The study of lipoprotein metabolism at the population level can provide valuable information for the organisation of lipoprotein related processes in the body. To use this information towards interventional hypotheses generation and testing, we need to be able to identify the mechanistic connections among the large number of observed correlations between the measured components of the system.To use population level metabolomics information to gain insight on their biochemical networks and metabolism.Genetic and metabolomics information for 230 metabolic measures, predominately lipoprotein related, from a targeted nuclear magnetic resonance approach, in two samples of an established European cohort, totalling more than 9400 individuals analysed using phenotypic and genetic correlations, as well as Mendelian Randomisation.More than 20,500 phenotypic correlations were identified in the data, with almost 2000 also showing evidence of strong genetic correlation. Mendelian randomisation, provided evidence for a causal effect between 9496 pairs of metabolic measures, mainly between lipoprotein traits. The results provide insights on the organisation of lipoproteins in three distinct classes, the heterogeneity between HDL particles, and the association, or lack of, between CLA, glycolysis markers, such as glucose and citrate, and glycoproteins with lipids subfractions. Two examples for the use of the approach in systems biology of lipoproteins are presented.Genetic variation can be used to infer the underlying mechanisms for the associations between lipoproteins for hypothesis generation and confirmation, and, together with biological information, to map complex biological processes.The online version contains supplementary material available at 10.1007/s11306-021-01856-6. Metabolomics is the study of the quantitative complement of small molecules in biological systems. The metabolic measures obtained are mostly organic compounds involved in the biochemical reactions of the organism and represent the final stage of the flow of information from the genome to the biological phenotype . The study recruited 14,541 pregnancies and has since followed participants in a number of phases during development and maturity. Full details of the study have been published previously  is a population based, prospective birth cohort (A high-throughput serum nuclear magnetic resonance (NMR) metabolomics platform was used to quantify 230 metabolic measures representing a broad molecular signature of systemic metabolism , the measures were obtained from overnight, or at least 6 h, fasted samples.p < 5 × 10−7)  Team, ., . Only inTeam, ., , to obtaTeam, ., . The rooTeam, .,  or ggploTeam, ., .–5 for correlation tests and 2.64 × 10–5 for regression based tests. In total 465,740 genotyped SNPs passed the quality control criteria in the two samples, with 12,516 SNPs found to be independent (LD r2 < 0.01) and used to estimate the correlation of SNP effects between the traits and in selecting MR instruments.In the Avon Longitudinal Study of Parents and Children (ALSPAC), excluding all subjects with missing values, resulted in 5353 unrelated children at age 7 and 4120 mothers with available metabolomics measurements. In the ALSPAC offspring, principal component analysis showed that 44 principal components (PCs) accounted for 0.99 of the metabolic measures variance. The corresponding figure in the mothers sample was 0.98. Based on this, our Bonferroni adjusted p-value threshold for the pairwise correlations was set at 5.28 × 10p-values can be found in the Supplementary material (Table S1) and plotted in Fig. S1. When the lipoprotein measures were ordered by size, as the current understanding links size to function, four clusters of high correlation were evident in their concentration measures, two major and two minor. The first major cluster included Chylomicrons and extra-large very low density lipoprotein (VLDL) and extended to very-large, large, medium and small VLDL particles. The second major cluster included very-small VLDL, intermediate density lipoprotein (IDL) and the various sizes of low density lipoprotein (LDL). The majority of measures were associated across the two clusters except triglycerides and free cholesterol measures. Both of the minor clusters of strong correlations were in the high-density lipoprotein (HDL) measures, with one cluster including particles of very-large and large size and the second measures of very-small HDL. Medium size HDL measures were associated with both. Again, cross cluster correlations were present throughout. The other prominent feature of the phenotypic correlations matrix was the complex correlations pattern between glycolysis, amino acids, ketone bodies, fluid balance, and inflammation markers with lipids, although most were consistently associated with the larger VLDL particles measures. Results from the mothers were similar, replicating 20,758 of the associations, and showed the same major features. The results can be seen in Table S2 and Fig. S2.Of the 26,335 Pearson’s correlation coefficients tested between the metabolic measures, 23,032 showed evidence of phenotypic association. A table showing all correlation coefficients and their respective ., Of the 26,335 possible pairs of metabolic measures, 5050 were tested through LD score regression , and 199 one-directional causal effects, from the 2533 observed initially (Table S10), with the rest being bidirectional associations replicated in one direction. Figure Using the MR-Egger approach, as a sensitivity analysis for the effect of pleiotropy in our estimates, resulted in 7074 associations, in accordance with the lower statistical power of the method, with 2224 pairs being bidirectional associations and 2626 in one direction only (Tables S11 and S12). Of the MR-Egger observed associations 6000 were also observed when the standard approach was used. In the children sample, 1719 associations also had evidence for the presence of pleiotropy based on their intercept (Table S13 and S14). MR-Egger analysis of the ALSPAC mothers revealed 176 pairs of bidirectional associations and 305 in one direction, with 137 of these also showing evidence of pleiotropy (Tables S15–S18). Compared to the standard approach 521 pairs of metabolites also had evidence of association in the MR-Egger analysis. In total, the mothers sample provided replication for 486 associations seen in the children.To demonstrate the potential use of the results in trying to infer the mechanistic relationships between the metabolic measures, we focused on two examples. VLDL is the main transport form of endogenous triglycerides in the body. It is produced in the hepatocytes and released into circulation progressively losing its triglyceride content to give rise to remnant VLDL, IDL and LDL particles of smaller sizes . CLA is a popular dietary supplement associated with a number of suggested beneficial effects on common diseases and BMI, while recent studies correlated CLA with a decrease of LDL , on large and medium VLDL concentration measures and particle diameter were observed. AGP is an acute-phase protein believed to be involved in a wide range of biological processes, including immuno-modulation, drug compound transport, maintaining capillary function, sphingolipid biosynthesis and glucose and insulin metabolism  Figure S2:Heatmap of Pearson’s correlation coefficients for the pairwise comparison between the metabolic measures in mothers, below the diagonal, and their respective statistical significance, above the diagonal. The same clusters as before can be seen but the associations are stronger between the clusters. Supplementary file5 (JPEG 12326 kb)Figure S3: Heatmap of genetic correlations for the pairwise comparison between the metabolic measures in children, below the diagonal, and their respective statistical significance, above the diagonal. The main clusters observed in the phenotypic data are still visible. Supplementary file5 (JPEG 12725 kb)Figure S4: Heatmap of genetic correlations for the pairwise comparison between the metabolic measures in mothers, below the diagonal, and their respective statistical significance, above the diagonal. The main clusters observed in the phenotypic data are still visible. Supplementary file5 (JPEG 12730 kb)Supplementary file5 (XLS 15053 kb)Below is the link to the electronic supplementary material.


