Genome-wide meta-analyses in 708,480 individuals shed light on the genetic conflict between productivity and fertility in dairy cattle

Genome-wide meta-analyses in 708,480 individuals shed light on the genetic conflict between productivity and fertility in dairy cattle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genome-wide meta-analyses in 708,480 individuals shed light on the genetic conflict between productivity and fertility in dairy cattle Hans Daetwyler, Irene van den Berg, Marie-Pierre Sanchez, Christy Vander Jagt, and 44 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8631217/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Milk production and female fertility are negatively correlated in cattle, reducing the efficiency of artificial selection. The genomic loci underlying these antagonistic effects are unknown. We carried out one of the largest cattle meta-analyses of genome-wide association studies (Meta-GWAS) for 10 milk production and fertility traits in 12 breeds numbering 281,841 animals. The quantitative trait loci (QTL) regions identified were validated in an additional dataset of 426,639 animals. Up to 595 QTL per trait were confirmed across traits, including regions with both antagonistic and synergistic effects between milk yield, protein yield, and fertility. Combining GWAS results with functional genomic information highlighted candidate variants in several genes ( ABO, PAEP, LIF, ESR2, GC, and LALBA ). The large Holstein population dominated the Meta-GWAS and negatively influenced QTL discovery in smaller breed groups. These results reveal the genetic basis of milk-fertility trade-off and pave the way for more balanced cattle selection. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Genetics/Genomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Intro/Background Dairy cattle is a key global agricultural species, securing human nutritional needs 1 through milk and meat products. However, dairy cattle also contribute significantly to enteric methane emissions 2 . Dairy cattle productivity and reduction of emissions depend crucially on both milk production (including milk protein and fat yield) and female fertility, the ability of cows to conceive, give birth to a calf and initiate a new lactation at regular intervals. Lactation onset initiates a period of high milk production with large energy demands. This negatively influences a cow’s conception ability 3 , 4 . There are slightly unfavourable genetic correlations between production and fertility traits in several dairy cattle populations 5 – 7 , resulting in deterioration of fertility when selection was primarily for milk production 8 . Furthermore, dairy cattle are selected based on standardized 305-day production and cows with delayed conception will on average produce more in the absence of pregnancy energy demands. These issues prompted revised breeding goals 9 before the turn of the century, balancing selection pressure between production and fertility. This shift, assisted by genomic selection, has, over the last 30 years, reversed the negative fertility genetic trend 10 . Genome-wide association studies (GWAS) of multiple traits enable understanding of genetic architecture and pleiotropic variants underlying dairy cattle milk production and fertility. The power of GWAS depends on sample size, quantitative trait locus (QTL) effect size, allele frequency as well as the extent of population linkage disequilibrium (LD) 11 , 12 . Meta-analyses of GWAS (Meta-GWAS) combine results of individual GWAS, increasing sample size and power to detect QTL shared across populations. To maximise power of the analyses, causal mutations should be included by using imputed whole-genome sequence (WGS), now ubiquitous due to decreasing sequencing costs and data sharing 13 . The 1000 Bull Genomes Project, underpinning this study, has enabled hundreds of cattle WGS studies and several large Meta-GWAS 14 – 17 . Combining populations from different ancestries can reduce LD surrounding causal mutations, improving QTL mapping precision 18 . Global dairy cattle populations are differentiated into breeds with small effective population size and limited migration among most breeds. Combining multiple breeds in a Meta-GWAS is advantageous as within-breed GWAS produce wide QTL regions and, for minor breeds with less data, a high proportion of QTL remain undetected due to low statistical power. However, strongly unbalanced population sizes can negatively affect detection power in smaller populations and render population-specific QTL undetected in a Meta-GWAS 19 . This study is one of the largest dairy cattle multi-breed WGS Meta-GWAS for milk production and fertility traits. We aimed to identify regions, variants and genes of importance in these traits, particularly focusing on regions affecting both trait groups. We explored population properties of QTL regions, including QTL region size, and ancestral versus derived QTL alleles. In addition, we investigated the effect of unbalanced numbers of animals in the different populations. Effects of the QTL regions were confirmed in an independent dataset of 426,639 animals. Finally, segregation within breeds was explored, as well as association with known molecular phenotypes (e.g., expression QTL) to highlight key regions with putative causal variants. Results In 1000 Bulls Genome Project Runs 7 and 8 13 , we compiled WGS data from 3,094 and 4,109 Bos taurus taurus cattle, respectively (Supp Table 1). This multi-breed reference population, or breed-specific subsets thereof that were enriched with additional sequence data 20 , was used by each of 12 collaborators in 10 countries to impute sequence variants into their twenty dairy populations made up of twelve breeds that we categorised into six breed groups: Holstein, Jersey, Normande, Red breeds, Dual Purpose, and Brown breeds (Supp Table 2). Up to 10 traits were phenotyped in each breed group: milk yield (MY), protein yield (PY), fat yield (FY), protein content (PC), fat content (FC), heifer fertility (HFert), interval between calving to first mating (CalvMate), conception rate (Conc), interval of first to last mating (Int1stLast), and calving interval (time from calving to calving, CalvInt). Note that fertility trait measures were coded such that high values mean improved fertility (see Methods). The total number of individuals with imputed sequence and phenotypes was highest for MY (N=281,841) and lowest for Int1stLast (N=56,431). Phenotypic data were either own performance for females or accurate daughter trait deviations for bulls (Supp Table 2). A total of 27,862,477 variants occurred in at least one of the GWAS after filtering on imputation accuracy (Supp Table 3). Meta-GWAS QTL regions were defined by grouping the top third of significant markers within 1 megabase windows. We validated the significant (p-value < 5x10 -8 ) multi-breed Meta-GWAS QTL regions using GWAS in three French populations of young cows recorded for all 5 production traits as well as HFert, CalvMate, and Conc (Holstein N=254,796, Montbéliarde N=135,295, Normande N=36,548). CalvMate and Conc were used to validate results for Int1stLast and CalvInt. The populations used for validation were not included in the Meta-GWAS (Supp Table 4). A high proportion of Meta-GWAS QTL regions were validated ranging from 80.1% for PY to 98.4% for Int1stLast (Table 1, Suppl Figs. 1 and 2, Supp Tables 4, 5 and 6). We found a higher number of QTL for production (e.g., MY N=515) than fertility (e.g., HFert N=84) traits. This was expected due to smaller sample size and lower heritability of dairy cattle fertility traits. We tested different sample sizes in the Holstein only and multi-breed Meta-GWAS with the number of QTL regions still increasing with sample size, indicating further efforts to increase sample size are warranted (Fig. 1a). Favourable effects for milk yield traits were generally from derived alleles and milk content traits were ancestral (additional detail in Supp Results). Figure 1. Impact of sample size and breed composition on Meta-GWAS. a) Meta-GWAS at increasing sample sizes in the Holstein and Multi-Breed datasets. b) Average percentage of MY, FY, PY QTL regions detected with the Meta-GWAS with increasing percentage of Holstein individuals included, when compared to single breed group GWAS. c) Percentage of QTL regions overlapping with mouse lactation genes (mouse) or variants predicted to have a high to moderate effect by Variant Effect Predictor (VEP) detected in the five breed groups with the Meta-GWAS compared to single breed group GWAS with (with HOL) and without including the Holstein breed (no HOL). Candidate genes were identified in the QTL regions using MAGMA 21 (Suppl table 7). We performed an over-representation analysis of MAGMA gene sets, using gProfiler 22,23 , to identify associated Gene Ontology (GO) terms and biological pathways (Supp Table 8 and Supp Fig. 3-11. Significant (p < 0.05) terms and pathways associated with gene sets for fertility traits included the spermato-proteasome complex (Hfert; GO_CC:1990111); cGMP kinase signalling complex (CalvMate; CORUM:638); with member gene PRKG1 which promotes primordial follicle activation and oocyte growth 24 (CalvInt, GO_BP:0001892). Gene sets for production traits were significantly associated (p<0.05) to growth hormone and prolactin signalling, development of the mammary gland and lactation, blood cell formation and oxygen transport capacity, cytokine and interleukin signalling for maintaining mammary gland health, and neuronal regulation (additional results in Supp Results). We evaluated enrichment of the QTL regions across gene expression QTL discovered in the Cattle GTEx 25,26 functional genomic datasets. Production QTL enrichment for expression (eQTL) and splice (sQTL) QTL was greatest for macrophage (eQTL, 24.1-fold) and mammary (eQTL, 24.1-fold) tissues, while fertility QTL, though less enriched overall, showed the most enriched tissues being monocytes (eQTL, 12.7-fold) and oviduct (sQTL, 9.1-fold) (Supp Tables 9 and 10). Some VEP annotation classes for variants in QTL regions were enriched (range 0.04 – 2.7 times, Supp Table 10), though not as strongly as eQTL and sQTL. Conserved variants were less enriched than non-conserved, except for intergenic and intronic variants. We tested whether variants in QTL regions were enriched for ChIPseq variants from four studies 27–30 . Variants associated with most traits were enriched in at least some studies (Supp Table 11 and 12). Our interest in regions with pleiotropic effects on production and fertility traits led us to categorize all validated QTL regions into four groups based on whether they affected only production, only fertility, or both production and fertility in either a synergistic or antagonistic manner. Variants for alleles associated with increased milk yield or protein yield (the main production traits of interest in dairy breeding) and improved fertility were considered synergistic, whereas variants for alleles associated with increased milk yield or protein yield and reduced fertility were considered antagonistic. Production-only QTL were by far the largest group with 324 QTL regions, followed by fertility-only (84 QTL regions), antagonistic (13 QTL regions) and synergistic QTL (4 QTL regions) (Supp Table 13). Four regions on chromosomes 11, 18 and 25 harboured synergistic QTL that increase both production and fertility, the opposite to the overall negative genetic correlation (Supp Table 13). Antagonistic QTL were spread across chromosomes 5, 6, 9, 13, 14, 20, and 23. Antagonistic QTL putatively play a role in causing the unfavourable genetic correlation between fertility and production. HFert was not associated with any pleiotropic QTL, whereas CalvInt, for which a low to moderate unfavourable genetic correlation with production is observed 31 , was affected by the highest number of antagonistic QTL. We investigated how the allele frequency of variants in QTL regions changed over three decades (1981-2015) in the Holstein and Jersey breeds (Supp Fig. 12, Supp Table 14). Favourable alleles only affecting production or fertility increased in frequency (range 0.00-0.05), with production allele frequency changes slightly larger than fertility. Synergistic favourable allele frequency changes over time were more variable and antagonistic QTL tended to show stable or slightly negative allele frequency over time, except Conc affected by only one antagonistic QTL (Supp Table 14). For both production and fertility traits, QTL intervals were wider in within-breed GWAS than in the Meta-GWAS, differing by 24Kb for production and 15Kb for fertility, suggesting that combining data across breeds narrowed QTL intervals (Supp Table 15). The size of QTL intervals also varied across the four QTL classes, where QTL affecting only production or fertility were considerably narrower (>60Kb) than synergistic and antagonistic QTL affecting both trait groups. We inferred causality between production and fertility traits using the GCTA Mendelian Randomization module Generalised Summary-data-based Mendelian Randomisation 32 in a Dutch Holstein bull population included in the Meta-GWAS. While primarily interested in causality of production (intermediate phenotype) on fertility (target phenotype), we performed a bidirectional analysis. We detected causal associations for MY and PY for CalvMate, CalvInt, fertility index, and Int1stLast (Supp Table 16, Supp Figs. 13 and 14). While previously hypothesized that high milk production affects fertility negatively, bidirectional analysis also indicated causality of fertility on milk and protein production. Nevertheless, directions of effects were consistent with known genetic correlations (i.e., higher production vs. lower fertility, and vice versa). Interestingly, MY did not have a significant causal relationship with Conc and 56-day non-return rate, suggesting milk production may influence resumption of cyclicity, but not the cow’s ability to conceive once cyclic or to maintain a pregnancy. Highlighted QTL regions Two distinct QTL in region 103-104 Mb on chromosome 11 On chromosome 11, two highly significant QTL regions lie within 1 Mb of each other that appear to be independent 33–35 . Both QTL showed an increased favourable allele frequency (Figs. 2b&c). The first QTL (103.2 Mb, Fig. 2, Supp Table 17) has synergistic effects on MY, PY, and fertility. Although this QTL is near PAEP (reviewed by Lopdell 36 ), it has not been reported also to affect cattle fertility traits possibly due to limited power in previous analyses. In cattle, PAEP (encoding beta-lactoglobulin in milk) has been shown to be highly expressed in the mammary gland, but also expressed in the infundibulum (ipsilateral to the corpus luteum) and the vas deferens 37 , suggesting an additional reproductive role. In humans, progestagen associated endometrial protein ( PAEP ) gene expression has been shown to be restricted to male and female reproductive tissues 38,39 , where the protein plays a key role in reproduction 40,41 . Due to the allergenicity of beta-lactoglobulin, which is an alternative name for PAEP , in cow milk 42–44 , unlike in human milk 45 , gene editing of PAEP in cattle 46 has been attempted, but its putative role in fertility would advise caution. Figure 2. Two QTL regions on chr 11 between 103Mb and 105Mb. a) LocusZoom plots and LD ( r 2 ) in top 3 panels respectively, overlap with cattle GTEx (middle panel) and genes (bottom panel) in the region; b) allele frequencies in 4 populations across time for most significant variant for CalvInt in QTL at 103Mb ( PAEP ); c) allele frequencies in 4 populations across time for most significant variant for PC in QTL at 104Mb ( ABO ). The second QTL (104.2Mb, Fig. 2, Supp Table 17), partially overlapping the ABO gene, has no