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Abstract and Figures

Music is an integral part of the cultural heritage of all known human societies, with the capacity for music perception and production present in most people. Researchers generally agree that both genetic and environmental factors contribute to the broader realization of music ability, with the degree of music aptitude varying, not only from individual to individual, but across various components of music ability within the same individual. While environmental factors influencing music development and expertise have been well investigated in the psychological and music literature, the interrogation of possible genetic influences has not progressed at the same rate. Recent advances in genetic research offer fertile ground for exploring the genetic basis of music ability. This paper begins with a brief overview of behavioral and molecular genetic approaches commonly used in human genetic analyses, and then critically reviews the key findings of genetic investigations of the components of music ability. Some promising and converging findings have emerged, with several loci on chromosome 4 implicated in singing and music perception, and certain loci on chromosome 8q implicated in absolute pitch and music perception. The gene AVPR1A on chromosome 12q has also been implicated in music perception, music memory, and music listening, whereas SLC6A4 on chromosome 17q has been associated with music memory and choir participation. Replication of these results in alternate populations and with larger samples is warranted to confirm the findings. Through increased research efforts, a clearer picture of the genetic mechanisms underpinning music ability will hopefully emerge.
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REVIEW ARTICLE
published: 27 June 2014
doi: 10.3389/fpsyg.2014.00658
The genetic basis of music ability
Yi Ting Tan1*, Gary E. McPherson1, Isabelle Peretz2, Samuel F. Berkovic3and Sarah J. Wilson3, 4
1Melbourne Conservatorium of Music, University of Melbourne, Parkville, VIC, Australia
2International Laboratory for Brain, Music and Sound Research and Department of Psychology, Université de Montréal, Montreal, QC, Canada
3Department of Medicine, Epilepsy Research Centre, University of Melbourne, Heidelberg, VIC, Australia
4Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia
Edited by:
Eckart Altenmüller, University of
Music and Drama Hannover,
Germany
Reviewed by:
Thomas F. Münte, University of
Magdeburg, Germany
Erin E. Hannon, University of
Nevada, Las Vegas, USA
*Correspondence:
Yi Ting Tan, Melbourne
Conservatorium of Music,
University of Melbourne, Gate 12,
Building 141, Royal Parade, Parkville,
VIC 3010, Australia
e-mail: yitt@student.unimelb.edu.au
Music is an integral part of the cultural heritage of all known human societies, with
the capacity for music perception and production present in most people. Researchers
generally agree that both genetic and environmental factors contribute to the broader
realization of music ability, with the degree of music aptitude varying, not only from
individual to individual, but across various components of music ability within the same
individual. While environmental factors influencing music development and expertise
have been well investigated in the psychological and music literature, the interrogation
of possible genetic influences has not progressed at the same rate. Recent advances
in genetic research offer fertile ground for exploring the genetic basis of music ability.
This paper begins with a brief overview of behavioral and molecular genetic approaches
commonly used in human genetic analyses, and then critically reviews the key findings of
genetic investigations of the components of music ability. Some promising and converging
findings have emerged, with several loci on chromosome 4 implicated in singing and
music perception, and certain loci on chromosome 8q implicated in absolute pitch and
music perception. The gene AVPR1A on chromosome 12q has also been implicated in
music perception, music memory, and music listening, whereas SLC6A4 on chromosome
17q has been associated with music memory and choir participation. Replication of these
results in alternate populations and with larger samples is warranted to confirm the
findings. Through increased research efforts, a clearer picture of the genetic mechanisms
underpinning music ability will hopefully emerge.
Keywords: music, music ability, music perception, music production, genetic, genome, review
INTRODUCTION
Music is ubiquitous in all known human cultures. The general
capacity for human beings to perceive, produce, and enjoy music
even in the absence of formal music training suggest that music
may be “hardwired” in our genetic makeup. However, the diver-
sity of music competency across individuals adds impetus to the
long-standing debate of whether musicians are born or made.
Studies on the genetic basis of music ability have been relatively
scarce, compared with more extensive investigation conducted in
the language domain (for recent reviews of the genetic findings on
speech and language, see Newbury and Monaco, 2010; Carrion-
Castillo et al., 2013; Graham and Fisher, 2013; Raskind et al.,
2013; Szalontai and Csiszar, 2013). Moreover, the earlier behav-
ioral genetic investigations of music ability often lacked scientific
rigor or suffered from small sample sizes (see Coon and Carey,
1989 for examples). The advent of molecular genetics in the post-
genomic era holds much promise for this relatively underexplored
field.
Since the boom of molecular genetic research, the current
state of knowledge of the genetic basis of music ability has not
been reviewed. Thus, it is timely to consolidate behavioral and
molecular genetic findings and provide a critical overview of
what is currently known about the genetic basis of various music
phenotypes. Current challenges and possible directions for future
research are also considered. This review aims to facilitate a
greater understanding of this relatively new field among music
and genetics researchers, and encourage increased research effort
into uncovering the genetic basis of music ability.
HUMAN GENETIC METHODS: OVERVIEW
The relationship between phenotypes and genes can be inves-
tigated through various genetic analytical approaches. In order
to offer readers of diverse backgrounds a preliminary under-
standing, this section provides an introductory overview of the
behavioral and molecular genetic approaches commonly used in
human genetic analyses. A glossary of genetic terminology (terms
shown in bold in the text) is also available as Supplementary
Material online.
BEHAVIORAL GENETIC APPROACHES
Familial aggregation
One of the first questions asked in human genetic analysis is
whether a trait clusters in families above chance level. Familial
aggregation can address this question by comparing whether the
prevalence of a trait is higher within the family of a proband than
that in the general population (Naj et al., 2012). This approach
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Tan e t a l. The genetic basis of music ability
is non-invasive because only phenotypic information is gathered
from the families and controls. One common measure arising
from familial aggregation is the sibling recurrence-risk ratio
(λs), which determines the proportion of proband siblings also
exhibiting the studied trait, relative to the population prevalence.
The magnitude and patterns of familial correlations observed in
familial aggregation studies can yield useful clues to the roles
played by genes and the environment (Naj et al., 2012). While
aλsclose to 1 suggests genetic influences are unimportant,
higher values (>5) indicate that a genetic hypothesis is worth
pursuing (Mitry et al., 2011). A related measure is the sibling
relative risk (sib RR), which is the ratio of the proband λsto the
control λs.
Familial aggregation measures such as λsmay be inflated by
ascertainment bias (Guo, 1998). Moreover, familial aggregation
only serves to determine the existence of familial clustering. It
does not seek to explain how much of the familial clustering is
due to genetic or environmental factors. Such questions can be
addressed through follow-up studies, such as twin and adoption
studies.
Twin studies
Twin studies take a step further by disentangling the relative con-
tributions of genetic and environmental factors on trait variation
(Verweij et al., 2012). Monozygotic (MZ) twins share 100% of
their genes whereas dizygotic (DZ) twins share on average 50%
of their genes. By comparing the similarity of the MZ twin pairs
on the trait of interest with that of the DZ twin pairs, greater
similarity exhibited by MZ twin pairs indicates a possible genetic
influence. In other words, if the concordance for a trait is much
higher in the MZ twins, the trait is likely to have significant
heritability.
Through twin studies, the effects of genetic influence, shared
environment (family environment), and unique environment on
a trait can be estimated through structural equation modeling
using statistical programs such as Mx (Neale et al., 2006). Basic
twin designs can be extended to allow multiple traits to be studied
simultaneously or to analyze more complex genetic and environ-
mental influences. For instance, additional family members can
be incorporated into the design (Verweij et al., 2012).
There are some criticisms surrounding the validity of twin
studies given that the twin design is built on a number of assump-
tions (Richardson and Norgate, 2005). For instance, the “equal
environment” assumption presupposes that regardless of zygosity,
all twins raised together experience equally similar shared envi-
ronments. Some research, however, suggests that MZ twins may
be treated more similarly than DZ twins (Plomin et al., 1976;but
see Borkenau et al., 2002).
Besides twin studies, heritability can also be estimated from
family pedigrees. Heritability in the narrow sense (h2)is the ratio
of the additive genetic variance to the total phenotypic variance
of a trait. Since the magnitude of h2can indicate the statistical
power for discovering the causal genes of a trait, heritability esti-
mation serves as a good precursor to molecular genetic studies
(Bochud, 2012). If the h2estimates of multiple related traits are
available, the trait with the best h2estimate can be chosen for sub-
sequent gene mapping. In human research, the general consensus
is that h2estimates below 0.2 are considered low, those between
0.2 and 0.5 are moderate, and estimates above 0.5 indicate high
heritability. High h2estimates suggest that the genotype is closely
correlated with the trait phenotype, but it should be noted that
this does not necessarily imply that every gene associated with the
trait has a large effect on the phenotype (Visscher et al., 2008).
Some limitations of heritability estimation include a lack of infor-
mation on the mode of inheritance of the trait, and the possibility
that h2estimates may vary across populations or with time. In
large pedigrees, there may also be cohort effects across different
generations living under different socioeconomic circumstances,
which may confound the “equal environment” within the sample
population (Bochud, 2012).
Segregation analysis
After estimating the heritability of a trait of interest, the mode
of inheritance of the trait can be elucidated through segrega-
tion analysis. Different segregation models representing various
inheritance patterns of the trait are fitted to the family data.
Using maximum likelihood procedures, the genetic model of
best fit (i.e., the inheritance pattern which best explains how
the trait is transmitted down the family line) can be identified.
