ARTICLE
Exome Genotyping Identifies Pleiotropic Variants
Associated with Red Blood Cell Traits
Nathalie Chami,1,2,91 Ming-Huei Chen,3,91 Andrew J. Slater,4,5,91 John D. Eicher,3
Evangelos Evangelou,6,7 Salman M. Tajuddin,8 Latisha Love-Gregory,9 Tim Kacprowski,10,11
Ursula M. Schick,12 Akihiro Nomura,13,14,15,16,17 Ayush Giri,18 Samuel Lessard,1,2 Jennifer A. Brody,19
Claudia Schurmann,12,20 Nathan Pankratz,21 Lisa R. Yanek,22 Ani Manichaikul,23 Raha Pazoki,24
Evelin Mihailov,25 W. David Hill,26,27 Laura M. Raffield,28 Amber Burt,29 Traci M. Bartz,30
Diane M. Becker,22 Lewis C. Becker,31 Eric Boerwinkle,32,33 Jette Bork-Jensen,34 Erwin P. Bottinger,12
Michelle L. O’Donoghue,35 David R. Crosslin,36 Simon de Denus,2,37 Marie-Pierre Dubé,1,2 Paul Elliott,6
Gunnar Engström,38,39 Michele K. Evans,8 James S. Floyd,19 Myriam Fornage,40 He Gao,6
Andreas Greinacher,41 Vilmundur Gudnason,42,43 Torben Hansen,34 Tamara B. Harris,44
Caroline Hayward,45 Jussi Hernesniemi,46,47,48 Heather M. Highland,32,49
(Author list continued on next page)
Red blood cell (RBC) traits are important heritable clinical biomarkers and modifiers of disease severity. To identify coding genetic
variants associated with these traits, we conducted meta-analyses of seven RBC phenotypes in 130,273 multi-ethnic individuals
from studies genotyped on an exome array. After conditional analyses and replication in 27,480 independent individuals, we identified
16 new RBC variants. We found low-frequency missense variants in MAP1A (rs55707100, minor allele frequency [MAF] ¼ 3.3%, p ¼
2 3 10 10 for hemoglobin [HGB]) and HNF4A (rs1800961, MAF ¼ 2.4%, p < 3 3 10 8 for hematocrit [HCT] and HGB). In African Americans, we identified a nonsense variant in CD36 associated with higher RBC distribution width (rs3211938, MAF ¼ 8.7%, p ¼ 7 3 10 11)
and showed that it is associated with lower CD36 expression and strong allelic imbalance in ex vivo differentiated human erythroblasts.
We also identified a rare missense variant in ALAS2 (rs201062903, MAF ¼ 0.2%) associated with lower mean corpuscular volume and
mean corpuscular hemoglobin (p < 8 3 10 9). Mendelian mutations in ALAS2 are a cause of sideroblastic anemia and erythropoietic
protoporphyria. Gene-based testing highlighted three rare missense variants in PKLR, a gene mutated in Mendelian non-spherocytic
hemolytic anemia, associated with HGB and HCT (SKAT p < 8 3 10 7). These rare, low-frequency, and common RBC variants showed
pleiotropy, being also associated with platelet, white blood cell, and lipid traits. Our association results and functional annotation suggest the involvement of new genes in human erythropoiesis. We also confirm that rare and low-frequency variants play a role in the
architecture of complex human traits, although their phenotypic effect is generally smaller than originally anticipated.
Introduction
One in four cells in the human body is a mature enucleated
red blood cell (RBC), also called an erythrocyte. RBC mean
lifespan in adults is 100–120 days, requiring constant
renewal. To that end, we produce on average 2.4 million
RBCs per second in the bone marrow. This massive yet
well-orchestrated cell proliferation process is necessary to
1
Department of Medicine, Université de Montréal, Montréal, QC H3T 1J4, Canada; 2Montreal Heart Institute, Montréal, QC H1T 1C8, Canada; 3Population
Sciences Branch, National Heart, Lung, and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, USA; 4Genetics Target Sciences,
GlaxoSmithKline, Research Triangle Park, NC 27709, USA; 5OmicSoft Corporation, Cary, NC 27513, USA; 6Department of Epidemiology and Biostatistics,
MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK; 7Department of Hygiene and
Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece; 8Laboratory of Epidemiology and Population Sciences, National Institute
on Aging, NIH, Baltimore, MD 21224, USA; 9Department of Medicine, Center of Human Nutrition, Washington University School of Medicine, St Louis,
MO 63110, USA; 10Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine, Greifswald and
Ernst-Mortiz-Arndt University Greifswald, Greifswald 17475, Germany; 11DZHK (German Centre for Cardiovascular Research), partner site Greifswald,
Greifswald QA, Germany; 12The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10069,
USA; 13Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA; 14Program in Medical and Population Genetics,
Broad Institute, Cambridge, MA 02142, USA; 15Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; 16Department
of Medicine, Harvard Medical School, Boston, MA 02115, USA; 17Division of Cardiovascular Medicine, Kanazawa University, Graduate School of Medical
Science, Kanazawa, Ishikawa 9200942, Japan; 18Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt
Genetics Institute, Vanderbilt University, Nashville, TN 37235, USA; 19Department of Medicine, University of Washington, Seattle, WA 98101, USA;
20
The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY 10069, USA; 21Department of
Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55454, USA; 22Department of Medicine/Division of General Internal Medicine, Johns Hopkins University, School of Medicine, Baltimore, MD 21205, USA; 23Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; 24Department of Epidemiology, Erasmus, MC Rotterdam 3000, the Netherlands; 25Estonian Genome Center, University of Tartu, Tartu
51010, Estonia; 26Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; 27Department of Psychology,
University of Edinburgh, Edinburgh EH8 9JZ, UK; 28Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA; 29Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; 30Department of Biostatistics, University of Washington,
(Affiliations continued on next page)
Ó 2016 American Society of Human Genetics.
8
The American Journal of Human Genetics 99, 8–21, July 7, 2016
Joel N. Hirschhorn,14,50 Albert Hofman,24,51 Marguerite R. Irvin,52 Mika Kähönen,53,54 Ethan Lange,55
Lenore J. Launer,44 Terho Lehtimäki,46,47 Jin Li,56 David C.M. Liewald,26,27 Allan Linneberg,57,58,59
Yongmei Liu,60 Yingchang Lu,12,20 Leo-Pekka Lyytikäinen,46,47 Reedik Mägi,25 Rasika A. Mathias,61
Olle Melander,38,39 Andres Metspalu,25 Nina Mononen,46,47 Mike A. Nalls,62 Deborah A. Nickerson,63
Kjell Nikus,48,64 Chris J. O’Donnell,3,65 Marju Orho-Melander,38,39 Oluf Pedersen,34 Astrid Petersmann,66
Linda Polfus,32 Bruce M. Psaty,67,68 Olli T. Raitakari,69,70 Emma Raitoharju,46,47 Melissa Richard,40
Kenneth M. Rice,30 Fernando Rivadeneira,24,71,72 Jerome I. Rotter,73,74 Frank Schmidt,10
Albert Vernon Smith,42,43 John M. Starr,26,75 Kent D. Taylor,73,74 Alexander Teumer,76 Betina H. Thuesen,57
Eric S. Torstenson,18 Russell P. Tracy,77 Ioanna Tzoulaki,6,7 Neil A. Zakai,78 Caterina Vacchi-Suzzi,79
Cornelia M. van Duijn,24 Frank J.A. van Rooij,24 Mary Cushman,78 Ian J. Deary,26,27
Digna R. Velez Edwards,80 Anne-Claire Vergnaud,6 Lars Wallentin,81 Dawn M. Waterworth,82
Harvey D. White,83 James G. Wilson,84 Alan B. Zonderman,8 Sekar Kathiresan,13,14,15,16 Niels Grarup,34
Tõnu Esko,14,25 Ruth J.F. Loos,12,20,85 Leslie A. Lange,28 Nauder Faraday,86 Nada A. Abumrad,9
Todd L. Edwards,18 Santhi K. Ganesh,87,91 Paul L. Auer,88,91 Andrew D. Johnson,3,91
Alexander P. Reiner,89,90,91,* and Guillaume Lettre1,2,91,*
accommodate RBCs’ main function: to transport oxygen
from the lungs to the peripheral organs, and carbon dioxide from the organs to the lungs. Hemoglobin (HGB), the
metalloprotein that constitutes by far the most abundant
biomolecule found in mature RBCs, is responsible for
oxygen transport. In addition to their critical role in the
circulatory system, RBCs also have secondary, often lessappreciated, functions. Within blood vessels, they respond
Seattle, WA 98195, USA; 31Department of Medicine/Divisions of Cardiology and General Internal Medicine, Johns Hopkins University School of Medicine,
Baltimore, MD 21205, USA; 32Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030,
USA; 33Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA; 34The Novo Nordisk Foundation, Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2100, Denmark; 35TIMI Study Group, Cardiovascular
Division, Brigham and Women’s Hospital, Boston, MA 02115, USA; 36Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA; 37Faculty of Pharmacy, Université de Montréal, Montréal, QC H3T 1J4, Canada; 38Department of Clinical Sciences, Malmö,
Lund University, Malmö 221 00, Sweden; 39Skåne University Hospital, Malmö 222 41, Sweden; 40Institute of Molecular Medicine, The University of Texas
Health Science Center at Houston, Houston, TX 77030, USA; 41Institute for Immunology and Transfusion Medicine, University Medicine Greifswald,
Greifswald 17475, Germany; 42Icelandic Heart Association, 201 Kopavogur, Iceland; 43Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland;
44
Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Intramural Research Program, NIH, Bethesda, MD 20892, USA;
45
MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK; 46Department of Clinical
Chemistry, Fimlab Laboratories, Tampere 33520, Finland; 47Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014,
Finland; 48University of Tampere, School of Medicine, Tampere 33014, Finland; 49Department of Epidemiology, University of North Carolina at Chapel
Hill, Chapel Hill, NC 27514, USA; 50Department of Endocrinology, Boston Children’s Hospital, Boston, MA 02115, USA; 51Department of Epidemiology,
Harvard TH Chan School of Public Health, Boston, MA 02115, USA; 52Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35233, USA; 53Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland; 54Department of
Clinical Physiology, University of Tampere School of Medicine, Tampere 33014, Finland; 55Departments of Genetics and Biostatistics, University of North
Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; 56Department of Medicine, Division of Cardiovascular Medicine, Stanford University, School of Medicine, Palo Alto, CA 94305, USA; 57Research Centre for Prevention and Health, The Capital Region of Denmark, Copenhagen 2600, Denmark; 58Department
of Clinical Experimental Research, Rigshospitalet, Glostrup 2100, Denmark; 59Department of Clinical Medicine, Faculty of Health and Medical Sciences,
University of Copenhagen, Copenhagen 2200, Denmark; 60Center for Human Genetics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA; 61Department of Medicine, Divisions of Allergy and Clinical Immunology and General Internal Medicine, Johns
Hopkins University School of Medicine, Baltimore, MD 21205, USA; 62Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda,