association with fertility but increased PC, PY, FP, FY and reduced MY. This gene encodes for proteins related to the blood group system in humans (A, B & O) and although cattle have different blood groups, these genes were shown to be associated with cattle milk fat percentage 47 . The ABO gene encodes glycosyltransferases involved in glucose metabolism and synthesis of some oligosaccharides 48 . Previously, QTL have been reported in the ABO gene region associated with milk oligosaccharides in cattle 49,50 and humans 51 , milk glucose content 52 and milk mid-infra-red (MIR) spectra 33,53 . Additionally, ABO showed the highest differential gene expression in bovine mammary and kidney 54 . This gene likely influences multiple milk traits, as glucose availability in mammary epithelial cells supports lipid and lactose synthesis. Lactose synthesis generates osmotic pressure that drives water into milk influencing milk volume 55 . A previous GWAS using sequence data and milk MIR spectra from Holstein and Jersey cattle identified a putative causal splice variant in ABO (104187200, rs207688357) 53 . Similarly, many of our most significant variants were reported as GTEx splice expression QTL for ABO . QTL associated with cow fertility on chromosome 17 A QTL region on chromosome 17 (~69 Mb) was associated with CalvMate, Conc, Int1stLast, and CalvInt in the multi-breed meta-GWAS (Fig. 3, Supp Table 17). The strongest association was detected with Int1stLast (p-value of 1.5×10 -15 ), for a variant at 69,244,946 bp (Fig. 3a). Consistent with historical selection pressure on fertility, frequency of this allele reduced from the eighties to the nineties and then increased again more recently (Fig. 3b). For this QTL region (69,242,369 to 69,352,822 bp), MAGMA detected six significant genes, with the most significant associations detected for OSM , LIF , and CASTOR1 (also known as GATSL3 ). Additional evidence points to LIF as a candidate gene due to the association between its expression in the ventral hypothalamus and mid-oestrus heat score 56 (Supp Table 18, for complete overlap analysis with studies on brain expression and protein-protein interaction, see Supp Results) and because it is known to be essential for embryo implantation in mice 57 . GO-terms associated with LIF included embryonic placenta development, spongiotrophoblast layer development, and regulation of nuclear division. LIF has been reported to play a regulatory role in trophoblast growth and differentiation during pregnancy in human placenta 58 . Previous studies in dairy cattle found variants in this QTL region significantly associated with pregnancy rate 59 , conception rate 60 , a female fertility index 61 , and interval first to last insemination 60 . Interestingly a QTL associated with fertility in Brown Swiss cattle 62 was located in the same region as a QTL detected in our Meta-GWAS in the BROWN breed group only, but had distinct top variants, e.g., variants from the multi-breed analysis are not significant in BROWN and vice-versa. Figure 3. QTL region detected for Int1stLast on chr 17 at 69Mb. a) LocusZoom plots and LD ( r 2 ) in top panels, Cattle GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant detected for Int1stLast 17:69244946 in QTL ( LIF ). Fertility QTL near ESR2 on chromosome 10 We identified a QTL region on chromosome 10 (~76 Mb), associated with CalvMate, Int1stLast, and CalvInt (Fig. 4, Supp Table 17). The strongest association for CalvMate (p = 2.4×10 -14 ) was detected at an intergenic variant located at 76,478,558 bp (rs379543839)( Fig. 4a). The frequency of the C allele at this variant, associated with reduced calving to first mating interval (i.e., improved fertility), was high in all populations with allele frequency data, ranging from 0.82 in Australian Jerseys (born 1981-1985), to 0.99 in Australian Holstein (born 1986-1990, Fig. 4b). This variant has also been significantly associated with the expression of estrogen receptor β ( ESR2 ) in the dorsal part of the hypothalamus 63 . Disrupted expression of ESR2 has been associated with reduced female fertility in cattle 64 and mice 65,66 . Within the breed group meta-GWAS, QTL in this area were detected for CalvMate, Int1stLast, and CalvInt in HOL, and HFert in our Meta-GWAS in dual purpose (DUAL) cattle only. Interestingly, this QTL region overlaps with previously detected QTL associated with stillbirth 67 and dystocia 67,68 in German Holstein cattle. Unsurprisingly, dairy cows with prolonged or difficult calvings take longer to conceive again 69 . Figure 4. QTL region on chr 10 at 76Mb. a) LocusZoom plots and LD ( r 2 ) in top panels, Cattle GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant for CalvMate 10:76478558 in QTL. Antagonistic QTL near GC on chromosome 6 Chromosome 6 contained overlapping QTL regions associated with production (MY, FY, PY, and FC) and fertility (CalvMate, Int1stLast, and CalvInt) (Supp Fig. 15, Supp Table 17). MAGMA genes associated with this region were GC, NPFFR2, and SLC4A4. Most significant was an intergenic variant (87,018,377 bp, rs208837389, p-value of 5.3×10 -93 ), whose T allele was associated with increased MY, FY, PY, and reduced FC and fertility. Strong selection for production in the late 20 th century increased the frequency of this allele in Holstein from 0.45 to 0.56, but then decreased again to 0.39 in animals born 2016-2020 (Supp Fig. 15b), when fertility was prioritised in breeding selections. Lee et al. 70 proposed a 12 kb multiallelic copy number variant (CNV) in this QTL region as putative causal variant for mastitis resistance, with pleiotropic synergistic effect on fertility and an antagonistic effect on milk yield (other milk traits not tested). They showed higher copy number alleles were associated with increased GC expression and found evidence of an enhancer region within the CNV. Recently, significant differential GC gene expression was reported between two divergently selected lines of high and low fertility dairy cows 71 . Milk fat production QTL on chromosome 26 - SCD We found a highly significant QTL associated with FC on chromosome 26 (between 21,267,450-21,287,713 bp) (Fig. 5, Supp Table 17). Overlapping QTL regions were detected for MY, FY, and PC, but not fertility traits. The most significant variant associated with FC was an intronic variant in SCD (21,273,073 bp, p = 2.4 × 10 -52 ) and a nearby missense and splice region variant (21,272,422 bp, p-value of 6.6 × 10 -52 ) was the third most significant variant. Several variants in the region were associated with the expression of PKD2L1 , SCD , DNMBP, and BTRC in macrophage, as well as with gene splicing of BTRC in blood. Of these four genes, SCD is a strong putative candidate gene because it encodes stearoyl-CoA desaturase, an enzyme involved in fatty acid metabolism 72,73 . SCD is implicated in lipid metabolism in mice 74 and multiple studies have reported QTL for FC and FY in dairy cattle 35,75,76 . Given SCD ’s involvement in lipid metabolism, it may also impact traits like MY and PC; indeed, in mice, SCD knockout elevated insulin signalling in muscle and increased glucose uptake 77 . Please see Supplementary Information for results for region chr5:30-32Mb LALBA . Figure 5. QTL region on chr 26 at 21Mb. a) LocusZoom plots and LD ( r 2 ) in top panels, GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant in FC QTL at 21Mb ( SCD ), for allele associated with increased FC. Effect of disparate number of individuals in the different breed groups Numerically, the Holstein breed was by far the largest breed group (69% of all individuals), similar to human Meta-GWAS where the Caucasian group typically outnumbers other ethnicities 78 . There was a reduction in the percentage of within-breed QTL detected for all traits for non-Holstein breeds compared with the Meta-GWAS, a trend that accelerated once the percentage of Holstein individuals was greater than 30 to 40% (Fig. 1b). When the Multi-Breed Meta-GWAS was done without Holsteins, up to half of within-breed (Jersey) QTL not detected in the full Multi-Breed Meta-GWAS could again be detected, indicating that the Holstein breed had a very strong effect on results and including them caused a proportion of minor breed QTL to be missed (Fig. 1b). Conversely, of all QTL found in the full Meta-GWAS (Holstein included), only approximately 8% were detected in the Meta-GWAS without Holstein. To reduce the potential of a higher false positive rate in within-breed GWAS, we selected QTL variants that overlapped genes annotated as involved in mouse lactation 79 or predicted (by VEP) to have moderate-to-high impact on gene function 80 . This confirmed that the inclusion of Holstein individuals reduced the total number of QTL and the two QTL subsets within the smaller breeds compared to non-Holstein Meta-GWAS (Fig. 1c). QTL not detected in the Meta-GWAS with Holstein tended to be significant in fewer within-breed GWAS, and QTL present in the Holstein breed were more likely to be detected in the full Meta-GWAS. Discussion We carried out the largest WGS-based Meta-GWAS yet in dairy cattle that included both milk production and fertility traits across a wide diversity of breeds. The inclusion of multiple breeds substantially reduced QTL interval sizes compared to single breed GWAS, indicating a decrease in the effective extent of LD. Our validation sample was 1.5 times larger than the discovery population, resulting in a high proportion of QTL being validated. The large number of QTL we observed in our multi-breed Meta-GWAS is comparable to human Meta-GWAS of similar size (Supp Table 19), despite fundamental differences in evolutionary history. While cattle had large historical effective population size but current effective population size is small, humans, in contrast, have experienced a large increase in effective population size over time. In our study, 44% of discovery phenotypes were derived from bulls, represented by daughter trait deviations (i.e., mixed model adjusted daughter-group means). Due to the large daughter groups, these are highly reliable predictors of the sires’ genetic values with effective heritabilities often greater than 0.8. This is comparable to the heritability of human height for which a larger number of QTL have been found 81 . The smaller number of QTL detected in fertility traits is expected due to the smaller sample size and much lower heritabilities than production traits. In addition, METAL assumes consistent trait definitions across datasets, an assumption violated for heifer fertility, whose definition varies across breeds and countries, leading to the detection of fewer QTL in this trait. Gene ontology enrichment confirmed biologically meaningful associations, highlighting pathways involved in milk production (e.g., prolactin signaling) and fertility (e.g., placenta development). QTL regions were also significantly enriched for functional annotations, including cattle GTEx expression and splicing QTL, ChIP-seq QTL, as well as some VEP annotation classes, providing independent molecular confirmation of our findings. Our study has shed additional light on the genetic relationship between production and fertility traits in dairy cattle. Consistent with the antagonism between the traits supported by previous studies showing negative genetic correlations, we have detected more QTL regions with antagonistic rather than synergistic effects. As another independent validation, we showed that for major QTL the allele frequency of the most significant SNP follows the expected patterns based on the artificial selection pressure applied. The allele frequency for fertility associated alleles in antagonistic regions declined until the mid 2000s. This trend was reversed when selection pressure was rebalanced towards fertility 9 . In contrast, allele frequencies in synergistic regions consistently increased over the last few decades. The antagonistic patterns were also confirmed by our Mendelian sampling analysis, which indicated causal relationships between milk production and fertility traits. Of interest is that causality in both directions was indicated, highlighting that the relationship of the traits may be more complex than a one-way negative causal effect of production on fertility. This is reasonable as increasing fertility could decrease production in the second part of the lactation due to earlier conception and associated energy demands and endocrine modifications due to the developing calf. The largest number of antagonistic QTL were discovered for CalvInt and is in keeping with non-pregnant cows producing more milk in a standard 305 day lactation. However, no significant causal relationships were detected between milk yield and conception rate or 56-day non-return rate, this could be due to conception rate having the lowest heritability of the fertility traits 31 leading to reduced power. Holstein dominance in our Meta-GWAS clearly impacted QTL detection with many within-breed GWAS QTL in non-Holstein breeds not reaching significance in the Meta-GWAS, more strongly affecting breeds more distantly related to Holstein. For example, Jersey had the largest proportion of missed QTL and the lowest F st to Holstein. The proportion of missed within breed QTL was inversely proportional to the percentage of Holstein individuals included in the Meta-GWAS. An equal percentage of Holstein versus all other breeds combined was reached at 30% of the total Holstein individuals and when this threshold was exceeded, the loss of non-Holstein QTL accelerated. To focus on biologically meaningful loci, we further examined variants in QTL that overlapped with genes involved in mouse lactation and selected regions containing variants with at least a moderate VEP effect. In both datasets, the Meta-GWAS including all Holstein cattle consistently identified fewer QTL in the smaller breed groups compared to GWAS within the respective breed groups. This indicates that QTL architectures differ across breeds, even in a set of genes with similar function across species, in a manner to incur negative consequences when one breed dominates the dataset. This cautionary result could be due to different LD structures or minor allele frequency across breeds or that QTL regions in smaller breeds had a lower probability to contain causative mutations due to smaller population sizes 82 . Meta-Analysis approaches that explicitly model allelic effect heterogeneity may improve QTL detection in mixed ancestry populations 83 . Overall, this study provides new insights into the genetic architecture underlying the milk production–fertility relationship in dairy cattle. We confirm the antagonism between these traits but also identify regions with synergistic effects that may support more balanced genomic selection. The combination of large-scale WGS data, multiple breeds, and functional annotations improves mapping resolution and biological interpretation. Future work should focus on fine-mapping causal variants and developing selection programs that enhance both production and fertility while preserving breed diversity Methods 1000 bull dataset and breed composition The whole-genome resequence (WGS) data for the imputation reference population originated from Run 7 and 8 of the 1000 Bull Genomes Project 13 including 3,092 and 4,109 Bos taurus taurus animals, respectively, distributed across 109 breed groups (Supp Table 1). The largest breed groups were Holstein, Norwegian Red, Angus, Fleckvieh-Simmental, Jersey, Charolais, and Brown Swiss. Whole-genome Re-sequence Data Processing and Variant Calling All 1000 bull genomes project partners generated short read whole genome sequence data to greater than 10x average coverage. Processing of sequencing data up to and including production of gVCF files was undertaken according to a shared protocol. Fastq files were processed according to the 1000 bulls GATK fastq to GVCF guidelines which largely followed GATK best practices. Raw sequence reads were trimmed of adapter and low-quality bases (qscore <20) on either end and reads with mean qscore less than 20 or length less than 35bp were removed. For each individual, the remaining high-quality reads were aligned to the bovine genome reference, ARS-UCD1.2_Btau5.0.1Y, using BWA mem (v0.7.17) 84 defining read groups and sorting and indexing with Samtools (v1.8) 85 . This reference combined the Btau5.0.1 Y chromosome assembly from Baylor College 86 and ARS-UCD1.2 87 . Picard MarkDuplicates was used to mark PCR and optical duplicate reads, where OPTICAL_DUPLICATE_PIXEL_DISTANCE was 100 for data generated on non-arrayed flowcells (i.e., from GAIIx, HiSeq1500/2000/2500), or 2500 for arrayed flowcell data (eg HiSeqX, HiSeq3000/4000, NovaSeq). Base quality recalibration was performed according to GATK best practices guidelines using GATK (v3.8-1-0-gf15c1c3ef) 88 BaseRecalibrator, with bqsrBAQGapOpenPenalty of 45 and a list of known variant files, and PrintReads. The known variants file consisted of SNP and INDEL generated from Bos taurus and Bos indicus Run7 at tranche 99.9 stringency. Finally, we created GVCF files with GATK HaplotypeCaller. The 1000 Bull Genomes variant detection analysis pipeline mostly followed the "best practices" guidelines outlined by the Broad Institute 89 using the same GATK version. Joint genotyping of SNPs and INDELs was performed with GenotypeGVCFs using default settings, the ARS-UCD1.2_Btau5.0.1Y reference genome and all animal GVCF files as input. The raw VCFs were filtered to minimize false-positive variant calls using the GATK Variant Quality Score Recalibration (VQSR) tool. The truth and training sets used for VQSR in Runs 7 and 8 of the 1000 Bull Genomes Project are detailed in Supp Table 20. VQSR was a two-step process: 1) Variant Recalibration: Variants in the call set were assigned a Variant Quality Score Log-Odds (VQSLOD) value based on annotation values of the truth sets. For Runs 7 and 8, the annotations used were QD, MQ, MQRankSum, ReadPosRankSum, FS, SOR, and InbreedingCoeff. Variants in the training sets were also ranked by their VQSLOD scores. 