ArticleThe Polygenic and Monogenic Basis of Blood Traits
and DiseasesGraphical AbstractPolygenic modification 
of blood disease risk
Statistical fine-mapping
BCX
Population-level GWAS
29 blood cell
phenotypes
Functional variant
interpretation
Omnigenic/Infinitesimal
model evaluation
Cell state chromatin
mapping
C / AHighlightsd Largest genome-wide association study of blood cell traits
to date
d Empiric assessments of omnigenic and infinitesimal models
of polygenic variation
d Functional insights into how genetic variants impact human
hematopoiesis
d Assessment of the effect of polygenic trait scores upon
blood diseasesVuckovic et al., 2020, Cell 182, 1214–1231
September 3, 2020 ª 2020 The Authors. Published by Elsevier In
https://doi.org/10.1016/j.cell.2020.08.008Authors
Dragana Vuckovic, Erik L. Bao,
Parsa Akbari, ..., Guillaume Lettre,
Vijay G. Sankaran, Nicole Soranzo
Correspondence
ns6@sanger.ac.uk (N.S.),
sankaran@broadinstitute.org (V.G.S.)
In Brief
Analysis of blood cell traits in the UK
Biobank and other cohorts illuminates the
full genetic architecture of hematopoietic
phenotypes, with evidence supporting
the omnigenic model for complex traits
and linking polygenic burden with
monogenic blood diseases.c. ll
OPEN ACCESS
llArticle
The Polygenic and Monogenic Basis
of Blood Traits and Diseases
Dragana Vuckovic,1,2,101 Erik L. Bao,4,5,6,101 Parsa Akbari,7,2,8,1,101 Caleb A. Lareau,4,5,101 Abdou Mousas,9 Tao Jiang,7,10
Ming-Huei Chen,11,12 Laura M. Raffield,13 Manuel Tardaguila,1 Jennifer E. Huffman,14 Scott C. Ritchie,15,16,7,17,10
Karyn Megy,18,19,20 Hannes Ponstingl,1 Christopher J. Penkett,19,18 Patrick K. Albers,1 Emilie M. Wigdor,1
Saori Sakaue,21,22 Arden Moscati,23 Regina Manansala,24 Ken Sin Lo,9 Huijun Qian,25 Masato Akiyama,22,26
Traci M. Bartz,27 Yoav Ben-Shlomo,28 Andrew Beswick,29 Jette Bork-Jensen,30 Erwin P. Bottinger,31,23
Jennifer A. Brody,32 Frank J.A. van Rooij,33 Kumaraswamy N. Chitrala,34 Peter W.F. Wilson,35 He´le`ne Choquet,36
(Author list continued on next page)
1Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
2National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, University of
Cambridge, Cambridge, CB1 8RN, UK
3Department of Epidemiology, University of Washington, Seattle, WA, 98109, USA
4Division of Hematology/Oncology, BostonChildren’sHospital andDepartment of Pediatric Oncology, Dana-FarberCancer Institute, Harvard
Medical School, Boston, MA, 02115, USA
5Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
6Harvard-MIT Health Sciences and Technology, Harvard Medical School, Boston, MA, 02142, USA
7Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge,
Cambridge, CB1 8RN, UK
8MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
9Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada
10National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University
Hospitals, Cambridge, CB2 0QQ, UK
11The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA, 01702, USA
12Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, 01702, USA
13Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
14Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston
Healthcare System, Boston, MA, 02130, USA
15Department of Public Health and Primary Care, Cambridge Baker Systems Genomics Initiative, University of Cambridge, Cambridge, CB1
8RN, UK
(Affiliations continued on next page)SUMMARYBlood cells play essential roles in human health, underpinning physiological processes such as immunity, ox-
ygen transport, and clotting, which when perturbed cause a significant global health burden. Herewe integrate
data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European
ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell
phenotypes covering a range of variation impacting hematopoiesis.We holistically characterize the genetic ar-
chitecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate
relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel
splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood
traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power
of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum
of human variation.INTRODUCTION
A major aspiration in human genetics is to understand how ge-
netic variation impacts complex traits and diseases. Recent1214 Cell 182, 1214–1231, September 3, 2020 ª 2020 The Authors. P
This is an open access article under the CC BY license (http://creativegenome-wide association studies (GWAS) have identified thou-
sands of genetic variants associated with complex phenotypes
and provided insights into their genetic architecture. This has
led to the recognition that complex trait heritability is polygenic,ublished by Elsevier Inc.
commons.org/licenses/by/4.0/).
John Danesh,7,37,2,1,10,38 Emanuele Di Angelantonio,7,37,2,1,10,38 Niki Dimou,39,40 Jingzhong Ding,41 Paul Elliott,42,43,44,45,46
To˜nu Esko,5 Michele K. Evans,34 Stephan B. Felix,47,48 James S. Floyd,32,49 Linda Broer,50 Niels Grarup,30
Michael H. Guo,5,51 Qi Guo,7 Andreas Greinacher,52 Jeff Haessler,53 Torben Hansen,30 Joanna M.M. Howson,7,10,54
Wei Huang,55 Eric Jorgenson,36 Tim Kacprowski,56,57,48 Mika Ka¨ho¨nen,58,59 Yoichiro Kamatani,22,60 Masahiro Kanai,22,61
Savita Karthikeyan,7 Fotios Koskeridis,40 Leslie A. Lange,62 Terho Lehtima¨ki,63,64 Allan Linneberg,65,66 Yongmei Liu,67
Leo-Pekka Lyytika¨inen,63,64 Ani Manichaikul,68 Koichi Matsuda,69 Karen L. Mohlke,13 Nina Mononen,63,64
Yoshinori Murakami,70 Girish N. Nadkarni,23 Kjell Nikus,71,72 Nathan Pankratz,73 Oluf Pedersen,30 Michael Preuss,23
Bruce M. Psaty,74,32,75,76 Olli T. Raitakari,77,78,79 Stephen S. Rich,68 Benjamin A.T. Rodriguez,11,12 Jonathan D. Rosen,80
16Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, VIC 3004, Australia
17British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke’s Hospital, Cambridge, CB2 0QQ, UK
18Department of Haematology, University of Cambridge, Cambridge, CB2 0PT, UK
19National Institute for Health Research (NIHR) BioResource, Cambridge University Hospitals, Cambridge, CB2 0PT, UK
20National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, CB2 0PT, UK
21Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
22Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
23Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, 10029, USA
24Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, USA
25Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, NC, 27599, USA
26Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8581,
Japan
27Department of Biostatistics, University of Washington, Seattle, WA, 98101, USA
28Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 1QU, UK
29Translational Health Sciences, Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, BS10 5NB, UK
30Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen,
Copenhagen, 2200, Denmark
31Hasso-Plattner-Institut, Universita¨t Potsdam, Potsdam, 14469, Germany
32Department of Medicine, University of Washington, Seattle, WA, 98101, USA
33Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, 3015 GE, the Netherlands
34Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD, 21224, USA
35Atlanta VA Medical Center, Decatur, GA, 30033, USA
36Division of Research, Kaiser Permanente Northern California, Oakland, CA, 94612, USA
37Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, CB10 1SA, UK
38British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, UK
39Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, 69008, France
40Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece
41Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC,
27101, USA
42Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
43Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, London, W2 1NY, UK
44Medical Research Council Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
45UK Dementia Research Institute, Imperial College London, London, WC1E 6BT, UK
46Health Data Research UK London, London, W2 1PG, UK
47Department of Internal Medicine B, University Medicine Greifswald, Greifswald, 17475, Germany
48German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, 17475, Germany
49Department of Epidemiology, University of Washington, Seattle, WA, 98101, USA
50Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, 3015 GE, the Netherlands
51Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
52Institute for Immunology and Transfusion Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
53Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98101, USA
54Novo Nordisk Research Centre Oxford, Oxford, OX3 7FZ, UK
55Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and
Shanghai Industrial Technology Institute (SITI), Shanghai, 201203, China
56Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, 17475, Germany
57Chair of Experimental Bioinformatics, Research Group Computational Systems Medicine, Technical University of Munich,
Freising-Weihenstephan, 85354, Germany
58Department of Clinical Physiology, Tampere University Hospital, Tampere, 33521, Finland
59Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology,
Tampere University, Tampere, 33014, Finland
ll
OPEN ACCESSArticleresulting from the cumulative effects of many genetic loci
throughout the genome, each of modest effect size (Visscher
et al., 2017; Timpson et al., 2018).Hematopoiesis is a valuable paradigm for studying complex
trait genetic architecture, since blood cell phenotypes are
commonly measured in large population-based studies and theCell 182, 1214–1231, September 3, 2020 1215
Jerome I. Rotter,81 Petra Schubert,82 Cassandra N. Spracklen,13,83 Praveen Surendran,7,38,84,85 Hua Tang,86
Jean-Claude Tardif,9,87 Mohsen Ghanbari,33,88 Uwe Vo¨lker,56,48 Henry Vo¨lzke,89,48 Nicholas A. Watkins,20
Stefan Weiss,56,48 VA Million Veteran Program100, Na Cai,1 Kousik Kundu,1,18 Stephen B. Watt,1 Klaudia Walter,1
Alan B. Zonderman,34 Kelly Cho,82,91,92 Yun Li,80,13,93 Ruth J.F. Loos,23 Julian C. Knight,94 Michel Georges,95
Oliver Stegle,96 Evangelos Evangelou,42,40 Yukinori Okada,21,97 David J. Roberts,98,99 Michael Inouye,15,16,7,38,10,37,90
Andrew D. Johnson,11,12 Paul L. Auer,24 William J. Astle,8,2,20 Alexander P. Reiner,3 Adam S. Butterworth,7,37,2,1,10,38
Willem H. Ouwehand,18,20,1,2,38 Guillaume Lettre,9,87 Vijay G. Sankaran,4,5,102,* and Nicole Soranzo1,2,17,18,102,103,*
60Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier
Sciences, The University of Tokyo, Tokyo, 108-8639, Japan
61Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
62Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, 80045, USA
63Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33520, Finland
64Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology,
Tampere University, Tampere, 33014, Finland
65Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, 2000, Denmark
66Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
67DepartmentofMedicine,DivisionofCardiology,DukeMolecularPhysiology Institute,DukeUniversityMedicalCenter,Durham,NC,27701,USA
68Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22903, USA
69Department of Computational Biology andMedical Sciences,Graduate school of Frontier Sciences, TheUniversity of Tokyo, Tokyo, 108-8639,
Japan
70Division of Molecular Pathology, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
71Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, 33521, Finland
72Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere
University, Tampere, 33014, Finland
73Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, 55455, USA
74Departments of Epidemiology, University of Washington, Seattle, WA, 98101, USA
75Department of Health Services, University of Washington, Seattle, WA, 98101, USA
76Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
77Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, 20521, Finland
78Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20521, Finland
79Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20521, Finland
80Department of Biostatistics, University of North Carolina, Chapel Hill, NC, 27599, USA
81Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation
(formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
82Massachusetts VeteransEpidemiology Research and InformationCenter (MAVERIC), VABostonHealthcareSystem, Boston,MA, 02130,USA
83Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA, 01002, USA
84Health Data Research UK Cambridge, Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
85Department of Public Health and Primary Care, Rutherford Fund Fellow, University of Cambridge, Cambridge, CB1 8RN, UK
86Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
87Department of Medicine, Faculty of Medicine, Universite´ de Montre´al, Montreal, Quebec, H3T 1J4, Canada
88Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, 9177948564, Iran
89Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
90The Alan Turing Institute, London, NW1 2DB, UK
91Department of Medicine, Division on Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
92Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
93Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
94Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
95Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Lie`ge, Lie`ge, B-4000, Belgium
96European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, CB10 1SA, UK
97Laboratory of Statistical Immunology, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
98BRC Haematology Theme and Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
99NHSBT Blood and Transplant - Oxford Center, John Radcliffe Hospital, Oxford, OX3 9BQ, UK
100A list of members and their affiliations appears in the Extended Acknowledgments and Author Contributions
101These authors contributed equally
102These authors contributed equally
103Lead Contact
*Correspondence: sankaran@broadinstitute.org (V.G.S.), ns6@sanger.ac.uk (N.S.)
https://doi.org/10.1016/j.cell.2020.08.008
ll
OPEN ACCESS Articleproduction of blood cells is a highly regulated, hierarchical, and
intrinsic process that can be readily studied (Bao et al., 2019; Tar-
daguila and Soranzo, 2019). While there have been advances in1216 Cell 182, 1214–1231, September 3, 2020understanding genetic loci associated with blood cell production,
the spectrumof humangenetic variation impacting hematopoiesis
remains incompletely defined.
ll
OPEN ACCESSArticleMost variants contributing to complex trait heritability are non-
coding and located in genomic regulatory regions within relevant
cell types. The availability of epigenomic and transcriptomic pro-
files for hematopoietic stem and progenitor and lineage-
committed cells enable mechanistic dissection of the roles that
different classes of genes have in hematopoiesis. Prior studies
of blood cell traits have suggested that master transcription fac-
tors (TFs) may be impacted by genetic variation (Ulirsch et al.,
2019), and it is likely that further studies may uncover additional
roles for, and variation of, key hematopoietic regulators. Another
priority is to advance understanding of network connectivity be-
tween trait-associated genes and variants, and this understand-
ing can be informed by theoretical models. Recently, an ‘‘omni-
genic’’ model has been proposed in which two types of genes
(‘‘core’’ versus ‘‘peripheral’’) differentially contribute to complex
trait heritability (Boyle et al., 2017; Liu et al., 2019). However, the
extent to which the omnigenic model applies to various complex
traits and diseases remains unclear and controversial (Wray
et al., 2018).
