Fagus sylvatica L.) is one of the most widespread forest trees in Europe whose distribution and intraspecific diversity has been largely shaped by repeated glacial cycles. Previous studies, mainly based on palaeobotanical evidence and a limited set of chloroplast and nuclear genetic markers, highlighted a complex phylogeographic scenario, with southern and western Europe characterized by a rather heterogeneous genetic structure, as a result of recolonization from different glacial refugia. Despite its ecological and economic importance, the genome of this broad-leaved tree has only recently been assembled, and its intra-species genomic diversity is still largely unexplored. Here, we performed whole-genome resequencing of nine Italian beech individuals sampled from two stands located in the Alpine and Apennine mountain ranges. We investigated patterns of genetic diversity at chloroplast, mitochondrial and nuclear genomes and we used chloroplast genomes to reconstruct a temporally-resolved phylogeny. Results allowed us to test European beech differentiation on a whole-genome level and to accurately date their divergence time. Our results showed comparable, relatively high levels of genomic diversity in the two populations and highlighted a clear differentiation at chloroplast, mitochondrial and nuclear genomes. The molecular clock analysis indicated an ancient split between the Alpine and Apennine populations, occurred between the Günz and the Riss glaciations (approximately 660 kyrs ago), suggesting a long history of separation for the two gene pools. This information has important conservation implications in the context of adaptation to ongoing climate changes.The European beech ( Fagus sylvatica L.) is one of the most common and widely distributed broadleaved tree species in Europe. It highly contributes to European forest biodiversity, acting as a foundation and keystone species and providing many ecosystem services [The European beech  did not contribute to the recolonization of central and northern Europe , althougFagus sylvatica genome sequence has been released [Following the high-throughput sequencing revolution, a large number of genome sequencing projects have been completed in the last two decades, covering a wide variety of model and non-model species . In the released , followereleased . Newly areleased  with higreleased . MoreoveF. sylvatica samples from two Italian locations in the Alpine and Apennine mountain ranges, respectively, with the aims of: (1) assessing the current levels of genome-wide diversity for the species in the Italian peninsula, at both organellar and nuclear genomes ; (2) testing the previously identified distinctiveness [In the European beech, the role of glaciations and different refugia in shaping the current distribution of genetic diversity is not yet fully elucidated : more activeness , 8 of AlFagus sylvatica is the dominant or co-dominant tree species. In order to avoid the sampling of closely related individuals, we chose dominant adult trees growing at least 100m apart from each other; as a further check, pairwise relatedness coefficients (r) among individuals of the same stand were computed (see section 2.4.3). All conducted experiments complied with the current laws of Italy. No permission was required for sampling at the sites studied. Total genomic DNA was extracted from the leaves of each individual using the DNeasy Plant Mini Kit (QIAGEN), following the manufacturer’s protocol. Short-read Illumina whole-genome resequencing was performed at Novogene Inc., using an Illumina NovaSeq platform (PE150). The genomic DNA of each sample was randomly sheared into short fragments of about 350 bp, which were subjected to library construction using the NEBNext® DNA Library Prep Kit. The final libraries were purified, and library quality and size verification were assessed on an Agilent 2100 Bioanalyser (Agilent Technologies).In the present study, nine individual trees from two distinct European beech stands located in the Italian peninsula  were sampled . SpecifiIn addition, available reads derived from one previously sequenced German individual from the Kellerwald-Edersee National Park   as an outgroup, given that only a few mitochondrial genomes were available. A list of all sequences downloaded from NCBI can be found in We collected all available chloroplast and mitochondrial genomes and proteomes for the genus Fagus sylvatica samples sequenced in this study , the reference German and Italian samples , identihttps://github.com/andrea-silverj/BioinfoToolkit/blob/main/clean_seqs.py), removing low quality regions and unconventional bases.For mitochondrial data, given the scarce availability of genomes and calibrations that prevents from conducting a reliable phylogenetic study, only a dataset comprising the newly sequenced genomes was created. All genomes were quality checked using a custom Python script , we performed an orthology analysis with OrthoFinder v2.5.4 , identifAmino acid sequences of chloroplast single copy orthologs were individually aligned using MAFFT v.7, with the “—auto” option. Aligned files were concatenated independently using FASconCAT , creatinF. sylvatica genomes and one F. crenata genome , sequence alignment was performed using the same method described above, followed by trimming with trimAl v1.2 [For the second chloroplast dataset, including only mAl v1.2  with theggtree R package [Maximum-likelihood phylogenies were obtained for all matrices using RAxML  with opt package .F. sylvatica origin, and then used these estimates to calibrate a second dataset centered on F. sylvatica.Molecular clock analysis was performed using BEASTv2.6 . We usedFagus and all other genera  and a monophyletic constraint at the node Castanea-Castanopsis, with another log-normal distribution . We set a cp REV site model with gamma = 4, a relaxed-clock with a log-normal distribution, and a birth-death prior. The results of this first analysis were used to calibrate a second molecular clock, with the aim of dating the split between the two populations of Fagus sylvatica included in our study (ALP and APE). In particular, the median age estimates of the split between Fagus sylvatica and Fagus crenata  and tha. Thion data . In all ion data , making Fagus sylvatica genome [http://www.beechgenome.net) using the Burrows-Wheeler Aligner  with “-v0.7.17; ; the mapv0.7.17; . The obsVariant calling was performed using the Haplotype Caller (in -ERC GVCF mode) and GenotypeGVCFs tools in GATK v4.2.3.0 , 60. We https://pcingola.github.io/SnpEff/; [F. sylvatica reference genome and its annotation list (Bhaga_genes.gff3) (http://thines-lab.senckenberg.de/beechgenome/data.html). The VCF file produced as described in 2.4.1 was used as the input file, and SnpEff annotated each SNP according to its genomic position  and predicted effect , producing a list of annotated variants, in which each variant was noted for its impact. Based on the definition of the sequence ontology (SO) terms (http://www.sequenceontology.org/) that SnpEff assigns as variant effects, we divided the SNPs into functionals and neutrals, creating two VCF files. We considered functional variants all those SNPs that were assigned the following SO terms: ’missense variant’, ’initiator codon variant’, ’start lost’, ’stop gained’, ’stop retained variant’, ’3 prime UTR variant’, ’5 prime UTR premature start codon gain variant’, ’5 prime UTR variant’, ’splice acceptor variant’, ’splice donor variant’ and ’splice region variant’. The following terms were considered for neutral variants classification: ’downstream gene variant’, ’intergenic region’, ’intron variant’, ’upstream gene variant’ and ’synonymous variant’. The SnpSift ’filter’ command was used to filter the annotated VCF file. Subsequently, neutral variants were further filtered out of all terms considered for the functional SNPs categories because of multiple effects that can be assigned to individual SNPs. Overall genetic diversity levels were estimated separately for the functional and neutral SNPs subsets (see the following section).The SNPs annotation was performed using SnpEff software 5.1 . Due toIn order to make sure that the trees sampled within each forest stand were not closely related, we estimated genome-wide pairwise relatedness coefficients (r) using the—make-rel command in PLINK 1.9 , 64. TheFor the two Italian populations, we estimated standard measures of genetic diversity: genome-wide heterozygosity (GW-het), nucleotide diversity (π), and Tajima’s D. Genome-wide  heterozygosity was calculated per sample using ANGSD v0.934 . This isFor a more in-depth characterization of whole-genome patterns of variation, we assessed linkage disequilibrium (LD) decay and runs of homozygosity (ROH) in the two Italian populations.mgcv R package [LD decay may reveal important information about a species’ demographic history and mating system ; moreove package . Due to detectRUNS [Runs of homozygosity (ROH) are contiguous homozygous segments of the genome, which can arise from the mating of related individuals and the subsequent transmission of identical haplotypes to the offspring , 73. ThetectRUNS  was usedtectRUNS , 68. Briaboot function from the R-package poppr [tidyverse collection of R packages [To examine topological relationships among all the European beech genomes in the dataset, we constructed a phylogenetic neighbor-joining tree using the ge poppr , based oge poppr ) matrix ge poppr , 64. Thepackages . An addivcfR [2 < 0.50, since strongly linked markers and rare variants may introduce bias in population structure analysis from WGS data [2 threshold of 0.5.The level of genetic divergence between the two Italian populations was then assessed by estimating pairwise Nei’s GST  and JostvcfR . All theWGS data , 82. We An average of 7,302,798 and 27,519,618 reads per individual were mapped to the chloroplast (cp) and mitochondrial (mt) reference genomes, with 915x and 237x average coverage, respectively. The proportion of mapped reads was higher for mt genomes (average 4.8%) than cp genomes (average 1.2%).