• HMB shares a significant part of its genetic architecture with other disorders of the female genital tract.

  • F5-Leiden was the only common coagulation-affecting variant with a large and significant effect on HMB.

Abstract

Heavy menstrual bleeding (HMB) is a widespread occurrence among women of reproductive age and inflicts a substantial impact on their well-being and on health care expenses. To better characterize the genetic architecture of HMB, we conducted a meta-analysis of the summary statistics of genome-wide association studies (GWAS) from 5 biobanks that included up to 84 633 HMB cases and 598 195 controls from several ancestries. Of the 21 signals significantly associated with HMB in a discovery GWAS meta-analysis that combined 4 biobanks, 20 had a concordant direction of effect in the remaining cohort, including 10 that were significantly replicated. By combining the discovery and replication data sets, 15 additional signals were identified in subsequent meta-analyses. These genetic analyses identified 36 signals (33 novel) that were significantly associated with HMB, and gene prioritization techniques (eg, transcriptome-wide association studies, polygenic priority score) subsequently revealed likely causal genes. Notable discoveries included the strong protective effect of the F5-Leiden variant (rs6025-T; odds ratio, 0.75; P = 6.8 × 10−33); variants at the FSHB and LHB/CGB loci, both involved in hormone production regulation; and several signals near genes involved in the Wnt/β-catenin signaling pathway. We also observed strong and significant genetic correlations with disorders of the female genital tract, including uterine fibroids, endometriosis, or ovarian cysts. Overall, we identified 33 novel genetic loci associated with HMB, thereby significantly improving our understanding of the genetic etiology of this condition, which may provide new targets for the development of therapeutic strategies.

1.
Chidgey
J
,
Leng
G
,
Lacey
T
.
Implementing NICE guidance
.
J R Soc Med
.
2007
;
100
(
10
):
448
-
452
.
2.
Davies
J
,
Kadir
RA
.
Heavy menstrual bleeding: an update on management
.
Thromb Res
.
2017
;
151
(
suppl 1
):
S70
-
S77
.
3.
Fraser
IS
,
Critchley
HOD
,
Broder
M
,
Munro
MG
.
The FIGO recommendations on terminologies and definitions for normal and abnormal uterine bleeding
.
Semin Reprod Med
.
2011
;
29
(
5
):
383
-
390
.
4.
Nicholson
WK
,
Ellison
SA
,
Grason
H
,
Powe
NR
.
Patterns of ambulatory care use for gynecologic conditions: a national study
.
Am J Obstet Gynecol
.
2001
;
184
(
4
):
523
-
530
.
5.
Liu
Z
,
Doan
QV
,
Blumenthal
P
,
Dubois
RW
.
A systematic review evaluating health-related quality of life, work impairment, and health-care costs and utilization in abnormal uterine bleeding
.
Value Health
.
2007
;
10
(
3
):
183
-
194
.
6.
Sinharoy
SS
,
Chery
L
,
Patrick
M
, et al
.
Prevalence of heavy menstrual bleeding and associations with physical health and wellbeing in low-income and middle-income countries: a multinational cross-sectional study [published correction appears in Lancet Glob Health. 2023;11(12):e1862]
.
Lancet Glob Health
.
2023
;
11
(
11
):
e1775
-
e1784
.
7.
Hapangama
DK
,
Bulmer
JN
.
Pathophysiology of heavy menstrual bleeding
.
Womens Health
.
2016
;
12
(
1
):
3
-
13
.
8.
Karlsson
TS
,
Marions
LB
,
Edlund
MG
.
Heavy menstrual bleeding significantly affects quality of life
.
Acta Obstet Gynecol Scand
.
2014
;
93
(
1
):
52
-
57
.
9.
Kocaoz
S
,
Cirpan
R
,
Degirmencioglu
AZ
.
The prevalence and impacts heavy menstrual bleeding on anemia, fatigue and quality of life in women of reproductive age
.
Pak J Med Sci
.
2019
;
35
(
2
):
365
-
370
.
10.
Matteson
KA
,
Clark
MA
.
