Several regulatory mechanisms are hypothesized, including methylation of the promoter probe cg10385390 (chr1:8’022’505) increasing the risk for inflammatory bowel disease by reducing PARK7 expression. The proposed integrative framework can be extended to other omics layers to identify causal molecular chains, providing a powerful tool to map and interpret GWAS signals.High-dimensional omics datasets provide valuable resources to determine the causal role of molecular traits in mediating the path from genotype to phenotype. Making use of molecular quantitative trait loci (QTL) and genome-wide association study (GWAS) summary statistics, we propose a multivariable Mendelian randomization (MVMR) framework to quantify the proportion of the impact of the DNA methylome (DNAm) on complex traits that is propagated through the assayed transcriptome. Evaluating 50 complex traits, we find that on average at least 28.3% (95% CI: [26.9%–29.8%]) of DNAm-to-trait effects are mediated through  transcripts in the  The mechanism by which DNA methylation might affect complex traits is not well understood. Here, the authors use Mendelian randomization to reveal a substantial role of transcript levels in mediating DNA methylation effects on complex traits and diseases. GWAS signals of common diseases predominantly fall into the non-coding genome3 and both their enrichment in regulatory elements 4), as well as advances in omics technology5, have motivated the establishment of large-scale consortia providing publicly available QTL datasets for molecular phenotypes such as DNA methylation (DNAm)6, transcript8, protein11 and metabolite13 levels.In the past decade, genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits15, summary versions of transcriptome-wide association studies (TWAS)17 and Mendelian randomization (MR) studies19. Colocalization methods identify shared QTL and GWAS signals, and while this might indicate causality between the molecular and GWAS trait, signal overlap can also arise due to reverse causality  or horizontal pleiotropy . In comparison, MR studies, which are conceptually similar to TWAS, use multiple genetic variants as instrumental variables (IVs) and are less prone to reverse causality and artefacts arising from LD patterns21 - although horizontal pleiotropy can never be ruled out entirely. In addition, MR analyses allow the quantification - direction and magnitude - of the causal effect of the omic on the outcome trait.Integrative statistical methods combining GWAS and omics QTL summary data include colocalization tests8, opportunities to integrate GWAS data with multiple molecular traits are no longer hampered by low statistical power. Previous efforts integrating multiple QTL omics data either adopted colocalization strategies23 or combined pairwise MR associations (two-step MR)25 testing only a single molecular mediator. Multivariable MR (MVMR) approaches have been proposed to identify multiple mediators of exposure-outcome relationships27. These approaches enable the dissection of the total causal effect of an exposure on an outcome into a direct and indirect effect measured via mediators. Similar to classical MR approaches, the use of genetic instruments allows for robust causal inference and MVMR has proven to be an unbiased approach for mediation analyses, even in the presence of confounders27. Hence, in addition to identifying causal effects through multiple layers, MVMR allows the quantification of mediation effects.With the advent of QTL datasets with increased sample sizescis-transcripts in mediating DNAm → complex trait causal relationships  framework to quantify the role of ips Fig. . To do s28 . If weacis , heterogeneity tests (through heterogeneity Q-statistics and leaving the strongest instrument out) and estimating bias due to by-chance signal overlap (through simulations).We then applied our framework in a genome-wide screen to estimate Nmed and a true MP of 35%), the bias in Nmed,sig) out of the 2 (Nmed) relevant mediators detected at a sample size of 3000 . Using a too lenient or too stringent PEM threshold resulted in downward biased cis of the assessed DNAm probe and transcript levels, respectively, and were primarily restricted to significant mQTLs for the former. Thus, in the MVMR analysis SNP-exposure effects for mediator instruments are often non-significant (hence unreported) and set to zero to reduce regression dilution bias . Our simulation studies, which mimicked this scenario by setting non-significant effects to zero (Methods), confirmed that this did not introduce any bias.We performed simulation studies to assess the bias in estimated MPs  were then further assessed to examine what fraction of the DNAm → trait causal effect is mediated by transcripts in cis  and glucose levels, to diseases such as asthma and schizophrenia  is a magnitude lower than that of the whole transcriptome32.In addition to the 2069 DNAm-trait pairs with detectable mediation, there were 554 pairs testable for mediation, but with no detectable causally implicated transcript Fig. a. Settincis. This resulted in an top of 26.0% (range: [13.0%–46.8%]) averaged across the 41 traits, and an top of 26.6% (95% CI: [25.1%–28.1%]) when aggregating the 2069 DNAm-trait pairs  corroborates our initial hypothesis that DNAm sites regulate the expression of multiple transcripts in the cis region.The average number of mediator transcripts, potentially correlated, was 3.3 per methylation-trait pair with detectable mediation, indicating that the impact of methylation is not mediated by a single transcript. To further explore this observation, we assessed the extent to which DNAm → trait effects were mediated by the single most significantly DNAm-associated transcript , as opposed to all transcripts in We conducted MVMR sensitivity analyses to assess potential sources of bias of the MP estimates such as weak instruments and pleiotropy.33. These statistics reflect whether genetic variants sufficiently explain the variance in the exposure given the presence of mediators. As demonstrated by Sanderson et al., direct effect estimates . Pairs with F-statistics ≤10 (N=1008) had significantly more mediators , but not a significantly higher Pdiff= 0.08; Supplementary Figs. To test whether the MVMR estimates suffer from weak instrument bias, we calculated conditional F-statistics34. We calculated Q-statistics for the IV sets in both the univariable and multivariable MR analyses. Out of the 2069 DNAm-trait pairs, 1757 showed no signs of heterogeneity in the univariable MR analyses (PHET > 0.01) and 1405 in neither the univariable nor multivariable analyses. The Pdiff=0.7; Supplementary Fig. Pleiotropic IVs violate MR assumptions and heterogeneity tests, such as the Cochran’s Q-statistic, can be used to detect them, assuming that most IVs are validp-value threshold PEM to select mediators based on the exposure-to-mediator causal effect (default PEM=0.01 for which N=2069 DNAm-trait pairs with at least 1 mediator were found). With a more lenient threshold (PEM=0.05), more DNAm-trait pairs with mediators emerged (N=2189). Conversely, with a more stringent threshold (PEM=0.001), less pairs were detected (N =1881). No differences in MPs between the three settings were found in these detectable mediation analyses  on a common set of DNAm-trait pairs ), a significantly higher MP for the more lenient threshold ; Pdiff=1.1e-4), and significantly lower MP for the more stringent threshold were observed ; Pdiff=4.8e-3; Supplementary Fig. Next, we assessed the influence of the cis. As such, SNPs in LD with significant QTLs for both quantities could give rise to an association merely because of horizontal pleiotropy , an issue further exacerbated by the fact that molecular omics entities generally have fewer associated IVs than complex traits. To assess whether mediation results are only based on a single strong genetic instrument, we repeated the mediation analysis excluding the top IV  from both the total effect θT and direct effect θD calculations (Methods). The results show that while MR effect estimates remain concordant in magnitude and effect direction, the estimates are noisier due to the much weaker instruments . Additionally, we performed simulation analyses to assess the possibility of significant DNAm-transcript associations caused by cis-mQTL and -eQTL signals being in LD (Methods). The analysis shows that randomly picked eQTL-SNPs in the region result in slightly inflated, but much weaker MR associations than using the original eQTL data  of the top transcript  only 53.4% of DNAm → transcript causal effects were negative. Stratifying DNAm sites with respect to their location on the assessed transcript, we found that DNAm sites situated in the first exon and nearby the TSS were enriched for negative effects , whereas those in the gene body were enriched for positive ones .DNAm inhibiting the binding of transcription factors (TFs) thereby repressing gene expression is often alluded to as the classical mechanism of action for DNAmρ = 0.39, P = 4.4e-75; Fig. Rmed < 0.3; Methods). The mean number of selected mediators dropped by more than half, from 3.3 to 1.2 ), while remaining significantly higher than top (Pdiff = 6.6e-4). Decreasing the Rmed threshold to 0.2 and 0.1 did not significantly decrease uncorrelated (Pdiff > 0.05), which stabilized at 29.2% (95% CI: [27.5%–30.8%]) for Rmed < 0.1 . This is consistent with the observation that negative effects αEM were larger than positive ones and the positive correlation between αEM magnitudes and high Nmed being a strong predictor for high αEM values, the observation of higher Pdiff = 0.020).Furthermore, we investigated whether mentclass2pt{minimIn addition to providing insights into global patterns governing the mediation between different intermediate phenotypic layers and functional traits, our analyses generated plausible hypotheses regarding specific biological pathways. We chose to follow-up putative regulatory mechanisms of DNAm-to-complex traits through transcript levels which showed both strong total effects  decreased PARK7 transcript expression  resulting in an overall increased IBD risk upon DNAm (P = 8.2e-9).Involvement of the anti-oxidant and anti-inflammatory protein PARK7 in inflammatory bowel disease (IBD) has recently been brought to light35, our data provides examples of methylation boosting expression. For instance, DNAm of cg13428477 (chr3:122’748’086) increased PDIA5 expression (P = 7.3e-11), whose levels subsequently increased platelet count (P = 0.018), so that DNAm resulted in significantly increased platelet count (P = 1.3e-43)  by reducing FCER1G expression (P = 3.5e-18), a gene listed in the KEGG pathway for asthma (hsa05310) and whose expression associated with an increased risk for asthma (P = 3e-12) 51, which was later supported by the increased susceptibility for severe COVID-19 in individuals with rare loss-of-function mutations in IFNAR252. Indeed, we found that DNAm of the IFNAR2 promoter probe cg13208562 (chr21:34’603’264) decreased the gene’s expression (P = 2.4e-19) , DNAm of the locus increased the risk of severe infection (P = 8.5e-13).In another example, we observed that DNAm of cg09070378 (chr1:161’183’762) decreased asthma risk  that could not pinpoint any factor drastically biasing our MP estimates. Importantly, simulation studies indicated that MP estimates were likely to be lower bounds. Low sample size was shown to lead to MP underestimations, as do weak instruments, both for exposure- and mediator-associated IVs.We presented a framework to quantify mediation of complex trait-impacting effects through an omics layer and demonstrated its application to assayed blood-derived DNAm (exposure) and transcript levels (as mediator). Evidence for mediation of DNAm-to-trait effects through transcripts in PEnrichment=2.15e-10). Interestingly, MPs were higher when DNAm was downregulating rather than upregulating transcripts. Previous genome-wide methylation and gene expression association studies reported high fractions of positive correlations (30–41%)39 and further investigations indicated that our estimated methylation-to-transcript causal effects agree strongly with the respective correlations reported by Grundberg et al. (P=2.6e-18). While poorly understood38, several mechanisms have been proposed to explain the phenomenon of DNAm induced transcription: preferential binding of some transcription factors to methylated DNA54, prevention of repressor binding indirectly leading to increased expression through looping DNA55, or DNAm in the gene body promoting elongation efficiency and preventing spurious initiation of transcription56. Furthermore, MP estimates indicated that DNAm sites typically regulate multiple transcripts in cis and that mediation through transcripts decreased the further away the TSS of the mediator transcript was from the DNAm site. Collectively, these results describe a more diverse picture of the transcription machinery, going beyond the classical views that DNAm solely