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\begin{document}

\title{Genetic responses in sexual diploid and asexual triploid goldfish
(Carassius auratus) introduced into a high-altitude environment}



\author[1]{Xiu Feng}%
\author[2]{Shenglin Liu}%
\author[1]{Xiaoyun Sui}%
\author[1]{Yifeng Chen}%
\author[1]{Ren Zhu}%
\author[1]{Yintao Jia}%
\author[1]{Jingou Tong}%
\author[1]{Xiaomu Yu}%
\author[1]{Chunlong Liu}%
\author[2]{Michael Hansen}%
\affil[1]{Institute of Hydrobiology Chinese Academy of Sciences}%
\affil[2]{Aarhus University}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Anthropogenic biological invasions represent major concerns but enable
us to investigate rapid evolutionary changes and adaptation to novel
environments. The goldfish Carassius auratus with sexual diploids and
asexual triploids coexisting in natural waters, is one of the most
widespread invasive fishes in Tibet, providing an ideal model to study
evolutionary processes during invasion in different reproductive forms
from the same vertebrate. Here, using whole-genome resequencing data of
151 C. auratus individuals from invasive and native ranges, we found
different patterns of genomic responses between diploid and triploid
populations during their invasion to Tibet. For diploids, although
invasive individuals derived from two different genetically distinct
sources and had a relative higher diversity (\selectlanguage{greek}π\selectlanguage{english}) at the population level,
their individual genetic diversity (genome-wide observed heterozygosity)
was significantly lower (21.4\%) than that of source individuals.
Population structure analysis revealed that the invasive individuals
formed a specific genetic cluster distinct from the source populations.
Runs of homozygosity analysis showed low inbreeding only in invasive
individuals, and only the invasive population experienced a recent
decline in effective population size reflecting founder events. For
triploids, however, invasive populations showed no loss of individual
genetic diversity and no genetic differentiation relative to source
populations. Regions of putative selective sweeps between invasive and
source populations of diploids mainly involved genes associated with
mannosidase activity and embryo development. Our results suggest
invasive diploids deriving from distinct sources still lost individual
genetic diversity resulting from recent inbreeding and founder events
and selective sweeps, and invasive triploids experienced no genetic
change owing to their reproduction mode of gynogenesis that precludes
inbreeding and founder effects and may make them more powerful invaders.%
\end{abstract}%



\sloppy


\textbf{Genetic responses in sexual diploid and asexual triploid
goldfish (\emph{Carassius auratus}) introduced into a high-altitude
environment}

\textbf{Running title: Genetic responses in invasive goldfish}

Xiu Feng\textsuperscript{1}, Shenglin Liu\textsuperscript{2}, Xiaoyun
Sui\textsuperscript{1*}, Yifeng Chen\textsuperscript{1*}, Ren
Zhu\textsuperscript{1}, Yintao Jia\textsuperscript{1}, Jingou
Tong\textsuperscript{1}, Xiaomu Yu\textsuperscript{1}, Chunlong
Liu\textsuperscript{1}, Michael M. Hansen\textsuperscript{2}

\textsuperscript{1} Institute of Hydrobiology, Chinese Academy of
Sciences, Wuhan 430072, China

\textsuperscript{2} Department of Biology, Aarhus University, Aarhus C,
Denmark

* Authors for correspondence:

Xiaoyun Sui: xiaoyunsui@ihb.ac.cn

Yifeng Chen: chenyf@ihb.ac.cn

\textbf{Abstract}

Anthropogenic biological invasions represent major concerns but enable
us to investigate rapid evolutionary changes and adaptation to novel
environments. The goldfish \emph{Carassius auratus} with sexual diploids
and asexual triploids coexisting in natural waters, is one of the most
widespread invasive fishes in Tibet, providing an ideal model to study
evolutionary processes during invasion in different reproductive forms
from the same vertebrate. Here, using whole-genome resequencing data of
151 \emph{C. auratus}individuals from invasive and native ranges, we
found different patterns of genomic responses between diploid and
triploid populations during their invasion to Tibet. For diploids,
although invasive individuals derived from two different genetically
distinct sources and had a relative higher diversity (\selectlanguage{greek}π\selectlanguage{english}) at the
population level, their individual genetic diversity (genome-wide
observed heterozygosity) was significantly lower (21.4\%) than that of
source individuals. Population structure analysis revealed that the
invasive individuals formed a specific genetic cluster distinct from the
source populations. Runs of homozygosity analysis showed low inbreeding
only in invasive individuals, and only the invasive population
experienced a recent decline in effective population size reflecting
founder events. For triploids, however, invasive populations showed no
loss of individual genetic diversity and no genetic differentiation
relative to source populations. Regions of putative selective sweeps
between invasive and source populations of diploids mainly involved
genes associated with mannosidase activity and embryo development. Our
results suggest invasive diploids deriving from distinct sources still
lost individual genetic diversity resulting from recent inbreeding and
founder events and selective sweeps, and invasive triploids experienced
no genetic change owing to their reproduction mode of gynogenesis that
precludes inbreeding and founder effects and may make them more powerful
invaders.

\textbf{Keywords} : Genetic response, biological
invasion,\emph{Carassius auratus} , ploidy, Tibet, selective sweep



\textbf{1 Introduction}

Biological invasion denotes the phenomenon that an alien species spreads
outside its natural range and proliferates and persists in new habitats.
This is increasingly occurring as a result of human activities and
causes negative impacts on native ecosystems (Mack et al., 2000; Reid et
al., 2005). Nevertheless, invasive populations are also interesting
models to investigate rapid genetic response and adaptation to novel
environments, thus providing valuable insights into basic biological
processes (Prentis et al., 2008; Sakai et al., 2001). In general, an
introduced population tends to lose its genetic diversity because of
bottlenecks which can reduce the fitness and evolutionary potential
(Lee, 2002). The genetic paradox of invasion appears when a bottlenecked
introduced population becomes invasive (Estoup et al., 2016). However,
recent studies provide evidence that the genetic paradox of invasion is
to a large extent overrated. In some cases, no paradox exists, as
introduced populations shows no loss of genetic diversity possibly owing
to multiple introductions, or no adaptive challenge in the introduced
habitat (Blumenfeld et al., 2021; Facon et al., 2006; Rius et al.,
2015). In other cases, an apparent paradox is spurious, when the loss in
genetic diversity of introduced populations mainly occurs at neutral
genetic markers which are not reflected in adaptive traits, or the
diversity loss is a consequence of a successful response to strong
selection (Dlugosch \& Parker, 2008; Estoup et al., 2016; Gonzalez et
al., 2013). Genetic analysis of the genetic architecture and composition
of introduced populations would provide insights into the evolutionary
mechanisms facilitating the invasion success.

