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\begin{document}

\title{Evolution along a parallel/non-parallel continuum in Pacific and
Atlantic feral chickens, G. gallus}



\author[1]{Eben Gering}%
\author[2]{Martin Johnsson}%
\author[3]{Doortje Theunissen}%
\author[3]{Maria Luisa Martin Cerezo}%
\author[4]{Alec Steep}%
\author[4]{Thomas Getty}%
\author[5]{Rie Henriksen}%
\author[3]{Dominic Wright}%
\affil[1]{Halmos College of Arts and Sciences}%
\affil[2]{Swedish University of Agricultural Sciences}%
\affil[3]{Linköping University}%
\affil[4]{Michigan State University}%
\affil[5]{Linkopings universitet}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
Recent work indicates that feralisation is not a simple reversal of
domestication, and therefore raises questions about the predictability
of evolution across replicated feral populations. In the present study
we compare genes and traits of two independently established feral
populations of chickens (G. gallus) that inhabit archipelagos within the
Pacific and Atlantic regions to test for evolutionary parallelism and/or
divergence. We find that these two feral populations share close genetic
similarities despite the lack of any current gene flow between them.
Next, we used genome scans to contrast the targets of feralisation
(selective sweeps) between the two independently feral populations from
Bermuda and Hawaii. Three sweep loci (each identified by multiple
detection methods) were shared between feral populations, and this
overlap is inconsistent with a null model in which selection targets are
randomly distributed throughout the genome. In the case of the Bermudian
population, many of the genes present within the selective sweeps were
either not annotated or of unknown function. Of the nine genes that were
identifiable, five were related to behaviour, with the remaining genes
involved in bone metabolism, eye development, and the immune system. Our
findings suggest that a subset of feralisation loci (i.e. genomic
targets of recent selection in feral populations) are shared across
independently-established populations, raising the possibility that
feralisation involves some degree of parallelism or convergence. A
clearer understanding of whether these reflect selection for similar
functional traits (`feralisation syndromes') will require elucidating
genotype-phenotype relationships in any populations being compared.%
\end{abstract}%



\sloppy


Signals of selection and ancestry in independently feral~\emph{G.
gallus~} populations

Authors: Gering, E\#\textsuperscript{1}., Johnsson,
M.\#\textsuperscript{2,3}, Theunissen, D.\# \textsuperscript{2}, Martin
Cerezo, M.L.\#\textsuperscript{2}, Steep, A.\textsuperscript{4} Getty,
T.\textsuperscript{5}, Henriksen, R.\textsuperscript{2}, and Wright,
D.*\textsuperscript{2}

Affiliations:

\textsuperscript{1} Department of Biological Sciences, Halmos College of
Arts and Sciences, Nova Southeastern University, Florida 33314-7796. USA

\textsuperscript{2} AVIAN Behavioural Genomics and Physiology group, IFM
Biology, Linko\selectlanguage{english}ping University, Link\selectlanguage{ngerman}öping 58183, Sweden

\textsuperscript{3} Department of Animal Breeding and Genetics, Swedish
University of Agricultural Sciences, 750 07 Uppsala, Sweden

\textsuperscript{4} Genetics and Genome Sciences Program, Michigan State
University, East Lansing, MI 48824, USA

\textsuperscript{5} Kellogg Biological Station, Michigan State
University, 3700 East Gull Lake Road, Hickory Corners, MI 49060, USA

\# These authors contributed equally

*Corresponding author, email dominic.wright@liu.se/ domwright@gmail.com

Keywords: Feralisation, Population genomics, Invasion Biology, Adaptive
evolution

Abstract

Recent work indicates that feralisation is not a simple reversal of
domestication, and therefore raises questions about the predictability
of evolution across replicated feral populations. In the present study
we compare genes and traits of two independently established feral
populations of chickens (\emph{G. gallus} ) that inhabit archipelagos
within the Pacific and Atlantic regions to test for evolutionary
parallelism and/or divergence. We find that feral populations from each
region are genetically similar despite their geographical isolation and
divergent colonization histories.

Next, we used genome scans to identify genomic regions selected during
feralisation (selective sweeps) in two independently feral populations
from Bermuda and Hawaii. Three selective sweep regions (each identified
by multiple detection methods) were shared between feral populations,
and this overlap is inconsistent with a null model in which selection
targets are randomly distributed throughout the genome. In the case of
the Bermudian population, many of the genes present within the selective
sweeps were either not annotated or of unknown function. Of the nine
genes that were identifiable, five were related to behaviour, with the
remaining genes involved in bone metabolism, eye development, and the
immune system.

Our findings suggest that a subset of feralisation loci (i.e. genomic
targets of recent selection in feral populations) are shared across
independently-established populations, raising the possibility that
feralisation involves some degree of parallelism or convergence and the
potential for a shared feralisation `syndrome'.



\textbf{Introduction}

When wild organisms independently colonize ecologically similar
environments, their descendants can be studied as replicated
evolutionary experiments. This permits us to test for parallel or
non-parallel changes in genotypes and phenotypes in nature, and thereby
assess the predictability of evolution outside of laboratory settings.
Several classic studies that have taken this approach provide powerful
evidence of parallel evolution (see examples in stickleback and lizards
(Colosimo, Hosemann et al. 2005, Mahler, Ingram et al. 2013)) .
Conversely, some lab experiments have found that even under perfectly
replicated conditions, evolution can be highly contingent on population
ancestry and/or stochastic processes. For example, minor differences in
standing genetic variation can determine whether or not an adaptive
trait will evolve under relatively strong selection spanning thousands
of generations, when using \emph{E.coli} (Blount, Lenski et al. 2018).
Other theoretical studies also find that population history affects the
degree of repeatability and propensity of parallel evolution in
different populations (Barghi, Hermisson et al. 2020, Otte, Nolte et al.
2021).

Feral study systems are well suited for testing fundamental evolutionary
questions (Gering, Incorvaia et al. 2019, Mabry, Rowan et al. 2021),
including the degree to which responses to recent selection are
predictable and reversible in separate populations. When domesticated
animals re-colonize the wild, they confront natural selection pressures
that were often relaxed in their recent (captive) ancestors;
feralisation can therefore be expected to drive rapid evolution in
populations that harbour sufficient additive genetic variation in traits
undergoing selection (Henriksen, Gering et al. 2018, Gering, Incorvaia
et al. 2019, Gering, Incorvaia et al. 2019). Since independently feral
populations have often undergone similar environmental change (e.g.
increased social competition for territories and/or mates, requirements
to seek out food and shelter, and interactions with naturally-occurring
pathogen communities), the descendent populations can be compared to
learn if parallel environmental changes drive the evolution of
overlapping sets of genes and/or gene functions. These contrasts are
especially informative (with respect to general features of
feralisation) where colonization timelines and invaded habitats are
similar among the allopatric populations that are being compared.

Another compelling feature of feral study systems is that focal
populations often originate from relatively well-documented starting
points. The domesticated sources of feral populations are often well
studied and relatively well-known genetically, certainly in comparison
to the actual wild progenitors of domesticated species that existed
several thousands of years ago. Of course, independent feral populations
of a given domesticated species may also be non- identical because of
factors such as divergent founder (domesticated) source populations,
random genetic drift, and/or localized admixture between feral,
domesticated and wild relatives (Gering, Incorvaia et al. 2019). This
makes feral populations excellent models for studying whether, and how,
colonizing populations' sources impact contemporary evolution.