−5) than for mt ones (NucDiv = 1.9 × 10−5). A total of 12 and 8 haplotypes were inferred for the cp and mt genomes, respectively, leading to a haplotype diversity (HapDiv) of 0.98 and 0.93, respectively. Almost all haplotypes related to the cp genomes were unique except for haplotype X, which was shared by two Apennine individuals, while one mt haplotype (II) was shared by three Apennine individuals and another haplotype (V) by two Alpine samples and FASYL29 (Germany). The lists of haplotypes detected in each sample at cpDNA and mtDNA are reported in We identified a total of 20 SNPs and 54 InDels in the cp genomes of the 13 included individuals and 30 SNPs and 33 InDels in their mt genome. Overall nucleotide diversity was on average higher for cp genomes  was 15.9x while the mean depth of coverage for the five samples from the Apennines (APE) was 16.6x. After VCF filtering, a total of 1,261,484 biallelic SNPs were retained across 11 individuals . The final mean depth of coverage, No. of biallelic SNPs and mean SNP density are reported for each sample in Relatedness coefficients (r) between the individuals sampled within each forest stand resulted to be negative in all pairwise comparisons, indicating unrelated samples  ALP: av. Individ2 values: the half-decay distance, i.e. the distance at which LD is half of its maximum value was found to be of about 250 bp on average (corresponding to r2 = 0.25); however, r2 values remain > 0.2 at 1000 bp and the fitted LD decay curve approaches 0.1 slowly  analysis. The average number of detected ROHs was 355 ± 22 in the APE population, 354 ± 81 in the ALP populations; these values were not so different from those detected in the two Central European samples . In general, the length of the detected ROHs was very low, with only five ROHs > 0.5 Mb  and no ROHs > 1 Mb (max ROH length: 0.59 Mb). Results remained consistent even using different values of SNP density to call a ROH.After filtering for MAF ≥ 5% and LD pruning , 38,271 SNPs were retained for genetic differentiation and population structure analyses. In the whole genome-based NJ tree , the fouFagus genus and the other groups of Fagaceae of a first long-scale reconstruction to calibrate a second analysis on a shorter timescale.We employed phylogenetic methods to understand how and when the two populations differentiated. For the molecular clock analysis, we were constrained by the absence of reliable substitution rates for both mitochondria and chloroplasts in this taxon. To overcome this limitation, we followed a strategy based on a 2-steps molecular clock analysis, using the estimated divergence times between the F. sylvatica). This shows the necessity of improving sequencing effort of mitochondrial genomes of this taxon. Our newly sequenced mitochondrial genomes were almost identical in their coding regions, with polymorphisms concentrated in the non-coding part of the genome, which were sufficient to discriminate between the Alpine and Apennine populations . We dated the split between these two taxa at a median of 11.02 Mya  resulted to be highly consistent with the one calculated in the first analysis , with only a slight difference which could be expected due to the inherent nature of the Bayesian analysis and the different priors used to calibrate the clock.Bayesian molecular clock analysis suggests that the split between the Alpine and Apennine populations occurred at a median of 662.2 Kya, between the Günz and the Riss glaciations . The last common ancestors of the individuals belonging to the Alpine  and Apennine population , respectively, are both dated in the Würm glaciation. The same topology, with a sharp separation between ALP and APE populations, was retrieved across all datasets and methods. The estimate for the split between The average SNP rate over all chromosomes was 1 every 424 bases. SNPs distribution on each chromosome was analyzed to reveal the most polymorphic regions. The highest number of variants  was found on chromosome 1, as expected based on chromosome length. The highest variant rate (region length bp / number of variants) was found on chromosome 4 (851), and the lowest (328) on chromosome 5.By analyzing the annotations of all the SNPs  deriving from the whole-genome sequencing of the Italian samples, we found a total of 2,783,020 predicted effects. According to the type of change and its effect, only 9135 (0.3%) SNPs were predicted to have a high impact , whereas 222,199 (8%) represent SNPs with moderate to low impact effects . In addition, SnpEff also classified as missense variant 132,606 SNPs, as stop gained 6611 SNPs, and 79,861 SNPs as silent functional classes .The highest number of SNPs identified were located in the upstream (27.65%) and downstream (26.26%) gene regions. We found 778,498 (27.9%) SNPs in intergenic regions  and 486,−4 in the functional SNPs subset and 2.8 × 10−4 in the neutral SNPs subset, respectively). No differences were observed in the functional diversity levels of the two Italian populations, ALP and APE, both in terms of observed heterozygous sites and nucleotide diversity (data not shown).Results of the genetic diversity analysis performed separately on the neutral and functional SNPs subsets for the overall Italian samples highlighted a prevalence of neutral variation, as expected; however, functional variation represented a numerically important component, with an average value of observed heterozygous sites of 11,902, only about six times lower than the average values of observed heterozygous site observed in the neutral SNPs subset   and five from the central Apennines , provided a first genome-wide overview of genetic diversity for this important tree species in the Italian peninsula, close to the southern margin of its distribution.Identifying, quantifying, and understanding genome-wide patterns of genetic diversity is of crucial importance for forest trees under climate change , and par−3; APE: 3.7 × 10−3) were higher, albeit of the same magnitude, than those observed in previous studies in the European beech in other European areas [Genetic diversity levels were comparable in the two sampled Italian forest stands, ALP and APE, at both organellar and nuclear genomes. At organellar genomes, the same number of haplotypes was detected in ALP and APE: four at cpDNA and three at mtDNA, reflecting a relatively high diversity despite the low sample size . At nuclear genomes, no significant differences were detected in terms of mean heterozygosity, nucleotide diversity and Tajima’s D . Our genan areas , 86.2 = 0.25) at about 250 bp; however, after this major drop LD was found to decrease relatively slowly. Both findings are in line with the results reported by [Populus tremula [Eucalyptus globulus [A rapid genome-wide LD decay was highlighted, as expected for an outcrossing forest tree species, with the correlation coefficient decaying to half of its maximum value  and Polish (JAMY) samples, their relationships with the Italian populations and the apparent incongruence between nuclear and organellar trees for BHAGA see . These EF. sylvatica and F. crenata cpDNA genomes .Fagus sylvatica, a keystone and economically important tree species, in one of the most southern areas of its distribution: the Italian peninsula.Our results mark a significant improvement of the genomic information of Fagus sylvatica is currently experiencing drought-induced growth decline and increasing mortality due to climate change, particularly at its southern edge, with predicted serious ecological and economic consequences [Although being a widely distributed deciduous broadleaf temperate tree, equences , 6, 103.equences , 105. ThFagus sylvatica experienced a long history of separation, predating the last glaciation. As a consequence, the genomic diversity of the species in the Italian peninsula has been shaped by repeated interglacial–glacial cycles, possibly leading to distinct local adaptation trajectories: this scenario seems not to be unlikely, also considering the overall high levels of functional variation detected in the Italian samples; however more specific studies are needed to confirm this hypothesis.Here, we demonstrated that the Alpine and Apennine populations of To conclude, our findings clearly showed the informative power of whole-genome data for the identification and management of in situ genetic conservation units for forest trees.S1 TableFor each sample, the average depth of coverage, No. of SNPs and SNP density are reported (inferred from the filtered VCF files), together with individual-based estimates of genome-wide diversity  = Observed number of homozygous genotypes; E(Hom) = Expected number of homozygous genotypes).(DOCX)Click here for additional data file.S2 TableThe annotated proteomes correspondent to each accession were downloaded from the NCBI website.(DOCX)Click here for additional data file.S3 TableSummary of SnpEff annotation with the number of SNPs found in each effect type category.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 FigThe plot shows the results of a sliding window analysis (window length = 10 kb) of nucleotide diversity per site (π) on chromosome 11, where large organellar genome insertions (from about Mb 16 to Mb 18) had been identified in a previous study .(PDF)Click here for additional data file.S2 Fig2 < 0.50).The analysis is based on pairwise identity-by-state (IBS) distance calculated from the 38,271 genome-wide SNPs dataset (after filtering for MAF ≥ 5% and LD pruning at r(PDF)Click here for additional data file.S3 FigIndividuals are plotted on the biplot of the first two principal components; the dataset includes nine Italian samples from the Alpine (ALP) and Apennine (APE) populations, one German (BHAGA) and one Polish sample (JAMY).(PDF)Click here for additional data file.S4 FigMitochondrial data show a sharp separation between the Alpine and the Apennine populations (these two clades are indicated by dots of different colors at the node), with very high bootstrap values. The two clades cannot be further divided into subclades reliably, as shown by the very low bootstrap supports inside these two groups. The tree was midpoint rooted. A scale bar indicating the substitutions per site is indicated below the tree.(PDF)Click here for additional data file.S5 FigBig and crossed red circles indicate fossil calibrated nodes. Full red circles indicate nodes for which posterior probabilities were below 60. Blue bars show the 95% highest posterior densities (HPDs) of the date estimates, representing the shortest interval on the posterior density of age estimates for a 95% confidence level.(PDF)Click here for additional data file.S6 FigSNPs classification according to their effect and distribution across genomic regions. The X-axis displays the variants number, and the Y-axis the different regions.(PDF)Click here for additional data file.


