We also further resolved loci that were previously found to be associated with one or more of five elements , with two metal chelator and five metal transporter candidate causal genes identified. The nas5 (nicotianamine synthase5) gene involved in the synthesis of nicotianamine, a metal chelator, was found associated with both zinc and iron and suggests a common genetic basis controlling the accumulation of these two metals in the grain. Furthermore, moderate predictive abilities were obtained for the 11 elemental grain phenotypes with two whole-genome prediction models: Bayesian Ridge Regression (0.33–0.51) and BayesB (0.33–0.53). Of the two models, BayesB, with its greater emphasis on large-effect loci, showed ∼4–10% higher predictive abilities for nickel, molybdenum, and copper. Altogether, our findings contribute to an improved genotype-phenotype map for grain element accumulation in maize.Despite its importance to plant function and human health, the genetics underpinning element levels in maize grain remain largely unknown. Through a genome-wide association study in the maize Ames panel of nearly 2,000 inbred lines that was imputed with ∼7.7 million SNP markers, we investigated the genetic basis of natural variation for the concentration of 11 elements in grain. Novel associations were detected for the metal transporter genes  Elements are important in every aspect of organismal development. In higher plants, at least 20 elements are involved in key biological functions . To mainZea mays L.) is a globally important staple crop, serving as a critical source of calories in Sub-Saharan Africa and Latin America  Heynh.], rice (Oryza sativa L.), maize and other plant species, yet only a subset from each family have been functionally characterized (rotten ear1 (rte1), rte2, and tassel-less1 (tls1) for boron (B) (yellow stripe1 (ys1) and ys3 for Fe  associated with an elemental concentration in maize grain, especially for Fe and Zn (Genome-wide association studies (GWAS) that exploit the extensive phenotypic variation and ancient recombination of many individuals comprising a diversity population (association panel) offer higher mapping resolution to dissect complex traits than biparental mapping populations  could be used to improve trait prediction accuracy (Triticum aestivum L.)  with causal loci of large effect  assess the extent of phenotypic variation and heritability of 11 elemental grain phenotypes, (ii) conduct a GWAS to identify candidate causal genes controlling variation for 11 elemental phenotypes in maize grain, (iii) compare detected GWAS signals with genetic mapping results from the U.S. maize NAM panel, and (iv) evaluate the predictive abilities of two WGP models having different assumptions of the underlying genetic architecture for the elemental grain phenotypes.We evaluated more than 2,400 maize inbred lines from the North Central Regional Plant Introduction Station association panel   were added for quality assurance. In addition, 6 to 7 experimental samples were replicated twice within each batch, allowing the assessment of technical (measurement) error. Technical replicate sample pairs with a relative standard deviation >10% were removed, which resulted in the removal of three inbred lines. Samples (0.8%) with Al present at >5 μg g−1 were considered to have unacceptable levels of purported soil contamination . For inductively coupled plasma mass spectrometry (ICP-MS) analysis, ∼0.3 g of each ground sample, which had been oven dried at 80°C for 4 h to remove remaining moisture, was acid-digested in a closed tube as described in −1 value was approximated for the missing value of each of these samples by imputing a uniform random variable ranging from 0 to the minimum ICP-MS detection value for the given element within each year  for ICP-MS. The levels of Ca and Ni were below the LOD for 1.98% and 18.30% of samples, respectively. Separately for each of these two elements, a μg gWe screened the generated dataset of 11 elemental phenotypes without missing values from the 2,166 inbred lines and separate set of 11 repeated check lines for significant outliers according to the procedure of e.g., pericarp, endosperm, and embryo) and extreme grain phenotypes could have substantially altered elemental composition (https://www.ars-grin.gov/) as sweet corn, popcorn, or with an endosperm mutation were conservatively removed from the dataset. All of the remaining 1,919 inbred lines had BLUP values for 10 or more of the 11 elemental phenotypes.To generate the best linear unbiased predictor (BLUP) values, a best-fit model was selected for each outlier-screened phenotype through iteratively fitting the above full mixed linear model in ASReml-R version 3.0, and retention of only random effect terms found to be significant (α = 0.05) in a likelihood ratio test (http://datacommons.cyverse.org/browse/iplant/home/shared/panzea/genotypes/GBS/v27) scored at 943,455 loci were obtained for the Ames panel from IT ≥0.8, and linkage disequilibrium (LD) r2 ≤ 0.2] derived from the We used target and reference SNP genotype sets in B73 RefGen_v4 (B73 v4) coordinates to increase the marker density of the Ames panel with an approach similar to https://datacommons.cyverse.org/browse/iplant/home/shared/panzea/hapmap3/hmp321/unimputed/uplifted_APGv4/) consisting of ∼83 million variants identified from more than 1,200 lines (https://www.maizegdb.org/data_center/map) to provide further information on the recombination landscape. The quality of the imputed genotypes was further enhanced by retaining only biallelic SNPs with MAF ≥1% and predicted dosage r2 (DR2) ≥ 0.80, resulting in 12,211,420 SNPs. In PLINK version 1.9  or r2 < 0.10 .To construct the reference SNP genotype set, we used the maize HapMap 3.2.1 unimputed datasets  (We conducted marker-trait associations at the genome-wide level as previously described in c (R2LR)  was usedTo better clarify complex association signals, the multi-locus mixed-model (MLMM) approach , served as the input dataset for the estimation of population structure with fastSTRUCTURE (r2) between a peak SNP and all SNPs within ± 5 Mb were calculated in TASSEL v5.2.49  with E-values <1 were identified by BLASTP with default parameters at TAIR (https://www.arabidopsis.org) and RAP-DB (https://rapdb.dna.affrc.go.jp) databases, respectively (Supplementary Table S5).To identify candidate genes, we first constructed a set of distinct loci significantly associated with the elemental phenotypes. A locus was defined as an association signal composed of at least two SNPs significant at 5% FDR within 100 kb from one another, with the most significant SNP designated as the peak marker at a locus. Estimates of pairwise LD (The genetic mapping results from joint linkage (JL) analysis and GWAS of grain elemental phenotypes in the U.S. nested association mapping (NAM) panel  with ther2 < 0.10) dataset of 361,302 genome-wide SNPs was used for both computationally intensive WGP models with an expected minimal loss of information. A stratified fivefold cross-validation scheme that accounted for population structure was conducted 10 times for each of the 11 phenotypes, with predictive ability calculated as the mean Pearson’s correlation of transformed BLUP values with genomic estimated breeding values across folds. Both models used the same fold assignments, and each fold had the same subpopulation (SP) proportion  as calculated for the entire panel (Supplementary Table S4).We evaluated two WGP models, Bayesian ridge regression (BRR) and BayesB . The BGL−1) elements in grain from the Ames panel, followed by Ca at an almost two orders of magnitude lower average concentration  between Mo and Ni to very strong (r = 0.70) between P and Mg . Interestingly, we detected a strong correlation (r = 0.55) between Fe and Zn, which suggests that these two elements could have a partially shared genetic architecture. All 11 elements showed significant genotype-by-year interaction, but which accounted for only a small percentage of the total phenotypic variance  at each of the 33 loci, a window size considerate of high marker density, wide variance in rapid rate of LD decay (mean r2 of 0.2 within ∼1–10 kb) in the panel  was separated by a physical distance of ∼851 kb, with virtually no LD (r2 = 0.03) between them. The peak SNP of the second locus, 3-128693026, was located ∼59 kb from the open reading frame (ORF) of the rotten ear2 (rte2) gene (Zm00001d041590) encoding a B efflux transporter  with each other. Of the two, the more significant peak SNP  was located within a gene (Zm00001d011063)  coding for a protein 43%–60% identical at the amino acid sequence level to three type 2 metallothioneins (MTs) in rice (Supplementary Table S5)  resided within the calcium pump1 (cap1) gene (Zm00001d011013)  encoding a calmodulin-regulated P-type Ca2+-ATPase that had been shown to have slightly enhanced mRNA expression in maize roots under anoxic conditions  of this locus resided about 2.2 kb from a gene (Zm00001d030846) encoding a protein with 74% and 72% sequence identity to NRAMP3 and NRAMP4 of Arabidopsis (Supplementary Table S5) that in addition to Fe, export Mn from vacuoles to chloroplasts in leaf mesophyll cells  with Mn and in very strong LD (r2 > 0.90) with the peak SNP.Of the two loci associated with Mn , the strll cells . An addir2 = 0.79) with one another. The peak  and second SNPs  were approximately 29 and 0.78 kb, respectively, from a gene (Zm00001d042939) that codes for a protein with 80% sequence identity to METAL TOLERANCE PROTEIN 11 (MTP11) of Arabidopsis (Supplementary Table S5) that transports Mn2+  for Mn was defined by two significant SNPs. Both SNPs were in very strong LD  ∼71 kb from the molybdate transporter1 (mo1) gene (Zm00001d033053) that codes for a protein having 69% sequence identity to the mitochondrial-localized MOLYBDATE TRANSPORTER 1 (MOT1) from Arabidopsis (Supplementary Table S5) that specifically transports Mo (r2 = 0.95) with a highly significant SNP  located within the gene. The other four loci (13–16) collectively consisted of 10 significant SNPs across three chromosomes but were within ± 100 kb of less probable candidate genes (Supplementary Table S9).The strongest signal for Mo spanned from 246.5 to 250.3 Mb on chromosome 1 , with the peak SNP  at this locus (iron-regulated transporter1 (irt1) gene (Zm00001d033446)  with the peak SNP. The peak SNPs for the other 14 Ni-associated loci (18–31), however, were within ± 100 kb of candidate genes (Supplementary Table S9) with more speculative involvement in Ni accumulation.Of the 15 loci associated with Ni, the strongest signal mapped from 261.8 to 263.3 Mb on chromosome 1 . The peais locus  was locar2 = 0.69) with each other. Of these two SNPs, the peak SNP  was nearest (∼1.9 kb) to the nicotianamine synthase5 (nas5) gene (Zm00001d022557) that codes for a class