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Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium

Erythrocyte measures are heritable and have important health implications, yet their genetic determinants are largely unknown. We performed genome-wide association analyses in 24,167 European-ancestry individuals for six erythrocyte traits and identified associations at 23 loci (P values 5×10-8 to 1×10-57). Replication testing in an independent set of 9,456 European-ancestry individuals showed strong evidence of association in all 23 loci in meta-analysis of the discovery and replication cohorts. Our findings include previously identified loci (HBS1L/MYB, HFE, TMPRSS6, TFR2, SPTA1) and novel associations (EPO, TFRC, SH2B3, and 15 other loci). This study has identified novel determinants of erythrocyte traits, offering insight into common variants underlying variation in erythrocyte measures.


Supplementary Table 2: CHARGE cohort and meta-analysis genomic control inflation factors (λ GC )
Inflation factor (λ GC ) is shown for each trait in each individual CHARGE cohort genome-wide association analysis, as well as for the CHARGE meta-analysis of each trait. Erythrocyte traits are abbreviated as in legend to Table 1 Association results for the SNPs identified by the CHARGE meta-analyses yielded 45 SNPs in 23 loci, selecting the lead SNP per trait and per locus. These top 45 SNPs, organized by locus, were tested for association with systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension. Among the 45 SNPs, 37 are unique. SNPs highlighted in red showed P < 0.00135 (0.05/37), and SNPs highlighted yellow show nominal association (P < 0.05).

Supplementary Information Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium
16 Supplemental Association results for SNPs identified in the previously reported CHARGE BP and hypertension analyses in each of the CHARGE erythrocyte traits. Association statistics from each red cell trait meta-analysis are shown. Erythrocyte traits are abbreviated as in legend to Table 1. The CHARGE BP and hypertension analyses previously identified two loci on chromosome 12q21.3 which contains ATP2B1 and the 12q24.12 region containing the genes SH2B3 and ATXN2. We observed the prior association of this chromosome 12q24.12 SH2B3/ATXN2 region, with two SNPs (rs3184504, rs653178) identified in both the SBP and DBP analyses significant in the Hgb and Hct meta-analyses (P < 5x10-8). We did not find association in the 12q21.3 region. Another six SNPs in this region (rs4766578, rs10774625, rs17630235, rs11066188, rs11065987, rs17696736) were associated with DBP but not SBP, and all six were associated with Hgb and Hct (P < 5x10-8). Additional genes in this region include BRAP, PTPN11, TRAFD1, C12orf51, and C12orf30. A nominal association with RBC and MCV was noted on chromosome 15 q24.1 near the CSK gene (rs6495122, P = 0.0033 in RBC; P = 0.030 in MCV) in the RBC meta-analysis. SNPs highlighted in red showed P < 0.00116 (0.05/43), and SNPs highlighted yellow showed nominal association (P < 0.05). Figure 1 (b) Quantile-Quantile plot of observed (orange) versus expected (grey) -log10 (P value) for trait-loci SNPs identified in the CHARGE meta-analysis tested in the HaemGen replication metaanalysis. 39 of the 45 SNPs are shown in the plot below; the additional 6 SNPs had -log10 (P value) > 10 and are not shown.

Supplemental Figure 2
Plots of -log 10 (P) for association and gene expression analysis for each locus identified in the CHARGE meta-analyses. For each locus, SNPs identified in the meta-analyses are plotted along with recombination rates as observed in HapMap CEU, using a window of +/-500kb around the lead SNP identified in each locus, which is plotted in blue. SNPs identified by the CHARGE metaanalyses are colored according to correlation with the lead SNP (r 2 > 0.8 red; 0.5 < r 2 < 0.8 orange; 0.2 < r 2 < 0.5 yellow; r 2 < 0.2 white; no r 2 value provided). (b) A heatmap of gene expression levels in nine blood and endothelial cell lines is shown, including all genes, as annotated by ENSEMBL 54, within the +/-500kb window of the locus (MK = megakaryocyte; EB = erythroid bodies; HUVEC = human umbilical vein endothelial cells; CD14 = monocytes; CD66b = granulocytes; CD19 = B lymphocytes; CD56 = NK cells; CD8 = Tc lymphocytes; CD4 = Th lymphocytes).
The plots are shown on the next 23 pages (pages 20-42 of Supplementary Information).

