Lack of association between winter coat colour and genetic population ...

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Biological Journal of the Linnean Society, 2014, 111, 761–776. With 4 figures

Lack of association between winter coat colour and genetic population structure in the Japanese hare, Lepus brachyurus (Lagomorpha: Leporidae) MITSUO NUNOME1*, GOHTA KINOSHITA2, MORIHIKO TOMOZAWA3, HARUMI TORII4, RIKYU MATSUKI5, FUMIO YAMADA6, YOICHI MATSUDA1 and HITOSHI SUZUKI2 1

Laboratory of Animal Genetics, Department of Applied Molecular Biosciences, Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan 2 Laboratory of Ecology and Genetics, Faculty of Environmental Earth Science, Hokkaido University, Kita-ku, Sapporo 060-0810, Japan 3 Department of Biology, Keio University, Yokohama 223-8521, Japan 4 Center for Natural Environment Education, Nara University of Education, Takabatake-cho, Nara 630-8528, Japan 5 Environmental Science Research Laboratory, Central Research Institute of Electric Power Industry, 1646 Abiko, Chiba 270-1194, Japan 6 Forestry and Forest Products Research Institute, PO Box 16, Tsukuba Norin, Ibaraki 305-8687, Japan Received 3 October 2013; revised 11 December 2013; accepted for publication 11 December 2013

Seasonal changes in fur colour in some mammalian species have long attracted the attention of biologists, especially in species showing population variation in these seasonal changes. Genetic differences among populations that show differences in seasonal changes in coat colour have been poorly studied. Because the Japanese hare (Lepus brachyurus) has two allopatric morphotypes that show remarkably different coat colours in winter, we examined the population genetic structure of the species using partial sequences of the SRY gene and six autosomal genes: three coat colour-related genes (ASIP, TYR, and MC1R) and three putatively neutral genes (TSHB, APOB, and SPTBN1). The phylogenetic tree of SRY sequences exhibited two distinct lineages that diverged approsimately 1 Mya. Although the two lineages exhibited a clear allopatric distribution, it was not consistent with the distribution of morphotypes. In addition, six nuclear gene sequences failed to reveal genetic differences between morphotypes. Population network trees for 11 expedient populations divided the populations into four groups. Genetic structure analysis revealed an admixture of four genetic clusters in L. brachyurus, two of which showed large genetic differences. Our results suggest ancient vicariance in L. brachyurus, and we detected no genetic differences between the two morphotypes. © 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 111, 761–776.

ADDITIONAL KEYWORDS: adaptation – natural selection – phylogeography – seasonal change.

INTRODUCTION The coat colours of some mammalian and avian species that live in middle to high latitudinal areas, such as grouses, foxes, martens, weasels, hares, and

*Corresponding author. E-mail: [email protected]

hamsters, change seasonally, from dark colours in summer to light colours in winter. Such seasonal changes in coat colour have attracted the interest of numerous researchers for many years (Grange, 1932; Hewson, 1958; Rust, 1965; Flux, 1970; Watson, 1973; Walsberg, 1991; Russell & Tumlison, 1996; Stoner, Bininda-Emonds & Caro, 2003; Scherbarth & Steinlechner, 2010). Morphological studies have been

© 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 111, 761–776

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performed in hares aiming to understand seasonal variation in fur colour and to identify environmental cues that induce the change (Hewson, 1958; Otsu, 1967; Flux, 1970; Kuderling et al., 1984). These studies have indicated that day length is a definite signal for changes in coat colour, and that temperature is another important cue for the timing of these changes (Mills et al., 2013). Hare species also change reproductive activities in response to day length. In addition, some hare species show intraspecific variation in the seasonality of coat colour. In such species, animals in northern areas show seasonal coat colour changes, whereas those in southern areas retain dark pelages year-round. Intraspecific variation in winter coat colour is considered to originate from genetic differences among populations. This hypothesis is supported by various observations as described below. However, the genes that regulate seasonal changes in coat colour have not yet been identified in hares or other animals. Although variation in the melanocortin 1 receptor (MC1R) gene is known to be associated with variation in winter coat colour in the Arctic fox (Alopex lagopus) (Vage et al., 2005), the gene is not involved in the regulation of seasonal changes in coat colour in the Japanese marten (Martes melampus) (Hosoda et al., 2005) or the willow grouse (Skoglund & Hoglund, 2010). Moreover, few studies have examined genetic differences between populations that show differences in seasonal moult (Meinke, Kapel & Arctander, 2001; Sato, Yasuda & Hosoda, 2009). In these studies, no remarkable genetic differences were found between the two morphotypes. In Lepus species, which are well known to show seasonal moult, the genetic basis of morphotypic differences has not been well studied, although numerous morphological, physiological, and phylogeographical studies have been conducted (Grange, 1932; Hewson, 1958; Watson, 1963; Flux, 1970; Kuderling et al., 1984; Iason & Ebling, 1989; Koutsogiannouli et al., 2012). Thus, uncovering the genetic basis of the two morphotypes in hare species is crucial not only for identifying the gene(s) involved in seasonal moult, but also for understanding how differences in winter coat colour are related to population genetic structure. The Japanese hare Lepus brachyurus Temminck, 1845 is an appropriate model in which to examine the genetic basis of winter colour morphotypes because of its restricted geographical distribution and genetic status. Lepus brachyurus is endemic to the Japanese archipelago and is distributed throughout three of the main islands (Honshu, Shikoku, and Kyushu), as well as in peripheral islands, including Sado Island and the Oki Islands (Fig. 1). The species has a much smaller range than other Lepus species that are found on the continent. In addition, although several

