Mendelian randomization (MR) studies are susceptible to metadata errors  and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors.We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets  and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set.We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls.https://github.com/MRCIEU/CheckSumStats).In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package ( Key MessagesMetadata errors  and other analytical issues can introduce substantial bias into Mendelian randomization (MR) studies but have received relatively little attention in comparison to other sources of bias, such as violations of instrument variable assumptions.We found that 11.6% of the studies in the Fatty Acids in Cancer Mendelian Randomization Collaboration were subject to metadata errors or analytical issues with the potential to introduce substantial bias into MR analyses .Previously developed guidelines for conducting MR studies provided insufficient safeguards against such errors.https://github.com/MRCIEU/CheckSumStats) that can reliably identify and correct metadata errors and other analytical issues at the study design stage.We developed additional guidelines and the CheckSumStats R package  assumptions are met: (i) the instrument is associated with the exposure; (ii) the instrument is not associated with confounders of the exposure–outcome association; and (iii) the instrument is associated with the outcome exclusively through its effect on the exposure. Although violations of assumptions can be introduced by genomic confounding or horizontal pleiotropy, an increasingly sophisticated range of sensitivity analyses are available that can be used to model the impact of such bias on MR findings.In the two-sample approach to MR, genetic summary data for the exposure and the outcome are obtained from separate studies. This greatly increases the scope of MR, as it means the method can be applied to any disease case–control collection regardless of whether the exposure has been directly measured or not. The success of GWAS has greatly increased the number of traits with available genetic association or summary data. In principle, any heritable trait with summary genetic association data can be used to define an exposure or an outcome in a two-sample MR study and thus the scope for what counts as an exposure or an outcome is very broad. Exposure and outcome traits can vary from relatively simple molecular traits, such as expression or protein traits, to highly complex traits, such as human behaviours and disease outcomes.Within the GWAS field, quality control (QC) procedures have been developed that can detect a wide range of analytical issues and metadata errors, either at the GWAS stageSummary data from genome-wide association studies (GWAS) provide a rich resource for two-sample Mendelian randomization (MR) studies of exposure–disease pathways —a pan-cancer MR study that seeks to evaluate the causal relevance of fatty acids for risk for most major cancers. The basic principle of our QC approach is to identify metadata errors through comparison of study-specific statistics to external reference data sets [e.g. the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS catalogue and 1000 genome super populations] and to identify potential analytical issues or summary-data errors through comparison of reported to expected genetic effect sizes. Using the pipeline, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls in 51 studies. The size and complexity of the FAMRC make it an ideal collaboration in which to develop and evaluate QC processes for the detection of errors that can introduce biases into downstream MR analyses.The FAMRC had four key design components: (i) fatty acid instrument selection strategy; (ii) cancer outcome selection strategy; (iii) cancer data preparation and harmonization; and (iv) identification of summary-data errors, metadata errors and other analytical issues .https://www.ebi.ac.uk/gwas/) and Open GWAShttps://gwas.mrcieu.ac.uk/), using fatty acid-related search terms, including: fat, acid, fatty acid, DHA, omega, monounsaturated, monounsaturated, polyunsaturated, saturated, omega 3 and omega 6. Fifteen studies were identified by this strategy. When full summary association statistics were available, independent genetic associations with P < 5e-8 were identified through linkage disequilibrium (LD) clumping (r2 threshold set to 0.01), with LD reference panels based on either the European or East Asian 1000 genome superpopulations  in the Single Nucleotide Polymorphism Database (dbSNP) and 1000 genomes reference data, to make allowance for different rsids across different genome builds for the same SNP. We refer to the genetic associations for fatty acids, their r2 proxies and alias rsids as the ‘fatty acid SNP set’  or the GWAS catalogue, we invited the identified studies to share summary data for all SNPs in their GWAS analysis (defined as ‘full GWAS summary data’). If studies were unable to share full summary data, they were invited to share genetic association results for the ‘fatty acid SNP set’. We further downloaded summary association statistics for cancers from Biobank Japanhttp://jenger.riken.jp/en/), FinnGen [data freeze 1 (14 January 2020)] (https://www.finngen.fi/fi) and UK Biobank,We searched for studies of cancer in the GWAS catalogueP-values for Hardy–Weinberg equilibrium (HWE) and metrics of imputation quality, such as info scores. When the GWAS was a meta-analysis of multiple independent studies, we additionally requested P-values for between-study heterogeneity.For data sets obtained via correspondence, studies were invited to share summary data up until December 2019, after which data collection was closed. Example data sharing instructions can be found in the For each cancer with full summary data, we extracted the following three sets of SNPs:the fatty acid SNP set;the 1000 genomes reference set;the GWAS catalogue ‘top hits’ for cancer.P-values <5×10–8). When full summary data were not provided, QC analyses were restricted to the ‘fatty acid SNP set’. We next formatted the cancer summary-data sets to have similar tabular formats (e.g. where results were distributed across multiple files we merged these together) and to have consistently named data fields. SNPs without rsids were mapped to an rsid using the reported chromosome and base pair position. We excluded duplicate and triallelic SNPs as well as SNPs with missing effect sizes and standard errors or with a minor allele count of <50 in either cases or controls. If standard errors were not reported, we attempted to infer these from confidence intervals or P-values before excluding the SNP. We asked each study to confirm the identity of the effect allele and effect allele frequency columns in their data sets, unless this was unambiguously specified in the metadata or associated readme file. We manually mapped the cancer name for each data set to the experimental factor ontology (EFO).‘Top hits’ refers to the strongest or statistically significant genetic associations for the cancer of interest from published studies .To identify metadata errors, summary-data errors or other analytical issues, we developed a QC pipeline based on the R programming language and associated packages.r2=0.01 and kb = 10 000) to associations in the GWAS catalogue. We set the significance threshold for LD clumping to the threshold reported in the fatty acid GWAS: 5-e8 in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium),et al.et al. and TwinsUK/KORA, and the East Asian superpopulation as an LD reference panel for the SCHS and NHAPC/MESA. We searched the GWAS catalogue for the lead SNP, identified by the latter clumping procedure, as well as SNPs within 200 000 base pairs of the lead SNP . In recent years, the GWAS catalogue has started to host full GWAS summary statistics. However, for this QC step, we are referring exclusively to the manually curated database of published ‘top hits’.To identify incorrect specification of the effect allele column, we compared summary association statistics in the test data set (either a fatty acids or cancer data set) to summary association statistics in the NHGRI-EBI GWAS catalogueIn the first step, we searched the GWAS catalogue for SNPs associated with the EFO or reported trait of the test data set. Second, for each SNP associated with the EFO term, we extracted from the GWAS catalogue the effect size, standard error, effect allele, effect allele frequency and study ancestry  than 0.5 in both data sets. The level of conflict was further labelled as high if the minor allele frequency was ≤0.4 in both data sets, and as moderate if not. The latter step makes allowance for chance deviations in allele frequencies for SNPs with minor allele frequencies close to 0.5. Conflicts were also labelled as high if the allele frequency differed by >10 points between the test and reference data sets. When interpreting the scatter plots, it is important to take into account the total number of SNPs in the comparison as well as the ancestry of the test and reference data sets. Conflicting associations are more likely to reflect true effect allele coding issues when the conflict is systematic across a large number of SNPs and when the ancestry of the data sets being compared is the same. When a substantial proportion of SNPs displayed effect or allele frequency conflicts, we flagged the test data set as containing a potential effect allele metadata error.We then inspected the scatter plots for conflicting directions of association. For example, we declared a conflicting direction of association if the effect allele was associated with higher cancer risk in the GWAS catalogue but was associated with lower risk in the test data set. The level of conflict was further labelled as ‘high’ if the To confirm the effect allele frequency column, we compare the test data sets to two types of reference data sets: the 1000 genomes projectTo identify potential analytical issues or summary-data errors, we compare the expected and reported effect sizes. For continuous exposures, such as fatty acid levels, we generate expected betas using the formula:p is the minor allele frequency, n is the sample size and z is the ratio of the effect size to its standard error. The predicted effect size from this transformation can be interpreted as the standard deviation change in the exposure per copy of the effect allele (assuming that z itself was generated in an additive genetic model). When the expected effect size is a log odds ratio, e.g. for cancer status analysed in a logistic regression model, we generate the expected log odds ratio for each SNP using a simulation method that takes into account the SNP’s z-score, minor allele frequency and the number of cases and controlswhere We then regress the expected effect size  on the reported effect size and interpret a slope very different from 1 (which we define as either >1.20 or <0.8) as evidence for an unusual distribution in the reported effect sizes. We also assess the overall shape of the relationship between the expected and reported effect sizes in scatter plots, with the expectation of linearity. Deviations of the slope from one or non-linear patterns could reflect:errors in the reported effect sizes, reported sample sizes or reported allele frequencies;effect size scale conflicts [e.g. reported effect sizes have not been standardized (for continuous traits) or effect sizes have not been generated in a logistic regression model (for binary cancer outcomes)]);the impact of covariate adjustment in the regression model;deviations from HWE.If we found that the summary association statistics for cancer were generated in a linear model  to pz-scores  in scatter plots , 22 saturated fatty acids, 6 omega 3 polyunsaturated fatty acids (PUFAs), 8 omega six PUFAs, 8 trans-fatty acids and 14 other fatty acid characteristics  on chromosome 16, regions harbouring established GWAS hits for fatty acids . In the SCHS, the relationship between the expected and reported effect sizes was skewed by a single outlier SNP . In the SCHS, correspondence with the data provider indicated that a file sharing error had occurred and we therefore obtained a new set of GWAS results files . Conflicts with the GWAS catalogue were not observed for the FHS, TwinsUK/KORA and Kettunen r2 proxies and alias rsids was 2326 for European studies and 2596 in East Asians (excluding duplicate SNPs that overlapped amongst the fatty acid and proxy sets) . We identified a further 1841 SNP proxies using the 1000 genomes European super population, 2251 SNP proxies in the 1000 genomes East Asian super population and 197 alias rsids in dbSNP and 1000 genomes reference data. The total number of SNPs associated with fatty acids, their ,. Thirty-eight studies supplied a single data set, 10 studies supplied two to five data sets and 6 studies supplied more than five data sets. Of the 166 cancer data sets, 31 were from UK Biobank, 12 were from Biobank Japan and 29 were from FinnGen. Fifty-nine data sets (from 41 studies) were obtained via correspondence with study principal investigators, 6 data sets (from 3 studies) were downloaded from the GWAS catalogue and 101 data sets (from 12 studies) were downloaded from the Open GWAS project.r2 or info scores) were available in 53 data sets (from 25 studies) and P-values for deviations from HWE were available in 6 (from 6 studies). Of 65 data sets derived from 29 GWAS