reduces gene expression in the TSS region.Additionally, we quantified the causal connectivity and directionality between DNAm and transcript levels and its impact on MPs. We found that 46.6% of significant DNAm-to-transcript effects were of positive sign , particularly so when the DNAm site was situated in the gene body  or causal markers in very high LD. Lack of colocalization can happen even if there is a true causal relationship, but there are additional associations impacting only the outcome trait. On the other hand, the major weakness of MR is that it may falsely detect a causal relationship when the causal variants for each trait are in reasonably high LD. The comparison of these two approaches is out of the scope of this work, but to explore the above-mentioned weakness in our study, we performed simulations tailored to detect by-chance overlaps in the association signals for methylation and gene expression . These analyses indicated that indeed elevated false positive rates are expected for MR, but the resulting MR p-values under the null are much less significant than the ones observed for real methylation-transcript data.Statistical methods to integrate GWAS with omics data have seen a surge in recent years. Namely, colocalization methods based on a single genetic signal or corroborated by a secondary one, as well as methods supported by the SMR HEIDI statistic have been previously used in the study of DNAm-to-complex trait effects2. In particular, a challenge in identifying causal chains through omics layers is the attenuation in the genetic association strengths when moving up along layers. In a linear model, the genetic effect on the phenotype is assumed to be the product of causal effects between the preceding layers and it was previously shown that the variance explained by the top associated QTL of the first layer weakens with each successive omics layer24. In line with this observation, the examples depicted in Fig. Mapping genetic variants identified by GWASs to biological processes is notoriously difficultPT < 1e-6), which inherently focuses on DNAm-trait pairs with larger (and hence detectable) effects. In theory, MPs could depend on the magnitude of the total causal effect, thus the reported MP may differ for weaker total effects. A special case of these weaker total effects is when direct and indirect effects differ in sign, leading to a weak total effect with an MP potentially outside the  range. Furthermore, selected DNAm sites were those with the strongest DNAm-trait signal in their region (up to 1Mb). Thus, we omit secondary methylation signals, which may be mediated by transcripts to a different degree. Second, as for all MR-IVW approaches, included IVs might be pleiotropic, i.e., violating MR assumptions and potentially biasing effect estimates. Although, filtering out DNAm-trait pairs with signs of heterogeneous IV sets did not change MP estimates, the presence of invalid IVs cannot be entirely excluded and could therefore compromise causal effect estimates58. In particular, since selected IVs are in cis of the investigated molecular trait, they might be based on a single (pleiotropic) haplotype signal. Third, we select mediators based on their association to the exposure without taking into account their mediator potential, i.e., whether or not the mediator is additionally causally linked to the trait. Phrased differently, selected mediators are simply candidates and such selection serves as a first filter to remove non-mediators. In line with our simulations, it has been shown that an extremely large number of such “false" mediators (88 out of 92) can cause MVMR regression models to fail30, indicating that our framework is less suitable for large numbers of molecular mediators unless the selection threshold PEM is made more stringent. Finally, while molecular mechanisms ought to be tissue- or even cell type-specific, QTL data used in this study were derived from whole blood. However, not correcting for blood cell types when analyzing gene expression data can introduce important artefacts59. It is also known that different tissues express different isoforms60, with many splicing and expression QTLs shown to differ across tissues61. Accordingly, MPs for blood biomarkers were generally higher than those for diseases, for which blood might not be the most relevant tissue. Differences between biomarker and disease MPs might also be due to the fact that indirect pathways, through unmeasured mediators, play a greater role for the latter trait category. Once tissue-stratified multi-omics datasets of larger sample size become available, more accurate, and potentially higher MPs will be obtained in trait-relevant tissues.While our method highlights candidate pathways and provides MP estimates, several limitations are to be considered. First, our MP estimates are based on a selection of 2623 DNAm-trait pairs with significant total effects  and multivariable MR (MVMR) to estimate the direct causal effect (θD) of an exposure E on an outcome Y. The mediation proportion (MP) was defined as 1 − θD/θT. Under the MR assumptions, genetic variants G used as IVs must be i) associated with E, ii) independent of any confounder of the E − Y relationship, iii) conditionally independent of Y given E. We analysed exposures with at least five LD-pruned (r2 < 0.05) IVs associated (P < 1e-6) with the molecular exposure and located in cis ( < 1 Mb). To estimate θT we used the inverse-variance weighted (IVW) MR method, while accounting for (mildly) correlated instruments62 as follows:βE and βY are vectors of genetic effect sizes obtained from summary statistics for E and Y, respectively. C is the linkage disequilibrium (LD) matrix with pairwise correlations between IVs estimated from the UK10K reference panel63. Sensitivity analyses confirmed that accounting for the LD-matrix safeguards against MR estimates being influenced by the pruning threshold r2 , including IVs in mild LD  of θT can be approximated by the Delta method65:Σ is a diagonal matrix with each diagonal element i equalling the maximum of the regression variance s2 and var(34.Univariable Mendelian randomization (MR) was applied to estimate the total causal effect , θD can be estimated analogously to θT using a multivariable regression model28 as the first element of θD:B is a matrix with k + 1 columns containing the effect sizes of the IVs on the exposure in the first column and on each mediator in the subsequent columns. The remaining elements of θD represent the direct effects of the mediators on the outcome and were referred to as αkMY,. In the estimation of MPs, we were not interested in αkMY, values per se, but we took these effect sizes into account for inferring molecular mechanisms. If the number of mediator-associated instruments was sufficient (≥3) to conduct a univariable MR from the mediator to the outcome, we estimated αkMY, from this analysis instead. In fact, the  contribution of an individual mediator can be better disentangled in univariable analyses, when mediators are highly correlated.Through the inclusion of mediators 66, our model investigates the commonly assumed concept of DNAm regulating gene expression. In addition to meeting the Steiger criterion described above, exposure-associated IVs were required to pass that same threshold trev of no larger mediator than exposure effects for each of the mediators Mk. Similarly, to mitigate reverse causal effects from the outcome on the mediators, mediator-associated instruments with larger Y than M effects were removed if not passing the trev threshold. The SE of 19.As our MVMR model assumes a chain of causal effects from the exposure to the mediator and then to the outcome, we conducted several Steiger filtering steps to reduce biases due to reverse causation. Although it has been proposed that DNAm could be a consequence of gene expression in the same locusentclass1pt{minima33. This method involves the regression of the exposure on the mediators based on the IV effect sizes on each of these quantities. The residuals of this regression are then used to derive the conditional F-statistic. The original method additionally includes the phenotypic correlation matrix between the exposure and mediators, which we omitted by default due to the lack of these data and thus used the identity matrix instead. However, as a sensitivity analysis, we calculated conditional F-statistics incorporating the phenotypic correlations between transcript mediators. Transcript correlations were calculated on RNAseq data from the Cohorte Lausannoise (CoLaus) based on 555 samples67. Transcript correlations could be estimated for 19,517 transcript of which 15,021 overlapped with the eQTL dataset (Methods: Omics and trait summary statistics)8. We then calculated conditional F-statistics that included mediator correlations for all DNAm-trait pairs with at least 2 mediators and for which at least half of them had available correlation data. Conditional F-statistics > 10 allow to reject the null hypothesis that the IVs are too weak to reliably estimate the multivariable effect of the exposure in the presence of the mediators.Conditional F-statistics of the exposure were calculated following the approach of Sanderson et al.34. This test statistic quantifies the deviation of MR effect estimates of each individual IV from the IVW-estimate based on all IVs68. The null hypothesis of homogeneity within the IV set follows a chi-squared distribution with m − 1 degrees of freedom for the univariable MR, and m − k degrees of freedom for the MVMR, where m is the number of IVs and k the number of mediators.Heterogeneity Q-statistics were computed as implemented in the TwoSampleMR package cis of the DNAm exposure probe ( ± 500kb) and ii) causally associated to the DNAm probe. This latter condition was verified by univariable MR analyses .For transcripts to be included as mediators in the MVMR regression model they had to be i) in ses (Eq. ) of the θT and direct effect θD calculations. This analysis allowed to assess whether mediation results are solely driven by a single strong IV. Second, we performed simulation analyses to quantify the possibility that causal links between DNAm probes and transcripts are driven by increased horizontal pleiotropy stemming from potential LD between methylation and transcript instruments due to their close genomic distance.To quantify whether significant MR estimates between the exposure and mediators were observed due to horizontal pleiotropy, we conducted two sensitivity analyses. First, we repeated the mediation analysis excluding the top IV  from both the total effect PEM < 1e-6. For each of these selected DNAm-transcript pairs, we first fixed the SNP-DNAm and SNP-transcript effects as observed in the data. Then, using near-independent significant cis-eQTLs  with observed marginal (univariable) effect sizes βM (a vector of size mM) and the corresponding pair-wise local LD matrix CM, we calculated the multivariable SNP effects on the transcript, βmulti, as:In the following, we outline step-by-step the workflow of the horizontal pleiotropy simulation study for which a schematic representation is shown in Supplementary Fig. mE exposure  IVs, yielding the causal effect αEM with corresponding p-value, PEM. We then performed simulation analyses as follows to obtain MR effects for a hypothetical transcript with identical multivariable eQTL effect size distribution as the real transcript. To achieve this, for each simulation j, we randomly selected mM leniently pruned (r2 < 0.5) SNPs and assigned βmulti as their multivariable eQTL effects. Hence the marginal SNP-transcript effects for the mE exposure-associated SNPs can be calculated as follows:mE exposure-associated SNPs and the mM randomly chosen SNPs (with multivariable SNP-transcript effect βmulti). This way we assign marginal SNP-transcript effect sizes for the mE exposure-associated instruments, while keeping the multivariable eQTL effect size distribution identical to the one observed for the real transcript (but they are assigned to other SNPs). Univariable DNAm-transcript MR analyses could then be conducted  for 100,000 (Nsim) hypothetical transcripts for 100 randomly selected DNAm-transcript pairs throughout the genome. The simulation p-value was then derived as Psim = #/Nsim.Using the original data, we performed DNAm-transcript MR on mentclass2pt{minimr2 < 0.05) mQTLs after harmonization of the datasets. DNAm probes significantly associated to the outcome (PT < 0.05/50000=1e-6) were clumped based on the p-value of the total causal effect PT (distance-pruning at 1 Mb), to be independent of each other.A diagram of the workflow with each of the following steps is shown in Supplementary Fig. PEM") were included as mediators as well as their associated SNPs as additional instruments. Steiger filtering on mediator-associated IVs was applied using the same trev threshold as for exposure-associated IVs. Remaining IVs were then clumped based on a rank score determined as follows: 1) for each