An important measure from segregation analysis is the segrega-
tion ratio, which is the proportion of offspring who inherit the
trait of interest from a parent (Strachan and Read, 1999). The
expected segregation ratios for autosomal dominant inheritance
and autosomal recessive inheritance are 0.5 and 0.25, respec-
tively. As these expected segregation ratios are for Mendelian
traits, deviation from these values indicate that the trait of inter-
est may have incomplete penetrance, be predisposed by several
genes in different loci, or that it is determined by both genetic
and environmental factors. For instance in a study by Theusch
and Gitschier (2011), absolute pitch was reported to have a segre-
gation ratio of 0.089, which suggests that the trait is not inherited
in a simple Mendelian fashion. Similar to familial aggregation, a
potential problem with segregation analysis is ascertainment bias,
which may inflate the segregation ratio (Strachan and Read, 1999;
Nandram et al., 2011), however it is possible to statistically correct
for this (Li and Mantel, 1968; Li et al., 1987; Yao and Tai, 2000;
Nandram et al., 2011).
Segregation analysis often serves as a precursor to parametric
linkage analysis, as the latter requires the inheritance pattern of
the studied trait to be specified (Schnell and Sun, 2012).
MOLECULAR GENETIC APPROACHES
Linkage analysis
Once the genetic basis of a trait has been established using some of
the above-mentioned methods, the next step is to conduct linkage
analysis to map the potential genetic loci predisposing the trait.
Linkage analysis requires each family member of a large family, or
of several family pedigrees to be genotyped, typically using single-
nucleotide polymorphism (SNP) arrays.Ifageneticlocus is
thought to predispose a trait, family members who share the same
markers near this locus should exhibit greater trait resemblance
compared to those who do not share the markers. Therefore, link-
age analysis aims to identify the markers on the SNP array that
are commonly present in participants within or across pedigrees
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Tan e t a l. The genetic basis of music ability
who exhibit the trait of interest. It should be noted that the iden-
tified markers are not the actual genes predisposing a trait; they
are merely known locations on the genome that are near the gene
of interest (Carey, 2003a). “Linkage” is said to occur when two
gene loci that are in close proximity on the same chromosome
are inherited together. In other words, if a marker is commonly
shared among family members exhibiting the trait of interest,
this marker is likely to be in linkage with the actual locus of the
trait.
There are two common approaches to estimate linkage. The
non-parametric or model-free linkage analysis basically tests
whether relatives exhibiting the trait of interest share more alleles
than would be expected by chance (Xu et al., 2012). In contrast,
parametric or model-based linkage analysis requires the speci-
fication of the mode of inheritance, as ascertained by segregation
analysis. Linkage is then tested by comparing the probability of
obtaining the current test data if a marker locus and the trait
locus are linked, to the probability of obtaining the test data if the
twolociarenotlinked(Schnell and Sun, 2012). The ratio of the
two probabilities expressed on a logarithmic scale is known as the
LOD score. LOD scores are computed across all markers for each
pedigree and then summed across different pedigrees. A LOD
score 3 is typically considered significant evidence for linkage,
as it indicates that the odds that the two loci are linked and
inherited together are greater than 1000–1. Alternatively, a LOD
score ≤−2 is considered significant evidence to reject linkage. In
general, scores within the range of 2<x<3 are regarded as
inconclusive evidence for linkage, with those between 2 x<3
warranting additional study.
Linkage analysis is very successful in identifying Mendelian
traits with a simple mode of inheritance, such as Huntington’s
disease. It has been less effective for complex traits predisposed by
multiple genes as each gene individually exerts only a small effect
on the trait (Nsengimana and Bishop, 2012). Large sample sizes
are therefore needed to obtain adequate statistical power to detect
linkage. Statistical power can also be increased by performing
multipoint analysis (i.e., using multiple markers simultaneously),
allowing more precise identification of the trait locus (Lathrop
et al., 1984).
As parametric linkage analysis requires the pattern of inher-
itance to be specified, misspecification of the genetic model may
lead to loss of power (Schnell and Sun, 2012). While this potential
problem can be alleviated by testing more than one genetic model,
multiple-testing issues increase the likelihood of false positives
(Weeks et al., 1990). A further shortcoming of linkage analysis
is that the identified linkage region typically contains numerous
genes. Fine-mapping of the linkage region is therefore necessary
to narrow down the locus and determine the possible causative
gene (Carey, 2003b). With the development of exome sequencing
technologies (see below), fine-mapping is now less essential as all
genes in the linkage region can be quickly analyzed.
Association analysis
Association analysis is a statistical method used to investigate the
association between a genetic variant and a trait (Carey, 2003b).
Association analysis can be used to test potential genetic vari-
ants that lie in significant linkage regions. It can also be employed
when there are well-founded reasons to suspect a gene’s involve-
ment in predisposing a trait. An association analysis can either
adopt a population-based design involving unrelated cases and
controls, or a family-based design in which relatives of cases serve
as controls for the study.
In the candidate gene approach, statistical tests are performed
to determine if the cases have a higher frequency of a particu-
lar allelic variant of the candidate gene,ascomparedwiththe
controls. The possible association between an allelic variant and
the trait of interest may yield valuable information about the
variant’s role in the biological pathway of the trait. One major
limitation of candidate gene studies is that only specific allelic
variants are investigated; the success of an association study thus
hinges on the accuracy of a researcher’s “educated guess” of the
potential candidate genes. Moreover, if the trait of interest is com-
plex and multiple genes are involved, the candidate gene approach
is not able to detect the influence of the predisposing genes in
other loci.
By contrast, a genome-wide association study (GWAS) can
be conducted without prior knowledge of potential candidate
genes. It involves an “agnostic” or non-candidate driven search
of the entire human genome, typically using SNP arrays with a
large number of common SNP markers found throughout the
genome (Sun and Dimitromanolakis, 2012). If certain SNPs have
a higher incidence in the cases relative to the controls, this will
indicate a possible association of these SNPs with the trait of
interest.
GWAS is a useful approach to find novel candidate genes
predisposing a trait of interest, especially if the biological path-
way of the trait is not well-understood. It is also possible to
identify multiple genetic contributors to a complex trait, even
if each of these may be conferring only a small effect (Reich
and Lander, 2001).ThedownsideisthattoidentifysuchSNPs
of low effect size, large sample sizes are needed to achieve ade-
quate power (typically thousands or even tens of thousands of
cases and controls) (Spencer et al., 2009). However, large sam-
ple sizes may give rise to confounding factors such as population
stratification and cryptic relatedness that result in false positives
(Nsengimana and Bishop, 2012). Moreover, GWAS does not iden-
tify complete genes; it only identifies the genomic regions possibly
associated with the trait, which in some cases may not even have
a protein-coding gene in the vicinity.
A common problem faced by both the candidate gene
approach and GWAS is the minimal replication of significant
results across association studies (Lewis and Knight, 2012).
Researchers must therefore exercise caution and critically exam-
ine the validity of published association studies, and make pru-
dent design decisions for their own association studies (Attia
et al., 2009).
Exome sequencing
With the advancement of high-throughput next-generation
sequencing (NGS) technologies, exome sequencing has emerged
as a rapid and cost-effective method for human genetic analysis
(Singleton, 2011). The exome is the portion of the genome con-
taining protein-coding information and represents about 1.5% of
the whole genome. Exome sequencing is based on the assumption
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Tan e t a l. The genetic basis of music ability
that changes in exons may modify the function of proteins
coded by exons, thereby leading to changes in the phenotype.
By targeting the exome instead of the entire genome, exome
sequencing presents a cost- and time-effective means to find pos-
sible genetic variants predisposing a trait. Exome sequencing uses
sequence capture methods to selectively capture exons from a
DNA sample and enrich the available information from each sam-
ple, before using high-throughput sequencing to identify coding
variants in the exome (Ku et al., 2012).
Since the publication of the first proof-of-concept study which
used exome sequencing to identify causal variants for a rare
Mendelian disorder (Ng et al., 2009), exome sequencing has
shown much promise in discovering coding variants for both
Mendelian and non-Mendelian traits (Singleton, 2011). Like
GWAS, exome sequencing can be performed without the fore-
knowledge of potential candidate genes or genetic variants. Its
usefulness has also been shown in the diagnosis of disorders char-
acterized by genetic heterogeneity (Singleton, 2011). Another
advantage of exome sequencing is that it can identify uncom-
mon causal variants predisposing rare Mendelian traits, which
cannot be identified through linkage studies due to inadequate
study power (Singleton, 2011).
Ultimately, exome sequencing may become a valuable genetic
screening and diagnostic tool, allowing robust diagnoses to be
reached rapidly and cost-effectively (Ku et al., 2012). However,
it has some limitations. For instance, some regions of interest in
the exome are difficult to sequence with the current technology,
and the large amount of sequencing data generated poses com-
putational challenge for analysis. Moreover, exome sequencing is
unable to detect large deletions or rearrangements in the genome,
and there is a major risk of false positives due to difficulties deter-
mining the biological relevance of coding variants to the trait of
interest.
Copy number variation (CNV) analysis
Copy number variants are structural variants (>1000 base pairs)
whose copy numbers deviate from those found in the human
reference genome. Such variation in the genome (known as
copy number variation) includes insertions, deletions, duplica-
tions, inversions and translocations (Wain et al., 2009). More
than 300 known causal genes for diseases are found to over-
lap with CNVs (Sebat et al., 2004), highlighting the impact
CNVs may exert on genetic mechanisms and phenotypic vari-
ation. Understanding the functional impact of CNVs, therefore,
offers a fruitful means of elucidating the genetic basis of complex
phenotypes.
There are several approaches to CNV detection. Microarray-
based CNV analysis techniques typically use SNP arrays or aCGH
(array comparative genomic hybridization) platformstodetect
copy number gains or losses in the test sample compared with a
reference sample (Alkan et al., 2011). CNV detection can also be
achieved through a sequencing-based approach.
CNV analysis has shown its utility in the diagnosis of syn-
dromes with heterogeneous phenotypes (Coughlin et al., 2012).
However, understanding the significance and effect of detected
CNVs on a trait of interest remains challenging. In addition,
microarray-based CNV analysis has a number of drawbacks.
Copy neutral alterations (such as inversions and translocations)
that do not cause a change in the total amount of genetic
material cannot be identified by the microarray-based approach,
even though such alterations are potentially deleterious. The
microarray-based approach is also less sensitive in detecting
duplications than deletions.
