Ambrosia artemisiifolia—an aggressively invasive weed and primary cause of pollen-induced hayfever—to track the phenotypic and genetic causes of recent local adaptation across its native and invasive ranges in North America and Europe, respectively. Large haploblocks—indicative of chromosomal inversions—contain a disproportionate share (26%) of genomic regions conferring parallel adaptation to local climates between ranges, are associated with rapidly adapting traits, and exhibit dramatic frequency shifts over space and time. These results highlight the importance of large-effect standing variants in rapid adaptation, which have been critical to A. artemisiifolia’s global spread across vast climatic gradients.Adaptation is the central feature and leading explanation for the evolutionary diversification of life. Adaptation is also notoriously difficult to study in nature, owing to its complexity and logistically prohibitive timescale. Here, we leverage extensive contemporary and historical collections of  Ambrosia artemisiifolia is an invasive weed and primary cause of pollen-induced hayfever. Here, the authors report its chromosome-level phased genome assembly, examine genome-wide variation among modern and historic accessions, and identify large haploblocks underling rapid adaptation. Adaptation can be rapid, yet changes in the traits and genes that underlie adaptation are difficult to observe in real time because speed is relative: that which is fast in evolutionary terms is slow from the human perspective. Thus, while we know that adaptation is central to evolutionary diversification, species persistence, and invasiveness, the genetic and phenotypic dynamics of adaptation are difficult to document outside of the laboratory.3, observable over human lifespans, as invasive populations can encounter drastically different environmental conditions from those of their source populations. Many invasions are, moreover, documented in large, geo-referenced herbarium collections, which can be phenotyped and sequenced to identify and track adaptive evolutionary changes through time—feats that are rarely achieved in natural populations. Invasive species frequently inhabit broad and climatically diverse ranges, which favors the evolution of adaptations to local environmental conditions3, along with evolved tolerance of environmental extremes, which may be conducive to invasiveness4. Because they often occupy geographically broad native and invasive ranges, invasive species allow for tests of the predictability of evolution—a major puzzle in biology—as local adaptation across native and invasive ranges may favor either parallel or unique genetic solutions to shared environmental challenges. However, despite the promise of historical records and other features of invasive species that make them tractable systems for capturing adaptation in action, this treasure trove of data has not been fully utilized to elucidate the genetic basis of local adaptation during recent range expansions.Invasive species are powerful systems for characterizing adaptation in nature, owing to several features that make them unique. In particular, biological invasions coincide with exceptionally rapid evolutionAmbrosia artemisiifolia, an annual weed native to North America, has mounted successful invasions on all continents except Antarctica5. The species thrives in disturbed habitats and has experienced extensive range shifts, historically documented in pollen records and herbarium collections. It also produces highly allergenic pollen, which is the chief cause of seasonal allergic rhinitis and asthma in the United States6. In Europe, approximately 13.5 million people suffer from Ambrosia-induced allergies, costing ~7.4 billion euros annually7. Continued invasion and climate change are predicted to more than double sensitization to Ambrosia pollen8, further magnifying the health burden of this pest. Pollen monitoring has demonstrated that climate change has already significantly lengthened the ragweed pollen season, particularly at high latitudes9. Consequently, there is considerable incentive to understand the factors that contribute to Ambrosia pollen production, including the species’ invasive spread, the timing of pollen production, plant size, and fecundity.Ambrosia artemisiifolia populations are characterized by strong local adaptation and high gene flow between populations11. In Europe, invasive populations have been established through multiple genetically diverse introductions from North America over the last ~160 years13. Remarkably, latitudinal clines observed for multiple traits in the native range, including flowering time and size, have re-evolved in Europe and Australia14, suggesting rapid local adaptation following invasion. Moreover, this trait-level parallelism is echoed in signals of parallelism at the genetic level10.15 and the effects of anthropogenic climate change intensify, understanding the genetic architecture of adaptation to sudden environmental shifts—a classical question in evolutionary research—becomes ever more important. While long-standing theory suggests that evolution in response to incremental environmental change should proceed through mutations of infinitesimally small16 or moderate17 effect, large-effect mutations are predicted to be useful in bridging extreme, sudden environmental shifts18. Moreover, alleles of large effect will, in cases of local adaptation, be better able to persist in the face of the swamping effects of gene flow19. Large-effect mutations are also more likely to produce patterns of evolutionary repeatability, or genetic parallelism, between species’ ranges20, particularly if adaptive responses make use of standing genetic variation (as would be expected during a bout of rapid adaptation), rather than de novo mutations21. These features of large-effect mutations may, however, be achieved by groups of mutations in tight genetic linkage19, including mutations captured by chromosomal inversions22. There is substantial empirical evidence for the involvement of inversions in local adaptation23. For example, Drosophila melanogaster’s In(3R)Payne inversion shows parallel environmental associations across multiple continents24, and several plant inversions have been identified as contributing to local adaptation and ecotype divergence26. Theory also predicts that inversions can drive range expansions27, though their actual contributions to biological invasions are not well-understood.As biological invasions continue to increase worldwideA. artemisiifolia samples from throughout North America and Europe11. Using these data of unparalleled spatial and temporal resolution, we first identify regions of the genome experiencing climate-mediated selection in the native North American and introduced European ranges leveraging landscape genomic approaches and genome-wide association studies of adapting traits such as flowering time. Second, motivated by evidence that European and North American populations show similar trait clines with respect to climate14, we examine the extent of between-range parallelism at the genetic level. Although adaptive traits such as flowering time and size are polygenic, we expect to see substantial levels of parallelism if large and moderate effect standing variants were contributing to adaptive divergence. Third, we determine if these same regions show temporal signatures of selection in Europe, which would be expected if some invading populations were initially maladapted to their local climates. We couple this temporal genomic analysis with a temporal analysis of phenological trait changes in European herbarium samples to further support our findings of genomic signatures of selection on flower time genes. Finally, we identify haplotype blocks with multiple features consistent with inversions, in genomic regions enriched for signatures of parallel adaptation. Four of these colocalize with inversions identified in our diploid assembly, confirming their identity as inversions. To determine if these haploblocks contribute to rapid local adaptation in Europe, we assess spatial and temporal changes in their frequency as well as their associations with adaptive traits.Here, we develop a chromosome-level phased diploid reference assembly, and examine genome-wide variation in over 600 modern and historic Ambrosia artemisiifolia reference genome , with 94.3 and 96.5% of each respective genome assembled into 18 large scaffolds  genes were identified on the 18 chromosomes of each haploid assembly, with 183 (71.8%) single-copy and 72 (28.2%) duplicated  species at the base of the tribe33. This species also retained a large number of duplicated BUSCO genes33. A large fraction of the genome consisted of repetitive sequence  and European 35, an evolutionary hotspot for parallel flowering time adaptation in A. thaliana, barley and rice36  using 121 modern samples across North American n = 4 and Euroies Fig. . While tA. artemisiifolia modern samples that were both highly divergent between populations (BayPass XtX)37, and correlated with 19 WorldClim temperature and precipitation variables 39. In North America , 2167 (80.1%) of the 2704 outlier windows for genomic divergence (XtX) were also outlier windows for at least one environmental variable (XtX-EAA), while in Europe , only 1357 (50.3%) of the 2697 XtX outlier windows overlapped environmental variable outlier windows. Signatures of local adaptation were much stronger in North America than Europe, with the North American range showing more extreme XtX values . Consequently, many of the same regions of the genome are involved in climate adaptation in both ranges.Previous studies in p < 0.05) in North America , as well as “gibberellin biosynthetic process”  where in northern populations, more recently collected plants were more likely to be flowering than older historic specimens. For this trait, collection day also significantly interacted with collection year and more recently collected plants were more likely to be flowering later in the year and less likely to be flowering earlier in the year. Similar patterns were identified with the presence of fruit, as older samples were less likely to produce fruit later in the season compared to recent samples . The frequency of the variant increased from 2.6% to 73.9% between historic and modern samples, an allele frequency shift greater than 10,000 putatively neutral loci sampled from the same geographic region; similar shifts were not observed in North American samples within a 200 km radius of Quebec City (ten historic and eleven modern), where the ELF3 allele is at comparably high frequencies  contributing to local adaptation in A. artemisiifolia. Briefly, we identified genomic regions in which population structure was divergent and fell into three clusters, putatively representing the heterozygous and two homozygous genotypic classes of an inversion. Further, we looked for pronounced shifts in population structure (indicating inversion breakpoints), elevated local heterozygosity in the heterozygous cluster, and increased linkage disequilibrium across the region . One haploblock also corresponds to the European selective