Hybridization load introduced by Alpine ibex hybrid swarms

Hybridization load introduced by Alpine ibex hybrid swarms | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Hybridization load introduced by Alpine ibex hybrid swarms View ORCID Profile F. Gözde Çilingir , View ORCID Profile Fabio Landuzzi , Alice Brambilla , Debora Charrance , Federica Furia , Sara Trova , Alberto Peracino , Glauco Camenisch , Dominique Waldvogel , Jo Howard-McCombe , Yeraldin Chiquinquira Castillo De Spelorzi , Edoardo Henzen , Andrea Bernagozzi , Alessandro Coppe , Jean Marc Christille , Manuela Vecchi , Diego Vozzi , Andrea Cavalli , Bruno Bassano , Stefano Gustincich , View ORCID Profile Daniel Croll , View ORCID Profile Luca Pandolfini , View ORCID Profile Christine Grossen doi: https://doi.org/10.1101/2024.06.24.599926 F. Gözde Çilingir 1 Swiss Federal Research Institute WSL, Biodiversity and Conservation Biology Research Unit , Birmensdorf, Switzerland 2 University of Zurich, Department of Evolutionary Biology and Environmental Studies , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for F. Gözde Çilingir Fabio Landuzzi 3 Computational and Chemical Biology, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fabio Landuzzi Alice Brambilla 2 University of Zurich, Department of Evolutionary Biology and Environmental Studies , Zurich, Switzerland 4 Alpine Wildlife Research Center, Gran Paradiso National Park , Turin, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Debora Charrance 3 Computational and Chemical Biology, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Federica Furia 3 Computational and Chemical Biology, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sara Trova 5 Non-coding RNA and RNA-based therapeutics, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alberto Peracino 4 Alpine Wildlife Research Center, Gran Paradiso National Park , Turin, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Glauco Camenisch 2 University of Zurich, Department of Evolutionary Biology and Environmental Studies , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dominique Waldvogel 2 University of Zurich, Department of Evolutionary Biology and Environmental Studies , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jo Howard-McCombe 6 WildGenes Laboratory, Royal Zoological Society of Scotland , Edinburgh, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yeraldin Chiquinquira Castillo De Spelorzi 7 Genomics Facility, Istituto Italiano di Tecnologia (IIT) , Genoa, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Edoardo Henzen 7 Genomics Facility, Istituto Italiano di Tecnologia (IIT) , Genoa, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Bernagozzi 8 Fondazione Clément Fillietroz ONLUS, Astronomical Observatory of Autonomous Region of Aosta Valley , Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alessandro Coppe 3 Computational and Chemical Biology, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jean Marc Christille 8 Fondazione Clément Fillietroz ONLUS, Astronomical Observatory of Autonomous Region of Aosta Valley , Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manuela Vecchi 5 Non-coding RNA and RNA-based therapeutics, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Diego Vozzi 7 Genomics Facility, Istituto Italiano di Tecnologia (IIT) , Genoa, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Cavalli 3 Computational and Chemical Biology, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy 9 CECAM-EPFL , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bruno Bassano 4 Alpine Wildlife Research Center, Gran Paradiso National Park , Turin, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefano Gustincich 5 Non-coding RNA and RNA-based therapeutics, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy 10 Non-coding RNA and RNA-based therapeutics, Center for Human Technology, Italian Institute of Technology (IIT) , Genoa, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Croll 11 Institute of Biology, University of Neuchâtel , Neuchâtel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Croll For correspondence: daniel.croll{at}unine.ch luca.pandolfini{at}iit.it christine.grossen{at}wsl.ch Luca Pandolfini 5 Non-coding RNA and RNA-based therapeutics, Italian Institute of Technology (IIT), CMP 3VdA, Aosta, Italy 10 Non-coding RNA and RNA-based therapeutics, Center for Human Technology, Italian Institute of Technology (IIT) , Genoa, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luca Pandolfini For correspondence: daniel.croll{at}unine.ch luca.pandolfini{at}iit.it christine.grossen{at}wsl.ch Christine Grossen 1 Swiss Federal Research Institute