Dioscorea rotundata) breeding lines using a genome-wide association study (GWAS).Improvement of tuber yield and tolerance to viruses are priority objectives in white Guinea yam breeding programs. However, phenotypic selection for these traits is quite challenging due to phenotypic plasticity and cumbersome screening of phenotypic-induced variations. This study assessed quantitative trait nucleotides (QTNs) and the underlying candidate genes related to tuber yield per plant (TYP) and yam mosaic virus (YMV) tolerance in a panel of 406 white Guinea yam  analysis using the multi-locus linear model (mrMLM) identified seventeen QTN regions significant for TYP and five for YMV with various effects. The seveteen QTNs were detected on nine chromosomes, while the five QTNs were identified on five chromosomes. We identified variants responsible for predicting higher yield and low virus severity scores in the breeding panel through the marker-effect prediction. Gene annotation for the significant SNP loci identified several essential putative genes associated with the growth and development of tuber yield and those that code for tolerance to mosaic virus.Application of different multi-locus models of GWAS identified 22 QTNs. Our results provide valuable insight for marker validation and deployment for tuber yield and mosaic virus tolerance in white yam breeding. The information on SNP variants and genes from the present study would fast-track the application of genomics-informed selection decisions in breeding white Guinea yam for rapid introgression of the targeted traits through markers validation.The online version contains supplementary material available at 10.1186/s12870-021-03314-w. Dioscorea species extensively cultivated in the tropics and subtropics by smallholder farmers for its starchy underground tuber and aerial bulbils [Dioscorea species, of which 11 are economically significant [D. rotundata), indigenous to Africa, is the most produced and consumed among cultivated species, supporting the livelihood of over 300 million people [Root and tuber crops are significant contributors to global food supply next to cereal crops. Yam is among the principal root and tuber crops, after cassava and potato, that are widely grown and consumed as subsistence staples . Yam is  bulbils , 3. The  bulbils , 5. Thernificant . White Gn people . Yam is n people .D. rotundata.Despite its socio-economic importance, a significant yield increase has not been achieved over the decades compared to cereal crops . ImproveFor tuber yield, limited knowledge exists regarding QTL mapping studies . The QTLSupporting yam breeding efforts based on quantitative genetics principles and genomics tools is indispensable to increase the program’s effectiveness for increasing productivity. Yam cultivar development using conventional strategies spans at least ten years from crossing to variety release recommendation , 6. The This study's objective was to dissect the genetic control of tuber yield and YMV tolerance in white Guinea yam.The study panel comprised 406 white Guinea yam clones, of which, 36 were trait progenitors, 49 elite clones, and 321 early generation breeding lines from the IITA's yam breeding program (Supplementary Table http://www.cropontology.org/ontology/CO_343/Yam) and yam standard operation protocol [Phenotypic data on tuber yield per plant (TYP) and yam mosaic virus (YMV) severity were recorded on the plant materials assessed at different breeding stages at IITA in Nigeria. The TYP and YMV severity were recorded on plants in the field using the procedure described in yam ontology  at the ith observation, and n = total number of observations.where We applied a one-step linear mixed model that used G-matrix to compute the best linear unbiased predictor (BLUP) values of an individual clone for a trait from the best fit model using the average information criterion (AIC) in restricted maximum likelihood (REML) algorithm  in the Ayij is the phenotypic value, μ is the overall average , βi is the effect of block i, τj is the specific effect to genotype j, γk is the specific effect to trials k and ℇij is an effect specific to each experimental unit (combination block and genotype ) and Zuu is the the vectors of random additive and non-additive genetic within location effects, respectively, with corresponding design matrix Zu. Accordingly, the genetic variance was partitioned into the additive effects, which were associated with a covariance structure proportional to genetic relationships derived from the molecular markers and the non-additive genetic effect. The non-additive genetic variance is explained by individual identity rather than the genomic relationship matrix following the approach described by Borgognone et al. [where e et al.  and Ovene et al. .2) estimates for the traits were calculated from phenotypic variance (σ2p) and the genotypic variance (σ2g). The BLUP values of the genotypes for the traits extracted from the best fit model were used as input for the GWAS model.Broad sense heritability (HDioscorea rotundata reference genome version 2 (https://drive.google.com/drive/folders/1H5T4xjKAEl9LliR-4qK_IR6TypCDe8nj) with Hisat2 [For each genotype, total genomic DNA was isolated from lyophilized young and fully expanded healthy leaves. Deoxyribonucleic acid (DNA) was extracted from the leaf samples using the CTAB procedure with slight modification . DNA quah Hisat2 . The rawCmplot’ function implemented in the CMplot R package [Various population genetic analysis methods were conducted to explore the structure and level of genetic diversity in the study material. The SNP distribution and the density were estimated using the ‘ package . For the package .The genetic relationship among the plant materials was explored using the principal component analysis (PCA) in FactorMiner R package . For theStructure software version 2.3.3 , 26 was The GWAS were performed using the R package mrMLM v4.0.2  with six2) and the markers effect were estimated in the mrMLM (v 4.0.2) R package (https://cran.r-project.org/web/packages/mrMLM/index.html).In the mrMLM analysis, we accounted for population structure (Q) generated from Structure analysis. For each trait, the optimal number Q value included in the GWAS models was determined based on the highest ΔK value. The percentage of variation explained by the associated marker (RThe possible candidate genes within the significant QTL region were searched in the defined range window of 1 MB at 500 Kb (downstream and upstream) from the yam Generic File Format (GFF3) file. Linkage disequilibrium (LD) was assessed between the significant SNPs using the LDheatmap library . The yamrstatix” package implemented in R, and the sequence of each haplotype was defined based on the 406 genetic material considered as testing and or identification population. The variant effect prediction was evaluated through the adjusted posterior probability, and the markers with high segregation were identified. Marker effects were then plotted for vizualization.Haplotype associated with significant QTL was developed using “-1 with an average of 1.19 kg. The area under the disease progress curve for YMV ranged from 100.56 to 2900.45 with an average of 936.16. (Supplementary Table Table The DArT genotyping of 406 white Guinea yam clones detected the highest number of SNPs (637) mapped on chromosome 5 and the lowest of 123 on chromosome 11    Table ; Fig. 3.1) Table . The SNPWe found five SNP loci that showed a significant association with the reaction to mosaic virus infection Table , Fig. 4.Four multi-locus models (MLMs) including FASTmrMLM, mrMLM, pKWmEB and pLARmEB detected a total of 22 QTNs across the 20 chromosomes of white yam for TYP and YMV traits , YMV Yam mosaic virus severity score , LOD Logarithm of odds, Chr chromosomes, Pos position, bp base-pair, MAF Minor allele frequency, r2 r-square, QTN quantitative trait nucleotideWe explored the association of the identified QTN regions on the physical map with the potential candidate genes and their functions using the white Guinea yam genome sequence. The LD heatmap of the significant SNPs on chromosomes 4, 5, 8, 13, 14, 15, 17 and 19 displayed a high genetic correlation (0.3 to 0.85) between the specific SNPs in the vicinity of the peak adjacent to the putative gene Fig. . On chroWe identified four candidate genes, namely AP2/ERF domain, Major facilitator, sugar transporter-like, and AUX/IAA protein on chromosome 3 near the SNP found in association with the YMV. The four identified candidate genes, AP2/ERF domain and AUX/IAA protein, were reported to confer essential gene functions related to plant defense and growth. The pairwise LD between the SNP of chromosome 3, 5, 10, 15 and 16 situated in genomic regions associated with YMV displayed a higher correlation with the three main haplotypes block . In the present study, the phenotypic effect values of the favorable alleles of TYP and YMV were evaluated and inferred to positively and negatively affect the individual traits. Based on the stringent criterion of −log10, we identified 17 significant markers trait associations ranging between 1.01 e-20 and 0.044 for tuber yield per plant; and 5 significant markers trait associations ranging between 5.25 e-14 and 0.029 for yam mosaic virus. The information on SNP variants from the present study would fast-track the application of genomics-informed selection decisions in breeding white Guinea yam for higher tuber yield and resistance to mosaic virus. Such great potential of GWAS has been reported for some root and tuber crops such as cassava [The whole-genome scan for phenotypic and allelic variation in tuber yield and yam mosaic virus resistance identified genome regions on ten chromosomes  with significant −log10 values. Both Q matrix (population structure) were considered in a mixed linear model for the association analysis to reduce false-positive associations. The model used for tuber yield and tolerance to yam mosaic virus showed no inflation of p-values indicating that the structure of relationships was well accounted for in the GWAS analysis. These findings are consistent with the view that traits with no inflation of p-values show that the structural relationship is adequate for GWAS analysis . Genome- cassava , potatoe cassava .This study used different MLMs  to identify genomic region associated with TYP and YMV. A total of 17 SNPs were significantly associated with TYP by the four MLM models across 9 out of the 20 chromosomes viz: chrs 4, 5, 8, 10, 14, 15, 17 and 19. Each of the four models detected different and complemeneted numbers of the SNPs: pKWmEB and pLARmEB (7 QTNs each) > FASTmrMLM, mrMLM (2 QTNs each). This indicates varied detection of each model. The MLMs used in this study detected putative candidate genes for the studied traits indicating its usefulness in GWAS. These results support the view that MLMs are useful for identifying QTNs and candidate genes in plants . The finOur results identified SNP markers that associate significantly with allelic variation for tuber yield and YMV tolerance in white yam. The detected markers offer good targets for further validation and analysis due to their location in proximity to candidate genes regulating growth, development and disease resistance. The SNP in chromosome 3 is near to AP2/ERF domain, AUX/IAA protein, major facilitator, sugar transporter-like genes. Zarei et al.  reportedOur findings