Shorea macrophylla is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to S. macrophylla.S. macrophylla in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for diameter at breast height and total height in the 7th year from planting (D7 and H7).To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of p-value was used, However, D7 showed the contrary tendency, which might have originated from the difference in genetic architecture between primary and secondary growth of the species. This study suggests that GS with GWAS-based SNP selection can be used in breeding for non-cultivated tree species to improve initial growth and reduce genotyping costs for next-generation seedlings.These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 6 methods and their derivatives with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability for D7 and H7 with the sets of selected SNPs and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high predictive abilities for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. Predictive abilities were higher in H7 when smaller number of SNP subsets selected by GWAS  Many studies have compared the performance  of different methods for predicting GEBVs, which is important because the genetic gains from GS depend on the accuracy of the GEBVs . HoweverMachine learning using deep learning (DL) methods has recently been remarkably successful in diverse applications involving the processing and analysis of complex datasets including image recognition and natural language processing. Many traits important in forestry, such as growth, are considered to be complex traits because they are regulated by groups of many polygenes. DL appears to be well-suited to the analysis of such complex traits and is therefore increasingly being used for this purpose . Among DP, small N problem” because the number of explanatory variables (genetic markers in this case) greatly exceeds the number of observations . To overcome these problems, some studies have focused on subsets of genetic markers selected using ranking values. The predictive accuracy of such models can rival or exceed that of conventional models using every available marker  identify the methods that yield the highest GEBVs predictability, (2) evaluate the effectiveness of DLs and GBDTs in genomic prediction, and (3) determine the impact of genetic marker selection on predictive accuracy.This is the second empirical study on GS for Dipterocarpaceae and builds on an earlier work  showing 22.1S. macrophylla at the PT. Sari Bumi Kusuma forest concession, Central Kalimantan, Indonesia was targeted for genomic prediction modeling. The progeny trial was established in 2006 using open-pollinated seeds from 94 mother trees in natural populations in the forest concession. The progeny was cultivated in a nursery for ten months and then planted with a 6 x 3 meter spacing. Five trees derived from each mother tree were planted using a Randomized Complete Block Design (RCBD) in a single block and eight block replicates were prepared, giving 3760 progeny trees in total. The phenotypic variance of the progeny population was deemed adequate because significant between-family differences in diameter at breast height (DBH) and total height (HT) were observed 4 years after planting , as described by MseI and MluCI are 4-base cutters, while PstI and BglII are 6-base cutters. The resulting DNA fragments were then ligated with adapter cassettes at restriction enzyme cleavage sites. Since the 4-base cutters had greater numbers of cleavage sites, the corresponding adapters were designed to form Y-shaped structures that suppressed fragment amplification in cases where MseI or MluCI sites were on both sides. Indexed PCR fragments were amplified using KAPA HiFi DNA Polymerase  and the Access Array Barcode Library for Illumina® Seqencers―384, Single Direction  following the manufacturer’s instructions. We then selected 430-470 bp fragments using a 2% agarose gel with the BluePippin system . These size-selected PCR fragments were adjusted to a concentration of 10 nM by assuming an average fragment size of 450 bp. Finally, each library solution was sequenced using 2 lanes on the Illumina Hiseq X platform  to obtain 151 bp paired-end sequences.Leaf samples (n=361) were individually pulverized in liquid nitrogen, then total genomic DNA was extracted from 60 mg of the resulting powder using a modified Cetyltrimethylammonium bromide (CTAB) method . The cruMseI/PstI library. Version 2.9.4 of the same software was used to perform the same tasks for the MluCI/BglII library. During the read mapping step, all reads were mapped to the draft genome of S. leprosula , which was named “TotalRawSNPs.vcf”, was then filtered using VCFtools version 0.1.16 . First, SNPs within 1000 bp of one-another were thinned using VCFtools (–thin 1000) to prevent detection of biased structure due to neighboring SNPs. The vcf data were converted to matrices with the following coding: -1 for homozygous reference alleles, 0 for heterozygous alleles, and 1 for homozygous alternative alleles: 1. PCA was performed in R using the “prcomp” function. We also analyzed the linkage disequilibrium of each SNP using TASSEL 5.2 with a window size of 870 to complete all combinations within each scaffold. To obtain a trendline for linkage disequilibrium decay within the inter-SNP distance, the 2.4The observed values of the phenotypic data in the progeny trial were corrected by performing a spatial structure analysis to reduce the impact of micro-environment effects, which could otherwise introduce noise that would reduce the accuracy of the genomic prediction models . This an2.5We analyzed genotype and phenotype data to check heritability and SNP effects on each phenotype. Samples were removed if at least one of them was NA within the same year in these analyses. To identify well-predicted phenotypes, narrow-sense genomic heritability was calculated using the equation: on 1.1.0 .2.6A genome-wide association study (GWAS) was conducted using DBH and HT in the 7th year (D7 and H7) as the traits of interest. These traits were selected (1) because they have relatively high heritability, (2) to avoid maternal effects expressed strongly after germination and during initial growth, (3) because the observation values of HT in the 11th year were discrete, and (4) since measurements using measurement rods are more accurate than those obtained using the Vertex system.P-values for each SNP. Second, LOD scores are calculated for strongly trait-associated SNPs; these scores indicate the probability of association between the SNP and quantitative trait loci (QTL) of the trait. Population and kinship structures were flattened using principal components 1 to 4 and a covariance matrix (K) inferred by the genetic markers as default setting of the “mrMLM” function  to reduce the incidence of false positive SNPs.The aim of the GWAS was to identify SNPs significantly associated with the traits of interest and to verify that both traits are complex, therefore, all individuals were used in the first GWAS. This was done in R using the “mrMLM” function of the mrMLM package version 5.0.1  with FASfunction . P-value2.7Genomic prediction models were generated using each of 12 methods described in the section 2.8. All models were built using 10 training/validation sets and 10 model construct replicates were generated to assess split effects in a half-sib population. After the split of training/validation sets, we confirmed that all SNP markers maintain their polymorphism in each of training populations Table S4P-values for each training population using FASTmrEMMA. Genomic prediction models were then built for each SNP subset in the same manner as the models for all SNPs. It should be noted that models using all SNPs were not generated using CNN1D and CNN2D because the structures of these methods are unsuitable for models with many SNPs.The second GWAS was performed on the training population to attempt to reduce the number of markers used in the genome prediction model by selecting markers with relatively strong additive genetic effects  explained 4.58% and 2.60% of the genetic variance, respectively. Additionally, clusters of samples were observed in the left (PC1) and central (PC2) regions of the space Figure 13.2A spatial structure analysis using a two-dimensional spline model was performed to detect and remove spatial bias in both focal traits (DBH and HT). Trees with lower values of both traits were concentrated on the left side of the progeny trial, which was captured by the analysis and the estimated spatial effect values were also smaller in this area. On the other hand, trees located around the center of the trial had higher phenotypic and spatial effect values. On the right side of the trial, there were some trees whose phenotypic values were lower, accentuating the relatively high phenotypic values of both traits in trees at the center of the trial area. Since the planting locations of progenies from each mother tree were arranged according to a RCBD, the spatial effects observed in the trial can be largely attributed to environmental heterogeneity. We therefore adjusted the raw phenotypic values to minimize the impact of this heterogeneity. The raw phenotypes, spatial effects, and adjusted phenotypes for D7 and H7 were visualized using heatmaps Figure 33.3Narrow-sense genomic heritability for DBH and HT from year 1 to year 12.5 was calculated based on variance components obtained from Bayesian ridge regression for sample sets without missing data Table 1.3.4P-values and LOD scores of SNPs for D7 and H7 were calculated using FASTmrEMMA for the sample sets considered in the genomic heritability analysis. The results were visualized using a Q-Q plot and a Manhattan plot showing all P-values and LOD scores above 3, which was taken as the significance threshold . The median of predictive abilities within model building replicates and sample split replicates were positive and majority of the predictive abilities was significantly deviated from zero except CNN1D of D7 , GBLUP of H7 , LGB of D7 , RKHS of D7 (96 SNPs), XGB of D7 , XGB of H7   Table S7Table 2Median predictive ability values of genomic prediction models when used with 48, 96, and 192 or with the full set of 18037 SNPs.44.1S. macrophylla genome size was estimated to be 363.816 Mbp using the formula of C-value of S. macrophylla is 0.372  > 192 > 96 > 48 SNP for D7 and 48 > 96 > 192 > 18037  for H7, and its difference were statistically significant in a few modeling methods Table 2. general . The difOther genomic prediction studies on disease resistance in aquaculture species have similarly found that SNP marker selection improves the accuracy of genomic prediction models . The preThe differences in predictive accuracy between semiparametric and nonparametric methods were greater than those between parametric methods Table 2.5S. macrophylla: DBH (D7) and HT (H7) in the seventh year from transplantation at the PT. Sari Bumi Kusuma forest concession in central Kalimantan, Indonesia. Unlike species with established lineages and pedigrees such as crops and livestock, the studied progeny trial retains high genetic diversity due to the large effective population size of mother trees and the reliance on open-pollinated mating to regenerate the reproductive source of the trial. Despite this high genetic diversity, the measured values correlated positively with the outputs of genomic prediction models for D7 and H7. When GWAS data were used to select SNP subsets, different sets of SNPs were selected for each split of the training/test population and the highest predictive accuracies were achieved with the full set of SNPs for D7 and 48 selected SNPs for H7, these opposite tendencies might be originated from difference of genetic architecture between primary and secondary growth of the species. These results show that the SNP subset with the highest predictive ability in H7 can be used for genotyping next-generation populations in breeding programs in order to reduce costs while maximizing genetic gains. Although it is necessary to further increase the marker density and the power of LD detection by increasing the number of individuals, the genomic prediction models and subsequent selection at seedling stage using the models show potential to accelerate breeding cycles for non-cultivated tree species.Genomic prediction models were constructed for two growth traits in the tropical timber tree species https://www.ncbi.nlm.nih.gov/bioproject/PRJNA988453/; https://doi.org/10.5281/zenodo.8051012; https://doi.org/10.5061/dryad.kkwh70s8d.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NT, KU, and YT conceived the ideas of the project. MN and SP established progeny trials and scientific data from the progeny trials were obtained by MN, SP, W, and SI, and NT, MN, W, SI, S, and SP carried out field work. NT, MN, W, SI, S, and YT contributed to searching for funds. HA, NT and KU performed the experiment and data analysis and HA and NT wrote the first draft of the manuscript and contributed to the writing and revision of the manuscript. All authors have approved the manuscript for publication. All authors contributed to the article.