A final benefit of studying feralisation is that evolutionary changes
accompanying domestication have been the subject of intensive recent
study (Wright 2015, Wright, Henriksen et al. 2020). This background
knowledge permits testing, across a wide array of organisms, whether
genes and/or functions that were previously modified under artificial
selection undergo further change when domesticated taxa recolonize the
wild (Johnsson, Williams et al. 2016).

Prior studies of feralisation have often focused on individual
population case studies. For example, genomic analyses of feral chickens
(\emph{Gallus gallus} ) on Kauai Island, Hawaii, recently found evidence
of rapid recent evolution at loci controlling traits that were also
modified under domestication (e.g. genes that regulate behavior,
reproduction, and growth) (Johnsson, Gering et al. 2016). Despite this
functional overlap, selective sweeps found in Kauai's feral fowl were
largely different from known \emph{G. gallus} domestication genes and
improvement genes that were identified by selective sweep analyses
(Johnsson, Gering et al. 2016)\emph{.} Strong selective sweeps in modern
domesticated birds are more likely to represent `improvement' genes that
have been selected relatively recently during intensive modern breeding
for layer and broiler chickens. For example, a coding change in the
gene\emph{TSHR} is found in virtually all modern domesticated birds
(Rubin, Zody et al. 2010), but this mutation is largely lacking in
archaeological domesticated chickens (Girdland Flink, Allen et al. 2014,
Loog, Thomas et al. 2017). Given this distinction between selective
sweep regions found in Kauai's feral chickens vs. recent domestication/
improvement selective sweeps found in captive \emph{G. gallus} , it is
now important to compare additional (independently feral) gene pools.
This will help determine how replicable feralisation processes are, and
whether they are a model for parallel evolution.

The sources of feral populations are obviously of importance when
comparing their gene pools. In the specific case of Kauai's chickens,
admixture between wild-living Red Junglefowl and escaped domesticated
birds appears to have capacitated recent adaptation (Gering, Johnsson et
al. 2015, Johnsson, Gering et al. 2016). This admixture most likely
followed the releases of large numbers of domesticated birds during
hurricanes Iniki (1992) and Iwa (1982) into the wild, as reported by
locals and supported by chromosomal painting techniques (Martin Cerezo,
Lopez et al. 2023). This timeframe also coincided with exponential
growth of the feral population's density on Kauai Island (Gering,
Johnsson et al. 2015). Chickens were first brought to the Hawaiian
Islands (including Kauai) by Polynesian settlers (circa 400 to 1200),
and were thought to be Red Junglefowl imported for ritual and/or
cockfighting purposes (though may also have been partially domesticated)
(Kirch 2011, Thomson, Lebrasseur et al. 2014). Subsequent introductions
of domesticated \emph{G. gallus} to Hawaii followed European contact
that began centuries later (1778) and involved domesticated chicken
breeds of largely western origins. For example, large numbers of animals
have been imported to the Hawaiian Islands via a local hatchery (Asagi)
that was established in 1935 and specializes in Cornish Rocks (a broiler
breed), White Leghorns (a layer breed), and smaller numbers of heritage
breeds (www.asagihatchery.com). Importation and release of live \emph{G.
gallus} from commercial sources also coincided with introductions of
non-commercial `backyard' chickens kept by residents for egg production,
meat production, and cockfighting which, though illegal in the US,
remains highly popular on Kauai and throughout the Pacific region (Young
2014). Contemporary genomes of feral Kauai chickens are consistent with
recent interbreeding among these diverse and asynchronously introduced
source populations (Martin Cerezo, Lopez et al. 2023).

The clearest distinction between the histories of feral chickens on
Kauai vs. Bermuda is that Red Junglefowl were never imported and
released into Bermuda. This is largely because ancient Polynesians did
not venture into the Atlantic region. Additionally, the landraces and
`backyard' varieties found in the two regions are likely non-identical,
because the European explorers and slaves who colonized Bermuda
beginning 1505 were closely connected (via culture and commerce) to
European, African, and Caribbean locales whereas Hawaii was connected
via more global recent trade routes and fashions (including Asian,
Pacific, American, and European). Finally, there are no large commercial
poultry operations in Bermuda and chickens are largely kept by Bermuda
residents as fancy pets (vs. for food and cockfighting). The Bermuda
Poultry Fancier's Society was formed there more than a century ago, and
current members reported to us that diverse domesticated breeds have
long been imported to the island, including breeds developed in Europe
(e.g. d'Uccles, Orpingtons, Faverolles, Friesians, Old English Game) and
the Americas (e.g. Plymouth Rock, Brahma), from diverse stock sources
(personal communication Ronnie Lopes, of Bermuda Poultry Fancier's
Society and US Master's Cup Poultry Show). Escaped domestic \emph{G.
gallus} have been highly successful in colonizing the tiny 53.2
km\textsuperscript{2} Bermudian archipelago, reaching an estimated size
of \textgreater{}30,000 individuals by 2011 and displaying a panoply of
phenotypes seen in fancy chickens (e.g. rose, walnut, and duplex combs,
polydactyly, barred and silver plumage patterns, and bantam morphology)
(Government of Bermuda 2013). Similar to Kauai, Bermuda's feral \emph{G.
gallus} densities have only recently reached their current high
densities, with local authorities hypothesizing that damage from
Hurricane Emily (1987) kickstarted exponential growth (Government of
Bermuda 2013).

Feral populations that lack recent Red Junglefowl ancestry (like
Bermuda) could be predicted to show evolutionary divergence from Kauai's
admixed fowl in their contemporary gene pools. On the other hand,
feralisation might filter input sources and/or select for parallel
genomic and phenotypic changes if a common feralisation `syndrome' is
adaptive in both regions. To evaluate these predictions, we examined
genomic and phenotypic data from feral chickens of Bermuda and compared
them to feral chickens from the Hawaiian island of Kauai. Kauai and
Bermuda chickens have colonized strikingly similar environments. Both
locales have subtropical climates in which feral chickens inhabit a
variety of urban, suburban, rural, and wild habitats. Both Kauai and
Bermuda ecosystems lack native terrestrial carnivores and, perhaps
consequentially, have sustained unusually large and dense populations of
feral \emph{G. gallus} for decades (average group size for wild Red
Junglefowl is around 6-8 birds, whilst groups on these islands can be as
large as 30 or more birds (personal observation)). In the present study,
feral birds from Bermuda (n=21) and Kauai (n=25) were sampled,
phenotyped, and genotyped (10X density). They were then used to identify
candidate feralisation selective sweeps, with the sweep regions further
characterised using chromosomal painting techniques (see methods).

\textbf{Results}

\textbf{Summary of Sequencing Statistics}

An average of 262.90 million reads were obtained per sample (min:
208.02M, max: 312,93M, std: 25.90M), generating an average coverage of
36.77x (min: 19.19x, max: 42.89x, std: 3.57x). On average, 98.58\% of
the reads mapped to the reference genome (min: 97.00\%, max: 99.06\%,
std: 0.52\%). Mapping quality was consistent between samples, ranging
from 56.00 to 56.90 (average: 56.53, std: 0.22). In total, 98.42\% of
the genome (min: 97.63\%, max: 98.66\%, std: 0.28\%) had a coverage of
at least 10x.