Finally, although rare variants with large effects generally do
not individually contribute substantially to overall complex trait
variance, they can often highlight important biologic mecha-
nisms and contribute to rare hematologic disorders, many of
which are characterized by variable penetrance or expressivity.
In addition, polygenic contributions of many variants with small
effects can yield disease risk odds ratios comparable to or larger
than that of known monogenic variants (Oetjens et al., 2019).
Therefore, large population-based datasets can help to both re-
classify the pathogenicity and penetrance of disease-associated
variants, aswell as understand the contribution of polygenic vari-
ation to the risk of blood diseases or as modifiers of rare variants
that contribute to presumed monogenic blood disorders.
RESULTS
Genetic Variants Associated with Blood Count
Phenotypes
We leveraged the power of the UK Biobank cohort to perform a
genome-wide discovery analysis in N = 408,112 participants of
European ancestry, investigating 29 blood cell phenotypes (Ta-
ble S1). In parallel, we also performed tests for genetic associa-
tions with a subset of 15 phenotypes available in an additional
154,973 European ancestry participants from the Blood Cell
Consortium (BCX) (Figure 1A, Table S2). A separate analysis of
non-European participants is reported in a companion paper
(Chen et al., 2020). Overall, this discovery effort identified
16,643 autosomal and 257 X-linked conditionally independent
(Method Details) trait-variant associations from the first stage
discovery and an additional 141 from the BCXmeta-analysis (Ta-
bles S3 and S4). The 16,900 associations were assigned to 7,122
genomic loci (5,106 not described before) using a linkage
disequilibrium (LD) clumping approach (Astle et al., 2016).
Each locus was represented by a unique tag variant (between-
tag pairwise LD r2% 0.8), and for simplicity, throughout the pa-
per we use the term ‘‘sentinel variant’’ to refer to either a clump
tag variant or a trait-specific conditionally independent signal.
Overall, we nearly tripled the number of loci reported prior to
this study (Astle et al., 2016). We assessed replication ratesacross three exemplar phenotypes (platelet count [PLT],
lymphocyte count, and red blood cell count) for 210 variants
on chromosome 1 in the Million Veteran Program (MVP, N =
271,280). We found that nearly all of them had directionally
concordant effect size estimates (Pearson’s R2 = 0.94; Fig-
ure S1A), and 196 (93%) variants replicated at a nominal signifi-
cance threshold (p < 0.05). The non-replicating ones exhibited
similar effect sizes as in the discovery cohort but lacked power
due toMVP having less than half the sample size of the discovery
cohort (Figure S1A, zoom-in panel). Using a Bayesian method
that accounts for multiple independent signals (Benner et al.,
2016) (Method Details, Figure 1B), we fine-mapped 3,100 (19%
of 16,643 autosomal) associations to a single putative causative
variant (> 95% posterior probability [PPFM]) (Table S5), and more
than half of the associated signals (n = 9,149, 55%) to fewer than
10 variants (Figure 1C). As expected, rare signals are more likely
to be fine-mapped to smaller credible sets (Figure 1C). We as-
signed sentinels to genes using a stringent variant effect predic-
tor (VEP) worst-consequence annotation (McLaren et al., 2016)
to obtain a distribution of functional categories. Overall, 8,866
sentinels (83%) were annotated to a gene using this approach,
of which 69% were intronic, 24% were in regulatory regions,
and 7% were in protein-coding regions (5.5% non-synonymous
and 1.5% synonymous; Figure 1D). The credible set size distri-
bution (number of variants per credible set) was consistent
across traits (Figure 1E).
Genetic Architecture andNetworkConnectivity of Blood
Cell Traits
Hematopoiesis is a finely tuned process involving coordinated
expression of hundreds of genes, and it is likely that a subset
of the variants associated with peripheral blood cell counts
and indices acts uponmaster regulators of this process. To iden-
tify whether genes discovered by GWAS identify networks of
coregulated genes, we accessed a published coexpression
network of 7,509 protein-coding genes expressed in whole
blood (Nath et al., 2017) (Figures 2A and 2B;MethodDetails). Un-
der the stringent VEP worst-consequence criteria used earlier,
25% of network genes (n = 1,874 genes) were annotated to a
GWAS signal. A more permissive VEP any-consequence criteria
annotated an additional 2.5% (27.5%, n = 2,070) genes. When all
genes in the fine-mapping regions were considered (± 250-kb
window), 78% of network genes could be linked to a GWAS lo-
cus, and 88% of sentinels were in proximity (< 250 kb) to a
network gene, suggesting that genes linked to association sig-
nals are likely to be coregulated. Where possible, gene assign-
ments were also validated using colocalization (Giambartolomei
et al., 2018) with (expression quantitative trait loci) cis-eQTLs
derived from six trait-matched blood cell types (platelets n =
424; CD19+ B cells, CD8+ T cells, CD4+ T cells and CD15+ neu-
trophils n = 300; CD14+monocytes n = 1,490). Across 667 coloc-
alizing cis-eQTLs, eGenes matched VEP worst-consequence
genes in 65% of the cases (Figure S1B) and were contained in
fine-mapping regions in 97% of the cases (Kreuzhuber, 2019).
Biological networks are organized hierarchically (Ravasz et al.,
2002; Ravasz and Baraba´si, 2003; Carlson et al., 2006). The
recently proposed ‘‘omnigenic’’ model (Boyle et al., 2017; Liu
et al., 2019) postulates that a small number of genes at the centerCell 182, 1214–1231, September 3, 2020 1217
AB
C
D
E
Figure 1. GWAS Study Design and Results
(A–E) (A) Study design, (B) illustration for fine-mapping (FM) strategy showing how the FM blocks and the relevant number of causative signals were defined, (C)
distribution of FM results by MAF, (D) distribution of FM results by sentinel annotation and MAF, and (E) FM 95% credible set size distribution for each sentinel,
across all traits: different colors indicate different cell type groups.
ll
OPEN ACCESS Article(or ‘‘core’’) of the network are directly implicated in diseases or
phenotypes of interest, but the variants in these genes contribute
only a small proportion of the overall trait heritability. Most of the
trait heritability is attributable to a much larger number of ‘‘pe-
ripheral’’ gene variants with small effect sizes that contribute to
subtler physiological perturbations of phenotypes through
trans-regulatory effects on core genes.We thus sought to empir-
ically test the main assumptions of the omnigenic model,
compared to a more continuous ‘‘infinitesimal’’ model of disease
heritability (Wray et al., 2018) in order to inform its utility for dis-
ease gene discovery. We accessed a manually curated list of
genes causative for stem cell and myeloid disorders (SMD, 206
genes); bleeding, thrombotic, and platelet disorders (BPD, 104
genes); and bone-marrow failure (BMF) syndromes (80 genes;
Table S7) (Turro et al., 2020). GWAS loci for blood cell indices
tended to be strongly enriched in and near Mendelian blood dis-
order genes (by 2.1-fold, p = 1.9310
22), a phenomenon already
described for many complex traits (Gieger et al., 2011; Durand1218 Cell 182, 1214–1231, September 3, 2020and Rappold, 2013; Flannick et al., 2016). We then asked
whether these Mendelian genes had properties expected of
core genes.
A first assumption of the model is that core genes are
strongly enriched at the center of biological networks (Fig-
ure 2C). Overall, we observed strong enrichments of both
GWAS (fold enrichment [FE] = 1.86, permutation p < 10
4)
and Mendelian (i.e., core, FE = 3.86, p < 10
4) genes in the
full blood coexpression network (Nath et al., 2017) compared
to permuted sets of protein-coding genes of similar size (Fig-
ure 2D; Table S6). Importantly, Mendelian genes had more
connections in the coexpression network compared to other
(non-Mendelian) genes, consistent with a centrality scenario
(valid for coexpression cut-offs at 0.4–0.8, p ranging from
4310
4 to 0.02, Wilcoxon test; Figure 2E). Finally, the expres-
sion of Mendelian genes was more correlated with other Men-
delian genes (median coexpression coefficient = 0.11) than
random sets of genes (median = 0.095, p = 0.007 permutation
A C E
B D F
Figure 2. Network Connectivity
(A–B) Coexpression network in whole blood. For illustrative purposes, a subset of highly coexpressed genes is shown (correlation > 0.7). Edges are omitted for
clarity, and the node size summarizes the number and strength of coexpression links. Blue dots represent genes detected by GWAS, violet dots are Mendelian
genes, and red dots show the intersection. Grey dots are genes in the coexpression network that do not belong to any of the previous categories. GWAS genes
are defined by two different variant annotation approaches: VEP all consequences (A) and 500kb FM regions (B).
(C) Diagram showing the hypothesized genetic architecture of healthy blood traits. At the core of the underlying molecular network is the set of Mendelian genes
which cause blood disorders when mutated. Peripherally to the core lie regulatory genes which affect the phenotype through core genes. Cis and trans-eQTLs
can give insights about cell-type specificity and can identify master regulators, i.e., genes that trans-regulate several core genes simultaneously.
(D) Enrichment of sets of genes in the coexpression network at different correlation cut-offs. Whiskers indicate 95% CI for the fold enrichment estimate.
(E) Proportion of network genes among Mendelian, GWAS, or other genes with > 1 edge, or average number of edges, at different correlation cut-offs.
(F) Example of a sub-network containing 3Mendelian genes involved in platelets (GP9, ITGA2B,GP1BB). As in (A), blue dots are GWAS genes, red dots are GWAS
and previously known Mendelian genes, and gray dots are other coexpressed genes.
ll
OPEN ACCESSArticletest). A second assumption is that variants assigned to core
genes have larger effect sizes than peripheral genes. When
compared to variants of comparable minor allele frequency
(MAF) assigned to other genes, variants assigned to Mende-
lian genes (including previously unreported ones) showed
significantly higher absolute effect sizes across all functional
categories tested (fold change ranging from 1.87- to 2.73-
fold increase; Figure S1C). Third, core genes should be
more phenotype-specific as opposed to peripheral associa-
tions which act as regulators and could be shared across
different phenotypes. We show by quartile-quartile (Q-Q)
plot enrichments that this pattern holds true for Mendelian
versus peripheral blood traits associations in a selection of
eight non-blood related traits (Figure S2).
Themodel also predicts that peripheral variants explain a large
proportion of trait heritability through trans-regulation of core
genes (Liu et al., 2019). To test this hypothesis, we accessed a
large set of recently reported blood trans-eQTLs (Vo˜sa et al.,
2018). Mendelian genes were strongly enriched as targets of
trans-eQTLs, compared to other GWAS genes (2.11-fold, Wil-
coxon test, p = 4.7310
5), after matching for expression levelsand trans-eQTL Z-scores to account for differences in detection
power, with the caveat that there may be other unaccounted fac-
tors involved. At a correlation cut-off of 0.8, a coexpression sub-
network of 26 GWAS-associated genes was centered on three
known Mendelian genes causative for spherocytosis (SLC4A1,
EPB42) and congenital anemia (KLF1; Figure S3A). Interestingly,
these factors all play key roles in red blood cell cytoskeleton for-
mation, a process regulated by KLF1 (Ludwig et al., 2019).
Another example is a subnetwork containing known platelet spe-
cific genes GP9, GP1BB, and ITGA2B, and eight other strongly
coexpressed genes (Figure 2F). All of these genes are trans-
regulated by the ARHGEF3 gene, a known master regulator of
megakaryopoiesis (Serbanovic-Canic et al., 2011). While these
results are broadly compatible with expectations of the omni-
genic model, first- and second-degree coexpression network
neighbors of Mendelian genes were also enriched for GWAS as-
sociations (p < 1310
3, permutation test) and thus had proper-
ties attributable to both core and peripheral genes. This indicates
either that these loci may fit a more continuous infinitesimal
model, or that our current proposed set of core genes is
incomplete.Cell 182, 1214–1231, September 3, 2020 1219
ll
OPEN ACCESS ArticleBlood Cell Trait Variants Map to Lineage-Specific
Hematopoietic Chromatin Landscapes
We next sought to delineate relevant cell states impacted by
core and peripheral gene networks. To this end, we integrated
all fine-mapped (FM) variants (PPFM > 0.1%) with chromatin
accessibility profiles (ATAC-seq) of 18 human hematopoietic
progenitor populations (Ulirsch et al., 2019). First, we noted
that FM variants falling within hematopoietic open chromatin
were strongly enriched in gene targets (assigned by VEP worst
consequence) compared to non-accessible variants (OR = 1.4,
Fisher’s p < 2.2310
16), consistent with variants acting via
trans-regulation of genes in hematopoietic cell states. Next, we
used g-chromVAR, a high-resolution cell type enrichment
method, to determine the hematopoietic populations most en-
riched for chromatin accessibility containing FM variants for 22
blood cell traits, including 6 new traits compared to a previous
study in a smaller subset of the UK Biobank (Ulirsch et al.,
2019). There were 43 lineage-specific enrichments surpassing
experiment-wide significance (corrected for 18 cell types 322
traits, p < 1.26310
4) (Figure 3A), of which 20 were new,
including novel enrichments in granulocyte-monocyte progeni-
tor (GMP) cell subsets for variants regulating monocyte, eosino-
phil, and neutrophil counts.