Acrocephalus paludicola is one of the rarest European passerines and is characterised by promiscuity, lack of pair-bonds and female-only parental care. This makes the species an important model for studying the function of avian courtship song. The song of the Aquatic Warbler consists of whistle and rattle phrases produced as discontinuous A-, B- and C-songs, which are built by a single rattle, a rattle and a whistle, and more than two phrases of both kinds, respectively. The A- and B-songs are thought to be aggressive signals in male-male interactions, while C-songs are thought to be important for female choice. Here, we analysed recordings of 40 individually marked males, and determined the phrase repertoire. The enumerated repertoire  ranged from 16 to 158 (mean 99), however, it did not capture the complete phrase repertoires. We then used models from species diversity ecology to estimate the actual phrase repertoire size, which ranged between 18 and 300 phrases (mean 155). The estimated repertoire was predicted by the number of C-songs. The rattle repertoire was larger than the whistle repertoire, and both positively correlated with the number of C-songs. Our study indicates that male Aquatic Warblers have highly complex phrase repertoires that vary widely in size. Their courtship song is flexible and efficient, enabling relative song complexity to be demonstrated in a short sample, thus facilitating both female attraction through the quick presentation of large phrase repertoires and rival deterrence through the production of many short and simple A- and B-songs.The Aquatic Warbler  These roles are typically performed by a repertoire of different vocal units (syllables or song types). The evolution of repertoires is therefore thought to be driven by intra- and inter-sexual selection. Repertoire size might serve as an honest signal of male quality and in several songbirds has been demonstrated to be an efficient female attractant. In polygynous species, larger repertoires can enable faster mating or the attraction of more females for copulation2.Bird song is a signal that has evolved into an extremely rich set of structurally and functionally diversified forms. In temperate species, song is primarily a male trait and has two well recognised functions, territorial defence and mate attraction, which are often fulfilled together, albeit with varying degrees of emphasisAlauda arvensis, males produce long sequences of syllables with short time gaps between them3. The discontinuous song, e.g. in the Yellowhammer Emberiza citrinella, song is organised in strophes that typically last a few seconds each, and the gaps between consecutive strophes are much longer than strophe duration4. Discontinuous singers usually have smaller repertoires than continuous ones, even if we consider syllables instead of strophes.Male bird song is often divided into two wide classes, continuous and discontinuous song. In continuous song, e.g. in the Skylark Acrocephalus as models1. However, not all Acrocephalus warblers sing continuously, and some species are much more flexible in the organisation of song output. An example is the Aquatic Warbler Acrocephalus paludicola, one of the rarest European passerines. Due to limited and inaccessible breeding sites, its song is much less known compared to other acrocephalids. It has some common features (e.g. types of sounds produced) with other well-studied Acrocephalus warblers, but it also has some striking differences5. Males produce relatively short, repetitive songs, which can be classified in terms of length and complexity7. The shortest ones, the A-songs, consist of a single rattle. B-songs are initiated by a rattle and followed by a slower phrase, containing a more complex whistle syllable. Finally, C-songs contain more than two phrases in a sequence, consisting of both rattles and whistles  best explains its variation.In the present study, we tried to fill the most important gaps in the knowledge about the Aquatic Warbler song. First, we analysed long recordings of individually marked males to find out how different song characteristics are related to each other. Second, we estimated, for the first time, the song phrase repertoire of the Aquatic Warbler with a new tool, the Koe software, which allows for computer-aided classification of syllables when repertoires are large. We tested whether the estimated phrase repertoire size is predicted by the number of different whistles and (or) different rattles, as these two song components are included in the A-, B- and C-songs in varying proportions, and, therefore, may have different functionsIn total, recordings of 40 focal males were analysed. We aimed at recording 10 min per male, but this was not always possible. We collected at least 10 min of recording for 29 males. On average . The enumerated phrase repertoire size correlated more strongly with the number of phrases recorded  than with the duration of a recording . Interestingly, the enumerated song phrase repertoire was not significantly correlated with the song duty cycle, understood as the percentage of time spent singing , while it correlated positively with the total time spent singing . The enumerated repertoires of whistles  than those of rattles (53.1 ± 25.76), and were significantly correlated .We found a significant correlation between the duration of a recording and the number of phrases recorded  or with the phrase type switch rate . Therefore, the song style described by these two variables , double-phrase B-songs 37%) or three-and-more-phrase C-songs (21%). The C-songs contained from 3 to 11 phrases , but not with the number of A-songs  or the number of B-songs recorded . The rattle repertoire was not significantly correlated with the number of A-songs  and B songs , while it was significantly correlated with the number of C-songs . Likewise, whistle repertoires were correlated significantly only with the number of C-songs , but not with B-  or A-songs .These statistics indicate that the phrase repertoire found in the recordings depends not only on the duration of a recording but also on how many phrases are sung and on the style of singing . The enumerated phrase repertoire size was positively and significantly (Bonferroni-adjusted significance level) correlated with the number of C-songs recorded .p < 0.0001). This discrepancy was still present when we compared the first 100 and 200 phrases for the males with ≥ 200 phrases recorded: 47.5 ± 14.67 vs. 82.6 ± 17.77 phrase types . More details on how the sample size relates to the enumerated phrase repertoire size are presented in Fig. The basic data on the enumerated phrase repertoire size presented above and the relationship between the enumerated phrase repertoire size and recording parameters as well as the style of singing strongly suggested that the real phrase repertoire of a male could be much larger than the phrase repertoire counted within recordings. The enumerated phrase type repertoire size based on small subsets of consecutive songs yielded underestimated values Fig. . For exaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaTo summarise, the true phrase repertoires of Aquatic Warbler males are likely larger than the enumerated ones, even when the latter are based on a long recording of around 10 min. The phrase repertoire size is formed by variation in both whistles and rattles, but the repertoire of rattles appears to be larger than that of whistles. Finally, only singing C-songs is related to the presentation of the real phrase repertoire size Fig. . Therefo12 revealed that the Aquatic Warbler males had relatively large enumerated repertoires, reaching on average 99 different phrases when recorded for ≥ 10 min. However, plotting cumulative curves of distinct phrases indicated that even our longest recordings did not reveal the entire phrase repertoires of all the males. The estimation of the phrase repertoire sizes with a species richness paradigm showed an average value around 150–155 (depending on the model used for estimation). The phrase repertoire sizes also showed high between-individual variation with an estimated range between 18 and 300 different phrases. It was the diversity of rattles, rather than the diversity of whistles, that contributed more to phrase repertoire variation, despite their being positively correlated. Since we did not have very long recordings, we cannot be completely certain how well the estimators worked. Nevertheless, even based on the enumerated repertoire sizes, it is clear that the repertoires are large and that males vary in song complexity to a great extent. Our results also indicate that the estimated repertoire sizes were larger the enumerated ones, although we should make a reservation that our samples (i.e. numbers of phrases recorded) might have been too small to capture the complete repertoires13. These findings shed new light on the singing abilities of the species. Although male Aquatic Warblers are not continuous singers, their song variability is impressive, and the phrase repertoire is larger by an order of magnitude than the previously suggested values14. The most surprising result for us was, however, not the size of the phrase repertoires, but the fact that both whistles and rattles were highly variable. The earliest descriptions of Aquatic Warbler song did not explore this aspect at length. However, it appears that in the past, it was whistles that were treated as diverse song units responsible for building repertoires, while rattles were considered to be largely invariable . Our results show that this is certainly not the case. We suspect that one of the reasons that variation in whistles was over-emphasized in earlier work is that differences in whistles are easier to discern than differences in rattles, both in terms of discriminating sounds by ear as well as visually on sonograms. Rattles consist of very short units produced at a distinctly faster rate than whistles. Moreover, they often differ from each other with respect to hard to detect, shifted peaks in maximum amplitude in the frequency domain. Some of rattles differ also in the temporal pattern, which is associated with the rhythm of pulses  there is a correlation between faster song rate and presentation of a larger repertoire17. However, this was not the case in the Aquatic Warblers. Males with larger repertoires did not sing faster and did not switch faster between different phrase categories or types. We found that it is the higher level of song organisation, i.e. producing A-, B- and C-songs that is important. Correlations between the number of these songs and both the enumerated and the estimated phrase repertoire size unequivocally revealed that only C-songs play a crucial role in presenting the repertoire size. This result adds support to earlier findings suggesting that C-songs are important in mate attraction7.Another question that we wanted to address in this study was whether and how the phrase repertoire size is related to the organisation of song output. In other words, do the males with larger (and likely more attractive) repertoires sing differently, or is repertoire variation merely limited to the number of different song units within the vocal output? The style of song output is often variable and in theory this variability could be linked to male quality. In fact, it is explained by two characteristics of the song output: syntax and rate. Even with a small repertoire, a singer may sing in a diverse way, switching between different units as often as possible, e.g. AB AB AB AB AB… This singing pattern is not equal to producing AB CD EF GH IJ…, where each song unit produced is different, although the switching pattern between the different units is exactly the same. In addition, both sequences could be—for example—produced during 10, 100 or 1000 s, i.e. sung with a different rate. In some acrocephalids, e.g. in the continuously singing Marsh Warbler  it was demonstrated that the song repertoire size has a significant effect on the pairing date18. In the polygynous Great Reed Warbler , males with larger repertoires attracted more females for copulation and sired more offspring than social mates with smaller repertoires2. This latter study also demonstrated that females engaging in extra-pair fertilisations benefit from having genetically better offspring. Therefore, the repertoire size and its individual variability in the Aquatic Warbler indicate that in this species song phrase repertoire could have evolved as an elaborated sexually selected trait, especially in the light of the promiscuous mating system.Here, we complement this knowledge by showing that C-songs are highly diverse in both content and duration, and that this leads to clear differences between individuals Fig. . Such va17. In both species males apparently differ in the repertoire size, which can be an honest signal of male quality2. The song communication system of the Aquatic Warbler should therefore be considered as very flexible and efficient. Within a short time, males are able to produce many simple A- and B-songs, with a high rate of production being more effective at deterring other males, and to smoothly switch to longer and complicated C songs, which are addressed to females searching for high-quality partners.Our analyses with the repertoire size estimators also show that reliable evaluation of the repertoire size depends more on the number of recorded song units than on the duration of recording. If a male sings continuously, a relatively short singing period could be used for evaluating the repertoire size. In other words, a few long and complicated C-songs are good predictors of a large repertoire. A similar result was previously found for the Marsh Warbler, in which two-minute-long recordings were used to estimate the repertoire size with capture-recapture and species-richness models, and the repertoire size correlated with male nesting success19. If we consider that the classic repertoire relies on physical entities remembered in the bird’s brain and presented (sung) in a particular context, the alternative mechanism could be flexible modification of such units to increase variation of the final output. It is a hypothesis, but the comparison of hundreds of whistles and rattles clearly showed that different whistles usually had different frequency track and duration, while different rattles quite often resembled more graded variation on the same theme. These variations do not appear when a male sings several A-songs in a sequence, but they are present if longer C-songs are sung.Finally, we want to point out one more observation. The comparison of thousands of phrases indicated that the Aquatic Warbler males were more flexible in producing different rattle than different whistle phrase types. As mentioned earlier, different rattle phrases often seemed to be created by slight modification of parameters such as the note rate or dominant frequency, which changed in a continuous rather than discrete way; moreover, these parameters often changed within a phrase  of the global population. Three study areas were used , with two 10–20-ha study plots in each study area.Males were detected by ear and caught with mist-nets. Each bird was marked with unique metal and colour rings. All the males were reproductively active i.e., in their second calendar year or older, as identified by plumage. The males were also blood-sampled for the purposes of another study. The recordings were made between 1 day and about 1 year after blood-sampling.20.All procedures were conducted in accordance with Polish and EU law and did not require approval by the Local Ethical Commission because no experimental procedures were performed in the study . The fieldwork procedures were conducted under permits from the Regional Directorate for Environmental Protection in Białystok (no. WPN.6401.244.2016.MN) and the Polish Ministry of Environment (no. DOP-WPN.286.38.2016.AN). This study has been designed and performed in accordance with the ARRIVE guidelinesSongs of the individually marked Aquatic Warbler males were recorded between 16:15 and 21:20 h, from 28 May to 29 July, 2018. The geographic position of each bird was stored using a GPS receiver. The code of the colour rings was identified with an 80-mm-lens and 20–60 magnification scope. The songs were recorded with a Marantz PMD 661 digital recorder and a Telinga Pro 7 microphone mounted in a Telinga Universal parabola, as 48 kHz/16 bit PCM .wav files.In total, 45 males were song-recorded. Aquatic Warbler males have a narrow window (around dusk) during which songs can be recorded effectively. Combined with remote and difficult habitat, the time necessary for identifying a male with a scope and the large home ranges of males, this makes data collection slow. We suppose that females might "sample" males in a similar manner, in the sense that the cost of sampling is time. If it is possible to get reliable information about the repertoire size in a shorter time, there is no point in spending more time on it.7 and the categorisation was then saved in a comma-separated (.csv) file. We also recorded the start and end time of a phrase in relation to the beginning of recording, as well as the minimal and maximal frequency. Each phrase was assigned to one of the three categories: whistle, rattle or rattle-whistle, based on visual and auditory inspection , songs of each individual were categorised into phrasesThe main difference between the variability of whistles and rattles was that whistles of a certain type differed distinctly from other types in the tonal course of sound, which was easy to distinguish visually on sonograms  with phrase selections from Raven Pro were imported into Koe. Each of the imported phrase selection was assigned a unique ID and the phrase category  was included as the ‘Family’ column .Classification of all the three phrase categories into discrete types was done with the Koe softwareIn the next step, we extracted unit (i.e. phrase) features in order to obtain data matrices for further comparison. Based on the visual inspection of phrases during manual selection, we decided to extract only such features which enabled assigning phrases automatically to the same category if they sounded consistently the same. Therefore, we chose all the features related to overall frequency and within-phrase changes of frequency and/or amplitude. We omitted all the features that may reflect the quality of recording (i.e. related to energy or power measurements), as all the recordings were obtained from a short distance and were of comparable and very good quality. We also omitted overall phrase duration features, as phrases often contained different numbers of syllables and differed by duration; while still consisting of the same shape syllables and sounding similarly, they were just shorter or longer. The final choice of features extracted was made after several tests in which we initially used all features as well as several combinations of different features. The full list of Koe features extracted and their descriptive statistics used for building the data matrices is presented in Table N, where N is the total number of units in an analysed database of sound units. The Koe similarity works as a rank scale. For example, if one compares 100 units , the unit with 0 similarity is the least similar to the unit with 99 similarity, while 5 and 6, or 83 and 84 are the most similar to each other, as they are neighbours on the scale. On the other hand, as with all rank scales, there is no information about similarity or dissimilarity distance between such values, hence 5 could be much more similar to 6 than 6–7, or when we compare similar 100 phrases, they will still be assigned to 0–99 similarity categories. However, this tool enables quick comparison of many units, as seeing and listening to neighbouring sounds in the unit table is easy. While assigning each phrase to the category we first visually checked the two-dimensional ordination, and with the use of the available tools  we added different labels to each phrase or group of phrases which separated as different on the ordination map  and the PCA  pre-processing Koe method. The combination of these two methods allows for retaining the overall variance of the dataset (characteristics of song phrases) with nonlinear dimensionality reduction allowing for efficient 2D data visualisation. Before we selected specific parameters for the final analysis, we ran several test by varying the available parameters to find out which set of parameters provided the best results, i.e. clustered together those phrases which sounded and looked similar on spectrograms. Finally, for all the recordings we constructed two-dimensional ordination with t-SNE perplexity parameter of 40 and the number of iterations equal to 10,000. In the next step, in Koe we calculated similarity of phrases for each male separately. The similarity is a value from 0 to map Fig. . After tmap Fig. .To summarise, each phrase within a male repertoire was classified to one of the three categories  and to a discrete types, which received unique number. Phrases of the same type were placed closer on the ordination map based on 23 feature measurements Table , t_SNE o7, we also characterised the song output of each male by showing how he uses the song phrase to compose songs into three syntactical categories: simple A-song (rattle), slightly more complicated B-songs (rattle + whistle) and the most complicated and longest C-songs.Recordings of each male were characterised by duration(s) and the total number of phrases that were assigned to the three general categories  and to several unique types (forming the phrase repertoire) within these categories Table . BecauseFinally, we characterised the singing effort and style of the males Table . We calc17. Preliminary analyses of correlations between the enumerated phrase repertoire size for the whole recording (full sample) and the phrase repertoire size based on subsamples (from 10 to 200 phrases) indicated that the enumerated phrase repertoire size does not reflect the real phrase repertoire size that a male may reveal during longer singing. Therefore, to estimate the phrase repertoire size, we used measures derived from species diversity ecology21. The most promising methods in the context of repertoire size estimation are those which treat syllables or other repertoire units (phrases in our case) as ‘species’ and use abundance or incidence-frequency data for measuring such ‘species’ richness. We used the SPADE (Species Prediction and Diversity Estimation) software22. We prepared data according to the SPADE manual by sorting the number of occurrences of each phrase type in descending order. We choose the default ‘Cut off point = 10’ for ACE and ICE estimators and set the number of bootstraps to 1000. The SPADE automatically calculates nine different estimators/models and outputs an estimate, its standard error and 95% confidence intervals. Among SPADE estimators are equivalents to those used by Garamszegi et al.10 for testing estimation of song complexity on 18 bird species, including M(t), M(h) and M(th) models, which were shown to be best for species with large repertoires23.We found that Aquatic Warbler males produced many different types of phrases with variation suggesting that the repertoire size could be very large. In long recordings with more than 100 or even 200 phrases recorded we still were finding new phrase types after initial 100 or 200 phrases. This was expected, as bird species with song organisation close to the continuous song often have large repertoires and deliver syllables with heterogeneous probabilityp < 0.001). Chao1 returns an estimate of species (here phrase) richness based on a vector or matrix of abundance data, while ACE is based on the concept of sample coverage, which was originally developed for cryptographic analyses by Alan Turing23. The Chao1 approach uses the number of singletons and doubletons  to estimate the number of undetected units. The ACE estimator separates phrases into abundant and rare groups and uses only the rare group to estimate the number of undetected species22. In our dataset, Chao1 had a slight tendency to overestimate the repertoire size, while ACE underestimated it. However, a comparison of the Chao1 estimates and ACE estimates with a paired t-test showed no significant difference . For illustrative purposes we use the ACE estimate throughout.We thoroughly inspected the enumerated repertoire sizes and estimated repertoire sizes calculated on different subsets of original data. Based on this and on theoretical assumptions of the different estimating methods, we finally chose the estimates which worked best in terms of giving the closest estimate based on the first 100 phrases of the enumerated repertoire for the whole male dataset with over 200 phrases recorded (N = 23). We found that the best estimators, which yielded very similar values, were Chao1 and ACE. However, all nine estimators gave highly correlated results  tests to seek correlation between song characteristics (Spearman’s and Pearson’s correlation) or test for differences between compared groups (t-test or Mann–Whitney test). We used the Bonferroni correction to reduce the chances of obtaining false-positive results when multiple tests were performed. In order to evaluate the relationship of the singing characteristics with the phrase repertoire size we used linear regression models with the song phrase repertoire as the explained variable. We performed the analysis in the R environment v. 4.0.1 (R Core Team 2021).Supplementary Information.


