Steroid hormones act as important regulators of physiological processes including gene expression. They provide possible mechanistic explanations of observed sex-dimorphisms in obesity and coronary artery disease (CAD). Here, we aim to unravel causal relationships between steroid hormones, obesity, and CAD in a sex-specific manner. In genome-wide meta-analyses of four steroid hormone levels and one hormone ratio, we identified 17 genome-wide significant loci of which 11 were novel. Among loci, seven were female-specific, four male-specific, and one was sex-related . As one of the loci was the human leukocyte antigen (HLA) region, we analyzed HLA allele counts and found four HLA subtypes linked to 17-OH-progesterone (17-OHP), including HLA-B*14*02. Using Mendelian randomization approaches with four additional hormones as exposure, we detected causal effects of dehydroepiandrosterone sulfate (DHEA-S) and 17-OHP on body mass index (BMI) and waist-to-hip ratio (WHR). The DHEA-S effect was stronger in males. Additionally, we observed the causal effects of testosterone, estradiol, and their ratio on WHR. By mediation analysis, we found a direct sex-unspecific effect of 17-OHP on CAD while the other four hormone effects on CAD were mediated by BMI or WHR. In conclusion, we identified the sex-specific causal networks of steroid hormones, obesity-related traits, and CAD. Male sex is an independent risk factor for cardiovascular disease (CVD), but the underlying molecular mechanisms are not fully understood. Genome-wide association studies have identified several risk loci in the autosomes , but nonThere are some studies supporting this relationship: estradiol (E2) was suspected of having a cardio-protective effect before menopause ,4,5 and Obesity is a disease defined by excessive fat accumulation that might impair health ; it alsoIt has been shown that steroid hormone signaling is relevant in adipose tissue (AT) regulation , e.g., aWhile the causal link of obesity to CAD is well established , the relIn the present study, we first validate previously published genetic loci associated with steroid hormones  and add To validate known and discover novel instruments for our MR analyses, we performed genome-wide association meta-analyses (GWAMA) of the levels of four hormones, namely progesterone (P4), hydroxyprogesterone (17-OHP), androstenedione (A4), and aldosterone (Aldo) in two independent studies: LIFE-Heart   and LIFE2 ≤ 0.9). A total of 10.9 Mio SNPs remained for evaluation, for which we did not observe signs of general inflation of test-statistics (λ in between 0.99 and 1.01). Genome-wide results across all hormones for the combined setting as well as the male- and female-stratified setting are shown in p < 5 × 10−8 and pairwise LD r2 < 0.1), of which 17 were best-associated in the combined setting, 11 in the male-stratified setting, and 7 in the female-stratified setting. Hits can be summarized to 16 unique genomic loci plus the major histocompatibility complex (MHC) stretching over three cytobands on chromosome 6 . We searched for genetic sex interactions of all genome-wide significant lead SNPs and applied hierarchical FDR correction to adjust for multiple testing (see Methods). Summary statistics, annotations, and interaction results can be found in We calculated fixed-effect meta-analysis models (FEM) and applied SNP filters for minor allele frequency , imputation info score , and heterogeneity of meta-analysis results  [VIPR2 at 7q36.3  [RBFOX1 at 16p13.3  [HSD3B1 at 1p12  [REL at 2p15 (sex-unspecific) [CYB5A at 18q22.3  [MGMT at 10q26.3  [HSD17B7 at 1q23.3  [CNST) [CYP19A1 at 15q21.2 (sex-unspecific) [PARK2 at 6q26 (sex-unspecific) [Eleven of these loci have not yet been described for the respective traits and, hence, are considered as novel findings. For P4, there were three novel loci: pecific) , VIPR2 afemales) , and RBFpecific) . For 17-pecific) , REL at pecific) , and CYBpecific) . There wpecific) ,32,33. F) [CNST) . Finallypecific) , and PARp < 1 × 10−6), and the other four traits were all associated on a nominal level, e.g., 716 of the 872 SNPs were associated with P4 in males (p < 0.05). Therefore, we tested whether the human leukocyte antigen (HLA) subtypes associate with our steroid hormones. HLA subtypes were estimated from imputed genotypes, and associations were calculated using an allele dosage model (see Methods).The hormone 17-OHP was associated with multiple variants at the MHC region . In addition, they were associated with P4 in males, but not in females or the combined setting. The effect difference was only significant for HLA-B*14*02 (qIA = 0.0254), suggesting that this subtype had a male-specific effect on P4, but not on 17-OHP. HLA-C*08*02 and HLA-B*14*02 are in high LD . We contrasted this observation to measured haplotype frequencies from Wilson et al. [CYP21A1 mutations [We derived 324 HLA-subtypes in LIFE-Adult and LIFE-Heart. After FDR correction, we detected significant associations for four of them: HLA-C*08*02, HLA-B*14*02, HLA-DQA1*01*01, and HLA-DQB1*05*01 see . The firn et al. , where tutations ,39.IA = 0.985, qIA = 0.985, respectively), and are also in high LD with each other  were only associated with 17-OHP in a sex-unspecific way (q819 from ). They asyndrome , and ourWe tested for causal effects of our hormones on obesity-related traits  and CAD. Regarding obesity, we also checked for reverse causality and mediation effects on the hormone–CAD link (see Methods). Instruments and summary statistics for BMI, WHR, and CAD were retrieved from the literature ,13, and IA = 0.043). The sex-specific effect difference of 17-OHP on WHR did not reach significance (pIA = 0.055).First, we tested for the causal effects of steroid hormones on BMI and WHR, stratified by sex. As instruments, we only used SNPs at loci with biologically plausible genes, e.g., coding for enzymes of the steroid biosynthesis pathway (max dist. 250 kb). There were 13 pairs of hormones and obesity-related traits showing significant causal relationships, of which 12 survived multiple testing corrections (see p < 1 × 10−6). We repeated the analyses for all combinations and detected four significant links: E2 on WHR in the combined setting, and, in males, T/E2 on WHR in the combined setting, and 17-OHP on WHR in females. We also repeated the interaction test as, now, instruments were available for both sexes, and found the causal effect of E2 on WHR to be male-specific (pIA = 1.92 × 10−7).In an explorative approach, we tested if the results could be replicated using more but weaker SNPs, e.g., considering loci of suggestive significance . In the sensitivity analysis, the effect in females was still nominally significant, but did not survive multiple testing correction . The mediation via WHR could also be replicated. All results are summarized in Since 17-OHP was the only hormone with both, direct and indirect effects on CAD, we aimed at replicating our causal estimates considering the identified associations with HLA subtypes. This analysis confirmed that 17-OHP causally affects CAD in a sex-unspecific way , hydroxyprogesterone (17-OHP), androstenedione (A4), and aldosterone. This is an extension of our previous work , in whicHSD3B1/B2 , HSD17B7 , and CYP19A1 . The HSD3B1/B2 gene codes for 3β-hydroxysteroid dehydrogenases  are required for the production of all biologically active steroid hormones [HSD17B7) is responsible for the transformation of estrone to estradiol [CYP19A1 gene has been previously reported for E2 [Three of these novel loci are directly linked to steroid hormone biosynthesis, namely hormones . The enzstradiol , which md for E2  and T [3d for E2 , but notCYP21A1 mutations [We were able to replicate the associations at 6p21.32, 6p21.33, and 6p22.1 for 17-OHP. In our previous work [utations ,39 and cutations . For ourCYP3A4 and SULT2A1, both catalyzing the reaction of DHEA to another metabolite, 16α-OH-DHEA and DHEA-S, respectively. In our previous work, we found sulfonation and de-sulfonation genetically regulated in females, but not males [In our MR analyses, we used as instruments our previously published data for cortisol, DHEA-S, T and E2 , and ourot males . The posCYP21A1 in the HLA region. This is in line with our findings. In women with polycystic ovary syndrome, a positive correlation between 17-OHP and epicardial fat thickness was reported [For 17-OHP, we detected sex-unspecific causal effects on BMI, WHR, and CAD. Both direct and indirect effects on CAD, mediated via obesity-related traits were observed. The hormone was proposed as an independent predictor of WHR , and abdreported . Epicardreported . In summFinally, we found the causal effects of E2, T, and T/E2 on WHR in both the combined setting and males. For the female subgroup, estimates could not be calculated since there were either no strong instruments for females  or the statistics of the outcome could not be matched to the available instrument (E2). Hence, the sex-specificity for these links could not be tested. The effects of E2 and T alone were negative, while the hormone ratio had a positive causal effect on WHR. In a study of young women, both E2 and T were negatively correlated with WHR, but there was a significant T × E2 interaction on WHR . Hence, This study was limited by the small number of instruments for the steroid hormones, which lowers the power of Mendelian randomization. However, for all hormones, instruments in or nearby genes with direct influence on the steroid hormone synthesis pathway were available, strengthening the assumption that the variants affect the analyzed outcomes via the respective hormone. A second limitation is that the summary statistics for CAD were only available for the combined setting. According to the latest CAD GWAMA , there aIn conclusion, we identified 11 novel genetic loci of steroid hormone levels with pronounced sex effects. In a fine-mapping approach of the MHC region, we found two HLA subtypes significantly associated with 17-OHP and P4. Based on these loci, we discovered the sex-specific causal networks of steroid hormones, obesity-related traits, and CAD.n = 10,000) [Two studies of the Leipzig Research Centre for Civilization Diseases (LIFE) were analyzed: LIFE-Adult is a population-based cohort of citizens of Leipzig, Germany  . RecruitLIFE-Heart is a cohort of patients with suspected or confirmed coronary artery disease or myocardial infarction . PatientBoth LIFE studies meet the ethical standards of the Declaration of Helsinki. They are approved by the Ethics Committee of the Medical Faculty of the University of Leipzig, Germany . Written informed consent, including agreement to genetic analyses was obtained from all participants.In LIFE-Adult, levels of the four steroid hormones—progesterone (P4), hydroxyprogesterone (17-OHP), androstenedione (A4), and aldosterone—were measured by liquid chromatography–tandem mass spectrometry (LC—MSMS) , while tSamples were excluded from hormone analyses if the participant was on steroid medication (ATC codes starting with “G03” or “H02AB”) or if quality control of the steroid profile suggested a mix-up of samples, or underlying diseases, e.g., hyperandrogenism, hypogonadism, adrenal insufficiency, congenital adrenal hyperplasia, or polycystic ovary syndrome.In both studies, participants were measured for height, weight, and waist and hip girths. Based on these characteristics, BMI and WHR were calculated as obesity-related traits. All LIFE-Heart patients received diagnostic coronary angiography, and CAD was defined as at least one stenosis of ≥50% of any major coronary vessel. Both, anthropometric and CAD data were used in MR sensitivity analyses using HLA subtypes as instruments.Both LIFE studies were genotyped using the Affymetrix Axiom SNP-array technology   < 0.01 were removed before imputation. Imputation was performed using the 1000 Genomes Project Phase 3 European reference panel [Samples with XY irregularities, including sex mismatches or cryptic relatedness, and genetic outliers  were excluded. Further, variants with a call rate less than 0.97, Hardy-Weinberg equilibrium ce panel  with IMPce panel . In summTo estimate the HLA subtypes, we selected all SNPs of the MHC region on chromosome 6  that could be matched to the Axiom HLA reference set . The besSingle study GWAS. The four hormones  and the hormone ratio (T/E2) were log-transformed for all analyses to obtain normally distributed traits. We performed genome-wide association analysis for each study (GWAS) and phenotype in all samples (combined setting) and sex-stratified samples , with adjustment for age, log-transformed BMI, and sex in the combined setting. For analyses, we used the additive frequentist model with expected genotype counts as implemented in PLINK 2.0 ,64.File QC. All SNPs were harmonized to the same effect allele and were filtered for minor allele frequency (MAF) <1%, imputation info score <0.5, and minor allele count (MAC) ≤6. In addition, we checked for mismatching alleles or chromosomal position with respect to 1000 Genomes Phase 3 European reference  and excl2 statistics to evaluate heterogeneity and filtered our results with I2 ≥ 0.9. Finally, we excluded SNPs with a minimum imputation info-score across studies of less than 0.8. The genome-wide and suggestive significance levels were set to αgw = 5 × 10−8 and αsug = 5 × 10−6, respectively.Meta analyses. For meta-analyses, single study results per phenotype and setting were combined using a fixed-effect model, assuming homogenous genetic effects across studies. We used I2 > 0.3, and known associated traits [2 > 0.3 using 1000 Genomes Phase 3 (European samples) [Annotation. SNPs reaching at least suggestive significance for one of the phenotypes were annotated with nearby genes , eQTLs  in linkad traits  in LD r2samples)  as the Lsamples) ).We used linear regression models to test for associations of the dosage of HLA subtypes with hormone levels. The same models as described in the GWAMA section were analyzed. There were 108 HLA subtypes available in both studies for meta-analyses. Regression models were run in R v.3.6.0. We also tested BMI, WHR, and CAD for association with HLA subtypes. Here, we used linear regression for analyses of BMI and WHR and logistic regression for analysis of CAD, and adjusted for age, log-BMI , and sex . CAD was only available in LIFE-Heart, while BMI and WHR were available in both LIFE cohorts.To identify independent subtypes, we estimated pairwise correlations between subtype allele dosages . In addition, we looked up asymmetric LD between HLA genes . While traditional LD estimates the correlation between bi-allelic loci, asymmetric LD captures the asymmetry of multi-allelic loci . We usedt-tests of β estimates) [We tested the 16 lead SNPs reaching genome-wide significance in any setting and the six significant HLA subtypes associated with steroid hormone levels regarding sex-specific effects. This was done by comparing the effect sizes of males and females for the best-associated phenotype (timates) . To adjutimates) . The firMR models. We investigated three possible causal links between steroid hormones, obesity-related traits, and CAD in a sex-specific manner. First, we tested for causal links between steroid hormones and obesity-related traits  in both directions. Then, we searched for causal links of steroid hormones on CAD and tested all significant links of steroid hormones and obesity-related traits for mediation effects on CAD by estimating direct and indirect effects (mediation MR). A graphical summary of this approach is given in −6 to get a comprehensive SNP list, and then selected for each exposure–outcome combination the best-associated SNP per locus for which outcome statistics are available. For 17-OHP, we repeated the analyses using the associated HLA subtypes as instruments to replicate our respective causal findings. As for these subtypes, association statistics for BMI, WHR, and CAD were not available in the literature; we estimated them in our LIFE studies.Data Source. As instruments for SH, we used SNPs associated with the analyzed hormones at biologically meaningful loci, e.g., genes coding for enzymes of the steroid hormone biosynthesis pathway. Statistics were obtained from the above-mentioned GWAMA and our previous work on cortisol, DHEAS, T, and E2 . While sCYP19A1). However, it is highly plausible that this condition holds because we only considered loci of the steroid hormone biosynthesis pathway, which should have a direct effect on hormones.Key Assumptions. SNPs were assumed to satisfy the three MR assumptions for instrumental variables (IVs): (1) The IVs were, genome-wide, significantly associated with the exposure of interest. This was shown by our GWAMA results. (2) The IVs were uncorrelated with confounders of the relationship of exposure and outcome. This might be a concern for sex, since the SNPs are partly sex-specific or sex-related, and the outcomes display sexual dimorphisms. Therefore, we ran all MR analyses in a sex-stratified manner using only those SNPs as IVs that were significant in the respective strata. (3) The IVs correlated with the outcome exclusively by affecting the exposure levels (no direct SNP effect on the outcome). Some loci are known to be associated with CAD or obesity , only one genome-wide significant locus was available. Hence, only one instrument was available and we applied the ratio method, which estimates the causal effect as the ratio of the SNP effect on the outcome by the SNP effect on the exposure . The stat-statistics of the differences:For mediation analyses, we used the total causal estimates α (SH → obesity-related trait), τ (SH → CAD), and β (obesity-related trait → CAD). While α and τ were calculated as described above, the causal effects of BMI and WHR on CAD were taken from  Table 1Table 1.


Feed efficiency is one of the key determinants of beef industry profitability and sustainability. However, the cellular and molecular background behind feed efficiency is largely unknown. This study combines imputed whole genome DNA variants and 31 plasma metabolites to dissect genes and biological functions/processes that are associated with residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) in beef cattle.P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using 10,488,742 imputed SNPs, 40, 66, 15, and 40 unique candidate genes were identified as associated with RFI, DMI, ADG, and MWT , respectively. These candidate genes were found to be involved in some key metabolic processes including metabolism of lipids, molecular transportation, cellular function and maintenance, cell morphology and biochemistry of small molecules.Regression analyses between feed efficiency traits and plasma metabolites in a population of 493 crossbred beef cattle identified 5 , 4 , 1 (citric acid), and 4  plasma metabolites associated with RFI, DMI, ADG, and MWT (This study identified metabolites, candidate genes and enriched biological functions/processes associated with RFI and its component traits through the integrative analyses of metabolites with phenotypic traits and DNA variants. Our findings could enhance the understanding of biochemical mechanisms of feed efficiency traits and could lead to improvement of genomic prediction accuracy via incorporating metabolite data.The online version contains supplementary material available at 10.1186/s12864-021-08064-5. Feeding-related costs are the major expense in beef cattle enterprises, representing 55 % - 75 % of total production costs –3. ReducResidual feed intake (RFI) is an important indicator of feed efficiency, which is usually defined as the difference between an animal’s actual daily dry matter intake (DMI) and the expected daily DMI given the animal’s average daily gain (ADG) and metabolic body weight (MWT) . CurrentP-value < 0.1) and the results of regression analyses are shown in Table P-values less than 0.05, and four metabolites  showed P-values ranging from 0.05 to 0.1 . At P-values less than 0.1, five metabolites, including L-valine, lysine, L-tyrosine, L-isoleucine, and L-leucine, were significantly associated with RFI, accounting for 5.90 % of the phenotypic variance in RFI. Lysine, L-lactic acid, L-tyrosine, and choline were significantly associated with DMI, and these four metabolites accounted for 4.04 % of phenotypic variance in DMI. Of note, lysine and L-tyrosine were significantly associated with both RFI and DMI. Citric acid was the only metabolite that was significantly associated with ADG and accounted for 0.93 % of phenotypic variance in ADG. Four metabolites, L-glutamine, glycine, citric acid, and dimethyl sulfone, were significantly associated with MWT and accounted for 3.39 % of phenotypic variance of MWT.Of the 31 metabolites analyzed, 11 were found to be significantly associated with the feed efficiency traits (n = 493). Thus, these estimates could be used as reference information and further study may be warranted.Heritability estimates of 11 metabolites associated with feed efficiency traits were calculated  and candidate genes identified for each metabolite are summarized in Table P-value and percentage of metabolite phenotypic variance explained by each SNP, for the 11 metabolites associated with feed efficiency traits are provided in Additional file Metabolome-genome wide association studies were performed for the 11 metabolites associated with the feed efficiency traits. The range of P-value < 0.05), respectively as presented in Additional file P-value for DMI and also significantly associated with RFI and MWT  were