Genetic Epidemiology Network of Arteriopathy (GENOA)

Study Description

The Genetic Epidemiology Network of Arteriopathy (GENOA)

From its inception in 1995, GENOA's long-term objective was to elucidate the genetics of hypertension and its arteriosclerotic target-organ damage, including both atherosclerotic (macrovascular) and arteriolosclerotic (microvascular) complications involving the heart, brain, kidneys, and peripheral arteries. Two GENOA cohorts were originally ascertained (1995-2000) through sibships in which at least 2 siblings had essential hypertension diagnosed prior to 60 years of age. All siblings in the sibship were invited to participate, both normotensive and hypertensive. These include non-Hispanic White Americans from Rochester, MN (n =1583 at the 1st exam) and African Americans from Jackson, MS (N=1854 at the 1st exam). A third Hispanic American cohort from Starr County, TX, was ascertained through diabetic rather than hypertensive sibships (N=1812). The Hispanic American cohort is not included in this genetic analysis because no genome-wide genotype data is currently available for this cohort.

Exclusion criteria for the white and African American cohorts were secondary hypertension, alcoholism or drug abuse, pregnancy, insulin-dependent diabetes mellitus, or active malignancy. The GENOA data consists of biological samples (DNA, serum, urine) as well as demographic, anthropometric, environmental, clinical, biochemical, physiological, and genetic data for understanding the genetic predictors of diseases of the heart, brain, kidney, and peripheral arteries. Data were collected during two phases (Phase I from 1995-2000, Phase II from 2001-2004) and multiple ancillary studies (eg., chronic kidney disease assessments, brain MRIs, 24-hour urine and blood pressure assessments, etc). Written informed consent was obtained from all subjects and approval was granted by participating institutional review boards.

Given that our informed consent documentation limits data sharing to GENOA investigators and our collaborators, we are unable to make individual-level GENOA phenotype and genotype data available on dbGAP at this time. We are working on this consent issue. However, we have made our analysis results available and we fully welcome collaboration with researchers that would like to include the GENOA sample in their analyses. We can easily allow transfer of the individual-level data with an appropriate Data Transfer Agreement.

If you would like to collaborate with GENOA, please contact: Sharon L.R. Kardia, Ph.D. Professor and Chair, Department of Epidemiology School of Public Health, University of Michigan 1415 Washington Heights, Room 4659 Ann Arbor, MI 48109 (734) 647-1029 [email protected] Family Blood Pressure Program (FBPP)

GENOA is one of four research networks that form the NHLBI Family Blood Pressure Program (FBPP). GENOA’s parent program, the FBPP, is an unprecedented collaboration to identify genes influencing blood pressure (BP) levels, hypertension, and its target-organ damage. This program has conducted over 21,000 physical examinations, assembled a shared database of several hundred BP and hypertension-related phenotypic measurements, completed genome-wide linkage analyses for BP, hypertension, and hypertension associated risk factors and complications, and published over 130 manuscripts on program findings. The FBPP emerged from what was initially funded as four independent networks of investigators (HyperGEN, GenNet, SAPPHIRe and GENOA) competing to identify genetic determinants of hypertension in multiple ethnic groups. Realizing the greater likelihood of success through collaboration, the investigators began working together during the first funding cycle (1995-2000) and formalized this arrangement in the second cycle (2000-2005), creating a single confederation with program-wide and network-specific goals.

Hypertension case definition and exclusion criteria for GENOA

Individuals in GENOA belong to sibships identified in which at least two siblings had essential hypertension diagnosed prior to 60 years of age. After identification of the initial pairs of hypertensive siblings, all siblings in the sibship were invited to participate regardless of hypertension status.

Hypertension case definition: Essential hypertension diagnosed prior to age 60 years of age, defined as: 1) average of the last 2 out of 3 systolic BP readings ≥ 140mmHg, or 2) an average of the last 2 out of 3 diastolic BP readings ≥ 90 mmHg, or 3) previous diagnosis of hypertension and antihypertensive medication prescribed by a physician to be taken daily during the last month.