Questioning our questions: do frequently asked questions adequately cover the aspects of women’s lives most affected by abnormal uterine bleeding? Opinions of women with abnormal uterine bleeding participating in focus group discussions
.
Women Health
.
2010
;
50
(
2
):
195
-
211
.
11.
Schoep
ME
,
Adang
EMM
,
Maas
JWM
,
De Bie
B
,
Aarts
JWM
,
Nieboer
TE
.
Productivity loss due to menstruation-related symptoms: a nationwide cross-sectional survey among 32 748 women
.
BMJ Open
.
2019
;
9
(
6
):
e026186
.
12.
Andrews
NC
.
Disorders of iron metabolism [published correction appears in N Engl J Med. 2000;342(5):364]
.
N Engl J Med
.
1999
;
341
(
26
):
1986
-
1995
.
13.
Camaschella
C
.
Iron-deficiency anemia
.
N Engl J Med
.
2015
;
372
(
19
):
1832
-
1843
.
14.
Mansour
D
,
Hofmann
A
,
Gemzell-Danielsson
K
.
A review of clinical guidelines on the management of iron deficiency and iron-deficiency anemia in women with heavy menstrual bleeding
.
Adv Ther
.
2021
;
38
(
1
):
201
-
225
.
15.
Munro
MG
,
Critchley
HOD
,
Broder
MS
,
Fraser
IS
;
FIGO Working Group on Menstrual Disorders
.
FIGO classification system (PALM-COEIN) for causes of abnormal uterine bleeding in nongravid women of reproductive age
.
Int J Gynaecol Obstet
.
2011
;
113
(
1
):
3
-
13
.
16.
Djambas Khayat
C
,
Gouider
E
,
von Mackensen
S
,
Abdul Kadir
R
.
Heavy menstrual bleeding in women with inherited bleeding disorders
.
Haemophilia
.
2020
;
26
(
suppl 3
):
16
-
19
.
17.
McLintock
C
.
Women with bleeding disorders: clinical and psychological issues
.
Haemophilia
.
2018
;
24
(
suppl 6
):
22
-
28
.
18.
Gallagher
CS
,
Mäkinen
N
,
Harris
HR
, et al
.
Genome-wide association and epidemiological analyses reveal common genetic origins between uterine leiomyomata and endometriosis [published correction appears in Nat Commun. 2022;13(1):5543]
.
Nat Commun
.
2019
;
10
(
1
):
4857
.
19.
Gaziano
JM
,
Concato
J
,
Brophy
M
, et al
.
Million Veteran Program: a mega-biobank to study genetic influences on health and disease
.
J Clin Epidemiol
.
2016
;
70
:
214
-
223
.
20.
All of Us Research Program Investigators
,
Denny
JC
,
Rutter
JL
, et al
.
The “All of Us” Research Program
.
N Engl J Med
.
2019
;
381
(
7
):
668
-
676
.
21.
Kurki
MI
,
Karjalainen
J
,
Palta
P
, et al
.
FinnGen provides genetic insights from a well-phenotyped isolated population [published correction appears in Nature. 2023;615(7952):E19]
.
Nature
.
2023
;
613
(
7944
):
508
-
518
.
22.
Leitsalu
L
,
Haller
T
,
Esko
T
, et al
.
Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu
.
Int J Epidemiol
.
2015
;
44
(
4
):
1137
-
1147
.
23.
Bycroft
C
,
Freeman
C
,
Petkova
D
, et al
.
The UK Biobank resource with deep phenotyping and genomic data
.
Nature
.
2018
;
562
(
7726
):
203
-
209
.
24.
Willer
CJ
,
Li
Y
,
Abecasis
GR
.
METAL: fast and efficient meta-analysis of genomewide association scans
.
Bioinforma Oxf Engl
.
2010
;
26
(
17
):
2190
-
2191
.
25.
Yang
J
,
Lee
SH
,
Goddard
ME
,
Visscher
PM
.
Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations
.
Methods Mol Biol
.
2013
;
1019
:
215
-
236
.
26.
Myers
TA
,
Chanock
SJ
,
Machiela
MJ
.
LDlinkR: an R package for rapidly calculating linkage disequilibrium statistics in diverse populations
.