hare species are known to have experienced genetic introgression from other species (Alves et al., 2008; Liu et al., 2011; Kinoshita et al., 2012), mitochondrial phylogenetic studies have confirmed the genetic purity of L. brachyurus (Yamada, Takaki & Suzuki, 2002; Wu et al., 2005; Nunome et al., 2010). Thus, compared to other hare species on the continent, the examination of genetic population structure is relatively simple in almost all populations of L. brachyurus. Lepus brachyurus is divided principally into northern and southern groups based on winter coat colour (Fig. 1). The coat colours of hares in northern areas change from brown in summer to white in winter, whereas hares in southern areas have brown pelages year-round (Imaizumi, 1960; Hirata, 1999). This intraspecific difference in winter pelage has led to the consideration of the northern and southern groups as subspecies; white winter pelage animals are designated Lepus brachyurus angustidens Hollister, 1912, and pigmented winter pelage animals are classified as Lepus brachyurus brachyurus Temminck, 1845, excluding two subspecies on Sado Island and the Oki Islands. Hereafter, for descriptive purposes, hares with white and pigmented winter coats on the main islands are referred to as ‘Lba’ and ‘Lbb’, respectively. When Lba is bred in areas where Lbb is dominant, Lba changes its coat colour from brown in summer to white in winter (Hirata, 1999), suggesting that the seasonal change in coat colour of Lba is not triggered by environmental cues but, instead, is determined by genetic factor(s). The distribution ranges of Lba and Lbb do not display simple north–south parapatry but are strongly related to annual snowfall in the archipelago. On Honshu Island, the amount of annual snowfall differs distinctly between the heavy-snow region on the Japan Sea side and the low-snow region on the Pacific side as a result of the presence of high mountain chains that run north–south through the middle of the island. Winter coat colour may provide cryptic coloration in snowy environments. That is, clear geographical differences in annual snowfall within the archipelago strongly affect the ranges of the two morphotypes, resulting into genetic divergence between them. A previous phylogeographical study of mitochondrial DNA variation in L. brachyurus found two distinct lineages. However, the geographical distributions of the lineages were not consistent with the distributions of the morphotypes, and genetic differences between the morphotypes remain to be examined (Fig. 2) (Nunome et al., 2010). In the present study, we examined genetic variation in L. brachyurus using multiple nuclear DNA sequences to clarify genetic differences between the two morphotypes. First, a phylogenetic tree of L. brachyurus was constructed using the


POPULATION GENETIC STRUCTURE IN JAPANESE HARE

763

50°

Tohoku 40°

1 5

30°

4

pop1 2

3

20°

Sado isl.

pop2

10°N

10 80°E

90°

100°

110°

120°

130°

140°

pop3 Oki isls.

pop9

23 22

40 39

pop6

Chugoku 38

pop8

28

pop7 pop11 46 45

37

36

48 47 49

35 41 42 44 43

pop10

34 29 33 30 32 31

8 9 7 19 17 21 6 18 14 16 20 13 12 11 15 pop5 27 26 24 25

pop4

Kanto

Chubu

Honshu Kinki

Shikoku 50

Kyushu

Figure 1. Map of sampling locations. The darkly shaded area indicates the distribution area of animals that have white pelages in winter. Samples were divided into 11 populations according to their geographical locations and phylogenetic trees of CYT B and SRY.

Y-linked, sex-determining gene SRY to confirm the two distinct lineages of the species that were exhibited in cytochrome b gene (CYT B) sequences in a previous study (Nunome et al., 2010). Second, to determine whether any of the three coat colour genes is the basis of the two morphotypes of L. brachyurus, we assessed genetic variation in six nuclear DNA loci among 11 expedient populations. Three of the six markers were thyrotropin beta subunit (TSHB), beta spectrin (SPTBN1), and apolipoprotein B 100 (APOB), which have been used widely as neutral markers in phylogenetic analyses of mammals, including Lepus species (TSHB and SPTBN1: Matthee et al., 2004; Hoofer et al., 2008; APOB: Amrine-Madsen et al., 2003). The other three markers were agouti signalling protein (ASIP), Tyrosinase (TYR) and MC1R, which are known to be responsible for light and dark coatcolour variations in mammalian species, including lagomorphs (Chen, Duhl & Barsh, 1996; Aigner et al., 2000; Miltenberger et al., 2002; Nachman, Hoekstra & D’Agostino, 2003; Kambe et al., 2011). Third, we conducted population genetic structure analyses with the six autosomal loci to evaluate genetic differences between the two morphotypes.

MATERIAL AND METHODS SAMPLING AND DNA EXTRACTION Total genomic DNA was extracted from skin, liver (sampled from road-killed or hunted animals) and faeces using a traditional phenol/chloroform protocol (Sambrook & Russell, 2001). Tissue samples of L. brachyurus were obtained from 50 localities across the distribution area (Fig. 1, Table 1). Partial sequences of genes were amplified for one Y-linked gene (SRY) and six autosomal genes (ASIP, MC1R, TYR, APOB, SPTBN1, and TSHB) using the polymerase chain reaction (PCR) method. Amplifications were performed in a total volume of 20 μL containing approximately 10 ng of genomic DNA, 10 pmol of each primer and 10 μL of AmpliTaq Gold® 360 Master Mix (Life Technologies). Cycling conditions for PCR were: initial denaturation at 95 °C for 10 min, followed by 35 cycles at 95 °C for 30 s, 52 °C–62 °C for 30 s, and 70 °C for 30 s. The primers and exact annealing temperature for each locus are shown in the Supporting information (Table S1). Double-stranded PCR products were purified using the 20% polyethylene glycol/ 2.5 M NaCl precipitation method. The PCR products


