Supplementary Appendix

This appendix has been provided by the authors to give readers additional information about their work.

Supplement to: Parsa A, Kao WHL, Xie D, et al. APOL1 risk variants, race, and progression of chronic kidney disease. N Engl J Med 2013;369:2183-96. DOI: 10.1056/NEJMoa1310345

1

Online Appendix Table of Contents

Page

Author Affiliations 2

AASK and CRIC Research Groups 3-8

Expanded Methods in AASK and CRIC 9-17

Bibliography 18-19

AASK Supplementary Tables

Supplementary Table S1: Baseline Characteristics of 1) AASK Participants Included in the Present Analysis (N=693), 2) Participants who Failed Genotyping (N=143), and 3) Participants who did not provide DNA/Genetic Consent or Reached ESRD Prior to DNA/Genetic Consent (N=258)

20

Supplementary Table S2: Hazard Ratio of the Composite Renal Outcome of Incident ESRD or Doubling of Serum Creatinine Associated with APOL1 Risk, Stratified by Baseline Proteinuria (trial and cohort phases of AASK)

21

Supplementary Table S3: Hazard Ratio of the Composite Renal Outcome of Incident ESRD or a 50% Reduction in GFR Associated with Intervention Groups, Stratified by APOL1 Risk Group (trial phase only of AASK)

22

Supplementary Table S4: Distribution of Incident Outcomes by APOL1/MYH9 Haplotypes in 693 AASK Participants

23

CRIC Supplementary Tables

Supplementary Table S5: Expanded Table of Baseline Characteristics and Events in CRIC, Stratified by Baseline Diabetes, Ancestry, and APOL1 Status

24

Supplementary Table S6: APOL1 Alleles and Genotype Distribution in African Americans, CRIC Study

25

Supplementary Table S7: APOL1 High-risk vs. Low-risk Genotype Dffect on eGFR Slope and Time to Renal Event in African Americans, CRIC Study

26

Supplementary Table S8: Hazard Ratios for Time to ESRD Alone, Comparing all African-Americans and African-Americans Stratified by APOL1 Genotype with all European Americans in Multivariable Models, Stratified by Diabetes Status, CRIC Study

27

Supplementary Table S9: Sensitivity Analysis, iGFR and Differences in Slope between African Americans Stratified by APOL1 Genotype compared to all European Americans, CRIC Study

28

Supplementary Table S10: MYH9 Risk vs. Low-Risk Genotype Effect on eGFR Slope and Time to Renal Event in African Americans, CRIC Study

29

Supplementary Table S11: Multivariable Analyses of Differences in eGFR Slope and Hazard Ratios Comparing all African Americans and African Americans stratified by APOL1 Risk Status vs all European Americans, Stratified by Baseline Diabetes Status, Model 3 without systolic BP, CRIC Study

30

2

Author Affiliations

Author Affiliation

Afshin Parsa, MD, MPH* University of Maryland School of Medicine, Division of Nephrology

WH Linda Kao, Ph.D. , M.H.S.* Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology; Johns Hopkins University, Welch Center for Prevention, Epidemiology and Clinical Research; Johns Hopkins University School of Medicine, Division of General Internal Medicine

Dawei Xie, Ph.D. University of Pennsylvania Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics

Brad C Astor, Ph.D, M.P.H. University of Wisconsin School of Medicine and Public Health, Departments of Medicine and Population Health Sciences

Man Li, M.S. Johns Hopkins University School of Public Health, Department of Epidemiology

Chi-yuan Hsu, M.D., M.Sc. University of California at San Francisco, Division of Nephrology; Kaiser Permanente of Northern California, Division of Research

Harold I. Feldman, M.D., MSCE University of Pennsylvania Perelman School of Medicine, , Center for Clinical Epidemiology and Biostatistics

Rulan S Parekh, M.D., M.S. University of Toronto, Hospital for Sick Children and University Health Network, Departments of Pediatrics & Medicine

John W. Kusek, Ph.D. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Tom H Greene, Ph.D. University of Utah School of Medicine, Division of Clinical Epidemiology

Jeffrey C. Fink, M.D., M.S. University of Maryland School of Medicine, Department of Medicine

Amanda H. Anderson, Ph.D., M.P.H. University of Pennsylvania Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics

Michael J Choi, M.D. Johns Hopkins University School of Medicine, Division of Nephrology

Jackson T Wright, M.D., Ph.D. Case Western Reserve University, Department of Medicine

James P. Lash, M.D. University of Illinois School of Medicine, Division of Nephrology

Barry I Freedman, M.D. Wake Forest School of Medicine, Department of Internal Medicine, Section on Nephrology

Akinlolu Ojo, M.D. ,Ph.D. University of Michigan School of Medicine, Departments of Internal Medicine and Epidemiology

Cheryl A Winkler, Ph.D. Center for Cancer Research, SAIC-Frederick, Frederick National Laboratory for Cancer Research

Dominic S Raj, M.D. The George Washington University School of Medicine, Division of Renal Diseases and Hypertension

Jeffrey B Kopp, M.D. Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health

Jiang He, M.D., Ph.D Tulane University School of Public Health and Tropical Medicine, Department of Epidemiology

Nancy G. Jensvold, M.P.H Kaiser Permanente of Northern California, Division of Research

Kaixiang Tao, Ph.D University of Pennsylvania Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics

Michael S Lipkowitz, M.D.** Georgetown University School of Medicine, Division of Nephrology

Lawrence J Appel, MD, MPH** Johns Hopkins University School of Medicine, Division of General Internal Medicine; Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology; Johns Hopkins University, Welch Center for Prevention, Epidemiology and Clinical Research

*Co-Lead Authors, **Co-Senior Authors

3

AASK Collaborative Research Group

The AASK Collaborative Group deeply appreciates the impressive and sustained commitment of the

AASK participants and staff. The following individuals, by center, participated in the conduct of

AASK:

Case Western Reserve University – Principal Investigator – Jackson T. Wright, Jr., M.D., Ph.D.,

Mahboob Rahman, M.D., Study Coordinator - Renee Dancie, C.M.A., Louise Strauss, R.N.;

Emory University – Principal Investigator - Janice Lea, M.D., Study Coordinator - Beth Wilkening,

PA-C, Arlene Chapman, M.D. and Diane Watkins, MA;

Harbor-UCLA Medical Center – Principal Investigator - Joel D. Kopple, M.D., Study Coordinator -

Linda Miladinovich, R.N., Jooree Choi, M.D., Patricia Oleskie, and Connie Secules;

Harlem Hospital Center – Principal Investigator - Velvie Pogue, M.D., Study Coordinator - Donna

Dowie, M.D., Herman Anderson, M.D., Leroy Herbert, M.D., Roberta Locko, M.D., Hazeline Nurse,

M.D., Jen-Tse Cheng, M.D., Fred Darkwa, Victoria Dowdy, R.N., Beverly Nicholas;

Howard University – Principal Investigator - Otelio Randall, M.D., Tamrat Retta, M.D., Ph.D., Study

Coordinator - Shichen Xu, M.D., Muluemebet Ketete, M.D., Debra Ordor, R.N., Carl Tilghman, R.N.;

