Second, we analyzed for enrichments of association signals in miRNAs and their miRNA-target networks. Our results demonstrated that genomic regions harboring miRNA genes were significantly (P < 0.05) enriched with GWAS signals for milk production traits and mastitis, and that enrichments within miRNA-target gene networks were significantly higher than in random gene-sets for the majority of traits. Furthermore, most between-trait and across-breed correlations of enrichments with miRNA-target networks were significantly greater than with random gene-sets, suggesting pleiotropic effects of miRNAs. Intriguingly, genes that were differentially expressed in response to mammary gland infections were significantly enriched in the miRNA-target networks associated with mastitis. All these findings were consistent across three breeds. Collectively, our observations demonstrate the importance of miRNAs and their targets for the expression of complex traits.MicroRNAs (miRNA) are key modulators of gene expression and so act as putative fine-tuners of complex phenotypes. Here, we hypothesized that causal variants of complex traits are enriched in miRNAs and miRNA-target networks. First, we conducted a genome-wide association study (GWAS) for seven functional and milk production traits using imputed sequence variants (13~15 million) and >10,000 animals from three dairy cattle breeds,  Complex phenotypes consist of contributions from multiple genomic loci, and accurate and reliable prediction of future complex traits and diseases based on genomic information is critical for human personalized medicine as well as for breeding of agricultural animal and plant species11. In the past decade, genome-wide association studies (GWAS) have been a productive way to achieve this goal5. Yet, big challenges remained in extending GWAS results to informative biological hypotheses underlying complex traits variation primarily due to 1) poor detection of many causal loci/genes with small effects by GWAS with limited sample sizes13, and 2) masking of true association signals by linkage disequilibrium (LD) between causal loci and neighboring markers14, especially in livestock with high degree of relatedness15. Recently, we have shown that the predictive ability of the genomic best linear unbiased prediction model increases in accuracy when the model utilizes and quantifies the combined contributions of markers in genomic regions associated with the genetic architecture of the underlying trait18. Also, biological knowledge has been incorporated to improve the understanding of GWAS results25. For instance, by integrating genome functional annotations with GWAS data, Finucane et al. (2015) revealed conserved genomic regions that were strongly enriched with genetic variation of many complex traits in human25. Holmans et al. (2009) provided insights into the biology of bipolar disorder through incorporation of Gene Ontology (GO) annotation into GWAS results23. Fang et al. (2017) revealed novel insights into the genetic and biological basis underpinning mastitis by incorporating transcriptome data22.Understanding the genetic architecture, 26. Thus, most transcripts in eukaryotic cells are non-coding RNA, and much of the regulatory capability and function of the genome are provided by non-coding RNA transcripts28. Among the many classes of non-coding RNA species are microRNAs, which act posttranscriptionally to fine-tune protein synthesis. The vertebrate genome encodes in the order of several hundred microRNAs comprising both evolutionary conserved and species-specific genes. The encoded miRNAs are ~22 nucleotide long RNA molecules that align to sequences in target mRNA molecules, leading to either deadenylation and decay of mRNAs or repression of protein translation29. In more rare cases, miRNAs can also activate and up-regulate gene expression in presence of distinct cofactors and under specific cellular conditions30. A single miRNA can target several different mRNAs, and moreover, mRNAs are often controlled by several different miRNAs29. Computational estimates suggest that more than 30% of all protein-coding genes in human are regulated by miRNAs34. This strongly indicates that miRNAs have widespread roles in vivo, and that all genetic networks and pathways are expected to be regulated to some degree by miRNAs. Genomic sequence variation can affect miRNAs functions in several ways: Variants can occur in sequences that are responsible for driving the expression of miRNA genes; variants can disrupt or create miRNA binding sites in target mRNAs; and the miRNAs themselves can exist as variants35. In this study, we aimed to investigate the joint effect of genetic variations in miRNA genes and in their targets-networks on complex traits of economic importance in dairy cattle.GWAS signals often map to non-coding regions of the genome, demonstrating that sequence variants in protein coding genes alone cannot explain most quantitative phenotypes. Indeed, conventional protein coding genes constitute only a very small percentage of the genome; yet ~75% of the genome generates RNA transcriptshttp://www.nordicebv.info). We used imputed sequence variants (13~15 million single-nucleotide polymorphism (SNPs)) to ensure the coverage of most genes and miRNAs in the bovine genome (UMD 3.1). We performed within breed analyses for Holstein , Nordic red  and Jersey  cattle, to confirm our findings. Infection-induced transcriptome data were then integrated to validate the significant miRNA-target networks detected for mastitis, and to investigate the underlying genetic and biological basis of our findings.The overall study design is shown in Fig. 10P values of all tested sequence variants in the seven traits across HOL, RDC and JER are shown in the Manhattan plots, Supplementary Fig. P-value < 3.3 × 10−9 (Bonferroni correction), a total of 6, 4, 12, 0, 5, 1 and 2 quantitative trait loci (QTL) were detected for MY, FY, PY, BC, mastitis, health and fertility, respectively in HOL, which jointly explained 24.1%, 22.3%, 19.3%, 0%, 9.7%, 8.2% and 5.3% of genomic variance in each corresponding trait, respectively. In RDC with the same P-value threshold as in HOL, 6, 6, 6, 4, 2, 0 and 4 QTLs were detected for MY, FY, PY, BC, mastitis, health and fertility respectively, which jointly explained 13.0%, 13.3%, 9.3%, 33.3%, 6.8%, 0% and 8.1% of genomic variance for each trait, respectively. In JER at P-value < 3.7 × 10−9, only 2 and 1 QTLs were detected for MY and FY, respectively, accounting for 3.9% and 3.1% of genomic variance for each trait, respectively. All the results demonstrated that the detected QTLs explained only a small fraction of genomic variance in complex traits, particularly in JER with a small sample size. The details of QTLs for all seven traits in the three breeds are summarized in Supplementary Table The single-marker GWAS was performed to identify genomic variants associated with the traits being studied. The −log36 (http://www.mirbase.org/ftp.shtml) were observed to be distributed with an average of 26 (range: 7 to 77) miRNAs per chromosome  enrichments were observed for most traits in the three breeds, especially for three milk production traits and mastitis  were correlated between breeds  that were in close proximity to the well-known milk/fat gene, DGAT1, on Bos taurus chromosome 14 (BTA 14)37, similar enrichments were still observed for the three production traits across the three breeds, particularly in RDC and JER . A total of 750 bovine autosome miRNA genes expressed in different tissues from miRbaseome Fig. . The aveome Fig. . Since vome Fig. , the anatis Fig. , and theeds Fig. . Of notein silico predict targets of all miRNAs38 (http://mirmap.ezlab.org/). Computational target prediction remains a challenge as they predicted targets only rely on the physical properties of miRNA regulation , hence only the top 25% of predicted targets were analyzed in the downstream analysis, including 11,455 out of 24,616 bovine genes . The average number of targets of miRNAs was 434   and only SNPs located in the open reading frame (ORF) of targets were included. Furthermore, for comparison, 1,000 random target networks were generated for each miRNA, and 769 GO terms together with 916 Reactome terms were also analyzed (see Methods).The targets of trait-associated miRNAs are likely to be enriched for GWAS signals. To test this, we employed the miRmap software to 25) Fig. . To testP < 0.05, Wilcoxon-test) higher for all seven traits across three breeds, except for health and fertility in HOL  correlated between traits in HOL, and the highest correlations were observed among milk production traits  in GWAS  greater than the random correlation coefficients, demonstrating that the genetic architecture of multiple complex traits shared certain similarities in the underlying miRNA-target networks. Only the significant between-traits correlations in HOL, RDC and JER are shown in Fig. vs. BC in RDC, and mastitis vs. health in JER. Additionally, the between-trait correlations with miRNA-target networks were typically higher than those with GO and Reactome terms, and the patterns were consistent across the three breeds . Furthermore, the between-breed correlations with miRNA-target networks tended to be higher than those with GO and Reactome terms  across all three breeds for a trait (P < 0.053 = 1.25e-04), this miRNA target-network was considered significant, suggesting that this miRNA and its target genes may participate in biological processes pertinent to this particular trait. A total of 55 significant miRNA-target networks were detected for seven traits and twelve miRNAs were involved in several traits  with DEGs detected in most of the conditions  of DETs across all experimental conditions are shown in Fig. P < 0.05, t-test)  Fig. .Figure 72FC > 0 (up) or <0 (down) across all infection conditions. In total, 1,002 unique up-regulated DETs were detected with an average of 142 (range: 31 to 231) for each miRNA, and 1,111 down-regulated DETs were detected with an average of 146 (range: 24 to 255) for each miRNA. The mastitis GWAS signals were significantly more enriched in up-regulated DETs of miRNAs than in down-regulated DETs across all three breeds , an indicator trait for mastitis, and bta-miR-10a, bta-miR-2448, bta-miR-2284 family members and bta-miR-2285 family members55. Also in other species several of these eighteen miRNAs have been linked to mastitis and immune protection. In porcine mammary epithelial cells, coliform mastitis induced differential expression of miR-10a/10b and miR-30b/c/f56. Moreover, miR-221 was differentially expressed in colostrum and peak lactation periods in the caprine mammary gland57.Eighteen miRNA-target networks were associated with mastitis many of which have previously been observed in connection with mammary gland infections. Thus, 58. Our work showed a genetic link between the two traits mediated by miRNA-target networks, which may in part help understanding the unfavorable correlation between mastitis and MY.Finally we defined genomic features for mastitis by integrating both transcriptome and miRNA-target networks with GWAS data. The results showed consistently across all three breeds that the mastitis associated GWAS signals were significantly more enriched in DETs than in non-DETs. Additionally, partitioning of DETs showed that up-regulated targets had higher enrichment compared to down-regulated targets. Furthermore, three miRNA-target networks showed enrichment for GWAS signals for both MY and mastitis. Intriguingly, when integrated with the mastitis-related transcriptome data, the analysis revealed that up-regulated transcripts belonging the network targets of all three miRNAs were significantly more enriched