The current changing climate trend poses a threat to the productive efficacy and welfare of livestock across the globe. This review is an attempt to synthesize information pertaining to the applications of various genomic tools and statistical models that are available to identify climate-resilient dairy cows. The different functional and economical traits which govern milk production play a significant role in determining the cost of milk production. Thus, identification of these traits may revolutionize the breeding programs to develop climate-resilient dairy cattle. Moreover, the genotype–environment interaction also influences the performance of dairy cattle especially during a challenging situation. The recent advancement in molecular biology has led to the development of a few biotechnological tools and statistical models like next-generation sequencing (NGS), microarray technology, whole transcriptome analysis, and genome-wide association studies (GWAS) which can be used to quantify the molecular mechanisms which govern the climate resilience capacity of dairy cows. Among these, the most preferred option for researchers around the globe was GWAS as this approach jointly takes into account all the genotype, phenotype, and pedigree information of farm animals. Furthermore, selection signatures can also help to demarcate functionally important regions in the genome which can be used to detect potential loci and candidate genes that have undergone positive selection in complex milk production traits of dairy cattle. These identified biomarkers can be incorporated in the existing breeding policies using genomic selection to develop climate-resilient dairy cattle. With the growing global human population, a huge demand for livestock products can be expected in the coming years . At the Milk production is one of the most important economic traits in cattle which, being polygenic, is affected by many genes. Advancements in the field of molecular genetics have aided in identifying various candidate genes and quantitative trait loci (QTL) regions which were found to have an association with milk yield, fat yield, protein yield, and other milk production traits . This inApart from the genetic factors, environmental factors also influence milk production, among which ambient temperature is one of the most important abiotic factors . An annuWith this background, this review was targeted to synthesize information pertaining to the application of various advanced genomic tools and statistical models that are currently available to identify climate-resilient dairy cows.Globally, around 15 million households are engaged in milk production, as it provides food security and nutrition and is also an important source of income especially for small-scale producers . More thMilk production in dairy cattle are predominantly governed by genetic and environmental factors, and thus this particular trait has been reported to be influenced by various factors such as breed , genetich2). In general, milk production traits have low to medium heritability ranging from 0.17 to 0.30  of 79. In general, the h2 estimates for milk yield in these cattle ranged from 0.111 to 0.176. Similarly, Kimiya et al.  model  and multIn the study conducted by Cheruiyot et al. , the G ×−2 hair length, diameter, and density, respectively. Conversely, a coat layer with longer (25 mm) and denser hair  of lesser diameter (35 mm) was recorded in Holstein cows reared in temperate regions , which confers the animals with a short and sleek hair coat  and DGV in each trait (DGVHT_ASI) were derived. Finally, the HT ABVg was calculated using the following equation:Use of test-day models, which account for different recording schemes, utilizes incomplete lactation records and considers that the time-dependent effect for each test-day can be thought to be more efficient to evaluate thermo-tolerance in dairy cattle. Nguyen et al.  estimateHT_ASI – mean (DGVHT_ASI)] / SD (DGVHT_ASI)HTABGg = 100 + 5 × [DGVAdditionally, the reliability of HT ABVg in genotyped Holstein and Jersey bulls was estimated to be 38%, which was moderate and comparable with that of other economically important traits such as the feed saved. However, it is always recommended to further improve the reliability, which could be accomplished by increasing the size of the reference population. Furthermore, it would be ideal to identify and consider other heat tolerance traits like fertility and health , which wIn a recently concluded study, Hagiya et al.  assessedIn general, the use of statistical models to assess the genetic variables in a heat stress study varies depending on several factors starting from the data quality up to the possibility of incorporating computationally relevant traits. On the comparative basis of various statistical models discussed above, it was quite evident that the herd-test-day model provided a reliable approach to identify climate-resilient dairy cattle.Conventional breeding strategies have played a potent role toward the production of superior animals; however, these methods were time consuming and most often would not consider all sources of genetic variability . The advSPP1  played a significant role in modulating milk protein gene expression. Functional genomics could establish an assured link between gene expression and phenotype and is expected to have a major positive impact on sustainable livestock production. Incorporating such holistic approaches in heat-stressed dairy cattle could reveal novel pathways affected due to heat stress. Furthermore, it may also unfold the hidden pathways associated with adaptation during heat stress. There are several specific functional genomics approaches focusing on the DNA level (genomic and microarray technologies), RNA level (transcriptomic studies), protein level (proteomics), and metabolite level (metabolomics).Functional genomics is a field of molecular biology that describes the interaction and functions of genes and proteins using genome-wide approaches . Sheehy The microarray technology follows the principle of complementarity based on which target DNA sequence hybridized with the immobilized DNA molecules on the nylon chip or glass slide . The tarMeuwissen et al.  proposedThe NGS technologies have been widely applied in dairy cattle to study the genetic variability underlying traits of economic interests . Gao et Whole transcriptome analysis was used by many researchers to explore various fields of animal genetics . Liu et Li et al.  studied In livestock, genome-wide association studies (GWAS) have been used extensively to map the QTL of important traits like production traits , 86, repIso-Touru et al.  conducteYodklaew et al.  conducteAnother study was conducted in Holstein dairy cattle to establish the genetic correlation and GWAS of the length of 305-days milk yield for the first lactation (FM305), length of productive life, and days open . In totacadherin-2 (NCAD) gene, small nucleolar RNA (SNORA19), ubiquitin-protein ligase (RFWD2), Cajal body-specific RNA 3 (SCARNA3), solute carrier organic anion transporter family member 1C1 (SLCO1C1), phosphodiesterase (PDE3A), kelch repeat and BTB domain-containing protein (KBTBD2), U6 snRNA-associated Sm-like protein LSM5 (LSM5), and glutamine-oxaloacetic transaminase, soluble (GOT1). These genes were predicted to have a direct or indirect role in regulating thermal stress. Thus, from this study, it was concluded that these SNPs could also be useful in the genomic selection of climate-resilient dairy animals.Dikmen et al.  performeThe extent of decline in milk production due to heat stress varies between animals which have a low to moderate level (0.13–0.23) of heritability . This trNguyen et al.  adopted GBLUP has been proven to be more accurate to predict the breeding value of an animal and further improve selection accuracy and gain. However, this methodology assumes genomic markers to contribute equally toward the variability of a trait , which mSeveral other statistical models have been used in GWAS like principal component regression , partialMateescu et al.  conducteST and pairwise FST , which plays a role in adaptation to heat stress, was also identified in a candidate region in Butana cattle. Such studies aid in identifying potential genes and QTLs having a significant association with various production and functional traits in cattle which are under positive selection. Such information plays an influential role while deciding breeding programs in dairy