MD 20892, USA; 63Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA; 64Department of Cardiology, Heart Center, Tampere University Hospital, Tampere 33521, Finland; 65Cardiology Section and Center for Population Genomics, Boston Veteran’s Administration (VA)
Healthcare, Boston, MA 02118, USA; 66Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald 17475,
Germany; 67Cardiovascular Health Research Unit, Departments of Medicine Epidemiology and Health Services, University of Washington, Seattle, WA
98101, USA; 68Group Health Research Institute, Group Health Cooperative, Seattle, WA 98101, USA; 69Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland; 70Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku
20520, Finland; 71Department of Internal Medicine, Erasmus MC, Rotterdam 3000, the Netherlands; 72Netherlands Consortium for Healthy Ageing
(NCHA), Rotterdam 3015, the Netherlands; 73Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Torrance, CA 90502, USA; 74Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA 90502, USA; 75Alzheimer Scotland Research Centre, Edinburgh EH8 9JZ, UK; 76Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany; 77Departments of Pathology and
Laboratory Medicine and Biochemistry, University of Vermont College of Medicine, Colchester, VT 05446, USA; 78Departments of Medicine and Pathology,
University of Vermont College of Medicine, Burlington, VT 05405, USA; 79Department of Family Population and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA; 80Vanderbilt Epidemiology Center, Department of Obstetrics & Gynecology, Institute for Medicine and Public Health,
Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37203, USA; 81Department of Medical Sciences, Cardiology and Uppsala Clinical
Research Center, Uppsala University, Uppsala 751 85, Sweden; 82Genetics Target Sciences, GlaxoSmithKline, King of Prussia, PA 19406, USA; 83Green
Lane Cardiovascular Service, Auckland City Hospital and University of Auckland, Auckland 1142, New Zealand; 84Department of Physiology and
Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA; 85The Mindich Child Health and Development Institute, Icahn School
of Medicine at Mount Sinai, New York, NY 10069, USA; 86Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of
Medicine, Baltimore, MD 21205, USA; 87Departments of Internal Medicine and Human Genetics, University of Michigan, Ann Arbor, MI 48108, USA;
88
Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53205, USA; 89Department of Epidemiology, University of Washington, Seattle, WA 98195, USA; 90Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
91
These authors contributed equally to this work
*Correspondence: apreiner@u.washington.edu (A.P.R.), guillaume.lettre@umontreal.ca (G.L.)
http://dx.doi.org/10.1016/j.ajhg.2016.05.007.
The American Journal of Human Genetics 99, 8–21, July 7, 2016 9
to shear stress and produce the vasodilator nitric oxide to
regulate vascular tonus.1 RBCs participate in antimicrobial
strategies to fight hemolytic pathogens2 and in the inflammatory response, acting as a reservoir for multiple chemokines.3 Furthermore, the direct involvement of RBCs in
adhering to the vascular endothelium or supporting
thrombin generation may help to promote blood coagulation or thrombosis.4,5
Given the paramount importance of RBCs in physiology, it is not surprising that monitoring their features
is common practice in medicine to assess the overall
health of patients. An excessive number of circulating
RBCs (erythrocytosis [MIM: 133100]) can suggest a primary bone marrow disease, a myeloproliferative neoplasm
such as polycythemia vera (MIM: 263300), or chronic
hypoxemia due to congenital heart defects. Low HGB
concentration and hematocrit (HCT) levels (anemia) can
indicate inherited HGB or RBC structural gene mutations,
malnutrition, or kidney diseases. By considering the volume (mean corpuscular volume [MCV]), hemoglobin content (mean corpuscular hemoglobin [MCH] and mean
corpuscular hemoglobin concentration [MCHC]) or the
distribution width (RDW) of RBCs, a physician can distinguish between the different causes of anemia (e.g., microcytic/hypochromic due to iron deficiency6). In addition,
epidemiological studies have correlated high RDW values
with a worse prognosis in heart failure patients.7 RDW is
also an independent predictor of overall mortality in
healthy individuals, as well as a predictor of mortality in
patients with various conditions such as cardiovascular
diseases, obesity, malignancies, and chronic kidney disease.8–12
RBC count and indices vary among individuals, and
40%–90% of this phenotypic variation is heritable.13–16
Identifying the genes and biological pathways that
contribute to this inter-individual variation in RBC traits
could highlight modifiers of severity and/or therapeutic
options for several hematological diseases. Already, largescale genome-wide association studies (GWASs) have
found dozens of SNPs associated with one or more of
these RBC traits.17,18 However, owing to their design,
GWASs are largely insensitive to rare (minor allele frequency [MAF] < 1%) and low-frequency (1% % MAF <
5%) genetic variants. Using an exome array, we previously
performed an association study for HGB and HCT in
31,340 European-ancestry individuals and identified rare
coding or splice site variants in the erythropoietin and
b-globin genes.19 Within the framework of the BloodCell Consortium (BCX),20,21 we now report a larger genotyping-based exome survey of seven RBC traits conducted
in up to 130,273 individuals, including 23,896 participants of non-European ancestry. With this experiment,
our initial goals were to expand the list of rare and lowfrequency coding or splice site variants associated with
RBC traits and to explore whether the exome array
can complement the GWAS approach to fine map RBC
causal genes.
10 The American Journal of Human Genetics 99, 8–21, July 7, 2016
Subjects and Methods
Study Participants
The Blood-Cell Consortium (BCX) aims to identify novel common
and rare variants associated with blood-cell traits using an exome
array. BCX is comprised of more than 134,021 participants from
24 discovery cohorts and five ancestries: European, African American, Hispanic, East Asian, and South Asian. Detailed description
of the participating cohorts is provided in Table S1. BCX is interested in the genetics of all main hematological measures and is
divided into three main working groups: RBC, white blood cell
(WBC),21 and platelet (PLT).20 For the RBC working group, we
analyzed seven traits available in up to 130,273 individuals: RBC
count (31012/L), HGB (g/dL), HCT (%), MCV (fL), MCH (pg),
MCHC (g/dL), and RDW (%) (Table S2). The BCX procedures
were in accordance with the institutional and national ethical
standards of the responsible committees and proper informed
consent was obtained.
Genotyping and Quality-Control Steps
Participants from the different studies were genotyped on one of
the following exome chip genotyping arrays: Illumina ExomeChip v.1.0, Illumina ExomeChip v.1.1_A, Illumina ExomeChip12 v.1.1, Affymetrix Axiom Biobank Plus GSKBB1, or Illumina
HumanOmniExpressExome Chip. Genotypes were then called
either (1) with the Illumina GenomeStudio GENCALL and subsequently recalled using zCALL or (2) by the Cohorts for Heart and
Aging Research in Genomic Epidemiology (CHARGE) Consortium
Exome Chip effort22 (Table S3). The same quality-control steps
were followed by each participating study. We excluded variants
with low genotyping success rate (<95%, except for WHI that
used a cutoff <90%) (Table S3). Samples with call rate < 95%
(except for SOLID-TIMI 52 and STABILITY that used 94.5% and
93.5% cutoffs, respectively) after joint or zCALL calling and with
outlying heterozygosity rate were also excluded. Other exclusions
were deviation from Hardy-Weinberg equilibrium (p < 1 3 10 6)
and gender mismatch. We performed principal-component analysis (PCA) or multidimensional scaling (MDS) and excluded sample outliers from the resulting plots through visual inspection,
using populations from the 1000 Genomes Project to anchor these
analyses. Keeping only autosomal and X chromosome variants for
the analysis, we aligned all variants on the forward strand and
created a uniform list of reference alleles using the --force alleles
command in PLINK.23 Finally, an indexed variant call format file
(VCF) was created by each study and checked for allele alignment
and any allele or strand flips using the checkVCF package.24 Prior
to performing meta-analyses of the association results provided by
each participating study, we ran the EasyQC protocol25 to check
for population allele frequency deviations and proper trait transformation in each cohort.
Phenotype Modeling and Association Analyses
When possible, we excluded individuals with blood cancer, leukemia, lymphoma, bone marrow transplant, congenital or hereditary anemia, HIV, end-stage kidney disease, dialysis, splenectomy,
or cirrhosis and those with extreme measurements of RBC traits
(Table S1). We also excluded individuals on erythropoietin treatment or chemotherapy. Additionally, we excluded pregnant
women and individuals with acute medical illness at the time
the complete blood count (CBC) was done. For the seven RBC
traits, within each study, we adjusted for age, age-squared, gender,
the first ten principal components, and, where applicable, other
study-specific covariates such as study center via a linear regression model. Within each study, we then applied inverse normal
transformation on the residuals and tested the phenotypes for association with the ExomeChip variants using either RVtests
(v.20140416)26 or RAREMETALWORKER.0.4.9.27
Discovery Meta-analyses
Score files generated by RVtests or RAREMETALWORKER from
each participating study were used to carry out meta-analyses of
the single variant association results using RareMETALS v.5.9.28
All analyses were performed separately in each of European
American (EA) and African American (AA) ancestries. In the
multi-ancestry meta-analyses, we combined individuals of European, African American, Hispanic, East-Asian, and South-Asian ancestries (All). We included variants with allele frequency difference
between the highest and lowest MAF < 0.3 for EA and AA ancestries and < 0.6 for the combined ancestry meta-analyses. For the
gene-based analyses, we used score files and variance-covariance
matrices from the study-specific association results and applied
the sequence kernel association test (SKAT)29 and variable
threshold (VT) algorithms30 in RareMETALS considering only
missense, nonsense, and splice site variants with a MAF < 1%.