We then wondered whether certain trait-cell type enrichments
would strengthen when restricting to core genes for correspond-
ing blood diseases. To this end, we calculated enrichments for
four platelet traits, considering only the variants mapping to
core genes for BPD. Whereas the gene-agnostic analysis pro-
duced significant enrichments in both megakaryocytes (n = 4)
and its less differentiated myeloid precursors (n = 7), the core-
gene restricted approach led to a strong signal for megakaryo-
cytes (n = 4) but a lack of enrichment in any other population (Fig-
ure 3B). This suggests that in addition to their roles in Mendelian
disease, core genes are also enriched for trans-regulatory vari-
ants acting specifically in their causal cell type.
Next, we sought to predict nucleotide-specific effects of vari-
ants on chromatin accessibility. We used deltaSVM, a support-
vector machine classifier, to train genomic sequence features
of the ATAC-seq from 18 hematopoietic cell populations (Figures
S3B and S3C), and then applied the model to predict the allele-
specific, cell type-specific impact of FM variants on chromatin
accessibility (Lee et al., 2015). Out of 215,694 variants with
PPFM > 0.001 for one or more blood traits, we identified 22,152
variants with an absolute deltaSVM score above the 99th
percentile for at least one hematopoietic cell type. Absolute del-
taSVM score was negatively associated with MAF (linear regres-
sion p < 2.2310
16) and positively associated with FM PPFM
(linear regression p = 1.0310
3) (Figure S3D). Variants assigned
to a gene by VEP worst consequence had stronger predicted ef-
fects on chromatin accessibility compared to intergenic variants
(Student’s t test, p = 1.2310
3); however, there was no signifi-
cant difference in deltaSVM between variants assigned to
‘‘core’’ versus ‘‘peripheral’’ genes, suggesting that variant-medi-
ated modulation of hematopoietic transcription occurs across
the entire gene regulatory network rather than disproportionately
impacting core genes.
To further characterize the regulatory effects of these vari-
ants, we predicted the potential for FM variants to disrupt 4261220 Cell 182, 1214–1231, September 3, 2020human TF motifs (Coetzee et al., 2015). Across motif-disrupting
variants, alternative alleles predicted to increase chromatin
accessibility (deltaSVM score > 99th percentile) had a signifi-
cantly higher motif matching score compared to the reference
allele (Method Details). The reverse was also true, indicating
that deltaSVM scores track with the potential for variants to
break or create TF motifs (Figure 3C). Moreover, this trend
was cell type specific, as evidenced by the fact that variants
affecting lineage-determining TFs had a higher deltaSVM score
within lineage-specific cell types, such as GATA1-disrupting
variants within erythroid progenitors (Wakabayashi et al.,
2016), compared to other hematopoietic populations (Figures
3D–3F). We next sought to integrate these functional annota-
tions in order to gain novel insights into biologically relevant var-
iants. For example, variant rs72928038, previously identified in
a locus associated with lymphocyte count (Astle et al., 2016),
was fine-mapped here as the likely causal variant (PPFM =
0.78), with the minor allele A (MAF = 18%) corresponding to
decreased lymphocyte count. The variant maps to intron 1 of
lymphoid TF BACH2 (Richer et al., 2016) colocalizes with a
H3K27ac histone QTL in CD4+ T cells (Kundu et al., 2020) and
has high chromatin accessibility in the CD4+ and CD8+ T
lymphoid populations. Interestingly, the lymphocyte count-
decreasing minor allele has strongly negative deltaSVM scores
(i.e., predicted to decrease chromatin accessibility) in the
lymphoid lineage and is predicted to disrupt multiple TF motifs
at the BACH2 locus, including those with known roles in
lymphocyte development, such as STAT3 and ETS1 (Figures
3G and 3H). This variant has been previously implicated in
risk for several autoimmune conditions including rheumatoid
arthritis (McAllister et al., 2013) and vitiligo (Jin et al., 2016).
These lines of evidence suggest that rs72928038 may affect
lymphocyte count by altering the binding of specific lymphoid
TFs within T cell progenitors. Altogether, our functional charac-
terization of non-coding blood trait variants highlights the value
of incorporating lineage-specific chromatin accessibility profiles
and motif disruption analyses to nominate high-confidence
mechanisms.
Clinical Impact of Rare Genetic Variants
The large sample size and dense imputation in this study gave us
unprecedented statistical power to discover variants with low
MAF and to assess their impact on human disease. First, we
identified 574 rare (minor allele count [MAC] > 20, MAF < 1%)
blood trait variants which were either conditionally independent
lead variants and/or strongly fine-mapped (PPFM>0.5), of which
512 (89.2%) were previously unreported (Astle et al., 2016; Bu-
niello et al., 2019). These variants had larger effect sizes (p <
2310
16, t test) on blood traits as expected and were enriched
for protein-coding consequences compared to other variants
with similar PPFM and/or lead conditional independence
(27.2% versus 4.86%, c2-test p < 2.2310
16; Figure 4A, Fig-
ure S3E). Remarkably, these rare variants were strongly enriched
for assignment to Mendelian blood genes (OR = 3.2, Fisher’s p =
4.22310
14), even after excluding known pathogenic variants
(Table 1; OR = 2.9, Fisher’s p = 4.46310
11), but were not
enriched for non-Mendelian genes (OR = 1.2, Fisher’s p =
0.18). These data support the hypothesis that a small group of
Figure 3. Functional Annotation of Blood Trait Variants
(A) g-chromVAR results for FM variants (PPFM>0.1%) across 22 hematological traits. The Bonferroni-adjusted significance level (p = 0.05/22 traits318 cell types)
is indicated by the dotted line. New traits are labeled in red. Novel enrichments are starred. The color legend for cell types is shared by panels (A), (B), and the
trackplot in (H). mono = monocyte; gran = granulocyte; ery = erythroid; mega = megakaryocyte; CD4 = CD4+ T cell; CD8 = CD8+ T cell; B = B cell; NK = natural
killer cell; mDC = myeloid dendritic cell; pDC, = plasmacytoid dendritic cell; MPP = multipotent progenitor; LMPP = lymphoid-primed multipotent progenitor;
CMP = common myeloid progenitor; CLP = common lymphoid progenitor; GMP = granulocyte-macrophage progenitor; MEP = megakaryocyte–erythroid
progenitor.
(B) g-chromVAR enrichment results across 4 platelet traits (MPV, mean platelet volume; PCT, platelet crit; PDW, platelet distribution width; PLT, platelet count),
using either all trait-associated variants (all), variants with any gene assignment (any gene), or only variants assigned to genes causative for BPD. The original
Bonferroni-adjusted significance level is indicated by the dotted line.
(C) The allelic effects of blood trait variants with (1) high (> 99th percentile) versus low (< 1st percentile) deltaSVM scores and (2) one or more predicted motif
disruptions, on normalizedmotif scores. The normalizedmotif score represents the score for a variant-containing sequence as a percentage of the best score that
motif could achieve on an ideal sequence.
(D–F) Cell type-specific deltaSVM scores for variants disrupting the (D) GATA1, (E) CEBPA, or (F) GABPA motif compared to scores in non-motif-disrupting
controls and non-lineage-specific cell types. Non-motif group indicates all other variants that do not disrupt the target TF. Gain or lost motif group contains
variants predicted to create or disrupt the target TF motif, respectively, with the deltaSVM score for a lineage-specific cell type (erythroblast for GATA1, GMP for
CEBPA, CD8 for GABPA). Non-lineage gain or lost indicates variants predicted to create or disrupt the target TF motif, but with the deltaSVM score for non-
lineage-specific populations (CD8, CD4, and B cells for GATA1 and CEBPA; erythroblast and megakaryocytes for GABPA).
(G) Lymphocyte count-associated variant rs72928038 has high chromatin accessibility (left) and deltaSVM score (right) in CD4 and CD8 populations.
(H) rs72928038 is located within intron 1 of BACH2, and its minor allele A is predicted to break the motifs of TFs ETS1 and STAT3. In the bottom ATAC-seq plot,
stacked colors represent accessibility for 18 hematopoietic cell types shown in (A).
ll
OPEN ACCESS
Cell 182, 1214–1231, September 3, 2020 1221
Article
BA
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BASO MONO NEUT PLT RBC WBCLYMPHEO
Hypothyroidism
Ulcerative colitis
Breast cancer
Psoriasis
Spondylosis 
Breast cancer
Diverticulosis
Hypercholesterolemia
Hyperlipidemia
Breast cancer
Other disorders of bladder
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CD3EAP
ULK3 TFR2
2148
392
175
0
1000
2000
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10638
2476
0
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45909900 45910100 45910300 45910500
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100225600 100225900
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75129700 75129900 75130100
rs12898397 rs139178017rs8113779
Figure 4. Characterization of Rare Blood Trait Variants
(A) Distribution of coding consequences of 456 rare variants (MAC > 20, MAF < 1%), annotated using VEP.
(B) Phenome-wide association study of these 456 rare variants across 529 well-represented clinical phenotypes in the UK Biobank (n up to 408,961). Variants are
grouped by the hematopoietic lineage with which they are associated (BASO, basophil; EO, eosinophil; LYMPH, lymphocyte; MONO, monocyte; NEUT,
neutrophil; PLT, platelet; RBC, red blood cell; WBC, white blood cell). Some variants appear in more than one category if they are associated with traits from
distinct lineages. Text labels indicate the clinical outcomes with the strongest association per category. The dotted line denotes the Bonferroni-adjusted sig-
nificance level (corrected for 529 phenotypes).
(C–E) Sashimi plots depicting splice alterations at 3 loci as determined by RNA-sequencing analysis, comparing carriers of a specified blood trait variant (top
track) versus non-carriers (bottom track). (C) Intronic donor gain splicing event in CD3EAP among carriers of rs8113779 (PPFM = 0.23 for PLT, 2nd highest in
credible set). Numbers within the splice junctions represent the number of reads supporting the junction. The x axis marks genomic coordinates. (D) Exonic donor
gain splicing alteration inULK3 associated with rs12898397 (PPFM = 0.071 for lymphocyte percent, 5th highest in credible set). (E) Donor loss splicing event in the
TFR2 locus, induced by variant rs139178017 (PPFM = 0.73 for RDW, highest in credible set; PPFM = 0.4 for MCV, 2nd highest in credible set).
ll
OPEN ACCESS Articlehigh-effect rare variants disproportionately affect core genes for
a complex trait.
Given the large effects of these variants on blood traits, we
next sought to test their pleiotropic associations with other clin-
ical and disease traits. Thus, we performed a phenome-wide
association study (PheWAS) for 456/574 rare variants using
summary statistics for 529 well-represented clinical phenotypes
from the UK Biobank cohort (https://www.leelabsg.org/
resources) (Zhou et al., 2018). There were 112 significant asso-
ciations involving 27 variants (Bonferroni-corrected p threshold
of 9.45310
5; Figure 4B, Table S8), of which 110 (98.2%) are
currently unreported in the GWAS Catalog. Several biologically
coherent associations stand out, including the missense variant
rs78534766 in ADCY7, associated with autoimmune conditions1222 Cell 182, 1214–1231, September 3, 2020(hypothyroidism, inflammatory bowel disease (Luo et al., 2017))
and eosinophil traits; several variants near PIEZO1 associated
with varicose veins (Fotiou et al., 2015; Van Hout et al., 2019)
and erythroid traits; and a variant (rs45611741) in the 50 UTR
of APOA5 associated with hypercholesterolemia (Nielsen
et al., 2019) and Mean Corpuscular Volume (MCV), as well as
Mean Corpuscular Hemoglobin Concentration (MCHC). Alto-
gether, the PheWAS analysis revealed a variety of novel and
relevant disease associations for rare blood trait variants and
could point toward common mechanistic roles for these pleio-
tropic loci.
