There is currently a dearth of accessible whole genome sequencing (WGS) data for individuals residing in the Americas with Sub-Saharan African ancestry. We generated whole genome sequencing data at intermediate (15×) coverage for 2,294 individuals with large amounts of Sub-Saharan African ancestry, predominantly Atlantic African admixed with varying amounts of European and American ancestry. We performed extensive comparisons of variant callers, phasing algorithms, and variant filtration on these data to construct a high quality imputation panel containing data from 2,269 unrelated individuals. With the exception of the TOPMed imputation server (which notably cannot be downloaded), our panel substantially outperformed other available panels when imputing African American individuals. The raw sequencing data, variant calls and imputation panel for this cohort are all freely available via dbGaP and should prove an invaluable resource for further study of admixed African genetics. O’Connell et al. construct a new genome-wide imputation reference panel comprising 2,269 individuals of Sub-Saharan African ancestries. They adapt DeepVariant to create best practices for reference panel development and generate a high quality, publicly available resource that will further empower high resolution genome-wide imputation efforts in individuals of African ancestries. A crucial component of GWAS is genotype imputation3, which requires a large reference panel of sequenced individuals with similar ancestry to the cohort being studied. Existing publicly available panels are predominantly composed of individuals of European descent. For example, the public release of the Haplotype Reference Consortium (HRC)4 panel consists of 27,166 individuals who are largely of European descent, except for 2001 individuals included from the 1000 Genomes Project (1KGP)5, only 661 of whom have substantial African ancestry. There are two imputation panels that focus on African genomic content: the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA)6 and the African Genome Resources (AGR) panel7. CAAPA contains individuals with African ancestry residing in the Americas and some Atlantic African individuals, making it very relevant for imputing African Americans (AFAMs) but it is a relatively small panel (N = 883). AGR has limited data from Atlantic African individuals, making it less appropriate for imputation of AFAM individuals. The recently available TOPMed8 imputation server provides imputation with substantially more individuals with African ancestry  but the TOPMed panel is not downloadable due to consent restrictions, limiting its utility to data that can be uploaded for imputation. A full (to our knowledge) list of imputation panels with African content is available in Supplementary Table Genome-wide association studies (GWAS) have greatly improved our understanding of human genetics over the past decade. To date, GWAS have largely been conducted in cohorts of European descent, leaving the genetic architecture of complex traits in non-Europeans underexploredphs001798.v2.p2).This biased reference panel composition generally leads to substantially poorer imputation quality for non-Europeans relative to Europeans. To help remedy this situation, we introduce an AFAM reference panel, composed of 2,269 American individuals with high amounts of (mainly Atlantic) African ancestry sequenced at ~15× coverage. We evaluated multiple single-sample variant callers, joint genotyping methods, and imputation panel creation methods, and ultimately generated an optimized reference panel using DeepVariant for single-sample calling, GLnexus for joint calling, and SHAPEIT-4 for genotype phasing. The optimized reference panel contains 45,802,366 single-nucleotide polymorphisms (SNPs) and 9,160,064 indels, after excluding all singleton variants. Many of the remaining SNP and indel calls are not present in publicly available panels such as 1KGP/HRC and impute well. This reference panel substantially improves imputation accuracy for individuals with Atlantic African ancestry compared to other publicly available panels, in particular for lower frequency variants. The panel and its associated sequencing data are publicly available on the database of Genotypes and Phenotypes (dbGaP)  failures. Sequencing was performed to an average aligned coverage of 14.8× , with a small number of individuals from Caribbean countries (4%), Africa 2%), and Europe (1.7%), and fewer than five individuals from each of Canada, Asia, South America, and Oceania  dimensionality reduction on the first 15 principal components of the AFAM samples along with six ethnicities from 1KGP as an unsupervised complement to our ancestry classifierImputation reference panel quality depends on both the breadth of haplotypes represented within the panel and the accuracy of the variants called in the individuals. With a fixed sequencing budget (as was the case in this project), these are competing requirements. Total sequencing cost is driven largely by total sequencing coverage and higher per-individual sequencing coverage produces more accurate variant calls but reduces the number of individuals able to be sequenced. At the ~15× coverage level chosen for this reference panel, we sought to optimize reference panel quality by performing three independent experiments to identify the best-performing single-sample variant caller and joint genotyping method.16. Given that GIAB benchmark currently does not include any individual of African ancestry, we used the most extensively characterized and reliable truth set of HG002 for the evaluation of the bioinformatics pipelines. We synthetically downsampled HG002 sequence coverage to all coverages from 15 to 50×, performed variant calling on the downsampled BAM with GATK417, DeepVariant v0.1018, and Strelka219, and assessed the resulting variant-calling accuracy in the HG002 v4.1 truth set using hap.py20. At all sequence coverages, the total number of errors produced by DeepVariant was lower than either GATK or Strelka2, with a more pronounced impact at lower coverages  using truth data carefully curated from a 23andMe microarray containing 387,493 SNPs and 73 indels after stringent quality control (“Methods”). Based on the above downsampling analysis, we evaluated GATK-3.5, GATK-4.1.0.0, and DeepVariant-0.10.0 pipelines for single-sample performance in each of the 292 AFAM samples  and the other generated with the DeepVariant-GLnexus (DV-GLx) Best Practices optimized pipeline23 (“Methods”) . For these analyses, we treated variants that were present in the truth set but missing from a panel as being imputed to homozygous reference, which penalizes panels with missing variation.Third, we evaluated the imputation performance of the 23andMe AFAM panels in samples with Atlantic African ancestry. The evaluation samples were all 240 individuals from the populations ASW, ACB, and YRI in 1KGP, who possessed both deep-sequencing data and Illumina Omni2.5 genotype array calls. Imputation was performed with Beagle 5.117 and DV-GLx23. For quality control, we measured the distributions of read coverage depth, duplication rate, variant call confidence, and transition-transversion ratio, and found that the majority of samples had similar properties . For SNPs, genotypes imputed with the DV-GLx AFAM panel showed higher aggregate R2 with the ground truth than variants imputed with the GATK AFAM panel, consistently in all allele-frequency bins and regardless of whether the ground truth used was generated with DV-GLx or GATK  and a GATK panel with genotype likelihoods refined into discrete genotypes using Beagle 4.1ATK Fig. . For indATK Fig. .Fig. 3Im28, who identified as AFAM (“Methods”). Microarray genotypes for these individuals were emulated for the current 23andMe microarray from WGS data and were pre-phased with SHAPEIT-4 . Imputation was then performed with five different reference panels: (1) the DV-GLx AFAM panel , (2) the HRC panel 4, (3) the 1KGP phase 3 panel 5, (4) the TOPMed panel 8, and the CAAPA panel (N = 883)6. Imputed genotypes were binned by alternate allele frequency taken from the AFAM allele frequency in gnomAD r329. TopMED and CAAPA results used their respective imputation servers, whereas AFAM, HRC, and 1KGP panels were imputed locally using Minimac 430 (the imputation software used by the imputation servers). We calculated aggregate R2 in two ways: first, treating variants missing from the truth set as being imputed as homozygous reference  truth set from 103 individuals in GTEx v8nce Fig.  and, secnce Fig. . The forl N = 2,29, (2) thR2 of 0.75, followed by DV-GLx AFAM (0.59), 1KGP (0.49), HRC (0.35), and CAAPA (0.35), when using the more stringent performance metric  are efficient for identification of causal polymorphisms within a candidate sequence32, but further emphasize the need for African haplotypes in imputation reference panels. Here we have introduced an imputation reference panel that is enriched for Atlantic African ancestry as a resource for researchers investigating AFAM genetics. Extensive evaluations of single-sample variant calling showed that DeepVariant consistently outperformed GATK across a spectrum of sequencing coverage on these data. These improvements in single-sample variant calling yielded a modest improvement in downstream imputation performance. In particular, due to greater sensitivity, the DV-GLx reference panel provided a much larger set of variants for association testing than the GATK Best Practices reference panel. When contrasted with the 1KGP, HRC, and CAAPA panels, the DV-GLx panel