indicated that multiple loci having unequal effects can influence the variation for TYP and YMV in white yam. The identified novel candidate genomic regions with growth, development and disease resistance genes in our study require further validation and testing in yam germplasm. This could be done by converting these MTAs into low cost Kompetitive Allele-Specific PCR (KASP) markers that can efficiently transfer alleles into elite yam genotypes as reported for wheat . These vThe SNP marker-TYP trait association exhibited high haplotype segregation. The marker effects alleles CC and CT are responsible for predicting high tuber yield per plant in the diversity panel used in the study, while alleles TT and GG were identified to associate with low yield. For the YMV, we found alleles GG, AG and TT to be responsible for low YMV disease scoring prediction. These findings suggest that data mining of favorable alleles is essential for improving the quantitative trait for tuber yield and YMV in yam using marker-assisted selection. Moreover, the results could be helpful for marker validation and deployment in yam breeding. Our findings agree with the view that information on marker effect based on segregation pattern is fundamental for marker validation and deployment in a breeding program , 59. AssUseful genetic variability exists in the 406 genotypes studied. The genetic architecture of TYP and YMV are regulated by varied QTNs unevenly distributed on the 20 chromosomes of white yam. Among the 4 MLM models, pKWmEB and pLARmEB are most robust in identifying more QTNs. The associated SNP markers could be potentially employed for targeted and accelerated tuber yield per plant and YMV resistance in white yam. The information from our study could help design new breeding strategies to hoard superior alleles for tuber yield per plant and yam mosaic virus in future marker-based breeding. The chromosomal regions controlling these studied traits could be exploited for selection and effective pyramiding of favorable alleles in white yam population improvement. Findings are relevant for population improvement of desirable TYP and YMV traits using marker assisted breeding (MAB) and haplotype-based scheme.Additional file 1: Supplementary Table 1. Description of trait progenitors utilized for the study. Supplementary Table 2. BLUP values of tuber yield per plant (TYP) and yam mosaic virus (YMV) among 406 clones of white yam. Supplementary Table 3. Cluster membership of 406 genotypes of white yam based on structure and phylogeny tree analyses. Supplementary Table 4. Single nucleotide polymorphism (SNP) markers associated with the yield per plant (TYP) and yam mosaic virus (YMV) and putative genes identified in chromosomes of 406 clones of white yam


APOB and either PCSK9 or LPL (“human double knock-outs”) plasma lipid levels change additively. Genetics and coRNAi identify overlapping AEs for 12 additional gene pairs. Overlapping GIs are observed for TOMM40/APOE with SORT1 and NCAN. Our study identifies distinct gene pairs that modulate plasma and cellular lipid levels primarily via AEs and nominates putative drug target pairs for improved lipid-lowering combination therapies.Complex traits are characterized by multiple genes and variants acting simultaneously on a phenotype. However, studying the contribution of individual pairs of genes to complex traits has been challenging since human genetics necessitates very large population sizes, while findings from model systems do not always translate to humans. Here, we combine genetics with combinatorial RNAi (coRNAi) to systematically test for pairwise additive effects (AEs) and genetic interactions (GIs) between 30 lipid genome-wide association studies (GWAS) genes. Gene-based burden tests from 240,970 exomes show that in carriers with truncating mutations in both,  Studying the contribution of pairs of genes to complex traits has been challenging. Here, the authors combine exome and genotype data with RNAi to screen for genetic interactions between 30 genes identified in lipid GWAS to hint at pairs whose joint modulation may improve lipid-lowering therapies. In rare instances, susceptibility to altered blood lipids can be attributed to mutations in individual genes such as LDLR, PCSK9 or APOB that lead to familial forms of disease. For the vast majority of dyslipidemic individuals, however, no single gene mutation can be identified or remain undetected without substantial follow-up. Recent evidence suggests that in the majority of such cases inherited susceptibility is caused by a cumulative effect of numerous common alleles within and across GWAS loci. Individually, such common alleles have only a minor effect, but when summarized in polygenic scores they can modify a phenotype to a similar extent as single high-impact mutations5, or further magnify the penetrance of individual mutations causing Mendelian disease6. The biological mechanisms behind the cumulative effect of risk alleles in different genes remain largely unclear.Genome-wide association studies (GWAS) have firmly established that a substantial fraction of variation in blood lipid levels and the risk of coronary artery disease (CAD) is heritable. Hundreds of genetic loci have been identified that reach genome-wide significant associations with plasma levels of low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), triglycerides (TG), total cholesterol (TC) and CAD8, it has made it increasingly difficult to decide which individual genes could be the most suitable targets for developing new drugs. Drug development is traditionally focused on discrete targets with well-understood biology. For certain diseases, an additive therapeutic benefit has been demonstrated through combination therapies that simultaneously modulate two or more targets at once. For instance, combinations of statins, inhibitors of HMG-CoA-reductase (HMGCR), with distinct other cholesterol-lowering medications including NPC1L1, PCSK9 and APOB inhibitors have been demonstrated to lower LDLc levels and CAD risk further than statin treatment alone10. Despite such successes, systematic strategies to predict that joint modulation of drug target pairs in combination therapies will show benefits beyond standard of care have yet to be explored.While the refined understanding of the polygenic nature of complex disease is starting to show promise for improved risk prediction and treatment decisions12. We thus hypothesized that genetics might also assist in nominating drug target pairs that, when addressed jointly, will have a higher probability to reach a desired therapeutic benefit. A particularly attractive approach to prioritize optimal target pairs would be to leverage synergistic gene-gene interactions, where genetic variants in two disease risk genes induce a phenotype that is more pronounced than what would be expected from each of the variants’ individual effects. Genetic interactions (GIs), or epistasis, have been extensively studied in model organisms and cell models with the aim to identify functional relationships among genes and gene products16. In humans, however, the contribution of GIs to complex traits has been controversial. While there is increasing evidence for modifier genes that modulate Mendelian phenotypes in non-additive manners17, most of the variance of complex traits appears to be explained by genes acting additively within or between loci 18.Genetic support for a drug target increases the probability that a medicine directed against the respective target will succeed by several foldHere we systematically test for pairwise genetic effects, both GIs and AEs, that regulate blood lipid levels by studying whether 30 genes prioritized based on known lipid regulatory functions from GWAS loci interact. For this, we use three complementary tools: protein-truncating variants (PTVs) identified through exome sequencing in the UK Biobank; reported GWAS lead SNPs genotyped or imputed in the UK Biobank; and combinatorial RNA interference (coRNAi) screening measuring LDLc uptake into cultured cells. Our combined genetics and functional genomics approach provides evidence that pairwise effects between lipid genes are foundational elements in controlling blood lipid levels and highlights distinct gene pairs as promising targets for lipid-lowering combination therapies.19. Causality for several of these genes to drive GWAS associations was further supported through systematic colocalization of plasma LDLc GWAS lead SNPs with GTEx liver eQTLs1 (2 genes), cis-pQTL signals20 (3 genes) and independently reported biological evidence for lipid-relevant functions (15 genes) (Supplementary Data 21) to model the data. For each gene pair, both the additive effects (AEs) (model 3), defined by the sum of effects from each gene or variant individually, as well as the genetic interactions (GIs) (model 4), represented by observed effects different than the expected additive effect, were calculated, with GIs being divided further into either negative (aggravating) or positive   and CAD as available from UK Biobank23 (see ‘Methods’).To explore combined effects between genes in GWAS loci and how these impact plasma lipid levels and LDLc uptake into cultured cells, we followed three parallel approaches: first, we extracted protein-truncating variants (PTVs) from whole-exome sequencing data of 302,331 participants of the UK Biobank. Second, we utilized GWAS lead SNPs commonly used to construct polygenic risk scores from the full set of 378,033 unrelated participants of European ancestry in the UK Biobank. And third, we conducted systematic RNAi-based combinatorial knockdown experiments in cells Fig. . We focu25. We obtained the quality-controlled exome sequences of 302,331 UK Biobank participants, annotated PTVs using Variant Effect Predictor v9626 and the LOFTEE plugin25, and identified 573,369 high confidence PTVs in the canonical transcripts of 19,076 genes. Within the 30 lipid GWAS genes, we detected a total of 983 unique rare PTVs . PTVs are expected to cause loss-of-function and compared to other types of mutations are rare at the population level due to purifying selectionp < 0.05) with both LDLc and TC , one with LDLc (LPL), two with HDLc  and two with TG , respectively , reflecting that joint loss-of-function of both genes modulates the respective lipid measures significantly more than if only one of the two genes is truncated  who carried both, PCSK9 and APOB PTVs, showed on average a further reduction in plasma LDLc by 41.2 mg/dl compared to individuals with PTVs in only one of the two genes, and by 91.7 mg/dl compared to individuals with no PTV in either of the two genes , although only one gene-pair showed strong  and two weak LD  between corresponding lead SNPs. GIs were also identified between loci on different chromosomes (‘trans-GIs’), such as between ZNF259 and APOE, or SORT1/CELSR2 and TOMM40 for LDLc and TC, or between LPL and ZNF259, or LPL and SIK3 for TG. Overall, our data support the hypothesis that AEs between GWAS loci are pervasive and individually small, yet if summed up across many loci in polygenic scores modulate complex traits5. Conversely, GIs are considerably less prevalent, with the APOE locus being a potential contributor to GIs for lipid traits.We next tested for pairwise genetic effects using 28 lipid/CAD GWAS lead SNPs representing the 30 loci in 378,033 unrelated individuals of European ancestry in the UK