Given the continued advancement in sequencing technologies,
sequencing-based approaches are likely to supersede microarray-
based CNV analysis. In time, the ability of NGS to detect various
forms of genetic variants (including small insertions/deletions
and copy neutral variants) will improve and the cost of mas-
sive parallel sequencing will become less expensive. Nevertheless,
this approach has not yet reached maturity and standard pro-
tocols and measures for sequencing-based CNV analysis have
not been established. Moreover, the bioinformatics infrastruc-
ture, support, and cost required for managing, analyzing, and
storing large amounts of sequencing data need to be care-
fully considered (Teo et al., 2012). In addition, the relatively
short read lengths of NGS pose mapping issues in align-
ing the sequenced reads with the reference genome, espe-
cially in the duplicated regions of the genome. Thus, as
with a microarray-based approach, copy number duplications
remain less detectable than deletions with the sequencing-
based method. Longer read lengths afforded by improve-
ments in sequencing technology will potentially mitigate this
issue.
SCOPE OF THE REVIEW
Indexed searches were performed in Scopus by searching for the
following keywords: musicand (gene or genetic or genome or
heritability or hereditary or innate) in the keyword field. Terms
such as music therapy, algorithm, dystonia, musicola, animal,
bird, and songbird were excluded. Subject areas such as computer
science, engineering, health professions, physics and astronomy,
mathematics, nursing and business, management and account-
ing were also excluded. Only papers published in English between
1988 and 2014 were considered since earlier genetic studies were
often fraught with methodological limitations, such as small sam-
ple sizes and lack of scientific rigor. Based on these criteria the
search identified 97 articles that were then manually canvassed to
select those directly addressing the genetic bases of various aspects
of music ability using behavioral genetic or molecular genetic
methods. In addition, the reference section of each article was
examined to identify additional studies not captured in the ini-
tial search. In total, this process identified 21 papers included in
this review.
SUMMARY OF THE REVIEW FINDINGS
Table 1 shows the various music traits investigated and the num-
ber of genetic studies conducted on each trait to date. It indicates
that the majority of studies have focused on music perception
abilities (74%), of which absolute pitch has been most extensively
investigated (39%). In light of this, findings pertaining to music
perception abilities are examined first, followed by those relating
to music production abilities. Tables 2,3that follow provide a
more detailed summary of the genetic findings from behavioral
and molecular studies, respectively.
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Tan e t a l. The genetic basis of music ability
Table 1 | The number of genetic studies investigating various music
traits.
Music trait Studies Authors
Music
perception
Absolute
pitch
9Profita and Bidder, 1988;
Gregersen and Kumar, 1996;
Baharloo et al., 1998, 2000;
Gregersen et al., 1999, 2001,
2013; Theusch et al., 2009;
Theusch and Gitschier, 2011
Congenital
amusia
1Peretz et al., 2007
Music
perception
(in general)
5Drayna et al., 2001; Pulli et al.,
2008; Ukkola et al., 2009;
Ukkola-Vuoti et al., 2013;
Oikkonen et al., 2014
Melodic
memory
1Granot et al., 2007
Music
listening
1Ukkola-Vuoti et al., 2011
Music
production
Singing
accuracy
1Park et al., 2012
Singing
participation
2Coon and Carey, 1989; Morley
et al., 2012
Music
creativity
2Ukkola et al., 2009;
Ukkola-Vuoti et al., 2013
Self-reported music ability 1 Vinkhuyzen et al., 2009
MUSIC PERCEPTION ABILITIES
Absolute pitch ability
Absolute pitch (AP) or “perfect pitch” is the rare music ability of
being able to identify or produce pitches without relying on an
external reference. It has an estimated prevalence of less than 1 in
10,000 (Bachem, 1955; Profita and Bidder, 1988). While AP is nei-
ther a prerequisite nor a predictor for outstanding musicianship,
its rarity and the musical advantages it confers have generated
much research interest into its etiology.
Several studies have explored the genetic basis of AP through
family studies. One of the earliest was a segregation study con-
ducted by Profita and Bidder (1988), who reported significant
familial incidence in 35 AP probands from 19 families. AP was
more common in females of this sample, and vertical trans-
mission was commonly observed. The segregation ratio was
estimated to lie between 0.24 and 0.37, suggesting a possible auto-
somal dominant gene with incomplete penetrance. Recurrence
risk ratios could not be determined because the study did not
recruit any control participants.
A subsequent familial aggregation study yielded a sibling
recurrence risk-ratio (λs) estimate of 20, meaning that siblings
of AP possessors are approximately 20 times more likely to pos-
sess AP relative to the general population (Gregersen and Kumar,
1996). Gregersen’s team subsequently conducted another two
familial aggregation studies and obtained sibling relative risk (sib
RR) estimates of 8.3 and 12.2, respectively (Gregersen et al., 1999,
2001). This measure indicates that the siblings of the AP posses-
sors were 8.3 and 12.2 times more likely to have AP compared to
the siblings of the controls. Sib RR generally provides a more con-
servative measure than λs(Naj et al., 2012), likely accounting for
the lower sib RR values. As noted in the previous section, familial
aggregation does not distinguish between genetic and environ-
mental contributions to a trait, making it possible that the high
familial aggregation estimates stem from environmental influ-
encessuchasearlymusictraining,whichhasbeenidentiedasa
key environmental determinant of AP (Sergeant, 1969; Miyazaki,
1988; Profita and Bidder, 1988; Gregersen et al., 2007; Wilson
et al., 2012).
In view of this, Baharloo et al. (1998) controlled for early
music training by only analyzing families where the participants
and one or more of their siblings had received music training
before 6 years of age. The λswas estimated to be approximately
7.5 (Gregersen, 1998). In a subsequent study, Baharloo and col-
leagues estimated λsfor the most stringent form of AP, termed
“AP- 1 ” ( Baharloo et al., 2000). The AP-1 phenotype was charac-
terized by a consistently high level of pitch naming ability, falling
at least three standard errors above the mean score of a ran-
domized group of AP and non-AP musicians. Also controlling
for early music training, the λsforAP-1wasestimatedtofall
within 7.8–15.1, with a greater likelihood of the true value being
found near the upper end of this range. It is possible, however,
that even after controlling for early music training, the estimated
λsmay still be influenced by other shared environmental factors
experienced by the AP-1 probands and their concordant sib-
lings. The authors therefore noted that the λsestimate may not
entirely reflect genetic factors. Nonetheless, the high estimates
of λsfrom various familial aggregation studies suggest a major
role for genetic influences on the development of AP and the
possibility of a major-gene effect.
More recently, evidence suggests that multiple genetic factors
may be involved in the etiology of AP (Theusch and Gitschier,
2011). A segregation analysis performed on 1463 AP-1 probands
yielded a segregation ratio of 0.089, which is considerably lower
than the estimate from the small AP sample in Profita and Bidder
(1988) and the segregation ratios of 0.25 and 0.5 for autosomal
recessive and autosomal dominant inheritance, respectively. This
suggests that AP was not inherited in a simple Mendelian fashion,
however, genetic factors likely play a role since within this larger
sample, 11 out of 14 MZ twin pairs were concordant for AP-1
in comparison to only 14 out of 31 DZ twin pairs. These results
yielded a significantly different casewise concordance rate of 78.6
and 45.2%, respectively.
OthersupportingevidenceforageneticbasisforAPcomes
from ethnicity effects observed in music students. Gregersen et al.
(2001) reported that Chinese, Korean and Japanese music theory
students had a substantially higher incidence of AP (47.5%) com-
pared to Caucasian students (9%). Although some researchers
have attributed this “Asian advantage” to environmental factors,
such as early tone-language exposure (Henthorn and Deutsch,
2007), this does not fully account for the higher incidence of
AP among all Asian ethnic subgroups since essentially, Korean
and Japanese are not tone languages (Sohn, 1999; Zatorre, 2003;
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Tan e t a l. The genetic basis of music ability
Table 2 | Summary of behavioral genetic studies investigating various music traits.
Trait Type of study Participants Findings
Absolute pitch (AP) Family segregation analysis
(Profita and Bidder, 1988)
35 AP probands from 19 families SR: 0.24–0.37
Familial aggregation
(Gregersen and Kumar, 1996)
101 AP probands λs:20
Familial aggregation
(Baharloo et al., 1998)
92 self-reported AP probands
520 non-AP probands
λs:7. 5
(controlled for early music training)
Familial aggregation
(Gregersen et al., 1999)
2707 tertiary music students Sib RR: 8.3
Familial aggregation
(Baharloo et al., 2000)
74 AP-1 probands with 113 siblings
625 controls
λs: 7.8–15.1
(controlled for early music training)
Familial aggregation
(Gregersen et al., 2001)
1067 music theory students Sib RR: 12.2
Family segregation analysis
(Theusch and Gitschier, 2011)
1463 families with AP-1 probands SR: 0.089
Twin study
(Theusch and Gitschier, 2011)
14MZand31DZfemaletwinpairs CCR
MZ: 78.6%; CCRDZ : 45.2%
Congenital amusia Familial aggregation
(Peretz et al., 2007)
13 probands, 58 family members
(9 families)
17 controls, 58 family members
(10 families)
λs:10.8
λo:2.3
Melodic perception
(tune recognition)
Twin study
(Drayna et al., 2001)
136 MZ and 148 DZ twin p air s A: 71–80%; C: 0%; E: 20–29%
Perception of pitch and
rhythm
Pedigree study
(Pulli et al., 2008)
15 Finnish musical families (N=234) Heritability estimates for KMT: 42%; SP: 57%; ST:
21%; COMB: 48%
Pedigree study
(Ukkolaetal.,2009)
19 Finnish musical families (N=343) Heritability estimates for KMT: 39%; SP: 52%; ST:
10%; COMB: 44%
Pedigree study
(Oikkonen et al., 2014)
76 Finnish families (N=767) Heritability estimates for KMT: 46%; SP: 68%; ST:
21%; COMB: 60%
Singing
(pitch accuracy)
Pedigree study
(Park et al., 2012)
73 extended families (N=1008) Heritability: 40%
Participation in singing
activities
Twin study
(Coon and Carey, 1989)
850 twin pairs
(from Loehlin and Nichols, 1976)
A: 71% (males) & 20% (females);
C: 8% (males) & 59% (females)
Music creativity Pedigree study
(Ukkolaetal.,2009)
19 Finnish musical families (N=343) Heritability for music creativity: 84%
(Composing: 40%; Arranging: 46%;
Improvising: 62%)
Self-reported music
ability
Twin study
(Coon and Carey, 1989)
850 twin pairs
(from Loehlin and Nichols, 1976)
Out-of-school music performances:
A: 38% (males) & 10% (females);
C: 18% (males) & 63% (females)
Twin study
(Vinkhuyzen et al., 2009)
1685 Netherlands twin pairs
(12–24 years)
Musical aptitude:
A: 66%; C: 8%; E: 25% (males)
A: 30%; C: 54%; E: 16% (fema les)
Exceptional musical talent:
A: 92%; C: 0%; E: 8%
λs, sibling recurrence risk; λo, offspring recurrence risk; CCRMZ, casewise concordance rate for MZ twins; CCRDZ , casewise concordance rate for DZ twins; COMB,
Combined music aptitude (SP+ST+KMT) score; A, additive genetic variance (heritability); C, common/shared environmental variance; E, unique environmental
variance; KMT, Karma music test; SP, Seashore’s pitch discrimination test; ST, Seashore’s time discrimination test; Sib RR, sibling relative risk; SR, segregation ratio.