Gene-based analyses were also stratified by ancestry. Significance
thresholds were determined using Bonferroni correction assuming
~250,000 independent variants (p < 2 3 10 7 for the singlevariant analyses) and ~17,000 genes tested on the ExomeChip
(p < 3 3 10 6 for the gene-based tests).
Conditional Analysis and Replication
In order to identify independent signals, we performed conditional analyses. In each round of conditional analysis, we conditioned on the most significant single variant in a 1 Mb window.
These conditional analyses were performed at the meta-analysis
level using RareMETALS. We repeated this step until there
were no new signals identified in each region, defined as p <
2 3 10 7. We then checked for linkage disequilibrium (LD) within
the list of variants that was retained from the conditional analyses.
For variants that were in moderate-to-strong LD (r2 R 0.3), we kept
the most significant. We attempted replication of the final list of
independent variants in eight additional studies that contributed
a total of 27,480 individuals (n ¼ 21,473 for EA and n ¼ 6,007
for AA) (Table S4). The division of discovery and replication samples was dictated by timing because we collected all groups we
were aware of for initial discovery and then found others who
could participate only much later and hence were used for replication. These studies followed similar analytical procedures and
steps as those followed by the discovery analysis (see above).
A joint meta-analysis of the discovery and the replication results
was carried out using a fixed-effects model and inverse-variance
weighting as implemented in METAL.31 We considered as replicated markers those with a nominal preplication < 0.05 and an effect
on phenotype in the same direction as in the discovery results.
Allelic Imbalance and Expression of CD36
We checked for allelic imbalance (AI) of the rs3211938 variant in
CD36 (MIM: 173510) as well as the expression of the gene in 12
samples of fetal liver erythroblasts obtained from anonymous donors. Details on the protocol including RNA extraction
and sequencing can be found elsewhere.32 We calculated the
difference in the ratio of reads of the reference allele (T) and the
alternate allele (G) of rs3211938. In brief, reads overlapping
rs3211938 were counted with samtools (v.1.1) mpileup software
using genome build hg19. We kept uniquely mapping reads using
-q 50 argument (mapping quality > 50) and sites with base
quality > 10. Statistical significance of the difference in the ratio
of reads between the reference allele and the alternate allele was
assessed with a binomial test. For each sample, we summed all
reads overlapping all heterozygous SNPs and calculated the expected ratio within each SNP allele combination. Reads that fall
in the top 25th coverage percentile were down-sampled so that
the highest covered sites do not bias the expected ratio.33 For
rs3211938, the expected T:G ratio was 0.507.
Expression Quantitative Trait Loci Analysis
We cross-referenced our list of RBC novel variants with more than
100 separate expression quantitative trait loci (eQTL) published
datasets. Datasets were collected through publications, publically
available sources, and private collaborations. A general overview
of a subset of >50 eQTL studies has been published,34 with specific
citations for >100 datasets included in the current query followed
here. A complete list of tissues and studies used can be found in the
Supplemental Data. We considered SNPs that are themselves
expression SNPs (eSNP) when they meet a p < 0.0001 threshold
or when they are in LD (r2 > 0.3) with the best eSNP (p < 0.0001).
Results
Single-Variant Meta-analyses
We meta-analyzed ExomeChip results for seven RBCrelated phenotypes (RBC count, HCT, HGB, MCH,
MCHC, MCV, and RDW) available in up to 130,273 individuals from 24 studies and 5 ancestries (Tables S1–S3
and Figure S1). Across these different phenotypes, a total
of 226 variants reached exome-wide significance (p <
2 3 10 7) in the combined ancestry analyses (Figures 1
and S2). Given that some of these RBC traits are correlated
(Figure S3), these associated variants highlight 71 different
loci (defined using a 1 Mb interval). Overall, we observed
only modest inflation of the test statistics (lGC ¼ 1.03–
1.05), consistent with little confounding due to technical
artifacts, population stratification, or cryptic relatedness.
In order to identify independent variants, we performed
conditional analyses at the meta-analysis level adjusting
for the effect of the most significant variant in a 1 Mb
region in a stepwise manner (Subjects and Methods).
After this analysis, we obtained a list of 126 independent variants associated with at least one RBC trait at p <
2 3 10 7 (Table S5). Selecting only variants that lie more
than 1 Mb away from a known GWAS locus resulted in
23 independent variants located within 20 novel RBC
loci, where novel is used to define loci not found in the existing literature (Table 1). We attempted to replicate these
126 variants in 8 independent cohorts totaling 27,480
participants (Table S5). Overall, we observed a strong replication, with 94 of the 126 variants showing consistent
direction of effect between the discovery and replication
analyses (binomial p ¼ 3 3 10 8; Table S5). Of the 23 novel
RBC variants, we replicated 16 at nominal p < 0.05 for at
The American Journal of Human Genetics 99, 8–21, July 7, 2016 11
Figure 1. Quantile-Quantile Plots of Single-Variant Association Results in the All Ancestry Meta-analyses for the Seven Red Blood Cell
Traits Analyzed
(A) Distribution of the single variant results for all variants tested on the exome array.
(B) Only markers with a minor allele frequency < 5% are shown here.
(C) Variants outside of known RBC GWAS regions. Variants that are within 1 Mb from a previously published RBC GWAS locus were
excluded for this QQ plot.
Abbreviations are as follows: HCT, hematocrit; HGB, hemoglobin; RBC, red blood cell count; MCV, mean corpuscular volume; MCHC,
mean corpuscular hemoglobin concentration; MCH, mean corpuscular hemoglobin; RDW, red blood cell distribution width.
least one RBC trait (binomial p ¼ 3 3 10 16; Table 1). Out
of these 16 novel and replicated RBC variants, there are five
missense variants, including two variants with MAF < 5%
in MAP1A (MIM: 600178) and HNF4A (MIM: 600281) and
one nonsense variant in CD36 (Table 1). Among the remaining nine novel and replicated RBC variants, there
are five intronic, one synonymous, one 50 UTR, and one intergenic marker (Table 1).
Prioritization of Candidate Genes and Genetic
Variants
Our single-variant analyses in EA samples identified one
rare missense variant in ALAS2 (MIM: 301300) associated
with MCV and MCH (rs201062903, p.Pro507Leu
[c.1559C>T], MAF ¼ 0.2%) (Table 1). The association
with this variant did not replicate, potentially because of
limited statistical power (the replication sample size for
this rare marker was 5,044; see also Discussion). ALAS2 encodes 5-aminolevulinate synthase 2, the rate-controlling
enzyme of erythroid heme synthesis. Additionally, rare
mutations in ALAS2 cause X-linked sideroblastic anemia
(MIM: 300751) and erythropoietic protoporphyria (MIM:
300752). Thus, despite the lack of replication, ALAS2
remains an excellent candidate gene to modulate RBC
traits. The ALAS2 p.Pro507Leu variant, which is not reported in the ClinVar database, maps between two amino
acids (Tyr506 and Thr508) that are important for catalytic
activity and known to be mutated in cases of sideroblastic
anemia.35
Two low-frequency missense variants identified in our
analyses implicate MAP1A and HNF4A in RBC biology
(Table 1). MAP1A encodes microtubule-associated protein
1A, a gene highly expressed in the nervous system and
mostly studied in the context of neuronal diseases,
although it is expressed in many additional tissues,
12 The American Journal of Human Genetics 99, 8–21, July 7, 2016
including hematopoietic cells.36 Deletion of MAP1A in
the mouse causes defects in synaptic plasticity.37 This
observation is interesting given that inactivation of
ANK1 (MIM: 612641), another gene that encodes a cytoskeleton protein and is expressed in neurons and RBCs, is
associated with neurological dysfunction in the mouse
and spherocytosis and hemolytic anemia in humans
(MIM: 182900). Our meta-analyses confirmed two known
independent ANK1 variants associated with MCHC: an intronic SNP (rs4737009, MAF ¼ 19.8%, p ¼ 1.3 3 10 8)
and a low-frequency missense variant (rs34664882,
p.Ala1462Val, MAF ¼ 2.9%, p ¼ 1.7 3 10 16) (Table S5;
N.P., U.M.S., J.B.-J., and M.-H.C., unpublished data).17
In the accompanying BCX PLT article,20 we report that
the same MAP1A rs55707100 allele (p.Pro2349Leu
[c.7046C>T]) associated here with decreased HGB concentration is also associated with increased PLT count. Furthermore, recent studies have identified associations between
rs55707100 and HDL-cholesterol and triglyceride levels
(S. Mukherjee, 2015, ASHG, conference). Adding to the
complexity, the GTEx dataset indicates that rs55707100
is an expression quantitative trait locus (eQTL) for ADAL
(peQTL ¼ 9 3 10 11) but not for MAP1A.38 ADAL is a poorly
characterized adenosine deaminase-like protein that is
highly expressed in human erythroblasts. However, the
eQTL association between rs55707100 and ADAL could
simply reflect ‘‘LD shadowing’’ from nearby markers that
are much stronger eQTL variants for ADAL. Indeed,
rs3742971 (a common variant located in ADAL’s 50 UTR)
is in partial LD with rs55707100 (r2 ¼ 0.18 in European populations from the 1000 Genomes Project) and strongly associated with ADAL expression levels
(peQTL ¼ 6 3 10 49).
The second low-frequency missense variant associated
with HGB and HCT maps within the coding sequence of
Table 1.