R2 > 0.8) with surrounding SNPs/insertions and deletions (Indels), indicating that SNP-based association testing and genomic prediction are blind to a nonnegligible portion of genetic variation. We showed that both SVs and STRs were more than 2-fold enriched among expression and splicing QTL (e/sQTL) relative to SNPs/Indels and were often associated with differential expression and splicing of multiple genes. Deletions and duplications had larger impacts on splicing and expression than any other type of SV. Exonic duplications predominantly increased gene expression either through alternative splicing or other mechanisms, whereas expression- and splicing-associated STRs primarily resided in intronic regions and exhibited bimodal effects on the molecular phenotypes investigated. Most e/sQTL resided within 100 kb of the affected genes or splicing junctions. We pinpoint candidate causal STRs and SVs associated with the expression of SLC13A4 and TTC7B and alternative splicing of a lncRNA and CAPP1. We provide a catalog of STRs and SVs for taurine cattle and show that these variants contribute substantially to gene expression and splicing variation.Structural variants (SVs) and short tandem repeats (STRs) are significant sources of genetic variation. However, the impacts of these variants on gene regulation have not been investigated in cattle. Here, we genotyped and characterized 19,408 SVs and 374,821 STRs in 183 bovine genomes and investigated their impact on molecular phenotypes derived from testis transcriptomes. We found that 71% STRs were multiallelic. The vast majority (95%) of STRs and SVs were in intergenic and intronic regions. Only 37% SVs and 40% STRs were in high linkage disequilibrium (LD)  and short tandem repeats (STRs) are significant sources of genetic variation; however, genotyping them is challenging. Bhati  Genome-wide association studies (GWAS) and expression and splicing QTL (e/sQTL) mapping establish links between genotype and (molecular) phenotype (7 indicates that a trinucleotide (3 bp) AGC motif is repeated 7 times, yielding a total length of 21 bp. Polymorphic STRs can vary in length due to a contraction or expansion of the repeat motif. These variants can arise due to recombination errors, insertions of mobile genetic elements, slippage during DNA replication, or imperfect DNA repair (SVs can be classified into deletions (DEL), duplications (DUP), insertions, inversions (INV), translocations, segmental DUP, mobile element insertions, or complex rearrangements, which may be a combination of multiple types . STRs spHistorically, SVs and STRs had been genotyped using microarray- and PCR-based methods. The resulting genotypes were used to validate parentage, construct genetic linkage maps, assess genetic diversity, and map QTL  software (We used paired-end (2 × 150 bp) whole-genome sequencing data of 183 individual cattle (mean coverage 12×) from the Brown Swiss (BSW), Original Braunvieh (OB), Grauvieh (GV), HOL, and Fleckvieh (FV) breeds and their crosses. Reference-guided alignment and variant discovery were performed as described in software  with optsoftware . Subsequsoftware  and retahttps://github.com/HipSTR-Tool/HipSTR-references/tree/master/mouse) was applied to compile a set of reference STRs from the soft-masked ARS-UCD1.2 reference genome [available from Ensembl (v. 104)]. Briefly, we ran the Tandem Repeat Finder (TRF) software for each chromosome with settings 2,7,7,80,10,5,500 -h -d -l 6 -ngs (A previously proposed HipSTR (l 6 -ngs . We retaThe STRs were genotyped in the cohort of 183 cattle using the default mode of the HipSTR software tool (We applied the smoove workflow to discover and genotype SVs from short sequencing reads . These rBoth STRs and SVs were annotated according to the Ensembl annotation (v. 104) of the bovine genome in a hierarchical manner using BEDTOOLS intersect  (exon > R2) with the dosage of each surrounding SNP or Indel (maf > 0.05) where dosage is 0 for the 0/0, 1 for the 0/1, and 2 for the 1/1 genotype  transformed expression values. One sample was excluded as it appeared as an outlier in the PCA. Finally, TPM values were quantile normalized and inverse normal transformed across samples per gene using the R package RNOmni (Gene-level expression [in transcript per million (TPM)] was quantified with the QTLtools quan function with default settings  .cis-eQTL mapping between expressed genes and all variants in cis (±1 Mb) with QTLtools using the cis permutation mode  and accounting for covariates , RNA integrity number (RIN), and age). To account for multiple testing per molecular phenotype (Genes), we used the Storey and Tibshirani false discovery rate procedure that was implemented with the R/qvalue package on beta-approximated P-values (eGene) as described by P-values for all genes. Finally, we performed conditional analyses using QTLtools with threshold P-values to identify all significant independent eVariants per eGene that were used for all subsequent comparison.We used the difference in length between reference and alternate (computed from the sum of the GB format tag in the output VCF file from HipSTR) alleles as dosage for the STRs for eQTL mapping . We employed grouped permutations (--grp_best option) to collectively calculate an empirical P-value across all introns within an intron cluster. Normalized intron excision ratios  were used as molecular phenotypes. We considered sQTL to be an sVariant per sIntron cluster pair. Significant intron clusters were annotated (candidate intron boundaries per cluster) based on the ARS-UCD1.2 gene annotation and strand match (Ensembl release 104). Intron cluster coordinates that mapped to multiple genes were considered as unannotated although the number of such intron clusters was less than 100 in each sQTL analysis.From each sQTL/eQTL analysis, we annotated each e/sVariant type with their respective annotation category as described above. For all enrichment analyses, we used Fisher's exact test (2 sided). All plots were created in R (v 3.6.3) with ggplot2. Gene structure was plotted with the ggtranscript R package .We used paired-end whole-genome sequencing data of 183 cattle from 5 breeds to genotype SVs, STRs, SNPs, and Indels. The average sequencing coverage was 12.8-fold, and it ranged from 5.0- to 30.4-fold.Bos taurus taurus reference sequence (ARS-UCD1.2) spanning 24.9 Mb autosomal sequence (1.0%) . The number of STRs on each chromosome was correlated (r = 0.99) with chromosome length . Mono- and hexanucleotide loci were the most and least frequent types of STRs, respectively, amounting to 35.9 and 9.8% of all identified STRs . Repeats of A, T, and AT were most prevalent among mono- and dinucleotide STRs. GC-rich repeats (e.g. AGC) were most frequent among trinucleotide STRs . The overall length of the STRs varied from 11 to 10,427 bp with a median size of 18 bp. The vast majority of the STRs  were shorter than 100 bp, facilitating short-read–based genotyping.We identified 1,202,536 STRs with a motif size between 1 and 6 bp in the current Supplementary Table 3 in File 2). We identified between 73,791 and 189,658  STRs in each cattle genome, and the number of STRs detected correlated (r = 0.94) with sequencing depth . As expected, given their prevalence in the bovine reference genome, mono- (52.9%) and hexanucleotide STRs (2.5%) were respectively the most and least frequent types of polymorphic STRs  were multiallelic and had between 1 and 41 alternate alleles, but more than 20 alternate alleles were rarely seen . Dinucleotide STRs had the highest number of alternative alleles among all STRs . Heterozygosity and allelic diversity were higher for dinucleotide loci than any other type of STRs . Forward and reverse primer sequences of the 16 STRs were subjected to a BLASTn search against the ARS-UCD1.2 reference sequence to identify their positions, motifs, and flanking sequences. Twelve STRs from our STR reference panel had matching coordinates and motif sequences. Among them, 10 STRs were genotyped in 183 samples with allele counts ranging from a minimum of 5 to a maximum of 11, whereas the remaining 2 STRs were excluded during quality filtering.We investigated the overlap between the STRs detected in our study and 16 STRs that had been frequently used for parentage testing [including those that had been approved by the International Society for Animal Genetics (ISAG)]  = 1.26). STRs were depleted in exonic regions  and promoter regions  . The proportion of STRs that overlapped exons was highest for tri- (1.9%) and hexanucleotide (1.4%) motifs .Functional annotation showed an enrichment of STRs in intergenic regions  motifs , which wSupplementary Fig. 2a in File 1 and Table 5 in File 2). The number of polymorphic SVs identified per chromosome was correlated (r = 0.94) with chromosome length . We found between 4,259 and 6,835 SVs in each cattle genome , and this number was correlated with sequencing coverage (r = 0.60) . A total of 6,728 (34.6%) SVs had minor allele frequency below 0.05  DUP were smaller than 1 kb, whereas 3,465 (23%) DEL were larger than 1 kb .We applied the smoove workflow to discover and genotype 61,806 SVs in the 183 cattle genomes, of which we retained 19,408 polymorphic autosomal loci  after stringent filtering for downstream analyses  were only present in the heterozygous state, and most of these  had minor allele frequency less than 0.05 , 5,383 (27.7%), 823 (4.2%), and 802 (4.1%) SVs overlapped intergenic, intronic, promoter, and exonic regions, respectively . SVs parhan 0.05 . Among tWe also discovered and genotyped SNPs and Indels in the 183 animals using the GATK haplotype caller. We considered 12,222,397 SNPs and 1,317,363 Indels with minor allele frequency greater than 5% for the downstream analyses, of which 55,010 SNPs and 89,673 Indels overlapped with STRs, and 387,593 SNPs and 47,129 Indels overlapped with SVs. The large overlap between SNP, SV, and STR variants is possibly due to nested variation but can also indicate that short sequencing reads are unable to resolve complex DNA variation.Supplementary