provided substantially better imputation performance for rarer variants. The TOPMed imputation server yielded far better imputation for SNPs than our panel due to its much larger sample size. However, the TOPMed panel cannot be downloaded due to consent restrictions, so only data consented to be uploaded to a cloud imputation service can take advantage of the large TOPMed panel. The TOPMed indel set also appeared to be very stringently filtered, perhaps at the cost of sensitivity.Increasing the representation of samples of non-European ancestry in genomic data sets is critical for reducing the potential of polygenic risk scores to exacerbate health disparities34. However, it is a computationally expensive step that introduces substantial complexity into the processing pipeline to parallelize efficiently genomewide (“Methods”). The DV-GLx panels evaluated here showed no performance improvement from genotype refinement, likely owing at least in part to the relatively high accuracy of single-sample DeepVariant calls and well-calibrated genotype likelihood estimates on low-coverage samples, enabling more accurate joint genotyping by GLnexus23, mitigating the need for further refinement using linkage disequilibrium-based context. This somewhat surprising result further increased the relative computational efficiency to create the DV-GLx AFAM panel compared to the GATK AFAM panel.Refinement of genotype likelihoods into hard genotypes is a common practice for generating imputation panels from low-coverage sequencing dataR2 metrics based on allele frequencies computed in an independent data set.Although this study demonstrates the importance of increasing genetic diversity in imputation panels, there are limitations that must be taken into account. Evaluation of imputation panels generated by different variant-calling pipelines is sensitive to selected metrics and the ground truth calls used. Ground truth variants generated using a particular joint-calling method bias result toward imputation panels generated with the same method. Restricting the evaluated sites to those called consistently among all calling pipelines ignores differences in variant detection sensitivity and biases toward easily called variants. To mitigate these issues, we evaluated candidate panel performance on multiple ground truth sets generated using both candidate panel joint-calling methods and binned aggregate The DV-GLx AFAM imputation panel and related sequencing data are available via NCBI dbGaP. As a standalone imputation panel, it can freely be used to improve imputation in AFAM cohorts. In addition, combining the raw data with other publicly available data such as the recently released high-coverage 1KGP individuals would increase the European and American content, and the resulting multi-ethnic panel would likely lead to even better imputation for underrepresented admixed populations, in particular AFAM and Latino cohorts. We believe that these resources are a valuable contribution to further research of complex trait genetics in non-European populations.35  using BWA-MEM36 (version 0.7.16a-r1181) and PCR duplicates were marked with Picard (version 2.1.0). As DNA was extracted from saliva, bacterial contamination resulted in an average aligned coverage of 14.8 × with high variation in coverage  > 5% from downstream analysis. This resulted in 2,294 individuals passing  single-sample QC.The full study was approved by the Ethical & Independent Review Services Institutional Review Board (IRB). Individuals were sequenced to an expected 17 × coverage. Reads were aligned to GRCh3839. These were used to remove 25 individuals with close relatives (first cousin or nearer) to create a panel of 2,269 unrelated individuals. There were 15 parent–child pairs (including one full trio), three sibling pairs, and seven first cousin (or similar) pairs. Relatedness pruning was simple; children in duos/trios were first excluded . After this, only familial cliques of size two remained; we chose the higher coverage individual from each clique to maximize data quality. It is noteworthy that although these related individuals are not in the imputation panel, their raw sequencing data are available in dbGaP.We estimated robust kinship coefficients and IBD0 proportions using AKT17 (GATK-3.5.0) and DV-GLx -optimized pipeline23 were restricted to chromosome 20. Refinement of genotype likelihoods into hard genotype calls was performed with Beagle 4.127 in approximate chunks of 1.4 Mbp (chunk size varied to keep the number of markers constant) with a 400 kbp overlap between each chunk. SHAPEIT-4.1.3 and Eagle-2.4.1 were then applied to the resulting hard genotypes in ~10 Mbp chunks with a 400 kbp overlap between each chunk. Chunks were ligated into whole chromosomes using bcftools40. Code for this analysis is included in our repository41  thresholds. In addition to VQSR filtering, we removed singletons and variants where >10% of genotypes were missing. The resulting reference panel contained 36.1 million SNPs and 7.7 million indels across all autosomes. Based on the results in the previous section, we refined genotype likelihoods using Beagle 4.1 followed by phasing with SHAPEIT-4.1.3. See the script in Supplementary Table 22 to determine the optimal GLnexus merging parameters for ~15× coverage reads. After merging, we removed singletons and applied additional variant-level filters using (1) the Hardy-Weinberg equilibrium p-value (≥10−20), (2) the proportion of missing genotype calls in all samples (≤20%), and (3) the expected proportion of correct genotypes computed using Genotype Qualities (GQs) of all genotype calls (≥60%). Then, 43.7 million SNPs and 8.8 million indels were retained after filtering across the autosomes. Finally, we phased the variants with SHAPEIT-4.1.3 to generate the imputation reference panel. Notably, we did not perform genotype refinement with Beagle for DV-GLx, as it is computationally expensive and did not improve quality for DV-GLx calls. See The reference panel using DeepVariant-0.10.0 and GLnexus-1.2.6 was created independently of the GATK panel. We used results from a previous study on DV-GLx best practices28. For each individual, we took to the intersection of GATK-3.5 variants and DeepVariant-0.10.0 variants, then set genotypes where the variant callers disagreed or either caller had GQ < 20 to missing. Any resulting variants with >10% missing genotypes across the cohort of 103 samples were excluded. We only considered HG001/HG002/HG005 GIAB regions that were outside of segmental duplications. Variants were then lifted to hg19 using Picard to accommodate the older 1KGP/HRC/CAAPA panels. Only the set of successfully lifted variants were considered in the final evaluation (on both hg19 and GRCh38). Finally, to provide an objective estimate of AFAM allele frequency, we evaluated accuracy within frequency bins from gnomAD r3. This meant that the variant set was further limited to mutations present in gnomAD. The resulting truth set contained 21,642,652 SNPs and 1,939,396 indels.We sought to create a fair truth set using high-coverage WGS data that could be imputed on the TOPMed imputation server. We extracted the 103 individuals who identified as AFAM from the GTEx V8 databaseWe applied stringent filtering to create a high-quality truth set from 23andMe genotype microarray data for evaluating the performance of single-sample variant calling. It is noteworthy that these filters are much more stringent than what would typically be applied in a GWAS setting. In addition to typical filters on allele frequency and call rate, probes were aligned with BWA-MEM to ensure high specificity to the reference genome, and that the vendor-provided coordinates were consistent with alignment. We also excluded variants whose probes overlapped other common variants. This was due to the inability of probes to distinguish between certain alleles at multi-allelic sites and because a probe may fail to hybridize if it overlaps a flanking variant near the targeted variant.Located on autosome.≥90% Call rate across entire research-consented database.≥0.001% Minor allele frequency across the entire research-consented database.Minor allele count ≥ 1 within our cohort of 2,294 sequenced individuals.Probes aligned by BWA had Mapping Quality (MAPQ field) = 60, edit distance to reference (NM tag) = 0, and no clipping.BWA alignment agreed with vendor-provided coordinates.Entire 50-mer probe did not overlap any variant occurring in TOPMed with >1% MAF.Probes did not overlap one another on the chip.The following filters were applied to a custom 23andMe genotype microarray (version 4):The resulting truth set contained 387,493 SNPs and 73 indels. Genotypes from these variants were provided to hap.py as both a Variant Call Format (VCF) file (for non-reference genotypes) and a confident region Browser-Extensible Data (BED) file (derived from both homozygous reference and non-reference genotypes) for evaluation of single-sample variant calling.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Associations of low lung function with features of poor cardio-metabolic health have been reported. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability or are confounded by environmental factors.N > 82,000).We performed three analyses: (1) cardio-metabolic health to lung function association tests in Northern Finland Birth cohort 1966, (2) cross-trait linkage disequilibrium score regression (LDSC) to compare genetic backgrounds and (3) Mendelian randomisation (MR) analysis to assess the causal effect of cardio-metabolic traits and disease on lung function, and vice versa . Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies , we found associations between type 2 diabetes (T2D) instruments and forced vital capacity (FVC) as well as FEV1/FVC. Body mass index (BMI) instruments were associated to all lung function traits and C-reactive protein (CRP) instruments to FVC. These genetic associations provide evidence for a causal effect of cardio-metabolic traits on lung function. Multivariable MR suggested independence of these causal effects from other tested cardio-metabolic traits and diseases. Analysis of lung function specific SNPs revealed a potential causal effect of FEV1/FVC on blood pressure.The present study overcomes many limitations of observational studies by using Mendelian Randomisation. We provide evidence for an independent causal effect of T2D, CRP and BMI on lung function with some of the T2D effect on lung function being attributed to inflammatory mechanisms. Furthermore, this analysis suggests a potential causal effect of FEV1/FVC on blood pressure. Our detailed analysis of the interplay between cardio-metabolic traits and impaired lung function provides the opportunity to improve the quality of existing intervention strategies.The online version contains supplementary material available at 10.1186/s13073-021-00914-x. Obesity and cardio-metabolic traits have become an increasing public health problem in most parts of the world. By 2025, global obesity prevalence is predicted to reach 18% in men and 21% in women [Several observational studies have reported an association between low lung function and cardio-metabolic traits, including obesity –5. In thIn this study, we assess associations between eleven cardio-metabolic traits representing wider range of traits than usually accounted in pure metabolic syndrome definition , 7 body Mendelian randomisation has been used to estimate the causal effect of body mass index (BMI) on two lung function parameters  . HoweverIn this study, we assessed associations between eleven cardio-metabolic traits and three lung function outcomes forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and the ratio (FEV1/FVC)  (Table 1The Northern Finland Birth Cohort 1966 (NFBC1966), described in detail previously , 24, tarType 2 diabetes in NFBC1966 was defined as either prescription of metformin , diagnosed by a physician , or screen-detected by OGTT at the age of 46 years . Coronary heart disease was defined based on participants answer to the following questions: ‘Do you now or have you had following the doctor diagnosed or treated the symptoms, diseases or injuries: Congenital heart disease’.For spirometry measurements, we used a MasterScreen Pneumo Spirometer , with a volumetric accuracy of ±2% or ±50 mL whichever was greater. The machines were calibrated every day the medical examination took place. The spirometric manoeuvre was performed three times in an upright sitting position while wearing a nose clip, but repeated if the coefficient of variation between two maximal readings was >4%.-8) with the cardio-metabolic traits from the most recent published GWAS including 82,000 up to 322,000 individuals  from the UK Biobank  using BOLT-LMM adjusted for assessment centre, sex, age, height, current smoking status and pack-years  . BrieflyMendelianrandomization, unless stated otherwise  associated with lung function in the univariable MR (B–path), and we could find evidence for a causal effect of the exposure on the mediator (A-path). These assumptions were fulfilled for the CRP-mediated effect of BMI on lung function. For this trait, we compared the direct effect estimates (IVW MR of risk factor) with the total effect estimate (MMR estimate of risk factor plus mediator) [Traits were interpreted as mediators when they were consistently significantly (ediator) Fig. 1B)Bp<0.05) We repeated the MR analysis in the opposite direction to determine possible causal effects of lung function on cardio-metabolic traits Fig. B. For thCardio-metabolic traits are closely related and correlation across traits could generate horizontal pleiotropy. For example, if an instrument for trait A is also associated with trait B (e.g. variants in FTO for BMI and CRP), it would be challenging to find out whether the association with outcome (e.g. lung function) is reflecting the causal effects of traits A or B. This is a major challenge in this study as 17% of the variants we used as instruments in this MR are associated with more than one cardio-metabolic trait  change in BMI. Low P values obtained from multivariable MR (MMR) suggest that the effects of BMI on lung function are independent from other tested risk factors  but a positive correlation with FEV1/FVC (P=0.012), with the opposite for HDL cholesterol . Furthermore, we found evidence for causal effect of some cardio-metabolic traits  on lung function measures and a possible causal effect of FEV1/FVC on blood pressure. As the assessed cardio-metabolic traits were highly correlated, we used multivariable Mendelian randomisation (MMR) to validate the findings from univariable MR and investigate the interplay between cardio-metabolic traits as the method simultaneously accounts for multiple causal factors Figs. .We have used the state-of-the-art statistical methods for assessing genetic correlation, using publicly available summary statistics of genetic associations. This method has been previously used to assess genetic correlations for example between 24 traits  , betweenWe went on to determine whether the observed correlations could reflect causal associations using MR. LD score regression is not the same as Mendelian randomisation as it uses information from the whole genome and thus does not model one trait as a function of the other. Also, it makes no assumption of the causal direction of association (whereas MR tests the effect of one factor on the other). We show through MR that higher BMI is causally associated with lower FEV1 and FVC, with greater effects on the latter, explaining its positive effect on the derived parameter FEV1/FVC. This is consistent with previous reports from multiple large-scale population-based epidemiological studies , as wellOur analysis showed stronger effects of increased BMI on restrictive ventilation patterns than on airway obstruction. The mechanisms for how BMI affects lung function remain elusive although it is likely that fat accumulation between the muscles around the lungs and in the abdomen may have mechanical effects on the diaphragm and impede full inspiration as well as decreasing chest wall compliance . ObesityCross-sectional and longitudinal studies have shown that middle-aged adults with T2D have worse lung function and slightly increased lung function decline . The proSimilar to BMI, we observed the causal effect of T2D having restrictive effects on the lung rather than obstructive Fig. . These aSome longitudinal observational studies have reported that low lung function is an independent predictor of incident T2D , 38, andOur proxy for systemic inflammation in this study is serum CRP. In this context, we acknowledge that altered serum CRP levels may be affected by factors not discussed in this study such as infection or immune disease. We observed statistically significant effects of CRP on FVC Fig. . These aObservational studies of associations of lipid levels and the presence of COPD have been inconsistent and a recent meta-analysis found no evidence for associations of COPD with serum levels of HDL-C, LDL-C, TC and TG . In crosAssociation between lung function and CAD, suggesting an effect of lung function on CAD, have not been stable across sensitivity analysis (Additional File We hope that the presented findings will increase awareness of the relationship between lung function and cardiometabolic disease amongst clinicians, particularly general practitioners, and encourage clinicians to regularly conduct spirometry on their patients even amongst those without symptoms of lung disease. Furthermore, this work highlights the notion that efforts to reduce obesity and T2D will also improve lung function and lung health. Thus, measures taken to reduce obesity in the general population can also be viewed as measures to improve lung health. Finally, this study also suggests weight loss as a measure to improve respiratory health.-8 (Additional File Mendelian randomisation analysis are a great tool to use large-scale GWAS results to gain public health relevant insights; however, one of the major limitations in MR is weak instrument bias, meaning the variant explains little variation of the exposure. To overcome this source of bias, we selected variants from large-scale GWAS Table  in combiP values for the associations presented in the study (Additional File Another major challenge in this MR study is pleiotropy, the potential for a SNP used as instrument for a risk factor to affect more than one phenotype. Due to this complexity in the present MR study, we relied on consistency of results of multiple MR methods with different assumptions as well as results from multivariable MR.  