meta-analyses, P-values for between-study heterogeneity were available in 18  and the number of studies per SNP was available in 16 .As of January 2020, we had collated 166 summary genetic data sets from 54 cancer studiesWe extracted three sets of genetic associations from each data set for which full GWAS results were available (132 data sets from 31 studies): (i) the fatty acid SNP set, (ii) the 1000 genomes reference set and (iii) known cancer hits in the GWAS catalogue. For 34 data sets from 25 studies, only a subset of GWAS results, corresponding to the fatty acid SNP set, was available. We excluded duplicate and triallelic SNPs; SNPs with missing effect allele, effect sizes or standard errors; SNPs that could not be mapped to an rsid; and SNPs with a minor allele count in cases of <50. After these exclusions, there were 401 026 genetic associations with cancer across 163 data sets in 52 studies. Of these, 223 970 genetic associations corresponded to the fatty acid SNP set, 93 121 corresponded to the 1000 genomes reference set and 24 860 corresponded to known cancer associations in the GWAS catalogue. Three studies providing genetic associations for the fatty acid SNP set provided an additional 40 582 genetic associations for SNPs within 500 kb of a fatty acid index SNP  (cancer study abbreviations explained in z ≤ 2.5). Therefore, we could not rule out chance deviations from the GWAS catalogue for this data set. In addition, the ancestry of the BC-NHL (East Asian) was different to the ancestry of the reference data set (European). Therefore, the observed conflict for the BC-NHL data set could also reflect differences in LD between populations. In the NBS (ID 106), equal numbers of SNPs were highly discordant and highly concordant to the GWAS catalogue. Due to the ambiguity of the effect allele we decided to drop the BC-NHL (ID 5) and NBS (ID 106) data sets. Reported effect alleles were compatible with reported cancer hits in the GWAS catalogue for other cancer data sets  in which the reported effect sizes had an unusual distribution when compared with expected log odds ratios, suggesting potential summary-data errors or analytical issues (Of the remaining 25 data sets (from 14 studies), we confirmed that the reported effect sizes were log odds ratios by consulting the original study publications. For 18 data sets (from nine studies), the discrepancy between reported and expected effect sizes was largely attributable to incorrect sample sizes, low imputation quality or a small number of outlier SNPs with unusually large log odds ratios (e.g. log odds ratios >1 or < –1). For example, in eight data sets from the ACCC, GICC/MDA, GECCO and HNMSC studies, we incorrectly assumed that the number of participants contributing to analyses was constant across SNPs  and bz-scores (z-scores inferred from reported effect sizes and standard errors) (z > 99) but with P-values very close to 1 (>0.9). The second data set showed a very irregular non-linear relationship between the two sets of z-scores  , including errors in 7 studies with the potential to introduce substantial bias into downstream MR analyses. These included a large number of false positive genetic associations for fatty acids and incorrect specification of the effect allele and effect allele frequency columns. Other more minor issues included inconsistent effect size scales amongst cancer studies, incorrect assumptions about sample sizes across SNPs and outlier SNPs with unusually large effect sizes.,Of the issues identified, incorrect specification of the effect allele column is the most serious, as it will lead to inferences of causal effect in the wrong directionFalse positive genetic associations for fatty acids were identified in two of six fatty acid consortia. Failure to account for false positive hits could lead to the inclusion of genetic variants in MR analyses that are not truly associated with the exposure [a violation of instrumental variable assumptions see ], which It is important to consider the impact of sample size when interpreting the presence of GWAS hits in the test data set that are absent from the GWAS catalogue. For example, if the GWAS being investigated is unpublished and is larger than any previously published study, we can reasonably expect a number of genetic associations to be identified that are absent from the GWAS catalogue but are nevertheless true novel hits. When the GWAS being investigated is smaller than any previously published study, one should be more sceptical of any GWAS hits that are previously unreported.We also found that cancer studies did not consistently express effect sizes as log odds ratios, with a substantial proportion of cancer analyses within UK Biobank expressing effect sizes as absolute changes in disease risk. The cancer analyses in question employed BOLT-LMM—a linear mixed model that allows the inclusion of related individuals, is more powerful and efficient than conventional regression proceduresPotential summary-data errors were flagged by mismatches between expected and reported effect sizes. We found that a substantial proportion of the mismatches were attributable to imputed SNPs, SNPs with incorrect sample sizes and SNPs with unusually large effect sizes. The sample size errors were due to the strategy of using the maximum reported sample size to represent sample size across all SNPs. However, not all samples in a GWAS necessarily contribute to the analysis of every SNP, which is particularly common in large meta-analysis consortia with many independent studies. Incomplete sample overlap amongst SNPs within a GWAS could introduce bias into methods that assume a constant sample size, such as summary-data methods that rely on an external LD reference panel to model the correlation structure amongst SNPs in a genetic instrument. In the presence of incomplete sample overlap amongst SNPs, the use of an external LD reference panel could lead to the overestimation of the covariance in SNP effect sizes. For example, in the most extreme case of zero sample overlap, the correlation in effect sizes for two SNPs will be zero even if those two SNPs are in LD.When obtaining summary GWAS data via correspondence with study authors, we recommend that researchers should request access to full GWAS summary data, as this allows a far more comprehensive assessment of summary-data reliability than is possible with only subsets of data. When full access is not possible, researchers should request summary data for SNPs that are established GWAS hits for their outcome of interest (i.e. not just the SNPs being used to instrument the exposure), which can then be used to confirm the identity of the effect allele through comparisons with the GWAS catalogue. In addition, researchers could request summary data corresponding to the SNPs in our 1000 genomes reference set, which contains 2297 SNPs with the same minor allele across all 1000 genomes super populations, and which can be used to identify allele frequency issues. An advantage of using our 1000 genomes reference set is that effect allele frequency conflicts can be identified without knowledge of the ancestral background of the test data set. Alternatively, a similar QC check can be achieved by comparing allele frequencies between the exposure and outcome studies of interest (assuming they are closely matched on ancestry). Where possible, researchers should also confirm the identity of the effect allele metadata through correspondence with the data providers.,,We also recommend that researchers confirm with data providers the nature of all post-GWAS filtering procedures that have been applied to the summary data. For example, in our own collaboration, we ask each cancer study to confirm that their summary data have been through the same QC procedures as described in their GWAS publications. Failure to perform this check could lead to the inclusion of large numbers of low-quality and unreliable genetic associations. It is also advisable to confirm effect size scales, to support the correct interpretation of results. These considerations supplement previously developed guidelines for conducting MR studies.Our approach of comparing expected to reported effect sizes, and of comparing summary association statistics to external reference data sets, offers an additional safeguard against the aforementioned errors and analytical issues. A limitation of this approach is that not all flagged data sets will necessarily be problematic because other factors, such as covariate adjustment in the original GWAS or deviations from HWE for reasons other than measurement error, could also cause deviations between expected and reported effect sizes. Therefore, SNPs flagged by this approach may still be suitable for downstream MR analyses.https://gwas.mrcieu.ac.uk/), GWAS ATLAS (https://atlas.ctglab.nl/), GWAS Central , PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/) and Global Biobank Engine (https://biobankengine.stanford.edu/).A limitation of our comparative approach is that it may be less effective when there are zero, or few, known genetic associations for the trait of interest. This could happen, for example, when working with understudied or rare characteristics, for which existing published GWAS may be underpowered. In such a situation, comparisons with genetic associations for closely related traits could still be informative. Alternatively, there are a growing number of online platforms that collate summary data from multiple GWAS, which in principle could also be considered as reference data sets when the trait of interest is absent from the GWAS catalogue. These include OpenGWAS (https://www.ebi.ac.uk/spot/zooma) ontology mapping service, which supports command line access via a REST API.We manually mapped the text descriptions for each cancer type to the EFO, which could be inefficient when working with hundreds or thousands of traits. A more efficient approach would be to use the EMBL-EBI Zooma , indicating that the reported study ancestries were broadly accurate. However, our QC procedure was not designed to specifically test for ancestral origins and was restricted to SNPs with a narrow allele frequency range. A more efficient approach would be to select SNPs with a much wider range of variation in minor allele frequency than chosen here. The need to assess the ‘same population’ assumption is becoming more urgent with the growing diversity of GWAS, including a growing number of trans-ethnic and admixed studies.https://github.com/MRCIEU/CheckSumStats).We have developed a QC pipeline that can be used to flag metadata and summary-data errors and a range of analytical issues in GWAS results, which in turn can be used to enhance the integrity of downstream two-sample MR analyses. We applied the pipeline to the FAMRC, identifying errors with potential to introduce substantial bias in seven studies. After resolving analytical issues and excluding problematic studies, 160 data sets from 51 studies were retained, representing 90 unique cancer types generated in analyses of 566 665 cancer cases and 1 622 374 controls. The methods developed here are available to other researchers via the CheckSumStats R package (Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer /World Health Organization.Fatty Acids in Cancer Mendelian Randomization Collaboration: Nathan Tintle, Ulrike Peters, Terri Rice, Iona Cheng, Mark Jenkins, Steve Gallinger, Alex J. Cornish, Amit Sud, Jayaram Vijayakrishnan, Margaret Wrensch, Mattias Johansson, Aaron D. Norman, Alison Klein, Alyssa Clay-Gilmour, Andre Franke, Andres V. Ardisson Korat, Bill Wheeler, Björn Nilsson, Caren Smith, Chew-Kiat Heng, Ci Song, David Riadi, Elizabeth B. Claus, Eva Ellinghaus, Evgenia Ostroumova, Florent de Vathaire, Giovanni Cugliari, Giuseppe Matullo, Irene Oi-Lin Ng, James R. Cerhan, Jeanette E. Passow, Jia Nee Foo, Jiali Han, Jianjun Liu, Jill Barnholtz-Sloan, Joellen M. Schildkraut, John Maris, Joseph L. Wiemels, Kari Hemminki, Keming Yang, Lambertus A Kiemeney, Lang Wu, Laufey T Amundadottir, Marc-Henri Stern, Marie-Christine Boutron, Mark Martin Iles, Mark P. Purdue, Martin Stanulla, Melissa Bondy, Mia Gaudet, Lenha Mobuchon, Nicki J. Camp, Pak Chung Sham, Pascal Guénel, Paul Brennan, Philip R. Taylor, Puya Gharahkhani, Quinn Ostrom, Rachael Stolzenberg-Solomon, Rajkumar Dorajoo, Richard Houlston, Robert B Jenkins, Sharon Diskin, Sonja I. Berndt, Spiridon Tsavachidis, Stefan Enroth, Stephen J. Chanock, Tabitha Harrison, Tessel Galesloot, Ulf Gyllensten, Vijai Joseph, Y Shi, Wenjian Yang, Yi Lin, Stephen K. Van Den Eeden, Maria Carolina Borges, Kimberley Burrows, Rozenn N. Lemaitre, Sean Harrison, Stephen Burgess, Xuling Chang, Jason Westra, Nikhil K. Khankari, Kostas Tsilidis, Tom Gaunt, Gibran Hemani, Jie Zheng, Therese Truong, Tracy O’Mara, Amanda B. Spurdle, Matthew H. Law, Susan L. Slager, Brenda M. Birmann, Fatemeh Saberi Hosnijeh, Daniela Mariosa, Christopher I. Amos, Rayjean J. Hung, Wei Zheng, Marc J. Gunter, George Davey Smith, Caroline Relton, Richard M. Martin, Philip C. Haycock.https://doi.org/10.5523/bris.aed0u12w0ede20olb0m77p4b9. Relevant approvals were obtained by each of the previously published studies. An ethics statement for each of the included GWAS can be found in This work used summary data from previously published GWAS or summary data from GWAS conducted in UK Biobank under application number 15825 and deposited at dyad018_Supplementary_DataClick here for additional data file.