involved  involved in the lipid metabolism were also involved in the carbohydrate metabolism for DMI, which engaged three subfunctions including oxidation of D-glucose, concentration of D-glucose, and quantity of carbohydrate  was used. Furthermore, we would like to highlight that only 31 metabolites were detected by the targeted method of NMR used in the current study. We therefore recommend that metabolomic profiles with more metabolites should be investigated in future with larger samples in order to identify more metabolites that are associated with RFI or its component traits.In this study, 5 , 4 , 1 (citric acid) and 4  plasma metabolites were identified to be associated with RFI, DMI, ADG, and MWT, respectively . A previous study reported a higher positive correlation  between lysine and tyrosine [rg = 0.66±0.11 to 0.75±0.10) [It is worth noting that, the three metabolites  associated with RFI are three essential branched-chain amino acids. These three metabolites share the first enzymatic steps in their oxidative pathways, including a reversible transamination followed by an irreversible oxidative decarboxylation to coenzyme-A derivatives . The restyrosine . The ass75±0.10) . Further75±0.10) , furtherADGRF1 and ADGRF5 which were associated with both RFI and DMI [SLC28A3 was associated with ADG and MWT [ADGRF1, ADGRF5, GTPBP8, and NEPRO as associated with both RFI and DMI [ADGRF1, ADGRF5 with RFI and DMI, because we identified that ADGRF1 and ADGRF5 were associated with L-tyrosine which was a metabolite associated with both RFI and DMI. As for GTPBP8 and NEPRO, both genes were associated with another common metabolite called lysine that was identified to be associated with both RFI and DMI in the current study. Additionally, we observed that certain genes might be associated with the same feed efficiency trait through different metabolites. For example, SHROOM3 was associated with L-valine and L-leucine and these two metabolites were associated with RFI , heifers (n = 125) and steers (n = 275) that were born between 2002 and 2011. These animals were from five different commercial herds and they were tested in feedlots from 2003 to 2012 [n = 73), Hereford-Angus crosses (n = 191) and a Beefbooster composite breed . The GrowSafe system  was used to measure the feed intake of finishing calves at the feeding test station for a period of 76 to 112 days. Serial body weights (BW) in kg were measured for each animal at the beginning and end of the test and at approximately 14-day intervals during the test. The daily DMI in kg was calculated as an average of dry matter intake over the test period and further standardized according to the energy content of the diet. The initial BW in kg at the start of the feeding test and the ADG in kg were derived from a linear regression of the serial BW measurements against time (day) [0.75 while the midpoint BW was computed as the sum of the initial BW in kg and the product of ADG multiplied by half of the days on test [All animals in this study were cared for according to the guidelines of the Canadian Council on Animal Care (1993). The population of animals was obtained from the Phenomic Gap Project that aimed to generate phenotypes of feed efficiency, carcass and meat quality as well as genomic data for Canadian crossbred beef animals . Details to 2012 . The majme (day) , 42. The on test , 42. Theers n = 25 that we on test . Thirty-P-value < 0.0001). Finally, a total of 10,488,742 SNPs remained after quality control and were used in further analyses.Theoretically, a higher marker density could improve the power of GWAS to identify significant SNPs, therefore, the 50 K genotypes were imputed to whole genome sequence variants using Beagle 5.1 software . The SNPBreed composition of each animal was predicted based on the 50 K genotypes using ADMIXTURE software to account for population stratification , 46. In P-value < 0.05). Therefore, phenotypic values of the feed efficiency traits and metabolites were pre-adjusted for the above factors using liner regression models. Residuals with more or less than 3 standard deviations from the mean of residuals were considered as outliers and were excluded. Additional file The variation in feed efficiency traits and metabolites could be affected by multiple systematic effects. A linear regression model implemented in R statistical software was used to assess the significant systematic effects that were associated with feed efficiency traits or metabolites. Animal type , birth year, herd, feedlot pen, age at the feedlot test, and breeding composition were found to be the significantly associated factors for both the feed efficiency traits and metabolites  were performed for metabolites that were significantly associated with the feed efficiency traits using the mlma (mixed linear model association) option as implemented in the GCTA package [After quality control and pre-adjustment of phenotypic data, regression analyses were conducted to identify associations between four feed efficiency traits and thirty-one metabolites using R statistical software. A feed efficiency trait and a metabolite were considered to be significantly associated when a  package  based onwhere g et al. . The samP-value < 1 × 10−5 were classified to be significantly associated with the metabolite according to the recommendation of The Wellcome Trust Case Control Consortium [The SNPs with nsortium . The phewww.Ingenuity.com) to predict the enriched biological functions and gene networks for feed efficiency traits. Biological functions were considered significantly enriched if the P-value for the overlap comparison test between the input gene list and the knowledge base of IPA for a given biological function was less than 0.05. In order to provide insight into cellular and molecular functions associated with feed efficiency traits, gene networks for some significant biological functions were constructed in IPA.To identify candidate genes for concentration of each metabolite, a 140-kbp window (70-kbp upstream and 70-kbp downstream) of each significant SNP was surveyed based on SNP annotation information from ARS-UCD 1.2 bovine genome assembly from the Ensembl BioMart database . The 70-kbp was the chromosomal length within which a high linkage disequilibrium phase correlation  variants in a beef cattle multibreed population; Table S2. Uniquely common candidate genes for feed efficiency traits in a beef cattle multibreed population; Table S3. Enriched biological functions significantly associated with RFI in a beef cattle multibreed population; Table S4. Enriched biological functions significantly associated with DMI in a beef cattle multibreed population; Table S5. Enriched biological functions significantly associated with ADG in a beef cattle multibreed population; Table S6. Enriched biological functions significantly associated with MWT in a beef cattle multibreed population; Table S7. Uniquely common biological functions for feed efficiency traits in a beef cattle multibreed population; Table S8. The comparison of candidate genes between the current study and Zhang et al; Table S9. Genomic breed composition for animals; Table S10. Descriptive statistics of phenotypic data on feed efficiency traits and metabolites.Additional file 2. This file contains a summary of SNPs significantly associated with the 11 metabolites.Additional file 3. This file contains a summary of candidate genes for the 11 metabolites.Additional file 4: Figure S1. Uniquely common candidate genes for feed efficiency traits in a beef cattle multibreed population; Figure S2. Uniquely common biological functions for feed efficiency traits RFI, DMI, ADG, MWT in a beef cattle multibreed population


Using an experimental video recording trial, feed conversion ratio (FCR), body weight gain (BWG), residual feed intake (RFI) and feed intake (FI) traits were recorded in 40 full-sibling families from the GIFT  Nile tilapia breeding population. Fish were genotyped with a ThermoFisher Axiom 65 K Nile tilapia SNP array. Significant heritabilities, ranging from 0.12 to 0.22, were estimated for all the assessed traits using the genomic relationship matrix. A negative but favourable genetic correlation was found between BWG and the feed-efficiency related traits; −0.60 and −0.63 for FCR and RFI, respectively. While the genome-wide association analyses suggested a polygenic genetic architecture for all the measured traits, there were significant QTL identified for BWG and FI on chromosomes seven and five respectively. Candidate genes previously found to be associated with feed-efficiency traits were located in these QTL regions, including ntrk3a, ghrh and eif4e3. The accuracy of breeding value prediction using the genomic data was up to 34% higher than using pedigree records. A SNP density of approximately 5,000 SNPs was sufficient to achieve similar prediction accuracy as the full genotype data set. Our results highlight the potential of genomic selection to improve feed efficiency traits in Nile tilapia breeding programmes.Nile tilapia is a key aquaculture species with one of the highest production volumes globally. Genetic improvement of feed efficiency via selective breeding is an important goal, and genomic selection may expedite this process. The aims of this study were to  One of the key features of aquaculture species compared with terrestrial farmed species is their greater feed efficiency (FE) . For exaOreochromis niloticus) is the third most produced farmed species globally (1) dissect the genetic architecture of feed-efficiency traits in a Nile tilapia breeding population, 2) map the genomic regions associated with these traits and identify candidate genes, 3) evaluate the accuracy of prediction of breeding values using genomic data, and 4) assess the impact of the SNP density on the genomic prediction accuracies. To the best of our knowledge, this is the first study to assess genomic prediction for feed-efficiency traits in Nile tilapia. Our results highlight the feasibility of including feed-efficiency traits into Nile tilapia breeding programs. Furthermore, they show the potential improvement in breeding value prediction using low density SNP panels compared to using pedigree methods, which can enhance genetic gain and improve production efficiency.Nile tilapia  breeding program based in Jitra, Malaysia, and managed by WorldFish (This study used tissue samples archived by orldFish  These fi1) Generally 1 week is sufficient for an adaptation period. However, this was increased to 2 weeks as a precaution because some fish could adapt faster than others. The 10 days of fasting 2) represents the longest period permitted while maintaining acceptable fish welfare, while fewer days would probably have a low impact in terms of weight loss. The longest