\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[greek,ngerman,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Genome-wide scan for selection signatures in wild African harlequin
quail (Coturnix delegorguei delegorguei) reveals candidate genes
associated with immune response, morphological and production traits}



\author[1]{Stephen Ogada}%
\author[1]{Philip  Panyako}%
\author[2]{Jacqueline  Lichoti}%
\author[3]{Min-sheng Peng}%
\author[1]{Sheila Ommeh}%
\affil[1]{Jomo Kenyatta University of Agriculture and Technology}%
\affil[2]{Government of Kenya Ministry of Agriculture Livestock and Fisheries}%
\affil[3]{Kunming Institute of Zoology Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The wild African harlequin quails (Coturnix delegorguei delegorguei) of
Western Kenya suffer from incessant hunting, habitat fragmentation, and
the effects of climate change. These challenges, among others, have
forced them to breed under intensive pressure, disrupting normal
evolutionary processes. Here, we provide the first overview of the
selection signatures in wild African harlequin quails using
genotyping-by-sequencing information from 76 captured individuals.
Additionally, 19 domestic Japanese quails (Coturnix coturnix japonica)
were incorporated for comparative signatures of selection analysis
between wild and domesticated quail species that undergo different
selection pressures. Composite likelihood ratio test (CLR) and
integrated haplotype score (iHS) methods were used to detect selection
signatures. As a result, 252 and 424 candidate genes were detected in
wild African harlequin and domestic Japanese quails, respectively,
through the CLR test, whereas 150 and 457 candidate genes were
identified through iHS. Some of the essential candidate genes identified
in the wild African harlequin quail were associated with important
traits such as immune response (MAPK13, MAPK14, CREB1, ITGB3, and
PPP1CA) and morphological traits (WNT5A, GRIA1, CREB1, ADCY8, and ALK)
whereas, in domestic Japanese quail, primarily production-related genes
such as VIPR2, DYNLL2, COL6A3, MSX2, PRF1and GNA12 were identified. Our
findings provide insights into the role of selection in shaping both
wild and domestic quail genomes in terms of significant immune response,
growth, reproduction, and morphological and behavioral traits.%
\end{abstract}%



\sloppy


\textbf{Genome-wide scan for selection signatures in wild African
harlequin quail (\emph{Coturnix delegorguei delegorguei}) reveals
candidate genes associated with immune response, morphological and
production traits}

Stephen Ogada\textsuperscript{1}, Philip Murunga
Panyako\textsuperscript{1}, Jacqueline Lichoti\textsuperscript{2},
Min-Sheng Peng\textsuperscript{3,4} and Sheila Cecily
Ommeh\textsuperscript{1*}

\emph{\textsuperscript{1}Institute For Biotechnology Research,Jomo
Kenyatta University of Agriculture and Technology, P.O. Box 62000, City
Square 00200, Nairobi-Kenya}

\emph{stevogada@gmail.com, pphilipmurunga@gmail.com, sommeh@jkuat.ac.ke}

\emph{\textsuperscript{2} Central Veterinary Laboratories Kabete, State
Department of Livestock, Ministry of Agriculture, Livestock and
Fisheries, P.O Private Bag 00625, Nairobi-Kenya}

\emph{kasiiti.orengo@gmail.com}

\emph{\textsuperscript{3} State Key Laboratory of Genetic Resources and
Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals,
Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming,
China}

\emph{\textsuperscript{4} Sino-Africa Joint Research Center, Chinese
Academy of Sciences, Nairobi, Kenya}

\emph{pengminsheng@mail.kiz.ac.cn}

\emph{*Author for correspondence: S C Ommeh}

\emph{Email: sommeh@jkuat.ac.ke}

\textbf{ABSTRACT}

The wild African harlequin quails (\emph{Coturnix delegorguei
delegorguei} ) of Western Kenya suffer from incessant hunting, habitat
fragmentation, and the effects of climate change. These challenges,
among others, have forced them to breed under intensive pressure,
disrupting normal evolutionary processes. Here, we provide the first
overview of the selection signatures in wild African harlequin quails
using genotyping-by-sequencing information from 76 captured individuals.
Additionally, 19 domestic Japanese quails (\emph{Coturnix coturnix
japonica} ) were incorporated for comparative signatures of selection
analysis between wild and domesticated quail species that undergo
different selection pressures. Composite likelihood ratio test (CLR) and
integrated haplotype score (iHS) methods were used to detect selection
signatures. As a result, 252 and 424 candidate genes were detected in
wild African harlequin and domestic Japanese quails, respectively,
through the CLR test, whereas 150 and 457 candidate genes were
identified through iHS. Some of the essential candidate genes identified
in the wild African harlequin quail were associated with important
traits such as immune response (MAPK13, MAPK14, CREB1, ITGB3, and
PPP1CA) and morphological traits (WNT5A, GRIA1, CREB1, ADCY8, and ALK)
whereas, in domestic Japanese quail, primarily production-related genes
such as VIPR2, DYNLL2, COL6A3, MSX2, PRF1and GNA12 were identified. Our
findings provide insights into the role of selection in shaping both
wild and domestic quail genomes in terms of significant immune response,
growth, reproduction, and morphological and behavioral traits.

\textbf{Keywords:} Selection pressure, GBS, candidate genes,

\emph{Coturnix}

\section*{INTRODUCTION}

{\label{introduction}}

The wild African harlequin quail (\emph{Coturnix delegorguei
delegorguei} ) is endemic to Eastern and Southern Africa and is commonly
found in grassland areas with scattered bush cover (Lewis \& Pomeroy,
1989). For generations, rural smallholder farmers in Kenya have hunted
quail for consumption as a complementary source of poultry protein
(Urban et al., 1986, Ogada et al., 2022). However, in addition to
continuous and uncontrolled harvesting, breeding attempts by rural
farmers, climate change, habitat destruction by humans, migration,
population bottlenecks, and inbreeding are among the main challenges
facing wild African harlequin quail in Kenya (Wamuyu et al., 2017; Ogada
et al., 2021). These factors interfere with the normal evolutionary
processes experienced by wild African harlequin quails and may influence
natural, artificial, or sexual selection (Allendorf \& Hard, 2009).

In contrast, commercial quail breeds, such as the domestic Japanese
quail, have become the most commonly consumed quail species globally
since their domestication in the late 19\textsuperscript{th} century and
early 20\textsuperscript{th} century (Nishibori et al., 2001). The
domestic Japanese quails have undergone intense selection pressure since
their domestication through modern breeding methods aimed at increased
egg and meat production (Mills et al., 1997; Lukanov \& Pavlova, 2020).
The domestication and artificial selection of the Japanese quail have
brought about genetic variation changes, altering its phenotype and
increasing its size and number of eggs laid (Lukanov \& Pavlova, 2020).

The main differences between wild and domestic quail species lie in the
morphological, behavioral, and productivity characteristics (Chang et
al., 2009). In our previous study, the wild African harlequin quail meat
and eggs were found to contain higher protein content and minerals
(zinc, potassium, calcium, and iron) when compared to domestic Japanese
quail, helmeted guinea fowl, indigenous and commercial chickens
(Chepkemoi et al., 2017). Therefore, understanding the genetic
architecture of the wild African harlequin quail, among other factors,
could help elucidate this finding, among other observed traits.

Genomic regions affected by selection pressures tend to develop
signatures such as high allele frequencies, substantial linkage
disequilibrium, increased homozygous genotypes, and long haplotypes
(Nielsen, 2005; Qanbari \& Simianer, 2014). Signatures of selection
tests are crucial for understanding genomes as they can provide an
accurate and deep understanding of the processes that affect population
diversity and trait selection in the genome (Oleksyk et al., 2010; Ma et
al., 2015). This study used the composite likelihood ratio test (CLR)
and integrated haplotype score (iHS) methods to detect selection
signatures. The CLR method uses site frequency spectrum (SFS) patterns
of single-nucleotide polymorphisms (Williamson et al., 2007). In
contrast, the iHS test examines the homozygosity of extended haplotypes
generated by a selective sweep (Voight et al., 2006).