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95/97

1

lineage N

63/62

1_AKT251 3_AKT238 3_AKT242 6_GNM214 6_GNM225 7_GNM216 8_NIG218 18_TCG101 18_TCG102

3

15_IBRK83 17_IBRK82 16_IBRK81 12_IBRK71

8

EF437189LU EF437190LU

outgroup

62/64

39

7

18 12

38

lineage S

22_TYM128 23_TYM129 24_SZOK04 32_NARA57 35_AWJ_18 36_HRSM25 36_HRSM27 37_HRSM32 95/95 38_SHMN39 39_OKNS43 41_KCH136 46_FKOK85 47_OHIT88

6

23 22

17 16 15

24

36 37

33

35 32

30

41

46 47

30_MIE_70 33_NARA59 33_NARA84 38_SHMN36

0.001

Figure 2. Phylogenetic tree of partial DNA sequences of SRY. Codes at the tips of the tree represent the numbers of sampling locations, followed by the sample codes. Values on branches indicate bootstrap values for the Neighbour-joining (left) and maximum-likelihood (right) methods. The geographical border between the two lineages in the tree is indicated by a dotted line on the map of sampling localities. The grey dotted line on the map indicates the geographical border between two clades of the mitochondrial CYT B gene (Nunome et al., 2010).

were sequenced from both directions with a BigDye Terminator cycle sequencing kit, version 3.1 (Life Technologies) and analyzed using an ABI 3100 automated sequencer (Applied Biosystems). DNA sequences were aligned visually using PROSEQ, version 2.9.1 (Filatov, 2001).

PHYLOGENETIC

ANALYSIS AND DIVERGENCE TIME

ESTIMATION FOR

SRY

A Neighbour-joining (NJ) tree (Saitou & Nei, 1987) was constructed using PAUP 4.0b10 (Swofford, 2002). As a distance model for the NJ tree, the Hasegawa– Kishino–Yano (HKY) distance model was chosen based on a hierarchical likelihood ratio test implemented with MODELTEST, version 3.7 (Posada & Crandall, 1998). Published SRY sequences (DDBJ/EMBL/ GenBank) for the European hare (L. europaeus; EF437189, EF437190) were used as an outgroup. One thousand bootstrap replicates were performed to assess the robustness of each node. A maximumlikelihood tree was reconstructed using PHYML, version 3.0 (Guindon & Gascuel, 2003; Guindon et al., 2005). Node robustness in the tree was evaluated using

100 bootstrap replicates. Divergence times between Japanese hare lineages were inferred using BEAST, version 1.4.8 (Drummond et al., 2005). SRY sequences for rabbit (Oryctolagus cuniculus; AY785433) were included in the dataset to set a calibration point for estimating divergence times. The divergences between O. cuniculus and the Lepus group, and between L. europaeus and L. brachyurus, were set at 11 Mya and 3.5 Mya, respectively, based on estimates from previous molecular phylogenetic studies (Matthee et al., 2004; Wu et al., 2005). The HKY with the SRD06 model was used as a substitution model. Analyses were run for 10 million generations, with sampling conducted every 1000 generations following one million burn-in generations. Convergence was assessed using TRACER, version 1.5 (Rambaut & Drummond, 2007).

SEQUENCE

ANALYSIS FOR SIX AUTOSOMAL LOCI

The minimum numbers of recombinations (Rms; Hudson & Kaplan, 1985) in the sequences of six autosomal genes were examined using DNASP, version 5.0.0 (Librado & Rozas, 2009), and the longest



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Table 1. Details of samples used in the present study Population number 1

2

3

4

Sample name

Locality number

District

Prefecture

City (Gun)

Latitude

Longitude

LbAKT251 LbAKT235 LbAKT236 LbAKT237 LbAKT238 LbAKT239 LbAKT240 LbAKT241 LbAKT242 LbAKT243 LbIWT205 LbIWT206 LbGNM210 LbGNM211 LbGNM212 LbGNM214 LbGNM225 LbGNM215 LbGNM216 LbGNM217 LbNIG218 LbNIG219 LbNIG220 LbSAD189 LbSAD190 LbSAD191 LbIBRK55 LbIBRK77 LbIBRK71 LbIBRK73 LbIBRK75 LbIBRK76 LbIBRK78 LbIBRK83 LbIBRK81 LbIBRK82 LbTCG100 LbTCG101 LbTCG102 LbTCG103 LbTCG104 LbTCG106 LbTCG107 LbTCG209 LbNGN221 LbNGN222 LbNGN223 LbNGN224 LbNGN226 LbNGN227 LbTYM128 LbTYM129

1 2 2 3 3 3 3 3 3 3 4 5 6 6 6 6 6 7 7 7 8 8 9 10 10 10 20 20 20 20 21 21 22 23 11 11 12 13 14 14 15 15 16 17 18 18 18 18 18 18 18 19

Tohoku Tohoku Tohoku Tohoku Tohoku Tohoku Tohoku Tohoku Tohoku Tohoku Tohoku Tohoku Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Chubu Chubu Chubu Sado isl. Sado isl. Sado isl. Chubu Chubu Chubu Chubu Chubu Chubu Chubu Chubu Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto Kanto