Association Results of Variants in Novel Loci Associated with Red Blood Cell Traits
Marker Info
Trait
Position
Discovery
A1/A2 SNP
Annotation Gene
n
Replication
AF (A2) Beta (SE)
p Value
n
Combined
AF (A2) Beta (SE)
p Value
RDW-EA 1: 25,768,937
A/G
rs10903129* intron
TMEM57-RHD 45,573
0.544
0.037 (0.007) 1.19 3 10
7
RDW-All 1: 25,768,937
A/G
rs10903129* intron
TMEM57-RHD 56,194
0.568
0.034 (0.006) 9.58 3 10
8
24,474 0.600
0.021 (0.01)
HCT-All
C/T
rs4072037*
109,875 0.554
0.025 (0.005) 5.82 3 10
8
25,006 0.563
0.038 (0.009) 5.96 3 10
3,162
1: 155,162,067
synonymous MUC1
Beta (SE)
p Value
18,475 0.560
0.023 (0.011) 0.0373
0.033 (0.006) 2.41 3 10
8
0.03 (0.005)
8
0.0252
5
1.32 3 10
0.027 (0.004) 3.47 3 10
11
0.012 (0.026) 0.6410
0.023 (0.044) 1.68 3 10
7
HGB-All
2: 27,741,237
T/C
rs780094
intron
GCKR
130,273 0.626
0.024 (0.004) 7.14 3 10
8
RBC-All
2: 219,509,618
C/A
rs2230115*
missense
ZNF142
74,488
0.509
0.033 (0.006) 9.74 3 10
9
27,442 0.477
0.024 (0.01)
0.0167
0.031 (0.005) 7.11 3 10
10
HCT-All
3: 56,771,251
A/C
rs3772219*
missense
ARHGEF3
109,875 0.338
0.028 (0.005) 2.38 3 10
9
25,006 0.366
0.021 (0.01)
0.0292
0.027 (0.004) 2.56 3 10
10
HGB-All
3: 56,771,251
A/C
rs3772219*
missense
ARHGEF3
130,273 0.336
0.026 (0.004) 3.76 3 10
9
27,749 0.367
0.02 (0.009)
0.0331
0.025 (0.004) 4.33 3 10
10
HCT-EA
4: 88,008,782
G/A
rs236985
intron
AFF1
87,444
0.394
0.032 (0.005) 3.89 3 10
10
19,968 0.405
0.02 (0.011)
0.0626
0.03 (0.005)
10
RBC-EA
4: 88,008,782
G/A
rs236985*
intron
AFF1
60,231
0.393
0.034 (0.006) 3.50 3 10
8
21,435 0.405
0.023 (0.011) 0.0273
0.031 (0.005) 4.22 3 10
9
21,743 0.586
0.029 (0.01)
0.0052
0.033 (0.004) 8.23 3 10
15
0.626
1.14 3 10
AFF1
106,377 0.595
0.034 (0.005) 3.97 3 10
13
rs10063647* intron
LINC01184SLC12A2
45,573
0.463
0.05 (0.007)
1.72 3 10
13
18,475 0.480
0.033 (0.011) 0.0018
0.045 (0.006) 2.88 3 10
15
A/G
rs10063647* intron
LINC01184SLC12A2
56,194
0.506
0.044 (0.006) 2.11 3 10
12
24,474 0.545
0.03 (0.01)
0.04 (0.005)
2.37 3 10
14
RDW-EA 5: 127,522,543
C/T
rs10089*
utr_5p
LINC01184SLC12A2
45,573
0.21
0.051 (0.008) 8.45 3 10
10
16,692 0.215
0.058 (0.014) 2.71 3 10
0.053 (0.007) 1.15 3 10
13
RDW-All 5: 127,522,543
C/T
rs10089*
utr_5p
LINC01184SLC12A2
56,194
0.207
0.044 (0.008) 4.08 3 10
9
22,691 0.208
0.045 (0.012) 0.0001
0.044 (0.006) 2.73 3 10
12
HGB-All
C/A
rs35742417* missense
RREB1
130,273 0.174
0.030 (0.005) 1.17 3 10
8
4,074
0.207
0.065 (0.028) 0.0190
0.032 (0.005) 1.50 3 10
9
RDW-AA 7: 80,300,449
T/G
rs3211938*
nonsense
CD36
6,666
0.087
0.174 (0.031) 2.36 3 10
8
5,999
0.086
0.139 (0.032) 1.83 3 10
5
0.161 (0.025) 7.09 3 10
11
RDW-All 7: 80,300,449
T/G
rs3211938*
nonsense
CD36
55,510
0.012
0.171 (0.029) 5.29 3 10
9
22,691 0.023
0.139 (0.032) 1.61 3 10
5
0.157 (0.022) 5.12 3 10
13
16,692 0.466
0.026 (0.011) 0.0210
0.034 (0.006) 1.29 3 10
8
HGB-EA
4: 88,030,261
G/T
rs442177*
RDW-EA 5: 127,371,588
A/G
RDW-All 5: 127,371,588
The American Journal of Human Genetics 99, 8–21, July 7, 2016 13
6: 7,247,344
intron
0.0014
5
RDW-EA 8: 126,490,972
A/T
rs2954029*
intergenic
TRIB1
45,573
0.46
0.036 (0.007) 1.53 3 10
7
RDW-All 8: 126,490,972
A/T
rs2954029*
intergenic
TRIB1
56,194
0.439
0.032 (0.006) 1.83 3 10
7
22,691 0.432
0.021 (0.01)
0.0298
0.029 (0.005) 2.54 3 10
8
MCH-All 10: 105,659,826 T/C
rs2487999
missense
OBFC1
66,318
0.869
0.047 (0.009) 4.12 3 10
8
26,749 0.861
0.025 (0.013) 0.0601
0.041 (0.007) 1.75 3 10
8
MCH-AA 11: 92,722,761
G/A
rs1447352
intergenic
MTNR1B
8,273
0.557
0.089 (0.016) 1.85 3 10
8
5,038
0.022 (0.02)
0.07 (0.014)
6
HGB-EA
C/T
rs55707100* missense
MAP1A
106,377 0.033
0.071 (0.013) 1.65 3 10
8
21,743 0.0223
0.099 (0.033) 0.0028
A/G
rs2667662*
TELO2
10,849
0.099 (0.015) 1.79 3 10
10
5,034
0.724
0.093 (0.022) 3.02 3 10
0.134 (0.025) 7.08 3 10
8
6,002
0.124
0.106 (0.027) 0.0001
15: 43,820,717
MCV-AA 16: 1,551,082
MCV-AA 16: 2,812,939
C/A
rs2240140*
intron
missense
SRRM2
8,525
0.725
0.118
0.562
0.2713
5
1.08 3 10
0.075 (0.012) 2.31 3 10
10
0.098 (0.014) 7.32 3 10
12
0.128 (0.022) 5.24 3 10
9
(Continued on next page)
14 The American Journal of Human Genetics 99, 8–21, July 7, 2016
Table 1.
Continued
Marker Info
Trait
Position
Discovery
A1/A2 SNP
Annotation Gene
n
Replication
AF (A2) Beta (SE)
p Value
n
Combined
AF (A2) Beta (SE)
p Value
HCT-EA
17: 59,017,025
T/C
rs8080784
intron
BCAS3-TBX2
79,344
0.158
0.039 (0.007) 2.62 3 10
8
HGB-EA
17: 59,483,766
C/T
rs8068318
intron
BCAS3-TBX2
106,377 0.722
0.026 (0.005) 1.53 3 10
7
21,743 0.730
0.021 (0.011) 0.0565
MCV-EA 20: 31,140,165
C/T
rs4911241*
intron
NOL4L
61,462
0.04 (0.007)
1.25 3 10
8
21,714 0.252
0.025 (0.012) 0.0302
18,475 0.240
0.241
Beta (SE)
p Value
19,968 0.148
0.011 (0.014) 0.4349
0.029 (0.006) 3.39 3 10
6
0.025 (0.005) 2.55 3 10
8
0.036 (0.006) 2.01 3 10
9
0.049 (0.012) 7.44 3 10
5
0.045 (0.007) 2.01 3 10
11
5
0.04 (0.006)
4.60 3 10
11
RDW-EA 20: 31,140,165
C/T
rs4911241*
intron
NOL4L
45,573
0.242
0.043 (0.008) 5.79 3 10
8
RDW-All 20: 31,140,165
C/T
rs4911241*
intron
NOL4L
56,194
0.235
0.038 (0.007) 1.56 3 10
7
24,474 0.222
0.044 (0.011) 6.10 3 10
HCT-EA
20: 43,042,364
C/T
rs1800961*
missense
HNF4A
79,344
0.024
0.083 (0.015) 1.44 3 10
8
19,968 0.033
0.082 (0.028) 0.0037
0.083 (0.013) 1.91 3 10
10
HGB-EA
20: 43,042,364
C/T
rs1800961*
missense
HNF4A
98,277
0.032
0.073 (0.013) 2.53 3 10
8
21,743 0.032
0.062 (0.027) 0.0232
0.071 (0.012) 1.93 3 10
9
HCT-All
20: 43,042,364
C/T
rs1800961*
missense
HNF4A
100,313 0.022
0.077 (0.014) 2.31 3 10
8
25,006 0.027
0.091 (0.028) 0.0010
0.08 (0.012)
11
HGB-All
22: 44,324,727
C/G
rs738409
missense
PNPLA3
130,273 0.223
0.028 (0.005) 2.24 3 10
8
4,074
0.218
0.053 (0.027) 0.0504
0.029 (0.005) 4.81 3 10
9
5,855
0.001
0.291 (0.235) 0.215
0.323 (0.052) 5.81 3 10
10
9.88 3 10
MCH-EA X: 55,039,960
G/A
rs201062903 missense
ALAS2
52,758
0.002
0.324 (0.053) 7.32 3 10
10
MCH-All X: 55,039,960
G/A
rs201062903 missense
ALAS2
65,067
0.002
0.322 (0.051) 3.36 3 10
10
10,893 0.001
0.276 (0.224) 0.218
0.321 (0.051) 2.68 3 10
10
MCV-EA X: 55,039,960
G/A
rs201062903 missense
ALAS2
60,211
0.002
0.285 (0.049) 7.11 3 10
9
5,044
0.178 (0.248) 0.472
0.282 (0.049) 6.11 3 10
9
0.001
Variants in novel loci with p < 2 3 10 7 and that were retained after conditional analyses are presented here. All variants are >1 Mb apart from a known GWAS signal for RBC traits. Chromosome positions are given on human
genome build hg19. Allele frequency and effect size are given for the alternate (A2) allele. Replication was carried out in six cohorts for EA and two cohorts for AA and was performed in RareMetals; meta-analyses of the
discovery and replication cohorts are presented under ‘‘Combined’’ and were carried out in METAL. Asterisks (*) indicate variants that replicated with a nominal p < 0.05. Abbreviations are as follows: EA, European American;
AA, African American; All, combined ancestry (EA þ AA þ Asians þ Hispanics); A1, reference allele; A2, alternate allele; N, sample size; AF, allele frequency; SE, standard error; HCT, hematocrit; HGB, hemoglobin; RBC, red
blood cell count; MCV, mean corpuscular volume; MCHC, mean corpuscular hemoglobin concentration; MCH, mean corpuscular hemoglobin; RDW, red blood cell distribution width.
