Download Article Artificial Selection on Brain

Artificial Selection on Brain-Expressed Genes during the
Domestication of Dog
Yan Li,1,2 Bridgett M. vonHoldt,3 Andy Reynolds,4 Adam R. Boyko,5 Robert K. Wayne,6
Dong-Dong Wu,*,2 and Ya-Ping Zhang*,1,2
1
Laboratory for Conservation and Utilization of Bio-Resource, Yunnan University, Kunming, China
State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming,
China
3
Department of Ecology and Evolutionary Biology, University of California, Irvine
4
Department of Biological Statistics and Computational Biology, Cornell University
5
Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University
6
Department of Ecology and Evolutionary Biology, University of California, Los Angeles
*Corresponding authors: E-mail: [email protected]; [email protected].
Associate editor: Joshua Akey
2
Abstract
Domesticated dogs have many unique behaviors not found in gray wolves that have augmented their interaction and
communication with humans. The genetic basis of such unique behaviors in dogs remains poorly understood. We found
that genes within regions highly differentiated between outbred Chinese native dogs (CNs) and wolves show high bias for
expression localized to brain tissues, particularly the prefrontal cortex, a specific region responsible for complex cognitive
behaviors. In contrast, candidate genes showing high population differentiation between CNs and German Shepherd dogs
(GSs) did not demonstrate significant expression bias. These observations indicate that these candidate genes highly
expressed in the brain have rapidly evolved. This rapid evolution was probably driven by artificial selection during the
primary transition from wolves to ancient dogs and was consistent with the evolution of dog-specific characteristics, such
as behavior transformation, for thousands of years.
Key words: artificial selection, dog domestication, brain evolution, behavioral evolution.
Introduction
ß The Author 2013. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please
e-mail: [email protected]
Mol. Biol. Evol. 30(8):1867–1876 doi:10.1093/molbev/mst088 Advance Access publication May 8, 2013
1867
Article
As the premier domesticated animal in human society, the
domesticated dog (Canis familiaris) is both a companion and
a powerful model for the study of morphology, disease, and
behavior (Ostrander and Wayne 2005; Wayne and Ostrander
2007). One of the critical behavioral traits that differentiate
the domesticated dog from wolves is that of interspecific
communication with humans. For example, dogs can locate
hidden food items by following human pointing gestures or
tracking human gazes in a manner that is much more sophisticated than those of primates or wolves (Hare et al. 2002;
Miklósi et al. 2003; Hare and Tomasello 2005; Tomasello and
Kaminski 2009; Topál et al. 2009). Even more surprising is the
observation that young puppies, before any major interaction
with humans, display developmental levels of social–cognitive
skills that parallel those seen in human children (Hare et al.
2002; Tomasello and Kaminski 2009; Topál et al. 2009). In
contrast to dogs, wolves reared by humans are not as skilled
as dogs in following human signals, suggesting that such
social–cognitive skills are derived traits and evolved during
the process of domestication in dogs (Hare et al. 2002; Miklósi
et al. 2003; Hare and Tomasello 2005). Research with domesticated foxes (reviews see [Trut 1999, 2001]) suggests that this
social–cognitive ability might have initially appeared as an
incidental by-product of behavioral selection for tameness
toward humans (Hare et al. 2005). It was hypothesized
more than 50 years ago that this behavioral transformation
toward tameness was the primary target of selection during
domestication and was produced through gradual changes of
developmental regulation (Belyaev 1969; Trut et al. 2009), for
example, temporal parameters of developmental maturation
of sensory systems and locomotion (Scott 1962; Fox 1971;
Serpelle and Jagoe 1997). These changes increased the developmental period sensitive to human conditioning, which
enhanced social adaption and the development of human
temperament-like traits (Trut et al. 2004; Hare and Tomasello
2005; Kukekova, Temnykh, et al. 2011). Indeed, a reduced
fearful–aggressive response with increased exploration was
observed in both dogs and domestic foxes and was lacking
in their respective untamed relatives (Trut et al. 2004).
Directly following from this hypothesis, it could be inferred
that the expected underlying genetic changes are not in the
genes themselves but rather in their regulation. A previous
comparison of brain-specific gene expression profiles across
domesticated dogs, gray wolves, and coyotes identified
that gene expression in the hypothalamus of the domestic
dogs rapidly diverged from that of the wolf and coyote
and was suspected to be a result of the selection for tameness
and other neuroendocrine responses to domestication
(Saetre et al. 2004). Further, examination of the brains of
tame and aggressive foxes showed notable differences in
Li et al. . doi:10.1093/molbev/mst088
gene expression in the amygdale, frontal cortex, and hypothalamus (Lindberg et al. 2005), with significant biochemical
changes in neurotransmitter metabolism in the brain of the
tamed fox (Popova et al. 1997; Trut 2001). However, candidate gene studies have failed to identify genetic variants associated with dog behavior (Masuda et al. 2004; Takeuchi
et al. 2005; Ogata et al. 2006), with this failure attributed to
the limitations of the candidate gene approach for investigating the genetic mechanisms of phenotypically complex
behavioral traits (Kukekova et al. 2008). In contrast, a population-based approach had identified a candidate canine locus
linked to dog behavior, which was independently verified as
an orthologous region in the tame fox model (vonHoldt et al.
2010; Kukekova, Trut, et al. 2011). Further quantification of
differential gene expression in the brain of the tame and aggressive foxes revealed a likely candidate (HTR2C) that may be
linked to this behavioral transformation, as the gene functions
in the serotonergic and dopaminergic signaling pathway of
the prefrontal cortex (Kukekova, Johnson, et al. 2011).
Despite these efforts, the genetic basis of the behavioral
transformation during the domestication of dogs is far less
well understood than for morphological traits such as body
mass, coat type, hair color, and orientation (Ostrander and
Wayne 2005; Wayne and Ostrander 2007). In this report, we
study how artificial selection has targeted and shaped the
expression profiles of a specific subset of developmental
genes during the evolution of dogs. We find evidence that
directional selection has driven the evolution of brain-associated genes during the domestication of dogs.