Exclusion criteria: Pregnancy or breast feeding, Type I diabetes mellitus (juvenile onset, insulin dependent), diagnosis of hypertension ≥ 60 yrs of age, or secondary causes of hypertension including but not limited to prior knowledge of renal parenchymal disease or serum creatinine ≥ 2.5 mg/dL, renal vascular disease, primary aldosteronism, pheochromocytoma, coarctation of aorta, hypertension associated with current use of oral contraceptive agents, prescription or non-prescription drugs, or active alcohol abuse.

Sample size

Non-Hispanic whites from Rochester, MN Phase I: N=1,583 Phase II: N=1,241

African Americans from Jackson, MS Phase I: N=1,854 Phase II: N=1,482

Key GENOA Publications

Daniels PR, Kardia SL, Hanis CL, Brown CA, Hutchinson R, Boerwinkle E, Turner ST, Genetic Epidemiology Network of Arteriopathy study. Familial Aggregation of Hypertension Treatment and Control in the Genetic Epidemiology Network of Arteriopathy (GENOA) Study. Am J Med 2004; 116(10): 676-681. PMID: 15121494.

FBPP Investigators. Multi-Center Genetic Study of Hypertension: The Family Blood Pressure Program (FBPP). Hypertension 2002; 39(1): 3-9. PMID: 11799070.

Kardia SL, Greene MT, Boerwinkle E, Turner ST, Kullo IJ. Investigating the complex genetic architecture of ankle- brachial index, a measure of peripheral arterial disease, in non-Hispanic whites. BMC Med Genomics 2008; 1:16. PMID: 18482449.

Khawaja FJ, Bailey KR, Turner ST, Kardia SL, Mosley TH Jr, Kullo IJ. Association of novel risk factors with the ankle brachial index in African American and non-Hispanic white populations. Mayo Clin Proc. 2007; 82(6): 709-16. PMID: 17550751.

Knopman DS, Mosley TH Jr, Bailey KR, Jack CR Jr, Schwartz GL, Turner ST. Associations of microalbuminuria with brain atrophy and white matter hyperintensities in hypertensive sibships. J Neurol Sci 2008; 271(1-2): 53-60. PMID: 18442832.

Lange LA, Lange EM, Bielak LF, Langefeld CD, Kardia SL, Royston P, Turner ST, Sheedy PF 2nd, Boerwinkle E, Peyser PA. Autosomal genome-wide scan for coronary artery calcification loci in sibships at high risk for hypertension. Arterioscler Thromb Vasc Biol 2002; 22(3): 418-23. PMID: 11884284.

Meyers KJ, Mosley TH, Fox E, Boerwinkle E, Arnett DK, Devereux RB, Kardia SL. Genetic variations associated with echocardiographic left ventricular traits in hypertensive blacks. Hypertension 2007; 49(5): 992-9. PMID: 17339538.

Rule AD, de Andrade M, Matsumoto M, Mosley TH, Kardia S, Turner ST. Association between SLC2A9 transporter gene variants and uric acid phenotypes in African American and white families. Rheumatology (Oxford) 2010; Dec 24. [Epub ahead of print] PMID: 21186168.

Smith JA, Turner ST, Sun YV, Fornage M, Kelly RJ, Mosley TH, Jack CR, Kullo IJ, Kardia SL. Complexity in the genetic architecture of leukoaraiosis in hypertensive sibships from the GENOA Study. BMC Med Genomics 2009; 2:16. PMID: 19351393.

Turner ST, Kardia SL, Mosley TH, Rule AD, Boerwinkle E, de Andrade M. Influence of genomic loci on measures of chronic kidney disease in hypertensive sibships. J Am Soc Nephrol 2006; 17(7): 2048-55. PMID: 16775034.

Turner ST, Fornage M, Jack CR Jr, Mosley TH, Knopman DS, Kardia SL, Boerwinkle E, de Andrade M. Genomic susceptibility Loci for brain atrophy, ventricular volume, and leukoaraiosis in hypertensive sibships. Arch Neurol 2009; 66(7): 847-57. PMID: 19597086.

