Rabbit genome analysis reveals a polygenic basis for
phenotypic change during domestication
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Citation
Carneiro, M., C.-J. Rubin, F. Di Palma, F. W. Albert, J. Alfoldi, A.
M. Barrio, G. Pielberg, et al. “Rabbit Genome Analysis Reveals a
Polygenic Basis for Phenotypic Change During Domestication.”
Science 345, no. 6200 (August 28, 2014): 1074–1079.
As Published
http://dx.doi.org/10.1126/science.1253714
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American Association for the Advancement of Science (AAAS)
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Author's final manuscript
Accessed
Wed May 25 15:28:17 EDT 2016
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http://hdl.handle.net/1721.1/98345
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Detailed Terms
Rabbit genome analysis reveals a polygenic basis for phenotypic change
during domestication
Miguel Carneiro1,*, Carl-Johan Rubin2,*, Federica Di Palma3,4,*, Frank W. Albert5,24, Jessica Alföldi3,
Alvaro Martinez Barrio2, Gerli Pielberg2, Nima Rafati2, Shumaila Sayyab6, Jason Turner-Maier3, Shady
Younis2,7, Sandra Afonso1, Bronwen Aken8,18, Joel M. Alves1,9, Daniel Barrell8,18, Gerard Bolet10, Samuel
Boucher11, Hernán A. Burbano5,25, Rita Campos1, Jean L. Chang3, Veronique Duranthon12, Luca
Fontanesi13, Hervé Garreau10, David Heiman3, Jeremy Johnson3, Rose G. Mage14, Ze Peng15, Guillaume
Queney16, Claire Rogel-Gaillard17, Magali Ruffier8,18, Steve Searle8, Rafael Villafuerte19, Anqi Xiong20,
Sarah Young3, Karin Forsberg-Nilsson20, Jeffrey M. Good5,21, Eric S. Lander3, Nuno Ferrand1,22,*, Kerstin
Lindblad-Toh2,3,*, Leif Andersson2,6,23,*
1
CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de
Vairão, Universidade do Porto, 4485-661, Vairão, Portugal.
2
Science of Life Laboratory Uppsala, Department of Medical Biochemistry and Microbiology, Uppsala
University, Uppsala, Sweden.
3
Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142, USA.
4
Vertebrate and Health Genomics, The Genome Analysis Center, Norwich, UK.
5
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig,
Germany.
6
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala,
Sweden.
7
Department of Animal Production, Ain Shams University, Shoubra El-Kheima, Cairo, Egypt.
8
Wellcome Trust Sanger Institute, Hinxton, UK.
9
Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK.
1 10
INRA, UMR1388 Génétique, Physiologie et Systèmes d’Elevage, F-31326 Castanet-Tolosan, France.
11
Labovet Conseil, Les Herbiers Cedex, France.
12
INRA, UMR1198 Biologie du Développement et Reproduction, F-78350 Jouy-en-Josas, France.
13
Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna,
40127 Bologna Italy.
14
Laboratory of Immunology, NIAID, NIH, Bethesda, MD, 20892, USA.
15
DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 2800 Mitchell Drive, Walnut
Creek, CA 94598.
16
ANTAGENE, Animal Genomics Laboratory, Lyon, France.
17
INRA, UMR1313 Génétique Animale et Biologie Intégrative, F- 78350, Jouy-en-Josas, France.
18
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome
Campus, Hinxton, Cambridge CB10 1SD, UK.
19
Instituto de Estudios Sociales Avanzados, (IESA-CSIC) Campo Santo de los Mártires 7, Córdoba Spain.
20
Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University,
Uppsala, Sweden.
21
Division of Biological Sciences, The University of Montana, Missoula, MT 59812, USA.
22
Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s⁄n.
4169-007 Porto, Portugal
23
Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical
Sciences, Texas A&M University, College Station, USA.
*contributed equally within group
Correspondence should be addressed to: kersli@broadinstitute.org and leif.andersson@imbim.uu.se.
Present addresses:
24
Department of Human Genetics, University of California, Los Angeles, Gonda Center, 695 Charles E.
Young Drive South, Los Angeles, CA 90095, USA.
2 25
Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen,
Germany.
3 Abstract
The genetic changes underlying the initial steps of animal domestication are still poorly
understood. We generated a high-quality reference genome for rabbit and compared it to
resequencing data from populations of wild and domestic rabbits. We identified over 100 selective
sweeps specific to domestic rabbits, but only a relatively small number of fixed (or nearly fixed)
SNPs for derived alleles. SNPs with marked allele frequency differences between wild and domestic
rabbits were enriched for conserved non-coding sites. Enrichment analyses suggest that genes
affecting brain and neuronal development have often been targeted during domestication. We
propose that due to a truly complex genetic background, tame behavior in rabbits and other
domestic animals evolved by shifts in allele frequencies at many loci, rather than by critical changes
at only a few ‘domestication loci’.
Introduction
Domestication of animals - the evolution of wild species into tame forms – has resulted in striking
changes in behavior, morphology, physiology and reproduction (1). However, the genetic underpinnings
of the initial steps of animal domestication are poorly understood but likely involved changes in behavior
that allowed the animals to survive and reproduce under conditions that might be too stressful for wild
animals. Given the differences in behavior between wild and domesticated animals, we investigated to
what extent this process involved fixation of new mutations with large phenotypic effects as opposed to
selection on standing variation. Such studies are hampered in most domestic animals due to ancient
domestication events, extinct wild ancestors, or geographically widespread wild ancestors.
Rabbit domestication was initiated in monasteries in Southern France as recent as ~1,400 years
ago (2). At this time, wild rabbits were mostly restricted to the Iberian Peninsula, where two subspecies
occurred (Oryctolagus cuniculus cuniculus and O. c. algirus), and to France, colonized by O. c. cuniculus
(Fig. 1). Additionally, the area of origin of domestic rabbit is still populated with wild rabbits related to
4 the ancestors of the domestic rabbit (3). This recent and well-defined origin provides a major advantage
for inferring genetic changes underlying domestication.
A female rabbit genome was Sanger sequenced and assembled (4). The draft OryCun2.0
assembly size is 2.66 Gb with a contig N50 size of 64.7 kb and a scaffold N50 size of 35.9 Mb (Tables
S1-S2). The genome assembly was annotated using the Ensembl gene annotation pipeline (Ensembl
release 73, Sept. 2013) and with both rabbit RNA-seq data and the annotation of human orthologs (4)
(Table S3). Our analysis of rabbit domestication used Ensembl annotations as well as a custom pipeline
for annotation of UTRs (168,286 unique features), non-coding RNA (n=9,666) and non-coding conserved
elements (2,518,476 unique features).
To identify genomic regions under selection during domestication we performed whole genome
resequencing (10X coverage) of pooled samples (Table S4) of six different breeds of domestic rabbits
(Fig. 1A), three pools of wild rabbits from Southern France, and 11 pools of wild rabbits from the Iberian
Peninsula, representing both subspecies (Fig. 1B). We also sequenced a close relative, snowshoe hare
(Lepus americanus), to deduce the ancestral state at polymorphic sites. Short sequence reads were aligned
to our assembly; SNP calling resulted in the identification of 50 million high quality SNPs and 5.6 million
insertion/deletion polymorphisms after filtering (Table S5). The numbers of SNPs at non-coding
conserved sites and in coding sequences were 719,911and 154,489, respectively. The per site nucleotide
diversity (π) within populations of wild rabbits was in the range of 0.6-0.9% (Fig. 1C). Thus, rabbit is one
of the most polymorphic mammals sequenced so far, presumably due to a larger long-term effective
population size relative to other sequenced mammals (5). Identity scores confirm that the domestic rabbit
is most closely related to wild rabbits from Southern France (Fig. S1A), and we inferred a strong
correlation (r = 0.94) in allele frequencies at most loci between these groups (Fig. S1B). The average
nucleotide diversity of each sequenced population is consistent with a bottleneck and reduction in genetic
diversity when rabbits from the Iberian Peninsula colonized Southern France and a second bottleneck
during domestication (3)(Figs. 1B,C).
5 Selective sweeps occur when beneficial genetic variants increase in frequency due to positive
selection together with linked neutral sequence variants (6). This results in genomic islands of reduced
heterozygosity, and increased differentiation between populations around the selected site. We compared
genetic diversity between domestic rabbits as one group to wild rabbits representing 14 different locations
in France and the Iberian Peninsula. We calculated FST values between wild and domestic rabbits, and
average pooled heterozygosity (H) in domestic rabbits, in 50 kb sliding windows across the genome
(hereafter referred to as FST-H outlier approach). We identified 78 outliers with FST>0.35 and H<0.05
(Figs. 2A, S2, Database S1). We also used SweepFinder (7), which calculates maximum composite
likelihoods for the presence of a selective sweep, taking into account the background pattern of genetic
variation in the data, and with a significance threshold set by coalescent simulations incorporating the
recent demographic history of domestic and wild rabbits (Figs. S3, S4, Databases S1, S2) (4). This
analysis resulted in the identification of 78 significant sweeps (false discovery rate 5%, Fig. 2A, Database
S1). Thirty-one (40%) of these were also detected with the FST-H approach (Fig. 2A). This incomplete
overlap is likely explained by the fact that SweepFinder primarily assesses the distribution of genetic
diversity within the selected population, while the FST-H analysis identifies the most differentiated regions
of the genome between wild and domestic rabbits. We carried out an additional screen using targeted
sequence capture on an independent sample of individual French wild and domestic rabbits. We targeted
over 6 Mb of DNA sequence split into 5,000 1.2 kb intronic fragments that were distributed across the
genome and selected independently of the genome resequencing results above. Coalescent simulations,
using the targeted resequencing dataset and incorporating the recent demographic history of domestic
rabbit as a null model (Figs. S3, S4, Databases S1, S2) (4), revealed that the majority of the sweep regions
detected by whole genome resequencing showed levels and patterns of genetic variation that were
observed less than 5% of the times in the simulated dataset (76.0% with SweepFinder and 73.7% with
FST-H outlier regions, excluding regions without targeted fragments), a highly significant overlap
(Fisher’s exact test, P<1x10-5 for both tests). Furthermore, 26 of the 31 sweep regions detected with both
SweepFinder and the FST-H approach were targeted in the capture experiment and an even greater
6 proportion (88.5%) showed levels and patterns of genetic variation unlikely to be generated under the
specified demographic model.
An example of a selective sweep overlapping the 3’-part of GRIK2 (glutamate receptor,
ionotropic, kainate 2) is shown in Fig. 2B. Parts of this region have low heterozygosity in domestic
rabbits, and at position chr12:90,153,383 bp domestic rabbits carry a nearly fixed derived allele at a site
with 100% sequence conservation among 29 mammals except for the allele present in domestic rabbits
(8) suggesting functional significance. GRIK2 encodes a subunit of a glutamate receptor highly expressed
in brain and has been associated with recessive mental retardation in humans (9). Both SweepFinder and
the FST–H outlier analysis identified two sweeps near SOX2 (SRY-BOX 2) separated by a region of high
heterozygosity (Fig. 2C). SOX2 encodes a transcription factor that is required for stem-cell maintenance
(10).