Results
Intensive selective breeding occurred very recently in dogs
and has followed primary selection for characteristics such
as behavior transformation that have occurred for thousands
of years. To avoid potentially misleading conclusions derived
from the extremely small gene pool that resulted from breed
creation and mating rules, outbred dogs, which retain great
genetic diversity from ancient dogs, would be the best model
for exploring the genetic foundation of dog domestication.
Previous studies have found that dogs in South China have
among the highest levels of canine mtDNA and Y chromosome diversity, implying high levels of nuclear diversity
(Savolainen et al. 2002; Pang et al. 2009; Brown et al. 2011;
Ding et al. 2012). We, therefore, used outbred Chinese native
dogs (CNs) to capture the representation of dog diversity
before recent breed creation of purebred dogs although our
sampled individuals likely represent only part of dog diversity
and 22 wild gray wolves from Northern China and the
Mongolian plain for comparison. The single-nucleotide polymorphism (SNP) genotypes of these dogs and wolves were
assayed with Affymetrix v2 canine SNP mapping array
chips. German Shepherd dogs (GSs) have distinct breed
behavioral characteristics and are famous for their intelligence
and obedience (American Kennel Club 1998). To determine
whether the genetic basis of the behavior transformation
from wolf to primitive dog to modern purebred dog breeds
was associated with specific genetic changes, we also
1868
MBE
compared the genome-wide divergence between the outbred
CNs and eight purebred GSs imported from Germany.
A total of 48,445 SNPs passed quality control filters, yielding a call rate of 95.3% and a concordance rate of over 99.9%
for the biological replicated samples. Over 90% of SNPs are
within 111 kb of another SNP (median = 23 kb). Total missingness for the 1,143 SNPs differed significantly between
wolves and dogs (P < 0.05). After false discovery rate (FDR)
multiple correction (Benjamini and Hochberg 2000), seven
SNPs with abnormally high rates of missingness between
the wolves and dogs were excluded from the analyses.
Finally, after the control for genetic relatedness among individuals, 20 CNs, 14 wild gray wolves, and 8 GSs remained for
the downstream analyses.
Based on a pruned subset of SNPs (23,816 SNPs) that were
in linkage equilibrium with each other, three distinct clusters
formed with K = 3, consisting solely of all individuals from that
population (supplementary fig. S1, Supplementary Material
online). To investigate the alteration of dog genomic landscape between outbred and modern breed, we quantified
pairwise SNP linkage disequilibrium (LD). The long LD
decay pattern of GSs was observed, which was nearly the
same as that reported in a previous study (Boyko et al.
2010), consistent with breed-forming history (supplementary
fig. S2, Supplementary Material online). The CNs’ LD was
much shorter than GSs, indicating lack of bottleneck due to
breed formation, but slightly greater than the wolf LD, consistent with a bottleneck in dogs during the primary domestication event (supplementary fig. S2, Supplementary
Material online). Additionally, average extend haplotype homozygosity (EHH) value at a distance of 500 kb from the core
region was negatively correlated with haplotype diversity
within the core region in GSs, consistent with the ROH
(runs of homozygosity) pattern observed in breed dogs
(Boyko et al. 2010). On the contrary, similar to human populations (Auton et al. 2009), CN and wolf did not show the
negative correlation with haplotype diversity (supplementary
fig. S3, Supplementary Material online). Furthermore, we analyzed haplotype sharing between dogs and wolves for 500-kb
haplotype windows containing 5 and 10 SNPs drawn at
random similar with previous study (vonHoldt et al. 2010)
and found that the CNs had much more haplotype sharing
with wolves than GSs, indicating that CNs have a rich genetic
diversity (supplementary fig. S4, Supplementary Material
online).
Differentiation between Dog and Wolf Populations
The hypothesis that behavioral transformation was the necessary prerequisite for domestication (Hare et al. 2005) is
supported by the finding of differential gene expression profiles in the brains of domesticated and wild canines (Saetre
et al. 2004; Lindberg et al. 2005; Kukekova, Johnson, et al.
2011). Therefore, we focused on exploring the expression profile and biological function of the genes identified as being
targets of artificial selection.
We conducted a pair-wise population differentiation (FST)
analysis between CNs and the gray wolves and identified
MBE
Artificial Selection on Brain-Expressed Genes . doi:10.1093/molbev/mst088
1,878 SNPs within the top 5% of the distribution (FST > 0.50,
mean FST = 0.63), indicative of strong selection. These SNPs
mapped to 610 putative genes, which have no significance in
their gene ontology (GO) category enrichment, suggesting
potential effect of SNP ascertainment bias. However, some
GO categories, such as locomotory behavior ontology, were
enriched with 31 genes when the top 10% FST outliers were
considered (FDR correct P < 0.02, table 1).
We explored the potential biological function of these FSToutlier genes based on the expression level from 10 different
dog tissues. A brain-biased expression pattern was observed
with highest expression level and lowest ranking value in the
brain (fig. 1). However, a detailed neurological-function analysis of these FST-outlier candidate genes was limited by the
lack of expression profiles from different regions of the dog
brain and the failure to detect expression of many genes due
to the incompleteness of the dog expression array. Because
cross-species comparisons of orthologs between dog and
human share similar tissue enrichment and tissue selective
expression patterns (Briggs et al. 2011), with expression in
brain being even more similar (supplementary fig. S5,
Supplementary Material online), the expression pattern of
genes in the human brain can be used as a proxy for the
expression pattern of orthologs in the dog. We, therefore,
examined the expression pattern of the human orthologs
of these FST-outlier genes to get an approximate localization
of their functional brain regions (fig. 2). This analysis showed
that the normalized mean expression level of these FST-outlier
genes was highest in the prefrontal cortex (fig. 2A).
Correspondingly, the mean ranking value was lowest in the
prefrontal cortex (fig. 2B). The tissue expression bias of these
genes was also highest in prefrontal cortex, followed by 10
other brain-associated tissues (Student’s t-test and Mann–
Whitney test, P < 0.05) (fig. 2C); however, none of these
tissues remained significant after multiple test correction
(the adaptive step-up FDR test [Benjamini and Hochberg
2000]). For example, the significance of the expression bias
for the FST-outlier genes was 0.002 in the prefrontal cortex
(Mann–Whitney test), but the adjusted P value was 0.135.