sweep region on chromosome 2 , hb-chr4 (6.2 Mbp), hb-chr5a (4.1 Mbp), hb-chr5b (5.4 Mbp), hb-chr6a (1.1 Mbp), hb-chr6b (3.9 Mbp), hb-chr7a (7.3 Mbp), hb-chr7b (7.0 Mbp), hb-chr8 (17.3 Mbp), hb-chr10 (6.5 Mbp), hb-chr11 (2.2 Mbp), hb-chr14 (4.9 Mbp), hb-chr16a (1.7 Mbp), hb-chr16b (13.7 Mbp) and hb-chr17 (2.8 Mbp). These haploblocks contained 77 of the 291 parallel XtX-EAA windows . We also found that changes in the haploblock cline slopes between historic and modern time points within Europe were significantly correlated with our estimates of the relative strength of spatially varying selection of the haploblocks across the European range . Cline slopes for latitude were also shallower in historic relative to modern European populations , but not so in North America , consistent with initial maladaptation in Europe, followed by adaptation to local climates.To further investigate whether European populations showed evidence of recent local selection on the haploblocks, we tested whether estimates of selection inferred from contemporary spatial data were associated with temporal changes in haploblock frequencies between historical and contemporary European populations. We used spatial variation in contemporary haploblock frequencies to estimate the relative strength of local selection on these haploblocks , although individually only hb-chr5b was significantly enriched  of the genome’s 210 genes annotated with this term falling within this haploblock , which encodes the A. artemisiifolia protein responsible for the majority of allergic reactions46. A detailed analysis of the hb-chr5a region reveals a cluster of six closely-related pectate lyase genes, which correspond with elevated XtX and XtX-EAA outlier windows in both ranges  were observed for four haploblocks, including for traits related to flowering time  contribute substantially to these genetic signals of parallel adaptation. 27% of these haploblocks correspond to inversions segregating in our diploid assembly. We propose that these haploblocks maintain cassettes of co-selected genes that effectively segregate as single alleles of large effectA. artemisiifolia was present in regions throughout Europe well before many of these adaptive variants became locally common, suggesting the species’ extensive phenotypic plasticity may have facilitated its initial expansion. Strong local selection further improved the match between genotypes and local environments, even appearing to affect reproductive output in herbarium specimens. Many of the selected variants we identified are linked to traits that are key factors in the timing, length, and severity of the local pollen season . Consequently, local adaptation has played a central role in shaping the allergy season in Europe and will likely continue to be critical as climate change and continued range expansion further amplify the damaging effects of this hazardous weed48.An important question during this era of environmental upheaval is the role of adaptation during range expansion and its necessity during colonization. Through our analysis of historic samples, we have shown that Ambrosia artemisiifolia population in Novi Sad, Serbia  were sown in potting soil at a greenhouse facility at the Ringve Botanical Garden, NTNU University Museum (Trondheim. Norway). After 160 days of growth under stable light and watering conditions, young leaf tissue from mature individual plant “NSS02/B” was sampled and flash-frozen in liquid nitrogen. These tissues were then shipped to Dovetail Genomics for high molecular weight DNA extraction and library building.Seeds collected from a wild 49.DNA samples were quantified using Qubit 2.0 Fluorometer . The PacBio SMRTbell library (~20 kbp mean insert length) for PacBio Sequel was constructed using SMRTbell Express Template Prep Kit 2.0  using the manufacturer recommended protocol. The library was bound to polymerase using the Sequel II Binding Kit 2.0 (PacBio) and loaded onto PacBio Sequel II. Sequencing was performed on PacBio Sequel II 8M SMRT cells generating 65.9 Gbp of data. These PacBio CCS reads were used as an input to HifiasmFor each Dovetail Omni-C library, chromatin was fixed in place with formaldehyde in the nucleus and then extracted. Fixed chromatin was digested with DNAse I, chromatin ends were repaired and ligated to a biotinylated bridge adapter followed by proximity ligation of adapter containing ends. After proximity ligation, crosslinks were reversed and the DNA purified. Purified DNA was treated to remove biotin that was not internal to ligated fragments. Sequencing libraries were generated using NEBNext Ultra enzymes and Illumina-compatible adapters. Biotin-containing fragments were isolated using streptavidin beads before PCR enrichment of each library. The library was sequenced on an Illumina HiSeqX platform to produce ~30X sequence coverage. The PacBio CCS reads and Omni-C reads were then used as input for Hifiasm to produce two haplotype-resolved assemblies (hap1 and hap2) using default parameters.28. Dovetail OmniC library sequences were aligned to the draft input assembly using bwa50. The separations of Dovetail OmniC read pairs mapped within draft scaffolds were analyzed by HiRise to produce a likelihood model for genomic distance between read pairs, and the model was used to identify and break putative misjoins, to score prospective joins, and make joins above a default threshold  and 26  scaffolds in haplotypes 1 and 2 respectively containing bacterial contamination which were subsequently removed from the final assembly.HiRise was used (see read-pair above) to scaffold each haplotype-resolved assembly. Each de novo assembly and Dovetail OmniC library reads were used as input data for HiRise, a software pipeline designed specifically for using proximity ligation data to scaffold genome assemblies52. Genomescope estimated the haploid genome size to be 1.04 Gbp using a diploid model , and measured average sequencing depth and heterozygosity of the alignment in non-overlapping 1 Mbp windows across the genome. Window depth was never greater than two times higher or 0.5 times lower than the mean, and furthermore regions of both low depth and low heterozygosity were distributed throughout the genome. The fact that there were no large regions with both low read-depth and low heterozygosity points to the success of the haplotype-resolved assembly  were used for environmental-allele associations. 97 historic and 100 modern samples divided into twelve populations were used for historic-modern population comparisons  and 21 samples within 200 km of Quebec City (ten historic and eleven modern) were used. Genotyping and analysis of haploblocks was performed using 311 modern and 305 historic samples. For details of each sample see Supplementary Data Whole-genome resequencing data used in this study have previously been described in Bieker et al.A. artemisiifolia samples from North America and Europe11 were aligned to haplotype 1 of our new reference genome using the Paleomix pipeline v.1.2.13.454, which incorporates AdapterRemoval v.2.3.153, BWA-MEM v.0.7.1750, Picard v.2.19.0 MarkDuplicates (https://broadinstitute.github.io/picard/) and GATK v.3.7 IndelRealigner55. Mean depths of alignments ranged from 0.37X to 19.95X with a mean of 4.05X for historic samples, and 1.75X to 44.03X with a mean of 6.86X for modern samples . In addition, SNPs and indels were separately filtered for sites with depth (DP) less than one standard deviation below the mean, and greater than 1.5 standard deviations above the mean. Individual genotypes were set to missing if their depth was less than three, then variants with greater than 20% missing across all samples were removed. Samples with greater than 50% missing variants were removed. For the remaining 311 modern samples, genotypes were phased and imputed using Beagle v.5.258.FASTQ files from historic and modern https://github.com/PacificBiosciences/IsoSeq). Briefly, the pipeline takes PacBio subread files as an input and undergoes steps of consensus generation, demultiplexing of primers, IsoSeq3 refinement, followed by a final clustering of the reads.To obtain RNA transcript sequences for annotation of the genome, after 160 days of growth additional samples of leaf, stem, flower, root, and branch were taken from individual “NSS02/B” and flash-frozen in liquid nitrogen. From these we extracted RNA from seven tissues  using a Spectrum Plant Total RNA Kit  with on-column DNA digestion following the manufacturer’s protocol. RNA extracts from all five tissues were pooled into a single sample. mRNA was enriched using oligo (dT) beads, and the first-strand cDNA was synthesized using the Clontech SMARTer PCR cDNA Synthesis Kit, followed by first-strand synthesis with SMARTScribeTM Reverse Transcriptase. After cDNA amplification, a portion of the product was used directly as a non-size selected SMRTbell library. In parallel, the rest of amplification was first selected using either BluePippin or SageELF, and then used to construct a size-selected SMRTbell library after size fractionation. DNA damage and ends were then repaired, followed by hairpin adapter ligation. Finally, sequencing primers and polymerase were annealed to SMRTbell templates, and IsoSeq isoform sequencing was performed by Novogene Europe  using a PacBio Sequel II instrument, yielding 97,819,215 HiFi reads. To prepare the raw IsoSeq RNA data for downstream use in the annotation of the genome, we first identified the transcripts in the PacBio single-molecule sequencing data by following the IsoSeq v3 pipeline provided by PacificBiosciences  and protein homology evidence from a plant protein database which combines the Swissprot plant protein database and NCBI Refseq for plants excluding transposable elements were used as the input files for the first run of the annotation pipeline. The custom repeat library was used to mask the repetitive regions. Additional regions with low complexity were soft masked using RepeatMasker v.4.1.161. Gene predictors SNAP v.2013-11-2962 and AUGUSTUS v.3.3.363 were trained by running iterative runs of MAKER as recommended by34. As the first round of annotation was based on the alignment of the EST evidence to the genome, est2genome option in the MAKER control file was set to allow MAKER to infer gene models directly from the EST evidence. After the completion of the first round of annotations, gene models with an AED (Annotation Edit Distance) score of 0.25 or greater and a length of 50 or more amino acids were retained and used to train SNAP v.2013-11-2962 to obtain a SNAP hmm file. We then trained AUGUSTUS v.3.3.363 using BUSCO version 5.1.331. First, training sequences were identified using the gene models predicted by MAKER from the first run by excising regions with mRNA annotations and 1000 bp on either side. These were used to run BUSCO using the embryophyte set of conserved genes. After training both SNAP and Augustus, MAKER was run again, with SNAP hmm and Augustus files. A total of three rounds of training for each gene predictor were run. We used the script genestats64 to calculate the numbers and lengths of genes, exons, introns, and UTR (untranslated region) sequences present in the predicted gene models by the final MAKER run. We ran BUSCO v.5.1.331 with the eukaryota odb10 lineage data set on the predicted transcript fasta file by MAKER to assess the quality and the completeness of the annotated genome.Genome annotation was performed using the MAKER v.3.01.03Arabidopsis thaliana annotations (TAIR10 representative gene model proteins65) and the UniProtKB plants database using the blastp command in BLAST+66. Using an E-value threshold of 1 × 10−6, 32,370 (87.9%) genes matched TAIR10 annotations and 28,092 (76.3%) matched UniProtKB. We identified 98.4% of the core eukaryotic genes amongst our annotated genes, 73.3% being single copy, 20% being duplicated and 5.1% fragmented compared to BUSCO markers present in the library eukaryota_odb10.2020-09.10 39, with the top 5% of windows designated outliers.Imputed genotype data from modern samples were divided for between-range and within-range analyses in PLINK v.1.970. Non-SNP sites and sites with more than two alleles were removed. The 121 samples overlapping those phenotyped by van Boheemen, Atwater, and Hodgins14 were retained (Supplementary Data n = 43) and European (n = 78) ranges using EMMAX v.beta-7Mar201073, and incorporating an identity-by-state kinship matrix (generated in PLINK 1.9)70 to account for genetic structure among samples. The kinship matrix was computed using 790,209 SNPs which remained after pruning for linkage disequilibrium using a window size of 50 kb, a step size of 5 bp, and an r2 of 0.5. Candidate SNPs were identified using a conservative threshold of Bonferroni-corrected p-values <0.05.Imputed genotypes from modern samples were filtered in PLINK v.1.9A. artemisiifolia European herbarium specimens for which there was a digitized image of the individual in the database alongside corresponding metadata (location and collection date). The collection date spanned 1849 to 2020 (median 1975) and comprised 985 specimens. We determined the stage of flowering  for each image. The presence of fruit was also recorded. The male inflorescence was used as an indicator of flowering as these structures are more visually prominent than female flowers and the onset of male and female flowering is highly correlated14. Male florets consist of prominent spike-like racemes of male capitula, and are found at the terminus of the stem, whereas female florets are observed to be in inconspicuous cyme-like clusters and are arranged in groups at the axils of main and lateral stem leaves. The dates when the specimens were collected were converted to Julian day of the year. We conducted a generalized linear model with a binomial response and logit link (glm R). Both binary traits  were included as response variables in two separate models. The significance of the effects were tested using the anova function (Car package R)74 using type 3 tests. For both models, the predictors of latitude, day of the year, and collection year as well as all interactions were included. Non-significant interactions were removed in a stepwise fashion, starting with the highest order. Latitude of origin strongly correlates with flowering time in common garden experiments14 and we expected northern populations to evolve early flowering relative to the start of the growing season to match the shorter growing seasons in these areas. As a result, if local phenology has evolved to better match the local growing season we predicted a collection year by latitude interaction, as the relationship between latitude and the probability of flowering in wild collected accessions should change over time when controlling for the day of collection.We conducted a trait-based analysis of herbarium specimens found in the Global Biodiversity Information Facility database (gbif.org 2021). We compiled information from all 75 using genotype likelihoods. For each population location we calculated pairwise nucleotide diversity (θπ) for historic and modern populations separately, and FST between historic and modern populations at each location. Statistics were calculated in non-overlapping 10 kbp windows, and windowed θπ values were normalized by dividing by the number of sites in each window. At each location, windows with θπ more than two standard deviations below the mean in both historic and modern populations were excluded from the analysis. We identified putative selective sweeps in each population as windows with extreme shifts over time in allele frequency as well as extreme reductions in diversity . To obtain further evidence for selective sweeps in these populations, we also performed genome scans of Fay and Wu’s H in each modern population. We first generated an ancestral consensus sequence in ANGSD (-doFasta 2 -minMapQ 25 -minQ 20 -remove_bads 1 -uniqueOnly 1 -doCounts 1) from alignments of Ambrosia carduacea and Ambrosia chamissonis to our A. artemisiifolia reference genome. We then used this ancestral sequence in calculating Fay and Wu’s H in 10 kbp windows in ANGSD.To identify targets of recent selection, we compared historic and modern samples from twelve locations  was used to calculate the allele frequency of the early flowering ELF3 allele (11:41517231) in 15 historic and eleven modern samples from within 200 km of Berlin, whilst the frequency of hb-chr2 in Europe was ascertained using haploblock frequency estimates from across the European range (see below). To understand the magnitude of these allele frequency shifts relative to putatively neutral alleles elsewhere in the genome, we calculated a standardized measure of frequency change, yt, using estimates of historic, t is the number of generations separating the frequency estimates . As we show in Supplementary Methods yt estimates under neutrality are predictable and roughly independent of the initial frequency of each neutral variant once the loci with low-frequency initial minor allele frequencies are filtered out , resulting in null distributions of between 6913 and 8303 SNPs. We then compared the distributions to the yt values of candidate adaptation loci to test whether candidate regions were more divergent than the putatively neutral distribution. As a point of comparison we repeated this analysis for hb-chr2 in North America and the ELF3 allele in Quebec. As in Berlin, the ELF3 allele is at high frequencies, but substantial temporal change was not expected as the populations were predicted to be closer to the equilibrium over the temporal sampling period in the native range. Samples within 200 km of Quebec City (ten historic and eleven modern) were pooled at both timepoints. Allele frequency changes of the 10,000 randomly-sampled SNPs and the non-synonymous ELF3 allele were assessed as above.In order to track allele frequency shifts over time, we estimated contemporary and historical allele frequencies of the 43. Using SNP data from 311 modern samples, we extracted the first ten multidimensional scaling (MDS) coordinates across each chromosome in windows of 100 SNPs. These MDS coordinates were then plotted along each scaffold to observe regions of local structure, indicative of segregating haploblocks. We focused on outlier MDS signals that overlapped parallel outlier windows for both XtX and at least one environmental variable, and also showed well-defined boundaries indicative of chromosomal inversions. We tested for additional evidence of inversions using PCA of MDS outlier regions and kmeans clustering in R72 to identify regions containing three distinct clusters representing heterozygotes and two homozygotes. In addition, we assessed heterozygosity from genotype data in each haploblock region and in each modern sample, and measured linkage disequilibrium  across each scaffold bearing a haploblock for all modern samples and for modern samples homozygous for the more common haploblock genotype using scripts from Todesco et al.26.To identify signatures of large, segregating haploblocks across the genome, we performed local windowed principal component analysis with Lostruct v.0.0.0.900076, so as to allow genotype estimation of these haploblocks in 305 historic samples alongside the 311 modern samples. For this local PCA we used only the chromosomal regions already defined as haploblocks in order to obtain population wide clustering for both historic and modern datasets, which we then used to infer haploblock genotypes. We also conducted a PCA on 10,000 SNPs randomly-sampled from the 311 modern genomes that were located outside annotated genes and haploblocks, and pruned for linkage disequilibrium using a window size of 50 kb, a step size of 5 bp and an r2 of 0.5 in PLINK70. Following this, we used generalized linear models (glm R) to assess how haplotype frequency  changed over time and space. A count of each haplotype at a geographic location and year was the binomial response variable and time period (historic or modern), range (North America or Europe), latitude, and all interactions between these three main effects were used as predictors. Non-significant interactions were removed in a stepwise fashion, starting with the highest order. PC1 from the PCA of 10,000 randomly-sampled SNPs was included as a covariate to control for the effects of population structure on haplotype frequency. We tested the significance of the effects in our model using the anova function (Car package R)74 with type 3 tests. Significant differences among groups for means or slopes were tested with the emmeans package using an FDR correction77. To determine if the classification of samples into modern or historic timepoints influenced our results we ran a second set of generalized linear models examining haplotype frequency as a function of collection year, range (North America or Europe), latitude, and all interactions between these three main effects as well as PC1, using the same approach as above. For interactions involving two continuous variables  we tested if the slope estimates of one variable were significant at specific values of the other using the package emmeans. This allowed us to estimate when and where the haplotype frequencies were changing. The results from both approaches (time as two categories or time as continuous) provided qualitatively similar patterns.For fifteen candidate inversions, a local PCA of each region and kmeans clustering was then repeated in PCAngsd77; Supplementary Table t-test and compared slopes for modern and historic timepoints in each range. We also expected that the magnitude of change in the slope over time would be the greatest in haploblocks showing the largest estimates of selection in Europe , we fit a generalized linear model with latitude as the only predictor. We did this for each null SNP and each haploblock that was statistically associated with latitude.42. Markers were generated using genotype by sequencing and alignments to the haplotype 1 of our diploid reference genome. Details of the sequencing, alignments, and variant calling can be found in Prapas et al.42. We developed sex-specific genetic maps  using Lep-MAP378 for each chromosome of interest and in each mapping population (an F1 mapping population and two F2 mapping populations). Multiple maps were constructed since the haploblocks may have been segregating in different frequencies in the parents of the mapping populations derived from outcrossing. For the recombination rates, linkage map construction was constrained by the physical order of the markers along each scaffold of interest. Genetic distance (cM) was plotted against physical position along the chromosome for each map and the intervals of the QTL and the boundaries of the haploblocks were visualized and inspected for reduced recombination compared to the rest of the scaffold. We also used the genetic map (pink family) to confirm the OmniC scaffolding, and assess the accuracy of the manual correction of chromosome 18 in both assemblies , all maps should show reduced recombination rates. However, if inversions were the cause, recombination would only be suppressed in genotypes heterozygous for the inversion, while homozygous individuals would not show suppressed recombination. To determine if there were genotype-specific reductions in recombination rate in the haploblocks, which would be consistent with inversions, we made use of three previously generated genetic mapsFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-9Reporting Summary