Sparus aurata) has long been considered resistant to viral nervous necrosis (VNN), until recently, when significant mortalities caused by a reassortant nervous necrosis virus (NNV) strain were reported. Selective breeding to enhance resistance against NNV might be a preventive action. In this study, 972 sea bream larvae were subjected to a NNV challenge test and the symptomatology was recorded. All the experimental fish and their parents were genotyped using a genome-wide single nucleotide polymorphism (SNP) array consisting of over 26,000 markers.The gilthead sea bream (r) of the predicted estimated breeding values (EBV) provided by three Bayesian genomic regression models  were consistent and on average were equal to 0.90 when assessed in a set of cross-validation (CV) procedures. When genomic relationships between training and testing sets were minimized, accuracy decreased greatly . Classification of the phenotype using the genomic predictions of the phenotype or using the genomic predictions of the pedigree-based, all data included, EBV as classifiers was moderately accurate .Estimates of pedigree-based and genomic heritabilities of VNN symptomatology were consistent with each other : 0.1–0.4; 0.19, HPD95%: 0.1–0.3, respectively). The genome-wide association study suggested one genomic region, i.e., in linkage group (LG) 23 that might be involved in sea bream VNN resistance, although it was far from the genome-wide significance threshold. The accuracies (The estimate of the heritability for VNN symptomatology indicates that it is feasible to implement selective breeding programs for increased resistance to VNN of sea bream larvae/juveniles. Exploiting genomic information offers the opportunity of developing prediction tools for VNN resistance, and genomic models can be trained on EBV using all data or phenotypes, with minimal differences in classification performance of the trait phenotype. In a long-term view, the weakening of the genomic ties between animals in the training and test sets leads to decreased genomic prediction accuracies, thus periodical update of the reference population with new data is mandatory. Viral nervous necrosis (VNN), known also as viral encephalopathy and retinopathy (VER), is one of the most important diseases in aquaculture. VNN is endemic in the Mediterranean area  and the The causative agent of VNN is Nodavirus [or nervous necrosis virus (NNV)], a non-enveloped icosahedral virus of the Nodaviridae family, genus Betanodavirus. In fish, four Betanodavirus genotypes have been detected according to a partial nucleotide sequence of the coat protein gene: striped jack nervous necrosis virus , tiger puffer nervous necrosis virus (TPNNV), barfin flounder nervous necrosis virus (BFNNV) and red-spotted grouper nervous necrosis virus (RGNNV) .Sparus aurata) has long been considered not susceptible to VNN and a potential asymptomatic vector of the infection [Gilthead sea bream , significant additive genetic variation for VNN resistance has been reported –15, whicPhenotyping for disease resistance based on challenge trials is difficult, due to the complexity of experimental infections, and limited to the relatives of breeding candidates due to biosecurity reasons. In this context, the implementation of genomic selection procedures is of great interest and is made feasible by the recent availability of high-density marker arrays for sea bream . GenomicTesting the consistency of prediction equations in a long-term view is of great importance: in commercial breeding, genomic models are first trained using a reference population consisting of full- and half-sibs of the candidate breeders. Random k-fold CV have been demonstrated to provide overly optimistic genomic prediction accuracies –25, thusTo date, in gilthead sea bream, neither the genetic variation in nor the genomic prediction accuracy for VNN resistance have been investigated. Hence, the aims of this study were to estimate the genetic and genomic parameters of VNN resistance in challenged gilthead sea bream larvae, to estimate the effects of single nucleotide polymorphisms (SNPs) on the VNN resistance phenotype through a genome-wide association study (GWAS) and to assess the accuracy of genomic predictions of the trait or EBV using a novel SNP array for sea bream. The accuracy of genomic prediction models was assessed using both random CV and two validation settings that mimic a reduction of the genomic relatedness between training and testing sets: a CV based on genomic clustering and a leave-one-family-out validation focused on the parents of the challenged fish.7.80 TCID50/mL) (TCID50 for median tissue culture infectious dose 50%) was added to the tank. The final infectious titre, obtained by back titration of the water, was 105.45 TCID50/mL. Larvae were exposed to the same amount of virus at the same time. A few hours after distributing the viral suspension, the water in the tank was discarded and replaced with clean water. Fish were checked three times a day to identify individuals showing typical clinical signs of VNN . As soon as a symptomatic larva was detected, it was sacrificed and stored in 96% ethanol for subsequent genomic DNA extraction. The experiment ended 14 d post-challenge and non-symptomatic larvae were sacrificed and stored in 96% ethanol. Overall, 1229 symptomatic and non-symptomatic larvae were collected.The experimental fish were produced in a commercial hatchery  in three independent full-factorial mating designs . At 16 d post-hatching, nearly 1300 sea bream larvae, NNV-free tested, were transferred to the Istituto Zooprofilattico Sperimentale delle Venezie . Larvae were distributed into one close-system 250-L tank filled with artificial saltwater at a salinity of 35–37‰ , a temperature of 19 ± 2 °C and oxygen 6–8 ppm, and exposed to an artificial photoperiod of 8 h of light and 16 h of darkness. After five days of acclimation, larvae were infected by immersion using the reassortant betanodavirus strain (RGNNV/SJNNV) isolated from animals belonging to a fish farm during a disease outbreak (VNNV/S.aurata/Farm1/461-1/Nov2014) [The experimental infection protocol was evaluated by the Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe) Animal Welfare Body and Ethics Committee (Opinion CE.IZSVE.3/2016 of 24/10/216) and approved by the Italian Ministry of Health (Law decree 101/2017-PR of 02/02/2017).Genomic DNA (gDNA) was extracted from the whole larva using a modified salting-out protocol described by Pardo et al. . gDNA cop < 0.001) were discarded. Missing genotypes were imputed using the FImpute software [Genotyping of the experimental fish and their parents was performed at IdentiGEN (Ireland) using the MedFish SNP array  which cosoftware , resultiThe APIS/R  softwareThe Kaplan–Meier product-limit survival curve of the challenged fish was estimated using survival/R .Estimation of the variance components, parameters, and prediction of breeding values were based on the solution of a threshold (probit) animal model. In such a model, the observed binary phenotypes for VNN symptomatology  were assumed to depend on the value of an unobservable and normally distributed latent variable The model used to interpret the variation in the liability was:Marginal posterior distributions of the genetic variance in VNN symptomatology were estimated using a Bayesian approach employing Monte-Carlo Markov chain (MCMC) and the Gibbs sampler, as implemented in the software TM . A singlHeritability of VNN symptomatology was estimated as:software .Estimated breeding values for VNN symptomatology were obtained using Model , using aGenomic variance and genomic heritability for VNN symptomatology were estimated with Bayesian procedures as implemented in BGLR/R  and follmentclass2pt{minimThree regression models were implemented: Bayes B, Bayes C  and BayeA GWAS was performed to test the association between the VNN symptomatology phenotype and SNP genotypes  using gaston/R  followin ggplot2 .Genotypes at 26,591 SNPs were used as predictors of the phenotype or pseudo-phenotypes (EBV) for VNN symptomatology. Three Bayesian regression models were fitted to the data using BGLR/R : Bayes BEach Bayesian analysis was carried out generating a single Gibbs chain of 800,000 samples, with a burn-in of 100,000 iterations and a thinning interval of 50 samples.In the analysis of the phenotype for VNN symptomatology, the probit function was used as a function