Front Genet
.
2020
;
11
:
157
.
27.
Lee
SH
,
Goddard
ME
,
Wray
NR
,
Visscher
PM
.
A better coefficient of determination for genetic profile analysis
.
Genet Epidemiol
.
2012
;
36
(
3
):
214
-
224
.
28.
Robin
X
,
Turck
N
,
Hainard
A
, et al
.
pROC: an open-source package for R and S+ to analyze and compare ROC curves
.
BMC Bioinformatics
.
2011
;
12
:
77
.
29.
Privé
F
,
Albiñana
C
,
Arbel
J
,
Pasaniuc
B
,
Vilhjálmsson
BJ
.
Inferring disease architecture and predictive ability with LDpred2-auto
.
Am J Hum Genet
.
2023
;
110
(
12
):
2042
-
2055
.
30.
Gusev
A
,
Ko
A
,
Shi
H
, et al
.
Integrative approaches for large-scale transcriptome-wide association studies
.
Nat Genet
.
2016
;
48
(
3
):
245
-
252
.
31.
1000 Genomes Project Consortium
,
Auton
A
,
Brooks
LD
, et al
.
A global reference for human genetic variation
.
Nature
.
2015
;
526
(
7571
):
68
-
74
.
32.
Giambartolomei
C
,
Vukcevic
D
,
Schadt
EE
, et al
.
Bayesian test for colocalisation between pairs of genetic association studies using summary statistics
.
PLOS Genet
.
2014
;
10
(
5
):
e1004383
.
33.
Weeks
EM
,
Ulirsch
JC
,
Cheng
NY
, et al
.
Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases
.
Nat Genet
.
2020
;
55
(
8
):
1267
-
1276
.
34.
Usoltsev
D
,
Kolosov
N
,
Rotar
O
, et al
.
Complex trait susceptibilities and population diversity in a sample of 4,145 Russians
.
Nat Commun
.
2024
;
15
(
1
):
6212
.
35.
Nagai
A
,
Hirata
M
,
Kamatani
Y
, et al
.
Overview of the BioBank Japan Project: study design and profile
.
J Epidemiol
.
2017
;
27
(
3S
):
S2
-
S8
.
36.
Awadalla
P
,
Boileau
C
,
Payette
Y
, et al;
CARTaGENE Project
.
Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics
.
Int J Epidemiol
.
2013
;
42
(
5
):
1285
-
1299
.
37.
Feng
Y-CA
,
Chen
C-Y
,
Chen
T-T
, et al
.
Taiwan Biobank: a rich biomedical research database of the Taiwanese population
.
Cell Genom
.
2022
;
2
(
11
):
100197
.
38.
Huffman
JE
,
Nicholas
J
,
Hahn
J
, et al
.
Whole-genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles
.
Blood
.
2024
;
144
(
21
):
2248
-
2265
.
39.
de Vries
PS
,
Reventun
P
,
Brown
MR
, et al
.
A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels
.
Blood
.
2024
;
143
(
18
):
1845
-
1855
.
40.
Smith
NL
,
Huffman
JE
,
Strachan
DP
, et al
.
Genetic predictors of fibrin D-dimer levels in healthy adults
.
Circulation
.
2011
;
123
(
17
):
1864
-
1872
.
41.
Huang
J
,
Sabater-Lleal
M
,
Asselbergs
FW
, et al;
CARDIoGRAM Consortium
C4D Consortium
CARDIOGENICS Consortium
.
Genome-wide association study for circulating levels of PAI-1 provides novel insights into its regulation
.
Blood
.
2012
;
120
(
24
):
4873
-
4881
.
42.
Huang
J
,
Huffman
JE
,
Yamakuchi
M
, et al;
Cohorts for Heart and Aging Research in Genome Epidemiology (CHARGE) Consortium Neurology Working Group
CARDIoGRAM Consortium
CHARGE Consortium Hemostatic Factor Working Group
.
Genome-wide association study for circulating tissue plasminogen activator levels and functional follow-up implicates endothelial STXBP5 and STX2 [published correction appears in Arterioscler Thromb Vasc Biol. 2014;34(8):E19]
.