mediator, IVs were ranked according to their association p-value to the mediator and assigned an integer score, 2) for each IV, a final score was calculated as the sum of its individual mediator scores. Following the establishment of the B effect size matrix, PkEM, as sole mediator. If no transcript causally associated with the DNAm probe, mediation is not detectable, and hence mE ≥ 5) could decrease. Therefore, to avoid scenarios of reverse causality where the mediator exerts an effect on the outcome through the exposure, we required ≥ 3 exposure-associated IVs.Second, MVMR analyses were performed to estimate the direct effect PEM) were clumped at various correlation thresholds Rmed . Correlations among mediators were calculated based on QTL effect sizes of independent exposure and mediator IVs and priority was given to the mediator with the lowest PkEM,.We additionally conducted mediation analyses on independent mediators. To this end, selected mediators (those that passed 31 (Eq. (Mediation proportions (MPs) were estimated on sets of DNAm-trait pairs with significant total causal effects 26. Note that yielded θM are estimated by subtracting direct effects from total effects, which is also referred to as the difference in coefficients method26. Alternatively, the indirect effect can be estimated by the product of coefficients method26, where univariable MR estimates from the exposure on the mediator are multiplied with the direct effects of the mediator on the outcome  and sumPEM), we made use of To test the statistical significance between PEM thresholds), hence the resulting p-values may be lenient.Significant difference between n=32,851)6, which contains > 170,000 whole blood DNAm sites with at least one significant cis-mQTL . Cis-eQTL data were taken from the eQTLGen consortium 8 which includes cis-eQTLs  for 19,250 transcripts .We used mQTL data from the GoDMC consortium , predominantly European-descent, publicly available studies, as listed in Supplementary Data 69 . Remaining GWAS data came mostly from case/control studies made available by the consortium of the respective disease. For binary outcome traits, log-odds ratios were used as effect sizes and results should be interpreted on the liability scale.GWAS summary statistics for outcome traits came from the largest , as well as SNPs with an allele frequency difference > 0.05 between any pairs of datasets were removed. If allele frequencies were not reported by the GWAS summary statistics, allele frequencies from the UK Biobank were used. Z-scores of summary statistics (molecular and outcome GWAS) were standardized by the square root of the sample size to be on the same SD scale.cis ( ± 500 kb) following the same procedure as in the univariable MR to obtain total effects r2 < 0.05) and significant (P < 1e-6) exposure IVs were selected and IVs not passing the aforementioned Steiger filter were discarded. MR causal effects were then computed based on Eq. 70.Pearson correlation coefficient with previously reported DNAm-transcript correlationsWe conducted simulation studies to assess the robustness of our model and to identify sources of bias in the estimated MP. Simulation settings were set up post-hoc to replicate mediation results obtained for real data  and of the mediator on the outcome  for Nmed mediators were drawn from a bivariate normal distribution Σ the covariance matrix:ρ is the correlation between αkEM, and αkMY,. For the remaining Nmed,pot - Nmed mediators, αkEM, and αkMY, causal effects were set to zero. The vector of effect sizes mE + Nmed ⋅ mM for each mediator k was constructed to have effect sizes equalling mE exposure SNPs and effect sizes equalling mM mediator-associated SNPs. The effect sizes of remaining IVs associated to mediators i ≠ k were set to zero. Likewise, effect sizes of the Nmed ⋅ mM IVs on the exposure in the βE vector were set to zero.We considered an exposure with heritability θM, direct effect θD and total effect θT were calculated as:The indirect effect βY:These quantities allowed to generate the outcome effect size vector βE, βY. Normally distributed noise, as a function of the sample size N, PEM  denoted Nmed,sig. Causal effects For each scenario, we simulated 500 data sets to each time get Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Finding definitive examples of such a response in fossil time series has proven challenging, however, as has controlling for the effects of a changing physical environment. We take a novel approach to this issue by quantifying variation in trait values that capture almost the entirety of function for steam locomotives (SL), a known example of competitive exclusion from material culture, with the goal of identifying patterns suitable for assessing clade replacement in the fossil record. Our analyses find evidence of an immediate, directional response to the first appearance of a direct competitor, with subsequent competitors further reducing the realized niche of SLs, until extinction was the inevitable outcome. These results demonstrate when interspecific competition should lead to extinction and suggest that clade replacement may only occur when niche overlap between an incumbent and its competitors is near absolute and where the incumbent is incapable of transitioning to a new adaptive zone. Our findings provide the basis for a new approach to analyse putative examples of competitive exclusion that is largely free of  While a reciprocal, contradictory response by two functionally similar and overlapping clades has typically been invoked as evidence of antagonistic clade displacement, confirming that a correlative or causal relationship exists between time series remains problematic . Entireli) functional trait data relevant to survival of individuals within the extinct clade can be quantified; these data are important, as existing theory predicts displacement of traits that impact resource use due to competition between one or more overlapping species [ii) direct competitors have been identified that overlap spatially, temporally and in their resource requirements [iii) a complete (birth to death), well-constrained time series is available for the extinct clade, where the vast majority of individuals that make up the extinct clade are known; at a minimum, the date of first appearance for relevant competitors must also be known; and (iv) non-competitive factors  do not apply or are irrelevant. Arguably, identifying fossil clades that adhere to these four criteria ranges from highly refractory to impossible, with criterion (iv) particularly problematic.We propose several key prerequisites  needed f species ,20; (ii)irements ; (iii) aWhile there have been previous attempts to generate theoretical predictions of the expected patterns arising from interspecific competition ,21, moreCompetition and extinction are phenomena that extend beyond the biological realm and previous studies have demonstrated how both can apply to well-recognized commercial, cultural and technological entities –27. Thisi–iv) for identifying interspecific competition, we consider functional trait data for SLs using a quantitative framework, evaluating if functional trait values exhibited directional shifts as competitors first appeared, or whether any shifts are better explained by other factors considered relevant to SL production but not related to competition. We examine these results in light of the principles of competitive exclusion and conclude with a discussion of the implications of our results for macroevolutionary studies.We assess the impact of direct competition on the SL throughout its lifespan with the aim of identifying diagnostic patterns relevant to presumed examples of competitive exclusion and associated extinction in the fossil record. After first verifying how SLs satisfy our criteria  is applicable to SLs.The primary function of a locomotive is to move a train of railroad cars, whether that train be composed of goods wagons (freight) or passenger coaches. To do so, a locomotive must generate sufficient force to overcome the drag imposed by the train it is pulling or pushing. The force a locomotive is capable of generating is encapsulated by a parameter known as tractive effort (TE) ,34. Becaii). All locomotive types compete for the same primary resource (passengers and freight), their capability to do so is captured by the same functional trait (TE) and spatial overlap is total, all utilizing railroad track, with the caveat of overhead wires or an electrified third rail as an additional requirement for electric locomotives. The third competitor for SLs is motor vehicles (e.g. automobiles and trucks) [Production and maintenance of any land transport technology will only continue while the benefit-to-cost ratio for that technology to move passengers and/or freight is equal to or greater than existing alternatives . Any com trucks) . Despite trucks) . A decliiii). Issues such as provincialism, preservation potential and taxonomic inconsistencies can make overlap difficult to establish for fossil taxa [iii).When attempting to determine if interspecific competition is a primary driver of an extinction event, it is necessary to establish overlap between the competing clades and demonstrate a relevant transformation in the clade (such as character displacement) that has putatively been driven to extinction by competitive forces during the period of overlap, i.e. criterion (sil taxa . By contiv)'s veracity in the case of SLs is well supported by historical data (e.g. [Criterion (ta e.g. –32). Non. Noniv)'ta , we employ the simplifying assumption that competition was initiated once the competitor was produced/employed in a commercial context or readily acquired in the marketplace, and thus actually competing with steam locomotives for the same resources (transportable passengers and freight). In the case of electric locomotives, a term used herein to capture all forms of electrified light and heavy rail, we use the completion of the first electric tramway constructed in a major metropolitan centre, the Gross-Lichterfelde Tramway, which began service in 1881 [sensu [To determine the first appearance dates for known SL competitors  events a priori assumptions about the form of nonlinearity, MARS can serve as a neutral model to identify potential changes in trend direction and slope. Because a number of cut-points in the resulting hinge function may contribute little to model accuracy, model fit was assessed using k-fold cross-validation (k = 10), with the asymptote of R2 used to determine the number of knots to be retained. MARS models were implemented using the earth package for R [Initial assessment of TE data was undertaken by generating hinge functions using multivariate adaptive regression splines (MARS), a non-parametric regression method developed to model high-dimensional nonlinear data while avoiding overfitting . With noge for R .paleoTS package for R, using the standard protocols outlined by Hunt [Random walk models previously applied in macroevolutionary studies –58 provi by Hunt ,57. Beca by Hunt ,58, this by Hunt .To assess how results for SLs may inform analyses of competitive exclusion for fossil taxa, maximum, mean and minimum body size for the Borophaginae, a subfamily of the Canidae purported to have been competitively displaced in North America by multiple carnivore clades, were calculated using data from .. 