We used 1, FVC and their ratio. These correlations reflect causal associations for the effects of BMI on all lung parameters and for T2D on FVC and FEV1/FVC. These associations are broadly independent from each other and of other metabolic traits with a small proportion of the effect of T2D and BMI on impaired lung function being mediated by serum CRP. There was evidence that FEV1/FVC ratio have a causal effect on blood pressure but not on the other tested cardio-metabolic traits. Our results strongly support efforts to reduce obesity and T2D as measures to improve lung function and lung health in the general population.In conclusion, we provide evidence for genetic correlations between BMI, CRP, T2D and coronary artery disease with FEVAdditional file 1. This file contains supplementary methods describing the analysis in more detail. Table S1. Summary of analyzed NFBC1966 data. Table S2. published datasets used in this study. Table S5. Cohort characteristic of UK Biobank. Table S8. Regression results between lung function and cardiometabolic traits in NFBC1966. Table S9. Results of LD-score regression. Figure S3. Correlation and Principal component analysis in NFBC1966. Figure S4. Forest plot of multivariable MR with anthropomorphic trats and smoking added to the model. Figure S8. Forest plots showing effect of cardio-metabolic traits on FEV1. Figure S9. Forest plots showing effect of cardio-metabolic traits on FVC. Figure S10. Forest plots showing effect of cardio-metabolic traits on FEV1pFVC. Figure S11. Forest plots showing effect of FEV1 on cardio-metabolic traits. Figure S12. Forest plots showing effect of FVC on cardio-metabolic traits. Figure S13. Forest plots showing effect of FEV1pFVC on cardio-metabolic traits. Figure S14. Forest plots of sex stratified analysis. Figure S15. Power analysis. Figure S16. Sensitivity analysis: Forest plots showing effect of cardio-metabolic traits on FEV1. Figure S17. Sensitivity analysis: Forest plots showing effect of cardio-metabolic traits on FVC. Figure S18. Sensitivity analysis: Forest plots showing effect of cardio-metabolic traits on FEV1pFVC. Figure S19. Sensitivity analysis: Forest plots showing effect of FEV1 on cardio-metabolic traits. Figure S20. Sensitivity analysis: Forest plots showing effect of FVC on cardio-metabolic traits. Figure S21. Sensitivity analysis: Forest plots showing effect of FEV1pFVC on cardio-metabolic traits.Additional file2: Table S3. contains a detailed overview of all variants used as instruments in this study including chromosomal position, nearest gene, allele frequencies and P value of association to all tested cardiometabolic traits. Table S4. Linkage disequilibrium between SNPs subdivided into traits and chromosomes. Table S6. Sensitivity analysis including effect estimates, standard errors and P-values for associations between all exposures and all outcomes for all applied MR techniques. Table S7. Multivariable MR including effect estimates, standard errors from Multivariable MR. Table S10. Effect attenuation gives differences in effect size estimates when adding an additional predictor to multivariable MR analysis. Table S11. Sex stratified analysis including effect estimates, standard errors and P-values.


The use of antimicrobials in animal production is under public debate, mainly due to the risk of transfer of resistance to pathogenic bacteria in humans. Therefore, measures have been taken during the last few decades to reduce antibiotic usage in animals, for instance, by national monitoring programmes and by improving animal health management. Although some initiatives exist in molecular genetic selection, quantitative genetic selection of animals towards decreased antibiotic usage is an underexplored area to reduce antibiotic usage. However, this strategy could yield cumulative effects. In this study, we derived new phenotypes from on-farm parenteral antibiotic records from commercially grown crossbred finishing pigs used in the progeny test of Piétrain terminal sires to investigate the heritability of antibiotics usage. Parenteral antibiotic records, production parameters and pedigree records of 2238 full-sib pens from two experimental farms in Belgium between 2014 and 2020 were analysed. Heritability estimates were moderate (18–44%) for phenotypes derived from all antibiotic treatments, and low (1–15%) for phenotypes derived from treatments against respiratory diseases only. Moreover, genetic correlations between these new phenotypes and mortality were low to moderate (0.08–0.60) and no strong adverse genetic correlations with production traits were found. The high heritabilities and favourable genetic correlations suggest these new phenotypes, derived from on-farm antibiotics records, to be promising for inclusion in future pig breeding programs to breed for a decrease in antibiotics usage. Antimicrobials are commonly used in animal production systems, not only for treatment, but also for metaphylaxis, prophylaxis and in some countries still for improvement of feed efficiency and growth ,2. HowevReducing antibiotic usage in animals by genetic selection has been underexplored so far . BreedinIn this study, we investigate the value of field data on antibiotic usage in finishing pigs on a pen level as a new, easy to measure phenotype in pig breeding. Genetic parameters such as heritability and genetic correlations were estimated for antimicrobial usage in finishing pigs.Mycoplasma hyopneumoniae before weaning and received anthelminthic medication upon arrival at the experimental farm. Pigs were kept in commercial conditions representative for Belgian pig production  but were kept in pens of only 6 to 7 full-sib pigs. Farm A had 18 compartments, each containing 8 pens (4.7 m2 per pen) of usually 6 pigs. Pens in farm A were of mixed sex with females and immunocastrated males to prevent boar taint . Farm B had 7 compartments, each with 16 pens (5.4 m2) of usually 7 pigs. Pens in farm B were of mixed sex composed of females and surgically castrated males (barrows). Only pens starting with 5 to 8 full-sib pigs were retained for further analyses.Data were collected on crossbred progeny of Piétrain sires and hybrid sows  kept in two experimental fattening units (farm A and B) of the breeding organization “Vlaamse Piétrain Fokkerij” . After quality control, data were available on 2238 full-sib pens with known pedigree  and treatment duration (days). Other medication such as vaccines, anthelmintic products and anti-inflammatory drugs were not considered in this study.Aside from antimicrobial usage, feed intake (FI) during the testing period was also recorded at the pen level. Average daily gain (ADG), mortality during the finishing period and carcass quality traits (meat%) were collected at the individual level. Pigs that died before slaughter were individually weighed so that all weight data could be adjusted and retained in the analysis.As the antimicrobial usage data in our study were collected at pen level, these data were summarised and analysed per pen. In total, 779 pens were medicated at least once. In farm A, however, 104 pens  were group medicated at the compartment level with an orally applied antibiotic . These pens were excluded due to the different route of administration  of the antibiotic.all) and another set with antibiotic treatments for respiratory problems only (ABrespiratory). The ABrespiratory dataset was created because the majority of antibiotics were used to treat respiratory diseases . In ABrespiratory, treatments with fluoroquinolones (1 pen) and macrolides (7 pens) were rare and therefore these treatments were removed from the dataset. An overview of the antibiotics reported in this study is shown in Hereafter, two datasets were created: one dataset containing all antibiotic treatments (ABtreatment) in a pen was calculated as the treatment duration in days for a specific treatment.The treatment length (NmL) was calculated for each treatment by multiplying the administered volume per pig by the number of treated pigs in the pen and the treatment duration:The total administered volume of an antibiotic (ABmg) was computed afterwards, using the concentration (mg/mL) of the active compound in the administered product , as suggested by Timmerman et al. .Pen; mg/kg) was calculated as the mg of active compound administered per kg of live weight of pig in the pen at the time of administration UDPen (t is the time at treatment (age in days). The growth curve parameters were estimated using the available weight data at start and at death or slaughter (The weight at treatment was estimated based on the age of pigs using a Gompertz growth curve function:laughter .pig) and animal daily dose per pig (ADDpig) were calculated following Timmerman et al. [pig is the administered dose of a drug per kg pig per day, whereas the ADDpig is the nationally recommended dose of a drug per kg pig per day. These parameters were used to assess whether dosing had been done according to label directions. Antibiotic administrations where pig were based on dose recommendations by the Belgian expertise center on antimicrobial use in animals .ADD and TIUDD) were calculated at the pen level for each parenterally administered treatment [mg; UDD or ADD is as specified before; LA is a correction factor for long acting antibiotics [ADD and TIUDD can be interpreted as the percentage of days in a period ‘at risk’ a pig is treated with one reference dose (ADDpig or UDDpig). For example, a TIUDD of 1 indicates that an average pig was treated with one reference dose of antibiotics for 1% of the period at risk.Finally, treatment incidences , whereas traits in the ABrespiratory dataset received a suffix ‘-respiratory’ .The parameters NAverage daily gain (ADG) was calculated individually as the difference between weight at slaughter/death and start of test (kg), divided by duration of test (days). The percentage of lean meat (meat%) was estimated using a fully automated classification system  at two slaughterhouses from the Belgian Pork Group. Mortality counts were summed at the pen level. Feed conversion ratio (FCR) was calculated as