P < 5 × 10−8), which were revealed to actively participate in heart development and contraction pathways. Significant causal relationships were observed between the LVRWT traits and hypertrophic cardiomyopathy (HCM) using genetic correlation and Mendelian randomization analyses (P < 0.01). The polygenic risk score of inferoseptal LVRWT at end systole exhibited a notable association with incident HCM, facilitating the identification of high-risk individuals. The findings yield insights into the genetic determinants of LVRWT phenotypes and shed light on the biological basis for HCM etiology.Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype  in a large population, identifying 72 significant genetic loci linked to LVRWT traits. Likewise, the LV wall thickness is an independent predictor of morbidity and mortality of hypertrophic cardiomyopathy (HCM)4, diastolic dysfunction5, and ventricular arrhythmias6. Various LV regional wall thicknesses (LVRWT) occurred with different clinical implications8, and its quantitative analysis is of great significance for the diagnosis of cardiovascular disease (CVD)9. Hence, LVRWT serving as intermediate phenotypes for CVD is needed for timely detection.The critical role of left ventricular (LV) structure and function in adversely affecting the prognosis of individuals is increasingly acknowledged. Changes in LV structure and function have been associated with acute myocardial infarction10. However, the highly variable cardiac structures across different subjects and quantification tasks of small targets pose the biggest challenge to accurate estimations of cardiac wall thicknesses in large investigations11. As a result, such a large-scale genome-wide association (GWAS) study of the LVRWT imaging phenotype has not been performed to date. Previous research on the genetic architecture of CMR-derived traits concentrated primarily on LV volumetric and functional metrics such as stroke volume and ejection fraction12. To solve this issue, we established a novel deep-learning framework named Myocardial Segmentation and Measurement Method (MSMM) to precisely estimate LVRWT. The UK Biobank (UKB), one of the largest population imaging studies, has acquired both high-quality standardized CMR examinations and genotype data13, offering a tremendous opportunity to investigate the unknown genetic determinants of LVRWT traits.Cardiovascular magnetic resonance (CMR) is one of the most popular medical imaging modalities to quantify the LV phenotypes due to its noninvasive and versatile natureIn this study, we aimed to investigate the genetic basis of 12 LVRWTs: anterior, anteroseptal, inferoseptal, inferior, inferolateral, and anterolateral LVRWT at end diastole and end systole. Using a deep learning-based method to segment LV structure and design an automatic measurement algorithm to quantify myocardial wall thickness, we performed GWASs on 12 LVRWT traits  phase and half during end-diastole (ED) phase) in 42,194 UKB participants. We then evaluated the putative causal relationships between the LVRWT traits and CVDs using genetic correlation and Mendelian randomization (MR) analyses. Finally, we established the polygenic risk score (PRS) of each LVRWT trait, and validated whether the LVRWT-PRSs have the ability to identify HCM in 439,981 individuals without CMR imaging. Overall, this study substantially enhances our knowledge of the genetic basis of LVRWT and may provide valuable risk stratification guidance to identify high-risk sub-populations in individualized HCM prevention.15 annotated by one clinical expert, which has a total of 1902 annotated images. We randomly selected 1420 short axis images for training, 100 short axis images for validation and 382 short axis images for testing. Within the 382 testing images, 80 images belong to the two mid-cavity slices. Since only the mid-cavity slice quantification results were used for the subsequent analysis, we evaluated the methods on two mid-cavity slices of each case to ensure a consistent comparison. Moreover, we included 500 mid-cavity images from the UKB dataset for independent evaluation because that GWASs are performed on the UKB dataset. The Deep Layer Aggregation based deep learning model17 as the network architecture (DLANet) was trained from 1420 ACDC images. The Deep Layer Aggregation (DLA)-based deep learning model as the network architecture was trained from these data. The trained deep learning model was then used to output LV segmentation results in the UKB imaging substudy of over 45,000 people18. Detailed information about model training, as well as quantitative and qualitative results, can be found in the “Methods“ section and The MSMM procedure we established to measure the thicknesses were presented in Fig. r2 = 0.82 between ES-IL and ES-AL), and the end-diastolic LVRWT phenotypes and end-systolic LVRWT phenotypes were modestly positively correlated with one another , end-systolic anteroseptal (ES-AS), end-systolic inferoseptal (ES-IS), end-systolic inferior (ES-I), end-systolic inferolateral (ES-IL), end-systolic anterolateral (ES-AL) LVRWT] and 6 LVRWT phenotypes at end diastole  were available. Following the exclusion of poor image quality and sample quality-control procedures, 42,194 individuals free from a diagnosis of myocardial infarction or heart failure remained , including 16 variants for IS, 2 variants for I, 7 variants for IL, 10 variants for AL, 3 variants for A and 7 variants for AS at end systole stage; 5 variants for IS, 3 variants for I, 3 variants for IL, 4 variants for AL, 3 variants for A and 9 variants for AS at end diastole stage, respectively  with 62 lead variants. To characterize the features for risk variants of 12 LVRWT traits, we first generated the control variants set and functionally defined the genomic position distribution of variants via SnpEff19. Compared with control variants, we found risk variants were significant enriched within intron region, gene upstream and downstream regions, and intergenic region   in most LVRWT traits  to low , followed by inferoseptal LVRWT at end diastole , while heritabilities were lower for anterior and anterolateral LVRWT at end diastole, which had a heritability of 17% Fig. . We obse12. The LVRWT traits had highly positive genetic correlations with LV volumetric and functional traits such as LV ejection fraction (LVEF) and stroke volume (SV) (rg range: 0.16–0.60), and negative genetic correlations with LVRWT traits were observed between LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV) and the body-surface-area indexed versions of these traits, including LVEDVi and LVESVi (rg range: −0.58 to −0.13), which emphasized their strong physiological connections  analyses using our LVRWT summary statistics and the summary data from a recently published LV GWASons Fig. . Additiorg = 0.65, P = 7.0 × 10−4). For inferoseptal LVRWT, we observed positive genetic correlations with HCM at both end systole and end diastole . Furthermore, HCM as the disease had the highest genetic correlation estimates with LVRWT than other CVDs (rg range: 0.37–0.65)  Fig. . In fullP < 0.01) . However, there is no statistically significant effect on hypertension, angina pectoris, myocardial infarction, chronic ischemic heart disease, and ischemic stroke (P > 0.05). Taken together, these findings suggest that the genetic relationships between LVRWT traits and HCM may partly reflect underlying causal processes.The genetic correlations between 12 LVRWT traits and CVDs risk led us to the hypothesis that the increased LVRWT traits are causally associated with CVDs. We then tested such potential causality between 12 LVRWT exposures and CVDs outcomes using two-sample Mendelian randomization (MR). Although heterogeneity and the number of variants in the exposure-outcome effects are limitations, the findings of the inverse-variance-weighted method and sensitivity analyses support causal relations between 10 of 12 LVRWT traits and increased HCM risk  Fig. . We alsoP < 1 × 10−5) from each LVRWT traits GWAS To our knowledge, this study is the first largest individual-level GWAS to investigate the genetic architecture of LVRWT traits. We established a novel deep learning algorithm to assess 12 LVRWTs accurately using CMR imaging in a large population-based biobank. We identified 72 significant genetic loci associated with at least one LVRWT phenotype, and candidate genes were actively participating in heart development and heart contraction pathways. Furthermore, we evaluated the causal relationships between the LVRWT traits and CVDs with MR analyses and found supporting evidence of LVRWT being causal for increased HCM. We demonstrated that PRSs derived from LVRWT traits are associated with incident HCM. This suggests that the genetic basis of LVRWT traits may offer valuable information for the risk stratification of individuals, aiding in the early screening of HCM  encodes a kinase previously implicated as the causal gene in HCM21. ALPK3 regulates the expression of transcription factors (like HEY and HAND proteins) to induce differentiation and maturation of cardiomyocytes at the beginning of the process34. Mutations in ALPK3 cause familial cardiomyopathy and demonstrate loss of function as the underlying genetic mechanism35. Additionally, HCM is a disease of sarcomere proteins, which are composed of thick and thin filaments and Z discs20. In agreement with this notion, we also discovered loci like SYNPO2L, GBAP1, MYOZ1, CAPN9, DMTN, and MTSS that had not been identified in earlier research on cardiac volumetric and functional traits. Interestingly, these genes were revealed to actively participate in Z disc, myofilament, sarcomere, and actin pathways. As an illustration, the scaffolding and actin/myosin regulatory protein known as synaptopodin 2 Like (SYNPO2L), which is expressed in the cardiac muscle and localizes at the Z-disc and interacts with a number of other actin proteins, has been linked to cardiac arrhythmia36. Altogether, several candidate genes