Figure 2. CD36 Expression in Human
Erythroblasts
(A) In a dataset of 12 human fetal liver
erythroblasts, all samples were homozygous at rs3211938 for the reference T-allele
with the exception of one heterozygous
sample (FL11). FL11 demonstrated strong
allelic imbalance: we observed 705 reads
for the reference allele (T) and 126
reads for the alternate allele (G) (binomial
p ¼ 4.9 3 10 95).
(B) FL11 (in green) shows the lowest
CD36 expression level when compared to
the other 11 samples. Abbreviation is as
follows: FPKM, fragments per kilobase of
transcript per million mapped reads.
the transcription factor HNF4A (Table 1). This marker,
rs1800961 (p.Thr117Ile [c.350C>T]), has previously been
associated with HDL and total cholesterol, C-reactive protein, fibrinogen, and coagulation factor VII levels.39–42 Mutations in HNF4A cause maturity-onset diabetes of the
young (MODY [MIM: 125851]) and a common intronic
SNP in HNF4A (rs4812829) has been associated with
type 2 diabetes (MIM: 125853) risk.43 The missense
rs1800961 associated with HGB and HCT is only in weak
LD with rs4812829 (r2 ¼ 0.021 in EA populations from
the 1000 Genomes Project). Querying recently released
ExomeChip data from Type 2 Diabetes Genetics (Web Resources), we found that rs1800961 is also associated with
T2D risk in ~82,000 participants (p ¼ 9.5 3 10 7, odds
ratio ¼ 1.16). HNF4A is expressed in the kidney and could
influence HGB and HCT through the regulation of erythropoietin production.44 It is also abundantly expressed in the
liver, where it could indirectly affect HGB and HCT levels
through an effect on blood lipid levels (see Discussion).
HNF4A is detectable at low levels in erythroblasts, and
the BLUEPRINT Project has found that some HNF4A isoforms may be more highly expressed in this cell type
(Figure S4).45
In AA, we identified a nonsense variant (rs3211938,
p.Tyr325Ter [c.975T>G], MAF ¼ 8.7%, p ¼ 7.1 3 10 11)
in CD36 associated with RDW. This variant displays a
wide variation in allele frequency between AA and EA
(MAFEA ¼ 0.01%). The association is slightly improved in
the ancestry-combined meta-analysis (p ¼ 5.1 3 10 13)
because there is also evidence of association in Hispanics
(MAF ¼ 1.9%, p ¼ 0.022) (Table 1). We examined a dataset
of ex vivo differentiated human erythroblasts to determine
whether this CD36 nonsense variant shows allelic imbalance (AI).32 All samples were homozygous at rs3211938
for the reference allele with the exception of one heterozygous sample (FL11). FL11 had the lowest level of CD36
expression among the 12 samples tested and demonstrated
strong AI where we observe 705 sequence reads for the
reference allele (T) versus 126 for the alternate allele (G)
(p ¼ 4.9 3 10 95; Figure 2). To confirm this finding in independent samples, we queried the GTEx dataset, which has
compiled RNA-sequencing and genotype information
from multiple human tissues.38 GTEx does not include
data for human erythroblasts. However, it detected a
strong eQTL effect of rs3211938 on CD36 expression in
whole blood (peQTL ¼ 1.1 3 10 15), with carriers of the
G-allele expressing less CD36 (Figure S5). Furthermore,
GTEx reported evidence for moderate AI in multiple tissues
for CD36-rs3211938, with the G-allele being under-represented among sequence reads (Figure S5). These results
strongly support our observations in human erythroblasts.
eQTL Analysis
To prioritize additional causal genes at RBC loci that
contain non-coding variants, we cross-referenced our list
of novel variants with more than 100 published eQTL datasets (Subjects and Methods). Overall, 12 variants were significant eQTLs in a wide variety of tissues (Table S6). The
most interesting eQTL finding is the association between
rs10903129, a common marker associated with RDW in
our analyses and located within an intron of TMEM57
(MIM: 610301), and the expression of RHD (MIM:
111680) in whole blood. RHD is located 112 kb downstream of TMEM57 and encodes the D antigen of the clinically significant Rhesus (Rh) blood group. rs10903129 has
also been associated with total cholesterol levels and erythrocyte sedimentation rate (ESR).46,47 The association with
ESR is particularly intriguing given that it is considered a
non-specific indicator of inflammation. As described
above, RDW is also abnormal in chronic diseases, such as
atherosclerosis and diabetes, which have an important
inflammation component.
Gene-Based Association Testing
Despite our large sample size, statistical power remains
limited for rare variants of weak-to-moderate phenotypic effect. To try to capture these genetic factors, we performed
gene-based testing by aggregating coding and splice site variants with MAF < 1% within each gene (Subjects and
Methods). The SKAT analyses identified two genes: ALAS2
associated with MCH and PKLR (MIM: 609712) associated
with HGB and HCT (Table 2). The ALAS2 signal was driven
The American Journal of Human Genetics 99, 8–21, July 7, 2016 15
Table 2.
Gene-Based Association Results
VT
Trait
Gene
n
Number of
Variants Analyzed
SKAT
p Value
HGB-EA
PKLR
106,377
15
1.92 3 10
HGB-All
PKLR
130,273
15
0.00016
p Value
5
Top Variant
Top-Variant MAF
Top-Variant p Value
7.02 3 10
7
rs116100695
0.003
1.17 3 10
5
6.57 3 10
7
rs116100695
0.003
1.94 3 10
5
7.95 3 10
7
rs116100695
0.003
2.49 3 10
5
7
rs201062903
0.002
7.32 3 10
10
rs202037221
3.0 3 10
HCT-All
PKLR
109,875
15
3.96 3 10
5
MCH-EA
ALAS2
54,009
11
4.78 3 10
6
5.79 3 10
MCHC-All
ALPK3
84,841
28
1.95 3 10
6
0.793
5
0.0005
6
Gene-based results of the VT and SKAT algorithms for genes associated with RBC traits at p < 3 3 10 . We analyzed non-synonymous coding (nonsense,
missense) and splice site variants with a minor allele frequency (MAF) < 1%. Abbreviations are as follows: EA, European American; All, combined ancestry
(EA þ AA þ Asians þ Hispanics); n, sample size; HCT, hematocrit; HGB, hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCH, mean corpuscular hemoglobin.
by a single rare missense variant (rs201062903) and was
described above. PKLR encodes the erythrocyte pyruvate kinase (PK) that catalyzes the last step of glycolysis. PK deficiency, usually caused by recessive mutations, is one of the
main causes of non-spherocytic hemolytic anemia (MIM:
266200). In fact, one of the variants identified in our metaanalysis (rs116100695, p.Arg486Trp [c.1456T>G], MAF ¼
0.3%, betaHGB ¼ 0.242 g/dl, pHGB ¼ 1.2 3 10 5) is a
frequent cause of PK deficiency in Italian and Spanish subjects.48,49 This variant was confirmed in the replication cohorts (preplication ¼ 0.039; Table S7). Two additional PKLR
rare missense variants contribute to the gene-based association statistic with HGB and HCT: rs61755431 (p.Arg569Gln
[c.1706G>A], MAF ¼ 0.2%, betaHGB ¼ 0.179 g/dl, pHGB ¼
0.006) and rs8177988 (p.Val506Ile [c.1516G>A], MAF ¼
0.6%, betaHGB ¼ þ0.116 g/dl, pHGB ¼ 0.003). It is noteworthy that the p.Val506Ile substitution is associated with
increased HGB concentration given that this amino acid
maps to a PKLR structural domain necessary for protein
interaction.50 This heterogeneity of effect among the PKLR
missense variants might explain why SKAT’s result is
more significant than VT’s for this gene (Table 2). A third
gene, ALPK3, was identified only in the VT analysis for
association with MCHC (Table 2). ALPK3 encodes a kinase
previously implicated in cardiomyocyte differentiation.51
We could not test for replication because of the rarity of
ALPK3’s coding alleles (Table S7).
RBC Variants and Pleiotropic Effects
Besides the overlap within the RBC traits themselves, we
identified seven novel RBC variants associated with other
blood-cell type traits or with lipid levels (Figure 3 and Table 3). To assess whether the genetic associations with
RBC traits are independent of lipid levels, we performed
additional analyses in a subset of BCX participants from
three of our studies (FHS, MHIBB, and WHI) ranging
from ~10,000 to 23,000 individuals. We repeated the
association analyses for five RBC loci (TMEM57-RHD
rs10903129, AFF1 rs442177, TRIB1 rs2954029, MAP1A
rs55707100, and HNF4A rs1800961) additionally adjusting
for the respective lipid trait and combined the results
across the three studies using fixed-effect meta-analysis
16 The American Journal of Human Genetics 99, 8–21, July 7, 2016
(Table S8). There was little or no change in the effect size
or p values associated with the five RBC trait loci upon
adjustment for the corresponding lipid trait, suggesting
that the RBC and lipid associations are independent of
one another and thus represent true ‘‘pleiotropic’’ genetic
effects.