cattle to improve their production.A study was led by Bahbahani et al.  to identST, haplotype homozygosity (H1), modified haplotype homozygosity (H12), Tajima's D index, and nucleotide diversity (Pi). On comparing the data with that of other breeds of both European and Asian origins, they identified some novel and previously reported candidate genes having a significant association with economically important traits and environmental adaptations in cattle. They identified some putative selection signature regions covering some important genes influencing milk production traits in cattle like DGAT1 (influencing milk fat content), ABCG2, LCORL, GHR, and NCAPG (affecting milk yield), FKBP2 (associated with milk protein percentage and yield), KLHL1 (influencing milk yield and lactation persistency), KDM5A (associated with fatty acid level), and many more. Furthermore, their study highlights the usage of such candidate regions while improving crossbreeding programs in an effort to improve the commercial breeds such that they could acclimatize better to the harsh environment and thus perform better.Local cattle breeds, though not highly productive, are well-acclimatized to the local environment. Yurchenko et al.  conducteUsing suitable selection signature methods, one can identify selection signatures, thereby detecting candidate genes and their location, which have a potential role in various economical traits in cattle. This would further give a better insight into the history of selection and also the genetic adaptability of the animal/population to environmental distress .HSP70.1 (ATP1B2 (HSF1 and HSPA6 (90); HSP90AA1  which play a vital role in protecting cells from environmental stress. Kumar et al.  in this breed of indigenous cattle, among which two, A1209G and A3292C, had a significant association with heat tolerance traits. Bharati et al. . It was inferred that the first peak would be to ameliorate the negative effects of thermal stress and the second would be to tackle the chronic exposure to heat stress, and thereby this gene could be used as a biomarker to select thermo-tolerant cattle  in HT cows (12.5% decline from baseline) than the HS cows (17.4% decline from baseline). Incorporation of such holistic approaches of GWAS which would depict candidate genes followed by its incorporation into GEBV will aid in the selection of climate-resilient cows having the ability to maintain its production. This would further reduce the cost on expensive management practices and reduce production losses due to rising temperature  with the Holstein  and its genetic correlations with production and reproduction traits (with Gibbs sampling) during the summer in 1,695 lactating Holstein cows that were sired by 509 bulls. As per their report, rectal temperature was found to have a moderate h2 of 0.17 ± 0.13, having genetic correlation with milk production traits. Lassen and Løvendahl (h2 of three CH4 phenotypes: the CH4 and CO2 ratio in the breath of the cows (CH4_RATIO), estimated quantified amount of CH4 (in g/d) recorded over a week (CH4_GRAMSw), and CH4 intensity, which was grams of CH4 per liter of milk produced (CH4_MILK). The h2 of CH4_GRAMSw and CH4_MILK was estimated to be 0.21 ± 0.06, while that of CH4_RATIO was 0.16 ± 0.04. They also observed that methane production has a strong negative correlation with milk production traits.In the current climate change scenario, researchers are now focusing to incorporate thermo-tolerance and low methane emission traits into breeding programs which were primarily looking into productive traits. Such a holistic breeding approach would ensure sustainable livestock production. Limited studies have been conducted in this aspect. However, there are a few reports of n et al.  estimateøvendahl  likewiseHSP70.1, ATP1B2, HSF1, HSPA6, HSP90AA1, TNF1/4, IL2/6, EIF2AK4, KIF9, SELENBP1, and XDH were found to be associated with thermo-tolerance in dairy cows. Furthermore, GWAS analysis revealed LIF, OSM, TXNRD2, and DGCR8 as candidate genes influencing the biological processes which alleviate the harsh effects of heat stress in heat-stressed animals. Incorporation of such candidate genes through marker-assisted selection program can improve the climate resilience capacity of dairy cattle.This review has projected the information pertaining to the impacts of heat stress on milk production of dairy cows and the various genomic tools that are available to identify climate-resilient dairy cows. The different functional and economical traits which govern milk production can be identified using advanced biotechnological tools and statistical models to improve thermo-tolerance in dairy cattle. Although several refinements have taken place in these areas over the years, the GWAS and selection signatures were the most preferred options for researchers across the globe to identify robust animals to withstand adversities associated with climate change. These tools can be used to detect potential loci and candidate genes that have undergone positive selection in complex milk production traits of dairy cattle during exposure to tropical climate. Thus, few biomarkers such as Although tremendous progress has been made in the last decade by employing these advanced molecular biology tools to identify various QTLs and genes associated with thermo-tolerance in dairy cattle, such research is still in infancy state. Most of these studies stopped at identification of various biomarkers in the laboratory condition, and it is still a long way to go before these technologies are implemented to develop robust animals which have the ability to survive in adverse environmental conditions without compromising their production potential. For this to happen, genomic selection breeding has to be intensified based on the already identified biomarkers/traits governing heat stress regulation and uncompromised milk production in dairy cattle. These advanced biotechnological tools should also be used to identify more traits pertaining to adaptation, production, and low methane emission. Based on this knowledge, efforts are also equally needed to develop a robust animal which has the ability to survive in multiple locations, produce milk optimally, and emit less methane per unit of feed consumed. Such efforts not only can ensure the livelihood security of poor and marginal farmers but also can make the livestock enterprises more profitable. As the world is battling to meet the huge food demand of the growing human population by 2050, such efforts of developing multi-location thermo-tolerant dairy cattle breeds may prove highly beneficial to ensure food security.MS and VS conceptualized the review and developed the outline. MS, VS, PM, MN, VF, and AM prepared the manuscript covering various sections. SK and RB edited and corrected the manuscript. All authors proof read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees.Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ.Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required.This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.The online version contains supplementary material available at 10.1186/s12711-021-00654-x. Currently, genomic selection is applied in various livestock species –3 but noProgress in other livestock is faster than in honey bees, which is due to the specific reproduction characteristics of honey bees. Queens can only mate during the first weeks of their life, and at this time, the unfertilized queens undertake nuptial flights during which they mate with several drones , and sto. We call the selection of phenotyped queens ‘colony-based selection’ (CBS), since phenotyping requires a colony. Figure Performance testing for relevant traits, such as honey yield, gentleness, or disease resistance, is only possible when the colony is fully developed. A colony contains up to 50,000 workers during spring, which are usually all offspring of the same queen and considered as a single worker group. The workers perform a wide range of tasks, such as foraging, cleaning, and feeding the queen, drones, and larvae. Collecting data on all the relevant traits is usually completed in the year after the queen hatches. All queens that were or will be performance-tested, and queens which are or were candidates for a preselection step before phenotyping, are referred to as breeding queens (BQ)The use of genomic selection in honey bee breeding programs is expected to enhance genetic gain. Gupta et al.  reportedTo estimate genomic breeding values in honey