are involved in heart development and contraction, and Z disc pathway activation, suggesting that candidate genes may contribute to the development of cardiomyopathies.Extensive multilayered bioinformatic annotations identified candidate genes that have an important role in cardiac muscle cell development and heart contraction. In light of recent Khurshid’s study, it conducted a GWAS of CMR-derived left ventricular mass indexed (LVMI) in the UKB. By comparing the loci of the 13 genes from Khurshid’s study, we found an overlap with 5 genes also identified in our research37. Previous studies have indicated that LVRWT traits are independent predictors of a number of clinical outcomes such as stroke38, ventricular arrhythmia39, and hypertension40. Although human measurement of ventricular wall thickness is prone to variability, a multi-center longitudinal cohort study has reported that LVRWT is a key imaging biomarker in HCM, guiding diagnosis, risk stratification, and clinical management24. In consistent with these findings, we also systematically examined the genetic relations between LVRWT traits and CVDs obtained from published GWASs. We observed genetic associations between LVRWT traits and CVDs, such as HCM, hypertension, pulmonary hypertension, chronic ischemic heart disease, and ischemic stroke, which further reinforced the importance of LVRWT in maintaining efficient circulatory physiology. The results of the MR analysis also showed that LVRWT traits are potentially causally linked with CVDs. Notably, LVRWT traits have the strongest causative effects on HCM than other CVDs. Specifically, these findings point to the notion that HCM may represent the extreme of LVRWT phenotypic variations in certain people, which is helpful to demonstrate that LV wall thickening is the most consistent clinical marker of HCM41.As the strong interdependence and genetic correlations between LVRWT and LV volumetric and functional traits, a significant fraction of the LVRWT loci were linked to previously identified loci for LV volumetric and functional imaging traits. This finding supports the justification for investigating the genetics of LVRWT traits as a complementary gateway to understanding the drivers of cardiac remodeling42. In agreement with this development, our analyses of PRSs shed light on the linkage between genetically determined LVRWT phenotypic variations and the incidence of HCM. Interestingly, the top-performing PRS, which included 115 variants linked to inferoseptal LVRWT at end systole, successfully identifies individuals at high risk of HCM, especially in those without CMR data. In accordance with recommendations, inferoseptal hypertrophy with a thickness greater than 15 mm was a major component of the diagnostic criteria for HCM44. The PRSs might identify high-risk, asymptomatic individuals who would benefit from CMR to screen for HCM. Likewise, previous studies also have reported that the PRSs of LV structure and function traits predict heart failure events2 and incident dilated cardiomyopathy12. Collectively, PRS may provide complementary information within guideline-supported frameworks to better stratify different trajectories of HCM risk and inform clinical decision-making for primary prevention and early screening.GWAS has identified genetic variations that contribute to complex traits and diseases, leading to the development of PRS. The effectiveness of the PRS is often evaluated by determining whether it can help screen the high-risk population to guide clinical or personal decision-makingWe acknowledge some limitations in our study. First, our study population largely consisted of European ancestry, limiting generalizability to other populations, and validation of our findings in more diverse cohorts is needed to assess their applicability to non-European populations. Second, further validation of PRS performance is required in independent cohorts. While PRSs derived from LVRWT traits showed associations with an increased risk of HCM, it’s important to recognize that the predictive performance of these PRSs were relatively limited. Our future research will focus on identifying additional genetic loci and molecular markers, with the goal of enhancing the predictive capabilities of our models. Furthermore, while our study provides strong statistical support for the loci that are highly specific for LVRWT phenotypic variation, future experimental studies using gene-editing techniques in cellular and animal models are warranted to elucidate the functional roles of the highlighted risk genes and the underlying mechanisms modulating LVRWT. Lastly, although our findings provide valuable insights into the genetic basis of LVRWT, the complexities of genetic regulation and functional mechanisms within specific cardiac cell types might require more advanced methodologies, such as emerging single-cell genomics techniques, to fully unravel the intricate genetic networks underlying LVRWT and related cardiac remodeling.In conclusion, using a novel deep learning algorithm to quantify LVRWT measured by CMR, we uncover common variants at 72 significant genetic loci associated with LVRWT, and implicate candidate genes linked to HCM. Moreover, we demonstrate that PRSs derived from LVRWT traits are associated with incident HCM, even in individuals without CMR data. Altogether, these findings represent a substantial advance in our understanding of the genetic architecture of LVRWT phenotypes and shed light on the biological basis for HCM etiology, which may lead to potential novel therapeutic targets and personalized risk stratification strategies in the future.The UKB is a large prospective cohort study of over 500,000 participants recruited at 22 assessment centers across the UK between 2006 and 2010. It has gathered a wealth of information on participants, including health and lifestyle data, physical measurements, biological samples, imputed genome-wide genotypes and imaging data. All participants provided informed consent. The ethical committees from the North West Multi-Center Research Ethics approved the study.2, 42,194 individuals were included in the analysis following the quality-control procedures outlined in Supplementary Fig. Disease information based on the UKB Field ID and date of first in-patient diagnosis is provided in Supplementary Data 2. The definitions were used for GWAS participant exclusion and PRS assessment.All covariates recorded at the imaging visit were used in the analysis where possible. The UKB Data-Fields of covariates were listed in Supplementary Data 13. In brief, CMR was performed with 1.5 Tesla scanners  using electrocardiographic gating for cardiac synchronization. Long-axis cines and a complete short-axis stack of balanced steady-state free precession cines were acquired at one slice per breath hold.CMR protocol had previously been described in detailhttps://www.creatis.insa-lyon.fr/Challenge/acdc/index.html) dataset are annotated by one clinical expert. There are 1420 images for training process, 100 images for validation and 382 images for testing process. Additionally, we resorted to a doctor who has 7 years of clinical experience to label the LV cavity and LV myocardium on the UKB dataset for a comprehensive evaluation. During the testing stage, 80 mid-cavity slices from the ACDC dataset and 500 mid-cavity slices from the UKB dataset were employed for evaluating the performance of the segmentation model.Semantic segmentation aims to assign a label to each pixel in an image. In the proposed MSMM framework, the deep learning model for segmentation learns to extract MR image features and output one of three different classes for each pixel, including background, LV cavity, and LV myocardium. Labels of LV structures in the ACDC  and the Iterative Deep Aggregation (IDA). With the HDA and IDA, the DLA architecture can better merge information from multiple layers and scales, and achieve state-of-the-art performance in the segmentation task. DLANet with 166 hidden layers and 20,576,932 parameters was implemented in Pytorch. Training and inference processes were performed on NVIDIA GeForce GTX 1080 Ti GPUs. During the training process, the DLANet was trained from scratch for 1000 epochs. We employed the Adam45 optimizer for minimizing the Dice Loss46. The initial learning rate was set to 1 × 10−3 and decayed by 0.99 every epoch. The weight decay was 1 × 10−5. At the inference stage, one frame of a patient with all slices in the size of (8–12)*256*256 will take about 0.5 s on one GPU. Additionally, we compared the performance of the DLANet with state-of-the-art methods by re-implementing them and reporting the results in Supplementary Data We utilized the DLANetBefore the training process, we first performed data preprocessing operations. The in-plane resolution of the original cardiac MR images was resampled to 1.25 mm × 1.25 mm, and then the resampled images were cropped or padded to a size of 256 × 256. During training, data augmentations were employed including random shifting, rotation, scaling and so on. For images at the base or apex of the LV, we over-sampled them more than one times in an epoch to increase their frequency during the training data and improve the performance on these images for their difficulty. We ran validation every 20 epochs of training, and when the training process was complete, we selected the DLANet that performs best on the validation set for testing. Finally, the trained model output LV segmentation results on the UKB dataset and the results were used for subsequent quantification of the myocardium.47 by rotation to the fixed arrangement in order to perform subsequent measurements. The thicknesses rely on the calculation of the distance between the center and the contour of the mask, either epicardium or endocardium, for some direction. To this end, we first converted the Cartesian coordinate system into a polar coordinate system and then generated the distance based on the direction angle. Finally, we averaged ten measurements for wall thicknesses. We then used MSMM to calculate the myocardial thicknesses in the UKB. We obtained accurate estimations of 12 LVRWT traits which included 6 LVRWT phenotypes at end systole  and 6 LVRWT phenotypes at end diastole . Additionally, we assessed the DLANet’s performance across various training dataset sizes, as detailed in Supplementary Data For quantification, we designed a measurement-based method to calculate wall thicknesses. Before the measurement, we normalize the MR images48. Briefly, participants were genotyped based on UK BiLEVE Axiom™ Array by Affymetrix  and UKB Axiom Array by Affymetrix . Genotype imputation was based on merged UK10K sequencing and 1000 Genomes phase3 reference panels with SHAPEIT3 and IMPUTE349. Variant positions were keyed to the GRCh37 human genome reference.Detailed information on genotyping and imputation in the UKB has been described previouslyh2g variant) using BOLT-RELM. Both GWAS and heritability analysis models were adjusted for age, sex, BMI, and principal component (PC) 1–10.We calculated the residuals by regressing LVRWT phenotypes on the covariates age, sex, BMI, and imaging center. We performed the