Supplementary Information Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium
Supplementary Information Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium

CHARGE Consortium Organization
The Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) consortium 1 includes five cohort studies that have genotyped high density SNP markers and have phenotypic data on erythrocyte traits. We have additionally incorporated data from the InCHIANTI Study for these analyses. Representatives from each constituent cohort formed a hematology working group. The group was responsible for phenotype harmonization, covariate selection, analytic plans for within-study analyses, meta-analysis of results and reporting of results. Each participating study was reviewed and approved by the corresponding IRB, and all subjects used for genetic analyses provided specific informed consent for genetic research. All participating studies approved guidelines for this collaboration, including data sharing and data security procedures.

Age, Gene/Environment Susceptibility-Reykjavik Study (AGES)
The AGES-Reykjavik Study cohort originally comprised a random sample of 30,795 men and women born in 1907-1935 and living in Reykjavik in 1967. A total of 19,381 people attended, resulting in 71% recruitment rate. The study sample was divided into six groups by birth year and birth date within month. One group was designated for longitudinal follow up and was examined in all stages. One group was designated a control group and was not included in examinations until 1991. Other groups were invited to participate in specific stages of the study. Between 2002 and 2006, the AGES-Reykjavik study re-examined 5764 survivors of the original cohort who had participated before in the Reykjavik Study. The AGES-Reykjavik Study GWAS was approved by the National Bioethics Committee (VSN: 00-063) and the Data Protection Authority.
Hgb, Hct, MCH, MCHC, MCV and RBC were measured in all participants of the AGES-Reykjavik Study. The parameters were measured in fasting whole blood on a Beckman Coulter (HMX). Between assay quality control procedures were used and the coefficient of variation of the method for each component was 1.10%, 0.84%, 1.42% for RBC, Hgb and Hct, respectively. For the erythrocyte indices the coefficient of variation of the method was 0.72%, 1.08% and 1.32% for MCV, MCH and MCHC respectively. Before removal of individuals outside of the +/-3 SD range, there were 3218 subjects, and exclusion on this basis led to the removal of 27 subjects for HB, 16 for HCT, 39 for MCH, 22 for MCHC, 30 for MCV, 13 for RBC. The final sample size is summarized in Table 1.
For AGES, DNA was genotyped in 3,219 participants who were eligible for this study using the Illumina 370CNV BeadChip array. Samples were excluded from the dataset based on sample failure, genotype mismatch with reference panel, and sex mismatch. Standard protocols for working with Illumina data were followed. Prior to genotype imputation, SNPs were excluded using filters based on call rate (<97%), Hardy-Weinberg Equilibrium (<1e -6 ), mishap (<1e -9 ), and mismatched positions between Illumina, dbSNP and/or HapMap resulting in 325,094 SNPs passing all QC (of 353,202 prior to cleaning steps). Imputation was done using MACH against all the HapMap CEPH haplotypes (release 22/NCBI build 36) resulting in 2,533,153 total SNPs for analysis

The Atherosclerosis Risk in Communities Study (ARIC)
The ARIC Study is a prospective population-based study of atherosclerotic disease in 15,792 mostly Caucasian and African American study participants sponsored by the National Heart, Lung, and Blood Institute (NHLBI). Participants aged 45-64 years were recruited by probability sampling and underwent a baseline examination and three subsequent examinations approximately every three years 3 . Ancestry was self-reported, and only self-identified Caucasians were included in this analysis. A detailed study protocol is available on the ARIC study website (http://www.cscc.unc.edu/aric).
Data were collected from the second ARIC visit (1990-92). Blood was drawn following procedures described elsewhere 9 . Hgb was measured using automated hematology analyzers: Coulter S + IV (calibration S -Cal, Beckman Coulter, Inc, Fullerton, CA) at 2 sites, Coulter S + III and Coulter S + IV (calibration S-Cal) at 1 site, and Technicon H-6000 (calibration Fisher, Technicon Corporation, Tarrytown, NY) at 1 site. Of 10,694 self-reported white individuals who attended visit 2 and consented to genetic research, Hgb and Hct was available for 10,629 individuals, and MCV for 10,627. After removal of individuals with values outside of the +/-3SD range, there were 10,581 individuals for the analysis of Hgb, 10,588 for the analyses of Hct, and 10,535 for those of MCV.
Genotypes were assayed using the Affymetrix Genome-Wide Human SNP Array 6.0 platform and genotypes were called using the Birdseed algorithm. For this analysis, the final sample with genotype data consisted of 8,861 samples from Caucasian participants among which call rates and deviations from HWE for all SNPs were calculated. Genotype call rates were 95% or greater for all samples. Of these, 734 samples were removed in data cleaning steps for sex mismatch, discordance with previously-genotyped markers, first-degree relative of an included individual, and genetic outlier based on allele sharing 10 and principal components analyses 11 . Data management and statistical analysis used R, ProbABEL and PLINK software.