A correlated response to or role in inflammation might
explain why some of the RBC variants are also associated
with WBC, PLT, or lipid traits. Another plausible explanation for the concomitant association of several markers
with RBC, WBC, and PLT phenotypes could be a more general effect of these genes on the proliferation or differentiation of hematopoietic progenitor cells. This is most
likely the case for JAK2 (MIM: 147796) and SH2B3
(MIM: 605093), two key regulators of hematopoietic cells
(Figure 3). In this category, we also observed two novel findings, AFF1 (MIM: 159557) and NOL4L, which are associated
with RBC and WBC phenotypes and have been previously
implicated in leukemia.53,54 Finally, we identified a novel
missense variant in ARHGEF3 (MIM: 612115) associated
with HGB and HCT. In addition to its association with
PLT traits, ARHGEF3 plays a role in the regulation of iron
uptake and erythroid cell maturation.55
Discussion
We present multi-ethnic meta-analyses of seven RBC traits
using ExomeChip results of 130,273 individuals. Our statistical thresholds to declare significance at the discovery
stage (p < 2 3 10 7 in the single-variant analyses) was
adjusted for the approximate number of variants genotyped on the ExomeChip (Bonferroni correction for
250,000 variants), but we decided not to adjust it for the
seven RBC phenotypes tested because of the high correlation between some of these traits (Figure S3). Instead, we
relied on independent replication to distinguish true
from probably false positive associations. Despite the
limited size of our replication set (27,480 individuals), it
was encouraging to detect a strong replication of direction
of effect for known and novel RBC variants, suggesting a
low false discovery rate. In total, we identified 23 novel
Figure 3. Venn Diagram Summarizing Pleiotropic Effects for
Genetic Variants Associated with Red Blood Cell Traits
We considered variants only if their association p values with
white blood cell (WBC) traits, platelet (PLT) traits, or with lipid
levels was p < 1 3 10 4. Results for WBC and PLT are from the
accompanying Blood-Cell Consortium articles.20,21 Results for
lipids have previously been published (Table 3). Genes highlighted
in red are novel RBC trait findings.
variants associated with RBC traits in the single-variant analyses and a collection of three rare missense variants in
PKLR associated with HGB and HCT in the gene-based analyses. Out of the 23 novel RBC variants, 16 were replicated
at p < 0.05 in the independent samples (Table 1). To
inform our replication criteria, we conducted a power analysis using a sample size of 20,000 and considering multiple
combinations of allele frequencies and effect sizes. Based
on allele frequency and effect size, one of our most difficult
to replicate variants was rs1800961 (MAF ¼ 0.022, Beta ¼
0.028). However, we still had approximately 56% power
to detect this association in the replication stage.
We identified a nonsense variant in CD36 associated
with RDW in African Americans. CD36 is a type B scavenger receptor located on the surface of many cell types,
including endothelial cells, platelets, monocytes, and
erythrocytes. CD36 is a marker of erythroid progenitor differentiation56 and might also be involved in macrophagemediated clearance of red blood cells.57 Furthermore,
CD36 plays a role in many biological pathways such as
lipid metabolism/transport and atherosclerosis, hemostasis, and inflammation.58 The nonsense CD36 variant identified in our RDW meta-analysis (rs3211938, Table 1) has
previously been associated with platelet count, HDL
cholesterol, and C-reactive protein levels in African Americans59,60 and malaria resistance in Africans.61,62 The CD36
locus shows a signature of natural selection in AA populations63 and the MAF of rs3211938 varies widely between
continents: in the 1000 Genomes Project, the minor allele
is absent from European populations but reaches frequency of 24%–29% in some African populations.64 To
characterize the molecular mechanism by which
rs3211938 can impact RDW, we identified one heterozygous sample among a collection of ex vivo differentiated
human erythroblasts.32 In erythroblasts from this donor,
we noted a strong allelic imbalance (Figure 2). Importantly,
this result was confirmed in independent samples from the
GTex dataset (Figure S5). At the molecular level, this CD36
expression phenotype could be explained by nonsensemediated mRNA decay or the regulatory effect of non-coding genetic variants in LD with rs3211938.
We observed that many new RBC variants are pleiotropic, being often associated with more than one RBC index as well as with WBC, PLT, and lipid traits (Figure 3).
These shared effects could imply that the underlying
causal genes at these RBC loci generally controlled blood
cell proliferation or modulate inflammatory responses.
An additional explanation for the link between RBC traits
and lipid variants might be the cholesterol content of RBC
membranes. As mentioned earlier, RBC corresponds to a
large fraction (~25%) of the cells found in the human
body. Genetic variation that modulates RBC count or volume could impact circulating lipid levels. In support of this
hypothesis, it has been observed that a thalassemia allele is
strongly associated with cholesterol levels in the Sardinian
population.65 In total, we found ten loci associated with
lipid levels and RBC indices, including four novel RBC variants (AFF1, TMEM57-RHD, TRIB1, HNF4A) (Figure 3).
In summary, our multi-ethnic meta-analyses have
expanded the genetic knowledge of erythrocyte biology
and identified new common, low-frequency, and rare RBC
variants. Many of the new RBC variants are pleiotropic,
affecting other complex traits such as WBC, PLT, and blood
lipid levels. Although our report demonstrates the utility of
the ExomeChip for genetic discovery, it also highlights the
challenge to attribute gene causality based only on association results. This is particularly evident for loci with common variants, for which coding and non-coding markers
are often statistically equivalent. For instance, we found
no examples of RBC coding variants that entirely explain
RBC GWAS signals among the seven loci that had both a
sentinel GWAS variant and ExomeChip coding markers.
Although increasing sample sizes will continue to yield
additional RBC loci, it has become incredibly clear that
only a combination of well-powered genetic studies, transcriptomic and epigenomic surveys, and functional experiments (e.g., using genome editing) will ultimately pinpoint
causal variants and genes that control RBC phenotypes.
Supplemental Data
Supplemental Data include a note on the eQTL analyses, information on supplementary funding, five figures, and eight tables and
can be found with this article online at http://dx.doi.org/10.1016/
j.ajhg.2016.05.007.
The American Journal of Human Genetics 99, 8–21, July 7, 2016 17
Table 3.
Overlap of Red Blood Cell Markers with Other Blood Cell Traits and/or Lipid
SNP
Position
A1/A2
AF (A2)
Annotation
Gene
Trait
rs10903129
1: 25,768,937
A/G
0.568
intron
TMEM57-RHD
RDW
0.037
1.19 3 10
7
TC46
0.061
5.40 3 10
10
PLT
0.021
7.06 3 10
6
HCT*
0.028
2.38 3 10
9
HGB*
0.026
3.76 3 10
9
PLT
0.031
5.93 3 10
10
HGB
0.034
3.97 3 10
13
TG40
0.031
1.00 3 10
18
BASO
0.030
1.99 3 10
5
RDW
0.036
1.53 3 10
7
TG40
0.076
1.00 3 10
7
HGB
0.071
1.65 3 10
8
PLT
0.095
7.03 3 10
14
TG52
0.090
1.40 3 10
17
MCV
0.040
1.25 3 10
8
RDW
0.043
5.79 3 10
8
BASO
0.051
1.35 3 10
10
MONO
0.033
3.57 3 10
5
HCT
0.083
1.44 3 10
8
HGB
0.073
2.53 3 10
8
HDL40
0.127
2.00 3 10
34
rs3772219
rs442177
rs2954029
rs55707100
rs4911241
rs1800961
3: 56,771,251
4: 88,030,261
8: 126,490,972
15: 43,820,717
20: 31,140,165
20: 43,042,364
A/C
G/T
A/T
C/T
C/T
C/T
0.338
0.595
0.439
0.033
0.241
0.032
missense
ARHGEF3
intron
AFF1
intergenic
missense
intron
missense
TRIB1
MAP1A
NOL4L
HNF4A
Beta
p Value
Shown here are significant novel variants from the RBC traits association analyses that overlap with other blood-cell traits or with lipids. Results for the white blood
cell and platelet traits are from the Blood Cell Consortium, and results for lipids are from the published literature. Results are presented for European-ancestry
individuals, except in the presence of an asterisk (*), which stands for result from ‘‘All’’ ancestry. The allele frequency and direction of the effect (beta) is given
for the A2 allele. Abbreviations are as follows: A1, reference allele; A2, alternate allele; AF, allele frequency; HCT, hematocrit; HGB, hemoglobin; MCV, mean
corpuscular volume; RDW, red blood cell distribution width; TC, total cholesterol; PLT, platelet; TG, triglycerides; WBC, white blood cells; BASO, basophils;
MONO, monocytes; HDL, HDL cholesterol.
Acknowledgments
We thank all participants, staff, and study coordinating centers.
We also thank Raymond Doty and Jan Abkowitz for discussion
of the ALAS2 finding. We would like to thank Liling Warren for
contributions to the genetic analysis of the SOLID-TIMI-52 and
STABILITY datasets. Young Finns Study (YFS) acknowledges the
expert technical assistance in the statistical analyses by Ville
Aalto and Irina Lisinen. Estonian Genome Center, University of
Tartu (EGCUT) thanks co-workers at the Estonian Biobank, especially Mr. V. Soo, Mr. S. Smith, and Dr. L. Milani. Airwave thanks
Louisa Cavaliero who assisted in data collection and management as well as Peter McFarlane and the Glasgow CARE, Patricia
Munroe at Queen Mary University of London, and Joanna
Sarnecka and Ania Zawodniak at Northwick Park for their contributions to the study. This work was supported by the Fonds de
Recherche du Queébec-Santeé (FRQS, scholarship to N.C.), the
Canadian Institute of Health Research (Banting-CIHR, scholarship to S.L. and operating grant MOP#123382 to G.L.), and the
Canada Research Chair program (to G.L.). P.L.A. was supported
by NHLBI R21 HL121422-02. N.A.A. is funded by NIH
DK060022. A.N. was supported by the Yoshida Scholarship Foundation. S.K. was supported by a Research Scholar award from the
18 The American Journal of Human Genetics 99, 8–21, July 7, 2016
Massachusetts General Hospital (MGH), the Howard Goodman
Fellowship from MGH, the Donovan Family Foundation,
R01HL107816, and a grant from Fondation Leducq. Additional
acknowledgments and funding information is provided in the
Supplemental Data.