Outcome Measures

Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Pulse Pressure (PP)

Blood pressure measures were obtained at the Phase I examination. Examinations were conducted in the morning after an overnight fast of at least eight hours. Using random zero sphygmomanometers and cuffs appropriate for arm size, three readings of blood pressure were taken in the right arm after the participant rested in the sitting position for at least five minutes; the last two readings were averaged for the analyses. Pulse pressure (PP) was calculated as the difference between the average systolic and diastolic blood pressures.

Body Mass Index (BMI)

Body mass index (BMI) was calculated using height and weight measures obtained at the Phase I examination. Height was measured by stadiometer, weight by electronic balance, and body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters.

Ankle-Brachial Index (ABI)

Ankle-brachial index (ABI) was measured during the Phase II examination. Measurements were taken while participants were in the supine position following a 5-min rest. Appropriately sized BP cuffs were placed on each arm and ankle, and a Doppler ultrasonic instrument (Medisonics, Minneapolis MN) was used to detect each pulse. The cuff was inflated to 10 mm Hg above SBP and deflated at 2 mm Hg/s. The first reappearance of the pulse was taken as the SBP. To calculate ABI, the SBP at each ankle site (posterior tibial and dorsalis pedis) was divided by the higher of the 2 brachial pressures. The lowest of the 4 ratios was designated as the ABI. The correlation of the lowest ABI with the average of the 2 ABIs from the same leg was 0.98, and inferences were similar using the lowest ABI or the average ABI.

Estimated Glomerular Filtration Rate (eGFR)

Estimated GFR was calculated using serum creatinine measures from blood collected on the morning of the Phase I examination after an overnight fast. Serum creatinine was measured by standard methods on the Hitachi 911 Chemistry analyzer (Roche Diagnostics, Indianapolis, IN). Estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD) equation: eGFR = 186.3 x (serum creatinine)–1.154 x (age)–0.203 x (0.742 if female) x (1.212 if black) (Levey, 1999; Levey, 2003). For calibration with the Cleveland Clinic assay that was used to develop the MDRD equation, 0.22 mg/dl (17 µmol/L) was added to all serum creatinine values. This adjustment for calibration bias was based on 255 serum creatinine samples that were measured both with the GENOA rate-Jaffe assay and with the Cleveland Clinic rate-Jaffe assay (on a Beckman CX3; Beckman Coulter, Fullerton, CA).

Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999; 130: 461–470.

Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW, Hogg RJ, Perrone RD, Lau J, Eknoyan G: National Kidney Foundation practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Ann Intern Med 2003; 139: 137–147.

Urinary Albumin-Creatinine Ratio (UACR)

UACR was calculated from urine collected on the morning of the Phase II examination after an overnight fast. Urinary albumin was measured by immunoturbidimetry utilizing antibody to human albumin in an automated immunoprecipitin analysis system (Diasorin Inc., Stillwater, MN) and urinary creatinine was measured by a colorometric dye-binding technique in the Hitachi 911 Chemistry analyzer (Roche Diagnostics, Indianapolis, IN) as previously described (Freedman, 2003).

Freedman BI, Beck SR, Rich SS, Heiss G, Lewis CE, Turner S, et al. A genome-wide scan for urinary albumin excretion in hypertensive families. Hypertension 2003; 42:291–296.

Coronary Artery Calcification (CAC)

Measures of CAC were taken as a supplement to the Phase II examination in the non-Hispanic white participants only. Participants were imaged with an Imatron C-150 electron beam computed tomography scanner (Imatron Inc., South San Francisco, Calif). A scan run consisted of 40 contiguous 3- mm-thick tomographic slices from the root of the aorta to the apex of the heart. Scan time was 100 ms per tomogram. Electrocardiographic gating was used and all images were triggered at end-diastole during 2 to 4 breath-holds. A radiological technologist scored the tomograms with an automated scoring system (Reed, 1994). CAC was defined as a hyperattenuating focus within 5 mm of the arterial midline and at least 4 adjacent pixels in size (ie, 1.04 mm2), with CT number above 130 HU throughout the focus. An experienced radiologist inspected the technical quality and scoring accuracy of each tomogram and interpreted their findings. Quantity of CAC was defined as the total CAC score summed from the 4 major epicardial arteries using the method of Agatston et al. (1990). The average CAC score from 2 sequential scans was used.

