Transcript Age at Natural Menopause

2nd International Conference on Endocrinology
Oct 20-22, 2014 Chicago
GWAS of Age at Menarche and Age at
Natural Menopause
Chunyan He, Sc.D.
Assistant Professor
Department of Epidemiology
Richard M. Fairbanks School of Public Health
Melvin and Bren Simon Cancer Center
Indiana University
Outline

Why do we study menarche and natural menopause
timing?

How do we identify their genetic determinants?
– Candidate gene association studies
– Genome-wide association studies (GWAS)

What do we learn from the GWAS findings?

How do we translate the GWAS findings?

What ‘s next?
Why are We Interested?

Menarche and menopause signal the beginning
and the end of normal reproductive life

Timing of two events varies between individuals

Associated with several chronic diseases and
conditions
–
–
–
–
–
Breast cancer
Endometrial cancer
Cardiovascular diseases
Osteoporosis
Obesity and Type 2 diabetes
What factors influence the timing?

Environmental factors
– Obesity and BMI
– Smoking

Genetic factors
– Both traits are strongly correlated between mothers
and daughters
– High heritability estimated from family and twin
studies
» Age at menarche: 53-74%
» Age at natural menopause: 44-65%
How to identify genetic determinants ?
Genetic Variants
Genome-wide
Association Study
(GWAS)
Candidate Gene
Association Study
Age at menarche
Age at natural menopause
Candidate Gene vs. GWAS Approach
Candidate gene association studies
5’
promoter exon1 intron1
3’
exon2 intron2
gene
Genotype
DNA
Phenotype
Age at menarche
Age at natural menopause
Genome-wide association studies
(GWAS)
exon3
Candidate Gene Association Studies

Genes involved in estrogen biosynthesis and
metabolism pathway
– ESR1 and ESR2
– CYP family genes: CYP1B1, CYP19A1