Arterioscler Thromb Vasc Biol
.
2014
;
34
(
5
):
1093
-
1101
.
43.
Thibord
F
,
Hardy
L
,
Ibrahim-Kosta
M
, et al
.
A Genome Wide Association Study on plasma FV levels identified PLXDC2 as a new modifier of the coagulation process
.
J Thromb Haemost
.
2019
;
17
(
11
):
1808
-
1814
.
44.
de Vries
PS
,
Sabater-Lleal
M
,
Huffman
JE
, et al;
INVENT Consortium
MEGASTROKE Consortium of the International Stroke Genetics Consortium
.
A genome-wide association study identifies new loci for factor VII and implicates factor VII in ischemic stroke etiology
.
Blood
.
2019
;
133
(
9
):
967
-
977
.
45.
Sennblad
B
,
Basu
S
,
Mazur
J
, et al
.
Genome-wide association study with additional genetic and post-transcriptional analyses reveals novel regulators of plasma factor XI levels
.
Hum Mol Genet
.
2017
;
26
(
3
):
637
-
649
.
46.
Tang
W
,
Schwienbacher
C
,
Lopez
LM
, et al
.
Genetic associations for activated partial thromboplastin time and prothrombin time, their gene expression profiles, and risk of coronary artery disease
.
Am J Hum Genet
.
2012
;
91
(
1
):
152
-
162
.
47.
Thibord
F
,
Klarin
D
,
Brody
JA
, et al;
Global Biobank Meta-Analysis Initiative; Estonian Biobank Research Team
23andMe Research Team
Biobank Japan
CHARGE Hemostasis Working Group
.
Cross-ancestry investigation of venous thromboembolism genomic predictors
.
Circulation
.
2022
;
146
(
16
):
1225
-
1242
.
48.
Bulik-Sullivan
BK
,
Loh
P-R
,
Finucane
HK
, et al;
Schizophrenia Working Group of the Psychiatric Genomics Consortium
.
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
.
Nat Genet
.
2015
;
47
(
3
):
291
-
295
.
49.
Chen
S
,
Francioli
LC
,
Goodrich
JK
, et al
.
A genomic mutational constraint map using variation in 76,156 human genomes [published correction appears in Nature. 2024;626(7997):E1]
.
Nature
.
2024
;
625
(
7993
):
92
-
100
.
50.
Machiela
MJ
,
Chanock
SJ
.
LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants
.
Bioinforma Oxf Engl
.
2015
;
31
(
21
):
3555
-
3557
.
51.
Ghouse
J
,
Tragante
V
,
Ahlberg
G
, et al
.
Genome-wide meta-analysis identifies 93 risk loci and enables risk prediction equivalent to monogenic forms of venous thromboembolism
.
Nat Genet
.
2023
;
55
(
3
):
399
-
409
.
52.
Nagirnaja
L
,
Rull
K
,
Uusküla
L
,
Hallast
P
,
Grigorova
M
,
Laan
M
.
Genomics and genetics of gonadotropin beta-subunit genes: Unique FSHB and duplicated LHB/CGB loci
.
Mol Cell Endocrinol
.
2010
;
329
(
1-2
):
4
-
16
.
53.
Renault
L
,
Patiño
LC
,
Magnin
F
, et al
.
BMPR1A and BMPR1B missense mutations cause primary ovarian insufficiency
.
J Clin Endocrinol Metab
.
2020
;
105
(
4
):
dgz226
.
54.
Regan
SLP
,
Knight
PG
,
Yovich
JL
, et al
.
Infertility and ovarian follicle reserve depletion are associated with dysregulation of the FSH and LH receptor density in human antral follicles
.
Mol Cell Endocrinol
.
2017
;
446
:
40
-
51
.
55.
Wang
F
,
Guo
S
,
Li
P
.
Two novel mutations in the MCM8 gene shared by two Chinese siblings with primary ovarian insufficiency and short stature
.
Mol Genet Genomic Med
.
2020
;
8
(
9
):
e1396
.
56.