4a). These shifts can be attributed to increasing electrification of suburban commuter routes, where lower TE values were sufficient, and increasing demand for high-speed intercity passenger and freight services, tasks both requiring higher TE values [The appearance of direct competitors results in measurable displacement of SL functional trait values figures  and 2. Tfigure 2E values .Figure a). The cut-point for minimum TE in 1921 (b), however, is probably due to competition from motor vehicles. Rail passenger numbers in North America and Europe sharply declined in 1921, corresponding with a rise in motor vehicle usage . This sudden preference for motor vehicles is due to the development and expansion of highway networks in the 1920s [2c) also probably represents a lagged response, specifically to the dieselization of rail networks. Diesel possessed multiple advantages over steam [The emergence of subsequent competitors initially had a lesser impact, with only an increase in the rate of positive change for maximum TE coincident with the first appearance of the motor car a. The cu in 1921 b, howevehe 1920s , allowiner steam , but warer steam . With ther steam .sensu [sensu [The peak in maximum TE coincides with nationalization of US railroads during WW1 . Nationasensu ,64) and u [sensu ), with Tu [sensu .Unlike competitive factors, non-competitive contingent events had limited impact on SL TE values. Economic depressions caused only brief declines in mean TE and both World Wars coincide with a sharp, but short-lived, increase in mean TE . These esensu [The results of our analyses provide the basis of a model suitable for identifying when interspecific competition is the primary driver of extinction . In the sensu ,64), witsensu  through sensu . Becausesensu , and evesensu , extinctidentical niches cannot coexist indefinitely. The extinction of the SL may only have come to pass because the niche overlap between steam and competing technologies far exceeds the maximum allowable overlap for two clades to coexist [We do not mean to imply that the above scenario represents the only means by which competitive exclusion can drive a clade to extinction, or that competition for the same resource pool must result in extinction. Support for character displacement is generally confined to animal groups, specifically carnivores  and the  coexist  and SLs  coexist . Figure  coexist  may thusa priori, but only that a putative one exist. While directional change in the mean trait value need not be indicative of interspecific competition when identified in isolation, when considered in light of the full spectrum of variation [The proposed model we present herein both establishes that the impact of competition on functional traits can be essentially immediate and provides a previously unlooked-for pattern to investigate clade replacement in the fossil record by focusing on functional traits that can be measured for fossil organisms, such as body size and metabolic rate . The immariation , such chariation . Multiplariation .Comparison of our results with a fossil example of putative competitive exclusion, specifically competition between subfamilies in the Canidae in North America, affirms that the patterns we identify for SLs can be observed in natural systems where competition between clades is considered significant . The onsOur results thus cannot, at this time, resolve what role, if any, competition plays in driving extinction at the macroevolutionary scale. They do, however, provide a path forward that may serve to resolve the issue by addressing the existing impasse in identifying causal relationships in fossil time series . There i


Average daily gain (ADG) is influenced by both an individual’s direct genetic effect (DGE) and by a social genetic effect (SGE) derived from pen mates. Therefore, identifying the DGE and SGE on ADG is essential for a better understanding of pig breeding systems. We conducted this study to elucidate the genetic characteristics and relationships of DGE and SGE on ADG using purebred and crossbred pigs. We found that the DGE and SGE both contributed to ADG in both populations. In addition, the SGE of purebred pigs was highly correlated with the DGE of crossbred pigs. Furthermore, we identified several genomic regions that may be associated with the DGE and SGE on ADG. Our findings will contribute to future genomic evaluation studies of socially affected traits.Average daily gain (ADG) is an important growth trait in the pig industry. The direct genetic effect (DGE) has been studied mainly to assess the association between genetic information and economic traits. The social genetic effect (SGE) has been shown to affect ADG simultaneously with the DGE because of group housing systems. We conducted this study to elucidate the genetic characteristics and relationships of the DGE and SGE of purebred Korean Duroc and crossbred pigs by single-step genomic best linear unbiased prediction and a genome-wide association study. We used the genotype, phenotype, and pedigree data of 1779, 6022, and 7904 animals, respectively. Total heritabilities on ADG were 0.19 ± 0.04 and 0.39 ± 0.08 for purebred and crossbred pigs, respectively. The genetic correlation was the greatest (0.77 ± 0.12) between the SGE of purebred and DGE of crossbred pigs. We found candidate genes located in the quantitative trait loci (QTLs) for the SGE that were associated with behavior and neurodegenerative diseases, and candidate genes in the QTLs for DGE that were related to body mass, size of muscle fiber, and muscle hypertrophy. These results suggest that the genomic selection of purebred animals could be applied for crossbred performance. In the swine production industry, many pigs can be managed in a house grouping system. The development of piglets is known to be substantially affected by group mates in such a system . SociallAverage daily gain (ADG) is an important indicator of growth performance in pigs, and many genome-wide association studies (GWAS) have been conducted to better understand the relationship between genetic information and ADG ,7,8. TheIn the livestock industry, genomic selection for productive traits is used to rapidly improve livestock. Genomic prediction studies for the ADG trait have been extensively conducted in different pig populations ,16,17,18The pedigree data of 7904 animals, including 5408 purebred Korean Duroc (DUC) , 21 KoreADG (g/day) was measured from 30 kg (start weight) to 100 kg (end weight) as follows.Sus scrofa 11.1 or sex chromosomes; (2) SNPs with a call rate < 90%; (3) SNPs with minor allele frequency < 0.05; (4) monomorphic SNPs; (5) animals with a call rate < 90%; (6) animals with Mendelian conflicts. This quality control process removed 18,871 SNPs and 12 animals, leaving a total of 42,694 SNPs for 1767 animals for further analyses.Genomic DNA was extracted from blood or hair root samples and genotyped using an Illumina porcineSNP60K BeadChip v2 , which includes 61,565 SNPs for 1779 animals (864 DUC and 915 crossbreds). The quality control process was conducted for SNP markers and animals as the following criteria: (1) SNPs unmapped in Variance and covariance components for the ADG trait for the DUC and crossbred pigs were estimated using the multi-trait model based on the Bayesian approach in GIBBS2F90 . The Gibb is the vector of fixed effects, d is the vector of the random group with l is the vector of random birth litter, e is the vector for the residuals, X,,, T, and U are the corresponding incidence matrixes. Because the pen sizes were different, we added a dilution factor Sex , birth year–2 months (54 levels), and group size (6 levels) were used as the fixed effects, and start weight (g) and age at target weight (100 kg) were fitted as covariates. Group (2246 levels), birth litter (1478 levels), and animal (7904 levels) were used as random effects. Genetic analysis was performed using the animal model as follows:For the ADG trait affected by both heritable DGE and SGE, the variances in total breeding value (TBV) were estimated as follows :σTBV2=σai-th individual was calculated as described by Bijma [n is the average pen size, In addition, the TBV for the by Bijma , using tThe phenotypic variance for the multi-trait model was obtained as follows:Total heritability (H matrix) that combines genomic and pedigree information. The relationship among the matrices is as follows:G weight matrix generated by reciprocals of expected variance of markers as proposed by VanRaden [Z is the incidence matrix of genetic content that is altered for allele frequencies, D is the diagonal weight matrix of SNPs, and q is a normalizing factor. The effects and weights of the SNPs were obtained as follows:D = I in the first step;Calculation of breeding values;Calculation of SNP effects, i is the i-th SNP;Calculation of SNP weight for each SNP, Normalization of SNP weight for retaining constant total genetic variance;Then loop to step 2.We performed a GWAS on random additive DGE and SGE  using thVanRaden  as folloD matrix were recalculated as described by Wang et al. [i-th region consisting of x = 0.4 Mb.This process was run for three iterations and SNP effects, breeding values, and the g et al. . In thisg et al. :(2)Var. We also mapped the QTLs discovered in this study to previously reported pig QTLs to identify the overlapping regions using the Pig QTL Database . Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using ClueGO v2.5.9 and CluePedia v1.5.9 plug-ins in Cytoscape (v3.9.1) [p-value ≤ 0.05, were considered to be significantly enriched and were used to annotate the candidate genes. The GeneCards database  was used to retrieve phenotype information of the annotated genes.To identify the candidate genes associated with DGE and SGE in the crossbred and purebred pig populations, we first determined the threshold for significant SNPs that explained >0.4% of the additive genetic variance. Then, 1-Mb regions that had significant SNPs in their centers were defined as quantitative trait loci (QTLs). We annotated genes within the QTLs based on the (v3.9.1) ,28. GO tThe average ADG (g) was higher in the DUC (986.04 ± 125.25) than it was in the crossbred (849.43 ± 110.87) pigs . Duroc pThe ectively . HoweverThis finding confirmed the contribution of SGE to total heritable variance. We found that the DUC had lower heritability for ADG than the crossbred pigs, and they also had lower heritability for ADG than other Duroc populations reported previously ,31,32,33To observe the relationship between genetic effects, we estimated the genetic correlations between the DGE and SGE for DUC and crossbred pigs . The genThis result is similar to that of a previous report of statistically significant and moderate genetic correlations between the DGE on ADG of a crossbred dam  and that of landrace (0.46 ± 0.18) or Yorkshire (0.41 ± 0.17) . The genAs shown in These SNPs explained 24.9% of the total genetic variance and were located on SSC1, SSC4, SSC8, SSC10, and SSC13. The gene annotations for the 1-Mb QTLs that centered those SNPs identified 97 genes associated with these QTLs . BecauseFor SGE in DUC, 70 significant SNPs located on SSC1, SSC2, SSC6, SSC8, SSC12, SSC13, SSC15, and SSC18 explained 33.6% of the total genetic variance . A QTL lSGE is an established concept in behavioral ecology . SGE is DBX1, PAX7, and SHH, which were annotated with the neuron fate commitment term (GO: 0048663). DBX1 is expressed in hypothalamic progenitors and restriction of DBX1 was found to be critical in establishing the neuronal fate of V0 and V1, which are derived from adjacent progenitor domains [DBX1 is associated with diminished responses to feeding stressors and abnormal GABAergic neuron morphology [PAX7 likely causes a neuromuscular syndrome in humans. Dysregulation of the SHH pathway in the brain was reported to be associated with neurodegenerative diseases, such as amyotrophic lateral sclerosis and Parkinson’s disease [We identified genes that were annotated with 17 GO terms that might be associated with the SGE in DUC , includi domains . The prerphology ,52. Prosrphology  suggeste disease .CCL19, CCL21, and SOX9) that might be associated with SGE in crossbreds. These genes were annotated with the response to interleukin-1 term (GO: 0070555). Interleukin-1 (IL-1) is a master regulator of inflammation by controlling innate immune processes [We also identified three genes  that were annotated with GO terms associated with the regulation of NIK/NF-kappaB signaling   . DiverseADCY7, IL17RC, and NOD2) were annotated with terms related to inflammatory response   . A previpulation . IL17RC ignaling , and IL1ignaling . NOD2 istivation . As noteWe identified the genes that were annotated with 14 and 13 significant GO terms that might be associated with the DGE in DUC and crossbred pigs, respectively .CAMK1, CAV3, FOXP1, and SHH) were involved in the biological process of positive regulation of muscle cell differentiation (GO: 0051149) (CAMK1 and FOXP1 were reported to be associated with increased lean body mass in mice [CAV3 null mice showed mild myopathic changes with the presence of necrotic fiber and variability in muscle fiber size [SHH were shown to have significant associations with body size traits in Chinese beef cattle breeds [Four of the genes (0051149) . Growth  in mice . CAV3 nuber size . Copy nue breeds . Becausee breeds .FGF3, FGF4, and FGF19) that were annotated with terms related to the response to the fibroblast growth factor (GO: 0071774 and GO: 0044344) and receptor signaling pathway of the fibroblast growth factor (GO: 0008543)  . Fibroblwith SHH . FGF4 ha embryos  and with embryos  reportedin vitro .In this study, we conducted the estimation of genetic parameters and GWAS for the DGE and SGE on the ADG trait in DUC and crossbred pigs. Our results showed that not only DGE, but also SGE contributed to the total heritable variance in ADG. The genetic correlation between the DGE and SGE was neutral to weak in DUC and crossbred pigs, respectively. We also identified genetic correlations among the DGE and SGE on ADG for DUC and crossbred pigs, showing that the SGE of DUC was highly correlated with the DGE of crossbred pigs. The QTLs for both the DGE and SGE overlapped with previously reported QTLs associated with production- and meat-related traits. QTLs for the SGE also overlapped with QTLs associated with coping behavior and exploration during stress. Furthermore, the candidate genes  for the SGE on ADG are associated with aggression and neurodegenerative diseases. These findings provide genomic information that will contribute to a better understanding of the DGE and SGE on ADG in pigs.