High-throughput sequencing technologies, such as whole genome
resequencing and reduced representation sequencing, provide huge number
of molecular markers for genome-wide population genetic studies in
non-model organisms (Ellegren, 2014). Genome-wide markers, such as
single nucleotide polymorphisms (SNPs), can be used for accurately
estimating genome-wide genetic diversity both within individuals and at
population levels and provide powerful tools to uncover population
genetic structure and reconstruct invasion history (Austin et al., 2006;
Baltazar-Soares et al., 2020; Le Moan et al., 2021; Liu et al., 2018).
Moreover, genome scans allow detecting possible footprints of selection
associated with local adaptation, albeit not without challenges (Haasl
\& Payseur, 2016). Finally, significant progress has been made in using
genomic data for reconstructing long-term demographic history and
estimating genomic inbreeding level and recent demographic history which
affect genetic diversity (Beichman et al., 2018; Ceballos et al., 2018;
Dong et al., 2021).

The Tibetan Plateau is one of the largest and highest plateaus on Earth.
The fish diversity of the Tibetan Plateau is very sensitive and
vulnerable to biological invasion due to the fragile ecosystem and
unique fish fauna (Favre et al., 2015; He et al., 2020; Jia et al.,
2019; Tao et al., 2018). One of the most widespread invasive species in
the region is the goldfish \emph{Carassius auratus} which is originally
distributed throughout the East Asian region except for the Tibetan
Plateau (Luo \& Yue, 2000). It represents a remarkable species complex
containing individuals with different ploidy levels in natural waters
(mainly diploid and triploid, and in rare cases tetraploid) (Liu et al.,
2017b; Xiao et al., 2011). They have different reproduction modes, with
diploids exhibiting sexual reproduction and triploids exhibiting
unisexual gynogenesis (Gui \& Zhou, 2010; Zhou et al., 2000). Triploids
produce chromosome number-unreduced eggs by suppression of the first
meiotic division. Eggs are subsequently activated by the sperm of
sympatric sexual species to initiate embryogenesis, resulting in clonal
offspring from their mothers (Wang et al., 2022). Such parthenogenesis
in vertebrates is also observed in a few other fishes, such
as\emph{Poecilia formosa} (Warren et al., 2018) and \emph{Phoxinus
eos-neogaeus} (Angers \& Schlosser, 2007). Unisexual vertebrates are
generally predicted to have low evolutionary potentials due to lack of
meiotic recombination which thus result in the accumulation of
deleterious mutations and hindrance of the creation of genetic diversity
(Butlin, 2002). On the other hand, asexual organisms may have advantages
to be better colonizers than sexual organisms, because they can populate
new habitats without mates thus avoiding inbreeding (Avise, 2008). The
unisexual triploid \emph{C. auratus} derives from sympatric diploids by
multiple independent polyploidization events and possesses a comparable
or slightly higher genetic diversity compared with diploids (Liu et al.,
2017c; Luo et al., 2014; Ren et al., 2018). If both sexual diploids and
unisexual triploids have been introduced into the Tibetan Plateau, this
raises an interesting possibility to compare the genetic responses of
the two forms.

\emph{C. auratus} is one of the most popular fishes in Tibet markets and
are bought and released into local waters for religious reasons (Jia et
al., 2019), resulting in repeated introductions of the species into the
waters of Tibet. Based on our preliminary market survey (data not
published), \emph{C. auratus} in Tibet is directly imported from the
aquatic product markets in Ningxia (NX: located on the upper Yellow
River) and Sichuan (SC: located on the upper Yangtze River) Provinces
mainly by the two main transportation routes (Qinghai-Tibet Highway and
Sichuan-Tibet Highway) connecting Tibet and its east (Figure 1). In
addition, \emph{C. auratus} in Ningxia was also imported from the
central and eastern regions along the Yangtze River and Huai River.
Hence, invasive populations of \emph{C. auratus} in Tibet came from
diverse sources which may be genetically distinct. Previous studies of
multiple invasive species have shown that multiple introductions from
the same or distinct source populations may have prevented or even
reversed loss of genetic diversity within the invasive range owing to
founder events (Blumenfeld et al., 2021; Kelager et al., 2013; van
Boheemen et al., 2017). However, given the extreme environmental
conditions on the Tibetan Plateau (Feng et al., 2019), this begs the
question to which extent genetic diversity of \emph{C. auratus} has been
shaped by the extreme environments that it has been transplanted into
during multiple introductions.

In this study, by analyzing whole-genome sequence data of 151 goldfish
individuals from two invasive and 11 native populations, we investigated
how genomic changes occurred in invasive populations in Tibet derived
from diverse sources. We specifically tested the hypothesis that
different genetic imprints of the colonization process would be detected
between invasive diploid and triploid populations, with loss of
variation and inbreeding occurring in diploids, but with no loss of
variation in triploids. To address this, we first determined the ploidy
levels of each individual and called SNPs for diploids and triploids
separately. Subsequently, genetic diversity and population genetic
structure were analyzed in both invasive and native populations, with a
specific focus on detecting inbreeding and footprints of recent founder
events in invasive populations. Finally, putative signatures of
selection in invasive populations were analyzed, with the expectation
that such patterns would be evident only in diploids. Our results
provide insights into evolutionary processes facilitating the invasion
success in Tibet and also insights into different genetic responses to
novel habitats between sexual and asexual forms of vertebrate species.

\textbf{2 Materials and Methods}

\textbf{2.1 Sample collection and whole-genome sequencing}

In the invasive range, a total of 62 \emph{C. auratus} juveniles with an
average weight of 2.9 g were collected in 2019 from the Lala (LL) and
Chabalang (CBL) wetlands within the Yarlung Zangbo River basin in Tibet
(Table 1). In the native range, 73 \emph{C. auratus} adults were
collected in 2015-2021 from 11 sites belonging to five river systems,
including the Yangtze River (SC, Sichuan; SS, Shishou; ZDL, Zhangdu
lake; CL, Chao lake; TH, Tai lake), Yellow River (NX, Ningxia), Huai
River (RR, Ru River; HZL, Hongze lake), Songhua River (DQ, Daqing; JPL,
Jingpo lake) and Pearl River (WR, You River). As the Ningxia and Sichuan
regions assumed to be the sources of goldfish introduced into Tibet,
samples were collected from these two regions across a wide geographical
range; the coordinate of each sample can be found in Table S1. Here
populations and individuals sampled from invasive and native ranges are
denoted ``invasive'' and ``native'' populations and individuals,
respectively. A small piece of fin tissue was sampled from each fish
individual and stored in absolute ethanol until DNA extraction.

Genomic DNA was extracted from fin clips using a phenol-chloroform
method (Taggart et al., 1992). At least 0.5 \selectlanguage{greek}μ\selectlanguage{english}g of genomic DNA from each
sample was used to construct the resequencing library with an insert
size of \textasciitilde{}350 bp using the NEBNext Ultra DNA Library Prep
Kit for 150 bp paired-end sequencing on the Illumina X Ten platform
(Illumina USA). Sequencing was outsourced to Biomarker Technologies
Corporation (Beijing, China). In addition, resequencing data of 16
individuals from the native range were downloaded from a previous study
(Chen et al., 2020). A total of 151 goldfish genomes were included in
this study. Additional information of each sample is shown in Table S1.

