Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals. G × E is a central issue in genetics, evolution, and ecology3, and cattle represent a unique opportunity to address these questions due to a large number of genotyped animals4 reared over a wide range of climatic and topographic regions. G × E may contribute to poor adaptation, which negatively affects the profitability and sustainability of the beef cattle production systems5. Studies using the relationship between breeding values and the environment have reported the existence of G × E for body weight in beef cattle7; however, limited research has focused on understanding the genetic loci of G × E underlying such traits. This knowledge could be used to better drive selection and mating decisions8 as well as in statistical models incorporating environmental information to accomplish more accurate genomic predictions9. In addition, G × E information could also be useful to predict the vulnerability of populations to climate change3 and to understand environmental adaptation in other mammalian species.Genotype-by-environment interactions (G × E) refer to a variable response of genotypes across environments, resulting in different trait valuesHere, we present a comprehensive study of G × E for birth weight (BW), weaning weight (WW), and yearling weight (YW) using SNP marker information and over 13,000 Simmental cattle. First, we estimated the contribution of G × E variance on the total phenotypic variances using a restricted maximum likelihood (REML) approach. Then, we searched for SNPs enriched for G × E using four linear mixed model approaches: (i) direct G × E genome-wide association analyses (G × E GWAA) using continuous environmental variables; (ii) direct G × E GWAA using discrete environmental variables (United States ecoregions); (iii) variance-heterogeneity genome-wide association analyses (vGWAA), which enable identification of interactions, using phenotypes adjusted for additive relationship matrix or additive, dominance and epistatic relationship matrices; and (iv) meta-analysis of ecoregion-specific GWAA. Lastly, we performed enrichment analyses using G × E candidate genes to investigate the biological mechanisms by which environmental factors modulate such phenotypes. A flowchart overview of this study is shown in Fig. 10. Each 1 km square of the United States was then assigned to one of 9 resulting ecoregions. We found that G × E variances contribute in 10.1%, 3.8%, and 2.8% to the BW, WW, and YW total phenotypic variances, respectively, suggesting that there is a decrease in the effect of G × E on the animal body weight throughout life. These results indicate an inverse relationship between farm management and environmental stress and that management practices  are more relevant for later weights than climate environmental factors. One possible explanation for these findings is that birth weight reflects intrauterine growth which is highly affected by environmental conditions11; however, as the animal ages physiological, morphological, endocrine, cellular and molecular mechanisms are altered in order to cope up with environmental challenges12. These results also suggest that as management inputs are decreased, animals become more sensitive to environmental stress, which has ramifications for animal performance as we seek to decrease inputs to make beef production more sustainable.Phenotypes were pre-adjusted for fixed effect of sex and random effect of contemporary groups , which represent differences in management, to remove any confounding between environment and management effects. The amount of BW, WW, and YW variation explained by contemporary group effects was 19%, 53%, and 61%, respectively, showing that the effect of farm management on the animal body weight increases with age. After adjusting the phenotypes, we estimated the contribution of G × E variance on the total phenotypic variances using additive relationship matrix calculated based on SNP marker information, and United States ecoregions as environmental factor. Ecoregions were determined based on the combination of 30-year normal values of mean temperature, precipitation, and elevationThe G × E GWAA for BW, WW, and YW were performed by fitting univariate and multivariate linear mixed models considering continuous or discrete environmental variables. The continuous environmental variables used were minimum, mean, maximum, and mean dew point temperatures; elevation; precipitation; and minimum and maximum vapor pressure deficit, separately  with effect of 6.6 kg on YW when interacting with minimum vapor pressure deficit  with effect of 18.5 kg on YW when comparing animals reared in the Fescue Belt ecoregion and the total dataset  when interacting with mean dew point temperature has an effect of 0.07 kg, 2 kg, and 1.2 kg on BW, WW, and YW, respectively  with G × E effect of 2.2 kg, 0.46 kg, and 9.1 kg on BW, WW, and YW, respectively, when comparing animals from the Southeast ecoregion and the total dataset , compared with other ecoregions. No common G × E QTL was identified for two or more different ecoregions indicating that the loci interacting with abiotic and biotic stressors are different across divergent environments, and consequently, the ecoregions have specific genetic architecture involved in G × E.Consistent with our G × E variance component results, a larger number of G × E QTL was detected for BW than for WW or YW Table . TemperaP < 1e−5) to scan for genes in their vicinity and to identify possible regulatory elements , metabolic processes , signal transduction , cellular processes (endocytosis), and nervous system activity (glutamatergic synapse). Reactome pathways such as formation of the cornified envelope and keratinization were also enriched. In addition, many G × E genes participate in nitrogen compound metabolic process, response to stimulus, protein metabolic process, developmental process, anatomical structure development, gene expression, nervous system development, immune system, among other biological processes  and compared the results with those from the G × E GWAA without maternal effects (MA). Results showed that 876 SNPs were not associated with G × E effects when accounting for the effect of dam. After adjustment for genomic control, 94 G × E SNP were not detected when accounting for the effect of dam , or some combination of the two27. Therefore, we performed a REML analysis to estimate how much of the BW, WW, and YW variance is explained by additive, dominance and epistasis effects. We fitted models (M) using only additive relationship matrix (MA) or using additive, dominance, and epistasis relationship matrices (MADE), see Additional file 2: Table 2), which are a measure of phenotypic variation28, as dependent variables fitting univariate and multivariate models. Using z2 from the MA, we expected to detect loci with both G × E and G × G effects, since this phenotype was adjusted only for the additive relationship matrix. However, using z2 from the MADE, we should detect exclusively G × E loci as the phenotype was adjusted for additive, dominance and epistatic relationship matrices. In total, 44 SNPs with effect on z2 (P < 1e−5) were identified on chromosomes 3, 5, 8, 10, 11, 13, 14, 16, 17, 18, 21, 27, and 29  were not identified by the univariate analyses.The vGWAA identify loci associated with differences in variance, rather than differences in mean, between genotype classes, called vQTL, which can be explained by G × E or G × G (epistatic) interactions2 from MA . Among these vQTL likely involved in G × G, one vQTL for BW is located on chromosome 14 at 23 Mb and resides near or within the genes LYN, RPS20, MOS, PLAG1, and CHCHD7. This region has been reported to have epistatic effects on several chromosomes affecting post-weaning weight in beef cattle29. These effects could be due to the gene PLAG1 which encodes a transcription factor that regulates the expression of several genes involved in a variety of cellular processes30, and has been previously associated with stature and growth traits in many beef cattle breeds33. In addition, we detected a G × G vQTL on chromosome 3 at 106 Mb within TRIT1, a gene that was also reported to harbor a vQTL involved in epistatic interactions in humans34.Nine vQTL were only significant using z2 from MADE, 15 vQTL were detected , which likely identify G × E. Based on GO information, genes near or within these vQTL are involved in response to stimulus , inflammation and immunity mechanisms , neurogenesis , regulation of transcription , developmental process , transmembrane transport , neurotransmission regulation (SHANK2), and cell proliferation . Some of those genes were previously reported to be involved in environmental adaptation in cattle , and heat tolerance in buffalo (XCL138) and dairy cattle (SHANK239).Using z2 as dependent variables in the analyses allowed the estimation of the proportion of residual variance that is explained by the SNPs in GEMMA (PVE). For BW z2, WW z2 and YW z2, SNPs explained 4.9% (SD = 0.8), 4.7% (SD = 0.8), and 5.9% (SD = 1.1), respectively, showing that residual variance is heritable and could be changed by selection, supporting previous studies42. Rönnegard et al.41 reported advantages in using breeding values explaining differences in residual variance (vEBV) for milk yield uniformity in Holstein cattle. According to Mulder et al.40 the reduction of phenotypic variance by selecting for reduced residual variance could be more beneficial for low heritability traits.Using z43. In addition, we calculated the m-value statistic for the significant SNPs , which is the posterior probability of an effect being present in an ecoregion given the observations from all other ecoregions43 allowing us to identify which ecoregions a given variant has an effect. Southeast and Desert and Arid Prairie ecoregions were not included in the meta-analysis due to their small sample