The most differentiated SNP (chr11.43544043, FST = 0.9698)
between wolves and CNs located within gene FOCAD/
KIAA1797, which encodes a focal adhesion protein and function as tumor suppressor in gliomas (Brockschmidt et al.
2012). The human ortholog of this gene is expressed highest
in brain-related regions, for example, pineal, prefrontal cortex,
amygdale, pituitary, and hypothalamus, except that it is also
expressed highly in B lymphoblasts (data from http://biogps.
org).
In total, 615 FST-outlier candidate genes were identified
between GSs and CNs. Of these genes, 75 overlapped with
the sets of differentiated genes between wolf and CNs. Five
genes (EIF3L, SCFD2, C1QTNF7, PEMT, and MED9) contain the
most differentiated SNPs (chr10.29844427, chr13.48921799,
chr3.67712095, chr5.44958674, and chr5.44978999, FST = 1)
between CNs and GSs. PEMT has highest expression level in
the digestion system in humans, whereas EIF3L and MED9 are
expressed highest in the immune system. The other two
genes showed modest expression among a number of different tissues (data from http://biogps.org).
Although, overlapping with FST-outlier genes between
wolves and CNs, the 615 FST-outlier candidate genes between
GSs and CNs did not show higher expression level in the brain
based on the expression data from 10 dog tissues (supplementary fig. S6, Supplementary Material online), but these
genes did show a tendency for higher expression levels in
brain related tissues relative other tissues based on the
human expression data (fig. 3). No statistical significance in
this pattern of expression was detected, and the normalized
expression levels were much lower than that of genes differentiated between the gray wolf and the native dog (the mean
Table 1. Over-Represented GO Categories among the Top 10% Differentiated Genes between Gray Wolves and CNs.
Category
Description
Gene Number
P
Corrected
P Value
All
TdG
E[TdG]
347
224
10
42
31
5
20.58
13.29
0.59
8.5088E-6
9.2094E-6
1.4247E-4
7.1815E-3
7.1815E-3
4.6290E-2
583
8
107
476
65
5
18
49
34.58
0.47
6.35
28.24
6.1476E-7
3.5000E-5
5.4135E-5
1.1614E-4
2.3970E-3
2.2744E-2
2.8736E-2
4.5285E-2
177
19
269
14
543
27
8
33
6
54
10.50
1.13
15.96
0.83
32.21
5.6258E-6
6.2473E-6
5.8961E-5
8.5220E-5
1.3086E-4
7.1815E-3
7.1815E-3
2.8736E-2
3.6919E-2
4.6290E-2
Biological process
GO:0007610
GO:0007626
GO:0060122
Behavior
Locomotory behavior
Inner ear receptor stereocilium organization and biogenesis
Molecular function
GO:0005509
GO:0030675
GO:0019199
GO:0004713
Calcium ion binding
Rac GTPase activator activity
Transmembrane receptor protein kinase activity
protein tyrosine Kinase activity
Cellular component
GO:0005578
GO:0032421
GO:0031012
GO:0032420
GO:0044421
Proteinaceous extracellular matrix
Stereocilium bundle
extracellular matrix
Stereocilium
Extracellular region part
NOTE.—All, all genes apportioned to each category or a descendant category; TdG, genes containing top 10% differentiated SNPs; E[TdG], expected TdG number in each category.
1869
MBE
Li et al. . doi:10.1093/molbev/mst088
normalized expression level of the top 10 brain-associated
tissues was 1.016 and 1.055, respectively; Mann–Whitney
test P < 105).
As mentioned by Boyko et al. (2009), village dogs lived as
human commensals and were not subjected to the intense
artificial selection and breed-formation practices of breed
1.08
relative expression level
1.04
ranking value
1
0.96
0.92
0.88
0.84
FIG. 1. Relative expression level and ranking value in 10 dog tissues
of genes showing high population differentiation between wolves
and CNs.
dogs. Although, to our knowledge, there is no clear definition
of the indigenous degree of village dogs, CNs developed genetic signatures characteristic of their geographic location,
similar to that of ancient human populations. Because previous studies have found that native dogs in South China have
almost the highest levels of canine mtDNA and Y chromosome diversity, implying high levels of nuclear diversity as well
(Savolainen et al. 2002; Pang et al. 2009; Brown et al. 2011;
Ding et al. 2012), we expect that the genetic structure of CNs
should be most similar to that of the ancient dogs. To validate
the power of CNs as a comparison model, we compared our
candidates of differentiated regions between the CNs and GSs
with the selected regions specific to the GSs compiled by
Akey et al. (2010). Among the 20 regions identified by Akey
et al. with top selective signatures in GSs differentiating them
from other 9 breeds, 17 were found to contain highly differentiated SNPs identified in our analysis, with several SNPs in
each region. Additionally, the SNP associated with ear erectness (chr10.11440860) (Boyko et al. 2010; Vaysse et al. 2011)
was also identified among our top 5% FST outlier between
wolves and CNs (chr10.11440860, FST = 0.61). Therefore, as
expected, the CNs represents nearly the full genetic diversity
of dogs.
B 1
A 1.08
1.06
1.04
1 02.
1
0.98
0.96
0.96
0.92
0.88
log(P)
C
0
-0.5
-1
-1.5
-2
-2.5
-3
Prefrontal Cortex
Whole brain
Medulla Oblongata
Occipital Lobe
Cingulate Cortex
Amygdala
Hypothalamus
Parietal Lobe
Subthalamic Nucleus
pineal_night
Fetal brain
Thalamus
Pons
Cerebellum
Cerebellum Peduncles
Globus Pallidus
pineal_day
Caudate nucleus
Testis Germ Cell
Temporal Lobe
retina
Olfactory Bulb
Testis Intersitial
Superior Cervical Ganglion
Atrioventricular Node
Prefrontal Cortex
Medulla Oblongata
Whole brain
Occipital Lobe
Cingulate Cortex
Amygdala
Subthalamic Nucleus
Fetal brain
Globus Pallidus
Parietal Lobe
Temporal Lobe
Pons
Thalamus
Hypothalamus
Caudatenucleus
Cerebellum
Cerebellum Peduncles
pineal_night
pineal_day
retina
Olfactory Bulb
Testis Germ Cell
Spinal cord
Testis Intersitial
Superior Cervical Ganglion
0.84
t-test
Mann-Whitney test
FIG. 2. Expression pattern of human orthologs of one-to-one orthologs of dog genes showing high population differentiation between wolves and CNs.