Quails are increasingly becoming an essential source of poultry protein
in developing countries, thus helping to ensure food security (Jeke et
al., 2018). At present, there is no information on the impacts of
selection on the wild African harlequin quail. Furthermore, wild and
domestic quails are exposed to different selection pressures; thus, it
is important to investigate the effects of natural and artificial
selection on the quail genome. Gaining knowledge about the selection
signatures in quail genomes will form the basis for comprehending the
underlying mechanisms that influence their key traits.

\section*{MATERIALS AND METHODS}

{\label{materials-and-methods}}

\textbf{Sample collection and genotyping-by-sequencing data}

One hundred quails were sampled for this study; 78 captured wild African
harlequin quails and 22 domestic Japanese quails from Siaya and Kajiado
Counties of Kenya, respectively. This comprised an equal number of male
and female individuals for every visited farm. Wild African harlequin
quails were captured around homesteads using traditional methods
described by Wamuyu et al. (2017). DNA extracted from blood samples was
sequenced using the genotyping-by-sequencing (GBS) approach and the
generated sequence data was extracted from our previous publication
(Ogada et al., 2021).

\textbf{Sequence data processing and SNP calling}

Raw reads were processed through a series of quality control procedures
which involved; (i) removing reads with [?]10\% unidentified nucleotides
(N); (ii) reads with \textgreater{}50\% bases with a Phred quality
\textless{} 5; (iii) removing reads with \textgreater{}10 nucleotides
alignment to the adapter; (iv) allowing [?]10\% mismatches; and (v)
removing reads that contain the~\emph{Hae} III enzyme sequence.

Burrows-Wheeler Aligner (BWA) version 0.7.17 was used to align the
retained reads against the \emph{Coturnix Japonica} 2.0 genome (Assembly
accession number GCF\_001577835.1) with the parameters mem -t 4 -k 32
-M' (Li \& Durbin, 2009). Variant calling was performed using SAMtools
version 1.11\emph{mpileup} command (Li et al., 2009) in conjunction with
BCFtools version 1.11 \emph{call} command (Li, 2011). Variant filtering
was performed by restricting the dataset to biallelic SNPs found in at
least 80\% of samples, with a minimum depth of 2 reads, minimum Phred
score of 30, and minimum minor allele frequency (MAF) of 0.01 using
VCFtools version 0.1.13 (Danecek et al., 2011). Additional filtering was
also done using PLINK version 1.9 (Purcell et al., 2007) to remove
individuals with missing genotype data (--mind 0.1), variants with
missing genotype data (--geno 0.05), minor allele frequency threshold
(--maf 0.05) and Hardy--Weinberg exact test threshold (--hwe 1e6).

\textbf{Signatures of selection detection}

Two complementary methods (CLR and iHS) that were found to have
power \textgreater{} 70\% to detect selection signatures were applied
(Ma et al., 2015). CLR values based on the variation in the
site-frequency spectrum along the chromosome were computed at 1000 grids
(-grid 1000) using the SweeD program version 4.0.0 (Pavlidis et al.,
2013) at each SNP. To generate phased haplotypes, SHAPEIT version 2.r900
(Delaneau et al., 2013) was used to perform haplotype phasing with
default settings. The ratio of extended haplotype homozygosities (EHH)
associated with each allele was calculated into standardized iHS
(\textbar{}iHS\textbar{}) values using the function ``\emph{ihh2ihs''}in
\emph{rehh} version 3.2.1 R package (Gautier et al., 2017). The iHS
analysis was conducted using unpolarized alleles, which is ideal for
non-model organisms that lack representative studies to allow for the
correct designation of `ancestral' or `derived' alleles (Santos et al.,
2021).

\textbf{Identification of Candidate Genes Under Selection and Gene
Annotation}

Candidate genes under selection were determined using an outlier
approach where SNPs above the cutoff value were highly considered (Wang
et al., 2018). The 99th percentile of the observed genome-wide
distribution of all standardized iHS values and CLR values was used as
the threshold to identify outliers. SNPs that met the threshold and were
located within a 250 kb window were highly considered and used to
determine candidate genes under positive selection.

The annotation of the candidate regions was based on the Japanese quail
genome assembly (Accession number GCF\_001577835.1) from NCBI. Gene
Ontology (GO) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes)
pathways identification was performed using the Database for Annotation,
Visualization, and Integrated Discovery (DAVID) version 6.8 browser tool
(Huang et al., 2009) and Metascape v3.5.20230101 (Zhou et al., 2019).
The biological significance of the enriched terms and pathways was
considered if \emph{P \textless{} 0.05} .

\section*{RESULTS}

{\label{results}}

After quality control, 95 individuals (76 wild African harlequin quails
and 19 domestic Japanese quails) with 499,461 SNPs were retained for
further analysis. The wild African harlequin and domestic Japanese quail
datasets had 8,024 and 491,441 SNPs, respectively.

\subsection*{Signatures of selection detection using CLR
approach}

{\label{signatures-of-selection-detection-using-clr-approach}}

Genome-wide selection signatures were detected using the composite
likelihood ratio statistical approach in wild African harlequin and
domestic Japanese quails. Potential candidate regions under selection
were identified with outliers that fell into the 99th percentile of CLR
values distribution in wild African harlequin (CLR \textgreater{} 1.39)
and domestic Japanese (CLR \textgreater{} 8.33) quails. Wild African
harlequin quail chromosomes 1 and 2 had the highest number of
significant CLR values (Figure 1). In contrast, outliers were observed
in most Japanese quail chromosomes except chromosomes 10 and 12 (Figure
2). 252 and 424 potential candidate genes were identified across 28
autosomes in wild African harlequin and Japanese quails, respectively.

Figure 1. Manhattan plot showing CLR values distribution across all wild
African harlequin quail autosomes. The red line indicates the 99th
percentile threshold.

In wild African harlequin quails, functional analysis of the candidate
genes showed an association with key biological processes, metabolic
pathways, and molecular functions. Wild African harlequin quail
candidate genes were implicated in melanogenesis (FZD7, WNT5A, WNT8B,
LOC107320948, WNT3, WNT4), Wnt signaling pathway (PPP3R1, FZD7, WNT5A,
WNT8B, WNT3, NKD1, WNT4), follicle-stimulating hormone signaling pathway
(FSHB, FSHR), ear morphogenesis (SOX2, PTPN11, PROX1), positive
regulation of skeletal muscle satellite cell proliferation (HGF, GPC1),
among others (see Supporting Information Table S1). Enrichment analysis
of the candidate genes revealed cluster groups with enriched terms such
as behavior (GO:0007610), \selectlanguage{english}brain and sensory organ development
(\selectlanguage{english}GO:0007420; GO:0007423), inner ear development (GO:0048839), response
to growth factor (GO:0070848), regulation of MAPK cascade (GO:0043408),
cell morphogenesis (GO:0000902), \selectlanguage{english}gliogenesis and glial cell
differentiation (GO:0042063, GO:0010001), and tissue morphogenesis
(GO:0048729).

Figure 2. Manhattan plot showing CLR values distribution across all
domestic Japanese quail autosomes. The red line indicates the 99th
percentile threshold.

Some of the top enrichment clusters identified in domestic Japanese
quail include \selectlanguage{english}cell morphogenesis (GO:0000902), regulation of
transmembrane transport (GO:0034762), \selectlanguage{english}embryonic morphogenesis
(GO:0048598), focal adhesion (GO:0005925), negative regulation of
cellular component organization (\selectlanguage{english}GO:0051129), \selectlanguage{english}platelet activation,
signaling and aggregation (GO:0030168), regulation of cell growth
(\selectlanguage{english}GO:0001558), heart development (GO:0007507), cell-cell adhesion
(GO:0098609), \selectlanguage{english}mesenchymal cell differentiation (GO:0048762) and brain
development (GO:0007420), among others (see Supporting Information Table
S2). Key domestic Japanese quail candidate genes identified include
VIPR2, RAC1, COL6A3, SLC9A1, MSX2, and PRF1.

