Value by Chi-Square test.

*P-Value by Fisher-Exact test.

Table 2

Base line biochemical characteristics of the CKD cohort and comparison between rapid progressors and stable CKD patients.

	Total CKD (n = 514)	Stable patients (n = 255)	Rapid progressors (n = 259)	p-Value
Creatinine (umol/L)	190 (150–242)	206 (160–262)	176 (142–223)	<0.001
eGFR(CKD-EPI) (ml/min/1.73m2)	28 (19.7–37.5)	24.7 (18.2–33.1)	31.4 (23.1–41.4)	<0.001
Delta eGFR (ml/min/1.73m2/year)	-3 (-4.6 to 0.06)	0.07 (-0.25 to 0.49)	-4.7 (-6.4 to -3.7)	<0.001
Urea (mmol/L)	13.2(10.3–17.4)	14 (10.7–18.4)	12.4 (10.1–16.3)	0.003
Albumin (g/L)	43 (40–45)	44 (42–45)	42 (39–44)	<0.001
Corrected calcium (mmol/L)	2.29 (2.2–2.3)	2.29 (2.2–2.36)	2.3 (2.2–2.37)	0.16
Phosphorus (mmol/L)	1.1 (0.9–1.27)	1.07 (0.94–1.24)	1.16 (1.02–1.28)	0.001
PTH (pmol/L)	6.8 (4.1–11.2)	6.7 (3.8–11)	6.9 (4.46–11.3)	0.33
Vitamin Da (nmol/L)	35.9(19.9–55)	38.9 (23.5–60)	30.6 (17.3–50)	0.004
CRP (mg/L)	2.7 (1.2–5.7)	2.4 (1.1–5.3)	3.1 (1.3–6)	0.06
uPCR (mg/mmol)	34.2 (14–135)	20 (10–44)	85 (25–223)	<0.001
Urateb(mmol/L)	0.44(0.38–0.53)	0.44 (0.38–0.54)	0.44 (0.38–0.51)	0.53
Cholesterol(mmol/L)	4.5 (3.7–5.4)	4.3 (3.6–5.1)	4.7 (4–5.6)	<0.001
Haemoglobin (g/L)	124 (114–136)	125 (116–137)	122 (113.5–134.5)	0.035
HbA1cc(mmol/mol)	40.7 (36–48)	41 (37–49)	40 (36–45.4)	0.09

eGFR-estimated glomerular filtration rate calculated using CKD-EPI equation, PTH-parathyroid hormone, CRP- C-reactive protein, uPCR-urine protein:creatinine ratio, HbA1c-haemoglobin A1c.

Variables are expressed as median (interquartile range) and p-Value by Man-Whitney U test.

a- Vitamin D results were only available in142 (55.7%) of slow progressors and 143 (55.2%) of rapid progressors.

b- Urate results were only available in 199 (78%) of slow progressors and 202 (78%) of rapid progressors.

c- HbA1C were only available in 201(78.8%) of slow progressors and 214(82.6%) of rapid progressors.

The median age of the total cohort was 62.8 years(IQR50.5–73.7) with more males (62.2%) and Caucasian ethnicity (96%).The RP patients were significantly younger in age (median 56; IQR45.6–69.3 years) than SP (median68.4; IQR 57.4–76.5 years); p-value <0.001 and included fewer males (52.1%) than SP (73.3%). Diabetes mellitus was the commonest cause of CKD in the total cohort (19.6%), while membranous nephropathy was the least (2.3%). Autosomal dominant polycystic kidney disease constituted the commonest cause in the RP group (21.2%). Median ΔeGFR was 0.07; (IQR -0.25 to 0.49)ml/min/1.73m2/yrin SP vs. -4.7(IQR-6.4 to -3.7)ml/min/1.73m2/yrin RP. Baseline eGFR was significantly higher in RP (median 31.4, IQR 23.1–41.4ml/min/1.73m2/yr) than that in SP (median 24.7, IQR 18.2–33.1ml/min/1.73m2/yr).

The R102G C3 polymorphism (rs2230199)

The distribution of the polymorphism frequencies in both the CKD cohort and the controls were consistent with the Hardy-Weinberg equation (S1 Table). There were significant differences between the CKD group in genotypic frequencies(FF 9.3%, FS 32.7%, SS 58%) and allele frequencies (F 25.7%, S 74.3%) compared with the control group which had genotype variants (FF 5.7%, FS 29.7%, SS 64.6%) and allele frequencies(F 20.6%, S 79.4%), with p-value = 0.039 and 0.008, respectively (Table 3). Comparison of clinical and biochemical characteristics between CKD patients with the rare genotype (C3FF) and the commoner genotypes (C3FS and C3SS) is summarized in S2 Table.

Table 3

Comparison of genotype variant and allele frequency between CKD and control group, and between RP and SP patients.