Melospiza melodia) subspecies that vary ~300% in body mass (17 − 50 g). Comparing large- and smaller-bodied subspecies revealed 9 candidate genes in three genomic regions associated with body mass. Further comparisons to the five smallest subspecies endemic to California revealed eight SNPs within four of the candidate genes  associated with body mass and varying as predicted by Bergmann’s Rule. Our results support the hypothesis that co-variation in environment, body mass and genotype reflect the influence of natural selection on local adaptation and a capacity for contemporary evolution in this diverse species.Ecogeographic rules denote spatial patterns in phenotype and environment that may reflect local adaptation as well as a species’ capacity to adapt to change. To identify genes underlying Bergmann’s Rule, which posits that spatial correlations of body mass and temperature reflect natural selection and local adaptation in endotherms, we compare 79 genomes from nine song sparrow ( Ecogeographic rules link spatial patterns in phenotype and environment, potentially reflecting adaptation. This study identifies nine genes associated with body mass variation in song sparrow populations, supporting Bergmann’s Rule and highlighting the role of natural selection in local adaptation. More recently, questions about local adaptation and the adaptive capacity of species to persist given environmental change have reinvigorated efforts to understand the micro-evolutionary processes involved. For example, correlations between ambient temperature, phenotype, and life-history traits affecting fitness and subject to natural selection are widely reported in vertebrates8 and summarized as ecogeographical rules9. Of particular relevance to climate adaptation, Bergmann’s rule posits an inverse correlation between ambient temperature and body size in endotherms10 and has garnered substantial empirical support . If such patterns that follow Bergman’s rule reflect local adaptation in traits affecting fitness, such as thermal tolerance, we expect genes underlying such trait variation to show signs of historical or ongoing selection and to vary predictably with phenotype.Explicating correlations among phenotypes and environmental variables is a historic focus of theoretical and empirical research on natural selection, local adaptation, and speciation16. We focused on song sparrows (Melospiza melodia), which are among the world’s most variable species18 as indexed by the number of recognized subspecies (25), and which accordingly exhibit a stunning range of co-variation in phenotype, life history, and environment . Song sparrows inhabit highly heterogeneous environments, spanning about 36 degrees of latitude (26 °N to 62 °N) and 15 °C in mean annual temperatures (ca. 1 °C to 16 °C). Because many phenotypic traits of song sparrows are known to have an additive genetic basis, respond to selection, and affect individual fitness , we hypothesized that the phenotypic clines in body mass exemplified in song sparrows at least partly reflect local adaptation to environment. If so, we further suggest that identifying the genomic mechanisms underlying such patterns can inform us about the adaptive capacity of other species displaying local adaptation to heterogeneous environments.Species with large ranges that encompass steep environmental gradients are excellent candidates in which to test for a genomic basis of local adaptationM. m. maxima, M. m. sanaka; mean mass [range]: 45.9 [41.9–50.0 g]) and two smaller-bodied subspecies  that breed in northern British Columbia and Alaska in order to evaluate the genomic landscape of divergence and identify candidate genes associated with body mass. Maxima and sanaka reside year-round from the Alaskan Peninsula to the Aleutian Islands, experience cold winters and are nearly twice the mass of rufina and merrilli, which experience warmer winters24  and smaller-bodied (merrilli and rufina) subspecies  indicated substantial divergence, with pairwise genome-wide values from 0.035 ± 0.026 in the control comparisons (merrilli-rufina) to 0.247 ± 0.112  was lowest between the control comparisons  and highest between maxima-merrilli (0.425 ± 0.046). Overall, the strongest signals of genetic differentiation were best explained by body size.Body mass (g) and mean winter and summer temperatures (°C) were strongly negatively related in the subspecies we studied Fig. , consistFST that were shared by at least two pairwise comparisons of large- and smaller-bodied subspecies . Overall, the mean FST values in the 50 kb outlier windows were elevated for each of the four pairwise comparisons , relative to low genome-wide FST averages , and two shared by three comparisons , with six candidate genes occurring on the same scaffold  and smaller (merrilli and rufina) subspecies pairs, as expected if these differentiated regions arose via divergent selection on body size . Six genes were shared by two subspecies comparisons , whereas the genome-wide average was an order of magnitude higher  , sanaka (0.229), and merrilli (−0.789), as compared to genome-wide means , but not in rufina . Dxy estimated in candidate regions was also elevated in size-related pairwise comparisons  compared to controls of within-size comparisons  test statistic to test for the presence of selective sweeps on the 3 contigs containing the 9 candidate genes , 1 SNP in GARNL3, and 3 SNPs in COL15A1  falls within the coding region of the gene, while the remaining SNPs are located within non-coding regions of genes. Notably, non-reference allele frequencies of the 8 candidate SNPs were also highly negatively correlated with average winter and summer temperature , for which we had prior genomic dataR2: 0.820–0.953; p < 0.001; Supplementary Table R2: 0.619–0.722; p < 0.05; Supplementary Table r = 0.183; p = 0.004).We observed consistent positive regressions of mass on non-reference allele frequency at each of the 8 focal SNPs  = 31.96–142.66; range FST estimates for the 8 focal SNPs noted ranged from 0.63 to 0.96 among all large- and smaller-bodied subspecies , two large Aleutian endemics , and five small California endemics . Whilst these findings suggest compelling support for our hypothesis that historical and/or ongoing natural selection has contributed to genetic differentiation of body size between subspecies, lower coverage for the San Francisco Bay populations may limit our ability to fully resolve heterozygotes and should be validated using higher coverage, phased sequence data.A phylogenetic tree reconstructed from individual genotypes of the 8 focal SNPs and 1–3 additional SNPs immediately up and downstream  that predicted allele frequency of small-bodied subspecies in California  and COL15A1  similarly associate with percent and distribution of body fat, size and BMI in humans32. ANGPTL2 contained several highly differentiated SNPs correlated with body mass and temperature . ANGPTL2 codes for angiopoietin-like protein 2 which has important roles in lipid, glucose and energy metabolism across taxa, and has also been found to be associated with human height and obesity34. In contrast, GARNL3  was informative in differentiating subspecies by body size and predicting the genotype of smaller-bodied song sparrows, but it has not yet been linked to phenotype in other species. Notably, there were high proportions of fixed or nearly fixed SNPs (FST > 0.95) within RALGPS1, GARNL3, and ANGTPL2 . These results support further our supposition that some or all of the genes we identified are directly or indirectly associated with thermoregulatory capacity and influential in their contributions to the roughly 300% increase in the body mass of song sparrows observed from California to Alaska.Having identified candidate genes capable of differentiating large- and smaller-bodied subspecies in Alaska, we then validated these patterns by predicting the genotypes of 39 birds from the five smallest subspecies endemic to California. Of the 9 candidate genes in regions shared by all large- and smaller-bodied pairwise comparisons, four contained SNPs  and M. m. sanaka , and two smaller-bodied subspecies, M. m. merrilli  and M. m. rufina . Tissue samples for these individuals were provided by the University of Alaska Museum. All birds were collected between 1997 and 2000 ; M. m. heermanni ; M. m. samuelis ; M. m. pusillula ; and M. m. maxillaris . Because extreme cold and heat are both expected to influence body size we used mean winter  and summer  temperature data, which were acquired from ClimateNA v.6.0049 for each of the sample locations to visualize the relationship between body mass and temperature . Whole-genome sequencing yielded a mean of 48,428,940 reads per individual in our British Columbia and Alaska populations.Genomic DNA was extracted using the DNeasy Blood and Tissue Kit . DNA concentrations were quantified using the Qubit BR dsDNA Assay Kit (Life Technologies). Using 150 ng of DNA from each sample, we prepared individually barcoded libraries with a 300 bp insert size following the protocol for the NEBNext Ultra II FS DNA Library Prep Kit . Libraries for http://www.bioinformatics.babraham.ac.uk/projects/fastqc). We used AdapterRemoval v.2.1.1 for sequence trimming, adapter removal, and quality filtering , and we merged overlapping paired-end reads. We aligned filtered reads to the song sparrow reference genome based on a small-bodied subspecies (M. m. morphna) from Southern British Columbia50 using the default settings in Bowtie2 v.2.4.251 and obtained alignment statistics from Qualimap v.2.2.152. Mean alignment rates for the four subspecies comparisons were 97.98%. Mean coverage and mapping quality were 7X (range: 4X–16X), and 18.62  to add index groups and mark duplicates. We used mpileup module in Bcftools v.1.12 for SNP variant discovery and genotyping for all song sparrows and filtered out variants that were not biallelic, had minor allele frequencies less than 5%, mean coverage less than 2X or more than 50X, and more than 20% missing data. This resulted in a total of 13,089,663 SNPs across the four subspecies. The mean missing data across individuals was 16.9%  of our subspecies were sampled from islands and are likely to have higher levels of inbreeding54, this signal is likely biologically relevant. To assess whether retaining related individuals had any impact on population structure, exploratory PCA plots were run both