\subsection*{Signatures of selection detection using the iHS
approach}

{\label{signatures-of-selection-detection-using-the-ihs-approach}}

The standardized iHS values were used to identify genomic regions that
showed signatures of selection patterns. Outliers that fell into the
99th percentile in domestic Japanese (\textbar{}iHS\textbar{}
\textgreater{} 3.2) and wild African harlequin (\textbar{}iHS\textbar{}
\textgreater{} 3.55) quails were considered to be potential candidate
regions under selection. Chromosome 2 had the highest number of outlier
SNPs in wild African harlequin quail (Figure 3), whereas, in domestic
Japanese quails, outlier SNPs numbers were high in all chromosomes
(Figure 4). 150 and 457 potential candidate genes were detected across
28 autosomes in wild African harlequin and domestic Japanese quails,
respectively.

Enrichment analysis of the wild African harlequin quail candidate genes
showed their association with platelet activation (MAPK14, ITGB3,
MAPK13), hematopoietic cell lineage (LOC107324620, ITGB3, LOC107317826),
Th1 and Th2 cell differentiation (MAPK14, LOC107317826, MAPK13), Th17
cell differentiation (MAPK14, LOC107317826, MAPK13), adrenergic
signaling in cardiomyocytes (CREB1, MAPK14, MAPK13, PPP1CA), among
others. Key enrichment terms include focal adhesion (GO:0005925),
regulation of secretion (\selectlanguage{english}GO:0051046), olfactory transduction
(GO:0004984), actin binding, and cytoskeleton organization (GO:0003779;
GO:0030036), Osteoclast differentiation (GO:0030316), negative
regulation of transcription by competitive promoter binding
(GO:0010944), and the regulation of peptidyl-tyrosine phosphorylation
(GO:0050730), among others (see Supporting Information Table S3).

Figure 3. Manhattan plot showing standardized iHS values distribution
across all wild African harlequin quail autosomes. The red line
indicates the 99th percentile threshold.

Some of the top enriched clusters of the domestic Japanese quail
candidate genes include the regulation of lipid metabolic process
(\selectlanguage{english}GO:0019216), cholesterol transport (\selectlanguage{english}GO:0030301), spermatogenesis
(GO:0007283), cell proliferation (GO:0008283), phagosome (GO:0045335),
the intestinal immune network for IgA production (GO:0002387), antigen
processing and presentation (GO:0019882), autoimmune thyroid disease
(cjo05320), and Th1 and Th2 cell differentiation (GO: 0045063,
GO:0045064), among others (see Supporting Information Table S4). Key
candidate genes involved in crucial biological processes of the domestic
Japanese quail include DYNLL2 and genes from the APOA, ABCA, G12/G13
gene families.

Figure 4. Manhattan plot showing standardized iHS values distribution
across all domestic Japanese quail autosomes. The red line indicates the
99th percentile threshold.

\section*{DISCUSSION}

{\label{discussion}}

Signatures of selection detection in both wild and domesticated species
is considered a critical step toward understanding the molecular basis
of adaptive evolution. This information can be used to identify genomic
regions and underlying biological mechanisms affecting their essential
traits. There are different methods of detecting selection signatures.
However, two complementary approaches (composite likelihood ratio test
(CLR) and integrated haplotype score (iHS)) that were found to have
power \textgreater{} 70\% to detect selection signatures were applied in
this study. Unfortunately, there is currently no information on the
effects of selection on the wild African harlequin quail, despite the
incessant harvesting by rural smallholder farmers, effects of habitat
destruction, and climate change, among other challenges. Moreover, there
is limited information on selection signatures detected in the domestic
Japanese quail, the most common domesticated quail species reared in
Kenya. It has experienced constant evolutionary and genetic changes in
its phenotype and genome architecture due to modern breeding methods.
Such information could have been a foundation for understanding
biological pathways governing important traits in wild quails and how
modern breeding methods will likely affect them if breeding initiatives
were to be made.

The application of CLR and iHS in detecting selective sweeps in wild
African harlequin and domestic Japanese quail genomes was successful.
Several complementary methods for signatures of selection detection
allow for improved testing as each statistical test captures patterns in
data differently (Fariello et al., 2013). The linkage disequilibrium
(LD)-based iHS approach, which requires haplotypes per individual of one
population to detect selection, was anticipated to be more reliable for
this study due to the single populations that were sampled and the lack
of a good reference population about quail (Voight et al., 2006). In
addition, the CLR method was also adopted for this study as it uses
allele frequency data to compare a neutral and a selective sweep model,
and it is not highly sensitive to assumptions about the underlying
recombination rate or recombination hotspots (Williamson et al., 2007).
Furthermore, the CLR method can detect a beneficial mutation that spread
in the entire population. In contrast, methods based on extended
haplotype length and high linkage disequilibrium only detect the
beneficial mutation that has yet to spread throughout the entire
population (Voight et al., 2006; Wang et al., 2006; Williamson et al.,
2007).

Both signatures of selection detection methods identified candidate
genes associated with crucial biological, molecular, and cellular
processes such as immune response, growth, reproduction, and
morphological and behavioral traits. Some essential candidate genes
involved in various immune response processes and pathways include
MAPK1, MAPK13, MAPK14, CREB1, DYNLL2, RAC1, and ITGB3. The MAPK
signaling pathway genes were enriched in the wild African harlequin and
domestic Japanese quail. Mitogen-activated protein kinases (MAPKs) are a
group of serine/threonine protein kinases that are highly conserved and
have essential roles in cellular processes, such as proliferation,
stress responses, apoptosis, and immune response regulation (Liu et al.,
2007). The MAPK13 and MAPK14 (p38 MAPK pathway) genes, which play an
important role in inflammatory responses, were positively selected in
the wild African harlequin quail. The p38 pathway participates in the
innate and adaptive immune response process by controlling the
production of inflammatory cytokines (TNF\selectlanguage{greek}α, \selectlanguage{english}interleukin (IL)-1, IL-10,
and IL-12) by specialized dendritic and macrophage cells, CD40-induced
gene expression, and proliferation of B cells, antigen processing in
CD8\textsuperscript{+} conventional dendritic cells, and T cell
homeostasis and function (Arthur \& Ley, 2013; Soares-Silva et al.,
2016; Han et al., 2020).

In addition to the p38 pathway, the extracellular signal-regulated
kinase (ERK1/2) is also a MAPK pathway involved in positive selection
and the differentiation of DP thymocytes to either CD4 or CD8 T cells
(Fischer et al., 2005). The MAPK1, a key component of the ERK1/2
pathway, was positively selected in domestic Japanese quails. It was
shown to participate in adaptive immune responses during bacterial
infection in Nile tilapia (Wei et al., 2020). MAPK1, through the
MAPK/ERK pathway, facilitates the activation and proliferation of immune
cells, including T cells, B cells, and macrophages (Sun et al., 2015).
Bacterial pathogens such as \emph{Salmonella} , \emph{Shigella}
,\emph{Yersinia} and \emph{Escherichia} species are known to manipulate
pathways like the MAPK in their hosts to propagate infection (Krachler
et al., 2011). Another positively selected candidate gene in domestic
Japanese quails associated with \emph{Salmonella} infection was the
DYNLL2 gene, which is predicted to be involved in cytoskeletal motor
activity and cytoskeletal protein binding. An observed increase in
DYNLL2 protein in gamma delta T-lymphocytes (\selectlanguage{greek}γδ \selectlanguage{english}T-lymphocytes) of
chickens infected with \emph{Salmonella} Enteritidis showed its role in
regulating immune response (Sekelova et al., 2017). Like commercial
chicken, domestic Japanese quails are reared on farms under different
housing systems such as battery cages, free-range and floor-raised
systems, among others. Choice of the housing system in connection to
other production factors such as infrastructure, stocking density (farm
and flock size), manure collection, disease status of the flock, and
rodent and insect load does determine the risk of
\emph{Salmonella}infection (Van Hoorebeke et al., 2011).