Frontiers in Psychology | Auditory Cognitive Neuroscience June 2014 | Volume 5 | Article 658 |6
Tan e t a l. The genetic basis of music ability
Table 3 | Summary of molecular genetic studies investigating various music traits.
Trait Study type Participants Ancestry Locus implicated Genetic variant(s)
implicated
Gene
impli-
cated
Possible function(s) of
the gene
Absolute
pitch (AP)
Genome-wide linkage
study
(Theusch et al., 2009)
73 AP families*European,
Ashkenazi
Jewish,
Indian,
East
Asian
8q24.21 SNP rs3057
LOD =2.330 Eu/AJ/I
LOD =3.464 Eu
ADCY8 Learning and memory
7q22.3 SNP rs2028030
LOD =2.074 Eu
LOD1–1.5 E Asian
8q21.11 SNP rs1007750
LOD =2.069 Eu/AJ/I
LOD =2.236 for Eu
9p21.3 SNP rs2169325
LOD =2.048 Eu
Genome-wide linkage
study; exome sequencing
(Gregersen et al., 2013)
53 AP multiplex
families,
36 synaesthesia
multiplex
families
Caucasian,
Asian
6q14.1–16.1
Peak LOD =4.68
EPHA7 Neural connectivity and
development
2q SNP rs1482308
HLOD =4.7
(combined data set)
SNP rs6759330
HLOD =3.93
(AP families)
Perception
of pitch
and
rhythm
Genome-wide linkage
study
(Pulli et al., 2008)
15 musical
families
(N=234)
Finnish 4q22 Near markers D4S423
and D4S2460
LOD =3.33
UNC5C Netrin receptor.
Netrins direct axon
extension and cell
migration during neural
development
8q13-21
LOD =2.29
TRPA1 Implicated in the hair
cell transduction
channel of the inner ear
Candidate gene
association study
(Ukkolaetal.,2009)
19 musical
families
(N=343)
Finnish 12q14.2 RS1 +RS3 haplotype
(corrected p<0.001 for
KMT and COMB)
AVPR1A Social cognition and
behavior; spatial
memory
Genome-wide CNV
analysis
(Ukkola-Vuoti et al., 2013)
5 extended
families,
172 unrelated
individuals
Finnish Deletion on 5q31.1
(linked to low
COMB)
Pcdha 1-9 Neural migration,
differentiation and
synaptogenesis;
learning and memory
Duplication on
8q24.22 (linked to
low COMB)
ADCY8 Learning and memory
Genome-wide linkage
and association study
(Oikkonen et al., 2014)
76 families
(N=767)
Finnish 3q21.3 SNP rs9854612 (PPLD =
0.98 for COMB)
GATA2 Development of
cochlear hair cells and
the inferior colliculus
4p15-q24 (linked
to SP, ST, KMT,
COMB)
SNP rs13146789 & SNP
rs13109270
(PPLD =0.81 for COMB)
PCDH7 Expressed in cochlea
(chicken) & amygdala
(mice)
(Continued)
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Tan e t a l. The genetic basis of music ability
Table 3 | Continued
Trait Study type Participants Ancestry Locus implicated Genetic variant(s)
implicated
Gene
impli-
cated
Possible function(s) of
the gene
Music
memory
Candidate gene
association study
(Granot et al., 2007)
82 university
students
12q14.2 RS1+RS3 haplotype AVPR1A Social cognition and
behavior; spatial
memory
17q11.2 HTTLPR SLC6A4 Reward-seeking
behavior;
neuropsychiatric
conditions
Music
listening
Candidate gene
association study
(Ukkola-Vuoti et al., 2011)
31 families
(N=437)
Finnish 12q14.2 RS1+AVR haplotype:
current active music
listening (p=0.0019);
RS1+RS3 haplotype:
lifelong active music
listening (p=0.0022)
AVPR1A Social cognition and
behavior; spatial
memory
Singing
(pitch
accuracy)
Genome-wide linkage
and association study;
exome sequencing; CNV
analysis (Park et al., 2012)
73 families
(N=1008)
Mongolian 4q23 Marker D4S2986
LOD =3.1
4q26 SNP rs12510781
(p=8.4×1017 )
SNP rs4148254
(p=8.0×1017 )
UGT8 Catalyzes the transfer
of galactose to
ceramide
Deletion at 5.6 kb
upstream of UGT8
(linked to low pitch
accuracy)
Choir par-
ticipation
Candidate gene
association study
(Morley et al., 2012)
262 amateur
choir singers,
261 controls
17q11.2 STin2.9, STin2.12: choir
singers (p=0.04);
STin2.10:
controls (p=0.009)
SLC6A4 Reward-seeking
behavior;
neuropsychiatric
conditions
Music
creativity
Genome-wide CNV
analysis
(Ukkola-Vuoti et al., 2013)
5 extended
families,
172 unrelated
individuals
Finnish Deletion on
5p15.33
(creative
phenotype)
ZDHHC11
Duplication on
2p22.1
(creative
phenotype)
GALM Associated with
serotonin transporter
binding potential in
human thalamus, which
is implicated in music
perception
Deletion on 2p12,
3p14.1, 3q28
(non-creative
phenotype)
*Families with at least two AP possessors who were not simply a parent-child relative pair.
ADCY8, adenylate cyclase 8; AJ, Ashkenazi Jewish ancestry; AVPR1A, arginine vasopressin receptor 1A; CNV, copy number variation; COMB, Combined music
aptitude (SP+ST+KMT) score; E Asian, East Asian ancestry; EPHA7, ephrin type-A receptor 7; Eu, European ancestr y; GALM, galactose mutarotase (aldose 1-
epimerase); GATA2, GATA binding protein 2; HLOD, heterogeneity logarithm of odds score; I, Indian ancestry; KMT, Karma Music Test; LOD, logarithm of odds score;
PCDH7, protocadherin 7; Pcdha 1-9, protocadherin alpha 1 to 9; PPLD, posterior probability of linkage disequilibrium; SLC6A4, solute carrier family 6 (neurotransmitter
transporter, serotonin), member 4; SNP, single nucleotide polymorphism; SP, Seashore’s pitch discrimination test; ST, Seashore’s time discrimination test; TRPA1,
transient receptor potential cation channel, subfamily A, member 1; UGT8, uridine diphosphate glycosyltransferase 8; UNC5C, UNC-5 homolog C (C. elegans);
ZDHHC11, zinc finger, DHHC-type containing 11.
Frontiers in Psychology | Auditory Cognitive Neuroscience June 2014 | Volume 5 | Article 658 |8
Tan e t a l. The genetic basis of music ability
Kubozono, 2012). Further analysis of Gregersen’s et al. (2001)
study data revealed that the age of onset of music training and
exposure to a “fixed do” training method before the age of 7
were the only strong predictors of AP acquisition in this sample
(Gregersen et al., 2007).
Possible ethnicity effects for AP can also be observed from
a genome-wide linkage study of 73 families of European, East
Asian, Ashkenazi Jewish and Indian descent (Theusch et al., 2009)
in the United States and Canada. In each family there were at
least two AP possessors, not limited to a parent-child relative pair.
Non-parametric multipoint linkage analyses were suggestive of
linkage on chromosomes 8q24.21 (LOD =2.330) and 8q21.11
(LOD =2.069) for the European/Ashkenazi Jewish/Indian com-
bined dataset (Table 3). Notably, one of the four genes found near
the linkage peak on 8q24.21 was ADCY8 (adenylate cyclase 8),
which is expressed almost exclusively in the brain and is impli-
cated in learning and memory processes (Wong et al., 1999;
Ludwig and Seuwen, 2002; De Quervain and Papassotiropoulos,
2006). When only the subset of 45 AP families of European
descent was examined, there was strong evidence of linkage on
chromosome 8q24.21 (LOD =3.464 at SNP rs3057), suggesting
that at least one gene within this linkage region could predis-
pose AP in individuals of European descent. A number of other
linkage peaks were found in the European families, namely on
loci 8q21.11 (LOD =2.236), 7q22.3 (LOD =2.074) and 9p21.3
(LOD =2.048). These peaks suggest that different genetic factors
may underpin the etiology of AP, even within the same popula-
tion. The linkage region on 7q22.3 was also observed in a subset
of 19 AP families of East Asian ancestry, albeit with a smaller
linkage peak (LOD approximately between 1 and 1.5). These find-
ings support a strong genetic contribution to AP, which is likely
to be heterogeneous. In other words, AP may be predisposed by
different genetic variants at different chromosomal regions, both
within and across populations of different ancestries.