WSL, Biodiversity and Conservation Biology Research Unit , Birmensdorf, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christine Grossen For correspondence: daniel.croll{at}unine.ch luca.pandolfini{at}iit.it christine.grossen{at}wsl.ch Abstract Full Text Info/History Metrics Supplementary material Preview PDF Summary Species restoration efforts can be threatened by the accumulation of deleterious mutations and inbreeding depression associated with the historic population contraction. However, even successfully restored species could face deleterious mutation swamping from hybridization with an abundant and closely related species. Here, we analyze this risk for Alpine ibex ( Capra ibex ), a flagship species of large mammal restoration in the Alps. The Alpine ibex faced near-extinction two centuries ago, resulting in exceptionally low genome-wide diversity and increased inbreeding, which facilitated the purging of severe deleterious mutation load. For this, we produced a highly contiguous chromosome-level genome assembly of the Alpine ibex capturing structural divergence from domestic goat ( Capra hircus ) and mapping immune-relevant MHC genes. Analyses of eight recent ibex-goat hybrids from two swarms in Northern Italy, combined with 29 non-hybrid Alpine ibex and 22 domestic goats, identified 215 masked loss-of-function (LOF) mutations introduced via hybridization. Yet, we found no evidence for counter-selection in early backcrosses. This exposes Alpine ibex to further backcrosses compounding the deleterious mutation load of the species by a factor of up to two. Our work provides one of the first direct estimates of hybridization load and guides conservation efforts to preserve endangered species gene pools. Results and discussion Human activity has profoundly affected species and populations through habitat degradation and over-exploitation leading to severe population reductions and extinctions 1 . A particular concern for species conservation is increased levels of inbreeding and the expression of deleterious mutations in small populations. Recent studies provided compelling evidence for changes in deleterious mutation load in natural populations (reviewed in Dussex et al. 2 ; Robinson et al. 3 ). As predicted by theory, population bottlenecks were repeatedly shown to lead to the selective removal (purging) of highly deleterious (usually recessive) mutations, while less severely deleterious mutations can accumulate ( e.g., Xue et al. 4 , Grossen et al. 5 , Dussex et al. 6 ; reviewed in Dussex et al. 2 ). Mutation load is defined as load resulting from newly introduced deleterious mutations 7 . Empirical assessments of fitness effects confirmed the detrimental impact of mutation load and inbreeding on both individual and population fitness 8 , 9 . However, the origin of deleterious mutations remains often unresolved. In addition to de novo mutations, a potentially significant source of mutation load could be deleterious variants introduced into a population through hybridization. Hybridization in nature contributes to genetic variation and fosters adaptation 10 , 11 . Hybridization in bottlenecked species may introduce new adaptive genetic variation, compensating previously lost variation 12 . In addition, high levels of heterozygosity in hybrids can also initially mask deleterious mutations, thereby causing hybrid vigor ( i.e. , heterosis) and mitigating inbreeding. Yet, this initial masking of deleterious mutations prevents their counter-selection in early generations. Hybridization also poses the risk of disrupting local adaptation through introgression of non-native alleles, in particular if the new species contact is caused by human activities 13 – 15 . Such introduction of deleterious mutations is defined as hybridization load 12 . Overall, deleterious mutations played a significant role in hybridization events in animals such as fish 16 , 17 , plants including crops 18 – 20 , and trees 21 . These studies indicate that while hybridization can introduce beneficial alleles, linked deleterious variants can reduce fitness and create partial barriers to introgression. The interplay of adaptive and maladaptive variation introduced through hybridization is likely complex and poorly understood. As hybridization can introduce new adaptive genetic variation and alleviate the effects of inbreeding through hybrid vigour, genetically impoverished species may be particularly susceptible to the introduction of hybridization load. To quantify load in genomes and to predict effects of introgression, high-quality genomic resources are essential 22 . The Alpine ibex ( Capra ibex ), a