II NAS purportedly involved in synthesizing the metal ion chelator nicotianamine  at a distance of ∼112 kb from nas5 . The minor allele of each peak SNP, which was associated with a higher mean concentration of either Zn or Fe, occurred at very low frequencies in the tropical (∼5%) and Stiff Stalk (∼1%) subpopulations (Supplementary Table S10).Two significant SNPs comprised the locus associated with Zn at ∼179.9 Mb on chromosome 7 (Supplementary Table S8). Also, these two SNPs were in moderately strong LD  only 1.2 kb from the yellow stripe-like2 (ysl2) gene (Zm00001d017427). The protein encoded by ysl2 has 58% and 63% sequence identity with AtYSL1 and AtYSL3  within this gene were in moderately strong LD (mean r2 of 0.39) with the peak SNP.On chromosome 5, the association signal for Zn ranged from 195.6 to 195.8 Mb , with the peak SNP , Ni , and Zn . Furthermore, the MLMM had selected an additional two SNPs, 9-1875785 (locus 29) and 9-745061 (locus 28), for Ni. When a conditional mixed linear model analysis was conducted separately for Cu, Mn, Mo, Ni, and Zn with their respective MLMM-selected SNPs as covariates, there were no remaining significant SNPs at a genome-wide 5% FDR . This suggests the MLMM had identified and conditional models had accounted for the major loci contributing to phenotypic variation.The MLMM approach, which helps resolve complex association signals, selected one or more SNPs for Cu, Mn, Mo, Ni, and Zn, but none for the presumably weaker signals of the other six elements (Supplementary Table S11). The top one or two most significant peak SNP markers that had been detected with the mixed linear model in GAPIT were selected by the MLMM for Cu , Mn , Mo , Zm00001d030846 (NRAMP), nas5 for Zn, and ysl2 were distant from their respective NAM GWAS peak variants within JL-QTL support intervals , whereas nas5 for Fe and Zm00001d042939 (MTP) were 126.9 and 0.203 kb, respectively, from their closest peak NAM GWAS variant. The joint consideration of GWAS results suggests that the large-effect loci associated with natural variation for the six-grain elements in the NAM panel were all resolved to the level of highly probable causal genes in the Ames panel.We assessed the findings of our study through a comparison involving the JL-QTL analysis and GWAS results of these elemental grain phenotypes in the U.S. maize NAM panel (Supplementary Tables S6 and S7) . While the predictive abilities from both models were essentially equivalent for most phenotypes, the predictive abilities of Ni, Mo, and Cu increased by 10.42%, 4.00%, and 3.92%, respectively, with BayesB relative to BRR.We evaluated the predictive ability of WGP for the 11 elemental phenotypes with two models that have different assumptions about the distribution of underlying genetic effects, BRR and BayesB . On averElemental homeostasis is critically important, with prolonged deficiencies or toxicities in essential elements negatively affecting plants . To daterte2 and irt1 with levels of B and Ni in maize grain, respectively. The rte2 gene, coding for a B efflux transporter, is one of six members of a small gene family (rte1-6)  and rte2 (maize2 subgenome) functionally diverged following the most recent tetraploidization event of the maize genome , they were more finely mapped in the Ames panel than in the U.S. NAM panel. The proteins with the highest identity (80% and 93%) to Zm00001d042939 in Arabidopsis (AtMTP11) and rice (OsMTP11) (Supplementary Table S5) are Golgi-localized Mn transporters involved in conferral of Mn tolerance by a mechanism hypothesized to involve one or both of vesicular transport to the vacuole or extracellular secretion  to be fumo1 gene, inferred to be orthologous to MOT1 proteins in Arabidopsis and rice (Supplementary Table S5), underlied the Mo association signal on chromosome 1. AtMOT1 was the first cloned and characterized Mo-specific transporter in plants , with the Mo transport activity of this causal gene confirmed via genetic and transgenic complementation  (Supplementary Table S5), had altered transcript abundance in rice shoot and root seedling tissues after Cu treatment  that are involved in homeostasis and remobilization of Cu -PS transporter (ysl2 (ys1 and ysl2 merit joint consideration in future fine mapping and mutagenesis studies to more conclusively determine their independent or collective contribution to Zn accumulation in grain.The er (ysl2 . TherefoR2 = 0.8 to 37.6%) in the U.S. NAM panel that affords higher statistical power  and BayesB (0.46) across all phenotypes that are comparable to those obtained for elemental phenotypes in wheat grain  the BRR rte2 and irt1 with B and Ni, respectively, in combination with enhanced pinpointing of seven candidate casual genes for Cu, Fe, Mn, Mo, and/or Zn illustrate the high level of statistical power and mapping resolution conferred by the Ames panel for genetically dissecting complex trait variation in maize. However, not all detected GWAS signals were resolved down to an individual gene with a definitive role in metal transport or chelation, thus potentially revealing novel candidate genes that could be further assessed for function in mutagenesis experiments. In addition, we identified two loci (nas5 and ysl2) that could be leveraged with marker-based breeding approaches to increase Zn levels in maize grain. Notably, the nas5 gene also associated with the concentration of Fe in grain, thus helping to enable multi-trait selection . The maize HapMap 3.2.1 unimputed datasets  are available on CyVerse (at https://datacommons.cyverse.org/browse/iplant/home/shared/panzea/hapmap3/hmp321/unimputed/uplifted_APGv4/). The untransformed and transformed BLUP values of the phenotypes are provided in Supplementary Tables S1 and S3, respectively. Supplemental Material available at figshare: https://doi.org/10.25387/g3.13644098.The raw genotypes of GBS SNPs (ZeaGBSv27_publicSamples_raw_AGPv4-181023.h5) are available on CyVerse (at


A decline in the level of genetic diversity in livestock can result in reduced response to selection, greater incidence of genetic defects, and inbreeding depression. In this context, various metrics have been proposed to assess the level of genetic diversity in selected populations. Therefore, the main goals of this study were to: 1) investigate the population structure of 16 cattle populations from 15 different pure breeds or composite populations, which have been selected for different breeds goals; and, 2) identify and compare runs of homozygosity (ROH) and heterozygosity-enriched regions (HER) based on different single nucleotide polymorphism (SNP) panels and whole-genome sequence data (WGS), followed by functional genomic analyses.HOM), uniting of gametes (FUNI), and genotype additive variance (FGRM) showed strong correlations among them. All methods yielded low to moderate correlations with the inbreeding coefficients based on runs of homozygosity (FROH). For the HER, 3576 runs and 26 islands, distributed across all autosomal chromosomes, were found in regions containing genes mainly related to the immune system, indicating potential balancing selection. Although the analyses with WGS did not enable detection of the same island patterns, it unraveled novel regions not captured when using SNP panel data.A total of 24,187 ROH were found across all cattle populations, with 55% classified in the 2-4 Mb size group. Fourteen homozygosity islands were found in five populations, where four ROH islands located on BTA1, BTA5, BTA16, and BTA19 overlapped between the Brahman (BRM) and Gyr (GIR) breeds. A functional analysis of the genes found in these islands revealed candidate genes known to play a role in the melanogenesis, prolactin signaling, and calcium signaling pathways. The correlations between inbreeding metrics ranged from 0.02 to 0.95, where the methods based on homozygous genotypes (FThe cattle populations that showed the largest amount of ROH and HER were Senepol (SEN) and Montana (MON), respectively. Overlapping ROH islands were identified between GIR and BRM breeds, indicating a possible historical connection between the populations. The distribution and pattern of ROH and HER are population specific, indicating that different breeds have experienced divergent selection processes or different genetic processes.The online version contains supplementary material available at 10.1186/s12864-022-08384-0. Since cattle domestication, which occurred around 10,000 years ago , 3 have One effect of these concentrations of homozygous alleles is the emergence of ROH. A ROH is defined as the continuous length of homozygous genotypes that are present in an individual genome due to the progenitors transmitting identical haplotypes to their descendants . The ideHeterozygosity-enriched regions, also known as runs of heterozygosity, consists on the identification of genomic regions with high variability, and can provide information about the population diversity and evolutionary history . For insGenetic diversity is not static, being a continuous process of creation and loss according to evolutionary and selection changes in a population. The maintenance of sufficient genetic diversity is important for the long-term sustainability of livestock populations , 8, 16. The main objectives of this study were to: 1) characterize the population structure of 16 cattle populations from 15 pure or composite breeds selected for different breeding goals; 2) quantify and classify ROH and HER in each population based on their length and alternative detection parameters; 3) perform functional annotation analyses to identify candidate genes and pathways involved in the genomic regions with higher concentration of ROH and HER; 4) estimate inbreeding coefficients and effective population size based on genomic information; and, 5) compare the results obtained from the analyzes of SNP and WGS.The group that presented the highest number of ROH is the SEN breed Fig.  with 419In total, 24,187 ROHs were identified, distributed throughout the autosomal chromosomes. The majority of ROHs were classified as 2-4 Mb, representing 55% of all ROHs found. In summary, only 14% of the ROHs were larger than 8 Mb; of these, 24% were larger than 16 Mb. The distribution and classification of ROHs by chromosome and population can be visualized in Additional file ROH method . The Hereford (HFD) population showed the highest inbreeding rate for the methodologies FHOM1 (0.086), FGRM (0.087), FHOM2 (0.087), and FUNI (0.087), while the SEN population showed the highest inbreeding rate (0.075) when FROH was used.The ANGSIM population showed the lowest inbreeding rate (− 0.026), except for the Fod Table . For FROHOM1 and FHOM2, FHOM1 and FUNI, and FGRM and FUNI  was found between FThe effective population size (Ne) for each population from the 54th to 13th generation ago is shown in Fig. The longest