Wolbachia wMel is the most commonly used strain in rear and release strategies for Aedes aegypti mosquitoes that aim to inhibit the transmission of arboviruses such as dengue, Zika, Chikungunya and yellow fever. However, the long-term establishment of wMel in natural Ae. aegypti populations raises concerns that interactions between Wolbachia wMel and Ae. aegypti may lead to changes in the host genome, which could affect useful attributes of Wolbachia that allow it to invade and suppress disease transmission.Ae. aegypti from the Cairns region, Australia, where Wolbachia wMel was first introduced more than 10 years ago. Mosquito samples were collected at three different time points in Gordonvale, Australia, covering the phase before (2010) and after (2013 and 2018) Wolbachia releases. An additional three locations where Wolbachia replacement happened at different times across the last decade were also sampled in 2018. We found that the genomes of mosquito populations mostly remained stable after Wolbachia release, with population differences tending to reflect the geographic location of the populations rather than Wolbachia infection status. However, outlier analysis suggests that Wolbachia may have had an influence on some genes related to immune response, development, recognition and behavior.We applied an evolve-and-resequence approach to study genome-wide genetic changes in Ae. aegypti populations remained geographically distinct after Wolbachia wMel releases in North Australia despite their Wolbachia infection status. At some specific genomic loci, we found signs of selection associated with Wolbachia, suggesting potential evolutionary impacts can happen in the future and further monitoring is warranted.The online version contains supplementary material available at 10.1186/s12864-021-08200-1. Wolbachia are bacteria that live inside the cells of many insects and induce important phenotypic effects on their hosts that can be harnessed for pest and disease control. Wolbachia-infected Aedes aegypti mosquitoes have now been released in multiple locations of the world [Wolbachia wMel, which was transferred artificially from Drosophila melanogaster into Ae. aegypti [Wolbachia strain, and these offspring are infected [Wolbachia to invade and be self-sustained in a population but may also increase population divergence because it can reduce the “effective migration rate” [Wolbachia can also impact mitochondrial DNA (mtDNA) variation through indirect linkage disequilibrium [he world –3 to helhe world –6. Wolba aegypti  where itinfected . This alon rate” , 10 betwilibrium –13.Wolbachia in suppressing dengue following invasion [Wolbachia wMel-infected Ae. aegypti as well as in the bacterial genome itself following releases in the field have raised concerns about the long-term effectiveness of the strategy. The genetic background of the mosquito host can affect the capacity of Wolbachia to invade populations and suppress arboviruses [Aedes aegypti has a short generation interval of about one month, and so if the introduction of Wolbachia triggers an evolutionary process in the mosquito genome this could be observable within a few years after invasion. Evolutionary changes in response to natural Wolbachia infections have previously been noted and appear to involve both the Wolbachia and host genomes [Wolbachia infections. Adaptations can occur to counter any negative fitness effects of Wolbachia, as documented in Drosophila [With the success of invasion , 3, manyinvasion , fitnessinvasion , 16 and invasion , 18. Theoviruses , 19, 20. genomes , 22, affosophila , 23, andosophila .Wolbachia release. Any putative changes may guide further phenotypic comparisons based on the types of candidate genes identified. However, in Ae. aegypti, there are challenges in characterizing genome changes after Wolbachia wMel infection. First, the genome of Ae. aegypti contains a large proportion (47%) of transposable elements (TEs), which result in a large genome size (1.38Gb) compared to other mosquitoes [Wolbachia release will increase the difficulty of outlier analysis. Finally, compared to model organisms, the genome of Ae. aegypti is still relatively poorly annotated, with only 256 proteins (< 1%) reviewed in the Swiss-Prot database (https://www.uniprot.org/taxonomy/7159).There are no published studies investigating evolutionary changes in wild host populations at the genomic level following a squitoes –27. TEs squitoes . MoreoveWolbachia replacement happened at different times across the last decade. In these locations, the population released involved a wMel transinfected strain that had been repeatedly backcrossed to a Cairns field population background, with the expectation that the released background would be 99.9% Cairns [Wolbachia-infected mosquitoes after the release [Wolbachia infection status to reveal genetic diversity in the mosquito populations and the potential impact of Wolbachia wMel on the genome of its host Ae. aegypti.In this study, we analyze pooled whole genome sequencing (WGS) data of mosquitoes from Gordonvale, Australia, where releases first took place, covering three different time points from the pre- and post-release phase. As a comparison, we also sequenced samples from the nearby Cairns area in Edge Hill, Redlynch and Yorkeys Knob, where % Cairns . For rel release . On the Aedes aegypti were collected from four sites around Cairns, Australia, at different times pre- and post-Wolbachia releases  sample which shows a high proportion of negative values Fig. . This pap = 0.12).We investigated genetic distances from the average of pairwise Fst (Fixation index) values through 100 kbp non-overlapping windows. The temporally-separated Gordonvale samples tended to have lower pairwise Fst than population pairs from different locations Table . For isoWolbachia infection status either across the genome or at the gene level. On the other hand, these populations clustered geographically  left after filtering with minimal depth of 50 in all populations and an average minor allele frequency (MAF) > 0.1. A principal components analysis (PCA) based on these SNPs showed the first two PCs accounted for 24.2 and 22.9% of the variance respectively  in dB units) > 5, and 391 showing “strongly-selected” signature of selection with an average BF* > 10 , suggesting selection due to Wolbachia (Table Wolbachia infection.We found that 950 (prior probability: 0.74 in each Mbp) of the “substantial” SNPs fell into the intersection of top 10% XTX values in the GV10, GV13 comparison and the GV10, GV18 comparison. We then found 113 SNPs that were highly associated with Wolbachia infection and were considered as “strong” outliers.We also used a stricter criterion: average BF* > 10 and falling into the intersection of top 5% XD. melanogaster for the gene ontology (GO) enrichment analysis, 108 gene sets were significantly enriched in biological process  are infected [We show that infected , 10. In infected , they doWolbachia, there is not only complete replacement of the uninfected mosquito population by Wolbachia-infected mosquitoes but also replacement of the mtDNA that hitchhikes along with the Wolbachia [Wolbachia and nuclear DNA variants is expected to break down relatively quickly [Following invasion by olbachia . Also, w quickly , new all quickly , 7.Populations were more differentiated at the gene level than at the genome level, which may be a consequence of a large proportion (47%) of TEs and satellites associated with non-coding regions , 26. TheWolbachia on the Ae. aegypti genome, selection in response to local conditions and the impact of wMel on Ae. aegypti may only influence patterns of genetic differentiation at specific loci [Wolbachia wMel on the genome of Ae. aegypti in the field suggest that further monitoring is warranted, although at this stage other factors unrelated to Wolbachia appear to have a larger impact on genomic differentiation among samples.When considering the impacts of fic loci . Our BaywMel and wMelPop-infected Ae. aegypti [We found signs of selection on Toll and Imd signaling pathways in KEGG analysis; these are important pathways in immune system , 45 and  aegypti , 49. Gen aegypti ; these g aegypti , 51. We wMel infection itself has not evolved in terms of either sequence composition or density since establishment in Ae. aegypti in northern Queensland, Australia [Ae. aegypti populations [In the past decade, the ustralia . Phenotyustralia , 18, altustralia , 54, andulations . HoweverWolbachia wMel-infected Ae. aegypti mosquitoes have been released successfully in the field to help reduce the transmission of arboviruses, but interactions between wMel and Ae. aegypti could result in adaptation [Ae. aegypti populations as being geographically distinct despite their Wolbachia infection status. However, selection associated with Wolbachia wMel may still have influenced variation at some loci. This is the first time that genome evolution associated with Wolbachia infection has been examined in field populations where there has been a deliberate