the mean FI (kg/day) divided by the mean ADG (kg/day) per pen. Details of trait distributions are shown in The final dataset after data cleaning included 2238 pens: 14,742 finishing pigs from 699 Piétrain sires and 1148 crossbred dams. Pedigree comprised 24,081 animals, where the median pedigree depth of Piétrain sires was 11 generations  and 5  for crossbred dams.2) were estimated using single-trait genetic animal models with average information REML, implemented in airemlf90 and invoked with the R-package breedR [g) between traits related to antibiotic usage  were estimated using bivariate animal models (airemlf90). The h2 was estimated as the proportion of additive genetic variance divided by total variance, whereas c2 was estimated as the proportion of variance explained by random environmental effects (c), divided by total variance. Standard errors of h2, c2 and rg were estimated using the se_covar_function option in airemlf90.Heritabilities  was fitted on the complete datasets. From these results, observed phenotypes were adjusted for fixed and non-genetic random effects: nt-level .all dataset). Most of the 1538 antibiotic treatments were from the class of lincosamides (44.5%), cephalosporins (18.4%), aminopenicillins (18.3%) and florfenicol (11.4%). The majority of treatments were applied for respiratory diseases (70.5%). Other indications included locomotion problems (8.7%), diarrhoea (5.6%), severe weight loss (4.5%), oedema disease (3.6%), ear infections (2.9%) and others (4.2%). The number of treatments per month is given in In total, 626 pens had a minimum of one pig treated, at least once, with antibiotics  and mortality were zero-bound and showed a heavy right-tailed distribution. Other traits were approximately normally distributed based on visual inspection of trait distributions.Descriptive statistics of the studied traits at a pen level are shown in all dataset, pens were treated 0.7 times with 3.6 mL of antibiotics and 445.1 mg of active compound , whereas 49.4% of treatments were underdosed and 17.1% were overdosed. Treatment incidences  ranged between 0.08 and 0.17, indicating that an average pig was treated with one reference dose of antibiotics for 0.08–0.17% of the period at risk (119 days median).On average for the ABcompound . The mea2, c2 and rg are shown in mg, TIADD and UDpen were most heritable and significantly >0 based on a 95% confidence interval (mean ± 1.96×se), which is not explicitly given. In general, c2 was moderate to high for antibiotic usage (12–38%) and estimates were larger in the respiratory problems dataset. UDpen and Ntreatment across datasets were highly genetically correlated (rg = 0.65 to 0.95). Genetic correlations with production traits were low with high standard errors, but the genetic correlation with mortality was consistently positive, ranging from 0.08 to 0.60.Estimates of hpen and Ntreatment are shown in respiratory dataset using the across-family sampling strategy. Within-family sampling yielded—as expected due to the exploitation of close relationships—higher predictive abilities with lower standard errors. Predictive ability of UDPen was higher than for Ntreatment. Heritabilities obtained in cross-validations were similar to the full dataset.Results of cross-validation analyses for UDThis study is the first to report genetic parameters of antibiotic usage derived from medication records in finishing pigs. New phenotypes for antibiotic usage at a pen level in finishing pigs were derived from on-farm medication records and were found to be moderately heritable and with a favourable (low to moderate) genetic correlation with mortality.mL, ABmg, Ntreatment, TIADD, TIUDD and UDPen. Data were summed over different antibiotics at a pen level with an equal weight to the administered mg of active compound, although potency (strength) of active compound can differ among products [Six different phenotypes derived from medication records at pen level were investigated: ABproducts . Howeverpig/ADDpig ratios showed that most treatments were underdosed (61.9%), while 12.6% were overdosed and 25.5% correctly dosed  were low, ranging from 0.09 to 0.15 units, meaning that pigs were treated with one reference dose of antibiotics for 0.09–0.15% during the period at risk (fattening period). Sjölund et al. [ADD and TIUDD are thus rather low, especially compared to the median Belgian value of 0.83.Treatment incidences  and/or ordinal traits , assuming that a higher health status would decrease antimicrobial usage and mortality ,30,31. F2) estimates for traits related to antibiotic usage were moderate to high in the dataset containing all parenteral antibiotic treatments (18–44%) and low to moderate in the respiratory problems dataset (1–15%)  or mL product (ABmL) might be interesting as description for the used antibiotics, however it seems less relevant for estimates of h2: 10 mg (or mL) of antibiotic x is not necessarily equivalent to 10 mg (or mL) of antibiotic y. Therefore, UD and/or TIADD seem better parameters for quantifying antibiotic usage, since they take into account the mg of active compound administered per kg of animal at risk.Heritability (h (1–15%) . The h2 2 estimates were robust against random sampling of data and also the reduction in data points.Heritability estimates remained relatively consistent in the cross-validations , implyintreatment-all and Ntreatment-respiratory were 25% and 3%, respectively. Putz et al. [Heritability estimates of Nz et al.  found a z et al.  found hez et al. ).2) were moderate to high for traits related to antibiotic usage. This was expected since disease prevalence and antimicrobial usage are known to show seasonal variation in pigs, depend on the presence of infectious agents and are linked to the stable climate [2-estimates were consistently higher in the respiratory problems dataset, meaning these environmental effects within a compartment are larger for respiratory problems. Indeed, the stable climate is well known to play a pivotal role for the respiratory health of pigs [Common environmental effects  were exploited. This indicates the potential of UDPen for use in pig breeding programs. Applying one generation of single-trait selection to reduce UDPen-all (additive genetic variance is 3.1 mg2 active compound/kg2 pig) with a selection intensity of 1, a generation interval of 3 years and an accuracy of 0.50 would yield an annual reduction of UDPen-all by 0.29 mg active compound/kg pig when using the breeder’s equation and assuming an equal potency between products.Predictive abilities using five-fold cross-validation were highest and significantly larger than zero for UDPen and Ntreatment with mortality at pen level were low to moderately high , mortality at the pen level would simultaneously decrease as a correlated response. We believe this is an extremely interesting result, as mortality is one of the most important and ignored traits in pig breeding programs. Furthermore, no strong adverse genetic correlations were found with production traits such as FI, ADG and FCR. However, we would like to note that not all genetic correlations were significantly different from zero. Only between Ntreatment and Meat% unfavourable genetic correlations were found (0.45 and 0.11), whereas low favourable genetic correlations were found between UDpen and ADG (−0.30 and −0.21) and FCR (0.25 and 0.26). These results indicate that UDPen or Ntreatment can be included in current breeding programs without adversely affecting other economically important traits. Moreover, breeding pigs for lower UDPen or Ntreatment will not only decrease antimicrobial usage by increasing general pig health, but may also decrease costs  for pig farmers and reduce the risk of antibiotic resistance [Genetic correlations of UD.60; Pen . This gesistance .The present study is based on data from finishing pigs at pen level in two experimental farms considered representative for the Belgian pig sector. The dataset contained 2238 full-sib pens, which is a reasonable size to estimate heritabilities, but might still be insufficient to accurately estimate genetic correlations although standard errors of genetic correlations were low to moderate . FurtherIn the present study, data on antibiotic usage were recorded on paper. However, recent technology allows automated recording of treatments of individual pigs in real time using RFID tags. Such new precision livestock farming solutions may offer a user-friendly solution to record antibiotic usage in large scale pig breeding programs. Individual data may increase accuracy of breeding values and, hence, genetic progress. Moreover, individual recording is required in pens of non-siblings in order to exploit pedigree or genomic relationships.Individual tracking using RFIDs would also allow to study the suckling and weaning periods during which most antibiotics are used. Sarrazin et al.  and SjölThis study shows that new phenotypes, derived from on-farm drug records at pen level from Piétrain-sired crossbred finishing pigs in a commercial environment, were heritable. Moreover, low to moderate favourable genetic correlations were found with mortality and  favourable genetic correlations were estimated with production traits such as average daily gain and feed conversion ratio. Hence, these new phenotypes are promising traits to be included in the pig breeding programs, although more research is still necessary at this point. Further improvements are expected by recording antibiotic usage on an individual level throughout a pig’s lifespan via emerging precision livestock farming technologies.