12,819,455 SNPs were found for the Bermudian samples. After filtering,
12,053,478 (94.02\%) of those SNPs were kept and used for further
analysis of selective sweep regions. For the Hawaiian samples,
11,913,016 SNPs were kept. The Bermudian and Hawaiian samples had
9,734,945 of the analysed SNP locations in common. 7,174,074 SNPs were
kept after merging with data from wild and domesticated populations for
the Chromopainter analyses, due to missing data in the domesticated and
Red Junglefowl populations.

\textbf{Feral Chicken Ancestry}

An Admixture analysis was used to compare the two feral populations with
two Red Junglefowl populations (one collected from the wild, the other
from a zoo population), two broiler populations, and two layer
populations (all from Qanbari et al. 2019). This analysis found that
these populations showed the most support for six founder populations
(CV-error n=6, 0.79268, next lowest scores were n=5 Cve=0.79773, n=7
Cve=0.79791, n=8 Cve=0.79800, n=4 Cve=0.80401). The feral chickens
clustered with one another, as did the Red Junglefowl populations
(though some White Layer introgression appeared in the Indian Red
Junglefowl population). The two broiler populations appeared to have
different origins, as did the White and Brown layer populations (see
Figure 1). Focusing solely on the two feral populations, the Bermudian
population had more introgression from Brown layers and Broilers from
population A, whilst the Kauai feral population had more introgression
from the White layer population. All other Admixture plots are shown in
Supplementary Figure 1.

A PCA plot of the data displays a similar pattern to the Admixture
results (see Figure 1B). Initially, the two Layer populations were both
related and showed pronounced divergence to the other analyzed breeds
(most likely due to being the most intensively selected of all the
modern domesticated breeds). When these two populations were removed to
better visualize the remaining population structure (see Figure 1C),
feral, broiler and Red Junglefowl all clustered by themselves with their
respective population pairs. A similar pattern was observed when
comparing Principal Component 3 with Principal Component 4 (Figure 1D).

As a further analysis, we expressed the shared chunk count matrix from
Chromopainter as a dissimilarity matrix by subtracting each element from
the maximum shared chunk count using the Splitstree v4 package (Huson
and Bryant 2005). This allowed us to again visualize the relationships
between the feral, wild and domestic populations (see Supplementary
Figure 2). Once again, the two feral populations clustered adjacent to
one another, whilst the other domestic and wild sub-populations were all
distinct from one another.

\textbf{Selective Sweep Detection}

Selective sweep detection was performed to identify genomic regions
undergoing selection. Three separate techniques were used: extended
haplotype homozygosity mapping, a composite likelihood ratio test
(Huber, DeGiorgio et al. 2016), and a Tajima's D approach (see methods).
The Extended Haplotype Homozygosity test identifies genomic regions with
high local haplotype homozygosity, considered to be a strong indication
of signatures of positive selection (Sabeti, Reich et al. 2002). The
Composite Likelihood Ratio test calculates the likelihood ratio of the
null hypothesis, calculated from the neutral (genome-wide) frequency
spectrum, whilst the alternative hypothesis is calculated using a model
where neutral selection has been altered by recent selection. This
technique can separate out footprints of positive selection from
background selection (Charlesworth 2012). Bermuda (n=21) and Hawaii
(n=25) samples were analysed using each sweep detection technique
separately.

\textbf{Extended Haplotype Homozygosity (EHH) Mapping}

For the Bermuda dataset, a total of 386 putative selective sweep regions
were identified within Bermuda \emph{G. gallus} genomes using EHH
mapping (Supplementary Table 1). A threshold of 1\% of the genome was
selected. The mean selective sweep length was 24kb bases (median 20kb,
sd 12.2kb). For the Kauai population, 351 selective sweeps were
detected, with a mean selective sweep length of 26kb (median 20kb, sd
14.6kb), see Supplementary Table 1. Seventeen of the selective sweeps
detected in the two feral populations overlapped with one another
(permutation test, p \textless{} 0.001, see methods and Supplementary
Table 2). To ascertain how many of the selective sweep regions
overlapped with domestication selective sweeps, the selective sweeps
identified in prior studies that focused on \emph{G.
gallus}domestication were compared with the Kauai and Bermuda selective
sweeps. Qanbari et al (Qanbari, Rubin et al. 2019) identified 304
domestication/ improvement selective sweeps in their analysis, with five
of these overlapping Bermudian EHH sweeps (permutation test, p
\textgreater{} 0.05), and four of these overlapping Kauai EHH selective
sweeps (permutation test p \textgreater{} 0.05) (see Supplementary Table
3).

\textbf{Composite Likelihood Ratio (CLR) Mapping}

In the Bermudian population we detected a total of 455 selective sweep
regions using CLR mapping (mean 32kb, median 20kb, SD 24.8kb;
Supplementary Table 4), with a significance threshold of 1\% of the
genome used. In the Kauai population a total of 456 selective sweep
regions were identified with CLR (mean 32kb, median 20kb, SD 29.6kb). Of
the selective sweep regions identified using CLR in the feral
populations, 44 of these overlap (permutation test, p \textless{} 0.001)
(see Supplementary Table 5). When comparing with the domesticated
selective sweeps identified by Qanbari et al, ten of the selective
sweeps detected in Bermuda overlap (permutation test, p \textgreater{}
0.05), and 21 of the Kauai selective sweeps overlap (permutation test, p
\textless{} 0.001; see Supplementary Table 3).

\textbf{Tajimas's D Mapping}

In the Bermuda population, 168 selective sweep regions were identified
(mean 57kb, median 40kb, SD 51.9kb), whilst in the Kauai population 165
selective sweep regions were identified (mean 58kb, median 40kb, SD 38.1
kb), see Supplementary Table 6. Between these two feral populations, 39
selective sweep regions were shared (permutation test, p \textless{}
0.001) (Supplementary Table 7). In contrast, Bermuda had 18 overlaps
(permutation test, p \textless{} 0.001) with the domestication selective
sweeps from Qanbari et al, and Kauai had 17 overlaps (permutation test,
p \textless{} 0.001) (see Supplementary Table 3).

\emph{Consensus Feral Sweeps and Overlaps}

For the feral populations, we considered that if a selective sweep was
detected by at least two selective sweep finding methods it represented
stronger evidence of a true selective sweep. Note that this may
sometimes not be the case, as any given detection technique may be
better tuned to detecting a specific sweep type that is `missed' by
other methods. Nonetheless, we chose to minimalize false positives by
focusing on consensus selective sweeps (though individual selective
sweep types are also presented in the Supplementary Tables). In the case
of the Bermudian population, 59 regions were detected in such a manner
(Supplementary Table 8), whilst in the case of the Kauai population 106
selective sweep regions were detected (Supplementary Table 9). When we
check for overlaps between these two sets of selective sweep regions,
three are detected in both population samples (Table 1), with this being
a significant enrichment over the null distribution (permutation test, p
\textless{} 0.001). Of these, one selective sweep also overlapped with
the domestication sweeps identified by Qanbari et al (Qanbari, Rubin et
al. 2019)(see Table 1). An overview figure of the locations of selective
sweeps identified by the different methods is presented in Supplementary
Figure 3.