period was ascribed to the feeding stage 3), which allows the fish to recover from the fasting period but also enables successful measurement of compensatory growth, ensuring that this does not impact on the measurements of the feeding and growth during the last stage. Previous results by Full details of the experimental challenge are given in previous studies, which reported methodology development and quantitative genetic analysis of feed efficiency traits ; 2018b. Body weight was measured at the beginning and end of each stage. Due to aggression between fish, only 1,029 fish were available at the beginning of the FI stage where fish were fed twice a day (07:00 and 13:00), pellet by pellet, and videos of each meal were recorded. Fish received a total of 13 meals during this stage . Uneaten pellets were removed at the end of each meal. The number of pellets consumed by each fish was counted through video analysis. Throughout the experiment, water temperature was fixed at 28°C ± 1°C, while the photoperiod was 12L:12D cycle. The phenotypic sex of each fish was registered by visual observation of the gonads at the end of the FI recording period. The fish were sufficiently developed to show sexual differentiation, but were not yet exhibiting any reproductive behaviour. Mortality was recorded daily throughout the challenge. Thirty two fish died during the FI stage and were not included in the analyses.As a practical calculation of pellet weight, a total of 500 pellets (16.4 ± 1.76 mg) were weighed, and as the variation in weight per pellet was considered low enough, the average weight of each pellet was used. Thus, the FI in grams for each fish could then be calculated as the sum of pellets consumed during the 13 meals. Body weight gain (BWG) was calculated during the FI stage as the difference between the body weight at the end and at the beginning of this stage. The quotient between these two traits (FI/BWG) was used to estimate the feed conversion ratio (FCR). Finally, residual feed intake (RFI) was estimated as the difference between the amount of feed intake by each fish and the amount predicted .Outliers were highlighted using the boxplot. stats function of the R package “stats”  and were® SNP array (1) call rate <0.95, 2) minor allele frequency (MAF) < 0.05 or 3) deviation from Hardy-Weinberg Equilibrium (p < 1 × 10−6). Simultaneously, animals with a call rate <0.95 were also excluded. Finally, to excluded potentially duplicated samples, both of each pair of fish with a proportion of IBD higher than 0.7 were removed from further analyses. Thus, a total of 755 fish and 48,431 SNPs remained after these filters, representing the final data for the SNP array  by using a 65 K AxiomNP array . The rawNP array  was perfNP array ..To assess the genetic structure within the Nile tilapia population, a principal component analysis (PCA) was performed through PLINK v1.09. The two main components were plotted along the two axes in R.c is the vector of random effect associated with the common environmental effects (aquarium effect), e is the vector of random residuals errors, whereas ,W are the incidences matrices for the fixed, genetic and environmental effects, respectively. A normal distribution was assumed for the random genetic, common, and residuals effects, with a mean of zero and the following variance:I are the numerator relationship and identity matrix, respectively. For all the traits, heritability was estimated as follows:Only fish with phenotype and genotype data were used for the pedigree-based BLUP (PBLUP) model, to enable a fair comparison between pedigree and genomic prediction performance. To predict the Estimated Breeding Values (EBVs) and estimate the variance components, the following univariate linear model was used:(G) relationship matrix. In this case, the vector of random additive genetic polygenic effects g has a distribution ∼ N  model was the same as for PBLUP and GBLUP. However, a combined kinship matrix H  which in program .1)Random animal: for each replicate, 80% of all the fish were randomly drawn independently of their family, and used as a training dataset. The remaining 20% of the samples were used as a validation dataset.2)Random families: for each replicate, 80% of the families were randomly drawn and used as a training set. For the remaining 20% of the families, the phenotype was masked and used as a validation dataset. Thus, breeding value predictions were made in the validation set using phenotypic data from different families.The ability to predict the breeding values for the traits using pedigree and genomic approaches was assessed using a fivefold cross-validation approach. To assess the predictive ability among the different models, a total of 100 replicates were used. For each replicate, 80% of fish were used as a training dataset and, for the remaining 20%, the phenotypes were masked and used as the validation dataset. For each model, two cross-validation approaches were applied:For each approach, prediction accuracy was calculated as follows:p values were adjusted by the number of independent tests.Significant differences in predicted accuracies among models were assessed for each trait using both approaches. A Shapiro-Wilk test was done to evaluate the normality of the distribution of the predicted accuracies. The “rstatix” R package , was useSince the relatively high cost of high density SNP array genotyping may present a barrier to routine genotyping of large number of animals within a population, the efficacy of reduced density SNP panels for predicting breeding values was assessed. A total of 23 subsets of random SNPs were selected from the high density SNP array. These densities included 0.1, 0.3, 0.5, 0.7, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80 and 90% of the entire SNP dataset. For each SNP density, 100 replicates of a random animal five-fold cross validation were generated. The GBLUP model fitted for each density is the same as that described for the full genotype data set, and the accuracy was predicted as detailed previously.leaving-one-chromosome-out (LOCO) approach from the GCTA software v.1.92.2  and used as the basis for a literature search for relevant functions connected to feed intake and efficiency. This size was selected based on Summary statistics for the feed efficiency and growth traits calculated after the trial are shown in The additive genetic variance estimated through PBLUP models was higher compared with GBLUP and ssGBLUP. The PBLUP model also had the lowest estimates for the environmental and residual variance .2 among models.Heritability estimates using PBLUP, GBLUP and ssGBLUP were significant, and low to moderate in magnitude . In all A high genetic correlation of 0.98 ± 0.03 between FCR and RFI was found, suggesting that both measurements are essentially representations of the same trait . NegativOni5 associated with both FCR and RFI  and growth hormone-releasing hormone (ghrh) were found within the QTL for BWG, eif4e3 being situated approximately 89 Kbp upstream from the most significant SNP (AX-317169164), whereas ghrh is ∼2 Kb upstream of the SNP AX-317150877. In the case of FI, the neurotrophic tyrosine kinase receptor type 3 (ntrk3), hyaluronidase 4  and Sus scrofa sperm adhesion molecule 1 (spam1), genes were found within the QTL region.The genes within a 500 Kb window surrounding the most significant SNPs associated with these traits are summarized in Two different cross-validation approaches were tested to estimate prediction accuracies; a random individual and a random family approach. For the former, higher accuracies were achieved for BWG and FI when genomic data were used, with an increase of 34% compared with PBLUP . HoweverAs expected, all the models resulted in lower prediction accuracies when a random family approach was used, compared with the random animal approach. Furthermore, two main trends were observed. Firstly, for FCR and BWG, there were no significant differences in prediction accuracies using any of the models incorporating genomic data . SecondlA decrease in the estimated heritabilities were observed with the low density panels (data not shown). Therefore, for the calculation of prediction accuracy at all SNP densities, a single heritability estimated with all the available markers was used, as is considered the most accurate estimate . Only a Tilapia  population (Oncorhynchus mykiss), but not for FI, whereas Genetic parameters estimates for FE traits are relatively rare in aquaculture species, but have been extensively assessed in livestock species with significant heritabilites detected, ranging from 0.13 up to 0.84 . The stupulation . FurtherDicentrarchus labrax) and rainbow trout  would result in favourable correlated responses for feed efficiency traits within the current population. Although these results are in agreement with previous studies in sea bass , several QTLs were detected for ADG, feed conversion efficiency and feed intake . Fish and higher vertebrates growth is partly controlled by the GH/insulin-like growth factor-I (IGF-I) axis, which is also involved in the regulation of several physiological process in fish such as lipid and protein metabolism, immune function and feeding behaviour , a hybrid fish with an increased growth rate compared with their parental species.Another interesting candidate gene, response . PreviouNeurotrophic Tyrosine Kinase Receptor Type 3, ntrk3, the ligand of neurotrophin 3 (ntf3) may act as a modulator of feeding and satiety in mice and is associated with eating disorders in humans  are likely to be explained by the genetic distance between fish from the training and validation groups. It has been shown that predicting EBV using data from fish from different full-sib families (i.e. random-family approach) affects mainly the pedigree-based model and traits that are not possible to measure on the candidate themselves . HoweverThe impact of SNP density on predicted accuracies has been studied thoroughly for a wide variety of traits and species in aquaculture . The resSignificant heritabilities were confirmed for feed-efficiency traits in a Nile tilapia breeding population, highlighting that genetic improvement is feasible. A negative, but favourable, genetic correlation was detected between BWG and FCR using the genomic data, suggesting selection for BWG may indirectly improve FCR. The traits were polygenic, suggesting the genomic selection may be the most effective route to incorporating genotype data into selection decisions. Genomic prediction markedly outperformed pedigree-based prediction, and this was the case even for relatively low density SNP markers. This study therefore highlights the potential for genomic selection to improve feed efficiency traits in Nile tilapia breeding programmes.