Reed JE, Rumberger JA, Davitt PJ, et al. System for quantitative analysis of coronary calcification via electron-beam computed tomography. In:Medical Imaging 1994: Physiology and Function from Multidimensional Images. Hoffman EA, Acharya RS, eds.Proc SPIE. 1994; 2168:43–53. Agatston AS, Janowitz WR, Hildner FJ, et al. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990; 15: 827–832.

Relative Wall Thickness (RWT) and Left Ventricular Mass Index (LVMI)

Echocardiographs were taken as a supplement to the Phase II examination in the African American participants only. LVM and RWT were derived using phased-array echocardiographs with M-mode, 2D and pulsed, continuous wave, and colorflow Doppler capabilities. Standardized methods, along with training and certification, were used by field-center technicians to achieve high-quality recordings. Readings were performed at the New York Presbyterian Hospital–Weill Cornell Medical Center and verified by a single highly experienced investigator. To measure LVM and RWT, the parasternal acoustic window was used to record 10 consecutive beats of 2D and M-mode recordings of the left ventricular internal diameter and wall thicknesses at, or just below, the tips of the anterior mitral leaflet in long- and short-axis views. Correct orientation of planes for imaging and Doppler recordings was verified using standardized protocols. Measurements were made using a computerized review station equipped with digitizing tablet and monitor screen overlay for calibration and performance of each measurement. Left ventricular internal dimension and interventricular septal and posterior wall thicknesses were measured at end diastole and end systole according to the recommendations of the American Society of Echocardiography in 3 cardiac cycles (Lang, 2005). Calculations of LVM were made using a necropsy- validated formula (Devereux, 1986), and RWT was calculated as 2*(posterior wall thicknesses)/left ventricular internal dimension. LVM index (LVMI) was defined as LV mass/height2.7 (g/m2.7). Lang RM, Bierig M, Devereux RB, Flachskampf FA, Foster E, Pellikka PA, Picard MH, Roman MJ, Seward J, Shanewise JS, Solomon SD, Spencer KT, Sutton MS, Stewart WJ. Recommendations for chamber quantification: a report from the Am Society of Echocardiography’s Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. J Am Soc Echocardiogr. 2005; 18: 1440–1463 Devereux RB, Alonso DR, Lutas EM, Gottlieb GJ, Camp E, Sachs I, Reichek N. Echocardiographic assessment of left ventricular hypertrophy: Comparison to necropsy findings. Am J Cardiol. 1986; 57: 450–458

Leukoaraiosis

Leukoaraiosis volume (cm3) was measured during an examination for the Genetics of Microangiopathic Brain Injury (GMBI) ancillary study of GENOA (2001-2006). The GMBI study was undertaken to investigate susceptibility genes for ischemic brain injury in Phase II GENOA participants that had a sibling willing and eligible to participate in the GMBI study. Ischemic brain damage to the subcortical and periventricular white matter (leukoaraiosis) was quantified by MRI in subjects who had no history of stroke or neurological disease and no implanted metal devices.