bees , the genA sufficiently large reference population must be gathered and maintained to ensure accurate genomic prediction of breeding values . BrascamThe aim of our simulation study was to provide insights into the optimization of genomic selection for honey bee breeding programs by using stochastic simulations to generate a breeding population. Genomic breeding values were estimated for the population using either the genotypes of BQ only, or the genotypes of both DPQ and BQ. The obtained accuracies of genomic predictions allowed us to compare the quality of the different analyses. Genetic gain was predicted by using a deterministic model for breeding schemes. We implemented GPS of BQ and/or DPQ with different selection intensities. Furthermore, the breeding schemes under consideration covered different budgets for genotyping queens, and different sizes of the reference population.Our study can be divided into two steps, each using very different methods. In the first step, honey bee populations were stochastically simulated using the program BeeSim . The breThe phenotypes of economically-relevant traits of a honey bee colony are influenced by the queen and her workers. While all non-reproductive tasks are performed by the workers, the egg-laying rate of the queen is one example of her essential qualities, since it is crucial for the number of workers. Therefore, in honey bees, the genetic model for most traits includes direct effects due to the contribution of the workers and maternal effects due to the contribution of the queen and the phenotype, where For a queen, mentclasspt{minimaups , i.e. BLUP without marker data. When the simulation of the population over 10 years was complete, breeding values were estimated with PBLUP and single-step genomic BLUP. Two versions of the genomic relationship matrix, The final pedigree contained 10,000 BQ, with 10,000 worker groups, and 2800 DPQ on 350 mating stations, since we did not include mating stations from years 0 to 2 in the pedigree. Worker groups from year 8 represented phenotyped colonies. BQ from year 9 represented unphenotyped queens, but had worker groups. Since unphenotyped worker groups do not affect the EBV of the other individuals, the accuracies of the EBV of queens from year 9 represented those of unfertilized queens. Older BQ were included in the reference population to increase the accuracy of genomic EBV.Proportions (Two quantitative traits that were affected by direct (worker group) and maternal (queen) genetic effects were simulated, with parameters as specified in Table The genetic variances and heritabilities of the direct and maternal effects take the specificities of the honey bee into account. The phenotypic variance was calculated (Eq.  in 27]))27]) as:where  (6c) in , respectwhere The heritability of the sum of the queens’ maternal and direct effects, which was the selection objective for queens in GPS,  (7a) in . The genwhere The genetic variance in worker groups equals the variance of the selection criterion in CBS. Therefore, the heritability of the selection criterion in CBS  is equaWe simulated a genome of 16 chromosomes with a recombination rate of 19 cM/Mb , with leU-shaped distribution. Loci with an allele frequency lower than 0.05 were discarded, which decreased the number of SNPs from an initial 100,000 to 48,419, with an average LD of To achieve this, a historical population of 50 queens per year was simulated, spanning 20,000 years, with a mutation rate of 0.0005 per locus where where The genomic relationship matrix with entries for individual DPQ, g to Eq.  by inclug to Eq.  to the gDPQ+BQ does not change the EBV of BQ or worker groups  TBV and EBV. Bias was evaluated based on deviations of the regression coefficient of TBV on EBV, BQ and ssGBLUPDPQ+BQ were calculated for the genotyped BQ. The variance of TBV for phenotyped colonies, The accuracies for phenotyped colonies, Accuracies for queens and worker groups cannot be directly compared, since different genetic variances must be used to estimate genetic gain from them. Therefore, we rescaled This can interpreted as an accuracy of fictional queens. If a daughter was reared from each of the colonies in year 8, then the correlation between the daughters’ EBV and the daughters’ TBV would be Equation  was derientclass1pt{minimaTable where The average TBV of a generation of colonies is given by the average TBV of the selected BQ and DPQ in the parental generation. The average TBV of a queen reared from a colony is equal to the breeding value of the colony’s worker group . ConsequAlternatively, from Eq.  by replaBased on the average TBV of colonies when BQ and DPQ were preselected and the dams of BQ and dams of DPQ have a TBV of 0, response to GPS, Response to selection in a genomic breeding program with CBS and GPS is given by DPQ+BQ.Response to pedigree-based selection, d on Eq. , which cd on Eq. . Equatiod on Eq. , 19), a, a\documd on Eq.  were useTo evaluate the impact of increasing the annual budget for genotyping queens, Accuracy was measured as the correlation between EBV and TBV. The results obtained with ssGBLUP and PBLUP are shown in Fig. DPQ+BQ, since the accuracy with ssGBLUPBQ was just slighlty lower with 5000 BQ in the reference population. The accuracies for unfertilized queens with ssGBLUPDPQ+BQ were higher than those with PBLUP by 160.1 (MOD) and 126.2% (HGC). The accuracies for replacement queens from phenotyped colonies with ssGBLUPDPQ+BQ were higher than those with PBLUP by 9.9 (MOD) and 12.5% (HGC).We focused on ssGBLUPDPQ+BQ and ssGBLUPBQ were larger when fewer BQ were included in the reference population. We focused on the accuracies for unfertilized queens. For a reference population size of 250 BQ, accuracies obtained with ssGBLUPBQ differed from those obtained with PBLUP by − 2.1 (MOD) and 6.9% (HGC). Addition of the genotypes of all 2800 DPQ increased the accuracy considerably. With 250 BQ in the reference population, accuracies obtained with ssGBLUPDPQ+BQ were higher than those obtained with PBLUP by 47.9 (MOD) and 54.4% (HGC). However, genotyping phenotyped BQ instead of DPQ yields more accurate genomic EBV for a smaller number of genotypes. E.g., with a reference population of 1500 BQ, accuracies obtained with ssGBLUPBQ were higher than those with PBLUP by 91.7 (MOD) and 63.2% (HGC).The differences in the accuracies of ssGBLUPBQ were 73.6 (MOD) and 53.8% (HGC) higher than those with PBLUP, while the accuracies for direct effects with ssGBLUPBQ were 41.4 (MOD) and 28.6% (HGC) higher than with PBLUP. With ssGBLUPDPQ+BQ, the accuracies for direct effects were higher than those with ssGBLUPBQ. For maternal effects, the accuracy with ssGBLUPDPQ+BQ was almost equal to that with ssGBLUPBQ.The results for the accuracies for estimates of direct and maternal effects with ssGBLUP and PBLUP are shown in Fig. Figure Up to ow Table . GeneticDPQ+BQ, as the optimal scenarios with ssGBLUPBQ increased genetic gain by 18.7–20.8% compared to pedigree-based selection . Worker groups in years 4–7 showed a greater bias than Results for the coefficient of regression of TBV on EBV for PBLUP and ssGBLUP are shown in Fig. BQ, and ssGBLUPDPQ+BQ and investigated the prediction accuracy and bias of overall, direct, and maternal EBV for queens and worker groups, as well as their change with the number of genotyped queens. We used these statistics to estimate genetic gain in different breeding programs.We used a honey bee specific relationship matrix to perform genetic evaluations with PBLUP, ssGBLUPBQ, are similar to results for young bulls with 25% unknown sires in beef cattle [We compared the quality of the EBV for different reference population sizes by genotyping different proportions of phenotyped BQ see Fig. . Rather f cattle . For theWe found a bias of up to LUP Fig. , which iorted by  for younWe examined the effect of including the genotypes of DPQ into the genomic relationship matrix. This proved useful, when a small number of phenotyped BQ and a large number of DPQ were genotyped. This situation is likely to occur in practice when genomic preselection is applied to DPQ  than with its dam (= 0.5), regardless of whether it is a queen or worker group. The reason is that the daughter is related by 0.5 to exactly one of the DPQ which are daughters of the pseudo-father, but it is uncertain which DPQ from the mating station it is. The relationship of a daughter to its pseudo-father is its average relationship to all DPQ from the mating station.DPQ+BQ with non-phenotyped DPQ, separating pseudo-fathers into individual DPQ in the relationship matrix and identifying sires from genomic data does not increase the accuracy of genomic EBV of BQ and their worker groups , a, aAdditiquations  were useAdditional file 7. Regression coefficients of true on estimated breeding values, BQ and PBLUP are presented for queens and worker groups from year 9, year 8, and years 4–7.Additional file 8. Genetic in the first generation of a genomic breeding program applying a shorter generation interval. In honey bees, the generation interval could be shortened at least by 1 year. We consider such a scheme. The results rely on the predicted accuracy due to LD for which we use an estimate from Habier et al. [r et al. . Genetic