\textbf{2.2 Read mapping, ploidy identification and variant calling}

The raw reads were filtered using fastp 0.20.0 (Chen et al., 2018) (with
parameters -3 -W 4 -M 20 -q 20 -u 40 -c -l 50) to eliminate reads with
low quality or short length. The clean reads were aligned to the recent
chromosome-level goldfish reference genome (Luo et al., 2020) using the
BWA-MEM algorithm from BWA (Li, 2012).The resulting SAM file was
converted to a coordinate-sorted and indexed BAM file using Samtools (Li
et al., 2009). Duplicated read pairs were removed using Sambamba
(Tarasov et al., 2015).

The ploidy level of each sample was determined by the nQuire software
(Wei\selectlanguage{ngerman}ß et al., 2018) using the BAM file. The denoised input of base
frequencies at biallelic sites generated with default parameters was
utilized to estimate ploidy levels with a Gaussian Mixture Model which
describes the histogram as a mixture of Gaussians distributions between
0 and 1. The expected distributions of read frequencies at biallelic
sites are one Gaussian with mean 0.5 for diploid, two Gaussians with
means 0.33 and 0.67 for triploid. The likelihood of certain assumptions
of di, tri- and tetraploidy based on this model given the empirical data
is maximized using an Expectation-Maximization (EM) algorithm.

Variant calling was carried out for diploids and triploids separately
using the Genome Analysis Toolkit (GATK version 4.2.2.0) (McKenna et
al., 2010), with the parameter of -ploidy being 2 for diploids and 3 for
triploids. Briefly, the genomic variants for each sample (in GVCF
format) were identified with the HaplotypeCaller module. The GVCF files
of all samples were merged into a single GVCF with the CombineGVCFs
module, followed by variant calling to generate a genotype file (in VCF
format) using the GenotypeGVCFs module based on a joint calling
approach. The SNPs were selected using the SelectVariants module and
were initially filtered with the VariantFiltration module using the
parameters `QUAL \textless{} 30.0 \textbar{}\textbar{} QD \textless{}
2.0 \textbar{}\textbar{} FS \textgreater{} 60.0 \textbar{}\textbar{} MQ
\textless{} 40.0'. The SNPs were further filtered using the VCFtools
software (Danecek et al., 2011) for diploids and BCFtools software
(Danecek et al., 2021) combined with a custom script for triploids using
the following criteria: (1) SNPs with more than two alleles, (2)
genotype calls with a depth \textless{} 4 or \textgreater{} 24, (3)
minor allele frequency (MAF) \textless{} 0.05, (4) SNPs with missing
rates \textgreater{} 0.1.

\textbf{2.3 Assembly of mitogenomes and phylogenetic analysis}

To assess the phylogenetic relationships among individuals,
maximum-likelihood phylogenetic trees were constructed for diploids and
triploids separately, as well as all individuals together, using the
IQ-TREE software version 2.0.3 (Nguyen et al., 2015), based on their
complete mitochondrial genome sequences. The mitogenome sequences
were\emph{de novo} assembled from the resequencing data using procedures
described in Liu et al. (2016). Briefly, the first 5 M paired-end reads
were selected for further analysis to fragmentize the assembly of
nuclear sequences. \emph{De novo} assembly of relatively short sequences
(e.g. mitogenomes) was conducted using Spades v3.13.0 (Bankevich et al.,
2012). The mitogenome sequence was then captured from the assembly using
a probe sequence which is conserved in \emph{C.
auratus}(GCTAGCGTAGCTTAATACAAAGC).

\textbf{2.4 Population structure analysis}

As linkage disequilibrium (LD) may affect the inference of population
structure, the diploid SNPs were firstly filtered with LD\selectlanguage{english} (\emph{r}
\textsuperscript{2}) \textless{}\selectlanguage{english} 0.2 using PLINK 1.9 (Chang et al.,
2015) with the parameters `--indep-pairwise 100 10 0.2'. To analyze data
of diploids and triploids together, the pruned diploid SNPs were then
compared with the triploid SNPs using the isec function in BCFtools
(Danecek et al., 2021), and the intersection of SNPs was used for
population structure analysis. Three methods were used to infer
population genetic structure including principal component analysis
(PCA), structure analysis and genetic distance analysis. The first
method was used for diploids and triploids separately as well as all
samples together, while the other two methods were used for diploids and
triploids separately.

For PCA, the genotype data at each locus was firstly converted into the
frequency of the reference allele, that is, 0/0.5/1 for diploids and
0/0.33/0.67/1 for triploids. The PCA was then performed using the R
built-in function \emph{prcomp} with default parameters. The STRUCTURE
software Version 2.3.4 (Pritchard et al., 2000) was used for genetic
structure analysis with five run times for each \emph{K} value ranging
from 1 to 12. The optimal \emph{K} , which indicates the most likely
number of genetic clusters, was determined according to the method
described in Evanno et al. (2005) (Evanno et al., 2005). For genetic
distance analysis, the identity-by-state (IBS) which describes the
genetic relationship among individuals was calculated using a custom R
script. The minimum evolution phylogeny trees were constructed based on
the genetic distance matrix of 1-IBS values using the FastME program
(Lefort et al., 2015) and visualized using the online tool iTOL
(http://itol.embl.de) (Letunic \& Bork, 2019).

Pairwise genetic differentiation (\emph{F} \textsubscript{ST}) and tests
for significance were estimated for invasive populations and regionally
defined genetic clusters of native populations using the R
package\emph{StAMPP} (Pembleton et al., 2013). Additionally, genetic
differentiation among populations was analyzed by an Analysis of
Molecular Variance (AMOVA) using 100 permutations, where the variance
components were partitioned between regions (invasive and source
ranges), among populations within regions and within populations.

\textbf{2.5 Individual and population genetic diversity}

Genome-wide genetic diversity was measured at both individual and
population level. For the individual level, genome-wide observed
heterozygosity (\emph{H} \textsubscript{O}) was calculated by averaging
the values of both variant and non-variant sites. The genotype data that
include both variant and non-variant sites were re-analysed, and
mono/bi-allelic SNPs with read depth ranging from 4 to 24 were retained
for estimation of genetic diversity. \emph{H} \textsubscript{O} at each
locus was calculated as the generalized gametic heterozygosity which
estimates the probability of randomly sampling two different alleles
from an individual (Meirmans et al., 2018; Moody et al., 1993). Hence, a
bi-allelic SNP had a \emph{H} \textsubscript{O} value of 1 for diploids,
and 2/3 for triploids because of three homologous chromosomes. For
population level, genome-wide nucleotide diversity (\selectlanguage{greek}π\selectlanguage{english}) was calculated
using the R package \emph{PopGenome} (Pfeifer et al., 2014) with a 10 kb
window size.