with association signals for mastitis than for MY across the three breeds; and vice versa, the down-regulated transcripts were more associated with MY than with mastitis. High milk yield is often associated with risk of mastitisIn conclusion, the marker-set test of joint effects of SNPs linked to miRNAs enabled us to discover contributions from miRNAs as well as of their target networks to multiple complex traits in three cattle breeds. Overall, this work underscores that the use of biological priors such as genomic features defined by miRNAs and their targets enhances our insight into the genetic architecture underlying phenotypic diversity. With the rapid progress in the field of annotation of functional elements in livestock genomes, the genomic feature based analysis like marker-set test or GFBLUP will be increasingly useful for discovering small effect loci or for increasing the accuracy and reliability of prediction of consequences of genomic selection in animal and plant breeding.All experimental procedures involving animals have been approved by the Danish Animal Experiments Inspectorate and complied with Danish Ministry of Justice Laws concerning animal experimentation and care of experimental animals. All the experiments were performed in strict accordance with guidelines and regulations established by these committees. Members from these committees carried out inspections during the entire animal infection experiments.et al.59 and Jorgensen et al.60. Briefly, in the first study liver biopsies were sampled at −22, 3, 6, 9, 12, and 48 h relative to LPS intra-mammary treatment in all three studied animals . In the second study liver biopsies were collected from the six animals at −144, 12 and 24 h post intra-mammary infection with E. coli, and the mammary gland samples of the infected and control udder quarters were collected from the same animals at 24 h post infection . Finally, a total of 48 samples were applied with RNA sequencing using a 100 bp pair-end approach with llumina HiSeq 2000 by AROS Applied Biotechnology . Time was considered as the only effect in the statistical model for analyzing liver samples data, while infection status was included in the model for mammary gland samples. The details of RNA-seq data analyses were previously described by Fang et al.22. FDR method was employed to control multiple testing, and genes with FDR < 0.05 were considered as DEGs.All the transcriptome data used in this study came from two intra-mammary infection studies involving three and six healthy HOL animals, respectively at the very early stage of their first lactation. The details of the involved animals and the sample collection procedures were previously described by Jiang i.e., MY, FY and PY), BC, mastitis, health and fertility. These DRPs were obtained from a routine genetic evaluation by Nordic Cattle Genetic Evaluation (http://www.nordicebv.info/) and were available for 5,056 HOL, 4,310 RDC and 1,231 JER bulls. All known systematic effects have been corrected.The phenotypes currently analysed were de-regressed proofs (DRPs) of seven complex traits of economic importance in cattle, including three milk production traits  that had been genotyped by Illumina BovineHD chips . In the next step the imputed HD genotypes were imputed to the whole genome sequence level using Minimac264 based on the reference population of 1,228 animals from both Run4 of the 1,000 Bull Genomes Project65 and in-house whole genome sequence data62. In total, 22,751,039 biallelic variants were obtained after the imputation. The imputed sequence genotypes were then filtered to keep markers with deviation from Hardy-Weinberg proportions (HWP) > 10−6 and minor allele frequency (MAF) > 0.01. After the data editing, 15,355,382, 15,243,827 and 13,403,916 SNPs remained for the following analyses in HOL, RDC and JER, respectively with an average imputation accuracy of larger than 0.90. The imputation accuracy of a SNP increased with the increase of its MAF, and the details of the imputation accuracy were described by Wu et al. and Iso-Touru et al.66.The whole genome sequence genotypes of these animals were obtained from the imputation of 50 K and High Density (HD) genotypes. The imputation was conducted using a two-step strategy as recommended by Brøndum 67. The EMMAX was developed to take account for genetic relatedness and population structure as well as to improve the computational speed. In the first step, the polygenic and residual variances were estimated byi.e., DRP); 0, 68, and 0,1 is a vector of ones. Z is a design matrix relating phenotypes to polygenic effects. In the second step, the individual association signal was assessed by a linear regression modely, μ and 1 are the same as described above, −9, 3.3 × 10−9, 3.7 × 10−9 for HOL, RDC and JER, respectively. Manhattan plots were generated using qqman v.0.1.2 implemented in the R package69.The single-marker GWAS for the imputed sequence genotypes was conducted using a two-step variance component-based method that was implemented in EMMAXP-values) higher than −log10 minus three of the top SNP70. The proportion of genomic variance explained by the top SNP within a QTL was calculated as b was the allele substitution effect, and Regions showing association signal could be very large due to high LD structure owing to small effective population sizes in dairy cattle breeds. Therefore, we demarcated a region around the top SNP, which most likely harbours the causal mutation. A QTL region boundaries were defined through extending the position of the top SNP up- and down- stream to include SNPs with −log10. The Bioconductor package “org.Bt.eg.db” v. 3.3.0 (https://bioconductor.org/packages/release/data/annotation/html/org.Bt.eg.db.html) and “reactome.db” v 3.3.0 (https://bioconductor.org/packages/release/data/annotation/html/reactome.db.html) were used to link genes to the GO and Reactome terms, respectively. Here, we focused on the GO and Reactome terms consisting of at least 10 directly evidenced genes. In GO terms, only biological processes terms were considered. The imputed sequence variants were mapped to the bovine reference genome (UMD3.1). A SNP was assigned to a gene if its chromosome position was between the start and end positions of the gene .Genes grouped into a specific GO or Reactome term were considered to be a single marker set  divided by its standard error. It should be noticed that the LD patterns of SNPs among multiple genes in a genomic feature were typically low, especially for genes in different chromosomes. Although the LD patterns among SNPs and the sizes of marker sets were not directly taken into account in the summary statistics, they have been taken in account by the following cyclical permutation strategy as previously described21. Briefly, the test statistics . Therefore, any association of SNPs with genomic features was uncoupled while maintaining the correlation structure among test statistics of SNPs. Afterwards, a new summary statistic of the genomic feature was computed according to its original chromosome position. The permutation procedure was repeated 1,000 times for each tested genomic feature, and an empirical P-value was computed according to one-tailed tests of the proportion of random summary statistics larger than that observed. This marker set-test method together with multiple quantitative genomic tools have been implemented in our QGG package (http://psoerensen.github.io/qgg/).In which r ~ N, where µ and σ2 can be approximately estimated as the mean and standard deviation (SD) of the 1000 randomly generated correlations. The significant level for an observed correlation based on miRNA target-networks was then determined by using one-sided test.The between-trait correlations within a breed and the between-breed correlations for a same trait were computed based on the GWAS signal enrichments of miRNA target-networks. In order to determine the significant level of an observed correlation, we randomly generated targets with the same size as predicted for each miRNA, then a random correlation was calculated using the enrichments of random target-networks. We repeated this procedure for 1000 times, thereby 1000 random correlations were generated for each particular correlation. Assuming that a correlation approximately follows a normal distribution 71, where a hypergeometric test, based on a KEGG pathway database, was applied, and the P-values for each pathway were adjusted using the FDR method. We used pathways with FDR < 0.1 to indicate the putative biological function of gene lists being studied.The functional enrichment analyses of a list of genes were conducted using R package clusterProfilerhttp://www.mirbase.org/ftp.shtml), and their best quantile predicted target genes is available in (http://mirmap.ezlab.org/). The GO annotation database can be publicly accessed (https://bioconductor.org/packages/release/data/annotation/html/org.Bt.eg.db.html). The Reactome annotation database can be publicly accessed (http://bioconductor.org/packages/release/data/annotation/html/reactome.db.html). The whole genome sequencing data from the 1000 Bull Genomes Project are publicly available from NBCI under SRA no. SRP039339 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA238491) and variations in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). The genotype and phenotype data, and genome sequence data from Aarhus University are available only upon agreement with the commercial breeding organization (http://www.vikinggenetics.com/) and should be requested directly from the authors or the breeding organization.All genomic annotation data defining gene regions is available for download (ftp://ftp.ensembl.org/pub/release-84/gtf/bos_taurus). The miRNA annotation data is available for download (Supplementary infomation


Gadus morhua), by exploring a unique data set combining whole‐genome sequencing data and behavioural data obtained with acoustic telemetry. Within a confined fjord environment, we find three genetically differentiated Atlantic cod types belonging to the oceanic North Sea population, the western Baltic population and a local fjord‐type cod. Continuous behavioural tracking over 4 year revealed temporally stable sympatry of these types within the fjord. Despite overall weak genetic differentiation consistent with high levels of gene flow, we detected significant frequency shifts of three previously identified inversions, indicating an adaptive barrier to gene flow. In addition, behavioural data indicated that North Sea cod and individuals homozygous for the LG12 inversion had lower fitness in the fjord environment. However, North Sea and fjord‐type cod also occupy different depths, possibly contributing to prezygotic reproductive isolation and representing a behavioural barrier to gene flow. Our results provide the first insights into a complex interplay of genomic and behavioural isolating barriers in Atlantic cod and establish a new model system towards an understanding of the role of genomic structural variants in adaptation and diversification.Genetic divergence among populations arises through natural selection or drift and is counteracted by connectivity and gene flow. In sympatric populations, isolating mechanisms are thus needed to limit the homogenizing effects of gene flow to allow for adaptation and speciation. Chromosomal inversions act as an important mechanism maintaining isolating barriers, yet their role in sympatric populations and divergence with gene flow is not entirely understood. Here, we revisit the question of whether inversions play a role in the divergence of connected populations of the marine fish Atlantic cod ( Yet, our understanding of the genetic mechanisms behind speciation with gene