Emmetropization requires coordinated scaling of the major ocular components, corneal curvature and axial length. This coordination is achieved in part through a shared set of genetic variants that regulate eye size. Poorly coordinated scaling of corneal curvature and axial length results in refractive error. We tested the hypothesis that genetic variants regulating eye size in emmetropic eyes are distinct from those conferring susceptibility to refractive error.A genome-wide association study (GWAS) for corneal curvature in 22,180 adult emmetropic individuals was performed as a proxy for a GWAS for eye size. A polygenic score created using lead GWAS variants was tested for association with corneal curvature and axial length in an independent sample: 437 classified as emmetropic and 637 as ametropic. The genetic correlation between eye size and refractive error was calculated using linkage disequilibrium score regression for approximately 1 million genetic variants.P < 5.0e-08). A polygenic score created using these 32 genetic markers explained 3.5% (P < 0.001) and 2.0% (P = 0.001) of the variance in corneal curvature and axial length, respectively, in the independent sample of emmetropic individuals but was not predictive of these traits in ametropic individuals. The genetic correlation between eye size and refractive error was close to zero .The GWAS for corneal curvature in emmetropes identified 32 independent genetic variants (These results support the hypothesis that genetic variants regulating eye size in emmetropic eyes do not overlap with those conferring susceptibility to myopia. This suggests that distinct biological pathways regulate normal eye growth and myopia development. This structural relationship between excessive axial eye length and refractive error gives rise to the rule of thumb that an axial elongation of 1 mm causes a myopic shift of approximately –2.70 diopters (D).–In a chick model of experimentally induced myopia, the genetic variants regulating eye size in eyes with normal visual experience were found to be distinct from the genetic variants conferring susceptibility to myopia induced by altered visual experience.,The UK Biobank is a longitudinal study of the health and well-being of approximately half a million UK residents 40 to 70 years old at baseline.,http://www.bristol.ac.uk/alspac/researchers/our-data/). Genotyping and imputation of ALSPAC participants were performed as described.Ethical approval for the Avon Longitudinal Study of Parents and Children (ALSPAC) was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants. Pregnant women residing in Avon, UK, with expected dates of delivery between April 1, 1991, and December 31, 1992, were invited to take part in the study. When the oldest children were approximately 7 years of age, an attempt was made to bolster the initial sample with eligible cases who had failed to join the study originally. As a result, an additional 913 children were enrolled. The phases of enrollment are described in more detail in two cohort profile papers.A very large sample size is required for a GWAS analysis. No such sample is available worldwide in which both the axial eye length and corneal curvature of participants have been measured. Therefore, in the current study, we took advantage of the reported coordinated genetic scaling of axial length and corneal curvature in humansP < 0.01 for a test of Hardy–Weinberg equilibrium, A/T or G/C variants, and those not present in the summary statistics from the CREAM+23andMe consortium refractive error GWAS meta-analysis (see below). A set of independent markers associated at genome-wide significance (P < 5.0e-08) with corneal curvature in emmetropes were selected by sequentially choosing the most strongly associated marker, excluding all markers within ±500 kb of the top marker or having pairwise LD r2 > 0.2 with the top marker, and so on until there were no further markers with P < 5.0e-08.The eyes of UK Biobank participants were classified as emmetropic if the spherical (SPH) and astigmatic (CYL) refractive error lay within the range of 0.00 ≤ SPH ≤ +1.00 D and |CYL| ≤ 1.00 D, respectively, and with a habitual visual acuity < 0.2 logMAR. If both eyes were classified as emmetropic, the average corneal curvature of the two eyes was taken as the phenotype.n = 22,180) (n = 5556) with the sample of 22,180 UK Biobank participants used for the GWAS of corneal curvature in emmetropes.To provide GWAS summary statistics for traits related to eye size that could be utilized in genetic correlation analyses, three additional GWAS analyses were performed: (1) a GWAS for refractive error, (2) a GWAS for corneal curvature, and (3) a GWAS for body height. These three additional GWAS analyses were performed with a sample size equal to that of the GWAS for corneal curvature in emmetropes   and with 22,180) . GWAS anThe CREAM Consortium and the 23andMe personal genomics company jointly published a GWAS meta-analysis for a combined phenotype of refractive error and age-of-onset of myopia.A total of 1848 ALSPAC participants with genetic data available also had information available at the age of 15 years for their refractive error, axial length, corneal curvature, and body height. Among the 1848 participants, 437 were assigned to an Emmetropes sample . AdoptinP value below 0.1 .To test for replication of the GWAS results, each of the 32 genetic variants identified in the UK Biobank GWAS for corneal curvature in emmetropes was tested for association with each of the traits of interest  in the 1848 ALSPAC participants using linear regression. The trait of interest was considered the outcome variable. Single-nucleotide polymorphism (SNP) genotype, age in months, and sex were modeled as predictor variables. A binomial test was performed to evaluate if the variants tested for replication had a matched direction of association or achieved a ,R2) for axial length, which was defined as the increase in R2 of a linear regression model with axial length as the outcome variable and including the baseline predictors age and sex, compared with a full model including the polygenic score, age, and sex. The incremental R2 was evaluated separately in the ALSPAC Emmetropes sample (n = 437) and the ALSPAC Ametropes sample (n = 637). An incremental R2 for corneal curvature, describing the capacity of the polygenic score for eye size to predict corneal curvature, was calculated analogously. Linear regression analyses were performed using R 3.5.1 .The 32 genome-wide significant genetic variants identified in the GWAS for corneal curvature in emmetropes were combined to create a polygenic score.,,The genetic correlation is the proportion of the observed (phenotypic) correlation between two traits that can be explained by genetic variants shared between the traits. SNP heritability is the proportion of phenotype variation that can be explained by a set of genotyped genetic variants. The SNP heritability of each trait and the genetic correlation between pairs of traits were calculated using LD score regression, with the GWAS summary statistics as input.Two samples of adult participants from the UK Biobank were studied: (1) a sample of 22,180 emmetropes, and (2) a sample of 22,180 participants selected at random from those with information available for all of the traits of interest. A sample of 1848 teenagers from the ALSPAC birth cohort were studied as a replication sample. Among the ALSPAC participants, 637 individuals were classified as ametropes and 437 individuals were classified as emmetropes . The demographic characteristics of the participants are shown in P < 5.0e-08). Details of these 32 genetic variants are listed in P = 2.0e-71), is an intronic variant in WNT7B, the gene coding for Wnt family member 7B. This variant is in strong LD with WNT7B variant rs10453441 (r2 = 0.77), which has previously been associated with eye size.,,P = 3.6e-21), lies upstream of the FGF9 gene and is in perfect LD (r2 = 1.00) with rs9506725, previously associated with axial length, corneal curvature, and refractive error.P = 2.8e-16), is an intronic variant in RSPO1, which encodes R-spondin-1. This variant has also been associated with axial length, corneal curvature, and high myopia.