Given the comprehensive sampling in our study and the correlation in allele frequencies between
domestic and French wild rabbits (Fig. S1B), highly differentiated individual SNPs are likely to have
been either directly targeted by selection or occur in the vicinity of loci under selection. For each SNP, we
calculated the absolute allele frequency difference between wild and domestic rabbits (ΔAF) and sorted
these into 5% bins (ΔAF=0-0.05, etc.). The majority of SNPs showed low ΔAF between wild and
domestic rabbits (Fig. 2D). We examined exons, introns, UTRs and evolutionarily conserved sites for
enrichment of SNPs with high ΔAF, as would be expected under directional selection on many
independent mutations (Fig. 2D, Table S6). We observed no consistent enrichment for high ΔAF SNPs in
introns, but significant enrichments in exons, UTRs and conserved non-coding sites (χ2 test, P<0.05). Of
note, we detected a significant excess of SNPs at conserved non-coding sites for each bin ΔAF>0.45 (χ2
test, P=1.8x10-3 - 7.3x10-17), whereas in coding sequence, a significant excess was only found at
ΔAF>0.80 (χ2 test, P=3.0x10-2 - 1.0x10-3). Compared to the relative proportions in the entire dataset,
there was an excess of 3,000 SNPs at conserved non-coding sites with ΔAF>0.45, whereas for exonic
SNPs with ΔAF>0.80 the excess was only 83 SNPs (Table S6). Thus, changes at regulatory sites have
7 played a much more prominent role in rabbit domestication, at least numerically, than changes in coding
sequences.
We selected the 1,635 SNPs at conserved non-coding sites with ΔAF>0.80, which represent 681
non-overlapping 1 Mb blocks of the rabbit genome. In order not to inflate significances due to inclusion
of SNPs in strong linkage disequilibrium we selected only one SNP per 50 kb, leaving 1,071 SNPs. More
than 60% of the SNPs were located 50 kb or more from the closest transcriptional start site (TSS; Fig.
2E), suggesting that many differentiated SNPs are located in long-range regulatory elements. A gene
ontology (GO) overrepresentation analysis (11) examining all genes located within 1 Mb from high ΔAF
SNPs showed that the most enriched categories of biological processes involved ‘cell fate commitment’
(Bonferroni P=3.1x10-3-5.4x10-5; Table 1, Database S3), while the statistically most significant categories
involved brain and nervous system cell development (Bonferroni P=2.9x10-3-3.7x10-10). Many of the
mouse orthologs of genes associated with non-coding high ΔAF SNPs were expressed in brain or sensory
organs, and this enrichment was highly significant (Table 1). We also examined phenotypes observed in
mouse mutants (http://www.informatics.jax.org) for these genes, revealing a significant (Bonferroni
P=3.7x10-2-7.5x10-17) enrichment of genes associated with defects in brain and neuronal development,
development of sensory organs (hearing and vision), ectoderm development and respiratory system
phenotypes (Fig. S5). These highly significant overrepresentations were obtained because there were
many genes in the overrepresented categories (Table 1). For example, we observed high ΔAF SNPs
associated with 191 genes (113 expected by chance) from the nervous system development GO category
(Bonferroni P=3.7x10-10). Thus, rabbit domestication must have a highly polygenic basis with many loci
responding to selection, and where genes affecting brain and neuronal development have been particularly
targeted.
None of the coding SNPs that differed between wild and domestic rabbits was a nonsense or
frame-shift mutation - consistent with data from chicken (12) and pigs (13), suggesting that gene loss has
not played a major role during animal domestication. This is an important finding as it has been suggested
that gene inactivation could be an important mechanism for rapid evolutionary change, for instance
8 during domestication (14). Of 69,985 autosomal missense mutations, there were no fixed differences and
only 14 showed a ΔAF above 90%. On the basis of poor sequence conservation, similar chemical
properties of the substituted amino acids, and/or the derived state of the domestic allele we assume that
most of these result from hitchhiking, rather than being functionally important (Database S4). However,
two missense mutations stand out; these may be direct targets of selection because at these two positions
the domestic rabbit differs from all other sequenced mammals (>40 species). The first is a Q813R
substitution in TTC21B (tetratricopeptide repeat domain 21B protein), which is part of the ciliome and
modulates sonic hedgehog signaling during embryonic development (15). The other is a R1627W
substitution in KDM6B (lysine-specific demethylase 6B) that encodes a histone H3K27 demethylase
involved in HOX gene regulation during development (16).
Deletions unique to domestic rabbits were difficult to identify, because the genome assembly is
based on a domestic rabbit, but some convincing duplications were detected with striking frequency
differences between wild and domestic rabbits (Database S5). We observed a one bp insertion/deletion
polymorphism located within an intron of IMMP2L (inner mitochondrial membrane peptidase-2 like
protein), where domestic and wild rabbits were fixed for different alleles. The polymorphism occurs in a
sweep region and it is the sequence polymorphism with highest ΔAF in the region (Fig. S6). Mutations in
IMMP2L have been associated with Tourette syndrome and autism in humans (17).
Cell fate determination was a strongly enriched GO category (enrichment factor=4.9; Database
S3) for genes near variants with high ΔAF. We examined the functional significance of twelve SOX2,
four KLF4 and one PAX2 high ΔAF SNPs associated with this GO category and where all 17 SNPs were
unique to domestic rabbits compared with other sequenced mammals. Electrophoretic mobility shift assay
(EMSA) with nuclear extracts from mouse ES-cell derived neural stem cells revealed specific DNAprotein interactions (Figs. 3, S7, Table S7). Four probes, all from the SOX2 region, showed a gel shift
difference between wild and domestic alleles. Nuclear extracts from a mouse P19 embryonic carcinoma
cell line before and after neuronal differentiation recapitulated these four gel shifts and revealed three
additional probes, one in PAX2 and two more in SOX2 that showed gel shift differences between wild 9 type and mutant probes only after neuronal differentiation. Thus, altered DNA-protein interactions for 7
of the 17 high ΔAF SNPs tested were identified, qualifying them as candidate causal SNPs that may have
contributed to rabbit domestication.
Our results show that very few loci have gone to complete fixation in domestic rabbits, none at
coding sites nor any at non-coding conserved sites. However, allele frequency shifts were detected at
many loci spread across the genome and the great majority of ‘domestic’ alleles were also found in wild
rabbits, implying that directional selection events associated with rabbit domestication are consistent with
polygenic and soft sweep modes of selection (18) that primarily acted on standing genetic variation in
regulatory regions of the genome. This stands in contrast with breed-specific traits in many domesticated
animals that often show a simple genetic basis with complete fixation of causative alleles (19). Our
finding that many genes affecting brain and neuronal development have been targeted during rabbit
domestication is fully consistent with the view that the most critical phenotypic changes during the initial
steps of animal domestication likely involved behavioral traits that allowed animals to tolerate humans
and the environment humans offered. On the basis of these observations, we propose that the reason for
the paucity of specific fixed domestication genes in animals is that no single genetic change is either
necessary or sufficient for domestication and because of the complex genetic background for tame
behavior we propose that domestic animals evolved by means of many mutations of small effect, rather
than by critical changes at only a few domestication loci.
Author contributions
KLT, FDP, and ESL oversaw genome sequencing, assembly and annotation performed by JA,
JTM, JJ, DH, JLC, and SaY. BA, DB, MR, and SS did Ensembl annotations. CRG, VD, LF, RGM, and
ZP contributed to the genome project. LA, MC, CJR, NF, and KLT led the domestication study and
AMB, NR, and SS contributed with bioinformatic analyses. ShY and GP performed EMSA, AX and KFN
developed neural stem cells for EMSA. MC, FWA, JMG, SA, JMA, GB, SB, HAB, RC, HG, GQ, and
10 RV designed, performed and analyzed the sequence capture experiment. LA, MC, CJR, KLT, NF and
FDP wrote the paper with input from other authors.
Acknowledgements
The work was supported by grants from NHGRI (U54 HG003067 to ESL), ERC project BATESON to
LA, Wellcome Trust (grant numbers WT095908 and WT098051), the intramural research program of the
NIH, NIAID (RGM), the European Molecular Biology Laboratory, POPH-QREN funds from the
European Social Fund and Portuguese MCTES [postdoc grants to M.C (SFRH/BPD/72343/2010) and
R.C. (SFRH/BPD/64365/2009), and PhD grant to J. Alves (SFRH/BD/72381/2010)], a NSF international
postdoctoral fellowship to J.M.G. (OISE- 0754461), by FEDER funds through the COMPETE program
and Portuguese national funds through the FCT – Fundação para a Ciência e a Tecnologia –
(PTDC/CVT/122943/2010; PTDC/BIA-EVF/115069/2009; PTDC/BIA-BDE/72304/2006; PTDC/BIABDE/72277/2006), by the projects “Genomics and Evolutionary Biology” and “Genomics Applied to
Genetic Resources” co-financed by North Portugal Regional Operational Programme 2007/2013 (ON.2 –
O Novo Norte) under the National Strategic Reference Framework (NSRF) and European Regional
Development Fund (ERDF), by travel grants to M.C. (COST Action TD1101) and S.S. was supported by
Higher Education Commission in Pakistan. We are grateful to L. Gaffney for assistance with figure
preparation, Paulo C. Alves and Scott Mills for providing the snowshoe hare sample and S. Pääbo for
hosting M.C., S.A. and R.C. Sequencing was performed by the Broad Institute Genomics Platform.
Computer resources were supplied by BITS and UPPNEX at Science for Life Laboratory. The O.
cuniculus genome assembly has been deposited in Genbank under the accession number
AAGW02000000. The RNA-seq data have been deposited there under the bioproject PRJNA78323, the
rabbit genome resequencing data under the bioproject PRJNA242290, and the sequence capture data
under the bioproject PRJNA221358.
11 Table 1 Summary of results from enrichment analysis of ΔAF >0.8 SNPs located in conserved non-coding elements. One
significantly enriched term was chosen from each group of significantly enriched inter-correlated terms. Full lists of enriched
terms and inter-correlations are presented in Database S3 and the most enriched inter-correlated terms are presented in Figure S5
Enrich
Unique
Number
Enriched term
P1
ment
loci
of genes
O/R)2
Gene Ontology Biological Process
GO:0007399
nervous system development
191
3.7x10-10
1.7
154/155
GO:0045595
regulation of cell differentiation
107
4.5x10-6
1.8
94/91
GO:0045935
positive regulation of nucleobase-containing compound
122
2.0x10-5
1.7
101/100
metabolic process
GO:0045165
cell fate commitment
36
5.5x10-5
2.9
31/32
GO:0007389
pattern specification process
57
1.4x10-4
2.2
43/44
GO:0009887
organ morphogenesis
85
2.0x10-3
1.8
72/73
GO:0048646
anatomical structure formation involved in morphogenesis
75
2.8x10-3
1.8
65/64
-2
GO:0045892
negative regulation of transcription, DNA-dependent
82
1.4x10
1.7
62/62
GO:0034332
adherens junction organization
13
1.5x10-2
4.7
11/11
Mouse Genome Informatics gene expression3
-25
11853
TS23 diencephalon; lateral wall; mantle layer
109
3.9x10
3.3
86/85
12449
TS23 medulla oblongata; lateral wall; basal plate; mantle layer
115
2.6x10-13
2.3
90/89
2257
TS17 sensory organ
113
3.4 x10-13
2.3
98/99
1324
TS15 future brain
72
8.5x10-9
2.4
61/61
Mouse Genome Informatics phenotype
MP:0010832
lethality during fetal growth through weaning
240
7.5x10-17
1.8
197/189
MP:0003632
abnormal nervous system morphology
237
1.2x10-13
1.7
191/193
-7
MP:0005388
respiratory system phenotype
127
1.7x10
1.8
101/102
MP:0000428
abnormal craniofacial morphology
109
1.4x10-6
1.9
93/92
MP:0002925
abnormal cardiovascular development
88
3.3x10-5
1.9
73/73
-4
MP:0005377
hearing/vestibular/ear phenotype
73
1.8x10
2.0
61/62
1
Bonferroni corrected P-value. 2Number of unique non-overlapping 1Mb windows observed (O) and the average number of 1
Mb windows observed in 1000 random (R) samplings of the same number of genes (rounded to the nearest integer). 3TS=Thieler
stage.