Fig. 12 in File 1). Due to variation in sample size, coverage, and insert size between breeds, we did not investigate within- and across-breed diversity in SVs and STRs. Next, we investigated if SVs and STRs can be tagged by SNPs/Indels. We calculated the LD between SNPs/Indels within 100 kb of each SV and STR. We observed that 40.1% of STRs  were in high LD (R2 > 0.8) with at least 1 SNP or Indel while this fraction ranged from 3.1 to 52.2% for the different SV types . BND and DUP were poorly tagged, possibly indicating low genotyping accuracy for these loci. The LD between SNPs/Indels and STRs was consistent across the different STR types .We calculated the principal components from genomic relationship matrices built with SNP, STR, and SV genotypes of the 183 cattle. All three analyses correctly separated the individuals by breed , and all (ALL) variants to assess the contribution of different types of DNA variation to gene expression.The impact of polymorphic SVs, STRs, SNPs, and Indels on gene expression was investigated in a subset of 75 sequenced bulls that also had testis RNA-sequencing data. We performed P = 1.4 × 10−8) and STRs  were enriched among the eVariants indicating that these variant types contribute disproportionally to gene expression variation. The SV–STR eQTL mapping revealed 5,641 eGenes associated with 5,971 unique eVariants . Most eGenes  were shared between the separate eQTL analyses, but 1,420 eGenes were shared only between SNPs and Indels and ALL suggesting that many eGenes are only associated with SNPs and Indels. A larger proportion of eSV (24.1% of eSV) and eSTR (8.0% of eSTR) than eSNV/eIndel (2.9% of eSNP/Indel) were associated with the expression of multiple eGenes . Both SVs . The eVariants were more strongly enriched for SVs than STRs , but most eVariants were STRs . Among the different SV types, DEL were enriched , and BND were depleted  among the eVariants compared to STRs (Supplementary Table 8 in File 2). The proportion of eVariants associated with multiple eGenes was higher for eDUP (21.4%) than eSTRs (9.6%). Overall, eDUP affected on average 1.35 eGenes (eSV 1.12 eGenes) whereas eSTR and eSNP and eIndel affected 1.11 and 1.01 eGenes, respectively. The maximum number of eGenes per eVariant was larger for STR (n = 6) than any other variant type . Overall, 19.9% eVariants  overlapped with their eGenes; 64 (0.9%), 166 (2.5%), and 964 (14.7%) overlapped with exons, promoters, and introns, respectively. Most eQTLs were in introns (48.1%) or intergenic regions (39.4%). eDUP were enriched in exons of their eGenes  while eDEL were enriched in the exons of other genes . In contrast, eSTRs were depleted in the exons of their eGenes  and other genes  (Supplementary Table 10 in File 2). These results suggest that eDUP impact gene expression by increasing the copy of their eGenes, which agrees with previous research (We examined the distance between eVariants and eGenes [5′-UTR or transcription start site (TSS)] and found that most eSVs and eSTRs were located within 250 kb of eGenes  but eBNDresearch . The higresearch . Such STR2 > 0.8) with surrounding SNP/Indel. In contrast, eDUP and eBND were poorly tagged by SNP/Indel.Exonic eDUP predominantly increased gene expression, while exonic eDEL mostly decreased gene expression. All other eVariant types exhibited a bimodal effect size distribution. We then explored the LD between eSTRs and eSVs and nearby SNP/Indel. More than three-quarters (78.4%) of the eDEL and two-thirds of the eSTR (65.9%) were in high LD . We observed a similar distribution of eGenes across all eVariant types except for eINV.We found that 92.2% of the eGenes were protein-coding genes, 4.5% were long noncoding RNA (lncRNA), 0.98% were pseudogenes, and 1.8% were other genes (i.e. genes that are not classified in the above 3 categories)  in the seventh intron of TTC7B encoding tetratricopeptide repeat domain 7B (TTC7B mRNA (mean TPM 4.9 ± 1.3) increased with an expansion of the GT repeat motif (P = 3.4 × 10−12). This STR was the top eVariant in both the ALL and SV–STR eQTL analyses  and SLC13A4   . This DEn = 135,342, 56.0%) of the introns overlapped with 14,583 genes, but the annotation-free splicing event identification by the LeafCutter software also detected many introns that did not overlap with annotated features. The intron excision ratios were normalized for each intron and subsequently used as input phenotypes for cis-sQTL mapping. We mapped cis-sQTL with an approach that was similar to the eQTL mapping, i.e. we separately considered SNPs and Indels, SVs, STRs, SV–STR, and ALL.We calculated intron excision ratios of 241,427 introns assigned to 76,083 intron clusters. More than half  overlapped with 4,890 sGenes whereas the remaining did not overlap with annotated features. Both SVs  and STRs  were enriched among the sVariants when compared to SNPs and Indels. The SV–STR analysis revealed 9,065 sIntron clusters associated with 8,857 unique sVariants  and 11,588 (15.2%) intron clusters (sIntron cluster). The 12,835 sVariants included 25 SVs, 712 STRs, 1,593 Indels, and 10,505 SNPs, and 286 of the sVariants were associated with more than 1 intron cluster. More than half of the sIntron clusters  and sGenes  overlapped between the SNPs and Indels, STR, SV–STR, and ALL sQTL analyses suggesting that distinct variant types in LD tag the same splicing event . A total of 3,126 (24.8%) sIntron clusters and 1,126 (21.3%) sGenes were shared only between SNPs and Indels and ALL suggesting that a substantial fraction of sGenes is only associated with SNPs and Indels.We then assessed the overlap of sGenes/sIntron clusters between all sQTL analyses. Approximately half of the sIntron clusters (Supplementary Table 11 in File 2). We observed that DEL were more likely to be sVariants than STRs  (Supplementary Table 12 in File 2). Conversely, BND  were less likely to be sVariants compared to STRs. We further examined how many intron clusters are affected by an sVariant. A similar proportion of sDEL (12.2%) and sSTRs (11.2%) were associated with multiple sIntron clusters whereas this fraction was considerably lower or negligible for all other types of sVariants (Supplementary Table 13 in File 2).The impact of STRs and SVs on alternative splicing was assessed based on the results from the SV–STR sQTL analysis (Variants . BetweenSupplementary Table 14 in File 2). On the other hand, sSTRs were depleted in exons of other genes, but they were enriched in introns of other genes (Supplementary Table 14 in File 2). We observed a high proportion of trinucleotide sSTRs among those that overlapped exons. These trinucleotide sSTRs were GC rich (R2 > 0.8) with surrounding (±50 kb) SNP/Indel, but sDUP (31.5%) and sBND (39.5%) were less frequently tagged (Most of the sQTL overlapped with either introns (49.7%) or intergenic regions (41.0%) but only few with promoter (6.9%) and exons (2.3%). Interestingly, sDEL were enriched in exons and depleted in introns of other genes, whereas sDUP showed enrichment in exons of sGenes . This comparison revealed that 1,988 genes and 505 QTL overlapped between both analyses. Out of the 505 shared QTLs, 479 were due to STR, while 23 were due to DEL . The eQTL that were also sQTL mainly regulated expression due to alterations in gene transcript level abundance, and these changes were mainly modulated by STR and DEL.Finally, we compared genes and molecular QTL (eQTL and sQTL as gene–variant pair) from both the eQTL and sQTL SV–STR analyses  upstream of the lncRNA ENSBTAG00000054182 (P = 4.5 × 10−25) in both SV–STR and ALL sQTL analyses. This splicing junction extends from upstream the lncRNA to the first intron of the lncRNA (ENSBTAG00000054182 (mean TPM 5.1 ± 1.5) decreased with the insertion of an additional repeat unit  but not in ALL eQTL analysis where a SNP  in LD (R2 = 0.93) was the top eVariant.Among the sQTL, we identified a candidate causal sSTR  with reduced excision ratios of a splicing junction  overlapping CAAP1 (CAAP1 (mean TPM 26.4 ± 2.8) (CAAP1 in the ALL eQTL and SV–STR eQTL (P = 5.1 × 10−12).A candidate sSV is a 729-bp DEL on chromosome 8 , which resides in the promoter region of ibitor 1 . The DELng CAAP1  and expr4 ± 2.8) . This sDWe generated a catalog of bovine polymorphic STRs, which contain motifs that vary in size, but some may also contain variation between the repeat motifs. A large number of cattle from different breeds enabled us to genotype 6-fold more STRs compared to a previous study  that considered only 5 HOL cattle genomes (R2 > 0.8). The LD between SNPs and other types of SVs such as BND, DUP, and INV is even lower, which could be due to incorrect genotyping, alignment error, or their occurrence in complex regions such as segmental DUP. Thus, the direct genotyping of these variants is required to enable powerful association studies.We observed more than twice the number of DEL compared to other SV types likely because they are easier to identify from short-read sequencing data  and DUP (Our study examines impacts of polymorphic SVs and STRs on gene expression and splicing variation in bovine testis tissue. Further research is needed to extend this analysis to additional tissues as gene regulation is predominantly tissue specific (ENSBTAG00000054182, which produces a transcript that is not included in the current Ensembl annotation. This further emphasizes the need for an improved bovine annotation, particularly with respect to noncoding elements of the genome such as lncRNAs. Although the association of expression and splicing variation with STRs and SVs in e/sQTL studies does not necessarily provide the underpinning molecular mechanism of action, these variants contribute significantly to complex trait variation (Our analysis showed that most e/sSTRs and e/sSVs were in intronic regions rather than intergenic regions, which contrasts with their overall distribution along the genome. This pattern agrees with the position of human e/sSTRs and e/sSVs (