with and without these individuals. This comparison produced similar results  for all individuals. We then calculated Tajima’s D, nucleotide diversity (π), and absolute genetic divergence (Dxy) using the popgenWindows.py script  to further provide insight into the evolutionary processes that have shaped population-level genetic variation. Putative chromosomal locations of different scaffolds were obtained by aligning them to the zebra finch (Taeniopygia guttata) assembly  using the pseudo-chromosome scaffolding command in SatsumaSynteny60. To analyze patterns of genetic structure among the four subspecies, Admixture v.1.2361 analyses were run using a filtered dataset  that contained no missing data and was pruned to avoid linkage using the script ldPruning.sh (https://github.com/speciationgenomics/scripts/blob/master/ldPruning.sh). We investigated one to four population clusters with 200 bootstrap resampling iterations.We visualized genetic clustering in the SNP dataset by performing a PCA using snpgdsPCA function in the SNPRelate packageFST estimates. Elevated values of FST averaged over non-overlapping 50 kb windows were considered elevated if exceeding the 99.9th percentile of genome-wide mean value .We identified regions of elevated divergence among the four pairwise (or between size) comparisons including large- and smaller-bodied northern populations using the sliding window M. m. merrilli and M. m. rufina, that also differ in migratory behavior as a measure of ensuring that the candidate genes are correlated with our trait of interest. We also compared the two large-bodied populations, M. m. sanaka and M. m. maxima as an additional control for our smaller- vs large-bodied comparisons. There were no overlaps in elevated peaks between our large-small bodied (between size) comparisons with those identified within our smaller-bodied or large-bodied (within size) control comparisons, increasing our confidence that we have identified genes related to body size variation as opposed to migratory behavior or other traits differing between subspecies.To control for phenotypic differences between subspecies in traits that are not body size , we compared the two smaller-bodied populations, Among our large- and smaller-bodied (between size) comparisons, we classified outlier regions as shared if two or more paired comparisons identified the same gene within 50 kb of an elevated window  from a different geographic region, the San Francisco Bay area of California. Whole-genome sequences for the 39 San Francisco Bay individuals were generated in a separate study25 using library preparation and sequencing methods as detailed above. For the whole-genome sequencing summary statistics of the 39 San Francisco Bay individuals see Mikles and colleagues25.To test the hypothesis that our narrowed set of 9 candidate genes is related to body size, we predicted the genotypes of more distantly related small-bodied subspecies  distance.We first performed a partial Mantel test on the 3 contigs containing the 9 candidate genes across all 9 subspecies to assess whether patterns of divergence were due to neutral, including isolation by distance, or selective processes. Using the veganr) to quantify the relationship between the non-reference allele frequency and body mass (g), and between non-reference allele frequency and average summer and winter temperature (°C) at each of the SNPs. We selected the top 8 SNPs with a r > 0.90 between non-reference allele frequency and mass: contig 391 (chr 17) positions: 19152, 57980, 61835, 86656, 122228; contig 3361 (chr 2) positions: 255216, 255218, 262569. We also performed linear regression analyses to further explore the relationships between allele frequency and mass and temperature at each of the focal SNPs. For each analysis, allele frequency served as the independent variable, while mass and temperature were the dependent variables. Model parameters, including regression coefficients, intercepts, confidence intervals, effect sizes, R-squared values, p-values, and leverage and influence statistics can be found in Supplementary Tables 69  for the 467 SNPs. We used Pearson correlation coefficient . Less than three SNPs were used in cases where the SNP was located at the end of a contig or adjacent to another focal SNP. We then used the Ward algorithm as a grouping method to express the relationships between sites, which was implemented using the hclust function in vegan. We plotted the output using the plot.phylo function in the R package ape70 and visualized genotypes by constructing a modified heatmap in which the base pairs of each individual at a locus are represented in the two halves of a diagonally split pixel . To do this, we used the R package veganFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting SummarySource Data


MTHFR-MTHFD1 and MTHFR-SLC4A5 were significantly associated with aggressive PCa. For the AA PCa patients, the interaction of DHFR-19bp polymorphism and rs4652 (LGALS3) was significantly associated with aggressive PCa. These findings can provide valuable information for precision intervention and medicine of PCa aggressiveness.Prostate cancer (PCa) is a complex disease. Identifying inherited genetic variants or single nucleotide polymorphisms (SNPs) for predicting PCa aggressiveness is essential for improving PCa clinical outcomes. However, the interactions of folate-related SNPs associated with PCa aggressiveness are understudied. The study’s objective is to evaluate interactions among the DHFR 19-bp polymorphism and 10 SNPs in folate metabolism and the one-carbon metabolism pathway associated with PCa aggressiveness. We evaluated 1294 PCa patients, including 690 European Americans (EAs) and 604 African Americans (AAs). None of the 11 individual polymorphisms were significant for EAs and AAs. For the EA PCa patients, the two SNP–SNP interaction pairs in MTHFR) and rs2236225 (MTHFD1), and rs1801131 (MTHFR) and rs7587117 (SLC4A5) were significantly associated with aggressive PCa. For the AA PCa patients, the interaction of DHFR-19bp polymorphism and rs4652 (LGALS3) was significantly associated with aggressive PCa. Conclusions: These SNP–SNP interactions in the folate metabolism-related genes have a larger impact than SNP individual effects on tumor aggressiveness for EA and AA PCa patients. These findings can provide valuable information for potential biological mechanisms of PCa aggressiveness.Background: Studies showed that folate and related single nucleotide polymorphisms (SNPs) could predict prostate cancer (PCa) risk. However, little is known about the interactions of folate-related SNPs associated with PCa aggressiveness. The study’s objective is to evaluate SNP–SNP interactions among the DHFR 19-bp polymorphism and 10 SNPs in folate metabolism and the one-carbon metabolism pathway associated with PCa aggressiveness. Methods: We evaluated 1294 PCa patients, including 690 European Americans (EAs) and 604 African Americans (AAs). Both individual SNP effects and pairwise SNP–SNP interactions were analyzed. Results: None of the 11 individual polymorphisms were significant for EAs and AAs. Three SNP–SNP interaction pairs can predict PCa aggressiveness with a medium to large effect size. For the EA PCa patients, the interaction between rs1801133 ( Prostate cancer (PCa) is the most common incident cancer and the second leading cause of cancer death 11%) among American men 1% among . In addiDHFR) and methylenetetrahydrofolate reductase (MTHFR)  = 7.7), and 53.8% were from the Louisiana site. For the 604 AA PCa patients, the mean age was 61.8 years (SD = 7.8), and 55.5% were from the Louisiana site. As shown in DHFR 19-bp polymorphism and 10 selected SNPs from theseven genes for EAs and AAs are listed in LGALS3. For rs4652, the ‘C’ allele was a minor allele  for EAs but was a major allele for AAs . Most minor allele frequencies for the 10 SNPs differed by race. Using rs1801133 in MTHFR as an example, the MAF of the ‘A’ allele was 33% for EAs and 13% for AAs. As shown in DHFR 19-bp polymorphism were significantly different by race. For rs10380 in MTRR, the TT genotype was only 1.5% for EAs but was 11.3% for AAs . For rs4652 in LGALS3, the CC genotype was 19.2% for EAs but 72.4% for AAs . The DHFR 19-bp del/del genotype prevalence was 17.7% for EA and 31.0% for AA PCa patients . The individual effects of the selected SNPs/polymorphism associated with PCa aggressiveness by considering three inheritance modes for EAs and AAs are shown in p-values > 0.05).Details regarding the p < 0.05 associated with PCa aggressiveness for EAs and AAs are shown in p > 0.0009). Among them, three pairs (two pairs for EAs and one pair for AAs) were selected based on the bootstrap approach with >65% times of significance out of 500 bootstrap samples. For EAs, the SNP pairs rs1801133- rs2236225 in MTHFR and MTHFD1  and rs1801131- rs7587117 in MTHFR and SLC4A5  were significantly associated with PCa aggressiveness  compared to those with other genotypes in this SNP pair. As shown in MTHFR and rs7587117 in SLC4A5 was associated with PCa aggressiveness (p = 0.018). The SIPI selected the RR_int_oo pattern, an interaction-only model with an original-recessive mode for both SNPs. As shown in p = 0.018). For the EA PCa patients, the top two high-risk groups of PCa aggressiveness were the CC+ CC genotype of rs1801131- rs7587117 (57%) and the AA+TT genotype of rs1801133- rs2236225 (44%), while the overall PCa aggressiveness prevalence was 21%.The top SNP–SNP interaction pairs with a siveness . The intly model A. This pp-value = 0.012, 65.4% bootstrap significance) out of the seven pairs with a p < 0.05. This SNP pair was the interaction of DHFR-19bp polymorphism and rs4652 in LGALS3 with an interaction pattern of DD_int_ro, an interaction-only model with a reverse-dominant for DHFR-19bp polymorphism and original-dominant mode for rs4652. As shown in p = 0.012). The PCa aggressiveness prevalence for AA PCa patients with DHFR-19bp del/del and CA/AA in rs4652 was 14–15% compared with the overall PCa aggressiveness prevalence of 30%. For the AA PCa patients, the high-risk group of PCa aggressiveness was the del/ins+ AA genotype of DHFR-19bp-rs4652 (70%), while the overall PCa aggressiveness prevalence was 30%.For SNP–SNP interaction analyses for AAs , there wMTHFR)-rs2236225 (MTHFD1) and rs1801131 (MTHFR)-rs7587117 (SLC4A5) for EAs and DHFR-19bp-rs4652 (LGALS3) for AAs. However, none