Human Molecular Genetics, 2013, Vol. 22, No. 7
doi:10.1093/hmg/dds551
Advance Access published on January 9, 2013
1465–1472
A genome-wide association study of early
menopause and the combined impact of identified
variants
1
Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK 2Wellcome Trust
Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK 3Department of Twin
Research and Genetic Epidemiology, King’s College London, Lambeth Palace Rd, London SE1 7EH, UK 4Department
of Medical Genetics, University of Lausanne, Lausanne, Switzerland 5Swiss Institute of Bioinformatics, Lausanne,
Switzerland 6Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy 7Estonian Genome
Center, 8Institute of Molecular and Cell Biology, 9Department of Obstetrics and Gynecology, University of Tartu, Tartu,
Estonia 10Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02215, USA 11Harvard
Medical School, Boston MA 02115, USA 12Department of Epidemiology, 13Department of Biostatistics, University of
North Carolina at Chapel Hill, Chapel Hill, NC, USA 14Department of Public Health, Indiana University School of
Medicine, Indianapolis, Indiana, USA 15Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis,
Indiana, USA 16Icelandic Heart Association, Kopavogur, Iceland 17University of Iceland, Reykjavik, Iceland
18
Department of Internal Medicine, 19Department of Epidemiology, 20Division of Reproductive Medicine, Erasmus MC,
21
Netherlands Consortium of Healthy Aging, Rotterdam, The Netherlands 22Decode genetics, Sturlugata 8, Reykjavik
105, Iceland 23Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA 24Department
of Biostatistics, 25Department of Medicine, University of Washington, Seattle, WA, USA 26Breakthrough Research
Centre, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK 27Harvard School of Public
Health, Boston, MA 02115, USA 28Department of Epidemiology, 29Department of Nutrition, Harvard School of Public
∗
To whom correspondence should be addressed at: Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter,
UK EX1 2LU. Tel: +44 1392722976; Fax: +44 1392722926; Email:
[email protected]
#
These authors contributed equally to this work.
# The Author 2013. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/
3.0/), which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial
re-use, please contact
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John R. B. Perry1,2,3,#, Tanguy Corre4,5,6,#, Tõnu Esko7,8,#, Daniel I. Chasman10,11, Krista Fischer7,
Nora Franceschini12, Chunyan He14,15, Zoltan Kutalik4,5, Massimo Mangino3, Lynda M. Rose10,
Albert Vernon Smith16,17, Lisette Stolk18,21, Patrick Sulem22, Michael N. Weedon1, Wei V.
Zhuang23, Alice Arnold24, Alan Ashworth26, Sven Bergmann4,5, Julie E. Buring10,11,27, Andrea
Burri3, Constance Chen28, Marilyn C. Cornelis29, David J. Couper13, Mark O. Goodarzi30,
Vilmundur Gudnason16,17, Tamara Harris31, Albert Hofman19,21, Michael Jones32, Peter
Kraft28,29,33, Lenore Launer31, Joop S. E. Laven20, Guo Li25, Barbara McKnight24, Corrado
Masciullo6, Lili Milani7, Nicholas Orr26, Bruce M. Psaty34,35, ReproGen Consortium, Paul M.
Ridker10,11,27, Fernando Rivadeneira18,21, Cinzia Sala6, Andres Salumets9,36, Minouk
Schoemaker32, Michela Traglia6, Gérard Waeber37, Stephen J. Chanock38, Ellen W. Demerath39,
Melissa Garcia31, Susan E. Hankinson28,33, Frank B. Hu28,29,33, David J. Hunter28,29,33, Kathryn L.
Lunetta23, Andres Metspalu7,8, Grant W. Montgomery40, Joanne M. Murabito41,42, Anne B.
Newman43, Ken K. Ong44,45, Tim D. Spector3, Kari Stefansson22, Anthony J. Swerdlow32, Unnur
Thorsteinsdottir22, Rob M. Van Dam29,46, André G. Uitterlinden18,21,19, Jenny A. Visser18, Peter
Vollenweider37, Daniela Toniolo6,47,# and Anna Murray1,#,∗
1466
Human Molecular Genetics, 2013, Vol. 22, No. 7
Received July 24, 2012; Revised October 12, 2012; Accepted December 24, 2012
Early menopause (EM) affects up to 10% of the female population, reducing reproductive lifespan considerably.