GWAS of each normalized LVRWT trait using a linear mixed-model method using ~8.5 million well-imputed variants with minor allele frequency (MAF) ≥ 1% and imputation quality (INFO) score >0.3 by BOLT-LMM. We next estimated the heritability explained by the genotyped variants (50 to estimate the genetic correlations (1) between 12 LVRWT traits; (2) between 12 LVRWT traits and 10 cardiac structure and function traits12: heart rate, diastolic blood pressure, systolic blood pressure, LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), stroke volume (SV), the body-surface-area (BSA) indexed versions of these traits , and LV ejection fraction (LVEF); and (3) between 12 LVRWT traits and 11 CVDs: hypertension, pulmonary hypertension, dilated cardiomyopathy, HCM, cardiomyopathy, HEART FAILURE, atrial fibrillation, myocardial infarction, angina pectoris, chronic ischemic heart disease, ischemic stroke. Data sources of summary statistics for genetic correlation analyses were listed in Supplementary Data Using summary statistics, we applied LDSC softwareGiven the unavailability of GWAS summary data for hypertrophic cardiomyopathy, pulmonary arterial hypertension, and dilated cardiomyopathies, we performed GWAS analyses utilizing ~8.5 million well-imputed variants, each with a MAF ≥ 1%, and an INFO score exceeding 0.3, using data from the UKB. Each GWAS analysis was adjusted for covariates including age, sex, BMI, smoking status, alcohol intake frequency, and the first ten principal components (PC1-10). The sample sizes for each disease were as follows: hypertrophic cardiomyopathy (552 cases and 2208 controls), pulmonary arterial hypertension (2047 cases and 8301 controls), and dilated cardiomyopathies (1303 cases and 5281 controls).https://fuma.ctglab.nl/)51. Genome-wide significant variants were defined by trait associations P < 5 × 10−8 and r2 < 0.1 with correlated variants in 250 kb region using FUMA.The web tool Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) has previously been described in detail (human reference assembly (GRCh37/hg19) are mapped; (2) eQTL mapping whereby allelic variations at a variant is significantly linked to expression of a gene, where we considered eQTLs within heart atrial appendage and heart left ventricle from GTEx v8.We utilized two main approaches to map genome-wide significant loci to genes via FUMA default settings and specialized datasets, as described follows: (1) positional mapping of variants, whereby variants within a 10kB window from known protein-coding genes in the χ2) of GWAS for the variants in a gene was used to determine the gene-based P value52. The Bonferroni method was used to calculate the P value significance threshold, which is 2.64 × 10−6 when 0.05 is divided by the total number of genes .We also performed a generalized gene-set analysis using MAGMA within FUMA. variants within exonic, intronic, and untranslated regions were chosen for each gene. The 18,888 protein-coding genes were used in MAGMA. The mean of the summary statistic (http://gusevlab.org/projects/fusion/). A P < 8.49 × 10−6 was considered significant in LV tissue, a P < 7.42 × 10−6 was considered significant in atrial appendage tissue, a P < 1.28 × 10−5 was considered significant in artery coronary tissue. FUSION was then run with its default settings.For each of the 12 LVRWT phenotypes, we performed a TWAS to identify the most strongly associated gene at each locus based on imputed cis-regulated gene expression. We used FUSION with eQTL data from GTEx v8. Precomputed transcript expression reference weights for the LV (5886 genes), the atrial appendage (6740 genes), and the artery coronary (3989 genes) were obtained from the FUSION authors’ website (http://kobas.cbi.pku.edu.cn/) to obtain Gene Ontology (GO) terms. Tissue expression analyses were obtained from GTEx which were also integrated in FUMA. Average gene expression per tissue type was utilized as a gene covariate to test for a positive link between gene expression in a given tissue type and genetic correlations.Functional enrichment and pathway characterization of the 127 candidate genes at end systole and 95 candidate genes at end diastole were done in the website  for each LVRWT trait, and remove variants in LD using default settings  to generate independent variations. A P < 0.05/5 traits = 0.01 was considered statistically significant. All analyses were performed using the R package TwoSampleMR.We used MR to estimate causal effects between the 12 LVRWT exposures and 11 CVDs. For two sample MR analyses, we used MR-Egger regression, weighted median and mode-based estimations as sensitivity analyses, along with the inverse-variance weighted (IVW) technique as our major model. To determine whether any discernible influence was mediated by outliers, the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO)54 to construct the polygenic risk score (PRS) of each LVRWT trait based on the effect sizes derived from the LVRWT GWASs. The PRS was calculated through a weighted model, as shown below:β values (the log of odds ratio) is the summary statistic for the effective allele and G is the number of the effective allele observed. We used variants with genome-wide significant (P < 1 × 10−5) and clumping window  to derive PRS. We repeated this procedure for each of the 12 traits, producing 12 PRSs. We categorized participants into three genetic risk levels: low (lowest tertile), intermediate (second tertile) and high (highest tertile).We used the C + T (clumping + thresholding) methodAdditional and detailed analyses are available in the Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19Supplementary Data 20Supplementary Data 21Supplementary Data 22Supplementary Data 23Supplementary Data 24Reporting SummarySource Data


Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains.Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models.While wood quality traits were mostly additively inherited, considerable non-additive effects and lower heritabilities were detected for growth traits. For growth, GBLUP-AD better partitioned the additive and dominance effects into roughly equal variances, while ABLUP-AD strongly overestimated dominance. The predictive abilities of breeding and total genetic value estimates were similar between ABLUP-AD and GBLUP-AD when predicting individuals from the same families as those included in the training dataset. However, GBLUP-AD outperformed ABLUP-AD when predicting for new unphenotyped families that were not represented in the training dataset, with, on average, 22% and 53% higher predictive ability of breeding and genetic values, respectively. Resampling simulations showed that GBLUP-AD required smaller sample sizes than ABLUP-AD to produce precise estimates of genetic variances and accurate predictions of genetic values. Still, regardless of the method used, large training datasets were needed to estimate additive and non-additive genetic variances precisely.This study highlights the different quantitative genetic architectures between growth and wood traits. Furthermore, the usefulness of genomic additive-dominance models for predicting new families should allow practicing mating allocation to maximize the total genetic values for the propagation of elite material. Hence, accelerating breeding for improved reforestation material is becoming essential for traits related to wood quality , biotic To fully harness the power of GS, models need to fit and rely on both additive and non-additive genetic variances for delivering the most optimal selections for the propagation of elite material. Non-additive genetic variance can be partitioned into dominance and epistasis components; that is the interaction of alleles within genetic loci, and the interaction of alleles among different loci, respectively . SimulatIn forest genetics, several studies have attempted to disentangle additive and non-additive effects. Dominance may be separated from additive effects with structured schemes of full-sib crosses, such as in diallel experiments, where each parent is mated with several others. In addition, the partition of additive, dominance, and epistasis components requires clonal repetition of individual genotypes from full-sibs . In treeG) used in GBLUP can estimate the realized relationships based on the fraction of the genome shared between individuals. Furthermore, the G-matrix allows the detection of inbreeding, hidden co-ancestry, and unknown parentage in breeding populations with shallow pedigree, such as for forest trees , a widespread Canadian conifer being the subject of major reforestation and breeding efforts  evaluate the ability of GS-based GBLUP and conventional pedigree-based ABLUP models to partition additive and dominance variances for growth and wood traits; (2) compare the predictive ability of ABLUP and GBLUP when predicting for the same families as those included in the training population, or for new unphenotyped full-sib families that were not part of the training population; and (3) investigate the effect of sample size on the precision of genetic parameter estimates and the accuracy of genetic values.22.1The material was sampled from a test series designed to assess the genetic merit of first-generation selections of the white spruce breeding program in Québec, Canada, which had been subdivided into six breeding groups (BGs) delineated by their geographic region Figure 13), excluding the bark, was calculated following For the present study, 4,245 trees were sampled in the six BGs from 151 full-sib families involving 101 parents. The sampled trees were roughly equally distributed among the two test sites . The phenotypic traits tree height (HT), diameter at breast height (DBH), average wood density (WD), and acoustic velocity (AV) were assessed at age 16 since plantation for BGs 1, 2, 5, and 6, and at age 13 since plantation for BGs 3 and 4. The total volume . Trees were genotyped using an Infinium iSelect SNP array  as described in Ns):Genetic diversity within each BG was estimated with the status effective number , and 2) the phenotypes measured at age 13 in BGs 3 and 4, hereafter referred to as “dataset 2” . These two datasets were analyzed separately because the measurements were taken at different ages, thus having different means and variances, and also because there was no genetic relatedness between these two datasets (no parents in common). All analyses were conducted in the R v.4.0.2 environment  the phenotypes measured at age 16 in BGs 1, 2, 5, and 6, hereafter referred to as “dataset 1” , referred to as “ABLUP” models, or using realized genomic relationship matrices , referred to as “GBLUP” models. We ran two additive models (ABLUP-A and GBLUP-A) and two additive-dominance models (ABLUP-AD and GBLUP-AD). The full additive-dominance models were fitted using ASReml-R v.4.1  is the random block within site effect; p(s) is the random plot within site effect; and e is the residual term. The terms b(s), p(s), and e were fitted with heterogeneous  variance among sites, as bg is the random breeding group (BG) effect, with s:bg is the random effect of site-by-BG interaction, with a(s) is the random additive genetic effect nested