Mytilus are an abundant component of the benthic community, found in the high latitude habitats. These foundation species are relevant to the aquaculture industry, with over 2 million tonnes produced globally each year. Mussels withstand a wide range of environmental conditions and species from the Mytilus edulis complex readily hybridize in regions where their distributions overlap. Significant effort has been made to investigate the consequences of environmental stress on mussel physiology, reproductive isolation, and local adaptation. Yet our understanding on the genomic mechanisms underlying such processes remains limited. In this study, we developed a multi species medium‐density 60 K SNP‐array including four species of the Mytilus genus. SNPs included in the platform were called from 138 mussels from 23 globally distributed mussel populations, sequenced using a whole‐genome low coverage approach. The array contains polymorphic SNPs which capture the genetic diversity present in mussel populations thriving across a gradient of environmental conditions (~59 K SNPs) and a set of published and validated SNPs informative for species identification and for diagnosis of transmissible cancer (610 SNPs). The array will allow the consistent genotyping of individuals, facilitating the investigation of ecological and evolutionary processes in these taxa. The applications of this array extend to shellfish aquaculture, contributing to the optimization of this industry via genomic selection of blue mussels, parentage assignment, inbreeding assessment and traceability. Further applications such as genome wide association studies (GWAS) for key production traits and those related to environmental resilience are especially relevant to safeguard aquaculture production under climate change.Blue mussels from the genus  They are also the species that, wherever their geographic range overlaps, readily hybridize. This includes regions occurring in the south west of the United Kingdom  are the most common form of genetic variation and, therefore, the marker of choice for GS. Other forms of structural genetic variation, including large insertions and deletions, are known to be prevalent in blue mussels  and European flat oysters (Ostrea edulis)    , together with the southern hemisphere, Mytilus chilensis, given the high economic relevance of these species to the mussel industry, as well as the ecological significance of this group given their propensity to hybridize. A subset of this database comprising 60 K SNPs was then used to develop a medium density SNP‐array on the Affymetrix ThermoFisher platform. Polymorphic markers were discovered using low‐coverage whole‐genome sequencing (lcWGS) data of 23 globally distributed blue‐mussel populations, including multiple pure and hybrid genotypes. This tool will facilitate the investigation of population genetic processes such as local adaptation and reproductive isolation in this species complex, and increase the potential of selective breeding in mussel aquaculture.In this study, we generate a SNP database for the 22.1Mytilus spp populations were sampled from across their global distribution in 2018  and their hybrids, together with the southern M. chilensis. DNA was extracted from ethanol (96%) preserved adductor muscle tissue using the E.Z.N.A.® mollusc DNA kit (Omega Bio‐Tek). Extraction followed the manufacturers protocol except the following adjustment: 30 mg of tissue was first homogenized  for 1 min (30 + 30 s) at 6 m s−1 in 350 mL of ML1 buffer, and subsequently digested for 1–4 h at 50°C  with 25 μL of proteinase K, prior to extraction. DNA was assessed using a NanoDrop One Spectrophotometer (A260/280 and 260/230 ratios) and a Qubit BR assay. Whole‐genome sequencing (WGS) library preparation and sequencing of six individuals per population (n = 138) was performed by the University of Exeter Sequencing Service. Libraries were prepared with the NEBNext Ultra II FS DNA Sample Preparation Kit (NEB) and Illumina TruSeq adapters following the manufacturer's guidelines. Library quality was initially checked by Tapestation D1000 before samples were pooled, the accuracy of pooling was checked on an Illumina Miseq, before sequencing (150 paired‐end) on an Illumina Novaseq 6000  to 4‐fold coverage, calculated using as a reference the M. galloprovincialis genome, which has a size of 1.7G , using BWA mem v0.7.17 . Variants were called using Freebayes v1.3.1  (Plink v1.9) and conducted admixture analysis (admixture v1.3)  error), we ran admixture testing K values between 2 and 10. Data missingness was estimated using the vcfR package in R v4.0.2  and, we excluded those which were present in <50% of individuals within each of the 23 populations.2.4The set of SNPs with putative monomorphic flanking probes (71‐meter bp) was submitted to Affymetrix ThermoFisher for in silico evaluation. During the quality control (QC) process, each submitted SNP received a design score  for each 35 bp probe flanking the variant. Based on p‐convert value, probes were classified as ‘Recommended’, ‘Neutral’, ‘Not recommended’, or ‘Not possible’. For subsequent analysis, only variants that scored as ‘Recommended’ for both flanking probes were included.M. edulis, M. galloprovincialis, M. trossulus and M. chilensis) by removing markers with a MAF <0.02 within species. Samples were visually assigned to each species group, by analysing sample clusters in the PCA  to segregate alleles from a single locus.M. galloprovinciallis (run‐GALLO), M. edulis (run‐EDU); M. trossulus (run‐TROS), and M. chilensis (run‐CHIL). These were performed to understand whether a higher proportion of SNPs were called when grouping all species together rather than when analysing each individual species independently. For each of the runs, we analyzed the final number of SNPs called with a marker CR equal or above 90% to 95%. Finally, we accounted for polymorphism among markers within and between species.The standard protocol for marker quality check (QC) using the AxaS software includes selecting thresholds for the following filters: marker call rate (CR), average sample CR and DishQC (DQC). DQC is a quality control metric that measures the amount of overlap between two homozygous peaks created by a subset of non‐polymorphic probes. In this study, we did not identify a subset of non‐polymorphic probes shared by all the genotyped subspecies, most likely due to not identifying polymorphisms present in the flaking regions of the markers during initial filtering, rendering DQC inappropriate as a sample QC metric. For this reason, we used marker CR as a direct sample QC for the array validation, and five different runs were performed in the software using the following approach: firstly, combining all discovery population samples (run‐ALL) and subsequently using samples classified as 2.5.2M. galloprovincialis, M. edulis, M. trossulus and M. chilensis). For the second dataset, we extracted genotype information on the same set of markers for the four species groups from the lcWGS data. Markers and their genotypes were extracted from this dataset using VCFtools v0.1.15.To assess the array performance, we compared genotype information within the four species generated by two different methods. The first dataset, obtained from the Axiom Analysis Software, consisted of genotype information generated by the 60 K Blue mussel SNP‐array obtained by filtering markers with a CR of ≥95%, in the four individual species groups (We explored marker frequency and data missingness in both datasets using Plink (v1.9) , suggesting the presence of hybrids in the sampled populations. The percentage of missing data across this reduced 4367 SNP set was 33% ± 5.The structure of the 23 s Figure . The fins Figure  with theK (built‐in the software), the most likely number of identified clusters in our dataset was K = 4, corresponding to the four Mytilus species we expected to uncover in the lcWGS dataset . As we have not included any M. trossulus samples originating from ‘pure’ populations, as previously described in the literature  inference of each individual are presented in Figure M. chilensis, the dark blue cluster M. galloprovincialis, the red cluster M. edulis, and the light blue cluster M. trossulus  which was assigned M. galloprovincialis. Samples from the north east Pacific were assigned either to M. galloprovincialis or M. trossulus, with some hybridization in one of the populations between these two species. Hybrids of all 3 species of the Mytilus species complex  are present in north Atlantic populations. Baltic populations sampled in this study were assigned to be admixed between the M. trossulus and M. edulis clusters, in a pattern common to introgressed populations.Clustering roughly recapitulated predicted species distribution with the yellow cluster representing s Figure . Populat3.2Mytilus galloprovincialis reference genome (genome accession JAKGDF000000000), attainment of bi‐allelic SNPs and post‐alignment QC filters led to the discovery of ~63 million putative polymorphisms. Variants were called from ~26 K contigs larger than >10,000 bp and with <20× coverage across aligned individuals. Biallelic SNPs with a depth coverage of <4 and >10 were discarded, resulting in a dataset of ~31 Million SNPs. Following additional filtering criteria of excluding SNPs with MAF <0.01, those present in less than 50% of genotypes within each population, and those with polymorphic alleles in the 35 bp flanking regions, 1,018,259 SNPs (as 71‐mer nucleotide sequences) were assessed through the Affymetrix ThermoFisher in silico probe scoring. Markers classified as ‘Recommended’ on both flanking probes by ThermoFisher probe QC  were used in subsequent analysis. From this set, SNPs with a MAF <0.02 were filtered from each of the four species groups. The final 60,142 SNP‐array design  and (iii) 691 SNPs from the 810 previously identified informative SNPs detailed in Table The alignment of the QC filtered reads against the 3.33.3.1M. edulis, 22,165 for M. chilenesis, 20,504 for M. galloprovincialis and 20,149 for M. trossulus.In this study, SNPs were retained for genotyping based on marker CR and not sample DQC. In all