In diploid species, genetic loci can show additive, dominance, and epistatic effects. To characterize the contributions of these different types of genetic effects to heritable traits, we use a double barcoding system to generate and phenotype a panel of ~200,000 diploid yeast strains that can be partitioned into hundreds of interrelated families. This experiment enables the detection of thousands of epistatic loci, many whose effects vary across families. Here, we show traits are largely specified by a small number of hub loci with major additive and dominance effects, and pervasive epistasis. Genetic background commonly influences both the additive and dominance effects of loci, with multiple modifiers typically involved. The most prominent dominance modifier in our data is the mating locus, which has no effect on its own. Our findings show that the interplay between additivity, dominance, and epistasis underlies a complex genotype-to-phenotype map in diploids. Heritable traits can be affected by additive, dominance, and epistatic effects at genetic loci. Here, the authors use chromosomally-encoded barcodes to perform linkage mapping in diploid cross progeny in budding yeast, finding that epistasis in diploids frequently modifies both additivity and dominance. How alleles at these loci collectively produce the heritable trait variation in genetically diverse populations remains unresolved3. While additive loci play a major role in most traits, non-additive genetic effects are also likely important9. However, loci with non-additive genetic effects are often difficult to detect, limiting knowledge of their properties11.Most complex traits, including many phenotypes of agricultural, clinical, and evolutionary significance, are specified by multiple loci13. Most empirical studies of non-additive genetic effects have focused on haploid or inbred individuals16, which provide higher statistical power to detect loci due to their minimal levels of heterozygosity. However, by design, these populations cannot furnish insight into dominance or its relationship with epistasis. This is a problem because many eukaryotic species that matter to humans, including our species itself, exist predominantly as diploids that outbreed and have high levels of heterozygosity19. Dominance may be an important contributor to traits in these species.The two purely genetic sources of non-additivity are dominance among alleles of individual loci and epistasis between alleles at different loci (or genetic interactions)22. Such interplay has implications for efforts to genetically dissect phenotypes, predict heritable traits from genotypes, and understand the evolutionary trajectories of beneficial and deleterious alleles. Yet, exploration of the relationship between additivity, dominance, and epistasis has mainly been limited to theory because of technical challenges in identifying non-additive loci.When epistasis occurs in diploids, a locus may influence only the additive effects, only the dominance effects, or both the additive and dominance effects of its interactor(s)Saccharomyces cerevisiae is a potentially powerful system for studying non-additive genetics in diploids. Haploid yeast segregants with known genotypes can be mated to produce diploid strains that also have known genotypes23. This strategy facilitates the generation of diploid mapping populations that are roughly the square of the number of haploid progenitors. However, phenotyping large diploid populations of more than ~10,000 individuals has been technically difficult24, limiting the use of this strategy.The budding yeast Here, we develop a chromosomally-encoded barcoding system that enables phenotyping of large yeast diploid mapping populations. We fuse two genomic barcodes, one from each haploid parent, in vivo to create a unique double barcode for each diploid strain Fig. . This syS. cerevisiae isolates, the commonly used lab strain BY4716 (BY) and a haploid derivative of the clinical isolate 322134S (3S)  Fig. . These sMATa and MATα strains (2 barcodes per segregant) were mated as pairs and grown on media that induced site-directed recombination between the MATa barcode and MATα barcode on homologous chromosomes . BY/3S and 3S/BY segregants were more fit than the BY/BY or 3S/3S diploids in all environments but one , with 40% 19–53%) being additive and 21% (19–26%) being non-additive 38 to identify an average of 18 loci per environment (10–26).Using quantile normalized fitness estimates from barcode sequencing, we mapped loci that contribute to growth , >2.5-fold more loci than detected by FaST-LMM Fig.  and b. AMany of the loci identified by FaST-LMM overlapped with the conservative set of enriched family-level loci, but there were also a number of differences between the methods Fig. . 29% of 23. Using these values accounts for family structure and enables mapping of non-additive loci in the full segregant panel with fixed effects linear models. Regarding dominance effects, we identified an average of 18 loci showing dominance per environment (12–30). 49% of these loci were also identified by FaST-LMM, while 72% of these loci were detected in the conservative set of loci from the family-level scans. Among loci with dominance effects, the average degree of dominance was ~51% , with 82% of the loci showing incomplete dominance  of all two- and three-locus interactions involved at least one hub. Fine-mapping localized the Chromosome VI, VIII, X, and XII hubs to genes involved in amino acid sensing (PTR3), copper resistance (CUP1), vacuolar protein sorting (VPS70), and a gene of unknown function (YLR257W), respectively.We next analyzed the relationship between individual loci and their genetic interactions. We found a strong positive relationship between the effect of a locus and its involvement in two- and three-locus interactions Fig. . This su22. To better characterize how loci are modified, each two-locus interaction was partitioned into additive and dominance components. We found that changes in dominance account for ~44% of the average epistatic effect .Although not a hub, the Chromosome III locus nevertheless had a prominent impact on phenotype by modifying the dominance effects of multiple variable effect loci. Interactions with the Chromosome III locus had greater impacts on dominance than additivity at all focal loci. For example, in hydrogen peroxide, dominance at the Chromosome X variable effect locus depended on the Chromosome III locus, ranging from complete to nearly absent in a genotype-dependent manner Fig. . We deliMATa, MATα, and diploid transcriptional programs40. This region of the genome is unique because four genotype classes segregate , and as a result, the two heterozygotes are not identical  and a haploid derivative of the clinical isolate 322134S (3S). To generate counterselectable markers in each strain, we first introduced clean deletions of FCY1 and URA3. Each gene was deleted using a two step approach: first, genes were replaced with a KanMX cassette44 via lithium acetate transformation45. Next, the cassette was targeted using CRISPR/Cas9 and a gRNA specific to the pTEF promoter region in each cassette46. A repair template homologous to the upstream and downstream region of the target gene was co-transformed with the CRISPR system to generate a clean deletion. The MATa BY and 3S strains were then mating-type-switched by transforming a plasmid containing galactose-inducible HO and URA3 using the lithium acetate protocol47. Strains with the plasmid were selected on SCM-Ura plates and inoculated into SCM-Ura + 2% galactose media overnight. Individual colonies were then obtained by plating out 102 cells onto YPD plates. Colonies were tested for their mating type using the yeast mating halo assay48. Successfully mating-type-switched BY and 3S MATα clones were cured of the HO plasmid by growing the cells on YPD + 5-FOA plates. The BY MATa and MATα cross parents were preserved at −80 °C with the unique identifiers IEY1176 and IEY1177, respectively. The 3S MATa and MATα cross parents were preserved at −80 °C with the unique identifiers IEY1178 and IEY1179, respectively.All haploid segregants and diploid segregants described in this paper were generated from a cross using two isolates of FLO8, a transcriptional activator of many flocculins49, and FLO11, the flocculin responsible for many aggregation phenotypes in S. cerevisiae50, were first knocked out in both mating types of the BY fcy1∆ ura3∆ and 3 S fcy1∆ ura3∆ strains. Each parental strain was then engineered to have a genomic ‘landing pad’29 containing two partially crippled LoxP sites51, Lox71 and Lox2272/71, and a galactose-inducible Cre recombinase52 at the YBR209W locus via CRISPR/Cas9-mediated homologous recombination  to maximize the number of unique recombination breakpoints. To avoid segregants carrying aneuploidies, only tetrads that produced 4 spores were utilized.Opposite mating types of BY MATα segregants were barcoded by integrating a randomly barcoded plasmid via Cre-mediated homologous recombination at lox2272/71  to determine where crossover events occured. A sequencing library was prepared using the Illumina Nextera Kit with custom multiplexing barcodes26. Libraries from different segregants were pooled in equimolar fractions and these multiplex pools were size selected using the Qiagen Gel Extraction Kit. Multiplexed samples were then sequenced on an Illumina HiSeq 2500 using 150 bp × 150 bp paired-end reads. For each strain, reads were mapped against the S288c genome using BWA with default settings55. Alignments were converted to a bam format and sorted using SAMTOOLS (default settings)56. Read duplicates were then removed and bam files were converted to pileups using SAMTOOLS (default settings). Base calls and coverage values were obtained from the pileup files for 43,865 high-confidence SNPs that segregate in the BYx3S cross. Any segregants that showed signs of cross-contamination, where the allele frequency at the SNP markers significantly deviated from the expected 0% or 100% 3S allele, were excluded from further analysis. To avoid segregants with aneuploidy, all segregants whose average coverage of each individual chromosome or segments of the chromosome significantly deviated from the overall average coverage were also removed. Additionally, all segregants with a mean coverage of less than 2 were removed. In total, 76 MATα and 11 MATa segregants were removed after filtering for quality. For the remaining 927 MATa and 489 MATα segregants, a vector containing the fraction of 3S calls at each SNP was generated and used to make initial genotype calls with sites above and below 50% classified as 3S and BY, respectively. This vector of initial genotype calls was then corrected with a Hidden Markov Model (HMM), implemented using the HMM package version 1.057 in R58. We used the following transition and emission probability matrices: transProbs = matrix, emissionProbs = matrix. All SNP markers within the first and last 30 kb of each chromosome were omitted because we observed higher sequencing error rate and/or lower read mapping quality for these specific regions. Adjacent SNPs in the HMM-corrected genotype calls that lack recombination in the segregants were collapsed into a single SNP, reducing the number of SNP markers in subsequent analysis from 43,865 to 7742.Clones containing different barcodes for 1003 MATa segregants, the genomic region containing the barcode was PCR amplified using primer pairs where one primer was located on the partial TruSeq read 1 adapter sequence and the other primer was located downstream of the barcode. Similarly, barcodes for MATα segregants were PCR amplified using primer pairs where one primer was located on the partial TruSeq read 2 adapter sequence and the other primer was located upstream of the barcode. PCR products were purified using a Qiagen MinElute PCR purification kit, amplified using Illumina P1 and P2 primers, and then size selected via gel extraction prior to sequencing. The 20-mer barcode sequences for each segregant were then extracted from the sequencing reads and barcodes within a Hamming distance of 2 were clustered with Bartender30. Only barcode clusters comprising >5% of the total reads for each sample were considered true barcodes. One possible concern with using random barcodes is that sequencing errors from an abundant barcode could erroneously contribute to read counts of a barcode with a similar sequence. To prevent this, we