Received: February 18, 2016
Accepted: May 3, 2016
Published: June 23, 2016
Web Resources
BCX
ExomeChip
association
results,
http://www.mhihumangenetics.org/en/resources
CheckVCF, https://github.com/zhanxw/checkVCF
ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/
OMIM, http://www.omim.org/
RareMETALS, http://genome.sph.umich.edu/wiki/RareMETALS
RareMetalWorker,
http://genome.sph.umich.edu/wiki/
RAREMETALWORKER
RvTests, http://genome.sph.umich.edu/wiki/RvTests
Type 2 Diabetes Genetics, http://www.type2diabetesgenetics.org/
References
1. Ulker, P., Sati, L., Celik-Ozenci, C., Meiselman, H.J., and
Baskurt, O.K. (2009). Mechanical stimulation of nitric oxide
synthesizing mechanisms in erythrocytes. Biorheology 46,
121–132.
2. Jiang, N., Tan, N.S., Ho, B., and Ding, J.L. (2007). Respiratory
protein-generated reactive oxygen species as an antimicrobial
strategy. Nat. Immunol. 8, 1114–1122.
3. Schnabel, R.B., Baumert, J., Barbalic, M., Dupuis, J., Ellinor,
P.T., Durda, P., Dehghan, A., Bis, J.C., Illig, T., Morrison, A.C.,
et al. (2010). Duffy antigen receptor for chemokines (Darc)
polymorphism regulates circulating concentrations of monocyte chemoattractant protein-1 and other inflammatory mediators. Blood 115, 5289–5299.
4. Colin, Y., Le Van Kim, C., and El Nemer, W. (2014). Red
cell adhesion in human diseases. Curr. Opin. Hematol. 21,
186–192.
5. Whelihan, M.F., and Mann, K.G. (2013). The role of the red
cell membrane in thrombin generation. Thromb. Res. 131,
377–382.
6. Brugnara, C. (2003). Iron deficiency and erythropoiesis: new
diagnostic approaches. Clin. Chem. 49, 1573–1578.
7. Huang, Y.L., Hu, Z.D., Liu, S.J., Sun, Y., Qin, Q., Qin, B.D.,
Zhang, W.W., Zhang, J.R., Zhong, R.Q., and Deng, A.M.
(2014). Prognostic value of red blood cell distribution width
for patients with heart failure: a systematic review and metaanalysis of cohort studies. PLoS ONE 9, e104861.
8. Nada, A.M. (2015). Red cell distribution width in type 2 diabetic patients. Diabetes Metab. Syndr. Obes. 8, 525–533.
9. Zalawadiya, S.K., Zmily, H., Farah, J., Daifallah, S., Ali, O., and
Ghali, J.K. (2011). Red cell distribution width and mortality in
predominantly African-American population with decompensated heart failure. J. Card. Fail. 17, 292–298.
10. Zalawadiya, S.K., Veeranna, V., Panaich, S.S., and Afonso, L.
(2012). Red cell distribution width and risk of peripheral artery disease: analysis of National Health and Nutrition Examination Survey 1999-2004. Vasc. Med. 17, 155–163.
11. Patel, K.V., Semba, R.D., Ferrucci, L., Newman, A.B., Fried, L.P.,
Wallace, R.B., Bandinelli, S., Phillips, C.S., Yu, B., Connelly, S.,
et al. (2010). Red cell distribution width and mortality in older
adults: a meta-analysis. J. Gerontol. A Biol. Sci. Med. Sci. 65,
258–265.
12. Patel, H.H., Patel, H.R., and Higgins, J.M. (2015). Modulation
of red blood cell population dynamics is a fundamental homeostatic response to disease. Am. J. Hematol. 90, 422–428.
13. Whitfield, J.B., and Martin, N.G. (1985). Genetic and environmental influences on the size and number of cells in the
blood. Genet. Epidemiol. 2, 133–144.
14. Pilia, G., Chen, W.M., Scuteri, A., Orrú, M., Albai, G., Dei, M.,
Lai, S., Usala, G., Lai, M., Loi, P., et al. (2006). Heritability of
cardiovascular and personality traits in 6,148 Sardinians.
PLoS Genet. 2, e132.
15. Evans, D.M., Frazer, I.H., and Martin, N.G. (1999). Genetic
and environmental causes of variation in basal levels of blood
cells. Twin Res. 2, 250–257.
16. Lin, J.P., O’Donnell, C.J., Jin, L., Fox, C., Yang, Q., and Cupples, L.A. (2007). Evidence for linkage of red blood cell size
and count: genome-wide scans in the Framingham Heart
Study. Am. J. Hematol. 82, 605–610.
17. van der Harst, P., Zhang, W., Mateo Leach, I., Rendon, A., Verweij, N., Sehmi, J., Paul, D.S., Elling, U., Allayee, H., Li, X.,
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
et al. (2012). Seventy-five genetic loci influencing the human
red blood cell. Nature 492, 369–375.
Chen, Z., Tang, H., Qayyum, R., Schick, U.M., Nalls, M.A.,
Handsaker, R., Li, J., Lu, Y., Yanek, L.R., Keating, B., et al.;
BioBank Japan Project; CHARGE Consortium (2013).
Genome-wide association analysis of red blood cell traits in
African Americans: the COGENT Network. Hum. Mol. Genet.
22, 2529–2538.
Auer, P.L., Teumer, A., Schick, U., O’Shaughnessy, A., Lo, K.S.,
Chami, N., Carlson, C., de Denus, S., Dubé, M.P., Haessler, J.,
et al. (2014). Rare and low-frequency coding variants in
CXCR2 and other genes are associated with hematological
traits. Nat. Genet. 46, 629–634.
Eicher, J.D., Chami, N., Kacprowski, T., Nomura, A., Chen,
M.-H., Yanek, L.R., Tajuddin, S.M., Schick, U.M., Slater, A.J.,
Pankratz, N., et al. (2016). Platelet-related variants identified
by exomechip meta-analysis in 157,293 individuals. Am. J.
Hum. Genet. 99, this issue, 40–55.
Tajuddin, S.M., Schick, U.M., Eicher, J.D., Chami, N., Giri, A.,
Brody, J.A., Hill, W.D., Kacprowski, T., Li, J., Lyytikäinen, L.-P.,
et al. (2016). Large-scale exome-wide association analysis
identifies loci for white blood cell traits and pleiotropy with
immune-mediated diseases. Am. J. Hum. Genet. 99, this issue,
22–39.
Grove, M.L., Yu, B., Cochran, B.J., Haritunians, T., Bis, J.C.,
Taylor, K.D., Hansen, M., Borecki, I.B., Cupples, L.A., Fornage,
M., et al. (2013). Best practices and joint calling of the
HumanExome BeadChip: the CHARGE Consortium. PLoS
ONE 8, e68095.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira,
M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly,
M.J., and Sham, P.C. (2007). PLINK: a tool set for wholegenome association and population-based linkage analyses.
Am. J. Hum. Genet. 81, 559–575.
Wells, Q.S., Becker, J.R., Su, Y.R., Mosley, J.D., Weeke, P.,
D’Aoust, L., Ausborn, N.L., Ramirez, A.H., Pfotenhauer, J.P.,
Naftilan, A.J., et al. (2013). Whole exome sequencing identifies a causal RBM20 mutation in a large pedigree with
familial dilated cardiomyopathy. Circ Cardiovasc Genet 6,
317–326.
Winkler, T.W., Day, F.R., Croteau-Chonka, D.C., Wood, A.R.,
Locke, A.E., Mägi, R., Ferreira, T., Fall, T., Graff, M., Justice,
A.E., et al.; Genetic Investigation of Anthropometric Traits
(GIANT) Consortium (2014). Quality control and conduct of
genome-wide association meta-analyses. Nat. Protoc. 9,
1192–1212.
Limongelli, G., Elliott, P., Charron, P., Mogensen, J., and
McKeown, P.P. (2012). Approaching genetic testing in cardiomyopathies (ESC Council for Cardiology Practice).
Olson, T.M., Michels, V.V., Thibodeau, S.N., Tai, Y.S., and Keating, M.T. (1998). Actin mutations in dilated cardiomyopathy,
a heritable form of heart failure. Science 280, 750–752.
Liu, D.J., Peloso, G.M., Zhan, X., Holmen, O.L., Zawistowski,
M., Feng, S., Nikpay, M., Auer, P.L., Goel, A., Zhang, H., et al.
(2014). Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204.
Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M., and Lin, X.
(2011). Rare-variant association testing for sequencing data
with the sequence kernel association test. Am. J. Hum. Genet.
89, 82–93.
Price, A.L., Kryukov, G.V., de Bakker, P.I., Purcell, S.M., Staples,
J., Wei, L.J., and Sunyaev, S.R. (2010). Pooled association tests
The American Journal of Human Genetics 99, 8–21, July 7, 2016 19
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
for rare variants in exon-resequencing studies. Am. J. Hum.
Genet. 86, 832–838.
Willer, C.J., Li, Y., and Abecasis, G.R. (2010). METAL: fast and
efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191.
Lessard, S., Beaudoin, M., Benkirane, K., and Lettre, G. (2015).
Comparison of DNA methylation profiles in human fetal and
adult red blood cell progenitors. Genome Med. 7, 1.
Lappalainen, T., Sammeth, M., Friedländer, M.R., ’t Hoen, P.A.,
Monlong, J., Rivas, M.A., Gonzàlez-Porta, M., Kurbatova, N.,
Griebel, T., Ferreira, P.G., et al.; Geuvadis Consortium (2013).