The Cardiovascular Health Study (CHS)
The CHS is a population-based cohort study of risk factors for coronary heart disease and stroke in adults ≥65 years conducted across four field centers 4 . The original predominantly Caucasian cohort of 5201 persons was recruited in 1989-1990 from random samples of the Medicare eligibility lists and an additional 687 African-Americans were enrolled subsequently for a total sample of 5,888. 1,908 persons were excluded from the GWAS study sample due to the presence at study baseline of coronary heart disease, congestive heart failure, peripheral vascular disease, valvular heart disease, stroke or transient ischemic attack or lack of available DNA.
Phlebotomy was performed fasting on the morning of the baseline visit, with Hgb and Hct measured on automated instruments at local hematology laboratories near each field center. Internal and external quality-assurance reports were examined with concurrently obtained duplicate samples from 3% of participants to establish assay precision 12 . Of the 3,295 self-reported white subjects with genotyping data, 20 were excluded due to lack of lab values, and 28 for falling outside of the +/-3SD range of the distribution.
Genotyping was performed at the General Clinical Research Center's Phenotyping/Genotyping Laboratory at Cedars-Sinai using the Illumina 370CNV BeadChip system. Genotypes were called using the Illumina BeadStudio software. Samples were excluded for genotypic sex mismatch, discordance with prior genotyping, or call rate <95%. Genotyping was successful in 96% of samples. The following exclusions were then applied to SNPs: call rate <97%, Hardy Weinberg Equilibrium p-value <10 -5 , >2 duplicate errors or Mendelian inconsistency, heterozygote frequency = 0, or SNP not found in HapMap. SNPs were further excluded from analysis if the variance of the allele dosage was <0.01. R software was used for data management and statistical analysis.

Framingham Heart Study (FHS)
The FHS is a prospective cohort study that aims to identify the risk factors for cardiovascular disease. Under the direction of the National Heart, Lung, and Blood Institute (NHLBI), the FHS recruited 5,209 men and women, the Original Cohort from the town of Framingham, Massachusetts in 1948; 5,124 of the second generation of the Original Cohort, the Offspring Cohort in 1971; and 4,095 participants of the Third Generation in 2002. The FHS cohort studied here is the Offspring Cohort, whose first exam was completed in 1975.
For FHS, Hgb, Hct, MCH, MCHC, MCV and RBC were measured at Exam 1 of the Offspring Cohort taken place between 1971 and 1975, as previously described 5,13,14 . Blood was collected and spun at 5,000 rpm for 20 min in a balanced oxalate tube. Hematocrit was measured by the Wintrobe method. The percent of total blood volume that was due to RBCs was determined visually against a calibrated scale. Before removal of individuals outside of the +/-3SD range, there were 3,200 subjects for all red blood cell indices except 3,381 for Hct and exclusion on this basis led to the removal of 21 subjects for Hgb, 22 for Hct, 57 for MCH, 34 for MCHC, 56 for MCV, 21 for RBC.
DNA was genotyped for the FHS participants with the Affymetrix 500K array and an additional gene-focused 50K array. MACH software was used to impute ~2.5 million SNPs based on the HapMap CEU phased haplotypes (build 22) and the SNPs that met the following criteria: minor allele frequency (MAF) >=0.01, Hardy Weinberg Equilibrium P value >10 -6 , SNP call rate >= 97%, MISHAP test P value >10 -9 , Mendelian errors <= 100. Linear mixed effects model was used to account for familial correlation in the FHS GWAS.