flow is far from complete , four large (5–17 Mb) chromosomal rearrangements, each possessing high internal linkage disequilibrium (LD), have been detected , as well as behavioural differences  may also play a role  from seven sites were sampled , North Sea , Skagerrak Tvedestrand fjord , and the western  and eastern Baltic Sea . For all TVE individuals, both genomic and behavioural data were collected. DNA extraction, library preparation , and sequencing  was performed at the Norwegian Sequencing Centre according to the Centre's protocols , and intrachromosomal LD . Additionally, repetitive regions were excluded  and 10‐fold cross‐validation was performed. Maximum likelihood phylogenetic inference was performed using raxml version 8.2.4 . The species tree and population sizes were estimated using the multispecies coalescence approach in the add‐on snapp version 1.3.0 , but allowing no missing data. As snapp runtimes are very long on larger SNP sets, the data set was reduced by applying a minimum distance of 70,000 bp between SNPs, resulting in 3,307 SNPs. The script “snapp_prep.rb”  using smartPCA in  1.3 Li,  for site2.3FST; as in Weir & Cockerham, dxy; as in Ruegg, Anderson, Boone, Pouls, & Smith, df; see Ruegg et al., bayescan version 2.1  > 1, false discovery rate [FDR] 0.05) as well as genes within 5 kb of such outlier SNPs. Enrichment tests for GO terms were performed for genes detected in comparisons TVEf–TVEn and TVEf–KIE, but not TVEn–KIE using Fisher's exact test with the algorithm “weight01” in the topgo package version 2.26  was calculated using all 204 individuals. SNPs in regions of putative inversions  from each of the identified co‐occurring North Sea (TVEn), western Baltic (KIE) and fjord‐type (TVEf) populations  were captured and tagged in the Tvedestrand fjord in May 2011–2013 as described elsewhere  were calculated in 30‐min time bins following Simpfendorfer, Heupel, and Hueter . Depth dIndividual fate was assigned based on detection patterns. Fish were classified as either: (a) alive within the study area , (b) dispersed from the study area  or (c) dead within the study area .BT). The study area (a no‐take marine reserve) holds a key spawning locality for local cod , inversion state for the LG02, LG07, and LG12 inverted regions , body size (L), and genetically determined sex , cod inversion states as factors with three levels , body size as a continuous variable, and genetic sex as a factor with two levels .Linear mixed effects models were used to analyse variation in cod behavioural traits (We included the following interaction effects: (a) between season and each of the inversions to explore whether any behavioural changes between feeding and spawning season depended on inversion state, (b) between season and body size because smaller fish might not be sexually mature and therefore not participate in spawning, and (c) between season and sex because spawning behaviour can differ between females and males, where males are known to defend territories associated with seafloor features during the spawning season . Fish that dispersed permanently from the study area were excluded from this analysis because their fate cannot be determined. The full model included effects of inversion states , body size (L) and genetic sex (GS):Next, we used the Lande–Arnold linear regression approach  or fjord‐type (TVEf) based on genetic results (see Section We used the Akaike information criterion (AIC) for model selection  grouping with the North Sea cluster, while the remaining ones (N = 27) formed a well‐defined private cluster  were distributed between the main clusters on PC2 and could not be clearly assigned to a particular cluster  were obtained assuming one to three clusters. As in the PCA results, the admixture ancestry proportions yielded three main clusters represented by the eastern Baltic Sea sample, the North Sea samples and half of the TVE sample, while the ancestry proportions of the western Baltic and Norwegian coastal samples appeared admixed . To identify genomic regions that may act as structural barriers to restrict gene flow between these groups, we performed genome scans testing for LD and differentiation. We identified three previously described large regions of strong LD and high interpopulation divergence on LG 02, 07, and 12  as well as in TVEn (p < 0.01), and an overrepresentation of the inverted LG02 arrangement in BOR (p < 0.001) as well as in TVEf (p < 0.01) , whereas the inverted arrangement on LG12 was overrepresented in NOR (p < 0.01) and AVE (p < 0.001). For TVEf, we also observed an overrepresentation of the inverted LG07 (p = 0.0043) and the ancestral LG12 arrangement (p = 0.0041); however, both comparisons were not significant after correction for multiple comparisons. No significant yearly difference was detected within the TVE sample . All of the inverted regions segregated independently, and all but three of the 27 possible combinations of homozygous ancestral, heterozygous and homozygous inverted arrangements on the three LGs were present. The nonsampled combinations are: (a) homozygous for all inverted arrangements, (b) homozygous for the inverted arrangement on LG02 and LG07 but heterozygous on LG12, and (c) heterozygous on LG02 but homozygous for the inverted arrangement on LG07 and 12. However, due to few inverted arrangements, the chance of sampling all combinations within 204 individuals is small .Frequency comparisons of the ancestral and inverted arrangements among populations revealed a significant overrepresentation of the ancestral LG02 arrangement in LOW  Figure . On LG07q < 0.05) between TVEf–TVEn and TVEf–KIE, excluding SNPs also detected in TVEn–KIE, in 20 genes on four LGs (Supporting Information Table p < 0.05); however, none of these remained significant after FDR correction  was correlated with behavioural differences constituting an additional prezygotic barrier to gene flow, we used data from acoustically tagged specimens to calculate home range, mean depth, diel vertical migration, and survival.3.3.1p > 0.50).A mixed effects model supported two‐way interaction effects between the LG07 inversion state and season on cod home range size Table . Specifi3.3.2p = 0.85), while there was statistical support for an additive effect of genotype where TVEf individuals tended to occupy deeper waters compared to the TVEn individuals , accounting for effects of body size and sex .As for mean depth, a mixed effects model supported two‐way interaction effects between body size and season, as well as sex and season, on cod diel vertical migrations Table c. Altern3.3.4NNorth Sea = 3, Nfjord = 1, Nindermediate = 2). Of those fish that did not leave , a generalized linear model supported effects of the LG12 inversion state and body size on cod survival, although the support for an effect of body size was marginal . However, many weakly differentiated loci across the genome possibly under positive selection, as well as behavioural differences of depth usage between North Sea and fjord types suggest that further prezygotic barriers to gene flow may also exist.4.1Based on whole‐genome sequencing data (excluding the inversion‐carrying LGs), we demonstrated that the Atlantic cod community in the southern Norwegian fjord of Tvedestrand consists of three co‐occurring genetically differentiated cod types, of which two types are associated with the adjacent Atlantic cod populations in the North Sea and the western Baltic Sea, while the third forms a distinct unit that was only detected within the fjord Figure b. SeveraTwo questions then arise: Which barriers limit gene flow between sympatric cod types and maintain genetic differentiation? How does genetic exchange between North Sea and western Baltic‐type cod in the fjords with the respective offshore populations occur? Our phylogenetic relationships showed that the fjord (TVEf) and North Sea‐type (TVEn) appear in two different well‐supported clades, with TVEn nested among the North Sea populations with large topological uncertainties, underlining that these cod belong to the same genotypic unit Figure c. The ge4.2Chromosomal inversions can promote divergence through the suppression of recombination  and the inverted arrangements suggests that adaptive alleles may perhaps have manifested elsewhere in the genome. In our genome scans, we did not detect other large regions (more than few kb) with tightly linked loci (indicating reduced recombination) that are also differentiated between TVEf and TVEn/KIE and thus may be protected from gene flow and have a barrier effect. However, it has been shown that chromosomal rearrangements are not always required to maintain a barrier to gene flow  on LG16 controlling influx and efflux of solutes, which may be important for egg buoyancy regulation under different salinities  on LG01, found to be up‐regulated under salinity stress in blue mussels  gene located on LG18, which is involved in synthesizing phosphatidylcholine, a major component of membranes shown to occur in different compositions related to salinity and temperature  that have previously been associated with temperature adaptation  located on LG08, possibly acting as an NO‐dependent sensor and transcriptional regulator , a marine fish with similarly high levels of connectivity as Atlantic cod, seems to be immensely polygenic , temporal isolation , and assortative mating. In Atlantic cod, spawning site fidelity  cod utilize generally deeper habitats within the fjord as compared to North Sea‐type (TVEn) cod Figure . DiffereBehavioural differences between cod types during the spawning period might also be an indication for assortative mating where alike individuals mate with their kind, leading to reduced gene flow between different ecotypes (Kopp et al., Nevertheless, assortative mating is also hypothesized to increase the likelihood of producing fit offspring and has been observed in many animals (Jiang, Bolnick, & Kirkpatrick, 5Isolating barriers to gene flow are best studied in differentiated populations that occur in sympatry, where such barriers actively prevent admixture. Here, we demonstrate extensive evidence for the temporally stable sympatric occurrence of genetically and behaviourally differentiated Atlantic cod types within a confined fjord environment. We show that these differences are likely to be maintained through a combination of structural and behavioural barriers to gene flow, both of which may reflect a fitness advantage in local environments. Our study thus emphasizes the high value of genomic analyses for conservation and fisheries management (Bernatchez et al., The study was conceived and designed by J.M.I.B., S.J., E.M.O, and facilitated by S.J. and K.S.J. Genomic data were compiled by B.S. and J.M.I.B. Genomic analyses were performed by J.M.I.B. Behavioural analyses were carried out by C.F., E.M., D.V.‐R. and E.M.O. Samples were provided by C.A., H.K., I.B., J.D., C.P., D.R., J.M. and E.M.O. The manuscript was written by J.M.I.B. with contributions from C.F., D.V.‐R., E.M., E.M.O. and H.K. All authors read and critically revised the manuscript. Click here for additional data file.