		CKD			Controls (n = 454)	p-Value
	Total (n = 514)	Stable patients (n = 255)	Rapid progressors (n = 259)	p-Value
Complement 3 (rs2230199)
FF	48 (9.3%)	19 (7.5%)	29 (11.2%)		26 (5.7%)
FS	168 (32.7%)	86 (33.7%)	82 (31.7%)	0.34	135 (29.7%)	0.039
SS	298 (58%)	150 (58.8%)	148 (57.1%)		293 (64.6%)
Allele frequency
F	264 (25.7%)	124 (24.3%)	140 (27%)	0.32	187(20.6%)	0.008
S	764 (74.3%)	386 (75.7%)	378 (73%)		721(79.4%)

p-Value by Chi-square test.

FF-homozygous complement 3 fast, FS-heterozygous complement 3, SS-homozygous complement 3 slow.

The difference between the RP genotype variants (FF 11.2%, FS 31.7%, SS 57.1%) and allele frequencies (F 27%, S 73%), and the SP genotype variants (FF 7.5%, FS 33.7%, SS 58.8%) and allele frequencies (F 24.3%, S 75.7%) was not significant with p-value 0.34 and 0.32, respectively (Table 3). However, in the subgroup analysis of patients stratified by CKD cause, the IgAN group showed a significant difference between RP (n = 21) allele frequencies (F 40.5%, S 59.5%), and SP (n = 16) allele frequencies (F 9.4%, S 90.6%), p-value = 0.003, with odds ratio = 6.6 (S3 Table). The comparison of the clinical and biochemical characteristics between IgAN patients with either RP or SP is summarized in S4 Table.

CKD progression: median follow-up for the CKD patients was 56(IQR 36–83) months with significantly longer follow-up in SP (median 71; IQR 47–104 months) than RP (median 43; IQR 30–62 months), p < 0.001;238 (91.9%) of the RP and 23 (9%) of the SP patients reached the progression end-point during the study follow-up period.

Mortality: median follow-up for the CKD patients was 57(IQR38-84) months, with significantly longer follow-up in SP (median 71; IQR 47–104 months) than RP (median 48; IQR 32.5–64.5 months), p < 0.001;36 (13.9%) of the RP and 58 (22.7%) of the SP had died during the study follow-up period.

Study outcomes

C3 polymorphism and CKD progression

There was no significant association between C3 polymorphism and CKD progression by Cox regression analysis, hazard ratio (HR) = 1.1(95% confidence interval (CI) 0.75–1.6; p = 0.59)for C3FF, HR = 1.0(CI 0.8–1.3; p = 0.78) for C3FS and HR = 0.95(CI 0.74–1.2; p = 0.70) for C3SS. The factors which showed significant association with progression in our CKD cohort in multivariate Cox regression analysis were age, gender, smoking, haemoglobin, baseline eGFR, and urinary protein:creatinine ratio (S5 Table).

C3 polymorphism and mortality

There was a significant association between C3 homozygous variant (C3FF) polymorphism and mortality in univariate regression analysis with HR = 1.8(CI 1.4–3.1; p = 0.037). This association remained significant after adjustment for other risk factors (model 1), but lost significance after adjustment for C-reactive protein (CRP) in model 2 with HR = 1.6 (CI 0.9–2.8; p = 0.14). The factors which showed significant association with mortality in our CKD cohort in the multivariate Cox regression were age, CCF, haemoglobin and CRP (Table 4).

Table 4

Cox regression analysis (death) univariate and multivariate models (CKD cohort n = 514, events n = 94).

Factor	Univariate model HR (95% CI)	p-Value	Multivariate model 1* HR (95% CI)	p-Value	Multivariate model 2* HR (95% CI)	p-Value
Complement 3 FF	1.8 (1.04–3.1)	0.037	1.9 (1.1–3.4)	0.033	1.6 (0.9–2.8)	0.14
Complement 3 FS	0.82 (0.52–1.3)	0.41
Complement 3SS	0.9 (0.59–1.4)	0.62
Age	1.1 (1.06–1.1)	<0.001	1.1 (1.05–1.09)	<0.001	1.1 (1.04–1.09)	<0.001
Gender (female)	0.84 (0.54–1.3)	0.43
Smoking	1.68 (1.06–2.6)	0.027	1.5 (0.91–2.4)	0.12	1.4 (0.85–2.3)	0.18
HTN	1.2 (0.4–3.7)	0.81
DM	2.0 (1.35–3.07)	0.001	1.1 (0.71–1.7)	0.68	1.1 (0.71–1.7)	0.70
Tumor	1.8 (1.05–3.0)	0.034	1.5 (0.86–2.7)	0.15	1.5 (0.85–2.6)	0.17
MI	2.6 (1.5–4.3)	<0.001	1.4 (0.83–2.5)	0.19	1.4 (0.83–2.5)	0.19
CCF	4.0 (2.1–7.7)	<0.001	2.5 (1.3–5.0)	0.009	2.5 (1.3–5.1)	0.008
eGFR (CKD-EPI)	0.97 (0.95–0.98)	0.001	0.99 (0.97–1.0)	0.52	0.99 (0.97–1.0)	0.32
Albumin (g/L)	0.96 (0.92–1.0)	0.05
Haemoglobin (g/L)	0.97 (0.96–0.99)	<0.001	0.97 (0.96–0.99)	0.002	0.98 (0.96–0.99)	0.005
UPCR (g/mol)	0.99 (0.97–1.0)	0.17
CRP (mg/L)	1.07 (1.04–1.1)	<0.001			1.1 (1.02–1.09)	<0.001