\subsection*{Selective sweeps origin
identification.}

{\label{selective-sweeps-origin-identification.}}

To identify the origin of the three shared Hawaiian and Bermudian feral
selective sweeps (i.e. are they domesticated haplotypes that have become
fixed vs. wild Red Junglefowl haplotypes) Chromopainter software was
used (Lawson, Hellenthal et al. 2012). This takes the feral genomes and
`paints' on the domesticated and wild haplotypes to ascertain which are
most likely to be the donor population of a given sweep haplotype. Two
separate layer and broiler populations were used as donors, as well as
two Red Junglefowl populations (from Thailand and India, respectively).
These were then used as donors for the Bermuda and Hawaii populations,
whilst an additional run with Bermuda was also performed, also including
Hawaii as an additional donor. In the case of the Bermudian feral
selective sweeps, domesticated haplotypes were the most likely donor
populations, with broilers and layers donating similar amounts for two
of the shared selective sweeps (chr1@17.5Mb, chr1@32.8Mb). In the case
of the third sweep (chr2@78.3Mb), the broiler donors were the strongest
donor for the Bermudian population. A similar pattern was also seen for
the Hawaiian population, though in this case the broiler population was
the largest donor for each selective sweep (see Figure 2 A-D). The
degree of donorship from Red Junglefowl alleles was broadly similar for
the two feral populations.

By further breaking down the domesticated populations into the
individual donor sub-populations, we once again see a similar pattern,
but also that it is often only one of the two candidate donor
sub-populations that contributes the most to a particular sweep. In
particular, the layer donors are actually more prevalent when
sub-populations are used, with the brown layer donorship high for Hawaii
at chr1@17.5Mb, the white layer a high donor for Hawaii at chr2@78.3Mb,
and the brown layer a high donor for Bermuda at chr1@32.8Mb (see figure
2B). When focusing on the Bermuda population, with the addition of
Hawaii as a donor, we can see a strong overlap between the two feral
populations, with Hawaii the largest donor for the sweep at chr2@78.3
and the joint largest for the sweep at chr1@17.5Mb (see figure 2C, D).

Of the three shared sweeps, one also overlapped with a domestication
sweep (chr1@32.8Mb). In this case, the domesticated donor is much more
evident than either the Hawaiian or Red Junglefowl donors.

\subsection*{}

{\label{section}}

\subsection*{Gene annotation and
Function}

{\label{gene-annotation-and-function}}

The 59 selective sweeps regions detected by both methods for the
Bermudian population contain a total of 61 genes when using the Ensembl
genome annotation browser, though a large percentage are long non-coding
RNA and other gene-free regions (see Supplementary Table 8 and below). A
total of 9 fully annotated genes were identified in selective sweeps
(\emph{ADCY1, CALU, CRAMP1, DRD3, MYST/Esa1-associated, PTPRB, TAFA5,
TSNARE1, ZC3H12A} ). These genes are involved in anxiety, schizophrenia,
depression and related behaviours (\emph{ADCY1, DRD3, PTRB, TAFA5,
tSNARE1} ) bone remodelling (\emph{MYST/Esa1-assctd} ), eye development/
vision (\emph{PTRB} ), the immune system (\emph{ZCH3H12a} ) and
metabolism (\emph{CALU} ). There was no enrichment for these types of
gene functions, however. Of the 61 genes, only 21 were able to be
assessed in the PANTHER over-representation analysis (using complete GO
processes setting), with the above mentioned nine genes plus a further
12 unannotated genes still included. The only enrichment was for
unclassified processes/ genes in this set (p\textless{}0.0001). Of the
61 genes, 40 were long non-coding RNAs (lncRNA). Similar results were
found with the Hawaii consensus sweep regions -- in this case 171 genes
were present in these sweeps, with 131 being useable by Gene ontology
software PANTHER (Protein Analysis Through Evolutionary Relationships)
(Mi, Muruganujan et al. 2012). The only enriched GO process was once
again unclassified (p\textless{}0.0001), whilst 25 of the genes were
lncRNAs, 10 were micro, miscellaneous, or sno RNAs, 25 were unknown, and
the remainder protein coding (see Supplementary Table 9). Gene function
was very diverse in the case of the Hawaiian gene set, though of note is
the presence of \emph{SEMA3A}(chr1@9.4Mb), which was one of the three
most significant improvement/ domestication-related genes detected by
Rubin et al (Rubin, Zody et al. 2010). Similarly, the gene\emph{tSNARE1}
was also found in a selective sweep identified by Rubin et al. 2010.

\textbf{Recent Improvement/ Domestication Signals in Feral Populations}

Very little overlap existed between the selective sweeps detected in
modern domesticated populations, and those detected in the two feral
populations. Several genes have been previously identified as likely
being highly important in modern domestication, by dint of being present
in selective sweeps detected in multiple domesticated breeds (Rubin,
Zody et al. 2010). In particular, the genes \emph{TSHR} , \emph{BCDO2} ,
and \emph{SEMA3A} were the three genes present in the strongest domestic
sweep regions in the Rubin et al study, and in the case of
\emph{TSHR}and \emph{BCDO2} also contained exonic non-synonymous
mutations. It is important to note that these selective sweeps
identified in (Rubin, Zody et al. 2010) and Qanbari et al (2019) almost
certainly represent modern improvement-related domestication genes,
whereby very strong recent selection is occurring in modern broiler and
layer breeds, as opposed to early domestication-related genes that were
selected during the initial phases of chicken domestication.

In the case of \emph{TSHR} , the gene is located at 40.97Mb-41.02Mb on
chromosome 5. A sweep covering 40.4-40.8Mb was detected in both feral
populations using Tajima's D (see Supplementary Table 6), whilst a sweep
was detected using CLR in the Bermudian population at 40.17-40.19Mb, and
at 41.3Mb in the Kauai population (see Supplementary Table 4). None of
these actually overlap the \emph{TSHR} gene itself, and the allele
frequency of the exonic mutation locus (present at 41020256-61bp) was
found to be variable. In the case of the Bermudian population, the SNP
present had a frequency of 3 homozygote reference individuals, 11
heterozygote individuals, and 7 homozygote variant individuals, whilst
the Hawaiian population had an allele frequency of 7 variant
homozygotes, 4 heterozygotes, and 12 homozygous alternate individuals.
When assessing the Tajima'sD result for the specific sweep region in the
Kauai population we get a Tajima'sD statistic of 1.4, with the 1\%
cut-off threshold being 3.4. In the case of the Bermudian population,
two Tajima's D statistics were calculated (for 40960000-41000000 and
41000000-41040000) with values of 2.9 and 3.5, with one being above the
1\% threshold of 3.3.

Similarly, only one sweep was present within the relative vicinity
of\emph{BCDO2} (located at 6140660-6152816bp), with this sweep being
detected at 6.5Mb in the Bermudian population using CLR (see
Supplementary Table 4). In the case of the Tajima'sD statistic for this
specific sweep region, for the Bermudian population this was 1.3 (with
the cut-off being 3.3) and for the Kauai population this value was 1.1
(with the cut-off being 3.4), showing no significant fixation.

In contrast, a consensus sweep (found using both CLR and Tajimas'D) was
detected in the Kauai population at 9.4-9.44Mb on chromosome 1, that
fully overlapped the gene \emph{SEMA3A} (9.34Mb-9.51Mb), and is
therefore the only one of these three improvement/ domestication genes
that has reliable support for being present in a feral population (see
Supplementary Table 8). In this case it appears that \emph{SEMA3A} is
either undergoing further positive selection within feral populations,
or it retains a signal of earlier artificial selection (e.g. due to
selective neutrality and/or loss of non-domesticated alleles as the
population became feral). One further point is the gene
\emph{tSNARE1,}which was identified in the feralisation-related
selective sweeps, is also identified (albeit not as strongly as the
above-mentioned genes) in an earlier analysis of domestication-related
G. gallus loci (Rubin et al 2010). This once again highlights the
possibility that some of the polymorphisms selected during domestication
may still be advantageous beyond captive environments, regardless of
whether they serve the same functional role(s) in feral habitats.