Transcriptome and genome sequencing uncovers functional
variation in humans. Nature 501, 506–511.
Zhang, X., Gierman, H.J., Levy, D., Plump, A., Dobrin, R., Goring, H.H., Curran, J.E., Johnson, M.P., Blangero, J., Kim, S.K.,
et al. (2014). Synthesis of 53 tissue and cell line expression
QTL datasets reveals master eQTLs. BMC Genomics 15, 532.
Astner, I., Schulze, J.O., van den Heuvel, J., Jahn, D., Schubert,
W.D., and Heinz, D.W. (2005). Crystal structure of 5-aminolevulinate synthase, the first enzyme of heme biosynthesis, and
its link to XLSA in humans. EMBO J. 24, 3166–3177.
Halpain, S., and Dehmelt, L. (2006). The MAP1 family of
microtubule-associated proteins. Genome Biol. 7, 224.
Takei, Y., Kikkawa, Y.S., Atapour, N., Hensch, T.K., and Hirokawa, N. (2015). Defects in synaptic plasticity, reduced
NMDA-receptor transport, and instability of postsynaptic
density proteins in mice lacking microtubule-associated protein 1A. J. Neurosci. 35, 15539–15554.
GTEx Consortium (2015). Human genomics. The GenotypeTissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660.
Dehghan, A., Dupuis, J., Barbalic, M., Bis, J.C., Eiriksdottir, G.,
Lu, C., Pellikka, N., Wallaschofski, H., Kettunen, J., Henneman, P., et al. (2011). Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for
C-reactive protein levels. Circulation 123, 731–738.
Willer, C.J., Schmidt, E.M., Sengupta, S., Peloso, G.M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., Buchkovich,
M.L., Mora, S., et al.; Global Lipids Genetics Consortium
(2013). Discovery and refinement of loci associated with lipid
levels. Nat. Genet. 45, 1274–1283.
Taylor, K.C., Lange, L.A., Zabaneh, D., Lange, E., Keating, B.J.,
Tang, W., Smith, N.L., Delaney, J.A., Kumari, M., Hingorani,
A., et al. (2011). A gene-centric association scan for Coagulation Factor VII levels in European and African Americans:
the Candidate Gene Association Resource (CARe) Consortium. Hum. Mol. Genet. 20, 3525–3534.
de Vries, P.S., Chasman, D.I., Sabater-Lleal, M., Chen, M.H.,
Huffman, J.E., Steri, M., Tang, W., Teumer, A., Marioni, R.E.,
Grossmann, V., et al. (2016). A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration.
Hum. Mol. Genet. 25, 358–370.
Kooner, J.S., Saleheen, D., Sim, X., Sehmi, J., Zhang, W., Frossard, P., Been, L.F., Chia, K.S., Dimas, A.S., Hassanali, N., et al.;
DIAGRAM; MuTHER (2011). Genome-wide association study
in individuals of South Asian ancestry identifies six new
type 2 diabetes susceptibility loci. Nat. Genet. 43, 984–989.
GTEx Consortium (2013). The Genotype-Tissue Expression
(GTEx) project. Nat. Genet. 45, 580–585.
Pradel, L.C., Vanhille, L., and Spicuglia, S. (2015). [The European Blueprint project: towards a full epigenome characterization of the immune system]. Med. Sci. (Paris) 31, 236–238.
20 The American Journal of Human Genetics 99, 8–21, July 7, 2016
46. Aulchenko, Y.S., Ripatti, S., Lindqvist, I., Boomsma, D.,
Heid, I.M., Pramstaller, P.P., Penninx, B.W., Janssens, A.C.,
Wilson, J.F., Spector, T., et al.; ENGAGE Consortium
(2009). Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat. Genet.
41, 47–55.
47. Kullo, I.J., Ding, K., Shameer, K., McCarty, C.A., Jarvik, G.P.,
Denny, J.C., Ritchie, M.D., Ye, Z., Crosslin, D.R., Chisholm,
R.L., et al. (2011). Complement receptor 1 gene variants are
associated with erythrocyte sedimentation rate. Am. J. Hum.
Genet. 89, 131–138.
48. Döbbeling, U. (1997). The effects of cyclosporin A on V(D)J
recombination activity. Scand. J. Immunol. 45, 494–498.
49. Zarza, R., Alvarez, R., Pujades, A., Nomdedeu, B., Carrera, A.,
Estella, J., Remacha, A., Sánchez, J.M., Morey, M., Cortes, T.,
et al.; Red Cell Pathology Group of the Spanish Society of Haematology (AEHH) (1998). Molecular characterization of the
PK-LR gene in pyruvate kinase deficient Spanish patients. Br.
J. Haematol. 103, 377–382.
50. Valentini, G., Chiarelli, L.R., Fortin, R., Dolzan, M., Galizzi, A.,
Abraham, D.J., Wang, C., Bianchi, P., Zanella, A., and Mattevi,
A. (2002). Structure and function of human erythrocyte pyruvate kinase. Molecular basis of nonspherocytic hemolytic anemia. J. Biol. Chem. 277, 23807–23814.
51. Van Sligtenhorst, I., Ding, Z.M., Shi, Z.Z., Read, R.W., Hansen,
G., and Vogel, P. (2012). Cardiomyopathy in a-kinase 3
(ALPK3)-deficient mice. Vet. Pathol. 49, 131–141.
52. Peloso, G.M., Auer, P.L., Bis, J.C., Voorman, A., Morrison, A.C.,
Stitziel, N.O., Brody, J.A., Khetarpal, S.A., Crosby, J.R., Fornage, M., et al.; NHLBI GO Exome Sequencing Project
(2014). Association of low-frequency and rare codingsequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am. J. Hum. Genet. 94,
223–232.
53. Gu, Y., Nakamura, T., Alder, H., Prasad, R., Canaani, O., Cimino, G., Croce, C.M., and Canaani, E. (1992). The t(4;11)
chromosome translocation of human acute leukemias fuses
the ALL-1 gene, related to Drosophila trithorax, to the AF-4
gene. Cell 71, 701–708.
54. Guastadisegni, M.C., Lonoce, A., Impera, L., Di Terlizzi, F.,
Fugazza, G., Aliano, S., Grasso, R., Cluzeau, T., Raynaud, S.,
Rocchi, M., and Storlazzi, C.T. (2010). CBFA2T2 and
C20orf112: two novel fusion partners of RUNX1 in acute
myeloid leukemia. Leukemia 24, 1516–1519.
55. Serbanovic-Canic, J., Cvejic, A., Soranzo, N., Stemple, D.L.,
Ouwehand, W.H., and Freson, K. (2011). Silencing of RhoA
nucleotide exchange factor, ARHGEF3, reveals its unexpected
role in iron uptake. Blood 118, 4967–4976.
56. Okumura, N., Tsuji, K., and Nakahata, T. (1992). Changes in
cell surface antigen expressions during proliferation and
differentiation of human erythroid progenitors. Blood 80,
642–650.
57. Kiefer, C.R., and Snyder, L.M. (2000). Oxidation and erythrocyte senescence. Curr. Opin. Hematol. 7, 113–116.
58. Nicholson, A.C., Han, J., Febbraio, M., Silversterin, R.L., and
Hajjar, D.P. (2001). Role of CD36, the macrophage class B scavenger receptor, in atherosclerosis. Ann. N Y Acad. Sci. 947,
224–228.
59. Auer, P.L., Johnsen, J.M., Johnson, A.D., Logsdon, B.A., Lange,
L.A., Nalls, M.A., Zhang, G., Franceschini, N., Fox, K., Lange,
E.M., et al. (2012). Imputation of exome sequence variants
into population- based samples and blood-cell-trait-associated
loci in African Americans: NHLBI GO Exome Sequencing
Project. Am. J. Hum. Genet. 91, 794–808.
60. Elbers, C.C., Guo, Y., Tragante, V., van Iperen, E.P., Lanktree,
M.B., Castillo, B.A., Chen, F., Yanek, L.R., Wojczynski, M.K.,
Li, Y.R., et al. (2012). Gene-centric meta-analysis of lipid traits
in African, East Asian and Hispanic populations. PLoS ONE 7,
e50198.
61. Ayodo, G., Price, A.L., Keinan, A., Ajwang, A., Otieno, M.F.,
Orago, A.S., Patterson, N., and Reich, D. (2007). Combining
evidence of natural selection with association analysis increases power to detect malaria-resistance variants. Am. J.
Hum. Genet. 81, 234–242.
62. Aitman, T.J., Cooper, L.D., Norsworthy, P.J., Wahid, F.N., Gray,
J.K., Curtis, B.R., McKeigue, P.M., Kwiatkowski, D., Greenwood, B.M., Snow, R.W., et al. (2000). Malaria susceptibility
and CD36 mutation. Nature 405, 1015–1016.
63. Bhatia, G., Patterson, N., Pasaniuc, B., Zaitlen, N., Genovese,
G., Pollack, S., Mallick, S., Myers, S., Tandon, A., Spencer, C.,
et al. (2011). Genome-wide comparison of African-ancestry
populations from CARe and other cohorts reveals signals of
natural selection. Am. J. Hum. Genet. 89, 368–381.
64. Auton, A., Brooks, L.D., Durbin, R.M., Garrison, E.P., Kang,
H.M., Korbel, J.O., Marchini, J.L., McCarthy, S., McVean,
G.A., and Abecasis, G.R.; 1000 Genomes Project Consortium
(2015). A global reference for human genetic variation. Nature
526, 68–74.
65. Sidore, C., Busonero, F., Maschio, A., Porcu, E., Naitza, S.,
Zoledziewska, M., Mulas, A., Pistis, G., Steri, M., Danjou,
F., et al. (2015). Genome sequencing elucidates Sardinian
genetic architecture and augments association analyses for
lipid and blood inflammatory markers. Nat. Genet. 47,
1272–1281.
The American Journal of Human Genetics 99, 8–21, July 7, 2016 21