A recently published study investigated the genetic relationship
between AP and synaesthesia through a combined genome-wide
linkage analysis of 53 multiplex families with AP (i.e., families
in which multiple family members have AP) and 36 multiplex
families with synaesthesia (Gregersen et al., 2013). Interestingly,
28 of the 126 AP possessors from the AP families reported
synaesthesia, while eight synaesthesia families had a member
with AP. Separate non-parametric linkage analysis of the AP and
synaesthesia datasets revealed overlaps in several linkage regions
(LOD >2), especially on chromosomes 2 and 6. Given this over-
lap and the hypothesis that the two phenotypes may be jointly
influenced by genes underpinning brain structural and functional
connectivity, the researchers combined the AP and synaesthesia
datasets for analysis. This revealed significant linkage on chro-
mosome 6q14.1–6q16.1 (LOD =4.68), where notably, a small
linkage peak (LOD =1.72) had been reported for the subset of 45
AP families of European ancestry studied by Theusch et al. (2009).
Upon sequencing several potential candidate genes in this region,
Gregersen et al. found that AP possessors from four of the AP
multiplex families shared one or more of three non-synonymous
variants of the gene EPHA7 (Ephrin type-A receptor 7). EPHA7
has been implicated in brain development, particularly estab-
lishing neural connectivity between auditory cortex and other
cortical regions with the thalamus (North et al., 2013; Torii et al.,
2013). Since neuroimaging studies have reported that both AP
and synaesthesia are marked by atypical structural and functional
connectivity (Rouw and Scholte, 2007; Loui et al., 2011; Dovern
et al., 2012), it is conceivable that EPHA7 variants may influ-
ence these two phenotypes. More research involving extensive
resequencing on the EPHA7 gene is needed in order to confirm
its involvement. Using parametric linkage analysis, a more com-
plex pattern of linkage was also observed on chromosome 2 in
the combined AP and synaesthesia dataset, with a heterogeneity
LOD score of 4.7 at SNP rs1482308. When only the AP families
were considered, a maximum heterogeneity LOD score of 3.93
was observed at SNP rs6759330 on chromosome 2.
Inherent neuroanatomical differences between AP and non-
AP possessors may also be genetically influenced. AP possessors
show increased leftward asymmetry of the planum temporale
(PT) due to a significantly smaller right mean PT volume com-
pared with non-AP possessors (Keenan et al., 2001; Wilson et al.,
2009). Keenan et al. (2001) suggested that the “pruning” of the
right PT may be prenatally determined rather than due to early
music training, since non-AP musicians with early music training
do not manifest similar asymmetry. Wilson et al. (2009) subse-
quently demonstrated a striking difference in the right mean PT
volumes of musicians with AP and quasi-absolute pitch (partici-
pants who scored between 20 and 90% on a note-naming test),
even though the age of onset of music training did not differ
significantly between these groups. Other supporting evidence
comes from the discovery of an adult AP possessor, R.M., who
had minimal music training but was able to perform a pitch mem-
ory task at a level indistinguishable from AP musicians. This case
indicates that an early onset of music training (or any music train-
ing) may not be essential for AP to emerge (Ross et al., 2003),
suggesting that AP and non-AP possessors may be using different
pitch processing mechanisms (McLachlan et al., 2013b)thatin
part, reflect genetically influenced neuroanatomical differences.
Congenital amusia
Congenital amusia (commonly known as “tone deafness”) is a
fine-grained pitch perception deficit characterized by the inability
to detect “wrong” notes in melodies, despite normal intellect, lan-
guage and hearing abilities (Peretz and Hyde, 2003). Congenital
amusia is uncommon in the general population, with an esti-
mated population prevalence of 4% (Kalmus and Fry, 1980).
While the neurological basis of congenital amusia has been well-
investigated (Peretz and Hyde, 2003; Hyde et al., 2007; Mandell
et al., 2007; Loui et al., 2009; Mignault Goulet et al., 2012), few
studies have explored its genetic basis.
In the first familial aggregation study on congenital amusia,
Peretz et al. (2007) administered an online amusic diagnostic
test to 13 amusic probands and 17 controls, as well as 58 family
members of the probands (from 9 large families) and 58 family
members of the controls (from 10 families). The results showed
that 39% of first-degree relatives have congenital amusia, whereas
only 3% of controls were similarly diagnosed. Notably, the λs
for congenital amusia was 10.8, whereas the offspring recurrence
risk was much lower at 2.3. While the high λssuggests a prob-
able genetic basis for congenital amusia, Peretz et al. speculated
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Tan e t a l. The genetic basis of music ability
that exposure to an enriched musical environment may mitigate
the risk for offspring of amusic probands. However, Mignault
Goulet et al. (2012) reported that after four weeks of daily music
listening, the music perception scores and electrophysiological
measures of seven amusic children (aged 10–12 years) did not
vary essentially. This suggests that daily music listening is insuf-
ficient to improve pitch perception performance or stimulate
neural plasticity in amusic children.
Music perception: pitch, rhythm and sound patterns
While music training may be necessary to develop specific
music skills, there exists a “commonplace musical competence”
that is possessed or easily acquired by most (Trehub, 2003).
Investigations of infant musical behavior have shown that infants
are capable of detecting melodic or rhythmic changes in musi-
cal patterns, as well as perceiving changes in pitch and rhythm
(Trehub et al., 1984, 1987, 1999; Trainor and Trehub, 1992, 1993;
Zentner and Kagan, 1996; Trainor and Heinmiller, 1998; Trainor
et al., 2002; Trehub, 2006; He and Trainor, 2009)(Honing et al.,
2009; Winkler et al., 2009). Coupled with the ubiquity of music
across all cultures (McDermott and Hauser, 2005), these find-
ings suggest that all humans are endowed with an intrinsic form
of musicality, and that genetic factors may play a role in its
manifestation.
In particular, individual differences in the ease of auditory skill
acquisition point to predisposed differences in auditory ability.
In one study, participants were classified as slow or fast learn-
ers in an auditory discrimination training task. Differences in
behavioral performance were reflected in differential patterns of
training-induced functional activation between the two groups
(Gaab et al., 2006). Compared with the slow learners, fast learn-
ers recruited the left supramarginal gyrus and left Heschl’s gyrus
to a greater extent during the post-training phase. Jäncke et al.
(2001) obtained similar findings, with different short-term func-
tional activation patterns for participants who improved at a
frequency discrimination task compared to those who showed no
improvement. Likewise, Zatorre et al. (2012) found that partici-
pants who learned a micromelody task more quickly had steeper
fMRI BOLD responses to pitch changes in their auditory cortex,
even before they trained on the task. These findings suggest that
predisposed differences in brain functioning may influence an
individual’s music perception abilities and the capacity to acquire
musical skills.
Relative pitch (RP) perception may also be genetically influ-
enced, as inferred from an ethnicity study by Hove et al. (2010).
These researchers examined the RP ability of secondary school
students with minimal music background using an interval iden-
tification task. Students from China or Taiwan (mean score
=72%) significantly outperformed Caucasian (mean score =
45.5%) and Hmong students (mean score =45.5%), even though
both Mandarin and Hmong are tonal languages. The researchers
then conducted a similar study with Caucasian, Chinese, and
Korean undergraduates with minimal music training (Hove et al.,
2010). The Chinese (mean score =72.2%) and Korean students
(mean score =78.2%) significantly outperformed the Caucasian
students (mean score =58.2%) but performed similarly to each
other. Interestingly, these ethnicity effects only occurred in the
pitch domain with no differences observed on a rhythm-pattern
task. Moreover, as most of the Korean participants spoke Seoul
or standard South Korean, both of which are non-pitch-accented
(Sohn, 1999), it is unlikely that the ethnicity effects stemmed from
tone-language experience. Neither the degree of tone-language
experience (fluent or non-fluent), the primary language spoken
at home (tonal or non-tonal), nor time spent in East Asia dur-
ing early childhood were associated with RP ability of the East
Asian participants. It is therefore possible that the ethnicity effects
observed for RP processing have a genetic basis.
Foster and Zatorre (2010) observed that gray matter volume
and cortical thickness in the right Heschl’s sulcus and bilateral
intraparietal sulcus predicted performance on a relative pitch
task, even after accounting for music training. As significant
heritabilities (65–97%) for overall brain volume and gray and
white-matter volumes have been consistently documented across
behavioral genetics research (Peper et al., 2007), these findings
are consistent with genetic influences on RP processing. In addi-
tion, a longitudinal twin study reported considerable genetic
influences (up to 56% heritability) on structural plasticity in
the frontal and temporal cortices (Brans et al., 2010). Although
music training-induced structural neuroplasticity has been well-
documented (see in this Research topic Barrett et al., 2013 and
Merrett et al., 2013), this finding suggests that structural plasticity
effects may also be genetically influenced.
In a large twin study conducted in 2001, 136 MZ twin
pairs and 148 DZ twin pairs undertook the Distorted Tunes
Test (DTT), in which they judged whether simple well-known
melodies contained incorrect pitches that rendered them “out-of-
tune” (Drayna et al., 2001). Twin structural modeling revealed a
very high heritability estimate of 71–80% with no effect of shared
environment, thus indicating a substantial genetic component
influencing melodic perception ability.