BiobankLDLRPTV-APOBSNP and LDLRPTV-PCSK9SNP, both with LDLc as well as TC  of individually small effect size subsumed in polygenic risk scores (PRS). We created PRS for the four lipid species using PRS-CS29 (‘Methods’) and tested for genetic effects between PRS and PTV burden for each of the 30 genes. Five AEs were identified . Of all combinations tested, only PTV burden in LPL showed evidence for a GI with the PRS for TG   in a sensitivity analysis using the p.S447X variant alone instead of LPL PTV-burden, proposing an alleviating GI of p.S447X variant on TG-PRS. These results are consistent with the hypothesis that a high polygenic risk for elevated TG can be mitigated by a concomitant gain-of-function mutation in LPL.Next, we queried for GIs between different types of genetic variation. Pairwise interaction testing between gene-based PTV burden and GWAS lead SNPs identified two GIs, 31. Each of the 30 lipid genes was profiled with a single siRNA that had previously been validated to significantly enhance or reduce cellular uptake of fluorescently-labelled LDL (DiI-LDL) or free cellular cholesterol levels, and/or to efficiently downregulate mRNA or protein levels of its respective target gene  were assayed in a total of 16,128 experiments . We corrected these p-values for multiple comparison testing with FDR. This identified 37 gene pairs, for which the interaction coefficient had a p-value(FDR) < 0.005, proposing them as GIs that differentially impact cellular LDLc uptake  , such as NPC1L1, the target of ezetimibe, or PCSK99. Genetic analyses in extreme phenotypes have identified a small number of individuals with concomitant mutations in two distinct FH genes, such as LDLR and APOB37, LDLR and LDLRAP139 or APOB and PCSK940. However, due to the rarity of highly penetrant FH mutations such findings have thus far remained limited to individual families. Conversely, on a population level, a previous GI analysis based on common alleles from ~24,000 individuals ascertained for lipid traits reported 14 replicated GIs between lipid GWAS loci, most notably, like in our study, with SNPs at the APOE locus being a key contributor41. Additional support for the relevance of genetic effects for modulating lipid traits comes from a study that includes a subset of the UK Biobank exomes analysed here and proposes an interplay of genetic variation across the allelic spectrum6. Notably, that study reports that carriers of monogenic CAD risk variants show an up to 12.6-fold higher risk to manifest disease if they are in the highest quintile of the polygenic risk distribution.Several of the genetic effects identified in our study can be expected to be high potential starting points for the development of advanced lipid-lowering combination therapies. Lowering LDLc with statins is the first-line pharmacological strategy to treat or prevent CAD and ischaemic heart disease as its clinical manifestation. However, many patients do not reach their recommended goals of LDLc lowering through statins alone, or they are intolerant against statins. For these, combination therapies have become available that aim to lower atherogenic lipid levels further. A motivation for this is that every 1 mmol/l (39 mg/dl) reduction in blood LDLc is associated with a 19% reduction in coronary mortality and a 21% reduction in major vascular events, supporting that, at least for secondary prevention, the lower blood LDLc levels, the betterHMGCR, the rate-limiting enzyme during cholesterol biosynthesis and target of statins, and APOB encoding apolipoprotein B, a critical constituent of LDLc particles. Consistent with the known biological functions of these genes, joint knockdown increased levels of functional LDLc receptor on the cell surface and stimulated internalization of exogenous, fluorescently-labelled LDLc. This observation is well in line with results from clinical trials showing that in patients with Familial Hypercholesterolemia and other hyperlipidemias a combination of statins with an antisense inhibitor of apolipoprotein B (mipomersen) efficiently reduces plasma LDLc levels more strongly than high-intensity statin treatment alone45. Importantly, the AE identified from UK Biobank participants carrying PTVs in both APOB and PCSK9 suggests that similarly beneficial effects can be expected when APOB antisense therapies are applied in combination with PCSK9 inhibitors. Recently, inclisiran, an siRNA targeting PCSK9 in individuals on maximally tolerated statin doses46 led to a persistent, highly significant lowering of LDLc in treated individuals relative to placebo in a phase 3 study47, introducing siRNAs as an attractive therapeutic modality for lipid-lowering therapies. Our results strongly propose that, on a population level, combination therapies inhibiting both PCSK9 and APOB may lower LDLc levels and CAD risk even more substantially than drugs targeting only one of the two genes.Our analyses here propose distinct gene pairs that modulate plasma and cellular lipid levels via additive and non-additive effects. Among others, we identify pairwise effects for several prominent cardiovascular risk genes that individually are established targets for lipid-lowering drugs. For instance, coRNAi proposed a synergistic, aggravating GI between APOB PTV burden was associated not only with LDLc and TC, but also HDLc and TG, and our analyses propose that joint disruption of APOB together with gain-of LPL function reduces TG and increases HDLc, most likely in an additive manner. LPL encodes for lipoprotein lipase which hydrolyzes TG from apolipoprotein B containing lipoproteins, releasing fatty acids48. PTV burden in LPL was dominated by the stop-gain variant p.S447X  which in our exome-sequenced UK Biobank sub-cohort showed an allele frequency of 9.95%. This variant is known to cause gain-of LPL activity leading to a 0.8-fold reduced risk for ischaemic heart disease49, an effect that is likely to be further enhanced by concomitant reduction of apolipoprotein B. The p.S447X allele was also the main driver behind the only GI detected between PTV burden and polygenic risk for plasma lipids and conferred that in LPL PTV-carriers polygenic risk for TG is reduced, with assumed non-additive effects being the most pronounced in the upper percentile range of the PRS distribution.APOE and apart from plasma lipids and CAD is associated with Alzheimer’s disease, longevity and macular degeneration among others20. Interestingly, our findings indicate that genes other than APOE at this locus might contribute to lipid GIs, which is consistent with our earlier findings that knockdown of several genes at this locus independently modulate cellular LDLc uptake19. For instance, both SNP-based GI testing and coRNAi suggested alleviating GIs for TOMM40 with SORT1 and NCAN, respectively. Variants in TOMM40 have been hypothesized to modify onset of Alzheimer’s disease independently of and in conjunction with APOE50. Our analyses suggest TOMM40 might exert similar modifying effects on lipid phenotypes and CAD risk, which will need to be clarified in future studies. Such studies will also need to critically assess whether GI signals involving the APOE locus might be inflated due to its large effect sizes51. Importantly, our integrated analysis revealed AEs between MLXIPL, NCAN and SIK3 with HMGCR, nominating these poorly characterized genes to be explored as potentially attractive new targets for lipid-lowering therapies on top of statins.A prominent driver of GIs in both our SNP- and coRNAi-based analyses was the 19q13.32 locus which includes 18, our results show that for regulating plasma lipid levels, additive effects between gene or variant pairs are common, while non-additive epistasis is rare. Indeed, despite a sample size of over 240,000 exomes, our gene-based PTV burden GI analyses did not find evidence for pairwise GIs between lipid genes disrupted by PTVs. Further increasing sample sizes might help uncover non-additive effects, however, at least for lipid traits and based on the analysed candidate gene set, the contribution of GIs to the overall variance appears to be small. This is consistent with the existence of evolutionary mechanisms that suppress epistatic interactions13. Since pairwise GIs can be expected to be identified the most easily for genes that are disrupted sufficiently frequently in a population by PTVs of large-enough effect size, sequencing of consanguineous or bottlenecked populations might improve the detection rate of GIs52. Interestingly, as observed also here, GIs seem to be more easily detectable in cell and animal models, for instance through synthetic lethality mapping14.Consistent with previous assumptionsIntegration of population-scale genetics and functional coRNAi screening results yielded a total of ten AEs and two putative GIs that influence plasma and cellular lipid levels. Such validation via two systematic approaches substantially increases the confidence for committing to time and resource-intense follow-up analyses of such findings, e.g., when exploring the suitability of a gene pair to be jointly targeted in combination therapies. Interestingly, a significant number of GIs identified through genetics and coRNAi in our study do not yet overlap. This may be explained by several reasons: First, our functional analyses were limited to measuring LDLc uptake into cells, which reflects a relevant, yet only a partial aspect of the many possible mechanisms by which a gene can modulate plasma lipid levels. Second, siRNA-based gene knockdown captures acute and rather severe functional effects, which may differ from the chronic and often compensated consequences upon lifelong modulation of a gene’s function through genetic variation. Third, despite the large number of samples used for genetics-based GI testing, the number of informative high-impact variants in the human germline may still be too discrete to comprehensively identify GIs. Conversely, the large numbers of individually low-impact GWAS lead SNPs may not unambiguously be mapped to a respective gene, so that observed associations may stem from a causal variant from another gene in LD. Regardless, the availability and rapid development of advanced high-throughput microscopy technology joint with the constantly increasing cohort sizes for genetic analyses will allow up-scaling of the approach taken here in future studies and with a high probability identify and validate further genetic effects.In conclusion, our study introduces a strategy to link large-scale genetic data from a population biobank with quantitative, cell-based coRNAi to map pairwise genetic effects that affect blood lipid levels and CAD, an approach that can be applied to other diseases and complex traits. Our analyses support that mechanisms exist through which pairs of genes help maintain blood lipid homoeostasis in additive and non-additive manners. CAD and ischaemic heart disease remain a substantial global health burden, and doubling-down on lowering atherogenic plasma lipids remains one of the most promising therapeutic approaches. With the encouraging results from recent gene- and antisense-based clinical trials for CAD, our results may help prioritize drug target pairs for the development of lipid-lowering combination therapies rooted in human genetics.19 (Supplementary Data 53) and queries using the PhenoScanner platform54 (http://www.phenoscanner.medschl.cam.ac.uk/). Twenty-eight genes were validated