mountain ungulate endemic to the European Alps ( Figure 1A , B), has experienced severe bottlenecks since the early 19th century and repeated hybridization events 12 , 23 , 24 . However, the evolution of deleterious mutation load driven by hybridization remains poorly understood. Download figure Open in new tab Figure 1: Sampling and genome description. A) Picture of a female Alpine ibex with her offspring (photo by Dario de Siena, Archivio PNGP). B) Map of species distribution and zoom in on the sampling location of Odissea (shown in yellow), the hybrids (green), and domestic goats (blue). C) Snail plot visualizing assembly statistics: scaffold statistics, sequence composition proportions, and BUSCO completeness scores. D) Riparian plot showing the results of the gene synteny analysis conducted on the cattle, sheep, domestic goat, and Alpine ibex genomes using GENESPACE. The human genome was used as an outgroup. See also Figures S1-S4. Conservation efforts have restored the species to its original distribution area, with a current population of about 55,000 individuals 25 . However, these bottlenecks have resulted in Alpine ibex having one of the lowest levels of genetic variation among mammals 5 , 26 . This reduced genetic diversity has led to increased inbreeding and inbreeding depression, with negative effects both at the individual 27 and population level 28 . Severe genetic load was likely reduced through purging of highly deleterious mutations, while less severe mutations accumulated 5 . Genome-wide analyses showed that successful hybridizations with domestic goats ( Capra hircus ) likely occurred during the severe historic bottleneck when ibex numbers were at their lowest 29 , 30 . Introgression was particularly evident at immune-relevant genes, where genetic variability in Alpine ibex is very low. Such introgressed variants may have been under positive selection due to heterozygote advantage at immune loci 29 . However, hybridization is also expected to have detrimental consequences for Alpine ibex, with potential loss of adaptations to the alpine environment and the introduction of deleterious variants. Of particular concern are the recently discovered hybrid swarms between Alpine ibex and domestic goats in Northern Italy 24 , 31 . Such populations of hybrids have survived beyond the initial hybrid (F1) generation, and there is continued backcrossing between hybrid individuals and parental species 24 . This extensive hybridization raises concerns of maladaptation and loss of ibex identity. Here, we generated the first reference-quality genome for Alpine ibex to quantify the hybridization load generated by high rates of hybridization as observed in hybrid swarms. A high-quality chromosome-level assembly We assembled a chromosome-level genome of a wild female Alpine ibex (Odissea) from the Gran Paradiso National Park population (Lauson Valley, Cogne, Italy). Using 130 Gb of high-quality ONT data, we generated a 2.66 Gb draft genome with 1040 contigs (N50 58.7 Mb), polished with short read sequencing data. The contigs showed high completeness, with BUSCO and k-mer-based scores of 96.1% and 98.6%, respectively ( Figure 1C ). We generated Hi-C data to anchor contigs into 30 pseudo-chromosomes (Figure S1) with a resulting N50 of 101.2 Mb and matching the chromosome numbers of domestic goats 32 (Table S1, Figure 1D ). The Alpine ibex assembly also includes the full mitochondrial genome (16.7 Kb, Figure S2). The nuclear genome is constituted of 43.4% repetitive elements (1.15 Gb), dominated by retroelements (38.22%), including Long Interspersed Nuclear Elements (LINEs: 32.56%) and Long Terminal Repeats (LTRs: 4.87%) (Table S2). We integrated gene model evidence using transcriptome-assisted predictions as well as annotations lifted over from the sheep ( Ovis aries ) genome, yielding a BUSCO completeness of 99.7% at the transcript level (Table S3). The annotated gene set consisted of 32,783 protein-coding genes and 61,420 transcripts (Table S4). Recent structural variants arising in Alpine ibex We used gene order conservation analyses between Alpine ibex and three related domesticated mammals, including cattle ( Bos taurus ), sheep ( Ovis aries ), and domestic goat, as well as the human genome as an outgroup to detect chromosomal rearrangements ( Figure 1D ; Tables S5 and S6). Chromosomal synteny is largely conserved between the domestic goat and the Alpine ibex. The sheep genome experienced three chromosome fusions, combining in each two distinct chromosomes found in cattle and goats. The Alpine ibex genome includes a fully assembled X chromosome compared to the domestic goat (contigs Xa