(A) Top 25 tissues/cells with high expression level. The value in each tissue/cell was calculated as the averaged log 2 transformed expression values of
genes showing high population differentiation in the tissue/cell divided by the averaged log 2 transformed genome-wide expression in the tissue/cell.
(B) Top 25 tissues/cells with lowest ranking values. The expression levels of each gene in 84 tissues/cells were sorted from 1st to 84th. The ranking values
in each tissue/cell for genes were further averaged and then normalized by dividing by the genome-wide averaged ranking values. (C) The log 10
transformed statistical significances of the expression levels of genes showing high population differentiation higher than genome-wide expression level
in each tissue/cell by Student’s t-test and Mann–Whitney test.
1870
Artificial Selection on Brain-Expressed Genes . doi:10.1093/molbev/mst088
A 1.08
MBE
B 1
1.06
1.04
1.02
1
0.98
0.96
0.96
0.92
0.88
Prefrontal Cortex
Amygdala
Occipital Lobe
Whole brain
retina
Cerebellum
Medulla Oblongata
Hypothalamus
Fetal brain
Cerebellum Peduncles
Caudate nucleus
Uterus
Cingulate Cortex
Parietal Lobe
Thyroid
Placenta
pineal_night
Lung
Pons
Liver
Prostate
Spinal cord
Subthalamic Nucleus
Thalamus
Uterus Corpus
Amygdala
Prefrontal Cortex
Occipital Lobe
Fetal brain
Whole brain
Cingulate Cortex
Cerebellum
Medulla Oblongata
Caudate nucleus
Parietal Lobe
Cerebellum Peduncles
Subthalamic Nucleus
Globus Pallidus
Pons
Temporal Lobe
Thalamus
Hypothalamus
Uterus
pineal_night
Placenta
Fetal Thyroid
retina
Thyroid
Superior Cervical Ganglion
pineal_day
0.84
C 0.9
P
0.7
t-test
Mann-Whitney test
0.5
0.3
0.1
FIG. 3. Expression pattern of human one-to-one ortholog of dog genes showing high population differentiation between CNs and GSs. (A) Top 25
tissues/cells with high expression level. The value in each tissue/cell was calculated as the averaged log 2 transformed expression values of genes showing
high population differentiation in the tissue/cell divided by the averaged log 2 transformed genome-wide expression in the tissue/cell. (B) Top 25 tissues/
cells with lowest ranking values. The expression levels of each gene in 84 tissues/cells were sorted from 1st to 84th. The ranking values in each tissue/cell
for genes were further averaged and then normalized by dividing by the genome-wide averaged ranking values. (C) The statistical significances of the
expression levels of genes showing high population differentiation higher than genome-wide expression level in each tissue/cell by Student’s t-test and
Mann–Whitney test.
Candidate Regions under Artificial Selection in
Native Dogs
Individual high values for FST may reflect population structure
(Weir et al. 2005). In addition, we may have missed genes
under selection due to differences in the density of the SNP
markers. Consequently, we scanned the dog and wolf
genomes using a sliding window analysis to search for significantly differentiated segments (500 kb) with window-averaged FST values beyond the 99th quantile of the empirical
distribution (see Materials and Methods) (fig. 4). We found
94 regions that extended for at least 500 kb, which show significant high population differentiation between the gray
wolves and the CNs (P < 0.01), and these regions rarely overlap (13 regions) with regions of high differentiation between
the GSs and CNs (99 regions). Among the 94 identified regions, a fragment on chromosome 19 is largest and extends
over 1.6 Mb (P < 0.01). Within this fragment, the FST value
peak (chr19.6599631, FST = 0.84) was located about 6 kb
downstream from CCRN4L, a gene regulating the circadian
clock with maximal level in early evening, which has also been
implicated in lipid metabolism, adipogenesis, glucose homeostasis, inflammation, and osteogenesis (review see
[Stubblefield et al. 2012]).
Archeological and genetic evidence suggests that the dog
was domesticated 16,000 or more years ago (Clutton-Brock
1995; Sablin and Khlopachev 2002; Pang et al. 2009). Genomic
regions under recent strong selection should demonstrate
extended haplotype homozygosity (Sabeti et al. 2002, 2006).
Here, we defined core regions following a previous method
(Gabriel et al. 2002) and employed the parameters of EHH
and relative EHH (REHH) to search for regions under selection
in the CN population. This test is designed for detecting selection using genotyped SNP data, and is robust to ascertainment bias and choice of genetic markers used, and is powerful
for the detection of very recent selection (Sabeti et al. 2006).
Combined with the previously identified 94 highly differentiated regions, six of them showed significantly higher EHH and
REHH values 500 kb upstream or downstream of their core
regions (95%) (table 2). Among the six regions, four contain
only a single highly differentiated SNP located within a single
gene (including the one mentioned earlier, CCRN4L, which
affects circadian rhythms) (table 2). One region is located
in an intergenic region on chromosome 5 and has four
highly differentiated SNPs. Another contains the highly differentiated SNP located within two overlapping genes (CRYAB
and FDXACB1), with the major core haplotype of this region
(including SNPs at positions 24,267,730 and 24,381,098 on
chromosome5) being “CT” (cytosine and thymine) with a
frequency 85% in CNs but rare in wolves (7%). At 500 kb
downstream of the core region, EHH reached 0.58 (the 95%
1871
MBE
Li et al. . doi:10.1093/molbev/mst088
The FST distribution
1.0
A
0.999
0.9999
0.0
0.2
0.4
0.6
0.8
0.99
chr_position (between gray wolf and Chinese Native dog)
0.99
0.999
0.9999
0.0
0.2
0.4
0.6
0.8
1.0
B
chr_position (between German Shepherd and Chinese native dog)
FIG. 4. Genome-wide FST distribution. (A) The FST between wolves and CNs. (B) The FST between GSs and CNs. The 99th, 99.9th, and 99.99th percentile
lines are drawn.
Table 2. Summary of Highly Differentiated SNPs with Significant EHH and REHH Values.