GG, p = 3.93 × 10−4, Δβ = 9%, effect size = 0.38 . Using a repeated stratified k-fold cross-validation approach, a series of balanced random forest classifiers with random under-sampling were built to evaluate the addition of GxMeth terms to cg19693031 models to discriminate between normoglycemic controls versus T2D subjects. The results were compared to those obtained from models incorporating only the covariates  and the addition of cg19693031. We found a post-pruned classifier incorporating 10 GxMeth SNPs and cg19693031 adjusted for covariates predicted the T2D status, with the AUC, sensitivity, specificity and precision of the positive target class being 0.76, 0.81, 0.70 and 0.63, respectively. Comparatively, the AUC, sensitivity, specificity and precision using the covariates and cg19693031 were only 0.71, 0.74, 0.67 and 0.59, respectively. Collectively, we demonstrate correcting for genetic confounding of cg19693031 improves its ability to detect type 2 diabetes. We conclude that an integrated genetic–epigenetic approach could inform personalized medicine programming for more effective prevention and treatment of T2D.Type 2 diabetes mellitus (T2D) has a complex genetic and environmental architecture that underlies its development and clinical presentation. Despite the identification of well over a hundred genetic variants and CpG sites that associate with T2D, a robust biosignature that could be used to prevent or forestall clinical disease has not been developed. Based on the premise that underlying genetic variation influences DNA methylation (DNAm) independently of or in combination with environmental exposures, we assessed the ability of local and distal gene x methylation (GxMeth) interactive effects to improve cg19693031 models for predicting T2D status in an African American cohort. Using genome-wide genetic data from 506 subjects, we identified a total of 1476 GxMeth terms associated with HbA1c values. The GxMeth SNPs map to biological pathways associated with the development and complications of T2D, with genetically contextual differences in methylation observed only in diabetic subjects for two GxMeth SNPs (rs2390998 AG vs. GG,  Type 2 diabetes mellitus (T2D) is the seventh leading cause of morbidity and mortality in the US and is responsible for $327 billion annually in economic damage . T2D is T2D has a complex genetic and environmental architecture that underlies its development and clinical presentation . Genome-In this communication, we use an omni-genic approach in an attempt to explain how the interactions between cg19693031 methylation and common variants in the cellular context of hyperglycemia can increase disease susceptibility. We then implement functional analyses to investigate possible mechanisms explaining the association between the methylation status of TXNIP’s 3′UTR and prevalent T2D. Finally, we examine if correcting for genetic confounding improves the ability of cg19693031 to detect T2D.This study uses genomic and biological data from Wave 5 of primary caregivers (PC) of the Family and Community Health Study (FACHS). The overall design and methods used in the FACHS study have been described previously . Each of1c (HbA1c) levels were determined by the University of Iowa Diagnostic Laboratories  using turbidimetric immune inhibition [DNA was prepared using standard cold protein precipitation . Glycosyhibition  in complhibition .http://genomics.umn.edu/, accessed on 10 March 2018) using the Infinium MethylationEpic Beadchip  according to the manufacturer’s protocol. Standard sample and probe level quality control of the resulting data were conducted as previously described [Genome-wide methylation was determined by the University of Minnesota Genome Center  according to the manufacturer’s suggested protocol. Quality control was performed at both the sample and single nucleotide polymorphism (SNP) probe levels in PLINK . SNP proGenotypes at rs7211, rs7212 and rs9245 were determined by our standard primer probe genotyping procedures using fluorescent hydrolysable probe assays and reagents from ThermoFisher  according to manufacturer’s instructions .2) and used as a continuous variable.Phenotypes that were considered in the analyses include age, gender, body mass index (BMI) and type 2 diabetes mellitus (T2D) status. The age used was the age of the subject at the time of the Wave 5 interview. Gender was coded as a binary variable representing self-reported biological sex. BMI was calculated using the measured height and weight of the subject  were retained for further analyses.The distribution and variability of methylation values were evaluated using boxplots for each of the identified HbA1c associated DNAm loci. Stochastic epimutations (SEMs) were defined as methylation outliers exceeding three times the interquartile range (IQR), consistent with the definition of Gentilini and colleagues . SEMs weSimilarly, low methylation outliers (LMO) were defined using the following equation:HbA1c-associated DNAm probes containing SEMs were removed from further analyses.DNAm probes potentially affected by polymorphisms at the target site were identified using methods previously described. In brief, the Infinium MethylationEPIC BeadChip manifest file was used to identify SNPs found at the target site for both Infinium Type I and Type II probes. To identify those associated with HbA1c values, a linear regression model was fit in R using the following equation:A total of 302,943 independent analyses were conducted. The DNAm loci with a genome-wide significance after FDR correction (α = 0.05) were retained for further analyses.To understand the genetically contextual effects on cg19693031 methylation, meQTL analyses in both cis and trans were conducted. Linear regression models were fit in R using the following equation:A total of 117 independent cis analyses of potential interaction effects with cg19693031 were conducted. Further, meQTLs acting in cis were defined as SNPs located on chromosome 1 and within 1Mb of cg19693031. A total of 38750 independent long-range cis analyses were conducted, defined as SNPs located on chromosome 1 and located > 1 Mb of cg19693031. A total of 459464 independent trans analyses were conducted, defined as SNPs not residing on chromosome 1. SNPs with nominally significant effects were retained for further analyses (α = 0.05).The additive genetic and environmental effects on cg19693031 methylation were assessed using the following equation in R:SNPs with nominally significant effects were retained for further analyses (α = 0.05).The genetically contextual effects of HbA1c on cg19693031 methylation were assessed using the following equation in R:p-value was used for the selection and ranking of SNPs with nominally significant GxE effects. A total of 117, 38,750 and 459,464 independent cis, long-range cis and trans analyses were conducted, respectively.In particular, the interaction term Training and test datasets were prepared to develop and evaluate each classifier for T2D status in Python v.3.8.10 . A totalThe identification of HbA1c-associated distal significant Genetic x Methylation (GxMeth) interactions was determined by fitting a linear regression using the following equation in R:CMplot was used to construct the Manhattan plot [Additive and interactive GxMeth terms were evaluated for each SNP that survived data cleaning and filtering, with a total of 614,729 independent analyses conducted. The SNPs of each GxMeth interactive term with genome-wide significance after Bonferroni correction (α = 0.05) were retained for further analyses. The R package tan plot .Each SNP with a significant GxMeth interaction term was annotated using the Multi-Ethnic Global-8 Annotation File. The 560 mapped genes were used to generate a protein–protein interaction (PPI) network using the STRING database . To invescikit-learn in Python v.3.8.10 to evaluate the predictive ability of integrated GxMeth models for discriminating normoglycemic controls versus diabetic subjects. Each BRF classifier was built using a repeated stratified cross-validation (10 splits and 3 repeats) approach. The dimensionality of the dataset was reduced by taking advantage of the implicit feature selection of the random forest using the SelectFromModel function in sklearn. A BRF classifier was built as previously described to evaluate the selected features for predicting T2D status (normoglycemic controls versus diabetic subjects). The selected features were then ranked according to their Gini importance, with the top 13 features retained.A series of balanced random forest classifiers (BRF) were built using p = 1.29 × 10−9 and p = 1.12 × 10−6, respectively). While the systolic and diastolic blood pressures were not significantly different between the groups, there was a higher prevalence of self-reported hypertension and anti-hypertensive therapy in the diabetic subjects. A total of 73 subjects self-reported a history of diabetes, with 70% of these subjects having an HbA1c ≥ 6.5% despite only 4% reporting anti-diabetic pharmacological treatment.The key demographic and clinical characteristics of the 506 subjects are given in 2 = 0.1976, p = 4.43 × 10−12). As illustrated in p = 1.06 × 10−6) and prediabetic subjects .A total of 514,292 methylation probes survived the data cleaning and quality control measures. To identify HbA1c-associated DNAm loci, each probe was regressed against HbA1c values, controlling for age, sex and BMI. A total of 23 DNAm probes were associated with HbA1c at a false-discovery rate (FDR) of <5% . Consistp-value = 1.40 × 10−11, two-tailed Fischer’s exact test), with 30 of the 49 total number of SEMs found in diabetic subjects. Further, six subjects had more than one SEM across the twenty-three probes, four of whom were diabetic.The distribution and variability of the methylation values were evaluated for each of the identified HbA1c-associated DNAm loci. With the exception of cg19693031 and cg26823705 , the assn = 11) or LMOs (n = 11); however, none contained both. Notably, 14 (54%) and 23 (70%) of the identified HMOs and LMOs were observed in diabetic subjects.The SEMs were further classified into two groups based on the methylation value compared to the population. A total of 26 SEMs were hypermethylated and classified as high