to reside within loci that are associated at genome-wide significance (p < 5e−8) with plasma lipid levels or CAD. For all lipid loci analysed we selected the more recently published sentinel SNP. When a respective locus was associated with multiple lipid phenotypes, the SNP with the lowest reported p-value association with LDLc was chosen to be the lead SNP. SNPs near FAM174A (rs383830) and SEZ6L (rs688034) had originally been reported as associated with CAD55, but failed to replicate at genome-wide significance in more recent meta-GWAS. However, since knockdown of both genes had scored as significantly impacting lipid parameters in cells19 the two genes were maintained for this current study.We chose to study 30 candidate genes from 18 loci reported as associated through common-variant genome-wide association studies (GWAS) with plasma lipid levels and the risk for CAD. Twenty-eight of these genes had been identified and validated as functional regulators of LDLc uptake and/or cholesterol levels into cells in a previous RNAi-screen analysing a total of 133 genes in 56 lipid and CAD GWAS loci1 (http://csg.sph.umich.edu/willer/public/lipids2013/) and the GTEx liver cis-eQTL dataset (N = 153)56. When a respective locus was associated with multiple lipid phenotypes, the SNP with the lowest reported p-value association with LDLc was chosen to be the lead SNP. There was no GTEx liver expression data for four genes , therefore there were no cis-eQTL for these genes to colocalize with. Colocalization analysis was conducted following the methods in Giambartolomei et al., 57 using the R ‘coloc’ package on a ±500 kb window around each lead SNP against SNP-to-expression data of all neighbouring genes within that locus. Positive colocalization between liver cis-eQTL and GWAS signal was defined as showing a posterior probability of sharing the same SNP (PP4) of larger than 0.8. A lead SNP at the SORT1/CELSR2 locus (rs629301) showed a positive colocalization signal, but the cis-eQTL co-localized with both genes, so SNP-based genetic effects for these genes could not be analysed separately.Colocalization analysis was performed between the 28 GWAS lead SNPs using summary statistics from the 2013 Global Lipid Genetics Consortium GWAS23. Baseline biochemical measures including LDL cholesterol (LDLc), HDL cholesterol (HDLc), triglycerides (TG), and serum total cholesterol (TC) had been obtained in UK Biobank’s purpose-built facility in Stockport as described in the UK Biobank online data showcase and protocol (www.ukbiobank.ac.uk). Demographic and other relevant phenotypic information was obtained from standard questionnaire data. Individual lipid phenotypes  were first modelled as dependent variables using linear regression models against covariates including age, sex, smoking, alcohol drinking status, BMI, lipid medication use and top ten genetic principle components. Residuals were then used as outcome variables in genetic analyses. Lipid medication use was obtained from self-reported questionnaire data (UK Biobank fields 6153 and 6177). CAD cases were recognized based on both self-reported diagnosis and Hospital Episode Statistics data in the UK Biobank with a code-based CAD definition as presented in the most recent CAD GWAS that included UK Biobank53. In total, 30,125 CAD cases were identified and the CAD analysis was conducted using logistic regression models adjusted for age, sex, smoking status, alcohol drinking status, BMI, lipid medication use and top ten genetic principle components. All phenotype data were derived from the UK Biobank basket ‘ukb27390’ released on March 11, 2019.The UK Biobank is a prospective study of over 500,000 participants recruited at an age of 40–69 years from 2006 to 2010 in the United Kingdom. Participant data include health records, medication history and self-reported survey information, together with imputed genome-wide genotypes and biochemical measures54. PTVs were called from a Regeneron QC-passing ‘Goldilocks’ set of genetic variants using Variant Effect Predictor v9626 and the LOFTEE plugin25. LOFTEE applies a range of filters on stop-gained, splice-site disrupting and frameshift variants in order to exclude putative PTVs due to variant annotation and sequencing mapping errors that are unlikely to significantly disrupt gene function. For instance, stop-gained and frameshift variants that are within 50 kb of the end of the transcript will be flagged as ‘low-confidence’. For our analysis, we only considered variants predicted as PTVs and flagged as ‘high confidence’ by LOFTEE for each canonical transcript (as defined in Ensembl). We identified 573,369 high-confidence PTVs in the canonical transcripts of 19,076 genes. This set included 983 rare PTVs in the 30 lipid genes analysed in this study. PTVs per gene were enumerated, and a PTV burden association analysis was conducted in 240,970 unrelated (>2nd degree relatedness) UK Biobank participants of European ancestry, as defined by principle components analysis of the genotyping data23.High-impact protein-truncating variants (PTVs) expected to disrupt protein functions were identified from 302,331 whole-exome sequencing (WES) data of UK Biobank participants to conduct pairwise interaction analyses. WES data were generated and quality controlled (QCed) as described in Van Hout et al. at the Regeneron Genetics Center as part of a collaboration between AbbVie, Alnylam Pharmaceuticals, AstraZeneca, Biogen, Bristol-Myers Squibb, Pfizer, Regeneron and Takeda and the UK Biobank consortiumFor pairwise PTV-based interaction testing, QCed UK Biobank lipid phenotypes  after residualization against the covariates were modelled as dependent variables using the following four robust linear regression models:1Model 1 for gene1 PTV burden only: lipids ~ PTV2Model 2 for gene2 PTV burden only: lipids ~ PTV1 + PTV2Model 3 for gene1 PTV burden and gene2 PTV burden : lipids ~ PTV1 + PTV2 + PTV1 * PTV2Model 4 for gene1 PTV burden and gene2 PTV burden (GI): lipids ~ PTV58 scoring was used to determine the best model to explain the data and goodness of fit, with the lowest BIC value indicating the best-fitting model describing each gene pair. Model 3 reflected additive effects (AEs), Model 4 gene interactions (GIs). The model with the lowest BIC was chosen as describing most adequately the type of interaction between each corresponding gene pair. All models were fitted using the rlm function in R package ‘MASS’.Schwarz’s Bayesian Information Criterion (BIC)p-values from the t-statistics of the robust linear regression full model (Model 3 or Model 4 depending on the gene pair), including the interaction term (if interaction testable), as well as the PTV effect for each single gene, for all gene pairs and all four lipid measures. To correct for multiple comparisons, the p-values were then adjusted using the false discovery rate (FDR) method59, after which genetic interactions, as well as additive effects, were identified in a two-step manner: if the FDR-corrected p-value of the interaction term was less than 0.005 and the BIC indicated Model 4 as the best fitting model, then the gene pair was considered as being a significant GI; if the FDR-corrected p-value of the interaction term was larger than 0.01 or untestable, but FDR-corrected p-value for each single-gene PTV effect was less than 0.005 and the BIC indicates Model 3 as the best fitting model, the gene pair was considered as a significant AE.In order to also estimate statistical significance of gene interaction and additive effect for each PTV combination, we calculated normal distribution approximated To assess whether GWAS lead SNPs modulate plasma lipid levels through joint effects within and across GWAS loci, we conducted pairwise SNP-SNP interaction analysis using genome-wide genotyping data and biochemical measures of lipid species from the UK Biobank. Twenty-eight lead SNPs mapped to the 30 lipid GWAS genes were extracted from genotyping data of 378,033 unrelated (removed up to 2nd degree relatedness) participants of European ancestry. A total of 378 pairwise interaction effects were tested for each of the four lipids species after residualization against the covariates by running four robust linear models using rlm function from R package ‘MASS’:1Model 1 for SNP1 only: lipids ~ SNP2Model 2 for SNP2 only: lipids ~ SNP1 + SNP2Model 3 for SNP1 and SNP2 (AE): lipids ~ SNP1 + SNP2 + SNP1 * SNP2Model 4 for SNP1 and SNP2 (GI): lipids ~ SNPSchwarz’s Bayesian Information Criterion (BIC) scoring was used to determine the best model to explain the data and goodness of fit, with the lowest BIC value indicating the best-fitting model describing each SNP pair. If Model 3 had the lowest BIC value, it reflected an AE, and if Model 4 had the lowest BIC value, it reflected a GI.A similar strategy was applied for pairwise interaction testing to explore potential joint effects between the 30 genes on CAD risk by running the following four logistic regression models adjusted for age, sex, smoking status, alcohol drinking status, BMI, lipid medication use and ten principle components:1Model 1 for SNP1 only: CAD ~ SNP2Model 2 for SNP2 only: CAD ~ SNP1 + SNP2Model 3 for SNP1 and SNP2 (AE): CAD ~ SNP1 + SNP2 + SNP1 * SNP2Model 4 for SNP1 and SNP2 (GI): CAD ~ SNPAs above, the model with the lowest BIC was chosen as describing most adequately the type of interaction between each corresponding SNP pair.p-values from the t-statistics of the robust linear regression and logistic regression full model (Model 4), including the interaction term, as well as the SNP effect for each single gene, for all gene pairs and all four lipid measures as well as CAD. To correct for multiple comparisons, the p-values were then adjusted using the false discovery rate (FDR) method59, after which genetic interactions, as well as additive effects, were identified in a two-step manner: if the FDR-corrected p-value of the interaction term was less than 0.005 and the BIC indicated Model 4 as the best fitting model, then the gene pair was considered as being a significant GI; if the FDR-corrected p-value of the interaction term was larger than 0.01 or untestable, but FDR-corrected p-value for each single-gene PTV effect was less than 0.005 and the BIC indicates Model 3 as the best fitting model, the gene pair was considered as a significant AE.We also calculated normal distribution approximated In order to conduct pairwise interaction analyses between GWAS lead SNPs and PTVs, we assessed the interaction of the 28 lead SNPs with rare PTV burden for each of the 30 genes. For SNP-PTV interaction testing, UK Biobank lipid phenotypes  after residualization against the covariates were modelled as dependent variables using the following four robust linear regression models fitted by rlm function in R package ‘MASS’:1Model 1 for gene1 lead SNP only: lipids ~ SNP2Model 2 for gene2 PTV burden only: lipids ~ PTV1 + PTV2Model 3 for gene1 lead SNP and gene2 PTV burden (AE): lipids ~ SNP1 + PTV2 + SNP1 * PTV2Model 4 for gene1 lead SNP and gene2 PTV burden (GI): lipids ~ SNPAs above, the model with the lowest BIC was chosen as describing most adequately the type of interaction between each corresponding SNP-gene pair.p-values from the t-statistics of the robust linear regression full model (Model 3 or Model 4 depending on the gene pair), including the interaction term (if interaction testable), as well as the PTV or SNP effect for each single