SNP
chr2.9229099
chr4.37752306
chr5.5080128
chr5.5160480
chr5.5160498
chr5.5323685
chr5.24267730
chr18.18141067
chr19.6599631
Gene Symbol
ENSCAFG00000014021
BMPR-1A
Description
Bone morphogenetic protein receptor type-1A
CRYAB
FDXACB1
SRPK2
CCRN4L
crystallin, alpha B
ferredoxin-fold anticodon binding domain containing 1
SRSF protein kinase 2
CCR4 carbon catabolite repression 4-like
Core Region
9,229,099–9,919,076
37,752,306–38,013,294
5,080,128–5,323,685
24267730–24381098
18,131,350–18,210,935
6,599,631–7,020,181
NOTE.—Highly differentiated SNPs included in each core region with significant EHH and REHH value were listed, together with the genes of which these SNPs locate in 5,000 bp
upstream or downstream. Core region was defined by method in Sweep program.
in the frequency range of 0.85–0.90 is 0.44) and REHH reached
4.36 (the 99% in the frequency range of 0.85–0.90 is 3.47).
Although the pathological consequences of mutations in
CRYAB is unknown, elevated expression occurs in many neurological diseases (Hagemann et al. 2009), suggesting a role in
the nervous system.
Discussion
Behavior evolution is important for domestication. The primary key for early domestication is the transformation of
negative defensive reactions toward humans (the fearful–
aggressive response) to positive reactions, which means physiological changes in the systems that govern neurochemical
production (Trut 1999). Specifically, such physiological
changes has been characterized by fearful response and a
reduced locomotion in a novel environment and increased
glucocorticoids that regulate the fear response by mediating
neurotransmitter serotonin metabolism (Serpelle and Jagoe
1997; Trut 1999; Korte 2001; Trut et al. 2004). The behavior
1872
evolution is mostly attributable to brain evolution. For example, parallel brain-associated expression differences were identified in the domestication dogs from gray wolves (Saetre et al.
2004). Here, although no GO overrepresentation was observed among the top 5% FST-outlier genes between wolves
and CNs, the locomotory behavior ontology was enriched
with 31 genes when the top 10% FST outliers were considered
(table 1). More strikingly, the top 5% FST-outlier genes were
expressed most preferentially in the prefrontal cortex, which
is responsible for both emotional and rational aspects of decision making (Striedter 2005). In addition, we identified a
highly differentiated region between wolves and CNs
(P < 0.01) containing a serotonin receptor (HTR4). The serotonin system is particularly noteworthy as the density of
serotonin receptor 1A (HTR1A) was significantly lower in
the hypothalamus of tame foxes (Popova et al. 1997), whereas
serotonin receptor 2C (HTR2C) was consistently overexpressed in specific brain regions in tame foxes and tame
rats (Popova et al. 2010; Kukekova, Johnson, et al. 2011).
Artificial Selection on Brain-Expressed Genes . doi:10.1093/molbev/mst088
Further, the binding index of serotonin receptor 2A (HTR2A)
in cortical regions is highly related with dog impulsive aggression (Peremans et al. 2005). Previous findings have associated
specific patterns of gene expression and serotonin regulation
in the dog brain with behaviors likely selected for during the
domestication process (Popova et al. 1997; Saetre et al. 2004;
Peremans et al. 2005; Kukekova, Johnson, et al. 2011).
Our finding of highly differentiated genes between wolves
and dogs with brain-biased expression provided new genetic
evidence consistent with behavioral transformation at the
onset of domestication. We reasoned that it might be attributable to 1) artificial selection for specific behavioral traits in
domesticated dogs, such as tameness, temperament, cognitive ability or 2) relaxation of selective constraint due to a
population bottleneck. For the latter, however, it would be
difficult to generate a group of genes involved in a similar
function that are more divergent than the genomic background, although the influence of the latter could not be
absolutely excluded. Unlike the genes that are highly differentiated between wolves and CNs, divergent genes between
GSs and CNs did not show significantly notable brain-biased
expression pattern. During dog breed formation, intense selection pressures differed among breeds and were likely focused on other aspects of phenotype generation and
morphology diversification, such as body mass, coat color,
and hair (Wayne and Ostrander 2007).
However, there are several important caveats to our study.
First, both DNA sequence and the expression patterns evolve.
We used expression data of the human one-to-one orthologs
to infer and evaluate the expression pattern of dog genes in
different tissues, especially in the brain-related regions. Here,
we reasoned that the human and canine genes have high
correlation in their expression profiles, provided that the conservative one-to-one orthologs are often associated with conservative expression patterns under stabilizing selection
(Gilad et al. 2006). Genome-wide expression data from various canine brain-related regions would undoubtedly help to
confirm the correspondence of brain-biased expression pattern. Second, the genome-wide SNP data were ascertained
primarily from the dog genome assemblies and likely underrepresent the genetic diversity in the gray wolf genome.
Indeed, there are significant differences between the
frequency spectrum of the actual genotyped SNPs and the
calculated corrected frequency spectrum, suggesting the
effect of ascertainment bias (supplementary fig. S7,
Supplementary Material online). Although the sliding
window analysis provides a reasonable correction for single
SNP-based diversity estimates, multiple-sequenced genomes
are needed for the study of artificial selection and identification of the key genes responsible for the domestication.
Indeed, a very recent study-based whole-genome sequencing
data found evidence of artificial selection on a gene having a
function in nervous system development during dog domestication (Axelsson et al. 2013), which supports our finding
that artificial selection drives population differentiation of
brain-biased genes between wolves and dogs, rather than
SNP ascertainment bias.
MBE
Conclusion
We found that genes showing population differentiation between wolves and native dogs based on the population genetics data showed brain-biased expression. These results
indicate that during the primary transition from wolves to
ancient dogs, genes expressed in the brain evolved rapidly,
driven by artificial selection, consistent with the evolution of
dog-specific behaviors during domestication.
Materials and Methods
SNP Genotyping and Calling
We genotyped 21 CNs, 22 wild gray wolves, and 8 GSs using
Affymetrix v2 Canine arrays. The CNs are outbred village dogs
collected across geographical regions (eight provinces) of
China, most of which had some specialized morphology
(see supplementary fig. S8, Supplementary Material online).