and Xb; Figure 1D ). In addition, the Alpine ibex chromosomes 12 and 17 also include sequences matching to unplaced contigs in domestic goat. Overall, domestic goat and Alpine ibex genomes share 2.4 Gb of syntenic regions (∼90.2%). Structural variant analyses with SyRI confirmed major rearrangements on chromosomes 7, 18, and 23 (Figure S3, S4A), mainly involving indels (38 Mb, 1.4% of the genome) and inversions (49 Mb, 1.8%), with fewer duplications and translocations (Figure S4B, C). Highly divergent regions (HDR) covered a total of 43 Mb (1.6%; Figure S4B, C). Nearly half (45%) of all insertions, deletions, and HDRs between domestic goat and Alpine ibex overlapped with gene bodies, and the proportion was even higher for inversions (52%), duplications (54%), and translocations (78%). MHC conservation and high heterozygosity in recent hybrids High levels of polymorphisms at the major histocompatibility complex (MHC) are typically favored within species, and the locus is prone to duplications 33 . Consequently, assembling the MHC regions can be challenging [see e.g., challenges for human MHC assembly 34 – 36 ]. Here, we report a contiguous assembly of the Alpine ibex MHC. High-level synteny is well-conserved among cattle, sheep, domestic goat, and Alpine ibex in both their main MHC regions (class I and II, Figure 2A ). We detected a ∼110 kb translocation between Alpine ibex and the domestic goat near the MHC class II cluster that does not affect MHC coding sequences ( Figure 2B ). An additional translocation of ∼1.92 Mb occurred further away from the MHC class II on chromosome 23 ( Figure 2B ). In contrast to the human MHC, both the domestic goat and Alpine ibex MHC class II clusters encode DR-and DQ-genes but no DP-genes 37 , 38 . The DR-and DQ-genes are generally exceptionally polymorphic. Over 100 DRB1 alleles for sheep and dozens for goats have been deposited in GenBank, reflecting their crucial role in immune diversity. Alpine ibex populations have previously been shown to carry introgressed alleles from the domestic goat at the DRB gene 30 and other immune-related genes 29 . Download figure Open in new tab Figure 2: Conserved synteny at the MHC and high diversity in hybrids. A) Close-up of the synteny analysis, conducted with GENESPACE, highlighting two MHC regions within Alpine ibex chromosome 23 (light blue and orange, respectively). A) Structural variation along chromosome 23 based on SyRI, including annotations of domestic goat (top) and Alpine ibex (bottom) in the range encompassing the major MHC II region. B) Average individual heterozygosity is shown as observed heterozygosity in Alpine ibex (blue), domestic goat (orange), and hybrids (purple). Heterozygosity excess and deficit in hybrids were shown with green (positive values) and orange (negative values), respectively. The grey box highlights a region corresponding to 25.47-25.55 Mb (encoding DQA and DQB ), which showed a local lack of coverage in domestic goats while being evenly covered in Alpine ibex (except for Alpine ibex individuals with previously reported historic introgression in the MHC region, see Figures S5-11 and Grossen et al. 30 ). To assess whether hybridization load may pose a risk to Alpine ibex, we generated whole-genome sequencing data of eight recent hybrid individuals sampled from two hybrid swarms in Northern Italy 24 and four domestic goats representing the local breed, and collected in the same areas as the hybrids (Table S7). We analysed the new sequencing data together with publicly available data representing 29 non-hybrid Alpine ibex as well as 18 domestic goats. Average individual heterozygosity was consistently low in non-hybrid Alpine ibex individuals compared to hybrids and domestic goats, including particularly striking contrasts in the DR-and DQ-gene regions (25.40-25.75 Mb; Figure 2C ). Diversity at the MHC is associated with disease resistance in a number of species 39 – 45 , including Alpine ibex 46 . The high diversity at the MHC in hybrids may therefore confer a selective advantage under disease pressure. Hence, hybridization and MHC introgression may be favored in the wild, posing a risk of disrupting local adaptation in wild Alpine ibex. Emerging recombinant haplotypes in an Alpine ibex hybrid swarm The recent observation of hybrid swarms in Northern Italy renewed concerns about Alpine ibex conservation efforts 24 . The high collinearity of Alpine ibex and domestic goat genomes should facilitate recombination between haplotypes. At the phenotypic level, recent hybrids carry well-recognizable domestic goat features such as horn shape and fur coloration 24 ( Figure 3A , B). We analyzed