The ITGB3 gene, which was positively selected in the wild African
harlequin quail, is involved in the blood coagulation pathway through
adrenergic signaling in platelet activation. ITGB3 encodes for
glycoprotein IIIa (GPIIIa) and, along with the alpha IIb chain, forms
the platelet adhesive protein receptor complex glycoprotein IIb/IIIa (GP
IIb/IIIa), which mediates platelet aggregation by acting as a receptor
for fibrinogen (Cerhan et al., 2007). It plays a critical role in many
cellular processes, such as cytoskeletal organization, cell adhesion,
migration, proliferation, and survival (Ridley et al., 1992; Arthur et
al., 2002; Guo et al., 2008). Its immune roles in the T cell development
(Luo et al., 2013), B cell development and signaling (Walmsley et al.,
2003), epidermal homeostasis resulting in wound healing (Winge \&
Marinkovich, 2019), and phagocytosis ( Lee et al., 2000; Han et al.,
2019) are continuously being studied. Phagocytosis is a necessary
component of the innate immune response. It plays an essential role in
host-defense mechanisms by enabling the uptake and destruction of
infectious pathogens through specialized cell types like macrophages,
neutrophils, and monocytes (Lee et al., 2020). Similar to the ITGB3
gene, the RAC1 candidate gene that was detected in domestic Japanese
quail is also associated with wound healing by regulating the innate
immune response in keratinocytes (Bustelo et al., 2007; Pedersen et al.,
2012; Winge \& Marinkovich, 2019).

PPP1CA, WNT5A, GRIA1, CREB1 and ADCY8 candidate genes, associated with
adaptation and behavior, were detected in the wild African harlequin
quail. Melanogenesis and ear morphogenesis biological processes are
crucial for wild African harlequin quail breeding behavior, social
interactions, and survival. Melanogenesis involves melanin production,
which plays a significant role in structural plumage in birds (Jeon et
al., 2021). Plumage color and patterns are helpful in camouflage,
mating, and differentiating between male and female wild African
harlequin quails (Mason \& Bowie, 2020). Melanin-based patterns are
influenced by melanocyte migration, differentiation, cell death, and/or
interaction with neighboring skin cells at the cellular level (Inaba \&
Chuong, 2020). The wild African harlequin quail songs and calls are also
essential to their communication and behavior; hence, ear morphogenesis
is a vital biological process for their survival and reproduction. The
WNT5A gene, among other genes, was implicated in melanogenesis, ear
morphogenesis, sex differentiation, and muscle development. Through
activating multiple intracellular signaling cascades, the WNT gene
family controls cell proliferation, differentiation, apoptosis,
survival, migration, and polarity (Kikuchi et al., 2012). According to
Kikuchi et al. (2012), regulating cellular functions, including
migration and differentiation, makes the WNT5A gene a target for
selection.

The GluA1 subunit has been implicated in the regulation of circadian
rhythms and behavior (Ang et al., 2021). The Glutamate receptor 1
(GluA1), encoded by the GRIA1 gene, forms part of the AMPA receptor that
mediates fast glutamate signaling in the central nervous system
(synaptic plasticity). Positive selection of genes associated with
behavioral response to stress and circadian entrainment (GRIA1, CREB1,
ADCY8) could contribute to the observed timely seasonal migration and
behavioral patterns of wild African harlequin quails (Bossu et al.,
2022). The PPP1CA gene encodes for a protein that is part of the three
catalytic subunits of protein phosphatase 1 (PP1). The PP1 protein was
found to be a key regulator of period and light-induced resetting of the
circadian clock in the common fruit fly (Fang et al., 2007) and
different mammals (Eide et al., 2005; Gallego et al., 2006; Schmutz et
al., 2011). It regulates the circadian period length, in counterbalance
with casein kinase 1\selectlanguage{greek}δ \selectlanguage{english}and \selectlanguage{greek}ε \selectlanguage{english}(CK1\selectlanguage{greek}δ/ε\selectlanguage{english}), through the regulation of the
speed and rhythmicity of period circadian regulator 1 and 2 (PER1 and 2)
phosphorylation (Lee et al., 2011; Schmutz et al., 2011).

Candidate genes associated with growth and reproduction were detected in
the wild African harlequin and domestic Japanese quail. The Wnt--Hippo
signaling pathway-related genes identified in wild African harlequin
quails include SOX2, FZD7, WNT3, NKD1, and WNT4. The Hippo signaling
pathway plays a crucial role in cellular differentiation, tissue, and
organ development by controlling organ size through the regulation of
cell proliferation and apoptosis (Justice et al., 1995; Xu et al., 1995;
Heallen et al., 2011; Wu \& Guan, 2021). It has also been implicated in
other diverse roles, such as tissue homeostasis, wound healing and
regeneration, immunity, tumorigenesis, and embryogenesis (Wu \& Guan,
2021). The transcription factor SOX2 is involved in osteoblast
differentiation (Seo et al., 2013), inner ear development (Kiernan et
al., 2005), neurogenesis, and the proliferation and/or maintenance of
stem cells (Oesterle et al., 2008), among others. The positive selection
of genes associated with tissue and organ size, among other factors,
could contribute to how the wild African harlequin quails have managed
to maintain their small body size, supporting their growth and survival
in the wild. The wild African harlequin quail is known to fly longer
distances during migration and at faster speeds (Wamuyu et al., 2017).

CREB1 gene is involved in growth hormone synthesis, secretion, and
action. CREB1 encodes a phosphorylation-dependent transcription factor
that mediates the response to various cellular processes, including
regulation of transcription, signal transduction, glucose homeostasis,
and growth-factor-dependent cell survival, proliferation, and memory
(Kinjo et al., 2005). CREB1 stimulates transcription upon binding to the
DNA cAMP response element (CRE) located in the promoter region of target
genes, leading to the recruitment of transcriptional coactivators and
the initiation of gene transcription (Shankar et al., 2005). In humans,
the CREB/CREB1 gene is involved in immune function (Cerhan et al., 2007)
as it promotes proliferation and survival and differentially regulates
Th1, Th2, and Th17 responses (Wen et al., 2010). It has also been shown
to be a critical driver of vaccine efficacy in non-human primates
(Tomalka et al., 2021).

Several candidate genes associated with lipid and cholesterol transport
and metabolism in domestic Japanese quails, such as the APOA (APOA1 and
APOA4) and ABCA (ABCA2, ABCA5, ABCA7) gene families, were identified
(Albrecht \& Viturro, 2007; Dominiczak \& Caslake, 2011). Additionally,
muscle cell differentiation, tissue, and structure development genes
(COL6A3, SLC9A1, SMARCD3, MSX2, and PRF1) were identified. The
positively selected DYNLL2 gene, apart from its role in immune response,
was also found to be a regulator of chicken myogenesis, providing
insights into breast muscle development in chickens and other birds (Li
et al., 2022). VIPR2, one of the positively selected cAMP signaling
pathway genes identified in domestic Japanese quails, has been
implicated in the regulation of egg production as it has been associated
with brooding behavior in chicken and geese (Luan et al., 2014; Huang et
al., 2022).

In this study, only two genes (CBFB and RET) overlapped between the CLR
and iHS tests in the domestic Japanese quail, in contrast to the wild
African harlequin quail, where no overlapped genes were observed. CBFB
protein is mainly associated with definitive hemopoiesis (Speck et al.,
1999). In contrast, RET is known for its protein kinase activity,
leading to the activation of signaling pathways involved in cell growth,
differentiation, and survival, such as MAPK and AKT (Schuchardt et al.,
1994; Taraviras et al., 1999).

\section*{CONCLUSION}

{\label{conclusion}}

In the present study, we used genotyping-by-sequence data to detect
selection signatures in wild African harlequin and domestic Japanese
quails. Our results revealed candidate genes associated with immune
response, growth, reproductive, morphological, and behavioral traits.
The wild African harlequin candidate genes were primarily associated
with morphological and behavioral traits, whereas the domestic Japanese
quail candidate genes were mainly associated with growth and production
traits.

Identifying true signatures can be complicated due to the likelihood of
detecting false positives. Therefore, further studies are always
encouraged to reaffirm and refine results from less-studied species such
as the wild African harlequin quail. Nevertheless, this study was able
to give a glimpse of how natural and artificial selective pressures have
shaped the genomic landscape of wild and domestic quail species.
Furthermore, information on the effects of artificial selection on
domestic Japanese quails may inform conservation policies for wild
quails, especially in regions where domestic quails have been used to
restock wild quail populations.