The Canine array includes 25.5% of the SNPs found in the
boxer dog genome assembly, 11.4% of SNPs from a comparison between the boxer and poodle assemblies, 59.9% of SNPs
from a comparison of boxer to low coverage sequencing from
nine other breeds, and 3.2% of SNPs from a comparison of the
dog and wolf sequences. More than 127,000 SNP markers
were scanned with highest signal-to-noise intensity ratios.
Because of the consistent overcalling of heterozygous genotypes yielded by the BRLMM-P algorithm, we applied
multidimensional analysis for genotype intensity clustering
calling algorithm discussed in previous study (Boyko et al.
2010) for SNP calling analysis. We assessed the missing rate
for each SNP between dogs and gray wolves using the Fisher’s
exact test in PLINK (Purcell et al. 2007). In addition, SNPs that
failed in exact tests for Hardy-Weinberg equilibrium at
P < 0.001 or had more than 10% missing data within each
population were excluded using PLINK (Purcell et al. 2007).
Individuals with more than 10% missing genotype data were
removed as well. A pairwise similarity matrix among individual was calculated. From our recorded pedigree in the data
set, known parental relationships were used to calibrate identity-by-state scores (0.84), and values below this level were
judged as unrelated. Pairwise LD was summarized robustly
with the method described before (Boyko et al. 2010).
Population Structure Analysis
The population structure was constructed using Structure
software (Pritchard et al. 2000; Falush et al. 2003, 2007;
Hubisz et al. 2009), with K = 3 based on a pruned subset of
SNPs (23,816 SNPs) that were in linkage equilibrium with each
other.
Population Differentiation (FST) Analysis
FST values of each SNP were calculated as described previously
(Weir and Cockerman 1984; Akey et al. 2002) to evaluate the
degree of population differentiation between populations.
Negative values have no biological explanation and were arbitrarily set to 0. The FST values of SNPs on X chromosome
XÞ
, where A and X
were simply adjusted as X^ ¼ X ðA=
denote the averaged FST value among autosomes and X
1873
MBE
Li et al. . doi:10.1093/molbev/mst088
chromosome, and X is FST value of the SNP on X chromosome. FST-outlier SNPs were extracted by 95 percentile. SNP
was mapped to genes if it locates in 5,000 bp upstream or
downstream of this gene.
As the hitchhiking effect under strong artificial selection
over a short period of time would lead to a rapid allele frequency increase for linked loci, we also focused on specific
regions with high differentiation between populations. Here,
we performed a sliding-window analysis in which FST values
were averaged in 500-kb windows with a step of 100 kb. This
procedure may decrease the stochastic variation inherent in
single-locus estimates of population structure (Weir et al.
2005). We calculated the significant threshold for 0.99,
0.999, and 0.9999 confidence by bootstrap resampling 1,000
times (sampling size = 2,000). The analyses were done by Perl
and R program.
EHH/REHH Analysis
The haplotype of each chromosome was inferred by
fastPHASE (Scheet and Stephens 2006). EHH and REHH
values upstream and downstream 500 kb of each core
region defined by method in Gabriel et al. (2002) were calculated for each core haplotype using SWEEP software (http://
www.broadinstitute.org/mpg/sweep/) (Sabeti et al. 2002). The
genome-wide data of each population were used as an empirical distribution to calculate the statistical P value.
GO Analysis
We used the Biological Networks Gene Ontology tool, BiNGO
2.44 (Maere et al. 2005), a Java-based tool implemented as a
plugin for Cytoscape (Shannon et al. 2003), to determine
which GO categories are statistically over-represented in
each set of selected genes. All the GO term accession
number for each gene through canine genome was downloaded from Ensembl (www.ensembl.org, version 62), which
included the whole annotation and was considered as reference set. Any predominant functional themes of interested
gene set on the GO hierarchy were shown if the P value
obtained from Benjamini and Hochberg FDR-corrected
Hypergeometric test is less than 0.05.
Analysis on the Expression of Dog Genes and Their
Human Orthologs
The genomic expression data from 10 different dog tissues
(Affymetrix Canine Version 2.0 array) were downloaded from
Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/
geo) with accession number GSE20113. According to the
chip annotation, probes that might hybridize to multiple
loci across the genome were discarded. The expression
value of each gene was averaged across four biological replicates. The one-to-one human orthologs of dog genes were
obtained using the BioMart tool in the Ensembl (http://
ensembl.org). Human gene expression data (Human U133A
Gene Atlas) in 84 tissues or cells were downloaded from
BioGPS (http://biogps.gnf.org/#goto=welcome) (Wu et al.
2009) with GEO code GSE1133. According to the chip annotation, probes that might hybridize to multiple loci across the
1874
genome were discarded. Expression values were log 2 transformed. For genes having more than one probe, the expression in each tissue was averaged. The expression similarity of
orthologous dog and human genes was estimated using
Pearson and Spearman methods in each matched tissue in
R program.
To avoid bias expressed in different tissues, the expression
levels of differentiated genes were normalized by dividing by
the average whole-genome expression levels in each tissue.
Further, to test whether the tissue-biased expression pattern
was affected by several genes with very high expression profiles, we estimated a rank value. Specifically, we sorted the
expression levels of each gene in different tissues from highest
to lowest. Then, the ranks of selected gene set were averaged
within each tissue, so as to the whole-genome genes. Similarly,
the average ranking value for selected genes was normalized
by dividing by whole-genome background.
In addition, we also assessed the statistical significance of
expression level of selected genes higher than genome-wide
expression level in each tissue by Student’s t-test and Mann–
Whitney test.
Supplementary Material
Supplementary figures S1–S7 are available at Molecular
Biology and Evolution online (http://www.mbe.oxfordjour
nals.org/).
Acknowledgments
The authors thank Prof. David Irwin for revising the manuscript. This work was supported by grants from the National
Basic Research Program of China (973 Program, Grant
2007CB815702), National Natural Science Foundation of
China (31061160189), and the National Science Foundation
(US) to R.K.W. (for the wolf SNP genotyping).
References
American Kennel Club. 1998. The complete dog book. New York:
Howell Book House.