Splice-altering genetic variants are a prevalent and under-
recognized class of variation underlying genetic disorders and
complex trait regulation (Park et al., 2018). We hypothesized
Table 1. Annotation of Pathogenic Variants
Variant Gene AA change
Imputed/
Genotyped Disease (ICD10 code)
Incidence in
UKBB (ICD10
codes/total
N = 410,293)
Variant
prevalence
(carriers with
disease/total) -
matched to
inheritance
Dominant/
recessive
pattern
GWAS blood
phenotype
Pathogenic
annotation db
rs113403872 PKLR p.Arg110Gln G pyruvate kinase
deficiency of red
cells (D552)
7.3E-06 0/0 R HCT, RBC, HGB Clinvar, HGMD
rs116100695 PKLR p.Arg486Trp G pyruvate kinase deficiency
of red cells (D552)
7.3E-06 0/2 R HGB, RET, RET%, IRF,
HCT, RBC, HLR, HLR%
Clinvar, HGMD
rs61755431 PKLR p.Arg569Gln G pyruvate kinase deficiency
of red cells (D552)
7.3E-06 0/6 R RET% HGMD
rs35897051 MPO c.2031-2A>C I myeloperoxidase
deficiency (D7289)
0.00099441
(D728)
0/14 D MONO, MONO% Clinvar, HGMD
rs119468010 MPO p.Arg569Trp G myeloperoxidase
deficiency (D7289)
0.00099441
(D728)
0/4 D MONO, MONO% HGMD
rs1799945 HFE p.His63Asp G haemochromatosis
(E83119)
0.002371
(E831)
27/10,230 R RET, RET%, MCHC, RDW Clinvar, HGMD
rs1800730 HFE p.Ser65Cys I haemochromatosis (E83119) 0.002371
(E831)
0/107 R MCH, MCHC, MCV Clinvar, HGMD
rs1800562 HFE p.Cys282Tyr I haemochromatosis (E83119) 0.002371 (E831) 418/2,889 R RET,RET%,MCV,RBC,
MSCV,HGB,PLT,HCT,
MCH,MCHC,RDW,PDW,
MONO%,HLR,HLR%
HGMD
rs138156467 CSF3R p.Trp547Ter G Neutropenia/philia
(D709, D72828)
0.0119768 (D70),
0.0009944 (D728)
0/1 R NEUT HGMD
rs28928907 MPL p.Arg102Pro G Congenital amegakaryocytic
thrombocytopenia (D610)
5.1E-05 0/0 R PCT HGMD
rs33946267 HBB p.Glu122Gln I Beta-thalassemia (D561) 1.7E-04 0/0 R MCV HGMD
rs61745086 PIEZO1 p.Pro2510Leu I Stomatocytosis dehydrated
(D588)
4.9E-06 0/24 R RET, RET%, HLR, HLR%,
HCT, RBC, MCHC, HGB
HGMD
rs137853120 TMPRSS6 p.Asp521Asn G Iron-refractory iron deficiency
anemia (IRIDA) (D508)
9.7E-03 0/0 R MCV, MCH, RDW HGMD
rs5030764 GP9 p.Asn61Ser I Bernard-Soulier Syndrome
(D691)
4.1E-05 0/0 R PDW, MPV, PLT, PCT HGMD
rs41316003 JAK2 p.Arg1063Hist G Erythrocytosis (D750) with
megakaryocytic atypia
9.0E-05 0/19 R PCT, PLT HGMD
rs146220228 WAS p.Glu131Lys G X-linked thrombocytopenia,
Wiskott-Aldrich syndrome
(D820)
0 0/0 X PDW HGMD
The table shows sentinels that were annotated as pathogenic by either ClinVar or HGMD, using stringent criteria in each database. For each variant, we report the gene, the amino acid (AA)
change caused, if the variant was genotyped or imputed, the associated disease, its incidence in UK Biobank, its prevalence among variant carriers (matched by disease inheritance, e.g., ho-
mozygous carriers are counted for recessive disorders), the phenotype associated by GWAS, and the database of origin (ClinVar or HGMD).
ll
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ll
OPEN ACCESS Articlethat a subset of blood trait variants, especially those that are rare
with large effect sizes,may bemediated by splice alterations.We
utilized a state-of-the-art neural net classifier, SpliceAI, to pre-
dict FM variants with splice-altering consequences (Jaganathan
et al., 2019). The delta score has been shown to closely track
with the validation rate of cryptic splice variants, thus approxi-
mating its splice-altering probability. Across 215,694 FM vari-
ants (PPFM>0.1%), we identified 109 variants with a putative
splicing consequence in 106 unique genes (delta score > 0.2)
(Figure S3F). Of these, 11 (10%) were rare (MAF < 1%) and confi-
dently fine-mapped (PPFM>0.5; Table S9). Strikingly, 85% (93/
109) of the variants, including 9/16 with delta score > 0.8, fell in
non-canonical splice sites, meaning they lie outside the essential
GT and AG splice junction dinucleotides. In addition, putative
splice variants had lower MAF (Mann-Whitney U p =
5.08310
8) and higher PPFM (Mann-Whitney U p = 9.89310
6)
compared to other FM variants (Figures S3G and S3H). Even
when matched by MAF and PPFM, splice variants also had a
1.5-fold higher GWAS effect size (Mann-Whitney U p = 0.007).
To validate these in silico predictions, we examined isoform vari-
ation in RNA-sequencing data of 465 participants from the Geu-
vadis project (Lappalainen et al., 2013) and used the LeafCutter
tool to identify splicing quantitative trait loci (sQTLs) (Li et al.,
2018). After excluding variants with insufficient statistical power
in GEUVADIS, LeafCutter quantified differential splicing effects
for 28/109 (26%) putative splice variants. Of these, 23/28
(82%) were identified as sQTLs at a 5% false discovery rate.
For example, two common variants falling within 95% credible
sets for PLT and lymphocyte count (rs8113779, MAF = 16%
and rs12898397, MAF = 37%) were predicted to produce donor
gain splice alterations in CD3EAP and ULK3 respectively. These
effects were validated by LeafCutter (rs8113779, adjusted p =
3.54310
47; rs12898397, adjusted p = 3.39310
104), with alter-
native splice sites produced by these variants in Geuvadis (Fig-
ures 4C and 4D). Finally, we highlight a previously unreported
splice variant which was too rare to be quantified by LeafCutter
but has interesting biological connections. rs139178017 (MAF =
0.53%) is a strongly FM variant in a novel association locus for
red cell distribution width (RDW) (PPFM = 0.73) and MCV
(PPFM = 0.4). It is predicted to induce a donor loss splice alter-
ation for transferrin receptor 2 (TFR2), a partner of the erythro-
poietin receptor and a known regulator of erythropoiesis (Nai
et al., 2015; Nandakumar et al., 2019). Compared to non-car-
riers, the 4 carriers of rs139178017 harbored substantially
increased transcripts with intron retention adjacent to this
variant (Figure 4E). These findings support the idea that large
GWAS are well powered to identify splice variants with large
phenotypic effects (Li et al., 2016), and these splice variants
represent a currently under-appreciated mechanism of trait
regulation in GWAS loci.
Contribution of Polygenic Variation to Blood Cell Traits
and Complex Human Diseases
Our study has identified the largest number of variants ever
associated with a single group of correlated phenotypes. While
each common (MAF R1%) variant accounts for a small effect,
their joint effect may be substantial. We used different variant
selection criteria to build weighted polygenic scores (PGSs)1224 Cell 182, 1214–1231, September 3, 2020based on the UK Biobank study and selected the one yielding
most predictive power for the 29 blood measurements in an in-
dependent cohort (INTERVAL study; Method Details; Table
S10). Remarkably, PGS based on hundreds of common sentinel
variants (135–689 depending on trait) were shown to be more
predictive than larger SNP sets employing more liberal signifi-
cance thresholds, in line with findings for autoimmune diseases
(Abraham et al., 2014) but in contrast to other common human
diseases (Khera et al., 2019). The proportion of phenotypic vari-
ance explained (R2) by the PGS ranged between 2.5% for baso-
phil count to 27.3% for mean platelet volume. Estimates ob-
tained for the same score in an independent cohort of 2,314
French Canadians (CARTaGENE) for 15 available traits were
broadly comparable, confirming portability of the PGS between
European-ancestry groups (Figure 5A). The causal relationship
between genetic variants determining eosinophil count and
asthma risk has been previously demonstrated (Astle et al.,
2016). Focusing on this exemplar disease, we can show that
the eosinophil count PGS was also significantly associated
with asthma incidence in UK Biobank (odds ratio [OR] = 1.17,
95% confidence interval [CI] = 1.13–1.21, p = 1.02310
19), sug-
gesting the potential utility of PGSs for blood biomarkers in the
clinic.
Intriguingly, the behavior of the PGSs suggests that the cur-
rent discovery sample sizes may have achieved saturation of
biological signals for blood cell traits. To begin to test this hy-
pothesis, we modeled different discovery measures (total num-
ber of variants, loci, genes, and heritability explained) as a func-
tion of increasing discovery sample sizes. The best-fitting
model shows a quadratic rate of discovery decrease across
all tested measures and traits (Figures S4A–S4D). However,
while the total numbers of associations detected does not
seem to reach a plateau, the heritability explained does (Fig-
ures 5B and 5C), suggesting that GWAS with larger sample
sizes will provide new discoveries, but of smaller and smaller
effects, with the exception of unobserved rare variants. In line
with the fact that variants assigned to Mendelian genes have
higher effect sizes, these showed a faster saturation curve
compared to other genes (Figure S4D). Larger independent dis-
covery datasets will be required to conclusively validate this
observation.
Finally, we wondered if multiple sentinels at a single locus
could underlie associations with complex diseases and help
define an allelic series at pharmacologically relevant genes.
20% of blood trait loci had R2 sentinels, including some un-
usually large sets (Figure 5D). We overlapped these regions
with colocalization results for 18 common human diseases
(Figure 5E). Figures 5F–5J show 6 instances of such condition-
ally independent variant sets, of which 3 involve a known drug
target. For example, the type I diabetes (T1D) locus tagged by
rs5845323 on chromosome 9 contains one rare and six com-
mon variants, all associated with eosinophil percentage (Fig-
ure 5I). While the colocalizing T1D variant is intronic in
C1QTNF6 gene, the coding-synonymous one from the series
is in IL2RB (interleukin 2 receptor subunit beta). It has recently
been proposed that the cancer drug Aldesleukin (recombinant
IL-2, which binds IL2RB) may be repurposed to treat T1D
at low doses, and the drug is currently in phase II clinical
A100000 200000 300000 400000
0.
26
0.
30
0.
34
Cohort size
He
rit
ab
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y 
ex
pl
ai
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100000 200000 300000 400000
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00
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00
Cohort size
N.
 o
f a
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oc
iat
ed
 va
ria
nt
s D
lymphocyte count
white blood cell count
eosinophil count
monocyte count
neutrophil count
basophil count
platelet count
mean platelet volume
mean corp. haemoglobin
mean corp. haemoglob. concent.
mean corp. volume
red blood cell count
haematocrit
haemoglobin
red cell distrib. width 
0.0 0.1 0.2 0.3 0.4 0.5
INTERVAL CARTaGENE
R
HGF
KJI
Multiple Sclerosis
Type 1 diabetes
Allergic Disease
Arthritis
Coronary artery disease
Celiac Disease
Primary biliary cirrhosis
Type 2 diabetes
Asthma
Lupus
Ankylosing spondylitis
Eczema
Inflammatory bowel disese
Schizophrenia
Cholangitis
Alzheimers
Hayfever
Psoriasis
0 5 10 15 20
Associated Loci
Known
New
Chromosome 2
UBE2E3
MIR4437
ITGA4
CERKL
NEUROD1
181800000 182000000 182200000 182400000 182600000
BANK1
SLC39A8
RN7SL728P
NFKB1
MANBA
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36000000 36250000 36500000 36750000 37000000
Chromosome 21
CNG2
IFT27
PVALB
NCF4
CSF2RB
CSF2RBP1
TEX33
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RN7SKP214
TMPRSS6
IL2RB
C1QTNF6
SSTR3
RAC2
CYTH4
ELFN2
37000000 37200000 37400000 37600000 37800000
Chromosome 22
P4HA2
PDLIM4
SLC22A4
MIR3936
SLC22A5
C5orf56
IRF1
IL5
RAD50
IL13
IL4
KIF3A
CCNI2
SEPT8
SOWAHA
SHROOM1
RNA5SP192
GDF9
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Figure 5. Polygenic Prediction of Blood Traits and Contribution to Common Diseases
(A) Portability of the PGS across populationswith European ancestry for 15 available traits. The red bar represents the Pearson’s correlation (R) between the score
and the trait in the validation cohort (INTERVAL). Blue bars show the same in a French Canadian cohort called CARTAgENE.
(B and C) Saturation analysis showing the number of discovered variants (B) and the proportion of heritability explained (C) as a function of GWAS sample size for
mean platelet volume. The black dotted line is a linear projection of the first 3 points, the red dotted line is a linear interpolation of all points, and the red solid curve
is the best model fitting the 4 points.
(D) Number of loci with multiple sentinel variants, stratified by trait group.
(E) Number of disease loci colocalizing (posterior probability > 99%) with at least one blood count locus, colored by known vs. new loci.