Currently, it constitutes the leading cause of infertility in the western world, affecting mainly those women who
postpone their first pregnancy beyond the age of 30 years. The genetic aetiology of EM is largely unknown in
the majority of cases. We have undertaken a meta-analysis of genome-wide association studies (GWASs) in
3493 EM cases and 13 598 controls from 10 independent studies. No novel genetic variants were discovered,
but the 17 variants previously associated with normal age at natural menopause as a quantitative trait (QT)
were also associated with EM and primary ovarian insufficiency (POI). Thus, EM has a genetic aetiology
which overlaps variation in normal age at menopause and is at least partly explained by the additive effects
of the same polygenic variants. The combined effect of the common variants captured by the single nucleotide
polymorphism arrays was estimated to account for ∼30% of the variance in EM. The association between the
combined 17 variants and the risk of EM was greater than the best validated non-genetic risk factor, smoking.
INTRODUCTION
Menopause represents a major hormonal change, characterized
by a decline in oestrogen and progesterone levels and cessation of female reproductive function as the ovarian reserve
is exhausted (1). It influences a woman’s well-being and
early menopause (EM) is associated with increased risk of
age-related diseases including cardiovascular disease, osteoarthritis and osteoporosis, but reduced risk of breast cancer (2).
The average age at natural menopause in women of Northern European descent is 50 to 51 years (3,4). Early entry into
menopause has implications for women’s fertility. Fertility
starts to decrease on average at about age 30 years and is considerably diminished after age 35. It is estimated that natural
fecundity ceases at a mean age of 41 years, i.e. 10 years
before menopause (5). In recent decades, the average age at
which a woman gives birth to her first child has increased
from around 25 up to 30 years of age (6). As a consequence,
women who are at risk of EM and who delay childbearing
until their 30’s are more likely to have problems conceiving
(2). This tendency has led to an increase in age-related infertility, subsequently increasing the utilization of assisted
reproductive technologies (ARTs). Better understanding of
the mechanisms that lead to EM, and even the ability to
predict it, could greatly improve family planning and reduce
the need for invasive and costly ART treatments (5,7).
Heritability estimates for age at natural menopause, from
twin and family studies, range from 44 – 65%, suggesting a
substantial genetic component to the trait (8 – 12). Initial
genome-wide association studies (GWASs) identified 4 loci
associated with variation in age at natural menopause in the
normal range (40 –60 years) (13,14) and more recent
GWASs have added a further 13 loci, bringing the total to
17, including genes implicated in DNA repair and immune
function (15). The effect size ranged from 8.7 weeks to
nearly 1 year (50.5 weeks) per allele and the 17 single nucleotide polymorphisms (SNPs) together explained 2.5– 4.1% of
the population variation in natural menopausal age.
EM, defined as menopause occurring before 45 years of age,
occurs in ≏5 – 10% of women and primary ovarian insufficiency (POI) when menstruation ceases before 40 years,
affects ≏1% of women (3,16,17). Premature ovarian ageing
may be the consequence of a precocious decline of the primordial follicle pool, which is established during fetal life,
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Health, Boston, MA, USA 30Division of Endocrinology, Diabetes & Metabolism, Cedars-Sinai Medical Center
31
National Institutes on Aging, NIH, Bethesda, MD, USA 32Section of Epidemiology, The Institute of Cancer Research,
Sutton, Surrey SM2 5NG, UK 33Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, and
Harvard Medical School, Boston, MA, USA 34Departments of Medicine, Epidemiology and Health Services, University
of Washington, Seattle, WA, USA 35Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
36
Competence Centre on Reproductive Medicine and Biology, Tartu, Estonia 37Division of Internal Medicine,
Lausanne University Hospital, CHUV, Lausanne, Switzerland 38Division of Cancer Epidemiology and Genetics,
National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 39Division of Epidemiology and
Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA 40Queensland Institute of
Medical Research, Brisbane, Queensland, Australia 41National Heart, Lung and Blood Institute’s Framingham Heart
Study, Framingham, MA, USA 42Section of General Internal Medicine, Boston University School of Medicine, Boston,
MA 02118, USA 43Departments of Epidemiology and Medicine, University of Pittsburgh, Pittsburgh, PA, USA
44
Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital,
Cambridge, UK 45Department of Paediatrics, University of Cambridge, Cambridge, UK 46Saw Swee Hock School of
Public Health and Yong Loo Lin School of Medicine, National University of Singapore, Singapore 47Institute of
Molecular Genetics, 27100 Pavia, Italy
Human Molecular Genetics, 2013, Vol. 22, No. 7
1467
prevalence of 5 or 10%, heritability of EM due to the SNP
array genotypes was estimated to be 27 and 33%, respectively
(P ¼ 0.006, se ¼ 0.11; P ¼ 0.006, se ¼ 0.13, respectively).
To identify associations at the gene level, where combinations of multiple SNPs may contribute in aggregate, we ran
the Versatile Gene-Based Association Study’ (VEGAS) test.