release. However, it is hard to draw conclusions about long-term impacts of Wolbachia on the mosquito genome, which may take more time to develop, and which may be different in regions where dengue is endemic, unlike in Australia. Our findings highlight the possibility that the effect of Wolbachia can interact with the host genomic background, which has been shown previously in phenotypic assays of the longevity effects of wMelPop in Drosophila [aptation , 56, altaptation , 57, hosaptation  and inseaptation . In thisosophila .Aedes aegypti samples were collected from four sites around Cairns  for 2010 and 2013 samples, and using Roche High Pure™ PCR Template Preparation Kits  for 2018 samples. lsewhere . The conRaw sequences were trimmed using Trimmomatic v. 0.39 to truncate and remove low quality reads by requiring all reads to have all bases with a phred score > 20 and read length > 70 bp. The reference genome AaegL5.0  was indeWe investigated patterns of genetic variation within populations using PoPoolation v. 1.2.2  with theWolbachia infection status in a pairwise comparison, and the same geographic distance in a pairwise comparison.For genetic variation between populations, we used PoPoolation2 v. 1.201  to obtaiWe further investigated IBD patterns among 2018 samples from the four locations by computing Fst* = Fst/(1 − Fst)  based onBayesian outlier approach BayPass v. 2.2 [Wolbachia infection. Firstly, we used a standard covariate model [* = 10 × log10(BF)); the association with environmental variance was considered as “substantial” when BF* > 5, “strongly-selected” when BF* > 10 and “decisively-selected” when BF* > 13 following H Jeffreys [Wolbachia infection status as a binary covariate by setting each infected sample as 1 and each uninfected sample as 0. The measures of BF values are based on an Importance Sampling Approximation, which is unstable for single runs in particular when the number of population is small, so we averaged the BF values of three runs with different seeds from the random number generators following the suggestion in the manual of BayPass [Wolbachia infection at the genome level; SNPs that could not be assigned a position on one of the three autosomes were discarded.We used two models from a s v. 2.2  to identte model , 74, white model . BF wereJeffreys . We mode BayPass . The int BayPass . Based oWolbachia was considered as the main variable across time. An XTX algorithm approach was used in this model, which was analogous to an Fst comparison [TX value was used to identify selection pressure, with higher values suggesting positive selection and smaller values suggesting balancing selection [TX values greater than the 90% threshold or 95% threshold in both the GV10 and GV13 comparison and the GV10 and GV18 comparison as candidates for intersecting with the “substantial” SNPs and “strongly-selected” SNPs from the first analysis. However, this model is unable to exclude the noise from gene flow or genetic drift due to the lack of duplicates, which can also cause false positives. We therefore considered the intersection of outliers from the above two models as SNPs potentially associated with Wolbachia infection. These SNPs were then matched with the GTF annotation file from NCBI to obtain a list of outlier genes.Secondly, we used a BayPass core model to identify outliers from comparisons between GV10 and GV13, GV10 and GV18, given that Gordonvale was the only location where we had samples at time points before and after the release. The infection of mparison , 32. TheD. melanogaster. Only best matches were retained and further filtered with an e-value cut off 1.0E-10 and > 60% identity [We considered SNPs from the intersection of the two BayPass models above as outliers potentially under selection and searched the open reading frames to obtain a list of potentially important genes within a 100 kbp region around each SNP (50 kbp upstream and 50 kbp downstream) . These gidentity , 79. Genidentity  and DOSEidentity , with a Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.Additional file 8.Additional file 9.


After the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions. However, so far, for most complex diseases and quantitative traits, all identified genetic markers can only explain a small proportion of genetic components of complex diseases and quantitative traits, suggesting that there are still missing heritability to be discovered by genetic markers.After the genome-wide association studies7 and explain additional disease risks or traits of heritability. A rare variation is usually defined as the minor allele frequency (MAF) < 0.5%.Genome-wide association studies have focused on the genetic association of common variants with complex diseases. However, rare variants may also play a key role in influencing certain complex diseases and traitsWith the advances in sequencing technology, new and useful whole-exome sequencing has been developed. As a result, robust and efficient statistical methods for the association between rare variants and complex diseases and traits are desired.9. Therefore, the genomic region-based assessment considers multiple variants and traits, such as the collapsing method10 and the sequence kernel association test (SKAT)11, a flexible and efficient regression method for the associations between genomic regions and quantitative traits with consideration of covariates. The SKAT is based on a mixed effect model and overcomes the power issue in the collapsing method, especially when the genetic effects are positive in some variants and negative in the other ones. The kernel function interprets the gene segment, the random effect in the mixed effect model. If the subjects are correlated with family structures, the fast family-based SKAT (FFBSKAT) was developed to avoid invalid results13.A conventional association test uses one genetic marker at one time to identify common variations that are associated with a disease or trait. Although one could repeatedly implement the simple technique to discover some rare variations, this approach suffers an insufficient statistical power after adjusting for multiple testing. As a result, the decision of the overall statistical testing is too conservative14. For better statistical power, such types of interactions using genomic regions are also discussed16. Recent studies also showed surprising findings with gene-environment interactions18.In genetic studies, common variations could partially explain most diseases. SNPs may affect traits, but the environmental factors may modify the effect of SNPs. Tests for gene-environment interactions using one SNP and one environmental factor have been proposed19 function with a detailed manual and an automatic pipeline for GWAS are ready for easy implementation of the new method FGE-SKAT, where six essential packages are also integrated, including "CompQuadForm", "kinship2", "SKAT", "survey", "rareGE", "ggplot2", and "quadprog".This research aims to develop a novel and efficient statistical model for the genomic region-based assessment using multiple variants and traits to test gene-environment interactions under the complex familiar structures. Therefore, we extend the SKAT model under the family-based design to identify gene-environment interactions and the gene effect, named the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT). Simulation studies with ten thousand repetitions confirm the validity of the new strategy. In addition to simulation studies, the new strategy is applied to the whole genome sequence data from the Genetic Analysis Workshop 18 (GAW18). Finally, a freely available R20, the inheritance of a quantitative trait with gene-environment interactions in the sample of "n" genetically related subjects could be presented in a linear mixed effect model, which is defined as 16. Vector Following the previous works12. Note that Let 13. The likelihood function is 24, test statistics of the FGE-SKAT are based on the second term of the likelihood function.In the mixed effect model, 24, and p-value could be obtained by Kuonen’s method25.In the proposed model, the quantitative trait follows a multivariate normal distribution with the vector of means 26 provided the whole genome sequencing data that involved 8,348,674 single nucleotide variations (SNVs), longitudinal phenotype data for hypertension, and related traits in 20 pedigrees. Raw data were processed, and the final sample included 835 individuals. Table The Genetic Analysis Workshop 18 (GAW18) 27 was applied before the analysis.Fixed effects are age, sex, smoking, and medications for blood pressure controls (BPMEDS), where SNPs and familial structures are random effects. Dependent variables include systolic (SBP) and diastolic blood pressure (DBP). According to the Shapiro–Wilk normality test, p-values for DBP and SBP are The linear mixed effect model for FFBSKAT is12.The sample size n is 835. h is a random effect ~ The newly proposed mixed effect model for FGE-SKAT is:13. G, W, and b are identical to that of the FFBSKAT, where 29. A well-known