12 Figures
Fig. 1. Experimental design and population data (A) Images of the six rabbit breeds, sized to reflect
differences in body weight, included in the study and of a wild rabbit. (B) Map of the Iberian Peninsula
and Southern France with sample locations marked (orange dots). Demographic history of this species is
indicated and a logarithmic time scale is indicated to the left. The hybrid zone between the two subspecies
is marked with dashes. (C) Nucleotide diversities in domestic and wild populations. The French (FRW13) and Iberian (IW1-11) wild rabbit populations are ordered according to a northeast to southwest
transection. Sample locations are provided in Table S4.
Fig. 2. Selective sweep and delta allele frequency analyses. (A) Plot of FST values between wild and
domestic rabbits. Sweeps detected with the FST-H outlier approach, SweepFinder and their overlaps are
marked on top. Unassigned scaffolds were not included in the analysis. (B) and (C) Selective sweeps at
GRIK2 (B) and SOX2 (C). Heterozygosity plots for wild (red) and domestic (black) rabbits together with
plots of FST values and SNPs with ΔAF>0.75 (HΔAF). The bottom panel shows putative sweep regions,
detected with the FST-H outlier approach and SweepFinder, marked with horizontal bars. Gene
annotations in sweep regions are indicated; *represents ENSOCUT000000. **SOX2-OT represents the
manually annotated SOX2 overlapping transcript (4). (D) Number of SNPs in non-overlapping ΔAF bins
(black lines, left y-axis). M-values (log2-fold changes) of the relative frequencies of SNPs at non-coding
evolutionary conserved sites, in untranslated regions (UTR), exons and introns according to ΔAF bins
(colored lines, right y-axis); M-values were calculated by comparing the frequency of SNPs in a given
annotation category in a specific bin with the corresponding frequency across all bins. (E) Location of
SNPs at conserved non-coding sites with ΔAF≥0.8 SNPs (n=1,635) and with ΔAF<0.8 SNPs (n=502,343)
in relation to the transcription start site (TSS) of the most closely linked gene; **, P<0.01).
13 Fig. 3. Bioinformatic and functional analysis of candidate causal mutations. Three examples of SNPs
near SOX2 and PAX2 where the domestic allele differs from other mammals. Fourteen such SNPs
assessed with electrophoretic mobility shift assays (EMSA) are indicated by red crosses on top. EMSA
with nuclear extracts from ES-cell derived neural stem cells or from mouse P19 embryonic carcinoma
cells before (un-diff) or after neuronal differentiation (diff) are shown for three SNPs; exact nucleotide
positions of polymorphic sites are indicated. Allele-specific gel shifts are indicated by arrows. WT=wildtype allele; Dom=domestic, the most common allele in domestic rabbits. Cold probes at 100-fold excess
were used to verify specific DNA-protein interactions.
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Supplementary Materials
www.sciencemag.org
Methods
Tables S1 to S7
Figs. S1 to S7
Databases S1 to S5
References (20-61)
16 Rabbit genome analysis reveals a polygenic basis for phenotypic change during
domestication
Carneiro et al.
Supplementary Materials
www.sciencemag.org
Methods
Tables S1 to S7
Figs. S1 to S7
Databases S1 to S5
Full reference list
List of Supplementary material:
Table S1. Summary statistics of the rabbit OryCun2.0 assembly.
Table S2. Comparison between rabbit genome assembly versions OryCun1.0 and OryCun2.0.
Table S3. Summary of RNA-sequencing data used for annotation of the rabbit genome.
Table S4. Sample details of wild and domestic rabbits used in this study both for whole-genome
resequencing and DNA sequence capture using hybridization on microarrays. Average sequence
coverage per sample is provided.
1 Table S5. Summary of SNPs and Insertions/Deletions detected in the rabbit genome.
Table S6. Distributions of SNP counts in the different delta allele frequency bins for conserved
non-coding elements, UTRs, coding sequences and introns. Deviations from expected values
were tested with a standard Χ2–analysis (d.f.=1). M-values were calculated using the average
frequency of the corresponding annotation category as reference.
Table S7. Summary of electrophoretic mobility shift assays using nuclear extracts from ES-cell
derived neural stem cells (ES) or from mouse P19 embryonic carcinoma cells before (un-diff) or
after neuronal differentiation (diff). "-" = no shift, "+" = shifted, "++" = lower band intensity,
"+++" = whole band/complex disappeared and "?" = unclear.
Fig. S1. Genetic relatedness between populations sampled in this study (A) Heat map (color code
to the right) of identity scores based on comparing resequencing data with the assembly. The xaxis represents genome coordinates with chromosome 1 to the left and chromosome X to the
right. The first row (R) represents the reference rabbit against itself, rows 8-21 correspond to
locations marked with red dots in Fig. 1A, ordered according to a northeast to southwest
transection. (B) Strong correlation in allele frequencies between domestic and French wild
rabbits; RAF French and RAF Domestic represent the frequency of the reference allele in wild
French and domestic rabbits, respectively.
2 Fig. S2. Distributions and scatterplot of heterozygosity and FST in 50 kb windows across the
rabbit genome. (A) Distribution of pooled heterozygosity in pools of domestic rabbits. The arrow
indicates the upper heterozygosity threshold for opening a sweep. (B) Distribution of FST values
observed in the contrast domestics vs. wild French. The arrow indicates the lower FST threshold
for opening a sweep. (C) Scatter plot of FST and pool heterozygosity. Red dots represent windows
fulfilling the sweep opening requirement (FST ≥ 0.35 and heterozygosity ≤0.05).
Fig. S3. Demographic model and magnitude of the domestication bottleneck estimated using the
targeted capture dataset. (A) Schematic representation of the coalescent model used to represent
the main events of the demographic history of wild and domestic rabbits. See Supplementary
Methods for a detailed description of all parameters and assumptions. (B) Diagram representing
the multilocus maximum-likelihood (ML) estimate for the magnitude of the domestication
bottleneck (kdom). Multilocus ML (y axis) of the parameter kdom (x axis) was estimated as the
product of the likelihood for each locus.
Fig. S4. Comparison between observed and simulated data under the estimated demographic
model. Observed and simulated data are represented in red and black, respectively. The first two
panels (A and B) illustrate the relationship between values of both π and θw in wild rabbits from
France (x axis) and domestic rabbits (y axis). The two histograms on the bottom row (C and D)
illustrate the distribution of FST and percentage of shared mutations between the two populations.
These figures include the full observed dataset, including outliers, explaining the discrepancy
between the observed and simulated data.
3 Fig. S5. Gene overrepresentation analysis using the GREAT software based on genes associated
with high delta allele frequency SNPs (ΔAF≥0.8) at non-coding conserved sites. (A) Gene
Ontology categories within Biological Processes, (B) MGI mouse expression data, and (C) MGI
mouse phenotypes. Each row in the heat maps represents one specific category and colors on that
row indicate the proportion of shared genes in relation to the other categories (ordered in the
same way on the x-axis as on y-axis). To the left of each cluster, the type of terms in that cluster
is summarized. Bars immediately left of heat-maps visualize the significance-, enrichment- and
number of genes of each significant term (P= -log10 Bonferroni-corrected P-value;
E=Enrichment: N=number of genes). For P, E, and N, the ranges are indicated below the plot.
The full results are presented in Database S3.
Fig. S6. Selective sweep at IMMP2L on chromosome 7. Heterozygosity plots for wild (red) and
domestic (black) rabbits together with plots of FST values and high ΔAF (HΔAF) SNPs with
ΔAF>0.75. Putative sweep regions detected with the FST -Heterozygosity outlier approach and
SweepFinder are marked with horizontal bars. Gene annotations in sweep regions are indicated.
IMMP2L was not predicted by Ensembl but by our in-house predictions based on RNAsequencing data. The human consensus model features all human IMMP2L transcripts in a
collapsed fashion. Asterisks (*) represent ENSOCUT000000. Red dots indicate the location of
the high ΔAF insertion/deletion in a conserved element for IMMP2L.
Fig. S7. Results for all 17 SNPs functionally examined by electrophoretic mobility shift assays
(EMSA). Results of EMSA using nuclear extracts from ES-cell derived neural stem cells or from
mouse P19 embryonic carcinoma cells before (un-diff) or after neuronal differentiation (diff) are
4 presented. WT=wild-type allele; D=domestic, the most common allele in domestic rabbits. Cold
probes at 100-fold excess were used to verify specific DNA-protein interactions. The results are
summarized in Table S7.
Database S1. Regions inferred to have been targeted by directional selection using: 1) a FST-H
outlier approach contrasting genetic diversity between wild and domestic rabbits, 2) allele
frequency spectra (SweepFinder), and 3) an explicit demographic model contrasting genetic
diversity between wild and domestic rabbits (capture arrays data).
Database S2. P-values obtained using coalescent simulations of the demographic scenario
inferred in this study for the SweepFinder and targeted capture analyses. The obtained value for
the magnitude of the domestication bottleneck in this study (kdom = 1.3) was lower than a previous
estimate (kdom = 2.8). Under the model used to infer directional selection in the domesticated
lineage using the targeted capture dataset, the estimation from this study describes a stronger
bottleneck and thus renders our selection tests conservative. However, this is not the case for the
SweepFinder analysis and P-values using both bottlenecks estimates are provided.
Database S3. Full results of overrepresentation analysis using SNPs at conserved non-coding
sited with ΔAF>0.80 performed using the GREAT tool.
Database S4. Missense SNPs showing a delta allele frequency difference of 0.90 or higher
between wild and domestic rabbits.
5 Database S5. Duplications and deletions showing allele frequency differences between wild and
domestic rabbits according to an ANOVA analysis.
6 Supplementary Methods
Genome assembly and annotation
Genome sample collection. Eight adult female rabbits of the Thorbecke New Zealand white
partially inbred line were obtained from Covance Research Products. This entire line was
destroyed in a fire in 2005, and thus is no longer available. Sequence heterozygosity of these
samples was assessed at up to 267 random nuclear loci through PCR and Sanger sequencing at
the Broad Institute. Heterozygosity was calculated based on high quality heterozygous single
nucleotide polymorphisms (SNPs) inferred from the chromatograms. The sample with the lowest
heterozygosity was selected as reference individual to whole genome sequencing.
Genome sequencing and assembly. An initial 2X assembly of the rabbit genome (OryCun1)
was constructed using paired-end reads from 4 kb plasmids and 40 kb fosmids from a single
female rabbit (Table S2). This assembly was described in Lindblad-Toh et al. (8). The assembly
described in this paper (OryCun2) used the sequencing reads from OryCun1 as well as additional
paired-end reads from 4 kb plasmids, 10 kb plasmids, 40 kb fosmids, and bacterial artificial
chromosomes (BACs). All sequenced reads derive from the same individual rabbit, except for
those from BACs (see below). Genome assembly was performed using the software package
Arachne 2.0 (20). The 6.55X OryCun2 assembly is comprised of 5.04X 4 kb reads, 1.14X 10 kb
reads, 0.33X 40 kb reads, and 0.04X BAC-derived reads. This assembly has an N50 contig size
of 64.65 kb (i.e. half of all bases reside in a contiguous sequence of 64.65 kb or more), an N50
scaffold size of 35.92 Mb and a total assembly size of 2.66 Gb (Table S1). It shows a
heterozygosity of 1/3,506 bp, a GC content of 43.8% and is 16.7% repetitive as defined by 48-
7 mer counts. OryCun2 used 92.3% of all reads produced, comprising 94.3% of all bases produced.
It is of similar quality to other Sanger-based draft assemblies.
OryCun2 was anchored to chromosomes using a cytogenetically anchored microsatellite
map (21). 364 BACs were end-sequenced, resulting in the anchoring of 99 scaffolds, comprising
82% of the genome assembly, or 2.178 Gb. Of the 2.178 Gb anchored, 238 Mb were only
ordered, while 1.940 Gb were ordered and oriented.