\textbf{CONFLICT OF INTEREST}

The authors declare that there are no competing interests regarding the
publication of this paper.

\textbf{FUNDING STATEMENT}

This work was supported by grants awarded to Dr. Sheila Ommeh by Jomo
Kenyatta University of Agriculture and Technology (JKUAT) under research
grant number JKU/2/4/RP/181. This research was also supported by the
Sino-Africa Joint Research Center, Chinese Academy of Sciences
(SAJC201611).

\textbf{ACKNOWLEDGEMENTS}

The authors thank the local farmers, veterinary officers, Kenya Wildlife
Service, Ministry of Agriculture, Livestock and Fisheries, and the
National Quail Breeding Program. Sincere gratitude to the veterinary
officers for their aid during sampling.

\textbf{AUTHOR CONTRIBUTIONS}

\textbf{Stephen Ogada:} Formal analysis (lead); Methodology (lead);
Software (lead); Validation (equal); Writing-original draft (lead);
Writing-review \& editing (equal). \textbf{Philip Murunga
Panyako:}Formal analysis (supporting); Methodology (supporting);
Validation (equal); Writing-review \& editing (equal).
\textbf{Jacqueline Lichoti:}Supervision (supporting); Validation
(equal); Writing-review \& editing (supporting). \textbf{Min-Sheng
Peng:} Formal analysis (supporting); Validation (equal); Writing --
review \& editing (equal). \textbf{Sheila Cecily Ommeh:}
Conceptualization (lead); Formal analysis (supporting); Validation
(equal); Investigation (lead); Methodology (supporting); Resources
(lead); Supervision (lead); Writing-original draft (supporting);
Writing-review \& editing (supporting).

\textbf{ETHICAL APPROVAL}

This study received ethical clearance from the Kenya Wildlife Service
under permit number KWS/BRM/5001 to sample wild African harlequin quails
and a ``no objection for the research'' from the Director of Veterinary
Services, Ministry of Agriculture, Livestock and Fisheries in Kenya
under permit number RES/POL/VOL.XXVII/162 to sample domestic quails.

\textbf{DATA AVAILABILITY STATEMENT}

The aligned GBS reads in the format of bam files were deposited in the
National Centre for Biotechnology Information (NCBI) repository under
project ID PRJNA748759 and PRJNA748896.