2) Recalibration Application: Recalibration was applied using tranche sensitivity thresholds, which determined the VQSLOD score or percentage above which variants were retained. For Runs 7 and 8, tranche thresholds of 100.0, 99.9, 99.0, and 90.0 were defined during the first step, with 90.0 used for the second step. Variants passing the 90.0 tranche threshold were marked as "PASS" in the VCF FILTER field, while lower-confidence variants were retained and annotated with their tranche (Supp Table 20). This preserved all variant data and allowed project partners to apply custom filters as needed. Using the final recalibrated variant call set, for each animal, QC metrics were calculated for variants in the 90.0, 99.0 and 99.9 tranches (e.g., opposing homozygotes, heterozygosity, concordance with available high-density SNP-chip data available for each animal, number of unique variants) using custom scripts. Animals failing multiple QC metric thresholds were removed from the VCF. Autosomal chromosome variants from tranches 90.0, 99.0, and 99.9 were phased using Beagle v4.0 90 to generate a high-accuracy call set for imputation. Imputation Genotype imputation was undertaken by each collaborating group following two main steps: Medium density (50k) SNP array genotypes were imputed within breed to high density (700k) genotypes corresponding to the BovineHD beadchip (Illumina Inc). The high density genotypes were then imputed to the full genome sequence, using the 1000 Bull Genomes Project sequence database (Run7 or Run8) as the imputation reference. Details of the software used at each step by each contributing institute are given in Supplementary Table 21. Typically, the imputation of sequence data has been found to be highly accurate using the 1000 Bull Genomes Project data 91 . GWAS Each collaborator performed GWAS with a modified version of GCTA 92 that included allele dosage with imputation uncertainty and the accuracy of the phenotypes within trait, breed and sex when relevant (available at https://git.wur.nl/vande018/gcta/-/tree/dosages). Summary statistics were combined in a within-breed Meta-GWAS in each breed group (Holstein, Jersey, Red breeds, Brown breeds, Dual purpose, Normande) and in a Multi-Breed Meta-GWAS containing all available data. While production traits were the same in each population, namely milk yield (MY, kg), fat yield (FY, kg), protein yield (PY, kg), fat percentage (FC) and protein percentage (PC), the fertility phenotypes measured varied among populations. Interbull fertility categories 93 were used to group similar traits into five categories: HFert, heifer traits (Interbull T1); CalvMate, conception rate, non-return rate, interval first to last insemination and age at first insemination (Interbull T2); Conc, conception rate and non-return rate (Interbull T3); Int1stLast, interval first to last insemination (Interbull T4); and CalvInt, calving interval and days open (Interbull T5). Phenotypic data were either own performance for females or accurate daughter trait deviations for bulls. To ensure all directions of effects were consistent, we changed the sign of effects for the fertility GWAS where a positive effect meant a reduction in fertility (i.e., longer calving interval correspond to lower fertility), so that in the GWAS effects reported in our analyses for fertility, a larger value always means improved fertility, regardless of the fertility trait. Hence, positive effects for interval traits (CalvMate, Int1stLast and CalvInt) represent improvements in fertility and therefore a reduction in interval. We only used variants with an imputation accuracy (approximated by Minimac or Beagle r 2 value) ≥ 0.10 and a minor allele count ≥ 10. Multi-breed meta-GWAS including defining QTL regions Meta-GWAS was performed for each trait category, both combining all GWAS in a large multi breed GWAS, as well as additional meta-GWAS within each breed group. We used the weighted z-score model implemented in METAL 94 , that uses the direction of effect and p-value obtained in individual GWAS weighted by sample size. We used a p-value significance threshold of 5x10 -8 approximately corresponding to nominal p-value of 0.03 following a Bonferroni correction for up to 562,159 simultaneous independent tests. Significant variants were grouped into QTL regions (maximum 1Mb size) by selecting all variants within the bottom third p-values of a QTL region. Validations A validation GWAS was performed for all variants in the QTL regions using phenotypes of 254,796 French Holstein, 135,295 Montbéliarde, and 36,548 Normande cows, for MY, FY, PY, FC, PC, HFert, CalvMate, and Conc. The validation dataset consisted of cows with first calving after September 2019, to avoid double counting performances of bulls used in the meta-GWAS. The validation GWAS was done using a weighted GWAS, as described in Tribout and Boichard 95 . In brief, the model was y = 1 µ + Ms + x b + e , where y is a vector of phenotypes adjusted for environmental effects and averaged when records were repeated, µ the mean, s is a vector of 14,205 random SNP effects to account for relationships among cows, M a genotype matrix assigning s to cows, b the fixed effect of the tested SNP, x the corresponding doses and e has heterogeneous variances proportional to 1/weights. Polygenic and residual variances were assumed to be known. The SNPs used to account for relationships excluded those on the chromosome of the tested variant. The 14,205 SNP were selected from the BovineSNP50 SNP chip panel (Illumina Inc), by selecting the variants with highest MAF (averaged across the three breeds) in 150kb intervals. The within breed validation GWAS were combined in a meta-GWAS using METAL 94 . We then considered all variants that were significant (p ≤ 5×10 -8 ) in the validation meta-GWAS as validated. Because Int1stLast and CalvInt were not included in the validation meta-GWAS, variants in QTL regions detected for those traits were considered validated if they were significant for either CalvMate or Conc in the validation meta-GWAS. Meta-GWAS Power Analysis To investigate the relationship between QTL detection power and sample size, we repeated both the multi breed meta-GWAS and the within HOL meta-GWAS with different sample sizes. For this, we randomly sampled approximately 25%, 50%, or 75% of samples, and repeated the meta-GWAS on the subset. This was repeated 10 times. We then compared the number of QTL regions and the number of significant variants with the number of QTL regions in the full meta-GWAS. MAGMA and gene ontology over-representation analysis We used MAGMA 21 to perform a gene-set enrichment analysis on the results of the full multi breed Meta-GWAS including all breed groups for all production and fertility traits, using a window size of 10kb. Over-representation analysis was conducted using the gprofiler2 22 package (version 0.2.3) in R 96 version 4.4.1 (released 2024-06-14). Gene sets were derived from Bos taurus (organism code: bos_taurus). Enrichment was tested using a hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) 97 correction (significance threshold of 0.05). Only terms with 1–500 annotated genes were included. Queried databases included Gene Ontology (GO) 98,99 subdivided into biological process (BP), molecular function (MF), and cellular component (CC), KEGG 100 , Reactome 101 , WikiPathways 102 , and CORUM 103 . GeneRatio was defined as the number of overlapping genes between the query and a term (intersection size) divided by the number of input genes (query size). Top terms were visualized using tidyverse 104 (version 2.0.0). Cattle GTEx For all variants in the QTL regions significantly associated with a trait in the multi-breed meta-GWAS and significant associated in the validation analyses, we investigated whether they were significantly associated (p ≤ 5×10 -8 ) with gene expression or gene splicing in Cattle GTEx 25 . This was done for all variants in validated QTL regions for production and fertility. The cattle GTEx dataset comprised 23 and 27 tissues with summary statistics of gene expression and gene splicing, respectively, with at least 40 samples per tissue (Supp Table 9). Cattle GTEx summary statistics were downloaded from https://cgtex.roslin.ed.ac.uk/. Enrichment was calculated by dividing the percentage of significant gene expression or gene splicing variants in QTL regions by the percentage of significant variants for any variants that overlapped between our dataset and cattle GTEx. Conserved variants Variants within conserved sites across 100 vertebrates were lifted over from human genome sites (hg38, http://hgdownload.cse.ucsc.edu/goldenpath/hg38/phastCons100way), following previous procedures 105–107 . Conserved sites with the PhastCon score >0.9 were kept. The liftover used the established pipeline from UCSC liftover: https://genome.ucsc.edu/cgi-bin/hgLiftOver based on UCSC curated chain files (http://genome.ucsc.edu/goldenPath/help/chain.html) from the human genome (hg38) to cattle (ARS-UCD1.2). Chain files have every base pair in the source assembly either not mapping to the destination assembly or mapping to a unique position in the target assembly 108 . QTL regions overlapping with gene expression and protein/protein interactions in previous studies The dairy fertility interval traits, especially CalvMate and Int1stlast are related to oestrous behaviour which has been linked to conception rates. Kommadath et al. 56 studied association patterns between heat score of oestrous behaviour and gene expression in the anterior pituitary and four brain areas (amygdala, dorsal and ventral hypothalamus, and hippocampus) collected from 14 cows at the start of the oestrous cycle (d0) and another 14 cows at midpoint of the oestrous cycle (d12). The gene names from the Bovine 24K oligonucleotide microarrays (Bovine Oligonucleotide Microarray Consortium (BOMC), USA) association to heat scores (on day 0, day 12 and combined) in Kommadath et al. 56 (Additional file 1) were placed on the ARS-UCD1.2 reference genome based on their matching RefSeq gene names (GCF_002263795.1_ARS-UCD1.2_RefSeqGene.gff). For 268 genes out of 372 genes listed in Kommadath et al. 56 (Additional file 1), the positions were recovered and were included in further analysis. In a follow-up study, Hulsegge et al. 63 (Supplementary Table S1) found 36 genes differentially expressed (p<0.05) across the 2 oestrous cycle stages (day 0 and day 12). For 13 genes, out of 36 genes listed 63 (Supplementary Table S1), positions were recovered matching RefSeq gene names, and were included in further analysis. Moreover, Hulsegge et al. 63 (Supplementary Table S2) prioritized candidate genes based on protein-protein interactions, gene expression, and text-mining per tissue type. In brief, genes with protein-protein interactions, that were expressed in the 5 tissues types (Zt>2) and were also found using text mining of PubMed abstract for terms in the Reproductive Trait and Phenotype Ontology (REPO; https://bioportal.bioontology.org/ontologies/REPO) 109 (Zp>2), and had a combined Z-score (Zc) > 2 were identified as candidate genes. We recovered gene positions of 179 out of 221 unique genes identified across the 5 tissue types by Hulsegge et al. 63 . For the QTL regions of each fertility trait in the Meta-GWAS, we checked if the start (-10kb) or the end (+10kb) of the genes reported in those 2 publications were located in the respective QTL regions. Resulting in trait specific QTL region-gene overlaps for genes associated with heat score at different stages of the oestrous cycle. As well as, in trait specific QTL region-gene overlaps for prioritized candidate genes in the 5 tissues related to reproduction. Antagonistic and synergistic genes for production and fertility We made the following subsets of validated variants in QTL regions: OnlyProd: variants that were only present in QTL regions associated with production traits, but not in QTL regions associated with any of the fertility traits. OnlyFert: variants that were only present in QTL regions associated with fertility traits, but not in QTL regions associated with any of the production traits. ProdAndFert: variants that were present in at least one QTL region associated with a production trait and at least one QTL region associated with a fertility trait. The ProdAndFert set of variants was further subdivided in two subsets: synergistic and antagonistic. Allele frequencies For validated variants in the QTL regions, we calculated the allele frequency in Australian, German and Irish Holstein, as well as Australian Jersey in multi-year bins. Supp Table 14 shows the number of individuals per population and birth year cohort used for these analyses. For each of these birth years and populations, allele frequencies calculated for the following sets of variants: only production (validated variants in QTL regions detected for production that were not in any QTL region detected for fertility), only fertility (validated variants in QTL regions detected for fertility that were not in any QTL region detected for production), synergistic (validated variants in QTL regions for both fertility and production; where the allele that improved milk yield or protein yield improved fertility), and antagonistic (validated variants in QTL regions for both fertility and production; where the allele that improved milk yield or protein yield reduced fertility). Mendelian Randomisation The GCTA module GSMR 32 (Generalised Summary-data based Mendelian Randomization) was used to infer causality between milk production traits and fertility traits. Causal