to link the probability of each category  to the linear predictor, whereas a linear model was used for pseudo-phenotypes (EBV); the residual variance was set to 1.Prediction performance of the Bayesian models was assessed in three CV strategies: (a) a fourfold random CV, (b) a fourfold CV based on genomic clustering (K-means clustering), and (c) a leave-one-family-out (LOFO) validation, focused on the parents of the challenged animals. These strategies were used to mimic three genomic selection scenarios, where the genomic prediction equations are obtained by training models with information from a reference population that could be more closely (random CV) or more distantly (K-means clustering and LOFO) related to the animals to be predicted.In the first strategy (a), the dataset consisting of EBV that were obtained according to the methodology described above and Model , was ranThe accuracy in the estimation of the additive genetic value, which depends on the accuracy of EBV and on the accuracy of genomic-predicted EBV, was calculated as in :6\documeIn the second strategy (b), the K-means method clustered the animals according to distance of genomic relationships, reducing the relatedness between training and testing populations while maximizing the relatedness within each cluster. K-means clustering was performed using the function kmeans (stats/R) , which iClusters were used one at a time as a test set. As in (a), pseudo-phenotypes (EBV obtained according to the methodology described above and Model ) of the In the third procedure (c), the EBV obtained according to the methodology described above and Model , were reDifferent classifiers of the phenotype for VNN symptomatology were tested, including the EBV, the genomic predictions of the phenotype and of the EBV provided by Bayesian models. Classification performance was assessed in a set of five independently generated fourfold CV. For each CV, three metrics were used to evaluate the performance for the classification of the phenotype for VNN symptomatology: the area under the receiving operating characteristics (ROC) curve (AUC), the classification accuracy (ACC) computed as (true positives + true negatives)/total number of samples, and the Matthews correlation coefficient (MCC). All the metrics were computed using the package ROCR/R .The Kaplan–Meier product-limit survival curve for the challenged fish is reported in Fig. The median of the marginal posterior density for the additive genetic . The GWAS did not identify any SNP exceeding the genome-wide significance level  equal to 1.01 Table . The comThe prediction accuracy decreased to 0.53 when the genomic relatedness between training and testing sets was minimized (K-means clustering) and dropped remarkably (0.12) when the most penalizing validation (LOFO), which is able to break down the family linkage disequilibrium due to the experimental design, was applied  exhibited an AUC of 0.905, an ACC of 0.864 and a MCC of 0.730. The classification of the observed symptomatology based on the genomic-predicted phenotype resulted in moderate performances for all methods, showing AUC between 0.581 and 0.601, ACC between 0.569 and 0.583, and MCC between 0.175 and 0.178  and of small size compared to previous studies  –48. In sDue to the small size of the fish, the experimental infection was carried out by immersion which, when compared to infection by injection, leads to outcomes which depend on the same defence mechanisms as those activated in natural disease outbreaks. The limited size of the experimental tank guaranteed an equal level of exposure to the virus for all animals, which is important when assessment of across-family differences in resistance is the key aspect of the study.The mortality rate in our study 46.1%) was comparable to that reported in sea bream of similar age experiencing natural infections (40%) . Previou.1% was cPenaeus vannamei) for susceptibility and endurance to taura syndrome [Gadus morhua) for susceptibility and endurance to VNN and vibriosis [Frequently, investigations on the genetic variation in disease resistance are focused only on mortality assessed in challenge tests, where fish are classified as dead or survived after a controlled viral, bacterial or parasitic infection. In future studies, it could be worth disentangling the genetic variation of the different mechanisms implied in survival, as already performed in pacific white shrimp  [The heritability estimated for VNN symptomatology 14–0.38) –48, 50 a–0.38 –48r; 0.25) , 51, 52.r; 0.25) , they alThe genomic heritability estimate , but, since this strategy may not be informative in the long-term due to the disruption of family-based linkage disequilibrium, two other validation strategies (K-means clustering and LOFO) were tested to mimic more realistic scenarios where the genomic ties between animals in the training and testing sets due to close relationships, as those occurring in full- and half-sib families, were weakened. As expected, both K-means clustering and LOFO accuracies decreased in comparison with that estimated in random fourfold CV. Although in the K-means clustering strategy the accuracy remained moderate, suggesting that genomic selection could be feasible in a mid-term view, in the LOFO validation strategy the inflection was marked (r = 0.12). Such differences across validation strategies may also be ascribed to the use of the EBV as pseudo-phenotypes. Due to the mating design and the moderate heritability of VNN symptomatology, EBV include a family component, which, in our case, represents the largest proportion of the EBV variance. The genomic prediction accuracies obtained in the CV procedures, where the animals included in the training and testing sets were randomly chosen, are expected to be high because many sibs are in both validation and training sets and family means are predicted with family information. When family data are not considered in the validation procedure, as in LOFO and, to a lower extent, in K-means clustering, the accuracy of the genomic prediction is expected to be low. Even with individual phenotypes, the difference between validation strategies would have been observed, but it would have been more limited.In our study, the accuracy of genomic prediction of EBV provided by random fourfold CV was high  the creation of a breeding nucleus which develops the élite broodstock, (2) the design of breeding strategies to produce both full-sib and half-sib families, (3) the development of a high-depth pedigree database including all the information of breeding candidates and tested fish, and (4) the exploitation of genomic information, which can be combined to the aforementioned information to accurately select the breeding candidates.The estimated heritability for VNN mortality indicates that selective breeding programmes are feasible for increasing resistance to VNN of fish larvae/juveniles, which would overcome the problem of vaccination. Traditional selective breeding approaches for VNN resistance are expected to have a limited efficiency, mainly due to the low accuracy of the estimated breeding values of the breeding candidates which, for sanitary reasons, cannot be included in VNN challenge tests. The results of the GWAS suggest a complex polygenic nature for VNN resistance with many genomic regions each with a small effect on the phenotypic variation of the trait. In view of the implementation of genome-enabled selection in sea bream to enhance resistance to the NNV reassortant strain, the accuracies of the genomic predictions were high when assessed in CV. However, the application of validation procedures, which can cancel the favourable effects of family-based linkage disequilibrium that are determined by the experimental design, decreased dramatically the estimated accuracies. This raises concerns on the use of factorial mating design paired with CV as a tool to assess genomic prediction accuracies and emphasizes the need of the periodical update of the training set with new data to keep prediction accuracies at a satisfactory level in the case of populations where, like the one investigated in this study, genomic relationships across families are weak. Further studies should focus on disentangling the genetic variation in the different mechanisms that have a role in VNN survival, such as susceptibility and endurance, rather than recording only mortality of experimentally-infected animals.