\textbf{2.6 Detection ofruns of homozygosity (ROH)}

To assess whether inbreeding occurred in invasive populations, the
genome-wide inbreeding level of each individual was estimated by
detecting the total length and number of ROHs using the R
package\emph{detectRUNs} (Biscarini et al., 2018). As ROHs were detected
only based on the genotype state (homozygous/heterozygous), the triploid
VCF file was converted into diploid format which could be analyzed
with\emph{detectRUNs} . The following constraints were applied to the
sliding-window-based run detection: (1) The minimum number of SNPs in a
ROH was 138 for diploids and 116 for triploids which was determined by
the method described in Purfield et al. (2012) (Purfield et al., 2012).
(2) One heterozygote and two missing genotypes were allowed in a window.
(3) The maximum gap between consecutive SNPs was 300 kb. (4) The minimum
length of a run was 500 kb. (5) The minimum SNP density was set to one
SNP per 100 kb. The genomic inbreeding coefficient based on ROHs
(\emph{F} \textsubscript{ROH}) was calculated following the method
described in McQuillan et al. (2008) (McQuillan et al., 2008):\emph{F}
\textsubscript{ROH,\emph{i}} =\emph{L} \textsubscript{ROH}/\emph{L}
\textsubscript{AUTO}, where\emph{L} \textsubscript{ROH} is the total
length of ROHs in individual\emph{i} , and \emph{L} \textsubscript{AUTO}
is the length of autosomal genome, here assumed to be 1.54 Gb. The ROH
segment length (\emph{E} (\emph{L\textsubscript{IBD-H}}
\textbar{}\emph{gcA} )) represents the number of generations from the
common ancestor (\emph{gcA} ), estimated as\emph{E}
(\emph{L\textsubscript{IBD-H}} \textbar{}\emph{gcA} )= 41/(2\emph{gcA} )
(Curik et al., 2014) assuming that 1 cM is roughly equivalent to 0.41 Mb
(Liu et al., 2017a).

\textbf{2.7 Demographic history reconstruction}

Demographic history was reconstructed, both to assess ancient population
history and detect possible recent population declines associated with
founder events in invasive populations. As triploids exhibit unisexual
gynogenesis, the demographic history was only constructed for diploid
populations. The fluctuation of effective population size was estimated
using SMC++ (version 1.15.4) which combines the computational efficiency
of site frequency spectra (SFS) and the advantage of using LD
information in coalescent HMMs and provides a high resolution of recent
demography (Terhorst et al., 2017). The VCF file for each population was
converted to the SMC input format using the vcf2smc module. The
population size was estimated using the estimate module with the option
--knots 20 and a per-generation mutation rate of 1e-8. The plot module
was used for plotting fitted size histories using a generation length of
one year.

\textbf{2.8 Scanning for putative signatures of selection}

Regions in the genome showing signatures of selective sweeps were
detected by comparing the invasive and source populations using two
different approaches. The first method estimated the Fixation index
(\emph{F} \textsubscript{ST}) values between populations by using the R
package \emph{PopGenome} (Pfeifer et al., 2014). Loci under selection
should exhibit large \emph{F} \textsubscript{ST} values. The second
method calculated the absolute XP-EHH (Cross Population Extended
Haplotype Homozygosity) scores to detect selective sweeps in which the
selected allele has approached or achieved fixation in one population
but remains polymorphic across both populations (Sabeti et al., 2007).
Phasing of each individual's genotype data was inferred using WhatsHap
(Patterson et al., 2015) and Shapeit4 (Delaneau et al., 2019) and XP-EHH
scores were calculated with the R package \emph{rehh} (Gautier \&
Vitalis, 2012). A sliding window of 10 kb was applied for both of the
methods, and windows with values above the 99th percentile of the
empirical distribution and with more than 10 SNPs were retained for
further analyses (Pujolar et al., 2022). Overlapping windows between the
two methods were considered as potential regions under selection. The
candidate genes were obtained by the SNP annotation within the candidate
regions using the ANNOVAR software (Wang et al., 2010). Gene Ontology
(GO) enrichment analysis of these genes were performed using the
hypergeometric Fisher exact test in an online tool (OmicShare,
www.omicshare.com/tools).

\textbf{3 Results}

\textbf{3.1 Ploidy identification and variant calling}

Whole genome resequencing of the 151 individuals generated a total of
\textasciitilde{} 10.6 G of clean paired-end reads (70.2 M per
individual), giving an average sequencing depth of 12.2\selectlanguage{ngerman}× (Table S1).
Among them, 99.14\% of reads were on average mapped to the reference
genome of goldfish. The analysis of allele frequency distributions at
biallelic variants clearly showed one Gaussian distribution with mean
0.5 for diploids and bimodal distributions with means 0.33 and 0.67 for
triploid (Figure S1). Among all individuals, 73 and 78 were identified
as diploids and triploids, respectively. In invasive populations, the
number of diploids and triploids were 29 and 9 in the LL population, and
2 and 22 in the CBL population, respectively (Table 1). In direct source
regions, samples from Ningxia and Sichuan were almost all triploids with
only one diploid detected from Sichuan. In indirect source regions, 29
diploids and 6 triploids were detected. After variant calling and
filtering, a total of 16,888,283 and 17,954,020 SNPs were identified
from diploids and triploids, respectively, with 10,719,815 SNPs being
common to both ploidy categories.

\textbf{3.2 Phylogenetic relationships based on mitogenome sequences}

In the phylogenetic trees constructed based on mitogenome sequences,
four distinct lineages were represented among diploids (Figure 2a). They
followed a pattern of geographical origin for the native samples, with
the invasive individuals from Tibet belonging to two of the lineages.
The first lineage (A) mainly contained 24 invasive individuals and
source individuals from the lower Yangzte River (TH and CL) and Huai
River (RR and HZL). The single individual from SC and one individual
from DQ also belonged to lineage A. The second lineage (B) was composed
of the remaining seven invasive individuals and source individuals from
the mid Yangzte River (SS and ZDL). The third lineage (C) contained
eight native individuals from the Songhua River (DQ and JPL), and the
single individual from Fujian separately formed the fourth lineage (D).

For triploids, four lineages E, F, G and H were observed with 59, 15, 3
and 1 individuals being included, respectively (Figure 2b). However, the
phylogenetic pattern of triploids was not related to geographical
origin, with each large lineage containing individuals from different
river systems and including invasive individuals. By combining diploids
and triploids together, four lineages were found with A, B, E and H
being clustered into one super lineage, C and F into another big
lineage, D and G into single lineages (Figure S2). Hence, the
phylogenetic pattern was not associated with the ploidy level.