marker CR filtering scenarios, the number of usable SNPs generated was higher when analysing individual species groups than when analysing all discovery population samples in combination Table . We set Mytilus spp with a CR of ≥95% resulting from the analysis of individual species , M. galloprovincialis (11.9%) and M. trossulus (10.6%).The majority of markers passing a CR threshold ≥95% were polymorphic within species , M. edulis (64.45% ± 0.55), M. trossulus (77.24% ± 0.6) and M. chilensis (66% ± 0.56) in comparison to the array genotyping dataset: M. galloprovincialis (1% ± 0.5), M. edulis (2% ± 0.1), M. trossulus (0 ± 0) and M. chilensis (2% ± 0.1), meaning that across the SNPs commonly called using both technologies, a far greater percentage was successfully called using the array.Individual missing genotypes per individual species in the lcWGS dataset was generally high in the four species group: M. galloprovincialis, an average of 71.02% ± 4.07 of the genotypes were called equivalently using the two technologies, followed by M. chilensis, for which an average of 57.5% ± 1.59 of genotypes were consistently called in both genotyping platforms, M. edulis (50.1% ± 3.6) and M. trossulus (37.58% ± 3).For 4Mytilus genus, a multi‐species medium density 60 K SNP‐array. SNPs were selected from 138 individual mussels, originating from 23 wild, globally‐distributed populations. These successfully called markers were either shared by three species , two species  or all four species (3268 SNPs), or unique to each of the individual groups: M. galloprovincialis (1343 SNPs), M. edulis (1384 SNPs), M. trossulus (1360 SNPs), and M. chilenses (1361 SNPs). In addition, 691 SNPs representing markers generated by previous studies were included in the genotyping platform  than when genotyping all samples combined (run‐ALL). Clusters generated by the Axiom Analysis Software classify the genotypes as either homozygous for one of the two alleles in the probe, or heterozygous. As a consequence of the lcWGS approach chosen to sequence individuals in this study, hemizygous loci, which are missing one of the alleles at a specific position of a marker included on the array, would be incorrectly called by the platform as a missing genotype. Whilst this is a limitation of the genotyping platform, the above mentioned genomic variations are commonly expected to occur in the mussel genome . The proportion of unique polymorphic probes per species was considerable . Polymorphism within each of the mussel species in the array was higher in comparison to other multi‐species arrays, such as the ~49.9 K European Pine tree multispecies array, in which approximately one quarter of the converted SNPs was polymorphic in each species in the array  the higher number of M. galloprovincialis individual in the founding and validation populations and (ii) the alignment to a Mediterranean M. galloprovincialis genome assembly, may also be contributing to a biased comparison of genotypes in the array. Besides, the high level of missing data across the lcWGS data suggests that this sequencing method was not the optimal approach for SNP selection for this species complex. Especially when taking into consideration the high hemizygous fraction of the mussel genome, the low coverage approach most likely contributed to incorrectly call individuals with a missing locus as a homozygous with a missing loci. Furthermore, these hemizygous regions are highly associated with the dispensable portion of the mussel genome, which, in turn, suffer massive presence and absence variation among individuals. Using a non‐annotated genome assembled at a contig level did not allow the identification of these specific regions, or the selection of SNPs located in core regions. In future, when working with these highly polymorphic species, a different approach, either pool sequencing larger numbers of individuals from each population  in Europe have a similar admixture pattern between M. galloprovincialis and M. edulis  in the contact zone show a mixing of introgressed M. edulis and hybrid genotypes, which is in agreement with previous literature , contributing to the understanding of genetic architecture of traits of interest, and leading to the optimization of blue mussel aquaculture via genomic selection. Equally, this tool can be used to deepen our understanding of population genetic processes in these taxa. Shedding light on such relevant phenomena can contribute to the development of conservation guidelines and effective management strategies of wild mussel populations as well as those exploited for aquaculture purposes.We have developed the first medium density multi species blue mussel SNP panel, subsequently validating its performance on 127 individuals from 23 globally‐distributed populations. The blue mussel array includes variants present in each of the four main species: The authors declare no conflict of interest for this article.Appendix S1.Click here for additional data file.


We demonstrated in two case studies that genotype imputation using this resource can dramatically improve the resolution of genetic mapping. A public web server has been developed to allow the pig genetics community to fully utilize this resource. We expect this resource to facilitate genetic mapping and accelerate genetic improvement in pigs.Genetic mapping to identify genes and alleles associated with or causing economically important quantitative trait variation in livestock animals such as pigs is a major goal in animal genetic improvement. Despite recent advances in high-throughput genotyping technologies, the resolution of genetic mapping in pigs remains poor due in part to the low density of genotyped variant sites. In this study, we overcame this limitation by developing a reference haplotype panel for pigs based on 2259 whole genome-sequenced animals representing 44 pig breeds. We evaluated software combinations and breed composition to optimize the imputation procedure and achieved an average concordance rate in excess of 96%, a non-reference concordance rate of 88%, and an  The SWine IMputation (SWIM) haplotype reference panel and web server offer a vast public resource to improve sequence-level imputation and genetic discoveries in pigs. Sus scrofa) is an important livestock species and a model organism for biomedical research1. Historically, domestication and intense artificial selection have created many pig breeds that are genetically and phenotypically distinct from each other and from their wild relatives4. More recently, high-throughput DNA sequencing and genotyping technologies5 have facilitated the genetic improvement of pigs. For example, hundreds of genome-wide association and quantitative trait locus (QTL) mapping studies have identified numerous genomic regions associated with various production, physiological, and behavioral phenotypes6. These studies are important for understanding the genetic and biological basis of economically and biomedically important traits such as growth7, fertility8, and disease resistance9.The domestic pig  genotyping arrays. One proven, cost-effective approach to overcome the limitation in resolution is through genotype imputation, leveraging linkage disequilibrium to infer genotypes at unobserved polymorphic lociIn this study, we produced whole-genome sequence data from 1530 newly sequenced pigs and combined them with 729 additional animals from public databases to call variants and develop by far the largest and most diverse reference panel of haplotypes in pigs to date. This substantial increase in the number of available genomes allowed us to impute SNP array genotypes to whole genome sequences rapidly and accurately. We evaluated the accuracy of imputation and demonstrated the utility of this haplotype reference panel in genome-wide association mapping. We introduce a new public web server (swimgeno.org) where users may submit array genotypes and retrieve imputed whole-genome sequence-level genotypes. This resource will greatly improve access to high-accuracy genotype imputation, facilitating potentially nucleotide resolution genetic mapping in pigs.n = 1530) and publicly available data (Supplementary Data n = 651), Yorkshire (n = 543), and Duroc (n = 485), three major commercial breeds. The uniquely aligned sequence depth was approximately 12.86 X averaged across all animals  between variants in this population was extensive but differed by breed Fig. . LD in wr2) between imputed and observed genotypes. We focused on Landrace as the target set because it has the largest number of animals in the dataset. We held out 100 Landrace pigs sequenced at high coverage (>15X) and compared observed genotypes with imputed genotypes starting from sequencing-based genotypes at sites on a 50 K SNP array (GeneSeek GGP). Regardless of breed composition in the haplotype reference panel of fixed size, SHAPEIT4/IMPUTE5 outperformed Beagle5.2/Beagle5.2 and Eagle2.4/Minimac4 in all three metrics  segregating at a minor allele frequency (MAF) > 0.005 to construct the haplotype reference panel. To investigate factors that influence imputation accuracy, we considered different combinations of commonly used phasing and imputation software, including SHAPEIT4/IMPUTE5, Beagle5.2/Beagle5.2, and Eagle2.4/Minimac4. We defined imputation accuracy using three metrics, the overall concordance rate between imputed and observed genotypes, non-reference concordance rate summarizing accuracy for non-reference genotypes only, and squared correlation  as the haplotype reference panel. While the overall concordance rate was uniformly high (>94.24%), imputation using the SWIM panel developed in the present study was consistently higher than PHARP within each breed  resource using the multi-breed reference panel with an imputation server for pigs (PHARP) that utilized 1006 animals publicly available in the SRAted Fig. . When eveed Fig. . The impncy Fig. . The difr2  for two important growth traits in pigs, using both SNP arrays and imputed genotypes. The two traits, backfat thickness and body length, were chosen