A study conducted on 15 musical Finnish families investigated
the genetic basis of music aptitude using three widely-used music
perception tests: the Karma Music Test, and Seashore’s pitch and
rhythm discrimination tests (Pulli et al., 2008). The Seashore
tasks use paired discrimination to assess pitch and rhythm per-
ception (Radocy and Boyle, 2012), while the Karma Music Test
assesses the ability to recognize patterns in sound sequences
(Karma, 2007). Heritability estimates of 42, 57, 21, and 48% were
obtained for the Karma Music Test, Seashore’s pitch and rhythm
discrimination tests, and the combined score on all three tests,
respectively. Genome-wide linkage analysis revealed significant
evidence of linkage on chromosome 4q22 (LOD =3.33 near
markers D4S423 and D4S2460) and suggestive linkage evidence
on chromosome region 8q13-21 (LOD =2.29) for the combined
score. Interestingly, the suggestive linkage peak at 8q13-21 was
close to the linkage on chromosome 8q21.11 identified in the
AP study by Theusch et al. (2009), pointing to a possible con-
vergence of AP and general music perception abilities. A possible
candidate gene at the tallest linkage peak of chromosome 4q22
is the netrin receptor UNC5C. Netrins are proteins that direct
axon extension and cell migration during neural development,
with studies showing interactions between netrins and robo fam-
ily receptors (Stein and Tessier-Lavigne, 2001). One such receptor,
ROBO1, is a candidate gene for dyslexia (Carrion-Castillo et al.,
Frontiers in Psychology | Auditory Cognitive Neuroscience June 2014 | Volume 5 | Article 658 |10
Tan e t a l. The genetic basis of music ability
2013). One of the genes found on 8q13-21 is TRPA1, which was
proposed as a non-essential subunit of the hair-cell transduction
channel in the vertebrate inner ear (Corey, 2006). The authors of
this study posited that the low selection pressure of TRPA1 may
make it susceptible to mutations and perhaps lead to variability in
the sound perception ability of individuals. Taken together, these
linkage results suggest a genetic contribution to music perception
underpinned by several predisposing genes on 4q and 8q (Pulli
et al., 2008).
A follow-up candidate gene study involving 19 musical Finnish
families found that the AV P R 1 A (arginine vasopressin 1a) haplo-
type RS1+RS3 on chromosome 12q has significant associations
with performance on the Karma Music Test and the combined
score on the Karma and Seashore music tasks (Ukkola et al.,
2009). Analyses on the polymorphisms of other candidate genes
such as SLC6A4,TPH1,andDRD2 yielded weak and inconclu-
sive results. Previous studies have shown that arginine vasopressin
(AVP) plays a key role in social cognition and behavior (Ferguson
et al., 2002; Bielsky et al., 2004; Depue and Morrone-Strupinsky,
2005; Hammock and Young, 2005) and in social and spatial
memory (Aarde and Jentsch, 2006). Its association with auditory
pattern perception in this study suggests a potential link between
music perception and human social functioning.
Using the same music perception measures as the two afore-
mentioned studies, a recent study analyzed genome-wide CNVs
in five multigenerational Finnish families and in 172 unre-
lated individuals (Ukkola-Vuoti et al., 2013). The CNV analysis
detected several copy number variable regions (CNVRs) con-
taining genes that influence neurodevelopment, learning and
memory. Notably, a deletion on 5q31.1 was present in some par-
ticipants who obtained a low combined score on the Karma and
Seashore music tasks, accounting for 54% of the low-scoring
individuals from two families and 7% of low-scoring unrelated
participants. This particular CNVR covers the protocadherin-α
gene cluster (Pcdha 1-9), which is implicated in neural migra-
tion, differentiation, and synaptogenesis, as well as learning and
memory (Fukuda et al., 2008). Since learning and memory are
crucial to music skill acquisition, including music perception
(McLachlan et al., 2013a), the authors proposed that Pcdha may
be a potential candidate gene influencing music perception and
practice. Also noteworthy was the identification of a novel large
1.3Mb duplication on 8q24.22 in an individual with a low com-
bined score. This region was previously reported as a major
linkage region for AP (Theusch et al., 2009). As large duplications
may have detrimental effects on neurodevelopment (Almal and
Padh, 2012; Grayton et al., 2012), the authors speculated that a
duplication in this region may have negatively impacted the par-
ticipant’s pitch perception accuracy. Due to the relatively small
sample size and a lack of screening for neurocognitive deficits,
the authors acknowledge that these results are preliminary, and
there remains a possibility that the identified CNVs may not be
predisposing for music perception per se.
In the first study to demonstrate a link between music per-
ception and genes that are expressed in the auditory pathway, the
same Finnish research group conducted a large-scale genome-
wide linkage and association study on the music perception
abilities of 767 people from 76 Finnish families (Oikkonen et al.,
2014). Participants were again assessed using the Karma Music
Test and the Seashore pitch and rhythm discrimination tasks,
with estimated heritabilities for each test and the combined test
score reported as 46, 68, 21, and 60%, respectively. While the
heritability estimates for the Karma Music Test and the rhythm
discrimination task are similar to the estimates reported by Pulli
et al. (2008), the estimate for pitch discrimination is higher,
which in turn may have inflated the heritability estimate for the
combined test score. SNP linkage and association analyses uncov-
ered multiple chromosomal regions containing auditory pathway
genes. Specifically, the strongest association was observed on
3q21.3 (at SNP rs9854612) for the combined test score. Located
close to 3q21.3 is the GATA2 (GATA binding protein 2) gene,
which has been implicated in the development of the inner ear
(Haugas et al., 2010) and the inferior colliculus (Lahti et al.,
2013). The inferior colliculus is a key structure in the periph-
eral auditory pathways that supports the initial integration of
pitch, direction and loudness information necessary for music
perception (McLachlan and Wilson, 2010). Linkage analysis also
revealed several linkage regions on chromosome 4, spanning 4p15
to 4q24. The strongest linkage was observed for the pitch discrim-
ination task on chromosome 4p14, which is located next to the
PCDH7 (protocadherin 7) gene. Notably, two SNPs (rs13146789
and rs13109270) of PCDH7 also showed strong associations with
the combined test score. PCDH7 is expressed in the developing
cochlea of the chicken and the amygdala of the mouse (Hertel
et al., 2012; Lin et al., 2012), providing possible support for its
role in music perception. Finally, some evidence of linkage was
found on 4q21.23-22.1 and 4q24 for performance of the Karma
Music Test, replicating the significant linkage on 4q22 reported
by Pulli et al. (2008) for the combined test score. The current
study, however, did not replicate the previously reported associa-
tion between AV P R 1 A and music perception (Ukkola et al., 2009),
nor did it observe any linkage or association evidence on 8q24.21,
the putative AP region (Theusch et al., 2009).
In the rhythm domain, one study has reported that mutation
of the FOXP2 (Forkhead box protein P2) gene on chromosome
7q31 impairs rhythm perception and production, while leaving
pitch perception and production abilities intact (Alcock et al.,
2000). As FOXP2 has been implicated in an inherited speech
and language disorder (Lai et al., 2001), these findings suggest a
possible shared genetic basis for speech and rhythm, while pitch-
based music abilities are likely influenced by other genetic factors
(Peretz, 2009).
Music memory
There is evidence that six to eight-month old infants have already
developed long-term memories for music and are able to dis-
tinguish between familiar and novel music (Saffran et al., 2000;
Plantinga and Trainor, 2003).Inaddition,exposuretomelodies
presented prenatally for three weeks elicits significant heart rate
change in one-month old infants compared to unexposed con-
trols, suggesting that newborn infants are capable of retaining
music representations up to six weeks following prenatal exposure
(Granier-Deferre et al., 2011). Genetic determinants of mem-
ory have been reported in the broader literature, with some
studies indicating that memory ability can be predicted by a
www.frontiersin.org June 2014 | Volume 5 | Article 658 |11
Tan e t a l. The genetic basis of music ability
particular SNP Val66Met variant of the BDNF (brain-derived
neurotrophic factor) gene (Egan et al., 2003; Hariri et al., 2003).
BDNF is evident in the hippocampus (a structure fundamental
to new learning and memory) and has been implicated in neu-
ronal growth, survival and maturation, including arborization
and synaptic plasticity in the adult brain (Park and Poo, 2012).
In the only genetic study of music memory to date, Granot
et al. (2007) investigated the possible association of phonological
and music memory with the genes AV P R 1 A and SLC6A4 (solute
carrier family 6 [neurotransmitter transporter serotonin], mem-
ber 4). The rationale for targeting these two genes included a pre-
viously reported relationship between arginine vasopressin (AVP)
and spatial and social memory (Ferguson et al., 2002; Aarde and
Jentsch, 2006). There is also evidence that serotonin interacts with
AVP in the hypothalamus (Albers et al., 2002) and that serotonin
increases the secretion of AVP (Gálfi et al., 2005). This points to a
possible epistatic relationship between the gene AV P R 1 A ,which
contains the blueprint to synthesize the AVP receptor, and the
gene SLC6A4, which is the serotonin transporter protein crucial
for regulating serotonin supply to serotonin receptors. In view of
this, Granot et al. genotyped 82 university students with mini-
mal music training for the AV P R 1 A (RS1 and RS3 haplotypes)
and the SLC6A4 (HTTLPR) polymorphisms using population-
based and family-based association analyses. The phonological
and music memory performance of the participants were assessed
using an extensive battery of tests. Results revealed significant
gene by gene epistatic interactions between the AV P R 1 A and
SLC6A4 polymorphisms for two melodic memory tasks, one
rhythmic memory task, and one phonological memory task, even
after applying conservative Bonferroni corrections for multiple
testing. This provides initial evidence for an epistatic relation-
ship between AV P R 1 A and SLC6A4 polymorphisms that may be
linked to short-term memory for music, or more generally, to
phonological memory.
Music Listening
In another association study involving AV P R 1 A and SLC6A4 poly-
morphisms, Ukkola-Vuoti et al. (2011) investigated the music
listening habits of 31 Finnish families using surveys. Family-
based association analysis revealed positive associations between
AV P R 1 A haplotypes and active music listening. The most sig-
nificant associations occurred between the RS1+AVR haplotype
and current active music listening, as well as the RS1+RS3 hap-
lotype and lifelong active music listening. No significant asso-
ciation was observed between music listening and the SLC6A4
polymorphisms. In this study, active listening referred to atten-
tive music listening, such as going to concerts. Since the same
AV P R 1 A promoter region (RS1+RS3) was shown by Ukkola
et al. (2009) to be associated with music perception, these find-
ings suggest a common genetic background for the frequency of
active music listening and music perception ability. Moreover,
the authors reported that when music perception test scores
and music education were covaried with music listening in the
association analysis, the significant effect remained, indicating
that music listening is independently associated with AV P R 1 A .