Oncorhynchus mykiss)—insectivores and piscivores—that coexist for the non-breeding portion of the year in Kootenay Lake, southeastern British Columbia. “Gerrards” are large-bodied, rapidly growing piscivores with high metabolic rates that spawn north of Kootenay Lake in the Lardeau River, in contrast to the insectivorous populations that are on average smaller in body size, with lower growth and metabolic rates, mainly forage on aquatic insects, and spawn in tributaries immediately surrounding Kootenay Lake. We used pool-seq data representing ~ 60% of the genome and 80 fish per population to assess the level of genomic divergence between ecomorphs and to identify and interrogate loci that may play functional or selective roles in their divergence.Identifying ecologically significant phenotypic traits and the genomic mechanisms that underly them are crucial steps in understanding traits associated with population divergence. We used genome-wide data to identify genomic regions associated with key traits that distinguish two ecomorphs of rainbow trout (Genomic divergence was high between sympatric insectivores and piscivores (Our results reveal a high degree of genomic differentiation between piscivorous and insectivorous populations and indicate that the large body piscivorous phenotype is likely not due to one or a few loci of large effect. Rather, the piscivore phenotype may be controlled by several loci of small effect, thus highlighting the power of whole-genome resequencing in identifying genomic regions underlying population-level phenotypic divergences.The online version contains supplementary material available at 10.1186/s12862-021-01795-9. Heliconius butterflies; ).iversity  and recoiversity . Withoutentclass1pt{minimaGrowth was one of the functions enriched in the most divergent loci , eight insectivores each from 10 tributaries immediately surrounding Kootenay Lake , and 80 insectivorous rainbow trout from the Blackwater River of central British Columbia , then Samblaster [http://broadinstitute.github.io/picard/) and Samtools. Lastly, indel regions were then identified using the PoPoolation2 [identify-genomic-indel-regions.pl script, and SNPs were discarded if within 5 bp of the indel regions using the filter-sync-by-gtf.pl script in PoPoolation2. SNPs were retained that had (1) two alleles, (2) a minor allele frequency (MAF) cutoff > 0.05 globally , (3) at least two copies of the minor allele at a locus (to mitigate the effect of sequencing errors), and (4) a sequencing depth of 15 xRaw sequence data (fastq format) were processed using the PoolParty pipeline  as follotion v1; ) based otion v1;  to alignmblaster  and SamTmblaster  were useolation2  identifyqqman [We used the fixation index (ockerham  (R scripge qqman .n = 20) as a first step in identifying the genomic regions that may be associated with the phenotypic differentiation of these two ecomorphs. Non-overlapping windows were used because a sliding-window approach leads to non-independent tests from overlapping windows with no clear statistical adjustment for multiple comparisons.Two approaches were used to identify genomic regions that were highly differentiated between piscivores and insectivores and therefore potentially responsible in underlying their phenotypic divergence. The first method was with a window-based ockerham  calculatariation . We focudddddOutlier detection via election . In lieun NPStat , 69, andF”; [p-values that were calculated in R scripts provided by Bonhomme et al. [As a complementary approach to outlier identification via o et al. . The locrix “F”; ) single-e et al. , as the p-values (from either the FLK or LK test statistic). However, in the case of a non-uniform distribution of p-values, p-value distribution, and we explored various values in this range. p-value distribution) resulted in no outliers identified, and p-value distribution) produced hundreds of significant outlier regions (results not shown). We therefore set p-value distribution.The value of the “tuning parameter”  has not been previously reported. We therefore estimated it through simulations mimicking our empirical dataset as follows. A single ancestral population of 1000 individuals evolved for 1000 generations and then split into three populations, each consisting of 1000 individuals. These three populations evolved independently  for 2500 more generations . And secondly, by separating our data into four bins based on estimates from our window-based n windows = 7219), 14% , 4.4% (854), and 0.9% (187) most divergent regions in the genome, respectively. We organized our data into bins based on divergence estimates because we expected the loci associated with biological processes most likely involved in the divergence of these populations to emerge in the more highly differentiated genomic regions  enrichment analyses to identify putative functions of genes in the most divergent genomic regions between piscivores and insectivores. The GO database is designed to be species-neutral, and therefore annotations were transferred from . mykiss . We lookp-value of this score corrected for multiple testing is then calculated. All GO analyses were performed with a significance threshold of pp-value . Beyond the GO analyses, we also used annotations of genes in the five most divergent We used bedtools  to extrah Revigo  as “treeAdditional file 1. Additional tables and figures.


Populus, reduce the risk of cavitation and hydraulic failure under water stress by producing water-conducting vessel elements with narrow lumens, which are more numerous and more interconnected with each other. Here, we determined the genetic architecture of vessel traits affecting hydraulic physiology and resilience to water stress. Vessel traits were measured for clonally replicated genotypes of a unique Populus deltoides x nigra population carrying genomically defined insertions and deletions that create gene dosage variation. We found significant phenotypic variation for all traits measured , and that all traits were under genetic control and showed moderate heritability values, ranging from 0.32 to 0.53. Whole-genome scans of correlations between gene dosage and phenotypic traits identified quantitative trait loci for tree height, mean vessel diameter, height-corrected mean vessel diameter, height-corrected vessel frequency, and vessel grouping index. Our results demonstrate that vessel traits affecting hydraulic physiology are under genetic control, and both pleiotropic and trait-specific quantitative trait loci are found for these traits.During secondary growth, forest trees can modify the anatomy of the wood produced by the vascular cambium in response to environmental conditions. Notably, the trees of the model angiosperm genus,  Populus. Vessel differentiation starts with the commitment of terminal cell fate and cessation of cell division, followed by cell expansion, construction of a rigid secondary cell wall, and programmed cell death to produce water conducting cell corpse  is the water conducting tissue of tree stems, and the anatomical features of wood can have profound effects on water transport and vulnerability to hydraulic failure during water stress . Vessel l corpse . In Popu vessels . Pits be vessels . To whatTogether, the anatomical properties of vessels have a profound effect on water conduction physiology . Vessel Several stem and wood anatomical traits have been shown to be correlated to each other  althoughirregular xylem (irx) mutants with physically compromised vessels with crumpled cell walls are not significantly defective in terms of cell diameter , tree height (TH), non-lumen fraction (NF), and bark thickness (BT). We report correlations among all traits measured including tree height and vessel traits. All traits showed modest heritabilities, indicating that there is a significant genetic component underlying the observed phenotypic variation for vessel traits. Additionally, dQTL are reported for MVD, tree height-corrected mean vessel diameter (cMVD), tree height-corrected vessel frequency (cVF), and VGI. We provide evidence of dQTL commonly shared by correlated traits, as well as trait-specific dQTL, suggesting that independent regulatory factors exist for both correlated and uncorrelated traits. Together, our results show that vessel traits are under genetic regulation and are not simply a passive consequence of tree height, and that system genetic approaches could now be used to further dissect these traits and identify candidate genes using this same genomics resource.P. deltoides with gamma-irradiated pollen of a male P. nigra. The F1 hybrids were then completely sequenced using Illumina short reads. Relative sequencing read coverage values were used to detect the insertions and deletions (indel mutations) across F1 hybrids that together cover the entire poplar genome multiple times. The pedigree consists of nearly 800 replicated lines maintained in an outdoor plantation at the US Forest Service Institute of Forest Genetics in Placerville, CA.A subset of 201 poplar hybrid genotypes was included from a larger dosage mutant pedigree developed and described by All lines within the subset were clonally propagated through stem cuttings from the field in multiple replicates (ramets) during the spring of 2018. Cuttings were planted with rooting hormone (Bontone) in individual 2.83-L pots filled with horticultural soil (Sungro Sunshine Mix #4) and fertilizer  inside a greenhouse at the IFG. After a 2-month growth period, three healthy clonal replicates per line were randomly selected to include in a randomized complete block design. Plants were grown in a lathe house for 