Johns Hopkins University – Principal Investigator - Edgar Miller, M.D., Ph.D., Brad Astor, PhD,

MPH, MS, Study Coordinator - Charalett Diggs, R.N., Jeanne Charleston, R.N., Charles Harris,

Thomas Shields, B.S., Steering Committee Chair - Lawrence Appel, M.D., M.P.H.;

Charles R. Drew University – Principal Investigator - Keith Norris, M.D., David Martins, M.D., Study

Coordinator - Melba Miller, R.N., Holly Howell, B.A., Laurice Pitts, LVN;

Medical University of South Carolina – Principal Investigator - DeAnna Cheek, M.D. Study

Coordinator - Deborah Brooks, M.S.N., R.N.;

Meharry Medical College – Principal Investigator - Marquetta Faulkner, M.D., Olufemi Adeyele,

M.D., Study Coordinator - Karen Phillips, R.N., Ginger Sanford, R.N., Cynthia Weaver, M.T.;

Morehouse School of Medicine – Principal Investigator - William Cleveland, M.D., Kimberly

Chapman, B.S., Study Coordinator - Winifred Smith, M.P.H., Sherald Glover;

4

Mount Sinai School of Medicine and University of Massachusetts – Principal Investigator -

Robert Phillips, M.D., Ph.D., Michael Lipkowitz, M.D., Mohammed Rafey, M.D., Study Coordinator -

Avril Gabriel, R.N., MPA, Eileen Condren, Natasha Coke;

Ohio State University – Principal Investigator - Lee Hebert, M.D., Ganesh Shidham, M.D., Study

Coordinator - Leena Hiremath, Ph.D., Stephanie Justice, RN;

University of Chicago, Chicago – Principal Investigator - George Bakris, M.D., James Lash, M.D.,

Study Coordinator - Linda Fondren, R.N., B.S.N., Louise Bagnuolo, R.N., N.P., Janet Cohan, R.N.,

MSN, Anne Frydrych, R.N., MSN;

University of Alabama, Birmingham – Principal Investigator - Stephen Rostand, M.D., Denyse

Thornley-Brown, M.D., Study Coordinator - Beverly Key, R.N.;

University of California, San Diego – Principal Investigator - Francis B. Gabbai, M.D., Daniel T.

O'Connor, M.D., Study Coordinator - Brenda Thomas, L.V. N.;

University of Florida – Principal Investigator - C. Craig Tisher, M.D., Geraldine Bichier, M.D., Study

Coordinator - Cipriano Sarmiento, R.N., Amado Diaz, R.N., Carol Gordon;

University of Miami – Principal Investigator - Gabriel Contreras, M.D., Jacques Bourgoignie, M.D.,

Dollie Florence-Green, M.D., Study Coordinator - Jorge Junco, Jacqueline Vassallo;

University of Michigan – Principal Investigator - Kenneth Jamerson, M.D., Akinlou Ojo, M.D., Tonya

Corbin, M.D., Study Coordinator - Denise Cornish-Zirker, R.N., A.D.N., Tanya Graham, M.A., Wendy

Bloembergen, M.D.;

University of Southern California – Principal Investigator - Shaul Massry, M.D., Miroslav

Smogorzewski, M.D.; Study Coordinator - Annie Richardson, L.V.N., Laurice Pitts, LVN;

University of Texas Southwestern Medical Center, Dallas – Principal Investigator - Robert Toto,

M.D., Gail Peterson, M.D., FACC, Rames Saxena, M.D., Ph.D. Study Coordinator - Tammy

Lightfoot, R.N., Sherry-Ann Blackstone, R.N., Carlos Loreto

Vanderbilt University – Principal Investigator - Julie Lewis, M.D., Gerald Schulman, M.D. Study

Coordinator - Mo Sika, Ph.D., Sandy McLeroy, M.S., R.D.;

5

National Institute of Diabetes and Digestive and Kidney Diseases – Lawrence. Y. Agodoa, M.D.,

Josephine. P. Briggs, M.D., John. W. Kusek, Ph.D.;

Data Coordinating Center (Cleveland Clinic Foundation) – Jennifer Gassman, Ph.D., Gerald

Beck, Ph.D., Tom Greene, Ph.D., Bo Hu, Ph.D., Study Coordinator – Karen Brittain, Susan Sherer,

B.S., Laurie Tuason, M.S., Cynthia Kendrick, B.S., Sharon Bi, MCIS, Harvey Litowitz, M.S., Xianyou

Liu, MCIC, Xuelei Wang, M.S., Kimberly Wiggins, AAB, Cheryl A. Tatum, Nancy Patterson;

Central Biochemistry Laboratory – Frederick Van Lente, Ph.D., Joan Waletzky, M.S., Cathy

O'Laughlin, M.L.T. (ASCP), LaChauna Burton, B.S.;

External Advisory Committee – William McClellan, M.D., M.P.H., Lucile Adams-Campbell, Ph.D.,

Kathy Faber-Langendoen, M.D., Bryce Kiberd, M.D., Elisa Lee, Ph.D., Timothy Meyer, M.D., David

Nathan, M.D., John Stokes, M.D., Herman Taylor, M.D., FACC, Peter W. Wilson, M.D..;

Cardiovascular Research Foundation – Tine deBacker, M.D., Alexandra Lansky, M.D., Steve

Slack.

6

Chronic Renal Insufficiency Cohort (CRIC) Study Collaborative Research Group University of Pennsylvania Scientific & Data Coordinating Center – Harold I. Feldman, MD,

MSCE (PI) J. Richard Landis, PhD (Co-PI) Amanda Hyre Anderson, PhD, MPH Shawn Ballard, MS

Boyang Chai, MS Laura M. Dember, MD Jennifer Dickson, Marie Durborow, Chuck Girard, Elizabeth

S. Helker, RN, Peter A. Kanetsky, PhD, MPH Scott Kasner, MD, MSCE, FAHA Stephen E. Kimmel,

MD, MSCE Susan Kramlik, MS, Steven R. Messe, MD Lisa Nessel, MSS, MLSP Qiang Pan, MA,

Nancy Robinson, PhD Jason Roy, PhD, Kaixiang (Kelvin) Tao, PhD, MS Krista Whitehead, MS,

Melanie Wolman, MPH Dawei Xie, PhD, Wei (Peter) Yang, PhD Xiaoming Zhang, MS

University of Pennsylvania Medical Center- Raymond R. Townsend, MD (PI) Debbie Cohen, MD,

Magdalena M. Cuevas, MT Mark J. Duckworth, Virginia Ford, MSN, CRNP Yonghong Huan, MD,

Radhakrishna R. Kallem, MD, MPH Juliet Leshner, Stephanie McDowell Emile R. Mohler, III, MD

Wanda M. Seamon, Angie Sheridan, MPH Jillian Strelsin, Karen Teff, PhD Sarah VanderVeen

The Johns Hopkins University- Lawrence J. Appel, MD, MPH (PI), Cheryl Anderson, PhD, MPH

(UC, San Diego) Pam Bowers, Teresa Chan, MD, MHS Alexander Chang, MD Jeanne Charleston,

RN Pat Crowley, Tara Harrison, Bernard Jaar, MD, MPH Dawn Jiggets, Carla Martin, Edgar “Pete”

Miller, MD, PhD Patience Ngoh, Steve Sozio, MD, MHS Letitia Thomas, Sharon Turban, MD, MHS

University of Maryland- Jeffrey Fink, MD, MS (Co-PI) Clarissa Diamantidis, MD Wanda Fink, MS,

BSN, RN Lisa Lucas, Tiffany Page, Afshin Parsa, MD, MPH Beth Scism, Stephen Seliger, MD, MS

Matthew Weir, MD

University Hospitals of Cleveland Case Medical Center- Mahboob Rahman, MD (PI), Mirela A.