of the individual effects of the DHFR 19-bp polymorphism and 10 target SNPs associated with PCa aggressiveness were significant. To our knowledge, the three SNP–SNP interaction pairs for PCa aggressiveness have not been reported. However, SNPs in some genes involved in these SNP pairs associated with PCa outcomes have been reported. The SNP of rs1801133 in MTHFR is related to PCa risk [MTHFR SNP (rs9651118) has been reported to be associated with PCa recurrence with and without adjusting for known risk factors [MTHFR and rs2236225 in MTHFD1 with PCa risk, localized, and advanced PCa [We identified three SNP–SNP interaction pairs significantly associated with PCa aggressiveness: rs1801133  in this study. It has been shown that the DHFR 19-bp deletion polymorphism is common with del/del genotype frequencies ranging from 10.5% to 48% in different populations [For racial differences, most of the top SNP–SNP interaction pairs associated with PCa for EAs and AAs were different , and racl survey . For DHFulations .MTHFR, MTHFD1, and DHFR), SLC4A5, and LGALS3 associated with PCa aggressiveness, although the individual effects of these SNPs were not significant. To our knowledge, this paper is the first to assess SNP–SNP interactions in folate-related pathways associated with PCa aggressiveness. Our study demonstrated that SNP–SNP interaction findings could provide better prediction than individual SNP effects in the folate-related pathways. The strengths of this study are the inclusion of two race groups (EAs and AAs) and the application of a powerful statistical approach for SNP–SNP interaction analyses. The limitation is a lack of external validation due to a relatively small sample size, so the bootstrap internal validation approach was applied to reduce false positivity. Thus, future large-scale studies are warranted to verify these findings and elucidate the biological mechanism of the identified SNP–SNP interactions. The SNP-SNP interactions discovered in this study may lead to further understanding of the mechanistic pathways regarding interactions of these genes and the discovery of future therapeutic options for PCa.In summary, we identified three novel interactions of SNPs or polymorphisms in the folate metabolism pathway, one-carbon metabolism pathway (


Viral nervous necrosis (VNN) is a major disease that affects European sea bass, and understanding the biological mechanisms that underlie VNN resistance is important for the welfare of farmed fish and sustainability of production systems. The aim of this study was to identify genomic regions and genes that are associated with VNN resistance in sea bass.ZDHHC14, which encodes a palmitoyltransferase, and IFI6/IFI27-like, which encodes an interferon-alpha induced protein. The most significant SNP in this QTL region was only 1.9 kb downstream of the coding sequence of the IFI6/IFI27-like gene. An unrelated population of four large families was used to validate the effect of the QTL. Survival rates of susceptible genotypes were 40.6% and 45.4% in populations A and B, respectively, while that of the resistant genotype was 66.2% in population B and 78% in population A.We generated a dataset of 838,451 single nucleotide polymorphisms (SNPs) identified from whole-genome sequencing (WGS) in the parental generation of two commercial populations  of European sea bass with phenotypic records for binary survival in a VNN challenge. For each population, three cohorts were submitted to a red-spotted grouper nervous necrosis virus (RGNNV) challenge by immersion and genotyped on a 57K SNP chip. After imputation of WGS SNPs from their parents, quantitative trait loci (QTL) were mapped using a Bayesian sparse linear mixed model (BSLMM). We found several QTL regions that were specific to one of the populations on different linkage groups (LG), and one 127-kb QTL region on LG12 that was shared by both populations and included the genes ZDHHC14 and IFI6/IFI27-like genes. The potential involvement of the interferon pathway, a well-known anti-viral defense mechanism in several organisms , in survival to VNN infection is of particular interest. Our results can lead to major improvements for sea bass breeding programs through marker-assisted genomic selection to obtain more resistant fish.We have identified a genomic region that carries a major QTL for resistance to VNN and includes the The online version contains supplementary material available at 10.1186/s12711-023-00805-2. Sustainable development of aquaculture depends strongly on the ability to control the epidemics that can impact farms . Viral nSeveral vaccination studies have been conducted for this disease, with very positive recent results  but thesAll infection challenges were carried out according to the European guidelines (Directive 2010–63-EU) and the corresponding French legislation. Animal experimental procedures were approved by the ethics committee on animal experimentation COMETH ANSES/ENVA/UPC No.16 and were authorized by the French Ministry of Higher Education, Research and Innovation under numbers 2017022816255366, 29/01/13-5 and 10/03/15-1.Dicentrarchus labrax) from two commercial populations were used to identify QTL for resistance to VNN. These animals were produced by artificial mating and originated from two different French hatcheries designated as pop A and pop B. These populations are independent of each other but both originated from Mediterranean and Atlantic European sea bass  of these individuals were sampled for genome sequencing. For pop B, cohorts B_1 and B_2 were related and B_3 was more distantly related. Cohort B_1 included 1083 individuals derived from a partly factorial mating of 40 sires and 14 dams, cohort B_2 included 1087 individuals derived from a partly factorial mating of 41 sires and 15 dams, and cohort B_3 included 1258 individuals derived from a partly factorial mating of 48 sires and 27 dams. For pop B, all parents of cohorts B_2 and B_3 were sampled for sequencing, while only some pairs of parents were sampled for cohort B_1 but only one parent (mostly the mothers) provided enough DNA to be sequenced for some individuals. Thus, for pop B, 159 parents were sampled for whole-genome sequencing. Full information is in Table 3. All fish used for the challenge were individually tagged with RFID glass tags and acclimatized for a three-week period before the challenge. Infection with the red-spotted grouper nervous necrosis virus (RGNNV) strain W80 was performed during 4 h in a static bath of aerated seawater containing 1 × 105 TCID50/mL of the virus. Mortality was recorded daily during the challenge period of on average 32 days. Fish that died during the challenge were categorized as susceptible and those that survived were considered resistant. To confirm the presence of the NNV virus in fish that died and eliminate the possible impact of unwanted bacterial coinfections, bacteriological and viral analyses were performed before the challenges and during peak mortality. Virologic analyses consisted of injecting a homogenized mixture of eye and brain sampled from a random dead fish on SSN1  cells [The fish from the cohorts were challenged with VNN on the SYSAAF-ANSES Fortior Genetics platform . For all challenges, fish were maintained in filtered seawater in a flow-through system at a temperature of 27 ± 2 °C. Infectious challenges were performed separately but followed the same procedure for all cohorts, i.e. in tanks of the same size (400 L) and with a density below 35 kg/ms) cells . After 5For the validation population, four full-sib families that were previously experimentally challenged with VNN, as described by Griot et al.   data, including 174 parents from pop A and 159 parents from pop B. The eight parents of the validation population were also sequenced (see details in Table http://broadinstitute.github.io/picard v.2.21.1). After mapping, SNPs and InDels were called using the DeepVariant (v.1.1.0) tool [The resulting sequencing reads were aligned to the European sea bass reference genome that consists of 24 LG (seabass_V1.0) using the Burrows-Wheeler Aligner  method with default parameters . SAMtool.0) tool  to retai.0) tool . The sec.0) tool , 31 to r–8 threshold. The output VCF files were converted to PLINK format and were coded as 0, 1, and 2, as described above for the sequencing data. Parentage assignment of the challenged fish to their sequenced parents was performed with the APIS R package [Samples from the challenged cohorts were genotyped for 56,730 SNPs using the ThermoFisher Axiom™ Sea Bass 57k SNP array DlabChip, at the Gentyane genotyping platform . In total, 3059, 3876, and 1536 individuals from pop A, pop B, and the validation population, respectively, were genotyped. Genotype calling was done using the ThermoFisher AxiomAnalysisSuite™ software. Preliminary quality controls were applied with cut-off values of 95% for SNP calling rate, 90% for sample calling rate, and the "Run PS Supplemental" option to regenerate SNP metrics to select the SNPs that were identified as polymorphic by the software. Two other filtering steps were applied to the genotypes of challenged individuals from pop A and B with the PLINK software (v.1.9) , to remo package  based onImputation of the WGS SNPs to the offspring that were genotyped with the 57k SNP array DlabChip was performed using the FImpute software (v.3) , while a2 = 0.8 within 200-kb windows, using the PLINK software (v.1.9) [To reduce the number of high-effect SNPs in the association analyses of populations A and B, a subset of the imputed WGS SNPs was created from filters on linkage disequilibrium. Two successive filters were applied to have the same set of SNPs across the populations and to avoid redundancy among SNPs. First, we retained the SNPs that were shared among pop A, pop B, and the validation populations. Second, within each population, we filtered the remaining SNPs based on linkage disequilibrium (LD) between all pairs of SNPs, with an upper limit of r (v.1.9) , 31. AndThe GWAS was conducted separately for each population by using a BSLMM that assumes that each of the SNPs has at least a relatively small effect but some SNPs may have a large effect , 31. TheThe BSLMM was implemented using the genome-wide efficient mixed model association (GEMMA) software . In GEMMThe BSLMM uses a MCMC algorithm for sampling from the posterior distribution to obtain all parameters values, including