3 months, harvested, and moved inside a greenhouse to coppice and grown for a second 3-month period before harvesting a second time. Both crops were kept under well-watered conditions using a drip-irrigation system and monitored stem height and diameter growth until the end of the growing season in the fall of 2018. Greenhouse trees were kept at a near constant temperature of 23°C. All analyses and results involve the latter greenhouse-grown crop. A 6–8-cm woody stem segment was harvested from each tree at a fixed height of 10 cm from the point of emergence from the original cutting. Stems were immediately stored in 60% ethyl alcohol solution in 50 ml Falcon tubes.Stem internodes were cut in 40 μm thick cross-sections with a sliding microtome (Spencer Lens) or a vibratome (Vibratome Series 1000). Sections were stained with and mounted in a mixture of phloroglucinol and Astra Blue, which stain lignin and cellulose, respectively. Sections were then photographed at ×5 or ×10 magnification under a microscope with a digital camera with standardized settings (Leica Microsystems). A 100-μm scale bar was included in each image, adjusted for specific magnification. Lastly, a high-quality micrograph of each replicate within a line was selected for image analysis.2). NF was calculated by multiplying the mean vessel area by vessel frequency (Stem cross-section micrographs were processed using Fiji ImageJ software (v2) to obtain wood anatomical trait data. Images were spatially calibrated using the known scale bar length to determine the number of pixels per micrometer. Bark thickness was calculated as the average distance between the cambial zone and the outer cork in unprocessed images. All non-xylem areas (bark and pith) were then manually cut out of the image before converting to grayscale. Images were divided into a “foreground” consisting of vessel lumens and a “background” made up of non-vessel lumen area by setting a standardized pixel thresholding value. Vessel lumen area and circularity values were obtained directly through the Analyze Particles tool with equivalent circle vessel diameters calculated from vessel lumen areas . A MVD arequency . A VGI vrequency . Vessel Because tree height is correlated with vessel diameter , cMVD, aH2) of each trait was estimated using the repeatability function in the CRAN heritability R package (version 1.3) developed by The broad-sense heritability  were performed on all traits. The start and end sites of all the indels of the genotypes included in the analysis were used to create the boundaries of genomic bins as previously described , 2021. Amal bins . The nummal bins . The oriP. deltoides x nigra carrying genomically mapped indels . For each tree, a basal stem cross section was stained, digitally imaged, and analyzed to extract anatomical trait data as illustrated in 2 of xylem, with an average of 365 and a standard deviation of 166 vessels/mm2. VGI had a narrow distribution  and tree height (p < 0.0001) had a statistically significant effect on MVD. There was a significant interaction between genotype and tree height (p < 0.0001), indicating a departure from what would be expected if MVD was directly attributed simply to tree height. Genotype (p < 0.0001) and tree height (p < 0.0001) had significant effects on VF, but there was no significant interaction between genotype and tree height (p = 0.0597). Genotype also had a significant effect on cMVD (p < 0.0001) and cVF (p < 0.0001), indicating significant genetic variance for these traits not directly attributable to tree size. An example of genotypes that significantly deviate from expected MVD based on tree height are shown in p < 0.0001), but not tree height (p = 0.193), explained significant portions of VGI phenotypic variation. Genotype (p < 0.0001), but not tree height (p = 0.186), had a significant effect on MVC. Both genotype (p < 0.0001) and tree height (p < 0.0001) had a significant effect on NF.ANOVA tests showed that genotype had a significant effect on all measured traits . Both geH2 = 0.32) with confidence intervals ranging from 0.22 to 0.41. MVC had the highest broad-sense heritability estimate (H2 = 0.51), with confidence intervals ranging from 0.42 to 0.59. As a reference to the estimation of vessel and stem anatomical trait heritabilities, tree height had a heritability of 0.45 in this study. All anatomical traits showed significant heritabilities suggest that the observed phenotypic variation for each trait has a substantial genetic basis. Together with the continuous phenotypic trait distributions, these results are consistent with the traits under study being quantitative traits influenced by multiple genes.Broad-sense heritabilities (repeatabilities) were calculated for all traits and are shown in R = 0.76, p < 0.01) and strongly negatively correlated with VF , similar to previous findings , indicating potential interdependence of the size and frequency of vessel elements in wood that is independent of organ size. There was a weaker, but significant positive correlation between tree height and BT . VGI, MVC, and NF, however, were not significantly correlated to tree height or to any other trait, suggesting that these traits might be independently regulated from each other. MVD and VF were strongly negatively correlated , as previously reported  and negatively correlated to VF . BT could be considered as a proxy for radial growth, and thus correlated with greater MVD and lower VF as expected for faster growing and larger trees.Correlations among all traits were calculated  and are summarized in findings . Even afreported , and sugp were adjusted for multiple testing based on the effective number of uncorrelated bins .We used our previously described approach , 2021 foCorrelations of dosage at genomic bins and each trait are shown in The regulation of vessel element morphological traits remains poorly understood, despite the key role of vessel traits in determining the hydraulic physiology of plants, and trees in particular. One fundamental feature of vessel elements that makes their study complex is their extreme developmental plasticity. Vessel elements show dramatic morphogenic variation, as seen by contrasting vessel elements with narrow lumens and spiral secondary cell wall thickenings produced during elongative primary growth, and vessels with wide lumens and extensive secondary cell walls produced during secondary growth. Vessel element diameter and frequency have also been shown to be positively correlated with tree size . And vesP. deltoides x nigra to provide new insights into the genetic control of vessel traits associated with water transport physiology. This study is unusual as it presents trait distributions, correlations, heritabilities, and dQTL for wood anatomical traits directly in a forest tree species within a pedigree carrying indels that create additional, dosage-based variation. All the anatomical and vessel traits under study here showed significant heritabilities, indicating a significant degree of genetic control. As anticipated, some stem anatomical and vessel traits showed significant correlations with tree height. Specifically, significant positive correlations were found between tree height and MVD (0.76) and BT (0.52), while VF showed a significant negative correlation (−0.76) with height. One goal was thus to determine the amount of phenotypic variation in the MVD and VF is attributed to genetic variation not associated with height. We analyzed this by removing the effect of tree height in ANOVA to calculate cMVD and cVF that were “corrected” for the effect of tree height. This approach was effective and showed that genetic variation independent of tree height had significant heritabilities for cMVD (0.44) and cVF (0.32). Thus, in addition to scaling by tree height, we observed additional significant variation among genotypes for MVD and VF, suggesting that these are traits amenable to genetic manipulation and breeding independent of height or tree size.Here, we took advantage of a genomically characterized population of Our irradiation hybrid germplasm also enabled analysis genome-wide scans for quantitative trait loci that are both responsive to gene dosage variation and that affect vessel traits. We found significant QTL for tree height, MVD, cMVD, cVF, VGI, and BT. Other traits, VF, MVC, and NF, did not reveal significant QTL, however. In each case, there are several types of genetic variation that would be transparent to our analysis here. These include genes that are involved in vessel trait regulation but that do not show dosage sensitivity, genes with segregating allelic variation, genes whose variation is masked by genetic redundancy or physiological compensation mechanisms, and genes with smaller effects. Regardless, some interesting conclusions can be reached about the genetic architecture and interplay of the traits under study here. For example, the correlation of tree height with MVD is reflected in a common dQTL on chromosome 11, perhaps representing a common regulator influencing each trait, or else a factor directly influencing one trait with indirect influence on the other. After correcting for tree height, significant genetic variation was still detected for cMVD, as reflected by a unique dQTL on chromosome 9. Interestingly, the cMVD trait was inversely proportional to dosage at this dQTL, predicting that a dosage-sensitive negative regulator of growth may be uncovered at this locus. Additionally, unique dQTL not shared by other traits were identified for cVF, VGI, and BT, suggesting potential trait-specific regulators at these loci. In addition to furthering our basic