Prader–Willi Syndrome (PWS) is a genetic neurodevelopmental disorder that is caused by either the deletion of the paternal allele of 15q11-q13, maternal uniparental disomy of chromosome 15 or defects in the chromosome 15 imprinting centre and is characterized by cognitive impairment, hyperphagia and low metabolic rate with significant risk of obesity, as well as a variety of other maladaptive behaviours and autistic spectrum disorder (ASD). Many of the features seen in PWS are thought to be due to hypothalamic dysfunction resulting in hormonal abnormalities and impaired social functioning. The preponderance of evidence indicates that the Oxytocin system is dysregulated in PWS individuals and that this neuropeptide pathways may provide promising targets for therapeutic intervention although the process by which this dysregulation occurs in PWS awaits mechanistic investigation. PWS individuals present abnormalities in thermoregulation an impaired detection for temperature change and altered perception of pain indicating an altered autonomic nervous system. Recent studies indicate that Oxytocin is involved in thermoregulation and pain perception. This review will describe the update on PWS and the recent discoveries on Oxytocin regulation of thermogenesis together with the potential link between Oxytocin regulation of thermogenesis and PWS to create a new groundwork for the treatment of this condition. Oxyll force . Oxtr−/−ogenesis ,9,10. Thsumption . Cold exsumption  since insumption . This absumption . PWS indsumption . Indeed sumption ,18,19, asumption . Finallysumption . In thisPWS is a rare complex genetic disorder arising from the lack of expression of paternally inherited genes on chromosome 15q11-q13 due to the deletion of the paternally inherited region (delPWS) or maternal uniparental disomy, where both copies are inherited from the mother (mUPD), or to an imprinting defect due to microdeletions or epimutations and paternal chromosomal rearrangements such as translocations . Magel2 −/− mice have lower adrenalin levels than control [−/− mice needs to be further researched. Indeed, Oxt plays an essential role in glucose metabolism but the lack of research about Oxt and insulin resistance in PWS prevents further analysis. Moreover, the underlying mechanisms by which Oxt influences glucose metabolism in PWS are unknown [PWS individuals have a dysfunctional Oxt system with less Oxt-producing neurons in the PVN and a decreased Oxtr gene function which impairs the satiety response. Specifically, in adults with PWS the number of Oxt-expressing neurons in the hypothalamus was decreased and plasma level of Oxt is reported altered and, in some cases, low in relation to their obesity. However, in children between 5 and 11 years of age, plasmatic Oxt is reported higher than in healthy siblings . This is control , but whe unknown . However unknown . The aut unknown . In thisPharmacological interventions in which Oxt was given intranasally to PWS patients are focused on reducing mostly symptoms of hyperphagia, reducing BMI and improving anxiety and behavior in general. The activity of Oxt neurons located in the hypothalamus is involved in feeding behavior in animal studies and could also be altered in PWS patients during food intake. PWS individuals have high circulating Oxt, and, given the anorexic effect of Oxt, it seems plausible that diminished Oxt activity could even worsen hyperphagia, delaying the satiety response . TogetheIn conclusion, the best results with IN-Oxt are obtained in infants, toddler and sometimes in adolescents ,38,39, a−/− mice have impaired thermoregulation, resulting in thermosensory deficit and the incapacity to maintain a stable temperature. PWS and ASD individuals are hyposentive or hypersensitive to hot and cold and this seems to be related to the dysfunctional Oxt system that can lead to atypical sensory processing. Conversely, the atypical sensory understanding of the surrounding environment could lead to altered socio-cognitive behaviors [Thermogenesis is the physiological response to thermoregulation, leading to heat production and retention under CS, while thermosensitivity to external temperatures begins at the skin level where different types of thermoreceptors are expressed allowing not only humans to differentiate between cold, warm and hot. Oxt regulates thermogenesis since Oxt/Oxtr signaling is a modulator of the thermogenic response to cold exposure by controlling BAT thermogenesis and energy expenditure by muscle contraction . Oxt/Oxtehaviors . Thermorehaviors . Moreoveehaviors . In anotehaviors . Finallyehaviors . This ev−/− mice show late-onset obesity but are normophagic for reduced metabolic rate [Oxt/Oxtrlic rate . For thilic rate ,43,44. Flic rate ,11,45. Alic rate ,11. Oxt lic rate ; howeverlic rate , as showlic rate . In the lic rate ,47.Shivering thermogenesis is the involuntary contraction of skeletal muscles during cold exposure to generate heat. This process requires a great amount of energy and ATP  to sustaThe Transient-Receptor-Potential-Vanilloid-1 (TRPV1) cation channel is a thermal and analgesic effector that interacts with Oxt/Oxtr mediating the Oxt analgesic effects ,61. SpecThe genetic studies described above  were furThe Oxt system in PWS appears dysfunctional. The main physiological features of PWS are reduced gene expression and density of Oxtr in PVN, as shown by post-mortem analysis , and incIn our model of CS, where the Oxt system is functional, low temperature increased Oxtr in PVN and Sol and decreased circulating Oxt . This mePWS children have disrupted thermoregulation that can lead to hyperpyrexia and hypothermia with no detectable causes and that can be fatal ,19,41. TThe question arises if Prader–Willi Syndrome is an Oxytocin disease, albeit the lack of positive results from clinical trials suggest that simply providing exogenous Oxt may not compensate for the dysfunctional Oxt system. The high level of circulating Oxt detected in PWS individuals is in contrast with the appetite suppressant effect of Oxt and can be explained by the establishment of a sort of “Oxt resistance”, as seen with leptin . At leasIn summary, we need to develop a better understanding of Oxt abnormality in PWS also in light of the new discoveries on Oxt regulation of thermogenesis that will hopefully lead to more targeted and effective interventions. Of note are also the latest results on the effects of Oxt on muscle contraction that require better investigation in the specific muscle phenotypes of PWS individuals. Oxt’s regulation of thermogenesis and Oxt-driven muscle contraction are well related, especially in the manifestation of PWS. We hope that our study will soon translate into clinical research to understand to which extent Oxt is involved in PWS.


This constellation of variables impacts the expression of countless genes. Numerous studies have uncovered changes in DNA methylation (DNAm), a form of epigenetic regulation, in offspring related to maternal risk factors. How these changes work together to link maternal-child risks to childhood cardiometabolic and neurocognitive traits remains unknown. This question is a key research priority as such traits predispose to future non-communicable diseases (NCDs). We propose viewing risk and the genome through a multidimensional lens to identify n = 15,454, Avon Longitudinal Study of Parents and Children (ALSPAC)). Using cord blood HumanMethylation450 BeadChip data, we identified genome-wide patterns of DNAm that co-vary with these MRPs. We tested the prospective relation of these DNAm patterns (n = 914) to future outcomes using decision tree analysis. We then tested the reproducibility of these patterns in (1) DNAm data at age 7 and 17 years within the same cohort  and (2) cord DNAm in an independent cohort, the Generation R Study (n = 686).We identified multifactorial Maternal Risk Profiles (MRPs) generated from population-based data (We identified twenty MRP-related DNAm patterns at birth in ALSPAC. Four were prospectively related to cardiometabolic and/or neurocognitive childhood outcomes. These patterns were replicated in DNAm data from blood collected at later ages. Three of these patterns were externally validated in cord DNAm data in Generation R. Compared to previous literature, DNAm patterns exhibited novel spatial distribution across the genome that intersects with chromatin functional and tissue-specific signatures.To our knowledge, we are the first to leverage multifactorial population-wide data to detect patterns of variability in DNAm. This context-based approach decreases biases stemming from overreliance on specific samples or variables. We discovered molecular patterns demonstrating prospective and replicable relations to complex traits. Moreover, results suggest that patterns harbour a genome-wide organisation specific to chromatin regulation and target tissues. These preliminary findings warrant further investigation to better reflect the reality of human context in molecular studies of NCDs.The online version contains supplementary material available at 10.1186/s12916-023-03168-z. Identifying the impact of maternal risk factors on foetal health and child outcomes is challenging because each mother–child dyad represents a unique combination of genetic, environmental, and random chance effects. This complexity accounts for why identical twins demonstrate differences in health outcomes or why foetal alcohol exposure can have a broad spectrum of features rather than a single phenotype. Moreover, the range and complexity of maternal risk factors that impact the foetus go beyond direct in utero effects and can include intergenerational effects .Regardless of the sources or types of risk, research suggests that such multifactorial risk exposures set off a ripple effect across the human genome, impacting the regulation of a multitude of biological pathways . It is b, theoretical and empirical evidence suggests that DNAm can provide insight into an individual’s past experiences.Most research attempts to pinpoint single or neighbouring clusters of CpG sites associated with a given risk or outcome, such as smoking or birth weight. Researchers now venture to connect these points across multiple sites on different chromosomes. For instance, Elliott and colleagues (2014) generated a smoking methylation score in adults that combined 183 CpG sites to distinguish never and former smokers from current smokers. This methylation score separated these two categories with 100% sensitivity and 97% specificity in a male population of European descent. When multiple sites are linkedEpigenetic analyses that focus on discrete risk exposures and molecular sites create models that are easier to understand and analyse, but these models do not reflect the clinical reality of non-communicable diseases (NCDs). Each person experiences a unique array of risks whose genomic targets are interactive and multidimensional in nature. Hence, we propose to view risk exposure and the genome as interconnected parts of an engine that drives the trajectory of health. This proof-of-concept study tests this idea of integrating context into statistical models using basic multidimensional methods.To begin, we asked how to address the challenge of examining the phenomenon of unique risk exposures in each individual across a population. We decided to develop a maternal risk profile (MRP) centred around the maternal behaviour of smoking while pregnant. This health behaviour is a telling marker of stress among physical, mental, and social determinants of disease . SmokingFrom a physiological perspective, exposure to MRP may behave like other stresses and have a normative range within the general population. Therefore, we used multiple variables related to maternal smoking in previous literature to generate a population-based MRP. This reduces the chance of drawing conclusions that only pertain to a subset of the population.Next, we considered how to view the molecular components. A multivariate model of associated DNAm changes may be particularly applicable in the study of NCDs. These diseases are etiologically distinct from processes like cancers that typically can be traced to single molecular events. NCDs tend to arise from many small and dynamic cellular changes that lead to an overall shift in the cellular and physiological state of the whole person. These multiple small changes reflect the interplay of genes, environment, and random chance.We theorise that coordinated shifts in genome-wide DNAm (gwDNAm) could be the common mechanistic link between individuals who encounter different maternal influences yet share similar phenotypic traits. We tested this by seeking recurring patterns of gwDNAm in cord blood that relate to population-based MRPs. In this way, gwDNAm patterns represent population-normalised molecular signatures of risk exposure. We investigated how these gwDNAm patterns may relate to the development of NCDs by observing their relation to anthropometric and neurodevelopmental child outcomes. We repeated this analysis over time as children aged and in an independent cohort. We outline our analysis workflow in Fig. Model discovery was conducted in the population-based