Dobre, MD, MPH (Co-PI) Kathryn Clark, RA, Valori Corrigan RN Genya Kisin, CTC, Radhika

Kanthety, MD, MSHS Louise Strauss, RN, Jackson T. Wright, Jr., MD, PhD

Kaiser Permanente of Northern California- Alan S. Go, MD (PI) Arthur Choi, Pete Dorin, MPA,

Nancy G. Jensvold, MPH Joan C. Lo, MD, Liliana Metzger, Elisa Frances Nasol, Juan D. Ordonez,

MD, MPH Rachel Perloff, Nina Sasso, Daphne Thompson, Jingrong Yang, MA

7

University of California, San Francisco- Chi-yuan Hsu, MD, MSc (Co-PI), Glenn M. Chertow, MD,

MPH (Stanford University) NIDDK, John W. Kusek, PhD Andrew S. Narva, MD

External Expert Panel - William McClellan, MD, MPH Linda Fried, MD, MPH David T. Gilbertson,

PhD, Peter A. McCullough, MD, MPH David Nathan, MD, Ann M. O’Hare, MD Paul M. Palevsky, MD,

Stephen S. Rich, PhD Richard D. Toto, MD Gina S. Wei, MD, MPH Peter W. Wilson, MD,

Ana Ricardo, MD, MPH

Tulane University Health Science Center- Jiang He, MD, PhD (PI) Lee Hamm, MD (Co-PI) Brent

Alper, MD, Vecihi Batuman, MD, Lydia A. Bazzano, MD, PhD Bernadette Borja, Jing Chen, MD, MSc,

Catherine Cooke, Patrice Delafontaine, MD, Karen B. DeSalvo, MD, MPH, MSc, Jacquelyn Dolan,

Lee Hamm, MD, Eva Lustigova, MPH, Erin Mahone, RN, BSN, MPH Lanie Sansing, Claire Starcke,

Maria Waight

MetroHealth Medical Center - Jeffrey Schelling, MD (Co-PI) Ed Horwitz, MD (Co-PI), Lori Guillon,

RN, Noreen O’Malley, RN, BSN Marleen Schachere, RN John R. Sedor, MD, Mary Ann Shella,

RN,BSN Anne Slaven, MSSA, J. Daryl Thornton, MD, MPH

Translational Core Lab-University of Pennsylvania- Megan Donovan, Steve Master, MD, PhD Ted

Mifflin, PhD, Linda Morrell

Cleveland Clinic Foundation- Sankar D. Navaneethan, MD, MPH (PI) Martin J. Schreiber, MD (Co-

PI), Jon Taliercio, DO (Co-PI) Martha Coleman, RN, BSN Kim Hopkins, RN, Teresa Markle Melanie

Ramos, RN Annette Russo Stephanie Slattery, RN Kay Stelmach, RN, Velma Stephens, LPN

University of Michigan at Ann Arbor- Akinlolu Ojo, MD, PhD (PI) Baskaran Sundaram, MD Jeff

Briesmiester, Denise Cornish-Zirker, RN, BSN Nancy Hill, Kenneth Jamerson, MD Marie Ringbloom

Rajiv Saran, MD Donna Smith, Jillian Wilson, MHA Eric Young, MD, MS Julie Wright, MD,

St. John’s Health System- Susan P. Steigerwalt, MD, FACP (Co-PI) Keith Bellovich, DO, Jennifer

DeLuca, Gail Makos, RN, MSN Kathleen Walls

Wayne State University- John M. Flack, MD, MPH (Co-PI) James Sondheimer, MD, Jennifer Mahn

Mary Maysura Stephen Migdal, MD M. Jena Mohanty, MD Carol Muzyk, CCRP Yanni Zhuang

8

University of Illinois at Chicago- James P. Lash, MD (PI) Carolyn Brecklin, MD Eunice Carmona

Janet Cohan, MSN, Michael Fischer, MD, MSPH Anne Frydrych, MS, RD Amada Lopez, Claudia

Lora, MD Monica Martinez Alejandro Mercado Brenda Moreno Patricia Meslar, MSN

ECG Reading Center- Wake Forest- Elsayed Z. Soliman, MD, MSc, MS Zhu-Ming Zhang, MD

9

EXPANDED METHODS

AASK

Study Population

AASK participants were self-identified African-Americans with CKD attributed to

hypertension.1 Major inclusion criteria were age between 18 and 70 year, hypertension

defined by a diastolic blood pressure > 95 mmHg, and an iothalamate glomerular filtration

rate (GFR) between 20 and 65 ml/min/1.73m2. Principal exclusion criteria were a cause for

CKD other than hypertension; diabetes mellitus or fasting blood glucose level > 140 mg/dl;

urine protein/creatinine ratio >2.5g/g; secondary hypertension; or accelerated/malignant

hypertension in the last 6 months.

Design and Data Collection

The study had two phases, an initial 5-year trial phase (February 1995 through

September 2001), which was followed by a 5-year cohort phase with a maximum follow-up of

12.2 years. Initially, 1094 participants were randomly assigned to receive either intensive BP

control (mean arterial pressure of 92 mmHg or less) or standard control (mean arterial

pressure of 102-107 mmHg). Participants were also randomly assigned to one of three initial

drug therapies: ramipril, an angiotensin-converting-enzyme inhibitor (ACE-I); metoprolol, a

sustained-release beta-blocker; or amlodipine, a dihydropyridine calcium channel blocker. If

the blood pressure target could not be achieved with the highest tolerated dose of the

randomly assigned drug, other antihypertensive drugs (furosemide, doxazosin, clonidine, and

hydralazine or minoxidil) were sequentially added. In April 2002, patients who had not

reached ESRD were invited to enroll in the cohort phase, in which they received protocol-

driven blood pressure management on the basis of the results of the trial.

After the start of the trial, participants were asked to provide DNA as part of an ancillary

study that was approved by the IRB at each participating study site. 836 participants

10

provided written informed consent. For the present analysis, 693 individuals had adequate

genotyping for all markers and were included.