regularized regression method, the elastic net30, is a convex combination of the ridge31 and lasso32 regressions. The size of the respective penalty terms is tuned via cross-validations to find the model's best fit. Regardless of the number of scenarios fitted to find the optimal combination, the searching procedure does not adjust for the multiple testing. The elastic net's methodology concept is similar to the FGE-SKAT that combines the FFBSKAT and rareGE via the hyper-parameter Although the default of the FGE-SKAT software examines only 11 points . In this way, the first and the second sliding windows contain the genetic effect. However, the third and fourth sliding windows do not cover the main genetic effect but have linkage disequilibrium (LD). We examined four scenarios for each disease model in the FFBSKAT and FGE-SKAT with one thousand repetitions. The first scenario is the pure genetic effect without the smoking effect on the trait, where we expect that the FFBSKAT and FGE-SKAT should demonstrate similar statistical power. In the second scenario, we want to ensure that the FGE-SKAT would not detect the wrong environmental effect when the genetic effect is absent. Thus, the SBP only depends on the smoking variable but not the SNV. In the third scenario, we want to show that the FGESKAT could discover gene-environment interactions, but the FFBSKAT failed in this situation. Therefore, only the SNV by smoking interaction contributes to the SBP variations. Finally, we simulated a weaker interaction effect with some environmental and genetic effects to show dose–response in power evaluations such that we could have more confidence in the performance of the new strategy.We summarize the permutation results in Table Table The second scenario  means that the SBP is higher, about 180 only when the smoking effect is present, but this value is not affected by the SNV. Both methods have valid results since the chance of detecting such erroneous information is less than 5%.The third scenario  means that the mean SBP could be 180, higher than the other three groups only when the SNV by smoking interaction effect is present. The FGE-SKAT demonstrated much superior power to the FFBSKAT. This phenomenon explains the need for our new approach in genetic research.The fourth scenario  has a weaker interaction effect and an extra genetic effect than the third scenario. Therefore, the FFBSKAT has small power but inferior to the FGE-SKAT. Simultaneously, we observed the dose–response effect since the FGE-SKAT has smaller power than itself in the third scenario.When the disease model of the FGE-SKAT is correctly specified in the analysis, the power is almost 100%, and the relative comparisons are not clear. Therefore, we added additional four scenarios at the bottom of Table After the validity and performance of the FGE-SKAT are confirmed, this approach is applied to the GAW18 data. Results of GWAS are displayed in Fig. In Table In Fig. The FGE-SKAT is the first proposed in the family-based SKAT method to detect genetic environment interactions based on rare variations. This research also provides a free R function that facilitates the implementation. The manual clearly describes the usage of FGE-SKAT with similar settings in the FFBSKAT. An automatic pipeline using the R code with an illustrative example facilitates the implementation of this new approach.In the application of GAW18 data, FGE-SKAT identified the most significant genetic region with interaction signals. Note that all analyses were based on Blom's transformation of SBP or DBP. However, analyses were also conducted for the original BP values. Although the p-values were much more significant than the normalized results, the regions discovered were very similar and hence not shown. Besides, using normalized outcomes avoids concerns regarding the validity of analyses.The FGE-SKAT results were based on the smallest p-value among 11 points of 12. The user could easily change the settings of sliding windows in the FGE-SKAT software. Besides, this research uses SKAT settings with minor allele frequency follows Beta distribution . This method's kernel function is linear, but researchers could also adopt polynomial or Identity of State (IBS) for the kernel functions in the FGE-SKAT software.In this research, the sliding window is chosen as 20 SNVs, with 10 SNVs shifted for the next window. If more biological information is available, the parameters could be modified to increase statistical power33 (SAIGE: https://github.com/weizhouUMICH/SAIGE).Since mixed models are used in family-based studies as well as in studies with unrelated samples (Kinship replaced by GRM), the FGE-SKAT has the potential to be extended using other strategies such as efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studiesThis work is based on samples from the Genetic Analysis Workshop 18 (GAW18). The samples were longitudinal, and the majority of participants had three measurements collected at approximately 5-year intervals. Datasets included systolic and diastolic blood pressure measurements from a human whole-genome sequencing (WGS) study. Thus, this research is dealing with human data.Evaluations of robustness for this approach against gene-environment correlation and miss-specified environmental main effects require a tremendous effort. It is cumbersome research when several advanced methods are compared under more complicated scenarios. On the other hand, this issue is an excellent topic for future research to examine further such impact for FFBSKAT, rareGE, FGE-SKAT, and other existing methods.The FGE-SKAT deals with one environmental factor. If there are multiple factors, one could apply the FGE-SKAT repeatedly and control for multiple testing. Extending this new strategy to accommodate multiple factors further is also a promising future research plan.34 has demonstrated extraordinary prediction abilities, this approach requires tons of training samples with available features and outcomes. Besides, the computational burden is high. If the quality of training samples is not guaranteed, the model performance may not be satisfactory. In contrast, our statistical approach does not require these assumptions and is ready to use with a satisfying speed.Although the deep learning modelSupplementary Information.


In recent years, animal welfare and health has become more and more important in pig breeding. So far, numerous parameters have been considered as important biomarkers, especially in the immune reaction and inflammation. Previous studies have shown moderate to high heritabilities in most of these traits. However, the genetic background of health and robustness of pigs needs to be extensively clarified. The objective of this study was to identify genomic regions with a biological relevance for the immunocompetence of piglets. Genome-wide Association Studies (GWAS) in 535 Landrace (LR) and 461 Large White (LW) piglets were performed, investigating 20 immune relevant traits. Besides the health indicators of the complete and differential blood count, eight different cytokines and haptoglobin were recorded in all piglets and their biological dams to capture mediating processes and acute phase reactions. Additionally, all animals were genotyped using the Illumina PorcineSNP60v2 BeadChip.In summary, GWAS detected 25 genome-wide and 452 chromosome-wide significant SNPs associated with 17 immune relevant traits in the two maternal pig lines LR and LW. Only small differences were observed considering the maternal immune records as covariate within the statistical model. Furthermore, the study identified across- and within-breed differences as well as relevant candidate genes. In LR more significant associations and related candidate genes were detected, compared with LW. The results detected in LR and LW are partly in accordance with previously identified quantitative trait loci (QTL) regions. In addition, promising novel genomic regions were identified which might be of interest for further detailed analysis. Especially putative pleiotropic regions on SSC5, SSC12, SSC15, SSC16 and SSC17 are of major interest with regard to the interacting structure of the immune system. The comparison with already identified QTL gives indications on interactions with traits affecting piglet survival and also production traits.In conclusion, results suggest a polygenic and breed-specific background of immune relevant traits. The current study provides knowledge about regions with biological relevance for health and immune traits. Identified markers and putative pleiotropic regions provide first indications in the context of balancing a breeding-based modification of the porcine immune system.The online version contains supplementary material available at (10.1186/s12864-021-07997-1). Health and immune traits are characterising the immunocompetence of piglets –3. ThrouPiglets are extremely susceptible to infections in their first stage of life . For thiPrevious studies have estimated heritabilities under various conditions regarding e.g., breed, age , number of animals and the experimental design e.g., , 16–20).