the whole-genome sequencing data to recapitulate introgression patterns in two hybrid swarms. We furthermore sequenced four domestic goat individuals from the same two regions ( Figure 3 , Table S7). Non-hybrid individuals of each species showed clear separation based on a principal component analysis (PCA, n = 29 Alpine ibex, n = 22 domestic goats; Figure 3C ) performed on 86,650 biallelic SNPs. All eight hybrid swarm members showed clear evidence for recent admixture ( Figure 3C ). Next, we performed chromosome painting using genome-wide polymorphism of the hybrids to identify parental contributions. Individual 12 matched expectations for a F1 hybrid with uninterrupted goat and Alpine ibex haplotypes over all chromosomes ( Figure 3D ). Hybrids that underwent backcrosses revealed evidence for recombination events as interrupted haplotypes ( Figure 3D ). Genome-wide ancestry matched well with recombination patterns and parental haplotype contributions. Individual 5 showed both the highest degree of ancestry and the largest haplotypes of domestic goat. In contrast, individual 8 carried the longest Alpine ibex haplotype blocks consistent with its strong Alpine ibex ancestry ( Figure 3D ). The highly diverse set of recombinants in the Alpine ibex swarm raises significant concerns for further introgression in the two respective regions and other populations with a high incidence of contacts between domestic goat and Alpine ibex. Download figure Open in new tab Figure 3: Genome-wide patterns of hybridization A) Picture of hybrid individual 10. B) Picture of hybrid individual 8. C) Principal Component Analysis of biallelic autosomal SNPs showing the clustering of species and the distribution of hybrids along the conjunction between Alpine ibex and domestic goat/bezoar. D) Chromosome paintings drawn with Mosaic, ordered according to the proportion of domestic goat ancestry (decreasing proportion of goat genome shown in orange from left to right). Hybridization load in two Alpine ibex hybrid swarms We assessed the extent of hybridization load, which could be introduced into Alpine ibex through a hybrid swarm. The cross-species polymorphism included 59,544,737 SNPs of which 0.009%, 0.311%, and 0.499% were classified as having putatively high, moderate, and low impacts on encoded proteins, respectively (see also Tables S8 and S9). High-impact variants, such as those causing premature stop codons or frameshifts, are predicted to severely disrupt protein function ( e.g ., loss-of-function, LOF variants). Moderate and low impact variants cover, respectively, missense mutations that may alter protein structure without severe impairment and synonymous changes with minimal expected effects on protein function. To evaluate the robustness of predicted high-impact mutations, we tested whether these mutations were disproportionately located in potential pseudogenes. However, we found that the presence of deleterious mutations cannot be explained by pseudogene-like regions alone (Table S10). We investigated deleterious mutations derived from either domestic goat or Alpine ibex as a proxy for genetic load ( Figure 4A ). First, we analyzed candidate mutations underpinning heterosis in hybrids. We defined these mutations as fixed derived LOF variants in Alpine ibex (fixed for the ancestral allele in all other species). Seven LOF variants were identified as candidates for heterosis (Table S11). Severely disruptive variants such as LOF are generally recessive; hence, these should be masked in the heterozygous state, conferring increased fitness (heterosis) in hybrids compared to non-hybrid Alpine ibex. We found that the analyzed hybrids each carried between zero and all seven candidates for heterosis in the heterozygous state (in a backcrossed individual into Alpine ibex and a F1 hybrid, respectively, Figure 4B ). This suggests that the positive effects of masking LOF variants in hybrids may quickly get lost through backcrossing. The observed signs of hybrid vigour in Alpine ibex-domestic goat hybrids 24 may reflect a combination of such masking effects alongside other genetic ( e.g. , overdominance) or epigenetic factors 47 . Download figure Open in new tab Figure 4: Deleterious mutation load in hybrids A) Phylogenetic tree showing species relationships and the definition of “goat-load” and “ibex-load” defined as newly derived mutations private to the respective species. B) Counts of loss-of-function alleles found in the heterozygous state for each sequenced hybrid individual (ordered according to proportion of goat). C) Total counts of loss-of-function alleles standardized by the total counts of modifier