association was inferred in the Dutch Holstein population using whole genome sequence level GWAS summary statistics for three milk production traits: kilograms of milk (MY), kilograms of protein (PY) and kilograms of fat (FY), and eight fertility traits: age at first insemination (HFert), conception rates in heifers (HFert), conception rate (Conc), non-return rate (NRR, very similar to Conc), interval between calving and first insemination (CalvMate), interval between first and last insemination (Int1stLast), and the fertility index (FI). A threshold p-value of 0.0001 was used to select significant SNPs from the GWAS for clumping. SNPs with a minor allele frequency (MAF) below 0.05 were excluded from the analysis. Individual genotypes of the same 5,798 bulls included in the Dutch Holstein GWAS were used for LD estimation. All bull genotypes in the dataset were imputed to whole genome sequence level. Although we were interested in the causal effect of milk production (intermediate phenotypes) on fertility (target phenotypes), a bidirectional GSMR analysis was chosen, meaning the causal effects of both a forward (effect of the intermediate phenotypes on the target phenotypes) and a reverse (effect of the target phenotypes on the intermediate phenotypes) were estimated. We performed 48 tests (3 (milk production traits) * 8 (fertility traits) * 2 (bi-directional)) and correcting for multiple testing considering causal associations with a p-value of less than 0.001/48=2.08e-05 significant. Breed comparisons For all QTL regions detected within each breed group, we compared the percentage of QTL regions that overlapped with QTL regions detected in other breed groups, the percentage of QTL regions that contained at least one variant significant (p ≤ 5×10 -8 ) in other breed groups, and the percentage of variants in QTL regions that had the same direction of effect on other breed groups. These comparisons were only made for the production traits, because the fertility data available for each breed group varied widely. Furthermore, we report variants in QTL regions that were detected for the same trait(s) in all five breed groups (HOL, JER, BROWN, RDC, and DUAL). Impact of unbalanced breed group size As the full meta-GWAS was highly dominated by Holstein, we repeated the meta-GWAS including only the non-Holstein GWAS, as well as 11 meta-GWAS with different percentages (3.8%, 7.5%, 12.7%, 17.5%, 22.1%, 27.7%, 33.3%, 39.4%, 50.5%, 61.1%, and 68.9%) of Holstein in the within population GWAS. Then, we compared the percentage of QTL regions detected in the within breed group meta-GWAS, that were also detected in each of the multi-breed meta-GWAS with varying percentage of Holstein. Additionally, we repeated the QTL overlap analysis comparing within breed group meta-GWAS, the full meta-GWAS and the meta-GWAS that excluded Holstein for genes associated with lactation in the Mouse Genome Informatics database 79 , and regions that contained variants with a moderate to high effect on gene function, as predicted by VEP 80 . LD in within-breed and multi-breed QTL Data from Run8 of the 1000 Bull Genomes 13 project was used to estimate LD between the most significant variant in each of the QTL regions and adjacent variants within 10Mb distance. LD was estimated using PLINK v2.00a6LM 110 on a subset of 2,372 individuals of the Run8 dataset, aiming to have approximately similar breed proportions in the dataset used for LD estimation as in the full multi-breed Meta-GWAS. We used LocusZoom 111 to visualize the meta-GWAS results, LD between variants in the QTL region, nearby genes and overlap with cattle GTEx for a number of QTL regions. Ancestral alleles Ancestral and derived allele classifications were obtained from a previous study that defined ancestral alleles for 70 million cattle variants 112 . We used this information to compare the direction of effect of the validation variants in QTL regions between the ancestral and derived alleles. This comparison was performed separately for each trait, to investigate if the direction of effect of the ancestral alleles differed between traits. Data availability The Run7 imputation reference database was developed by the 1000 Bull Genomes project members. Full access to this database is available to members and access can also be requested by external collaborators. Additionally, a large portion of the 1000 Bull Genome Project imputation reference database has been publicly released (2881 sequences) and these can be accessed at the European Nucleotide Archive in Projects PRJEB42783 (Run8) and PRJEB56689 (Run9). Declarations Acknowledgements The authors thank all 1000 Bull Genome Project partners for provision of genome sequence data. IvdB, HDD, CJV, TVN, RX, MEG, IMM acknowledge funding from the DairyBio project: a joint venture between Agriculture Victoria (Melbourne, Australia), Dairy Australia (Melbourne, Australia) and the Gardiner Foundation (Melbourne, Australia). HP, NKK, ACB, IH, JV, LZ, RFV, MPS, TT, MB, DR, AB, MC, DB, CH, GS, ZC, MSL, JV, TIT acknowledge the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 815668 (BovReg). AMMT, RF are grateful for funding for FOTiGe - Forschungsverbund Tiergesundheit durch Genomik. HP, NKK thank Braunvieh Schweiz for providing genotype data. ACB, IH, JV, LZ, RFV were financially supported by the Dutch Ministry of Economic Affairs (TKI Agri & Food project LWV20054) together with the Breed4Food partners CRV, Hendrix Genetics and Topigs Norsvin. 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Size of Meta-GWAS discovery population (nInd), number of QTL discovered in the full Meta-GWAS and in validation GWAS in milk, fat and protein yield (MY, FY, PY), milk fat and protein content (FC, PC), heifer fertility (Hfert), interval from calving to first mating (CalvMate), conception rate (Conc), interval from first to last insemination (Int1stLast), and calving interval (CalvInt). number of QTL number of validated QTL % Validated QTL Trait nInd nIndV Regions Variants Regions Variants Regions Variants MY 281,841 426,634 595 22,600 515 22,015 86.6 97.4 FY 281,883 426,634 474 11,094 389 10,211 82.1 92.0 PY 281,643 426,634 584 14,142 468 13,278 80.1 93.9 FC 259,085 426,634 330 10,409 266 9,809 80.6 94.2 PC 259,085 426,634 562 17,277 476 16,409 84.7 95.0 Hfert 98,741 426,634 93 2,048 84 1,953 90.3 95.4 CalvMate 87,560 426,634 158 3,998 153 3,938 96.8 98.5 Conc 95,656 426,634 123 2,672 115 2,520 93.5 94.3 Int1stLast 56,431 426,634 123 3,451 121 3,258 98.4 94.4 CalvInt 83,731 426,634 153 5,361 149 5,206 97.4 97.1 Additional Declarations There is NO Competing Interest. Supplementary Files SuppTablesLarge.xlsx Large Supplementary Tables 1000BullsSupplementaryInformation.docx Supplementary results, figures and smaller tables Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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composition on Meta-GWAS. a) Meta-GWAS at increasing sample sizes in the Holstein and Multi-Breed datasets. b) Average percentage of MY, FY, PY QTL regions detected with the Meta-GWAS with increasing percentage of Holstein individuals included, when compared to single breed group GWAS. c) Percentage of QTL regions overlapping with mouse lactation genes (mouse) or variants predicted to have a high to moderate effect by Variant Effect Predictor (VEP) detected in the five breed groups with the Meta-GWAS compared to single breed group GWAS with (with HOL) and without including the Holstein breed (no HOL).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/4e7ded48a68c6939d1e5080c.png"},{"id":102747011,"identity":"c06319e1-4501-4d08-97ec-e849f0a513c6","added_by":"auto","created_at":"2026-02-16 09:03:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269914,"visible":true,"origin":"","legend":"\u003cp\u003eTwo QTL regions on chr 11 between 103Mb and 105Mb. a) LocusZoom plots and LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) in top 3 panels respectively, overlap with cattle GTEx (middle panel) and genes (bottom panel) in the region; b) allele frequencies in 4 populations across time for most significant variant for CalvInt in QTL at 103Mb (\u003cem\u003ePAEP\u003c/em\u003e); c) allele frequencies in 4 populations across time for most significant variant for PC in QTL at 104Mb (\u003cem\u003eABO\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/2a76e12a0b85758931bf10cc.png"},{"id":102747259,"identity":"876b465c-0b8b-4f90-afe5-ddeb3b6b84a8","added_by":"auto","created_at":"2026-02-16 09:04:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":404779,"visible":true,"origin":"","legend":"\u003cp\u003eQTL region detected for Int1stLast on chr 17 at 69Mb. a) LocusZoom plots and LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) in top panels, Cattle GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant detected for Int1stLast 17:69244946 in QTL (\u003cem\u003eLIF\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/5ead26414b5b5a287ec569f6.png"},{"id":102580734,"identity":"66df40f2-3d19-4a4b-8343-7184601ac2e5","added_by":"auto","created_at":"2026-02-13 09:14:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":278851,"visible":true,"origin":"","legend":"\u003cp\u003eQTL region on chr 10 at 76Mb. a) LocusZoom plots and LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) in top panels, Cattle GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant for CalvMate 10:76478558 in QTL.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/e89669adffbf1eebda35196f.png"},{"id":102580732,"identity":"d23912fd-e021-4527-b82c-a8a33f5b5c61","added_by":"auto","created_at":"2026-02-13 09:14:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":330681,"visible":true,"origin":"","legend":"\u003cp\u003eQTL region on chr 26 at 21Mb. a) LocusZoom plots and LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) in top panels, GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant in FC QTL at 21Mb (\u003cem\u003eSCD\u003c/em\u003e), for allele associated with increased FC.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/0821b4f22f418684c34583a5.png"},{"id":102962538,"identity":"ad7b8646-74c8-4739-a0a1-4f45e29de2f4","added_by":"auto","created_at":"2026-02-19 04:09:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2765213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/56cc84ac-3014-4c0a-89b6-ae8c2b9a4fb4.pdf"},{"id":102580733,"identity":"abd4bb2c-79fa-4aeb-9e2e-037755822ce8","added_by":"auto","created_at":"2026-02-13 09:14:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4051777,"visible":true,"origin":"","legend":"Large Supplementary Tables","description":"","filename":"SuppTablesLarge.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/f1cf72a0e4bfe299871a24d1.xlsx"},{"id":102580735,"identity":"6eb28860-33db-4f94-9df5-653a0bbe381d","added_by":"auto","created_at":"2026-02-13 09:14:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9542333,"visible":true,"origin":"","legend":"Supplementary results, figures and smaller tables","description":"","filename":"1000BullsSupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8631217/v1/e69ac6b06901d514563bb492.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Genome-wide meta-analyses in 708,480 individuals shed light on the genetic conflict between productivity and fertility in dairy cattle","fulltext":[{"header":"Intro/Background","content":"\u003cp\u003eDairy cattle is a key global agricultural species, securing human nutritional needs\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e through milk and meat products. However, dairy cattle also contribute significantly to enteric methane emissions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Dairy cattle productivity and reduction of emissions depend crucially on both milk production (including milk protein and fat yield) and female fertility, the ability of cows to conceive, give birth to a calf and initiate a new lactation at regular intervals. Lactation onset initiates a period of high milk production with large energy demands. This negatively influences a cow’s conception ability\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. There are slightly unfavourable genetic correlations between production and fertility traits in several dairy cattle populations\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, resulting in deterioration of fertility when selection was primarily for milk production\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, dairy cattle are selected based on standardized 305-day production and cows with delayed conception will on average produce more in the absence of pregnancy energy demands. These issues prompted revised breeding goals\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e before the turn of the century, balancing selection pressure between production and fertility. This shift, assisted by genomic selection, has, over the last 30 years, reversed the negative fertility genetic trend\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) of multiple traits enable understanding of genetic architecture and pleiotropic variants underlying dairy cattle milk production and fertility. The power of GWAS depends on sample size, quantitative trait locus (QTL) effect size, allele frequency as well as the extent of population linkage disequilibrium (LD)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Meta-analyses of GWAS (Meta-GWAS) combine results of individual GWAS, increasing sample size and power to detect QTL shared across populations. To maximise power of the analyses, causal mutations should be included by using imputed whole-genome sequence (WGS), now ubiquitous due to decreasing sequencing costs and data sharing\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The 1000 Bull Genomes Project, underpinning this study, has enabled hundreds of cattle WGS studies and several large Meta-GWAS\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Combining populations from different ancestries can reduce LD surrounding causal mutations, improving QTL mapping precision\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Global dairy cattle populations are differentiated into breeds with small effective population size and limited migration among most breeds. Combining multiple breeds in a Meta-GWAS is advantageous as within-breed GWAS produce wide QTL regions and, for minor breeds with less data, a high proportion of QTL remain undetected due to low statistical power. However, strongly unbalanced population sizes can negatively affect detection power in smaller populations and render population-specific QTL undetected in a Meta-GWAS\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study is one of the largest dairy cattle multi-breed WGS Meta-GWAS for milk production and fertility traits. We aimed to identify regions, variants and genes of importance in these traits, particularly focusing on regions affecting both trait groups. We explored population properties of QTL regions, including QTL region size, and ancestral versus derived QTL alleles. In addition, we investigated the effect of unbalanced numbers of animals in the different populations. Effects of the QTL regions were confirmed in an independent dataset of 426,639 animals. Finally, segregation within breeds was explored, as well as association with known molecular phenotypes (e.g., expression QTL) to highlight key regions with putative causal variants.