–P = 4.9e-11) is located in the promoter region of the PDGFRA gene, which codes for platelet-derived growth factor receptor A. Variants in this region have previously been associated with corneal curvature ,,r2 = 0.84).A GWAS for corneal curvature in emmetropes was performed as a proxy for a GWAS for eye size. . After testing 6,961,902 genetic markers in a sample of 22,180 UK Biobank participants, a total of 32 independent genetic regions harbored markers associated with corneal curvature in emmetropes at genome-wide significance (n = 1848) compared with the GWAS discovery cohort  resulted in limited statistical power for replication. However, the direction of association  in the original GWAS matched that in the ALSPAC replication sample more often than expected by chance for both corneal curvature and axial length (P = 9.65e-06), whereas for axial length 25 of the 32 variants had the same direction of association (P = 1.05e-03). Conversely, concordance in the direction of association was no higher than expected by chance for refractive error and body height. For refractive error, the direction matched for 17 of the 32 variants (P = 0.430). For height, the direction matched for 18 of the 32 variants (P = 0.298). Furthermore, the association P values were lower than expected by chance for corneal curvature and axial length , and seven of the 32 variants achieved P < 0.10 for axial length (P = 3.59e-02). However, the association P values were not lower than expected by chance for the other two traits: 1 of the 32 variants achieved P < 0.10 342 for refractive error (P = 0.966) and 4 of the 32 variants achieved P < 0.10 for body height (P = 0.399).Participants from the ALSPAC cohort with information available for corneal curvature, axial length, refractive error, and body height at the age of 15 years were evaluated as a replication sample. The much smaller size of the ALSPAC sample (l length . Specifil length . For exaP = 5.69e-05) of the variance in corneal curvature and 2.0% (P = 1.41e-03) of the variance in axial length in a sample of emmetropes from the ALSPAC cohort (P = 0.070) or axial length (P = 0.747) in a sample of ametropes from the ALSPAC cohort, nor was the polygenic score associated with refractive error or body height in either the sample of emmetropes or the sample of ametropes  between the eye size surrogate and a series of other traits: refractive error, corneal curvature, and body height (gr = 0.00 ± 0.06 (P = 0.953) and gr = −0.03 ± 0.05 (P = 0.513) when the genetic correlation was computed using two separate sets of GWAS summary statistics  and the genetic correlation between eye size and body height was also well above zero . All of the traits considered had moderate SNP-heritability  but that the FGF9 variant was strongly associated with all three traits. Taken in the context of the near-zero genetic correlation between eye size and refractive error found in the current study, which was based on an analysis of more than a million variants distributed across the genome, these variants in FGF9 and RSPO1 appear to be exceptions to the rule, as they exert effects on both eye size and refractive error.To assess whether genetic variants associated with eye size are, in general, also associated with susceptibility to refractive error, we calculated the genetic correlation between eye size and refractive error for a set of approximately 1 million SNPs distributed across the genome. This yielded a genetic correlation very close to zero, implying that there is very little genetic overlap between these two traits. This finding replicates the genetic correlation of zero between baseline (pre-treatment) eye size and susceptibility to form-deprivation myopia reported in chickens.PRSS56, ADAMTS19, and PIEZO2 – are of particular interest by virtue of their known or suggested role in causing nanophthalmos or microphthalmos.–PIEZO2 encodes piezo-type mechanosensitive ion channel component 2, a transmembrane protein that senses mechanical forces experienced by tissues and translates them into cellular signals. Thus, we suggest PIEZO2 may act as a sensor of eye size. Genes such as PRSS56 (and MFRP), for which loss of function causes nanophthalmos, have been considered potential targets for slowing the progression of myopia, because reducing their functional activity may prevent further ocular elongation.–Three of the candidate genes discovered in our GWAS for eye size – Strengths of this work were that it took advantage of the very large sample of individuals with emmetropia in the UK Biobank study; the use of a replication cohort (ALSPAC) with information about refractive error, axial length, corneal curvature, and body height; the standardized ocular phenotyping methods adopted by the UK Biobank and ALSPAC; and the implementation of a series of sensitivity analyses to confirm that the findings were robust. The major weakness of the current work was the need to use corneal curvature in emmetropes as a proxy for eye size rather than a dataset with information on both corneal curvature and axial length in emmetropes. Axial length was not measured in the UK Biobank, and no very large sample of emmetropes with information about both axial length and genetic data has been collected to our knowledge .PRSS56, MFRP, and ADAMTS19 may modify distinct biological pathways compared with those upregulated to produce myopic eye enlargement. Currently, it is unknown if this is an advantage or a disadvantage.In summary, a GWAS analysis identified 32 genetic variants strongly associated with corneal curvature in emmetropes, 14 of which were in regions not previously associated with eye size or refractive error. In an independent group of participants, a polygenic score derived using these 32 variants was associated with eye size in emmetropic eyes that had well-coordinated scaling of corneal curvature and axial length. However, the polygenic score was not associated with eye size in ametropic eyes. More generally, our work suggests, in humans, there is little overlap of genetic variants controlling normal eye enlargement during juvenile development and variants causing susceptibility to refractive error and myopia. These findings imply that therapies for myopia that target the products of genes such as


Host genetics has recently been shown to be a driver of plant microbiome composition. However, identifying the underlying genetic loci controlling microbial selection remains challenging. Genome-wide association studies (GWAS) represent a potentially powerful, unbiased method to identify microbes sensitive to the host genotype and to connect them with the genetic loci that influence their colonization. Here, we conducted a population-level microbiome analysis of the rhizospheres of 200 sorghum genotypes. Using 16S rRNA amplicon sequencing, we identify rhizosphere-associated bacteria exhibiting heritable associations with plant genotype, and identify significant overlap between these lineages and heritable taxa recently identified in maize. Furthermore, we demonstrate that GWAS can identify host loci that correlate with the abundance of specific subsets of the rhizosphere microbiome. Finally, we demonstrate that these results can be used to predict rhizosphere microbiome structure for an independent panel of sorghum genotypes based solely on knowledge of host genotypic information. Recent work has shown that root-associated microbial communities are in part shaped by host genetics –4. A stuDespite consistent evidence of the interaction between host genetics and plant microbiome composition, identifying specific genetic elements driving host-genotype dependent microbiome acquisition and assembly in plants remains a challenge. Recent efforts guided by a priori hypotheses of gene involvement have begun to dissect the impact of individual genes on microbiome composition , 7. HoweGenome-wide association studies (GWAS) represent a powerful approach to map loci that are associated with complex traits in a genetically diverse population. Though pioneered