All MRI scans were performed on identically equipped Signa 1.5 T MRI scanners (GE Medical Systems, Waukesha, WI, USA) and images were centrally processed at the Mayo Clinic. Symmetric head positioning with respect to orthogonal axes was verified by a series of short scout scans. Total intracranial volume (head size) was measured from T1-weighted spin echo sagittal images, each set consisting of 32 contiguous 5 mm thick slices with no interslice gap, field of view = 24 cm, matrix = 256 x 192, obtained with the following sequence: scan time = 2.5 min, echo time = 14 ms, repetitions = 2, replication time = 500 ms (Jack, 1989). Total brain and leukoaraiosis volumes were determined from axial fluid-attenuated inversion recovery (FLAIR) images, each set consisting of 48 contiguous 3-mm interleaved slices with no interslice gap, field of view = 22 cm, matrix = 256 x 160, obtained with the following sequence: scan time = 9 min, echo time = 144.8 ms, inversion time = 2,600 ms, repetition time = 26,002 ms, bandwidth = +/- 15.6 kHz, one signal average. A FLAIR image is a T2-weighted image with the signal of the cerebrospinal fluid nulled, such that brain pathology appears as the brightest intracranial tissue. Interactive imaging processing steps were performed by a research associate who had no knowledge of the subjects’ personal or medical histories or biological relationships. A fully automated algorithm was used to segment each slice of the edited multi-slice FLAIR sequence into voxels assigned to one of three categories: brain, cerebrospinal fluid, or leukoaraiosis. The mean absolute error of this method is 1.4% for brain volume and 6.6% for leukoaraiosis volume, and the mean test-retest coefficient of variation is 0.3% for brain volume and 1.4% for leukoaraiosis volume (Jack, 2001). White matter hyperintensities in the corona-radiata and periventricular zone, as well as central gray infarcts (ie, lacunes) were included in the global leukoaraiosis measurements. Brain scans with cortical infarctions were excluded from the analysis because of the distortion of the leukoaraiosis volume estimates that would be introduced in the automated segmentation algorithm.

Jack CR Jr, Twomey CK, Zinsmeister AR, Sharbrough FW, Petersen RC, and Cascino GD. Anterior Temporal Lobes and Hippocampal Formations: Normative Volumetric Measurements from MR Images in Young Adults. Radiology 1989; 172(2): 549-554. Jack CR Jr, O'Brien PC, Rettman DW, Shiung MM, Xu Y, Muthupillai R, Manduca A, Avula R, and Erickson BJ. FLAIR Histogram Segmentation for Measurement of Leukoaraiosis Volume. J Magn Reson Imaging 2001; 14(6): 668-676.

Descriptive Statistics

Non-Hispanic Whites

Adjustment Variables Variable N Mean St. Dev. Minimum Maximum Age at Phase I, years 1439 55.3 10.8 24.9 89.9 Age at Phase II, years 1120 59.0 10.1 29.6 84.0 Age at GMBI Examination, years 797 60.4 9.9 33.5 84.2 Total Intracranial Volume, cm3 797 1466 147 1011 1970

Outcome Variables (Before and After Transformations) Variable N Mean St. Dev. Minimum Maximum Systolic Blood Pressure, mm Hg 1438 133.0 17.1 87.0 215.0 Diastolic Blood Pressure, mm Hg 1438 78.5 9.6 38.0 111.0 Pulse Pressure, mm Hg 1438 54.5 14.5 20.0 129.0 Body Mass Index, g/m2 1438 30.4 6.4 15.8 67.9 Ankle Brachial Index 1063 1.1 0.1 0.4 1.8 Estimated GFR, mL/min/1.73m2 1437 60.5 11.0 7.1 105.5 Urinary Albumin-Creatinine Ratio 909 10.1 51.7 0.4 1178 Ln(Urinary Albumin-Creatinine Ratio) 909 1.4 1.0 -0.9 7.1 Coronary Artery Calcification Score 1043 2.9 2.6 0.0 8.4 Ln(Coronary Artery Calcification+1) 1043 1.1 0.8 0.0 2.2 Leukoaraiosis, cm3 797 7.8 6.5 1.2 61.9 Ln(Leukoaraiosis+1) 797 2.0 0.5 0.8 4.1

Residual Values for Outcome Variables (After Adjustment) Variable N Mean St. Dev. Minimum Maximum Systolic Blood Pressure, mm Hg 1438 133 15.9 79 209.6 Diastolic Blood Pressure, mm Hg 1438 78.5 9.2 42.2 107.8 Pulse Pressure, mm Hg 1438 54.5 12.2 22.5 120.1 Body Mass Index, g/m2 1438 30.4 6.3 15.3 67.2 Ankle Brachial Index 1063 1.1 0.1 0.4 1.8 Estimated GFR, mL/min/1.73m2 1437 60.5 9.8 10.8 103.9 Ln(Urinary Albumin-Creatinine Ratio) 909 1.4 1 -1 6.9 Ln(Coronary Artery Calcification+1) 1043 1.1 0.7 -1 2.9 Ln(Leukoaraiosis+1) 784 2 0.4 0.5 4.3