methylation outliers (HMO), and a total of 23 SEMs were hypomethylated and classified as low methylation outliers (LMO). Each of the 22 probes contained either HMOs (n = 7), with the remainder in open seas (n = 3). Notably, the Hidden Markov Model predicts an additional three of these loci are located within an island. Comparatively, the majority of DNAm loci with LMOs resided in the open sea (n = 8), with the remainder in subsequent shores (n = 1), shelves (n = 1) and islands (n = 1). These findings are consistent with those previously reported by Wang et al. [To further characterize the HMOs and LMOs, we examined their locations in relation to CpG island regions. The majority of DNAm loci with HMOs resided in CpG islands  of the 21269 HbA1c-associated DNAm probes with nominal significance. The total number of SEMs per probe ranged from 0 (n = 7740) to 102 across the 506 subjects. Of the 13,528 probes with SEMs detected, a majority (84%) contained less than five SEMs across the cohort. Additionally, a majority of the SEMs detected were HMOs . The total number of SEMs per subject ranged from 3 to 7271 across the 21,269 probes, with a median of 20 ± 410 SEMs per subject. No relationship was observed in the number of SEMs with T2D status, age, gender or BMI. With the exception of cg19693031 and cg26823705, the association of each probe to HbA1c diminished after the removal of SEM observations.A total of 37009 SEMs were detected in 64% , with the remainder in subsequent islands (n = 3) and shelves (n = 1). The 54 probes mapped to 37 unique genes, with functional enrichment using UniProt for alternative splicing . These genes also mapped to several relevant KEGG pathways, including type 1 diabetes mellitus, insulin secretion, PI3K-Akt signaling and diabetic cardiomyopathy.Of the 54 polymorphic DNAm probes, the island status of 11 were identified. The majority resided in shores (n = 117), (2) long-range cis meQTL  and (3) trans meQTL . A total of 7 and 2052 meQTLs acting in cis and long-range cis were detected with nominal significance, with the degree of explained methylation variance ranging from 0.45% to 1.35% and 0.41% to 4.16%, respectively. While the cis long-range meQTLs explained more variance than those acting in cis, the magnitude of explained variance did not correlate with the SNP’s proximity to cg19693031. The meQTLs acting in cis are described in Methylation quantitative trait loci (meQTL) analyses were conducted in both cis and trans to identify genetic factors that confound the methylation status of cg19693031. As underlying genomic mechanisms may differ according to proximity, our analyses were separated into: (1) cis meQTL  and trans (n = 18)). Using an additive step-wise approach, genetic factors were ranked and fed into the model based on their adj R2. The associations between two meQTLs and cg19693031 methylation were no longer observed when incorporated into the model and were dropped from further analyses. The final meQTL model captured 43.23% of the variance of cg19693031 methylation (p < 2.2 × 10−16).To determine the magnitude of meQTL effects on cg19693031 methylation, a multivariate linear regression model was built using meQTLs that demonstrated an R2 = 0.1314, p < 2.2 × 10−16). With the incorporation of HbA1c values to the final meQTL model (G + E), the amount of variance captured increased by 6% . Using ANOVA, the additive G + E model significantly outperformed the genetic factors alone (p = 2.62 × 10−14).It has been previously suggested that some of the meQTL effects may in fact be occurrences of genetic and environmental interactions (GxE). To better understand the genetic and environmental confounding architecture influencing the methylation of cg19693031, we assessed whether additive or interactive effects explain the variability of cg19693031 methylation better than genetic factors alone. As a means of comparison, HbA1c was first regressed against cg19693031 methylation adjusted for age (Rp = 0.0146). The GxE effect explained ~ 1% more variance than the G + E model (R2 = 0.1712 vs. R2 = 0.1630) and demonstrated a significantly better fit (ANOVA: p = 0.0156). Incorporating the rs74439 GxE term to the final G + E meQTL model captured an additional 1% of the variance , and explained the variability of cg19693031 methylation better than genetic or additive G + E factors alone .Next, GxE analyses were conducted and compared to the G + E effects for each of the 21 retained meQTLs. Only one meQTL, rs74439, demonstrated a significant GxE term  or indirectly.p = 2.09 × 10−25), ABC transporters (p = 1.7 × 10−4), insulin secretion (p = 8.1 × 10−4), lymphocyte mediated immunity (p = 2.61 × 10−4), immunoglobulin production (p = 1.19 × 10−4), complement activation (p = 2.50 × 10−6), immune system process (p = 2.32 × 10−7) and calcium ion transmembrane transporter activity (p = 6.23 × 10−4).Of the 25,792 meQTLs, a total of 21 had confirmed associations with T2D via genome-wide association studies. Additionally, a total of 986 are located within exons and 335 are missense mutations. The meQTL SNPs mapped to 4687 genes, with enrichment of two or more SNPs found within 2075 genes. These genes functionally map to several processes, including: alternative splicing , a total of 14,826 cis-eQTL associations with 5955 unique trans-meQTLs were identified with Bonferroni-corrected significance, with 3249 meQTL SNPs influencing the expression of 2+ genes. The identified cis-eQTLs mapped to 6861 unique genes, with enrichment of two or more cis-eQTLs influencing 3542 genes.To gain insights into the molecular networks involved in nuclear regulation underlying the relationship between genetic variation and cg19693031 methylation, we assessed whether the proximal candidate gene at a trans-acting locus showed covariation with cg19693031 methylation. Using data from the eQTLGen Consortium , readers  and erasers (TDF and SMUG1). Further, several of the eQTL genes are known regulators of TXNIP, including: transcription factors , chromatin modifications (HDACs and NF-YA) and factors regulating mRNA turnover . Provided that distal residues can be brought into physical proximity by 3D structures, an SNP could affect DNAm levels at CpG sites in trans by acting either through cis-meQTL mechanisms or by disrupting the formation of structural loops. To that end, two of the three cohesion subunits were identified as an eQTL gene (SMC3 and RAD21).n = 504), PI3K-Akt signaling (n = 95), MAPK signaling (n = 81), cytokine-cytokine receptor interaction (n = 72), Rap1 signaling (n = 66), lipid and atherosclerosis (n = 65), ras signaling (n = 59), calcium signaling (n = 56), mTOR (n = 55), cAMP signaling (n = 52), diabetic cardiomyopathy (n = 49), insulin signaling pathway (n = 48), apoptosis (n = 46), Wnt signaling (n = 45), insulin resistance (n = 45), AMPK signaling (n = 45), FoxO signaling (n = 44), natural killer cell mediated cytotoxicity (n = 44), non-alcoholic fatty liver disease (n = 43), TNF signaling (n = 39), leukocyte transendothelial migration (n = 37), NK-kappa B signaling (n = 35), AGE-RAGE signaling pathway in diabetic complications (n = 34), TGF-β signaling (n = 33), b cell receptor signaling (n = 33), glucagon signaling (n = 27), type 1 diabetes (n = 23), PPAR signaling (n = 21), hypertrophic cardiomyopathy (n = 21), regulation of lipolysis in adipocytes (n = 18), glycolysis and gluconeogenesis (n = 18), insulin secretion (n = 18), ABC transporters (n = 17) and type 2 diabetes (n = 12). The eQTLs identified in type I diabetes and AGE-RAGE signaling in diabetic complications are illustrated in n = 159), pathways in neurodegeneration (n = 135), Alzheimer’s disease (n = 105), Huntington disease (n = 81), Parkinson’s disease (n = 68), microRNAs in cancer (n = 66), proteoglycans in cancer (n = 65), chemical carcinogenesis—ROS (n = 60), transcriptional regulation in cancer (n = 59), hepatocellular carcinoma (n = 52), Hippo signaling pathway (n = 50), cellular senescence (n = 49), breast cancer (n = 34) and p53 pathway (n = 29).To gain insights into the potential role of trans meQTL effects on cg19693031 in the pleiotropic functions of TXNIP, functional gene mapping was conducted utilizing KEGG and GO. The eQTL genes mapped to key pathways that are associated with TXNIP’s pathophysiological role in diabetes and coronary heart disease, including: metabolic pathways .To gain a better understanding of the genetically contextual effect of HbA1c levels on cg19693031 methylation, genome-wide GxE analyses were conducted. A total of seven and 1355 GxE terms acting in cis and long-range cis were detected with nominal significance, with the degree of explained methylation variance ranging from 13.31% to 14.05% and 12.32% to 16.67%, respectively. A total of 16,760 GxE terms acting in trans were detected with nominal significance, with the degree of explained variance ranging from 10.98% to 17.80%. Of the 18,797 GxE terms, a total of 964 were identified in our meQTL analyses . Notably, a majority of the retained GxE terms were previously identified as meQTLs (n = 32). The features were ranked and fed into the model based on their adj R2, with retainment dependent on each feature remaining significant and a significant increase in the captured variance. A total of six GxE terms were retained for the final GxE model. The final model explained 27.78% of the variance of cg19693031 methylation (p < 2.2 × 10−16), and significantly improved the fit compared to HbA1c alone (ANOVA: p < 2.2 × 10−16).To determine the magnitude of GxE effects on cg19693031 methylation, an additive step-wise linear regression was fit using GxE terms that demonstrated an adj Rp = 1.183 × 10−36, the same as the HbA1c-associated polymorphic DNAm probes and meQTLs. Similar to the meQTLs, the GxE network mapped to several key TXNIP mechanistic pathways, including the cell adhesion (p = 1.74 × 10−11), glutamatergic synapse (p = 1.89 × 10−5), calcium signaling pathway (p = 1.7 × 10−4), insulin secretion (p = 1.6 × 10−3) and PI3K-Akt signaling pathway (p = 1.6 × 10−3).Next, we performed secondary analyses to better characterize the functional effects of the genetic variation of the GxE loci on cg19693031 methylation. Of the 18,797 SNPs, a total of nine had confirmed associations with T2D via genome-wide association