ROH island was found on BTA16 of the BRM breed Table , with anThe ROH island present in the highest proportion (92%) of the population was found in the BTA5 for the GIR breed. This island was also present in the BRM breed, in a lower proportion of animals (51.43%). An overlap of ROH islands were observed in the BTA1, BTA16, and BTA19 in both GIR and BRM breeds. These breeds also showed the highest number of ROH islands (5 islands in each breed). In total, the ROH islands in these breeds contain 556 and 863 genes in the GIR and BRM breeds, respectively, which are involved in key biological processes, cellular components, and molecular functions, as detailed in Additional file Two methods were used to detect HER: the windows method, and the consecutive-SNP method . DespiteIn total, 3576 HERs were found for all populations and the highest number of HERs was found in the MON population 1702 runs). The smallest number of HER was found in the CGU population (2 HERs). In general, the highest percentage of HER was classified as having a length of 0.5-1 Mb (45.13%) and 1-1.5 Mb (41.11%). The percentage of HER higher than 2 Mb were only detected in 1% of HER. The HER distribution by chromosome in each population indicated that the BTA5 had the highest number of HER, with 12% of all HER  was found on BTA1 in NEL50. This region harbors 28 genes , DVL1 in BTA16, and FZD2 and WNT3 in BTA19.In summary, 1662 positional genes were found in the ROH islands and 850 genes in the HER. The genes found in ROH islands are related to 379 pathways, 389 cellular components, 400 biological processes, and 319 molecular functions. The genes found in HER islands are related to 217 pathways, 180 cellular components, 373 biological processes, and 224 molecular functions ; in the GIR breed, three genes are involved , while for Jersey (JER) and SEN, only the genes MPK10 and PRLR were involved, respectively. Another pathway in common between groups was the calcium signaling pathway in ANGSIM and JER, where the CACNA1E and DRD5 genes were found related to this pathway.Prolactin signaling was another important pathway observed in this study. Four genes identified for BRM are involved in this pathway , with the selection of SNPs with higher MAF in Zebu cattle populations and designed to optimize equidistant spacing of markers [The difference in the SNP panel density affected the number of ROHs detected. For NEL35 and NEL50 (same breed but genotyped using different SNP panels), there was a considerable discrepancy between the amount of ROH detected Fig. : Fig. S1 markers . These fSome studies reported that the SNP panel density affect ROH detection , 19, 34.HOM1, FHOM2, FGRM) are dependent on genotype allele frequency different to the methodology by uniting gamete [In general, the inbreeding estimates of the first four methodologies showed similar results. Each one of the methodologies used in the present study has their particularities regarding inbreeding estimation and are dependent on some factors. For example, the first three methods  is responsible for the development and differentiation of mammary gland cells in different life stages [MAPK10 gene (mitogen-activated protein kinase 10) is linked with the inflammatory response and immunity of epithelial cells in the mammary gland. According to Silva et al. [MAPK10 is a candidate gene to somatic cell score (SCS). The PRLR gene (prolactin receptor), also known as the SLICK gene, was associated with heat tolerance [Another pathway found to be in common in four populations that presented the homozygosity islands was the prolactin signaling pathway, which is responsible for many biological processes. Various genes in this pathway were previously associated with important traits in cattle. For example, the e stages . The MAPa et al. , MAPK10 olerance , becauseolerance .CACNA1E . Hay and Roberts [CACNA1E as a candidate gene for growth and carcass-related traits. In JER breed, the DRD5 gene (dopamine receptor D5) was associated with the calcium signaling pathway, which has been associated with feed intake regulation and energy homeostasis [The calcium signaling pathway found in the common islands was observed in ANGSIM and JER. In the ANGSIM breed, the gene related to this pathway is  Roberts  reportedeostasis . Other pDifferent from ROHs, it is expected that the HER occur in regions under balancing selection or with high recombination rate, as low linkage disequilibrium leads to high region diversity. Normally, HER are concentrated in genomic regions associated with disease resistance, where higher levels of diversity can help the population to deal with novel (and changing) potential infirmity issues . DespiteBos taurus indicus and Bos taurus taurus [The population that showed the highest amount of HER was MON (a composite population), which represented more than 47% of HER found in this study. This result was expected as the MON population is a composite breed that combines different biological groups, including breeds from s taurus .It is also important to point out the difference found between NEL35 and NEL50 groups for HER, mostly because the difference on the density among SNP panels. The length classification of HER was different between SNP panels: the NEL50 group presented a smaller length of HER compared to the NEL35 group. As the same regions were detected based on both SNP panels, this result suggests that many of the long HER regions are composed of small HER, broken by regions with homozygous SNPs.KHDRBS2 . This gene has been associated with fertility and reproductive performance in Sanmartinero cattle [IFNG (interferon-gamma), which was found in the HER island for MON breed. IFNG has been previously associated with tick resistance in taurine and zebu cattle [ANXA1 gene (Annexin A1), a protein regulated by glucocorticoids, which was identified for SEN, plays an inhibitory role in the synthesis of arachidonic acid, a precursor of inflammatory processes.Only one gene was found in the smaller HER – o cattle . Howeveru cattle . The ANXThese high heterozygosity concentrations in the region of immune response control are an interesting point to be investigated in these populations, as they can contribute to disease resilience . AdditioAlthough only few studies have focused on identifying HER islands, it seems that the use of the consecutive approach is preferred, as the case of Biscarini et al ., Marras Currently, some WGS studies have been used to detect population structure and identify the regions influencing economically-important traits in livestock . The preSLICK gene, already discussed previously and originally found in SEN animals. Recent studies have already shown that the SLICK gene has been introduced into some HOL populations in a selection process to control heat stress [One interesting observation in the comparison between breeds and SNP panels that was not previously identified with the analysis only with SNP panels, was a small ROH concentration observed in the WGS analysis for Holstein breed close to an island region found in SNP panel analysis of the SEN population. This region contains the t stress .As found in our study, the number of ROH and HER regions differ among populations and provide insights on their differences in selective breeding and evolutionary histories. These differences are expected once the events that acted in each population are different or show different intensity . Some paThe comparison between the 35 K and 50 K SNP panels in the Nellore breed evidenced a divergence in the identification and classification of ROH and HER  from 16 worldwide cattle populations selected for different purposes were used in this study. The sample size of each breed and the density of the SNP panels used are presented in Table http://widde.toulouse.inra.fr/widde/) provided the data of Angus (ANG), Borgou (BOR), Brahman (BRM), Creolo from Guadalupe (CGU), Charolais (CHL), Gyr (GIR), Hereford (HFD), Holstein (HOL), Jersey (JER), Limousin (LMS), Senepol (SEN), and Santa Gertrudis (SGT). To increase the sample size for some breeds, datasets from high-density and 50 K SNP panels were merged for the ANG, BRM, GIR, HFD, JER, and LIM breeds. This was done on the WIDDE platform prior to downloading the data. All SNP coordinates were updated to the ARS-UCD 1.2 [Genomic datasets n = 241 from 16 -UCD 1.2  referenc− 6) parameters were also used to filter out SNPs. The density of genotype panels and the amount of discarded SNP in each QC are shown in Table The genotype quality control (QC) was done separately for each analysis. For ROH and HER, we removed SNPs with call rate lower than 0.90, duplicated position, non-autosomes, or without a defined position. For the other analyses, the minor frequency allele (MAF < 0.05) and Hardy-Weinberg equilibrium , number of homozygous SNPs allowed in a HER (0 to 5), and number of missing SNPs allowed in a HER (0 to 5). In this case, the two population with the highest amount of HER were used to test the impact of parameters alterations on HER results (MON and NEL50).HOM1), calculated as [Hexp is the expected value for homozygous genotypes and Hobs is the observed value for the homozygous genotypes.Five models of inbreeding coefficient estimates were analyzed. The first method was based on the homozygous genotypes observed and expected (Flated as :\documenGRM), using the following model [i is the number of reference allele copies of ith SNP, pi is the reference allele frequency in the population. Similar to the first method, the methodology FHOM2 was based on homozygous genotype following the model:The second method was based on genotype additive variance (Fng model :\documenUNI) using the following model [The above models are all dependents of genotype allele frequency, for this reason, a fourth model was a test based on the correlation between uniting gametes (Fng model :\documenROH) using the following eq. [f(ROHi) is the ROH length of individual ith, n is the total intact homozygous genomic regions of each individual, h(j) is the length of chromosome jth, and A is the number of autosomal chromosomes (A = 29). Still, for each class of ROH , inbreeding estimates were made dividing the total sum of ROH segments by the total length of the cattle autosomal genome covered by SNPs. All the genomic inbreeding coefficients were calculated using the PLINK v1.9 software [The last method was based on the sum of ROH individual length divided by the total length of the autosomal genome  was investigated with the relationship method between linkage disequilibrium variances and the effective population size using the SNeP software  and the Bos taurus from the Ensembl database (https://www.ensembl.org/Bos_taurus/Info/Index), version ARS-UCD1.2 [The genomic regions considered as ROH and HER islands were used for genomic annotations. The GALLO package  was usedS-UCD1.2 . SubsequS-UCD1.2  was usedBos taurus taurus and Bos taurus indicus and their crosses. In our analyses, we only considered breeds in common for the analyses in the SNP panel, this configured a sum of 914 animals. We removed SNPs with call-rate lower than 0.90, duplicated positions, non-autosomes, or without a defined position for the quality control. The analyses were made separately for each chromosome. The parameters used to identify both ROHs and HER in the WGS data was basically the same than those used in the SNP panel analyses, except the number of heterozygous/homozygous SNPs (3) and missing SNPs (5). These parameters were adapted from Ceballos et al. [The results of the SNP panel analysis, for both ROH and HER analyses were also carried out using WGS data. The WGS data was obtained from the “The 1000 Bull Genomes Project – Run 8” database . A totals et al. .Additional file 1: Figure S1. Classification of runs of homozygosity according to length size by chromosome in the different breeds.Additional file 2: Figure S2. Classification of heterozygous-enriched regions according to length size by chromosome in the different breeds.Additional file 3: Figure S3. Comparison between runs of homozygosity SNP panel and whole-genome sequence (WGS) analyzes.Additional file 4: Figure S4. Comparison between heterozygous-enriched regions SNP panel and whole-genome sequence (WGS) analyzes.Additional file 5: Table S1. Genes found in Brahman’s runs homozygosity islands and the processes that are involved.Additional file 6: Table S2. Genes found in AngusxSimmental crossbreed’s runs homozygosity islands and the processes that are involved. Additional file 7: Table S3. Genes found in Gyr’s runs of homozygosity islands and the processes that are involved.Additional file 8: Table S4. Genes found in Montana’s heterozygous-enriched regions and the processes that are involved.Additional file 9: Table S5. Genes found in Nellore 50 K’s heterozygous-enriched regions and the processes that are involved.Additional file 10: Table S6. Genes found in Jersey’s runs of homozygosity islands and the processes that are involved.Additional file 11: Table S7. Genes found in Angus’ heterozygous-enriched regions and the processes that are involved.Additional file 12: Table S8. Genes found in Hereford’s heterozygous-enriched regions and the processes that are involved.Additional file 13: Table S9. Genes found in Nellore 35 K’s heterozygous-enriched regions and the processes that are involved.Additional file 14: Table S10. Genes found in Senepol’s heterozygous-enriched regions and the processes that are involved.Additional file 15: Table S11. Genes found in Senepol’s runs of homozygosity islands and the processes that are involved.Additional file 16: Table S12. Genes found in Santa Gertrudis’ heterozygous-enriched regions and the processes that are involved.


Marker-trait association tests identified 96 SNPs related to stem weight (SW), WSCs, and GY, among other traits, at the different sites, growing seasons, and growth periods. The percentage of SNPs that were part of the gene-coding regions was 34%. Most of these genes are involved in the defensive response to drought and biotic stress. A complimentary analysis detected significant effects of different haplotypes on WSC and SW, in anthesis and maturity. Our results evidence both genetic and environmental influence on WSC dynamics in spring wheat. At the same time, they provide a series of markers suitable for supporting assisted selection approaches and functional characterization of genes.Water deficit represents an important challenge for wheat production in many regions of the world. Accumulation and remobilization of water-soluble carbohydrates (WSCs) in stems are part of the physiological responses regulated by plants to cope with water stress and, in turn, determine grain yield (GY). The genetic mechanisms underlying the variation in WSC are only partially understood. In this study, we aimed to identify Single Nucleotide Polymorphism (SNP) markers that account for variation in a suite of WSC and GY, evaluated in 225 cultivars and advanced lines of spring wheat. These genotypes were established in two sites in the Mediterranean region of Central Chile, under water-limited and full irrigation conditions, and assessed in two growing seasons, namely anthesis and maturity growth periods. A genome-wide association study (GWAS) was performed by using 3243 SNP markers. Genetic variance accounted for 5 to 52% of phenotypic variation of the assessed traits. A rapid linkage disequilibrium decay was observed across chromosomes ( Triticum aestivum L.) is one of the world’s most important cereal crops in terms of cultivated area and economic activity [Bread wheat  of bread wheat genotypes in Australia revealed significant associations between stem WSCs at post-anthesis and molecular markers (SNPs and diversity arrays technology (DArT)) located on chromosomes 1A, 1B, 1D, 2D, and 4A [Physiological traits associated with tolerance to water deficit, such as accumulation and remobilization of stem WSCs, are included in some breeding programs, to increase yield in rainfed environments ,20. Thes, and 4A . Recentl, and 4A  conducteIn the present study, we performed a GWAS and a haplotype analysis, to identify SNP markers accounting for variation in a suite of stem traits, including weight, the WSC content at anthesis and maturity, and the apparent remobilization of stem WSCs. We analyzed 225 advanced spring wheat lines, which were established at two sites in the Mediterranean-climate region of Central Chile, under water-limited and full irrigation conditions, respectively. These wheat genotypes were obtained from different breeding programs developed by research institutions in Chile, Uruguay, and Mexico. We hypothesized that, in a diverse set of accessions, a high-density genotyping with SNP markers and the linkage disequilibrium present in specific regions of the genome would allow identification of new genome-phenotype associations for stem carbohydrate traits related to tolerance to water deficit. This study complements our previous research ,16,30 on−1 at Cauquenes and Santa Rosa, respectively  and full irrigated conditions, respectively. Large differences in GY were observed between both sites, with an average of 3140 ± 550 and 9820 ± 970 kg haectively a. The opectively b–f, indiectively . Mean vaively  of significantly associated SNPs was located within intergenic regions, whereas nearly a third of them (34%) were part of gene coding regions a; for thp-value < 0.001 and q-value < 0.15) with the assessed traits is presented in 2 in Santa Rosa, 2011, some significant SNPs were also located in intergenic regions. In terms of the specific effect of each SNP on trait variation (R2), it ranged from 8.4% (0.0837) to 14.9% (0.1491).A list of SNP-markers significantly associated  and the slowest decay on chromosome 6D (r2 ≤ 0.2 at 8.31 kbp). Graphs showing the LD decay per chromosome are included in The extent of linkage disequilibrium (LD) was analyzed across each chromosome. On average, LD over physical distance decayed to 2 . Finally, for the gene encoding an endoglucanase, present on chromosome 7B, we identified two significant haplotypes in association with WSCa and WSCCa/m2, in Santa Rosa, 2011 (mild water stress). From these results, we further considered SWa as a study case, to analyze the specific effect of haplotypes on phenotype. In Santa Rosa, 2011 (full irrigation), haplotypes H1 (ACACCAGGGCCT) and H3 (CCGTTAGGGTTC), belonging to the intergenic region, differed significantly in terms of their mean values for SWa , we identified four significant haplotypes that were associated with SWa and another three associated with SWm, WSCCm, and WSCCa in Cauquenes, 2012 . Analysi for SWa a. Simila for SWa b.2 (at approximately 180 degree-days post-anthesis) across environments differing in water availability. In the current work, the differential influence of genetics relative to other factors  was represented by a low-to-moderate σ2G/σ2P ratio, which ranged from 0.05 to 0.52 for WSCm and SWa, respectively , a significant effect of genotype, site, or GxS interaction was observed on all the analyzed traits . These eectively . These er2 ≤ 0.2 at 2.52 kbp). A similar pattern was reported by Sela et al. [Triticum dicoccoides), which decayed from r2 = 0.5 to r2 = 0.1 within a 2 kbp span. These authors suggested a significant effect of recombination operating as a diversifying force. The breakdown in LD is primarily driven by recombination, so the extent of LD is expected to vary as an inverse relation to the local recombination rate [Genotyping and analysis of LD detected a relatively rapid decay in this parameter over physical distance .The GWAS analysis using 3243 SNP-markers identified 96 significant associations for the assessed traits . Signifiw2 (1-FEH w2), and fructan 1-exohydrolase (1-FEH). These genes have been mapped in different chromosomes. The 1-FFT-A1 gene was located on chromosome 4A [w1, w2, and w3 genes were mapped on chromosomes 6A, 6B, and 6D [Our association tests showed that most of the significant SNPs (64%) were within intergenic regions . This isosome 4A ; the 1-F, and 6D ; and 1-S, and 6D  of Chine, and 6D .TaSnRK2.4) have been implicated in the regulation of enhanced osmotic potential, strengthening tolerance to drought, salt, and freezing stresses [Catharanthus roseus, STR is involved in the response to salinity, low temperature stress [From single-SNP associations , a set ostresses . The secstresses . In planstresses . The thistresses . In Cathe stress , and PEGe stress . The foue stress . The fife stress . Finallye stress .We performed an SNP-haplotype analysis for three gene regions harboring significant single-SNP markers. This approach allowed for the detection of wider genomic regions associated with the different phenotypes analyzed . HaplotyA collection of 225 different genotypes of spring wheat, generated by breeding programs in Uruguay , Chile , and Mexico , was utilized as an association population. This set is part of the 384 lines described and analyzed previously by del Pozo , in term2. The experiment was established as an alpha-lattice design with 20 incomplete blocks, and 20 genotypes per block. A replicate comprised 400 plots and 384 genotypes. Two replicates were included in both trials, except for the MWS treatment in 2011, at both Cauquenes and Santa Rosa, where only single replicates were established.Wheat genotypes were assessed in two sites located in the Mediterranean climate region of central Chile: Cauquenes  and Santa Rosa . Cauquenes is a rainfed area and represented the water-limited conditions; annual precipitation was 580 and 600 mm in 2011 and 2012, respectively. Santa Rosa represented full irrigation (FI) and moderate water stress (MWS) conditions, which were