\textbf{3.3 Population genetic structure and differentiation}

After LD filtering, 1,545,006 SNPs common to di- and triploids were
selected and used for population structure analysis. The three methods
including PCA, structure analysis and genetic distance analysis all
showed the same patterns of population genetic structure (Figure 3 and
4, and Figure S3). For diploids, two clear genetic clusters were shaped
and consisted of individuals from invasive and native populations,
respectively. The genetic cluster owned by native individuals split into
three subclusters and followed a pattern of geographical separation:
individuals from the mid Yangzte River (SS and ZDL); individuals from
the lower Yangzte River (TH and CL) and Huai River (RR and HZL) and
individuals from the Songhua River (DQ and JPL). For triploids, although
several genetic clusters were observed, the pattern was not related to
the geographical origin, with each cluster containing individuals from
different rivers. The invasive triploids were assigned into different
clusters. Results of PCA for all individuals revealed that both PCA1 and
PCA2 did not clearly separate diploids and triploids, indicating
patterns of population structure were not linked to the ploidy level.

For diploids, highly significant genetic differentiation (\emph{F}
\textsubscript{ST}) was detected for all pairwise comparisons among the
invasive population (LL) and three genetic subclusters from the native
range (Table 2). The \emph{F} \textsubscript{ST} values between LL and
two source genetic subclusters (i.e., the mid Yangzte River, the lower
Yangzte River and Huai River) were 0.1045 and 0.0926, respectively,
which were both higher than that between the two subclusters (0.0449).
AMOVA analysis revealed that genetic variation between regions (invasive
and source), among populations within regions and within populations
were all significant and accounted for 6.29\%, 8.65\% and 85.06\% of the
total variation (\emph{P} \textless{} 0.01), respectively. For
triploids, low \emph{F} \textsubscript{ST} values ranging from 0.0073 to
0.0281 (\emph{P} \textgreater{} 0.05) were detected for pairwise
comparisons between invasive (LL and CBL) and source (SC and NX)
populations and a relative higher\emph{F} \textsubscript{ST} (0.0345)
was obtained between the two source populations (\emph{P} \textless{}
0.01). The variation between regions, among populations within regions
and within populations accounted for -2.93\%, 4.41\% and 98.52\%,
respectively. Overall, the\emph{F} \textsubscript{ST} values between
invasive and source populations were 0.0866 in diploids and 0.0027 in
triploids.

\textbf{3.4 Genome-wide genetic diversity}

At the individual level for diploids (Figure 5a), the genome-wide
observed heterozygosity (\emph{H} \textsubscript{O}) values of invasive
individuals (mean: 0.00861) was significantly lower than those of native
individuals (mean: 0.01062) (paired-sample \emph{t} -test, \emph{p} =
2.2e\textsuperscript{-16}). For triploids, \emph{H}
\textsubscript{O}values of invasive individuals (mean: 0.00922) was
comparable to those of the native individuals (mean: 0.00914). Compared
with triploids, invasive diploids showed significantly lower observed
heterozygosity than the invasive (\emph{p} = 5.1e\textsuperscript{-5})
and native triploids (\emph{p} = 2.5e\textsuperscript{-7}), while native
diploids had significant higher genetic diversity than the invasive
(\emph{p} = 5.4e\textsuperscript{-10}) and native triploids (\emph{p} =
3.2e\textsuperscript{-12}).

As population structure inferred three genetic subclusters among diploid
native samples, the population genetic diversity was estimated for these
subclusters. For diploids, the genome-wide nucleotide diversity (\selectlanguage{greek}π\selectlanguage{english})
value of the invasive population LL was 0.00297, which was slightly
higher than those of native subclusters (0.00266 for mid Yangzte River,
0.00279 for lower Yangzte River and Huai River, and 0.00251 for Songhua
River) (Figure 5b and Table 3). For triploids, \selectlanguage{greek}π \selectlanguage{english}values of the invasive
populations LL and CBL were 0.00286 and 0.00284, respectively, which
were also slightly higher than those of source populations (0.00273 for
SC and 0.00277 for NX).

\textbf{3.5 Estimation of inbreeding level}

A total of 78 ROHs were detected, with almost all of them (76) found in
25 invasive diploid individuals from the LL population (Figure 6 and
Table S1). The number of ROHs per individual ranged from one to eight
(mean: 3.04), and the total length ranged from 0.518 to 5.989 Mb (mean:
2.129 Mb). The genomic inbreeding coefficient (\emph{F}
\textsubscript{ROH}) was low, ranging from 0.0003 to 0.0038 across
individuals, with an average value of 0.0014. The ROHs of a length from
0.518 to 5.989 Mb suggested inbreeding events that have occurred 3-39
generations ago.

\textbf{3.6 Recent demographic history}

The results of SMC++ analysis revealed a difference in recent
demographic history between invasive and native populations (Figure 7).
Among all diploid populations, only the invasive population (LL)
experienced a recent decline in effective population size reflecting
founder events, while all native populations experienced a recent
expansion during the same time period. For more ancient time periods,
the population trajectories were almost similar across all populations,
suggesting an expansion since about 150 ka followed by a peak at about
60 ka and a subsequent decline, assuming mutation rate of 1e-8 and
generation length of one year.

\textbf{3.7 Putative signatures of selection}

The 99th percentile threshold values of the \emph{F} \textsubscript{ST}
and \textbar{}XP-EHH\textbar{} were used to detect the putative
signatures of selective sweeps between invasive and source populations.
These thresholds were 0.300 and 2.399 for diploids, and 0.021 and 2.021
for triploids, respectively (Figure 8). For diploids, 324 overlapping
outlier windows between the two methods were detected on 37 chromosomes,
with most outliers on Chr24 (66) followed by Chr13 (38), Chr9 (34) and
Chr30 (29) (Table S2). A total of 16,754 SNPs were observed within these
outlier regions, among which 4.16\% and 52.75\% were annotated to the
exonic and intronic regions, respectively, with the percentages being
higher than those found in the whole genome (2.51\% and 49.56\%). The
nucleotide diversity within these outliers in the source range was more
than two times higher than that in the invasive range. The SNPs within
exonic and intronic regions were annotated to 191 genes (Table S3). GO
enrichment of these genes revealed one enriched cluster namely
`mannosyl-oligosaccharide mannosidase activity' (Pvalue \textless{} 0.01
and Qvalue \textless{} 0.05) involving in three genes (\emph{Endoplasmic
reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase}
,\emph{Mannosyl-oligosaccharide 1,2-alpha-mannosidase IB}
and\emph{Alpha-mannosidase 2} ). Other enriched clusters with Pvalue
\textless{} 0.01 but Qvalue \textgreater{} 0.05 were mainly involved in
mannosidase and embryo development (Table S4).