BAC library. The white blood cells of a New Zealand white rabbit of unknown sex were
embedded in agarose plugs at the concentration of 107 per ml, treated with ESP buffer (50mg/ml
proteinase K, 1% Sarkosyl, and 0.5M EDTA, pH 8.0), and rinsed with TE until suitable for
enzymatic digests. The DNA was partially digested with EcoRI and EcoRI methylase, size
selected, and cloned into the pBACe3.6 vector as described (22). The ligated DNA was then
transformed into DH10B electro-competent cells (Invitrogen). The library was arrayed into 322
384-well plates. The average insert size of 269 randomly selected clones is 175 kb, and about
92% of these clones are greater than 140 kb. This library represents about 7-fold coverage of the
rabbit genome.
RNA sequencing, transcriptome assembly, and annotation. A panel of 19 RNA samples
derived from New Zealand white rabbits was used. Ten RNA samples (nine different tissues from
a single female and testis from a male) were purchased from the company Zyagen while the other
nine samples were isolated from INRA 1077 New Zealand white rabbits (Table S3). Nineteen
strand-specific dUTP libraries (23) were produced from Oligo dT polyA-isolated RNA. The
libraries were sequenced using Illumina Hi-Seq instruments, producing 76 bp single end reads (34 Gb of sequence/tissue). All nineteen RNA-seq datasets were assembled via the genomeindependent RNA-seq assembler Trinity (24).
8 The genome assembly was annotated both by the Ensembl gene annotation pipeline
(Ensembl release 73, Sept. 2013) and by a novel methodology using both RNA-seq and
orthologous annotation in human. The Ensembl gene annotation pipeline created gene models
using UniProt protein alignments and RNA-seq data. This pipeline produced 24,964 transcripts
arising from 19,293 protein coding genes and 3,375 short non-coding transcripts. The custom
pipeline created gene models using the same RNA-seq panel, and older Ensembl rabbit
(Genebuild 71.3) and human annotations (Genebuild 71.37). It produced 19,118 high-confidence
protein-coding genes, 881 low-confidence protein-coding genes, 1,318 spliced antisense
transcripts, 2,243 unspliced antisense loci, 2,746 high confidence lncRNA genes and 48,794 lowconfidence non-coding transcripts. Our analysis of rabbit domestication used Ensembl
annotations as well as the custom pipeline for annotation of UTRs, non-coding RNA and noncoding conserved elements.
Genome resequencing and data analyses
Sampling. To identify regions of the genome likely to have been targeted by selective breeding
at domestication and shared across breeds, we obtained whole genome sequence data for both
domestic and wild animals using a pool-sequencing approach (Table S4). Our sampling of
domestic rabbits followed that of Carneiro et al. (3), and includes individuals from breeds
representing a wide range of phenotypes and for which historical records indicate a mostly
unrelated origin. This sampling design focusing on divergent breeds should assure an
approximation to the ancestral genetic diversity prior to breed formation and captured in the early
years of rabbit domestication. In total, we sampled six domestic breeds (Table S4). From the
wild, we sampled individuals from three localities in southern France (i.e. the ancestral
population that gave origin to domestic rabbits) and from 11 localities within the ancestral range
9 of rabbits in the Iberian Peninsula, including localities within the range of both rabbit subspecies
(Table S4). The number of individuals sampled for each breed and locality varied from 10-20. A
single snowshoe hare (Lepus americanus) individual was sampled as outgroup.
Resequencing, alignment and SNP calling. Paired-end sequencing libraries were generated
from domestic and wild rabbit DNA pools using standard procedures. The resulting libraries were
sequenced as 2X 76bp paired-end reads using Genome Analyzer II (Illumina), to a coverage of
~10X per pool (Table S4). Sequence reads were then mapped to the reference genome assembly
(OryCun2) with the Burrows-Wheeler alignment tool (25) using default parameters, except for
the q-parameter, indicating the base quality cut-off for soft-clipping reads, which was set to 5.
We then marked duplicate reads using Picard (26). The mapping distance distribution of pairedend reads revealed that a large proportion of paired-end reads had been generated from fragments
smaller than the length of the two reads (76bp x 2). In order not to bias allele frequency
estimations by counting overlapping parts of the same molecule twice, we merged overlapping
paired-end reads into a single read by using a custom python script. For mismatching bases in
overlapping parts, the highest quality base and its quality value were retained and for overlapping
bases that agreed the base qualities were rescaled (PhredOL=PhredR1+PhredR2) to reflect the
increased confidence of base calling.
SNP calling was performed using Samtools (27) (version 0.1.19-44428cd) and the output
was further filtered using the mpileup2snp option of VarScan v2.3.3 (28). This approach enabled
the detection of more than 61 x 106 raw SNPs, a set further processed by using a custom script
requiring that: i) the sum of read depths (RD) across all populations 100 ≥ RD ≤350 for
autosomes and 100 ≥ RD ≤270 for the X chromosome; ii) the least abundant SNP allele was
observed in at least three independent reads at a frequency ≥0.01; iii) the most commonly
10 observed variant allele constituted ≥ 85% of the total count of all three possible variant alleles;
iv) the reference allele was represented by at least one read in the dataset; v) that the variant allele
was not observed in the reference individual; and vi) the average Phred scaled base quality of the
variant allele was ≥15. Application of these filters retained 50,165,386 SNPs for downstream
analyses and the allele counts for each of the sequenced rabbit pools were extracted using
mpileup from the Samtools package.
Read alignments from the Snowshoe hare (Lepus americanus) individual were
interrogated at rabbit SNP positions to infer the ancestral and derived states of alleles. For a
position to be assessed it was required that either the rabbit reference or variant allele was the
only observed allele in the hare and that this allele was supported by ≥3 reads with an average
mapping quality of ≥20.
Genome-wide identity scores from pool-sequencing data. To visualize genetic relatedness
between populations we calculated identity scores (IS) of individual SNPs in relation to the
reference assembly using the reference allele frequencies (AFREF) observed for each sequenced
pool as well as for the resequenced reference individual (IS= AFREF). Identity scores for 50 kb
windows presented along the genome in Fig. S1A were calculated as the average IS of all SNPs
in a window.
Genome-wide estimates of nucleotide diversity from pool-sequencing data. To estimate levels
of nucleotide diversity (π) across all sampled populations we used the PoPoolation package (29),
which implements corrections for biases introduced by pooling and sequencing errors. We
obtained genome-wide values of π by averaging estimates computed along each chromosome in
50 kb non-overlapping windows. We required per position a minimum coverage of 4, a
maximum coverage of 30, at least two reads supporting the minor allele in polymorphic
11 positions, and a Phred quality score of 20 or higher. Windows with less than 60% of positions
passing these quality filters were discarded.
Identification of regions consistent with directional selection in the domesticated lineage.
The merits of model-based methods using simulations versus outlier methods based on genomewide empirical distributions of summary statistics for detecting genomic regions targeted by
directional selection have frequently been discussed. Here, we use both these approaches. We
started by using an outlier-based approach that is free from any assumptions regarding domestic
rabbits’ demographic history and we searched for regions showing unusual levels and patterns of
genetic diversity using statistics summarizing heterozygosity and differentiation. We then
estimated a neutral demographic scenario for rabbit domestication taking advantage of individual
genotypes resulting from an additional dataset where we resequenced to high coverage 5,000
fragments enriched by DNA hybridization on microarrays. This null demographic model was
then used to search for signatures of selection in the domesticated lineage using methods relying
on different aspects of the data, including the allele frequency spectra, heterozygosity, and
genetic differentiation. We described these approaches in detail below.
Inference of selection using heterozygosity and FST. In order to define candidate regions having
undergone directional selection during rabbit domestication we started by using an approach
combining heterozygosity estimates for the six sequenced domestic breeds with estimates of FST
between domestic and French wild rabbits. We calculated pooled heterozygosity (HP) for 50 kb
windows iterated every 25 kb along each chromosome. HP was calculated independently for the
three main populations considered in this study: 1) six sequenced pools of domestic rabbits
(HPDOM); 2) three pools of wild rabbits from France (WF); and 3) eleven pools of wild rabbits
sampled across the Iberian Peninsula (WI). For each SNP in each pool combination, we
12 determined the major allele from the sum of observed reference and variant alleles and then
proceeded to calculate the average frequency of the major allele (MajFreq) and minor allele
(MinFreq) in the individual pools included in that particular pool combination: HP was then
calculated for individual SNPs in each sequenced pool by HP = 2(MajFreq * MinFreq). HPDOM of
individual SNPs were calculated as the mean HP of the individual pools in which read depth ≥ 3
and HPDOM of 50 kb windows was calculated as the average HP of all SNPs in that window. FST
was calculated for individual SNPs between domestic rabbits and wild rabbits from France using
the formula by Weir & Cockerham (30) and FST of 50 kb windows were then calculated as the
average values of all SNPs in a window.
For selective sweep calling based on extreme HPDOM and FST values of 50 kb windows we
consulted the distributions of HPDOM and FST (Fig. S2) and selected the joint criterion of HPDOM ≤
0.05 + FST ≥ 0.35 to open a selective sweep and then extended the sweep to each side for as long
as windows fulfilled either HPDOM ≤ 0.05 or FST ≥ 0.35. In order not to excessively fragment
predicted selective sweeps, regions separated by two or fewer windows not meeting the above
extension criteria were collapsed into a single putative sweep region.
Inference of selection using the site frequency spectrum and a demographic model. To detect
evidence of directional selection we used an additional approach (SweepFinder) that uses a
likelihood framework to search for local deviations in the site frequency spectrum when
compared to the remainder of the genome, evaluating for each location a selective sweep with a
neutral model (7). The genomic background frequency spectrum was obtained for each
chromosome independently and the likelihood ratio between neutral and selective models was
calculated for a different grid size for each chromosome in order to obtain estimates
approximately every 10 kb. Following Williamson et al. (31), we included SNPs that were
13 monomorphic within the targeted subpopulation (i.e. domestic rabbits), but variable in the
combined sample (i.e. domestic and wild rabbits from France). This should increase the power to
detect recent population-specific sweeps in the domesticated lineage that have eliminated most
genetic variation, while accounting for mutation rate heterogeneity across the genome. The
number of chromosomes sampled for each position was equal to the number of reference and
alternative allele counts. The ancestral state of mutations was polarized using the French wild
rabbits (i.e. the most common allele was picked as the ancestral allele).
A null distribution is required to infer the statistical significance of the selective sweep
hypothesis. To create a null distribution of the likelihood ratio statistic we used neutral coalescent
simulations according to a non-equilibrium demographic model based on historical records and
incorporating the estimated magnitude of the domestication bottleneck obtained from genetic data
(Fig. S3A) (see below for details). We performed 1,000 simulations of 4Mb segments for a total
of 200 chromosomes (the total number of chromosomes of domestic rabbits for the six breeds)
and a SNP density equal to the observed data. Sequencing pooled DNA results in an additional
source of error associated with sampling with replacement of the sequenced alleles. The average
coverage (~60X) for domestic rabbits in our dataset was much lower than the total number of
chromosomes in the pools (200 chromosomes of domestic origin), and thus allele counts for each
position are likely to mostly represent different chromosomes. Nevertheless, in order to more
closely mimic the pool sequencing data structure we resampled alleles in the simulated data for
each position by randomly drawing values from the empirical distribution of coverage in the
observed data. The background frequency spectrum was obtained for each simulation
independently and, similarly to the observed data, the likelihood ratio was calculated every 10 kb.