Akey JM, Ruhe AL, Akey DT, Wong AK, Connelly CF, Madeoy J, Nicholas
TJ, Neff MW. 2010. Tracking footprints of artificial selection in the
dog genome. Proc Natl Acad Sci U S A. 107:1160–1165.
Akey JM, Zhang G, Zhang K, Jin L, Shriver MD. 2002. Interrogating a
high-density SNP map for signatures of natural selection. Genome
Res. 12:1805–1814.
Auton A, Bryc K, Boyko AR, Lohmueller KE, Novermbre J. 2009. Global
distribution of genomic diversity underscores rich complex history
of continental human populations. Genome Res. 19:795–803.
Axelsson E, Ratnakumar A, Arendt M-L, Maqbool K, Webster MT,
Perloski M, Liberg O, Arnemo JM, Hedhammar A, Lindblad-Toh K.
2013. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature 495:360–364.
Belyaev DK. 1969. Domestication of animals. Science 5:47–52.
Benjamini Y, Hochberg Y. 2000. On the adaptive control of the false
discovery rate in multiple testing with independent statistics. J
Behav Educ Stat. 25:60–83.
Boyko AR, Boyko RH, Boyko CM, et al. (15 co-authors). 2009. Complex
population structure in African village dogs and its implications for
inferring dog domestication history. Proc Natl Acad Sci U S A. 106:
13903–13908.
Boyko AR, Quignon P, Li L, Schoenebeck JJ, Degenhardt JD, Lohmueller
KE, Zhao K, Brisbin A, Parker HG. 2010. A simple genetic architecture
underlies morphological variation in dogs. PLoS Biol. 8:e1000451.
Artificial Selection on Brain-Expressed Genes . doi:10.1093/molbev/mst088
Briggs J, Paoloni M, Chen QR, Wen X, Khan J, Khanna C. 2011. A compendium of canine normal tissue gene expression. PLoS One 6:
e17107.
Brockschmidt A, Trost D, Peterziel H, et al. (22 co-authors). 2012.
KIAA1797/FOCAD encodes a novel focal adhesion protein with
tumour suppressor function in gliomas. Brain 135:1027–1041.
Brown SK, Pedersen NC, Jafarishorijeh S, Bannasch DL, Ahrens KD, Wu J,
Okon M, Sacks BN. 2011. Phylogenetic distinctiveness of Middle
Eastern and Southeast Asian village dog Y chromosomes illuminates
dog origins. PLoS One 6:e28496.
Clutton-Brock J. 1995. Origins of the dog: domestication and early history. Cambridge: Cambridge University Press.
Ding ZL, Oskarsson M, Ardalan A, Angleby H, Dahlgren LG, Tepeli C,
Kirkness E, Savolainen P, Zhang Y-P. 2012. Origins of domestic dog in
Southern East Asia is supported by analysis of Y-chromosome DNA.
Heredity 108:507–514.
Falush D, Stephens M, Pritchard JK. 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele
frequencies. Genetics 164:1567–1587.
Falush D, Stephens M, Pritchard JK. 2007. Inference of population structure using multilocus genotype data: dominant markers and null
alleles. Mol Ecol Notes. 7:574–578.
Fox M. 1971. Behavior of wolves, dogs and related canids. New York:
Harper-Row.
Gabriel SB, Schaffner SF, Nguyen H, et al. (18 co-authors). 2002. The
structure of haplotype blocks in the human genome. Science 296:
2225–2229.
Gilad Y, Oshlack A, Rifkin SA. 2006. Natural selection on gene expression.
Trends Genet. 22:456–461.
Hagemann TL, Boelens WC, Wawrousek EF, Messing A. 2009.
Suppression of GFAP toxicity by alpha-B-crystallin in mouse
models of Alexander disease. Hum Mol Genet. 18:1190–1199.
Hare B, Brown M, Williamson C, Tomasello M. 2002. The domestication
of social cognition in dogs. Science 298:1634–1636.
Hare B, Plyusnina I, Ignacio N, Schepina O, Stepika A, Wrangham R, Trut
L. 2005. Social cognitive evolution in captive foxes is a correlated byproduct of experimental domestication. Curr Biol. 15:226–230.
Hare B, Tomasello M. 2005. Human-like social skills in dogs? Trends Cogn
Sci. 9:439–444.
Hubisz MJ, Falush D, Stephens M, Pritchard JK. 2009. Inferring weak
population structure with the assistance of sample group information. Mol Ecol Notes. 9:1322–1332.
Korte SM. 2001. Corticosteroids in relation to fear, anxiety and psychopathology. Neurosci Biobehav Rev. 25:117–142.
Kukekova A, Temnykh S, Johnson J, Trut L, Acland G. 2011. Genetics of
behavior in the silver fox. Mamm Genome. 23:164–177.
Kukekova AV, Johnson JL, Teiling C, et al. (17 co-authors). 2011.
Sequence comparison of prefrontal cortical brain transcriptome
from a tame and an aggressive silver fox (Vulpes vulpes). BMC
Genomics 12:482.
Kukekova AV, Trut LN, Chase K, et al. (11 co-authors). 2008.
Measurement of segregating behaviors in experimental silver fox
pedigrees. Behav Genet. 38:185–194.
Kukekova AV, Trut LN, Chase K, et al. (14 co-authors). 2011. Mapping
loci for fox domestication: deconstruction/reconstruction of a behavioral phenotype. Behav Genet. 41:593–606.
Lindberg J, Bjornerfeldt S, Saetre P, Svartberg K, Seehuus B, Bakken M,
Vila C, Jazin E. 2005. Selection for tameness has changed brain gene
expression in silver foxes. Curr Biol. 15:915–916.
Maere S, Heymans K, Kuiper M. 2005. BiNGO: a Cytoscape plugin to
assess overrepresentation of gene ontology categories in biological
networks. Bioinformatics 21:3448–3449.
Masuda K, Hashizume C, Kikusui T, Takeuchi Y, Mori Y. 2004. Breed
differences in genotype and allele frequency of catechol o-methyltransferase gene polymorphic regions in dogs. J Vet Med Sci. 66:
183–187.
Miklósi A, Kubinyi E, Topál J, Gácsi M, Virányi Z, Csányi V. 2003. A simple
reason for a big difference: wolves do not look back at humans, but
dogs do. Curr Biol. 13:763–766.