(F–K) Examples of loci with multiple sentinels associated with blood cell counts, and with at least one disease-colocalization (red diamond) or PheWAS asso-
ciation (green diamond) for the following genes and diseases: ITGA4 and Inflammatory Bowel Disease (IBD) (F), RUNX1 and Rheumatoid Arthritis (G),NFKB1 and
IBD (H), C1QTNF6 and Type-1 Diabetes (I), JAK2 and IBD (J), IL4 and asthma (K). In each panel, black dots show MAF (right y axis) and red dots show the effect
size (in SD for the phenotype between brackets, left y axis) of each variant as a function of the variant’s position in the genomic interval.
ll
OPEN ACCESSArticletrial for this therapeutic application (Todd et al., 2016)
(ClinicalTrials.gov ID: NCT01862120). There were 3 colocaliz-
ing loci between asthma and eosinophil count and/or percent-
age and a further three novel PheWAS associations of rare
non-coding variants near known asthma genes (GATA3,
RAD50, and IL33). One of the rare variants is part of a 270-
kb set of sentinels on chromosome 5 associated with eosino-
phil count, including another rare variant and 5 common sig-
nals (Figure 5K). The genes implicated are C5orf56 (IRF1-AS1
or IRF1 antisense RNA 1), IRF1, IL5, RAD50, IL13, KIF3A,
and IL4. Interestingly, both IL5 and IL4 are current therapeutic
targets for treating a number of allergic diseases (Ortega et al.,
2014; Chang and Nadeau, 2017). Overall, this large set of
conditionally independent variants informs future efforts to
define allelic series to study genes of pharmacological impor-
tance (Claussnitzer et al., 2020).The Influence of Polygenic Variation on Blood Disorders
Mendelian blood disorders display considerable heterogeneity
in penetrance and expressivity. Furthermore, estimates of ef-
fect size and penetrance of pathogenic variants tend to be in-
flated when ascertained from patient populations (Wright
et al., 2019). While the PGSs defined by the common variants
discovered in this study explain a substantial proportion of vari-
ance of respective phenotypes, the extent to which polygenic
variation contributes to the manifestation of rare diseases re-
mains to be determined. To address this question, we first
explored the genetic landscape of classical blood disorders in
UKBiobank.We annotated each protein-coding sentinel variant
using (1) ClinVar (Landrum et al., 2014), (2) Human Gene Muta-
tion Database (HGMD) (Stenson et al., 2017), and (3) a recently
curated list of variants for rare blood disorders from the Rare
Disease Pilot for the 100,000 Genomes Project (NIHR-RD)Cell 182, 1214–1231, September 3, 2020 1225
AC
B D
Figure 6. Contribution of Polygenic and Rare Variation to Blood Diseases
(A) Density distribution of PLT (109/liter) for UK Biobank participants who are heterozygous carriers (HET, red line) or wild-type (WT, black line) of the GP9
rs5030764 c.182A>G (p.Asn61Ser) variant pathogenic for Bernard-Soulier syndrome, plotted for participants whose PGS is above or below 2 SDs of the
population platelet PGS.
(B) Proportion of participants below the normal range for PLT (1503109/l) depending on PGS quintiles and GP9 rs5030764 carriage status.
(C) Absolute effect sizes comparison between different rare variant annotations and the common polygenic score. A subset of previously unreported missense
variants shows high effect sizes comparable to known pathogenic ones, nominating them as putative new pathogenic candidates. The contribution of the
polygenic score is comparable to that of a pathogenic variant in heterozygosity. Diamond shapes represent median values.
(D) Forest plot showing the association of PGS with rare blood disorders, top 30 results (by p-value) are shown. Significant associations, after Bonferroni
correction, are indicated by the * symbol for the discovery stage, while replication effects shown are all nominally significant. Diamonds represent odds ratios and
whiskers show the 95% confidence interval.
ll
OPEN ACCESS Article(Turro et al., 2020). Overall, 101 sentinels were included in one
or more databases above, of which 80% were coding, 10%
were annotated to 30 or 50 UTRs, and the remaining were splice
or intronic variants. 16/101 (16%) were annotated to be patho-
genic in either ClinVar or HGMD using strict criteria (Method De-
tails), involving 11 genes (PKLR,HFE,HBB,PIEZO1, TMPRSS6,
JAK2, MPO, CSF3R, MPL, GP9, and WAS; Table 1). Only 5/16
variants satisfied the pathogenicity criteria in both ClinVar and
HGMD. Of these five, two variants previously reported as path-
ogenic for autosomal recessive diseases (rs116100695 in
PKLR for pyruvate kinase deficiency of red cells and
rs1800730 in HFE for hemochromatosis) were found in appar-
ently healthy homozygous UK Biobank participants. Similarly,
we found apparently healthy homozygous carriers for other
four recessive variants, reported as pathogenic in HGMD, but
not in ClinVar (rs61755431 in PKLR for pyruvate kinase defi-
ciency or red cells, rs138156467 in CSF3R for neutropenia,
rs61745086 in PIEZO1 for dehydrated stomatocytosis and
rs41316003 in JAK2 for erythrocytosis and thrombocytosis).
This lack of disease phenotype may be indicative of low pene-1226 Cell 182, 1214–1231, September 3, 2020trance, missing health record data, misannotation of the path-
ogenicity, or undiscovered compensatory effects either by rare
variants or polygenic variation. For two additional recessive
variants (rs137853120 in TMPRSS6 for iron-refractory iron
deficiency anemia and rs5030764 in GP9 for Bernard-Soulier
Syndrome) we observed no homozygous carriers, but hetero-
zygous carriers were around 3 times more likely to have blood
indices outside the normal range (hemoglobin < 12 g/dl, PLT <
1503109/l), demonstrating previously unreported dosage-
dependent effects (OR = 3.25, 95% CI = 1.85, 5.37, p =
5310
5 [Figures 6A and 6B] and OR = 3.79, 95% CI = 2.40–
5.68, p = 1.1310
9, respectively). Data were inconclusive for
the remaining 8 variants, either because there were no
homozygous carriers in UK Biobank (rs113403872 in PKLR,
rs28928907 in MPL, rs33946267 in HBB, rs146220228 in
WAS), or because the disease presented mild symptoms that
are not easily detectable (rs35897051 and rs119468010 in
MPO for myeloperoxidase deficiency).
We next compared the effects of PGS and rare monogenic
variants. The average effect of each standard deviation of PGS
ll
OPEN ACCESSArticleranged from 0.16 to 0.47 SD (depending on trait) and was thus
comparable to that of a rare pathogenic variant carried in hetero-
zygosity (Figure 6C). We hypothesized that in the previous exam-
ples, a low penetrance in a rare disease could be explained by
background polygenic variation, for instance in cases where
the rare disease mutation carriers have a polygenic effect in
the opposite direction that compensated for a high-impact rare
mutation. However, the PGS of identified homozygous rare
variant carriers was not different from the population mean
(defined arbitrarily as PGS > 2SDPGS or tested by logistic regres-
sion for variants with more than 10 homozygotes). Hence, the
polygenic effects alone were not sufficiently extreme to explain
the lower disease prevalence in homozygous rare variant car-
riers, at least with our current PGS definition.
Comparing effect sizes can also be used to screen for potential
new pathogenic mutations. Variants of uncertain significance
(VUS) and missense variants showed a broad distribution of
effect sizes, with tails approaching the range of pathogenic
ones, and could harbor putative new pathogenic variants. Among
the 16 missense variants with the largest effect sizes, two previ-
ously uncharacterized ones were in known Mendelian genes
(rs139473150 in TUBB1, associated with platelet count and
rs201514157 in SPTA1 associated with immature reticulocytes).
Platelet count was also associated with rs149254521 in PEAR1,
a gene previously identified by an intronic variant in a platelet
GWAS (Eicher et al., 2016). Subsequent functional studies
showed that this gene is involved in platelet aggregation, which
is consistent with the phenotypes observed here (Eicher et al.,
2016; Keramati et al., 2019). Two missense variants associated
with monocyte count (rs140221307 and rs149771513) were in
IL17RA (CHARGE Consortium Hematology Working Group,
2016; Tajuddin et al., 2016), which has been implicated in mono-
cyte function in mice (Ge et al., 2014). Similarly, E2F4 is known to
be essential in mouse erythropoiesis (Humbert et al., 2000), and
here its missense variant rs61735430 was strongly associated
with mean reticulocyte volume. The other variants were in TIE1
and PLEKHO2 associated to platelets (rs140190628 and
rs143331139 respectively), IFRD2 (rs200622087) associated
with reticulocytes, TF (rs8177318 and rs150854910) with mean
corpuscular hemoglobin (MCH) and MCV, CXCR2 (rs61733609
and rs55799208) with white blood cells (Auer et al., 2014),
C10orf54 (rs201859625, in VSIR encoding V-set immunoregula-
tory receptor) with monocytes, and FAM46C (rs148397151)
with MCH. The high predictive value of the PGS, alongside future
catalogs of additional rare and private variants from whole
genome sequencing (WGS), will enable robust modeling and
could help explain the heterogeneity in many monogenic blood
disorders.
As shown earlier, polygenic contributions can yield effect sizes
on blood traits comparable to or larger than that of known mono-
genic variants.We therefore sought to explorewhether they affect
predisposition to rare diseases of the blood. In UK Biobank, we
extracted ICD10 codes for a total of 29,080 patients and controls,
with 423 blood diseases. We then considered the subset of these
participants thatwere excluded from the discoveryGWASandwe
estimated their weighted sentinel-based PGS (as detailed earlier).
We then fit logistic regression models to test the associations of
the PGS with rare disorders of the blood. For the first time, weshowed that PGSs derived for blood parameters can influence
risk for several rare blood disorders (Figure 6D). For instance,
we showed that a higher PGS for red cell count was positively
associated with incidence of secondary polycythemia, a disorder
characterized by elevated hematocrit (OR = 1.55, 95%CI = 1.21–
2.00, p = 6.5310
4). A high PGS for MCH was protective for iron
deficiency anemia (OR = 0.86, 95% CI = 0.79–0.94, p =
9.1310
4). A high PGS for neutrophil count decreased the risk
of aplastic anemia (OR = 0.74, 95% CI = 0.64–0.87, p =
2.9310
4), which manifests as cytopenias due to depletion of he-
matopoietic stem cells and failure of blood cell production (Pas-
cutti et al., 2016). Finally, the PLT PGS was negatively associated
with thrombocytopenia or low PLT (OR = 0.83, 95% CI = 0.76–
0.91, p = 3.8310
5). We replicated findings for platelet related
PGSs in an independent cohort of 1,199 BPD patients and
7,308 controls with WGS (Turro et al., 2020) (Figure 6D). We
showed that an increased PGS for PLT resulted in a protective ef-
fect against BPD disorders, including thrombocytopenia (OR =
0.92, 95% CI = 0.86–0.98, p = 0.007). These results refine our un-
derstanding of rare blood disease heterogeneity and the contribu-
tion of the polygenic background of an individual to the manifes-
tations of a rare disease known to be caused by high-impact
pathogenic variants.
DISCUSSION
Hematopoiesis is a highly regulated hierarchical process. Ge-
netic variation leading to alteration of blood cell counts can
teach us fundamental lessons about this process and serve
as a paradigm for studying complex trait genetic architecture
(Bao et al., 2019; Tardaguila and Soranzo, 2019). Here we
present a large set of GWAS association results for a set of
traits that illuminate numerous aspects of hematopoiesis. The
magnitude of this discovery set enabled unprecedented statis-
tical power to explore current paradigms in complex trait ge-
netics as well as build a bridge between GWAS in general pop-
ulation cohorts and existing knowledge of monogenic blood
disorders.
While an omnigenic model has recently been proposed to
explain complex disease or trait architecture (Boyle, Li and
Pritchard, 2017), this framework has been met with some skep-
ticism (Wray et al., 2018). Demonstrating the validity of the omni-
genic model has relevance for disease gene discovery for differ-
entiating genes with a potential to underpin pathological
variation in human traits from those that control variation within
healthy physiological ranges. Here we leveraged the knowledge
accrued on the hematopoietic system to carry out a first empir-
ical assessment of this model in the context of blood cell trait
variation. By defining core genes as those that are found in Men-
delian blood disorders and using a coexpression network, we
describe properties consistent with this omnigenic model. Spe-
cifically, when compared to other GWAS-associated genes,
Mendelian genes (1) were enriched among GWAS hits, (2)
harbored variants with larger effect sizes, (3) had predominantly
blood-specific effects, (4) were coregulated, (5) had central
properties in the coexpression network, and (6) were enriched
among trans-eQTL targets. Other observations were inconclu-
sive or suggestive of a more continuous (infinitesimal) patternCell 182, 1214–1231, September 3, 2020 1227
ll
OPEN ACCESS Articleof inheritance: (1) there was an apparent continuum of effect
sizes of variants on disease odds, (2) there was no significant dif-
ference in functional scores (chromatin accessibility, deltaSVM)
between variants assigned to ‘‘core’’ versus ‘‘peripheral’’ genes,
and (3) first- and second-degree neighbors of Mendelian genes
were compatible with ‘‘core’’ and ‘‘peripheral’’ functions. The
relatively incomplete ascertainment of rare and private DNA
sequence variation in both population cohorts and rare disease
cases implies that our knowledge of core genes is likely incom-
plete. Despite these uncertainties, the emerging picture of
underlying network connectivity regulating blood traits harbors
potential for discovering new pathogenic genes and drug
targets. As an example, we identified a subset of 11 closely
coexpressed genes (including three known platelet genes
(GP9, ITGA2B, and GP1BB) that is coregulated by the same
trans-acting eQTL in the ARHGEF3 gene. The use of a large da-
taset with concurrent genetic and gene expression data in
different cell states will be necessary for further quantitative vali-
dation of this model (Liu et al., 2019).