SNP effect estimates d Berger , regionsd Berger  proposedAs proposed by Kass and Raftery , the natTo determine how QTL regions affected VNN resistance, we analyzed survival of challenged bars as a function of the genotype of SNPs located in those regions. For this purpose, we standardized the survival rate in each cohort to a survival rate of 50% after a probit transformation to allow all cohorts to be compared. Then, the percent survival of each genotype for the peak SNP was recalculated for each cohort. Finally, survival rates of the studied SNPs were averaged by cohort to facilitate discussion of the results. In addition, an ANOVA was performed using R to calculate statistically significant differences in survival between Resistant (R) and Susceptible (S) genotypes within a population (by cohort) and across the three populations.Functional analysis of the identified QTL regions was performed by linking variants detected in the whole-genome sequences of the parents with the annotated reference genome assembly of the European sea bass. All annotated genes positioned within the QTL region were reported. To understand the role of the variants in the coding regions of the identified genes, all SNPs identified in the variant calling step were annotated based on the UCSC annotation  of the EThe six challenges for the six cohorts of pop A and B were conducted over approximately 32 days. Presence and absence of NNV and pathogenic bacteria were confirmed on several subsets of dead fish during the infection, validating NNV-related deaths. Survival rates ranged from 44.5 to 76.3% for the pop A cohorts and from 38.6 to 66.5% for the pop B cohorts. For the validation population, the average survival rate was 56% Table . The pea–8) filters are reported in Additional file We generated WGS at an average coverage of 19-fold for 174 sires from pop A, 159 parents (126 sires and 53 dams) from pop B, and the eight parents (4 sires and 4 dams) of the validation population. Variant calling identified around 8 million raw variants. Quality control of the WGS SNPs was first carried out by population. All sequenced individuals were retained. The number of SNPs removed based on MAF (less than 15%), SNP call rate (less than 95%), and Hardy-Weindberg , call frequency (less than 95%), and the Hardy–Weinberg equilibrium, 40,743 SNPs and 36,862 SNPs were retained for pop A and B, respectively. Thus, genotypes of 2371 pop A animals for 40,743 SNPs and genotypes of 3428 pop B animals for 36,862 SNPs were retained for further analyses  was shared between the two populations. This QTL explained 2.6% of the total genetic variance in pop A and 3.1% in pop B. Based on logBF, four shared QTL regions were identified on LG12: LG12_QTL_1 between 4,717,307 pb and 7,246,406 pb, LG12_QTL_2 between 8,671,217 pb and 8,797,942 pb, LG12_QTL_3 between 9,118,505 pb and 9,821,502 pb and LG12_QTL_4 between 12,167,000 pb and 19,133,722 pb. The 126,725-pb long LG12_QTL_2 QTL region had a major effect in both populations, with the major SNP, at 8,797,936 pb, explaining 21.8% of the total genetic variance in pop A and 20.3% in pop B. For all the other shared QTL regions on LG12, the major SNPs explained only a small proportion of the genetic variance (less than 2%).Survival rates for the genotypes at the SNP with the highest logBF value were calculated for the LG12_QTL_2 for both commercial populations and in the validation population.G corresponds to the resistant allele (R) and the alternative allele T is the susceptibility allele (S). The flanking sequences of the LG12_8797936 SNP for the reference and alternative alleles are in Additional file In Fig. IFI6/IFI27-like, which encodes an interferon alpha-induced protein, and ZDHHC14, which encodes a zinc finger palmitoyltransferase. The IFI6/IFI27-like gene is 903 pb long with four exons and the ZDHHC14 gene is 44.4 kb long with nine exons, respectively, and they are at 1.9 and 6 kb from the SNP with the highest logBF of the LG12_QTL_2.Based on the UCSC genome annotation database, two genes were located in the LG12_QTL_2 region Fig. , i.e. IFIFI6/IFI27-like gene since all SNPs are positioned in non-coding downstream sequences, in particular the LG12_8797936 SNP with the strongest effect in the GWAS. For the ZDHHC14 gene, LG12_8746942 was found to be the SNP most strongly associated with the resistance phenotype and the SnpEff approach showed that it is located within the coding sequence of the ZDHHC14 gene, but with no impact on protein structure.Analyses of the SNPs in the LG12_QTL_2 region did not identify causal variants that impact the coding region of the In the present study, our aim was to use a combination of whole-genome sequencing, 57K genotyping, and GWAS approaches to identify QTL for VNN resistance in European sea bass and to characterize the QTL using public functional annotation. Our results show that heritability estimates for VNN survival using imputed genotypes  are of the same magnitude as those obtained in previous studies on NNV-infected sea bass, i.e. 0.08 to 0.23 on the observed scale in , 23 and Previous studies on the genetic architecture of VNN resistance in European sea bass revealed several genomic regions with one or more significant SNPs. The majority of the previously reported QTL were based on a defined chromosome-wide significance threshold. Based on RAD sequencing, Palaiokostas et al.  identifiWhole-genome sequencing of the parents of 57K genotyped offspring was used to finely map SNPs and InDels to identify candidate genes for VNN resistance. Whole-genome sequence variants contain more causal mutations than the SNP genotyping arrays that are available for European sea bass, which means that our study is more likely to detect causal mutations. The relevance of using sequence data for mapping traits in farm animals was shown in the 1000 bull genomes program . We condZDHHC14 and IFI6/IFI27-like were identified as strong candidates. Neither of these genes were reported in previous association studies for survival to VNN. Interestingly, LG12_QTL_2 and these two genes are located more than 2.5 Mb away from the QTL region identified by Vela-Avitúa et al. [HSP70 gene. This may be a consequence of the much higher precision of our localization, as the LG12_QTL_2 region is only 127 kb long, while the region identified in [HSP70 gene was also as identified differentially expressed in a study that compared NNV infected and non-infected cell lines of European sea bass [Finding the causal variants that underlie a QTL is a very difficult task, and very few causal variants have been identified to date. Using sequence data, we were able to refine the QTL region associated with VNN resistance and identify the genes located in this region, although prioritization of the putative causal genes is challenging with the current state of annotation of the European sea bass genome, which counts 23,382 coding genes and 35,707 transcribed genes . In spitsea bass . This stZDHHC14 gene, which encodes a palmitoyltransferase that can catalyze the addition of palmitate onto various protein substrates. A recent study linked this reaction with inhibition of NNV in vitro and showed that inhibition of protein palmitoylation and phospholipid synthesis, which involve lipid metabolism, significantly decreased RGNNV replication [ZDHHC14 gene is of great interest and deserves to be analyzed further via functional analyses.As for the variants identified in our study, the LG12_8746942 SNP is located in the coding region of the lication , noting lication . In addilication . As thesIFI6/IFI27-like gene that encodes an interferon-inducible protein. In most cells, interferon response is a major first line of defense against viral infection. Viral infection triggers the production of interferon, which binds to ubiquitously expressed receptors on nearby cells and induces a powerful transcriptional program that comprises hundreds of interferon-stimulated genes [IFI6/IFI27-like gene, which encodes a small 130 amino acid interferon-inducible protein from the IFI6 family and has four conserved paralogues in humans , is involved in several processes of the regulation of human viral attacks in response to type I interferon (I-IFN) [IFI6/IFI27-like expression plasmid and HBV revealed significant inhibition of HBV DNA replication and gene expression [IFI6/IFI27-like and ZDHHC14 genes to evaluate their impact on immune mechanisms of European sea bass against VNN.The SNP with the strongest effect on the survival rate in the LG12_QTL_2 region is 3.7 kb downstream from the ed genes . Initialed genes . The IFI (I-IFN) , 48. Forpression . The sampression . In sevepression –55, VNN pression , 55. Baspression . Thus, bIFI6/IFI27-like and ZDHHC14 genes, the role of which should be further clarified using other functional approaches. For practical application in selective breeding, the LG12_8797936 SNP identified in this QTL and located near the IFI6/IFI27-like gene, has a high potential for successful marker-assisted selection, since we show that the susceptible and resistant genotypes had a similar effect on survival to VNN infection in independent populations. This is a major advance to improve the resistance of cultured populations of European sea bass to one of its main infectious diseases.We identified candidate genes associated with resistance to VNN in European sea bass using sequence data GWAS. Based on dense genotypic information, the BSLMM approach was performed to refine the GWAS. Among the identified putative QTL, two were shared between the two commercial populations that were studied, among which QTL LG12_QTL_2 that carries the Additional file 1: Figure S1. PCA on genotypes of commercial populations (pop A and pop B) and wild populations in the Atlantic (Atl) and Eastern Mediterranean (East-Med). Principal component analysis (PCA) using genotypic data from two commercial populations and two wild populations, one from the Atlantic strain (Atl) and the other from the West Mediterranean strain (East-Med).Additional file 2: Table S1. Number of SNPs removed after several quality filters. Details of the number of SNPs removed with quality controls applied on whole-genome sequences and 57K SNP chip data.Additional file 3: Flanking 50-bp sequences for the top associated SNPs in LG12_QTL_2. Genomic sequences flanking the IFI6/IFI27-like and ZDHHC14 SNPs computed from Dicentrarchus labrax genome (seabass_V1.0).