\textbf{DISCUSSION}

\textbf{Summary of feral gene pool comparisons}

Analyses of Bermuda's feral chickens (and comparisons to counterparts on
Kauai) revealed both unique and common features of independently
feral\emph{G. gallus} in the Atlantic vs. Pacific regions\emph{.}
Admixture analyses suggest the two feral populations originate from
genetically similar colonization source(s), yet they also show
asymmetrical signals of recent gene flow and selection. Bermudian feral
chickens appeared to have more similarity to broiler and brown layer
populations, whilst Hawaiian feral chickens had introgression from White
Layer birds (or their ancestors). Hawaiian feral birds also had more
introgression from Red Junglefowl (the source of domesticated chickens)
in comparison to their feral Bermuda counterparts. These findings mirror
what we know of the two feral population histories, with large numbers
of White Layers released by the Asagi hatchery in Hawaii following
tropical storms and attendant opportunity for introgression from the Red
Junglefowl already present in the Hawaiian islands.

\textbf{Summary of selective sweep analyses}

Common selective sweeps were detected among both Kauai and Bermuda feral
populations, at least two of which involved loci and variants that were
not found in earlier studies of domestication or breed improvement
selection. Haplotypes in this narrow set of shared (Bermuda and Kauai)
feral sweeps chiefly exhibit signs of artificially selected (vs. Red
Junglefowl) origin, though Red Junglefowl donorship is also evident in
at least one case. However, in the case of Bermuda, the comparison with
the most shared regions appeared to consistently be the Hawaiian
population sample. Once again, this highlights the genetic similarity
between these two distinct feral populations, and a feasible role for
shared recent selection in the filtering and/or allele-frequency shifts
of contemporary feral gene pools.

When the panel of candidate sweeps from Bermuda chickens is considered
alone, very little overlap is found with recent improvement/
domestication-related selective sweeps, a pattern previously reported in
Kauai ferals. Thus, the available data suggest feral populations are
more likely to share selective sweep loci with each other than with
contemporary domesticated breeds, though selected polymorphisms may
still share the same biological pathways, even though they occur in
different genes. This confirms that feralisation is not a mere reversal
of artificially-selected genomic changes, but also highlights the
limitations of genomic analyses that lack representation from
historically present breeds and landraces -- which could further
elucidate apparent connectivity between allopatric feral populations.
Additionally, the majority of Bermudian selective sweeps involved
different genomic loci to those found in the Kauai gene pool. Thus,
Bermuda and Kauai feral chickens' local environments, ancestries, and/or
stochastic processes have taken the two populations down somewhat
divergent evolutionary paths, though selected polymorphisms may still
share the same biological pathways, even though they occur in different
genes. Nonetheless, further study may reveal functional overlap in
selected polymorphisms of these, and other, feral populations (e.g. via
changes at divergent loci controlling shared biological pathway/s and
traits).

Despite the overarching differences in feral sweep loci between Kauai
and Bermuda, the significant overlap of a subset of selective sweeps
between these two populations offers intriguing evidence of potential
evolutionary parallelism. Future genotype-phenotype studies, both for
traits involving shared feral sweeps and those produced by potentially
convergent ones, can help determine if feral genomics can yield
predictable outcomes of feralisation-related selection.

\textbf{On the phenotypes of independently feral populations}

Biologists since Darwin have predicted that feral phenotypes will revert
from domestic to wildtype forms across generations, and observed that
admixture between domestic breeds can produce such reversions rapidly
(Li and Liu 2014). Observations from both Hawaii and Bermuda feral
chickens support this general prediction, despite these populations'
colonization of islands separated by \textasciitilde{}9000km linear
distance in the Pacific and Atlantic oceans. Among the traits comprising
a shared putative ``feralisation syndrome'' are reduced body mass and
comb size (as seen in Red Junglefowl), and plumage colour and patterning
that resemble the Red Junglefowl's to varying degrees. In a similar
manner to domestication, there is the potential that feralisation is
driven by a distinct set of alleles that are common between distinct
populations. However, even with the commonality of this feralisation
syndrome, certain heritable, domestication-related phenotypes were
observed in Bermudian chickens, but not Hawaiian chickens. These include
polydactyly (additional toes), yellow leg-skin, and comb variants (e.g.
rose and duplex combs (see Table 2)).

\textbf{Functional implications of outlier loci in Bermuda's feral
chickens}

Exploring gene function and annotation within sweep loci from feral gene
pools offer clues as to which traits are selected in ferals.
Unfortunately, the large number of unannotated genes found in the
Bermudian sweeps limits our ability to test for enrichments of
particular gene functions. Recognizing this limitation, it is still of
interest to examine gene functions in those genes that are annotated. In
the case of the nine genes within Bermuda sweeps that were annotated,
several had previously been identified as affecting anxiety-related
behaviours (see below). Additional roles for annotated genes included
bone remodelling, eye development or vision, and the immune system,
suggesting possible connections to life-histories, sensory ecology,
and/or pathogen resistance. In the case of the Hawaiian sweeps, function
was highly variable.

Concerning behavioural genetics, five of the nine genes from Bermuda
sweeps were strongly associated with anxiety, schizophrenia, depression,
and their related behaviours (\emph{ADCY1} (Chen, Xu et al. 2023)
(Sundararajan, Manzardo et al. 2018, Chen, Ding et al. 2022)\emph{,
DRD3} (Staal 2015) (Liu, Xu et al. 2022, Sofronov, Dobrovolskaya et al.
2022)\emph{, PTRB} (Ishiguro, Gong et al. 2008)\emph{, TAFA5} (Huang,
Zheng et al. 2021) (Li, Li et al. 2020)\emph{, tSNARE1} (Sleiman, Wang
et al. 2013, Gu, Jiang et al. 2015, Fromer, Roussos et al. 2016, Whelan,
St Clair et al. 2018, Li, Whelan et al. 2019, Schrode, Ho et al. 2019,
Li, Shen et al. 2020, Plooster, Rossi et al. 2021)). Although the
studies linking these genes to behaviour come mostly from human
literature, we have previously demonstrated a strong cross-species
replication for anxiety-related genes in chickens, humans and mice
(Johnsson, Williams et al. 2016). Thus, the relationship between
anxiety-related traits and adaptive (e.g. anti-predator) behaviour may
explain the presence of such behavioural genes in the Bermudian
population. In particular, Red Junglefowl display much more
anxiety-related/ anti-predator behaviour than their domesticated
counterparts (Johnsson, Williams et al. 2016), and hence the feral
chickens appear to be reverting to ancestral behaviour in wild habitats
where feral cats, humans, and other wild animals present credible
mortality risks. It is also possible that these genes are influenced by
other sources of behavioural selection in wild settings (such as
high-stakes social interactions, and the demands of successful foraging
in complex and changing environments).

The remaining known annotated genes affected bone remodelling via
osteoblast differentiation (MYST/Esa1)(Hu, Yin et al. 2021), the immune
system via the regulation of inflammation (Zc3h12a/ MCPIP1) (Matsushita,
Takeuchi et al. 2009, Lin, Chien et al. 2013, Miao, Huang et al. 2013),
and cancer metastasis and development (Nasri Nasrabadi, Nayeri et al.
2020). Once again, these raise exciting questions about how pathogens
and life history selection might influence feral gene pools that merit
targeted future study -- particularly since feral G. gallus are known to
host zoonotic diseases (Dubey 2010).