sizes. Meta-analysis of ecoregion-specific GWAA detected 12 G × E SNPs for BW located on chromosome 6  and PB-plot  of the G × E SNPs detected by the meta-analysis, we observed that some G × E SNPs on chromosome 6 at 30 Mb have an effect on animal’s BW from Forested Mountain and Upper Midwest and Northeast ecoregions, however with different certainty  to look for significant differences (Cochran’s Q statistics) in effect size of SNPs between the ecoregions, which are interpreted as G × EWe also performed traditional GWAA using univariate and multivariate linear mixed models in order to compare with the G × E GWAA results. We detected major QTL on chromosomes 6 (26–48 Mb), 7 88–90 Mb), 14 (15–30 Mb), and 20 (41–78 Mb) for univariate BW, WW and YW  as fixed effects in the model. In this way, if those SNPs are causal mutations accounting for QTL effect the loci association signals would be missed. Results showed that even including six significant SNPs (not in LD) simultaneously as fixed effects in the model, a small number of SNPs were still associated (six out of 531 SNPs), indicating that there are possibly multiple causal mutations in this region. Based on the linkage disequilibrium between adjacent significant SNP, we found 166, 49, and 95 haplotype blocks in this region for univariate BW, WW, and YW, respectively, and 131 haplotype blocks for multivariate analysis.Out of 948 total significant SNPs using multivariate GWAA analysis for the growth traits, 531 SNPs were located on chromosome 6 (from 26.8 to 48.8 Mb). Likewise, several SNPs interacting with Forested Mountains ecoregion, elevation, precipitation and, mainly, with the temperatures were also detected in the same positions on chromosome 6. Given the importance of chromosome 6 underlying the growth traits, we repeated the multivariate GWAA including six independent significant SNPs (e.g. P < 1e−5) was used to equally interpret the results of all GWAA approaches, including continuous and discrete G × E GWAA, vGWAA, ecoregion-specific GWAA, and traditional GWAA. Therefore, the false discovery rate (FDR) was estimated for each analysis  has an additive effect of 0.41 kg for BW in the total dataset, but its G × E effects is even more impactful (0.67 kg) on the BW of animals from the Forested Mountains ecoregion. These findings show the importance of G × E effects on quantitative traits and the need for precision, ecoregion-specific genomic predictions to optimize animal performance.Comparing traditional GWAA with all G × E GWAA results, we verified that 312 SNPs with additive effects also have G × E effects . After adjusting the G × E GWAA for genomic control, 68 SNP on chromosome 6 remained significant in both analyses. Some SNPs showed G × E effects even greater than their additive effects on the traits. For example, the SNP LYN, MOS, RPS20, PLAG1, and CHCHD7, was also found as a direct effect QTL for BW, meaning that this locus affects both mean and variance of BW in this population. Five vQTL were also identified by the G × E GWAA, which were found to interact with multiple continuous environmental variables as well as interacting with ecoregions. However, after genomic control adjustment on the G × E GWAA, only one vQTL was also identified by the G × E GWAA, which was found to interact with mean and minimum temperature. G × E GWAA detected a greater number of G × E loci when compared with the vGWAA results.Comparing vGWAA with the traditional GWAA results, only one vQTL, on chromosome 14 at 23.1–23.3 Mb harboring the genes P-value < 1e−5), which were also identified by the G × E GWAA. In addition, using the meta-analysis approach the Southeast and Desert and Arid Prairie ecoregions could not be included in the analysis due to their small sample sizes, suggesting that this was not an optimal approach to detect G × E associations for our dataset. However, creating PM-plots 44, and PB-plots  helped interpret results of our G × E GWAA and vGWAA. For significant G × E SNPs detected by the G × E GWAA, there were clear differences in the SNP effect sizes between the significant ecoregion and the remaining ecoregions , which causes pulmonary hypertension and vasoconstriction leading to right-side heart failure in cattle49. This condition is known as brisket disease and accounts for significant losses in growth and reproductive performance, being one of the top causes of mortality in cattle residing at high altitudes50. Therefore, the genomic regions detected interacting with Forested Mountain and High Plains ecoregions may be related to adaptation to oxygen deprivation in beef cattle.Among the environmental variables analyzed, birth weight was most affected by temperature. Also, a greater number of G × E QTL for birth weight were detected in animals born in the Fescue Belt and Forested Mountain ecoregions, which have high and low temperatures, respectively. The effect of higher temperature on calf birth weight may be due to the reduced feed intake or the decreased uterine blood flow and placental function often found in heat-stressed cows, factors that contribute to impaired cow-to-fetal nutrient exchange, negatively affecting fetal growth52. Specifically, GABA receptor activity and glutamatergic synapse pathway were enriched. Genes with G × E associations with roles in the activity of the GABA receptor  were found interacting with Fescue Belt ecoregion. This may be due to the consumption of tall fescue, a grass commonly used in grazing cattle systems in the Southeastern United States53, which usually contains an endophytic fungus that produces ergot alkaloids54. Ergot alkaloids have vasoconstrictive effects and interact with GABAergic receptor affecting multiple aspects of animal’s physiology57. Animals consuming the endophyte-infected tall fescue forage often develop fescue toxicosis, which is characterized by reduced feed intake, weight gain, and blood flow, as well as elevated body temperature associated with heat stress60. The glutamatergic synapse pathway was enriched mainly by G × E genes interacting with Forested Mountains and/or elevation , supporting that neurotransmitters respond to the effects of hypoxia61.Molecular functions involved in neurotransmission processes were enriched by G × E genes. The release of specific neurotransmitters in the brain, such as dopamine, acetylcholine, glutamate, serotonin and GABA, is the main physiological mechanism that organizes the response to a stressful situation62. VEGF signaling is involved in growth of blood vessels promoting angiogensis and have an important role in response to hypoxia63. Both endocytosis and VEGF signaling pathways have previously been related to high altitude adaptation in cattle64 and other species66.Endocytosis and VEGF signaling pathways were also enriched by candidate G × E genes interacting with elevation or ecoregions with high altitudes. Endocytosis pathway plays an important role in cell survival under hypoxia conditionKRT15, KRT31, KRT32, KRT33A, KRT34, and KRT36) are candidates to interact with Fescue Belt ecoregion. Such G × E genes enriched pathways involved in the keratinocyte differentiation (formation of the cornified envelope and keratinization). Such process is involved in the formation of cornified cell envelope  which provides the barrier function of epidermis helping to protect the body against environmental stimuli67. Keratin proteins are also involved in the hair-fiber formation cyclical process, which involves growth, regression, resting phases and shedding of the hair shaft68. In cattle, hair shedding is an adaptive process that help the animal to cope with heat stress, which is aggravated by fescue toxicosis condition69. Durbin et al.70 detected genes of the KRT family associated with hair shedding in Angus cattle reared in the Fescue Belt ecoregion. Interestingly, the KRT family genes also enriched the estrogen signaling pathway, which is involved in fertility in cattle71. This result may explain, at least in part, fertility problems in heifers grazing endophyte-infected tall fescue72.Genes of the keratin (KRT) family  across environments while other genotypes at the same locus are more variable across environments, indicating that selection for low variance vQTL genotypes may provide sustainability advantages. Therefore, genetic variation in residual variance may be utilized to increase uniformity of production in livestock populations40. Improvement of uniformity through selection is particularly interesting for traits with an intermediate optimum value , or for decreasing variation in performance within sire’s daughter group41. Nonetheless, many genes harboring the vQTL identified here were previously reported to be involved in environmental adaptation in cattle39 supporting that phenotypic variability may be an adaptive evolutionary solution to environmental changes92. However, our results support Paré et al.93 which reported that when an environmental factor of interest has been measured on all the genotyped individuals, the direct G × E GWAA will be able to detect a larger number of G × E loci.We showed that vGWAA may be able to detect loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. A vGWAA may capture G × E loci that interact with multiple environmental variables, possibly even those not included in the G × E GWAA model . When multiple interacting factors induce variance heterogeneity, the power of identification of any single one of them or all together may be lower than the power of the variance heterogeneity testFTO gene variants on obesity in humans96 until a study with large sample size  confirmed this interaction97. Therefore, studies using a larger population size could validate our results.Our study also demonstrated the challenges of detecting G × E effects. Although we have used a relatively large beef cattle population to date, it still seems that a greater number of animals representing all possible environments are