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn 1000 Bulls Genome Project Runs 7 and 8\u003csup\u003e13\u003c/sup\u003e, we compiled WGS data from 3,094 and 4,109 \u003cem\u003eBos taurus taurus\u003c/em\u003e cattle, respectively (Supp Table 1). This multi-breed reference population, or breed-specific subsets thereof that were enriched with additional sequence data\u003csup\u003e20\u003c/sup\u003e, was used by each of 12 collaborators in 10 countries to impute sequence variants into their twenty dairy populations made up of twelve breeds that we categorised into six breed groups: Holstein, Jersey, Normande, Red breeds, Dual Purpose, and Brown breeds (Supp Table 2). Up to 10 traits were phenotyped in each breed group: milk yield (MY), protein yield (PY), fat yield (FY), protein content (PC), fat content (FC), heifer fertility (HFert), interval between calving to first mating (CalvMate), conception rate (Conc), interval of first to last mating (Int1stLast), and calving interval (time from calving to calving, CalvInt). \u0026nbsp;Note that fertility trait measures were coded such that high values mean improved fertility (see Methods). The total number of individuals with imputed sequence and phenotypes was highest for MY (N=281,841) and lowest for Int1stLast (N=56,431). Phenotypic data were either own performance for females or accurate daughter trait deviations for bulls (Supp Table 2). \u0026nbsp;A total of 27,862,477 variants occurred in at least one of the GWAS after filtering on imputation accuracy (Supp Table 3). \u0026nbsp;Meta-GWAS QTL regions were defined by grouping the top third of significant markers within 1 megabase windows. We validated the significant (p-value \u0026lt; 5x10\u003csup\u003e-8\u003c/sup\u003e) multi-breed Meta-GWAS QTL regions using GWAS in three French populations of young cows recorded for all 5 production traits as well as HFert, CalvMate, and Conc (Holstein N=254,796, Montb\u0026eacute;liarde N=135,295, Normande N=36,548). CalvMate and Conc were used to validate results for Int1stLast and CalvInt. The populations used for validation were not included in the Meta-GWAS (Supp Table 4). \u0026nbsp;A high proportion of Meta-GWAS QTL regions were validated ranging from 80.1% for PY to 98.4% for Int1stLast (Table 1, Suppl Figs. 1 and 2, Supp Tables 4, 5 and 6). \u0026nbsp;We found a higher number of QTL for production (e.g., MY N=515) than fertility (e.g., HFert N=84) traits. This was expected due to smaller sample size and lower heritability of dairy cattle fertility traits. We tested different sample sizes in the Holstein only and multi-breed Meta-GWAS with the number of QTL regions still increasing with sample size, indicating further efforts to increase sample size are warranted (Fig. 1a). Favourable effects for milk yield traits were generally from derived alleles and milk content traits were ancestral (additional detail in Supp Results). \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1. Impact of sample size and breed composition on Meta-GWAS. a) Meta-GWAS at increasing sample sizes in the Holstein and Multi-Breed datasets. b) Average percentage of MY, FY, PY QTL regions detected with the Meta-GWAS with increasing percentage of Holstein individuals included, when compared to single breed group GWAS. c) Percentage of QTL regions overlapping with mouse lactation genes (mouse) or variants predicted to have a high to moderate effect by Variant Effect Predictor (VEP) detected in the five breed groups with the Meta-GWAS compared to single breed group GWAS with (with HOL) and without including the Holstein breed (no HOL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCandidate genes were identified in the QTL regions using MAGMA\u003csup\u003e21\u003c/sup\u003e (Suppl table 7). We performed an over-representation analysis of MAGMA gene sets, using gProfiler\u003csup\u003e22,23\u003c/sup\u003e, to identify associated Gene Ontology (GO) terms and biological pathways (Supp Table 8 and Supp Fig. 3-11. Significant (p \u0026lt; 0.05) terms and pathways associated with gene sets for fertility traits included the spermato-proteasome complex (Hfert; GO_CC:1990111); cGMP kinase signalling complex (CalvMate; CORUM:638); with member gene PRKG1 which promotes primordial follicle activation and oocyte growth\u003csup\u003e24\u003c/sup\u003e(CalvInt, GO_BP:0001892). Gene sets for production traits were significantly associated (p\u0026lt;0.05) to growth hormone and prolactin signalling, development of the mammary gland and lactation, blood cell formation and oxygen transport capacity, cytokine and interleukin signalling for maintaining mammary gland health, and neuronal regulation (additional results in Supp Results).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe evaluated enrichment of the QTL regions across gene expression QTL discovered in the Cattle GTEx\u003csup\u003e25,26\u003c/sup\u003e functional genomic datasets. Production QTL enrichment for expression (eQTL) and splice (sQTL) QTL was greatest for macrophage (eQTL, 24.1-fold) and mammary (eQTL, 24.1-fold) tissues, while fertility QTL, though less enriched overall, showed the most enriched tissues being monocytes (eQTL, 12.7-fold) and oviduct (sQTL, 9.1-fold) (Supp Tables 9 and 10). \u0026nbsp; Some VEP annotation classes for variants in QTL regions were enriched (range 0.04 \u0026ndash; 2.7 times, Supp Table 10), though not as strongly as eQTL and sQTL. Conserved variants were less enriched than non-conserved, except for intergenic and intronic variants. We tested whether variants in QTL regions were enriched for ChIPseq variants from four studies\u003csup\u003e27\u0026ndash;30\u003c/sup\u003e. Variants associated with most traits were enriched in at least some studies (Supp Table 11 and 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur interest in regions with pleiotropic effects on production and fertility traits led us to categorize all validated QTL regions into four groups based on whether they affected only production, only fertility, or both production and fertility in either a synergistic or antagonistic manner. \u0026nbsp; Variants for alleles associated with increased milk yield or protein yield (the main production traits of interest in dairy breeding) and improved fertility were considered synergistic, whereas variants for alleles associated with increased milk yield or protein yield and reduced fertility were considered antagonistic. \u0026nbsp;Production-only QTL were by far the largest group with 324 QTL regions, followed by fertility-only (84 QTL regions), antagonistic (13 QTL regions) and synergistic QTL (4 QTL regions) (Supp Table 13). \u0026nbsp; Four regions on chromosomes 11, 18 and 25 harboured synergistic QTL that increase both production and fertility, the opposite to the overall negative genetic correlation (Supp Table 13). \u0026nbsp; Antagonistic QTL were spread across chromosomes 5, 6, 9, 13, 14, 20, and 23. Antagonistic QTL putatively play a role in causing the unfavourable genetic correlation between fertility and production. HFert was not associated with any pleiotropic QTL, whereas CalvInt, for which a low to moderate unfavourable genetic correlation with production is observed\u003csup\u003e31\u003c/sup\u003e, was affected by the highest number of antagonistic QTL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe investigated how the allele frequency of variants in QTL regions changed over three decades (1981-2015) in the Holstein and Jersey breeds (Supp Fig. 12, Supp Table 14). \u0026nbsp;Favourable alleles only affecting production or fertility increased in frequency (range 0.00-0.05), with production allele frequency changes slightly larger than fertility. \u0026nbsp;Synergistic favourable allele frequency changes over time were more variable and antagonistic QTL tended to show stable or slightly negative allele frequency over time, except Conc affected by only one antagonistic QTL (Supp Table 14).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor both production and fertility traits, QTL intervals were wider in within-breed GWAS than in the Meta-GWAS, differing by 24Kb for production and 15Kb for fertility, suggesting that combining data across breeds narrowed QTL intervals (Supp Table 15). The size of QTL intervals also varied across the four QTL classes, where QTL affecting only production or fertility were considerably narrower (\u0026gt;60Kb) than synergistic and antagonistic QTL affecting both trait groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe inferred causality between production and fertility traits using the GCTA Mendelian Randomization module Generalised Summary-data-based Mendelian Randomisation\u003csup\u003e32\u003c/sup\u003e in a Dutch Holstein bull population included in the Meta-GWAS. While primarily interested in causality of production (intermediate phenotype) on fertility (target phenotype), we performed a bidirectional analysis. We detected causal associations for MY and PY for CalvMate, CalvInt, fertility index, and Int1stLast (Supp Table 16, Supp Figs. 13 and 14). While previously hypothesized that high milk production affects fertility negatively, bidirectional analysis also indicated causality of fertility on milk and protein production. Nevertheless, directions of effects were consistent with known genetic correlations (i.e., higher production vs. lower fertility, and vice versa). Interestingly, MY did not have a significant causal relationship with Conc and 56-day non-return rate, suggesting milk production may influence resumption of cyclicity, but not the cow\u0026rsquo;s ability to conceive once cyclic or to maintain a pregnancy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHighlighted QTL regions\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTwo distinct QTL in region 103-104 Mb on chromosome 11\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOn chromosome 11, two\u0026nbsp;highly significant QTL regions lie within 1 Mb of each other that appear to be independent\u003csup\u003e33\u0026ndash;35\u003c/sup\u003e. Both QTL showed an increased favourable allele frequency (Figs. 2b\u0026amp;c). The first QTL (103.2 Mb, Fig. 2, Supp Table 17) has synergistic effects on MY, PY, and fertility. Although this QTL is near \u003cem\u003ePAEP\u003c/em\u003e (reviewed by Lopdell\u003csup\u003e36\u003c/sup\u003e), it has not been reported also to affect cattle fertility traits possibly due to limited power in previous analyses. \u0026nbsp;In cattle, \u003cem\u003ePAEP\u003c/em\u003e (encoding beta-lactoglobulin in milk) has been shown to be highly expressed in the mammary gland, but also expressed in the infundibulum (ipsilateral to the corpus luteum) and the vas deferens\u003csup\u003e37\u003c/sup\u003e, suggesting an additional reproductive role. In humans, progestagen associated\u0026nbsp;endometrial protein (\u003cem\u003ePAEP\u003c/em\u003e) gene expression has been shown to be restricted to male and female reproductive tissues\u003csup\u003e38,39\u003c/sup\u003e, where the protein plays a key role in reproduction\u003csup\u003e40,41\u003c/sup\u003e. Due to the allergenicity of beta-lactoglobulin, which is an alternative name for \u003cem\u003ePAEP\u003c/em\u003e, in cow milk\u003csup\u003e42\u0026ndash;44\u003c/sup\u003e, unlike in human milk\u003csup\u003e45\u003c/sup\u003e, gene editing of \u003cem\u003ePAEP\u003c/em\u003e in cattle\u003csup\u003e46\u003c/sup\u003e has been attempted, but its putative role in fertility would advise caution. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2. Two QTL regions on chr 11 between 103Mb and 105Mb. a)\u0026nbsp;LocusZoom plots and LD (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) in top 3 panels respectively, overlap with cattle GTEx (middle panel) and genes (bottom panel) in the region; b) allele frequencies in 4 populations across time for most significant variant for CalvInt in QTL at 103Mb (\u003cem\u003ePAEP\u003c/em\u003e); c) allele frequencies in 4 populations across time for most significant variant for PC in QTL at 104Mb (\u003cem\u003eABO\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second QTL (104.2Mb, Fig. 2, Supp Table 17), partially overlapping the \u003cem\u003eABO\u003c/em\u003e gene, has no association with fertility but increased PC, PY, FP, FY and reduced MY. This gene encodes for proteins related to the blood group system in humans (A, B \u0026amp; O) and although cattle have different blood groups, these genes were shown to be associated with cattle milk fat percentage\u003csup\u003e47\u003c/sup\u003e. \u0026nbsp;The \u003cem\u003eABO\u003c/em\u003e gene encodes glycosyltransferases involved in glucose metabolism and synthesis of some oligosaccharides\u003csup\u003e48\u003c/sup\u003e. Previously, QTL have been reported in the \u003cem\u003eABO\u003c/em\u003e gene region associated with milk oligosaccharides in cattle\u003csup\u003e49,50\u003c/sup\u003e and humans\u003csup\u003e51\u003c/sup\u003e, milk glucose content\u003csup\u003e52\u003c/sup\u003e and milk mid-infra-red (MIR) spectra\u003csup\u003e33,53\u003c/sup\u003e. Additionally, \u003cem\u003eABO\u003c/em\u003e showed the highest differential gene expression in bovine mammary and kidney\u003csup\u003e54\u003c/sup\u003e. This gene likely influences multiple milk traits, as glucose availability in mammary epithelial cells supports lipid and lactose synthesis. Lactose synthesis generates osmotic pressure that drives water into milk influencing milk volume\u003csup\u003e55\u003c/sup\u003e. \u0026nbsp;A previous GWAS using sequence data and milk MIR spectra from Holstein and Jersey cattle identified a putative causal splice variant in \u003cem\u003eABO\u003c/em\u003e (104187200, rs207688357)\u003csup\u003e53\u003c/sup\u003e. \u0026nbsp; Similarly, many of our most significant variants were reported as GTEx splice expression QTL for \u003cem\u003eABO\u003c/em\u003e. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQTL associated with cow fertility on chromosome 17\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA QTL region on chromosome 17 (~69 Mb) was associated with CalvMate, Conc, Int1stLast, and CalvInt in the multi-breed meta-GWAS (Fig. 3, Supp Table 17). The strongest association was detected with Int1stLast (p-value of 1.5\u0026times;10\u003csup\u003e-15\u003c/sup\u003e), for a variant at 69,244,946 bp (Fig. 3a). Consistent with historical selection pressure on fertility, frequency of this allele reduced from the eighties to the nineties and then increased again more recently (Fig. 3b). For this QTL region (69,242,369 to 69,352,822 bp), MAGMA detected six significant genes, with the most significant associations detected for \u003cem\u003eOSM\u003c/em\u003e, \u003cem\u003eLIF\u003c/em\u003e, \u003cem\u003eand CASTOR1\u0026nbsp;\u003c/em\u003e(also known as \u003cem\u003eGATSL3\u003c/em\u003e). Additional evidence points to \u003cem\u003eLIF\u003c/em\u003e as a candidate gene due to the association between its expression in the ventral hypothalamus and mid-oestrus heat score\u003csup\u003e56\u003c/sup\u003e (Supp Table 18, for complete overlap analysis with studies on brain expression and protein-protein interaction, see Supp Results) and because it is known to be essential for embryo implantation in mice\u003csup\u003e57\u003c/sup\u003e. GO-terms associated with \u003cem\u003eLIF\u0026nbsp;\u003c/em\u003eincluded embryonic placenta development, spongiotrophoblast layer development, and regulation of nuclear division. LIF has been reported to play a regulatory role in trophoblast growth and differentiation during pregnancy in human placenta\u003csup\u003e58\u003c/sup\u003e. Previous studies in dairy cattle found variants in this QTL region significantly associated with pregnancy rate\u003csup\u003e59\u003c/sup\u003e, conception rate\u003csup\u003e60\u003c/sup\u003e, a female fertility index\u003csup\u003e61\u003c/sup\u003e, and interval first to last insemination\u003csup\u003e60\u003c/sup\u003e. Interestingly a QTL associated with fertility in Brown Swiss cattle\u003csup\u003e62\u003c/sup\u003e was located in the same region as a QTL detected in our Meta-GWAS in the BROWN breed group only, but had distinct top variants, e.g., variants from the multi-breed analysis are not significant in BROWN and vice-versa.