Akita Akita Akita Akita Akita Akita Akita Akita Akita Akita Iwate Iwate Gunma Gunma Gunma Gunma Gunma Gunma Gunma Gunma Niigata Niigata Niigata Niigata Niigata Niigata Nagano Nagano Nagano Nagano Nagano Nagano Toyama Toyama Ibaraki Ibaraki Ibaraki Ibaraki Ibaraki Ibaraki Ibaraki Ibaraki Ibaraki Ibaraki Tochigi Tochigi Tochigi Tochigi Tochigi Tochigi Tochigi Tochigi

Kitaakita Nikaho Nikaho Yuzawa Yuzawa Yuzawa Yuzawa Yuzawa Yuzawa Yuzawa Shimohei Morioka Azuma Azuma Azuma Azuma Azuma Tone Tone Tone Tokamachi MinamiUonuma MinamiUonuma Sado Sado Sado KitaSaku KitaSaku KitaSaku KitaSaku Shimotakai Shimotakai Toyama Oyabe Tsukuba Tsukuba Bando – Kasama Kasama Kasumigaura Kasumigaura Mito Takahagi Nikko Nikko Nikko Nikko Nikko Nikko Nikko Nasushiobara

140.37 139.91 139.91 140.49 140.49 140.49 140.49 140.49 140.49 140.49 141.87 141.19 138.50 138.50 138.50 138.50 138.50 139.05 139.05 139.05 138.76 138.82 138.82 138.37 138.37 138.37 138.55 138.55 138.55 138.55 138.43 138.43 137.21 137.00 140.07 140.07 139.89 140.28 140.24 140.24 140.32 140.32 140.45 140.70 139.60 139.60 139.60 139.60 139.60 139.60 139.60 140.05

40.23 39.20 39.20 39.16 39.16 39.16 39.16 39.16 39.16 39.16 39.69 39.67 36.50 36.50 36.50 36.50 36.50 36.73 36.73 36.73 37.13 36.95 36.95 38.02 38.02 38.02 36.33 36.33 36.33 36.33 36.81 36.81 36.70 36.68 36.04 36.04 36.04 36.25 36.38 36.38 36.08 36.08 36.37 36.72 36.74 36.74 36.74 36.74 36.74 36.74 36.74 36.96


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Table 1. Continued Population number 5

6

7

8

9

10

11

Sample name

Locality number

District

Prefecture

City (Gun)

Latitude

Longitude

LbSZOK02 LbSZOK04 LbSZOK07 LbSZOK09 LbYMN130 LbYMN131 LbYMN134 LbMIE_68 LbMIE_69 LbMIE_70 LbNARA12 LbNARA57 LbNARA59 LbNARA84 LbSHIG97 LbAWJ_17 LbAWJ_18 LbAWJ_19 LbAWJ_20 LbAWJ_21 LbHRSM25 LbHRSM26 LbHRSM27 LbHRSM28 LbHRSM32 LbHRSM34 LbSHMN36 LbSHMN37 LbSHMN39 LbOKI_42 LbOKI_43 LbOKI_44 LbOKI_46 LbOKI_47 LbOKI_49 LbOKI_53 LbOKI_54 LbKCH136 LbKCH138 LbKCH140 LbKCH229 LbKCH230 LbFKO163 LbFKOK85 LbOITA86 LbOITA88 LbOITA92 LbOITA87 LbOITA91 LbOITA93 LbKGS233 LbKGS234

24 24 25 25 26 27 27 28 29 30 31 32 33 33 34 35 35 35 35 35 36 36 36 36 37 37 38 38 38 39 39 39 40 40 40 40 40 41 42 42 43 44 45 46 47 47 48 49 49 49 50 50

Chubu Chubu Chubu Chubu Chubu Chubu Chubu Kinki Kinki Kinki Kinki Kinki Kinki Kinki Kinki Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Chugoku Oki isl. Oki isl. Oki isl. Oki isl. Oki isl. Oki isl. Oki isl. Oki isl. Shikoku Shikoku Shikoku Shikoku Shikoku Kyushu Kyushu Kyushu Kyushu Kyushu Kyushu Kyushu Kyushu Kyushu Kyushu

Shizuoka Shizuoka Shizuoka Shizuoka Yamanashi Yamanashi Yamanashi Mie Mie Mie Nara Nara Nara Nara Shiga Hyogo Hyogo Hyogo Hyogo Hyogo Hiroshima Hiroshima Hiroshima Hiroshima Hiroshima Hiroshima Shimane Shimane Shimane Shimane Shimane Shimane Shimane Shimane Shimane Shimane Shimane Kochi Kochi Kochi Kochi Kochi Fukuoka Fukuoka Oita Oita Oita Oita Oita Oita Kagoshima Kagoshima

Fujinomiya Fujinomiya Susono Susono Fujiyoshida Minamitsuru Minamitsuru Inabe Tsu – Odaigahara Katsuragi Nara Nara Otsu Awaji Awaji Awaji Awaji Awaji Mihara Mihara Mihara Mihara Onomichi Onomichi Nita Nita Nita Nishinoshima Nishinoshima Nishinoshima Dogo Dogo Dogo Dogo Dogo Nagaoka Tosa (gun) Tosa (gun) Kami Tosa (city) Kama KitaKyushu Beppu Beppu Usa Yufu Yufu Yufu Satsumasendai Satsumasendai

138.62 138.62 138.91 138.91 138.81 138.80 138.80 136.56 136.51 136.51 135.88 135.71 135.81 135.81 135.85 134.92 134.92 134.92 134.92 134.92 133.08 133.08 133.08 133.08 133.20 133.20 133.05 133.05 133.05 132.98 132.98 132.98 133.28 133.28 133.28 133.28 133.28 133.65 133.53 133.53 133.69 133.43 130.77 130.88 131.49 131.49 131.33 131.43 131.43 131.43 130.30 130.30

35.22 35.22 35.17 35.17 35.49 35.48 35.48 35.12 34.72 34.73 34.39 34.51 34.69 34.69 35.02 34.44 34.44 34.44 34.44 34.44 34.40 34.40 34.40 34.40 34.41 34.41 35.18 35.18 35.18 36.10 36.10 36.10 36.25 36.25 36.25 36.25 36.25 33.77 33.74 33.74 33.60 33.50 33.56 33.88 33.28 33.28 33.53 33.18 33.18 33.18 31.81 31.81

Sampling locations are represented by the city or area where the samples were collected. The longitude and latitude of each sampling location were determined using Google Earth 6.02; (Google, Inc.).