Using our full discovery meta-analysis, VEGAS produced
gene-level results for 17 580 genes. No genes passed our conservative Bonferroni correction at the 0.05 level (P ¼ 2.8 ×
1026). There were 48 genes with P , 0.001 from the
VEGAS analysis, we used GRAIL to identify any of these
48 genes which shared functional links with any gene within
the 17 known menopause regions. Four genes reached a
GRAIL P , 0.05; MCM6 (most similar to MCM8, top SNP
rs2164210—P ¼ 7 × 1025), C6orf150 (similar to SYCP2L,
top SNP rs311686—P ¼ 7 × 1024), CRHR1 (similar to
UCN, top SNP rs4640231—P ¼ 2 × 1024), SLC25A13
(similar to POLG, top SNP rs2375044—P ¼ 2 × 1025).
Pathway analysis with Magenta revealed no significant enrichment of biological pathways in EM.
RESULTS
The role of loci associated with variation in normal age at
menopause in women with early menopause and POI
To identify common genetic variants associated with EM, we
followed a two-stage, case – control approach. From the ReproGen consortium cohorts with GWAS data, we selected cases
as women with age at menopause before 45 years (N ¼ 3493)
and controls as women with age at menopause between 50 and
60 years (N ¼ 13598). Only cohorts with ≥100 cases were
included, giving 10 independent studies (Supplementary Material, Table S1). Meta-analysis of this EM discovery dataset identified four independent signals with P-values stronger than the
genome-wide significant threshold of P , 5 × 1028 (Supplementary Material, Table S2). All the four signals had been identified in the ReproGen quantitative trait (QT) GWAS of normal
menopause age (15). A further four SNPs were borderline significant for EM (P , 5 × 1027, Supplementary Material,
Table S2) and two of these had not been previously identified
in the QT GWAS: rs1867631 in SGIP1 at chromosome 1p31.3
and rs1473307 near NYAP2 at chromosome 2q36.3. Both
SNPs were carried forward for replication by de novo genotyping or in silico analyses in an additional sample of 3412 cases
and 4928 controls, from four cohorts (Supplementary Material,
Table S1). For both SNPs the association P-value increased
when the replication data were combined with the EM discovery
data (Supplementary Material, Table S3); thus, we found no evidence for novel genetic loci associated with EM.
To estimate the proportion of variance explained by all
common variants captured on the SNP arrays in a polygenic
model, we used genome-wide complex trait analysis (analysis
tools available at: http://www.complextraitgenomics.com/
software/gcta/). We estimated the variance explained in
the WGHS cohort, one of the largest cohorts used in the
meta-analysis (N ¼ 10 302). For menopause as a QT, the
SNPs explain 21% of the variance (P ¼ 1 × 10211, se ¼
0.03) in a model taking residuals of menopause age with
body mass index, smoking and population eigenvectors.
Using the same approach and assuming a population
We next investigated the risk of EM for each of the 17 variants
that were associated with normal variation in menopause age
reported in the ReproGen QT GWAS. In silico data were
available for 3840 individuals with EM (those with age at
menopause 40– 44 years were included in the previous QT
GWAS15, individuals with age at menopause ,40 years
have not been included previously). A further 1365 cases
and 2475 controls from three studies not included in that QT
GWAS were directly genotyped or had in silico data for the
17 SNPs. The odds ratios (ORs) for EM were in the same direction and of a similar magnitude in the discovery EM GWAS
and in the meta-analysis of the three additional independent
cohorts (Supplementary Material, Table S4). Combining
both datasets, all 17 QT GWAS SNPs were nominally associated with EM (P-value ,0.05) and were all directionally
consistent with their effects on normal age at menopause
(Table 1 and Supplementary Material, Table S4). The SNPs
with the largest association with age at menopause in the
normal range had the greatest OR for EM (Fig. 1).
In five of the studies (two from the discovery EM GWAS and
three of the additional independent studies), there were more than
100 individuals with menopause before 40 years (Supplementary
Material, Table S1). We tested the association of the 17 menopause SNPs in 1108 POI cases and 7727 controls who were not
part of the sample for the QT GWAS. Despite limited power
from the relatively small sample size, rs11668344 on chromosome 19 was significantly associated with POI in the
meta-analysis [OR ¼ 1.30 (CI 1.21–1.47), P ¼ 5.39 × 1028;
after Bonferroni correction accounting for 17 tests]. Of the
remaining 16 SNPs, all had an effect in the expected direction
and eight were nominally associated with POI (P , 0.05)
(Table 1). We also explored associations with EM and POI
using dominant and recessive models for each the 17 menopause
variants and found no evidence for any SNP acting in a nonadditive fashion (Supplementary Material, Table S5).