alleles. Box plots per species. Local domestic goat refers to breeds kept in the region where hybrid swarms were detected. See also Figure S12. D) Masked loss-of-function load (heterozygote counts) per individual. New goats, as in C. We also investigated the extent to which hybridization load could affect Alpine ibex. We found that the mutation load for LOF variants (standardized by neutral variation) was higher in domestic goat than in Alpine ibex ( Figure 4C , p < 10^-9). As a consequence, hybrids carried a total of 215 newly acquired LOF variants (Table S11), more than doubling the total number of LOF variants in Alpine ibex ( n = 177 LOF variants detected among 29 non-hybrid Alpine ibex). With an average of 76 masked LOF variants per hybrid individual compared to 29 in non-hybrid Alpine ibex, hybrids carried on average a 2.5 times higher severe masked load ( Figure 4D ). Hybrids backcrossed with Alpine ibex saw their mutation load decreased with each generation as expected ( Figure 4D ). Yet standardized LOF load did not decrease over the first few generations of back-crossing into Alpine ibex (Figure S12). Hence, there is no indication that selection acted to reduce mutation load. Such counterselection is expected to face less interference after further back-crossings (and recombination). Since the hybrid swarm acquired hybridization load ( i.e., masked mutation load), our data highlights that Alpine ibex are at risk of increasing their mutation load through leakage from the hybrid swarm. Concluding remarks The detrimental effects of deleterious variants are well-recognized as a risk factor in endangered species 2 , 3 . While a large number of recent studies investigated the evolution of deleterious mutation load as a consequence of small population size 4 – 6 , 48 – 52 , much less consideration was given to the source of deleterious mutation load. Here, we investigated the role of hybridization with domestic goat as a source of mutation load for Alpine ibex. The high heterozygosity in hybrids may confer early-generation hybrids an advantage over non-hybrid individuals, compounding the risk of further introgression and a potential loss of local adaptation. Here, we show that high rates of hybridization can introduce deleterious mutations from a closely related species without evident counterselection. Given the increased rate of human-induced hybridization 53 , tracking the deleterious effects of hybridizations should be a crucial element of species conservation actions. Resource availability Lead contact Further information and requests for resources and reagents should be directed to, and will be fulfilled by, the lead contact, Christine Grossen ( Christine.Grossen{at}wsl.ch ). Materials availability This study did not generate novel biological materials. Data and code availability Raw sequencing experiment data were submitted to the SRA archive under the project identifier PRJNA1104824 (reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1104824?reviewer=kij4fipl6g0eheslrgertb0ak9 ). The final C. ibex genome assembly and high-confidence coding gene annotations will be publicly available in the NCBI repository XXXXXX (currently undergoing NCBI processing). The CapIbe1.0 genome fasta file, full transcriptome models, gene functional annotation, and GERP scores relative to the novel genome can be downloaded from Zenodo ( https://doi.org/10.5281/zenodo.15632776 ; https://doi.org/10.5281/zenodo.15632854 ; https://doi.org/10.5281/zenodo.15357984 ). This paper does not report original code. The lead contact can provide any additional information required to reanalyze the data reported in this paper upon request. Author contributions AB conceived the study and managed DNA sample collection. AB, AP, GC, DW, JHM, and BB performed and coordinated the sampling. FGÇ, FL, DC, LP, and CG designed experiments and analyses. EH, YCCDS, and DV generated sequencing data under the supervision of FGÇ, LP, MV, and ST. FGÇ, FL, FF, LP, and CG performed analyses with input from DC. FGÇ, FL, AB, DC, LP, and CG wrote the manuscript with contributions from all authors. JMC, ACa, SG, AB, and CG provided funding and resources for the study. Declaration of interests The authors declare no competing interests. Supplemental information Document S1 contains Figures S1–S12. Figure S1: HiC contact map showing the data used for genome scaffolding, related to Figure 1 . Figure S2: Scheme of the Alpine ibex mitochondrial genome, related to Figure 1 . Figure S3: Structural variant calling between domestic goat and Alpine ibex (based on SyRI) across all 29 autosomes, related to Figure 1 . Figure S4: Major translocations