POPULATION GENETIC STRUCTURE IN JAPANESE HARE portions without recombination signals were used in the present study. We subdivided the MC1R sequence into two parts (MC1R-a and MC1R-b) based on the results of the recombination test. Tajima’s neutrality tests (Tajima’s D; Tajima, 1989) and linkage disequilibrium tests were performed with 10 000 permutations using ARLEQUIN, version 3.5 (Excoffier & Lischer, 2010). We subdivided the samples into 11 populations according to their geographical locations and phylogenetic data of previous CYT B sequences and the SRY sequences from this study (populations 1–11; Fig. 1). First, samples were partitioned according to the three main islands of the Japanese archipelago (Honshu, Shikoku, and Kyushu). Samples from the Oki Islands were treated as one population. Then, samples from Honshu were further subdivided based on the borders of the two mitochondrial lineages (population 8 and the others), the two SRY lineages (such as populations 2 and 3), the two morphotypes (such as populations 3 and 5) and geographical distances (such as populations 1 and 2). Geographical variations in the six genes were surveyed by examining haplotype frequencies in 11 populations. Isolationby-distance (IBD) was evaluated for each gene dataset using a Mantel test with 1000 replicates using ALLELE IN SPACE (Miller, 2005). Genetic diversity [expected heterozygosity (HE) and pairwise nucleotide differences (pi)] within populations for each locus were examined using ARLEQUIN, verison 3.5.

POPULATION

NETWORK CONSTRUCTION AND

POPULATION GENETIC STRUCTURE ANALYSIS

To infer genetic differences between the two morphotypes, exact tests (Raymond & Rousset, 1995) were performed using ARLEQUIN, version 3.5, for two representative groups of populations (populations 1–3 and 4–11), based on the distributions of Lba and Lbb. In addition, according to two CYT B lineages, an exact test was performed for two other population groupings (populations 1–6 and 7–11). Genetic differences among populations were also examined through two independent analyses. First, we constructed population network trees to determine the genetic relationships among the 11 populations, with a special focus on significant differences in the pairwise fixation index (FST) among populations. Second, population genetic structure was inferred using STRUCTURE, version 2.3 (Pritchard, Stephens & Donnelly, 2000) to determine the genetic backgrounds of the 11 populations. Genetic relationships among populations have commonly been constructed as unrooted trees based on pairwise FST (Wright, 1951) or Nei’s genetic distance (Nei’s DA; Nei & Li, 1979). However, these trees are rectilinear and cannot present significant genetic differences among popula-

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tions. Thus, to survey population genetic relationships and identify significant genetic differences, we used significant FST distances in network reconstruction in addition to performing conventional network reconstruction using pairwise FST distances. First, pairwise FST differences between populations and their corresponding P values were calculated for the six nuclear genes using ARLEQUIN, version 3.5, with 1000 permutation replicates. Then, a conventional FST network based on the NJ method was reconstructed using TREEFIT (Kalinowski, 2009). Second, we assigned values of ‘1’ or ‘0’ to P values in the resulting data matrix according to their significance (P < 0.05) or nonsignificance (P ≥ 0.05), respectively. We used ‘0’ (meaning nonsignificant difference) for within-population values, which were diagonal blank elements in the P value matrix. Finally, a binary alignment of 0 and 1 for each population was generated. Then, for the binary data, reduced median networks were constructed using NETWORK, version 4.6.1.0 (http://www.fluxus-engineering.com; Bandelt, Forster & Rohl, 1999). The network analyses and subsequent Bayesian clustering analysis were performed for each locus and for the combined dataset that included all six loci. MC1R-a and -b were included in combined data A and combined data B, respectively. Population genetic structure was inferred by Bayesian clustering analysis using STRUCTURE, version 2.3. This analysis estimates the number of genetic clusters (K) based on allelic frequencies of loci in the dataset and survey proportions of genetic clusters in the populations. K was estimated from 5 000 000 Markov chain Monte Carlo (MCMC) generations, with data sampled every 10 000 generations after a burn-in period of 2 000 000 generations. Convergence of MCMC chains was checked based on the similarity of estimated log probability values for data [lnP(D)] and the variance of log likelihood {Var [lnP(D)]} in an independent analysis. Admixture and allele frequency-correlated models were assumed in the analyses. To determine an appropriate K, we used Evanno’s method in STRUCTURE HARVESTER (Earl & vonHoldt, 2012).