African Americans

Adjustment Variables Variable N Mean St. Dev. Minimum Maximum Age at Phase I, years 1589 58.7 9.9 20.5 91.2 Age at Phase II, years 1275 63.6 9.2 26.4 94.7 Age at GMBI Examination, years 683 64.2 8.8 28.4 91.6 Total Intracranial Volume, cm3 683 1372 134 1010 1755

Outcome Variables (Before and After Transformations) Variable N Mean St. Dev. Minimum Maximum Systolic Blood Pressure, mm Hg 1589 136.5 23.0 80.0 243.0 Diastolic Blood Pressure, mm Hg 1589 77.5 12.3 35.0 129.0 Pulse Pressure, mm Hg 1589 59.0 18.8 22.0 142.0 Body Mass Index, g/m2 1589 31.1 6.7 14.4 61.4 Ankle Brachial Index 1242 1.0 0.1 0.2 1.3 Estimated GFR, mL/min/1.73m2 1588 69.6 15.2 4.6 151.7 Urinary Albumin-Creatinine Ratio 1194 68.8 330.3 0.4 6054 Ln(Urinary Albumin-Creatinine Ratio) 1194 2.3 1.6 -0.9 8.7 Relative Wall Thickness 1238 0.3 0.1 0.2 0.6 Ln(Relative Wall Thickness) 1238 -1.2 0.2 -1.7 -0.6 Left Ventricular Mass Index, g/m2.7 1235 81.2 21.5 38.0 231.4 Ln(Left Ventricular Mass Index) 1235 4.4 0.2 3.6 5.4 Leukoaraiosis, cm3 683 10.4 11.2 2.0 126.0 Ln(Leukoaraiosis+1) 683 2.2 0.6 1.1 4.8

Residual Values for Outcome Variables (After Adjustment) Variable N Mean St. Dev. Minimum Maximum Systolic Blood Pressure, mm Hg 1589 136.5 21.9 82.3 238.3 Diastolic Blood Pressure, mm Hg 1589 77.5 12.0 37.4 125.5 Pulse Pressure, mm Hg 1589 59.0 16.5 21.8 141.4 Body Mass Index, g/m2 1589 31.1 6.4 15.5 59.9 Ankle Brachial Index 1242 1.0 0.1 0.2 1.3 Estimated GFR, mL/min/1.73m2 1588 69.6 14.1 2.5 147.2 Ln(Urinary Albumin-Creatinine Ratio) 1194 2.3 1.6 -1.3 8.8 Ln(Relative Wall Thickness) 1238 -1.2 0.1 -1.6 -0.6 Ln(Left Ventricular Mass Index) 1235 4.4 0.2 3.6 5.4 Ln(Leukoaraiosis+1) 683 2.2 0.5 1.1 4.3

Genetic Data

Genotyping

A total of 1386 white participants and 1355 African American participants from GENOA were genotyped on the Affymetrix® Genome-Wide Human SNP Array 6.0 array using the protocol outlined by Affymetrix (Affymetrix, 2007) at the Mayo Clinic in Rochester, Minnesota, or at the Broad Institute for a subset (N=92) of African Americans. Some of the stored blood samples contained DNA of poor quality, and we were unable to genotype these samples using the Affymetrix 6.0 platform. However, we were able to obtain high quality genotyping using the Illumina® Human1M-Duo or Human660W-Quad BeadChips (Illumina, 2010) for an additional 123 whites and 269 African Americans. For all genotyping platforms used, samples and SNPs with a call rate <95% were removed. Samples demonstrating sex mismatch, duplicate samples, and samples with low identity-by-state with all other samples were also removed. Pedigree information was used as a quality check to identify mislabeled samples. Affymetrix. Affymetrix® Genome-Wide Human SNP Nsp/Sty 6.0 User Guide. 2007. Illumina. Genome-Wide DNA Analysis Beadchips. 2010. Imputation