–20.16–20α) in LR and 0.4 (Interleukin (IL)-8) in LW. Therefore, genetic foundation of immune relevant traits and responsiveness for selective breeding is assumed.Heritability estimation in a Landrace and Large White population under non-challenging conditions has confirmed findings to a large extent , 23. RBChttps://www.animalgenome.org/cgi-bin/QTLdb/SS/summary) )  (PRRSV) , 40 and  (PRRSV) , 42. Ano (PRRSV) .The aim of this study was to identify biological relevant markers associated with health and immune traits in piglets under non-challenge conditions in a Landrace (LR) and a Large White (LW) population. Besides, the genetic background of the piglets’ immune system and the role of the maternal immunocompetence is discussed.In total, 535 piglets of LR and 461 piglets of LW were phenotyped for the complete and differential blood count (15 traits), eight cytokines and haptoglobin in an experiment conducted under mostly practical, but high hygienic conditions and without challenging the animals. The number of samples per trait, least-squares means and standard errors are listed in Table p-values of 0.1, the amount of validated SNPs was increased up to 17.9% (LR) and 13.2% (LW).Different genetic backgrounds led to breed-specific investigations of LR and LW see Fig. . For ideMost of the associations are variants within intergenic  or intronic  regions. A lower number of significant SNPs is located within untranslated  or upstream and downstream regions of a gene. In addition, GWAS on immune relevant traits identified a few synonymous variants and a variant within a splice site  or a non-coding exon sequence  as well as a missense variant . Each significant SNP explained between 1.7-5.2% and 2.2-5.5% of the phenotypic variance in LR and LW, respectively.Tables *). The effect was significant in twelve (LR) and ten (LW) traits out of 20. GWAS using model (1*) confirmed the majority  of the significant genomic regions from basic analysis using model (1). In some traits without any significant SNP , model (1*) revealed single markers which were among the leading SNPs identified by model (1), but did not exceed the chromosome-wide threshold. Consequently, the benefit of model (1*) is limited. Conspicuous deviations from results based on model (1) were identified in IL-6 in LW. Markers associated with IL-6 diverged in the chromosomal region.Due to the unclear influence of the dam on the immunocompetence of the piglets, model (1) was extended by the corresponding trait measured in the biological dam, included as a fixed covariate  are discussed further.SNPs = 296) compared to LW (n SNPs = 197). Significant genomic regions of different traits did not overlap in a window of 70 kb. Window definition followed the twofold mean distance of markers identified by Ramos et al. [Regarding the genomic background within the breeds, remarkably more significances were found within LR  and mean corpuscular haemoglobin (MCH), significant SNPs were identified for all traits analysed. In most cases, SNPs were only found within the LR or LW. For five traits , IL-10), detected markers were relevant in both breeds, but not in overlapping chromosomal regions. Moreover, WBC, neutrophils, IL-1 Identified SNPs were located across all autosomes with the exception of SSC8. But there were chromosome-specific clusters for certain trait complexes. For example, SSC12 showed associations with cell parameters such as WBC, neutrophils, monocytes, and RBC, whereas on SSC16 only associations with traits characterising RBC in LW were observed.β, IL-10 and IL-4 in LR were identified. These SNPs explained up to 3.7% of the phenotypic variance and associations with IL-10 were additionally confirmed with the more conservative Bonferroni correction at 5%.The largest number of significant markers was found for cytokines Table  with an α in LW exceeded the genome-wide significance level according to q-value and partly the Bonferroni correction and explained up to 5.1% of the phenotypic variance.In addition, 23 SNPs for IL-8 in LR and two SNPs for TNF- Blood cell characteristics accounted for the second largest proportion of chromosome-wide significant SNPs in LR. Monocyte percentage, as the only subset of WBC showing significant SNPs in LR and LW, revealed SNPs on SSC12 and SSC18, respectively.ALGA0085557, 56.0 Mb) and SSC16  in LW as well as a genomic region on SSC17 in LR, affecting several cytokines.Putative pleiotropic SNPs were detected on SSC15 , whereas LR tended towards a granulocytic blood count (N:L ratio = 1.06). This led to the assumption of a more specific and higher responding immunocompetence of LW in the first weeks of age. On the other side, the neutrophil-to-lymphocyte ratio is influenced by non-genetic factors like stress  or age aThe impact of age at blood sampling has been discussed in contrasting ways. For one thing, neutrophils have been described as age-independent at an age of around seven weeks , whereasSNPs = 280) more significant associated SNPs were found, compared to LW (n SNPs = 197). Moreover, all of the markers were breed-specific. This might be due to a strong impact of the breed on baseline values of the immune system [In total, GWAS revealed 477 SNPs associated with 17 immune relevant traits in LR and LW. With the exception of some traits, at least one SNP within most of the genomic regions of interest, identified according to q-value, was also significant after Bonferroni correction. Annotation to the reference genome Sscrofa 11.1 identified 113 genes in LR and 71 genes in LW. Results are partly in accordance with immune-related QTL published in previous studies e.g., , 42, 47), 4742, 4e system . Across-Due to the comprehensive results, the discussion focuses on the importance of the networking structure of immune relevant traits and trade-offs between the immune system of piglets and economically important fertility and production factors.Analyses on WBC in LR identified significant associations on SSC4, SSC12 and SSC14. The genomic region on SSC14 (41.7 - 48.5 Mb) confirms a QTL for WBC in a German LR population  and overMARC0040388, 34.5 Mb) within the gene lactoperoxidase (LPO) on SSC12 showed chromosome-wide significance for neutrophils in LR. Pathway analysis indicates LPO to affect neutrophils in their phagocytolytic activity [An intron variant  at 36 Mb to 37 Mb without any intersection. From the physiological point of view, relationships between neutrophils and monocytes are well known, especially in the innate immune system and the defense against pathogens . Use of ASGA0070620, 120.4 Mb) within the gene transmembrane BAX inhibitor motif containing 1 (TMBIM1). TMBIM1 is involved in the neutrophil degranulation and thereby, for instance, in regulating the exocytosis of inflammatory mediators [Especially cytokines, as intercellular signalling molecules, are pleiotropic and interconnected in the mechanisms of the immune system , 40. Nevediators , 52, 53.RNA binding motif single stranded interacting protein 3 (RBMS3). Significant markers within RBMS3 were located within a QTL previously described to be associated with IL-10 level [GWAS on IL-12 concentration in LW piglets provided a moderate number of immune relevant candidate genes, inter alia 10 level . Signifi10 level .α in LW detected significant SNPs on SSC1 and SSC9. An intron variant on SSC1  and an intergenic variant on SSC9  exceeded the genome-wide level.Furthermore, association analysis on TNF- The genetic component of the biological dam in the development of piglets’ immune system still remains unclear. The results indicate consequences on IL-6 in LW piglets depending on the corresponding cytokine status of the biological dam. Nevertheless, most of the genomic regions from basic analysis with model (1) were confirmed after the environmental correction. However, putative pleiotropic mechanisms in cytokines were supported in all statistical models.Knowledge about pleiotropic mechanisms might help to clarify genetic relationships between the traits . IndicatALGA0085557, 56.0 Mb) on SSC15 in haemoglobin and haematocrit. Located on SSC16, a SNP  was found which was significantly associated with haemoglobin, haematocrit as well as RBC. Therefore, in red blood cell characteristics, pleiotropy in LW is assumed. These potentially pleiotropic SNPs underline the highly interacting mechanisms of the immune system. According to the biological context, this relationship is not unexpected. Yan et al. [rg 0.77-0.85) and day 46 (rg 0.50-0.77).In LW, association studies identified a chromosome-wide significant SNP . Among others, ROMO1 is classified in the biological process of a defense to a bacterium [In LR, a genomic region on SSC17 showed nine identical SNPs significantly associated with at least two of the cytokines IL-1 acterium , 56. Humacterium .Furthermore, we identified two regions on SSC5 and SSC12 with a size of around 3.6 and 3.9 Mb including three and 16 SNPs, respectively. Markers were associated with up to three different