For triploids, 244 overlapping outliers between the two methods were
observed on 40 chromosomes. However, these outliers only formed visual
peaks on Chr14, Chr15, Chr18 and Chr35. Among 2,543 SNPs within these
outliers, 3.54\% and 33.23\% were annotated to exonic and intronic
regions, respectively, with the percentages being lower than those found
in the whole genome (3.85\% and 49.31\%). These SNPs were annotated to
60 genes with enriched GO terms related to metabolic process and immune
response (Table S4). Interestingly, one enriched GO term was
`development of secondary sexual characteristics' with two involved
genes (\emph{Beta-1,4-galactosyltransferase 1} and \emph{Signal
transducer and activator of transcription 5B} ). When comparing outliers
for diploids and triploids, only four were overlapped on Chr2 and Chr18
with all SNPs being annotated to intergenic regions.

\textbf{4 Discussion}

The results of our study document a highly different genetic and
phylogeographic structure of diploid and triploid goldfish, particularly
reflected in a clear geographic structure in diploids and no obvious
association between structure and geographical origin in triploids.
Also, and in accordance with our main hypothesis, we found a major
difference in patterns of genetic variation between invasive populations
of diploids and triploids in Tibet, with decreased variation in diploids
and evidence for recent population decline reflecting founder events,
but in contrast no detectable loss of variation in triploids.
Nevertheless, inbreeding was low in diploids suggesting that multiple
introductions from diverse sources have buffered bottlenecks associated
with founding. Finally, we found evidence for selective sweeps in
invasive diploid goldfish, with genes within outlier regions enriched
for functions related to mannosidase activity and embryo development. We
discuss these findings in more detail below.

\textbf{4.1 Comparison between diploids and triploids in the native
range}

Our analyses of genetic diversity and structure between diploid and
triploid \emph{C. auratus} showed that overall nucleotide diversity of
diploids was comparable to that of triploids based on both mitogenome
and nuclear genome (Table 3). This is inconsistent with results of
previous studies showing a slightly higher genetic diversity in
triploids than diploids (Liu et al., 2017b; Luo et al., 2014). This may
reflect the fact that our sampling work mainly focused on the native
range of source populations for introduction into Tibet, with a higher
percentage of diploids than triploids, while triploids actually have
wider geographic distribution than diploids (Liu et al., 2017b; Liu et
al., 2017c). By using the same procedures of variant calling, total
number of detected SNPs in triploids was slightly higher than that in
diploids, which is consistent with a previous study based on SNPs from
transcriptome data (Ren et al., 2018). Furthermore, at the individual
level, although the mean observed heterozygosity of diploids was
significantly higher than that of triploids when considering ploidy
level (Figure 5), the mean frequency of heterozygous loci across genomes
in diploids was significantly lower than that in triploids. Similarly in
other unisexual organisms that are derived from auto-polyploidization
(uneven ploidy in general), they show levels of genetic diversity close
to that of their sexual diploid counterparts (Joly \& Bruneau, 2004; Lee
et al., 2016; Liu et al., 2015b). However, unisexual organisms with
interspecific hybrid origins, such as vertebrate \emph{P. formosa}
and\emph{P. eos-neogaeus} and some plants, usually have a much higher
genetic diversity than their sexual parental species (Angers \&
Schlosser, 2007; Robertson et al., 2010; Vallejo-Marin \& Lye, 2013;
Warren et al., 2018). Hence, genetic diversity of unisexual organisms
depends on their specific origin relative to related sexual species.

Previous studies revealed that triploid \emph{C. auratus} have undergone
multiple independent polyploidy origins from sympatric diploids (Liu et
al., 2017b; Luo et al., 2014). Our results also supported this point by
showing that native diploids were genetically clustered with triploids
within some lineages. Based on mitogenomes, diploids and triploids
coexist in almost each lineages, indicating the same origin within the
species. However, by using genomic SNPs, diploids were clustered with
triploids only within two clusters (Figure 3), and triploids shaped
three more clusters. Given that the genetic variation of triploids
remains almost frozen across generations because of unisexual
gynogenesis (Wang et al., 2022), we assumed that the two shared clusters
reflected recent triploidization events and the three triploid-specific
clusters originated from more ancient triploidization events. Genetic
structure of contemporary diploids linked to a geographical separation
pattern should be shaped by local adaptation or other factors. However,
the genetic structure of triploids is accumulated by previous
triploidization from diploids. Based on results of this study, the
triploid \emph{C. auratus} has at least five polyploidy origins and
might possess more origins when investigating more samples from a
broader range of locations.

\textbf{4.2 Genetic changes in invasive diploid populations}

The loss of genetic diversity within invasive populations resulting from
genetic bottlenecks can be prevented or even reversed by multiple
introductions from the same or distinct sources, which have been
reported in many animals and plants, such as \emph{Pseudorasbora parva}
,\emph{Coptotermes formosanus} , \emph{Aedes albopictus} ,
\emph{Ambrosia artemisiifolia} and \emph{Rosa rugosa} (Baltazar-Soares
et al., 2020; Blumenfeld et al., 2021; Kelager et al., 2013; Sherpa et
al., 2019; van Boheemen et al., 2017). The invasive \emph{C. auratus} in
Tibet was introduced from different sources, which was also evidenced by
phylogenetic analysis of diploid mitogenomes in this study, showing that
invasive individuals were clustered into two regionally defined lineages
of native individuals (i.e., the mid Yangzte River, the lower Yangzte
River and Huai River). The two lineages showed a low but significant
genetic differentiation (\emph{F} \textsubscript{ST}: 0.0449) estimated
based on genomic SNPs. In general, recent hybrid populations from
diverse intraspecific source populations should possess higher genetic
diversity than that of individual source populations (Baltazar-Soares et
al., 2020; Simon et al., 2011; Smith et al., 2020). However, the genetic
diversity of invasive diploid population of \emph{C. auratus} was only
slightly (9.0\%) higher than that of source populations. Interestingly,
at the individual level, the genome-wide observed heterozygosity of
invasive individuals was significantly (21.4\%) lower than that of
source individuals (Figure 5a). The discordance between genetic
diversity at the population and individual levels suggests that the
total invasive population derives from genetically distinct sources,
which may mask loss of variation at the population level during
invasion. Nevertheless, the observed low levels of inbreeding suggest
that founder events have still not had a significant biological impact.
In that sense, the results provide an illustration of factors that may
cause biological invasions to be associated with lower loss of genetic
variation than expected (Blumenfeld et al., 2021; Facon et al., 2006).
On a longer time scale, triploid \emph{C. auratus} now dominate the
market (Liu, 2010), and the LL wetland is padlocked since becoming a
natural reserve zone since 1999 (Chen et al. 2018). Therefore, the
diploid invasive population is expected to receive very low supplements
of new invaders recently and in the future, which could accelerate loss
of genetic diversity.