More broadly, this association suggests that music listening may
share common neurobiological pathways with social attachment
and communication, given the well-established findings of AVP’s
mediating role in social behavior (Ferguson et al., 2002; Bielsky
et al., 2004; Depue and Morrone-Strupinsky, 2005; Hammock
and Young, 2005).
MUSIC PRODUCTION ABILITIES
Singing
Across all cultures humans have a propensity to sing. From two
months onwards, infants begin to produce “musical babbling”
containing definite music features such as pitch and rhythmic
patterns (Welch, 2006). Most children begin imitating songs at
approximately age two, by age four they can sing complete songs,
and by age 5 most of them can accurately reproduce entire songs
(McPherson and Williamon, 2006; Parncutt, 2006).
Although it is likely that variability in children’s singing
competency is, in part, attributable to environmental fac-
tors, such as early music exposure and training, a behavioral
study has suggested there may also be an inborn aspect to
singing accuracy. Watts et al. (2003) identified individuals who
received no professional vocal training yet were described by
professional voice teachers as “exhibiting expressed singing tal-
ent.” These individuals showed consistently superior perfor-
mance on pitch-matching tasks, especially in the absence of
externalauditoryfeedback,evenwhencomparedwithtrained
singers who had at least three years of professional vocal
training.
Park et al. (2012) investigated the genetic factors underpinning
singing ability by conducting family-based linkage and associa-
tion analyses on 1008 participants from 73 extended Mongolian
families. They administered a pitch production accuracy test and
found that 357 of the participants (35.4%) were accurate pitch-
matchers, reliably singing the target pitches with deviations less
than a semitone. Using pedigree data, the heritability of singing
accuracy was reported as 40%. A genome-wide linkage analy-
sis was then conducted, with the most significant linkage peak
observed on 4q23 (LOD =3.1 at marker D4S2986). The findings
overlap with regions on chromosome 4q, where there is linkage
evidence for music perception ability (Pulli et al., 2008; Oikkonen
et al., 2014). A family-based association analysis performed at
the putative linkage region revealed that SNP rs12510781 on
4q26 was most significantly associated with singing accuracy.
This is an intergenic SNP near the gene UGT8 (UDP glycosyl-
transferase 8), whose encoded protein is highly expressed in the
brain, especially the substantia nigra (see online Supplementary
Figure S2 of Park et al., 2012). The authors also utilized exome
sequencing to find other potential candidate SNPs and discov-
ered a non-synonymous SNP (rs4148254) in UGT8 on 4q26 that
was significantly associated with singing accuracy. In addition,
CNV analysis using an array comparative genomic hybridization
(aCGH) platform showed that a copy number loss at 5.6 kb (5600
base pairs) upstream of UGT8 may be negatively associated with
singing accuracy. Although environmental factors such as edu-
cation and music training were not considered in this study, the
authors argued that because the participants resided in an isolated
region with homogeneous culture and most were educated in the
same school without additional music training, environmental
factors were unlikely to impact greatly on the results. In other
Frontiers in Psychology | Auditory Cognitive Neuroscience June 2014 | Volume 5 | Article 658 |12
Tan e t a l. The genetic basis of music ability
words, this study yields evidence that singing accuracy may be
heritable in this population and possibly associated with a region
on chromosome 4q.
Participation in singing activities
Coon and Carey (1989) analyzed the music ability of 11th grade
twins by extracting music-related questionnaire data from an
earlier study (Loehlin and Nichols, 1976). For participation in
singing activities, the heritability estimates were reported as 71%
for males and 20% for females, while the corresponding shared
environment estimates were 8 and 59% respectively. The signif-
icant gender difference in the estimates indicates that a shared
environment exerted a stronger effect on females than males,
while heritability was much higher in males than females. The
authors suggested this may be due to a stereotypical perception
that singing is a feminine activity and therefore, males might
require greater interest and intrinsic ability to take part in such
activities. As this study relied on self-reported data and did not
objectively assess the singing ability of the twin pairs, more
investigation is warranted to ascertain the genetic contribution
to singing ability.
Similar to the association study on music memory by Granot
et al. (2007), a candidate gene association study by Morley et al.
(2012) tested the relationship between choir membership and
allelic variants of the genes AV P R 1 A and SLC6A4. An overall asso-
ciation with choral singer status was observed at the STin2 (intron
2) polymorphism in the SLC6A4 gene, with the STin2 9-repeat
and 12-repeat alleles being more common in choral singers, and
the 10-repeat alleles more common in non-musically active con-
trols. No significant differences in allele frequencies were observed
between the two groups for other SLC6A4 and AV P R 1 A poly-
morphisms. Previous studies have reported possible involvement
of STin2 in personality traits and reward behavior (Kazantseva
et al., 2008; Zhong et al., 2009; Saiz et al., 2010). SLC6A4 poly-
morphisms (together with AV P R 1 A )havealsobeenlinkedto
participation in creative dance (Bachner-Melman et al., 2005).
As several studies have observed associations between AVP R 1 A
polymorphisms and certain music traits (Granot et al., 2007;
Ukkola et al., 2009; Ukkola-Vuoti et al., 2011), the non-significant
AV P R 1 A association in this study led the authors to speculate
that the observed STin2 effect may predispose social behavioral
characteristics (i.e., a “predisposed to group activity” phenotype)
rather than music ability per se.
Music creativity
The genetic basis of music creativity was investigated in 19
Finnish musical families using a web-based questionnaire.
Participants were asked about their music background and par-
ticipation in creative music activities, such as music composition,
improvisation or arrangement (Ukkola et al., 2009). The find-
ings indicated that creative functions in music may have a strong
genetic component in this sample population, with a heritability
estimate of 84%. A significant positive association between music
creativity and high music perception test scores was also observed.
However, no significant associations between music creativity and
the polymorphisms of candidate genes such as TPH1,COMT,and
AV P R 1 A were found.
Ukkola-Vuoti et al. (2013) performed a subsequent CNV anal-
ysis on five multigenerational Finnish families and 172 unrelated
individuals using the same music creativity questionnaire. A “cre-
ative phenotype” was characterized by engagement in one or
more creative music activities (composing, improvising or music
arranging). Results showed that a deletion on 5p15.33 was present
in 48% of family members and 28% of unrelated participants who
exhibited the creative phenotype, while a duplication on 2p22.1
was present in 27% of creative family members. The region 2p22.1
contains the gene GALM, which is associated with serotonin
transporter binding potential in the human thalamus (Liu et al.,
2011). The medial geniculate nucleus of the thalamus forms part
of the auditory pathways, and more generally has been implicated
in music-related functions such as beat perception (McAuley
et al., 2012), sensorimotor synchronization (Krause et al., 2010)
and musical imagery (Goycoolea et al., 2007). Other studies have
found a link between the serotonin transporter gene (SLC6A4)
and music-related functions such as choir participation (Morley
et al., 2012) and creative dance (Bachner-Melman et al., 2005).
On the other hand, deletions in three CNV regions (2p12, 3p14.1,
and 3q28) occurred quite commonly in individuals from two or
more families without the creative phenotype, with frequencies
ranging from 19 to 31%. The authors acknowledged the prelimi-
nary nature of their findings, highlighting their use of uncorrected
multiple comparisons. Replication of the findings is clearly war-
ranted, including the use of objective measures of music creativity
in future research.
Self-reported music ability
In addition to examining participation in singing activities in
11th grade twins, Coon and Carey (1989) used the music-related
questionnaire data from Loehlin and Nichols (1976) to investi-
gate other aspects of self-reported music ability, including interest
in a music career, participation in music activities, out-of-school
music performance experience, and receiving music prizes. Their
results showed that while there were genetic influences, the effects
of a shared environment were almost always larger across all the
variables, with the exception of singing participation (described
above) and out-of-school music performances in male twins. For
participation in out-of-school music performances, the heritabil-
ity estimates were reported as 38% for males and 10% for females,
while the corresponding shared environment estimates were 18
and 63% respectively. The researchers concluded that music abil-
ity is generally more influenced by shared environment than by
shared genes in this young adult sample.
Interestingly, contrasting results were obtained in a more
recent Netherlands twin study involving 1685 twin pairs aged 12–
24 years. Using a self-report questionnaire, this study examined
the heritability of domain-specific aptitude (defined as ability
within the normal range) and exceptional talent. In particular,
the participants were asked to rate their level of competence
in various domains such as music, arts, language and sports
(Vinkhuyzen et al., 2009). For self-reported music aptitude, the
heritability estimates for males and females were 66% and 30%
and the shared environment estimates were 8 and 54%, respec-
tively. As for self-reported exceptional music talent, the heritabil-
ity estimate was 92% with no shared environment effect. The
www.frontiersin.org June 2014 | Volume 5 | Article 658 |13
Tan e t a l. The genetic basis of music ability
authors concluded that genetic influences possibly account for
the variation in aptitude and exceptional talent across domains,
including intellect, creativity, and sporting ability to a large extent.
Similar to the music creativity research, both of the above
twin studies lacked objective measures of music ability, raising
concerns about the reliability of the findings.
DISCUSSION
As reviewed in this paper, a number of studies have begun to
yield insights into the genetic basis of music ability. To date,
some promising and converging findings have begun to emerge.
Several loci on chromosome 8q have been implicated in more
than one music trait. For instance, loci 8q21 and 8q24 have been
implicated in AP ability and music perception (Pulli et al., 2008;
Theusch et al., 2009; Ukkola-Vuoti et al., 2013). Similarly, loci
4p14 and 4q22 on chromosome 4 have been implicated in music
perception, particularly pitch discrimination (Pulli et al., 2008;
Oikkonen et al., 2014), while the neighboring locus 4q23 has been
implicated in pitch accuracy of singing (Park et al., 2012).