Genes involved in vascular pathway
– F5, APOE, NOS3

Genes involved in other pathway
– IGF1, CCR3, AMHR2, HDC, VDR, IL-1RA

Results were inconsistent
A comprehensive candidate gene
association study
Group
Description
Age at Menarche and Age at Natural Menopause
38
24
49
1
2
3
32
36
4
5
Steroid-hormone metabolism and biosynthesis pathway
Insulin-like growth factor (IGF) pathway
Transforming growth factor-beta (TGF-β) superfamily and
signaling pathway
Thrombophilia and vascular homeostasis pathway
Obesity and obesity-related phenotypes
6
Age at Menarche Only
Precocious or delayed puberty
19
Age at Natural Menopause Only
13
18
49
7
8
9
Premature ovarian failure
Polycystic ovary syndrome
Smoking and nicotine dependence
Total 278 genes, total 18,862 SNPs
He et al. Hum Genet, 2010
Gene Level Test
Age at Natural Menopause (16/259)
Age at Menarche (9/198)
Group Gene
1
FSHB
LHCGR
POMC
UGT2B4
2
2
4
0.023
0.007
0.016
0.001
rs11031010 5'-UTR
0.004
rs11031010
5'-UTR
0.035
rs1782507
5'-UTR
0.021
LHCGR
2
rs1464729
3'-UTR
0.004
rs4953616
Intronic
0.006
PGR
11
rs619487
Intronic
0.026
rs6729809
Intronic
0.010
SRD5A1
5
rs494958
Intronic
0.037
rs7579411
Intronic
0.013
IGF1
12
rs1019731
Intronic
0.005
rs4374421
Intronic
0.017
IGF2R
6
rs9457827
Intronic
0.038
rs2164808
3'-UTR
0.038
rs2297362
Intronic
0.041
rs7589318
3'-UTR
0.047
rs2013573
Intronic
0.019
rs13111134
Intronic
0.042
2
GHRH
20
rs1073768
3'-UTR
0.045
4
CD40LG
X
rs5930973
Intronic
0.007
rs3092921
3'-UTR
0.037
6
Intronic
5'-UTR
3'-UTR
5'-UTR
FGFR1
8
rs2288696
Intronic
0.033
KISS1
1
rs7538038
Intronic
0.012
NKX2-1
14
rs999460
3'-UTR
0.039
He et al. Hum Genet, 2010
3
5
7
8
9
15
11
Adj.P
rs11856927
rs621686
rs7951733
2
CYP19A1
FSHB
Function
Adj.P
rs555621
1
Chr. SNP
Function
Chr. SNP
11
Group Gene
SMAD7
18
rs4939833
Intronic
0.029
TGFBR1
9
rs1590
3'-UTR
0.042
PCSK1
5
rs271924
Intronic
0.034
PPARG
TNF
3
6
rs4135280
rs909253
Intronic
5'-UTR
0.005
0.003
EIF2B4
2
rs7586601
3'-UTR
0.002
rs12476704
5'-UTR
0.005
POLG
15
rs2351002
Intronic
0.019
NBN
8
rs2697679
Intronic
0.000
rs2735387
rs7011299
Intronic
5'-UTR
0.003
0.004
rs6279
3'-UTR
0.044
ANKK1
11
Pathway/Group Level Test
Group
Gene Functional Group
#genes
#SNPs
Obs
Exp
O/E
Pval
Age at Menarche
1 Steroid-hormone metabolism and biosynthesis
2 IGF pathway
3 TGF-β superfamily and signaling pathway
4 Thrombophilia and vascular homeostasis
5 Obesity and related phenotypes
6 Precocious/ delayed puberty
38
24
49
32
36
19
2627
1547
2878
2069
2437
1343
4
1
0
1
0
3
1.9
1.2
2.5
1.6
1.8
1.0
2.1
0.8
0.0
0.6
0.0
3.2
0.04
0.34
0.92
0.48
0.84
0.013
Total
198
12901
9
9.9
0.9
0.53
Age at Natural Menopause
1 Steroid-hormone metabolism and biosynthesis
2 IGF pathway
3 TGF-β superfamily and signaling pathway
4 Thrombophilia and vascular homeostasis
5 Obesity and related phenotypes
7 Premature ovarian failure
8 Polycystic ovary syndrome
9 Smoking and nicotine dependence
38
24
49
32
36
13
18
49
2627
1547
2878
2069
2437
648
2013
3300
5
2
2
0
3
2
1
1
1.9
1.2
2.5
1.6
1.8
0.7
0.9
2.5
2.6
1.7
0.8
0.0
1.7
3.1
1.1
0.4
0.011
0.12
0.45
0.81
0.1
0.025
0.23
0.71
Total
259
17519
16
13.0
1.2
0.16
Genes that lead to the extremes of the traits also influence normal phenotypic
variation in the general population
He et al. Hum Genet, 2010
GWAS

Require no a priori information about causal
gene location and function

An unbiased yet comprehensive approach

Detect common genetic variants with
moderate effect

Study chronic diseases and complex traits
Our GWAS Study Design

Two independent GWAS
– Nurses’ Health Study (NHS)
» 2,287 women, Illumina HumanHap550
– Women’s Genome Health Study (WGHS)
» 15,151 women, Illumina HumanHap300

Joint-analysis
– Total 17,438 women
– 317,759 common SNPs with MAF>1% in both studies
He C et al, Nat Genet, 2009
Manhattan Plots: Age at Menarche
He C et al, Nat Genet, 2009
Loci for age at menarche
66q21
69q31.2
Manhattan Plots: Age at Natural Menopause
Loci for age at natural menopause
Nat. Genet. May 2009
Sample Size is Key for Success

Is the sample size large enough ?
– Studies designed to find common variants of modest to high risk
– Detecting small effects requires large sample sizes
– Combine data across available GWAS to boost sample size
GWAS Consortium !!!!
The ReproGen Consortium

The ReproGen consortium is an international network
of investigators interested in better understanding the
genetic basis of reproductive aging mainly among
women of European ancestry.

Initially included approximately 30 studies and was
later expanded to more than 50 studies from the United
States, Europe and Australia and from more than 100
institutions.

http://www.reprogen.org/
http://www.reprogen.org/
The ReproGen Consortium
Meta-analysis 1: Age at Menarche

Meta-analysis of 32 GWAS in 87,802 women of European
ancestry; replication in up to 14,731 women

Confirmed the 2 previously known loci, and identified 30
new and 10 possible loci
Elks et al, Nat Genet, 2010
Major Findings
Meta-analysis 1: Age at Menarche

Variance in age at menarche explained by the 42 known
loci: 1.31%

Overlapping heritability of body size and menarche timing
– included four genetic loci previously linked to body mass index
(BMI) in or near the FTO, SEC16B, TRA2B, and TMEM18 genes.