*Multivariate model 1 included variables that showed significant association in univariate model except CRP, model 2 after addition of CRP to model 1.FF-homozygous complement 3 fast, FS-heterozygous complement 3,SS homozygous complement 3 slow, HTN-hypertension, DM-diabetes mellitus, MI-myocardial infarction, CCF-congestive cardiac failure, eGFR-estimated glomerular filtration rate calculated using CKD-EPI equation, CRP-C-reactive protein

Study 2 (biopsy-proven GN)

The GN group was mainly Caucasian (99.3%), hypertensive (92.2%), predominantly male (72.5%), with a median age of 59.9(IQR 47.9–68.5)years.The median baseline eGFR was 33.5(IQR 21.3–46.6)ml/min/1.73m2/yr and median ΔeGFR was-1.6(IQR -4.2 to 0.06)ml/min/1.73m2/yr. Clinical and biochemical baseline characteristics of this group are summarized in S6 Table.

The genotype variants were (FF 8.6%, FS 29.7%, SS 61.7%) and allele frequencies were (F 23.4%, S 76.6%) in total GN. The highest C3F frequency was in FSGS (26%) and IgAN (25.5%) and the lowest in membranous nephropathy (16%), (Table 5).

Table 5

Genotype variant and allele frequency of the different GN groups.

	Total GN (n = 269)	IgA nephropathy (n = 114)	FSGS (n = 50)	Membranous nephropathy (n = 59)	Other GN (n = 46)
Complement 3 (rs2230199)
FF	23(8.6%)	9 (7.9%)	5 (10%)	5 (8.5%)	4 (8.7%)
FS	80(29.7%)	40 (35%)	16 (32%)	9 (15.3%)	15 (32.6%)
SS	166(61.7%)	65 (57%)	29 (58%)	45 (76.3%)	27 (58.7%)
Allele frequency
F	126(23.4%)	58 (25.5%)	26 (26%)	19 (16%)	23 (25%)
S	412(76.6%)	170 (74.5%)	74 (74%)	99 (84%)	69 (75%)

FF-homozygous complement 3 fast, FS-heterozygous complement 3, SS-homozygous complement 3 slow

C3 polymorphism and progression in IgAN patients

IgAN group was the only GN group to show an association between C3 polymorphism and progression. 59 (51.8%) of the IgAN patients reached the progression endpoint during the study follow-up period, with median follow-up of 44 (IQR 25–82) months. Cox regression showed a significant association between C3 polymorphism and CKD progression in the IgAN group with HR = 1.9 (95% CI 1.1–3.1; p = 0.018) for heterozygous C3FS, increasing further for homozygous C3FF to HR = 2.8 (95% CI 1.2–6.2; p = 0.014). C3SS showed a protective benefit for progression with HR = 0.41 (95% CI 0.24–0.68; p = 0.001).These associations remained significant after adjustment for several progression risk factors including treatment with immunosuppressive therapy (Table 6).In a Kaplan–Meier analysis, the incidence rate of renal outcomes was significantly higher in IgAN patients with C3FF and C3FS genotypes compared with patients with the C3SS genotype (Fig 3).

Table 6

Coxregression analysis (progression) univariate and multivariate models in IgA nephropathy group (patients n = 114, events n = 59).

Factor	Univariate model HR (95% CI)	p-Value	Multivariate Model 1 HR (95% CI)	p-Value	Multivariate Model 2 HR (95% CI)	p-Value
Complement 3 FF	2.8 (1.2–6.2)	0.014	7.8 (3.0–20.2)	<0.001
Complement 3 FS	1.9 (1.1–3.1)	0.018	3.5 (1.9–6.4)	<0.001
Complement 3 SS	0.41 (0.24–0.68)	0.001			0.25 (0.14–0.45)	<0.001
Age	0.99 (0.97–1.0)	0.15
Gender (female)	0.99 (0.49–1.9)	0.95
Smoking	0.96 (0.57–1.6)	0.86
HTN	1.1 (0.3–4.4)	0.93
DM	1.2 (0.51–2.8)	0.69
eGFR (CKD-EPI)	0.94 (0.92–0.96)	<0.001	0.94 (0.92–0.97)	<0.001	0.94 (0.92–0.97)	<0.001
Albumin (g/L)	0.91 (0.85–0.97)	0.005	1.1 (9.7–1.2)	0.16	1.05 (0.96–1.1)	0.27
Haemoglobin (g/L)	0.97 (0.95–0.98)	<0.001	0.97 (0.95–0.99)	0.005	0.98 (0.95–0.99)	0.011
UPCR (g/mol)	1.02 (1.01–1.02)	0.001	1.02 (1.00–1.03)	0.009	1.01 (1.00–1.03)	0.027
CRP (mg/L)	1.01 (0.97–1.05)	0.63
Treatment*	0.58 (0.36–0.95)	0.031	0.84 (0.49–1.4)	0.53	0.84 (0.49–1.4)	0.53