Bos taurus autosome (BTA) 7. The JER breed displayed the highest level of inbreeding across all inbreeding coefficients. The mean (± standard deviation) pedigree-based inbreeding coefficient (FPED) ranged from 0.051 (±0.020) for AYR to 0.062 (±0.027) for JER, whereas SNP-based inbreeding coefficients (FSNP) ranged from 0.020 (HST) to 0.190 (JER) and ROH-based inbreeding coefficients, considering all ROH segment coverage (FROH), ranged from 0.053 (AYR) to 0.085 (JER). Within-breed Spearman correlations between pedigree-based and genome-based estimates ranged from weak  to moderate (HST: 0.584 between FPED and FSNP). Correlations strengthened between FPED and FROH as the ROH length category was considered lengthened, suggesting a dependency on breed-specific pedigree depth. The genomic homozygosity-based parameters studied proved useful in investigating the current inbreeding status of reference populations genotyped to implement genomic selection in the three most prominent South African dairy cattle breeds.The erosion of genetic diversity limits long-term genetic gain and impedes the sustainability of livestock production. In the South African (SA) dairy industry, the major commercial dairy breeds have been applying estimated breeding values (EBVs) and/or have been participating in Multiple Across Country Evaluations (MACE). The transition to genomic estimated breeding values (GEBVs) in selection strategies requires monitoring of the genetic diversity and inbreeding of current genotyped animals, especially considering the comparatively small population sizes of global dairy breeds in SA. This study aimed to perform a homozygosity-based evaluation of the SA Ayrshire (AYR), Holstein (HST), and Jersey (JER) dairy cattle breeds. Three sources of information, namely 1) single nucleotide polymorphism (SNP) genotypes  2) pedigree records , and 3) identified runs of homozygosity (ROH) segments were used to quantify inbreeding related parameters. The lowest pedigree completeness was for the HST population reducing from a value of 0.990 to 0.186 for generation depths of one to six. Across all breeds, 46.7% of the detected ROH were between 4 megabase pairs (Mb) and 8 Mb in length. Two conserved homozygous haplotypes were identified in more than 70% of the JER population on  AlthougThe years 1881 and 1890 have been reported as the most probable dates for the arrival of the first Jersey and Ayrshire cattle in South Africa, respectively . The AyrOfficial animal recording for dairy cattle in South Africa dates back to 1917, with the inception of a milk recording scheme , which wThe breeding objectives for all three breeds include milk yield, milk quality, fertility, and functional traits . More reInbreeding results from consanguineous mating inevitably leading to an increased frequency of homozygosity. The phenomenon of reduced performance due to inbreeding known as inbreeding depression has been reported for a plethora of traits in dairy cows including milk production, reproduction, and fitness , thereby impacting overall herd profitability . HistoriRuns of homozygosity (ROH) segments are detected using genome-wide genotype information with the profiling of these segments providing a well-established methodology to quantify genetic autozygosity and genetic diversity . DetecteThe widespread use of certain local and international bulls may contribute to greater genome-wide and location-specific homozygosity with downstream repercussions on productivity. In the South African dairy industry, the majority of bull semen used is of foreign origin with more than 40% of Holstein semen imported from the United States of America (USA); Canadian bloodlines predominate in the Ayrshire bulls used in South Africa while most Jersey bulls used in South Africa are of USA origin. Semen from countries such as Great Britain, Denmark, France, the Netherlands, Australia, and New Zealand have also contributed to the South African dairy cattle gene pool . The ancThe objectives of the present study were to 1) classify and quantify runs of homozygosity in three South African dairy cattle populations; 2) estimate inbreeding coefficients using various sources of information, and 3) compare the inbreeding statistics generated from either recorded ancestry or genomic information.Ethical approval was granted by the University of Pretoria’s Ethics Committee for external data use (EC170627-135). Consent was provided from the respective breeders’ societies to allow access to the available pedigree and genotypic data.Pedigree data of the genotyped South African Ayrshire (AYR), Holstein (HST), and Jersey (JER) populations used in the present study were provided by SA Stud Book. The pedigree information included 7,885 AYR , 28,391 HST , and 18,755 JER  records as summarized in A total of 3,199 genotyped animals  with a sample call rate above 95% were available for this study consisting of 510 AYR, 1,360 HST, and 1,329 JER cattle. The animals included in this study originated from the national Dairy Genomic Program (DGP) with the aim of establishing reference populations for genomic selection. Animals that were included in this program represented the local populations and were selected based on EBV accuracies of at least 60%. The data structure of the genotyped populations is summarized in ® Genomic Profiler™ 150K , International Dairy and Beef (IDB) version 3 , Weatherbys Scientific VersaSNP 50K™ , and the Unistel-SA Stud Book 50K version 1  panels. The GeneSeek® Genomic Profiler™ 150K genotypes were generated through the South African DGP that was initiated in 2016 to benefit herds that participate in pedigree-based genetic evaluations and/or milk recording schemes provided by the Agricultural Research Council (ARC) or SA Stud Book. Genotype calling was done through various local and international service providers using their respective protocols, and the raw genotype files were converted into PLINK software version 1.9  array containing 53,218 SNPs. Genotypes of HST and JER animals originated from five different genotyping panels, namely, the Bovine SNP50 versions 1  and 3 , GeneSeeksion 1.9  input fiSample- and marker-based quality control edits were performed using PLINK software version 1.9  to filteGCTA version 1.24  used across all livestock species  for all genotyped animals were detected using the R package tectRUNS  by executectRUNS . The SW tectRUNS , is gene species  and has  species . Dixit eentified . Both apns and ni were the numbers of SNPs and individuals, respectively, α represented the proportion of false-positive identifications (set to 0.05) and detectRUNS (http://asia.ensembl.org/biomart/martview/244b07db6f169a19f1e0362778df6ab5). Gene ontology and pathway analyses were carried out by PANTHER version 13.1 software tool (http://pantherdb.org).For CR, the following ROH defining parameters were set: i) a minimum length of 1Mb, ii) a maximum distance (gap) between consecutive SNPs of 500kb, iii) a lower density limit of one SNP per 75kb, and iv) a maximum of two missing and no opposing (heterozygous) genotypes were allowed. The aforementioned parameters were the same for the SW approach, but, the sliding window size was set to 50 SNPs. The minimum number of SNP that constituted an ROH segment was set to 54 based on the formula implemented by tectRUNS  package PED represented a pedigree-derived estimate, 2) FSNP represented an SNP-by-SNP excess in homozygosity, and 3) FROH represented genome-wide ROH coverage. The FPED and FSNP coefficients were calculated using optiSel  calculated based on the estimated allele frequencies of the sample, and N is the total number of SNPs. Additionally, the observed as well as expected heterozygosity rates  were estimated as the total number of non-missing genotypes (NNM) minus the number of observed homozygous genotypes (HOM) divided by the total non-missing genotypes (NNM).Three methods were used to estimate the inbreeding coefficients of all genotyped individuals: 1) F optiSel  and PLIN optiSel , respectROH coefficients were based on the ROH detected with the SW approach for comparability. The FROH coefficient was estimated as  of an actual population can be defined as the size of a hypothetical ideal population resulting in the same amount of genetic diversity as is present in the real population . The Ne The mean, interquartile range (IQR), and median years of birth for the genotyped AYR population was 1974, 1956 to 1994, and 1972, respectively; 1973, 1953 to 1992, and 1969 for the genotyped HST population, respectively and 1980, 1960 to 1998, and 1982 for the JER population, respectively. The mean pedigree completeness index (PCI) of the genotyped populations was 0.976 for the AYR, 0.967 for the HST, and 0.993 for the JER populations. The average pedigree depth based on CGE was equal to 9.75 for AYR, 11.70 for HST, and 10.05 for JER.The mean six-generation deep pedigree completeness for genotyped animals born in the 10-year period between 2011 and 2021 for the HST and JER breeds, and between 2007 and 2017 for the AYR breed is summarized in −3 for AYR to 0.002 for