immune traits in LR and LW.Fibroblast growth factor 6 (FGF6), as a putative candidate gene within the described genomic region, is involved in signalling processes and thereby in cell growth and survival processes due to the Phosphatidylinositol 3-kinase cascade [ASGA0025952, 65.8 Mb) is located within the gene A-kinase anchoring protein 3 (AKAP3). It is a missense variant probably causing an amino acid change. Biological pathways in humans indicate a link to signalling by regulating the mitogen-activated protein kinase (MAPK) pathway [The adjoining SNPs on SSC5 were associated with MCHC in LR and haemoglobin in LW.  cascade , 50, 58. cascade . Another pathway , 50.CST telomere replication complex component 1 (CTC1), directly associated with the percentage of neutrophils in LR on SSC12. Findings in humans outline CTC1 is involved in immune system processes and the development of the immune system [A genomic region on SSC12 was detected around 50.1 to 54 Mb associated with different cell parameters of the immune system and the blood. SNPs revealed a significance for monocytes and neutrophils in LR and RBC in LW. A putative candidate gene is e system , 56, 60.Conserved functions might have an effect across species , and, thGenetics of immunocompetence and economic traitsTaking the identified markers into consideration for further analyses, we need to check for undesirable relationships with traits involved in the survivability and diseases susceptibility but also regarding production traits and meat quality.In this context, the current study identified markers associated with neutrophils in LR (SSC12), IL-8 concentration in LR (SSC15) as well as IL-10 and IL-12 concentration in LW (SSC1 and SSC13). Genomic regions were identified close to QTL for intramuscular fat content –63 or wiAdditionally, our findings for IL-8 in LR and MCHC in LW indicate a connection to QTL regions associated with feed conversion ratio and average daily gain in Duroc . The genResults of this study indicate overlaps between regions significantly associated with immune relevant phenotypes and QTL for survivability of piglets and growing pigs. A putative relationship is supported by several studies which were summarized in the review of Heuß et al. . In thisFurthermore, significant SNPs on SSC14, associated with WBC in LR and RBC in LW, have been previously described in the context of the reproductive traits litter size (WBC)  and mummThe relationship between the immune system and the survivability of piglets gains in importance. Besides, several putative pleiotropic regions have been discussed in “In summary, the study identified 477 SNP markers associated with immune relevant traits. Analyses were performed in piglets of a LR and LW population kept under high hygienic standards and without challenging the immune system. GWAS results pointed out clear differences between the breeds LR and LW. Comparison between previous studies and findings in this study indicate a complex genetic background of immune relevant traits and health indicators. Several interesting genomic regions suggest a pleiotropic background. Further investigations need to follow, considering relationships and antagonistic mechanisms between the immune system, survivability, performance traits and other economically important traits. In general, as health and immune traits are expected to become an essential part of balanced pig breeding, this study provides first insights into regions with special importance for the immune system of piglets.A total of 535 piglets (♂190/♀345) of LR and 461 piglets (♂170/♀291) of LW were analysed. Animals were a subset of the nucleus populations which aim to reflect the genetic variability of both populations with respect to their different breeding objectives. All piglets were born between 2015 and 2017 on five farms of the German breeding organization BHZP GmbH (Bundeshybridzuchtprogramm GmbH). All herds were subjected to high hygienic standards. The experimental design aimed to sample a triplet consisting of the mother and two full siblings born alive.Vena jugularis.Piglets were routinely blood sampled in the week around weaning (⌀ 42 to 44 days), sows around piglets’ birth. Sampling was performed by collecting blood via the ® 2120. Band cells as well as basophils and eosinophils were excluded from further analyses due to low expression, resulting in a variation close to zero. Haptoglobin concentration was measured using a photometric technology. Cytokines were analysed with Millipore’s Milliplex Map Kit Porcine Cytokine/Chemokine Magnetic Bead Panel on a Luminex® 200, as well as the software xPonent 3.1. Milliplex Map Kit enabled simultaneous analysis of eight cytokines. Cytokine concentrations below the analytical threshold were replaced by one-half of the threshold [Traits investigated are listed in Table hreshold , 71. MeaΔBR), age (ΔA) and weight (ΔW) at sample collection and their interaction, a combined herd, year, season, sex effect (HYSS) and litter number (L). The intercept is represented by μ and regression coefficients are given as b1,b2,b3 and b4 and environmental residual effects as e. In the analyses of cytokines, batch (B) was an additional fixed effect and biological dam (u d) an additional random effect.Descriptive statistics including least-squares means and breed differences are listed in Table Model (0): Note: Terms in  are only used in the analysis of cytokines.Blood and tissue samples were analysed using the PorcineSNP60v2 BeadChip .r2>0.8) in a region of 3 kb were excluded from further analysis, resulting in 41’872 (LR) and 42’388 (LW) markers in 534 (LR) and 461 (LW) piglets.Breed-specific quality control on autosomes was applied using the R-package GenABEL . SamplesGenetic distances were visualised using multidimensional scaling based on a genomic relationship matrix calculated within the R-package GenABEL  Fig. 1)1). ConseΔA) and weight (ΔW) at sample collection and their interaction, a fixed SNP effect, herd, year, season and sex, combined to a HYSS-effect , and litter number . The intercept is represented by μ and regression coefficients are given as b1,b2,b3 and b4. The environmental residual effects were denoted by e. In the analyses of cytokines, a fixed batch effect  and the random effect “biological dam of the piglet” (u d) were added to the model. Both additional effects have been consistently identified as relevant for cytokines.In a first step, data were analysed with a generalized linear mixed model (model 1). Observations (y) were corrected for the fixed environmental effects age (Model (1): Note: Terms in  are only used in the analysis of cytokines.*). Accordingly, model (1) was extended by the corresponding blood parameter of the biological dam, modelled as a fixed covariate. Phenotypic information of all biological dams in LR (n = 262) and LW (n = 226) was available. In case of a significant effect of the covariate, the trait was considered for GWAS using model (1*).In a second step, the impact of direct environmental maternal effects on the piglet’s immunocompetence were verified using model (1*), were standardised to a mean value of zero.Covariables age, weight and blood parameter of the biological dam, used in models (1) and  values were low to moderate in LR (0.99-1.87) and LW (0.95-1.67). Adjustment for biases through the population structure was performed according to the Genomic Control approach [λ values were in an acceptable range <1.08. In addition, correction for a modified false discovery rate [p.adjust R-command. Markers with adjusted p-values <0.05 and 0.1 were highlighted in Additional files Lambda (approach . After cery rate , 76 of 5 LR 0.99-.87 and LThe phenotypic variance explained by a single SNP was calculated according to the transformation of a student’s t-distribution into a z-distribution  using thMarkers were mapped to the porcine reference genome Sscrofa 11.1 and variants were identified according to Ensembl release 100 . In addiAdditional file 1 Complete GWAS results in Landrace. Significant SNPs in Landrace associated with immune relevant traits identified by GWAS. MCHC: Mean corpuscular haemoglobin concentration, IL: Interleukin, SSC: Sus Scrofa Chromosome, MAF: Minor allele frequency, *: genome-wide significance, adj. p-value: adjusted p-value after Bonferroni correction, B5: Additionally significant after Bonferroni correction with adjusted p-value <0.05, B10: Additionally significant after Bonferroni correction with adjusted p-value <0.1.Additional file 2 Complete GWAS results in Large White. Significant SNPs in Large White associated with immune relevant traits identified by GWAS. MCHC: Mean corpuscular haemoglobin concentration, IFN: Interferon, IL: Interleukin, TNF: Tumor necrosis factor, SSC: Sus Scrofa Chromosome, MAF: Minor allele frequency, *: genome-wide significance, adj. p-value: adjusted p-value after Bonferroni correction, B5: Additionally significant after Bonferroni correction with adjusted p-value <0.05, B10: Additionally significant after Bonferroni correction with adjusted p-value <0.1.