RESULTS PHYLOGENETIC

ANALYSIS OF

SRY

SEQUENCES

In total, 1002 bp from the SRY region were obtained from 30 samples (see Supporting information, Table S2), except for a simple tandem repeat of (GT)n that was approximately 36 bp in length. The dataset comprised four haplotypes with six variable sites, including one base indel. The four haplotypes were divided into two major lineages (N and S) by five of


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Table 2. Summary of nondifferentiation exact P values between two putative groups based on winter coat colour: CYT B (previous study) and the results for SRY Exact P value

Gene fragment

Colour (Lba versus Lbb)

North versus South of CYT B

North versus South of SRY

ASIP TYR MC1R-a MC1R-b MC1R (full length) APOB SPTBN1 TSHB

< 0.01 0.13 0.08 0.23 0.11 < 0.01 < 0.01 < 0.01

0.01 0.06 0.06 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01

0.01 0.43 0.01 < 0.01 < 0.01 < 0.01 < 0.01 0.03

Significant P values are shown in bold.

the six variable sites. The two lineages showed a clear allopatric distribution with the border located approximately halfway between Kanto and Chubu districts (Fig. 2). The bootstrap value of lineage S (95%) was substantially high, although the value for lineage N (62%) was not. Lineage N was composed of two haplotypes (Haps N1 and N2) from 13 samples from populations 1, 2, and 4. For lineage S, two haplotypes (Haps S1 and S2) were found from 17 samples from populations 3 and 5–11. The divergence time between the two lineages was estimated to be 1.07 Mya, with 95% highest-probability density ranging from 0.95 to 2.99 Mya.

GENETIC

VARIATIONS IN SIX NUCLEAR GENE SEQUENCES

For six nuclear loci, 104 samples were used in the subsequent analyses, including missing loci for which amplification failed. The sequence lengths for the six nuclear genes ranged from 316 bp for SPTBN1 to 668 bp for TSHB (see Supporting information, Table S3; accession number in Table S2). Because the MC1R gene was estimated to have a recombination point halfway along the fragment, we partitioned the sequences into the first 456 bp (MC1R-a) and the last 375 bp (MC1R-b) and used them separately in the analyses. The number of polymorphic sites ranged from two (TYR) to nine (ASIP). The number of alleles ranged from four (TYR) to 12 (ASIP). Although MC1R had five nonsynonymous substitutions at V95I, A101V, V154M, A179T, and V199I (site numbers for substitutions refer to the MC1R sequence for O. cuniculus), in 11 substitutions within sequences that totalled 831 bp, the geographical distributions of the variations were restricted to a few populations and did not appear to match the distributions of

the two morphotypes (see Supporting information, Table S4). The significance of IBD varied among genes, and the r-values of the Mantel tests were generally very small (see Supporting information, Table S3). SPTBN1 and TSHB exhibited lower genetic diversity than the other genes, especially in northern populations. In addition, TSHB had a significantly negative Tajima’s D.

GENETIC

DIFFERENCES AMONG POPULATIONS BASED ON SIX NUCLEAR GENES

For the exact test, we also examined allelic differences between groups that represented the two SRY lineages discovered in the present study (Table 2). Almost all genes exhibited significant differences between the two winter coat groups, between the two CYT B lineage groups, and between the two SRY lineage groups (P < 0.05). TYR and MC1R did not differ significantly between Lba and Lbb. Population networks were constructed using the NJ method (Saitou & Nei, 1987), pairwise FST distances, and the significance levels of FST values for the six autosomal genes (Fig. 3). Here, we describe networks based on pairwise FST distances (Fig. 3A) and the significance of FST (Fig. 3B) as ‘distance-based’ and ‘significance-based’ networks, respectively. The relationships among populations estimated by the two types of network were generally similar. For example, population 5 was placed at one end of both TYR networks. Populations 4, 7, 2, 6, 8, 10, and 11 proceeded in sequence from the other end of the distancebased network for TYR (Fig. 3A) and were clustered together in one node in the significance-based network (Fig. 3B). However, some points differed between the two types of network for TYR. Population 9 was closely related to population 4 in the



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Figure 3. Population networks for six nuclear genes and combined data for all loci. A, networks calculated based on pairwise FST distance. B, networks constructed based on significance of FST. Numbers on branches of networks in (B) are population numbers and indicate the cut-off point for significant differences for a certain population. Combined data A includes all sequences except MC1R-b (the latter half of MC1R) and combined data B includes all sequences except MC1R-a (the first half of MC1R).

distance-based network, whereas it was assigned to the node of populations 1 and 3 and was separated from population 4 in the significance-based network. In the networks for ASIP, population 3 was located near population 11 in the distance-based network, whereas the significance-based network placed population 3 at the same node as population 9, which was

very far from population 11. Similar partial discrepancies were observed between networks for APOB, in which the relationship between populations 2 and 10 differed. Remarkable differences were found between networks for combined data B, which consisted of MC1R-b and the other five genes (without MC1R-a). The significance-based network showed


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Figure 3. Continued.

close connections among populations 1, 2, 9, and 11, despite large genetic and geographical distances among these populations in the distance-based network. Although several differences were observed between the two types of population network, populations 1, 2, 9, and 11 tended to be located at external nodes of the networks for all six genes. Combined data A and B subdivided the other populations (populations 3–8 and 10) into two similar groups: populations 3–6 and populations 7, 8, and 10.

Bayesian clustering analysis indicated that the Japanese hare population is composed of four genetic clusters (N1 and S1–S3), the geographical distributions of which show a north–south cline (Fig. 4). STRUCTURE HARVESTER indicated that K = 4 was the most appropriate parameter for combined data A and B. Because these datasets produced very similar results, we used only the results from combined data A. FST genetic distances among the four clusters indicated that cluster N was very distant from the other three.



