Genetic Basis of Male Pattern Baldness

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Genetic Basis of Male Pattern Baldness
To the Editor:
Common pattern baldness (androgenetic alopecia) is the most
common form of hair loss in humans. In Caucasians, normal
male hair loss, commonly known as ‘‘male pattern baldness’’
(MPB; MIM 109200), is noticeable in about 20% of men aged
20, and increases steadily with age, so that a male in his 90s has
a 90% chance of having some degree of MPB. In addition to
being among the most common natural conditions that make
men self-conscious, recent studies indicate associations of MPB
with: (1) benign prostatic hyperplasia (MIM 600082; odds ratio
(OR) ¼3.23; 95% con¢dence interval (CI): 1.81^5.79) (Hawk
et al, 2000); (2) coronary heart disease (relative risk ¼1.36; 95%
CI: 1.11^1.67) (Lotufo et al, 2000); (3) hyperinsulinemia
(OR ¼1.91; 95% CI: 1.02^3.56); and (4) insulin-resistance-asso-
ciated disorders, such as obesity (MIM 601665; OR ¼2.90; 95%
CI: 1.76^4.79), hypertension (MIM 145500; OR ¼2.09; 95% CI:
1.14^3.82), and dyslipidemia (OR ¼4.45; 95% CI: 1.74^11.34)
(Matilainen et al, 2000). MBP is also a risk factor for clinical pros-
tate cancer (MIM 176807; relative risk ¼1.50; 95% CI: 1.12^2.00)
(Oh et al, 1998). Although it is a widely accepted opinion that
common baldness is an autosomal dominant phenotype in men
and an autosomal recessive phenotype in women, or indeed that
baldness is genetically in£uenced, it is based on surprisingly little
empirical data. Here we grade MBP, in 476 monozygotic (MZ)
and 408 dizygotic (DZ) male twin pairs aged between 25 and 36
y and ¢nd a heritability of 0.81 (95% CI: 0.77^0.85), thus con-
¢rming that genetic e¡ects play a major part in the progression
of common hair loss.
Measures of hair loss were obtained in the course of an exten-
sive semistructured telephone interview with respondent book-
let, designed to assess physical, psychologic, and social
manifestations of alcoholism and related disorders, conducted
with 6265 twins born 1964 to 1971 from the volunteer-based Aus-
tralian Twin Registry. All males (45% of the sample) were asked
to rate their degree of hair loss, if any, using the Hamilton^Nor-
wood Baldness scale (Norwood, 1975) (a standard classi¢cation
scheme shown to have good test^retest reliability) (Hamilton,
1951; Norwood, 1975), which was printed i n the respondent book-
let (Fig 1). This data collection scheme was validated i n a study by
Ellis et al (1998), which compared participant self-assessment hair
loss against that determined by an independent trained observer
in their research clinic. Speci¢cally, the self-assessed rating of
score I in nine subjects was concurred by the trained observer in
all but one individual who received a score of II (p ¼0.317, Wil-
coxon matched pairs signed rank test), whereas no discrepancies
with observer’s scores were detected in ¢ve individuals with self-
assessed scores ranging from III to VII (Ellis et al,1998).
Data collected from 476 MZ and 408 DZ male pairs, plus 143
MZ and 154 DZ male individual twins (mean ages for the MZ
and DZ twins were 30.3 and 30.5 y, respectively) were analyzed
using structural equation modeling, to estimate parameters of a
model that include additive genetic e¡ects (A), nonadditive ge-
netic e¡ects (i.e., dominance or epistasis) (D), shared or family
environment (C), and random or unique environment (E) (Neale
and Cardon, 1992). In addition to the 12 Hamilton^Norwood ca-
tegories, scoring individuals who answered ‘‘no’’ to the question
‘‘have you experienced hair loss?’’, as zero, resulted in a 13 -point
scale.