\section*{REFERENCES}

{\label{references}}

\begin{enumerate}
\tightlist
\item
  Albrecht, C., \& Viturro, E. (2007). The ABCA subfamily---gene and
  protein structures, functions and associated hereditary
  diseases.\selectlanguage{ngerman}\emph{Pflügers Archiv - European Journal of Physiology}
  ,\emph{\textbf{453},} 581--589.
  https://doi.org/10.1007/s00424-006-0047-8
\item
  Allendorf, F. W., \& Hard, J. J. (2009). Human-induced evolution
  caused by unnatural selection through harvest of wild
  animals.\emph{Proceedings of the National Academy of Sciences}
  ,\emph{\textbf{106},} 9987--9994.
  https://doi.org/10.1073/PNAS.0901069106
\item
  Ang, G., Brown, L. A., Tam, S. K. E., Davies, K. E., Foster, R. G.,
  Harrison, P. J., Sprengel, R., Vyazovskiy, V. V, Oliver, P. L.,
  Bannerman, D. M., \& Peirson, S. N. (2021). Deletion of AMPA receptor
  GluA1 subunit gene (Gria1) causes circadian rhythm disruption and
  aberrant responses to environmental cues. \emph{Translational
  Psychiatry} , \emph{\textbf{11},} 588.
  https://doi.org/10.1038/s41398-021-01690-3
\item
  Arthur, J. S. C., \& Ley, S. C. (2013). Mitogen-activated protein
  kinases in innate immunity. \emph{Nature Reviews Immunology}
  ,\emph{\textbf{13},} 679--692. https://doi.org/10.1038/nri3495
\item
  Arthur, W. T., Noren, N. K., \& Burridge, K. (2002). Regulation of Rho
  family GTPases by cell-cell and cell-matrix adhesion. \emph{Biological
  Research} , \textbf{35} , 239--246.
  http://dx.doi.org/10.4067/S0716-97602002000200016
\item
  Bossu, C. M., Heath, J. A., Kaltenecker, G. S., Helm, B., \& Ruegg, K.
  C. (2022). Clock-linked genes underlie seasonal migratory timing in a
  diurnal raptor. \emph{Proceedings of the Royal Society B}
  ,\textbf{289} , 20212507. https://doi.org/10.1098/rspb.2021.2507
\item
  Bustelo, X. R., Sauzeau, V., \& Berenjeno, I. M. (2007). GTP\selectlanguage{english}-binding
  proteins of the Rho/Rac family: regulation, effectors and functions in
  vivo. \emph{Bioessays} , \emph{\textbf{29},} 356--370.
  https://doi.org/10.1002/bies.20558
\item
  Cerhan, J. R., Ansell, S. M., Fredericksen, Z. S., Kay, N. E., Liebow,
  M., Call, T. G., Dogan, A., Cunningham, J. M., Wang, A. H., Liu-Mares,
  W., Macon, W. R., Jelinek, D., Witzig, T. E., Habermann, T. M., \&
  Slager, S. L. (2007). Genetic variation in 1253 immune and
  inflammation genes and risk of non-Hodgkin lymphoma. \emph{Blood} ,
  110 , 4455--4463. https://doi.org/10.1182/blood-2007-05-088682
\item
  Chang, G. B., Liu, X. P., Chang, H., Chen, G. H., Zhao, W. M., Ji, D.
  J., Chen, R., Qin, Y. R., Shi, X. K., \& Hu, G. S. (2009). Behavior
  differentiation between wild Japanese quail, domestic quail, and their
  first filial generation. \emph{Poultry Science} , 88 , 1137--1142.
  https://doi.org/10.3382/ps.2008-00320
\item
  Chepkemoi, M., Macharia, J. W., Sila, D., Oyier, P., Malaki, P.,
  Ndiema, E., Agwanda, B., Obanda, V., Ngeiywa, K. J., \& Lichoti, J.
  (2017). Physical characteristics and nutritional composition of meat
  and eggs of five poultry species in Kenya. \emph{Livestock Research
  for Rural Development} , 29 , 1--11.
\item
  Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E.,
  DePristo, M. A., Handsaker, R. E., Lunter, G., Marth, G. T., \&
  Sherry, S. T. (2011). The variant call format and
  VCFtools.\emph{Bioinformatics} , 27 , 2156--2158.
  https://doi.org/10.1093/bioinformatics/btr330
\item
  Delaneau, O., Howie, B., Cox, A. J., Zagury, J.-F., \& Marchini, J.
  (2013). Haplotype estimation using sequencing reads. \emph{The
  American Journal of Human Genetics} , 93 , 687--696.
  https://doi.org/10.1016/j.ajhg.2013.09.002
\item
  Dillon, L. A. L., Suresh, R., Okrah, K., Corrada Bravo, H., Mosser, D.
  M., \& El-Sayed, N. M. (2015). Simultaneous transcriptional profiling
  of Leishmania major and its murine macrophage host cell reveals
  insights into host-pathogen interactions. \emph{BMC Genomics} , 16 ,
  1108. https://doi.org/10.1186/s12864-015-2237-2
\item
  Dominiczak, M. H., \& Caslake, M. J. (2011). Apolipoproteins:
  metabolic role and clinical biochemistry applications. \emph{Annals of
  Clinical Biochemistry} , 48 , 498--515.
  https://doi.org/10.1258/acb.2011.011111
\item
  Eide Erik., J., F., W. M., Heeseog, K., Peter, W., William, H.,
  Fernando, C., L., V. E., Andrew, G., \& M., V. D. (2005). Control of
  Mammalian Circadian Rhythm by CKI\selectlanguage{greek}ε-\selectlanguage{english}Regulated Proteasome-Mediated PER2
  Degradation. \emph{Molecular and Cellular Biology} , 25 , 2795--2807.
  https://doi.org/10.1128/MCB.25.7.2795-2807.2005
\item
  Fang, Y., Sathyanarayanan, S., \& Sehgal, A. (2007).
  Post-translational regulation of the Drosophila circadian clock
  requires protein phosphatase 1 (PP1). \emph{Genes \& Development} , 21
  , 1506--1518. https://doi.org/10.1101/gad.1541607
\item
  Fariello, M. I., Boitard, S., Naya, H., SanCristobal, M., \& Servin,
  B. (2013). Detecting Signatures of Selection Through Haplotype
  Differentiation Among Hierarchically Structured
  Populations.\emph{Genetics} , 193 , 929--941.
  https://doi.org/10.1534/genetics.112.147231
\item
  Fischer, A. M., Katayama, C. D., Pag\selectlanguage{ngerman}ès, G., Pouysségur, J., \&
  Hedrick, S. M. (2005). The Role of Erk1 and Erk2 in Multiple Stages of
  T Cell Development. \emph{Immunity} , 23 , 431--443.
  https://doi.org/10.1016/j.immuni.2005.08.013
\item
  Gallego, M., Kang, H., \& Virshup, D. M. (2006). Protein phosphatase 1
  regulates the stability of the circadian protein
  PER2.\emph{Biochemical Journal} , 399 , 169--175.
  https://doi.org/10.1042/BJ20060678
\item
  Gautier, M., Klassmann, A., \& Vitalis, R. (2017). rehh 2.0: a
  reimplementation of the R package rehh to detect positive selection
  from haplotype structure. \emph{Molecular Ecology Resources} , 17 ,
  78--90. https://doi.org/10.1111/1755-0998.12634
\item
  Guo, F., Cancelas, J. A., Hildeman, D., Williams, D. A., \& Zheng, Y.
  (2008). Rac GTPase isoforms Rac1 and Rac2 play a redundant and crucial
  role in T-cell development. \emph{Blood} , 112 , 1767--1775.
  https://doi.org/10.1182/blood-2008-01-132068
\item
  Han, F., Li, W., Liu, X., Zhang, D., Liu, L., \& Wang, Z. (2019). Rac1
  GTPase is a critical factor in phagocytosis in the large yellow
  croaker Larimichthys crocea by interacting with tropomyosin.\emph{Fish
  \& Shellfish Immunology} , 91 , 148--158.
  https://doi.org/https://doi.org/10.1016/j.fsi.2019.04.056
\item
  Han, J., Wu, J., \& Silke, J. (2020). An overview of mammalian p38
  mitogen-activated protein kinases, central regulators of cell stress
  and receptor signaling. \emph{F1000Research} , 9 .
  https://doi.org/10.12688/f1000research.22092.1
\item
  Heallen, T., Zhang, M., Wang, J., Bonilla-Claudio, M., Klysik, E.,
  Johnson, R. L., \& Martin, J. F. (2011). Hippo Pathway Inhibits Wnt
  Signaling to Restrain Cardiomyocyte Proliferation and Heart
  Size.\emph{Science} , 332 , 458--461.
  https://doi.org/10.1126/science.1199010
\item
  Huang, D. W., Sherman, B. T., \& Lempicki, R. A. (2009).
  Bioinformatics enrichment tools: paths toward the comprehensive
  functional analysis of large gene lists. \emph{Nucleic Acids Research}
  , 37 , 1--13. https://doi.org/10.1093/nar/gkn923
\item
  Huang, X., Zhang, H., Cao, H., Zhou, W., Xiang, X., \& Yin, Z. (2022).
  Transcriptomics and Metabolomics Analysis of the Ovaries of High and
  Low Egg Production Chickens. \emph{Animals, \textbf{12},} 2010.
  https://doi.org/10.3390/ani12162010
\item
  Inaba, M., \& Chuong, C.M. (2020). Avian Pigment Pattern Formation:
  Developmental Control of Macro- (Across the Body) and Micro- (Within a
  Feather) Level of Pigment Patterns. \emph{Frontiers in Cell and
  Developmental Biology, \textbf{8},} 620.
  https://doi.org/10.3389/fcell.2020.00620
\item
  Jeke, A., Phiri, C., Chitiindingu, K., \& Taru, P. (2018). Nutritional
  compositions of Japanese quail (Coturnix coturnix japonica) breed
  lines raised on a basal poultry ration under farm conditions in Ruwa,