\u003c/p\u003e\n\u003cp\u003eFigure 3. QTL region detected for Int1stLast on chr 17 at 69Mb. a)\u0026nbsp;LocusZoom plots and LD (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) in top panels, Cattle GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant detected for Int1stLast 17:69244946 in QTL (\u003cem\u003eLIF\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFertility QTL near ESR2 on chromosome 10\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe identified a QTL region on chromosome 10 (~76 Mb), associated with CalvMate, Int1stLast, and CalvInt (Fig. 4, Supp Table 17). The strongest association for CalvMate (p = 2.4\u0026times;10\u003csup\u003e-14\u003c/sup\u003e) was detected at an intergenic variant located at 76,478,558 bp (rs379543839)( Fig. 4a). The frequency of the C allele at this variant, associated with reduced calving to first mating interval (i.e., improved fertility), was high in all populations with allele frequency data, ranging from 0.82 in Australian Jerseys (born 1981-1985), to 0.99 in Australian Holstein (born 1986-1990, Fig. 4b). This variant has also been significantly associated with the expression of \u003cem\u003eestrogen receptor \u0026beta;\u003c/em\u003e (\u003cem\u003eESR2\u003c/em\u003e) in the dorsal part of the hypothalamus\u003csup\u003e63\u003c/sup\u003e. \u0026nbsp;Disrupted expression of \u003cem\u003eESR2\u003c/em\u003e has been associated with reduced female fertility in cattle\u003csup\u003e64\u003c/sup\u003e and mice\u003csup\u003e65,66\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin the breed group meta-GWAS, QTL in this area were detected for CalvMate, Int1stLast, and CalvInt in HOL, and HFert in our Meta-GWAS in dual purpose (DUAL) cattle only. Interestingly, this QTL region overlaps with previously detected QTL associated with stillbirth\u003csup\u003e67\u003c/sup\u003e and dystocia\u003csup\u003e67,68\u003c/sup\u003e in German Holstein cattle. Unsurprisingly, dairy cows with prolonged or difficult calvings take longer to conceive again\u003csup\u003e69\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFigure 4. QTL region on chr 10 at 76Mb. a)\u0026nbsp;LocusZoom plots and LD (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) in top panels, Cattle GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant for CalvMate 10:76478558 in QTL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAntagonistic QTL near GC on chromosome 6\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChromosome 6 contained overlapping QTL regions associated with production (MY, FY, PY, and FC) and fertility (CalvMate, Int1stLast, and CalvInt) (Supp Fig. 15, Supp Table 17). MAGMA genes associated with this region were \u003cem\u003eGC, NPFFR2, and SLC4A4.\u0026nbsp;\u003c/em\u003eMost significant was an intergenic variant (87,018,377 bp, rs208837389, p-value of 5.3\u0026times;10\u003csup\u003e-93\u003c/sup\u003e), whose T allele was associated with increased MY, FY, PY, and reduced FC and fertility. Strong selection for production in the late 20\u003csup\u003eth\u003c/sup\u003e century increased the frequency of this allele in Holstein from 0.45 to 0.56, but then decreased again to 0.39 in animals born 2016-2020 (Supp Fig. 15b), when fertility was prioritised in breeding selections. Lee et al.\u003csup\u003e70\u003c/sup\u003e proposed a 12 kb multiallelic copy number variant (CNV) in this QTL region as putative causal variant for mastitis resistance, with pleiotropic synergistic effect on fertility and an antagonistic effect on milk yield (other milk traits not tested). They showed higher copy number alleles were associated with increased GC expression and found evidence of an enhancer region within the CNV. Recently, significant differential \u003cem\u003eGC\u003c/em\u003e gene expression was reported between two divergently selected lines of high and low fertility dairy cows\u003csup\u003e71\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMilk fat production QTL on chromosome 26 - SCD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe found a highly significant QTL associated with FC on chromosome 26 (between 21,267,450-21,287,713 bp) (Fig. 5, Supp Table 17). Overlapping QTL regions were detected for MY, FY, and PC, but not fertility traits. The most significant variant associated with FC was an intronic variant in \u003cem\u003eSCD\u003c/em\u003e (21,273,073 bp, p = 2.4 \u0026times; 10\u003csup\u003e-52\u003c/sup\u003e) and a nearby missense and splice region variant (21,272,422 bp, p-value of 6.6 \u0026times; 10\u003csup\u003e-52\u003c/sup\u003e) was the third most significant variant. Several variants in the region were associated with the expression of \u003cem\u003ePKD2L1\u003c/em\u003e, \u003cem\u003eSCD\u003c/em\u003e, \u003cem\u003eDNMBP,\u003c/em\u003e and \u003cem\u003eBTRC\u003c/em\u003e in macrophage, as well as with gene splicing of \u003cem\u003eBTRC\u003c/em\u003e in blood. Of these four genes, \u003cem\u003eSCD\u003c/em\u003e is a strong putative candidate gene because it encodes stearoyl-CoA desaturase, an enzyme involved in fatty acid metabolism\u003csup\u003e72,73\u003c/sup\u003e. \u003cem\u003eSCD\u003c/em\u003e is implicated in lipid metabolism in mice\u003csup\u003e74\u003c/sup\u003e and multiple studies have reported QTL for FC and FY in dairy cattle\u003csup\u003e35,75,76\u003c/sup\u003e. \u0026nbsp; Given \u0026nbsp;\u003cem\u003eSCD\u003c/em\u003e\u0026rsquo;s involvement in lipid metabolism, it may also impact traits like MY and PC; indeed, in mice, \u003cem\u003eSCD\u003c/em\u003e knockout elevated insulin signalling in muscle and increased glucose uptake\u003csup\u003e77\u003c/sup\u003e. Please see Supplementary Information for results for region chr5:30-32Mb \u003cem\u003eLALBA\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eFigure 5. QTL region on chr 26 at 21Mb. a)\u0026nbsp;LocusZoom plots and LD (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) in top panels, GTEx eQTL and sQTL (middle panels) and genes (bottom panels) in the region; b) allele frequencies across time for most significant variant in FC QTL at 21Mb (\u003cem\u003eSCD\u003c/em\u003e), for allele associated with increased FC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of disparate number of individuals in the different breed groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumerically, the Holstein breed was by far the largest breed group (69% of all individuals), similar to human Meta-GWAS where the Caucasian group typically outnumbers other ethnicities\u003csup\u003e78\u003c/sup\u003e. \u0026nbsp;There was a reduction in the percentage of within-breed QTL detected for all traits for non-Holstein breeds compared with the Meta-GWAS, a trend that accelerated once the percentage of Holstein individuals was greater than 30 to 40% (Fig. 1b). When the Multi-Breed Meta-GWAS was done without Holsteins, up to half of within-breed (Jersey) QTL not detected in the full Multi-Breed Meta-GWAS could again be detected, indicating that the Holstein breed had a very strong effect on results and including them caused a proportion of minor breed QTL to be missed (Fig. 1b). \u0026nbsp;Conversely, of all QTL found in the full Meta-GWAS (Holstein included), only approximately 8% were detected in the Meta-GWAS without Holstein. To reduce the potential of a higher false positive rate in within-breed GWAS, we selected QTL variants that overlapped genes annotated as involved in mouse lactation\u003csup\u003e79\u003c/sup\u003e or predicted (by VEP) to have moderate-to-high impact on gene function \u003csup\u003e80\u003c/sup\u003e. This confirmed that the inclusion of Holstein individuals reduced the total number of QTL and the two QTL subsets within the smaller breeds compared to non-Holstein Meta-GWAS (Fig. 1c). QTL not detected in the Meta-GWAS with Holstein tended to be significant in fewer within-breed GWAS, and QTL present in the Holstein breed were more likely to be detected in the full Meta-GWAS.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe carried out the largest WGS-based Meta-GWAS yet in dairy cattle that included both milk production and fertility traits across a wide diversity of breeds. The inclusion of multiple breeds substantially reduced QTL interval sizes compared to single breed GWAS, indicating a decrease in the effective extent of LD. Our validation sample was 1.5 times larger than the discovery population, resulting in a high proportion of QTL being validated. The large number of QTL we observed in our multi-breed Meta-GWAS is comparable to human Meta-GWAS of similar size (Supp Table\u0026nbsp;19), despite fundamental differences in evolutionary history. While cattle had large historical effective population size but current effective population size is small, humans, in contrast, have experienced a large increase in effective population size over time. In our study, 44% of discovery phenotypes were derived from bulls, represented by daughter trait deviations (i.e., mixed model adjusted daughter-group means). Due to the large daughter groups, these are highly reliable predictors of the sires\u0026rsquo; genetic values with effective heritabilities often greater than 0.8. This is comparable to the heritability of human height for which a larger number of QTL have been found\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The smaller number of QTL detected in fertility traits is expected due to the smaller sample size and much lower heritabilities than production traits. In addition, METAL assumes consistent trait definitions across datasets, an assumption violated for heifer fertility, whose definition varies across breeds and countries, leading to the detection of fewer QTL in this trait. Gene ontology enrichment confirmed biologically meaningful associations, highlighting pathways involved in milk production (e.g., prolactin signaling) and fertility (e.g., placenta development). QTL regions were also significantly enriched for functional annotations, including cattle GTEx expression and splicing QTL, ChIP-seq QTL, as well as some VEP annotation classes, providing independent molecular confirmation of our findings.\u003c/p\u003e \u003cp\u003eOur study has shed additional light on the genetic relationship between production and fertility traits in dairy cattle. Consistent with the antagonism between the traits supported by previous studies showing negative genetic correlations, we have detected more QTL regions with antagonistic rather than synergistic effects. As another independent validation, we showed that for major QTL the allele frequency of the most significant SNP follows the expected patterns based on the artificial selection pressure applied. The allele frequency for fertility associated alleles in antagonistic regions declined until the mid 2000s. This trend was reversed when selection pressure was rebalanced towards fertility\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In contrast, allele frequencies in synergistic regions consistently increased over the last few decades. The antagonistic patterns were also confirmed by our Mendelian sampling analysis, which indicated causal relationships between milk production and fertility traits. Of interest is that causality in both directions was indicated, highlighting that the relationship of the traits may be more complex than a one-way negative causal effect of production on fertility. This is reasonable as increasing fertility could decrease production in the second part of the lactation due to earlier conception and associated energy demands and endocrine modifications due to the developing calf. The largest number of antagonistic QTL were discovered for CalvInt and is in keeping with non-pregnant cows producing more milk in a standard 305 day lactation. However, no significant causal relationships were detected between milk yield and conception rate or 56-day non-return rate, this could be due to conception rate having the lowest heritability of the fertility traits\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e leading to reduced power.\u003c/p\u003e \u003cp\u003eHolstein dominance in our Meta-GWAS clearly impacted QTL detection with many within-breed GWAS QTL in non-Holstein breeds not reaching significance in the Meta-GWAS, more strongly affecting breeds more distantly related to Holstein. For example, Jersey had the largest proportion of missed QTL and the lowest \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e to Holstein. The proportion of missed within breed QTL was inversely proportional to the percentage of Holstein individuals included in the Meta-GWAS. An equal percentage of Holstein versus all other breeds combined was reached at 30% of the total Holstein individuals and when this threshold was exceeded, the loss of non-Holstein QTL accelerated. To focus on biologically meaningful loci, we further examined variants in QTL that overlapped with genes involved in mouse lactation and selected regions containing variants with at least a moderate VEP effect. In both datasets, the Meta-GWAS including all Holstein cattle consistently identified fewer QTL in the smaller breed groups compared to GWAS within the respective breed groups. This indicates that QTL architectures differ across breeds, even in a set of genes with similar function across species, in a manner to incur negative consequences when one breed dominates the dataset. This cautionary result could be due to different LD structures or minor allele frequency across breeds or that QTL regions in smaller breeds had a lower probability to contain causative mutations due to smaller population sizes\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Meta-Analysis approaches that explicitly model allelic effect heterogeneity may improve QTL detection in mixed ancestry populations\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOverall, this study provides new insights into the genetic architecture underlying the milk production\u0026ndash;fertility relationship in dairy cattle. We confirm the antagonism between these traits but also identify regions with synergistic effects that may support more balanced genomic selection. The combination of large-scale WGS data, multiple breeds, and functional annotations improves mapping resolution and biological interpretation. Future work should focus on fine-mapping causal variants and developing selection programs that enhance both production and fertility while preserving breed diversity\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1000 bull dataset and breed composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-genome resequence (WGS) data for the imputation reference population originated from Run 7 and 8 of the 1000 Bull Genomes Project\u003csup\u003e13\u003c/sup\u003e including 3,092 and 4,109 \u003cem\u003eBos taurus taurus\u003c/em\u003e animals, respectively, distributed across 109 breed groups (Supp Table 1). \u0026nbsp;The largest breed groups were Holstein, Norwegian Red, Angus, Fleckvieh-Simmental, Jersey, Charolais, and Brown Swiss.