Polygenic variation has a substantial contribution to variation
in complex quantitative traits and disease risk, sometimes
yielding effects comparable to those of rare pathogenic vari-
ants. Using only the sentinel signals from our discovery
GWAS, we built PGSs explaining up to 28% of phenotypic vari-
ance. We explored the polygenic effects jointly with pathogenic
variants and as phenotype modulators in patients with rare
blood disorders. While we found that 16 known monogenic var-
iants were each associated with quantitative blood traits, 52
participants homozygous for five rare recessive pathogenic var-
iants appeared to be healthy with normal blood count and
indices. This suggests that the penetrance of pathogenic vari-
ants may be overestimated in many instances, as was recently
shown (Oetjens et al., 2019). Differences in PGS could not
explain the reduced penetrance, but our analysis may be
limited by the diseases we had adequate statistical power to
assess. Conversely, we observed strong allele dosage-depen-
dent effect sizes for two heterozygous variants (previously re-
ported as recessive), that could lead to disease especially if co-
inherited with an adverse PGS. For example, heterozygous
carriers of the GP9 variant rs5030764 were three times more
likely to have a PLT below the normal range (< 150K/ul). Finally,
we observed a significant association between phenotype-rele-
vant PGSs and rare blood disorders for thrombocytopenia, sec-
ondary polycythemia, anemia, and aplastic anemia, regardless
of the presence or absence of known rare variants in patients.
This highlights a substantial polygenic modulating effect on pre-
sumably monogenic disorders and lays the groundwork for
future studies aiming to define the impact of genetic back-
ground on the variable penetrance and expressivity in blood
disorders.
In summary, through the largest study of blood cell trait varia-
tion to date, we provide new insights into the regulation of blood
cell parameters and how genetic variation may contribute to the
variability observed in rare blood disorders that are presumed to
have a monogenic etiology. Our findings provide a novel frame-
work for considering an individual’s genetic background and
how this may impact the presentation of blood diseases. Finally,
the lessons learned from this study of hematopoiesis will likely be1228 Cell 182, 1214–1231, September 3, 2020more broadly applicable to a wide range of other complex dis-
eases and traits.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
d KEY RESOURCES TABLE
d RESOURCE AVAILABILITYB Lead Contact
B Materials Availability
B Data and Code Availability
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
d METHOD DETAILS
B Genotyping, quality control and imputation
d QUANTIFICATION AND STATISTICAL ANALYSIS
B Phenotype modeling and cohort level GWAS
B QC and pre-processing of cohort level GWAS
B Meta-analysis
B Exact conditional analysis
B Meta-analysis conditional analysis
B Replication
B Fine-mapping
B eQTL Colocalization
B Mendelian genes
B Co-expression network
B g-chromVAR
B DeltaSVM
B Transcription factor motif analysis
B Phenome-wide association study
B Splice variant analysis
B Polygenic scores
B Discovery saturation
B Allelic series and Disease Colocalization
B Polygenic effects in rare blood disorders
B Pathogenic variants annotation
SUPPLEMENTAL INFORMATION
Supplemental Information can be found online at https://doi.org/10.1016/j.
cell.2020.08.008.
ACKNOWLEDGMENTS
We thank all participants. This research has been conducted using the UK Bio-
bank Resource under Application Number 13745. A full list of acknowledg-
ments appears in the Extended Acknowledgments and Author Contributions
(Document S1).
AUTHOR CONTRIBUTIONS
Writing – Original Draft, D.V., E.L.B., P.A., C.A.L., N.S., V.G.S., P.A., W.J.A.,
P.L.A., A.S.B., A.P.R., A.D.J., G.L., and W.H.O.; Writing – Review & Editing,
All authors contributed and discussed the results and commented on the
manuscript; Data curation, methodology, software and formal analysis group,
D.V., E.L.B., P.A., C.A.L., T.J., S.C.R., A.M., M.-H.C., L.M.R., J.E.H., Q.G.,
E.M.W., M.T., K.M., C.J.P., H.P., P.S., and P.K.A.; Conceptualization and su-
pervision, N.S., V.G.S., G.L., W.H.O., A.S.B., A.P.R., W.J.A., P.L.A., A.D.J.,
M.I., D.J.R., E.D.A., and J.D. A full list of contributions appears in the Extended
Acknowledgments and Author Contributions (Document S1).
ll
OPEN ACCESSArticleDECLARATION OF INTERESTS
Adam Butterworth has received grants (outside of this work) from
AstraZeneca, Biogen, BioMarin, Bioverativ, Merck, Novartis, and Sanofi;
James Floyd has consulted for Shionogi; Qi Guo is a full-time employee of Be-
nevolentAI; JoannaHowson is a full-time employee of NovoNordisk. Parsa Ak-
bari is a full-time employee of Regeneron Pharmaceuticals.
Received: January 18, 2020
Revised: June 29, 2020
Accepted: August 3, 2020
Published: September 3, 2020
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OPEN ACCESS ArticleSTAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCE SOURCE IDENTIFIER
Deposited Data
The NIHR BioResource, on behalf of the
100,000 Genomes Project
The NIHR BioResource, on behalf of the
100,000 Genomes Project (2019).
Whole-genome sequencing of rare
disease patients in a national healthcare
system (bioRxiv)
https://www.genomicsengland.co.uk/
about-gecip/joining-researchcommunity/
Ensembl v99 EMBL-EBI https://www.ensembl.org/index.html;
HOCOMOCO (Kulakovskiy et al., 2018) https://repository.kaust.edu.sa/handle/
10754/325453
PheWAS summary statistics from
UK Biobank
Lee Lab, Seoul National University https://www.leelabsg.org/resources
Geuvadis (Lappalainen et al., 2013) http://www.geuvadis.org
ClinVar database https://www.ncbi.nlm.nih.gov/clinvar/ accessed 2018-11-05
Human Gene Mutation Database
(HGMD)
http://www.hgmd.cf.ac.uk/ac/index.php accessed 2019-01-17
eQTL-gen University of Groningen https://www.eqtlgen.org/
BIOS BIOS Consortium http://bbmri.researchlumc.nl/atlas/#data
ATAC-seq profiles Gene Expression Omnibus (GEO) and
Sequence Read Archive (SRA)
Accession GSE119453 (GEO) and PRJNA491478
(SRA)
UK Biobank summary statistics This manuscipt GWAS Catalog:GCST90002379-GCST90002407
Software and Algorithms
R 3.6.1 R Core Team https://www.r-project.org/
BOLT-LMM (Loh et al. 2015) https://alkesgroup.broadinstitute.org/
BOLT-LMM/
checkVCF Abecasis Lab - University of Michigan https://genome.sph.umich.edu/wiki/CheckVCF.py
Sanger Imputation service Wellcome Sanger Institute https://imputation.sanger.ac.uk/
Michigan Imputation Server University of Michigan http://imputationserver.sph.umich.edu/index.html
EPACTS Hyun Min Kang (University of Michigan) https://github.com/statgen/EPACTS
rvtests (Zhan et al., 2016) https://github.com/zhanxw/rvtests
GWAMA (Ma¨gi and Morris, 2010) https://www.well.ox.ac.uk/GWAMA
GCTA (Yang et al., 2011) https://gump.qimr.edu.au/gcta
FINEMAP v1.3.1 (Benner et al., 2016) http://www.christianbenner.com/
gwas-pw (Pickrell et al., 2016) https://github.com/joepickrell/gwas-pw
g-chromVAR (Ulirsch et al., 2019) https://caleblareau.github.io/gchromVAR
DeltaSVM (Lee et al., 2015) http://www.beerlab.org/deltasvm/
SpliceAI (Jaganathan, et al., 2019) https://github.com/Illumina/SpliceAI.
ggsashimi (Garrido-Martı´n et al., 2018) https://github.com/guigolab/ggsashimi
PLINK v1.9 PLINK working group https://www.cog-genomics.org/plink/1.9/
VEP: Variant Effect Predictor EMBL-EBI https://www.ensembl.org/Tools/VEP
LeafCutter (Li et al., 2017) https://davidaknowles.github.io/leafcutter/RESOURCE AVAILABILITY
Lead Contact
Further information and requests may be directed to the Lead Contact, Nicole Soranzo (ns6@sanger.ac.uk).e1 Cell 182, 1214–1231.e1–e6, September 3, 2020
ll
OPEN ACCESSArticleMaterials Availability
This study did not generate new unique reagents.
Data and Code Availability
Summary statistics are available to download from: ftp://ftp.sanger.ac.uk/pub/project/humgen/summary_statistics/UKBB_blood_
cell_traits/ for UK Biobank and http://www.mhi-humangenetics.org/en/resources for the meta-analysis. The accession numbers
for the UK Biobank summary statistics reported in this paper are GWAS Catalog: GCST90002379–GCST90002407.
The code generated during this study is publicly available at GitHub https://github.com/bloodcellgwas/manuscript_code/.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Following the success of the Blood Cell Consortium ((Chami et al., 2016; Eicher et al., 2016) and (Tajuddin et al., 2016)), the Blood Cell
Consortium Phase 2 (BCX2) continues to identify novel common and rare variants associated with blood cell traits using imputed
genotype data based on Haplotype Reference Consortium (HRC) or the 1000 Genomes Project (Phase 3, version 5) for European
ancestry cohorts and non-European ancestry cohorts, respectively. BCX2 comprises 746,667 participants from 40 discovery cohorts
and five ancestries: European, African American, Hispanic, East Asian, and South Asian. BCX2 is divided into two working groups:
European that consists of 563,946 participants from 26 cohorts and that is the focus of this study, and trans-ethnic. Detailed descrip-
tions of the participating cohorts are summarized in Table S1. All participants provided written informed consent, and local research
ethics committees and institutional review boards approved the individual studies.
METHOD DETAILS
Genotyping, quality control and imputation
Genotyping array and pre-imputation quality control (QC) for each participating cohort is provided in Table S2. Genotype QCmetrics
includedMAF (> 0), call rate (> 98%) and Hardy-Weinberg equilibrium p (> 10
6). The pre-imputation sample exclusion criteria (Table
S2) included call rate (> 95%), heterozygosity rate (> median+3*IQR), gender mismatches, duplicates, and outliers from principal
component analysis with reference samples from 1000Genomes Project. All genotypes were onGenomeReference ConsortiumHu-
man Build 37 (GRCh37) forward strand (https://www.well.ox.ac.uk/wrayner/strand/). All the cohorts checked strand and allele
orientation in the variant call format files prior to imputation using checkVCF (https://genome.sph.umich.edu/wiki/CheckVCF.py).
Finally the imputation was performed using servers available at https://imputation.sanger.ac.uk/ or http://imputationserver.sph.
umich.edu/index.html with requesting the HRCr1.1 2016 reference panel, EUR population and Quality Control & Imputation
Mode. All cohorts followed this procedure for the imputation of autosomal variants except for UK Biobank (UKBB) and INTERVAL
that had their genotype imputation described elsewhere (Astle et al., 2016).
QUANTIFICATION AND STATISTICAL ANALYSIS
Phenotype modeling and cohort level GWAS
When possible, we excluded samples with any of the following: pregnancy (when complete blood count (CBC) done), acute
medical/surgical illness (when CBC done), blood cancer, leukemia, lymphoma, chemotherapy, myelodysplastic syndrome,
bone marrow transplant, congenital or hereditary anemia (e.g., hemoglobinopathy such as sickle cell anemia or thalassemia),
HIV, end-stage kidney disease, dialysis, EPO treatment, splenectomy, cirrhosis and those with any of the following extreme
measurements: WBC count > 100*109/L with > 5% immature cell or blasts, WBC > 200*109/L, Hemoglobin > 20 g/dL, Hemat-
ocrit > 60%, Platelet > 1000*109/L. For the WBC subtypes (e.g., basophils count) we used the relative count, i.e., the total WBC
count multiplied by the proportion for each cell type (e.g., basophils percentage). Raw phenotypes were regressed on age, age-
squared, sex, principal components and cohort specific covariates (e.g., study center, cohort, etc) if needed, WBC related traits
were log10 transformed before regression modeling. Residuals from the modeling were obtained and then inverse normalized for
cohort level association analysis or GWAS. All cohorts followed the same exclusions and phenotype modeling except for UKBB
and INTERVAL that had their procedure described elsewhere (Astle et al., 2016). The cohort level association analyses were
then conducted using a linear mixed effects model in order to account for known or cryptic relatedness (e.g., BOLT-LMM
(Loh et al., 2015, 2018), EPACTS https://github.com/statgen/EPACTS and rvtests (Zhan et al., 2016) with the additive genetic
model. Linear mixed effects models have been shown to effectively account for both population structure and inter-individual
relatedness within the UK Biobank cohort, along with having increased discovery power over simple linear regression with prin-
cipal components.
QC and pre-processing of cohort level GWAS
Cohort level association analysis results went through a standard QCprocedure (Winkler et al., 2014; Zhan et al., 2016) using EasyQC
R package (https://www.uni-regensburg.de/medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/). The
mapping file and allele frequency reference data (GRCh37/hg19) from HRC were used to harmonize variant names across cohortsCell 182, 1214–1231.e1–e6, September 3, 2020 e2
ll
OPEN ACCESS Articleand to check allele frequency discrepancies between cohorts and the HRC reference panel, respectively. We generated a unique ID
for each variant using the form of chromosome:position_allele1_allele2 where alleles were ordered lexicographically or based on in-
del length as tri-allelic variants and/or indels of the same chromosome:position were observed. In addition to allele frequency plots,
quantile-quantile (Q-Q) plots and SE-N (i.e., inverse of the median standard error versus square root of sample size) plots were also
checked to detect systematic inflation, and different phenotypic variances due to mis-specified phenotype transformation or regres-
sion model, different study design or different study population, etc.