determined the number of mismatches away from a nearest neighbor for each barcode. All barcodes were at least 4 mismatches away from each other. Thus, a sequencing read was unlikely to be assigned to the wrong barcode cluster unless it contained 3 or more errors. Overall, we recovered 403 MATa segregants and 679 MATα segregants with good genotype calls and at least 3 barcodes that are unique from all others.For all segregants, the associated barcodes were determined by pooling all clones and sequencing the genomic region containing the barcode using Novogene Illumina HiSeq 2500 150 bp × 150 bp paired-end reads at ~2000× coverage per barcode. For the MATa and 600 MATα segregants were mated to create a panel of ~240,000 diploid segregants, each labeled with 4 unique pairs of barcodes . To minimize skews in the initial frequency distribution of genotypes, each MATa segregant was individually mated to each MATα segregant to generate the panel of diploid segregants. Specifically, two barcoded versions of each segregant were first grown to saturation and mixed in equal proportion. Each MATa segregant mixture was then systematically mated with a MATα segregant mixture using a LabCyte Echo 550 acoustic liquid handling robot. For each pairwise mating, the Echo transferred 100 nl of MATa and MATα segregants onto overlapping positions on a new YPD plate, resulting in the formation of diploid segregants labeled with ~4 double barcodes. After growing the cells overnight at 30°C, successfully mated diploids were isolated from unmated haploids by pinning the colonies onto YPD + 200 μg/ml G418 + 300 μg/ml Hygromycin B agar plates using Singer ROTOR pinning robot and grown for 2 days. Haploid strains carried either KanMX or HphMX, therefore only mated diploids that carry both selection markers should grow. In addition to the diploid segregants, different barcoded versions of the homozygous and heterozygous parental diploid strains were made as controls, using the same approaches described for segregants .400 URA3 marker onto the same chromosome, resulting in the reconstitution of a functional URA3 marker. Diploids that have undergone successful recombination were selected by pinning the diploid colonies onto SCM -Ura agar plates and growing for 2 days. To minimize bias due to differences in growth rate between diploids, the colonies were then pinned onto fresh SCM -Ura plates and pooled immediately with SC -Ura media. The pooled sample of ~960,000 double barcoded segregants was then spun down, re-suspended in SC -Ura media at a concentration of 2 × 109 cells/ml, and frozen in 25% glycerol for later use. In parallel, the BY/BY, 3S/3S, BY/3S, and 3S/BY parental diploids were generated in the same manner and stored at the same concentration as the segregants.After mating, the two barcodes are located on different chromosomes  was inoculated in 198 ml of SC -Ura media and then grown in a 500 ml flask at 30 °C with 300 rpm shaking for 48 h. At saturation, the cell concentration was 1.5 × 108 cells/ml for a total of 3 × 1010 cells, or ~3 × 104 cells per double barcode. In parallel, 10 μl of the parental frozen stocks were inoculated in 990 μl of SCM-Ura media and grown at 30 °C with 300 rpm for 48 h. The parental diploids were then spiked into the segregant culture at a concentration of 10−5. This mixed culture served as the seed culture (time point 0 or T0) for subsequent growth time points.The panel of diploid segregants was evolved for 15 generations by serial batch culture under carbon limitation in 100 ml of SC -Ura media with 4% ammonium sulfate and 2% dextrose. First, 2 ml of the segregant frozen stock (2 × 103 cells per double barcode at bottleneck) was transferred into eight 500 ml flasks, each containing 87.5 ml of SC -Ura media supplemented with different drugs or chemicals . The sample was again mixed at 30 rpm for 10 min, centrifuged at 16,000 × g for 10 min, and the upper aqueous layer was transferred to a new tube. Finally, to remove residual chloroform, the DNA sample was ethanol precipitated using one-tenth volume of 3 M sodium acetate (pH 5.5) and 2.5 volume of 100% ethanol. The resulting DNA pellet was washed with ice-cold 70% ethanol twice, and dissolved in 10 mM Tris-HCl pH 8.0. After the DNA was extracted and cleaned, a two-step PCR was used to amplify the double barcodes for sequencing. Because only a small fraction of the total genome contains relevant information (~250 bp amplicon out of a 12 Mb genome size), we amplified 30 µg of template per time point sample, which corresponds to ~2 × 109 genomes or ~2000 copies per double barcode.Frozen cultures were thawed and DNA was extracted for library preparation using the MasterPure Yeast DNA Purification kit. Any remaining RNA was removed by adding RNaseA (10 mg/ml) and incubating the sample at 37 °C for one hour. DNA was then cleaned by adding one volume of phenol:chloroform:isoamyl alcohol (25:24:1). The sample was gently mixed using a tube rotator at 30 rpm for 10 min and then centrifuged at 16,000 × 2, 3% DMSO, and 125 µL total volume per well. Primers for this reaction were: AATGATACGGCGACCACCGAGATCTACACNXXXXXNNACACTCTTTCCCTACACGACGCTCTT and CAAGCAGAAGACGGCATACGAGATNXXXXXNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT.First, a 4-cycle PCR with OneTaq polymerase (New England Biolabs) was performed in 60 wells of 96-well PCR plates, with ~500 ng of DNA template, 4.5 mM MgClThe Ns in these sequences correspond to any random nucleotide and are used as unique molecular identifiers (UMIs) in downstream analysis to identify PCR duplicates. The Xs correspond to one of several multiplexing tags, which were used to distinguish different samples when loaded on the same sequencing flow cell. Multiplexing tags were designed to have a Levenshtein distance of 3 from each other, such that reads with 1 or less sequencing errors in the multiplexing tag can be assigned to the correct sample.After amplification, 16 wells of PCR products were pooled , run through a NucleoSpin PCR cleanup column, and eluted in 120 μL of elution buffer. The pooled PCR products were then cleaned for the second time to remove any residual primers using a NucleoSpin column and eluted in 120 μL of elution buffer. A second 22-cycle PCR was performed with PrimeStar HS DNA polymerase (Takara) in 16 reaction tubes, with 30 μl of cleaned product from the first PCR as template and 125 μL total volume per tube. Illumina primers P1 and P2 were used for this reaction.PCR products from all reaction tubes were concentrated into 100 μl using ethanol precipitation. The appropriate PCR band (264 bp) was isolated by agarose gel electrophoresis and quantified by Bioanalyzer (Agilent) and Qubit fluorometer (Life Technologies).MATa and MATα barcodes were extracted from the sequencing reads and fused into one double barcode.Sequencing (150 bp paired-end) was performed on an Illumina HiSeq 4000 or NovaSeq 6000. Each flow cell contained at least 2 multiplexed time points and 25% genomic DNA or PhiX. Genomic DNA/PhiX was included to increase the read complexity for proper calibration of the instrument since most of the bases in each barcode read are fixed. Sequencing reads were analyzed using custom written code in Python and R, which are available on GitHub. Reads were sorted by their multiplexing tags (the Xs in the primers above) and removed if they failed to pass either of two quality filters: 1) The average Illumina quality score for the double barcode insert region must be greater than 30, and 2) the double barcode region must contain the fixed sequence ATAACTTCGTATAATGTATGCTAT with less than 2 mismatches. After filtering, 30. One source of bias in these counts that we wanted to avoid is PCR duplicates or other non-linearities between the amount of template for a double barcode and the number of sequences observed for that double barcode. We removed these errors by using UMIs (the Ns in the primers above). Specifically, 2 random 3-mers were attached to each template DNA molecule in the first few rounds of PCR (see section ‘Library preparation’ for more detail). Because the total sequence space of 2 random 3-mers  is much larger than the target coverage for each double barcode (~100×), it is unlikely that any two template DNA molecules from the same time point that contain the same double barcode will be attached to identical pairs of 3-mers. Thus, sequence reads for a double barcode that contained the same pair of 3-mers were counted as PCR duplicates and removed from our final counts.We next used Bartender to cluster similar sequences into consensus double barcodes, with the maximum allowable sequence distance (-d) set to 2MATa and MATα segregant and parental barcodes were extracted. All double barcodes with an average read count of <5 across the 4–5 time points were removed, as accurately estimating fitness for samples with very low read counts is difficult  separated by a fixed region can lead to formation of undesired chimeric molecules due to template switching29. This can result in erroneous double barcode counts, leading to errors in fitness estimation.Previous studies have shown that PCR amplification of DNA sequences containing two variable regions   double barcodes per environment were used for the remaining downstream analysis  with a spectrally decomposed genetic similarity matrix (GSM).Our experimental design resulted in genotypes that are not equally related to each other. For example, two diploid segregants that share a parental haploid segregant are more related to each other than two diploids that do not share a parental haploid segregant. We found that differences in relatedness had a large effect on the fitness of the diploid segregants. Thus, failing to account for family structure could inflate type I error and result in false positives. To account for these potential errors, we detected one-locus effect loci using FaST-LMM61. To avoid proximal contamination, all SNP markers that are located on the same chromosome as the SNP marker that is being tested were omitted from the calculation of the GSM by setting the leave_out_one_chrom function as TRUE. To determine appropriate significance thresholds, 1000 permutations were conducted with the correspondence between genotypes and phenotypes randomly shuffled each time. Among the minimum p values obtained in the permutations, the fifth quantile was identified and used as the threshold for determining significant loci.We used FaST-LMM’s single_snp function to perform a genome-wide linear mixed effect analysis. The genotype table was reformatted as a binary biallelic genotype table (BED) prior to running FaST-LMM using PLINK version 1.07p-value) of the maximal significance observed at the peak position (hereafter referred to as “3-LOD drop”).To detect loci that are closely linked to a nearby locus, we ran multiple rounds of stepwise forward regression in FaST-LMM. For the second round of forward regression, we included the most significant SNP marker from each chromosome that was above the significance threshold from the initial genome-wide scan as a covariate in the mixed effect linear model. In the third round, all SNP markers detected in the first two rounds were added as covariates in the mixed effect model. This process was repeated until no significant SNP markers were detected. The confidence interval of a locus was defined as the region surrounding a peak marker in which the significance of detection was within three −log10 had a huge impact on the statistical power to detect loci. Because all genetic differences between diploids within a family are random, they do not need to be corrected for family structure prior to mapping. Genome-wide mapping was conducted in each family using a fixed effects linear model in R using the lm function: lm(fitness ~ locus). The fitness term corresponded to the vector of quantile normalized fitness values of individuals within a family and the locus term corresponded to the vector of these individuals’ genotypes at a given marker. Because diploids within a family are derived from the same MATa haploid, they possess only two possible genotype states: BY/BY and BY/3S or BY/3S and 3S/3S, depending on the allele present in the MATa progenitor. Although the locus term was encoded as a categorical variable here, the coding of the locus term should not matter. The categorical coding should be equivalent to a count of an allele because either defines two categories. The p-value of the locus term was obtained using the summary.aov