for use in human genetics, to date the majority of GWAS have been conducted in plants , and it Arabidopsis thaliana used phyllosphere microbial community data as the phenotypic trait in a GWAS to demonstrate that plant loci responsible for defense and cell wall integrity affect microbial community variation [In plants, a recent study in ariation . Severalariation –18. Compariation . Previouariation , 4. ThesSorghum bicolor (L.) as an ideal plant system for GWAS-based dissection of host-genetic control of microbiome composition. Sorghum is a heavy producer of root exudates [In the context of the root and rhizosphere, we propose exudates , and theexudates . In addiexudates . Severalexudates , providiIn this study, we dissect the host-genetic control of bacterial microbiome composition in the sorghum rhizosphere. Using 16S rRNA sequencing, we profiled the microbiome of a panel of 200 diverse genotypes of field-grown sorghum. We test the hypothesis that a subset of the sorghum microbiome responds to the host genotype and demonstrate that this subset shares considerable overlap with lineages shown to be susceptible to host genetic control in another plant host. In addition, we tested whether GWAS can be used to identify specific genetic loci within the host genome that are correlated with the abundance of specific heritable lineages and whether differences in microbiome composition can be predicted solely from genotypic information. Collectively, this work demonstrates the utility of GWAS for analyzing host-mediated control of rhizosphere microbiome phenotypes.In order to ensure that microbiome profiling was performed on a representative subset of the broad genetic diversity present in the 378 members sorghum association panel (SAP) , 22, subwww.ars-grin.gov). To ensure a uniform starting soil microbiome for all sorghum seedlings and to control their planting density, seeds were first sown into a thoroughly homogenized field soil mix in a growth chamber with controlled environmental factors  followed by transplantation to the field site. To prepare the soil for seed germination, 0.54 cubic meters of soil was collected at a depth of 0–20 cm from the field site subsequently used for planting, and homogenized by separately mixing four equally sized batches with irrigation water in a sterilized cement mixer followed by manual homogenization on a sterilized tarp surface. The soil was then transferred to sterilized 72-cell plant trays. To prepare seeds for planting, seeds were surface-sterilized through soaking 10 min in 10% bleach +0.1% Tween-20, followed by four washes in sterile water. Following planting, sorghum seedlings were watered with ~5 ml of water using a mist nozzle every 24 h for the first 3 days, and bottom watered every three days until the 12th day, then transplanted to the field.The experimental field used in this study is an agricultural field site located in Albany, California , characterized by a silty loam soil with pH 5.2  was obtaThe field consisted of three replicate blocks, with each block containing 200 plots for each of 200 selected genotypes. Six healthy sorghum seedlings of each genotype were transplanted to their respective plots, separated by 15.2 cm, and thinning to three seedlings per plot was performed at two weeks post transplanting. Plots were organized in an alternating pattern with respect to the irrigation line to maximize the distance between each plant  for individual OTU abundances, we fit the following linear mixed model to OTU abundances of each individual OTU (n = 1189) following a cumulative sum scaling [To calculate the broad-sense heritability , will impact both phylogenetic assignment and resolution at lower taxonomic ranks [To identify the degree to which highly heritable taxa were shared between maize and sorghum, we compared the top 100 most heritable OTUs reported from both maize datasets (referred to as NAM 2010 and NAM 2015) and the sorghum dataset generated in this study. This cutoff, which resulted in a more stringent cutoff than ic ranks . Second,n = 18) containing highly heritable OTUs in the maize dataset was not detected in either the high or lowly heritable fractions of the sorghum dataset and was excluded from subsequent comparative analyses. Of the remaining bacterial orders represented by these highly heritable OTUs, we determined the number (n = 26) that contained highly heritable OTUs in at least two of the datasets, and the number (n = 15) that contained highly heritable OTUs in all three datasets. To understand the taxonomic diversity contained within these 65 orders, the family and genus classification of the 300 heritable OTUs are provided in Supplemental Table n = 10,000) in which we resampled 100 random OTUs from the 1189 total sorghum OTUs and recomputed intersections with the two maize datasets. These resamplings were not based on OTU abundance; as such, it would be equally likely to draw an abundant or rare OTU, which avoids the possible confounding issue of heritability being correlated with abundance. P-values are reported as the number of instances that these permutations returned a greater degree of overlap in these permutations divided by the total number of permutations.A subset of the orders (Q) and relative kinship (K) to control spurious association. The Q model (y = Sα + Qν + e), the K model (y = Sα + Zu + e), and the Q + K model (y = Xβ + Sα + Qν + Zu + e) described previously [y is a vector of phenotypic observation; α is a vector of allelic effects; e is a vector of residual effects; ν is a vector of population effects; β is a vector of fixed effects other than allelic or population group effects; u is a vector of polygenic background effects; Q is the matrix relating y to ν; and X, S, and Z are incidence matrices of 1 s and 0 s relating y to β, α, and u, respectively. To account for the population structure and genetic relatedness, the first three principal components (PCs) and kinship matrix were calculated using the SNPs obtained from [A genome-wide SNP map of sorghum SAP accessions used in this study was obtained from the community resource generated previously by genotyping-by-sequencing and included characterization at 265,487 SNPs . GWAS waeviously , were usned from  and fittned from .n = 343) and minor (n = 14) allele for the two haplotypes found at the peak on chromosome 4 described in the text. Including the 178 genotypes not selected for the GWAS, a total of nine sorghum genotypes belonging to the minor allele were selected, with an effort to include genotypes spanning the phylogenetic tree. For each of these nine minor allele lines, another genotype containing the major allele with close overall genetic relatedness was selected, resulting in nine major and nine minor allele-containing lines. Two replicates of each line were grown in growth chambers  in a 10% vermiculite/90% calcined clay mixture rinsed with a soil wash prepared from a 2:1 ratio of field soil to water from the field site used in the GWAS. Plants were watered daily with approximately 5 ml of autoclaved Milli-Q water using a spray bottle for the first 3 days, followed by top watering with 15 ml of water every three days. An additional misting was performed to the soil surface every 24 h to prevent drying. Following two weeks of growth, plants were harvested and rhizosphere microbiomes extracted as described for the field experiment.For the GWAS validation experiment, the 378 genotypes of the SAP were the first subset into lines containing the major  was performed using the capscale function in the R package vegan [All statistical analyses of the amplicon datasets were performed in R using the normalized reduced dataset unless stated otherwise. For alpha-diversity measurement, Shannon’s Diversity was calculated with the diversity function in the R package vegan . Principge vegan ; an ANOVge vegan .Publicly available sorghum RNA-Seq data for 27 annotated genes in the 1.15 Mb interval of chromosome 4 (Sobic.004G153000–Sobic.004G155900), were downloaded from phytozome v12.1 (ref. ). ExpresR2 = 0.13, Df = 1, p = 0.02). Based on these results, subsequent investigation of the microbiomes of the full panel of 200 lines, including heritability and GWAS analyses, was performed using rhizosphere samples.In this study, the relationship between host genotype and microbiome composition was explored through a field experiment involving 200 genotypes selected from the SAP germplasm collection , 22 Sup. While aTo investigate host genotype-dependent variation