Sweep regions were considered significant at a P<0.001 (i.e. likelihood ratio values higher than
14 the top value observed in our simulated data) and the borders of these regions were extended by
aggregating genomic positions while P<0.01. Sweep regions separated by less than 10 genomic
positions with P-values not meeting the above criteria were collapsed into a single region. Using
a P-value < 0.001 approximately 250 windows are expected to be significant just by chance alone
(225,368 * 0.001), but we identified 4758 windows resulting in a false discovery rate of ~5%.
The magnitude of the domestication bottleneck (kdom) estimated in this study (Fig. S3B)
describes a more stringent bottleneck (kdom = 1.3) when compared to a previous estimate (kdom =
2.8) (3). The power to uncover regions consistent with directional selection using the allele
frequency spectra has been shown to vary with the magnitude of the bottleneck, and not always in
the more intuitive direction (31). For example, CLR values and their variance are lower in
stronger bottlenecks than that in bottlenecks of intermediate strength or even in equilibrium
models. Although the previous estimate in rabbits was based on a much smaller dataset (16
fragments and biased towards the X-chromosome) when compared to the 5,000 fragments used
here, we created an additional null distribution using the same non-equilibrium demographic
model but incorporating this less stringent estimate. The distribution is slightly inflated but 63%
of regions still displayed CLR values that were not observed in the null distribution and the
remaining regions were highly significant (P≤0.003). Given that our current estimate is based on
a much larger dataset, and thus likely to be more robust and more representative of genome-wide
patterns of genetic diversity, we list in the main manuscript all regions identified using this
estimate but both P-values are available in Database S2. Genes within regions robust to these
different demographic models are the most promising candidates to follow up with functional
studies. For instance, regions containing GRIK2 and SOX2 (Fig. 2) are amongst these regions.
15 A potential confounding factor in both our sweep analysis is artificial selection associated
with breed formation. These later events in the domestication process could potentially result in
selection being inferred for genomic regions not associated with the initial steps of domestication.
We examined this by testing whether two genes (ASIP and TYR) that control breed-characteristic
coat colour phenotypes overlap regions detected in our genomic scan. Three of the six breeds
carried mutations at these loci, New Zealand (TYR), Champagne d’Argent (ASIP) and Dutch
(ASIP) (32, 33). Although these phenotypes follow Mendelian inheritance patterns and certainly
represent some of the strongest signatures of selection imposed by the process of breed
formation, none of the genes were found within the regions inferred to be under selection. This
finding, although anecdotal, suggests that our catalog of regions targeted by directional selection
should be highly enriched for genes selected before breed formation, validating the utility of our
approach.
Confirmation of selective sweeps using a targeted capture dataset. To confirm our selective
sweep regions obtained using the pool sequencing approach we generated an additional dataset
by sequencing genomic regions enriched by DNA hybridization on microarrays (34) and focused
on a slightly different set of breeds (six in total and three in common with the pool sequencing
data) and wild individuals from France (five localities in total and none in common with the pool
sequencing data; Table S4). Published sequence data for the same genomic regions from six wild
rabbits from the Iberian Peninsula (35) were used in data analysis.
The full methodological details are given elsewhere (35) so here we provide a brief
description. We designed a custom Agilent array for the selective enrichment of 6 Mb of intronic
sequence throughout the rabbit genome (5000 fragments of 1.2 kb). This array was designed
initially for the study described above and thus the sequenced fragments are located randomly
16 with regard to gene function or to the location of the sweeps inferred from the pool sequencing
approach. All sequencing runs of the barcoded Illumina sequencing libraries were performed on
an Illumina GAII platform using a combination of single-end and paired-end 76 bp reads. As
before, read mapping to the rabbit reference genome was performed using the Burrows-Wheeler
alignment tool using default parameters and PCR duplicates were removed from further analysis.
SNP and genotype/consensus calling were also carried out using Samtools. SNPs with a
minimum quality of 20, minimum mapping quality of 20 (root mean square), and distancing 10
bp from indel polymorphisms were kept for individual genotype calling. Homozygote and
heterozygote genotypes were accepted according to the algorithm implemented in Samtools if the
total effective sequence coverage was equal or higher than 8X and genotype quality equal or
higher than 20, otherwise that specific genotype was coded as missing data. Consensus calling for
positions where no SNPs were identified was performed using these same criteria, otherwise that
specific position was coded as missing data. The average effective coverage per individual (i.e.
after quality filtering and duplicates removal) was 30X±4.3, and >91% of targeted positions were
covered by ≥8 reads in all individuals (Table S4).
This dataset consisting of DNA sequence variation in individual (rather than pooled) wild
and domesticated rabbits allows a formal investigation of the main demographic events in the
recent history of domestic rabbits, and its impact upon levels and patterns of genetic diversity
across the genome. We carried out computer simulations of the coalescent process, according to
the model depicted in Fig. S3A, using a modified version of the computer program ms (36). Our
main goal was to estimate the magnitude of the domestication bottleneck which is described as
kdom = Nbdom/ddom, where Nbdom is the size of the bottlenecked population and ddom the duration of
the bottleneck in generations. Similar models have been applied previously to other domestic
17 species (37-42) and also to rabbits (3). Our model consisted of three populations and describes
two bottleneck events occurring from an ancestral source population. First, we incorporated the
recent colonization of France from the Iberian Peninsula after the last glacial maximum and
consequent bottleneck (3, 43), in which an ancestral population (rabbits from the Iberian
Peninsula) of constant size (Na) gives rise at time t2fr to a small founder population (Nbfr). More
recently, the bottlenecked population at time t1fr (dfr = t2fr – t1fr, where dfr is the duration of the
bottleneck in generations) expands into its current size (Npfr). Second, we incorporated the
domestication bottleneck from French wild populations. The overall configuration of this second
event is similar to the colonization of France and consists of similar parameters (Nbdom, Npdom,
ddom, and kdom). Several summary statistics were calculated both for observed and simulated data.
We summarized levels and patterns of genetic diversity using two standard estimators of the
neutral mutation parameter: Watterson's θw (44), the proportion of segregating sites in a sample,
and π (45), the average number of pairwise differences per sequence in a sample. Genetic
differentiation between populations was described by means of the fixation index (FST) (46). All
these statistics were calculated for each 1.2 kb fragment independently.
We simulated data varying the magnitude of the domestication bottleneck (kdom). To find
the best fitting model we compared summary statistics computed on the observed data to those
computed on simulated data for each fragment independently. The observed missing data for
each fragment was incorporated in the simulations and calculations of the summary statistics
(36). Owing to the complex and mostly unknown demography of the natural populations of
rabbits, our model is certainly a simplified scenario. Therefore, we conditioned the simulations
using rejection sampling (47). Briefly, we simulated the population of rabbits from the Iberian
Peninsula as the ancestral population, and forced the simulations to fit the data observed in
18 French wild rabbits. Simulations were kept if the values obtained were within 20% of the
observed values of π, θw and FST, and run until 1,000 genealogies were recorded. This should, in
principle, help remove uncertainty associated with the demographic scenario prior to
domestication that is not historically documented, which might otherwise have negatively
impacted our estimation of kdom (39). The best-fitting value of kdom for each fragment was then
estimated as the proportion of 1,000 simulations whose summary statistics (θw and FST) were
contained within 20% of the observed values in domestic rabbits. The overall maximumlikelihood across loci was estimated as the product of likelihoods for each locus. Loci
incompatible with the multilocus estimate of kdom (see below) were removed and the kdom estimate
updated until no deviations were found.
We used several key assumptions. First, previously published effective population size
estimates for wild rabbits from Iberian Peninsula and France were used as current population
sizes (Na = 1,000,000 and Npfr = 500,000 (3, 48)), and the current population size of domestic
rabbits was assumed to be 50,000. Second, variation in mutation rate among fragments was
incorporated in the simulations from the variation in the population mutation parameter (θw =
4Neµ for autosomal and θw = 3Neµ for X-linked loci) estimated from the wild rabbits from the
Iberian Peninsula (i.e. the ancestral population). Third, using available historical evidence (43,
49) and assuming a generation time of 1 year (50), we assumed a split time between wild rabbits
from Iberian Peninsula and wild rabbits from France of 10,000 generations (i.e. after the last
glacial maximum), and the initial domestication event 1,400 generations ago. We have previously
shown that because the short time scale since the initial rabbit domestication provides little
opportunity for new mutations to have a substantial impact, several fold changes in Ne or split
times result in little or no qualitative change in the estimation of kdom 25). Fourth, the parameter k
19 is the ratio of Nb and d, which are positively correlated (37, 38). Therefore, we fixed ddom at 500
generations and used 35 different values of Nbdom so that kdom ranged between 0.1 and 15.
Because our previous estimate of kdom based on a smaller dataset was 2.8 (3), we used a finer grid
of values between 0.5 and 3.6. Fifth, we included the population recombination parameter (θ =
4Ner) in our simulations assuming the genome-wide average recombination rate in rabbits
(~1cM/Mb) (21) and estimates of Ne described above. Finally, for computational efficiency we
used a previous estimate of kfr = 2 for the magnitude of the bottleneck associated with the
colonization of France (3). Our rejection sampling approach should, in principle, attenuate the
potential uncertainty associated with this parameter.
Although several features of the demography of both wild and domestic rabbits may not
have been accurately captured by our model, the estimated model generated shifts in genetic
diversity between wild rabbits from France and domestic rabbits (measured using both π and θw)
as well as levels of differentiation between populations (FST and percentage of shared mutations)
that were qualitatively similar to those estimated from the observed data (Fig. S4). Average
values for all statistics were also similar between observed [πdom/(πdom + πfr) = 0.355;
θdom/(θdom + θfr) = 0.337; FST = 0.141; % shared = 0.470] and simulated data [πdom/(πdom + πfr) =
0.376; θdom/(θdom + θfr) = 0.354; FST = 0.090; % shared = 0.535], and even more similar when loci
incompatible with genome-wide background levels and patterns of genetic diversity were
removed as described below from the observed data [πdom/(πdom + πfr) = 0.393; θdom/(θdom + θfr) =
0.367; FST = 0.091; % shared = 0.519]. Overall, our model is generally consistent with observed
patterns of sequence diversity.
If directional selection resulted in a sweep of a favorable allele associated with a
domestication trait, we expect to find one, or both of the following signatures: 1) a loss of
20 heterozygosity in the genomic region under selection significantly greater than the loss in genetic
diversity produced by the domestication bottleneck; and 2) higher differentiation between wild
and domestic populations due to the potential for directional selection to generate elevated levels
of population differentiation in structured populations. Both these signatures under the described
model make no assumptions as to whether selection happened on new or on previously existing
genetic variation. In order to identify regions under selection, we used the multilocus value of
kdom and interrogated for each locus individually whether levels of genetic diversity (θw) and
differentiation (FST) were significantly lower and higher (P≤0.05), respectively, than expected
from the null demographic model alone. Given that these two signatures, although partially
interrelated because both depend on a within-population component of variation, may respond
differently to distinct forms of selection, we performed a significance test for each summary
statistic independently (Database S2). Unlike the previous implementation based on
SweepFinder, this method using a more stringent kdom and focused on reductions of
heterozygosity and increased differentiation renders our test conservative. Due to the small
number of individuals used and the relatively short size of the sequenced fragments, we note that
P-values associated with individual fragments are not robust to multiple testing. However, our
main intent with this additional screen for selection was not to attach great significance to
individual loci but to corroborate the findings of the pool-sequencing approach (see main
manuscript). In fact, under the specified significance threshold (P<0.05) we identified a strong
excess of outlier fragments (FST, n = 703; θw, n= 403) compared to the chance expectations alone
(5000*0.05 = 250) This suggests that our combined list of outlier regions based on FST and θw (n
= 936) contains false positives but should be highly enriched for regions displaying levels and
patterns of genetic diversity that are incompatible with the estimated demographic model.