Multivariate model 1 included the variables that showed significance in univariate model with C3FF/FS. Multivariate model 2 included the variables that showed significance in univariate model with C3SS.

FF-homozygous complement 3 fast, FS-heterozygous complement 3, HTN-hypertension, DM-diabetes mellitus, eGFR-estimated glomerular filtration rate calculated using CKD-EPI equation, UPCR-urine protein:creatinine ratio, CRP- C-reactive protein.

.*Treatment with corticosteroids and or immunosuppressive therapy.

Fig 3

Kaplan–Meier survival curve for progression in IgAN.

(Patients n = 114, event n = 59); Log Rank 14.8, p-value 0.001.

C3 deposition in the renal biopsies of the GN patients

Information regarding C3 deposition assessed by immunoflourescence or immunoperoxidase in the non-sclerosed glomeruli of the renal biopsies was available in 180 of the 269 GN patients. In the all-cause GN group, C3 deposition was positive in 71% of patients with the C3FF genotype, 75% of those with C3FS genotype and 55% of those with the C3SS genotype. In the subgroup analysis by GN type only the IgAN patients (n = 82) showed significant difference in the C3 deposition between patients with different genotypes: 100% of patients with the C3FF genotype, 97% of those withC3FS genotype and 64% of those with the C3SS genotype had C3 deposition in their biopsies, p = 0.002 (Table 7).

Table 7

C3 deposition in biopsies of the GN patients.

GN type	C3 deposition	C3FF	C3FS	C3SS	p-Value
IgAN (n = 82)		6	31	45
	Yes	6 (100%)	30 (97%)	29 (64%)	0.002
	No	0 (0.00%)	1 (3%)	16 (36%)
FSGS (n = 30)		4	10	16
	Yes	2 (50%)	3 (30%)	6 (37.5%)	0.88
	No	2 (50%)	7 (70%)	10 (62.5%)
MN (n = 34)		1	6	27
	Yes	0 (0.00%)	6 (100%)	20 (74%)	0.89
	No	1 (100%)	0 (0.00%)	7 (26%)
Others (n = 34)		3	13	18
	Yes	2 (67%)	6 (47%)	3 (17%)	0.11
	No	1 (33%)	7 (53%)	15 (83%)
Total (n = 180)		14	60	106
	Yes	10 (71%)	45 (75%)	58 (55%)	0.03
	No	4 (29%)	15 (25%)	48 (45%)

GN glomerulonephritis, IgANimunoglobulin A nephropathy, FSGS focal and segmental glomerulosclerosis, MN membranous nephropathy, C3 complement 3, C3FF fast homozygous complement 3, C3FS heterozygous, C3SS slow homozygous complement 3. Categorical variables are expressed as number (%) and p-Value by Monte-Carlo test.

Discussion

Our study has provided insights into the association of the C3 variant R102G with CKD progression and mortality in a large non-dialysis CKD cohort.

We observed that the C3F allele had a significantly increased frequency in CKD patients than the normal healthy controls. This observation replicates the finding of a previous study undertaken in a Caucasian cohort from Madrid[19]. However, that study reported a C3F allele frequency of 40%, much higher than that observed in our CKD cohort (25.7%). Nevertheless, our results include a larger number of CKD patients compared to the Madrid cohort (514 vs. 20). In our healthy controls the C3F frequency was 20.7% which is similar to reports in the literature for Caucasian populations[16].

C3F allele frequencies in CKD patients with rapid or slow CKD progression have not been reported in previous studies. We found that the C3F allele was more common in RP (27%) than in SP (24.3%) but this difference was not significant. Despite the C3F allele frequency being higher in RP than in healthy controls (20.7%), we cannot conclude that an association between C3F allele frequency and progression of all cause CKD exists as the difference may simply be due to the fact that C3F allele frequency is greater in CKD patients than healthy people. This point was also confirmed by the Cox regression analysis that showed no significant association between C3F homozygous or heterozygous status and CKD progression in our CKD cohort.