Genotyping

Seven single nucleotide polymorphisms (SNPs) in APOL1 and MYH9 were typed

using ABI Taqman (frequency of high risk alleles in parentheses): rs73885319 (G

allele=0.29) and rs60910145 (G allele = 0.29) which constitute the APOL1 G1 locus;

rs71785313 (the deletion =0.16) and represents the APOL1 G2 locus; and rs4821480 (T

allele=0.27), rs2032487 (T allele=0.30), rs4821481 (T allele=0.30), and rs3752462 ( C

allele=0.24) which constitute the MYH9 E1 haplotype. In addition, 140 ancestry informative

markers randomly spread across the entire genome were genotyped to estimate global

ancestry using the program ANCESTRYMAP.2

Outcomes

The primary outcome was the composite renal outcome defined as a doubling of serum

creatinine (roughly equivalent to a 50% reduction in GFR) or incident ESRD. Serum

creatinine was assessed twice at baseline and then every 6 months. ESRD was defined by

the initiation of dialysis or receipt of a kidney transplant. For the interaction analysis, which

was performed only for the trial phase, the composite renal outcome was defined as a 50%

reduction of GFR (as measured by iothalamate) or incident ESRD. Iothalamate GFR was

measured twice at baseline (mean of the two measurements was used), once at month 3,

once at month 6, then every 6 months thereafter during the trial phase of the study.

Statistical Analysis

Our primary exposure was APOL1 haplotype status. Two APOL1 missense variants

(rs73885319A>G, S342G; rs60910145T>G, I384M) together constitute the G1 high-risk allele

and the 6-base pair deletion allele (rs71785313 RRRATAA/-, N388Y399/--) is the G2 high-

11

risk allele. Since these variant alleles typically occur on mutually exclusive chromosomes,

there are two risk haplotypes: the G1 haplotype comprising of the two tightly linked missense

variants and the G2 haplotype, which contains the deletion. The low-risk haplotype at

APOL1 in this case is composed of the low-risk alleles at both G1 and G2. Individuals were

classified as having 0, 1, or 2 copies of the high-risk haplotype and they are grouped as

follows: 1) the reference group comprised of individuals with two low-risk haplotype (e.g.

they did not have either the high-risk G1 or G2 haplotypes); 2) those with 1 copy of the high-

risk haplotype (i.e. they either have the high-risk G1 or G2 haplotype but not both); and 3)

those with 2 copies of high-risk haplotype (i.e. their haplotypes were G1/G1, G1/G2, or

G2/G2).

To differentiate the effect of APOL1 from MYH9, the 3 APOL1 and 4 MYH9 genetic

markers were estimated into more haplotypes consisting of all 7 SNPs using PLINK3 for each

individual. To distinguish the genetic effect of APOL1 and MYH9, we defined the following

groups of individuals: 1) reference group consists of those with 2 copies of the low-risk

haplotypes (low-risk alleles at all 7 genetic markers); 2) intermediate risk group are those

with only 1 copy of a haplotype that contains the APOL1 high-risk variants or 1 copy of the

haplotype that contains the MYH9 high-risk variants; 3) individuals with 2 copies of the high-

risk MYH9 haplotype but had low-risk APOL1 haplotypes; and 4) individuals with 2 copies of

the high-risk APOL1 haplotypes. Since MYH9 E1 is highly correlated with APOL1 G1 and G2

(95% people who have G1 also have the MYH9 E1 high-risk alleles, 97% people who have

G2 also have E1 high-risk alleles), we were not able to identify individuals who only had the

APOL1 high-risk haplotype but not the MYH9 high-risk haplotype.

Baseline characteristics were examined across the 3 APOL1 risk groups, as

previously defined, and were tested with analysis of variance or chi-square tests. Pairwise

comparisons of baseline characteristics were tested with t-tests or Kruskal-Wallis tests. The

association between APOL1 groups and outcome was assessed using Cox proportional

12

hazards model, adjusted for age, sex, ancestry and baseline GFR. We initially used a co-

dominant genetic model for APOL1, i.e. individuals with either 1 or 2 copies of the high-risk

haplotype were compared to those with 0 copy separately. After verifying that those with just

with 1 copy of the high-risk haplotype were not at increased risk of progression compared to

the reference group, we used a recessive genetic model (those carrying 2 copies of the high-

risk haplotype were compared to everyone else) for the rest of the analyses, which is

consistent with prior literature.4 Evaluation of interactions between genetic factors and trial

interventions were limited to the 5-year trial data only and were tested with the inclusion of an

interaction term in the Cox proportional hazards model.

CRIC

Study Population

We studied participants of the Chronic Renal Insufficiency Cohort (CRIC) Study. A

total of 3,939 men and women were enrolled between June 2003 and August 2008.

Participants from primary care and nephrology practices were recruited using a variety of

approaches, with most relying on computerized searches of clinical databases.

Requirements for study entry and baseline characteristics of the study participants have been

previously published.5-7 Persons were eligible for the study if they were between 21 and 74

years of age and had an estimated glomerular filtration rate (eGFR) between 20 and 70

ml/min/1.73m2. Exclusion criteria were glomerulonephritis which required

immunosuppressive therapy, advanced heart failure, cirrhosis, or polycystic kidney disease.

We restricted our analyses to African Americans and European Americans with adequate

DNA samples (n=2,955). Institutional review boards at participating institutions approved the

study protocol. All study participants provided written informed consent.

Design and Data Collection

13

Demographic characteristics, self-reported medical history, anthropometric measures,

and medication use were ascertained at baseline.5 Serum creatinine was measured annually

using an enzymatic method on an Ortho Vitros 950 through October 2008, and by the Jaffe

method on a Beckman Synchron System, thereafter. All serum creatinine measures were

standardized to isotope-dilution mass spectrometry traceable values. Serum cystatin C was

measured using a particle-enhanced nephelometric immunoassay on a Dade-Behring BNII.

GFR was estimated (eGFR) from an equation developed with measures from CRIC Study

participants.8 About one- third of the study participants had kidney function measured by

clearance of125I-iothalamate (iGFR).6 Total proteinuria was measured from 24 hour urine

collections. Diabetes was defined as a fasting glucose >126 mg/d, a non-fasting glucose

>200 mg/dL, or use of insulin or an oral hypoglycemic agent.

Genotyping

The APOL1 risk genotype determination in African Americans (nearly absent in

European Americans) was based on having two risk variants consisting of either possible

combination of the G1 and G2 risk alleles (i.e., G1/G1, G1/G2 or G2/G2 variants). The G1

haplotype can be tagged by either non-synonymous SNP rs73885319 or rs60910145, which

are in near complete linkage disequilibrium occur together as part of the same haplotype.9

The G2 haplotype can be tagged by the rs71785313, 6 base pair insertion/deletion marker.

The G2 haplotype is in complete negative linkage disequilibrium with the G1 haplotype and is

never observed on the same chromosome. We used TaqMan SNP Genotyping Assays

(Applied Biosystems, Foster City, CA) to detect the APOL1 G1 risk allele and presumed

causative SNP rs73885319 and the APOL1 G2 risk allele marker rs71785313 (6 base-pair

insertion/deletion).10 MYH9 E1 tagging SNPs were determined on the Illumina Omni-1-quad

chip array. MYH9 risk genotype was derived based on the E1 haplotype tagging SNPs

rs4821481 and 3752462.11 All call and replication rates for our markers exceeded 97 and

14

99%, respectively. G1 haplotype frequency was in Hardy Weinberg Equilibrium (HWE) in our

diabetic cohort and showed slight deviation with excess risk homozygotes in our non-DM

cohort, consistent with selection of excess risk alleles in our CKD cases. G2 allele frequency

was in HWE in both our diabetic and non-diabetic cohorts.