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Figure 4. Results of genetic structuring analyses using STRUCTURE, version 2.3. A, genetic component proportions of individuals (upper) and populations (lower) are represented by thin horizontal bars composed of four coloured clusters. The component proportions of a cluster indicate the proportions of assignment of the individual (population) into each of the four subgroups. B, genetic relationships among clusters are illustrated using networks based on FST genetic distance.

Clusters S2 and S3 branched off from cluster S1, with a relatively shorter FST distance compared to that observed between S1 and N. Cluster N prevailed in the northernmost population (population 1). Populations 2 and 4 also had relatively high frequencies for cluster N relative to the other populations. The S clusters were broadly observed in populations 2–11. Cluster S1 generally included individuals from populations 8 and 11 (Fig. 4A), and clusters S2 and S3 were equally distributed over all populations, except population 1.

DISCUSSION USEFULNESS

OF A POPULATION NETWORK BASED ON

THE SIGNIFICANCE OF

FST

VALUES FOR DEPICTING

POPULATION GENETIC STRUCTURE

Population networks based on the (non)significance of pairwise FST distances proved rather effective for clustering populations compared to conventional networks of FST distance. Significance-based networks had almost the same topologies as conventional networks of FST distance for every gene and combined data A; in combined data B, populations 1, 2, 9, and 11 were placed near one another (Fig. 3B). Although populations 1 and 2 and populations 9 and 11 showed substantial genetic differences from one another, they were similar in that they also had significant genetic differences from the other populations in combined data B. Although the distance-based network

could recognize the large genetic differences between populations 1 and 2 and populations 9 and 11, the significance-based network treated all significant genetic differences among populations as having the same value of ‘1’, which did not reflect genetic distance. For this reason, distance- and significancebased networks of combined data B produced different topologies. Thus, combined data A provided a more reasonable significance-based network, in which the four groups of populations could be recognized more easily than in the conventional FST network: (1) populations 1 and 2; (2) populations 3–6; (3) populations 7, 8, and 10; and (4) populations 9 and 11. The significance-based network with combined data A appeared to reflect the results of the population clustering analyses (Fig. 4). For example, the primary genetic component in populations 1 and 2 was cluster N1, and populations 3–6 contained the four genetic clusters with similar proportions. Although simulation studies would be needed to evaluate the efficiency of the significance-based network, this may provide an easy method for surveying clusters of populations that showed significant genetic differences.

TWO DIVERGENT GENETIC L. BRACHYURUS AS A RESULT OF

GROUPS IN PAST VICARIANCE

The SRY and nuclear genes showed two genetically divergent groups in L. brachyurus, as demonstrated


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previously using mitochondrial data (Nunome et al., 2010), even though genetic differences between the two morphotypes were obscure in our molecular data (Figs 2, 4). A geographical border between the two divergent lineages in SRY was clearly located across the eastern part of Honshu Island, running from north to south, although the location did not match the distribution of either morphotype. Interestingly, the border did not even match the two CYT B lineages in Chugoku district on the western part of Honshu Island (Fig. 2). The presence of two lineages within the species was well supported by nuclear genes, with two divergent genetic clusters (N and S) alternately in the northern and southern areas (Fig. 4). When STRUCTURE analysis was performed on the basis of the K = 2 model, clusters S1, S2, and S3 were combined into one cluster ‘S’ and then only two clusters ‘N’ and ‘S’ were exhibited without any changes in frequency in populations (data not shown). The existence of two SRY lineages and the presence of two genetic clusters in the nuclear genes could be explained by past vicariance in the species, as hypothesized in a previous mitochondrial study (Nunome et al., 2010). In addition, the timing of the vicariance in the two SRY lineages was estimated to be approximately 1 Mya, which is similar to the estimate of 1.2 Mya for the two CYT B lineages, although the evolutionary rate of the SRY sequences used in the present study would have been slow, given the small numbers of substitutions observed in the sequences. Genetic subdivisions in morphology, karyotype, and DNA sequences have been found commonly at interor intraspecific levels in various Japanese mammals (Nagata et al., 1999; Tsuchiya et al., 2000; Iwasa & Abe, 2006; Kawamoto et al., 2007; Tomozawa & Suzuki, 2008; Oshida, Masuda & Ikeda, 2009; Yasuda et al., 2012). One possible explanation for these divergences is repeated habitat fragmentation during glacial periods in the Late Pleistocene. The four SRY haplotypes (Fig. 2) and three subclusters of cluster S (S1–S3; Fig. 4) would also be products of fragmentation during a more recent period. Although we could not determine a relationship between past vicariance and morphological divergence in L. brachyurus, a vestige of the vicariance would remain in the strong genetic population structure.

NUCLEAR

GENES IMPLY A GENETIC MIXTURE

BETWEEN TWO MORPHOTYPES

The results of the present study suggest that Lba and Lbb were genetically indistinguishable from each other, in contrast to our initial expectation that the two morphotypes with different winter coat colours would be genetically differentiated as a result of different evolutionary trajectories. The topologies of the

population networks were not consistent among the six genes. The absence of consistency among the networks for the six genes implies that the populations are not genetically structured, as is evident in the mixing states of the four genetic clusters in the nuclear genes among the populations. Similar findings were reported in willow grouse and Arctic fox, which also showed little genetic divergence between populations with different winter coat colours (Meinke et al., 2001; Skoglund & Hoglund, 2010). Genetic variation at neutral loci that are not correlated with a colour polymorphism or genes involved in a polymorphism have been commonly observed in other mammals, such as the oldfield mouse (Peromyscus polionotus) (Mullen & Hoekstra, 2008), the rock pocket mouse (Chaetodipus intermedius) (Hoekstra, Drumm & Nachman, 2004), and the grey wolf (Canis lupus) (Anderson et al., 2009). The two morphotypes of L. brachyurus may be only differentiated at genes involved in the differences in seasonal changes in coat colour. Thus, genome-wide surveys using abundant genetic markers, such as microsatellites or single-nucleotide polymorphism analyses, in individuals around the boundary between Lba and Lbb are required to fully appreciate gene flow between the two morphotypes and to determine which loci show affinities with the morphotypes.