Loci implicate genes involved in energy balance (BSX,
CRTC1, MCHR2) and hormone regulation (INHBA,
PCSK2, RXRG).

The timing of puberty is related to fatty acid metabolic
pathways.
Elks et al, Nat Genet, 2010
The ReproGen Consortium
Meta-analysis 2: Age at Menarche

Meta-analysis of 57 GWAS in 132,989 women of
European ancestry; replication in up to 49,427 women

Identified total 106 genetic loci (explain 2.71% menarche
variation) , and confirmed 41/42 previously known loci.
Perry et al, Nature, 2014
Major Findings
Meta-analysis 2: Age at Menarche

90/106 menarche loci showed consistent directions of
association with Tanner stage in boys and girls

Menarche signals overlapped with reported GWAS loci for
other traits: BMI, adult height, birth weight, various
immunity or inflammation-related traits, breast cancer and
bone mineral density

Menarche signals were enriched in imprinted regions, with
loci demonstrating parent-of-origin-specific associations
concordant with known parental expression patterns.

Pathway analyses implicated novel mechanisms that
regulate pubertal timing, including nuclear hormone
receptors, particularly retinoic acid and GABAB receptor
signalling
Perry et al, Nature, 2014
The ReproGen Consortium
Meta-analysis: Age at Natural Menopause

Meta-analysis of 22 GWAS in 38,968 women of European
ancestry; replication in up to 14,435 women

Confirmed the 4 previously loci, and identified 13 new loci
Stolk, et al, Nat Genet, 2012
Major Findings
Meta-analysis: Age at Natural Menopause

Variance in age at natural menopause explained by the
17 known loci: 2.5-4.1%

Loci implicate genes involved in DNA repair and Immune
functions.

Pleotropic effect at GCKR locus: Kidney function, type 2
diabetes, continuous glycemic traits, serum albumin, C
reactive protein, serum urate, and triglycerides

NF-kB signaling and mitochondrial dysfunction were
identified as biological processes related to timing of
menopause
Stolk, et al, Nat Genet, 2012
Summary of GWAS Findings

Genetic loci implicate novel mechanisms
that regulate reproductive timing

Pleiotropic genetic effects of shared loci
between reproductive timing and other
diseases

Few shared loci between age at menarche
and age at natural menopause
Translation of GWAS Findings
“Predictors of predictors”
SNPs
predictors
Age at Menarche
Age at Natural Menopause
Age at Menarche
Age at Natural Menopause
Breast Cancer
predictors
??????
SNPs
Age at Menarche
Age at Natural Menopause
Breast Cancer
“Mendelian Randomization”
Singe- SNP Tests
Test 19 menarche SNPs and 17 menopause SNPs
He et al. Breast Cancer Res. 2012
Genetic Risk Score (GRS) Tests
He et al. Breast Cancer Res. 2012
ER Status: Single-SNP Tests
He et al. Breast Cancer Res. 2012
ER Status: GRS Tests
What’s next ?
 Missing heritability
 Gene-gene interactions
? more variants
? rare variants
? GxG, GXE
 Gene-environment interactions
 Causal variants
 Other ethnic populations
 African Americans, Hispanic, Asian
 Functional characterization of GWAS hits
•
Functional consequences of variation at the identified loci
•
The underlying molecular mechanism
•
Use of genome annotation
Acknowledgments

IU Richard M. Fairbanks
School of Public Health

John Perry
Lisette Stolk
Anna Murray
Kathryn Lunetta
Ellen Demerath
Andre Uitterlinden
Joanne Murabito
Erin Wagner
Jiali Han

HSPH and NHS
David Hunter
Frank Fu
Sue Hankinson
Peter Kraft


WGHS
NCI
Stephen Chanock
IUSM and IUSCC
Harikrishna Nakshatri
Kathy Miller
Bryan Schneider
Kenneth Nephew
Yunlong Liu
Daniel Chasman
Julie Buring
Paul Ridker
Guillaume Paré

The ReproGen Consortium
Anna Maria Storniolo
Jill Henry
Funding
The Indiana CTSI
V-Foundation for Cancer Research
EA-Illumina GWAS Grant