needed to detect such interactions. Also, several G × E QTL have small effect sizes on quantitative traits increasing the difficulty to differentiate them from potential bias. These limitations directly impact the statistical power to detect G × E as previously reported. For example, G × E studies observed unclear evidence of physical activity modulating the effects of 77, and adaptation processes99, revealing the role that G × E plays in adaptation and evolution at the level of the genome in cattle and potentially across mammals. It is important to understand such biological mechanisms since the inability of an animal to cope with its environment results in failure to produce optimally leading to lost potential profitability of livestock operations101. Further, these biological insights may help us understand how adaptation occurs in wild populations and the potential effect of climate change on animal physiological functions.The environmentally affected biological mechanisms that modulate growth traits identified in this study have been reported to be involved in resilienceG × E GWAA and vGWAA detected several loci affecting growth traits that the traditional GWAA did not, showing that functional loci may have non-additive genetic effects between genotype classes regardless of differences in genotypic means. We revealed biological mechanisms by which beef cattle respond to changes in their environment. Neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways were highlighted, indicating that climate change may become a burden on animal health and productivity. In addition, ecoregion-specific G × E SNPs detected in this study may play a crucial role in resilience and adaptation of beef cattle to divergent environments. Although conventional genomic selection has shown great promise in improving genetic gain, it only considers additive effects and, according to our findings, ecoregion-specific genomic predictions should be created to identify animals best suited for given environments. This could help producers in making optimal decisions given their geographical location. Our results revealed novel trait associations and alternative genetic mechanisms involved in shaping the total phenotypic variance of growth traits providing new insights into how the environment influences such traits and adaptation in beef cattle and other mammalian species.Data from the American Simmental Association males and females born between 1975 and 2016 and genotyped for genomic-enhanced expected progeny differences  were transferred to the University of Missouri. Bulls who were born decades ago were genotyped from cryopreserved semen used for artificial insemination. The traits studied were birth weight (BW), 205-d adjusted weaning weight (WW) and 365-d adjusted yearling weight (YW), with the traits also adjusted for age of dam as per breed association practice. The animals were assigned to contemporary groups (CG), defined by the combination of farm, season  and year of birth.102. Briefly, the imputation pipeline begins with genotype filtering to remove non-autosomal variants, as well as both variants and individuals with call rates less than 0.90, using PLINK 1.9103. SNP positions were based on the ARS-UCD1.2 Bovine reference genome assembly104 and all genotypes were coded as reference versus alternate alleles as A, C, G, and T bases. The genotypes were then phased using Eagle 2.4105 and imputed using Minimac3106. The final imputed genotype data contained a total of 835,947 bi-allelic variants with imputation accuracy of 99.6%102. Variants with minor allele frequency less than 0.01 were removed using PLINK 1.9 for further analysis, leaving 710,202, 709,054, and 706,771 SNP markers for 13,427, 11,847, and 8546 animals for BW, WW and YW, respectively.The DNA samples were genotyped with various low-density assays  and were imputed to the combination of Illumina BovineHD  and the GeneSeek Genomic Profiler F250  according to Rowan et al.107. Contemporary groups were included in the univariate model as random effect since 19% of individuals analyzed belong to CG with only a single animal108. After removing confounding management effects, the pre-adjusted phenotypes were used in the downstream analyses.Phenotypes were adjusted for fixed effect of sex and random effect of CG. A random maternal permanent environmental effect was also included in the WW adjustment to account for differences in milk production and maternal care of the animal’s dam. For G × E GWAS models, birth weights were analyzed twice, once adjusted for a maternal effect and once without. Fixed effect of sex was estimated using ‘--reml-est-fix’ option, and random effects were predicted by the BLUP method using ‘--reml-pred-rand’ flag while controlling for population stratification using single-trait animal model implemented in GCTA softwareoC); elevation (m); precipitation (ml); and minimum and maximum vapor pressure deficit (hPs) data, which were drawn from the PRISM climate dataset109. The United States was partitioned into nine regions  based on similar topographic and environmental conditions (ecoregions), including mean temperature, elevation and precipitation information, using k-means clustering implemented in ‘RStoolbox’ R package111. The optimal ecoregions were identified using ‘pamk’ function from the R package ‘fpc’112. The ecoregions were named Desert (D), High Plains (HP), Arid Prairie (AP), Forested Mountains (FM), Upper Midwest and Northeast (UN), Southeast (SE), Rainforest (R), Foothills (FH), and Fescue Belt (FB)10. Animals were assigned to the ecoregions based on the zip code of their breeder , and only ecoregions with more than 200 records were analyzed. Thus, animals located in the Rainforest ecoregion were removed from the dataset, and those that were reared in the Desert and Arid Prairie ecoregions were analyzed together (in a group referred to as DA) due to their similar environmental conditions. No animal record was assigned to the Foothills ecoregion. A complete description of the dataset, including weight records and environmental variable measurements is shown in Additional file 2: Table Eight continuous environmental variables were used for the G × E GWAA, including information of 30-year normal of minimum, mean, maximum temperatures and mean dew point temperature ; u is the vector of random additive genetic effects; G × E is the vector of random G × E effects; and e is the vector of random residuals. Assuming that I is the identity matrix and G is the genomic relationship matrix. The G matrix was calculated using SNP marker information according to107 as: N is the number of SNPs. GG × Ei,j][ if i and j are from the same ecoregion and zero otherwise113.In order to estimate the amount of phenotypic variance explained by the G × E interactions among the ecoregions, G × E variance component for pre-adjusted BW, WW, and YW were estimated. This analysis was performed using multi-component restricted maximum likelihood (REML) approach, implemented in GCTA software, considering the ecoregions as an environmental factor in the univariate linear mixed model as follows:114 as follows:y* is an n-vector of pre-adjusted phenotypes; X is the incidence matrix of genotypes; Z is the design matrix for random animal effects, u is an n-vector of random additive genetic effects; and e is an n-vector of random residual. Assuming that G is the n × n genomic relationship matrix; I is an n × n identity matrix. Genomic relationship matrices were estimated using the standardized genotypes of 710,202, 709,054, and 706,771 SNP markers for 13,427, 11,847, and 8546 animals for BW, WW and YW, respectively.Univariate linear mixed model analyses were performed for the pre-adjusted BW, WW or YW using GEMMA software115 in the following form:Y* is an n × d matrix of d pre-adjusted phenotypes for n individuals; X is the incidence matrix of genotypes; d vector of the genotype effects for the d phenotypes; Z is the design matrix for random animal effects; U is an n × d matrix of random additive genetic effects; and E is an n × d matrix of random residuals. G is the n × n genomic relationship matrix; Va is the d × d additive genetic (co)variance matrix; Ve is a d × d residual (co)variance matrix; I is an n × n identity matrix. ⊗ denotes Kronecker product operation. For all GWAA performed, single-marker P-values were used to generate Manhattan and QQ plots using ‘manhattan’ and ‘qq’ functions, respectively, implemented in ‘qqman’ R package116.We also fitted a multivariate linear mixed model for the pre-adjusted BW, WW and YW using GEMMA1: 0:P-values correspond to the significance of the G × E interaction31. This model can be described as:y* is an n-vector of pre-adjusted phenotypes; xi is an n-vector of the genotypes of the SNP i; i; xj is an n-vector of environmental variable j; j; i and the environmental variable j; u is an n-vector of random additive genetic effects [e is an n-vector of random residual [The G × E GWAA were performed using GEMMA through two approaches: using eight continuous environmental variables  separately; and using the ecoregions as discrete environmental variable, where each ecoregion was compared against the total dataset using 0 and 1 dummy coding. In the G × E GWAA, for each SNP in turn, GEMMA fits a linear mixed model that controls both the SNP main effect and environmental main effect, while testing for the interaction effect and controlling for population stratification, evaluating the alternative hypothesis : including only additive effects (MA), or using additive, dominance, and epistasis effects (MADE) as follows:y* is the vector of pre-adjusted phenotypes; u is the vector of random additive genetic effects; d is the vector of random dominance effects; ep is the vector of random epistatic effects; and eA and eADE are vectors of random residuals for MA and MADE models, respectively. Z matrices are incidence matrices relating observations