\textbf{4.3 No genetic change in invasive triploid populations}

Triploids are considered an evolutionary `dead end' because they lack
meiotic recombination and thus accumulate deleterious mutations (Butlin,
2002). Such `disadvantage' in triploids may become an advantage in terms
of avoiding inbreeding and thus being better colonizers than diploids
(Avise, 2008). In the present study, the lack of change of genetic
diversity and structure in invasive triploid populations is to be
expected, given the pattern of asexual gynogenesis which produces clonal
offspring from mothers (Wang et al., 2022). This reproductive mode may
make triploid \emph{C. auratus} a more powerful invader in novel
environments compared with the sexual diploid form. Firstly,
parthenogenesis enables triploids to avoid negative impacts of
bottlenecks and inbreeding on genetic diversity during invasion (Avise,
2008; Roman \& Darling, 2007). Secondly, eggs of triploids can be
activated by the sperm of both conspecific and non-conspecific fishes,
providing more chances for reproduction. Concerning adaptability, the
pros and cons of invaders being triploid are less clear-cut. On one
hand, triploids possess slightly higher genetic diversity and frequency
of heterozygous loci (Liu et al., 2017b; Luo et al., 2014) which would
be an advantage in the case of overdominance. On the other hand, the
lack of meiosis and sexual reproduction precludes reshuffling of allelic
variation at loci influencing polygenic traits, hence precluding
adaptive responses. As a whole, experiences from different organisms do
suggest stronger invasiveness of asexual forms as compared to sexual
forms (Donne et al., 2020; Pandit et al., 2011).

\textbf{4.4 Candidate genes within genomic regions of putative sweeps}

The regions with potential selective sweeps for invasive diploid
populations of \emph{C. auratus} were widely detected across the genome.
The SNPs within these regions were annotated to 191 candidate genes with
enriched GO terms mainly related to mannosidase activity and embryo
development (Table S4). The biological significance of selection in
genes related to mannosidase are not immediately obvious, although in
humans mannosidase deficiency can cause disease, such as congenital
dyserythropoietic anemia (Moremen, 2002). The selective sweeps related
to genes involved in embryo development may be attributed to an
advancement of spawning time in Tibet (Liu et al., 2015a) and subsequent
exposure of embryos to the cold waters. The water temperature during
reproduction period is about 20 \selectlanguage{ngerman}°C in the native range (e.g. the mid and
lower Yangtze River) and is below 10 °C in the invasive range (Huo et
al., 2013; Zhang et al., 2016). Low temperature exposure can increase
the mortality and frequency of abnormal larvae in \emph{C.
auratus}(Wiegand et al., 1989), resulting in individual selection during
embryonic development.

For triploids, selection occurs at the whole individual level owing to
the asexual gynogenesis and would be expected to be low, as numbers of
mitogenome haplotypes and genetic diversity in invasive populations were
not reduced as compared to the source populations. Hence, it is doubtful
whether signals of selective sweeps found in invasive triploids were
actual targets of selection; more likely these signals would derive from
the process of establishment of triploids from diploid ancestors. It is
nevertheless interesting that one enriched GO term found in triploids is
`development of secondary sexual characteristics'. A previous study has
shown that the ratio of males among invasive triploid \emph{C.
auratus}in Croatia was only 2.3\% which was lower than that reported in
the native range (Gui \& Zhou, 2010; Jakovlic \& Gui, 2011). It would be
of interest to further investigate the ratio of males in Tibet.

\textbf{5 Conclusion}

Our study represents a unique case of diploid and triploid individuals
from the same species invading an extreme high-altitude environment. The
results demonstrated profoundly different outcomes of the invasion
process at the genomic level for diploids and triploids. For diploids,
in spite of introductions from two genetically distinct sources, the
invasive individuals possessed significantly lower heterozygosity than
source individuals and formed a genetic cluster distinct from the source
populations. Demographic history reconstruction also documented recent
population declines coinciding with invasion. Nevertheless, inbreeding
was still low, presumably reflecting buffering effects of multiple
continuous events of introduction. For triploids, no change in genetic
diversity and structure was found in invasive populations, which is
owing to their reproduction mode of gynogenesis that precludes
inbreeding and reduces founder effects. Finally, there evidence for
selective sweeps in invasive diploid goldfish, pointing to a role of
mannosidase activity that nevertheless requires further clarification
and embryo development, the latter of which may play important roles for
adaptive processes of individuals introduced into this extreme
environment.

\textbf{Acknowledgments}

This study was supported by the Second Tibetan Plateau Scientific
Expedition and Research (STEP) program (2019QZKK0501 and 2019QZKK0304),
the National Natural Science Foundation of China (31601844), the
Strategic Priority Research Program of the Chinese Academy of Sciences
(XDA20050204) and the China Scholarship Council (CSC, 202004910172). We
would like to thank Prof. Yaping Zhang from Chinese academy science,
Prof. Shaojun Liu from Hunan Normal University, Prof. Jing Luo from
Yunnan University and Dr. Zhen Huang from Fujian Normal University for
their assistance in data collection and analysis.

\textbf{Data Availability} \textbf{Statement}

The genome resequencing data have been deposited in the Genome Sequence
Archive (GSA; http://gsa.big.ac.cn/) under accession number PRJCA010043.

\textbf{Author Contributions}

YC, XS and XF conceived and designed the study. XF, RZ, YJ, XY, and CL
collected fish samples and conducted experiments. XF, SL, JT and MMH
performed the data analysis. XF drafted the manuscript, and SL, XS, YC
and MMH revised the manuscript. All authors read and approved the final
manuscript.

\textbf{Conflict of Interest}

The authors declare no conflict of interest.

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Zhang, H., Wu, J. M., Wang, C. Y., Du, H., Liu, Z. G., Shen, L., Chen,
D., \& Wei, Q. W. (2016). River Temperature Variations and Potential
Effects on Fish in a Typical Yangtze River Reach: Implications for
Management. \emph{Applied Ecology and Environmental Research, 14} (4),
553-567.\emph{https://doi.org/10.15666/aeer/1404\_553567}

Zhou, L., Wang, Y., \& Gui, J. F. (2000). Genetic evidence for
gonochoristic reproduction in gynogenetic silver crucian carp
(\emph{Carassius auratus gibelio} bloch) as revealed by RAPD
assays.\emph{Journal of Molecular Evolution, 51} (5),
498-506.\emph{https://doi.org/10.1007/s002390010113}