Most of the GWAS that have been conducted in CKD patients have tested the association between different SNPs and the prevalence of CKD [5-8], There are only 2 studies that have searched for the genetic factors that are associated with progression rather than prevalence of CKD: Boger CA et al [34] and Parsa A et al [35].Boger et al`s study tested only 16 SNPs and found that 11 of them associated with incident CKD. Our targeted SNP, the R102G, was not one of the 16 tested SNPs in this candidate gene association study. Parsa et al`s GWAS tested a million SNPs in a large CKD cohort with 5 years follow up and found 12 SNPs associated with time to ESRD in black patients and 6 SNPs in white patients; the R102G SNP was not one of the latter 6 SNPs. This agrees with our finding that the R102G was not associated with progression in all-cause CKD. However in Parsa et al`s study the specific causes of CKD in their cohort were not defined and so they could not relate primary renal disease-specific progression to specific SNPs.

To the best of our knowledge our study is the first to explore the association between C3 polymorphism and mortality. We found that there was a significant association of C3FF and mortality in the entire CKD cohort, but this became non-significant after adjustment for the CRP levels. It is well-known that higher CRP level is associated with all-cause mortality in the general population as well as in CKD patients[36,37]. In our study, CKD patients with the C3FF status had a significantly higher level of CRP than those with C3FS and C3SS. This may explain the significant association between C3FF and mortality that was found in univariate Cox regression, but which was lost after adjustment for CRP levels in the multivariate analysis.

In the second study, the IgAN was in keeping with the published literature showed strong link between C3F and IgAN[20]. In our group of 114 IgAN patients we found a C3F allele frequency of 25.5%, which was higher than in normal healthy controls (20.7%).This finding agrees with the previous studies, except for one study that has been conducted in a group of Chinese IgAN patients in which no difference in C3F allele frequency between the IgAN and control group was reported[38]. This can be explained by the rarity of this SNP in the Asian population in general (1%) [16] and points to the difference in the genetic background of the IgAN in different ethnicities.

IgAN is considered the commonest GN worldwide. However, the role of the complement system in the pathogenesis of IgAN is still unclear; mesangial deposits of some complement components including C3, complement factor H (CFH) and complement factor H related protein 5 (CFHR5) are found in the renal biopsies from IgAN patients[39]. Whether galactose-deficient IgA1 containing immune complexes can activate the complement system in IgAN patients while circulating in plasma or after their deposition in the kidney still needs to be elucidated [40]. We found a significant difference between the three C3 genotypes with regards the deposition of C3 in the renal biopsies of IgAN patients with significantly more deposition in patients with C3FF and C3FS genotypes than in those with the C3SS genotype. This may indicate a difference in the affinity of complement binding to the galactose-deficient IgA1 containing immune complexes in IgAN patients according to their C3 genotype, but this is speculative and requires further investigation.

The previous study that found increased C3F frequency in IgAN patients also reported that this increase was particularly noted in those with renal impairment or hypertension[20]. In subgroup analysis of the first part of our study we found that C3F allelefrequency was significantly higher in RP IgAN patients than SP with more than six-fold increased risk. Also, in the Cox regression analysis we found a strong association between C3F homozygous and heterozygous status and CKD progression in 114 IgAN patients. We have thus provided novel data highlighting that IgAN patients who are carriers of the C3F allele are at increased risk for rapid progression, while the C3SS status confers a protective benefit against progression.

Although our study is limited by a sample size, it benefits from a robust methodology used to select the RP and the SP patient status resulting in exclusion of a significant number of potentially confounding data from other patients. Beside the robust methodology our study is also strengthened by the data being derived from an advanced CKD cohort (stages 3–5 not on dialysis) with a long follow-up period (average of 4 years) which therefore included a large number of events that strengthened the determination of genetic associations.

Conclusion

C3 SNP (R102G) is associated with rapid CKD progression in IgAN patients but not in other causes of CKD. Further research is required to replicate the association and fully elucidate the pathophysiological mechanism of this association, which could help unravel novel targets for treatments.

Calculation to determine whether observed genotype frequencies are consistent with Hardy-Weinberg equation.

(DOCX)

Click here for additional data file.

Comparison of baseline characteristics (clinical and biochemical) between CKD patients with the C3FF, and those with C3FS or SS.

(DOCX)

Click here for additional data file.

Comparison of allele frequency between rapid and stable CKD patients in different causes of CKD.

(DOCX)

Click here for additional data file.

Comparison of clinical and biochemical characteristics between RP IgAN patients and SP IgAN patients is summarized in S4 Table.

(DOCX)

Click here for additional data file.

Cox regression analysis (renal progression) univariate and multivariate models.

(DOCX)

Click here for additional data file.

Baseline characteristics (clinical and biochemical) of the different GN groups.

(DOCX)

Click here for additional data file.