in R. Among markers exceeding a significance threshold, the most significant marker from each chromosome was identified and utilized in subsequent rounds of forward regression. Specifically, identified loci were included in the fixed effects linear model as a covariate, e.g. fitness ~ known_locus1 + known_locus2 + … known_locusN + locus. This process was repeated until no additional locus terms were discovered within an environment. The significance threshold for these forward scans was established by pooling the p values from 1000 permutations of 10 randomly selected families. In each permutation, the correspondence between genotype and phenotype was shuffled within a family. From the resulting distribution of minimum p-values obtained in each permutation, the fifth quantile was identified and used as the significance threshold. After calling peaks, loci detected in multiple families were consolidated using 3-LOD drops within each family.We also performed scans for one-locus effects within families generated from the same 39. We divided the genome into nonoverlapping 20 kb bins and determined the maximum number of detections expected by chance at a Bonferroni-corrected p = 0.05/(the number of 20 kb nonoverlapping bins). The threshold was then obtained from a Poisson distribution with λ = /(the number of 20 kb nonoverlapping bins tiling the genome) using the qpois function in R, with lower.tail set to FALSE.We identified distinct loci that were enriched for detections across families23. Specifically, we first estimated the fitnesses of each parental haploid segregant by calculating the mean fitness of all diploids that originated from each parental strain (additive genetic background contribution to fitness). Next, the expected midparent value for each diploid was obtained by taking the average of the 2 parental fitnesses. If phenotypic variation is due to only additive effects, then we expect the fitness of a diploid to be the same as its midparent value. Deviations from the midparent value were then determined by fitting a linear model between fitness and the midparent value, fitness ~ midparent value, and taking the residuals . Not only does this correct out the additive portion of each diploid’s phenotype, it also effectively accounts for family structure , we ran multiple rounds of forward regression. In the first round, a genome-wide scan was conducted using the following fixed effects linear model: fitness value for each genotype state was calculated. Additive effect sizes of the 3S allele were calculated by subtracting the mean fitness of diploids that are BY/BY at the focal locus from the mean fitness of diploids that are 3 S/3 S at the focal locus and dividing by two. Dominance effect sizes for each focal locus were calculated by subtracting the mean fitness of diploids that are BY/3S at the focal locus from the average of the mean fitness of diploids that are BY/BY and 3S/3S at the focal locus . Dominance effect sizes were then normalized based on the absolute additive effect sizes of the 3S allele relative to the BY allele at the same loci. Normalized dominance values ranging from −0.9 to 0 were classified as incomplete dominance for the deleterious allele, while values ranging from 0 to 0.9 were classified as incomplete dominance for the allele conferring higher fitness. Values that were between −0.9 to −1.1 or 0.9 to 1.1 were classified as complete dominance for the deleterious and fit allele, respectively. Values < −1.1 or >1.1 were classified as underdominant or overdominant, respectively.For each locus detected in the scan for dominance effects, the magnitude of dominance was calculated in the following manner. First, the data was subsetted based on the genotype state at the focal locus . Then, the mean residuals ~ locus1 + locus2 + locus1:locus2. Here, the significance of the locus1:locus2 interaction term was tested. Simpler terms were included in each model to ensure that variances due to one-locus non-additive effects  were not erroneously attributed to more complex terms. All locus terms were treated as categorical values, including the locus1:locus2 interaction term, such that all 9 genotype classes  are treated as independent categories rather than continuous numerical values. The significance threshold was determined by conducting 1,000 permutations with the correspondence between genotype and residuals shuffled each time. Among the minimum p-values obtained in each permutation, the fifth quantile was identified and used as the significance threshold for our comprehensive scan of all two-loci interactions. To reduce computational time, 10,000 random pairs of loci were randomly selected for each permutation rather than all possible pairs of loci. All significant pairs of loci where both loci were within 3-LOD drop of each other were consolidated. For each set of overlapping loci, the SNP marker with the most significant p-value was used for downstream analysis while less significant markers were recorded but treated as the same genetic effect and not used downstream.We conducted a genome-wide scan for pairwise interactions using the following linear fixed effect model: locus1 fixed for a hub locus, e.g., residuals ~ hub + locus2 + hub:locus2. The significance of the hub:locus2 interaction term was tested. Significance thresholds were determined using the same permutation strategy as the comprehensive scan. The most significant interacting locus from each chromosome that was above the significance threshold was identified as a significant interactor of a hub locus. For subsequent rounds of forward regression, all identified interactions were included in the fixed effects linear model as covariates, including the simpler terms, e.g. residuals ~ hub + locus2 + known_locus1 + known_locus2 + … known_locusN + hub:known_locus1 + hub:known_locus2 + … hub:known_locusN + hub:locus2. The most significant interacting locus from each chromosome that was above the significance threshold was again identified as a significant interactor with a hub locus. This process was repeated until no additional hub:locus2 terms were discovered for each environment. To avoid double counting interactions, all pairwise interactions identified in the comprehensive scan that involved a locus within 50 kb of a hub were removed.During our comprehensive scan for pairwise interactions, we detected several hubs that were involved in large numbers of interactions. These interactions often spanned the entire length of the genome, making it difficult to differentiate sites interacting with these hubs. In such cases, a forward regression approach was conducted. Specifically, we first ran the same fixed effects linear model as the comprehensive scan for pairwise interactions, but with residuals ~ locus1 + locus2 + locus3 + locus1:locus2 + locus1:locus3 + locus2:locus3 + locus1:locus2:locus3. Similar to the scan for pairwise interactions, all locus and interaction terms were treated as categorical values. Here, the significance of the locus1:locus2:locus3 interaction term was tested. Simpler terms were included in each model to ensure that variances due to one-locus non-additive effects  and pairwise interactions were not erroneously attributed to more complex terms. As before, the significance threshold was determined by conducting 1000 permutations with the correspondence between genotype and residuals shuffled each time. Among the minimum p-values obtained in each permutation, the fifth quantile was identified and used as the significance threshold for our comprehensive scan of all two-loci interactions. Similar to the pairwise interaction scan, for each permutation, 10,000 trios of loci were randomly selected for each permutation rather than all possible trios of loci.A comprehensive scan for three-locus effects using all SNP markers is computationally expensive. Instead, we scanned for three-locus effects using a smaller subset of 579 SNP markers, where each SNP marker was at least 5 cM apart. The following fixed effects linear model was used: MATa families. The peak position of each hub was defined as the most significant marker for that locus across all families. Family-level detections with confidence intervals overlapping the peak position of a hub were identified. The first and last coordinates of all confidence intervals overlapping a peak position were compared, and the coordinates closest to each side of a peak position were recorded as the minimum bounds for a given hub.Hub loci were resolved using data from family-level scans in each of the additive-additive, dominance-additive, additive-dominance, and dominance-dominance62. The following linear fixed effect model was used: residuals ~ locus1 + locus2 + additive_locus1:additive_locus2(a1:a2) + dominance_locus1:additive_locus2(d1:a2) + additive_locus1:dominance_locus2(a1:d2) + dominance_locus1:dominance_locus2(d1:d2). Additive_locus terms were treated as numerical values, while locus and dominance_locus terms were treated as categorical values. The percent variance explained (PVE) by each of the four epistatic types was then estimated by taking the sum of squares of each interaction term and dividing it by the total sum of squares. Using these PVE values for each interaction term, the fraction of epistatic effect involving dominance was then calculated. To estimate the fraction in which locus1’s modifying effect on locus2 acts on dominance, the following equation was used: (d1:a2 + d1:d2)/(a1:a2 + d1:a2 + a1:d2 + d1:d2). Conversely, to estimate the fraction in which locus2’s modifying effect on locus1 acts on dominance, the following equation was used: (a1:d2 + d1:d2)/(a1:a2 + d1:a2 + a1:d2 + d1:d2).To understand how a locus modifies the effect of an interacting locus in a pairwise interaction, each pairwise interaction was partitioned into four epistasis types: a1:a2 and a1:d2 interaction terms, where locus1 is the hub. The magnitude in which the hub modifier affects the dominance component of the hub (‘dom_mod’) was determined by adding the PVE of d1:a2 and d1:d2 interaction terms. Significantly large outliers in add_mod or dom_mod, defined as values more than 1.5 interquartile range above the third quartile, were identified as major effect modifiers.Using the PVE values for the four epistatic types (see section ‘Estimating the fraction of epistatic effect that involves dominance’), we examined how loci that interact with a hub contribute to additive and dominance effect size variance across different genetic backgrounds. For each two-locus interaction involving a hub, the magnitude in which the hub modifier affects the additive component of the hub (‘add_mod’) was determined by adding the PVE of fitness of diploids that are BY/BY at the focal hub from the mean fitness of diploids that are 3S/3S at the focal hub. Dominance effect sizes for each genotype class were calculated by subtracting the mean fitness of diploids that are heterozygous at the focal hub from the average of the mean fitness of diploids that are BY/BY and 3S/3S at the focal hub.To examine how modifiers collectively change the effect size of hubs, four modifiers with the largest modifying effects on additivity and dominance were chosen for each hub. Data was then subsetted into 81 genotype classes based on the genotype state at the four modifiers. For each genotype class, the data were further subsetted based on the genotype state of the focal hub locus. Additive effect sizes for each genotype class were calculated by subtracting the mean Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