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhole-genome Re-sequence Data Processing and Variant Calling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 1000 bull genomes project partners generated short read whole genome sequence data to greater than 10x average coverage. Processing of sequencing data up to and including production of gVCF files was undertaken according to a shared protocol. Fastq files were processed according to the 1000 bulls GATK fastq to GVCF guidelines which largely followed GATK best practices. Raw sequence reads were trimmed of adapter and low-quality bases (qscore \u0026lt;20) on either end and reads with mean qscore less than 20 or length less than 35bp were removed. For each individual, the remaining high-quality reads were aligned to the bovine genome reference, ARS-UCD1.2_Btau5.0.1Y, using BWA mem (v0.7.17)\u003csup\u003e84\u003c/sup\u003e defining read groups and sorting and indexing with Samtools (v1.8)\u003csup\u003e85\u003c/sup\u003e. \u0026nbsp;This reference combined the Btau5.0.1 Y chromosome assembly from Baylor College\u003csup\u003e86\u003c/sup\u003e and ARS-UCD1.2\u003csup\u003e87\u003c/sup\u003e. Picard MarkDuplicates was used to mark PCR and optical duplicate reads, where OPTICAL_DUPLICATE_PIXEL_DISTANCE was 100 for data generated on non-arrayed flowcells (i.e., from GAIIx, HiSeq1500/2000/2500), or 2500 for arrayed flowcell data (eg HiSeqX, HiSeq3000/4000, NovaSeq). Base quality recalibration was performed according to GATK best practices guidelines using GATK (v3.8-1-0-gf15c1c3ef)\u003csup\u003e88\u003c/sup\u003e BaseRecalibrator, with bqsrBAQGapOpenPenalty of 45 and a list of known variant files, and PrintReads. The known variants file consisted of SNP and INDEL generated from \u003cem\u003eBos taurus\u003c/em\u003e and \u003cem\u003eBos indicus\u003c/em\u003e Run7 at tranche 99.9 stringency. Finally, we created GVCF files with GATK HaplotypeCaller.\u003c/p\u003e\n\u003cp\u003eThe 1000 Bull Genomes variant detection analysis pipeline mostly followed the \u0026quot;best practices\u0026quot; guidelines outlined by the Broad Institute\u003csup\u003e89\u003c/sup\u003e using the same GATK version. Joint genotyping of SNPs and INDELs was performed with GenotypeGVCFs using default settings, the ARS-UCD1.2_Btau5.0.1Y reference genome and all animal GVCF files as input. The raw VCFs were filtered to minimize false-positive variant calls using the GATK Variant Quality Score Recalibration (VQSR) tool. The truth and training sets used for VQSR in Runs 7 and 8 of the 1000 Bull Genomes Project are detailed in Supp Table 20. VQSR was a two-step process: 1) Variant Recalibration: Variants in the call set were assigned a Variant Quality Score Log-Odds (VQSLOD) value based on annotation values of the truth sets. For Runs 7 and 8, the annotations used were QD, MQ, MQRankSum, ReadPosRankSum, FS, SOR, and InbreedingCoeff. Variants in the training sets were also ranked by their VQSLOD scores. 2) Recalibration Application: Recalibration was applied using tranche sensitivity thresholds, which determined the VQSLOD score or percentage above which variants were retained. For Runs 7 and 8, tranche thresholds of 100.0, 99.9, 99.0, and 90.0 were defined during the first step, with 90.0 used for the second step. Variants passing the 90.0 tranche threshold were marked as \u0026quot;PASS\u0026quot; in the VCF FILTER field, while lower-confidence variants were retained and annotated with their tranche (Supp Table 20). This preserved all variant data and allowed project partners to apply custom filters as needed.\u003c/p\u003e\n\u003cp\u003eUsing the final recalibrated variant call set, for each animal, QC metrics were calculated for variants in the 90.0, 99.0 and 99.9 tranches (e.g., opposing homozygotes, heterozygosity, concordance with available high-density SNP-chip data available for each animal, number of unique variants) using custom scripts. Animals failing multiple QC metric thresholds were removed from the VCF. Autosomal chromosome variants from tranches 90.0, 99.0, and 99.9 were phased using Beagle v4.0\u003csup\u003e90\u003c/sup\u003e to generate a high-accuracy call set for imputation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImputation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotype imputation was undertaken by each collaborating group following two main steps:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eMedium density (50k) SNP array genotypes were imputed within breed to high density (700k) genotypes corresponding to the BovineHD beadchip (Illumina Inc).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe high density genotypes were then imputed to\u0026nbsp;the full\u0026nbsp;genome sequence, using the 1000 Bull Genomes Project sequence database (Run7 or Run8) as the imputation reference.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDetails of the software used at each step by each contributing institute are given in Supplementary Table 21. Typically, the imputation of sequence data has been found to be highly accurate using the 1000 Bull Genomes Project data\u003csup\u003e91\u003c/sup\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach collaborator performed GWAS with a modified version of GCTA\u003csup\u003e92\u003c/sup\u003e that included allele dosage with imputation uncertainty and the accuracy of the phenotypes within trait, breed and sex when relevant (available at https://git.wur.nl/vande018/gcta/-/tree/dosages). Summary statistics were combined in a within-breed Meta-GWAS in each breed group (Holstein, Jersey, Red breeds, Brown breeds, Dual purpose, Normande) and in a Multi-Breed Meta-GWAS containing all available data. While production traits were the same in each population, namely milk yield (MY, kg), fat yield (FY, kg), protein yield (PY, kg), fat percentage (FC) and protein percentage (PC), the fertility phenotypes measured varied among populations. Interbull fertility categories\u003csup\u003e93\u003c/sup\u003e were used to group similar traits into five categories: HFert, heifer traits (Interbull T1); CalvMate, conception rate, non-return rate, interval first to last insemination and age at first insemination (Interbull T2); Conc, conception rate and non-return rate (Interbull T3); Int1stLast, interval first to last insemination (Interbull T4); and CalvInt, calving interval and days open (Interbull T5). Phenotypic data were either own performance for females or accurate daughter trait deviations for bulls. To ensure all directions of effects were consistent, we changed the sign of effects for the fertility GWAS where a positive effect meant a reduction in fertility (i.e., longer calving interval correspond to lower fertility), so that in the GWAS effects reported in our analyses for fertility, a larger value always means improved fertility, regardless of the fertility trait. Hence, positive effects for interval traits (CalvMate, Int1stLast and CalvInt) represent improvements in fertility and therefore a reduction in interval. We only used variants with an imputation accuracy (approximated by Minimac or Beagle r\u003csup\u003e2\u003c/sup\u003e value) \u0026ge; 0.10 and a minor allele count \u0026ge; 10.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-breed meta-GWAS including defining QTL regions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeta-GWAS was performed for each trait category, both combining all GWAS in a large multi breed GWAS, as well as additional meta-GWAS within each breed group. We used the weighted z-score model implemented in METAL\u003csup\u003e94\u003c/sup\u003e, that uses the direction of effect and p-value obtained in individual GWAS weighted by sample size. We used a p-value significance threshold of 5x10\u003csup\u003e-8\u0026nbsp;\u003c/sup\u003eapproximately corresponding to nominal p-value of 0.03 following a Bonferroni correction for up to 562,159 simultaneous independent tests. Significant variants were grouped into QTL regions (maximum 1Mb size) by selecting all variants within the bottom third p-values of a QTL region. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA validation GWAS was performed for all variants in the QTL regions using phenotypes of 254,796 French Holstein, 135,295 Montb\u0026eacute;liarde, and 36,548 Normande cows, for MY, FY, PY, FC, PC, HFert, CalvMate, and Conc. The validation dataset consisted of cows with first calving after September 2019, to avoid double counting performances of bulls used in the meta-GWAS. The validation GWAS was done using a weighted GWAS, as described in Tribout and Boichard\u003csup\u003e95\u003c/sup\u003e. In brief, the model was \u003cstrong\u003ey\u003c/strong\u003e = \u003cstrong\u003e1\u003c/strong\u003e\u0026micro; + \u003cstrong\u003eMs\u003c/strong\u003e + \u003cstrong\u003ex\u003c/strong\u003eb + \u003cstrong\u003ee\u003c/strong\u003e, where y is a vector of phenotypes adjusted for environmental effects and averaged when records were repeated, \u0026micro; the mean, \u003cstrong\u003es\u003c/strong\u003e is a vector of 14,205 random SNP effects to account for relationships among cows, \u003cstrong\u003eM\u003c/strong\u003e a genotype matrix assigning \u003cstrong\u003es\u003c/strong\u003e to cows, b the fixed effect of the tested SNP, \u003cstrong\u003ex\u003c/strong\u003e the corresponding doses and \u003cstrong\u003ee\u003c/strong\u003e has heterogeneous variances proportional to 1/weights. Polygenic and residual variances were assumed to be known. The SNPs used to account for relationships excluded those on the chromosome of the tested variant. The 14,205 SNP were selected from the BovineSNP50 SNP chip panel (Illumina Inc), by selecting the variants with highest MAF (averaged across the three breeds) in 150kb intervals. The within breed validation GWAS were combined in a meta-GWAS using METAL\u003csup\u003e94\u003c/sup\u003e. We then considered all variants that were significant (p \u0026le; 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e) in the validation meta-GWAS as validated. Because Int1stLast and CalvInt were not included in the validation meta-GWAS, variants in QTL regions detected for those traits were considered validated if they were significant for either CalvMate or Conc in the validation meta-GWAS. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeta-GWAS Power Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the relationship between QTL detection power and sample size, we repeated both the multi breed meta-GWAS and the within HOL meta-GWAS with different sample sizes. For this, we randomly sampled approximately 25%, 50%, or 75% of samples, and repeated the meta-GWAS on the subset. This was repeated 10 times. We then compared the number of QTL regions and the number of significant variants with the number of QTL regions in the full meta-GWAS. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAGMA and gene ontology over-representation analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used MAGMA\u003csup\u003e21\u003c/sup\u003e to perform a gene-set enrichment analysis on the results of the full multi breed Meta-GWAS including all breed groups for all production and fertility traits, using a window size of 10kb. Over-representation analysis was conducted using the gprofiler2\u003csup\u003e22\u003c/sup\u003e package (version 0.2.3) in R\u003csup\u003e96\u003c/sup\u003e version 4.4.1 (released 2024-06-14). Gene sets were derived from \u003cem\u003eBos taurus\u003c/em\u003e (organism code: bos_taurus). Enrichment was tested using a hypergeometric test with Benjamini\u0026ndash;Hochberg false discovery rate (FDR)\u003csup\u003e97\u003c/sup\u003e correction (significance threshold of 0.05). Only terms with 1\u0026ndash;500 annotated genes were included. Queried databases included Gene Ontology (GO)\u003csup\u003e98,99\u003c/sup\u003e subdivided into biological process (BP), molecular function (MF), and cellular component (CC), KEGG\u003csup\u003e100\u003c/sup\u003e, Reactome\u003csup\u003e101\u003c/sup\u003e, WikiPathways\u003csup\u003e102\u003c/sup\u003e, and CORUM\u003csup\u003e103\u003c/sup\u003e. GeneRatio was defined as the number of overlapping genes between the query and a term (intersection size) divided by the number of input genes (query size). Top terms were visualized using tidyverse\u003csup\u003e104\u003c/sup\u003e (version 2.0.0).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCattle GTEx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all variants in the QTL regions significantly associated with a trait in the multi-breed meta-GWAS and significant associated in the validation analyses, we investigated whether they were significantly associated (p \u0026le; 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e) with gene expression or gene splicing in Cattle GTEx\u003csup\u003e25\u003c/sup\u003e. This was done for all variants in validated QTL regions for production and fertility. The cattle GTEx dataset comprised 23 and 27 tissues with summary statistics of gene expression and gene splicing, respectively, with at least 40 samples per tissue (Supp Table 9). Cattle GTEx summary statistics were downloaded from https://cgtex.roslin.ed.ac.uk/. Enrichment was calculated by dividing the percentage of significant gene expression or gene splicing variants in QTL regions by the percentage of significant variants for any variants that overlapped between our dataset and cattle GTEx. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConserved variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariants within conserved sites across 100 vertebrates were lifted over from human genome sites (hg38, http://hgdownload.cse.ucsc.edu/goldenpath/hg38/phastCons100way), following previous procedures\u003csup\u003e105\u0026ndash;107\u003c/sup\u003e. Conserved sites with the PhastCon score \u0026gt;0.9 were kept. The liftover used the established pipeline from UCSC liftover: https://genome.ucsc.edu/cgi-bin/hgLiftOver based on UCSC curated chain files (http://genome.ucsc.edu/goldenPath/help/chain.html) from the human genome (hg38) to cattle (ARS-UCD1.2). Chain files have every base pair in the source assembly either not mapping to the destination assembly or mapping to a unique position in the target assembly\u003csup\u003e108\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQTL regions overlapping with gene expression and protein/protein interactions in previous studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dairy fertility interval traits, especially CalvMate and Int1stlast are related to oestrous behaviour which has been linked to conception rates. Kommadath et al.\u003csup\u003e56\u003c/sup\u003e studied association patterns between heat score of oestrous behaviour and gene expression in the anterior pituitary and four brain areas (amygdala, dorsal and ventral hypothalamus, and hippocampus) collected from 14 cows at the start of the oestrous cycle (d0) and another 14 cows at midpoint of the oestrous cycle (d12). The gene names from the Bovine 24K oligonucleotide microarrays (Bovine Oligonucleotide Microarray Consortium (BOMC), USA) association to heat scores (on day 0, day 12 and combined) in Kommadath et al.\u003csup\u003e56\u003c/sup\u003e (Additional file 1) were placed on the ARS-UCD1.2 reference genome based on their matching RefSeq gene names (GCF_002263795.1_ARS-UCD1.2_RefSeqGene.gff). For 268 genes out of 372 genes listed in Kommadath et al.\u003csup\u003e56\u003c/sup\u003e (Additional file 1), the positions were recovered and were included in further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a follow-up study, Hulsegge et al.\u003csup\u003e63\u003c/sup\u003e (Supplementary Table S1) found 36 genes differentially expressed (p\u0026lt;0.05) across the 2 oestrous cycle stages (day 0 and day 12). For 13 genes, out of 36 genes listed\u003csup\u003e63\u003c/sup\u003e (Supplementary Table S1), positions were recovered matching RefSeq gene names, and were included in further analysis. Moreover, Hulsegge et al.\u003csup\u003e63\u003c/sup\u003e (Supplementary Table S2) prioritized candidate genes based on protein-protein interactions, gene expression, and text-mining per tissue type. In brief, genes with protein-protein interactions, that were expressed in the 5 tissues types (Zt\u0026gt;2) and were also found using text mining of PubMed abstract for terms in the Reproductive Trait and Phenotype Ontology (REPO; https://bioportal.bioontology.org/ontologies/REPO)\u003csup\u003e109\u003c/sup\u003e (Zp\u0026gt;2), and had a combined Z-score (Zc) \u0026gt; 2 were identified as candidate genes. We recovered gene positions of 179 out of 221 unique genes identified across the 5 tissue types by Hulsegge et al.