  Zimbabwe. \emph{Cogent Food \& Agriculture} , 4 , 1473009.
  https://doi.org/10.1080/23311932.2018.1473009
\item
  Jeon, D.J., Paik, S., Ji, S., \& Yeo, J.S. (2021). Melanin-based
  structural coloration of birds and its biomimetic
  applications.\emph{Applied Microscopy} , 51 , 1-11.
  https://doi.org/10.1186/s42649-021-00063-w
\item
  Justice, R. W., Zilian, O., Woods, D. F., Noll, M., \& Bryant, P. J.
  (1995). The Drosophila tumor suppressor gene warts encodes a homolog
  of human myotonic dystrophy kinase and is required for the control of
  cell shape and proliferation. \emph{Genes \& Development} , 9 ,
  534--546. https://doi.org/10.1101/gad.9.5.534
\item
  Kiernan, A. E., Pelling, A. L., Leung, K. K. H., Tang, A. S. P., Bell,
  D. M., Tease, C., Lovell-Badge, R., Steel, K. P., \& Cheah, K. S. E.
  (2005). Sox2 is required for sensory organ development in the
  mammalian inner ear. \emph{Nature} , 434 , 1031--1035.
  https://doi.org/10.1038/nature03487
\item
  Kikuchi, A., Yamamoto, H., Sato, A., \& Matsumoto, S. (2012). Wnt5a:
  its signalling, functions and implication in diseases. \emph{Acta
  Physiologica} , 204 , 17--33.
  https://doi.org/https://doi.org/10.1111/j.1748-1716.2011.02294.x
\item
  Kinjo, K., Sandoval, S., Sakamoto, K. M., \& Shankar, D. B. (2005).
  The Role of CREB as a Proto-oncogene in Hematopoiesis. \emph{Cell
  Cycle} , 4 , 1134--1135. https://doi.org/10.4161/cc.4.9.1991
\item
  Krachler, A. M., Woolery, A. R., \& Orth, K. (2011). Manipulation of
  kinase signaling by bacterial pathogens. \emph{The Journal of Cell
  Biology} , 195 , 1083--1092. https://doi.org/10.1083/jcb.201107132
\item
  Lee, D. J., Cox, D., Li, J., \& Greenberg, S. (2000). Rac1 and Cdc42
  Are Required for Phagocytosis, but Not NF-kB-dependent Gene
  Expression, in Macrophages Challenged with Pseudomonas
  aeruginosa*.\emph{Journal of Biological Chemistry} , 275 , 141--146.
  https://doi.org/10.1074/jbc.275.1.141
\item
  Lee, H.-J., Woo, Y., Hahn, T.-W., Jung, Y. M., \& Jung, Y.-J. (2020).
  Formation and Maturation of the Phagosome: A Key Mechanism in Innate
  Immunity against Intracellular Bacterial
  Infection.\emph{Microorganisms} , 8 , 1298.
  https://doi.org/10.3390/microorganisms8091298
\item
  Lee, H., Chen, R., Kim, H., Etchegaray, J.-P., Weaver, D. R., \& Lee,
  C. (2011). The period of the circadian oscillator is primarily
  determined by the balance between casein kinase 1 and protein
  phosphatase 1. \emph{Proceedings of the National Academy of Sciences}
  , 108 , 16451--16456. https://doi.org/10.1073/pnas.1107178108
\item
  Lewis, A., \& Pomeroy, D. (1989). \emph{A bird atlas of Kenya} (1st
  ed.). Routledge. https://doi.org/10.1201/9781315136264
\item
  Li, H. (2011). A statistical framework for SNP calling, mutation
  discovery, association mapping and population genetical parameter
  estimation from sequencing data. \emph{Bioinformatics} , 27 ,
  2987--2993.
\item
  Li, H., \& Durbin, R. (2009). Fast and accurate short read alignment
  with Burrows--Wheeler transform. \emph{Bioinformatics} , 25 ,
  1754--1760. https://doi.org/10.1093/bioinformatics/btp324
\item
  Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N.,
  Marth, G., Abecasis, G., \& Durbin, R. (2009). The sequence
  alignment/map format and SAMtools. \emph{Bioinformatics} , 25 ,
  2078--2079. https://doi.org/10.1093/bioinformatics/btp352
\item
  Li, Y., Yuan, P., Fan, S., Zhai, B., Jin, W., Li, D., Li, H., Sun, G.,
  Han, R., Liu, X., Tian, Y., Li, G., \& Kang, X. (2022). Weighted gene
  co-expression network indicates that the DYNLL2 is an important
  regulator of chicken breast muscle development and is regulated by
  miR-148a-3p. \emph{BMC Genomics} , 23 , 258.
  https://doi.org/10.1186/s12864-022-08522-8
\item
  Liu, Y., Shepherd, E. G., \& Nelin, L. D. (2007). MAPK phosphatases
  --- regulating the immune response. \emph{Nature Reviews Immunology} ,
  7 , 202--212. https://doi.org/10.1038/nri2035
\item
  Luan, X., Liu, D., Cao, Z., Luo, L., Liu, M., Gao, M., \& Zhang, X.
  (2014). Transcriptome Profiling Identifies Differentially Expressed
  Genes in Huoyan Goose Ovaries between the Laying Period and Ceased
  Period. \emph{PLOS ONE} , 9 , e113211.
  https://doi.org/10.1371/journal.pone.0113211
\item
  Lukanov, H., \& Pavlova, I. (2020). Domestication changes in Japanese
  quail (Coturnix japonica): a review. \emph{World's Poultry Science
  Journal} , 76 , 787--801.
  https://doi.org/10.1080/00439339.2020.1823303
\item
  Luo, C., Qu, H., Ma, J., Wang, J., Li, C., Yang, C., Hu, X., Li, N.,
  \& Shu, D. (2013). Genome-wide association study of antibody response
  to Newcastle disease virus in chicken. \emph{BMC Genetics} , 14 , 42.
  https://doi.org/10.1186/1471-2156-14-42
\item
  Ma, Y., Ding, X., Qanbari, S., Weigend, S., Zhang, Q., \& Simianer, H.
  (2015). Properties of different selection signature statistics and a
  new strategy for combining them. \emph{Heredity} , 115 , 426--436.
  https://doi.org/https://doi.org/10.1038/hdy.2015.42
\item
  Mason, N. A., \& Bowie, R. C. K. (2020). Plumage patterns: Ecological
  functions, evolutionary origins, and advances in
  quantification.\emph{The Auk} , 137 , ukaa060.
  https://doi.org/10.1093/auk/ukaa060
\item
  Mills, A. D., Crawford, L. L., Domjan, M., \& Faure, J. M. (1997). The
  behavior of the Japanese or domestic quail Coturnix
  japonica.\emph{Neuroscience \& Biobehavioral Reviews} , 21 , 261--281.
  https://doi.org/10.1016/S0149-7634(96)00028-0
\item
  Nielsen, R. (2005). Molecular signatures of natural
  selection.\emph{Annu. Rev. Genet.} , 39 , 197--218.
  https://doi.org/10.1146/annurev.genet.39.073003.112420
\item
  Nishibori, M., Hayashi, T., Tsudzuki, M., Yamamoto, Y., \& Yasue, H.
  (2001). Complete sequence of the Japanese quail (Coturnix japonica)
  mitochondrial genome and its genetic relationship with related
  species. \emph{Animal Genetics} , 32 , 380--385.
  https://doi.org/10.1046/j.1365-2052.2001.00795.x
\item
  Oesterle, E. C., Campbell, S., Taylor, R. R., Forge, A., \& Hume, C.
  R. (2008). Sox2 and Jagged1 Expression in Normal and Drug-Damaged
  Adult Mouse Inner Ear. \emph{Journal of the Association for Research
  in Otolaryngology} , 9 , 65--89.
  https://doi.org/10.1007/s10162-007-0106-7
\item
  Ogada, S., Otecko, N. O., Moraa Kennedy, G., Musina, J., Agwanda, B.,
  Obanda, V., \ldots{} \& Ommeh, S. (2021). Demographic history and
  genetic diversity of wild African harlequin quail (Coturnix
  delegorguei delegorguei) populations of Kenya. \emph{Ecology and
  evolution,}11 , 18562-18574. https://doi.org/10.1002/ece3.8458
\item
  Ogada, S., Lichoti, J., Musina, J., \& Ommeh, S. (2022). A review of
  the distribution, nutritional value and conservation status of wild
  harlequin quails (Coturnix delegorguei delegorguei) in
  Kenya.\emph{Journal of Agriculture, Science and Technology} , 21 ,
  43-48. 10.4314/jagst.v21i1.5
\item
  Oleksyk, T. K., Smith, M. W., \& O'Brien, S. J. (2010). Genome-wide
  scans for footprints of natural selection. \emph{Philosophical
  Transactions of the Royal Society B: Biological Sciences} , 365 ,
  185--205. https://doi.org/https://doi.org/10.1098/rstb.2009.0219
\item
  Pavlidis, P., \selectlanguage{polish}Ž\selectlanguage{english}ivkovi\selectlanguage{polish}ć, \selectlanguage{english}D., Stamatakis, A., \& Alachiotis, N. (2013).
  SweeD: likelihood-based detection of selective sweeps in thousands of
  genomes. \emph{Molecular Biology and Evolution} , 30 , 2224--2234.
  https://doi.org/10.1093/molbev/mst112
\item
  Pedersen, E., Wang, Z., Stanley, A., Peyrollier, K., R\selectlanguage{ngerman}ösner, L. M.,
  Werfel, T., Quondamatteo, F., \& Brakebusch, C. (2012). RAC1 in
  keratinocytes regulates crosstalk to immune cells by Arp2/3-dependent
  control of STAT1. \emph{Journal of Cell Science} , 125 , 5379--5390.
  https://doi.org/10.1242/jcs.107011
\item
  Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A.
  R., Bender, D., Maller, J., Sklar, P., De Bakker, P. I. W., \& Daly,