achieved through support irrigation. Trials were assayed during two consecutive growing seasons, 2011 and 2012, except for the MWS trial, which was only set up during 2011. Cauquenes corresponds to the Mediterranean drought-prone area of Chile. According to del Pozo et al. , the ave2) for the same phenological stages (WSCCa/m2 and WSCCm/m2). The apparent WSC remobilization (ARWSC) was calculated as the difference in WSC content between anthesis and maturity. Grain yield (GY) was determined by harvesting the whole plot.Stem weights at anthesis (SWa) and maturity (SWm) were evaluated in five main stems per plot per genotype. The concentration of WSC in stems was determined at anthesis (WSCa) and maturity (WSCm), using the anthrone reactive method . The WSC2, for both the anthesis and maturity measurements. An ANOVA was carried out for each trait, using the following simplified model:ijkly is the response measured in the l-th plot of the i-th genotype within the j-th site and the k-th replicate, μ is the overall mean, iG is the random effect of genotype i, jS is the fixed effect of site j, k(j)R(S) is the fixed effect of replicate k within site j, ijGxS is the random interaction effect of genotype i with site k, and ijkle is the random error in replicate k at site j within genotype i. ANOVA was executed by using the GLM procedure in SAS 9.2 [σ2G) on phenotypic variation (σ2p) was estimated by the σ2G/σ2p ratio, where σ2p = σ2G + σ2GxS + σ2e, and σ2GxS and σ2e are the variance components estimated for the GxS interaction and residual, respectively. These analyses were done only with data from Cauquenes and Santa Rosa, for the 2012 season, considering that both datasets included two full replicates.An approach based on a mixed linear model was applied to assess the genetic variation underlying the different traits. Initially the normal distribution was evaluated. A transformation utilizing natural logarithms was used to normalize WSC, WSCC, and WSCC/m SAS 9.2 . AdditioGenotyping by sequencing (GBS) was performed, using the Illumina HiSeq 2000 sequencing platform and the bioinformatic approach for SNP identification described previously by Lado et al. . SNP-marr2. The r2 value between pairs of SNP markers, within each chromosome, was estimated, using TASSEL 5.2 [r2 values [Linkage disequilibrium (LD) was estimated among pairwise combinations of SNPs per chromosome. It was expressed in terms of the squared correlation of allele frequencies SSEL 5.2 , with th2 values . Analysi2 values .SNP-marker trait association tests were performed using a mixed linear model (MLM), adjusted for population structure and genetic relatedness, utilizing using TASSEL 5.2 . The popAssociation tests between traits and selected genomic regions containing significantly associated single-SNPs were carried out, using the Haplo.stats package in R . HaplotyTriticum aestivum genome sequence available at the Ensembl platform [Gene models and annotations for genes containing significant SNPs were obtained from the platform . Functioplatform .Wheat is a key crop for world food sustainability, so genetic improvement in yield for sites with limited water availability must be a priority under the climate-change scenarios projected for this century. In that framework, identification and characterization of the genetic mechanisms underlying adaptive and productive traits are fundamental. In this study, we identified a series of SNPs controlling the variation in WSC traits in genotypes grown under different water regimes, in the Mediterranean-climate region of Central Chile. Most of these significant SNPs are part of gene-encoding proteins involved in the defensive response to drought and biotic stress. These SNPs represent a starting point to define candidate genes for functional characterization of their biological role under water stress. Our results also showed a complex and polygenic inheritance of the assessed traits. In that sense, the relative influence of genetic or environment (sites and irrigation conditions) factors was variable, depending on traits. Results from this study provided a set of SNPs that could be utilized to support marker-assisted selection suitable for future genetic improvement of spring wheat lines under water-limited areas.


Oreochromis niloticus); a key aquaculture species globally. A pond outbreak of TiLV in a pedigreed population of the GIFT strain was observed, with 950 fish classified as either survivor or mortality, and genotyped using a 65 K SNP array. A significant QTL of large effect was identified on chromosome Oni22. The average mortality rate of tilapia homozygous for the resistance allele at the most significant SNP  was 11%, compared to 43% for tilapia homozygous for the susceptibility allele. Several candidate genes related to host response to viral infection were identified within this QTL, including lgals17, vps52, and trim29. These results provide a rare example of a major QTL affecting a trait of major importance to a farmed animal. Genetic markers from the QTL region have potential in marker-assisted selection to improve host resistance, providing a genetic solution to an infectious disease where few other control or mitigation options currently exist.Enhancing host resistance to infectious disease has received increasing attention in recent years as a major goal of farm animal breeding programs. Combining field data with genomic tools can provide opportunities to understand the genetic architecture of disease resistance, leading to new opportunities for disease control. In the current study, a genome-wide association study was performed to assess resistance to the Tilapia lake virus (TiLV), one of the biggest threats affecting Nile tilapia ( Oreochromis niloticus) has the third-highest production of all finfish species, with more than 4.5 million tons farmed in 2018  populations, explaining between 80 and 100% of the genetic variation for the trait  in two different Atlantic salmon  breeding program and had been selected for improved growth rate for 16 generations. The breeding nucleus was based in Jittra, Malaysia, and managed by WorldFish . The population sample consisted of 124 nuclear families (GIFT generation 16th) produced by crossing 124 dams and 115 sires. Pedigree data for these fish included records of approximately 86,000 fish from generation 16 back to the first generation of GIFT in Malaysia. Each fish from the current generation was tagged using a passive integrated transponder (PIT tag) at an average weight of 4.97 g, which corresponded to an average age of 110.5 days post-hatching. At typical harvest weight, fish were transferred to a single pond where a natural TiLV outbreak was observed shortly thereafter. A summary of the phenotypes measured on these fish, such as weight and age at tagging and harvest for this population can be found in Barría et al. .After the fish were transferred to the single pond, a natural field outbreak of TiLV was observed (in February and March 2018). Mortalities were collected and sampled daily for 14 days (22 February to 7 March 2018), and once the mortality levels had returned to baseline, all remaining fish in the pond were euthanized (using 400 mg/l clove oil) and sampled over five days (8–12 March 2018). A total of 1,821 fish were classified as survivors or mortalities, and the phenotypic sex of all fish was determined. On average, each full-sibling family comprised 14 fish (ranging from 2 to 21). Clinical signs of TiLV were observed throughout the outbreak. The presence of TiLV was assessed on the spleen of a subset of 35 fish, through one-step RT-qPCR. Briefly, the EvaGreen assay RT-qPCR was performed using the following primer set; Forward primer: 5′–CTGAGCTAAAGAGGCAATATGGATT–3′ and Reverse primer: 5′–CGTGCGTACTCGTTCAGTATAAGTTCT–3′. Finally, the PCR product was detected by measuring the fluorescence generated by the EvaGreen dye bound to dsDNA. More details about this RT-qPCR assay, including reproducibility and sensitivity, can be found on Tattiyapong et al. (p < 1 × 10−6) were excluded from further analyses. Thus, 94% of the SNPs  passed all the QC filters, with most of them being removed due to low MAF (~2 K SNPs). Furthermore, using trio information, the resulting data set was tested for putative Mendelian errors in any fish and SNPs. A total of 166 fish (25 parents and 141 offspring) and 3 K SNPs were excluded for subsequent analyses due to a Mendelian error rate >5%, which could reflect either errors in pedigree recording or samples with poor quality genotype data. Following all QC steps, the final filtered dataset comprised 1,112 fish and 47,915 SNPs. This comprised genotype and phenotype data for 950 offspring, and genotype data for 162 parents. From this filtered dataset, the genotyped offspring represent fish from 117 families (108 full-sib families), with an average of 8 fish per family, ranging from 1 to 16.Total DNA from fin clips of 2,016 fish, including 195 parents and all the 1,821 offspring collected from the outbreak, was extracted using a salt-extraction protocol (Aljanabi and Martinez y is the vector of phenotypes (BS or TD records), μ is the population mean, b is the vector of fixed effects , u is the vector of the additive genetic effects, and X and Z are incidences matrices. The following distributions were assumed; G is the GRM and I is the identity matrix. Heritability was estimated as the ratio of the additive genetic variance to the phenotypic variance. A genetic correlation was estimated as the ratio of the genetic covariance between BS and TD to the square root of the product of the genetic variance of BS and TD.Host resistance to TiLV was defined as binary survival (BS)  and as a time to death (TD). In the case of BS, the survivors and mortalities were designated as 1 or 0, respectively. The TD trait was considered as a continuous trait, with values ranging from 1 up to 14, representing the day of the first observed mortality and the last day before mortality returned to baseline levels, respectively. The heritability for BS and TD in the genotyped population was estimated using a genomic-relationship matrix (GRM) calculated with the genome-wide complex trait analysis (GCTA) software v.1.92.2  approach was applied using the GCTA v.1.92.2 software. This approach estimates the GRM between individuals by removing the SNPs located in the tested chromosome and including SNPs from all the other chromosomes. The SNP effect from the chromosome being tested is not included twice in the model, and therefore a higher statistical power is achieved  was computed as the median of the quantile χ2 distribution of the obtained P-values/0.455. For practical reasons, SNPs not placed in chromosomes in the reference genome assembly , a mixed linear model using the lot” Yin  in R  flanking the significantly associated SNPs, again using the Nile