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leading to a loss of negative feedback from ovarian sex steroids and inhibins on the hypothalamic – pituitary axis.
Oocyte quality decreases with increasing age and EM may
reflect the damage accumulated during reproductive life,
and/or age-related changes in granulosa cell – oocyte communication (18). EM may be caused by genetic defects (eg.
Turner syndrome or FMR1 premutations), autoimmunity or
iatrogenic (as a consequence of surgery, chemotherapy or radiation) or might be the consequence of environmental
factors. Unexplained EM also has a substantial genetic component (19). A woman whose mother had an EM has ≏6-fold
increased risk of having EM (8,20). However, in the majority
of cases, the genes involved in EM are largely unknown and
may be different from the genes regulating age at menopause
in the normal range.
We have addressed this issue by conducting a GWAS comparing EM cases with controls who had menopause at ages
50– 60 years, in the ReproGen consortium. We find considerable overlap between the genetic variation that contributes to
normal menopause age and EM.
1468
Table 1. Effect of 17 SNPs, identified by the GWAS of normal menopause QT, in EM and POI cases versus controls
20
19
8
12
11
6
19
1
13
16
2
6
2
15
5
1
4
Location
(bp)
Effect allele
5896227
60525476
38096889
55432336
30339475
31710946
61012475
39152972
60011740
11924420
1.72E+08
11005474
27568920
87664932
1.76E+08
2.4E+08
84592646
g
g
t
g
c
a
a
c
g
t
t
g
t
g
g
c
a
Effect allele
frequency
Normal menopause QT GWAS
Effect (years) SE
P-value
0.93
0.36
0.83
0.1
0.83
0.35
0.36
0.73
0.67
0.58
0.37
0.51
0.39
0.4
0.51
0.48
0.51
20.948
20.416
20.262
20.38
20.225
20.213
20.158
20.24
20.17
20.168
20.196
20.165
20.175
20.184
20.287
20.164
20.228
0.052
0.026
0.034
0.042
0.033
0.026
0.026
0.029
0.026
0.025
0.026
0.024
0.025
0.025
0.025
0.027
0.025
1.4E 2 73
1.5E 2 59
9.3E 2 16
2.5E 2 19
1.5E 2 11
1.6E 2 16
8.7E 2 10
9.0E 2 17
9.5E 2 11
1.0E 2 11
2.2E 2 12
7.8E 2 12
2.3E 2 12
3.6E 2 13
9.1E 2 32
8.5E 2 10
2.4E 2 19
EM cases versus controls
OR [95% CI]
P-value
Dir
POI cases versus controls
OR [95% CI]
P-value
Dir
1.55 [1.41– 1.71]
1.33 [1.27– 1.4]
1.23 [1.15– 1.32]
1.19 [1.1–1.28]
1.18 [1.11– 1.26]
1.16 [1.11– 1.22]
1.16 [1.1–1.21]
1.14 [1.08– 1.21]
1.14 [1.08– 1.2]
1.13 [1.08– 1.19]
1.13 [1.08– 1.19]
1.12 [1.07– 1.18]
1.12 [1.07– 1.18]
1.12 [1.07– 1.17]
1.11 [1.06– 1.16]
1.1 [1.05 –1.16]
1.09 [1.04– 1.14]
22222
?2222
?2+++
?2222
?2222
?22++
?++++
?+222
?2222
? 2+ + +
?++++
?2222
?2+++
?+222
?+222
?2222
?2+++
1.17 [0.99– 1.39]
1.34 [1.21– 1.47]
1.22 [1.05– 1.41]
1.21 [1.04– 1.4]
1.21 [1.07– 1.38]
1.16 [1.05– 1.28]
1.14 [1.03– 1.26]
1.08 [0.97– 1.2]
1.2 [1.08– 1.33]
1.03 [0.93– 1.13]
1.12 [1.02– 1.23]
1.10 [1.0– 1.21]
1.09 [0.99– 1.2]
1.18 [1.07– 1.3]
1.05 [0.96– 1.16]
1.04 [0.94– 1.14]
1.04 [0.94– 1.14]
2222+
2? 2 2 2
+?2+2
2? 2 2 2
2? 2 2 2
+?2++
+?+++
2?+2+
2? 2 2 2
2? 2 + +
+?++2
2? 2 + 2
+? 2 + +
2?+22
+?222
2?+2+
+?2++
5.8E 2 20
2.2E 2 32
1.5E 2 09
5.8E 2 06
2.2E 2 07
9.2E 2 10
4.0E 2 09
1.2E 2 06
2.5E 2 07
2.0E 2 07
3.6E 2 07
6.2E 2 07
1.1E 2 06
3.0E 2 06
1.1E 2 05
0.0002
0.0005
0.07
3.7E 2 09
0.008
0.01
0.004
0.003
0.01
0.15
0.0006
0.60
0.02
0.06
0.09
0.0009
0.27
0.45
0.45
SNPs are ordered by OR for EM. Direction of effects for individual studies given in the following order: BGS, Colaus, EGCUT, NIDO, discovery for EM and Aric, BGS, Colaus, NIDO, WGHS for POI. ?