gene, for all gene pairs and all four lipids measurements. To correct for multiple comparisons, the p-values were then adjusted using the false discovery rate (FDR) method59, after which genetic interactions, as well as additive effects, were identified in a two-step manner: if the FDR-corrected p-value of the interaction term was less than 0.005 and the BIC indicated Model 4 as the best fitting model, then the gene pair was considered as being a significant GI; if the FDR-corrected p-value of the interaction term was larger than 0.01 or untestable, but FDR-corrected p-value for each single-gene PTV effect was less than 0.005 and the BIC indicates Model 3 as the best fitting model, the gene pair was considered as a significant AE.We also calculated normal distribution approximated 29, which is a Bayesian regression-based algorithm, and publicly available summary statistics from lipid GWAS1. We applied derived PRS weights to imputed genotypes  of UK Biobank samples and calculated PRS for each lipid, based on the corresponding PRS weights. Note that all SNPs in the gene of interest were excluded from the PRS when testing for PRS–PTV gene interaction. To test GIs between PRS and PTV burden for each of the 30 genes, UK Biobank lipid phenotypes  after residualization against the covariates were modelled as dependent variables by fitting the four robust linear regression models using rlm function in R package ‘MASS’:We assessed the interaction effects between polygenic risk score (PRS) and PTVs for each of the four lipid phenotypes. To construct PRS for UK Biobank samples, we first derived the PRS weights for each SNP across the genome using PRS-CSModel 1 for PRS only: lipids ~ PRSModel 2 for gene PTV burden only: lipids ~ PTVModel 3 for PRS and gene PTV burden (AE): lipids ~ PRS + PTVModel 4 for PRS and gene PTV burden (GI): lipids ~ PRS + PTV + PRS ∗ PTVAs above, the model with the lowest BIC was chosen as describing most adequately the type of interaction between each corresponding PRS-gene pair.p-values from the t-statistics of the robust linear regression full model (Model 4), including the interaction term, as well as the PRS and PTV effect, for all PRS–PTV pairs and all four lipids measurements. To correct for multiple comparisons, the p-values were then adjusted using the false discovery rate (FDR) method59, after which genetic interactions, as well as additive effects, were identified in a two-step manner: if the FDR-corrected p-value of the interaction term was less than 0.005 and the BIC indicated Model 4 as the best fitting model, then the gene pair was considered as being a significant GI; if the FDR-corrected p-value of the interaction term was larger than 0.01 or untestable, but FDR-corrected p-value for each single-gene PTV effect was less than 0.005 and the BIC indicates Model 3 as the best fitting model, the gene pair was considered as a significant AE.We also calculated normal distribution approximated 59. P-values were calculated based on t-values extracted from the rlm result for the fitted effects of single genes and interaction terms. For both genetics and primary coRNAi screen, we set the threshold to call a gene pair as GI to an FDR-corrected p-value for the interaction term as p < 0.005, with FDR correction being based on the number of interactions between gene/variant pairs tested  times the number of genetic effects tested . FDR-correction was done separately for PTV-PTV, SNP-SNP, PTV-SNP, and PTV-PRS interaction for genetic analyses given the different nature of genetic variations tested, but a rather stringent significance cut off was chosen at FDR corrected p < 0.005 for all analyses. For the validation RNAi experiments, significance threshold was set at an FDR corrected p < 0.01.We performed FDR-correction as described by Benjamini and Hochberg, 199531. DiI-LDL (Life Technologies), DRAQ5 (Biostatus), Dapi (Molecular Probes), 2-hydroxy-propyl-beta-cyclodextrin (HPCD) (Sigma), Lipofectamine 2000 (Invitrogen) and Benzonase (Novagen) were purchased from the respective suppliers.HeLa-Kyoto cells are a strongly adherent Hela isolate  that, as we demonstrated earlier, enable reliable measurements of LDL-cholesterol uptake dynamics and show lipid homoeostatic mechanisms similar to those described for liver-derived cell models60. Each gene under study was targeted with a single siRNA  that had been selected with the EMBL-generated software tool bluegecko  based on the alignment to the reference genome, a maximal number of protein-coding transcripts per gene targeted and expected specificity for the target gene. The 28 siRNAs in this study had been validated earlier to significantly enhance or reduce cellular uptake of fluorescent-labelled LDLc (DiI-LDL) or free cellular cholesterol levels19 and were shown to efficiently downregulate mRNA or protein levels of their respective target genes , siRNAs used in the current study were prioritized from 3 and 5 siRNAs per gene based on bluegecko in silico analyses, knockdown efficiency on target mRNA/protein levels  and/or efficiency to modulate cellular DiI-LDL uptake in preparatory individual single gene knock-down experiments (not shown). The 75% (12/16) of siRNAs that had scored as individually modulating cellular DiI-LDL uptake in our earlier study19 also met the more stringent criteria of our current study to score as LDLc uptake modulator when used either alone or together with non-silencing control siRNA Neg9  screening was conducted in glass-bottomed single-well chambered cell culture (Lab-Tek) slides with solid-phase reverse siRNA-transfection of cultured cells (‘cell microarrays’) as described previouslyINCENP-siRNA (s7424) to control for transfection efficiency19; eight spots containing non-silencing control siRNA Neg1 (s229174), and eight spots containing non-silencing control siRNA (denoted as CTRL throughout the text) Neg9 (s444246). Furthermore, eight spots were added with siRNA targeting LDLR (s224006) as a positive control for LDLc uptake, as well as eight spots with siRNA targeting NPC1 (s237198) knockdown of which increases free cellular cholesterol signals30. For pairwise combinatorial RNAi-screening, siRNAs against two genes were printed simultaneously on a respective siRNA-spot, with equal amounts (~0.053 pmol/siRNA) of siRNA per gene. As positive controls, eight spots containing both, non-silencing control siRNA Neg9 (CTRL) (s444246) and siRNA targeting LDLR (s224006), and eight spots containing both, non-silencing control siRNA (CTRL) Neg9 (s444246) and siRNA targeting NPC1 (s237197) were included per array. For all genes, ‘single-gene knockdown’ scenarios [siRNAgeneA + Neg9] were added on two spots per array. Each pairwise ‘combinatorial knockdown’ scenario [siRNAgeneA + siRNAgeneB] was analysed on one spot per array, with a single spot covering 50-100 informative cells61  in replication analyses and for which interaction values showed the same as in the primary coRNAi screen, were considered as validated  were performed as described also in the previous publications31. Cells cultured in serum-free medium (DMEM/2 mM L-glutamine/0.2% (w/v) BSA) and exposed to 1% 2-hydroxy-propyl-beta-cyclodextrin for 45 min were labelled with 50 µg/ml DiI-LDL (Invitrogen) for 30 min at 4 °C. DiI-LDL uptake was stimulated for 20 min at 37.0 °C. Endocytosis of labelled LDLc was stopped by washing with ice-cold media. We removed non-internalized DiI-LDL from the plasma membrane by acidic wash for 1 min in medium at pH 3.5 performed also at 4 °C. This was followed by fixation, counterstaining for nuclei (Dapi) and cell outlines (DRAQ5). For RNAi-based gene interaction screening, each of the five cell microarrays was assayed in 7–10 biological replicas.HeLa Kyoto cells were grown in DMEM medium (Gibco) supplemented with 10% (w/v) foetal calf serum (FCS)(PAA) and 2 mM L-glutamine (Sigma) at 37 °C with 5% CO31. Images from a total of 42 cell microarrays were visually quality controlled. Arrays with insufficient knockdown efficiency where INCENP siRNA treated cells did not show the expected multinucleated phenotype in the DAPI channel were excluded. Also, arrays with plate effects as evaluated through diagnostic plots with the splot function in R, and arrays where knockdown of LDLR, or LDLR together with negative control siRNA Neg9, did not show a significant difference from controls, were discarded as well. Following these QC criteria, 29 cell microarrays with a total of 11,047 image frames per channel were further analysed. The in-house developed tool HTM Explorer  was then used to select images fulfilling pre-defined criteria for cell number, image sharpness quality, and image background intensity, resulting in a total number of 9539 (86.35%) QCed image frames that were used for subsequent analyses.Image acquisition was performed using an Olympus IX81 automated microscope with Scan^R software and an UPlanSApo 20×/0.4NA air objective as described62http://www.cellprofiler.org as described31. In brief, areas of individual cells were approximated by stepwise dilation of masks on the DAPI (nuclei) and DRAQ5 (cell outlines) channels63. For each individual cell, DiI-LDL signal was determined from masks representing intracellular endosome-like vesicular areas that were determined by local adaptive thresholding according to predefined criteria for size and shape , or double siRNA knock-downs ([siRNAgeneA + siRNAgeneB]), these mean values from different images from the same biological replicate were averaged ]) and the median absolute deviation of these controls (madIF(controls)) as follows65:Automated image analysis was performed using a specifically developed pipeline (available upon request) in the open-source software CellProfilerA median robust Z-score was calculated per treatment across all biological replicates and is represented on the heatmap Fig.  and in tgeneA + Neg9] and [siRNAgeneB + Neg9]) or double knock-downs ([siRNAgeneA + siRNAgeneB]) were considered to be response variable values. Negative control values [Neg9] were included in each fitted dataset to correctly account for baseline LDLc uptake. The full regression model considered in the study wasTo identify pairs of genes for which simultaneous knock-down results in additive effects (AEs) or gene interactions (GIs) on LDLc uptake we conducted a Robust Linear Model fitting in R (rlm function of ‘MASS’ package). Robust Z-score values calculated from different biological replicates in the presence of single .In both formulas Subsequently, two strategies were used to evaluate functional interactions for each gene pair using a defined statistical model:First, we used primary screen data to identify likely gene pairs for which GIs and AEs observed upon combinatorial knockdowns. For this we compared fitting of the whole model to the fitting of reduced model versions. Following models were compared:β0 in case of either single or double knockdown): y ~ 1Model 0 - (only baseline effect geneA only: y ~ xAModel 1 - effect of siRNAgeneB only: y ~ xBModel 2 - effect of siRNAy ~ xA + xBModel 3 for additive effect of both siRNAs (AE): y ~ xA + xB + xA ∗ xB.Model 4 – full model including genetic interaction (GI): 58. BIC score was calculated for each model fitted to the data, then the model with the lowest BIC value (BIC*) was selected as the best-fitting model. Co-knockdown effects of each gene pair were classified as AEs or GIs when model 3 or model 4 accordingly were defined to fit data