Twinning in dairy cattle is caused by many different factors, both genetic  and non-genetic . In dairy operations, twinning is an undesirable trait associated with other reproductive and metabolic diseases, higher operational costs, and higher rates of culling on farm. The animal welfare and economic impacts have resulted in the development of a genomic prediction for twinning  by Zoetis such that producers can make informed breeding decisions for breeding Holstein females that are less likely to become pregnant with twins in a given lactation. This prediction is included in a holistic breeding tool  for producers so that they can improve multiple health, fertility, and production traits in parallel with reducing twinning when making breeding decisions for future generations. The objectives of the present study were (1) to describe how the twinning prediction was developed (and included in a selection index), (2) show that the prediction works effectively using real life farm data, and (3) propose how this genetic tool can be used in collaboration with management practices to proactively reduce twin pregnancies on farm. The results of this study provide evidence that twinning can be proactively managed on dairy farms using genetically powered tools.Twinning is a multifactorial trait influenced by both genetic and environmental factors that can negatively impact animal welfare and economic sustainability on commercial dairy operations. To date, using genetic selection as a tool for reducing twinning rates on commercial dairies has been proposed, but not yet implemented. In response to this market need, Zoetis  has developed a genomic prediction for twin pregnancies, and included it in a comprehensive multitrait selection index. The objectives of this study were to (1) describe a genetic evaluation for twinning in Holstein cattle, (2) demonstrate the efficacy of the predictions, (3) propose strategies to reduce twin pregnancies using this information. Data were retrieved from commercial dairies and provided directly by producers upon obtaining their permission. The twin pregnancies trait (TWIN) was defined as a pregnancy resulting in birth or abortion of twin calves, classified as a binary  event, and analysed using a threshold animal model. Predictions for a subset of cows were compared to their on-farm twin records. The heritability for twin pregnancies was 0.088, and genomic predicted transmitting abilities ((g)PTAs) ranged from −7.45–20.79. Genetic correlations between TWIN and other traits were low, meaning that improvement for TWIN will not negatively impact improvement for other traits. TWIN was effectively demonstrated to identify cows most and least likely to experience a twin pregnancy in a given lactation, regardless of reproductive protocol used. Effective inclusion of the prediction in a multitrait selection index offers producers a comprehensive tool to inform selection and management decisions. When combined with sound management practices, this presents a compelling opportunity for dairy producers to proactively reduce the incidence of twin pregnancies on commercial dairy operations. Twinning in cattle is a complex trait that can be influenced by a multitude of factors such as milk production, season, breed, parity and ovulation rate, previous twin calving events, pharmaceutical use, and genetics ,5,6,7,8.2α, and (4) culling. Limitations of these methods include an increased likelihood of pregnancy loss in the embryo that was not selected for manual reduction, and in the case of pregnancy maintenance, the aforementioned periparturient diseases and abortion, stillbirth, calf mortality, and dystocia [Once diagnosed, current intervention and management practices for twin pregnancies include (1) GnRH treatment to maintain the gestation, (2) induced embryo reduction through aspiration of the allanto-amniotic fluid or manual rupture, (3) induced abortion using PGFdystocia ,27,30,31Bos taurus autosome 1, 5, 6, 7, 8, 10, 14, 15, 19, 23, and 24 [There is an established historical and ongoing effort by research groups across the globe to quantify the genetic component of twinning in cattle. Notably, since the 1980′s the USDA Meat Animal Research Center (USDA-MARC) has quantified the genetic parameters of, and identified areas in the bovine genome associated with twinning in cattle, in addition to demonstrating the effectiveness of selective breeding for twinning in cattle based on their observed twinning rates ,8,21,32., and 24 ,49,50,51Recent advancements in implementing hormonal synchronization protocols which proactively reduce the incidence of double ovulation in dairy cattle has shown to be an effective non-genetic  tool in this space . HoweverHarnessing such genetic- and genomically powered tools, in tandem with practical and strategic breeding and management strategies can holistically offer producers the ability to proactively reduce the incidence of twinning in their herds, helping improve animal wellbeing, economic outcomes ,54, and ® Plus . CLARIFIDE® Plus provides producers with a comprehensive suite of genomic predictions, plus a multitrait selection index tool, the Dairy Wellness ProfitTM (DWP$) index to facilitate sound management and selection decisions based on the genetic predisposition of their heifer calves for these traits that will be expressed later in life. DWP$ is an economic multitrait selection index that was formulated to estimate the potential lifetime profitability an animal would generate under US dairy economic conditions and includes cow and calf wellness, production, fertility, functional type, longevity, livability, calving ability, and milk quality traits, as well as polled test results [® Plus and DWP$ were updated to include additional traits shown to impact the lifetime profitability of a dairy animal; one of those traits included in the 2020 update was twinning . The objective of the present study was to (1) describe a genetic and genomic evaluation for twinning in Holstein cattle (including how this information is incorporated into a comprehensive and easy to interpret multitrait selection index), (2) demonstrate the ability of the evaluation’s predictions to accurately predict twinning incidences (regardless of on-farm reproductive protocol used), and (3) propose practical ways to holistically apply this information to offer a proactive solution that producers can implement to reduce the incidence of twinning in Holstein dairy herds.In response to industry needs for genetic improvement of dairy wellness traits, Zoetis Genetics developed an industry-available genetic and genomic evaluation for wellness traits in Holstein cattle in 2016, known as CLARIFIDE results ,61. In 2Data was available from 276 herds located in 26 states in the US and collected approximately from 1990 to date. Each herd supplied, on average 22,998 phenotypic records. Data were obtained directly from producers upon obtaining their signed permissions; the herds were not routinely monitored or compensated for event recording by Zoetis. Production, reproduction, and pedigree information was extracted from on-farm herd management software backup files using proprietary scripts. Genotypes were obtained from the Zoetis Genotyping Lab . Animals were genotyped with a variety of low-density SNP chips with a number of SNPs ranging from about 3000 to over 35,000, and several types of medium-density chips with 50,000–80,000 SNPs. All animals genotyped on chips with <40,000 SNPs were imputed using the program FImpute  to 45,24Initial data editing included checking animal identification for accuracy and consistency across data files, using similar criteria as described in Norman et al. (1994) . Each anThe genetic trait of twin pregnancies  was defined as a pregnancy resulting in birth or abortion of twin calves . TWIN was treated as a binary event-having a value of one if the cow was recorded as carrying or giving birth to twins and zero otherwise. Records of the same cow without TWIN recorded, as well as records of all herdmates without recorded twins, were added as ‘singleton pregnancy’ records . Twinning information was extracted from a combination of the events and remarks recorded on the herd management software. Terminology used to record TWIN events varied across the herds, which were standardized and collapsed into the binary TWIN outcome as shown Further, each herd by year and season of calving (HYS) group was required to have a minimum of 20 records and at least one recorded twinning event. HYS groups that did not meet these criteria were omitted from analysis, as it was assumed that the herd did not record twinning at all, or did not record it during that time period.