because putative causal genes and mutations have been previously well characterized. Our objective was to see if imputation-based GWAS was able to find previously validated functional genes and variants.IGF220, MC4R21, and LEPR22, have been consistently associated with BF variation in pigs. In particular, a missense mutation in the MC4R gene (chr1:160773437:G>A) has been suggested as the causative mutation21 and extensively replicated in multiple genetic backgrounds23. Furthermore, mutations in MC4R are strongly associated with early onset obesity in humans24, and its role in the regulation of energy homeostasis is well established25. Importantly, the putative causal mutation in MC4R has been included in one of the commercially available SNP genotyping arrays, the Geneseek GGP Porcine 50K SNP Chip . However, the same SNP is not present in the more widely used Illumina PorcineSNP60 chip. To see if genotype imputation was able to correctly impute the genotypes of this SNP, we excluded the MC4R SNP and imputed whole-genome genotypes from a population of 3769 Duroc pigs genotyped using the GGP Porcine 50K SNP arrays. Remarkably, the concordance rate and r2 between the imputed and array MC4R SNP genotypes were 99.71% and 0.9916, respectively. We performed GWAS using array and imputed genotypes; both showed a major peak on chromosome 1  explained 2.85% of the total phenotypic variance  with the top hit, including the MC4R SNP  is one of the most important economic traits in pigs and has been intensively interrogated for its genetic basis. Genomic heritabilities estimated using either array SNPs or imputed SNPs were similar and indicated a moderately heritable trait Fig. . Allelesnce Fig. . Under tSNP Fig. . The higBE1 Fig. . Howeverh2 ~ 0.32) and imputed (h2 ~ 0.34) genotypes . Remarkably, this variant explained 13.65% of the total phenotypic variance, and the homozygous C/C animals were, on average, 4.01 cm longer than the T/T homozygotes  in an SNP chip-based GWAS was about 184 kb away from the lead SNP and explained a substantially smaller variance (8.22% versus 13.65%).We next considered body length. We imputed genotypes from an Affymetrix 55K SNP chip (Wens55K) to a whole genome sequence using our imputation platform and performed GWAS in a population of 1694 Yorkshire boars Fig. . The trapes Fig. . Using Gpes Fig. , we fountes Fig. . BMP2 hahttps://www.swimgeno.org and https://swim.scau.pigselection.com/swim), on which users can upload SNP chip genotypes and retrieve imputed genotypes. The user interface is extremely simple, which only requires users to upload the genotypes in gzipped ped/map format and leave their email addresses. Unlike other servers, such as PHARP, allele matching and flipping are performed on the server end, further simplifying the process on the user end. Imputation status can be monitored and results can be downloaded from a dynamic link without having to register an account. The server is set up to accommodate multiple users at the same time while limiting multiple jobs of the same user. Our tests indicated that a typical job with 2000 individuals and 50K SNP chip genotypes can be completed in approximately 12 h for all chromosomes.To enable the broad research community to efficiently utilize the resource developed in this study, we developed a public SWine IMputation (SWIM) web server  and BGI (n = 667) platforms with 150 bp paired-end reads. Among them, 610 Landrace, 413 Duroc, 391 Yorkshire, 18 Taiwanhei, and 17 Lichahei were from Wen’s Food Group Co., Ltd. , 21 Dahuabai, 21 Lantanghei, 20 Guangdong Xiaoerhua, and 19 Yuedonghei from Guangdong Gene Bank of Livestock and Poultry . Additionally, sequences for 729 animals were downloaded from the sequence read archive (SRA). A complete breakdown, including accession numbers, sample sizes, and average sequencing coverage, can be found in Supplementary Data We consolidated WGS data from multiple sources. A total of 1530 animals are first reported in this study using Illumina  using GATK-4.1.8.1 HaplotypeCaller31 after several post-alignment processing steps including duplicate removal using PicardTools-2.23.331, and base quality recalibration using GATK. A population VCF was generated by combining GVCFs across all samples. Variants with excessive heterozygosity (“ExcessHet > 54.69”) were removed. Variant quality score recalibration (VQSR) on SNPs was performed with truth SNP sets compiled from commercial SNP arrays, including 50K, 60K, and 80K SNP chips (prior = 15.0) on the Illumina platform and the 660K (prior = 12.0), SowPro90 (prior = 15.0) SNP chips from the Affymetrix platform. SNPs were filtered with a truth sensitivity filter level at 99.0. Without a truth set of indels, we applied hard filtering on them by excluding indels with QD < 2.0, QUAL < 50.0, FS > 100.0, ReadPosRankSum < −20.0, as recommended by GATK’s best practices. Additionally, we filtered out animals with a missing rate >0.20, heterozygosity >0.20, and retained bi-allelic sites with a missing rate <0.2 and mean sequencing depth between 5 and 500. Filtering was performed using a combination of VCFtools 0.1.1332 and BCFtools 1.1333 commands.We aligned sequence reads to the pig reference genome 34 on individuals within the same breed after removing close relatives (GRM > 0.5) and low-frequency variants (MAF < 0.05). To understand the genetic structure in the population, we retained variants with MAF > 0.05 and missing rate <0.1 and pruned SNPs with LD  using PLINK 1.935. Principal component analysis (PCA) was performed on the filtered list of 1,223,882 variants using GCTA 1.93.236 for all individuals. Ancestries were estimated using ADMIXTURE 1.337 on 185 individuals randomly selected according to breed representation in the dataset or at least four individuals per breed. The downsampling was necessary to properly visualize population structure.Linkage disequilibrium was computed using PopLDdecayP-value < 10−10 implemented separately in PLINK in all three of the Duroc, Landrace, and Yorkshire pigs were removed. Only autosomal variants were retained for imputation.We further filtered variants prior to phasing haplotypes in the reference population. Variants with missing rate >0.1 and MAF < 0.005 were removed. Additionally, variants with a Hardy–Weinberg equilibrium test n = 2159): all individuals except the 100 Landraces; L (n = 550): Landrace pigs only; DLY (n = 550): 250 Landraces + 150 Durocs + 150 Yorkshires; and LO (n = 550): 250 Landraces + 300 randomly selected pigs other than Durocs and Yorkshires. Phasing was independently performed in these reference sets. In addition, we also tested imputation using the PHARP web server , which contains reference haplotypes constructed from 1006 individuals in the SRA.We extracted 100 Landrace pigs with the highest sequencing depth  and designated these individuals as the target population to evaluate imputation accuracy. To test the effect of breed composition of the reference population, we constructed four reference haplotype panels using different sets of individuals, including All , but the effective population size was set to 100. Imputed genotypes were called by the ones with the highest posterior genotype probability. However, users of the imputation web server also receive genotype probabilities.We tested three combinations of software for phasing and imputation, including SHAPEIT 4.243, and r2. Concordance rate is defined as the proportion of individuals with imputed genotypes in concordance with observed genotypes. Non-reference concordance rate is similar to the concordance rate but is restricted to only individuals that are not homozygous for the reference allele. r2 is the squared Pearson correlation coefficient between observed and imputed genotypes. We measured concordance rates and r2 on a per SNP basis and averaged them over SNPs in MAF bins or across the whole genome.We considered three commonly used metrics of imputation accuracy, concordance rate, non-reference concordance rateTo demonstrate the utility of imputation in genetic mapping, we collected phenotypes and genotypes for three populations of pigs, which were managed by three core breeding farms of Wen’s Food Group Co., Ltd. , all under standard management practices. For backfat thickness, the phenotypes were collected on 3769 Duroc pigs from 2013 to 2018, and SNP genotyping was performed using the Geneseek GGP Porcine 50K SNP chip . Backfat thickness was measured between the 10th and 11th ribs using an Aloka 500 V SSD B ultrasound  when live weights of pigs reached about 100 kg (100 ± 5 kg). For body length, phenotypes from a total of 1694 Yorkshire boars were collected from 2012 to 2018, and SNP genotyping was performed using the Affymetrix PorcineWens55K SNP chip . Body length was measured from the base of the ear to the base of the tail in pigs at approximately 100 kg (100 ± 5 kg) body weight. All samples were collected according to the guidelines for the care and use of experimental animals approved by the Ministry of Agriculture and Rural Affairs of the People’s Republic of China. The ethics committee of South China Agricultural University specifically approved the animal use in this study.P = 5 × 10−8 to declare significance. Variance explained by a single significant SNP was estimated by fitting a mixed linear model with the genomic relationship matrix determined by a single SNP.We used GCTA 1.92.1 to perform a mixed linear model (MLM) based association analysis. The following statistical model was used: https://github.com/qgg-lab/swim-public) as well as a Zenodo repository44 (10.5281/zenodo.7900470). The sample size for the entire SWIM haplotype reference panel is 2259, with subsets selected for the different designs to answer specific questions. Sample sizes for the backfat thickness and body length GWAS were 3769 and 1694, respectively.All statistical analyses are performed using either software packages as described or in R 4.2.2. We supply all scripts, including those to generate figures in a GitHub (Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-12Reporting Summary