in the sorghum rhizosphere microbiome, the rhizospheres of 600 field-grown plants (three replicates of each of the 200 genotypes) were profiled using V3–V4 16S rRNA amplicon sequencing. The resulting data set included 1189 OTUs representing 29 bacterial phyla. Compositional analysis of the microbiome dataset exhibited profiles consistent with recent microbiome studies involving the sorghum rhizosphere , 46 fromH2) of individual OTUs in our sorghum dataset was evaluated; H2 quantifies the proportion of variance that is explained by genetic rather than environmental effects. More generally, this approach treats bacterial abundance as a continuously varying phenotype, similar to plant height, biomass, and yield [H2 ranged from 0 to 66% for individual OTUs , while the second exhibited a maximum of 54% (performed in 2015) [h2). Consistent with our expectation, the h2 was lower than the H2 estimated from the phenotypic data, likely due to overcorrection of the spatial components in the spline analysis. Nevertheless, h2 was significantly  correlated with previous phenotype-based estimates  and lowly heritable fractions (n = 842) using an H2 cutoff score of 0.15 , we noted that more than half were observed in at least two of the datasets, and approximately one-third (n = 15) contained highly heritable OTUs in all three datasets  , the baets Fig. . To dete44) Fig. . Collectn = 100) by total sorghum OTU counts (n = 1189) belonging to each order  in the highly heritable fraction not expected to be influenced by abundance alone.In an effort to identify the bacterial lineages with the greatest propensity for high heritability, we calculated the number of highly heritable OTUs in each of the shared highly heritable bacterial orders identified above. We noted that among bacterial orders containing the greatest number of highly heritable OTUs across all three datasets were several that represent large lineages frequently observed within the root microbiome;  Fig. . We hypoder Fig. . These rH2 equal to or greater than 0.25  to our GWAS. However, no significant SNPs were identified using this method, suggesting this threshold was too stringent and masking potential true positive associations, due to a combination of small sample size (n = 200) and relatively low heritability (H2 = 0.35 for PC1) in our data. Despite the common use of applying multiple testing corrections, including Bonferroni corrections, to GWAS to define significance cutoffs, it is understood that these cutoffs are overly conservative due to the assumption that every genetic variant tested is independent of the rest [10 (p = 10–4)  to generate a list of top candidate SNPs. The GWAS analysis performed for PC1, which explained 21% of the total variance and had the second-highest heritability (H2 = 0.35), revealed a correlation between community composition and a locus on chromosome 4 that was among these top candidates (n = 6)  Fig. .Fig. 4A The MAF of candidate SNPs at the chromosome 4 locus ranged from 0.021 to 0.036. We carried out LD analysis between the most significant SNP (leading SNP) and the SNPs around the region. Results revealed that SNPs exhibiting high LD with the leading SNP were physically close (<1.6 Mb) and showed relatively high association signals  and PC10   for alln = 9), we included a phylogenetically related major allele line (n = 9) , with genotype explaining approximately 7.5% (CAP1) of variance in the dataset.To validate that allelic variation at the candidate locus on chromosome 4 contributes to differences in rhizosphere composition, we conducted a follow-up growth chamber experiment with eighteen additional sorghum lines, including genotypes not present in the original study. To help disentangle phylogenetic-relatedness from locus-specific effects, we selected sorghum genotypes that spanned the diversity panel; additionally, for each minor allele genotype ( 9) Fig. . Followi 9) Fig. . A CAP oq < 0.05) from each allele group in the validation dataset (n = 65) demonstrated similar trends in abundance of indicator OTUs as observed in the main experiment (Fig. To identify which taxa drive the clustering observed in our CAP analysis, and to compare this to taxa responsive to the chromosome 4 allele group in our main experiment, we performed an indicator species analysis on the validation dataset. A comparison of the significant indicator OTUs (ent Fig. , with OTent Fig.  and was ent Fig. . CollectSorghum bicolor, and demonstrate that of the three, the rhizosphere represents the most promising compartment for conducting experiments to untangle the heritability of the sorghum microbiome. Notably, the degree of correlation between sorghum phylogenetic distance and microbiome distance was highest in the rhizosphere and lowest in the leaves. This greater correlation observed in the root and rhizosphere could be in part due to the phyllosphere’s relative compositional simplicity. Even Arabidopsis rosette leaves, which are in close proximity to the soil, harbor a distinct and relatively simple bacterial community compared to the root [Previous GWAS of plant-associated microbiome traits have often been conducted with leaf samples –18, and the root .By contrast, the rhizosphere represents a highly diverse and populated subset of the soil microbiome and potentially offers a greater pool of microbes upon which the host may exert influence . AlternaH2, and highly heritable taxa show strong overlap with highly heritable lineages identified in maize, spanning fifteen different bacterial orders [Burkholderia spp., this could be facilitated by their relatively large pan-genome, with diversity driven by large multi-replicon genomes and abundant genomic islands [Within the rhizosphere, we demonstrate that microbiome constituents vary in l orders . In partl orders , 58, andl orders , it is f islands .These observations suggest that evaluating bacterial heritability may identify new lineages for which close or symbiotic but previously undetected associations with plant hosts exist. For example, we observed several lineages with high heritability that are common in soil, yet prior evidence of plant-microbe interactions in the literature is lacking, including verrucomicrobiales and planctomycetales. Interestingly, high heritability in these lineages might be facilitated by the presence of a recently discovered shared bacterial microcompartment gene cluster present in both Planctomycetes and Verrucomicrobia, which confers the ability to degrade certain plant polysaccharides . Indeed,Our GWAS correlated host genetic loci and the abundance of specific bacteria within the host microbiome, as well as overall rhizosphere community structure. Our study builds upon previous research that applied GWAS to the Arabidopsis root microbiome , demonstAlthough the underlying host genetic causes of shifts in the microbiome are not well understood, candidate-driven approaches have implicated disease resistance , 7, nutrSupplemental FiguresSupplemental Tables


This study reports a genomic analysis of purebred Angus and Hereford × Angus maternal productivity, which is a key driver of sustainable systems. Heterozygosity effects quantify hybrid vigour or heterosis and were significant for growth and puberty. Breed differences were mostly due to heterosis, but there was an advantage of Hereford genes for reproductive performance. Days to calving was the most important Estimated Breeding Value (EBV) as a predictor of attainment of puberty and subsequent reproductive performance. Genomics offers a cheaper and faster strategy for the development of multi-breed EBVs in commercial herds.Multiple studies have quantified the production differences of Hereford Angus crossbreds compared to purebred Angus for a range of traits including growth, carcass, and reproductive traits. This study aims to quantify breed and heterosis effects on maternal performance using genomics. Thirty Hereford and thirty Angus sires were mated to 1100 Angus heifers and cows in a large commercial herd run on pasture at Musselroe Bay, Tasmania, Australia. Approximately 1650 calves were born. Heifers were weaned, scanned for attainment of puberty prior to joining at approximately 15 months of age, joined, and then recorded for status of pregnancy, calving, lactating, 2nd pregnancy, and weaning of second calf. Heterozygosity effects were significant for heifer pre-joining weight and height as well as proportion pubertal. Breed differences were significant for the same traits plus pregnancy rate at second