13 Nov 2019

PONE-D-19-25255

Genetic polymorphism in C3 is associated with progression in chronic kidney disease (CKD) patients with IgA nephropathy but not in other causes of CKD

PLOS ONE

Dear Dr Ibrahim,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The manuscript focuses on a topic of potential interest. However, the study has several shortcomings that should be addressed. To mention few of them, i) concern about the novelty regarding the association of the R102G and CKD; ii) need to analyze the results of the study considering the already available GWAS data on CKD; iii) concern about the sample size of the 37 patients with IgA nephropathy too small to be convincing of a different distribution of the polymorphism on the RP and SP groups; iv) need to evaluate the pathophysiological link between the R102G polymorphism and complement activation; v) need to include in the multivariate Cox regression model the evaluation of the IgAN subgroup CRP; vi) concern about the fact that the study featured a cohort that was primarily Caucasian which limits the generalization of the results; vii) concern about the significant differences in age between RP ad SP likely contributing to findings; viii) need to indicate how common the C3F variant is in the general population; ix) concern about the fact that Table 6 shows a regression analysis of the IgAN group in which the multivariate regression analysis did not include the C3SS genotype as a variant.

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Reviewer #1: In this paper the authors provide evidence for the association of a genetic variant with CKD in a clinical study. Their goal was to identify novel genetic risk factors that lead to progression of kidney diseases. The target of interest was a variant in the human complement component C3. The study shows that within the cohort, there was a statistical overrepresentation of the C3FF variant in patients with fast progressing CKD and IgAN.

The study featured a cohort that was primarily caucasian which limits the impact of the results.

Significant differences in age between RP and SP likely contributes to findings, given likely strong survival bias. More clarification on how age when adjusted for affects the results.

The data in tables 1, 2, and 3 features multiple observations on various measurements, with a p-value determined by a Mann-Whitney U test or chi-square analysis. In the figure there are 15 observations made, and there should be some sort of adjustment for the false discovery rate as it is misleading to mark observations as significant otherwise.

The paper does not state how common the C3F variant is in the general population, which would affect the interpretation of the observed frequency of the C3FF variant in the patients.

Table 6 shows a regression analysis of the IgAN group in which the multivariate regression analysis did not include the C3SS genotype as a variant, although the p-value for the univariate model seems to be most statistically significant.

Overall, the manuscript presents interesting findings for which the methods are clearly written out and presented. However, the statistical analysis of the findings need further explanation and clarification, with the issues presented above.

Reviewer #2: In this study Sara T Ibrahim and collaborators evaluated the role of the R102G variant in complement 3 (C3) in a coohort of CKD patients. They analyzed the distribution of the polymorphism compared to a healthy control group, in the CKD rapid progressive group (RP) against the CKD stable function subjects (SP). Finally, they evaluated the role of the polymorphism in the sub group of the biopsy proven glomerulonephritis affected patients. C3F allele frequency was found to be significantly higher in the CKD cohort compared with the healthy control group. There was no significant difference in C3F allele frequency between the RP and SP groups. In the glomerulonephritis subgroup Cox regression showed an association between C3F and progression only in those with IgA nephropathy.

Major concerns

The role of complement in many renal conditions and in particular in many glomerulonephrites (IgAN, C3 glomerulonephritis, HUS, membranous nephropathy, MPGN IgG mediated and others) is well known. The evaluation of a C3 polymorphism can be interesting. The authors recognize the small size of their cohort, but justify the importance of their study in consideration of the better clinical characterization of their sample compared to much larger GWAS studies.

However the results of the study are not novel regarding the association of the R102G and CKD: in particular the same evaluation could be better analyzed considering the already available GWAS data on CKD.

The sample size of the 37 patients with IgA nephropathy is too small to be convincing of a different distribution of the polymorphism in the RP and SP groups. Before publication it is necessary to increase the IgAN sample size of a factor of 10 times at least.

Most important the authors did not try to make any evaluation of the pathophysiological link between the R102G polymorphism and complement activation. I understand that the C3F and C3S are two variants with different electrophoretic migration characteristic. What is the difference in C3 activity of the two variants.

Minor concern

In the multivariate Cox regression evaluation of the IgAN subgroup CRP has not been included in the model. Because it was highly colinear with R102G polymorphism in the analysis of the outcome of risk of death it should be maintained even in the other analyses.

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Reviewer #2: Yes: RICCARDO MAGISTRONI

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27 Nov 2019

Response to reviewers

Dear editor and reviewers,

Thank you so much for reviewing our manuscript and providing us with your comments and queries, aiming to improve our work. The following is our reply to your comments and queries.

1- Concern about the novelty regarding the association of the R102G and CKD

The association between the R102G polymorphism of C3 and CKD or GN is not a novel one and it has been addressed by some studies before. This is the main point we have relied upon in the rationale of our study that the R102G may be associated with progression of all-cause CKD or in specific sub-types of GN (hence study 2 which included all the biopsy proven GN patients in our cohort). The novel point in our study is the association between the R102G and progression in IgA nephropathy patients. This association has not been addressed by any of the previous studies.