Altogether, annotated genes in sweeps conform with our predictions as to
how feral birds should adapt to the natural environment, with the return
of natural and sexual selection pressures driving adaptive responses
within sufficiently diverse gene pools such as those of Kauai and
Bermuda chickens. The fact that anxiety-related genes are present in
selected regions of the feral gene pools' genomes provides a tantalizing
suggestion of selection on behavior that requires further study. We have
found previously that domesticated birds are less anxious than their
wild counterparts the Red Junglefowl (Johnsson, Williams et al. 2016,
Johnsson, Henriksen et al. 2018, Fogelholm, Inkabi et al. 2019). The
observation that the feral sweeps involve anxiety-related genes, and
also harbor alleles of domesticated origin, indicates that such
polymorphisms are still potentially circulating in domesticated
populations but are not fixed or maintained at a low frequency.

\textbf{Broader implications for feralisation}

As well as the genetic similarities that we observe between our two
feral chicken populations, with a significant number of overlapping
selective sweep regions, we also see a return to the `wild' phenotype in
both cases. Other examples of such reversion in coat colour in feral
populations also come from feral boar populations in the US and dingo
populations in Australia. In the case of the feral boar, one
longitudinal study found that domestic coat colour individuals showed a
mark decrease over a 13 year period, with a commensurate increase in the
black (wild type) coloration over the same period (Gipson, Lee et al.
2006), with another study finding the same black coloration to be the
dominant adult phenotype in Texan populations (Mapston 2007). However,
another longitudinal study found the reverse, with an increase in
spotted individuals at the expense of black individuals (Mayer, Brisbin
Jr et al. 1989), though they ascribe this to human hunting preferences.
Similarly, dingo populations show an excess of classical ginger coat
colour phenotypes over white domestic ones, even when a high degree of
domestic introgression/ hybridisation is present (Tatler, Prowse et al.
2021) (Newsome and Corbett 1985).

Thus, it appears that feralisation still has strong selection against
more novelty traits produced through domestication, but that
domestication also has many advantageous polymorphisms for feral
populations, but that these are at low/ intermediate frequencies in
normal domesticated populations. The importance of domestication-related
alleles controlling other, less visible phenotypes during feralisation
is also shown by the location of a sweep at a well-known
domestication-related gene (\emph{SEMA3A} ) that participates in nervous
system development, and the fact that chromopainter indicates that many
of the alleles in feralisation-related sweeps appear to be domestic in
origin.

\textbf{Conclusion}

This study demonstrates that feralisation is at least partly repeatable
at the genetic and phenotypic level in chickens, and involves
significant enrichment of shared sweeps between independently feral
populations. These sweeps also show little overlap to those produced by
recent improvement/ domestication in modern domestic birds, with a
single exception that shows how artificially-selected genome regions can
either retain signals of selection, or be continuously selected, during
the recolonization of wild habitats. As mentioned earlier, it is
important to note that most known improvement selective sweeps may not
be representative of the earliest domestication-related genes (i.e.
those polymorphisms/ mutations selected during the initial domestication
process), as pointed out by Flink et al (2014). Unfortunately, unless
(or until) all domestication genes are known, we cannot fully exclude
the possibility of their involvement in feralisation. Nonetheless, our
collective findings suggest that further studies of feralisation
genomics will continue to produce novel and complex discoveries. For
example, the two populations we compared herein exhibit both parallels
and differences in their recent evolutionary trajectories, and have
recruited haplotypes to high frequency that stem from both domesticated
and wild sources. This complexity is perhaps unsurprising, given that
feral populations consist of novel and uniquely human-impacted
organisms, and that these organisms exhibit, to remarkable and often
vexing degrees, a capacity to thrive in an increasingly human impacted
world.

\textbf{METHODS}

\subsection*{\texorpdfstring{Field sampling of Bermuda's feral chickens
(\emph{G. Gallus}
)}{Field sampling of Bermuda's feral chickens (G. Gallus )}}

{\label{field-sampling-of-bermudas-feral-chickens-g.-gallus}}

Field samples of fresh blood were collected on Whatman Filter cards from
chickens that were culled by the Bermudian government in 2015.
Individuals were chosen for sequencing (see below) in order to include
1) a range of sublocalities spanning the small archipelago's main
islands and 2) a range of microhabitats including developed and
undeveloped areas. In total, n=15 males and n=6 females were selected
for sequencing. Phenotypic data was not available for the individuals
that were used for Whole Genome Sequencing, and was instead collected
from individuals sampled in the field in Bermuda during 2018 (table 2).
This consisted of body weight and comb weight measures taken post-mortem
(collected from 95 Bermudian feral individuals, 55 males and 40
females), as well as comb morphology recordings (presence of pea comb,
rose comb and duplex comb morphs), and the number of different leg
colorations present in the population (yellow and/ or grey leg colours),
taken from 134 Bermudian feral individuals. In addition, comb and body
weight, and morphological measures were also taken from 36 male and 36
female Kauai feral birds. For the Kauai birds, 25 birds were blood
sampled and sequenced, with samples once again taken from a variety of
sublocalities.

\subsection*{\texorpdfstring{Publicly available sequence data for
domestic and wild \emph{G.
gallus}}{Publicly available sequence data for domestic and wild G. gallus}}

{\label{publicly-available-sequence-data-for-domestic-and-wild-g.-gallus}}

Whole-genome sequence data for domestic and Red Junglefowl contrasts
were obtained by from data published by Qanbari et al (Qanbari, Rubin et
al. 2019). We downloaded the reads from the European Nucleotide Archive
(ENA, http://ebi.ac.uk/ena), and called variants using the same workflow
as for the Bermuda samples. The accession numbers and sample labels used
are listed in Supplementary Table 10.

\subsection*{DNA Sampling and whole genome
sequencing}

{\label{dna-sampling-and-whole-genome-sequencing}}

We extracted genomic DNA using the DNEasy Blood and Tissue Kit (Qiagen),
according to the manufacturer's protocol. We sequenced 21 Bermuda
chicken samples at 30X coverage on the Illumina HiSeq X platform at
SciLifeLab, Stockholm, and 25 Kauai chicken samples on an Illumina
novoseq X6000. In the Bermuda samples, we trimmed residual adapter
sequences with Trimmomatic version 0.36 (Bolger, Lohse, \& Usadel,
2014), aligned the sequences to the chicken genome (version Galgal6)
using bwa mem version 0.7.17(Li, 2013) and processed the alignments
using a workflow inspired by GATK best practices (DePristo et al., 2011;
McKenna \emph{et al.} , 2010; Van der Auwera \emph{et al} ., 2013),
including the use of the functions MergeSamFiles, SortSam,
BaseRecalibrator, ApplyBQSR, GenomicsDBImport and GenotypeGVCFs from
GATK version 4.3.0.0. Sequencing duplicates were removed with the
MarkDuplicates function from picard versions 2.27.5
(https://broadinstitute.github.io/picard/index.html). In the Kauai
samples, poly-G tails were trimmed using fastp (Chen, Zhou et al. 2018),
after which the workflow mentioned above was followed, but with bwa mem
version 0.7.15, GATK version 3.8.1.0 and picard version 1.118. Finally,
we filtered the resulting SNPs with the GATK VariantFiltration tool. We
retained variants that passed filters QD \textless{} 2.0, FS
\textgreater{} 60.0, MQ \textless{} 40.0, MQRankSum \textless{} -12.5,
and ReadPosRankSum \textless{} -8.0. In a second filtering step using
bcftools version 1.14 (Danecek, Bonfield et al. 2021), SNPs with up to
40\% of missingness and a minor allele frequency larger than 5\% per
population were kept. All SNPs for the selective sweep analyses were
phased using 50 iterations in Beagle 5.4 (Browning, Tian et al. 2021).