between domestic goat and Alpine ibex observed on chromosomes 7 and 18 based on SyRI. B) Total counts and cumulative sizes of the structural variants identified by SyRI between the Alpine ibex and domestic goat genomes. C) Size distribution of translocations shown as a histogram from the same variants as in A (INDEL: insertion/deletion, INV: inversion, DUP: duplication, TRANS: translocation, HDR: highly divergent region), related to Figure 1 . Figure S5: Read depth profiles of already available whole genome sequencing samples of Alpine ibex (N=29), domestic goat (N=18), Iberian ibex (N=4), and 12 newly sequenced individual samples (domestic goat, N=4; hybrids, N=8). The profiles are displayed for the genomic interval 25.47-25.55 Mb on chromosome 23, which includes portions of DQA and DQB loci, related to Figure 2 . Figure S6: Figure S5 continued, related to Figure 2 . Figure S7: Figure S5 continued, related to Figure 2 . Figure S8: Figure S5 continued, related to Figure 2 . Figure S9: Figure S5 continued, related to Figure 2 . Figure S10: Figure S5 continued, related to Figure 2 . Figure S11: Figure S5 continued, related to Figure 2 . Figure S12: Total counts of loss-of-function alleles standardized by the total counts of modifier alleles for each hybrid individual ordered by proportion of domestic goat (indicated in parentheses), related to Figure 4 . Document S2 contains Tables S1-S13. Table S1: Genome contiguity statistics of the draft and scaffolded Alpine ibex assemblies, related to Figure 1 . Table S2: Summary of repeat annotations, related to Figure 1 . Table S3: BUSCO statistics for the scaffolded assembly and the protein-coding gene annotation of Alpine ibex, related to Figure 1 . Table S4: Genome annotation stats, related to Figure 1 . Table S5: Syntenic blocks identified in the GENESPACE analysis, related to Figure 1 . Table S6: Gene orthogroups identified in the GENESPACE analysis, related to Figure 1 . Table S7: Goat and goat/ibex hybrid individuals sequenced in the present study, related to Figures 2 - 4 . Table S8: Number of effects of genetic variants by type, related to Figure 4 . Table S9: Number of effects of genetic variants by region, related to Figure 4 . Table S10: Summary of deleterious mutations’ PFAM annotations, related to Figure 4 . Table S11: Newly acquired loss-of-function (LOF) variants and candidates for heterosis, related to Figure 4 . Table S12: Summary of the ONT runs used for genome assembly, related to Figure 1 . Table S13: Species and IDs of the available datasets used in this study, related to Figures 3 and 4 . Document S3 Supplementary Text Methods We collected fresh samples from thirteen individuals: one female Alpine ibex (Odissea), eight hybrids, and four domestic goats. For the female Alpine ibex, we extracted multiple organs and performed high molecular weight DNA extractions to enable Oxford Nanopore Technologies (ONT; Oxford, UK) long-read (including ultra-long) and Illumina NovaSeq 6000 short-read sequencing (Table S12). These data were used to produce a draft genome assembly, which was then integrated with Hi-C sequencing data from the same individual’s liver. The samples from the other individuals were prepared for whole-genome paired-end sequencing on an Illumina NovaSeq 6000. Detailed protocols are provided in the Supplementary Text . Genome assembly and evaluation We assembled the ONT data into a draft genome using Flye v2.9 54 , after which short-read data were aligned and used for polishing with Pilon v1.23 55 . We scanned the assembly for laboratory or environmental contaminants using the NCBI Foreign Contamination Screen tool 56 . Following quality filtering, we mapped Hi-C reads following the Arima Genomics pipeline ( https://github.com/ArimaGenomics/mapping_pipeline ) and performed scaffolding with YaHS v1.1 57 . Finally, we evaluated the final assembly for contiguity, k-mer completeness, reference-free QV, and expected gene content (further methodological details are in the Supplementary Text ). Genome assembly statistics were visualized with a snail plot in BlobToolKit v4.3.2 58 ( Figure 1C ). Gene model prediction, curation, and functional annotation We softmasked the Alpine ibex genome by merging repeat coordinates identified via RepeatModeler v2.0.3 59 and TRF 60 . Then we incorporated RNA-seq and homology-based evidence into the braker2 v2.1.6 pipeline 61 – 69 using pre-trained parameter sets for human ( Homo sapiens) , which is the evolutionarily closest taxon for Alpine ibex available within the software. Subsequently, an annotation lift-over from the sheep reference genome [ARS-UI_Ramb_v2.0, NCBI Genome GCF_016772045.1 