A number of genes have featured quite prominently in music
genetics research to date. The gene AV P R 1 A on chromosome
12q has been implicated in music listening (Ukkola-Vuoti et al.,
2011), music perception (Ukkola et al., 2009), and music memory
(Granot et al., 2007). On the other hand, the gene SLC6A4 has
been associated with music memory (Granot et al., 2007)and
choir participation (Morley et al., 2012). The role of AV P R 1 A
in social cognition and behavior has been well-investigated, as
has the possible interaction between AV P R 1 A and SLC6A4 in
communicative behavior. The associations of these two genes
with various music functions raises the intriguing possibility of
an overlap in the neurobiological basis of music functions and
social behavior.
Replication of the results of existing studies is necessary to con-
firm the findings, especially in those studies with small sample
sizes (e.g., Granot et al., 2007; Pulli et al., 2008; Ukkola-Vuoti
et al., 2013). For instance, a recent large genome-wide linkage
and association study (Oikkonen et al., 2014)failedtoobserve
an association between AV P R 1 A and music perception, which
had previously been reported in a candidate gene study by the
same research group (Ukkola et al., 2009). In many of the candi-
date gene association studies, the polymorphisms of genes such as
AV P R 1 A and SLC6A4 were chosen as candidates based on sugges-
tive results from other music studies. However, the multi-faceted
nature of music ability may render a candidate gene associated
with one musical function a weak candidate for another musi-
cal function. This is illustrated by the study of Morley et al.
(2012), which found no association between AV P R 1 A polymor-
phisms and choir participation despite having adequate statistical
power. It may thus be more prudent for researchers to select can-
didate genes based on supporting evidence from linkage analysis
or GWAS of related music abilities. It is also important to note
that candidate gene studies have a poor record of replication, with
negative findings likely under-reported due to publication bias for
positive findings (Ott, 2004; Lewis and Knight, 2012).
In replicating the findings of current studies, it will be impor-
tant for future studies to use alternate populations and larger
samples. It is apparent from Tab l e s 2 ,3that a significant number
of molecular genetic studies have been conducted on several
multigenerational Finnish families. Extending the findings from
these families to other ethnic populations would serve to validate
the reported associations. More generally, conducting GWAS in
populations of different ancestries has been identified as a key area
for future medical genetics research, as the different linkage dis-
equilibrium (LD) structure of different populations may help to
refine a gene locus of interest (Stranger et al., 2011),andinsome
cases it may increase the statistical power to detect an association
(Pulit et al., 2010). Increased research efforts toward replication
will also add to the number of independent molecular genetic
studies available for meta-analysis, which in turn, will increase
the sample size and statistical power of meta-analyses to detect
associations with modest effects (Stranger et al., 2011; Rowe and
Tenesa, 2012).
As molecular genetics research in music is still in its infancy,
many of the molecular studies reviewed in this paper utilized
earlier molecular genetic methods such as linkage mapping or
the candidate gene approach. Technological advances now make
it possible for commercial arrays to include a combination of
SNPs and structural genetic variants (such as CNVs) (McCarroll,
2008). Music genetics researchers can consider integrating these
approaches, with findings yielded from CNV analysis able to
complement those from SNP analysis (Stranger et al., 2007).
Other recent methods include exome sequencing, which provides
a more cost-effective and efficient alternative to whole genome
sequencing, as well as methylation studies, which can be used
to investigate the potential contribution of epigenetic influences
and the underlying molecular and biological mechanisms of a
trait (Rowe and Tenesa, 2012). Future studies of the genetic basis
of music would therefore likely benefit from a shift toward more
current molecular genetic methods to investigate complex traits,
especially while new approaches for integrating and analyzing
diversedatatypesarebeingdeveloped(Battle et al., 2010).
Another important, but as yet under-researched avenue of
music genetics research involves exploration of the potential
contributions of epistasis, gene-environment interactions, and
epigenetic influences on music ability. These factors may explain
why many of the genetic variants and loci implicated in complex
traits could only account for a small percentage of the heritabil-
ity estimated from family studies (Stranger et al., 2011). Music
researchers need to move away from the dichotomous view of
nature vs. nurture and develop an awareness of the intricate inter-
play between genes and environment. For instance, there may
be possible genetic influences on ostensibly environmental com-
ponents such as training-induced neural plasticity (Brans et al.,
2010; Vinkhuyzen et al., 2010). Conversely, environmental fac-
tors may alter gene expression through epigenetic mechanisms
(Fagiolini et al., 2009; Sweatt, 2013).
While the results garnered to date are promising, a more
comprehensive investigation of music ability is warranted, includ-
ing more precise characterization of music phenotypes to iden-
tify their genetic basis. As evident from Table 1, the majority
of studies have used tests of music aptitude to operationalize
music ability, producing an undue emphasis on certain perceptual
skills, such as pitch and rhythm discrimination, while ignor-
ing others. This means that other equally important perceptual
Frontiers in Psychology | Auditory Cognitive Neuroscience June 2014 | Volume 5 | Article 658 |14
Tan e t a l. The genetic basis of music ability
skills, as well as music production abilities, creativity, sensitiv-
ity and expressivity have received minimal investigation. The
multifaceted nature of music ability calls for identification and
then careful delineation of the range of music phenotypes, with
greater research efforts directed toward phenotypes that have
been scantly researched. Conceivably, proper characterization of
music phenotypes will facilitate identification of relevant genes
predisposing these phenotypes through rigorous genetic stud-
ies (Levitin, 2012). Music deficits, such as tone deafness (Peretz
et al., 2007) and beat deafness (Phillips-Silver et al., 2011), also
offer fruitful avenues for further investigation as deficits often
have more distinct phenotypic outcomes than abilities. Currently,
few genetic studies have focused on music deficits. In addition, a
dearth of research effort has been directed toward investigating a
possible overlap in the genetic bases of language and music abili-
ties. Our current knowledge is limited to the finding that FOXP2
may play a role in music rhythm processing as well as language
and speech (Alcock et al., 2000; Lai et al., 2001). Comparative
genetic research between music and language abilities promises to
advance our understanding of the shared and non-shared genetic
and neurobiological mechanisms underpinning music and lan-
guage, and may help elucidate important questions about the
origins of music and language (Peretz, 2009).
In conclusion, although currently there is only a handful of
research studies in this area, music genetics research has yielded
promising preliminary results, highlighting the need for increased
research effort in this emerging field. Elucidating the genetic basis
of music ability may be challenging due to its multifaceted nature,
necessitating careful identification, characterization, and genetic
investigation of its many different facets. Coupled with the propi-
tious rate at which molecular genetics and statistical designs are
advancing, an increasingly clearer picture of the genetic mech-
anisms underpinning the etiology of music traits will begin to
emerge. These mechanisms may then be linked to established
neuroscientific findings of the neurobiological basis of specific
music functions and behaviors. Ultimately, this will allow us to
gain a deeper understanding of the way in which interactions
between nature and nurture shape the development of human
music ability over the lifespan.
ACKNOWLEDGMENT
The authors would like to thank the reviewers for their valuable
suggestions and comments on this review article.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online
at: http://www.frontiersin.org/journal/10.3389/fpsyg.2014.
00658/abstract
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Frontiers in Psychology | Auditory Cognitive Neuroscience June 2014 | Volume 5 | Article 658 |18
Tan e t a l. The genetic basis of music ability
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 24 December 2013; accepted: 08 June 2014; published online: 27 June 2014.
Citation: Tan YT, McPherson GE, Peretz I, Berkovic SF and Wilson SJ (2014) The
genetic basis of music ability. Front. Psychol. 5:658. doi: 10.3389/fpsyg.2014.00658
This article was submitted to Auditory Cognitive Neuroscience, a section of the journal
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www.frontiersin.org June 2014 | Volume 5 | Article 658 |19
... Even so, genetics have contributed only very discretely to the understanding of the biological bases of music; the genetic architecture underlying music-related skills is largely unknown, remaining a field that is still in its infancy [9,10]. The arrival of new genotyping and sequencing genomic technologies provides us with great opportunities to further investigate this field. ...
... In the most recent literature, genomic research emerges as a fundamental approach to understanding the biological bases of musical abilities. It seems evident that some genetic conditioning underlies musical abilities and musical perception, given the presence and importance of music in any culture [10] and our longstanding ancestral relationship (co-evolution?) with it. At the same time, it has long been debated whether musical talent is related to genes ("nature") or to training and environmental stimulus ("nurture"). ...
... There is an incipient field of research focusing on the genetic bases that underlie musical ability [10], musical creativity [16], or AP [17][18][19]. ...
Article
Full-text available
What is the actual impact of music on the human being and the scope for scientific research in this realm? Compared to other areas, the study of the relationship between music and human biology has received limited attention. At the same time, evidence of music’s value in clinical science, neuroscience, and social science keeps increasing. This review article synthesizes the existing knowledge of genetics related to music. While the success of genomics has been demonstrated in medical research, with thousands of genes that cause inherited diseases or a predisposition to multifactorial disorders identified, much less attention has been paid to other human traits. We argue for the development of a new discipline, sensogenomics, aimed at investigating the impact of the sensorial input on gene expression and taking advantage of new, discovery-based ‘omic’ approaches that allow for the exploration of the whole transcriptome of individuals under controlled experiments and circumstances.
... The GATA-binding protein 2 (GATA2) regulates SNCA in dopaminergic neurons. GATA2 links DNA-and RNA-studies of music aptitude [see Oikkonen and Järvelä (2014) review for related genetic studies of music aptitude; see also Tan et al. (2014) for a comprehensive review on behavioral and molecular genetic studies of music aptitude]. Genomic analyses conducted by Park et al. (2012) research group on 1008 Mongolian individuals identified an intergenic single nucleotide polymorphism (SNP) and a nonsynonymous SNP in UDP Glycosyltransferase 8 (UGT8) to be strong determinants of music aptitude, as assessed by a pitchproduction accuracy test. ...