tilapia reference genome assembly (Genbank accession number GCA_001858045.3).a) and dominance (d) effect were estimated as follows: a = (AA – BB)/2 and d = AB – [AA + BB/2] where AA, AB, and BB are the predicted trait value for each genotype. The proportion of genetic variance explained for each of the selected SNPs was estimated as [2pq(a + d(q – p))2]/VA, where p and q are the frequencies of the SNP alleles, and VA is the total additive genetic variance explained by the model when no individual SNP is fitted.Following the GWAS, the top three SNPs significantly associated with BS and/or TD on each of the significant chromosomes were tested for the estimation of the additive and dominance effect, by using ASReml v.4.1.0 . From the 35 sampled fish, 15 were survivors and 20 were mortalities, with 40% of the survivors testing positive and 100% of the mortalities testing positive. The survivors which tested positive had a substantially lower viral load, with 127 viral copies/μl ± 150, compared to 188,491 ± 233,167 copies/μl for mortalities.After 14 days of the first observed mortality, the daily mortality level had returned close to the baseline, and all remaining fish in the pond were euthanized and sampled. The total cumulative mortality in the outbreak in the entire sampled population was 39.6%. Further details about the pattern of mortality during the TiLV outbreak can be found in Barría et al. .The estimates of heritability in this dataset based on the GRM, were consistent with the estimates using the pedigree on a larger dataset described in Barría et al. . ModeratOni22  with a P-value of 4.5 × 10−10 and 4.8 × 10−07, for BS and TD, respectively . The minor allele was associated with resistance to TiLV for 25 out of the 29 significant SNPs. The frequency of these resistance-associated alleles ranges from 0.11 to 0.48 , the equivalent allele substitution effect is 0.07.A high genetic correlation was found between both TiLV resistance definitions , trappc1, and psmb6 were identified. The other SNP found to be associated with TD and BS (AX-317647630) is flanked by two genes belonging to the tripartite motif family, trim21 and trim29.The SNP with the lowest The complete list of genes flanking the SNPs with the strongest association, within each chromosome, for host resistance to TiLV and their position within the QTL regions, are shown in Table Experimental challenges are commonly used as a means to collect measurements of host disease resistance for the purposes of genetic improvement in aquaculture  is located at a position of 0.25 Mb on Oni22, specifically within the second intron of the lgals17 gene. This gene encodes one of a family of proteins with functions as pattern-recognition receptors (PRRs) which are important in viral infections due to their role in activating the innate and adaptive immune response  (O’Farrell et al. lgals1 has been found to reduce influenza virus load and that knockout mice have higher susceptibility than their wild-type counterparts (Yang et al. Due to the relatively recent discovery of TiLV, there is a relative paucity of information about the entry, dissemination, and replication of the virus, and the host immune response. In the current study, the SNP with the lowest P value is the vps52 subunit of the GARP complex gene. This gene is required for the replication and extracellular formation of two DNA viruses; vaccinia virus and monkeypox virus, and plays a crucial role in virus egress and cell-to-cell spread (Harrison et al. rnf2 gene is located within the QTL region and has been linked to inhibition of ifn1, which plays a crucial role in the innate response by inducing multiple antiviral genes in mice (Liu et al. Another gene of interest proximal to the SNP with the lowest cdc42 gene is also located in the major QTL region, and several RNA viruses are known to hijack the actin-regulating pathways to internalize into human cells (Swaine and Dittmar cdc42 in the host response to viral infection has been observed in some aquaculture species. For example, Xu et al. (cdc42 to inhibit the replication of the white spot syndrome virus and IPNV, respectively. Studies in Atlantic salmon populations revealed that this gene mapped to a QTL associated with host resistance to cardiomyopathy syndrome (Boison et al. trim29 and trim21 were also located within the significant QTL. The former negatively regulates the IFN production in response to an infection mediated by RNA virus (Xing et al. trim29−/− mice have higher survival and body weight than their wild-type counterparts following the influenza challenge (Xing et al. The u et al.  and Leviu et al.  showed tA recent study performed by Wang et al. , examineThe presence of a large QTL underlying an important production trait is a relatively rare situation in farmed animal breeding. However, its usefulness has been effectively exploited in the aquaculture industry, by reducing the IPN-related mortalities in Norwegian Atlantic salmon populations to near zero Hjeltnes . As suchSupplementary FiguresSupplementary Table 1


FROH is associated with a 60% reduction in the odds of survival in lambs, though inbreeding depression decreases with age. Finally, a genome-wide association scan on ROH shows that many loci with small effects and five loci with larger effects contribute to inbreeding depression in survival.Inbreeding depression is ubiquitous, but we still know little about its genetic architecture and precise effects in wild populations. Here, we combine long-term life-history data with 417 K imputed SNP genotypes for 5952 wild Soay sheep to explore inbreeding depression on a key fitness component, annual survival. Inbreeding manifests in long runs of homozygosity (ROH), which make up nearly half of the genome in the most inbred individuals. The ROH landscape varies widely across the genome, with islands where up to 87% and deserts where only 4% of individuals have ROH. The fitness consequences of inbreeding are severe; a 10% increase in individual inbreeding  Without understanding the genetic architecture of inbreeding depression, its effects are hard to pinpoint. Long-term data from wild Soay sheep shows that inbreeding manifests in long runs of homozygosity, which made up nearly half of the genome in the most inbred individuals with severe fitness consequences. The detrimental effects of inbreeding on a broad range of traits, individual fitness and population viability have now been recognised across the animal and plant kingdoms9. With the ongoing decline of animal populations10 and global habitat fragmentation11, rates of inbreeding are likely to accelerate, and so it is increasingly important to have a detailed understanding of its genetic causes and fitness consequences to inform conservation strategies. However, we still know very little about some of the most fundamental features of inbreeding depression in wild populations12, such as its precise strength, how it varies across life-history stages15, and if it is driven by loci with weak and/or strong deleterious effects on fitness. As genomic data proliferates for wild populations, it is increasingly possible to quantify the distribution of effect sizes at loci underpinning inbreeding depression. By determining this genetic architecture, we can improve our understanding of the relationship between inbreeding, purging and genetic rescue16, with important implications for the persistence and conservation of small populations19.Inbreeding depression, the reduced fitness of offspring from related parents, has been a core theme in evolutionary and conservation biology since Darwin20. While the probability of IBD at a genetic locus was traditionally estimated as the expected inbreeding coefficient based on a pedigree22, modern genomic approaches enable us to gain a much more detailed picture. Genome-wide markers or whole-genome sequences are now helping to unravel the genomic mosaic of homo- and heterozygosity and, unlike pedigree-based approaches, capture individual variation in homozygosity due to the stochastic effects of Mendelian segregation and recombination23. This makes it possible to quantify realised rather than expected individual inbreeding, and to measure IBD precisely along the genome25.Inbreeding decreases fitness because it increases the fraction of the genome which is homozygous and identical-by-descent (IBD). This unmasks the effects of  recessive deleterious alleles or in rarer cases may decrease fitness at loci with heterozygote advantage26. An ROH arises when two IBD haplotypes come together in an individual, which happens more frequently with increasing parental relatedness. The frequency and length of ROH in a population vary along the genome due to factors such as recombination, gene density, genetic drift and linkage disequilibrium31 and extreme regions can potentially pinpoint loci under natural selection31. Regions with high ROH density, known as ‘ROH islands’32, have low genetic diversity and high homozygosity and have been linked to loci under positive selection in humans31. Regions where ROH are rare in the population, known as ‘ROH deserts’, could be due to loci under balancing selection or loci harbouring strongly deleterious mutations under purifying selection35. Moreover, the abundance of ROH also varies among ROH length classes, which are shaped by the effective population size (Ne)37 and can vary in their deleterious allele load41. Longer ROH are a consequence of closer inbreeding and their relative abundance is indicative of recent Ne. This is because their underlying IBD haplotypes have a most recent common ancestor (MRCA) in the recent past with fewer generations for recombination to break them up. In contrast, shorter ROH are derived from more distant ancestors and their relative abundance in the population reflects Ne further back in time42.An intuitive and powerful way of measuring IBD is through runs of homozygosity (ROH), which are long stretches of homozygous genotypesFROH), which is an estimate of realised individual inbreeding F43. FROH has helped to uncover inbreeding depression in a wide range of traits in humans and farm animals44 and is often preferable to other SNP-based inbreeding estimators in terms of precision and bias47. While FROH condenses the information about an individual’s IBD into a single number, quantifying the genomic locations of ROH across individuals makes it possible to identify the loci contributing to inbreeding depression and estimate their effect sizes12. As mapping inbreeding depression in fitness and complex traits requires large samples in addition to dense genomic data37, the genetic architecture of inbreeding depression has mostly been studied in humans and livestock49. However, individual fitness will be different under natural conditions and consequently, there is a need to study inbreeding depression in wild populations