Estimated Ancestry

Ancestry informative genetic markers were based on the IBC Illumina chip array

panel. QC metrics for our Illumina based BeadChip genotype results included testing for

HWE, sex concordance, excess heterozygosity, call rates and relatedness. We used 1053

ancestry informative markers that were shared between our genotyping platform and with

HapMap3. We employed an admixture model using the software Structure to derive global

European and African continental genetic ancestry measures. We found 96.7% and 98.8%

concordance rate between self-reported white and black race with our genotype derived

European and African ancestry classifications, respectively.

Outcomes

Two measures of CKD progression were our primary outcomes - the rate of decline of

kidney function (eGFR slope) and the composite renal event of either ESRD (initiation of

maintenance dialysis or kidney transplantation) or a decline in eGFR of at least one-half from

baseline.6 Our primary exposures were genotype derived African or European ancestry and

among AAs the presence or absence of APOL1 risk genotype.

Statistical Analysis

We used descriptive statistics to compare clinical characteristics across subgroups

defined by genetically inferred race, APOL1 risk genotype, and the presence or absence of

diabetes. We described mean duration of follow-up, crude event rates (expressed per 100

15

person years of follow-up), number of events, and means of eGFR slopes (estimated by

ordinary least squares). We also fit a series of multivariable models to estimate the

associations between the primary exposures and the outcomes. We compared outcomes

among all African Americans, and African Americans with and without the APOL1 risk

genotype to European Americans.

For each outcome, we fit a set of hierarchical models retaining all covariates from

each prior model regardless of statistical significance. In model 1, we adjusted for

demographic variables, clinical site and baseline eGFR. In Model 2, we added

socioeconomic variables (education level, nephrologist use, and ACE/ARB use). In Model 3,

we added clinical risk factors (systolic blood pressure, body mass index, hemoglobin A1c,

and smoking status). In Model 4, we added 24-hour urine protein excretion. Because

proteinuria may be on the causal pathway for CKD progression, we selected model 3 as our

primary explanatory model.

In time-to-event analyses, we used Cox proportional hazards regression models to

evaluate the relationship of exposures with the composite renal outcome. Participants were

censored either at the time of death, study withdrawal, their last study visit (telephone or in

person), or on 3/31/2011 (the administratively defined end of follow-up for this report),

whichever occurred first. Baseline eGFR was modeled using quadratic splines (with one

knot at the median). The natural log of baseline 24 hour urine protein excretion was used

because of the non-normal distribution of this variable expressed on its natural scale.12

Analyses of the rate of decline of kidney function were based on mixed effects models

of repeated eGFR values measured over time. In these analyses, the difference in eGFR

slope between comparison groups was expressed as ml/min/1.73m2 per year. Baseline

eGFR was modeled without spline terms, and 24 hour urine protein was modeled on its

natural scale.

16

Analyses of the effect of the APOL1 genotype among African Americans were adjusted for

age, gender, clinical site, baseline eGFR and global percent African ancestry to avoid

confounding by admixture.

Sensitivity analyses

We also tested for any association between the MYH9 E1 haplotype based on two E1

tagging SNPs rs4821481 and 3752462.11 Risk genotype was determined based on the

recessive model with 2 risk alleles vs. 0 or 1 risk alleles. We found an approximate

0.5ml/min/year faster decline in participants with and without diabetes with the MYH9 risk

genotype, however this did not reach statistical significance (Supplementary Table S9). For

our time to renal event outcome we similarly found a modest HR of 1.1 for both diabetic and

non-diabetic cases, which did not reach statistical significance. The inclusion of MYH9 did

not modify the associations between APOL1 genotype and our renal outcomes. We also did

not find an association between having a single APOL1 risk allele versus having no risk

allele, confirming a recessive model best suited for APOL1 risk alleles and CKD progression.

Measured iothalamate glomerular filtration rate (iGFR) and slope was available in a

subset of 334 African Americans and 444 European Americans participants and used in a

sensitivity analysis against our eGFR derived slopes. Since we found similar effect of APOL1

in diabetic and non-diabetic cases, we did not stratify by diabetes for this smaller subgroup

analysis. Results from our adjusted analysis for African Americans vs. European Americans

iGFR and estimated GFR slope were relatively similar (Supplementary Table S8).

Since our Renal Events outcome consisted of a composite outcome (i.e., ESRD or

halving of eGFR), we conducted a sensitivity analysis looking at African Americans vs.

European Americans for time to ESRD alone. Results were generally consistent with those

obtained from our composite Renal Event outcomes, demonstrating no skewing of our

17

African Americans vs. European Americans increased HR for time to event due to

differentials in ESRD vs. halving of eGFR measures (Supplementary Table S7).

While blood pressure may act as a confounder between the differential progression

rate of CKD in African Americans vs. European American participants, we cannot rule out

that in may also be on the causal pathway for CKD progression. Accordingly, in order to

better assess the effect of adjustment of blood pressure on our effect estimates, separate

from other co-variates in our main multivariate model, we looked at our main explanatory

Model 3 with and without the inclusion of blood pressure. The exclusion of systolic BP from

our multivariate analysis did not meaningfully modify the relationship between European

Americans and African Americans (Supplementary Table S11).

All analyses were conducted using SAS version 9.3 (SAS Institute, Cary, NC). All

statistical tests were 2-sided. P values <0.05 were considered significant.

18

Bibliography for Expanded Methods

1. Appel LJ, Middleton J, Miller ER, 3rd, et al. The rationale and design of the AASK

cohort study. J Am Soc Nephrol 2003;14:S166-72.

2. Patterson N, Hattangadi N, Lane B, et al. Methods for high-density admixture mapping

of disease genes. American journal of human genetics 2004;74:979-1000.

3. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome

association and population-based linkage analyses. American journal of human

genetics 2007;81:559-75.

4. Genovese G, Friedman DJ, Ross MD, et al. Association of trypanolytic ApoL1 variants

with kidney disease in African Americans. Science 2010;329:841-5.

5. Lash JP, Go AS, Appel LJ, et al. Chronic Renal Insufficiency Cohort (CRIC) Study:

baseline characteristics and associations with kidney function. Clinical journal of the

American Society of Nephrology : CJASN 2009;4:1302-11.

6. Feldman HI, Appel LJ, Chertow GM, et al. The Chronic Renal Insufficiency Cohort

(CRIC) Study: Design and Methods. J Am Soc Nephrol 2003;14:S148-53.

7. Fischer MJ, Go AS, Lora CM, et al. CKD in Hispanics: Baseline characteristics from

the CRIC (Chronic Renal Insufficiency Cohort) and Hispanic-CRIC Studies. Am J

Kidney Dis 2011;58:214-27.

8. Anderson AH, Yang W, Hsu CY, et al. Estimating GFR among participants in the

Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis 2012;60:250-61.