to animals. Assuming that I is the identity matrix; and G, D, and E are the additive (genomic), dominance and epistatic genetic relationship matrices, respectively. The G and D matrices were calculated using SNP marker information117 as: N is the number of SNPs. According to Henderson118E can be derived from the additive genomic relationship matrix as: Variance-heterogeneity GWAA (vGWAA) can detect loci affecting the difference in variance between genotypes (vQTL), which can be explained by G × E or GxG (epistatic) interactionseA and eADE) from the MA and MADE models, respectively, were standardized to z score by the rank-based inverse-normal transformation and squared (z2), which is a measure of phenotypic variance120. The vGWAA were performed for BW z2, WW z2, and YW z2 using univariate and multivariate linear mixed models implemented in GEMMA software as denoted in “Genome-wide association analyses (GWAA)” section, where y* is an n-vector of z2; and 2.Residual effects (43 we combined the outputs from univariate within-ecoregion GWAA (GWAA for each ecoregion separately) into a single meta-analysis for each phenotype using METASOFT software121. Only ecoregions with more than 1000 animals were analyzed. In addition, the statistic m-value (posterior probability of an effect) for the significant SNPs  were calculated, which is an estimate of the posterior probability of a locus having an effect in a particular ecoregion122. The results were visualized through PM-plots and PB-plots, in which ecoregion-specific P-values are simultaneously visualized with the m-values and beta-values (SNP effects estimates obtained from the ecoregion-specific GWAA) at each tested locus, respectively.In order to evaluate how significantly the effect sizes of SNPs (Cochran’s Q test) varied between the ecoregions (indicative of G × E)13 similar to cryptic relatedness or population structure. Thus, we calculated the genomic control inflation factor (λGC) for all analyses123 to improve the calibration of the G × E GWAS P-values. For those G × E GWAA with λGC > 1, P-values were divided by the λGC.In G × E GWAA samples can have shared environmental effects outside the environmental variable used in the G × E GWAA which may cause spurious signalsP < 1e−5) was used to equally compare the results of all GWAA approaches, including continuous and discrete G × E GWAA, vGWAA, ecoregion-specific GWAA, and traditional GWAA. Therefore, false discovery rate (FDR) of the nominal significant threshold (P < 1e−5) was estimated for all analyses performed ; A is the number of SNP that were significant at the nominal significant threshold; and T is the total number of SNPs tested. Although the true FDR cannot exceed 1, the estimated FDR can exceed 1 if the number of significant SNPs is smaller than expected by chance, meaning that all SNPs that met the nominal significant threshold are likely false positives29.125 using HaploView software, version 4.2126. This methodology defines that pairs of SNP markers are in strong linkage disequilibrium (LD) if the confidence interval is equal to or greater than 95% with D′ equal to 0.98 and the lower limit above 0.70.For all analysis done in this study, QTL were defined based on haplotype blocksP < 1e−5) to scan for putative G × E genes located in their vicinity and to identify possible regulatory elements based on the ARS-UCD1.2 bovine reference genomic positions. Enrichment analysis using the G × E candidate genes based on Gene Ontology (GO), and KEGG127 and Reactome pathways were performed using g:Profiler128, in which the analyses were adjusted for FDR of 10%. GO annotations were also retrieved from the AmiGO browser129.We explored 100 kb sequence windows that flanked the significant SNPs (Animal Care and Use Committee approval was not required or obtained for data that were extracted from the existing American Simmental Association database.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.


Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) that predict a woman’s risk of breast cancer based on mammographically dense areas when defined by different levels of brightness. We measured these MRS for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated how much these MRSs were correlated in relatives (ρ), how much of the differences between women were due to genetic factors (heritability), and how much these MRS explained why breast cancer runs in families. The ρ estimates ranged from: 0.41 to 0.60 for MZ pairs, 0.16 to 0.26 for DZ pairs, and 0.19 to 0.29 sister pairs, respectively. Heritability estimates were 36% to 69%. Genetic factors explain most of why twins and sisters are similar in their MRS, and these genetic factors explain one-quarter to one-half as much breast cancer risk as to the current best genetic risk score.Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score. Conventionally, mammographic density refers to the white or bright areas on a mammographic image. These regions are critical for breast screening because they are used by radiologists to identify potential cancers, but at the same time, they can make it difficult to detect existing cancers . The extent of mammographically dense regions on a breast image is also associated with a woman’s risks of being diagnosed with breast cancer: (i) at the time of screening, (ii) in the interval before the next regular screen, and (iii) at a future screen .There are many paradoxes involved here, not the least being that because masking increases the risk of missing cancer at mammographic screening, it reduces, not increases, the incidence of screen-detected breast cancer . Another½) [Therefore, we define a mammogram risk score (MRS) as the residual of a mammographic measure  after making an adjustment for age and BMI based on the analysis of controls or another sample to represent the population and then standardizing it to have mean = 0 and variance = 1. This approach allows the simple estimation of the odds ratio per adjusted standard deviation (OPERA), where adjusted standard deviation refers to the standard deviation of the adjusted measure, not of the unadjusted measure . The log½) .Cumulus.Conventionally, mammographic density is defined at the area of the breast covered by white or bright regions and can be measured using the computer software CUMULUS . We nameAltocumulus and Cirrocumulus are the transformed, adjusted, and standardized MRSs based on these two measures, respectively. Therefore, Cumulus is the MRS for Cumulus, Altocumulus is the MRS for Altocumulus. and Cirrocumulus is the MRS for Cirrocumulus.Over the last few years, we introduced other mammographic density measures based on defining density at, in effect, higher pixel brightness thresholds ,7,8,9. WAltocumulus and Cirrocumulus provide more information on risk than Cumulus [Cumulus attenuates and often becomes null after adjusting for Altocumulus or Cirrocumulus [From a series of studies of Australian and Korean women, we consistently found that  Cumulus ,7,8,9,10Cumulus-percent, based on Cumulus as a percentage of the total breast area and adjusted for age and BMI, found that the correlations for monozygotic (MZ) twin pairs were about 0.6, whereas they were about one-half of this (about 0.3) for dizygotic (DZ) twin pairs. The twin pair correlations and total variances did not differ between countries. Given the strong correlation of >0.9 between Cumulus and Cumulus-percent, this is consistent with additive genetic factors explaining about 60% of the variation in both Cumulus and Cumulus-percent [The potential role of familial, if not genetic, factors in explaining variation in the MRS has been considered for more than two decades. In 2001, a joint Australian–Canadian study of -percent . Based o-percent , we predCumulus by enlarging the Australian twin data set and recruiting sisters of these twin pairs [We more critically addressed the causes of familial variation in in pairs . We founCumulus, Cumulus-percent, Altocumulus, and Cirrocumulus, but this time the MRS were measured by a different set of measures.In this study, we aimed to estimate the familial correlations of all the MRS and again try to decompose the variance into genetic and non-genetic components. We used the enlarged twins and sisters data set to consider MRS based on Cumulus-white, based on transforming, adjusting, and standardizing the difference between the raw Cumulus density measure and the raw Altocumulus density measure. This new measure represents the white but not bright areas on a mammogram.Here we introduced a new mammogram risk score called We again considered the extent to which the familial aspects of these MRSs explain the familial aggregation of breast cancer based on their published risk gradients. We also compared the proportion of familial aggregation in breast cancer explained by these MRS with that of the latest polygenic risk score (PRS) based on 313 single-nucleotide polymorphisms (SNPs) [We used data from the Australian Mammographic Density Twins and Sisters Study . BrieflyTelephone-administered questionnaires were used to record demographic information and self-reported weight, height, smoking history, alcohol consumption, reproductive history, onset and cessation of menstruation, use of oral contraceptives, use of hormone replacement therapy, and personal and family history of cancer. For twin pairs, zygosity was determined from genome-wide analysis study data .All available episodes of mammograms were retrieved with the participants’ written consent, mostly from Australian BreastScreen services, but also from private clinics and private hospitals. We also retrieved mammograms from the participants themselves. The craniocaudal views for left and right breasts were selected and digitized using the Lumysis 85 scanner at the Australian Mammographic Density Research Facility. For each woman, the most recent right breast craniocaudal view was selected for