ALSPAC longitudinal cohort. This initial cohort collected data on 14,541 pregnancies and 14,676 foetuses in the former county of Avon, United Kingdom (UK) with expected delivery dates from 1 April 1991 to 31 December 1992. This cohort has been extensively described , 18 withTo develop a multidimensional risk profile from the viewpoint of foetal programming, we apply a context-based approach that uses population-based data to integrate multiple MRP-related variables. The normative values for blood pressure in children exist as continuous ranges that are based on sex, age, and height percentiles. Similarly, we regard to foetal risk exposure as a continuous and interdependent variable. The risk factors traverse gestational , family , and social effects . These data were gathered from maternal self-reports and linked clinical records. z-scores (sex-specific) using data from the entire ALSPAC cohort unless otherwise specified. To control for the impact of early weight and length accrual on later life physical outcomes, individual infant growth trajectories calculated for this cohort using multilevel modelling were included in relevant models [We selected outcomes based on their relevance to maternal smoking exposure Table  as identt models . At age ARIES is a sub-study of ALSPAC child-mother pairs with available DNAm data. Blood samples were collected at three time points: birth (cord blood) and at ages 7 and 17 years . At lateThe Generation R Study (GenR) is a population-based prospective pregnancy cohort study. It included 9778 women and their children born between April 2002 and January 2006. We used cord blood 450K BeadChip DNAm data from a subgroup consisting of 969 children of European descent described in detail . In totaIn part one of the study see Fig. , aqua, wMuch like the clinical observation of a patient, component-based analysis enables us to view prenatal risk exposure as multifactorial and existing on a continuous normal distribution. Since we link gwDNAm patterns to MRPs, inferences about DNAm are made relative to the whole population. This diminishes the risk of drawing statistically flashy results that only exist in the subset of individuals with DNAm data.In part two of the study Fig. , purple,Random forests (RFs) are a type of decision tree analysis that generates thousands of trees through an algorithm that randomly shuffles data values. The collation of these results is akin to the consensus of thousands of doctors who have each encountered thousands of unique patients. This inherent property of RF diminishes overfitting to a specific subset of a population. Further details on Parts 1 and 2 of our analysis are discussed below and in Additional file We used component-based analysis to extract MRP-related DNAm patterns in three steps. We summarise these steps  below.FactoMineR R-package [imputeFAMD function. The best-fit model included eight variables: birth weight, maternal smoking during pregnancy, household smoking, maternal grandfather’s history of smoking, maternal partner’s smoking history, maternal grandmother’s history of smoking, maternal history of smoking, and grandmother’s smoking while pregnant with mother, hereafter referred to as MRP-related variables  method, a principal component-based technique, as described in the -package . We refees Table . The detsPLS function in the sgPLS R-package. The objective of PLS modelling is to predict MRP with DNAm data , the model can estimate the likelihood a real variable will exceed the importance of a shadow variable. Thus, shadow variables introduce additional randomness to the model, making it more robust to random chance or correlations that can lead to false positives. We included the following previously associated covariates in all models: sex, maternal educational level, paternal social class, and in models using DNAm data, blood cell type proportions [We used the Boruta algorithm (from R-package of the same name) to select all variables relevant to an outcome . This isportions –34.caret R-package [caret R-package, [R2). In this way, we compared the predictive performance of models using DNAm data, MRPs, and MRP-related variables. We further tested the performance of models using DNAm data from peripheral blood and GenR cord blood.We calculated model metrics using the -package . We calcpackage,  ntree = All models included sex, maternal education, and paternal social status as covariates. We selected the latter two variables among other social confounders after evaluation of multicollinearity and data loss due to missingness  0.1) and 17  years in 914, 973, and 974 children, respectively. Previous studies comparing the baseline characteristics of families in the ALSPAC versus the ARIES datasets found that the ARIES mothers were older, reported less maternal smoking, attained a higher educational level, and were more likely employed in non-manual labour , 37. ChaWe performed factor analysis to represent the MRP-related variables as a composite score see the “. Thus, cEach of the first two dimensions captured nearly 20% of total data variability. We can evaluate the risk represented by each dimension by viewing its most prominent contributory variables  at age 10. Early growth rates (i.e. before age 36 months) were the top-ranked variables in all three models. In Model 1, we observed that smoking-related variables were not ranked as relevant. This was consistent with traditional linear models . This was the general observation across various outcomes, except for two academic performance measures , as well as Dimension 1 and Component 9 (blue boxes), were selected in Models 2 and 3, respectively. These Dimension/Component pairs are correlated in Fig. In Step 2-B, we compared the performances between Models 1, 2, and 3. The differences between the three models were minor Fig. —inset, sWe first review results from Model 3 using DNAm data collected at birth from cord blood. Component 9 was one of the most consistently relevant components among cardiometabolic outcomes  Raw—using all variables and only complete (i.e. no missing data) observations; 2) Boruta—using only Boruta selected variables and only complete observations; and (3) Imputed—using all variables and all observations (i.e. missing data was imputed). As an example, Additional file  Boruta—uWe examined whether DNAm patterns could be observed in children as they aged and in another cohort. Previous literature has shown that complex trait-related DNAm variability in cord blood has small effect sizes in the 1–2% range , a levelThe performance of models using DNAm data at older ages was similar compared to those using cord blood  at age 6 years  in thousands of post hoc multiple hypothesis tests, such as performed in an epigenome-wide association study (EWAS).We took a more detailed look at the genomic localisation of gwDNAm patterns. PLS enables the empirical selection of DNAm sites that most consistently and strongly represent MRPs  DNase I hypersensitivity sites (DHSs) and 2) promoter-anchored chromatin interaction (PAI) and chromatin loop sites. DNAm locations that spatially intersect with these two features are positioned in a 3D chromatin environment that is open and conformationally poised to interact with gene regulatory factors. Sites representative of gwDNAm patterns were enriched for DHSs and PAI/loop features . Given that the 450K BeadChip was designed to interrogate DNAm at well-annotated genes, enrichment in such annotations would be unsurprising. We wondered whether the enrichments observed were simply due to the bias of the 450K BeadChip for certain genes, particularly genes that could demonstrate smoking-related changes but may have no biological effect on future health.n = 6685). The study team identified 2965 CpGs after correcting for multiple hypothesis testing, and 89% of these were associated with gene expression levels.We challenged this query by testing whether genomic sites of a given gwDNAm pattern would still show greater than expected enrichment when directly compared to candidates identified by the largest-to-date meta-analysis of the association between sustained maternal smoking and cord blood DNAm (meta-EWAS) using the 450K BeadChip . This stWe used the XSTREME (meme-suite.org) analysis tool to evaluate enrichment in genomic sequences, called motifs, that exhibit affinity for binding to transcription factors. An enrichment is identified if the interrogated sites  can generate the same or more motifs of equal width and number of occurrences as the background sites (meaning meta-EWAS sites) based on log-likelihood ratios . Most ofq-value < 0.05) by chromatin marks specific to certain cell lineages, often with multiple enrichment clusters falling on the same or embryologically-related tissues  and the curated LOLA database to determine whether gwDNAm patterns localised to tissue-specific chromatin marks . gwDNAm ues Fig. . For insWe first examined the overlap of our DNAm component candidates to the 28 maternal smoking-related CpG sites in cord blood identified in the same ARIES dataset by Richmond et al. . Seven o. overlap with EWAS hits in other cohorts [Confounding from covariates like sex and cell count affect DNAm data distributions differently and can be cohort-specific , 50. Mos cohorts , 51–53. The rise in NCD prevalence and the resources needed to treat their lifelong complications are draining to health systems and societies. Current detection methods require an adequate degree of cellular damage to indicate entry into the diseased state. The most cost-effective means of stemming this global crisis is detection that is generalisable to the whole population yet targets intervention before disease onset. We believe the key to achieving this feat is by considering the complex and context-specific nature of NCDs.In this study, we adopted a view of the early origins of NCDs where (1) foetal risk exposure is a multidimensional and population-normalised entity and (2) gene regulation is a transformer of cell fate that requires numerous coordinated changes across the genome. We believe this is the first identification of patterns of DNAm underlying population-based risk profiles. We employed component-based analysis and model-based imputation to lessen the loss and/or distortion of information due to reliance on single points of data from subjects or variables alike. Importantly, this can reduce the impact of error and/or bias from subpopulation-specific characteristics , 54, 55.As a proof-of-concept, we used MRPs that modelled the risks surrounding why and how mothers smoke while pregnant. We used this constellation of risks to reveal how early experiences influence the root of health during foetal development, branching into tissues and organs as phenotypic offshoots after birth, as hypothesised in DOHaD theory –58. ThisPopulation-based gwDNAm patterns can help capture biological heterogeneity that comes from the interaction of genetic, environmental, and random effects. Decision tree-based analysis has the ability to account for these interactions, revealing relations that are difficult to detect using traditional linear models. This facilitates a more realistic view of interactive, robust, and prospective relations between gwDNAm patterns and future cardiometabolic and neurocognitive outcomes. We tested these models in DNAm data from mid- and late childhood and in an independent cohort. It was remarkable to observe the persistent association of DNAm to child phenotypes regardless of data influences such as developmental maturation, technical artefacts, and data set-specific variability .SLC6A4 gene. They first found an association between COVID-19-related stress and methylation and, in a subsequent model, found the latter associated with only one out of three measured factors of infant behaviour. Most other studies use retrospective data or cannot capture foetal effects because of risk assessment or tissue collection at later ages .R2 and related metrics. Moreover, by avoiding the assumption that individuals fit into homogeneous categories, we cannot enhance the reported differential effect between groups. The non-linear and interactive effects detected by decision tree analysis are difficult to translate into straightforward results of causal, mediating, or dose–response effects, which are more familiar to clinicians, patients, and researchers [Our aim to view each subject using a multidimensional and unique perspective could be described as “self-defeating” if judged by traditional statistical metrics. For instance, the PLS methodology seeks shared information between two datasets and does not maximise the variability explained. This directly undermines earchers .DNAm data is also expensive and more invasive than routine history and physical measurements. However, we believe the value of gwDNAm patterns lies in how they may connect to real-life biological context. For example, the genome-wide distribution of these patterns intersects with various features that are integral to interactions with regulatory elements  and the 3D topology of chromatin (e.g. chromatin loop domains). This suggests that gwDNAm patterns have a spatial organisation linked to the functional units of cell