\textbf{Detecting selective sweeps}

We detected selective sweeps in the Bermuda and Kauai populations by
calculating three relevant statistics, keeping the top 1\% regions per
statistic, and then selecting regions that were recognised as a sweep by
at least two out of the three methods, using the R package GenomicRanges
version 1.46.1 (Lawrence, Huber et al. 2013). Only autosomal regions
were used. First, we estimated pooled Tajima's D. Each statistic was
calculated in 40 kb sliding windows along the autosomal genome. We
standardized the Tajima's D values by subtracting the mean and dividing
by the standard deviation. VCFTools version 0.1.16 was used
(Danecek\emph{et al} ., 2011).

As a second method, we ran a composite likelihood ratio test for
positive selection (Kim and Stephan 2002). With this, the likelihood
ratio of the null hypothesis is calculated from the neutral
(genome-wide) frequency spectrum, whilst the alternative hypothesis is
calculated using a model where neutral selection has been altered by
recent selection. This was calculated by using SweepFinder2 version 1.0
(DeGiorgio, Huber et al. 2016). In particular, this technique can
separate out footprints of positive selection from background selection
(with this being a loss of neutral variation due to a purging of linked
deleterious alleles via negative selection (Charlesworth 2012)). To
conduct this, we first computed an empirically-derived allele frequency
file based on all chromosomal data. This file serves as a null
hypothesis in order to calculate a likelihood ratio to detect positive
selection. A whole genome scan for selective sweeps was then conducted
as per the recommendations given in (DeGiorgio, Huber et al. 2016). A 20
kb window was used to detect selective sweeps.

The third method used the concept of `Extended Haplotype Homozygosity'.
Where genomic regions with high local haplotype homozygosity are
detected, this can be an excellent indication of signatures of positive
selection, with such haplotype structure useful in detecting selective
sweeps (Sabeti, Reich et al. 2002). Strong selection with commensurate
Linkage Disequilibrium should lead to an expansion of such haplotypes in
the population, prior to them being slowly broken down by recombination.
This premise led Sabati et al to develop the Extended Haplotype
Homozygosity test, with this later expanded upon by Voight et al
(Voight, Kudaravalli et al. 2006), Sabati et al (Sabeti, Varilly et al.
2007) and Tang et al (Tang, Thornton et al. 2007). This test measures
the extent to which an extended haplotype has been transmitted without
recombination. Firstly, an allele-specific integrated Haplotype
Homozygosity (iHH) is calculated, with this then used to calculate the
iHS (a ratio of the iHH for its ancestral and derived alleles). We used
the R package rehh version 3.2.2 (Gautier, Klassmann et al. 2017) to
calculate the iHS statistic for each individual SNP, by running the
data2haplohh, scan\_hh and ihh2ihs functions . We then used the per SNP
iHS statistic to calculate the maximum iHS statistic for each 20 kb
window using the R package tidyverse version 2.0.0 (Wickham, Averick et
al. 2019).

\subsection*{Gene annotation}

{\label{gene-annotation}}

We downloaded the Gallus gallus Biomart files from the Ensembl ftp
server
(https://ftp.ensembl.org/pub/release-104/mysql/gallus\_gallus\_core\_104\_6/)
and added them to a local PostgreSQL v15 database. Earlier detected
selective sweep regions were added to the same database. Custom SQL
queries were written to select all the known genes that were found in
these regions.

\subsection*{Overlap tests}

{\label{overlap-tests}}

We used a simulation test to determine the number of overlaps observed
between sweep regions on Bermuda and Kauai was greater than expected by
random chance. The test consisted of placing two sets of regions
uniformly at random on an interval the size of the autosomal sequenced
chicken genome, and counting the overlaps. The two sets had numbers and
lengths equal to the number and average length of sweeps observed on
Bermuda and Kauai. A permutation procedure was used to calculate the
significance, with 5000 replicates used and the number of observed
overlaps compared to the probability of obtaining the same number of
overlaps by chance (https://github.com/mrtnj/bermuda\_overlaps).

\subsection*{Chromosome painting}

{\label{chromosome-painting}}

We used CHROMOPAINTERV2 (Lawson \emph{et al.} , 2012) to compare the
Bermuda sweep regions to the other populations (Kauai, Red Junglefowl
and Domestic chickens). First we combined the vcf files from the
separate populations into one vcf file per chromosome using bcftools
v1.14 (Danecek, Bonfield et al. 2021). Then, we lifted an earlier Gallus
gallus recombination map (Elferink, van As et al. 2010) to Galgal6 using
LiftOver(https://genome.ucsc.edu/cgi-bin/hgLiftOver). Then we converted
our vcf files and the newly acquired recombination map to an accepted
chromopainter format by using the vcf2cp.pl and convertracfile.pl
scripts include in the fineSTRUCTURE version 4.1.1 library (Lawson,
Hellenthal et al. 2012). SNPs were then phased using SHAPEIT v5.1
(Hofmeister, Ribeiro et al. 2023). Then we ran ChromopainterV2, using
the default parameters, in each selective sweep regions flanked with 20
kb on each side. We painted Kauai and Bermudian populations using Red
Junglefowl and domestic sequences as donors. Images were created with
the R package tidyverse version 2.0.0 (Wickham, Averick et al. 2019).

\textbf{Principal Component Analysis}

The principal components were calculated after linkage pruning the SNPs
- using window size 50, step size 10 and -- pairwise
r\textsuperscript{2} threshold of 0.1 -, both with PLINK 1.9 (Chang,
Chow et al. 2015). Images were created with the R package tidyverse
version 2.0.0 (Wickham, Averick et al. 2019).

\textbf{Admixture}

Individual ancestries were estimated by running ADMIXTURE v1.3.0 for K
ranging from 1 to 10 and with cross validation enabled. CV-errors for
all values of K were taken directly from the ADMIXTURE output, with the
.bed file used for the analysis taken from PLINK output used for the
principal component analysis above. Images were created with the R
package tidyverse version 2.0.0 (Wickham, Averick et al. 2019).

\textbf{SplitsTree Analysis}

We expressed the shared chunk count matrix from Chromopainter as a
dissimilarity matrix by subtracting each element from the maximum shared
chunk count. We visualised this distance matrix as a Neighbour-Net
(Grünewald, Forslund et al. 2007) using Splitstree version 4 (Huson and
Bryant 2006).

\textbf{Acknowledgements}

The research was carried out within the framework of the Linköping
University Neuro-network. Sequencing was performed by the Uppsala
Sequencing Center, part of the SciLifeLab. The project was supported by
grants from the European Research Council (Consolidator grant FERALGEN
772874), the Swedish Research Council (VR), and the Linköping University
Neuro-network. The computations and data handling were enabled by
resources provided by the Swedish National Infrastructure for Computing
(SNIC) at Uppsala and Stockholm, partially funded by the Swedish
Research Council through grant agreement no. 2018-05973.