21 Analyses of allele frequency differences between domestic and wild rabbits at individual
SNPs. For estimations of allele frequencies of single SNPs we required minimum read depth
sums of 20, 10, and 40 reads across the six domestic population pools, the three French wild
pools and the 11 wild Iberian pools, respectively. This filter retained 34,293,238 SNPs where
reliable allele frequencies could be estimated. The per-SNP absolute allele frequency difference
(ΔAF) between domestic and wild rabbits was then calculated using the formula: ΔAF=
abs(RefAFdom-mean(RefAFfrench+ RefAFiberian)). We next binned SNPs by ΔAF in steps of
0.05 (i.e. ΔAF=0–0.05, 0.05–0.10, etc. until 0.95-1.00) and intersected these binned SNPs with
coding exons, UTRs, introns and non-coding elements identified as under evolutionary constraint
in mammals using a rate-based score (omega) (8). Coding exons and introns were based on rabbit
Ensembl version 73. UTRs of Ensembl gene models were frequently either missing or were
truncated in relation to those annotated in human. For the intersections of ΔAF SNPs with UTRs
we therefore utilized a custom gene prediction pipeline less reliant on cross-species homology
and more reliant on our rabbit RNA-sequencing data. This pipeline defined UTRs as elements of
predicted protein coding exons where no open reading frame was identified. In order not to
include retained introns due to incomplete splicing in our UTR set we removed sequences
extending an Ensembl intron by more than 90% of intron length. For intersections with ΔAF
binned SNPs we also used elements identified as under evolutionary constraint in analysis of 29
mammalian genomes (8) (Omega set) that were lifted over from hg18 to OryCun2 using the
liftOver tool (51) from the University of Santa Cruz (UCSC) and the resulting rabbit elements
were then stripped of those overlapping Ensembl version 73 exons. For each ΔAF bin, the
proportions of SNPs falling into coding exons, UTRs, introns and the 29 mammals conserved
elements were then determined by intersections using the software bedops (52).
22 Coding variants and assessment of functional significance. To investigate the location of
SNPs within genes and their potential coding properties we started by using ANNOVAR (53) and
relied on Ensembl 73 annotations. We specifically focused on missense SNPs showing a
ΔAF>0.90 between wild and domestic rabbits. To infer whether these missense mutations are
likely to have functional significance we used several approaches. First, we analyzed sequence
conservation for each SNP among 100 vertebrates and placental mammals using the UCSC
(University of California Santa Cruz) genome browser with rabbit coordinates obtained with the
liftover tool. To identify potential artefacts due to errors in gene predictions, we used the Basic
Local Alignment Search Tool (BLAST) (54) for alignment of proteins to the non-redundant
database in UniProtKB (UniProt Knowledgebase), aligned the orthologs, and visually inspected
the alignments. For example, this information allowed us to identify Ensembl gene models that
we believe represent nonfunctional retrogenes due to errors in gene prediction, which was further
supported by lack of expression data for those genes using the RNA-seq data generated to
annotate the reference genome (see above). Second, the nature of each mutation was assessed
using standalone versions of SIFT v4.0.5 (55) and PolyPhen2 v2.2.2 (56) using default
parameters. Finally, we inferred the ancestral or derived state of each allele using the outgroup
Lepus americanus as mentioned above.
Detection of insertions/deletions. We called small insertions/deletions (indels) with GATK (57)
using the INDEL model of UnifiedGenotyper with default parameters. In total, 9,331,686 indels
in relation to the reference genome were discovered. Because indels are often a product of read
misalignment, sequence reads overlapping called indel positions were realigned with the GATK
pipeline. We could validate that every initially called indel was recalled again and although
restricted by previously discovered indel positions, no novel indels were reported for the
23 realigned dataset. However, the number of alleles counted had been readjusted giving a better
estimation of the allelic frequencies at indel positions. In order to calculate the frequency spectra
of the distribution, we extracted counts of reads supporting the observed alleles from the GATK
vcf files and calculated the reference allele frequency for each sequenced pool.
In order to limit the effect of misalignment on called indels we discarded those where the
resequenced reference individual had any reads supporting a non-reference allele (n=1,223,493).
Then, we established restrictive depth filters to distinguish real indels from putative artefacts.
Only indel positions that were supported by a read depth of reads within a 50% range from the
average for each population, inclusive the reference individual, were analysed and counted and
1,112,286 positions were removed by this filter. In this analysis, different median depth
thresholds were used for autosomes, X-chromosome and the mitochondrion. All the unplaced
(Un) scaffolds were treated as autosomal. We ran GATK with default parameters to report a
maximum of six allelic variants. From those resulting indels (n= 6,995,907), we found 6,380,621
bi-allelic, 502,852 tri-allelic, 103,723 tetra-allelic, 8,550 penta-allelic, and 161 hexa-allelic
indels.
There were 1,413,933 indels that supported different major alleles in the two wild subspecies included in this study (O. c. cuniculus and O. c. algirus). These positions were discarded
from further analyses. We then grouped the remaining indels (n= 5,581,974) into 5% bins of
absolute allele frequency difference (ΔAF) between wild and domestic rabbits to create the
background genome-wide distribution. We intersected indels in these ΔAF bins with Ensembl
v73 UTRs and coding sequences as well as elements under evolutionary constraint in mammals
and identified 2,331, 251 and 9,213 indels falling in these three categories, respectively. Sweeps
detected by SweepFinder and the FST-Het combination were merged in one unique dataset and we
24 enlarged these loci by taking 20 kb surrounding them and intersected with indels. Thus, we
discovered 15,923 indels overlapping sweeps.
Detection of structural changes. To identify duplications/deletions that could differ between
wild and domestic rabbits we performed an analysis of variance (ANOVA) on depth of coverage
using the package implemented in R (58). We scanned the genome in 1 kb non-overlapping
windows and all depths were normalized against the Flemish giant breed, which had higher
average depth compared to the other breeds. The contrast was made between all domestic breeds
and wild populations. For each window we also calculated a M-value to measure fold-coverage
difference between domestic and wild populations as follows:
𝑀 − 𝑣𝑎𝑙𝑢𝑒 = 𝑙𝑜𝑔! (
𝐷𝑜𝑚𝑒𝑠𝑡𝚤𝑐 𝑑𝑒𝑝𝑡ℎ
)
𝑊𝚤𝑙𝑑 𝑑𝑒𝑝𝑡ℎ
In total, 2,165,484 windows were analysed from which 710 windows were filtered out
due to low coverage. 29,792 windows were significant with a P ≤ 0.001. Using the P-value of the
ANOVA analysis together with M-values, we merged significant windows to identify regions
indicating the same signature of duplication/deletion using the following criteria. First, we
opened a locus when a given window fulfilled two criteria: 1) P ≤ 0.001; and 2) absolute (Mvalue) ≥ 0.6. Second, we continued scanning by setting a dynamic cut-off for |M-value|. It was
redefined when a given window had higher |M-value| than the starting threshold (|M-value| ≥ 0.6).
To merge adjacent windows, we employed a more lenient cut-off for the M-value (≥ 0.5 *
redefined |M-value|) and P-value (≤ 0.05). Third, we allowed two tandem outliers and closed the
locus when three or more consecutive outliers were detected. Finally, we rescanned windows
upstream of the opening site using the criteria mentioned in previous steps.
25 Gene overrepresentation analyses. In order to assess enrichment of gene ontology terms and
other ontology terms such as those associated with murine phenotypes and gene expression
among ΔAF≥0.8 SNP loci we utilized the software Genomic Regions Enrichment of Annotations
Tool (GREAT) (11). In order not to inflate significances due to inclusion of SNPs in strong
linkage disequilibrium we selected only one SNP per 50 kb from the set of 1635 SNPs in
conserved elements with ΔAF ≥0.80, leaving 1,071 SNPs. We next lifted this selected set of
rabbit SNPs over to the human assembly hg18 and used the resulting human coordinates as input
for GREAT, all genes with a Transcription Start Site (TSS) within 1 Mb from a high ΔAF SNP
were included in the analysis. Overrepresentation was tested using hypergeometric tests, and
Bonferroni corrected P-values were used to evaluate statistically significant overrepresentation of
terms. For extraction of data in order to condense the GREAT output for visualization in Fig. S5,
we first required ≥6 genes associated with a term and then further filtered the GREAT output
requiring a Bonferroni P <0.05 and an enrichment >1.7 for GO Biological Process as well as for
mouse phenotype gene association terms (MGI Phenotypes) in Fig. S5 and a Bonferroni P<1x108
and an enrichment >2.2 for MGI expression. Next we determined the fractions of genes shared
in each pairwise contrast of associated terms (in relation to the term containing fewest genes) and
this matrix of gene sharing fractions was then subjected to hierarchical clustering (Pearson
correlation) to construct heat maps. Annotations summarizing clustered terms to the left of heatmaps were manually inferred from included terms to represent a description of the individual
terms in each cluster.
Electrophoretic mobility shift assays (EMSA)
Cells and preparation of nuclear extracts. Mouse ES-cell derived neural stem cells were
obtained by in vitro differentiation of R1 ES cells using the protocol by Conti et al. (59). The
26 resulting neural stem cells were propagated in N2B27 medium supplemented with EGF and
FGF2 (60). After dissociation into single cells by trypsinization, cell pellets were stored at -70°C
for further analysis. The P19 embryonic carcinoma cells were maintained in Alpha Modified
Eagle’s Medium (αMEM, Invitrogen) supplemented with 10% heat-inactivated fetal bovine
serum and penicillin (0.2 U/mL)/streptomycin (0.2 µg/ml)/L-glutamine (0.2 µg/ml) (Gibco) at
37ºC in a 5% CO2 humidified atmosphere. In order to induce the neuronal differentiation of P19,
the cells were cultured in αMEM growth medium containing 1 µM all-trans-Retinoic Acid (RA,
Sigma Aldrich R-2625) in bacterial garde Petri dishes to promote the aggregation of embryonic
bodies (EB). After 48h the EBs were plated in adherent culture plates in RA-free growth medium
and after 48h the differentiated P19 cells were harvested for preparation of nuclear extracts.
The nuclear extracts were prepared from embryonic stem cell-derived neural stem cells
(ES-NSC) and P19 cells using NucBuster Protein Extraction Kit (Novagen).
EMSA. Seventeen selected SNPs were functionally assessed using EMSA to reveal their
potential to affect DNA-protein interaction. 5’Biotin-labelled probes were synthesized (Integrated
DNA Technologies) and can be obtained upon request. Double-stranded probes were generated
by annealing single-stranded complementary oligonucleotides in 1X NEB2 buffer (New England
Biolabs). Two µg of the nuclear extracts were added to the binding reaction (10X binding buffer,
30.1 mM KCl, 2 mM MgCl2, 7.5% Glycerol, 0.1 mM EDTA, 0.063% NP-40 and 1µg/ml
Poly(dI•dC)) and then preincubated for 20 min on ice. For competition reactions, 20 pmol of
unlabeled double-stranded oligos were added to the reaction. Thereafter, 20 fmol of biotinylated
oligos were added to the reactions and incubated for 20 min at RT. The DNA-protein complexes
were separated by electrophoresis on 5% non-denaturing polyacrylamide gel at 100 V for 1.5 h at
RT in 0.5×TBE running buffer. The separated complexes were transferred to nylon membrane
27 (Perkin Elmer) at 45 V for 1 h in cold 0.5×TBE. The DNA-protein complexes were crosslinked
using a transilluminator with 312 nm UV bulbs. The biotinylated probes were detected using
Lightshift Electrophoretic Mobility-Shift Assay kit (Thermo Scientific).