fate decisions –74. Thisrelative to all other CpG sites across the genome to uncover consistent and coordinated patterns among individuals. Thus, DNAm sites within a given pattern are inherently related by design. Genotype-dependent methylation must act in magnitude and direction synchronously within gwDNAm patterns to give consistent false positive associations. If this biased the extraction of MRP-related gwDNAm patterns, then this same bias must also align itself to relate to outcomes across data sets spanning time, populations, and cardiometabolic and neurocognitive phenotypes. If that were the case, we reason that there should be far more consistent relations between differential methylation and child outcomes in the current literature [It is possible that genotype-dependent methylation is responsible for our findings. However, we note that each gwDNAm pattern uses the magnitude and directionality of methylation at a given site terature .within patterns reveals differences between patterns. We speculate these differences may underlie distinct processes such as cell- or stimuli-specific biological signalling. This was suggested by the degree of specificity in DNAm patterns, such as for certain tissues  and with Components 9 and 11 (which were related to child outcomes). A similar scenario was observed for sites annotated to GFI1 and AHRR genes. One interpretation of these observations is that DNAm at these sites is confounded and should be discarded. However, we speculate that methylation (like for CYP1A1) is relevant to clinical outcomes depending on their interactions with genetic and epigenetic mechanisms at other sites that will vary in each individual. As such, there are instances where differential methylation at these same sites may be driven by “bystanders”, such as sex or cell composition that may covary or even interact with the exposure but do not lie at the biological root underpinning the phenotype. In other words, there may be specificity in the function of DNA methylation based on a broad scope of clinical and, thus, molecular contexts.CpGs representing a gwDNAm pattern are also conceptually different from differential DNAm sites identified in EWAS research because the latter have no innate relational organisation. This connectivity of sites ues Fig.  and for Previous literature has identified thousands of maternal smoking-related DNAm sites, many with theoretically plausible links to biological phenotypes. Compared to these , gwDNAm patterns were implicated in novel genomic sites Fig.  and tranA disease-based bias currently exists, as the vast majority of gene annotations originate from repositories collected for cancers, rare diseases, or diseases mainly relevant to populations of European ancestry . These pSLC6A4) and mental health disorders [We anticipate that a context-based approach may complement traditional research findings by helping to explain why some DNAm sites that have received intense research focus  have provided no clinical traction in improving the management of NCDs , 78. Theisorders ), our wotowards context-based design and away from the use of discrete categories that invite stereotype-based labels.Instead of interpreting these findings to suggest any degree of determinism conferred by life exposures, we believe they demonstrate the potential to uncover common molecular pathways underlying diverse vulnerabilities to risk. This capability will help the scientific and medical communities to foster resilience on a population scale. Further, our aim in exploring a context-based approach was to find a means to realistically model the intersection between genetic, environmental, and random chance effects that underlie maternal-foetal health. The uncovered models of gwDNAm patterns demonstrate reproducibility and specificity. As well, they probe novel interactions and functions among networks of DNAm sites that may offer insight into pathways to future health. We believe such efforts better harmonise science and bedside medicine, shifting the field We used DNAm data from blood cells. The extent of extrapolation of these findings to target tissues such as adipose and brain cells remains unknown, even though there are clear links between immune function in cardiometabolic and neurocognitive diseases , 86. Thocoordinated patterns of molecular signals—signals that can funnel a wide diversity of risk profiles to a number of shared clinically relevant traits [Our findings are preliminary, but they join a growing body of research arguing for the exploration of t traits –93. The Additional file 1. Methodological details [ Figure S1. Bisulphite-converted DNA plate-related components obtained using normFact R function  on cord ARIES DNAm data. Figure S2. Correlation matrix of maternal risk profile variables. Figure S3. Graphical representation of the decomposition of data into two sparser matrices. details –142. FigAdditional file 2. Supplementary results [ Figure S1. Scree plot of factor analysis of maternal smoking profiles data. Figure S2. Heat map of confounder variables represented by top 20 singular value decomposition (SVD) components. Figure S3. Correlation matrix of DNAm components and sex-related singular value decomposition principal components. Figure S4. Correlation matrix of DNAm components and singular value decomposition principal components related to social confounders, V13 and V19. Figure S5. Correlation matrix of gwDNAm patterns and estimated cell count proportions. Figure S6. Blood pressure (z-score by sex), systolic and diastolic. Figure S7. Fat mass (z-score by sex) obtained through dual-energy x-ray absorptiometry (DEXA) scanning. Figure S8. Lean mass (z-score by sex) obtained through DEXA scanning. Figure S9. Weight (z-score by sex). Figure S10. Denver Developmental Screening Test – II performance . Figure S11. School performance as assessed on the UK Department of Education scores from standard assessment tests linked to ALSPAC subjects for ages 5-7, 8-11, and 12-14 years. Figure S12. Weschler Intelligence Scale for Children-III (WISC) performance at age 8 years. Table S1. Random forest metrics – a comparison of three models using cord blood DNAm components and waist circumference as the outcome. Figure S13. Replication of gwDNAm patterns at birth– Model testing in peripheral blood at age 7 and age 17. Table S2. Performance metrics comparing models in DNAm data (Model 3) at birth and mid- and late childhood in ARIES. Table S3. Performance metrics of Model 1 (risk-related variables) in ARIES. Figure S14. Component 9 overlaps with DNase I hypersensitivity sites (DHSs) sites more than randomly expected in the genome. Figure S15. Component 7 versus control group: meta-EWAS (44). Figure S16. Component 19 versus control group: meta-EWAS (44). Figure S17. Component 18 versus control group: meta-EWAS (44). Figure S18. Locus overlap enrichment analysis (LOEA) using tissue-clustered DHSs (47) (available from LOLA core database) for meta-EWAS and Components 7, 9 and 18. Figure S19. Locus overlap enrichment analysis (LOLA) using chromatin marks (available from LOLA Roadmap database) for Component 4. Figure S20. All CpG sites overlapping between EWAS candidates from Richmond (48) and each DNAm component. results –149. FigAdditional file 3. List of CpGs representative of gwDNAm Components 1-20.


The prenatal environment can influence the postnatal performance of cattle. Especially in tropical regions, pregnant beef cows may experience nutritional restriction during gestation, which coincides with the season of poor quality and quantity of feed. Thus, it was verified that the offspring of cows subjected to a better gestation environment exhibited better productive and reproductive performances throughout their lives. In terms of genetic merit, it was found that the best animals in a restricted gestational environment are not necessarily the same in a favorable gestational environment. In other words, for each condition of the gestational environment, there are animals specifically suited to perform better. In addition, regions in the genome of these animals responsible for several traits of economic importance in cattle were identified. Thus, for a more efficient selection process, breeders must consider the effect of genotype by prenatal environment interaction and provide adequate management and nutrition care for pregnant cows.The prenatal environment is recognized as crucial for the postnatal performance in cattle. In tropical regions, pregnant beef cows commonly experience nutritional restriction during the second half of the gestation period. Thus, the present study was designed to analyze the genotype by prenatal environment interaction (G × Epn) and to identify genomic regions associated with the level and response in growth and reproduction-related traits of beef cattle to changes in the prenatal environment. A reaction norm model was applied to data from two Nelore herds using the solutions of contemporary groups for birth weight as a descriptor variable of the gestational environment quality. A better gestational environment favored weights until weaning, scrotal circumference at yearling, and days to first calving of the offspring. The G × Epn was strong enough to result in heterogeneity of variance components and genetic parameters in addition to reranking of estimated breeding values and SNPs effects. Several genomic regions associated with the level of performance and specific responses of the animals to variations in the gestational environment were revealed, which harbor QTLs and can be exploited for selection purposes. Therefore, genetic evaluation models considering G × Epn and special management and nutrition care for pregnant cows are recommended. The prenatal environment is recognized as crucial for the development of the bovine fetus and as one of the determining factors of postnatal performance in these animals. Several studies have demonstrated the short- and long-term consequences of developmental programming on growth, reproduction, and offspring health. Some of the most relevant and studied environmental effects during the gestational period include maternal nutrition and thermal environment ,2,3.Maternal nutritional restriction during gestation has been associated with various effects, including reduced growth during the prenatal and postnatal periods ,4,5, decIn tropical regions, beef cattle are predominantly raised in grazing systems and exposed to various stressful climatic variables and parasites. In these regions, pregnant cows commonly experience nutritional restriction during the second half of the gestation period, which coincides with the season with low quantity and quality of forage . TherefoEnvironmental effects in typical tropical beef cattle production systems commonly interact with animal genotypes . HoweverThe present study is designed to identify and characterize the G × Epn on growth and reproduction-related traits in beef cattle raised under tropical conditions. Additionally, we aim to identify genomic regions associated with the level and response in the postnatal performance of the animals to changes in the prenatal environment.The present study considered two different beef cattle databases of the Nelore breed from Brazil. Both Nelore populations underwent routine genetic evaluations within their respective breeding programs with minimal connection. Therefore, independent analyses were conducted.The first dataset comprised information from animals born between 1978 and 2018 in an experimental herd (EXP) maintained by the Instituto de Zootecnia, APTA, in Sertãozinho, São Paulo state. Of these animals, approximately 17% were from the control line , 34% were from the line selected for higher post-weaning weight, 44% were from the line selected for higher post-weaning weight and lower residual feed intake, and 5% of the animals were from the founder herd.The second dataset consisted of a large herd from Agropecuária CFM company (COM) distributed across 12 farms located in the states of Mato Grosso do Sul (49%), São Paulo (45.7%), Bahia (3.7%), and Mato Grosso (1.6%). It is very common for genetic material to be shared among all COM farms, especially in the case of sires, ensuring strong connectivity between farms. The COM animals were born between 1984 and 2019. They were selected over the years for various growth and reproduction-related traits, in addition to utilizing a selection index mainly based on weaning weight, post-weaning weight gain, scrotal circumference, and muscling score.Brachiaria spp., Panicum spp.) and received mineral supplements. The breeding season occurred between October and February and lasted 90 days. Heifers were either artificially inseminated or multi-sire natural serviced. The cow-to-bull ratio ranged from 15:1 to 35:1. Calves were kept with their mothers until approximately seven months of age.The animals were kept In high-quality pastures , around 120 days , and 210 days of age . Scrotal circumference (SC), only available for COM, was measured at approximately 550 days of age. For EXP, the days to first calving (DFC) was obtained for all heifers that entered the breeding season, calculated as the difference between the start date