studies. Additionally, a total of 681 are located within exons and 261 are missense mutations. The 18,797 GxE SNPs mapped to 3808 genes, with enrichment of two or more SNPs found within 1533 genes. Interestingly, these genes functionally mapped to alternative splicing (p < 2.2 × 10−16) and performed significantly better than the genetic factors alone (ANOVA: 3.79 × 10−8).Next, we assessed whether an integrated genetic and environmental model accounting for additive and interactive effects explains cg19693031 methylation variability better than either alone. Using a similar step-wise approach, a total of 20 G + E and 6 GxE terms were used to build the integrated model. The final integrated model captured 55.15% of the methylation variance , and one SNP was located in the 5′UTR. The incorporation of any local SNP or the addition of all three did not significantly improve the cg19693031 models for T2D status .Based on the understanding that trans genetic variation can confound DNA methylation status, we conducted a genome-wide analysis of SNP interactions with cg19693031 using the Multi-Ethnic Beadchip. The HbA1c values were regressed against additive and interactive GxMeth terms for each SNP, controlling for age, sex and BMI, with a total of 614,729 independent analyses conducted. After correcting for multiple comparisons, a total of 1476 and 1987 SNPs had significant GxMeth and additive effects, respectively . The topp = 0.045) with a small effect size . As illustrated in Holm-corrected = 0.005, Δβ = 2%). Notably, the genetic influence of rs2390998 on methylation was only prominent in diabetic subjects, with the most significant difference in methylation seen between the homozygote genotypes . Next, we conducted a genetic x environment analysis (GxE) to determine the genetically contextual effect of HbA1c levels on cg19693031 methylation. After Bonferroni correction, rs1074390 had a significant GxE effect , a significant difference was seen when stratified by T2D status  analysis to determine the extent to which genetic variation in the GxMeth loci influences cg19693031 methylation. After Bonferroni correction, rs2390998 was identified as a meQTL (D status . Similarp = 4.1 × 10−9), with an average node degree of 0.662. This network mapped to several biological process GO pathways that are highly associated with the development and complications of T2D, including regulation of voltage-gated sodium channel activity (p = 1.33 × 10−2), protein kinase C-activating G protein-coupled receptor signaling pathway (p = 1.26 × 10−2), cell communication involved in cardiac conduction (p = 1.41 × 10−2), cardiac muscle cell action potential involved in contraction (p = 3.65 × 10−2), regulation of heart rate (p = 6.63 × 10−3), calcium-mediated signaling (p = 2.30 × 10−2) and angiogenesis (p = 1.13 × 10−2). This network also mapped to several KEGG pathways, including glutamatergic synapse (p = 3.8 × 10−3), dilated cardiomyopathy (p = 7.5 × 10−3), Rap1 signaling pathway (p = 8.1 × 10−3), PI3K-Akt signaling pathway (p = 1.56 × 10−2), phospholipase D signaling pathway (p = 2.2 × 10−2), hypertrophic cardiomyopathy (p = 2.27 × 10−2) and Wnt signaling pathway (p = 2.42 × 10−2).The significant GxMeth SNPs mapped to 560 unique genes, with enrichment of two or more SNPs found within 103 genes. Of those 560 genes, 167, 144 and 22 genes had confirmed associations to metabolic and cardiovascular disorders and type 2 diabetes, respectively [To determine if correcting for genetic factors with significant GxMeth effects improves the predictive ability of cg19693031 for T2D status, a series of balanced random forest (BRF) classifiers were built, adjusting for age, sex and BMI . Using aTo resolve this, we performed multivariate ensemble feature selection using random forest with a subsequent BRF classifier to reduce the dimensionality of the dataset. Multivariate approaches consider conditional higher-order interactions between several or all features simultaneously when measuring each feature’s relevance to the overall classification task. A total of 554 features were selected by the random forest learner, including the covariates, cg19693031 and 550 non-local GxMeth SNPs. Using a repeated stratified k-fold cross-validation approach with random under-samplings of the minority class to counteract class imbalance, a BRF model was built to evaluate the selected features for predicting control versus diabetic subjects. The performance of the model improved, with an AUC of 0.74. To further reduce the dimensionality of the dataset, we used the Gini importance to select the top-ranked relevant features. The selected features were ranked according to their Gini Importance, and the top 13 features were retained . Using tUsing data from a group of African American adults, we confirm and extend the prior findings, showing that the methylation of cg19693031 has a significant dose-dependent relationship with HbA1c. We identify confounding genetic variations influencing the methylation status of cg19693031 both independent of and in combination with HbA1c. The pathway analyses identified the regulatory mechanisms related to the 3D genomic structure and TXNIP’s role in disease susceptibility. Finally, we demonstrate that correcting for genetic confounding improves the ability of cg19693031 to detect type 2 diabetes. Collectively, our analyses suggest that the demethylation response of cg19693031 to sustained hyperglycemia in white blood cells may play a pivotal role in the regulatory network effects that underlie several clinical phenotypes, with insights into TXNIP’s pleiotropic functions for disease onset and progression.To the best of our knowledge, our study is the first full integration of genetic and hyperglycemia effects on cg19693031 methylation, laying the groundwork for a more comprehensive understanding of the molecular mechanisms linking the methylation status of TXNIP’s 3′UTR to disease. Collectively, we identify 11,240 genes that participated in significant interactions. The enrichment and functional genomic analyses suggest the crosstalk between genetic variants and the demethylation response of cg19693031 may contribute to the dysregulation of gene expression seen in diabetes. For example, TXNIP has been shown to be induced by glucose and overexpressed in diabetic patients . In addiPrior studies have suggested the utility of TXNIP as a biomarker for diabetes due to its hyperglycemia-induced overexpression and the strong association of cg19693031 with HbA1c . While tn = 27) and type 2 diabetes (n = 26). In addition, biological pathways related to diabetes were identified, including: insulin section, downstream signaling of insulin and glucagon and sensory perception of chemical stimulus. TXNIP emerged as a viable therapeutic target for diabetes due to its role in pancreatic β-cell apoptosis via activation of the mitochondrial death pathway [n = 102), diabetic cardiomyopathy (n = 73), insulin resistance (n = 66) and AGE-RAGE diabetic complications (n = 59). This suggests that the pathophysiological role of the demethylation response of cg19693031 may generalize across the diabetes subtypes. As the diagnosis of diabetes mellitus is based on clinical presentation rather than specific molecular defects, overlaps in their etiologies have been documented. Indeed, prior studies have demonstrated the association of cg19693031 methylation and T1D. Further, current clinical trials are investigating the use of Verapamil for treatment of T1D, which has been shown to decrease the expression of TXNIP.Our findings will be particularly useful for those seeking to better understand the pathophysiological function of TXNIP in order to optimize diabetes medicine. For example, several cg19693031 interacting genes were enriched in both type 1 ( pathway . The act pathway . Critica pathway . Pathwayn = 274), pathways in neurodegeneration (n = 205), Alzheimer’s disease (n = 155), microRNAs in cancer (n = 106), transcriptional misregulation in cancer (n = 100) and the Hippo signaling pathway (n = 84). Moreover, prior studies have demonstrated an association between cg19693031 methylation and chylomicrons (Class A), triglycerides, hexose, α-hydrobutyrate, systolic hypertension, waist circumference, gender, metabolic syndrome, type 1 diabetes, coronary heart disease, breast cancer and colon cancer [Altered TXNIP activity has been implicated in the development of several diseases aside from diabetes, including atherosclerosis, cancer and neurodegenerative diseases ,34,35,36n cancer ,38,39,40We also note that, among those with HbA1c confirmed diabetes, only 57% had come to clinical attention, and less than 1% were on diabetic medications. This observation is reflective of the long asymptomatic phase of the disease, and health disparities seen in African American communities, resulting in a high rate of undiagnosed T2D. Additionally, control and pre-diabetic subjects reported a DM diagnosis, 3% and 9%, respectively, but they did not report the use of anti-diabetic pharmacological therapy. This discordance may be reflective of subjects reporting a pre-diabetic diagnosis, or lowered glucose levels due to lifestyle changes or bariatric surgery.The findings of this study should be considered in the context of its limitations. First, it is important to note that the FACHS cohort is almost exclusively mature adult African American women. Further examinations using a larger number of subjects of other ethnicities, age groups and equal proportions of biological sexes will be required to demonstrate generalizability. Second, although the HbA1c test is the clinical gold standard, it is not sensitive to all pre-diabetes phenotypes . TherefoIn summary, we confirm and extend the prior findings of the demethylation response of cg19693031 to increasing HbA1c values in an African American cohort. This study is the first to extensively examine the genetic and hyperglycemia contextual effects on cg19693031 methylation, laying the groundwork for a better understanding of TXNIP’s pleiotropic pathophysiological effects in the development of disease. Future studies examining the extent of non-diabetes cellular-context-confounding cg19693031 methylation are needed to assess the feasibility for translation and potential clinical utility.