9. Genovese G, Friedman DJ, Pollak MR. APOL1 variants and kidney disease in people

of recent African ancestry. Nat Rev Nephrol 2013;9:240-4.

10. Kopp JB, Nelson GW, Sampath K, et al. APOL1 genetic variants in focal segmental

glomerulosclerosis and HIV-associated nephropathy. J Am Soc Nephrol

2011;22:2129-37.

19

11. Winkler CA, Nelson G, Oleksyk TK, Nava MB, Kopp JB. Genetics of focal segmental

glomerulosclerosis and human immunodeficiency virus-associated collapsing

glomerulopathy: the role of MYH9 genetic variation. Seminars in nephrology

2010;30:111-25.

12. Finkle WD, Greenland S, Miettinen OS, Ziel HK. Endometrial cancer risk after

discontinuing use of unopposed conjugated estrogens (California, United States).

Cancer causes & control : CCC 1995;6:99-102.

20

Supplementary Table S1. Baseline Characteristics of 1) AASK Participants Included in the Present Analysis (N=693), 2) Participants who Failed Genotyping (N=143), and 3) Participants who did not provide DNA/Genetic Consent or Reached ESRD Prior to DNA/Genetic Consent (N=258)

All Genotyped

and included Failed

genotyping P-value1

No DNA/consent

collected P-value2

N 1094 693 143 - 258 -

Age, years 54.6 (10.7) 54.1 (10.6) 54.0 (10.8) 0.90 56.4 (10.6) 0.003

Female, % 38.8 40.3 37.1 0.48 36.0 0.24

BMI, kg/m2 30.6 (6.6) 31.1 (6.7) 30.5 (5.6) 0.30 29.2 (6.7) <0.001

Mean baseline GFR, ml/min/1.73m

2

46.4 (13.6) 47.3 (13.5) 48.9 (13.3) 0.21 42.6 (13.4) <0.001

Serum creatinine, mg/dL 2.0 (0.7) 2.0 (0.7) 2.0 (0.7) 0.91 2.1 (0.7) 0.02

Urinary protein:creatinine,* mg/g

80.8 (29.8, 359.3)

74.2 (27.4, 307.4)

61.1 (28.4, 251.9)

0.44 111.6

(39.6, 359.3) <0.001

Proteinuria, % 32.7 30.4 26.6 0.36 42.2 0.001

History of heart disease, % 51.6 50.5 49.0 0.74 55.8 0.15

Baseline mean arterial pressure, mmHg

113.8 (16.0) 114.1 (16.4) 112.8 (13.3) 0.40 113.6 (16.3)

0.70

P-value 1 pertains to comparison of “failed genotyping” to “genotyped and included” p-value 2 pertains to comparison of “No DNA/consent collected” to “genotyped and included” *UPC shown as median (interquartile ranges)

21

Supplementary Table S2. Hazard Ratio of the Composite Renal Outcome of Incident ESRD or Doubling of Serum Creatinine Associated with APOL1 Risk, Stratified by Baseline Proteinuria (trial and cohort phases of AASK)

Proteinuria No Proteinuria

Low-Risk APOL1 group 1.00 (ref) 1.00 (ref)

High-Risk APOL1 group 1.30 (0.94, 1.81) 1.95 (1.30, 2.93)

P interaction 0.16

22

Supplementary Table S3. Hazard Ratio of the Composite Renal Outcome of Incident ESRD or a 50% Reduction in GFR Associated with Intervention Group, Stratified by APOL1 Risk Group (trial phase only of AASK)

Blood Pressure Goal Initial Drug Therapy

Standard Control

Intensive Control Other ACE-I

High-Risk APOL1 group

1.00 (ref) 1.13 (0.77, 1.67) 1.00 (ref) 0.71 (0.48, 1.07)

Low-Risk APOL1 group

1.00 (ref) 1.22 (0.69, 2.17) 1.00 (ref) 0.74 (0.42, 1.31)

P interaction 0.72 0.72

23

Supplementary Table S4. Distribution of Incident Outcomes by APOL1/MYH9 Haplotypes in 693 AASK Participants

To distinguish the genetic effect of APOL1 and MYH9, we defined the following groups: 1. Individuals with zero copies of the APOL1 G1, G2, and MYH9 E1 haplotypes (reference) 2. Individuals with 1 copy of APOL1 G1, 0 of APOL1 G2, and any copy of MYH9 E1 3. Individuals with 1 copy of APOL1 G2, 0 of APOL1 G1, and any copay of MYH9 E1 4. Individuals with 1 copy of MYH9 E1 and 0 of APOL1 G1 or G2 5. Individuals with 2 copies of APOL1 G1 and any copies of MYH9 E1 6. Individuals with 1 copy of APOL1 G1 and 1 copy of APOL1G2 and any copy of MYH9 E1 7. Individuals with 2 copies of APOL1 G2 and any copy of MYH9 E1 8. Individuals with 2 copies of MYH9 E1 and 0 copy of either APOL1 G1 or G2

Group Haplotype N ESRD ESRD or Doubling of Serum Creatinine

Death before ESRD

1 Reference 64 18 (28.1%) 24 (37.5%) 5 (7.8%)

2 1 copy of G1 191 47 (24.6%) 72 (37.7%) 21 (11.0%)

3 1 copy of G2 108 22 (20.4%) 40 (37.0%) 12 (11.1%)

4 1 copy of E1 136 29 (21.3%) 49 (36.0%) 20 (14.7%)

5 2 copies of G1 67 30 (44.8%) 37 (55.2%) 6 (9.0%)

6 1 copy of G1 / 1 copy of G2

70 39 (55.7%) 46 (65.7%) 7 (10.0%)

7 2 copies of G2 23 10 (43.5%) 10 (43.5%) 3 (13.0%)

8 2 copies of E1 and no G1, G2

34 9 (26.5%) 10 (29.4%) 3 (8.8%)

All - 693 204 (29.4%) 288 (41.6%) 77 (11.1%)

24

Supplementary Table S5. Expanded Table of Baseline Characteristics and Events in CRIC, Stratified by Baseline Diabetes, Ancestry, and APOL1 Status

With Diabetes Without Diabetes

Characteristic

EA (n=624)

All AA (n=722)

AA APOL1 Low-Risk

(n=610)

AA APOL1 High-Risk

(n=112)

EA

(n=920)

All AA (n=689)

AA APOL1 Low-Risk

(n=531)

AA APOL1 High-Risk

(n=158)

Age (S.D) 59.5 (9.8) 60.0 (9.4) 60.0 (9.2) 59.6 (10.2) 58.7 (11.5) 56.1 (11.6)* 57.8 (10.6) 50.5 (13.0)++

Male sex 411 (65.9%) 345 (47.8%)* 296 (48.5%) 49 (43.8%) 509 (55.3%) 348 (50.5%) 265 (49.9%) 83 (52.5%)

Education

Less than high school 48 (7.7%) 216 (29.9%)* 180 (29.5%) 36 (32.1%) 38 (4.1%) 157 (22.8%)* 127 (23.9%) 30 (19.0%)

High school graduate 122 (19.6%) 157 (21.7%)† 140 (23.0%) 17 (15.2%) 154 (16.7%) 157 (22.8%)* 123 (23.2%) 34 (21.5%)