within site, using the pedigree-based relationship matrix A for ABLUP, with a(s)~NaV, and using the additive genomic relationship matrix aG for GBLUP, with a(s)~N ; d(s) is the random dominance genetic effect nested within site, using the pedigree-based dominant relationship matrix D for ABLUP, with d(s)~NV, and using the dominant genomic relationship matrix dG for GBLUP, with d(s)~NV. The term d(s) was not included for the additive ABLUP-A and GBLUP-A models. The matrices X and Zx  are incidence matrices of their corresponding effects. The matrices I are identity matrices of their appropriate size. The symbols ⊕ and ⊗ refer to the direct sum and Kronecker product of matrices, respectively.where Va is a 2 x 2 variance-covariance matrix defined by the correlation of additive effects between sites (rBa) and unique additive variances for site ASS (The matrix Vd was defined by the correlation of dominance effects between sites (rBd) and unique dominance variances for site ASS  and its inverse were computed from the corrected pedigree using the “Amatrix” and the “ainverse” functions of the R packages AGHmatrix  following D matrix using the same proportions as in Eq. [6] before computing its inverse (“solve” function). Blending aG with A, or dG with D, using different proportions of xG  did not change the genetic parameter estimates (results not shown).The dominant relationship matrix nd nadiv , respectbg and s:bg were small with large standard errors, we removed these terms from the final models. The final additive-dominance models were then expressed as:Variance components estimated using the full GBLUP-AD models (Eq. [3]) are presented in where the terms are defined in Eq. [3]. The final models (Eq. [7]) had a similar AIC compared to the full models , but the BIC was always smaller for the final models (ΔBIC: -7 to -15), indicating that the final models were the most parsimonious for all traits S2. SimiThe equations used to obtain genetic parameter estimates within each site from the final additive and additive-dominance models are presented in where For the additive-dominance models, across-site estimates of individual narrow-sense heritability ). Estimated genetic values of individual trees on each site were obtained by adding the dominance deviations (BLUPs of the dominance effect d(s)) to the breeding values.Standard errors of genetic parameter estimates were obtained using the delta method (“vpredict” function from the ASReml-R v.4.1 package). Estimated breeding values of individual trees on each site were obtained as the best linear unbiased predictions (BLUPs) of the random additive effect (2.4PA) and prediction accuracy (PACC) of ABLUP and GBLUP models were estimated using two cross-validation (CV) procedures. The CV1 scenario evaluated the potential for predicting additional unphenotyped progeny trees within the same families as those included in the training population, while the CV2 scenario evaluated the prediction of new unphenotyped full-sib families. For CV1, trees were randomly split into 10 folds, making sure that each fold contained ~10% of the trees from each family . For CV2, families were randomly split into 10-folds such that there were only half-sib relationships between the training and validation datasets . For each round of CV, nine folds were used in model training, which was used to predict the breeding and genetic values for the remaining fold . This 10-fold cross-validation was repeated 10 times to obtain the standard deviation of estimates.The predictive ability , within each repetition , which can in turn affect PA and PACC estimates. We reported the mean and standard deviation of PA and PACC estimates across repetitions.All petition . For eacBVPA) or total genetic values (GVPA) and the observed phenotypes, within each site. The prediction accuracy of breeding value estimates (BVPACC) was obtained as GVPACC) was obtained as BVPACC and GVPACC of both ABLUP and GBLUP models, we used the within-site PACC between the corresponding ABLUP and GBLUP models were possible because we used the same heritability estimates for both models.The predictive ability of the models was evaluated as the Pearson’s correlation coefficient between the predicted breeding . This subset of 72 families was used for the two resampling scenarios.To investigate the ability of ABLUP-AD and GBLUP-AD models to estimate additive and dominance effects at different sample sizes, we ran two scenarios of resampling simulations: 1) varying the number of families while keeping the number of trees per family constant, and 2) varying the number of trees per family while keeping constant the number of families. For this analysis, we used dataset 1  and identified 72 families in which at least 26 trees per family were sampled. This was the number of trees per family that allowed us to keep the largest maximum number of trees for these resampling simulations  was conducted to estimate 33.1Genetic parameters were estimated by modeling additive effects in the pedigree-based ABLUP-A and genomic-based GBLUP-A models, or by modeling both additive and dominance genetic effects in ABLUP-AD and GBLUP-AD. In both datasets, the within-site narrow-sense and broad-sense heritabilities differed between sites (16) and average wood density (WD16), were moderately to highly heritable (ABLUP-A: 16), DBH16, and volume (VOL16) exhibited low to moderate heritabilities (ABLUP-A: 16 and VOL16 to as much as 0.77 for WD16. The estimates of 16 to 0.41 for WD16. The two wood traits showed very low genotype-by-environment interactions (GxE), as indicated by high narrow-sense .For wood traits and dataset 1, only small dominance effects were detected Figure 2For growth traits and dataset 1, both the AIC and BIC values favored the additive-dominance models over the additive models Table 3.For the second dataset, we found results similar to those of dataset 1, that is moderate to high across-site heritabilities for wood traits, while growth traits presented lower heritabilities (3.2BVPA), that is the correlation between the predicted breeding values and the observed phenotypes, considerably differed between sites for most traits. For simplicity, we present the averaged estimates across sites. For the first cross-validation scenario  in dataset 1 and the additive ABLUP-A and GBLUP-A models, we found BVPA values ranging from 0.48–0.55 for wood traits, and from 0.36–0.39 for growth traits (BVPACC) were similar between wood (BVPACC=0.84–0.90) and growth traits (BVPACC=0.76–0.91).The predictive abilities of breeding value estimates PABV, that isPA). The results were identical in terms of prediction accuracy (PACC) since we used the same heritability estimates for both methods. For CV1, The BVPA were similar between ABLUP-A and GBLUP-A for all traits.To compare ABLUP and GBLUP models, we simply use the predictive ability  than for wood traits, with BVPA values remaining above 0.41. These results translated into much smaller BVPACC for growth (0.44–0.52) than for wood traits (0.71–0.74) under CV2. Again, we obtained similar BVPA values between ABLUP-A and GBLUP-A for all traits.For the second cross-validation scenario (CV2) in dataset 1, in which the predicted unphenotyped full-sib families were not part of the model training , the BVPA and BVPACC estimates for wood than for growth traits.The results for dataset 2 were similar to those of dataset 1 Table S93.3GVPA) for growth traits by 0.02–0.04 compared with that of breeding values (BVPA) from the additive models, but little to no improvement was observed for wood traits  under the additive-dominance models, after standardizing by the broad-sense heritability, compared with that of breeding values (BVPACC) under the additive models, which were standardized by the narrow-sense heritability.For dataset 1 under CV1, and for both ABLUP and GBLUP, the additive-dominance models improved the predictive ability of genetic values , but not for wood traits . We found the opposite trend for ABLUP models under CV2, with a reduction of 0–0.03 from BVPA (ABLUP-A) to GVPA (ABLUP-AD), and a large reduction of 0.11 from BVPACC to GVPACC for the three growth traits.When predicting for new unphenotyped families under CV2, the GVPA) between ABLUP-AD and GBLUP-AD  than for wood traits (increase of up to 0.03 in GVPA). Interestingly, GBLUP-AD also increased the predictive ability of breeding values (BVPA) over ABLUP-AD, again only under CV2, and especially for growth traits (increase of BVPA up to 0.05). Thus, GBLUP-AD was better than ABLUP-AD for predicting both breeding and genetic values for unphenotyped families.Under CV1, we found almost equal predictive ability of genetic values PAGV between GVPA almost doubled using GBLUP-AD versus ABLUP-AD for DBH  and volume .For dataset 2, we obtained very similar results Table S93.416, WD16), the dominance ratio (16), or sometimes both parameters  were generally overestimated using ABLUP-AD, leading to higher broad-sense heritabilities  or accuracy (GVPACC) of genetic values under CV2 steadily increased with increasing the number of families sampled in the training dataset up to the maximum number of families  or 20 (HT16) trees per family for both ABLUP-AD and GBLUP-AD.We found similar results when varying the number of trees per family while keeping constant the number of families. Increasing the number of trees per family decreased standard deviations of 44.1We found that the studied wood traits, acoustic velocity, a proxy for wood stiffness, and average wood density, were under moderate to high genetic control, with almost no GxE interactions, indicating very little rank changes of families between the two contrasting environments tested. These conclusions based on GxE are limited as we only considered two sites. However, these results were confirmed in the two datasets analyzed here, which can be seen as independent replications since they consisted of different breeding populations with no relatedness between them. Our results are also in line with previous studies, which generally found higher across-site heritability and lower GxE estimates for wood quality versus growth traits in white spruce , Norway 13, HT13, and DBH13) were used, although we performed additional SNP and individual quality control filtering following pedigree correction. The reported within-site narrow-sense heritabilities using the ABLUP-A models in Our results for the second dataset can be directly compared to those of Eucalyptus species and their hybrids, the genetic variance of wood density was found to be mostly additive  over the additive-dominance models (GBLUP-AD) for both datasets. The literature is sparse regarding the evaluation of non-additive effects, especially in white spruce , and eveGBLUP-AD . These cGBLUP-AD . In fullGBLUP-AD  and in SGBLUP-AD . In the additive . Thus, oadditive .High narrow-sense heritabilities make wood traits excellent candidates for genetic improvement. The additive genetic variation is of utmost importance to tree breeders because it can be utilized in a simple and efficient random mating design such as open-pollinated seed orchards for the deployment of improved genetic material. Considerable genetic gains