Meta-analysis
Post-QC’ed and pre-processed European cohort results were thenmeta-analyzed by GWAMA (Ma¨gi andMorris, 2010) using inverse
variance weighted fixed effects approach. We applied an imputation quality filter of INFO score%0.4 and a minor allele count (MAC)
filter ofMAC%5 for each variant in themeta-analysis, except for three large cohorts UKBB (N = 487,409),WHI (N = 17,682) andGERA
(N = 53,822), where amore stringentMAC filter ofMAC% 20was applied to exclude extremely rare variants with extreme effects prior
to meta-analysis.
Exact conditional analysis
We performed an exact conditional analysis using a stepwise multiple linear regression (Astle et al., 2016) approach in UKBB. Step-
wise multiple linear regression aims to identify a parsimonious subset of variants which explain the significant associations identified
by univariable GWAS. For each blood phenotype, the set of genome wide significant variants was partitioned into the largest number
of blocks such that no pair of blocks are separated by fewer than 5Mb, subject to the restriction that no block contained more than
2,500 variants. Blocks were generated independently for each phenotype. For each phenotype and each block, we identified a parsi-
monious set of variants explaining the signal in that block using a stepwise conditional linear regression algorithm. Each iteration of
the algorithm had two stages: i) addition of variants to the model and ii) removal of variants from the model. Convergence occurred
when neither addition nor removal of any variant improved themodel fit sufficiently for a t test p < 8.31x10
9. The variants in themodel
at convergence represent a parsimonious set for the block.
Let ‘‘M’’ represent the ‘current model,’ The algorithm is initialised withM as the emptymodel, containing just an intercept term, and
develops with the following steps:
1. Update M by inserting the variant in the block with the lowest univariable association p value, into the model.
2. In turn for each variant* in the block not inM, compareM to the model generated by augmentingMwith the variant using a t test.
Record the p value from each comparison.
3. If the least p value recorded in step 2 is greater than 8.31x10
9 terminate the algorithm.
4. Update M by adding the variant with the least p value recorded in step 2.
5. In turn for each variant in M, compare M to the model generated by removing the variant from M, using a t test. Record the p
values from each comparison.
6. If the greatest p value recorded in step 5 is smaller than 8.31x10
9 go to step 2.
7. Update M by removing the variant with the greatest p value recorded in step 5.
8. Go to step 5.
* When comparingM toM augmented by a variant, we are testing to see if this new variant represents a genetic signal independent
of the variants inM. In the situation where a potential new variant is in high LD (r2 > 0.9) with a variant already inMwe assume that the
this variant cannot represent an independent signal and we do not proceed to calculate its P value.
All linear regression was performed using the fastLM from the R package RcppEigen.
For each phenotype, following identification of conditionally significant variants in each block, all conditionally significant variants
within each chromosome were put into a single linear model and tested with the same multiple stepwise linear regression algorithm
described above, but starting at step 5. The union across chromosomes of the resulting sets of variants is the ‘conditionally signif-
icant’ list of variants for the blood cell phenotype, also referred to as ‘‘sentinel variants’’ throughout the text.
Meta-analysis conditional analysis
Using conditional and joint analysis as implemented in GCTA (Yang et al., 2011) (https://gump.qimr.edu.au/gcta), we iden-
tified independent association results in the meta-analyses at p < 5x10
9. To define novel associations, we tested these
variants using the same exact multivariate approach as above, in the UKBB, while conditioning on the variants identified by the
previous step.
Replication
We checked replication in the Million Veteran Project Cohort (Gaziano et al., 2016), for chromosome 1 and three different traits, one
per each major cell type (platelet counts, lymphocyte counts and red cell counts). The replication significance threshold was set to a
nominal level (p < 0.05) with the same direction of effect.e3 Cell 182, 1214–1231.e1–e6, September 3, 2020
ll
OPEN ACCESSArticleFine-mapping
Statistical fine-mappingwas performed in theUKBB cohort, using FINEMAP v1.3.1 (Benner et al., 2016) (http://www.christianbenner.
com/). Input windows were defined as +- 250 kb from a conditionally independent signal. In case of multiple sentinels generating
overlapping windows these weremerged together, resulting in window size ranging from 500kb to 1.38Mb. The number of condition-
ally independent signals in each windowwas used as prior knowledge for the maximum number of causative variants to be searched
(–n-causal-snps option) and the prior standard deviation for effect sizes was set to 0.08 (–prior-std option). The LD structure was
computed from the same samples included in the GWAS analysis. 95% credible sets were defined as minimal sets of variants jointly
covering at least 95% of the posterior probability of including the true causative signals.
eQTL Colocalization
We performed colocalization using gwas-pw (Pickrell et al., 2016) between GWAS of 10 hematological traits and transcriptomic
profiling of Platelets, CD4+, CD8+, CD14+, CD15+, and CD19+ cells. Where MPV, PDW, PLT#, and PCT were colocalized with eQTLs
from Platelets, NEUT# and NEUT% were colocalized with eQTLs from CD15+ cells, LYMPH# and LYMPH% were colocalized with
eQTLs from CD4+, CD8+, and CD19+ cells, and MONO# and MONO%were colocalized with eQTLs from CD14+ cells. Loci of coloc-
alization were defined by the recombination regions identified by (Berisa and Pickrell, 2016). Colocalization was only performed if the
locus contained a conditionally independent variant in LD r2 > 0.8 with the eQTL sentinel. Results were filtered to include only those
with posterior probability for colocalization higher than 80% resulting in a set of colocalized loci which are considered ‘highly likely’ to
be colocalized (Sun et al., 2018).
Mendelian genes
The list of Mendelian genes was retrieved from a manually curated list, compiled as part of the NIHR BioResource rare disease
sequencing project (Turro et al., 2020). It includes genes causative for stem cell and myeloid disorders (SMDs, 206 genes), bleeding,
thrombotic and platelet disorders (BPD, 104 genes) and bone-marrow failure syndromes (BMF 80 genes). We refer to Mendelian
SNPs as those assigned by VEP (worst consequence) to one of the Mendelian genes. To test for differences in absolute effect sizes
we matched for MAF between Mendelian and other SNPs using the R package ‘‘MatchIt’’ (Ho et al., 2007), separately for each func-
tional annotation, with at least 10 variants per group. We then tested the absolute effect size distribution shift using theWilcoxon test
as implemented in R.
Co-expression network
A co-expression matrix computed from the whole blood of 2,168 participants was used (Nath et al., 2017). The matrix quantifies cor-
relations between genes, replicated across 2 different cohorts. The edges between genes were defined by imposing variable hard
cut-offs on co-expression coefficients, e.g., two genes are linked in the network if their co-expression is higher than the cut-off. The
following cut-offs were used (0.05, 0.1, 0.2,., 0.8) but the results did not generally depend on the specific cut-off (unless otherwise
stated). The overlap enrichment between GWAS genes and network genes was computed by random permutations of gene sets, in
particular we used the following steps:
1. Annotate GWAS associations by VEP - this gives us a gene annotation for 83% of GWAS variants, which we refer to as
‘‘GWAS genes’’
2. Iterations:
- Randomly select a set of genes from the Ensembl v99 list of protein coding genes (using the R function ‘‘sample’’). The size of the
set matches the size of the test set
- Overlap the random set of genes with GWAS genes to calculate how many of the random genes are among the GWAS
associations
- Repeat 10,000 times
3. Compare the observed overlap with the background distribution
The numbers of links per gene were compared between Mendelian genes and all other genes by Wilcoxon test. For co-expression
among Mendelian genes, median absolute co-expression coefficients were computed for equal sized random draws of genes.
The enrichment of trans-eQTLs targeting Mendelian genes was computed similarly to the above comparison between effect sizes
of Mendelian and other GWAS genes. First, we downloaded median gene expression levels in whole blood from one of the cohorts
included in eQTLGen (Vo˜sa et al., 2018), BIOS (http://bbmri.researchlumc.nl/atlas/#data). We rank-inverse normalized the median
expression levels. Then, to account for differences in power detection due to higher expression levels of Mendelian genes and higher
Z-scores of Mendelian-targeting trans-eQTLs, we used the R package ‘‘MatchIt’’ (Ho et al., 2007) to select a matched subset. This
included trans-eQTLs targeting non-Mendelian genes with matched Mendelian expression levels and Z-scores for trans-eQTLs tar-
getingMendelian genes.We included themaximumpossible number of trans-eQTL-gene pairs with these characteristics (N = 9,258).
Finally, we compared the number of trans-eQTLs per gene in the two groups by Wilcoxon test.
The enrichment of GWAS genes among first and second degree neighbors to Mendelian genes was computed as follows: i) deter-
mine the list of neighboring genes based on the specific cut-off, ii) intersect with 1000 random permutations of gene sets of the same
size as the GWAS list, iii) compare to the actual intersection. The second degree neighbors were defined as neighboring genes to all
first-degree neighbors.Cell 182, 1214–1231.e1–e6, September 3, 2020 e4
ll
OPEN ACCESS Articleg-chromVAR
Bias-corrected enrichment of blood trait variants for chromatin accessibility of 18 hematopoietic populations was performed using
g-chromVAR, whose methodology has been previously described in detail (Ulirsch et al., 2019). In brief, this method weights
chromatin features by fine-mapped variant posterior probabilities and computes the enrichment for each cell type versus an empir-
ical background matched for GC content and feature intensity. For chromatin feature input, we used a consensus peak set for all
hematopoietic cell types with a uniform width of 500 bp centered at the summit. For variant input, we included all variants with
fine-mapped PPFM > 0.1%.
DeltaSVM
DeltaSVM is a machine learning model which uses sequence composition to predict cell type-specific open chromatin (Lee et al.,
2015). It then uses this sequence vocabulary to predict the change in chromatin accessibility from each variant. We trained on
two ATAC-Seq datasets: 1) 18 hematopoietic populations sorted from bone marrow, and 2) 8 stages of primary erythroid differen-
tiation. For each dataset, we trained on strong ATAC peaks in the > 80th percentile of counts matrix from each cell type. Standard
5-fold cross-validation was used to calculate AUROC.We then scored each variant with a posterior probability of association greater
than 0.001 for all populations to determine variants predicted to alter chromatin accessibility. Here, a positive deltaSVM score is in-
terpreted as a prediction where the variant increases chromatin accessibility whereas a negative score would reduce chromatin
accessibility.
Transcription factor motif analysis
Prediction of the effects of fine-mapped variants on transcription factor binding sites (TFBS) was performed by using themotifbreakR
package and a collection of 426 human TFBS models (HOCOMOCO) (Kulakovskiy et al., 2018). For 115,609 fine-mapped variants
with PPFM > 0.1%, we applied the ‘information content’ scoring algorithm and used a p cutoff of 13 10

4 for TFBSmatches; all other
parameters were kept at default settings.
Phenome-wide association study
To identify associations between blood trait variants and clinical phenotypes, we conducted a phenome-wide association study
(PheWAS) using summary statistics of 1,403 clinical phenotypes analyzed from the UK Biobank (https://www.leelabsg.org/
resources). As input, we started with 574 rare variants with 0.00005 < MAF < 0.01 which were either conditionally independent
lead signals or had fine-mapped PPFM > 0.50. To avoid studying phenotypes with too few cases to capture these low allele fre-
quencies, we only included phenotype-variant results for which the expected_case_minor_AC, calculated as 2 * variant_MAF *
num_cases, was greater than 25. This resulted in the final inclusion of 529 clinical phenotypes (case numbers ranging from 1,236
- 77,977) across 456 variants which had pheWAS data. The Bonferroni-corrected significance threshold for pheWAS was calculated
as 0.05 / 529 phenotypes = 9.45x10
5.
Splice variant analysis
To predict splice variants, we used SpliceAI, a deep neural network that accurately predicts splice junctions from genomic sequence
(Jaganathan et al., 2019). We obtained prediction scores for all possible single nucleotide variants in the reference genome, which
were released along with the SpliceAI tool, and extracted scores for all variants with fine-mapped PPFM > 0.001 in one or more blood
traits from the UK Biobank GWAS. We considered a variant to have a putative splicing consequence if it had a delta score > 0.2 for
one or more splicing consequences (acceptor gain, acceptor loss, donor gain, donor loss); this threshold was shown to be enriched
for splice variants and have high sensitivity.
Validation of SpliceAI predictions was performed using RNA-seq data on lymphoblastoid cell lines (LCL) of 465 participants
from the Geuvadis project 23 (http://www.geuvadis.org). We aligned paired-end reads to the hg19 reference genome with STAR,
allowing for novel splice junctions. To systematically evaluate predicted splice-altering variants, we processed junction files for all
465 samples using the LeafCutter workflow and evaluated changes in splicing clusters that overlapped the variant (Li et al.,
2018). For each variant, read alignments were merged into two groups, all carriers versus all non-carriers, and visualized in the
form of sashimi plots using the ggsashimi tool (Garrido-Martı´n et al., 2018). The threshold for the minimum number of reads support-
ing a junction to be drawn was 100 for rs8113779 and rs12898397, and reduced to 15 for rs139178017 given the low number of
carriers.