indicates that a study did not contribute data for that SNP, either because not genotyped or failed QC.
Figure 1. Effect on normal age of menopause as a QT plotted against the odds
of EM (,45 years) or POI (,40 years) for each of 17 ReproGen age at menopause GWAS SNPs.
Observed versus expected estimates of the normal
menopause range loci on early menopause risk
We estimated the association between the 17 QT normal
menopause variants and the odds of having EM and POI,
based on the associations with menopause age in the QT analysis (15) by comparing the expected with the observed odds
(Supplementary Material, Table S6, Fig. S1). The expected
odds were derived using published estimates for the incidence
of POI and EM (1 and 5%, respectively) and using those to dichotomize a normal distribution of age at menopause. It was
not possible to determine the actual incidence of EM and
POI in participating cohorts because of the cross-sectional
study design of most of the participating studies. We, therefore, conducted a sensitivity analysis taking cut-offs either
side of 1 and 5% of the age at menopause distribution. The
method assumes a normal distribution for menopause age,
which may not be the case; thus, the results should be interpreted with caution. For the majority of SNPs, the effect on
EM and POI was within the range expected from the QT
study. However, there was evidence that one SNP varied significantly from expected. The allele associated with lower age
at menopause at rs16991615 on chromosome 20 was significantly less strongly associated with POI than expected (P ¼
1.04 × 1026). A significant difference between the observed
and expected ORs for rs16991615 was seen at both 0.05 and
2.5% for the POI cases.
Testing the combined effect of the 17 age at menopause
SNPs on EM risk
We sought to assess the combined association of the 17
QT age at menopause SNPs with the risk of EM, in two datasets independent of the QT and EM discovery samples
(NIDO—691 cases, 1394 controls, and EGCUT—647 cases,
848 controls). The number of age at menopause lowering
alleles carried per individual was calculated, and the distribution of these alleles in cases and controls is shown in Fig. 2.
A per risk allele OR for EM of 1.13 ([CI 1.08 –1.17],
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rs16991615
rs11668344
rs2517388
rs2277339
rs12294104
rs1046089
rs12461110
rs4246511
rs4886238
rs10852344
rs10183486
rs2153157
rs2303369
rs2307449
rs365132
rs1635501
rs4693089
Chr
Human Molecular Genetics, 2013, Vol. 22, No. 7
SNPID
Human Molecular Genetics, 2013, Vol. 22, No. 7
1469
P ¼ 7.75 × 10210) was observed in the NIDO cohort,
which was similar to the estimate in EGCUT (OR ¼ 1.14
([CI 1.08– 1.19], P ¼ 6 × 1028).
We divided the case–control samples into risk quintiles,
based on the number of risk alleles they carried, weighted by
the relative effect sizes of those alleles from the EM
discovery + replication GWAS meta-analysis. The risk of
EM associated with being in each quintile relative to the
median quintile is shown in Figure 2. An OR of 2.47([CI
1.94–3.14], P ¼ 2.7 × 10213) for EM risk was observed
when comparing the top 20%, with the most EM risk alleles,
with the bottom 20%. This difference was higher when combined with smoking status. The smoking status alone (current
versus former/never smokers) was associated with a doubling
in risk for EM (OR 1.96 [CI 1.51–2.56], P ¼ 6 × 1027).
Those women with the combination of the top 20% EM risk
allele group plus current smoking had an OR of 3.38 ([CI
1.74–6.59], P ¼ 0.003) higher risk of EM than those in the
lowest 20% EM risk allele group who were former/never
smokers.
We tested the ability of the 17 SNPs to discriminate EM
cases from controls by calculating a receiver operating characteristic (ROC) area under the curve (AUC), using individual’s
weighted EM risk allele score and smoking status. Data from
the NIDO and EGCUT cohorts gave highly concordant results,
with an AUC of 0.60 for the 17 SNPs. This showed a significant improvement over smoking status alone (AUC ¼ 0.55).