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Accuracy and coverage assessment of Oryctolagus cuniculus (rabbit) genes encoding
immunoglobulins in the whole genome sequence assembly (OryCun2.0) and localization of the
IGH locus to chromosome 20. Immunogenetics 65, 749-762 (2013).
34 R
French Domestic
A
Identity
score
Iberian
1.0
0.8
0.6
0.4
0.2
0
Windows along rabbit genome
B
1.0
6.5
0.9
RAF Wild French
0.7
5.5
0.6
5.0
0.5
4.5
0.4
4.0
0.3
3.5
0.2
log10(nSNPs)
6.0
0.8
3.0
0.1
2.5
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
RAF Domestic
Fig. S1: Genetic relatedness between populations sampled in this study. (A) Heat map
(color code to the right) of identity scores based on comparing resequencing data with
the assembly. The x-axis represents genome coordinates with chromosome 1 to the left
and chromosome X to the right. The first row (R) represents the reference rabbit against
itself, rows 8-21 correspond to locations marked with red dots in Fig. 1A, ordered according
to a northeast to southwest transection. (B) Strong correlation in allele frequencies between
domestic and French wild rabbits; RAF French and RAF Domestic represent the frequency
of the reference allele in wild French and domestic rabbits, respectively.
A
Heterozygosity domestic pools
number of windows
3000
2500
2000
1500
1000
500
0
0
0.05
B
0.15
0.2
0.25
Heterozygosity
0.3
0.35
0.4
0.45
0.5
FST domestics vs. wild French
6000
number of windows
0.1
5000
4000
3000
2000
1000
0
0
0.1
0.2
0.3
FST domestics vs. wild French
C
FST
0.4
0.5
0.6
0.7
Scatter plot FST and heterozygosity
0.8
0.6
0.4
0.2
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Heterozygosity domestic pools
0.4
0.45
0.5
Fig. S2: Distributions and scatterplot of heterozygosity and FST of 50 kb windows.
(A) Distribution of pool heterozygosity in the domestic pools sequenced. The arrow indicates
the upper heterozygosity treshold for opening a sweep. (B) Distribution of FST values observed
in the contrast domestics vs. wild French. The arrow indicates thelower FST treshold for opening
a sweep. (C) Scatter plot of FST and pool heterozygosity. Red dots represent windows fulfilling
the sweep opening requirement (FST >= 0.35 and heterozygosity <= 0.05)
Nbfr
t2fr
dfr
t1fr
Na
Npfr
d
Nbdom dom
t2dom
t1dom
Npdom
Present
Wild Iberia Wild France
Domestics
12000
Past
0
B
Relative
Relative ln
ln likelihood
likelihood
4000
8000
A
0
3
6
9
12
Kdom
Kdom
Fig. S3: Demographic model and magnitude of the domestication bottleneck estimated
using the targeted capture dataset.(A) Schematic representation of the coalescent model
used to represent the main events of the demographic history of wild and domestic rabbits.
See Supplementary Methods for a detailed description of all parameters and assumptions.
(B) Diagram representing the multilocus maximum-likelihood (ML) estimate for the magnitude
of the domestication bottleneck (kdom). Multilocus ML (y axis) of the parameter kdom (x axis)
was estimated as the product of the likelihood for each locus.
15
0.25
0.020
0.015
0.010
0.005
0
0
0.005
0.010
π Domestics
0.015
0.020
0.25
A
0
0.005
0.010
0.015
π France
0.25
0
0.005
0.010
0.015
0.020
0.25
D
0
0
100
200
1000
300
400
Frequency
Frequency
2000
500
600
3000
700
C
0.020
0.1
0.2
0.3
0.4
0.5
0.6
FST
0.7
0.8
0.9
1
10
20
30
40
50
60
70
% Shared mutations
80
90
100
Fig. S4: Comparison between observed and simulated data under the estimated demographic model.
Observed and simulated data are represented in red and black, respectively. The first two panels (A and B)
illustrate the relationship between values of both π and θw in wild rabbits from France (x axis) and domestic
rabbits (y axis). The two histograms on the bottom row (C and D) illustrate the distribution of FST and
percentage of shared mutations between the two populations.These figures include the full observed
dataset, including outliers, explaining the discrepancy between the observed and simulated data.
80
160
140
120
100
0
3
4
2
60
40
20
3.5
2.5
80
1
0
0.5
1.5
5
0
0
0
0
0
0
1.0
0.9
5
5
20
20
30
15
25
30
15
20
25
30
0
Brain parts and sensory
organ Thieler stage 17
Positive regulation
of gene expression
20
Negative regulation
of gene expression
0.7
0.6
20
20
20
0.5
0.4
30
30
30
0.3
0.2
Hearing phenotype
0.1
160
proportion of genes shared
10
10
0.8
Abnormal sensory
capabilities
40
40
50
50
60
60
70
70
40
Abnormal cranial
nerve morphology
1.0
0.9
10
Abnormal
ectoderm
development
16
0
Respiratory
system
phenotype
14
12
6
5
4
8
6
4
2
0
10
3
2
1
250
200
150
50
100
MGI: Mouse Phenotypes
Lethality
associated
terms
4
0
0
P E N
P E N
C
25
0
200
5
0
0
Future brain + CNS
Thieler stage 15
10
0
Adherens junction
organization
0.1
Cerebellum, forebrain
and sensory organs
Thieler stage 20
0
0.3
0.2
15
20
25
Cell fate
20
Eye, sensory organ
and organ
morphogenesis
0.1
0.4
10
Morphogenesis
0.5
10
0.2
0.6
Different brain regions
Thieler stage 23
10
15
15
0.3
0.7
5
0.4
0.8
5
10
10
0.5
0.9
5
0.6
15
Pattern and axis
specification
0.8
0.7
10
Brain and
nervous system
development
5
Regulation of
cell development
and neurogenesis
proportion of genes shared
1.0
proportion of genes shared
0
25
20
15
10
MGI: Gene expression detected
80
80
80
60
B
200
180
160
140
120
100
0
40
20
5
5
5
5
5
4
5
3
1
2
0
9
8
7
6
5
4
3
2
1
0
10
Gene Ontology: Biological Process
A
0
0
70
250
60
6
0
50
Abnormal brain and
nervous system
morphology terms
18
0
Abnormal eye
development
P E N
Fig. S5: Gene overrepresentation analysis using the GREAT software based on genes associated with
high delta allele frequency SNPs (ΔAF≥0.8) at non-coding conserved sites. (A) Gene Ontology categories
within Biological Processes, (B) MGI mouse expression data and (C) MGI mouse phenotypes. Each row
in the heat-maps represents one specific category and colors on that row indicate the proportion of shared
genes in relation to the other categories (ordered in the same way on the x-axis as on y-axis). To the left
of each cluster of enriched terms the types of terms in that group is indicated in text. Bars immediately left
of heat-maps visualize the significance-, enrichment- and number of genes of each significant term
(P= -log10 Bonferroni-corrected P-value; E= fold enrichment: N=number of genes). The ranges for P, E,
and N are indicated below the plot. The full results are presented in Database S3.
Heterozygosity
0.4
0
FST
0.5
0
Delta > 0.75
1.00
0.75
Fst-Het
Sweepfinder
IMMP2L^ LRRN3 IMMP2L
DOCK4 ZNF277
*15263
LSMEM1
IMMP2L H. sapiens consensus
Chr7
47
47.5
48
48.5
49
49.5
Mb
Fig. S6: Selective sweep at IMMP2L on chromosome 7. Heterozygosity plots for wild (red)
and domestic (black) rabbits together with plots of FST values and high ΔAF (HΔAF) SNPs
with ΔAF>0.75. Putative sweep regions detected with the FST-Heterozygosity outlier approach
and SweepFinder are marked with horizontal bars. Gene annotations in sweep regions are
indicated. IMMP2L was not predicted by Ensembl but by our in-house predictions based on
RNA-sequencing data. The human consensus model shows all human IMMP2L transcripts
in a collapsed fashion. Asterisks (*) represent ENSOCUT000000. Red dots indicate the
location of the high ΔAF insertion/deletion in a conserved element for IMMP2L.
Mouse ES-cell derived neural stem cells
SNP-1 SNP-2
Cold-probe
D
WT
WT
SNP-6
SNP-3 SNP-4 SNP-5
D
WT
D
WT
D
D
D
WT
WT
SNP-7
D
SNP-8
WT WT
D
SNP-13 SNP-14 SNP-15 SNP-16 SNP-17
+ - + - + - + - + - + -
+ - + - + - + - + - + - + - + - + - + -
SNP-10
D
WT
+ - + - + - + - + - + - + - + -
+ - + - + - + -+ - + -+ - + - + - + -
SNP-12
SNP-9
WT
D
SNP-11
D
WT
WT
D
WT
D
WT
D
D
WT WT
D
WT
D
Un-differentiated mouse P19 embryonic carcinoma cells
SNP-1
WT D
SNP-2
WT D
SNP-3
WT D
SNP-4
WT D
SNP-5
D WT
SNP-6
D WT
SNP-7
D WT
SNP-8
WT D
SNP-9
WT D
SNP-10 SNP-11
D WT WT D
SNP-12
WT D
SNP-13
WT D
SNP-14
WT D
SNP-15
D WT
SNP-16 SNP-17
WT D WT D
Differentiated mouse P19 embryonic carcinoma cells
SNP-1
WT D
SNP-2
WT D
SNP-3
D WT
SNP-4
WT D
SNP-5
D WT
SNP-6
D WT
SNP-7
D WT
SNP-8
WT D
SNP-9
WT D
SNP-10
D WT
SNP-11
WT
SNP-12
SNP-13
SNP-14
SNP-15
WT D
WT D
WT D
D WT
D
SNP-16 SNP-17
WT D
WT D
Un-differentiated and differentiated mouse P19 embryonic carcinoma cells repeated for SNPs 1,2,3,5 and 13
SNP-1
Cold-probe
un-diff
WT
D
+ - + -
diff
WT
D
+ - + -
SNP-2
un-diff
WT
D
+ - + -
diff
WT
D
+ - + -
SNP-3
un-diff
WT
D
+ - + -
diff
WT
D
+ - + -
SNP-13
SNP-5
un-diff
D
WT
+ - + -
D
diff
+ -
WT
+ -
un-diff
WT
D
+ - + -
diff
WT
D
+ - + -
Fig. S7: Results for all 17 SNPs functionally examined by electrophoretic mobility shift assays (EMSA).
Results of EMSA using nuclear extracts from ES-cell derived neural stem cells or from mouse
P19 embryonic carcinoma cells before (un-diff) or after neuronal differentiation (diff) are presented.
WT=wild-type allele; D=domestic, the most common allele in domestic rabbits. Cold probes at
100-fold excess were used to verify specific DNA-protein interactions. The results are summarized
in Table S7.
Table S1. Summary statistics of the rabbit OryCun2.0 assembly
Coverage (Q20)
Contig N50 (kb)
Scaffold N50 (Mb)
Assembly size with gaps (Gb)
Assembly size without gaps (Gb)
Number of anchored scaffolds
% of assembly in anchored scaffolds
Sequence in anchored scaffolds (Gb)
OryCun2.0
6.55X
64.7
35.9
2.66
2.60
99
82.0
2.18
Table S2. Comparison between rabbit genome assembly versions OryCun1.0 and OryCun2.0.