understanding of how these various wood anatomical traits are related to each other genetically, these results also provide insights into what traits may be independently targeted through breeding approaches. In this regard, an identification of dQTL for vessel traits that are not shared by tree height is encouraging for the ability to select or breed for trees through selection on both height growth and vessel traits.P. deltoides mapping population identified a single QTL with the causative locus ENLARGED VESSEL ELEMENT (EVE) encoding a plasma membrane-localized protein affecting potassium uptake and presumably turgor in differentiating vessel elements  can be found at: AG, FDRZ, and IH designed the study. FDRZ led the data collection and the data analysis with direction from AG. FDRZ drafted the manuscript. AG and IH edited the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genomic selection is a promising approach to select superior wheat lines with better resistance to Fusarium head blight. The accuracy of genomic selection is determined by many factors. In this study, we found a training population with large size, genomic selection models incorporating biological information, and multi-environment modelling led to considerably better predictabilities. A training population designed by the coefficient of determination (CDmean) could increase accuracy of prediction. Relatedness between training population (TP) and testing population is the key for accuracies of genomic selection across populations. Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.  Fusarium graminearum Schwabe [telomorph: Gibberella zeae Schw. (Petch)], is becoming the most destructive fungal disease for wheat production in both Canada and USA [F. graminearum pathogen also accumulate the mycotoxin deoxynivalenol (DON), which represents a severe food and feed safety concern [Fusarium head blight (FHB), or scab, mainly caused by the fungus  and USA . Fusariu and USA . This re and USA ,3. In ad concern . Annual  concern . While i concern . Integration of agronomic practices and chemical fungicides with genetic resistance is the most effective means to manage FHB ,7. Due tThe nature of FHB resistance traits also represents challenges in the application of the QTL approach, which relies on sufficient statistical power of the mapping populations to improve resistance. Genomics selection/prediction (GS) that can address these limitations, is therefore an ideal approach to improve FHB resistance ,29,30,31N = 200 for FHB resistance traits in barley [N = 192 for wheat FHB resistance traits [N = 150 in rice for flower time and plant height traits [The success of applying GS for complex traits is mainly determined by the prediction accuracy/precision (PA). A broad range of factors affect the PA of GS. As reviewed by Voss-Fels et al.  and Xu en barley , N = 192e traits  and N = t traits . In genet traits ,43. In at traits ,45,46,47A broad range of statistical models can be implemented in GS to predict the complex traits, as reviewed by Xu et al. . These mGS has been evaluated to predict FHB resistance traits in a number of previous reports ,31,57,58Triticum aestivum L.) population (double haploid population) derived from the cross of FL62R1  with Stettler, Muchmore, and Emerson was developed as described previously [A multi-parental spring wheat  and Ottawa, Ontario (ON) in 2015 and 2016 with three biological replications and a random complete block design (RCBD) in single meter rows. Disease trait evaluation and phenotyping were described in detail by Zhang et al. . The evaThe multi-parental population was genotyped with the Illumina iSelect 90K SNP array  and raw 2ε). I is defined as the identity matrix and δ2ε is the residual variance. When QTL were incorporated as fixed effects, the peak markers identified from Zhang et al. [Seven genomics selection (GS) models, including BayesA, BayesB, BayesC, and Bayesian least absolute shrinkage and selection operator (BLASSO), Bayesian Ridge Regression (BRR), reproducing kernel Hilbert space (RKHS) and RR-BLUP were evaluated by R packages BGLR or rrBLUP ,64. Markg et al.  were pasTwo optimization algorithms, mean of the coefficient of determination (CDmean) and mean of predictor error variance (PEVmean) proposed by Rincent et al.  were impEB + Z1b1 + Z2b2 + e, where Y is defined as the phenotypic response with a n × L matrix, n represents observations of a trait and L is the number of traits. XE is a design matrix for the environmental effects with an n × I order, B is defined as beta coefficients with an I × L matrix. Z1 is the design matrix of genotypes with n × J order, and Z2 is the design matrix of genotype x environment interactions with an order of n × JI. b1 is the matrix for genotypic random effects with an order of J × L, followed a distribution as b1 ~ M N , where M N represents matrix normal distribution with a mean vector 0 and within and between variance-covariance matrices G and Σt. G is calculated according to Van Raden [t is a L × L matrix for unknown variance -covariance of traits and b2 stands for random effects of genotype x environment interactions with a matrix of JI × L order with an assumed distribution as b2 ~ M N . e is the matrix for random residual errors with a n × L dimension, distributed as M N . Multi-trait and multi-environment prediction were performed with Bayesian Multi-Trait Multi-Environment (BMTME) models, proposed by Montesinos-López et al.  which waan Raden  and assuPrediction accuracies of TP sizes and TP optimizations were obtained by 100 iterations from these defined TP with the genomic selection model rrBLUP . For allWe used the FS population, with data from MB, to assess the effects of the population size on the accuracy of genomics prediction. For all traits, the prediction accuracy considerably increased by increasing the size of the TP from 25 to 150 lines in both year-independent tests . The accIn addition, for all test TP sizes, for DA, a higher accuracy was observed in 2015 A than inIn addition to the TP size, we also evaluated the effect of TP optimization algorithms with same datasets above. As shown in Seven genomic prediction models, including five parametric  and one semiparametric approach (RKPHS) were evaluated with the same datasets. In the year 2015, each model had a similar performance for all FHB related traits A. In theWe evaluated the effect of including QTL as a fixed effect in the genomics prediction model, with the datasets of the FS population from MB. As shown in We first compared the single trait and multi-trait model analysis in the year 2015 with datasets of FS population from MB. As shown in To evaluate the accuracy of the multi-environment prediction model, we adopted a scenario to simplify the complexity of GEI. We first assessed a scenario of prediction accuracy in the same location but across different years with datasets of the FS population from MB. For all FHB related traits, a single environment model built from a one-year trial always had lower accuracy when applied to the other year A,B. WhenThe multi-parental population including FS, FM and FE was evaluated for FHB resistance with datasets from MB. This multi-parental population was designed as a nested association mapping (NAM) approach with the FHB resistance line FL62R1, as common parental line. Principal component analysis (PCA) showed that lines from each individual population were grouped together . PC1 difTo evaluate predictabilities across populations, we set the FS population as the reference population and used it to predict performance on the other two test populations. As shown in The gradual increase in accuracies for FHB related traits along with the increased TP size agrees with many previous research, including FHB resistance in wheat ,58 and bLarger training population size tends to increase the accuracy of the genomic selection, however, a reasonable accuracy can be achieved with a small TP size, given an appropriate design ,45,46,70There are a large number of models that can be used to predict phenotypic traits and these methods can be simply classified into parametric and non-parametric (or semi-parametric) approaches . These mPrevious studies with both simulated and empirical datasets in different crops, including rice, maize, sorghum and wheat demonstrated that incorporating large effect QTL as fixed effects into GS can lead to better predictability ,74,75,76Many studies, including our previous study on the FHB resistance of FL62R1 identified that flowering time and plant height are associated with FHB resistance ,21,23,25In contrast, when we applied multi-environment (MET) models across different years or locations, MET models that incorporated GEI always performed better than single environment models for all FHB related traits and for DA and HT. Lado et al.  performeThe relatedness between the TP and tested population is critical for the prediction of complex traits . With FSIn this study, with multiple connected bi-parental populations, we evaluated factors that can affect accuracies for the prediction of FHB related traits in wheat. We found a TP size around 200 lines, or more, is needed to achieve good accuracies for FHB resistance. Incorporating prior known QTL information of these traits can significantly improve predictabilities. Multi-environment modelling and including phenotypic data from the TP with relevance to the test population led to considerably better predictabilities across environments. Relatedness between TP and testing population is the key for accuracies, but accurately applying GS across less related populations remains a challenge.Not Applicable.