Combining genetic and smoking risk factors gave an AUC
of 0.63 (sensitivity ¼ 35.4%, specificity ¼ 81.3%).
Prior to the recent identification of 13 new variants associated with normal age at menopause, there were four loci
reported, which were replicated in the more recent study,
(13,14, 15). The AUC for the first four published loci associated with age at menopause was 0.55.
DISCUSSION
Shared aetiology of EM/POI and normal menopause
A recent GWAS has identified 17 loci associated with age at
natural menopause in the normal range (40 – 60 years),
explaining ≏4% of the variation in menopause age (15).
However, this GWAS excluded women who had menopause
before 40 years (POI), a condition affecting ≏1% of the
female population. EM leads to short reproductive lifespan
and is also associated with several harmful health outcomes including increased risk of cardiovascular diseases (21). Up to
30% of POI cases have an affected relative suggesting a substantial genetic burden in these women, but candidate gene
studies have been unable to determine a genetic cause in the
majority of cases (22). The definition of POI is arbitrarily
based on the population distribution of menopause age,
affected women representing the extreme 1% tail (≏2.5 SDs
from the mean), rather than distinct clinical characteristics.
A small proportion of women with POI spontaneously conceive and thus, it is a heterogeneous condition. We hypothesized that very EM has distinct genetic aetiology compared
with menopause age within the normal range, caused by
either independent deleterious variants in the known age at
menopause genes, or by variants at different loci, which
have a larger effect on menopause age. In order to understand the genetic aetiology of menopause at the extreme of
the age distribution, we performed a GWAS in women
with menopause before 45 years of age. This ensured that
we captured the full spectrum of ovarian insufficiency and
gave us a large enough sample size to make it feasible to
perform a GWAS; however, a clinical diagnosis of POI
was not recorded in any of our studies. It is also possible
that rare variants, poorly captured by the SNP chips, are
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Figure 2. Distribution of the age at menopause-lowering allele score (quintiles) in women with EM and controls and ORs (95% CIs) for EM. Data shown are
from the two replication cohorts combined. OR’s are calculated relative to the median quintile.
1470
Human Molecular Genetics, 2013, Vol. 22, No. 7
New SNPs increase discriminative power over previous four
SNPs
By combining the effect of the 17 variants in a weighted allele
score, we demonstrated a larger effect on EM risk than the
best-known non-genetic risk factor, smoking (23,24).
However, the increased OR for EM for carriers of the most
risk alleles compared with the fewest was 2.47, which is still
significantly lower than the OR associated with having a
mother with EM, which is about six in most reported studies
(8, 20). However, the current 17 variants only explain ,5%
of the variance in menopause age and thus as more genetic
variants are discovered the discriminative power is likely to
increase. We observed a significant improvement in discriminative power for EM when the 13 most recently described
variants were added to the first four previously published
signals (25).
In conclusion, while much of the genetic aetiology of EM is
yet to be discovered, we have demonstrated that the combined
effect of multiple genes involved in determining the age at
normal menopause plays a role. This of course does not
exclude the possibility that rarer variants with larger effects
are also involved, as these may not have been well captured
by the SNP arrays used in the GWAS. Genetic markers of
ovarian ageing are present throughout life and thus may be superior to current best predictors, e.g. AMH, inhibin B and FSH
levels, which are only reliable indicators up to about 5 – 10
years prior to menopause. As more genetic components of
this trait are discovered, we will be able to include additional
genetic data in predictive models for menopause age, giving
women information about potential reproductive lifespan and
enabling them to make informed reproductive choices.
MATERIALS AND METHODS
GWAS for EM
EM cases were selected from studies which contributed to the
ReproGen GWAS of normal menopause (15). EM cases were
defined as women who had menopause before 45 years of age,
and controls were women with age at menopause from 50 to
60 years. Age at menopause was assessed through questionnaires, as detailed in Supplementary Material, Table S1.
Women of self-reported non-European ancestry were
excluded, as were women with menopause due to hysterectomy and/or bilateral ovariectomy, or chemotherapy/irradiation, if validated by medical records, and women using
hormone replacement therapy before menopause. Other variables associated with age at menopause, e.g. smoking, were
not excluded. We only included studies which had .100
EM cases. There were 10 studies included in the
meta-analysis, from the ReproGen consortium, with a total
of 3493 cases and 13598 controls (Supplementary Material,
Table S7). All samples were of European ancestry. All
cohorts performed SNP array genotyping followed by imputation to HapMapII, to generate a common set of ≏2.5
million autosomal SNPs with a minor allele frequency of
.1% (Supplementary Material, Table S7). Each individual
study performed their own quality control for imputation
quality, deviation from Hardy– Weinberg equilibrium, SNP
call rate and lambda GC correction (Supplementary Material,
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more prevalent in individuals at the extreme of the menopause distribution, but these cannot be assessed by our
current GWAS approach.
In our sample of ≏3500 cases, we found no evidence for
novel genetic associations with EM that reached genome-wide
significance thresholds. We did, however, find genome-wide
significant associations with four loci previously identified in
the normal menopause age QT GWAS (15). Our study was,
therefore, powered to detect associations with ORs of 1.17 –
1.59, depending on the minor allele frequency. There was,
however, considerable overlap between the samples used in
the normal menopause QT GWAS and the current EM
GWAS, which may have increased our chances of detecting
such signals due to the winner’s curse phenomenon. Despite
following up two borderline signals in replication cohorts, we
were unable to detect any new variants for EM. With our
sample size of ≏3500 cases and ≏13500 controls, we had
≏80% power to detect ORs of 1.2 with 30% minor allele frequency SNPs. We estimated that all common variants captured
by our SNP arrays account for ≏20% of the variance in natural
age at menopause, thus a significant proportion of the genetic
component to the trait is likely to be due to rarer or complex variants not captured by the SNP arrays. We did not include nongenetic variables in our association analyses and it is possible
that that there are genetic interactions with known environmental risk factors for EM, e.g. smoking. EM is a heterogeneous trait
and it is possible that clinical classification of sub-types would
increase our power to detect genetic factors associated with
the condition. We found no evidence for a distinct genetic aetiology in EM cases. If there were a genetically distinct group of
individuals at the extreme end of the distribution, by choosing a
relatively broad extreme category, representing ≏10% of the
menopause age distribution, there may be too much overlap
with the normal range of menopause age, thus masking any differences. We did not have sufficient number of cases with menopause at ages ,40 years to perform a GWAS on this category,
but we were able to investigate the role of known QT menopause
signals in this group of women representing the extreme ≏1%
tail of the distribution.
We tested the 17 variants identified in the ReproGen QT
GWAS of normal menopause, in cases of EM and POI. For
all 17 variants, the allele that was associated with younger
menopause age was also associated with increased risk of
EM and POI. Only four SNPs reached genome-wide levels
of significance for EM, but all 17 for EM and 3 for POI
were below the Bonferroni-corrected P-value of ,0.0015, assuming 34 independent tests. There was some evidence that
the association with POI was weaker than expected for the
SNP with largest effect on normal menopause, but this
requires a formal confirmation. Stolk et al. determined
common pathways for the variants associated with age at
menopause in the normal range and highlighted DNA repair/
replication, hormonal regulation and immune function as key
pathways (15). However, there was no evidence that genes
from a particular biological pathway were more important in
EM or POI. Our data support the hypothesis that EM and
POI represent the tail of the menopause distribution and thus
have overlapping polygenic aetiology, with individuals carrying more age at menopause-lowering variants having
increased risk of EM and POI.
Human Molecular Genetics, 2013, Vol. 22, No. 7
Table S7). Meta-analysis was performed using inverse variance weighting in METAL with genomic control correction.
Heterogeneity between studies was assessed using Cochrane’s
Q statistic test in METAL. Replication was carried out in four
independent cohorts, including 3412 cases and 4928 controls
(Supplementary Material, Table S1). In silico genome-wide
SNP data were available from COLAUS, while the other
three studies performed de-novo genotyping by Taqman SNP
assay.
Analysis of 17 QT menopause SNPs in EM and POI
Pathway analysis
We implemented two methods to assess whether particular
gene pathways were enriched in our EM GWAS data: (i)
We used a GSEA-based approach with MAGENTA (26),
where each gene in the genome is mapped to a single index
SNP with the lowest P-value within a 110 kb upstream,
40 kb downstream window. This P-value, representing a
gene score, is then corrected for confounding factors such as
gene size, SNP density and LD-related properties in a regression model. Each mapped gene in the genome is then ranked
by its adjusted gene score. At a given significance threshold
(95th and 75th percentiles of all gene scores), the observed
number of gene scores in a given pathway, with a ranked
score above the specified threshold percentile, is calculated.
This observed statistic is then compared with 1 000 000 randomly permuted pathways of identical size. This generates
an empirical GSEA P-value for each pathway. Significance
was determined when an individual pathway reached a false
discovery rate of ,0.05 in either analysis. In total, 2580 pathways from Gene Ontology, PANTHER, KEGG and Ingenuity
were tested for enrichment of multiple modest associations
with EM status.