A major goal of the genetic analysis was to test the multiple
threshold model (Reich et al, 1972; Kendler, 1993), which posits
that di¡erent types of hair loss re£ect di¡erent levels of severity
on a single dimension, rather than distinct etiologies. These
thresholds can be regarded as the z-value of the normal distribu-
tion that divides the area under the curve in such a way that it
gives the right proportion of individuals in each (hair loss) group,
thus re£ecting the prevalence of each group (Neale and Cardon,
1992). For each of the two zygosity groups, the ¢t of a mul-
tiple threshold model was tested by calculating the poly-
choric correlation for the Hamilton^Norwood hair loss gradings,
using POLYCORR (http://ourworld.compuserve.com/homepages/
jsuebersax/xpc.htm) or PRELIS 2.30 ( J˛reskog and S˛rbom,
1999). The polychoric correlation, also termed the ‘‘correlation of
liability’’, assumes that underlying the observed polychotomous
distribution of hair loss status there exists a continuous, normally
distributed latent liability (Kendler, 1993). A w
2
goodness-of-¢t
test is used to test whether the multiple threshold model provides
a good ¢t to the observed data. Calculation of 95% CI for the
polychoric correlations, the comparison of threshold values with-
in twin pairs and across zygosity groups, and genetic model
¢tting by maximum likelihood univariate analysis of raw data
were performed using the Mx program (Neale et al,1999).
Multiple threshold model tests performed on the 13 categories,
assuming equal thresholds for twin 1 and twin 2, indicated no
signi¢cant departure from normality in either MZ (w
2
155
¼117.94,
p¼0.99) or DZ twins (w
2
155
¼118.47, p ¼0.99), supporting a single
liability dimension model of hair loss. As contingency tables
using all 13 categories may be too sparse to yield a meaningful
test of the multiple threshold model, however (e.g., the w
2
statis-
tic may not be asymptotically distributed), the MZ and DZ data
were combined and the 13 score categories were collapsed into
the following eight groups: group 1 (0, I, II, IIa; representing
nonbaldness); group 2 (III); group 3 (IIIa); group 4 (IIIv, IV);
group 5 (IVa); group 6 (V); group 7 (Va), and group 8 (VI, VII).
Groups 2 to 8 represent signi¢cant cosmetic hair loss (Norwood,
1975), while maximizing counts for vertex and recessive hair loss.
Multiple threshold model tests performed on both the full 8 8
table and after combining frequencies in the two o¡-diagonal quad-
rants, also indicated no signi¢cant departure from normality
(w
2
48
¼55.47, p ¼0.21 and w
2
18
¼19.5 8, p ¼0.36, respectively). These re-
sults strongly support a single liability dimension model of hair loss,
with frontal recession not etiologically distinct from vertex balding.
Subsequently, a single liability dimension-threshold model was
applied to our hair loss data, using the full distribution of ordered
hair loss scores (0^I^II^IIa^III^IIIa^IIIv^IV^IVa^V^Va^VI^VII)
as an ordered sequence re£ecting the severity of hair loss (see
Address correspondence and reprint requests to: Dr Dale R. Nyholt,
Queensland Institute of Medical Research, Post O⁄ce Royal Brisbane
Hospital, Brisbane QLD 4029, Australia. Email: daleN@qimr.edu.au
Electronic Database Information: accession number and URL for data in
this article are as follows: Online Mendelian Inheritance in Man (OMIM),
http://www.ncbi.nlm.nih.gov/Omim/(for MPB (MIM 109200), benign
prostatic hyperplasia (MIM 600082), obesity (MIM 601665), hypertension
(MIM 145500), and prostate cancer (MIM 176807)).
Manuscript received July 14, 2003; accepted for publication July 28, 2003
0022- 202X/03/$15.00 .Copyright r2003 by The Society for Investigative Dermatology, Inc.
1561
LETTER TO THE EDITOR
See related Commentary on pages v and vi

Fig 2). No signi¢cant di¡erences in threshold liability distribu-
tions were observed within twin pairs and across zygosity groups.
The age corrected maximum likelihood (ML) twin pair polycho-
ric correlation for hair loss gradings in MZ twin pairs (r¼0.81;
95% CI: 0.77^0.85) was over twice as large as the DZ correlation
(r ¼0.39; 95% CI: 0.28^0.49), indicating a strong genetic e¡ect.
Furthermore, genetic model ¢tting by ML univariate analysis of
raw data using Mx (Neale et al, 1999) (Ta b l e I ), indicated that an
additive genetic and nonshared environmental (AE) model best
explained individual di¡erences in MPB, and that 81% of the
total variance could be attributed to additive genetic e¡ects
(i.e., 81% heritability, 95% CI: 77^85%).