mammographic density measurement, and the left breast craniocaudal view was selected if the right breast mammogram was missing or unavailable. Mammographic measurements of total area and dense area were performed using CUMULUS 4.0, a computer-assisted thresholding technique, after randomization and blind to information, by three independent measurers  with high repeatability; see for example . Dense aCumulus and Cumulus-percent, even though these are highly correlated, due to both measures often being reported in the literature. For Altocumulus density and Cirrocumulus density, we reported only the MRSs based on the absolute density measures. To create Cumulus, the absolute density measure was cube root transformed, while for Cumulus-percent, the percent density measure was square-root transformed. To create Altocumulus, the absolute density measure was cube root transformed and to create Cirrocumulus, the absolute density measure was log-transformed. For Cumulus-white, the difference between the Cumulus and Altocumulus absolute density measures was log-transformed. All transformed measures were adjusted for age and the inverse of BMI, and the residuals were standardized to generate unit variance to create the MRS. This was performed using linear regression applied to data for controls only to estimate the two regression coefficients and the constant term and thereby the expected value as a function of age and BMI and the variance of the residuals for controls. For both cases and controls, their observed transformed values were then subtracted from their expected values based on the regression analysis of controls above to derive the residuals, which were then divided by the variance of the residuals for controls. We previously found that breast cancer risk factors measured by questionnaire, other than age and BMI, explained, at most, a slight percentage of variance in the conventional density measure, Cumulus, and this was trivial compared with the variance explained by age and BMI [Altocumulus and Cirrrocumulus. These statistical transformations were conducted using R version 4.0.2 software [The Box–Cox procedure was used to test the normality of the distributions of the mammographic density measures and, if necessary, select an appropriate power transformation. We present the MRSs based on both the absolute density measure and the percent density measure,  and BMI . We alsosoftware .We applied a multivariate normal model for pedigree analyses fitted using the software FISHER, with statistical inference based on asymptotic likelihood theory, to estimate the correlation between pairs of relatives and to fit the variance components models ,18. ThisijA + δC)/V where 2φij = 1 if MZ else 0.5 and δ = 1 for all pairs.We also fitted models estimating independent genetic and environmental components of variance to represent additive genetic factors (A), environment factors shared by twins and sisters (C), and individual specific environmental factors and measurement error (E), where A + C + E = total residual variance (V). MZ pairs share all their genes while DZ pairs and sister pairs share, on average, half their genes, so the correlation in additive genetic factors is 1.0 for MZ pairs and 0.5 for DZ and sister pairs . Under tMZ, so that the correlation between a pair will be (2φijA + δCMZ)/V, where δ = 1 for MZ pairs, otherwise it is 0. Finally, we also fitted a purely genetic model which did not include any shared environmental factors but instead included a term D to represent dominance genetic factors such that the correlation between a pair is (2φijA + δD)/V, where δ = 1 if MZ, otherwise it is 0.25 [We also fitted a model in which C was defined as an MZ pair-specific shared non-genetic environmental factor, which we refer to as C is 0.25 . Note thρ is the pair correlation. These results are consistent with those of Aalen [We estimated the proportion of familial aggregation in breast cancer explained by the familial correlations in the MRS and PRS by reference to of Aalen  and the of Aalen . Based op < 10−4). Model 2 constrains the DZ pair and sister correlations to be the same, and comparisons of the log-likelihoods with those of Model 1 show that for no MRS, there is evidence that the DZ pair and sister-pair correlations differed.Cirrocumulus, there was at least nominally significant evidence that the MZ correlations were more than twice the corresponding correlations for DZ and sister pairs combined . The best-fitting models were Model 2 for Cumulus, Altocumulus, and Cumulus-white, and Model 4 for Cumulus-percent and Cirrocumulus.Model 3 constrains all pair correlations to be the same, and comparisons of the log-likelihoods with those of Model 1 show that there is highly significant evidence that this model is not consistent with the data; a similar statement applies to comparisons with Model 2. Therefore, we conclude that the MZ pair correlations are greater than those for the DZ pairs and sister pairs, which are similar to one another. Model 4 constrains the MZ pair correlation to be twice the correlation for DZ and sister pairs combined, and the comparison of the log-likelihoods with those of Model 2 shows that, except for MZ, the estimates of CMZ were all positive, and nominally so, except for Cirrocumulus. These significant CMZ estimates ranged from 0.10 to 0.15, and the corresponding A estimate was reduced by a similar amount when compared with their estimates using Model 1.We considered the extent to which the familial correlations accorded with different variance components models; see Cumulus; 0.43 and 0.20 for Cumulus-percent; 0.34 and 0.27 for Altocumulus; 0.32 and 0.08 for Cirrocumulus; and 0.30 and 0.31 for Cumulus-white, respectively. The correlations between the A and D estimates were approximately −0.98. These model fits had the same log-likelihoods as their corresponding ACE fits.When the ADE model was fitted, the estimates of A and D  were: 0.41 and 0.30 for Cumulus, consistent across studies, and up to 5.0 for Altocumulus and Cirrocumulus from some studies [Cumulus, and up to 1.12 or 1.17 for Altocumulus and Cirrocumulus, respectively.The inter-quartile risk ratio for the MRSs as predictors of breast cancer are in the range of 2.5 for  studies . We foun studies   means that they explain a proportion of familial aggregation of breast cancer, as explained in Hopper and Carlin [In terms of heritability, under the equal environments assumption of the classic twin model, extended to become that all twins and sister pairs share, to the same extent, all of the non-genetic factors that determine variation in the MRS, genetic factors would explain about 39% to 69% of the total variance of the MRS; see Model 1 of d Carlin . The famd Carlin , and eved Carlin . MZ twind Carlin .We predicted that the FRR generated by the MRSs are about 1.08 to 1.17 and therefore explain about one-quarter to one-half of as much of the familial aggregation explained by the current PRS. In the typical screening age range of 50 to 70 years, the PRS explains about one-quarter of all familial aggregation and the MRS up to another one-eighth.The question then remains regarding the genetic, and perhaps non-genetic, familial factors that determine these mammographic density MRS and how they relate to the PRS and its components. We will address this issue in a concurrent paper in this series .There are substantial familial correlations in the MRSs that could have a genetic cause and explain about one-quarter to one-half of the familial aggregation of breast cancer that is explained by the current PRS.


Theobroma cacao is the only source that allows the production of chocolate. It is of major economic importance for producing countries such as Ecuador, which is the third-largest cocoa producer in the world. Cocoa is classified into two groups: bulk cocoa and aromatic fine flavour cocoa. In contrast to bulk cocoa, fine flavour cocoa is characterised by fruity and floral notes. One of the characteristics of Nacional cocoa, the emblematic cocoa of Ecuador, is its aromatic ARRIBA flavour. This aroma is mainly composed of floral notes whose genetic and biochemical origin is not well-known. This research objective is to study the genetic and biochemical determinism of the floral aroma of modern Nacional cocoa variety from Ecuador. Genome-Wide Association Study (GWAS) was conducted on a population of 152 genotypes of cocoa trees belonging to the population variety of modern Nacional. Genome-Wide Association Study was conducted by combining SSR and SNP genotyping, assaying biochemical compounds (in roasted and unroasted beans), and sensory evaluations from various tastings. This analysis highlighted different areas of association for all types of traits. In a second step, a search for candidate genes in these association zones was undertaken, which made it possible to find genes potentially involved in the biosynthesis pathway of the biochemical compound identified in associations. Our results show that two biosynthesis pathways seem to be mainly related to the floral note of Nacional cocoa: the monoterpene biosynthesis pathway and the L-phenylalanine degradation pathway. As already suggested, the genetic background would therefore appear as largely explaining the floral note of cocoa. Theobroma cacao L. is native to the tropical rainforests of northern South America and is a member of the family Malvaceae  with a small genome that is now sequenced and of which 96.7% of the assembly is anchored on all 10 chromosomes . Criollo is not widely cultivated because of its high susceptibility to diseases and low vigour  to linalool by linalool synthase (LIS). Subsequently, the linalool was transformed into 6,7-epoxylinalool. The 6,7-epoxylialool was then converted to pyranoid linalool oxide or furanoid linalool oxide. Other studies showed that cytochrome P450 is responsible for the transformation of linalool to 6,7-epoxylinalool and