of the breeding season and the date of the first calving. Records of heifers that did not calve were included in the analyses by assigning a projected value. This projected value was determined following Johnston and Bunter [A total of 1561 EXP animals were genotyped, of which 773 were genotyped using the Illumina BovineHD BeadChip , and 788 were genotyped using the GeneSeek Genomic Profiler HDi 75 k . The genotypes of animals genotyped with the lower-density panel (75 k) were imputed to the high-density (HD) panel using FImpute v.3 software . A datasThe objective of this study was not to analyze BW directly; however, this trait was essential for defining the prenatal environment to which the animals were exposed during the entire gestation period. The contemporary group (CG) solutions for BW from each dataset were used as environmental covariates in a reaction norm model in the subsequent stage of the research. The gestational environment was standardized in this study, with a mean of zero and a standard deviation of 1. This standardization allowed for a better comparison and interpretation of the results by eliminating the scale differences between the environmental descriptors of EXP and COM. For EXP, only the CG solutions corresponding to the control line were used in subsequent analyses of all data, as they would more reliably describe the environmental conditions . The CG For the two datasets considered in this study, phenotypes of animals in CG with fewer than five animals, records of animals with an unknown sire or dam, and data exceeding 3.5 standard deviations (except for DFC) above or below the overall trait mean were excluded. For EXP, the CG values for W120 and W210 were defined by selection line, year, and birth month. For SC and DFC, the CG was defined by the selection line and birth year for EXP, and by farm, management group, and birth year for COM. The farm and management group for COM corresponded to those from birth until the time of measurement.p-value for the Hardy–Weinberg equilibrium > 10−5, a call rate > 92% for SNPs, and a call rate > 90% for samples. After quality control, the total number of SNPs was 383,570. The overall description of the data after quality control is presented in Quality control of genomic data was performed, retaining only autosomal single nucleotide polymorphisms (SNPs) with minor allele frequencies > 0.02, the A reaction norm model was applied separately in multiple-trait analyses to each dataset. The adopted animal model can be described as follows:ormation . This apSamples of the posterior distributions of the covariance components were obtained using the GIBBS2F90 program . Chains www.ensembl.org/index.html, accessed on 22 March 2021) and the ARS-UCD1.2 bovine genome assembly [https://www.animalgenome.org/cgi-bin/QTLdb/BT/index, accessed on 22 March 2021; [The effects of markers on the intercept and slope of the reaction norms for each trait were estimated using the weighted single-step GBLUP method (scenario S1 with two iterations) proposed by Wang et al. . The perassembly . Additioch 2021; ) was expThe top 0.5% SNPs in EXP were sampled for each effect and trait in the most unfavorable and favorable environments to illustrate the G × Epn on the studied traits at the SNP level. Thus, the reaction norms of each SNP group were presented along the environmental gradient in terms of the percentage of genetic variance explained. Additionally, 100 EXP and COM bulls (with at least 25 progeny records) were randomly sampled, and their reaction norms were displayed along the scale of gestational environment values.The animals showed a variation in performance across the gestational environment (CG solutions for BW) for all studied traits . The W12The mean genetic correlations between the intercept and slope of reaction norms for direct and maternal effects ranged from −0.073 (DFC) to 0.215 (W120) for the studied traits in the EXP herd . These eHeritability estimates exhibited variations to a greater or lesser extent along the gestational environment for all studied traits and herds . The meaThe genetic correlations for the direct and maternal effects of W120 and W210 showed considerably different estimates from unity between the distant values of the gestational environment . These eThe mean posterior estimates of genetic correlations between W120 and W210 in EXP were consistently high, exceeding 0.77 and 0.89 for direct and maternal effects, respectively . The genThe genomic windows that explained at least 0.5% of the total genetic variance for the level and slope of the reaction norms for direct and maternal effects were spread across all autosomal chromosomes except BTA 17, 27, and 28 . The topFor the slope of the reaction norms of the direct effect , the regAmong all the important genomic regions found, nine were exclusively related to the slope of the reaction norms and explained 6.83% of the genetic variance for the direct effect of W120. For W210, six genomic windows associated exclusively with the reaction norms’ slopes were identified and explained 3.65% of the genetic variance for the direct effect. For the maternal effect, 14 and five genomic windows exclusively related to the slope of the reaction norms explained 9.78% and 3.17% of the genetic variances for W120 and W210, respectively. For DFC, nine genomic windows exclusively related to the reaction norms’ slopes were identified and explained 6.99% of the genetic variance for this component.A substantial SNP × prenatal environment interaction was observed for all the studied traits in the EXP herd . Some SNThe reaction norms of bulls from the EXP and COM herds along the environmental gradient demonstrated a substantial re-ranking of estimated breeding values . Both hiThe primary motivation of the present study was that beef cattle raised in tropical pasture environments, such as those in Brazil, are subjected to breeding seasons ranging from October to February (rainy season in the southern hemisphere). Therefore, calves are typically born between July and October (dry season in the southern hemisphere), which is an appropriate time for early calf care . On the other hand, pregnant cows experience the final stage of gestation during the dry season, when the quality and quantity of forage are typically limited. If appropriate supplementation is not provided during this period, cows inevitably experience nutritional restriction, which can affect the development of their calves in utero. Additionally, some heat stress can be experienced by cows and their offspring, as regions, such as central Brazil  commonly have temperatures above 30 °C even during winter (June to September).The literature evidence indicates that intrauterine growth retardation can lead to slower growth throughout the life of cattle ,4. In thTaking the ratio between the slope and intercept of reaction norms as a measure of the magnitude of G × Epn, SC exhibited low, body weight traits exhibited moderate, and DFC exhibited high G × Epn values. These findings are similar to those reported by Chiaia et al.  for NeloConsistent with the magnitude of G × Epn discussed above, while SC exhibited relatively stable heritability estimates along the environmental gradient, the other traits studied showed substantial variation in their estimates. In this sense, selection responses can be considerably different depending on the prenatal environment to which the animals are exposed. Hay and Roberts  also obsA general trend of higher heritability estimates was observed for extreme gestational environments, while lower estimates were found near the zero environment. This behavior can be explained by at least two reasons. The first factor is the smaller number of observations in extreme environments, which leads to less accurate estimates of genetic parameters at the ends of the evaluated environmental scale. This is evident through the larger standard deviations obtained in the extreme environments. The second factor seems related to the point on the environmental gradient where a higher number of interceptions of linear reaction norms occur. In other words, some animals show an improvement, while others show a decline in terms of genetic merit, as observed by Alves et al. .Except for SC, all genetic correlation estimates for within-trait direct and maternal effects were below unity in distant discrepant environments. DFC exhibited the lowest estimates of genetic correlation between distant environments, as it was the trait that showed the highest environmental sensitivity and the greatest G × Epn effect. In general, the standard deviations associated with the estimates of genetic correlation within traits were relatively small compared to the mean, indicating that the true value of the parameter in question was likely to fall within a narrow interval with high probability. DFC showed higher standard deviations due to the smaller number of records available for this trait. When the genetic correlation between two character states deviated from unity, it indicated that phenotypes in each environment were influenced by different alleles or by the same alleles in different ways, suggesting the possibility of independent evolution . TherefoInter-trait genetic correlations showed varying degrees along the environmental gradient for all traits, with the strongest correlations observed between W120 and DFC and between W210 and DFC in EXP. The posterior means of the genetic correlation estimates between W120 and W210 were similar to those reported by Boligon et al.  for direThe posterior means of the genetic correlation estimates between W210 and SC were relatively low and showed slight variations across different gestational environments. Santana et al. (2015) reported relatively close genetic correlation estimates (0.03 to 0.20) between post-weaning weight gain and SC for Nelore cattle in different production environments. The means of the genetic association estimates between SC and DFC in COM were consistently negative and favorable for all gestational environments. This finding is similar to that Chiaia et al.  reportedThe behavior of reaction norms at the level of (G)EBV or SNP for all traits demonstrated, to a greater or lesser degree, the presence of G × Epn. The re-ranking of breeding values for Nelore cattle raised in pasture-based systems has been consistently reported in the literature ,44,45. BThe approach adopted here to describe the quality of the gestational environment did not allow for separately determining the factors that were the most relevant to the future performance of progeny. On the other hand, contemporary groups represent the most elementary entities for characterizing the production environments of beef cattle . This apThe GWAS revealed important regions associated with the level of performance and specific responses of animals to variations in the quality of the gestational environment. The published bovine QTL database allowed us to identify several genomic regions that overlapped with previously related regions harboring QTLs that influenced milk quality, growth, meat characteristics, adaptation, health, productive life, and reproduction of animals. Not surprisingly, for W120 and W210, common candidate genes were identified due to the close additive genetic relationship between these traits. The genes GNA12 and AMZ1 on BTA25 were reported as important for carcass gain in Holsteins . AMZ1, BRAT1, and PRUNE2 affected residual feed intake and maintenance efficiency in Nelore cattle ,52. For Cooperia oncophora infections [Cooperia. Additionally, Hoelker et al. [For DFC, genes, such as CDKN3, AOX2, AOX4, and BZW1, related to bovine health and reproduction were identified for the intercept of reaction norm. The cyclin-dependent kinase inhibitor 3 gene (CDKN3) was associated with metritis in first-lactation Holstein cows. BZW1 was significantly induced in the bovine small intestine by fections . In Brazr et al.  showed tr et al. .For the slope of the reaction norm of DFC, the SERPINE2 gene was identified. This gene was reported as an important protease inhibitor in growing follicles and corpora lutea of crossbred heifers . In thisSubstantial genotype by prenatal environment interaction has been identified for traits related to the growth and reproduction of beef cattle raised under tropical grazing conditions. This interaction was strong enough to result in heterogeneity of variance components and genetic parameters in addition to re-ranking of estimated breeding values and SNPs effects. Therefore, pregnant cows’ nutrition, health, and well-being can affect the development of the bovine fetus and the offspring’s future productive and reproductive performances. Genetic evaluation models considering genotype by prenatal environment interaction and special management and nutrition care for pregnant cows are recommended.Several genomic regions associated with the level of performance and specific responses of the animals to variations in the quality of the gestational environment were revealed. These regions overlapped with previously identified regions harboring QTLs that influence economically important traits in cattle and can be further explored for selection purposes.