Some college 207 (33.2%) 240 (33.2%) 199 (32.6%) 41 (36.6%) 238 (25.9%) 246 (35.7%)* 187 (35.2%) 59 (37.3%)

College graduate or higher

247 (39.6%) 109 (15.1%)* 91 (14.9%) 18 (16.1%) 490 (53.3%) 129 (18.7%)* 94 (17.7%) 35 (22.2%)

Current non-smoker 568 (91.0%) 603 (83.5%)* 512 (83.9%) 91 (81.3%) 826 (89.8%) 539 (78.2%)* 415 (78.2%) 124 (78.5%)

Hypertension 550 (88.1%) 688 (95.3%)* 582 (95.4%) 106 (94.6%) 669 (72.7%) 625 (90.7%)* 476 (89.6%) 149 (94.3%)

Systolic blood pressure (S.D)

125.6 (19.1) 136.6 (23.6)* 136.2 (24.2) 138.7 (20.0) 119.3 (17.7) 128.9 (21.7)* 129.9 (21.7) 125.6 (21.4)+

Diastolic blood pressure (S.D)

66.9 (11.3) 71.6 (13.5)* 71.0 (13.5) 74.5 (13.3)+ 70.4 (11.0) 76.4 (13.8)* 76.1 (13.4) 77.2 (15.0)

ACE-I or ARB use 505 (81.5%) 579 (80.6%) 492 (81.1%) 87 (78.4%) 524 (57.3%) 419 (61.3%) 314 (59.7%) 105 (66.9%)

Body Mass Index (kg/m2) 33.9 (8.2) 35.3 (8.2)

‡ 35.2 (8.3) 35.4 (7.8) 29.5 (6.3) 31.6 (7.6)* 31.5 (7.6) 31.6 (7.8)

Baseline eGFR 43.4 (14.7) 41.3 (14.8)* 41.4 (14.8) 40.8 (15.1) 50.8 (17.9) 46.6 (17.4)* 46.2 (17.0) 48.0 (18.5)

Hemoglobin A1C 7.5 (1.5) 7.9 (1.8)* 7.9 (1.8) 7.9 (1.8) 5.6 (0.5) 5.8 (0.6)* 5.8 (0.6) 5.8 (0.6)

Uric Acid (mg/dL) 7.3 (1.9) 7.8 (1.9)* 7.9 (1.9) 7.6 (1.7) 6.8 (1.8) 7.6 (1.8)* 7.6 (1.8) 7.6 (1.8)

24H Urine Protein (g/24H)

Mean(S.D) 1.1 (2.6) 1.5 (2.8)‡ 1.5 (2.7) 1.9 (3.4) 0.4 (1.1) 0.6 (1.3)

‡ 0.5 (1.1) 0.9 (1.6)++

Median 0.2 0.4 0.4 0.6 0.1 0.1 0.1 0.4

Events

Time F/U in years (S.D) 4.2 (2.2) 3.8 (2.2)* 3.8 (2.2) 3.5 (2.2) 4.9 (2.0) 4.4 (2.2)* 4.5 (2.2) 4.2 (2.2)

eGFR slope (S.D) -1.5 (4.3) -2.9 (4.9)* -2.7 (4.7) -4.3 (5.6)+ -0.7 (3.1) -1.4 (4.2)* -1.0 (4.0) -2.9 (4.5)

++

Number of Renal Events 152 (24.4%) 274 (38%)* 220 (36.1%) 54 (48.2%)+ 95 (10.3%) 155 (22.5%)* 106 (20.0%) 49 (31.0%)**

Renal Event rate§ 5.8 10.1* 9.5 13.7+ 2.1 5.1* 4.4 7.5

++

Number of iESRD (rate) 100 (3.2) 219 (6.5)* 177 (6.2) 42 (8.1) 67 (1.3) 122 (3.4)* 82 (2.9) 40 (5.0)**

Number of deaths (rate) 82 (3.3) 95 (4.1) 82 (4.0) 13 (4.3) 69 (1.5) 58 (1.9) 47 (2.0) 11 (1.7)

Number of withdrawals (percent)

28 (5.9%) 29 (6.5%) 24 (6.2%) 5 (8.6%) 44 (5.3%) 30 (5.6%) 27 (6.4%) 3 (2.8%)

AA = African American, EA = European American. APOL1 risk genotype based on recessive model. AA APOL1 high-risk = all African Americans with APOL1 high-risk genotype, AA APOL1 low-Risk= all African Americans with APOL1 low-risk genotype. S.D= standard deviation; Hypertension= blood pressure >140/90 or on anti-hypertensive medication. eGFR = estimated glomerular filtration rate in ml/min/1.73m

2. Renal event= end-stage renal disease (ESRD) or halving of eGFR,

rate§ = event rate per 100 person year, iESRD= incident ESRD, number of withdrawals= number of participants who

withdrew from study prior to having a renal event. † P<0.05 between European Americans and all African Americans; ‡ P<0.01 between European Americans and all African Americans * P<0.001 between European Americans and all African Americans; + P<0.05 between APOL1 high-risk and low-risk group; ++ P<0.001 between APOL1 high-risk and low-risk group; ** P<0.01 between APOL1 high-risk and low-risk group

25

Supplementary Table S6. APOL1 Alleles and Genotype Distribution in African-Americans, CRIC Study

With Diabetes (n=722 )

Without Diabetes (n=689)

0 Risk allele

1 Risk allele

2 Risk alleles

0

Risk allele 1

Risk allele 2

Risk alleles

G1 allele 59.1% 34.8% 6.1% 55.3% 33.1% 11.6%

G2 allele 73.6% 23.4% 3.1% 72% 25.7% 2.3%

APOL1 risk alleles 39.1% 45.4% 15.5% 36.3% 40.8% 22.9%

G1 allele based on rs73885319, G2 allele based on rs71785313 APOL1 risk allele = total number of either risk G1 or G2 risk alleles

26

Supplementary Table S7. APOL1 High-Risk vs. Low-Risk Genotype Effect on eGFR Slope and Risk of the Composite Renal Outcome in African Americans, CRIC Study

eGFR slope Risk of Renal Event

Estimate(95%CI) P value

HR (95%CI) P value

Diabetic -1.07(-1.83, -0.32) 0.005 1.46 (1.08, 1.98) 0.015

Non-diabetic -1.21(-1.83, -0.60) <0.001 1.61 (1.11, 2.33) 0.012

eGFR = estimated glomerular filtration rate. AA = African American. APOL1 risk genotype based on recessive model. eGFR slope based on mixed effect linear regression. Renal event = composite of incident end stage renal disease or a reduction of 50% in the eGFR from baseline on cox-modeling. HR = hazard ratio. Both models adjusted for age, gender, clinical site, baseline eGFR and percent global African Ancestry.