70 , 71 ] was conducted with Liftoff v1.6.3 72 . In order to reduce the redundancy of the transcript set obtained, we employed an isoform pruning pipeline as previously described 73 . Finally, gene models were annotated using eggNOG mapper v2.1.11 74 in DIAMOND-search mode. The details of used external evidence for gene prediction and the detailed methodology can be found in the Supplementary Text . Functional annotation of the gene models showed that 54,205 out of 61,420 (88.2%) transcripts had matches in the eggNOG protein database (corresponding to 81.1% of genes). The mitochondrial genome encodes 13 proteins, including seven complex I proteins (ND1-3, ND4L, ND4-6), four complex IV proteins (COXI, COXII, COXIII, and CytB), and 2 ATP synthase subunits (ATPase 6 and 8; Figure S2). Synteny analysis and structural rearrangement identification We ran GENESPACE v1.2.3 75 with default parameters to examine synteny among the Alpine ibex genome and the human (GRCh38.p14), cattle (ARS-UCD1.3), sheep (ARS-UI_Ramb_v2.0), and domestic goat (ARS1.2, GCF_001704415.2) references, focusing on primary chromosome scaffolds. We further explored syntenic regions and structural rearrangements between domestic goat and Alpine ibex reference genomes with SyRI v1.6.3 76 (see Supplementary Text ). Results were visualized using plotsr v1.1.1 77 . SNP genotyping and hybridization analyses Whole-genome sequencing data from eight newly sequenced hybrids and goats were combined with available datasets for Alpine ibex (N=29), domestic goat (N=18), bezoar (N=4), Iberian ibex (N=4), Nubian ibex (N=2), Siberian ibex (N=2), and markhor (N=1), plus four outgroup samples (chamois, cattle, sheep, bighorn sheep; see Table S13) After alignment to the new Alpine ibex genome following the germline pipeline of the NVIDIA Clara Parabricks v3.8 78 , variants were called, and hard filtering was performed with GATK v4.1.0 79 . Eventually, we retained SNPs polymorphic among Capra spp. (see Supplementary Text for selection criteria), which were autosomal, bi-allelic, with a minimal genotype quality of 20 and a minimal genotyping rate of 80% using bcftools v1.2 80 . We estimated average per-site heterozygosity with VCFtools 81 and used PLINK v2.0 82 for principal component analysis. After data phasing using beagle v22Jul22.46e 83 , the Mosaic R-toolbox 84 was employed to examine Alpine ibex and goat haplotypes in hybrids; chromosome painting was visualized via the karyogram function. Finally, putative functional changes were annotated with SnpEff v4.3t 85 . Individual (derived) genetic load was computed as masked LOF load (number of heterozygotes) or total load (number of heterozygotes + 2* number of homozygotes) and standardized by the respective numbers of MODIFIER genotypes. These standardized estimates of LOF load in domestic goat vs. Alpine ibex were then compared using a Welch two-sample t-test (see also Supplementary Text ). Acknowledgments For domestic goat sampling, we thank Stefania Zanet, Alice Naudin, and Emilio Gugliermetti. For hybrid sampling, we thank the Surveillance Service of Gran Paradiso National Park, the Forestry Service of Ragione Valle d’Aosta, Claudio Scaini, and the personnel of Città Metropolitana di Torino and of CATO4, Noel Zehnder and Luca Rossi. We thank Sara Gottardo, Francesca Groppo, and the sequencing facility of CMP 3 VdA in Aosta for technical assistance with Illumina whole-genome sequencing. We thank Sergio Decherchi, Alessandro Parodi, and Mattia Pini for their support for high-performance computing and data storage, and Dario de Siena and Raffaele Turvani for the beautiful ibex pictures. F. Gözde Çilingir was funded by the Swiss National Science Foundation (IZCOZ0_198147 to A.B. and C.G. and 31003A_182343 to C.G.). This work was supported by the 5000genomi@VdA Project co-founded by “Fondo Europeo di Sviluppo Regionale (FESR CUPB68H19005520007) to SG and AC; FL, ST, ACa, MV and LP were supported by “Fondo Europeo di Sviluppo Regionale (FESR CUPB68H19005520007); DC and FF were recipient of a fellowship from Programma Investimenti per la crescita e l’occupazione 2014/20” (European Social Fund, ESF CUP B65F19001200009) and continued their activity with a fellowship from Programma Regionale Fondo Sociale Europeo Plus 2021/2027 (“European Social Fund”, ESF CUPJ51B24000170002). This work is part of the “Technologies for Sustainability” Flagship program of IIT (L.P.). Footnotes lead contact ( christine.grossen{at}wsl.ch ) Revised text and added results of deleterious mutation load (new Figure 4). References 1. ↵ Bongaarts , J . ( 2019 ). IPBES, 2019. 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