Imputation was performed separately by ethnic group using the single-step approach implemented in Markov Chain Haplotyper (MaCH) 1.0.16 (Li, 2006). In whites, imputation was performed once using all genotyped subjects from the Affymetrix and Illumina platforms. The reference panel for imputation in whites was composed of the HapMap phased haplotypes (release 22) from 60 unrelated CEU samples (Utah residents with Northern and Western European ancestry) (The International HapMap Consortium, 2003). In African Americans, imputation was performed separately for subjects genotyped on the Affymetrix and Illumina platforms, and post-imputation comparison between the two groups revealed no substantial differences in genotype and allele frequencies. The reference panel for African Americans was composed of the HapMap phased haplotypes (release 22) from 60 unrelated CEU and 60 unrelated YRI samples (Yoruba from Ibadan, Nigeria). Li, Y., and Abecasis, G.R. (2006). Mach 1.0: Rapid Haplotype Reconstruction and Missing Genotype Inference. American Journal of Human Genetics 2006; S79: 2290.

Quality Control

As quality control measures, we removed SNPs that had poor imputation quality as measured by the estimated r2 between imputed and true genotypes (r2<0.3) from MaCH output (N=40,629 SNPs in whites; N=16,479 SNPs in African Americans). In order to prevent false positive associations due to a small number of people in a single genotype category, SNPs with a minor allele frequency (MAF) less than 0.01 were also removed (N=52,948 SNPs in whites; N=2,050 SNPs in African Americans). Finally, we removed SNPs that were out of Hardy-Weinberg Equilibrium (HWE p-value < 10-6) (N=17 SNPs in whites; N=17 SNPs in African Americans). A total of 2,450,293 SNPs in whites and 2,185,063 SNPs in African Americans were available for GWAS analysis after these quality control procedures were completed.

Total Number of SNPs Included in GWAS Analysis Group Number of SNPs Non-Hispanic Whites 2,450,293 African Americans 2,185,063

Principal Components Analysis

Principal components analysis of genome-wide genotypes was conducted separately within each ethnic group for the purposes of 1) identifying and removing samples with outlying genotype profiles to ensure that there was minimum variation due to poor quality genotyping or single individuals with a dramatically different admixture profile than the remainder of the sample, and 2) constructing principal components (PCs) to use as covariates in modeling to control for population stratification in the African American sample.

Since GENOA is composed of sibships, we calculated PCs in an unrelated sample of individuals separately within each ethnic group. First, we removed SNPs that had moderate to poor imputation quality as measured by the estimated r2 between imputed and true genotypes (r2<0.8) from MaCH output. Next, we obtained the maximum number of unrelated individuals in our total sample (N=1509 whites; N=1624 African Americans) by selecting one sibling randomly from each sibship (N=570 whites; N=671 African Americans). In these samples, we calculated the first ten PCs on the set of SNPs that were common to both genotyping platforms and were also in HapMap. The imputed best guess genotypes were used to ensure no missing values for SNPs (N=226,619 SNPs in whites; N=207,565 SNPs in African Americans). An additive model was assumed for the SNPs, which are standardized with a mean of 0 and variance of 1. We then used the loading matrix for these PCs to calculate the PC values in the full samples. Next, participants that had outlying values (more than 6 standard deviations) on any of the ten PCs were removed from the analysis sample (N= 45 whites and 35 African Americans).

Finally, we again selected an unrelated sample of individuals by randomly selecting one individual from each sibship (N=556 whites; N=661 African Americans) and recalculated the first ten PCs in this sample. We then used the loading matrix to calculate the first ten PCs in all participants. The final analysis sample consisted of 1464 whites (1345 genotyped on the Affymetrix platform and 119 on the Illumina platforms) and 1589 African Americans (1326 genotyped on the Affymetrix platform and 263 genotyped on the Illumina platforms).

Total Number of Participants Genotyped on Each Platform Number of Participants Number of Participants Genotyped on Genotyped on Illumina Total Number Group Affymetrix 6.0 660W-Quad or 1M-Duo of Participants Non-Hispanic Whites 1345 119 1464* African Americans 1326 263 1589 * This number includes 25 individuals who did not participate in Phase I, but were added to GENOA during Phase II since their siblings had been recruited in Phase I.