λ represents a vector of unobserved liabilities to twinning; β is the vector of fixed parity effects, with the corresponding incidence matrix X; parities 1, 2, 3, 4, and ≥5 were considered; h is the random herd-year-season effect, where h~N, with the variance hσ2. Four seasons were defined within each calving year: Winter (December–February), Spring (March–May), Summer (June–August), and Fall (September–November); a is the random animal effect with a~N, where σa2 is the additive genetic variance and H is the pedigree relationship matrix augmented using genotypes; p is the random effect of permanent environment with p~N, with σp2 is the permanent environment variance and e is the residual, where e~N.The analyses for the present study were conducted largely as described by Gonzalez-Peña et al. (2020) ; the fol−1, was replaced by the inverse of H matrix that combines pedigree and genomic relationships [−1 is an inverse of the pedigree relationship matrix, 𝐺−1 is an inverse of the genomic relationship matrix and 𝐴22−1 is an inverse of the pedigree-relationship matrix for genotyped animals only. The ‘algorithm for proven and young animals’ (APY) developed by Ignacy Misztal’s group from the University of Georgia in Athens  was applied. The program CBLUP90IOD2 version 3.39 was used to obtain genomic breeding values using preconditioned conjugate gradient (PCG) with the number of rounds set to 200. The core consisted of 25,000 randomly selected animals. The genomic matrix conditioning parameters tau and omega were set to 1.0. Inbreeding was considered when constructing the pedigree relationship matrix. The reliabilities of estimated breeding values (EBVs) were obtained with the program ACCF90GS version 2.54, which approximates reliabilities using contribution from genotypes, phenotypes, and pedigree. To reduce computational requirements, the contribution from genotypes is replaced by the value of the diagonal of the G matrix, iig. Reliabilities for genotyped animals were approximated as per Gonzales-Peña et al. (2020) using the guidance of Daniela Lourenco, University of Georgia, Athens, personal communication, 2016 [Variance components were estimated using the same model, but without genotypes using the program THRGIBBS1F90 version 2.108 from the BLUPF90 family . Geneticionships ,68:(2)H−on, 2016 .The solutions from the CBLUP90IOD2 program (raw EBVs) were transformed into probabilities of exceeding the threshold value, with threshold values being calculated from the current data. For each animal solution, we calculated probability that a standard normal variable with the mean equal to this solution and the variance of one exceeds the threshold. The probabilities were then multiplied by 100 (to represent percent), divided by 2 (to obtain predicted transmitting abilities (PTAs)), and expressed as the deviation from the average PTA of all animals ) born in 2015 with a phenotypic record for TWIN. The resulting (g)PTAs may be interpreted as differences of the individual animal’s risk of having twins from the average (base) risk. Higher values of (g)PTAs indicate a higher chance of twinning. For example, an animal with a TWIN (g)PTA of −2.0 has a 2% lower risk, whereas an animal with a TWIN (g)PTA of 2.0 has a 2% higher risk of having twins in a given lactation than the base animal . For easAn in-depth description of the development of DWP$ is detailed in Fessenden et al. (2020) . Brieflyn = 3, defined as: no synchronization protocol, presynch ovsynch protocol, or Double Ovsynch protocol). Table 7 outlines the overview 5 herds used in the demonstration cohort.To demonstrate the efficacy of the TWIN predictions generated by the evaluation, a subset of 8,219 females from 5 US herds that used commercial genomic tests were selected, and the relationship between the TWIN phenotype and genetic(omic) merit for TWIN investigated for each herd. Criteria for inclusion in these demonstration cohorts included (1) the animals/herds did not contribute phenotypes to the genetic evaluation so as to not bias results, (2) the herds demonstrated adequate recording of TWIN events on a per-animal basis, and (3) a single reproductive protocol was required to be used consistently on farm  for each herd were analyzed using PROC GLIMMIX with a binomial distribution and a logit link function using the following model:p-values are reported. Finally, cost per cow per genetic group for TWIN was calculated as per McNeel et al. (2017) [Once selected, TWIN STAs for these animals were adjusted to reflect what their STA values would have been in 2013. This measure was included to reduce bias in the TWIN estimate predictions such that the predictions closely reflect what the producer would have received before the animals entered the milking string. This measure reduces the bias that could arise from having 2014–2020 records (pedigree and performance) feeding into the evaluation. Females were then ranked based on TWIN STA predictions within each herd, and allocated to one of three genetic groups : worst 33%, 34–66%, and best 33%, similar to what has been reported by others ,56,71. D. (2017) , using t. (2017) . A totalSecondarily, the association of individual animal TWIN STAs, lactation and TWIN outcomes was estimated as supporting data, with the model’s β term updated to represent the fixed effects of TWIN STA  and lactation (1st–4th), and μ updated to represent the random effect of animal. Plots of the association between the individual animal STAs and TWIN outcome are reported.Tertiarily, the association between TWIN STA, season of conception , and peak lactation output (from the previous lactation) with TWIN outcome was estimated as supporting data. This was completed given previous reports of associations between season of conception and peak lactation with TWIN ,2,3,5. IThe total number of pedigree records, phenotypic records, animals with phenotypes, animals with genotypes, animals with both genotypes and phenotypes, and the incidence of twinning as of August 2020 are given in The means, standard deviations, minimum and maximum of (g)PTAs, STAs, and reliabilities for TWIN are presented in ® Plus, as well as the correlations of TWIN with economically important traits in the genetic evaluation produced by the Council on Dairy Cattle Breeding (CDCB). As expected, the strongest correlations exist between TWIN and RETP, ABORT and METR. The weak correlation between TWIN and production traits communicate that making genetic progress for TWIN would not negatively impact genetic progress for those production traits, and vice versa.An overview of the herd data is presented in p < 0.0001). As shown in Differences in twinning incidence  were statistically significant between the genetic groups  translate into appreciable differences in expected economic costs on a US dairy operation . Cows inp-values ranging from 0.0033 (TWIN STA 2013 for herd 5) to 0.9129 (season of conception for herd 1). TWIN STA was a significant predictor of TWIN for 4 out of the 5 herds, with the exception of herd 2 . Season of conception was not a significant predictor for any herds . Peak lactation (previous lactation) was a significant predictor for TWIN for 1 of the 5 herds, with a p-value of 0.0495 for herd 3 (and ranging from 0.0650–0.8849 for the other herds).The incidence of twinning records in the genetic evaluation of 3.25% reported here is similar to incidences reported in previous studies, which range from <1–5.8% ,15,16,37Previous studies that estimated genetic parameters for twinning in cattle suggest that heritability estimates for the trait range from 0.004–0.29, with repeatabilities (where reported) ranging from 0.04–0.55 ,39,40,41There exists both qualitative and quantitative evidence of an association between twinning and other traits of (economic and wellness) importance in dairy production. Previous phenotypic associations reported implicate correlations between twinning and mortality, stillbirths and abortions, and reduced birth weights for calves ,17,18,19In the present study, we reported genetic correlations between genomic predictions for TWIN and genomic predictions for an index and traits of importance for dairy wellness and profitability . As expeAs with Ron et al. 1990) , the pre990 , theMost importantly, the correlation between TWIN and DWP$ was positive at 0.115, inferring selecting on DWP$ will result in genetic progress for TWIN, in parallel with all other trait categories in the index . TWIN’s inclusion in the 2020 update of the DWP$ index was predicated on the fact that it was identified as a trait that impacts the lifetime profitability of a dairy female, and which occurs at an appreciable incidence in the industry. Subsequently, to quantify how selecting on DWP$ would alter genetic progress of its (aforementioned) underlying traits, the expected response to selection per standard deviation of genetic improvement of the index has been estimated previously PTAs) and STAs using a threshold animal model and ssGBLUP methodology for TWIN in Holstein dairy females. The means, standard deviations, and extreme values of gPTAs for TWIN are comparable to those previously obtained for other wellness traits in Holstein  and JersThese present estimates are based on a large, longitudinal dataset which has undergone rigorous data editing procedures, and demonstrates wide-ranging variability in genomic merit within the study population. The inclusion of producer-recorded on-farm records shows that even with the potential intra-herd variation in phenotype recording , the data editing and modeling approach employed yielded heritability and (g)PTA/STA estimates demonstrating exploitable variation, whilst reflecting actual on-farm situations. Moreover, the advantages of using an animal model lies in the fact that they are more suited to calculating variance components given that they account for all relationships amongst animals by taking in to account selection and assortative mating  . FurtherWe converted the TWIN (g)PTAs to STAs for ease of interpretation and thus to facilitate ease of selection decisions. As McNeel et al. (2017)  outlinedIn the current study, we hypothesized that cows with the highest genetic risk (lowest STAs) for TWIN would have a higher phenotypic incidence of TWIN than cows with the lowest genetic risk (highest STAs). As outlined by McNeel et al. (2017) , an acceThe association between individual TWIN STAs and twinning incidence illustrated in This demonstration shows that producers can use the TWIN prediction to effectively reduce twinning incidence on-farm, with associated economic benefits  , selectiTo our knowledge, this is the first time a prediction for TWIN has been included in a multitrait selection index for the dairy industry, which constitutes a novel step forward when it comes to harnessing genetic(omic) technologies in a practical way for producers to proactively reduce TWIN in Holstein populations. Previous genetic(omic) merit estimates generated for TWIN served other purposes such as (1) demonstrating variation in PTAs in cattle populations, (2) identifying QTLs associated with twinning rates and (3) inclusion as an input for genome-wide association studies ,37,39,40This empirical theoretical and practical evidence demonstrating the exploitable genetic component to TWIN in Holsteins offers a solution for reducing TWIN in dairy cattle populations. One major advantage of this genetic component is that genetic progress made upon selection is cumulative, and can be permanent provided it is consistently selected for by producers. Environmental influences that