can be achieved for wood traits under such conditions . Here, w4.2Improvement for growth has been the main focus in most forest tree improvement programs, yet non-additive effects have not been frequently evaluated or used in tree breeding, including for white spruce . Compare16 in dataset 1; DBH13 in dataset 2), clearly showing the important confounding of genetic variances occurring in the ABLUP-AD models. The reduction of the estimated additive variance when non-additive effects are included in the model has also been reported previously  variances using open-pollinated , 2018 or4.3PA) is obtained as the correlation between the predicted breeding/genetic values and the observed phenotypes, and the prediction accuracy (PACC) is obtained as the correlation between the predicted breeding/genetic values and the true breeding/genetic values. In operational breeding populations, the true breeding/genetic values are unknown and many previous studies used, for this purpose, the BLUPs obtained using the complete dataset  with either ABLUP or GBLUP as a surrogate for the true breeding values  techniques can be used to evaluate model predictive ability and accuracy. The predictive ability . Similar results were found for GBLUP-AD and for dataset 2. Given the differences observed between sites, we suggest that computing PA and PACC within sites as done here is more accurate, especially for traits with higher GxE, and when there is a sufficient number of samples per site.Here, we evaluated n spruce , 2020b. es e.g., , 2020b. en sites . For exa4.4BVPACC) for all traits, datasets, and models .In our first CV scenario (CV1), individuals were divided into 10 folds, with ~10% of individuals from each family in each fold so that all families were well represented in training and validation datasets . Using the CV1 scenario and the additive models, we obtained very high prediction accuracies of breeding values (BVPA) or accuracy (BVPACC as BVPACC is a better estimator to compare across studies because it accounts for the different heritabilities. We found that the accuracies obtained in this study for the GBLUP-A models and dataset 1 were similar to those reported for Norway spruce (Ns). The accuracies obtained for both datasets in this study were larger than those obtained for a white spruce polycross trial with similar Ns, which used a smaller sample size  found either no improvements  or to the expected smaller within- versus among-family genetic variances  and 7K SNPs, but were still able to show an advantage of GBLUP when ranking individuals within full-sib families. Other studies reported significantly positive within-family PA or PACC values, either from full-sib family trials without clonal replication  compared with the predictive ability of breeding values (BVPA) obtained from the additive models. This was observed only for growth traits because wood traits showed little dominance variance. For growth traits, the PA increased from the additive to the additive-dominance models by 6.4% and 8.2% on average for ABLUP and GBLUP, respectively. This increase was expected because the additive-dominance models could predict a larger portion of the phenotypic variation due to the inclusion of significant dominance genetic effects. However, this improvement in PA was rather small considering that the dominance variance accounted for about half of the genetic variance for growth traits and that the broad-sense heritabilities obtained from the additive-dominance models were ~55% higher on average than the narrow-sense heritabilities estimated by the additive models. The standardization of PA as PACC) from the additive to the additive-dominance models for all traits, but more so for growth traits . These results indicated that the additive-dominance models could predict with less accuracy the total genetic values compared with breeding values under CV1.Under CV1 with both datasets 1 and 2, the inclusion of dominance in the additive-dominance models increased the predictive ability of genetic values , which is highly amenable for a large diversity of clonal lines in spruce species (GVPA) for growth traits in this study; 2) the selection intensity could be largely increased by genotyping more individuals; and 3) it dramatically reduces the breeding cycle length because no field testing is required for the SE lines. Thus, we expect that the predictions of total genetic values by GS models, although less accurate than those of breeding values, should still allow increasing genetic gains.The CV1 scenario simulated the prediction of additional genotypes from the same families as those included in the training population, which is within the current generation, and has direct applications in forest tree breeding. For example, GS models could be beneficial for the selection of somatic embryogenesis (SE) lines, for which the pedigree-based methods do not allow within-family ranking. As such, thousands of cryo-conserved SE lines can be genotyped to predict their genetic values. The best individuals, both across and within families, can then be deployed  species . Such cl species . This st4.6BVPA) between the additive ABLUP-A and GBLUP-A models, the results were different for the additive-dominance models. We found that the predictive ability of breeding (BVPA) and of genetic values (GVPA) for growth traits were substantially increased by 22% and 53% on average, respectively, for GBLUP-AD versus ABLUP-AD. For wood traits, there was a slight advantage of GBLUP-AD for wood density (~5–10%), which presented small dominance effects, but no advantage was observed for acoustic velocity.We found different trends under the second cross-validation scenario (CV2), in which the training and validation datasets included different families, thus simulating the prediction of breeding and genetic values for new unphenotyped full-sib families. However, one must keep in mind that those predicted families were half-sib related to a few families in the training dataset. While there were no differences in predictive ability of breeding values  by 5%, but substantially increased the predictive ability of genetic values (GVPA) by 24%, on average, compared with GBLUP-A. This increase in GVPA led to an increase in GVPACC of 10% from GBLUP-A to GBLUP-AD, on average. The trait that showed the largest improvement was DBH in dataset 2 (+54% in GVPA and +27% in GVPACC), which was also the trait that had the highest proportion of genetic variation attributed to dominance under GBLUP-AD /2=1,891 possible ways to combine the 62 parents of the four breeding groups, including crosses between breeding groups, but only 90 crosses were tested in the present study. Although the accuracy was smaller when predicting new families (-25% on average for CV2 versus CV1 in dataset 1), this could be compensated by the large increase in selection intensity from predicting additional parent-pairs and thus, ultimately, could lead to higher genetic gains.Mate allocation using GS models was found to increase the selection response in animal breeding . This onAt the operational level, this strategy would be relatively quick to implement because the parents are already sexually mature and good phenotypic data is available for their tested progeny, also allowing the measurement of new relevant traits, for instance in relation to adaptation to climate . Another4.7GVPA or GVPACC under CV2. Evaluating the minimum number of families needed for accurate predictions of genetic values was more difficult because, as the number of families sampled decreased, some families in the validation datasets became unrelated with the training population in CV2  or trees per family (6–26) sampled in dataset 1. For our breeding population, we found that a large sample size of 60-72 families and the maximum of 26 trees per family was required for accurate estimation of broad-sense heritabilities H^ind2, An important finding of our study is that GBLUP-AD estimated genetic parameters and predicted genetic values with greater accuracy than ABLUP-AD at all sample sizes tested, especially for growth traits. For these traits, smaller sample sizes were needed in GBLUP-AD to obtain similar prediction abilities of genetic values compared to ABLUP-AD under CV2. Similarly, 4.8Using two large full-sib datasets, we evaluated the inclusion of dominance in pedigree-based ABLUP and genomic-based GBLUP models for wood and growth traits. Wood traits were found to be optimal candidates for tree breeding efficiency, as they presented higher narrow-sense heritabilities and lower GxE than growth traits. High accuracies of GS models were even maintained when predicting for new unphenotyped families, which were half-sib related to a few families in the training dataset, a scenario that is closer to the production of the next generation.Predictions for growth traits will highly benefit from application of GS instead of using pedigree-based methods. GBLUP led to more realistic estimates of genetic variances and better partitioning of additive and non-additive variances, thus allowing to better plan the methods of selection for breeding purposes and the propagation of reforestation material. For growth traits, the use of GBLUP-AD led to higher predictive abilities for new families (CV2) than ABLUP-AD, mainly due to the ability of GBLUP-AD to predict dominance. The predictive ability of breeding values was slightly improved under GBLUP-AD for new families, compared with ABLUP-AD. Dominance was generally predicted with smaller accuracy than breeding values in GBLUP-AD (CV1). Still, it was sufficiently accurate to substantially increase predictive abilities and accuracies for unphenotyped families (CV2), and to outperform ABLUP-AD. It can be concluded that the dominance term should always be included into models when significant dominance variance is expected, such as for growth traits in this study.By subsampling various sets of families and trees per family, we found that GBLUP produced better estimates of genetic variances and higher predictive abilities than ABLUP for all subsamples, especially for growth traits. Results also highlight the need to rely on sufficiently large sample sizes to obtain accurate estimates of genetic parameters and predictions of breeding and genetic values.The ability of GBLUP to predict for new unphenotyped families also provided useful insights for next-generation prediction accuracies. Nonetheless, there is a need for next-generation studies in white spruce and other important tree breeding species. Meanwhile, predicting for new crosses may be used to perform mating allocation and maximize the total genetic values for elite family or clonal propagation in the current generation.https://treesource.rncan.gc.ca/en.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: SN, JBe, JBo, and PRNL conceived the study. SN and PRNL wrote the manuscript. JBo, PRNL, SG, JBe, and MP edited the manuscript. SN and SG performed the statistical analyses. JBe, JBo, PRNL, and MP set up and maintained the field trial. JBo, JBe, and PRNL obtained funding to support genotyping and phenotyping. All authors contributed to the article and approved the submitted version.