(ii) We searched for evidence of multiple-SNP signal enrichment at the gene level using the (VEGAS algorithm
(http://gump.qimr.edu.au/VEGAS/). This method is described
in detail by Liu et al. (http://www.cell.com/AJHG/retrieve/p
ii/S0002929710003125), but briefly, test statistics across a
UCSC gene region (+50 kb) are collapsed into a single statistic representing the gene. The statistic is adjusted for confounding factors such as gene size, LD and SNP density.
The analysis was run on the full discovery meta-analysis
summary statistics, using the default settings of the online
tool. Genes reaching a P-value of ,0.001 were analysed by
GRAIL (http://www.broadinstitute.org/mpg/grail/) for literaturebased homology to the genes within the 17 known menopause
regions. A nominal GRAIL similarity P , 0.05 was chosen
to highlight genes of interest.
Expected versus observed OR
We estimated the expected OR for both EM (,46 years) and
POI (,40 years) for each of the 17 variants, based on the
coefficient estimate from the QT effect size in the normal
menopause age GWAS15. We calculated the expected ORs
for both the point estimate QT coefficient and the upper and
lower 95% CIs, by using the ‘Case – Control for thresholdselected QTs’ analysis on the Genetic Power Calculator
website (http://pngu.mgh.harvard.edu/~purcell/gpc/). Using
the proportion of variation explained by an SNP and the
allele frequency, the program generates expected allele frequencies in cases and controls, where cases and controls are
defined by standard deviation thresholds. We tested three
standard deviation thresholds for EM (equivalent to the 2.5,
5 and 10% tail of the menopause distribution) and three for
POI (0.05, 1 and 2.5% tails). We then tested for heterogeneity
by a Z-test of ln(observed_OR)-ln(expected_OR)/sqrt[se_squared
(observed_OR) + se_squared(expected_OR)]. The method
assumes that menopause age is normally distributed, but this
was not tested in individual studies.
Risk prediction
Two datasets independent of the ReproGen discovery studies
were used to assess the predictive impact of the 17 menopause
SNPs—NIDO and EGCUT. First, the number of EM risk
alleles carried per individual was calculated using the
‘score’ command in PLINK. Any individuals with less than
half of the genotyped SNPs missing were excluded from analysis. The same command then creates a genotypic score for
each individual, imputing any missing genotypes based on
the sample allele frequency and gives a weighting based on
SNP effect sizes from the combined EM + replication
meta-analysis (Table 1). This score was then used to calculate
the ROC curve statistics using the ‘lroc’ command in Stata.
The results were repeated using a raw risk allele sum score
from only individuals with all genotypes present. Total
sample sizes available with a genotypic risk score and phenotype were 691 cases and 1394 controls (NIDO) and 647 cases
and 848 controls (EGCUT). Smoking status was available in
the EGCUT samples, indicating ‘current’, ‘former’ or
‘never’ smoking based on questionnaire data. The individuals
in these datasets were additionally partitioned into quintiles
based on their genotypic risk score. ORs were calculated for
the risk of EM based on the quintile membership, relative to
the median (third) quintile. The two cohorts were combined
in this analysis, with adjustment for cohort as an additional dichotomous trait in the logistic regression model.
SUPPLEMENTARY MATERIAL
Supplementary Material is available at HMG online.
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Association statistics for the 17 SNPs previously identified to
influence normal age at menopause were extracted from the
EM data. This included the 10 EM discovery GWAS cohorts
and 3 of the 4 replication cohorts, giving a combined sample
size of 5205 EM cases and 16926 controls. Four studies had
genotype data for the 17 SNPs on more than 100 cases with
POI (ARIC, WGHS, COLAUS, NIDO), giving a total of
1108 POI cases and 7727 controls. BGS had data on a
subset of SNPs in 2121 EM and 260 POI cases and were
added to the meta-analysis for those SNPs. Meta-analyses
were carried in METAL.
1471
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Human Molecular Genetics, 2013, Vol. 22, No. 7
ACKNOWLEDGEMENTS
We are grateful to the study participants and staff from
all cohorts involved in this study. We would like to acknowledge ReproGen consortium members for initial analyses
which were not included in the final draft of the paper
and the CHARGE consortium for organizing conference
calls and face-to-face meetings of ReproGen. A full list of
members of ReproGen members is available as supplementary
information.
Conflict of Interest statement. None declared.
J.R.B.P. is funded by the Wellcome Trust as a Sir Henry
Wellcome Postdoctoral Research Fellow (092447/Z/10/Z).
Funding details for individual studies is provided in supplementary information. Funding to pay the Open Access publication charges for this article was provided by the Wellcome
Trust.
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