The objective of the present study was to investigate how variation in the faecal microbial composition is associated with variation in average daily gain (ADG), backfat thickness (BFT), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI), using data from two experimental pig lines that were divergent for feed efficiency. Estimates of microbiability were obtained by a Bayesian approach using animal mixed models. Microbiome-wide association analyses (MWAS) were conducted by single-operational taxonomic units (OTU) regression and by back-solving solutions of best linear unbiased prediction using a microbiome covariance matrix. In addition, accuracy of microbiome predictions of phenotypes using the microbiome covariance matrix was evaluated.Estimates of heritability ranged from 0.31 ± 0.13 for FCR to 0.51 ± 0.10 for BFT. Estimates of microbiability were lower than those of heritability for all traits and were 0.11 ± 0.09 for RFI, 0.20 ± 0.11 for FCR, 0.04 ± 0.03 for DFI, 0.03 ± 0.03 for ADG, and 0.02 ± 0.03 for BFT. Bivariate analyses showed a high microbial correlation of 0.70 ± 0.34 between RFI and FCR. The two approaches used for MWAS showed similar results. Overall, eight OTU with significant or suggestive effects on the five traits were identified. They belonged to the genera and families that are mainly involved in producing short-chain fatty acids and digestive enzymes. Prediction accuracy of phenotypes using a full model including the genetic and microbiota components ranged from 0.60 ± 0.19 to 0.78 ± 0.05. Similar accuracies of predictions of the microbial component were observed using models that did or did not include an additive animal effect, suggesting no interaction with the genetic effect.Our results showed substantial associations of the faecal microbiome with feed efficiency related traits but negligible effects with growth traits. Microbiome data incorporated as a covariance matrix can be used to predict phenotypes of animals that do not (yet) have phenotypic information. Connecting breeding environment between training sets and predicted populations could be necessary to obtain reliable microbiome predictions.The online version contains supplementary material available at 10.1186/s12711-022-00717-7. Recent advances in the collection of microbiota information make it possible to analyze the interplay between complex traits and the microbial community of the gastrointestinal tract (GIT) in animals and humans. This is especially essential in the pig industry since previous studies have suggested that the GIT microbiome could contribute to the variability of feed efficiency in pigs –3. From ment}m^) . Howeverment}m^) , 8. In ament}m^) . In an ement}m^) , 11. Givhttps://doi.org/10.15454/1.5572415481185847E12), as described in Aliakbari et al. [Phenotypic records and faecal samples (604 pigs) were collected from the two last generations (G9 and G10) of two experimental French Large White pig lines that were developed over 11 generations of divergent selection for RFI during 18 years at INRAE  had records for feed intake (DFI), feed efficiency traits (FCR and RFI), growth rate (ADG) from 35 to 95 kg of body weight (BW), and live body composition traits at 95 kg BW (BFT). The remaining animals, from G10 , had records for growth rate, feed efficiency from 10 weeks of age until slaughter (115 kg BW), and backfat thickness at 23 weeks of age (BFT23). Different multiple linear regression equations were used to compute realized RFI for selection candidates versus G10 females and castrated males, considering their test differences, as described in . First, The faecal samples were collected at 15 weeks of age to obtain the gut microbial information. Immediately after collection, the samples were homogenized and placed in dry ice, before storage at − 80 °C until DNA extraction (see next section).https://www.ncbi.nlm.nih.gov/bioproject/) with accession number PRJNA701065. Reads were filtered and trimmed for high-quality sequences using the DADA2 package in the R software [The Quick-DNA™ Faecal Microbe Miniprep Kit™  was used to extract microbial DNA based on a 15-min bead-beating step at 30 Hz. PCR amplification of the V3-V4 region of the 16S rRNA gene obtained from diluted genomic DNA was carried out by using two primers: F343 (CTTTCCCTACACGACGCTCTTCCGATCTTACGGRAGGCAGCAG) and R784 (GGAGTTCAGACGTGTGCTCTTCCGATCTTACCAGGGTATCTAATCCT), in 30 cycles with an annealing temperature of 65 °C. To assemble pair-end sequences, the Flash software v1.2.6 [software .To account for differences in sequencing depth across samples, a rarefaction step was applied: for all samples with more than 9000 reads, an equal number of 9000 reads were randomly selected. Sixteen samples had a smaller number of reads and were discarded for later analyses. After this rarefication step, the final abundance table contained 5689 OTU for 588 samples (295 LRFI and 293 HRFI). Finally, following Rothschild et al. , OTU in For each trait, the following four univariate linear models were fitted:In addition, to assess if covariance between traits could be due to similar microbiota compositions , microbiAll models were fitted using the Bayesian framework using the GIBBSF90 software . In tota, two scenarios for cross-validation were considered for each of the five traits using Models (2) and (4): the first scenario was designed to run predictions for 50 random animals in 20 successive replicates, and the second scenario was designed to run predictions for the animals of each of the 14 CG, successively. The objective with these designs was to evaluate the effect of the presence of contemporary animals on the prediction accuracy.To evaluate the accuracy of prediction of the phenotypes using The part-whole linear regression (LR) method of Legarra and Reverter  was usedThe objective of the MWAS was to identify OTU that have significant associations with the phenotype of each of the five traits. Two approaches were used.Single-OTU regression analyses were applied to test the effect of the 2630 OTU, one at a time, and obtain the associated p-value, which is the most common approach . The modAn alternative approach is the back-solving of predicted  Garrick  and Gual Garrick  to obtai Garrick , howeverEstimates for the effect of each OTU Therefore, estimates for the effects of OTU given the  follows :7\documeAnd the variance of the OTU estimates is defined as in :8\documeThe predictor error variance (PEV) of Therefore,Finally:Then, the Z-score for the estimate for each OTU The corresponding p-values can then be calculated by applying a Chi-square test to these Z-scores.The back-solving method was run using a local script to construct and solve the mixed model equations based on the variance component estimates of Model (4) for each trait. It should be noted that both the single-OTU regression and the back-solving approach accounted for additive genetic effects in order to obtain comparable results.To obtain significance thresholds for the MWAS to control the family-wise type I error rate while accounting for multiple testing using non-independent variables, a principal component analysis (PCA) was applied to the correlation matrix of OTU o et al. . The PCAThe results of univariate analyses of the five studied traits with the four models are in Table  (2) and  , (3), and 4) are in Table  are in TThe mentclass2pt{minimmentclass2pt{minimEstimates of entclass1pt{minimaThe results of MWAS with single-OTU regression are shown in Fig. Streptococcaceae (1 OTU), Prevotellaceae (3 OTU), Ruminococcaceae (3 OTU), and Lachnospiraceae (1 OTU) families  for DFI, ADG and BFT, the MWAS results were equivalent between the single-OTU regression and the back-solving approaches, which can be considered as an indirect confirmation of the estimated microbiome variances. Indeed, if variances deviated strongly from the actual values, biased MWAS results could be expected from the back-solving approach.The tests indicated that the abundance of some of the OTU may be associated with the variability of phenotypic traits. We have previously shown that some microbiota genera differ between lines . The aimLachnospiraceae, Ruminococcaceae, Prevotellaceae and Streptococcaceae families with RFI, DFI, and BFT [Streptococcus genus in our present MWAS is opposite to the positive genetic correlation that was estimated between RFI and the Streptococcus genus in our earlier study. This suggests potential antagonistic relationships between microbiota and traits at the phenotypic and genetic levels. Weishaar et al. [Lachnospiraceae and Prevotellaceae families had strong effects on FCR and RFI, but without indication of the direction of these effects. The Prevotellaceae, Lachnospiraceae and Ruminococcaceae families are involved in the digestion of fibrous material and provide short-chain fatty acids to the host [Streptococcaceae family are known to be lactic acid producer bacteria [In a previous study at the genera level, using the same microbiome dataset but filtered for the most abundant genera, we showed significant genetic correlations of abundance of genera from the  and BFT . Our finr et al.  also repthe host , 33. Bacbacteria  that havbacteria . TherefoWe have shown that microbiota information can be used to better predict traits in pigs, especially feed efficiency traits. The sizable Additional file 1: Figure S1. Average correlations between . Figure S2. Average correlations between Figure S3. Results of microbiome wide association analyses using back solving of BLUP solutions between operational taxonomic units and residual feed intake (RFI), feed conversion ratio (FCR), daily feed intake (DFI), average daily gain (ADG) and back fat thickness (BFT).


Both annual and perennial sorghum biomass serve as important forage for ruminant animals around the world. Unfortunately, sorghum can produce hydrogen cyanide (HCN), which, if occurring in high enough concentrations, can be toxic or lethal to animals that consume it. The objectives of this study were to develop a fast and inexpensive colorimetric assay to measure the hydrogen cyanide potential (HCN‐P) as well as to compare this with existing visual assays while assessing the range of variation for HCN‐P among perennial and annual sorghum biomass. The HCN‐P of 100 sorghum lines derived from an interspecific hybridization program was determined over 2 years (establishment and regrowth) using both visual and colorimetric assays. Visual assessment underestimated the HCN‐P and was less accurate than colorimetry. Repeatability for HCN‐P across all sampling dates was functionally zero in the visual assessment and low for the colorimetric assay. This was mostly explained by the significant pedigree × year interaction effects and growth stage. Growth stage substantially influenced HCN‐P, which should be considered when feeding animals on fresh forage. Among the perennial sorghum species, Sorghum halepense (Johnson grass) was originally introduced to the United States as a forage species  have potential as a perennial forage  has colonized large land masses and become one of the 10 worst weeds of field crops , enzymatic reactions cleave the dhurrin molecule to produce hydrogen cyanide (HCN). This HCN production is thought to have evolved as a defense against insect herbivory  and S. halepense (gypsum). These progenies were selected in Salina, Kansas, for both perenniality and grain productivity using methods described by Nabukalu a Cox  and (2) S. bicolor , and four hybrid grain sorghums  , primarily comprising 79 BClu a Cox . In addi2.2S. bicolor) was replanted on April 8, 2020. A randomized complete block design with two replications and single row plots of 6 m length and 1.5 m wide was used. Agronomic practices standard for forage sorghum production in Texas were followed , and maintained through 2020 (regrowth year). Annual sorghum , biomass sampling for HCN‐P occurred in late summer (September 3–6) when the plants were at physiological maturity stage . At this time, daily air temperatures ranged from 21 to 38°C, soil temperature ranged from 27 to 30°C, and relative humidity ranged from 28% to 92%. Further, the plots were under drought and heat stress.In 2020, HCN‐P was first sampled in late spring May (27–29) when sorghum plants were at the five‐ to seven‐leaf stage (vegetative), daily air temperatures ranged from 18 to 30°C, soil temperature ranged from 26 to 27°C, and the relative humidity was 48%–99%. Third and fourth sample dates were collected July (1–3) at 50% flowering  and at physiological maturity (August 18–20) when daily air temperatures ranged from (28–29°C and 30–31°C), soil temperature (27–28°C and 31–32°C), and relative humidity (71%–76% and 65%–71%), respectively.2.4The visual color assessment of HCN‐P was based on the modified protocol of Buck et al. . An indeThe visual scale is based on the intensity of color change Figure . An exceFor each plot, 300 g was weighed from the 10 whole leaves harvested per plot and cut into 1 cm pieces and placed into a 250‐ml Erlenmeyer flask. Chloroform (1.5 ml) was added to the sample flask to create a vapor to lyse cells and an indicator paper suspended by the tightly inserted stopper. A positive control was also prepared by adding 100 μl β‐glucosidase to 1000 μl of aqueous 1 mg/ml amygdalin. Samples were incubated in batches and scored. The color scale . A frequency distribution for color across all plots was made to assess how many fell under each category on the color scale. Subsequently, comparisons were made with the colorimetric distribution.A HCN colorimetric curve was built based on the change in color (ΔE) compared with the actual HCN released from the different amygdalin concentrations. Correlations were run between HCN from the amygdalin and the values from the colorimetry. The values from the colorimetry were dependent on the standard as described earlier on in Section Y represented individual observations of each pedigree, μ was the grand mean, and pedigreei represented effect of the pedigree (fixed). All other effects were random; yearj, effect of the year; replicationk, effect of the replication; rangel, effect of range; rowm, effect of row; batchm, effect of batch of HCN laboratory processing; pedigreexyearij interaction, pedigreexbatchnj interaction; and errorijklmn represented the pooled error for all the mentioned factors in Equation , flowering and physiological maturity]) using variance component analysis to estimate error variation and determine precision between the two methods. Correlation analysis was also run to assess the relationship between the colorimetric and visual assessment assay. Repeatability for HCN‐P was calculated as in Equation  was performed as that was the only year where data could be taken at all three growth stages.The model was reduced by eliminating effects that did not minimize (<0.1%) error variation and non‐significant interaction effects Equation .(4)Yij33.12 values for all variables  by the action of β‐glucosidase enzyme versus the actual HCN from varying amygdalin concentrations  across all sampling dates and thus produced similar results; for example, HCN‐P was lower in more mature sorghum. However, the visual color score consistently produced lower HCN‐P estimates than the colorimetric assay with a greater range in variation  were faster than the previous studies, because results obtained within an hour of laboratory work, freeing up space, glassware, and time to increase the number of samples. Colorimetry only added approximately 2 min per sample. It has been found that lower accuracy methods can still result in better decisions if offset by higher throughput and low cost that allows greater sample collection  and from 6 to 60 for 2020 (May 27–29) for the colorimetric measure . The comparison made across the growth stages confirmed the findings of Zahid et al.  produced for perennial and grain sorghum across 2019 and 2020.Click here for additional data file.