The Invechhiare in Chianti (InCHIANTI)
The InCHIANTI Study is a population-based epidemiological cohort study in the Chianti region of Tuscany, Italy. The study employs two clinical sites, in the towns of Greve and Bagno a Ripoli (study site was used as an additional covariate in all analyses), with participants recruited from the population registries of these immediate areas. Further details on this cohort has been previously published elsewhere 7 .
Overnight fasted blood samples were used for genomic extraction and hematological analyses at baseline. Hematological assays were carried out using a Coulter LH 750 (Beckman Coulter, Instrumentation Laboratory, Milan, Italy), with fasting blood sample aliquots stored at -80 Celsius and thawed at time of assay.
DNA extracted from InCHIANTI participants was genotyped at the Laboratory of Neurogenetics, National Institute on Aging, using Illumina 550K beadchips. After standard QC measures, genotypes were imputed using MACH. Maximum likelihood genotypes were filtered for quality of imputation prior to analysis.

Rotterdam Study (RS)
The RS is a prospective population-based cohort study that addresses determinants and occurrence of cardiovascular, neurological, ophthalmologic, psychiatric, and locomotor diseases in the elderly 8,15 . For the first cohort, all residents of Ommoord, a suburb of Rotterdam, aged 55 years and over were invited to participate. A total of 7,983 men and women entered the study (response rate 78 percent). Baseline data were collected from 1990 until 1993. Trained research assistants interviewed the participants at home. A total of 7,129 participants visited the research centre twice for a physical examination.
Venous blood samples were drawn from non-fasting participants. Hgb, Hct, MCH, MCHC, MCV and RBC were assessed immediately after blood collection with an automated blood cell counter (Coulter Counter T660) using Beckman reagents. After exclusion of participants with values outside the 3 SD range, participants with complete genetic and phenotypic information were 5,475 for Hgb, 5,466 for Hct, 5,405 for MCV, 5,378 for MCHC, 5,413 for MCH, and 5,523 for RBC.
Plated DNA was available for 6,680 (83.7%) of 7,129 participants who visited the research center. Genotyping was conducted using the Illumina 550K array among self-reported Caucasian individuals, and succeeded in 6,240 individuals (sample call rate ≥ 97.5%). We excluded subjects for excess autosomal heterozygosity, mismatch between called and phenotypic gender, or being outliers identified by the IBS clustering analysis. The final population for genetic analysis comprised 5,974 subjects. SNPs were excluded for minor allele frequency ≤1%, Hardy-Weinberg equilibrium p-value<10 -6 , or SNP call rate ≤90% resulting in data on 530683 SNPs.
Phenotype harmonization and modeling All of the traits we studied are quantitative traits and were drawn from existing cohort databases. In order to focus on determinants of variation of these traits in the general populations, rather than on specific hematologic diseases which are over-represented at the tails of the distribution for each of the traits, we restricted analysis to those individuals within three standard deviations of the population mean of each cohort. Hgb was measured and reported in g/dL, Hct is reported as a percentage, MCH is reported in picograms, MCHC is reported as g/dL, MCV is reported in femtoliters and RBC is reported as millions of cells per cubic millimeter. Natural log transformations were applied to MCH, MCHC and MCV, and square root transformation was applied to RBC prior to analysis to normalize the distribution.

Imputation
In all studies, genotypes were imputed to approximately 2.5 million autosomal SNPs in HapMap, using the Phase II CEU individuals as a reference panel. For imputation software, ARIC, FHS, InCHIANTI. and RS used MACH (http://www.sph.umich.edu/ csg/abecasis/MACH), and CHS used BIMBAM 16,17 . Imputation results are summarized as an "allele dosage" (a fractional value between 0 and 2), defined as the expected number of copies of the minor allele at that SNP. SNP imputation inferred genotypes probabilistically according to shared haplotype stretches between each study's samples and HapMap release 22 build 36.
Association and meta-analysis Genome-wide analyses were conducted within each cohort. Using an additive genetic model, we used linear regression to evaluate the association between the allele dosage and the trait of interest and to quantify the regression slope (β) and standard error (SE(β)). We adjusted all analyses for age and sex. ARIC and CHS additionally adjusted for study site, and we accounted for relatedness of individuals in FHS as detailed previously 9 . Separate regression models were fitted for each of the six traits studied. The genomic control parameter was calculated to adjust each study's standard errors. Meta-analysis was conducted using a fixed effects model and inverse-variance weighting as implemented in METAL (http://www.sph.umich.edu/csg/abecasis/ Metal/index.html).