MBE
Ogata N, Hashizume C, Momozawa Y, Masuda K, Kikusui T, Takeuchi Y,
Mori Y. 2006. Polymorphisms in the canine glutamate transporter-1
gene: identification and variation among five dog breeds. J Vet Med
Sci. 68:157–159.
Ostrander EA, Wayne RK. 2005. The canine genome. Genome Res. 15:
1706–1716.
Pang J-F, Kluetsch C, Zou X-J, et al. (14 co-authors). 2009. mtDNA data
indicate a single origin for dogs south of Yangtze river, less than
16,300 years ago, from numerous wolves. Mol Biol Evol. 26:
2849–2864.
Peremans K, Audenaert K, Hoybergs Y, Otte A, Goethals I, Gielen I,
Blankaert P, Vervaet M, van Heeringen C, Dierckx R. 2005. The
effect of citalopram hydrobromide on 5-HT2A receptors in the impulsive-aggressive dog, as measured with 123I-5-I-R91150 SPECT. Eur
J Nucl Med Mol Imaging. 32:708–716.
Popova NK, Kulikov AV, Avgustinovich DF, Voitenko NN, Trut LN. 1997.
Effect of domestication of the silver fox on the main enzymes of
serotonin metabolism and serotonin receptors. Genetika 33:
370–374.
Popova NK, Naumenko VS, Kozhemyakina RV, Plyusnina IZ. 2010.
Functional characteristics of serotonin 5-HT2A and 5-HT2C receptors in the brain and the expression of the 5-HT2A and 5-HT2C
receptor genes in aggressive and non-aggressive rats. Neurosci Behav
Physiol. 40:357–361.
Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population
structure using multilocus genotype data. Genetics 155:945–959.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D,
Maller J, Sklar P, De Bakker PIW, Daly MJ. 2007. PLINK: a tool set for
whole-genome association and population-based linkage analyses.
Am J Hum Genet. 81:559–575.
Sabeti PC, Reich DE, Higgins JM, et al. (17 co-authors). 2002. Detecting
recent positive selection in the human genome from haplotype
structure. Nature 419:832–837.
Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, Shamovsky O,
Palma A, Mikkelsen TS, Altshuler D, Lander ES. 2006. Positive natural
selection in the human lineage. Science 312:1614–1620.
Sablin MV, Khlopachev GA. 2002. The earliest ice age dogs: evidence
from Eliseevichi 1. Curr Anthropol. 43:795–798.
Saetre P, Lindberg J, Leonard JA, Olsson K, Pettersson U, Ellegren H,
Bergström TF, Vil C, Jazin E. 2004. From wild wolf to domestic
dog: gene expression changes in the brain. Mol Brain Res. 126:
198–206.
Savolainen P, Zhang YP, Luo J, Lundeberg J, Leitner T. 2002. Genetic
evidence for an East Asian origin of domestic dogs. Science 298:
1610–1613.
Scheet P, Stephens M. 2006. A fast and flexible statistical model for largescale population genotype data: applications to inferring missing
genotypes and haplotypic phase. Am J Hum Genet. 78:629–644.
Scott JP. 1962. Critical periods in behavioral development. Science 138:
949–958.
Serpelle J, Jagoe JA. 1997. Early experience and the development of
behavior. In: Serpell J, editor. The domestic dog: its evolution, behavior and interactions with people. Cambridge: Cambridge
University Press. p. 79–102.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N,
Schwikowski B, Ideker T. 2003. Cytoscape: a software environment
for integrated models of biomolecular interaction networks.
Genome Res. 13:2498–2504.
Striedter GF. 2005. Principles of brain evolution. Sunderland (MA):
Sinauer Associates.
Stubblefield JJ, Terrien J, Green CB. 2012. Nocturnin: at the crossroads of
clocks and metabolism. Trends Endocrinol Metab. 23:326–333.
Takeuchi Y, Hashizume C, Chon EM, Momozawa Y, Masuda K, Kikusui
T, Mori Y. 2005. Canine tyrosine hydroxylase (th) gene and dopamine beta-hydroxylase (dbh) gene: their sequences, genetic polymorphisms, and diversities among five different dog breeds. J Vet
Med Sci. 67:861–867.
Tomasello M, Kaminski J. 2009. Like infant, like dog. Science 325:
1213–1214.
1875
Li et al. . doi:10.1093/molbev/mst088
Topál J, Gergely G, Erdöhegyi Á, Csibra G, Miklósi Á. 2009. Differential
sensitivity to human communication in dogs, wolves, and human
infants. Science 325:1269–1272.
Trut L, Oskina I, Kharlamova A. 2009. Animal evolution during
domestication: the domesticated fox as a model. Bioessays 31:
349–360.
Trut LN. 1999. Early canid domestication: the farm-fox experiment. Am
Sci. 87:160–169.
Trut LN. 2001. Experimental studies in early canid domestication. In:
Ruvinsky A, Sampson J, editors. The genetics of the dog. New York:
CABI Publishing. p. 15–41.
Trut LN, Plyusnina IZ, Oskina IN. 2004. An experiment on fox domestication and debatable issues of evolution of the dog. Russ J Genet.
40:644–655.
Vaysse A, Ratnakumar A, Derrien T, et al. (22 co-authors). 2011.
Identification of genomic regions associated with phenotypic
1876
MBE
variation between dog breeds using selection mapping. PLoS
Genet. 7:e1002316.
vonHoldt BM, Pollinger JP, Lohmueller KE, et al. (36 co-authors). 2010.
Genome-wide SNP and haplotype analyses reveal a rich history
underlying dog domestication. Nature 464:898–902.
Wayne RK, Ostrander EA. 2007. Lessons learned from the dog genome.
Trends Genet. 23:557–567.
Weir B, Cockerman C. 1984. Estimating F-statistics for the analysis of
population structure. Evolution 38:1358–1370.
Weir BS, Cardon LR, Anderson AD, Nielsen DM, Hill WG. 2005.
Measures of human population structure show heterogeneity
among genomic regions. Genome Res. 15:1468–1476.
Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL,
Haase J, Janes J, Huss JW 3rd. 2009. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10:R130.