GENETIC

VARIATION OF COAT COLOUR-RELATED

GENES AND NEUTRAL GENES AMONG POPULATIONS

Initially, we expected that genes involved in differences in winter coat colour would show particular geographical differences in genetic diversity. None of the markers, including the three coat colour-related genes, exhibited genetic differentiation between the two morphotypes. Tajima’s neutrality tests did not suggest that natural selection was affecting the genes (Fig. 3; see also Supporting information, Table S3). No differences in genetic diversity (HE or pi) among populations were observed in the three genes. The results did not suggest that environmental differences between locations were affecting the genes. We found five amino acid variants of MC1R (V154M, A101V, V95I, V199I, and A179T) (see Supporting information, Table S4). One of the variants, A101V, is placed in the region where the jaguar (Panthera onca) has a 15-bp deletion related to its coat colour variation (Eizirik et al., 2003). The remaining four amino acid variants did not correspond to any of the amino acid changes related to coat colour variations of other animals (Majerus & Mundy, 2003). However, all five mutations appeared to be distributed without any relation to the areas of Lba and Lbb. Similar findings were reported in the Japanese marten (Hosoda et al., 2005; Sato et al., 2009) and the willow grouse


POPULATION GENETIC STRUCTURE IN JAPANESE HARE (Skoglund & Hoglund, 2010). MC1R and other neutral markers showed no clear genetic differences between the two morphotypes in the Japanese marten. In addition, genetic variation in TYR was not related to differences in winter colour in the willow grouse. Although a study of the Arctic fox found a relationship between substitutions in MC1R and variation in winter coat colour (Vage et al., 2005), MC1R, as well as ASIP and TYR, are involved in lifetime coat-colour variations in other mammals (Klungland & Vage, 2003; Nachman et al., 2003; Fontanesi et al., 2006; Kambe et al., 2011). Thus, the seasonal change in coat colour in L. brachyurus may be controlled by other regulatory genes and not by melanogenesisrelated genes such as MC1R and ASIP. The negative value of Tajima’s D for TSHB and its lower genetic variation in northern populations suggested that the locus is under natural selection. Although TSHB has been used as a neutral marker for phylogenetic analyses (Matthee et al., 2004; Hoofer et al., 2008; Stoffberg et al., 2010), it has recently been reported to regulate seasonal reproduction through photoperiodic signalling in birds and mammals (Nakao et al., 2008; Ono et al., 2008). Wild stickleback populations showed differences in gene expression for TSHβ2, a paralogue of TSHB, between marine and stream ecotypes, especially under shortphotoperiod conditions (Kitano et al., 2010). Genetic variation in photoperiod-related genes could vary with latitude, as was shown in European populations of Drosophila melanogaster (Tauber et al., 2007) and Alaskan populations of Chinook salmon (Oncorhynchus tshawytscha) (O’Malley & Banks, 2008). Because L. brachyurus is known to show seasonal changes in reproductive activity with photoperiodic signals (Otsu, 1971), TSHB could be subject to the latitudinal cline in seasonal photoperiod in the Japanese archipelago. To further examine this issue, studies of the seasonal and geographical expression patterns of the gene in wild mammals that show seasonal changes in breeding activity or coat colour, such as Lepus and Martes species, are needed.

CONCLUSIONS The results of the present study suggest that genetic diversity in the Japanese hare is attributable to vicariance events, which may have occurred approximately 1 Mya, rather than to differences in winter coat colour. Although the two morphotypes of Japanese hare have been considered to be different subspecies, the difference in winter coat colour did not appear to restrain gene flow. However, some functional genes related to seasonal changes in coat colour and reproductive status may vary geographically

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according to environmental differences within the Japanese archipelago. In this case, we can carry out population genetic surveys of the Japanese hare across its range because the Japanese hare inhabits a restricted area: the Japanese archipelago. In addition, the accumulation of genomic information for rabbit (O. cuniculus) would facilitate genome-wide research on the Japanese hare, although these two species are not closely related. Indeed, several primers used in the present study were designed based on the published genome sequences of the rabbit. Many rabbit microsatellite markers have been applied for population genetic studies of Lepus species (Hamill, Doyle & Duke, 2006; Thulin, Fang & Averianov, 2006) and a high karyotype similarity was revealed between Lepus and Oryctolagus (Robinson, Yang & Harrison, 2002). Thus, to detect a candidate gene for seasonal changes in coat colour and to survey gene flow between populations that show differences in winter coat colour, the Japanese hare represents an appropriate model species.

ACKNOWLEDGEMENTS We thank Kimiyuki Tsuchiya, Shimane Prefecture Mountainous Region Research Centre and Toyama Family Park, for providing tissue specimens. We also express our appreciation to our colleagues for their valuable advice during the present study. This study was supported in part by a Grant-in-Aid for Fellows and for Research Activity Start-Up of the Japan Society for the Promotion of Science from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Table S1. Primer information. Table S2. List of accession numbers of samples. Alleles of heterozygous individuals have two accession numbers. Samples that could not be amplified are represented by hyphens. Table S3. Molecular indices for loci. Table S4. Geographical distribution of nonsynonymous substitutions of MC1R.