Chadchan
SB
,
Popli
P
,
Liao
Z
, et al
.
A GREB1-steroid receptor feedforward mechanism governs differential GREB1 action in endometrial function and endometriosis
.
Nat Commun
.
2024
;
15
(
1
):
1947
.
57.
Scott
TG
,
Sathyan
KM
,
Gioeli
D
,
Guertin
MJ
.
TRPS1 modulates chromatin accessibility to regulate estrogen receptor alpha (ER) binding and ER target gene expression in luminal breast cancer cells
.
PLOS Genet
.
2024
;
20
(
2
):
e1011159
.
58.
Bleach
R
,
Sherlock
M
,
O’Reilly
MW
,
McIlroy
M
.
Growth hormone/insulin growth factor axis in sex steroid associated disorders and related cancers
.
Front Cell Dev Biol
.
2021
;
9
:
630503
.
59.
Wang
D
,
Wu
S
,
He
J
, et al
.
FAT4 overexpression promotes antitumor immunity by regulating the β-catenin/STT3/PD-L1 axis in cervical cancer
.
J Exp Clin Cancer Res
.
2023
;
42
(
1
):
222
.
60.
Wei
Y
,
Chen
W
,
Li
Z
,
Xie
K
,
Liu
F
.
EIF3H stabilizes CCND1 to promotes intrahepatic cholangiocarcinoma progression via Wnt/β-catenin signaling
.
FASEB J Off Publ Fed Am Soc Exp Biol
.
2022
;
36
(
12
):
e22647
.
61.
Jin
Y-R
,
Yoon
JK
.
The R-spondin family of proteins: emerging regulators of WNT signaling
.
Int J Biochem Cell Biol
.
2012
;
44
(
12
):
2278
-
2287
.
62.
Zhang
G
,
Michener
CM
,
Yang
B
.
Low-grade ovarian stromal tumors with genetic alterations of the Wnt/β-catenin pathway that is crucial in ovarian follicle development and regulation
.
Cancers
.
2022
;
14
(
22
):
5622
.
63.
Venteicher
AS
,
Meng
Z
,
Mason
PJ
,
Veenstra
TD
,
Artandi
SE
.
Identification of ATPases pontin and reptin as telomerase components essential for holoenzyme assembly
.
Cell
.
2008
;
132
(
6
):
945
-
957
.
64.
Park
J-I
,
Venteicher
AS
,
Hong
JY
, et al
.
Telomerase modulates Wnt signalling by association with target gene chromatin
.
Nature
.
2009
;
460
(
7251
):
66
-
72
.
65.
McMellen
A
,
Woodruff
ER
,
Corr
BR
,
Bitler
BG
,
Moroney
MR
.
Wnt signaling in gynecologic malignancies
.
Int J Mol Sci
.
2020
;
21
(
12
):
4272
.
66.
El Sabeh
M
,
Saha
SK
,
Afrin
S
,
Islam
MS
,
Borahay
MA
.
Wnt/β-catenin signaling pathway in uterine leiomyoma: role in tumor biology and targeting opportunities
.
Mol Cell Biochem
.
2021
;
476
(
9
):
3513
-
3536
.
67.
Xia
Z
,
Cochrane
DR
,
Anglesio
MS
, et al
.
LINE-1 retrotransposon-mediated DNA transductions in endometriosis associated ovarian cancers
.
Gynecol Oncol
.
2017
;
147
(
3
):
642
-
647
.
68.
Terré
B
,
Piergiovanni
G
,
Segura-Bayona
S
, et al
.
GEMC1 is a critical regulator of multiciliated cell differentiation
.
EMBO J
.
2016
;
35
(
9
):
942
-
960
.
69.
Yang
T
,
Zhao
J
,
Liu
F
,
Li
Y
.
Lipid metabolism and endometrial receptivity
.
Hum Reprod Update
.
2022
;
28
(
6
):
858
-
889
.
70.
Dorsey
KA
.
Menorrhagia, active component service women, U.S. Armed Forces, 1998-2012
.
MSMR
.
2013
;
20
(
9
):
20
-
24
.
You do not currently have access to this content.
Sign in via your Institution