cyclases for the transformation of 6,7-epoxylinalool to pyranoid linalool oxide or furanoid linalool oxide  has been found in muscadine grape juice, wine, and roses  on a modern cultivated Nacional population, composed of trees resulting from hybridizations between three contrasting main ancestors: Criollo, Amelonado, and the ancestral Nacional variety. This population was characterised by VOCs and sensory analyses and presented a high degree of variability. Thanks to the availability of the genome sequence and high-density SNP genotyping, candidate genes involved in key traits could be proposed.The plant material used for these experiments was composed of a collection of 152 cocoa trees from Ecuador conserved in the Pichilingue experimental station of the “Instituto Nacional de Investigaciones Agropecurias” (INIAP) and the “Colecion de Cacao de Aroma Tenguel” (CCAT) of Tenguel. This population represents the Nacional variety currently grown in Ecuador and has been described by Loor .Micro-fermentations of cocoa beans were carried out in a wooden box in the most homogeneous way possible with a homogeneous cocoa mass. The process lasted 4 days with two turns at 24 and 72 h after the beginning of the fermentation. Each clone sample (152) was placed in a protective laundry bag and micro-fermented in a cocoa mass. After fermentation, the samples were put in a dry place. They were considered dried when their moisture content was below 8%.One hundred and forty-six individuals were characterised by sensory analyses based on blind tastings carried out on three repetitions per sample. The tastings were carried out on cocoa liquor. The cocoa liquor corresponds to merchant cocoa (dried fermented beans) which have been roasted and crushed. Sixteen floral notes were judged with a score ranging from zero  to 10. We used the average of the three replicates for the phenotype of the GWAS analysis ISCQF, .This study was managed by Mr. Edward Seguine, whose work consists of conducting sensory analyses of chocolate samples (see attached documents). This study does not require the approval of an ethics committee.The analysis of VOCs was carried out on dried fermented beans and roasted beans. For each sample, 50 g of beans were taken. The beans were deshelled and crushed to obtain nibs. Then, nibs were put in liquid nitrogen and ground with a blender , to obtain cocoa powder, which was stored at −80°C until analysis. In a 10 ml vial, 2.85 g of powder, 1 ml of standard internal solution (butan-1-ol at a concentration of about 600 μg/ml), and 2 ml of distilled water were added.−1. Injection mode was split less at 250°C for 2 min. The selective mass detector was a quadrupole , with an electronic impact ionisation system at 70 eV and at 230°C  using a 50/30-μm divinylbenzene/carboxene/polydimethylsiloxane (DVB/CAR/PDMS) fibre provided by Supelco to extract volatiles. The fibre was previously conditioned at 250°C for 3 min and then exposed to the sample headspace at 50°C for 45 min. Extracted aroma VOCs were analysed using an Agilent 6890 N gas chromatography–mass spectrometer (GC–MS) equipped with a Hewlett Packard capillary column DBWAX, 30 m length × 0.25 mm internal diameter × 0.25 μm film thickness . The GC oven temperature was initially set at 40°C for 5 min, increased to 140°C at a rate of 2°C/min and then increased at a rate of 10 to 250°C for 66 min. The carrier gas was high-purity helium at 1 ml minThe identification was done by comparing the mass spectra with the commercial NIST Wiley 275L database. No deconvolution was applied. Co-eluted VOCs were excluded from this study, with the exception of cis-ocimene co-eluted with ethyl hexanoate (cis-ocimene + ethyl hexanoate) which showed interesting results.DNA extraction was conducted according to Risterucci et al.  protocolThis population was genotyped using SSR markers by Loor . SSR lochttp://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=COCOAinthegenotypessectionandtheCocoa-Nacional-aromasub-section.DNA samples were genotyped by sequencing (GBS) using DArTseq (Diversity Arrays Technology Sequencing) technology  and sensory  traits using TASSEL v5.For all the traits, we used a mixed model (MLM) on the one hand. The MLM was carried out with a structure matrix, determined by running a principal component analyses (PCA integrated with TASSEL v5 software), considered as a fixed effect, and also with a kinship matrix considered as a random effect as covariates to control the false-positive rate. The option of not compressing and re-evaluating the components of variance for each marker was chosen. The kinship matrix using the identity by state (IBS) pairwise method proposed by Tassel v5 was established.On the other hand, we used a fixed-effect model (GLM) with a structure matrix, determined by running a PCA. The option of 500 permutations was chosen.P-value of approximately 1.79e−05. The significance of all markers was plotted using Manhattan plots with the R QQman package.For both methods, quantile-quantile plots were used to graphically evaluate the false-positive numbers observed in the selected model, based on deviations from the uniform law. The threshold was determined using the Bonferonni correction formula as proposed by Gao et al.  with the−04.We performed an analysis with SSR markers associated with biochemical (180 accessions × 180 markers) and sensory (197 accessions × 180 markers) traits using TASSEL v3. We used a fixed-effect model (GLM) with a structure matrix; the option of 500 permutations was chosen. The threshold was determined using the Bonferonni correction corresponding to a p-value about 2.78eThe borders of the association zones were calculated using Haploview . The size of the dots is correlated to the R2.Theobroma cacao genome sequence  and sensory analysis data.To identify the areas of Sixteen floral notes were determined by sensory analyses performed on cocoa liquor. A total of 16 sensorial traits were therefore used for this study .Principal component analysis for sensory traits showed continuous variation in the population . Axis 1 The biochemical characterisation was done on unroasted and roasted beans. Among 160 VOCs identified, 26 VOCs are known to have a floral taste or are involved in biosynthetic pathways of known floral compounds . EighteePrincipal component analyses of aroma VOCs was made , 3. AxisCorrelation analyses between the different traits showed positive correlations between several biochemical compounds in roasted and unroasted beans . The higThese various correlations between compounds can be partly explained by the fact that they belong to the same biosynthesis pathway. This is the case for the different terpenes which are strongly correlated or compounds resulting from the degradation of L-phenylalanine . On the other hand, no strong correlation between biochemical and sensory traits was detected .The linkage disequilibrium observed in this population amounts to 15 cM Loor, . Genome-p-values  was done at 5% (MAF5). The population being very heterozygous, the sorting by MAF allowed to eliminate the alleles with a total frequency lower than 5% but left homozygous genotypes very poorly represented . The hypothesis was that the low representation of genotypic classes could induce a bias in the analyses, in the same way as a minor allele. It was therefore undertaken to do a further sorting of markers by discarding markers for which genotype classes had <5% representation of the total population . We conserved markers that had at least seven individuals per genotype class (G7). Several tests were performed such as the comparison of Q–Q plots or the comparison of T. cocoa. However, a decrease in marker density is observed in the centromeric and peri-centromeric areas  also made it possible to highlight more association zones: 333 against 295 for the GLM method and 152 against 94 for the MLM method. The MLM method, therefore, appears to be more stringent.Some areas of the association are common for different methods. For example, in the case of terpene relatives' traits, 63 co-locations between positive associations for different methods for the same trait was found on all chromosomes except chromosome 4 and 8. A co-localisation between GLM_MAF5 and GLM_G7 methods for linalool cis pyranic oxide (UR) was observed on chromosome 2 as shown in Among all the associations, only 38 are related to the sensory data with floral notes. Out of a total of 16 floral perceptions, significant associations were detected for 11 of them, on all chromosomes except chromosome 5 and chromosome 7. Only one area of association was revealed for each of the six floral notes: the floral notes bark woody, dark wood, mushrooms, orange blossom, other spice, and tobacco . Four asThe GWAS analyses brought to light 393 association zones. Some of them were detected with several VOCs. All the associations found can be consulted in the Significant associations for 18 VOCs in unroasted beans and 17 volatile compounds in roasted beans were identified . No assoTwo major pathways for the biosynthesis of compounds known to have a floral taste, among those compounds for which a significant association was detected, seem to be particularly represented: the monoterpene biosynthesis pathway and, the L-phenylalanine degradation pathway that allows the synthesis of, among others, acetophenone and 2-phenylethanol.The results obtained were mapped to visualise the areas of significant associations, their locations, as well as possible co-locations between them. Two maps were made. A map with the results of significant associations related to the compounds involved in the terpene biosynthesis pathway and the floral traits from the sensorial evaluation. A second map includes the results of the significant associations of floral tastes and of compounds involved in the degradation pathway of L-phenylalanine which allows, the synthesis of acetophenone and 2-phenylethanol known