can impact TWIN risks are numerous and have been outlined by Fricke (2001), Fricke (2015), López-Gatius et al. (2017) and Mur-Novales et al. (2018) ,12,27,30All methods previously proposed have merit and their various combinations should be employed depending on the specific circumstances of each herd e.g., resource availability and allocation . An important consideration is that the positive effects of environmental management decisions to reduce twinning events are short-lived considering they impact the animal today and cannot be transmitted to the next generation, whereas the genetic(omic) merit of a cow is a function of all selection decisions that were made throughout her ancestral generations . TWIN isThe first step of a genomically enhanced management strategy involves genomically testing all females in the herd, with the aim to select on indexes that include TWIN predictions (such as DWP$). Applying an appropriate level of selection pressure based on the index merit values  should result in genomic progress for TWIN along with production and wellness traits thus breeding a robust cow. Sire selection should consider the genomic merit for DWP$ and TWIN (high STAs) in order to minimize TWIN pregnancies. Coupled with Double Ovsynch protocols, this strategy should further reduce the incidence on TWIN on-farm. Optionally, the top 80% females retained annually on DWP$ could then be secondarily ranked on TWIN, and Double Ovsynch strategies employed for those with the lowest TWIN STAs, and/or those with the lowest STAs are only bred to bulls of a high TWIN STA merit . In line with recommendations suggested by Lopez-Gatius (2020) , cows wiOur evaluation results are based on over one million producer-recorded twinning records, and over one million genotypes. Continuous data collection and inclusion of both new data from existing herds and historical data from new herds will increase the sampling depth in the Holstein population, simultaneously allowing for the variation in (g)PTA estimates observed to converge toward the true population variation, and reliability of records to increase given sufficient time. A concurrent and complementary approach when combining genetic(omic) tools and reproductive protocols ushers in the new era of precision management of dairy cattle, which should extract the maximum value of genetic(omic) predictions in relation to improving dairy wellness and herd sustainability.This study showed that on-farm, producer-recorded twinning data can be successfully used in routine genetic and genomic evaluations for Holstein cattle. Moreover, the twinning predictions outputted by the evaluation have been demonstrated to effectively identify cows that are more genetically predisposed to experiencing twin pregnancies (regardless of reproduction protocol) when they enter the milking herd. Genetic correlations with Zoetis dairy reproduction wellness traits were low yet positive and indicate that they are controlled by the overall reproductive system of the cow. Successful inclusion of this trait in to a multitrait selection index whose aim is to improve the overall herd wellness and profitability offers a new selection tool for dairy reproduction and wellness improvement which was not available to producers until now. Using such a tool to drive on-farm genetic selection for improved reproduction and wellness traits will confer cumulative improvement of a herd’s reproductive and health status. In the shorter-term temporary relief offered by other management interventions (therapeutic or otherwise) currently available today can be used in tandem. Moreover, concurrent management interventions can complement genetic selection strategies to further reduce the risk/incidence of twinning in the herd concomitantly. The inclusion of novel reproduction traits such as twinning in a selection index offers producers a more comprehensive tool for selecting potentially more robust, sustainable and profitable animals.


Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs.Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways.Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS  and FTD , and X-11529  and X-13429  significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as “urea cycle” (P = 0.036), “arginine biosynthesis” (P = 0.004) on AD and “phenylalanine, tyrosine and tryptophan biosynthesis” (P = 0.046) on ALS.Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases. Neurodegenerative diseases NDDs; , such asAs part of efforts to understand such mechanism, the relationship between metabolites and NDDs has been attracted active research attention . MetabolRecent advances in statistical genetic approaches of causal inference, along with publicly available summary statistics from large-scale genome-wide association studies (GWASs) of metabolites and NDDs provide an unprecedented opportunity to systematically evaluate their relationship through Mendelian randomization MR; ,b. In brHereby making full use of the latest GWAS summary statistics of 486 metabolites and five major NDDs, we conducted a two-sample MR analysis to assess the causal effects of metabolites on these diseases or vice versa. Extensive sensitivity analyses, including linkage disequilibrium (LD) score regression (LDSC) and colocalization analysis , were alWe obtained summary statistics of metabolites from the metabolomics GWAS , which wP-values are shown in 1000 and the intercept obtained by LDSC  . Specifianalysis .F statistic to quantitatively measure the strength of instruments  by instruments using summary statistics using the approach proposed in truments . The Coctruments . We thentruments . We decltruments .causality from metabolites to NDDs, which indicates that the metabolites are risk factors related to the diseases; (2) reverse causality from NDDs to metabolites, which implies that the metabolites are biomarkers of the diseases; (3) undetected horizontal pleiotropy, which suggests the diseases and the metabolites may share common genetic foundation; and (4) confounding by LD among leading causal SNPs shared by metabolites and NDDs, which means that the observed associations are spurious (via the similar PLINK clumping procedure described above but with a genome-wide significance level of 5E-8 (rg) using all available SNPs; (vii) the colocalization analysis to investigate whether the identified association between metabolites and NDDs was attributable to shared causal genetic variants  spurious . Therefospurious  and the spurious  to evaluspurious  to detecspurious ; (iv) th of 5E-8 ; (v) the of 5E-8 ; (vi) LDvariants .via MetaboAnalyst  for our MR analysis (P < 0.05), including 99 associations for 85 known metabolites and 65 associations for 51 unknown metabolites , implying shared metabolic mechanisms might exist among these NDDs. Moreover, 12 metabolites are suggestively related to at least two NDDs (P < 0.05) (P = 0.031) and FTD , hexadecanedioate on AD  and FTD , stearate (18:0) on AD  and FTD , ADpSGEGDFXAEGGGVR on ALS  and MS , suggesting potentially different functional roles of these metabolites implicated in NDDs. In brief, these findings provide important knowledge for understanding the metabolic mechanism underlying the relationship between metabolites and NDDs.It is worth noting that 2-methoxyacetaminophen sulfate also exhibits a suggestive association with AD  . For exaPMR–PRESSOglobal > 0.05) and instrumental outliers (P > 0.05)  , 6. Thir > 0.05) . For theW method , partly P < 0.05) in PMR–Egger Intercept = 0.065, and PMR–PRESSO global = 0.444), guanosine on AD , mannose on PD , citrate on PD  and isovalerylcarnitine on MS .We report another nine metabolites which have suggestive associations with the five NDDs (P = 0.020), FTD on 2-methoxyacetaminophen sulfate , and FTD on X-11529 , implying that the emergence of possible bidirectional causal relationships between these metabolites and ALS/FTD.For the four significant associations between metabolites and ALS/FTD identified above, we further carried out a MR analysis using instrumental variables of ALS/FTD to estimate their reverse causal effects on metabolites . We obsePLikelihood = 0.022 and PMR–PRESSO = 0.038 for ALS on 2-methoxyacetaminophen sulfate; PWeight–median = 0.030, PLikelihood = 0.021 and PMR–PRESSO = 3.44E-05 for FTD on 2-methoxyacetaminophen sulfate; PWeight–median = 0.009, PLikelihood = 0.002 and PMR–PRESSO = 0.001 for FTD on X-11529). Moreover, there is no evidence of horizontal pleiotropy for any association   , 8.P = 0.012), X-11529 on X-13429 , suggesting that metabolites may interact to affect NDDs . However, we cannot fully rule out the possibility of low statistical power due to small sample sizes of metabolites. Furthermore, upon performing colocalization analysis , and identified seven significant metabolic pathways for these diseases except PD (P = 0.036), “arginine biosynthesis” (P = 0.004), “purine metabolism” (P = 0.009), and “D-glutamine and D-glutamate metabolism” (P = 0.042). There exists a common metabolic pathway  shared by ALS (P = 0.046) and MS (P = 0.023).We were also interested in elucidating plausible metabolic pathways for the five NDDs. Therefore, we further carried out the metabolic pathway analysis using all metabolites discovered through the IVW approach  and NDDs (such as ALS); however, the small effect size would limit its potential utility as important biomarkers or therapeutic targets in practice. Third, we used a multivariate MR analysis to investigate whether metabolites interacted with each other to influence the causal effect of disease, but this method may be not unsuitable for unknown pleiotropy . Fourth,In summary, our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.The original contributions presented in the study are included in the article/PZ conceived the idea for the study. PZ, TW, SH, and HC obtained the genetic data. PZ and SH developed the study methods. PZ, TW, ZS, and HC performed the data analyses. PZ, ZS, JQ, and HC interpreted the results of the data analyses. PZ, JQ, and HC wrote the manuscript with suggestions from other authors. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.