SNP Annotation

Since GENOA samples were genotyped on two different genotyping platforms (Affymetrix and Illumina), we only had a small number of SNPs that were directly genotyped for all participants. Therefore, all of our analyses were conducted using imputed SNPs, and thus all of our annotation files contain data pertaining to imputed SNPs.

GENOA is composed of sibships, and thus measures of minor allele frequency and Hardy-Weinberg equilibrium estimated in the full sample would not accurately reflect true population genetic parameters (which assume unrelated individuals). Therefore, our annotation files contain MAF and HWE p-values calculated using a bootstrapping procedure with samples of unrelated individuals from GENOA. The imputed best guess genotypes of SNPs whose posterior probability are greater than 0.9 were used for the calculation of HWE p-values (the genotype was set as missing otherwise) and the imputed dosages were used for the calculation of MAF. To calculate these parameters, we first took 100 random draws of the largest possible sample of unrelated individuals within each ethnic group (N=556 in whites; N=661 in African Americans). The MAF that we report in our annotation files is the average MAF over these 100 samples. The reported HWE p-value was calculated as the p-value associated with the mean Chi-square statistic from a test of HWE in each of the 100 samples.

Analysis Methods

Transformations of Outcome Variables

Outcome variables that demonstrated skewness were transformed using the natural logarithm. Since some participants had coronary artery calcification and/or leukoaraiosis measurements that were close to zero, the natural logarithm of the outcome plus one was taken for these variables. Transformed outcome variables are given below.

Outcome variables transformed with natural logarithm: Urinary Albumin-Creatinine Ratio, Relative Wall Thickness, Left Ventricular Mass Index

Outcome variables transformed with natural logarithm of the outcome plus one:

Coronary Artery Calcification, Leukoaraiosis

Outcome Variable Adjustments Prior to GWAS Analysis

The residual values of the outcome variables were calculated using linear least squares regression according to the models below. For all outcome variables, sex and the appropriate age (at Phase I, Phase II, or an ancillary examination) were used to as adjustment variables. For all analyses conducted in African Americans, the top 10 PCs from genome-wide genotypes were included as adjustment variables in all models to prevent confounding from population substructure.

Systolic Blood Pressure = Age at Phase I + Sex + Top 10 PCs (African Americans only) Diastolic Blood Pressure = Age at Phase I + Sex + Top 10 PCs (African Americans only) Pulse Pressure = Age at Phase I + Sex + Top 10 PCs (African Americans only) Body Mass Index = Age at Phase I + Sex + Top 10 PCs (African Americans only) Estimated GFR = Age at Phase I + Sex + Top 10 PCs (African Americans only) Ln(Urinary Albumin-Creatinine Ratio) = Age at Phase II + Sex + Top 10 PCs (African Americans only) Ankle Brachial Index = Age at Phase II + Sex + Top 10 PCs (African Americans only) Ln(Coronary Artery Calcification + 1) = Age at Phase II + Sex Ln(Relative Wall Thickness) = Age at Phase II + Sex + Top 10 PCs Ln(Left Ventricular Mass Index) = Age at Phase II + Sex + Top 10 PCs Ln(Leukoaraiosis + 1) = Age at GMBI Exam + Sex + Total Intracranial Volume + Top 10 PCs (African Americans only)

GWAS Analysis

Genotypes: We used allelic dosages (the expected number of copies of a specific allele, ranging from 0 to 2) of imputed SNPs for all analyses in order to incorporate the uncertainty of genotype designation through the imputation procedure. An additive genetic model was assumed.

Modeling: Since the GENOA cohort is composed of siblings, linear mixed effects modeling with “family” as a random variable was used to test all associations between SNPs and the residual outcome measures. SNP effects were assumed to be fixed across families, but each family had its own intercept.

Statistical Testing: For each association test between a SNP and a residual outcome measure, the test statistics reported are 1) the regression coefficient (beta value) associated with the SNP, 2) the T statistic for the regression coefficient, and 3) the p-value for a Wald test of the T statistic.