to have a floral taste. Some results differ between the different methods (GLM and MLM) or the sorting of SNP markers MAF5 or G7) or between the type of SNP and SSR markers. All results are shown on the maps in  or betweAmong the 27 compounds related to the floral note, six VOCs derived from the terpene biosynthesis pathway: linalool (UR and R), trans furanic oxide linalool (UR), cis pyranic oxide linalool (UR), epoxylinalool (R), and cis ocimene co-eluted with ethyl hexanoate (UR) . EighteeThe map with the results for terpenes  shows seSixteen co-locations between different biochemical compounds were also observed on chromosomes 2, 4, 5, 7, 9, and 10, for example on chromosome 2 between the linalool cis pyranic oxide (UR) and cis-ocimene co-eluted with ethyl hexanoate . VariousSeven co-locations between at least one biochemical compound and a floral note were detected on chromosomes 1 and 2. On chromosome 1, two co-locations were observed between epoxylinalool (R) and the floral note lightwood and one between epoxylinalool (R), floral notes lightwood and floral notes tobacco . On chroEighteen compounds for which significant associations have been identified appear to be involved in the degradation pathway of L-phenylalanine to either 2-phenylethanol or acetophenone . Among tThe map showing the results for compounds of the L-phenylalanine degradation pathway  shows seOne hundred and eleven co-locations between different VOCs were also observed on all chromosomes. An example of co-localisation was observed between 4-hydroxyacetophenone (UR) and acetophenone (UR) on chromosome 5 .Thirteen co-locations between at least one aroma VOC and one sensory trait were observed on chromosomes 1, 2, 8, and 9 .Several areas of association were highlighted for seven other compounds known also to have a floral taste: ethyl dodecanoate (R), guaiacol (UR and R), hexyl acetate (UR), furfural (UR and R), propyl acetate (R), and nonanal (UR). One hundred and seventeen association zones were detected for guaiacol (UR) against zero for guaiacol (R). Twelve association zones were observed for furfural (UR) compared to 30 for furfural (R) . The varOf the 393 association zones exposed, 27 with candidate genes with predicted functions were identified.Candidate genes related to the terpene biosynthetic pathway were found on chromosomes 1, 2, 5, 7, 9, and 10. The association zone number and candidate genes are reported in Geranylgeranyl pyrophosphate synthase, chloroplastic.” This enzyme allows the synthesis of geranylgeranyl pyrophosphate in chloroplasts. This compound is a precursor of terpenes. As the monoterpene biosynthesis pathway is located in the plastids, the indication of chloroplastic synthesis seems to confirm the correspondence to another compound derived from linalool also synthesised in Chloroplast  linked to epoxylinalool (R) contains a gene coding for a “Dehydrodolichyl diphosphate synthase 6” (DDS 6) in Probable 3-hydroxyisobutyryl-CoA hydrolase 2.” The enzyme 3-hydroxyisobutyryl-CoA hydrolase 2 can enable the production of acetyl-CoA by releasing a CoA. Acetyl-CoA is a precursor of the mevalonate biosynthetic pathway that allows the production of geranyl diphosphate  contains candidate genes. It is linked to linalool (UR) and linalool trans furanic oxide (UR) and contains six candidate genes, five of which are known to code for “50 89A9” . The pre50 89A9” .Probable terpene synthase 9.” Terpene synthases 9 are known to be involved in the synthesise of linalool, one of the precursors of linalool cis pyranic oxide  linked to 1-phenylethyl acetate (R), benzaldehyde (R), and cinnamaldehyde (R) contains a gene coding for an “ acetate . AssociaALD1 Aminotransferase.” Several aminotransferases have been identified in the shikimate biosynthesis pathway that allows the synthesis of L-phenylalanine  linked to 1-phenylethyl acetate (R) contains two candidate genes coding for: a “Chalcone synthase 2” and a “3-ketoacyl-CoA thiolase 2, peroxisomal.” Chalcone synthases participate in the flavonoid and isoflavonoid biosynthesis pathway that follows the degradation of phenylalanine to CA  is linked to 2-phenylethanol (UR) (the same to terpene association zone 6). It contains six genes, five of which code for Cytochrome P450 89A2 and one for Cytochrome P450 89A9. Cytochrome P450 has redox activities. Several of these reactions are involved in the synthesis of 2-phenylethanol  linked to 4-hydroxy acetophenone (UR), acetophenone (UR), and benzyl acetate (UR) contains a candidate gene encoding a “ember C8 . An acetember C8 . The assGDSL esterase/lipase At1g28570. A lipase/esterase may be required for the formation of benzyl acetate from benzyl alcohol or the synthesis of 1-phenyl acetate from 1-phenyl ethanol  linked to benzyl acetate (UR) contains a candidate gene coding for a Putative GDSL esterase/lipase At1g29670 that may play a role in the degradation of benzyl acetate  trait  where weGenome-Wide Association Study analysis, two main biosynthesis pathways of compounds known for their floral notes seem to be involved in cocoa floral aromas: the monoterpene synthesis pathway and the L-phenylalanine degradation pathway. These biosynthesis pathways have already been identified in other such as grapes or its derivative wine as important contributors to their floral aromas  with this function been identified close to the association zones on chromosomes 7 and 9 , ethylphenyl acetate (UR), and ethyl hexanoate (UR). These compounds present after fermentation and before roasting could also be synthesised by yeasts during fermentation  produced by plants are involved in various processes and often released for defence, signalling, or pollinator attraction purposes Baldwin, . VolatilDuring fermentation, the change in environment and chemical composition of the medium induced by yeasts and bacteria can be taken as a threat and cause the seed to react. Then, they could release VOCs to defend themselves and would be responsible for the synthesis of VOCs involved in fine flavour, as suggested by Sabau et al.  who obseIf cocoa beans use VOCs as a defence mechanism against external microorganisms such as fermentative yeasts, lactic bacteria, or acetic bacteria, some questions remain unanswered: by which mechanisms do they detect such microorganisms? Knowing that different types of yeast have been identified according to the place of fermentation  analyses for example. Knowing the main molecules responsible for the floral taste as well as the mechanisms of synthesis and degradation of the compounds during fermentation and roasting could also, in the long term, allow the adaptation of the roasting process (temperatures and roasting time) to preserve the most fragile aromatic compounds. Knowledge of the biosynthesis pathway of cocoa aromatic compounds could provide a better mastering of the parameters of fermentations allowing the synthesis of these molecules.The identification of these molecules and their biosynthetic pathway within the cocoa tree is complex. A genomic selection approach could allow early prediction of aroma traits for the search of cocoa trees having good aroma potential, especially as certain genetic variation could explain a large extend of biochemical compounds in the beans. In this case, a marker-assisted selection could be envisaged in the selected programmes to make it easier for the selection of the cocoa trees aromatic quality.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants did not require approval in line with regional/national guidelines. The patients/participants provided their written informed consent to participate in this study.EC, CL, and RL conceived the experiment. J-CJ and AS conducted biochemical analyses. ES carried out sensorial analyses. OF carried out DNA experiments. KC, J-CJ, AS, RB, CL, FD, SA, and XA analysed data. KC, RB, and CL wrote the manuscript. All authors contributed to the article and approved the submitted version.This study was funded by the United States Department of State (U.S. Foreign Ministry), the U.S. Embassy, Quito, and the U.S. Department of Agriculture (USDA-ARS) with the agreement n° 58-4001-2-F128 and the MUSE Amazcacao project with the reference ANR-16-IDEX-0006.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables.We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR.In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding , even in the absence of horizontal pleiotropy . In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index.Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data  is required to direct sensitivity analyses and interpret results with appropriate caution. Summary genetic association results from large genome-wide associations studies (GWAS) have been increasingly used in two-sample Mendelian randomization (MR) analyses.Many GWAS adjust for heritable covariates in an attempt to estimate direct genetic effects on the trait of interest.In an extensive simulation study, we demonstrate that using covariable-adjusted summary associations may bias MR analyses.The bias largely depends on the underlying causal structure, specially the presence of unmeasured common causes between the covariable and the outcome.Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided.,Mendelian randomization (MR) uses genetic variants to assess the influence of modifiable exposures on health outcomes.Two-sample MR is an extension to the one-sample MR design, where estimates for the association of genetic variants with exposure and with outcome are derived from different (non-overlapping) samples from the same underlying population.,In GWAS, estimates for the association of genetic variants with the trait of interest are often conditioned on covariables. As an example, GWAS of waist-to-hip ratio have adjusted estimates for body mass index (BMI),The potential issue of adjusting for heritable covariates is illustrated in et al., Emdin et al., Borges et al., Au Yeung et al., Clarke et al.,,Several two-sample MR studies have used summary association data from GWAS that have estimated the effect of genetic variants on the trait of interest conditioned on heritable covariables  . This maWe performed simulations in two main ways:(i) All genetic variants have directionally consistent direct effects on the same traits (hereafter referred to as ‘homogeneous genetic variants’). For example, if there are 10 simulated genetic variants and one of them has positive direct effects on both (ii) Some genetic variants have direct effects only on some traits (hereafter referred to as ‘heterogeneous genetic variants’). This also varies according to the data-generating model (explained below).The distinction between situations (i) and (ii) is further clarified below, after presenting the causal structures underlying the simulations.Scenarios simulated under situation (i) are illustrated in IJE online), all direct effects are independent of one another. Therefore, the In scenarios A1-F1, there are no unmeasured confounders (other than R). To isolate the implications of unmeasured confounders when controlling for To simulate data under situation (ii), the same underlying causal structure used for situation (i) was used, with the exception that, in all simulations, there were four non-overlapping subgroups of genetic variants: some with direct effects on It is now easy to clarify the distinction between situations (i) and (ii). In situation (i), all genetic variants have direct effects on All simulations generated data for 40 independent candidate genetic instruments and 100 000 individuals, and the resulting dataset was divided into two halves at random: one was used to estimate instrument-exposure associations, and the other to estimate instrument-outcome associations, thus corresponding to the two-sample MR context. Briefly, for each measured phenotypic variable, all genetic variants with direct effects on the given phenotype were combined into an additive allele score which had an effect set to account for 10% of the variance of the given phenotype. For measured phenotypic and unmeasured variables with direct effects on the given phenotype, direct effects were set to account for 20% of the variance of the given phenotype. A detailed description on how effects were generated is provided in the For analyses using unadjusted variant-exposure summary association results , the outcome variables were systolic (SBP) and diastolic blood pressure (DBP) and the covariable was BMI. We selected genetic instruments of unadjusted WC and BMI-adjusted WC from the Genetic Investigation of ANthropometric Traits (GIANT) consortiumBMI was used as a covariable due to its strong correlation with WC, meaning that variants that affect WC might also affect BMI due to their effect on overall adiposity. Here, we assume that two distinct causal structures  are plauP-value <5 × 10–8. After quality control  are already available. Therefore, we selected two sets of WC genetic instruments: one using the unadjusted GWAS results; and another using the BMI-adjusted GWAS. In each case, independent genetic variants were selected as WC instruments if ,Causal effect estimates were obtained using multiplicative random effects inverse-variance weighting (IVW).IJE online, displays the number of select genetic instruments and mean IJE online). Although the absolute bias was generally larger when simulating homogeneous genetic variants, the results were generally in the same direction in both situations.In the absence of unmeasured confounders (Scenario 1), using adjusted instrument-In cases where In the unadjusted SNP-exposure and SNP-outcome analysis, each standard unit increase in WC was related to an increase of 0.06 mmHg in SBP  and of 0.12 mmHg in DBP . These effects changed in direction when only adjusting the SNP-outcome association for BMI . Effect estimates also changed direction, but were consistent with the null when adjusting only the SNP-exposure association  or using both adjusted SNP-exposure and SNP-outcome associations  .P <0.05), results were similar for the adjusted SNP-exposure analyses. The exclusion of BMI-related SNPs dramatically restricted the number of SNPs included in the unadjusted SNP-exposure analyses (two out of 37 SNPs) and, as a result, estimates are highly imprecise (IJE online).After excluding SNPs associated with BMI  between exposure-covariable, exposure-outcome and covariable-outcome, covariable adjustment eliminates bias due to horizontal pleiotropy mediated by such covariable (Scenarios E and F). However, absence of unobserved confounding is unrealistic in observational studies and is one of the primary motivations for performing MR. In the presence of unobserved confounding, mainly between the covariable and the outcome, covariable adjustment will likely lead to bias even in the absence of horizontal pleiotropy, due to collider bias when genetic instruments are marginally associated with the covariable. Therefore, minimizing horizontal pleiotropy is generally an invalid justification for using covariable-adjusted summary association results for two-sample MR.Bias was generally weaker when the true causal effect of the exposure on the outcome was null among several scenarios we evaluated. Indeed, even in the absence of any unmeasured confounding, covariable adjustment may lead to bias when the true causal effect is not null. As an example based on the directed acyclic graph (DAG) for Scenario A1 in Using covariable-adjusted instrument-outcome summary associations was also consistently related to larger biases compared with using covariable-adjusted instrument-exposure summary associations. The exception was scenarios where there was horizontal pleiotropy. However, since attempting to minimize horizontal pleiotropy via covariable adjustment is generally unjustified due to the likely presence of unmeasured confounders, we are primarily concerned with the scenarios where there is no horizontal pleiotropy.Results were generally consistent between simulations with homogeneous and heterogeneous instruments. The simulations with homogeneous instruments are useful to isolate the effect of covariable adjustment according to each underlying causal structure linking In an analysis using real data from GIANT consortium and UK Biobank, the estimated causal effect of waist circumference on blood pressure changed substantially upon adjustment for BMI. In this context, interpreting whether covariable adjustment could have affected the validity of results is particularly challenging, since adjustment for BMI  could remove bias due to horizontal pleiotropy and/or introduce bias due to conditioning on a collider (i.e. BMI) in the pathway between instrument-outcome and/or instrument-exposure. As illustrated in To our knowledge, this is the first study to systematically assess the impact of covariable adjustment in (two-sample) MR. Conditioning on a heritable covariable can introduce bias when the covariable is a collider in the pathway between instrument-exposure and/or instrument-outcome. Collider bias can also be introduced in MR studies in other settings, such as in disease progression studies which include a selected (i.e. case-only) group of individualsIt is important to emphasize that using covariable-adjusted GWAS summary association results in two-sample MR studies differs from applying multivariable MR (MVMR) on unadjusted GWAS summary association results to estimate the effect of two or more exposures on an outcome.One of the strengths of our study was that our simulations covered a wide range of scenarios, thus allowing a detailed evaluation of covariable-adjustment bias in a variety of situations. The simulation study was also complemented with a real data example illustrating the strong influence that covariable-adjustment may have not only on the magnitude, but also on the direction of the causal effect estimate. However, any simulation study is a simplification of a reality that is likely to be much more complex. It is impossible to simulate all possible scenarios that might be of relevance to this topic. Moreover some results, especially quantitative estimates of bias and coverage, are highly dependent on the data-generating model. Therefore, our results should be interpreted qualitatively, as general indications of some of the main aspects related to covariable-adjustment in MR. It was reassuring that some results (as described above) were consistent across scenarios, which indicates that they may apply generally  to covariable-adjusted MR.In conclusion, our findings indicate that using summary association results adjusted for heritable covariables may lead to bias in two-sample MR due to unmeasured confounding. We recommend avoiding adjustment for such covariables in the context of MR. When only covariable-adjusted data are available, it is important to carefully consider the causal structure underlying the research question to understand the potential impact on the results. In such cases, we recommend that researchers refrain from interpreting the causal estimate too literally . To account for horizontal pleiotropy due to measured covariables, MVMR should be preferred over non-genetic covariable adjustment.IJE online.K.T., G.D.S., D.A.L. and M.C.B. work in the MRC Integrative Epidemiology Unit at the University of Bristol which receives funding from the UK Medical Research Council . D.A.L.’s contribution is also supported by the European Research Council under the European Union's Seventh Framework Programme [FP/2007–2013] / ERC Grant Agreement  and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 733206 (LifeCycle). M.C.B. is supported by a Medical Research Council (MRC) Skills Development Fellowship [MR/P014054/1]. D.A.L. is a UK National Institute of Health Research Senior Investigator [NF-SI-0611–10196].dyaa266_Supplementary_DataClick here for additional data file.