Given the di¡erences between some of the Hamilton^Nor-
wood gradings are quite subtle, we re-analyzed our data using
more clear-cut (dichotomous) categories of hair loss. For these
analyses, males with gradings of III, IIIa, IIIv, IV, IVa, V, Va, VI,
or VII were classi¢ed as bald, whereas males with gradings of 0,
I, II, or IIa were classi¢ed as nonbald. Analogous to the previous
genetic analyses, an AE model best explained individual di¡er-
ences in MPB, with 80% of the total variance attributed to addi-
tive genetic e¡ects (95% CI: 70^87%). Furthermore, the AE
model best explained individual di¡erences in MPB for dichoto-
mized clear-cut vertex balding (0, I, or II vs. IIIv, IV, V,VI, or VII)
and recessive balding (0, I, or II vs. IIIa, IVa, or Va) producing
heritability estimates of 89% (95% CI: 75^95%) and 96% (95%
CI: 87^99%), respectively. As predicted under the multiple
threshold model, and re£ected in their overlapping con¢dence in-
tervals, the use of di¡erent grouping thresholds/schemes does not
produce signi¢cantly di¡erent heritabilities.
Surprisingly, there is only one known extensive family study
on androgenetic alopecia published (Osborn, 1916). This study of
hair growth patterns in 22 families concluded that common bald-
ness is an autosomal dominant phenotype in men and an autoso-
mal recessive phenotype in women. Owing to a lack of details
regarding examination methods and the practice of omitting
symptom-free women in some pedigrees, however, the validity
of these results remain controversial. Additionally, although the
results from the two other known twin studies produced concor-
dance rates of 100% and 92.3% for MZ, and 50% and 68.7% for
DZ twins, they are far too smallincluding only three MZ and
Figure1. Hamilton^Norwood standards for classi¢cation of the most common types of MBP. Adapted from Norwood (1975). Types I, II, III, IV, V,
VI, and VII represent the most common forms of MPB. Type IIIv has no more front temporal hair loss than type III, but has considerable hair loss at the
vertex. Type A variants (IIa, IIIa, Iva, and Va) have hair loss restricted to the anterior region, which eventually recedes to equivalence with type VI (Nor-
wood, 1975). Frequencies in our sample (2029 males aged 25^36 y) are: zero hair loss (61.2%), I (6.5%), II (14.2%), IIa (2.9%), III (4.3%), IIIa (1.5%), IIIv
(3.8%), IV (1.6%), IVa (1.0%), V (0.8%), Va (1.1%), VI (0.4%), and VII (0.5%).
Figure 2. The multiple threshold model for the level of severity of
hair loss for the best ¢tting AE model.
1562 LETTER TO THE EDITOR THE JOURNAL OF INVESTIGATIVE DERMATOLOGY

eight DZ male pairs (Niermann, 1964; Kuster and Happle, 1984),
and 65 MZ (42 male, 23 female) and 16 DZ (14 male, two female)
pairs (Hayakawa et al, 1992), respectivelyto permit reliable
conclusions.
Therefore, our results represent one of the ¢rst large-scale stu-
dies on the heritability of MBP and indicate that additive genetic
e¡ects play a major part in the progression of common hair loss.
Moreover, a recent study by Ellis et al (2001), which tested poly-
morphisms in the androgen receptor (AR)gene,foundaStuIre-
striction site in 98.1% of 54 young (18^30 y) bald men (p ¼
0.0005) and in 92.3% of 392 older (450 y) bald men
(p ¼0.000004) compared with 76.6% of 107 nonbald (450 y)
men, suggesting that a polymorphism in or near AR (and in link-
age disequilibrium with the AR StuI restriction site) is a contri-
buting, but not su⁄cient, component of the genetic pre-
disposition to MPB. Moreover, the AR gene is on chromosome
Xq11.2^q12 and therefore could not explain the similar hair loss
patterns shared between father and sons, as observed in an earlier
study on the same population, where 32 of 54 bald cases (59.3%)
had fathers with a greater degree of baldness, and only one of 65
sons of 50 nonbald controls had type III baldness or greater (Ellis
et al,1998).