JER  categories is depicted in LYPD8 (Gram-negative bacteria defense response) and various olfactory receptor genes, whilst the second region encompassed 82 protein-coding genes, including HSPA4 (heat-shock protein), ELANE (immune response), and LEAP2 . Manhattan plots of the incidence of each SNP within detected ROH per breed are illustrated in PPEF2 (Hsp90 protein binding), as well as the CXCL9 and CXCL10 . Three smaller ROH haplotypes, close in proximity, were identified on BTA20  in 28.31%, 28.16%, and 28.09% of the HST population, respectively. These 0.054Mb, 0.181Mb, and 0.112 Mb regions contained two, four, and three SNPs, respectively. The 0.181 Mb genomic region overlaps with the SPEF2 (sperm flagella 2 protein) protein-coding gene, whereas the 0.112 Mb overlaps with PRLR, a prolactin receptor.The proportion of times an SNP resided within a detected ROH was estimated per population. Two ROH haplotypes on BTA7 were identified in 70.96% of the JER population. The first preserved region consisted of 20 SNPs spanning 1.19 Mb and the second of 31 SNPs spanning 2.60 Mb. The first region encompasses 35 protein-coding genes, including versus genome-based estimates  is included in The variability in animal-specific inbreeding coefficients per breed for the genotyped animals is illustrated by box and whisker plots in PED for the AYR, HST, and JER genotyped populations was 0.051, 0.064, and 0.062, respectively. The highest frequency of AYR, HST, and JER animals fell within the 0.04-0.05, 0.05-0.06, and 0.07-0.08 coefficient classes, respectively. The greatest observed heterozygosity was in HST (HO = 0.356) while the lowest was in JER (HO = 0.332); hence, the FSNP-based inbreeding coefficient ranked the JER breed as the most inbred (FSNP = 0.190) followed by the AYR (FSNP = 0.088) and HST (FSNP = 0.020) breeds. For the JER breed, for example, the majority of animals  were categorized as having high FSNP values (>0.1) despite most of them having low  or moderate  FPED values (ROH was the highest for the JER breed (mean FROH=0.085), followed by the HST (mean FROH = 0.056) which was similar to the AYR (mean FROH = 0.053) breeds. The most AYR animals had FROH values in the 0.04 to 0.05 interval, whereas for HST and JER, most animals had FROH values of 0.05–0.06 and 0.06 to 0.07, respectively.The mean FD values . The ranROH coefficients, irrespective of what length category was used to calculate the ROH, were highest for the JER population; the largest FROH statistic was obtained for FROH calculated for ROH that were larger than  4 Mb but smaller than 8 Mb , whereas HST had higher mean values for FROH calculated on the basis of longer ROH. For both the AYR and HST breeds, the All F(ρ) between FPED and genome-based F-statistics are given in SNP-FROH correlations ranged from ρ = 0.857 for AYR to ρ = 0.896 for JER. The FPED coefficient was weak to moderately correlated with FSNP and FROH within all breeds; the pairwise correlations between FPED and each of the genome-based coefficients were similar . The FPED coefficient was most strongly related to ROH statistics, in both the AYR and HST breeds ; within the JER breed, however, the FPED coefficient was most strongly related to The Spearman correlations The pedigree-based Ne estimates increased for all three dairy cattle populations in this study  from 85 To ensure sustainable breeding programs within the South African dairy industry, and to optimize the adoption of genome-based selection strategies, it is important to characterize and routinely monitor the genetic variability and inbreeding levels of the prominent dairy breeds . In the Results from the present study indicate growing pedigree completeness over the past 10 generations with greater overall completeness in the AYR and JER populations. Traditionally, pedigree data has been used in the estimation of population diversity, but limitations on the quality and pedigree depth present limitations  aligns with previous studies of Canadian Holstein  were predominantly of international descent . The greThe profiling of genome-wide ROH has become an increasingly popular parameter for explaining genetic differences between populations; many ROH-based analyses have been conducted on global dairy breeds e.g., . CompariDespite the higher ROH counts observed for the JER population, the percentage genome coverage by autozygotic segments was the highest for the HST population (10.02%), followed by the AYR (8.80%) and JER (8.78%) populations. The percentage coverage was similar to the 10% reported by The percentage occurrence of SNPs residing within identified ROH was analyzed to identify overlapping genomic regions of autozygosity among animals within breeds. These overlapping regions could be the result of positive selection and could be indicative of adaptation to specific environmental conditions . The mosLYPD8 gene, which has been suggested to play a role in intestinal immunity in mice  70 gene and is well known for its integral role in cellular stress response to heat (PRLR) gene, highlights the higher selective pressure for milk productivity  further support the integral role that these breeds may play in the future sustainability of the South African dairy industry.Amongst the 35 protein-coding genes located in the first preserved region identified in the present study, is the  in mice  and more in mice . The HSP to heat . Becauseuctivity  in HST. PED values were generally lower than inbreeding estimates calculated from the genomic data. Perfect concordance was not expected between the FPED values and those estimated using genomic information for several reasons including: 1) pedigree information will not always be complete all the way to the founder population, 2) the PED are based on expected relationships among individuals and cannot consider the variability that exists around this expectation owing to Mendelian sampling during gametogenesis  or identical by state (IBS)  and SNP genotyping panel density. The suitability of each of the genomic measures is, therefore, dependent on the data available. The FROH coefficient is, however, more informative because of the additional information that the ROH length, for example, provides about the inbreeding history.As would be expected for dairy cattle populations, given factors such as the increased utilization of AI and other reproductive technologies compared to, for example, beef cattle, all inbreeding coefficients estimated in the present study suggested inbreeding is occurring. The Fogenesis . The diste (IBS)  whereas PED (ranging from 0.051 for AYR to 0.064 for HST), as well as FSNP (ranging from 0.02 for HST to 0.19 for JER) and FROH (ranging from 0.053 for AYR to 0.085 for JER), were similar in trend  to those observed by PED means ranging from 0.06 for AYR to 0.08 for HST and JER, and FROH means ranging from 0.11 for AYR to 0.17 for JER. The effect of incomplete pedigree on the estimates of inbreeding is well documented . PED and genome-based coefficients  in a small Mexican Holstein population genotyped for 100,806 SNPs. Nonetheless, the pairwise correlations between FPED and FROH improved as ROH length increased. Irrespective of breed, the correlation between FPED and FROH<4MB was the weakest of all correlations between FPED and length-specific FROH coefficients. This observation agrees with previous suggestions that correlations between FPED and FROH strengthen when the shortest ROH fragments  are not considered in the calculation  compared to AYR . Considering the generally low within-breed participation in pedigree recording for South African dairy breeds  and Ne, more recent , estimates of genome-based Ne are possible with a greater density of SNPs and, therefore, is better at capturing population-wide LD (Factors that influence Ne estimates include the constant change in the real population size, unequal sex ratios, and the variance in the number of offspring per parent . A reducnologies . The pednologies  but it mnologies  animals,nologies . Estimat-wide LD . Genomic-wide LD . Genetic-wide LD  and willThe South African AYR has always been a small population serving a niche market, while the South African HST and JER breeds are mainly responsible for the fresh milk supply. It will be important for these breeds to grow and maintain their reference populations and ensure that international bull families and genotypes are available for genetic evaluations and continuous monitoring of diversity and inbreeding. This study confirmed the usefulness of SNP genotypes for accurately assessing autozygosity and inbreeding levels, and the impact of these on the management of genetic resources. The analyzed results support the influence of globalized dairy germplasm and their observed influences on the genetic diversity within the JER, HST, and AYR reference populations in South Africa thus far. Since the erosion of genetic diversity limits long-term genetic gain and impedes resilience and sustainability amidst future challenges, these results may assist in strategies to improve and update reference populations for genomic selection.