Assembly
Covera
ge
(Q20)
Contig
N50
(kb)
Scaffold
N50 (Mb)
Assembly
size (with
gaps)
Assembly
size
(without
gaps)
OryCun1.0
1.95X
2.84
0.05
3.69
2.08
N/A
N/A
N/A
OryCun2.0
6.55X
64.65
35.92
2.66
2.60
99
82
2.18
Number
anchored
scaffolds
% assembly Sequence in
anchored
anchored
scaffolds
bases (Gb)
Table S3. Summary of RNA-sequencing data for annotation of the rabbit genome
Adult adrenal gland
85 775 334
Assembled
Transcripts
29 672
Blood
103 242 794
25 451
1226
Zyagen
Brain
118 014 338
59 029
1362
Zyagen
Fetal adrenal gland – gestation day 28
92 146 626
36 404
1465
INRA
Heart
100 862 716
35 557
1433
Zyagen
Kidney
108 298 292
39 556
1403
Zyagen
Liver
102 233 272
30 711
1435
Zyagen
Lung
93 531 078
41 731
1479
Zyagen
Mammary gland – gestation day 3
89 869 204
57 763
1336
INRA
Mammary gland - gestation day 14
84 998 914
58 136
1377
INRA
Mammary gland – lactation day 16
89 377 970
27 691
1403
INRA
Ovary
110 264 790
42 350
1381
Zyagen
Peripheral blood mononuclear cells
83 915 910
28 031
1493
INRA
Placenta – female fetus – gestation day 28
86 728 588
30 992
1388
INRA
Placenta – male fetus – gestation day 28
76 666 886
28 420
1389
INRA
Skeletal muscle
96 519 628
28 805
1489
Zyagen
Skin
87 995 594
43 294
1462
Zyagen
Spleen
87 432 290
48 398
1426
INRA
Testis
117 649 896
63 676
1191
Zyagen
Tissue Site
Pass Filter Reads
Median Transcript
Length (bp)
1380
Source
INRA
Table S4. Sample details of wild and domestic rabbits used in this study both
for whole genome resequencing and DNA sequence capture using hybridization
on microarrays. Average sequence coverage per sample is provided.
Population
Breed/Location
ID
Fig.1c
Sample size
Sequencing method
Coverage
Domestic
Belgian hare
17 (pool)
WGS
10.4
Domestic
Champagne d'argent
16 (pool)
WGS
11.2
Domestic
Dutch
13 (pool)
WGS
10.1
Domestic
Flemish giant
18 (pool)
WGS
12.1
Domestic
French lop
20 (pool)
WGS
11.9
Domestic
New Zealand white
Thorbecke
(reference)
16 (pool)
WGS
11.1
1 (individual)
WGS
10.9
1 (individual)
WGS
10.2
Domestic
Hare
(outgroup)
Wild France
Missoula, Montana
Caumont
FRW1
10 (pool)
WGS
10.7
Wild France
La Roque
FRW2
10 (pool)
WGS
11.5
Wild France
Villemolaque
FRW3
10 (pool)
WGS
12.0
Wild Iberian
Huelva
IW11
16 (pool)
WGS
11.0
Wild Iberian
IW10
11 (pool)
WGS
10.7
IW9
20 (pool)
WGS
11.0
Wild Iberian
Milmarcos
S. Agustin de
Guadalix
Mora
IW8
13 (pool)
WGS
11.9
Wild Iberian
Toledo
IW7
14 (pool)
WGS
10.7
Wild Iberian
Mazarambroz
IW6
16 (pool)
WGS
11.3
Wild Iberian
Urda
IW5
16 (pool)
WGS
11.4
Wild Iberian
IW4
17 (pool)
WGS
11.7
IW3
16 (pool)
WGS
12.1
Wild Iberian
Carrión de Calatrava
Calzada de
Calatrava
Pedroche
IW2
11 (pool)
WGS
11.2
Wild Iberian
Zaragoza
IW1
12 (pool)
WGS
6.1
Wild France
Aveyron
1 (individual)
Seq. capture
27.3
Wild France
Fos-su-Mer
2 (individual)
Seq. capture
31.7; 31.1
Wild France
Herauld
1 (individual)
Seq. capture
30.5
Wild France
Lancon
2 (individual)
Seq. capture
33.3; 34.3
Wild France
Vaucluse
1 (individual)
Seq. capture
34.0
Domestic
Belgian hare
1 (individual)
Seq. capture
35.5
Domestic
Champagne d'argent
1 (individual)
Seq. capture
31.3
Domestic
Angora
2 (individual)
Seq. capture
27.9; 30.7
Domestic
French lop
1 (individual)
Seq. capture
36.5
Domestic
Flemish giant
2 (individual)
Seq. capture
24.3; 20.8
Domestic
Rex
1 (individual)
Seq. capture
26.7
Wild Iberian
Wild Iberian
* WGS= whole genome resequencing, Seq. Capture = Targeted resequencing after sequence
capture
Table S5. Summary of SNPs and Insertions/Deletions detected in the
rabbit genome
Type
Total count In coding
Missense2 Conserved
1
sequence
non-coding3
SNPs
Indels
1
50,165,386
5,581,974
154,489
2,812
48,453
-
719,911
82,289
In coding exons based on Ensembl v73 gene models
Amino acid alteration predicted by the software Annovar (28)
3
Located within elements under evolutionary constraint but outside of Ensembl v73
coding exons
2
Table S6. Distributions of SNP counts in the different delta allele frequency bins for conserved non-coding elements, UTRs, coding sequences and introns.
Conserved non-coding sites
All
UTRs
Coding
ΔAF bin
a
0.95-1.0
0.90-0.95
0.85-0.90
0.80-0.85
0.75-0.80
0.70-0.75
0.65-0.70
0.60-0.65
0.55-0.60
0.50-0.55
0.45-0.50
0.40-0.45
0.35-0.40
0.30-0.35
0.25-0.30
0.20-0.25
0.15-0.20
0.10-0.15
0.05-0.10
0-0.05
a
Observed
o
1,030
6,987
22,968
55,438
108,850
184,708
287,540
417,269
578,178
771,964
1,003,765
1,294,445
1,650,687
2,095,342
2,640,617
3,317,366
4,268,059
5,451,393
6,017,447
4,119,252
o
Observed Expected
27
140
448
1,021
1,918
3,009
4,703
6,560
8,953
11,789
15,031
18,986
23,783
30,227
37,522
47,090
60,926
79,151
89,894
58,999
15
102
335
809
1,588
2,694
4,194
6,086
8,433
11,259
14,640
18,880
24,076
30,561
38,514
48,385
62,251
79,510
87,766
60,080
b
c
b
P
M
1.80E-03
1.50E-04
4.99E-10
5.98E-14
7.29E-17
9.74E-10
2.42E-15
9.32E-10
1.17E-08
4.86E-07
1.13E-03
4.37E-01
5.71E-02
5.43E-02
3.54E-07
3.02E-09
8.81E-08
2.00E-01
4.62E-13
8.88E-06
0.8
0.5
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Observed
22
113
378
875
1,720
2,782
4,397
6,304
8,375
10,936
14,266
18,459
23,277
29,643
36,784
45,569
59,111
75,301
83,900
57,428
o
Expected
14
98
321
775
1,522
2,583
4,022
5,836
8,087
10,797
14,039
18,105
23,087
29,306
36,933
46,398
59,695
76,245
84,162
57,614
b
P
c
3.13E-02
1.27E-01
1.36E-03
2.97E-04
3.20E-07
8.04E-05
2.60E-09
6.85E-10
1.26E-03
1.78E-01
5.37E-02
8.06E-03
2.08E-01
4.74E-02
4.35E-01
1.06E-04
1.61E-02
5.76E-04
3.63E-01
4.35E-01
M
b
0.7
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Observed
d
10
56
147
391
646
1,161
1,812
2,592
3,521
4,762
6,046
7,810
9,629
12,263
15,408
19,111
24,976
32,760
37,931
27,334
o
Expected
6
42
140
337
661
1,122
1,747
2,535
3,513
4,691
6,099
7,865
10,030
12,732
16,045
20,157
25,933
33,123
36,563
25,029
b
P
c
M
d
1.01E-01
3.03E-02
5.53E-01
3.17E-03
5.58E-01
2.43E-01
1.19E-01
2.56E-01
8.92E-01
2.98E-01
4.96E-01
5.34E-01
5.91E-05
3.06E-05
4.55E-07
1.47E-13
2.51E-09
4.54E-02
7.17E-13
2.28E-48
Introns
b
Observed
d
325
2,089
6,951
16,671
32,177
54,993
85,412
124,361
172,726
229,011
299,028
387,925
495,609
632,561
793,553
984,798
1,255,095
1,597,012
1,757,751
1,186,573
0.7
0.4
0.1
0.2
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
-0.1
-0.1
-0.1
-0.1
-0.1
0.0
0.1
0.1
o
Expected
b
304
2,061
6,774
16,351
32,105
54,479
84,808
123,071
170,530
227,687
296,055
381,789
486,861
618,010
778,835
978,439
1,258,840
1,607,858
1,774,813
1,214,951
c
M
1.51E-01
4.63E-01
1.04E-02
2.88E-03
6.32E-01
8.73E-03
1.35E-02
1.19E-05
2.40E-10
9.51E-04
7.65E-11
2.84E-32
2.07E-50
1.10E-107
8.75E-88
1.92E-14
7.03E-05
2.27E-24
1.58E-52
1.87E-206
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Binned delta allele frequencies for the contrast domestic vs. wild rabbits. Domestic and Wild reference allele frequencies (dRAF and wRAF) and ΔAF were calculated as:
dRAF= mean(RAF Domestics), wRAF= (mean(RAF Wild French)+mean(RAF Wild Iberian))/2, ΔAF=abs(dRAF-wRAF)
b
The expected number of SNPs of each category in each bin was calculated as p(category) X n(binX), where p(category) is the proportion of a specific SNP category (Conserved non-coding,
UTR, coding or intronic) in the entire genome and n(binX) is the total number of SNPs in a given bin. M-­‐values were then calculated as the log2 fold change of the observed SNP count
vs. the expected SNP count. c
d
b
P
Statistical significance of deviations from expected values were tested with a standard Χ2–analysis (d.f.=1)
For coding SNPs in the ΔAF bin 0.95-­‐1.0 4 SNPs annotated as nonsynonymous in CTDSP2 were shown to reside in a pseudogene (see Database S4), reducing the number of coding SNPs from 14 to 10, the P-­‐value from 0.001 to 0.1 and the M-­‐value from 1.2 to 0.7.
P-­‐values in bold font indicate those ≤ 0.05. M-­‐values in bold font indicate those with M ≥ 0.1 and with P ≤ 0.05
Table S7. Summary of electrophoretic mobility shift assays using nuclear extracts from ES-cell derived neural
stem cells (ES or from mouse P19 embryonic carcinoma cells before (un-diff) or after neuronal differentiation (diff).
Shifted bands
Difference domestic vs. wild
SNP
Chr
Pos
Gene*
ES
P19
P19
ES
P19
P19
un-diff.
diff.
un-diff.
diff.
77728853
1
chr14
SOX2
+
+
+
++
77734115
2
chr14
SOX2
+
+
+
++
78187181
3
chr14
SOX2
+
+
+
++
78228121
4
chr14
SOX2
48839466
5
chr18
PAX2
+
+
+
+++
5538210
6
chr1
KLF4
+
+
+
5609176
7
chr1
KLF4
77386300
8
chr14
SOX2
+
+
?
?
77458138
9
chr14
SOX2
+
+
+
++
++
++
77700191
10
chr14
SOX2
+
+
+
77885074
11
chr14
SOX2
+
+
+
++
++
++
78046337
12
chr14
SOX2
+
+
+
78227621
13
chr14
SOX2
+
+
+
+++
+++
+++
78223293
14
chr14
SOX2
+
+
+
+++
+++
+++
5609251
15
chr1
KLF4
78256018
16
chr14
SOX2
+
78290160
17
chr14
SOX2
+
+
?
"-" = no shift, "+" = shifted, "++" = lower band intensity, "+++" = whole band/complex disappeared and "?" = unclear