best. Additionally, for the coRNAi screen, we used the method published by Raftery, 1995 to define the strength of evidence for the respective model to be selected66. Namely, if the difference (ΔBIC) between the BIC value of the best fitting model  and the BIC value of any other model is bigger than 2, then it would indicate a significant evidence for this model (with BIC*) to truly represent the data. In other words, if ΔBIC > 2 then the model with lowest BIC value (BIC*) was considered as the one most correctly describing the data in comparison to other tested models. If the ΔBIC < 2, then two models were considered as possible alternatives for representing the dataset.To determine the best model explaining the data for each gene pair we used Schwarz’s Bayesian Information Criterion (BIC)t-value of the linear regression model terms describing effects of individual gene knockdowns (βA and βB) and genetic interaction (βAB) as pVal = 2-2 * pnorm). To correct for multiple comparisons, the p-values were adjusted using the false discovery rate (FDR) method62. We applied FDR-correction on the pulled together p-values of individual gene knockdown effects (βA and βB) and genetic interaction term (βAB), thus we calculated FDR-correction on the set of 3 × 435 = 1305 p-values. The FDR-corrected p-values pFDR < 0.005 were considered to correspond to significant GIs.Secondly, to estimate the statistical significance of gene interaction effect for each siRNA gene combination and to confirm potential AE and GI pairs identified by the BIC method, we calculated a p-value from the βA and βB) were significant having a p(FDR) < 0.005, whereas the interaction term (βAB) was not significant, namely p(FDR) > 0.01. Both were defined only when BIC analysis indicated Model 3 and each of the SNPs separately had significant effect.AEs were defined as pairs for which both individual effect terms (p(FDR) < 0.01 and Interaction Value had the same directionality (‘−’ or ‘+’) as in the primary screen.36 gene pairs were subsequently taken for validation experiments by liquid phase transfection. Of these, we considered 20 to be validated when https://www.ebi.ac.uk/ols/ontologies) as negative—aggravating or synergistic, i.e., simultaneous knockdown of both genes magnified the effect size beyond expectations for an additive effect and positive GIs. Positive GIs were further subdivided into: alleviating, i.e., the joint effect was approximately equal to the most severe of the phenotypes and suppressive, i.e., the joint effect was less severe  than the most severe of the phenotypes22.GIs identified through coRNAi were classified  was calculated . Reverse-transcription was performed with the SuperScript™ III First-Strand Synthesis SuperMix for RT-qPCR (Invitrogen). Raw data was collected with StepOne Software v2.3. RT-qPCR data were obtained from three biological replicates/siRNA treatment. For each siRNA treatment target mRNA was normalized to that of Cells were transfected and cultured as described above then fixed with 3% paraformaldehyde (PFA) at room temperature for 20 min, washed with PBS, incubated with 30 mM glycine for 5 min and washed again with PBS. For LDLR staining cells were permeabilized with 0.05% Filipin III (Sigma #F4767) in 10% FCS for 30 min at room temperature. Primary antibody: rabbit monoclonal anti-LDLR  was diluted 1:100 in 5% FCS overnight at 4 °C. Secondary antibody: goat polyclonal goat anti-rabbit IgG Alexa 568 (Invitrogen #A11011) was diluted 1:400 in 5% FCS. Fixed cells were imaged using a Zeiss LSM 780 confocal microscope using a 63×/1.4NA oil immersion objective. For plasma membrane LDLR staining, cells were not permeabilized, but directly stained for 1 hour at 4 °C with antibody recognising the extracellular part of LDLR, namely, mouse anti-LDLR-C7 antibody (Progen #61087) diluted 1:100 in PBS. As a secondary antibody, we used chicken anti-mouse IgG Alexa 488 (Invitrogen #A-21200) diluted 1:400. Cells were imaged on an Olympus FluoView 3000 confocal microscope using 60×1.3NA silicon oil immersion objective.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Supplementary DatasetSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


We constructed the first high-density linkage map for trevally, which included 19,861 single nucleotide polymorphism markers, and discovered 8 quantitative trait loci for height, length, and weight on linkage groups 3, 14, and 18. Using genome-wide association studies, we further identified 113 single nucleotide polymorphism-trait associations, uncovering 10 genetic hot spots involved in growth. Two of the markers found in the genome-wide association studies colocated with the quantitative trait loci previously mentioned, demonstrating that combining quantitative trait locus mapping and genome-wide association studies represents a powerful approach for the identification and validation of loci controlling complex traits. This is the first study of its kind for trevally. Our findings provide important insights into the genetic architecture of growth in this species and supply a basis for fine mapping quantitative trait loci, genomic selection, and further detailed functional analysis of the genes underlying growth in trevally.Growth directly influences production rate and therefore is one of the most important and well-studied traits in animal breeding. However, understanding the genetic basis of growth has been hindered by its typically complex polygenic architecture. Here, we performed quantitative trait locus mapping and genome-wide association studies for 10 growth traits that were observed over 2 years in 1,100 F Gadus morhua) [see review by Salmo salar) (Tilapia mossambica)  mapping experiments. Many variants associated with complex phenotypes have since been found in a variety of nonmodel species. However, QTL mapping approaches only allow the identification of genetic regions that are polymorphic between 2 parents, and relevant in a particular environment, therefore, potentially missing some common variants associated with the traits . For thiRecently, genome-wide association studies (GWAS) have been widely employed to detect QTLs both in captive . This tePerna canaliculus), Pacific oysters (Crassostrea gigas), and only 1 finfish, king/chinook salmon (Oncorhynchus tshawytscha), which is an introduced species  has been identified as a suitable candidate for New Zealand aquaculture. In Aotearoa, its Māori name is araara. Indigenous Māori people have a strong cultural connection to trevally, where it is considered as taonga . Trevally is a shoaling pelagic species found throughout the coastal waters of southern Australia and around New Zealand  generate a high-density linkage map; (2) detect rare QTLs associated with growth using linkage mapping in a subfamily comprising 89 individuals; (3) identify common SNPs associated with growth using GWAS on the whole population; and (4) overlap the results and annotate supported regions.In 2016, the Institute of Plant and Food Research (PFR) started a selective breeding program on trevally, and has induced a wild broodstock to create F0 broodstock and 1,100 F1 captive-bred offspring. In 2015, induced mass spawning of the F0 generation in a single tank was used to produce the offspring F1 generation. This resulted in a complex pedigree, including a combination of unrelated, full-, and half-siblings in the F1 generation. F1 offspring were held in a single tank from hatch receiving the same feeding regime, light, water flow, and aeration until the end of this experiment. The seawater tanks in the facility are located on the seaward side of Port Nelson and receive ambient seawater from an underground bore that is filtered using mesh filters and UV treatment.The trevally population used in this study was generated as part of a breeding program started at the PFR finfish research facility in Nelson, New Zealand. The full description of holding conditions and the pedigree are described in https://www.plantandfood.co.nz/page/morphometric-software-home/), the outline of each fish was extracted from images and morphometric were located, and then used to make measurements. PL was measured by assessing the distance between the upper lip and narrowest cross-section of the tail. Height was measured at 3 positions along the fish: 25%, 50%, and 75% of the PL. The weight estimations (EW) were done following a Bayesian hierarchical approach as described in Ten growth traits were used in the current study, namely peduncle length (PL), height at 25 (H25), 50 (H50), and 75% (H75) of the PL, estimated weight (EW), and related net gain traits   over 3 lanes of the HiSeq 2500 platform at the Australian Genome Research Facility . The F1 were genotyped using a modified GBS approach .The parents of each Fa v2.0.7  as repor al. 2021. The ful al. 2021 was usedn 1.47-9  with 1,01 population . SNPs were removed if the call rate was smaller than 0.8, MAF < 0.01, if Mendel error rate >5% , and they were LD-pruned using an r2 > 0.80 in a 50-kb sliding window with 5 variants. Association analysis were performed using the Fixed and random model Circulating Probability Unification (FarmCPU) method  method . A Bayese = 0.05 , to contAll research carried out in this study was reviewed and approved by the animal ethics committee of Victoria University of Wellington in New Zealand (application number 25976).All offspring were phenotyped at the first measurement (November 2017), while numbers decreased at the 2 subsequent time points because of natural mortality that occurred during the study. All traits showed a normal distribution and exhibited large levels of phenotypic variation, as discussed in 1 were successfully genotyped via GBS, resulting in 0.42× genome coverage per individual fish. Variant calling and basic quality filtering generated a dataset of 171,923 SNPs. Overall, 21 families from 10 out of the 13 sequenced F0 fishes were identified by Sequencing of the 13 parental fishes generated 1.23 billion reads, corresponding to an average genome coverage of 13× per individual. Of the 1,100 offspring, 1,094 FThe genome-wide significant thresholds for QTL mapping were established at LOD values between 4.60 and 4.81 for the 10 phenotypic traits across 3 years . In tota1 individuals had both genotypic and phenotypic data for all measurements. After filtering based on Mendelian errors, MAF, and LD pruning, 107,067 SNPs were left and used in the GWAS. Model selection in GAPIT resulted in no PCs to be used as covariates in any of the traits , the closest species to trevally for which a map has been constructed, was also resolved into 24 LGs (Chrysophrys auratus) (Dicentrarchus labrax)    and is rArabidopsis thaliana .https://repo.data.nesi.org.nz/. This was done to recognize Māori as important partners in science and innovation and as intergenerational guardians of significant natural resources and indigenous knowledge.Trevally (araara) are a taonga (treasured) species to Māori, the Indigenous people of Aotearoa New Zealand. All genomic data obtained from taonga species have whakapapa  and are therefore taonga in their own right. These data are tapu (sacred) and tikanga  determine how people interact with these data. Thus, all the genomic data have been deposited in a managed repository that controls access. Raw and analyzed data are available through the Genomics Aotearoa data repository at G3 online.jkac016_Supplementary_DataClick here for additional data file.