\textbf{Tables and Figures}

\textbf{Tables}

\textbf{Table 1} Information of sampling sites and ploidy distribution\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Location & Abbr. & River system & Range type & Elevation (m) & Number of
samples & Number of samples\tabularnewline
\midrule
\endhead
& & & & & Diploid & Triploid\tabularnewline
Chabalang wetland & CBL & Yarlung Zangbo River & Invasive & 3591 & 2 &
22\tabularnewline
Lala wetland & LL & Yarlung Zangbo River & Invasive & 3648 & 29 &
9\tabularnewline
Ningxia & NX & Yellow River & Native (source) & 1131 & 0 &
15\tabularnewline
Sichuan & SC & Upper Yangtze River & Native (source) & 519 & 1 &
11\tabularnewline
Shishou & SS & Mid Yangtze River & Native (source) & 35 & 6 &
1\tabularnewline
Zhangdu lake & ZDL & Mid Yangtze River & Native (source) & 16 & 8 &
1\tabularnewline
Chao lake & CL & Lower Yangtze River & Native (source) & 11 & 1 &
2\tabularnewline
Tai lake & TH & Lower Yangtze River & Native (source) & 10 & 4 &
0\tabularnewline
Hongze lake & HZL & Huai River & Native (source) & 10 & 6 &
0\tabularnewline
Ru River & RR & Huai River & Native (source) & 86 & 4 & 2\tabularnewline
Daqing & DQ & Songhua River & Native & 141 & 5 & 0\tabularnewline
Jingpo lake & JPL & Songhua River & Native & 349 & 3 & 1\tabularnewline
You River & WR & Pearl River & Native & 117 & 0 & 2\tabularnewline
Chen et al., 2020 & - & - & Native & - & 4 & 12\tabularnewline
Overall & - & - & - & - & 73 & 78\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} Pairwise genetic differentiation (\emph{F}
\textsubscript{ST}) among invasive and native populations or genetic
clusters\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Diploid & Diploid & Diploid & Diploid & Triploid & Triploid & Triploid &
Triploid\tabularnewline
\midrule
\endhead
Population/Genetic cluster & LL & LYR \& HR & Mid Yangzte River &
Population & CBL & LL & NX\tabularnewline
LL & & & & CBL & & &\tabularnewline
LYR \& HR & 0.0926** & & & LL & 0.0148 & &\tabularnewline
Mid Yangzte River & 0.1045** & 0.0449** & & NX & 0.0076 & 0.0073
&\tabularnewline
Songhua River & 0.1193** & 0.0348** & 0.0789** & SC & 0.0177 & 0.0281 &
0.0345**\tabularnewline
\bottomrule
\end{longtable}

LL, Lalu wetland; LYR \& HR, lower Yangzte River and Huai River; CBL,
Chabalang wetland; NX, Ningxia; SC, Sichuan; **, \emph{P} value
\textless{} 0.01.

\textbf{Table 3} Genome-wide genetic diversity of invasive and native
populations\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Population/Cluster/Range & Population/Cluster/Range & Range type &
Number of samples & Number of mitogenome haplotypes & Nucleotide
diversity (\selectlanguage{greek}π\selectlanguage{english}) of mitogenome & Nucleotide diversity (\selectlanguage{greek}π\selectlanguage{english}) of nuclear
genome\tabularnewline
\midrule
\endhead
Diploid & LL & Invasive & 29 & 10 & 0.00369 & 0.00297\tabularnewline
& LYR \& HR & Native (source) & 15 & 15 & 0.00175 &
0.00279\tabularnewline
& Mid Yangzte River & Native (source) & 14 & 14 & 0.00246 &
0.00266\tabularnewline
& Songhua River & Native & 9 & 9 & 0.00382 & 0.00251\tabularnewline
& Invasive range & - & 31 & 11 & 0.00358 & 0.00297\tabularnewline
& Source range & - & 30 & 30 & 0.00522 & 0.00280\tabularnewline
& Native range & - & 42 & 42 & 0.00795 & 0.00280\tabularnewline
& Overall & - & 73 & 53 & 0.00664 & 0.00304\tabularnewline
Triploid & LL & Invasive & 9 & 7 & 0.00731 & 0.00286\tabularnewline
& CBL & Invasive & 22 & 13 & 0.00454 & 0.00284\tabularnewline
& NX & Native (source) & 11 & 9 & 0.00668 & 0.00273\tabularnewline
& SC & Native (source) & 15 & 11 & 0.00412 & 0.00277\tabularnewline
& Invasive range & - & 31 & 17 & 0.00535 & 0.00287\tabularnewline
& Source range & - & 32 & 23 & 0.00530 & 0.00281\tabularnewline
& Native range & - & 47 & 34 & 0.00774 & 0.00274\tabularnewline
& Overall & - & 78 & 42 & 0.00680 & 0.00280\tabularnewline
\bottomrule
\end{longtable}

LL, Lalu wetland; LYR \& HR, lower Yangzte River and Huai River; CBL,
Chabalang wetland; NX, Ningxia; SC, Sichuan.



\textbf{Figure legends}

\textbf{Figure 1} Sampling sites and introduction pathways
of\emph{Carassius auratus} in this study. The figures in brackets below
site names represents the number of diploid and triploid samples. The
two dotted lines represents main transportation routes (Qinghai-Tibet
Highway and Sichuan-Tibet Highway) connecting Tibet and its east.

\textbf{Figure 2} The maximum-likelihood phylogenetic trees based on
mitogenome sequences of diploids (a) and triploids (b).

\textbf{Figure 3} Principal component analysis based on genomic
LD-pruned SNPs for diploids (a), triploids (b) and all samples (c).

\textbf{Figure 4} Population structure analysis based on genomic
LD-pruned SNPs for diploids (a) and triploids (b).

\textbf{Figure 5} Genome-wide genetic diversity of invasive and
native\emph{Carassius auratus} at individual (a) and population (b)
levels. The \emph{p} value of the paired-sample \emph{t} -test in figure
a was showed for each pair-wise comparison. The light red and light blue
bars figure b represented the invasive and native populations,
respectively.

\textbf{Figure 6} The genome-wide distribution of ROHs detected in
invasive diploids (above) triploids (below).

\textbf{Figure 7} Reconstruction of the population size histories with
the SMC++ method.

\textbf{Figure 8} Detection of putative signatures of selective sweeps
between invasive and source populations by sliding-window analysis of
the \emph{F} \textsubscript{ST} and \textbar{}XP-EHH\textbar{} values in
diploids (a and b) and triploids (c and d). Dashed lines indicated
genome-wide 1\% outlier cut-off.

\textbf{Supplemental Tables \& Figures}

\textbf{Table S1} The detailed information of each sample of goldfish
(\emph{Carassius auratus} ), including summary for genome resequencing
data, individual genetic diversity, ROH detection and mitogenome

\textbf{Table S2} The putative selective sweep regions identified
between invasive and source populations

\textbf{Table S3} Candidate genes obtained by annotation of SNPs within
selective sweep regions

\textbf{Table S4} The top 20 of enriched GO terms for candidate genes
under selection in goldfish (\emph{Carassius auratus} ) after invasion
to the Tibet Plateau

\textbf{Figure S1} The analysis of allele frequency distributions at
biallelic variants clearly showed one Gaussian with 0.5 in diploids (a)
and two Gaussians with 0.33 and 0.67 (b) in triploids.

\textbf{Figure S2} The maximum-likelihood phylogenetic tree based on
mitogenome sequences of all samples. The circles and triangles
represented the diploid and triploid samples, respectively.

\textbf{Figure S3} Neighbor-joining clustering of diploids (a) and
triploids (b) based on pairwise genetic distances calculated from
genomic LD-pruned SNPs.\selectlanguage{english}
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