A major problem in the swine meat industry is significant weight loss due to pigs overheating. Our approach to solving this problem is breeding swine based on their ability to survive in high temperatures. We determined in this study that genetically there is a difference between the most successful animal in hot conditions and the most successful animal in cooler conditions. This can be applied to selection of breeding animals for swine companies. Additionally, we found a few genes that may be related to this heat resistance, but more studies need to be completed to validate this finding.The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri, U.S. Individuals were from a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8232 animals with 33,581 SNPs. The pedigree file contained a total of 553,448 animals. A threshold of 78 on the Temperature Humidity Index (THI) was used to signify heat stress. A two-trait analysis was used with the phenotypes heat stress (Trait One) and non-heat stress (Trait Two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for Traits One and Two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The benchmark was the GEBV with complete data, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies. Both methods showed a maximum accuracy of 0.32 for Trait One and 0.54 for Trait Two. Manhattan Plots for Trait One, Trait Two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining more than 0.8% of the genetic variance were isolated. Chromosomes 1 and 14 showed peaks in the difference between the two traits. The genetic correlation suggests a different mechanism for Hot Carcass Weight under heat stress. The GWAS results show that both traits are highly polygenic, with only a few genomic regions explaining more than 1% of variance. Heat stress is a problem for almost all livestock producers around the world. However, swine are disproportionately sensitive to high temperatures because they are unable to sweat. In response to high temperatures, swine have been shown to experience weight loss due to reduced feed intake and physiological changes . This weGenetic evaluations of heat stress have been assessed utilizing multiple models for calculation, including multiple-trait and random regression models. Zumbach et al. performed both a two-trait and a random regression analysis of heat stress in crossbred swine in 2008 . The twoThe incorporation of genomic data has the ability to increase accuracy of prediction for young animals . MoreoveThis study will attempt to establish a useable method for heat stress resistance in swine breeding programs. The primary objective of this study is to utilize a two-trait animal model analysis to evaluate the efficacy of selecting for heat stress resistant pigs. Additionally, we will identify the SNP windows that may play a role in swine ability or inability to regulate temperature.Animal Care and Use Committee approval was not obtained for this study because data were obtained from an existing database.Data were collected at packing plants in North Carolina and Missouri on a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. All data were collected within a single company which maintained consistent management practices for feed and housing. Phenotypes were measured as Hot Carcass Weight (HCW) at an average slaughter age of 185 days. Phenotype data were collected between August 2008 and July 2014. There was overlap between the two farms with approximately 30% of sires having progeny in both states. Descriptive statistics for the study population are given in Cleaning of the data set consisted of removing animals missing both parents, animals with pedigree conflicts, and any outliers with records above 4 standard deviations from the mean. The pedigree file contained a total of 553,448 animals after cleaning. Of this group, 227,043 animals had records and 8232 had genotypic information available. Genotype data is from the Infinium PorcineSNP60 v2 BeadChip . Genotypes were imputed following the same procedures as Fragomeni et al. [t was the maximum temperature in degrees Celsius and rh was the minimum relative humidity on a scale of 0 to 100. A regression analysis was run prior to the start of this study to determine the threshold for heat stress. A THI value of 78 was the threshold used for this study to separate heat stress and thermo-normative conditions based on convergence and fit of the model established in a previous study done by Fragomeni et al. [Weather data was collected using the R package weatherData  using air=0.99) . For NorGenetic analysis was performed using a two-trait animal model:In this model, To include genomic information into the analysis, a ssGBLUP approach was utilized ,8. Such H .The inverse of the matrix Here, lculated (9)G=MDMIn addition to the two-trait analysis, a single-trait analysis was run for both Trait One and Trait Two to compare results. The same model was adopted for the single-trait approach, and the assumptions for the variances of the random effects were the same as the two-trait approach.SNP effects were calculated by back solving GEBVs, following VanRaden and Zhang ,17.(10)ith marker. This approach follows the methodology from Wang et al. [The elements of the g et al. . The valg et al. , withoutn BayesA . In thise values .GWAS results were compiled in Manhattan Plots including the position of the SNPs windows within chromosomes and the percent of the total additive genetic variance explained by that window. Moving windows containing 20 markers each were calculated all over the genome based on the average variance of the SNPs in that window. The SNP variance of a SNP that SNP ,17. UponGenetics of thermotolerance was assessed as the difference between the additive genetic effect below or above the threshold for the heat load. However, the single-step GWAS generates a single Manhattan Plot for each trait, which makes interpretation of the results difficult. Therefore, a third Manhattan Plot was generated by plotting the difference in the variance explained by each region in the two traits. The working hypothesis for this approach is that genomic regions associated with thermotolerance will have different effects under normal and heat stress conditions.For validation calculations, estimated breeding values were calculated both with a reduced and complete dataset. The reduced dataset excluded records collected after 1 October 2013. Validation was calculated for 163 sires with at least 200 progeny with records in the complete data, but no progeny in the reduced data.Accuracy estimates of each model were evaluated using the correlation between the estimated breeding value (EBV) and the “true” breeding value (TBV). The EBVs were calculated for the validation boars in each SNP weighting method with the reduced data, and the hereby termed TBV were calculated using the two-trait unweighted ssGBLUP with the complete data, following the validation performed by Fragomeni et al. and the LR method ,19. AccuTo estimate variance components, an average information restricted maximum likelihood (AIREML) method was used. Calculation of the variance components was done utilizing the AIREMLF90 software version 1.149. Additionally, breeding values were predicted as the best linear unbiased predictor (BLUP) using Henderson’s mixed model equation. An ssGBLUP approach was used for genomic enhanced breeding values (GEBV). Breeding value calculations were done using the BLUPF90 program version 1.71 . For theHeritability, genetic correlation, and variance components results are displayed in The GWAS results for both traits are illustrated in Manhattan Plots in Trait One, representing HCW under heat stress, is shown in the Manhattan Plot in To fully demonstrate the differences and overlaps between the Trait One and Trait Two results, a Manhattan Plot for the difference between results was generated, shown in Utilizing the R package Gallo , SNP regThe manual search of Quantitative Trait Loci (QTL) in the regions at this threshold revealed two QTLs for production traits and one for health traits. An isolated peak on Chromosome 1 at 289 kbp had a variance explained of approximately 1% and was linked to body mass and growth traits. An isolated peak on Chromosome 14 at 16 kbp also explained 1% of the variance in the trait and was linked to traits involving diseases susceptibility and back fat thickness.The weighted single step GBLUP analysis was tested using the reduced set of data. Accuracy was calculated using the homo-correlation and hetero-correlation approaches mentioned above. The results showed no difference in accuracy between these two approaches for both Nonlinear and Quadratic weighting methods. Accuracy calculations for the two methods of both traits are shown in Nonlinear “A” weighting methods showed a slight decrease in accuracy as SNP weights were allowed to vary. For both Trait One and Trait Two, the accuracy for the Nonlinear “A” method peaked at iteration one. The initial accuracy was calculated at 0.32 for Trait One and at 0.54 for Trait Two. For the homo-correlation calculations, Trait One showed a drop in accuracy of Quadratic weighting methods showed a greater decrease in accuracy as SNP weights varied. For both Trait One and Trait Two, the accuracy for the Quadratic method peaked at iteration one as well. The initial accuracies were calculated to be the same as the Nonlinear “A” method. For both homo-correlation and hetero-correlation calculations, Quadratic method showed a greater decrease in accuracy after the first iteration than was found in the Nonlinear “A” calculations. Traits One and Two showed a drop in accuracy of 0.1 from iteration one to iteration two for both calculations. Accuracies continued to drop as iterations progressed.Hot Carcass Weight under heat stress had a higher heritability when compared to that without heat stress. This pattern of greater heritability at high temperatures has been found in previous studies as well ,24. The The genetic correlation in this study was found to be less than 0.70, which is the critical threshold to assume genotype × environment interaction . ZumbachThe GWAS results further illustrate the differences in genomic regions underlying each trait. Both traits presented as polygenic in nature, with few regions explaining more than 0.8% of the genetic variance. The differences in the peak patterns between the Trait One and Trait Two Manhattan Plots were used to identify SNP associations in heat tolerance only . This MaThe GWAS results demonstrated the overall complexities of both traits for HCW. Results from the two weighted ssGBLUP analyses provided further evidence that Traits One and Two are polygenic. As SNP weights varied using Quadratic weighting methods there was a decrease in accuracy. This indicates the polygenic nature of heat stress traits as it has been shown in previous studies that Quadratic weighting will only improve accuracy when there are only a few major QTLs or genes affecting a trait . There wThe GWAS results allowed for isolation of chromosome regions that explained different proportions of genetic variance for HCW under heat stress and HCW not under heat stress. The genes located in those regions may provide insight into the biological mechanisms involved in heat tolerance. However, changes in the effects of SNPs may also be consequence of the small number of independent chromosome segments (og(2NeL) ,32. Usinog(2NeL)  and a chog(2NeL) , would mog(2NeL) ,36,37,38og(2NeL) . For thiGenes isolated through genomic annotation included the protein coding regions of CEP44, FBXO8, HPGD, and GLRA3. The primary function of the CEP44 protein is to stabilize cell division at the centrosome and is highly conserved in eukaryotes . This geQTL regions found through manual search revealed some interesting studies that could explain the decrease in HCW when swine undergo heat stress. The peak isolated on Chromosome 1 at 289 kbp was linked to body mass and growth and is associated with transforming growth factor, beta receptor 1 (TGFBR1). This growth factor is known to impact cell proliferation, differentiation, and apoptosis . This haThese candidate genes identified through the GWAS may have an impact on phenotype or inheritance, though it is difficult to truly identify candidate genes because of the overwhelming lack on annotation in the swine genome. Using this approach may help to understand some of the underlying genetics, but it is not useful for application in breeding. For this reason, we recommend a whole genome approach for use in breeding programs.This study demonstrated that HCW under heat stress and HCW not under heat stress are two different traits. This conclusion was supported first by the genetic correlation and additionally from the GWAS results. This discovery suggests that you can successfully select for heat tolerance in swine through a breeding program. Peaks were isolated at Chromosomes 1 and 14 which explained more than 1% of the variance each through the GWAS. Each region should be further investigated through functional studies. In combination with the weighted ssGBLUP, the GWAS also revealed that HCW under heat stress and not under heat stress are highly polygenic traits. The conclusions of this study hold implications for industry applications of breeding value calculations, specifically the ineffectiveness of using a weighted ssGBLUP approach for polygenic traits.