Beef production has expanded worldwide through cattle adaptation to diverse environmental and husbandry conditions. The beef industry faces societal challenges from animal welfare perspectives, including dehorning and disbudding, which are common farm practices to limit animal and handler injuries by the horned cattle. Most cattle breeds were originally horned, and reluctance to poll breeding existed because of perceived negative correlations between polledness and production. In Australia, population trends indicate a recent rise to above 50% of poll types in six breeds , and two breeds with lower but increasing rates of polledness (Brahman and Santa Gertrudis). Overall, recently estimated breeding values of 12 investigated production traits have not shown consistently negative trends within and across breeds. Thus, polledness should not be considered as detrimental. Cautious breeding plans are warranted to avoid inbreeding depression in breeds with lower poll frequency. These findings should support the augmented poll breeding across the beef industry.p = 0.05) genetic merits for live (birth to maturity) and carcass weights, milk, meat quality, and fat content traits. Substantial gains of genetic potential were achieved during 2000 to 2018 in each breed, such that polled animals have significantly improved for the majority of traits studied. Generally, polled cohorts showed advantageous EBVs for live and carcass weights irrespective of the lower birth weights in some breeds. While Polled Brahman showed inferior production parameters, the poll genetics’ effect size (d) and correlation (r) were very small on recent birth weight , 200 days , 400 days , 600 days , mature cow live weight , and carcass weight . In conclusion, although there is some evidence that historical selection for polled breeding animals may have reduced productivity, there is strong evidence that more recent selection for polled genotypes in the breeds studied has not resulted in any adverse effects on genetic merit.Breeding for polled animals is deemed the most practical solution to eradicate horns naturally and circumvent management costs and risks on health and welfare. However, there has been a historical reluctance by some farmers to select polled animals due to perceived lower productivity of their calves. This study has compared estimated breeding values (EBVs) between horned and polled animals  for 12 production and carcass traits to assess historical (before 2000) and recent (2000–2018) genetic implications of poll breeding. Older generations of the polled animals in most breeds had significantly lower (Bonferroni-corrected  Livestock production and associated industries have been expanding globally due to increasing demands for high-quality protein by the rapidly growing human population. The beef sector has been highly dynamic by attaining animal population growth and farm production efficiency in both developing and developed countries  through Polledness—the absence of horns—is an example of a recently in-demand trait in cattle for improved animal welfare and economics. Once, horned animals were perceived as genetically superior and were extensively used in breeding programs for dairy and beef production. However, the cattle industry also incurs significant economic costs associated with horns, including extra housing requirements and bruising in feedlots, during transport, and in abattoirs, and fatal dangers to animal handlers ,5. IncreIn some traditionally horned breeds, poll breeding initially faced resistance for fear of losing earlier genetic gains in production traits achieved by selecting horned animals during the commercial establishment of the specialized breeds . This rehttps://breedplan.une.edu.au) is an advanced genetic evaluation system implemented for a national beef recording scheme in Australia to compute EBVs of various economically important traits [Modern beef production systems have included many new traits and efficient selection tools to assess the management strategies, production performance, and breeding programs across many breeds ,18. Thert traits . It has Bos taurus , Bos indicus (Brahman), and their composites (Droughtmaster and Santa Gertrudis).This study was undertaken to compare historic (1950s to 1999) and recent (2000 to 2018) changes in genetic gains for 12 beef production traits  and meat quality . The genetic merit of the horned and polled animals was compared within eight major breeds of Australian beef cattle including Genetic merit of beef cattle in Australia is evaluated using Best Linear Unbiased Prediction (BLUP) technology. The BLUP model performs a multi-trait genetic evaluation on a breed-by-breed basis as explained in Reference . Brieflyhttps://breedplan.une.edu.au/search-login/, accessed in January and May 2020) archives information on registered cattle of several breeds including on-farm records and the genetic evaluation. Each animal has recorded information on head status , birth date, parents, and EBVs of several traits in categories such as growth (weight), fertility, carcass, behavior, and feed efficiency. A total of 2,467,000 records of animals across eight Australian breeds with known head status were acquired from the BREEDPLAN database  on each trait due to polledness were computed using the Cohen’s d [psych” (function: cohen.d.ci) was used to compute 95% confidence intervals for ES [r of the strength of association between continuous-level variables  and a binary variable of head status —were computed with biserial.cor function in R-package “ltm” [The filtered and structured dataset analyses were performed by using the R program  to compuohen’s d ,25: d=Mes for ES . Point Bge “ltm” .p-values were deemed significant above the adjusted n is total number of comparisons , i.e., n = i × j, where i is recorded traits with EBVs and j is number of breeds. ES (d) and PBC (r) values provide direction for interpretation of which of the two cohorts are better in a comparison to communicate the practical significance of results. Our analyses were performed such that positive values indicate polled-as-favorable while negative values indicate horned-as-favorable. Two traits  can be considered in either direction depending on the breeding objectives. Summary graphs are shown to compare EBVs of horn vs. poll cohorts as Violin-plots using modified functions of R-package “vioplot” [Results of these analyses were interpreted as follows: vioplot” .Commercial beef producers and feedlots in Australia have seen changing preferences for polled cattle in the past two decades due to increased awareness of the critical importance of improving animal welfare, consumer choices, and costs and risks associated with physical dehorning and disbudding.The proportion of polled animals in commercial herds have also trended higher during the period (birth years) from 2000 to 2018 across all breeds . Only thAccuracy thresholds are important considerations for comparing genetic merit between polled and horned types across the eight breeds. The accuracy of the BREEDPLAN EBVs ranged from 20% to above 90% . As the t-tests) of producing polled cattle, as shown in the following sections.Comparison of horn and poll population distributions of EBVs are presented from three perspectives: (i) historical status , (ii) yearly changes from 2000 to 2018, and (iii) 2000–2018 combined. Ongoing selective breeding for or against the set of traits has modulated the production dynamics of the beef industry. As expected, overall genetic merit for recorded traits in the eight breeds studied have experienced noteworthy changes within 2000–2018  higher than horned in Hereford, Limousin, Simmental, and Santa Gertrudis, and (iii) horned animals had significantly higher weights at birth than their polled contemporaries in Shorthorn and Brahman  non-significant differences between the birth-weight means in Charolais and Droughtmaster, (ii) polled cattle were significantly  may be a disadvantage. Instead, the distribution of birth weights of polled cohorts in Shorthorn, Droughtmaster, and Santa Gertrudis showed an advantage of relatively small magnitude over their horned herd-mates. Overall, polled cohorts presented higher genetic merit for birth weight.Weight traits measured at different life stages  showed essentially similar trends across all breeds . HistoriEBVs of mature cow  and carcEBVs of milk—indicators of maternal effect on calf weaning (200 days) weight—were historically higher in horned cohorts of five breeds  as compared to polled in two breeds (Charolais and Limousin) . Four brRetail beef yield EBVs  has surpEBVs of eye muscle area  were alsThree traits of body fat deposition in intra-muscles , rib Su, and rumEarlier results noted a contrasting pattern of EBVs between birth weights as compared to other weight traits at the next life stages . d) on a particular trait  and Simmental (−0.87). However, substantially low-spectrum EBVs of retail beef yield (%) have been noted. The magnitude of difference between the polled and horned cohorts has reduced in recent five years , 200 days (−0.19), 400 days (−0.06), 600 days (−0.05), mature cow (−0.08), and carcass (−0.19) will have limited practical implications on beef production of Brahman herds. Furthermore, using EBVs with higher accuracy (70%) demonstrated a further drop in significance occurrences and constricted the effect sizes of polledness on target traits in most breeds, although a few opposite trends were seen, probably impacted by lower sample sizes  and correlations  of poll status with the beef traits investigated here were consistently in the small range (d < 0.2 and r < 0.1), such that statistically significant differences will have very small practical significances. Mostly, the trends of trait-wise EBVs differences and their effect sizes were neither consistent nor unidirectional across the eight breeds, hence genetics controlling the head status is not deemed directly causal for differences observed.The magnitude of an occurrence is quantitatively measured by its effect size. The larger the effect size, the stronger the relationship between two variables, e.g., polledness and birth weight. The effect sizes (Cohen’s d) and correlation (r) with polledness were very small for EBVs of weight at birth , 200 days , 400 days , 600 days , and mature cow  and carcass  weights. These findings are novel contributions to augment poll breeding across the beef industry for sustainable production and objectively better animal welfare.Interestingly, a promising observation in favor of poll breeding was that the polled cohorts that had lower EBVs at early age (birth weight) showed above average EBVs for later growth and weight  traits. In later life, poll types became as good as or better than their horned contemporaries, which was also consistent with previous findings using live weights . Some coGenetic gain for different traits within each breed remains variable , explainDespite the increasing trends in all breeds, poll gene frequency remains low in the tropically adapted Brahman and Santa Gertrudis. Hence, loss of genetic diversity and consequently elevated levels of inbreeding may occur in the progeny of closely related polled animals. Inbreeding depression in production traits, as well as the potential increase of detrimental allelic homozygosity  and assoAn eminent challenge is the accuracy of head status phenotyping and subsequent recording in the BREEDPLAN database . New gend < 0.2), such that there would be no significant genetic gains for evaluated traits through selection of polled animals over horned animals, and vice versa. This study disputed earlier perceptions and provided novel contributions to promote poll breeding in beef cattle for increased sustainability and lowered animal welfare risks. Nevertheless, the potential risks of inbreeding depression should be carefully managed as the proportion of polled animals remains low in Brahman and Santa Gertrudis. Genomic evaluation and gene-editing tools can potentially augment genetic gains while conserving genetic diversity.Historically, for the eight breeds examined in this study, the frequency of polled cattle was lower, and their genetic merit was variable and often lower than that for horned cattle. Growing awareness about animal welfare and non-invasive alternatives concerning dehorning practices has surged the progress of poll breeding in recent years. Currently, the trends of 12 traits have shown that polledness has no detrimental effects on beef production and meat quality, and indeed, for many traits, polled are statistically superior. However, the effect size in all cases is relatively small (Cohen’s