\emph{Data Accessibility Statement}

Bam files are deposited in the ENA, with general accession ID PRJEB67702
(see also Supplementary Table 10).

\textbf{Figure Legends}

Figure 1. (A) Admixture Plot assessing the relatedness of Bermudian
chickens with a variety of different domestic breeds and Red Junglefowl.
Graph shown for k=6. (B) PCA plot of the same data (PC1 vs PC2) showing
the relatedness between the feral, domestic and RJF sub-populations. (C)
PCA plot of the same data excluding the layer populations (PC1 vs PC2).
(D) PCA plot of the same data (PC3 vs PC4)

Figure 2. Chromopainter analysis for sweeps origins. A) Chromopainter
analysis showing Hawaiian (left side) and Bermudian (right side)
populations at the three shared feral sweeps locations sweeps (y-axis,
labelled chr1\_17340927, chr1\_32751411, and chr2\_78310987). For the
two feral populations, domestic (DOM, grey bar) and wild RJF (RJF, red
bar) birds were used as donors, with the size of the red and grey bars
indicating the number of chunks that were similar from each donor.
Therefore, the larger the bar, the greater the donor similarity to the
feral population. B) This is the same analysis as (A), however the donor
populations have now been further subdivided into sub-populations (Brown
Layer, White Layer, Broiler A, Broiler B, Red Junglefowl Thailand, Red
Junglefowl India). (C) Chromopainter analysis of Bermudian sweeps, with
this analysis also including Hawaii as a donor population, as well as
domestic and RJF birds. The same y-axis notation as in (A) is used. (D)
The same analysis type as in (C) however donor populations are once
again subdivided as in (B).

SUPPLEMENTARY TABLE LEGENDS

Supplementary Table 1. All Extended Haplotype Homozygosity sweeps
identified in Bermudian and Kauaian samples. Population, chromosome,
start and end coordinates are given (in bp).

Supplementary Table 2. Selective Sweep regions identified by Extended
Haplotype Homozygosity mapping in both Bermuda and Hawaii populations.
The chromosome, region start and end, gene type (protein coding, lncRNA,
etc), and any gene IDs are given, as well as the number of regions
identified (region nr).

Supplementary Table 3. Overlaps between sweeps in domestic and feral
populations. Technique used to detect the sweep, pairwise population,
and genomic coordinates (in bp) are given.

Supplementary Table 4. All Composite Likelihood Ratio sweeps detected in
Bermudian and Kauain samples. Population, chromosome, start and end
coordinates are given (in bp).

Supplementary Table 5. Selective Sweep regions identified by Composite
Likelihood Ratio mapping in both Bermuda and Hawaii populations. The
chromosome, region start and end, gene type (protein coding, lncRNA,
etc), and any gene IDs are given, as well as the number of regions
identified (region nr).

Supplementary Table 6. Selective Sweep regions identified by Tajima'sD
mapping in both Bermuda and Hawaii populations. The population,
chromosome, and region start and end are given.

Supplementary Table 7. Selective Sweep regions identified by Tajima's D
in both Bermuda and Hawaii populations. The chromosome, region start and
end, gene type (protein coding, lncRNA, etc), and any gene IDs are
given, as well as the number of regions identified (region nr).

Supplementary Table 8. Selective Sweep regions identified by at least
two sweep detection techniques in the Bermuda population. The
chromosome, region start and end, gene type (protein coding, lncRNA,
etc), and any gene IDs are given, as well as the number of regions
identified (region nr).

Supplementary Table 9. Selective Sweep regions identified by at least
two sweep detection techniques in the Hawaii (Kauai) population. The
chromosome, region start and end, gene type (protein coding, lncRNA,
etc), and any gene IDs are given, as well as the number of regions
identified (region nr).

Supplementary Table 10. Overview of the domestic, wild and feral samples
used and links to their repository website.

SUPPLEMENTARY FIGURE LEGENDS

Supplementary Figure 1. Admixture plots for K=2,3,4,5,7,8,9,10 showing
number of founder populations for domestic, wild and feral chicken
populations used in this study.

Supplementary Figure 2. Splitstree plot showing the relationships
between feral, domestic and Red Junglefowl samples.

Supplementary Figure 3. Overview of selective sweeps detected by
Tajima's D, Composite Likelihood Ratio (CLR) and Extended Haplotype
Homozygosity (EHH) mapping, as well as the domestication/ improvement
selective sweeps identified by Qanbari et al. 2019. Each chromosome
where a selective sweep was present is presented in its own panel, with
location in Megabases (Mb) on the x-axis of each, with the results from
the four different selective sweep mapping methods presented on the
y-axis.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}



\textbf{Figure 2.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Table 1. Selective Sweep regions shared between Hawaii and Bermuda that
were identified by at least two different mapping approaches in each
population.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
region\_nr & chr\_name & region\_start & region\_end & gene\_start &
gene\_end & biotype & stable\_id &
overlap\_with\_domestic\_sweeps\tabularnewline
\midrule
\endhead
1 & 1 & 17550927 & 17570928 & 17209559 & 17632970 & protein\_coding &
ENSGALG00000023584 & no\tabularnewline
2 & 1 & 32761411 & 32781411 & NULL & NULL & NULL & NULL &
yes\tabularnewline
3 & 2 & 78323570 & 78338404 & 78327060 & 78338570 & protein\_coding &
ENSGALG00000035925 & no\tabularnewline
\bottomrule
\end{longtable}

Table 2. Morphometric comparison of feral Bermudian chickens with Red
Junglefowl and White Leghorn chickens. Bermudian data taken in the field
from 40 female adults, 55 male adults. Kauai data taken in the field
from 36 male adults, 36 female adults. Red Junglefowl data and White
Leghorn female data taken from captive adults (230 days old) as
presented in (Wright, Kerje et al. 2008). Data for male White Leghorn
males taken from 29-week old sub-adults (von Schantz, Tufvesson et al.
1995), which are nearly at adult body mass but below well below their
final/adult comb mass.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{Chicken Population} &  \textbf{bodyweight (\emph{+}SD)} & 
\textbf{comb mass (\emph{+}SD)} &  \textbf{bodyweight (\emph{+}SD)} & 
\textbf{comb mass (\emph{+}SD)} & \textbf{duplex comb (\%)} &
\textbf{pea comb (\%)} & \textbf{rose comb (\%)} & \textbf{leg
colour}\tabularnewline
\midrule
\endhead
Red Junglefowl (wild) & 1119 (\emph{+}138) & NA & 799 (\emph{+}130) &
1.1 (\emph{+}0.3) & 0 & 0 & 0 & grey\tabularnewline
Kauai feral (feral hybrid) & 1106 (\emph{+}232\emph{)} & 2.73
(\emph{+}2.38) & 833.6 (\emph{+}157) & 0.31 (\emph{+}0.28) & 0 & 0 & 0 &
grey + yellow\tabularnewline
Bermudian feral (feral domestic) & 1153 (\emph{+}340) & 3.4
(\emph{+}2.9) & 799 (\emph{+}187) & 0.34 (\emph{+}0.29) & 3\% & 3\% &
1\% & grey + yellow\tabularnewline
White Leghorn (domestic) & 1900 (\emph{+}138) & 7.15 (\emph{+}1.38)* &
1629 (\emph{+}110) & 7.3 (\emph{+}0.6) & 0 & 0 & 0 &
yellow\tabularnewline
\bottomrule
\end{longtable}

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