\u003csup\u003e63\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the QTL regions of each fertility trait in the Meta-GWAS, we checked if the start (-10kb) or the end (+10kb) of the genes reported in those 2 publications were located in the respective QTL regions. Resulting in trait specific QTL region-gene overlaps for genes associated with heat score at different stages of the oestrous cycle. As well as, in trait specific QTL region-gene overlaps for prioritized candidate genes in the 5 tissues related to reproduction. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntagonistic and synergistic genes for production and fertility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe made the following subsets of validated variants in QTL regions:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eOnlyProd: variants that were only present in QTL regions associated with production traits, but not in QTL regions associated with any of the fertility traits.\u003c/li\u003e\n \u003cli\u003eOnlyFert: variants that were only present in QTL regions associated with fertility traits, but not in QTL regions associated with any of the production traits.\u003c/li\u003e\n \u003cli\u003eProdAndFert: variants that were present in at least one QTL region associated with a production trait and at least one QTL region associated with a fertility trait.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe ProdAndFert set of variants was further subdivided in two subsets: synergistic and antagonistic. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAllele frequencies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor validated variants in the QTL regions, we calculated the allele frequency in Australian, German and Irish Holstein, as well as Australian Jersey in multi-year bins. Supp Table 14 shows the number of individuals per population and birth year cohort used for these analyses. For each of these birth years and populations, allele frequencies calculated for the following sets of variants: only production (validated variants in QTL regions detected for production that were not in any QTL region detected for fertility), only fertility (validated variants in QTL regions detected for fertility that were not in any QTL region detected for production), synergistic (validated variants in QTL regions for both fertility and production; where the allele that improved milk yield or protein yield improved fertility), and antagonistic (validated variants in QTL regions for both fertility and production; where the allele that improved milk yield or protein yield reduced fertility). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian Randomisation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GCTA module GSMR\u003csup\u003e32\u003c/sup\u003e (Generalised Summary-data based Mendelian Randomization) was used to infer causality between milk production traits and fertility traits. Causal association was inferred in the Dutch Holstein population using whole genome sequence level GWAS summary statistics for three milk production traits: kilograms of milk (MY), kilograms of protein (PY) and kilograms of fat (FY), and eight fertility traits: age at first insemination (HFert), conception rates in heifers (HFert), conception rate (Conc), non-return rate (NRR, very similar to Conc), interval between calving and first insemination (CalvMate), interval between first and last insemination (Int1stLast), and the fertility index (FI). A threshold p-value of 0.0001 was used to select significant SNPs from the GWAS for clumping. SNPs with a minor allele frequency (MAF) below 0.05 were excluded from the analysis. Individual genotypes of the same 5,798 bulls included in the Dutch Holstein GWAS were used for LD estimation. All bull genotypes in the dataset were imputed to whole genome sequence level. Although we were interested in the causal effect of milk production (intermediate phenotypes) on fertility (target phenotypes), a bidirectional GSMR analysis was chosen, meaning the causal effects of both a forward (effect of the intermediate phenotypes on the target phenotypes) and a reverse (effect of the target phenotypes on the intermediate phenotypes) were estimated. We performed 48 tests (3 (milk production traits) * 8 (fertility traits) * 2 (bi-directional)) and correcting for multiple testing considering causal associations with a p-value of less than 0.001/48=2.08e-05 significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBreed comparisons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all QTL regions detected within each breed group, we compared the percentage of QTL regions that overlapped with QTL regions detected in other breed groups, the percentage of QTL regions that contained at least one variant significant (p \u0026le; 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e) in other breed groups, and the percentage of variants in QTL regions that had the same direction of effect on other breed groups. These comparisons were only made for the production traits, because the fertility data available for each breed group varied widely. Furthermore, we report variants in QTL regions that were detected for the same trait(s) in all five breed groups (HOL, JER, BROWN, RDC, and DUAL). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of unbalanced breed group size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the full meta-GWAS was highly dominated by Holstein, we repeated the meta-GWAS including only the non-Holstein GWAS, as well as 11 meta-GWAS with different percentages (3.8%, 7.5%, 12.7%, 17.5%, 22.1%, 27.7%, 33.3%, 39.4%, 50.5%, 61.1%, and 68.9%) of Holstein in the within population GWAS. Then, we compared the percentage of QTL regions detected in the within breed group meta-GWAS, that were also detected in each of the multi-breed meta-GWAS with varying percentage of Holstein. Additionally, we repeated the QTL overlap analysis comparing within breed group meta-GWAS, the full meta-GWAS and the meta-GWAS that excluded Holstein for genes associated with lactation in the Mouse Genome Informatics database\u003csup\u003e79\u003c/sup\u003e, and regions that contained variants with a moderate to high effect on gene function, as predicted by VEP\u003csup\u003e80\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLD in within-breed and multi-breed QTL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from Run8 of the 1000 Bull Genomes\u003csup\u003e13\u003c/sup\u003e project was used to estimate LD between the most significant variant in each of the QTL regions and adjacent variants within 10Mb distance. LD was estimated using PLINK v2.00a6LM\u003csup\u003e110\u003c/sup\u003e on a subset of 2,372 individuals of the Run8 dataset, aiming to have approximately similar breed proportions in the dataset used for LD estimation as in the full multi-breed Meta-GWAS. \u0026nbsp;We used LocusZoom\u003csup\u003e111\u003c/sup\u003e to visualize the meta-GWAS results, LD between variants in the QTL region, nearby genes and overlap with cattle GTEx for a number of QTL regions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAncestral alleles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAncestral and derived allele classifications were obtained from a previous study that defined ancestral alleles for 70 million cattle variants\u003csup\u003e112\u003c/sup\u003e. We used this information to compare the direction of effect of the validation variants in QTL regions between the ancestral and derived alleles. This comparison was performed separately for each trait, to investigate if the direction of effect of the ancestral alleles differed between traits. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Run7 imputation reference database was developed by the 1000 Bull Genomes project members. Full access to this database is available to members and access can also be requested by external collaborators. Additionally, a large portion of the 1000 Bull Genome Project imputation reference database has been publicly released (2881 sequences) and these can be accessed at the European Nucleotide Archive in Projects PRJEB42783 (Run8) and PRJEB56689 (Run9).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank all 1000 Bull Genome Project partners for provision of genome sequence data. IvdB, HDD, CJV, TVN, RX, MEG, IMM acknowledge funding from the DairyBio project: a joint venture between Agriculture Victoria (Melbourne, Australia), Dairy Australia (Melbourne, Australia) and the Gardiner Foundation (Melbourne, Australia). HP, NKK, ACB, IH, JV, LZ, RFV, MPS, TT, MB, DR, AB, MC, DB, CH, GS, ZC, MSL, JV, TIT acknowledge the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under Grant Agreement No. 815668 (BovReg). AMMT, RF are grateful for funding for FOTiGe - Forschungsverbund Tiergesundheit durch Genomik. HP, NKK thank Braunvieh Schweiz for providing genotype data. ACB, IH, JV, LZ, RFV were financially supported by the Dutch Ministry of Economic Affairs (TKI Agri \u0026amp; Food project LWV20054) together with the Breed4Food partners CRV, Hendrix Genetics and Topigs Norsvin. MPS, TT, MB, DR, AB, MC, DB, CH thank France Genetique Elevage and Valogene for providing phenotypic and genotype data, respectively. GS, ZC, MSL, JV, TIT thank the Nordic Cattle Genetic Evaluation (NAV, Aarhus, Denmark) for phenotypic data and Viking Genetics (Randers, Denmark) for genotypes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith NW, Fletcher AJ, Hill JP, McNabb WC (2022) Modeling the contribution of milk to global nutrition. Front Nutr 8:716100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrero M et al (2016) Greenhouse gas mitigation potentials in the livestock sector. Nat Clim Change 6:452\u0026ndash;461\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWathes D et al (2007) Influence of negative energy balance on cyclicity and fertility in the high producing dairy cow. 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Gigascience 4:s13742\u0026ndash;s13015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePruim RJ et al (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26:2336\u0026ndash;2337\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDorji J et al (2024) Ancestral alleles defined for 70 million cattle variants using a population-based likelihood ratio test. Genet Sel Evol 56:11\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Size of Meta-GWAS discovery population (nInd), number of QTL discovered in the full Meta-GWAS and in validation GWAS in milk, fat and protein yield (MY, FY, PY), milk fat and protein content (FC, PC), heifer fertility (Hfert), interval from calving to first mating (CalvMate), conception rate (Conc), interval from first to last insemination (Int1stLast), and calving interval (CalvInt).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003enumber of QTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003enumber of validated QTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Validated QTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003enInd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003enIndV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e281,841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e22,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e22,015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eFY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e281,883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e11,094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10,211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e92.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e281,643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e14,142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13,278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e80.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e93.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e259,085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e10,409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9,809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e80.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e94.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e259,085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e17,277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16,409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e84.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e95.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHfert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e98,741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2,048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e95.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eCalvMate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e87,560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3,998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3,938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e96.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eConc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e95,656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2,672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2,520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e94.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eInt1stLast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e56,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3,451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3,258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e94.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eCalvInt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e83,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e426,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e5,361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8631217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8631217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMilk production and female fertility are negatively correlated in cattle, reducing the efficiency of artificial selection. The genomic loci underlying these antagonistic effects are unknown. We carried out one of the largest cattle meta-analyses of genome-wide association studies (Meta-GWAS) for 10 milk production and fertility traits in 12 breeds numbering 281,841 animals. The quantitative trait loci (QTL) regions identified were validated in an additional dataset of 426,639 animals. Up to 595 QTL per trait were confirmed across traits, including regions with both antagonistic and synergistic effects between milk yield, protein yield, and fertility. Combining GWAS results with functional genomic information highlighted candidate variants in several genes (\u003cem\u003eABO, PAEP, LIF, ESR2, GC, and LALBA\u003c/em\u003e). The large Holstein population dominated the Meta-GWAS and negatively influenced QTL discovery in smaller breed groups. These results reveal the genetic basis of milk-fertility trade-off and pave the way for more balanced cattle selection.\u003c/p\u003e","manuscriptTitle":"Genome-wide meta-analyses in 708,480 individuals shed light on the genetic conflict between productivity and fertility in dairy cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 09:14:37","doi":"10.21203/rs.3.rs-8631217/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"65bf0748-b4f7-4f14-ada1-4449294a5ccd","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62000601,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"},{"id":62000602,"name":"Biological sciences/Genetics/Genomics"}],"tags":[],"updatedAt":"2026-02-13T09:14:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 09:14:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8631217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8631217","identity":"rs-8631217","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}