Polygenic scores
The polygenic scores (PGSs) were computed as weighted sums of genotypes, weighted by their effect size on the phenotype (beta
coefficient), using the PLINK score function. Beta coefficient estimateswere computed in UKBiobank and PGS scores were tested in
an independent cohort (INTERVAL). Positive effect alleles were included in order to get a positive contribution for each carried allele
and consequently a positive correlation with the phenotype. The following SNP inclusion criteria were compared: a) all genome-wide
SNPs after LD pruning with PLINK at 0.8 cut-off; b) LD-pruned SNPs with GWAS p < (0.05, 5x10
4, 5x10
6, 5x10
8); c) conditionally
independent variants and fine-mapped variants with posterior probability > 0.5; d) conditionally independent variants. Resulting
PGSs were then standardized and Pearson’s R coefficients of correlation between each PGS and its relevant trait were computede5 Cell 182, 1214–1231.e1–e6, September 3, 2020
ll
OPEN ACCESSArticlefor comparison. The phenotypic variance explained was computed as R2. Validation in a further independent cohort of French-
Canadians (European ancestry) called CARTaGENEwas performed using the same protocol, for the best performing PGS (condition-
ally independent variants). A linear regression model between the adjusted phenotypes and the PGS, adjusted by sex, age and
principal components, was used to determine the PGS’s effect sizes per SD.
Discovery saturation
To explore discovery saturation we chose 4 large GWAS analyses in cohorts of increasing sample size: INTERVAL (N35k), UK Bi-
LEVE (N43,5k), UK Biobank 1st release (N83k) and UKBiobank full cohort (N400k). For all of these we had conditionally inde-
pendent associations identified by the same method as described above. For each trait and cohort we determined the number of
conditionally independent variants detected by GWAS; the number of genes identified by these variants (using VEP worst conse-
quence annotation); the number of associated loci and we further subset the genes as Mendelian or others. Associated loci were
defined based on LD-blocks computed in Pickrell et al. (Berisa and Pickrell, 2016) which had at least one conditionally independent
signal. First, a linear projection of the first 3 data points was visually inspected to determine in the 4th data point fitted the expected.
Then 4 different regression models were tested to determine which one best described the full dataset: i) yx; ii) ysqrt(x); iii)
ysqrt(x)+x; (iv) ylog(x). Here y represents the counts (number of variants/genes/loci associated) and x represents the cohort
size. Similarly we computed the heritability explained by the set of variants identified by each cohort and searched for the best fitting
model. The heritability was computed as R2 of themultivariatemodel including the relevant variants in the full UK Biobank cohort. The
model fitting the Mendelian genes versus others was computed across all pooled phenotypes.
Allelic series and Disease Colocalization
We performed pairwise colocalization analysis between GWAS studies of 29 hematological parameters from the UK Biobank cohort
and 18 different autoimmune and inflammatory related disorders. Our analysis was performed using summary statistics collected
following GWAS of the respective studies. An inner merge was performed with variants tested for each hematological parameter
and each respective disease risk GWAS. Colocalization analysis was then performed following the same protocol described above
for eQTLs. Allelic series were defined as fine-mapping blocks including 2 or more associated sentinels.
Polygenic effects in rare blood disorders
We used UK Biobank participants with ICD10 codes in Chapter III (‘‘Diseases of the blood and blood-forming organs,’’ D500-D899
codes), who were excluded from the GWAS discovery. After computing the PGS, we performed a logistic regression for the disease
status, including sex, age, 10 principal components and any other co-occurring blood disorders as covariates. P values were cor-
rected by Bonferroni correction for the number of diseases (i.e., ICD10 codes) tested. We included only ICD10 codes with at least
40 cases for a total of 49 disorders.
Pathogenic variants annotation
Conditionally independent variants for each trait, as well as fine-mapped variants with posterior probability of being causative greater
than 50% were pulled together for the pathogenicity annotation. Genes were assigned to variants by VEP worst consequence
[release 84]. The set of Mendelian genes was manually curated by the NIHR-RD project. We focused on three different sources of
pathogenicity annotations: the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/ - accessed 2018-11-05), the Human Gene
Mutation Database, version pro 2018.4 (HGMD) (http://www.hgmd.cf.ac.uk/ac/index.php - accessed 2019-01-17) and a manually
curated list of novel pathogenic variants, produced by the NIHR-RD sequencing project (Turro et al., 2020). Variants were matched
by chromosome, position and alleles, in GRCh37. The following parameters were considered: a) ClinVar: categorical pathogenicity
assignment (yes/no/unknown), the star rating (1-4, 1 being the most uncertain and 4 the most certain) and the Review Status; b)
HGMD: categorial pathogenicity assignment and Rank Score indicating the pathogenicity confidence on a continuous scale from
0 to 1, 1 being certain pathogenicity assignment and 0 being very uncertain. The set of pathogenic variants was defined with high
confidence, imposing pathogenicity in ClinVar with at least 2 stars or pathogenicity in HGMD (‘‘DM’’) with rank score greater than
0.1. Variants reported by NIHR-RD for the first time were assigned to the ‘‘variants of uncertain significance’’ category (VUS). To
assess the effects of such pathogenic variants and their penetrance, two types of data were considered: full blood count diagnostic
cut-offs as used in the clinics and ICD-10 codes for blood disorders (Chapter III), as recorded by UKBB. Participants with full blood
counts in the normal range and no ICD-10 code were considered healthy. The joint modeling of rare variants and PGSwas performed
only for variants with more than 10 homozygotes, using logistic regression and relevant covariates, as above. For variants with less
than 10 homozygotes, we checked if these for systematic PGS deviation from the population mean (defined as PGS > 2*SDPGS).Cell 182, 1214–1231.e1–e6, September 3, 2020 e6
Supplemental Figures
A B
C
N=24 N=49 N=37 N=117 N=355 N=5 N=7 N=52
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no yes no yes no yes no yes no yes
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N=34 N=17 N=612 N=306 N=98 N=49 N=24 N=12 N=68 N=34
FC = 2.17 FC = 1.89 FC = 1.87 FC = 1.99 FC = 2.73
−0.6 −0.4 −0.2 0.0 0.2 0.4 0.6
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Figure S1. Replication and Mendelian Genes, Related to Figures 1 and 2
A, Comparison of replication effect size estimates, the x-axes shows effect sizes in MVP, the y-axes shows effect sizes in UK Biobank. The zoom-in panel
highlights non-replicating variants in red.B, Proportions of correct gene to variant assignments for VEPworst consequence and VEP all consequences divided by
functional annotation. Only known eQTLs in matched cell-types are shown and the correct gene is assumed to be the one identified by the eQTL experiment
(eGene). C, Variants assigned by VEP to Mendelian genes across different functional annotations have higher effect sizes compared to other variants, after
matching for MAF. The top 5 panels show absolute effect size distributions across all sentinel variants, where sentinels associated with multiple traits were
included only once with the highest effect size. The middle 5 panels show the same distributions but after matching the non-Mendelian variants to the Mendelian
ones by MAF. Stars denote significance: * 0.005 < p value < 0.05; ** 0.0005 < p value < 0.005; ***p value < 0.0005; FC =median fold change. The bottom 5 panels
show the distributions of minor allele frequencies after matching.
ll
OPEN ACCESSArticle
Mean platelet volume Mean reticulocyte volume Monocyte count Lymphocyte count
recnac tsaerBrecnac etatsorPerocs ecnegilletnIsisairosP
IMBainerhpozihcSDBIthgieH
Figure S2. Mendelian and Peripheral Enrichment Q-Q Plots, Related to Figure 2
EachQ-Qplot shows the enrichment for variants assigned to a 100kb interval surroundingMendelian genes. Different GWAS traits are included: 4 exemplar blood
traits and 8 unrelated traits, selected to have at least 500 significant GWAS associations. Overall, with the exception of the ‘‘Intelligence’’ trait, most non-blood
phenotypes do not show enrichment for variantsmapped toMendelian blood disorder genes. Conversely, peripheral associationsweremore likely to be enriched
in non-blood traits, showing enrichment for six out of eight traits. SMD= stem cell andmyeloid disorders, BPD = bleeding and thrombotic disorders, BMF = bone-
marrow failure; GW = genome-wide.
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OPEN ACCESS Article
CA1
BCL2L1
UBXN6
RNF123
SRRD
RANBP10
GYPC
MKRN1
EPB49
ASCC2 CARM1
ST6GALNAC4
ADIPOR1
HMBS
AHSP
RNF10
STRADB
SNCA IGF2BP2
RBM38
TRIM58
LOC440359
KLF1
CDC34
FAM46C
MAF1
SLC25A37
TMOD1
KEL
RAB3IL1
FBXO7
UBE2O
TESC
RAB2B
IFIT1L
C22ORF13
HDGF
HAGH
SLC4A1
FECH
DNAJA4
E2F2
OSBP2
CSDA
C1ORF128
WDR40A
C22ORF25
MAP2K3
GLRX5
TSPAN5
SLC14A1
TNS1
RNF11
PLEK2
OR2W3
EPB42
PIP5K2A
EIF2AK1
XPO7
PSMF1
RIOK3
0.00
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0.00 0.25 0.50 0.75 1.00
1 − Specificity
Se
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Celltype
B
CD4
CD8
CLP
CMP
Ery
GMP−A
GMP−B
GMP−C
HSC
LMPP
mDC
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MEP
Mono
MPP
NK
pDC
A B
0.00
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1.00
0.00 0.25 0.50 0.75 1.00
1 − Specificity
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Celltype
myeloid progenitor 
colony forming unit-ery
proerythroblasts 1
proerythroblasts 2
basophilic erythroblasts
polychromatic erythroblast
orthochromatic erythroblast
OrthoE and reticulocytes
C
E
H
coding promoter accessible intron intergenic
UTR
27.2% 14.1% 15% 25.4% 15.9%
2.4%
0
1
2
0.1 0.2 0.3 0.4
MAF
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all finemap splice variants
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MAF
m
a
xS
VM
Genomic Annotation
all finemap
splice variants
F
rs8113779
no yes
rs139178017 rs8113779
rs12898397
putative splice altering
n = 109 variants
spliceAI
Δ score > 0.2
Fine-mapped (PPFM > 0.001)
n = 215,694 variants
RNA + genotype
in GEUVADIS
detected
n = 57
not detected
n = 52
FDR > 0.01
n = 5
FDR < 0.01
n = 23
significant
spliceQTL
yesno
quantifiable 
by leafcutter
yesno
rare
n = 29
G
(legend on next page)
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OPEN ACCESSArticle
Figure S3. Network Examples and Functional Annotation, Related to Figure 2
A, A zoom-in example of the coexpression network, including connected genes with a very high correlation cut-off (0.8). Blue dots represent genes detected by
GWAS, according to VEP worst-consequence annotation, red dots represent GWAS genes that are also Mendelian genes for blood disorders. Three Mendelian
genes are identified, all of them involved in spherocytosis and other red-cell disorders. B-C, Receiver operating characteristic (ROC) curves for measuring
classification performance of deltaSVM in two datasets: B) 18 hematopoietic populations sorted from bone marrow, and C) 8 stages of primary erythroid dif-
ferentiation.D, Association between variant absolute deltaSVM score (maxSVM), reflecting a variant’s predicted disruption of chromatin accessibility, and bins of
MAF. Dotted line indicates the median maxSVM score for the MAF 0.3-0.5 bin. E, Rare variants (MAC > 20, MAF < 1%, PPFM > 0.50, conditionally independent)
grouped by genomic annotation. F, Flow-chart depicting the steps involved in the identification and validation of blood trait-associated splice variants.G,Density
distribution of variant MAF, comparing 109 putative splice variants to all fine-mapped blood trait variants. H, Violin plot of the fine-mapped posterior probability
(PPFM) for putative splice variants versus all fine-mapped variants. For variants fine-mapped to multiple blood traits, we used the maximum PPFM.
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OPEN ACCESS Article
ret ret_p
rbc hct hgb hlr hlr_p irf
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linear regression 
linear projection of first 3 
data points 
linear regression 
linear projection of first 3 
data points 
linear regression 
Best model fit
Y~x+sqrt
linear regression 
Best model fit
Y~x+sqrt
Figure S4. Saturation Models, Related to Figure 5
A, For each trait, we show the number of conditionally independent variants (y-axes) discovered by GWAS in four cohorts of increasing sample size. The sample
size is shown on x-axes in 10,000 s. Two linear regression lines are shown: the full black line represents a regression including all 4 data points, the dotted black
line represents a linear projection of the first three data points for comparison. A decreasing trend can be observed for almost all traits. B, Similarly to panel a, the
number of GWAS-identified genes is shown on the y-axes. Genes were identified by VEP worst-consequence annotations.C, The same data points as in panel a
are now shown with the best fitting model line in red, which correspond to a square-root growth model.D, The same data points as in panel b are now shownwith
the best fitting model line in red, which corresponds to a square-root growth model. E, The plot shows the saturation analysis of the number of discovered
Mendelian genes (red color) and peripheral genes (black color) as a function of the discovery sample size. Both lines represent the best fitting model interpolating
the dots and are defined as a function of the square-root of the sample size.
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