  M. J. (2007). PLINK: a tool set for whole-genome association and
  population-based linkage analyses. \emph{The American Journal of Human
  Genetics} , 81 , 559--575. https://doi.org/10.1086/519795
\item
  Qanbari, S., \& Simianer, H. (2014). Mapping signatures of positive
  selection in the genome of livestock. \emph{Livestock Science} , 166 ,
  133--143. https://doi.org/10.1016/j.livsci.2014.05.003
\item
  Ridley, A. J., Paterson, H. F., Johnston, C. L., Diekmann, D., \&
  Hall, A. (1992). The small GTP-binding protein rac regulates growth
  factor-induced membrane ruffling. \emph{Cell} , 70 , 401--410.
  https://doi.org/10.1016/0092-8674(92)90164-8
\item
  Santos, W. B., Schettini, G. P., Maiorano, A. M., Bussiman, F. O.,
  Balieiro, J. C., Ferraz, G. C., \ldots{} \& Curi, R. A. (2021).
  Genome-wide scans for signatures of selection in Mangalarga Marchador
  horses using high-throughput SNP genotyping.~\emph{BMC genomics} ,~22
  , 1-17. https://doi.org/10.1186/s12864-021-08053-8
\item
  Schmutz, I., Wendt, S., Schnell, A., Kramer, A., Mansuy, I. M., \&
  Albrecht, U. (2011). Protein Phosphatase 1 (PP1) Is a
  Post-Translational Regulator of the Mammalian Circadian
  Clock.\emph{PLOS ONE} , 6 , e21325.
  https://doi.org/10.1371/journal.pone.0021325
\item
  Schuchardt, A., D'Agati, V., Larsson-Blomberg, L., Costantini, F., \&
  Pachnis, V. (1994). Defects in the kidney and enteric nervous system
  of mice lacking the tyrosine kinase receptor Ret. \emph{Nature} , 367
  , 380--383. https://doi.org/10.1038/367380a0
\item
  Sekelova, Z., Polansky, O., Stepanova, H., Fedr, R., Faldynova, M.,
  Rychlik, I., \& Vlasatikova, L. (2017). Different roles of CD4, CD8
  and \selectlanguage{greek}γδ \selectlanguage{english}T-lymphocytes in naive and vaccinated chickens during
  Salmonella Enteritidis infection. \emph{PROTEOMICS} , 17 , 1700073.
  https://doi.org/https://doi.org/10.1002/pmic.201700073
\item
  Seo, E., Basu-Roy, U., Gunaratne, P. H., Coarfa, C., Lim, D.-S.,
  Basilico, C., \& Mansukhani, A. (2013). SOX2 Regulates YAP1 to
  Maintain Stemness and Determine Cell Fate in the Osteo-Adipo
  Lineage.\emph{Cell Reports} , 3 , 2075--2087.
  https://doi.org/https://doi.org/10.1016/j.celrep.2013.05.029
\item
  Shankar, D. B., Cheng, J. C., Kinjo, K., Federman, N., Moore, T. B.,
  Gill, A., Rao, N. P., Landaw, E. M., \& Sakamoto, K. M. (2005). The
  role of CREB as a proto-oncogene in hematopoiesis and in acute myeloid
  leukemia. \emph{Cancer Cell} , 7 , 351--362.
  https://doi.org/https://doi.org/10.1016/j.ccr.2005.02.018
\item
  Soares-Silva, M., Diniz, F. F., Gomes, G. N., \& Bahia, D. (2016). The
  Mitogen-Activated Protein Kinase (MAPK) Pathway: Role in Immune
  Evasion by Trypanosomatids. \emph{Frontiers in Microbiology}
  ,\emph{\textbf{7},} 183. https://doi.org/10.3389/fmicb.2016.00183
\item
  Speck, N. A., Stacy, T., Wang, Q., North, T., Gu, T.-L., Miller, J.,
  Binder, M., \& Mar\selectlanguage{ngerman}ín-Padilla, M. (1999). Core-Binding Factor: A
  Central Player in Hematopoiesis and Leukemia1. \emph{Cancer Research}
  , 59 , 1789s-1793s.
\item
  Sun, Y., Liu, W.-Z., Liu, T., Feng, X., Yang, N., \& Zhou, H.-F.
  (2015). Signaling pathway of MAPK/ERK in cell proliferation,
  differentiation, migration, senescence and apoptosis. \emph{Journal of
  Receptors and Signal Transduction} , 35 , 600--604.
  https://doi.org/10.3109/10799893.2015.1030412
\item
  Taraviras, S., Marcos-Gutierrez, C. V, Durbec, P., Jani, H.,
  Grigoriou, M., Sukumaran, M., Wang, L.-C., Hynes, M., Raisman, G., \&
  Pachnis, V. (1999). Signalling by the RET receptor tyrosine kinase and
  its role in the development of the mammalian enteric nervous
  system.\emph{Development} , 126 , 2785--2797.
  https://doi.org/10.1242/dev.126.12.2785
\item
  Tomalka, J. A., Pelletier, A. N., Fourati, S., Latif, M. B., Sharma,
  A., Furr, K., Carlson, K., Lifton, M., Gonzalez, A., Wilkinson, P.,
  Franchini, G., Parks, R., Letvin, N., Yates, N., Seaton, K., Tomaras,
  G., Tartaglia, J., Robb, M. L., Michael, N. L., \ldots{} Sekaly, R. P.
  (2021). The transcription factor CREB1 is a mechanistic driver of
  immunogenicity and reduced HIV-1 acquisition following ALVAC
  vaccination. \emph{Nature Immunology} , 22 , 1294--1305.
  https://doi.org/10.1038/s41590-021-01026-9
\item
  Urban, E. K., Fry, C. H., Keith, S., Woodcock, M., \& Willis, I.
  (1986). \emph{The Birds of Africa: Volume II} (1st ed.). Bloomsbury
  Publishing.
\item
  Van Hoorebeke, S., Van Immerseel, F., Haesebrouck, F., Ducatelle, R.,
  \& Dewulf, J. (2011). The influence of the housing system on
  Salmonella infections in laying hens: a review. \emph{Zoonoses and
  Public Health} , 58 , 304--311.
  https://doi.org/10.1111/j.1863-2378.2010.01372.x
\item
  Voight, B. F., Kudaravalli, S., Wen, X., \& Pritchard, J. K. (2006). A
  map of recent positive selection in the human genome. \emph{PLoS
  Biology} , 4 , e72--e72. https://doi.org/10.1371/journal.pbio.0040072
\item
  Walmsley, M. J., Ooi, S. K. T., Reynolds, L. F., Smith, S. H., Ruf,
  S., Mathiot, A., Vanes, L., Williams, D. A., Cancro, M. P., \&
  Tybulewicz, V. L. J. (2003). Critical roles for Rac1 and Rac2 GTPases
  in B cell development and signaling. \emph{Science} , 302 , 459--462.
  DOI: 10.1126/science.10897
\item
  Wamuyu, L., Mberu, M., Imboma, T., Obanda, V., Agwanda, B., Lichoti,
  J., \& Ommeh, S. C. (2017). Phenotypic variations between wild and
  farm-reared quails of Kenya. \emph{Livestock Res for Rural
  Development} , \emph{\textbf{29},} 111.
\item
  Wang, E. T., Kodama, G., Baldi, P., \& Moyzis, R. K. (2006). Global
  landscape of recent inferred Darwinian selection for Homo
  sapiens.\emph{Proceedings of the National Academy of Sciences} , 103 ,
  135--140. https://doi.org/10.1073/pnas.0509691102
\item
  Wang, K., Wu, P., Yang, Q., Chen, D., Zhou, J., Jiang, A., Ma, J.,
  Tang, Q., Xiao, W., \& Jiang, Y. (2018). Detection of selection
  signatures in Chinese Landrace and Yorkshire pigs based on
  genotyping-by-sequencing data. \emph{Frontiers in Genetics} , 9 , 119.
  https://doi.org/10.3389/fgene.2018.00119
\item
  Wei, X., Zhang, Y., Li, C., Ai, K., Li, K., Li, H., \& Yang, J.
  (2020). The evolutionarily conserved MAPK/Erk signaling promotes
  ancestral T-cell immunity in fish via c-Myc--mediated
  glycolysis.\emph{Journal of Biological Chemistry} , 295 , 3000--3016.
  https://doi.org/https://doi.org/10.1074/jbc.RA119.012231
\item
  Wen, A. Y., Sakamoto, K. M., \& Miller, L. S. (2010). The role of the
  transcription factor CREB in immune function. \emph{The Journal of
  Immunology} , 185 , 6413--6419.
  https://doi.org/10.4049/jimmunol.1001829
\item
  Williamson, S. H., Hubisz, M. J., Clark, A. G., Payseur, B. A.,
  Bustamante, C. D., \& Nielsen, R. (2007). Localizing Recent Adaptive
  Evolution in the Human Genome. \emph{PLOS Genetics} , 3 , e90.
  https://doi.org/10.1371/journal.pgen.0030090
\item
  Winge, M. C. G., \& Marinkovich, M. P. (2019). Epidermal activation of
  the small GTPase Rac1 in psoriasis pathogenesis. \emph{Small GTPases}
  , 10 , 163--168. https://doi.org/10.1080/21541248.2016.1273861
\item
  Wu, Z., \& Guan, K.-L. (2021). Hippo Signaling in Embryogenesis and
  Development. \emph{Trends in Biochemical Sciences} , 46 , 51--63.
  https://doi.org/10.1016/j.tibs.2020.08.008
\item
  Xu, T., Wang, W., Zhang, S., Stewart, R. A., \& Yu, W. (1995).
  Identifying tumor suppressors in genetic mosaics: the Drosophila lats
  gene encodes a putative protein kinase. \emph{Development} , 121 ,
  1053--1063. https://doi.org/10.1242/dev.121.4.1053
\item
  Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H.,
  Tanaseichuk, O., Benner, C., \& Chanda, S. K. (2019). Metascape
  provides a biologist-oriented resource for the analysis of
  systems-level datasets. \emph{Nature Communications} , 10 , 1523.
  https://doi.org/10.1038/s41467-019-09234-6
\end{enumerate}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

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

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