joining and proportion rearing two calves. Genetic parameters were reported for 13 traits. On average, higher genetic merit  Hereford bulls were used than Angus for growth and puberty, but they were similar for fat and reproduction. Days to calving BREEDPLAN EBVs of the sires were related to puberty and reproduction. Scrotal size BREEDPLAN EBVs of the sires were related to attainment of puberty genomic EBVs calculated. In summary, breed differences in growth and puberty were due to heterosis, but there was an advantage of Hereford genes for reproductive performance. Ongoing emphasis on selection for reduced days to calving and estimation of multi-breed EBVs is important. Breeder herd or maternal productivity is important for the sustainability of beef cattle industries worldwide because of the large proportion of feed across the production system required for cow maintenance. Indeed, achieving a calving interval of 365 days represents the single most important production issue affecting maternal productivity ,2. In AuIn the research herds, the genetically High-Fat heifers (higher Rib Fat EBV) had higher conception rates than Low-Fat, especially under a shorter joining period . At secoLimitations of the Beef Cooperative Research Centre (CRC) maternal productivity trials  were thaSince that time, there have been ongoing reports of the advantages of crossbred cows. More recently, Daley and Earley  reportedThe purpose of this paper is to report genetic effects on the components of female reproduction in the Australian Black Baldy trial. The first aim is to quantify the heterosis (or at least heterozygosity) effects separately from the breed effects. Second, to report genomic parameters for the traits. The third aim is to report the relationship between current BREEDPLAN EBVs  on the sThe Black Baldy trial was conducted over five years, 2014 to 2019 . The heiOver three years , Angus heifers  and cows  were mated to leading Angus or Hereford bulls by artificial insemination to produce the heifers of interest. The sires included bulls that were used in benchmarking programmes or were widely used in their respective breeds . Angus bCalving occurred in winter/spring. Cohort 1 calved in June–August 2015, Cohort 2 in June–August 2016, and Cohort 3 in July–September 2017. Approximately 1100 cows were mated, and many were mated over multiple years. There were 1562 calves  sampled at weaning in February–March and subsequently genotyped. The 755  heifers Not all heifers completed the two reproductive cycles, with culls at each stage. The number that were scanned for puberty is a proportion of the number at weaning . The numGenomic data: Cattle were genotyped on multiple platforms with 12 animals on GGP100K, 25 on GGPHD140K, 352 on GGPLD30K, and 1106 GGP50K chips. Most but not all sires were genotyped. There were ample purebred Angus genotypes, but 1398 purebred Hereford genotypes (GGP50K) from outside this study were added to assist with the imputation of Hereford haplotypes to a common chip (GGP50K) prior to genotype cleaning and genomic relationship matrix (GRM) construction. The 37 sires with >100K single nucleotide polymorhpisms (SNPs) were cut down to 50K and included in the reference population. There were 12,340 SNPs in common between the GGPLD30K and GGP50K chips. Imputation was performed using Fimpute 3 . SNPs weZ denotes a centred matrix of allele effects with a mean of zero of size number of animals by number of markers, Z’ is the transpose of Z, n is number of animals, ip is the frequency of the minor allele at locus G matrix to be similar in magnitude  to the numerator relationship matrix constructed from genealogy [Homozygous genotypes for the major allele were coded as 0, those for the minor allele were coded as 2, and heterozygous genotypes were coded as 1. The GRM was constructed as per VanRaden’s first method ;ZZ′2∑i=enealogy .Only those animals genotyped are included in the analyses, so the numbers differ slightly from those of Pitchford et al. . There wStatistical analysis: To account for variation between cows of different age and for different seasons between calving years, a management group combining cow age and calving year was created with eight levels .As mainly Angus bulls were used as back-up bulls, there were more Angus sired calves born late, and so, they tended to be younger than Hereford sired calves. To account for this, a Calving Time factor was developed based on ovarian cycle length where calves were born early , medium (next 21 days), or late . The contemporary group was defined as Calving Time factor nested within the management group. Day of birth was fitted as a linear covariate within contemporary group. Of 1562 calves at weaning, 652 were sired by Hereford and 910 by Angus bulls.Data were analysed with a linear mixed model using ASReml-R version 4 . Fixed eHereford sired heifers were 5.6% heavier and 1.4% taller pre-joining . There wThe mean heterozygosity for Angus and Hereford sired calves was 41.1% and 44.6%, respectively. Thus, the difference of 3.5% can be multiplied by the heterozygosity value  to obtaiThe heterozygosity effect on proportion of cows weaning two calves was not significant, and when calculated as a heterosis effect, it would only account for a difference of 5%, which is less than the 12% difference between the sire breeds . Thus, tPre-joining (yearling) weight (37%) and height (39%) were moderate to highly heritable, as expected. P8 fat depth was less so (22%) but likely as expected given that the mean fat depth was only 2 mm . The herGenetic and phenotypic correlations have been estimated . The genPhenotypically, the pre-joining measures of weight, height, fat, and puberty were lowly correlated with proportion rearing two calves (0.10–0.16). Pregnancy outcomes at first (0.46) and second (0.81) joining were highly phenotypically correlated with proportion rearing two calves. However, this was largely causal, as they are components of the trait that had the highest variation.The purpose of the analysis was not to benchmark the sire breed performance relative to the national system (BREEDPLAN) as described by Pitchford et al. , becausep < 0.10) with a slope of 0.37% lower pregnancy rate per 1 kg maternal EBV. If the genetic standard deviation (SD) for milk EBV based on percentile tables is 2.8 kg, then this relationship represents an approximately 1% pregnancy rate per milk EBV SD and so is not a large effect. The first pregnancy rate was also significant but a 0.27% increase per kg. The 200 day direct or growth EBV was associated with (p < 0.10) a 0.23% lower proportion pubertal per kg. The sire P8 fat EBV was not related to any of the traits despite the same trait being measured. It is assumed this is due to the lower mean (2.35 mm) and variation (σP = 0.81) than would commonly be the case.Weaning weight was used rather than yearling weight, as it is separated into both direct and maternal components and provided the hypothesis of whether milk production was associated with the pregnancy rate of lactating cows. While there were only weak relationships, the regression of second pregnancy rate on milk EBV was significant  and heritability (0.11) were similar (Johnston and Bunter  modelled similar . They fo similar .Meyer and Johnston  reportedAs covariances between weaned two calves and some component traits were difficult to estimate , the corThe regression of sire GEBV on BREEDPLAN EBV for scrotal size was only significant for attainment of puberty. Morris et al.  publisheMaternal productivity is key driver of sustainability due to the large feed requirement for the maintenance of breeder herds and also the importance of weaning rate during periods of herd rebuilding after liquidation during drought. This project has quantified the differences in components of maternal productivity when using Hereford compared with Angus sires in an Angus cow herd. Using genomics allowed the separation of heterosis and additive genetic effects, thus enabling quantification of the effect of current BREEDPLAN EBVs on maternal traits. As expected, the days to calving EBV was the most related to maternal performance, and the scrotal size EBV was the most related to attainment of puberty in heifers. There was an advantage of using Hereford sires on maternal performance beyond that accounted for by heterosis. Breeders are encouraged to utilise breed differences as well as selecting superior sires within breeds especially focusing on days to calving for maternal performance.