-We have now illustrated this point in a clearer way in the introduction and the discussion of the revised manuscript.

2- Need to analyze the results of the study considering the already available GWAS data on CKD

Most available GWAS that have been conducted in CKD [1-5] or IgA nephropathy [6-8] patients aimed to investigate the genetic factors associated with prevalent CKD or IgAN; as these studies lacked the detailed clinical characteristics of the patients and follow up data they were unable to include progression as an end-point. The only 2 GWAS that attempted to search for genetic factors associated with progression rather than prevalence of CKD are Boger CA et al [9] and Parsa A et al [10].Boger et al`s GWAS tested only 16 SNPs and found that 11 of them associated with incident CKD (during a follow up period of 7 years). Our targeted SNP, the R102G, was not one of the 16 tested SNPs in this GWAS. Parsa et al`s GWAS tested a million SNPs in a large CKD cohort with 5 years follow up and found 12 SNPs that were associated with time to ESRD in black patients and 6 in white patients; the R102G SNP was not one of the latter 6 SNPs and this agrees with our finding that the R102G was not associated with progression in all-cause CKD. However in Pars et al`s study the specific causes of CKD in their cohort were not defined and so they could not relate primary renal disease-specific progression to specific SNPs.

-We have added a paragraph about this in our discussion in the revised manuscript.

3- Concern about the sample size of the 37 patients with IgA nephropathy too small to be convincing of a different distribution of the polymorphism on the RP and SP groups

This is correct and we mentioned this point in our manuscript`s discussion as a limitation. However in study 2 with larger number of IgAN patients (114) we found that C3F is strongly and independently associated with the progression in IgAN patients. We believe that our study could underpin future studies in larger cohorts of IgAN patients which could then replicate this association.

4- Need to evaluate the pathophysiological link between the R102G polymorphism and complement activation

The pathophysiological link between the R102G and complement activation has been evaluated previously by Heurich M et al [11]. They used C3FF and C3SS from plasma of healthy individuals and added them separately to antibody coated sheep RBCs and then added factor B (FB) to them. They found that the samples containing C3SS needed larger amount of FB to lyse the RBCs. Demonstrating that plasma containing C3FF has more complement activity than that containing C3SS. They further investigated this issue and proved that factor H (one of the complement regulator proteins) bound less well to C3FF than C3SS.

-We have added a paragraph about this issue in the introduction of the revised manuscript.

5- Need to include in the multivariate Cox regression model the evaluation of the IgAN subgroup CRP

-We have added the CRP to the univariate Cox regression in table 6 in the revised manuscript. It showed no significant association with progression in IgAN patients so we have not added it to the multivariate Cox model. The variables which showed significant association in the univariate model are the only variables that were used in the multivariate model to be consistent with the statistical rule that one variable can be included in the Cox model for every 8-10 patients with the event of interest (we had 59 events).

6- Concern about the fact that the study featured a cohort that was primarily Caucasian which limits the generalization of the results

All genetic studies should define the ethnicity of the studied cohort according to the prevalence of studied SNPs. If the studied SNP prevalence is ˂ 5% in a certain ethnicity the presence or the absence of the association between this SNP and the disease will not be accurate due to the rarity of the SNP in this ethnicity. The frequency of the C3F allele differs in different ethnicities: Caucasian (20%), black (5%) and Asian (1%) [12]. Hence the Caucasian cohort appears to be the optimal cohort to be used to investigate the R102G SNP in different diseases.

7- Concern about the significant differences in age between RP and SP likely contributing to findings.

The difference between the age of patients in RP and SP groups was only significant in the whole CKD cohort but it was not significant in the sub group of IgAN patients (37 patients: 21 RP and 16 SP). Also the age was not significantly associated with progression in the IgAN patients in the Cox regression (table 6). The Cox regression analysis for death was undertaken in the whole cohort without separation into SP and RP; adjustment for age in the multivariate analysis was also performed (table 4).

-We have added the table comparing the baseline characteristics of the IgAN subgroup to the supplementary tables in the revised manuscript (S4 table).

8- Need to indicate how common the C3F variant is in the general population

As mentioned above the frequency of the C3F allele differs in different ethnicities (Caucasian (20%), black (5%) and Asian (1%)). We have previously referred to its frequency in Caucasian cohorts and in our healthy control group in the discussion section.

-We have now illustrated this point in the introduction of the revised manuscript.

9- Concern about the fact that Table 6 shows a regression analysis of the IgAN group in which the multivariate regression analysis did not include the C3SS genotype as a variant.

Statistically we cannot include C3SS in the same multivariate model with C3FF and C3FS as they are constant or linearly dependent covariates; C3SS = 1- (C3FF + C3FS).