Hair loss similarities between father and son have also been
observed in a study on the frequency of MPB in brothers of
men having prematurely bald fathers (66%) compared with
brothers of men with una¡ected fathers (46%; Harris, 1946; Kus-
ter and Happle, 1984). Further evidence against a single and/or
X-linked gene of major e¡ect comes from a study by Smith and
Wells (1964), which observed hair loss in only 33% of the fathers
of 18 women su¡ering from severe pattern baldness (Kuster and
Happle, 1984). Additionally, a study examining 410 men with
premature baldness found evidence of a genetic in£uence from
the father’s side in 236 cases (Galewsky, 1932; Jackson, 1932; Kuster
and Happle, 1984). Hence, other (autosomal) genes, possibly of
large e¡ect, remain to be found.
It is worth noting that these heritabilities are based on a
relatively young populationranging in age from 25 to 36 with
a mean of 30 y. As some of the nonbald subjects will inevitably
develop baldingwith the rate of baldness known to increase
steadily with ageit is possible that heritability (A) will di¡er
with age. For example, through the age-dependent expression of
genes, and/or a change in the body’s resilience to the major e¡ects
of a genetic in£uence in early phases of life. Also, the accumula-
tion of environmental in£uences (E) may play a larger part in
older ages. Twin studies in older cohorts are required to investi-
gate these possibilities.
The negative psychosocial e¡ects associated with male hair loss
include decreased self-esteem, dissatisfaction with body image or
appearance, self-consciousness, perception of aging, and often
emotional stress. Furthermore, these e¡ects tend to be more
pronounced in younger men (Girman et al, 1998). Certainly,
MPB in itself has a considerable e¡ect on the quality of life for
many men. Because it is a clearly observable trait, however,
which generally precedes the diagnosis of benign prostatic hyper-
plasia and clinical prostate cancer by decades (Hawk et al,2000),
genes in£uencing MPB may prove valuable in determining sus-
ceptibility to life-threatening prostatic disorders. Moreover, genes
in£uencing MPB, may lead to the identi¢cation of novel me-
chanisms, which may in£uence cardiovascular disease and/or in-
sulin resistance.
The authors wish to thank Dr David L Du¡y for many helpful discussions. This
re s ea rc h was sup p or t ed in pa r t b y gr ant s fro m N IA A A ( US A) no. AA0 7535, an d
NHMRC (Australia) no. 941177 and no. 951023. DRN was supported in part by
an NHMRC Peter Doherty Fellowship and NHMRC grant no. 241916.
Dale R. Nyholt, Nathan A. Gillespie, Andrew C. Heath,

and
Nicholas G. Martin
Genetic Epidemiology Laboratory, Queensland Institute of Medical
Research, Brisbane, Queensland, Australia;

Department of
Psychiatry, Washington University School of Medicine, St Louis,
Missouri, USA
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Table I. Genetic model ¢tting results using m aximum likelihood raw data methods
Goodness of ¢t
Model A C D E 2LL d.f. D-2LL Ddf p vs. Model
ADE 0.75 0.06 0.19 5485.58 2013
ACE 0.81 0.0 0 0.19 5 4 85.6 8 2013
AE
a
0.81 0.19 5 485.68 2 014 0.09 1 0.76 ADE
CE 0.62 0.38 5552.84 2014 67.16 1 o0.001 ACE
Liability thresholds, computed using PRELIS 2.30 ( J˛reskog and S˛rbom, 1999), were utilized as starti ng values for the maximum likelihood univariate genetic ana-
lysis of raw data, performed using the Mx program (Neale et al, 1999). The correlation between age and baldness was accounted for by simultaneously estimating and
applying a single age displacement (normalized regression coe⁄cient) (b¼^0.06) to the threshold distribution. First, a fully ‘‘saturated’’ model (ADE or ACE) was tested
to evaluate the statistical properties of the data, then the e¡ect of dropping one of the parameters (A, C, D, or E) was examined by testing the respective di¡erence (D-
2LL) for statistical signi¢cance.
a
The AE model was found to provide the most parsimonious ¢t to the data.
LETTER TO THE EDITOR 1563VOL. 121, NO. 6 DECEMBER 2003

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156 4 LETTER TO THE EDITOR THE JOURNAL OF INVESTIGATIVE DERM ATOLOGY