Genomic resources for the domestic dog have improved with the widespread adoption of a 173k SNP array platform and updated reference genome. SNP arrays of this density are sufficient for detecting genetic associations within breeds but are underpowered for finding associations across multiple breeds or in mixed-breed dogs, where linkage disequilibrium rapidly decays between markers, even though such studies would hold particular promise for mapping complex diseases and traits. Here we introduce an imputation reference panel, consisting of 365 diverse, whole-genome sequenced dogs and wolves, which increases the number of markers that can be queried in genome-wide association studies approximately 130-fold. Using previously genotyped dogs, we show the utility of this reference panel in identifying potentially novel associations, including a locus on CFA20 significantly associated with cranial cruciate ligament disease, and fine-mapping for canine body size and blood phenotypes, even when causal loci are not in strong linkage disequilibrium with any single array marker. This reference panel resource will improve future genome-wide association studies for canine complex diseases and other phenotypes. Complex traits are controlled by more than one gene and as such are difficult to map. For complex trait mapping in the domestic dog, researchers use the current array of 173,000 variants, with only minimal success. Here, we use a method called imputation to increase the number of variants–from 173,000 to 24 million–that can be queried in canine association studies. We use sequence data from the whole genomes of 365 dogs and wolves to accurately predict variants, in a separate cohort of dogs, that are not present on the array. Using dog body size, blood phenotypes, and a common orthopedic disease that involves rupture of the cranial cruciate ligament, we show that the increase in variants results in an increase in mapping power, through the identification of new associations and the narrowing of regions of interest. This imputation panel is particularly important because of its usefulness in improving complex trait mapping in the dog, which has significant implications for discovery of variants in humans with similar diseases. Canis lupus familiaris) consists of over 500 breeds selected for diverse roles and subject to wildly different disease prevalences [https://omi.org/home/). With an average spacing of 1 SNP every 13kb, the CanineHD array  has been successfully implemented in many genome-wide association studies (GWAS), especially within single breeds where linkage disequilibrium (LD) often extends beyond 1Mb [for example, see [https://omia.org/home/).The modern domestic dog , a complex genetic disease that involves the partial to complete rupture of the cranial cruciate ligament and is a well-established model for anterior cruciate ligament rupture in humans . SeveralWe posit that improving the density of variants by using an imputation panel will greatly improve the power to identify causal loci for canine complex traits, due to increased LD. We use 365 canine whole genome sequences to create a reference panel of 24 million variants and impute these variants in 6,112 dogs previously genotyped on a semi-custom 185k CanineHD array. We show that using an imputation panel increases our power to detect variants affecting complex canine traits–including a common orthopedic disease, CCLD–by identifying potentially novel loci, and by refining intervals for previously-identified QTLs for use in fine-mapping. To our knowledge, this is the first study to use an imputation panel based on WGS for across-breed canine mapping studies.We used IMPUTE2 to impute the WGS reference panel across the 6,112 genotyped dogs resulting in 24 million variants. By comparing 33,144 imputed variants to directly genotyped sites on a second custom array, we were able to calculate the accuracy for our imputation panel, which was 88.4% overall. Across all sites, purebred dogs had the highest accuracy , followed by mixed-breed dogs , and then village dogs . This result is expected given that 210 of our 365 WGS panel were purebred dogs, and also due to the long-range haplotypes found in purebreds that make calling imputed variants easier. For all three dog types , imputation accuracy increased with decreasing minor allele frequency (MAF) , which iLooking at the imputation accuracy in the four breeds with the highest number of dogs in the WGS panel , the overall imputation accuracy was 92.7% (n = 32), a 3% increase from the larger panel of 276 purebred dogs described above. The same trends of increasing accuracy with decreasing MAF and vice versa for heterozygous sites are observed for the four breeds analysis but at higher imputation accuracies .In general, the larger chromosomes and chromosome X had higher imputation accuracies than the smaller chromosomes, such as 35, 36, 37, and 38, due to lower recombination rates . For allP = 1.3×10−8) but was significant in the male breed-average height imputed GWAS (P = 2.8×10−22).We performed two separate GWAS, firstly using the semi-custom CanineHD array data of 185k markers, and secondly using our imputed panel of 24 million variants. The phenotypes used in these GWAS were male breed-average weight, male breed-average height, and individual sex-corrected weight. We were then able to compare the results from the array GWAS and the imputed GWAS using the exact same phenotypes see . For theHMGA2 on CFA10 and fgf4 on CFA18 for height (2 > 0.2) with the exception of the CFA12 and two CFA26 associations (2 < 0.8) with array QTLs, the imputed variants generally had stronger effect sizes and lower minor allele frequencies , with P-values of 1.1×10−5, 2.1×10−7 and 1.8×10−8 for individual sex-corrected weight, male breed-average weight and male breed-average height respectively  [P-values within two orders of magnitude of the most associated marker  ,32,33. Fd marker , confirmIGF1R  [STC2 , GHR , and HMGA2  [IGF1R QTL  did not reach significance, but with the imputed data we saw a significant association signal , and the causal variant was the 6th most associated SNP   putative causal variants were the most significant variants at those loci in the imputed GWAS  with the most significant marker at this locus in the imputed GWAS  , STC2  . The STCted GWAS . Note thTC2 4:39,2,836, GHted GWAS .IGF1, SNP5 , which is in LD with the SINE element [nd most associated SNP and the SNP that tags the SINE element  was the 4th most associated SNP, and these SNPs were nearly in complete LD with the most significant marker in the GWAS (r2 = 0.98 and 0.97 respectively) (IGSF1 missense mutation [2 = 0.97)  . The IGSmutation  was in h = 0.97) . Note thentified .Seventeen QTLs have been previously associated with body size in dogs . Here, ugrowth hormone 1 (GH1) , and an in-frame deletion in exon 5 . Both of these variants were at <5% frequency in the WGS panel but all occurrences were in small-sized breeds .The most significant SNP at CFA9:12 is located about 200kb upstream of the gene  1 (GH1) , which i 1 (GH1) . The gen 1 (GH1) . Using oANAPC5, a member of the anaphase-promoting complex gene family that includes ANAPC13, which has been associated with height in humans [KDM2B (lysine-specific demethylase 2B), which has been associated with body mass index in humans in a CpG methylation study [ANAPC5 or KDM2B in the snpEff-annotated files that are in LD with the associated imputation variant. The non-reference, derived allele was found at high frequency in the small breeds Shiba Inu, Papillon, and Chihuahua  as covariates in the GWAS (hereafter referred to as “top 4 covariates”), and then implemented a region-specific stepwise approach, including further associated SNPs in the region as covariates, until no significant association signal remained. For male breed-average weight, when we regressed out the most significant association for a QTL, we expected the association signal to disappear, as seen with the MAD2 QTL .ANAPC13 and LCORL. The residual signal in the CFA10 region lies close to the ear flop association [MSRB3) but is not in LD with the imputed ear flop locus at CFA10: 8,097,650 (r2 = 0.147). The variant in this residual signal region may be regulatory, as the significance peak lies approximately 248kb upstream of HMGA2. We did not identify any coding variants in these two residual signal regions from the snpEff annotations. Two other QTLs (CFA4:67 and CFA15:41) showed evidence of residual signal but these did not reach significance .Our results showed two QTLs  that retain significant association signal after regressing out the most associated locus in the respective region . For botociation ,16,17 , we took the most significant SNP from the top 4 covariates GWAS. We used that significant SNP as a covariate in a GWAS to see if we were able to recover the most significant SNP from the initial GWAS with no covariates. For both CFA3 and CFA10, we did recover the initial associated SNP, suggesting that these are real associations and not midway between two imperfectly tagged SNPs.P = 4.5×10−10 and 5.9×10−9 respectively), neither of which were previously identified in the array GWAS [LC22A1) and about 30kb downstream of the insulin-like growth factor 2 receptor gene (CI-MPR/IGF2R). During gestation, IGF2R binds insulin-like growth factor 2 (IGF2), the presence of which stimulates the uptake of glucose [Using our imputed panel for GWAS on blood phenotypes revealed several potentially novel associations. For example, we saw significant associations with the phenotypes of albumin  and calcray GWAS  (S4 Tabl glucose . The SNPP = 1.0×10−8) we saw with the imputed data, only two were not novel–alanine aminotransferase (ALT) and amylase–although both increased in significance  in the gene LIM Domain-Containing 1 (LIMD1), which is involved in regulating stress osteoclastogenesis, and osteoblast function and differentiation [Using a quantitative GWAS design that separates the CCLD cases into partial rupture and complete rupture, we identified a significant association using the imputation data that is not identified using the array data . This asntiation ,42. 10% Imputation increases GWAS power by including additional sites that are not well-tagged by any single array marker, and has been successfully implemented in human studies, for example, low-density lipoprotein GWAS –45. HereAlthough the price of WGS is decreasing, it is still more cost-effective to use a panel of WGS individuals to create an imputation dataset based on genotyped samples than it is to directly WGS all samples . Our impWith our imputation panel, we improved association mapping for previously studied phenotypes, such as body size. Previous mapping studies of canine body size and other morphological traits using CanineHD array data have identified many significant QTLs. This success is largely the result of selection for body size during the formation of dog breeds, leading to selective sweeps around large-effect loci that facilitated mapping efforts. Nevertheless, using the imputation panel, we were able to identify two additional potentially novel loci (at CFA9:12 and CFA26:7) that influence body size. Using imputation, we were also able to narrow intervals for previously known associated QTLs, and find evidence of possible allelic heterogeneity at two loci. Furthermore, imputation provides a more accurate analysis of the genetic architecture underlying canine body size and, in turn, allows a more accurate prediction for body size in dogs.Imputation is especially helpful in across-breed and/or mixed-breed study designs, where LD breaks down very rapidly making it more difficult to identify associations. Increasing the number and density of queried variants (as done by imputation) increases the chance that a variant will be in LD with the causal variant, especially when compared to a within-breed study design. We used our imputation panel for across-breed GWAS of blood phenotypes and the orthopedic disease CCLD. For blood phenotypes, we show several potentially novel associations and the narrowing of associated intervals when compared to array data alone, and for CCLD, we identified a significant association (at CFA20:42) that was not identified with array genotype data. Previously identified loci that influence CCLD risk include five in Labrador retrievers , three in Newfoundlands , one in Rottweilers (CFA9), and another two from an across-breed GWAS design  using the same phenotyped dogs as this study –30. ThisImputation is a type of statistical inference, whereby genotype data are filled in based on observed haplotype patterns from WGS. As we have shown above, this is not 100% accurate, especially in dog breeds not well-represented in our WGS panel and, as a result, some significant associations identified in our imputation panel alone may be spurious. However, in general, we expect the effect of low imputation accuracy to be a reduction in power to detect significant associations, as the LD between an imputed marker and the causal variant will be reduced.In summary, using our canine imputation panel of 24 million variants results in an increase in GWAS power, even for phenotypes that have multiple significant associations. The improvements to canine GWAS, especially for complex phenotypes, will not only further the field of canine genetics, but may also have beneficial implications for human medical genetics–especially for complex diseases, such as cancer and specific orthopedic diseases, for which the domestic dog is a good model organism .The 365 whole genome sequences include 210 breed dogs (from 76 breeds), 107 village dogs (from 13 countries), and 28 wolves . 