to understand its genetic basis in an evolutionary and ecological context. To date, only a handful of studies have estimated inbreeding depression using genomic data in the wild53. While these studies show that inbreeding depression in wild populations is more prevalent and more severe than previously thought, all of them used genome-wide inbreeding coefficients and did not explore the underlying genetic basis of depression.Individual inbreeding can be measured as the proportion of the autosomal genome in ROH  and individuals with imputed SNPs (median FROH = 0.241), indicating no obvious bias in the abundance of inferred ROH based on imputed data -shaped patterns of ROH in Soay sheep  contained ROH in 87% of individuals, while only 4.4% of individuals had an ROH in the top ROH desert on chromosome 11  measured in 500 Kb windows as response variable, window recombination rate in cM/Mb based on the Soay sheep linkage map58 and window SNP heterozygosity as fixed effects as well as a chromosome identifier as a random effect.The wide variation in ROH density along the genome could be partially explained by recombination because regions with high recombination rate produce shorter ROH, and these are less likely to be detected by ROH calling algorithmsR2 = 0.42, 95% CI , Supplementary Table R2 = 0.38, 95% CI , Fig. R2 = 0.04, 95% CI , Fig. R2 = 0.89, 95% CI , Fig. R2 = 0.07, 95% CI , Fig. Recombination rate and heterozygosity together explained 42% of the variation in ROH density  and more variation in the dataset consisting only of longer ROH . Consequently, recombination rate variation has a larger impact on the detected abundance of longer ROH across the genome.Lastly, we explored how much variation in ROH density was explained by recombination when using different minimum ROH thresholds. We repeated the analysis with a dataset based on a minimum ROH length threshold of 0.4 Mb, and a second dataset with a minimum ROH length of 3 Mb , a 10% increase in ges Fig.  with the59. We followed suggestions by Nietlisbach et al.60 and refitted the survival model with a Poisson distribution and logarithmic link function using a simplified model structure without interactions for better comparability across studies. This gave an estimate of 2B = 4.57 (95% CI 2.61–6.55) lethal equivalents for Soay sheep annual survival.We next estimated the inbreeding load in Soay sheep as the diploid number of lethal equivalents 2B. Lethal equivalents are a concept rooted in population genetics, where one lethal equivalent is equal to a group of mutations which, if dispersed across individuals, would cause one death on averagep-values of additive SNP effects are of interest, we analysed the effects of ROH status for both alleles at every SNP. Specifically, at a diallelic locus, ROH result either from two IBD haplotypes containing allele A or from two IBD haplotypes containing allele B. If strongly deleterious recessive alleles exist in the population, they could be associated with ROH based on allele A haplotypes or ROH based on allele B haplotypes. To test this, we constructed a binomial mixed model of annual survival for each SNP position. In each model, we fitted two ROH status predictors. The first predictor was assigned a 1 if allele A was homozygous and part of an ROH and a 0 otherwise. The second predictor was assigned a 1 if allele B was homozygous and part of an ROH and a 0 otherwise. Model estimates and p-values for these two predictors, therefore, reflect whether ROH are associated with survival consequences at each SNP location and for each allele. In the GWAS model, we also controlled for the additive SNP effect and mean individual inbreeding FROH , alongside a range of other individual traits and environmental effects (see ‘Methods’ section for details).To quantify the survival consequences of being IBD at each SNP location, we used a modified genome-wide association study (GWAS). Unlike in traditional GWAS where p = 2.2 * 10−16). Moreover, the proportion of negative ROH effects increases for larger model estimates  and when their p-value was smaller . Consequently, it is likely that a large number of recessive deleterious alleles contribute to inbreeding depression, which manifests in negative ROH effects spread across many loci.A GWAS on allele-wise ROH status can detect deleterious recessive alleles at specific regions when ROH effects reach genome-wide significance. Moreover, the distribution of ROH status effects across the genome can also be informative of the overall number of deleterious recessive alleles contributing to inbreeding depression through ROH. Under the null hypothesis that ROH status does not have an effect on survival at any SNP position, we would expect a 50/50 distribution of negative and positive ROH status estimates due to chance. In contrast, we found many more negative than positive effects of ROH status on survival across the genome than expected by chance , 10, 14, 18, 19 and 23 Fig. . In five54. As a consequence, levels of IBD are high in the population and ROH make up nearly a quarter of the average autosomal Soay sheep genome. Although this is still an underestimate as we only analysed ROH longer than 1.2 Mb, it is three times as high as the average FROH estimated across 78 mammal species based on genome-sequence data61 and only slightly lower than in some extremely inbred and very small populations such as mountain gorillas62, Scandinavian grey wolves24 or Isle Royale wolves17.The Soay sheep on St. Kilda have existed at a small population size in relative isolation for thousands of years36. In Soay sheep, the largest fraction of IBD was comprised of ROH with lengths between 1.2 and 4.9 Mb. These ROH originate from haplotypes around 8–32 generations ago and their relative abundance reflects a smaller Ne of the population in the recent past. While there is considerably higher uncertainty in estimating the time to the MRCA for ROH when these are measured based on physical rather than genetic map lengths12, this corroborates with historical knowledge: the Soay sheep population was indeed smaller in the early twentieth century when 107 sheep were translocated from the island of Soay to their current location on Hirta, after the last humans left St. Kilda54. Current levels of inbreeding were most visible in the variation in long ROH (>19.5 Mb), which made up <1% of the genome of the least inbred individuals on average, but 18% of the genome of highly inbred individuals with pedigree inbreeding coefficients Fped > 0.2 . While this is a low to moderate inbreeding load compared to the few available estimates from wild mammals obtained from appropriate statistical models60, none of these estimates are based on genomic data and they vary in their exact fitness measure as well as the degree to which they control for environmental and life-history variation. As such, average estimates of lethal equivalents might change in magnitude with the increasing use of genomics in individual-based long-term studies.We have only recently begun to understand the precise consequences of inbreeding for individual fitness in natural populations. In Soay sheep, we found that the odds of survival decreased by 60% with a mere 10% increase in 50. Under the mutation accumulation hypothesis63, the adverse effects of deleterious mutations expressed late in life should become stronger as selection becomes less efficient. Assuming mutation accumulation, inbreeding depression is expected to increase with age too64, but empirical evidence is sparse66. In contrast, we showed that inbreeding depression in Soay sheep becomes weaker at later life stages. In addition, the sample for each successive age class consists of increasingly outbred individuals  recessive alleles will depend on many factors, such as the distribution of dominance and selection coefficients for mutations relative to the effective population size, and the frequency of inbreeding69. However, we could expect that small populations purge largely deleterious recessive mutations more efficiently as these are more frequently exposed to selection in the homozygous state41, while weakly deleterious mutations can more often drift to higher frequencies. We estimated the effect of ROH status on Soay sheep survival for each of the two alleles at every SNP position within a GWAS framework. The effect size distribution revealed predominantly negative effects of ROH status on survival, particularly towards larger model estimates, showing that many alleles with weakly deleterious effects (or at low frequencies) probably contribute to inbreeding depression in survival.The effect size distribution of loci underpinning inbreeding depression has to our knowledge not been studied in wild populations using fitness data, although deleterious mutations have been predicted from sequencing data, for example in ibex and Isle Royale wolvesNe of 19757, and strongly deleterious mutations should be rapidly purged. On the one hand, it is possible that genetic drift counteracted the effects of purifying selection and has allowed deleterious mutations to increase in frequency and be detected in a GWAS. On the other hand, a relatively recent admixture event with the Dunface sheep breed around 150 years ago55 could have introduced deleterious variants into the population and recent selection has not been efficient enough to purge them from the population yet. Identifying the loci harbouring these strongly deleterious alleles will be challenging as ROH overlapping a given SNP vary in length among individuals, which makes it difficult to pinpoint an exact effect location. Nevertheless, we have shown that it is possible to identify the haplotypes carrying deleterious alleles with large effects. The frequencies of such haplotypes could be monitored in natural populations, and individuals carrying them could be selected against in conservation breeding programs. To sum up, our study shows how genome-wide marker information for a large sample of individuals with known fitness can deepen our understanding of the genetic architecture and lifetime dynamics of inbreeding depression in the wild.Associations between ROH and survival reached genome-wide significance in seven regions on six chromosomes. In two of these regions, allele-specific ROH are predicted to increase survival, a fascinating observation we intend to explore in more detail but which is beyond the scope of this manuscript. In five further regions, ROH caused significant depression in survival, presumably due to loci harbouring strongly deleterious recessive alleles. This is unexpected, as Soay sheep have a long-term small population size with an estimated Ovis aries) is a primitive sheep breed descended from Bronze Age domestic sheep and has lived unmanaged on island of Soay in St. Kilda archipelago, Scotland for thousands of years. When the last