Replication samples and analysis
The HaemGen Consortium included samples from 5 population-based cohorts of individuals of European ancestry (SHIP from West Pomerania N=3200; UK Blood Services Common Control (UKBS1-CC1 18 ) N=1290; Twins UK adult twin registry N=1510; KORAF3 500K study population 19, 20 N =1643; and KORA-F4 N=1814). Summary statistics for erythrocyte traits are provide in Table 1.

UK Blood Services Donor Panel 1 (UKBS-CC1)
The UKBS collection of Common Controls is an anonymised collection of DNA samples from 3,000 healthy blood donors. The collection has been established by the three British Blood Services of England, Scotland and Wales as part of the Wellcome Trust Case Control Consortium study and 1,500 of the samples (panel 1) served as shared controls in this study 18 .

KORA F3 500K
The study population for the KORA F3 500K, GWAS (http://epi.gsf.de/kora-gen/) was recruited from the KORA S3 survey (4,856 subjects, response 75%). It is an independent population based sample from the general population living in the region of Augsburg, Southern Germany, examined in the years 1994/95 (KORA S3). The standardized examinations have been described in detail elsewhere 19,21 . A total of 3,006 subjects participated in a follow-up examination of S3 in 2004/05 (KORA F3). For KORA F3 500K we selected 1,643 subjects of these participants then aged 35 to 79 years 19 . Informed consent has been given. The study has been approved by the local ethics committee.
Genotyping of KORA F3 samples was performed using Affymetrix 500K Array Set. For fine scale analysis genotypes were imputed for all polymorphic HapMap SNPs using a Hidden Markov Model as programmed in MACH. For association testing of imputed genotypes, we used the program MACH2QTL, which uses dosage value (0.0 -2.0) as predictor in a linear regression framework.

KORA F4 1000K
The KORA S4 survey, an independent population-based sample from the general population living in the region of Augsburg, Southern Germany, was conducted in 1999/2001. The standardized examinations applied in the survey (4261 participants, response 67%) have been described in detail elsewhere 21 . A total of 3,080 subjects participated in a follow-up examination of S4 in 2006/08 (KORA F4). For KORA 1000K we selected 1,814 subjects of these participants 22 . Informed consent has been given. The study has been approved by the local ethical committee. The KORA S3 and S4 samples do not overlap. DNA was extracted from fresh blood, and was stored at -80°C. FBCs were performed on fresh venous EDTA-anticoagulated blood using an automatic blood counter (Beckman Coulter LH 750). Mean +/-SD for the traits were: Hgb 14.11 +/-1. 16  Genotyping of KORA F4 GWAS samples was performed using Affymetrix Human SNP Array 6.0. For fine scale analysis genotypes were imputed for all polymorphic HapMap SNPs using IMPUTE. For association testing of imputed genotypes, we used the software snptest which uses posteriori probabilities for genotypes in the association testing.

TwinsUK
The TwinsUK cohort (KCL, www.twinsuk.ac.uk) is an adult twin British registry shown to be representative of singleton populations and the United Kingdom population 6. A total of 1,763 twins (100 % females) were available with blood count data. Ethics approval was obtained from the Guy's and St. Thomas' Hospital Ethics Committee. Written informed consent was obtained from every participant to the study.

Study of Health in Pomerania (SHIP)
SHIP is a longitudinal population-based cohort study conducted in West Pomerania, the northeast area of Germany 23 . For the baseline examinations, a sample of 6,267 eligible subjects aged 20 to 79 years was drawn from population registries. Only individuals with German citizenship and main residency in the study area were included. Selected persons received a maximum of three written invitations. In case of non-response, letters were followed by a phone call or by home visits if contact by phone was not possible. The SHIP population finally comprised 4,310 participants (response 68.8%), of which 4,291 are part of this study. Baseline examinations were conducted between 1997 and 2001.
Non-fasting blood samples were taken in the supine position. The blood count was measured within 60 minutes: Hgb, Hct, MCH, MCHC, MCV and RBC, platelets and leukocytes. Samples were analyzed either at the hospital laboratory in Greifswald with a Coulter Max M analyzer (Coulter Electronics, Miami, USA) or at the hospital laboratory in Stralsund with a Coulter T660 analyzer (Coulter Electronics, Miami, USA). Both analyzers were calibrated and maintained according to the manufacturers' instructions. Quality control was performed internally as well as externally by participating in external proficiency testing programmes. Mean +/-SD for the traits were: Hgb 14.07 +/-1. 30