88 of tSHAPEIT v2.r790  was used276 purebred, 86 village, and 13 mixed-breed dogs were also genotyped on a second custom Illumina CanineHD 215k array, which contains 33,144 SNPs that are not on the 185k semi-custom CanineHD array but do feature in our imputed dataset. These 33,144 SNPs were used to determine the accuracy of our imputation across the 375 total dogs. Accuracy was calculated for each SNP as the number of sites that are correctly called in the imputed dataset divided by the total number of dogs. For example, if a G/C SNP was called G/G in 14 dogs, C/C in 10 dogs, and G/C in the remaining 351 dogs, then the imputation accuracy for that SNP is 93.6%. MAF was calculated for each SNP as the number of occurrences of the allele across all village, purebred, and mixed-breed dogs in the genotyped dataset. Imputation accuracy and MAF were plotted in Jupyter notebook  using MaIn addition, imputation accuracy was also calculated for a subset of these 375 dogs, namely 32 dogs of the four breeds  most highly represented in the WGS reference panel . This was done to show the increase in imputation accuracy when using a reference panel containing dogs of the same breed as the genotyping dataset.For the genotype data, individuals were run on a semi-custom Illumina CanineHD array of 185k SNPs, and quality control steps were performed as previously described  binary fP-values. All LD plots were created using Matplotlib library [We ran GWAS using a linear mixed model in the program GEMMA v 0.94 , with a  library  in Jupyt library .P = 1.0×10−8, based on thresholds calculated using the effective number of independent markers for human 1000 Genomes datasets [see For the imputed panel, GWAS was performed for each canine chromosome (CFA1-39) separately. The kinship matrix calculated using the array data in GEMMA was also used in the imputed GWAS for the same phenotype. The significance threshold was set to sets see  and givesets see .To identify QTLs associated with body size, we ran a GWAS of male breed-average weights (n = 1926) and heights (n = 1926), and individual sex-corrected weights (n = 3095), using both the semi-custom 185k CanineHD array data and the imputed panel data. Effect sizes were recorded from the GEMMA output, and MAFs and LD statistics were calculated using PLINK.0.303) to normalize the distribution of weights across the breeds, based on a Box-Cox transformation performed in R [The male breed-average weight data included 1926 dogs from 175 breeds with a maximum of 25 dogs per breed. The phenotype of male breed-average weight, in kg, was assigned to all dogs in the breed for the GWAS. We used weight to the power of 0.303 .Individual sex-corrected weight GWAS was performed using 3,095 dogs including 417 village dogs and 427 mixed-breed dogs. The average raw weight in this individual dataset is 24.7kg, ranging from 1.6kg to 99.7kg. 164 breeds are represented, with 52 small breeds, 57 medium breeds, 34 large, and 21 giant breeds. Individual weights were sex-corrected by 16.47% , and also transformed, as above (weightHMGA2 (CFA10), IGF1 (CFA15), LCORL (CFA3), and SMAD2 (CFA7). In order to control for possible spurious associations, we ran an imputed GWAS, using male breed-average weights, including the four most-associated loci as covariates and then observed which of our significant associations remained.GWAS of male breed-average weight using imputed variants shows the four most-associated loci are We used snpEff version 4.3T  to annotWe used all the individual sex-corrected weights  and specific body size QTLs as a training set, and then randomly set 20% of the weights to missing and used a Bayesian sparse linear mixed model (with a ridge regression/GBLUP fit) in GEMMA to predict these missing weights. We did this randomization and prediction 50 times, and then compared the actual weights to the predicted weights using a correlation coefficient.In order to reduce phenotypic noise, we again included all four most-associated QTLs as covariates in a follow-up GWAS of male breed-average weight and then, for those regions that still had residual association signal, we also included the most associated SNP (in addition to the four) as a covariate in the next GWAS. We continued this stepwise process of including the most-associated SNP in the next GWAS until there was no significant association signal in the respective region remaining.Using previously published data of 39 phenotypes from complete blood count (CBC) and serum chemistry diagnostic panels of 353 dogs from a range of breeds , GWAS weA quantitative GWAS was performed in GEMMA, as described above, using both the semi-custom CanineHD array data and the imputed data for the phenotype of cranial cruciate ligament disease (CCLD). The across-breed GWAS included 602 dogs total: 377 dogs had no rupture (controls), 141 dogs had a partial rupture, and 84 dogs had a complete rupture–this is the same dataset recently analyzed in a GWAS using FarmCPU . ResultsS1 FigIGF1R, b) CFA4:39 near the gene STC2, c) CFA4:67 near the gene GHR, d) CFA7:43 near the gene SMAD2, e) CFA10:8 near the gene HMGA2, f) CFA12:33, g) CFA15:41 near the gene IGF1, h) CFA18:20, i) CFAX near the gene IGSF1. Dashed lines show the significant interval . Asterisks show the location of the putative causal locus. Note that for d), f), g), and h) the putative causal locus is a deletion, retrogene insertion, SINE insertion, and retrogene insertion respectively, so these locations are labeled with asterisks at the top of the plot. Array genotypes are shown as o, imputed data are shown as +.a) CFA3:41 near the gene (TIF)Click here for additional data file.S2 FigLCORL and ANAPC13, b) CFA7:30 near the gene TBX19. The significant interval, drawn with dashed vertical lines, is defined by P-values within two orders of magnitude of the most associated SNP. Array genotypes are shown as o, imputed data are shown as +. Black arrows point to the most significant SNP in the array GWAS and pink arrows point to the most significant variant in the imputed GWAS. Colors indicate amount of LD with the most significantly associated SNP, ranging from black (r2<0.2) to red (r2>0.8).a) CFA3:91 near the gene (TIF)Click here for additional data file.S1 TableAverage recombination rate (cM/Mb) calculated from Campbell et al. 2016  for each(XLSX)Click here for additional data file.S2 TableValues are shown for individual sex-corrected weight, male breed-average weight, and male breed-average height phenotypes with array genotypes and the imputed panel. Potentially novel body size loci are shown in bold.(XLSX)Click here for additional data file.S3 Tablea) Minor allele frequencies (MAF) by dog breed for the two potentially novel body size loci  identified by imputed GWAS and b) proportion of dogs with different genotypes in different weight  classes for the two potentially novel body size loci  identified by imputed GWAS.(XLSX)Click here for additional data file.S4 TableP-value from the GWAS using the array genotype data.Also shown is the location and (XLSX)Click here for additional data file.S5 Tablea) Proportion of dogs of different phenotypes  with different genotypes at the CFA20:42,827,199 significant locus identified by imputed GWAS and b) frequencies, effect sizes and P-values for the CFA20:42,827,199 significant locus in different breeds.(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Human population isolates provide a snapshot of the impact of historical demographic processes on population genetics. Such data facilitate studies of the functional impact of rare sequence variants on biomedical phenotypes, as strong genetic drift can result in higher frequencies of variants that are otherwise rare. We present the first whole genome sequencing (WGS) study of the VIKING cohort, a representative collection of samples from the isolated Shetland population in northern Scotland, and explore how its genetic characteristics compare to a mainland Scottish population. Our analyses reveal the strong contributions played by the founder effect and genetic drift in shaping genomic variation in the VIKING cohort. About one tenth of all high-quality variants discovered are unique to the VIKING cohort or are seen at frequencies at least ten fold higher than in more cosmopolitan control populations. Multiple lines of evidence also suggest relaxation of purifying selection during the evolutionary history of the Shetland isolate. We demonstrate enrichment of ultra-rare VIKING variants in exonic regions and for the first time we also show that ultra-rare variants are enriched within regulatory regions, particularly promoters, suggesting that gene expression patterns may diverge relatively rapidly in human isolates. Population isolates provide a valuable window into the roles of rare genetic variation in human phenotypes, as a result of their unusual evolutionary histories, that often lead to relatively high frequencies of variants that are exceptionally rare elsewhere. Such populations show increased levels of background relatedness among individuals and are often subject to stronger genetic drift, leading to a higher frequency of deleterious variants. Here, for the first time, we present whole genome sequencing data from the Shetland population in Northern Scotland, encompassing 500 individuals, and compare these genomes to the mainland Scottish population. As expected we find the imprint of Shetland population history in the Shetland genome, with strong evidence for founder effects and genetic drift, but we also discover a relaxation of selective constraint across the genome. These influences have combined to endow the Shetland genome with thousands of ultra-rare genetic variants, not observed previously in other populations. Surprisingly these variants are significantly enriched in functional regions including protein coding regions of genes and regulatory elements. Among regulatory regions, promoters are particularly enriched for ultra-rare variants, suggesting the potential for rapid divergence of gene expression in isolates. Population isolates are subpopulations that originated from a small number of founders and subsequently remained relatively isolated for long periods of time due to geographical, cultural and social barriers. Such populations have been recognised to be of significant interest for some time , due to With the recent advances in high throughput sequencing (HTS) technologies, the traditional approach of investigating the genomic architecture of isolated populations via SNP genotyping arrays –13 has si) is there any significant difference between the variant load observed in the two populations, ii) if so, what are the characteristics and the driving forces behind it and iii) which identified variants should be further examined for potential phenotype/trait associations?For the first time our study used high-coverage WGS to compare the genomic landscapes of samples from an isolated population from the Shetland Islands to a more cosmopolitan mainland Scottish population. By investigating common and rare single nucleotide polymorphisms (SNPs) and short (up to 75bp) insertions/deletions (INDELs) in coding as well as in regulatory regions, we aimed to answer the following questions: th century, the extreme geographic location in the north Atlantic served to isolate the population from further major immigration. In common with neighbouring areas, Shetland was variously affected by smallpox epidemics and famines over the centuries. Analyses of uniparental genetic systems reveal Shetland, like Orkney, to be a Norse-Scots hybrid population [The Shetland Islands lie scattered between ~160–290 km (~100–180 miles) north of the Scottish mainland and consist of a group of ~100 islands, of which 16 are inhabited, with a population of ~23,000 . First spulation –30, withpulation . The prepulation .A total of 2,122 participants of the VIKING Health Study–Shetland  were genThe WGS data for the VIKING  and LBC  cohorts were processed in an identical manner to identify and retain only high-quality SNP and INDEL variants . Overall concordance analysis between the SNP array data and WGS-derived genotypes for the Shetland cohort was performed to ensure there were no sample mix-ups by using the GenotypeConcordance tool from the GATK 3.6 toolkit  and the vice versa. Such samples are representative of the fact that, although the Shetland population is isolated, there has been some gene flow to and from the capital city of Scotland and its surrounding area, where the LBC cohort were recruited. Inclusion of these individuals implies that any observed differences between the variant loads in the two cohorts will tend to be underestimated.A multidimensional scaling (MDS) analysis revealed that while similar, the two populations are genetically distinct from each other , and thiNFE ≤ 5%, “common” with 5% < MAFNFE ≤ 10%, and “very common” with MAFNFE > 10%. To quantify the observed differences accurately for each frequency class, we bootstrapped the LBC data by generating 10,000 random subsets (with replacement) of size 269 individuals each to match the size of the VIKING dataset. For each of these subsets we counted the numbers of variants per individual in the VIKING and LBC cohorts and used the Wilcoxon rank sum test to evaluate the difference in distribution of number of variants between the two cohorts. To annotate the number of variants in a frequency class as significantly different  and no overlap between the 95% CI for the LBC and VIKING median values.To compare the genome-wide variant load in the two cohorts we stratified the variants found in the mappable regions of the 22 autosomal chromosomes based on their presence and MAF observed in the gnomAD genomes dataset (r2.0.1 ). We annOur results indicate that the VIKING samples are significantly enriched for ultra-rare SNPs (1.16 fold) and INDELs (1.22 fold) not observed in gnomAD . Importap = 6.5x10-11, Wilcoxon rank sum test) and ultra-rare INDELs are enriched 1.20 fold  in the VIKING cohort; these values are very similar to the results obtained for the 269 VIKING and 1156 LBC sets (p = 2.4x10-12) and 2.65 fold ultra-rare INDEL enrichment (p = 1.7x10-12)—whereas the two cohorts exhibit very similar levels of individual-specific ultra-rare variation and their difference is not significant.To evaluate the potential effect of distant relatedness remaining in the chosen sets of 269 VIKING and 1156 LBC individuals on the ultra-rare variant load, we selected from them the 34 VIKING and 68 LBC individuals with no detectable relationships within each cohort (pi_hat = 0 within cohort). Using the discussed bootstrapping approach on these stricter subsets, we found that ultra-rare SNPs are enriched 1.14 fold  in VIKING, again due to the action of genetic drift whereby many rare variants are expected to be lost in the population.These data suggest that genetic drift has increased the frequency of many ultra-rare variants in Shetland compared to those in Lothian. On average, a Shetland individual carries about 2.6 times more ultra-rare variants shared with at least one other Shetlander, compared to the ultra-rare variants shared within the Lothian individuals . There iUsing data provided by Ensembl , we annOur results show that VIKING samples are significantly enriched for ultra-rare SNPs in all coding related regions–including exonic regions–while potentially more damaging ultra-rare INDELs are restricted to non-coding and intronic regions. The observed exonic enrichment of ultra-rare SNPs is similar to the levels of enrichment seen genome-wide and in non-coding regions, demonstrating that exonic regions in the VIKING cohort have not been protected from the general accumulation of ultra-rare variation in spite of their functional importance. Indeed, the median enrichments seen in exons, 3’UTR and 5’UTR regions are somewhat higher than the genome-wide median enrichment.We also annotated variants within predicted functional non-coding regions using the coordinates of 15 chromatin states generated for nine cell types by the NIH Roadmap Epigenomics Consortium , includiAs for exonic regions, the median enrichment for promoters is generally somewhat higher than the genome-wide enrichment, particularly for predicted promoters active in H1 embryonic stem cells, HMEC primary mammary epithelial cells and NHEK epidermal keratinocyte cells .The results for ultra-rare INDELs  are simiA likely source of the observed enrichment of ultra-rare variants in the isolated Shetland population is the founder effect . Among tNFE ≤ 1%), there are 359,275 SNPs (10.49%) and 31,713 (9.35%) INDELs with allele frequency in VIKING at least ten times higher than the maximum allele frequency observed in LBC and all gnomAD populations. Collectively, these enriched frequency variants represent 3.50% and 3.08% of all SNPs and INDELs identified in the VIKING cohort, respectively, highlighting the strength of genetic drift.There is also evidence of genetic drift for VIKING variants shared only with LBC, as well as for variants shared with geographically more distant populations . Among tThe above analyses reveal the extent of the contributions made by the founder effect and genetic drift in shaping the genomic variation in the isolated VIKING cohort. About one tenth of all high-quality variants discovered– 10.06% of the SNPs and 8.95% of the INDELs–are either unique to the VIKING cohort or seen at least ten times more frequently in it compared to cosmopolitan WGS populations (LBC and gnomAD).Another line of evidence supporting the founder effect / genetic drift in the VIKING cohort is based on the analysis of the distribution of allele frequencies across polymorphic sites, also known as the site frequency spectrum (SFS) analysis . Our analysis is based on the high-quality variants discovered in the callable regions of the 22 autosomal chromosomes in the two cohorts of unrelated individuals, split to known variants (present in gnomAD at any frequency) and ultra-rare variants (not found in any gnomAD population).-17) versus 22% (s.d. 1x10-16) and 19% (s.d. 5x10-3) versus 21% (s.d. 3x10-3) for SNPs and INDELs, respectively, whereas the opposite is true for known variants found in two or more individuals. A similar trend was previously observed comparing the SFS of Finnish against non-Finnish Europeans [The proportion of known variants  found asuropeans , consist-3) versus 98% (s.d. 5x10-16) and 86% (s.d. 7x10-3) versus 97% (s.d. 8x10-16) for SNPs and INDELs, respectively. Notably, 12% of the ultra-rare SNPs are shared by two or more among 50 randomly-chosen VIKING subjects compared to only 2% ultra-rare SNPs for LBC; 14% of the ultra-rare INDELs are shared by two or more VIKING subjects compared to 3% for LBC. These results support our finding of increased sharing of ultra-rare variants in VIKING compared to LBC , followed by 5’UTR, 3’UTR, ncRNA and intronic regions. The VIKING cohort exhibit higher Tajima’s D scores in all interrogated categories reflecting the specific demographic characteristics of this isolated population. Notably, the consistency of the Tajima’s D upwards shifts in VIKING compared to LBC (~ 0.3–0.4), even in exonic regions, is suggestive of potential relaxation of purifying selection in the VIKING cohort, which we address in the next section.p = 3x10-22) and the total length of ROH in VIKING , consistent with increased parental kinship in the Shetland population.Lastly, we compared the runs of homozygosity (ROH) identified in the two cohorts. ROHs were identified in VIKING and LBC individuals  and split into intermediate (length 0.5-2Mb) and long (≥ 2Mb) ROH . The totPurifying (negative) selection is a powerful evolutionary mechanism of removing harmful genetic variation. It has been shown previously that isolated populations, due to their smaller effective population size, exhibit weaker purifying selection . The strWe sought formal evidence for the relaxation of purifying selection by examining the accumulation of extremely rare (i.e. singleton) variants predicted to have a loss of function (LOF) impact using the SVxy statistic (a comparison of the ratios of damaging to synonymous variants between isolate and other populations), which has previously been shown to identify weakened purifying selection in isolates . As a baLOF/SYN = 1.40 and VIKINGLOF/SYN = 1.47), which indicates a 5.3% enrichment of ultra-rare singleton LOF SNP alleles in the VIKING cohort compared to LBC .For more rigorous evaluation of the potential relaxation of purifying selection in VIKING compared to LBC, we repeated the ultra-rare singleton comparison with an additional requirement of considering only genes for which there is at least one LOF or SYN variant observed in both cohorts . This leNFE ≤ 1% in Non-Finnish Europeans. Given their low frequencies, these variants from the ancestral European population are likely to be enriched for SNPs that have been subject to purifying selection. We repeatedly (1000x) randomly selected 269 LBC individuals  and selected MAFNFE ≤ 1% variants shared between this LBC subset and the VIKING cohort. We then computed the mean MAF of such variants for each LBC subset and the VIKING cohort in exonic, promoter, intronic and non-functional intergenic (NFIG) regions  test, which is designed to assess relaxation of selection in non-coding regions, based on the change in the allele frequency of variants within specific genomic regions across populations, as follows. We selected all SNPs in the VIKING and LBC cohorts found in the gnomAD genome dataset with MAF regions . We alsoWe estimated the strength of the purifying selection in each cohort as the difference between the mean MAF of the selected variants observed in the NFIG regions, where the effect of purifying selection is assumed to be negligible, and the mean MAF in regions assumed to be subject to active purifying selection. If purifying selection acts with the same strength in two populations there will be equivalent MAF differences in the two cohorts between the NFIG regions and the regions being tested. However, in the scenario where purifying selection is weakened in one of the populations, we expect to observe a bias towards smaller MAF differences in this population. The significance of these shifts can then be measured by a nonparametric statistic comparing the distributions of MAF differences between cohorts.We applied the ASB test on exonic, promoter and intronic regions . Our res-5 , 17 variants (31%) with MAF ≤ 5x10-5  and the remaining 2 variants with MAF ≤ 1x10-4 . As of Aug 27, 2019 only one of these 54 variants—rs779590262, a missense variant of uncertain significance (Variation ID 423006)–was present in ClinVar [Our analysis of the WGS data of the 269 Shetland individuals revealed 79 exonic variants predicted to impact gene function as significantly enriched (Fisher’s exact test) in VIKING compared to gnomAD, and occurring in 74 unique genes predicted to be largely intolerant to variation ; 54 of these variants (68%) are ultra-rare (i.e. not found in gnomAD genomes dataset). A lookup for these 54 exonic variants in the order of magnitude larger gnomAD exomes dataset   confirms ClinVar , a databp ≤ 5x10-8) is reported in the GWAS Catalog (v1.0.1) [Given our small sample size, in order to reduce the search space and the multiple testing correction burden, from the 79 enriched exonic variants predicted to be functional we selected the 40 variants (26 of which ultra-rare) within 38 genes for which a strong evidence of gene-trait association ((v1.0.1) ; among tVIKING variants in promoter regions show higher levels of enrichment for ultra-rare variants than other regulatory regions , and anaComparison of high-coverage WGS data for 269 unrelated individuals in the VIKING cohort from the Shetland Islands to similar data from LBC–a more cosmopolitan Scottish sample from the city of Edinburgh and around–reveals evidence of founder effects, genetic drift, and relaxation of purifying selection in Shetland. VIKING individuals exhibit genome-wide enrichment of ultra-rare variants . On averThe existing literature reports similar proportions of ultra-rare variants detected in isolated populations as a fraction of all variants in the population ,19,20, aThe VIKING individuals in this study were recruited as phenotypically ‘normal’ healthy individuals and represent only our first view of the Shetland isolate, with further recruitment underway. The detailed demographics and history of the Norse diaspora is still an area of active research (e.g. ). We looThe availability of high-coverage WGS data allows the interrogation of both SNP and INDEL variant loads in regulatory as well as coding regions. Our results suggest that due to the reduced efficiency of purifying selection, the exonic and regulatory regions in the Shetland isolate exhibit ultra-rare SNP loads equal to the genome-wide level. We observe the same trend for higher levels of ultra-rare INDELs in many VIKING regulatory regions, particularly promoters, but VIKING exonic regions appear to be protected from short ultra-rare INDELs (of length up to 75bp), consistent with the higher expected intolerance to variation in exonic compared to regulatory regions, as well as with the previously reported finding that exonic regions are depleted of long  copy number variant deletions . ExcesseIt has previously been shown that primate promoters exhibit an increased rate of evolution compared to other genomic regions  and thisAll participants in the Viking Health Study—Shetland (VIKING) gave informed consent for WGS and the study was given a favourable opinion by the South East Scotland Research Ethics Committee (REC Ref 12/SS/0151).Ethical permissions were obtained from the Lothian Research Ethics Committee , the Multi-Centre Research Ethics Committee for Scotland (MREC/01/0/56) and the Scotland A Research Ethics Committee (07/MRE00/58). Written informed consent was obtained from all participants.The Viking Health Study—Shetland (VIKING) is a family-based, cross-sectional study that seeks to identify genetic factors influencing cardiovascular and other disease risk in the population isolate of the Shetland Isles in northern Scotland. The 2105 participants were recruited between 2013 and 2015, 95% of them having at least three grandparents from Shetland. Fasting blood samples were collected and many health-related phenotypes and environmental exposures were measured in each individual.The Lothian Birth Cohort (LBC) study sampled people living in Edinburgh and the Lothians who were recruited and tested in the Scottish Mental Surveys of 1932 and 1947 as described elsewhere ,36; 1369The WGS sequencing and initial processing of the samples used in this study was performed at Edinburgh Genomics, University of Edinburgh. The starting point of our analyses were the gVCF files (GRCh38) we received for the 500 VIKING and 1369 LBC individuals, generated as follows. Demultiplexing is performed using bcl2fastq , allowing 1 mismatch when assigning reads to barcodes; adapters are trimmed during the demultiplexing process. BCBio-Nextgen (0.9.7) is used to perform alignment, bam file preparation and variant detection. BCBio uses bwa mem (v0.7.13 ) to aligWe called the variants in each sample individually from its gVCF using GenotypeGVCFs (GATK 3.6); the identified INDELs are limited to 75bp, i.e. about half of the read length. The discovered variants for each sample were decomposed and normalized using VT (v0.5772-60f436c3 ). The VaAn attempt to filter the variants using GATK’s VQSR approach did not produce convincing results–there was no clear separation between the filtered and retained variants in the generated plots. Instead, we adopted a hard-filtering strategy based on the variant call parameters suggested as suitable for hard-filtering by GATK . The cuthttp://broadinstitute.github.io/picard) and merged into cohort-wide SNP and INDEL VCFs .The individual SNP and INDEL VCFs were lifted over to the human_g1k_v37 reference genome  using the UCSC tracks for the CRg dataset (36mers) , the DukDespite the cohort-specific cut-off values used in the hard-filtering step, we further evaluated our data for the presence of potential technical artefacts due to the different kits used for sequencing of the VIKING (“PCR free”) and LBC (“PCR plus”) cohorts. We were advised  that the use of the “PCR free” kit may result in a higher number of discovered raw INDELs genome-wide due to the elimination of the PCR amplification step in the “PCR plus” kit which may not perform optimally in regions with extreme GC content (resulting in drop of coverage in such regions for “PCR plus”). To address this, we split the mappable regions in the reference human genome to ~ 1.75 billion consecutive blocks of length 100bp, computed the GC content for each block and assigned it to one of the 100 bins based on its GC content (one bin for each percent difference in the GC content). We then counted and compared the total number of VIKING and LBC variants for all the blocks in each of the 100 bins. As a control, we considered variants from 139 unrelated individuals from the island of Korcula, Croatia, which were sequenced with the “PCR plus” kit (same as LBC), by the same sequencing centre (Edinburgh Genomics) at a time point between the LBC and VIKING cohorts and processed by us in the same manner as for the other two cohorts. The results , S13 FigIn order to avoid bias in the variant load analyses, we first excluded 165 samples from the LBC cohort with mean sequencing coverage < 30x, given that all but two of the 500 Shetland samples have mean coverage > = 30x. Next, we identified and excluded related samples in each cohort. We based this analysis on the discovered biallelic SNPs from the mappable regions in the 22 autosomal chromosomes with MAF > = 2% in the VIKING and LBC cohorts: 5,732,180 and 5,711,775 such markers, respectively. As a relatedness metric, we used PLINK’s  pi_hat sAnother potential source of bias could be the presence of individuals with non-European genomic heritage. The VIKING cohort samples were investigated using the genotype array data and only those with no evidence of non-European heritage were submitted for WGS. For the LBC cohort, using data available from the 1000G Project (Phase 3)  as contrThe variants were annotated with their predicted functional effect using VEP (v90 ) and witAdmixture analysis of the 269 VIKING and 1156 LBC unrelated individuals was performed using the ADMIXTURE tool ,74. The SFS analysis of the 269 VIKING and 1156 LBC unrelated individuals was performed using VCFtools (v0.1.13)  using thTajima’s D analysis of the 269 VIKING and 1156 LBC unrelated individuals was performed using VCFtools (v0.1.13) using the—TajimaD option and sliding windows of size 1Mb. The analysis was based on the cohorts’ known SNPs  identified in the callable regions of the 22 autosomal chromosomes. The variants were then split into six subsets based on the functional region they reside in: 5’UTR, exon, intron, 3’UTR, ncRNA and non-coding regions. For the VIKING cohort, we computed the median Tajima’s D value and the 95% CI for each region aggregating the results observed for the 269 individuals in the ~3000 genomic windows of size 1Mb, excluding any window with no SNPs present. For the LBC cohort, we generated 100 random subsets of size 269 unrelated individuals to match the VIKING size  and computed the cohort’s median and 95% CI aggregating the 1Mb window medians observed for each of these 100 subsets.The runs of homozygosity (ROH) tracts were called using the roh function in bcftools (v1.6)  interrogTo establish suitable cut-offs for partitioning the discovered ROH into intermediate and long based on their length, we used the available data for 10 populations of European ancestry, reported in . Based oNext, we examined the density of SNP markers in the detected long and intermediate ROHs . For lonUsing the Ensembl  data, we split the mappable regions in the reference human genome into six categories– 5’UTR , exon (30Mb), intron (906Mb), 3’UTR (27.6Mb), ncRNA (7.3Mb) and non-coding (1.1Gb) regions. Note that some regions may be overlapping, e.g. a 3’UTR region of one gene might be 5’UTR region for another, etc. The non-coding regions are defined as genome regions which do not fall in any of the above five categories.For the regulatory regions we used the chromatin states data generated for nine cell types by Ernst and colleagues , downloaFirst, we selected exonic variants which are more frequent in VIKING compared to LBC and any gnomAD population and are predicted (VEP 90) to have one of the following effects on the gene’s canonical transcript(s): stop gained, splice acceptor/donor variant, start/stop lost, missense, frameshift or inframe insertion/deletion. Next, we annotated these variants with their CADD score (CADD v1.3) and with the pLI and missense z-score values for the harbouring gene . The latp = 0.05 / 1257 = 4.x10-5. For variants found in gnomAD, we used the AC_POPMAX and AN_POPMAX ; for variants not seen in gnomAD (AC = 0) we computed the corresponding AN value based on the number of individuals with coverage at least 30x at this position. In summary, we discovered 79 significantly enriched and potentially functional exonic variants in 74 unique genes.From the set of 1257 potentially functional variants which were more frequent in VIKING compared to LBC/gnomAD, we extracted the variants which were significantly enriched compared to gnomAD. For each variant, we performed Fisher’s exact test on the number of variant alleles (AC) and total alleles (AN) at a given position using a Bonferroni corrected p = 0.05 /153381 = 3x10-7), which resulted in 2782 significantly enriched promoter region variants.From the 470,180 Shetland variants in the aggregated promoter regions (computed as the union of the promoter regions identified in each of the nine cell types ), we ideS1 FigThe Shetland Islands lie scattered between ~160–290 km (~100–180 miles) north of the Scottish mainland and consist of a group of ~100 islands, of which 16 are inhabited, with a population of ~23,000.(PDF)Click here for additional data file.S2 FigMDS analysis performed with PLINK (1.90b4) of the 269 Shetland (VIK) and 1156 unrelated Lothian (LBC) individuals, using 1000G Gujarati Indians in Houston, Texas , Han Chinese in Beijing, China  and Yoruba in Ibadan, Nigeria  populations as outgroups. The analysis is based on 9,070,695 marker loci from the callable regions in the 22 autosomal chromosomes for which a SNP with MAF ≥ 1% is found in the full 1000G dataset. MDS1 separates African (YRI) from European (LBC+VIK) samples, MDS2: African (YRI) vs East Asian (CHB), MDS3: East Asian (CHB) vs South Asian (GIH), and MDS4 separates VIKING from LBC/rest.(PDF)Click here for additional data file.S3 FigAdmixture analysis (admixture_linux-1.3.0 with K = 2) of the 269 VIKING and 1156 LBC individuals based on 4,320,501 not LD-pruned SNPs found in the callable regions in the 22 autosomal chromosomes with combined MAF ≥ 5% in the two cohorts and also present in gnomAD genomes dataset. The tool was run with default parameters with 4 threads in unsupervised mode with K = 1, 2 and 3. The cross-validation error for each K computed using the—cv option (5 folds) identified K = 2 as the most suitable modelling choice.(PDF)Click here for additional data file.S4 FigCircles represent the ratio of the median number of variants in a VIKING individual to the median number of variants in an LBC individual; whiskers are 95% CI based in 10k randomly selected LBC subsets (n = 269). Significance: at least 95% of the 10k subsets have p-value ≤ 8x10-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC median and the VIKING median value see .(PDF)Click here for additional data file.S5 Fig-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC median and the VIKING median value. Red vertical lines represent the median genome-wide enrichment for ultra-rare INDELs and its 95% CI. No significant difference was observed for any of the cell types in the insulator regions (not plotted).Circle dots represent the ratio of the median number of variants in a VIKING individual to the median number of variants in an LBC individual; whiskers are 95% CI based in 10k randomly selected LBC subsets (n = 269). Significance: at least 95% of the 10k subsets have p-value ≤ 2x10(PDF)Click here for additional data file.S6 FigThe analysis is based on high-quality SNPs/INDELs discovered in the callable regions of the 22 autosomal chromosomes in the two cohorts of unrelated individuals, split to known variants (present in gnomAD at any frequency) and ultra-rare variants (not found in any gnomAD population). All sites with missing genotype(s) were excluded. The means and standard deviations for each frequency were computed based on subsampling the two cohorts to 50 individuals each repeated 100 times see .(PDF)Click here for additional data file.S7 FigLeft panel: intermediate length ROH = 0.5 – 2Mb; Right panel: long ROH ≥ 2Mb. Each marker represents a VIKING or LBC individual. Significance: at least 95% of the 10k subsets have p-value ≤ 0.0125 (Bonferroni corrected) and no overlap between the 95% CI for the LBC median and the VIKING median value.(PDF)Click here for additional data file.S8 FigFor long ROHs (top panel), we observe a bi-modal distribution for the number of SNP markers discovered per 1Kb ROH length indicating potentially poor coverage/reliability for some ROHs. Long ROHs with less than 2 or 3.5 markers per 1Kb ROH length  in the VIKING and LBC cohorts, respectively, were excluded from further analysis. The chosen density cut-offs also appear suitable for intermediate ROHs (bottom panel).(PDF)Click here for additional data file.S9 FigLOF/SYN ratio = 1.504 (median = 1.286), VIKING LOF/SYN ratio = 1.492 (median = 1.444); singleton ultra-rare LBC LOF/SYN ratio = 1.720 (median = 1.400), VIKING LOF/SYN ratio = 1.714 (median = 1.523). The LOF/SYN ratios reported here are computed at individual level and then aggregated , while those reported in the main text (LBC LOF/SYN = 1.40 and VIKING LOF/SYN = 1.47) are computed directly at cohort level .LOF: loss-of-function variant ; SYN: synonymous variant. Left panel: based on singleton ultra-rare SNPs only; Right panel: based on all ultra-rare SNPs. Black horizontal lines represent cohort means: all ultra-rare LBC (PDF)Click here for additional data file.S10 FigHistograms of the mean AF of very rare Non-Finnish European SNPs observed both in the 269 VIKING individuals and 1000 randomly selected LBC subsets (n = 269). Mean number of shared very rare SNPs (1000 LBC subsets): non-synonymous exonic = 13,590; all exonic = 22,802; promoter (CADD ≥ 10) = 14,533; all promoter = 78,781; intronic = 786,271 and non-functional intergenic = 483,429 variants.(PDF)Click here for additional data file.S11 FigThe red points depict data from the 34 unrelated (pi_hat = 0) VIK individuals. Black boxplots represent the data from 10 control subsets of 34 VIK individuals randomly selected from the remaining 466 VIK individuals . The upper and lower "hinges" correspond to the first and third quartiles (the 25th and 75th percentiles); the upper whisker extends from the hinge to the highest value that is within 1.5 * IQR of the hinge, where IQR is the inter-quartile range, or distance between the first and third quartiles; the lower whisker extends from the hinge to the lowest value within 1.5 * IQR of the hinge. Top panel: sites split to those with minor AC from 1 to 9 and 10+, lower panel: zoom in onto rarer sites with minor AC = 1, 2, 3, 4 and 5.(PDF)Click here for additional data file.S12 FigUpper panel: SNPs, lower panel: INDELs. VIKING sequenced with TruSeq PCR-Free High Throughput library kit (“PCR-free”); LBC and Korcula with TruSeqNano High Throughput library kit (“PCR plus”). The main difference between the number of variants discovered in samples processed with the “PCR-free” and “PCR plus” kits is in regions with extreme GC content (GC ≤ 15% and GC ≥ 75%), due to the different coverage efficiency by the two kits in such regions see .(PDF)Click here for additional data file.S13 FigVIKING sequenced with TruSeq PCR-Free High Throughput library kit (“PCR-free”); LBC sequenced with TruSeqNano High Throughput library kit (“PCR plus”). S13 Fig is based on 100 VIK and 100 LBC samples, randomly selected.(PDF)Click here for additional data file.S1 TableThe variants are stratified by their presence in the full gnomAD genomes dataset  and their prevalence in gnomAD Non-Finnish Europeans (NFE) population .(PDF)Click here for additional data file.S2 TableNFE ≤ 10%; rare: 1% < MAFNFE ≤ 5%; very rare: MAFNFE ≤ 1%; ultra-rare: not observed in any gnomADg individual . Median number and 95% CI of LBC alleles (forth column) for each frequency class is computed based on 10,000 random subsets ; last two columns represent the median p-value (and 95% CI) and the number of tests with p-value smaller than the Bonferroni corrected threshold. To annotate the number of variants in a frequency class as significantly different (shown in bold), we required at least 95% of the 10,000 subsets to have p-value ≤ 8x10-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC and VIKING median values. Region annotation  is based on Ensembl data  for the mappable sections of the 22 autosomal chromosomes; the remaining 1.1Gb of the mappable regions in the reference human genome is annotated as “non-coding”.Very common: variants with MAF > 10% in Non-Finnish Europeans ; common: 5% < MAF(PDF)Click here for additional data file.S3 TableNFE ≤ 10%; rare: 1% < MAFNFE ≤ 5%; very rare: MAFNFE ≤ 1%; ultra-rare: not observed in any gnomADg individual . Median number and 95% CI of LBC alleles (forth column) for each frequency class is computed based on 10,000 random subsets ; last two columns represent the median p-value (and 95% CI) and the number of tests with p-value smaller than the Bonferroni corrected threshold. To annotate the number of variants in a frequency class as significantly different (shown in bold), we required at least 95% of the 10,000 subsets to have p-value ≤ 8x10-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC and VIKING median values. Region annotation  is based on Ensembl data  for the mappable sections of the 22 autosomal chromosomes; the remaining 1.1Gb of the mappable regions in the reference human genome is annotated as “non-coding”.Very common: variants with MAF > 10% in Non-Finnish Europeans ; common: 5% < MAF(PDF)Click here for additional data file.S4 Table-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC and VIKING median values; VIKING is enriched for ultra-rare SNPs in all considered states/cell types.To annotate the number of variants in a state/cell type class as significantly different, we required at least 95% of the 10,000 subsets to have p-value ≤ 2x10(PDF)Click here for additional data file.S5 Table-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC and VIKING median values; similar to ultra-rare SNPs, VIKING is enriched for ultra-rare INDELs in almost all considered states/cell types, except the Insulator chromatin state (shown in grey).To annotate the number of variants in a state/cell type class as significantly different, we required at least 95% of the 10,000 subsets to have p-value ≤ 2x10(PDF)Click here for additional data file.S6 Table-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC and VIKING median values; there is no significant difference between the two cohorts for known SNPs in any of the considered states/cell types.To annotate the number of variants in a state/cell type class as significantly different, we required at least 95% of the 10,000 subsets to have p-value ≤ 2x10(PDF)Click here for additional data file.S7 Table-4 (Bonferroni corrected) and no overlap between the 95% CI for the LBC and VIKING median values; non-significant differences shown in grey.To annotate the number of variants in a state/cell type class as significantly different, we required at least 95% of the 10,000 subsets to have p-value ≤ 2x10(PDF)Click here for additional data file.S8 TableTo annotate a difference as significantly different, we required at least 95% of the 10,000 subsets to have p-value ≤ 0.0125 (Bonferroni corrected) and no overlap between the 95% CI for the LBC and VIKING median values. The ROHs used for the analysis are filtered to exclude ROH regions with poor SNP density see .(PDF)Click here for additional data file.S9 TableFrom the gnomAD dataset we report the MAF for the population with the maximum MAF for the variant; gnomADg is WGS data  and gnomADe is WES data . The p-value for the VIKING vs gnomADg enrichment for a variant is calculated using Fisher’s Exact Test.(PDF)Click here for additional data file.S10 TableFrom the gnomAD dataset we report the MAF for the population with the maximum MAF for the variant; gnomADg is WGS data . The p-value for the VIKING vs gnomADg enrichment for a variant is calculated using Fisher’s Exact Test.(PDF)Click here for additional data file.S11 TableThe analysis is based on high-quality SNPs/INDELs discovered in the callable regions of the 22 autosomal chromosomes in the two cohorts of unrelated individuals, split to known variants (present in gnomADg at any frequency) and ultra-rare variants (not found in any gnomADg population). All sites with missing genotype(s) were excluded. The means and standard deviations for each frequency were computed based on subsampling the two cohorts to 50 individuals each repeated 100 times also see .(PDF)Click here for additional data file.S12 TableApplied filtering criteria are denoted with ‘yes’.(PDF)Click here for additional data file.