27

Supplementary Table S8. Hazard Ratios for Time to ESRD Alone, Comparing all African Americans and African Americans Stratified by APOL1 Genotype with all European Americans in Multivariable Models, Stratified by Diabetes Status, CRIC study

With Diabetes Without Diabetes

Cox model Comparison groups HR P value

HR P value

Model 1 all AA vs. all EA 1.94

(1.50, 2.51) <0.001

2.56 (1.82, 3.60)

<0.001

Model 1 APOL1 High-Risk AA vs. all EA 2.41

(1.66, 3.52) <0.001

3.69 (2.36, 5.77)

<0.001

Model 1 APOL1 Low-Risk AA vs. all EA 1.85

(1.42, 2.41) <0.001

2.26 (1.58, 3.25)

<0.001

Model 2 all AA vs. all EA 1.97

(1.51, 2.57) <0.001

2.48 (1.74 ,3.53)

<0.001

Model 2 APOL1 High-Risk AA vs. all EA 2.48

(1.69, 3.62) <0.001

3.74 (2.37 ,5.92)

<0.001

Model 2 APOL1 Low-Risk AA vs. all EA 1.87

(1.42, 2.46) <0.001

2.14 (1.47 ,3.13)

<0.001

Model 3 all AA vs. all EA 1.57

(1.19, 2.06) 0.002

2.08 (1.44, 3.01)

<0.001

Model 3 APOL1 High-Risk AA vs. all EA 1.87

(1.26, 2.76) 0.002

3.53 (2.20, 5.66)

<0.001

Model 3 APOL1 Low-Risk AA vs. all EA 1.50

(1.13, 2.0) 0.005

1.74 (1.18, 2.58)

0.006

Model 4 all AA vs. all EA 1.44

(1.09, 1.92) 0.011

2.14 (1.48, 3.09)

<0.001

Model 4 APOL1 High-Risk AA vs. all EA 1.49

(0.99, 2.23) 0.054

3.59 (2.18, 5.89)

<0.001

Model 4 APOL1 Low-Risk AA vs. all EA 1.43

(1.07, 1.92) 0.017

1.85 (1.25, 2.73)

0.002

AA = African American, EA = European American. ESRD = end-stage renal disease. APOL1 risk based on recessive model. all AA risk vs. all EA = all African Americans with APOL1 high-risk genotype compare to all European Americans. APOL1 high-risk AA vs. all EA= all African Americans with APOL1 high-risk genotype vs. all European Americans APOL1 low-risk AA vs. all EA= all African Americans without APOL1 high-risk genotype vs. all European Americans. Model 1= Multivariate base model: adjusted for age, gender, clinical site and baseline eGFR Model 2= Model 1 + education, nephrologist use and ACE/ARB use Model 3= model 2 + systolic blood pressure, body mass index, HBA1c and smoking Model 4= model 3 + 24 hour proteinuria

28

Supplementary Table S9. Sensitivity Analysis, iGFR and eGFR Differences in Slope between African Americans Stratified by APOL1 Genotype compared to all European Americans, CRIC Study

iGFR

(n=334 AA, 444 EA )

eGFR

(n=1255 AA, 1438 EA)

Comparison groups

Estimate (95%CI)

P value Estimate (95%CI)

P value

APOL1 High-Risk AA vs. all EA -1.08

(-2.27, 0.10) 0.07

-1.09 (-1.50, -0.69)

<0.001

APOL1 Low-Risk AA vs. all EA 0.43

(-0.28, 1.13) 0.23

-0.05 (-0.30, 0.21)

0.72

iGFR slope = iothalamate glomerular filtration rate in ml/min/1.73m2/year, eGFR = estimated glomerular filtration rate in ml/min/1.73m2/year. AA = African American, EA = European American. APOL1 risk genotype based on recessive model. All AA vs. all EA = all African Americans compare to all European Americans. APOL1 high-risk AA vs. all EA = all African Americans with APOL1 high-risk genotype compare to all European Americans. APOL1 low-risk AA vs. all EA= all African Americans without APOL1 high-risk genotype vs. all European Americans. Adjusted for age, gender, clinical site, baseline eGFR, education, nephrologist use and ACE/ARB, systolic blood pressure, body mass index, HBA1c and smoking.

29

Supplementary Table S10. MYH9 Risk vs. Low-Risk Genotype Effect on eGFR Slope and Time to Renal Event in African Americans, CRIC Study

eGFR slope

Time to Renal Event

Estimate(95%CI) P value

HR (95%CI) P value

With Diabetes -0.45(-1.02, 0.12) 0.12 1.1 (0.8, 1.4) 0.55

Without Diabetes -0.50(-1.01, 0.01) 0.06 1.1 (0.8, 1.6) 0.49

MYH9 risk genotype based on E1 tagging SNPs rs4821481 and 3752462. Risk genotype was determined based on the recessive model with 2 risk haplotypes vs. 0 or 1 risk haplotype. eGFR slope = estimated glomerular filtration rate in ml/min/1.73m2/year . AA = African American. eGFR slope based on mixed effect linear regression and time to Renal Event = incident end stage renal disease or a reduction of 50% in the eGFR, based on cox-modeling. Both models adjusted for age, gender, clinical site, baseline eGFR and percent global African ancestry.

30

Supplementary Table S11. Multivariable Analyses of Differences in eGFR Slope and Hazard Ratios Comparing all African Americans and African Americans stratified by APOL1 Risk Status vs all European Americans, Stratified by Baseline Diabetes Status, Model 3 without systolic BP, CRIC Study

Difference in eGFR Slope

Hazard Ratio (HR)

With Diabetes Without Diabetes With Diabetes Without Diabetes

Multivariate Model

Comparison groups

Estimate ml/min/year

(95%CI) P value

Estimate ml/min/yr (95%CI)

P value HR

(95%CI) P value

HR

(95%CI) P value

Model 3 without BP

all AA vs. all EA -0.84

(-1.25, -0.43) <0.001

-0.38 (-0.69, -0.07)

0.017 1.78

(1.42, 2.24) <0.001

2.26

(1.64, 3.09) <0.001

Model 3 without BP

APOL1-Risk AA vs. all EA

-1.84 (-2.56, -1.11)

<0.001 -1.26

(-1.76, -0.76) <0.001

2.51

(1.79, 3.52) <0.001

3.34

(2.23, 5.01) <0.001

Model 3 without BP

APOL1 Low-risk AA vs. all EA

-0.67 (-1.09, -0.25)

0.0018 -0.13

(-0.46, 0.20) 0.44

1.66

(1.31, 2.11) <0.001

1.95

(1.39, 2.74) <0.001

eGFR slope = estimated glomerular filtration rate in ml/min/1.73m2/year. AA = African American, EA = European American. APOL1 risk based on recessive model. All AA risk vs. all EA = all AA with APOL1 high-risk genotype compared to all EA. APOL1 high-risk AA vs. all EA = all AA with APOL1 high-risk genotype vs. all EA. APOL1 low-risk AA vs. all EA = all AA without APOL1 high-risk genotype vs. all EA. HR = hazard ratio for composite renal outcome of incident end stage renal disease or a reduction of 50% in the eGFR from baseline. w/o BP = without blood pressure. Model 3 w/o BP = Multivariable-adjusted for: age, gender, clinical site, baseline eGFR, education, nephrologist use, ACE/ARB use, body mass index, HBA1c and smoking