-We have added a second multivariate model to table 6 in the revised manuscript with C3SS (but without C3FF and C3FS) to show that it is still protective of progression in the multivariate model.

All of the above modifications have been highlighted in yellow in the revised manuscript.

Thank you so much,

Sara T Ibrahim

References

1. Kottgen A, Glazer NL, Dehghan A, Hwang SJ, Katz R, Li M, et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nature Genetics. 2009;41:712-7.

2. Kottgen A, Pattaro C, Boger CA, Fuchsberger C, Olden M, Glazer NL, et al. New loci associated with kidney function and chronic kidney disease. Nature genetics. 2010;42(5):376-84.

3. Pattaro C, Kottgen A, Teumer A, Garnaas M, Boger CA, Fuchsberger C, et al. Genome-wide association and functional follow-up reveals new loci for kidney function. PLoS genetics. 2012;8(3):e1002584.

4. Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023.

5. Chambers J, Zhang W, Lord G, van der Harst P, Lawlor DA, Sehmi JS, et al: Genetic loci influencing kidney function and chronic kidney disease. Nat Genet. 2010;42:373-5.

6. Gharavi AG, Kiryluk K, Choi M, Li Y, Hou P, Xie J, et al: Genome-wide association study identifies susceptibility loci for IgA nephropathy. Nat Genet. 2011;43:321-7.

7. Yu XQ, Li M, Zhang H, Low HQ, Wei X, Wang JQ, et al: A genome-wide association study in Han Chinese identifies multiple susceptibility loci for IgA nephropathy. Nat Genet. 2011;44:178-82.

8. Kiryluk K, Li Y, Scolari F, Sanna-Cherchi S, Choi M, Verbitsky M, et al: Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. Nat Genet. 2014;46:1187-96.

9. Böger CA, Gorski M, Li M, Hoffmann MM, Huang C, Yang Q, et al. Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD. PLoS Genet. 2011;7(9):e1002292.

10. Parsa A, Kanetsky PA, Xiao R, Gupta J, Mitra N, Limou S, et al. Genome-Wide Association of CKD Progression: The Chronic Renal Insufficiency Cohort Study. J Am Soc Nephrol. 2017;28(3):923-34.

11. Heurich M, Martínez-Barricarte R, Francis NJ, Roberts DL, Rodríguez de Córdoba S, Morgan BP, et al. Common polymorphisms in C3, factor B, and factor H collaborate to determine systemic complement activity and disease risk. Proc Natl Acad Sci U S A. 2011;108(21):8761–6.

12. Bazyar N, Azarpira N, Khatami RS, Galehdari H. The investigation of allele and genotype frequencies of human C3 (rs2230199). Mol Biol Rep. 2012;39(9):8919–24.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

18 Dec 2019

PONE-D-19-25255R1

Genetic polymorphism in C3 is associated with progression in chronic kidney disease (CKD) patients with IgA nephropathy but not in other causes of CKD

PLOS ONE

Dear Dr Ibrahim,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The revised manuscript is definitely improved. It remains, however, a minor issue raised by Reviewer #2 that should be easily addressed.

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PLOS ONE

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #2: Yes

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Reviewer #1: Appreciate the additional explanations in the introduction and discussion sections to address concerns.

Reviewer #2: The authors have answered to the questions I raised. However the Parsa et al. study should not be reported as a GWAS (Genome Wide Association Study) but rather as a Candidate Genes Association Study.

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Reviewer #2: Yes: RICCARDO MAGISTRONI

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19 Dec 2019

Dear 2nd reviewer,

Thank you so much for your comment (The Parsa et al. study should not be reported as a GWAS (Genome Wide Association Study) but rather as a Candidate Genes Association Study).

You are right, but I think you meant the Böger et al study not the Parsa et al study. Parsa et al study is a large GWAS which tested million SNPs but Böger et al study is the one which tested only 16 SNPs that were identified by a previous GWAS. I have now corrected this and highlighted it in yellow in the revised manuscript.

Thanks

Sara T Ibrahim

8 Jan 2020

Genetic polymorphism in C3 is associated with progression in chronic kidney disease (CKD) patients with IgA nephropathy but not in other causes of CKD

PONE-D-19-25255R2

Dear Dr. Ibrahim,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

The re-revised version of the manuscript is definitely improved. The authors have adequately addressed all the reviewers’ comments.

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: As stated on prior review of submission, the authors have already addressed my comments. Although there are still some limitations, the authors were responsive.

Reviewer #2: (No Response)

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Reviewer #1: No

Reviewer #2: Yes: Riccardo Magistroni

17 Jan 2020

PONE-D-19-25255R2

Genetic polymorphism in C3 is associated with progression in chronic kidney disease (CKD) patients with IgA nephropathy but not in other causes of CKD

Dear Dr. Ibrahim:

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