human inhabitants left St. Kilda in 1932, 107 Soays were transferred to the largest island, Hirta, and have roamed the island freely and unmanaged ever since. The population increased and fluctuates nowadays between 600 and 2200 individuals. A part of the population in the Village Bay area of Hirta  has been the subject of a long-term individual-based study since 198554. Most individuals born in the study area (95%) are ear-tagged and DNA samples are obtained from ear punches or blood sampling. Routine mortality checks are conducted throughout the year with peak mortality occurring at the end of winter and beginning of spring. Overall, around 80% of deceased animals are found50. For the analyses in this paper, survival was defined as dying (0) or surviving (1) from the 1st May of the previous year to the 30th April of that year, with measures available for 5952 individuals from 1979 to 2018. Annual survival data were complete for all individuals in the analysis, as the birth year was known and the death year of an individual was known when it has been found dead during one of the regular mortality checks on the island. We focused on annual measures as this allowed us to incorporate the effects of age and environmental variation.The Soay sheep  or microsatellites72. All animal work was carried out in compliance with all relevant ethical regulations for animal testing and research according to UK Home Office procedures and was licensed under the UK Animals (Scientific Procedures) Act of 1986 (Project License no. PPL70/8818).To assemble the pedigree, we inferred parentage for each individual using 438 unlinked SNP markers from the Ovine SNP50 BeadChip, on which most individuals since 1990 have been genotyped73 with the same thresholds, including identity by state with another individual <0.9. This resulted in a dataset containing 39,368 polymorphic SNPs in 7700 sheep. In addition, we genotyped 189 sheep on the Ovine Infinium HD SNP BeadChip containing 606,066 SNP loci. These sheep were specifically selected to maximise the genetic diversity represented in the full population as described in Johnston et al.58. As a quality control, monomorphic SNPs were discarded, and SNPs with SNP locus genotyping success >0.99 and individual sheep with genotyping success >0.95 were retained. This resulted in 430,702 polymorphic SNPs for 188 individuals. All genotype positions were based on the Oar_v3.1 sheep genome assembly (GenBank assembly ID GCA_000298735.174).We genotyped a total of 7,700 Soay on the Illumina Ovine SNP50 BeadChip containing 51,135 SNP markers. To control for marker quality, we first filtered for SNPs with minor allele frequency (MAF) > 0.001, SNP locus genotyping success >0.99 and individual sheep genotyping success >0.95. We then used the check.marker function in GenABEL version 1.8-075. This resulted in a dataset with 436,117 SNPs including 33,068 SNPs genotyped on both SNP chips. For genotype imputation, we discarded SNPs on the X chromosome and focused on the 419,281 SNPs located on autosomes. The merged dataset contained nearly complete genotype information for 188 individuals who have been genotyped on the HD chip, and genotypes at 38,130 SNPs for 7700 individuals who have been genotyped on the 50 K chip. To impute the missing SNPs, we used AlphaImpute v1.9856, which combines information on shared haplotypes and pedigree relationships for phasing and genotype imputation. AlphaImpute works on a per-chromosome basis, and phasing and imputation are controlled using a parameter file . Briefly, we phased individuals using core lengths ranging from 1 to 5% of the SNPs on a given chromosome over 10 iterations, resulting in a haplotype library. Based on the haplotype library, missing alleles were imputed using the heuristic method over five iterations which allowed us to use genotype information imputed in previous iterations. We only retained imputed genotypes for which all phased haplotypes matched and did not allow for errors. We also discarded SNPs with call rates below 95% after imputation. Overall, this resulted in a dataset with 7691 individuals, 417,373 SNPs and a mean genotyping rate per individual of 99.5% (range 94.8–100%).In order to impute genotypes to high density, we merged the datasets from the 50 K SNP chip and from the HD SNP chip using PLINK v1.90b6.12 with –-bmerge58 for details).To evaluate the accuracy of the imputation we used 10-fold leave-one-out cross-validation. In each iteration, we masked the genotypes unique to the high-density chip for one random individual that had been genotyped at high-density (HD) and imputed the masked genotypes. This allowed a direct comparison between the true and imputed genotypes. The imputation accuracy of the HD individuals should reflect of the average imputation accuracy across the whole population because HD individuals were selected to be representative of the genetic variation observed across the pedigree ) cM/1.28 cM/Mb where g is 32 generations and 1.28 is the sex-averaged genome-wide recombination rate in Soay sheep58. To plot the ROH length distribution, we used the same formula to cluster ROH according to their physical length into length classes with expected MRCA ranging from 2 to 32 generations ago .The final dataset contained genotypes at 417,373 SNPs autosomal SNPs for 5925 individuals for which annual survival data was available. We called runs of homozygosity (ROH) with a minimum length of 1200Kb and spanning at least 50 SNPs with the --homozyg function in Plinkago Fig. . Notably30; each window contained 85 SNPs on average. Specifically, we first calculated the number of ROH overlapping each SNP position in the population using PLINK --homozyg. We then calculated the mean number of ROH overlapping SNPs in 500 Kb non-overlapping sliding windows in the population  were discarded. To estimate the impact of recombination rate on ROH frequency across the genome, we then quantified the recombination rate in 500 Kb windows using genetic distances from the Soay sheep linkage map76. Window heterozygosity was calculated as the mean SNP heterozygosity of all SNPs in a given window. Next, we constructed a linear mixed model in lme477 with population-wide ROH density  per window as response, window recombination rate and heterozygosity as fixed effects and chromosome ID as random intercept . As fixed effects, we fitted the individual inbreeding coefficient FROH (continuous), the life stage of the individual , early life , mid-life , late-life (age = 5+), sex ), and a variable indicating whether an individual was born as a twin . We also fitted an interaction term of FROH with life stage to estimate how inbreeding depression changes across the lifetime. As life stage was fitted as a factor, the model estimates three main effects for the differences in the log-odds of survival between lambs (reference category) and early life, mid-life and late-life respectively. Similarly, the interaction term between FROH and life stage estimates the difference in the effect of FROH on survival (i.e. inbreeding depression) between lambs and individuals in early life, mid-life and late-life, respectively. As random intercepts, we fitted the birth year of an individual, the observation year to account for survival variation among years and the sheep identity to account for repeated measures. For all random effects, which are estimated in terms of precision rather than the variance in INLA, we used log-gamma priors with both shape and inverse-scale parameter values of 0.5. Supplementary Table We modelled the effects of inbreeding depression in annual survival using a Bayesian animal model in INLA84 and transformed FROH from its original range 0–1 to 0–10, which allowed us to directly interpret model estimates as resulting from a 10% increase in genome-wide IBD rather than the difference between a completely outbred and a completely inbred individual. Finally, we report model estimates as odds ratios in the main paper, and on the link scale (as log-odds ratios) in the Supplementary Material.Before modelling, we mean-centred59. However, differences in statistical methodology and inbreeding measures make it difficult to compare the strength of inbreeding depression in terms of lethal equivalents among studies60. Here, we used the approach suggested by Nietlisbach et al.60 and refitted the survival animal model with a Poisson distribution and a logarithmic link function. We were interested in lethal equivalents for the overall strength of inbreeding depression rather than its lifetime dynamics, so we fitted a slightly simplified animal model with FROH, age, age2, twin and sex as fixed effects and birth year, capture year, individual id and pedigree relatedness as random effects. The slope of FROH estimates the decrease in survival due to a 10% increase in FROH, so we calculated the number of diploid lethal equivalents 2B as –(ßFROH)/0.10 ßFROH is the Poisson model slope for FROH.The traditional way of comparing inbreeding depression among studies is to quantify the inbreeding load in terms of lethal equivalents, i.e. a group of genes that would cause on average one death if dispersed in different individuals and made homozygous85. For each of the ~417 K SNPs, we fitted a binomial mixed model with logit link in lme477 with the following model structure:ADD is the additive effect for the focal SNP , and controls for the possibility that a potential negative effect of ROH status is simply an additive effect. FROH as we were interested in the effect of ROH status at a certain locus on top of the average individual inbreeding coefficient. Sex and twin are again binary variables representing the sex of the individual and whether it is a twin. Age and age2 control for the linear and quadratic effects of age, and are fitted as continuous covariates. Because it was computationally impractical to fit 417 K binomial animal models with our sample size and because the additive genetic variance in survival was very small  as fixed effects75. Again, we added birth year, capture year and individual id as random effects. For each fitted model, we extracted the estimated slope of the two SNPROH predictors and their p-values, which were calculated based on a Wald-Z test. To determine a threshold for genome-wide significance we used the ‘simpleM’ procedure86. The method uses composite linkage disequilibrium to create a SNP correlation matrix and calculates the effective number of independent tests, which was much lower than the number of SNPs (neff = 39184) because LD stretches over large distances in Soay sheep (Supplementary Fig. 87 of p-values, resulting in a genome-wide significance threshold of p < 6.38 −7.To map the effects of inbreeding depression in survival across the genome, we used a modification of a genome-wide association studyFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary